ce32073d07
- 面板: 会话持久化(oc_sess.json)/余额实时/文件栏(搜索+MD+拖拽)/朗读(edge-tts)/工具过程实时显示/漫剧画布 - 面板: 经验自动检索注入+存经验按钮(experience.py) - 管线: local_motion本地运镜/IMAGE-FIRST-GUIDE/LOCAL-PIPELINE-V1 - 经验: EED-EXPER-014 面板工程+管线+飞书扒取全记录 - 资料: 飞书《清欢AIGC伪真人短剧全流程》归档
244 lines
9.5 KiB
Python
244 lines
9.5 KiB
Python
#!/usr/bin/env python3
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"""
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generate_assets_local.py — 本地ComfyUI批量生成角色/场景/道具资产图
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替代线上Seedream API（¥0.15-1/张）→ 本地ComfyUI（¥0电费）
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用法:
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python tools/generate_assets_local.py <项目目录> [--comfy-url URL]
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python tools/generate_assets_local.py <项目目录> --list # 列出待生成的资产
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"""
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import sys, os, json, argparse, subprocess
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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# 默认资产生成prompt模板
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ASSET_PROMPTS = {
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"CHAR": {
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"template": "{name}, {description}, {style}, full body character design, character sheet, consistent clothing, clean background, high quality, detailed",
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"default_style": "digital art, anime style, vibrant colors"
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},
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"ENV": {
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"template": "{name}, {description}, {style}, wide angle view, environmental design, detailed scene, high quality",
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"default_style": "digital painting, cinematic lighting, detailed environment"
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},
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"PROP": {
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"template": "{name}, {description}, {style}, isolated object, clean background, detailed, high quality",
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"default_style": "product photography, sharp focus, detailed texture"
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}
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}
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def generate_workflow(prompt, asset_type, asset_name, output_prefix="asset"):
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"""生成ComfyUI API调用JSON"""
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workflow = {
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"3": {
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"class_type": "KSampler",
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"inputs": {
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"seed": abs(hash(asset_name)) % 1000000,
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"steps": 25,
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"cfg": 7,
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"sampler_name": "euler",
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"scheduler": "normal",
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"denoise": 1,
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"model": ["4", 0],
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"positive": ["6", 0],
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"negative": ["7", 0],
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"latent_image": ["5", 0]
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}
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},
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"4": {
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"class_type": "CheckpointLoaderSimple",
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"inputs": {"ckpt_name": "realvisxlV40_v40BDFPonNoobVae.safetensors"}
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},
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"5": {
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"class_type": "EmptyLatentImage",
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"inputs": {"width": 1024, "height": 1024, "batch_size": 1}
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},
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"6": {
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"class_type": "CLIPTextEncode",
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"inputs": {"text": prompt, "clip": ["4", 1]}
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},
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"7": {
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"class_type": "CLIPTextEncode",
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"inputs": {"text": "worst quality, low quality, blurry, deformed", "clip": ["4", 1]}
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},
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"8": {
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"class_type": "VAEDecode",
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"inputs": {"samples": ["3", 0], "vae": ["4", 2]}
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},
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"9": {
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"class_type": "SaveImage",
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"inputs": {"filename_prefix": f"{output_prefix}_{asset_name}", "images": ["8", 0]}
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}
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}
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return workflow
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def load_asset_list(project_dir):
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"""加载项目资产清单"""
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# 检查是否有 ASSET-LIST 文件
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for pattern in ['ASSET-LIST*', 'assets*.json', 'CHARACTERS*']:
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import glob
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matches = glob.glob(os.path.join(project_dir, pattern))
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if matches:
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with open(matches[0], 'r', encoding='utf-8') as f:
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return json.load(f)
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# 检查项目protocols目录
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protocols_dir = os.path.join(project_dir, 'protocols')
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if os.path.isdir(protocols_dir):
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for fname in os.listdir(protocols_dir):
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if 'ASSET' in fname.upper() or 'CHAR' in fname.upper():
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with open(os.path.join(protocols_dir, fname), 'r', encoding='utf-8') as f:
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content = f.read()
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try:
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return json.loads(content)
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except:
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pass
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return None
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def scan_project_assets(project_dir):
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"""扫描项目目录，识别需要生成的资产"""
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assets = []
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# 检查 characters/ envs/ props/ 目录
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for asset_type, subdir in [('CHAR', 'characters'), ('ENV', 'envs'), ('PROP', 'props')]:
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asset_dir = os.path.join(project_dir, 'assets', subdir)
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if os.path.isdir(asset_dir):
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for f in os.listdir(asset_dir):
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if f.endswith(('.json', '.hdlp', '.txt')):
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with open(os.path.join(asset_dir, f), 'r', encoding='utf-8') as fh:
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content = fh.read()
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assets.append({
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'type': asset_type,
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'name': os.path.splitext(f)[0],
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'description': content[:200],
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'file': os.path.join(asset_dir, f)
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})
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# 检查分镜JSON中出现的角色/场景
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for root, dirs, files in os.walk(project_dir):
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for f in files:
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if f.startswith('STORYBOARD') and f.endswith('.json'):
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try:
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with open(os.path.join(root, f), 'r', encoding='utf-8') as fh:
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sb = json.load(fh)
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for shot in sb.get('shots', []):
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for char in shot.get('characters', []):
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if not any(a['name'] == char for a in assets):
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assets.append({
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'type': 'CHAR',
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'name': char,
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'description': f'角色 {char}',
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'source': f
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})
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for scene in shot.get('scenes', []):
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if not any(a['name'] == scene for a in assets):
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assets.append({
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'type': 'ENV',
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'name': scene,
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'description': f'场景 {scene}',
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'source': f
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})
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except:
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pass
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return assets
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def main():
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parser = argparse.ArgumentParser(description='本地ComfyUI批量生成资产图')
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parser.add_argument('project_dir', help='项目目录')
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parser.add_argument('--comfy-url', default='http://127.0.0.1:8188', help='ComfyUI API地址')
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parser.add_argument('--style', default='', help='全局风格')
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parser.add_argument('--list', action='store_true', help='只列出待生成资产')
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parser.add_argument('--output-dir', help='资产输出目录 (默认: project_dir/assets/)')
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parser.add_argument('--dry-run', action='store_true', help='只生成workflow JSON，不调API')
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args = parser.parse_args()
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if not os.path.isdir(args.project_dir):
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print(f'❌ 项目目录不存在: {args.project_dir}')
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sys.exit(1)
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# 扫描资产
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assets = scan_project_assets(args.project_dir)
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if not assets:
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print('⚠️ 未发现需要生成的资产')
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print(' 提示: 将角色描述放在 assets/characters/ 目录下')
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print(' 或: 先运行 storyboard_to_workflow.py 生成分镜后再扫描')
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sys.exit(0)
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# 去重
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seen = set()
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unique_assets = []
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for a in assets:
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key = f"{a['type']}-{a['name']}"
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if key not in seen:
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seen.add(key)
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unique_assets.append(a)
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assets = unique_assets
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print(f'📦 发现 {len(assets)} 个待生成资产:')
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for a in assets:
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print(f' [{a["type"]}] {a["name"]:20s} {a.get("description", "")[:40]}')
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if args.list:
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return
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# 输出目录
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out_dir = args.output_dir or os.path.join(args.project_dir, 'assets')
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assets_char_dir = os.path.join(out_dir, 'characters')
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assets_env_dir = os.path.join(out_dir, 'envs')
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assets_prop_dir = os.path.join(out_dir, 'props')
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for d in [assets_char_dir, assets_env_dir, assets_prop_dir]:
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os.makedirs(d, exist_ok=True)
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# 生成并调用
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for i, asset in enumerate(assets):
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atype = asset['type']
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name = asset['name']
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desc = asset.get('description', name)
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style = args.style or ASSET_PROMPTS.get(atype, {}).get('default_style', '')
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template = ASSET_PROMPTS.get(atype, {}).get('template', '{description}, {style}')
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prompt = template.format(name=name, description=desc, style=style)
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print(f'\n🎨 [{i+1}/{len(assets)}] {name} ({atype})')
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print(f' Prompt: {prompt[:80]}...')
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workflow = generate_workflow(prompt, atype, name)
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# 确定输出位置
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dir_map = {'CHAR': assets_char_dir, 'ENV': assets_env_dir, 'PROP': assets_prop_dir}
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asset_out_dir = dir_map.get(atype, out_dir)
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wf_path = os.path.join(asset_out_dir, f'_{name}_workflow.json')
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with open(wf_path, 'w', encoding='utf-8') as f:
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json.dump(workflow, f, ensure_ascii=False, indent=2)
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if args.dry_run:
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print(f' ✅ 工作流JSON: {wf_path}')
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continue
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# 调ComfyUI API
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print(f' 📤 发送ComfyUI...')
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try:
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resp = subprocess.run([
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'curl', '-s', '-X', 'POST',
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f'{args.comfy_url}/prompt',
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'-H', 'Content-Type: application/json',
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'-d', json.dumps({"prompt": workflow})
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], capture_output=True, text=True, timeout=30)
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result = json.loads(resp.stdout) if resp.stdout else {}
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if 'error' in result:
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print(f' ❌ API错误: {result["error"]}')
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else:
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print(f' ✅ 已提交 (task: {result.get("task_id", "?")})')
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except Exception as e:
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print(f' ⚠️ 调用失败: {e}')
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print(f'\n✅ 完成! 资产将保存到: {out_dir}')
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if __name__ == '__main__':
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main()
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