DFloat11 / stable-diffusion-3.5-large-DF11

DFloat11 Compressed Model: stabilityai/stable-diffusion-3.5-large

This is a losslessly compressed version of 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) :

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

2. Install or upgrade the diffusers package.

   pip install -U diffusers
   

3. To use the DFloat11 model, run the following example code in 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
• GitHub : https://github.com/LeanModels/DFloat11
• HuggingFace : https://huggingface.co/DFloat11