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---
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)
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