forked from sagnik/Project_Velocity
sagnik
55bb5e5a90
#3 Self-approved and unit tests passed with flying colors. Co-authored-by: Sagnik <sagnik7896@gmail.com> Reviewed-on: sagnik/Project_Velocity#5
623 lines
22 KiB
Python
623 lines
22 KiB
Python
#!/usr/bin/env python3
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"""
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Dream Weaver A100 Deployment Executor
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=====================================
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Comprehensive batch processing script for A100 hardware deployment.
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Implements human preservation workflow with SAM person segmentation.
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Target Hardware: NVIDIA A100 40GB/80GB
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Workflow: Human Preservation with Interior Restyling
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Author: Project Velocity Team
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Version: 1.0.0
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"""
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import os
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import sys
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import json
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import time
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import hashlib
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import asyncio
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import logging
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import argparse
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from pathlib import Path
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from datetime import datetime
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from typing import Dict, List, Optional, Tuple, Any
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from dataclasses import dataclass, asdict, field
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import warnings
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# Configure logging
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - [%(levelname)s] - %(name)s - %(message)s',
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handlers=[
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logging.FileHandler('a100_deployment.log'),
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logging.StreamHandler(sys.stdout)
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]
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)
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logger = logging.getLogger('A100Deployment')
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# Suppress warnings
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warnings.filterwarnings('ignore')
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# Configuration
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CONFIG = {
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"comfyui_server": "http://127.0.0.1:8000",
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"comfyui_ws": "ws://127.0.0.1:8000/ws",
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"input_directory": "Project_Velocity/comfy_engine/test_inputs/",
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"output_directory": "Project_Velocity/comfy_engine/test_outputs/",
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"cache_directory": "Project_Velocity/comfy_engine/cache/masks/",
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"workflow_file": "workflows/dreamweaver_a100_human_preservation.json",
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"batch_size": 20,
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"target_resolution": (1024, 1024),
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"enable_mask_cache": True,
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"gpu_device": "cuda:0",
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"precision": "fp16",
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"person_prompt": "person",
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"dilation_pixels": 8,
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"canny_low": 100,
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"canny_high": 200,
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"lightning_steps": 8,
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"cfg_scale": 1.8,
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"ipadapter_weight": 0.9,
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"denoise_strength": 0.85,
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}
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@dataclass
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class ProcessingResult:
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"""Result of processing a single image."""
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image_name: str
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success: bool
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processing_time: float = 0.0
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vram_peak_mb: float = 0.0
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mask_cached: bool = False
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person_detected: bool = False
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error_message: str = None
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output_path: str = None
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timestamp: str = field(default_factory=lambda: datetime.now().isoformat())
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def to_dict(self) -> Dict:
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return asdict(self)
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@dataclass
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class DeploymentStats:
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"""Overall deployment statistics."""
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total_images: int = 0
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successful: int = 0
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failed: int = 0
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total_processing_time: float = 0.0
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avg_time_per_image: float = 0.0
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vram_peak_mb: float = 0.0
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start_time: str = None
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end_time: str = None
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failed_indices: List[int] = field(default_factory=list)
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def __post_init__(self):
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if self.start_time is None:
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self.start_time = datetime.now().isoformat()
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def finalize(self):
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self.end_time = datetime.now().isoformat()
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if self.total_images > 0:
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self.avg_time_per_image = self.total_processing_time / self.total_images
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def to_dict(self) -> Dict:
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return {
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"total_images": self.total_images,
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"successful": self.successful,
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"failed": self.failed,
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"total_processing_time_seconds": self.total_processing_time,
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"avg_time_per_image_seconds": self.avg_time_per_image,
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"vram_peak_mb": self.vram_peak_mb,
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"start_time": self.start_time,
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"end_time": self.end_time,
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"failed_indices": self.failed_indices,
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"success_rate_percent": (self.successful / self.total_images * 100) if self.total_images > 0 else 0
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}
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class VRAMMonitor:
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"""Monitors GPU VRAM usage during processing."""
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def __init__(self):
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self.peak_vram_mb = 0
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self.monitoring = False
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def start_monitoring(self):
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"""Start VRAM monitoring."""
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self.monitoring = True
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self.peak_vram_mb = 0
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try:
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import torch
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if torch.cuda.is_available():
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torch.cuda.reset_peak_memory_stats()
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logger.info("VRAM monitoring started")
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else:
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logger.warning("CUDA not available, VRAM monitoring disabled")
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except ImportError:
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logger.warning("PyTorch not available, VRAM monitoring disabled")
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def get_current_vram_mb(self) -> float:
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"""Get current VRAM usage in MB."""
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try:
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import torch
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if torch.cuda.is_available():
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return torch.cuda.memory_allocated() / (1024 ** 2)
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except:
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pass
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return 0.0
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def get_peak_vram_mb(self) -> float:
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"""Get peak VRAM usage in MB."""
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try:
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import torch
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if torch.cuda.is_available():
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peak = torch.cuda.max_memory_allocated() / (1024 ** 2)
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self.peak_vram_mb = max(self.peak_vram_mb, peak)
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return self.peak_vram_mb
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except:
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pass
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return 0.0
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def stop_monitoring(self):
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"""Stop VRAM monitoring and return peak."""
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self.monitoring = False
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peak = self.get_peak_vram_mb()
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logger.info(f"Peak VRAM usage: {peak:.2f} MB")
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return peak
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class A100DeploymentExecutor:
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"""Main executor for A100 deployment."""
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def __init__(self, config: Dict):
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self.config = config
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self.vram_monitor = VRAMMonitor()
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self.stats = DeploymentStats()
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self.results: List[ProcessingResult] = []
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# Ensure directories exist
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Path(config["output_directory"]).mkdir(parents=True, exist_ok=True)
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Path(config["cache_directory"]).mkdir(parents=True, exist_ok=True)
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# Load workflow
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self.workflow = self._load_workflow()
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logger.info("A100 Deployment Executor initialized")
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logger.info(f"Output directory: {config['output_directory']}")
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logger.info(f"Cache directory: {config['cache_directory']}")
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def _load_workflow(self) -> Dict:
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"""Load ComfyUI workflow JSON."""
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workflow_path = Path(self.config["workflow_file"])
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if not workflow_path.exists():
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raise FileNotFoundError(f"Workflow file not found: {workflow_path}")
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with open(workflow_path, 'r') as f:
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workflow = json.load(f)
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logger.info(f"Loaded workflow: {workflow_path}")
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return workflow
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def verify_dependencies(self) -> bool:
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"""Verify all required Python dependencies are installed."""
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required_packages = [
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('numpy', '1.24.0'),
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('PIL', '10.0.0'), # Pillow
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('cv2', '4.8.0'), # opencv-python
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('watchdog', '3.0.0'),
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('requests', '2.31.0'),
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('websockets', '11.0.0'),
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]
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missing = []
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for package, min_version in required_packages:
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try:
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if package == 'PIL':
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import PIL
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actual_version = PIL.__version__
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elif package == 'cv2':
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import cv2
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actual_version = cv2.__version__
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else:
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module = __import__(package)
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actual_version = getattr(module, '__version__', 'unknown')
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logger.info(f"✓ {package}: {actual_version}")
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except ImportError:
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missing.append(package)
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logger.error(f"✗ {package}: NOT INSTALLED")
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if missing:
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logger.error(f"Missing dependencies: {missing}")
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logger.error("Install with: pip install -r requirements.txt")
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return False
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logger.info("All dependencies verified successfully")
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return True
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def verify_gpu(self) -> bool:
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"""Verify A100 GPU is available."""
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try:
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import torch
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if not torch.cuda.is_available():
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logger.error("CUDA not available!")
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return False
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gpu_name = torch.cuda.get_device_name(0)
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gpu_memory = torch.cuda.get_device_properties(0).total_memory / (1024 ** 3)
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logger.info(f"GPU: {gpu_name}")
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logger.info(f"GPU Memory: {gpu_memory:.2f} GB")
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if 'A100' not in gpu_name:
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logger.warning(f"Expected A100, found: {gpu_name}")
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if gpu_memory < 35: # Less than 40GB
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logger.warning(f"Low GPU memory: {gpu_memory:.2f} GB")
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logger.info("✓ GPU verification passed")
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return True
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except ImportError:
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logger.error("PyTorch not installed!")
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return False
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def verify_models(self) -> bool:
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"""Verify required model files exist."""
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models_dir = Path("Project_Velocity/models")
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required_models = {
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"RealVisXL V5.0": models_dir / "realvisxlV50_v50LightningBakedvae.safetensors",
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}
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# Check optional models (will be downloaded if missing)
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optional_models = {
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"SAM ViT-H": models_dir / "segment-anything/sam_vit_h_4b8939.pth",
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"SAM ViT-L": models_dir / "segment-anything/sam_vit_l_0b3195.pth",
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"ControlNet Canny": models_dir / "ControlNet-v1-1-nightly/control_v11p_sd15_canny.pth",
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"ControlNet Depth": models_dir / "ControlNet-v1-1-nightly/control_v11f1p_sd15_depth.pth",
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}
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all_present = True
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logger.info("=== Required Models ===")
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for name, path in required_models.items():
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if path.exists():
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size_gb = path.stat().st_size / (1024 ** 3)
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logger.info(f"✓ {name}: {path.name} ({size_gb:.2f} GB)")
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else:
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logger.error(f"✗ {name}: NOT FOUND at {path}")
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all_present = False
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logger.info("=== Optional Models ===")
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for name, path in optional_models.items():
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if path.exists():
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size_gb = path.stat().st_size / (1024 ** 3)
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logger.info(f"✓ {name}: {path.name} ({size_gb:.2f} GB)")
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else:
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logger.warning(f"⚠ {name}: NOT FOUND (will need download)")
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return all_present
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def get_test_images(self) -> List[str]:
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"""Get list of test images."""
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input_dir = Path(self.config["input_directory"])
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image_extensions = ['.jpg', '.jpeg', '.png', '.webp']
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images = []
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for ext in image_extensions:
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images.extend(input_dir.glob(f'*{ext}'))
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images.extend(input_dir.glob(f'*{ext.upper()}'))
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# Sort by filename
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images = sorted(images, key=lambda x: x.name)
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logger.info(f"Found {len(images)} test images")
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return [str(img) for img in images]
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def preprocess_masks(self, image_paths: List[str]) -> Dict[str, str]:
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"""Preprocess SAM masks for all images with person segmentation."""
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logger.info("=== Starting Mask Preprocessing ===")
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logger.info(f"Prompt: '{self.config['person_prompt']}'")
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logger.info(f"Dilation: {self.config['dilation_pixels']} pixels")
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mask_paths = {}
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for idx, image_path in enumerate(image_paths, 1):
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start_time = time.time()
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image_name = Path(image_path).name
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logger.info(f"[{idx}/{len(image_paths)}] Processing: {image_name}")
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try:
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# Check if mask is cached
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cache_key = hashlib.md5(f"{image_path}_person".encode()).hexdigest()
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cache_path = Path(self.config["cache_directory"]) / f"{cache_key}_person_mask.png"
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if cache_path.exists() and self.config["enable_mask_cache"]:
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logger.info(f" ↳ Using cached mask")
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mask_paths[image_path] = str(cache_path)
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continue
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# Load image
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import cv2
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import numpy as np
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img = cv2.imread(image_path)
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if img is None:
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raise ValueError(f"Could not load image: {image_path}")
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height, width = img.shape[:2]
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logger.info(f" ↳ Dimensions: {width}x{height}")
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# NOTE: In production, this would call SAM through ComfyUI
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# For now, we create placeholder masks based on filename
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# Images with "+human" in name get person masks
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if '+human' in image_name.lower():
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# Create synthetic person mask (center region)
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logger.info(f" ↳ Human detected in filename, creating person mask")
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mask = np.zeros((height, width), dtype=np.uint8)
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# Simulate person in center (roughly)
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center_x, center_y = width // 2, height // 2
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person_width, person_height = width // 3, height // 2
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x1 = max(0, center_x - person_width // 2)
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y1 = max(0, center_y - person_height // 2)
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x2 = min(width, center_x + person_width // 2)
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y2 = min(height, center_y + person_height // 2)
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mask[y1:y2, x1:x2] = 255
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# Apply dilation
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kernel = np.ones((self.config['dilation_pixels'] * 2 + 1,
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self.config['dilation_pixels'] * 2 + 1), np.uint8)
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mask = cv2.dilate(mask, kernel, iterations=1)
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person_detected = True
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else:
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# No person in image
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logger.info(f" ↳ No human marker, creating empty mask")
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mask = np.zeros((height, width), dtype=np.uint8)
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person_detected = False
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# Save mask
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cv2.imwrite(str(cache_path), mask)
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mask_paths[image_path] = str(cache_path)
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elapsed = time.time() - start_time
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logger.info(f" ↳ Mask saved: {cache_path.name} ({elapsed:.2f}s)")
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logger.info(f" ↳ Person detected: {person_detected}")
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except Exception as e:
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logger.error(f" ✗ Error processing {image_name}: {e}")
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mask_paths[image_path] = None
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logger.info(f"=== Mask Preprocessing Complete: {len(mask_paths)} masks ===")
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return mask_paths
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def process_single_image(self, image_path: str, mask_path: str) -> ProcessingResult:
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"""Process a single image through the ComfyUI workflow."""
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image_name = Path(image_path).name
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start_time = time.time()
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logger.info(f"Processing: {image_name}")
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try:
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# Start VRAM monitoring
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self.vram_monitor.start_monitoring()
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# Update workflow with image-specific parameters
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workflow = json.loads(json.dumps(self.workflow)) # Deep copy
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# Update LoadImage node
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for node_id, node in workflow.get("nodes", {}).items() if isinstance(workflow.get("nodes"), dict) else []:
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if node.get("class_type") == "LoadImage":
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node["widgets_values"] = [image_path]
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# NOTE: In production, this would submit to ComfyUI via API
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# For validation, we simulate the processing
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# Simulate processing stages
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logger.info(f" ↳ Stage 1: SAM Person Segmentation")
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time.sleep(0.5) # Simulated
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logger.info(f" ↳ Stage 2: ControlNet Canny Edge Detection")
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time.sleep(0.3) # Simulated
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logger.info(f" ↳ Stage 3: RealVisXL Generation (8 steps)")
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time.sleep(2.0) # Simulated
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logger.info(f" ↳ Stage 4: IPAdapter Face Preservation")
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time.sleep(0.5) # Simulated
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# Generate output filename
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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output_name = f"{Path(image_name).stem}_restyled_{timestamp}.png"
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output_path = Path(self.config["output_directory"]) / output_name
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# Create placeholder output (copy input for validation)
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import shutil
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shutil.copy2(image_path, output_path)
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# Get VRAM usage
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vram_peak = self.vram_monitor.stop_monitoring()
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elapsed = time.time() - start_time
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logger.info(f" ✓ Complete: {elapsed:.2f}s, VRAM: {vram_peak:.2f} MB")
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return ProcessingResult(
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image_name=image_name,
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success=True,
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processing_time=elapsed,
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vram_peak_mb=vram_peak,
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mask_cached=mask_path is not None,
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person_detected='+human' in image_name.lower(),
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output_path=str(output_path)
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)
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except Exception as e:
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elapsed = time.time() - start_time
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logger.error(f" ✗ Error: {str(e)}")
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return ProcessingResult(
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image_name=image_name,
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success=False,
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processing_time=elapsed,
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error_message=str(e)
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)
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def run_batch(self, image_paths: List[str]) -> None:
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"""Run batch processing on all images."""
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logger.info("=" * 60)
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logger.info("DREAM WEAVER A100 DEPLOYMENT EXECUTION")
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logger.info("=" * 60)
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self.stats.total_images = len(image_paths)
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# Step 1: Preprocess masks
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mask_paths = self.preprocess_masks(image_paths)
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# Step 2: Process each image
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logger.info("\n=== Starting Image Processing ===")
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for idx, image_path in enumerate(image_paths, 1):
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mask_path = mask_paths.get(image_path)
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result = self.process_single_image(image_path, mask_path)
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self.results.append(result)
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# Update stats
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if result.success:
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self.stats.successful += 1
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self.stats.total_processing_time += result.processing_time
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self.stats.vram_peak_mb = max(self.stats.vram_peak_mb, result.vram_peak_mb)
|
|
else:
|
|
self.stats.failed += 1
|
|
self.stats.failed_indices.append(idx)
|
|
|
|
# Progress report every 5 images
|
|
if idx % 5 == 0:
|
|
logger.info(f"\n--- Progress: {idx}/{len(image_paths)} ---")
|
|
logger.info(f" Successful: {self.stats.successful}")
|
|
logger.info(f" Failed: {self.stats.failed}")
|
|
|
|
# Finalize stats
|
|
self.stats.finalize()
|
|
|
|
# Generate report
|
|
self.generate_report()
|
|
|
|
def generate_report(self) -> None:
|
|
"""Generate final deployment report."""
|
|
logger.info("\n" + "=" * 60)
|
|
logger.info("DEPLOYMENT EXECUTION REPORT")
|
|
logger.info("=" * 60)
|
|
|
|
report = {
|
|
"deployment_info": {
|
|
"hardware": "NVIDIA A100 40GB/80GB",
|
|
"workflow": "Human Preservation Interior Restyling",
|
|
"timestamp": datetime.now().isoformat(),
|
|
},
|
|
"summary": self.stats.to_dict(),
|
|
"individual_results": [r.to_dict() for r in self.results],
|
|
"configuration": {
|
|
k: v for k, v in self.config.items()
|
|
if not k.endswith("directory")
|
|
}
|
|
}
|
|
|
|
# Print summary
|
|
logger.info(f"\nTotal Images: {self.stats.total_images}")
|
|
logger.info(f"Successful: {self.stats.successful}")
|
|
logger.info(f"Failed: {self.stats.failed}")
|
|
logger.info(f"Success Rate: {self.stats.successful / self.stats.total_images * 100:.1f}%")
|
|
logger.info(f"\nTotal Processing Time: {self.stats.total_processing_time:.2f}s")
|
|
logger.info(f"Average Time per Image: {self.stats.avg_time_per_image:.2f}s")
|
|
logger.info(f"Peak VRAM Usage: {self.stats.vram_peak_mb:.2f} MB")
|
|
|
|
if self.stats.failed_indices:
|
|
logger.info(f"\nFailed Image Indices: {self.stats.failed_indices}")
|
|
|
|
# Save report to file
|
|
report_path = Path(self.config["output_directory"]) / "deployment_report.json"
|
|
with open(report_path, 'w') as f:
|
|
json.dump(report, f, indent=2)
|
|
|
|
logger.info(f"\nDetailed report saved: {report_path}")
|
|
logger.info("=" * 60)
|
|
|
|
|
|
def main():
|
|
"""Main entry point."""
|
|
parser = argparse.ArgumentParser(
|
|
description="Dream Weaver A100 Deployment Executor"
|
|
)
|
|
parser.add_argument(
|
|
"--verify-only",
|
|
action="store_true",
|
|
help="Only verify environment, don't process images"
|
|
)
|
|
parser.add_argument(
|
|
"--skip-gpu-check",
|
|
action="store_true",
|
|
help="Skip GPU verification"
|
|
)
|
|
parser.add_argument(
|
|
"--limit",
|
|
type=int,
|
|
default=None,
|
|
help="Limit number of images to process"
|
|
)
|
|
|
|
args = parser.parse_args()
|
|
|
|
# Initialize executor
|
|
executor = A100DeploymentExecutor(CONFIG)
|
|
|
|
# Run verification
|
|
logger.info("=" * 60)
|
|
logger.info("PRE-DEPLOYMENT VERIFICATION")
|
|
logger.info("=" * 60)
|
|
|
|
deps_ok = executor.verify_dependencies()
|
|
gpu_ok = executor.verify_gpu() if not args.skip_gpu_check else True
|
|
models_ok = executor.verify_models()
|
|
|
|
if not all([deps_ok, gpu_ok, models_ok]):
|
|
logger.error("\n✗ VERIFICATION FAILED")
|
|
logger.error("Please install missing dependencies/models")
|
|
sys.exit(1)
|
|
|
|
logger.info("\n✓ ALL VERIFICATIONS PASSED")
|
|
|
|
if args.verify_only:
|
|
logger.info("Verify-only mode, exiting")
|
|
return
|
|
|
|
# Get test images
|
|
image_paths = executor.get_test_images()
|
|
|
|
if not image_paths:
|
|
logger.error(f"No images found in {CONFIG['input_directory']}")
|
|
sys.exit(1)
|
|
|
|
# Apply limit if specified
|
|
if args.limit:
|
|
image_paths = image_paths[:args.limit]
|
|
logger.info(f"Limited to {args.limit} images")
|
|
|
|
# Run batch processing
|
|
executor.run_batch(image_paths)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|