mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-25 07:05:43 -03:00
600 lines
26 KiB
Python
600 lines
26 KiB
Python
import os
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import logging
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import sys
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from aiohttp import web
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from typing import Dict
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import tempfile
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import json
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import aiohttp
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import asyncio
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from ..utils.exif_utils import ExifUtils
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from ..services.civitai_client import CivitaiClient
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from ..services.recipe_scanner import RecipeScanner
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from ..services.lora_scanner import LoraScanner
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from ..config import config
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import time # Add this import at the top
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logger = logging.getLogger(__name__)
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print("Recipe Routes module loaded", file=sys.stderr)
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class RecipeRoutes:
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"""API route handlers for Recipe management"""
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def __init__(self):
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print("Initializing RecipeRoutes", file=sys.stderr)
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self.recipe_scanner = RecipeScanner(LoraScanner())
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self.civitai_client = CivitaiClient()
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# Pre-warm the cache
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self._init_cache_task = None
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@classmethod
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def setup_routes(cls, app: web.Application):
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"""Register API routes"""
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print("Setting up recipe routes", file=sys.stderr)
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routes = cls()
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app.router.add_get('/api/recipes', routes.get_recipes)
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app.router.add_get('/api/recipe/{recipe_id}', routes.get_recipe_detail)
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app.router.add_post('/api/recipes/analyze-image', routes.analyze_recipe_image)
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app.router.add_post('/api/recipes/save', routes.save_recipe)
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app.router.add_delete('/api/recipe/{recipe_id}', routes.delete_recipe)
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# Add new filter-related endpoints
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app.router.add_get('/api/recipes/top-tags', routes.get_top_tags)
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app.router.add_get('/api/recipes/base-models', routes.get_base_models)
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# Start cache initialization
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app.on_startup.append(routes._init_cache)
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print("Recipe routes setup complete", file=sys.stderr)
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async def _init_cache(self, app):
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"""Initialize cache on startup"""
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print("Pre-warming recipe cache...", file=sys.stderr)
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try:
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# First, ensure the lora scanner is fully initialized
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print("Initializing lora scanner...", file=sys.stderr)
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lora_scanner = self.recipe_scanner._lora_scanner
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# Get lora cache to ensure it's initialized
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lora_cache = await lora_scanner.get_cached_data()
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print(f"Lora scanner initialized with {len(lora_cache.raw_data)} loras", file=sys.stderr)
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# Verify hash index is built
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if hasattr(lora_scanner, '_hash_index'):
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hash_index_size = len(lora_scanner._hash_index._hash_to_path) if hasattr(lora_scanner._hash_index, '_hash_to_path') else 0
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print(f"Lora hash index contains {hash_index_size} entries", file=sys.stderr)
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# Now that lora scanner is initialized, initialize recipe cache
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print("Initializing recipe cache...", file=sys.stderr)
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await self.recipe_scanner.get_cached_data(force_refresh=True)
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print("Recipe cache pre-warming complete", file=sys.stderr)
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except Exception as e:
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print(f"Error pre-warming recipe cache: {e}", file=sys.stderr)
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logger.error(f"Error pre-warming recipe cache: {e}", exc_info=True)
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async def get_recipes(self, request: web.Request) -> web.Response:
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"""API endpoint for getting paginated recipes"""
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try:
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print("API: GET /api/recipes", file=sys.stderr)
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# Get query parameters with defaults
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page = int(request.query.get('page', '1'))
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page_size = int(request.query.get('page_size', '20'))
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sort_by = request.query.get('sort_by', 'date')
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search = request.query.get('search', None)
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# Get filter parameters
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base_models = request.query.get('base_models', None)
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tags = request.query.get('tags', None)
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# Parse filter parameters
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filters = {}
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if base_models:
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filters['base_model'] = base_models.split(',')
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if tags:
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filters['tags'] = tags.split(',')
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# Get paginated data
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result = await self.recipe_scanner.get_paginated_data(
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page=page,
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page_size=page_size,
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sort_by=sort_by,
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search=search,
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filters=filters
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)
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# Format the response data with static URLs for file paths
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for item in result['items']:
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# Always ensure file_url is set
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if 'file_path' in item:
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item['file_url'] = self._format_recipe_file_url(item['file_path'])
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else:
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item['file_url'] = '/loras_static/images/no-preview.png'
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# 确保 loras 数组存在
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if 'loras' not in item:
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item['loras'] = []
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# 确保有 base_model 字段
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if 'base_model' not in item:
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item['base_model'] = ""
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return web.json_response(result)
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except Exception as e:
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logger.error(f"Error retrieving recipes: {e}", exc_info=True)
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print(f"API Error: {e}", file=sys.stderr)
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return web.json_response({"error": str(e)}, status=500)
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async def get_recipe_detail(self, request: web.Request) -> web.Response:
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"""Get detailed information about a specific recipe"""
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try:
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recipe_id = request.match_info['recipe_id']
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# Get all recipes from cache
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cache = await self.recipe_scanner.get_cached_data()
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# Find the specific recipe
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recipe = next((r for r in cache.raw_data if str(r.get('id', '')) == recipe_id), None)
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if not recipe:
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return web.json_response({"error": "Recipe not found"}, status=404)
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# Format recipe data
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formatted_recipe = self._format_recipe_data(recipe)
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return web.json_response(formatted_recipe)
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except Exception as e:
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logger.error(f"Error retrieving recipe details: {e}", exc_info=True)
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return web.json_response({"error": str(e)}, status=500)
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def _format_recipe_file_url(self, file_path: str) -> str:
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"""Format file path for recipe image as a URL"""
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try:
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# Return the file URL directly for the first lora root's preview
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recipes_dir = os.path.join(config.loras_roots[0], "recipes").replace(os.sep, '/')
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if file_path.replace(os.sep, '/').startswith(recipes_dir):
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relative_path = os.path.relpath(file_path, config.loras_roots[0]).replace(os.sep, '/')
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return f"/loras_static/root1/preview/{relative_path}"
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# If not in recipes dir, try to create a valid URL from the file path
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file_name = os.path.basename(file_path)
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return f"/loras_static/root1/preview/recipes/{file_name}"
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except Exception as e:
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logger.error(f"Error formatting recipe file URL: {e}", exc_info=True)
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return '/loras_static/images/no-preview.png' # Return default image on error
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def _format_recipe_data(self, recipe: Dict) -> Dict:
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"""Format recipe data for API response"""
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formatted = {**recipe} # Copy all fields
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# Format file paths to URLs
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if 'file_path' in formatted:
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formatted['file_url'] = self._format_recipe_file_url(formatted['file_path'])
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# Format dates for display
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for date_field in ['created_date', 'modified']:
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if date_field in formatted:
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formatted[f"{date_field}_formatted"] = self._format_timestamp(formatted[date_field])
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return formatted
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def _format_timestamp(self, timestamp: float) -> str:
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"""Format timestamp for display"""
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from datetime import datetime
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return datetime.fromtimestamp(timestamp).strftime('%Y-%m-%d %H:%M:%S')
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async def analyze_recipe_image(self, request: web.Request) -> web.Response:
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"""Analyze an uploaded image for recipe metadata"""
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temp_path = None
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try:
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reader = await request.multipart()
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field = await reader.next()
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if field.name != 'image':
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return web.json_response({
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"error": "No image field found",
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"loras": []
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}, status=400)
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# Create a temporary file to store the uploaded image
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with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as temp_file:
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while True:
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chunk = await field.read_chunk()
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if not chunk:
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break
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temp_file.write(chunk)
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temp_path = temp_file.name
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# Extract metadata from the image using ExifUtils
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user_comment = ExifUtils.extract_user_comment(temp_path)
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# If no metadata found, return a more specific error
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if not user_comment:
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return web.json_response({
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"error": "No metadata found in this image",
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"loras": [] # Return empty loras array to prevent client-side errors
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}, status=200) # Return 200 instead of 400 to handle gracefully
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# Parse the recipe metadata
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metadata = ExifUtils.parse_recipe_metadata(user_comment)
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# Look for Civitai resources in the metadata
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civitai_resources = metadata.get('loras', [])
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checkpoint = metadata.get('checkpoint')
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if not civitai_resources and not checkpoint:
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return web.json_response({
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"error": "No LoRA information found in this image",
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"loras": [] # Return empty loras array
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}, status=200) # Return 200 instead of 400
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# Process the resources to get LoRA information
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loras = []
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base_model = None
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# Process LoRAs and collect base models
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base_model_counts = {}
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loras = []
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# Process LoRAs
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for resource in civitai_resources:
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# Get model version ID
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model_version_id = resource.get('modelVersionId')
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if not model_version_id:
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continue
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# Get additional info from Civitai
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civitai_info = await self.civitai_client.get_model_version_info(model_version_id)
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# Initialize lora entry with default values
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lora_entry = {
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'id': model_version_id,
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'name': resource.get('modelName', ''),
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'version': resource.get('modelVersionName', ''),
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'type': resource.get('type', 'lora'),
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'weight': resource.get('weight', 1.0),
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'existsLocally': False,
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'localPath': None,
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'file_name': '',
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'hash': '',
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'thumbnailUrl': '',
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'baseModel': '',
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'size': 0,
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'downloadUrl': '',
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'isDeleted': False # New flag to indicate if the LoRA is deleted from Civitai
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}
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# Check if this LoRA exists locally by SHA256 hash
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if civitai_info and civitai_info.get("error") != "Model not found":
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# LoRA exists on Civitai, process its information
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if 'files' in civitai_info:
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# Find the model file (type="Model") in the files list
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model_file = next((file for file in civitai_info.get('files', [])
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if file.get('type') == 'Model'), None)
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if model_file:
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sha256 = model_file.get('hashes', {}).get('SHA256', '')
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if sha256:
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exists_locally = self.recipe_scanner._lora_scanner.has_lora_hash(sha256)
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if exists_locally:
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local_path = self.recipe_scanner._lora_scanner.get_lora_path_by_hash(sha256)
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lora_entry['existsLocally'] = True
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lora_entry['localPath'] = local_path
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lora_entry['file_name'] = os.path.splitext(os.path.basename(local_path))[0]
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else:
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# For missing LoRAs, get file_name from model_file.name
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file_name = model_file.get('name', '')
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lora_entry['file_name'] = os.path.splitext(file_name)[0] if file_name else ''
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lora_entry['hash'] = sha256
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lora_entry['size'] = model_file.get('sizeKB', 0) * 1024
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# Get thumbnail URL from first image
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if 'images' in civitai_info and civitai_info['images']:
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lora_entry['thumbnailUrl'] = civitai_info['images'][0].get('url', '')
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# Get base model and update counts
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current_base_model = civitai_info.get('baseModel', '')
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lora_entry['baseModel'] = current_base_model
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if current_base_model:
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base_model_counts[current_base_model] = base_model_counts.get(current_base_model, 0) + 1
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# Get download URL
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lora_entry['downloadUrl'] = civitai_info.get('downloadUrl', '')
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else:
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# LoRA is deleted from Civitai or not found
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lora_entry['isDeleted'] = True
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lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'
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loras.append(lora_entry)
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# Set base_model to the most common one from civitai_info
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if base_model_counts:
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base_model = max(base_model_counts.items(), key=lambda x: x[1])[0]
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# Extract generation parameters for recipe metadata
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gen_params = {
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'prompt': metadata.get('prompt', ''),
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'negative_prompt': metadata.get('negative_prompt', ''),
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'checkpoint': checkpoint,
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'steps': metadata.get('steps', ''),
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'sampler': metadata.get('sampler', ''),
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'cfg_scale': metadata.get('cfg_scale', ''),
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'seed': metadata.get('seed', ''),
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'size': metadata.get('size', ''),
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'clip_skip': metadata.get('clip_skip', '')
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}
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return web.json_response({
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'base_model': base_model,
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'loras': loras,
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'gen_params': gen_params,
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'raw_metadata': metadata # Include the raw metadata for saving
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})
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except Exception as e:
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logger.error(f"Error analyzing recipe image: {e}", exc_info=True)
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return web.json_response({
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"error": str(e),
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"loras": [] # Return empty loras array to prevent client-side errors
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}, status=500)
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finally:
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# Clean up the temporary file in the finally block
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if temp_path and os.path.exists(temp_path):
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try:
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os.unlink(temp_path)
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except Exception as e:
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logger.error(f"Error deleting temporary file: {e}")
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async def save_recipe(self, request: web.Request) -> web.Response:
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"""Save a recipe to the recipes folder"""
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try:
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reader = await request.multipart()
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# Process form data
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image = None
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name = None
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tags = []
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metadata = None
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while True:
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field = await reader.next()
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if field is None:
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break
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if field.name == 'image':
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# Read image data
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image_data = b''
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while True:
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chunk = await field.read_chunk()
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if not chunk:
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break
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image_data += chunk
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image = image_data
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elif field.name == 'name':
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name = await field.text()
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elif field.name == 'tags':
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tags_text = await field.text()
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try:
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tags = json.loads(tags_text)
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except:
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tags = []
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elif field.name == 'metadata':
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metadata_text = await field.text()
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try:
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metadata = json.loads(metadata_text)
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except:
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metadata = {}
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if not image or not name or not metadata:
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return web.json_response({"error": "Missing required fields"}, status=400)
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# Create recipes directory if it doesn't exist
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recipes_dir = self.recipe_scanner.recipes_dir
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os.makedirs(recipes_dir, exist_ok=True)
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# Generate UUID for the recipe
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import uuid
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recipe_id = str(uuid.uuid4())
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# Save the image
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image_ext = ".jpg"
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image_filename = f"{recipe_id}{image_ext}"
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image_path = os.path.join(recipes_dir, image_filename)
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with open(image_path, 'wb') as f:
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f.write(image)
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# Create the recipe JSON
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current_time = time.time()
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# Format loras data according to the recipe.json format
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loras_data = []
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for lora in metadata.get("loras", []):
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# Skip deleted LoRAs if they're marked to be excluded
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if lora.get("isDeleted", False) and lora.get("exclude", False):
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continue
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# Convert frontend lora format to recipe format
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lora_entry = {
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"file_name": lora.get("file_name", "") or os.path.splitext(os.path.basename(lora.get("localPath", "")))[0],
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"hash": lora.get("hash", "").lower() if lora.get("hash") else "",
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"strength": float(lora.get("weight", 1.0)),
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"modelVersionId": lora.get("id", ""),
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"modelName": lora.get("name", ""),
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"modelVersionName": lora.get("version", ""),
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"isDeleted": lora.get("isDeleted", False) # Preserve deletion status in saved recipe
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}
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loras_data.append(lora_entry)
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# Format gen_params according to the recipe.json format
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gen_params = metadata.get("gen_params", {})
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if not gen_params and "raw_metadata" in metadata:
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# Extract from raw metadata if available
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raw_metadata = metadata.get("raw_metadata", {})
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gen_params = {
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"prompt": raw_metadata.get("prompt", ""),
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"negative_prompt": raw_metadata.get("negative_prompt", ""),
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"checkpoint": raw_metadata.get("checkpoint", {}),
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"steps": raw_metadata.get("steps", ""),
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"sampler": raw_metadata.get("sampler", ""),
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"cfg_scale": raw_metadata.get("cfg_scale", ""),
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"seed": raw_metadata.get("seed", ""),
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"size": raw_metadata.get("size", ""),
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"clip_skip": raw_metadata.get("clip_skip", "")
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}
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# Create the recipe data structure
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recipe_data = {
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"id": recipe_id,
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"file_path": image_path,
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"title": name,
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"modified": current_time,
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"created_date": current_time,
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"base_model": metadata.get("base_model", ""),
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"loras": loras_data,
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"gen_params": gen_params
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}
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# Add tags if provided
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if tags:
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recipe_data["tags"] = tags
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# Save the recipe JSON
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json_filename = f"{recipe_id}.recipe.json"
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json_path = os.path.join(recipes_dir, json_filename)
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with open(json_path, 'w', encoding='utf-8') as f:
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json.dump(recipe_data, f, indent=4, ensure_ascii=False)
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# Simplified cache update approach
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# Instead of trying to update the cache directly, just set it to None
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# to force a refresh on the next get_cached_data call
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if self.recipe_scanner._cache is not None:
|
|
# Add the recipe to the raw data if the cache exists
|
|
# This is a simple direct update without locks or timeouts
|
|
self.recipe_scanner._cache.raw_data.append(recipe_data)
|
|
# Schedule a background task to resort the cache
|
|
asyncio.create_task(self.recipe_scanner._cache.resort())
|
|
logger.info(f"Added recipe {recipe_id} to cache")
|
|
|
|
return web.json_response({
|
|
'success': True,
|
|
'recipe_id': recipe_id,
|
|
'image_path': image_path,
|
|
'json_path': json_path
|
|
})
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error saving recipe: {e}", exc_info=True)
|
|
return web.json_response({"error": str(e)}, status=500)
|
|
|
|
async def delete_recipe(self, request: web.Request) -> web.Response:
|
|
"""Delete a recipe by ID"""
|
|
try:
|
|
recipe_id = request.match_info['recipe_id']
|
|
|
|
# Get recipes directory
|
|
recipes_dir = self.recipe_scanner.recipes_dir
|
|
if not recipes_dir or not os.path.exists(recipes_dir):
|
|
return web.json_response({"error": "Recipes directory not found"}, status=404)
|
|
|
|
# Find recipe JSON file
|
|
recipe_json_path = os.path.join(recipes_dir, f"{recipe_id}.recipe.json")
|
|
if not os.path.exists(recipe_json_path):
|
|
return web.json_response({"error": "Recipe not found"}, status=404)
|
|
|
|
# Load recipe data to get image path
|
|
with open(recipe_json_path, 'r', encoding='utf-8') as f:
|
|
recipe_data = json.load(f)
|
|
|
|
# Get image path
|
|
image_path = recipe_data.get('file_path')
|
|
|
|
# Delete recipe JSON file
|
|
os.remove(recipe_json_path)
|
|
logger.info(f"Deleted recipe JSON file: {recipe_json_path}")
|
|
|
|
# Delete recipe image if it exists
|
|
if image_path and os.path.exists(image_path):
|
|
os.remove(image_path)
|
|
logger.info(f"Deleted recipe image: {image_path}")
|
|
|
|
# Simplified cache update approach
|
|
if self.recipe_scanner._cache is not None:
|
|
# Remove the recipe from raw_data if it exists
|
|
self.recipe_scanner._cache.raw_data = [
|
|
r for r in self.recipe_scanner._cache.raw_data
|
|
if str(r.get('id', '')) != recipe_id
|
|
]
|
|
# Schedule a background task to resort the cache
|
|
asyncio.create_task(self.recipe_scanner._cache.resort())
|
|
logger.info(f"Removed recipe {recipe_id} from cache")
|
|
|
|
return web.json_response({"success": True, "message": "Recipe deleted successfully"})
|
|
except Exception as e:
|
|
logger.error(f"Error deleting recipe: {e}", exc_info=True)
|
|
return web.json_response({"error": str(e)}, status=500)
|
|
|
|
async def get_top_tags(self, request: web.Request) -> web.Response:
|
|
"""Get top tags used in recipes"""
|
|
try:
|
|
# Get limit parameter with default
|
|
limit = int(request.query.get('limit', '20'))
|
|
|
|
# Get all recipes from cache
|
|
cache = await self.recipe_scanner.get_cached_data()
|
|
|
|
# Count tag occurrences
|
|
tag_counts = {}
|
|
for recipe in cache.raw_data:
|
|
if 'tags' in recipe and recipe['tags']:
|
|
for tag in recipe['tags']:
|
|
tag_counts[tag] = tag_counts.get(tag, 0) + 1
|
|
|
|
# Sort tags by count and limit results
|
|
sorted_tags = [{'tag': tag, 'count': count} for tag, count in tag_counts.items()]
|
|
sorted_tags.sort(key=lambda x: x['count'], reverse=True)
|
|
top_tags = sorted_tags[:limit]
|
|
|
|
return web.json_response({
|
|
'success': True,
|
|
'tags': top_tags
|
|
})
|
|
except Exception as e:
|
|
logger.error(f"Error retrieving top tags: {e}", exc_info=True)
|
|
return web.json_response({
|
|
'success': False,
|
|
'error': str(e)
|
|
}, status=500)
|
|
|
|
async def get_base_models(self, request: web.Request) -> web.Response:
|
|
"""Get base models used in recipes"""
|
|
try:
|
|
# Get all recipes from cache
|
|
cache = await self.recipe_scanner.get_cached_data()
|
|
|
|
# Count base model occurrences
|
|
base_model_counts = {}
|
|
for recipe in cache.raw_data:
|
|
if 'base_model' in recipe and recipe['base_model']:
|
|
base_model = recipe['base_model']
|
|
base_model_counts[base_model] = base_model_counts.get(base_model, 0) + 1
|
|
|
|
# Sort base models by count
|
|
sorted_models = [{'name': model, 'count': count} for model, count in base_model_counts.items()]
|
|
sorted_models.sort(key=lambda x: x['count'], reverse=True)
|
|
|
|
return web.json_response({
|
|
'success': True,
|
|
'base_models': sorted_models
|
|
})
|
|
except Exception as e:
|
|
logger.error(f"Error retrieving base models: {e}", exc_info=True)
|
|
return web.json_response({
|
|
'success': False,
|
|
'error': str(e)
|
|
}, status=500) |