slahiriandClaude Opus 4.5
5086598eb8
## New Features - Add web-based Prompt Editor at /sid/prompt-editor - Monaco editor with TOML syntax highlighting - File browser for prompt templates - Save with TOML validation and auto-backup - Reset to git default functionality - Add prompt_generator_core.py for standalone batch processing - Add sid_prompt_generator_v2.py with simplified interface - Add sid_prompt_debug.py for prompt quality evaluation - Add cv_analyzer.py for YOLO-based human detection ## Prompt Template Improvements - Restructure prompts into separate TOML files (portrait, scene, classification) - Add sentence-by-sentence structure for local models - Add comprehensive hair texture vocabulary (straight/wavy/curly/bangs) - Add makeup vocabulary (smoky eyes, lipstick colors) - Improve basic tier prompts to output sentences instead of keyword lists ## API Call Optimization - Standard mode: Single API call (ignores reasoning) - Detailed + reasoning OFF: Single API call with detailed prompts - Detailed + reasoning ON: Multi-step agentic analysis ## Dependencies - Add aiohttp for web routes - Add tomlkit for TOML editing with comment preservation 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
106 lines
3.2 KiB
Python
106 lines
3.2 KiB
Python
# -*- coding: utf-8 -*-
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"""
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System Information Gathering
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Collects system information for debug evaluation context.
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"""
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import platform
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import sys
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from typing import Dict, Any
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def get_system_info() -> Dict[str, Any]:
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"""
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Gather comprehensive system information.
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Returns:
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Dictionary with system details
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"""
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info = {
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"platform": sys.platform,
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"os_version": platform.platform(),
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"python_version": platform.python_version(),
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"architecture": platform.machine(),
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}
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# PyTorch and CUDA info
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try:
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import torch
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info["torch_version"] = torch.__version__
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info["cuda_available"] = torch.cuda.is_available()
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if torch.cuda.is_available():
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info["cuda_version"] = torch.version.cuda
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info["gpu"] = {
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"name": torch.cuda.get_device_name(0),
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"vram_total_gb": round(torch.cuda.get_device_properties(0).total_memory / (1024**3), 1),
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"vram_available_gb": round((torch.cuda.get_device_properties(0).total_memory - torch.cuda.memory_allocated(0)) / (1024**3), 1),
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}
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# Try to get driver version
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try:
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import subprocess
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result = subprocess.run(['nvidia-smi', '--query-gpu=driver_version', '--format=csv,noheader'],
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capture_output=True, text=True, timeout=5)
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if result.returncode == 0:
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info["gpu"]["driver_version"] = result.stdout.strip()
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except:
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pass
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else:
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info["gpu"] = None
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except ImportError:
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info["torch_version"] = "not installed"
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info["cuda_available"] = False
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info["gpu"] = None
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# RAM info
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try:
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import psutil
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mem = psutil.virtual_memory()
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info["ram_total_gb"] = round(mem.total / (1024**3), 1)
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info["ram_available_gb"] = round(mem.available / (1024**3), 1)
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except ImportError:
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info["ram_total_gb"] = None
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info["ram_available_gb"] = None
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# ComfyUI version (if available)
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try:
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# Try to get ComfyUI version from various sources
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import importlib.metadata
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try:
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info["comfyui_version"] = importlib.metadata.version("comfyui")
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except:
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info["comfyui_version"] = "unknown"
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except:
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info["comfyui_version"] = "unknown"
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return info
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def get_gpu_memory_info() -> Dict[str, float]:
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"""
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Get current GPU memory usage.
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Returns:
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Dictionary with VRAM info in GB
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"""
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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 {
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"total_gb": round(torch.cuda.get_device_properties(0).total_memory / (1024**3), 2),
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"allocated_gb": round(torch.cuda.memory_allocated(0) / (1024**3), 2),
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"cached_gb": round(torch.cuda.memory_reserved(0) / (1024**3), 2),
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"available_gb": round((torch.cuda.get_device_properties(0).total_memory - torch.cuda.memory_allocated(0)) / (1024**3), 2),
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}
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except:
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pass
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return {
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"total_gb": 0,
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"allocated_gb": 0,
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"cached_gb": 0,
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"available_gb": 0,
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}
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