Cascade w/ new varnames produces funky images.

[Beinsezii]

Describe the bug

Using Stable Cascade with the diffusers main branch and the HF PRs 2/44 as intended, it appears as though there's some misconfiguration that results in super funky images as shown.

Old Cascade branch before the variable renaming, using the current main Cascade revisions

Diffusers master branch w/ the updated Cascade revision PRs

Reproduction

import torch
from math import ceil
from diffusers import (
    StableCascadeCombinedPipeline,
    StableCascadeDecoderPipeline,
    StableCascadePriorPipeline,
)

torch.set_grad_enabled(False)

prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", revision="refs/pr/2", torch_dtype=torch.bfloat16)
decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade", revision="refs/pr/44", torch_dtype=torch.bfloat16)
pipe = StableCascadeCombinedPipeline(
    decoder.tokenizer,
    decoder.text_encoder,
    decoder.decoder,
    decoder.scheduler,
    decoder.vqgan,
    prior.prior,
    prior.text_encoder,
    prior.tokenizer,
    prior.scheduler,
    prior.feature_extractor,
    prior.image_encoder,
)

del prior, decoder
pipe = pipe.to("cuda")

# CPU noise for cross-machine reproducibility
size = [
    1,
    pipe.prior_pipe.prior.config.in_channels,
    ceil(1024 / pipe.prior_pipe.config.resolution_multiple),
    ceil(1024 / pipe.prior_pipe.config.resolution_multiple),
]
generator = torch.Generator("cpu").manual_seed(-2060472805)
latent_input = torch.randn(size, generator=generator, dtype=torch.float32, device="cpu").to(torch.bfloat16)

pipe(
    prompt="photorealistic portrait artwork of an floral robot with a dark night cyberpunk city background",
    negative_prompt="",
    num_inference_steps=50,
    prior_num_inference_steps=50,
    prior_guidance_scale=3.0,
    latents=latent_input,
    generator=generator,
    width=1024,
    height=1024,
).images[0].save("/tmp/cascade_repro.png")

Logs

No response

System Info

Diffusers master, torch 2.3.0+ROCm-6.0

Who can help?

No response

[sayakpaul]

Cc: @DN6 @kashif

[Beinsezii]

It looks like #7287 fixed the coloration but not the smeary-ness.

Original

Diffusers 0.27

Made with the same script, only changing the diffusers version and cascade refs

[yiyixuxu]

@Beinsezii

I swapped back to the previous checkpoint here https://huggingface.co/stabilityai/stable-cascade/discussions/50
can you test out to see if this checkpoint is better?

import torch
from diffusers import StableCascadeDecoderPipeline
from diffusers.utils.testing_utils import load_pt

torch.set_grad_enabled(False)
prompt="photorealistic portrait artwork of an floral robot with a dark night cyberpunk city background"
negative_prompt=""

path_prior_out = "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/sd_cascade_image_embeds.ipadpt"
image_embeddings = load_pt(path_prior_out)

# current checkpoint

decoder = StableCascadeDecoderPipeline.from_pretrained(
    "stabilityai/stable-cascade", 
    variant="bf16", 
    torch_dtype=torch.bfloat16)
decoder.enable_model_cpu_offload()

generator = torch.Generator("cpu").manual_seed(-2060472805)
decoder_output = decoder(
    image_embeddings=image_embeddings,
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_inference_steps=50,
    guidance_scale= 0.0,
    generator=generator,
)
decoder_output.images[0].save("yiyi_test_9_out_current.png")

# with the new checkpoint
decoder = StableCascadeDecoderPipeline.from_pretrained(
    "stabilityai/stable-cascade", 
    variant="bf16", 
    revision="refs/pr/50",
    torch_dtype=torch.bfloat16)
decoder.enable_model_cpu_offload()

generator = torch.Generator("cpu").manual_seed(-2060472805)
decoder_output = decoder(
    image_embeddings=image_embeddings,
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_inference_steps=50,
    guidance_scale= 0.0,
    generator=generator,
)
decoder_output.images[0].save("yiyi_test_9_out_new.png")

current outpput

new output with the PR

[Beinsezii]

@yiyixuxu you madwoman, that's exactly it. The robot lines up 1:1 with the original cascade PR now.

I rendered a few other prompts demonstrating the problem. Current weights vs PR 50 reverted weights.

The robot flowers, frog hands, and armor plate decorations are all much more artifacted / smeary on the current weights.

What even are the 'new' weights that were introduced in the PR 44? All the commit says is "update diffusers format".

Reminds me of when XL 1.0 first released and the VAE weights were updated to a version that produces artifacts, needing to be reverted.

Should I close now or wait for a HF merge?

[yiyixuxu]

@Beinsezii
it's merged!

[yiyixuxu]

@Beinsezii
the new weights in PR 44 were converted from https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_b.safetensors - so I think the issue still exists if you use from_single_file; I also think the comfyUI checkpoints have the same issue

What even are the 'new' weights that were introduced in the PR 44? All the commit says is "update diffusers format".

[Beinsezii]

None of the Cascade pipes implement the FromSingleFileMixin trait in the first place, and the official ComfyUI Cascade example errs on unet load for me so further validations are going to be fun...

Edit: I didn't realize Comfy had it's own entirely separate single file checkpoints. I'll see how reproducible it is after the movie.

[Beinsezii]

ComfyUI uses what looks like totally different sampling for Cascade which means its not seed compatible.

However, the robot flowers and the dog paws/clothes have some of that sorta smeary look that the diffusers weights used to have before you merged, so while not completely conclusive I think you're right.

Should probably have a note to be investigated if/when someone adds the single file mixin to Cascade.

[sayakpaul]

FWIW, I think it makes sense to go through the section on from_single_file for the Stable Cascade pipeline docs to understand the scopes of its support.

It also helps to go through the Cascade conversion script for getting the fuller context: https://github.com/huggingface/diffusers/blob/main/scripts/convert_stable_cascade.py.

TL;DR is (IIUC) that the original checkpoints ship with either the prior or the decoder but not with the other components involved in the pipeline (unlike other models such as SVD, SDXL, etc.). This is why, from_single_file() support is implemented for prior and decoder.

However, I also invite @kashif and @DN6 to correct me if I am completely wrong here.

[Beinsezii]

Ah, my bad I scrolled right passed that. I wasn't expecting "single file" to be only the unet, especially when it's already present as a "single file" in the decoder/ folder?

Swapping the unet with the file @yiyixuxu linked and it's very clearly the source of the issue. Using the following script

import torch
from math import ceil
from diffusers import (
    StableCascadeDecoderPipeline,
    StableCascadePriorPipeline,
)
from diffusers.models import StableCascadeUNet

torch.set_grad_enabled(False)

prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", torch_dtype=torch.bfloat16).to("cuda")

# CPU noise for cross-machine reproducibility
size = [
    1,
    prior.prior.config.in_channels,
    ceil(1024 / prior.config.resolution_multiple),
    ceil(1024 / prior.config.resolution_multiple),
]
generator = torch.Generator("cpu").manual_seed(-2060472805)
prior_out = prior(
    prompt="photorealistic portrait artwork of an floral robot with a dark night cyberpunk city background",
    negative_prompt="",
    num_inference_steps=50,
    guidance_scale=3.0,
    width=1024,
    height=1024,
    latents=torch.randn(size, generator=generator, dtype=torch.float32, device="cpu").to(torch.bfloat16),
    generator=generator,
)
del prior
decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade", torch_dtype=torch.bfloat16).to("cuda")
dgen2 = torch.Generator("cpu")
dgen2.set_state(generator.get_state())

decoder(num_inference_steps=50, generator=generator, **prior_out).images[0].save("/tmp/cascade_normal.png")

decoder.decoder = StableCascadeUNet.from_single_file("/tmp/stage_b.safetensors", torch_dtype=torch.bfloat16).to("cuda")

decoder(num_inference_steps=50, generator=dgen2, **prior_out).images[0].save("/tmp/cascade_single.png")

It 1:1 reproduces both the good image after the PR50 partial revert

and the smeary image I raised in this issue when you swap the unet

[sayakpaul]

Cc: @DN6 ^.

I wasn't expecting "single file" to be only the unet, especially when it's already present as a "single file" in the decoder/ folder?

My understanding is that even then the single-file checkpoints don't contain all the necessary components (i.e., image encoder, VQGAN, etc.).