just use an assert to make sure clip image channels is never different than the channels of the diffusion prior and decoder, if clip is given

block in a section that links to available pre-trained models for those who are interested

README.md

@@ -24,6 +24,11 @@ There was enough interest for a <a href="https://github.com/lucidrains/dalle2-ja
 
 *ongoing at 21k steps*
 
+## Pre-Trained Models
+- LAION is training prior models. Checkpoints are available on <a href="https://huggingface.co/zenglishuci/conditioned-prior">🤗huggingface</a> and the training statistics are available on <a href="https://wandb.ai/nousr_laion/conditioned-prior/reports/LAION-DALLE2-PyTorch-Prior--VmlldzoyMDI2OTIx">🐝WANDB</a>.
+- Decoder 🚧
+- DALL-E 2 🚧
+
 ## Install
 
 ```bash
@@ -1079,6 +1084,7 @@ This library would not have gotten to this working state without the help of
 - [x] use pydantic for config drive training
 - [x] for both diffusion prior and decoder, all exponential moving averaged models needs to be saved and restored as well (as well as the step number)
 - [x] offer save / load methods on the trainer classes to automatically take care of state dicts for scalers / optimizers / saving versions and checking for breaking changes
+- [x] allow for creation of diffusion prior model off pydantic config classes - consider the same for tracker configs
 - [ ] become an expert with unets, cleanup unet code, make it fully configurable, port all learnings over to https://github.com/lucidrains/x-unet (test out unet² in ddpm repo) - consider https://github.com/lucidrains/uformer-pytorch attention-based unet
 - [ ] transcribe code to Jax, which lowers the activation energy for distributed training, given access to TPUs
 - [ ] train on a toy task, offer in colab

dalle2_pytorch/dalle2_pytorch.py

@@ -890,6 +890,8 @@ class DiffusionPrior(BaseGaussianDiffusion):
         )
 
         if exists(clip):
+            assert image_channels == clip.image_channels, f'channels of image ({channels}) should be equal to the channels that CLIP accepts ({clip.image_channels})'
+
             if isinstance(clip, CLIP):
                 clip = XClipAdapter(clip, **clip_adapter_overrides)
             elif isinstance(clip, CoCa):
@@ -1710,12 +1712,19 @@ class Decoder(BaseGaussianDiffusion):
         )
 
         self.unconditional = unconditional
-        assert not (condition_on_text_encodings and unconditional), 'unconditional decoder image generation cannot be set to True if conditioning on text is present'
 
-        assert self.unconditional or (exists(clip) ^ (exists(image_size) or exists(image_sizes))), 'either CLIP is supplied, or you must give the image_size and channels (usually 3 for RGB)'
+        # text conditioning
+
+        assert not (condition_on_text_encodings and unconditional), 'unconditional decoder image generation cannot be set to True if conditioning on text is present'
+        self.condition_on_text_encodings = condition_on_text_encodings
+
+        # clip
 
         self.clip = None
         if exists(clip):
+            assert not unconditional, 'clip must not be given if doing unconditional image training'
+            assert channels == clip.image_channels, f'channels of image ({channels}) should be equal to the channels that CLIP accepts ({clip.image_channels})'
+
             if isinstance(clip, CLIP):
                 clip = XClipAdapter(clip, **clip_adapter_overrides)
             elif isinstance(clip, CoCa):
@@ -1725,13 +1734,20 @@ class Decoder(BaseGaussianDiffusion):
             assert isinstance(clip, BaseClipAdapter)
 
             self.clip = clip
-            self.clip_image_size = clip.image_size
-            self.channels = clip.image_channels
-        else:
-            self.clip_image_size = default(image_size, lambda: image_sizes[-1])
-            self.channels = channels
 
-        self.condition_on_text_encodings = condition_on_text_encodings
+        # determine image size, with image_size and image_sizes taking precedence
+
+        if exists(image_size) or exists(image_sizes):
+            assert exists(image_size) ^ exists(image_sizes), 'only one of image_size or image_sizes must be given'
+            image_size = default(image_size, lambda: image_sizes[-1])
+        elif exists(clip):
+            image_size = clip.image_size
+        else:
+            raise Error('either image_size, image_sizes, or clip must be given to decoder')
+
+        # channels
+
+        self.channels = channels
 
         # automatically take care of ensuring that first unet is unconditional
         # while the rest of the unets are conditioned on the low resolution image produced by previous unet
@@ -1773,7 +1789,7 @@ class Decoder(BaseGaussianDiffusion):
 
         # unet image sizes
 
-        image_sizes = default(image_sizes, (self.clip_image_size,))
+        image_sizes = default(image_sizes, (image_size,))
         image_sizes = tuple(sorted(set(image_sizes)))
 
         assert len(self.unets) == len(image_sizes), f'you did not supply the correct number of u-nets ({len(self.unets)}) for resolutions {image_sizes}'
@@ -1811,6 +1827,7 @@ class Decoder(BaseGaussianDiffusion):
         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
 

dalle2_pytorch/train_configs.py

@@ -3,7 +3,7 @@ from torchvision import transforms as T
 from pydantic import BaseModel, validator, root_validator
 from typing import List, Iterable, Optional, Union, Tuple, Dict, Any
 
-from dalle2_pytorch.dalle2_pytorch import Unet, Decoder
+from dalle2_pytorch.dalle2_pytorch import Unet, Decoder, DiffusionPrior, DiffusionPriorNetwork
 
 # helper functions
 
@@ -18,6 +18,43 @@ def ListOrTuple(inner_type):
 
 # pydantic classes
 
+class DiffusionPriorNetworkConfig(BaseModel):
+    dim: int
+    depth: int
+    num_timesteps: int = None
+    num_time_embeds: int = 1
+    num_image_embeds: int = 1
+    num_text_embeds: int = 1
+    dim_head: int = 64
+    heads: int = 8
+    ff_mult: int = 4
+    norm_out: bool = True
+    attn_dropout: float = 0.
+    ff_dropout: float = 0.
+    final_proj: bool = True
+    normformer: bool = False
+    rotary_emb: bool = True
+
+class DiffusionPriorConfig(BaseModel):
+    # only clip-less diffusion prior config for now
+    net: DiffusionPriorNetworkConfig
+    image_embed_dim: int
+    image_size: int
+    image_channels: int = 3
+    timesteps: int = 1000
+    cond_drop_prob: float = 0.
+    loss_type: str = 'l2'
+    predict_x_start: bool = True
+    beta_schedule: str = 'cosine'
+
+    def create(self):
+        kwargs = self.dict()
+        diffusion_prior_network = DiffusionPriorNetwork(**kwargs.pop('net'))
+        return DiffusionPrior(net = diffusion_prior_network, **kwargs)
+
+    class Config:
+        extra = "allow"
+
 class UnetConfig(BaseModel):
     dim: int
     dim_mults: ListOrTuple(int)
@@ -31,7 +68,7 @@ class UnetConfig(BaseModel):
         extra = "allow"
 
 class DecoderConfig(BaseModel):
-    unets: Union[List[UnetConfig], Tuple[UnetConfig]]
+    unets: ListOrTuple(UnetConfig)
     image_size: int = None
     image_sizes: ListOrTuple(int) = None
     channels: int = 3
@@ -39,6 +76,8 @@ class DecoderConfig(BaseModel):
     loss_type: str = 'l2'
     beta_schedule: str = 'cosine'
     learned_variance: bool = True
+    image_cond_drop_prob: float = 0.1
+    text_cond_drop_prob: float = 0.5
 
     def create(self):
         decoder_kwargs = self.dict()

dalle2_pytorch/trainer.py

@@ -288,7 +288,7 @@ class DiffusionPriorTrainer(nn.Module):
 
         self.register_buffer('step', torch.tensor([0]))
 
-    def save(self, path, overwrite = True):
+    def save(self, path, overwrite = True, **kwargs):
         path = Path(path)
         assert not (path.exists() and not overwrite)
         path.parent.mkdir(parents = True, exist_ok = True)
@@ -298,7 +298,8 @@ class DiffusionPriorTrainer(nn.Module):
             optimizer = self.optimizer.state_dict(),
             model = self.diffusion_prior.state_dict(),
             version = get_pkg_version(),
-            step = self.step.item()
+            step = self.step.item(),
+            **kwargs
         )
 
         if self.use_ema:
@@ -319,7 +320,7 @@ class DiffusionPriorTrainer(nn.Module):
         self.step.copy_(torch.ones_like(self.step) * loaded_obj['step'])
 
         if only_model:
-            return
+            return loaded_obj
 
         self.scaler.load_state_dict(loaded_obj['scaler'])
         self.optimizer.load_state_dict(loaded_obj['optimizer'])
@@ -328,6 +329,8 @@ class DiffusionPriorTrainer(nn.Module):
             assert 'ema' in loaded_obj
             self.ema_diffusion_prior.load_state_dict(loaded_obj['ema'], strict = strict)
 
+        return loaded_obj
+
     def update(self):
         if exists(self.max_grad_norm):
             self.scaler.unscale_(self.optimizer)
@@ -449,7 +452,7 @@ class DecoderTrainer(nn.Module):
 
         self.register_buffer('step', torch.tensor([0.]))
 
-    def save(self, path, overwrite = True):
+    def save(self, path, overwrite = True, **kwargs):
         path = Path(path)
         assert not (path.exists() and not overwrite)
         path.parent.mkdir(parents = True, exist_ok = True)
@@ -457,7 +460,8 @@ class DecoderTrainer(nn.Module):
         save_obj = dict(
             model = self.decoder.state_dict(),
             version = get_pkg_version(),
-            step = self.step.item()
+            step = self.step.item(),
+            **kwargs
         )
 
         for ind in range(0, self.num_unets):
@@ -485,7 +489,7 @@ class DecoderTrainer(nn.Module):
         self.step.copy_(torch.ones_like(self.step) * loaded_obj['step'])
 
         if only_model:
-            return
+            return loaded_obj
 
         for ind in range(0, self.num_unets):
             scaler_key = f'scaler{ind}'
@@ -500,6 +504,8 @@ class DecoderTrainer(nn.Module):
             assert 'ema' in loaded_obj
             self.ema_unets.load_state_dict(loaded_obj['ema'], strict = strict)
 
+        return loaded_obj
+
     @property
     def unets(self):
         return nn.ModuleList([ema.ema_model for ema in self.ema_unets])

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.4.8',
+  version = '0.4.12',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',