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fix(import): discover all resources from CivitAI modelVersionIds

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CivitAI image API returns modelVersionIds at the root level of the
response (not inside meta), containing ALL model version IDs across
all resources (checkpoint + LoRAs). Two bugs prevented LoRAs from
being discovered:

1. _download_remote_media only extracted the first modelVersionId for
   enrichment, dropping the rest.
2. CivitAI API meta parsing only ran as an EXIF fallback, but most
   images have embedded EXIF metadata (prompt, steps, etc.), so the
   fallback was never triggered.
3. When civitai_meta_raw itself has a nested 'meta' key, unwrapping
   it stripped the injected modelVersionIds.

Also fixed gen_params merge: API gen_params now overlays EXIF at the
field level instead of full replacement, preserving EXIF-only fields
like detailed generation parameters.
This commit is contained in:
Will Miao
2026-05-16 22:12:30 +08:00
parent 31c54ff068
commit 94edfaa190
2 changed files with 74 additions and 15 deletions
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81
py/routes/handlers/recipe_handlers.py
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@@ -871,28 +871,47 @@ class RecipeManagementHandler:
"Failed to extract embedded metadata during import: %s", exc
)
# Fallback: if EXIF extraction yielded nothing, parse Civitai API meta directly
# (same approach as analyze_remote_image — downloaded Civitai images often
# have no embedded EXIF but the API meta contains resources/hashes)
if parsed_embedded is None and civitai_meta_raw:
# Parse CivitAI API meta to discover all resources from modelVersionIds
# (modelVersionIds is injected at root level by _download_remote_media).
# Run unconditionally — EXIF parsing may succeed for gen_params but miss
# LoRAs since modelVersionIds is NOT embedded in the image EXIF.
civitai_parsed = None
if civitai_meta_raw:
civitai_inner_meta = civitai_meta_raw
if isinstance(civitai_meta_raw, dict) and "meta" in civitai_meta_raw:
civitai_inner_meta = civitai_meta_raw["meta"]
# modelVersionIds lives at outer meta level; propagate after unwrap
_mvids = civitai_meta_raw.get("modelVersionIds")
if _mvids and isinstance(civitai_inner_meta, dict):
civitai_inner_meta["modelVersionIds"] = _mvids
if isinstance(civitai_inner_meta, dict):
parser = self._analysis_service._recipe_parser_factory.create_parser(
civitai_inner_meta
)
if parser:
parsed_embedded = await parser.parse_metadata(
civitai_parsed = await parser.parse_metadata(
civitai_inner_meta, recipe_scanner=recipe_scanner
)
if parsed_embedded and "gen_params" in parsed_embedded:
embedded_gen_params = parsed_embedded["gen_params"]
if civitai_parsed and "gen_params" in civitai_parsed:
# Merge: API gen_params override EXIF at field level,
# EXIF fills in fields the API doesn't have.
embedded_gen_params = {
**(embedded_gen_params or {}),
**civitai_parsed["gen_params"],
}
if embedded_gen_params:
metadata["gen_params"] = embedded_gen_params
if parsed_embedded:
# Merge LoRAs: prefer frontend resources, supplement with CivitAI modelVersionIds
if civitai_parsed:
civitai_loras = civitai_parsed.get("loras", [])
if civitai_loras and not metadata.get("loras"):
metadata["loras"] = civitai_loras
civitai_model = civitai_parsed.get("model")
if civitai_model and not metadata.get("checkpoint"):
metadata["checkpoint"] = civitai_model
elif parsed_embedded:
parsed_loras = parsed_embedded.get("loras")
if parsed_loras and not metadata.get("loras"):
metadata["loras"] = parsed_loras
@@ -1270,16 +1289,29 @@ class RecipeManagementHandler:
with open(temp_path, "rb") as file_obj:
model_ver_id = None
civitai_meta_raw = (
image_info.get("meta") if civitai_image_id and image_info else None
)
if civitai_image_id and image_info:
model_ver_id = image_info.get("modelVersionId")
if not model_ver_id:
ids = image_info.get("modelVersionIds")
if isinstance(ids, list) and ids:
model_ver_id = ids[0]
# Inject root-level modelVersionIds into meta so downstream
# parsers (CivitaiApiMetadataParser) can discover ALL resources
# (checkpoint + LoRAs), not just the first model version ID.
# CivitAI API returns modelVersionIds at the root level of
# the image response, NOT inside the meta object.
mvids = image_info.get("modelVersionIds")
if mvids and isinstance(civitai_meta_raw, dict):
civitai_meta_raw["modelVersionIds"] = mvids
return (
file_obj.read(),
extension,
image_info.get("meta") if civitai_image_id and image_info else None,
civitai_meta_raw,
model_ver_id,
)
except RecipeDownloadError:
@@ -1467,20 +1499,34 @@ class RecipeManagementHandler:
"Failed to extract embedded metadata: %s", exc
)
if parsed_embedded is None and civitai_meta_raw:
# Parse CivitAI API meta to discover all resources from modelVersionIds.
# Run unconditionally — EXIF parsing succeeds for gen_params but misses
# LoRAs (modelVersionIds is NOT in the image EXIF).
civitai_parsed = None
if civitai_meta_raw:
civitai_inner_meta = civitai_meta_raw
if isinstance(civitai_meta_raw, dict) and "meta" in civitai_meta_raw:
civitai_inner_meta = civitai_meta_raw["meta"]
# Propagate modelVersionIds into unwrapped meta — it lives
# at the outer meta level in the CivitAI API response.
_mvids = civitai_meta_raw.get("modelVersionIds")
if _mvids and isinstance(civitai_inner_meta, dict):
civitai_inner_meta["modelVersionIds"] = _mvids
if isinstance(civitai_inner_meta, dict):
parser = self._analysis_service._recipe_parser_factory.create_parser(
civitai_inner_meta
)
if parser:
parsed_embedded = await parser.parse_metadata(
civitai_parsed = await parser.parse_metadata(
civitai_inner_meta, recipe_scanner=recipe_scanner
)
if parsed_embedded and "gen_params" in parsed_embedded:
embedded_gen_params = parsed_embedded["gen_params"]
if civitai_parsed and "gen_params" in civitai_parsed:
# Merge: API gen_params override EXIF at field level,
# EXIF fills in fields the API doesn't have.
embedded_gen_params = {
**(embedded_gen_params or {}),
**civitai_parsed["gen_params"],
}
metadata: Dict[str, Any] = {
"base_model": "",
@@ -1489,7 +1535,14 @@ class RecipeManagementHandler:
"source_path": image_url,
}
if parsed_embedded:
if civitai_parsed:
civitai_loras = civitai_parsed.get("loras", [])
if civitai_loras and not metadata.get("loras"):
metadata["loras"] = civitai_loras
civitai_model = civitai_parsed.get("model")
if civitai_model and not metadata.get("checkpoint"):
metadata["checkpoint"] = civitai_model
elif parsed_embedded:
parsed_loras = parsed_embedded.get("loras")
if parsed_loras and not metadata.get("loras"):
metadata["loras"] = parsed_loras