now completely OpenAI CLIP compatible for training

README.md

@@ -430,8 +430,8 @@ 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.clip.embed_image(images).image_embed
+clip_image_embeds = diffusion_prior.get_image_embed(images)
-clip_text_embeds = diffusion_prior.clip.embed_text(text).text_embed
+clip_text_embeds = diffusion_prior.get_text_cond(text).get('text_embed')
 
 # feed text and images into diffusion prior network
 
@@ -741,7 +741,6 @@ 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

@@ -3,7 +3,6 @@ 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
@@ -103,9 +102,6 @@ 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__()
@@ -157,7 +153,7 @@ 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 EmbeddedText(l2norm(text_embed), text_encodings, text_mask)
+        return l2norm(text_embed), text_encodings, text_mask
 
     @torch.no_grad()
     def embed_image(self, image):
@@ -165,7 +161,7 @@ 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 EmbeddedImage(l2norm(image_embed), image_encodings)
+        return l2norm(image_embed), image_encodings
 
 class OpenAIClipAdapter(BaseClipAdapter):
     def __init__(
@@ -223,7 +219,7 @@ class OpenAIClipAdapter(BaseClipAdapter):
         text_embed = self.clip.encode_text(text)
         text_encodings = self.text_encodings
         del self.text_encodings
-        return EmbeddedText(text_embed.float(), text_encodings.float(), text_mask)
+        return text_embed.float(), text_encodings.float(), text_mask
 
     @torch.no_grad()
     def embed_image(self, image):
@@ -231,7 +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 EmbeddedImage(image_embed.float(), None)
+        return image_embed.float(), None
 
 # classifier free guidance functions
 

dalle2_pytorch/optimizer.py

@@ -1,29 +0,0 @@
-from torch.optim import AdamW, Adam
-
-def separate_weight_decayable_params(params):
-    no_wd_params = set([param for param in params if param.ndim < 2])
-    wd_params = set(params) - no_wd_params
-    return wd_params, no_wd_params
-
-def get_optimizer(
-    params,
-    lr = 3e-4,
-    wd = 1e-2,
-    betas = (0.9, 0.999),
-    filter_by_requires_grad = False
-):
-    if filter_by_requires_grad:
-        params = list(filter(lambda t: t.requires_grad, params))
-
-    if wd == 0:
-        return Adam(params, lr = lr, betas = betas)
-
-    params = set(params)
-    wd_params, no_wd_params = separate_weight_decayable_params(params)
-
-    param_groups = [
-        {'params': list(wd_params)},
-        {'params': list(no_wd_params), 'weight_decay': 0},
-    ]
-
-    return AdamW(param_groups, lr = lr, weight_decay = wd, betas = betas)

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.0.71',
+  version = '0.0.67',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',