AlekseyCalvin/WanVACE_1.3B_nf4_umT5fp8_Diffusers

Bits&Bytes NF4 Conversion of Wan2.1 1.3B + VACE

The umT5XXL Text Encoder is in FP8.

Use the same way as the official Diffusers-format checkpoint.

Here's the example Diffusers code from the official repo:

import torch
import PIL.Image
from diffusers import AutoencoderKLWan, WanVACEPipeline
from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
from diffusers.utils import export_to_video, load_image
def prepare_video_and_mask(first_img: PIL.Image.Image, last_img: PIL.Image.Image, height: int, width: int, num_frames: int):
    first_img = first_img.resize((width, height))
    last_img = last_img.resize((width, height))
    frames = []
    frames.append(first_img)
    # Ideally, this should be 127.5 to match original code, but they perform computation on numpy arrays
    # whereas we are passing PIL images. If you choose to pass numpy arrays, you can set it to 127.5 to
    # match the original code.
    frames.extend([PIL.Image.new("RGB", (width, height), (128, 128, 128))] * (num_frames - 2))
    frames.append(last_img)
    mask_black = PIL.Image.new("L", (width, height), 0)
    mask_white = PIL.Image.new("L", (width, height), 255)
    mask = [mask_black, *[mask_white] * (num_frames - 2), mask_black]
    return frames, mask
model_id = "AlekseyCalvin/WanVACE_1.3B_nf4_umT5fp8_Diffusers"
vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
pipe = WanVACEPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
flow_shift = 5.0  # 5.0 for 720P, 3.0 for 480P
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=flow_shift)
pipe.to("cuda")
prompt = "CG animation style, a small blue bird takes off from the ground, flapping its wings. The bird's feathers are delicate, with a unique pattern on its chest. The background shows a blue sky with white clouds under bright sunshine. The camera follows the bird upward, capturing its flight and the vastness of the sky from a close-up, low-angle perspective."
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"
first_frame = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flf2v_input_first_frame.png")
last_frame = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flf2v_input_last_frame.png")
height = 512
width = 512
num_frames = 81
video, mask = prepare_video_and_mask(first_frame, last_frame, height, width, num_frames)
output = pipe(
    video=video,
    mask=mask,
    prompt=prompt,
    negative_prompt=negative_prompt,
    height=height,
    width=width,
    num_frames=num_frames,
    num_inference_steps=30,
    guidance_scale=5.0,
    generator=torch.Generator().manual_seed(42),
).frames[0]
export_to_video(output, "output.mp4", fps=16)

PS: Don't mind the extra hqq-4bit T5 checkpoint/configs in the TE folder. That is, unless you want to download and use it locally (it should definetely work for Flux, etc). I renamed the files to preclude accidental paths mixups.