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--- |
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base_model: |
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- black-forest-labs/FLUX.1-Kontext-dev |
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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: `black-forest-labs/FLUX.1-Kontext-dev` |
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This is a **DFloat11 losslessly compressed** version of the original `black-forest-labs/FLUX.1-Kontext-dev` model. It reduces model size by **32%** compared to the original BFloat16 model, while maintaining **bit-identical outputs** and supporting **efficient GPU inference**. |
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🔥🔥🔥 Thanks to DFloat11 compression, FLUX.1-Kontext-dev can now run smoothly on a single 24GB GPU without any quality loss. 🔥🔥🔥 |
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### 📊 Performance Comparison |
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| Metric | FLUX.1-Kontext-dev (BFloat16) | FLUX.1-Kontext-dev (DFloat11) | |
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| ----------------------------------------------- | ------------------- | ------------------- | |
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| Model Size | 23.80 GB | 16.33 GB | |
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| Peak GPU Memory<br>(1024×1024 image generation) | 24.86 GB | 18.12 GB | |
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| Generation Time<br>(A100 GPU) | 72 seconds | 83 seconds | |
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### 🔧 How to Use |
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1. Install or upgrade the DFloat11 pip package *(installs the CUDA kernel automatically; requires a CUDA-compatible GPU and PyTorch installed)*: |
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```bash |
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pip install -U dfloat11[cuda12] |
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# or if you have CUDA version 11: |
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# pip install -U dfloat11[cuda11] |
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``` |
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2. Install diffusers from the main branch until future stable release. |
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```bash |
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pip install git+https://github.com/huggingface/diffusers.git |
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``` |
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3. To use the DFloat11 model, run the following example code in Python: |
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```python |
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import torch |
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from diffusers import FluxKontextPipeline |
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from diffusers.utils import load_image |
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from dfloat11 import DFloat11Model |
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pipe = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16) |
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DFloat11Model.from_pretrained( |
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"DFloat11/FLUX.1-Kontext-dev-DF11", |
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device="cpu", |
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bfloat16_model=pipe.transformer, |
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) |
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pipe.enable_model_cpu_offload() |
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input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") |
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image = pipe( |
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image=input_image, |
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prompt="Add a hat to the cat", |
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guidance_scale=2.5, |
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).images[0] |
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image.save("kontext.png") |
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``` |
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### 🔍 How It Works |
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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. |
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The result is a model that is **~32% smaller**, delivers **bit-identical outputs**, and achieves performance **comparable to the original** BFloat16 model. |
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Learn more in our [research paper](https://arxiv.org/abs/2504.11651). |
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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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