[LoRA] Support Wan

[a-r-r-o-w]

Training: a-r-r-o-w/finetrainers#281

Dummy model: https://huggingface.co/finetrainers/Wan2.1-T2V-1.3B-crush-smol-v0

cc @yiyixuxu for pipeline related changes

[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]

Image-to-Video works as expected after the changes to the pipeline. Most of the changes are to address consistency across implementations and fixes to default values.

import torch
from diffusers import AutoencoderKLWan, WanImageToVideoPipeline, UniPCMultistepScheduler
from diffusers.utils import export_to_video, load_image

# Available models: Wan-AI/Wan2.1-I2V-14B-480P, Wan-AI/Wan2.1-I2V-1.3B-720P
model_id = "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
scheduler = UniPCMultistepScheduler(prediction_type="flow_prediction", use_flow_sigmas=True, flow_shift=6.0)
pipe = WanImageToVideoPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
pipe.scheduler = scheduler
pipe.to("cuda")

# height, width = 480, 832
height, width = 480, 704
image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg")
prompt = (
    "An astronaut hatching from an egg, on the surface of the moon, the darkness and depth of space realised in "
    "the background. High quality, ultrarealistic detail and breath-taking movie-like camera shot."
)
negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"

output = pipe(
    image=image,
    prompt=prompt,
    negative_prompt=negative_prompt,
    height=height,
    width=width,
    num_frames=81,
    num_inference_steps=30,
    guidance_scale=3.0
).frames[0]
export_to_video(output, "output2.mp4", fps=15)

output2.mp4