mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-06-09 20:39:25 -03:00
feat(recipe): add Create As Recipe from example images with import dedup check (#945)
This commit is contained in:
Will Miao
@@ -16,7 +16,7 @@ from aiohttp import web
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from ...config import config
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from ...services.server_i18n import server_i18n as default_server_i18n
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from ...services.settings_manager import SettingsManager
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from ...services.settings_manager import SettingsManager, get_settings_manager
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from ...services.recipes import (
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RecipeAnalysisService,
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RecipeDownloadError,
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@@ -26,7 +26,12 @@ from ...services.recipes import (
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RecipeValidationError,
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)
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from ...services.metadata_service import get_default_metadata_provider
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from ...utils.civitai_utils import extract_civitai_image_id, rewrite_preview_url
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from ...utils.civitai_utils import (
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build_civitai_image_page_url,
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extract_civitai_image_id,
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extract_civitai_image_id_from_cdn_url,
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rewrite_preview_url,
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)
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from ...utils.exif_utils import ExifUtils
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from ...recipes.merger import GenParamsMerger
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from ...recipes.enrichment import RecipeEnricher
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@@ -96,6 +101,7 @@ class RecipeHandlerSet:
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"browse_directory": self.batch_import.browse_directory,
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"check_image_exists": self.management.check_image_exists,
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"import_from_url": self.management.import_from_url,
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"create_from_example": self.management.create_from_example,
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}
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@@ -1668,6 +1674,246 @@ class RecipeManagementHandler:
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)
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return web.json_response(result.payload, status=result.status)
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async def create_from_example(self, request: web.Request) -> web.Response:
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"""Create a recipe from a model's example image using cached metadata.
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Uses the image's meta data (already cached in .metadata.json from the
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CivitAI model-versions API) to create a recipe without additional
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CivitAI API calls.
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If the image metadata doesn't contain any resources of the parent
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model's type (LoRA-type or Checkpoint), the parent model is
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auto-populated as a fallback.
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Request body:
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image_data (dict): The full image object from model-versions API
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(includes meta, additionalResources, url, etc.)
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model_hash (str): SHA256 hash of the parent model
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model_name (str): Filename of the parent model
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model_type (str): Page type (``"loras"``, ``"checkpoints"``, etc.)
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local_image_path (str, optional): Local filesystem path to read
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the image bytes for the recipe preview
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"""
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try:
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await self._ensure_dependencies_ready()
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recipe_scanner = self._recipe_scanner_getter()
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if recipe_scanner is None:
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raise RuntimeError("Recipe scanner unavailable")
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data = await request.json()
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image_data = data.get("image_data")
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model_hash = data.get("model_hash")
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model_name = data.get("model_name")
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model_type = data.get("model_type", "")
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if not image_data or not model_hash or not model_name:
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raise RecipeValidationError(
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"Missing required fields: image_data, model_hash, model_name"
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)
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# Merge nested meta into top level so the parser finds everything.
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# CivitaiApiMetadataParser expects prompt, seed, resources, etc.
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# at the top level or wrapped under a "meta" key.
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inner_meta = image_data.get("meta") or {}
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parsed_input = {**image_data, **inner_meta}
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parsed_input.pop("meta", None)
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parser = self._analysis_service._recipe_parser_factory.create_parser(
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parsed_input
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)
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if not parser:
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raise RecipeValidationError("Unable to parse image metadata")
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parsed = await parser.parse_metadata(
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parsed_input, recipe_scanner=recipe_scanner
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)
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loras = list(parsed.get("loras") or [])
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checkpoint = parsed.get("model")
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is_lora_type = model_type.startswith("lora")
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is_ckpt_type = model_type.startswith("checkpoint")
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# Look up parent model's cached CivitAI metadata (version ID,
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# version name, model ID) from the scanner cache. Used to fix
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# isDeleted entries and enrich auto-populated ones.
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parent_civitai_id: int | None = None
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parent_model_id: int | None = None
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parent_version_name: str | None = None
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parent_model_name: str | None = None
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lora_scanner = getattr(recipe_scanner, "_lora_scanner", None)
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if lora_scanner and model_hash:
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try:
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parent_cache = await lora_scanner.get_cached_data()
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for item in getattr(parent_cache, "raw_data", []):
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if item.get("sha256", "").lower() == model_hash.lower():
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civ = item.get("civitai") or {}
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if isinstance(civ, dict):
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parent_civitai_id = civ.get("id")
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parent_model_id = civ.get("modelId")
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parent_version_name = civ.get("name")
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# model_name is a flat SQLite column holding the
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# CivitAI model display name (not nested under
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# civitai.model which only stores type).
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parent_model_name = item.get("model_name")
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break
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else:
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pass
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except Exception:
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pass
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# Reconcile isDeleted entries against the parent model.
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# When the CivitAI hash lookup fails (known issue — hashes not
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# yet computed), the parser marks the entry isDeleted even though
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# the model exists locally.
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if is_lora_type:
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for lora in loras:
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if lora.get("isDeleted") and lora.get("file_name") == model_name:
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lora["isDeleted"] = False
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lora["existsLocally"] = True
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lora["hash"] = model_hash
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if parent_civitai_id is not None:
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lora["id"] = parent_civitai_id
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if parent_model_id is not None:
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lora["modelId"] = parent_model_id
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if parent_version_name is not None:
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lora["version"] = parent_version_name
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if parent_model_name is not None:
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lora["name"] = parent_model_name
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elif is_ckpt_type and checkpoint and checkpoint.get("isDeleted"):
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if checkpoint.get("file_name") == model_name:
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checkpoint["isDeleted"] = False
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checkpoint["existsLocally"] = True
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checkpoint["hash"] = model_hash
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if parent_civitai_id is not None:
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checkpoint["id"] = parent_civitai_id
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if parent_model_id is not None:
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checkpoint["modelId"] = parent_model_id
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if parent_version_name is not None:
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checkpoint["version"] = parent_version_name
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# Auto-populate parent model only when the image metadata didn't
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# contain any resources of that type.
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if is_lora_type and not loras:
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lora_entry = {
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"name": model_name,
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"type": "lora",
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"weight": 1.0,
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"hash": model_hash,
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"existsLocally": True,
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"localPath": None,
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"file_name": model_name,
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"thumbnailUrl": "/loras_static/images/no-preview.png",
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"baseModel": parsed.get("base_model", ""),
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"size": 0,
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"downloadUrl": "",
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"isDeleted": False,
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}
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if parent_civitai_id is not None:
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lora_entry["id"] = parent_civitai_id
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if parent_model_id is not None:
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lora_entry["modelId"] = parent_model_id
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if parent_version_name is not None:
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lora_entry["version"] = parent_version_name
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if parent_model_name is not None:
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lora_entry["name"] = parent_model_name
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loras.insert(0, lora_entry)
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elif is_ckpt_type and not checkpoint:
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checkpoint = {
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"name": model_name,
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"type": "checkpoint",
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"hash": model_hash,
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"file_name": model_name,
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"existsLocally": True,
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"baseModel": parsed.get("base_model", ""),
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"isDeleted": False,
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}
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if parent_civitai_id is not None:
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checkpoint["id"] = parent_civitai_id
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if parent_model_id is not None:
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checkpoint["modelId"] = parent_model_id
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if parent_version_name is not None:
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checkpoint["version"] = parent_version_name
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if parent_model_name is not None:
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checkpoint["name"] = parent_model_name
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image_url = image_data.get("url") or ""
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image_id = extract_civitai_image_id_from_cdn_url(image_url)
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settings_mgr = get_settings_manager()
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civitai_host = settings_mgr.get("civitai_host") if settings_mgr else None
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page_url = build_civitai_image_page_url(image_id, host=civitai_host) or image_url
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recipe_metadata: dict[str, Any] = {
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"base_model": parsed.get("base_model") or "",
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"loras": loras,
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"gen_params": parsed.get("gen_params") or {},
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"source_path": page_url,
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}
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nsfw_level = image_data.get("nsfwLevel")
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if isinstance(nsfw_level, int):
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recipe_metadata["preview_nsfw_level"] = nsfw_level
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if checkpoint:
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recipe_metadata["checkpoint"] = checkpoint
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image_bytes: bytes | None = None
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extension: str | None = None
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local_image_path = data.get("local_image_path")
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if local_image_path and os.path.exists(local_image_path):
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with open(local_image_path, "rb") as f:
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image_bytes = f.read()
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ext = os.path.splitext(local_image_path)[1].lower()
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if ext in (".jpg", ".jpeg", ".png", ".webp", ".gif"):
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extension = ext
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elif image_data.get("url"):
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try:
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downloader = await self._downloader_factory()
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url = image_data["url"]
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tmp = tempfile.NamedTemporaryFile(delete=False)
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tmp.close()
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success, result = await downloader.download_file(
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url, tmp.name, use_auth=False
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)
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if success:
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with open(tmp.name, "rb") as f:
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image_bytes = f.read()
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url_path = url.split("?")[0].split("#")[0]
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ext = os.path.splitext(url_path)[1].lower()
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if ext:
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extension = ext
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if os.path.exists(tmp.name):
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os.unlink(tmp.name)
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except Exception as exc:
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self._logger.warning(
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"Failed to download image for recipe: %s", exc
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)
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prompt = (
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(parsed.get("gen_params") or {}).get("prompt") or ""
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)
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if prompt:
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name = " ".join(str(prompt).split()[:10])
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else:
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name = f"Recipe from {model_name}"
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save_result = await self._persistence_service.save_recipe(
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recipe_scanner=recipe_scanner,
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image_bytes=image_bytes,
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image_base64=None,
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name=name,
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tags=[],
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metadata=recipe_metadata,
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extension=extension,
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)
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return web.json_response(save_result.payload, status=save_result.status)
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except RecipeValidationError as exc:
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return web.json_response({"error": str(exc)}, status=400)
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except Exception as exc:
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self._logger.error(
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"Error creating recipe from example: %s", exc, exc_info=True
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)
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return web.json_response({"error": str(exc)}, status=500)
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class RecipeAnalysisHandler:
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"""Analyze images to extract recipe metadata."""
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