soon is now

.gitignore

@@ -0,0 +1,7 @@
+.pt2
+.pt2_2
+.pt13
+*.egg-info
+build
+/outputs
+/checkpoints

LICENSE

@@ -0,0 +1,75 @@
+SDXL 0.9 RESEARCH LICENSE AGREEMENT
+Copyright (c) Stability AI Ltd.
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README.md

@@ -0,0 +1,187 @@
+# Generative Models by Stability AI
+
+![sample1](assets/000.jpg)
+
+## News
+
+**June 22, 2023**
+
+
+- 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).** 
+
+## 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.
+
+### 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:
+
+- 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`).
+- 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
+cd generative-models
+```
+
+#### 2. Setting up the virtualenv
+
+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** 
+
+
+```shell
+# install required packages from pypi
+python3 -m venv .pt2
+source .pt2/bin/activate
+pip3 install wheel
+pip3 install -r requirements_pt2.txt
+```
+
+## Inference:
+
+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)
+
+**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/`. 
+Next, start the demo using
+
+```
+streamlit run scripts/demo/sampling.py --server.port <your_port>
+```
+
+### Invisible Watermark Detection
+
+Images generated with our code use the
+[invisible-watermark](https://github.com/ShieldMnt/invisible-watermark/)
+library to embed an invisible watermark into the model output. We also provide
+a script to easily detect that watermark. Please note that this watermark is
+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
+
+pip install "numpy>=1.17" "PyWavelets>=1.1.1" "opencv-python>=4.1.0.25"
+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>
+# test multiple files at once
+python scripts/demo/detect.py <filename 1> <filename 2> ... <filename n>
+# test all files in a specific folder
+python scripts/demo/detect.py <your folder name here>/*
+```
+
+## Training:
+
+We are providing example training configs in `configs/example_training`. To launch a training, run
+
+```
+python main.py --base configs/<config1.yaml> configs/<config2.yaml>
+```
+
+where configs are merged from left to right (later configs overwrite the same values).
+This can be used to combine model, training and data configs. However, all of them can also be
+defined in a single config. For example, to run a class-conditional pixel-based diffusion model training on MNIST,
+run
+
+```bash
+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 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.
+
+### Building New Diffusion Models
+
+#### Conditioner
+
+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.
+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.
+Note that the order of the embedders in the `conditioner_config` is important.
+
+#### Network
+
+The neural network is set through the `network_config`. This used to be called `unet_config`, which is not general
+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`.
+
+#### Sampler config
+
+As discussed above, the sampler is independent of the model. In the `sampler_config`, we set the type of numerical
+solver, number of steps, type of discretization, as well as, for example, guidance wrappers for classifier-free
+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).
+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.,
+
+```python
+example = {"jpg": x,  # this is a tensor -1...1 chw
+           "txt": "a beautiful image"}
+```
+
+where we expect images in -1...1, channel-first format.

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

@@ -0,0 +1,115 @@
+model:
+  base_learning_rate: 4.5e-6
+  target: sgm.models.autoencoder.AutoencodingEngine
+  params:
+    input_key: jpg
+    monitor: val/rec_loss
+
+    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
+
+    regularizer_config:
+      target: sgm.modules.autoencoding.regularizers.DiagonalGaussianRegularizer
+
+    encoder_config:
+      target: sgm.modules.diffusionmodules.model.Encoder
+      params:
+        attn_type: none
+        double_z: True
+        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
+
+    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
+
+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

@@ -0,0 +1,188 @@
+model:
+  base_learning_rate: 1.0e-4
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.13025
+    disable_first_stage_autocast: True
+    log_keys:
+      - cls
+
+    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.]
+
+    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:
+          target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+    network_config:
+      target: sgm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_checkpoint: True
+        use_fp16: True
+        in_channels: 4
+        out_channels: 4
+        model_channels: 256
+        attention_resolutions: [1, 2, 4]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4]
+        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
+
+    conditioner_config:
+      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
+              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
+          - 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
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper
+      params:
+        ckpt_path: CKPT_PATH
+        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
+
+    loss_fn_config:
+      target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
+      params:
+        sigma_sampler_config:
+          target: sgm.modules.diffusionmodules.sigma_sampling.DiscreteSampling
+          params:
+            num_idx: 1000
+
+            discretization_config:
+              target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+    sampler_config:
+      target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
+      params:
+        num_steps: 50
+
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+        guider_config:
+          target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+          params:
+            scale: 5.0
+
+data:
+  target: sgm.data.dataset.StableDataModuleFromConfig
+  params:
+    train:
+      datapipeline:
+        urls:
+          # USER: adapt this path the root of your custom dataset
+          - "DATA_PATH"
+        pipeline_config:
+          shardshuffle: 10000
+          sample_shuffle: 10000 # USER: you might wanna adapt depending on your available RAM
+
+        decoders:
+          - "pil"
+
+        postprocessors:
+          - target: sdata.mappers.TorchVisionImageTransforms
+            params:
+              key: 'jpg' # USER: you might wanna adapt this for your custom dataset
+              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 # USER: you might wanna adapt this for your custom dataset
+              w_key: width # USER: you might wanna adapt this for your custom dataset
+
+      loader:
+        batch_size: 64
+        num_workers: 6
+
+lightning:
+  modelcheckpoint:
+    params:
+      every_n_train_steps: 5000
+
+  callbacks:
+    metrics_over_trainsteps_checkpoint:
+      params:
+        every_n_train_steps: 25000
+
+    image_logger:
+      target: main.ImageLogger
+      params:
+        disabled: False
+        enable_autocast: False
+        batch_frequency: 1000
+        max_images: 8
+        increase_log_steps: True
+        log_first_step: False
+        log_images_kwargs:
+          use_ema_scope: False
+          N: 8
+          n_rows: 2
+
+  trainer:
+    devices: 0,
+    benchmark: True
+    num_sanity_val_steps: 0
+    accumulate_grad_batches: 1
+    max_epochs: 1000

configs/example_training/toy/cifar10_cond.yaml

@@ -0,0 +1,99 @@
+model:
+  base_learning_rate: 1.0e-4
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    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:
+            sigma_data: 1.0
+
+    network_config:
+      target: sgm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_checkpoint: True
+        in_channels: 3
+        out_channels: 3
+        model_channels: 32
+        attention_resolutions: []
+        num_res_blocks: 4
+        channel_mult: [1, 2, 2]
+        num_head_channels: 32
+        num_classes: sequential
+        adm_in_channels: 128
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+          - is_trainable: True
+            input_key: cls
+            ucg_rate: 0.2
+            target: sgm.modules.encoders.modules.ClassEmbedder
+            params:
+              embed_dim: 128
+              n_classes: 10
+
+    first_stage_config:
+      target: sgm.models.autoencoder.IdentityFirstStage
+
+    loss_fn_config:
+      target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
+      params:
+        sigma_sampler_config:
+          target: sgm.modules.diffusionmodules.sigma_sampling.EDMSampling
+
+    sampler_config:
+      target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
+      params:
+        num_steps: 50
+
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.EDMDiscretization
+
+        guider_config:
+          target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+          params:
+            scale: 3.0
+
+data:
+  target: sgm.data.cifar10.CIFAR10Loader
+  params:
+    batch_size: 512
+    num_workers: 1
+
+lightning:
+  modelcheckpoint:
+    params:
+      every_n_train_steps: 5000
+
+  callbacks:
+    metrics_over_trainsteps_checkpoint:
+      params:
+        every_n_train_steps: 25000
+
+    image_logger:
+      target: main.ImageLogger
+      params:
+        disabled: False
+        batch_frequency: 1000
+        max_images: 64
+        increase_log_steps: True
+        log_first_step: False
+        log_images_kwargs:
+          use_ema_scope: False
+          N: 64
+          n_rows: 8
+
+  trainer:
+    devices: 0,
+    benchmark: True
+    num_sanity_val_steps: 0
+    accumulate_grad_batches: 1
+    max_epochs: 20

configs/example_training/toy/mnist.yaml

@@ -0,0 +1,80 @@
+model:
+  base_learning_rate: 1.0e-4
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    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:
+            sigma_data: 1.0
+
+    network_config:
+      target: sgm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_checkpoint: True
+        in_channels: 1
+        out_channels: 1
+        model_channels: 32
+        attention_resolutions: []
+        num_res_blocks: 4
+        channel_mult: [1, 2, 2]
+        num_head_channels: 32
+
+    first_stage_config:
+      target: sgm.models.autoencoder.IdentityFirstStage
+
+    loss_fn_config:
+      target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
+      params:
+        sigma_sampler_config:
+          target: sgm.modules.diffusionmodules.sigma_sampling.EDMSampling
+
+    sampler_config:
+      target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
+      params:
+        num_steps: 50
+
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.EDMDiscretization
+
+data:
+  target: sgm.data.mnist.MNISTLoader
+  params:
+    batch_size: 512
+    num_workers: 1
+
+lightning:
+  modelcheckpoint:
+    params:
+      every_n_train_steps: 5000
+
+  callbacks:
+    metrics_over_trainsteps_checkpoint:
+      params:
+        every_n_train_steps: 25000
+
+    image_logger:
+      target: main.ImageLogger
+      params:
+        disabled: False
+        batch_frequency: 1000
+        max_images: 64
+        increase_log_steps: False
+        log_first_step: False
+        log_images_kwargs:
+          use_ema_scope: False
+          N: 64
+          n_rows: 8
+
+  trainer:
+    devices: 0,
+    benchmark: True
+    num_sanity_val_steps: 0
+    accumulate_grad_batches: 1
+    max_epochs: 10

configs/example_training/toy/mnist_cond.yaml

@@ -0,0 +1,99 @@
+model:
+  base_learning_rate: 1.0e-4
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    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:
+            sigma_data: 1.0
+
+    network_config:
+      target: sgm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_checkpoint: True
+        in_channels: 1
+        out_channels: 1
+        model_channels: 32
+        attention_resolutions: [ ]
+        num_res_blocks: 4
+        channel_mult: [ 1, 2, 2 ]
+        num_head_channels: 32
+        num_classes: sequential
+        adm_in_channels: 128
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+          - is_trainable: True
+            input_key: "cls"
+            ucg_rate: 0.2
+            target: sgm.modules.encoders.modules.ClassEmbedder
+            params:
+              embed_dim: 128
+              n_classes: 10
+
+    first_stage_config:
+      target: sgm.models.autoencoder.IdentityFirstStage
+
+    loss_fn_config:
+      target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
+      params:
+        sigma_sampler_config:
+          target: sgm.modules.diffusionmodules.sigma_sampling.EDMSampling
+
+    sampler_config:
+      target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
+      params:
+        num_steps: 50
+
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.EDMDiscretization
+
+        guider_config:
+          target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+          params:
+            scale: 3.0
+
+data:
+  target: sgm.data.mnist.MNISTLoader
+  params:
+    batch_size: 512
+    num_workers: 1
+
+lightning:
+  modelcheckpoint:
+    params:
+      every_n_train_steps: 5000
+
+  callbacks:
+    metrics_over_trainsteps_checkpoint:
+      params:
+        every_n_train_steps: 25000
+
+    image_logger:
+      target: main.ImageLogger
+      params:
+        disabled: False
+        batch_frequency: 1000
+        max_images: 16
+        increase_log_steps: True
+        log_first_step: False
+        log_images_kwargs:
+          use_ema_scope: False
+          N: 16
+          n_rows: 4
+
+  trainer:
+    devices: 0,
+    benchmark: True
+    num_sanity_val_steps: 0
+    accumulate_grad_batches: 1
+    max_epochs: 20

configs/example_training/toy/mnist_cond_discrete_eps.yaml

@@ -0,0 +1,104 @@
+model:
+  base_learning_rate: 1.0e-4
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+      params:
+        num_idx: 1000
+
+        weighting_config:
+          target: sgm.modules.diffusionmodules.denoiser_weighting.EDMWeighting
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.EDMScaling
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+    network_config:
+      target: sgm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_checkpoint: True
+        in_channels: 1
+        out_channels: 1
+        model_channels: 32
+        attention_resolutions: [ ]
+        num_res_blocks: 4
+        channel_mult: [ 1, 2, 2 ]
+        num_head_channels: 32
+        num_classes: sequential
+        adm_in_channels: 128
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+          - is_trainable: True
+            input_key: "cls"
+            ucg_rate: 0.2
+            target: sgm.modules.encoders.modules.ClassEmbedder
+            params:
+              embed_dim: 128
+              n_classes: 10
+
+    first_stage_config:
+      target: sgm.models.autoencoder.IdentityFirstStage
+
+    loss_fn_config:
+      target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
+      params:
+        sigma_sampler_config:
+          target: sgm.modules.diffusionmodules.sigma_sampling.DiscreteSampling
+          params:
+            num_idx: 1000
+
+            discretization_config:
+              target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+    sampler_config:
+      target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
+      params:
+        num_steps: 50
+
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+        guider_config:
+          target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+          params:
+            scale: 5.0
+
+data:
+  target: sgm.data.mnist.MNISTLoader
+  params:
+    batch_size: 512
+    num_workers: 1
+
+lightning:
+  modelcheckpoint:
+    params:
+      every_n_train_steps: 5000
+
+  callbacks:
+    metrics_over_trainsteps_checkpoint:
+      params:
+        every_n_train_steps: 25000
+
+    image_logger:
+      target: main.ImageLogger
+      params:
+        disabled: False
+        batch_frequency: 1000
+        max_images: 16
+        increase_log_steps: True
+        log_first_step: False
+        log_images_kwargs:
+          use_ema_scope: False
+          N: 16
+          n_rows: 4
+
+  trainer:
+    devices: 0,
+    benchmark: True
+    num_sanity_val_steps: 0
+    accumulate_grad_batches: 1
+    max_epochs: 20

configs/example_training/toy/mnist_cond_l1_loss.yaml

@@ -0,0 +1,104 @@
+model:
+  base_learning_rate: 1.0e-4
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    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:
+            sigma_data: 1.0
+
+    network_config:
+      target: sgm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_checkpoint: True
+        in_channels: 1
+        out_channels: 1
+        model_channels: 32
+        attention_resolutions: []
+        num_res_blocks: 4
+        channel_mult: [1, 2, 2]
+        num_head_channels: 32
+        num_classes: "sequential"
+        adm_in_channels: 128
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+          - is_trainable: True
+            input_key: "cls"
+            ucg_rate: 0.2
+            target: sgm.modules.encoders.modules.ClassEmbedder
+            params:
+              embed_dim: 128
+              n_classes: 10
+
+    first_stage_config:
+      target: sgm.models.autoencoder.IdentityFirstStage
+
+    loss_fn_config:
+      target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
+      params:
+        sigma_sampler_config:
+          target: sgm.modules.diffusionmodules.sigma_sampling.EDMSampling
+
+    sampler_config:
+      target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
+      params:
+        num_steps: 50
+
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.EDMDiscretization
+
+        guider_config:
+          target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+          params:
+            scale: 3.0
+
+    loss_config:
+      target: sgm.modules.diffusionmodules.StandardDiffusionLoss
+      params:
+        type: l1
+
+data:
+  target: sgm.data.mnist.MNISTLoader
+  params:
+    batch_size: 512
+    num_workers: 1
+
+lightning:
+  modelcheckpoint:
+    params:
+      every_n_train_steps: 5000
+
+  callbacks:
+    metrics_over_trainsteps_checkpoint:
+      params:
+        every_n_train_steps: 25000
+
+    image_logger:
+      target: main.ImageLogger
+      params:
+        disabled: False
+        batch_frequency: 1000
+        max_images: 64
+        increase_log_steps: True
+        log_first_step: False
+        log_images_kwargs:
+          use_ema_scope: False
+          N: 64
+          n_rows: 8
+
+  trainer:
+    devices: 0,
+    benchmark: True
+    num_sanity_val_steps: 0
+    accumulate_grad_batches: 1
+    max_epochs: 20

configs/example_training/toy/mnist_cond_with_ema.yaml

@@ -0,0 +1,101 @@
+model:
+  base_learning_rate: 1.0e-4
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    use_ema: True
+
+    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:
+            sigma_data: 1.0
+
+    network_config:
+      target: sgm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_checkpoint: True
+        in_channels: 1
+        out_channels: 1
+        model_channels: 32
+        attention_resolutions: []
+        num_res_blocks: 4
+        channel_mult: [1, 2, 2]
+        num_head_channels: 32
+        num_classes: sequential
+        adm_in_channels: 128
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+          - is_trainable: True
+            input_key: cls
+            ucg_rate: 0.2
+            target: sgm.modules.encoders.modules.ClassEmbedder
+            params:
+              embed_dim: 128
+              n_classes: 10
+
+    first_stage_config:
+      target: sgm.models.autoencoder.IdentityFirstStage
+
+    loss_fn_config:
+      target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
+      params:
+        sigma_sampler_config:
+          target: sgm.modules.diffusionmodules.sigma_sampling.EDMSampling
+
+    sampler_config:
+      target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
+      params:
+        num_steps: 50
+
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.EDMDiscretization
+
+        guider_config:
+          target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+          params:
+            scale: 3.0
+
+data:
+  target: sgm.data.mnist.MNISTLoader
+  params:
+    batch_size: 512
+    num_workers: 1
+
+lightning:
+  modelcheckpoint:
+    params:
+      every_n_train_steps: 5000
+
+  callbacks:
+    metrics_over_trainsteps_checkpoint:
+      params:
+        every_n_train_steps: 25000
+
+    image_logger:
+      target: main.ImageLogger
+      params:
+        disabled: False
+        batch_frequency: 1000
+        max_images: 64
+        increase_log_steps: True
+        log_first_step: False
+        log_images_kwargs:
+          use_ema_scope: False
+          N: 64
+          n_rows: 8
+
+  trainer:
+    devices: 0,
+    benchmark: True
+    num_sanity_val_steps: 0
+    accumulate_grad_batches: 1
+    max_epochs: 20

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

@@ -0,0 +1,185 @@
+model:
+  base_learning_rate: 1.0e-4
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.13025
+    disable_first_stage_autocast: True
+    log_keys:
+      - txt
+
+    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. ]
+
+    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:
+          target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+    network_config:
+      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 ]
+        num_res_blocks: 2
+        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
+
+    conditioner_config:
+      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
+          - 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
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper
+      params:
+        ckpt_path: CKPT_PATH
+        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
+
+    loss_fn_config:
+      target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
+      params:
+        sigma_sampler_config:
+          target: sgm.modules.diffusionmodules.sigma_sampling.DiscreteSampling
+          params:
+            num_idx: 1000
+
+            discretization_config:
+              target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+    sampler_config:
+      target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
+      params:
+        num_steps: 50
+
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+        guider_config:
+          target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+          params:
+            scale: 7.5
+
+data:
+  target: sgm.data.dataset.StableDataModuleFromConfig
+  params:
+    train:
+      datapipeline:
+        urls:
+          # USER: adapt this path the root of your custom dataset
+          - "DATA_PATH"
+        pipeline_config:
+          shardshuffle: 10000
+          sample_shuffle: 10000 # USER: you might wanna adapt depending on your available RAM
+
+        decoders:
+          - "pil"
+
+        postprocessors:
+          - target: sdata.mappers.TorchVisionImageTransforms
+            params:
+              key: 'jpg' # USER: you might wanna adapt this for your custom dataset
+              transforms:
+                - target: torchvision.transforms.Resize
+                  params:
+                    size: 256
+                    interpolation: 3
+                - target: torchvision.transforms.ToTensor
+          - target: sdata.mappers.Rescaler
+          - target: sdata.mappers.AddOriginalImageSizeAsTupleAndCropToSquare
+            # USER: you might wanna use non-default parameters due to your custom dataset
+
+      loader:
+        batch_size: 64
+        num_workers: 6
+
+lightning:
+  modelcheckpoint:
+    params:
+      every_n_train_steps: 5000
+
+  callbacks:
+    metrics_over_trainsteps_checkpoint:
+      params:
+        every_n_train_steps: 25000
+
+    image_logger:
+      target: main.ImageLogger
+      params:
+        disabled: False
+        enable_autocast: False
+        batch_frequency: 1000
+        max_images: 8
+        increase_log_steps: True
+        log_first_step: False
+        log_images_kwargs:
+          use_ema_scope: False
+          N: 8
+          n_rows: 2
+
+  trainer:
+    devices: 0,
+    benchmark: True
+    num_sanity_val_steps: 0
+    accumulate_grad_batches: 1
+    max_epochs: 1000

configs/example_training/txt2img-clipl.yaml

@@ -0,0 +1,186 @@
+model:
+  base_learning_rate: 1.0e-4
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.13025
+    disable_first_stage_autocast: True
+    log_keys:
+      - txt
+
+    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. ]
+
+    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:
+          target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+    network_config:
+      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 ]
+        num_res_blocks: 2
+        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
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+          # crossattn cond
+          - is_trainable: True
+            input_key: txt
+            ucg_rate: 0.1
+            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
+          - 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
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper
+      params:
+        ckpt_path: CKPT_PATH
+        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
+
+    loss_fn_config:
+      target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss
+      params:
+        sigma_sampler_config:
+          target: sgm.modules.diffusionmodules.sigma_sampling.DiscreteSampling
+          params:
+            num_idx: 1000
+
+            discretization_config:
+              target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+    sampler_config:
+      target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
+      params:
+        num_steps: 50
+
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+        guider_config:
+          target: sgm.modules.diffusionmodules.guiders.VanillaCFG
+          params:
+            scale: 7.5
+
+data:
+  target: sgm.data.dataset.StableDataModuleFromConfig
+  params:
+    train:
+      datapipeline:
+        urls:
+          # USER: adapt this path the root of your custom dataset
+          - "DATA_PATH"
+        pipeline_config:
+          shardshuffle: 10000
+          sample_shuffle: 10000
+
+
+        decoders:
+          - "pil"
+
+        postprocessors:
+          - target: sdata.mappers.TorchVisionImageTransforms
+            params:
+              key: 'jpg' # USER: you might wanna adapt this for your custom dataset
+              transforms:
+                - target: torchvision.transforms.Resize
+                  params:
+                    size: 256
+                    interpolation: 3
+                - target: torchvision.transforms.ToTensor
+          - target: sdata.mappers.Rescaler
+            # USER: you might wanna use non-default parameters due to your custom dataset
+          - target: sdata.mappers.AddOriginalImageSizeAsTupleAndCropToSquare
+            # USER: you might wanna use non-default parameters due to your custom dataset
+
+      loader:
+        batch_size: 64
+        num_workers: 6
+
+lightning:
+  modelcheckpoint:
+    params:
+      every_n_train_steps: 5000
+
+  callbacks:
+    metrics_over_trainsteps_checkpoint:
+      params:
+        every_n_train_steps: 25000
+
+    image_logger:
+      target: main.ImageLogger
+      params:
+        disabled: False
+        enable_autocast: False
+        batch_frequency: 1000
+        max_images: 8
+        increase_log_steps: True
+        log_first_step: False
+        log_images_kwargs:
+          use_ema_scope: False
+          N: 8
+          n_rows: 2
+
+  trainer:
+    devices: 0,
+    benchmark: True
+    num_sanity_val_steps: 0
+    accumulate_grad_batches: 1
+    max_epochs: 1000

configs/inference/sd_2_1.yaml

@@ -0,0 +1,66 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.18215
+    disable_first_stage_autocast: True
+
+    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:
+          target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+    network_config:
+      target: sgm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_checkpoint: True
+        use_fp16: True
+        in_channels: 4
+        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_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
+            params:
+              freeze: true
+              layer: penultimate
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          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

configs/inference/sd_2_1_768.yaml

@@ -0,0 +1,66 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.18215
+    disable_first_stage_autocast: True
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+      params:
+        num_idx: 1000
+
+        weighting_config:
+          target: sgm.modules.diffusionmodules.denoiser_weighting.VWeighting
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.VScaling
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+    network_config:
+      target: sgm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_checkpoint: True
+        use_fp16: True
+        in_channels: 4
+        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_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
+            params:
+              freeze: true
+              layer: penultimate
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          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

configs/inference/sd_xl_base.yaml

@@ -0,0 +1,98 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.13025
+    disable_first_stage_autocast: True
+
+    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:
+          target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+    network_config:
+      target: sgm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        adm_in_channels: 2816
+        num_classes: sequential
+        use_checkpoint: True
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [4, 2]
+        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
+        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
+            params:
+              arch: ViT-bigG-14
+              version: laion2b_s39b_b160k
+              freeze: True
+              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
+          - is_trainable: False
+            input_key: crop_coords_top_left
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256  # multiplied by two
+          # vector cond
+          - is_trainable: False
+            input_key: target_size_as_tuple
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256  # multiplied by two
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper
+      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

configs/inference/sd_xl_refiner.yaml

@@ -0,0 +1,91 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.13025
+    disable_first_stage_autocast: True
+
+    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:
+          target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+    network_config:
+      target: sgm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        adm_in_channels: 2560
+        num_classes: sequential
+        use_checkpoint: True
+        in_channels: 4
+        out_channels: 4
+        model_channels: 384
+        attention_resolutions: [4, 2]
+        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
+        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
+            params:
+              arch: ViT-bigG-14
+              version: laion2b_s39b_b160k
+              legacy: False
+              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
+          - is_trainable: False
+            input_key: crop_coords_top_left
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256  # multiplied by two
+          # vector cond
+          - is_trainable: False
+            input_key: aesthetic_score
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256  # multiplied by one
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper
+      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

@@ -0,0 +1,947 @@
+import argparse
+import datetime
+import glob
+import inspect
+import os
+import sys
+from inspect import Parameter
+from typing import Union
+
+import numpy as np
+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 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,
+)
+
+MULTINODE_HACKS = True
+
+
+def default_trainer_args():
+    argspec = dict(inspect.signature(Trainer.__init__).parameters)
+    argspec.pop("self")
+    default_args = {
+        param: argspec[param].default
+        for param in argspec
+        if argspec[param] != Parameter.empty
+    }
+    return default_args
+
+
+def get_parser(**parser_kwargs):
+    def str2bool(v):
+        if isinstance(v, bool):
+            return v
+        if v.lower() in ("yes", "true", "t", "y", "1"):
+            return True
+        elif v.lower() in ("no", "false", "f", "n", "0"):
+            return False
+        else:
+            raise argparse.ArgumentTypeError("Boolean value expected.")
+
+    parser = argparse.ArgumentParser(**parser_kwargs)
+    parser.add_argument(
+        "-n",
+        "--name",
+        type=str,
+        const=True,
+        default="",
+        nargs="?",
+        help="postfix for logdir",
+    )
+    parser.add_argument(
+        "--no_date",
+        type=str2bool,
+        nargs="?",
+        const=True,
+        default=False,
+        help="if True, skip date generation for logdir and only use naming via opt.base or opt.name (+ opt.postfix, optionally)",
+    )
+    parser.add_argument(
+        "-r",
+        "--resume",
+        type=str,
+        const=True,
+        default="",
+        nargs="?",
+        help="resume from logdir or checkpoint in logdir",
+    )
+    parser.add_argument(
+        "-b",
+        "--base",
+        nargs="*",
+        metavar="base_config.yaml",
+        help="paths to base configs. Loaded from left-to-right. "
+        "Parameters can be overwritten or added with command-line options of the form `--key value`.",
+        default=list(),
+    )
+    parser.add_argument(
+        "-t",
+        "--train",
+        type=str2bool,
+        const=True,
+        default=True,
+        nargs="?",
+        help="train",
+    )
+    parser.add_argument(
+        "--no-test",
+        type=str2bool,
+        const=True,
+        default=False,
+        nargs="?",
+        help="disable test",
+    )
+    parser.add_argument(
+        "-p", "--project", help="name of new or path to existing project"
+    )
+    parser.add_argument(
+        "-d",
+        "--debug",
+        type=str2bool,
+        nargs="?",
+        const=True,
+        default=False,
+        help="enable post-mortem debugging",
+    )
+    parser.add_argument(
+        "-s",
+        "--seed",
+        type=int,
+        default=23,
+        help="seed for seed_everything",
+    )
+    parser.add_argument(
+        "-f",
+        "--postfix",
+        type=str,
+        default="",
+        help="post-postfix for default name",
+    )
+    parser.add_argument(
+        "--projectname",
+        type=str,
+        default="stablediffusion",
+    )
+    parser.add_argument(
+        "-l",
+        "--logdir",
+        type=str,
+        default="logs",
+        help="directory for logging dat shit",
+    )
+    parser.add_argument(
+        "--scale_lr",
+        type=str2bool,
+        nargs="?",
+        const=True,
+        default=False,
+        help="scale base-lr by ngpu * batch_size * n_accumulate",
+    )
+    parser.add_argument(
+        "--legacy_naming",
+        type=str2bool,
+        nargs="?",
+        const=True,
+        default=False,
+        help="name run based on config file name if true, else by whole path",
+    )
+    parser.add_argument(
+        "--enable_tf32",
+        type=str2bool,
+        nargs="?",
+        const=True,
+        default=False,
+        help="enables the TensorFloat32 format both for matmuls and cuDNN for pytorch 1.12",
+    )
+    parser.add_argument(
+        "--startup",
+        type=str,
+        default=None,
+        help="Startuptime from distributed script",
+    )
+    parser.add_argument(
+        "--wandb",
+        type=str2bool,
+        nargs="?",
+        const=True,
+        default=False,  # TODO: later default to True
+        help="log to wandb",
+    )
+    parser.add_argument(
+        "--no_base_name",
+        type=str2bool,
+        nargs="?",
+        const=True,
+        default=False,  # TODO: later default to True
+        help="log to wandb",
+    )
+    if version.parse(torch.__version__) >= version.parse("2.0.0"):
+        parser.add_argument(
+            "--resume_from_checkpoint",
+            type=str,
+            default=None,
+            help="single checkpoint file to resume from",
+        )
+    default_args = default_trainer_args()
+    for key in default_args:
+        parser.add_argument("--" + key, default=default_args[key])
+    return parser
+
+
+def get_checkpoint_name(logdir):
+    ckpt = os.path.join(logdir, "checkpoints", "last**.ckpt")
+    ckpt = natsorted(glob.glob(ckpt))
+    print('available "last" checkpoints:')
+    print(ckpt)
+    if len(ckpt) > 1:
+        print("got most recent checkpoint")
+        ckpt = sorted(ckpt, key=lambda x: os.path.getmtime(x))[-1]
+        print(f"Most recent ckpt is {ckpt}")
+        with open(os.path.join(logdir, "most_recent_ckpt.txt"), "w") as f:
+            f.write(ckpt + "\n")
+        try:
+            version = int(ckpt.split("/")[-1].split("-v")[-1].split(".")[0])
+        except Exception as e:
+            print("version confusion but not bad")
+            print(e)
+            version = 1
+        # version = last_version + 1
+    else:
+        # in this case, we only have one "last.ckpt"
+        ckpt = ckpt[0]
+        version = 1
+    melk_ckpt_name = f"last-v{version}.ckpt"
+    print(f"Current melk ckpt name: {melk_ckpt_name}")
+    return ckpt, melk_ckpt_name
+
+
+class SetupCallback(Callback):
+    def __init__(
+        self,
+        resume,
+        now,
+        logdir,
+        ckptdir,
+        cfgdir,
+        config,
+        lightning_config,
+        debug,
+        ckpt_name=None,
+    ):
+        super().__init__()
+        self.resume = resume
+        self.now = now
+        self.logdir = logdir
+        self.ckptdir = ckptdir
+        self.cfgdir = cfgdir
+        self.config = config
+        self.lightning_config = lightning_config
+        self.debug = debug
+        self.ckpt_name = ckpt_name
+
+    def on_exception(self, trainer: pl.Trainer, pl_module, exception):
+        if not self.debug and trainer.global_rank == 0:
+            print("Summoning checkpoint.")
+            if self.ckpt_name is None:
+                ckpt_path = os.path.join(self.ckptdir, "last.ckpt")
+            else:
+                ckpt_path = os.path.join(self.ckptdir, self.ckpt_name)
+            trainer.save_checkpoint(ckpt_path)
+
+    def on_fit_start(self, trainer, pl_module):
+        if trainer.global_rank == 0:
+            # Create logdirs and save configs
+            os.makedirs(self.logdir, exist_ok=True)
+            os.makedirs(self.ckptdir, exist_ok=True)
+            os.makedirs(self.cfgdir, exist_ok=True)
+
+            if "callbacks" in self.lightning_config:
+                if (
+                    "metrics_over_trainsteps_checkpoint"
+                    in self.lightning_config["callbacks"]
+                ):
+                    os.makedirs(
+                        os.path.join(self.ckptdir, "trainstep_checkpoints"),
+                        exist_ok=True,
+                    )
+            print("Project config")
+            print(OmegaConf.to_yaml(self.config))
+            if MULTINODE_HACKS:
+                import time
+
+                time.sleep(5)
+            OmegaConf.save(
+                self.config,
+                os.path.join(self.cfgdir, "{}-project.yaml".format(self.now)),
+            )
+
+            print("Lightning config")
+            print(OmegaConf.to_yaml(self.lightning_config))
+            OmegaConf.save(
+                OmegaConf.create({"lightning": self.lightning_config}),
+                os.path.join(self.cfgdir, "{}-lightning.yaml".format(self.now)),
+            )
+
+        else:
+            # ModelCheckpoint callback created log directory --- remove it
+            if not MULTINODE_HACKS and not self.resume and os.path.exists(self.logdir):
+                dst, name = os.path.split(self.logdir)
+                dst = os.path.join(dst, "child_runs", name)
+                os.makedirs(os.path.split(dst)[0], exist_ok=True)
+                try:
+                    os.rename(self.logdir, dst)
+                except FileNotFoundError:
+                    pass
+
+
+class ImageLogger(Callback):
+    def __init__(
+        self,
+        batch_frequency,
+        max_images,
+        clamp=True,
+        increase_log_steps=True,
+        rescale=True,
+        disabled=False,
+        log_on_batch_idx=False,
+        log_first_step=False,
+        log_images_kwargs=None,
+        log_before_first_step=False,
+        enable_autocast=True,
+    ):
+        super().__init__()
+        self.enable_autocast = enable_autocast
+        self.rescale = rescale
+        self.batch_freq = batch_frequency
+        self.max_images = max_images
+        self.log_steps = [2**n for n in range(int(np.log2(self.batch_freq)) + 1)]
+        if not increase_log_steps:
+            self.log_steps = [self.batch_freq]
+        self.clamp = clamp
+        self.disabled = disabled
+        self.log_on_batch_idx = log_on_batch_idx
+        self.log_images_kwargs = log_images_kwargs if log_images_kwargs else {}
+        self.log_first_step = log_first_step
+        self.log_before_first_step = log_before_first_step
+
+    @rank_zero_only
+    def log_local(
+        self,
+        save_dir,
+        split,
+        images,
+        global_step,
+        current_epoch,
+        batch_idx,
+        pl_module: Union[None, pl.LightningModule] = None,
+    ):
+        root = os.path.join(save_dir, "images", split)
+        for k in images:
+            if isheatmap(images[k]):
+                fig, ax = plt.subplots()
+                ax = ax.matshow(
+                    images[k].cpu().numpy(), cmap="hot", interpolation="lanczos"
+                )
+                plt.colorbar(ax)
+                plt.axis("off")
+
+                filename = "{}_gs-{:06}_e-{:06}_b-{:06}.png".format(
+                    k, global_step, current_epoch, batch_idx
+                )
+                os.makedirs(root, exist_ok=True)
+                path = os.path.join(root, filename)
+                plt.savefig(path)
+                plt.close()
+                # TODO: support wandb
+            else:
+                grid = torchvision.utils.make_grid(images[k], nrow=4)
+                if self.rescale:
+                    grid = (grid + 1.0) / 2.0  # -1,1 -> 0,1; c,h,w
+                grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1)
+                grid = grid.numpy()
+                grid = (grid * 255).astype(np.uint8)
+                filename = "{}_gs-{:06}_e-{:06}_b-{:06}.png".format(
+                    k, global_step, current_epoch, batch_idx
+                )
+                path = os.path.join(root, filename)
+                os.makedirs(os.path.split(path)[0], exist_ok=True)
+                img = Image.fromarray(grid)
+                img.save(path)
+                if exists(pl_module):
+                    assert isinstance(
+                        pl_module.logger, WandbLogger
+                    ), "logger_log_image only supports WandbLogger currently"
+                    pl_module.logger.log_image(
+                        key=f"{split}/{k}",
+                        images=[
+                            img,
+                        ],
+                        step=pl_module.global_step,
+                    )
+
+    @rank_zero_only
+    def log_img(self, pl_module, batch, batch_idx, split="train"):
+        check_idx = batch_idx if self.log_on_batch_idx else pl_module.global_step
+        if (
+            self.check_frequency(check_idx)
+            and hasattr(pl_module, "log_images")  # batch_idx % self.batch_freq == 0
+            and callable(pl_module.log_images)
+            and
+            # batch_idx > 5 and
+            self.max_images > 0
+        ):
+            logger = type(pl_module.logger)
+            is_train = pl_module.training
+            if is_train:
+                pl_module.eval()
+
+            gpu_autocast_kwargs = {
+                "enabled": self.enable_autocast,  # torch.is_autocast_enabled(),
+                "dtype": torch.get_autocast_gpu_dtype(),
+                "cache_enabled": torch.is_autocast_cache_enabled(),
+            }
+            with torch.no_grad(), torch.cuda.amp.autocast(**gpu_autocast_kwargs):
+                images = pl_module.log_images(
+                    batch, split=split, **self.log_images_kwargs
+                )
+
+            for k in images:
+                N = min(images[k].shape[0], self.max_images)
+                if not isheatmap(images[k]):
+                    images[k] = images[k][:N]
+                if isinstance(images[k], torch.Tensor):
+                    images[k] = images[k].detach().float().cpu()
+                    if self.clamp and not isheatmap(images[k]):
+                        images[k] = torch.clamp(images[k], -1.0, 1.0)
+
+            self.log_local(
+                pl_module.logger.save_dir,
+                split,
+                images,
+                pl_module.global_step,
+                pl_module.current_epoch,
+                batch_idx,
+                pl_module=pl_module
+                if isinstance(pl_module.logger, WandbLogger)
+                else None,
+            )
+
+            if is_train:
+                pl_module.train()
+
+    def check_frequency(self, check_idx):
+        if ((check_idx % self.batch_freq) == 0 or (check_idx in self.log_steps)) and (
+            check_idx > 0 or self.log_first_step
+        ):
+            try:
+                self.log_steps.pop(0)
+            except IndexError as e:
+                print(e)
+                pass
+            return True
+        return False
+
+    @rank_zero_only
+    def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
+        if not self.disabled and (pl_module.global_step > 0 or self.log_first_step):
+            self.log_img(pl_module, batch, batch_idx, split="train")
+
+    @rank_zero_only
+    def on_train_batch_start(self, trainer, pl_module, batch, batch_idx):
+        if self.log_before_first_step and pl_module.global_step == 0:
+            print(f"{self.__class__.__name__}: logging before training")
+            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
+    ):
+        if not self.disabled and pl_module.global_step > 0:
+            self.log_img(pl_module, batch, batch_idx, split="val")
+        if hasattr(pl_module, "calibrate_grad_norm"):
+            if (
+                pl_module.calibrate_grad_norm and batch_idx % 25 == 0
+            ) and batch_idx > 0:
+                self.log_gradients(trainer, pl_module, batch_idx=batch_idx)
+
+
+@rank_zero_only
+def init_wandb(save_dir, opt, config, group_name, name_str):
+    print(f"setting WANDB_DIR to {save_dir}")
+    os.makedirs(save_dir, exist_ok=True)
+
+    os.environ["WANDB_DIR"] = save_dir
+    if opt.debug:
+        wandb.init(project=opt.projectname, mode="offline", group=group_name)
+    else:
+        wandb.init(
+            project=opt.projectname,
+            config=config,
+            settings=wandb.Settings(code_dir="./sgm"),
+            group=group_name,
+            name=name_str,
+        )
+
+
+if __name__ == "__main__":
+    # custom parser to specify config files, train, test and debug mode,
+    # postfix, resume.
+    # `--key value` arguments are interpreted as arguments to the trainer.
+    # `nested.key=value` arguments are interpreted as config parameters.
+    # configs are merged from left-to-right followed by command line parameters.
+
+    # model:
+    #   base_learning_rate: float
+    #   target: path to lightning module
+    #   params:
+    #       key: value
+    # data:
+    #   target: main.DataModuleFromConfig
+    #   params:
+    #      batch_size: int
+    #      wrap: bool
+    #      train:
+    #          target: path to train dataset
+    #          params:
+    #              key: value
+    #      validation:
+    #          target: path to validation dataset
+    #          params:
+    #              key: value
+    #      test:
+    #          target: path to test dataset
+    #          params:
+    #              key: value
+    # lightning: (optional, has sane defaults and can be specified on cmdline)
+    #   trainer:
+    #       additional arguments to trainer
+    #   logger:
+    #       logger to instantiate
+    #   modelcheckpoint:
+    #       modelcheckpoint to instantiate
+    #   callbacks:
+    #       callback1:
+    #           target: importpath
+    #           params:
+    #               key: value
+
+    now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
+
+    # add cwd for convenience and to make classes in this file available when
+    # running as `python main.py`
+    # (in particular `main.DataModuleFromConfig`)
+    sys.path.append(os.getcwd())
+
+    parser = get_parser()
+
+    opt, unknown = parser.parse_known_args()
+
+    if opt.name and opt.resume:
+        raise ValueError(
+            "-n/--name and -r/--resume cannot be specified both."
+            "If you want to resume training in a new log folder, "
+            "use -n/--name in combination with --resume_from_checkpoint"
+        )
+    melk_ckpt_name = None
+    name = None
+    if opt.resume:
+        if not os.path.exists(opt.resume):
+            raise ValueError("Cannot find {}".format(opt.resume))
+        if os.path.isfile(opt.resume):
+            paths = opt.resume.split("/")
+            # idx = len(paths)-paths[::-1].index("logs")+1
+            # logdir = "/".join(paths[:idx])
+            logdir = "/".join(paths[:-2])
+            ckpt = opt.resume
+            _, melk_ckpt_name = get_checkpoint_name(logdir)
+        else:
+            assert os.path.isdir(opt.resume), opt.resume
+            logdir = opt.resume.rstrip("/")
+            ckpt, melk_ckpt_name = get_checkpoint_name(logdir)
+
+        print("#" * 100)
+        print(f'Resuming from checkpoint "{ckpt}"')
+        print("#" * 100)
+
+        opt.resume_from_checkpoint = ckpt
+        base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*.yaml")))
+        opt.base = base_configs + opt.base
+        _tmp = logdir.split("/")
+        nowname = _tmp[-1]
+    else:
+        if opt.name:
+            name = "_" + opt.name
+        elif opt.base:
+            if opt.no_base_name:
+                name = ""
+            else:
+                if opt.legacy_naming:
+                    cfg_fname = os.path.split(opt.base[0])[-1]
+                    cfg_name = os.path.splitext(cfg_fname)[0]
+                else:
+                    assert "configs" in os.path.split(opt.base[0])[0], os.path.split(
+                        opt.base[0]
+                    )[0]
+                    cfg_path = os.path.split(opt.base[0])[0].split(os.sep)[
+                        os.path.split(opt.base[0])[0].split(os.sep).index("configs")
+                        + 1 :
+                    ]  # cut away the first one (we assert all configs are in "configs")
+                    cfg_name = os.path.splitext(os.path.split(opt.base[0])[-1])[0]
+                    cfg_name = "-".join(cfg_path) + f"-{cfg_name}"
+                name = "_" + cfg_name
+        else:
+            name = ""
+        if not opt.no_date:
+            nowname = now + name + opt.postfix
+        else:
+            nowname = name + opt.postfix
+            if nowname.startswith("_"):
+                nowname = nowname[1:]
+        logdir = os.path.join(opt.logdir, nowname)
+        print(f"LOGDIR: {logdir}")
+
+    ckptdir = os.path.join(logdir, "checkpoints")
+    cfgdir = os.path.join(logdir, "configs")
+    seed_everything(opt.seed, workers=True)
+
+    # move before model init, in case a torch.compile(...) is called somewhere
+    if opt.enable_tf32:
+        # pt_version = version.parse(torch.__version__)
+        torch.backends.cuda.matmul.allow_tf32 = True
+        torch.backends.cudnn.allow_tf32 = True
+        print(f"Enabling TF32 for PyTorch {torch.__version__}")
+    else:
+        print(f"Using default TF32 settings for PyTorch {torch.__version__}:")
+        print(
+            f"torch.backends.cuda.matmul.allow_tf32={torch.backends.cuda.matmul.allow_tf32}"
+        )
+        print(f"torch.backends.cudnn.allow_tf32={torch.backends.cudnn.allow_tf32}")
+
+    try:
+        # init and save configs
+        configs = [OmegaConf.load(cfg) for cfg in opt.base]
+        cli = OmegaConf.from_dotlist(unknown)
+        config = OmegaConf.merge(*configs, cli)
+        lightning_config = config.pop("lightning", OmegaConf.create())
+        # merge trainer cli with config
+        trainer_config = lightning_config.get("trainer", OmegaConf.create())
+
+        # default to gpu
+        trainer_config["accelerator"] = "gpu"
+        #
+        standard_args = default_trainer_args()
+        for k in standard_args:
+            if getattr(opt, k) != standard_args[k]:
+                trainer_config[k] = getattr(opt, k)
+
+        ckpt_resume_path = opt.resume_from_checkpoint
+
+        if not "devices" in trainer_config and trainer_config["accelerator"] != "gpu":
+            del trainer_config["accelerator"]
+            cpu = True
+        else:
+            gpuinfo = trainer_config["devices"]
+            print(f"Running on GPUs {gpuinfo}")
+            cpu = False
+        trainer_opt = argparse.Namespace(**trainer_config)
+        lightning_config.trainer = trainer_config
+
+        # model
+        model = instantiate_from_config(config.model)
+
+        # trainer and callbacks
+        trainer_kwargs = dict()
+
+        # default logger configs
+        default_logger_cfgs = {
+            "wandb": {
+                "target": "pytorch_lightning.loggers.WandbLogger",
+                "params": {
+                    "name": nowname,
+                    # "save_dir": logdir,
+                    "offline": opt.debug,
+                    "id": nowname,
+                    "project": opt.projectname,
+                    "log_model": False,
+                    # "dir": logdir,
+                },
+            },
+            "csv": {
+                "target": "pytorch_lightning.loggers.CSVLogger",
+                "params": {
+                    "name": "testtube",  # hack for sbord fanatics
+                    "save_dir": logdir,
+                },
+            },
+        }
+        default_logger_cfg = default_logger_cfgs["wandb" if opt.wandb else "csv"]
+        if opt.wandb:
+            # TODO change once leaving "swiffer" config directory
+            try:
+                group_name = nowname.split(now)[-1].split("-")[1]
+            except:
+                group_name = nowname
+            default_logger_cfg["params"]["group"] = group_name
+            init_wandb(
+                os.path.join(os.getcwd(), logdir),
+                opt=opt,
+                group_name=group_name,
+                config=config,
+                name_str=nowname,
+            )
+        if "logger" in lightning_config:
+            logger_cfg = lightning_config.logger
+        else:
+            logger_cfg = OmegaConf.create()
+        logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg)
+        trainer_kwargs["logger"] = instantiate_from_config(logger_cfg)
+
+        # modelcheckpoint - use TrainResult/EvalResult(checkpoint_on=metric) to
+        # specify which metric is used to determine best models
+        default_modelckpt_cfg = {
+            "target": "pytorch_lightning.callbacks.ModelCheckpoint",
+            "params": {
+                "dirpath": ckptdir,
+                "filename": "{epoch:06}",
+                "verbose": True,
+                "save_last": True,
+            },
+        }
+        if hasattr(model, "monitor"):
+            print(f"Monitoring {model.monitor} as checkpoint metric.")
+            default_modelckpt_cfg["params"]["monitor"] = model.monitor
+            default_modelckpt_cfg["params"]["save_top_k"] = 3
+
+        if "modelcheckpoint" in lightning_config:
+            modelckpt_cfg = lightning_config.modelcheckpoint
+        else:
+            modelckpt_cfg = OmegaConf.create()
+        modelckpt_cfg = OmegaConf.merge(default_modelckpt_cfg, modelckpt_cfg)
+        print(f"Merged modelckpt-cfg: \n{modelckpt_cfg}")
+
+        # https://pytorch-lightning.readthedocs.io/en/stable/extensions/strategy.html
+        # default to ddp if not further specified
+        default_strategy_config = {"target": "pytorch_lightning.strategies.DDPStrategy"}
+
+        if "strategy" in lightning_config:
+            strategy_cfg = lightning_config.strategy
+        else:
+            strategy_cfg = OmegaConf.create()
+            default_strategy_config["params"] = {
+                "find_unused_parameters": False,
+                # "static_graph": True,
+                # "ddp_comm_hook": default.fp16_compress_hook  # TODO: experiment with this, also for DDPSharded
+            }
+        strategy_cfg = OmegaConf.merge(default_strategy_config, strategy_cfg)
+        print(
+            f"strategy config: \n ++++++++++++++ \n {strategy_cfg} \n ++++++++++++++ "
+        )
+        trainer_kwargs["strategy"] = instantiate_from_config(strategy_cfg)
+
+        # add callback which sets up log directory
+        default_callbacks_cfg = {
+            "setup_callback": {
+                "target": "main.SetupCallback",
+                "params": {
+                    "resume": opt.resume,
+                    "now": now,
+                    "logdir": logdir,
+                    "ckptdir": ckptdir,
+                    "cfgdir": cfgdir,
+                    "config": config,
+                    "lightning_config": lightning_config,
+                    "debug": opt.debug,
+                    "ckpt_name": melk_ckpt_name,
+                },
+            },
+            "image_logger": {
+                "target": "main.ImageLogger",
+                "params": {"batch_frequency": 1000, "max_images": 4, "clamp": True},
+            },
+            "learning_rate_logger": {
+                "target": "pytorch_lightning.callbacks.LearningRateMonitor",
+                "params": {
+                    "logging_interval": "step",
+                    # "log_momentum": True
+                },
+            },
+        }
+        if version.parse(pl.__version__) >= version.parse("1.4.0"):
+            default_callbacks_cfg.update({"checkpoint_callback": modelckpt_cfg})
+
+        if "callbacks" in lightning_config:
+            callbacks_cfg = lightning_config.callbacks
+        else:
+            callbacks_cfg = OmegaConf.create()
+
+        if "metrics_over_trainsteps_checkpoint" in callbacks_cfg:
+            print(
+                "Caution: Saving checkpoints every n train steps without deleting. This might require some free space."
+            )
+            default_metrics_over_trainsteps_ckpt_dict = {
+                "metrics_over_trainsteps_checkpoint": {
+                    "target": "pytorch_lightning.callbacks.ModelCheckpoint",
+                    "params": {
+                        "dirpath": os.path.join(ckptdir, "trainstep_checkpoints"),
+                        "filename": "{epoch:06}-{step:09}",
+                        "verbose": True,
+                        "save_top_k": -1,
+                        "every_n_train_steps": 10000,
+                        "save_weights_only": True,
+                    },
+                }
+            }
+            default_callbacks_cfg.update(default_metrics_over_trainsteps_ckpt_dict)
+
+        callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg)
+        if "ignore_keys_callback" in callbacks_cfg and ckpt_resume_path is not None:
+            callbacks_cfg.ignore_keys_callback.params["ckpt_path"] = ckpt_resume_path
+        elif "ignore_keys_callback" in callbacks_cfg:
+            del callbacks_cfg["ignore_keys_callback"]
+
+        trainer_kwargs["callbacks"] = [
+            instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg
+        ]
+        if not "plugins" in trainer_kwargs:
+            trainer_kwargs["plugins"] = list()
+
+        # cmd line trainer args (which are in trainer_opt) have always priority over config-trainer-args (which are in trainer_kwargs)
+        trainer_opt = vars(trainer_opt)
+        trainer_kwargs = {
+            key: val for key, val in trainer_kwargs.items() if key not in trainer_opt
+        }
+        trainer = Trainer(**trainer_opt, **trainer_kwargs)
+
+        trainer.logdir = logdir  ###
+
+        # data
+        data = instantiate_from_config(config.data)
+        # NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html
+        # calling these ourselves should not be necessary but it is.
+        # lightning still takes care of proper multiprocessing though
+        data.prepare_data()
+        # data.setup()
+        print("#### Data #####")
+        try:
+            for k in data.datasets:
+                print(
+                    f"{k}, {data.datasets[k].__class__.__name__}, {len(data.datasets[k])}"
+                )
+        except:
+            print("datasets not yet initialized.")
+
+        # configure learning rate
+        if "batch_size" in config.data.params:
+            bs, base_lr = config.data.params.batch_size, config.model.base_learning_rate
+        else:
+            bs, base_lr = (
+                config.data.params.train.loader.batch_size,
+                config.model.base_learning_rate,
+            )
+        if not cpu:
+            ngpu = len(lightning_config.trainer.devices.strip(",").split(","))
+        else:
+            ngpu = 1
+        if "accumulate_grad_batches" in lightning_config.trainer:
+            accumulate_grad_batches = lightning_config.trainer.accumulate_grad_batches
+        else:
+            accumulate_grad_batches = 1
+        print(f"accumulate_grad_batches = {accumulate_grad_batches}")
+        lightning_config.trainer.accumulate_grad_batches = accumulate_grad_batches
+        if opt.scale_lr:
+            model.learning_rate = accumulate_grad_batches * ngpu * bs * base_lr
+            print(
+                "Setting learning rate to {:.2e} = {} (accumulate_grad_batches) * {} (num_gpus) * {} (batchsize) * {:.2e} (base_lr)".format(
+                    model.learning_rate, accumulate_grad_batches, ngpu, bs, base_lr
+                )
+            )
+        else:
+            model.learning_rate = base_lr
+            print("++++ NOT USING LR SCALING ++++")
+            print(f"Setting learning rate to {model.learning_rate:.2e}")
+
+        # allow checkpointing via USR1
+        def melk(*args, **kwargs):
+            # run all checkpoint hooks
+            if trainer.global_rank == 0:
+                print("Summoning checkpoint.")
+                if melk_ckpt_name is None:
+                    ckpt_path = os.path.join(ckptdir, "last.ckpt")
+                else:
+                    ckpt_path = os.path.join(ckptdir, melk_ckpt_name)
+                trainer.save_checkpoint(ckpt_path)
+
+        def divein(*args, **kwargs):
+            if trainer.global_rank == 0:
+                import pudb
+
+                pudb.set_trace()
+
+        import signal
+
+        signal.signal(signal.SIGUSR1, melk)
+        signal.signal(signal.SIGUSR2, divein)
+
+        # run
+        if opt.train:
+            try:
+                trainer.fit(model, data, ckpt_path=ckpt_resume_path)
+            except Exception:
+                if not opt.debug:
+                    melk()
+                raise
+        if not opt.no_test and not trainer.interrupted:
+            trainer.test(model, data)
+    except RuntimeError as err:
+        if MULTINODE_HACKS:
+            import requests
+            import datetime
+            import os
+            import socket
+
+            device = os.environ.get("CUDA_VISIBLE_DEVICES", "?")
+            hostname = socket.gethostname()
+            ts = datetime.datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S")
+            resp = requests.get("http://169.254.169.254/latest/meta-data/instance-id")
+            print(
+                f"ERROR at {ts} on {hostname}/{resp.text} (CUDA_VISIBLE_DEVICES={device}): {type(err).__name__}: {err}",
+                flush=True,
+            )
+        raise err
+    except Exception:
+        if opt.debug and trainer.global_rank == 0:
+            try:
+                import pudb as debugger
+            except ImportError:
+                import pdb as debugger
+            debugger.post_mortem()
+        raise
+    finally:
+        # move newly created debug project to debug_runs
+        if opt.debug and not opt.resume and trainer.global_rank == 0:
+            dst, name = os.path.split(logdir)
+            dst = os.path.join(dst, "debug_runs", name)
+            os.makedirs(os.path.split(dst)[0], exist_ok=True)
+            os.rename(logdir, dst)
+
+        if opt.wandb:
+            wandb.finish()
+        # if trainer.global_rank == 0:
+        #    print(trainer.profiler.summary())

requirements_pt13.txt

@@ -0,0 +1,41 @@
+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

@@ -0,0 +1,41 @@
+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

@@ -0,0 +1,157 @@
+import argparse
+
+import cv2
+import numpy as np
+
+try:
+    from imwatermark import WatermarkDecoder
+except ImportError as e:
+    try:
+        # Assume some of the other dependencies such as torch are not fulfilled
+        # import file without loading unnecessary libraries.
+        import importlib.util
+        import sys
+
+        spec = importlib.util.find_spec("imwatermark.maxDct")
+        assert spec is not None
+        maxDct = importlib.util.module_from_spec(spec)
+        sys.modules["maxDct"] = maxDct
+        spec.loader.exec_module(maxDct)
+
+        class WatermarkDecoder(object):
+            """A minimal version of
+            https://github.com/ShieldMnt/invisible-watermark/blob/main/imwatermark/watermark.py
+            to only reconstruct bits using dwtDct"""
+
+            def __init__(self, wm_type="bytes", length=0):
+                assert wm_type == "bits", "Only bits defined in minimal import"
+                self._wmType = wm_type
+                self._wmLen = length
+
+            def reconstruct(self, bits):
+                if len(bits) != self._wmLen:
+                    raise RuntimeError("bits are not matched with watermark length")
+
+                return bits
+
+            def decode(self, cv2Image, method="dwtDct", **configs):
+                (r, c, channels) = cv2Image.shape
+                if r * c < 256 * 256:
+                    raise RuntimeError("image too small, should be larger than 256x256")
+
+                bits = []
+                assert method == "dwtDct"
+                embed = maxDct.EmbedMaxDct(watermarks=[], wmLen=self._wmLen, **configs)
+                bits = embed.decode(cv2Image)
+                return self.reconstruct(bits)
+
+    except:
+        raise e
+
+
+# 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:]]
+MATCH_VALUES = [
+    [27, "No watermark detected"],
+    [33, "Partial watermark match. Cannot determine with certainty."],
+    [
+        35,
+        (
+            "Likely watermarked. In our test 0.02% of real images were "
+            'falsely detected as "Likely watermarked"'
+        ),
+    ],
+    [
+        49,
+        (
+            "Very likely watermarked. In our test no real images were "
+            'falsely detected as "Very likely watermarked"'
+        ),
+    ],
+]
+
+
+class GetWatermarkMatch:
+    def __init__(self, watermark):
+        self.watermark = watermark
+        self.num_bits = len(self.watermark)
+        self.decoder = WatermarkDecoder("bits", self.num_bits)
+
+    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.
+
+        Args:
+            x: ([B], h w, c) in range [0, 255]
+
+        Returns:
+           number of matched bits ([B],)
+        """
+        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)
+        for k in range(bs):
+            detected[k] = self.decoder.decode(x[k], "dwtDct")
+        result = np.sum(detected == self.watermark, axis=-1)
+        if squeeze:
+            return result[0]
+        else:
+            return result
+
+
+get_watermark_match = GetWatermarkMatch(WATERMARK_BITS)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "filename",
+        nargs="+",
+        type=str,
+        help="Image files to check for watermarks",
+    )
+    opts = parser.parse_args()
+
+    print(
+        """
+        This script tries to detect watermarked images. Please be aware of
+        the following:
+        - As the watermark is supposed to be invisible, there is the risk that
+          watermarked images may not be detected.
+        - To maximize the chance of detection make sure that the image has the same
+          dimensions as when the watermark was applied (most likely 1024x1024
+          or 512x512).
+        - Specific image manipulation may drastically decrease the chance that
+          watermarks can be detected.
+        - There is also the chance that an image has the characteristics of the
+          watermark by chance.
+        - The watermark script is public, anybody may watermark any images, and
+          could therefore claim it to be generated.
+        - All numbers below are based on a test using 10,000 images without any
+          modifications after applying the watermark.
+        """
+    )
+
+    for fn in opts.filename:
+        image = cv2.imread(fn)
+        if image is None:
+            print(f"Couldn't read {fn}. Skipping")
+            continue
+
+        num_bits = get_watermark_match(image)
+        k = 0
+        while num_bits > MATCH_VALUES[k][0]:
+            k += 1
+        print(
+            f"{fn}: {MATCH_VALUES[k][1]}",
+            f"Bits that matched the watermark {num_bits} from {len(WATERMARK_BITS)}\n",
+            sep="\n\t",
+        )

scripts/demo/sampling.py

@@ -0,0 +1,328 @@
+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/"
+
+SD_XL_BASE_RATIOS = {
+    "0.5": (704, 1408),
+    "0.52": (704, 1344),
+    "0.57": (768, 1344),
+    "0.6": (768, 1280),
+    "0.68": (832, 1216),
+    "0.72": (832, 1152),
+    "0.78": (896, 1152),
+    "0.82": (896, 1088),
+    "0.88": (960, 1088),
+    "0.94": (960, 1024),
+    "1.0": (1024, 1024),
+    "1.07": (1024, 960),
+    "1.13": (1088, 960),
+    "1.21": (1088, 896),
+    "1.29": (1152, 896),
+    "1.38": (1152, 832),
+    "1.46": (1216, 832),
+    "1.67": (1280, 768),
+    "1.75": (1344, 768),
+    "1.91": (1344, 704),
+    "2.0": (1408, 704),
+    "2.09": (1472, 704),
+    "2.4": (1536, 640),
+    "2.5": (1600, 640),
+    "2.89": (1664, 576),
+    "3.0": (1728, 576),
+}
+
+VERSION2SPECS = {
+    "SD-XL base": {
+        "H": 1024,
+        "W": 1024,
+        "C": 4,
+        "f": 8,
+        "is_legacy": False,
+        "config": "configs/inference/sd_xl_base.yaml",
+        "ckpt": "checkpoints/sd_xl_base_0.9.safetensors",
+        "is_guided": True,
+    },
+    "sd-2.1": {
+        "H": 512,
+        "W": 512,
+        "C": 4,
+        "f": 8,
+        "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": {
+        "H": 768,
+        "W": 768,
+        "C": 4,
+        "f": 8,
+        "is_legacy": True,
+        "config": "configs/inference/sd_2_1_768.yaml",
+        "ckpt": "checkpoints/v2-1_768-ema-pruned.safetensors",
+    },
+    "SDXL-Refiner": {
+        "H": 1024,
+        "W": 1024,
+        "C": 4,
+        "f": 8,
+        "is_legacy": True,
+        "config": "configs/inference/sd_xl_refiner.yaml",
+        "ckpt": "checkpoints/sd_xl_refiner_0.9.safetensors",
+        "is_guided": True,
+    },
+}
+
+
+def load_img(display=True, key=None, device="cuda"):
+    image = get_interactive_image(key=key)
+    if image is None:
+        return None
+    if display:
+        st.image(image)
+    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 = np.array(image.convert("RGB"))
+    image = image[None].transpose(0, 3, 1, 2)
+    image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
+    return image.to(device)
+
+
+def run_txt2img(
+    state, version, version_dict, is_legacy=False, return_latents=False, filter=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]
+    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
+        )
+    C = version_dict["C"]
+    F = version_dict["f"]
+
+    init_dict = {
+        "orig_width": W,
+        "orig_height": H,
+        "target_width": W,
+        "target_height": H,
+    }
+    value_dict = init_embedder_options(
+        get_unique_embedder_keys_from_conditioner(state["model"].conditioner),
+        init_dict,
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+    )
+    num_rows, num_cols, sampler = init_sampling(
+        use_identity_guider=not version_dict["is_guided"]
+    )
+
+    num_samples = num_rows * num_cols
+
+    if st.button("Sample"):
+        st.write(f"**Model I:** {version}")
+        out = do_sample(
+            state["model"],
+            sampler,
+            value_dict,
+            num_samples,
+            H,
+            W,
+            C,
+            F,
+            force_uc_zero_embeddings=["txt"] if not is_legacy else [],
+            return_latents=return_latents,
+            filter=filter,
+        )
+        return out
+
+
+def run_img2img(
+    state, version_dict, is_legacy=False, return_latents=False, filter=None
+):
+    img = load_img()
+    if img is None:
+        return None
+    H, W = img.shape[2], img.shape[3]
+
+    init_dict = {
+        "orig_width": W,
+        "orig_height": H,
+        "target_width": W,
+        "target_height": H,
+    }
+    value_dict = init_embedder_options(
+        get_unique_embedder_keys_from_conditioner(state["model"].conditioner),
+        init_dict,
+    )
+    strength = st.number_input(
+        "**Img2Img Strength**", value=0.5, min_value=0.0, max_value=1.0
+    )
+    num_rows, num_cols, sampler = init_sampling(
+        img2img_strength=strength,
+        use_identity_guider=not version_dict["is_guided"],
+    )
+    num_samples = num_rows * num_cols
+
+    if st.button("Sample"):
+        out = do_img2img(
+            repeat(img, "1 ... -> n ...", n=num_samples),
+            state["model"],
+            sampler,
+            value_dict,
+            num_samples,
+            force_uc_zero_embeddings=["txt"] if not is_legacy else [],
+            return_latents=return_latents,
+            filter=filter,
+        )
+        return out
+
+
+def apply_refiner(
+    input,
+    state,
+    sampler,
+    num_samples,
+    prompt,
+    negative_prompt,
+    filter=None,
+):
+    init_dict = {
+        "orig_width": input.shape[3] * 8,
+        "orig_height": input.shape[2] * 8,
+        "target_width": input.shape[3] * 8,
+        "target_height": input.shape[2] * 8,
+    }
+
+    value_dict = init_dict
+    value_dict["prompt"] = prompt
+    value_dict["negative_prompt"] = negative_prompt
+
+    value_dict["crop_coords_top"] = 0
+    value_dict["crop_coords_left"] = 0
+
+    value_dict["aesthetic_score"] = 6.0
+    value_dict["negative_aesthetic_score"] = 2.5
+
+    st.warning(f"refiner input shape: {input.shape}")
+    samples = do_img2img(
+        input,
+        state["model"],
+        sampler,
+        value_dict,
+        num_samples,
+        skip_encode=True,
+        filter=filter,
+    )
+
+    return samples
+
+
+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)
+    st.write("__________________________")
+
+    if version == "SD-XL 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"]
+
+    is_legacy = version_dict["is_legacy"]
+
+    prompt = st.text_input(
+        "prompt",
+        "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+    )
+    if is_legacy:
+        negative_prompt = st.text_input("negative prompt", "")
+    else:
+        negative_prompt = ""  # which is unused
+
+    if add_pipeline:
+        st.write("__________________________")
+
+        version2 = "SDXL-Refiner"
+        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)
+        st.info(state2["msg"])
+
+        stage2strength = st.number_input(
+            "**Refinement strength**", value=0.3, min_value=0.0, max_value=1.0
+        )
+
+        sampler2 = init_sampling(
+            key=2,
+            img2img_strength=stage2strength,
+            use_identity_guider=not version_dict["is_guided"],
+            get_num_samples=False,
+        )
+        st.write("__________________________")
+
+    if mode == "txt2img":
+        out = run_txt2img(
+            state,
+            version,
+            version_dict,
+            is_legacy=is_legacy,
+            return_latents=add_pipeline,
+            filter=filter,
+        )
+    elif mode == "img2img":
+        out = run_img2img(
+            state,
+            version_dict,
+            is_legacy=is_legacy,
+            return_latents=add_pipeline,
+            filter=filter,
+        )
+    else:
+        raise ValueError(f"unknown mode {mode}")
+    if isinstance(out, (tuple, list)):
+        samples, samples_z = out
+    else:
+        samples = out
+
+    if add_pipeline:
+        st.write("**Running Refinement Stage**")
+        samples = apply_refiner(
+            samples_z,
+            state2,
+            sampler2,
+            samples_z.shape[0],
+            prompt=prompt,
+            negative_prompt=negative_prompt if is_legacy else "",
+            filter=filter,
+        )
+
+    if save_locally and samples is not None:
+        perform_save_locally(save_path, samples)

scripts/demo/streamlit_helpers.py

@@ -0,0 +1,668 @@
+import os
+from typing import Union, List
+
+import math
+import numpy as np
+import streamlit as st
+import torch
+from PIL import Image
+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 safetensors.torch import load_file as load_safetensors
+
+from sgm.modules.diffusionmodules.sampling import (
+    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)
+
+
+@st.cache_resource()
+def init_st(version_dict, load_ckpt=True):
+    state = dict()
+    if not "model" in state:
+        config = version_dict["config"]
+        ckpt = version_dict["ckpt"]
+
+        config = OmegaConf.load(config)
+        model, msg = load_model_from_config(config, ckpt if load_ckpt else None)
+
+        state["msg"] = msg
+        state["model"] = model
+        state["ckpt"] = ckpt if load_ckpt else None
+        state["config"] = config
+    return state
+
+
+def load_model_from_config(config, ckpt=None, verbose=True):
+    model = instantiate_from_config(config.model)
+
+    if ckpt is not None:
+        print(f"Loading model from {ckpt}")
+        if ckpt.endswith("ckpt"):
+            pl_sd = torch.load(ckpt, map_location="cpu")
+            if "global_step" in pl_sd:
+                global_step = pl_sd["global_step"]
+                st.info(f"loaded ckpt from global step {global_step}")
+                print(f"Global Step: {pl_sd['global_step']}")
+            sd = pl_sd["state_dict"]
+        elif ckpt.endswith("safetensors"):
+            sd = load_safetensors(ckpt)
+        else:
+            raise NotImplementedError
+
+        msg = None
+
+        m, u = model.load_state_dict(sd, strict=False)
+
+        if len(m) > 0 and verbose:
+            print("missing keys:")
+            print(m)
+        if len(u) > 0 and verbose:
+            print("unexpected keys:")
+            print(u)
+    else:
+        msg = None
+
+    model.cuda()
+    model.eval()
+    return model, msg
+
+
+def get_unique_embedder_keys_from_conditioner(conditioner):
+    return list(set([x.input_key for x in conditioner.embedders]))
+
+
+def init_embedder_options(keys, init_dict, prompt=None, negative_prompt=None):
+    # Hardcoded demo settings; might undergo some changes in the future
+
+    value_dict = {}
+    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"
+                )
+            if negative_prompt is None:
+                negative_prompt = st.text_input("Negative prompt", "")
+
+            value_dict["prompt"] = prompt
+            value_dict["negative_prompt"] = negative_prompt
+
+        if key == "original_size_as_tuple":
+            orig_width = st.number_input(
+                "orig_width",
+                value=init_dict["orig_width"],
+                min_value=16,
+            )
+            orig_height = st.number_input(
+                "orig_height",
+                value=init_dict["orig_height"],
+                min_value=16,
+            )
+
+            value_dict["orig_width"] = orig_width
+            value_dict["orig_height"] = orig_height
+
+        if key == "crop_coords_top_left":
+            crop_coord_top = st.number_input("crop_coords_top", value=0, min_value=0)
+            crop_coord_left = st.number_input("crop_coords_left", value=0, min_value=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":
+            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"] = target_width
+            value_dict["target_height"] = target_height
+
+    return value_dict
+
+
+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)
+    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
+
+
+def init_save_locally(_dir, init_value: bool = False):
+    save_locally = st.sidebar.checkbox("Save images locally", value=init_value)
+    if save_locally:
+        save_path = st.text_input("Save path", value=os.path.join(_dir, "samples"))
+    else:
+        save_path = None
+
+    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):
+    guider = st.sidebar.selectbox(
+        f"Discretization #{key}",
+        [
+            "VanillaCFG",
+            "IdentityGuider",
+        ],
+    )
+
+    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
+        )
+
+        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},
+        }
+    else:
+        raise NotImplementedError
+    return guider_config
+
+
+def init_sampling(
+    key=1, img2img_strength=1.0, use_identity_guider=False, get_num_samples=True
+):
+    if get_num_samples:
+        num_rows = 1
+        num_cols = st.number_input(
+            f"num cols #{key}", value=2, min_value=1, max_value=10
+        )
+
+    steps = st.sidebar.number_input(
+        f"steps #{key}", value=50, min_value=1, max_value=1000
+    )
+    sampler = st.sidebar.selectbox(
+        f"Sampler #{key}",
+        [
+            "EulerEDMSampler",
+            "HeunEDMSampler",
+            "EulerAncestralSampler",
+            "DPMPP2SAncestralSampler",
+            "DPMPP2MSampler",
+            "LinearMultistepSampler",
+        ],
+        0,
+    )
+    discretization = st.sidebar.selectbox(
+        f"Discretization #{key}",
+        [
+            "LegacyDDPMDiscretization",
+            "EDMDiscretization",
+        ],
+    )
+
+    discretization_config = get_discretization(discretization, key=key)
+
+    guider_config = get_guider(key=key)
+
+    sampler = get_sampler(sampler, steps, discretization_config, guider_config, key=key)
+    if img2img_strength < 1.0:
+        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
+
+
+def get_discretization(discretization, 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)
+        discretization_config = {
+            "target": "sgm.modules.diffusionmodules.discretizer.EDMDiscretization",
+            "params": {
+                "sigma_min": sigma_min,
+                "sigma_max": sigma_max,
+                "rho": rho,
+            },
+        }
+
+    return discretization_config
+
+
+def get_sampler(sampler_name, steps, discretization_config, guider_config, key=1):
+    if sampler_name == "EulerEDMSampler" or sampler_name == "HeunEDMSampler":
+        s_churn = st.sidebar.number_input(f"s_churn #{key}", value=0.0, min_value=0.0)
+        s_tmin = st.sidebar.number_input(f"s_tmin #{key}", value=0.0, min_value=0.0)
+        s_tmax = st.sidebar.number_input(f"s_tmax #{key}", value=999.0, min_value=0.0)
+        s_noise = st.sidebar.number_input(f"s_noise #{key}", value=1.0, min_value=0.0)
+
+        if sampler_name == "EulerEDMSampler":
+            sampler = EulerEDMSampler(
+                num_steps=steps,
+                discretization_config=discretization_config,
+                guider_config=guider_config,
+                s_churn=s_churn,
+                s_tmin=s_tmin,
+                s_tmax=s_tmax,
+                s_noise=s_noise,
+                verbose=True,
+            )
+        elif sampler_name == "HeunEDMSampler":
+            sampler = HeunEDMSampler(
+                num_steps=steps,
+                discretization_config=discretization_config,
+                guider_config=guider_config,
+                s_churn=s_churn,
+                s_tmin=s_tmin,
+                s_tmax=s_tmax,
+                s_noise=s_noise,
+                verbose=True,
+            )
+    elif (
+        sampler_name == "EulerAncestralSampler"
+        or sampler_name == "DPMPP2SAncestralSampler"
+    ):
+        s_noise = st.sidebar.number_input("s_noise", value=1.0, min_value=0.0)
+        eta = st.sidebar.number_input("eta", value=1.0, min_value=0.0)
+
+        if sampler_name == "EulerAncestralSampler":
+            sampler = EulerAncestralSampler(
+                num_steps=steps,
+                discretization_config=discretization_config,
+                guider_config=guider_config,
+                eta=eta,
+                s_noise=s_noise,
+                verbose=True,
+            )
+        elif sampler_name == "DPMPP2SAncestralSampler":
+            sampler = DPMPP2SAncestralSampler(
+                num_steps=steps,
+                discretization_config=discretization_config,
+                guider_config=guider_config,
+                eta=eta,
+                s_noise=s_noise,
+                verbose=True,
+            )
+    elif sampler_name == "DPMPP2MSampler":
+        sampler = DPMPP2MSampler(
+            num_steps=steps,
+            discretization_config=discretization_config,
+            guider_config=guider_config,
+            verbose=True,
+        )
+    elif sampler_name == "LinearMultistepSampler":
+        order = st.sidebar.number_input("order", value=4, min_value=1)
+        sampler = LinearMultistepSampler(
+            num_steps=steps,
+            discretization_config=discretization_config,
+            guider_config=guider_config,
+            order=order,
+            verbose=True,
+        )
+    else:
+        raise ValueError(f"unknown sampler {sampler_name}!")
+
+    return sampler
+
+
+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(display=True, key=None):
+    image = get_interactive_image(key=key)
+    if image is None:
+        return None
+    if display:
+        st.image(image)
+    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),
+        ]
+    )
+    img = transform(image)[None, ...]
+    st.text(f"input min/max/mean: {img.min():.3f}/{img.max():.3f}/{img.mean():.3f}")
+    return img
+
+
+def get_init_img(batch_size=1, key=None):
+    init_image = load_img(key=key).cuda()
+    init_image = repeat(init_image, "1 ... -> b ...", b=batch_size)
+    return init_image
+
+
+def do_sample(
+    model,
+    sampler,
+    value_dict,
+    num_samples,
+    H,
+    W,
+    C,
+    F,
+    force_uc_zero_embeddings: List = None,
+    batch2model_input: List = None,
+    return_latents=False,
+    filter=None,
+):
+    if force_uc_zero_embeddings is None:
+        force_uc_zero_embeddings = []
+    if batch2model_input is None:
+        batch2model_input = []
+
+    st.text("Sampling")
+
+    outputs = st.empty()
+    precision_scope = autocast
+    with torch.no_grad():
+        with precision_scope("cuda"):
+            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("cuda"), (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("cuda")
+
+                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)
+
+                grid = 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_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
+
+
+@torch.no_grad()
+def do_img2img(
+    img,
+    model,
+    sampler,
+    value_dict,
+    num_samples,
+    force_uc_zero_embeddings=[],
+    additional_kwargs={},
+    offset_noise_level: int = 0.0,
+    return_latents=False,
+    skip_encode=False,
+    filter=None,
+):
+    st.text("Sampling")
+
+    outputs = st.empty()
+    precision_scope = autocast
+    with torch.no_grad():
+        with precision_scope("cuda"):
+            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("cuda"), (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]
+
+                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.
+
+                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)
+
+                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

scripts/util/detection/nsfw_and_watermark_dectection.py

@@ -0,0 +1,104 @@
+import os
+import torch
+import numpy as np
+import torchvision.transforms as T
+from PIL import Image
+import clip
+
+RESOURCES_ROOT = "scripts/util/detection/"
+
+
+def predict_proba(X, weights, biases):
+    logits = X @ weights.T + biases
+    proba = np.where(
+        logits >= 0, 1 / (1 + np.exp(-logits)), np.exp(logits) / (1 + np.exp(logits))
+    )
+    return proba.T
+
+
+def load_model_weights(path: str):
+    model_weights = np.load(path)
+    return model_weights["weights"], model_weights["biases"]
+
+
+def clip_process_images(images: torch.Tensor) -> torch.Tensor:
+    min_size = min(images.shape[-2:])
+    return T.Compose(
+        [
+            T.CenterCrop(min_size),  # TODO: this might affect the watermark, check this
+            T.Resize(224, interpolation=T.InterpolationMode.BICUBIC, antialias=True),
+            T.Normalize(
+                (0.48145466, 0.4578275, 0.40821073),
+                (0.26862954, 0.26130258, 0.27577711),
+            ),
+        ]
+    )(images)
+
+
+class DeepFloydDataFiltering(object):
+    def __init__(self, verbose: bool = False):
+        super().__init__()
+        self.verbose = verbose
+        self.clip_model, _ = clip.load("ViT-L/14", device="cpu")
+        self.clip_model.eval()
+
+        self.cpu_w_weights, self.cpu_w_biases = load_model_weights(
+            os.path.join(RESOURCES_ROOT, "w_head_v1.npz")
+        )
+        self.cpu_p_weights, self.cpu_p_biases = load_model_weights(
+            os.path.join(RESOURCES_ROOT, "p_head_v1.npz")
+        )
+        self.w_threshold, self.p_threshold = 0.5, 0.5
+
+    @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"))
+        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)
+        print(f"p_pred = {p_pred}, w_pred = {w_pred}") if self.verbose else None
+        query = p_pred > self.p_threshold
+        if query.sum() > 0:
+            print(f"Hit for p_threshold: {p_pred}") if self.verbose else None
+            images[query] = T.GaussianBlur(99, sigma=(100.0, 100.0))(images[query])
+        query = w_pred > self.w_threshold
+        if query.sum() > 0:
+            print(f"Hit for w_threshold: {w_pred}") if self.verbose else None
+            images[query] = T.GaussianBlur(99, sigma=(100.0, 100.0))(images[query])
+        return images
+
+
+def load_img(path: str) -> torch.Tensor:
+    image = Image.open(path)
+    if not image.mode == "RGB":
+        image = image.convert("RGB")
+    image_transforms = T.Compose(
+        [
+            T.ToTensor(),
+        ]
+    )
+    return image_transforms(image)[None, ...]
+
+
+def test(root):
+    from einops import rearrange
+
+    filter = DeepFloydDataFiltering(verbose=True)
+    for p in os.listdir((root)):
+        print(f"running on {p}...")
+        img = load_img(os.path.join(root, p))
+        filtered_img = filter(img)
+        filtered_img = rearrange(
+            255.0 * (filtered_img.numpy())[0], "c h w -> h w c"
+        ).astype(np.uint8)
+        Image.fromarray(filtered_img).save(
+            os.path.join(root, f"{os.path.splitext(p)[0]}-filtered.jpg")
+        )
+
+
+if __name__ == "__main__":
+    import fire
+
+    fire.Fire(test)
+    print("done.")

setup.py

@@ -0,0 +1,13 @@
+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

@@ -0,0 +1,3 @@
+from .data import StableDataModuleFromConfig
+from .models import AutoencodingEngine, DiffusionEngine
+from .util import instantiate_from_config

sgm/data/__init__.py

@@ -0,0 +1 @@
+from .dataset import StableDataModuleFromConfig

sgm/data/cifar10.py

@@ -0,0 +1,67 @@
+import torchvision
+import pytorch_lightning as pl
+from torchvision import transforms
+from torch.utils.data import DataLoader, Dataset
+
+
+class CIFAR10DataDictWrapper(Dataset):
+    def __init__(self, dset):
+        super().__init__()
+        self.dset = dset
+
+    def __getitem__(self, i):
+        x, y = self.dset[i]
+        return {"jpg": x, "cls": y}
+
+    def __len__(self):
+        return len(self.dset)
+
+
+class CIFAR10Loader(pl.LightningDataModule):
+    def __init__(self, batch_size, num_workers=0, shuffle=True):
+        super().__init__()
+
+        transform = transforms.Compose(
+            [transforms.ToTensor(), transforms.Lambda(lambda x: x * 2.0 - 1.0)]
+        )
+
+        self.batch_size = batch_size
+        self.num_workers = num_workers
+        self.shuffle = shuffle
+        self.train_dataset = CIFAR10DataDictWrapper(
+            torchvision.datasets.CIFAR10(
+                root=".data/", train=True, download=True, transform=transform
+            )
+        )
+        self.test_dataset = CIFAR10DataDictWrapper(
+            torchvision.datasets.CIFAR10(
+                root=".data/", train=False, download=True, transform=transform
+            )
+        )
+
+    def prepare_data(self):
+        pass
+
+    def train_dataloader(self):
+        return DataLoader(
+            self.train_dataset,
+            batch_size=self.batch_size,
+            shuffle=self.shuffle,
+            num_workers=self.num_workers,
+        )
+
+    def test_dataloader(self):
+        return DataLoader(
+            self.test_dataset,
+            batch_size=self.batch_size,
+            shuffle=self.shuffle,
+            num_workers=self.num_workers,
+        )
+
+    def val_dataloader(self):
+        return DataLoader(
+            self.test_dataset,
+            batch_size=self.batch_size,
+            shuffle=self.shuffle,
+            num_workers=self.num_workers,
+        )

sgm/data/dataset.py

@@ -0,0 +1,80 @@
+from typing import Optional
+
+import torchdata.datapipes.iter
+import webdataset as wds
+from omegaconf import DictConfig
+from pytorch_lightning import LightningDataModule
+
+try:
+    from sdata import create_dataset, create_dummy_dataset, create_loader
+except ImportError as e:
+    print("#" * 100)
+    print("Datasets not yet available")
+    print("to enable, we need to add stable-datasets as a submodule")
+    print("please use ``git submodule update --init --recursive``")
+    print("and do ``pip install -e stable-datasets/`` from the root of this repo")
+    print("#" * 100)
+    exit(1)
+
+
+class StableDataModuleFromConfig(LightningDataModule):
+    def __init__(
+        self,
+        train: DictConfig,
+        validation: Optional[DictConfig] = None,
+        test: Optional[DictConfig] = None,
+        skip_val_loader: bool = False,
+        dummy: bool = False,
+    ):
+        super().__init__()
+        self.train_config = train
+        assert (
+            "datapipeline" in self.train_config and "loader" in self.train_config
+        ), "train config requires the fields `datapipeline` and `loader`"
+
+        self.val_config = validation
+        if not skip_val_loader:
+            if self.val_config is not None:
+                assert (
+                    "datapipeline" in self.val_config and "loader" in self.val_config
+                ), "validation config requires the fields `datapipeline` and `loader`"
+            else:
+                print(
+                    "Warning: No Validation datapipeline defined, using that one from training"
+                )
+                self.val_config = train
+
+        self.test_config = test
+        if self.test_config is not None:
+            assert (
+                "datapipeline" in self.test_config and "loader" in self.test_config
+            ), "test config requires the fields `datapipeline` and `loader`"
+
+        self.dummy = dummy
+        if self.dummy:
+            print("#" * 100)
+            print("USING DUMMY DATASET: HOPE YOU'RE DEBUGGING ;)")
+            print("#" * 100)
+
+    def setup(self, stage: str) -> None:
+        print("Preparing datasets")
+        if self.dummy:
+            data_fn = create_dummy_dataset
+        else:
+            data_fn = create_dataset
+
+        self.train_datapipeline = data_fn(**self.train_config.datapipeline)
+        if self.val_config:
+            self.val_datapipeline = data_fn(**self.val_config.datapipeline)
+        if self.test_config:
+            self.test_datapipeline = data_fn(**self.test_config.datapipeline)
+
+    def train_dataloader(self) -> torchdata.datapipes.iter.IterDataPipe:
+        loader = create_loader(self.train_datapipeline, **self.train_config.loader)
+        return loader
+
+    def val_dataloader(self) -> wds.DataPipeline:
+        return create_loader(self.val_datapipeline, **self.val_config.loader)
+
+    def test_dataloader(self) -> wds.DataPipeline:
+        return create_loader(self.test_datapipeline, **self.test_config.loader)

sgm/data/mnist.py

@@ -0,0 +1,85 @@
+import torchvision
+import pytorch_lightning as pl
+from torchvision import transforms
+from torch.utils.data import DataLoader, Dataset
+
+
+class MNISTDataDictWrapper(Dataset):
+    def __init__(self, dset):
+        super().__init__()
+        self.dset = dset
+
+    def __getitem__(self, i):
+        x, y = self.dset[i]
+        return {"jpg": x, "cls": y}
+
+    def __len__(self):
+        return len(self.dset)
+
+
+class MNISTLoader(pl.LightningDataModule):
+    def __init__(self, batch_size, num_workers=0, prefetch_factor=2, shuffle=True):
+        super().__init__()
+
+        transform = transforms.Compose(
+            [transforms.ToTensor(), transforms.Lambda(lambda x: x * 2.0 - 1.0)]
+        )
+
+        self.batch_size = batch_size
+        self.num_workers = num_workers
+        self.prefetch_factor = prefetch_factor if num_workers > 0 else 0
+        self.shuffle = shuffle
+        self.train_dataset = MNISTDataDictWrapper(
+            torchvision.datasets.MNIST(
+                root=".data/", train=True, download=True, transform=transform
+            )
+        )
+        self.test_dataset = MNISTDataDictWrapper(
+            torchvision.datasets.MNIST(
+                root=".data/", train=False, download=True, transform=transform
+            )
+        )
+
+    def prepare_data(self):
+        pass
+
+    def train_dataloader(self):
+        return DataLoader(
+            self.train_dataset,
+            batch_size=self.batch_size,
+            shuffle=self.shuffle,
+            num_workers=self.num_workers,
+            prefetch_factor=self.prefetch_factor,
+        )
+
+    def test_dataloader(self):
+        return DataLoader(
+            self.test_dataset,
+            batch_size=self.batch_size,
+            shuffle=self.shuffle,
+            num_workers=self.num_workers,
+            prefetch_factor=self.prefetch_factor,
+        )
+
+    def val_dataloader(self):
+        return DataLoader(
+            self.test_dataset,
+            batch_size=self.batch_size,
+            shuffle=self.shuffle,
+            num_workers=self.num_workers,
+            prefetch_factor=self.prefetch_factor,
+        )
+
+
+if __name__ == "__main__":
+    dset = MNISTDataDictWrapper(
+        torchvision.datasets.MNIST(
+            root=".data/",
+            train=False,
+            download=True,
+            transform=transforms.Compose(
+                [transforms.ToTensor(), transforms.Lambda(lambda x: x * 2.0 - 1.0)]
+            ),
+        )
+    )
+    ex = dset[0]

sgm/lr_scheduler.py

@@ -0,0 +1,135 @@
+import numpy as np
+
+
+class LambdaWarmUpCosineScheduler:
+    """
+    note: use with a base_lr of 1.0
+    """
+
+    def __init__(
+        self,
+        warm_up_steps,
+        lr_min,
+        lr_max,
+        lr_start,
+        max_decay_steps,
+        verbosity_interval=0,
+    ):
+        self.lr_warm_up_steps = warm_up_steps
+        self.lr_start = lr_start
+        self.lr_min = lr_min
+        self.lr_max = lr_max
+        self.lr_max_decay_steps = max_decay_steps
+        self.last_lr = 0.0
+        self.verbosity_interval = verbosity_interval
+
+    def schedule(self, n, **kwargs):
+        if self.verbosity_interval > 0:
+            if n % self.verbosity_interval == 0:
+                print(f"current step: {n}, recent lr-multiplier: {self.last_lr}")
+        if n < self.lr_warm_up_steps:
+            lr = (
+                self.lr_max - self.lr_start
+            ) / self.lr_warm_up_steps * n + self.lr_start
+            self.last_lr = lr
+            return lr
+        else:
+            t = (n - self.lr_warm_up_steps) / (
+                self.lr_max_decay_steps - self.lr_warm_up_steps
+            )
+            t = min(t, 1.0)
+            lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * (
+                1 + np.cos(t * np.pi)
+            )
+            self.last_lr = lr
+            return lr
+
+    def __call__(self, n, **kwargs):
+        return self.schedule(n, **kwargs)
+
+
+class LambdaWarmUpCosineScheduler2:
+    """
+    supports repeated iterations, configurable via lists
+    note: use with a base_lr of 1.0.
+    """
+
+    def __init__(
+        self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0
+    ):
+        assert (
+            len(warm_up_steps)
+            == len(f_min)
+            == len(f_max)
+            == len(f_start)
+            == len(cycle_lengths)
+        )
+        self.lr_warm_up_steps = warm_up_steps
+        self.f_start = f_start
+        self.f_min = f_min
+        self.f_max = f_max
+        self.cycle_lengths = cycle_lengths
+        self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths))
+        self.last_f = 0.0
+        self.verbosity_interval = verbosity_interval
+
+    def find_in_interval(self, n):
+        interval = 0
+        for cl in self.cum_cycles[1:]:
+            if n <= cl:
+                return interval
+            interval += 1
+
+    def schedule(self, n, **kwargs):
+        cycle = self.find_in_interval(n)
+        n = n - self.cum_cycles[cycle]
+        if self.verbosity_interval > 0:
+            if n % self.verbosity_interval == 0:
+                print(
+                    f"current step: {n}, recent lr-multiplier: {self.last_f}, "
+                    f"current cycle {cycle}"
+                )
+        if n < self.lr_warm_up_steps[cycle]:
+            f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[
+                cycle
+            ] * n + self.f_start[cycle]
+            self.last_f = f
+            return f
+        else:
+            t = (n - self.lr_warm_up_steps[cycle]) / (
+                self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle]
+            )
+            t = min(t, 1.0)
+            f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * (
+                1 + np.cos(t * np.pi)
+            )
+            self.last_f = f
+            return f
+
+    def __call__(self, n, **kwargs):
+        return self.schedule(n, **kwargs)
+
+
+class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2):
+    def schedule(self, n, **kwargs):
+        cycle = self.find_in_interval(n)
+        n = n - self.cum_cycles[cycle]
+        if self.verbosity_interval > 0:
+            if n % self.verbosity_interval == 0:
+                print(
+                    f"current step: {n}, recent lr-multiplier: {self.last_f}, "
+                    f"current cycle {cycle}"
+                )
+
+        if n < self.lr_warm_up_steps[cycle]:
+            f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[
+                cycle
+            ] * n + self.f_start[cycle]
+            self.last_f = f
+            return f
+        else:
+            f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (
+                self.cycle_lengths[cycle] - n
+            ) / (self.cycle_lengths[cycle])
+            self.last_f = f
+            return f

sgm/models/__init__.py

@@ -0,0 +1,2 @@
+from .autoencoder import AutoencodingEngine
+from .diffusion import DiffusionEngine

sgm/models/autoencoder.py

@@ -0,0 +1,335 @@
+import re
+from abc import abstractmethod
+from contextlib import contextmanager
+from typing import Any, Dict, Tuple, Union
+
+import pytorch_lightning as pl
+import torch
+from omegaconf import ListConfig
+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.ema import LitEma
+from ..util import default, get_obj_from_str, instantiate_from_config
+
+
+class AbstractAutoencoder(pl.LightningModule):
+    """
+    This is the base class for all autoencoders, including image autoencoders, image autoencoders with discriminators,
+    unCLIP models, etc. Hence, it is fairly general, and specific features
+    (e.g. discriminator training, encoding, decoding) must be implemented in subclasses.
+    """
+
+    def __init__(
+        self,
+        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:
+            self.monitor = monitor
+
+        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)
+
+        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}")
+
+    @abstractmethod
+    def get_input(self, batch) -> Any:
+        raise NotImplementedError()
+
+    def on_train_batch_end(self, *args, **kwargs):
+        # for EMA computation
+        if self.use_ema:
+            self.model_ema(self)
+
+    @contextmanager
+    def ema_scope(self, context=None):
+        if self.use_ema:
+            self.model_ema.store(self.parameters())
+            self.model_ema.copy_to(self)
+            if context is not None:
+                print(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")
+
+    @abstractmethod
+    def encode(self, *args, **kwargs) -> torch.Tensor:
+        raise NotImplementedError("encode()-method of abstract base class called")
+
+    @abstractmethod
+    def decode(self, *args, **kwargs) -> torch.Tensor:
+        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")
+        return get_obj_from_str(cfg["target"])(
+            params, lr=lr, **cfg.get("params", dict())
+        )
+
+    def configure_optimizers(self) -> Any:
+        raise NotImplementedError()
+
+
+class AutoencodingEngine(AbstractAutoencoder):
+    """
+    Base class for all image autoencoders that we train, like VQGAN or AutoencoderKL
+    (we also restore them explicitly as special cases for legacy reasons).
+    Regularizations such as KL or VQ are moved to the regularizer class.
+    """
+
+    def __init__(
+        self,
+        *args,
+        encoder_config: Dict,
+        decoder_config: Dict,
+        loss_config: Dict,
+        regularizer_config: Dict,
+        optimizer_config: Union[Dict, None] = None,
+        lr_g_factor: float = 1.0,
+        **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.optimizer_config = default(
+            optimizer_config, {"target": "torch.optim.Adam"}
+        )
+        self.lr_g_factor = lr_g_factor
+
+    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)
+        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())
+        )
+        return params
+
+    def get_discriminator_params(self) -> list:
+        params = list(self.loss.get_trainable_parameters())  # e.g., discriminator
+        return params
+
+    def get_last_layer(self):
+        return self.decoder.get_last_layer()
+
+    def encode(self, x: Any, return_reg_log: bool = False) -> Any:
+        z = self.encoder(x)
+        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)
+        return x
+
+    def forward(self, x: Any) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        z, reg_log = self.encode(x, return_reg_log=True)
+        dec = self.decode(z)
+        return z, dec, reg_log
+
+    def training_step(self, batch, batch_idx, optimizer_idx) -> Any:
+        x = self.get_input(batch)
+        z, xrec, regularization_log = self(x)
+
+        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",
+            )
+
+            self.log_dict(
+                log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True
+            )
+            return aeloss
+
+        if 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",
+            )
+            self.log_dict(
+                log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True
+            )
+            return discloss
+
+    def validation_step(self, batch, batch_idx) -> 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:
+        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,
+        )
+
+        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()
+
+        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
+        )
+
+        return [opt_ae, opt_disc], []
+
+    @torch.no_grad()
+    def log_images(self, batch: Dict, **kwargs) -> Dict:
+        log = dict()
+        x = self.get_input(batch)
+        _, xrec, _ = self(x)
+        log["inputs"] = x
+        log["reconstructions"] = xrec
+        with self.ema_scope():
+            _, xrec_ema, _ = self(x)
+            log["reconstructions_ema"] = xrec_ema
+        return log
+
+
+class AutoencoderKL(AutoencodingEngine):
+    def __init__(self, embed_dim: int, **kwargs):
+        ddconfig = kwargs.pop("ddconfig")
+        ckpt_path = kwargs.pop("ckpt_path", None)
+        ignore_keys = kwargs.pop("ignore_keys", ())
+        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"),
+            **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.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)
+
+    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 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 IdentityFirstStage(AbstractAutoencoder):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def get_input(self, x: Any) -> Any:
+        return x
+
+    def encode(self, x: Any, *args, **kwargs) -> Any:
+        return x
+
+    def decode(self, x: Any, *args, **kwargs) -> Any:
+        return x

sgm/models/diffusion.py

@@ -0,0 +1,324 @@
+from contextlib import contextmanager
+from typing import Any, Dict, List, Tuple, Union
+
+import pytorch_lightning as pl
+import torch
+from omegaconf import ListConfig, OmegaConf
+from safetensors.torch import load_file as load_safetensors
+from torch.optim.lr_scheduler import LambdaLR
+
+from ..modules import UNCONDITIONAL_CONFIG
+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,
+)
+
+
+class DiffusionEngine(pl.LightningModule):
+    def __init__(
+        self,
+        network_config,
+        denoiser_config,
+        first_stage_config,
+        conditioner_config: Union[None, Dict, ListConfig, OmegaConf] = None,
+        sampler_config: Union[None, Dict, ListConfig, OmegaConf] = None,
+        optimizer_config: Union[None, Dict, ListConfig, OmegaConf] = None,
+        scheduler_config: Union[None, Dict, ListConfig, OmegaConf] = None,
+        loss_fn_config: Union[None, Dict, ListConfig, OmegaConf] = None,
+        network_wrapper: Union[None, str] = None,
+        ckpt_path: Union[None, str] = None,
+        use_ema: bool = False,
+        ema_decay_rate: float = 0.9999,
+        scale_factor: float = 1.0,
+        disable_first_stage_autocast=False,
+        input_key: str = "jpg",
+        log_keys: Union[List, None] = None,
+        no_cond_log: bool = False,
+        compile_model: bool = False,
+    ):
+        super().__init__()
+        self.log_keys = log_keys
+        self.input_key = input_key
+        self.optimizer_config = default(
+            optimizer_config, {"target": "torch.optim.AdamW"}
+        )
+        model = instantiate_from_config(network_config)
+        self.model = get_obj_from_str(default(network_wrapper, OPENAIUNETWRAPPER))(
+            model, compile_model=compile_model
+        )
+
+        self.denoiser = instantiate_from_config(denoiser_config)
+        self.sampler = (
+            instantiate_from_config(sampler_config)
+            if sampler_config is not None
+            else None
+        )
+        self.conditioner = instantiate_from_config(
+            default(conditioner_config, UNCONDITIONAL_CONFIG)
+        )
+        self.scheduler_config = scheduler_config
+        self._init_first_stage(first_stage_config)
+
+        self.loss_fn = (
+            instantiate_from_config(loss_fn_config)
+            if loss_fn_config is not None
+            else None
+        )
+
+        self.use_ema = use_ema
+        if self.use_ema:
+            self.model_ema = LitEma(self.model, decay=ema_decay_rate)
+            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+        self.scale_factor = scale_factor
+        self.disable_first_stage_autocast = disable_first_stage_autocast
+        self.no_cond_log = no_cond_log
+
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path)
+
+    def init_from_ckpt(
+        self,
+        path: str,
+    ) -> 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
+
+        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 _init_first_stage(self, config):
+        model = instantiate_from_config(config).eval()
+        model.train = disabled_train
+        for param in model.parameters():
+            param.requires_grad = False
+        self.first_stage_model = model
+
+    def get_input(self, batch):
+        # assuming unified data format, dataloader returns a dict.
+        # image tensors should be scaled to -1 ... 1 and in bchw format
+        return batch[self.input_key]
+
+    @torch.no_grad()
+    def decode_first_stage(self, z):
+        z = 1.0 / self.scale_factor * z
+        with torch.autocast("cuda", enabled=not self.disable_first_stage_autocast):
+            out = self.first_stage_model.decode(z)
+        return out
+
+    @torch.no_grad()
+    def encode_first_stage(self, x):
+        with torch.autocast("cuda", enabled=not self.disable_first_stage_autocast):
+            z = self.first_stage_model.encode(x)
+        z = self.scale_factor * z
+        return z
+
+    def forward(self, x, batch):
+        loss = self.loss_fn(self.model, self.denoiser, self.conditioner, x, batch)
+        loss_mean = loss.mean()
+        loss_dict = {"loss": loss_mean}
+        return loss_mean, loss_dict
+
+    def shared_step(self, batch: Dict) -> Any:
+        x = self.get_input(batch)
+        x = self.encode_first_stage(x)
+        batch["global_step"] = self.global_step
+        loss, loss_dict = self(x, batch)
+        return loss, loss_dict
+
+    def training_step(self, batch, batch_idx):
+        loss, loss_dict = self.shared_step(batch)
+
+        self.log_dict(
+            loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=False
+        )
+
+        self.log(
+            "global_step",
+            self.global_step,
+            prog_bar=True,
+            logger=True,
+            on_step=True,
+            on_epoch=False,
+        )
+
+        if self.scheduler_config is not None:
+            lr = self.optimizers().param_groups[0]["lr"]
+            self.log(
+                "lr_abs", lr, prog_bar=True, logger=True, on_step=True, on_epoch=False
+            )
+
+        return loss
+
+    def on_train_start(self, *args, **kwargs):
+        if self.sampler is None or self.loss_fn is None:
+            raise ValueError("Sampler and loss function need to be set for training.")
+
+    def on_train_batch_end(self, *args, **kwargs):
+        if self.use_ema:
+            self.model_ema(self.model)
+
+    @contextmanager
+    def ema_scope(self, context=None):
+        if self.use_ema:
+            self.model_ema.store(self.model.parameters())
+            self.model_ema.copy_to(self.model)
+            if context is not None:
+                print(f"{context}: Switched to EMA weights")
+        try:
+            yield None
+        finally:
+            if self.use_ema:
+                self.model_ema.restore(self.model.parameters())
+                if context is not None:
+                    print(f"{context}: Restored training weights")
+
+    def instantiate_optimizer_from_config(self, params, lr, cfg):
+        return get_obj_from_str(cfg["target"])(
+            params, lr=lr, **cfg.get("params", dict())
+        )
+
+    def configure_optimizers(self):
+        lr = self.learning_rate
+        params = list(self.model.parameters())
+        for embedder in self.conditioner.embedders:
+            if embedder.is_trainable:
+                params = params + list(embedder.parameters())
+        opt = self.instantiate_optimizer_from_config(params, lr, self.optimizer_config)
+        if self.scheduler_config is not None:
+            scheduler = instantiate_from_config(self.scheduler_config)
+            print("Setting up LambdaLR scheduler...")
+            scheduler = [
+                {
+                    "scheduler": LambdaLR(opt, lr_lambda=scheduler.schedule),
+                    "interval": "step",
+                    "frequency": 1,
+                }
+            ]
+            return [opt], scheduler
+        return opt
+
+    @torch.no_grad()
+    def sample(
+        self,
+        cond: Dict,
+        uc: Union[Dict, None] = None,
+        batch_size: int = 16,
+        shape: Union[None, Tuple, List] = None,
+        **kwargs,
+    ):
+        randn = torch.randn(batch_size, *shape).to(self.device)
+
+        denoiser = lambda input, sigma, c: self.denoiser(
+            self.model, input, sigma, c, **kwargs
+        )
+        samples = self.sampler(denoiser, randn, cond, uc=uc)
+        return samples
+
+    @torch.no_grad()
+    def log_conditionings(self, batch: Dict, n: int) -> Dict:
+        """
+        Defines heuristics to log different conditionings.
+        These can be lists of strings (text-to-image), tensors, ints, ...
+        """
+        image_h, image_w = batch[self.input_key].shape[2:]
+        log = dict()
+
+        for embedder in self.conditioner.embedders:
+            if (
+                (self.log_keys is None) or (embedder.input_key in self.log_keys)
+            ) and not self.no_cond_log:
+                x = batch[embedder.input_key][:n]
+                if isinstance(x, torch.Tensor):
+                    if x.dim() == 1:
+                        # class-conditional, convert integer to string
+                        x = [str(x[i].item()) for i in range(x.shape[0])]
+                        xc = log_txt_as_img((image_h, image_w), x, size=image_h // 4)
+                    elif x.dim() == 2:
+                        # size and crop cond and the like
+                        x = [
+                            "x".join([str(xx) for xx in x[i].tolist()])
+                            for i in range(x.shape[0])
+                        ]
+                        xc = log_txt_as_img((image_h, image_w), x, size=image_h // 20)
+                    else:
+                        raise NotImplementedError()
+                elif isinstance(x, Union[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:
+                    raise NotImplementedError()
+                log[embedder.input_key] = xc
+        return log
+
+    @torch.no_grad()
+    def log_images(
+        self,
+        batch: Dict,
+        N: int = 8,
+        sample: bool = True,
+        ucg_keys: List[str] = None,
+        **kwargs,
+    ) -> Dict:
+        conditioner_input_keys = [e.input_key for e in self.conditioner.embedders]
+        if ucg_keys:
+            assert all(map(lambda x: x in conditioner_input_keys, ucg_keys)), (
+                "Each defined ucg key for sampling must be in the provided conditioner input keys,"
+                f"but we have {ucg_keys} vs. {conditioner_input_keys}"
+            )
+        else:
+            ucg_keys = conditioner_input_keys
+        log = dict()
+
+        x = self.get_input(batch)
+
+        c, uc = self.conditioner.get_unconditional_conditioning(
+            batch,
+            force_uc_zero_embeddings=ucg_keys
+            if len(self.conditioner.embedders) > 0
+            else [],
+        )
+
+        sampling_kwargs = {}
+
+        N = min(x.shape[0], N)
+        x = x.to(self.device)[:N]
+        log["inputs"] = x
+        z = self.encode_first_stage(x)
+        log["reconstructions"] = self.decode_first_stage(z)
+        log.update(self.log_conditionings(batch, N))
+
+        for k in c:
+            if isinstance(c[k], torch.Tensor):
+                c[k], uc[k] = map(lambda y: y[k][:N].to(self.device), (c, uc))
+
+        if sample:
+            with self.ema_scope("Plotting"):
+                samples = self.sample(
+                    c, shape=z.shape[1:], uc=uc, batch_size=N, **sampling_kwargs
+                )
+            samples = self.decode_first_stage(samples)
+            log["samples"] = samples
+        return log

sgm/modules/__init__.py

@@ -0,0 +1,6 @@
+from .encoders.modules import GeneralConditioner
+
+UNCONDITIONAL_CONFIG = {
+    "target": "sgm.modules.GeneralConditioner",
+    "params": {"emb_models": []},
+}

sgm/modules/attention.py

@@ -0,0 +1,947 @@
+import math
+from inspect import isfunction
+from typing import Any, Optional
+
+import torch
+import torch.nn.functional as F
+from einops import rearrange, repeat
+from packaging import version
+from torch import nn
+
+if version.parse(torch.__version__) >= version.parse("2.0.0"):
+    SDP_IS_AVAILABLE = True
+    from torch.backends.cuda import SDPBackend, sdp_kernel
+
+    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,
+        },
+        None: {"enable_math": True, "enable_flash": True, "enable_mem_efficient": True},
+    }
+else:
+    from contextlib import nullcontext
+
+    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."
+    )
+
+try:
+    import xformers
+    import xformers.ops
+
+    XFORMERS_IS_AVAILABLE = True
+except:
+    XFORMERS_IS_AVAILABLE = False
+    print("no module 'xformers'. Processing without...")
+
+from .diffusionmodules.util import checkpoint
+
+
+def exists(val):
+    return val is not None
+
+
+def uniq(arr):
+    return {el: True for el in arr}.keys()
+
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if isfunction(d) else d
+
+
+def max_neg_value(t):
+    return -torch.finfo(t.dtype).max
+
+
+def init_(tensor):
+    dim = tensor.shape[-1]
+    std = 1 / math.sqrt(dim)
+    tensor.uniform_(-std, std)
+    return tensor
+
+
+# feedforward
+class GEGLU(nn.Module):
+    def __init__(self, dim_in, dim_out):
+        super().__init__()
+        self.proj = nn.Linear(dim_in, dim_out * 2)
+
+    def forward(self, x):
+        x, gate = self.proj(x).chunk(2, dim=-1)
+        return x * F.gelu(gate)
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = default(dim_out, dim)
+        project_in = (
+            nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU())
+            if not glu
+            else GEGLU(dim, inner_dim)
+        )
+
+        self.net = nn.Sequential(
+            project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+def zero_module(module):
+    """
+    Zero out the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+
+def Normalize(in_channels):
+    return torch.nn.GroupNorm(
+        num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
+    )
+
+
+class LinearAttention(nn.Module):
+    def __init__(self, dim, heads=4, dim_head=32):
+        super().__init__()
+        self.heads = heads
+        hidden_dim = dim_head * heads
+        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)
+        self.to_out = nn.Conv2d(hidden_dim, dim, 1)
+
+    def forward(self, x):
+        b, c, h, w = x.shape
+        qkv = self.to_qkv(x)
+        q, k, v = rearrange(
+            qkv, "b (qkv heads c) h w -> qkv b heads c (h w)", heads=self.heads, qkv=3
+        )
+        k = k.softmax(dim=-1)
+        context = torch.einsum("bhdn,bhen->bhde", k, v)
+        out = torch.einsum("bhde,bhdn->bhen", context, q)
+        out = rearrange(
+            out, "b heads c (h w) -> b (heads c) h w", heads=self.heads, h=h, w=w
+        )
+        return self.to_out(out)
+
+
+class SpatialSelfAttention(nn.Module):
+    def __init__(self, in_channels):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = Normalize(in_channels)
+        self.q = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.k = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.v = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.proj_out = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+
+    def forward(self, x):
+        h_ = x
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        b, c, h, w = q.shape
+        q = rearrange(q, "b c h w -> b (h w) c")
+        k = rearrange(k, "b c h w -> b c (h w)")
+        w_ = torch.einsum("bij,bjk->bik", q, k)
+
+        w_ = w_ * (int(c) ** (-0.5))
+        w_ = torch.nn.functional.softmax(w_, dim=2)
+
+        # attend to values
+        v = rearrange(v, "b c h w -> b c (h w)")
+        w_ = rearrange(w_, "b i j -> b j i")
+        h_ = torch.einsum("bij,bjk->bik", v, w_)
+        h_ = rearrange(h_, "b c (h w) -> b c h w", h=h)
+        h_ = self.proj_out(h_)
+
+        return x + h_
+
+
+class CrossAttention(nn.Module):
+    def __init__(
+        self,
+        query_dim,
+        context_dim=None,
+        heads=8,
+        dim_head=64,
+        dropout=0.0,
+        backend=None,
+    ):
+        super().__init__()
+        inner_dim = dim_head * heads
+        context_dim = default(context_dim, query_dim)
+
+        self.scale = dim_head**-0.5
+        self.heads = heads
+
+        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
+        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
+        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
+
+        self.to_out = nn.Sequential(
+            nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)
+        )
+        self.backend = backend
+
+    def forward(
+        self,
+        x,
+        context=None,
+        mask=None,
+        additional_tokens=None,
+        n_times_crossframe_attn_in_self=0,
+    ):
+        h = self.heads
+
+        if additional_tokens is not None:
+            # get the number of masked tokens at the beginning of the output sequence
+            n_tokens_to_mask = additional_tokens.shape[1]
+            # add additional token
+            x = torch.cat([additional_tokens, x], dim=1)
+
+        q = self.to_q(x)
+        context = default(context, x)
+        k = self.to_k(context)
+        v = self.to_v(context)
+
+        if n_times_crossframe_attn_in_self:
+            # reprogramming cross-frame attention as in https://arxiv.org/abs/2303.13439
+            assert x.shape[0] % n_times_crossframe_attn_in_self == 0
+            n_cp = x.shape[0] // n_times_crossframe_attn_in_self
+            k = repeat(
+                k[::n_times_crossframe_attn_in_self], "b ... -> (b n) ...", n=n_cp
+            )
+            v = repeat(
+                v[::n_times_crossframe_attn_in_self], "b ... -> (b n) ...", n=n_cp
+            )
+
+        q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (q, k, v))
+
+        ## old
+        """
+        sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
+        del q, k
+
+        if exists(mask):
+            mask = rearrange(mask, 'b ... -> b (...)')
+            max_neg_value = -torch.finfo(sim.dtype).max
+            mask = repeat(mask, 'b j -> (b h) () j', h=h)
+            sim.masked_fill_(~mask, max_neg_value)
+
+        # attention, what we cannot get enough of
+        sim = sim.softmax(dim=-1)
+
+        out = einsum('b i j, b j d -> b i d', sim, v)
+        """
+        ## new
+        with sdp_kernel(**BACKEND_MAP[self.backend]):
+            # print("dispatching into backend", self.backend, "q/k/v shape: ", q.shape, k.shape, v.shape)
+            out = F.scaled_dot_product_attention(
+                q, k, v, attn_mask=mask
+            )  # scale is dim_head ** -0.5 per default
+
+        del q, k, v
+        out = rearrange(out, "b h n d -> b n (h d)", h=h)
+
+        if additional_tokens is not None:
+            # remove additional token
+            out = out[:, n_tokens_to_mask:]
+        return self.to_out(out)
+
+
+class MemoryEfficientCrossAttention(nn.Module):
+    # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
+    def __init__(
+        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}."
+        )
+        inner_dim = dim_head * heads
+        context_dim = default(context_dim, query_dim)
+
+        self.heads = heads
+        self.dim_head = dim_head
+
+        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
+        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
+        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
+
+        self.to_out = nn.Sequential(
+            nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)
+        )
+        self.attention_op: Optional[Any] = None
+
+    def forward(
+        self,
+        x,
+        context=None,
+        mask=None,
+        additional_tokens=None,
+        n_times_crossframe_attn_in_self=0,
+    ):
+        if additional_tokens is not None:
+            # get the number of masked tokens at the beginning of the output sequence
+            n_tokens_to_mask = additional_tokens.shape[1]
+            # add additional token
+            x = torch.cat([additional_tokens, x], dim=1)
+        q = self.to_q(x)
+        context = default(context, x)
+        k = self.to_k(context)
+        v = self.to_v(context)
+
+        if n_times_crossframe_attn_in_self:
+            # reprogramming cross-frame attention as in https://arxiv.org/abs/2303.13439
+            assert x.shape[0] % n_times_crossframe_attn_in_self == 0
+            # n_cp = x.shape[0]//n_times_crossframe_attn_in_self
+            k = repeat(
+                k[::n_times_crossframe_attn_in_self],
+                "b ... -> (b n) ...",
+                n=n_times_crossframe_attn_in_self,
+            )
+            v = repeat(
+                v[::n_times_crossframe_attn_in_self],
+                "b ... -> (b n) ...",
+                n=n_times_crossframe_attn_in_self,
+            )
+
+        b, _, _ = q.shape
+        q, k, v = map(
+            lambda t: t.unsqueeze(3)
+            .reshape(b, t.shape[1], self.heads, self.dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b * self.heads, t.shape[1], self.dim_head)
+            .contiguous(),
+            (q, k, v),
+        )
+
+        # 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
+        )
+
+        # TODO: Use this directly in the attention operation, as a bias
+        if exists(mask):
+            raise NotImplementedError
+        out = (
+            out.unsqueeze(0)
+            .reshape(b, self.heads, out.shape[1], self.dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b, out.shape[1], self.heads * self.dim_head)
+        )
+        if additional_tokens is not None:
+            # remove additional token
+            out = out[:, n_tokens_to_mask:]
+        return self.to_out(out)
+
+
+class BasicTransformerBlock(nn.Module):
+    ATTENTION_MODES = {
+        "softmax": CrossAttention,  # vanilla attention
+        "softmax-xformers": MemoryEfficientCrossAttention,  # ampere
+    }
+
+    def __init__(
+        self,
+        dim,
+        n_heads,
+        d_head,
+        dropout=0.0,
+        context_dim=None,
+        gated_ff=True,
+        checkpoint=True,
+        disable_self_attn=False,
+        attn_mode="softmax",
+        sdp_backend=None,
+    ):
+        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__}"
+            )
+            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."
+            )
+            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.")
+                attn_mode = "softmax-xformers"
+        attn_cls = self.ATTENTION_MODES[attn_mode]
+        if version.parse(torch.__version__) >= version.parse("2.0.0"):
+            assert sdp_backend is None or isinstance(sdp_backend, SDPBackend)
+        else:
+            assert sdp_backend is None
+        self.disable_self_attn = disable_self_attn
+        self.attn1 = attn_cls(
+            query_dim=dim,
+            heads=n_heads,
+            dim_head=d_head,
+            dropout=dropout,
+            context_dim=context_dim if self.disable_self_attn else None,
+            backend=sdp_backend,
+        )  # is a self-attention if not self.disable_self_attn
+        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
+        self.attn2 = attn_cls(
+            query_dim=dim,
+            context_dim=context_dim,
+            heads=n_heads,
+            dim_head=d_head,
+            dropout=dropout,
+            backend=sdp_backend,
+        )  # is self-attn if context is none
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(dim)
+        self.norm3 = nn.LayerNorm(dim)
+        self.checkpoint = checkpoint
+        if self.checkpoint:
+            print(f"{self.__class__.__name__} is using checkpointing")
+
+    def forward(
+        self, x, context=None, additional_tokens=None, n_times_crossframe_attn_in_self=0
+    ):
+        kwargs = {"x": x}
+
+        if context is not None:
+            kwargs.update({"context": context})
+
+        if additional_tokens is not None:
+            kwargs.update({"additional_tokens": additional_tokens})
+
+        if n_times_crossframe_attn_in_self:
+            kwargs.update(
+                {"n_times_crossframe_attn_in_self": n_times_crossframe_attn_in_self}
+            )
+
+        # return mixed_checkpoint(self._forward, kwargs, self.parameters(), self.checkpoint)
+        return checkpoint(
+            self._forward, (x, context), self.parameters(), self.checkpoint
+        )
+
+    def _forward(
+        self, x, context=None, additional_tokens=None, n_times_crossframe_attn_in_self=0
+    ):
+        x = (
+            self.attn1(
+                self.norm1(x),
+                context=context if self.disable_self_attn else None,
+                additional_tokens=additional_tokens,
+                n_times_crossframe_attn_in_self=n_times_crossframe_attn_in_self
+                if not self.disable_self_attn
+                else 0,
+            )
+            + x
+        )
+        x = (
+            self.attn2(
+                self.norm2(x), context=context, additional_tokens=additional_tokens
+            )
+            + x
+        )
+        x = self.ff(self.norm3(x)) + x
+        return x
+
+
+class BasicTransformerSingleLayerBlock(nn.Module):
+    ATTENTION_MODES = {
+        "softmax": CrossAttention,  # vanilla attention
+        "softmax-xformers": MemoryEfficientCrossAttention  # on the A100s not quite as fast as the above version
+        # (todo might depend on head_dim, check, falls back to semi-optimized kernels for dim!=[16,32,64,128])
+    }
+
+    def __init__(
+        self,
+        dim,
+        n_heads,
+        d_head,
+        dropout=0.0,
+        context_dim=None,
+        gated_ff=True,
+        checkpoint=True,
+        attn_mode="softmax",
+    ):
+        super().__init__()
+        assert attn_mode in self.ATTENTION_MODES
+        attn_cls = self.ATTENTION_MODES[attn_mode]
+        self.attn1 = attn_cls(
+            query_dim=dim,
+            heads=n_heads,
+            dim_head=d_head,
+            dropout=dropout,
+            context_dim=context_dim,
+        )
+        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(dim)
+        self.checkpoint = checkpoint
+
+    def forward(self, x, context=None):
+        return checkpoint(
+            self._forward, (x, context), self.parameters(), self.checkpoint
+        )
+
+    def _forward(self, x, context=None):
+        x = self.attn1(self.norm1(x), context=context) + x
+        x = self.ff(self.norm2(x)) + x
+        return x
+
+
+class SpatialTransformer(nn.Module):
+    """
+    Transformer block for image-like data.
+    First, project the input (aka embedding)
+    and reshape to b, t, d.
+    Then apply standard transformer action.
+    Finally, reshape to image
+    NEW: use_linear for more efficiency instead of the 1x1 convs
+    """
+
+    def __init__(
+        self,
+        in_channels,
+        n_heads,
+        d_head,
+        depth=1,
+        dropout=0.0,
+        context_dim=None,
+        disable_self_attn=False,
+        use_linear=False,
+        attn_type="softmax",
+        use_checkpoint=True,
+        # sdp_backend=SDPBackend.FLASH_ATTENTION
+        sdp_backend=None,
+    ):
+        super().__init__()
+        print(
+            f"constructing {self.__class__.__name__} of depth {depth} w/ {in_channels} channels and {n_heads} heads"
+        )
+        from omegaconf import ListConfig
+
+        if exists(context_dim) and not isinstance(context_dim, (list, ListConfig)):
+            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."
+                )
+                # depth does not match context dims.
+                assert all(
+                    map(lambda x: x == context_dim[0], context_dim)
+                ), "need homogenous context_dim to match depth automatically"
+                context_dim = depth * [context_dim[0]]
+        elif context_dim is None:
+            context_dim = [None] * depth
+        self.in_channels = in_channels
+        inner_dim = n_heads * d_head
+        self.norm = Normalize(in_channels)
+        if not use_linear:
+            self.proj_in = nn.Conv2d(
+                in_channels, inner_dim, kernel_size=1, stride=1, padding=0
+            )
+        else:
+            self.proj_in = nn.Linear(in_channels, inner_dim)
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                BasicTransformerBlock(
+                    inner_dim,
+                    n_heads,
+                    d_head,
+                    dropout=dropout,
+                    context_dim=context_dim[d],
+                    disable_self_attn=disable_self_attn,
+                    attn_mode=attn_type,
+                    checkpoint=use_checkpoint,
+                    sdp_backend=sdp_backend,
+                )
+                for d in range(depth)
+            ]
+        )
+        if not use_linear:
+            self.proj_out = zero_module(
+                nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
+            )
+        else:
+            # self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
+            self.proj_out = zero_module(nn.Linear(inner_dim, in_channels))
+        self.use_linear = use_linear
+
+    def forward(self, x, context=None):
+        # note: if no context is given, cross-attention defaults to self-attention
+        if not isinstance(context, list):
+            context = [context]
+        b, c, h, w = x.shape
+        x_in = x
+        x = self.norm(x)
+        if not self.use_linear:
+            x = self.proj_in(x)
+        x = rearrange(x, "b c h w -> b (h w) c").contiguous()
+        if self.use_linear:
+            x = self.proj_in(x)
+        for i, block in enumerate(self.transformer_blocks):
+            if i > 0 and len(context) == 1:
+                i = 0  # use same context for each block
+            x = block(x, context=context[i])
+        if self.use_linear:
+            x = self.proj_out(x)
+        x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w).contiguous()
+        if not self.use_linear:
+            x = self.proj_out(x)
+        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"
+            )
+        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.")

sgm/modules/autoencoding/losses/__init__.py

@@ -0,0 +1,246 @@
+from typing import Any, Union
+
+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

sgm/modules/autoencoding/regularizers/__init__.py

@@ -0,0 +1,53 @@
+from abc import abstractmethod
+from typing import Any, Tuple
+
+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()
+
+
+class DiagonalGaussianRegularizer(AbstractRegularizer):
+    def __init__(self, sample: bool = True):
+        super().__init__()
+        self.sample = sample
+
+    def get_trainable_parameters(self) -> Any:
+        yield from ()
+
+    def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]:
+        log = dict()
+        posterior = DiagonalGaussianDistribution(z)
+        if self.sample:
+            z = posterior.sample()
+        else:
+            z = posterior.mode()
+        kl_loss = posterior.kl()
+        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/diffusionmodules/__init__.py

@@ -0,0 +1,7 @@
+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

@@ -0,0 +1,63 @@
+import torch.nn as nn
+
+from ...util import append_dims, instantiate_from_config
+
+
+class Denoiser(nn.Module):
+    def __init__(self, weighting_config, scaling_config):
+        super().__init__()
+
+        self.weighting = instantiate_from_config(weighting_config)
+        self.scaling = instantiate_from_config(scaling_config)
+
+    def possibly_quantize_sigma(self, sigma):
+        return sigma
+
+    def possibly_quantize_c_noise(self, c_noise):
+        return c_noise
+
+    def w(self, sigma):
+        return self.weighting(sigma)
+
+    def __call__(self, network, input, sigma, cond):
+        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
+
+
+class DiscreteDenoiser(Denoiser):
+    def __init__(
+        self,
+        weighting_config,
+        scaling_config,
+        num_idx,
+        discretization_config,
+        do_append_zero=False,
+        quantize_c_noise=True,
+        flip=True,
+    ):
+        super().__init__(weighting_config, scaling_config)
+        sigmas = instantiate_from_config(discretization_config)(
+            num_idx, do_append_zero=do_append_zero, flip=flip
+        )
+        self.register_buffer("sigmas", sigmas)
+        self.quantize_c_noise = quantize_c_noise
+
+    def sigma_to_idx(self, sigma):
+        dists = sigma - self.sigmas[:, None]
+        return dists.abs().argmin(dim=0).view(sigma.shape)
+
+    def idx_to_sigma(self, idx):
+        return self.sigmas[idx]
+
+    def possibly_quantize_sigma(self, sigma):
+        return self.idx_to_sigma(self.sigma_to_idx(sigma))
+
+    def possibly_quantize_c_noise(self, c_noise):
+        if self.quantize_c_noise:
+            return self.sigma_to_idx(c_noise)
+        else:
+            return c_noise

sgm/modules/diffusionmodules/denoiser_scaling.py

@@ -0,0 +1,31 @@
+import torch
+
+
+class EDMScaling:
+    def __init__(self, sigma_data=0.5):
+        self.sigma_data = sigma_data
+
+    def __call__(self, sigma):
+        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
+        c_noise = 0.25 * sigma.log()
+        return c_skip, c_out, c_in, c_noise
+
+
+class EpsScaling:
+    def __call__(self, sigma):
+        c_skip = torch.ones_like(sigma, device=sigma.device)
+        c_out = -sigma
+        c_in = 1 / (sigma**2 + 1.0) ** 0.5
+        c_noise = sigma.clone()
+        return c_skip, c_out, c_in, c_noise
+
+
+class VScaling:
+    def __call__(self, sigma):
+        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

sgm/modules/diffusionmodules/denoiser_weighting.py

@@ -0,0 +1,24 @@
+import torch
+
+
+class UnitWeighting:
+    def __call__(self, sigma):
+        return torch.ones_like(sigma, device=sigma.device)
+
+
+class EDMWeighting:
+    def __init__(self, sigma_data=0.5):
+        self.sigma_data = sigma_data
+
+    def __call__(self, sigma):
+        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:
+    def __call__(self, sigma):
+        return sigma**-2.0

sgm/modules/diffusionmodules/discretizer.py

@@ -0,0 +1,65 @@
+import torch
+import numpy as np
+from functools import partial
+
+from ...util import append_zero
+from ...modules.diffusionmodules.util import make_beta_schedule
+
+
+class Discretization:
+    def __call__(self, n, do_append_zero=True, device="cuda", flip=False):
+        sigmas = self.get_sigmas(n, device)
+        sigmas = append_zero(sigmas) if do_append_zero else sigmas
+        return sigmas if not flip else torch.flip(sigmas, (0,))
+
+
+class EDMDiscretization(Discretization):
+    def __init__(self, sigma_min=0.02, 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):
+        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)
+        sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** self.rho
+        return sigmas
+
+
+class LegacyDDPMDiscretization(Discretization):
+    def __init__(
+        self,
+        linear_start=0.00085,
+        linear_end=0.0120,
+        num_timesteps=1000,
+        legacy_range=True,
+    ):
+        self.num_timesteps = num_timesteps
+        betas = make_beta_schedule(
+            "linear", num_timesteps, linear_start=linear_start, linear_end=linear_end
+        )
+        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):
+        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:]
+
+            alphas_cumprod = self.alphas_cumprod[timesteps]
+        else:
+            alphas_cumprod = self.alphas_cumprod
+
+        to_torch = partial(torch.tensor, dtype=torch.float32, device=device)
+        sigmas = to_torch((1 - alphas_cumprod) / alphas_cumprod) ** 0.5
+        return torch.flip(sigmas, (0,))

sgm/modules/diffusionmodules/guiders.py

@@ -0,0 +1,53 @@
+from functools import partial
+
+import torch
+
+from ...util import default, instantiate_from_config
+
+
+class VanillaCFG:
+    """
+    implements parallelized CFG
+    """
+
+    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 __call__(self, x, sigma):
+        x_u, x_c = x.chunk(2)
+        scale_value = self.scale_schedule(sigma)
+        x_pred = self.dyn_thresh(x_u, x_c, scale_value)
+        return x_pred
+
+    def prepare_inputs(self, x, s, c, uc):
+        c_out = dict()
+
+        for k in c:
+            if k in ["vector", "crossattn", "concat"]:
+                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 IdentityGuider:
+    def __call__(self, x, sigma):
+        return x
+
+    def prepare_inputs(self, x, s, c, uc):
+        c_out = dict()
+
+        for k in c:
+            c_out[k] = c[k]
+
+        return x, s, c_out

sgm/modules/diffusionmodules/loss.py

@@ -0,0 +1,69 @@
+from typing import List, Optional, Union
+
+import torch
+import torch.nn as nn
+from omegaconf import ListConfig
+from taming.modules.losses.lpips import LPIPS
+
+from ...util import append_dims, instantiate_from_config
+
+
+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,
+    ):
+        super().__init__()
+
+        assert type in ["l2", "l1", "lpips"]
+
+        self.sigma_sampler = instantiate_from_config(sigma_sampler_config)
+
+        self.type = type
+        self.offset_noise_level = offset_noise_level
+
+        if type == "lpips":
+            self.lpips = LPIPS().eval()
+
+        if not batch2model_keys:
+            batch2model_keys = []
+
+        if isinstance(batch2model_keys, str):
+            batch2model_keys = [batch2model_keys]
+
+        self.batch2model_keys = set(batch2model_keys)
+
+    def __call__(self, network, denoiser, conditioner, input, batch):
+        cond = conditioner(batch)
+        additional_model_inputs = {
+            key: batch[key] for key in self.batch2model_keys.intersection(batch)
+        }
+
+        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
+            )
+        noised_input = input + noise * append_dims(sigmas, input.ndim)
+        model_output = denoiser(
+            network, noised_input, sigmas, cond, **additional_model_inputs
+        )
+        w = append_dims(denoiser.w(sigmas), input.ndim)
+        return self.get_loss(model_output, input, w)
+
+    def get_loss(self, model_output, target, w):
+        if self.type == "l2":
+            return torch.mean(
+                (w * (model_output - target) ** 2).reshape(target.shape[0], -1), 1
+            )
+        elif self.type == "l1":
+            return torch.mean(
+                (w * (model_output - target).abs()).reshape(target.shape[0], -1), 1
+            )
+        elif self.type == "lpips":
+            loss = self.lpips(model_output, target).reshape(-1)
+            return loss

sgm/modules/diffusionmodules/model.py

@@ -0,0 +1,743 @@
+# pytorch_diffusion + derived encoder decoder
+import math
+from typing import Any, Callable, Optional
+
+import numpy as np
+import torch
+import torch.nn as nn
+from einops import rearrange
+from packaging import version
+
+try:
+    import xformers
+    import xformers.ops
+
+    XFORMERS_IS_AVAILABLE = True
+except:
+    XFORMERS_IS_AVAILABLE = False
+    print("no module 'xformers'. Processing without...")
+
+from ...modules.attention import LinearAttention, MemoryEfficientCrossAttention
+
+
+def get_timestep_embedding(timesteps, embedding_dim):
+    """
+    This matches the implementation in Denoising Diffusion Probabilistic Models:
+    From Fairseq.
+    Build sinusoidal embeddings.
+    This matches the implementation in tensor2tensor, but differs slightly
+    from the description in Section 3.5 of "Attention Is All You Need".
+    """
+    assert len(timesteps.shape) == 1
+
+    half_dim = embedding_dim // 2
+    emb = math.log(10000) / (half_dim - 1)
+    emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
+    emb = emb.to(device=timesteps.device)
+    emb = timesteps.float()[:, None] * emb[None, :]
+    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+    if embedding_dim % 2 == 1:  # zero pad
+        emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
+    return emb
+
+
+def nonlinearity(x):
+    # swish
+    return x * torch.sigmoid(x)
+
+
+def Normalize(in_channels, num_groups=32):
+    return torch.nn.GroupNorm(
+        num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True
+    )
+
+
+class Upsample(nn.Module):
+    def __init__(self, in_channels, with_conv):
+        super().__init__()
+        self.with_conv = with_conv
+        if self.with_conv:
+            self.conv = torch.nn.Conv2d(
+                in_channels, in_channels, kernel_size=3, stride=1, padding=1
+            )
+
+    def forward(self, x):
+        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
+        if self.with_conv:
+            x = self.conv(x)
+        return x
+
+
+class Downsample(nn.Module):
+    def __init__(self, in_channels, with_conv):
+        super().__init__()
+        self.with_conv = with_conv
+        if self.with_conv:
+            # no asymmetric padding in torch conv, must do it ourselves
+            self.conv = torch.nn.Conv2d(
+                in_channels, in_channels, kernel_size=3, stride=2, padding=0
+            )
+
+    def forward(self, x):
+        if self.with_conv:
+            pad = (0, 1, 0, 1)
+            x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
+            x = self.conv(x)
+        else:
+            x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
+        return x
+
+
+class ResnetBlock(nn.Module):
+    def __init__(
+        self,
+        *,
+        in_channels,
+        out_channels=None,
+        conv_shortcut=False,
+        dropout,
+        temb_channels=512,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+        self.out_channels = out_channels
+        self.use_conv_shortcut = conv_shortcut
+
+        self.norm1 = Normalize(in_channels)
+        self.conv1 = torch.nn.Conv2d(
+            in_channels, out_channels, kernel_size=3, stride=1, padding=1
+        )
+        if temb_channels > 0:
+            self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
+        self.norm2 = Normalize(out_channels)
+        self.dropout = torch.nn.Dropout(dropout)
+        self.conv2 = torch.nn.Conv2d(
+            out_channels, out_channels, kernel_size=3, stride=1, padding=1
+        )
+        if self.in_channels != self.out_channels:
+            if self.use_conv_shortcut:
+                self.conv_shortcut = torch.nn.Conv2d(
+                    in_channels, out_channels, kernel_size=3, stride=1, padding=1
+                )
+            else:
+                self.nin_shortcut = torch.nn.Conv2d(
+                    in_channels, out_channels, kernel_size=1, stride=1, padding=0
+                )
+
+    def forward(self, x, temb):
+        h = x
+        h = self.norm1(h)
+        h = nonlinearity(h)
+        h = self.conv1(h)
+
+        if temb is not None:
+            h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None]
+
+        h = self.norm2(h)
+        h = nonlinearity(h)
+        h = self.dropout(h)
+        h = self.conv2(h)
+
+        if self.in_channels != self.out_channels:
+            if self.use_conv_shortcut:
+                x = self.conv_shortcut(x)
+            else:
+                x = self.nin_shortcut(x)
+
+        return x + h
+
+
+class LinAttnBlock(LinearAttention):
+    """to match AttnBlock usage"""
+
+    def __init__(self, in_channels):
+        super().__init__(dim=in_channels, heads=1, dim_head=in_channels)
+
+
+class AttnBlock(nn.Module):
+    def __init__(self, in_channels):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = Normalize(in_channels)
+        self.q = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.k = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.v = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.proj_out = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+
+    def attention(self, h_: torch.Tensor) -> torch.Tensor:
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        b, c, h, w = q.shape
+        q, k, v = map(
+            lambda x: rearrange(x, "b c h w -> b 1 (h w) c").contiguous(), (q, k, v)
+        )
+        h_ = torch.nn.functional.scaled_dot_product_attention(
+            q, k, v
+        )  # scale is dim ** -0.5 per default
+        # compute attention
+
+        return rearrange(h_, "b 1 (h w) c -> b c h w", h=h, w=w, c=c, b=b)
+
+    def forward(self, x, **kwargs):
+        h_ = x
+        h_ = self.attention(h_)
+        h_ = self.proj_out(h_)
+        return x + h_
+
+
+class MemoryEfficientAttnBlock(nn.Module):
+    """
+    Uses xformers efficient implementation,
+    see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
+    Note: this is a single-head self-attention operation
+    """
+
+    #
+    def __init__(self, in_channels):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = Normalize(in_channels)
+        self.q = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.k = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.v = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.proj_out = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.attention_op: Optional[Any] = None
+
+    def attention(self, h_: torch.Tensor) -> torch.Tensor:
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        B, C, H, W = q.shape
+        q, k, v = map(lambda x: rearrange(x, "b c h w -> b (h w) c"), (q, k, v))
+
+        q, k, v = map(
+            lambda t: t.unsqueeze(3)
+            .reshape(B, t.shape[1], 1, C)
+            .permute(0, 2, 1, 3)
+            .reshape(B * 1, t.shape[1], C)
+            .contiguous(),
+            (q, k, v),
+        )
+        out = xformers.ops.memory_efficient_attention(
+            q, k, v, attn_bias=None, op=self.attention_op
+        )
+
+        out = (
+            out.unsqueeze(0)
+            .reshape(B, 1, out.shape[1], C)
+            .permute(0, 2, 1, 3)
+            .reshape(B, out.shape[1], C)
+        )
+        return rearrange(out, "b (h w) c -> b c h w", b=B, h=H, w=W, c=C)
+
+    def forward(self, x, **kwargs):
+        h_ = x
+        h_ = self.attention(h_)
+        h_ = self.proj_out(h_)
+        return x + h_
+
+
+class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention):
+    def forward(self, x, context=None, mask=None, **unused_kwargs):
+        b, c, h, w = x.shape
+        x = rearrange(x, "b c h w -> b (h w) c")
+        out = super().forward(x, context=context, mask=mask)
+        out = rearrange(out, "b (h w) c -> b c h w", h=h, w=w, c=c)
+        return x + out
+
+
+def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
+    assert attn_type in [
+        "vanilla",
+        "vanilla-xformers",
+        "memory-efficient-cross-attn",
+        "linear",
+        "none",
+    ], f"attn_type {attn_type} unknown"
+    if (
+        version.parse(torch.__version__) < version.parse("2.0.0")
+        and attn_type != "none"
+    ):
+        assert XFORMERS_IS_AVAILABLE, (
+            f"We do not support vanilla attention in {torch.__version__} anymore, "
+            f"as it is too expensive. Please install xformers via e.g. 'pip install xformers==0.0.16'"
+        )
+        attn_type = "vanilla-xformers"
+    print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
+    if attn_type == "vanilla":
+        assert attn_kwargs is None
+        return AttnBlock(in_channels)
+    elif attn_type == "vanilla-xformers":
+        print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...")
+        return MemoryEfficientAttnBlock(in_channels)
+    elif type == "memory-efficient-cross-attn":
+        attn_kwargs["query_dim"] = in_channels
+        return MemoryEfficientCrossAttentionWrapper(**attn_kwargs)
+    elif attn_type == "none":
+        return nn.Identity(in_channels)
+    else:
+        return LinAttnBlock(in_channels)
+
+
+class Model(nn.Module):
+    def __init__(
+        self,
+        *,
+        ch,
+        out_ch,
+        ch_mult=(1, 2, 4, 8),
+        num_res_blocks,
+        attn_resolutions,
+        dropout=0.0,
+        resamp_with_conv=True,
+        in_channels,
+        resolution,
+        use_timestep=True,
+        use_linear_attn=False,
+        attn_type="vanilla",
+    ):
+        super().__init__()
+        if use_linear_attn:
+            attn_type = "linear"
+        self.ch = ch
+        self.temb_ch = self.ch * 4
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+
+        self.use_timestep = use_timestep
+        if self.use_timestep:
+            # timestep embedding
+            self.temb = nn.Module()
+            self.temb.dense = nn.ModuleList(
+                [
+                    torch.nn.Linear(self.ch, self.temb_ch),
+                    torch.nn.Linear(self.temb_ch, self.temb_ch),
+                ]
+            )
+
+        # downsampling
+        self.conv_in = torch.nn.Conv2d(
+            in_channels, self.ch, kernel_size=3, stride=1, padding=1
+        )
+
+        curr_res = resolution
+        in_ch_mult = (1,) + tuple(ch_mult)
+        self.down = nn.ModuleList()
+        for i_level in range(self.num_resolutions):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_in = ch * in_ch_mult[i_level]
+            block_out = ch * ch_mult[i_level]
+            for i_block in range(self.num_res_blocks):
+                block.append(
+                    ResnetBlock(
+                        in_channels=block_in,
+                        out_channels=block_out,
+                        temb_channels=self.temb_ch,
+                        dropout=dropout,
+                    )
+                )
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(make_attn(block_in, attn_type=attn_type))
+            down = nn.Module()
+            down.block = block
+            down.attn = attn
+            if i_level != self.num_resolutions - 1:
+                down.downsample = Downsample(block_in, resamp_with_conv)
+                curr_res = curr_res // 2
+            self.down.append(down)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(
+            in_channels=block_in,
+            out_channels=block_in,
+            temb_channels=self.temb_ch,
+            dropout=dropout,
+        )
+        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
+        self.mid.block_2 = ResnetBlock(
+            in_channels=block_in,
+            out_channels=block_in,
+            temb_channels=self.temb_ch,
+            dropout=dropout,
+        )
+
+        # upsampling
+        self.up = nn.ModuleList()
+        for i_level in reversed(range(self.num_resolutions)):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_out = ch * ch_mult[i_level]
+            skip_in = ch * ch_mult[i_level]
+            for i_block in range(self.num_res_blocks + 1):
+                if i_block == self.num_res_blocks:
+                    skip_in = ch * in_ch_mult[i_level]
+                block.append(
+                    ResnetBlock(
+                        in_channels=block_in + skip_in,
+                        out_channels=block_out,
+                        temb_channels=self.temb_ch,
+                        dropout=dropout,
+                    )
+                )
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(make_attn(block_in, attn_type=attn_type))
+            up = nn.Module()
+            up.block = block
+            up.attn = attn
+            if i_level != 0:
+                up.upsample = Upsample(block_in, resamp_with_conv)
+                curr_res = curr_res * 2
+            self.up.insert(0, up)  # prepend to get consistent order
+
+        # end
+        self.norm_out = Normalize(block_in)
+        self.conv_out = torch.nn.Conv2d(
+            block_in, out_ch, kernel_size=3, stride=1, padding=1
+        )
+
+    def forward(self, x, t=None, context=None):
+        # assert x.shape[2] == x.shape[3] == self.resolution
+        if context is not None:
+            # assume aligned context, cat along channel axis
+            x = torch.cat((x, context), dim=1)
+        if self.use_timestep:
+            # timestep embedding
+            assert t is not None
+            temb = get_timestep_embedding(t, self.ch)
+            temb = self.temb.dense[0](temb)
+            temb = nonlinearity(temb)
+            temb = self.temb.dense[1](temb)
+        else:
+            temb = None
+
+        # downsampling
+        hs = [self.conv_in(x)]
+        for i_level in range(self.num_resolutions):
+            for i_block in range(self.num_res_blocks):
+                h = self.down[i_level].block[i_block](hs[-1], temb)
+                if len(self.down[i_level].attn) > 0:
+                    h = self.down[i_level].attn[i_block](h)
+                hs.append(h)
+            if i_level != self.num_resolutions - 1:
+                hs.append(self.down[i_level].downsample(hs[-1]))
+
+        # middle
+        h = hs[-1]
+        h = self.mid.block_1(h, temb)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h, temb)
+
+        # upsampling
+        for i_level in reversed(range(self.num_resolutions)):
+            for i_block in range(self.num_res_blocks + 1):
+                h = self.up[i_level].block[i_block](
+                    torch.cat([h, hs.pop()], dim=1), temb
+                )
+                if len(self.up[i_level].attn) > 0:
+                    h = self.up[i_level].attn[i_block](h)
+            if i_level != 0:
+                h = self.up[i_level].upsample(h)
+
+        # end
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h)
+        return h
+
+    def get_last_layer(self):
+        return self.conv_out.weight
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        *,
+        ch,
+        out_ch,
+        ch_mult=(1, 2, 4, 8),
+        num_res_blocks,
+        attn_resolutions,
+        dropout=0.0,
+        resamp_with_conv=True,
+        in_channels,
+        resolution,
+        z_channels,
+        double_z=True,
+        use_linear_attn=False,
+        attn_type="vanilla",
+        **ignore_kwargs,
+    ):
+        super().__init__()
+        if use_linear_attn:
+            attn_type = "linear"
+        self.ch = ch
+        self.temb_ch = 0
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+
+        # downsampling
+        self.conv_in = torch.nn.Conv2d(
+            in_channels, self.ch, kernel_size=3, stride=1, padding=1
+        )
+
+        curr_res = resolution
+        in_ch_mult = (1,) + tuple(ch_mult)
+        self.in_ch_mult = in_ch_mult
+        self.down = nn.ModuleList()
+        for i_level in range(self.num_resolutions):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_in = ch * in_ch_mult[i_level]
+            block_out = ch * ch_mult[i_level]
+            for i_block in range(self.num_res_blocks):
+                block.append(
+                    ResnetBlock(
+                        in_channels=block_in,
+                        out_channels=block_out,
+                        temb_channels=self.temb_ch,
+                        dropout=dropout,
+                    )
+                )
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(make_attn(block_in, attn_type=attn_type))
+            down = nn.Module()
+            down.block = block
+            down.attn = attn
+            if i_level != self.num_resolutions - 1:
+                down.downsample = Downsample(block_in, resamp_with_conv)
+                curr_res = curr_res // 2
+            self.down.append(down)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(
+            in_channels=block_in,
+            out_channels=block_in,
+            temb_channels=self.temb_ch,
+            dropout=dropout,
+        )
+        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
+        self.mid.block_2 = ResnetBlock(
+            in_channels=block_in,
+            out_channels=block_in,
+            temb_channels=self.temb_ch,
+            dropout=dropout,
+        )
+
+        # end
+        self.norm_out = Normalize(block_in)
+        self.conv_out = torch.nn.Conv2d(
+            block_in,
+            2 * z_channels if double_z else z_channels,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+        )
+
+    def forward(self, x):
+        # timestep embedding
+        temb = None
+
+        # downsampling
+        hs = [self.conv_in(x)]
+        for i_level in range(self.num_resolutions):
+            for i_block in range(self.num_res_blocks):
+                h = self.down[i_level].block[i_block](hs[-1], temb)
+                if len(self.down[i_level].attn) > 0:
+                    h = self.down[i_level].attn[i_block](h)
+                hs.append(h)
+            if i_level != self.num_resolutions - 1:
+                hs.append(self.down[i_level].downsample(hs[-1]))
+
+        # middle
+        h = hs[-1]
+        h = self.mid.block_1(h, temb)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h, temb)
+
+        # end
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h)
+        return h
+
+
+class Decoder(nn.Module):
+    def __init__(
+        self,
+        *,
+        ch,
+        out_ch,
+        ch_mult=(1, 2, 4, 8),
+        num_res_blocks,
+        attn_resolutions,
+        dropout=0.0,
+        resamp_with_conv=True,
+        in_channels,
+        resolution,
+        z_channels,
+        give_pre_end=False,
+        tanh_out=False,
+        use_linear_attn=False,
+        attn_type="vanilla",
+        **ignorekwargs,
+    ):
+        super().__init__()
+        if use_linear_attn:
+            attn_type = "linear"
+        self.ch = ch
+        self.temb_ch = 0
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+        self.give_pre_end = give_pre_end
+        self.tanh_out = tanh_out
+
+        # compute in_ch_mult, block_in and curr_res at lowest res
+        in_ch_mult = (1,) + tuple(ch_mult)
+        block_in = ch * ch_mult[self.num_resolutions - 1]
+        curr_res = resolution // 2 ** (self.num_resolutions - 1)
+        self.z_shape = (1, z_channels, curr_res, curr_res)
+        print(
+            "Working with z of shape {} = {} dimensions.".format(
+                self.z_shape, np.prod(self.z_shape)
+            )
+        )
+
+        make_attn_cls = self._make_attn()
+        make_resblock_cls = self._make_resblock()
+        make_conv_cls = self._make_conv()
+        # z to block_in
+        self.conv_in = torch.nn.Conv2d(
+            z_channels, block_in, kernel_size=3, stride=1, padding=1
+        )
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = make_resblock_cls(
+            in_channels=block_in,
+            out_channels=block_in,
+            temb_channels=self.temb_ch,
+            dropout=dropout,
+        )
+        self.mid.attn_1 = make_attn_cls(block_in, attn_type=attn_type)
+        self.mid.block_2 = make_resblock_cls(
+            in_channels=block_in,
+            out_channels=block_in,
+            temb_channels=self.temb_ch,
+            dropout=dropout,
+        )
+
+        # upsampling
+        self.up = nn.ModuleList()
+        for i_level in reversed(range(self.num_resolutions)):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_out = ch * ch_mult[i_level]
+            for i_block in range(self.num_res_blocks + 1):
+                block.append(
+                    make_resblock_cls(
+                        in_channels=block_in,
+                        out_channels=block_out,
+                        temb_channels=self.temb_ch,
+                        dropout=dropout,
+                    )
+                )
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(make_attn_cls(block_in, attn_type=attn_type))
+            up = nn.Module()
+            up.block = block
+            up.attn = attn
+            if i_level != 0:
+                up.upsample = Upsample(block_in, resamp_with_conv)
+                curr_res = curr_res * 2
+            self.up.insert(0, up)  # prepend to get consistent order
+
+        # end
+        self.norm_out = Normalize(block_in)
+        self.conv_out = make_conv_cls(
+            block_in, out_ch, kernel_size=3, stride=1, padding=1
+        )
+
+    def _make_attn(self) -> Callable:
+        return make_attn
+
+    def _make_resblock(self) -> Callable:
+        return ResnetBlock
+
+    def _make_conv(self) -> Callable:
+        return torch.nn.Conv2d
+
+    def get_last_layer(self, **kwargs):
+        return self.conv_out.weight
+
+    def forward(self, z, **kwargs):
+        # assert z.shape[1:] == self.z_shape[1:]
+        self.last_z_shape = z.shape
+
+        # timestep embedding
+        temb = None
+
+        # z to block_in
+        h = self.conv_in(z)
+
+        # middle
+        h = self.mid.block_1(h, temb, **kwargs)
+        h = self.mid.attn_1(h, **kwargs)
+        h = self.mid.block_2(h, temb, **kwargs)
+
+        # upsampling
+        for i_level in reversed(range(self.num_resolutions)):
+            for i_block in range(self.num_res_blocks + 1):
+                h = self.up[i_level].block[i_block](h, temb, **kwargs)
+                if len(self.up[i_level].attn) > 0:
+                    h = self.up[i_level].attn[i_block](h, **kwargs)
+            if i_level != 0:
+                h = self.up[i_level].upsample(h)
+
+        # end
+        if self.give_pre_end:
+            return h
+
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h, **kwargs)
+        if self.tanh_out:
+            h = torch.tanh(h)
+        return h

sgm/modules/diffusionmodules/sampling.py

@@ -0,0 +1,365 @@
+"""
+    Partially ported from https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/sampling.py
+"""
+
+
+from typing import Dict, Union
+
+import torch
+from omegaconf import ListConfig, OmegaConf
+from tqdm import tqdm
+
+from ...modules.diffusionmodules.sampling_utils import (
+    get_ancestral_step,
+    linear_multistep_coeff,
+    to_d,
+    to_neg_log_sigma,
+    to_sigma,
+)
+from ...util import append_dims, default, instantiate_from_config
+
+DEFAULT_GUIDER = {"target": "sgm.modules.diffusionmodules.guiders.IdentityGuider"}
+
+
+class BaseDiffusionSampler:
+    def __init__(
+        self,
+        discretization_config: Union[Dict, ListConfig, OmegaConf],
+        num_steps: Union[int, None] = None,
+        guider_config: Union[Dict, ListConfig, OmegaConf, None] = None,
+        verbose: bool = False,
+        device: str = "cuda",
+    ):
+        self.num_steps = num_steps
+        self.discretization = instantiate_from_config(discretization_config)
+        self.guider = instantiate_from_config(
+            default(
+                guider_config,
+                DEFAULT_GUIDER,
+            )
+        )
+        self.verbose = verbose
+        self.device = device
+
+    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
+        )
+        uc = default(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 denoise(self, x, denoiser, sigma, cond, uc):
+        denoised = denoiser(*self.guider.prepare_inputs(x, sigma, cond, uc))
+        denoised = self.guider(denoised, sigma)
+        return denoised
+
+    def get_sigma_gen(self, num_sigmas):
+        sigma_generator = range(num_sigmas - 1)
+        if self.verbose:
+            print("#" * 30, " Sampling setting ", "#" * 30)
+            print(f"Sampler: {self.__class__.__name__}")
+            print(f"Discretization: {self.discretization.__class__.__name__}")
+            print(f"Guider: {self.guider.__class__.__name__}")
+            sigma_generator = tqdm(
+                sigma_generator,
+                total=num_sigmas,
+                desc=f"Sampling with {self.__class__.__name__} for {num_sigmas} steps",
+            )
+        return sigma_generator
+
+
+class SingleStepDiffusionSampler(BaseDiffusionSampler):
+    def sampler_step(self, sigma, next_sigma, denoiser, x, cond, uc, *args, **kwargs):
+        raise NotImplementedError
+
+    def euler_step(self, x, d, dt):
+        return x + dt * d
+
+
+class EDMSampler(SingleStepDiffusionSampler):
+    def __init__(
+        self, s_churn=0.0, s_tmin=0.0, s_tmax=float("inf"), s_noise=1.0, *args, **kwargs
+    ):
+        super().__init__(*args, **kwargs)
+
+        self.s_churn = s_churn
+        self.s_tmin = s_tmin
+        self.s_tmax = s_tmax
+        self.s_noise = s_noise
+
+    def sampler_step(self, sigma, next_sigma, denoiser, x, cond, uc=None, gamma=0.0):
+        sigma_hat = sigma * (gamma + 1.0)
+        if gamma > 0:
+            eps = torch.randn_like(x) * self.s_noise
+            x = x + eps * append_dims(sigma_hat**2 - sigma**2, x.ndim) ** 0.5
+
+        denoised = self.denoise(x, denoiser, sigma_hat, cond, uc)
+        d = to_d(x, sigma_hat, denoised)
+        dt = append_dims(next_sigma - sigma_hat, x.ndim)
+
+        euler_step = self.euler_step(x, d, dt)
+        x = self.possible_correction_step(
+            euler_step, x, d, dt, next_sigma, denoiser, cond, uc
+        )
+        return x
+
+    def __call__(self, denoiser, x, cond, uc=None, num_steps=None):
+        x, s_in, sigmas, num_sigmas, cond, uc = self.prepare_sampling_loop(
+            x, cond, uc, num_steps
+        )
+
+        for i in self.get_sigma_gen(num_sigmas):
+            gamma = (
+                min(self.s_churn / (num_sigmas - 1), 2**0.5 - 1)
+                if self.s_tmin <= sigmas[i] <= self.s_tmax
+                else 0.0
+            )
+            x = self.sampler_step(
+                s_in * sigmas[i],
+                s_in * sigmas[i + 1],
+                denoiser,
+                x,
+                cond,
+                uc,
+                gamma,
+            )
+
+        return x
+
+
+class AncestralSampler(SingleStepDiffusionSampler):
+    def __init__(self, eta=1.0, s_noise=1.0, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        self.eta = eta
+        self.s_noise = s_noise
+        self.noise_sampler = lambda x: torch.randn_like(x)
+
+    def ancestral_euler_step(self, x, denoised, sigma, sigma_down):
+        d = to_d(x, sigma, denoised)
+        dt = append_dims(sigma_down - sigma, x.ndim)
+
+        return self.euler_step(x, d, dt)
+
+    def ancestral_step(self, x, sigma, next_sigma, sigma_up):
+        x = torch.where(
+            append_dims(next_sigma, x.ndim) > 0.0,
+            x + self.noise_sampler(x) * self.s_noise * append_dims(sigma_up, x.ndim),
+            x,
+        )
+        return x
+
+    def __call__(self, denoiser, x, cond, uc=None, num_steps=None):
+        x, s_in, sigmas, num_sigmas, cond, uc = self.prepare_sampling_loop(
+            x, cond, uc, num_steps
+        )
+
+        for i in self.get_sigma_gen(num_sigmas):
+            x = self.sampler_step(
+                s_in * sigmas[i],
+                s_in * sigmas[i + 1],
+                denoiser,
+                x,
+                cond,
+                uc,
+            )
+
+        return x
+
+
+class LinearMultistepSampler(BaseDiffusionSampler):
+    def __init__(
+        self,
+        order=4,
+        *args,
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+
+        self.order = order
+
+    def __call__(self, denoiser, x, cond, uc=None, num_steps=None, **kwargs):
+        x, s_in, sigmas, num_sigmas, cond, uc = self.prepare_sampling_loop(
+            x, cond, uc, num_steps
+        )
+
+        ds = []
+        sigmas_cpu = sigmas.detach().cpu().numpy()
+        for i in self.get_sigma_gen(num_sigmas):
+            sigma = s_in * sigmas[i]
+            denoised = denoiser(
+                *self.guider.prepare_inputs(x, sigma, cond, uc), **kwargs
+            )
+            denoised = self.guider(denoised, sigma)
+            d = to_d(x, sigma, denoised)
+            ds.append(d)
+            if len(ds) > self.order:
+                ds.pop(0)
+            cur_order = min(i + 1, self.order)
+            coeffs = [
+                linear_multistep_coeff(cur_order, sigmas_cpu, i, j)
+                for j in range(cur_order)
+            ]
+            x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds)))
+
+        return x
+
+
+class EulerEDMSampler(EDMSampler):
+    def possible_correction_step(
+        self, euler_step, x, d, dt, next_sigma, denoiser, cond, uc
+    ):
+        return euler_step
+
+
+class HeunEDMSampler(EDMSampler):
+    def possible_correction_step(
+        self, euler_step, x, d, dt, next_sigma, denoiser, cond, uc
+    ):
+        if torch.sum(next_sigma) < 1e-14:
+            # Save a network evaluation if all noise levels are 0
+            return euler_step
+        else:
+            denoised = self.denoise(euler_step, denoiser, next_sigma, cond, uc)
+            d_new = to_d(euler_step, next_sigma, denoised)
+            d_prime = (d + d_new) / 2.0
+
+            # apply correction if noise level is not 0
+            x = torch.where(
+                append_dims(next_sigma, x.ndim) > 0.0, x + d_prime * dt, euler_step
+            )
+            return x
+
+
+class EulerAncestralSampler(AncestralSampler):
+    def sampler_step(self, sigma, next_sigma, denoiser, x, cond, uc):
+        sigma_down, sigma_up = get_ancestral_step(sigma, next_sigma, eta=self.eta)
+        denoised = self.denoise(x, denoiser, sigma, cond, uc)
+        x = self.ancestral_euler_step(x, denoised, sigma, sigma_down)
+        x = self.ancestral_step(x, sigma, next_sigma, sigma_up)
+
+        return x
+
+
+class DPMPP2SAncestralSampler(AncestralSampler):
+    def get_variables(self, sigma, sigma_down):
+        t, t_next = [to_neg_log_sigma(s) for s in (sigma, sigma_down)]
+        h = t_next - t
+        s = t + 0.5 * h
+        return h, s, t, t_next
+
+    def get_mult(self, h, s, t, t_next):
+        mult1 = to_sigma(s) / to_sigma(t)
+        mult2 = (-0.5 * h).expm1()
+        mult3 = to_sigma(t_next) / to_sigma(t)
+        mult4 = (-h).expm1()
+
+        return mult1, mult2, mult3, mult4
+
+    def sampler_step(self, sigma, next_sigma, denoiser, x, cond, uc=None, **kwargs):
+        sigma_down, sigma_up = get_ancestral_step(sigma, next_sigma, eta=self.eta)
+        denoised = self.denoise(x, denoiser, sigma, cond, uc)
+        x_euler = self.ancestral_euler_step(x, denoised, sigma, sigma_down)
+
+        if torch.sum(sigma_down) < 1e-14:
+            # Save a network evaluation if all noise levels are 0
+            x = x_euler
+        else:
+            h, s, t, t_next = self.get_variables(sigma, sigma_down)
+            mult = [
+                append_dims(mult, x.ndim) for mult in self.get_mult(h, s, t, t_next)
+            ]
+
+            x2 = mult[0] * x - mult[1] * denoised
+            denoised2 = self.denoise(x2, denoiser, to_sigma(s), cond, uc)
+            x_dpmpp2s = mult[2] * x - mult[3] * denoised2
+
+            # apply correction if noise level is not 0
+            x = torch.where(append_dims(sigma_down, x.ndim) > 0.0, x_dpmpp2s, x_euler)
+
+        x = self.ancestral_step(x, sigma, next_sigma, sigma_up)
+        return x
+
+
+class DPMPP2MSampler(BaseDiffusionSampler):
+    def get_variables(self, sigma, next_sigma, previous_sigma=None):
+        t, t_next = [to_neg_log_sigma(s) for s in (sigma, next_sigma)]
+        h = t_next - t
+
+        if previous_sigma is not None:
+            h_last = t - to_neg_log_sigma(previous_sigma)
+            r = h_last / h
+            return h, r, t, t_next
+        else:
+            return h, None, t, t_next
+
+    def get_mult(self, h, r, t, t_next, previous_sigma):
+        mult1 = to_sigma(t_next) / to_sigma(t)
+        mult2 = (-h).expm1()
+
+        if previous_sigma is not None:
+            mult3 = 1 + 1 / (2 * r)
+            mult4 = 1 / (2 * r)
+            return mult1, mult2, mult3, mult4
+        else:
+            return mult1, mult2
+
+    def sampler_step(
+        self,
+        old_denoised,
+        previous_sigma,
+        sigma,
+        next_sigma,
+        denoiser,
+        x,
+        cond,
+        uc=None,
+    ):
+        denoised = self.denoise(x, denoiser, sigma, cond, uc)
+
+        h, r, t, t_next = self.get_variables(sigma, next_sigma, previous_sigma)
+        mult = [
+            append_dims(mult, x.ndim)
+            for mult in self.get_mult(h, r, t, t_next, previous_sigma)
+        ]
+
+        x_standard = mult[0] * x - mult[1] * denoised
+        if old_denoised is None or torch.sum(next_sigma) < 1e-14:
+            # Save a network evaluation if all noise levels are 0 or on the first step
+            return x_standard, denoised
+        else:
+            denoised_d = mult[2] * denoised - mult[3] * old_denoised
+            x_advanced = mult[0] * x - mult[1] * denoised_d
+
+            # apply correction if noise level is not 0 and not first step
+            x = torch.where(
+                append_dims(next_sigma, x.ndim) > 0.0, x_advanced, x_standard
+            )
+
+        return x, denoised
+
+    def __call__(self, denoiser, x, cond, uc=None, num_steps=None, **kwargs):
+        x, s_in, sigmas, num_sigmas, cond, uc = self.prepare_sampling_loop(
+            x, cond, uc, num_steps
+        )
+
+        old_denoised = None
+        for i in self.get_sigma_gen(num_sigmas):
+            x, old_denoised = self.sampler_step(
+                old_denoised,
+                None if i == 0 else s_in * sigmas[i - 1],
+                s_in * sigmas[i],
+                s_in * sigmas[i + 1],
+                denoiser,
+                x,
+                cond,
+                uc=uc,
+            )
+
+        return x

sgm/modules/diffusionmodules/sampling_utils.py

@@ -0,0 +1,48 @@
+import torch
+from scipy import integrate
+
+from ...util import append_dims
+
+
+class NoDynamicThresholding:
+    def __call__(self, uncond, cond, scale):
+        return uncond + scale * (cond - uncond)
+
+
+def linear_multistep_coeff(order, t, i, j, epsrel=1e-4):
+    if order - 1 > i:
+        raise ValueError(f"Order {order} too high for step {i}")
+
+    def fn(tau):
+        prod = 1.0
+        for k in range(order):
+            if j == k:
+                continue
+            prod *= (tau - t[i - k]) / (t[i - j] - t[i - k])
+        return prod
+
+    return integrate.quad(fn, t[i], t[i + 1], epsrel=epsrel)[0]
+
+
+def get_ancestral_step(sigma_from, sigma_to, eta=1.0):
+    if not eta:
+        return sigma_to, 0.0
+    sigma_up = torch.minimum(
+        sigma_to,
+        eta
+        * (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5,
+    )
+    sigma_down = (sigma_to**2 - sigma_up**2) ** 0.5
+    return sigma_down, sigma_up
+
+
+def to_d(x, sigma, denoised):
+    return (x - denoised) / append_dims(sigma, x.ndim)
+
+
+def to_neg_log_sigma(sigma):
+    return sigma.log().neg()
+
+
+def to_sigma(neg_log_sigma):
+    return neg_log_sigma.neg().exp()

sgm/modules/diffusionmodules/sigma_sampling.py

@@ -0,0 +1,31 @@
+import torch
+
+from ...util import default, instantiate_from_config
+
+
+class EDMSampling:
+    def __init__(self, p_mean=-1.2, p_std=1.2):
+        self.p_mean = p_mean
+        self.p_std = p_std
+
+    def __call__(self, n_samples, rand=None):
+        log_sigma = self.p_mean + self.p_std * default(rand, torch.randn((n_samples,)))
+        return log_sigma.exp()
+
+
+class DiscreteSampling:
+    def __init__(self, discretization_config, num_idx, do_append_zero=False, flip=True):
+        self.num_idx = num_idx
+        self.sigmas = instantiate_from_config(discretization_config)(
+            num_idx, do_append_zero=do_append_zero, flip=flip
+        )
+
+    def idx_to_sigma(self, idx):
+        return self.sigmas[idx]
+
+    def __call__(self, n_samples, rand=None):
+        idx = default(
+            rand,
+            torch.randint(0, self.num_idx, (n_samples,)),
+        )
+        return self.idx_to_sigma(idx)

sgm/modules/diffusionmodules/util.py

@@ -0,0 +1,308 @@
+"""
+adopted from
+https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
+and
+https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
+and
+https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
+
+thanks!
+"""
+
+import math
+
+import torch
+import torch.nn as nn
+from einops import repeat
+
+
+def make_beta_schedule(
+    schedule,
+    n_timestep,
+    linear_start=1e-4,
+    linear_end=2e-2,
+):
+    if schedule == "linear":
+        betas = (
+            torch.linspace(
+                linear_start**0.5, linear_end**0.5, n_timestep, dtype=torch.float64
+            )
+            ** 2
+        )
+    return betas.numpy()
+
+
+def extract_into_tensor(a, t, x_shape):
+    b, *_ = t.shape
+    out = a.gather(-1, t)
+    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
+
+
+def mixed_checkpoint(func, inputs: dict, params, flag):
+    """
+    Evaluate a function without caching intermediate activations, allowing for
+    reduced memory at the expense of extra compute in the backward pass. This differs from the original checkpoint function
+    borrowed from https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py in that
+    it also works with non-tensor inputs
+    :param func: the function to evaluate.
+    :param inputs: the argument dictionary to pass to `func`.
+    :param params: a sequence of parameters `func` depends on but does not
+                   explicitly take as arguments.
+    :param flag: if False, disable gradient checkpointing.
+    """
+    if flag:
+        tensor_keys = [key for key in inputs if isinstance(inputs[key], torch.Tensor)]
+        tensor_inputs = [
+            inputs[key] for key in inputs if isinstance(inputs[key], torch.Tensor)
+        ]
+        non_tensor_keys = [
+            key for key in inputs if not isinstance(inputs[key], torch.Tensor)
+        ]
+        non_tensor_inputs = [
+            inputs[key] for key in inputs if not isinstance(inputs[key], torch.Tensor)
+        ]
+        args = tuple(tensor_inputs) + tuple(non_tensor_inputs) + tuple(params)
+        return MixedCheckpointFunction.apply(
+            func,
+            len(tensor_inputs),
+            len(non_tensor_inputs),
+            tensor_keys,
+            non_tensor_keys,
+            *args,
+        )
+    else:
+        return func(**inputs)
+
+
+class MixedCheckpointFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx,
+        run_function,
+        length_tensors,
+        length_non_tensors,
+        tensor_keys,
+        non_tensor_keys,
+        *args,
+    ):
+        ctx.end_tensors = length_tensors
+        ctx.end_non_tensors = length_tensors + length_non_tensors
+        ctx.gpu_autocast_kwargs = {
+            "enabled": torch.is_autocast_enabled(),
+            "dtype": torch.get_autocast_gpu_dtype(),
+            "cache_enabled": torch.is_autocast_cache_enabled(),
+        }
+        assert (
+            len(tensor_keys) == length_tensors
+            and len(non_tensor_keys) == length_non_tensors
+        )
+
+        ctx.input_tensors = {
+            key: val for (key, val) in zip(tensor_keys, list(args[: ctx.end_tensors]))
+        }
+        ctx.input_non_tensors = {
+            key: val
+            for (key, val) in zip(
+                non_tensor_keys, list(args[ctx.end_tensors : ctx.end_non_tensors])
+            )
+        }
+        ctx.run_function = run_function
+        ctx.input_params = list(args[ctx.end_non_tensors :])
+
+        with torch.no_grad():
+            output_tensors = ctx.run_function(
+                **ctx.input_tensors, **ctx.input_non_tensors
+            )
+        return output_tensors
+
+    @staticmethod
+    def backward(ctx, *output_grads):
+        # additional_args = {key: ctx.input_tensors[key] for key in ctx.input_tensors if not isinstance(ctx.input_tensors[key],torch.Tensor)}
+        ctx.input_tensors = {
+            key: ctx.input_tensors[key].detach().requires_grad_(True)
+            for key in ctx.input_tensors
+        }
+
+        with torch.enable_grad(), torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs):
+            # Fixes a bug where the first op in run_function modifies the
+            # Tensor storage in place, which is not allowed for detach()'d
+            # Tensors.
+            shallow_copies = {
+                key: ctx.input_tensors[key].view_as(ctx.input_tensors[key])
+                for key in ctx.input_tensors
+            }
+            # shallow_copies.update(additional_args)
+            output_tensors = ctx.run_function(**shallow_copies, **ctx.input_non_tensors)
+        input_grads = torch.autograd.grad(
+            output_tensors,
+            list(ctx.input_tensors.values()) + ctx.input_params,
+            output_grads,
+            allow_unused=True,
+        )
+        del ctx.input_tensors
+        del ctx.input_params
+        del output_tensors
+        return (
+            (None, None, None, None, None)
+            + input_grads[: ctx.end_tensors]
+            + (None,) * (ctx.end_non_tensors - ctx.end_tensors)
+            + input_grads[ctx.end_tensors :]
+        )
+
+
+def checkpoint(func, inputs, params, flag):
+    """
+    Evaluate a function without caching intermediate activations, allowing for
+    reduced memory at the expense of extra compute in the backward pass.
+    :param func: the function to evaluate.
+    :param inputs: the argument sequence to pass to `func`.
+    :param params: a sequence of parameters `func` depends on but does not
+                   explicitly take as arguments.
+    :param flag: if False, disable gradient checkpointing.
+    """
+    if flag:
+        args = tuple(inputs) + tuple(params)
+        return CheckpointFunction.apply(func, len(inputs), *args)
+    else:
+        return func(*inputs)
+
+
+class CheckpointFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, run_function, length, *args):
+        ctx.run_function = run_function
+        ctx.input_tensors = list(args[:length])
+        ctx.input_params = list(args[length:])
+        ctx.gpu_autocast_kwargs = {
+            "enabled": torch.is_autocast_enabled(),
+            "dtype": torch.get_autocast_gpu_dtype(),
+            "cache_enabled": torch.is_autocast_cache_enabled(),
+        }
+        with torch.no_grad():
+            output_tensors = ctx.run_function(*ctx.input_tensors)
+        return output_tensors
+
+    @staticmethod
+    def backward(ctx, *output_grads):
+        ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
+        with torch.enable_grad(), torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs):
+            # Fixes a bug where the first op in run_function modifies the
+            # Tensor storage in place, which is not allowed for detach()'d
+            # Tensors.
+            shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
+            output_tensors = ctx.run_function(*shallow_copies)
+        input_grads = torch.autograd.grad(
+            output_tensors,
+            ctx.input_tensors + ctx.input_params,
+            output_grads,
+            allow_unused=True,
+        )
+        del ctx.input_tensors
+        del ctx.input_params
+        del output_tensors
+        return (None, None) + input_grads
+
+
+def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
+    """
+    Create sinusoidal timestep embeddings.
+    :param timesteps: a 1-D Tensor of N indices, one per batch element.
+                      These may be fractional.
+    :param dim: the dimension of the output.
+    :param max_period: controls the minimum frequency of the embeddings.
+    :return: an [N x dim] Tensor of positional embeddings.
+    """
+    if not repeat_only:
+        half = dim // 2
+        freqs = torch.exp(
+            -math.log(max_period)
+            * torch.arange(start=0, end=half, dtype=torch.float32)
+            / half
+        ).to(device=timesteps.device)
+        args = timesteps[:, None].float() * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat(
+                [embedding, torch.zeros_like(embedding[:, :1])], dim=-1
+            )
+    else:
+        embedding = repeat(timesteps, "b -> b d", d=dim)
+    return embedding
+
+
+def zero_module(module):
+    """
+    Zero out the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+
+def scale_module(module, scale):
+    """
+    Scale the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().mul_(scale)
+    return module
+
+
+def mean_flat(tensor):
+    """
+    Take the mean over all non-batch dimensions.
+    """
+    return tensor.mean(dim=list(range(1, len(tensor.shape))))
+
+
+def normalization(channels):
+    """
+    Make a standard normalization layer.
+    :param channels: number of input channels.
+    :return: an nn.Module for normalization.
+    """
+    return GroupNorm32(32, channels)
+
+
+# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
+class SiLU(nn.Module):
+    def forward(self, x):
+        return x * torch.sigmoid(x)
+
+
+class GroupNorm32(nn.GroupNorm):
+    def forward(self, x):
+        return super().forward(x.float()).type(x.dtype)
+
+
+def conv_nd(dims, *args, **kwargs):
+    """
+    Create a 1D, 2D, or 3D convolution module.
+    """
+    if dims == 1:
+        return nn.Conv1d(*args, **kwargs)
+    elif dims == 2:
+        return nn.Conv2d(*args, **kwargs)
+    elif dims == 3:
+        return nn.Conv3d(*args, **kwargs)
+    raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def linear(*args, **kwargs):
+    """
+    Create a linear module.
+    """
+    return nn.Linear(*args, **kwargs)
+
+
+def avg_pool_nd(dims, *args, **kwargs):
+    """
+    Create a 1D, 2D, or 3D average pooling module.
+    """
+    if dims == 1:
+        return nn.AvgPool1d(*args, **kwargs)
+    elif dims == 2:
+        return nn.AvgPool2d(*args, **kwargs)
+    elif dims == 3:
+        return nn.AvgPool3d(*args, **kwargs)
+    raise ValueError(f"unsupported dimensions: {dims}")

sgm/modules/diffusionmodules/wrappers.py

@@ -0,0 +1,34 @@
+import torch
+import torch.nn as nn
+from packaging import version
+
+OPENAIUNETWRAPPER = "sgm.modules.diffusionmodules.wrappers.OpenAIWrapper"
+
+
+class IdentityWrapper(nn.Module):
+    def __init__(self, diffusion_model, compile_model: bool = False):
+        super().__init__()
+        compile = (
+            torch.compile
+            if (version.parse(torch.__version__) >= version.parse("2.0.0"))
+            and compile_model
+            else lambda x: x
+        )
+        self.diffusion_model = compile(diffusion_model)
+
+    def forward(self, *args, **kwargs):
+        return self.diffusion_model(*args, **kwargs)
+
+
+class OpenAIWrapper(IdentityWrapper):
+    def forward(
+        self, x: torch.Tensor, t: torch.Tensor, c: dict, **kwargs
+    ) -> torch.Tensor:
+        x = torch.cat((x, c.get("concat", torch.Tensor([]).type_as(x))), dim=1)
+        return self.diffusion_model(
+            x,
+            timesteps=t,
+            context=c.get("crossattn", None),
+            y=c.get("vector", None),
+            **kwargs
+        )

sgm/modules/distributions/distributions.py

@@ -0,0 +1,102 @@
+import torch
+import numpy as np
+
+
+class AbstractDistribution:
+    def sample(self):
+        raise NotImplementedError()
+
+    def mode(self):
+        raise NotImplementedError()
+
+
+class DiracDistribution(AbstractDistribution):
+    def __init__(self, value):
+        self.value = value
+
+    def sample(self):
+        return self.value
+
+    def mode(self):
+        return self.value
+
+
+class DiagonalGaussianDistribution(object):
+    def __init__(self, parameters, deterministic=False):
+        self.parameters = parameters
+        self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
+        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
+        self.deterministic = deterministic
+        self.std = torch.exp(0.5 * self.logvar)
+        self.var = torch.exp(self.logvar)
+        if self.deterministic:
+            self.var = self.std = torch.zeros_like(self.mean).to(
+                device=self.parameters.device
+            )
+
+    def sample(self):
+        x = self.mean + self.std * torch.randn(self.mean.shape).to(
+            device=self.parameters.device
+        )
+        return x
+
+    def kl(self, other=None):
+        if self.deterministic:
+            return torch.Tensor([0.0])
+        else:
+            if other is None:
+                return 0.5 * torch.sum(
+                    torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,
+                    dim=[1, 2, 3],
+                )
+            else:
+                return 0.5 * torch.sum(
+                    torch.pow(self.mean - other.mean, 2) / other.var
+                    + self.var / other.var
+                    - 1.0
+                    - self.logvar
+                    + other.logvar,
+                    dim=[1, 2, 3],
+                )
+
+    def nll(self, sample, dims=[1, 2, 3]):
+        if self.deterministic:
+            return torch.Tensor([0.0])
+        logtwopi = np.log(2.0 * np.pi)
+        return 0.5 * torch.sum(
+            logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
+            dim=dims,
+        )
+
+    def mode(self):
+        return self.mean
+
+
+def normal_kl(mean1, logvar1, mean2, logvar2):
+    """
+    source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
+    Compute the KL divergence between two gaussians.
+    Shapes are automatically broadcasted, so batches can be compared to
+    scalars, among other use cases.
+    """
+    tensor = None
+    for obj in (mean1, logvar1, mean2, logvar2):
+        if isinstance(obj, torch.Tensor):
+            tensor = obj
+            break
+    assert tensor is not None, "at least one argument must be a Tensor"
+
+    # Force variances to be Tensors. Broadcasting helps convert scalars to
+    # Tensors, but it does not work for torch.exp().
+    logvar1, logvar2 = [
+        x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
+        for x in (logvar1, logvar2)
+    ]
+
+    return 0.5 * (
+        -1.0
+        + logvar2
+        - logvar1
+        + torch.exp(logvar1 - logvar2)
+        + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
+    )

sgm/modules/ema.py

@@ -0,0 +1,86 @@
+import torch
+from torch import nn
+
+
+class LitEma(nn.Module):
+    def __init__(self, model, decay=0.9999, use_num_upates=True):
+        super().__init__()
+        if decay < 0.0 or decay > 1.0:
+            raise ValueError("Decay must be between 0 and 1")
+
+        self.m_name2s_name = {}
+        self.register_buffer("decay", torch.tensor(decay, dtype=torch.float32))
+        self.register_buffer(
+            "num_updates",
+            torch.tensor(0, dtype=torch.int)
+            if use_num_upates
+            else torch.tensor(-1, dtype=torch.int),
+        )
+
+        for name, p in model.named_parameters():
+            if p.requires_grad:
+                # remove as '.'-character is not allowed in buffers
+                s_name = name.replace(".", "")
+                self.m_name2s_name.update({name: s_name})
+                self.register_buffer(s_name, p.clone().detach().data)
+
+        self.collected_params = []
+
+    def reset_num_updates(self):
+        del self.num_updates
+        self.register_buffer("num_updates", torch.tensor(0, dtype=torch.int))
+
+    def forward(self, model):
+        decay = self.decay
+
+        if self.num_updates >= 0:
+            self.num_updates += 1
+            decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates))
+
+        one_minus_decay = 1.0 - decay
+
+        with torch.no_grad():
+            m_param = dict(model.named_parameters())
+            shadow_params = dict(self.named_buffers())
+
+            for key in m_param:
+                if m_param[key].requires_grad:
+                    sname = self.m_name2s_name[key]
+                    shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
+                    shadow_params[sname].sub_(
+                        one_minus_decay * (shadow_params[sname] - m_param[key])
+                    )
+                else:
+                    assert not key in self.m_name2s_name
+
+    def copy_to(self, model):
+        m_param = dict(model.named_parameters())
+        shadow_params = dict(self.named_buffers())
+        for key in m_param:
+            if m_param[key].requires_grad:
+                m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
+            else:
+                assert not key in self.m_name2s_name
+
+    def store(self, parameters):
+        """
+        Save the current parameters for restoring later.
+        Args:
+          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+            temporarily stored.
+        """
+        self.collected_params = [param.clone() for param in parameters]
+
+    def restore(self, parameters):
+        """
+        Restore the parameters stored with the `store` method.
+        Useful to validate the model with EMA parameters without affecting the
+        original optimization process. Store the parameters before the
+        `copy_to` method. After validation (or model saving), use this to
+        restore the former parameters.
+        Args:
+          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+            updated with the stored parameters.
+        """
+        for c_param, param in zip(self.collected_params, parameters):
+            param.data.copy_(c_param.data)

sgm/modules/encoders/modules.py

@@ -0,0 +1,960 @@
+from contextlib import nullcontext
+from functools import partial
+from typing import Dict, List, Optional, Tuple, Union
+
+import kornia
+import numpy as np
+import open_clip
+import torch
+import torch.nn as nn
+from einops import rearrange, repeat
+from omegaconf import ListConfig
+from torch.utils.checkpoint import checkpoint
+from transformers import (
+    ByT5Tokenizer,
+    CLIPTextModel,
+    CLIPTokenizer,
+    T5EncoderModel,
+    T5Tokenizer,
+)
+
+from ...modules.autoencoding.regularizers import DiagonalGaussianRegularizer
+from ...modules.diffusionmodules.model import Encoder
+from ...modules.diffusionmodules.openaimodel import Timestep
+from ...modules.diffusionmodules.util import extract_into_tensor, make_beta_schedule
+from ...modules.distributions.distributions import DiagonalGaussianDistribution
+from ...util import (
+    autocast,
+    count_params,
+    default,
+    disabled_train,
+    expand_dims_like,
+    instantiate_from_config,
+)
+
+
+class AbstractEmbModel(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self._is_trainable = None
+        self._ucg_rate = None
+        self._input_key = None
+
+    @property
+    def is_trainable(self) -> bool:
+        return self._is_trainable
+
+    @property
+    def ucg_rate(self) -> Union[float, torch.Tensor]:
+        return self._ucg_rate
+
+    @property
+    def input_key(self) -> str:
+        return self._input_key
+
+    @is_trainable.setter
+    def is_trainable(self, value: bool):
+        self._is_trainable = value
+
+    @ucg_rate.setter
+    def ucg_rate(self, value: Union[float, torch.Tensor]):
+        self._ucg_rate = value
+
+    @input_key.setter
+    def input_key(self, value: str):
+        self._input_key = value
+
+    @is_trainable.deleter
+    def is_trainable(self):
+        del self._is_trainable
+
+    @ucg_rate.deleter
+    def ucg_rate(self):
+        del self._ucg_rate
+
+    @input_key.deleter
+    def input_key(self):
+        del self._input_key
+
+
+class GeneralConditioner(nn.Module):
+    OUTPUT_DIM2KEYS = {2: "vector", 3: "crossattn", 4: "concat", 5: "concat"}
+    KEY2CATDIM = {"vector": 1, "crossattn": 2, "concat": 1}
+
+    def __init__(self, emb_models: Union[List, ListConfig]):
+        super().__init__()
+        embedders = []
+        for n, embconfig in enumerate(emb_models):
+            embedder = instantiate_from_config(embconfig)
+            assert isinstance(
+                embedder, AbstractEmbModel
+            ), f"embedder model {embedder.__class__.__name__} has to inherit from AbstractEmbModel"
+            embedder.is_trainable = embconfig.get("is_trainable", False)
+            embedder.ucg_rate = embconfig.get("ucg_rate", 0.0)
+            if not embedder.is_trainable:
+                embedder.train = disabled_train
+                for param in embedder.parameters():
+                    param.requires_grad = False
+                embedder.eval()
+            print(
+                f"Initialized embedder #{n}: {embedder.__class__.__name__} "
+                f"with {count_params(embedder, False)} params. Trainable: {embedder.is_trainable}"
+            )
+
+            if "input_key" in embconfig:
+                embedder.input_key = embconfig["input_key"]
+            elif "input_keys" in embconfig:
+                embedder.input_keys = embconfig["input_keys"]
+            else:
+                raise KeyError(
+                    f"need either 'input_key' or 'input_keys' for embedder {embedder.__class__.__name__}"
+                )
+
+            embedder.legacy_ucg_val = embconfig.get("legacy_ucg_value", None)
+            if embedder.legacy_ucg_val is not None:
+                embedder.ucg_prng = np.random.RandomState()
+
+            embedders.append(embedder)
+        self.embedders = nn.ModuleList(embedders)
+
+    def possibly_get_ucg_val(self, embedder: AbstractEmbModel, batch: Dict) -> Dict:
+        assert embedder.legacy_ucg_val is not None
+        p = embedder.ucg_rate
+        val = embedder.legacy_ucg_val
+        for i in range(len(batch[embedder.input_key])):
+            if embedder.ucg_prng.choice(2, p=[1 - p, p]):
+                batch[embedder.input_key][i] = val
+        return batch
+
+    def forward(
+        self, batch: Dict, force_zero_embeddings: Optional[List] = None
+    ) -> Dict:
+        output = dict()
+        if force_zero_embeddings is None:
+            force_zero_embeddings = []
+        for embedder in self.embedders:
+            embedding_context = nullcontext if embedder.is_trainable else torch.no_grad
+            with embedding_context():
+                if hasattr(embedder, "input_key") and (embedder.input_key is not None):
+                    if embedder.legacy_ucg_val is not None:
+                        batch = self.possibly_get_ucg_val(embedder, batch)
+                    emb_out = embedder(batch[embedder.input_key])
+                elif hasattr(embedder, "input_keys"):
+                    emb_out = embedder(*[batch[k] for k in embedder.input_keys])
+            assert isinstance(
+                emb_out, (torch.Tensor, list, tuple)
+            ), f"encoder outputs must be tensors or a sequence, but got {type(emb_out)}"
+            if not isinstance(emb_out, (list, tuple)):
+                emb_out = [emb_out]
+            for emb in emb_out:
+                out_key = self.OUTPUT_DIM2KEYS[emb.dim()]
+                if embedder.ucg_rate > 0.0 and embedder.legacy_ucg_val is None:
+                    emb = (
+                        expand_dims_like(
+                            torch.bernoulli(
+                                (1.0 - embedder.ucg_rate)
+                                * torch.ones(emb.shape[0], device=emb.device)
+                            ),
+                            emb,
+                        )
+                        * emb
+                    )
+                if (
+                    hasattr(embedder, "input_key")
+                    and embedder.input_key in force_zero_embeddings
+                ):
+                    emb = torch.zeros_like(emb)
+                if out_key in output:
+                    output[out_key] = torch.cat(
+                        (output[out_key], emb), self.KEY2CATDIM[out_key]
+                    )
+                else:
+                    output[out_key] = emb
+        return output
+
+    def get_unconditional_conditioning(
+        self, batch_c, batch_uc=None, force_uc_zero_embeddings=None
+    ):
+        if force_uc_zero_embeddings is None:
+            force_uc_zero_embeddings = []
+        ucg_rates = list()
+        for embedder in self.embedders:
+            ucg_rates.append(embedder.ucg_rate)
+            embedder.ucg_rate = 0.0
+        c = self(batch_c)
+        uc = self(batch_c if batch_uc is None else batch_uc, force_uc_zero_embeddings)
+
+        for embedder, rate in zip(self.embedders, ucg_rates):
+            embedder.ucg_rate = rate
+        return c, uc
+
+
+class InceptionV3(nn.Module):
+    """Wrapper around the https://github.com/mseitzer/pytorch-fid inception
+    port with an additional squeeze at the end"""
+
+    def __init__(self, normalize_input=False, **kwargs):
+        super().__init__()
+        from pytorch_fid import inception
+
+        kwargs["resize_input"] = True
+        self.model = inception.InceptionV3(normalize_input=normalize_input, **kwargs)
+
+    def forward(self, inp):
+        # inp = kornia.geometry.resize(inp, (299, 299),
+        #                              interpolation='bicubic',
+        #                              align_corners=False,
+        #                              antialias=True)
+        # inp = inp.clamp(min=-1, max=1)
+
+        outp = self.model(inp)
+
+        if len(outp) == 1:
+            return outp[0].squeeze()
+
+        return outp
+
+
+class IdentityEncoder(AbstractEmbModel):
+    def encode(self, x):
+        return x
+
+    def forward(self, x):
+        return x
+
+
+class ClassEmbedder(AbstractEmbModel):
+    def __init__(self, embed_dim, n_classes=1000, add_sequence_dim=False):
+        super().__init__()
+        self.embedding = nn.Embedding(n_classes, embed_dim)
+        self.n_classes = n_classes
+        self.add_sequence_dim = add_sequence_dim
+
+    def forward(self, c):
+        c = self.embedding(c)
+        if self.add_sequence_dim:
+            c = c[:, None, :]
+        return c
+
+    def get_unconditional_conditioning(self, bs, device="cuda"):
+        uc_class = (
+            self.n_classes - 1
+        )  # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000)
+        uc = torch.ones((bs,), device=device) * uc_class
+        uc = {self.key: uc.long()}
+        return uc
+
+
+class ClassEmbedderForMultiCond(ClassEmbedder):
+    def forward(self, batch, key=None, disable_dropout=False):
+        out = batch
+        key = default(key, self.key)
+        islist = isinstance(batch[key], list)
+        if islist:
+            batch[key] = batch[key][0]
+        c_out = super().forward(batch, key, disable_dropout)
+        out[key] = [c_out] if islist else c_out
+        return out
+
+
+class FrozenT5Embedder(AbstractEmbModel):
+    """Uses the T5 transformer encoder for text"""
+
+    def __init__(
+        self, version="google/t5-v1_1-xxl", device="cuda", max_length=77, freeze=True
+    ):  # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
+        super().__init__()
+        self.tokenizer = T5Tokenizer.from_pretrained(version)
+        self.transformer = T5EncoderModel.from_pretrained(version)
+        self.device = device
+        self.max_length = max_length
+        if freeze:
+            self.freeze()
+
+    def freeze(self):
+        self.transformer = self.transformer.eval()
+
+        for param in self.parameters():
+            param.requires_grad = False
+
+    # @autocast
+    def forward(self, text):
+        batch_encoding = self.tokenizer(
+            text,
+            truncation=True,
+            max_length=self.max_length,
+            return_length=True,
+            return_overflowing_tokens=False,
+            padding="max_length",
+            return_tensors="pt",
+        )
+        tokens = batch_encoding["input_ids"].to(self.device)
+        with torch.autocast("cuda", enabled=False):
+            outputs = self.transformer(input_ids=tokens)
+        z = outputs.last_hidden_state
+        return z
+
+    def encode(self, text):
+        return self(text)
+
+
+class FrozenByT5Embedder(AbstractEmbModel):
+    """
+    Uses the ByT5 transformer encoder for text. Is character-aware.
+    """
+
+    def __init__(
+        self, version="google/byt5-base", device="cuda", max_length=77, freeze=True
+    ):  # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
+        super().__init__()
+        self.tokenizer = ByT5Tokenizer.from_pretrained(version)
+        self.transformer = T5EncoderModel.from_pretrained(version)
+        self.device = device
+        self.max_length = max_length
+        if freeze:
+            self.freeze()
+
+    def freeze(self):
+        self.transformer = self.transformer.eval()
+
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def forward(self, text):
+        batch_encoding = self.tokenizer(
+            text,
+            truncation=True,
+            max_length=self.max_length,
+            return_length=True,
+            return_overflowing_tokens=False,
+            padding="max_length",
+            return_tensors="pt",
+        )
+        tokens = batch_encoding["input_ids"].to(self.device)
+        with torch.autocast("cuda", enabled=False):
+            outputs = self.transformer(input_ids=tokens)
+        z = outputs.last_hidden_state
+        return z
+
+    def encode(self, text):
+        return self(text)
+
+
+class FrozenCLIPEmbedder(AbstractEmbModel):
+    """Uses the CLIP transformer encoder for text (from huggingface)"""
+
+    LAYERS = ["last", "pooled", "hidden"]
+
+    def __init__(
+        self,
+        version="openai/clip-vit-large-patch14",
+        device="cuda",
+        max_length=77,
+        freeze=True,
+        layer="last",
+        layer_idx=None,
+        always_return_pooled=False,
+    ):  # clip-vit-base-patch32
+        super().__init__()
+        assert layer in self.LAYERS
+        self.tokenizer = CLIPTokenizer.from_pretrained(version)
+        self.transformer = CLIPTextModel.from_pretrained(version)
+        self.device = device
+        self.max_length = max_length
+        if freeze:
+            self.freeze()
+        self.layer = layer
+        self.layer_idx = layer_idx
+        self.return_pooled = always_return_pooled
+        if layer == "hidden":
+            assert layer_idx is not None
+            assert 0 <= abs(layer_idx) <= 12
+
+    def freeze(self):
+        self.transformer = self.transformer.eval()
+
+        for param in self.parameters():
+            param.requires_grad = False
+
+    @autocast
+    def forward(self, text):
+        batch_encoding = self.tokenizer(
+            text,
+            truncation=True,
+            max_length=self.max_length,
+            return_length=True,
+            return_overflowing_tokens=False,
+            padding="max_length",
+            return_tensors="pt",
+        )
+        tokens = batch_encoding["input_ids"].to(self.device)
+        outputs = self.transformer(
+            input_ids=tokens, output_hidden_states=self.layer == "hidden"
+        )
+        if self.layer == "last":
+            z = outputs.last_hidden_state
+        elif self.layer == "pooled":
+            z = outputs.pooler_output[:, None, :]
+        else:
+            z = outputs.hidden_states[self.layer_idx]
+        if self.return_pooled:
+            return z, outputs.pooler_output
+        return z
+
+    def encode(self, text):
+        return self(text)
+
+
+class FrozenOpenCLIPEmbedder2(AbstractEmbModel):
+    """
+    Uses the OpenCLIP transformer encoder for text
+    """
+
+    LAYERS = ["pooled", "last", "penultimate"]
+
+    def __init__(
+        self,
+        arch="ViT-H-14",
+        version="laion2b_s32b_b79k",
+        device="cuda",
+        max_length=77,
+        freeze=True,
+        layer="last",
+        always_return_pooled=False,
+        legacy=True,
+    ):
+        super().__init__()
+        assert layer in self.LAYERS
+        model, _, _ = open_clip.create_model_and_transforms(
+            arch,
+            device=torch.device("cpu"),
+            pretrained=version,
+        )
+        del model.visual
+        self.model = model
+
+        self.device = device
+        self.max_length = max_length
+        self.return_pooled = always_return_pooled
+        if freeze:
+            self.freeze()
+        self.layer = layer
+        if self.layer == "last":
+            self.layer_idx = 0
+        elif self.layer == "penultimate":
+            self.layer_idx = 1
+        else:
+            raise NotImplementedError()
+        self.legacy = legacy
+
+    def freeze(self):
+        self.model = self.model.eval()
+        for param in self.parameters():
+            param.requires_grad = False
+
+    @autocast
+    def forward(self, text):
+        tokens = open_clip.tokenize(text)
+        z = self.encode_with_transformer(tokens.to(self.device))
+        if not self.return_pooled and self.legacy:
+            return z
+        if self.return_pooled:
+            assert not self.legacy
+            return z[self.layer], z["pooled"]
+        return z[self.layer]
+
+    def encode_with_transformer(self, text):
+        x = self.model.token_embedding(text)  # [batch_size, n_ctx, d_model]
+        x = x + self.model.positional_embedding
+        x = x.permute(1, 0, 2)  # NLD -> LND
+        x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
+        if self.legacy:
+            x = x[self.layer]
+            x = self.model.ln_final(x)
+            return x
+        else:
+            # x is a dict and will stay a dict
+            o = x["last"]
+            o = self.model.ln_final(o)
+            pooled = self.pool(o, text)
+            x["pooled"] = pooled
+            return x
+
+    def pool(self, x, text):
+        # take features from the eot embedding (eot_token is the highest number in each sequence)
+        x = (
+            x[torch.arange(x.shape[0]), text.argmax(dim=-1)]
+            @ self.model.text_projection
+        )
+        return x
+
+    def text_transformer_forward(self, x: torch.Tensor, attn_mask=None):
+        outputs = {}
+        for i, r in enumerate(self.model.transformer.resblocks):
+            if i == len(self.model.transformer.resblocks) - 1:
+                outputs["penultimate"] = x.permute(1, 0, 2)  # LND -> NLD
+            if (
+                self.model.transformer.grad_checkpointing
+                and not torch.jit.is_scripting()
+            ):
+                x = checkpoint(r, x, attn_mask)
+            else:
+                x = r(x, attn_mask=attn_mask)
+        outputs["last"] = x.permute(1, 0, 2)  # LND -> NLD
+        return outputs
+
+    def encode(self, text):
+        return self(text)
+
+
+class FrozenOpenCLIPEmbedder(AbstractEmbModel):
+    LAYERS = [
+        # "pooled",
+        "last",
+        "penultimate",
+    ]
+
+    def __init__(
+        self,
+        arch="ViT-H-14",
+        version="laion2b_s32b_b79k",
+        device="cuda",
+        max_length=77,
+        freeze=True,
+        layer="last",
+    ):
+        super().__init__()
+        assert layer in self.LAYERS
+        model, _, _ = open_clip.create_model_and_transforms(
+            arch, device=torch.device("cpu"), pretrained=version
+        )
+        del model.visual
+        self.model = model
+
+        self.device = device
+        self.max_length = max_length
+        if freeze:
+            self.freeze()
+        self.layer = layer
+        if self.layer == "last":
+            self.layer_idx = 0
+        elif self.layer == "penultimate":
+            self.layer_idx = 1
+        else:
+            raise NotImplementedError()
+
+    def freeze(self):
+        self.model = self.model.eval()
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def forward(self, text):
+        tokens = open_clip.tokenize(text)
+        z = self.encode_with_transformer(tokens.to(self.device))
+        return z
+
+    def encode_with_transformer(self, text):
+        x = self.model.token_embedding(text)  # [batch_size, n_ctx, d_model]
+        x = x + self.model.positional_embedding
+        x = x.permute(1, 0, 2)  # NLD -> LND
+        x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
+        x = x.permute(1, 0, 2)  # LND -> NLD
+        x = self.model.ln_final(x)
+        return x
+
+    def text_transformer_forward(self, x: torch.Tensor, attn_mask=None):
+        for i, r in enumerate(self.model.transformer.resblocks):
+            if i == len(self.model.transformer.resblocks) - self.layer_idx:
+                break
+            if (
+                self.model.transformer.grad_checkpointing
+                and not torch.jit.is_scripting()
+            ):
+                x = checkpoint(r, x, attn_mask)
+            else:
+                x = r(x, attn_mask=attn_mask)
+        return x
+
+    def encode(self, text):
+        return self(text)
+
+
+class FrozenOpenCLIPImageEmbedder(AbstractEmbModel):
+    """
+    Uses the OpenCLIP vision transformer encoder for images
+    """
+
+    def __init__(
+        self,
+        arch="ViT-H-14",
+        version="laion2b_s32b_b79k",
+        device="cuda",
+        max_length=77,
+        freeze=True,
+        antialias=True,
+        ucg_rate=0.0,
+        unsqueeze_dim=False,
+        repeat_to_max_len=False,
+        num_image_crops=0,
+        output_tokens=False,
+    ):
+        super().__init__()
+        model, _, _ = open_clip.create_model_and_transforms(
+            arch,
+            device=torch.device("cpu"),
+            pretrained=version,
+        )
+        del model.transformer
+        self.model = model
+        self.max_crops = num_image_crops
+        self.pad_to_max_len = self.max_crops > 0
+        self.repeat_to_max_len = repeat_to_max_len and (not self.pad_to_max_len)
+        self.device = device
+        self.max_length = max_length
+        if freeze:
+            self.freeze()
+
+        self.antialias = antialias
+
+        self.register_buffer(
+            "mean", torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False
+        )
+        self.register_buffer(
+            "std", torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False
+        )
+        self.ucg_rate = ucg_rate
+        self.unsqueeze_dim = unsqueeze_dim
+        self.stored_batch = None
+        self.model.visual.output_tokens = output_tokens
+        self.output_tokens = output_tokens
+
+    def preprocess(self, x):
+        # normalize to [0,1]
+        x = kornia.geometry.resize(
+            x,
+            (224, 224),
+            interpolation="bicubic",
+            align_corners=True,
+            antialias=self.antialias,
+        )
+        x = (x + 1.0) / 2.0
+        # renormalize according to clip
+        x = kornia.enhance.normalize(x, self.mean, self.std)
+        return x
+
+    def freeze(self):
+        self.model = self.model.eval()
+        for param in self.parameters():
+            param.requires_grad = False
+
+    @autocast
+    def forward(self, image, no_dropout=False):
+        z = self.encode_with_vision_transformer(image)
+        tokens = None
+        if self.output_tokens:
+            z, tokens = z[0], z[1]
+        z = z.to(image.dtype)
+        if self.ucg_rate > 0.0 and not no_dropout and not (self.max_crops > 0):
+            z = (
+                torch.bernoulli(
+                    (1.0 - self.ucg_rate) * torch.ones(z.shape[0], device=z.device)
+                )[:, None]
+                * z
+            )
+            if tokens is not None:
+                tokens = (
+                    expand_dims_like(
+                        torch.bernoulli(
+                            (1.0 - self.ucg_rate)
+                            * torch.ones(tokens.shape[0], device=tokens.device)
+                        ),
+                        tokens,
+                    )
+                    * tokens
+                )
+        if self.unsqueeze_dim:
+            z = z[:, None, :]
+        if self.output_tokens:
+            assert not self.repeat_to_max_len
+            assert not self.pad_to_max_len
+            return tokens, z
+        if self.repeat_to_max_len:
+            if z.dim() == 2:
+                z_ = z[:, None, :]
+            else:
+                z_ = z
+            return repeat(z_, "b 1 d -> b n d", n=self.max_length), z
+        elif self.pad_to_max_len:
+            assert z.dim() == 3
+            z_pad = torch.cat(
+                (
+                    z,
+                    torch.zeros(
+                        z.shape[0],
+                        self.max_length - z.shape[1],
+                        z.shape[2],
+                        device=z.device,
+                    ),
+                ),
+                1,
+            )
+            return z_pad, z_pad[:, 0, ...]
+        return z
+
+    def encode_with_vision_transformer(self, img):
+        # if self.max_crops > 0:
+        #    img = self.preprocess_by_cropping(img)
+        if img.dim() == 5:
+            assert self.max_crops == img.shape[1]
+            img = rearrange(img, "b n c h w -> (b n) c h w")
+        img = self.preprocess(img)
+        if not self.output_tokens:
+            assert not self.model.visual.output_tokens
+            x = self.model.visual(img)
+            tokens = None
+        else:
+            assert self.model.visual.output_tokens
+            x, tokens = self.model.visual(img)
+        if self.max_crops > 0:
+            x = rearrange(x, "(b n) d -> b n d", n=self.max_crops)
+            # drop out between 0 and all along the sequence axis
+            x = (
+                torch.bernoulli(
+                    (1.0 - self.ucg_rate)
+                    * torch.ones(x.shape[0], x.shape[1], 1, device=x.device)
+                )
+                * x
+            )
+            if tokens is not None:
+                tokens = rearrange(tokens, "(b n) t d -> b t (n d)", n=self.max_crops)
+                print(
+                    f"You are running very experimental token-concat in {self.__class__.__name__}. "
+                    f"Check what you are doing, and then remove this message."
+                )
+        if self.output_tokens:
+            return x, tokens
+        return x
+
+    def encode(self, text):
+        return self(text)
+
+
+class FrozenCLIPT5Encoder(AbstractEmbModel):
+    def __init__(
+        self,
+        clip_version="openai/clip-vit-large-patch14",
+        t5_version="google/t5-v1_1-xl",
+        device="cuda",
+        clip_max_length=77,
+        t5_max_length=77,
+    ):
+        super().__init__()
+        self.clip_encoder = FrozenCLIPEmbedder(
+            clip_version, device, max_length=clip_max_length
+        )
+        self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length)
+        print(
+            f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder) * 1.e-6:.2f} M parameters, "
+            f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder) * 1.e-6:.2f} M params."
+        )
+
+    def encode(self, text):
+        return self(text)
+
+    def forward(self, text):
+        clip_z = self.clip_encoder.encode(text)
+        t5_z = self.t5_encoder.encode(text)
+        return [clip_z, t5_z]
+
+
+class SpatialRescaler(nn.Module):
+    def __init__(
+        self,
+        n_stages=1,
+        method="bilinear",
+        multiplier=0.5,
+        in_channels=3,
+        out_channels=None,
+        bias=False,
+        wrap_video=False,
+        kernel_size=1,
+        remap_output=False,
+    ):
+        super().__init__()
+        self.n_stages = n_stages
+        assert self.n_stages >= 0
+        assert method in [
+            "nearest",
+            "linear",
+            "bilinear",
+            "trilinear",
+            "bicubic",
+            "area",
+        ]
+        self.multiplier = multiplier
+        self.interpolator = partial(torch.nn.functional.interpolate, mode=method)
+        self.remap_output = out_channels is not None or remap_output
+        if self.remap_output:
+            print(
+                f"Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing."
+            )
+            self.channel_mapper = nn.Conv2d(
+                in_channels,
+                out_channels,
+                kernel_size=kernel_size,
+                bias=bias,
+                padding=kernel_size // 2,
+            )
+        self.wrap_video = wrap_video
+
+    def forward(self, x):
+        if self.wrap_video and x.ndim == 5:
+            B, C, T, H, W = x.shape
+            x = rearrange(x, "b c t h w -> b t c h w")
+            x = rearrange(x, "b t c h w -> (b t) c h w")
+
+        for stage in range(self.n_stages):
+            x = self.interpolator(x, scale_factor=self.multiplier)
+
+        if self.wrap_video:
+            x = rearrange(x, "(b t) c h w -> b t c h w", b=B, t=T, c=C)
+            x = rearrange(x, "b t c h w -> b c t h w")
+        if self.remap_output:
+            x = self.channel_mapper(x)
+        return x
+
+    def encode(self, x):
+        return self(x)
+
+
+class LowScaleEncoder(nn.Module):
+    def __init__(
+        self,
+        model_config,
+        linear_start,
+        linear_end,
+        timesteps=1000,
+        max_noise_level=250,
+        output_size=64,
+        scale_factor=1.0,
+    ):
+        super().__init__()
+        self.max_noise_level = max_noise_level
+        self.model = instantiate_from_config(model_config)
+        self.augmentation_schedule = self.register_schedule(
+            timesteps=timesteps, linear_start=linear_start, linear_end=linear_end
+        )
+        self.out_size = output_size
+        self.scale_factor = scale_factor
+
+    def register_schedule(
+        self,
+        beta_schedule="linear",
+        timesteps=1000,
+        linear_start=1e-4,
+        linear_end=2e-2,
+        cosine_s=8e-3,
+    ):
+        betas = make_beta_schedule(
+            beta_schedule,
+            timesteps,
+            linear_start=linear_start,
+            linear_end=linear_end,
+            cosine_s=cosine_s,
+        )
+        alphas = 1.0 - betas
+        alphas_cumprod = np.cumprod(alphas, axis=0)
+        alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1])
+
+        (timesteps,) = betas.shape
+        self.num_timesteps = int(timesteps)
+        self.linear_start = linear_start
+        self.linear_end = linear_end
+        assert (
+            alphas_cumprod.shape[0] == self.num_timesteps
+        ), "alphas have to be defined for each timestep"
+
+        to_torch = partial(torch.tensor, dtype=torch.float32)
+
+        self.register_buffer("betas", to_torch(betas))
+        self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
+        self.register_buffer("alphas_cumprod_prev", to_torch(alphas_cumprod_prev))
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.register_buffer("sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod)))
+        self.register_buffer(
+            "sqrt_one_minus_alphas_cumprod", to_torch(np.sqrt(1.0 - alphas_cumprod))
+        )
+        self.register_buffer(
+            "log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod))
+        )
+        self.register_buffer(
+            "sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod))
+        )
+        self.register_buffer(
+            "sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod - 1))
+        )
+
+    def q_sample(self, x_start, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+        return (
+            extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+            + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)
+            * noise
+        )
+
+    def forward(self, x):
+        z = self.model.encode(x)
+        if isinstance(z, DiagonalGaussianDistribution):
+            z = z.sample()
+        z = z * self.scale_factor
+        noise_level = torch.randint(
+            0, self.max_noise_level, (x.shape[0],), device=x.device
+        ).long()
+        z = self.q_sample(z, noise_level)
+        if self.out_size is not None:
+            z = torch.nn.functional.interpolate(z, size=self.out_size, mode="nearest")
+        # z = z.repeat_interleave(2, -2).repeat_interleave(2, -1)
+        return z, noise_level
+
+    def decode(self, z):
+        z = z / self.scale_factor
+        return self.model.decode(z)
+
+
+class ConcatTimestepEmbedderND(AbstractEmbModel):
+    """embeds each dimension independently and concatenates them"""
+
+    def __init__(self, outdim):
+        super().__init__()
+        self.timestep = Timestep(outdim)
+        self.outdim = outdim
+
+    def forward(self, x):
+        if x.ndim == 1:
+            x = x[:, None]
+        assert len(x.shape) == 2
+        b, dims = x.shape[0], x.shape[1]
+        x = rearrange(x, "b d -> (b d)")
+        emb = self.timestep(x)
+        emb = rearrange(emb, "(b d) d2 -> b (d d2)", b=b, d=dims, d2=self.outdim)
+        return emb
+
+
+class GaussianEncoder(Encoder, AbstractEmbModel):
+    def __init__(
+        self, weight: float = 1.0, flatten_output: bool = True, *args, **kwargs
+    ):
+        super().__init__(*args, **kwargs)
+        self.posterior = DiagonalGaussianRegularizer()
+        self.weight = weight
+        self.flatten_output = flatten_output
+
+    def forward(self, x) -> Tuple[Dict, torch.Tensor]:
+        z = super().forward(x)
+        z, log = self.posterior(z)
+        log["loss"] = log["kl_loss"]
+        log["weight"] = self.weight
+        if self.flatten_output:
+            z = rearrange(z, "b c h w -> b (h w ) c")
+        return log, z

sgm/util.py

@@ -0,0 +1,231 @@
+import functools
+import importlib
+import os
+from functools import partial
+from inspect import isfunction
+
+import fsspec
+import numpy as np
+import torch
+from PIL import Image, ImageDraw, ImageFont
+from safetensors.torch import load_file as load_safetensors
+
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+
+def get_string_from_tuple(s):
+    try:
+        # Check if the string starts and ends with parentheses
+        if s[0] == "(" and s[-1] == ")":
+            # Convert the string to a tuple
+            t = eval(s)
+            # Check if the type of t is tuple
+            if type(t) == tuple:
+                return t[0]
+            else:
+                pass
+    except:
+        pass
+    return s
+
+
+def is_power_of_two(n):
+    """
+    chat.openai.com/chat
+    Return True if n is a power of 2, otherwise return False.
+
+    The function is_power_of_two takes an integer n as input and returns True if n is a power of 2, otherwise it returns False.
+    The function works by first checking if n is less than or equal to 0. If n is less than or equal to 0, it can't be a power of 2, so the function returns False.
+    If n is greater than 0, the function checks whether n is a power of 2 by using a bitwise AND operation between n and n-1. If n is a power of 2, then it will have only one bit set to 1 in its binary representation. When we subtract 1 from a power of 2, all the bits to the right of that bit become 1, and the bit itself becomes 0. So, when we perform a bitwise AND between n and n-1, we get 0 if n is a power of 2, and a non-zero value otherwise.
+    Thus, if the result of the bitwise AND operation is 0, then n is a power of 2 and the function returns True. Otherwise, the function returns False.
+
+    """
+    if n <= 0:
+        return False
+    return (n & (n - 1)) == 0
+
+
+def autocast(f, enabled=True):
+    def do_autocast(*args, **kwargs):
+        with torch.cuda.amp.autocast(
+            enabled=enabled,
+            dtype=torch.get_autocast_gpu_dtype(),
+            cache_enabled=torch.is_autocast_cache_enabled(),
+        ):
+            return f(*args, **kwargs)
+
+    return do_autocast
+
+
+def load_partial_from_config(config):
+    return partial(get_obj_from_str(config["target"]), **config.get("params", dict()))
+
+
+def log_txt_as_img(wh, xc, size=10):
+    # wh a tuple of (width, height)
+    # xc a list of captions to plot
+    b = len(xc)
+    txts = list()
+    for bi in range(b):
+        txt = Image.new("RGB", wh, color="white")
+        draw = ImageDraw.Draw(txt)
+        font = ImageFont.truetype("data/DejaVuSans.ttf", size=size)
+        nc = int(40 * (wh[0] / 256))
+        if isinstance(xc[bi], list):
+            text_seq = xc[bi][0]
+        else:
+            text_seq = xc[bi]
+        lines = "\n".join(
+            text_seq[start : start + nc] for start in range(0, len(text_seq), nc)
+        )
+
+        try:
+            draw.text((0, 0), lines, fill="black", font=font)
+        except UnicodeEncodeError:
+            print("Cant encode string for logging. Skipping.")
+
+        txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
+        txts.append(txt)
+    txts = np.stack(txts)
+    txts = torch.tensor(txts)
+    return txts
+
+
+def partialclass(cls, *args, **kwargs):
+    class NewCls(cls):
+        __init__ = functools.partialmethod(cls.__init__, *args, **kwargs)
+
+    return NewCls
+
+
+def make_path_absolute(path):
+    fs, p = fsspec.core.url_to_fs(path)
+    if fs.protocol == "file":
+        return os.path.abspath(p)
+    return path
+
+
+def ismap(x):
+    if not isinstance(x, torch.Tensor):
+        return False
+    return (len(x.shape) == 4) and (x.shape[1] > 3)
+
+
+def isimage(x):
+    if not isinstance(x, torch.Tensor):
+        return False
+    return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
+
+
+def isheatmap(x):
+    if not isinstance(x, torch.Tensor):
+        return False
+
+    return x.ndim == 2
+
+
+def isneighbors(x):
+    if not isinstance(x, torch.Tensor):
+        return False
+    return x.ndim == 5 and (x.shape[2] == 3 or x.shape[2] == 1)
+
+
+def exists(x):
+    return x is not None
+
+
+def expand_dims_like(x, y):
+    while x.dim() != y.dim():
+        x = x.unsqueeze(-1)
+    return x
+
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if isfunction(d) else d
+
+
+def mean_flat(tensor):
+    """
+    https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86
+    Take the mean over all non-batch dimensions.
+    """
+    return tensor.mean(dim=list(range(1, len(tensor.shape))))
+
+
+def count_params(model, verbose=False):
+    total_params = sum(p.numel() for p in model.parameters())
+    if verbose:
+        print(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.")
+    return total_params
+
+
+def instantiate_from_config(config):
+    if not "target" in config:
+        if config == "__is_first_stage__":
+            return None
+        elif config == "__is_unconditional__":
+            return None
+        raise KeyError("Expected key `target` to instantiate.")
+    return get_obj_from_str(config["target"])(**config.get("params", dict()))
+
+
+def get_obj_from_str(string, reload=False, invalidate_cache=True):
+    module, cls = string.rsplit(".", 1)
+    if invalidate_cache:
+        importlib.invalidate_caches()
+    if reload:
+        module_imp = importlib.import_module(module)
+        importlib.reload(module_imp)
+    return getattr(importlib.import_module(module, package=None), cls)
+
+
+def append_zero(x):
+    return torch.cat([x, x.new_zeros([1])])
+
+
+def append_dims(x, target_dims):
+    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
+    dims_to_append = target_dims - x.ndim
+    if dims_to_append < 0:
+        raise ValueError(
+            f"input has {x.ndim} dims but target_dims is {target_dims}, which is less"
+        )
+    return x[(...,) + (None,) * dims_to_append]
+
+
+def load_model_from_config(config, ckpt, verbose=True, freeze=True):
+    print(f"Loading model from {ckpt}")
+    if ckpt.endswith("ckpt"):
+        pl_sd = torch.load(ckpt, map_location="cpu")
+        if "global_step" in pl_sd:
+            print(f"Global Step: {pl_sd['global_step']}")
+        sd = pl_sd["state_dict"]
+    elif ckpt.endswith("safetensors"):
+        sd = load_safetensors(ckpt)
+    else:
+        raise NotImplementedError
+
+    model = instantiate_from_config(config.model)
+    sd = pl_sd["state_dict"]
+
+    m, u = model.load_state_dict(sd, strict=False)
+
+    if len(m) > 0 and verbose:
+        print("missing keys:")
+        print(m)
+    if len(u) > 0 and verbose:
+        print("unexpected keys:")
+        print(u)
+
+    if freeze:
+        for param in model.parameters():
+            param.requires_grad = False
+
+    model.eval()
+    return model