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feat(recipes): explain empty LoRA lists with collapsible "Why no LoRAs?" panel

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Record import provenance on every recipe: a new import_info block
(channel, machine-readable no-LoRA reason, diagnostic details) built at
import time across all channels (batch import, single URL, local file,
upload, widget save, re-imports) and persisted in the recipe JSON plus
the SQLite persistent cache (new import_info_json column with ALTER
TABLE migration).

The recipe modal renders the empty LoRA list with a collapsed details
panel showing the import method, the reason (CivitAI API returned no
LoRA resource data, API meta missing, no embedded metadata, ComfyUI
workflow metadata, video, unparsable format), and recorded diagnostics.
Legacy recipes without import_info fall back to heuristics labeled as
inferred. Genuine no-LoRA generations show no panel.

CivitAI images are always classified by API meta shape: the onsite
generator writes A1111-style EXIF without LoRA references, so parsed
EXIF cannot prove "no LoRAs used".

Adds recipes.resources.noLoras* i18n keys (all 10 locales) plus
frontend vitest and backend pytest coverage.
This commit is contained in:
Will Miao
2026-08-30 16:28:41 +08:00
parent 3fd29f6943
commit bccd494a56
23 changed files with 1342 additions and 10 deletions
Download Patch File Download Diff File Expand all files Collapse all files
py/services/recipes/analysis_service.py
+53 -4
View File
@@ -72,15 +72,28 @@ class RecipeAnalysisService:
metadata = self._exif_utils.extract_image_metadata(temp_path)
if not metadata:
return AnalysisResult(
{"error": "No metadata found in this image", "loras": []}
{
"error": "No metadata found in this image",
"loras": [],
"diagnostics": {
"channel": "upload",
"exif_present": False,
},
}
)
return await self._parse_metadata(
result = await self._parse_metadata(
metadata,
recipe_scanner=recipe_scanner,
image_path=None,
include_image_base64=False,
)
result.payload["diagnostics"] = {
"channel": "upload",
"exif_present": True,
"exif_parser": result.payload.get("parser"),
}
return result
finally:
self._safe_cleanup(temp_path)
@@ -104,9 +117,13 @@ class RecipeAnalysisService:
image_info: Optional[dict[str, Any]] = None
is_video = False
extension = ".jpg" # Default
# Diagnostics collected during analysis; surfaced in the payload so
# callers can persist an import_info block explaining empty LoRA lists.
diagnostics: dict[str, Any] = {"channel": "url"}
try:
civitai_image_id = extract_civitai_image_id(url)
diagnostics["civitai_image"] = bool(civitai_image_id)
if civitai_image_id:
image_info = await civitai_client.get_image_info(
civitai_image_id, source_url=url
@@ -147,11 +164,23 @@ class RecipeAnalysisService:
):
metadata = metadata["meta"]
# Diagnostics: capture the API meta shape before injecting
# modelVersionIds / browsingLevel so the recipe modal can
# explain why an import ended up without LoRAs.
diagnostics["api_meta_present"] = isinstance(metadata, dict)
if isinstance(metadata, dict):
diagnostics["api_meta_keys"] = sorted(metadata.keys())
# Include modelVersionIds from root level if available.
# CivitAI API returns modelVersionIds at root level, not in meta.
# When meta is null (None), create a minimal dict so downstream
# parsers can still discover LoRAs and checkpoints.
model_version_ids = image_info.get("modelVersionIds")
diagnostics["api_model_version_ids"] = (
len(model_version_ids)
if isinstance(model_version_ids, list)
else 0
)
if model_version_ids:
if isinstance(metadata, dict):
metadata["modelVersionIds"] = model_version_ids
@@ -229,6 +258,8 @@ class RecipeAnalysisService:
finally:
self._safe_cleanup(orig_temp_path)
diagnostics["exif_present"] = bool(exif_metadata)
# Parse EXIF data (typically a string like parameters/prompt/workflow)
# and API metadata (dict with modelVersionIds, browsingLevel) separately,
# then merge: API loras/checkpoint override, EXIF gen_params fill in gaps.
@@ -237,6 +268,7 @@ class RecipeAnalysisService:
if isinstance(exif_metadata, str):
exif_parser = self._recipe_parser_factory.create_parser(exif_metadata)
if exif_parser:
diagnostics["exif_parser"] = exif_parser.__class__.__name__
exif_data = await exif_parser.parse_metadata(
exif_metadata, recipe_scanner=recipe_scanner,
)
@@ -324,6 +356,8 @@ class RecipeAnalysisService:
if isinstance(bl, int) and bl > 0:
result.payload["preview_nsfw_level"] = bl
diagnostics["is_video"] = is_video
result.payload["diagnostics"] = diagnostics
return result
finally:
if temp_path:
@@ -348,14 +382,25 @@ class RecipeAnalysisService:
self._exif_utils.extract_image_metadata, normalized_path
)
if not metadata:
return self._metadata_not_found_response(normalized_path)
result = self._metadata_not_found_response(normalized_path)
result.payload["diagnostics"] = {
"channel": "local",
"exif_present": False,
}
return result
return await self._parse_metadata(
result = await self._parse_metadata(
metadata,
recipe_scanner=recipe_scanner,
image_path=normalized_path,
include_image_base64=True,
)
result.payload["diagnostics"] = {
"channel": "local",
"exif_present": True,
"exif_parser": result.payload.get("parser"),
}
return result
async def analyze_widget_metadata(self, *, recipe_scanner) -> AnalysisResult:
"""Analyse the most recent generation metadata for widget saves."""
@@ -452,6 +497,10 @@ class RecipeAnalysisService:
metadata, recipe_scanner=recipe_scanner
)
# Record which parser handled the metadata so import diagnostics
# can distinguish e.g. ComfyUI workflow sources.
result["parser"] = parser.__class__.__name__
if include_image_base64 and image_path:
result["image_base64"] = self._encode_file(image_path)