alphard/ComfyUI-Lora-Manager
1
0
Fork 0
mirror of https://github.com/willmiao/ComfyUI-Lora-Manager.git synced 2026-03-21 21:22:11 -03:00
Code Issues Packages Projects Releases Wiki Activity

fix: improve Civitai recipe import by extracting EXIF when API metadata is empty

Browse Source 
- Add validation to check if Civitai API metadata contains recipe fields
- Fall back to EXIF extraction when API returns empty metadata (meta.meta=null)
- Improve error messages to distinguish between missing metadata and unsupported format
- Add _has_recipe_fields() helper method to validate metadata content

This fixes import failures for Civitai images where the API returns
metadata wrapper but no actual generation parameters (e.g., images
edited in Photoshop that lost their original generation metadata)
This commit is contained in:
Will Miao
2026-03-18 22:30:36 +08:00
parent 701a6a6c44
commit fefcaa4a45
1 changed files with 60 additions and 15 deletions
Download Patch File Download Diff File Expand all files Collapse all files

75
py/services/recipes/analysis_service.py
View File

@@ -1,4 +1,5 @@
"""Services responsible for recipe metadata analysis."""
from __future__ import annotations
import base64
@@ -69,7 +70,9 @@ class RecipeAnalysisService:
try:
metadata = self._exif_utils.extract_image_metadata(temp_path)
if not metadata:
return AnalysisResult({"error": "No metadata found in this image", "loras": []})
return AnalysisResult(
{"error": "No metadata found in this image", "loras": []}
)
return await self._parse_metadata(
metadata,
@@ -105,29 +108,33 @@ class RecipeAnalysisService:
if civitai_match:
image_info = await civitai_client.get_image_info(civitai_match.group(1))
if not image_info:
raise RecipeDownloadError("Failed to fetch image information from Civitai")
raise RecipeDownloadError(
"Failed to fetch image information from Civitai"
)
image_url = image_info.get("url")
if not image_url:
raise RecipeDownloadError("No image URL found in Civitai response")
is_video = image_info.get("type") == "video"
# Use optimized preview URLs if possible
rewritten_url, _ = rewrite_preview_url(image_url, media_type=image_info.get("type"))
rewritten_url, _ = rewrite_preview_url(
image_url, media_type=image_info.get("type")
)
if rewritten_url:
image_url = rewritten_url
if is_video:
# Extract extension from URL
url_path = image_url.split('?')[0].split('#')[0]
url_path = image_url.split("?")[0].split("#")[0]
extension = os.path.splitext(url_path)[1].lower() or ".mp4"
else:
extension = ".jpg"
temp_path = self._create_temp_path(suffix=extension)
await self._download_image(image_url, temp_path)
metadata = image_info.get("meta") if "meta" in image_info else None
if (
isinstance(metadata, dict)
@@ -135,15 +142,23 @@ class RecipeAnalysisService:
and isinstance(metadata["meta"], dict)
):
metadata = metadata["meta"]
# Validate that metadata contains meaningful recipe fields
# If not, treat as None to trigger EXIF extraction from downloaded image
if isinstance(metadata, dict) and not self._has_recipe_fields(metadata):
self._logger.debug(
"Civitai API metadata lacks recipe fields, will extract from EXIF"
)
metadata = None
else:
# Basic extension detection for non-Civitai URLs
url_path = url.split('?')[0].split('#')[0]
url_path = url.split("?")[0].split("#")[0]
extension = os.path.splitext(url_path)[1].lower()
if extension in [".mp4", ".webm"]:
is_video = True
else:
extension = ".jpg"
temp_path = self._create_temp_path(suffix=extension)
await self._download_image(url, temp_path)
@@ -211,7 +226,9 @@ class RecipeAnalysisService:
image_bytes = self._convert_tensor_to_png_bytes(latest_image)
if image_bytes is None:
raise RecipeValidationError("Cannot handle this data shape from metadata registry")
raise RecipeValidationError(
"Cannot handle this data shape from metadata registry"
)
return AnalysisResult(
{
@@ -222,6 +239,22 @@ class RecipeAnalysisService:
# Internal helpers -------------------------------------------------
def _has_recipe_fields(self, metadata: dict[str, Any]) -> bool:
"""Check if metadata contains meaningful recipe-related fields."""
recipe_fields = {
"prompt",
"negative_prompt",
"resources",
"hashes",
"params",
"generationData",
"Workflow",
"prompt_type",
"positive",
"negative",
}
return any(field in metadata for field in recipe_fields)
async def _parse_metadata(
self,
metadata: dict[str, Any],
@@ -234,7 +267,12 @@ class RecipeAnalysisService:
) -> AnalysisResult:
parser = self._recipe_parser_factory.create_parser(metadata)
if parser is None:
payload = {"error": "No parser found for this image", "loras": []}
# Provide more specific error message based on metadata source
if not metadata:
error_msg = "This image does not contain any generation metadata (prompt, models, or parameters)"
else:
error_msg = "No parser found for this image"
payload = {"error": error_msg, "loras": []}
if include_image_base64 and image_path:
payload["image_base64"] = self._encode_file(image_path)
payload["is_video"] = is_video
@@ -257,7 +295,9 @@ class RecipeAnalysisService:
matching_recipes: list[str] = []
if fingerprint:
matching_recipes = await recipe_scanner.find_recipes_by_fingerprint(fingerprint)
matching_recipes = await recipe_scanner.find_recipes_by_fingerprint(
fingerprint
)
result["matching_recipes"] = matching_recipes
return AnalysisResult(result)
@@ -269,7 +309,10 @@ class RecipeAnalysisService:
raise RecipeDownloadError(f"Failed to download image from URL: {result}")
def _metadata_not_found_response(self, path: str) -> AnalysisResult:
payload: dict[str, Any] = {"error": "No metadata found in this image", "loras": []}
payload: dict[str, Any] = {
"error": "No metadata found in this image",
"loras": [],
}
if os.path.exists(path):
payload["image_base64"] = self._encode_file(path)
return AnalysisResult(payload)
@@ -305,7 +348,9 @@ class RecipeAnalysisService:
if hasattr(tensor_image, "shape"):
self._logger.debug(
"Tensor shape: %s, dtype: %s", tensor_image.shape, getattr(tensor_image, "dtype", None)
"Tensor shape: %s, dtype: %s",
tensor_image.shape,
getattr(tensor_image, "dtype", None),
)
import torch # type: ignore[import-not-found]