[scheduler] Support custom sigmas/timesteps in UniPCMultistepScheduler

[a-r-r-o-w]

Fixes #12108

import torch
from diffusers import QwenImagePipeline, UniPCMultistepScheduler

pipe = QwenImagePipeline.from_pretrained("Qwen/Qwen-Image", torch_dtype=torch.bfloat16)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, prediction_type="flow_prediction", use_flow_sigmas=True)
pipe.to("cuda")
image = pipe("a cat holding a sign that says 'hello world'", num_inference_steps=30).images[0]
image.save("output.png")

The quality is not great, not sure why yet. Still needs debugging.

cc @Vargol

[HuggingFaceDocBuilderDev]

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

[a-r-r-o-w]

I think it's working:

import torch
from diffusers import QwenImagePipeline, UniPCMultistepScheduler

pipe = QwenImagePipeline.from_pretrained("Qwen/Qwen-Image", torch_dtype=torch.bfloat16)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, prediction_type="flow_prediction", use_flow_sigmas=True)
pipe.to("cuda")

prompt = """A coffee shop entrance features a chalkboard sign reading "Qwen Coffee 😊 $2 per cup," with a neon light beside it displaying "通义千问". Next to it hangs a poster showing a beautiful Chinese woman, and beneath the poster is written "π≈3.1415926-53589793-23846264-33832795-02384197". Ultra HD, 4K, cinematic composition."""
image = pipe(prompt, negative_prompt=" ", width=1664, height=928, num_inference_steps=30, generator=torch.Generator().manual_seed(42)).images[0]
image.save("output.png")

FM Euler (50 steps)	UniPC (30 steps)

cc @asomoza in case you want to do some tests