AttributeError: 'XClipAdapter' object has no attribute 'max_text_len'

[etali]

When I run the code in README.md, it finally shown that "AttributeError: 'XClipAdapter' object has no attribute 'max_text_len'". Any hint?

[lucidrains]

@etali hey! which script in the readme are you running?

[lucidrains]

seems to be working for me, but feel free to reopen the issue with the script that reproduces the error

[etali]

I run the script described here.

[etali]

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)

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

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

decoder = Decoder( unet = (unet1, unet2), image_sizes = (128, 256), clip = clip, timesteps = 100, image_cond_drop_prob = 0.1, text_cond_drop_prob = 0.5, condition_on_text_encodings = False # set this to True if you wish to condition on text during training and sampling ).cuda()

for unet_number in (1, 2): loss = decoder(images, unet_number = unet_number) # this can optionally be decoder(images, text) if you wish to condition on the text encodings as well, though it was hinted in the paper it didn't do much 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 (in this example, of size 256x256)

[1073521013]

same error in same place