wan_t2v_fp32_example.py

import torch
from transformers import AutoTokenizer, UMT5EncoderModel
from diffusers import AutoencoderKLWan, WanPipeline, WanTransformer3DModel, FlowMatchEulerDiscreteScheduler
from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
from diffusers.utils import export_to_video
from torchvision import transforms
import os
import cv2
import numpy as np


from pathlib import Path
import json
from safetensors.torch import safe_open

device = "cuda"
seed = 0

# TODO: impl AutoencoderKLWan
vae = vae.from_pretrained("StevenZhang/Wan2.1-VAE_Diff")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
vae = vae.to(device)

# TODO: impl FlowDPMSolverMultistepScheduler
scheduler = UniPCMultistepScheduler(prediction_type='flow_prediction', use_flow_sigmas=True, num_train_timesteps=1000, flow_shift=1.0)

text_encoder = UMT5EncoderModel.from_pretrained("google/umt5-xxl", torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained("google/umt5-xxl")

# 14B
transformer = WanTransformer3DModel.from_pretrained('StevenZhang/Wan2.1-T2V-14B-Diff', torch_dtype=torch.bfloat16)
# transformer = WanTransformer3DModel.from_pretrained('StevenZhang/Wan2.1-T2V-1.3B-Diff', torch_dtype=torch.bfloat16)

components = {
    "transformer": transformer,
    "vae": vae,
    "scheduler": scheduler,
    "text_encoder": text_encoder,
    "tokenizer": tokenizer,
}
pipe = WanPipeline(**components)

pipe.to(device)

negative_prompt = '色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走'

generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
    "prompt": "两只拟人化的猫咪身穿舒适的拳击装备，戴着鲜艳的手套，在聚光灯照射的舞台上激烈对战",
    "negative_prompt": negative_prompt, # TODO
    "generator": generator,
    "num_inference_steps": 50,
    "flow_shift": 3.0,
    "guidance_scale": 5.0,
    "height": 480,
    "width": 832,
    "num_frames": 81,
    "max_sequence_length": 512,
    "output_type": "np"
}

video = pipe(**inputs).frames[0]

print(video.shape)

export_to_video(video, "output.mp4", fps=16)