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[lijain]

I run first:
import 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),
timesteps = 1000,
cond_drop_prob = 0.1
).cuda()

mock images (get a lot of this) and text encodings from large T5

text_embeds = torch.randn(4, 256, 768).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, 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 = 3.)

images.shape # (3, 3, 256, 256)

FileNotFoundError: [Errno 2] No such file or directory: '/mnt/cache/xxx/.cac
he/huggingface/hub/models--google--t5-v1_1-base/refs/main'
srun: error: xx-IDC1-10-142-5-20: task 0: Exited with exit code 1