438 lines
16 KiB
Python
438 lines
16 KiB
Python
import os
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import re
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import yaml
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import requests
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import markdownify
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from abc import ABC, abstractmethod
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from urllib.parse import urlparse, parse_qs
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from . import utils
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class ModelSearcher(ABC):
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"""
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Abstract class for model searcher.
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"""
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@abstractmethod
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def search_by_url(self, url: str) -> list[dict]:
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pass
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@abstractmethod
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def search_by_hash(self, hash: str) -> dict:
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pass
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class UnknownWebsiteSearcher(ModelSearcher):
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def search_by_url(self, url: str):
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raise RuntimeError(f"Unknown Website, please input a URL from huggingface.co or civitai.com.")
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def search_by_hash(self, hash: str):
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raise RuntimeError(f"Unknown Website, unable to search with hash value.")
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class CivitaiModelSearcher(ModelSearcher):
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def search_by_url(self, url: str):
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parsed_url = urlparse(url)
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pathname = parsed_url.path
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match = re.match(r"^/models/(\d*)", pathname)
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model_id = match.group(1) if match else None
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query_params = parse_qs(parsed_url.query)
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version_id = query_params.get("modelVersionId", [None])[0]
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if not model_id:
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return []
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response = requests.get(f"https://civitai.com/api/v1/models/{model_id}")
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response.raise_for_status()
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res_data: dict = response.json()
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model_versions: list[dict] = res_data["modelVersions"]
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if version_id:
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model_versions = utils.filter_with(model_versions, {"id": int(version_id)})
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models: list[dict] = []
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for version in model_versions:
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model_files: list[dict] = version.get("files", [])
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model_files = utils.filter_with(model_files, {"type": "Model"})
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shortname = version.get("name", None) if len(model_files) > 0 else None
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for file in model_files:
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fullname = file.get("name", None)
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extension = os.path.splitext(fullname)[1]
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basename = os.path.splitext(fullname)[0]
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metadata_info = {
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"website": "Civitai",
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"modelPage": f"https://civitai.com/models/{model_id}?modelVersionId={version.get('id')}",
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"author": res_data.get("creator", {}).get("username", None),
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"baseModel": version.get("baseModel"),
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"hashes": file.get("hashes"),
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"metadata": file.get("metadata"),
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"preview": [i["url"] for i in version["images"]],
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}
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description_parts: list[str] = []
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description_parts.append("---")
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description_parts.append(yaml.dump(metadata_info).strip())
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description_parts.append("---")
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description_parts.append("")
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description_parts.append(f"# Trigger Words")
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description_parts.append("")
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description_parts.append(", ".join(version.get("trainedWords", ["No trigger words"])))
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description_parts.append("")
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description_parts.append(f"# About this version")
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description_parts.append("")
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description_parts.append(markdownify.markdownify(version.get("description", "<p>No description about this version</p>")).strip())
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description_parts.append("")
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description_parts.append(f"# {res_data.get('name')}")
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description_parts.append("")
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description_parts.append(markdownify.markdownify(res_data.get("description", "<p>No description about this model</p>")).strip())
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description_parts.append("")
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model = {
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"id": file.get("id"),
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"shortname": shortname or basename,
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"fullname": fullname,
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"basename": basename,
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"extension": extension,
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"preview": metadata_info.get("preview"),
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"sizeBytes": file.get("sizeKB", 0) * 1024,
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"type": self._resolve_model_type(res_data.get("type", "unknown")),
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"pathIndex": 0,
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"description": "\n".join(description_parts),
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"metadata": file.get("metadata"),
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"downloadPlatform": "civitai",
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"downloadUrl": file.get("downloadUrl"),
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"hashes": file.get("hashes"),
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}
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models.append(model)
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return models
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def search_by_hash(self, hash: str):
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if not hash:
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raise RuntimeError(f"Hash value is empty.")
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response = requests.get(f"https://civitai.com/api/v1/model-versions/by-hash/{hash}")
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response.raise_for_status()
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version: dict = response.json()
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model_id = version.get("modelId")
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version_id = version.get("id")
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model_page = f"https://civitai.com/models/{model_id}?modelVersionId={version_id}"
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models = self.search_by_url(model_page)
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for model in models:
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sha256 = model.get("hashes", {}).get("SHA256")
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if sha256 == hash:
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return model
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return models[0]
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def _resolve_model_type(self, model_type: str):
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map_legacy = {
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"TextualInversion": "embeddings",
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"LoCon": "loras",
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"DoRA": "loras",
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"Controlnet": "controlnet",
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"Upscaler": "upscale_models",
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"VAE": "vae",
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"unknown": "unknown",
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}
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return map_legacy.get(model_type, f"{model_type.lower()}s")
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class HuggingfaceModelSearcher(ModelSearcher):
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def search_by_url(self, url: str):
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parsed_url = urlparse(url)
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pathname = parsed_url.path
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space, name, *rest_paths = pathname.strip("/").split("/")
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model_id = f"{space}/{name}"
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rest_pathname = "/".join(rest_paths)
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response = requests.get(f"https://huggingface.co/api/models/{model_id}")
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response.raise_for_status()
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res_data: dict = response.json()
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sibling_files: list[str] = [x.get("rfilename") for x in res_data.get("siblings", [])]
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model_files = utils.filter_with(
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utils.filter_with(sibling_files, self._match_model_files()),
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self._match_tree_files(rest_pathname),
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)
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image_files = utils.filter_with(
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utils.filter_with(sibling_files, self._match_image_files()),
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self._match_tree_files(rest_pathname),
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)
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image_files = [f"https://huggingface.co/{model_id}/resolve/main/{filename}" for filename in image_files]
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models: list[dict] = []
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for filename in model_files:
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fullname = os.path.basename(filename)
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extension = os.path.splitext(fullname)[1]
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basename = os.path.splitext(fullname)[0]
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description_parts: list[str] = []
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metadata_info = {
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"website": "HuggingFace",
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"modelPage": f"https://huggingface.co/{model_id}",
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"author": res_data.get("author", None),
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"preview": image_files,
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}
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description_parts: list[str] = []
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description_parts.append("---")
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description_parts.append(yaml.dump(metadata_info).strip())
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description_parts.append("---")
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description_parts.append("")
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description_parts.append(f"# Trigger Words")
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description_parts.append("")
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description_parts.append("No trigger words")
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description_parts.append("")
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description_parts.append(f"# About this version")
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description_parts.append("")
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description_parts.append("No description about this version")
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description_parts.append("")
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description_parts.append(f"# {res_data.get('name')}")
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description_parts.append("")
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description_parts.append("No description about this model")
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description_parts.append("")
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model = {
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"id": filename,
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"shortname": filename,
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"fullname": fullname,
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"basename": basename,
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"extension": extension,
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"preview": image_files,
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"sizeBytes": 0,
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"type": "unknown",
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"pathIndex": 0,
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"description": "\n".join(description_parts),
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"metadata": {},
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"downloadPlatform": "huggingface",
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"downloadUrl": f"https://huggingface.co/{model_id}/resolve/main/{filename}?download=true",
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}
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models.append(model)
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return models
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def search_by_hash(self, hash: str):
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raise RuntimeError("Hash search is not supported by Huggingface.")
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def _match_model_files(self):
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extension = [
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".bin",
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".ckpt",
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".gguf",
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".onnx",
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".pt",
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".pth",
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".safetensors",
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]
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def _filter_model_files(file: str):
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return any(file.endswith(ext) for ext in extension)
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return _filter_model_files
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def _match_image_files(self):
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extension = [
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".png",
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".webp",
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".jpeg",
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".jpg",
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".jfif",
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".gif",
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".apng",
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]
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def _filter_image_files(file: str):
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return any(file.endswith(ext) for ext in extension)
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return _filter_image_files
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def _match_tree_files(self, pathname: str):
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target, *paths = pathname.split("/")
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def _filter_tree_files(file: str):
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if not target:
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return True
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if target != "tree" and target != "blob":
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return True
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prefix_path = "/".join(paths)
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return file.startswith(prefix_path)
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return _filter_tree_files
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def get_model_searcher_by_url(url: str) -> ModelSearcher:
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parsed_url = urlparse(url)
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host_name = parsed_url.hostname
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if host_name == "civitai.com":
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return CivitaiModelSearcher()
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elif host_name == "huggingface.co":
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return HuggingfaceModelSearcher()
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return UnknownWebsiteSearcher()
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import folder_paths
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from . import config
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from aiohttp import web
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class Information:
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def add_routes(self, routes):
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@routes.get("/model-manager/model-info")
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async def fetch_model_info(request):
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"""
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Fetch model information from network with model page.
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"""
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try:
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model_page = request.query.get("model-page", None)
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result = self.fetch_model_info(model_page)
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return web.json_response({"success": True, "data": result})
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except Exception as e:
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error_msg = f"Fetch model info failed: {str(e)}"
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utils.print_error(error_msg)
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return web.json_response({"success": False, "error": error_msg})
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@routes.post("/model-manager/model-info/scan")
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async def download_model_info(request):
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"""
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Create a task to download model information.
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"""
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post = await utils.get_request_body(request)
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try:
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scan_mode = post.get("scanMode", "diff")
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await self.download_model_info(scan_mode, request)
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return web.json_response({"success": True})
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except Exception as e:
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error_msg = f"Download model info failed: {str(e)}"
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utils.print_error(error_msg)
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return web.json_response({"success": False, "error": error_msg})
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@routes.get("/model-manager/preview/{type}/{index}/{filename:.*}")
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async def read_model_preview(request):
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"""
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Get the file stream of the specified image.
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If the file does not exist, no-preview.png is returned.
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:param type: The type of the model. eg.checkpoints, loras, vae, etc.
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:param index: The index of the model folders.
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:param filename: The filename of the image.
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"""
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model_type = request.match_info.get("type", None)
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index = int(request.match_info.get("index", None))
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filename = request.match_info.get("filename", None)
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extension_uri = config.extension_uri
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try:
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folders = folder_paths.get_folder_paths(model_type)
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base_path = folders[index]
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abs_path = utils.join_path(base_path, filename)
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except:
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abs_path = extension_uri
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if not os.path.isfile(abs_path):
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abs_path = utils.join_path(extension_uri, "assets", "no-preview.png")
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return web.FileResponse(abs_path)
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@routes.get("/model-manager/preview/download/{filename}")
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async def read_download_preview(request):
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filename = request.match_info.get("filename", None)
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extension_uri = config.extension_uri
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download_path = utils.get_download_path()
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preview_path = utils.join_path(download_path, filename)
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if not os.path.isfile(preview_path):
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preview_path = utils.join_path(extension_uri, "assets", "no-preview.png")
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return web.FileResponse(preview_path)
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def fetch_model_info(self, model_page: str):
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if not model_page:
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return []
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model_searcher = get_model_searcher_by_url(model_page)
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result = model_searcher.search_by_url(model_page)
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return result
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async def download_model_info(self, scan_mode: str, request):
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utils.print_info(f"Download model info for {scan_mode}")
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model_base_paths = utils.resolve_model_base_paths()
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for model_type in model_base_paths:
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folders, extensions = folder_paths.folder_names_and_paths[model_type]
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for path_index, base_path in enumerate(folders):
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files = utils.recursive_search_files(base_path, request)
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models = folder_paths.filter_files_extensions(files, folder_paths.supported_pt_extensions)
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for fullname in models:
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fullname = utils.normalize_path(fullname)
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basename = os.path.splitext(fullname)[0]
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abs_model_path = utils.join_path(base_path, fullname)
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image_name = utils.get_model_preview_name(abs_model_path)
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abs_image_path = utils.join_path(base_path, image_name)
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has_preview = os.path.isfile(abs_image_path)
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description_name = utils.get_model_description_name(abs_model_path)
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abs_description_path = utils.join_path(base_path, description_name) if description_name else None
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has_description = os.path.isfile(abs_description_path) if abs_description_path else False
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try:
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utils.print_info(f"Checking model {abs_model_path}")
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utils.print_debug(f"Scan mode: {scan_mode}")
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utils.print_debug(f"Has preview: {has_preview}")
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utils.print_debug(f"Has description: {has_description}")
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if scan_mode != "full" and (has_preview and has_description):
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continue
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utils.print_debug(f"Calculate sha256 for {abs_model_path}")
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hash_value = utils.calculate_sha256(abs_model_path)
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utils.print_info(f"Searching model info by hash {hash_value}")
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model_info = CivitaiModelSearcher().search_by_hash(hash_value)
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preview_url_list = model_info.get("preview", [])
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preview_image_url = preview_url_list[0] if preview_url_list else None
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if preview_image_url:
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utils.print_debug(f"Save preview image to {abs_image_path}")
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utils.save_model_preview_image(abs_model_path, preview_image_url)
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description = model_info.get("description", None)
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if description:
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utils.save_model_description(abs_model_path, description)
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except Exception as e:
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utils.print_error(f"Failed to download model info for {abs_model_path}: {e}")
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utils.print_debug("Completed scan model information.")
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