Should the first Unet have cond_on_image_embeds=True

[xiankgx]

With reference to your example usage for decoder training. The Unet code seems to have argument cond_on_image_embeds set to False by default, which is alright since it is used in various stages in the decoding process. However, shouldn't the first Unet have it set to True; otherwise, the decoder is just conditioning on time and does not respond to the CLIP image embeddings input?

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 = 6,
    text_seq_len = 256,
    text_heads = 8,
    visual_enc_depth = 6,
    visual_image_size = 256,
    visual_patch_size = 32,
    visual_heads = 8
).cuda()

# 2 unets for the decoder (a la cascading DDPM)

unet1 = Unet(
    dim = 32,
    image_embed_dim = 512,
    cond_dim = 128,
    channels = 3,
    dim_mults = (1, 2, 4, 8)
).cuda()

unet2 = Unet(
    dim = 32,
    image_embed_dim = 512,
    cond_dim = 128,
    channels = 3,
    dim_mults = (1, 2, 4, 8, 16)
).cuda()

# decoder, which contains the unet(s) and clip

decoder = Decoder(
    clip = clip,
    unet = (unet1, unet2),            # insert both unets in order of low resolution to highest resolution (you can have as many stages as you want here)
    image_sizes = (256, 512),         # resolutions, 256 for first unet, 512 for second. these must be unique and in ascending order (matches with the unets passed in)
    timesteps = 1000,
    cond_drop_prob = 0.2
).cuda()

# mock images (get a lot of this)

images = torch.randn(4, 3, 512, 512).cuda()

# feed images into decoder, specifying which unet you want to train
# each unet can be trained separately, which is one of the benefits of the cascading DDPM scheme

loss = decoder(images, unet_number = 1)
loss.backward()

loss = decoder(images, unet_number = 2)
loss.backward()

# do the above for many steps for both unets

[xiankgx]

Please enlighten me. Thanks.

[lucidrains]

@xiankgx thanks for catching this before anyone started training! https://github.com/lucidrains/DALLE2-pytorch/releases/tag/0.0.64 :pray:

[xiankgx]

Thanks!