659637c6e0
* pref: migrate fetch model info to end back * fix(download): can't select model type * feat: add scan model info * feat: add trigger button in setting * feat: add printing logs * chore: add explanation of scan model info
318 lines
10 KiB
Python
318 lines
10 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(
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f"Unknown Website, please input a URL from huggingface.co or civitai.com."
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)
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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(
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", ".join(version.get("trainedWords", ["No trigger words"]))
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)
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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(
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markdownify.markdownify(
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version.get(
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"description", "<p>No description about this version</p>"
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)
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).strip()
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)
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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(
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markdownify.markdownify(
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res_data.get(
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"description", "<p>No description about this model</p>"
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)
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).strip()
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)
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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(
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f"https://civitai.com/api/v1/model-versions/by-hash/{hash}"
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)
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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 = (
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f"https://civitai.com/models/{model_id}?modelVersionId={version_id}"
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)
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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] = [
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x.get("rfilename") for x in res_data.get("siblings", [])
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]
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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 = [
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f"https://huggingface.co/{model_id}/resolve/main/{filename}"
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for filename in image_files
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]
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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": "",
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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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