Fixes

Co-authored-by: Vikram Voleti <vikram@ip-26-0-153-234.us-west-2.compute.internal>

.github/workflows/black.yml

@@ -0,0 +1,15 @@
+name: Run black
+on: [pull_request]
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - name: Install venv
+        run: |
+          sudo apt-get -y install python3.10-venv
+      - uses: psf/black@stable
+        with:
+          options: "--check --verbose -l88"
+          src: "./sgm ./scripts ./main.py"

.github/workflows/test-build.yaml

@@ -0,0 +1,27 @@
+name: Build package
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+
+jobs:
+  build:
+    name: Build
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.8", "3.10"]
+        requirements-file: ["pt2", "pt13"]
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements/${{ matrix.requirements-file }}.txt
+          pip install .

.github/workflows/test-inference.yml

@@ -0,0 +1,34 @@
+name: Test inference
+
+on:
+  pull_request:
+  push:
+    branches:
+      - main
+
+jobs:
+  test:
+    name: "Test inference"
+    # This action is designed only to run on the Stability research cluster at this time, so many assumptions are made about the environment
+    if: github.repository == 'stability-ai/generative-models'
+    runs-on: [self-hosted, slurm, g40]
+    steps:
+      - uses: actions/checkout@v3
+      - name: "Symlink checkpoints"
+        run: ln -s ${{vars.SGM_CHECKPOINTS_PATH}} checkpoints
+      - name: "Setup python"
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.10"
+      - name: "Install Hatch"
+        run: pip install hatch
+      - name: "Run inference tests"
+        run: hatch run ci:test-inference --junit-xml test-results.xml
+      - name: Surface failing tests
+        if: always()
+        uses: pmeier/pytest-results-action@main
+        with:
+          path: test-results.xml
+          summary: true
+          display-options: fEX
+          fail-on-empty: true

.gitignore

@@ -1,7 +1,14 @@
-.pt2
-.pt2_2
-.pt13
+# extensions
 *.egg-info
-build
+*.py[cod]
+
+# envs
+.pt13
+.pt2
+
+# directories
+/checkpoints
+/dist
 /outputs
-/checkpoints
+/build
+/src

CODEOWNERS

@@ -0,0 +1 @@
+.github @Stability-AI/infrastructure

LICENSE-CODE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 Stability AI
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -4,86 +4,213 @@
 
 ## News
 
+**March 18, 2024**
+- We are releasing **[SV3D](https://huggingface.co/stabilityai/sv3d)**, an image-to-video model for novel multi-view synthesis, for research purposes:
+    - **SV3D** was trained to generate 21 frames at resolution 576x576, given 1 context frame of the same size, ideally a white-background image with one object.
+    - **SV3D_u**: This variant generates orbital videos based on single image inputs without camera conditioning..
+    - **SV3D_p**: Extending the capability of **SVD3_u**, this variant accommodates both single images and orbital views allowing for the creation of 3D video along specified camera paths.
+    - We extend the streamlit demo `scripts/demo/video_sampling.py` and the standalone python script `scripts/sampling/simple_video_sample.py` for inference of both models.
+    - Please check our [project page](https://sv3d.github.io), [tech report](https://sv3d.github.io/static/paper.pdf) and [video summary](https://youtu.be/Zqw4-1LcfWg) for more details.
+
+To run **SV3D_u** on a single image:
+- Download `sv3d_u.safetensors` from https://huggingface.co/stabilityai/sv3d to `checkpoints/sv3d_u.safetensors`
+- Run `python scripts/sampling/simple_video_sample.py --input_path <path/to/image.png> --version sv3d_u`
+
+To run **SV3D_p** on a single image:
+- Download `sv3d_p.safetensors` from https://huggingface.co/stabilityai/sv3d to `checkpoints/sv3d_p.safetensors`
+1. Generate static orbit at a specified elevation eg. 10.0 : `python scripts/sampling/simple_video_sample.py --input_path <path/to/image.png> --version sv3d_p --elevations_deg 10.0`
+2. Generate dynamic orbit at a specified elevations and azimuths: specify sequences of 21 elevations (in degrees) to `elevations_deg` ([-90, 90]), and 21 azimuths (in degrees) to `azimuths_deg` [0, 360] in sorted order from 0 to 360. For example: `python scripts/sampling/simple_video_sample.py --input_path <path/to/image.png> --version sv3d_p --elevations_deg [<list of 21 elevations in degrees>] --azimuths_deg [<list of 21 azimuths in degrees>]`
+
+To run SVD or SV3D on a streamlit server:
+`streamlit run scripts/demo/video_sampling.py`
+
+  ![tile](assets/sv3d.gif)
+
+
+**November 30, 2023**
+- Following the launch of SDXL-Turbo, we are releasing [SD-Turbo](https://huggingface.co/stabilityai/sd-turbo).
+
+**November 28, 2023**
+- We are releasing SDXL-Turbo, a lightning fast text-to image model.
+  Alongside the model, we release a [technical report](https://stability.ai/research/adversarial-diffusion-distillation)
+    - Usage:
+        - Follow the installation instructions or update the existing environment with `pip install streamlit-keyup`.
+        - Download the [weights](https://huggingface.co/stabilityai/sdxl-turbo) and place them in the `checkpoints/` directory.
+        - Run `streamlit run scripts/demo/turbo.py`.
+
+  ![tile](assets/turbo_tile.png)
+
+
+**November 21, 2023**
+- We are releasing Stable Video Diffusion, an image-to-video model, for research purposes:
+    - [SVD](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid): This model was trained to generate 14
+      frames at resolution 576x1024 given a context frame of the same size.
+      We use the standard image encoder from SD 2.1, but replace the decoder with a temporally-aware `deflickering decoder`.
+    - [SVD-XT](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt): Same architecture as `SVD` but finetuned
+      for 25 frame generation.
+    - You can run the community-build gradio demo locally by running `python -m scripts.demo.gradio_app`.
+    - We provide a streamlit demo `scripts/demo/video_sampling.py` and a standalone python script `scripts/sampling/simple_video_sample.py` for inference of both models.
+    - Alongside the model, we release a [technical report](https://stability.ai/research/stable-video-diffusion-scaling-latent-video-diffusion-models-to-large-datasets).
+
+  ![tile](assets/tile.gif)
+
+**July 26, 2023**
+
+- We are releasing two new open models with a
+  permissive [`CreativeML Open RAIL++-M` license](model_licenses/LICENSE-SDXL1.0) (see [Inference](#inference) for file
+  hashes):
+    - [SDXL-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0): An improved version
+      over `SDXL-base-0.9`.
+    - [SDXL-refiner-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0): An improved version
+      over `SDXL-refiner-0.9`.
+
+![sample2](assets/001_with_eval.png)
+
+**July 4, 2023**
+
+- A technical report on SDXL is now available [here](https://arxiv.org/abs/2307.01952).
+
 **June 22, 2023**
 
+- We are releasing two new diffusion models for research purposes:
+    - `SDXL-base-0.9`: The base model was trained on a variety of aspect ratios on images with resolution 1024^2. The
+      base model uses [OpenCLIP-ViT/G](https://github.com/mlfoundations/open_clip)
+      and [CLIP-ViT/L](https://github.com/openai/CLIP/tree/main) for text encoding whereas the refiner model only uses
+      the OpenCLIP model.
+    - `SDXL-refiner-0.9`: The refiner has been trained to denoise small noise levels of high quality data and as such is
+      not expected to work as a text-to-image model; instead, it should only be used as an image-to-image model.
 
-- We are releasing two new diffusion models:
-  - `SD-XL 0.9-base`: The base model was trained on a variety of aspect ratios on images with resolution 1024^2. The base model uses [OpenCLIP-ViT/G](https://github.com/mlfoundations/open_clip) and [CLIP-ViT/L](https://github.com/openai/CLIP/tree/main) for text encoding whereas the refiner model only uses the OpenCLIP model.
-  - `SD-XL 0.9-refiner`: The refiner has been trained to denoise small noise levels of high quality data and as such is not expected to work as a text-to-image model; instead, it should only be used as an image-to-image model.
-
-**We plan to do a full release soon (July).** 
+If you would like to access these models for your research, please apply using one of the following links:
+[SDXL-0.9-Base model](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9),
+and [SDXL-0.9-Refiner](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-0.9).
+This means that you can apply for any of the two links - and if you are granted - you can access both.
+Please log in to your Hugging Face Account with your organization email to request access.
+**We plan to do a full release soon (July).**
 
 ## The codebase
 
 ### General Philosophy
 
-Modularity is king. This repo implements a config-driven approach where we build and combine submodules by calling `instantiate_from_config()` on objects defined in yaml configs. See `configs/` for many examples.
+Modularity is king. This repo implements a config-driven approach where we build and combine submodules by
+calling `instantiate_from_config()` on objects defined in yaml configs. See `configs/` for many examples.
 
 ### Changelog from the old `ldm` codebase
 
-For training, we use [pytorch-lightning](https://www.pytorchlightning.ai/index.html), but it should be easy to use other training wrappers around the base modules. The core diffusion model class (formerly `LatentDiffusion`, now `DiffusionEngine`) has been cleaned up:
+For training, we use [PyTorch Lightning](https://lightning.ai/docs/pytorch/stable/), but it should be easy to use other
+training wrappers around the base modules. The core diffusion model class (formerly `LatentDiffusion`,
+now `DiffusionEngine`) has been cleaned up:
 
-- No more extensive subclassing! We now handle all types of conditioning inputs (vectors, sequences and spatial conditionings, and all combinations thereof) in a single class: `GeneralConditioner`, see `sgm/modules/encoders/modules.py`.
+- No more extensive subclassing! We now handle all types of conditioning inputs (vectors, sequences and spatial
+  conditionings, and all combinations thereof) in a single class: `GeneralConditioner`,
+  see `sgm/modules/encoders/modules.py`.
 - We separate guiders (such as classifier-free guidance, see `sgm/modules/diffusionmodules/guiders.py`) from the
   samplers (`sgm/modules/diffusionmodules/sampling.py`), and the samplers are independent of the model.
-- We adopt the ["denoiser framework"](https://arxiv.org/abs/2206.00364) for both training and inference (most notable change is probably now the option to train continuous time models):
-    * Discrete times models (denoisers) are simply a special case of continuous time models (denoisers); see `sgm/modules/diffusionmodules/denoiser.py`.
-    * The following features are now independent: weighting of the diffusion loss function (`sgm/modules/diffusionmodules/denoiser_weighting.py`), preconditioning of the network (`sgm/modules/diffusionmodules/denoiser_scaling.py`), and sampling of noise levels during training (`sgm/modules/diffusionmodules/sigma_sampling.py`).
+- We adopt the ["denoiser framework"](https://arxiv.org/abs/2206.00364) for both training and inference (most notable
+  change is probably now the option to train continuous time models):
+    * Discrete times models (denoisers) are simply a special case of continuous time models (denoisers);
+      see `sgm/modules/diffusionmodules/denoiser.py`.
+    * The following features are now independent: weighting of the diffusion loss
+      function (`sgm/modules/diffusionmodules/denoiser_weighting.py`), preconditioning of the
+      network (`sgm/modules/diffusionmodules/denoiser_scaling.py`), and sampling of noise levels during
+      training (`sgm/modules/diffusionmodules/sigma_sampling.py`).
 - Autoencoding models have also been cleaned up.
 
 ## Installation:
+
 <a name="installation"></a>
 
 #### 1. Clone the repo
 
 ```shell
-git clone git@github.com:Stability-AI/generative-models.git
+git clone https://github.com/Stability-AI/generative-models.git
 cd generative-models
 ```
 
 #### 2. Setting up the virtualenv
 
-This is assuming you have navigated to the `generative-models` root after cloning it. 
+This is assuming you have navigated to the `generative-models` root after cloning it.
 
-**NOTE:** This is tested under `python3.8` and `python3.10`. For other python versions, you might encounter version conflicts.
-
-
-**PyTorch 1.13** 
-
-```shell
-# install required packages from pypi
-python3 -m venv .pt1
-source .pt1/bin/activate
-pip3 install wheel
-pip3 install -r requirements_pt13.txt
-```
-
-**PyTorch 2.0** 
+**NOTE:** This is tested under `python3.10`. For other python versions, you might encounter version conflicts.
 
+**PyTorch 2.0**
 
 ```shell
 # install required packages from pypi
 python3 -m venv .pt2
 source .pt2/bin/activate
-pip3 install wheel
-pip3 install -r requirements_pt2.txt
+pip3 install -r requirements/pt2.txt
 ```
 
-## Inference:
+#### 3. Install `sgm`
 
-We provide a [streamlit](https://streamlit.io/) demo for text-to-image and image-to-image sampling in `scripts/demo/sampling.py`. The following models are currently supported:
-- [SD-XL 0.9-base](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9)
-- [SD-XL 0.9-refiner](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-0.9)
-- [SD 2.1-512](https://huggingface.co/stabilityai/stable-diffusion-2-1-base/blob/main/v2-1_512-ema-pruned.safetensors)
-- [SD 2.1-768](https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/v2-1_768-ema-pruned.safetensors)
+```shell
+pip3 install .
+```
+
+#### 4. Install `sdata` for training
+
+```shell
+pip3 install -e git+https://github.com/Stability-AI/datapipelines.git@main#egg=sdata
+```
+
+## Packaging
+
+This repository uses PEP 517 compliant packaging using [Hatch](https://hatch.pypa.io/latest/).
+
+To build a distributable wheel, install `hatch` and run `hatch build`
+(specifying `-t wheel` will skip building a sdist, which is not necessary).
+
+```
+pip install hatch
+hatch build -t wheel
+```
+
+You will find the built package in `dist/`. You can install the wheel with `pip install dist/*.whl`.
+
+Note that the package does **not** currently specify dependencies; you will need to install the required packages,
+depending on your use case and PyTorch version, manually.
+
+## Inference
+
+We provide a [streamlit](https://streamlit.io/) demo for text-to-image and image-to-image sampling
+in `scripts/demo/sampling.py`.
+We provide file hashes for the complete file as well as for only the saved tensors in the file (
+see [Model Spec](https://github.com/Stability-AI/ModelSpec) for a script to evaluate that).
+The following models are currently supported:
+
+- [SDXL-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+  ```
+  File Hash (sha256): 31e35c80fc4829d14f90153f4c74cd59c90b779f6afe05a74cd6120b893f7e5b
+  Tensordata Hash (sha256): 0xd7a9105a900fd52748f20725fe52fe52b507fd36bee4fc107b1550a26e6ee1d7
+  ```
+- [SDXL-refiner-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0)
+  ```
+  File Hash (sha256): 7440042bbdc8a24813002c09b6b69b64dc90fded4472613437b7f55f9b7d9c5f
+  Tensordata Hash (sha256): 0x1a77d21bebc4b4de78c474a90cb74dc0d2217caf4061971dbfa75ad406b75d81
+  ```
+- [SDXL-base-0.9](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9)
+- [SDXL-refiner-0.9](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-0.9)
+- [SD-2.1-512](https://huggingface.co/stabilityai/stable-diffusion-2-1-base/blob/main/v2-1_512-ema-pruned.safetensors)
+- [SD-2.1-768](https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/v2-1_768-ema-pruned.safetensors)
 
 **Weights for SDXL**:
-If you would like to access these models for your research, please apply using one of the following links: 
-[SDXL-0.9-Base model](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9), and [SDXL-0.9-Refiner](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-0.9). 
-This means that you can apply for any of the two links - and if you are granted - you can access both. 
-Please log in to your HuggingFace Account with your organization email to request access.
 
-After obtaining the weights, place them into `checkpoints/`. 
+**SDXL-1.0:**
+The weights of SDXL-1.0 are available (subject to
+a [`CreativeML Open RAIL++-M` license](model_licenses/LICENSE-SDXL1.0)) here:
+
+- base model: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/
+- refiner model: https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/
+
+**SDXL-0.9:**
+The weights of SDXL-0.9 are available and subject to a [research license](model_licenses/LICENSE-SDXL0.9).
+If you would like to access these models for your research, please apply using one of the following links:
+[SDXL-base-0.9 model](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9),
+and [SDXL-refiner-0.9](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-0.9).
+This means that you can apply for any of the two links - and if you are granted - you can access both.
+Please log in to your Hugging Face Account with your organization email to request access.
+
+After obtaining the weights, place them into `checkpoints/`.
 Next, start the demo using
 
 ```
@@ -100,6 +227,7 @@ not the same as in previous Stable Diffusion 1.x/2.x versions.
 
 To run the script you need to either have a working installation as above or
 try an _experimental_ import using only a minimal amount of packages:
+
 ```bash
 python -m venv .detect
 source .detect/bin/activate
@@ -111,6 +239,7 @@ pip install --no-deps invisible-watermark
 To run the script you need to have a working installation as above. The script
 is then useable in the following ways (don't forget to activate your
 virtual environment beforehand, e.g. `source .pt1/bin/activate`):
+
 ```bash
 # test a single file
 python scripts/demo/detect.py <your filename here>
@@ -137,11 +266,21 @@ run
 python main.py --base configs/example_training/toy/mnist_cond.yaml
 ```
 
-**NOTE 1:** Using the non-toy-dataset configs `configs/example_training/imagenet-f8_cond.yaml`, `configs/example_training/txt2img-clipl.yaml` and `configs/example_training/txt2img-clipl-legacy-ucg-training.yaml` for training will require edits depdending on the used dataset (which is expected to stored in tar-file in the [webdataset-format](https://github.com/webdataset/webdataset)). To find the parts which have to be adapted, search for comments containing `USER:` in the respective config.  
+**NOTE 1:** Using the non-toy-dataset
+configs `configs/example_training/imagenet-f8_cond.yaml`, `configs/example_training/txt2img-clipl.yaml`
+and `configs/example_training/txt2img-clipl-legacy-ucg-training.yaml` for training will require edits depending on the
+used dataset (which is expected to stored in tar-file in
+the [webdataset-format](https://github.com/webdataset/webdataset)). To find the parts which have to be adapted, search
+for comments containing `USER:` in the respective config.
 
-**NOTE 2:** This repository supports both `pytorch1.13` and `pytorch2`for training generative models. However for autoencoder training as e.g. in `configs/example_training/autoencoder/kl-f4/imagenet-attnfree-logvar.yaml`, only `pytorch1.13` is supported.
+**NOTE 2:** This repository supports both `pytorch1.13` and `pytorch2`for training generative models. However for
+autoencoder training as e.g. in `configs/example_training/autoencoder/kl-f4/imagenet-attnfree-logvar.yaml`,
+only `pytorch1.13` is supported.
 
-**NOTE 3:** Training latent generative models (as e.g. in `configs/example_training/imagenet-f8_cond.yaml`) requires retrieving the checkpoint from [Hugging Face](https://huggingface.co/stabilityai/sdxl-vae/tree/main) and replacing the `CKPT_PATH` placeholder in [this line](configs/example_training/imagenet-f8_cond.yaml#81). The same is to be done for the provided text-to-image configs.
+**NOTE 3:** Training latent generative models (as e.g. in `configs/example_training/imagenet-f8_cond.yaml`) requires
+retrieving the checkpoint from [Hugging Face](https://huggingface.co/stabilityai/sdxl-vae/tree/main) and replacing
+the `CKPT_PATH` placeholder in [this line](configs/example_training/imagenet-f8_cond.yaml#81). The same is to be done
+for the provided text-to-image configs.
 
 ### Building New Diffusion Models
 
@@ -150,7 +289,8 @@ python main.py --base configs/example_training/toy/mnist_cond.yaml
 The `GeneralConditioner` is configured through the `conditioner_config`. Its only attribute is `emb_models`, a list of
 different embedders (all inherited from `AbstractEmbModel`) that are used to condition the generative model.
 All embedders should define whether or not they are trainable (`is_trainable`, default `False`), a classifier-free
-guidance dropout rate is used (`ucg_rate`, default `0`), and an input key (`input_key`), for example, `txt` for text-conditioning or `cls` for class-conditioning.
+guidance dropout rate is used (`ucg_rate`, default `0`), and an input key (`input_key`), for example, `txt` for
+text-conditioning or `cls` for class-conditioning.
 When computing conditionings, the embedder will get `batch[input_key]` as input.
 We currently support two to four dimensional conditionings and conditionings of different embedders are concatenated
 appropriately.
@@ -163,7 +303,8 @@ enough as we plan to experiment with transformer-based diffusion backbones.
 
 #### Loss
 
-The loss is configured through `loss_config`. For standard diffusion model training, you will have to set `sigma_sampler_config`.
+The loss is configured through `loss_config`. For standard diffusion model training, you will have to
+set `sigma_sampler_config`.
 
 #### Sampler config
 
@@ -173,8 +314,9 @@ guidance.
 
 ### Dataset Handling
 
-
-For large scale training we recommend using the datapipelines from our [datapipelines](https://github.com/Stability-AI/datapipelines) project. The project is contained in the requirement and automatically included when following the steps from the [Installation section](#installation).
+For large scale training we recommend using the data pipelines from
+our [data pipelines](https://github.com/Stability-AI/datapipelines) project. The project is contained in the requirement
+and automatically included when following the steps from the [Installation section](#installation).
 Small map-style datasets should be defined here in the repository (e.g., MNIST, CIFAR-10, ...), and return a dict of
 data keys/values,
 e.g.,

configs/example_training/autoencoder/kl-f4/imagenet-attnfree-logvar.yaml

@@ -29,25 +29,14 @@ model:
         in_channels: 3
         out_ch: 3
         ch: 128
-        ch_mult: [ 1, 2, 4 ]
+        ch_mult: [1, 2, 4]
         num_res_blocks: 4
-        attn_resolutions: [ ]
+        attn_resolutions: []
         dropout: 0.0
 
     decoder_config:
       target: sgm.modules.diffusionmodules.model.Decoder
-      params:
-        attn_type: none
-        double_z: False
-        z_channels: 4
-        resolution: 256
-        in_channels: 3
-        out_ch: 3
-        ch: 128
-        ch_mult: [ 1, 2, 4 ]
-        num_res_blocks: 4
-        attn_resolutions: [ ]
-        dropout: 0.0
+      params: ${model.params.encoder_config.params}
 
 data:
   target: sgm.data.dataset.StableDataModuleFromConfig
@@ -55,18 +44,18 @@ data:
     train:
       datapipeline:
         urls:
-          - "DATA-PATH"
+          - DATA-PATH
         pipeline_config:
           shardshuffle: 10000
           sample_shuffle: 10000
 
         decoders:
-          - "pil"
+          - pil
 
         postprocessors:
           - target: sdata.mappers.TorchVisionImageTransforms
             params:
-              key: 'jpg'
+              key: jpg
               transforms:
                 - target: torchvision.transforms.Resize
                   params:

configs/example_training/autoencoder/kl-f4/imagenet-kl_f8_8chn.yaml

@@ -0,0 +1,105 @@
+model:
+  base_learning_rate: 4.5e-6
+  target: sgm.models.autoencoder.AutoencodingEngine
+  params:
+    input_key: jpg
+    monitor: val/loss/rec
+    disc_start_iter: 0
+
+    encoder_config:
+      target: sgm.modules.diffusionmodules.model.Encoder
+      params:
+        attn_type: vanilla-xformers
+        double_z: true
+        z_channels: 8
+        resolution: 256
+        in_channels: 3
+        out_ch: 3
+        ch: 128
+        ch_mult: [1, 2, 4, 4]
+        num_res_blocks: 2
+        attn_resolutions: []
+        dropout: 0.0
+
+    decoder_config:
+      target: sgm.modules.diffusionmodules.model.Decoder
+      params: ${model.params.encoder_config.params}
+
+    regularizer_config:
+      target: sgm.modules.autoencoding.regularizers.DiagonalGaussianRegularizer
+
+    loss_config:
+      target: sgm.modules.autoencoding.losses.GeneralLPIPSWithDiscriminator
+      params:
+        perceptual_weight: 0.25
+        disc_start: 20001
+        disc_weight: 0.5
+        learn_logvar: True
+
+        regularization_weights:
+          kl_loss: 1.0
+
+data:
+  target: sgm.data.dataset.StableDataModuleFromConfig
+  params:
+    train:
+      datapipeline:
+        urls:
+          - DATA-PATH
+        pipeline_config:
+          shardshuffle: 10000
+          sample_shuffle: 10000
+
+        decoders:
+          - pil
+
+        postprocessors:
+          - target: sdata.mappers.TorchVisionImageTransforms
+            params:
+              key: jpg
+              transforms:
+                - target: torchvision.transforms.Resize
+                  params:
+                    size: 256
+                    interpolation: 3
+                - target: torchvision.transforms.ToTensor
+          - target: sdata.mappers.Rescaler
+          - target: sdata.mappers.AddOriginalImageSizeAsTupleAndCropToSquare
+            params:
+              h_key: height
+              w_key: width
+
+      loader:
+        batch_size: 8
+        num_workers: 4
+
+
+lightning:
+  strategy:
+    target: pytorch_lightning.strategies.DDPStrategy
+    params:
+      find_unused_parameters: True
+
+  modelcheckpoint:
+    params:
+      every_n_train_steps: 5000
+
+  callbacks:
+    metrics_over_trainsteps_checkpoint:
+      params:
+        every_n_train_steps: 50000
+
+    image_logger:
+      target: main.ImageLogger
+      params:
+        enable_autocast: False
+        batch_frequency: 1000
+        max_images: 8
+        increase_log_steps: True
+
+  trainer:
+    devices: 0,
+    limit_val_batches: 50
+    benchmark: True
+    accumulate_grad_batches: 1
+    val_check_interval: 10000

configs/example_training/imagenet-f8_cond.yaml

@@ -21,8 +21,6 @@ model:
       params:
         num_idx: 1000
 
-        weighting_config:
-          target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting
         scaling_config:
           target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
         discretization_config:
@@ -32,7 +30,6 @@ model:
       target: sgm.modules.diffusionmodules.openaimodel.UNetModel
       params:
         use_checkpoint: True
-        use_fp16: True
         in_channels: 4
         out_channels: 4
         model_channels: 256
@@ -42,7 +39,6 @@ model:
         num_head_channels: 64
         num_classes: sequential
         adm_in_channels: 1024
-        use_spatial_transformer: true
         transformer_depth: 1
         context_dim: 1024
         spatial_transformer_attn_type: softmax-xformers
@@ -51,32 +47,31 @@ model:
       target: sgm.modules.GeneralConditioner
       params:
         emb_models:
-          # crossattn cond
           - is_trainable: True
             input_key: cls
             ucg_rate: 0.2
             target: sgm.modules.encoders.modules.ClassEmbedder
             params:
-              add_sequence_dim: True  # will be used through crossattn then
+              add_sequence_dim: True
               embed_dim: 1024
               n_classes: 1000
-          # vector cond
+
           - is_trainable: False
             ucg_rate: 0.2
             input_key: original_size_as_tuple
             target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
             params:
-              outdim: 256  # multiplied by two
-          # vector cond
+              outdim: 256
+
           - is_trainable: False
             input_key: crop_coords_top_left
             ucg_rate: 0.2
             target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
             params:
-              outdim: 256  # multiplied by two
+              outdim: 256
 
     first_stage_config:
-      target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper
+      target: sgm.models.autoencoder.AutoencoderKL
       params:
         ckpt_path: CKPT_PATH
         embed_dim: 4
@@ -98,7 +93,9 @@ model:
 
     loss_fn_config:
       target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
-      params:
+      params:        
+        loss_weighting_config:
+          target: sgm.modules.diffusionmodules.loss_weighting.EpsWeighting
         sigma_sampler_config:
           target: sgm.modules.diffusionmodules.sigma_sampling.DiscreteSampling
           params:
@@ -127,18 +124,18 @@ data:
       datapipeline:
         urls:
           # USER: adapt this path the root of your custom dataset
-          - "DATA_PATH"
+          - DATA_PATH
         pipeline_config:
           shardshuffle: 10000
           sample_shuffle: 10000 # USER: you might wanna adapt depending on your available RAM
 
         decoders:
-          - "pil"
+          - pil
 
         postprocessors:
           - target: sdata.mappers.TorchVisionImageTransforms
             params:
-              key: 'jpg' # USER: you might wanna adapt this for your custom dataset
+              key: jpg # USER: you might wanna adapt this for your custom dataset
               transforms:
                 - target: torchvision.transforms.Resize
                   params:

configs/example_training/toy/cifar10_cond.yaml

@@ -5,10 +5,6 @@ model:
     denoiser_config:
       target: sgm.modules.diffusionmodules.denoiser.Denoiser
       params:
-        weighting_config:
-          target: sgm.modules.diffusionmodules.denoiser_weighting.EDMWeighting
-          params:
-            sigma_data: 1.0
         scaling_config:
           target: sgm.modules.diffusionmodules.denoiser_scaling.EDMScaling
           params:
@@ -17,7 +13,6 @@ model:
     network_config:
       target: sgm.modules.diffusionmodules.openaimodel.UNetModel
       params:
-        use_checkpoint: True
         in_channels: 3
         out_channels: 3
         model_channels: 32
@@ -46,6 +41,10 @@ model:
     loss_fn_config:
       target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
       params:
+        loss_weighting_config:
+          target: sgm.modules.diffusionmodules.loss_weighting.EDMWeighting
+          params:
+            sigma_data: 1.0
         sigma_sampler_config:
           target: sgm.modules.diffusionmodules.sigma_sampling.EDMSampling
 

configs/example_training/toy/mnist.yaml

@@ -5,10 +5,6 @@ model:
     denoiser_config:
       target: sgm.modules.diffusionmodules.denoiser.Denoiser
       params:
-        weighting_config:
-          target: sgm.modules.diffusionmodules.denoiser_weighting.EDMWeighting
-          params:
-            sigma_data: 1.0
         scaling_config:
           target: sgm.modules.diffusionmodules.denoiser_scaling.EDMScaling
           params:
@@ -17,7 +13,6 @@ model:
     network_config:
       target: sgm.modules.diffusionmodules.openaimodel.UNetModel
       params:
-        use_checkpoint: True
         in_channels: 1
         out_channels: 1
         model_channels: 32
@@ -32,6 +27,10 @@ model:
     loss_fn_config:
       target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
       params:
+        loss_weighting_config:
+          target: sgm.modules.diffusionmodules.loss_weighting.EDMWeighting
+          params:
+            sigma_data: 1.0
         sigma_sampler_config:
           target: sgm.modules.diffusionmodules.sigma_sampling.EDMSampling
 

configs/example_training/toy/mnist_cond.yaml

@@ -5,10 +5,6 @@ model:
     denoiser_config:
       target: sgm.modules.diffusionmodules.denoiser.Denoiser
       params:
-        weighting_config:
-          target: sgm.modules.diffusionmodules.denoiser_weighting.EDMWeighting
-          params:
-            sigma_data: 1.0
         scaling_config:
           target: sgm.modules.diffusionmodules.denoiser_scaling.EDMScaling
           params:
@@ -17,13 +13,12 @@ model:
     network_config:
       target: sgm.modules.diffusionmodules.openaimodel.UNetModel
       params:
-        use_checkpoint: True
         in_channels: 1
         out_channels: 1
         model_channels: 32
-        attention_resolutions: [ ]
+        attention_resolutions: []
         num_res_blocks: 4
-        channel_mult: [ 1, 2, 2 ]
+        channel_mult: [1, 2, 2]
         num_head_channels: 32
         num_classes: sequential
         adm_in_channels: 128
@@ -33,7 +28,7 @@ model:
       params:
         emb_models:
           - is_trainable: True
-            input_key: "cls"
+            input_key: cls
             ucg_rate: 0.2
             target: sgm.modules.encoders.modules.ClassEmbedder
             params:
@@ -46,6 +41,10 @@ model:
     loss_fn_config:
       target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
       params:
+        loss_weighting_config:
+          target: sgm.modules.diffusionmodules.loss_weighting.EDMWeighting
+          params:
+            sigma_data: 1.0
         sigma_sampler_config:
           target: sgm.modules.diffusionmodules.sigma_sampling.EDMSampling
 

configs/example_training/toy/mnist_cond_discrete_eps.yaml

@@ -7,8 +7,6 @@ model:
       params:
         num_idx: 1000
 
-        weighting_config:
-          target: sgm.modules.diffusionmodules.denoiser_weighting.EDMWeighting
         scaling_config:
           target: sgm.modules.diffusionmodules.denoiser_scaling.EDMScaling
         discretization_config:
@@ -17,13 +15,12 @@ model:
     network_config:
       target: sgm.modules.diffusionmodules.openaimodel.UNetModel
       params:
-        use_checkpoint: True
         in_channels: 1
         out_channels: 1
         model_channels: 32
-        attention_resolutions: [ ]
+        attention_resolutions: []
         num_res_blocks: 4
-        channel_mult: [ 1, 2, 2 ]
+        channel_mult: [1, 2, 2]
         num_head_channels: 32
         num_classes: sequential
         adm_in_channels: 128
@@ -33,7 +30,7 @@ model:
       params:
         emb_models:
           - is_trainable: True
-            input_key: "cls"
+            input_key: cls
             ucg_rate: 0.2
             target: sgm.modules.encoders.modules.ClassEmbedder
             params:
@@ -46,6 +43,8 @@ model:
     loss_fn_config:
       target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
       params:
+        loss_weighting_config:
+          target: sgm.modules.diffusionmodules.loss_weighting.EDMWeighting
         sigma_sampler_config:
           target: sgm.modules.diffusionmodules.sigma_sampling.DiscreteSampling
           params:

configs/example_training/toy/mnist_cond_l1_loss.yaml

@@ -5,10 +5,6 @@ model:
     denoiser_config:
       target: sgm.modules.diffusionmodules.denoiser.Denoiser
       params:
-        weighting_config:
-          target: sgm.modules.diffusionmodules.denoiser_weighting.EDMWeighting
-          params:
-            sigma_data: 1.0
         scaling_config:
           target: sgm.modules.diffusionmodules.denoiser_scaling.EDMScaling
           params:
@@ -17,7 +13,6 @@ model:
     network_config:
       target: sgm.modules.diffusionmodules.openaimodel.UNetModel
       params:
-        use_checkpoint: True
         in_channels: 1
         out_channels: 1
         model_channels: 32
@@ -25,7 +20,7 @@ model:
         num_res_blocks: 4
         channel_mult: [1, 2, 2]
         num_head_channels: 32
-        num_classes: "sequential"
+        num_classes: sequential
         adm_in_channels: 128
 
     conditioner_config:
@@ -33,7 +28,7 @@ model:
       params:
         emb_models:
           - is_trainable: True
-            input_key: "cls"
+            input_key: cls
             ucg_rate: 0.2
             target: sgm.modules.encoders.modules.ClassEmbedder
             params:
@@ -46,6 +41,11 @@ model:
     loss_fn_config:
       target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
       params:
+        loss_type: l1
+        loss_weighting_config:
+          target: sgm.modules.diffusionmodules.loss_weighting.EDMWeighting
+          params:
+            sigma_data: 1.0
         sigma_sampler_config:
           target: sgm.modules.diffusionmodules.sigma_sampling.EDMSampling
 
@@ -62,11 +62,6 @@ model:
           params:
             scale: 3.0
 
-    loss_config:
-      target: sgm.modules.diffusionmodules.StandardDiffusionLoss
-      params:
-        type: l1
-
 data:
   target: sgm.data.mnist.MNISTLoader
   params:

configs/example_training/toy/mnist_cond_with_ema.yaml

@@ -7,10 +7,6 @@ model:
     denoiser_config:
       target: sgm.modules.diffusionmodules.denoiser.Denoiser
       params:
-        weighting_config:
-          target: sgm.modules.diffusionmodules.denoiser_weighting.EDMWeighting
-          params:
-            sigma_data: 1.0
         scaling_config:
           target: sgm.modules.diffusionmodules.denoiser_scaling.EDMScaling
           params:
@@ -19,7 +15,6 @@ model:
     network_config:
       target: sgm.modules.diffusionmodules.openaimodel.UNetModel
       params:
-        use_checkpoint: True
         in_channels: 1
         out_channels: 1
         model_channels: 32
@@ -48,6 +43,10 @@ model:
     loss_fn_config:
       target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
       params:
+        loss_weighting_config:
+          target: sgm.modules.diffusionmodules.loss_weighting.EDMWeighting
+          params:
+            sigma_data: 1.0
         sigma_sampler_config:
           target: sgm.modules.diffusionmodules.sigma_sampling.EDMSampling
 

configs/example_training/txt2img-clipl-legacy-ucg-training.yaml

@@ -10,19 +10,17 @@ model:
     scheduler_config:
       target: sgm.lr_scheduler.LambdaLinearScheduler
       params:
-        warm_up_steps: [ 10000 ]
-        cycle_lengths: [ 10000000000000 ]
-        f_start: [ 1.e-6 ]
-        f_max: [ 1. ]
-        f_min: [ 1. ]
+        warm_up_steps: [10000]
+        cycle_lengths: [10000000000000]
+        f_start: [1.e-6]
+        f_max: [1.]
+        f_min: [1.]
 
     denoiser_config:
       target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
       params:
         num_idx: 1000
 
-        weighting_config:
-          target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting
         scaling_config:
           target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
         discretization_config:
@@ -32,18 +30,16 @@ model:
       target: sgm.modules.diffusionmodules.openaimodel.UNetModel
       params:
         use_checkpoint: True
-        use_fp16: True
         in_channels: 4
         out_channels: 4
         model_channels: 320
-        attention_resolutions: [ 1, 2, 4 ]
+        attention_resolutions: [1, 2, 4]
         num_res_blocks: 2
-        channel_mult: [ 1, 2, 4, 4 ]
+        channel_mult: [1, 2, 4, 4]
         num_head_channels: 64
         num_classes: sequential
         adm_in_channels: 1792
         num_heads: 1
-        use_spatial_transformer: true
         transformer_depth: 1
         context_dim: 768
         spatial_transformer_attn_type: softmax-xformers
@@ -52,7 +48,6 @@ model:
       target: sgm.modules.GeneralConditioner
       params:
         emb_models:
-          # crossattn cond
           - is_trainable: True
             input_key: txt
             ucg_rate: 0.1
@@ -60,23 +55,23 @@ model:
             target: sgm.modules.encoders.modules.FrozenCLIPEmbedder
             params:
               always_return_pooled: True
-          # vector cond
+
           - is_trainable: False
             ucg_rate: 0.1
             input_key: original_size_as_tuple
             target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
             params:
-              outdim: 256  # multiplied by two
-          # vector cond
+              outdim: 256
+
           - is_trainable: False
             input_key: crop_coords_top_left
             ucg_rate: 0.1
             target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
             params:
-              outdim: 256  # multiplied by two
+              outdim: 256
 
     first_stage_config:
-      target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper
+      target: sgm.models.autoencoder.AutoencoderKL
       params:
         ckpt_path: CKPT_PATH
         embed_dim: 4
@@ -99,6 +94,8 @@ model:
     loss_fn_config:
       target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
       params:
+        loss_weighting_config:
+          target: sgm.modules.diffusionmodules.loss_weighting.EpsWeighting
         sigma_sampler_config:
           target: sgm.modules.diffusionmodules.sigma_sampling.DiscreteSampling
           params:
@@ -127,18 +124,18 @@ data:
       datapipeline:
         urls:
           # USER: adapt this path the root of your custom dataset
-          - "DATA_PATH"
+          - DATA_PATH
         pipeline_config:
           shardshuffle: 10000
           sample_shuffle: 10000 # USER: you might wanna adapt depending on your available RAM
 
         decoders:
-          - "pil"
+          - pil
 
         postprocessors:
           - target: sdata.mappers.TorchVisionImageTransforms
             params:
-              key: 'jpg' # USER: you might wanna adapt this for your custom dataset
+              key: jpg # USER: you might wanna adapt this for your custom dataset
               transforms:
                 - target: torchvision.transforms.Resize
                   params:

configs/example_training/txt2img-clipl.yaml

@@ -10,19 +10,17 @@ model:
     scheduler_config:
       target: sgm.lr_scheduler.LambdaLinearScheduler
       params:
-        warm_up_steps: [ 10000 ]
-        cycle_lengths: [ 10000000000000 ]
-        f_start: [ 1.e-6 ]
-        f_max: [ 1. ]
-        f_min: [ 1. ]
+        warm_up_steps: [10000]
+        cycle_lengths: [10000000000000]
+        f_start: [1.e-6]
+        f_max: [1.]
+        f_min: [1.]
 
     denoiser_config:
       target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
       params:
         num_idx: 1000
 
-        weighting_config:
-          target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting
         scaling_config:
           target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
         discretization_config:
@@ -32,18 +30,16 @@ model:
       target: sgm.modules.diffusionmodules.openaimodel.UNetModel
       params:
         use_checkpoint: True
-        use_fp16: True
         in_channels: 4
         out_channels: 4
         model_channels: 320
-        attention_resolutions: [ 1, 2, 4 ]
+        attention_resolutions: [1, 2, 4]
         num_res_blocks: 2
-        channel_mult: [ 1, 2, 4, 4 ]
+        channel_mult: [1, 2, 4, 4]
         num_head_channels: 64
         num_classes: sequential
         adm_in_channels: 1792
         num_heads: 1
-        use_spatial_transformer: true
         transformer_depth: 1
         context_dim: 768
         spatial_transformer_attn_type: softmax-xformers
@@ -52,30 +48,30 @@ model:
       target: sgm.modules.GeneralConditioner
       params:
         emb_models:
-          # crossattn cond
           - is_trainable: True
             input_key: txt
             ucg_rate: 0.1
+            legacy_ucg_value: ""
             target: sgm.modules.encoders.modules.FrozenCLIPEmbedder
             params:
               always_return_pooled: True
-          # vector cond
+
           - is_trainable: False
             ucg_rate: 0.1
             input_key: original_size_as_tuple
             target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
             params:
-              outdim: 256  # multiplied by two
-          # vector cond
+              outdim: 256
+
           - is_trainable: False
             input_key: crop_coords_top_left
             ucg_rate: 0.1
             target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
             params:
-              outdim: 256  # multiplied by two
+              outdim: 256
 
     first_stage_config:
-      target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper
+      target: sgm.models.autoencoder.AutoencoderKL
       params:
         ckpt_path: CKPT_PATH
         embed_dim: 4
@@ -88,9 +84,9 @@ model:
           in_channels: 3
           out_ch: 3
           ch: 128
-          ch_mult: [ 1, 2, 4, 4 ]
+          ch_mult: [1, 2, 4, 4]
           num_res_blocks: 2
-          attn_resolutions: [ ]
+          attn_resolutions: []
           dropout: 0.0
         lossconfig:
           target: torch.nn.Identity
@@ -98,6 +94,8 @@ model:
     loss_fn_config:
       target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
       params:
+        loss_weighting_config:
+          target: sgm.modules.diffusionmodules.loss_weighting.EpsWeighting
         sigma_sampler_config:
           target: sgm.modules.diffusionmodules.sigma_sampling.DiscreteSampling
           params:
@@ -126,19 +124,19 @@ data:
       datapipeline:
         urls:
           # USER: adapt this path the root of your custom dataset
-          - "DATA_PATH"
+          - DATA_PATH
         pipeline_config:
           shardshuffle: 10000
           sample_shuffle: 10000
 
 
         decoders:
-          - "pil"
+          - pil
 
         postprocessors:
           - target: sdata.mappers.TorchVisionImageTransforms
             params:
-              key: 'jpg' # USER: you might wanna adapt this for your custom dataset
+              key: jpg # USER: you might wanna adapt this for your custom dataset
               transforms:
                 - target: torchvision.transforms.Resize
                   params:

configs/inference/sd_2_1.yaml

@@ -9,8 +9,6 @@ model:
       params:
         num_idx: 1000
 
-        weighting_config:
-          target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting
         scaling_config:
           target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
         discretization_config:
@@ -20,7 +18,6 @@ model:
       target: sgm.modules.diffusionmodules.openaimodel.UNetModel
       params:
         use_checkpoint: True
-        use_fp16: True
         in_channels: 4
         out_channels: 4
         model_channels: 320
@@ -28,17 +25,14 @@ model:
         num_res_blocks: 2
         channel_mult: [1, 2, 4, 4]
         num_head_channels: 64
-        use_spatial_transformer: True
         use_linear_in_transformer: True
         transformer_depth: 1
         context_dim: 1024
-        legacy: False
 
     conditioner_config:
       target: sgm.modules.GeneralConditioner
       params:
         emb_models:
-          # crossattn cond
           - is_trainable: False
             input_key: txt
             target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder
@@ -47,7 +41,7 @@ model:
               layer: penultimate
 
     first_stage_config:
-      target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper
+      target: sgm.models.autoencoder.AutoencoderKL
       params:
         embed_dim: 4
         monitor: val/rec_loss

configs/inference/sd_2_1_768.yaml

@@ -9,8 +9,6 @@ model:
       params:
         num_idx: 1000
 
-        weighting_config:
-          target: sgm.modules.diffusionmodules.denoiser_weighting.VWeighting
         scaling_config:
           target: sgm.modules.diffusionmodules.denoiser_scaling.VScaling
         discretization_config:
@@ -20,7 +18,6 @@ model:
       target: sgm.modules.diffusionmodules.openaimodel.UNetModel
       params:
         use_checkpoint: True
-        use_fp16: True
         in_channels: 4
         out_channels: 4
         model_channels: 320
@@ -28,17 +25,14 @@ model:
         num_res_blocks: 2
         channel_mult: [1, 2, 4, 4]
         num_head_channels: 64
-        use_spatial_transformer: True
         use_linear_in_transformer: True
         transformer_depth: 1
         context_dim: 1024
-        legacy: False
 
     conditioner_config:
       target: sgm.modules.GeneralConditioner
       params:
         emb_models:
-          # crossattn cond
           - is_trainable: False
             input_key: txt
             target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder
@@ -47,7 +41,7 @@ model:
               layer: penultimate
 
     first_stage_config:
-      target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper
+      target: sgm.models.autoencoder.AutoencoderKL
       params:
         embed_dim: 4
         monitor: val/rec_loss

configs/inference/sd_xl_base.yaml

@@ -9,8 +9,6 @@ model:
       params:
         num_idx: 1000
 
-        weighting_config:
-          target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting
         scaling_config:
           target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
         discretization_config:
@@ -29,25 +27,22 @@ model:
         num_res_blocks: 2
         channel_mult: [1, 2, 4]
         num_head_channels: 64
-        use_spatial_transformer: True
         use_linear_in_transformer: True
-        transformer_depth: [1, 2, 10]  # note: the first is unused (due to attn_res starting at 2) 32, 16, 8 --> 64, 32, 16
+        transformer_depth: [1, 2, 10]
         context_dim: 2048
         spatial_transformer_attn_type: softmax-xformers
-        legacy: False
 
     conditioner_config:
       target: sgm.modules.GeneralConditioner
       params:
         emb_models:
-          # crossattn cond
           - is_trainable: False
             input_key: txt
             target: sgm.modules.encoders.modules.FrozenCLIPEmbedder
             params:
               layer: hidden
               layer_idx: 11
-          # crossattn and vector cond
+
           - is_trainable: False
             input_key: txt
             target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
@@ -58,27 +53,27 @@ model:
               layer: penultimate
               always_return_pooled: True
               legacy: False
-          # vector cond
+
           - is_trainable: False
             input_key: original_size_as_tuple
             target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
             params:
-              outdim: 256  # multiplied by two
-          # vector cond
+              outdim: 256
+
           - is_trainable: False
             input_key: crop_coords_top_left
             target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
             params:
-              outdim: 256  # multiplied by two
-          # vector cond
+              outdim: 256
+
           - is_trainable: False
             input_key: target_size_as_tuple
             target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
             params:
-              outdim: 256  # multiplied by two
+              outdim: 256
 
     first_stage_config:
-      target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper
+      target: sgm.models.autoencoder.AutoencoderKL
       params:
         embed_dim: 4
         monitor: val/rec_loss

configs/inference/sd_xl_refiner.yaml

@@ -9,8 +9,6 @@ model:
       params:
         num_idx: 1000
 
-        weighting_config:
-          target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting
         scaling_config:
           target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
         discretization_config:
@@ -29,18 +27,15 @@ model:
         num_res_blocks: 2
         channel_mult: [1, 2, 4, 4]
         num_head_channels: 64
-        use_spatial_transformer: True
         use_linear_in_transformer: True
         transformer_depth: 4
-        context_dim: [1280, 1280, 1280, 1280]  # 1280
+        context_dim: [1280, 1280, 1280, 1280]
         spatial_transformer_attn_type: softmax-xformers
-        legacy: False
 
     conditioner_config:
       target: sgm.modules.GeneralConditioner
       params:
         emb_models:
-          # crossattn and vector cond
           - is_trainable: False
             input_key: txt
             target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
@@ -51,27 +46,27 @@ model:
               freeze: True
               layer: penultimate
               always_return_pooled: True
-          # vector cond
+
           - is_trainable: False
             input_key: original_size_as_tuple
             target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
             params:
-              outdim: 256  # multiplied by two
-          # vector cond
+              outdim: 256
+
           - is_trainable: False
             input_key: crop_coords_top_left
             target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
             params:
-              outdim: 256  # multiplied by two
-          # vector cond
+              outdim: 256
+
           - is_trainable: False
             input_key: aesthetic_score
             target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
             params:
-              outdim: 256  # multiplied by one
+              outdim: 256
 
     first_stage_config:
-      target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper
+      target: sgm.models.autoencoder.AutoencoderKL
       params:
         embed_dim: 4
         monitor: val/rec_loss

configs/inference/sv3d_p.yaml

@@ -0,0 +1,118 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.18215
+    disable_first_stage_autocast: True
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.Denoiser
+      params:
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.VScalingWithEDMcNoise
+
+    network_config:
+      target: sgm.modules.diffusionmodules.video_model.VideoUNet
+      params:
+        adm_in_channels: 1280
+        num_classes: sequential
+        use_checkpoint: True
+        in_channels: 8
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [4, 2, 1]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4, 4]
+        num_head_channels: 64
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        spatial_transformer_attn_type: softmax-xformers
+        extra_ff_mix_layer: True
+        use_spatial_context: True
+        merge_strategy: learned_with_images
+        video_kernel_size: [3, 1, 1]
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+        - input_key: cond_frames_without_noise
+          is_trainable: False
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPImagePredictionEmbedder
+          params:
+            n_cond_frames: 1
+            n_copies: 1
+            open_clip_embedding_config:
+              target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+              params:
+                freeze: True
+
+        - input_key: cond_frames
+          is_trainable: False
+          target: sgm.modules.encoders.modules.VideoPredictionEmbedderWithEncoder
+          params:
+            disable_encoder_autocast: True
+            n_cond_frames: 1
+            n_copies: 1
+            is_ae: True
+            encoder_config:
+              target: sgm.models.autoencoder.AutoencoderKLModeOnly
+              params:
+                embed_dim: 4
+                monitor: val/rec_loss
+                ddconfig:
+                  attn_type: vanilla-xformers
+                  double_z: True
+                  z_channels: 4
+                  resolution: 256
+                  in_channels: 3
+                  out_ch: 3
+                  ch: 128
+                  ch_mult: [1, 2, 4, 4]
+                  num_res_blocks: 2
+                  attn_resolutions: []
+                  dropout: 0.0
+                lossconfig:
+                  target: torch.nn.Identity
+
+        - input_key: cond_aug
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: polars_rad
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 512
+
+        - input_key: azimuths_rad
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 512
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencodingEngine
+      params:
+        loss_config:
+          target: torch.nn.Identity
+        regularizer_config:
+          target: sgm.modules.autoencoding.regularizers.DiagonalGaussianRegularizer
+        encoder_config:
+          target: torch.nn.Identity
+        decoder_config:
+          target: sgm.modules.diffusionmodules.model.Decoder
+          params:
+            attn_type: vanilla-xformers
+            double_z: True
+            z_channels: 4
+            resolution: 256
+            in_channels: 3
+            out_ch: 3
+            ch: 128
+            ch_mult: [ 1, 2, 4, 4 ]
+            num_res_blocks: 2
+            attn_resolutions: [ ]
+            dropout: 0.0

configs/inference/sv3d_u.yaml

@@ -0,0 +1,106 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.18215
+    disable_first_stage_autocast: True
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.Denoiser
+      params:
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.VScalingWithEDMcNoise
+
+    network_config:
+      target: sgm.modules.diffusionmodules.video_model.VideoUNet
+      params:
+        adm_in_channels: 256
+        num_classes: sequential
+        use_checkpoint: True
+        in_channels: 8
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [4, 2, 1]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4, 4]
+        num_head_channels: 64
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        spatial_transformer_attn_type: softmax-xformers
+        extra_ff_mix_layer: True
+        use_spatial_context: True
+        merge_strategy: learned_with_images
+        video_kernel_size: [3, 1, 1]
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+        - input_key: cond_frames_without_noise
+          is_trainable: False
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPImagePredictionEmbedder
+          params:
+            n_cond_frames: 1
+            n_copies: 1
+            open_clip_embedding_config:
+              target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+              params:
+                freeze: True
+
+        - input_key: cond_frames
+          is_trainable: False
+          target: sgm.modules.encoders.modules.VideoPredictionEmbedderWithEncoder
+          params:
+            disable_encoder_autocast: True
+            n_cond_frames: 1
+            n_copies: 1
+            is_ae: True
+            encoder_config:
+              target: sgm.models.autoencoder.AutoencoderKLModeOnly
+              params:
+                embed_dim: 4
+                monitor: val/rec_loss
+                ddconfig:
+                  attn_type: vanilla-xformers
+                  double_z: True
+                  z_channels: 4
+                  resolution: 256
+                  in_channels: 3
+                  out_ch: 3
+                  ch: 128
+                  ch_mult: [1, 2, 4, 4]
+                  num_res_blocks: 2
+                  attn_resolutions: []
+                  dropout: 0.0
+                lossconfig:
+                  target: torch.nn.Identity
+
+        - input_key: cond_aug
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencodingEngine
+      params:
+        loss_config:
+          target: torch.nn.Identity
+        regularizer_config:
+          target: sgm.modules.autoencoding.regularizers.DiagonalGaussianRegularizer
+        encoder_config:
+          target: torch.nn.Identity
+        decoder_config:
+          target: sgm.modules.diffusionmodules.model.Decoder
+          params:
+            attn_type: vanilla-xformers
+            double_z: True
+            z_channels: 4
+            resolution: 256
+            in_channels: 3
+            out_ch: 3
+            ch: 128
+            ch_mult: [ 1, 2, 4, 4 ]
+            num_res_blocks: 2
+            attn_resolutions: [ ]
+            dropout: 0.0

configs/inference/svd.yaml

@@ -0,0 +1,131 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.18215
+    disable_first_stage_autocast: True
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.Denoiser
+      params:
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.VScalingWithEDMcNoise
+
+    network_config:
+      target: sgm.modules.diffusionmodules.video_model.VideoUNet
+      params:
+        adm_in_channels: 768
+        num_classes: sequential
+        use_checkpoint: True
+        in_channels: 8
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [4, 2, 1]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4, 4]
+        num_head_channels: 64
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        spatial_transformer_attn_type: softmax-xformers
+        extra_ff_mix_layer: True
+        use_spatial_context: True
+        merge_strategy: learned_with_images
+        video_kernel_size: [3, 1, 1]
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+        - is_trainable: False
+          input_key: cond_frames_without_noise
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPImagePredictionEmbedder
+          params:
+            n_cond_frames: 1
+            n_copies: 1
+            open_clip_embedding_config:
+              target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+              params:
+                freeze: True
+
+        - input_key: fps_id
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: motion_bucket_id
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: cond_frames
+          is_trainable: False
+          target: sgm.modules.encoders.modules.VideoPredictionEmbedderWithEncoder
+          params:
+            disable_encoder_autocast: True
+            n_cond_frames: 1
+            n_copies: 1
+            is_ae: True
+            encoder_config:
+              target: sgm.models.autoencoder.AutoencoderKLModeOnly
+              params:
+                embed_dim: 4
+                monitor: val/rec_loss
+                ddconfig:
+                  attn_type: vanilla-xformers
+                  double_z: True
+                  z_channels: 4
+                  resolution: 256
+                  in_channels: 3
+                  out_ch: 3
+                  ch: 128
+                  ch_mult: [1, 2, 4, 4]
+                  num_res_blocks: 2
+                  attn_resolutions: []
+                  dropout: 0.0
+                lossconfig:
+                  target: torch.nn.Identity
+
+        - input_key: cond_aug
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencodingEngine
+      params:
+        loss_config:
+          target: torch.nn.Identity
+        regularizer_config:
+          target: sgm.modules.autoencoding.regularizers.DiagonalGaussianRegularizer
+        encoder_config: 
+          target: sgm.modules.diffusionmodules.model.Encoder
+          params:
+            attn_type: vanilla
+            double_z: True
+            z_channels: 4
+            resolution: 256
+            in_channels: 3
+            out_ch: 3
+            ch: 128
+            ch_mult: [1, 2, 4, 4]
+            num_res_blocks: 2
+            attn_resolutions: []
+            dropout: 0.0
+        decoder_config:
+          target: sgm.modules.autoencoding.temporal_ae.VideoDecoder
+          params:
+            attn_type: vanilla
+            double_z: True
+            z_channels: 4
+            resolution: 256
+            in_channels: 3
+            out_ch: 3
+            ch: 128
+            ch_mult: [1, 2, 4, 4]
+            num_res_blocks: 2
+            attn_resolutions: []
+            dropout: 0.0
+            video_kernel_size: [3, 1, 1]

configs/inference/svd_image_decoder.yaml

@@ -0,0 +1,114 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.18215
+    disable_first_stage_autocast: True
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.Denoiser
+      params:
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.VScalingWithEDMcNoise
+
+    network_config:
+      target: sgm.modules.diffusionmodules.video_model.VideoUNet
+      params:
+        adm_in_channels: 768
+        num_classes: sequential
+        use_checkpoint: True
+        in_channels: 8
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [4, 2, 1]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4, 4]
+        num_head_channels: 64
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        spatial_transformer_attn_type: softmax-xformers
+        extra_ff_mix_layer: True
+        use_spatial_context: True
+        merge_strategy: learned_with_images
+        video_kernel_size: [3, 1, 1]
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+        - is_trainable: False
+          input_key: cond_frames_without_noise
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPImagePredictionEmbedder
+          params:
+            n_cond_frames: 1
+            n_copies: 1
+            open_clip_embedding_config:
+              target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+              params:
+                freeze: True
+
+        - input_key: fps_id
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: motion_bucket_id
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: cond_frames
+          is_trainable: False
+          target: sgm.modules.encoders.modules.VideoPredictionEmbedderWithEncoder
+          params:
+            disable_encoder_autocast: True
+            n_cond_frames: 1
+            n_copies: 1
+            is_ae: True
+            encoder_config:
+              target: sgm.models.autoencoder.AutoencoderKLModeOnly
+              params:
+                embed_dim: 4
+                monitor: val/rec_loss
+                ddconfig:
+                  attn_type: vanilla-xformers
+                  double_z: True
+                  z_channels: 4
+                  resolution: 256
+                  in_channels: 3
+                  out_ch: 3
+                  ch: 128
+                  ch_mult: [1, 2, 4, 4]
+                  num_res_blocks: 2
+                  attn_resolutions: []
+                  dropout: 0.0
+                lossconfig:
+                  target: torch.nn.Identity
+
+        - input_key: cond_aug
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          attn_type: vanilla-xformers
+          double_z: True
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult: [1, 2, 4, 4]
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity

main.py

@@ -12,22 +12,18 @@ import pytorch_lightning as pl
 import torch
 import torchvision
 import wandb
-from PIL import Image
 from matplotlib import pyplot as plt
 from natsort import natsorted
 from omegaconf import OmegaConf
 from packaging import version
+from PIL import Image
 from pytorch_lightning import seed_everything
 from pytorch_lightning.callbacks import Callback
 from pytorch_lightning.loggers import WandbLogger
 from pytorch_lightning.trainer import Trainer
 from pytorch_lightning.utilities import rank_zero_only
 
-from sgm.util import (
-    exists,
-    instantiate_from_config,
-    isheatmap,
-)
+from sgm.util import exists, instantiate_from_config, isheatmap
 
 MULTINODE_HACKS = True
 
@@ -469,9 +465,8 @@ class ImageLogger(Callback):
             self.log_img(pl_module, batch, batch_idx, split="train")
 
     @rank_zero_only
-    # def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
     def on_validation_batch_end(
-        self, trainer, pl_module, outputs, batch, batch_idx, **kwargs
+        self, trainer, pl_module, outputs, batch, batch_idx, *args, **kwargs
     ):
         if not self.disabled and pl_module.global_step > 0:
             self.log_img(pl_module, batch, batch_idx, split="val")
@@ -911,11 +906,12 @@ if __name__ == "__main__":
             trainer.test(model, data)
     except RuntimeError as err:
         if MULTINODE_HACKS:
-            import requests
             import datetime
             import os
             import socket
 
+            import requests
+
             device = os.environ.get("CUDA_VISIBLE_DEVICES", "?")
             hostname = socket.gethostname()
             ts = datetime.datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S")

model_licenses/LICENCE-SD-Turbo

@@ -0,0 +1,58 @@
+STABILITY AI NON-COMMERCIAL RESEARCH COMMUNITY LICENSE AGREEMENT        
+Dated: November 28, 2023
+
+
+By using or distributing any portion or element of the Models, Software, Software Products or Derivative Works, you agree to be bound by this Agreement.
+
+
+"Agreement" means this Stable Non-Commercial Research Community License Agreement.
+
+
+“AUP” means the Stability AI Acceptable Use Policy available at https://stability.ai/use-policy, as may be updated from time to time.
+
+
+"Derivative Work(s)” means (a) any derivative work of the Software Products as recognized by U.S. copyright laws and (b) any modifications to a Model, and any other model created which is based on or derived from the Model or the Model’s output. For clarity, Derivative Works do not include the output of any Model.
+
+
+“Documentation” means any specifications, manuals, documentation, and other written information provided by Stability AI related to the Software.
+
+
+"Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity's behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.
+
+
+“Model(s)" means, collectively, Stability AI’s proprietary models and algorithms, including machine-learning models, trained model weights and other elements of the foregoing, made available under this Agreement.
+
+
+“Non-Commercial Uses” means exercising any of the rights granted herein for the purpose of research or non-commercial purposes. Non-Commercial Uses does not include any production use of the Software Products or any Derivative Works. 
+
+
+"Stability AI" or "we" means Stability AI Ltd. and its affiliates.
+
+"Software" means Stability AI’s proprietary software made available under this Agreement. 
+
+
+“Software Products” means the Models, Software and Documentation, individually or in any combination. 
+
+
+
+1.     License Rights and Redistribution. 
+
+a.  Subject to your compliance with this Agreement, the AUP (which is hereby incorporated herein by reference), and the Documentation, Stability AI grants you a non-exclusive, worldwide, non-transferable, non-sublicensable, revocable, royalty free and limited license under Stability AI’s intellectual property or other rights owned or controlled by Stability AI embodied in the Software Products to reproduce the Software Products and produce, reproduce, distribute, and create Derivative Works of the Software Products for Non-Commercial Uses only, respectively. 
+
+b.  You may not use the Software Products or Derivative Works to enable third parties to use the Software Products or Derivative Works as part of your hosted service or via your APIs, whether you are adding substantial additional functionality thereto or not. Merely distributing the Software Products or Derivative Works for download online without offering any related service (ex. by distributing the Models on HuggingFace) is not a violation of this subsection. If you wish to use the Software Products or any Derivative Works for commercial or production use or you wish to make the Software Products or any Derivative Works available to third parties via your hosted service or your APIs, contact Stability AI at https://stability.ai/contact.    
+
+c.  If you distribute or make the Software Products, or any Derivative Works thereof, available to a third party, the Software Products, Derivative Works, or any portion thereof, respectively, will remain subject to this Agreement and you must (i) provide a copy of this Agreement to such third party, and (ii) retain the following attribution notice within a "Notice" text file distributed as a part of such copies: "This Stability AI Model is licensed under the Stability AI Non-Commercial Research Community License, Copyright (c) Stability AI Ltd. All Rights Reserved.” If you create a Derivative Work of a Software Product, you may add your own attribution notices to the Notice file included with the Software Product, provided that you clearly indicate which attributions apply to the Software Product and you must state in the NOTICE file that you changed the Software Product and how it was modified.
+
+2.     Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE SOFTWARE PRODUCTS  AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE SOFTWARE PRODUCTS, DERIVATIVE WORKS OR ANY OUTPUT OR RESULTS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE SOFTWARE PRODUCTS, DERIVATIVE WORKS AND ANY OUTPUT AND RESULTS. 
+
+3.     Limitation of Liability. IN NO EVENT WILL STABILITY AI OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY DIRECT, INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF STABILITY AI OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING. 
+
+4.     Intellectual Property.
+
+a.  No trademark licenses are granted under this Agreement, and in connection with the Software Products or Derivative Works, neither Stability AI nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Software Products or Derivative Works. 
+
+b.  Subject to Stability AI’s ownership of the Software Products and Derivative Works made by or for Stability AI, with respect to any Derivative Works that are made by you, as between you and Stability AI, you are and will be the owner of such Derivative Works 
+
+c.  If you institute litigation or other proceedings against Stability AI (including a cross-claim or counterclaim in a lawsuit) alleging that the Software Products, Derivative Works or associated outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Stability AI from and against any claim by any third party arising out of or related to your use or distribution of the Software Products or Derivative Works in violation of this Agreement. 
+
+5.      Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Software Products and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Stability AI may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of any Software Products or Derivative Works. Sections 2-4 shall survive the termination of this Agreement.

model_licenses/LICENSE-SDXL-Turbo

@@ -0,0 +1,58 @@
+STABILITY AI NON-COMMERCIAL RESEARCH COMMUNITY LICENSE AGREEMENT        
+Dated: November 28, 2023
+
+
+By using or distributing any portion or element of the Models, Software, Software Products or Derivative Works, you agree to be bound by this Agreement.
+
+
+"Agreement" means this Stable Non-Commercial Research Community License Agreement.
+
+
+“AUP” means the Stability AI Acceptable Use Policy available at https://stability.ai/use-policy, as may be updated from time to time.
+
+
+"Derivative Work(s)” means (a) any derivative work of the Software Products as recognized by U.S. copyright laws and (b) any modifications to a Model, and any other model created which is based on or derived from the Model or the Model’s output. For clarity, Derivative Works do not include the output of any Model.
+
+
+“Documentation” means any specifications, manuals, documentation, and other written information provided by Stability AI related to the Software.
+
+
+"Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity's behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.
+
+
+“Model(s)" means, collectively, Stability AI’s proprietary models and algorithms, including machine-learning models, trained model weights and other elements of the foregoing, made available under this Agreement.
+
+
+“Non-Commercial Uses” means exercising any of the rights granted herein for the purpose of research or non-commercial purposes. Non-Commercial Uses does not include any production use of the Software Products or any Derivative Works. 
+
+
+"Stability AI" or "we" means Stability AI Ltd. and its affiliates.
+
+"Software" means Stability AI’s proprietary software made available under this Agreement. 
+
+
+“Software Products” means the Models, Software and Documentation, individually or in any combination. 
+
+
+
+1.     License Rights and Redistribution. 
+
+a.  Subject to your compliance with this Agreement, the AUP (which is hereby incorporated herein by reference), and the Documentation, Stability AI grants you a non-exclusive, worldwide, non-transferable, non-sublicensable, revocable, royalty free and limited license under Stability AI’s intellectual property or other rights owned or controlled by Stability AI embodied in the Software Products to reproduce the Software Products and produce, reproduce, distribute, and create Derivative Works of the Software Products for Non-Commercial Uses only, respectively. 
+
+b.  You may not use the Software Products or Derivative Works to enable third parties to use the Software Products or Derivative Works as part of your hosted service or via your APIs, whether you are adding substantial additional functionality thereto or not. Merely distributing the Software Products or Derivative Works for download online without offering any related service (ex. by distributing the Models on HuggingFace) is not a violation of this subsection. If you wish to use the Software Products or any Derivative Works for commercial or production use or you wish to make the Software Products or any Derivative Works available to third parties via your hosted service or your APIs, contact Stability AI at https://stability.ai/contact.    
+
+c.  If you distribute or make the Software Products, or any Derivative Works thereof, available to a third party, the Software Products, Derivative Works, or any portion thereof, respectively, will remain subject to this Agreement and you must (i) provide a copy of this Agreement to such third party, and (ii) retain the following attribution notice within a "Notice" text file distributed as a part of such copies: "This Stability AI Model is licensed under the Stability AI Non-Commercial Research Community License, Copyright (c) Stability AI Ltd. All Rights Reserved.” If you create a Derivative Work of a Software Product, you may add your own attribution notices to the Notice file included with the Software Product, provided that you clearly indicate which attributions apply to the Software Product and you must state in the NOTICE file that you changed the Software Product and how it was modified.
+
+2.     Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE SOFTWARE PRODUCTS  AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE SOFTWARE PRODUCTS, DERIVATIVE WORKS OR ANY OUTPUT OR RESULTS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE SOFTWARE PRODUCTS, DERIVATIVE WORKS AND ANY OUTPUT AND RESULTS. 
+
+3.     Limitation of Liability. IN NO EVENT WILL STABILITY AI OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY DIRECT, INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF STABILITY AI OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING. 
+
+4.     Intellectual Property.
+
+a.  No trademark licenses are granted under this Agreement, and in connection with the Software Products or Derivative Works, neither Stability AI nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Software Products or Derivative Works. 
+
+b.  Subject to Stability AI’s ownership of the Software Products and Derivative Works made by or for Stability AI, with respect to any Derivative Works that are made by you, as between you and Stability AI, you are and will be the owner of such Derivative Works 
+
+c.  If you institute litigation or other proceedings against Stability AI (including a cross-claim or counterclaim in a lawsuit) alleging that the Software Products, Derivative Works or associated outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Stability AI from and against any claim by any third party arising out of or related to your use or distribution of the Software Products or Derivative Works in violation of this Agreement. 
+
+5.      Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Software Products and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Stability AI may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of any Software Products or Derivative Works. Sections 2-4 shall survive the termination of this Agreement.

model_licenses/LICENSE-SDXL1.0

@@ -0,0 +1,175 @@
+Copyright (c) 2023 Stability AI CreativeML Open RAIL++-M License dated July 26, 2023
+
+Section I: PREAMBLE Multimodal generative models are being widely adopted and used, and
+have the potential to transform the way artists, among other individuals, conceive and
+benefit from AI or ML technologies as a tool for content creation. Notwithstanding the
+current and potential benefits that these artifacts can bring to society at large, there
+are also concerns about potential misuses of them, either due to their technical
+limitations or ethical considerations. In short, this license strives for both the open
+and responsible downstream use of the accompanying model. When it comes to the open
+character, we took inspiration from open source permissive licenses regarding the grant
+of IP rights. Referring to the downstream responsible use, we added use-based
+restrictions not permitting the use of the model in very specific scenarios, in order
+for the licensor to be able to enforce the license in case potential misuses of the
+Model may occur. At the same time, we strive to promote open and responsible research on
+generative models for art and content generation. Even though downstream derivative
+versions of the model could be released under different licensing terms, the latter will
+always have to include - at minimum - the same use-based restrictions as the ones in the
+original license (this license). We believe in the intersection between open and
+responsible AI development; thus, this agreement aims to strike a balance between both
+in order to enable responsible open-science in the field of AI. This CreativeML Open
+RAIL++-M License governs the use of the model (and its derivatives) and is informed by
+the model card associated with the model. NOW THEREFORE, You and Licensor agree as
+follows: Definitions "License" means the terms and conditions for use, reproduction, and
+Distribution as defined in this document. "Data" means a collection of information
+and/or content extracted from the dataset used with the Model, including to train,
+pretrain, or otherwise evaluate the Model. The Data is not licensed under this License.
+"Output" means the results of operating a Model as embodied in informational content
+resulting therefrom. "Model" means any accompanying machine-learning based assemblies
+(including checkpoints), consisting of learnt weights, parameters (including optimizer
+states), corresponding to the model architecture as embodied in the Complementary
+Material, that have been trained or tuned, in whole or in part on the Data, using the
+Complementary Material. "Derivatives of the Model" means all modifications to the Model,
+works based on the Model, or any other model which is created or initialized by transfer
+of patterns of the weights, parameters, activations or output of the Model, to the other
+model, in order to cause the other model to perform similarly to the Model, including -
+but not limited to - distillation methods entailing the use of intermediate data
+representations or methods based on the generation of synthetic data by the Model for
+training the other model. "Complementary Material" means the accompanying source code
+and scripts used to define, run, load, benchmark or evaluate the Model, and used to
+prepare data for training or evaluation, if any. This includes any accompanying
+documentation, tutorials, examples, etc, if any. "Distribution" means any transmission,
+reproduction, publication or other sharing of the Model or Derivatives of the Model to a
+third party, including providing the Model as a hosted service made available by
+electronic or other remote means - e.g. API-based or web access. "Licensor" means the
+copyright owner or entity authorized by the copyright owner that is granting the
+License, including the persons or entities that may have rights in the Model and/or
+distributing the Model. "You" (or "Your") means an individual or Legal Entity exercising
+permissions granted by this License and/or making use of the Model for whichever purpose
+and in any field of use, including usage of the Model in an end-use application - e.g.
+chatbot, translator, image generator. "Third Parties" means individuals or legal
+entities that are not under common control with Licensor or You. "Contribution" means
+any work of authorship, including the original version of the Model and any
+modifications or additions to that Model or Derivatives of the Model thereof, that is
+intentionally submitted to Licensor for inclusion in the Model by the copyright owner or
+by an individual or Legal Entity authorized to submit on behalf of the copyright owner.
+For the purposes of this definition, "submitted" means any form of electronic, verbal,
+or written communication sent to the Licensor or its representatives, including but not
+limited to communication on electronic mailing lists, source code control systems, and
+issue tracking systems that are managed by, or on behalf of, the Licensor for the
+purpose of discussing and improving the Model, but excluding communication that is
+conspicuously marked or otherwise designated in writing by the copyright owner as "Not a
+Contribution." "Contributor" means Licensor and any individual or Legal Entity on behalf
+of whom a Contribution has been received by Licensor and subsequently incorporated
+within the Model.
+
+Section II: INTELLECTUAL PROPERTY RIGHTS Both copyright and patent grants apply to the
+Model, Derivatives of the Model and Complementary Material. The Model and Derivatives of
+the Model are subject to additional terms as described in
+
+Section III. Grant of Copyright License. Subject to the terms and conditions of this
+License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive,
+no-charge, royalty-free, irrevocable copyright license to reproduce, prepare, publicly
+display, publicly perform, sublicense, and distribute the Complementary Material, the
+Model, and Derivatives of the Model. Grant of Patent License. Subject to the terms and
+conditions of this License and where and as applicable, each Contributor hereby grants
+to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+(except as stated in this paragraph) patent license to make, have made, use, offer to
+sell, sell, import, and otherwise transfer the Model and the Complementary Material,
+where such license applies only to those patent claims licensable by such Contributor
+that are necessarily infringed by their Contribution(s) alone or by combination of their
+Contribution(s) with the Model to which such Contribution(s) was submitted. If You
+institute patent litigation against any entity (including a cross-claim or counterclaim
+in a lawsuit) alleging that the Model and/or Complementary Material or a Contribution
+incorporated within the Model and/or Complementary Material constitutes direct or
+contributory patent infringement, then any patent licenses granted to You under this
+License for the Model and/or Work shall terminate as of the date such litigation is
+asserted or filed. Section III: CONDITIONS OF USAGE, DISTRIBUTION AND REDISTRIBUTION
+Distribution and Redistribution. You may host for Third Party remote access purposes
+(e.g. software-as-a-service), reproduce and distribute copies of the Model or
+Derivatives of the Model thereof in any medium, with or without modifications, provided
+that You meet the following conditions: Use-based restrictions as referenced in
+paragraph 5 MUST be included as an enforceable provision by You in any type of legal
+agreement (e.g. a license) governing the use and/or distribution of the Model or
+Derivatives of the Model, and You shall give notice to subsequent users You Distribute
+to, that the Model or Derivatives of the Model are subject to paragraph 5. This
+provision does not apply to the use of Complementary Material. You must give any Third
+Party recipients of the Model or Derivatives of the Model a copy of this License; You
+must cause any modified files to carry prominent notices stating that You changed the
+files; You must retain all copyright, patent, trademark, and attribution notices
+excluding those notices that do not pertain to any part of the Model, Derivatives of the
+Model. You may add Your own copyright statement to Your modifications and may provide
+additional or different license terms and conditions - respecting paragraph 4.a. - for
+use, reproduction, or Distribution of Your modifications, or for any such Derivatives of
+the Model as a whole, provided Your use, reproduction, and Distribution of the Model
+otherwise complies with the conditions stated in this License. Use-based restrictions.
+The restrictions set forth in Attachment A are considered Use-based restrictions.
+Therefore You cannot use the Model and the Derivatives of the Model for the specified
+restricted uses. You may use the Model subject to this License, including only for
+lawful purposes and in accordance with the License. Use may include creating any content
+with, finetuning, updating, running, training, evaluating and/or reparametrizing the
+Model. You shall require all of Your users who use the Model or a Derivative of the
+Model to comply with the terms of this paragraph (paragraph 5). The Output You Generate.
+Except as set forth herein, Licensor claims no rights in the Output You generate using
+the Model. You are accountable for the Output you generate and its subsequent uses. No
+use of the output can contravene any provision as stated in the License.
+
+Section IV: OTHER PROVISIONS Updates and Runtime Restrictions. To the maximum extent
+permitted by law, Licensor reserves the right to restrict (remotely or otherwise) usage
+of the Model in violation of this License. Trademarks and related. Nothing in this
+License permits You to make use of Licensors’ trademarks, trade names, logos or to
+otherwise suggest endorsement or misrepresent the relationship between the parties; and
+any rights not expressly granted herein are reserved by the Licensors. Disclaimer of
+Warranty. Unless required by applicable law or agreed to in writing, Licensor provides
+the Model and the Complementary Material (and each Contributor provides its
+Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
+express or implied, including, without limitation, any warranties or conditions of
+TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are
+solely responsible for determining the appropriateness of using or redistributing the
+Model, Derivatives of the Model, and the Complementary Material and assume any risks
+associated with Your exercise of permissions under this License. Limitation of
+Liability. In no event and under no legal theory, whether in tort (including
+negligence), contract, or otherwise, unless required by applicable law (such as
+deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be
+liable to You for damages, including any direct, indirect, special, incidental, or
+consequential damages of any character arising as a result of this License or out of the
+use or inability to use the Model and the Complementary Material (including but not
+limited to damages for loss of goodwill, work stoppage, computer failure or malfunction,
+or any and all other commercial damages or losses), even if such Contributor has been
+advised of the possibility of such damages. Accepting Warranty or Additional Liability.
+While redistributing the Model, Derivatives of the Model and the Complementary Material
+thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty,
+indemnity, or other liability obligations and/or rights consistent with this License.
+However, in accepting such obligations, You may act only on Your own behalf and on Your
+sole responsibility, not on behalf of any other Contributor, and only if You agree to
+indemnify, defend, and hold each Contributor harmless for any liability incurred by, or
+claims asserted against, such Contributor by reason of your accepting any such warranty
+or additional liability. If any provision of this License is held to be invalid, illegal
+or unenforceable, the remaining provisions shall be unaffected thereby and remain valid
+as if such provision had not been set forth herein.
+
+END OF TERMS AND CONDITIONS
+
+Attachment A Use Restrictions
+You agree not to use the Model or Derivatives of the Model:
+In any way that violates any applicable national, federal, state, local or
+international law or regulation; For the purpose of exploiting, harming or attempting to
+exploit or harm minors in any way; To generate or disseminate verifiably false
+information and/or content with the purpose of harming others; To generate or
+disseminate personal identifiable information that can be used to harm an individual; To
+defame, disparage or otherwise harass others; For fully automated decision making that
+adversely impacts an individual’s legal rights or otherwise creates or modifies a
+binding, enforceable obligation; For any use intended to or which has the effect of
+discriminating against or harming individuals or groups based on online or offline
+social behavior or known or predicted personal or personality characteristics; To
+exploit any of the vulnerabilities of a specific group of persons based on their age,
+social, physical or mental characteristics, in order to materially distort the behavior
+of a person pertaining to that group in a manner that causes or is likely to cause that
+person or another person physical or psychological harm; For any use intended to or
+which has the effect of discriminating against individuals or groups based on legally
+protected characteristics or categories; To provide medical advice and medical results
+interpretation; To generate or disseminate information for the purpose to be used for
+administration of justice, law enforcement, immigration or asylum processes, such as
+predicting an individual will commit fraud/crime commitment (e.g. by text profiling,
+drawing causal relationships between assertions made in documents, indiscriminate and
+arbitrarily-targeted use).

model_licenses/LICENSE-SV3D

@@ -0,0 +1,41 @@
+STABILITY AI NON-COMMERCIAL COMMUNITY LICENSE AGREEMENT
+Dated: March 18, 2024
+
+"Agreement" means this Stable Non-Commercial Research Community License Agreement.
+
+“AUP” means the Stability AI Acceptable Use Policy available at https://stability.ai/use-policy, as may be updated from time to time.
+
+"Derivative Work(s)” means (a) any derivative work of the Software Products as recognized by U.S. copyright laws, (b) any modifications to a Model, and (c) any other model created which is based on or derived from the Model or the Model’s output. For clarity, Derivative Works do not include the output of any Model.
+
+“Documentation” means any specifications, manuals, documentation, and other written information provided by Stability AI related to the Software.
+
+"Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity's behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.
+
+“Model(s)" means, collectively, Stability AI’s proprietary models and algorithms, including machine-learning models, trained model weights and other elements of the foregoing, made available under this Agreement.
+
+“Non-Commercial Uses” means exercising any of the rights granted herein for the purpose of research or non-commercial purposes. Non-Commercial Uses does not include any production use of the Software Products or any Derivative Works.
+
+"Stability AI" or "we" means Stability AI Ltd and its affiliates.
+
+
+"Software" means Stability AI’s proprietary software made available under this Agreement.
+
+“Software Products” means the Models, Software and Documentation, individually or in any combination.
+
+
+
+1. 	License Rights and Redistribution.
+a.  	Subject to your compliance with this Agreement, the AUP (which is hereby incorporated herein by reference), and the Documentation, Stability AI grants you a non-exclusive, worldwide, non-transferable, non-sublicensable, revocable, royalty free and limited license under Stability AI’s intellectual property or other rights owned or controlled by Stability AI embodied in the Software Products to use, reproduce, distribute, and create Derivative Works of, the Software Products, in each case for Non-Commercial Uses only.
+b.   You may not use the Software Products or Derivative Works to enable third parties to use the Software Products or Derivative Works as part of your hosted service or via your APIs, whether you are adding substantial additional functionality thereto or not. Merely distributing the Software Products or Derivative Works for download online without offering any related service (ex. by distributing the Models on HuggingFace) is not a violation of this subsection. If you wish to use the Software Products or any Derivative Works for commercial or production use or you wish to make the Software Products or any Derivative Works available to third parties via your hosted service or your APIs, contact Stability AI at https://stability.ai/contact.
+c.	If you distribute or make the Software Products, or any Derivative Works thereof, available to a third party, the Software Products, Derivative Works, or any portion thereof, respectively, will remain subject to this Agreement and you must (i) provide a copy of this Agreement to such third party, and (ii) retain the following attribution notice within a "Notice" text file distributed as a part of such copies: "This Stability AI Model is licensed under the Stability AI Non-Commercial Research Community License, Copyright (c) Stability AI Ltd. All Rights Reserved.” If you create a Derivative Work of a Software Product, you may add your own attribution notices to the Notice file included with the Software Product, provided that you clearly indicate which attributions apply to the Software Product and you must state in the NOTICE file that you changed the Software Product and how it was modified.
+2.	Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE SOFTWARE PRODUCTS  AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE SOFTWARE PRODUCTS, DERIVATIVE WORKS OR ANY OUTPUT OR RESULTS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE SOFTWARE PRODUCTS, DERIVATIVE WORKS AND ANY OUTPUT AND RESULTS.
+3.	Limitation of Liability. IN NO EVENT WILL STABILITY AI OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY DIRECT, INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF STABILITY AI OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.
+4.   	Intellectual Property.
+a. 	No trademark licenses are granted under this Agreement, and in connection with the Software Products or Derivative Works, neither Stability AI nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Software Products or Derivative Works.
+b.	Subject to Stability AI’s ownership of the Software Products and Derivative Works made by or for Stability AI, with respect to any Derivative Works that are made by you, as between you and Stability AI, you are and will be the owner of such Derivative Works
+c. 	If you institute litigation or other proceedings against Stability AI (including a cross-claim or counterclaim in a lawsuit) alleging that the Software Products, Derivative Works or associated outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Stability AI from and against any claim by any third party arising out of or related to your use or distribution of the Software Products or Derivative Works in violation of this Agreement.
+5. 	Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Software Products and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Stability AI may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of any Software Products or Derivative Works. Sections 2-4 shall survive the termination of this Agreement.
+
+6.	Governing Law. This Agreement will be governed by and construed in accordance with the laws of the United States and the State of California without regard to choice of law
+principles.
+

model_licenses/LICENSE-SVD

@@ -0,0 +1,31 @@
+STABLE VIDEO DIFFUSION NON-COMMERCIAL COMMUNITY LICENSE AGREEMENT	
+Dated: November 21, 2023
+
+“AUP” means the Stability AI Acceptable Use Policy available at https://stability.ai/use-policy, as may be updated from time to time.
+
+"Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Software Products set forth herein.
+"Derivative Work(s)” means (a) any derivative work of the Software Products as recognized by U.S. copyright laws and (b) any modifications to a Model, and any other model created which is based on or derived from the Model or the Model’s output. For clarity, Derivative Works do not include the output of any Model.
+“Documentation” means any specifications, manuals, documentation, and other written information provided by Stability AI related to the Software.
+
+"Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity's behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.
+
+"Stability AI" or "we" means Stability AI Ltd. 
+
+"Software" means, collectively, Stability AI’s proprietary models and algorithms, including machine-learning models, trained model weights and other elements of the foregoing, made available under this Agreement.
+
+“Software Products” means Software and Documentation. 
+
+By using or distributing any portion or element of the Software Products, you agree to be bound by this Agreement.
+
+
+
+License Rights and Redistribution. 
+Subject to your compliance with this Agreement, the AUP (which is hereby incorporated herein by reference), and the Documentation, Stability AI grants you a non-exclusive, worldwide, non-transferable, non-sublicensable, revocable, royalty free and limited license under Stability AI’s intellectual property or other rights owned by Stability AI embodied in the Software Products to reproduce, distribute, and create Derivative Works of the Software Products for purposes other than commercial or production use.     
+b.	If you distribute or make the Software Products, or any Derivative Works thereof, available to a third party, the Software Products, Derivative Works, or any portion thereof, respectively, will remain subject to this Agreement and you must (i) provide a copy of this Agreement to such third party, and (ii) retain the following attribution notice within a "Notice" text file distributed as a part of such copies: "Stable Video Diffusion is licensed under the Stable Video Diffusion Research License, Copyright (c) Stability AI Ltd. All Rights Reserved.” If you create a Derivative Work of a Software Product, you may add your own attribution notices to the Notice file included with the Software Product, provided that you clearly indicate which attributions apply to the Software Product and you must state in the NOTICE file that you changed the Software Product and how it was modified.
+2. 	  Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE SOFTWARE PRODUCTS  AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE SOFTWARE PRODUCTS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE SOFTWARE PRODUCTS AND ANY OUTPUT AND RESULTS. 
+3.   Limitation of Liability. IN NO EVENT WILL STABILITY AI OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF STABILITY AI OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING. 
+3.   Intellectual Property.
+a. 	No trademark licenses are granted under this Agreement, and in connection with the Software Products, neither Stability AI nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Software Products. 
+Subject to Stability AI’s ownership of the Software Products and Derivative Works made by or for Stability AI, with respect to any Derivative Works that are made by you, as between you and Stability AI, you are and will be the owner of such Derivative Works. 
+If you institute litigation or other proceedings against Stability AI (including a cross-claim or counterclaim in a lawsuit) alleging that the Software Products or associated outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Stability AI from and against any claim by any third party arising out of or related to your use or distribution of the Software Products in violation of this Agreement. 
+4.   Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Software Products and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Stability AI may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Software Products. Sections 2-4 shall survive the termination of this Agreement. 

pyproject.toml

@@ -0,0 +1,48 @@
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[project]
+name = "sgm"
+dynamic = ["version"]
+description = "Stability Generative Models"
+readme = "README.md"
+license-files = { paths = ["LICENSE-CODE"] }
+requires-python = ">=3.8"
+
+[project.urls]
+Homepage = "https://github.com/Stability-AI/generative-models"
+
+[tool.hatch.version]
+path = "sgm/__init__.py"
+
+[tool.hatch.build]
+# This needs to be explicitly set so the configuration files
+# grafted into the `sgm` directory get included in the wheel's
+# RECORD file.
+include = [
+    "sgm",
+]
+# The force-include configurations below make Hatch copy
+# the configs/ directory (containing the various YAML files required
+# to generatively model) into the source distribution and the wheel.
+
+[tool.hatch.build.targets.sdist.force-include]
+"./configs" = "sgm/configs"
+
+[tool.hatch.build.targets.wheel.force-include]
+"./configs" = "sgm/configs"
+
+[tool.hatch.envs.ci]
+skip-install = false
+
+dependencies = [
+    "pytest"
+]
+
+[tool.hatch.envs.ci.scripts]
+test-inference = [
+    "pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 torchaudio==2.0.2+cu118 --index-url https://download.pytorch.org/whl/cu118",
+    "pip install -r requirements/pt2.txt",    
+    "pytest -v tests/inference/test_inference.py {args}",
+]

pytest.ini

@@ -0,0 +1,3 @@
+[pytest]
+markers = 
+  inference: mark as inference test (deselect with '-m "not inference"')

requirements/pt2.txt

@@ -0,0 +1,42 @@
+black==23.7.0
+chardet==5.1.0
+clip @ git+https://github.com/openai/CLIP.git
+einops>=0.6.1
+fairscale>=0.4.13
+fire>=0.5.0
+fsspec>=2023.6.0
+invisible-watermark>=0.2.0
+kornia==0.6.9
+matplotlib>=3.7.2
+natsort>=8.4.0
+ninja>=1.11.1
+numpy>=1.24.4
+omegaconf>=2.3.0
+open-clip-torch>=2.20.0
+opencv-python==4.6.0.66
+pandas>=2.0.3
+pillow>=9.5.0
+pudb>=2022.1.3
+pytorch-lightning==2.0.1
+pyyaml>=6.0.1
+rembg
+scipy>=1.10.1
+streamlit>=0.73.1
+tensorboardx==2.6
+timm>=0.9.2
+tokenizers==0.12.1
+torch>=2.0.1
+torchaudio>=2.0.2
+torchdata==0.6.1
+torchmetrics>=1.0.1
+torchvision>=0.15.2
+tqdm>=4.65.0
+transformers==4.19.1
+triton==2.0.0
+urllib3<1.27,>=1.25.4
+wandb>=0.15.6
+webdataset>=0.2.33
+wheel>=0.41.0
+xformers>=0.0.20
+gradio
+streamlit-keyup==0.2.0

requirements_pt13.txt

@@ -1,41 +0,0 @@
-omegaconf
-einops
-fire
-tqdm
-pillow
-numpy
-webdataset>=0.2.33
---extra-index-url https://download.pytorch.org/whl/cu117
-torch==1.13.1+cu117
-xformers==0.0.16
-torchaudio==0.13.1
-torchvision==0.14.1+cu117
-torchmetrics
-opencv-python==4.6.0.66
-fairscale
-pytorch-lightning==1.8.5
-fsspec
-kornia==0.6.9
-matplotlib
-natsort
-tensorboardx==2.5.1
-open-clip-torch
-chardet
-scipy
-pandas
-pudb
-pyyaml
-urllib3<1.27,>=1.25.4
-streamlit>=0.73.1
-timm
-tokenizers==0.12.1
-torchdata==0.5.1
-transformers==4.19.1
-onnx<=1.12.0
-triton
-wandb
-invisible-watermark
--e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
--e git+https://github.com/openai/CLIP.git@main#egg=clip
--e git+https://github.com/Stability-AI/datapipelines.git@main#egg=sdata
--e .

requirements_pt2.txt

@@ -1,41 +0,0 @@
-omegaconf
-einops
-fire
-tqdm
-pillow
-numpy
-webdataset>=0.2.33
-ninja
-torch
-matplotlib
-torchaudio>=2.0.2
-torchmetrics
-torchvision>=0.15.2
-opencv-python==4.6.0.66
-fairscale
-pytorch-lightning==2.0.1
-fire
-fsspec
-kornia==0.6.9
-natsort
-open-clip-torch
-chardet==5.1.0
-tensorboardx==2.6
-pandas
-pudb
-pyyaml
-urllib3<1.27,>=1.25.4
-scipy
-streamlit>=0.73.1
-timm
-tokenizers==0.12.1
-transformers==4.19.1
-triton==2.0.0
-torchdata==0.6.1
-wandb
-invisible-watermark
-xformers
--e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
--e git+https://github.com/openai/CLIP.git@main#egg=clip
--e git+https://github.com/Stability-AI/datapipelines.git@main#egg=sdata
--e .

scripts/demo/detect.py

@@ -83,7 +83,7 @@ class GetWatermarkMatch:
     def __call__(self, x: np.ndarray) -> np.ndarray:
         """
         Detects the number of matching bits the predefined watermark with one
-        or multiple images. Images should be in cv2 format, e.g. h x w x c.
+        or multiple images. Images should be in cv2 format, e.g. h x w x c BGR.
 
         Args:
             x: ([B], h w, c) in range [0, 255]
@@ -94,7 +94,6 @@ class GetWatermarkMatch:
         squeeze = len(x.shape) == 3
         if squeeze:
             x = x[None, ...]
-        x = np.flip(x, axis=-1)
 
         bs = x.shape[0]
         detected = np.empty((bs, self.num_bits), dtype=bool)

scripts/demo/discretization.py

@@ -0,0 +1,59 @@
+import torch
+
+from sgm.modules.diffusionmodules.discretizer import Discretization
+
+
+class Img2ImgDiscretizationWrapper:
+    """
+    wraps a discretizer, and prunes the sigmas
+    params:
+        strength: float between 0.0 and 1.0. 1.0 means full sampling (all sigmas are returned)
+    """
+
+    def __init__(self, discretization: Discretization, strength: float = 1.0):
+        self.discretization = discretization
+        self.strength = strength
+        assert 0.0 <= self.strength <= 1.0
+
+    def __call__(self, *args, **kwargs):
+        # sigmas start large first, and decrease then
+        sigmas = self.discretization(*args, **kwargs)
+        print(f"sigmas after discretization, before pruning img2img: ", sigmas)
+        sigmas = torch.flip(sigmas, (0,))
+        sigmas = sigmas[: max(int(self.strength * len(sigmas)), 1)]
+        print("prune index:", max(int(self.strength * len(sigmas)), 1))
+        sigmas = torch.flip(sigmas, (0,))
+        print(f"sigmas after pruning: ", sigmas)
+        return sigmas
+
+
+class Txt2NoisyDiscretizationWrapper:
+    """
+    wraps a discretizer, and prunes the sigmas
+    params:
+        strength: float between 0.0 and 1.0. 0.0 means full sampling (all sigmas are returned)
+    """
+
+    def __init__(
+        self, discretization: Discretization, strength: float = 0.0, original_steps=None
+    ):
+        self.discretization = discretization
+        self.strength = strength
+        self.original_steps = original_steps
+        assert 0.0 <= self.strength <= 1.0
+
+    def __call__(self, *args, **kwargs):
+        # sigmas start large first, and decrease then
+        sigmas = self.discretization(*args, **kwargs)
+        print(f"sigmas after discretization, before pruning img2img: ", sigmas)
+        sigmas = torch.flip(sigmas, (0,))
+        if self.original_steps is None:
+            steps = len(sigmas)
+        else:
+            steps = self.original_steps + 1
+        prune_index = max(min(int(self.strength * steps) - 1, steps - 1), 0)
+        sigmas = sigmas[prune_index:]
+        print("prune index:", prune_index)
+        sigmas = torch.flip(sigmas, (0,))
+        print(f"sigmas after pruning: ", sigmas)
+        return sigmas

scripts/demo/gradio_app.py

@@ -0,0 +1,310 @@
+# Adding this at the very top of app.py to make 'generative-models' directory discoverable
+import os
+import sys
+
+sys.path.append(os.path.join(os.path.dirname(__file__), "generative-models"))
+
+import math
+import random
+import uuid
+from glob import glob
+from pathlib import Path
+from typing import Optional
+
+import cv2
+import gradio as gr
+import numpy as np
+import torch
+from einops import rearrange, repeat
+from fire import Fire
+from huggingface_hub import hf_hub_download
+from omegaconf import OmegaConf
+from PIL import Image
+from torchvision.transforms import ToTensor
+
+from scripts.sampling.simple_video_sample import (
+    get_batch,
+    get_unique_embedder_keys_from_conditioner,
+    load_model,
+)
+from scripts.util.detection.nsfw_and_watermark_dectection import DeepFloydDataFiltering
+from sgm.inference.helpers import embed_watermark
+from sgm.util import default, instantiate_from_config
+
+# To download all svd models
+# hf_hub_download(repo_id="stabilityai/stable-video-diffusion-img2vid-xt", filename="svd_xt.safetensors", local_dir="checkpoints")
+# hf_hub_download(repo_id="stabilityai/stable-video-diffusion-img2vid", filename="svd.safetensors", local_dir="checkpoints")
+# hf_hub_download(repo_id="stabilityai/stable-video-diffusion-img2vid-xt-1-1", filename="svd_xt_1_1.safetensors", local_dir="checkpoints")
+
+
+# Define the repo, local directory and filename
+repo_id = "stabilityai/stable-video-diffusion-img2vid-xt-1-1"  # replace with "stabilityai/stable-video-diffusion-img2vid-xt" or "stabilityai/stable-video-diffusion-img2vid" for other models
+filename = "svd_xt_1_1.safetensors"  # replace with "svd_xt.safetensors" or "svd.safetensors" for other models
+local_dir = "checkpoints"
+local_file_path = os.path.join(local_dir, filename)
+
+# Check if the file already exists
+if not os.path.exists(local_file_path):
+    # If the file doesn't exist, download it
+    hf_hub_download(repo_id=repo_id, filename=filename, local_dir=local_dir)
+    print("File downloaded.")
+else:
+    print("File already exists. No need to download.")
+
+
+version = "svd_xt_1_1"  # replace with 'svd_xt' or 'svd' for other models
+device = "cuda"
+max_64_bit_int = 2**63 - 1
+
+if version == "svd_xt_1_1":
+    num_frames = 25
+    num_steps = 30
+    model_config = "scripts/sampling/configs/svd_xt_1_1.yaml"
+else:
+    raise ValueError(f"Version {version} does not exist.")
+
+model, filter = load_model(
+    model_config,
+    device,
+    num_frames,
+    num_steps,
+)
+
+
+def sample(
+    input_path: str = "assets/test_image.png",  # Can either be image file or folder with image files
+    seed: Optional[int] = None,
+    randomize_seed: bool = True,
+    motion_bucket_id: int = 127,
+    fps_id: int = 6,
+    version: str = "svd_xt_1_1",
+    cond_aug: float = 0.02,
+    decoding_t: int = 7,  # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.
+    device: str = "cuda",
+    output_folder: str = "outputs",
+    progress=gr.Progress(track_tqdm=True),
+):
+    """
+    Simple script to generate a single sample conditioned on an image `input_path` or multiple images, one for each
+    image file in folder `input_path`. If you run out of VRAM, try decreasing `decoding_t`.
+    """
+    fps_id = int(fps_id)  # casting float slider values to int)
+    if randomize_seed:
+        seed = random.randint(0, max_64_bit_int)
+
+    torch.manual_seed(seed)
+
+    path = Path(input_path)
+    all_img_paths = []
+    if path.is_file():
+        if any([input_path.endswith(x) for x in ["jpg", "jpeg", "png"]]):
+            all_img_paths = [input_path]
+        else:
+            raise ValueError("Path is not valid image file.")
+    elif path.is_dir():
+        all_img_paths = sorted(
+            [
+                f
+                for f in path.iterdir()
+                if f.is_file() and f.suffix.lower() in [".jpg", ".jpeg", ".png"]
+            ]
+        )
+        if len(all_img_paths) == 0:
+            raise ValueError("Folder does not contain any images.")
+    else:
+        raise ValueError
+
+    for input_img_path in all_img_paths:
+        with Image.open(input_img_path) as image:
+            if image.mode == "RGBA":
+                image = image.convert("RGB")
+            w, h = image.size
+
+            if h % 64 != 0 or w % 64 != 0:
+                width, height = map(lambda x: x - x % 64, (w, h))
+                image = image.resize((width, height))
+                print(
+                    f"WARNING: Your image is of size {h}x{w} which is not divisible by 64. We are resizing to {height}x{width}!"
+                )
+
+            image = ToTensor()(image)
+            image = image * 2.0 - 1.0
+
+        image = image.unsqueeze(0).to(device)
+        H, W = image.shape[2:]
+        assert image.shape[1] == 3
+        F = 8
+        C = 4
+        shape = (num_frames, C, H // F, W // F)
+        if (H, W) != (576, 1024):
+            print(
+                "WARNING: The conditioning frame you provided is not 576x1024. This leads to suboptimal performance as model was only trained on 576x1024. Consider increasing `cond_aug`."
+            )
+        if motion_bucket_id > 255:
+            print(
+                "WARNING: High motion bucket! This may lead to suboptimal performance."
+            )
+
+        if fps_id < 5:
+            print("WARNING: Small fps value! This may lead to suboptimal performance.")
+
+        if fps_id > 30:
+            print("WARNING: Large fps value! This may lead to suboptimal performance.")
+
+        value_dict = {}
+        value_dict["motion_bucket_id"] = motion_bucket_id
+        value_dict["fps_id"] = fps_id
+        value_dict["cond_aug"] = cond_aug
+        value_dict["cond_frames_without_noise"] = image
+        value_dict["cond_frames"] = image + cond_aug * torch.randn_like(image)
+        value_dict["cond_aug"] = cond_aug
+
+        with torch.no_grad():
+            with torch.autocast(device):
+                batch, batch_uc = get_batch(
+                    get_unique_embedder_keys_from_conditioner(model.conditioner),
+                    value_dict,
+                    [1, num_frames],
+                    T=num_frames,
+                    device=device,
+                )
+                c, uc = model.conditioner.get_unconditional_conditioning(
+                    batch,
+                    batch_uc=batch_uc,
+                    force_uc_zero_embeddings=[
+                        "cond_frames",
+                        "cond_frames_without_noise",
+                    ],
+                )
+
+                for k in ["crossattn", "concat"]:
+                    uc[k] = repeat(uc[k], "b ... -> b t ...", t=num_frames)
+                    uc[k] = rearrange(uc[k], "b t ... -> (b t) ...", t=num_frames)
+                    c[k] = repeat(c[k], "b ... -> b t ...", t=num_frames)
+                    c[k] = rearrange(c[k], "b t ... -> (b t) ...", t=num_frames)
+
+                randn = torch.randn(shape, device=device)
+
+                additional_model_inputs = {}
+                additional_model_inputs["image_only_indicator"] = torch.zeros(
+                    2, num_frames
+                ).to(device)
+                additional_model_inputs["num_video_frames"] = batch["num_video_frames"]
+
+                def denoiser(input, sigma, c):
+                    return model.denoiser(
+                        model.model, input, sigma, c, **additional_model_inputs
+                    )
+
+                samples_z = model.sampler(denoiser, randn, cond=c, uc=uc)
+                model.en_and_decode_n_samples_a_time = decoding_t
+                samples_x = model.decode_first_stage(samples_z)
+                samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
+
+                os.makedirs(output_folder, exist_ok=True)
+                base_count = len(glob(os.path.join(output_folder, "*.mp4")))
+                video_path = os.path.join(output_folder, f"{base_count:06d}.mp4")
+                writer = cv2.VideoWriter(
+                    video_path,
+                    cv2.VideoWriter_fourcc(*"mp4v"),
+                    fps_id + 1,
+                    (samples.shape[-1], samples.shape[-2]),
+                )
+
+                samples = embed_watermark(samples)
+                samples = filter(samples)
+                vid = (
+                    (rearrange(samples, "t c h w -> t h w c") * 255)
+                    .cpu()
+                    .numpy()
+                    .astype(np.uint8)
+                )
+                for frame in vid:
+                    frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
+                    writer.write(frame)
+                writer.release()
+
+        return video_path, seed
+
+
+def resize_image(image_path, output_size=(1024, 576)):
+    image = Image.open(image_path)
+    # Calculate aspect ratios
+    target_aspect = output_size[0] / output_size[1]  # Aspect ratio of the desired size
+    image_aspect = image.width / image.height  # Aspect ratio of the original image
+
+    # Resize then crop if the original image is larger
+    if image_aspect > target_aspect:
+        # Resize the image to match the target height, maintaining aspect ratio
+        new_height = output_size[1]
+        new_width = int(new_height * image_aspect)
+        resized_image = image.resize((new_width, new_height), Image.LANCZOS)
+        # Calculate coordinates for cropping
+        left = (new_width - output_size[0]) / 2
+        top = 0
+        right = (new_width + output_size[0]) / 2
+        bottom = output_size[1]
+    else:
+        # Resize the image to match the target width, maintaining aspect ratio
+        new_width = output_size[0]
+        new_height = int(new_width / image_aspect)
+        resized_image = image.resize((new_width, new_height), Image.LANCZOS)
+        # Calculate coordinates for cropping
+        left = 0
+        top = (new_height - output_size[1]) / 2
+        right = output_size[0]
+        bottom = (new_height + output_size[1]) / 2
+
+    # Crop the image
+    cropped_image = resized_image.crop((left, top, right, bottom))
+
+    return cropped_image
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """# Community demo for Stable Video Diffusion - Img2Vid - XT ([model](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt), [paper](https://stability.ai/research/stable-video-diffusion-scaling-latent-video-diffusion-models-to-large-datasets))
+#### Research release ([_non-commercial_](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/blob/main/LICENSE)): generate `4s` vid from a single image at (`25 frames` at `6 fps`). Generation takes ~60s in an A100. [Join the waitlist for Stability's upcoming web experience](https://stability.ai/contact).
+  """
+    )
+    with gr.Row():
+        with gr.Column():
+            image = gr.Image(label="Upload your image", type="filepath")
+            generate_btn = gr.Button("Generate")
+        video = gr.Video()
+    with gr.Accordion("Advanced options", open=False):
+        seed = gr.Slider(
+            label="Seed",
+            value=42,
+            randomize=True,
+            minimum=0,
+            maximum=max_64_bit_int,
+            step=1,
+        )
+        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+        motion_bucket_id = gr.Slider(
+            label="Motion bucket id",
+            info="Controls how much motion to add/remove from the image",
+            value=127,
+            minimum=1,
+            maximum=255,
+        )
+        fps_id = gr.Slider(
+            label="Frames per second",
+            info="The length of your video in seconds will be 25/fps",
+            value=6,
+            minimum=5,
+            maximum=30,
+        )
+
+    image.upload(fn=resize_image, inputs=image, outputs=image, queue=False)
+    generate_btn.click(
+        fn=sample,
+        inputs=[image, seed, randomize_seed, motion_bucket_id, fps_id],
+        outputs=[video, seed],
+        api_name="video",
+    )
+
+if __name__ == "__main__":
+    demo.queue(max_size=20)
+    demo.launch(share=True)

scripts/demo/sampling.py

@@ -1,6 +1,6 @@
 from pytorch_lightning import seed_everything
+
 from scripts.demo.streamlit_helpers import *
-from scripts.util.detection.nsfw_and_watermark_dectection import DeepFloydDataFiltering
 
 SAVE_PATH = "outputs/demo/txt2img/"
 
@@ -34,7 +34,16 @@ SD_XL_BASE_RATIOS = {
 }
 
 VERSION2SPECS = {
-    "SD-XL base": {
+    "SDXL-base-1.0": {
+        "H": 1024,
+        "W": 1024,
+        "C": 4,
+        "f": 8,
+        "is_legacy": False,
+        "config": "configs/inference/sd_xl_base.yaml",
+        "ckpt": "checkpoints/sd_xl_base_1.0.safetensors",
+    },
+    "SDXL-base-0.9": {
         "H": 1024,
         "W": 1024,
         "C": 4,
@@ -42,9 +51,8 @@ VERSION2SPECS = {
         "is_legacy": False,
         "config": "configs/inference/sd_xl_base.yaml",
         "ckpt": "checkpoints/sd_xl_base_0.9.safetensors",
-        "is_guided": True,
     },
-    "sd-2.1": {
+    "SD-2.1": {
         "H": 512,
         "W": 512,
         "C": 4,
@@ -52,9 +60,8 @@ VERSION2SPECS = {
         "is_legacy": True,
         "config": "configs/inference/sd_2_1.yaml",
         "ckpt": "checkpoints/v2-1_512-ema-pruned.safetensors",
-        "is_guided": True,
     },
-    "sd-2.1-768": {
+    "SD-2.1-768": {
         "H": 768,
         "W": 768,
         "C": 4,
@@ -63,7 +70,7 @@ VERSION2SPECS = {
         "config": "configs/inference/sd_2_1_768.yaml",
         "ckpt": "checkpoints/v2-1_768-ema-pruned.safetensors",
     },
-    "SDXL-Refiner": {
+    "SDXL-refiner-0.9": {
         "H": 1024,
         "W": 1024,
         "C": 4,
@@ -71,7 +78,15 @@ VERSION2SPECS = {
         "is_legacy": True,
         "config": "configs/inference/sd_xl_refiner.yaml",
         "ckpt": "checkpoints/sd_xl_refiner_0.9.safetensors",
-        "is_guided": True,
+    },
+    "SDXL-refiner-1.0": {
+        "H": 1024,
+        "W": 1024,
+        "C": 4,
+        "f": 8,
+        "is_legacy": True,
+        "config": "configs/inference/sd_xl_refiner.yaml",
+        "ckpt": "checkpoints/sd_xl_refiner_1.0.safetensors",
     },
 }
 
@@ -95,18 +110,19 @@ def load_img(display=True, key=None, device="cuda"):
 
 
 def run_txt2img(
-    state, version, version_dict, is_legacy=False, return_latents=False, filter=None
+    state,
+    version,
+    version_dict,
+    is_legacy=False,
+    return_latents=False,
+    filter=None,
+    stage2strength=None,
 ):
-    if version == "SD-XL base":
-        ratio = st.sidebar.selectbox("Ratio:", list(SD_XL_BASE_RATIOS.keys()), 10)
-        W, H = SD_XL_BASE_RATIOS[ratio]
+    if version.startswith("SDXL-base"):
+        W, H = st.selectbox("Resolution:", list(SD_XL_BASE_RATIOS.values()), 10)
     else:
-        H = st.sidebar.number_input(
-            "H", value=version_dict["H"], min_value=64, max_value=2048
-        )
-        W = st.sidebar.number_input(
-            "W", value=version_dict["W"], min_value=64, max_value=2048
-        )
+        H = st.number_input("H", value=version_dict["H"], min_value=64, max_value=2048)
+        W = st.number_input("W", value=version_dict["W"], min_value=64, max_value=2048)
     C = version_dict["C"]
     F = version_dict["f"]
 
@@ -122,10 +138,7 @@ def run_txt2img(
         prompt=prompt,
         negative_prompt=negative_prompt,
     )
-    num_rows, num_cols, sampler = init_sampling(
-        use_identity_guider=not version_dict["is_guided"]
-    )
-
+    sampler, num_rows, num_cols = init_sampling(stage2strength=stage2strength)
     num_samples = num_rows * num_cols
 
     if st.button("Sample"):
@@ -147,7 +160,12 @@ def run_txt2img(
 
 
 def run_img2img(
-    state, version_dict, is_legacy=False, return_latents=False, filter=None
+    state,
+    version_dict,
+    is_legacy=False,
+    return_latents=False,
+    filter=None,
+    stage2strength=None,
 ):
     img = load_img()
     if img is None:
@@ -163,13 +181,15 @@ def run_img2img(
     value_dict = init_embedder_options(
         get_unique_embedder_keys_from_conditioner(state["model"].conditioner),
         init_dict,
+        prompt=prompt,
+        negative_prompt=negative_prompt,
     )
     strength = st.number_input(
-        "**Img2Img Strength**", value=0.5, min_value=0.0, max_value=1.0
+        "**Img2Img Strength**", value=0.75, min_value=0.0, max_value=1.0
     )
-    num_rows, num_cols, sampler = init_sampling(
+    sampler, num_rows, num_cols = init_sampling(
         img2img_strength=strength,
-        use_identity_guider=not version_dict["is_guided"],
+        stage2strength=stage2strength,
     )
     num_samples = num_rows * num_cols
 
@@ -195,6 +215,7 @@ def apply_refiner(
     prompt,
     negative_prompt,
     filter=None,
+    finish_denoising=False,
 ):
     init_dict = {
         "orig_width": input.shape[3] * 8,
@@ -222,6 +243,7 @@ def apply_refiner(
         num_samples,
         skip_encode=True,
         filter=filter,
+        add_noise=not finish_denoising,
     )
 
     return samples
@@ -231,26 +253,30 @@ if __name__ == "__main__":
     st.title("Stable Diffusion")
     version = st.selectbox("Model Version", list(VERSION2SPECS.keys()), 0)
     version_dict = VERSION2SPECS[version]
-    mode = st.radio("Mode", ("txt2img", "img2img"), 0)
+    if st.checkbox("Load Model"):
+        mode = st.radio("Mode", ("txt2img", "img2img"), 0)
+    else:
+        mode = "skip"
     st.write("__________________________")
 
-    if version == "SD-XL base":
-        add_pipeline = st.checkbox("Load SDXL-Refiner?", False)
+    set_lowvram_mode(st.checkbox("Low vram mode", True))
+
+    if version.startswith("SDXL-base"):
+        add_pipeline = st.checkbox("Load SDXL-refiner?", False)
         st.write("__________________________")
     else:
         add_pipeline = False
 
-    filter = DeepFloydDataFiltering(verbose=False)
-
     seed = st.sidebar.number_input("seed", value=42, min_value=0, max_value=int(1e9))
     seed_everything(seed)
 
     save_locally, save_path = init_save_locally(os.path.join(SAVE_PATH, version))
 
-    state = init_st(version_dict)
-    if state["msg"]:
-        st.info(state["msg"])
-    model = state["model"]
+    if mode != "skip":
+        state = init_st(version_dict, load_filter=True)
+        if state["msg"]:
+            st.info(state["msg"])
+        model = state["model"]
 
     is_legacy = version_dict["is_legacy"]
 
@@ -263,30 +289,34 @@ if __name__ == "__main__":
     else:
         negative_prompt = ""  # which is unused
 
+    stage2strength = None
+    finish_denoising = False
+
     if add_pipeline:
         st.write("__________________________")
-
-        version2 = "SDXL-Refiner"
+        version2 = st.selectbox("Refiner:", ["SDXL-refiner-1.0", "SDXL-refiner-0.9"])
         st.warning(
             f"Running with {version2} as the second stage model. Make sure to provide (V)RAM :) "
         )
         st.write("**Refiner Options:**")
 
         version_dict2 = VERSION2SPECS[version2]
-        state2 = init_st(version_dict2)
+        state2 = init_st(version_dict2, load_filter=False)
         st.info(state2["msg"])
 
         stage2strength = st.number_input(
-            "**Refinement strength**", value=0.3, min_value=0.0, max_value=1.0
+            "**Refinement strength**", value=0.15, min_value=0.0, max_value=1.0
         )
 
-        sampler2 = init_sampling(
+        sampler2, *_ = init_sampling(
             key=2,
             img2img_strength=stage2strength,
-            use_identity_guider=not version_dict["is_guided"],
-            get_num_samples=False,
+            specify_num_samples=False,
         )
         st.write("__________________________")
+        finish_denoising = st.checkbox("Finish denoising with refiner.", True)
+        if not finish_denoising:
+            stage2strength = None
 
     if mode == "txt2img":
         out = run_txt2img(
@@ -295,7 +325,8 @@ if __name__ == "__main__":
             version_dict,
             is_legacy=is_legacy,
             return_latents=add_pipeline,
-            filter=filter,
+            filter=state.get("filter"),
+            stage2strength=stage2strength,
         )
     elif mode == "img2img":
         out = run_img2img(
@@ -303,16 +334,20 @@ if __name__ == "__main__":
             version_dict,
             is_legacy=is_legacy,
             return_latents=add_pipeline,
-            filter=filter,
+            filter=state.get("filter"),
+            stage2strength=stage2strength,
         )
+    elif mode == "skip":
+        out = None
     else:
         raise ValueError(f"unknown mode {mode}")
     if isinstance(out, (tuple, list)):
         samples, samples_z = out
     else:
         samples = out
+        samples_z = None
 
-    if add_pipeline:
+    if add_pipeline and samples_z is not None:
         st.write("**Running Refinement Stage**")
         samples = apply_refiner(
             samples_z,
@@ -321,7 +356,8 @@ if __name__ == "__main__":
             samples_z.shape[0],
             prompt=prompt,
             negative_prompt=negative_prompt if is_legacy else "",
-            filter=filter,
+            filter=state.get("filter"),
+            finish_denoising=finish_denoising,
         )
 
     if save_locally and samples is not None:

scripts/demo/streamlit_helpers.py

@@ -1,78 +1,48 @@
-import os
-from typing import Union, List
-
+import copy
 import math
+import os
+from glob import glob
+from typing import Dict, List, Optional, Tuple, Union
+
+import cv2
+import imageio
 import numpy as np
 import streamlit as st
 import torch
-from PIL import Image
+import torch.nn as nn
+import torchvision.transforms as TT
 from einops import rearrange, repeat
 from imwatermark import WatermarkEncoder
-from omegaconf import OmegaConf, ListConfig
-from torch import autocast
-from torchvision import transforms
-from torchvision.utils import make_grid
+from omegaconf import ListConfig, OmegaConf
+from PIL import Image
 from safetensors.torch import load_file as load_safetensors
-
+from scripts.demo.discretization import (
+    Img2ImgDiscretizationWrapper,
+    Txt2NoisyDiscretizationWrapper,
+)
+from scripts.util.detection.nsfw_and_watermark_dectection import DeepFloydDataFiltering
+from sgm.inference.helpers import embed_watermark
+from sgm.modules.diffusionmodules.guiders import (
+    LinearPredictionGuider,
+    TrianglePredictionGuider,
+    VanillaCFG,
+)
 from sgm.modules.diffusionmodules.sampling import (
+    DPMPP2MSampler,
+    DPMPP2SAncestralSampler,
+    EulerAncestralSampler,
     EulerEDMSampler,
     HeunEDMSampler,
-    EulerAncestralSampler,
-    DPMPP2SAncestralSampler,
-    DPMPP2MSampler,
     LinearMultistepSampler,
 )
-from sgm.util import append_dims
-from sgm.util import instantiate_from_config
-
-
-class WatermarkEmbedder:
-    def __init__(self, watermark):
-        self.watermark = watermark
-        self.num_bits = len(WATERMARK_BITS)
-        self.encoder = WatermarkEncoder()
-        self.encoder.set_watermark("bits", self.watermark)
-
-    def __call__(self, image: torch.Tensor):
-        """
-        Adds a predefined watermark to the input image
-
-        Args:
-            image: ([N,] B, C, H, W) in range [0, 1]
-
-        Returns:
-            same as input but watermarked
-        """
-        # watermarking libary expects input as cv2 format
-        squeeze = len(image.shape) == 4
-        if squeeze:
-            image = image[None, ...]
-        n = image.shape[0]
-        image_np = rearrange(
-            (255 * image).detach().cpu(), "n b c h w -> (n b) h w c"
-        ).numpy()
-        # torch (b, c, h, w) in [0, 1] -> numpy (b, h, w, c) [0, 255]
-        for k in range(image_np.shape[0]):
-            image_np[k] = self.encoder.encode(image_np[k], "dwtDct")
-        image = torch.from_numpy(
-            rearrange(image_np, "(n b) h w c -> n b c h w", n=n)
-        ).to(image.device)
-        image = torch.clamp(image / 255, min=0.0, max=1.0)
-        if squeeze:
-            image = image[0]
-        return image
-
-
-# A fixed 48-bit message that was choosen at random
-# WATERMARK_MESSAGE = 0xB3EC907BB19E
-WATERMARK_MESSAGE = 0b101100111110110010010000011110111011000110011110
-# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
-WATERMARK_BITS = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]
-embed_watemark = WatermarkEmbedder(WATERMARK_BITS)
+from sgm.util import append_dims, default, instantiate_from_config
+from torch import autocast
+from torchvision import transforms
+from torchvision.utils import make_grid, save_image
 
 
 @st.cache_resource()
-def init_st(version_dict, load_ckpt=True):
+def init_st(version_dict, load_ckpt=True, load_filter=True):
     state = dict()
     if not "model" in state:
         config = version_dict["config"]
@@ -85,9 +55,39 @@ def init_st(version_dict, load_ckpt=True):
         state["model"] = model
         state["ckpt"] = ckpt if load_ckpt else None
         state["config"] = config
+        if load_filter:
+            state["filter"] = DeepFloydDataFiltering(verbose=False)
     return state
 
 
+def load_model(model):
+    model.cuda()
+
+
+lowvram_mode = False
+
+
+def set_lowvram_mode(mode):
+    global lowvram_mode
+    lowvram_mode = mode
+
+
+def initial_model_load(model):
+    global lowvram_mode
+    if lowvram_mode:
+        model.model.half()
+    else:
+        model.cuda()
+    return model
+
+
+def unload_model(model):
+    global lowvram_mode
+    if lowvram_mode:
+        model.cpu()
+        torch.cuda.empty_cache()
+
+
 def load_model_from_config(config, ckpt=None, verbose=True):
     model = instantiate_from_config(config.model)
 
@@ -118,7 +118,7 @@ def load_model_from_config(config, ckpt=None, verbose=True):
     else:
         msg = None
 
-    model.cuda()
+    model = initial_model_load(model)
     model.eval()
     return model, msg
 
@@ -134,11 +134,12 @@ def init_embedder_options(keys, init_dict, prompt=None, negative_prompt=None):
     for key in keys:
         if key == "txt":
             if prompt is None:
-                prompt = st.text_input(
-                    "Prompt", "A professional photograph of an astronaut riding a pig"
-                )
+                prompt = "A professional photograph of an astronaut riding a pig"
             if negative_prompt is None:
-                negative_prompt = st.text_input("Negative prompt", "")
+                negative_prompt = ""
+
+            prompt = st.text_input("Prompt", prompt)
+            negative_prompt = st.text_input("Negative prompt", negative_prompt)
 
             value_dict["prompt"] = prompt
             value_dict["negative_prompt"] = negative_prompt
@@ -170,19 +171,30 @@ def init_embedder_options(keys, init_dict, prompt=None, negative_prompt=None):
             value_dict["negative_aesthetic_score"] = 2.5
 
         if key == "target_size_as_tuple":
-            target_width = st.number_input(
-                "target_width",
-                value=init_dict["target_width"],
-                min_value=16,
-            )
-            target_height = st.number_input(
-                "target_height",
-                value=init_dict["target_height"],
-                min_value=16,
-            )
+            value_dict["target_width"] = init_dict["target_width"]
+            value_dict["target_height"] = init_dict["target_height"]
 
-            value_dict["target_width"] = target_width
-            value_dict["target_height"] = target_height
+        if key in ["fps_id", "fps"]:
+            fps = st.number_input("fps", value=6, min_value=1)
+
+            value_dict["fps"] = fps
+            value_dict["fps_id"] = fps - 1
+
+        if key == "motion_bucket_id":
+            mb_id = st.number_input("motion bucket id", 0, 511, value=127)
+            value_dict["motion_bucket_id"] = mb_id
+
+        if key == "pool_image":
+            st.text("Image for pool conditioning")
+            image = load_img(
+                key="pool_image_input",
+                size=224,
+                center_crop=True,
+            )
+            if image is None:
+                st.info("Need an image here")
+                image = torch.zeros(1, 3, 224, 224)
+            value_dict["pool_image"] = image
 
     return value_dict
 
@@ -190,7 +202,7 @@ def init_embedder_options(keys, init_dict, prompt=None, negative_prompt=None):
 def perform_save_locally(save_path, samples):
     os.makedirs(os.path.join(save_path), exist_ok=True)
     base_count = len(os.listdir(os.path.join(save_path)))
-    samples = embed_watemark(samples)
+    samples = embed_watermark(samples)
     for sample in samples:
         sample = 255.0 * rearrange(sample.cpu().numpy(), "c h w -> h w c")
         Image.fromarray(sample.astype(np.uint8)).save(
@@ -209,65 +221,82 @@ def init_save_locally(_dir, init_value: bool = False):
     return save_locally, save_path
 
 
-class Img2ImgDiscretizationWrapper:
-    """
-    wraps a discretizer, and prunes the sigmas
-    params:
-        strength: float between 0.0 and 1.0. 1.0 means full sampling (all sigmas are returned)
-    """
-
-    def __init__(self, discretization, strength: float = 1.0):
-        self.discretization = discretization
-        self.strength = strength
-        assert 0.0 <= self.strength <= 1.0
-
-    def __call__(self, *args, **kwargs):
-        # sigmas start large first, and decrease then
-        sigmas = self.discretization(*args, **kwargs)
-        print(f"sigmas after discretization, before pruning img2img: ", sigmas)
-        sigmas = torch.flip(sigmas, (0,))
-        sigmas = sigmas[: max(int(self.strength * len(sigmas)), 1)]
-        print("prune index:", max(int(self.strength * len(sigmas)), 1))
-        sigmas = torch.flip(sigmas, (0,))
-        print(f"sigmas after pruning: ", sigmas)
-        return sigmas
-
-
-def get_guider(key):
+def get_guider(options, key):
     guider = st.sidebar.selectbox(
         f"Discretization #{key}",
         [
             "VanillaCFG",
             "IdentityGuider",
+            "LinearPredictionGuider",
+            "TrianglePredictionGuider",
         ],
+        options.get("guider", 0),
     )
 
+    additional_guider_kwargs = options.pop("additional_guider_kwargs", {})
+
     if guider == "IdentityGuider":
         guider_config = {
             "target": "sgm.modules.diffusionmodules.guiders.IdentityGuider"
         }
     elif guider == "VanillaCFG":
         scale = st.number_input(
-            f"cfg-scale #{key}", value=5.0, min_value=0.0, max_value=100.0
+            f"cfg-scale #{key}",
+            value=options.get("cfg", 5.0),
+            min_value=0.0,
         )
 
-        thresholder = st.sidebar.selectbox(
-            f"Thresholder #{key}",
-            [
-                "None",
-            ],
-        )
-
-        if thresholder == "None":
-            dyn_thresh_config = {
-                "target": "sgm.modules.diffusionmodules.sampling_utils.NoDynamicThresholding"
-            }
-        else:
-            raise NotImplementedError
-
         guider_config = {
             "target": "sgm.modules.diffusionmodules.guiders.VanillaCFG",
-            "params": {"scale": scale, "dyn_thresh_config": dyn_thresh_config},
+            "params": {
+                "scale": scale,
+                **additional_guider_kwargs,
+            },
+        }
+    elif guider == "LinearPredictionGuider":
+        max_scale = st.number_input(
+            f"max-cfg-scale #{key}",
+            value=options.get("cfg", 1.5),
+            min_value=1.0,
+        )
+        min_scale = st.sidebar.number_input(
+            f"min guidance scale",
+            value=options.get("min_cfg", 1.0),
+            min_value=1.0,
+            max_value=10.0,
+        )
+
+        guider_config = {
+            "target": "sgm.modules.diffusionmodules.guiders.LinearPredictionGuider",
+            "params": {
+                "max_scale": max_scale,
+                "min_scale": min_scale,
+                "num_frames": options["num_frames"],
+                **additional_guider_kwargs,
+            },
+        }
+    elif guider == "TrianglePredictionGuider":
+        max_scale = st.number_input(
+            f"max-cfg-scale #{key}",
+            value=options.get("cfg", 2.5),
+            min_value=1.0,
+            max_value=10.0,
+        )
+        min_scale = st.sidebar.number_input(
+            f"min guidance scale",
+            value=options.get("min_cfg", 1.0),
+            min_value=1.0,
+            max_value=10.0,
+        )
+
+        guider_config = {
+            "target": "sgm.modules.diffusionmodules.guiders.TrianglePredictionGuider",
+            "params": {
+                "max_scale": max_scale,
+                "min_scale": min_scale,
+                "num_frames": options["num_frames"],
+                **additional_guider_kwargs,
+            },
         }
     else:
         raise NotImplementedError
@@ -275,16 +304,22 @@ def get_guider(key):
 
 
 def init_sampling(
-    key=1, img2img_strength=1.0, use_identity_guider=False, get_num_samples=True
+    key=1,
+    img2img_strength: Optional[float] = None,
+    specify_num_samples: bool = True,
+    stage2strength: Optional[float] = None,
+    options: Optional[Dict[str, int]] = None,
 ):
-    if get_num_samples:
-        num_rows = 1
+    options = {} if options is None else options
+
+    num_rows, num_cols = 1, 1
+    if specify_num_samples:
         num_cols = st.number_input(
-            f"num cols #{key}", value=2, min_value=1, max_value=10
+            f"num cols #{key}", value=num_cols, min_value=1, max_value=10
         )
 
-    steps = st.sidebar.number_input(
-        f"steps #{key}", value=50, min_value=1, max_value=1000
+    steps = st.number_input(
+        f"steps #{key}", value=options.get("num_steps", 50), min_value=1, max_value=1000
     )
     sampler = st.sidebar.selectbox(
         f"Sampler #{key}",
@@ -296,7 +331,7 @@ def init_sampling(
             "DPMPP2MSampler",
             "LinearMultistepSampler",
         ],
-        0,
+        options.get("sampler", 0),
     )
     discretization = st.sidebar.selectbox(
         f"Discretization #{key}",
@@ -304,36 +339,41 @@ def init_sampling(
             "LegacyDDPMDiscretization",
             "EDMDiscretization",
         ],
+        options.get("discretization", 0),
     )
 
-    discretization_config = get_discretization(discretization, key=key)
+    discretization_config = get_discretization(discretization, options=options, key=key)
 
-    guider_config = get_guider(key=key)
+    guider_config = get_guider(options=options, key=key)
 
     sampler = get_sampler(sampler, steps, discretization_config, guider_config, key=key)
-    if img2img_strength < 1.0:
+    if img2img_strength is not None:
         st.warning(
             f"Wrapping {sampler.__class__.__name__} with Img2ImgDiscretizationWrapper"
         )
         sampler.discretization = Img2ImgDiscretizationWrapper(
             sampler.discretization, strength=img2img_strength
         )
-    if get_num_samples:
-        return num_rows, num_cols, sampler
-    return sampler
+    if stage2strength is not None:
+        sampler.discretization = Txt2NoisyDiscretizationWrapper(
+            sampler.discretization, strength=stage2strength, original_steps=steps
+        )
+    return sampler, num_rows, num_cols
 
 
-def get_discretization(discretization, key=1):
+def get_discretization(discretization, options, key=1):
     if discretization == "LegacyDDPMDiscretization":
-        use_new_range = st.checkbox(f"Start from highest noise level? #{key}", False)
         discretization_config = {
             "target": "sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization",
-            "params": {"legacy_range": not use_new_range},
         }
     elif discretization == "EDMDiscretization":
-        sigma_min = st.number_input(f"sigma_min #{key}", value=0.03)  # 0.0292
-        sigma_max = st.number_input(f"sigma_max #{key}", value=14.61)  # 14.6146
-        rho = st.number_input(f"rho #{key}", value=3.0)
+        sigma_min = st.sidebar.number_input(
+            f"sigma_min #{key}", value=options.get("sigma_min", 0.03)
+        )  # 0.0292
+        sigma_max = st.sidebar.number_input(
+            f"sigma_max #{key}", value=options.get("sigma_max", 14.61)
+        )  # 14.6146
+        rho = st.sidebar.number_input(f"rho #{key}", value=options.get("rho", 3.0))
         discretization_config = {
             "target": "sgm.modules.diffusionmodules.discretizer.EDMDiscretization",
             "params": {
@@ -422,8 +462,8 @@ def get_sampler(sampler_name, steps, discretization_config, guider_config, key=1
     return sampler
 
 
-def get_interactive_image(key=None) -> Image.Image:
-    image = st.file_uploader("Input", type=["jpg", "JPEG", "png"], key=key)
+def get_interactive_image() -> Image.Image:
+    image = st.file_uploader("Input", type=["jpg", "JPEG", "png"])
     if image is not None:
         image = Image.open(image)
         if not image.mode == "RGB":
@@ -431,8 +471,12 @@ def get_interactive_image(key=None) -> Image.Image:
         return image
 
 
-def load_img(display=True, key=None):
-    image = get_interactive_image(key=key)
+def load_img(
+    display: bool = True,
+    size: Union[None, int, Tuple[int, int]] = None,
+    center_crop: bool = False,
+):
+    image = get_interactive_image()
     if image is None:
         return None
     if display:
@@ -440,12 +484,15 @@ def load_img(display=True, key=None):
     w, h = image.size
     print(f"loaded input image of size ({w}, {h})")
 
-    transform = transforms.Compose(
-        [
-            transforms.ToTensor(),
-            transforms.Lambda(lambda x: x * 2.0 - 1.0),
-        ]
-    )
+    transform = []
+    if size is not None:
+        transform.append(transforms.Resize(size))
+    if center_crop:
+        transform.append(transforms.CenterCrop(size))
+    transform.append(transforms.ToTensor())
+    transform.append(transforms.Lambda(lambda x: 2.0 * x - 1.0))
+
+    transform = transforms.Compose(transform)
     img = transform(image)[None, ...]
     st.text(f"input min/max/mean: {img.min():.3f}/{img.max():.3f}/{img.mean():.3f}")
     return img
@@ -466,15 +513,18 @@ def do_sample(
     W,
     C,
     F,
-    force_uc_zero_embeddings: List = None,
+    force_uc_zero_embeddings: Optional[List] = None,
+    force_cond_zero_embeddings: Optional[List] = None,
     batch2model_input: List = None,
     return_latents=False,
     filter=None,
+    T=None,
+    additional_batch_uc_fields=None,
+    decoding_t=None,
 ):
-    if force_uc_zero_embeddings is None:
-        force_uc_zero_embeddings = []
-    if batch2model_input is None:
-        batch2model_input = []
+    force_uc_zero_embeddings = default(force_uc_zero_embeddings, [])
+    batch2model_input = default(batch2model_input, [])
+    additional_batch_uc_fields = default(additional_batch_uc_fields, [])
 
     st.text("Sampling")
 
@@ -483,34 +533,61 @@ def do_sample(
     with torch.no_grad():
         with precision_scope("cuda"):
             with model.ema_scope():
-                num_samples = [num_samples]
+                if T is not None:
+                    num_samples = [num_samples, T]
+                else:
+                    num_samples = [num_samples]
+
+                load_model(model.conditioner)
                 batch, batch_uc = get_batch(
                     get_unique_embedder_keys_from_conditioner(model.conditioner),
                     value_dict,
                     num_samples,
+                    T=T,
+                    additional_batch_uc_fields=additional_batch_uc_fields,
                 )
-                for key in batch:
-                    if isinstance(batch[key], torch.Tensor):
-                        print(key, batch[key].shape)
-                    elif isinstance(batch[key], list):
-                        print(key, [len(l) for l in batch[key]])
-                    else:
-                        print(key, batch[key])
+
                 c, uc = model.conditioner.get_unconditional_conditioning(
                     batch,
                     batch_uc=batch_uc,
                     force_uc_zero_embeddings=force_uc_zero_embeddings,
+                    force_cond_zero_embeddings=force_cond_zero_embeddings,
                 )
+                unload_model(model.conditioner)
 
                 for k in c:
                     if not k == "crossattn":
                         c[k], uc[k] = map(
                             lambda y: y[k][: math.prod(num_samples)].to("cuda"), (c, uc)
                         )
+                    if k in ["crossattn", "concat"] and T is not None:
+                        uc[k] = repeat(uc[k], "b ... -> b t ...", t=T)
+                        uc[k] = rearrange(uc[k], "b t ... -> (b t) ...", t=T)
+                        c[k] = repeat(c[k], "b ... -> b t ...", t=T)
+                        c[k] = rearrange(c[k], "b t ... -> (b t) ...", t=T)
 
                 additional_model_inputs = {}
                 for k in batch2model_input:
-                    additional_model_inputs[k] = batch[k]
+                    if k == "image_only_indicator":
+                        assert T is not None
+
+                        if isinstance(
+                            sampler.guider,
+                            (
+                                VanillaCFG,
+                                LinearPredictionGuider,
+                                TrianglePredictionGuider,
+                            ),
+                        ):
+                            additional_model_inputs[k] = torch.zeros(
+                                num_samples[0] * 2, num_samples[1]
+                            ).to("cuda")
+                        else:
+                            additional_model_inputs[k] = torch.zeros(num_samples).to(
+                                "cuda"
+                            )
+                    else:
+                        additional_model_inputs[k] = batch[k]
 
                 shape = (math.prod(num_samples), C, H // F, W // F)
                 randn = torch.randn(shape).to("cuda")
@@ -520,23 +597,49 @@ def do_sample(
                         model.model, input, sigma, c, **additional_model_inputs
                     )
 
+                load_model(model.denoiser)
+                load_model(model.model)
                 samples_z = sampler(denoiser, randn, cond=c, uc=uc)
+                unload_model(model.model)
+                unload_model(model.denoiser)
+
+                load_model(model.first_stage_model)
+                model.en_and_decode_n_samples_a_time = (
+                    decoding_t  # Decode n frames at a time
+                )
                 samples_x = model.decode_first_stage(samples_z)
                 samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
+                unload_model(model.first_stage_model)
 
                 if filter is not None:
                     samples = filter(samples)
 
-                grid = torch.stack([samples])
-                grid = rearrange(grid, "n b c h w -> (n h) (b w) c")
-                outputs.image(grid.cpu().numpy())
+                if T is None:
+                    grid = torch.stack([samples])
+                    grid = rearrange(grid, "n b c h w -> (n h) (b w) c")
+                    outputs.image(grid.cpu().numpy())
+                else:
+                    as_vids = rearrange(samples, "(b t) c h w -> b t c h w", t=T)
+                    for i, vid in enumerate(as_vids):
+                        grid = rearrange(make_grid(vid, nrow=4), "c h w -> h w c")
+                        st.image(
+                            grid.cpu().numpy(),
+                            f"Sample #{i} as image",
+                        )
 
                 if return_latents:
                     return samples, samples_z
                 return samples
 
 
-def get_batch(keys, value_dict, N: Union[List, ListConfig], device="cuda"):
+def get_batch(
+    keys,
+    value_dict: dict,
+    N: Union[List, ListConfig],
+    device: str = "cuda",
+    T: int = None,
+    additional_batch_uc_fields: List[str] = [],
+):
     # Hardcoded demo setups; might undergo some changes in the future
 
     batch = {}
@@ -544,21 +647,15 @@ def get_batch(keys, value_dict, N: Union[List, ListConfig], device="cuda"):
 
     for key in keys:
         if key == "txt":
-            batch["txt"] = (
-                np.repeat([value_dict["prompt"]], repeats=math.prod(N))
-                .reshape(N)
-                .tolist()
-            )
-            batch_uc["txt"] = (
-                np.repeat([value_dict["negative_prompt"]], repeats=math.prod(N))
-                .reshape(N)
-                .tolist()
-            )
+            batch["txt"] = [value_dict["prompt"]] * math.prod(N)
+
+            batch_uc["txt"] = [value_dict["negative_prompt"]] * math.prod(N)
+
         elif key == "original_size_as_tuple":
             batch["original_size_as_tuple"] = (
                 torch.tensor([value_dict["orig_height"], value_dict["orig_width"]])
                 .to(device)
-                .repeat(*N, 1)
+                .repeat(math.prod(N), 1)
             )
         elif key == "crop_coords_top_left":
             batch["crop_coords_top_left"] = (
@@ -566,30 +663,73 @@ def get_batch(keys, value_dict, N: Union[List, ListConfig], device="cuda"):
                     [value_dict["crop_coords_top"], value_dict["crop_coords_left"]]
                 )
                 .to(device)
-                .repeat(*N, 1)
+                .repeat(math.prod(N), 1)
             )
         elif key == "aesthetic_score":
             batch["aesthetic_score"] = (
-                torch.tensor([value_dict["aesthetic_score"]]).to(device).repeat(*N, 1)
+                torch.tensor([value_dict["aesthetic_score"]])
+                .to(device)
+                .repeat(math.prod(N), 1)
             )
             batch_uc["aesthetic_score"] = (
                 torch.tensor([value_dict["negative_aesthetic_score"]])
                 .to(device)
-                .repeat(*N, 1)
+                .repeat(math.prod(N), 1)
             )
 
         elif key == "target_size_as_tuple":
             batch["target_size_as_tuple"] = (
                 torch.tensor([value_dict["target_height"], value_dict["target_width"]])
                 .to(device)
-                .repeat(*N, 1)
+                .repeat(math.prod(N), 1)
+            )
+        elif key == "fps":
+            batch[key] = (
+                torch.tensor([value_dict["fps"]]).to(device).repeat(math.prod(N))
+            )
+        elif key == "fps_id":
+            batch[key] = (
+                torch.tensor([value_dict["fps_id"]]).to(device).repeat(math.prod(N))
+            )
+        elif key == "motion_bucket_id":
+            batch[key] = (
+                torch.tensor([value_dict["motion_bucket_id"]])
+                .to(device)
+                .repeat(math.prod(N))
+            )
+        elif key == "pool_image":
+            batch[key] = repeat(value_dict[key], "1 ... -> b ...", b=math.prod(N)).to(
+                device, dtype=torch.half
+            )
+        elif key == "cond_aug":
+            batch[key] = repeat(
+                torch.tensor([value_dict["cond_aug"]]).to("cuda"),
+                "1 -> b",
+                b=math.prod(N),
+            )
+        elif key == "cond_frames":
+            batch[key] = repeat(value_dict["cond_frames"], "1 ... -> b ...", b=N[0])
+        elif key == "cond_frames_without_noise":
+            batch[key] = repeat(
+                value_dict["cond_frames_without_noise"], "1 ... -> b ...", b=N[0]
+            )
+        elif key == "polars_rad":
+            batch[key] = torch.tensor(value_dict["polars_rad"]).to(device).repeat(N[0])
+        elif key == "azimuths_rad":
+            batch[key] = (
+                torch.tensor(value_dict["azimuths_rad"]).to(device).repeat(N[0])
             )
         else:
             batch[key] = value_dict[key]
 
+    if T is not None:
+        batch["num_video_frames"] = T
+
     for key in batch.keys():
         if key not in batch_uc and isinstance(batch[key], torch.Tensor):
             batch_uc[key] = torch.clone(batch[key])
+        elif key in additional_batch_uc_fields and key not in batch_uc:
+            batch_uc[key] = copy.copy(batch[key])
     return batch, batch_uc
 
 
@@ -600,12 +740,14 @@ def do_img2img(
     sampler,
     value_dict,
     num_samples,
-    force_uc_zero_embeddings=[],
+    force_uc_zero_embeddings: Optional[List] = None,
+    force_cond_zero_embeddings: Optional[List] = None,
     additional_kwargs={},
     offset_noise_level: int = 0.0,
     return_latents=False,
     skip_encode=False,
     filter=None,
+    add_noise=True,
 ):
     st.text("Sampling")
 
@@ -614,6 +756,7 @@ def do_img2img(
     with torch.no_grad():
         with precision_scope("cuda"):
             with model.ema_scope():
+                load_model(model.conditioner)
                 batch, batch_uc = get_batch(
                     get_unique_embedder_keys_from_conditioner(model.conditioner),
                     value_dict,
@@ -623,8 +766,9 @@ def do_img2img(
                     batch,
                     batch_uc=batch_uc,
                     force_uc_zero_embeddings=force_uc_zero_embeddings,
+                    force_cond_zero_embeddings=force_cond_zero_embeddings,
                 )
-
+                unload_model(model.conditioner)
                 for k in c:
                     c[k], uc[k] = map(lambda y: y[k][:num_samples].to("cuda"), (c, uc))
 
@@ -633,36 +777,145 @@ def do_img2img(
                 if skip_encode:
                     z = img
                 else:
+                    load_model(model.first_stage_model)
                     z = model.encode_first_stage(img)
+                    unload_model(model.first_stage_model)
+
                 noise = torch.randn_like(z)
-                sigmas = sampler.discretization(sampler.num_steps)
+
+                sigmas = sampler.discretization(sampler.num_steps).cuda()
                 sigma = sigmas[0]
 
                 st.info(f"all sigmas: {sigmas}")
                 st.info(f"noising sigma: {sigma}")
-
                 if offset_noise_level > 0.0:
                     noise = noise + offset_noise_level * append_dims(
                         torch.randn(z.shape[0], device=z.device), z.ndim
                     )
-                noised_z = z + noise * append_dims(sigma, z.ndim)
-                noised_z = noised_z / torch.sqrt(
-                    1.0 + sigmas[0] ** 2.0
-                )  # Note: hardcoded to DDPM-like scaling. need to generalize later.
+                if add_noise:
+                    noised_z = z + noise * append_dims(sigma, z.ndim).cuda()
+                    noised_z = noised_z / torch.sqrt(
+                        1.0 + sigmas[0] ** 2.0
+                    )  # Note: hardcoded to DDPM-like scaling. need to generalize later.
+                else:
+                    noised_z = z / torch.sqrt(1.0 + sigmas[0] ** 2.0)
 
                 def denoiser(x, sigma, c):
                     return model.denoiser(model.model, x, sigma, c)
 
+                load_model(model.denoiser)
+                load_model(model.model)
                 samples_z = sampler(denoiser, noised_z, cond=c, uc=uc)
+                unload_model(model.model)
+                unload_model(model.denoiser)
+
+                load_model(model.first_stage_model)
                 samples_x = model.decode_first_stage(samples_z)
+                unload_model(model.first_stage_model)
                 samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
 
                 if filter is not None:
                     samples = filter(samples)
 
-                grid = embed_watemark(torch.stack([samples]))
                 grid = rearrange(grid, "n b c h w -> (n h) (b w) c")
                 outputs.image(grid.cpu().numpy())
                 if return_latents:
                     return samples, samples_z
                 return samples
+
+
+def get_resizing_factor(
+    desired_shape: Tuple[int, int], current_shape: Tuple[int, int]
+) -> float:
+    r_bound = desired_shape[1] / desired_shape[0]
+    aspect_r = current_shape[1] / current_shape[0]
+    if r_bound >= 1.0:
+        if aspect_r >= r_bound:
+            factor = min(desired_shape) / min(current_shape)
+        else:
+            if aspect_r < 1.0:
+                factor = max(desired_shape) / min(current_shape)
+            else:
+                factor = max(desired_shape) / max(current_shape)
+    else:
+        if aspect_r <= r_bound:
+            factor = min(desired_shape) / min(current_shape)
+        else:
+            if aspect_r > 1:
+                factor = max(desired_shape) / min(current_shape)
+            else:
+                factor = max(desired_shape) / max(current_shape)
+
+    return factor
+
+
+def get_interactive_image(key=None) -> Image.Image:
+    image = st.file_uploader("Input", type=["jpg", "JPEG", "png"], key=key)
+    if image is not None:
+        image = Image.open(image)
+        if not image.mode == "RGB":
+            image = image.convert("RGB")
+        return image
+
+
+def load_img_for_prediction(
+    W: int, H: int, display=True, key=None, device="cuda"
+) -> torch.Tensor:
+    image = get_interactive_image(key=key)
+    if image is None:
+        return None
+    if display:
+        st.image(image)
+    w, h = image.size
+
+    image = np.array(image).astype(np.float32) / 255
+    if image.shape[-1] == 4:
+        rgb, alpha = image[:, :, :3], image[:, :, 3:]
+        image = rgb * alpha + (1 - alpha)
+
+    image = image.transpose(2, 0, 1)
+    image = torch.from_numpy(image).to(dtype=torch.float32)
+    image = image.unsqueeze(0)
+
+    rfs = get_resizing_factor((H, W), (h, w))
+    resize_size = [int(np.ceil(rfs * s)) for s in (h, w)]
+    top = (resize_size[0] - H) // 2
+    left = (resize_size[1] - W) // 2
+
+    image = torch.nn.functional.interpolate(
+        image, resize_size, mode="area", antialias=False
+    )
+    image = TT.functional.crop(image, top=top, left=left, height=H, width=W)
+
+    if display:
+        numpy_img = np.transpose(image[0].numpy(), (1, 2, 0))
+        pil_image = Image.fromarray((numpy_img * 255).astype(np.uint8))
+        st.image(pil_image)
+    return image.to(device) * 2.0 - 1.0
+
+
+def save_video_as_grid_and_mp4(
+    video_batch: torch.Tensor, save_path: str, T: int, fps: int = 5
+):
+    os.makedirs(save_path, exist_ok=True)
+    base_count = len(glob(os.path.join(save_path, "*.mp4")))
+
+    video_batch = rearrange(video_batch, "(b t) c h w -> b t c h w", t=T)
+    video_batch = embed_watermark(video_batch)
+    for vid in video_batch:
+        save_image(vid, fp=os.path.join(save_path, f"{base_count:06d}.png"), nrow=4)
+
+        video_path = os.path.join(save_path, f"{base_count:06d}.mp4")
+        vid = (
+            (rearrange(vid, "t c h w -> t h w c") * 255).cpu().numpy().astype(np.uint8)
+        )
+        imageio.mimwrite(video_path, vid, fps=fps)
+
+        video_path_h264 = video_path[:-4] + "_h264.mp4"
+        os.system(f"ffmpeg -i '{video_path}' -c:v libx264 '{video_path_h264}'")
+        with open(video_path_h264, "rb") as f:
+            video_bytes = f.read()
+        os.remove(video_path_h264)
+        st.video(video_bytes)
+
+        base_count += 1

scripts/demo/sv3d_helpers.py

@@ -0,0 +1,119 @@
+import os
+
+import matplotlib.pyplot as plt
+import numpy as np
+from PIL import Image
+
+
+def generate_dynamic_cycle_xy_values(
+    length=21,
+    init_elev=0,
+    num_components=84,
+    frequency_range=(1, 5),
+    amplitude_range=(0.5, 10),
+    step_range=(0, 2),
+):
+    # Y values generation
+    y_sequence = np.ones(length) * init_elev
+    for _ in range(num_components):
+        # Choose a frequency that will complete whole cycles in the sequence
+        frequency = np.random.randint(*frequency_range) * (2 * np.pi / length)
+        amplitude = np.random.uniform(*amplitude_range)
+        phase_shift = np.random.choice([0, np.pi])  # np.random.uniform(0, 2 * np.pi)
+        angles = (
+            np.linspace(0, frequency * length, length, endpoint=False) + phase_shift
+        )
+        y_sequence += np.sin(angles) * amplitude
+    # X values generation
+    # Generate length - 1 steps since the last step is back to start
+    steps = np.random.uniform(*step_range, length - 1)
+    total_step_sum = np.sum(steps)
+    # Calculate the scale factor to scale total steps to just under 360
+    scale_factor = (
+        360 - ((360 / length) * np.random.uniform(*step_range))
+    ) / total_step_sum
+    # Apply the scale factor and generate the sequence of X values
+    x_values = np.cumsum(steps * scale_factor)
+    # Ensure the sequence starts at 0 and add the final step to complete the loop
+    x_values = np.insert(x_values, 0, 0)
+    return x_values, y_sequence
+
+
+def smooth_data(data, window_size):
+    # Extend data at both ends by wrapping around to create a continuous loop
+    pad_size = window_size
+    padded_data = np.concatenate((data[-pad_size:], data, data[:pad_size]))
+
+    # Apply smoothing
+    kernel = np.ones(window_size) / window_size
+    smoothed_data = np.convolve(padded_data, kernel, mode="same")
+
+    # Extract the smoothed data corresponding to the original sequence
+    # Adjust the indices to account for the larger padding
+    start_index = pad_size
+    end_index = -pad_size if pad_size != 0 else None
+    smoothed_original_data = smoothed_data[start_index:end_index]
+    return smoothed_original_data
+
+
+# Function to generate and process the data
+def gen_dynamic_loop(length=21, elev_deg=0):
+    while True:
+        # Generate the combined X and Y values using the new function
+        azim_values, elev_values = generate_dynamic_cycle_xy_values(
+            length=84, init_elev=elev_deg
+        )
+        # Smooth the Y values directly
+        smoothed_elev_values = smooth_data(elev_values, 5)
+        max_magnitude = np.max(np.abs(smoothed_elev_values))
+        if max_magnitude < 90:
+            break
+    subsample = 84 // length
+    azim_rad = np.deg2rad(azim_values[::subsample])
+    elev_rad = np.deg2rad(smoothed_elev_values[::subsample])
+    # Make cond frame the last one
+    return np.roll(azim_rad, -1), np.roll(elev_rad, -1)
+
+
+def plot_3D(azim, polar, save_path=None, dynamic=True):
+    if save_path is not None:
+        os.makedirs(os.path.dirname(save_path), exist_ok=True)
+    elev = np.deg2rad(90) - polar
+    fig = plt.figure(figsize=(5, 5))
+    ax = fig.add_subplot(projection="3d")
+    cm = plt.get_cmap("Greys")
+    col_line = [cm(i) for i in np.linspace(0.3, 1, len(azim) + 1)]
+    cm = plt.get_cmap("cool")
+    col = [cm(float(i) / (len(azim))) for i in np.arange(len(azim))]
+    xs = np.cos(elev) * np.cos(azim)
+    ys = np.cos(elev) * np.sin(azim)
+    zs = np.sin(elev)
+    ax.scatter(xs[0], ys[0], zs[0], s=100, color=col[0])
+    xs_d, ys_d, zs_d = (xs[1:] - xs[:-1]), (ys[1:] - ys[:-1]), (zs[1:] - zs[:-1])
+    for i in range(len(xs) - 1):
+        if dynamic:
+            ax.quiver(
+                xs[i], ys[i], zs[i], xs_d[i], ys_d[i], zs_d[i], lw=2, color=col_line[i]
+            )
+        else:
+            ax.plot(xs[i : i + 2], ys[i : i + 2], zs[i : i + 2], lw=2, c=col_line[i])
+        ax.scatter(xs[i + 1], ys[i + 1], zs[i + 1], s=100, color=col[i + 1])
+    ax.scatter(xs[:1], ys[:1], zs[:1], s=120, facecolors="none", edgecolors="k")
+    ax.scatter(xs[-1:], ys[-1:], zs[-1:], s=120, facecolors="none", edgecolors="k")
+    ax.view_init(elev=40, azim=-20, roll=0)
+    ax.xaxis.set_ticklabels([])
+    ax.yaxis.set_ticklabels([])
+    ax.zaxis.set_ticklabels([])
+    if save_path is None:
+        fig.canvas.draw()
+        lst = list(fig.canvas.get_width_height())
+        lst.append(3)
+        image = Image.fromarray(
+            np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8).reshape(lst)
+        )
+    else:
+        plt.savefig(save_path, bbox_inches="tight")
+    plt.clf()
+    plt.close()
+    if save_path is None:
+        return image

scripts/demo/sv3d_p_gradio.py

@@ -0,0 +1,340 @@
+# Adding this at the very top of app.py to make 'generative-models' directory discoverable
+import os
+import sys
+
+sys.path.append(os.path.dirname(__file__))
+
+import random
+from glob import glob
+from pathlib import Path
+from typing import List, Optional
+
+import cv2
+import gradio as gr
+import imageio
+import numpy as np
+import torch
+from einops import rearrange, repeat
+from huggingface_hub import hf_hub_download
+from PIL import Image
+from rembg import remove
+from scripts.demo.sv3d_helpers import gen_dynamic_loop, plot_3D
+from scripts.sampling.simple_video_sample import (
+    get_batch,
+    get_unique_embedder_keys_from_conditioner,
+    load_model,
+)
+from sgm.inference.helpers import embed_watermark
+from torchvision.transforms import ToTensor
+
+version = "sv3d_p"  # replace with 'sv3d_p' or 'sv3d_u' for other models
+
+# Define the repo, local directory and filename
+repo_id = "stabilityai/sv3d"
+filename = f"{version}.safetensors"  # replace with "sv3d_u.safetensors" or "sv3d_p.safetensors"
+local_dir = "checkpoints"
+local_ckpt_path = os.path.join(local_dir, filename)
+
+# Check if the file already exists
+if not os.path.exists(local_ckpt_path):
+    # If the file doesn't exist, download it
+    hf_hub_download(repo_id=repo_id, filename=filename, local_dir=local_dir)
+    print("File downloaded.")
+else:
+    print("File already exists. No need to download.")
+
+device = "cuda"
+max_64_bit_int = 2**63 - 1
+
+num_frames = 21
+num_steps = 50
+model_config = f"scripts/sampling/configs/{version}.yaml"
+
+model, filter = load_model(
+    model_config,
+    device,
+    num_frames,
+    num_steps,
+)
+
+polars_rad = np.array([np.deg2rad(90 - 10.0)] * num_frames)
+azimuths_rad = np.linspace(0, 2 * np.pi, num_frames + 1)[1:]
+
+
+def gen_orbit(orbit, elev_deg):
+    if orbit == "dynamic":
+        azim_rad, elev_rad = gen_dynamic_loop(length=num_frames, elev_deg=elev_deg)
+        polars_rad = np.deg2rad(90) - elev_rad
+        azimuths_rad = azim_rad
+    else:
+        polars_rad = np.array([np.deg2rad(90 - elev_deg)] * num_frames)
+        azimuths_rad = np.linspace(0, 2 * np.pi, num_frames + 1)[1:]
+
+    plot = plot_3D(
+        azim=azimuths_rad,
+        polar=polars_rad,
+        save_path=None,
+        dynamic=(orbit == "dynamic"),
+    )
+    return plot
+
+
+def sample(
+    input_path: str = "assets/test_image.png",  # Can either be image file or folder with image files
+    seed: Optional[int] = None,
+    randomize_seed: bool = True,
+    decoding_t: int = 7,  # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.
+    device: str = "cuda",
+    output_folder: str = None,
+    image_frame_ratio: Optional[float] = None,
+):
+    """
+    Simple script to generate a single sample conditioned on an image `input_path` or multiple images, one for each
+    image file in folder `input_path`. If you run out of VRAM, try decreasing `decoding_t`.
+    """
+    if randomize_seed:
+        seed = random.randint(0, max_64_bit_int)
+
+    torch.manual_seed(seed)
+
+    path = Path(input_path)
+    all_img_paths = []
+    if path.is_file():
+        if any([input_path.endswith(x) for x in ["jpg", "jpeg", "png"]]):
+            all_img_paths = [input_path]
+        else:
+            raise ValueError("Path is not valid image file.")
+    elif path.is_dir():
+        all_img_paths = sorted(
+            [
+                f
+                for f in path.iterdir()
+                if f.is_file() and f.suffix.lower() in [".jpg", ".jpeg", ".png"]
+            ]
+        )
+        if len(all_img_paths) == 0:
+            raise ValueError("Folder does not contain any images.")
+    else:
+        raise ValueError
+
+    for input_img_path in all_img_paths:
+
+        image = Image.open(input_img_path)
+        if image.mode == "RGBA":
+            pass
+        else:
+            # remove bg
+            image.thumbnail([768, 768], Image.Resampling.LANCZOS)
+            image = remove(image.convert("RGBA"), alpha_matting=True)
+
+        # resize object in frame
+        image_arr = np.array(image)
+        in_w, in_h = image_arr.shape[:2]
+        ret, mask = cv2.threshold(
+            np.array(image.split()[-1]), 0, 255, cv2.THRESH_BINARY
+        )
+        x, y, w, h = cv2.boundingRect(mask)
+        max_size = max(w, h)
+        side_len = (
+            int(max_size / image_frame_ratio) if image_frame_ratio is not None else in_w
+        )
+        padded_image = np.zeros((side_len, side_len, 4), dtype=np.uint8)
+        center = side_len // 2
+        padded_image[
+            center - h // 2 : center - h // 2 + h,
+            center - w // 2 : center - w // 2 + w,
+        ] = image_arr[y : y + h, x : x + w]
+        # resize frame to 576x576
+        rgba = Image.fromarray(padded_image).resize((576, 576), Image.LANCZOS)
+        # white bg
+        rgba_arr = np.array(rgba) / 255.0
+        rgb = rgba_arr[..., :3] * rgba_arr[..., -1:] + (1 - rgba_arr[..., -1:])
+        input_image = Image.fromarray((rgb * 255).astype(np.uint8))
+
+        image = ToTensor()(input_image)
+        image = image * 2.0 - 1.0
+
+        image = image.unsqueeze(0).to(device)
+        H, W = image.shape[2:]
+        assert image.shape[1] == 3
+        F = 8
+        C = 4
+        shape = (num_frames, C, H // F, W // F)
+        if (H, W) != (576, 576) and "sv3d" in version:
+            print(
+                "WARNING: The conditioning frame you provided is not 576x576. This leads to suboptimal performance as model was only trained on 576x576."
+            )
+
+        cond_aug = 1e-5
+
+        value_dict = {}
+        value_dict["cond_aug"] = cond_aug
+        value_dict["cond_frames_without_noise"] = image
+        value_dict["cond_frames"] = image + cond_aug * torch.randn_like(image)
+        value_dict["cond_aug"] = cond_aug
+
+        value_dict["polars_rad"] = polars_rad
+        value_dict["azimuths_rad"] = azimuths_rad
+
+        output_folder = output_folder or f"outputs/gradio/{version}"
+        cond_aug = 1e-5
+
+        with torch.no_grad():
+            with torch.autocast(device):
+                batch, batch_uc = get_batch(
+                    get_unique_embedder_keys_from_conditioner(model.conditioner),
+                    value_dict,
+                    [1, num_frames],
+                    T=num_frames,
+                    device=device,
+                )
+                c, uc = model.conditioner.get_unconditional_conditioning(
+                    batch,
+                    batch_uc=batch_uc,
+                    force_uc_zero_embeddings=[
+                        "cond_frames",
+                        "cond_frames_without_noise",
+                    ],
+                )
+
+                for k in ["crossattn", "concat"]:
+                    uc[k] = repeat(uc[k], "b ... -> b t ...", t=num_frames)
+                    uc[k] = rearrange(uc[k], "b t ... -> (b t) ...", t=num_frames)
+                    c[k] = repeat(c[k], "b ... -> b t ...", t=num_frames)
+                    c[k] = rearrange(c[k], "b t ... -> (b t) ...", t=num_frames)
+
+                randn = torch.randn(shape, device=device)
+
+                additional_model_inputs = {}
+                additional_model_inputs["image_only_indicator"] = torch.zeros(
+                    2, num_frames
+                ).to(device)
+                additional_model_inputs["num_video_frames"] = batch["num_video_frames"]
+
+                def denoiser(input, sigma, c):
+                    return model.denoiser(
+                        model.model, input, sigma, c, **additional_model_inputs
+                    )
+
+                samples_z = model.sampler(denoiser, randn, cond=c, uc=uc)
+                model.en_and_decode_n_samples_a_time = decoding_t
+                samples_x = model.decode_first_stage(samples_z)
+                samples_x[-1:] = value_dict["cond_frames_without_noise"]
+                samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
+
+                os.makedirs(output_folder, exist_ok=True)
+                base_count = len(glob(os.path.join(output_folder, "*.mp4")))
+
+                imageio.imwrite(
+                    os.path.join(output_folder, f"{base_count:06d}.jpg"), input_image
+                )
+
+                samples = embed_watermark(samples)
+                samples = filter(samples)
+                vid = (
+                    (rearrange(samples, "t c h w -> t h w c") * 255)
+                    .cpu()
+                    .numpy()
+                    .astype(np.uint8)
+                )
+                video_path = os.path.join(output_folder, f"{base_count:06d}.mp4")
+                imageio.mimwrite(video_path, vid)
+
+        return video_path, seed
+
+
+def resize_image(image_path, output_size=(576, 576)):
+    image = Image.open(image_path)
+    # Calculate aspect ratios
+    target_aspect = output_size[0] / output_size[1]  # Aspect ratio of the desired size
+    image_aspect = image.width / image.height  # Aspect ratio of the original image
+
+    # Resize then crop if the original image is larger
+    if image_aspect > target_aspect:
+        # Resize the image to match the target height, maintaining aspect ratio
+        new_height = output_size[1]
+        new_width = int(new_height * image_aspect)
+        resized_image = image.resize((new_width, new_height), Image.LANCZOS)
+        # Calculate coordinates for cropping
+        left = (new_width - output_size[0]) / 2
+        top = 0
+        right = (new_width + output_size[0]) / 2
+        bottom = output_size[1]
+    else:
+        # Resize the image to match the target width, maintaining aspect ratio
+        new_width = output_size[0]
+        new_height = int(new_width / image_aspect)
+        resized_image = image.resize((new_width, new_height), Image.LANCZOS)
+        # Calculate coordinates for cropping
+        left = 0
+        top = (new_height - output_size[1]) / 2
+        right = output_size[0]
+        bottom = (new_height + output_size[1]) / 2
+
+    # Crop the image
+    cropped_image = resized_image.crop((left, top, right, bottom))
+
+    return cropped_image
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """# Demo for SV3D_p from Stability AI ([model](https://huggingface.co/stabilityai/sv3d), [news](https://stability.ai/news/introducing-stable-video-3d))
+#### Research release ([_non-commercial_](https://huggingface.co/stabilityai/sv3d/blob/main/LICENSE)): generate 21 frames orbital video from a single image, at variable elevation and azimuth.
+Generation takes ~40s (for 50 steps) in an A100.
+  """
+    )
+    with gr.Row():
+        with gr.Column():
+            image = gr.Image(label="Upload your image", type="filepath")
+            generate_btn = gr.Button("Generate")
+        video = gr.Video()
+    with gr.Row():
+        with gr.Column():
+            orbit = gr.Dropdown(
+                ["same elevation", "dynamic"],
+                label="Orbit",
+                info="Choose with orbit to generate",
+            )
+            elev_deg = gr.Slider(
+                label="Elevation (in degrees)",
+                info="Elevation of the camera in the conditioning image, in degrees.",
+                value=10.0,
+                minimum=-10,
+                maximum=30,
+            )
+        plot_image = gr.Image()
+    with gr.Accordion("Advanced options", open=False):
+        seed = gr.Slider(
+            label="Seed",
+            value=23,
+            randomize=True,
+            minimum=0,
+            maximum=max_64_bit_int,
+            step=1,
+        )
+        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+        decoding_t = gr.Slider(
+            label="Decode n frames at a time",
+            info="Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.",
+            value=7,
+            minimum=1,
+            maximum=14,
+        )
+
+    image.upload(fn=resize_image, inputs=image, outputs=image, queue=False)
+
+    orbit.change(gen_orbit, [orbit, elev_deg], plot_image)
+    elev_deg.change(gen_orbit, [orbit, elev_deg], plot_image)
+    # seed.change(gen_orbit, [orbit, elev_deg], plot_image)
+
+    generate_btn.click(
+        fn=sample,
+        inputs=[image, seed, randomize_seed, decoding_t],
+        outputs=[video, seed],
+        api_name="video",
+    )
+
+if __name__ == "__main__":
+    demo.queue(max_size=20)
+    demo.launch(share=True)

scripts/demo/sv3d_u_gradio.py

@@ -0,0 +1,295 @@
+# Adding this at the very top of app.py to make 'generative-models' directory discoverable
+import os
+import sys
+
+sys.path.append(os.path.dirname(__file__))
+
+import random
+from glob import glob
+from pathlib import Path
+from typing import Optional
+
+import cv2
+import gradio as gr
+import imageio
+import numpy as np
+import torch
+from einops import rearrange, repeat
+from huggingface_hub import hf_hub_download
+from PIL import Image
+from rembg import remove
+from scripts.sampling.simple_video_sample import (
+    get_batch,
+    get_unique_embedder_keys_from_conditioner,
+    load_model,
+)
+from sgm.inference.helpers import embed_watermark
+from torchvision.transforms import ToTensor
+
+version = "sv3d_u"  # replace with 'sv3d_p' or 'sv3d_u' for other models
+
+# Define the repo, local directory and filename
+repo_id = "stabilityai/sv3d"
+filename = f"{version}.safetensors"  # replace with "sv3d_u.safetensors" or "sv3d_p.safetensors"
+local_dir = "checkpoints"
+local_ckpt_path = os.path.join(local_dir, filename)
+
+# Check if the file already exists
+if not os.path.exists(local_ckpt_path):
+    # If the file doesn't exist, download it
+    hf_hub_download(repo_id=repo_id, filename=filename, local_dir=local_dir)
+    print("File downloaded.")
+else:
+    print("File already exists. No need to download.")
+
+device = "cuda"
+max_64_bit_int = 2**63 - 1
+
+num_frames = 21
+num_steps = 50
+model_config = f"scripts/sampling/configs/{version}.yaml"
+
+model, filter = load_model(
+    model_config,
+    device,
+    num_frames,
+    num_steps,
+)
+
+
+def sample(
+    input_path: str = "assets/test_image.png",  # Can either be image file or folder with image files
+    seed: Optional[int] = None,
+    randomize_seed: bool = True,
+    decoding_t: int = 7,  # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.
+    device: str = "cuda",
+    output_folder: str = None,
+    image_frame_ratio: Optional[float] = None,
+):
+    """
+    Simple script to generate a single sample conditioned on an image `input_path` or multiple images, one for each
+    image file in folder `input_path`. If you run out of VRAM, try decreasing `decoding_t`.
+    """
+    if randomize_seed:
+        seed = random.randint(0, max_64_bit_int)
+
+    torch.manual_seed(seed)
+
+    path = Path(input_path)
+    all_img_paths = []
+    if path.is_file():
+        if any([input_path.endswith(x) for x in ["jpg", "jpeg", "png"]]):
+            all_img_paths = [input_path]
+        else:
+            raise ValueError("Path is not valid image file.")
+    elif path.is_dir():
+        all_img_paths = sorted(
+            [
+                f
+                for f in path.iterdir()
+                if f.is_file() and f.suffix.lower() in [".jpg", ".jpeg", ".png"]
+            ]
+        )
+        if len(all_img_paths) == 0:
+            raise ValueError("Folder does not contain any images.")
+    else:
+        raise ValueError
+
+    for input_img_path in all_img_paths:
+
+        image = Image.open(input_img_path)
+        if image.mode == "RGBA":
+            pass
+        else:
+            # remove bg
+            image.thumbnail([768, 768], Image.Resampling.LANCZOS)
+            image = remove(image.convert("RGBA"), alpha_matting=True)
+
+        # resize object in frame
+        image_arr = np.array(image)
+        in_w, in_h = image_arr.shape[:2]
+        ret, mask = cv2.threshold(
+            np.array(image.split()[-1]), 0, 255, cv2.THRESH_BINARY
+        )
+        x, y, w, h = cv2.boundingRect(mask)
+        max_size = max(w, h)
+        side_len = (
+            int(max_size / image_frame_ratio) if image_frame_ratio is not None else in_w
+        )
+        padded_image = np.zeros((side_len, side_len, 4), dtype=np.uint8)
+        center = side_len // 2
+        padded_image[
+            center - h // 2 : center - h // 2 + h,
+            center - w // 2 : center - w // 2 + w,
+        ] = image_arr[y : y + h, x : x + w]
+        # resize frame to 576x576
+        rgba = Image.fromarray(padded_image).resize((576, 576), Image.LANCZOS)
+        # white bg
+        rgba_arr = np.array(rgba) / 255.0
+        rgb = rgba_arr[..., :3] * rgba_arr[..., -1:] + (1 - rgba_arr[..., -1:])
+        input_image = Image.fromarray((rgb * 255).astype(np.uint8))
+
+        image = ToTensor()(input_image)
+        image = image * 2.0 - 1.0
+
+        image = image.unsqueeze(0).to(device)
+        H, W = image.shape[2:]
+        assert image.shape[1] == 3
+        F = 8
+        C = 4
+        shape = (num_frames, C, H // F, W // F)
+        if (H, W) != (576, 576) and "sv3d" in version:
+            print(
+                "WARNING: The conditioning frame you provided is not 576x576. This leads to suboptimal performance as model was only trained on 576x576."
+            )
+
+        cond_aug = 1e-5
+
+        value_dict = {}
+        value_dict["cond_aug"] = cond_aug
+        value_dict["cond_frames_without_noise"] = image
+        value_dict["cond_frames"] = image + cond_aug * torch.randn_like(image)
+        value_dict["cond_aug"] = cond_aug
+
+        output_folder = output_folder or f"outputs/gradio/{version}"
+        cond_aug = 1e-5
+
+        with torch.no_grad():
+            with torch.autocast(device):
+                batch, batch_uc = get_batch(
+                    get_unique_embedder_keys_from_conditioner(model.conditioner),
+                    value_dict,
+                    [1, num_frames],
+                    T=num_frames,
+                    device=device,
+                )
+                c, uc = model.conditioner.get_unconditional_conditioning(
+                    batch,
+                    batch_uc=batch_uc,
+                    force_uc_zero_embeddings=[
+                        "cond_frames",
+                        "cond_frames_without_noise",
+                    ],
+                )
+
+                for k in ["crossattn", "concat"]:
+                    uc[k] = repeat(uc[k], "b ... -> b t ...", t=num_frames)
+                    uc[k] = rearrange(uc[k], "b t ... -> (b t) ...", t=num_frames)
+                    c[k] = repeat(c[k], "b ... -> b t ...", t=num_frames)
+                    c[k] = rearrange(c[k], "b t ... -> (b t) ...", t=num_frames)
+
+                randn = torch.randn(shape, device=device)
+
+                additional_model_inputs = {}
+                additional_model_inputs["image_only_indicator"] = torch.zeros(
+                    2, num_frames
+                ).to(device)
+                additional_model_inputs["num_video_frames"] = batch["num_video_frames"]
+
+                def denoiser(input, sigma, c):
+                    return model.denoiser(
+                        model.model, input, sigma, c, **additional_model_inputs
+                    )
+
+                samples_z = model.sampler(denoiser, randn, cond=c, uc=uc)
+                model.en_and_decode_n_samples_a_time = decoding_t
+                samples_x = model.decode_first_stage(samples_z)
+                samples_x[-1:] = value_dict["cond_frames_without_noise"]
+                samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
+
+                os.makedirs(output_folder, exist_ok=True)
+                base_count = len(glob(os.path.join(output_folder, "*.mp4")))
+
+                imageio.imwrite(
+                    os.path.join(output_folder, f"{base_count:06d}.jpg"), input_image
+                )
+
+                samples = embed_watermark(samples)
+                samples = filter(samples)
+                vid = (
+                    (rearrange(samples, "t c h w -> t h w c") * 255)
+                    .cpu()
+                    .numpy()
+                    .astype(np.uint8)
+                )
+                video_path = os.path.join(output_folder, f"{base_count:06d}.mp4")
+                imageio.mimwrite(video_path, vid)
+
+        return video_path, seed
+
+
+def resize_image(image_path, output_size=(576, 576)):
+    image = Image.open(image_path)
+    # Calculate aspect ratios
+    target_aspect = output_size[0] / output_size[1]  # Aspect ratio of the desired size
+    image_aspect = image.width / image.height  # Aspect ratio of the original image
+
+    # Resize then crop if the original image is larger
+    if image_aspect > target_aspect:
+        # Resize the image to match the target height, maintaining aspect ratio
+        new_height = output_size[1]
+        new_width = int(new_height * image_aspect)
+        resized_image = image.resize((new_width, new_height), Image.LANCZOS)
+        # Calculate coordinates for cropping
+        left = (new_width - output_size[0]) / 2
+        top = 0
+        right = (new_width + output_size[0]) / 2
+        bottom = output_size[1]
+    else:
+        # Resize the image to match the target width, maintaining aspect ratio
+        new_width = output_size[0]
+        new_height = int(new_width / image_aspect)
+        resized_image = image.resize((new_width, new_height), Image.LANCZOS)
+        # Calculate coordinates for cropping
+        left = 0
+        top = (new_height - output_size[1]) / 2
+        right = output_size[0]
+        bottom = (new_height + output_size[1]) / 2
+
+    # Crop the image
+    cropped_image = resized_image.crop((left, top, right, bottom))
+
+    return cropped_image
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """# Demo for SV3D_u from Stability AI ([model](https://huggingface.co/stabilityai/sv3d), [news](https://stability.ai/news/introducing-stable-video-3d))
+#### Research release ([_non-commercial_](https://huggingface.co/stabilityai/sv3d/blob/main/LICENSE)): generate 21 frames orbital video from a single image, at the same elevation.
+Generation takes ~40s (for 50 steps) in an A100.
+  """
+    )
+    with gr.Row():
+        with gr.Column():
+            image = gr.Image(label="Upload your image", type="filepath")
+            generate_btn = gr.Button("Generate")
+        video = gr.Video()
+    with gr.Accordion("Advanced options", open=False):
+        seed = gr.Slider(
+            label="Seed",
+            value=23,
+            randomize=True,
+            minimum=0,
+            maximum=max_64_bit_int,
+            step=1,
+        )
+        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+        decoding_t = gr.Slider(
+            label="Decode n frames at a time",
+            info="Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.",
+            value=7,
+            minimum=1,
+            maximum=14,
+        )
+
+    image.upload(fn=resize_image, inputs=image, outputs=image, queue=False)
+    generate_btn.click(
+        fn=sample,
+        inputs=[image, seed, randomize_seed, decoding_t],
+        outputs=[video, seed],
+        api_name="video",
+    )
+
+if __name__ == "__main__":
+    demo.queue(max_size=20)
+    demo.launch(share=True)

scripts/demo/turbo.py

@@ -0,0 +1,234 @@
+from st_keyup import st_keyup
+from streamlit_helpers import *
+
+from sgm.modules.diffusionmodules.sampling import EulerAncestralSampler
+
+VERSION2SPECS = {
+    "SDXL-Turbo": {
+        "H": 512,
+        "W": 512,
+        "C": 4,
+        "f": 8,
+        "is_legacy": False,
+        "config": "configs/inference/sd_xl_base.yaml",
+        "ckpt": "checkpoints/sd_xl_turbo_1.0.safetensors",
+    },
+    "SD-Turbo": {
+        "H": 512,
+        "W": 512,
+        "C": 4,
+        "f": 8,
+        "is_legacy": False,
+        "config": "configs/inference/sd_2_1.yaml",
+        "ckpt": "checkpoints/sd_turbo.safetensors",
+    },
+}
+
+
+class SubstepSampler(EulerAncestralSampler):
+    def __init__(self, n_sample_steps=1, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.n_sample_steps = n_sample_steps
+        self.steps_subset = [0, 100, 200, 300, 1000]
+
+    def prepare_sampling_loop(self, x, cond, uc=None, num_steps=None):
+        sigmas = self.discretization(
+            self.num_steps if num_steps is None else num_steps, device=self.device
+        )
+        sigmas = sigmas[
+            self.steps_subset[: self.n_sample_steps] + self.steps_subset[-1:]
+        ]
+        uc = cond
+        x *= torch.sqrt(1.0 + sigmas[0] ** 2.0)
+        num_sigmas = len(sigmas)
+        s_in = x.new_ones([x.shape[0]])
+        return x, s_in, sigmas, num_sigmas, cond, uc
+
+
+def seeded_randn(shape, seed):
+    randn = np.random.RandomState(seed).randn(*shape)
+    randn = torch.from_numpy(randn).to(device="cuda", dtype=torch.float32)
+    return randn
+
+
+class SeededNoise:
+    def __init__(self, seed):
+        self.seed = seed
+
+    def __call__(self, x):
+        self.seed = self.seed + 1
+        return seeded_randn(x.shape, self.seed)
+
+
+def init_embedder_options(keys, init_dict, prompt=None, negative_prompt=None):
+    value_dict = {}
+    for key in keys:
+        if key == "txt":
+            value_dict["prompt"] = prompt
+            value_dict["negative_prompt"] = ""
+
+        if key == "original_size_as_tuple":
+            orig_width = init_dict["orig_width"]
+            orig_height = init_dict["orig_height"]
+
+            value_dict["orig_width"] = orig_width
+            value_dict["orig_height"] = orig_height
+
+        if key == "crop_coords_top_left":
+            crop_coord_top = 0
+            crop_coord_left = 0
+
+            value_dict["crop_coords_top"] = crop_coord_top
+            value_dict["crop_coords_left"] = crop_coord_left
+
+        if key == "aesthetic_score":
+            value_dict["aesthetic_score"] = 6.0
+            value_dict["negative_aesthetic_score"] = 2.5
+
+        if key == "target_size_as_tuple":
+            value_dict["target_width"] = init_dict["target_width"]
+            value_dict["target_height"] = init_dict["target_height"]
+
+    return value_dict
+
+
+def sample(
+    model,
+    sampler,
+    prompt="A lush garden with oversized flowers and vibrant colors, inhabited by miniature animals.",
+    H=1024,
+    W=1024,
+    seed=0,
+    filter=None,
+):
+    F = 8
+    C = 4
+    shape = (1, C, H // F, W // F)
+
+    value_dict = init_embedder_options(
+        keys=get_unique_embedder_keys_from_conditioner(model.conditioner),
+        init_dict={
+            "orig_width": W,
+            "orig_height": H,
+            "target_width": W,
+            "target_height": H,
+        },
+        prompt=prompt,
+    )
+
+    if seed is None:
+        seed = torch.seed()
+    precision_scope = autocast
+    with torch.no_grad():
+        with precision_scope("cuda"):
+            batch, batch_uc = get_batch(
+                get_unique_embedder_keys_from_conditioner(model.conditioner),
+                value_dict,
+                [1],
+            )
+            c = model.conditioner(batch)
+            uc = None
+            randn = seeded_randn(shape, seed)
+
+            def denoiser(input, sigma, c):
+                return model.denoiser(
+                    model.model,
+                    input,
+                    sigma,
+                    c,
+                )
+
+            samples_z = sampler(denoiser, randn, cond=c, uc=uc)
+            samples_x = model.decode_first_stage(samples_z)
+            samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
+            if filter is not None:
+                samples = filter(samples)
+            samples = (
+                (255 * samples)
+                .to(dtype=torch.uint8)
+                .permute(0, 2, 3, 1)
+                .detach()
+                .cpu()
+                .numpy()
+            )
+    return samples
+
+
+def v_spacer(height) -> None:
+    for _ in range(height):
+        st.write("\n")
+
+
+if __name__ == "__main__":
+    st.title("Turbo")
+
+    head_cols = st.columns([1, 1, 1])
+    with head_cols[0]:
+        version = st.selectbox("Model Version", list(VERSION2SPECS.keys()), 0)
+        version_dict = VERSION2SPECS[version]
+
+    with head_cols[1]:
+        v_spacer(2)
+        if st.checkbox("Load Model"):
+            mode = "txt2img"
+        else:
+            mode = "skip"
+
+    if mode != "skip":
+        state = init_st(version_dict, load_filter=True)
+        if state["msg"]:
+            st.info(state["msg"])
+        model = state["model"]
+        load_model(model)
+
+    # seed
+    if "seed" not in st.session_state:
+        st.session_state.seed = 0
+
+    def increment_counter():
+        st.session_state.seed += 1
+
+    def decrement_counter():
+        if st.session_state.seed > 0:
+            st.session_state.seed -= 1
+
+    with head_cols[2]:
+        n_steps = st.number_input(label="number of steps", min_value=1, max_value=4)
+
+    sampler = SubstepSampler(
+        n_sample_steps=1,
+        num_steps=1000,
+        eta=1.0,
+        discretization_config=dict(
+            target="sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization"
+        ),
+    )
+    sampler.n_sample_steps = n_steps
+    default_prompt = (
+        "A cinematic shot of a baby racoon wearing an intricate italian priest robe."
+    )
+    prompt = st_keyup(
+        "Enter a value", value=default_prompt, debounce=300, key="interactive_text"
+    )
+
+    cols = st.columns([1, 5, 1])
+    if mode != "skip":
+        with cols[0]:
+            v_spacer(14)
+            st.button("↩", on_click=decrement_counter)
+        with cols[2]:
+            v_spacer(14)
+            st.button("↪", on_click=increment_counter)
+
+        sampler.noise_sampler = SeededNoise(seed=st.session_state.seed)
+        out = sample(
+            model,
+            sampler,
+            H=512,
+            W=512,
+            seed=st.session_state.seed,
+            prompt=prompt,
+            filter=state.get("filter"),
+        )
+        with cols[1]:
+            st.image(out[0])

scripts/demo/video_sampling.py

@@ -0,0 +1,280 @@
+import os
+import sys
+
+sys.path.append(os.path.realpath(os.path.join(os.path.dirname(__file__), "../../")))
+from pytorch_lightning import seed_everything
+from scripts.demo.streamlit_helpers import *
+from scripts.demo.sv3d_helpers import *
+
+SAVE_PATH = "outputs/demo/vid/"
+
+VERSION2SPECS = {
+    "svd": {
+        "T": 14,
+        "H": 576,
+        "W": 1024,
+        "C": 4,
+        "f": 8,
+        "config": "configs/inference/svd.yaml",
+        "ckpt": "checkpoints/svd.safetensors",
+        "options": {
+            "discretization": 1,
+            "cfg": 2.5,
+            "sigma_min": 0.002,
+            "sigma_max": 700.0,
+            "rho": 7.0,
+            "guider": 2,
+            "force_uc_zero_embeddings": ["cond_frames", "cond_frames_without_noise"],
+            "num_steps": 25,
+        },
+    },
+    "svd_image_decoder": {
+        "T": 14,
+        "H": 576,
+        "W": 1024,
+        "C": 4,
+        "f": 8,
+        "config": "configs/inference/svd_image_decoder.yaml",
+        "ckpt": "checkpoints/svd_image_decoder.safetensors",
+        "options": {
+            "discretization": 1,
+            "cfg": 2.5,
+            "sigma_min": 0.002,
+            "sigma_max": 700.0,
+            "rho": 7.0,
+            "guider": 2,
+            "force_uc_zero_embeddings": ["cond_frames", "cond_frames_without_noise"],
+            "num_steps": 25,
+        },
+    },
+    "svd_xt": {
+        "T": 25,
+        "H": 576,
+        "W": 1024,
+        "C": 4,
+        "f": 8,
+        "config": "configs/inference/svd.yaml",
+        "ckpt": "checkpoints/svd_xt.safetensors",
+        "options": {
+            "discretization": 1,
+            "cfg": 3.0,
+            "min_cfg": 1.5,
+            "sigma_min": 0.002,
+            "sigma_max": 700.0,
+            "rho": 7.0,
+            "guider": 2,
+            "force_uc_zero_embeddings": ["cond_frames", "cond_frames_without_noise"],
+            "num_steps": 30,
+            "decoding_t": 14,
+        },
+    },
+    "svd_xt_image_decoder": {
+        "T": 25,
+        "H": 576,
+        "W": 1024,
+        "C": 4,
+        "f": 8,
+        "config": "configs/inference/svd_image_decoder.yaml",
+        "ckpt": "checkpoints/svd_xt_image_decoder.safetensors",
+        "options": {
+            "discretization": 1,
+            "cfg": 3.0,
+            "min_cfg": 1.5,
+            "sigma_min": 0.002,
+            "sigma_max": 700.0,
+            "rho": 7.0,
+            "guider": 2,
+            "force_uc_zero_embeddings": ["cond_frames", "cond_frames_without_noise"],
+            "num_steps": 30,
+            "decoding_t": 14,
+        },
+    },
+    "sv3d_u": {
+        "T": 21,
+        "H": 576,
+        "W": 576,
+        "C": 4,
+        "f": 8,
+        "config": "configs/inference/sv3d_u.yaml",
+        "ckpt": "checkpoints/sv3d_u.safetensors",
+        "options": {
+            "discretization": 1,
+            "cfg": 2.5,
+            "sigma_min": 0.002,
+            "sigma_max": 700.0,
+            "rho": 7.0,
+            "guider": 3,
+            "force_uc_zero_embeddings": ["cond_frames", "cond_frames_without_noise"],
+            "num_steps": 50,
+            "decoding_t": 14,
+        },
+    },
+    "sv3d_p": {
+        "T": 21,
+        "H": 576,
+        "W": 576,
+        "C": 4,
+        "f": 8,
+        "config": "configs/inference/sv3d_p.yaml",
+        "ckpt": "checkpoints/sv3d_p.safetensors",
+        "options": {
+            "discretization": 1,
+            "cfg": 2.5,
+            "sigma_min": 0.002,
+            "sigma_max": 700.0,
+            "rho": 7.0,
+            "guider": 3,
+            "force_uc_zero_embeddings": ["cond_frames", "cond_frames_without_noise"],
+            "num_steps": 50,
+            "decoding_t": 14,
+        },
+    },
+}
+
+
+if __name__ == "__main__":
+    st.title("Stable Video Diffusion / SV3D")
+    version = st.selectbox(
+        "Model Version",
+        [k for k in VERSION2SPECS.keys()],
+        0,
+    )
+    version_dict = VERSION2SPECS[version]
+    if st.checkbox("Load Model"):
+        mode = "img2vid"
+    else:
+        mode = "skip"
+
+    H = st.sidebar.number_input(
+        "H", value=version_dict["H"], min_value=64, max_value=2048
+    )
+    W = st.sidebar.number_input(
+        "W", value=version_dict["W"], min_value=64, max_value=2048
+    )
+    T = st.sidebar.number_input(
+        "T", value=version_dict["T"], min_value=0, max_value=128
+    )
+    C = version_dict["C"]
+    F = version_dict["f"]
+    options = version_dict["options"]
+
+    if mode != "skip":
+        state = init_st(version_dict, load_filter=True)
+        if state["msg"]:
+            st.info(state["msg"])
+        model = state["model"]
+
+        ukeys = set(
+            get_unique_embedder_keys_from_conditioner(state["model"].conditioner)
+        )
+
+        value_dict = init_embedder_options(
+            ukeys,
+            {},
+        )
+
+        if "fps" not in ukeys:
+            value_dict["fps"] = 10
+
+        value_dict["image_only_indicator"] = 0
+
+        if mode == "img2vid":
+            img = load_img_for_prediction(W, H)
+            if "sv3d" in version:
+                cond_aug = 1e-5
+            else:
+                cond_aug = st.number_input(
+                    "Conditioning augmentation:", value=0.02, min_value=0.0
+                )
+            value_dict["cond_frames_without_noise"] = img
+            value_dict["cond_frames"] = img + cond_aug * torch.randn_like(img)
+            value_dict["cond_aug"] = cond_aug
+
+        if "sv3d_p" in version:
+            elev_deg = st.number_input("elev_deg", value=5, min_value=-90, max_value=90)
+            trajectory = st.selectbox(
+                "Trajectory",
+                ["same elevation", "dynamic"],
+                0,
+            )
+            if trajectory == "same elevation":
+                value_dict["polars_rad"] = np.array([np.deg2rad(90 - elev_deg)] * T)
+                value_dict["azimuths_rad"] = np.linspace(0, 2 * np.pi, T + 1)[1:]
+            elif trajectory == "dynamic":
+                azim_rad, elev_rad = gen_dynamic_loop(length=21, elev_deg=elev_deg)
+                value_dict["polars_rad"] = np.deg2rad(90) - elev_rad
+                value_dict["azimuths_rad"] = azim_rad
+        elif "sv3d_u" in version:
+            elev_deg = st.number_input("elev_deg", value=5, min_value=-90, max_value=90)
+            value_dict["polars_rad"] = np.array([np.deg2rad(90 - elev_deg)] * T)
+            value_dict["azimuths_rad"] = np.linspace(0, 2 * np.pi, T + 1)[1:]
+
+        seed = st.sidebar.number_input(
+            "seed", value=23, min_value=0, max_value=int(1e9)
+        )
+        seed_everything(seed)
+
+        save_locally, save_path = init_save_locally(
+            os.path.join(SAVE_PATH, version), init_value=True
+        )
+
+        if "sv3d" in version:
+            plot_save_path = os.path.join(save_path, "plot_3D.png")
+            plot_3D(
+                azim=value_dict["azimuths_rad"],
+                polar=value_dict["polars_rad"],
+                save_path=plot_save_path,
+                dynamic=("sv3d_p" in version),
+            )
+            st.image(
+                plot_save_path,
+                f"3D camera trajectory",
+            )
+
+        options["num_frames"] = T
+
+        sampler, num_rows, num_cols = init_sampling(options=options)
+        num_samples = num_rows * num_cols
+
+        decoding_t = st.number_input(
+            "Decode t frames at a time (set small if you are low on VRAM)",
+            value=options.get("decoding_t", T),
+            min_value=1,
+            max_value=int(1e9),
+        )
+
+        if st.checkbox("Overwrite fps in mp4 generator", False):
+            saving_fps = st.number_input(
+                f"saving video at fps:", value=value_dict["fps"], min_value=1
+            )
+        else:
+            saving_fps = value_dict["fps"]
+
+        if st.button("Sample"):
+            out = do_sample(
+                model,
+                sampler,
+                value_dict,
+                num_samples,
+                H,
+                W,
+                C,
+                F,
+                T=T,
+                batch2model_input=["num_video_frames", "image_only_indicator"],
+                force_uc_zero_embeddings=options.get("force_uc_zero_embeddings", None),
+                force_cond_zero_embeddings=options.get(
+                    "force_cond_zero_embeddings", None
+                ),
+                return_latents=False,
+                decoding_t=decoding_t,
+            )
+
+            if isinstance(out, (tuple, list)):
+                samples, samples_z = out
+            else:
+                samples = out
+                samples_z = None
+
+            if save_locally:
+                save_video_as_grid_and_mp4(samples, save_path, T, fps=saving_fps)

scripts/sampling/configs/sv3d_p.yaml

@@ -0,0 +1,132 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.18215
+    disable_first_stage_autocast: True
+    ckpt_path: checkpoints/sv3d_p.safetensors
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.Denoiser
+      params:
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.VScalingWithEDMcNoise
+
+    network_config:
+      target: sgm.modules.diffusionmodules.video_model.VideoUNet
+      params:
+        adm_in_channels: 1280
+        num_classes: sequential
+        use_checkpoint: True
+        in_channels: 8
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [4, 2, 1]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4, 4]
+        num_head_channels: 64
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        spatial_transformer_attn_type: softmax-xformers
+        extra_ff_mix_layer: True
+        use_spatial_context: True
+        merge_strategy: learned_with_images
+        video_kernel_size: [3, 1, 1]
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+        - input_key: cond_frames_without_noise
+          is_trainable: False
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPImagePredictionEmbedder
+          params:
+            n_cond_frames: 1
+            n_copies: 1
+            open_clip_embedding_config:
+              target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+              params:
+                freeze: True
+
+        - input_key: cond_frames
+          is_trainable: False
+          target: sgm.modules.encoders.modules.VideoPredictionEmbedderWithEncoder
+          params:
+            disable_encoder_autocast: True
+            n_cond_frames: 1
+            n_copies: 1
+            is_ae: True
+            encoder_config:
+              target: sgm.models.autoencoder.AutoencoderKLModeOnly
+              params:
+                embed_dim: 4
+                monitor: val/rec_loss
+                ddconfig:
+                  attn_type: vanilla-xformers
+                  double_z: True
+                  z_channels: 4
+                  resolution: 256
+                  in_channels: 3
+                  out_ch: 3
+                  ch: 128
+                  ch_mult: [1, 2, 4, 4]
+                  num_res_blocks: 2
+                  attn_resolutions: []
+                  dropout: 0.0
+                lossconfig:
+                  target: torch.nn.Identity
+
+        - input_key: cond_aug
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: polars_rad
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 512
+
+        - input_key: azimuths_rad
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 512
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencodingEngine
+      params:
+        loss_config:
+          target: torch.nn.Identity
+        regularizer_config:
+          target: sgm.modules.autoencoding.regularizers.DiagonalGaussianRegularizer
+        encoder_config:
+          target: torch.nn.Identity
+        decoder_config:
+          target: sgm.modules.diffusionmodules.model.Decoder
+          params:
+            attn_type: vanilla-xformers
+            double_z: True
+            z_channels: 4
+            resolution: 256
+            in_channels: 3
+            out_ch: 3
+            ch: 128
+            ch_mult: [ 1, 2, 4, 4 ]
+            num_res_blocks: 2
+            attn_resolutions: [ ]
+            dropout: 0.0
+
+    sampler_config:
+      target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
+      params:
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.EDMDiscretization
+          params:
+            sigma_max: 700.0
+
+        guider_config:
+          target: sgm.modules.diffusionmodules.guiders.TrianglePredictionGuider
+          params:
+            max_scale: 2.5

scripts/sampling/configs/sv3d_u.yaml

@@ -0,0 +1,120 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.18215
+    disable_first_stage_autocast: True
+    ckpt_path: checkpoints/sv3d_u.safetensors
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.Denoiser
+      params:
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.VScalingWithEDMcNoise
+
+    network_config:
+      target: sgm.modules.diffusionmodules.video_model.VideoUNet
+      params:
+        adm_in_channels: 256
+        num_classes: sequential
+        use_checkpoint: True
+        in_channels: 8
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [4, 2, 1]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4, 4]
+        num_head_channels: 64
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        spatial_transformer_attn_type: softmax-xformers
+        extra_ff_mix_layer: True
+        use_spatial_context: True
+        merge_strategy: learned_with_images
+        video_kernel_size: [3, 1, 1]
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+        - is_trainable: False
+          input_key: cond_frames_without_noise
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPImagePredictionEmbedder
+          params:
+            n_cond_frames: 1
+            n_copies: 1
+            open_clip_embedding_config:
+              target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+              params:
+                freeze: True
+
+        - input_key: cond_frames
+          is_trainable: False
+          target: sgm.modules.encoders.modules.VideoPredictionEmbedderWithEncoder
+          params:
+            disable_encoder_autocast: True
+            n_cond_frames: 1
+            n_copies: 1
+            is_ae: True
+            encoder_config:
+              target: sgm.models.autoencoder.AutoencoderKLModeOnly
+              params:
+                embed_dim: 4
+                monitor: val/rec_loss
+                ddconfig:
+                  attn_type: vanilla-xformers
+                  double_z: True
+                  z_channels: 4
+                  resolution: 256
+                  in_channels: 3
+                  out_ch: 3
+                  ch: 128
+                  ch_mult: [1, 2, 4, 4]
+                  num_res_blocks: 2
+                  attn_resolutions: []
+                  dropout: 0.0
+                lossconfig:
+                  target: torch.nn.Identity
+
+        - input_key: cond_aug
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencodingEngine
+      params:
+        loss_config:
+          target: torch.nn.Identity
+        regularizer_config:
+          target: sgm.modules.autoencoding.regularizers.DiagonalGaussianRegularizer
+        encoder_config:
+          target: torch.nn.Identity
+        decoder_config:
+          target: sgm.modules.diffusionmodules.model.Decoder
+          params:
+            attn_type: vanilla-xformers
+            double_z: True
+            z_channels: 4
+            resolution: 256
+            in_channels: 3
+            out_ch: 3
+            ch: 128
+            ch_mult: [ 1, 2, 4, 4 ]
+            num_res_blocks: 2
+            attn_resolutions: [ ]
+            dropout: 0.0
+
+    sampler_config:
+      target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
+      params:
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.EDMDiscretization
+          params:
+            sigma_max: 700.0
+
+        guider_config:
+          target: sgm.modules.diffusionmodules.guiders.TrianglePredictionGuider
+          params:
+            max_scale: 2.5

scripts/sampling/configs/svd.yaml

@@ -0,0 +1,146 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.18215
+    disable_first_stage_autocast: True
+    ckpt_path: checkpoints/svd.safetensors
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.Denoiser
+      params:
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.VScalingWithEDMcNoise
+
+    network_config:
+      target: sgm.modules.diffusionmodules.video_model.VideoUNet
+      params:
+        adm_in_channels: 768
+        num_classes: sequential
+        use_checkpoint: True
+        in_channels: 8
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [4, 2, 1]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4, 4]
+        num_head_channels: 64
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        spatial_transformer_attn_type: softmax-xformers
+        extra_ff_mix_layer: True
+        use_spatial_context: True
+        merge_strategy: learned_with_images
+        video_kernel_size: [3, 1, 1]
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+        - is_trainable: False
+          input_key: cond_frames_without_noise
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPImagePredictionEmbedder
+          params:
+            n_cond_frames: 1
+            n_copies: 1
+            open_clip_embedding_config:
+              target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+              params:
+                freeze: True
+
+        - input_key: fps_id
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: motion_bucket_id
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: cond_frames
+          is_trainable: False
+          target: sgm.modules.encoders.modules.VideoPredictionEmbedderWithEncoder
+          params:
+            disable_encoder_autocast: True
+            n_cond_frames: 1
+            n_copies: 1
+            is_ae: True
+            encoder_config:
+              target: sgm.models.autoencoder.AutoencoderKLModeOnly
+              params:
+                embed_dim: 4
+                monitor: val/rec_loss
+                ddconfig:
+                  attn_type: vanilla-xformers
+                  double_z: True
+                  z_channels: 4
+                  resolution: 256
+                  in_channels: 3
+                  out_ch: 3
+                  ch: 128
+                  ch_mult: [1, 2, 4, 4]
+                  num_res_blocks: 2
+                  attn_resolutions: []
+                  dropout: 0.0
+                lossconfig:
+                  target: torch.nn.Identity
+
+        - input_key: cond_aug
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencodingEngine
+      params:
+        loss_config:
+          target: torch.nn.Identity
+        regularizer_config:
+          target: sgm.modules.autoencoding.regularizers.DiagonalGaussianRegularizer
+        encoder_config: 
+          target: sgm.modules.diffusionmodules.model.Encoder
+          params:
+            attn_type: vanilla
+            double_z: True
+            z_channels: 4
+            resolution: 256
+            in_channels: 3
+            out_ch: 3
+            ch: 128
+            ch_mult: [1, 2, 4, 4]
+            num_res_blocks: 2
+            attn_resolutions: []
+            dropout: 0.0
+        decoder_config:
+          target: sgm.modules.autoencoding.temporal_ae.VideoDecoder
+          params:
+            attn_type: vanilla
+            double_z: True
+            z_channels: 4
+            resolution: 256
+            in_channels: 3
+            out_ch: 3
+            ch: 128
+            ch_mult: [1, 2, 4, 4]
+            num_res_blocks: 2
+            attn_resolutions: []
+            dropout: 0.0
+            video_kernel_size: [3, 1, 1]
+
+    sampler_config:
+      target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
+      params:
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.EDMDiscretization
+          params:
+            sigma_max: 700.0
+
+        guider_config:
+          target: sgm.modules.diffusionmodules.guiders.LinearPredictionGuider
+          params:
+            max_scale: 2.5
+            min_scale: 1.0

scripts/sampling/configs/svd_image_decoder.yaml

@@ -0,0 +1,129 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.18215
+    disable_first_stage_autocast: True
+    ckpt_path: checkpoints/svd_image_decoder.safetensors
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.Denoiser
+      params:
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.VScalingWithEDMcNoise
+
+    network_config:
+      target: sgm.modules.diffusionmodules.video_model.VideoUNet
+      params:
+        adm_in_channels: 768
+        num_classes: sequential
+        use_checkpoint: True
+        in_channels: 8
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [4, 2, 1]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4, 4]
+        num_head_channels: 64
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        spatial_transformer_attn_type: softmax-xformers
+        extra_ff_mix_layer: True
+        use_spatial_context: True
+        merge_strategy: learned_with_images
+        video_kernel_size: [3, 1, 1]
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+        - is_trainable: False
+          input_key: cond_frames_without_noise
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPImagePredictionEmbedder
+          params:
+            n_cond_frames: 1
+            n_copies: 1
+            open_clip_embedding_config:
+              target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+              params:
+                freeze: True
+
+        - input_key: fps_id
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: motion_bucket_id
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: cond_frames
+          is_trainable: False
+          target: sgm.modules.encoders.modules.VideoPredictionEmbedderWithEncoder
+          params:
+            disable_encoder_autocast: True
+            n_cond_frames: 1
+            n_copies: 1
+            is_ae: True
+            encoder_config:
+              target: sgm.models.autoencoder.AutoencoderKLModeOnly
+              params:
+                embed_dim: 4
+                monitor: val/rec_loss
+                ddconfig:
+                  attn_type: vanilla-xformers
+                  double_z: True
+                  z_channels: 4
+                  resolution: 256
+                  in_channels: 3
+                  out_ch: 3
+                  ch: 128
+                  ch_mult: [1, 2, 4, 4]
+                  num_res_blocks: 2
+                  attn_resolutions: []
+                  dropout: 0.0
+                lossconfig:
+                  target: torch.nn.Identity
+
+        - input_key: cond_aug
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          attn_type: vanilla-xformers
+          double_z: True
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult: [1, 2, 4, 4]
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    sampler_config:
+      target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
+      params:
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.EDMDiscretization
+          params:
+            sigma_max: 700.0
+
+        guider_config:
+          target: sgm.modules.diffusionmodules.guiders.LinearPredictionGuider
+          params:
+            max_scale: 2.5
+            min_scale: 1.0

scripts/sampling/configs/svd_xt.yaml

@@ -0,0 +1,146 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.18215
+    disable_first_stage_autocast: True
+    ckpt_path: checkpoints/svd_xt.safetensors
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.Denoiser
+      params:
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.VScalingWithEDMcNoise
+
+    network_config:
+      target: sgm.modules.diffusionmodules.video_model.VideoUNet
+      params:
+        adm_in_channels: 768
+        num_classes: sequential
+        use_checkpoint: True
+        in_channels: 8
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [4, 2, 1]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4, 4]
+        num_head_channels: 64
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        spatial_transformer_attn_type: softmax-xformers
+        extra_ff_mix_layer: True
+        use_spatial_context: True
+        merge_strategy: learned_with_images
+        video_kernel_size: [3, 1, 1]
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+        - is_trainable: False
+          input_key: cond_frames_without_noise
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPImagePredictionEmbedder
+          params:
+            n_cond_frames: 1
+            n_copies: 1
+            open_clip_embedding_config:
+              target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+              params:
+                freeze: True
+
+        - input_key: fps_id
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: motion_bucket_id
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: cond_frames
+          is_trainable: False
+          target: sgm.modules.encoders.modules.VideoPredictionEmbedderWithEncoder
+          params:
+            disable_encoder_autocast: True
+            n_cond_frames: 1
+            n_copies: 1
+            is_ae: True
+            encoder_config:
+              target: sgm.models.autoencoder.AutoencoderKLModeOnly
+              params:
+                embed_dim: 4
+                monitor: val/rec_loss
+                ddconfig:
+                  attn_type: vanilla-xformers
+                  double_z: True
+                  z_channels: 4
+                  resolution: 256
+                  in_channels: 3
+                  out_ch: 3
+                  ch: 128
+                  ch_mult: [1, 2, 4, 4]
+                  num_res_blocks: 2
+                  attn_resolutions: []
+                  dropout: 0.0
+                lossconfig:
+                  target: torch.nn.Identity
+
+        - input_key: cond_aug
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencodingEngine
+      params:
+        loss_config:
+          target: torch.nn.Identity
+        regularizer_config:
+          target: sgm.modules.autoencoding.regularizers.DiagonalGaussianRegularizer
+        encoder_config: 
+          target: sgm.modules.diffusionmodules.model.Encoder
+          params:
+            attn_type: vanilla
+            double_z: True
+            z_channels: 4
+            resolution: 256
+            in_channels: 3
+            out_ch: 3
+            ch: 128
+            ch_mult: [1, 2, 4, 4]
+            num_res_blocks: 2
+            attn_resolutions: []
+            dropout: 0.0
+        decoder_config:
+          target: sgm.modules.autoencoding.temporal_ae.VideoDecoder
+          params:
+            attn_type: vanilla
+            double_z: True
+            z_channels: 4
+            resolution: 256
+            in_channels: 3
+            out_ch: 3
+            ch: 128
+            ch_mult: [1, 2, 4, 4]
+            num_res_blocks: 2
+            attn_resolutions: []
+            dropout: 0.0
+            video_kernel_size: [3, 1, 1]
+
+    sampler_config:
+      target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
+      params:
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.EDMDiscretization
+          params:
+            sigma_max: 700.0
+
+        guider_config:
+          target: sgm.modules.diffusionmodules.guiders.LinearPredictionGuider
+          params:
+            max_scale: 3.0
+            min_scale: 1.5

scripts/sampling/configs/svd_xt_1_1.yaml

@@ -0,0 +1,146 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.18215
+    disable_first_stage_autocast: True
+    ckpt_path: checkpoints/svd_xt_1_1.safetensors
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.Denoiser
+      params:
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.VScalingWithEDMcNoise
+
+    network_config:
+      target: sgm.modules.diffusionmodules.video_model.VideoUNet
+      params:
+        adm_in_channels: 768
+        num_classes: sequential
+        use_checkpoint: True
+        in_channels: 8
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [4, 2, 1]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4, 4]
+        num_head_channels: 64
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        spatial_transformer_attn_type: softmax-xformers
+        extra_ff_mix_layer: True
+        use_spatial_context: True
+        merge_strategy: learned_with_images
+        video_kernel_size: [3, 1, 1]
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+        - is_trainable: False
+          input_key: cond_frames_without_noise
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPImagePredictionEmbedder
+          params:
+            n_cond_frames: 1
+            n_copies: 1
+            open_clip_embedding_config:
+              target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+              params:
+                freeze: True
+
+        - input_key: fps_id
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: motion_bucket_id
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: cond_frames
+          is_trainable: False
+          target: sgm.modules.encoders.modules.VideoPredictionEmbedderWithEncoder
+          params:
+            disable_encoder_autocast: True
+            n_cond_frames: 1
+            n_copies: 1
+            is_ae: True
+            encoder_config:
+              target: sgm.models.autoencoder.AutoencoderKLModeOnly
+              params:
+                embed_dim: 4
+                monitor: val/rec_loss
+                ddconfig:
+                  attn_type: vanilla-xformers
+                  double_z: True
+                  z_channels: 4
+                  resolution: 256
+                  in_channels: 3
+                  out_ch: 3
+                  ch: 128
+                  ch_mult: [1, 2, 4, 4]
+                  num_res_blocks: 2
+                  attn_resolutions: []
+                  dropout: 0.0
+                lossconfig:
+                  target: torch.nn.Identity
+
+        - input_key: cond_aug
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencodingEngine
+      params:
+        loss_config:
+          target: torch.nn.Identity
+        regularizer_config:
+          target: sgm.modules.autoencoding.regularizers.DiagonalGaussianRegularizer
+        encoder_config: 
+          target: sgm.modules.diffusionmodules.model.Encoder
+          params:
+            attn_type: vanilla
+            double_z: True
+            z_channels: 4
+            resolution: 256
+            in_channels: 3
+            out_ch: 3
+            ch: 128
+            ch_mult: [1, 2, 4, 4]
+            num_res_blocks: 2
+            attn_resolutions: []
+            dropout: 0.0
+        decoder_config:
+          target: sgm.modules.autoencoding.temporal_ae.VideoDecoder
+          params:
+            attn_type: vanilla
+            double_z: True
+            z_channels: 4
+            resolution: 256
+            in_channels: 3
+            out_ch: 3
+            ch: 128
+            ch_mult: [1, 2, 4, 4]
+            num_res_blocks: 2
+            attn_resolutions: []
+            dropout: 0.0
+            video_kernel_size: [3, 1, 1]
+
+    sampler_config:
+      target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
+      params:
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.EDMDiscretization
+          params:
+            sigma_max: 700.0
+
+        guider_config:
+          target: sgm.modules.diffusionmodules.guiders.LinearPredictionGuider
+          params:
+            max_scale: 3.0
+            min_scale: 1.5

scripts/sampling/configs/svd_xt_image_decoder.yaml

@@ -0,0 +1,129 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.18215
+    disable_first_stage_autocast: True
+    ckpt_path: checkpoints/svd_xt_image_decoder.safetensors
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.Denoiser
+      params:
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.VScalingWithEDMcNoise
+
+    network_config:
+      target: sgm.modules.diffusionmodules.video_model.VideoUNet
+      params:
+        adm_in_channels: 768
+        num_classes: sequential
+        use_checkpoint: True
+        in_channels: 8
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [4, 2, 1]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4, 4]
+        num_head_channels: 64
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        spatial_transformer_attn_type: softmax-xformers
+        extra_ff_mix_layer: True
+        use_spatial_context: True
+        merge_strategy: learned_with_images
+        video_kernel_size: [3, 1, 1]
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+        - is_trainable: False
+          input_key: cond_frames_without_noise
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPImagePredictionEmbedder
+          params:
+            n_cond_frames: 1
+            n_copies: 1
+            open_clip_embedding_config:
+              target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+              params:
+                freeze: True
+
+        - input_key: fps_id
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: motion_bucket_id
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: cond_frames
+          is_trainable: False
+          target: sgm.modules.encoders.modules.VideoPredictionEmbedderWithEncoder
+          params:
+            disable_encoder_autocast: True
+            n_cond_frames: 1
+            n_copies: 1
+            is_ae: True
+            encoder_config:
+              target: sgm.models.autoencoder.AutoencoderKLModeOnly
+              params:
+                embed_dim: 4
+                monitor: val/rec_loss
+                ddconfig:
+                  attn_type: vanilla-xformers
+                  double_z: True
+                  z_channels: 4
+                  resolution: 256
+                  in_channels: 3
+                  out_ch: 3
+                  ch: 128
+                  ch_mult: [1, 2, 4, 4]
+                  num_res_blocks: 2
+                  attn_resolutions: []
+                  dropout: 0.0
+                lossconfig:
+                  target: torch.nn.Identity
+
+        - input_key: cond_aug
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          attn_type: vanilla-xformers
+          double_z: True
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult: [1, 2, 4, 4]
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    sampler_config:
+      target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
+      params:
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.EDMDiscretization
+          params:
+            sigma_max: 700.0
+
+        guider_config:
+          target: sgm.modules.diffusionmodules.guiders.LinearPredictionGuider
+          params:
+            max_scale: 3.0
+            min_scale: 1.5

scripts/sampling/simple_video_sample.py

@@ -0,0 +1,349 @@
+import math
+import os
+import sys
+from glob import glob
+from pathlib import Path
+from typing import List, Optional
+
+sys.path.append(os.path.realpath(os.path.join(os.path.dirname(__file__), "../../")))
+import cv2
+import imageio
+import numpy as np
+import torch
+from einops import rearrange, repeat
+from fire import Fire
+from omegaconf import OmegaConf
+from PIL import Image
+from rembg import remove
+from scripts.util.detection.nsfw_and_watermark_dectection import DeepFloydDataFiltering
+from sgm.inference.helpers import embed_watermark
+from sgm.util import default, instantiate_from_config
+from torchvision.transforms import ToTensor
+
+
+def sample(
+    input_path: str = "assets/test_image.png",  # Can either be image file or folder with image files
+    num_frames: Optional[int] = None,  # 21 for SV3D
+    num_steps: Optional[int] = None,
+    version: str = "svd",
+    fps_id: int = 6,
+    motion_bucket_id: int = 127,
+    cond_aug: float = 0.02,
+    seed: int = 23,
+    decoding_t: int = 14,  # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.
+    device: str = "cuda",
+    output_folder: Optional[str] = None,
+    elevations_deg: Optional[float | List[float]] = 10.0,  # For SV3D
+    azimuths_deg: Optional[List[float]] = None,  # For SV3D
+    image_frame_ratio: Optional[float] = None,
+    verbose: Optional[bool] = False,
+):
+    """
+    Simple script to generate a single sample conditioned on an image `input_path` or multiple images, one for each
+    image file in folder `input_path`. If you run out of VRAM, try decreasing `decoding_t`.
+    """
+
+    if version == "svd":
+        num_frames = default(num_frames, 14)
+        num_steps = default(num_steps, 25)
+        output_folder = default(output_folder, "outputs/simple_video_sample/svd/")
+        model_config = "scripts/sampling/configs/svd.yaml"
+    elif version == "svd_xt":
+        num_frames = default(num_frames, 25)
+        num_steps = default(num_steps, 30)
+        output_folder = default(output_folder, "outputs/simple_video_sample/svd_xt/")
+        model_config = "scripts/sampling/configs/svd_xt.yaml"
+    elif version == "svd_image_decoder":
+        num_frames = default(num_frames, 14)
+        num_steps = default(num_steps, 25)
+        output_folder = default(
+            output_folder, "outputs/simple_video_sample/svd_image_decoder/"
+        )
+        model_config = "scripts/sampling/configs/svd_image_decoder.yaml"
+    elif version == "svd_xt_image_decoder":
+        num_frames = default(num_frames, 25)
+        num_steps = default(num_steps, 30)
+        output_folder = default(
+            output_folder, "outputs/simple_video_sample/svd_xt_image_decoder/"
+        )
+        model_config = "scripts/sampling/configs/svd_xt_image_decoder.yaml"
+    elif version == "sv3d_u":
+        num_frames = 21
+        num_steps = default(num_steps, 50)
+        output_folder = default(output_folder, "outputs/simple_video_sample/sv3d_u/")
+        model_config = "scripts/sampling/configs/sv3d_u.yaml"
+        cond_aug = 1e-5
+    elif version == "sv3d_p":
+        num_frames = 21
+        num_steps = default(num_steps, 50)
+        output_folder = default(output_folder, "outputs/simple_video_sample/sv3d_p/")
+        model_config = "scripts/sampling/configs/sv3d_p.yaml"
+        cond_aug = 1e-5
+        if isinstance(elevations_deg, float) or isinstance(elevations_deg, int):
+            elevations_deg = [elevations_deg] * num_frames
+        assert (
+            len(elevations_deg) == num_frames
+        ), f"Please provide 1 value, or a list of {num_frames} values for elevations_deg! Given {len(elevations_deg)}"
+        polars_rad = [np.deg2rad(90 - e) for e in elevations_deg]
+        if azimuths_deg is None:
+            azimuths_deg = np.linspace(0, 360, num_frames + 1)[1:] % 360
+        assert (
+            len(azimuths_deg) == num_frames
+        ), f"Please provide a list of {num_frames} values for azimuths_deg! Given {len(azimuths_deg)}"
+        azimuths_rad = [np.deg2rad((a - azimuths_deg[-1]) % 360) for a in azimuths_deg]
+        azimuths_rad[:-1].sort()
+    else:
+        raise ValueError(f"Version {version} does not exist.")
+
+    model, filter = load_model(
+        model_config,
+        device,
+        num_frames,
+        num_steps,
+        verbose=verbose,
+    )
+    torch.manual_seed(seed)
+
+    path = Path(input_path)
+    all_img_paths = []
+    if path.is_file():
+        if any([input_path.endswith(x) for x in ["jpg", "jpeg", "png"]]):
+            all_img_paths = [input_path]
+        else:
+            raise ValueError("Path is not valid image file.")
+    elif path.is_dir():
+        all_img_paths = sorted(
+            [
+                f
+                for f in path.iterdir()
+                if f.is_file() and f.suffix.lower() in [".jpg", ".jpeg", ".png"]
+            ]
+        )
+        if len(all_img_paths) == 0:
+            raise ValueError("Folder does not contain any images.")
+    else:
+        raise ValueError
+
+    for input_img_path in all_img_paths:
+        if "sv3d" in version:
+            image = Image.open(input_img_path)
+            if image.mode == "RGBA":
+                pass
+            else:
+                # remove bg
+                image.thumbnail([768, 768], Image.Resampling.LANCZOS)
+                image = remove(image.convert("RGBA"), alpha_matting=True)
+
+            # resize object in frame
+            image_arr = np.array(image)
+            in_w, in_h = image_arr.shape[:2]
+            ret, mask = cv2.threshold(
+                np.array(image.split()[-1]), 0, 255, cv2.THRESH_BINARY
+            )
+            x, y, w, h = cv2.boundingRect(mask)
+            max_size = max(w, h)
+            side_len = (
+                int(max_size / image_frame_ratio)
+                if image_frame_ratio is not None
+                else in_w
+            )
+            padded_image = np.zeros((side_len, side_len, 4), dtype=np.uint8)
+            center = side_len // 2
+            padded_image[
+                center - h // 2 : center - h // 2 + h,
+                center - w // 2 : center - w // 2 + w,
+            ] = image_arr[y : y + h, x : x + w]
+            # resize frame to 576x576
+            rgba = Image.fromarray(padded_image).resize((576, 576), Image.LANCZOS)
+            # white bg
+            rgba_arr = np.array(rgba) / 255.0
+            rgb = rgba_arr[..., :3] * rgba_arr[..., -1:] + (1 - rgba_arr[..., -1:])
+            input_image = Image.fromarray((rgb * 255).astype(np.uint8))
+
+        else:
+            with Image.open(input_img_path) as image:
+                if image.mode == "RGBA":
+                    input_image = image.convert("RGB")
+                w, h = image.size
+
+                if h % 64 != 0 or w % 64 != 0:
+                    width, height = map(lambda x: x - x % 64, (w, h))
+                    input_image = input_image.resize((width, height))
+                    print(
+                        f"WARNING: Your image is of size {h}x{w} which is not divisible by 64. We are resizing to {height}x{width}!"
+                    )
+
+        image = ToTensor()(input_image)
+        image = image * 2.0 - 1.0
+
+        image = image.unsqueeze(0).to(device)
+        H, W = image.shape[2:]
+        assert image.shape[1] == 3
+        F = 8
+        C = 4
+        shape = (num_frames, C, H // F, W // F)
+        if (H, W) != (576, 1024) and "sv3d" not in version:
+            print(
+                "WARNING: The conditioning frame you provided is not 576x1024. This leads to suboptimal performance as model was only trained on 576x1024. Consider increasing `cond_aug`."
+            )
+        if (H, W) != (576, 576) and "sv3d" in version:
+            print(
+                "WARNING: The conditioning frame you provided is not 576x576. This leads to suboptimal performance as model was only trained on 576x576."
+            )
+        if motion_bucket_id > 255:
+            print(
+                "WARNING: High motion bucket! This may lead to suboptimal performance."
+            )
+
+        if fps_id < 5:
+            print("WARNING: Small fps value! This may lead to suboptimal performance.")
+
+        if fps_id > 30:
+            print("WARNING: Large fps value! This may lead to suboptimal performance.")
+
+        value_dict = {}
+        value_dict["cond_frames_without_noise"] = image
+        value_dict["motion_bucket_id"] = motion_bucket_id
+        value_dict["fps_id"] = fps_id
+        value_dict["cond_aug"] = cond_aug
+        value_dict["cond_frames"] = image + cond_aug * torch.randn_like(image)
+        if "sv3d_p" in version:
+            value_dict["polars_rad"] = polars_rad
+            value_dict["azimuths_rad"] = azimuths_rad
+
+        with torch.no_grad():
+            with torch.autocast(device):
+                batch, batch_uc = get_batch(
+                    get_unique_embedder_keys_from_conditioner(model.conditioner),
+                    value_dict,
+                    [1, num_frames],
+                    T=num_frames,
+                    device=device,
+                )
+                c, uc = model.conditioner.get_unconditional_conditioning(
+                    batch,
+                    batch_uc=batch_uc,
+                    force_uc_zero_embeddings=[
+                        "cond_frames",
+                        "cond_frames_without_noise",
+                    ],
+                )
+
+                for k in ["crossattn", "concat"]:
+                    uc[k] = repeat(uc[k], "b ... -> b t ...", t=num_frames)
+                    uc[k] = rearrange(uc[k], "b t ... -> (b t) ...", t=num_frames)
+                    c[k] = repeat(c[k], "b ... -> b t ...", t=num_frames)
+                    c[k] = rearrange(c[k], "b t ... -> (b t) ...", t=num_frames)
+
+                randn = torch.randn(shape, device=device)
+
+                additional_model_inputs = {}
+                additional_model_inputs["image_only_indicator"] = torch.zeros(
+                    2, num_frames
+                ).to(device)
+                additional_model_inputs["num_video_frames"] = batch["num_video_frames"]
+
+                def denoiser(input, sigma, c):
+                    return model.denoiser(
+                        model.model, input, sigma, c, **additional_model_inputs
+                    )
+
+                samples_z = model.sampler(denoiser, randn, cond=c, uc=uc)
+                model.en_and_decode_n_samples_a_time = decoding_t
+                samples_x = model.decode_first_stage(samples_z)
+                if "sv3d" in version:
+                    samples_x[-1:] = value_dict["cond_frames_without_noise"]
+                samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
+
+                os.makedirs(output_folder, exist_ok=True)
+                base_count = len(glob(os.path.join(output_folder, "*.mp4")))
+
+                imageio.imwrite(
+                    os.path.join(output_folder, f"{base_count:06d}.jpg"), input_image
+                )
+
+                samples = embed_watermark(samples)
+                samples = filter(samples)
+                vid = (
+                    (rearrange(samples, "t c h w -> t h w c") * 255)
+                    .cpu()
+                    .numpy()
+                    .astype(np.uint8)
+                )
+                video_path = os.path.join(output_folder, f"{base_count:06d}.mp4")
+                imageio.mimwrite(video_path, vid)
+
+
+def get_unique_embedder_keys_from_conditioner(conditioner):
+    return list(set([x.input_key for x in conditioner.embedders]))
+
+
+def get_batch(keys, value_dict, N, T, device):
+    batch = {}
+    batch_uc = {}
+
+    for key in keys:
+        if key == "fps_id":
+            batch[key] = (
+                torch.tensor([value_dict["fps_id"]])
+                .to(device)
+                .repeat(int(math.prod(N)))
+            )
+        elif key == "motion_bucket_id":
+            batch[key] = (
+                torch.tensor([value_dict["motion_bucket_id"]])
+                .to(device)
+                .repeat(int(math.prod(N)))
+            )
+        elif key == "cond_aug":
+            batch[key] = repeat(
+                torch.tensor([value_dict["cond_aug"]]).to(device),
+                "1 -> b",
+                b=math.prod(N),
+            )
+        elif key == "cond_frames" or key == "cond_frames_without_noise":
+            batch[key] = repeat(value_dict[key], "1 ... -> b ...", b=N[0])
+        elif key == "polars_rad" or key == "azimuths_rad":
+            batch[key] = torch.tensor(value_dict[key]).to(device).repeat(N[0])
+        else:
+            batch[key] = value_dict[key]
+
+    if T is not None:
+        batch["num_video_frames"] = T
+
+    for key in batch.keys():
+        if key not in batch_uc and isinstance(batch[key], torch.Tensor):
+            batch_uc[key] = torch.clone(batch[key])
+    return batch, batch_uc
+
+
+def load_model(
+    config: str,
+    device: str,
+    num_frames: int,
+    num_steps: int,
+    verbose: bool = False,
+):
+    config = OmegaConf.load(config)
+    if device == "cuda":
+        config.model.params.conditioner_config.params.emb_models[
+            0
+        ].params.open_clip_embedding_config.params.init_device = device
+
+    config.model.params.sampler_config.params.verbose = verbose
+    config.model.params.sampler_config.params.num_steps = num_steps
+    config.model.params.sampler_config.params.guider_config.params.num_frames = (
+        num_frames
+    )
+    if device == "cuda":
+        with torch.device(device):
+            model = instantiate_from_config(config.model).to(device).eval()
+    else:
+        model = instantiate_from_config(config.model).to(device).eval()
+
+    filter = DeepFloydDataFiltering(verbose=False, device=device)
+    return model, filter
+
+
+if __name__ == "__main__":
+    Fire(sample)

scripts/tests/attention.py

@@ -0,0 +1,319 @@
+import einops
+import torch
+import torch.nn.functional as F
+import torch.utils.benchmark as benchmark
+from torch.backends.cuda import SDPBackend
+
+from sgm.modules.attention import BasicTransformerBlock, SpatialTransformer
+
+
+def benchmark_attn():
+    # Lets define a helpful benchmarking function:
+    # https://pytorch.org/tutorials/intermediate/scaled_dot_product_attention_tutorial.html
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    def benchmark_torch_function_in_microseconds(f, *args, **kwargs):
+        t0 = benchmark.Timer(
+            stmt="f(*args, **kwargs)", globals={"args": args, "kwargs": kwargs, "f": f}
+        )
+        return t0.blocked_autorange().mean * 1e6
+
+    # Lets define the hyper-parameters of our input
+    batch_size = 32
+    max_sequence_len = 1024
+    num_heads = 32
+    embed_dimension = 32
+
+    dtype = torch.float16
+
+    query = torch.rand(
+        batch_size,
+        num_heads,
+        max_sequence_len,
+        embed_dimension,
+        device=device,
+        dtype=dtype,
+    )
+    key = torch.rand(
+        batch_size,
+        num_heads,
+        max_sequence_len,
+        embed_dimension,
+        device=device,
+        dtype=dtype,
+    )
+    value = torch.rand(
+        batch_size,
+        num_heads,
+        max_sequence_len,
+        embed_dimension,
+        device=device,
+        dtype=dtype,
+    )
+
+    print(f"q/k/v shape:", query.shape, key.shape, value.shape)
+
+    # Lets explore the speed of each of the 3 implementations
+    from torch.backends.cuda import SDPBackend, sdp_kernel
+
+    # Helpful arguments mapper
+    backend_map = {
+        SDPBackend.MATH: {
+            "enable_math": True,
+            "enable_flash": False,
+            "enable_mem_efficient": False,
+        },
+        SDPBackend.FLASH_ATTENTION: {
+            "enable_math": False,
+            "enable_flash": True,
+            "enable_mem_efficient": False,
+        },
+        SDPBackend.EFFICIENT_ATTENTION: {
+            "enable_math": False,
+            "enable_flash": False,
+            "enable_mem_efficient": True,
+        },
+    }
+
+    from torch.profiler import ProfilerActivity, profile, record_function
+
+    activities = [ProfilerActivity.CPU, ProfilerActivity.CUDA]
+
+    print(
+        f"The default implementation runs in {benchmark_torch_function_in_microseconds(F.scaled_dot_product_attention, query, key, value):.3f} microseconds"
+    )
+    with profile(
+        activities=activities, record_shapes=False, profile_memory=True
+    ) as prof:
+        with record_function("Default detailed stats"):
+            for _ in range(25):
+                o = F.scaled_dot_product_attention(query, key, value)
+    print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
+
+    print(
+        f"The math implementation runs in {benchmark_torch_function_in_microseconds(F.scaled_dot_product_attention, query, key, value):.3f} microseconds"
+    )
+    with sdp_kernel(**backend_map[SDPBackend.MATH]):
+        with profile(
+            activities=activities, record_shapes=False, profile_memory=True
+        ) as prof:
+            with record_function("Math implmentation stats"):
+                for _ in range(25):
+                    o = F.scaled_dot_product_attention(query, key, value)
+        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
+
+    with sdp_kernel(**backend_map[SDPBackend.FLASH_ATTENTION]):
+        try:
+            print(
+                f"The flash attention implementation runs in {benchmark_torch_function_in_microseconds(F.scaled_dot_product_attention, query, key, value):.3f} microseconds"
+            )
+        except RuntimeError:
+            print("FlashAttention is not supported. See warnings for reasons.")
+        with profile(
+            activities=activities, record_shapes=False, profile_memory=True
+        ) as prof:
+            with record_function("FlashAttention stats"):
+                for _ in range(25):
+                    o = F.scaled_dot_product_attention(query, key, value)
+        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
+
+    with sdp_kernel(**backend_map[SDPBackend.EFFICIENT_ATTENTION]):
+        try:
+            print(
+                f"The memory efficient implementation runs in {benchmark_torch_function_in_microseconds(F.scaled_dot_product_attention, query, key, value):.3f} microseconds"
+            )
+        except RuntimeError:
+            print("EfficientAttention is not supported. See warnings for reasons.")
+        with profile(
+            activities=activities, record_shapes=False, profile_memory=True
+        ) as prof:
+            with record_function("EfficientAttention stats"):
+                for _ in range(25):
+                    o = F.scaled_dot_product_attention(query, key, value)
+        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
+
+
+def run_model(model, x, context):
+    return model(x, context)
+
+
+def benchmark_transformer_blocks():
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    import torch.utils.benchmark as benchmark
+
+    def benchmark_torch_function_in_microseconds(f, *args, **kwargs):
+        t0 = benchmark.Timer(
+            stmt="f(*args, **kwargs)", globals={"args": args, "kwargs": kwargs, "f": f}
+        )
+        return t0.blocked_autorange().mean * 1e6
+
+    checkpoint = True
+    compile = False
+
+    batch_size = 32
+    h, w = 64, 64
+    context_len = 77
+    embed_dimension = 1024
+    context_dim = 1024
+    d_head = 64
+
+    transformer_depth = 4
+
+    n_heads = embed_dimension // d_head
+
+    dtype = torch.float16
+
+    model_native = SpatialTransformer(
+        embed_dimension,
+        n_heads,
+        d_head,
+        context_dim=context_dim,
+        use_linear=True,
+        use_checkpoint=checkpoint,
+        attn_type="softmax",
+        depth=transformer_depth,
+        sdp_backend=SDPBackend.FLASH_ATTENTION,
+    ).to(device)
+    model_efficient_attn = SpatialTransformer(
+        embed_dimension,
+        n_heads,
+        d_head,
+        context_dim=context_dim,
+        use_linear=True,
+        depth=transformer_depth,
+        use_checkpoint=checkpoint,
+        attn_type="softmax-xformers",
+    ).to(device)
+    if not checkpoint and compile:
+        print("compiling models")
+        model_native = torch.compile(model_native)
+        model_efficient_attn = torch.compile(model_efficient_attn)
+
+    x = torch.rand(batch_size, embed_dimension, h, w, device=device, dtype=dtype)
+    c = torch.rand(batch_size, context_len, context_dim, device=device, dtype=dtype)
+
+    from torch.profiler import ProfilerActivity, profile, record_function
+
+    activities = [ProfilerActivity.CPU, ProfilerActivity.CUDA]
+
+    with torch.autocast("cuda"):
+        print(
+            f"The native model runs in {benchmark_torch_function_in_microseconds(model_native.forward, x, c):.3f} microseconds"
+        )
+        print(
+            f"The efficientattn model runs in {benchmark_torch_function_in_microseconds(model_efficient_attn.forward, x, c):.3f} microseconds"
+        )
+
+        print(75 * "+")
+        print("NATIVE")
+        print(75 * "+")
+        torch.cuda.reset_peak_memory_stats()
+        with profile(
+            activities=activities, record_shapes=False, profile_memory=True
+        ) as prof:
+            with record_function("NativeAttention stats"):
+                for _ in range(25):
+                    model_native(x, c)
+        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
+        print(torch.cuda.max_memory_allocated() * 1e-9, "GB used by native block")
+
+        print(75 * "+")
+        print("Xformers")
+        print(75 * "+")
+        torch.cuda.reset_peak_memory_stats()
+        with profile(
+            activities=activities, record_shapes=False, profile_memory=True
+        ) as prof:
+            with record_function("xformers stats"):
+                for _ in range(25):
+                    model_efficient_attn(x, c)
+        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
+        print(torch.cuda.max_memory_allocated() * 1e-9, "GB used by xformers block")
+
+
+def test01():
+    # conv1x1 vs linear
+    from sgm.util import count_params
+
+    conv = torch.nn.Conv2d(3, 32, kernel_size=1).cuda()
+    print(count_params(conv))
+    linear = torch.nn.Linear(3, 32).cuda()
+    print(count_params(linear))
+
+    print(conv.weight.shape)
+
+    # use same initialization
+    linear.weight = torch.nn.Parameter(conv.weight.squeeze(-1).squeeze(-1))
+    linear.bias = torch.nn.Parameter(conv.bias)
+
+    print(linear.weight.shape)
+
+    x = torch.randn(11, 3, 64, 64).cuda()
+
+    xr = einops.rearrange(x, "b c h w -> b (h w) c").contiguous()
+    print(xr.shape)
+    out_linear = linear(xr)
+    print(out_linear.mean(), out_linear.shape)
+
+    out_conv = conv(x)
+    print(out_conv.mean(), out_conv.shape)
+    print("done with test01.\n")
+
+
+def test02():
+    # try cosine flash attention
+    import time
+
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+    torch.backends.cudnn.benchmark = True
+    print("testing cosine flash attention...")
+    DIM = 1024
+    SEQLEN = 4096
+    BS = 16
+
+    print(" softmax (vanilla) first...")
+    model = BasicTransformerBlock(
+        dim=DIM,
+        n_heads=16,
+        d_head=64,
+        dropout=0.0,
+        context_dim=None,
+        attn_mode="softmax",
+    ).cuda()
+    try:
+        x = torch.randn(BS, SEQLEN, DIM).cuda()
+        tic = time.time()
+        y = model(x)
+        toc = time.time()
+        print(y.shape, toc - tic)
+    except RuntimeError as e:
+        # likely oom
+        print(str(e))
+
+    print("\n now flash-cosine...")
+    model = BasicTransformerBlock(
+        dim=DIM,
+        n_heads=16,
+        d_head=64,
+        dropout=0.0,
+        context_dim=None,
+        attn_mode="flash-cosine",
+    ).cuda()
+    x = torch.randn(BS, SEQLEN, DIM).cuda()
+    tic = time.time()
+    y = model(x)
+    toc = time.time()
+    print(y.shape, toc - tic)
+    print("done with test02.\n")
+
+
+if __name__ == "__main__":
+    # test01()
+    # test02()
+    # test03()
+
+    # benchmark_attn()
+    benchmark_transformer_blocks()
+
+    print("done.")

scripts/util/detection/nsfw_and_watermark_dectection.py

@@ -1,9 +1,10 @@
 import os
-import torch
+
+import clip
 import numpy as np
+import torch
 import torchvision.transforms as T
 from PIL import Image
-import clip
 
 RESOURCES_ROOT = "scripts/util/detection/"
 
@@ -36,10 +37,13 @@ def clip_process_images(images: torch.Tensor) -> torch.Tensor:
 
 
 class DeepFloydDataFiltering(object):
-    def __init__(self, verbose: bool = False):
+    def __init__(
+        self, verbose: bool = False, device: torch.device = torch.device("cpu")
+    ):
         super().__init__()
         self.verbose = verbose
-        self.clip_model, _ = clip.load("ViT-L/14", device="cpu")
+        self._device = None
+        self.clip_model, _ = clip.load("ViT-L/14", device=device)
         self.clip_model.eval()
 
         self.cpu_w_weights, self.cpu_w_biases = load_model_weights(
@@ -53,7 +57,9 @@ class DeepFloydDataFiltering(object):
     @torch.inference_mode()
     def __call__(self, images: torch.Tensor) -> torch.Tensor:
         imgs = clip_process_images(images)
-        image_features = self.clip_model.encode_image(imgs.to("cpu"))
+        if self._device is None:
+            self._device = next(p for p in self.clip_model.parameters()).device
+        image_features = self.clip_model.encode_image(imgs.to(self._device))
         image_features = image_features.detach().cpu().numpy().astype(np.float16)
         p_pred = predict_proba(image_features, self.cpu_p_weights, self.cpu_p_biases)
         w_pred = predict_proba(image_features, self.cpu_w_weights, self.cpu_w_biases)

setup.py

@@ -1,13 +0,0 @@
-from setuptools import find_packages, setup
-
-setup(
-    name="sgm",
-    version="0.0.1",
-    packages=find_packages(),
-    python_requires=">=3.8",
-    py_modules=["sgm"],
-    description="Stability Generative Models",
-    long_description=open("README.md", "r", encoding="utf-8").read(),
-    long_description_content_type="text/markdown",
-    url="https://github.com/Stability-AI/generative-models",
-)

sgm/__init__.py

@@ -1,3 +1,4 @@
-from .data import StableDataModuleFromConfig
 from .models import AutoencodingEngine, DiffusionEngine
-from .util import instantiate_from_config
+from .util import get_configs_path, instantiate_from_config
+
+__version__ = "0.1.0"

sgm/data/cifar10.py

@@ -1,7 +1,7 @@
-import torchvision
 import pytorch_lightning as pl
-from torchvision import transforms
+import torchvision
 from torch.utils.data import DataLoader, Dataset
+from torchvision import transforms
 
 
 class CIFAR10DataDictWrapper(Dataset):

sgm/data/mnist.py

@@ -1,7 +1,7 @@
-import torchvision
 import pytorch_lightning as pl
-from torchvision import transforms
+import torchvision
 from torch.utils.data import DataLoader, Dataset
+from torchvision import transforms
 
 
 class MNISTDataDictWrapper(Dataset):

sgm/inference/api.py

@@ -0,0 +1,385 @@
+import pathlib
+from dataclasses import asdict, dataclass
+from enum import Enum
+from typing import Optional
+
+from omegaconf import OmegaConf
+
+from sgm.inference.helpers import (Img2ImgDiscretizationWrapper, do_img2img,
+                                   do_sample)
+from sgm.modules.diffusionmodules.sampling import (DPMPP2MSampler,
+                                                   DPMPP2SAncestralSampler,
+                                                   EulerAncestralSampler,
+                                                   EulerEDMSampler,
+                                                   HeunEDMSampler,
+                                                   LinearMultistepSampler)
+from sgm.util import load_model_from_config
+
+
+class ModelArchitecture(str, Enum):
+    SD_2_1 = "stable-diffusion-v2-1"
+    SD_2_1_768 = "stable-diffusion-v2-1-768"
+    SDXL_V0_9_BASE = "stable-diffusion-xl-v0-9-base"
+    SDXL_V0_9_REFINER = "stable-diffusion-xl-v0-9-refiner"
+    SDXL_V1_BASE = "stable-diffusion-xl-v1-base"
+    SDXL_V1_REFINER = "stable-diffusion-xl-v1-refiner"
+
+
+class Sampler(str, Enum):
+    EULER_EDM = "EulerEDMSampler"
+    HEUN_EDM = "HeunEDMSampler"
+    EULER_ANCESTRAL = "EulerAncestralSampler"
+    DPMPP2S_ANCESTRAL = "DPMPP2SAncestralSampler"
+    DPMPP2M = "DPMPP2MSampler"
+    LINEAR_MULTISTEP = "LinearMultistepSampler"
+
+
+class Discretization(str, Enum):
+    LEGACY_DDPM = "LegacyDDPMDiscretization"
+    EDM = "EDMDiscretization"
+
+
+class Guider(str, Enum):
+    VANILLA = "VanillaCFG"
+    IDENTITY = "IdentityGuider"
+
+
+class Thresholder(str, Enum):
+    NONE = "None"
+
+
+@dataclass
+class SamplingParams:
+    width: int = 1024
+    height: int = 1024
+    steps: int = 50
+    sampler: Sampler = Sampler.DPMPP2M
+    discretization: Discretization = Discretization.LEGACY_DDPM
+    guider: Guider = Guider.VANILLA
+    thresholder: Thresholder = Thresholder.NONE
+    scale: float = 6.0
+    aesthetic_score: float = 5.0
+    negative_aesthetic_score: float = 5.0
+    img2img_strength: float = 1.0
+    orig_width: int = 1024
+    orig_height: int = 1024
+    crop_coords_top: int = 0
+    crop_coords_left: int = 0
+    sigma_min: float = 0.0292
+    sigma_max: float = 14.6146
+    rho: float = 3.0
+    s_churn: float = 0.0
+    s_tmin: float = 0.0
+    s_tmax: float = 999.0
+    s_noise: float = 1.0
+    eta: float = 1.0
+    order: int = 4
+
+
+@dataclass
+class SamplingSpec:
+    width: int
+    height: int
+    channels: int
+    factor: int
+    is_legacy: bool
+    config: str
+    ckpt: str
+    is_guided: bool
+
+
+model_specs = {
+    ModelArchitecture.SD_2_1: SamplingSpec(
+        height=512,
+        width=512,
+        channels=4,
+        factor=8,
+        is_legacy=True,
+        config="sd_2_1.yaml",
+        ckpt="v2-1_512-ema-pruned.safetensors",
+        is_guided=True,
+    ),
+    ModelArchitecture.SD_2_1_768: SamplingSpec(
+        height=768,
+        width=768,
+        channels=4,
+        factor=8,
+        is_legacy=True,
+        config="sd_2_1_768.yaml",
+        ckpt="v2-1_768-ema-pruned.safetensors",
+        is_guided=True,
+    ),
+    ModelArchitecture.SDXL_V0_9_BASE: SamplingSpec(
+        height=1024,
+        width=1024,
+        channels=4,
+        factor=8,
+        is_legacy=False,
+        config="sd_xl_base.yaml",
+        ckpt="sd_xl_base_0.9.safetensors",
+        is_guided=True,
+    ),
+    ModelArchitecture.SDXL_V0_9_REFINER: SamplingSpec(
+        height=1024,
+        width=1024,
+        channels=4,
+        factor=8,
+        is_legacy=True,
+        config="sd_xl_refiner.yaml",
+        ckpt="sd_xl_refiner_0.9.safetensors",
+        is_guided=True,
+    ),
+    ModelArchitecture.SDXL_V1_BASE: SamplingSpec(
+        height=1024,
+        width=1024,
+        channels=4,
+        factor=8,
+        is_legacy=False,
+        config="sd_xl_base.yaml",
+        ckpt="sd_xl_base_1.0.safetensors",
+        is_guided=True,
+    ),
+    ModelArchitecture.SDXL_V1_REFINER: SamplingSpec(
+        height=1024,
+        width=1024,
+        channels=4,
+        factor=8,
+        is_legacy=True,
+        config="sd_xl_refiner.yaml",
+        ckpt="sd_xl_refiner_1.0.safetensors",
+        is_guided=True,
+    ),
+}
+
+
+class SamplingPipeline:
+    def __init__(
+        self,
+        model_id: ModelArchitecture,
+        model_path="checkpoints",
+        config_path="configs/inference",
+        device="cuda",
+        use_fp16=True,
+    ) -> None:
+        if model_id not in model_specs:
+            raise ValueError(f"Model {model_id} not supported")
+        self.model_id = model_id
+        self.specs = model_specs[self.model_id]
+        self.config = str(pathlib.Path(config_path, self.specs.config))
+        self.ckpt = str(pathlib.Path(model_path, self.specs.ckpt))
+        self.device = device
+        self.model = self._load_model(device=device, use_fp16=use_fp16)
+
+    def _load_model(self, device="cuda", use_fp16=True):
+        config = OmegaConf.load(self.config)
+        model = load_model_from_config(config, self.ckpt)
+        if model is None:
+            raise ValueError(f"Model {self.model_id} could not be loaded")
+        model.to(device)
+        if use_fp16:
+            model.conditioner.half()
+            model.model.half()
+        return model
+
+    def text_to_image(
+        self,
+        params: SamplingParams,
+        prompt: str,
+        negative_prompt: str = "",
+        samples: int = 1,
+        return_latents: bool = False,
+    ):
+        sampler = get_sampler_config(params)
+        value_dict = asdict(params)
+        value_dict["prompt"] = prompt
+        value_dict["negative_prompt"] = negative_prompt
+        value_dict["target_width"] = params.width
+        value_dict["target_height"] = params.height
+        return do_sample(
+            self.model,
+            sampler,
+            value_dict,
+            samples,
+            params.height,
+            params.width,
+            self.specs.channels,
+            self.specs.factor,
+            force_uc_zero_embeddings=["txt"] if not self.specs.is_legacy else [],
+            return_latents=return_latents,
+            filter=None,
+        )
+
+    def image_to_image(
+        self,
+        params: SamplingParams,
+        image,
+        prompt: str,
+        negative_prompt: str = "",
+        samples: int = 1,
+        return_latents: bool = False,
+    ):
+        sampler = get_sampler_config(params)
+
+        if params.img2img_strength < 1.0:
+            sampler.discretization = Img2ImgDiscretizationWrapper(
+                sampler.discretization,
+                strength=params.img2img_strength,
+            )
+        height, width = image.shape[2], image.shape[3]
+        value_dict = asdict(params)
+        value_dict["prompt"] = prompt
+        value_dict["negative_prompt"] = negative_prompt
+        value_dict["target_width"] = width
+        value_dict["target_height"] = height
+        return do_img2img(
+            image,
+            self.model,
+            sampler,
+            value_dict,
+            samples,
+            force_uc_zero_embeddings=["txt"] if not self.specs.is_legacy else [],
+            return_latents=return_latents,
+            filter=None,
+        )
+
+    def refiner(
+        self,
+        params: SamplingParams,
+        image,
+        prompt: str,
+        negative_prompt: Optional[str] = None,
+        samples: int = 1,
+        return_latents: bool = False,
+    ):
+        sampler = get_sampler_config(params)
+        value_dict = {
+            "orig_width": image.shape[3] * 8,
+            "orig_height": image.shape[2] * 8,
+            "target_width": image.shape[3] * 8,
+            "target_height": image.shape[2] * 8,
+            "prompt": prompt,
+            "negative_prompt": negative_prompt,
+            "crop_coords_top": 0,
+            "crop_coords_left": 0,
+            "aesthetic_score": 6.0,
+            "negative_aesthetic_score": 2.5,
+        }
+
+        return do_img2img(
+            image,
+            self.model,
+            sampler,
+            value_dict,
+            samples,
+            skip_encode=True,
+            return_latents=return_latents,
+            filter=None,
+        )
+
+
+def get_guider_config(params: SamplingParams):
+    if params.guider == Guider.IDENTITY:
+        guider_config = {
+            "target": "sgm.modules.diffusionmodules.guiders.IdentityGuider"
+        }
+    elif params.guider == Guider.VANILLA:
+        scale = params.scale
+
+        thresholder = params.thresholder
+
+        if thresholder == Thresholder.NONE:
+            dyn_thresh_config = {
+                "target": "sgm.modules.diffusionmodules.sampling_utils.NoDynamicThresholding"
+            }
+        else:
+            raise NotImplementedError
+
+        guider_config = {
+            "target": "sgm.modules.diffusionmodules.guiders.VanillaCFG",
+            "params": {"scale": scale, "dyn_thresh_config": dyn_thresh_config},
+        }
+    else:
+        raise NotImplementedError
+    return guider_config
+
+
+def get_discretization_config(params: SamplingParams):
+    if params.discretization == Discretization.LEGACY_DDPM:
+        discretization_config = {
+            "target": "sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization",
+        }
+    elif params.discretization == Discretization.EDM:
+        discretization_config = {
+            "target": "sgm.modules.diffusionmodules.discretizer.EDMDiscretization",
+            "params": {
+                "sigma_min": params.sigma_min,
+                "sigma_max": params.sigma_max,
+                "rho": params.rho,
+            },
+        }
+    else:
+        raise ValueError(f"unknown discretization {params.discretization}")
+    return discretization_config
+
+
+def get_sampler_config(params: SamplingParams):
+    discretization_config = get_discretization_config(params)
+    guider_config = get_guider_config(params)
+    sampler = None
+    if params.sampler == Sampler.EULER_EDM:
+        return EulerEDMSampler(
+            num_steps=params.steps,
+            discretization_config=discretization_config,
+            guider_config=guider_config,
+            s_churn=params.s_churn,
+            s_tmin=params.s_tmin,
+            s_tmax=params.s_tmax,
+            s_noise=params.s_noise,
+            verbose=True,
+        )
+    if params.sampler == Sampler.HEUN_EDM:
+        return HeunEDMSampler(
+            num_steps=params.steps,
+            discretization_config=discretization_config,
+            guider_config=guider_config,
+            s_churn=params.s_churn,
+            s_tmin=params.s_tmin,
+            s_tmax=params.s_tmax,
+            s_noise=params.s_noise,
+            verbose=True,
+        )
+    if params.sampler == Sampler.EULER_ANCESTRAL:
+        return EulerAncestralSampler(
+            num_steps=params.steps,
+            discretization_config=discretization_config,
+            guider_config=guider_config,
+            eta=params.eta,
+            s_noise=params.s_noise,
+            verbose=True,
+        )
+    if params.sampler == Sampler.DPMPP2S_ANCESTRAL:
+        return DPMPP2SAncestralSampler(
+            num_steps=params.steps,
+            discretization_config=discretization_config,
+            guider_config=guider_config,
+            eta=params.eta,
+            s_noise=params.s_noise,
+            verbose=True,
+        )
+    if params.sampler == Sampler.DPMPP2M:
+        return DPMPP2MSampler(
+            num_steps=params.steps,
+            discretization_config=discretization_config,
+            guider_config=guider_config,
+            verbose=True,
+        )
+    if params.sampler == Sampler.LINEAR_MULTISTEP:
+        return LinearMultistepSampler(
+            num_steps=params.steps,
+            discretization_config=discretization_config,
+            guider_config=guider_config,
+            order=params.order,
+            verbose=True,
+        )
+
+    raise ValueError(f"unknown sampler {params.sampler}!")

sgm/inference/helpers.py

@@ -0,0 +1,305 @@
+import math
+import os
+from typing import List, Optional, Union
+
+import numpy as np
+import torch
+from einops import rearrange
+from imwatermark import WatermarkEncoder
+from omegaconf import ListConfig
+from PIL import Image
+from torch import autocast
+
+from sgm.util import append_dims
+
+
+class WatermarkEmbedder:
+    def __init__(self, watermark):
+        self.watermark = watermark
+        self.num_bits = len(WATERMARK_BITS)
+        self.encoder = WatermarkEncoder()
+        self.encoder.set_watermark("bits", self.watermark)
+
+    def __call__(self, image: torch.Tensor) -> torch.Tensor:
+        """
+        Adds a predefined watermark to the input image
+
+        Args:
+            image: ([N,] B, RGB, H, W) in range [0, 1]
+
+        Returns:
+            same as input but watermarked
+        """
+        squeeze = len(image.shape) == 4
+        if squeeze:
+            image = image[None, ...]
+        n = image.shape[0]
+        image_np = rearrange(
+            (255 * image).detach().cpu(), "n b c h w -> (n b) h w c"
+        ).numpy()[:, :, :, ::-1]
+        # torch (b, c, h, w) in [0, 1] -> numpy (b, h, w, c) [0, 255]
+        # watermarking libary expects input as cv2 BGR format
+        for k in range(image_np.shape[0]):
+            image_np[k] = self.encoder.encode(image_np[k], "dwtDct")
+        image = torch.from_numpy(
+            rearrange(image_np[:, :, :, ::-1], "(n b) h w c -> n b c h w", n=n)
+        ).to(image.device)
+        image = torch.clamp(image / 255, min=0.0, max=1.0)
+        if squeeze:
+            image = image[0]
+        return image
+
+
+# A fixed 48-bit message that was choosen at random
+# WATERMARK_MESSAGE = 0xB3EC907BB19E
+WATERMARK_MESSAGE = 0b101100111110110010010000011110111011000110011110
+# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
+WATERMARK_BITS = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]
+embed_watermark = WatermarkEmbedder(WATERMARK_BITS)
+
+
+def get_unique_embedder_keys_from_conditioner(conditioner):
+    return list({x.input_key for x in conditioner.embedders})
+
+
+def perform_save_locally(save_path, samples):
+    os.makedirs(os.path.join(save_path), exist_ok=True)
+    base_count = len(os.listdir(os.path.join(save_path)))
+    samples = embed_watermark(samples)
+    for sample in samples:
+        sample = 255.0 * rearrange(sample.cpu().numpy(), "c h w -> h w c")
+        Image.fromarray(sample.astype(np.uint8)).save(
+            os.path.join(save_path, f"{base_count:09}.png")
+        )
+        base_count += 1
+
+
+class Img2ImgDiscretizationWrapper:
+    """
+    wraps a discretizer, and prunes the sigmas
+    params:
+        strength: float between 0.0 and 1.0. 1.0 means full sampling (all sigmas are returned)
+    """
+
+    def __init__(self, discretization, strength: float = 1.0):
+        self.discretization = discretization
+        self.strength = strength
+        assert 0.0 <= self.strength <= 1.0
+
+    def __call__(self, *args, **kwargs):
+        # sigmas start large first, and decrease then
+        sigmas = self.discretization(*args, **kwargs)
+        print(f"sigmas after discretization, before pruning img2img: ", sigmas)
+        sigmas = torch.flip(sigmas, (0,))
+        sigmas = sigmas[: max(int(self.strength * len(sigmas)), 1)]
+        print("prune index:", max(int(self.strength * len(sigmas)), 1))
+        sigmas = torch.flip(sigmas, (0,))
+        print(f"sigmas after pruning: ", sigmas)
+        return sigmas
+
+
+def do_sample(
+    model,
+    sampler,
+    value_dict,
+    num_samples,
+    H,
+    W,
+    C,
+    F,
+    force_uc_zero_embeddings: Optional[List] = None,
+    batch2model_input: Optional[List] = None,
+    return_latents=False,
+    filter=None,
+    device="cuda",
+):
+    if force_uc_zero_embeddings is None:
+        force_uc_zero_embeddings = []
+    if batch2model_input is None:
+        batch2model_input = []
+
+    with torch.no_grad():
+        with autocast(device) as precision_scope:
+            with model.ema_scope():
+                num_samples = [num_samples]
+                batch, batch_uc = get_batch(
+                    get_unique_embedder_keys_from_conditioner(model.conditioner),
+                    value_dict,
+                    num_samples,
+                )
+                for key in batch:
+                    if isinstance(batch[key], torch.Tensor):
+                        print(key, batch[key].shape)
+                    elif isinstance(batch[key], list):
+                        print(key, [len(l) for l in batch[key]])
+                    else:
+                        print(key, batch[key])
+                c, uc = model.conditioner.get_unconditional_conditioning(
+                    batch,
+                    batch_uc=batch_uc,
+                    force_uc_zero_embeddings=force_uc_zero_embeddings,
+                )
+
+                for k in c:
+                    if not k == "crossattn":
+                        c[k], uc[k] = map(
+                            lambda y: y[k][: math.prod(num_samples)].to(device), (c, uc)
+                        )
+
+                additional_model_inputs = {}
+                for k in batch2model_input:
+                    additional_model_inputs[k] = batch[k]
+
+                shape = (math.prod(num_samples), C, H // F, W // F)
+                randn = torch.randn(shape).to(device)
+
+                def denoiser(input, sigma, c):
+                    return model.denoiser(
+                        model.model, input, sigma, c, **additional_model_inputs
+                    )
+
+                samples_z = sampler(denoiser, randn, cond=c, uc=uc)
+                samples_x = model.decode_first_stage(samples_z)
+                samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
+
+                if filter is not None:
+                    samples = filter(samples)
+
+                if return_latents:
+                    return samples, samples_z
+                return samples
+
+
+def get_batch(keys, value_dict, N: Union[List, ListConfig], device="cuda"):
+    # Hardcoded demo setups; might undergo some changes in the future
+
+    batch = {}
+    batch_uc = {}
+
+    for key in keys:
+        if key == "txt":
+            batch["txt"] = (
+                np.repeat([value_dict["prompt"]], repeats=math.prod(N))
+                .reshape(N)
+                .tolist()
+            )
+            batch_uc["txt"] = (
+                np.repeat([value_dict["negative_prompt"]], repeats=math.prod(N))
+                .reshape(N)
+                .tolist()
+            )
+        elif key == "original_size_as_tuple":
+            batch["original_size_as_tuple"] = (
+                torch.tensor([value_dict["orig_height"], value_dict["orig_width"]])
+                .to(device)
+                .repeat(*N, 1)
+            )
+        elif key == "crop_coords_top_left":
+            batch["crop_coords_top_left"] = (
+                torch.tensor(
+                    [value_dict["crop_coords_top"], value_dict["crop_coords_left"]]
+                )
+                .to(device)
+                .repeat(*N, 1)
+            )
+        elif key == "aesthetic_score":
+            batch["aesthetic_score"] = (
+                torch.tensor([value_dict["aesthetic_score"]]).to(device).repeat(*N, 1)
+            )
+            batch_uc["aesthetic_score"] = (
+                torch.tensor([value_dict["negative_aesthetic_score"]])
+                .to(device)
+                .repeat(*N, 1)
+            )
+
+        elif key == "target_size_as_tuple":
+            batch["target_size_as_tuple"] = (
+                torch.tensor([value_dict["target_height"], value_dict["target_width"]])
+                .to(device)
+                .repeat(*N, 1)
+            )
+        else:
+            batch[key] = value_dict[key]
+
+    for key in batch.keys():
+        if key not in batch_uc and isinstance(batch[key], torch.Tensor):
+            batch_uc[key] = torch.clone(batch[key])
+    return batch, batch_uc
+
+
+def get_input_image_tensor(image: Image.Image, device="cuda"):
+    w, h = image.size
+    print(f"loaded input image of size ({w}, {h})")
+    width, height = map(
+        lambda x: x - x % 64, (w, h)
+    )  # resize to integer multiple of 64
+    image = image.resize((width, height))
+    image_array = np.array(image.convert("RGB"))
+    image_array = image_array[None].transpose(0, 3, 1, 2)
+    image_tensor = torch.from_numpy(image_array).to(dtype=torch.float32) / 127.5 - 1.0
+    return image_tensor.to(device)
+
+
+def do_img2img(
+    img,
+    model,
+    sampler,
+    value_dict,
+    num_samples,
+    force_uc_zero_embeddings=[],
+    additional_kwargs={},
+    offset_noise_level: float = 0.0,
+    return_latents=False,
+    skip_encode=False,
+    filter=None,
+    device="cuda",
+):
+    with torch.no_grad():
+        with autocast(device) as precision_scope:
+            with model.ema_scope():
+                batch, batch_uc = get_batch(
+                    get_unique_embedder_keys_from_conditioner(model.conditioner),
+                    value_dict,
+                    [num_samples],
+                )
+                c, uc = model.conditioner.get_unconditional_conditioning(
+                    batch,
+                    batch_uc=batch_uc,
+                    force_uc_zero_embeddings=force_uc_zero_embeddings,
+                )
+
+                for k in c:
+                    c[k], uc[k] = map(lambda y: y[k][:num_samples].to(device), (c, uc))
+
+                for k in additional_kwargs:
+                    c[k] = uc[k] = additional_kwargs[k]
+                if skip_encode:
+                    z = img
+                else:
+                    z = model.encode_first_stage(img)
+                noise = torch.randn_like(z)
+                sigmas = sampler.discretization(sampler.num_steps)
+                sigma = sigmas[0].to(z.device)
+
+                if offset_noise_level > 0.0:
+                    noise = noise + offset_noise_level * append_dims(
+                        torch.randn(z.shape[0], device=z.device), z.ndim
+                    )
+                noised_z = z + noise * append_dims(sigma, z.ndim)
+                noised_z = noised_z / torch.sqrt(
+                    1.0 + sigmas[0] ** 2.0
+                )  # Note: hardcoded to DDPM-like scaling. need to generalize later.
+
+                def denoiser(x, sigma, c):
+                    return model.denoiser(model.model, x, sigma, c)
+
+                samples_z = sampler(denoiser, noised_z, cond=c, uc=uc)
+                samples_x = model.decode_first_stage(samples_z)
+                samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
+
+                if filter is not None:
+                    samples = filter(samples)
+
+                if return_latents:
+                    return samples, samples_z
+                return samples

sgm/models/autoencoder.py

@@ -1,18 +1,22 @@
+import logging
+import math
 import re
 from abc import abstractmethod
 from contextlib import contextmanager
-from typing import Any, Dict, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import pytorch_lightning as pl
 import torch
-from omegaconf import ListConfig
+import torch.nn as nn
+from einops import rearrange
 from packaging import version
-from safetensors.torch import load_file as load_safetensors
 
-from ..modules.diffusionmodules.model import Decoder, Encoder
-from ..modules.distributions.distributions import DiagonalGaussianDistribution
+from ..modules.autoencoding.regularizers import AbstractRegularizer
 from ..modules.ema import LitEma
-from ..util import default, get_obj_from_str, instantiate_from_config
+from ..util import (default, get_nested_attribute, get_obj_from_str,
+                    instantiate_from_config)
+
+logpy = logging.getLogger(__name__)
 
 
 class AbstractAutoencoder(pl.LightningModule):
@@ -27,10 +31,9 @@ class AbstractAutoencoder(pl.LightningModule):
         ema_decay: Union[None, float] = None,
         monitor: Union[None, str] = None,
         input_key: str = "jpg",
-        ckpt_path: Union[None, str] = None,
-        ignore_keys: Union[Tuple, list, ListConfig] = (),
     ):
         super().__init__()
+
         self.input_key = input_key
         self.use_ema = ema_decay is not None
         if monitor is not None:
@@ -38,38 +41,21 @@ class AbstractAutoencoder(pl.LightningModule):
 
         if self.use_ema:
             self.model_ema = LitEma(self, decay=ema_decay)
-            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
-
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
+            logpy.info(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
 
         if version.parse(torch.__version__) >= version.parse("2.0.0"):
             self.automatic_optimization = False
 
-    def init_from_ckpt(
-        self, path: str, ignore_keys: Union[Tuple, list, ListConfig] = tuple()
-    ) -> None:
-        if path.endswith("ckpt"):
-            sd = torch.load(path, map_location="cpu")["state_dict"]
-        elif path.endswith("safetensors"):
-            sd = load_safetensors(path)
-        else:
-            raise NotImplementedError
-
-        keys = list(sd.keys())
-        for k in keys:
-            for ik in ignore_keys:
-                if re.match(ik, k):
-                    print("Deleting key {} from state_dict.".format(k))
-                    del sd[k]
-        missing, unexpected = self.load_state_dict(sd, strict=False)
-        print(
-            f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys"
-        )
-        if len(missing) > 0:
-            print(f"Missing Keys: {missing}")
-        if len(unexpected) > 0:
-            print(f"Unexpected Keys: {unexpected}")
+    def apply_ckpt(self, ckpt: Union[None, str, dict]):
+        if ckpt is None:
+            return
+        if isinstance(ckpt, str):
+            ckpt = {
+                "target": "sgm.modules.checkpoint.CheckpointEngine",
+                "params": {"ckpt_path": ckpt},
+            }
+        engine = instantiate_from_config(ckpt)
+        engine(self)
 
     @abstractmethod
     def get_input(self, batch) -> Any:
@@ -86,14 +72,14 @@ class AbstractAutoencoder(pl.LightningModule):
             self.model_ema.store(self.parameters())
             self.model_ema.copy_to(self)
             if context is not None:
-                print(f"{context}: Switched to EMA weights")
+                logpy.info(f"{context}: Switched to EMA weights")
         try:
             yield None
         finally:
             if self.use_ema:
                 self.model_ema.restore(self.parameters())
                 if context is not None:
-                    print(f"{context}: Restored training weights")
+                    logpy.info(f"{context}: Restored training weights")
 
     @abstractmethod
     def encode(self, *args, **kwargs) -> torch.Tensor:
@@ -104,7 +90,7 @@ class AbstractAutoencoder(pl.LightningModule):
         raise NotImplementedError("decode()-method of abstract base class called")
 
     def instantiate_optimizer_from_config(self, params, lr, cfg):
-        print(f"loading >>> {cfg['target']} <<< optimizer from config")
+        logpy.info(f"loading >>> {cfg['target']} <<< optimizer from config")
         return get_obj_from_str(cfg["target"])(
             params, lr=lr, **cfg.get("params", dict())
         )
@@ -129,196 +115,435 @@ class AutoencodingEngine(AbstractAutoencoder):
         regularizer_config: Dict,
         optimizer_config: Union[Dict, None] = None,
         lr_g_factor: float = 1.0,
+        trainable_ae_params: Optional[List[List[str]]] = None,
+        ae_optimizer_args: Optional[List[dict]] = None,
+        trainable_disc_params: Optional[List[List[str]]] = None,
+        disc_optimizer_args: Optional[List[dict]] = None,
+        disc_start_iter: int = 0,
+        diff_boost_factor: float = 3.0,
+        ckpt_engine: Union[None, str, dict] = None,
+        ckpt_path: Optional[str] = None,
+        additional_decode_keys: Optional[List[str]] = None,
         **kwargs,
     ):
         super().__init__(*args, **kwargs)
-        # todo: add options to freeze encoder/decoder
-        self.encoder = instantiate_from_config(encoder_config)
-        self.decoder = instantiate_from_config(decoder_config)
-        self.loss = instantiate_from_config(loss_config)
-        self.regularization = instantiate_from_config(regularizer_config)
+        self.automatic_optimization = False  # pytorch lightning
+
+        self.encoder: torch.nn.Module = instantiate_from_config(encoder_config)
+        self.decoder: torch.nn.Module = instantiate_from_config(decoder_config)
+        self.loss: torch.nn.Module = instantiate_from_config(loss_config)
+        self.regularization: AbstractRegularizer = instantiate_from_config(
+            regularizer_config
+        )
         self.optimizer_config = default(
             optimizer_config, {"target": "torch.optim.Adam"}
         )
+        self.diff_boost_factor = diff_boost_factor
+        self.disc_start_iter = disc_start_iter
         self.lr_g_factor = lr_g_factor
+        self.trainable_ae_params = trainable_ae_params
+        if self.trainable_ae_params is not None:
+            self.ae_optimizer_args = default(
+                ae_optimizer_args,
+                [{} for _ in range(len(self.trainable_ae_params))],
+            )
+            assert len(self.ae_optimizer_args) == len(self.trainable_ae_params)
+        else:
+            self.ae_optimizer_args = [{}]  # makes type consitent
+
+        self.trainable_disc_params = trainable_disc_params
+        if self.trainable_disc_params is not None:
+            self.disc_optimizer_args = default(
+                disc_optimizer_args,
+                [{} for _ in range(len(self.trainable_disc_params))],
+            )
+            assert len(self.disc_optimizer_args) == len(self.trainable_disc_params)
+        else:
+            self.disc_optimizer_args = [{}]  # makes type consitent
+
+        if ckpt_path is not None:
+            assert ckpt_engine is None, "Can't set ckpt_engine and ckpt_path"
+            logpy.warn("Checkpoint path is deprecated, use `checkpoint_egnine` instead")
+        self.apply_ckpt(default(ckpt_path, ckpt_engine))
+        self.additional_decode_keys = set(default(additional_decode_keys, []))
 
     def get_input(self, batch: Dict) -> torch.Tensor:
         # assuming unified data format, dataloader returns a dict.
-        # image tensors should be scaled to -1 ... 1 and in channels-first format (e.g., bchw instead if bhwc)
+        # image tensors should be scaled to -1 ... 1 and in channels-first
+        # format (e.g., bchw instead if bhwc)
         return batch[self.input_key]
 
     def get_autoencoder_params(self) -> list:
-        params = (
-            list(self.encoder.parameters())
-            + list(self.decoder.parameters())
-            + list(self.regularization.get_trainable_parameters())
-            + list(self.loss.get_trainable_autoencoder_parameters())
-        )
+        params = []
+        if hasattr(self.loss, "get_trainable_autoencoder_parameters"):
+            params += list(self.loss.get_trainable_autoencoder_parameters())
+        if hasattr(self.regularization, "get_trainable_parameters"):
+            params += list(self.regularization.get_trainable_parameters())
+        params = params + list(self.encoder.parameters())
+        params = params + list(self.decoder.parameters())
         return params
 
     def get_discriminator_params(self) -> list:
-        params = list(self.loss.get_trainable_parameters())  # e.g., discriminator
+        if hasattr(self.loss, "get_trainable_parameters"):
+            params = list(self.loss.get_trainable_parameters())  # e.g., discriminator
+        else:
+            params = []
         return params
 
     def get_last_layer(self):
         return self.decoder.get_last_layer()
 
-    def encode(self, x: Any, return_reg_log: bool = False) -> Any:
+    def encode(
+        self,
+        x: torch.Tensor,
+        return_reg_log: bool = False,
+        unregularized: bool = False,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]:
         z = self.encoder(x)
+        if unregularized:
+            return z, dict()
         z, reg_log = self.regularization(z)
         if return_reg_log:
             return z, reg_log
         return z
 
-    def decode(self, z: Any) -> torch.Tensor:
-        x = self.decoder(z)
+    def decode(self, z: torch.Tensor, **kwargs) -> torch.Tensor:
+        x = self.decoder(z, **kwargs)
         return x
 
-    def forward(self, x: Any) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    def forward(
+        self, x: torch.Tensor, **additional_decode_kwargs
+    ) -> Tuple[torch.Tensor, torch.Tensor, dict]:
         z, reg_log = self.encode(x, return_reg_log=True)
-        dec = self.decode(z)
+        dec = self.decode(z, **additional_decode_kwargs)
         return z, dec, reg_log
 
-    def training_step(self, batch, batch_idx, optimizer_idx) -> Any:
+    def inner_training_step(
+        self, batch: dict, batch_idx: int, optimizer_idx: int = 0
+    ) -> torch.Tensor:
         x = self.get_input(batch)
-        z, xrec, regularization_log = self(x)
+        additional_decode_kwargs = {
+            key: batch[key] for key in self.additional_decode_keys.intersection(batch)
+        }
+        z, xrec, regularization_log = self(x, **additional_decode_kwargs)
+        if hasattr(self.loss, "forward_keys"):
+            extra_info = {
+                "z": z,
+                "optimizer_idx": optimizer_idx,
+                "global_step": self.global_step,
+                "last_layer": self.get_last_layer(),
+                "split": "train",
+                "regularization_log": regularization_log,
+                "autoencoder": self,
+            }
+            extra_info = {k: extra_info[k] for k in self.loss.forward_keys}
+        else:
+            extra_info = dict()
 
         if optimizer_idx == 0:
             # autoencode
-            aeloss, log_dict_ae = self.loss(
-                regularization_log,
-                x,
-                xrec,
-                optimizer_idx,
-                self.global_step,
-                last_layer=self.get_last_layer(),
-                split="train",
-            )
+            out_loss = self.loss(x, xrec, **extra_info)
+            if isinstance(out_loss, tuple):
+                aeloss, log_dict_ae = out_loss
+            else:
+                # simple loss function
+                aeloss = out_loss
+                log_dict_ae = {"train/loss/rec": aeloss.detach()}
 
             self.log_dict(
-                log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True
+                log_dict_ae,
+                prog_bar=False,
+                logger=True,
+                on_step=True,
+                on_epoch=True,
+                sync_dist=False,
+            )
+            self.log(
+                "loss",
+                aeloss.mean().detach(),
+                prog_bar=True,
+                logger=False,
+                on_epoch=False,
+                on_step=True,
             )
             return aeloss
-
-        if optimizer_idx == 1:
+        elif optimizer_idx == 1:
             # discriminator
-            discloss, log_dict_disc = self.loss(
-                regularization_log,
-                x,
-                xrec,
-                optimizer_idx,
-                self.global_step,
-                last_layer=self.get_last_layer(),
-                split="train",
-            )
+            discloss, log_dict_disc = self.loss(x, xrec, **extra_info)
+            # -> discriminator always needs to return a tuple
             self.log_dict(
                 log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True
             )
             return discloss
+        else:
+            raise NotImplementedError(f"Unknown optimizer {optimizer_idx}")
 
-    def validation_step(self, batch, batch_idx) -> Dict:
+    def training_step(self, batch: dict, batch_idx: int):
+        opts = self.optimizers()
+        if not isinstance(opts, list):
+            # Non-adversarial case
+            opts = [opts]
+        optimizer_idx = batch_idx % len(opts)
+        if self.global_step < self.disc_start_iter:
+            optimizer_idx = 0
+        opt = opts[optimizer_idx]
+        opt.zero_grad()
+        with opt.toggle_model():
+            loss = self.inner_training_step(
+                batch, batch_idx, optimizer_idx=optimizer_idx
+            )
+            self.manual_backward(loss)
+        opt.step()
+
+    def validation_step(self, batch: dict, batch_idx: int) -> Dict:
         log_dict = self._validation_step(batch, batch_idx)
         with self.ema_scope():
             log_dict_ema = self._validation_step(batch, batch_idx, postfix="_ema")
             log_dict.update(log_dict_ema)
         return log_dict
 
-    def _validation_step(self, batch, batch_idx, postfix="") -> Dict:
+    def _validation_step(self, batch: dict, batch_idx: int, postfix: str = "") -> Dict:
         x = self.get_input(batch)
 
         z, xrec, regularization_log = self(x)
-        aeloss, log_dict_ae = self.loss(
-            regularization_log,
-            x,
-            xrec,
-            0,
-            self.global_step,
-            last_layer=self.get_last_layer(),
-            split="val" + postfix,
+        if hasattr(self.loss, "forward_keys"):
+            extra_info = {
+                "z": z,
+                "optimizer_idx": 0,
+                "global_step": self.global_step,
+                "last_layer": self.get_last_layer(),
+                "split": "val" + postfix,
+                "regularization_log": regularization_log,
+                "autoencoder": self,
+            }
+            extra_info = {k: extra_info[k] for k in self.loss.forward_keys}
+        else:
+            extra_info = dict()
+        out_loss = self.loss(x, xrec, **extra_info)
+        if isinstance(out_loss, tuple):
+            aeloss, log_dict_ae = out_loss
+        else:
+            # simple loss function
+            aeloss = out_loss
+            log_dict_ae = {f"val{postfix}/loss/rec": aeloss.detach()}
+        full_log_dict = log_dict_ae
+
+        if "optimizer_idx" in extra_info:
+            extra_info["optimizer_idx"] = 1
+            discloss, log_dict_disc = self.loss(x, xrec, **extra_info)
+            full_log_dict.update(log_dict_disc)
+        self.log(
+            f"val{postfix}/loss/rec",
+            log_dict_ae[f"val{postfix}/loss/rec"],
+            sync_dist=True,
         )
+        self.log_dict(full_log_dict, sync_dist=True)
+        return full_log_dict
 
-        discloss, log_dict_disc = self.loss(
-            regularization_log,
-            x,
-            xrec,
-            1,
-            self.global_step,
-            last_layer=self.get_last_layer(),
-            split="val" + postfix,
-        )
-        self.log(f"val{postfix}/rec_loss", log_dict_ae[f"val{postfix}/rec_loss"])
-        log_dict_ae.update(log_dict_disc)
-        self.log_dict(log_dict_ae)
-        return log_dict_ae
-
-    def configure_optimizers(self) -> Any:
-        ae_params = self.get_autoencoder_params()
-        disc_params = self.get_discriminator_params()
+    def get_param_groups(
+        self, parameter_names: List[List[str]], optimizer_args: List[dict]
+    ) -> Tuple[List[Dict[str, Any]], int]:
+        groups = []
+        num_params = 0
+        for names, args in zip(parameter_names, optimizer_args):
+            params = []
+            for pattern_ in names:
+                pattern_params = []
+                pattern = re.compile(pattern_)
+                for p_name, param in self.named_parameters():
+                    if re.match(pattern, p_name):
+                        pattern_params.append(param)
+                        num_params += param.numel()
+                if len(pattern_params) == 0:
+                    logpy.warn(f"Did not find parameters for pattern {pattern_}")
+                params.extend(pattern_params)
+            groups.append({"params": params, **args})
+        return groups, num_params
 
+    def configure_optimizers(self) -> List[torch.optim.Optimizer]:
+        if self.trainable_ae_params is None:
+            ae_params = self.get_autoencoder_params()
+        else:
+            ae_params, num_ae_params = self.get_param_groups(
+                self.trainable_ae_params, self.ae_optimizer_args
+            )
+            logpy.info(f"Number of trainable autoencoder parameters: {num_ae_params:,}")
+        if self.trainable_disc_params is None:
+            disc_params = self.get_discriminator_params()
+        else:
+            disc_params, num_disc_params = self.get_param_groups(
+                self.trainable_disc_params, self.disc_optimizer_args
+            )
+            logpy.info(
+                f"Number of trainable discriminator parameters: {num_disc_params:,}"
+            )
         opt_ae = self.instantiate_optimizer_from_config(
             ae_params,
             default(self.lr_g_factor, 1.0) * self.learning_rate,
             self.optimizer_config,
         )
-        opt_disc = self.instantiate_optimizer_from_config(
-            disc_params, self.learning_rate, self.optimizer_config
-        )
+        opts = [opt_ae]
+        if len(disc_params) > 0:
+            opt_disc = self.instantiate_optimizer_from_config(
+                disc_params, self.learning_rate, self.optimizer_config
+            )
+            opts.append(opt_disc)
 
-        return [opt_ae, opt_disc], []
+        return opts
 
     @torch.no_grad()
-    def log_images(self, batch: Dict, **kwargs) -> Dict:
+    def log_images(
+        self, batch: dict, additional_log_kwargs: Optional[Dict] = None, **kwargs
+    ) -> dict:
         log = dict()
+        additional_decode_kwargs = {}
         x = self.get_input(batch)
-        _, xrec, _ = self(x)
+        additional_decode_kwargs.update(
+            {key: batch[key] for key in self.additional_decode_keys.intersection(batch)}
+        )
+
+        _, xrec, _ = self(x, **additional_decode_kwargs)
         log["inputs"] = x
         log["reconstructions"] = xrec
+        diff = 0.5 * torch.abs(torch.clamp(xrec, -1.0, 1.0) - x)
+        diff.clamp_(0, 1.0)
+        log["diff"] = 2.0 * diff - 1.0
+        # diff_boost shows location of small errors, by boosting their
+        # brightness.
+        log["diff_boost"] = (
+            2.0 * torch.clamp(self.diff_boost_factor * diff, 0.0, 1.0) - 1
+        )
+        if hasattr(self.loss, "log_images"):
+            log.update(self.loss.log_images(x, xrec))
         with self.ema_scope():
-            _, xrec_ema, _ = self(x)
+            _, xrec_ema, _ = self(x, **additional_decode_kwargs)
             log["reconstructions_ema"] = xrec_ema
+            diff_ema = 0.5 * torch.abs(torch.clamp(xrec_ema, -1.0, 1.0) - x)
+            diff_ema.clamp_(0, 1.0)
+            log["diff_ema"] = 2.0 * diff_ema - 1.0
+            log["diff_boost_ema"] = (
+                2.0 * torch.clamp(self.diff_boost_factor * diff_ema, 0.0, 1.0) - 1
+            )
+        if additional_log_kwargs:
+            additional_decode_kwargs.update(additional_log_kwargs)
+            _, xrec_add, _ = self(x, **additional_decode_kwargs)
+            log_str = "reconstructions-" + "-".join(
+                [f"{key}={additional_log_kwargs[key]}" for key in additional_log_kwargs]
+            )
+            log[log_str] = xrec_add
         return log
 
 
-class AutoencoderKL(AutoencodingEngine):
+class AutoencodingEngineLegacy(AutoencodingEngine):
     def __init__(self, embed_dim: int, **kwargs):
+        self.max_batch_size = kwargs.pop("max_batch_size", None)
         ddconfig = kwargs.pop("ddconfig")
         ckpt_path = kwargs.pop("ckpt_path", None)
-        ignore_keys = kwargs.pop("ignore_keys", ())
+        ckpt_engine = kwargs.pop("ckpt_engine", None)
         super().__init__(
-            encoder_config={"target": "torch.nn.Identity"},
-            decoder_config={"target": "torch.nn.Identity"},
-            regularizer_config={"target": "torch.nn.Identity"},
-            loss_config=kwargs.pop("lossconfig"),
+            encoder_config={
+                "target": "sgm.modules.diffusionmodules.model.Encoder",
+                "params": ddconfig,
+            },
+            decoder_config={
+                "target": "sgm.modules.diffusionmodules.model.Decoder",
+                "params": ddconfig,
+            },
             **kwargs,
         )
-        assert ddconfig["double_z"]
-        self.encoder = Encoder(**ddconfig)
-        self.decoder = Decoder(**ddconfig)
-        self.quant_conv = torch.nn.Conv2d(2 * ddconfig["z_channels"], 2 * embed_dim, 1)
+        self.quant_conv = torch.nn.Conv2d(
+            (1 + ddconfig["double_z"]) * ddconfig["z_channels"],
+            (1 + ddconfig["double_z"]) * embed_dim,
+            1,
+        )
         self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
         self.embed_dim = embed_dim
 
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
+        self.apply_ckpt(default(ckpt_path, ckpt_engine))
 
-    def encode(self, x):
-        assert (
-            not self.training
-        ), f"{self.__class__.__name__} only supports inference currently"
-        h = self.encoder(x)
-        moments = self.quant_conv(h)
-        posterior = DiagonalGaussianDistribution(moments)
-        return posterior
+    def get_autoencoder_params(self) -> list:
+        params = super().get_autoencoder_params()
+        return params
+
+    def encode(
+        self, x: torch.Tensor, return_reg_log: bool = False
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]:
+        if self.max_batch_size is None:
+            z = self.encoder(x)
+            z = self.quant_conv(z)
+        else:
+            N = x.shape[0]
+            bs = self.max_batch_size
+            n_batches = int(math.ceil(N / bs))
+            z = list()
+            for i_batch in range(n_batches):
+                z_batch = self.encoder(x[i_batch * bs : (i_batch + 1) * bs])
+                z_batch = self.quant_conv(z_batch)
+                z.append(z_batch)
+            z = torch.cat(z, 0)
+
+        z, reg_log = self.regularization(z)
+        if return_reg_log:
+            return z, reg_log
+        return z
+
+    def decode(self, z: torch.Tensor, **decoder_kwargs) -> torch.Tensor:
+        if self.max_batch_size is None:
+            dec = self.post_quant_conv(z)
+            dec = self.decoder(dec, **decoder_kwargs)
+        else:
+            N = z.shape[0]
+            bs = self.max_batch_size
+            n_batches = int(math.ceil(N / bs))
+            dec = list()
+            for i_batch in range(n_batches):
+                dec_batch = self.post_quant_conv(z[i_batch * bs : (i_batch + 1) * bs])
+                dec_batch = self.decoder(dec_batch, **decoder_kwargs)
+                dec.append(dec_batch)
+            dec = torch.cat(dec, 0)
 
-    def decode(self, z, **decoder_kwargs):
-        z = self.post_quant_conv(z)
-        dec = self.decoder(z, **decoder_kwargs)
         return dec
 
 
-class AutoencoderKLInferenceWrapper(AutoencoderKL):
-    def encode(self, x):
-        return super().encode(x).sample()
+class AutoencoderKL(AutoencodingEngineLegacy):
+    def __init__(self, **kwargs):
+        if "lossconfig" in kwargs:
+            kwargs["loss_config"] = kwargs.pop("lossconfig")
+        super().__init__(
+            regularizer_config={
+                "target": (
+                    "sgm.modules.autoencoding.regularizers"
+                    ".DiagonalGaussianRegularizer"
+                )
+            },
+            **kwargs,
+        )
+
+
+class AutoencoderLegacyVQ(AutoencodingEngineLegacy):
+    def __init__(
+        self,
+        embed_dim: int,
+        n_embed: int,
+        sane_index_shape: bool = False,
+        **kwargs,
+    ):
+        if "lossconfig" in kwargs:
+            logpy.warn(f"Parameter `lossconfig` is deprecated, use `loss_config`.")
+            kwargs["loss_config"] = kwargs.pop("lossconfig")
+        super().__init__(
+            regularizer_config={
+                "target": (
+                    "sgm.modules.autoencoding.regularizers.quantize" ".VectorQuantizer"
+                ),
+                "params": {
+                    "n_e": n_embed,
+                    "e_dim": embed_dim,
+                    "sane_index_shape": sane_index_shape,
+                },
+            },
+            **kwargs,
+        )
 
 
 class IdentityFirstStage(AbstractAutoencoder):
@@ -333,3 +558,58 @@ class IdentityFirstStage(AbstractAutoencoder):
 
     def decode(self, x: Any, *args, **kwargs) -> Any:
         return x
+
+
+class AEIntegerWrapper(nn.Module):
+    def __init__(
+        self,
+        model: nn.Module,
+        shape: Union[None, Tuple[int, int], List[int]] = (16, 16),
+        regularization_key: str = "regularization",
+        encoder_kwargs: Optional[Dict[str, Any]] = None,
+    ):
+        super().__init__()
+        self.model = model
+        assert hasattr(model, "encode") and hasattr(
+            model, "decode"
+        ), "Need AE interface"
+        self.regularization = get_nested_attribute(model, regularization_key)
+        self.shape = shape
+        self.encoder_kwargs = default(encoder_kwargs, {"return_reg_log": True})
+
+    def encode(self, x) -> torch.Tensor:
+        assert (
+            not self.training
+        ), f"{self.__class__.__name__} only supports inference currently"
+        _, log = self.model.encode(x, **self.encoder_kwargs)
+        assert isinstance(log, dict)
+        inds = log["min_encoding_indices"]
+        return rearrange(inds, "b ... -> b (...)")
+
+    def decode(
+        self, inds: torch.Tensor, shape: Union[None, tuple, list] = None
+    ) -> torch.Tensor:
+        # expect inds shape (b, s) with s = h*w
+        shape = default(shape, self.shape)  # Optional[(h, w)]
+        if shape is not None:
+            assert len(shape) == 2, f"Unhandeled shape {shape}"
+            inds = rearrange(inds, "b (h w) -> b h w", h=shape[0], w=shape[1])
+        h = self.regularization.get_codebook_entry(inds)  # (b, h, w, c)
+        h = rearrange(h, "b h w c -> b c h w")
+        return self.model.decode(h)
+
+
+class AutoencoderKLModeOnly(AutoencodingEngineLegacy):
+    def __init__(self, **kwargs):
+        if "lossconfig" in kwargs:
+            kwargs["loss_config"] = kwargs.pop("lossconfig")
+        super().__init__(
+            regularizer_config={
+                "target": (
+                    "sgm.modules.autoencoding.regularizers"
+                    ".DiagonalGaussianRegularizer"
+                ),
+                "params": {"sample": False},
+            },
+            **kwargs,
+        )

sgm/models/diffusion.py

@@ -1,5 +1,6 @@
+import math
 from contextlib import contextmanager
-from typing import Any, Dict, List, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import pytorch_lightning as pl
 import torch
@@ -8,15 +9,11 @@ from safetensors.torch import load_file as load_safetensors
 from torch.optim.lr_scheduler import LambdaLR
 
 from ..modules import UNCONDITIONAL_CONFIG
+from ..modules.autoencoding.temporal_ae import VideoDecoder
 from ..modules.diffusionmodules.wrappers import OPENAIUNETWRAPPER
 from ..modules.ema import LitEma
-from ..util import (
-    default,
-    disabled_train,
-    get_obj_from_str,
-    instantiate_from_config,
-    log_txt_as_img,
-)
+from ..util import (default, disabled_train, get_obj_from_str,
+                    instantiate_from_config, log_txt_as_img)
 
 
 class DiffusionEngine(pl.LightningModule):
@@ -40,6 +37,7 @@ class DiffusionEngine(pl.LightningModule):
         log_keys: Union[List, None] = None,
         no_cond_log: bool = False,
         compile_model: bool = False,
+        en_and_decode_n_samples_a_time: Optional[int] = None,
     ):
         super().__init__()
         self.log_keys = log_keys
@@ -82,6 +80,8 @@ class DiffusionEngine(pl.LightningModule):
         if ckpt_path is not None:
             self.init_from_ckpt(ckpt_path)
 
+        self.en_and_decode_n_samples_a_time = en_and_decode_n_samples_a_time
+
     def init_from_ckpt(
         self,
         path: str,
@@ -117,14 +117,35 @@ class DiffusionEngine(pl.LightningModule):
     @torch.no_grad()
     def decode_first_stage(self, z):
         z = 1.0 / self.scale_factor * z
+        n_samples = default(self.en_and_decode_n_samples_a_time, z.shape[0])
+
+        n_rounds = math.ceil(z.shape[0] / n_samples)
+        all_out = []
         with torch.autocast("cuda", enabled=not self.disable_first_stage_autocast):
-            out = self.first_stage_model.decode(z)
+            for n in range(n_rounds):
+                if isinstance(self.first_stage_model.decoder, VideoDecoder):
+                    kwargs = {"timesteps": len(z[n * n_samples : (n + 1) * n_samples])}
+                else:
+                    kwargs = {}
+                out = self.first_stage_model.decode(
+                    z[n * n_samples : (n + 1) * n_samples], **kwargs
+                )
+                all_out.append(out)
+        out = torch.cat(all_out, dim=0)
         return out
 
     @torch.no_grad()
     def encode_first_stage(self, x):
+        n_samples = default(self.en_and_decode_n_samples_a_time, x.shape[0])
+        n_rounds = math.ceil(x.shape[0] / n_samples)
+        all_out = []
         with torch.autocast("cuda", enabled=not self.disable_first_stage_autocast):
-            z = self.first_stage_model.encode(x)
+            for n in range(n_rounds):
+                out = self.first_stage_model.encode(
+                    x[n * n_samples : (n + 1) * n_samples]
+                )
+                all_out.append(out)
+        z = torch.cat(all_out, dim=0)
         z = self.scale_factor * z
         return z
 
@@ -258,14 +279,10 @@ class DiffusionEngine(pl.LightningModule):
                         xc = log_txt_as_img((image_h, image_w), x, size=image_h // 20)
                     else:
                         raise NotImplementedError()
-                elif isinstance(x, Union[List, ListConfig]):
+                elif isinstance(x, (List, ListConfig)):
                     if isinstance(x[0], str):
                         # strings
                         xc = log_txt_as_img((image_h, image_w), x, size=image_h // 20)
-                    elif isinstance(x[0], Union[ListConfig, List]):
-                        # # case: videos processed
-                        x = [xx[0] for xx in x]
-                        xc = log_txt_as_img((image_h, image_w), x, size=image_h // 20)
                     else:
                         raise NotImplementedError()
                 else:

sgm/modules/attention.py

@@ -1,3 +1,4 @@
+import logging
 import math
 from inspect import isfunction
 from typing import Any, Optional
@@ -7,6 +8,9 @@ import torch.nn.functional as F
 from einops import rearrange, repeat
 from packaging import version
 from torch import nn
+from torch.utils.checkpoint import checkpoint
+
+logpy = logging.getLogger(__name__)
 
 if version.parse(torch.__version__) >= version.parse("2.0.0"):
     SDP_IS_AVAILABLE = True
@@ -36,9 +40,10 @@ else:
     SDP_IS_AVAILABLE = False
     sdp_kernel = nullcontext
     BACKEND_MAP = {}
-    print(
-        f"No SDP backend available, likely because you are running in pytorch versions < 2.0. In fact, "
-        f"you are using PyTorch {torch.__version__}. You might want to consider upgrading."
+    logpy.warn(
+        f"No SDP backend available, likely because you are running in pytorch "
+        f"versions < 2.0. In fact, you are using PyTorch {torch.__version__}. "
+        f"You might want to consider upgrading."
     )
 
 try:
@@ -48,9 +53,9 @@ try:
     XFORMERS_IS_AVAILABLE = True
 except:
     XFORMERS_IS_AVAILABLE = False
-    print("no module 'xformers'. Processing without...")
+    logpy.warn("no module 'xformers'. Processing without...")
 
-from .diffusionmodules.util import checkpoint
+# from .diffusionmodules.util import mixed_checkpoint as checkpoint
 
 
 def exists(val):
@@ -146,6 +151,62 @@ class LinearAttention(nn.Module):
         return self.to_out(out)
 
 
+class SelfAttention(nn.Module):
+    ATTENTION_MODES = ("xformers", "torch", "math")
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = False,
+        qk_scale: Optional[float] = None,
+        attn_drop: float = 0.0,
+        proj_drop: float = 0.0,
+        attn_mode: str = "xformers",
+    ):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+        assert attn_mode in self.ATTENTION_MODES
+        self.attn_mode = attn_mode
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        B, L, C = x.shape
+
+        qkv = self.qkv(x)
+        if self.attn_mode == "torch":
+            qkv = rearrange(
+                qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads
+            ).float()
+            q, k, v = qkv[0], qkv[1], qkv[2]  # B H L D
+            x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
+            x = rearrange(x, "B H L D -> B L (H D)")
+        elif self.attn_mode == "xformers":
+            qkv = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.num_heads)
+            q, k, v = qkv[0], qkv[1], qkv[2]  # B L H D
+            x = xformers.ops.memory_efficient_attention(q, k, v)
+            x = rearrange(x, "B L H D -> B L (H D)", H=self.num_heads)
+        elif self.attn_mode == "math":
+            qkv = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+            q, k, v = qkv[0], qkv[1], qkv[2]  # B H L D
+            attn = (q @ k.transpose(-2, -1)) * self.scale
+            attn = attn.softmax(dim=-1)
+            attn = self.attn_drop(attn)
+            x = (attn @ v).transpose(1, 2).reshape(B, L, C)
+        else:
+            raise NotImplemented
+
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
 class SpatialSelfAttention(nn.Module):
     def __init__(self, in_channels):
         super().__init__()
@@ -289,9 +350,10 @@ class MemoryEfficientCrossAttention(nn.Module):
         self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, **kwargs
     ):
         super().__init__()
-        print(
-            f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using "
-            f"{heads} heads with a dimension of {dim_head}."
+        logpy.debug(
+            f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, "
+            f"context_dim is {context_dim} and using {heads} heads with a "
+            f"dimension of {dim_head}."
         )
         inner_dim = dim_head * heads
         context_dim = default(context_dim, query_dim)
@@ -352,9 +414,29 @@ class MemoryEfficientCrossAttention(nn.Module):
         )
 
         # actually compute the attention, what we cannot get enough of
-        out = xformers.ops.memory_efficient_attention(
-            q, k, v, attn_bias=None, op=self.attention_op
-        )
+        if version.parse(xformers.__version__) >= version.parse("0.0.21"):
+            # NOTE: workaround for
+            # https://github.com/facebookresearch/xformers/issues/845
+            max_bs = 32768
+            N = q.shape[0]
+            n_batches = math.ceil(N / max_bs)
+            out = list()
+            for i_batch in range(n_batches):
+                batch = slice(i_batch * max_bs, (i_batch + 1) * max_bs)
+                out.append(
+                    xformers.ops.memory_efficient_attention(
+                        q[batch],
+                        k[batch],
+                        v[batch],
+                        attn_bias=None,
+                        op=self.attention_op,
+                    )
+                )
+            out = torch.cat(out, 0)
+        else:
+            out = xformers.ops.memory_efficient_attention(
+                q, k, v, attn_bias=None, op=self.attention_op
+            )
 
         # TODO: Use this directly in the attention operation, as a bias
         if exists(mask):
@@ -393,21 +475,24 @@ class BasicTransformerBlock(nn.Module):
         super().__init__()
         assert attn_mode in self.ATTENTION_MODES
         if attn_mode != "softmax" and not XFORMERS_IS_AVAILABLE:
-            print(
-                f"Attention mode '{attn_mode}' is not available. Falling back to native attention. "
-                f"This is not a problem in Pytorch >= 2.0. FYI, you are running with PyTorch version {torch.__version__}"
+            logpy.warn(
+                f"Attention mode '{attn_mode}' is not available. Falling "
+                f"back to native attention. This is not a problem in "
+                f"Pytorch >= 2.0. FYI, you are running with PyTorch "
+                f"version {torch.__version__}."
             )
             attn_mode = "softmax"
         elif attn_mode == "softmax" and not SDP_IS_AVAILABLE:
-            print(
-                "We do not support vanilla attention anymore, as it is too expensive. Sorry."
+            logpy.warn(
+                "We do not support vanilla attention anymore, as it is too "
+                "expensive. Sorry."
             )
             if not XFORMERS_IS_AVAILABLE:
                 assert (
                     False
                 ), "Please install xformers via e.g. 'pip install xformers==0.0.16'"
             else:
-                print("Falling back to xformers efficient attention.")
+                logpy.info("Falling back to xformers efficient attention.")
                 attn_mode = "softmax-xformers"
         attn_cls = self.ATTENTION_MODES[attn_mode]
         if version.parse(torch.__version__) >= version.parse("2.0.0"):
@@ -437,7 +522,7 @@ class BasicTransformerBlock(nn.Module):
         self.norm3 = nn.LayerNorm(dim)
         self.checkpoint = checkpoint
         if self.checkpoint:
-            print(f"{self.__class__.__name__} is using checkpointing")
+            logpy.debug(f"{self.__class__.__name__} is using checkpointing")
 
     def forward(
         self, x, context=None, additional_tokens=None, n_times_crossframe_attn_in_self=0
@@ -456,9 +541,12 @@ class BasicTransformerBlock(nn.Module):
             )
 
         # return mixed_checkpoint(self._forward, kwargs, self.parameters(), self.checkpoint)
-        return checkpoint(
-            self._forward, (x, context), self.parameters(), self.checkpoint
-        )
+        if self.checkpoint:
+            # inputs = {"x": x, "context": context}
+            return checkpoint(self._forward, x, context)
+            # return checkpoint(self._forward, inputs, self.parameters(), self.checkpoint)
+        else:
+            return self._forward(**kwargs)
 
     def _forward(
         self, x, context=None, additional_tokens=None, n_times_crossframe_attn_in_self=0
@@ -518,9 +606,9 @@ class BasicTransformerSingleLayerBlock(nn.Module):
         self.checkpoint = checkpoint
 
     def forward(self, x, context=None):
-        return checkpoint(
-            self._forward, (x, context), self.parameters(), self.checkpoint
-        )
+        # inputs = {"x": x, "context": context}
+        # return checkpoint(self._forward, inputs, self.parameters(), self.checkpoint)
+        return checkpoint(self._forward, x, context)
 
     def _forward(self, x, context=None):
         x = self.attn1(self.norm1(x), context=context) + x
@@ -554,18 +642,20 @@ class SpatialTransformer(nn.Module):
         sdp_backend=None,
     ):
         super().__init__()
-        print(
-            f"constructing {self.__class__.__name__} of depth {depth} w/ {in_channels} channels and {n_heads} heads"
+        logpy.debug(
+            f"constructing {self.__class__.__name__} of depth {depth} w/ "
+            f"{in_channels} channels and {n_heads} heads."
         )
-        from omegaconf import ListConfig
 
-        if exists(context_dim) and not isinstance(context_dim, (list, ListConfig)):
+        if exists(context_dim) and not isinstance(context_dim, list):
             context_dim = [context_dim]
         if exists(context_dim) and isinstance(context_dim, list):
             if depth != len(context_dim):
-                print(
-                    f"WARNING: {self.__class__.__name__}: Found context dims {context_dim} of depth {len(context_dim)}, "
-                    f"which does not match the specified 'depth' of {depth}. Setting context_dim to {depth * [context_dim[0]]} now."
+                logpy.warn(
+                    f"{self.__class__.__name__}: Found context dims "
+                    f"{context_dim} of depth {len(context_dim)}, which does not "
+                    f"match the specified 'depth' of {depth}. Setting context_dim "
+                    f"to {depth * [context_dim[0]]} now."
                 )
                 # depth does not match context dims.
                 assert all(
@@ -633,315 +723,37 @@ class SpatialTransformer(nn.Module):
         return x + x_in
 
 
-def benchmark_attn():
-    # Lets define a helpful benchmarking function:
-    # https://pytorch.org/tutorials/intermediate/scaled_dot_product_attention_tutorial.html
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    import torch.nn.functional as F
-    import torch.utils.benchmark as benchmark
-
-    def benchmark_torch_function_in_microseconds(f, *args, **kwargs):
-        t0 = benchmark.Timer(
-            stmt="f(*args, **kwargs)", globals={"args": args, "kwargs": kwargs, "f": f}
-        )
-        return t0.blocked_autorange().mean * 1e6
-
-    # Lets define the hyper-parameters of our input
-    batch_size = 32
-    max_sequence_len = 1024
-    num_heads = 32
-    embed_dimension = 32
-
-    dtype = torch.float16
-
-    query = torch.rand(
-        batch_size,
-        num_heads,
-        max_sequence_len,
-        embed_dimension,
-        device=device,
-        dtype=dtype,
-    )
-    key = torch.rand(
-        batch_size,
-        num_heads,
-        max_sequence_len,
-        embed_dimension,
-        device=device,
-        dtype=dtype,
-    )
-    value = torch.rand(
-        batch_size,
-        num_heads,
-        max_sequence_len,
-        embed_dimension,
-        device=device,
-        dtype=dtype,
-    )
-
-    print(f"q/k/v shape:", query.shape, key.shape, value.shape)
-
-    # Lets explore the speed of each of the 3 implementations
-    from torch.backends.cuda import SDPBackend, sdp_kernel
-
-    # Helpful arguments mapper
-    backend_map = {
-        SDPBackend.MATH: {
-            "enable_math": True,
-            "enable_flash": False,
-            "enable_mem_efficient": False,
-        },
-        SDPBackend.FLASH_ATTENTION: {
-            "enable_math": False,
-            "enable_flash": True,
-            "enable_mem_efficient": False,
-        },
-        SDPBackend.EFFICIENT_ATTENTION: {
-            "enable_math": False,
-            "enable_flash": False,
-            "enable_mem_efficient": True,
-        },
-    }
-
-    from torch.profiler import ProfilerActivity, profile, record_function
-
-    activities = [ProfilerActivity.CPU, ProfilerActivity.CUDA]
-
-    print(
-        f"The default implementation runs in {benchmark_torch_function_in_microseconds(F.scaled_dot_product_attention, query, key, value):.3f} microseconds"
-    )
-    with profile(
-        activities=activities, record_shapes=False, profile_memory=True
-    ) as prof:
-        with record_function("Default detailed stats"):
-            for _ in range(25):
-                o = F.scaled_dot_product_attention(query, key, value)
-    print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
-
-    print(
-        f"The math implementation runs in {benchmark_torch_function_in_microseconds(F.scaled_dot_product_attention, query, key, value):.3f} microseconds"
-    )
-    with sdp_kernel(**backend_map[SDPBackend.MATH]):
-        with profile(
-            activities=activities, record_shapes=False, profile_memory=True
-        ) as prof:
-            with record_function("Math implmentation stats"):
-                for _ in range(25):
-                    o = F.scaled_dot_product_attention(query, key, value)
-        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
-
-    with sdp_kernel(**backend_map[SDPBackend.FLASH_ATTENTION]):
-        try:
-            print(
-                f"The flash attention implementation runs in {benchmark_torch_function_in_microseconds(F.scaled_dot_product_attention, query, key, value):.3f} microseconds"
+class SimpleTransformer(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        depth: int,
+        heads: int,
+        dim_head: int,
+        context_dim: Optional[int] = None,
+        dropout: float = 0.0,
+        checkpoint: bool = True,
+    ):
+        super().__init__()
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(
+                BasicTransformerBlock(
+                    dim,
+                    heads,
+                    dim_head,
+                    dropout=dropout,
+                    context_dim=context_dim,
+                    attn_mode="softmax-xformers",
+                    checkpoint=checkpoint,
+                )
             )
-        except RuntimeError:
-            print("FlashAttention is not supported. See warnings for reasons.")
-        with profile(
-            activities=activities, record_shapes=False, profile_memory=True
-        ) as prof:
-            with record_function("FlashAttention stats"):
-                for _ in range(25):
-                    o = F.scaled_dot_product_attention(query, key, value)
-        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
 
-    with sdp_kernel(**backend_map[SDPBackend.EFFICIENT_ATTENTION]):
-        try:
-            print(
-                f"The memory efficient implementation runs in {benchmark_torch_function_in_microseconds(F.scaled_dot_product_attention, query, key, value):.3f} microseconds"
-            )
-        except RuntimeError:
-            print("EfficientAttention is not supported. See warnings for reasons.")
-        with profile(
-            activities=activities, record_shapes=False, profile_memory=True
-        ) as prof:
-            with record_function("EfficientAttention stats"):
-                for _ in range(25):
-                    o = F.scaled_dot_product_attention(query, key, value)
-        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
-
-
-def run_model(model, x, context):
-    return model(x, context)
-
-
-def benchmark_transformer_blocks():
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    import torch.utils.benchmark as benchmark
-
-    def benchmark_torch_function_in_microseconds(f, *args, **kwargs):
-        t0 = benchmark.Timer(
-            stmt="f(*args, **kwargs)", globals={"args": args, "kwargs": kwargs, "f": f}
-        )
-        return t0.blocked_autorange().mean * 1e6
-
-    checkpoint = True
-    compile = False
-
-    batch_size = 32
-    h, w = 64, 64
-    context_len = 77
-    embed_dimension = 1024
-    context_dim = 1024
-    d_head = 64
-
-    transformer_depth = 4
-
-    n_heads = embed_dimension // d_head
-
-    dtype = torch.float16
-
-    model_native = SpatialTransformer(
-        embed_dimension,
-        n_heads,
-        d_head,
-        context_dim=context_dim,
-        use_linear=True,
-        use_checkpoint=checkpoint,
-        attn_type="softmax",
-        depth=transformer_depth,
-        sdp_backend=SDPBackend.FLASH_ATTENTION,
-    ).to(device)
-    model_efficient_attn = SpatialTransformer(
-        embed_dimension,
-        n_heads,
-        d_head,
-        context_dim=context_dim,
-        use_linear=True,
-        depth=transformer_depth,
-        use_checkpoint=checkpoint,
-        attn_type="softmax-xformers",
-    ).to(device)
-    if not checkpoint and compile:
-        print("compiling models")
-        model_native = torch.compile(model_native)
-        model_efficient_attn = torch.compile(model_efficient_attn)
-
-    x = torch.rand(batch_size, embed_dimension, h, w, device=device, dtype=dtype)
-    c = torch.rand(batch_size, context_len, context_dim, device=device, dtype=dtype)
-
-    from torch.profiler import ProfilerActivity, profile, record_function
-
-    activities = [ProfilerActivity.CPU, ProfilerActivity.CUDA]
-
-    with torch.autocast("cuda"):
-        print(
-            f"The native model runs in {benchmark_torch_function_in_microseconds(model_native.forward, x, c):.3f} microseconds"
-        )
-        print(
-            f"The efficientattn model runs in {benchmark_torch_function_in_microseconds(model_efficient_attn.forward, x, c):.3f} microseconds"
-        )
-
-        print(75 * "+")
-        print("NATIVE")
-        print(75 * "+")
-        torch.cuda.reset_peak_memory_stats()
-        with profile(
-            activities=activities, record_shapes=False, profile_memory=True
-        ) as prof:
-            with record_function("NativeAttention stats"):
-                for _ in range(25):
-                    model_native(x, c)
-        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
-        print(torch.cuda.max_memory_allocated() * 1e-9, "GB used by native block")
-
-        print(75 * "+")
-        print("Xformers")
-        print(75 * "+")
-        torch.cuda.reset_peak_memory_stats()
-        with profile(
-            activities=activities, record_shapes=False, profile_memory=True
-        ) as prof:
-            with record_function("xformers stats"):
-                for _ in range(25):
-                    model_efficient_attn(x, c)
-        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
-        print(torch.cuda.max_memory_allocated() * 1e-9, "GB used by xformers block")
-
-
-def test01():
-    # conv1x1 vs linear
-    from ..util import count_params
-
-    conv = nn.Conv2d(3, 32, kernel_size=1).cuda()
-    print(count_params(conv))
-    linear = torch.nn.Linear(3, 32).cuda()
-    print(count_params(linear))
-
-    print(conv.weight.shape)
-
-    # use same initialization
-    linear.weight = torch.nn.Parameter(conv.weight.squeeze(-1).squeeze(-1))
-    linear.bias = torch.nn.Parameter(conv.bias)
-
-    print(linear.weight.shape)
-
-    x = torch.randn(11, 3, 64, 64).cuda()
-
-    xr = rearrange(x, "b c h w -> b (h w) c").contiguous()
-    print(xr.shape)
-    out_linear = linear(xr)
-    print(out_linear.mean(), out_linear.shape)
-
-    out_conv = conv(x)
-    print(out_conv.mean(), out_conv.shape)
-    print("done with test01.\n")
-
-
-def test02():
-    # try cosine flash attention
-    import time
-
-    torch.backends.cuda.matmul.allow_tf32 = True
-    torch.backends.cudnn.allow_tf32 = True
-    torch.backends.cudnn.benchmark = True
-    print("testing cosine flash attention...")
-    DIM = 1024
-    SEQLEN = 4096
-    BS = 16
-
-    print(" softmax (vanilla) first...")
-    model = BasicTransformerBlock(
-        dim=DIM,
-        n_heads=16,
-        d_head=64,
-        dropout=0.0,
-        context_dim=None,
-        attn_mode="softmax",
-    ).cuda()
-    try:
-        x = torch.randn(BS, SEQLEN, DIM).cuda()
-        tic = time.time()
-        y = model(x)
-        toc = time.time()
-        print(y.shape, toc - tic)
-    except RuntimeError as e:
-        # likely oom
-        print(str(e))
-
-    print("\n now flash-cosine...")
-    model = BasicTransformerBlock(
-        dim=DIM,
-        n_heads=16,
-        d_head=64,
-        dropout=0.0,
-        context_dim=None,
-        attn_mode="flash-cosine",
-    ).cuda()
-    x = torch.randn(BS, SEQLEN, DIM).cuda()
-    tic = time.time()
-    y = model(x)
-    toc = time.time()
-    print(y.shape, toc - tic)
-    print("done with test02.\n")
-
-
-if __name__ == "__main__":
-    # test01()
-    # test02()
-    # test03()
-
-    # benchmark_attn()
-    benchmark_transformer_blocks()
-
-    print("done.")
+    def forward(
+        self,
+        x: torch.Tensor,
+        context: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        for layer in self.layers:
+            x = layer(x, context)
+        return x

sgm/modules/autoencoding/losses/__init__.py

@@ -1,246 +1,7 @@
-from typing import Any, Union
+__all__ = [
+    "GeneralLPIPSWithDiscriminator",
+    "LatentLPIPS",
+]
 
-import torch
-import torch.nn as nn
-from einops import rearrange
-from taming.modules.discriminator.model import NLayerDiscriminator, weights_init
-from taming.modules.losses.lpips import LPIPS
-from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss
-
-from ....util import default, instantiate_from_config
-
-
-def adopt_weight(weight, global_step, threshold=0, value=0.0):
-    if global_step < threshold:
-        weight = value
-    return weight
-
-
-class LatentLPIPS(nn.Module):
-    def __init__(
-        self,
-        decoder_config,
-        perceptual_weight=1.0,
-        latent_weight=1.0,
-        scale_input_to_tgt_size=False,
-        scale_tgt_to_input_size=False,
-        perceptual_weight_on_inputs=0.0,
-    ):
-        super().__init__()
-        self.scale_input_to_tgt_size = scale_input_to_tgt_size
-        self.scale_tgt_to_input_size = scale_tgt_to_input_size
-        self.init_decoder(decoder_config)
-        self.perceptual_loss = LPIPS().eval()
-        self.perceptual_weight = perceptual_weight
-        self.latent_weight = latent_weight
-        self.perceptual_weight_on_inputs = perceptual_weight_on_inputs
-
-    def init_decoder(self, config):
-        self.decoder = instantiate_from_config(config)
-        if hasattr(self.decoder, "encoder"):
-            del self.decoder.encoder
-
-    def forward(self, latent_inputs, latent_predictions, image_inputs, split="train"):
-        log = dict()
-        loss = (latent_inputs - latent_predictions) ** 2
-        log[f"{split}/latent_l2_loss"] = loss.mean().detach()
-        image_reconstructions = None
-        if self.perceptual_weight > 0.0:
-            image_reconstructions = self.decoder.decode(latent_predictions)
-            image_targets = self.decoder.decode(latent_inputs)
-            perceptual_loss = self.perceptual_loss(
-                image_targets.contiguous(), image_reconstructions.contiguous()
-            )
-            loss = (
-                self.latent_weight * loss.mean()
-                + self.perceptual_weight * perceptual_loss.mean()
-            )
-            log[f"{split}/perceptual_loss"] = perceptual_loss.mean().detach()
-
-        if self.perceptual_weight_on_inputs > 0.0:
-            image_reconstructions = default(
-                image_reconstructions, self.decoder.decode(latent_predictions)
-            )
-            if self.scale_input_to_tgt_size:
-                image_inputs = torch.nn.functional.interpolate(
-                    image_inputs,
-                    image_reconstructions.shape[2:],
-                    mode="bicubic",
-                    antialias=True,
-                )
-            elif self.scale_tgt_to_input_size:
-                image_reconstructions = torch.nn.functional.interpolate(
-                    image_reconstructions,
-                    image_inputs.shape[2:],
-                    mode="bicubic",
-                    antialias=True,
-                )
-
-            perceptual_loss2 = self.perceptual_loss(
-                image_inputs.contiguous(), image_reconstructions.contiguous()
-            )
-            loss = loss + self.perceptual_weight_on_inputs * perceptual_loss2.mean()
-            log[f"{split}/perceptual_loss_on_inputs"] = perceptual_loss2.mean().detach()
-        return loss, log
-
-
-class GeneralLPIPSWithDiscriminator(nn.Module):
-    def __init__(
-        self,
-        disc_start: int,
-        logvar_init: float = 0.0,
-        pixelloss_weight=1.0,
-        disc_num_layers: int = 3,
-        disc_in_channels: int = 3,
-        disc_factor: float = 1.0,
-        disc_weight: float = 1.0,
-        perceptual_weight: float = 1.0,
-        disc_loss: str = "hinge",
-        scale_input_to_tgt_size: bool = False,
-        dims: int = 2,
-        learn_logvar: bool = False,
-        regularization_weights: Union[None, dict] = None,
-    ):
-        super().__init__()
-        self.dims = dims
-        if self.dims > 2:
-            print(
-                f"running with dims={dims}. This means that for perceptual loss calculation, "
-                f"the LPIPS loss will be applied to each frame independently. "
-            )
-        self.scale_input_to_tgt_size = scale_input_to_tgt_size
-        assert disc_loss in ["hinge", "vanilla"]
-        self.pixel_weight = pixelloss_weight
-        self.perceptual_loss = LPIPS().eval()
-        self.perceptual_weight = perceptual_weight
-        # output log variance
-        self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init)
-        self.learn_logvar = learn_logvar
-
-        self.discriminator = NLayerDiscriminator(
-            input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=False
-        ).apply(weights_init)
-        self.discriminator_iter_start = disc_start
-        self.disc_loss = hinge_d_loss if disc_loss == "hinge" else vanilla_d_loss
-        self.disc_factor = disc_factor
-        self.discriminator_weight = disc_weight
-        self.regularization_weights = default(regularization_weights, {})
-
-    def get_trainable_parameters(self) -> Any:
-        return self.discriminator.parameters()
-
-    def get_trainable_autoencoder_parameters(self) -> Any:
-        if self.learn_logvar:
-            yield self.logvar
-        yield from ()
-
-    def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
-        if last_layer is not None:
-            nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
-            g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
-        else:
-            nll_grads = torch.autograd.grad(
-                nll_loss, self.last_layer[0], retain_graph=True
-            )[0]
-            g_grads = torch.autograd.grad(
-                g_loss, self.last_layer[0], retain_graph=True
-            )[0]
-
-        d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
-        d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
-        d_weight = d_weight * self.discriminator_weight
-        return d_weight
-
-    def forward(
-        self,
-        regularization_log,
-        inputs,
-        reconstructions,
-        optimizer_idx,
-        global_step,
-        last_layer=None,
-        split="train",
-        weights=None,
-    ):
-        if self.scale_input_to_tgt_size:
-            inputs = torch.nn.functional.interpolate(
-                inputs, reconstructions.shape[2:], mode="bicubic", antialias=True
-            )
-
-        if self.dims > 2:
-            inputs, reconstructions = map(
-                lambda x: rearrange(x, "b c t h w -> (b t) c h w"),
-                (inputs, reconstructions),
-            )
-
-        rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
-        if self.perceptual_weight > 0:
-            p_loss = self.perceptual_loss(
-                inputs.contiguous(), reconstructions.contiguous()
-            )
-            rec_loss = rec_loss + self.perceptual_weight * p_loss
-
-        nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar
-        weighted_nll_loss = nll_loss
-        if weights is not None:
-            weighted_nll_loss = weights * nll_loss
-        weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0]
-        nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
-
-        # now the GAN part
-        if optimizer_idx == 0:
-            # generator update
-            logits_fake = self.discriminator(reconstructions.contiguous())
-            g_loss = -torch.mean(logits_fake)
-
-            if self.disc_factor > 0.0:
-                try:
-                    d_weight = self.calculate_adaptive_weight(
-                        nll_loss, g_loss, last_layer=last_layer
-                    )
-                except RuntimeError:
-                    assert not self.training
-                    d_weight = torch.tensor(0.0)
-            else:
-                d_weight = torch.tensor(0.0)
-
-            disc_factor = adopt_weight(
-                self.disc_factor, global_step, threshold=self.discriminator_iter_start
-            )
-            loss = weighted_nll_loss + d_weight * disc_factor * g_loss
-            log = dict()
-            for k in regularization_log:
-                if k in self.regularization_weights:
-                    loss = loss + self.regularization_weights[k] * regularization_log[k]
-                log[f"{split}/{k}"] = regularization_log[k].detach().mean()
-
-            log.update(
-                {
-                    "{}/total_loss".format(split): loss.clone().detach().mean(),
-                    "{}/logvar".format(split): self.logvar.detach(),
-                    "{}/nll_loss".format(split): nll_loss.detach().mean(),
-                    "{}/rec_loss".format(split): rec_loss.detach().mean(),
-                    "{}/d_weight".format(split): d_weight.detach(),
-                    "{}/disc_factor".format(split): torch.tensor(disc_factor),
-                    "{}/g_loss".format(split): g_loss.detach().mean(),
-                }
-            )
-
-            return loss, log
-
-        if optimizer_idx == 1:
-            # second pass for discriminator update
-            logits_real = self.discriminator(inputs.contiguous().detach())
-            logits_fake = self.discriminator(reconstructions.contiguous().detach())
-
-            disc_factor = adopt_weight(
-                self.disc_factor, global_step, threshold=self.discriminator_iter_start
-            )
-            d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
-
-            log = {
-                "{}/disc_loss".format(split): d_loss.clone().detach().mean(),
-                "{}/logits_real".format(split): logits_real.detach().mean(),
-                "{}/logits_fake".format(split): logits_fake.detach().mean(),
-            }
-            return d_loss, log
+from .discriminator_loss import GeneralLPIPSWithDiscriminator
+from .lpips import LatentLPIPS

sgm/modules/autoencoding/losses/discriminator_loss.py

@@ -0,0 +1,306 @@
+from typing import Dict, Iterator, List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torchvision
+from einops import rearrange
+from matplotlib import colormaps
+from matplotlib import pyplot as plt
+
+from ....util import default, instantiate_from_config
+from ..lpips.loss.lpips import LPIPS
+from ..lpips.model.model import weights_init
+from ..lpips.vqperceptual import hinge_d_loss, vanilla_d_loss
+
+
+class GeneralLPIPSWithDiscriminator(nn.Module):
+    def __init__(
+        self,
+        disc_start: int,
+        logvar_init: float = 0.0,
+        disc_num_layers: int = 3,
+        disc_in_channels: int = 3,
+        disc_factor: float = 1.0,
+        disc_weight: float = 1.0,
+        perceptual_weight: float = 1.0,
+        disc_loss: str = "hinge",
+        scale_input_to_tgt_size: bool = False,
+        dims: int = 2,
+        learn_logvar: bool = False,
+        regularization_weights: Union[None, Dict[str, float]] = None,
+        additional_log_keys: Optional[List[str]] = None,
+        discriminator_config: Optional[Dict] = None,
+    ):
+        super().__init__()
+        self.dims = dims
+        if self.dims > 2:
+            print(
+                f"running with dims={dims}. This means that for perceptual loss "
+                f"calculation, the LPIPS loss will be applied to each frame "
+                f"independently."
+            )
+        self.scale_input_to_tgt_size = scale_input_to_tgt_size
+        assert disc_loss in ["hinge", "vanilla"]
+        self.perceptual_loss = LPIPS().eval()
+        self.perceptual_weight = perceptual_weight
+        # output log variance
+        self.logvar = nn.Parameter(
+            torch.full((), logvar_init), requires_grad=learn_logvar
+        )
+        self.learn_logvar = learn_logvar
+
+        discriminator_config = default(
+            discriminator_config,
+            {
+                "target": "sgm.modules.autoencoding.lpips.model.model.NLayerDiscriminator",
+                "params": {
+                    "input_nc": disc_in_channels,
+                    "n_layers": disc_num_layers,
+                    "use_actnorm": False,
+                },
+            },
+        )
+
+        self.discriminator = instantiate_from_config(discriminator_config).apply(
+            weights_init
+        )
+        self.discriminator_iter_start = disc_start
+        self.disc_loss = hinge_d_loss if disc_loss == "hinge" else vanilla_d_loss
+        self.disc_factor = disc_factor
+        self.discriminator_weight = disc_weight
+        self.regularization_weights = default(regularization_weights, {})
+
+        self.forward_keys = [
+            "optimizer_idx",
+            "global_step",
+            "last_layer",
+            "split",
+            "regularization_log",
+        ]
+
+        self.additional_log_keys = set(default(additional_log_keys, []))
+        self.additional_log_keys.update(set(self.regularization_weights.keys()))
+
+    def get_trainable_parameters(self) -> Iterator[nn.Parameter]:
+        return self.discriminator.parameters()
+
+    def get_trainable_autoencoder_parameters(self) -> Iterator[nn.Parameter]:
+        if self.learn_logvar:
+            yield self.logvar
+        yield from ()
+
+    @torch.no_grad()
+    def log_images(
+        self, inputs: torch.Tensor, reconstructions: torch.Tensor
+    ) -> Dict[str, torch.Tensor]:
+        # calc logits of real/fake
+        logits_real = self.discriminator(inputs.contiguous().detach())
+        if len(logits_real.shape) < 4:
+            # Non patch-discriminator
+            return dict()
+        logits_fake = self.discriminator(reconstructions.contiguous().detach())
+        # -> (b, 1, h, w)
+
+        # parameters for colormapping
+        high = max(logits_fake.abs().max(), logits_real.abs().max()).item()
+        cmap = colormaps["PiYG"]  # diverging colormap
+
+        def to_colormap(logits: torch.Tensor) -> torch.Tensor:
+            """(b, 1, ...) -> (b, 3, ...)"""
+            logits = (logits + high) / (2 * high)
+            logits_np = cmap(logits.cpu().numpy())[..., :3]  # truncate alpha channel
+            # -> (b, 1, ..., 3)
+            logits = torch.from_numpy(logits_np).to(logits.device)
+            return rearrange(logits, "b 1 ... c -> b c ...")
+
+        logits_real = torch.nn.functional.interpolate(
+            logits_real,
+            size=inputs.shape[-2:],
+            mode="nearest",
+            antialias=False,
+        )
+        logits_fake = torch.nn.functional.interpolate(
+            logits_fake,
+            size=reconstructions.shape[-2:],
+            mode="nearest",
+            antialias=False,
+        )
+
+        # alpha value of logits for overlay
+        alpha_real = torch.abs(logits_real) / high
+        alpha_fake = torch.abs(logits_fake) / high
+        # -> (b, 1, h, w) in range [0, 0.5]
+        # alpha value of lines don't really matter, since the values are the same
+        # for both images and logits anyway
+        grid_alpha_real = torchvision.utils.make_grid(alpha_real, nrow=4)
+        grid_alpha_fake = torchvision.utils.make_grid(alpha_fake, nrow=4)
+        grid_alpha = 0.8 * torch.cat((grid_alpha_real, grid_alpha_fake), dim=1)
+        # -> (1, h, w)
+        # blend logits and images together
+
+        # prepare logits for plotting
+        logits_real = to_colormap(logits_real)
+        logits_fake = to_colormap(logits_fake)
+        # resize logits
+        # -> (b, 3, h, w)
+
+        # make some grids
+        # add all logits to one plot
+        logits_real = torchvision.utils.make_grid(logits_real, nrow=4)
+        logits_fake = torchvision.utils.make_grid(logits_fake, nrow=4)
+        # I just love how torchvision calls the number of columns `nrow`
+        grid_logits = torch.cat((logits_real, logits_fake), dim=1)
+        # -> (3, h, w)
+
+        grid_images_real = torchvision.utils.make_grid(0.5 * inputs + 0.5, nrow=4)
+        grid_images_fake = torchvision.utils.make_grid(
+            0.5 * reconstructions + 0.5, nrow=4
+        )
+        grid_images = torch.cat((grid_images_real, grid_images_fake), dim=1)
+        # -> (3, h, w) in range [0, 1]
+
+        grid_blend = grid_alpha * grid_logits + (1 - grid_alpha) * grid_images
+
+        # Create labeled colorbar
+        dpi = 100
+        height = 128 / dpi
+        width = grid_logits.shape[2] / dpi
+        fig, ax = plt.subplots(figsize=(width, height), dpi=dpi)
+        img = ax.imshow(np.array([[-high, high]]), cmap=cmap)
+        plt.colorbar(
+            img,
+            cax=ax,
+            orientation="horizontal",
+            fraction=0.9,
+            aspect=width / height,
+            pad=0.0,
+        )
+        img.set_visible(False)
+        fig.tight_layout()
+        fig.canvas.draw()
+        # manually convert figure to numpy
+        cbar_np = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
+        cbar_np = cbar_np.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+        cbar = torch.from_numpy(cbar_np.copy()).to(grid_logits.dtype) / 255.0
+        cbar = rearrange(cbar, "h w c -> c h w").to(grid_logits.device)
+
+        # Add colorbar to plot
+        annotated_grid = torch.cat((grid_logits, cbar), dim=1)
+        blended_grid = torch.cat((grid_blend, cbar), dim=1)
+        return {
+            "vis_logits": 2 * annotated_grid[None, ...] - 1,
+            "vis_logits_blended": 2 * blended_grid[None, ...] - 1,
+        }
+
+    def calculate_adaptive_weight(
+        self, nll_loss: torch.Tensor, g_loss: torch.Tensor, last_layer: torch.Tensor
+    ) -> torch.Tensor:
+        nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
+        g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
+
+        d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
+        d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
+        d_weight = d_weight * self.discriminator_weight
+        return d_weight
+
+    def forward(
+        self,
+        inputs: torch.Tensor,
+        reconstructions: torch.Tensor,
+        *,  # added because I changed the order here
+        regularization_log: Dict[str, torch.Tensor],
+        optimizer_idx: int,
+        global_step: int,
+        last_layer: torch.Tensor,
+        split: str = "train",
+        weights: Union[None, float, torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, dict]:
+        if self.scale_input_to_tgt_size:
+            inputs = torch.nn.functional.interpolate(
+                inputs, reconstructions.shape[2:], mode="bicubic", antialias=True
+            )
+
+        if self.dims > 2:
+            inputs, reconstructions = map(
+                lambda x: rearrange(x, "b c t h w -> (b t) c h w"),
+                (inputs, reconstructions),
+            )
+
+        rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
+        if self.perceptual_weight > 0:
+            p_loss = self.perceptual_loss(
+                inputs.contiguous(), reconstructions.contiguous()
+            )
+            rec_loss = rec_loss + self.perceptual_weight * p_loss
+
+        nll_loss, weighted_nll_loss = self.get_nll_loss(rec_loss, weights)
+
+        # now the GAN part
+        if optimizer_idx == 0:
+            # generator update
+            if global_step >= self.discriminator_iter_start or not self.training:
+                logits_fake = self.discriminator(reconstructions.contiguous())
+                g_loss = -torch.mean(logits_fake)
+                if self.training:
+                    d_weight = self.calculate_adaptive_weight(
+                        nll_loss, g_loss, last_layer=last_layer
+                    )
+                else:
+                    d_weight = torch.tensor(1.0)
+            else:
+                d_weight = torch.tensor(0.0)
+                g_loss = torch.tensor(0.0, requires_grad=True)
+
+            loss = weighted_nll_loss + d_weight * self.disc_factor * g_loss
+            log = dict()
+            for k in regularization_log:
+                if k in self.regularization_weights:
+                    loss = loss + self.regularization_weights[k] * regularization_log[k]
+                if k in self.additional_log_keys:
+                    log[f"{split}/{k}"] = regularization_log[k].detach().float().mean()
+
+            log.update(
+                {
+                    f"{split}/loss/total": loss.clone().detach().mean(),
+                    f"{split}/loss/nll": nll_loss.detach().mean(),
+                    f"{split}/loss/rec": rec_loss.detach().mean(),
+                    f"{split}/loss/g": g_loss.detach().mean(),
+                    f"{split}/scalars/logvar": self.logvar.detach(),
+                    f"{split}/scalars/d_weight": d_weight.detach(),
+                }
+            )
+
+            return loss, log
+        elif optimizer_idx == 1:
+            # second pass for discriminator update
+            logits_real = self.discriminator(inputs.contiguous().detach())
+            logits_fake = self.discriminator(reconstructions.contiguous().detach())
+
+            if global_step >= self.discriminator_iter_start or not self.training:
+                d_loss = self.disc_factor * self.disc_loss(logits_real, logits_fake)
+            else:
+                d_loss = torch.tensor(0.0, requires_grad=True)
+
+            log = {
+                f"{split}/loss/disc": d_loss.clone().detach().mean(),
+                f"{split}/logits/real": logits_real.detach().mean(),
+                f"{split}/logits/fake": logits_fake.detach().mean(),
+            }
+            return d_loss, log
+        else:
+            raise NotImplementedError(f"Unknown optimizer_idx {optimizer_idx}")
+
+    def get_nll_loss(
+        self,
+        rec_loss: torch.Tensor,
+        weights: Optional[Union[float, torch.Tensor]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar
+        weighted_nll_loss = nll_loss
+        if weights is not None:
+            weighted_nll_loss = weights * nll_loss
+        weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0]
+        nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
+
+        return nll_loss, weighted_nll_loss

sgm/modules/autoencoding/losses/lpips.py

@@ -0,0 +1,73 @@
+import torch
+import torch.nn as nn
+
+from ....util import default, instantiate_from_config
+from ..lpips.loss.lpips import LPIPS
+
+
+class LatentLPIPS(nn.Module):
+    def __init__(
+        self,
+        decoder_config,
+        perceptual_weight=1.0,
+        latent_weight=1.0,
+        scale_input_to_tgt_size=False,
+        scale_tgt_to_input_size=False,
+        perceptual_weight_on_inputs=0.0,
+    ):
+        super().__init__()
+        self.scale_input_to_tgt_size = scale_input_to_tgt_size
+        self.scale_tgt_to_input_size = scale_tgt_to_input_size
+        self.init_decoder(decoder_config)
+        self.perceptual_loss = LPIPS().eval()
+        self.perceptual_weight = perceptual_weight
+        self.latent_weight = latent_weight
+        self.perceptual_weight_on_inputs = perceptual_weight_on_inputs
+
+    def init_decoder(self, config):
+        self.decoder = instantiate_from_config(config)
+        if hasattr(self.decoder, "encoder"):
+            del self.decoder.encoder
+
+    def forward(self, latent_inputs, latent_predictions, image_inputs, split="train"):
+        log = dict()
+        loss = (latent_inputs - latent_predictions) ** 2
+        log[f"{split}/latent_l2_loss"] = loss.mean().detach()
+        image_reconstructions = None
+        if self.perceptual_weight > 0.0:
+            image_reconstructions = self.decoder.decode(latent_predictions)
+            image_targets = self.decoder.decode(latent_inputs)
+            perceptual_loss = self.perceptual_loss(
+                image_targets.contiguous(), image_reconstructions.contiguous()
+            )
+            loss = (
+                self.latent_weight * loss.mean()
+                + self.perceptual_weight * perceptual_loss.mean()
+            )
+            log[f"{split}/perceptual_loss"] = perceptual_loss.mean().detach()
+
+        if self.perceptual_weight_on_inputs > 0.0:
+            image_reconstructions = default(
+                image_reconstructions, self.decoder.decode(latent_predictions)
+            )
+            if self.scale_input_to_tgt_size:
+                image_inputs = torch.nn.functional.interpolate(
+                    image_inputs,
+                    image_reconstructions.shape[2:],
+                    mode="bicubic",
+                    antialias=True,
+                )
+            elif self.scale_tgt_to_input_size:
+                image_reconstructions = torch.nn.functional.interpolate(
+                    image_reconstructions,
+                    image_inputs.shape[2:],
+                    mode="bicubic",
+                    antialias=True,
+                )
+
+            perceptual_loss2 = self.perceptual_loss(
+                image_inputs.contiguous(), image_reconstructions.contiguous()
+            )
+            loss = loss + self.perceptual_weight_on_inputs * perceptual_loss2.mean()
+            log[f"{split}/perceptual_loss_on_inputs"] = perceptual_loss2.mean().detach()
+        return loss, log

sgm/modules/autoencoding/lpips/loss/.gitignore

@@ -0,0 +1 @@
+vgg.pth

sgm/modules/autoencoding/lpips/loss/LICENSE

@@ -0,0 +1,23 @@
+Copyright (c) 2018, Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, Oliver Wang
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

sgm/modules/autoencoding/lpips/loss/lpips.py

@@ -0,0 +1,147 @@
+"""Stripped version of https://github.com/richzhang/PerceptualSimilarity/tree/master/models"""
+
+from collections import namedtuple
+
+import torch
+import torch.nn as nn
+from torchvision import models
+
+from ..util import get_ckpt_path
+
+
+class LPIPS(nn.Module):
+    # Learned perceptual metric
+    def __init__(self, use_dropout=True):
+        super().__init__()
+        self.scaling_layer = ScalingLayer()
+        self.chns = [64, 128, 256, 512, 512]  # vg16 features
+        self.net = vgg16(pretrained=True, requires_grad=False)
+        self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout)
+        self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout)
+        self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout)
+        self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout)
+        self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout)
+        self.load_from_pretrained()
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def load_from_pretrained(self, name="vgg_lpips"):
+        ckpt = get_ckpt_path(name, "sgm/modules/autoencoding/lpips/loss")
+        self.load_state_dict(
+            torch.load(ckpt, map_location=torch.device("cpu")), strict=False
+        )
+        print("loaded pretrained LPIPS loss from {}".format(ckpt))
+
+    @classmethod
+    def from_pretrained(cls, name="vgg_lpips"):
+        if name != "vgg_lpips":
+            raise NotImplementedError
+        model = cls()
+        ckpt = get_ckpt_path(name)
+        model.load_state_dict(
+            torch.load(ckpt, map_location=torch.device("cpu")), strict=False
+        )
+        return model
+
+    def forward(self, input, target):
+        in0_input, in1_input = (self.scaling_layer(input), self.scaling_layer(target))
+        outs0, outs1 = self.net(in0_input), self.net(in1_input)
+        feats0, feats1, diffs = {}, {}, {}
+        lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4]
+        for kk in range(len(self.chns)):
+            feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(
+                outs1[kk]
+            )
+            diffs[kk] = (feats0[kk] - feats1[kk]) ** 2
+
+        res = [
+            spatial_average(lins[kk].model(diffs[kk]), keepdim=True)
+            for kk in range(len(self.chns))
+        ]
+        val = res[0]
+        for l in range(1, len(self.chns)):
+            val += res[l]
+        return val
+
+
+class ScalingLayer(nn.Module):
+    def __init__(self):
+        super(ScalingLayer, self).__init__()
+        self.register_buffer(
+            "shift", torch.Tensor([-0.030, -0.088, -0.188])[None, :, None, None]
+        )
+        self.register_buffer(
+            "scale", torch.Tensor([0.458, 0.448, 0.450])[None, :, None, None]
+        )
+
+    def forward(self, inp):
+        return (inp - self.shift) / self.scale
+
+
+class NetLinLayer(nn.Module):
+    """A single linear layer which does a 1x1 conv"""
+
+    def __init__(self, chn_in, chn_out=1, use_dropout=False):
+        super(NetLinLayer, self).__init__()
+        layers = (
+            [
+                nn.Dropout(),
+            ]
+            if (use_dropout)
+            else []
+        )
+        layers += [
+            nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False),
+        ]
+        self.model = nn.Sequential(*layers)
+
+
+class vgg16(torch.nn.Module):
+    def __init__(self, requires_grad=False, pretrained=True):
+        super(vgg16, self).__init__()
+        vgg_pretrained_features = models.vgg16(pretrained=pretrained).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        self.slice5 = torch.nn.Sequential()
+        self.N_slices = 5
+        for x in range(4):
+            self.slice1.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(4, 9):
+            self.slice2.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(9, 16):
+            self.slice3.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(16, 23):
+            self.slice4.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(23, 30):
+            self.slice5.add_module(str(x), vgg_pretrained_features[x])
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, X):
+        h = self.slice1(X)
+        h_relu1_2 = h
+        h = self.slice2(h)
+        h_relu2_2 = h
+        h = self.slice3(h)
+        h_relu3_3 = h
+        h = self.slice4(h)
+        h_relu4_3 = h
+        h = self.slice5(h)
+        h_relu5_3 = h
+        vgg_outputs = namedtuple(
+            "VggOutputs", ["relu1_2", "relu2_2", "relu3_3", "relu4_3", "relu5_3"]
+        )
+        out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)
+        return out
+
+
+def normalize_tensor(x, eps=1e-10):
+    norm_factor = torch.sqrt(torch.sum(x**2, dim=1, keepdim=True))
+    return x / (norm_factor + eps)
+
+
+def spatial_average(x, keepdim=True):
+    return x.mean([2, 3], keepdim=keepdim)

sgm/modules/autoencoding/lpips/model/LICENSE

@@ -0,0 +1,58 @@
+Copyright (c) 2017, Jun-Yan Zhu and Taesung Park
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+--------------------------- LICENSE FOR pix2pix --------------------------------
+BSD License
+
+For pix2pix software
+Copyright (c) 2016, Phillip Isola and Jun-Yan Zhu
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+----------------------------- LICENSE FOR DCGAN --------------------------------
+BSD License
+
+For dcgan.torch software
+
+Copyright (c) 2015, Facebook, Inc. All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
+
+Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
+
+Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
+
+Neither the name Facebook nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

sgm/modules/autoencoding/lpips/model/model.py

@@ -0,0 +1,88 @@
+import functools
+
+import torch.nn as nn
+
+from ..util import ActNorm
+
+
+def weights_init(m):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        nn.init.normal_(m.weight.data, 0.0, 0.02)
+    elif classname.find("BatchNorm") != -1:
+        nn.init.normal_(m.weight.data, 1.0, 0.02)
+        nn.init.constant_(m.bias.data, 0)
+
+
+class NLayerDiscriminator(nn.Module):
+    """Defines a PatchGAN discriminator as in Pix2Pix
+    --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py
+    """
+
+    def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False):
+        """Construct a PatchGAN discriminator
+        Parameters:
+            input_nc (int)  -- the number of channels in input images
+            ndf (int)       -- the number of filters in the last conv layer
+            n_layers (int)  -- the number of conv layers in the discriminator
+            norm_layer      -- normalization layer
+        """
+        super(NLayerDiscriminator, self).__init__()
+        if not use_actnorm:
+            norm_layer = nn.BatchNorm2d
+        else:
+            norm_layer = ActNorm
+        if (
+            type(norm_layer) == functools.partial
+        ):  # no need to use bias as BatchNorm2d has affine parameters
+            use_bias = norm_layer.func != nn.BatchNorm2d
+        else:
+            use_bias = norm_layer != nn.BatchNorm2d
+
+        kw = 4
+        padw = 1
+        sequence = [
+            nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
+            nn.LeakyReLU(0.2, True),
+        ]
+        nf_mult = 1
+        nf_mult_prev = 1
+        for n in range(1, n_layers):  # gradually increase the number of filters
+            nf_mult_prev = nf_mult
+            nf_mult = min(2**n, 8)
+            sequence += [
+                nn.Conv2d(
+                    ndf * nf_mult_prev,
+                    ndf * nf_mult,
+                    kernel_size=kw,
+                    stride=2,
+                    padding=padw,
+                    bias=use_bias,
+                ),
+                norm_layer(ndf * nf_mult),
+                nn.LeakyReLU(0.2, True),
+            ]
+
+        nf_mult_prev = nf_mult
+        nf_mult = min(2**n_layers, 8)
+        sequence += [
+            nn.Conv2d(
+                ndf * nf_mult_prev,
+                ndf * nf_mult,
+                kernel_size=kw,
+                stride=1,
+                padding=padw,
+                bias=use_bias,
+            ),
+            norm_layer(ndf * nf_mult),
+            nn.LeakyReLU(0.2, True),
+        ]
+
+        sequence += [
+            nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)
+        ]  # output 1 channel prediction map
+        self.main = nn.Sequential(*sequence)
+
+    def forward(self, input):
+        """Standard forward."""
+        return self.main(input)

sgm/modules/autoencoding/lpips/util.py

@@ -0,0 +1,128 @@
+import hashlib
+import os
+
+import requests
+import torch
+import torch.nn as nn
+from tqdm import tqdm
+
+URL_MAP = {"vgg_lpips": "https://heibox.uni-heidelberg.de/f/607503859c864bc1b30b/?dl=1"}
+
+CKPT_MAP = {"vgg_lpips": "vgg.pth"}
+
+MD5_MAP = {"vgg_lpips": "d507d7349b931f0638a25a48a722f98a"}
+
+
+def download(url, local_path, chunk_size=1024):
+    os.makedirs(os.path.split(local_path)[0], exist_ok=True)
+    with requests.get(url, stream=True) as r:
+        total_size = int(r.headers.get("content-length", 0))
+        with tqdm(total=total_size, unit="B", unit_scale=True) as pbar:
+            with open(local_path, "wb") as f:
+                for data in r.iter_content(chunk_size=chunk_size):
+                    if data:
+                        f.write(data)
+                        pbar.update(chunk_size)
+
+
+def md5_hash(path):
+    with open(path, "rb") as f:
+        content = f.read()
+    return hashlib.md5(content).hexdigest()
+
+
+def get_ckpt_path(name, root, check=False):
+    assert name in URL_MAP
+    path = os.path.join(root, CKPT_MAP[name])
+    if not os.path.exists(path) or (check and not md5_hash(path) == MD5_MAP[name]):
+        print("Downloading {} model from {} to {}".format(name, URL_MAP[name], path))
+        download(URL_MAP[name], path)
+        md5 = md5_hash(path)
+        assert md5 == MD5_MAP[name], md5
+    return path
+
+
+class ActNorm(nn.Module):
+    def __init__(
+        self, num_features, logdet=False, affine=True, allow_reverse_init=False
+    ):
+        assert affine
+        super().__init__()
+        self.logdet = logdet
+        self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1))
+        self.scale = nn.Parameter(torch.ones(1, num_features, 1, 1))
+        self.allow_reverse_init = allow_reverse_init
+
+        self.register_buffer("initialized", torch.tensor(0, dtype=torch.uint8))
+
+    def initialize(self, input):
+        with torch.no_grad():
+            flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1)
+            mean = (
+                flatten.mean(1)
+                .unsqueeze(1)
+                .unsqueeze(2)
+                .unsqueeze(3)
+                .permute(1, 0, 2, 3)
+            )
+            std = (
+                flatten.std(1)
+                .unsqueeze(1)
+                .unsqueeze(2)
+                .unsqueeze(3)
+                .permute(1, 0, 2, 3)
+            )
+
+            self.loc.data.copy_(-mean)
+            self.scale.data.copy_(1 / (std + 1e-6))
+
+    def forward(self, input, reverse=False):
+        if reverse:
+            return self.reverse(input)
+        if len(input.shape) == 2:
+            input = input[:, :, None, None]
+            squeeze = True
+        else:
+            squeeze = False
+
+        _, _, height, width = input.shape
+
+        if self.training and self.initialized.item() == 0:
+            self.initialize(input)
+            self.initialized.fill_(1)
+
+        h = self.scale * (input + self.loc)
+
+        if squeeze:
+            h = h.squeeze(-1).squeeze(-1)
+
+        if self.logdet:
+            log_abs = torch.log(torch.abs(self.scale))
+            logdet = height * width * torch.sum(log_abs)
+            logdet = logdet * torch.ones(input.shape[0]).to(input)
+            return h, logdet
+
+        return h
+
+    def reverse(self, output):
+        if self.training and self.initialized.item() == 0:
+            if not self.allow_reverse_init:
+                raise RuntimeError(
+                    "Initializing ActNorm in reverse direction is "
+                    "disabled by default. Use allow_reverse_init=True to enable."
+                )
+            else:
+                self.initialize(output)
+                self.initialized.fill_(1)
+
+        if len(output.shape) == 2:
+            output = output[:, :, None, None]
+            squeeze = True
+        else:
+            squeeze = False
+
+        h = output / self.scale - self.loc
+
+        if squeeze:
+            h = h.squeeze(-1).squeeze(-1)
+        return h

sgm/modules/autoencoding/lpips/vqperceptual.py

@@ -0,0 +1,17 @@
+import torch
+import torch.nn.functional as F
+
+
+def hinge_d_loss(logits_real, logits_fake):
+    loss_real = torch.mean(F.relu(1.0 - logits_real))
+    loss_fake = torch.mean(F.relu(1.0 + logits_fake))
+    d_loss = 0.5 * (loss_real + loss_fake)
+    return d_loss
+
+
+def vanilla_d_loss(logits_real, logits_fake):
+    d_loss = 0.5 * (
+        torch.mean(torch.nn.functional.softplus(-logits_real))
+        + torch.mean(torch.nn.functional.softplus(logits_fake))
+    )
+    return d_loss

sgm/modules/autoencoding/regularizers/__init__.py

@@ -5,19 +5,9 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from ....modules.distributions.distributions import DiagonalGaussianDistribution
-
-
-class AbstractRegularizer(nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]:
-        raise NotImplementedError()
-
-    @abstractmethod
-    def get_trainable_parameters(self) -> Any:
-        raise NotImplementedError()
+from ....modules.distributions.distributions import \
+    DiagonalGaussianDistribution
+from .base import AbstractRegularizer
 
 
 class DiagonalGaussianRegularizer(AbstractRegularizer):
@@ -39,15 +29,3 @@ class DiagonalGaussianRegularizer(AbstractRegularizer):
         kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
         log["kl_loss"] = kl_loss
         return z, log
-
-
-def measure_perplexity(predicted_indices, num_centroids):
-    # src: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py
-    # eval cluster perplexity. when perplexity == num_embeddings then all clusters are used exactly equally
-    encodings = (
-        F.one_hot(predicted_indices, num_centroids).float().reshape(-1, num_centroids)
-    )
-    avg_probs = encodings.mean(0)
-    perplexity = (-(avg_probs * torch.log(avg_probs + 1e-10)).sum()).exp()
-    cluster_use = torch.sum(avg_probs > 0)
-    return perplexity, cluster_use

sgm/modules/autoencoding/regularizers/base.py

@@ -0,0 +1,40 @@
+from abc import abstractmethod
+from typing import Any, Tuple
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+
+class AbstractRegularizer(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]:
+        raise NotImplementedError()
+
+    @abstractmethod
+    def get_trainable_parameters(self) -> Any:
+        raise NotImplementedError()
+
+
+class IdentityRegularizer(AbstractRegularizer):
+    def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]:
+        return z, dict()
+
+    def get_trainable_parameters(self) -> Any:
+        yield from ()
+
+
+def measure_perplexity(
+    predicted_indices: torch.Tensor, num_centroids: int
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    # src: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py
+    # eval cluster perplexity. when perplexity == num_embeddings then all clusters are used exactly equally
+    encodings = (
+        F.one_hot(predicted_indices, num_centroids).float().reshape(-1, num_centroids)
+    )
+    avg_probs = encodings.mean(0)
+    perplexity = (-(avg_probs * torch.log(avg_probs + 1e-10)).sum()).exp()
+    cluster_use = torch.sum(avg_probs > 0)
+    return perplexity, cluster_use

sgm/modules/autoencoding/regularizers/quantize.py

@@ -0,0 +1,487 @@
+import logging
+from abc import abstractmethod
+from typing import Dict, Iterator, Literal, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from torch import einsum
+
+from .base import AbstractRegularizer, measure_perplexity
+
+logpy = logging.getLogger(__name__)
+
+
+class AbstractQuantizer(AbstractRegularizer):
+    def __init__(self):
+        super().__init__()
+        # Define these in your init
+        # shape (N,)
+        self.used: Optional[torch.Tensor]
+        self.re_embed: int
+        self.unknown_index: Union[Literal["random"], int]
+
+    def remap_to_used(self, inds: torch.Tensor) -> torch.Tensor:
+        assert self.used is not None, "You need to define used indices for remap"
+        ishape = inds.shape
+        assert len(ishape) > 1
+        inds = inds.reshape(ishape[0], -1)
+        used = self.used.to(inds)
+        match = (inds[:, :, None] == used[None, None, ...]).long()
+        new = match.argmax(-1)
+        unknown = match.sum(2) < 1
+        if self.unknown_index == "random":
+            new[unknown] = torch.randint(0, self.re_embed, size=new[unknown].shape).to(
+                device=new.device
+            )
+        else:
+            new[unknown] = self.unknown_index
+        return new.reshape(ishape)
+
+    def unmap_to_all(self, inds: torch.Tensor) -> torch.Tensor:
+        assert self.used is not None, "You need to define used indices for remap"
+        ishape = inds.shape
+        assert len(ishape) > 1
+        inds = inds.reshape(ishape[0], -1)
+        used = self.used.to(inds)
+        if self.re_embed > self.used.shape[0]:  # extra token
+            inds[inds >= self.used.shape[0]] = 0  # simply set to zero
+        back = torch.gather(used[None, :][inds.shape[0] * [0], :], 1, inds)
+        return back.reshape(ishape)
+
+    @abstractmethod
+    def get_codebook_entry(
+        self, indices: torch.Tensor, shape: Optional[Tuple[int, ...]] = None
+    ) -> torch.Tensor:
+        raise NotImplementedError()
+
+    def get_trainable_parameters(self) -> Iterator[torch.nn.Parameter]:
+        yield from self.parameters()
+
+
+class GumbelQuantizer(AbstractQuantizer):
+    """
+    credit to @karpathy:
+    https://github.com/karpathy/deep-vector-quantization/blob/main/model.py (thanks!)
+    Gumbel Softmax trick quantizer
+    Categorical Reparameterization with Gumbel-Softmax, Jang et al. 2016
+    https://arxiv.org/abs/1611.01144
+    """
+
+    def __init__(
+        self,
+        num_hiddens: int,
+        embedding_dim: int,
+        n_embed: int,
+        straight_through: bool = True,
+        kl_weight: float = 5e-4,
+        temp_init: float = 1.0,
+        remap: Optional[str] = None,
+        unknown_index: str = "random",
+        loss_key: str = "loss/vq",
+    ) -> None:
+        super().__init__()
+
+        self.loss_key = loss_key
+        self.embedding_dim = embedding_dim
+        self.n_embed = n_embed
+
+        self.straight_through = straight_through
+        self.temperature = temp_init
+        self.kl_weight = kl_weight
+
+        self.proj = nn.Conv2d(num_hiddens, n_embed, 1)
+        self.embed = nn.Embedding(n_embed, embedding_dim)
+
+        self.remap = remap
+        if self.remap is not None:
+            self.register_buffer("used", torch.tensor(np.load(self.remap)))
+            self.re_embed = self.used.shape[0]
+        else:
+            self.used = None
+            self.re_embed = n_embed
+        if unknown_index == "extra":
+            self.unknown_index = self.re_embed
+            self.re_embed = self.re_embed + 1
+        else:
+            assert unknown_index == "random" or isinstance(
+                unknown_index, int
+            ), "unknown index needs to be 'random', 'extra' or any integer"
+            self.unknown_index = unknown_index  # "random" or "extra" or integer
+        if self.remap is not None:
+            logpy.info(
+                f"Remapping {self.n_embed} indices to {self.re_embed} indices. "
+                f"Using {self.unknown_index} for unknown indices."
+            )
+
+    def forward(
+        self, z: torch.Tensor, temp: Optional[float] = None, return_logits: bool = False
+    ) -> Tuple[torch.Tensor, Dict]:
+        # force hard = True when we are in eval mode, as we must quantize.
+        # actually, always true seems to work
+        hard = self.straight_through if self.training else True
+        temp = self.temperature if temp is None else temp
+        out_dict = {}
+        logits = self.proj(z)
+        if self.remap is not None:
+            # continue only with used logits
+            full_zeros = torch.zeros_like(logits)
+            logits = logits[:, self.used, ...]
+
+        soft_one_hot = F.gumbel_softmax(logits, tau=temp, dim=1, hard=hard)
+        if self.remap is not None:
+            # go back to all entries but unused set to zero
+            full_zeros[:, self.used, ...] = soft_one_hot
+            soft_one_hot = full_zeros
+        z_q = einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight)
+
+        # + kl divergence to the prior loss
+        qy = F.softmax(logits, dim=1)
+        diff = (
+            self.kl_weight
+            * torch.sum(qy * torch.log(qy * self.n_embed + 1e-10), dim=1).mean()
+        )
+        out_dict[self.loss_key] = diff
+
+        ind = soft_one_hot.argmax(dim=1)
+        out_dict["indices"] = ind
+        if self.remap is not None:
+            ind = self.remap_to_used(ind)
+
+        if return_logits:
+            out_dict["logits"] = logits
+
+        return z_q, out_dict
+
+    def get_codebook_entry(self, indices, shape):
+        # TODO: shape not yet optional
+        b, h, w, c = shape
+        assert b * h * w == indices.shape[0]
+        indices = rearrange(indices, "(b h w) -> b h w", b=b, h=h, w=w)
+        if self.remap is not None:
+            indices = self.unmap_to_all(indices)
+        one_hot = (
+            F.one_hot(indices, num_classes=self.n_embed).permute(0, 3, 1, 2).float()
+        )
+        z_q = einsum("b n h w, n d -> b d h w", one_hot, self.embed.weight)
+        return z_q
+
+
+class VectorQuantizer(AbstractQuantizer):
+    """
+    ____________________________________________
+    Discretization bottleneck part of the VQ-VAE.
+    Inputs:
+    - n_e : number of embeddings
+    - e_dim : dimension of embedding
+    - beta : commitment cost used in loss term,
+        beta * ||z_e(x)-sg[e]||^2
+    _____________________________________________
+    """
+
+    def __init__(
+        self,
+        n_e: int,
+        e_dim: int,
+        beta: float = 0.25,
+        remap: Optional[str] = None,
+        unknown_index: str = "random",
+        sane_index_shape: bool = False,
+        log_perplexity: bool = False,
+        embedding_weight_norm: bool = False,
+        loss_key: str = "loss/vq",
+    ):
+        super().__init__()
+        self.n_e = n_e
+        self.e_dim = e_dim
+        self.beta = beta
+        self.loss_key = loss_key
+
+        if not embedding_weight_norm:
+            self.embedding = nn.Embedding(self.n_e, self.e_dim)
+            self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
+        else:
+            self.embedding = torch.nn.utils.weight_norm(
+                nn.Embedding(self.n_e, self.e_dim), dim=1
+            )
+
+        self.remap = remap
+        if self.remap is not None:
+            self.register_buffer("used", torch.tensor(np.load(self.remap)))
+            self.re_embed = self.used.shape[0]
+        else:
+            self.used = None
+            self.re_embed = n_e
+        if unknown_index == "extra":
+            self.unknown_index = self.re_embed
+            self.re_embed = self.re_embed + 1
+        else:
+            assert unknown_index == "random" or isinstance(
+                unknown_index, int
+            ), "unknown index needs to be 'random', 'extra' or any integer"
+            self.unknown_index = unknown_index  # "random" or "extra" or integer
+        if self.remap is not None:
+            logpy.info(
+                f"Remapping {self.n_e} indices to {self.re_embed} indices. "
+                f"Using {self.unknown_index} for unknown indices."
+            )
+
+        self.sane_index_shape = sane_index_shape
+        self.log_perplexity = log_perplexity
+
+    def forward(
+        self,
+        z: torch.Tensor,
+    ) -> Tuple[torch.Tensor, Dict]:
+        do_reshape = z.ndim == 4
+        if do_reshape:
+            #     # reshape z -> (batch, height, width, channel) and flatten
+            z = rearrange(z, "b c h w -> b h w c").contiguous()
+
+        else:
+            assert z.ndim < 4, "No reshaping strategy for inputs > 4 dimensions defined"
+            z = z.contiguous()
+
+        z_flattened = z.view(-1, self.e_dim)
+        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
+
+        d = (
+            torch.sum(z_flattened**2, dim=1, keepdim=True)
+            + torch.sum(self.embedding.weight**2, dim=1)
+            - 2
+            * torch.einsum(
+                "bd,dn->bn", z_flattened, rearrange(self.embedding.weight, "n d -> d n")
+            )
+        )
+
+        min_encoding_indices = torch.argmin(d, dim=1)
+        z_q = self.embedding(min_encoding_indices).view(z.shape)
+        loss_dict = {}
+        if self.log_perplexity:
+            perplexity, cluster_usage = measure_perplexity(
+                min_encoding_indices.detach(), self.n_e
+            )
+            loss_dict.update({"perplexity": perplexity, "cluster_usage": cluster_usage})
+
+        # compute loss for embedding
+        loss = self.beta * torch.mean((z_q.detach() - z) ** 2) + torch.mean(
+            (z_q - z.detach()) ** 2
+        )
+        loss_dict[self.loss_key] = loss
+
+        # preserve gradients
+        z_q = z + (z_q - z).detach()
+
+        # reshape back to match original input shape
+        if do_reshape:
+            z_q = rearrange(z_q, "b h w c -> b c h w").contiguous()
+
+        if self.remap is not None:
+            min_encoding_indices = min_encoding_indices.reshape(
+                z.shape[0], -1
+            )  # add batch axis
+            min_encoding_indices = self.remap_to_used(min_encoding_indices)
+            min_encoding_indices = min_encoding_indices.reshape(-1, 1)  # flatten
+
+        if self.sane_index_shape:
+            if do_reshape:
+                min_encoding_indices = min_encoding_indices.reshape(
+                    z_q.shape[0], z_q.shape[2], z_q.shape[3]
+                )
+            else:
+                min_encoding_indices = rearrange(
+                    min_encoding_indices, "(b s) 1 -> b s", b=z_q.shape[0]
+                )
+
+        loss_dict["min_encoding_indices"] = min_encoding_indices
+
+        return z_q, loss_dict
+
+    def get_codebook_entry(
+        self, indices: torch.Tensor, shape: Optional[Tuple[int, ...]] = None
+    ) -> torch.Tensor:
+        # shape specifying (batch, height, width, channel)
+        if self.remap is not None:
+            assert shape is not None, "Need to give shape for remap"
+            indices = indices.reshape(shape[0], -1)  # add batch axis
+            indices = self.unmap_to_all(indices)
+            indices = indices.reshape(-1)  # flatten again
+
+        # get quantized latent vectors
+        z_q = self.embedding(indices)
+
+        if shape is not None:
+            z_q = z_q.view(shape)
+            # reshape back to match original input shape
+            z_q = z_q.permute(0, 3, 1, 2).contiguous()
+
+        return z_q
+
+
+class EmbeddingEMA(nn.Module):
+    def __init__(self, num_tokens, codebook_dim, decay=0.99, eps=1e-5):
+        super().__init__()
+        self.decay = decay
+        self.eps = eps
+        weight = torch.randn(num_tokens, codebook_dim)
+        self.weight = nn.Parameter(weight, requires_grad=False)
+        self.cluster_size = nn.Parameter(torch.zeros(num_tokens), requires_grad=False)
+        self.embed_avg = nn.Parameter(weight.clone(), requires_grad=False)
+        self.update = True
+
+    def forward(self, embed_id):
+        return F.embedding(embed_id, self.weight)
+
+    def cluster_size_ema_update(self, new_cluster_size):
+        self.cluster_size.data.mul_(self.decay).add_(
+            new_cluster_size, alpha=1 - self.decay
+        )
+
+    def embed_avg_ema_update(self, new_embed_avg):
+        self.embed_avg.data.mul_(self.decay).add_(new_embed_avg, alpha=1 - self.decay)
+
+    def weight_update(self, num_tokens):
+        n = self.cluster_size.sum()
+        smoothed_cluster_size = (
+            (self.cluster_size + self.eps) / (n + num_tokens * self.eps) * n
+        )
+        # normalize embedding average with smoothed cluster size
+        embed_normalized = self.embed_avg / smoothed_cluster_size.unsqueeze(1)
+        self.weight.data.copy_(embed_normalized)
+
+
+class EMAVectorQuantizer(AbstractQuantizer):
+    def __init__(
+        self,
+        n_embed: int,
+        embedding_dim: int,
+        beta: float,
+        decay: float = 0.99,
+        eps: float = 1e-5,
+        remap: Optional[str] = None,
+        unknown_index: str = "random",
+        loss_key: str = "loss/vq",
+    ):
+        super().__init__()
+        self.codebook_dim = embedding_dim
+        self.num_tokens = n_embed
+        self.beta = beta
+        self.loss_key = loss_key
+
+        self.embedding = EmbeddingEMA(self.num_tokens, self.codebook_dim, decay, eps)
+
+        self.remap = remap
+        if self.remap is not None:
+            self.register_buffer("used", torch.tensor(np.load(self.remap)))
+            self.re_embed = self.used.shape[0]
+        else:
+            self.used = None
+            self.re_embed = n_embed
+        if unknown_index == "extra":
+            self.unknown_index = self.re_embed
+            self.re_embed = self.re_embed + 1
+        else:
+            assert unknown_index == "random" or isinstance(
+                unknown_index, int
+            ), "unknown index needs to be 'random', 'extra' or any integer"
+            self.unknown_index = unknown_index  # "random" or "extra" or integer
+        if self.remap is not None:
+            logpy.info(
+                f"Remapping {self.n_embed} indices to {self.re_embed} indices. "
+                f"Using {self.unknown_index} for unknown indices."
+            )
+
+    def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, Dict]:
+        # reshape z -> (batch, height, width, channel) and flatten
+        # z, 'b c h w -> b h w c'
+        z = rearrange(z, "b c h w -> b h w c")
+        z_flattened = z.reshape(-1, self.codebook_dim)
+
+        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
+        d = (
+            z_flattened.pow(2).sum(dim=1, keepdim=True)
+            + self.embedding.weight.pow(2).sum(dim=1)
+            - 2 * torch.einsum("bd,nd->bn", z_flattened, self.embedding.weight)
+        )  # 'n d -> d n'
+
+        encoding_indices = torch.argmin(d, dim=1)
+
+        z_q = self.embedding(encoding_indices).view(z.shape)
+        encodings = F.one_hot(encoding_indices, self.num_tokens).type(z.dtype)
+        avg_probs = torch.mean(encodings, dim=0)
+        perplexity = torch.exp(-torch.sum(avg_probs * torch.log(avg_probs + 1e-10)))
+
+        if self.training and self.embedding.update:
+            # EMA cluster size
+            encodings_sum = encodings.sum(0)
+            self.embedding.cluster_size_ema_update(encodings_sum)
+            # EMA embedding average
+            embed_sum = encodings.transpose(0, 1) @ z_flattened
+            self.embedding.embed_avg_ema_update(embed_sum)
+            # normalize embed_avg and update weight
+            self.embedding.weight_update(self.num_tokens)
+
+        # compute loss for embedding
+        loss = self.beta * F.mse_loss(z_q.detach(), z)
+
+        # preserve gradients
+        z_q = z + (z_q - z).detach()
+
+        # reshape back to match original input shape
+        # z_q, 'b h w c -> b c h w'
+        z_q = rearrange(z_q, "b h w c -> b c h w")
+
+        out_dict = {
+            self.loss_key: loss,
+            "encodings": encodings,
+            "encoding_indices": encoding_indices,
+            "perplexity": perplexity,
+        }
+
+        return z_q, out_dict
+
+
+class VectorQuantizerWithInputProjection(VectorQuantizer):
+    def __init__(
+        self,
+        input_dim: int,
+        n_codes: int,
+        codebook_dim: int,
+        beta: float = 1.0,
+        output_dim: Optional[int] = None,
+        **kwargs,
+    ):
+        super().__init__(n_codes, codebook_dim, beta, **kwargs)
+        self.proj_in = nn.Linear(input_dim, codebook_dim)
+        self.output_dim = output_dim
+        if output_dim is not None:
+            self.proj_out = nn.Linear(codebook_dim, output_dim)
+        else:
+            self.proj_out = nn.Identity()
+
+    def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, Dict]:
+        rearr = False
+        in_shape = z.shape
+
+        if z.ndim > 3:
+            rearr = self.output_dim is not None
+            z = rearrange(z, "b c ... -> b (...) c")
+        z = self.proj_in(z)
+        z_q, loss_dict = super().forward(z)
+
+        z_q = self.proj_out(z_q)
+        if rearr:
+            if len(in_shape) == 4:
+                z_q = rearrange(z_q, "b (h w) c -> b c h w ", w=in_shape[-1])
+            elif len(in_shape) == 5:
+                z_q = rearrange(
+                    z_q, "b (t h w) c -> b c t h w ", w=in_shape[-1], h=in_shape[-2]
+                )
+            else:
+                raise NotImplementedError(
+                    f"rearranging not available for {len(in_shape)}-dimensional input."
+                )
+
+        return z_q, loss_dict

sgm/modules/autoencoding/temporal_ae.py

@@ -0,0 +1,347 @@
+from typing import Callable, Iterable, Union
+
+import torch
+from einops import rearrange, repeat
+
+from sgm.modules.diffusionmodules.model import (XFORMERS_IS_AVAILABLE,
+                                                AttnBlock, Decoder,
+                                                MemoryEfficientAttnBlock,
+                                                ResnetBlock)
+from sgm.modules.diffusionmodules.openaimodel import (ResBlock,
+                                                      timestep_embedding)
+from sgm.modules.video_attention import VideoTransformerBlock
+from sgm.util import partialclass
+
+
+class VideoResBlock(ResnetBlock):
+    def __init__(
+        self,
+        out_channels,
+        *args,
+        dropout=0.0,
+        video_kernel_size=3,
+        alpha=0.0,
+        merge_strategy="learned",
+        **kwargs,
+    ):
+        super().__init__(out_channels=out_channels, dropout=dropout, *args, **kwargs)
+        if video_kernel_size is None:
+            video_kernel_size = [3, 1, 1]
+        self.time_stack = ResBlock(
+            channels=out_channels,
+            emb_channels=0,
+            dropout=dropout,
+            dims=3,
+            use_scale_shift_norm=False,
+            use_conv=False,
+            up=False,
+            down=False,
+            kernel_size=video_kernel_size,
+            use_checkpoint=False,
+            skip_t_emb=True,
+        )
+
+        self.merge_strategy = merge_strategy
+        if self.merge_strategy == "fixed":
+            self.register_buffer("mix_factor", torch.Tensor([alpha]))
+        elif self.merge_strategy == "learned":
+            self.register_parameter(
+                "mix_factor", torch.nn.Parameter(torch.Tensor([alpha]))
+            )
+        else:
+            raise ValueError(f"unknown merge strategy {self.merge_strategy}")
+
+    def get_alpha(self, bs):
+        if self.merge_strategy == "fixed":
+            return self.mix_factor
+        elif self.merge_strategy == "learned":
+            return torch.sigmoid(self.mix_factor)
+        else:
+            raise NotImplementedError()
+
+    def forward(self, x, temb, skip_video=False, timesteps=None):
+        if timesteps is None:
+            timesteps = self.timesteps
+
+        b, c, h, w = x.shape
+
+        x = super().forward(x, temb)
+
+        if not skip_video:
+            x_mix = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps)
+
+            x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps)
+
+            x = self.time_stack(x, temb)
+
+            alpha = self.get_alpha(bs=b // timesteps)
+            x = alpha * x + (1.0 - alpha) * x_mix
+
+            x = rearrange(x, "b c t h w -> (b t) c h w")
+        return x
+
+
+class AE3DConv(torch.nn.Conv2d):
+    def __init__(self, in_channels, out_channels, video_kernel_size=3, *args, **kwargs):
+        super().__init__(in_channels, out_channels, *args, **kwargs)
+        if isinstance(video_kernel_size, Iterable):
+            padding = [int(k // 2) for k in video_kernel_size]
+        else:
+            padding = int(video_kernel_size // 2)
+
+        self.time_mix_conv = torch.nn.Conv3d(
+            in_channels=out_channels,
+            out_channels=out_channels,
+            kernel_size=video_kernel_size,
+            padding=padding,
+        )
+
+    def forward(self, input, timesteps, skip_video=False):
+        x = super().forward(input)
+        if skip_video:
+            return x
+        x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps)
+        x = self.time_mix_conv(x)
+        return rearrange(x, "b c t h w -> (b t) c h w")
+
+
+class VideoBlock(AttnBlock):
+    def __init__(
+        self, in_channels: int, alpha: float = 0, merge_strategy: str = "learned"
+    ):
+        super().__init__(in_channels)
+        # no context, single headed, as in base class
+        self.time_mix_block = VideoTransformerBlock(
+            dim=in_channels,
+            n_heads=1,
+            d_head=in_channels,
+            checkpoint=False,
+            ff_in=True,
+            attn_mode="softmax",
+        )
+
+        time_embed_dim = self.in_channels * 4
+        self.video_time_embed = torch.nn.Sequential(
+            torch.nn.Linear(self.in_channels, time_embed_dim),
+            torch.nn.SiLU(),
+            torch.nn.Linear(time_embed_dim, self.in_channels),
+        )
+
+        self.merge_strategy = merge_strategy
+        if self.merge_strategy == "fixed":
+            self.register_buffer("mix_factor", torch.Tensor([alpha]))
+        elif self.merge_strategy == "learned":
+            self.register_parameter(
+                "mix_factor", torch.nn.Parameter(torch.Tensor([alpha]))
+            )
+        else:
+            raise ValueError(f"unknown merge strategy {self.merge_strategy}")
+
+    def forward(self, x, timesteps, skip_video=False):
+        if skip_video:
+            return super().forward(x)
+
+        x_in = x
+        x = self.attention(x)
+        h, w = x.shape[2:]
+        x = rearrange(x, "b c h w -> b (h w) c")
+
+        x_mix = x
+        num_frames = torch.arange(timesteps, device=x.device)
+        num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps)
+        num_frames = rearrange(num_frames, "b t -> (b t)")
+        t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False)
+        emb = self.video_time_embed(t_emb)  # b, n_channels
+        emb = emb[:, None, :]
+        x_mix = x_mix + emb
+
+        alpha = self.get_alpha()
+        x_mix = self.time_mix_block(x_mix, timesteps=timesteps)
+        x = alpha * x + (1.0 - alpha) * x_mix  # alpha merge
+
+        x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w)
+        x = self.proj_out(x)
+
+        return x_in + x
+
+    def get_alpha(
+        self,
+    ):
+        if self.merge_strategy == "fixed":
+            return self.mix_factor
+        elif self.merge_strategy == "learned":
+            return torch.sigmoid(self.mix_factor)
+        else:
+            raise NotImplementedError(f"unknown merge strategy {self.merge_strategy}")
+
+
+class MemoryEfficientVideoBlock(MemoryEfficientAttnBlock):
+    def __init__(
+        self, in_channels: int, alpha: float = 0, merge_strategy: str = "learned"
+    ):
+        super().__init__(in_channels)
+        # no context, single headed, as in base class
+        self.time_mix_block = VideoTransformerBlock(
+            dim=in_channels,
+            n_heads=1,
+            d_head=in_channels,
+            checkpoint=False,
+            ff_in=True,
+            attn_mode="softmax-xformers",
+        )
+
+        time_embed_dim = self.in_channels * 4
+        self.video_time_embed = torch.nn.Sequential(
+            torch.nn.Linear(self.in_channels, time_embed_dim),
+            torch.nn.SiLU(),
+            torch.nn.Linear(time_embed_dim, self.in_channels),
+        )
+
+        self.merge_strategy = merge_strategy
+        if self.merge_strategy == "fixed":
+            self.register_buffer("mix_factor", torch.Tensor([alpha]))
+        elif self.merge_strategy == "learned":
+            self.register_parameter(
+                "mix_factor", torch.nn.Parameter(torch.Tensor([alpha]))
+            )
+        else:
+            raise ValueError(f"unknown merge strategy {self.merge_strategy}")
+
+    def forward(self, x, timesteps, skip_time_block=False):
+        if skip_time_block:
+            return super().forward(x)
+
+        x_in = x
+        x = self.attention(x)
+        h, w = x.shape[2:]
+        x = rearrange(x, "b c h w -> b (h w) c")
+
+        x_mix = x
+        num_frames = torch.arange(timesteps, device=x.device)
+        num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps)
+        num_frames = rearrange(num_frames, "b t -> (b t)")
+        t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False)
+        emb = self.video_time_embed(t_emb)  # b, n_channels
+        emb = emb[:, None, :]
+        x_mix = x_mix + emb
+
+        alpha = self.get_alpha()
+        x_mix = self.time_mix_block(x_mix, timesteps=timesteps)
+        x = alpha * x + (1.0 - alpha) * x_mix  # alpha merge
+
+        x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w)
+        x = self.proj_out(x)
+
+        return x_in + x
+
+    def get_alpha(
+        self,
+    ):
+        if self.merge_strategy == "fixed":
+            return self.mix_factor
+        elif self.merge_strategy == "learned":
+            return torch.sigmoid(self.mix_factor)
+        else:
+            raise NotImplementedError(f"unknown merge strategy {self.merge_strategy}")
+
+
+def make_time_attn(
+    in_channels,
+    attn_type="vanilla",
+    attn_kwargs=None,
+    alpha: float = 0,
+    merge_strategy: str = "learned",
+):
+    assert attn_type in [
+        "vanilla",
+        "vanilla-xformers",
+    ], f"attn_type {attn_type} not supported for spatio-temporal attention"
+    print(
+        f"making spatial and temporal attention of type '{attn_type}' with {in_channels} in_channels"
+    )
+    if not XFORMERS_IS_AVAILABLE and attn_type == "vanilla-xformers":
+        print(
+            f"Attention mode '{attn_type}' is not available. Falling back to vanilla attention. "
+            f"This is not a problem in Pytorch >= 2.0. FYI, you are running with PyTorch version {torch.__version__}"
+        )
+        attn_type = "vanilla"
+
+    if attn_type == "vanilla":
+        assert attn_kwargs is None
+        return partialclass(
+            VideoBlock, in_channels, alpha=alpha, merge_strategy=merge_strategy
+        )
+    elif attn_type == "vanilla-xformers":
+        print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...")
+        return partialclass(
+            MemoryEfficientVideoBlock,
+            in_channels,
+            alpha=alpha,
+            merge_strategy=merge_strategy,
+        )
+    else:
+        return NotImplementedError()
+
+
+class Conv2DWrapper(torch.nn.Conv2d):
+    def forward(self, input: torch.Tensor, **kwargs) -> torch.Tensor:
+        return super().forward(input)
+
+
+class VideoDecoder(Decoder):
+    available_time_modes = ["all", "conv-only", "attn-only"]
+
+    def __init__(
+        self,
+        *args,
+        video_kernel_size: Union[int, list] = 3,
+        alpha: float = 0.0,
+        merge_strategy: str = "learned",
+        time_mode: str = "conv-only",
+        **kwargs,
+    ):
+        self.video_kernel_size = video_kernel_size
+        self.alpha = alpha
+        self.merge_strategy = merge_strategy
+        self.time_mode = time_mode
+        assert (
+            self.time_mode in self.available_time_modes
+        ), f"time_mode parameter has to be in {self.available_time_modes}"
+        super().__init__(*args, **kwargs)
+
+    def get_last_layer(self, skip_time_mix=False, **kwargs):
+        if self.time_mode == "attn-only":
+            raise NotImplementedError("TODO")
+        else:
+            return (
+                self.conv_out.time_mix_conv.weight
+                if not skip_time_mix
+                else self.conv_out.weight
+            )
+
+    def _make_attn(self) -> Callable:
+        if self.time_mode not in ["conv-only", "only-last-conv"]:
+            return partialclass(
+                make_time_attn,
+                alpha=self.alpha,
+                merge_strategy=self.merge_strategy,
+            )
+        else:
+            return super()._make_attn()
+
+    def _make_conv(self) -> Callable:
+        if self.time_mode != "attn-only":
+            return partialclass(AE3DConv, video_kernel_size=self.video_kernel_size)
+        else:
+            return Conv2DWrapper
+
+    def _make_resblock(self) -> Callable:
+        if self.time_mode not in ["attn-only", "only-last-conv"]:
+            return partialclass(
+                VideoResBlock,
+                video_kernel_size=self.video_kernel_size,
+                alpha=self.alpha,
+                merge_strategy=self.merge_strategy,
+            )
+        else:
+            return super()._make_resblock()

sgm/modules/diffusionmodules/__init__.py

@@ -1,7 +0,0 @@
-from .denoiser import Denoiser
-from .discretizer import Discretization
-from .loss import StandardDiffusionLoss
-from .model import Model, Encoder, Decoder
-from .openaimodel import UNetModel
-from .sampling import BaseDiffusionSampler
-from .wrappers import OpenAIWrapper

sgm/modules/diffusionmodules/denoiser.py

@@ -1,62 +1,74 @@
+from typing import Dict, Union
+
+import torch
 import torch.nn as nn
 
 from ...util import append_dims, instantiate_from_config
+from .denoiser_scaling import DenoiserScaling
+from .discretizer import Discretization
 
 
 class Denoiser(nn.Module):
-    def __init__(self, weighting_config, scaling_config):
+    def __init__(self, scaling_config: Dict):
         super().__init__()
 
-        self.weighting = instantiate_from_config(weighting_config)
-        self.scaling = instantiate_from_config(scaling_config)
+        self.scaling: DenoiserScaling = instantiate_from_config(scaling_config)
 
-    def possibly_quantize_sigma(self, sigma):
+    def possibly_quantize_sigma(self, sigma: torch.Tensor) -> torch.Tensor:
         return sigma
 
-    def possibly_quantize_c_noise(self, c_noise):
+    def possibly_quantize_c_noise(self, c_noise: torch.Tensor) -> torch.Tensor:
         return c_noise
 
-    def w(self, sigma):
-        return self.weighting(sigma)
-
-    def __call__(self, network, input, sigma, cond):
+    def forward(
+        self,
+        network: nn.Module,
+        input: torch.Tensor,
+        sigma: torch.Tensor,
+        cond: Dict,
+        **additional_model_inputs,
+    ) -> torch.Tensor:
         sigma = self.possibly_quantize_sigma(sigma)
         sigma_shape = sigma.shape
         sigma = append_dims(sigma, input.ndim)
         c_skip, c_out, c_in, c_noise = self.scaling(sigma)
         c_noise = self.possibly_quantize_c_noise(c_noise.reshape(sigma_shape))
-        return network(input * c_in, c_noise, cond) * c_out + input * c_skip
+        return (
+            network(input * c_in, c_noise, cond, **additional_model_inputs) * c_out
+            + input * c_skip
+        )
 
 
 class DiscreteDenoiser(Denoiser):
     def __init__(
         self,
-        weighting_config,
-        scaling_config,
-        num_idx,
-        discretization_config,
-        do_append_zero=False,
-        quantize_c_noise=True,
-        flip=True,
+        scaling_config: Dict,
+        num_idx: int,
+        discretization_config: Dict,
+        do_append_zero: bool = False,
+        quantize_c_noise: bool = True,
+        flip: bool = True,
     ):
-        super().__init__(weighting_config, scaling_config)
-        sigmas = instantiate_from_config(discretization_config)(
-            num_idx, do_append_zero=do_append_zero, flip=flip
+        super().__init__(scaling_config)
+        self.discretization: Discretization = instantiate_from_config(
+            discretization_config
         )
+        sigmas = self.discretization(num_idx, do_append_zero=do_append_zero, flip=flip)
         self.register_buffer("sigmas", sigmas)
         self.quantize_c_noise = quantize_c_noise
+        self.num_idx = num_idx
 
-    def sigma_to_idx(self, sigma):
+    def sigma_to_idx(self, sigma: torch.Tensor) -> torch.Tensor:
         dists = sigma - self.sigmas[:, None]
         return dists.abs().argmin(dim=0).view(sigma.shape)
 
-    def idx_to_sigma(self, idx):
+    def idx_to_sigma(self, idx: Union[torch.Tensor, int]) -> torch.Tensor:
         return self.sigmas[idx]
 
-    def possibly_quantize_sigma(self, sigma):
+    def possibly_quantize_sigma(self, sigma: torch.Tensor) -> torch.Tensor:
         return self.idx_to_sigma(self.sigma_to_idx(sigma))
 
-    def possibly_quantize_c_noise(self, c_noise):
+    def possibly_quantize_c_noise(self, c_noise: torch.Tensor) -> torch.Tensor:
         if self.quantize_c_noise:
             return self.sigma_to_idx(c_noise)
         else:

sgm/modules/diffusionmodules/denoiser_scaling.py

@@ -1,11 +1,24 @@
+from abc import ABC, abstractmethod
+from typing import Tuple
+
 import torch
 
 
+class DenoiserScaling(ABC):
+    @abstractmethod
+    def __call__(
+        self, sigma: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        pass
+
+
 class EDMScaling:
-    def __init__(self, sigma_data=0.5):
+    def __init__(self, sigma_data: float = 0.5):
         self.sigma_data = sigma_data
 
-    def __call__(self, sigma):
+    def __call__(
+        self, sigma: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
         c_skip = self.sigma_data**2 / (sigma**2 + self.sigma_data**2)
         c_out = sigma * self.sigma_data / (sigma**2 + self.sigma_data**2) ** 0.5
         c_in = 1 / (sigma**2 + self.sigma_data**2) ** 0.5
@@ -14,7 +27,9 @@ class EDMScaling:
 
 
 class EpsScaling:
-    def __call__(self, sigma):
+    def __call__(
+        self, sigma: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
         c_skip = torch.ones_like(sigma, device=sigma.device)
         c_out = -sigma
         c_in = 1 / (sigma**2 + 1.0) ** 0.5
@@ -23,9 +38,22 @@ class EpsScaling:
 
 
 class VScaling:
-    def __call__(self, sigma):
+    def __call__(
+        self, sigma: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
         c_skip = 1.0 / (sigma**2 + 1.0)
         c_out = -sigma / (sigma**2 + 1.0) ** 0.5
         c_in = 1.0 / (sigma**2 + 1.0) ** 0.5
         c_noise = sigma.clone()
         return c_skip, c_out, c_in, c_noise
+
+
+class VScalingWithEDMcNoise(DenoiserScaling):
+    def __call__(
+        self, sigma: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        c_skip = 1.0 / (sigma**2 + 1.0)
+        c_out = -sigma / (sigma**2 + 1.0) ** 0.5
+        c_in = 1.0 / (sigma**2 + 1.0) ** 0.5
+        c_noise = 0.25 * sigma.log()
+        return c_skip, c_out, c_in, c_noise

sgm/modules/diffusionmodules/discretizer.py

@@ -1,25 +1,37 @@
-import torch
-import numpy as np
+from abc import abstractmethod
 from functools import partial
 
-from ...util import append_zero
+import numpy as np
+import torch
+
 from ...modules.diffusionmodules.util import make_beta_schedule
+from ...util import append_zero
+
+
+def generate_roughly_equally_spaced_steps(
+    num_substeps: int, max_step: int
+) -> np.ndarray:
+    return np.linspace(max_step - 1, 0, num_substeps, endpoint=False).astype(int)[::-1]
 
 
 class Discretization:
-    def __call__(self, n, do_append_zero=True, device="cuda", flip=False):
-        sigmas = self.get_sigmas(n, device)
+    def __call__(self, n, do_append_zero=True, device="cpu", flip=False):
+        sigmas = self.get_sigmas(n, device=device)
         sigmas = append_zero(sigmas) if do_append_zero else sigmas
         return sigmas if not flip else torch.flip(sigmas, (0,))
 
+    @abstractmethod
+    def get_sigmas(self, n, device):
+        pass
+
 
 class EDMDiscretization(Discretization):
-    def __init__(self, sigma_min=0.02, sigma_max=80.0, rho=7.0):
+    def __init__(self, sigma_min=0.002, sigma_max=80.0, rho=7.0):
         self.sigma_min = sigma_min
         self.sigma_max = sigma_max
         self.rho = rho
 
-    def get_sigmas(self, n, device):
+    def get_sigmas(self, n, device="cpu"):
         ramp = torch.linspace(0, 1, n, device=device)
         min_inv_rho = self.sigma_min ** (1 / self.rho)
         max_inv_rho = self.sigma_max ** (1 / self.rho)
@@ -33,8 +45,8 @@ class LegacyDDPMDiscretization(Discretization):
         linear_start=0.00085,
         linear_end=0.0120,
         num_timesteps=1000,
-        legacy_range=True,
     ):
+        super().__init__()
         self.num_timesteps = num_timesteps
         betas = make_beta_schedule(
             "linear", num_timesteps, linear_start=linear_start, linear_end=linear_end
@@ -42,23 +54,15 @@ class LegacyDDPMDiscretization(Discretization):
         alphas = 1.0 - betas
         self.alphas_cumprod = np.cumprod(alphas, axis=0)
         self.to_torch = partial(torch.tensor, dtype=torch.float32)
-        self.legacy_range = legacy_range
 
-    def get_sigmas(self, n, device):
+    def get_sigmas(self, n, device="cpu"):
         if n < self.num_timesteps:
-            c = self.num_timesteps // n
-
-            if self.legacy_range:
-                timesteps = np.asarray(list(range(0, self.num_timesteps, c)))
-                timesteps += 1  # Legacy LDM Hack
-            else:
-                timesteps = np.asarray(list(range(0, self.num_timesteps + 1, c)))
-                timesteps -= 1
-                timesteps = timesteps[1:]
-
+            timesteps = generate_roughly_equally_spaced_steps(n, self.num_timesteps)
             alphas_cumprod = self.alphas_cumprod[timesteps]
-        else:
+        elif n == self.num_timesteps:
             alphas_cumprod = self.alphas_cumprod
+        else:
+            raise ValueError
 
         to_torch = partial(torch.tensor, dtype=torch.float32, device=device)
         sigmas = to_torch((1 - alphas_cumprod) / alphas_cumprod) ** 0.5

sgm/modules/diffusionmodules/guiders.py

@@ -1,31 +1,33 @@
-from functools import partial
+import logging
+from abc import ABC, abstractmethod
+from typing import Dict, List, Literal, Optional, Tuple, Union
 
 import torch
+from einops import rearrange, repeat
 
-from ...util import default, instantiate_from_config
+from ...util import append_dims, default
+
+logpy = logging.getLogger(__name__)
 
 
-class VanillaCFG:
-    """
-    implements parallelized CFG
-    """
+class Guider(ABC):
+    @abstractmethod
+    def __call__(self, x: torch.Tensor, sigma: float) -> torch.Tensor:
+        pass
 
-    def __init__(self, scale, dyn_thresh_config=None):
-        scale_schedule = lambda scale, sigma: scale  # independent of step
-        self.scale_schedule = partial(scale_schedule, scale)
-        self.dyn_thresh = instantiate_from_config(
-            default(
-                dyn_thresh_config,
-                {
-                    "target": "sgm.modules.diffusionmodules.sampling_utils.NoDynamicThresholding"
-                },
-            )
-        )
+    def prepare_inputs(
+        self, x: torch.Tensor, s: float, c: Dict, uc: Dict
+    ) -> Tuple[torch.Tensor, float, Dict]:
+        pass
 
-    def __call__(self, x, sigma):
+
+class VanillaCFG(Guider):
+    def __init__(self, scale: float):
+        self.scale = scale
+
+    def __call__(self, x: torch.Tensor, sigma: torch.Tensor) -> torch.Tensor:
         x_u, x_c = x.chunk(2)
-        scale_value = self.scale_schedule(sigma)
-        x_pred = self.dyn_thresh(x_u, x_c, scale_value)
+        x_pred = x_u + self.scale * (x_c - x_u)
         return x_pred
 
     def prepare_inputs(self, x, s, c, uc):
@@ -40,14 +42,90 @@ class VanillaCFG:
         return torch.cat([x] * 2), torch.cat([s] * 2), c_out
 
 
-class IdentityGuider:
-    def __call__(self, x, sigma):
+class IdentityGuider(Guider):
+    def __call__(self, x: torch.Tensor, sigma: float) -> torch.Tensor:
         return x
 
-    def prepare_inputs(self, x, s, c, uc):
+    def prepare_inputs(
+        self, x: torch.Tensor, s: float, c: Dict, uc: Dict
+    ) -> Tuple[torch.Tensor, float, Dict]:
         c_out = dict()
 
         for k in c:
             c_out[k] = c[k]
 
         return x, s, c_out
+
+
+class LinearPredictionGuider(Guider):
+    def __init__(
+        self,
+        max_scale: float,
+        num_frames: int,
+        min_scale: float = 1.0,
+        additional_cond_keys: Optional[Union[List[str], str]] = None,
+    ):
+        self.min_scale = min_scale
+        self.max_scale = max_scale
+        self.num_frames = num_frames
+        self.scale = torch.linspace(min_scale, max_scale, num_frames).unsqueeze(0)
+
+        additional_cond_keys = default(additional_cond_keys, [])
+        if isinstance(additional_cond_keys, str):
+            additional_cond_keys = [additional_cond_keys]
+        self.additional_cond_keys = additional_cond_keys
+
+    def __call__(self, x: torch.Tensor, sigma: torch.Tensor) -> torch.Tensor:
+        x_u, x_c = x.chunk(2)
+
+        x_u = rearrange(x_u, "(b t) ... -> b t ...", t=self.num_frames)
+        x_c = rearrange(x_c, "(b t) ... -> b t ...", t=self.num_frames)
+        scale = repeat(self.scale, "1 t -> b t", b=x_u.shape[0])
+        scale = append_dims(scale, x_u.ndim).to(x_u.device)
+
+        return rearrange(x_u + scale * (x_c - x_u), "b t ... -> (b t) ...")
+
+    def prepare_inputs(
+        self, x: torch.Tensor, s: torch.Tensor, c: dict, uc: dict
+    ) -> Tuple[torch.Tensor, torch.Tensor, dict]:
+        c_out = dict()
+
+        for k in c:
+            if k in ["vector", "crossattn", "concat"] + self.additional_cond_keys:
+                c_out[k] = torch.cat((uc[k], c[k]), 0)
+            else:
+                assert c[k] == uc[k]
+                c_out[k] = c[k]
+        return torch.cat([x] * 2), torch.cat([s] * 2), c_out
+
+
+class TrianglePredictionGuider(LinearPredictionGuider):
+    def __init__(
+        self,
+        max_scale: float,
+        num_frames: int,
+        min_scale: float = 1.0,
+        period: float | List[float] = 1.0,
+        period_fusing: Literal["mean", "multiply", "max"] = "max",
+        additional_cond_keys: Optional[Union[List[str], str]] = None,
+    ):
+        super().__init__(max_scale, num_frames, min_scale, additional_cond_keys)
+        values = torch.linspace(0, 1, num_frames)
+        # Constructs a triangle wave
+        if isinstance(period, float):
+            period = [period]
+
+        scales = []
+        for p in period:
+            scales.append(self.triangle_wave(values, p))
+
+        if period_fusing == "mean":
+            scale = sum(scales) / len(period)
+        elif period_fusing == "multiply":
+            scale = torch.prod(torch.stack(scales), dim=0)
+        elif period_fusing == "max":
+            scale = torch.max(torch.stack(scales), dim=0).values
+        self.scale = (scale * (max_scale - min_scale) + min_scale).unsqueeze(0)
+
+    def triangle_wave(self, values: torch.Tensor, period) -> torch.Tensor:
+        return 2 * (values / period - torch.floor(values / period + 0.5)).abs()

sgm/modules/diffusionmodules/loss.py

@@ -1,31 +1,34 @@
-from typing import List, Optional, Union
+from typing import Dict, List, Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
-from omegaconf import ListConfig
-from taming.modules.losses.lpips import LPIPS
 
+from ...modules.autoencoding.lpips.loss.lpips import LPIPS
+from ...modules.encoders.modules import GeneralConditioner
 from ...util import append_dims, instantiate_from_config
+from .denoiser import Denoiser
 
 
 class StandardDiffusionLoss(nn.Module):
     def __init__(
         self,
-        sigma_sampler_config,
-        type="l2",
-        offset_noise_level=0.0,
-        batch2model_keys: Optional[Union[str, List[str], ListConfig]] = None,
+        sigma_sampler_config: dict,
+        loss_weighting_config: dict,
+        loss_type: str = "l2",
+        offset_noise_level: float = 0.0,
+        batch2model_keys: Optional[Union[str, List[str]]] = None,
     ):
         super().__init__()
 
-        assert type in ["l2", "l1", "lpips"]
+        assert loss_type in ["l2", "l1", "lpips"]
 
         self.sigma_sampler = instantiate_from_config(sigma_sampler_config)
+        self.loss_weighting = instantiate_from_config(loss_weighting_config)
 
-        self.type = type
+        self.loss_type = loss_type
         self.offset_noise_level = offset_noise_level
 
-        if type == "lpips":
+        if loss_type == "lpips":
             self.lpips = LPIPS().eval()
 
         if not batch2model_keys:
@@ -36,34 +39,67 @@ class StandardDiffusionLoss(nn.Module):
 
         self.batch2model_keys = set(batch2model_keys)
 
-    def __call__(self, network, denoiser, conditioner, input, batch):
+    def get_noised_input(
+        self, sigmas_bc: torch.Tensor, noise: torch.Tensor, input: torch.Tensor
+    ) -> torch.Tensor:
+        noised_input = input + noise * sigmas_bc
+        return noised_input
+
+    def forward(
+        self,
+        network: nn.Module,
+        denoiser: Denoiser,
+        conditioner: GeneralConditioner,
+        input: torch.Tensor,
+        batch: Dict,
+    ) -> torch.Tensor:
         cond = conditioner(batch)
+        return self._forward(network, denoiser, cond, input, batch)
+
+    def _forward(
+        self,
+        network: nn.Module,
+        denoiser: Denoiser,
+        cond: Dict,
+        input: torch.Tensor,
+        batch: Dict,
+    ) -> Tuple[torch.Tensor, Dict]:
         additional_model_inputs = {
             key: batch[key] for key in self.batch2model_keys.intersection(batch)
         }
+        sigmas = self.sigma_sampler(input.shape[0]).to(input)
 
-        sigmas = self.sigma_sampler(input.shape[0]).to(input.device)
         noise = torch.randn_like(input)
         if self.offset_noise_level > 0.0:
-            noise = noise + self.offset_noise_level * append_dims(
-                torch.randn(input.shape[0], device=input.device), input.ndim
+            offset_shape = (
+                (input.shape[0], 1, input.shape[2])
+                if self.n_frames is not None
+                else (input.shape[0], input.shape[1])
             )
-        noised_input = input + noise * append_dims(sigmas, input.ndim)
+            noise = noise + self.offset_noise_level * append_dims(
+                torch.randn(offset_shape, device=input.device),
+                input.ndim,
+            )
+        sigmas_bc = append_dims(sigmas, input.ndim)
+        noised_input = self.get_noised_input(sigmas_bc, noise, input)
+
         model_output = denoiser(
             network, noised_input, sigmas, cond, **additional_model_inputs
         )
-        w = append_dims(denoiser.w(sigmas), input.ndim)
+        w = append_dims(self.loss_weighting(sigmas), input.ndim)
         return self.get_loss(model_output, input, w)
 
     def get_loss(self, model_output, target, w):
-        if self.type == "l2":
+        if self.loss_type == "l2":
             return torch.mean(
                 (w * (model_output - target) ** 2).reshape(target.shape[0], -1), 1
             )
-        elif self.type == "l1":
+        elif self.loss_type == "l1":
             return torch.mean(
                 (w * (model_output - target).abs()).reshape(target.shape[0], -1), 1
             )
-        elif self.type == "lpips":
+        elif self.loss_type == "lpips":
             loss = self.lpips(model_output, target).reshape(-1)
             return loss
+        else:
+            raise NotImplementedError(f"Unknown loss type {self.loss_type}")

sgm/modules/diffusionmodules/loss_weighting.py

@@ -0,0 +1,32 @@
+from abc import ABC, abstractmethod
+
+import torch
+
+
+class DiffusionLossWeighting(ABC):
+    @abstractmethod
+    def __call__(self, sigma: torch.Tensor) -> torch.Tensor:
+        pass
+
+
+class UnitWeighting(DiffusionLossWeighting):
+    def __call__(self, sigma: torch.Tensor) -> torch.Tensor:
+        return torch.ones_like(sigma, device=sigma.device)
+
+
+class EDMWeighting(DiffusionLossWeighting):
+    def __init__(self, sigma_data: float = 0.5):
+        self.sigma_data = sigma_data
+
+    def __call__(self, sigma: torch.Tensor) -> torch.Tensor:
+        return (sigma**2 + self.sigma_data**2) / (sigma * self.sigma_data) ** 2
+
+
+class VWeighting(EDMWeighting):
+    def __init__(self):
+        super().__init__(sigma_data=1.0)
+
+
+class EpsWeighting(DiffusionLossWeighting):
+    def __call__(self, sigma: torch.Tensor) -> torch.Tensor:
+        return sigma**-2.0