fix decoder needing separate conditional dropping probabilities for image embeddings and text encodings, thanks to @xiankgx !

Fix spelling and grammatical errors

README.md

@@ -47,7 +47,7 @@ clip = CLIP(
     use_all_token_embeds = True,            # whether to use fine-grained contrastive learning (FILIP)
     decoupled_contrastive_learning = True,  # use decoupled contrastive learning (DCL) objective function, removing positive pairs from the denominator of the InfoNCE loss (CLOOB + DCL)
     extra_latent_projection = True,         # whether to use separate projections for text-to-image vs image-to-text comparisons (CLOOB)
-    use_visual_ssl = True,                  # whether to do self supervised learning on iages
+    use_visual_ssl = True,                  # whether to do self supervised learning on images
     visual_ssl_type = 'simclr',             # can be either 'simclr' or 'simsiam', depending on using DeCLIP or SLIP
     use_mlm = False,                        # use masked language learning (MLM) on text (DeCLIP)
     text_ssl_loss_weight = 0.05,            # weight for text MLM loss
@@ -110,7 +110,8 @@ decoder = Decoder(
     unet = unet,
     clip = clip,
     timesteps = 100,
-    cond_drop_prob = 0.2
+    image_cond_drop_prob = 0.1,
+    text_cond_drop_prob = 0.5
 ).cuda()
 
 # mock images (get a lot of this)
@@ -229,7 +230,8 @@ decoder = Decoder(
     unet = (unet1, unet2),            # insert both unets in order of low resolution to highest resolution (you can have as many stages as you want here)
     image_sizes = (256, 512),         # resolutions, 256 for first unet, 512 for second. these must be unique and in ascending order (matches with the unets passed in)
     timesteps = 1000,
-    cond_drop_prob = 0.2
+    image_cond_drop_prob = 0.1,
+    text_cond_drop_prob = 0.5
 ).cuda()
 
 # mock images (get a lot of this)
@@ -348,7 +350,8 @@ decoder = Decoder(
     image_sizes = (128, 256),
     clip = clip,
     timesteps = 100,
-    cond_drop_prob = 0.2,
+    image_cond_drop_prob = 0.1,
+    text_cond_drop_prob = 0.5,
     condition_on_text_encodings = False  # set this to True if you wish to condition on text during training and sampling
 ).cuda()
 
@@ -430,8 +433,8 @@ images = torch.randn(4, 3, 256, 256).cuda()
 # precompute the text and image embeddings
 # here using the diffusion prior class, but could be done with CLIP alone
 
-clip_image_embeds = diffusion_prior.get_image_embed(images)
-clip_text_embeds = diffusion_prior.get_text_cond(text).get('text_embed')
+clip_image_embeds = diffusion_prior.clip.embed_image(images).image_embed
+clip_text_embeds = diffusion_prior.clip.embed_text(text).text_embed
 
 # feed text and images into diffusion prior network
 
@@ -499,9 +502,7 @@ loss.backward()
 
 Although there is the possibility they are using an unreleased, more powerful CLIP, you can use one of the released ones, if you do not wish to train your own CLIP from scratch. This will also allow the community to more quickly validate the conclusions of the paper.
 
-First you'll need to install <a href="https://github.com/openai/CLIP#usage">the prerequisites</a>
-
-Then to use a pretrained OpenAI CLIP, simply import `OpenAIClipAdapter` and pass it into the `DiffusionPrior` or `Decoder` like so
+To use a pretrained OpenAI CLIP, simply import `OpenAIClipAdapter` and pass it into the `DiffusionPrior` or `Decoder` like so
 
 ```python
 import torch
@@ -560,7 +561,8 @@ decoder = Decoder(
     image_sizes = (128, 256),
     clip = clip,
     timesteps = 100,
-    cond_drop_prob = 0.2,
+    image_cond_drop_prob = 0.1,
+    text_cond_drop_prob = 0.5,
     condition_on_text_encodings = False  # set this to True if you wish to condition on text during training and sampling
 ).cuda()
 
@@ -618,7 +620,7 @@ clip = CLIP(
 # 3 unets for the decoder (a la cascading DDPM)
 
 # first two unets are doing latent diffusion
-# vqgan-vae must be trained before hand
+# vqgan-vae must be trained beforehand
 
 vae1 = VQGanVAE(
     dim = 32,
@@ -671,7 +673,8 @@ decoder = Decoder(
     unet = (unet1, unet2, unet3),      # insert unets in order of low resolution to highest resolution (you can have as many stages as you want here)
     image_sizes = (256, 512, 1024),    # resolutions, 256 for first unet, 512 for second, 1024 for third
     timesteps = 100,
-    cond_drop_prob = 0.2
+    image_cond_drop_prob = 0.1,
+    text_cond_drop_prob = 0.5
 ).cuda()
 
 # mock images (get a lot of this)
@@ -741,6 +744,7 @@ Once built, images will be saved to the same directory the command is invoked
 - [ ] become an expert with unets, cleanup unet code, make it fully configurable, port all learnings over to https://github.com/lucidrains/x-unet
 - [ ] copy the cascading ddpm code to a separate repo (perhaps https://github.com/lucidrains/denoising-diffusion-pytorch) as the main contribution of dalle2 really is just the prior network
 - [ ] transcribe code to Jax, which lowers the activation energy for distributed training, given access to TPUs
+- [ ] just take care of the training for the decoder in a wrapper class, as each unet in the cascade will need its own optimizer
 - [ ] train on a toy task, offer in colab
 - [ ] think about how best to design a declarative training config that handles preencoding for prior and training of multiple networks in decoder
 - [ ] extend diffusion head to use diffusion-gan (potentially using lightweight-gan) to speed up inference

dalle2_pytorch/dalle2_pytorch.py

@@ -3,6 +3,7 @@ from tqdm import tqdm
 from inspect import isfunction
 from functools import partial
 from contextlib import contextmanager
+from collections import namedtuple
 
 import torch
 import torch.nn.functional as F
@@ -102,6 +103,9 @@ def unnormalize_img(normed_img):
 
 # clip related adapters
 
+EmbeddedText = namedtuple('EmbedTextReturn', ['text_embed', 'text_encodings', 'text_mask'])
+EmbeddedImage = namedtuple('EmbedImageReturn', ['image_embed', 'image_encodings'])
+
 class BaseClipAdapter(nn.Module):
     def __init__(self, clip):
         super().__init__()
@@ -153,7 +157,7 @@ class XClipAdapter(BaseClipAdapter):
         encoder_output = self.clip.text_transformer(text)
         text_cls, text_encodings = encoder_output[:, 0], encoder_output[:, 1:]
         text_embed = self.clip.to_text_latent(text_cls)
-        return l2norm(text_embed), text_encodings, text_mask
+        return EmbeddedText(l2norm(text_embed), text_encodings, text_mask)
 
     @torch.no_grad()
     def embed_image(self, image):
@@ -161,24 +165,20 @@ class XClipAdapter(BaseClipAdapter):
         encoder_output = self.clip.visual_transformer(image)
         image_cls, image_encodings = encoder_output[:, 0], encoder_output[:, 1:]
         image_embed = self.clip.to_visual_latent(image_cls)
-        return l2norm(image_embed), image_encodings
+        return EmbeddedImage(l2norm(image_embed), image_encodings)
 
 class OpenAIClipAdapter(BaseClipAdapter):
     def __init__(
         self,
         name = 'ViT-B/32'
     ):
-        try:
-            import clip
-        except ImportError:
-            print('you must install openai clip in order to use this adapter - `pip install git+https://github.com/openai/CLIP.git` - more instructions at https://github.com/openai/CLIP#usage')
-
-        openai_clip, _ = clip.load(name)
+        import clip
+        openai_clip, preprocess = clip.load(name)
         super().__init__(openai_clip)
 
         text_attention_final = self.find_layer('ln_final')
         self.handle = text_attention_final.register_forward_hook(self._hook)
-        self.clip_normalize = T.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
+        self.clip_normalize = preprocess.transforms[-1]
         self.cleared = False
 
     def find_layer(self,  layer):
@@ -219,7 +219,7 @@ class OpenAIClipAdapter(BaseClipAdapter):
         text_embed = self.clip.encode_text(text)
         text_encodings = self.text_encodings
         del self.text_encodings
-        return text_embed.float(), text_encodings.float(), text_mask
+        return EmbeddedText(text_embed.float(), text_encodings.float(), text_mask)
 
     @torch.no_grad()
     def embed_image(self, image):
@@ -227,7 +227,7 @@ class OpenAIClipAdapter(BaseClipAdapter):
         image = resize_image_to(image, self.image_size)
         image = self.clip_normalize(unnormalize_img(image))
         image_embed = self.clip.encode_image(image)
-        return image_embed.float(), None
+        return EmbeddedImage(image_embed.float(), None)
 
 # classifier free guidance functions
 
@@ -684,14 +684,14 @@ class DiffusionPriorNetwork(nn.Module):
 
         # classifier free guidance
 
-        cond_prob_mask = prob_mask_like((batch,), cond_drop_prob, device = device)
-        cond_prob_mask = rearrange(cond_prob_mask, 'b -> b 1')
+        keep_mask = prob_mask_like((batch,), 1 - cond_drop_prob, device = device)
+        keep_mask = rearrange(keep_mask, 'b -> b 1')
 
-        mask &= cond_prob_mask
+        mask &= keep_mask
 
         # whether text embedding is masked or not depends on the classifier free guidance conditional masking
 
-        mask = torch.cat((mask, cond_prob_mask), dim = 1)
+        mask = torch.cat((mask, keep_mask), dim = 1)
 
         # whether text embedding is used for conditioning depends on whether text encodings are available for attention (for classifier free guidance, even though it seems from the paper it was not used in the prior ddpm, as the objective is different)
         # but let's just do it right
@@ -1174,7 +1174,7 @@ class Unet(nn.Module):
         if cond_scale == 1:
             return logits
 
-        null_logits = self.forward(*args, cond_drop_prob = 1., **kwargs)
+        null_logits = self.forward(*args, text_cond_drop_prob = 1., image_cond_drop_prob = 1., **kwargs)
         return null_logits + (logits - null_logits) * cond_scale
 
     def forward(
@@ -1185,7 +1185,8 @@ class Unet(nn.Module):
         image_embed,
         lowres_cond_img = None,
         text_encodings = None,
-        cond_drop_prob = 0.,
+        image_cond_drop_prob = 0.,
+        text_cond_drop_prob = 0.,
         blur_sigma = None,
         blur_kernel_size = None
     ):
@@ -1204,8 +1205,10 @@ class Unet(nn.Module):
 
         # conditional dropout
 
-        cond_prob_mask = prob_mask_like((batch_size,), cond_drop_prob, device = device)
-        cond_prob_mask = rearrange(cond_prob_mask, 'b -> b 1 1')
+        image_keep_mask = prob_mask_like((batch_size,), 1 - image_cond_drop_prob, device = device)
+        text_keep_mask = prob_mask_like((batch_size,), 1 - text_cond_drop_prob, device = device)
+
+        image_keep_mask, text_keep_mask = rearrange_many((image_keep_mask, text_keep_mask), 'b -> b 1 1')
 
         # mask out image embedding depending on condition dropout
         # for classifier free guidance
@@ -1216,7 +1219,7 @@ class Unet(nn.Module):
             image_tokens = self.image_to_cond(image_embed)
 
             image_tokens = torch.where(
-                cond_prob_mask,
+                image_keep_mask,
                 image_tokens,
                 self.null_image_embed
             )
@@ -1228,7 +1231,7 @@ class Unet(nn.Module):
         if exists(text_encodings) and self.cond_on_text_encodings:
             text_tokens = self.text_to_cond(text_encodings)
             text_tokens = torch.where(
-                cond_prob_mask,
+                text_keep_mask,
                 text_tokens,
                 self.null_text_embed[:, :text_tokens.shape[1]]
             )
@@ -1318,7 +1321,8 @@ class Decoder(BaseGaussianDiffusion):
         clip,
         vae = tuple(),
         timesteps = 1000,
-        cond_drop_prob = 0.2,
+        image_cond_drop_prob = 0.1,
+        text_cond_drop_prob = 0.5,
         loss_type = 'l1',
         beta_schedule = 'cosine',
         predict_x_start = False,
@@ -1402,7 +1406,8 @@ class Decoder(BaseGaussianDiffusion):
 
         # classifier free guidance
 
-        self.cond_drop_prob = cond_drop_prob
+        self.image_cond_drop_prob = image_cond_drop_prob
+        self.text_cond_drop_prob = text_cond_drop_prob
 
     def get_unet(self, unet_number):
         assert 0 < unet_number <= len(self.unets)
@@ -1484,7 +1489,8 @@ class Decoder(BaseGaussianDiffusion):
             image_embed = image_embed,
             text_encodings = text_encodings,
             lowres_cond_img = lowres_cond_img,
-            cond_drop_prob = self.cond_drop_prob
+            image_cond_drop_prob = self.image_cond_drop_prob,
+            text_cond_drop_prob = self.text_cond_drop_prob,
         )
 
         target = noise if not predict_x_start else x_start
@@ -1632,4 +1638,3 @@ class DALLE2(nn.Module):
             return images[0]
 
         return images
-

dalle2_pytorch/openai_clip.py

@@ -1,84 +0,0 @@
-import torch
-from PIL import Image
-
-from dalle2_pytorch.dalle2_pytorch import BaseClipAdapter
-import torchvision.transforms as T
-
-def find_layer(model, layer):
-    modules = dict([*model.named_modules()])
-    return modules.get(layer, None)
-
-def hook(_, input, output):
-    print(output.shape)
-
-import clip
-# image = preprocess(Image.open("CLIP.png")).unsqueeze(0).to(device)
-text = clip.tokenize(["a diagram", "a dog", "a cat"]).cuda()
-image = torch.randn(1, 3, 224, 224).cuda()
-
-
-class OpenAIClipAdapter(BaseClipAdapter):
-    def __init__(self, name = 'ViT-B/32'):
-        try:
-            import clip
-        except ImportError:
-            print('you must install openai clip in order to use this adapter - `pip install git+https://github.com/openai/CLIP.git` - more instructions at https://github.com/openai/CLIP#usage')
-
-        openai_clip, _ = clip.load(name)
-        super().__init__(openai_clip)
-
-        text_attention_final = self.find_layer(self.clip, 'ln_final')
-        self.handle = text_attention_final.register_forward_hook(self._hook)
-        self.clip_normalize = T.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
-        self.cleared = False
-
-    def find_layer(self,  layer):
-        modules = dict([*self.clip.named_modules()])
-        return modules.get(layer, None)
-
-    def clear(self):
-        if self.cleared:
-            return
-
-        self.handle()
-
-    def _hook(self, _, inputs, outputs):
-        self.text_encodings = outputs
-
-    @property
-    def dim_latent(self):
-        return 512
-
-    @property
-    def image_size(self):
-        return self.clip.visual.input_resolution
-
-    @property
-    def image_channels(self):
-        return 3
-
-    @torch.no_grad()
-    def embed_text(self, text):
-        assert not self.cleared
-
-        text_embed = self.clip.encode_text(text)
-        text_encodings = self.text_encodings
-        del self.text_encodings
-        return text_embed, text_encodings
-
-    @torch.no_grad()
-    def embed_image(self, image):
-        assert not self.cleared
-
-        image = self.clip_normalize(image)
-        image_embed = self.clip.encode_image(image)
-        return image_embed, None
-
-clip_adapter = OpenAIClipAdapter().cuda()
-
-# print(model)
-with torch.no_grad():
-    image_features, _ = clip_adapter.embed_image(image)
-    text_features, text_encodings = clip_adapter.embed_text(text)
-    print(text_features.shape, image_features.shape)
-    print(text_encodings.shape)

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.0.70',
+  version = '0.0.74',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',
@@ -23,6 +23,7 @@ setup(
   ],
   install_requires=[
     'click',
+    'clip-anytorch',
     'einops>=0.4',
     'einops-exts>=0.0.3',
     'kornia>=0.5.4',