企业官网建设流程全解析

系统：Windows10/11 23H2+ 显卡：NVIDIA 8G/12G CUDA显卡 内存：≥16G（批量量产建议32G） 磁盘：SSD≥40G空闲空间（模型读写必须SSD） 运行模式：纯本地离线部署，无网络推理

git clone https://github.com/comfyanonymous/ComfyUI.git cd ComfyUI pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

cd custom_nodes # 模型管理核心 git clone https://github.com/ltdrdata/ComfyUI-Manager.git # 批量任务、文本解析 git clone https://github.com/Impact-Pack/ComfyUI-Impact-Pack.git # 显存分块、VAE优化 git clone https://github.com/comfyanonymous/ComfyUI_VAE_Tiled.git

ComfyUI/models/ ├─ checkpoints/ # 放置SDXL1.0 FP8量化底模 ├─ loras/ # 国漫/日漫/短剧专属LoRA ├─ vae/ # SDXL专用漫剧VAE ├─ embeddings/ # 正负向提示词嵌入 └─ workflows/ # AI漫剧标准化工作流模板

@echo off chcp 65001 echo ===== SDXL AI漫剧 8G显存优化启动 ===== :: 显存分片核心优化，解决显存碎片化 set PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256,expandable_segments:True :: 关闭冗余显存缓存 set TORCH_USE_CUDA_DSA=0 :: 重定向缓存目录，防止C盘爆满 set COMFYUI_CACHE_DIR=%cd%\cache set COMFYUI_OUTPUT_DIR=%cd%\output_comic :: 低显存强制优化启动参数 python main.py --lowvram --vae-tile --fp8-storage --listen --port 8188 pause

# SDXL AI漫剧 8G显存固定工程参数 COMIC_CONFIG = { "width": 768, "height": 1024, "steps": 24, "cfg_scale": 6.5, "sampler": "dpmpp_2m_sde", "scheduler": "karras", "vae_tile_size": 384, "loras_weight": 0.55, "ip_adapter_weight": 0.68 }

import json import requests import time import torch import gc import logging # 服务配置 COMFY_API = "http://127.0.0.1:8188/prompt" WORKFLOW_PATH = "./workflows/sdxl_comic_workflow.json" LOG_PATH = "./logs/batch_run.log" # 日志初始化 logging.basicConfig(filename=LOG_PATH, level=logging.INFO, format="%(asctime)s-%(message)s", encoding="utf-8") # 显存手动回收函数（8G核心） def clear_vram(): torch.cuda.empty_cache() gc.collect() logging.info("显存缓存强制清理完成") # 加载漫剧工作流模板 def load_workflow(): with open(WORKFLOW_PATH, "r", encoding="utf-8") as f: return json.load(f) # 推送渲染任务 def submit_task(workflow): res = requests.post(COMFY_API, json={"prompt": workflow}, timeout=120) return res.json()["prompt_id"] # 阻塞等待单镜头完成（串行防OOM） def wait_finish(pid): while True: res = requests.get(f"http://127.0.0.1:8188/history/{pid}") if pid in res.json(): return True time.sleep(2) if __name__ == "__main__": workflow = load_workflow() # 自定义批量漫剧镜头文案（可批量拓展） scene_list = [ "高清国漫，少年男主侧脸，古风场景，柔光质感，统一人物人设，静态镜头", "高清国漫，少年男主正面，微表情，室内古风房间，写实漫剧质感", "高清国漫，双人对视，古风夜景，氛围感拉满，画风统一无变形" ] for idx, prompt in enumerate(scene_list): try: # 替换工作流提示词节点（根据自己节点ID修改） workflow["12"]["inputs"]["text"] = prompt pid = submit_task(workflow) wait_finish(pid) logging.info(f"第{idx+1}镜头渲染成功") clear_vram() except Exception as e: logging.error(f"第{idx+1}镜头异常：{str(e)}") clear_vram() continue logging.info("===== AI漫剧批量本地渲染全部完成 =====")

python comic_batch_render.py

# 连载漫剧角色固化参数 SEED_FIX = 889966 IP_WEIGHT = 0.68 LORA_FIX_WEIGHT = 0.55

# 强制释放所有显存残留 python -c "import torch,gc;torch.cuda.empty_cache();gc.collect()"

# 校验模型完整性，查看目录文件 dir models\checkpoints

@echo off for %%i in (output_comic\*.png) do ( ffmpeg -i "%%i" -vf scale=1080:1440:flags=lanczos -y "output_comic\hd_%%~ni.png" ) echo AI漫剧高清超分完成 pause