Update utils.py from anycoder

utils.py

@@ -0,0 +1,111 @@
+import os
+import hashlib
+from typing import Optional
+from pathlib import Path
+import torch
+
+def get_device_info() -> dict:
+    """Get information about available devices."""
+    info = {
+        "cuda_available": torch.cuda.is_available(),
+        "device_count": torch.cuda.device_count() if torch.cuda.is_available() else 0,
+        "current_device": None,
+        "device_name": None,
+        "memory_allocated": None,
+        "memory_reserved": None,
+    }
+    
+    if torch.cuda.is_available():
+        info["current_device"] = torch.cuda.current_device()
+        info["device_name"] = torch.cuda.get_device_name(0)
+        info["memory_allocated"] = torch.cuda.memory_allocated(0) / 1024**3  # GB
+        info["memory_reserved"] = torch.cuda.memory_reserved(0) / 1024**3  # GB
+    
+    return info
+
+
+def format_time(seconds: float) -> str:
+    """Format seconds into human-readable time string."""
+    if seconds < 60:
+        return f"{seconds:.1f}s"
+    elif seconds < 3600:
+        minutes = seconds / 60
+        return f"{minutes:.1f}m"
+    else:
+        hours = seconds / 3600
+        return f"{hours:.1f}h"
+
+
+def estimate_generation_time(
+    num_frames: int,
+    height: int,
+    width: int,
+    num_steps: int,
+    has_lora: bool = False
+) -> float:
+    """Estimate generation time based on parameters."""
+    # Base time estimation (approximate for A100)
+    base_time_per_frame = 0.5  # seconds per frame per step
+    
+    # Calculate total operations
+    total_pixels = height * width * num_frames
+    base_time = total_pixels * num_steps * base_time_per_frame / (480 * 848 * 25 * 20)
+    
+    # LoRA speedup factor
+    if has_lora:
+        base_time *= 0.4  # ~2.5x speedup with fast LoRA
+    
+    return base_time
+
+
+def validate_prompt(prompt: str, max_length: int = 500) -> tuple[bool, str]:
+    """Validate the input prompt."""
+    if not prompt or not prompt.strip():
+        return False, "Prompt cannot be empty"
+    
+    if len(prompt) > max_length:
+        return False, f"Prompt too long (max {max_length} characters)"
+    
+    return True, "Valid prompt"
+
+
+def get_cache_directory() -> Path:
+    """Get the cache directory for models and LoRAs."""
+    cache_dir = Path.home() / ".cache" / "wan_video_generator"
+    cache_dir.mkdir(parents=True, exist_ok=True)
+    return cache_dir
+
+
+def clear_cache():
+    """Clear the model cache."""
+    import shutil
+    cache_dir = get_cache_directory()
+    if cache_dir.exists():
+        shutil.rmtree(cache_dir)
+    cache_dir.mkdir(parents=True, exist_ok=True)
+    return "Cache cleared successfully"
+
+
+def get_memory_usage() -> dict:
+    """Get current GPU memory usage."""
+    if not torch.cuda.is_available():
+        return {"error": "CUDA not available"}
+    
+    return {
+        "allocated_gb": torch.cuda.memory_allocated(0) / 1024**3,
+        "reserved_gb": torch.cuda.memory_reserved(0) / 1024**3,
+        "max_allocated_gb": torch.cuda.max_memory_allocated(0) / 1024**3,
+        "device_name": torch.cuda.get_device_name(0),
+    }
+
+
+def optimize_memory():
+    """Optimize GPU memory usage."""
+    import gc
+    
+    gc.collect()
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+        torch.cuda.synchronize()
+    
+    return "Memory optimized"