mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-21 21:22:11 -03:00
feat: implement batch import recipe functionality (frontend + backend fixes)
Backend fixes: - Add missing API route for /api/lm/recipes/batch-import/progress (GET) - Add missing API route for /api/lm/recipes/batch-import/directory (POST) - Add missing API route for /api/lm/recipes/browse-directory (POST) - Register WebSocket endpoint for batch import progress - Fix skip_no_metadata default value (True -> False) to allow no-LoRA imports - Add items array to BatchImportProgress.to_dict() for detailed results Frontend implementation: - Create BatchImportManager.js with complete batch import workflow - Add directory browser UI for selecting folders - Add batch import modal with URL list and directory input modes - Implement real-time progress tracking (WebSocket + HTTP polling) - Add results summary with success/failed/skipped statistics - Add expandable details view showing individual item status - Auto-refresh recipe list after import completion UI improvements: - Add spinner animation for importing status - Simplify results summary UI to match progress stats styling - Fix current item text alignment - Fix dark theme styling for directory browser button - Fix batch import button styling consistency Translations: - Add batch import related i18n keys to all locale files - Run sync_translation_keys.py to sync all translations Fixes: - Batch import now allows images without LoRAs (matches single import behavior) - Progress endpoint now returns complete items array with status details - Results view correctly displays skipped items with error messages
This commit is contained in:
Will Miao
@@ -10,7 +10,11 @@ import uuid
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from typing import Dict, List, Optional, Set, Tuple
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from urllib.parse import urlparse
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from ..utils.models import LoraMetadata, CheckpointMetadata, EmbeddingMetadata
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from ..utils.constants import CARD_PREVIEW_WIDTH, DIFFUSION_MODEL_BASE_MODELS, VALID_LORA_TYPES
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from ..utils.constants import (
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CARD_PREVIEW_WIDTH,
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DIFFUSION_MODEL_BASE_MODELS,
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VALID_LORA_TYPES,
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)
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from ..utils.civitai_utils import rewrite_preview_url
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from ..utils.preview_selection import select_preview_media
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from ..utils.utils import sanitize_folder_name
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@@ -352,10 +356,12 @@ class DownloadManager:
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# Check if this checkpoint should be treated as a diffusion model based on baseModel
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is_diffusion_model = False
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if model_type == "checkpoint":
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base_model_value = version_info.get('baseModel', '')
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base_model_value = version_info.get("baseModel", "")
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if base_model_value in DIFFUSION_MODEL_BASE_MODELS:
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is_diffusion_model = True
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logger.info(f"baseModel '{base_model_value}' is a known diffusion model, routing to unet folder")
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logger.info(
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f"baseModel '{base_model_value}' is a known diffusion model, routing to unet folder"
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)
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# Case 2: model_version_id was None, check after getting version_info
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if model_version_id is None:
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@@ -464,7 +470,7 @@ class DownloadManager:
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# 2. Get file information
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files = version_info.get("files", [])
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file_info = None
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# If file_params is provided, try to find matching file
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if file_params and model_version_id:
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target_type = file_params.get("type", "Model")
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@@ -472,23 +478,28 @@ class DownloadManager:
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target_size = file_params.get("size", "full")
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target_fp = file_params.get("fp")
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is_primary = file_params.get("isPrimary", False)
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if is_primary:
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# Find primary file
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file_info = next(
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(f for f in files if f.get("primary") and f.get("type") in ("Model", "Negative")),
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None
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(
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f
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for f in files
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if f.get("primary")
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and f.get("type") in ("Model", "Negative")
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),
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None,
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)
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else:
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# Match by metadata
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for f in files:
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f_type = f.get("type", "")
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f_meta = f.get("metadata", {})
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# Check type match
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if f_type != target_type:
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continue
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# Check metadata match
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if f_meta.get("format") != target_format:
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continue
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@@ -496,10 +507,10 @@ class DownloadManager:
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continue
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if target_fp and f_meta.get("fp") != target_fp:
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continue
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file_info = f
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break
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# Fallback to primary file if no match found
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if not file_info:
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file_info = next(
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@@ -510,7 +521,7 @@ class DownloadManager:
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),
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None,
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)
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if not file_info:
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return {"success": False, "error": "No suitable file found in metadata"}
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mirrors = file_info.get("mirrors") or []
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@@ -1220,7 +1231,13 @@ class DownloadManager:
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entries: List = []
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for index, file_path in enumerate(file_paths):
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entry = base_metadata if index == 0 else copy.deepcopy(base_metadata)
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entry.update_file_info(file_path)
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# Update file paths without modifying size and modified timestamps
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# modified should remain as the download start time (import time)
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# size will be updated below to reflect actual downloaded file size
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entry.file_path = file_path.replace(os.sep, "/")
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entry.file_name = os.path.splitext(os.path.basename(file_path))[0]
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# Update size to actual downloaded file size
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entry.size = os.path.getsize(file_path)
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entry.sha256 = await calculate_sha256(file_path)
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entries.append(entry)
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