---
base_model:
  - black-forest-labs/FLUX.1-Fill-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-Fill-dev`

This is a **losslessly compressed** version of [`black-forest-labs/FLUX.1-Fill-dev`](https://huggingface.co/black-forest-labs/FLUX.1-Fill-dev) using our custom **DFloat11** format. The outputs of this compressed model are **bit-for-bit identical** to the original BFloat16 model, while reducing GPU memory consumption by approximately **30%**.

### 🔍 How It Works

DFloat11 compresses model weights using **Huffman coding** of BFloat16 exponent bits, combined with **hardware-aware algorithmic designs** that enable efficient on-the-fly decompression directly on the GPU. During inference, the weights remain compressed in GPU memory and are **decompressed just before matrix multiplications**, then **immediately discarded after use** to minimize memory footprint.

Key benefits:

* **No CPU decompression or host-device data transfer**: all operations are handled entirely on the GPU.
* DFloat11 is **much faster than CPU-offloading approaches**, enabling practical deployment in memory-constrained environments.
* The compression is **fully lossless**, guaranteeing that the model’s outputs are **bit-for-bit identical** to those of the original model.

### 🔧 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 or upgrade the diffusers package.

    ```bash
    pip install -U diffusers
    ```

3. To use the DFloat11 model, run the following example code in Python:
    ```python
    import torch
    from diffusers import FluxFillPipeline
    from diffusers.utils import load_image
    from dfloat11 import DFloat11Model

    image = load_image("https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/cup.png")
    mask = load_image("https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/cup_mask.png")

    pipe = FluxFillPipeline.from_pretrained("black-forest-labs/FLUX.1-Fill-dev", torch_dtype=torch.bfloat16)
    pipe.enable_model_cpu_offload()

    DFloat11Model.from_pretrained('DFloat11/FLUX.1-Fill-dev-DF11', device='cpu', bfloat16_model=pipe.transformer)

    image = pipe(
        prompt="a white paper cup",
        image=image,
        mask_image=mask,
        height=1632,
        width=1232,
        guidance_scale=30,
        num_inference_steps=50,
        max_sequence_length=512,
        generator=torch.Generator("cpu").manual_seed(0)
    ).images[0]
    image.save(f"flux-fill-dev.png")
    ```

### 📄 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)