review

• pig is a lazy architecture for gguf node; it applies to all model, encoder and vae gguf file(s); if you try to run it in comfyui-gguf node, you might need to manually add pig in it's IMG_ARCH_LIST (under loader.py); easier than you edit the gguf file itself; btw, model architecture which compatible with comfyui-gguf, including wan, should work in gguf node
• 1.3b model: t2v, vace gguf is working fine; good for old or low end machine

run it with diffusers🧨 (alternative 1)

import torch
from transformers import UMT5EncoderModel
from diffusers import AutoencoderKLWan, WanVACEPipeline, WanVACETransformer3DModel, GGUFQuantizationConfig
from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
from diffusers.utils import export_to_video

model_path = "https://huggingface.co/calcuis/wan-gguf/blob/main/wan2.1-v5-vace-1.3b-q4_0.gguf"
transformer = WanVACETransformer3DModel.from_single_file(
    model_path,
    quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
    torch_dtype=torch.bfloat16,
    )

text_encoder = UMT5EncoderModel.from_pretrained(
    "chatpig/umt5xxl-encoder-gguf",
    gguf_file="umt5xxl-encoder-q4_0.gguf",
    torch_dtype=torch.bfloat16,
    )

vae = AutoencoderKLWan.from_pretrained(
    "callgg/wan-decoder",
    subfolder="vae",
    torch_dtype=torch.float32
    )

pipe = WanVACEPipeline.from_pretrained(
    "callgg/wan-decoder",
    transformer=transformer,
    text_encoder=text_encoder,
    vae=vae, 
    torch_dtype=torch.bfloat16
)

flow_shift = 3.0
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=flow_shift)
pipe.enable_model_cpu_offload()
pipe.vae.enable_tiling()

prompt = "a pig moving quickly in a beautiful winter scenery nature trees sunset tracking camera"
negative_prompt = "blurry ugly bad"

output = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    width=720,
    height=480,
    num_frames=57,
    num_inference_steps=24,
    guidance_scale=2.5,
    conditioning_scale=0.0,
    generator=torch.Generator().manual_seed(0),
).frames[0]
export_to_video(output, "output.mp4", fps=16)

run it with gguf-connector (alternative 2)

ggc v2

update

• wan2.1-v5-vace-1.3b: except block weights, all in f32 status (avoid triggering time/text embedding key error for inference usage)

reference

• base model from wan-ai
• comfyui from comfyanonymous
• pig architecture from connector
• gguf-connector (pypi)
• gguf-node (pypi|repo|pack)