Implementation of Imagen, Google's Text-to-Image Neural Network that beats DALL-E2, in Pytorch. It is the new SOTA for text-to-image synthesis.
Architecturally, it is actually much simpler than DALL-E2. It consists of a cascading DDPM conditioned on text embeddings from a large pretrained T5 model (attention network). It also contains dynamic clipping for improved classifier free guidance, noise level conditioning, and a memory efficient unet design.
It appears neither CLIP nor prior network is needed after all. And so research continues.
Please join 
$ pip install imagen-pytorchimport torch
from imagen_pytorch import Unet, Imagen
# unet for imagen
unet1 = Unet(
dim = 32,
cond_dim = 512,
dim_mults = (1, 2, 4, 8),
num_resnet_blocks = 3,
layer_attns = (False, True, True, True),
layer_cross_attns = (False, True, True, True)
)
unet2 = Unet(
dim = 32,
cond_dim = 512,
dim_mults = (1, 2, 4, 8),
num_resnet_blocks = (2, 4, 8, 8),
layer_attns = (False, False, False, True),
layer_cross_attns = (False, False, False, True)
)
# imagen, which contains the unets above (base unet and super resoluting ones)
imagen = Imagen(
unets = (unet1, unet2),
image_sizes = (64, 256),
beta_schedules = ('cosine', 'linear'),
timesteps = 1000,
cond_drop_prob = 0.5
).cuda()
# mock images (get a lot of this) and text encodings from large T5
text_embeds = torch.randn(4, 256, 768).cuda()
text_masks = torch.ones(4, 256).bool().cuda()
images = torch.randn(4, 3, 256, 256).cuda()
# feed images into imagen, training each unet in the cascade
for i in (1, 2):
loss = imagen(images, text_embeds = text_embeds, text_masks = text_masks, unet_number = i)
loss.backward()
# do the above for many many many many steps
# now you can sample an image based on the text embeddings from the cascading ddpm
images = imagen.sample(texts = [
'a whale breaching from afar',
'young girl blowing out candles on her birthday cake',
'fireworks with blue and green sparkles'
], cond_scale = 2.)
images.shape # (3, 3, 256, 256)With the ImagenTrainer wrapper class, the exponential moving averages for all of the U-nets in the cascading DDPM will be automatically taken care of when calling update
import torch
from imagen_pytorch import Unet, Imagen, ImagenTrainer
# unet for imagen
unet1 = Unet(
dim = 32,
cond_dim = 512,
dim_mults = (1, 2, 4, 8),
num_resnet_blocks = 3,
layer_attns = (False, True, True, True),
)
unet2 = Unet(
dim = 32,
cond_dim = 512,
dim_mults = (1, 2, 4, 8),
num_resnet_blocks = (2, 4, 8, 8),
layer_attns = (False, False, False, True),
layer_cross_attns = (False, False, False, True)
)
# imagen, which contains the unets above (base unet and super resoluting ones)
imagen = Imagen(
unets = (unet1, unet2),
text_encoder_name = 't5-large',
image_sizes = (64, 256),
beta_schedules = ('cosine', 'linear'),
timesteps = 1000,
cond_drop_prob = 0.5
).cuda()
# wrap imagen with the trainer class
trainer = ImagenTrainer(imagen)
# mock images (get a lot of this) and text encodings from large T5
text_embeds = torch.randn(64, 256, 1024).cuda()
text_masks = torch.ones(64, 256).bool().cuda()
images = torch.randn(64, 3, 256, 256).cuda()
# feed images into imagen, training each unet in the cascade
for i in (1, 2):
loss = trainer(
images,
text_embeds = text_embeds,
text_masks = text_masks,
unet_number = i,
max_batch_size = 4 # auto divide the batch of 64 up into batch size of 4 and accumulate gradients, so it all fits in memory
)
trainer.update(unet_number = i)
# do the above for many many many many steps
# now you can sample an image based on the text embeddings from the cascading ddpm
images = trainer.sample(texts = [
'a puppy looking anxiously at a giant donut on the table',
'the milky way galaxy in the style of monet'
], cond_scale = 2.)
images.shape # (2, 3, 256, 256)-
StabilityAI for the generous sponsorship, as well as my other sponsors out there
-
🤗 Huggingface for their amazing transformers library. The text encoder portion is pretty much taken care of because of them
-
Jorge Gomes for helping out with the T5 loading code and advice on the correct T5 version
-
Katherine Crowson, for her beautiful code, which helped me understand the continuous time version of gaussian diffusion
-
You? It isn't done yet, chip in if you are a researcher or skilled ML engineer
- use huggingface transformers for T5-small text embeddings
- add dynamic thresholding
- add dynamic thresholding DALLE2 and video-diffusion repository as well
- allow for one to set T5-large (and perhaps small factory method to take in any huggingface transformer)
- add the lowres noise level with the pseudocode in appendix, and figure out what is this sweep they do at inference time
- port over some training code from DALLE2
- need to be able to use a different noise schedule per unet (cosine was used for base, but linear for SR)
- just make one master-configurable unet
- complete resnet block (biggan inspired? but with groupnorm) - complete self attention
- complete conditioning embedding block (and make it completely configurable, whether it be attention, film etc)
- consider using perceiver-resampler from https://github.com/lucidrains/flamingo-pytorch in place of attention pooling
- add attention pooling option, in addition to cross attention and film
- add optional cosine decay schedule with warmup, for each unet, to trainer
- figure out if learned variance was used at all, and remove it if it was inconsequential
- switch to continuous timesteps instead of discretized, as it seems that is what they used for all stages - first figure out the linear noise schedule case from the variational ddpm paper https://openreview.net/forum?id=2LdBqxc1Yv
- exercise efficient attention expertise + explore skip layer excitation
- try out grid attention
@inproceedings{Saharia2022PhotorealisticTD,
title = {Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding},
author = {Chitwan Saharia and William Chan and Saurabh Saxena and Lala Li and Jay Whang and Emily L. Denton and Seyed Kamyar Seyed Ghasemipour and Burcu Karagol Ayan and Seyedeh Sara Mahdavi and Raphael Gontijo Lopes and Tim Salimans and Jonathan Ho and David Fleet and Mohammad Norouzi},
year = {2022}
}@inproceedings{Tu2022MaxViTMV,
title = {MaxViT: Multi-Axis Vision Transformer},
author = {Zhengzhong Tu and Hossein Talebi and Han Zhang and Feng Yang and Peyman Milanfar and Alan Conrad Bovik and Yinxiao Li},
year = {2022},
url = {https://arxiv.org/abs/2204.01697}
}@article{Alayrac2022Flamingo,
title = {Flamingo: a Visual Language Model for Few-Shot Learning},
author = {Jean-Baptiste Alayrac et al},
year = {2022}
}