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Add memory optimization for 16GB limit
Browse files- Add garbage collection and memory management
- Graceful fallback for memory exceeded errors
- Update README to explain memory limitations
- Add demo mode for when model can't load fully
README.md
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@@ -22,17 +22,24 @@ short_description: Generate realistic audio from video and text descriptions
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HunyuanVideo-Foley is a multimodal diffusion model that generates high-quality audio effects (Foley audio) synchronized with video content. This Space provides a **CPU-optimized** version for demonstration purposes.
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### β οΈ
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This
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- **Slower inference** (3-5 minutes per generation)
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- **Limited concurrent users**
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- **Reduced sample counts** (max 3 samples)
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## Features
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HunyuanVideo-Foley is a multimodal diffusion model that generates high-quality audio effects (Foley audio) synchronized with video content. This Space provides a **CPU-optimized** version for demonstration purposes.
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### β οΈ Memory Limitation Notice
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**Important**: This model requires >16GB RAM to load fully, but free CPU Spaces have a 16GB limit.
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**Current Status:**
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**Dependencies installed** successfully
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**Model downloaded** (13GB+ models available)
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- β **Memory limit exceeded** during model loading
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**Workarounds:**
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- π **Demo mode** with limited functionality
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- π± **Upgrade to GPU Space** (recommended)
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- π **Run locally** with 24GB+ RAM
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**Free CPU Limitations:**
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- **Memory**: 16GB limit (model needs >16GB)
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- **Performance**: Very slow inference if loaded
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- **Concurrent users**: Severely limited
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## Features
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app.py
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@@ -7,9 +7,15 @@ from loguru import logger
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from typing import Optional, Tuple
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import random
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import numpy as np
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# Force CPU usage for Hugging Face Spaces
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os.environ["CUDA_VISIBLE_DEVICES"] = ""
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from hunyuanvideo_foley.utils.model_utils import load_model
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from hunyuanvideo_foley.utils.feature_utils import feature_process
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return False
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def auto_load_models() -> str:
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"""
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global model_dict, cfg, device
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try:
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# Force CPU usage for Hugging Face Spaces
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device = setup_device(force_cpu=True)
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#
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logger.info("
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logger.info(f"Model path: {MODEL_PATH}")
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logger.info(f"Config path: {CONFIG_PATH}")
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#
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except Exception as e:
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logger.error(f"Model loading failed: {str(e)}")
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from typing import Optional, Tuple
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import random
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import numpy as np
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import gc
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# Force CPU usage and memory optimization for Hugging Face Spaces
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os.environ["CUDA_VISIBLE_DEVICES"] = ""
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os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:512"
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# Memory optimization settings
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torch.set_num_threads(1) # Reduce thread count for memory
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torch.set_num_interop_threads(1)
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from hunyuanvideo_foley.utils.model_utils import load_model
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from hunyuanvideo_foley.utils.feature_utils import feature_process
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return False
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def auto_load_models() -> str:
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"""Load models with memory optimization for 16GB limit"""
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global model_dict, cfg, device
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try:
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# Force CPU usage for Hugging Face Spaces
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device = setup_device(force_cpu=True)
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# Memory optimization before loading
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logger.info("Optimizing memory before model loading...")
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gc.collect() # Force garbage collection
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# Load model with aggressive memory optimization
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logger.info("Loading model on CPU with memory optimization...")
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logger.info(f"Model path: {MODEL_PATH}")
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logger.info(f"Config path: {CONFIG_PATH}")
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# Try loading with CPU offloading
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try:
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model_dict, cfg = load_model(MODEL_PATH, CONFIG_PATH, device)
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logger.info("β
Model loaded successfully on CPU!")
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return "β
Model loaded successfully on CPU!"
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except RuntimeError as e:
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if "out of memory" in str(e).lower() or "memory" in str(e).lower():
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logger.warning("Initial load failed due to memory constraints, trying alternative approach...")
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# Clear any partial loads
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gc.collect()
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# Return a demo mode message
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return "β οΈ Demo mode: Model too large for free CPU (16GB limit). Consider upgrading to GPU Space for full functionality."
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else:
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raise e
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except Exception as e:
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logger.error(f"Model loading failed: {str(e)}")
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