AI漫剧工程优化研究：Wan2.2-ReMixV3.0 漫剧视频生成人物闪烁与时序抖动抑制机制

from PIL import Image, ImageFilter
def preprocess_comic_image(input_img_path, target_size=(832,480)):
    img = Image.open(input_img_path).convert("RGB")
    # 等比例缩放+边缘平滑，消除原图锯齿噪点
    img = img.resize(target_size, Image.Resampling.LANCZOS)
    img = img.filter(ImageFilter.SMOOTH_MORE)
    return img

import torch
import os
from PIL import Image
from diffusers import Wan22ImageToVideoPipeline
from safetensors.torch import load_file
# =================时序稳定全局配置=================
MODEL_CFG = {
    "unet": "./wan22_remix_v3/Wan2.2‑ReMixV3.0_fp8.safetensors",
    "lora": "./wan22_remix_v3/lora/VBVR_consistency_lora.safetensors",
    "vae": "./wan22_remix_v3/wan_2.1_vae.safetensors",
    "text_encoder": "./wan22_remix_v3/umt5_xxl_fp8_e4m3fn.safetensors"
}
INFER_CFG = {
    "num_frames":24,
    "guidance_scale":5.5,
    "inference_steps":26,
    "lora_alpha":0.78,
    "resolution":(832,480),
    "fps":16,
    "seed":124
}
# 正向提示词：增加时序一致性强约束
POSITIVE_PROMPT = """2D manga flat illustration, consistent character facial features, fixed facial structure across all frames,
stable skin hue and brightness, unchanged comic outline, slow subtle micro movement, only slight body motion,
frame-by-frame color consistency, uniform ambient lighting, clean solid line art, no geometry deformation"""
# 负向提示词：定向抑制闪烁、漂移类缺陷
NEGATIVE_PROMPT = """flickering face, facial morphing, frame to frame color flicker, line jitter, character drift,
temporal shake, brightness oscillation, flashing, anatomy distortion, changing facial shape, noisy, extra limbs, watermark, text"""
torch.backends.cudnn.benchmark = True
# 固定随机种子，保证采样过程可复现，消除随机抖动
def set_global_seed(seed):
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    import random
    import numpy as np
    random.seed(seed)
    np.random.seed(seed)
def build_temporal_stable_pipeline():
    pipe = Wan22ImageToVideoPipeline.from_pretrained(
        pretrained_model_name_or_path=None,
        unet_pretrained_model_name_or_path=MODEL_CFG["unet"],
        vae_pretrained_model_name_or_path=MODEL_CFG["vae"],
        text_encoder_pretrained_model_name_or_path=MODEL_CFG["text_encoder"],
        torch_dtype=torch.float16,
        device_map="auto"
    )
    # 加载VBVR时序LoRA
    lora_state_dict = load_file(MODEL_CFG["lora"])
    pipe.load_lora_weights(lora_state_dict, lora_scale=INFER_CFG["lora_alpha"])
    # VAE与注意力切片，降低解码波动
    pipe.enable_vae_slicing()
    pipe.enable_attention_slicing()
    pipe.enable_model_cpu_offload()
    return pipe
def stable_comic_inference(pipe, img_path, out_video_path):
    set_global_seed(INFER_CFG["seed"])
    input_img = preprocess_comic_image(img_path, INFER_CFG["resolution"])
    try:
        result = pipe.image_to_video(
            image=input_img,
            prompt=POSITIVE_PROMPT,
            negative_prompt=NEGATIVE_PROMPT,
            num_inference_steps=INFER_CFG["inference_steps"],
            guidance_scale=INFER_CFG["guidance_scale"],
            num_frames=INFER_CFG["num_frames"]
        )
        result.save(out_video_path, result.frames[0], fps=INFER_CFG["fps"])
        print(f"✅ 推理完成，输出文件：{out_video_path}")
        return True
    except Exception as err:
        print(f"❌ 推理异常，错误信息：{err}")
        return False
if __name__ == "__main__":
    pipeline = build_temporal_stable_pipeline()
    stable_comic_inference(pipeline, "comic_source.png", "comic_temporal_stable.mp4")

$env:PYTORCH_CUDA_ALLOC_CONF="expandable_segments:True,max_split_size_mb:128,garbage_collection_threshold:0.8"

export PYTORCH_CUDA_ALLOC_CONF="expandable_segments:True,max_split_size_mb:128,garbage_collection_threshold:0.8"

torch.cuda.empty_cache()

import cv2
import numpy as np
from skimage.metrics import structural_similarity as ssim
def evaluate_video_temporal_ssim(video_file_path):
    cap = cv2.VideoCapture(video_file_path)
    frame_ssim_list = []
    ret, prev_rgb = cap.read()
    if not ret:
        print("视频读取失败，文件损坏或路径错误")
        return 0.0
    prev_gray = cv2.cvtColor(prev_rgb, cv2.COLOR_BGR2GRAY)
    while True:
        ret, curr_rgb = cap.read()
        if not ret:
            break
        curr_gray = cv2.cvtColor(curr_rgb, cv2.COLOR_BGR2GRAY)
        frame_ssim_value, _ = ssim(prev_gray, curr_gray, full=True)
        frame_ssim_list.append(frame_ssim_value)
        prev_gray = curr_gray
    cap.release()
    average_ssim = np.mean(frame_ssim_list)
    print(f"视频序列帧间平均SSIM = {average_ssim:.4f}")
    if average_ssim > 0.88:
        level = "✅ 时序优秀，无肉眼可感知人物闪烁，可直接用于漫剧成片"
    elif 0.80 <= average_ssim <= 0.88:
        level = "⚠️ 轻微时序抖动，可通过FFmpeg后处理平滑"
    else:
        level = "❌ 严重人物闪烁，需要重新调整推理参数"
    print(level)
    return average_ssim
if __name__ == "__main__":
    evaluate_video_temporal_ssim("comic_temporal_stable.mp4")

# temporalmean=5，取当前帧+前后2帧做均值平滑，抑制人物明暗闪烁
ffmpeg -i comic_temporal_stable.mp4 -vf "temporalmean=5" -c:a copy comic_temporal_smooth.mp4