eliohead / DALLE-pytorch

Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch

DALL-E in Pytorch (wip)

Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch. It will also contain CLIP for ranking the generations.

Yannic Kilcher's video

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 = 1024,      # number of visual tokens. iGPT had 512, so probably should have more
    codebook_dim = 512,     # codebook dimension
    hidden_dim = 64,        # hidden dimension
    temperature = 0.9       # gumbel softmax temperature, the lower this is, the more hard the discretization
)

images = torch.randn(4, 3, 256, 256)

loss = vae(images, return_recon_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 = 1024,
    codebook_dim = 512,
    hidden_dim = 64,
    temperature = 0.9
)

dalle = DALLE(
    dim = 512,
    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 = 6,                  # should be 64
    heads = 8
)

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 # (2, 3, 256, 256)

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

Citations

@misc{unpublished2021dalle,
    title   = {DALL·E: Creating Images from Text},
    author  = {Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray},
    year    = {2021}
}

@misc{unpublished2021clip,
    title  = {CLIP: Connecting Text and Images},
    author = {Alec Radford, Ilya Sutskever, Jong Wook Kim, Gretchen Krueger, Sandhini Agarwal},
    year   = {2021}
}

Those who do not want to imitate anything, produce nothing. - Dali