README.md

---
base_model:
  - black-forest-labs/FLUX.1-Kontext-dev
base_model_relation: quantized
pipeline_tag: text-to-image
tags:
- dfloat11
- df11
- lossless compression
- 70% size, 100% accuracy
---

# DFloat11 Compressed Model: `black-forest-labs/FLUX.1-Kontext-dev`

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**.

🔥🔥🔥 Thanks to DFloat11 compression, FLUX.1-Kontext-dev can now run smoothly on a single 24GB GPU without any quality loss. 🔥🔥🔥

### 📊 Performance Comparison

| Metric                                          | FLUX.1-Kontext-dev (BFloat16) | FLUX.1-Kontext-dev (DFloat11) |
| ----------------------------------------------- | ------------------- | ------------------- |
| Model Size                                      | 23.80 GB            | 16.33 GB            |
| Peak GPU Memory<br>(1024×1024 image generation) | 24.86 GB            | 18.12 GB            |
| Generation Time<br>(A100 GPU)                   | 72 seconds          | 83 seconds          |

### 🔧 How to Use

1. Install or upgrade the DFloat11 pip package *(installs the CUDA kernel automatically; requires a CUDA-compatible GPU and PyTorch installed)*:

    ```bash
    pip install -U dfloat11[cuda12]
    # or if you have CUDA version 11:
    # pip install -U dfloat11[cuda11]
    ```

2. Install diffusers from the main branch until future stable release.

    ```bash
    pip install git+https://github.com/huggingface/diffusers.git
    ```

3. To use the DFloat11 model, run the following example code in Python:

    ```python
    import torch
    from diffusers import FluxKontextPipeline
    from diffusers.utils import load_image
    from dfloat11 import DFloat11Model

    pipe = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16)
    DFloat11Model.from_pretrained(
        "DFloat11/FLUX.1-Kontext-dev-DF11",
        device="cpu",
        bfloat16_model=pipe.transformer,
    )
    pipe.enable_model_cpu_offload()

    input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png")

    image = pipe(
        image=input_image,
        prompt="Add a hat to the cat",
        guidance_scale=2.5,
    ).images[0]

    image.save("kontext.png")
    ```


### 🔍 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)