use the eval decorator

improvise and use rmsnorm in convnext blocks too

.github/FUNDING.yml

@@ -0,0 +1 @@
+github: [lucidrains]

README.md

@@ -2,7 +2,7 @@
 
 ## DALL-E 2 - Pytorch (wip)
 
-Implementation of <a href="https://openai.com/dall-e-2/">DALL-E 2</a>, OpenAI's updated text-to-image synthesis neural network, in Pytorch
+Implementation of <a href="https://openai.com/dall-e-2/">DALL-E 2</a>, OpenAI's updated text-to-image synthesis neural network, in Pytorch. <a href="https://youtu.be/RJwPN4qNi_Y?t=555">Yannic Kilcher summary</a>
 
 The main novelty seems to be an extra layer of indirection with the prior network (whether it is an autoregressive transformer or a diffusion network), which predicts an image embedding based on the text embedding from CLIP. Specifically, this repository will only build out the diffusion prior network, as it is the best performing variant (but which incidentally involves a causal transformer as the denoising network 😂)
 
@@ -12,6 +12,320 @@ It may also explore an extension of using <a href="https://huggingface.co/spaces
 
 Please join <a href="https://discord.gg/xBPBXfcFHd"><img alt="Join us on Discord" src="https://img.shields.io/discord/823813159592001537?color=5865F2&logo=discord&logoColor=white"></a> if you are interested in helping out with the replication
 
+Do let me know if anyone is interested in a Jax version https://github.com/lucidrains/DALLE2-pytorch/discussions/8
+
+For all of you emailing me (there is a lot), the best way to contribute is through pull requests. Everything is open sourced after all. All my thoughts are public. This is your moment to participate.
+
+## Install
+
+```bash
+$ pip install dalle2-pytorch
+```
+
+## Usage
+
+To train DALLE-2 is a 3 step process, with the training of CLIP being the most important
+
+To train CLIP, you can either use <a href="https://github.com/lucidrains/x-clip">x-clip</a> package, or join the LAION discord, where a lot of replication efforts are already <a href="https://github.com/mlfoundations/open_clip">underway</a>.
+
+This repository will demonstrate integration with `x-clip` for starters
+
+```python
+import torch
+from dalle2_pytorch import CLIP
+
+clip = CLIP(
+    dim_text = 512,
+    dim_image = 512,
+    dim_latent = 512,
+    num_text_tokens = 49408,
+    text_enc_depth = 1,
+    text_seq_len = 256,
+    text_heads = 8,
+    visual_enc_depth = 1,
+    visual_image_size = 256,
+    visual_patch_size = 32,
+    visual_heads = 8,
+    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
+    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
+    image_ssl_loss_weight = 0.05            # weight for image self-supervised learning loss
+).cuda()
+
+# mock data
+
+text = torch.randint(0, 49408, (4, 256)).cuda()
+images = torch.randn(4, 3, 256, 256).cuda()
+
+# train
+
+loss = clip(
+    text,
+    images,
+    return_loss = True              # needs to be set to True to return contrastive loss
+)
+
+loss.backward()
+
+# do the above with as many texts and images as possible in a loop
+```
+
+Then, you will need to train the decoder, which learns to generate images based on the image embedding coming from the trained CLIP above
+
+```python
+import torch
+from dalle2_pytorch import Unet, Decoder, CLIP
+
+# trained clip from step 1
+
+clip = CLIP(
+    dim_text = 512,
+    dim_image = 512,
+    dim_latent = 512,
+    num_text_tokens = 49408,
+    text_enc_depth = 1,
+    text_seq_len = 256,
+    text_heads = 8,
+    visual_enc_depth = 1,
+    visual_image_size = 256,
+    visual_patch_size = 32,
+    visual_heads = 8
+).cuda()
+
+# unet for the decoder
+
+unet = Unet(
+    dim = 128,
+    image_embed_dim = 512,
+    cond_dim = 128,
+    channels = 3,
+    dim_mults=(1, 2, 4, 8)
+).cuda()
+
+# decoder, which contains the unet and clip
+
+decoder = Decoder(
+    net = unet,
+    clip = clip,
+    timesteps = 100,
+    cond_drop_prob = 0.2
+).cuda()
+
+# mock images (get a lot of this)
+
+images = torch.randn(4, 3, 256, 256).cuda()
+
+# feed images into decoder
+
+loss = decoder(images)
+loss.backward()
+
+# do the above for many many many many steps
+# then it will learn to generate images based on the CLIP image embeddings
+```
+
+Finally, the main contribution of the paper. The repository offers the diffusion prior network. It takes the CLIP text embeddings and tries to generate the CLIP image embeddings. Again, you will need the trained CLIP from the first step
+
+```python
+import torch
+from dalle2_pytorch import DiffusionPriorNetwork, DiffusionPrior, CLIP
+
+# get trained CLIP from step one
+
+clip = CLIP(
+    dim_text = 512,
+    dim_image = 512,
+    dim_latent = 512,
+    num_text_tokens = 49408,
+    text_enc_depth = 6,
+    text_seq_len = 256,
+    text_heads = 8,
+    visual_enc_depth = 6,
+    visual_image_size = 256,
+    visual_patch_size = 32,
+    visual_heads = 8,
+).cuda()
+
+# setup prior network, which contains an autoregressive transformer
+
+prior_network = DiffusionPriorNetwork(
+    dim = 512,
+    depth = 6,
+    dim_head = 64,
+    heads = 8
+).cuda()
+
+# diffusion prior network, which contains the CLIP and network (with transformer) above
+
+diffusion_prior = DiffusionPrior(
+    net = prior_network,
+    clip = clip,
+    timesteps = 100,
+    cond_drop_prob = 0.2
+).cuda()
+
+# mock data
+
+text = torch.randint(0, 49408, (4, 256)).cuda()
+images = torch.randn(4, 3, 256, 256).cuda()
+
+# feed text and images into diffusion prior network
+
+loss = diffusion_prior(text, images)
+loss.backward()
+
+# do the above for many many many steps
+# now the diffusion prior can generate image embeddings from the text embeddings
+```
+
+Finally, to generate the DALL-E2 images from text. Insert the trained `DiffusionPrior` as well as the `Decoder` (which both contains `CLIP`, a unet, and a causal transformer)
+
+```python
+from dalle2_pytorch import DALLE2
+
+dalle2 = DALLE2(
+    prior = diffusion_prior,
+    decoder = decoder
+)
+
+# send the text as a string if you want to use the simple tokenizer from DALLE v1
+# or you can do it as token ids, if you have your own tokenizer
+
+texts = ['glistening morning dew on a flower petal']
+images = dalle2(texts) # (1, 3, 256, 256)
+```
+
+That's it!
+
+Let's see the whole script below
+
+```python
+import torch
+from dalle2_pytorch import DALLE2, DiffusionPriorNetwork, DiffusionPrior, Unet, Decoder, CLIP
+
+clip = CLIP(
+    dim_text = 512,
+    dim_image = 512,
+    dim_latent = 512,
+    num_text_tokens = 49408,
+    text_enc_depth = 6,
+    text_seq_len = 256,
+    text_heads = 8,
+    visual_enc_depth = 6,
+    visual_image_size = 256,
+    visual_patch_size = 32,
+    visual_heads = 8
+).cuda()
+
+# mock data
+
+text = torch.randint(0, 49408, (4, 256)).cuda()
+images = torch.randn(4, 3, 256, 256).cuda()
+
+# train
+
+loss = clip(
+    text,
+    images,
+    return_loss = True
+)
+
+loss.backward()
+
+# do above for many steps ...
+
+# prior networks (with transformer)
+
+prior_network = DiffusionPriorNetwork(
+    dim = 512,
+    depth = 6,
+    dim_head = 64,
+    heads = 8
+).cuda()
+
+diffusion_prior = DiffusionPrior(
+    net = prior_network,
+    clip = clip,
+    timesteps = 100,
+    cond_drop_prob = 0.2
+).cuda()
+
+loss = diffusion_prior(text, images)
+loss.backward()
+
+# do above for many steps ...
+
+# decoder (with unet)
+
+unet = Unet(
+    dim = 128,
+    image_embed_dim = 512,
+    cond_dim = 128,
+    channels = 3,
+    dim_mults=(1, 2, 4, 8)
+).cuda()
+
+decoder = Decoder(
+    net = unet,
+    clip = clip,
+    timesteps = 100,
+    cond_drop_prob = 0.2
+).cuda()
+
+loss = decoder(images)
+loss.backward()
+
+# do above for many steps
+
+dalle2 = DALLE2(
+    prior = diffusion_prior,
+    decoder = decoder
+)
+
+images = dalle2(
+    ['cute puppy chasing after a squirrel'],
+    cond_scale = 2. # classifier free guidance strength (> 1 would strengthen the condition)
+)
+
+# save your image
+```
+
+Everything in this readme should run without error
+
+For the layperson, no worries, training will all be automated into a CLI tool, at least for small scale training.
+
+## CLI Usage (work in progress)
+
+```bash
+$ dream 'sharing a sunset at the summit of mount everest with my dog'
+```
+
+Once built, images will be saved to the same directory the command is invoked
+
+## Training wrapper (wip)
+
+Offer training wrappers
+
+## Training CLI (wip)
+
+<a href="https://github.com/lucidrains/stylegan2-pytorch">template</a>
+
+## Todo
+
+- [x] finish off gaussian diffusion class for latent embedding - allow for prediction of epsilon
+- [x] add what was proposed in the paper, where DDPM objective for image latent embedding predicts x0 directly (reread vq-diffusion paper and get caught up on that line of work)
+- [x] make sure it works end to end to produce an output tensor, taking a single gradient step
+- [ ] augment unet so that it can also be conditioned on text encodings (although in paper they hinted this didn't make much a difference)
+- [ ] look into Jonathan Ho's cascading DDPM for the decoder, as that seems to be what they are using. get caught up on DDPM literature
+- [ ] figure out all the current bag of tricks needed to make DDPMs great (starting with the blur trick mentioned in paper)
+- [ ] train on a toy task, offer in colab
+- [ ] add attention to unet - apply some personal tricks with efficient attention
+- [ ] figure out the big idea behind latent diffusion and what can be ported over
+
 ## Citations
 
 ```bibtex
@@ -39,3 +353,24 @@ Please join <a href="https://discord.gg/xBPBXfcFHd"><img alt="Join us on Discord
     primaryClass = {cs.CV}
 }
 ```
+
+```bibtex
+@inproceedings{Liu2022ACF,
+    title   = {A ConvNet for the 2020s},
+    author  = {Zhuang Liu and Hanzi Mao and Chaozheng Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
+    year    = {2022}
+}
+```
+
+```bibtex
+@misc{zhang2019root,
+    title   = {Root Mean Square Layer Normalization},
+    author  = {Biao Zhang and Rico Sennrich},
+    year    = {2019},
+    eprint  = {1910.07467},
+    archivePrefix = {arXiv},
+    primaryClass = {cs.LG}
+}
+```
+
+*Creating noise from data is easy; creating data from noise is generative modeling.* - Yang Song's <a href="https://arxiv.org/abs/2011.13456">paper</a>

dalle2_pytorch/__init__.py

@@ -1 +1,2 @@
-from dalle2_pytorch.dalle2_pytorch import DALLE2
+from dalle2_pytorch.dalle2_pytorch import DALLE2, DiffusionPriorNetwork, DiffusionPrior, Unet, Decoder
+from x_clip import CLIP

dalle2_pytorch/cli.py

@@ -0,0 +1,9 @@
+import click
+
+def main():
+    pass
+
+@click.command()
+@click.argument('text')
+def dream(text):
+    return image

setup.py

@@ -4,7 +4,13 @@ setup(
   name = 'dalle2-pytorch',
   packages = find_packages(exclude=[]),
   include_package_data = True,
-  version = '0.0.1',
+  entry_points={
+    'console_scripts': [
+      'dalle2_pytorch = dalle2_pytorch.cli:main',
+      'dream = dalle2_pytorch.cli:dream'
+    ],
+  },
+  version = '0.0.12',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',
@@ -16,11 +22,16 @@ setup(
     'text to image'
   ],
   install_requires=[
+    'click',
     'einops>=0.4',
-    'einops-exts',
-    'torch>=1.6',
-    'x-clip>=0.4.1',
-    'yttm'
+    'einops-exts>=0.0.3',
+    'kornia>=0.5.4',
+    'pillow',
+    'torch>=1.10',
+    'torchvision',
+    'tqdm',
+    'x-clip>=0.4.4',
+    'youtokentome'
   ],
   classifiers=[
     'Development Status :: 4 - Beta',