fix EMA updating buffers with non-float tensors

* Converted decoder trainer to use accelerate

* Fixed issue where metric evaluation would hang on distributed mode

* Implemented functional saving
Loading still fails due to some issue with the optimizer

* Fixed issue with loading decoders

* Fixed issue with tracker config

* Fixed issue with amp
Updated logging to be more logical

* Saving checkpoint now saves position in training as well
Fixed an issue with running out of gpu space due to loading weights into the gpu twice

* Fixed ema for distributed training

* Fixed isue where get_pkg_version was reintroduced

* Changed decoder trainer to upload config as a file

Fixed issue where loading best would error

README.md

@@ -12,7 +12,7 @@ This model is SOTA for text-to-image for now.
 
 Please join <a href="https://discord.gg/xBPBXfcFHd"><img alt="Join us on Discord" src="https://img.shields.io/discord/823813159592001537?color=5865F2&logo=discord&logoColor=white"></a> if you are interested in helping out with the replication with the <a href="https://laion.ai/">LAION</a> community | <a href="https://www.youtube.com/watch?v=AIOE1l1W0Tw">Yannic Interview</a>
 
-There was enough interest for a <a href="https://github.com/lucidrains/dalle2-jax">Jax version</a>. I will also eventually extend this to <a href="https://github.com/lucidrains/dalle2-video">text to video</a>, once the repository is in a good place.
+As of 5/23/22, it is no longer SOTA. SOTA will be <a href="https://github.com/lucidrains/imagen-pytorch">here</a>. Jax versions as well as text-to-video project will be shifted towards the Imagen architecture, as it is way simpler.
 
 ## Status
 
@@ -24,6 +24,30 @@ There was enough interest for a <a href="https://github.com/lucidrains/dalle2-ja
 
 *ongoing at 21k steps*
 
+- <a href="https://twitter.com/Buntworthy/status/1529475416775434240?t=0GEge3Kr9I36cjcUVCQUTg">Justin Pinkney</a> successfully trained the diffusion prior in the repository for his CLIP to Stylegan2 text-to-image application
+
+## Pre-Trained Models
+
+- LAION is training prior models. Checkpoints are available on <a href="https://huggingface.co/zenglishuci/conditioned-prior">🤗huggingface</a> and the training statistics are available on <a href="https://wandb.ai/nousr_laion/conditioned-prior/reports/LAION-DALLE2-PyTorch-Prior--VmlldzoyMDI2OTIx">🐝WANDB</a>.
+- Decoder - <a href="https://wandb.ai/veldrovive/dalle2_train_decoder/runs/jkrtg0so?workspace=user-veldrovive">In-progress test run</a> 🚧
+- Decoder - <a href="https://wandb.ai/veldrovive/dalle2_train_decoder/runs/3d5rytsa?workspace=">Another test run with sparse attention</a>
+- DALL-E 2 🚧
+
+## Appreciation
+
+This library would not have gotten to this working state without the help of
+
+- <a href="https://github.com/nousr">Zion</a> for the distributed training code for the diffusion prior
+- <a href="https://github.com/Veldrovive">Aidan</a> for the distributed training code for the decoder as well as the dataloaders
+- <a href="https://github.com/krish240574">Kumar</a> for working on the initial diffusion training script
+- <a href="https://github.com/rom1504">Romain</a> for the pull request reviews and project management
+- <a href="https://github.com/Ciaohe">He Cao</a> and <a href="https://github.com/xiankgx">xiankgx</a> for the Q&A and for identifying of critical bugs
+- <a href="https://github.com/crowsonkb">Katherine</a> for her advice
+- <a href="https://stability.ai/">Stability AI</a> for the generous sponsorship
+- <a href="https://huggingface.co">🤗 Huggingface</a> and in particular <a href="https://github.com/sgugger">Sylvain</a> for the <a href="https://github.com/huggingface/accelerate">Accelerate</a> library
+
+... and many others. Thank you! 🙏
+
 ## Install
 
 ```bash
@@ -936,7 +960,7 @@ from dalle2_pytorch.dataloaders import ImageEmbeddingDataset, create_image_embed
 
 # Create a dataloader directly.
 dataloader = create_image_embedding_dataloader(
-    tar_url="/path/or/url/to/webdataset/{0000..9999}.tar", # Uses braket expanding notation. This specifies to read all tars from 0000.tar to 9999.tar
+    tar_url="/path/or/url/to/webdataset/{0000..9999}.tar", # Uses bracket expanding notation. This specifies to read all tars from 0000.tar to 9999.tar
     embeddings_url="path/or/url/to/embeddings/folder",     # Included if .npy files are not in webdataset. Left out or set to None otherwise
     num_workers=4,
     batch_size=32,
@@ -993,33 +1017,6 @@ The most significant parameters for the script are as follows:
 
 - `clip`, default = `None` # Signals the prior to use pre-computed embeddings
 
-#### Loading and Saving the DiffusionPrior model
-
-Two methods are provided, load_diffusion_model and save_diffusion_model, the names being self-explanatory. 
-
-```python
-from dalle2_pytorch.train import load_diffusion_model, save_diffusion_model
-```
-
-##### Loading
-
-    load_diffusion_model(dprior_path, device) 
-        dprior_path : path to saved model(.pth)
-        device      : the cuda device you're running on
-    
-##### Saving
-
-    save_diffusion_model(save_path, model, optimizer, scaler, config, image_embed_dim)
-        save_path : path to save at
-        model     : object of Diffusion_Prior
-        optimizer : optimizer object - see train_diffusion_prior.py for how to create one. 
-            e.g: optimizer = get_optimizer(diffusion_prior.net.parameters(), wd=weight_decay, lr=learning_rate)
-        scaler    : a GradScaler object.
-            e.g: scaler = GradScaler(enabled=amp)
-        config    : config object created in train_diffusion_prior.py - see file for example. 
-        image_embed_dim - the dimension of the image_embedding
-            e.g: 768
-
 ## CLI (wip)
 
 ```bash
@@ -1064,22 +1061,18 @@ Once built, images will be saved to the same directory the command is invoked
 - [x] bring in cross-scale embedding from iclr paper https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/crossformer.py#L14
 - [x] cross embed layers for downsampling, as an option
 - [x] use an experimental tracker agnostic setup, as done <a href="https://github.com/lucidrains/tf-bind-transformer#simple-trainer-class-for-fine-tuning">here</a>
+- [x] use pydantic for config drive training
+- [x] for both diffusion prior and decoder, all exponential moving averaged models needs to be saved and restored as well (as well as the step number)
+- [x] offer save / load methods on the trainer classes to automatically take care of state dicts for scalers / optimizers / saving versions and checking for breaking changes
+- [x] allow for creation of diffusion prior model off pydantic config classes - consider the same for tracker configs
+- [x] bring in skip-layer excitations (from lightweight gan paper) to see if it helps for either decoder of unet or vqgan-vae training (doesnt work well)
+- [x] test out grid attention in cascading ddpm locally, decide whether to keep or remove https://arxiv.org/abs/2204.01697 (keeping, seems to be fine)
+- [x] allow for unet to be able to condition non-cross attention style as well
 - [ ] become an expert with unets, cleanup unet code, make it fully configurable, port all learnings over to https://github.com/lucidrains/x-unet (test out unet² in ddpm repo) - consider https://github.com/lucidrains/uformer-pytorch attention-based unet
-- [ ] transcribe code to Jax, which lowers the activation energy for distributed training, given access to TPUs
+- [ ] speed up inference, read up on papers (ddim or diffusion-gan, etc)
-- [ ] train on a toy task, offer in colab
-- [ ] think about how best to design a declarative training config that handles preencoding for prior and training of multiple networks in decoder
-- [ ] extend diffusion head to use diffusion-gan (potentially using lightweight-gan) to speed up inference
 - [ ] figure out if possible to augment with external memory, as described in https://arxiv.org/abs/2204.11824
-- [ ] test out grid attention in cascading ddpm locally, decide whether to keep or remove
 - [ ] interface out the vqgan-vae so a pretrained one can be pulled off the shelf to validate latent diffusion + DALL-E2
-- [ ] make sure FILIP works with DALL-E2 from x-clip https://arxiv.org/abs/2111.07783
+- [ ] build infilling
-- [ ] offer save / load methods on the trainer classes to automatically take care of state dicts for scalers / optimizers / saving versions and checking for breaking changes
-- [ ] bring in skip-layer excitatons (from lightweight gan paper) to see if it helps for either decoder of unet or vqgan-vae training
-- [ ] decoder needs one day worth of refactor for tech debt
-- [ ] allow for unet to be able to condition non-cross attention style as well
-- [ ] for all model classes with hyperparameters that changes the network architecture, make it requirement that they must expose a config property, and write a simple function that asserts that it restores the object correctly
-- [ ] for both diffusion prior and decoder, all exponential moving averaged models needs to be saved and restored as well (as well as the step number)
-- [ ] read the paper, figure it out, and build it https://github.com/lucidrains/DALLE2-pytorch/issues/89
 
 ## Citations
 
@@ -1122,8 +1115,9 @@ Once built, images will be saved to the same directory the command is invoked
 ```bibtex
 @inproceedings{Tu2022MaxViTMV,
     title   = {MaxViT: Multi-Axis Vision Transformer},
-    author  = {Zhe-Wei Tu and Hossein Talebi and Han Zhang and Feng Yang and Peyman Milanfar and Alan Conrad Bovik and Yinxiao Li},
+    author  = {Zhengzhong Tu and Hossein Talebi and Han Zhang and Feng Yang and Peyman Milanfar and Alan Conrad Bovik and Yinxiao Li},
-    year    = {2022}
+    year    = {2022},
+    url     = {https://arxiv.org/abs/2204.01697}
 }
 ```
 
@@ -1177,4 +1171,22 @@ Once built, images will be saved to the same directory the command is invoked
 }
 ```
 
+```bibtex
+@misc{Saharia2022,
+    title   = {Imagen: unprecedented photorealism × deep level of language understanding},
+    author  = {Chitwan Saharia*, William Chan*, Saurabh Saxena†, Lala Li†, Jay Whang†, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho†, David Fleet†, Mohammad Norouzi*},
+    year    = {2022}
+}
+```
+
+```bibtex
+@article{Choi2022PerceptionPT,
+    title   = {Perception Prioritized Training of Diffusion Models},
+    author  = {Jooyoung Choi and Jungbeom Lee and Chaehun Shin and Sungwon Kim and Hyunwoo J. Kim and Sung-Hoon Yoon},
+    journal = {ArXiv},
+    year    = {2022},
+    volume  = {abs/2204.00227}
+}
+```
+
 *Creating noise from data is easy; creating data from noise is generative modeling.* - <a href="https://arxiv.org/abs/2011.13456">Yang Song's paper</a>

configs/README.md

@@ -4,11 +4,12 @@ For more complex configuration, we provide the option of using a configuration f
 
 ### Decoder Trainer
 
-The decoder trainer has 7 main configuration options. A full example of their use can be found in the [example decoder configuration](train_decoder_config.json.example).
+The decoder trainer has 7 main configuration options. A full example of their use can be found in the [example decoder configuration](train_decoder_config.example.json).
 
-**<ins>Unets</ins>:**
+**<ins>Unet</ins>:**
+
+This is a single unet config, which belongs as an array nested under the decoder config as a list of `unets`
 
-Each member of this array defines a single unet that will be added to the decoder.
 | Option | Required | Default | Description |
 | ------ | -------- | ------- | ----------- |
 | `dim`  | Yes      | N/A     | The starting channels of the unet. |
@@ -22,6 +23,7 @@ Any parameter from the `Unet` constructor can also be given here.
 Defines the configuration options for the decoder model. The unets defined above will automatically be inserted.
 | Option | Required | Default | Description |
 | ------ | -------- | ------- | ----------- |
+| `unets` | Yes | N/A | A list of unets, using the configuration above |
 | `image_sizes` | Yes | N/A | The resolution of the image after each upsampling step. The length of this array should be the number of unets defined. |
 | `image_size` | Yes | N/A | Not used. Can be any number. |
 | `timesteps` | No | `1000` | The number of diffusion timesteps used for generation. |
@@ -81,7 +83,7 @@ Defines which evaluation metrics will be used to test the model.
 Each metric can be enabled by setting its configuration. The configuration keys for each metric are defined by the torchmetrics constructors which will be linked.
 | Option | Required | Default | Description |
 | ------ | -------- | ------- | ----------- |
-| `n_evalation_samples` | No | `1000` | The number of samples to generate to test the model. |
+| `n_evaluation_samples` | No | `1000` | The number of samples to generate to test the model. |
 | `FID` | No | `None` | Setting to an object enables the [Frechet Inception Distance](https://torchmetrics.readthedocs.io/en/stable/image/frechet_inception_distance.html) metric. 
 | `IS` | No | `None` | Setting to an object enables the [Inception Score](https://torchmetrics.readthedocs.io/en/stable/image/inception_score.html) metric.
 | `KID` | No | `None` | Setting to an object enables the [Kernel Inception Distance](https://torchmetrics.readthedocs.io/en/stable/image/kernel_inception_distance.html) metric. |

configs/decoder_defaults.py

@@ -1,82 +0,0 @@
-"""
-Defines the default values for the decoder config
-"""
-
-from enum import Enum
-class ConfigField(Enum):
-    REQUIRED = 0  # This had more options. It's a bit unnecessary now, but I can't think of a better way to do it.
-
-default_config = {
-    "unets": ConfigField.REQUIRED,
-    "decoder": {
-        "image_sizes": ConfigField.REQUIRED,  # The side lengths of the upsampled image at the end of each unet
-        "image_size": ConfigField.REQUIRED,  # Usually the same as image_sizes[-1] I think
-        "channels": 3,
-        "timesteps": 1000,
-        "loss_type": "l2",
-        "beta_schedule": "cosine",
-        "learned_variance": True
-    },
-    "data": {
-        "webdataset_base_url": ConfigField.REQUIRED,  # Path to a webdataset with jpg images
-        "embeddings_url": ConfigField.REQUIRED,  # Path to .npy files with embeddings
-        "num_workers": 4,
-        "batch_size": 64,
-        "start_shard": 0,
-        "end_shard": 9999999,
-        "shard_width": 6,
-        "index_width": 4,
-        "splits": {
-            "train": 0.75,
-            "val": 0.15,
-            "test": 0.1
-        },
-        "shuffle_train": True,
-        "resample_train": False,
-        "preprocessing": {
-            "ToTensor": True
-        }
-    },
-    "train": {
-        "epochs": 20,
-        "lr": 1e-4,
-        "wd": 0.01,
-        "max_grad_norm": 0.5,
-        "save_every_n_samples": 100000,
-        "n_sample_images": 6,  # The number of example images to produce when sampling the train and test dataset
-        "device": "cuda:0",
-        "epoch_samples": None,  # Limits the number of samples per epoch. None means no limit. Required if resample_train is true as otherwise the number of samples per epoch is infinite.
-        "validation_samples": None,  # Same as above but for validation.
-        "use_ema": True,
-        "ema_beta": 0.99,
-        "amp": False,
-        "save_all": False,  # Whether to preserve all checkpoints
-        "save_latest": True,  # Whether to always save the latest checkpoint
-        "save_best": True,  # Whether to save the best checkpoint
-        "unet_training_mask": None  # If None, use all unets
-    },
-    "evaluate": {
-        "n_evalation_samples": 1000,
-        "FID": None,
-        "IS": None,
-        "KID": None,
-        "LPIPS": None
-    },
-    "tracker": {
-        "tracker_type": "console",  # Decoder currently supports console and wandb
-        "data_path": "./models",  # The path where files will be saved locally
-
-        "wandb_entity": "",  # Only needs to be set if tracker_type is wandb
-        "wandb_project": "",
-
-        "verbose": False  # Whether to print console logging for non-console trackers
-    },
-    "load": {
-        "source": None,  # Supports file and wandb
-
-        "run_path": "",  # Used only if source is wandb
-        "file_path": "",  # The local filepath if source is file. If source is wandb, the relative path to the model file in wandb.
-
-        "resume": False  # If using wandb, whether to resume the run
-    }
-}

configs/train_decoder_config.example.json

@@ -1,22 +1,21 @@
 {
-    "unets": [
-        {
-            "dim": 128,
-            "image_embed_dim": 768,
-            "cond_dim": 64,
-            "channels": 3,
-            "dim_mults": [1, 2, 4, 8],
-            "attn_dim_head": 32,
-            "attn_heads": 16
-        }
-    ],
     "decoder": {
+        "unets": [
+            {
+                "dim": 128,
+                "image_embed_dim": 768,
+                "cond_dim": 64,
+                "channels": 3,
+                "dim_mults": [1, 2, 4, 8],
+                "attn_dim_head": 32,
+                "attn_heads": 16
+            }
+        ],
         "image_sizes": [64],
-        "image_size": [64],
         "channels": 3,
         "timesteps": 1000,
         "loss_type": "l2",
-        "beta_schedule": "cosine",
+        "beta_schedule": ["cosine"],
         "learned_variance": true
     },
     "data": {
@@ -63,7 +62,7 @@
         "unet_training_mask": [true]
     },
     "evaluate": {
-        "n_evalation_samples": 1000,
+        "n_evaluation_samples": 1000,
         "FID": {
             "feature": 64
         },

configs/train_prior_config.example.json

@@ -0,0 +1,70 @@
+{
+    "prior": {
+        "clip": {
+            "make": "x-clip",
+            "model": "ViT-L/14",
+            "base_model_kwargs": {
+                "dim_text": 768,
+                "dim_image": 768,
+                "dim_latent": 768
+            }
+        },
+        "net": {
+            "dim": 768,
+            "depth": 12,
+            "num_timesteps": 1000,
+            "num_time_embeds": 1,
+            "num_image_embeds": 1,
+            "num_text_embeds": 1,
+            "dim_head": 64,
+            "heads": 12,
+            "ff_mult": 4,
+            "norm_out": true,
+            "attn_dropout": 0.0,
+            "ff_dropout": 0.0,
+            "final_proj": true,
+            "normformer": true,
+            "rotary_emb": true
+        },
+        "image_embed_dim": 768,
+        "image_size": 224,
+        "image_channels": 3,
+        "timesteps": 1000,
+        "cond_drop_prob": 0.1,
+        "loss_type": "l2",
+        "predict_x_start": true,
+        "beta_schedule": "cosine",
+        "condition_on_text_encodings": true
+    },
+    "data": {
+        "image_url": "https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion2B-en/img_emb/",
+        "text_url": "https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion2B-en/text_emb/",
+        "meta_url": "https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion2B-en/laion2B-en-metadata/",
+        "batch_size": 256,
+        "splits": {
+            "train": 0.9,
+            "val": 1e-7,
+            "test": 0.0999999
+        }
+    },
+    "train": {
+        "epochs": 1,
+        "lr": 1.1e-4,
+        "wd": 6.02e-2,
+        "max_grad_norm": 0.5,
+        "use_ema": true,
+        "amp": false,
+        "save_every": 10000
+    },
+    "load": {
+        "source": null,
+        "resume": false
+    },
+    "tracker": {
+        "tracker_type": "wandb",
+        "data_path": "./prior_checkpoints",
+        "wandb_entity": "laion",
+        "wandb_project": "diffusion-prior",
+        "verbose": true
+    }
+}

dalle2_pytorch/__init__.py

@@ -1,3 +1,4 @@
+from dalle2_pytorch.version import __version__
 from dalle2_pytorch.dalle2_pytorch import DALLE2, DiffusionPriorNetwork, DiffusionPrior, Unet, Decoder
 from dalle2_pytorch.dalle2_pytorch import OpenAIClipAdapter
 from dalle2_pytorch.trainer import DecoderTrainer, DiffusionPriorTrainer

dalle2_pytorch/dalle2_pytorch.py

@@ -1,6 +1,6 @@
 import math
+import random
 from tqdm import tqdm
-from inspect import isfunction
 from functools import partial, wraps
 from contextlib import contextmanager
 from collections import namedtuple
@@ -11,7 +11,7 @@ import torch.nn.functional as F
 from torch import nn, einsum
 import torchvision.transforms as T
 
-from einops import rearrange, repeat
+from einops import rearrange, repeat, reduce
 from einops.layers.torch import Rearrange
 from einops_exts import rearrange_many, repeat_many, check_shape
 from einops_exts.torch import EinopsToAndFrom
@@ -56,7 +56,7 @@ def maybe(fn):
 def default(val, d):
     if exists(val):
         return val
-    return d() if isfunction(d) else d
+    return d() if callable(d) else d
 
 def cast_tuple(val, length = 1):
     if isinstance(val, list):
@@ -313,11 +313,6 @@ def extract(a, t, x_shape):
     out = a.gather(-1, t)
     return out.reshape(b, *((1,) * (len(x_shape) - 1)))
 
-def noise_like(shape, device, repeat=False):
-    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
-    noise = lambda: torch.randn(shape, device=device)
-    return repeat_noise() if repeat else noise()
-
 def meanflat(x):
     return x.mean(dim = tuple(range(1, len(x.shape))))
 
@@ -372,7 +367,7 @@ def quadratic_beta_schedule(timesteps):
     scale = 1000 / timesteps
     beta_start = scale * 0.0001
     beta_end = scale * 0.02
-    return torch.linspace(beta_start**2, beta_end**2, timesteps, dtype = torch.float64) ** 2
+    return torch.linspace(beta_start**0.5, beta_end**0.5, timesteps, dtype = torch.float64) ** 2
 
 
 def sigmoid_beta_schedule(timesteps):
@@ -383,8 +378,8 @@ def sigmoid_beta_schedule(timesteps):
     return torch.sigmoid(betas) * (beta_end - beta_start) + beta_start
 
 
-class BaseGaussianDiffusion(nn.Module):
+class NoiseScheduler(nn.Module):
-    def __init__(self, *, beta_schedule, timesteps, loss_type):
+    def __init__(self, *, beta_schedule, timesteps, loss_type, p2_loss_weight_gamma = 0., p2_loss_weight_k = 1):
         super().__init__()
 
         if beta_schedule == "cosine":
@@ -449,6 +444,11 @@ class BaseGaussianDiffusion(nn.Module):
         register_buffer('posterior_mean_coef1', betas * torch.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))
         register_buffer('posterior_mean_coef2', (1. - alphas_cumprod_prev) * torch.sqrt(alphas) / (1. - alphas_cumprod))
 
+        # p2 loss reweighting
+
+        self.has_p2_loss_reweighting = p2_loss_weight_gamma > 0.
+        register_buffer('p2_loss_weight', (p2_loss_weight_k + alphas_cumprod / (1 - alphas_cumprod)) ** -p2_loss_weight_gamma)
+
     def q_posterior(self, x_start, x_t, t):
         posterior_mean = (
             extract(self.posterior_mean_coef1, t, x_t.shape) * x_start +
@@ -472,11 +472,10 @@ class BaseGaussianDiffusion(nn.Module):
             extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
         )
 
-    def sample(self, *args, **kwargs):
+    def p2_reweigh_loss(self, loss, times):
-        raise NotImplementedError
+        if not self.has_p2_loss_reweighting:
-
+            return loss
-    def forward(self, *args, **kwargs):
+        return loss * extract(self.p2_loss_weight, times, loss.shape)
-        raise NotImplementedError
 
 # diffusion prior
 
@@ -687,8 +686,7 @@ class Attention(nn.Module):
 
         # attention
 
-        sim = sim - sim.amax(dim = -1, keepdim = True).detach()
+        attn = sim.softmax(dim = -1, dtype = torch.float32)
-        attn = sim.softmax(dim = -1)
         attn = self.dropout(attn)
 
         # aggregate values
@@ -862,7 +860,7 @@ class DiffusionPriorNetwork(nn.Module):
 
         return pred_image_embed
 
-class DiffusionPrior(BaseGaussianDiffusion):
+class DiffusionPrior(nn.Module):
     def __init__(
         self,
         net,
@@ -883,13 +881,17 @@ class DiffusionPrior(BaseGaussianDiffusion):
         image_embed_scale = None,           # this is for scaling the l2-normed image embedding, so it is more suitable for gaussian diffusion, as outlined by Katherine (@crowsonkb) https://github.com/lucidrains/DALLE2-pytorch/issues/60#issue-1226116132
         clip_adapter_overrides = dict()
     ):
-        super().__init__(
+        super().__init__()
+
+        self.noise_scheduler = NoiseScheduler(
             beta_schedule = beta_schedule,
             timesteps = timesteps,
             loss_type = loss_type
         )
 
         if exists(clip):
+            assert image_channels == clip.image_channels, f'channels of image ({image_channels}) should be equal to the channels that CLIP accepts ({clip.image_channels})'
+
             if isinstance(clip, CLIP):
                 clip = XClipAdapter(clip, **clip_adapter_overrides)
             elif isinstance(clip, CoCa):
@@ -921,6 +923,13 @@ class DiffusionPrior(BaseGaussianDiffusion):
         self.training_clamp_l2norm = training_clamp_l2norm
         self.init_image_embed_l2norm = init_image_embed_l2norm
 
+        # device tracker
+        self.register_buffer('_dummy', torch.tensor([True]), persistent = False)
+
+    @property
+    def device(self):
+        return self._dummy.device
+
     def p_mean_variance(self, x, t, text_cond, clip_denoised = False, cond_scale = 1.):
         assert not (cond_scale != 1. and not self.can_classifier_guidance), 'the model was not trained with conditional dropout, and thus one cannot use classifier free guidance (cond_scale anything other than 1)'
 
@@ -931,7 +940,7 @@ class DiffusionPrior(BaseGaussianDiffusion):
             # not 100% sure of this above line - for any spectators, let me know in the github issues (or through a pull request) if you know how to correctly do this
             # i'll be rereading https://arxiv.org/abs/2111.14822, where i think a similar approach is taken
         else:
-            x_recon = self.predict_start_from_noise(x, t = t, noise = pred)
+            x_recon = self.noise_scheduler.predict_start_from_noise(x, t = t, noise = pred)
 
         if clip_denoised and not self.predict_x_start:
             x_recon.clamp_(-1., 1.)
@@ -939,21 +948,21 @@ class DiffusionPrior(BaseGaussianDiffusion):
         if self.predict_x_start and self.sampling_clamp_l2norm:
             x_recon = l2norm(x_recon) * self.image_embed_scale
 
-        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+        model_mean, posterior_variance, posterior_log_variance = self.noise_scheduler.q_posterior(x_start=x_recon, x_t=x, t=t)
         return model_mean, posterior_variance, posterior_log_variance
 
     @torch.no_grad()
-    def p_sample(self, x, t, text_cond = None, clip_denoised = True, repeat_noise = False, cond_scale = 1.):
+    def p_sample(self, x, t, text_cond = None, clip_denoised = True, cond_scale = 1.):
         b, *_, device = *x.shape, x.device
         model_mean, _, model_log_variance = self.p_mean_variance(x = x, t = t, text_cond = text_cond, clip_denoised = clip_denoised, cond_scale = cond_scale)
-        noise = noise_like(x.shape, device, repeat_noise)
+        noise = torch.randn_like(x)
         # no noise when t == 0
         nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
         return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
 
     @torch.no_grad()
     def p_sample_loop(self, shape, text_cond, cond_scale = 1.):
-        device = self.betas.device
+        device = self.device
 
         b = shape[0]
         image_embed = torch.randn(shape, device=device)
@@ -961,7 +970,7 @@ class DiffusionPrior(BaseGaussianDiffusion):
         if self.init_image_embed_l2norm:
             image_embed = l2norm(image_embed) * self.image_embed_scale
 
-        for i in tqdm(reversed(range(0, self.num_timesteps)), desc='sampling loop time step', total=self.num_timesteps):
+        for i in tqdm(reversed(range(0, self.noise_scheduler.num_timesteps)), desc='sampling loop time step', total=self.noise_scheduler.num_timesteps):
             times = torch.full((b,), i, device = device, dtype = torch.long)
             image_embed = self.p_sample(image_embed, times, text_cond = text_cond, cond_scale = cond_scale)
 
@@ -970,7 +979,7 @@ class DiffusionPrior(BaseGaussianDiffusion):
     def p_losses(self, image_embed, times, text_cond, noise = None):
         noise = default(noise, lambda: torch.randn_like(image_embed))
 
-        image_embed_noisy = self.q_sample(x_start = image_embed, t = times, noise = noise)
+        image_embed_noisy = self.noise_scheduler.q_sample(x_start = image_embed, t = times, noise = noise)
 
         pred = self.net(
             image_embed_noisy,
@@ -984,7 +993,7 @@ class DiffusionPrior(BaseGaussianDiffusion):
 
         target = noise if not self.predict_x_start else image_embed
 
-        loss = self.loss_fn(pred, target)
+        loss = self.noise_scheduler.loss_fn(pred, target)
         return loss
 
     @torch.no_grad()
@@ -995,7 +1004,7 @@ class DiffusionPrior(BaseGaussianDiffusion):
 
         img = torch.randn(shape, device = device)
 
-        for i in tqdm(reversed(range(0, self.num_timesteps)), desc = 'sampling loop time step', total = self.num_timesteps):
+        for i in tqdm(reversed(range(0, self.noise_scheduler.num_timesteps)), desc = 'sampling loop time step', total = self.noise_scheduler.num_timesteps):
             img = self.p_sample(img, torch.full((batch_size,), i, device = device, dtype = torch.long), text_cond = text_cond, cond_scale = cond_scale)
         return img
 
@@ -1067,7 +1076,7 @@ class DiffusionPrior(BaseGaussianDiffusion):
         # timestep conditioning from ddpm
 
         batch, device = image_embed.shape[0], image_embed.device
-        times = torch.randint(0, self.num_timesteps, (batch,), device = device, dtype = torch.long)
+        times = torch.randint(0, self.noise_scheduler.num_timesteps, (batch,), device = device, dtype = torch.long)
 
         # scale image embed (Katherine)
 
@@ -1082,8 +1091,9 @@ class DiffusionPrior(BaseGaussianDiffusion):
 def Upsample(dim):
     return nn.ConvTranspose2d(dim, dim, 4, 2, 1)
 
-def Downsample(dim):
+def Downsample(dim, *, dim_out = None):
-    return nn.Conv2d(dim, dim, 4, 2, 1)
+    dim_out = default(dim_out, dim)
+    return nn.Conv2d(dim, dim_out, 4, 2, 1)
 
 class SinusoidalPosEmb(nn.Module):
     def __init__(self, dim):
@@ -1105,13 +1115,20 @@ class Block(nn.Module):
         groups = 8
     ):
         super().__init__()
-        self.block = nn.Sequential(
+        self.project = nn.Conv2d(dim, dim_out, 3, padding = 1)
-            nn.Conv2d(dim, dim_out, 3, padding = 1),
+        self.norm = nn.GroupNorm(groups, dim_out)
-            nn.GroupNorm(groups, dim_out),
+        self.act = nn.SiLU()
-            nn.SiLU()
+
-        )
+    def forward(self, x, scale_shift = None):
-    def forward(self, x):
+        x = self.project(x)
-        return self.block(x)
+        x = self.norm(x)
+
+        if exists(scale_shift):
+            scale, shift = scale_shift
+            x = x * (scale + 1) + shift
+
+        x = self.act(x)
+        return x
 
 class ResnetBlock(nn.Module):
     def __init__(
@@ -1130,7 +1147,7 @@ class ResnetBlock(nn.Module):
         if exists(time_cond_dim):
             self.time_mlp = nn.Sequential(
                 nn.SiLU(),
-                nn.Linear(time_cond_dim, dim_out)
+                nn.Linear(time_cond_dim, dim_out * 2)
             )
 
         self.cross_attn = None
@@ -1150,11 +1167,14 @@ class ResnetBlock(nn.Module):
         self.res_conv = nn.Conv2d(dim, dim_out, 1) if dim != dim_out else nn.Identity()
 
     def forward(self, x, cond = None, time_emb = None):
-        h = self.block1(x)
 
+        scale_shift = None
         if exists(self.time_mlp) and exists(time_emb):
             time_emb = self.time_mlp(time_emb)
-            h = rearrange(time_emb, 'b c -> b c 1 1') + h
+            time_emb = rearrange(time_emb, 'b c -> b c 1 1')
+            scale_shift = time_emb.chunk(2, dim = 1)
+
+        h = self.block1(x, scale_shift = scale_shift)
 
         if exists(self.cross_attn):
             assert exists(cond)
@@ -1221,8 +1241,7 @@ class CrossAttention(nn.Module):
             mask = rearrange(mask, 'b j -> b 1 1 j')
             sim = sim.masked_fill(~mask, max_neg_value)
 
-        sim = sim - sim.amax(dim = -1, keepdim = True).detach()
+        attn = sim.softmax(dim = -1, dtype = torch.float32)
-        attn = sim.softmax(dim = -1)
 
         out = einsum('b h i j, b h j d -> b h i d', attn, v)
         out = rearrange(out, 'b h n d -> b n (h d)')
@@ -1331,12 +1350,15 @@ class Unet(nn.Module):
         cond_on_text_encodings = False,
         max_text_len = 256,
         cond_on_image_embeds = False,
+        add_image_embeds_to_time = True, # alerted by @mhh0318 to a phrase in the paper - "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and adding CLIP embeddings to the existing timestep embedding"
         init_dim = None,
         init_conv_kernel_size = 7,
         resnet_groups = 8,
+        num_resnet_blocks = 2,
         init_cross_embed_kernel_sizes = (3, 7, 15),
         cross_embed_downsample = False,
         cross_embed_downsample_kernel_sizes = (2, 4),
+        memory_efficient = False,
         **kwargs
     ):
         super().__init__()
@@ -1356,7 +1378,7 @@ class Unet(nn.Module):
         self.channels_out = default(channels_out, channels)
 
         init_channels = channels if not lowres_cond else channels * 2 # in cascading diffusion, one concats the low resolution image, blurred, for conditioning the higher resolution synthesis
-        init_dim = default(init_dim, dim // 3 * 2)
+        init_dim = default(init_dim, dim)
 
         self.init_conv = CrossEmbedLayer(init_channels, dim_out = init_dim, kernel_sizes = init_cross_embed_kernel_sizes, stride = 1)
 
@@ -1383,11 +1405,16 @@ class Unet(nn.Module):
             nn.Linear(time_cond_dim, time_cond_dim)
         )
 
-        self.image_to_cond = nn.Sequential(
+        self.image_to_tokens = nn.Sequential(
             nn.Linear(image_embed_dim, cond_dim * num_image_tokens),
             Rearrange('b (n d) -> b n d', n = num_image_tokens)
         ) if cond_on_image_embeds and image_embed_dim != cond_dim else nn.Identity()
 
+        self.to_image_hiddens = nn.Sequential(
+            nn.Linear(image_embed_dim, time_cond_dim),
+            nn.GELU()
+        ) if cond_on_image_embeds and add_image_embeds_to_time else None
+
         self.norm_cond = nn.LayerNorm(cond_dim)
         self.norm_mid_cond = nn.LayerNorm(cond_dim)
 
@@ -1408,6 +1435,7 @@ class Unet(nn.Module):
         # for classifier free guidance
 
         self.null_image_embed = nn.Parameter(torch.randn(1, num_image_tokens, cond_dim))
+        self.null_image_hiddens = nn.Parameter(torch.randn(1, time_cond_dim))
 
         self.max_text_len = max_text_len
         self.null_text_embed = nn.Parameter(torch.randn(1, max_text_len, cond_dim))
@@ -1419,6 +1447,7 @@ class Unet(nn.Module):
         # resnet block klass
 
         resnet_groups = cast_tuple(resnet_groups, len(in_out))
+        num_resnet_blocks = cast_tuple(num_resnet_blocks, len(in_out))
 
         assert len(resnet_groups) == len(in_out)
 
@@ -1434,16 +1463,17 @@ class Unet(nn.Module):
         self.ups = nn.ModuleList([])
         num_resolutions = len(in_out)
 
-        for ind, ((dim_in, dim_out), groups) in enumerate(zip(in_out, resnet_groups)):
+        for ind, ((dim_in, dim_out), groups, layer_num_resnet_blocks) in enumerate(zip(in_out, resnet_groups, num_resnet_blocks)):
             is_first = ind == 0
             is_last = ind >= (num_resolutions - 1)
             layer_cond_dim = cond_dim if not is_first else None
 
             self.downs.append(nn.ModuleList([
-                ResnetBlock(dim_in, dim_out, time_cond_dim = time_cond_dim, groups = groups),
+                downsample_klass(dim_in, dim_out = dim_out) if memory_efficient else None,
+                ResnetBlock(dim_out if memory_efficient else dim_in, dim_out, time_cond_dim = time_cond_dim, groups = groups),
                 Residual(LinearAttention(dim_out, **attn_kwargs)) if sparse_attn else nn.Identity(),
-                ResnetBlock(dim_out, dim_out, cond_dim = layer_cond_dim, time_cond_dim = time_cond_dim, groups = groups),
+                nn.ModuleList([ResnetBlock(dim_out, dim_out, cond_dim = layer_cond_dim, time_cond_dim = time_cond_dim, groups = groups) for _ in range(layer_num_resnet_blocks)]),
-                downsample_klass(dim_out) if not is_last else nn.Identity()
+                downsample_klass(dim_out) if not is_last and not memory_efficient else None
             ]))
 
         mid_dim = dims[-1]
@@ -1452,19 +1482,19 @@ class Unet(nn.Module):
         self.mid_attn = EinopsToAndFrom('b c h w', 'b (h w) c', Residual(Attention(mid_dim, **attn_kwargs))) if attend_at_middle else None
         self.mid_block2 = ResnetBlock(mid_dim, mid_dim, cond_dim = cond_dim, time_cond_dim = time_cond_dim, groups = resnet_groups[-1])
 
-        for ind, ((dim_in, dim_out), groups) in enumerate(zip(reversed(in_out[1:]), reversed(resnet_groups))):
+        for ind, ((dim_in, dim_out), groups, layer_num_resnet_blocks) in enumerate(zip(reversed(in_out), reversed(resnet_groups), reversed(num_resnet_blocks))):
-            is_last = ind >= (num_resolutions - 2)
+            is_last = ind >= (len(in_out) - 1)
             layer_cond_dim = cond_dim if not is_last else None
 
             self.ups.append(nn.ModuleList([
                 ResnetBlock(dim_out * 2, dim_in, cond_dim = layer_cond_dim, time_cond_dim = time_cond_dim, groups = groups),
                 Residual(LinearAttention(dim_in, **attn_kwargs)) if sparse_attn else nn.Identity(),
-                ResnetBlock(dim_in, dim_in, cond_dim = layer_cond_dim, time_cond_dim = time_cond_dim, groups = groups),
+                nn.ModuleList([ResnetBlock(dim_in, dim_in, cond_dim = layer_cond_dim, time_cond_dim = time_cond_dim, groups = groups)  for _ in range(layer_num_resnet_blocks)]),
-                Upsample(dim_in)
+                Upsample(dim_in) if not is_last or memory_efficient else nn.Identity()
             ]))
 
         self.final_conv = nn.Sequential(
-            ResnetBlock(dim, dim, groups = resnet_groups[0]),
+            ResnetBlock(dim * 2, dim, groups = resnet_groups[0]),
             nn.Conv2d(dim, self.channels_out, 1)
         )
 
@@ -1536,6 +1566,7 @@ class Unet(nn.Module):
         # initial convolution
 
         x = self.init_conv(x)
+        r = x.clone() # final residual
 
         # time conditioning
 
@@ -1549,7 +1580,23 @@ class Unet(nn.Module):
         image_keep_mask = prob_mask_like((batch_size,), 1 - image_cond_drop_prob, device = device)
         text_keep_mask = prob_mask_like((batch_size,), 1 - text_cond_drop_prob, device = device)
 
-        image_keep_mask, text_keep_mask = rearrange_many((image_keep_mask, text_keep_mask), 'b -> b 1 1')
+        text_keep_mask = rearrange(text_keep_mask, 'b -> b 1 1')
+
+        # image embedding to be summed to time embedding
+        # discovered by @mhh0318 in the paper
+
+        if exists(image_embed) and exists(self.to_image_hiddens):
+            image_hiddens = self.to_image_hiddens(image_embed)
+            image_keep_mask_hidden = rearrange(image_keep_mask, 'b -> b 1')
+            null_image_hiddens = self.null_image_hiddens.to(image_hiddens.dtype)
+
+            image_hiddens = torch.where(
+                image_keep_mask_hidden,
+                image_hiddens,
+                null_image_hiddens
+            )
+
+            t = t + image_hiddens
 
         # mask out image embedding depending on condition dropout
         # for classifier free guidance
@@ -1557,11 +1604,12 @@ class Unet(nn.Module):
         image_tokens = None
 
         if self.cond_on_image_embeds:
-            image_tokens = self.image_to_cond(image_embed)
+            image_keep_mask_embed = rearrange(image_keep_mask, 'b -> b 1 1')
+            image_tokens = self.image_to_tokens(image_embed)
             null_image_embed = self.null_image_embed.to(image_tokens.dtype) # for some reason pytorch AMP not working
 
             image_tokens = torch.where(
-                image_keep_mask,
+                image_keep_mask_embed,
                 image_tokens,
                 null_image_embed
             )
@@ -1616,12 +1664,20 @@ class Unet(nn.Module):
 
         hiddens = []
 
-        for block1, sparse_attn, block2, downsample in self.downs:
+        for pre_downsample, init_block, sparse_attn, resnet_blocks, post_downsample in self.downs:
-            x = block1(x, c, t)
+            if exists(pre_downsample):
+                x = pre_downsample(x)
+
+            x = init_block(x, c, t)
             x = sparse_attn(x)
-            x = block2(x, c, t)
+
+            for resnet_block in resnet_blocks:
+                x = resnet_block(x, c, t)
+
             hiddens.append(x)
-            x = downsample(x)
+
+            if exists(post_downsample):
+                x = post_downsample(x)
 
         x = self.mid_block1(x, mid_c, t)
 
@@ -1630,20 +1686,24 @@ class Unet(nn.Module):
 
         x = self.mid_block2(x, mid_c, t)
 
-        for block1, sparse_attn, block2, upsample in self.ups:
+        for init_block, sparse_attn, resnet_blocks, upsample in self.ups:
-            x = torch.cat((x, hiddens.pop()), dim=1)
+            x = torch.cat((x, hiddens.pop()), dim = 1)
-            x = block1(x, c, t)
+            x = init_block(x, c, t)
             x = sparse_attn(x)
-            x = block2(x, c, t)
+
+            for resnet_block in resnet_blocks:
+                x = resnet_block(x, c, t)
+
             x = upsample(x)
 
+        x = torch.cat((x, r), dim = 1)
         return self.final_conv(x)
 
 class LowresConditioner(nn.Module):
     def __init__(
         self,
         downsample_first = True,
-        blur_sigma = 0.1,
+        blur_sigma = 0.6,
         blur_kernel_size = 3,
     ):
         super().__init__()
@@ -1667,13 +1727,25 @@ class LowresConditioner(nn.Module):
             # when training, blur the low resolution conditional image
             blur_sigma = default(blur_sigma, self.blur_sigma)
             blur_kernel_size = default(blur_kernel_size, self.blur_kernel_size)
+
+            # allow for drawing a random sigma between lo and hi float values
+            if isinstance(blur_sigma, tuple):
+                blur_sigma = tuple(map(float, blur_sigma))
+                blur_sigma = random.uniform(*blur_sigma)
+
+            # allow for drawing a random kernel size between lo and hi int values
+            if isinstance(blur_kernel_size, tuple):
+                blur_kernel_size = tuple(map(int, blur_kernel_size))
+                kernel_size_lo, kernel_size_hi = blur_kernel_size
+                blur_kernel_size = random.randrange(kernel_size_lo, kernel_size_hi + 1)
+
             cond_fmap = gaussian_blur2d(cond_fmap, cast_tuple(blur_kernel_size, 2), cast_tuple(blur_sigma, 2))
 
         cond_fmap = resize_image_to(cond_fmap, target_image_size)
 
         return cond_fmap
 
-class Decoder(BaseGaussianDiffusion):
+class Decoder(nn.Module):
     def __init__(
         self,
         unet,
@@ -1686,36 +1758,44 @@ class Decoder(BaseGaussianDiffusion):
         image_cond_drop_prob = 0.1,
         text_cond_drop_prob = 0.5,
         loss_type = 'l2',
-        beta_schedule = 'cosine',
+        beta_schedule = None,
         predict_x_start = False,
         predict_x_start_for_latent_diffusion = False,
         image_sizes = None,                         # for cascading ddpm, image size at each stage
         random_crop_sizes = None,                   # whether to random crop the image at that stage in the cascade (super resoluting convolutions at the end may be able to generalize on smaller crops)
         lowres_downsample_first = True,             # cascading ddpm - resizes to lower resolution, then to next conditional resolution + blur
-        blur_sigma = 0.1,                           # cascading ddpm - blur sigma
+        blur_sigma = (0.1, 0.2),                    # cascading ddpm - blur sigma
         blur_kernel_size = 3,                       # cascading ddpm - blur kernel size
         condition_on_text_encodings = False,        # the paper suggested that this didn't do much in the decoder, but i'm allowing the option for experimentation
         clip_denoised = True,
         clip_x_start = True,
         clip_adapter_overrides = dict(),
         learned_variance = True,
+        learned_variance_constrain_frac = False,
         vb_loss_weight = 0.001,
         unconditional = False,
         auto_normalize_img = True,                  # whether to take care of normalizing the image from [0, 1] to [-1, 1] and back automatically - you can turn this off if you want to pass in the [-1, 1] ranged image yourself from the dataloader
+        use_dynamic_thres = False,                  # from the Imagen paper
+        dynamic_thres_percentile = 0.9,
+        p2_loss_weight_gamma = 0.,                  # p2 loss weight, from https://arxiv.org/abs/2204.00227 - 0 is equivalent to weight of 1 across time - 1. is recommended
+        p2_loss_weight_k = 1
     ):
-        super().__init__(
+        super().__init__()
-            beta_schedule = beta_schedule,
-            timesteps = timesteps,
-            loss_type = loss_type
-        )
 
         self.unconditional = unconditional
-        assert not (condition_on_text_encodings and unconditional), 'unconditional decoder image generation cannot be set to True if conditioning on text is present'
 
-        assert self.unconditional or (exists(clip) ^ exists(image_size)), 'either CLIP is supplied, or you must give the image_size and channels (usually 3 for RGB)'
+        # text conditioning
+
+        assert not (condition_on_text_encodings and unconditional), 'unconditional decoder image generation cannot be set to True if conditioning on text is present'
+        self.condition_on_text_encodings = condition_on_text_encodings
+
+        # clip
 
         self.clip = None
         if exists(clip):
+            assert not unconditional, 'clip must not be given if doing unconditional image training'
+            assert channels == clip.image_channels, f'channels of image ({channels}) should be equal to the channels that CLIP accepts ({clip.image_channels})'
+
             if isinstance(clip, CLIP):
                 clip = XClipAdapter(clip, **clip_adapter_overrides)
             elif isinstance(clip, CoCa):
@@ -1725,24 +1805,34 @@ class Decoder(BaseGaussianDiffusion):
             assert isinstance(clip, BaseClipAdapter)
 
             self.clip = clip
-            self.clip_image_size = clip.image_size
-            self.channels = clip.image_channels
-        else:
-            self.clip_image_size = image_size
-            self.channels = channels
 
-        self.condition_on_text_encodings = condition_on_text_encodings
+        # determine image size, with image_size and image_sizes taking precedence
+
+        if exists(image_size) or exists(image_sizes):
+            assert exists(image_size) ^ exists(image_sizes), 'only one of image_size or image_sizes must be given'
+            image_size = default(image_size, lambda: image_sizes[-1])
+        elif exists(clip):
+            image_size = clip.image_size
+        else:
+            raise Error('either image_size, image_sizes, or clip must be given to decoder')
+
+        # channels
+
+        self.channels = channels
 
         # automatically take care of ensuring that first unet is unconditional
         # while the rest of the unets are conditioned on the low resolution image produced by previous unet
 
         unets = cast_tuple(unet)
+        num_unets = len(unets)
+
         vaes = pad_tuple_to_length(cast_tuple(vae), len(unets), fillvalue = NullVQGanVAE(channels = self.channels))
 
         # whether to use learned variance, defaults to True for the first unet in the cascade, as in paper
 
         learned_variance = pad_tuple_to_length(cast_tuple(learned_variance), len(unets), fillvalue = False)
         self.learned_variance = learned_variance
+        self.learned_variance_constrain_frac = learned_variance_constrain_frac # whether to constrain the output of the network (the interpolation fraction) from 0 to 1
         self.vb_loss_weight = vb_loss_weight
 
         # construct unets and vaes
@@ -1771,9 +1861,30 @@ class Decoder(BaseGaussianDiffusion):
             self.unets.append(one_unet)
             self.vaes.append(one_vae.copy_for_eval())
 
+        # create noise schedulers per unet
+
+        if not exists(beta_schedule):
+            beta_schedule = ('cosine', *(('cosine',) * max(num_unets - 2, 0)), *(('linear',) * int(num_unets > 1)))
+
+        beta_schedule = cast_tuple(beta_schedule, num_unets)
+        p2_loss_weight_gamma = cast_tuple(p2_loss_weight_gamma, num_unets)
+
+        self.noise_schedulers = nn.ModuleList([])
+
+        for unet_beta_schedule, unet_p2_loss_weight_gamma in zip(beta_schedule, p2_loss_weight_gamma):
+            noise_scheduler = NoiseScheduler(
+                beta_schedule = unet_beta_schedule,
+                timesteps = timesteps,
+                loss_type = loss_type,
+                p2_loss_weight_gamma = unet_p2_loss_weight_gamma,
+                p2_loss_weight_k = p2_loss_weight_k
+            )
+
+            self.noise_schedulers.append(noise_scheduler)
+
         # unet image sizes
 
-        image_sizes = default(image_sizes, (self.clip_image_size,))
+        image_sizes = default(image_sizes, (image_size,))
         image_sizes = tuple(sorted(set(image_sizes)))
 
         assert len(self.unets) == len(image_sizes), f'you did not supply the correct number of u-nets ({len(self.unets)}) for resolutions {image_sizes}'
@@ -1810,10 +1921,24 @@ class Decoder(BaseGaussianDiffusion):
         self.clip_denoised = clip_denoised
         self.clip_x_start = clip_x_start
 
+        # dynamic thresholding settings, if clipping denoised during sampling
+
+        self.use_dynamic_thres = use_dynamic_thres
+        self.dynamic_thres_percentile = dynamic_thres_percentile
+
         # normalize and unnormalize image functions
+
         self.normalize_img = normalize_neg_one_to_one if auto_normalize_img else identity
         self.unnormalize_img = unnormalize_zero_to_one if auto_normalize_img else identity
 
+        # device tracker
+
+        self.register_buffer('_dummy', torch.Tensor([True]), persistent = False)
+
+    @property
+    def device(self):
+        return self._dummy.device
+
     def get_unet(self, unet_number):
         assert 0 < unet_number <= len(self.unets)
         index = unet_number - 1
@@ -1837,7 +1962,7 @@ class Decoder(BaseGaussianDiffusion):
         for unet, device in zip(self.unets, devices):
             unet.to(device)
 
-    def p_mean_variance(self, unet, x, t, image_embed, text_encodings = None, text_mask = None, lowres_cond_img = None, clip_denoised = True, predict_x_start = False, learned_variance = False, cond_scale = 1., model_output = None):
+    def p_mean_variance(self, unet, x, t, image_embed, noise_scheduler, text_encodings = None, text_mask = None, lowres_cond_img = None, clip_denoised = True, predict_x_start = False, learned_variance = False, cond_scale = 1., model_output = None):
         assert not (cond_scale != 1. and not self.can_classifier_guidance), 'the decoder was not trained with conditional dropout, and thus one cannot use classifier free guidance (cond_scale anything other than 1)'
 
         pred = default(model_output, lambda: unet.forward_with_cond_scale(x, t, image_embed = image_embed, text_encodings = text_encodings, text_mask = text_mask, cond_scale = cond_scale, lowres_cond_img = lowres_cond_img))
@@ -1848,38 +1973,55 @@ class Decoder(BaseGaussianDiffusion):
         if predict_x_start:
             x_recon = pred
         else:
-            x_recon = self.predict_start_from_noise(x, t = t, noise = pred)
+            x_recon = noise_scheduler.predict_start_from_noise(x, t = t, noise = pred)
 
         if clip_denoised:
-            x_recon.clamp_(-1., 1.)
+            # s is the threshold amount
+            # static thresholding would just be s = 1
+            s = 1.
+            if self.use_dynamic_thres:
+                s = torch.quantile(
+                    rearrange(x_recon, 'b ... -> b (...)').abs(),
+                    self.dynamic_thres_percentile,
+                    dim = -1
+                )
 
-        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+                s.clamp_(min = 1.)
+                s = s.view(-1, *((1,) * (x_recon.ndim - 1)))
+
+            # clip by threshold, depending on whether static or dynamic
+            x_recon = x_recon.clamp(-s, s) / s
+
+        model_mean, posterior_variance, posterior_log_variance = noise_scheduler.q_posterior(x_start=x_recon, x_t=x, t=t)
 
         if learned_variance:
             # if learned variance, posterio variance and posterior log variance are predicted by the network
             # by an interpolation of the max and min log beta values
             # eq 15 - https://arxiv.org/abs/2102.09672
-            min_log = extract(self.posterior_log_variance_clipped, t, x.shape)
+            min_log = extract(noise_scheduler.posterior_log_variance_clipped, t, x.shape)
-            max_log = extract(torch.log(self.betas), t, x.shape)
+            max_log = extract(torch.log(noise_scheduler.betas), t, x.shape)
             var_interp_frac = unnormalize_zero_to_one(var_interp_frac_unnormalized)
 
+            if self.learned_variance_constrain_frac:
+                var_interp_frac = var_interp_frac.sigmoid()
+
             posterior_log_variance = var_interp_frac * max_log + (1 - var_interp_frac) * min_log
             posterior_variance = posterior_log_variance.exp()
 
         return model_mean, posterior_variance, posterior_log_variance
 
     @torch.no_grad()
-    def p_sample(self, unet, x, t, image_embed, text_encodings = None, text_mask = None, cond_scale = 1., lowres_cond_img = None, predict_x_start = False, learned_variance = False, clip_denoised = True, repeat_noise = False):
+    def p_sample(self, unet, x, t, image_embed, noise_scheduler, text_encodings = None, text_mask = None, cond_scale = 1., lowres_cond_img = None, predict_x_start = False, learned_variance = False, clip_denoised = True):
         b, *_, device = *x.shape, x.device
-        model_mean, _, model_log_variance = self.p_mean_variance(unet, x = x, t = t, image_embed = image_embed, text_encodings = text_encodings, text_mask = text_mask, cond_scale = cond_scale, lowres_cond_img = lowres_cond_img, clip_denoised = clip_denoised, predict_x_start = predict_x_start, learned_variance = learned_variance)
+        model_mean, _, model_log_variance = self.p_mean_variance(unet, x = x, t = t, image_embed = image_embed, text_encodings = text_encodings, text_mask = text_mask, cond_scale = cond_scale, lowres_cond_img = lowres_cond_img, clip_denoised = clip_denoised, predict_x_start = predict_x_start, noise_scheduler = noise_scheduler, learned_variance = learned_variance)
-        noise = noise_like(x.shape, device, repeat_noise)
+        noise = torch.randn_like(x)
         # no noise when t == 0
         nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
         return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
 
     @torch.no_grad()
-    def p_sample_loop(self, unet, shape, image_embed, predict_x_start = False, learned_variance = False, clip_denoised = True, lowres_cond_img = None, text_encodings = None, text_mask = None, cond_scale = 1, is_latent_diffusion = False):
+    def p_sample_loop(self, unet, shape, image_embed, noise_scheduler, predict_x_start = False, learned_variance = False, clip_denoised = True, lowres_cond_img = None, text_encodings = None, text_mask = None, cond_scale = 1, is_latent_diffusion = False):
-        device = self.betas.device
+        device = self.device
 
         b = shape[0]
         img = torch.randn(shape, device = device)
@@ -1887,7 +2029,7 @@ class Decoder(BaseGaussianDiffusion):
         if not is_latent_diffusion:
             lowres_cond_img = maybe(self.normalize_img)(lowres_cond_img)
 
-        for i in tqdm(reversed(range(0, self.num_timesteps)), desc = 'sampling loop time step', total = self.num_timesteps):
+        for i in tqdm(reversed(range(0, noise_scheduler.num_timesteps)), desc = 'sampling loop time step', total = noise_scheduler.num_timesteps):
             img = self.p_sample(
                 unet,
                 img,
@@ -1898,6 +2040,7 @@ class Decoder(BaseGaussianDiffusion):
                 cond_scale = cond_scale,
                 lowres_cond_img = lowres_cond_img,
                 predict_x_start = predict_x_start,
+                noise_scheduler = noise_scheduler,
                 learned_variance = learned_variance,
                 clip_denoised = clip_denoised
             )
@@ -1905,7 +2048,7 @@ class Decoder(BaseGaussianDiffusion):
         unnormalize_img = self.unnormalize_img(img)
         return unnormalize_img
 
-    def p_losses(self, unet, x_start, times, *, image_embed, lowres_cond_img = None, text_encodings = None, text_mask = None, predict_x_start = False, noise = None, learned_variance = False, clip_denoised = False, is_latent_diffusion = False):
+    def p_losses(self, unet, x_start, times, *, image_embed, noise_scheduler, lowres_cond_img = None, text_encodings = None, text_mask = None, predict_x_start = False, noise = None, learned_variance = False, clip_denoised = False, is_latent_diffusion = False):
         noise = default(noise, lambda: torch.randn_like(x_start))
 
         # normalize to [-1, 1]
@@ -1916,7 +2059,7 @@ class Decoder(BaseGaussianDiffusion):
 
         # get x_t
 
-        x_noisy = self.q_sample(x_start = x_start, t = times, noise = noise)
+        x_noisy = noise_scheduler.q_sample(x_start = x_start, t = times, noise = noise)
 
         model_output = unet(
             x_noisy,
@@ -1936,7 +2079,12 @@ class Decoder(BaseGaussianDiffusion):
 
         target = noise if not predict_x_start else x_start
 
-        loss = self.loss_fn(pred, target)
+        loss = noise_scheduler.loss_fn(pred, target, reduction = 'none')
+        loss = reduce(loss, 'b ... -> b (...)', 'mean')
+
+        loss = noise_scheduler.p2_reweigh_loss(loss, times)
+
+        loss = loss.mean()
 
         if not learned_variance:
             # return simple loss if not using learned variance
@@ -1949,8 +2097,8 @@ class Decoder(BaseGaussianDiffusion):
 
         # if learning the variance, also include the extra weight kl loss
 
-        true_mean, _, true_log_variance_clipped = self.q_posterior(x_start = x_start, x_t = x_noisy, t = times)
+        true_mean, _, true_log_variance_clipped = noise_scheduler.q_posterior(x_start = x_start, x_t = x_noisy, t = times)
-        model_mean, _, model_log_variance = self.p_mean_variance(unet, x = x_noisy, t = times, image_embed = image_embed, clip_denoised = clip_denoised, learned_variance = True, model_output = model_output)
+        model_mean, _, model_log_variance = self.p_mean_variance(unet, x = x_noisy, t = times, image_embed = image_embed, noise_scheduler = noise_scheduler, clip_denoised = clip_denoised, learned_variance = True, model_output = model_output)
 
         # kl loss with detached model predicted mean, for stability reasons as in paper
 
@@ -1982,7 +2130,8 @@ class Decoder(BaseGaussianDiffusion):
         text_encodings = None,
         batch_size = 1,
         cond_scale = 1.,
-        stop_at_unet_number = None
+        stop_at_unet_number = None,
+        distributed = False,
     ):
         assert self.unconditional or exists(image_embed), 'image embed must be present on sampling from decoder unless if trained unconditionally'
 
@@ -1999,9 +2148,9 @@ class Decoder(BaseGaussianDiffusion):
         img = None
         is_cuda = next(self.parameters()).is_cuda
 
-        for unet_number, unet, vae, channel, image_size, predict_x_start, learned_variance in tqdm(zip(range(1, len(self.unets) + 1), self.unets, self.vaes, self.sample_channels, self.image_sizes, self.predict_x_start, self.learned_variance)):
+        for unet_number, unet, vae, channel, image_size, predict_x_start, learned_variance, noise_scheduler in tqdm(zip(range(1, len(self.unets) + 1), self.unets, self.vaes, self.sample_channels, self.image_sizes, self.predict_x_start, self.learned_variance, self.noise_schedulers)):
 
-            context = self.one_unet_in_gpu(unet = unet) if is_cuda else null_context()
+            context = self.one_unet_in_gpu(unet = unet) if is_cuda and not distributed else null_context()
 
             with context:
                 lowres_cond_img = None
@@ -2027,7 +2176,8 @@ class Decoder(BaseGaussianDiffusion):
                     learned_variance = learned_variance,
                     clip_denoised = not is_latent_diffusion,
                     lowres_cond_img = lowres_cond_img,
-                    is_latent_diffusion = is_latent_diffusion
+                    is_latent_diffusion = is_latent_diffusion,
+                    noise_scheduler = noise_scheduler
                 )
 
                 img = vae.decode(img)
@@ -2053,6 +2203,7 @@ class Decoder(BaseGaussianDiffusion):
         unet = self.get_unet(unet_number)
 
         vae                 = self.vaes[unet_index]
+        noise_scheduler     = self.noise_schedulers[unet_index]
         target_image_size   = self.image_sizes[unet_index]
         predict_x_start     = self.predict_x_start[unet_index]
         random_crop_size    = self.random_crop_sizes[unet_index]
@@ -2062,7 +2213,7 @@ class Decoder(BaseGaussianDiffusion):
         check_shape(image, 'b c h w', c = self.channels)
         assert h >= target_image_size and w >= target_image_size
 
-        times = torch.randint(0, self.num_timesteps, (b,), device = device, dtype = torch.long)
+        times = torch.randint(0, noise_scheduler.num_timesteps, (b,), device = device, dtype = torch.long)
 
         if not exists(image_embed) and not self.unconditional:
             assert exists(self.clip), 'if you want to derive CLIP image embeddings automatically, you must supply `clip` to the decoder on init'
@@ -2093,7 +2244,7 @@ class Decoder(BaseGaussianDiffusion):
             image = vae.encode(image)
             lowres_cond_img = maybe(vae.encode)(lowres_cond_img)
 
-        return self.p_losses(unet, image, times, image_embed = image_embed, text_encodings = text_encodings, text_mask = text_mask, lowres_cond_img = lowres_cond_img, predict_x_start = predict_x_start, learned_variance = learned_variance, is_latent_diffusion = is_latent_diffusion)
+        return self.p_losses(unet, image, times, image_embed = image_embed, text_encodings = text_encodings, text_mask = text_mask, lowres_cond_img = lowres_cond_img, predict_x_start = predict_x_start, learned_variance = learned_variance, is_latent_diffusion = is_latent_diffusion, noise_scheduler = noise_scheduler)
 
 # main class
 

dalle2_pytorch/dataloaders/README.md

@@ -15,7 +15,7 @@ from dalle2_pytorch.dataloaders import ImageEmbeddingDataset, create_image_embed
 
 # Create a dataloader directly.
 dataloader = create_image_embedding_dataloader(
-    tar_url="/path/or/url/to/webdataset/{0000..9999}.tar", # Uses braket expanding notation. This specifies to read all tars from 0000.tar to 9999.tar
+    tar_url="/path/or/url/to/webdataset/{0000..9999}.tar", # Uses bracket expanding notation. This specifies to read all tars from 0000.tar to 9999.tar
     embeddings_url="path/or/url/to/embeddings/folder",     # Included if .npy files are not in webdataset. Left out or set to None otherwise
     num_workers=4,
     batch_size=32,
@@ -39,3 +39,37 @@ dataset = ImageEmbeddingDataset(
 )
 ```
 
+### Diffusion Prior: Prior Embedding Dataset
+When training the prior it is much more efficient to work with pre-computed embeddings. The `PriorEmbeddingDataset` class enables you to leverage the same script (with minimal modification) for both embedding-only and text-conditioned prior training. This saves you from having to worry about a lot of the boilerplate code.
+
+To utilize the `PriorEmbeddingDataset`, all you need to do is make a single call to `get_reader()` which will create `EmbeddingReader` object(s) for you. Afterwards, you can utilize `make_splits()` to cleanly create DataLoader objects from for your training run.
+
+If you are training in a distributed manner, `make_splits()` accepts `rank` and `world_size` arguments to properly distribute to each process. The defaults for these values are `rank=0` and `world_size=1`, so single-process training can safely ignore these parameters.
+
+Usage:
+```python
+from dalle2_pytorch.dataloaders import get_reader, make_splits
+
+# grab embeddings from some specified location
+IMG_URL = "data/img_emb/"
+META_URL = "data/meta/"
+
+reader = get_reader(text_conditioned=True, img_url=IMG_URL, meta_url=META_URL)
+
+# some config for training
+TRAIN_ARGS = {
+    "world_size": 3,
+    "text_conditioned": True,
+    "start": 0,
+    "num_data_points": 10000,
+    "batch_size": 2,
+    "train_split": 0.5,
+    "eval_split": 0.25,
+    "image_reader": reader,
+}
+
+# specifying a rank will handle allocation internally
+rank0_train, rank0_eval, rank0_test = make_splits(rank=0, **TRAIN_ARGS)
+rank1_train, rank1_eval, rank1_test = make_splits(rank=1, **TRAIN_ARGS)
+rank2_train, rank2_eval, rank2_test = make_splits(rank=2, **TRAIN_ARGS)
+```

dalle2_pytorch/dataloaders/__init__.py

@@ -1,2 +1,2 @@
 from dalle2_pytorch.dataloaders.decoder_loader import ImageEmbeddingDataset, create_image_embedding_dataloader
-from dalle2_pytorch.dataloaders.embedding_wrapper import make_splits
+from dalle2_pytorch.dataloaders.prior_loader import make_splits, get_reader, PriorEmbeddingDataset

dalle2_pytorch/dataloaders/decoder_loader.py

@@ -164,9 +164,6 @@ class ImageEmbeddingDataset(wds.DataPipeline, wds.compat.FluidInterface):
             # There may be webdataset shards that do not have a embedding shard associated with it. If we do not skip these, they would cause issues.
             self.append(skip_unassociated_shards(embeddings_url=embedding_folder_url, handler=handler))
 
-        self.append(wds.split_by_node)
-        self.append(wds.split_by_worker)
-
         self.append(wds.tarfile_to_samples(handler=handler))
         self.append(wds.decode("pilrgb", handler=handler))
         if embedding_folder_url is not None:

dalle2_pytorch/dataloaders/embedding_wrapper.py

@@ -1,180 +0,0 @@
-from torch.utils.data import IterableDataset
-from torch import from_numpy
-from clip import tokenize
-from embedding_reader import EmbeddingReader
-
-
-class PriorEmbeddingLoader(IterableDataset):
-    def __init__(
-        self,
-        text_conditioned: bool,
-        batch_size: int,
-        start: int,
-        stop: int,
-        image_reader,
-        text_reader: EmbeddingReader = None,
-        device: str = "cpu",
-    ) -> None:
-        super(PriorEmbeddingLoader).__init__()
-
-        self.text_conditioned = text_conditioned
-
-        if not self.text_conditioned:
-            self.text_reader = text_reader
-
-        self.image_reader = image_reader
-        self.batch_size = batch_size
-        self.start = start
-        self.stop = stop
-        self.device = device
-
-    def __iter__(self):
-        self.n = 0
-        loader_args = dict(
-            batch_size=self.batch_size,
-            start=self.start,
-            end=self.stop,
-            show_progress=False,
-        )
-        if self.text_conditioned:
-            self.loader = self.image_reader(**loader_args)
-        else:
-            self.loader = zip(
-                self.image_reader(**loader_args), self.text_reader(**loader_args)
-            )
-        return self
-
-    def __next__(self):
-        try:
-            return self.get_sample()
-        except StopIteration:
-            raise StopIteration
-
-    def get_sample(self):
-        """
-        pre-proocess data from either reader into a common format
-        """
-        self.n += 1
-
-        if self.text_conditioned:
-            image_embedding, caption = next(self.loader)
-
-            image_embedding = from_numpy(image_embedding).to(self.device)
-            tokenized_caption = tokenize(
-                caption["caption"].to_list(), truncate=True
-            ).to(self.device)
-
-            return image_embedding, tokenized_caption
-
-        else:
-            (image_embedding, _), (text_embedding, _) = next(self.loader)
-
-            image_embedding = from_numpy(image_embedding).to(self.device)
-            text_embedding = from_numpy(text_embedding).to(self.device)
-
-            return image_embedding, text_embedding
-
-
-def make_splits(
-    text_conditioned: bool,
-    batch_size: int,
-    num_data_points: int,
-    train_split: float,
-    eval_split: float,
-    device: str,
-    img_url: str,
-    meta_url: str = None,
-    txt_url: str = None,
-):
-
-    assert img_url is not None, "Must supply some image embeddings"
-
-    if text_conditioned:
-        assert meta_url is not None, "Must supply metadata url if text-conditioning"
-        image_reader = EmbeddingReader(
-            embeddings_folder=img_url,
-            file_format="parquet_npy",
-            meta_columns=["caption"],
-            metadata_folder=meta_url,
-        )
-
-        # compute split points
-        if num_data_points > image_reader.count:
-            print("Specified point count is larger than the number of points available...defaulting to max length of reader.")
-            num_data_points = image_reader.count
-
-        train_set_size = int(train_split * num_data_points)
-        eval_set_size = int(eval_split * num_data_points)
-        eval_stop = int(train_set_size + eval_set_size)
-
-        train_loader = PriorEmbeddingLoader(
-            text_conditioned=text_conditioned,
-            image_reader=image_reader,
-            batch_size=batch_size,
-            start=0,
-            stop=train_set_size,
-            device=device,
-        )
-        eval_loader = PriorEmbeddingLoader(
-            text_conditioned=text_conditioned,
-            image_reader=image_reader,
-            batch_size=batch_size,
-            start=train_set_size,
-            stop=eval_stop,
-            device=device,
-        )
-        test_loader = PriorEmbeddingLoader(
-            text_conditioned=text_conditioned,
-            image_reader=image_reader,
-            batch_size=batch_size,
-            start=eval_stop,
-            stop=int(num_data_points),
-            device=device,
-        )
-
-    else:
-        assert (
-            txt_url is not None
-        ), "Must supply text embedding url if not text-conditioning"
-
-        image_reader = EmbeddingReader(img_url, file_format="npy")
-        text_reader = EmbeddingReader(txt_url, file_format="npy")
-
-        # compute split points
-        if num_data_points > image_reader.count:
-            print("Specified point count is larger than the number of points available...defaulting to max length of reader.")
-            num_data_points = image_reader.count
-
-        train_set_size = int(train_split * num_data_points)
-        eval_set_size = int(eval_split * num_data_points)
-        eval_stop = int(train_set_size + eval_set_size)
-
-        train_loader = PriorEmbeddingLoader(
-            text_conditioned=text_conditioned,
-            image_reader=image_reader,
-            text_reader=text_reader,
-            batch_size=batch_size,
-            start=0,
-            stop=train_set_size,
-            device=device,
-        )
-        eval_loader = PriorEmbeddingLoader(
-            text_conditioned=text_conditioned,
-            image_reader=image_reader,
-            text_reader=text_reader,
-            batch_size=batch_size,
-            start=train_set_size,
-            stop=eval_stop,
-            device=device,
-        )
-        test_loader = PriorEmbeddingLoader(
-            text_conditioned=text_conditioned,
-            image_reader=image_reader,
-            text_reader=text_reader,
-            batch_size=batch_size,
-            start=eval_stop,
-            stop=int(num_data_points),
-            device=device,
-        )
-
-    return train_loader, eval_loader, test_loader

dalle2_pytorch/dataloaders/prior_loader.py

@@ -0,0 +1,273 @@
+from math import ceil
+from clip import tokenize
+from embedding_reader import EmbeddingReader
+from torch import from_numpy
+from torch.utils.data import IterableDataset, DataLoader
+
+
+class PriorEmbeddingDataset(IterableDataset):
+    """
+    PriorEmbeddingDataset is a wrapper of EmbeddingReader.
+
+    It enables one to simplify the logic necessary to yield samples from
+    the different EmbeddingReader configurations available.
+    """
+
+    def __init__(
+        self,
+        text_conditioned: bool,
+        batch_size: int,
+        start: int,
+        stop: int,
+        image_reader,
+        text_reader: EmbeddingReader = None,
+    ) -> None:
+        super(PriorEmbeddingDataset).__init__()
+
+        self.text_conditioned = text_conditioned
+
+        if not self.text_conditioned:
+            self.text_reader = text_reader
+
+        self.image_reader = image_reader
+        self.start = start
+        self.stop = stop
+        self.batch_size = batch_size
+
+    def __len__(self):
+        return self.stop - self.start
+
+    def __iter__(self):
+        # D.R.Y loader args
+        loader_args = dict(
+            batch_size=self.batch_size,
+            start=self.start,
+            end=self.stop,
+            show_progress=False,
+        )
+
+        # if the data requested is text conditioned, only load images
+        if self.text_conditioned:
+            self.loader = self.image_reader(**loader_args)
+        # otherwise, include text embeddings and bypass metadata
+        else:
+            self.loader = zip(
+                self.image_reader(**loader_args), self.text_reader(**loader_args)
+            )
+
+        # return the data loader in its formatted state
+        return self
+
+    def __next__(self):
+        try:
+            return self.get_sample()
+        except StopIteration:
+            raise StopIteration
+
+    def __str__(self):
+        return f"<PriorEmbeddingDataset: start: {self.start}, stop: {self.stop}, len: {self.__len__()}>"
+
+    def get_sample(self):
+        """
+        pre-proocess data from either reader into a common format
+        """
+        if self.text_conditioned:
+            image_embedding, caption = next(self.loader)
+
+            image_embedding = from_numpy(image_embedding)
+            tokenized_caption = tokenize(caption["caption"].to_list(), truncate=True)
+
+            return image_embedding, tokenized_caption
+
+        else:
+            (image_embedding, _), (text_embedding, _) = next(self.loader)
+
+            image_embedding = from_numpy(image_embedding)
+            text_embedding = from_numpy(text_embedding)
+
+            return image_embedding, text_embedding
+
+
+# helper functions
+
+
+def distribute_to_rank(start, stop, rank, world_size):
+    """
+    Distribute data to each rank given the world size.
+
+    Return:
+        - New start and stop points for this rank.
+    """
+    num_samples = int(stop - start)
+
+    per_rank = int(ceil((num_samples) / float(world_size)))
+
+    assert (
+        per_rank > 0
+    ), f"Number of samples per rank must be larger than 0, (found: {per_rank})"
+
+    rank_start = start + rank * per_rank
+
+    rank_stop = min(rank_start + per_rank, stop)
+
+    new_length = rank_stop - rank_start
+
+    assert (
+        new_length > 0
+    ), "Calculated start and stop points result in a length of zero for this rank."
+
+    return rank_start, rank_stop
+
+
+def get_reader(
+    text_conditioned: bool, img_url: str, meta_url: str = None, txt_url: str = None
+):
+    """
+    Create an EmbeddingReader object from the specified URLs
+
+    get_reader() will always expect a url to image embeddings.
+
+    If text-conditioned, it will also expect a meta_url for the captions.
+    Otherwise, it will need txt_url for the matching text embeddings.
+
+    Returns an image_reader object if text-conditioned.
+    Otherwise it returns both an image_reader and a text_reader
+    """
+
+    assert img_url is not None, "Must supply a image url"
+
+    if text_conditioned:
+        assert meta_url is not None, "Must supply meta url if text-conditioned"
+
+        image_reader = EmbeddingReader(
+            embeddings_folder=img_url,
+            file_format="parquet_npy",
+            # will assume the caption column exists and is the only one requested
+            meta_columns=["caption"],
+            metadata_folder=meta_url,
+        )
+
+        return image_reader
+
+    # otherwise we will require text embeddings as well and return two readers
+    assert (
+        txt_url is not None
+    ), "Must supply text embedding url if not text-conditioning"
+
+    image_reader = EmbeddingReader(img_url, file_format="npy")
+    text_reader = EmbeddingReader(txt_url, file_format="npy")
+
+    return image_reader, text_reader
+
+
+def make_splits(
+    text_conditioned: bool,
+    batch_size: int,
+    num_data_points: int,
+    train_split: float,
+    eval_split: float,
+    image_reader: EmbeddingReader,
+    text_reader: EmbeddingReader = None,
+    start=0,
+    rank=0,
+    world_size=1,
+):
+    """
+    Split an embedding reader object as needed.
+
+    NOTE: make_splits() will infer the test set size from your train and eval.
+
+    Input:
+        - text_conditioned: whether to prepare text-conditioned training data
+        - batch_size: the batch size for a single gpu
+        - num_data_points: the total number of data points you wish to train on
+        - train_split: the percentage of data you wish to train on
+        - eval_split: the percentage of data you wish to validate on
+        - image_reader: the image_reader you wish to split
+        - text_reader: the text_reader you want to split (if !text_conditioned)
+        - start: the starting point within your dataset
+        - rank: the rank of your worker
+        - world_size: the total world size of your distributed training run
+
+    Returns:
+        - PyTorch Dataloaders that yield tuples of (img, txt) data.
+    """
+
+    assert start < image_reader.count, "start position cannot exceed reader count."
+
+    # verify that the num_data_points does not exceed the max points
+    if num_data_points > (image_reader.count - start):
+        print(
+            "Specified count is larger than what's available...defaulting to reader's count."
+        )
+        num_data_points = image_reader.count
+
+    # compute split points
+    train_set_size = int(train_split * num_data_points)
+    eval_set_size = int(eval_split * num_data_points)
+    eval_start = train_set_size
+    eval_stop = int(eval_start + eval_set_size)
+
+    assert (
+        train_split + eval_split
+    ) < 1.0, "Specified train and eval split is too large to infer a test split."
+
+    # distribute to rank
+    rank_train_start, rank_train_stop = distribute_to_rank(
+        start, train_set_size, rank, world_size
+    )
+    rank_eval_start, rank_eval_stop = distribute_to_rank(
+        train_set_size, eval_stop, rank, world_size
+    )
+    rank_test_start, rank_test_stop = distribute_to_rank(
+        eval_stop, num_data_points, rank, world_size
+    )
+
+    # wrap up splits into a dict
+    train_split_args = dict(
+        start=rank_train_start, stop=rank_train_stop, batch_size=batch_size
+    )
+    eval_split_args = dict(
+        start=rank_eval_start, stop=rank_eval_stop, batch_size=batch_size
+    )
+    test_split_args = dict(
+        start=rank_test_start, stop=rank_test_stop, batch_size=batch_size
+    )
+
+    if text_conditioned:
+        # add the text-conditioned args to a unified dict
+        reader_args = dict(
+            text_conditioned=text_conditioned,
+            image_reader=image_reader,
+        )
+
+        train_split_args = dict(**reader_args, **train_split_args)
+        eval_split_args = dict(**reader_args, **eval_split_args)
+        test_split_args = dict(**reader_args, **test_split_args)
+
+        train = PriorEmbeddingDataset(**train_split_args)
+        val = PriorEmbeddingDataset(**eval_split_args)
+        test = PriorEmbeddingDataset(**test_split_args)
+
+    else:
+        # add the non-conditioned args to a unified dict
+        reader_args = dict(
+            text_conditioned=text_conditioned,
+            image_reader=image_reader,
+            text_reader=text_reader,
+        )
+
+        train_split_args = dict(**reader_args, **train_split_args)
+        eval_split_args = dict(**reader_args, **eval_split_args)
+        test_split_args = dict(**reader_args, **test_split_args)
+
+        train = PriorEmbeddingDataset(**train_split_args)
+        val = PriorEmbeddingDataset(**eval_split_args)
+        test = PriorEmbeddingDataset(**test_split_args)
+
+    # true batch size is specifed in the PriorEmbeddingDataset
+    train_loader = DataLoader(train, batch_size=None)
+    eval_loader = DataLoader(val, batch_size=None)
+    test_loader = DataLoader(test, batch_size=None)
+
+    return train_loader, eval_loader, test_loader

dalle2_pytorch/optimizer.py

@@ -1,17 +1,20 @@
 from torch.optim import AdamW, Adam
 
 def separate_weight_decayable_params(params):
-    no_wd_params = set([param for param in params if param.ndim < 2])
+    wd_params, no_wd_params = [], []
-    wd_params = set(params) - no_wd_params
+    for param in params:
+        param_list = no_wd_params if param.ndim < 2 else wd_params
+        param_list.append(param)
     return wd_params, no_wd_params
 
 def get_optimizer(
     params,
     lr = 1e-4,
     wd = 1e-2,
-    betas = (0.9, 0.999),
+    betas = (0.9, 0.99),
     eps = 1e-8,
     filter_by_requires_grad = False,
+    group_wd_params = True,
     **kwargs
 ):
     if filter_by_requires_grad:
@@ -20,12 +23,12 @@ def get_optimizer(
     if wd == 0:
         return Adam(params, lr = lr, betas = betas, eps = eps)
 
-    params = set(params)
+    if group_wd_params:
-    wd_params, no_wd_params = separate_weight_decayable_params(params)
+        wd_params, no_wd_params = separate_weight_decayable_params(params)
 
-    param_groups = [
+        params = [
-        {'params': list(wd_params)},
+            {'params': wd_params},
-        {'params': list(no_wd_params), 'weight_decay': 0},
+            {'params': no_wd_params, 'weight_decay': 0},
-    ]
+        ]
 
-    return AdamW(param_groups, lr = lr, weight_decay = wd, betas = betas, eps = eps)
+    return AdamW(params, lr = lr, weight_decay = wd, betas = betas, eps = eps)

dalle2_pytorch/tokenizer.py

@@ -2,7 +2,6 @@
 # to give users a quick easy start to training DALL-E without doing BPE
 
 import torch
-import youtokentome as yttm
 
 import html
 import os
@@ -11,6 +10,8 @@ import regex as re
 from functools import lru_cache
 from pathlib import Path
 
+from dalle2_pytorch.utils import import_or_print_error
+
 # OpenAI simple tokenizer
 
 @lru_cache()
@@ -156,7 +157,9 @@ class YttmTokenizer:
         bpe_path = Path(bpe_path)
         assert bpe_path.exists(), f'BPE json path {str(bpe_path)} does not exist'
 
-        tokenizer = yttm.BPE(model = str(bpe_path))
+        self.yttm = import_or_print_error('youtokentome', 'you need to install youtokentome by `pip install youtokentome`')
+
+        tokenizer = self.yttm.BPE(model = str(bpe_path))
         self.tokenizer = tokenizer
         self.vocab_size = tokenizer.vocab_size()
 
@@ -167,7 +170,7 @@ class YttmTokenizer:
         return self.tokenizer.decode(tokens, ignore_ids = pad_tokens.union({0}))
 
     def encode(self, texts):
-        encoded = self.tokenizer.encode(texts, output_type = yttm.OutputType.ID)
+        encoded = self.tokenizer.encode(texts, output_type = self.yttm.OutputType.ID)
         return list(map(torch.tensor, encoded))
 
     def tokenize(self, texts, context_length = 256, truncate_text = False):

dalle2_pytorch/trackers.py

@@ -6,6 +6,8 @@ from itertools import zip_longest
 import torch
 from torch import nn
 
+from dalle2_pytorch.utils import import_or_print_error
+
 # constants
 
 DEFAULT_DATA_PATH = './.tracker-data'
@@ -15,23 +17,15 @@ DEFAULT_DATA_PATH = './.tracker-data'
 def exists(val):
     return val is not None
 
-def import_or_print_error(pkg_name, err_str = None):
+# load file functions
-    try:
-        return importlib.import_module(pkg_name)
-    except ModuleNotFoundError as e:
-        if exists(err_str):
-            print(err_str)
-        exit()
 
-# load state dict functions
+def load_wandb_file(run_path, file_path, **kwargs):
-
-def load_wandb_state_dict(run_path, file_path, **kwargs):
     wandb = import_or_print_error('wandb', '`pip install wandb` to use the wandb recall function')
     file_reference = wandb.restore(file_path, run_path=run_path)
-    return torch.load(file_reference.name)
+    return file_reference.name
 
-def load_local_state_dict(file_path, **kwargs):
+def load_local_file(file_path, **kwargs):
-    return torch.load(file_path)
+    return file_path
 
 # base class
 
@@ -61,12 +55,43 @@ class BaseTracker(nn.Module):
         """
         # TODO: Pull this into a dict or something similar so that we can add more sources without having a massive switch statement
         if recall_source == 'wandb':
-            return load_wandb_state_dict(*args, **kwargs)
+            return torch.load(load_wandb_file(*args, **kwargs))
         elif recall_source == 'local':
-            return load_local_state_dict(*args, **kwargs)
+            return torch.load(load_local_file(*args, **kwargs))
         else:
             raise ValueError('`recall_source` must be one of `wandb` or `local`')
 
+    def save_file(self, file_path, **kwargs):
+        raise NotImplementedError
+
+    def recall_file(self, recall_source, *args, **kwargs):
+        if recall_source == 'wandb':
+            return load_wandb_file(*args, **kwargs)
+        elif recall_source == 'local':
+            return load_local_file(*args, **kwargs)
+        else:
+            raise ValueError('`recall_source` must be one of `wandb` or `local`')
+
+# Tracker that no-ops all calls except for recall
+
+class DummyTracker(BaseTracker):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def init(self, config, **kwargs):
+        pass
+
+    def log(self, log, **kwargs):
+        pass
+
+    def log_images(self, images, **kwargs):
+        pass
+
+    def save_state_dict(self, state_dict, relative_path, **kwargs):
+        pass
+
+    def save_file(self, file_path, **kwargs):
+        pass
 
 # basic stdout class
 
@@ -83,6 +108,10 @@ class ConsoleTracker(BaseTracker):
     def save_state_dict(self, state_dict, relative_path, **kwargs):
         torch.save(state_dict, str(self.data_path / relative_path))
     
+    def save_file(self, file_path, **kwargs):
+        # This is a no-op for local file systems since it is already saved locally
+        pass
+
 # basic wandb class
 
 class WandbTracker(BaseTracker):
@@ -113,3 +142,11 @@ class WandbTracker(BaseTracker):
         full_path = str(self.data_path / relative_path)
         torch.save(state_dict, full_path)
         self.wandb.save(full_path, base_path = str(self.data_path))  # Upload and keep relative to data_path
+
+    def save_file(self, file_path, base_path=None, **kwargs):
+        """
+        Uploads a file from disk to wandb
+        """
+        if base_path is None:
+            base_path = self.data_path
+        self.wandb.save(str(file_path), base_path = str(base_path))

dalle2_pytorch/train_configs.py

@@ -0,0 +1,270 @@
+import json
+from torchvision import transforms as T
+from pydantic import BaseModel, validator, root_validator
+from typing import List, Iterable, Optional, Union, Tuple, Dict, Any
+
+from x_clip import CLIP as XCLIP
+from coca_pytorch import CoCa
+
+from dalle2_pytorch.dalle2_pytorch import (
+    CoCaAdapter,
+    OpenAIClipAdapter,
+    Unet,
+    Decoder,
+    DiffusionPrior,
+    DiffusionPriorNetwork,
+    XClipAdapter,
+)
+
+# helper functions
+
+def exists(val):
+    return val is not None
+
+def default(val, d):
+    return val if exists(val) else d
+
+def ListOrTuple(inner_type):
+    return Union[List[inner_type], Tuple[inner_type]]
+
+def SingularOrIterable(inner_type):
+    return Union[inner_type, ListOrTuple(inner_type)]
+
+# general pydantic classes
+
+class TrainSplitConfig(BaseModel):
+    train: float = 0.75
+    val: float = 0.15
+    test: float = 0.1
+
+    @root_validator
+    def validate_all(cls, fields):
+        actual_sum = sum([*fields.values()])
+        if actual_sum != 1.:
+            raise ValueError(f'{fields.keys()} must sum to 1.0. Found: {actual_sum}')
+        return fields
+
+class TrackerConfig(BaseModel):
+    tracker_type: str = 'console'           # Decoder currently supports console and wandb
+    data_path: str = './models'             # The path where files will be saved locally
+    init_config: Dict[str, Any] = None
+    wandb_entity: str = ''                  # Only needs to be set if tracker_type is wandb
+    wandb_project: str = ''
+    verbose: bool = False                   # Whether to print console logging for non-console trackers
+
+# diffusion prior pydantic classes
+
+class AdapterConfig(BaseModel):
+    make: str = "openai"
+    model: str = "ViT-L/14"
+    base_model_kwargs: Dict[str, Any] = None
+
+    def create(self):
+        if self.make == "openai":
+            return OpenAIClipAdapter(self.model)
+        elif self.make == "x-clip":
+            return XClipAdapter(XCLIP(**self.base_model_kwargs))
+        elif self.make == "coca":
+            return CoCaAdapter(CoCa(**self.base_model_kwargs))
+        else:
+            raise AttributeError("No adapter with that name is available.")
+
+class DiffusionPriorNetworkConfig(BaseModel):
+    dim: int
+    depth: int
+    num_timesteps: int = None
+    num_time_embeds: int = 1
+    num_image_embeds: int = 1
+    num_text_embeds: int = 1
+    dim_head: int = 64
+    heads: int = 8
+    ff_mult: int = 4
+    norm_out: bool = True
+    attn_dropout: float = 0.
+    ff_dropout: float = 0.
+    final_proj: bool = True
+    normformer: bool = False
+    rotary_emb: bool = True
+
+    def create(self):
+        kwargs = self.dict()
+        return DiffusionPriorNetwork(**kwargs)
+
+class DiffusionPriorConfig(BaseModel):
+    clip: AdapterConfig = None
+    net: DiffusionPriorNetworkConfig
+    image_embed_dim: int
+    image_size: int
+    image_channels: int = 3
+    timesteps: int = 1000
+    cond_drop_prob: float = 0.
+    loss_type: str = 'l2'
+    predict_x_start: bool = True
+    beta_schedule: str = 'cosine'
+    condition_on_text_encodings: bool = True
+
+    class Config:
+        extra = "allow"
+
+    def create(self):
+        kwargs = self.dict()
+
+        has_clip = exists(kwargs.pop('clip'))
+        kwargs.pop('net')
+
+        clip = None
+        if has_clip:
+            clip = self.clip.create()
+
+        diffusion_prior_network = self.net.create()
+        return DiffusionPrior(net = diffusion_prior_network, clip = clip, **kwargs)
+
+class DiffusionPriorTrainConfig(BaseModel):
+    epochs: int = 1
+    lr: float = 1.1e-4
+    wd: float = 6.02e-2
+    max_grad_norm: float = 0.5
+    use_ema: bool = True
+    ema_beta: float = 0.99
+    amp: bool = False
+    save_every: int = 10000 # what steps to save on
+
+class DiffusionPriorDataConfig(BaseModel):
+    image_url: str     # path to embeddings folder
+    meta_url: str      # path to metadata (captions) for images
+    splits: TrainSplitConfig
+    batch_size: int = 64
+
+class DiffusionPriorLoadConfig(BaseModel):
+    source: str = None
+    resume: bool = False
+
+class TrainDiffusionPriorConfig(BaseModel):
+    prior: DiffusionPriorConfig
+    data: DiffusionPriorDataConfig
+    train: DiffusionPriorTrainConfig
+    load: DiffusionPriorLoadConfig
+    tracker: TrackerConfig
+
+    @classmethod
+    def from_json_path(cls, json_path):
+        with open(json_path) as f:
+            config = json.load(f)
+        return cls(**config)
+
+# decoder pydantic classes
+
+class UnetConfig(BaseModel):
+    dim: int
+    dim_mults: ListOrTuple(int)
+    image_embed_dim: int = None
+    cond_dim: int = None
+    channels: int = 3
+    attn_dim_head: int = 32
+    attn_heads: int = 16
+
+    class Config:
+        extra = "allow"
+
+class DecoderConfig(BaseModel):
+    unets: ListOrTuple(UnetConfig)
+    image_size: int = None
+    image_sizes: ListOrTuple(int) = None
+    channels: int = 3
+    timesteps: int = 1000
+    loss_type: str = 'l2'
+    beta_schedule: ListOrTuple(str) = 'cosine'
+    learned_variance: bool = True
+    image_cond_drop_prob: float = 0.1
+    text_cond_drop_prob: float = 0.5
+
+    def create(self):
+        decoder_kwargs = self.dict()
+        unet_configs = decoder_kwargs.pop('unets')
+        unets = [Unet(**config) for config in unet_configs]
+        return Decoder(unets, **decoder_kwargs)
+
+    @validator('image_sizes')
+    def check_image_sizes(cls, image_sizes, values):
+        if exists(values.get('image_size')) ^ exists(image_sizes):
+            return image_sizes
+        raise ValueError('either image_size or image_sizes is required, but not both')
+
+    class Config:
+        extra = "allow"
+
+class DecoderDataConfig(BaseModel):
+    webdataset_base_url: str     # path to a webdataset with jpg images
+    embeddings_url: str          # path to .npy files with embeddings
+    num_workers: int = 4
+    batch_size: int = 64
+    start_shard: int = 0
+    end_shard: int = 9999999
+    shard_width: int = 6
+    index_width: int = 4
+    splits: TrainSplitConfig
+    shuffle_train: bool = True
+    resample_train: bool = False
+    preprocessing: Dict[str, Any] = {'ToTensor': True}
+
+    @property
+    def img_preproc(self):
+        def _get_transformation(transformation_name, **kwargs):
+            if transformation_name == "RandomResizedCrop":
+                return T.RandomResizedCrop(**kwargs)
+            elif transformation_name == "RandomHorizontalFlip":
+                return T.RandomHorizontalFlip()
+            elif transformation_name == "ToTensor":
+                return T.ToTensor()
+
+        transforms = []
+        for transform_name, transform_kwargs_or_bool in self.preprocessing.items():
+            transform_kwargs = {} if not isinstance(transform_kwargs_or_bool, dict) else transform_kwargs_or_bool
+            transforms.append(_get_transformation(transform_name, **transform_kwargs))
+        return T.Compose(transforms)
+
+class DecoderTrainConfig(BaseModel):
+    epochs: int = 20
+    lr: SingularOrIterable(float) = 1e-4
+    wd: SingularOrIterable(float) = 0.01
+    max_grad_norm: SingularOrIterable(float) = 0.5
+    save_every_n_samples: int = 100000
+    n_sample_images: int = 6                       # The number of example images to produce when sampling the train and test dataset
+    device: str = 'cuda:0'
+    epoch_samples: int = None                      # Limits the number of samples per epoch. None means no limit. Required if resample_train is true as otherwise the number of samples per epoch is infinite.
+    validation_samples: int = None                 # Same as above but for validation.
+    use_ema: bool = True
+    ema_beta: float = 0.999
+    amp: bool = False
+    save_all: bool = False                         # Whether to preserve all checkpoints
+    save_latest: bool = True                       # Whether to always save the latest checkpoint
+    save_best: bool = True                         # Whether to save the best checkpoint
+    unet_training_mask: ListOrTuple(bool) = None   # If None, use all unets
+
+class DecoderEvaluateConfig(BaseModel):
+    n_evaluation_samples: int = 1000
+    FID: Dict[str, Any] = None
+    IS: Dict[str, Any] = None
+    KID: Dict[str, Any] = None
+    LPIPS: Dict[str, Any] = None
+
+class DecoderLoadConfig(BaseModel):
+    source: str = None                      # Supports file and wandb
+    run_path: str = ''                      # Used only if source is wandb
+    file_path: str = ''                     # The local filepath if source is file. If source is wandb, the relative path to the model file in wandb.
+    resume: bool = False                    # If using wandb, whether to resume the run
+
+class TrainDecoderConfig(BaseModel):
+    decoder: DecoderConfig
+    data: DecoderDataConfig
+    train: DecoderTrainConfig
+    evaluate: DecoderEvaluateConfig
+    tracker: TrackerConfig
+    load: DecoderLoadConfig
+    seed: int = 0
+
+    @classmethod
+    def from_json_path(cls, json_path):
+        with open(json_path) as f:
+            config = json.load(f)
+        return cls(**config)

dalle2_pytorch/trainer.py

@@ -1,5 +1,6 @@
 import time
 import copy
+from pathlib import Path
 from math import ceil
 from functools import partial, wraps
 from collections.abc import Iterable
@@ -10,6 +11,12 @@ from torch.cuda.amp import autocast, GradScaler
 
 from dalle2_pytorch.dalle2_pytorch import Decoder, DiffusionPrior
 from dalle2_pytorch.optimizer import get_optimizer
+from dalle2_pytorch.version import __version__
+from packaging import version
+
+from ema_pytorch import EMA
+
+from accelerate import Accelerator
 
 import numpy as np
 
@@ -19,7 +26,9 @@ def exists(val):
     return val is not None
 
 def default(val, d):
-    return val if exists(val) else d
+    if exists(val):
+        return val
+    return d() if callable(d) else d
 
 def cast_tuple(val, length = 1):
     return val if isinstance(val, tuple) else ((val,) * length)
@@ -128,111 +137,6 @@ def split_args_and_kwargs(*args, split_size = None, **kwargs):
         chunk_size_frac = chunk_size / batch_size
         yield chunk_size_frac, (chunked_args, chunked_kwargs)
 
-# print helpers
-
-def print_ribbon(s, symbol = '=', repeat = 40):
-    flank = symbol * repeat
-    return f'{flank} {s} {flank}'
-
-# saving and loading functions
-
-# for diffusion prior
-
-def load_diffusion_model(dprior_path, device):
-    dprior_path = Path(dprior_path)
-    assert dprior_path.exists(), 'Dprior model file does not exist'
-    loaded_obj = torch.load(str(dprior_path), map_location='cpu')
-
-    # Get hyperparameters of loaded model
-    dpn_config = loaded_obj['hparams']['diffusion_prior_network']
-    dp_config = loaded_obj['hparams']['diffusion_prior']
-    image_embed_dim = loaded_obj['image_embed_dim']['image_embed_dim']
-
-    # Create DiffusionPriorNetwork and DiffusionPrior with loaded hyperparameters
-
-    # DiffusionPriorNetwork
-    prior_network = DiffusionPriorNetwork( dim = image_embed_dim, **dpn_config).to(device)
-
-    # DiffusionPrior with text embeddings and image embeddings pre-computed
-    diffusion_prior = DiffusionPrior(net = prior_network, **dp_config, image_embed_dim = image_embed_dim).to(device)
-
-    # Load state dict from saved model
-    diffusion_prior.load_state_dict(loaded_obj['model'])
-
-    return diffusion_prior, loaded_obj
-
-def save_diffusion_model(save_path, model, optimizer, scaler, config, image_embed_dim):
-    # Saving State Dict
-    print_ribbon('Saving checkpoint')
-
-    state_dict = dict(model=model.state_dict(),
-                      optimizer=optimizer.state_dict(),
-                      scaler=scaler.state_dict(),
-                      hparams = config,
-                      image_embed_dim = {"image_embed_dim":image_embed_dim})
-    torch.save(state_dict, save_path+'/'+str(time.time())+'_saved_model.pth')
-
-# exponential moving average wrapper
-
-class EMA(nn.Module):
-    def __init__(
-        self,
-        model,
-        beta = 0.9999,
-        update_after_step = 1000,
-        update_every = 10,
-    ):
-        super().__init__()
-        self.beta = beta
-        self.online_model = model
-        self.ema_model = copy.deepcopy(model)
-
-        self.update_every = update_every
-        self.update_after_step = update_after_step  // update_every # only start EMA after this step number, starting at 0
-
-        self.register_buffer('initted', torch.Tensor([False]))
-        self.register_buffer('step', torch.tensor([0.]))
-
-    def restore_ema_model_device(self):
-        device = self.initted.device
-        self.ema_model.to(device)
-
-    def copy_params_from_model_to_ema(self):
-        self.ema_model.state_dict(self.online_model.state_dict())
-
-    def update(self):
-        self.step += 1
-
-        if (self.step % self.update_every) != 0:
-            return
-
-        if self.step <= self.update_after_step:
-            self.copy_params_from_model_to_ema()
-            return
-
-        if not self.initted:
-            self.copy_params_from_model_to_ema()
-            self.initted.data.copy_(torch.Tensor([True]))
-
-        self.update_moving_average(self.ema_model, self.online_model)
-
-    def update_moving_average(self, ma_model, current_model):
-        def calculate_ema(beta, old, new):
-            if not exists(old):
-                return new
-            return old * beta + (1 - beta) * new
-
-        for current_params, ma_params in zip(current_model.parameters(), ma_model.parameters()):
-            old_weight, up_weight = ma_params.data, current_params.data
-            ma_params.data = calculate_ema(self.beta, old_weight, up_weight)
-
-        for current_buffer, ma_buffer in zip(current_model.buffers(), ma_model.buffers()):
-            new_buffer_value = calculate_ema(self.beta, ma_buffer, current_buffer)
-            ma_buffer.copy_(new_buffer_value)
-
-    def __call__(self, *args, **kwargs):
-        return self.ema_model(*args, **kwargs)
-
 # diffusion prior trainer
 
 def prior_sample_in_chunks(fn):
@@ -255,44 +159,190 @@ class DiffusionPriorTrainer(nn.Module):
         eps = 1e-6,
         max_grad_norm = None,
         amp = False,
+        group_wd_params = True,
+        device = None,
+        accelerator = None,
         **kwargs
     ):
         super().__init__()
         assert isinstance(diffusion_prior, DiffusionPrior)
+        assert not exists(accelerator) or isinstance(accelerator, Accelerator)
+        assert exists(accelerator) or exists(device), "You must supply some method of obtaining a device."
         ema_kwargs, kwargs = groupby_prefix_and_trim('ema_', kwargs)
 
+        # assign some helpful member vars
+        self.accelerator = accelerator
+        self.device = accelerator.device if exists(accelerator) else device
+        self.text_conditioned = diffusion_prior.condition_on_text_encodings
+
+        # save model
+
         self.diffusion_prior = diffusion_prior
 
-        # exponential moving average
-
-        self.use_ema = use_ema
-        if self.use_ema:
-            self.ema_diffusion_prior = EMA(diffusion_prior, **ema_kwargs)
-
         # optimizer and mixed precision stuff
 
         self.amp = amp
 
         self.scaler = GradScaler(enabled = amp)
 
+        self.optim_kwargs = dict(lr=lr, wd=wd, eps=eps, group_wd_params=group_wd_params)
+
         self.optimizer = get_optimizer(
-            diffusion_prior.parameters(),
+            self.diffusion_prior.parameters(),
-            lr = lr,
+            **self.optim_kwargs,
-            wd = wd,
-            eps = eps,
             **kwargs
         )
 
+        # distribute the model if using HFA
+        if exists(self.accelerator):
+            self.diffusion_prior, self.optimizer = self.accelerator.prepare(self.diffusion_prior, self.optimizer)
+
+        # exponential moving average stuff
+
+        self.use_ema = use_ema
+
+        if self.use_ema:
+            self.ema_diffusion_prior = EMA(self.unwrap_model(self.diffusion_prior), **ema_kwargs)
+
         # gradient clipping if needed
 
         self.max_grad_norm = max_grad_norm
 
-        self.register_buffer('step', torch.tensor([0.]))
+        # track steps internally
+
+        self.register_buffer('step', torch.tensor([0]))
+
+    # accelerator wrappers
+
+    def print(self, msg):
+        if exists(self.accelerator):
+            self.accelerator.print(msg)
+        else:
+            print(msg)
+
+    def unwrap_model(self, model):
+        if exists(self.accelerator):
+            return self.accelerator.unwrap_model(model)
+        else:
+            return model
+
+    def wait_for_everyone(self):
+        if exists(self.accelerator):
+            self.accelerator.wait_for_everyone()
+
+    def is_main_process(self):
+        if exists(self.accelerator):
+            return self.accelerator.is_main_process
+        else:
+            return True
+
+    def clip_grad_norm_(self, *args):
+        if exists(self.accelerator):
+            return self.accelerator.clip_grad_norm_(*args)
+        else:
+            return torch.nn.utils.clip_grad_norm_(*args)
+
+    def backprop(self, x):
+        if exists(self.accelerator):
+            self.accelerator.backward(x)
+        else:
+            try:
+                x.backward()
+            except Exception as e:
+                self.print(f"Caught error in backprop call: {e}")
+
+    # utility
+
+    def save(self, path, overwrite = True, **kwargs):
+        # ensure we sync gradients before continuing
+        self.wait_for_everyone()
+
+        # only save on the main process
+        if self.is_main_process():
+            self.print(f"Saving checkpoint at step: {self.step.item()}")
+            path = Path(path)
+            assert not (path.exists() and not overwrite)
+            path.parent.mkdir(parents = True, exist_ok = True)
+
+            save_obj = dict(
+                scaler = self.scaler.state_dict(),
+                optimizer = self.optimizer.state_dict(),
+                model = self.unwrap_model(self.diffusion_prior).state_dict(), # unwrap the model from distribution if applicable
+                version = version.parse(__version__),
+                step = self.step.item(),
+                **kwargs
+            )
+
+            if self.use_ema:
+                save_obj = {
+                    **save_obj,
+                    'ema': self.ema_diffusion_prior.state_dict(),
+                    'ema_model': self.ema_diffusion_prior.ema_model.state_dict() # save the ema model specifically for easy ema-only reload
+                }
+
+            torch.save(save_obj, str(path))
+
+    def load(self, path, overwrite_lr = True, strict = True):
+        """
+        Load a checkpoint of a diffusion prior trainer.
+
+        Will load the entire trainer, including the optimizer and EMA.
+
+        Params:
+            - path (str): a path to the DiffusionPriorTrainer checkpoint file
+            - overwrite_lr (bool): wether or not to overwrite the stored LR with the LR specified in the new trainer
+            - strict (bool): kwarg for `torch.nn.Module.load_state_dict`, will force an exact checkpoint match
+
+        Returns:
+            loaded_obj (dict): The loaded checkpoint dictionary
+        """
+
+        # all processes need to load checkpoint. no restriction here
+        path = Path(path)
+        assert path.exists()
+
+        loaded_obj = torch.load(str(path), map_location=self.device)
+
+        if version.parse(__version__) != loaded_obj['version']:
+            print(f'loading saved diffusion prior at version {loaded_obj["version"]} but current package version is at {__version__}')
+
+        # unwrap the model when loading from checkpoint
+        self.unwrap_model(self.diffusion_prior).load_state_dict(loaded_obj['model'], strict = strict)
+        self.step.copy_(torch.ones_like(self.step) * loaded_obj['step'])
+
+        self.scaler.load_state_dict(loaded_obj['scaler'])
+        self.optimizer.load_state_dict(loaded_obj['optimizer'])
+
+        if overwrite_lr:
+            new_lr = self.optim_kwargs["lr"]
+
+            self.print(f"Overriding LR to be {new_lr}")
+
+            for group in self.optimizer.param_groups:
+                group["lr"] = new_lr
+
+        if self.use_ema:
+            assert 'ema' in loaded_obj
+            self.ema_diffusion_prior.load_state_dict(loaded_obj['ema'], strict = strict)
+            # below not be necessary, but I had a suspicion that this wasn't being loaded correctly
+            self.ema_diffusion_prior.ema_model.load_state_dict(loaded_obj["ema_model"])
+
+        # sync and inform
+        self.wait_for_everyone()
+        self.print(f"Loaded model")
+
+        return loaded_obj
+
+    # model functionality
 
     def update(self):
+        # only continue with updates until all ranks finish
+        self.wait_for_everyone()
+
         if exists(self.max_grad_norm):
             self.scaler.unscale_(self.optimizer)
-            nn.utils.clip_grad_norm_(self.diffusion_prior.parameters(), self.max_grad_norm)
+            # utilize HFA clipping where applicable
+            self.clip_grad_norm_(self.diffusion_prior.parameters(), self.max_grad_norm)
 
         self.scaler.step(self.optimizer)
         self.scaler.update()
@@ -307,17 +357,26 @@ class DiffusionPriorTrainer(nn.Module):
     @cast_torch_tensor
     @prior_sample_in_chunks
     def p_sample_loop(self, *args, **kwargs):
-        return self.ema_diffusion_prior.ema_model.p_sample_loop(*args, **kwargs)
+        model = self.ema_diffusion_prior.ema_model if self.use_ema else self.diffusion_prior
+        return model.p_sample_loop(*args, **kwargs)
 
     @torch.no_grad()
     @cast_torch_tensor
     @prior_sample_in_chunks
     def sample(self, *args, **kwargs):
-        return self.ema_diffusion_prior.ema_model.sample(*args, **kwargs)
+        model = self.ema_diffusion_prior.ema_model if self.use_ema else self.diffusion_prior
+        return model.sample(*args, **kwargs)
 
     @torch.no_grad()
     def sample_batch_size(self, *args, **kwargs):
-        return self.ema_diffusion_prior.ema_model.sample_batch_size(*args, **kwargs)
+        model = self.ema_diffusion_prior.ema_model if self.use_ema else self.diffusion_prior
+        return model.sample_batch_size(*args, **kwargs)
+
+    @torch.no_grad()
+    @cast_torch_tensor
+    @prior_sample_in_chunks
+    def embed_text(self, *args, **kwargs):
+        return self.unwrap_model(self.diffusion_prior).clip.embed_text(*args, **kwargs)
 
     @cast_torch_tensor
     def forward(
@@ -335,8 +394,10 @@ class DiffusionPriorTrainer(nn.Module):
 
             total_loss += loss.item()
 
+            # backprop with accelerate if applicable
+
             if self.training:
-                self.scaler.scale(loss).backward()
+                self.backprop(self.scaler.scale(loss))
 
         return total_loss
 
@@ -362,20 +423,23 @@ class DecoderTrainer(nn.Module):
     def __init__(
         self,
         decoder,
+        accelerator = None,
         use_ema = True,
         lr = 1e-4,
         wd = 1e-2,
         eps = 1e-8,
         max_grad_norm = 0.5,
         amp = False,
+        group_wd_params = True,
         **kwargs
     ):
         super().__init__()
         assert isinstance(decoder, Decoder)
         ema_kwargs, kwargs = groupby_prefix_and_trim('ema_', kwargs)
 
-        self.decoder = decoder
+        self.accelerator = default(accelerator, Accelerator)
-        self.num_unets = len(self.decoder.unets)
+
+        self.num_unets = len(decoder.unets)
 
         self.use_ema = use_ema
         self.ema_unets = nn.ModuleList([])
@@ -387,56 +451,101 @@ class DecoderTrainer(nn.Module):
 
         lr, wd, eps = map(partial(cast_tuple, length = self.num_unets), (lr, wd, eps))
 
-        for ind, (unet, unet_lr, unet_wd, unet_eps) in enumerate(zip(self.decoder.unets, lr, wd, eps)):
+        optimizers = []
+
+        for unet, unet_lr, unet_wd, unet_eps in zip(decoder.unets, lr, wd, eps):
             optimizer = get_optimizer(
                 unet.parameters(),
                 lr = unet_lr,
                 wd = unet_wd,
                 eps = unet_eps,
+                group_wd_params = group_wd_params,
                 **kwargs
             )
 
-            setattr(self, f'optim{ind}', optimizer) # cannot use pytorch ModuleList for some reason with optimizers
+            optimizers.append(optimizer)
 
             if self.use_ema:
                 self.ema_unets.append(EMA(unet, **ema_kwargs))
 
-            scaler = GradScaler(enabled = amp)
-            setattr(self, f'scaler{ind}', scaler)
-
         # gradient clipping if needed
 
         self.max_grad_norm = max_grad_norm
 
         self.register_buffer('step', torch.tensor([0.]))
 
+        decoder, *optimizers = list(self.accelerator.prepare(decoder, *optimizers))
+
+        self.decoder = decoder
+
+        for opt_ind, optimizer in zip(range(len(optimizers)), optimizers):
+            setattr(self, f'optim{opt_ind}', optimizer)
+
+    def save(self, path, overwrite = True, **kwargs):
+        path = Path(path)
+        assert not (path.exists() and not overwrite)
+        path.parent.mkdir(parents = True, exist_ok = True)
+
+        save_obj = dict(
+            model = self.accelerator.unwrap_model(self.decoder).state_dict(),
+            version = __version__,
+            step = self.step.item(),
+            **kwargs
+        )
+
+        for ind in range(0, self.num_unets):
+            optimizer_key = f'optim{ind}'
+            optimizer = getattr(self, optimizer_key)
+            save_obj = {**save_obj, optimizer_key: self.accelerator.unwrap_model(optimizer).state_dict()}
+
+        if self.use_ema:
+            save_obj = {**save_obj, 'ema': self.ema_unets.state_dict()}
+
+        self.accelerator.save(save_obj, str(path))
+
+    def load(self, path, only_model = False, strict = True):
+        path = Path(path)
+        assert path.exists()
+
+        loaded_obj = torch.load(str(path), map_location = 'cpu')
+
+        if version.parse(__version__) != version.parse(loaded_obj['version']):
+            self.accelerator.print(f'loading saved decoder at version {loaded_obj["version"]}, but current package version is {__version__}')
+
+        self.accelerator.unwrap_model(self.decoder).load_state_dict(loaded_obj['model'], strict = strict)
+        self.step.copy_(torch.ones_like(self.step) * loaded_obj['step'])
+
+        if only_model:
+            return loaded_obj
+
+        for ind in range(0, self.num_unets):
+            optimizer_key = f'optim{ind}'
+            optimizer = getattr(self, optimizer_key)
+
+            self.accelerator.unwrap_model(optimizer).load_state_dict(loaded_obj[optimizer_key])
+
+        if self.use_ema:
+            assert 'ema' in loaded_obj
+            self.ema_unets.load_state_dict(loaded_obj['ema'], strict = strict)
+
+        return loaded_obj
+
     @property
     def unets(self):
         return nn.ModuleList([ema.ema_model for ema in self.ema_unets])
 
-    def scale(self, loss, *, unet_number):
-        assert 1 <= unet_number <= self.num_unets
-        index = unet_number - 1
-        scaler = getattr(self, f'scaler{index}')
-        return scaler.scale(loss)
-
     def update(self, unet_number = None):
         if self.num_unets == 1:
             unet_number = default(unet_number, 1)
 
         assert exists(unet_number) and 1 <= unet_number <= self.num_unets
         index = unet_number - 1
-        unet = self.decoder.unets[index]
 
         optimizer = getattr(self, f'optim{index}')
-        scaler = getattr(self, f'scaler{index}')
 
         if exists(self.max_grad_norm):
-            scaler.unscale_(optimizer)
+            self.accelerator.clip_grad_norm_(self.decoder.parameters(), self.max_grad_norm)  # Automatically unscales gradients
-            nn.utils.clip_grad_norm_(unet.parameters(), self.max_grad_norm)
+        optimizer.step()
-
-        scaler.step(optimizer)
-        scaler.update()
         optimizer.zero_grad()
 
         if self.use_ema:
@@ -449,15 +558,17 @@ class DecoderTrainer(nn.Module):
     @cast_torch_tensor
     @decoder_sample_in_chunks
     def sample(self, *args, **kwargs):
+        distributed = self.accelerator.num_processes > 1
+        base_decoder = self.accelerator.unwrap_model(self.decoder)
         if kwargs.pop('use_non_ema', False) or not self.use_ema:
-            return self.decoder.sample(*args, **kwargs)
+            return base_decoder.sample(*args, **kwargs, distributed = distributed)
 
-        trainable_unets = self.decoder.unets
+        trainable_unets = self.accelerator.unwrap_model(self.decoder).unets
-        self.decoder.unets = self.unets                  # swap in exponential moving averaged unets for sampling
+        base_decoder.unets = self.unets                  # swap in exponential moving averaged unets for sampling
 
-        output = self.decoder.sample(*args, **kwargs)
+        output = base_decoder.sample(*args, **kwargs, distributed = distributed)
 
-        self.decoder.unets = trainable_unets             # restore original training unets
+        base_decoder.unets = trainable_unets             # restore original training unets
 
         # cast the ema_model unets back to original device
         for ema in self.ema_unets:
@@ -479,13 +590,14 @@ class DecoderTrainer(nn.Module):
         total_loss = 0.
 
         for chunk_size_frac, (chunked_args, chunked_kwargs) in split_args_and_kwargs(*args, split_size = max_batch_size, **kwargs):
-            with autocast(enabled = self.amp):
+            # with autocast(enabled = self.amp):
+            with self.accelerator.autocast():
                 loss = self.decoder(*chunked_args, unet_number = unet_number, **chunked_kwargs)
                 loss = loss * chunk_size_frac
 
             total_loss += loss.item()
 
             if self.training:
-                self.scale(loss, unet_number = unet_number).backward()
+                self.accelerator.backward(loss)
 
         return total_loss

dalle2_pytorch/utils.py

@@ -1,4 +1,7 @@
 import time
+import importlib
+
+# time helpers
 
 class Timer:
     def __init__(self):
@@ -9,3 +12,19 @@ class Timer:
 
     def elapsed(self):
         return time.time() - self.last_time
+
+# print helpers
+
+def print_ribbon(s, symbol = '=', repeat = 40):
+    flank = symbol * repeat
+    return f'{flank} {s} {flank}'
+
+# import helpers
+
+def import_or_print_error(pkg_name, err_str = None):
+    try:
+        return importlib.import_module(pkg_name)
+    except ModuleNotFoundError as e:
+        if exists(err_str):
+            print(err_str)
+        exit()

dalle2_pytorch/version.py

@@ -0,0 +1 @@
+__version__ = '0.11.4'

dalle2_pytorch/vqgan_vae.py

@@ -68,8 +68,8 @@ def group_dict_by_key(cond, d):
         return_val[ind][key] = d[key]
     return (*return_val,)
 
-def string_begins_with(prefix, str):
+def string_begins_with(prefix, string_input):
-    return str.startswith(prefix)
+    return string_input.startswith(prefix)
 
 def group_by_key_prefix(prefix, d):
     return group_dict_by_key(partial(string_begins_with, prefix), d)

dalle2_pytorch/vqgan_vae_trainer.py

@@ -16,10 +16,11 @@ from torchvision.utils import make_grid, save_image
 
 from einops import rearrange
 
-from dalle2_pytorch.train import EMA
 from dalle2_pytorch.vqgan_vae import VQGanVAE
 from dalle2_pytorch.optimizer import get_optimizer
 
+from ema_pytorch import EMA
+
 # helpers
 
 def exists(val):
@@ -97,7 +98,7 @@ class VQGanVAETrainer(nn.Module):
         valid_frac = 0.05,
         random_split_seed = 42,
         ema_beta = 0.995,
-        ema_update_after_step = 2000,
+        ema_update_after_step = 500,
         ema_update_every = 10,
         apply_grad_penalty_every = 4,
         amp = False

setup.py

@@ -1,4 +1,5 @@
 from setuptools import setup, find_packages
+exec(open('dalle2_pytorch/version.py').read())
 
 setup(
   name = 'dalle2-pytorch',
@@ -10,7 +11,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.3.7',
+  version = __version__,
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',
@@ -23,15 +24,19 @@ setup(
     'text to image'
   ],
   install_requires=[
+    'accelerate',
     'click',
     'clip-anytorch',
     'coca-pytorch>=0.0.5',
+    'ema-pytorch>=0.0.7',
     'einops>=0.4',
     'einops-exts>=0.0.3',
     'embedding-reader',
     'kornia>=0.5.4',
     'numpy',
+    'packaging',
     'pillow',
+    'pydantic',
     'resize-right>=0.0.2',
     'rotary-embedding-torch',
     'torch>=1.10',
@@ -39,7 +44,6 @@ setup(
     'tqdm',
     'vector-quantize-pytorch',
     'x-clip>=0.4.4',
-    'youtokentome',
     'webdataset>=0.2.5',
     'fsspec>=2022.1.0',
     'torchmetrics[image]>=0.8.0'

train_decoder.py

@@ -1,21 +1,34 @@
-from dalle2_pytorch import Unet, Decoder
+from pathlib import Path
-from dalle2_pytorch.trainer import DecoderTrainer, print_ribbon
+
-from dalle2_pytorch.dataloaders import create_image_embedding_dataloader
+from dalle2_pytorch.trainer import DecoderTrainer
-from dalle2_pytorch.trackers import WandbTracker, ConsoleTracker
+from dalle2_pytorch.dataloaders import create_image_embedding_dataloader
-from dalle2_pytorch.utils import Timer
+from dalle2_pytorch.trackers import WandbTracker, ConsoleTracker, DummyTracker
+from dalle2_pytorch.train_configs import TrainDecoderConfig
+from dalle2_pytorch.utils import Timer, print_ribbon
+from dalle2_pytorch.dalle2_pytorch import resize_image_to
 
-from configs.decoder_defaults import default_config, ConfigField
-import json
 import torchvision
-from torchvision import transforms as T
 import torch
 from torchmetrics.image.fid import FrechetInceptionDistance
 from torchmetrics.image.inception import InceptionScore
 from torchmetrics.image.kid import KernelInceptionDistance
 from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
+from accelerate import Accelerator, DistributedDataParallelKwargs
+from accelerate.utils import dataclasses as accelerate_dataclasses
 import webdataset as wds
 import click
 
+# constants
+
+TRAIN_CALC_LOSS_EVERY_ITERS = 10
+VALID_CALC_LOSS_EVERY_ITERS = 10
+
+# helpers functions
+
+def exists(val):
+    return val is not None
+
+# main functions
 
 def create_dataloaders(
     available_shards,
@@ -32,6 +45,7 @@ def create_dataloaders(
     train_prop = 0.75,
     val_prop = 0.15,
     test_prop = 0.10,
+    seed = 0,
     **kwargs
 ):
     """
@@ -42,7 +56,7 @@ def create_dataloaders(
     num_test = round(test_prop*len(available_shards))
     num_val = len(available_shards) - num_train - num_test
     assert num_train + num_test + num_val == len(available_shards), f"{num_train} + {num_test} + {num_val} = {num_train + num_test + num_val} != {len(available_shards)}"
-    train_split, test_split, val_split = torch.utils.data.random_split(available_shards, [num_train, num_test, num_val], generator=torch.Generator().manual_seed(0))
+    train_split, test_split, val_split = torch.utils.data.random_split(available_shards, [num_train, num_test, num_val], generator=torch.Generator().manual_seed(seed))
 
     # The shard number in the webdataset file names has a fixed width. We zero pad the shard numbers so they correspond to a filename.
     train_urls = [webdataset_base_url.format(str(shard).zfill(shard_width)) for shard in train_split]
@@ -76,23 +90,6 @@ def create_dataloaders(
         "test_sampling": test_sampling_dataloader
     }
 
-
-def create_decoder(device, decoder_config, unets_config):
-    """Creates a sample decoder"""
-    unets = []
-    for i in range(0, len(unets_config)):
-        unets.append(Unet(
-            **unets_config[i]
-        ))
-    
-    decoder = Decoder(
-        unet=unets,
-        **decoder_config
-    )
-    decoder.to(device=device)
-
-    return decoder
-
 def get_dataset_keys(dataloader):
     """
     It is sometimes neccesary to get the keys the dataloader is returning. Since the dataset is burried in the dataloader, we need to do a process to recover it.
@@ -124,7 +121,6 @@ def get_example_data(dataloader, device, n=5):
         captions.extend(list(txt))
         if len(images) >= n:
             break
-    print("Generated {} examples".format(len(images)))
     return list(zip(images[:n], embeddings[:n], captions[:n]))
 
 def generate_samples(trainer, example_data, text_prepend=""):
@@ -144,80 +140,103 @@ def generate_grid_samples(trainer, examples, text_prepend=""):
     Generates samples and uses torchvision to put them in a side by side grid for easy viewing
     """
     real_images, generated_images, captions = generate_samples(trainer, examples, text_prepend)
+
+    real_image_size = real_images[0].shape[-1]
+    generated_image_size = generated_images[0].shape[-1]
+
+    # training images may be larger than the generated one
+    if real_image_size > generated_image_size:
+        real_images = [resize_image_to(image, generated_image_size) for image in real_images]
+
     grid_images = [torchvision.utils.make_grid([original_image, generated_image]) for original_image, generated_image in zip(real_images, generated_images)]
     return grid_images, captions
                     
-def evaluate_trainer(trainer, dataloader, device, n_evalation_samples=1000, FID=None, IS=None, KID=None, LPIPS=None):
+def evaluate_trainer(trainer, dataloader, device, n_evaluation_samples=1000, FID=None, IS=None, KID=None, LPIPS=None):
     """
     Computes evaluation metrics for the decoder
     """
     metrics = {}
     # Prepare the data
-    examples = get_example_data(dataloader, device, n_evalation_samples)
+    examples = get_example_data(dataloader, device, n_evaluation_samples)
+    if len(examples) == 0:
+        print("No data to evaluate. Check that your dataloader has shards.")
+        return metrics
     real_images, generated_images, captions = generate_samples(trainer, examples)
     real_images = torch.stack(real_images).to(device=device, dtype=torch.float)
     generated_images = torch.stack(generated_images).to(device=device, dtype=torch.float)
     # Convert from [0, 1] to [0, 255] and from torch.float to torch.uint8
     int_real_images = real_images.mul(255).add(0.5).clamp(0, 255).type(torch.uint8)
     int_generated_images = generated_images.mul(255).add(0.5).clamp(0, 255).type(torch.uint8)
-    if FID is not None:
+
-        fid = FrechetInceptionDistance(**FID)
+    def null_sync(t, *args, **kwargs):
+        return [t]
+
+    if exists(FID):
+        fid = FrechetInceptionDistance(**FID, dist_sync_fn=null_sync)
         fid.to(device=device)
         fid.update(int_real_images, real=True)
         fid.update(int_generated_images, real=False)
         metrics["FID"] = fid.compute().item()
-    if IS is not None:
+    if exists(IS):
-        inception = InceptionScore(**IS)
+        inception = InceptionScore(**IS, dist_sync_fn=null_sync)
         inception.to(device=device)
         inception.update(int_real_images)
         is_mean, is_std = inception.compute()
         metrics["IS_mean"] = is_mean.item()
         metrics["IS_std"] = is_std.item()
-    if KID is not None:
+    if exists(KID):
-        kernel_inception = KernelInceptionDistance(**KID)
+        kernel_inception = KernelInceptionDistance(**KID, dist_sync_fn=null_sync)
         kernel_inception.to(device=device)
         kernel_inception.update(int_real_images, real=True)
         kernel_inception.update(int_generated_images, real=False)
         kid_mean, kid_std = kernel_inception.compute()
         metrics["KID_mean"] = kid_mean.item()
         metrics["KID_std"] = kid_std.item()
-    if LPIPS is not None:
+    if exists(LPIPS):
         # Convert from [0, 1] to [-1, 1]
         renorm_real_images = real_images.mul(2).sub(1)
         renorm_generated_images = generated_images.mul(2).sub(1)
-        lpips = LearnedPerceptualImagePatchSimilarity(**LPIPS)
+        lpips = LearnedPerceptualImagePatchSimilarity(**LPIPS, dist_sync_fn=null_sync)
         lpips.to(device=device)
         lpips.update(renorm_real_images, renorm_generated_images)
         metrics["LPIPS"] = lpips.compute().item()
+
+    if trainer.accelerator.num_processes > 1:
+        # Then we should sync the metrics
+        metrics_order = sorted(metrics.keys())
+        metrics_tensor = torch.zeros(1, len(metrics), device=device, dtype=torch.float)
+        for i, metric_name in enumerate(metrics_order):
+            metrics_tensor[0, i] = metrics[metric_name]
+        metrics_tensor = trainer.accelerator.gather(metrics_tensor)
+        metrics_tensor = metrics_tensor.mean(dim=0)
+        for i, metric_name in enumerate(metrics_order):
+            metrics[metric_name] = metrics_tensor[i].item()
     return metrics
 
-def save_trainer(tracker, trainer, epoch, step, validation_losses, relative_paths):
+def save_trainer(tracker, trainer, epoch, sample, next_task, validation_losses, relative_paths):
     """
     Logs the model with an appropriate method depending on the tracker
     """
     if isinstance(relative_paths, str):
         relative_paths = [relative_paths]
-    trainer_state_dict = {}
-    trainer_state_dict["trainer"] = trainer.state_dict()
-    trainer_state_dict['epoch'] = epoch
-    trainer_state_dict['step'] = step
-    trainer_state_dict['validation_losses'] = validation_losses
     for relative_path in relative_paths:
-        tracker.save_state_dict(trainer_state_dict, relative_path)
+        local_path = str(tracker.data_path / relative_path)
+        trainer.save(local_path, epoch=epoch, sample=sample, next_task=next_task, validation_losses=validation_losses)
+        tracker.save_file(local_path)
     
 def recall_trainer(tracker, trainer, recall_source=None, **load_config):
     """
     Loads the model with an appropriate method depending on the tracker
     """
-    print(print_ribbon(f"Loading model from {recall_source}"))
+    trainer.accelerator.print(print_ribbon(f"Loading model from {recall_source}"))
-    state_dict = tracker.recall_state_dict(recall_source, **load_config)
+    local_filepath = tracker.recall_file(recall_source, **load_config)
-    trainer.load_state_dict(state_dict["trainer"])
+    state_dict = trainer.load(local_filepath)
-    print("Model loaded")
+    return state_dict.get("epoch", 0), state_dict.get("validation_losses", []), state_dict.get("next_task", "train"), state_dict.get("sample", 0)
-    return state_dict["epoch"], state_dict["step"], state_dict["validation_losses"]
 
 def train(
     dataloaders,
     decoder,
+    accelerator,
     tracker,
     inference_device,
     load_config=None,
@@ -236,271 +255,286 @@ def train(
     """
     Trains a decoder on a dataset.
     """
-    trainer = DecoderTrainer(  # TODO: Change the get_optimizer function so that it can take arbitrary named args so we can just put **kwargs as an argument here
+    is_master = accelerator.process_index == 0
+
+    trainer = DecoderTrainer(
+        accelerator,
         decoder,
         **kwargs
     )
+
     # Set up starting model and parameters based on a recalled state dict
-    start_step = 0
     start_epoch = 0
     validation_losses = []
+    next_task = 'train'
+    sample = 0
+    val_sample = 0
+    step = lambda: int(trainer.step.item())
 
-    if load_config is not None and load_config["source"] is not None:
+    if exists(load_config) and exists(load_config.source):
-        start_epoch, start_step, validation_losses = recall_trainer(tracker, trainer, recall_source=load_config["source"], **load_config)
+        start_epoch, validation_losses, next_task, recalled_sample = recall_trainer(tracker, trainer, recall_source=load_config.source, **load_config.dict())
+        if next_task == 'train':
+            sample = recalled_sample
+        if next_task == 'val':
+            val_sample = recalled_sample
+        accelerator.print(f"Loaded model from {load_config.source} on epoch {start_epoch} with minimum validation loss {min(validation_losses) if len(validation_losses) > 0 else 'N/A'}")
+        accelerator.print(f"Starting training from task {next_task} at sample {sample} and validation sample {val_sample}")
     trainer.to(device=inference_device)
 
-    if unet_training_mask is None:
+    if not exists(unet_training_mask):
         # Then the unet mask should be true for all unets in the decoder
         unet_training_mask = [True] * trainer.num_unets
     assert len(unet_training_mask) == trainer.num_unets, f"The unet training mask should be the same length as the number of unets in the decoder. Got {len(unet_training_mask)} and {trainer.num_unets}"
 
-    print(print_ribbon("Generating Example Data", repeat=40))
+    accelerator.print(print_ribbon("Generating Example Data", repeat=40))
-    print("This can take a while to load the shard lists...")
+    accelerator.print("This can take a while to load the shard lists...")
-    train_example_data = get_example_data(dataloaders["train_sampling"], inference_device, n_sample_images)
+    if is_master:
-    test_example_data = get_example_data(dataloaders["test_sampling"], inference_device, n_sample_images)
+        train_example_data = get_example_data(dataloaders["train_sampling"], inference_device, n_sample_images)
+        accelerator.print("Generated training examples")
+        test_example_data = get_example_data(dataloaders["test_sampling"], inference_device, n_sample_images)
+        accelerator.print("Generated testing examples")
     
     send_to_device = lambda arr: [x.to(device=inference_device, dtype=torch.float) for x in arr]
-    step = start_step
 
+    sample_length_tensor = torch.zeros(1, dtype=torch.int, device=inference_device)
+    unet_losses_tensor = torch.zeros(TRAIN_CALC_LOSS_EVERY_ITERS, trainer.num_unets, dtype=torch.float, device=inference_device)
     for epoch in range(start_epoch, epochs):
-        print(print_ribbon(f"Starting epoch {epoch}", repeat=40))
+        accelerator.print(print_ribbon(f"Starting epoch {epoch}", repeat=40))
-        trainer.train()
 
         timer = Timer()
+        last_sample = sample
+        last_snapshot = sample
 
-        sample = 0
+        if next_task == 'train':
-        last_sample = 0
+            for i, (img, emb) in enumerate(dataloaders["train"]):
-        last_snapshot = 0
+                # We want to count the total number of samples across all processes
+                sample_length_tensor[0] = len(img)
+                all_samples = accelerator.gather(sample_length_tensor)  # TODO: accelerator.reduce is broken when this was written. If it is fixed replace this.
+                total_samples = all_samples.sum().item()
+                sample += total_samples
+                img, emb = send_to_device((img, emb))
 
-        losses = []
+                trainer.train()
-        for i, (img, emb) in enumerate(dataloaders["train"]):
+                for unet in range(1, trainer.num_unets+1):
-            step += 1
+                    # Check if this is a unet we are training
-            sample += img.shape[0]
+                    if not unet_training_mask[unet-1]: # Unet index is the unet number - 1
-            img, emb = send_to_device((img, emb))
+                        continue
 
-            for unet in range(1, trainer.num_unets+1):
-                # Check if this is a unet we are training
-                if unet_training_mask[unet-1]: # Unet index is the unet number - 1
                     loss = trainer.forward(img, image_embed=emb, unet_number=unet)
                     trainer.update(unet_number=unet)
-                    losses.append(loss)
+                    unet_losses_tensor[i % TRAIN_CALC_LOSS_EVERY_ITERS, unet-1] = loss
                 
-            samples_per_sec = (sample - last_sample) / timer.elapsed()
+                samples_per_sec = (sample - last_sample) / timer.elapsed()
+                timer.reset()
+                last_sample = sample
 
-            timer.reset()
+                if i % TRAIN_CALC_LOSS_EVERY_ITERS == 0:
-            last_sample = sample
+                    # We want to average losses across all processes
+                    unet_all_losses = accelerator.gather(unet_losses_tensor)
+                    mask = unet_all_losses != 0
+                    unet_average_loss = (unet_all_losses * mask).sum(dim=0) / mask.sum(dim=0)
+                    loss_map = { f"Unet {index} Training Loss": loss.item() for index, loss in enumerate(unet_average_loss) if loss != 0 }
+                    log_data = {
+                        "Epoch": epoch,
+                        "Sample": sample,
+                        "Step": i,
+                        "Samples per second": samples_per_sec,
+                        **loss_map
+                    }
+                    # print(f"I am rank {accelerator.state.process_index}. Example weight: {trainer.decoder.state_dict()['module.unets.0.init_conv.convs.0.weight'][0,0,0,0]}")
+                    if is_master:
+                        tracker.log(log_data, step=step(), verbose=True)
 
-            if i % 10 == 0:
+                if is_master and last_snapshot + save_every_n_samples < sample:  # This will miss by some amount every time, but it's not a big deal... I hope
-                average_loss = sum(losses) / len(losses)
+                    # It is difficult to gather this kind of info on the accelerator, so we have to do it on the master
-                log_data = {
+                    print("Saving snapshot")
-                    "Training loss": average_loss,
+                    last_snapshot = sample
-                    "Epoch": epoch,
+                    # We need to know where the model should be saved
-                    "Sample": sample,
+                    save_paths = []
-                    "Step": i,
+                    if save_latest:
-                    "Samples per second": samples_per_sec
+                        save_paths.append("latest.pth")
-                }
+                    if save_all:
-                tracker.log(log_data, step=step, verbose=True)
+                        save_paths.append(f"checkpoints/epoch_{epoch}_step_{step()}.pth")
-                losses = []
+                    save_trainer(tracker, trainer, epoch, sample, next_task, validation_losses, save_paths)
+                    if exists(n_sample_images) and n_sample_images > 0:
+                        trainer.eval()
+                        train_images, train_captions = generate_grid_samples(trainer, train_example_data, "Train: ")
+                        tracker.log_images(train_images, captions=train_captions, image_section="Train Samples", step=step())
                 
-            if last_snapshot + save_every_n_samples < sample:  # This will miss by some amount every time, but it's not a big deal... I hope
+                if epoch_samples is not None and sample >= epoch_samples:
-                last_snapshot = sample
+                    break
-                # We need to know where the model should be saved
+            next_task = 'val'
-                save_paths = []
-                if save_latest:
-                    save_paths.append("latest.pth")
-                if save_all:
-                    save_paths.append(f"checkpoints/epoch_{epoch}_step_{step}.pth")
-                save_trainer(tracker, trainer, epoch, step, validation_losses, save_paths)
-                if n_sample_images is not None and n_sample_images > 0:
-                    trainer.eval()
-                    train_images, train_captions = generate_grid_samples(trainer, train_example_data, "Train: ")
-                    trainer.train()
-                    tracker.log_images(train_images, captions=train_captions, image_section="Train Samples", step=step)
-
-            if epoch_samples is not None and sample >= epoch_samples:
-                break
-
-        trainer.eval()
-        print(print_ribbon(f"Starting Validation {epoch}", repeat=40))
-        with torch.no_grad():
             sample = 0
-            average_loss = 0
+
+        all_average_val_losses = None
+        if next_task == 'val':
+            trainer.eval()
+            accelerator.print(print_ribbon(f"Starting Validation {epoch}", repeat=40))
+            last_val_sample = val_sample
+            val_sample_length_tensor = torch.zeros(1, dtype=torch.int, device=inference_device)
+            average_val_loss_tensor = torch.zeros(1, trainer.num_unets, dtype=torch.float, device=inference_device)
             timer = Timer()
+            accelerator.wait_for_everyone()
+            i = 0
             for i, (img, emb, txt) in enumerate(dataloaders["val"]):
-                sample += img.shape[0]
+                val_sample_length_tensor[0] = len(img)
+                all_samples = accelerator.gather(val_sample_length_tensor)
+                total_samples = all_samples.sum().item()
+                val_sample += total_samples
                 img, emb = send_to_device((img, emb))
 
                 for unet in range(1, len(decoder.unets)+1):
+                    if not unet_training_mask[unet-1]: # Unet index is the unet number - 1
+                        # No need to evaluate an unchanging unet
+                        continue
+                    
                     loss = trainer.forward(img.float(), image_embed=emb.float(), unet_number=unet)
-                    average_loss += loss
+                    average_val_loss_tensor[0, unet-1] += loss
 
-                if i % 10 == 0:
+                if i % VALID_CALC_LOSS_EVERY_ITERS == 0:
-                    print(f"Epoch {epoch}/{epochs} - {sample / timer.elapsed():.2f} samples/sec")
+                    samples_per_sec = (val_sample - last_val_sample) / timer.elapsed()
-                    print(f"Loss: {average_loss / (i+1)}")
+                    timer.reset()
-                    print("")
+                    last_val_sample = val_sample
+                    accelerator.print(f"Epoch {epoch}/{epochs} Val Step {i} -  Sample {val_sample} - {samples_per_sec:.2f} samples/sec")
+                    accelerator.print(f"Loss: {(average_val_loss_tensor / (i+1))}")
+                    accelerator.print("")
                 
-                if validation_samples is not None and sample >= validation_samples:
+                if validation_samples is not None and val_sample >= validation_samples:
                     break
+            print(f"Rank {accelerator.state.process_index} finished validation after {i} steps")
+            accelerator.wait_for_everyone()
+            average_val_loss_tensor /= i+1
+            # Gather all the average loss tensors
+            all_average_val_losses = accelerator.gather(average_val_loss_tensor)
+            if is_master:
+                unet_average_val_loss = all_average_val_losses.mean(dim=0)
+                val_loss_map = { f"Unet {index} Validation Loss": loss.item() for index, loss in enumerate(unet_average_val_loss) if loss != 0 }
+                tracker.log(val_loss_map, step=step(), verbose=True)
+            next_task = 'eval'
 
-            average_loss /= i+1
+        if next_task == 'eval':
-            log_data = {
+            if exists(evaluate_config):
-                "Validation loss": average_loss
+                accelerator.print(print_ribbon(f"Starting Evaluation {epoch}", repeat=40))
-            }
+                evaluation = evaluate_trainer(trainer, dataloaders["val"], inference_device, **evaluate_config.dict())
-            tracker.log(log_data, step=step, verbose=True)
+                if is_master:
+                    tracker.log(evaluation, step=step(), verbose=True)
+            next_task = 'sample'
+            val_sample = 0
 
-        # Compute evaluation metrics
+        if next_task == 'sample':
-        trainer.eval()
+            if is_master:
-        if evaluate_config is not None:
+                # Generate examples and save the model if we are the master
-            print(print_ribbon(f"Starting Evaluation {epoch}", repeat=40))
+                # Generate sample images
-            evaluation = evaluate_trainer(trainer, dataloaders["val"], inference_device, **evaluate_config)
+                print(print_ribbon(f"Sampling Set {epoch}", repeat=40))
-            tracker.log(evaluation, step=step, verbose=True)
+                test_images, test_captions = generate_grid_samples(trainer, test_example_data, "Test: ")
+                train_images, train_captions = generate_grid_samples(trainer, train_example_data, "Train: ")
+                tracker.log_images(test_images, captions=test_captions, image_section="Test Samples", step=step())
+                tracker.log_images(train_images, captions=train_captions, image_section="Train Samples", step=step())
 
-        # Generate sample images
+                print(print_ribbon(f"Starting Saving {epoch}", repeat=40))
-        print(print_ribbon(f"Sampling Set {epoch}", repeat=40))
+                # Get the same paths
-        test_images, test_captions = generate_grid_samples(trainer, test_example_data, "Test: ")
+                save_paths = []
-        train_images, train_captions = generate_grid_samples(trainer, train_example_data, "Train: ")
+                if save_latest:
-        tracker.log_images(test_images, captions=test_captions, image_section="Test Samples", step=step)
+                    save_paths.append("latest.pth")
-        tracker.log_images(train_images, captions=train_captions, image_section="Train Samples", step=step)
+                if all_average_val_losses is not None:
+                    average_loss = all_average_val_losses.mean(dim=0).item()
+                    if save_best and (len(validation_losses) == 0 or average_loss < min(validation_losses)):
+                        save_paths.append("best.pth")
+                    validation_losses.append(average_loss)
+                save_trainer(tracker, trainer, epoch, sample, next_task, validation_losses, save_paths)
+            next_task = 'train'
 
-        print(print_ribbon(f"Starting Saving {epoch}", repeat=40))
+def create_tracker(accelerator, config, config_path, tracker_type=None, data_path=None):
-        # Get the same paths
-        save_paths = []
-        if save_latest:
-            save_paths.append("latest.pth")
-        if save_best and (len(validation_losses) == 0 or average_loss < min(validation_losses)):
-            save_paths.append("best.pth")
-        validation_losses.append(average_loss)
-        save_trainer(tracker, trainer, epoch, step, validation_losses, save_paths)
-
-def create_tracker(config, tracker_type=None, data_path=None, **kwargs):
     """
     Creates a tracker of the specified type and initializes special features based on the full config
     """
-    tracker_config = config["tracker"]
+    tracker_config = config.tracker
-    init_config = {}
+    accelerator_config = {
-    init_config["config"] = config.config
+        "Distributed": accelerator.distributed_type != accelerate_dataclasses.DistributedType.NO,
-    if tracker_type == "console":
+        "DistributedType": accelerator.distributed_type,
-        tracker = ConsoleTracker(**init_config)
+        "NumProcesses": accelerator.num_processes,
+        "MixedPrecision": accelerator.mixed_precision
+    }
+    init_config = { "config": {**config.dict(), **accelerator_config} }
+    data_path = data_path or tracker_config.data_path
+    tracker_type = tracker_type or tracker_config.tracker_type
+
+    if tracker_type == "dummy":
+        tracker = DummyTracker(data_path)
+        tracker.init(**init_config)
+    elif tracker_type == "console":
+        tracker = ConsoleTracker(data_path)
+        tracker.init(**init_config)
     elif tracker_type == "wandb":
         # We need to initialize the resume state here
-        load_config = config["load"]
+        load_config = config.load
-        if load_config["source"] == "wandb" and load_config["resume"]:
+        if load_config.source == "wandb" and load_config.resume:
             # Then we are resuming the run load_config["run_path"]
-            run_id = config["resume"]["wandb_run_path"].split("/")[-1]
+            run_id = load_config.run_path.split("/")[-1]
             init_config["id"] = run_id
             init_config["resume"] = "must"
-        init_config["entity"] = tracker_config["wandb_entity"]
+
-        init_config["project"] = tracker_config["wandb_project"]
+        init_config["entity"] = tracker_config.wandb_entity
+        init_config["project"] = tracker_config.wandb_project
         tracker = WandbTracker(data_path)
         tracker.init(**init_config)
+        tracker.save_file(str(config_path.absolute()), str(config_path.parent.absolute()))
     else:
         raise ValueError(f"Tracker type {tracker_type} not supported by decoder trainer")
     return tracker
     
-def initialize_training(config):
+def initialize_training(config, config_path):
-    # Create the save path
+    # Make sure if we are not loading, distributed models are initialized to the same values
-    if "cuda" in config["train"]["device"]:
+    torch.manual_seed(config.seed)
-        assert torch.cuda.is_available(), "CUDA is not available"
-    device = torch.device(config["train"]["device"])
-    torch.cuda.set_device(device)
-    all_shards = list(range(config["data"]["start_shard"], config["data"]["end_shard"] + 1))
 
+    # Set up accelerator for configurable distributed training
+    ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
+    accelerator = Accelerator(kwargs_handlers=[ddp_kwargs])
+    
+    # Set up data
+    all_shards = list(range(config.data.start_shard, config.data.end_shard + 1))
+    world_size = accelerator.num_processes
+    rank = accelerator.process_index
+    shards_per_process = len(all_shards) // world_size
+    assert shards_per_process > 0, "Not enough shards to split evenly"
+    my_shards = all_shards[rank * shards_per_process: (rank + 1) * shards_per_process]
     dataloaders = create_dataloaders (
-        available_shards=all_shards,
+        available_shards=my_shards,
-        img_preproc = config.get_preprocessing(),
+        img_preproc = config.data.img_preproc,
-        train_prop = config["data"]["splits"]["train"],
+        train_prop = config.data.splits.train,
-        val_prop = config["data"]["splits"]["val"],
+        val_prop = config.data.splits.val,
-        test_prop = config["data"]["splits"]["test"],
+        test_prop = config.data.splits.test,
-        n_sample_images=config["train"]["n_sample_images"],
+        n_sample_images=config.train.n_sample_images,
-        **config["data"]
+        **config.data.dict(),
+        rank = rank,
+        seed = config.seed,
     )
 
-    decoder = create_decoder(device, config["decoder"], config["unets"])
+    # Create the decoder model and print basic info
+    decoder = config.decoder.create()
     num_parameters = sum(p.numel() for p in decoder.parameters())
-    print(print_ribbon("Loaded Config", repeat=40))
-    print(f"Number of parameters: {num_parameters}")
 
-    tracker = create_tracker(config, **config["tracker"])
+    # Create and initialize the tracker if we are the master
+    tracker = create_tracker(accelerator, config, config_path) if rank == 0 else create_tracker(accelerator, config, config_path, tracker_type="dummy")
 
-    train(dataloaders, decoder, 
+    accelerator.print(print_ribbon("Loaded Config", repeat=40))
+    accelerator.print(f"Running training with {accelerator.num_processes} processes and {accelerator.distributed_type} distributed training")
+    accelerator.print(f"Number of parameters: {num_parameters}")
+    train(dataloaders, decoder, accelerator,
         tracker=tracker,
-        inference_device=device,
+        inference_device=accelerator.device,
-        load_config=config["load"],
+        load_config=config.load,
-        evaluate_config=config["evaluate"],
+        evaluate_config=config.evaluate,
-        **config["train"],
+        **config.train.dict(),
     )
     
-
-class TrainDecoderConfig:
-    def __init__(self, config):
-        self.config = self.map_config(config, default_config)
-
-    def map_config(self, config, defaults):
-        """
-        Returns a dictionary containing all config options in the union of config and defaults.
-        If the config value is an array, apply the default value to each element.
-        If the default values dict has a value of ConfigField.REQUIRED for a key, it is required and a runtime error should be thrown if a value is not supplied from config
-        """
-        def _check_option(option, option_config, option_defaults):
-            for key, value in option_defaults.items():
-                if key not in option_config:
-                    if value == ConfigField.REQUIRED:
-                        raise RuntimeError("Required config value '{}' of option '{}' not supplied".format(key, option))
-                    option_config[key] = value
-        
-        for key, value in defaults.items():
-            if key not in config:
-                # Then they did not pass in one of the main configs. If the default is an array or object, then we can fill it in. If is a required object, we must error
-                if value == ConfigField.REQUIRED:
-                    raise RuntimeError("Required config value '{}' not supplied".format(key))
-                elif isinstance(value, dict):
-                    config[key] = {}
-                elif isinstance(value, list):
-                    config[key] = [{}]
-            # Config[key] is now either a dict, list of dicts, or an object that cannot be checked. 
-            # If it is a list, then we need to check each element
-            if isinstance(value, list):
-                assert isinstance(config[key], list)
-                for element in config[key]:
-                    _check_option(key, element, value[0])
-            elif isinstance(value, dict):
-                _check_option(key, config[key], value)
-            # This object does not support checking
-        return config
-
-    def get_preprocessing(self):
-        """
-        Takes the preprocessing dictionary and converts it to a composition of torchvision transforms
-        """
-        def _get_transformation(transformation_name, **kwargs):
-            if transformation_name == "RandomResizedCrop":
-                return T.RandomResizedCrop(**kwargs)
-            elif transformation_name == "RandomHorizontalFlip":
-                return T.RandomHorizontalFlip()
-            elif transformation_name == "ToTensor":
-                return T.ToTensor()
-        
-        transformations = []
-        for transformation_name, transformation_kwargs in self.config["data"]["preprocessing"].items():
-            if isinstance(transformation_kwargs, dict):
-                transformations.append(_get_transformation(transformation_name, **transformation_kwargs))
-            else:
-                transformations.append(_get_transformation(transformation_name))
-        return T.Compose(transformations)
-    
-    def __getitem__(self, key):
-        return self.config[key]
-
 # Create a simple click command line interface to load the config and start the training
 @click.command()
 @click.option("--config_file", default="./train_decoder_config.json", help="Path to config file")
 def main(config_file):
-    print("Recalling config from {}".format(config_file))
+    config_file_path = Path(config_file)
-    with open(config_file) as f:
+    config = TrainDecoderConfig.from_json_path(str(config_file_path))
-        config = json.load(f)
+    initialize_training(config, config_path=config_file_path)
-    config = TrainDecoderConfig(config)
-    initialize_training(config)
-
 
 if __name__ == "__main__":
     main()

train_diffusion_prior.py

@@ -1,75 +1,135 @@
-from pathlib import Path
+# TODO: add start, num_data_points, eval_every and group to config
+# TODO: switch back to repo's wandb
+
+START = 0
+NUM_DATA_POINTS = 250e6
+EVAL_EVERY = 1000
+GROUP = "distributed"
+
+import os
 import click
-import math
+import wandb
-import numpy as np
 
 import torch
-import clip
 from torch import nn
+from torch.utils.data import DataLoader
 
-from dalle2_pytorch.dataloaders import make_splits
+import numpy as np
-from dalle2_pytorch import DiffusionPrior, DiffusionPriorNetwork, OpenAIClipAdapter
-from dalle2_pytorch.trainer import DiffusionPriorTrainer, load_diffusion_model, save_diffusion_model, print_ribbon
 
-from dalle2_pytorch.trackers import ConsoleTracker, WandbTracker
+from accelerate import Accelerator
+
+from dalle2_pytorch.dataloaders import get_reader, make_splits
 from dalle2_pytorch.utils import Timer
+from dalle2_pytorch.train_configs import (
+    DiffusionPriorTrainConfig,
+    TrainDiffusionPriorConfig,
+)
+from dalle2_pytorch.trackers import BaseTracker, WandbTracker
+from dalle2_pytorch import DiffusionPriorTrainer
 
-from embedding_reader import EmbeddingReader
 
-from tqdm import tqdm
+# helpers
 
-# constants
 
-REPORT_METRICS_EVERY = 250 # for cosine similarity and other metric reporting during training
+cos = nn.CosineSimilarity(dim=1, eps=1e-6)
 
-tracker = WandbTracker()
-
-# helpers functions
 
 def exists(val):
-    val is not None
+    return val is not None
 
-# functions
 
-def eval_model(model, dataloader, text_conditioned, loss_type, phase="Validation"):
+def make_model(
-    model.eval()
+    prior_config, train_config, device: str = None, accelerator: Accelerator = None
+):
+    # create model from config
+    diffusion_prior = prior_config.create()
+
+    # instantiate the trainer
+    trainer = DiffusionPriorTrainer(
+        diffusion_prior=diffusion_prior,
+        lr=train_config.lr,
+        wd=train_config.wd,
+        max_grad_norm=train_config.max_grad_norm,
+        amp=train_config.amp,
+        use_ema=train_config.use_ema,
+        device=device,
+        accelerator=accelerator,
+    )
+
+    return trainer
+
+
+# eval functions
+
+
+def eval_model(
+    trainer: DiffusionPriorTrainer,
+    dataloader: DataLoader,
+    text_conditioned: bool,
+    loss_type: str,
+    tracker_context: str,
+    tracker: BaseTracker = None,
+    use_ema: bool = True,
+):
+    trainer.eval()
+    if trainer.is_main_process():
+        click.secho(f"Measuring performance on {tracker_context}", fg="green", blink=True)
 
     with torch.no_grad():
-        total_loss = 0.
+        total_loss = 0.0
-        total_samples = 0.
+        total_samples = 0.0
 
-        for image_embeddings, text_data in tqdm(dataloader):
+        for image_embeddings, text_data in dataloader:
+            image_embeddings = image_embeddings.to(trainer.device)
+            text_data = text_data.to(trainer.device)
 
             batches = image_embeddings.shape[0]
 
             input_args = dict(image_embed=image_embeddings)
+
             if text_conditioned:
-                input_args = dict(**input_args, text = text_data)
+                input_args = dict(**input_args, text=text_data)
             else:
                 input_args = dict(**input_args, text_embed=text_data)
 
-            loss = model(**input_args)
+            if use_ema:
+                loss = trainer.ema_diffusion_prior(**input_args)
+            else:
+                loss = trainer(**input_args)
 
             total_loss += loss * batches
             total_samples += batches
 
-        avg_loss = (total_loss / total_samples)
+        avg_loss = total_loss / total_samples
 
-        tracker.log({f'{phase} {loss_type}': avg_loss})
+        stats = {f"{tracker_context}-{loss_type}": avg_loss}
+        trainer.print(stats)
 
-def report_cosine_sims(diffusion_prior, dataloader, text_conditioned):
+        if exists(tracker):
-    diffusion_prior.eval()
+            tracker.log(stats, step=trainer.step.item() + 1)
 
-    cos = nn.CosineSimilarity(dim=1, eps=1e-6)
 
-    for test_image_embeddings, text_data in tqdm(dataloader):
+def report_cosine_sims(
+    trainer: DiffusionPriorTrainer,
+    dataloader: DataLoader,
+    text_conditioned: bool,
+    tracker: BaseTracker,
+    tracker_context: str = "validation",
+):
+    trainer.eval()
+    if trainer.is_main_process():
+        click.secho("Measuring Cosine-Similarity", fg="green", blink=True)
+
+    for test_image_embeddings, text_data in dataloader:
+        test_image_embeddings = test_image_embeddings.to(trainer.device)
+        text_data = text_data.to(trainer.device)
 
         # we are text conditioned, we produce an embedding from the tokenized text
         if text_conditioned:
-            text_embedding, text_encodings, text_mask = diffusion_prior.clip.embed_text(
+            text_embedding, text_encodings, text_mask = trainer.embed_text(text_data)
-                text_data)
+            text_cond = dict(
-            text_cond = dict(text_embed=text_embedding,
+                text_embed=text_embedding, text_encodings=text_encodings, mask=text_mask
-                             text_encodings=text_encodings, mask=text_mask)
+            )
         else:
             text_embedding = text_data
             text_cond = dict(text_embed=text_embedding)
@@ -80,8 +140,9 @@ def report_cosine_sims(diffusion_prior, dataloader, text_conditioned):
         # roll the text to simulate "unrelated" captions
         rolled_idx = torch.roll(torch.arange(text_embedding.shape[0]), 1)
         text_embed_shuffled = text_embed_shuffled[rolled_idx]
-        text_embed_shuffled = text_embed_shuffled / \
+        text_embed_shuffled = text_embed_shuffled / text_embed_shuffled.norm(
-            text_embed_shuffled.norm(dim=1, keepdim=True)
+            dim=1, keepdim=True
+        )
 
         if text_conditioned:
             text_encodings_shuffled = text_encodings[rolled_idx]
@@ -90,276 +151,276 @@ def report_cosine_sims(diffusion_prior, dataloader, text_conditioned):
             text_encodings_shuffled = None
             text_mask_shuffled = None
 
-        text_cond_shuffled = dict(text_embed=text_embed_shuffled,
+        text_cond_shuffled = dict(
-                                  text_encodings=text_encodings_shuffled, mask=text_mask_shuffled)
+            text_embed=text_embed_shuffled,
+            text_encodings=text_encodings_shuffled,
+            mask=text_mask_shuffled,
+        )
 
         # prepare the text embedding
         text_embed = text_embedding / text_embedding.norm(dim=1, keepdim=True)
 
         # prepare image embeddings
-        test_image_embeddings = test_image_embeddings / \
+        test_image_embeddings = test_image_embeddings / test_image_embeddings.norm(
-            test_image_embeddings.norm(dim=1, keepdim=True)
+            dim=1, keepdim=True
+        )
 
         # predict on the unshuffled text embeddings
-        predicted_image_embeddings = diffusion_prior.p_sample_loop(
+        predicted_image_embeddings = trainer.p_sample_loop(
-            test_image_embeddings.shape, text_cond)
+            test_image_embeddings.shape, text_cond
-        predicted_image_embeddings = predicted_image_embeddings / \
+        )
-            predicted_image_embeddings.norm(dim=1, keepdim=True)
+
+        predicted_image_embeddings = (
+            predicted_image_embeddings
+            / predicted_image_embeddings.norm(dim=1, keepdim=True)
+        )
 
         # predict on the shuffled embeddings
-        predicted_unrelated_embeddings = diffusion_prior.p_sample_loop(
+        predicted_unrelated_embeddings = trainer.p_sample_loop(
-            test_image_embeddings.shape, text_cond_shuffled)
+            test_image_embeddings.shape, text_cond_shuffled
-        predicted_unrelated_embeddings = predicted_unrelated_embeddings / \
+        )
-            predicted_unrelated_embeddings.norm(dim=1, keepdim=True)
+
+        predicted_unrelated_embeddings = (
+            predicted_unrelated_embeddings
+            / predicted_unrelated_embeddings.norm(dim=1, keepdim=True)
+        )
 
         # calculate similarities
-        original_similarity = cos(
+        original_similarity = cos(text_embed, test_image_embeddings).cpu().numpy()
-           text_embed, test_image_embeddings).cpu().numpy()
+        predicted_similarity = cos(text_embed, predicted_image_embeddings).cpu().numpy()
-        predicted_similarity = cos(
+        unrelated_similarity = (
-           text_embed, predicted_image_embeddings).cpu().numpy()
+            cos(text_embed, predicted_unrelated_embeddings).cpu().numpy()
-        unrelated_similarity = cos(
+        )
-           text_embed, predicted_unrelated_embeddings).cpu().numpy()
+        predicted_img_similarity = (
-        predicted_img_similarity = cos(
+            cos(test_image_embeddings, predicted_image_embeddings).cpu().numpy()
-           test_image_embeddings, predicted_image_embeddings).cpu().numpy()
+        )
-        tracker.log({"CosineSimilarity(text_embed,image_embed)": np.mean(original_similarity),
+
-            "CosineSimilarity(text_embed,predicted_image_embed)":np.mean(predicted_similarity),
+        stats = {
-            "CosineSimilarity(orig_image_embed,predicted_image_embed)":np.mean(predicted_img_similarity),
+            f"{tracker_context}/baseline similarity": np.mean(original_similarity),
-            "CosineSimilarity(text_embed,predicted_unrelated_embed)": np.mean(unrelated_similarity),
+            f"{tracker_context}/similarity with text": np.mean(predicted_similarity),
-            "Cosine similarity difference":np.mean(predicted_similarity - original_similarity)})
+            f"{tracker_context}/similarity with original image": np.mean(
+                predicted_img_similarity
+            ),
+            f"{tracker_context}/similarity with unrelated caption": np.mean(unrelated_similarity),
+            f"{tracker_context}/difference from baseline similarity": np.mean(
+                predicted_similarity - original_similarity
+            ),
+        }
+
+        for k, v in stats.items():
+            trainer.print(f"{tracker_context}/{k}: {v}")
+
+        if exists(tracker):
+            tracker.log(stats, step=trainer.step.item() + 1)
+
+
+# training script
+
+
+def train(
+    trainer: DiffusionPriorTrainer,
+    train_loader: DataLoader,
+    eval_loader: DataLoader,
+    test_loader: DataLoader,
+    config: DiffusionPriorTrainConfig,
+):
+    # distributed tracking with wandb
+    if trainer.accelerator.num_processes > 1:
+        os.environ["WANDB_START_METHOD"] = "thread"
+
+    tracker = wandb.init(
+        name=f"RANK:{trainer.device}",
+        entity=config.tracker.wandb_entity,
+        project=config.tracker.wandb_project,
+        config=config.dict(),
+        group=GROUP,
+    )
+
+    # sync after tracker init
+    trainer.wait_for_everyone()
+
+    # init a timer
+    timer = Timer()
+
+    # do training
+    for img, txt in train_loader:
+        trainer.train()
+        current_step = trainer.step.item() + 1
+
+        # place data on device
+        img = img.to(trainer.device)
+        txt = txt.to(trainer.device)
+
+        # pass to model
+        loss = trainer(text=txt, image_embed=img)
+
+        # display & log loss (will only print from main process)
+        trainer.print(f"Step {current_step}: Loss {loss}")
+
+        # perform backprop & apply EMA updates
+        trainer.update()
+
+        # track samples/sec/rank
+        samples_per_sec = img.shape[0] / timer.elapsed()
+
+        # samples seen
+        samples_seen = (
+            config.data.batch_size * trainer.accelerator.num_processes * current_step
+        )
+
+        # ema decay
+        ema_decay = trainer.ema_diffusion_prior.get_current_decay()
+
+        # Log on all processes for debugging
+        tracker.log(
+            {
+                "tracking/samples-sec": samples_per_sec,
+                "tracking/samples-seen": samples_seen,
+                "tracking/ema-decay": ema_decay,
+                "metrics/training-loss": loss,
+            },
+            step=current_step,
+        )
+
+        # Metric Tracking & Checkpointing (outside of timer's scope)
+        if current_step % EVAL_EVERY == 0:
+            eval_model(
+                trainer=trainer,
+                dataloader=eval_loader,
+                text_conditioned=config.prior.condition_on_text_encodings,
+                loss_type=config.prior.loss_type,
+                tracker_context="metrics/online-model-validation",
+                tracker=tracker,
+                use_ema=False,
+            )
+
+            eval_model(
+                trainer=trainer,
+                dataloader=eval_loader,
+                text_conditioned=config.prior.condition_on_text_encodings,
+                loss_type=config.prior.loss_type,
+                tracker_context="metrics/ema-model-validation",
+                tracker=tracker,
+                use_ema=True,
+            )
+
+            report_cosine_sims(
+                trainer=trainer,
+                dataloader=eval_loader,
+                text_conditioned=config.prior.condition_on_text_encodings,
+                tracker=tracker,
+                tracker_context="metrics",
+            )
+
+        if current_step % config.train.save_every == 0:
+            trainer.save(f"{config.tracker.data_path}/chkpt_step_{current_step}.pth")
+
+        # reset timer for next round
+        timer.reset()
+
+    # evaluate on test data
+
+    eval_model(
+        trainer=trainer,
+        dataloader=test_loader,
+        text_conditioned=config.prior.condition_on_text_encodings,
+        loss_type=config.prior.loss_type,
+        tracker_context="test",
+        tracker=tracker,
+    )
+
+    report_cosine_sims(
+        trainer,
+        test_loader,
+        config.prior.condition_on_text_encodings,
+        tracker,
+        tracker_context="test",
+    )
+
+
+def initialize_training(config, accelerator=None):
+    """
+    Parse the configuration file, and prepare everything necessary for training
+    """
+
+    # get a device
+
+    if accelerator:
+        device = accelerator.device
+        click.secho(f"Accelerating on: {device}", fg="yellow")
+    else:
+        if torch.cuda.is_available():
+            click.secho("GPU detected, defaulting to cuda:0", fg="yellow")
+            device = "cuda:0"
+        else:
+            click.secho("No GPU detected...using cpu", fg="yellow")
+            device = "cpu"
+
+    # make the trainer (will automatically distribute if possible & configured)
+
+    trainer = make_model(config.prior, config.train, device, accelerator).to(device)
+
+    # reload from chcekpoint
+
+    if config.load.resume == True:
+        click.secho(f"Loading checkpoint: {config.load.source}", fg="cyan")
+        trainer.load(config.load.source)
+
+    # fetch and prepare data
+
+    if trainer.is_main_process():
+        click.secho("Grabbing data from source", fg="blue", blink=True)
+
+    img_reader = get_reader(
+        text_conditioned=trainer.text_conditioned,
+        img_url=config.data.image_url,
+        meta_url=config.data.meta_url,
+    )
+
+    train_loader, eval_loader, test_loader = make_splits(
+        text_conditioned=trainer.text_conditioned,
+        batch_size=config.data.batch_size,
+        num_data_points=NUM_DATA_POINTS,
+        train_split=config.data.splits.train,
+        eval_split=config.data.splits.val,
+        image_reader=img_reader,
+        rank=accelerator.state.process_index if exists(accelerator) else 0,
+        world_size=accelerator.state.num_processes if exists(accelerator) else 1,
+        start=START,
+    )
+
+    # wait for everyone to load data before continuing
+    trainer.wait_for_everyone()
+
+    # start training
+    train(
+        trainer=trainer,
+        train_loader=train_loader,
+        eval_loader=eval_loader,
+        test_loader=test_loader,
+        config=config,
+    )
 
 
 @click.command()
-@click.option("--wandb-entity", default="laion")
+@click.option("--hfa", default=True)
-@click.option("--wandb-project", default="diffusion-prior")
+@click.option("--config_path", default="configs/prior.json")
-@click.option("--wandb-dataset", default="LAION-5B")
+def main(hfa, config_path):
-@click.option("--wandb-arch", default="DiffusionPrior")
+    # start HFA if requested
-@click.option("--image-embed-url", default="https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion2B-en/img_emb/")
+    if hfa:
-@click.option("--text-embed-url", default="https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion2B-en/text_emb/")
+        accelerator = Accelerator()
-@click.option("--meta-url", default="https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion2B-en/laion2B-en-metadata/")
-@click.option("--learning-rate", default=1.1e-4)
-@click.option("--weight-decay", default=6.02e-2)
-@click.option("--dropout", default=5e-2)
-@click.option("--max-grad-norm", default=0.5)
-@click.option("--num-data-points", default=250e6)
-@click.option("--batch-size", default=320)
-@click.option("--num-epochs", default=5)
-@click.option("--image-embed-dim", default=768)
-@click.option("--train-percent", default=0.9)
-@click.option("--val-percent", default=1e-7)
-@click.option("--test-percent", default=0.0999999)
-@click.option("--dpn-depth", default=12)
-@click.option("--dpn-dim-head", default=64)
-@click.option("--dpn-heads", default=12)
-@click.option("--dp-condition-on-text-encodings", default=True)
-@click.option("--dp-timesteps", default=1000)
-@click.option("--dp-normformer", default=True)
-@click.option("--dp-cond-drop-prob", default=0.1)
-@click.option("--dp-loss-type", default="l2")
-@click.option("--clip", default="ViT-L/14")
-@click.option("--amp", default=False)
-@click.option("--save-interval", default=120)
-@click.option("--save-path", default="./diffusion_prior_checkpoints")
-@click.option("--pretrained-model-path", default=None)
-@click.option("--gpu-device", default=0)
-def train(
-    wandb_entity,
-    wandb_project,
-    wandb_dataset,
-    wandb_arch,
-    image_embed_url,
-    text_embed_url,
-    meta_url,
-    learning_rate,
-    weight_decay,
-    dropout,
-    max_grad_norm,
-    num_data_points,
-    batch_size,
-    num_epochs,
-    image_embed_dim,
-    train_percent,
-    val_percent,
-    test_percent,
-    dpn_depth,
-    dpn_dim_head,
-    dpn_heads,
-    dp_condition_on_text_encodings,
-    dp_timesteps,
-    dp_normformer,
-    dp_cond_drop_prob,
-    dp_loss_type,
-    clip,
-    amp,
-    save_interval,
-    save_path,
-    pretrained_model_path,
-    gpu_device
-):
-    config = {
-        "learning_rate": learning_rate,
-        "architecture": wandb_arch,
-        "dataset": wandb_dataset,
-        "weight_decay": weight_decay,
-        "max_gradient_clipping_norm": max_grad_norm,
-        "batch_size": batch_size,
-        "epochs": num_epochs,
-        "diffusion_prior_network": {
-            "depth": dpn_depth,
-            "dim_head": dpn_dim_head,
-            "heads": dpn_heads,
-            "normformer": dp_normformer
-        },
-        "diffusion_prior": {
-            "condition_on_text_encodings": dp_condition_on_text_encodings,
-            "timesteps": dp_timesteps,
-            "cond_drop_prob": dp_cond_drop_prob,
-            "loss_type": dp_loss_type,
-            "clip": clip
-        }
-    }
-
-    # Check if DPRIOR_PATH exists(saved model path)
-
-    DPRIOR_PATH = pretrained_model_path
-    RESUME = exists(DPRIOR_PATH)
-
-    if not RESUME:
-        tracker.init(
-            entity = wandb_entity,
-            project = wandb_project,
-            config = config
-        )
-
-    # Obtain the utilized device.
-
-    has_cuda = torch.cuda.is_available()
-    if has_cuda:
-        device = torch.device(f"cuda:{gpu_device}")
-        torch.cuda.set_device(device)
-
-    # Training loop
-    # diffusion prior network
-
-    prior_network = DiffusionPriorNetwork( 
-        dim = image_embed_dim,
-        depth = dpn_depth,
-        dim_head = dpn_dim_head,
-        heads = dpn_heads,
-        attn_dropout = dropout,
-        ff_dropout = dropout,
-        normformer = dp_normformer
-    )
-    
-    # Load clip model if text-conditioning
-    if dp_condition_on_text_encodings:
-        clip_adapter = OpenAIClipAdapter(clip)
     else:
-        clip_adapter = None
+        accelerator = None
 
-    # diffusion prior with text embeddings and image embeddings pre-computed
+    # load the configuration file on main process
+    if not exists(accelerator) or accelerator.is_main_process:
+        click.secho(f"Loading configuration from {config_path}", fg="green")
 
-    diffusion_prior = DiffusionPrior( 
+    config = TrainDiffusionPriorConfig.from_json_path(config_path)
-        net = prior_network,
-        clip = clip_adapter,
-        image_embed_dim = image_embed_dim,
-        timesteps = dp_timesteps,
-        cond_drop_prob = dp_cond_drop_prob,
-        loss_type = dp_loss_type,
-        condition_on_text_encodings = dp_condition_on_text_encodings
-    )
 
-    # Load pre-trained model from DPRIOR_PATH
+    # send config to get processed
-
+    initialize_training(config, accelerator)
-    if RESUME:
-        diffusion_prior, loaded_obj = load_diffusion_model(DPRIOR_PATH, device)
-        tracker.init(entity = wandb_entity, project = wandb_project, config = config)
-
-    # diffusion prior trainer
-
-    trainer = DiffusionPriorTrainer(
-        diffusion_prior = diffusion_prior,
-        lr = learning_rate,
-        wd = weight_decay,
-        max_grad_norm = max_grad_norm,
-        amp = amp,
-    ).to(device)
-
-    # load optimizer and scaler
-
-    if RESUME:
-        trainer.optimizer.load_state_dict(loaded_obj['optimizer'])
-        trainer.scaler.load_state_dict(loaded_obj['scaler'])
-
-    # Create save_path if it doesn't exist
-
-    Path(save_path).mkdir(exist_ok = True, parents = True)
-
-    # Utilize wrapper to abstract away loader logic
-    print_ribbon("Downloading Embeddings")
-    loader_args = dict(text_conditioned=dp_condition_on_text_encodings, batch_size=batch_size, num_data_points=num_data_points,
-                       train_split=train_percent, eval_split=val_percent, device=device, img_url=image_embed_url)
-
-    if dp_condition_on_text_encodings:
-        loader_args = dict(**loader_args, meta_url=meta_url)
-    else:
-        loader_args = dict(**loader_args, txt_url=text_embed_url)
-
-    train_loader, eval_loader, test_loader = make_splits(**loader_args)
-
-    ### Training code ###
-
-    step = 1 
-    timer = Timer()
-    epochs = num_epochs
-
-    for _ in range(epochs):
-
-        for image, text in tqdm(train_loader):
-            
-            diffusion_prior.train()
-            
-            input_args = dict(image_embed=image)
-            if dp_condition_on_text_encodings:
-                input_args = dict(**input_args, text = text)
-            else:
-                input_args = dict(**input_args, text_embed=text)
-
-            loss = trainer(**input_args)
-
-            # Samples per second
-
-            samples_per_sec = batch_size * step / timer.elapsed()
-
-            # Save checkpoint every save_interval minutes
-            if(int(timer.elapsed()) >= 60 * save_interval):
-                timer.reset()
-
-                save_diffusion_model(
-                    save_path,
-                    diffusion_prior,
-                    trainer.optimizer,
-                    trainer.scaler,
-                    config,
-                    image_embed_dim)
-
-            # Log to wandb
-            tracker.log({"Training loss": loss,
-                        "Steps": step,
-                        "Samples per second": samples_per_sec})
-            # Log cosineSim(text_embed,predicted_image_embed) - cosineSim(text_embed,image_embed)
-            # Use NUM_TEST_EMBEDDINGS samples from the test set each time
-            # Get embeddings from the most recently saved model
-            if(step % REPORT_METRICS_EVERY) == 0:
-                report_cosine_sims(diffusion_prior, eval_loader, dp_condition_on_text_encodings)
-                ### Evaluate model(validation run) ###
-                eval_model(diffusion_prior, eval_loader, dp_condition_on_text_encodings, dp_loss_type, phase="Validation")
-
-            step += 1
-            trainer.update()
-
-    ### Test run ###
-    eval_model(diffusion_prior, test_loader, dp_condition_on_text_encodings, dp_loss_type, phase="Test")
 
 
 if __name__ == "__main__":
-    train()
+    main()