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---
base_model:
- black-forest-labs/FLUX.1-Canny-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-Canny-dev`
This is a **losslessly compressed** version of [`black-forest-labs/FLUX.1-Canny-dev`](https://huggingface.co/black-forest-labs/FLUX.1-Canny-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 and controlnet_aux packages.
```bash
pip install -U diffusers controlnet_aux
```
3. To use the DFloat11 model, run the following example code in Python:
```python
import torch
from controlnet_aux import CannyDetector
from diffusers import FluxControlPipeline
from diffusers.utils import load_image
from dfloat11 import DFloat11Model
pipe = FluxControlPipeline.from_pretrained("black-forest-labs/FLUX.1-Canny-dev", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
DFloat11Model.from_pretrained('DFloat11/FLUX.1-Canny-dev-DF11', device='cpu', bfloat16_model=pipe.transformer)
prompt = "A robot made of exotic candies and chocolates of different kinds. The background is filled with confetti and celebratory gifts."
control_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png")
processor = CannyDetector()
control_image = processor(control_image, low_threshold=50, high_threshold=200, detect_resolution=1024, image_resolution=1024)
image = pipe(
prompt=prompt,
control_image=control_image,
height=1024,
width=1024,
num_inference_steps=50,
guidance_scale=30.0,
).images[0]
image.save("output.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) |