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--- |
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base_model: |
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- Wan-AI/Wan2.1-T2V-14B-Diffusers |
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base_model_relation: quantized |
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pipeline_tag: text-to-image |
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tags: |
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- dfloat11 |
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- df11 |
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- lossless compression |
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- 70% size, 100% accuracy |
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--- |
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# DFloat11 Compressed Model: `Wan-AI/Wan2.1-T2V-14B-Diffusers` |
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This model uses **DFloat11** lossless compression. It's 30% smaller than the original BFloat16 model, yet produces bit-identical outputs and runs efficiently on GPUs. |
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### 📊 Performance Comparison |
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| Metric | Wan2.1-T2V-14B (BFloat16) | Wan2.1-T2V-14B (DFloat11) | |
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| ---------------------------------- | ------------------------- | ------------------------- | |
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| Model Size | 28.64 GB | 19.39 GB | |
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| Peak GPU Memory<br>(2s 480p Video) | 30.79 GB | 22.22 GB | |
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| Generation Time<br>(an A100 GPU) | 339 seconds | 348 seconds | |
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### 🔍 How It Works |
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We apply Huffman coding to the exponent bits of BFloat16 model weights, which are highly compressible. We leverage hardware-aware algorithmic designs to enable highly efficient, on-the-fly weight decompression directly on the GPU. Find out more in our [research paper](https://arxiv.org/abs/2504.11651). |
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### 🔧 How to Use |
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A complete usage guide is available in our GitHub repository: [https://github.com/LeanModels/DFloat11/tree/master/examples/wan2.1](https://github.com/LeanModels/DFloat11/tree/master/examples/wan2.1). |
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### 📄 Learn More |
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* **Paper**: [70% Size, 100% Accuracy: Lossless LLM Compression for Efficient GPU Inference via Dynamic-Length Float](https://arxiv.org/abs/2504.11651) |
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* **GitHub**: [https://github.com/LeanModels/DFloat11](https://github.com/LeanModels/DFloat11) |
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* **HuggingFace**: [https://huggingface.co/DFloat11](https://huggingface.co/DFloat11) |
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