DALL-E in Pytorch
Implementation / replication of DALL-E (paper), OpenAI's Text to Image Transformer, in Pytorch. It will also contain CLIP for ranking the generations.
Sid, Ben, and Aran over at Eleuther AI are working on DALL-E for Mesh Tensorflow! Please lend them a hand if you would like to see DALL-E trained on TPUs.
Before we replicate this, we can settle for Deep Daze or Big Sleep
Status
- Hannu has managed to train a small 6 layer DALL-E on a dataset of just 2000 landscape images! (2048 visual tokens)
- Kobiso, a research engineer from Naver, has trained on both the CUBS and COCO dataset here, using full and deepspeed sparse attention
- afiaka87 has managed one epoch using a 32 layer reversible DALL-E here
Install
$ pip install dalle-pytorch
Usage
Train VAE
import torch
from dalle_pytorch import DiscreteVAE
vae = DiscreteVAE(
image_size = 256,
num_layers = 3, # number of downsamples - ex. 256 / (2 ** 3) = (32 x 32 feature map)
num_tokens = 8192, # number of visual tokens. in the paper, they used 8192, but could be smaller for downsized projects
codebook_dim = 512, # codebook dimension
hidden_dim = 64, # hidden dimension
num_resnet_blocks = 1, # number of resnet blocks
temperature = 0.9, # gumbel softmax temperature, the lower this is, the harder the discretization
straight_through = False, # straight-through for gumbel softmax. unclear if it is better one way or the other
)
images = torch.randn(4, 3, 256, 256)
loss = vae(images, return_loss = True)
loss.backward()
# train with a lot of data to learn a good codebook
Train DALL-E with pretrained VAE from above
import torch
from dalle_pytorch import DiscreteVAE, DALLE
vae = DiscreteVAE(
image_size = 256,
num_layers = 3,
num_tokens = 8192,
codebook_dim = 1024,
hidden_dim = 64,
num_resnet_blocks = 1,
temperature = 0.9
)
dalle = DALLE(
dim = 1024,
vae = vae, # automatically infer (1) image sequence length and (2) number of image tokens
num_text_tokens = 10000, # vocab size for text
text_seq_len = 256, # text sequence length
depth = 12, # should aim to be 64
heads = 16, # attention heads
dim_head = 64, # attention head dimension
attn_dropout = 0.1, # attention dropout
ff_dropout = 0.1 # feedforward dropout
)
text = torch.randint(0, 10000, (4, 256))
images = torch.randn(4, 3, 256, 256)
mask = torch.ones_like(text).bool()
loss = dalle(text, images, mask = mask, return_loss = True)
loss.backward()
# do the above for a long time with a lot of data ... then
images = dalle.generate_images(text, mask = mask)
images.shape # (4, 3, 256, 256)
OpenAI's Pretrained VAE
You can also skip the training of the VAE altogether, using the pretrained model released by OpenAI! The wrapper class should take care of downloading and caching the model for you auto-magically.
import torch
from dalle_pytorch import OpenAIDiscreteVAE, DALLE
vae = OpenAIDiscreteVAE() # loads pretrained OpenAI VAE
dalle = DALLE(
dim = 1024,
vae = vae, # automatically infer (1) image sequence length and (2) number of image tokens
num_text_tokens = 10000, # vocab size for text
text_seq_len = 256, # text sequence length
depth = 1, # should aim to be 64
heads = 16, # attention heads
dim_head = 64, # attention head dimension
attn_dropout = 0.1, # attention dropout
ff_dropout = 0.1 # feedforward dropout
)
text = torch.randint(0, 10000, (4, 256))
images = torch.randn(4, 3, 256, 256)
mask = torch.ones_like(text).bool()
loss = dalle(text, images, mask = mask, return_loss = True)
loss.backward()
Taming Transformer's Pretrained VQGAN VAE
You can also use the pretrained VAE offered by the authors of Taming Transformers! Currently only the VAE with a codebook size of 1024 is offered, with the hope that it may train a little faster than OpenAI's, which has a size of 8192.
In contrast to OpenAI's VAE, it also has an extra layer of downsampling, so the image sequence length is 256 instead of 1024 (this will lead to a 16 reduction in training costs, when you do the math). Whether it will generalize as well as the original DALL-E is up to the citizen scientists out there to discover.
from dalle_pytorch import VQGanVAE1024
vae = VQGanVAE1024()
# the rest is the same as the above example
Ranking the generations
Train CLIP
import torch
from dalle_pytorch import CLIP
clip = CLIP(
dim_text = 512,
dim_image = 512,
dim_latent = 512,
num_text_tokens = 10000,
text_enc_depth = 6,
text_seq_len = 256,
text_heads = 8,
num_visual_tokens = 512,
visual_enc_depth = 6,
visual_image_size = 256,
visual_patch_size = 32,
visual_heads = 8
)
text = torch.randint(0, 10000, (4, 256))
images = torch.randn(4, 3, 256, 256)
mask = torch.ones_like(text).bool()
loss = clip(text, images, text_mask = mask, return_loss = True)
loss.backward()
To get the similarity scores from your trained Clipper, just do
images, scores = dalle.generate_images(text, mask = mask, clip = clip)
scores.shape # (2,)
images.shape # (2, 3, 256, 256)
# do your topk here, in paper they sampled 512 and chose top 32
Or you can just use the official CLIP model to rank the images from DALL-E
Scaling depth
In the blog post, they used 64 layers to achieve their results. I added reversible networks, from the Reformer paper, in order for users to attempt to scale depth at the cost of compute. Reversible networks allow you to scale to any depth at no memory cost, but a little over 2x compute cost (each layer is rerun on the backward pass).
Simply set the reversible keyword to True for the DALLE class
dalle = DALLE(
dim = 1024,
vae = vae,
num_text_tokens = 10000,
text_seq_len = 256,
depth = 64,
heads = 16,
reversible = True # <-- reversible networks https://arxiv.org/abs/2001.04451
)
Sparse Attention
The blogpost alluded to a mixture of different types of sparse attention, used mainly on the image (while the text presumably had full causal attention). I have done my best to replicate these types of sparse attention, on the scant details released. Primarily, it seems as though they are doing causal axial row / column attention, combined with a causal convolution-like attention.
By default DALLE will use full attention for all layers, but you can specify the attention type per layer as follows.
-
fullfull attention -
axial_rowaxial attention, along the rows of the image feature map -
axial_colaxial attention, along the columns of the image feature map -
conv_likeconvolution-like attention, for the image feature map
The sparse attention only applies to the image. Text will always receive full attention, as said in the blogpost.
dalle = DALLE(
dim = 1024,
vae = vae,
num_text_tokens = 10000,
text_seq_len = 256,
depth = 64,
heads = 16,
reversible = True,
attn_types = ('full', 'axial_row', 'axial_col', 'conv_like') # cycles between these four types of attention
)
Deepspeed Sparse Attention
You can also train with Microsoft Deepspeed's Sparse Attention, with any combination of dense and sparse attention that you'd like. However, you will have to endure the installation process.
First, you need to install Deepspeed with Sparse Attention
$ sh install_deepspeed.sh
Next, you need to install the pip package triton
$ pip install triton
If both of the above succeeded, now you can train with Sparse Attention!
dalle = DALLE(
dim = 512,
vae = vae,
num_text_tokens = 10000,
text_seq_len = 256,
depth = 64,
heads = 8,
attn_types = ('full', 'sparse') # interleave sparse and dense attention for 64 layers
)
Training
This section will outline how to train the discrete variational autoencoder as well as the final multi-modal transformer (DALL-E). We are going to use Weights & Biases for all the experiment tracking.
(You can also do everything in this section in a Google Colab, link below)
$ pip install wandb
Followed by
$ wandb login
VAE
To train the VAE, you just need to run
$ python train_vae.py --image_folder /path/to/your/images
If you installed everything correctly, a link to the experiments page should show up in your terminal. You can follow your link there and customize your experiment, like the example layout below.
You can of course open up the training script at ./train_vae.py, where you can modify the constants, what is passed to Weights & Biases, or any other tricks you know to make the VAE learn better.
Model will be saved periodically to ./vae.pt
In the experiment tracker, you will have to monitor the hard reconstruction, as we are essentially teaching the network to compress images into discrete visual tokens for use in the transformer as a visual vocabulary.
Weights and Biases will allow you to monitor the temperature annealing, image reconstructions (encoder and decoder working properly), as well as to watch out for codebook collapse (where the network decides to only use a few tokens out of what you provide it).
Once you have trained a decent VAE to your satisfaction, you can move on to the next step with your model weights at ./vae.pt.
DALL-E
Now you just have to invoke the ./train_dalle.py script, indicating which VAE model you would like to use, as well as the path to your folder if images and text.
The dataset I am currently working with contains a folder of images and text files, arbitraily nested in subfolders, where text file name corresponds with the image name, and where each text file contains multiple descriptions, delimited by newlines. The script will find and pair all the image and text files with the same names, and randomly select one of the textual descriptions during batch creation.
ex.
📂image-and-text-data
┣ 📜cat.png
┣ 📜cat.txt
┣ 📜dog.jpg
┣ 📜dog.txt
┣ 📜turtle.jpeg
┗ 📜turtle.txt
ex. cat.txt
A black and white cat curled up next to the fireplace
A fireplace, with a cat sleeping next to it
A black cat with a red collar napping
If you have a dataset with its own directory structure for tying together image and text descriptions, do let me know in the issues, and I'll see if I can accommodate it in the script.
$ python train_dalle.py --vae_path ./vae.pt --image_text_folder /path/to/data
You likely will not finish DALL-E training as quickly as you did your Discrete VAE. To resume from where you left off, just run the same script, but with the path to your DALL-E checkpoints.
$ python train_dalle.py --dalle_path ./dalle.pt --image_text_folder /path/to/data
DALL-E with OpenAI's VAE
You can now also train DALL-E without having to train the Discrete VAE at all, courtesy to their open-sourcing their model. You simply have to invoke the train_dalle.py script without specifying the --vae_path
$ python train_dalle.py --image_text_folder /path/to/coco/dataset
Generation
Once you have successfully trained DALL-E, you can then used the saved model for generation!
$ python generate.py --dalle_path ./dalle.pt --text 'fireflies in a field under a full moon'
You should see your images saved as ./outputs/{your prompt}/{image number}.jpg
Citations
@misc{ramesh2021zeroshot,
title = {Zero-Shot Text-to-Image Generation},
author = {Aditya Ramesh and Mikhail Pavlov and Gabriel Goh and Scott Gray and Chelsea Voss and Alec Radford and Mark Chen and Ilya Sutskever},
year = {2021},
eprint = {2102.12092},
archivePrefix = {arXiv},
primaryClass = {cs.CV}
}
@misc{unpublished2021clip,
title = {CLIP: Connecting Text and Images},
author = {Alec Radford, Ilya Sutskever, Jong Wook Kim, Gretchen Krueger, Sandhini Agarwal},
year = {2021}
}
@misc{kitaev2020reformer,
title = {Reformer: The Efficient Transformer},
author = {Nikita Kitaev and Łukasz Kaiser and Anselm Levskaya},
year = {2020},
eprint = {2001.04451},
archivePrefix = {arXiv},
primaryClass = {cs.LG}
}
@misc{esser2021taming,
title = {Taming Transformers for High-Resolution Image Synthesis},
author = {Patrick Esser and Robin Rombach and Björn Ommer},
year = {2021},
eprint = {2012.09841},
archivePrefix = {arXiv},
primaryClass = {cs.CV}
}
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