bump

Added config files for training

Changed example image generation to be more efficient

Added configuration description to README

Removed unused import

.gitignore

@@ -1,3 +1,12 @@
+# default experiment tracker data
+.tracker-data/
+
+# Configuration Files
+configs/*
+!configs/*.example
+!configs/*_defaults.py
+!configs/README.md
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

README.md

@@ -14,6 +14,16 @@ Please join <a href="https://discord.gg/xBPBXfcFHd"><img alt="Join us on Discord
 
 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.
 
+## Status
+
+- A research group has used the code in this repository to train a functional diffusion prior for their CLIP generations. Will share their work once they release their preprint. This, and <a href="https://github.com/crowsonkb">Katherine's</a> own experiments, validate OpenAI's finding that the extra prior increases variety of generations.
+
+- Decoder is now verified working for unconditional generation on my experimental setup for Oxford flowers. 2 researchers have also confirmed Decoder is working for them.
+
+<img src="./samples/oxford.png" width="600px" />
+
+*ongoing at 21k steps*
+
 ## Install
 
 ```bash
@@ -814,8 +824,8 @@ clip = CLIP(
 
 # mock data
 
-text = torch.randint(0, 49408, (32, 256)).cuda()
-images = torch.randn(32, 3, 256, 256).cuda()
+text = torch.randint(0, 49408, (512, 256)).cuda()
+images = torch.randn(512, 3, 256, 256).cuda()
 
 # prior networks (with transformer)
 
@@ -848,7 +858,7 @@ diffusion_prior_trainer.update()  # this will update the optimizer as well as th
 # after much of the above three lines in a loop
 # you can sample from the exponential moving average of the diffusion prior identically to how you do so for DiffusionPrior
 
-image_embeds = diffusion_prior_trainer.sample(text) # (4, 512) - exponential moving averaged image embeddings
+image_embeds = diffusion_prior_trainer.sample(text, max_batch_size = 4) # (512, 512) - exponential moving averaged image embeddings
 ```
 
 ## Bonus
@@ -861,7 +871,7 @@ ex.
 
 ```python
 import torch
-from dalle2_pytorch import Unet, Decoder
+from dalle2_pytorch import Unet, Decoder, DecoderTrainer
 
 # unet for the cascading ddpm
 
@@ -884,20 +894,24 @@ decoder = Decoder(
     unconditional = True
 ).cuda()
 
-# mock images (get a lot of this)
+# decoder trainer
+
+decoder_trainer = DecoderTrainer(decoder)
+
+# images (get a lot of this)
 
 images = torch.randn(1, 3, 512, 512).cuda()
 
 # feed images into decoder
 
 for i in (1, 2):
-    loss = decoder(images, unet_number = i)
-    loss.backward()
+    loss = decoder_trainer(images, unet_number = i)
+    decoder_trainer.update(unet_number = i)
 
-# do the above for many many many many steps
+# do the above for many many many many images
 # then it will learn to generate images
 
-images = decoder.sample(batch_size = 2) # (2, 3, 512, 512)
+images = decoder_trainer.sample(batch_size = 36, max_batch_size = 4) # (36, 3, 512, 512)
 ```
 
 ## Dataloaders
@@ -1065,6 +1079,7 @@ Once built, images will be saved to the same directory the command is invoked
 - [ ] 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
 
@@ -1153,4 +1168,13 @@ Once built, images will be saved to the same directory the command is invoked
 }
 ```
 
+```bibtex
+@article{ho2021cascaded,
+    title   = {Cascaded Diffusion Models for High Fidelity Image Generation},
+    author  = {Ho, Jonathan and Saharia, Chitwan and Chan, William and Fleet, David J and Norouzi, Mohammad and Salimans, Tim},
+    journal = {arXiv preprint arXiv:2106.15282},
+    year    = {2021}
+}
+```
+
 *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

@@ -0,0 +1,109 @@
+## DALLE2 Training Configurations
+
+For more complex configuration, we provide the option of using a configuration file instead of command line arguments.
+
+### 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).
+
+**<ins>Unets</ins>:**
+
+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. |
+| `image_embed_dim` | Yes | N/A | The dimension of the image embeddings. |
+| `dim_mults` | No | `(1, 2, 4, 8)` | The growth factors of the channels. |
+
+Any parameter from the `Unet` constructor can also be given here.
+
+**<ins>Decoder</ins>:**
+
+Defines the configuration options for the decoder model. The unets defined above will automatically be inserted.
+| Option | Required | Default | Description |
+| ------ | -------- | ------- | ----------- |
+| `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. |
+| `loss_type` | No | `l2` | The loss function. Options are `l1`, `huber`, or `l2`. |
+| `beta_schedule` | No | `cosine` | The noising schedule. Options are `cosine`, `linear`, `quadratic`, `jsd`, or `sigmoid`. |
+| `learned_variance` | No | `True` | Whether to learn the variance. |
+
+Any parameter from the `Decoder` constructor can also be given here.
+
+**<ins>Data</ins>:**
+
+Settings for creation of the dataloaders.
+| Option | Required | Default | Description |
+| ------ | -------- | ------- | ----------- |
+| `webdataset_base_url` | Yes | N/A | The url of a shard in the webdataset with the shard replaced with `{}`[^1]. |
+| `embeddings_url` | No | N/A | The url of the folder containing embeddings shards. Not required if embeddings are in webdataset. |
+| `num_workers` | No | `4` | The number of workers used in the dataloader. |
+| `batch_size` | No | `64` | The batch size. |
+| `start_shard` | No | `0` | Defines the start of the shard range the dataset will recall. |
+| `end_shard` | No | `9999999` | Defines the end of the shard range the dataset will recall. |
+| `shard_width` | No | `6` | Defines the width of one webdataset shard number[^2]. |
+| `index_width` | No | `4` | Defines the width of the index of a file inside a shard[^3]. |
+| `splits` | No | `{ "train": 0.75, "val": 0.15, "test": 0.1 }` | Defines the proportion of shards that will be allocated to the training, validation, and testing datasets. |
+| `shuffle_train` | No | `True` | Whether to shuffle the shards of the training dataset. |
+| `resample_train` | No | `False` | If true, shards will be randomly sampled with replacement from the datasets making the epoch length infinite if a limit is not set. Cannot be enabled if `shuffle_train` is enabled. |
+| `preprocessing` | No | `{ "ToTensor": True }` | Defines preprocessing applied to images from the datasets. |
+
+[^1]: If your shard files have the paths `protocol://path/to/shard/00104.tar`, then the base url would be `protocol://path/to/shard/{}.tar`. If you are using a protocol like `s3`, you need to pipe the tars. For example `pipe:s3cmd get s3://bucket/path/{}.tar -`.
+
+[^2]: This refers to the string length of the shard number for your webdataset shards. For instance, if your webdataset shard has the filename `00104.tar`, your shard length is 5.
+
+[^3]: Inside the webdataset `tar`, you have files named something like `001045945.jpg`. 5 of these characters refer to the shard, and 4 refer to the index of the file in the webdataset (shard is `001041` and index is `5945`). The `index_width` in this case is 4.
+
+**<ins>Train</ins>:**
+
+Settings for controlling the training hyperparameters.
+| Option | Required | Default | Description |
+| ------ | -------- | ------- | ----------- |
+| `epochs` | No | `20` | The number of epochs in the training run. |
+| `lr` | No | `1e-4` | The learning rate. |
+| `wd` | No | `0.01` | The weight decay. |
+| `max_grad_norm`| No | `0.5` | The grad norm clipping. |
+| `save_every_n_samples` | No | `100000` | Samples will be generated and a checkpoint will be saved every `save_every_n_samples` samples. |
+| `device` | No | `cuda:0` | The device to train on. |
+| `epoch_samples` | No | `None` | Limits the number of samples iterated through in each epoch. This must be set if resampling. None means no limit. |
+| `validation_samples` | No | `None` | The number of samples to use for validation. None mean the entire validation set. |
+| `use_ema` | No | `True` | Whether to use exponential moving average models for sampling. |
+| `ema_beta` | No | `0.99` | The ema coefficient. |
+| `save_all` | No | `False` | If True, preserves a checkpoint for every epoch. |
+| `save_latest` | No | `True` | If True, overwrites the `latest.pth` every time the model is saved. |
+| `save_best` | No | `True` | If True, overwrites the `best.pth` every time the model has a lower validation loss than all previous models. |
+| `unet_training_mask` | No | `None` | A boolean array of the same length as the number of unets. If false, the unet is frozen. A value of `None` trains all unets. |
+
+**<ins>Evaluate</ins>:**
+
+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. |
+| `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. |
+| `LPIPS` | No | `None` | Setting to an object enables the [Learned Perceptual Image Patch Similarity](https://torchmetrics.readthedocs.io/en/stable/image/learned_perceptual_image_patch_similarity.html) metric. |
+
+**<ins>Tracker</ins>:**
+
+Selects which tracker to use and configures it.
+| Option | Required | Default | Description |
+| ------ | -------- | ------- | ----------- |
+| `tracker_type` | No | `console` | Which tracker to use. Currently accepts `console` or `wandb`. |
+| `data_path` | No | `./models` | Where the tracker will store local data. |
+| `verbose` | No | `False` | Enables console logging for non-console trackers. |
+
+Other configuration options are required for the specific trackers. To see which are required, reference the initializer parameters of each [tracker](../dalle2_pytorch/trackers.py).
+
+**<ins>Load</ins>:**
+
+Selects where to load a pretrained model from.
+| Option | Required | Default | Description |
+| ------ | -------- | ------- | ----------- |
+| `source` | No | `None` | Supports `file` or `wandb`. |
+| `resume` | No | `False` | If the tracker support resuming the run, resume it. |
+
+Other configuration options are required for loading from a specific source. To see which are required, reference the load methods at the top of the [tracker file](../dalle2_pytorch/trackers.py).

configs/decoder_defaults.py

@@ -0,0 +1,82 @@
+"""
+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.json.example

@@ -0,0 +1,100 @@
+{
+    "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": {
+        "image_sizes": [64],
+        "image_size": [64],
+        "channels": 3,
+        "timesteps": 1000,
+        "loss_type": "l2",
+        "beta_schedule": "cosine",
+        "learned_variance": true
+    },
+    "data": {
+        "webdataset_base_url": "pipe:s3cmd get s3://bucket/path/{}.tar -",
+        "embeddings_url": "s3://bucket/embeddings/path/",
+        "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": {
+            "RandomResizedCrop": {
+                "size": [128, 128],
+                "scale": [0.75, 1.0],
+                "ratio": [1.0, 1.0]
+            },
+            "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,
+        "device": "cuda:0",
+        "epoch_samples": null,
+        "validation_samples": null,
+        "use_ema": true,
+        "ema_beta": 0.99,
+        "amp": false,
+        "save_all": false,
+        "save_latest": true,
+        "save_best": true,
+        "unet_training_mask": [true]
+    },
+    "evaluate": {
+        "n_evalation_samples": 1000,
+        "FID": {
+            "feature": 64
+        },
+        "IS": {
+            "feature": 64,
+            "splits": 10
+        },
+        "KID": {
+            "feature": 64,
+            "subset_size": 10
+        },
+        "LPIPS": {
+            "net_type": "vgg",
+            "reduction": "mean"
+        }
+    },
+    "tracker": {
+        "tracker_type": "console",
+        "data_path": "./models",
+
+        "wandb_entity": "",
+        "wandb_project": "",
+
+        "verbose": false
+    },
+    "load": {
+        "source": null,
+
+        "run_path": "",
+        "file_path": "",
+
+        "resume": false
+    }
+}

dalle2_pytorch/__init__.py

@@ -1,6 +1,6 @@
 from dalle2_pytorch.dalle2_pytorch import DALLE2, DiffusionPriorNetwork, DiffusionPrior, Unet, Decoder
 from dalle2_pytorch.dalle2_pytorch import OpenAIClipAdapter
-from dalle2_pytorch.train import DecoderTrainer, DiffusionPriorTrainer
+from dalle2_pytorch.trainer import DecoderTrainer, DiffusionPriorTrainer
 
 from dalle2_pytorch.vqgan_vae import VQGanVAE
 from x_clip import CLIP

dalle2_pytorch/dalle2_pytorch.py

@@ -61,6 +61,9 @@ def default(val, d):
 def cast_tuple(val, length = 1):
     return val if isinstance(val, tuple) else ((val,) * length)
 
+def module_device(module):
+    return next(module.parameters()).device
+
 @contextmanager
 def null_context(*args, **kwargs):
     yield
@@ -794,7 +797,7 @@ class DiffusionPriorNetwork(nn.Module):
         text_embed,
         text_encodings = None,
         mask = None,
-        cond_drop_prob = 0.2
+        cond_drop_prob = 0.
     ):
         batch, dim, device, dtype = *image_embed.shape, image_embed.device, image_embed.dtype
 
@@ -901,6 +904,7 @@ class DiffusionPrior(BaseGaussianDiffusion):
         self.channels = default(image_channels, lambda: clip.image_channels)
 
         self.cond_drop_prob = cond_drop_prob
+        self.can_classifier_guidance = cond_drop_prob > 0.
         self.condition_on_text_encodings = condition_on_text_encodings
 
         # in paper, they do not predict the noise, but predict x0 directly for image embedding, claiming empirically better results. I'll just offer both.
@@ -914,8 +918,10 @@ class DiffusionPrior(BaseGaussianDiffusion):
         self.training_clamp_l2norm = training_clamp_l2norm
         self.init_image_embed_l2norm = init_image_embed_l2norm
 
-    def p_mean_variance(self, x, t, text_cond, clip_denoised: bool):
-        pred = self.net(x, t, **text_cond)
+    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)'
+
+        pred = self.net.forward_with_cond_scale(x, t, cond_scale = cond_scale, **text_cond)
 
         if self.predict_x_start:
             x_recon = pred
@@ -933,17 +939,17 @@ class DiffusionPrior(BaseGaussianDiffusion):
         model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
         return model_mean, posterior_variance, posterior_log_variance
 
-    @torch.inference_mode()
-    def p_sample(self, x, t, text_cond = None, clip_denoised = True, repeat_noise = False):
+    @torch.no_grad()
+    def p_sample(self, x, t, text_cond = None, clip_denoised = True, repeat_noise = False, 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)
+        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)
         # 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.inference_mode()
-    def p_sample_loop(self, shape, text_cond):
+    @torch.no_grad()
+    def p_sample_loop(self, shape, text_cond, cond_scale = 1.):
         device = self.betas.device
 
         b = shape[0]
@@ -954,7 +960,7 @@ class DiffusionPrior(BaseGaussianDiffusion):
 
         for i in tqdm(reversed(range(0, self.num_timesteps)), desc='sampling loop time step', total=self.num_timesteps):
             times = torch.full((b,), i, device = device, dtype = torch.long)
-            image_embed = self.p_sample(image_embed, times, text_cond = text_cond)
+            image_embed = self.p_sample(image_embed, times, text_cond = text_cond, cond_scale = cond_scale)
 
         return image_embed
 
@@ -978,21 +984,21 @@ class DiffusionPrior(BaseGaussianDiffusion):
         loss = self.loss_fn(pred, target)
         return loss
 
-    @torch.inference_mode()
+    @torch.no_grad()
     @eval_decorator
-    def sample_batch_size(self, batch_size, text_cond):
+    def sample_batch_size(self, batch_size, text_cond, cond_scale = 1.):
         device = self.betas.device
         shape = (batch_size, self.image_embed_dim)
 
         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):
-            img = self.p_sample(img, torch.full((batch_size,), i, device = device, dtype = torch.long), text_cond = text_cond)
+            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
 
-    @torch.inference_mode()
+    @torch.no_grad()
     @eval_decorator
-    def sample(self, text, num_samples_per_batch = 2):
+    def sample(self, text, num_samples_per_batch = 2, cond_scale = 1.):
         # in the paper, what they did was
         # sample 2 image embeddings, choose the top 1 similarity, as judged by CLIP
         text = repeat(text, 'b ... -> (b r) ...', r = num_samples_per_batch)
@@ -1007,7 +1013,7 @@ class DiffusionPrior(BaseGaussianDiffusion):
         if self.condition_on_text_encodings:
             text_cond = {**text_cond, 'text_encodings': text_encodings, 'mask': text_mask}
 
-        image_embeds = self.p_sample_loop((batch_size, image_embed_dim), text_cond = text_cond)
+        image_embeds = self.p_sample_loop((batch_size, image_embed_dim), text_cond = text_cond, cond_scale = cond_scale)
 
         # retrieve original unscaled image embed
 
@@ -1387,7 +1393,8 @@ class Unet(nn.Module):
         self.text_to_cond = None
 
         if cond_on_text_encodings:
-            self.text_to_cond = nn.LazyLinear(cond_dim) if not exists(text_embed_dim) else nn.Linear(text_embed_dim, cond_dim)
+            assert exists(text_embed_dim), 'text_embed_dim must be given to the unet if cond_on_text_encodings is True'
+            self.text_to_cond = nn.Linear(text_embed_dim, cond_dim)
 
         # finer control over whether to condition on image embeddings and text encodings
         # so one can have the latter unets in the cascading DDPMs only focus on super-resoluting
@@ -1690,7 +1697,8 @@ class Decoder(BaseGaussianDiffusion):
         clip_adapter_overrides = dict(),
         learned_variance = True,
         vb_loss_weight = 0.001,
-        unconditional = False
+        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
     ):
         super().__init__(
             beta_schedule = beta_schedule,
@@ -1792,12 +1800,17 @@ class Decoder(BaseGaussianDiffusion):
 
         self.image_cond_drop_prob = image_cond_drop_prob
         self.text_cond_drop_prob = text_cond_drop_prob
+        self.can_classifier_guidance = image_cond_drop_prob > 0. or text_cond_drop_prob > 0.
 
         # whether to clip when sampling
 
         self.clip_denoised = clip_denoised
         self.clip_x_start = clip_x_start
 
+        # 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
+
     def get_unet(self, unet_number):
         assert 0 < unet_number <= len(self.unets)
         index = unet_number - 1
@@ -1811,13 +1824,19 @@ class Decoder(BaseGaussianDiffusion):
             unet = self.get_unet(unet_number)
 
         self.cuda()
-        self.unets.cpu()
 
+        devices = [module_device(unet) for unet in self.unets]
+        self.unets.cpu()
         unet.cuda()
+
         yield
-        unet.cpu()
+
+        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):
+        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))
 
         if learned_variance:
@@ -1846,7 +1865,7 @@ class Decoder(BaseGaussianDiffusion):
 
         return model_mean, posterior_variance, posterior_log_variance
 
-    @torch.inference_mode()
+    @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):
         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)
@@ -1855,14 +1874,15 @@ class Decoder(BaseGaussianDiffusion):
         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.inference_mode()
-    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):
+    @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):
         device = self.betas.device
 
         b = shape[0]
         img = torch.randn(shape, device = device)
 
-        lowres_cond_img = maybe(normalize_neg_one_to_one)(lowres_cond_img)
+        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):
             img = self.p_sample(
@@ -1879,16 +1899,17 @@ class Decoder(BaseGaussianDiffusion):
                 clip_denoised = clip_denoised
             )
 
-        unnormalize_img = unnormalize_zero_to_one(img)
+        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):
+    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):
         noise = default(noise, lambda: torch.randn_like(x_start))
 
         # normalize to [-1, 1]
 
-        x_start = normalize_neg_one_to_one(x_start)
-        lowres_cond_img = maybe(normalize_neg_one_to_one)(lowres_cond_img)
+        if not is_latent_diffusion:
+            x_start = self.normalize_img(x_start)
+            lowres_cond_img = maybe(self.normalize_img)(lowres_cond_img)
 
         # get x_t
 
@@ -1948,12 +1969,14 @@ class Decoder(BaseGaussianDiffusion):
 
         return loss + vb_loss
 
-    @torch.inference_mode()
+    @torch.no_grad()
     @eval_decorator
     def sample(
         self,
         image_embed = None,
         text = None,
+        text_mask = None,
+        text_encodings = None,
         batch_size = 1,
         cond_scale = 1.,
         stop_at_unet_number = None
@@ -1963,8 +1986,8 @@ class Decoder(BaseGaussianDiffusion):
         if not self.unconditional:
             batch_size = image_embed.shape[0]
 
-        text_encodings = text_mask = None
-        if exists(text):
+        if exists(text) and not exists(text_encodings) and not self.unconditional:
+            assert exists(self.clip)
             _, text_encodings, text_mask = self.clip.embed_text(text)
 
         assert not (self.condition_on_text_encodings and not exists(text_encodings)), 'text or text encodings must be passed into decoder if specified'
@@ -2000,7 +2023,8 @@ class Decoder(BaseGaussianDiffusion):
                     predict_x_start = predict_x_start,
                     learned_variance = learned_variance,
                     clip_denoised = not is_latent_diffusion,
-                    lowres_cond_img = lowres_cond_img
+                    lowres_cond_img = lowres_cond_img,
+                    is_latent_diffusion = is_latent_diffusion
                 )
 
                 img = vae.decode(img)
@@ -2016,6 +2040,7 @@ class Decoder(BaseGaussianDiffusion):
         text = None,
         image_embed = None,
         text_encodings = None,
+        text_mask = None,
         unet_number = None
     ):
         assert not (len(self.unets) > 1 and not exists(unet_number)), f'you must specify which unet you want trained, from a range of 1 to {len(self.unets)}, if you are training cascading DDPM (multiple unets)'
@@ -2040,7 +2065,6 @@ class Decoder(BaseGaussianDiffusion):
             assert exists(self.clip), 'if you want to derive CLIP image embeddings automatically, you must supply `clip` to the decoder on init'
             image_embed, _ = self.clip.embed_image(image)
 
-        text_encodings = text_mask = None
         if exists(text) and not exists(text_encodings) and not self.unconditional:
             assert exists(self.clip), 'if you are passing in raw text, you need to supply `clip` to the decoder'
             _, text_encodings, text_mask = self.clip.embed_text(text)
@@ -2059,12 +2083,14 @@ class Decoder(BaseGaussianDiffusion):
             image = aug(image)
             lowres_cond_img = aug(lowres_cond_img, params = aug._params)
 
+        is_latent_diffusion = not isinstance(vae, NullVQGanVAE)
+
         vae.eval()
         with torch.no_grad():
             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)
+        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)
 
 # main class
 
@@ -2087,22 +2113,23 @@ class DALLE2(nn.Module):
 
         self.to_pil = T.ToPILImage()
 
-    @torch.inference_mode()
+    @torch.no_grad()
     @eval_decorator
     def forward(
         self,
         text,
         cond_scale = 1.,
+        prior_cond_scale = 1.,
         return_pil_images = False
     ):
-        device = next(self.parameters()).device
+        device = module_device(self)
         one_text = isinstance(text, str) or (not is_list_str(text) and text.shape[0] == 1)
 
         if isinstance(text, str) or is_list_str(text):
             text = [text] if not isinstance(text, (list, tuple)) else text
             text = tokenizer.tokenize(text).to(device)
 
-        image_embed = self.prior.sample(text, num_samples_per_batch = self.prior_num_samples)
+        image_embed = self.prior.sample(text, num_samples_per_batch = self.prior_num_samples, cond_scale = prior_cond_scale)
 
         text_cond = text if self.decoder_need_text_cond else None
         images = self.decoder.sample(image_embed, text = text_cond, cond_scale = cond_scale)

dalle2_pytorch/dataloaders/README.md

@@ -0,0 +1,41 @@
+## Dataloaders
+In order to make loading data simple and efficient, we include some general dataloaders that can be used to train portions of the network.
+
+### Decoder: Image Embedding Dataset
+When training the decoder (and up samplers if training together) in isolation, you will need to load images and corresponding image embeddings. This dataset can read two similar types of datasets. First, it can read a [webdataset](https://github.com/webdataset/webdataset) that contains `.jpg` and `.npy` files in the `.tar`s that contain the images and associated image embeddings respectively. Alternatively, you can also specify a source for the embeddings outside of the webdataset. In this case, the path to the embeddings should contain `.npy` files with the same shard numbers as the webdataset and there should be a correspondence between the filename of the `.jpg` and the index of the embedding in the `.npy`. So, for example, `0001.tar` from the webdataset with image `00010509.jpg` (the first 4 digits are the shard number and the last 4 are the index) in it should be paralleled by a `img_emb_0001.npy` which contains a NumPy array with the embedding at index 509.
+
+Generating a dataset of this type: 
+1. Use [img2dataset](https://github.com/rom1504/img2dataset) to generate a webdataset.
+2. Use [clip-retrieval](https://github.com/rom1504/clip-retrieval) to convert the images to embeddings.
+3. Use [embedding-dataset-reordering](https://github.com/Veldrovive/embedding-dataset-reordering) to reorder the embeddings into the expected format.
+
+Usage:
+```python
+from dalle2_pytorch.dataloaders import ImageEmbeddingDataset, create_image_embedding_dataloader
+
+# 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
+    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,
+    shard_width=4,                                         # If a file in the webdataset shard 3 is named 0003039.jpg, we know the shard width is 4 and the last three digits are the index
+    shuffle_num=200,                                       # Does a shuffle of the data with a buffer size of 200
+    shuffle_shards=True,                                   # Shuffle the order the shards are read in
+    resample_shards=False,                                 # Sample shards with replacement. If true, an epoch will be infinite unless stopped manually
+)
+for img, emb in dataloader:
+    print(img.shape)  # torch.Size([32, 3, 256, 256])
+    print(emb.shape)  # torch.Size([32, 512])
+    # Train decoder only as shown above
+
+# Or create a dataset without a loader so you can configure it manually
+dataset = ImageEmbeddingDataset(
+    urls="/path/or/url/to/webdataset/{0000..9999}.tar",
+    embedding_folder_url="path/or/url/to/embeddings/folder",
+    shard_width=4,
+    shuffle_shards=True,
+    resample=False
+)
+```
+

dalle2_pytorch/dataloaders/__init__.py

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

dalle2_pytorch/dataloaders/decoder_loader.py

@@ -3,6 +3,7 @@ import webdataset as wds
 import torch
 import numpy as np
 import fsspec
+import shutil
 
 def get_shard(filename):
     """
@@ -20,7 +21,7 @@ def get_example_file(fs, path, file_format):
     """
     return fs.glob(os.path.join(path, f"*.{file_format}"))[0]
 
-def embedding_inserter(samples, embeddings_url, shard_width, handler=wds.handlers.reraise_exception):
+def embedding_inserter(samples, embeddings_url, index_width, handler=wds.handlers.reraise_exception):
     """Given a datum of {"__key__": str, "__url__": str, ...} adds the cooresponding embedding and yields"""
     previous_tar_url = None
     current_embeddings = None
@@ -50,8 +51,12 @@ def embedding_inserter(samples, embeddings_url, shard_width, handler=wds.handler
                 previous_tar_url = tar_url
                 current_embeddings = load_corresponding_embeds(tar_url)
                 
-            embedding_index = int(key[shard_width:])
-            sample["npy"] = current_embeddings[embedding_index]
+            embedding_index = int(key[-index_width:])
+            embedding = current_embeddings[embedding_index]
+            # We need to check if this sample is nonzero. If it is, this embedding is not valid and we should continue to the next loop
+            if torch.count_nonzero(embedding) == 0:
+                raise RuntimeError(f"Webdataset had a sample, but no embedding was found. ImgShard: {key[:-index_width]} - Index: {key[-index_width:]}")
+            sample["npy"] = embedding
             yield sample
         except Exception as exn:  # From wds implementation
             if handler(exn):
@@ -60,6 +65,28 @@ def embedding_inserter(samples, embeddings_url, shard_width, handler=wds.handler
                 break
 insert_embedding = wds.filters.pipelinefilter(embedding_inserter)
 
+def unassociated_shard_skipper(tarfiles, embeddings_url, handler=wds.handlers.reraise_exception):
+    """Finds if the is a corresponding embedding for the tarfile at { url: [URL] }"""
+    embeddings_fs, embeddings_path = fsspec.core.url_to_fs(embeddings_url)
+    embedding_files = embeddings_fs.ls(embeddings_path)
+    get_embedding_shard = lambda embedding_file: int(embedding_file.split("_")[-1].split(".")[0])
+    embedding_shards = set([get_embedding_shard(filename) for filename in embedding_files])  # Sets have O(1) check for member
+
+    get_tar_shard = lambda tar_file: int(tar_file.split("/")[-1].split(".")[0])
+    for tarfile in tarfiles:
+        try:
+            webdataset_shard = get_tar_shard(tarfile["url"])
+            # If this shard has an associated embeddings file, we pass it through. Otherwise we iterate until we do have one
+            if webdataset_shard in embedding_shards:
+                yield tarfile
+        except Exception as exn:  # From wds implementation
+            if handler(exn):
+                continue
+            else:
+                break
+    
+skip_unassociated_shards = wds.filters.pipelinefilter(unassociated_shard_skipper)
+
 def verify_keys(samples, handler=wds.handlers.reraise_exception):
     """
     Requires that both the image and embedding are present in the sample
@@ -86,7 +113,9 @@ class ImageEmbeddingDataset(wds.DataPipeline, wds.compat.FluidInterface):
             self,
             urls,
             embedding_folder_url=None,
-            shard_width=None,
+            index_width=None,
+            img_preproc=None,
+            extra_keys=[],
             handler=wds.handlers.reraise_exception,
             resample=False,
             shuffle_shards=True
@@ -97,13 +126,31 @@ class ImageEmbeddingDataset(wds.DataPipeline, wds.compat.FluidInterface):
         :param urls: A url pointing to the tar files of the webdataset formatted as /path/to/webdataset/{0000..9999}.tar
         :param embedding_folder_url: Required if webdataset does not contain embeddings. A url pointing to the npy files of the embeddings. Should have the same number of shards as the webdataset.
             Webdataset image keys should align with the index of the embedding. This means missing image indices must have a corresponding embedding of all zeros.
-        :param shard_width: The number of digits in the shard number. This is used to align the embedding index with the image index.
-            For example, if a file in the webdataset shard 3 is named 0003039.jpg, we know the shard with this 4 and the last three digits are the index.
+        :param index_width: The number of digits in the index. This is used to align the embedding index with the image index.
+            For example, if a file in the webdataset shard 3 is named 0003039.jpg, we know the shard is 4 digits and the last 3 digits are the index_width.
+        :param img_preproc: This function is run on the img before it is batched and returned. Useful for data augmentation or converting to torch tensor.
         :param handler: A webdataset handler.
         :param resample: If true, resample webdataset shards with replacement. You need to set your own epoch size if this is true since it will resample infinitely.
         :param shuffle_shards: If true, shuffle the shards before resampling. This cannot be true if resample is true.
+
+
         """
         super().__init__()
+        keys = ["jpg", "npy"] + extra_keys
+        self.key_map = {key: i for i, key in enumerate(keys)}
+        self.resampling = resample
+        self.img_preproc = img_preproc
+        # If s3, check if s3fs is installed and s3cmd is installed and check if the data is piped instead of straight up
+        if (isinstance(urls, str) and "s3:" in urls) or (isinstance(urls, list) and any(["s3:" in url for url in urls])):
+            # Then this has an s3 link for the webdataset and we need extra packages
+            if shutil.which("s3cmd") is None:
+                raise RuntimeError("s3cmd is required for s3 webdataset")
+        if "s3:" in embedding_folder_url:
+            # Then the embeddings are being loaded from s3 and fsspec requires s3fs
+            try:
+                import s3fs
+            except ImportError:
+                raise RuntimeError("s3fs is required to load embeddings from s3")
         # Add the shardList and randomize or resample if requested
         if resample:
             assert not shuffle_shards, "Cannot both resample and shuffle"
@@ -112,28 +159,43 @@ class ImageEmbeddingDataset(wds.DataPipeline, wds.compat.FluidInterface):
             self.append(wds.SimpleShardList(urls))
             if shuffle_shards:
                 self.append(wds.filters.shuffle(1000))
+        
+        if embedding_folder_url is not None:
+            # 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("torchrgb"))
+        self.append(wds.decode("pilrgb", handler=handler))
         if embedding_folder_url is not None:
-            assert shard_width is not None, "Reading embeddings separately requires shard length to be given"
-            self.append(insert_embedding(embeddings_url=embedding_folder_url, shard_width=shard_width, handler=handler))
+            # Then we are loading embeddings for a remote source
+            assert index_width is not None, "Reading embeddings separately requires index width length to be given"
+            self.append(insert_embedding(embeddings_url=embedding_folder_url, index_width=index_width, handler=handler))
         self.append(verify_keys)
-        self.append(wds.to_tuple("jpg", "npy"))
+        # Apply preprocessing
+        self.append(wds.map(self.preproc))
+        self.append(wds.to_tuple(*keys))
+
+    def preproc(self, sample):
+        """Applies the preprocessing for images"""
+        if self.img_preproc is not None:
+            sample["jpg"] = self.img_preproc(sample["jpg"])
+        return sample
 
 def create_image_embedding_dataloader(
     tar_url,
     num_workers,
     batch_size,
     embeddings_url=None,
-    shard_width=None,
+    index_width=None,
     shuffle_num = None,
     shuffle_shards = True,
     resample_shards = False, 
-    handler=wds.handlers.warn_and_continue
+    img_preproc=None,
+    extra_keys=[],
+    handler=wds.handlers.reraise_exception#warn_and_continue
 ):
     """
     Convenience function to create an image embedding dataseta and dataloader in one line
@@ -143,8 +205,8 @@ def create_image_embedding_dataloader(
     :param batch_size: The batch size to use for the dataloader
     :param embeddings_url: Required if webdataset does not contain embeddings. A url pointing to the npy files of the embeddings. Should have the same number of shards as the webdataset.
         Webdataset image keys should align with the index of the embedding. This means missing image indices must have a corresponding embedding of all zeros.
-    :param shard_width: The number of digits in the shard number. This is used to align the embedding index with the image index.
-        For example, if a file in the webdataset shard 3 is named 0003039.jpg, we know the shard width is 4 and the last three digits are the index.
+    :param index_width: The number of digits in the index. This is used to align the embedding index with the image index.
+            For example, if a file in the webdataset shard 3 is named 0003039.jpg, we know the shard is 4 digits and the last 3 digits are the index_width.
     :param shuffle_num: If not None, shuffle the dataset with this size buffer after sampling.
     :param shuffle_shards: If true, shuffle the shards before sampling. This cannot be true if resample is true.
     :param resample_shards: If true, resample webdataset shards with replacement. You need to set your own epoch size if this is true since it will resample infinitely.
@@ -153,9 +215,11 @@ def create_image_embedding_dataloader(
     ds = ImageEmbeddingDataset(
         tar_url,
         embeddings_url,
-        shard_width=shard_width,
+        index_width=index_width,
         shuffle_shards=shuffle_shards,
         resample=resample_shards,
+        extra_keys=extra_keys,
+        img_preproc=img_preproc,
         handler=handler
     )
     if shuffle_num is not None and shuffle_num > 0:

dalle2_pytorch/dataloaders/embedding_wrapper.py

@@ -0,0 +1,180 @@
+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/simple_image_only_dataloader.py

@@ -0,0 +1,59 @@
+from pathlib import Path
+
+import torch
+from torch.utils import data
+from torchvision import transforms, utils
+
+from PIL import Image
+
+# helpers functions
+
+def cycle(dl):
+    while True:
+        for data in dl:
+            yield data
+
+# dataset and dataloader
+
+class Dataset(data.Dataset):
+    def __init__(
+        self,
+        folder,
+        image_size,
+        exts = ['jpg', 'jpeg', 'png']
+    ):
+        super().__init__()
+        self.folder = folder
+        self.image_size = image_size
+        self.paths = [p for ext in exts for p in Path(f'{folder}').glob(f'**/*.{ext}')]
+
+        self.transform = transforms.Compose([
+            transforms.Resize(image_size),
+            transforms.RandomHorizontalFlip(),
+            transforms.CenterCrop(image_size),
+            transforms.ToTensor()
+        ])
+
+    def __len__(self):
+        return len(self.paths)
+
+    def __getitem__(self, index):
+        path = self.paths[index]
+        img = Image.open(path)
+        return self.transform(img)
+
+def get_images_dataloader(
+    folder,
+    *,
+    batch_size,
+    image_size,
+    shuffle = True,
+    cycle_dl = True,
+    pin_memory = True
+):
+    ds = Dataset(folder, image_size)
+    dl = data.DataLoader(ds, batch_size = batch_size, shuffle = shuffle, pin_memory = pin_memory)
+
+    if cycle_dl:
+        dl = cycle(dl)
+    return dl

dalle2_pytorch/optimizer.py

@@ -7,11 +7,12 @@ def separate_weight_decayable_params(params):
 
 def get_optimizer(
     params,
-    lr = 2e-5,
+    lr = 1e-4,
     wd = 1e-2,
     betas = (0.9, 0.999),
     eps = 1e-8,
-    filter_by_requires_grad = False
+    filter_by_requires_grad = False,
+    **kwargs
 ):
     if filter_by_requires_grad:
         params = list(filter(lambda t: t.requires_grad, params))

dalle2_pytorch/trackers.py

@@ -1,17 +1,45 @@
 import os
+from pathlib import Path
+import importlib
+from itertools import zip_longest
+
 import torch
 from torch import nn
 
+# constants
+
+DEFAULT_DATA_PATH = './.tracker-data'
+
 # helper functions
 
 def exists(val):
     return val is not None
 
+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()
+
+# load state dict functions
+
+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)
+
+def load_local_state_dict(file_path, **kwargs):
+    return torch.load(file_path)
+
 # base class
 
 class BaseTracker(nn.Module):
-    def __init__(self):
+    def __init__(self, data_path = DEFAULT_DATA_PATH):
         super().__init__()
+        self.data_path = Path(data_path)
+        self.data_path.mkdir(parents = True, exist_ok = True)
 
     def init(self, config, **kwargs):
         raise NotImplementedError
@@ -19,6 +47,27 @@ class BaseTracker(nn.Module):
     def log(self, log, **kwargs):
         raise NotImplementedError
 
+    def log_images(self, images, **kwargs):
+        raise NotImplementedError
+
+    def save_state_dict(self, state_dict, relative_path, **kwargs):
+        raise NotImplementedError
+
+    def recall_state_dict(self, recall_source, *args, **kwargs):
+        """
+        Loads a state dict from any source.
+        Since a user may wish to load a model from a different source than their own tracker (i.e. tracking using wandb but recalling from disk),
+            this should not be linked to any individual tracker.
+        """
+        # 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)
+        elif recall_source == 'local':
+            return load_local_state_dict(*args, **kwargs)
+        else:
+            raise ValueError('`recall_source` must be one of `wandb` or `local`')
+
+
 # basic stdout class
 
 class ConsoleTracker(BaseTracker):
@@ -28,22 +77,39 @@ class ConsoleTracker(BaseTracker):
     def log(self, log, **kwargs):
         print(log)
 
+    def log_images(self, images, **kwargs): # noop for logging images
+        pass
+    
+    def save_state_dict(self, state_dict, relative_path, **kwargs):
+        torch.save(state_dict, str(self.data_path / relative_path))
+
 # basic wandb class
 
 class WandbTracker(BaseTracker):
-    def __init__(self):
-        super().__init__()
-        try:
-            import wandb
-        except ImportError as e:
-            print('`pip install wandb` to use the wandb experiment tracker')
-            raise e
-
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.wandb = import_or_print_error('wandb', '`pip install wandb` to use the wandb experiment tracker')
         os.environ["WANDB_SILENT"] = "true"
-        self.wandb = wandb
 
     def init(self, **config):
         self.wandb.init(**config)
 
-    def log(self, log, **kwargs):
+    def log(self, log, verbose=False, **kwargs):
+        if verbose:
+            print(log)
         self.wandb.log(log, **kwargs)
+
+    def log_images(self, images, captions=[], image_section="images", **kwargs):
+        """
+        Takes a tensor of images and a list of captions and logs them to wandb.
+        """
+        wandb_images = [self.wandb.Image(image, caption=caption) for image, caption in zip_longest(images, captions)]
+        self.log({ image_section: wandb_images }, **kwargs)
+    
+    def save_state_dict(self, state_dict, relative_path, **kwargs):
+        """
+        Saves a state_dict to disk and uploads it 
+        """
+        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

dalle2_pytorch/trainer.py

@@ -47,6 +47,14 @@ def groupby_prefix_and_trim(prefix, d):
     kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
     return kwargs_without_prefix, kwargs
 
+def num_to_groups(num, divisor):
+    groups = num // divisor
+    remainder = num % divisor
+    arr = [divisor] * groups
+    if remainder > 0:
+        arr.append(remainder)
+    return arr
+
 # decorators
 
 def cast_torch_tensor(fn):
@@ -179,8 +187,8 @@ class EMA(nn.Module):
         self.online_model = model
         self.ema_model = copy.deepcopy(model)
 
-        self.update_after_step = update_after_step # only start EMA after this step number, starting at 0
         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.]))
@@ -189,14 +197,21 @@ class EMA(nn.Module):
         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_after_step or (self.step % self.update_every) != 0:
+        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.ema_model.state_dict(self.online_model.state_dict())
+            self.copy_params_from_model_to_ema()
             self.initted.data.copy_(torch.Tensor([True]))
 
         self.update_moving_average(self.ema_model, self.online_model)
@@ -220,6 +235,16 @@ class EMA(nn.Module):
 
 # diffusion prior trainer
 
+def prior_sample_in_chunks(fn):
+    @wraps(fn)
+    def inner(self, *args, max_batch_size = None, **kwargs):
+        if not exists(max_batch_size):
+            return fn(self, *args, **kwargs)
+
+        outputs = [fn(self, *chunked_args, **chunked_kwargs) for _, (chunked_args, chunked_kwargs) in split_args_and_kwargs(*args, split_size = max_batch_size, **kwargs)]
+        return torch.cat(outputs, dim = 0)
+    return inner
+
 class DiffusionPriorTrainer(nn.Module):
     def __init__(
         self,
@@ -278,17 +303,19 @@ class DiffusionPriorTrainer(nn.Module):
 
         self.step += 1
 
-    @torch.inference_mode()
+    @torch.no_grad()
     @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)
 
-    @torch.inference_mode()
+    @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)
 
-    @torch.inference_mode()
+    @torch.no_grad()
     def sample_batch_size(self, *args, **kwargs):
         return self.ema_diffusion_prior.ema_model.sample_batch_size(*args, **kwargs)
 
@@ -315,15 +342,31 @@ class DiffusionPriorTrainer(nn.Module):
 
 # decoder trainer
 
+def decoder_sample_in_chunks(fn):
+    @wraps(fn)
+    def inner(self, *args, max_batch_size = None, **kwargs):
+        if not exists(max_batch_size):
+            return fn(self, *args, **kwargs)
+
+        if self.decoder.unconditional:
+            batch_size = kwargs.get('batch_size')
+            batch_sizes = num_to_groups(batch_size, max_batch_size)
+            outputs = [fn(self, *args, **{**kwargs, 'batch_size': sub_batch_size}) for sub_batch_size in batch_sizes]
+        else:
+            outputs = [fn(self, *chunked_args, **chunked_kwargs) for _, (chunked_args, chunked_kwargs) in split_args_and_kwargs(*args, split_size = max_batch_size, **kwargs)]
+
+        return torch.cat(outputs, dim = 0)
+    return inner
+
 class DecoderTrainer(nn.Module):
     def __init__(
         self,
         decoder,
         use_ema = True,
-        lr = 2e-5,
+        lr = 1e-4,
         wd = 1e-2,
         eps = 1e-8,
-        max_grad_norm = None,
+        max_grad_norm = 0.5,
         amp = False,
         **kwargs
     ):
@@ -335,11 +378,6 @@ class DecoderTrainer(nn.Module):
         self.num_unets = len(self.decoder.unets)
 
         self.use_ema = use_ema
-
-        if use_ema:
-            has_lazy_linear = any([type(module) == nn.LazyLinear for module in decoder.modules()])
-            assert not has_lazy_linear, 'you must set the text_embed_dim on your u-nets if you plan on doing automatic exponential moving average'
-
         self.ema_unets = nn.ModuleList([])
 
         self.amp = amp
@@ -382,8 +420,11 @@ class DecoderTrainer(nn.Module):
         scaler = getattr(self, f'scaler{index}')
         return scaler.scale(loss)
 
-    def update(self, unet_number):
-        assert 1 <= unet_number <= self.num_unets
+    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]
 
@@ -406,15 +447,17 @@ class DecoderTrainer(nn.Module):
 
     @torch.no_grad()
     @cast_torch_tensor
+    @decoder_sample_in_chunks
     def sample(self, *args, **kwargs):
-        if self.use_ema:
-            trainable_unets = self.decoder.unets
-            self.decoder.unets = self.unets                  # swap in exponential moving averaged unets for sampling
+        if kwargs.pop('use_non_ema', False) or not self.use_ema:
+            return self.decoder.sample(*args, **kwargs)
+
+        trainable_unets = self.decoder.unets
+        self.decoder.unets = self.unets                  # swap in exponential moving averaged unets for sampling
 
         output = self.decoder.sample(*args, **kwargs)
 
-        if self.use_ema:
-            self.decoder.unets = trainable_unets             # restore original training unets
+        self.decoder.unets = trainable_unets             # restore original training unets
 
         # cast the ema_model unets back to original device
         for ema in self.ema_unets:
@@ -426,10 +469,13 @@ class DecoderTrainer(nn.Module):
     def forward(
         self,
         *args,
-        unet_number,
+        unet_number = None,
         max_batch_size = None,
         **kwargs
     ):
+        if self.num_unets == 1:
+            unet_number = default(unet_number, 1)
+
         total_loss = 0.
 
         for chunk_size_frac, (chunked_args, chunked_kwargs) in split_args_and_kwargs(*args, split_size = max_batch_size, **kwargs):

dalle2_pytorch/utils.py

@@ -0,0 +1,11 @@
+import time
+
+class Timer:
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.last_time = time.time()
+
+    def elapsed(self):
+        return time.time() - self.last_time

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.2.35',
+  version = '0.3.6',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',
@@ -41,7 +41,8 @@ setup(
     'x-clip>=0.4.4',
     'youtokentome',
     'webdataset>=0.2.5',
-    'fsspec>=2022.1.0'
+    'fsspec>=2022.1.0',
+    'torchmetrics[image]>=0.8.0'
   ],
   classifiers=[
     'Development Status :: 4 - Beta',

train_decoder.py

@@ -0,0 +1,506 @@
+from dalle2_pytorch import Unet, Decoder
+from dalle2_pytorch.trainer import DecoderTrainer, print_ribbon
+from dalle2_pytorch.dataloaders import create_image_embedding_dataloader
+from dalle2_pytorch.trackers import WandbTracker, ConsoleTracker
+from dalle2_pytorch.utils import Timer
+
+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
+import webdataset as wds
+import click
+
+
+def create_dataloaders(
+    available_shards,
+    webdataset_base_url,
+    embeddings_url,
+    shard_width=6,
+    num_workers=4,
+    batch_size=32,
+    n_sample_images=6,
+    shuffle_train=True,
+    resample_train=False,
+    img_preproc = None,
+    index_width=4,
+    train_prop = 0.75,
+    val_prop = 0.15,
+    test_prop = 0.10,
+    **kwargs
+):
+    """
+    Randomly splits the available shards into train, val, and test sets and returns a dataloader for each
+    """
+    assert train_prop + test_prop + val_prop == 1
+    num_train = round(train_prop*len(available_shards))
+    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))
+
+    # 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]
+    test_urls = [webdataset_base_url.format(str(shard).zfill(shard_width)) for shard in test_split]
+    val_urls = [webdataset_base_url.format(str(shard).zfill(shard_width)) for shard in val_split]
+    
+    create_dataloader = lambda tar_urls, shuffle=False, resample=False, with_text=False, for_sampling=False: create_image_embedding_dataloader(
+        tar_url=tar_urls,
+        num_workers=num_workers,
+        batch_size=batch_size if not for_sampling else n_sample_images,
+        embeddings_url=embeddings_url,
+        index_width=index_width,
+        shuffle_num = None,
+        extra_keys= ["txt"] if with_text else [],
+        shuffle_shards = shuffle,
+        resample_shards = resample, 
+        img_preproc=img_preproc,
+        handler=wds.handlers.warn_and_continue
+    )
+
+    train_dataloader = create_dataloader(train_urls, shuffle=shuffle_train, resample=resample_train)
+    train_sampling_dataloader = create_dataloader(train_urls, shuffle=False, for_sampling=True)
+    val_dataloader = create_dataloader(val_urls, shuffle=False, with_text=True)
+    test_dataloader = create_dataloader(test_urls, shuffle=False, with_text=True)
+    test_sampling_dataloader = create_dataloader(test_urls, shuffle=False, for_sampling=True)
+    return {
+        "train": train_dataloader,
+        "train_sampling": train_sampling_dataloader,
+        "val": val_dataloader,
+        "test": test_dataloader,
+        "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=tuple(unets),  # Must be tuple because of cast_tuple
+        **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.
+    """
+    # If the dataloader is actually a WebLoader, we need to extract the real dataloader
+    if isinstance(dataloader, wds.WebLoader):
+        dataloader = dataloader.pipeline[0]
+    return dataloader.dataset.key_map
+
+def get_example_data(dataloader, device, n=5):
+    """
+    Samples the dataloader and returns a zipped list of examples
+    """
+    images = []
+    embeddings = []
+    captions = []
+    dataset_keys = get_dataset_keys(dataloader)
+    has_caption = "txt" in dataset_keys
+    for data in dataloader:
+        if has_caption:
+            img, emb, txt = data
+        else:
+            img, emb = data
+            txt = [""] * emb.shape[0]
+        img = img.to(device=device, dtype=torch.float)
+        emb = emb.to(device=device, dtype=torch.float)
+        images.extend(list(img))
+        embeddings.extend(list(emb))
+        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=""):
+    """
+    Takes example data and generates images from the embeddings
+    Returns three lists: real images, generated images, and captions
+    """
+    real_images, embeddings, txts = zip(*example_data)
+    embeddings_tensor = torch.stack(embeddings)
+    samples = trainer.sample(embeddings_tensor)
+    generated_images = list(samples)
+    captions = [text_prepend + txt for txt in txts]
+    return real_images, generated_images, captions
+
+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)
+    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):
+    """
+    Computes evaluation metrics for the decoder
+    """
+    metrics = {}
+    # Prepare the data
+    examples = get_example_data(dataloader, device, n_evalation_samples)
+    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)
+        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:
+        inception = InceptionScore(**IS)
+        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:
+        kernel_inception = KernelInceptionDistance(**KID)
+        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:
+        # 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.to(device=device)
+        lpips.update(renorm_real_images, renorm_generated_images)
+        metrics["LPIPS"] = lpips.compute().item()
+    return metrics
+
+def save_trainer(tracker, trainer, epoch, step, 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)
+    
+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}"))
+    state_dict = tracker.recall_state_dict(recall_source, **load_config)
+    trainer.load_state_dict(state_dict["trainer"])
+    print("Model loaded")
+    return state_dict["epoch"], state_dict["step"], state_dict["validation_losses"]
+
+def train(
+    dataloaders,
+    decoder,
+    tracker,
+    inference_device,
+    load_config=None,
+    evaluate_config=None,
+    epoch_samples = None,  # If the training dataset is resampling, we have to manually stop an epoch
+    validation_samples = None,
+    epochs = 20,
+    n_sample_images = 5,
+    save_every_n_samples = 100000,
+    save_all=False,
+    save_latest=True,
+    save_best=True,
+    unet_training_mask=None,
+    **kwargs
+):
+    """
+    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
+        decoder,
+        **kwargs
+    )
+    # Set up starting model and parameters based on a recalled state dict
+    start_step = 0
+    start_epoch = 0
+    validation_losses = []
+
+    if load_config is not None and load_config["source"] is not None:
+        start_epoch, start_step, validation_losses = recall_trainer(tracker, trainer, recall_source=load_config["source"], **load_config)
+    trainer.to(device=inference_device)
+
+    if unet_training_mask is None:
+        # 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))
+    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)
+    test_example_data = get_example_data(dataloaders["test_sampling"], inference_device, n_sample_images)
+    
+    send_to_device = lambda arr: [x.to(device=inference_device, dtype=torch.float) for x in arr]
+    step = start_step
+
+    for epoch in range(start_epoch, epochs):
+        print(print_ribbon(f"Starting epoch {epoch}", repeat=40))
+        trainer.train()
+
+        timer = Timer()
+
+        sample = 0
+        last_sample = 0
+        last_snapshot = 0
+
+        losses = []
+        for i, (img, emb) in enumerate(dataloaders["train"]):
+            step += 1
+            sample += img.shape[0]
+            img, emb = send_to_device((img, emb))
+            
+            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)
+
+            samples_per_sec = (sample - last_sample) / timer.elapsed()
+
+            timer.reset()
+            last_sample = sample
+
+            if i % 10 == 0:
+                average_loss = sum(losses) / len(losses)
+                log_data = {
+                    "Training loss": average_loss,
+                    "Epoch": epoch,
+                    "Sample": sample,
+                    "Step": i,
+                    "Samples per second": samples_per_sec
+                }
+                tracker.log(log_data, step=step, verbose=True)
+                losses = []
+
+            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
+                last_snapshot = sample
+                # We need to know where the model should be saved
+                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
+            timer = Timer()
+            for i, (img, emb, txt) in enumerate(dataloaders["val"]):
+                sample += img.shape[0]
+                img, emb = send_to_device((img, emb))
+                
+                for unet in range(1, len(decoder.unets)+1):
+                    loss = trainer.forward(img.float(), image_embed=emb.float(), unet_number=unet)
+                    average_loss += loss
+
+                if i % 10 == 0:
+                    print(f"Epoch {epoch}/{epochs} - {sample / timer.elapsed():.2f} samples/sec")
+                    print(f"Loss: {average_loss / (i+1)}")
+                    print("")
+
+                if validation_samples is not None and sample >= validation_samples:
+                    break
+
+            average_loss /= i+1
+            log_data = {
+                "Validation loss": average_loss
+            }
+            tracker.log(log_data, step=step, verbose=True)
+
+        # Compute evaluation metrics
+        trainer.eval()
+        if evaluate_config is not None:
+            print(print_ribbon(f"Starting Evaluation {epoch}", repeat=40))
+            evaluation = evaluate_trainer(trainer, dataloaders["val"], inference_device, **evaluate_config)
+            tracker.log(evaluation, step=step, verbose=True)
+
+        # Generate sample images
+        print(print_ribbon(f"Sampling Set {epoch}", repeat=40))
+        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)
+
+        print(print_ribbon(f"Starting Saving {epoch}", repeat=40))
+        # 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"]
+    init_config = {}
+    init_config["config"] = config.config
+    if tracker_type == "console":
+        tracker = ConsoleTracker(**init_config)
+    elif tracker_type == "wandb":
+        # We need to initialize the resume state here
+        load_config = config["load"]
+        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]
+            init_config["id"] = run_id
+            init_config["resume"] = "must"
+        init_config["entity"] = tracker_config["wandb_entity"]
+        init_config["project"] = tracker_config["wandb_project"]
+        tracker = WandbTracker(data_path)
+        tracker.init(**init_config)
+    else:
+        raise ValueError(f"Tracker type {tracker_type} not supported by decoder trainer")
+    return tracker
+    
+def initialize_training(config):
+    # Create the save path
+    if "cuda" in config["train"]["device"]:
+        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))
+
+    dataloaders = create_dataloaders (
+        available_shards=all_shards,
+        img_preproc = config.get_preprocessing(),
+        train_prop = config["data"]["splits"]["train"],
+        val_prop = config["data"]["splits"]["val"],
+        test_prop = config["data"]["splits"]["test"],
+        n_sample_images=config["train"]["n_sample_images"],
+        **config["data"]
+    )
+
+    decoder = create_decoder(device, config["decoder"], config["unets"])
+    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"])
+
+    train(dataloaders, decoder, 
+        tracker=tracker,
+        inference_device=device,
+        load_config=config["load"],
+        evaluate_config=config["evaluate"],
+        **config["train"],
+    )
+
+
+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))
+    with open(config_file) as f:
+        config = json.load(f)
+    config = TrainDecoderConfig(config)
+    initialize_training(config)
+
+
+if __name__ == "__main__":
+    main()

train_diffusion_prior.py

@@ -1,15 +1,18 @@
 from pathlib import Path
 import click
 import math
-import time
 import numpy as np
 
 import torch
+import clip
 from torch import nn
 
-from dalle2_pytorch import DiffusionPrior, DiffusionPriorNetwork
-from dalle2_pytorch.train import DiffusionPriorTrainer, load_diffusion_model, save_diffusion_model, print_ribbon
+from dalle2_pytorch.dataloaders import make_splits
+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 dalle2_pytorch.utils import Timer
 
 from embedding_reader import EmbeddingReader
 
@@ -17,8 +20,7 @@ from tqdm import tqdm
 
 # constants
 
-NUM_TEST_EMBEDDINGS = 100 # for cosine similarity reporting during training
-REPORT_METRICS_EVERY = 100 # for cosine similarity and other metric reporting during training
+REPORT_METRICS_EVERY = 250 # for cosine similarity and other metric reporting during training
 
 tracker = WandbTracker()
 
@@ -27,90 +29,105 @@ tracker = WandbTracker()
 def exists(val):
     val is not None
 
-class Timer:
-    def __init__(self):
-        self.reset()
-
-    def reset(self):
-        self.last_time = time.time()
-
-    def elapsed(self):
-        return time.time() - self.last_time
 # functions
 
-def eval_model(model,device,image_reader,text_reader,start,end,batch_size,loss_type,phase="Validation"):
+def eval_model(model, dataloader, text_conditioned, loss_type, phase="Validation"):
     model.eval()
+
     with torch.no_grad():
         total_loss = 0.
         total_samples = 0.
 
-        for emb_images, emb_text in zip(image_reader(batch_size=batch_size, start=start, end=end),
-                text_reader(batch_size=batch_size, start=start, end=end)):
+        for image_embeddings, text_data in tqdm(dataloader):
 
-            emb_images_tensor = torch.tensor(emb_images[0]).to(device)
-            emb_text_tensor = torch.tensor(emb_text[0]).to(device)
+            batches = image_embeddings.shape[0]
 
-            batches = emb_images_tensor.shape[0]
+            input_args = dict(image_embed=image_embeddings)
+            if text_conditioned:
+                input_args = dict(**input_args, text = text_data)
+            else:
+                input_args = dict(**input_args, text_embed=text_data)
 
-            loss = model(text_embed = emb_text_tensor, image_embed = emb_images_tensor)
+            loss = model(**input_args)
 
-            total_loss += loss.item() * batches
+            total_loss += loss * batches
             total_samples += batches
 
         avg_loss = (total_loss / total_samples)
+
         tracker.log({f'{phase} {loss_type}': avg_loss})
 
-def report_cosine_sims(diffusion_prior,image_reader,text_reader,train_set_size,NUM_TEST_EMBEDDINGS,device):
+def report_cosine_sims(diffusion_prior, dataloader, text_conditioned):
     diffusion_prior.eval()
 
     cos = nn.CosineSimilarity(dim=1, eps=1e-6)
 
-    tstart = train_set_size
-    tend = train_set_size+NUM_TEST_EMBEDDINGS
+    for test_image_embeddings, text_data in tqdm(dataloader):
+
+        # 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_data)
+            text_cond = dict(text_embed=text_embedding,
+                             text_encodings=text_encodings, mask=text_mask)
+        else:
+            text_embedding = text_data
+            text_cond = dict(text_embed=text_embedding)
+
+        # make a copy of the text embeddings for shuffling
+        text_embed_shuffled = text_embedding.clone()
+
+        # 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.norm(dim=1, keepdim=True)
+
+        if text_conditioned:
+            text_encodings_shuffled = text_encodings[rolled_idx]
+            text_mask_shuffled = text_mask[rolled_idx]
+        else:
+            text_encodings_shuffled = None
+            text_mask_shuffled = None
+
+        text_cond_shuffled = dict(text_embed=text_embed_shuffled,
+                                  text_encodings=text_encodings_shuffled, mask=text_mask_shuffled)
 
-    for embt, embi in zip(text_reader(batch_size=NUM_TEST_EMBEDDINGS, start=tstart, end=tend), 
-            image_reader(batch_size=NUM_TEST_EMBEDDINGS, start=tstart, end=tend)):
-       # make a copy of the text embeddings for shuffling
-       text_embed = torch.tensor(embt[0]).to(device)
-       text_embed_shuffled = text_embed.clone()
-        # roll the text embeddings to simulate "unrelated" captions
-       rolled_idx = torch.roll(torch.arange(NUM_TEST_EMBEDDINGS), 1)
-       text_embed_shuffled = text_embed_shuffled[rolled_idx]
-       text_embed_shuffled = text_embed_shuffled / \
-           text_embed_shuffled.norm(dim=1, keepdim=True)
-       test_text_shuffled_cond = dict(text_embed=text_embed_shuffled)
         # prepare the text embedding
-       text_embed = text_embed / text_embed.norm(dim=1, keepdim=True)
-       test_text_cond = dict(text_embed=text_embed)
+        text_embed = text_embedding / text_embedding.norm(dim=1, keepdim=True)
+
         # prepare image embeddings
-       test_image_embeddings = torch.tensor(embi[0]).to(device)
-       test_image_embeddings = test_image_embeddings / \
-           test_image_embeddings.norm(dim=1, keepdim=True)
+        test_image_embeddings = test_image_embeddings / \
+            test_image_embeddings.norm(dim=1, keepdim=True)
+
         # predict on the unshuffled text embeddings
-       predicted_image_embeddings = diffusion_prior.p_sample_loop(
-           (NUM_TEST_EMBEDDINGS, 768), text_cond=test_text_cond)
-       predicted_image_embeddings = predicted_image_embeddings / \
-           predicted_image_embeddings.norm(dim=1, keepdim=True)
+        predicted_image_embeddings = diffusion_prior.p_sample_loop(
+            test_image_embeddings.shape, text_cond)
+        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(
-           (NUM_TEST_EMBEDDINGS, 768), text_cond=test_text_shuffled_cond)
-       predicted_unrelated_embeddings = predicted_unrelated_embeddings / \
-           predicted_unrelated_embeddings.norm(dim=1, keepdim=True)
+        predicted_unrelated_embeddings = diffusion_prior.p_sample_loop(
+            test_image_embeddings.shape, text_cond_shuffled)
+        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()
-       predicted_similarity = cos(
+        predicted_similarity = cos(
            text_embed, predicted_image_embeddings).cpu().numpy()
-       unrelated_similarity = cos(
+        unrelated_similarity = cos(
            text_embed, predicted_unrelated_embeddings).cpu().numpy()
-       predicted_img_similarity = cos(
+        predicted_img_similarity = cos(
            test_image_embeddings, predicted_image_embeddings).cpu().numpy()
-       tracker.log({"CosineSimilarity(text_embed,image_embed)": np.mean(original_similarity),
+        tracker.log({"CosineSimilarity(text_embed,image_embed)": np.mean(original_similarity),
             "CosineSimilarity(text_embed,predicted_image_embed)":np.mean(predicted_similarity),
             "CosineSimilarity(orig_image_embed,predicted_image_embed)":np.mean(predicted_img_similarity),
             "CosineSimilarity(text_embed,predicted_unrelated_embed)": np.mean(unrelated_similarity),
             "Cosine similarity difference":np.mean(predicted_similarity - original_similarity)})
 
+
 @click.command()
 @click.option("--wandb-entity", default="laion")
 @click.option("--wandb-project", default="diffusion-prior")
@@ -118,29 +135,32 @@ def report_cosine_sims(diffusion_prior,image_reader,text_reader,train_set_size,N
 @click.option("--wandb-arch", default="DiffusionPrior")
 @click.option("--image-embed-url", default="https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion2B-en/img_emb/")
 @click.option("--text-embed-url", default="https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion2B-en/text_emb/")
+@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("--batch-size", default=10**4)
+@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=0.7)
-@click.option("--val-percent", default=0.2)
-@click.option("--test-percent", default=0.1)
-@click.option("--dpn-depth", default=6)
+@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=8)
-@click.option("--dp-condition-on-text-encodings", default=False)
-@click.option("--dp-timesteps", default=100)
-@click.option("--dp-normformer", default=False)
+@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=None)
+@click.option("--clip", default="ViT-L/14")
 @click.option("--amp", default=False)
-@click.option("--save-interval", default=30)
+@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,
@@ -148,10 +168,12 @@ def train(
     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,
@@ -170,7 +192,8 @@ def train(
     amp,
     save_interval,
     save_path,
-    pretrained_model_path
+    pretrained_model_path,
+    gpu_device
 ):
     config = {
         "learning_rate": learning_rate,
@@ -197,7 +220,7 @@ def train(
 
     # Check if DPRIOR_PATH exists(saved model path)
 
-    DPRIOR_PATH = args.pretrained_model_path
+    DPRIOR_PATH = pretrained_model_path
     RESUME = exists(DPRIOR_PATH)
 
     if not RESUME:
@@ -211,7 +234,7 @@ def train(
 
     has_cuda = torch.cuda.is_available()
     if has_cuda:
-        device = torch.device("cuda:0")
+        device = torch.device(f"cuda:{gpu_device}")
         torch.cuda.set_device(device)
 
     # Training loop
@@ -227,11 +250,17 @@ def train(
         normformer = dp_normformer
     )
     
+    # Load clip model if text-conditioning
+    if dp_condition_on_text_encodings:
+        clip_adapter = OpenAIClipAdapter(clip)
+    else:
+        clip_adapter = None
+        
     # diffusion prior with text embeddings and image embeddings pre-computed
 
     diffusion_prior = DiffusionPrior( 
         net = prior_network,
-        clip = clip,
+        clip = clip_adapter,
         image_embed_dim = image_embed_dim,
         timesteps = dp_timesteps,
         cond_drop_prob = dp_cond_drop_prob,
@@ -265,33 +294,37 @@ def train(
 
     Path(save_path).mkdir(exist_ok = True, parents = True)
 
-    # Get image and text embeddings from the servers
+    # 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)
 
-    print_ribbon("Downloading embeddings - image and text")
-    image_reader = EmbeddingReader(embeddings_folder=image_embed_url, file_format="npy")
-    text_reader  = EmbeddingReader(embeddings_folder=text_embed_url, file_format="npy")
-    num_data_points = text_reader.count
+    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
 
-    train_set_size = int(train_percent*num_data_points)
-    val_set_size = int(val_percent*num_data_points)
-    eval_start = train_set_size
-
     for _ in range(epochs):
 
-        for emb_images,emb_text in zip(image_reader(batch_size=batch_size, start=0, end=train_set_size),
-                text_reader(batch_size=batch_size, start=0, end=train_set_size)):
-
-            trainer.train()
+        for image, text in tqdm(train_loader):
             
-            emb_images_tensor = torch.tensor(emb_images[0]).to(device)
-            emb_text_tensor = torch.tensor(emb_text[0]).to(device)
+            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(text_embed = emb_text_tensor, image_embed = emb_images_tensor)
+            loss = trainer(**input_args)
 
             # Samples per second
 
@@ -310,37 +343,23 @@ def train(
                     image_embed_dim)
 
             # Log to wandb
-            tracker.log({"Training loss": loss.item(),
+            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,
-                        image_reader,
-                        text_reader,
-                        train_set_size,
-                        NUM_TEST_EMBEDDINGS,
-                        device)
+                report_cosine_sims(diffusion_prior, eval_loader, dp_condition_on_text_encodings)
                 ### Evaluate model(validation run) ###
-                eval_model(diffusion_prior,
-                        device,
-                        image_reader,
-                        text_reader,
-                        eval_start,
-                        eval_start+NUM_TEST_EMBEDDINGS,
-                        NUM_TEST_EMBEDDINGS,
-                        dp_loss_type,
-                        phase="Validation")
+                eval_model(diffusion_prior, eval_loader, dp_condition_on_text_encodings, dp_loss_type, phase="Validation")
 
+            step += 1
             trainer.update()
 
     ### Test run ###
-    test_set_size = int(test_percent*train_set_size) 
-    start = train_set_size+val_set_size
-    end = num_data_points
-    eval_model(diffusion_prior,device,image_reader,text_reader,start,end,batch_size,dp_loss_type,phase="Test")
+    eval_model(diffusion_prior, test_loader, dp_condition_on_text_encodings, dp_loss_type, phase="Test")
+
 
 if __name__ == "__main__":
     train()