mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-21 21:22:11 -03:00
Add CivitaiApiMetadataParser and improve recipe parsing logic for Civitai images. Also fixes #197
Additional info: Now prioritizes using the Civitai Images API to fetch image and generation metadata. Even NSFW images can now be imported via URL.
This commit is contained in:
Will Miao
@@ -7,7 +7,8 @@ from .parsers import (
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RecipeFormatParser,
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ComfyMetadataParser,
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MetaFormatParser,
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AutomaticMetadataParser
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AutomaticMetadataParser,
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CivitaiApiMetadataParser
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)
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__all__ = [
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@@ -18,5 +19,6 @@ __all__ = [
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'RecipeFormatParser',
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'ComfyMetadataParser',
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'MetaFormatParser',
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'AutomaticMetadataParser'
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'AutomaticMetadataParser',
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'CivitaiApiMetadataParser'
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]
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@@ -5,7 +5,8 @@ from .parsers import (
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RecipeFormatParser,
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ComfyMetadataParser,
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MetaFormatParser,
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AutomaticMetadataParser
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AutomaticMetadataParser,
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CivitaiApiMetadataParser
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)
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from .base import RecipeMetadataParser
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@@ -15,29 +16,49 @@ class RecipeParserFactory:
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"""Factory for creating recipe metadata parsers"""
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@staticmethod
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def create_parser(user_comment: str) -> RecipeMetadataParser:
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def create_parser(metadata) -> RecipeMetadataParser:
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"""
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Create appropriate parser based on the user comment content
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Create appropriate parser based on the metadata content
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Args:
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user_comment: The EXIF UserComment string from the image
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metadata: The metadata from the image (dict or str)
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Returns:
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Appropriate RecipeMetadataParser implementation
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"""
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# Try ComfyMetadataParser first since it requires valid JSON
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# First, try CivitaiApiMetadataParser for dict input
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if isinstance(metadata, dict):
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try:
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if CivitaiApiMetadataParser().is_metadata_matching(metadata):
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return CivitaiApiMetadataParser()
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except Exception as e:
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logger.debug(f"CivitaiApiMetadataParser check failed: {e}")
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pass
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# Convert dict to string for other parsers that expect string input
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try:
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import json
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metadata_str = json.dumps(metadata)
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except Exception as e:
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logger.debug(f"Failed to convert dict to JSON string: {e}")
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return None
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else:
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metadata_str = metadata
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# Try ComfyMetadataParser which requires valid JSON
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try:
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if ComfyMetadataParser().is_metadata_matching(user_comment):
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if ComfyMetadataParser().is_metadata_matching(metadata_str):
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return ComfyMetadataParser()
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except Exception:
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# If JSON parsing fails, move on to other parsers
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pass
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if RecipeFormatParser().is_metadata_matching(user_comment):
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# Check other parsers that expect string input
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if RecipeFormatParser().is_metadata_matching(metadata_str):
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return RecipeFormatParser()
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elif AutomaticMetadataParser().is_metadata_matching(user_comment):
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elif AutomaticMetadataParser().is_metadata_matching(metadata_str):
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return AutomaticMetadataParser()
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elif MetaFormatParser().is_metadata_matching(user_comment):
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elif MetaFormatParser().is_metadata_matching(metadata_str):
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return MetaFormatParser()
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else:
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return None
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@@ -4,10 +4,12 @@ from .recipe_format import RecipeFormatParser
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from .comfy import ComfyMetadataParser
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from .meta_format import MetaFormatParser
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from .automatic import AutomaticMetadataParser
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from .civitai_image import CivitaiApiMetadataParser
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__all__ = [
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'RecipeFormatParser',
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'ComfyMetadataParser',
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'MetaFormatParser',
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'AutomaticMetadataParser',
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'CivitaiApiMetadataParser',
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]
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248
py/recipes/parsers/civitai_image.py
Normal file
248
py/recipes/parsers/civitai_image.py
Normal file
@@ -0,0 +1,248 @@
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"""Parser for Civitai image metadata format."""
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import json
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import logging
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from typing import Dict, Any, Union
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from ..base import RecipeMetadataParser
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from ..constants import GEN_PARAM_KEYS
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logger = logging.getLogger(__name__)
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class CivitaiApiMetadataParser(RecipeMetadataParser):
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"""Parser for Civitai image metadata format"""
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def is_metadata_matching(self, metadata) -> bool:
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"""Check if the metadata matches the Civitai image metadata format
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Args:
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metadata: The metadata from the image (dict)
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Returns:
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bool: True if this parser can handle the metadata
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"""
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if not metadata or not isinstance(metadata, dict):
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return False
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# Check for key markers specific to Civitai image metadata
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return any([
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"resources" in metadata,
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"civitaiResources" in metadata,
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"additionalResources" in metadata
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])
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async def parse_metadata(self, metadata, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
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"""Parse metadata from Civitai image format
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Args:
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metadata: The metadata from the image (dict)
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recipe_scanner: Optional recipe scanner service
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civitai_client: Optional Civitai API client
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Returns:
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Dict containing parsed recipe data
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"""
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try:
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# Initialize result structure
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result = {
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'base_model': None,
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'loras': [],
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'gen_params': {},
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'from_civitai_image': True
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}
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# Extract prompt and negative prompt
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if "prompt" in metadata:
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result["gen_params"]["prompt"] = metadata["prompt"]
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if "negativePrompt" in metadata:
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result["gen_params"]["negative_prompt"] = metadata["negativePrompt"]
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# Extract other generation parameters
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param_mapping = {
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"steps": "steps",
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"sampler": "sampler",
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"cfgScale": "cfg_scale",
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"seed": "seed",
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"Size": "size",
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"clipSkip": "clip_skip",
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}
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for civitai_key, our_key in param_mapping.items():
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if civitai_key in metadata and our_key in GEN_PARAM_KEYS:
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result["gen_params"][our_key] = metadata[civitai_key]
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# Extract base model information - directly if available
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if "baseModel" in metadata:
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result["base_model"] = metadata["baseModel"]
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elif "Model hash" in metadata and civitai_client:
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model_hash = metadata["Model hash"]
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model_info = await civitai_client.get_model_by_hash(model_hash)
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if model_info:
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result["base_model"] = model_info.get("baseModel", "")
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elif "Model" in metadata and isinstance(metadata.get("resources"), list):
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# Try to find base model in resources
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for resource in metadata.get("resources", []):
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if resource.get("type") == "model" and resource.get("name") == metadata.get("Model"):
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# This is likely the checkpoint model
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if civitai_client and resource.get("hash"):
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model_info = await civitai_client.get_model_by_hash(resource.get("hash"))
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if model_info:
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result["base_model"] = model_info.get("baseModel", "")
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base_model_counts = {}
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# Process standard resources array
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if "resources" in metadata and isinstance(metadata["resources"], list):
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for resource in metadata["resources"]:
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# Modified to process resources without a type field as potential LoRAs
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if resource.get("type", "lora") == "lora":
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lora_entry = {
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'name': resource.get("name", "Unknown LoRA"),
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'type': "lora",
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'weight': float(resource.get("weight", 1.0)),
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'hash': resource.get("hash", ""),
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'existsLocally': False,
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'localPath': None,
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'file_name': resource.get("name", "Unknown"),
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'thumbnailUrl': '/loras_static/images/no-preview.png',
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'baseModel': '',
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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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# Try to get info from Civitai if hash is available
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if lora_entry['hash'] and civitai_client:
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try:
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lora_hash = lora_entry['hash']
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civitai_info = await civitai_client.get_model_by_hash(lora_hash)
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populated_entry = await self.populate_lora_from_civitai(
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lora_entry,
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civitai_info,
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recipe_scanner,
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base_model_counts,
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lora_hash
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)
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if populated_entry is None:
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continue # Skip invalid LoRA types
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lora_entry = populated_entry
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except Exception as e:
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logger.error(f"Error fetching Civitai info for LoRA hash {lora_entry['hash']}: {e}")
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result["loras"].append(lora_entry)
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# Process civitaiResources array
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if "civitaiResources" in metadata and isinstance(metadata["civitaiResources"], list):
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for resource in metadata["civitaiResources"]:
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# Modified to process resources without a type field as potential LoRAs
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if resource.get("type") in ["lora", "lycoris"] or "type" not in resource:
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# Initialize lora entry with the same structure as in automatic.py
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lora_entry = {
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'id': str(resource.get("modelVersionId")),
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'modelId': str(resource.get("modelId")) if resource.get("modelId") else None,
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'name': resource.get("modelName", "Unknown LoRA"),
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'version': resource.get("modelVersionName", ""),
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'type': resource.get("type", "lora"),
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'weight': round(float(resource.get("weight", 1.0)), 2),
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'existsLocally': False,
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'thumbnailUrl': '/loras_static/images/no-preview.png',
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'baseModel': '',
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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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# Try to get info from Civitai if modelVersionId is available
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if resource.get('modelVersionId') and civitai_client:
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try:
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version_id = str(resource.get('modelVersionId'))
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# Use get_model_version_info instead of get_model_version
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civitai_info, error = await civitai_client.get_model_version_info(version_id)
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if error:
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logger.warning(f"Error getting model version info: {error}")
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continue
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populated_entry = await self.populate_lora_from_civitai(
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lora_entry,
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civitai_info,
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recipe_scanner,
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base_model_counts
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)
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if populated_entry is None:
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continue # Skip invalid LoRA types
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lora_entry = populated_entry
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except Exception as e:
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logger.error(f"Error fetching Civitai info for model version {resource.get('modelVersionId')}: {e}")
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result["loras"].append(lora_entry)
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# Process additionalResources array
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if "additionalResources" in metadata and isinstance(metadata["additionalResources"], list):
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for resource in metadata["additionalResources"]:
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# Modified to process resources without a type field as potential LoRAs
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if resource.get("type") in ["lora", "lycoris"] or "type" not in resource:
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lora_type = resource.get("type", "lora")
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name = resource.get("name", "")
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# Extract ID from URN format if available
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model_id = None
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if name and "civitai:" in name:
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parts = name.split("@")
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if len(parts) > 1:
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model_id = parts[1]
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lora_entry = {
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'name': name,
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'type': lora_type,
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'weight': float(resource.get("strength", 1.0)),
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'hash': "",
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'existsLocally': False,
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'localPath': None,
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'file_name': name,
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'thumbnailUrl': '/loras_static/images/no-preview.png',
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'baseModel': '',
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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 we have a model ID and civitai client, try to get more info
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if model_id and civitai_client:
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try:
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# Use get_model_version_info with the model ID
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civitai_info, error = await civitai_client.get_model_version_info(model_id)
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if error:
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logger.warning(f"Error getting model version info: {error}")
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else:
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populated_entry = await self.populate_lora_from_civitai(
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lora_entry,
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civitai_info,
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recipe_scanner,
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base_model_counts
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)
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if populated_entry is None:
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continue # Skip invalid LoRA types
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lora_entry = populated_entry
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except Exception as e:
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logger.error(f"Error fetching Civitai info for model ID {model_id}: {e}")
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result["loras"].append(lora_entry)
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# If base model wasn't found earlier, use the most common one from LoRAs
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if not result["base_model"] and base_model_counts:
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result["base_model"] = max(base_model_counts.items(), key=lambda x: x[1])[0]
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return result
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except Exception as e:
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logger.error(f"Error parsing Civitai image metadata: {e}", exc_info=True)
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return {"error": str(e), "loras": []}
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@@ -254,6 +254,7 @@ class RecipeRoutes:
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content_type = request.headers.get('Content-Type', '')
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is_url_mode = False
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metadata = None # Initialize metadata variable
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if 'multipart/form-data' in content_type:
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# Handle image upload
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@@ -287,17 +288,63 @@ class RecipeRoutes:
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"loras": []
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}, status=400)
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# Download image from URL
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temp_path = download_civitai_image(url)
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# Check if this is a Civitai image URL
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import re
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civitai_image_match = re.match(r'https://civitai\.com/images/(\d+)', url)
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if not temp_path:
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return web.json_response({
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"error": "Failed to download image from URL",
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"loras": []
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}, status=400)
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if civitai_image_match:
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# Extract image ID and fetch image info using get_image_info
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image_id = civitai_image_match.group(1)
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image_info = await self.civitai_client.get_image_info(image_id)
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if not image_info:
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return web.json_response({
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"error": "Failed to fetch image information from Civitai",
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"loras": []
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}, status=400)
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# Get image URL from response
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image_url = image_info.get('url')
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if not image_url:
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return web.json_response({
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"error": "No image URL found in Civitai response",
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"loras": []
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}, status=400)
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# Download image directly from URL
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session = await self.civitai_client.session
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# Create a temporary file to save the downloaded image
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with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as temp_file:
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temp_path = temp_file.name
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async with session.get(image_url) as response:
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if response.status != 200:
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return web.json_response({
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"error": f"Failed to download image from URL: HTTP {response.status}",
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"loras": []
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}, status=400)
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with open(temp_path, 'wb') as f:
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f.write(await response.read())
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# Use meta field from image_info as metadata
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if 'meta' in image_info:
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metadata = image_info['meta']
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else:
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# Not a Civitai image URL, use the original download method
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temp_path = download_civitai_image(url)
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if not temp_path:
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return web.json_response({
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"error": "Failed to download image from URL",
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"loras": []
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}, status=400)
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# Extract metadata from the image using ExifUtils
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metadata = ExifUtils.extract_image_metadata(temp_path)
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# If metadata wasn't obtained from Civitai API, extract it from the image
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if metadata is None:
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# Extract metadata from the image using ExifUtils
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metadata = ExifUtils.extract_image_metadata(temp_path)
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|
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# If no metadata found, return a more specific error
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if not metadata:
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@@ -346,3 +346,34 @@ class CivitaiClient:
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except Exception as e:
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logger.error(f"Error getting hash from Civitai: {e}")
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return None
|
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|
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async def get_image_info(self, image_id: str) -> Optional[Dict]:
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"""Fetch image information from Civitai API
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|
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Args:
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image_id: The Civitai image ID
|
||||
|
||||
Returns:
|
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Optional[Dict]: The image data or None if not found
|
||||
"""
|
||||
try:
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session = await self._ensure_fresh_session()
|
||||
headers = self._get_request_headers()
|
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url = f"{self.base_url}/images?imageId={image_id}&nsfw=X"
|
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|
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logger.debug(f"Fetching image info for ID: {image_id}")
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async with session.get(url, headers=headers) as response:
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if response.status == 200:
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data = await response.json()
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if data and "items" in data and len(data["items"]) > 0:
|
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logger.debug(f"Successfully fetched image info for ID: {image_id}")
|
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return data["items"][0]
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logger.warning(f"No image found with ID: {image_id}")
|
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return None
|
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|
||||
logger.error(f"Failed to fetch image info for ID: {image_id} (status {response.status})")
|
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return None
|
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except Exception as e:
|
||||
error_msg = f"Error fetching image info: {e}"
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logger.error(error_msg)
|
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return None
|
||||
|
||||
@@ -58,8 +58,17 @@ export class ImportManager {
|
||||
this.stepManager.removeInjectedStyles();
|
||||
});
|
||||
|
||||
// Verify visibility
|
||||
setTimeout(() => this.ensureModalVisible(), 50);
|
||||
// Verify visibility and focus on URL input
|
||||
setTimeout(() => {
|
||||
this.ensureModalVisible();
|
||||
|
||||
// Ensure URL option is selected and focus on the input
|
||||
this.toggleImportMode('url');
|
||||
const urlInput = document.getElementById('imageUrlInput');
|
||||
if (urlInput) {
|
||||
urlInput.focus();
|
||||
}
|
||||
}, 50);
|
||||
}
|
||||
|
||||
resetSteps() {
|
||||
|
||||
Reference in New Issue
Block a user