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README.md

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+---
+license: mit
+language: en
+pipeline_tag: text-to-audio
+base_model: cloud19/NSFW_MMaudio
+---
+
+# MMAudio NSFW - FP16 Optimized
+
+This repository contains an **FP16 safetensors** version of the fine-tuned MMAudio model from [cloud19/NSFW_MMaudio](https://huggingface.co/cloud19/NSFW_MMaudio), optimized for improved memory efficiency and faster loading times.
+
+**Base Model:** [cloud19/NSFW_MMaudio](https://huggingface.co/cloud19/NSFW_MMaudio)  
+**Original Project:** [hkchengrex/MMAudio](https://github.com/hkchengrex/MMAudio)
+
+## Model Details
+
+* **Base Architecture:** `large_44k` (from the original MMAudio)
+* **Fine-tuning:** Fine-tuned on NSFW content (see base model for details)
+* **Optimization:** Converted from FP32 PyTorch checkpoint to FP16 safetensors
+* **Capabilities:** Video-to-Audio, Image-to-Audio, Text-to-Audio
+* **Format:** Safetensors (`.safetensors`)
+* **Precision:** 16-bit floating point
+
+## Improvements Over Base Model
+
+✅ **~50% smaller file size** (FP32 → FP16 conversion)  
+✅ **Faster loading** with safetensors format  
+✅ **Lower GPU memory usage** during inference  
+✅ **Same quality output** (minimal precision loss with FP16)  
+✅ **Better compatibility** with modern ML frameworks
+
+## How to Use
+
+This model can be used as a drop-in replacement for the original model. Load the safetensors file instead of the original PyTorch checkpoint:
+
+```python
+from safetensors.torch import load_file
+
+# Load the FP16 model weights
+model_weights = load_file("model_fp16.safetensors")
+
+# Load into your MMAudio model architecture
+# (follow the same usage pattern as the base model)
+```
+
+**System Requirements:**
+* GPU: 8-12 GB VRAM (reduced from 12-16 GB due to FP16 optimization)
+* Python 3.10+
+* PyTorch with CUDA support
+
+## Installation
+
+For usage instructions, please refer to the [base model repository](https://huggingface.co/cloud19/NSFW_MMaudio) and simply replace the model loading with the FP16 safetensors version.
+
+## Technical Details
+
+* **Original Format:** FP32 PyTorch (.pth) - ~2.5GB
+* **Optimized Format:** FP16 Safetensors (.safetensors) - ~1.25GB
+* **Conversion Method:** Direct FP32 → FP16 tensor conversion
+* **Quality Impact:** Negligible quality loss in practice
+
+## Limitations
+
+* Same limitations as the base model apply
+* **Content Warning:** Due to the NSFW nature of the fine-tuning dataset, the model may generate explicit or mature audio content. User discretion is advised.
+* FP16 precision may introduce minimal numerical differences compared to FP32
+
+## Credits & Citation
+
+**Base Model:** [cloud19/NSFW_MMaudio](https://huggingface.co/cloud19/NSFW_MMaudio)  
+**Original MMAudio:** [hkchengrex/MMAudio](https://github.com/hkchengrex/MMAudio)  
+**Optimization:** FP16 conversion for improved efficiency
+
+All credit for the original architecture, fine-tuning, and model development goes to the respective authors. This repository only provides format optimization.
+
+```bibtex
+@inproceedings{cheng2025taming,
+  title={{MMAudio}: Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis},
+  author={Cheng, Ho Kei and Ishii, Masato and Hayakawa, Akio and Shibuya, Takashi and Schwing, Alexander and Mitsufuji, Yuki},
+  booktitle={CVPR},
+  year={2025}
+}
+```

convert_fp32_to_fp16.py

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+#!/usr/bin/env python3
+"""
+Simple FP32 to FP16 converter for safetensors files.
+Converts ALL FP32 tensors to FP16 without discrimination.
+
+USAGE:
+  python convert_fp16.py input.safetensors [output.safetensors]
+"""
+
+import argparse
+from pathlib import Path
+from typing import Dict
+
+import torch
+from safetensors.torch import load_file, save_file
+
+
+def convert_fp32_to_fp16(state_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
+    """Convert all FP32 tensors to FP16."""
+    converted = {}
+    fp32_count = 0
+    total_tensors = len(state_dict)
+    
+    for name, tensor in state_dict.items():
+        if tensor.dtype == torch.float32:
+            converted[name] = tensor.half()  # Convert to FP16
+            fp32_count += 1
+        else:
+            converted[name] = tensor  # Keep as-is
+    
+    print(f"Converted {fp32_count}/{total_tensors} tensors from FP32 to FP16")
+    return converted
+
+
+def calculate_size_reduction(original: Dict[str, torch.Tensor], converted: Dict[str, torch.Tensor]) -> None:
+    """Calculate and print size reduction."""
+    original_bytes = sum(t.numel() * t.element_size() for t in original.values())
+    converted_bytes = sum(t.numel() * t.element_size() for t in converted.values())
+    saved_bytes = original_bytes - converted_bytes
+    
+    original_gb = original_bytes / (1024**3)
+    converted_gb = converted_bytes / (1024**3)
+    saved_gb = saved_bytes / (1024**3)
+    
+    print(f"Size: {original_gb:.3f} GB → {converted_gb:.3f} GB (saved {saved_gb:.3f} GB)")
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Convert all FP32 tensors to FP16 in a safetensors file")
+    parser.add_argument("input", help="Input safetensors file")
+    parser.add_argument("output", nargs="?", help="Output safetensors file (optional)")
+    
+    args = parser.parse_args()
+    
+    # Generate output filename if not provided
+    if args.output is None:
+        input_path = Path(args.input)
+        stem = input_path.stem
+        if not stem.endswith("_fp16"):
+            stem += "_fp16"
+        args.output = str(input_path.with_name(stem + input_path.suffix))
+    
+    print(f"Loading: {args.input}")
+    state_dict = load_file(args.input)
+    
+    print("Converting FP32 → FP16...")
+    converted_state_dict = convert_fp32_to_fp16(state_dict)
+    
+    calculate_size_reduction(state_dict, converted_state_dict)
+    
+    print(f"Saving: {args.output}")
+    save_file(converted_state_dict, args.output)
+    print("Done!")
+
+
+if __name__ == "__main__":
+    main()

mmaudio_nsfw_gold_8.5k_final_fp16.safetensors

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