333 lines
14 KiB
Python
333 lines
14 KiB
Python
import os
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import json
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import time
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from pathlib import Path
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from aiohttp import web
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from server import PromptServer
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import jinja2
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from safetensors import safe_open
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from .utils.file_utils import get_file_info, save_metadata, load_metadata, update_civitai_metadata
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from .utils.lora_metadata import extract_lora_metadata
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from typing import Dict, Optional
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from .services.civitai_client import CivitaiClient
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class LorasEndpoint:
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def __init__(self):
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self.template_env = jinja2.Environment(
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loader=jinja2.FileSystemLoader(
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os.path.join(os.path.dirname(__file__), 'templates')
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),
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autoescape=True
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)
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# 配置Loras根目录（根据实际安装位置调整）
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self.loras_root = os.path.join(Path(__file__).parents[2], "models", "loras")
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# 添加 server 属性
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self.server = PromptServer.instance
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@classmethod
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def add_routes(cls):
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instance = cls()
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app = PromptServer.instance.app
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static_path = os.path.join(os.path.dirname(__file__), 'static')
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app.add_routes([
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web.get('/loras', instance.handle_loras_request),
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web.static('/loras_static/previews', instance.loras_root),
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web.static('/loras_static', static_path),
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web.post('/api/delete_model', instance.delete_model),
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web.post('/api/fetch-civitai', instance.fetch_civitai)
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])
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def send_progress(self, current, total, status="Scanning"):
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"""Send progress through websocket"""
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try:
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if hasattr(self.server, 'send_sync'):
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self.server.send_sync("lora-scan-progress", {
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"value": current,
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"max": total,
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"status": status
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})
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except Exception as e:
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print(f"Error sending progress: {str(e)}")
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async def scan_loras(self):
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loras = []
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for root, _, files in os.walk(self.loras_root):
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safetensors_files = [f for f in files if f.endswith('.safetensors')]
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total_files = len(safetensors_files)
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for idx, filename in enumerate(safetensors_files, 1):
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self.send_progress(idx, total_files, f"Scanning: {filename}")
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file_path = os.path.join(root, filename)
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# Try to load existing metadata first
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metadata = await load_metadata(file_path)
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if metadata is None:
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# Only get file info and extract metadata if no existing metadata
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metadata = await get_file_info(file_path)
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base_model_info = await extract_lora_metadata(file_path)
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metadata.base_model = base_model_info['base_model']
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await save_metadata(file_path, metadata)
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# Convert to dict for API response
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lora_data = metadata.to_dict()
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# Get relative path and remove filename to get just the folder structure
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rel_path = os.path.relpath(file_path, self.loras_root)
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folder = os.path.dirname(rel_path)
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# Ensure forward slashes for consistency across platforms
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lora_data['folder'] = folder.replace(os.path.sep, '/')
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loras.append(lora_data)
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self.send_progress(total_files, total_files, "Scan completed")
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return loras
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def clean_description(self, desc):
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"""清理HTML格式的描述"""
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return desc.replace("<p>", "").replace("</p>", "\n").strip()
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async def get_preview_url(self, preview_path, root_dir):
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"""生成预览图URL"""
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if os.path.exists(preview_path):
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rel_path = os.path.relpath(preview_path, self.loras_root)
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return f"/loras_static/previews/{rel_path.replace(os.sep, '/')}"
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return "/loras_static/images/no-preview.png"
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async def handle_loras_request(self, request):
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"""处理Loras请求并渲染模板"""
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try:
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scan_start = time.time()
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data = await self.scan_loras()
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print(f"Scanned {len(data)} loras in {time.time()-scan_start:.2f}s")
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# Format the data for the template
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formatted_loras = [self.format_lora(l) for l in data]
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folders = sorted(list(set(l['folder'] for l in data)))
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# Debug logging
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if formatted_loras:
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print(f"Sample lora data: {formatted_loras[0]}")
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else:
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print("Warning: No loras found")
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context = {
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"loras": formatted_loras,
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"folders": folders,
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# Only set single lora if we're viewing details
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"lora": formatted_loras[0] if formatted_loras else {
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"model_name": "",
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"file_name": "",
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"preview_url": "",
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"folder": "",
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"civitai": {
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"id": "",
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"model": "",
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"base_model": "",
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"trained_words": [],
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"creator": "",
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"downloads": 0,
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"images": [],
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"description": ""
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}
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}
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}
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template = self.template_env.get_template('loras.html')
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rendered = template.render(**context)
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return web.Response(
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text=rendered,
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content_type='text/html'
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)
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except Exception as e:
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print(f"Error handling loras request: {str(e)}")
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import traceback
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print(traceback.format_exc()) # Print full stack trace
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return web.Response(
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text="Error loading loras page",
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content_type='text/html',
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status=500
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)
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def format_lora(self, lora):
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"""格式化前端需要的数据结构"""
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try:
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return {
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"model_name": lora["model_name"],
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"file_name": lora["file_name"],
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"preview_url": lora["preview_url"],
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"base_model": lora["base_model"],
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"folder": lora["folder"],
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"sha256": lora["sha256"],
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"file_path": lora["file_path"],
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"modified": lora["modified"],
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"civitai": lora.get("civitai", {}) or {} # 确保当 civitai 为 None 时返回空字典
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}
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except Exception as e:
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print(f"Error formatting lora: {str(e)}")
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print(f"Lora data: {lora}")
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return {
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"model_name": lora.get("model_name", "Unknown"),
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"file_name": lora.get("file_name", ""),
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"preview_url": lora.get("preview_url", ""),
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"base_model": lora.get("base_model", ""),
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"folder": lora.get("folder", ""),
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"sha256": lora.get("sha256", ""),
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"file_path": lora.get("file_path", ""),
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"modified": lora.get("modified", ""),
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"civitai": {
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"id": "",
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"modelId": "",
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"model": "",
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"base_model": "",
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"trained_words": [],
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"creator": "",
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"downloads": 0,
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"images": [],
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"description": ""
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}
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}
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async def delete_model(self, request):
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try:
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data = await request.json()
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file_name = data.get('file_name')
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folder = data.get('folder') # 从请求中获取folder信息
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if not file_name:
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return web.Response(text='Model name is required', status=400)
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# 构建完整的目录路径
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target_dir = self.loras_root
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if folder and folder != "root":
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target_dir = os.path.join(self.loras_root, folder)
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# List of file patterns to delete
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required_file = f"{file_name}.safetensors" # 主文件必须存在
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optional_files = [ # 这些文件可能不存在
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f"{file_name}.metadata.json",
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f"{file_name}.preview.png",
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f"{file_name}.preview.jpg",
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f"{file_name}.preview.jpeg",
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f"{file_name}.preview.webp"
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]
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deleted_files = []
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# Try to delete the main safetensors file
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main_file_path = os.path.join(target_dir, required_file)
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if os.path.exists(main_file_path):
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try:
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os.remove(main_file_path)
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deleted_files.append(required_file)
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except Exception as e:
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print(f"Error deleting {main_file_path}: {str(e)}")
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return web.Response(text=f"Failed to delete main model file: {str(e)}", status=500)
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# Only try to delete optional files if main file was deleted
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for pattern in optional_files:
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file_path = os.path.join(target_dir, pattern)
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if os.path.exists(file_path):
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try:
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os.remove(file_path)
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deleted_files.append(pattern)
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except Exception as e:
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print(f"Error deleting optional file {file_path}: {str(e)}")
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else:
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return web.Response(text=f"Model file {required_file} not found in {folder}", status=404)
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return web.json_response({
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'success': True,
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'deleted_files': deleted_files
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})
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except Exception as e:
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return web.Response(text=str(e), status=500)
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async def update_civitai_info(self, file_path: str, civitai_data: Dict, preview_url: Optional[str] = None):
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"""Update Civitai metadata and download preview image"""
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# Update metadata file
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await update_civitai_metadata(file_path, civitai_data)
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# Download and save preview image if URL is provided
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if preview_url:
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preview_path = f"{os.path.splitext(file_path)[0]}.preview.png"
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try:
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# Add your image download logic here
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# Example:
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# await download_image(preview_url, preview_path)
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pass
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except Exception as e:
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print(f"Error downloading preview image: {str(e)}")
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async def fetch_civitai(self, request):
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print("Received fetch-civitai request") # Debug log
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try:
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data = await request.json()
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print(f"Request data: {data}") # Debug log
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client = CivitaiClient()
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try:
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# 1. 获取CivitAI元数据
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civitai_metadata = await client.get_model_by_hash(data["sha256"])
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if not civitai_metadata:
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return web.json_response(
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{"success": False, "error": "Not found on CivitAI"},
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status=404
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)
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# 2. 读取/创建本地元数据文件
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metadata_path = os.path.splitext(data['file_path'])[0] + '.metadata.json'
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# 合并元数据
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local_metadata = {}
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if os.path.exists(metadata_path):
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with open(metadata_path, 'r', encoding='utf-8') as f:
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local_metadata = json.load(f)
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# 3. 更新元数据字段
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local_metadata['civitai']=civitai_metadata
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# 更新模型名称（优先使用CivitAI名称）
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if 'model' in civitai_metadata:
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local_metadata['model_name'] = civitai_metadata['model'].get('name', local_metadata.get('model_name'))
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# update base model
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local_metadata['base_model'] = civitai_metadata.get('baseModel')
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# 4. 下载预览图
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first_preview = next((img for img in civitai_metadata.get('images', [])), None)
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if first_preview:
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preview_extension = '.mp4' if first_preview['type'] == 'video' else os.path.splitext(first_preview['url'])[-1] # Get the file extension
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preview_filename = os.path.splitext(os.path.basename(data['file_path']))[0] + preview_extension
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preview_path = os.path.join(os.path.dirname(data['file_path']), preview_filename)
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await client.download_preview_image(first_preview['url'], preview_path)
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# 存储相对路径，使用正斜杠格式
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local_metadata['preview_url'] = os.path.relpath(preview_path, self.loras_root).replace(os.sep, '/')
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# 5. 保存更新后的元数据
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with open(metadata_path, 'w', encoding='utf-8') as f:
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json.dump(local_metadata, f, indent=2, ensure_ascii=False)
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return web.json_response({
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"success": True
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})
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except Exception as e:
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print(f"Error in fetch_civitai: {str(e)}") # Debug log
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return web.json_response({
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"success": False,
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"error": str(e)
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}, status=500)
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finally:
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await client.close()
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except Exception as e:
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print(f"Error processing request: {str(e)}") # Debug log
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return web.json_response({
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"success": False,
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"error": f"Request processing error: {str(e)}"
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}, status=400)
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# 注册路由
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LorasEndpoint.add_routes() |