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---
base_model:
- stabilityai/stable-diffusion-3.5-large
base_model_relation: quantized
pipeline_tag: text-to-image
tags:
- dfloat11
- df11
- lossless compression
- 70% size, 100% accuracy
---
## DFloat11 Compressed Model: `stabilityai/stable-diffusion-3.5-large`
This is a **losslessly compressed** version of [`stabilityai/stable-diffusion-3.5-large`](https://huggingface.co/stabilityai/stable-diffusion-3.5-large) using our custom **DFloat11** format.
### 💡 Key Benefits
* ✅ **Bit-for-bit identical outputs** to the original BFloat16 model
* 📉 **\~30% reduction in model size** (from **16GB** → **11.3GB**)
* 🧠 **Lower memory requirements**: now runs on **16GB GPUs**
* ⚡ **Minimal performance overhead**: barely any slower than the full model
DFloat11 compresses the model weights while preserving full numerical precision. This allows you to run `stabilityai/stable-diffusion-3.5-large` on more accessible hardware, with **no compromise in output quality**.
### 🔍 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.
Advantages:
* **Fully GPU-based**: no CPU decompression or host-device data transfer.
* 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 StableDiffusion3Pipeline
from dfloat11 import DFloat11Model
pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3.5-large", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
DFloat11Model.from_pretrained('DFloat11/stable-diffusion-3.5-large-DF11', device='cpu', bfloat16_model=pipe.transformer)
image = pipe(
"A capybara holding a sign that reads Hello World",
num_inference_steps=28,
guidance_scale=3.5,
).images[0]
image.save("capybara.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)
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