Files
ComfyUI-Lora-Manager/py/routes/misc_routes.py

515 lines
22 KiB
Python

import logging
import os
import asyncio
import json
import time
import tkinter as tk
from tkinter import filedialog
import aiohttp
from aiohttp import web
from ..services.settings_manager import settings
from ..utils.usage_stats import UsageStats
from ..services.service_registry import ServiceRegistry
from ..utils.exif_utils import ExifUtils
from ..utils.constants import EXAMPLE_IMAGE_WIDTH, SUPPORTED_MEDIA_EXTENSIONS
logger = logging.getLogger(__name__)
# Download status tracking
download_task = None
is_downloading = False
download_progress = {
'total': 0,
'completed': 0,
'current_model': '',
'status': 'idle', # idle, running, paused, completed, error
'errors': [],
'last_error': None,
'start_time': None,
'end_time': None,
'processed_models': set() # Track models that have been processed
}
class MiscRoutes:
"""Miscellaneous routes for various utility functions"""
@staticmethod
def setup_routes(app):
"""Register miscellaneous routes"""
app.router.add_post('/api/settings', MiscRoutes.update_settings)
# Usage stats routes
app.router.add_post('/api/update-usage-stats', MiscRoutes.update_usage_stats)
app.router.add_get('/api/get-usage-stats', MiscRoutes.get_usage_stats)
# Example images download routes
app.router.add_post('/api/download-example-images', MiscRoutes.download_example_images)
app.router.add_get('/api/example-images-status', MiscRoutes.get_example_images_status)
app.router.add_post('/api/pause-example-images', MiscRoutes.pause_example_images)
app.router.add_post('/api/resume-example-images', MiscRoutes.resume_example_images)
@staticmethod
async def update_settings(request):
"""Update application settings"""
try:
data = await request.json()
# Validate and update settings
for key, value in data.items():
# Special handling for example_images_path - verify path exists
if key == 'example_images_path' and value:
if not os.path.exists(value):
return web.json_response({
'success': False,
'error': f"Path does not exist: {value}"
})
# Path changed - server restart required for new path to take effect
old_path = settings.get('example_images_path')
if old_path != value:
logger.info(f"Example images path changed to {value} - server restart required")
# Save to settings
settings.set(key, value)
return web.json_response({'success': True})
except Exception as e:
logger.error(f"Error updating settings: {e}", exc_info=True)
return web.Response(status=500, text=str(e))
@staticmethod
async def update_usage_stats(request):
"""
Update usage statistics based on a prompt_id
Expects a JSON body with:
{
"prompt_id": "string"
}
"""
try:
# Parse the request body
data = await request.json()
prompt_id = data.get('prompt_id')
if not prompt_id:
return web.json_response({
'success': False,
'error': 'Missing prompt_id'
}, status=400)
# Call the UsageStats to process this prompt_id synchronously
usage_stats = UsageStats()
await usage_stats.process_execution(prompt_id)
return web.json_response({
'success': True
})
except Exception as e:
logger.error(f"Failed to update usage stats: {e}", exc_info=True)
return web.json_response({
'success': False,
'error': str(e)
}, status=500)
@staticmethod
async def get_usage_stats(request):
"""Get current usage statistics"""
try:
usage_stats = UsageStats()
stats = await usage_stats.get_stats()
return web.json_response({
'success': True,
'data': stats
})
except Exception as e:
logger.error(f"Failed to get usage stats: {e}", exc_info=True)
return web.json_response({
'success': False,
'error': str(e)
}, status=500)
@staticmethod
async def download_example_images(request):
"""
Download example images for models from Civitai
Expects a JSON body with:
{
"output_dir": "path/to/output", # Base directory to save example images
"optimize": true, # Whether to optimize images (default: true)
"model_types": ["lora", "checkpoint"], # Model types to process (default: both)
"delay": 1.0 # Delay between downloads to avoid rate limiting (default: 1.0)
}
"""
global download_task, is_downloading, download_progress
if is_downloading:
# Create a copy for JSON serialization
response_progress = download_progress.copy()
response_progress['processed_models'] = list(download_progress['processed_models'])
return web.json_response({
'success': False,
'error': 'Download already in progress',
'status': response_progress
}, status=400)
try:
# Parse the request body
data = await request.json()
output_dir = data.get('output_dir')
optimize = data.get('optimize', True)
model_types = data.get('model_types', ['lora', 'checkpoint'])
delay = float(data.get('delay', 0.2))
if not output_dir:
return web.json_response({
'success': False,
'error': 'Missing output_dir parameter'
}, status=400)
# Create the output directory
os.makedirs(output_dir, exist_ok=True)
# Initialize progress tracking
download_progress['total'] = 0
download_progress['completed'] = 0
download_progress['current_model'] = ''
download_progress['status'] = 'running'
download_progress['errors'] = []
download_progress['last_error'] = None
download_progress['start_time'] = time.time()
download_progress['end_time'] = None
# Get the processed models list from a file if it exists
progress_file = os.path.join(output_dir, '.download_progress.json')
if os.path.exists(progress_file):
try:
with open(progress_file, 'r', encoding='utf-8') as f:
saved_progress = json.load(f)
download_progress['processed_models'] = set(saved_progress.get('processed_models', []))
logger.info(f"Loaded previous progress, {len(download_progress['processed_models'])} models already processed")
except Exception as e:
logger.error(f"Failed to load progress file: {e}")
download_progress['processed_models'] = set()
else:
download_progress['processed_models'] = set()
# Start the download task
is_downloading = True
download_task = asyncio.create_task(
MiscRoutes._download_all_example_images(
output_dir,
optimize,
model_types,
delay
)
)
# Create a copy for JSON serialization
response_progress = download_progress.copy()
response_progress['processed_models'] = list(download_progress['processed_models'])
return web.json_response({
'success': True,
'message': 'Download started',
'status': response_progress
})
except Exception as e:
logger.error(f"Failed to start example images download: {e}", exc_info=True)
return web.json_response({
'success': False,
'error': str(e)
}, status=500)
@staticmethod
async def get_example_images_status(request):
"""Get the current status of example images download"""
global download_progress
# Create a copy of the progress dict with the set converted to a list for JSON serialization
response_progress = download_progress.copy()
response_progress['processed_models'] = list(download_progress['processed_models'])
return web.json_response({
'success': True,
'is_downloading': is_downloading,
'status': response_progress
})
@staticmethod
async def pause_example_images(request):
"""Pause the example images download"""
global download_progress
if not is_downloading:
return web.json_response({
'success': False,
'error': 'No download in progress'
}, status=400)
download_progress['status'] = 'paused'
return web.json_response({
'success': True,
'message': 'Download paused'
})
@staticmethod
async def resume_example_images(request):
"""Resume the example images download"""
global download_progress
if not is_downloading:
return web.json_response({
'success': False,
'error': 'No download in progress'
}, status=400)
if download_progress['status'] == 'paused':
download_progress['status'] = 'running'
return web.json_response({
'success': True,
'message': 'Download resumed'
})
else:
return web.json_response({
'success': False,
'error': f"Download is in '{download_progress['status']}' state, cannot resume"
}, status=400)
@staticmethod
async def _download_all_example_images(output_dir, optimize, model_types, delay):
"""Download example images for all models
Args:
output_dir: Base directory to save example images
optimize: Whether to optimize images
model_types: List of model types to process
delay: Delay between downloads to avoid rate limiting
"""
global is_downloading, download_progress
# Create an independent session for downloading example images
# This avoids interference with the CivitAI client's session
connector = aiohttp.TCPConnector(
ssl=True,
limit=3,
force_close=False,
enable_cleanup_closed=True
)
timeout = aiohttp.ClientTimeout(total=None, connect=60, sock_read=60)
# Create a dedicated session just for this download task
independent_session = aiohttp.ClientSession(
connector=connector,
trust_env=True,
timeout=timeout
)
try:
# Get the scanners
scanners = []
if 'lora' in model_types:
lora_scanner = await ServiceRegistry.get_lora_scanner()
scanners.append(('lora', lora_scanner))
if 'checkpoint' in model_types:
checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
scanners.append(('checkpoint', checkpoint_scanner))
# Get all models from all scanners
all_models = []
for scanner_type, scanner in scanners:
cache = await scanner.get_cached_data()
if cache and cache.raw_data:
for model in cache.raw_data:
# Only process models with images and a valid sha256
if model.get('civitai') and model.get('civitai', {}).get('images') and model.get('sha256'):
all_models.append((scanner_type, model))
# Update total count
download_progress['total'] = len(all_models)
logger.info(f"Found {download_progress['total']} models with example images")
# Process each model
for scanner_type, model in all_models:
# Check if download is paused
while download_progress['status'] == 'paused':
await asyncio.sleep(1)
# Check if download should continue
if download_progress['status'] != 'running':
logger.info(f"Download stopped: {download_progress['status']}")
break
model_success = True # Track if all images for this model download successfully
try:
# Update current model info
model_hash = model.get('sha256', '').lower()
model_name = model.get('model_name', 'Unknown')
download_progress['current_model'] = f"{model_name} ({model_hash[:8]})"
# Skip if already processed
if model_hash in download_progress['processed_models']:
logger.debug(f"Skipping already processed model: {model_name}")
download_progress['completed'] += 1
continue
# Create model directory
model_dir = os.path.join(output_dir, model_hash)
os.makedirs(model_dir, exist_ok=True)
# Process images for this model
images = model.get('civitai', {}).get('images', [])
if not images:
logger.debug(f"No images found for model: {model_name}")
download_progress['processed_models'].add(model_hash)
download_progress['completed'] += 1
continue
# Download example images
for i, image in enumerate(images, 1):
image_url = image.get('url')
if not image_url:
continue
# Get image filename from URL
image_filename = os.path.basename(image_url.split('?')[0])
image_ext = os.path.splitext(image_filename)[1].lower()
# Handle both images and videos
is_image = image_ext in SUPPORTED_MEDIA_EXTENSIONS['images']
is_video = image_ext in SUPPORTED_MEDIA_EXTENSIONS['videos']
if not (is_image or is_video):
logger.debug(f"Skipping unsupported file type: {image_filename}")
continue
save_filename = f"image_{i}{image_ext}"
# Check if already downloaded
save_path = os.path.join(model_dir, save_filename)
if os.path.exists(save_path):
logger.debug(f"File already exists: {save_path}")
continue
# Download the file
try:
logger.debug(f"Downloading {save_filename} for {model_name}")
# Direct download using the independent session
async with independent_session.get(image_url, timeout=60) as response:
if response.status == 200:
if is_image and optimize:
# For images, optimize if requested
image_data = await response.read()
optimized_data, ext = ExifUtils.optimize_image(
image_data,
target_width=EXAMPLE_IMAGE_WIDTH,
format='webp',
quality=85,
preserve_metadata=False
)
# Update save filename if format changed
if ext == '.webp':
save_filename = os.path.splitext(save_filename)[0] + '.webp'
save_path = os.path.join(model_dir, save_filename)
# Save the optimized image
with open(save_path, 'wb') as f:
f.write(optimized_data)
else:
# For videos or unoptimized images, save directly
with open(save_path, 'wb') as f:
async for chunk in response.content.iter_chunked(8192):
if chunk:
f.write(chunk)
else:
error_msg = f"Failed to download file: {image_url}, status code: {response.status}"
logger.warning(error_msg)
download_progress['errors'].append(error_msg)
download_progress['last_error'] = error_msg
model_success = False # Mark model as failed
# Add a delay between downloads
await asyncio.sleep(delay)
except Exception as e:
error_msg = f"Error downloading file {image_url}: {str(e)}"
logger.error(error_msg)
download_progress['errors'].append(error_msg)
download_progress['last_error'] = error_msg
model_success = False # Mark model as failed
# Only mark model as processed if all images downloaded successfully
if model_success:
download_progress['processed_models'].add(model_hash)
else:
logger.warning(f"Model {model_name} had download errors, will not mark as completed")
# Save progress to file periodically
if download_progress['completed'] % 10 == 0 or download_progress['completed'] == download_progress['total'] - 1:
progress_file = os.path.join(output_dir, '.download_progress.json')
with open(progress_file, 'w', encoding='utf-8') as f:
json.dump({
'processed_models': list(download_progress['processed_models']),
'completed': download_progress['completed'],
'total': download_progress['total'],
'last_update': time.time()
}, f, indent=2)
except Exception as e:
error_msg = f"Error processing model {model.get('model_name')}: {str(e)}"
logger.error(error_msg, exc_info=True)
download_progress['errors'].append(error_msg)
download_progress['last_error'] = error_msg
# Update progress
download_progress['completed'] += 1
# Mark as completed
download_progress['status'] = 'completed'
download_progress['end_time'] = time.time()
logger.info(f"Example images download completed: {download_progress['completed']}/{download_progress['total']} models processed")
except Exception as e:
error_msg = f"Error during example images download: {str(e)}"
logger.error(error_msg, exc_info=True)
download_progress['errors'].append(error_msg)
download_progress['last_error'] = error_msg
download_progress['status'] = 'error'
download_progress['end_time'] = time.time()
finally:
# Close the independent session
try:
await independent_session.close()
except Exception as e:
logger.error(f"Error closing download session: {e}")
# Save final progress to file
try:
progress_file = os.path.join(output_dir, '.download_progress.json')
with open(progress_file, 'w', encoding='utf-8') as f:
json.dump({
'processed_models': list(download_progress['processed_models']),
'completed': download_progress['completed'],
'total': download_progress['total'],
'last_update': time.time(),
'status': download_progress['status']
}, f, indent=2)
except Exception as e:
logger.error(f"Failed to save progress file: {e}")
# Set download status to not downloading
is_downloading = False