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feat: enhance LoRA metadata handling by adding model IDs and updating recipe data structure. Fixes #246

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Will Miao
2025-06-24 11:12:21 +08:00
parent f6ef428008
commit d0ed1213d8
9 changed files with 38 additions and 58 deletions
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65
py/routes/recipe_routes.py
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@@ -648,7 +648,7 @@ class RecipeRoutes:
"file_name": lora.get("file_name", "") or os.path.splitext(os.path.basename(lora.get("localPath", "")))[0] if lora.get("localPath") else "",
"hash": lora.get("hash", "").lower() if lora.get("hash") else "",
"strength": float(lora.get("weight", 1.0)),
"modelVersionId": lora.get("id", ""),
"modelVersionId": lora.get("id", 0),
"modelName": lora.get("name", ""),
"modelVersionName": lora.get("version", ""),
"isDeleted": lora.get("isDeleted", False), # Preserve deletion status in saved recipe
@@ -1107,7 +1107,7 @@ class RecipeRoutes:
"file_name": lora_name,
"hash": lora_info.get("sha256", "").lower() if lora_info else "",
"strength": float(lora_strength),
"modelVersionId": lora_info.get("civitai", {}).get("id", "") if lora_info else "",
"modelVersionId": lora_info.get("civitai", {}).get("id", 0) if lora_info else 0,
"modelName": lora_info.get("civitai", {}).get("model", {}).get("name", "") if lora_info else lora_name,
"modelVersionName": lora_info.get("civitai", {}).get("name", "") if lora_info else "",
"isDeleted": False
@@ -1296,7 +1296,7 @@ class RecipeRoutes:
data = await request.json()
# Validate required fields
required_fields = ['recipe_id', 'lora_data', 'target_name']
required_fields = ['recipe_id', 'lora_index', 'target_name']
for field in required_fields:
if field not in data:
return web.json_response({
@@ -1304,7 +1304,7 @@ class RecipeRoutes:
}, status=400)
recipe_id = data['recipe_id']
lora_data = data['lora_data']
lora_index = int(data['lora_index'])
target_name = data['target_name']
# Get recipe scanner
@@ -1324,46 +1324,27 @@ class RecipeRoutes:
# Load recipe data
with open(recipe_path, 'r', encoding='utf-8') as f:
recipe_data = json.load(f)
# Find the deleted LoRA in the recipe
found = False
updated_lora = None
lora = recipe_data.get("loras", [])[lora_index] if lora_index < len(recipe_data.get('loras', [])) else None
if lora is None:
return web.json_response({"error": "LoRA index out of range in recipe"}, status=404)
# Update LoRA data
lora['isDeleted'] = False
lora['exclude'] = False
lora['file_name'] = target_name
# Identification can be by hash, modelVersionId, or modelName
for i, lora in enumerate(recipe_data.get('loras', [])):
match_found = False
# Try to match by available identifiers
if 'hash' in lora and 'hash' in lora_data and lora['hash'] == lora_data['hash']:
match_found = True
elif 'modelVersionId' in lora and 'modelVersionId' in lora_data and lora['modelVersionId'] == lora_data['modelVersionId']:
match_found = True
elif 'modelName' in lora and 'modelName' in lora_data and lora['modelName'] == lora_data['modelName']:
match_found = True
if match_found:
# Update LoRA data
lora['isDeleted'] = False
lora['file_name'] = target_name
# Update with information from the target LoRA
if 'sha256' in target_lora:
lora['hash'] = target_lora['sha256'].lower()
if target_lora.get("civitai"):
lora['modelName'] = target_lora['civitai']['model']['name']
lora['modelVersionName'] = target_lora['civitai']['name']
lora['modelVersionId'] = target_lora['civitai']['id']
# Keep original fields for identification
# Mark as found and store updated lora
found = True
updated_lora = dict(lora) # Make a copy for response
break
if not found:
return web.json_response({"error": "Could not find matching deleted LoRA in recipe"}, status=404)
# Update with information from the target LoRA
if 'sha256' in target_lora:
lora['hash'] = target_lora['sha256'].lower()
if target_lora.get("civitai"):
lora['modelName'] = target_lora['civitai']['model']['name']
lora['modelVersionName'] = target_lora['civitai']['name']
lora['modelVersionId'] = target_lora['civitai']['id']
updated_lora = dict(lora) # Make a copy for response
# Recalculate recipe fingerprint after updating LoRA
from ..utils.utils import calculate_recipe_fingerprint
recipe_data['fingerprint'] = calculate_recipe_fingerprint(recipe_data.get('loras', []))