--- base_model: - OmniGen2/OmniGen2 base_model_relation: quantized pipeline_tag: any-to-any tags: - dfloat11 - df11 - lossless compression - 70% size, 100% accuracy --- # DFloat11 Compressed Model: `OmniGen2/OmniGen2` Transformer This is a **DFloat11 losslessly compressed** version of the original `OmniGen2/OmniGen2` model. It reduces model size by **32%** compared to the original BFloat16 model, while maintaining **bit-identical outputs** and supporting **efficient GPU inference**. ### 📊 Performance Comparison | Metric | OmniGen2 (BFloat16) | OmniGen2 (DFloat11) | | ----------------------------------------------- | ------------------- | ------------------- | | Model Size | 16.23 GB | 11.11 GB | | Peak GPU Memory
(1024×1024 image generation) | 18.41 GB | 14.36 GB | | Generation Time
(A100 GPU) | 25 seconds | 27 seconds | ### 🔧 How to Use A complete usage guide is available in our GitHub repository (forked from the official OmniGen2 repository). 👉 [https://github.com/LeanModels/OmniGen2-DFloat11](https://github.com/LeanModels/OmniGen2-DFloat11) 👈 ### 🔍 How It Works We apply **Huffman coding** to losslessly compress the exponent bits of BFloat16 model weights, which are highly compressible (their 8 bits carry only ~2.6 bits of actual information). To enable fast inference, we implement a highly efficient CUDA kernel that performs on-the-fly weight decompression directly on the GPU. The result is a model that is **~32% smaller**, delivers **bit-identical outputs**, and achieves performance **comparable to the original** BFloat16 model. Learn more in our [research paper](https://arxiv.org/abs/2504.11651). ### 📄 Learn More * **Paper**: [70% Size, 100% Accuracy: Lossless LLM Compression for Efficient GPU Inference via Dynamic-Length Float](https://arxiv.org/abs/2504.11651) * **GitHub**: [https://github.com/LeanModels/DFloat11](https://github.com/LeanModels/DFloat11) * **HuggingFace**: [https://huggingface.co/DFloat11](https://huggingface.co/DFloat11)