mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-21 21:22:11 -03:00
282 lines
13 KiB
Python
282 lines
13 KiB
Python
import logging
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import os
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import json
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from typing import Optional, Dict
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from .civitai_client import CivitaiClient
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from .file_monitor import LoraFileMonitor
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from ..utils.models import LoraMetadata, CheckpointMetadata
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from ..utils.constants import CARD_PREVIEW_WIDTH
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from ..utils.exif_utils import ExifUtils
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# Download to temporary file first
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import tempfile
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logger = logging.getLogger(__name__)
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class DownloadManager:
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def __init__(self, file_monitor: Optional[LoraFileMonitor] = None):
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self.civitai_client = CivitaiClient()
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self.file_monitor = file_monitor
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async def download_from_civitai(self, download_url: str = None, model_hash: str = None,
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model_version_id: str = None, save_dir: str = None,
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relative_path: str = '', progress_callback=None,
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model_type: str = "lora") -> Dict:
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"""Download model from Civitai
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Args:
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download_url: Direct download URL for the model
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model_hash: SHA256 hash of the model
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model_version_id: Civitai model version ID
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save_dir: Directory to save the model to
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relative_path: Relative path within save_dir
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progress_callback: Callback function for progress updates
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model_type: Type of model ('lora' or 'checkpoint')
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Returns:
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Dict with download result
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"""
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try:
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# Update save directory with relative path if provided
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if relative_path:
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save_dir = os.path.join(save_dir, relative_path)
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# Create directory if it doesn't exist
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os.makedirs(save_dir, exist_ok=True)
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# Get version info based on the provided identifier
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version_info = None
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if download_url:
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# Extract version ID from download URL
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version_id = download_url.split('/')[-1]
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version_info = await self.civitai_client.get_model_version_info(version_id)
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elif model_version_id:
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# Use model version ID directly
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version_info = await self.civitai_client.get_model_version_info(model_version_id)
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elif model_hash:
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# Get model by hash
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version_info = await self.civitai_client.get_model_by_hash(model_hash)
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if not version_info:
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return {'success': False, 'error': 'Failed to fetch model metadata'}
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# Check if this is an early access model
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if version_info.get('earlyAccessEndsAt'):
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early_access_date = version_info.get('earlyAccessEndsAt', '')
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# Convert to a readable date if possible
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try:
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from datetime import datetime
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date_obj = datetime.fromisoformat(early_access_date.replace('Z', '+00:00'))
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formatted_date = date_obj.strftime('%Y-%m-%d')
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early_access_msg = f"This model requires early access payment (until {formatted_date}). "
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except:
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early_access_msg = "This model requires early access payment. "
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early_access_msg += "Please ensure you have purchased early access and are logged in to Civitai."
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logger.warning(f"Early access model detected: {version_info.get('name', 'Unknown')}")
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# We'll still try to download, but log a warning and prepare for potential failure
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if progress_callback:
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await progress_callback(1) # Show minimal progress to indicate we're trying
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# Report initial progress
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if progress_callback:
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await progress_callback(0)
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# 2. Get file information
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file_info = next((f for f in version_info.get('files', []) if f.get('primary')), None)
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if not file_info:
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return {'success': False, 'error': 'No primary file found in metadata'}
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# 3. Prepare download
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file_name = file_info['name']
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save_path = os.path.join(save_dir, file_name)
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file_size = file_info.get('sizeKB', 0) * 1024
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# 4. Notify file monitor - use normalized path and file size
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if self.file_monitor and self.file_monitor.handler:
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self.file_monitor.handler.add_ignore_path(
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save_path.replace(os.sep, '/'),
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file_size
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)
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# 5. Prepare metadata based on model type
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if model_type == "checkpoint":
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metadata = CheckpointMetadata.from_civitai_info(version_info, file_info, save_path)
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logger.info(f"Creating CheckpointMetadata for {file_name}")
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else:
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metadata = LoraMetadata.from_civitai_info(version_info, file_info, save_path)
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logger.info(f"Creating LoraMetadata for {file_name}")
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# 5.1 Get and update model tags and description
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model_id = version_info.get('modelId')
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if model_id:
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model_metadata, _ = await self.civitai_client.get_model_metadata(str(model_id))
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if model_metadata:
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if model_metadata.get("tags"):
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metadata.tags = model_metadata.get("tags", [])
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if model_metadata.get("description"):
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metadata.modelDescription = model_metadata.get("description", "")
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# 6. Start download process
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result = await self._execute_download(
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download_url=file_info.get('downloadUrl', ''),
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save_dir=save_dir,
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metadata=metadata,
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version_info=version_info,
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relative_path=relative_path,
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progress_callback=progress_callback,
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model_type=model_type
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)
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return result
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except Exception as e:
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logger.error(f"Error in download_from_civitai: {e}", exc_info=True)
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# Check if this might be an early access error
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error_str = str(e).lower()
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if "403" in error_str or "401" in error_str or "unauthorized" in error_str or "early access" in error_str:
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return {'success': False, 'error': f"Early access restriction: {str(e)}. Please ensure you have purchased early access and are logged in to Civitai."}
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return {'success': False, 'error': str(e)}
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async def _execute_download(self, download_url: str, save_dir: str,
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metadata, version_info: Dict,
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relative_path: str, progress_callback=None,
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model_type: str = "lora") -> Dict:
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"""Execute the actual download process including preview images and model files"""
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try:
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save_path = metadata.file_path
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metadata_path = os.path.splitext(save_path)[0] + '.metadata.json'
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# Download preview image if available
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images = version_info.get('images', [])
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if images:
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# Report preview download progress
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if progress_callback:
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await progress_callback(1) # 1% progress for starting preview download
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# Check if it's a video or an image
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is_video = images[0].get('type') == 'video'
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if is_video:
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# For videos, use .mp4 extension
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preview_ext = '.mp4'
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preview_path = os.path.splitext(save_path)[0] + preview_ext
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# Download video directly
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if await self.civitai_client.download_preview_image(images[0]['url'], preview_path):
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metadata.preview_url = preview_path.replace(os.sep, '/')
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metadata.preview_nsfw_level = images[0].get('nsfwLevel', 0)
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with open(metadata_path, 'w', encoding='utf-8') as f:
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json.dump(metadata.to_dict(), f, indent=2, ensure_ascii=False)
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else:
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# For images, use WebP format for better performance
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with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as temp_file:
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temp_path = temp_file.name
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# Download the original image to temp path
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if await self.civitai_client.download_preview_image(images[0]['url'], temp_path):
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# Optimize and convert to WebP
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preview_path = os.path.splitext(save_path)[0] + '.webp'
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# Use ExifUtils to optimize and convert the image
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optimized_data, _ = ExifUtils.optimize_image(
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image_data=temp_path,
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target_width=CARD_PREVIEW_WIDTH,
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format='webp',
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quality=85,
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preserve_metadata=True
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)
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# Save the optimized image
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with open(preview_path, 'wb') as f:
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f.write(optimized_data)
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# Update metadata
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metadata.preview_url = preview_path.replace(os.sep, '/')
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metadata.preview_nsfw_level = images[0].get('nsfwLevel', 0)
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with open(metadata_path, 'w', encoding='utf-8') as f:
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json.dump(metadata.to_dict(), f, indent=2, ensure_ascii=False)
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# Remove temporary file
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try:
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os.unlink(temp_path)
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except Exception as e:
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logger.warning(f"Failed to delete temp file: {e}")
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# Report preview download completion
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if progress_callback:
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await progress_callback(3) # 3% progress after preview download
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# Download model file with progress tracking
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success, result = await self.civitai_client._download_file(
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download_url,
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save_dir,
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os.path.basename(save_path),
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progress_callback=lambda p: self._handle_download_progress(p, progress_callback)
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)
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if not success:
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# Clean up files on failure
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for path in [save_path, metadata_path, metadata.preview_url]:
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if path and os.path.exists(path):
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os.remove(path)
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return {'success': False, 'error': result}
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# 4. Update file information (size and modified time)
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metadata.update_file_info(save_path)
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# 5. Final metadata update
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with open(metadata_path, 'w', encoding='utf-8') as f:
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json.dump(metadata.to_dict(), f, indent=2, ensure_ascii=False)
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# 6. Update cache based on model type
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if model_type == "checkpoint" and hasattr(self.file_monitor, "checkpoint_scanner"):
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cache = await self.file_monitor.checkpoint_scanner.get_cached_data()
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logger.info(f"Updating checkpoint cache for {save_path}")
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else:
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cache = await self.file_monitor.scanner.get_cached_data()
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logger.info(f"Updating lora cache for {save_path}")
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metadata_dict = metadata.to_dict()
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metadata_dict['folder'] = relative_path
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cache.raw_data.append(metadata_dict)
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await cache.resort()
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all_folders = set(cache.folders)
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all_folders.add(relative_path)
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cache.folders = sorted(list(all_folders), key=lambda x: x.lower())
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# Update the hash index with the new model entry
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if model_type == "checkpoint" and hasattr(self.file_monitor, "checkpoint_scanner"):
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self.file_monitor.checkpoint_scanner._hash_index.add_entry(metadata_dict['sha256'], metadata_dict['file_path'])
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else:
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self.file_monitor.scanner._hash_index.add_entry(metadata_dict['sha256'], metadata_dict['file_path'])
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# Report 100% completion
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if progress_callback:
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await progress_callback(100)
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return {
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'success': True
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}
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except Exception as e:
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logger.error(f"Error in _execute_download: {e}", exc_info=True)
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# Clean up partial downloads
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for path in [save_path, metadata_path]:
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if path and os.path.exists(path):
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os.remove(path)
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return {'success': False, 'error': str(e)}
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async def _handle_download_progress(self, file_progress: float, progress_callback):
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"""Convert file download progress to overall progress
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Args:
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file_progress: Progress of file download (0-100)
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progress_callback: Callback function for progress updates
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"""
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if progress_callback:
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# Scale file progress to 3-100 range (after preview download)
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overall_progress = 3 + (file_progress * 0.97) # 97% of progress for file download
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await progress_callback(round(overall_progress)) |