DFloat11/Chroma-DF11

DFloat11 Compressed Model: lodestones/Chroma

This is a DFloat11 losslessly compressed version of the original lodestones/Chroma (v39) 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, Chroma can now run smoothly on a single 16GB GPU without any quality loss. 🔥🔥🔥

📊 Performance Comparison

Metric	Chroma (BFloat16)	Chroma (DFloat11)
Model Size	17.80 GB	12.16 GB
Peak GPU Memory (1024×1024 image generation)	18.33 GB	13.26 GB
Generation Time (A100 GPU)	56 seconds	59 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):

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

2. Install or upgrade the diffusers library.

   pip install -U diffusers
   

3. To use the DFloat11 model, run the following example code in Python:

   import torch
   from diffusers import ChromaTransformer2DModel, ChromaPipeline
   from transformers.modeling_utils import no_init_weights
   from dfloat11 import DFloat11Model
   
   with no_init_weights():
       transformer = ChromaTransformer2DModel().to(torch.bfloat16)
   
   DFloat11Model.from_pretrained(
       "DFloat11/Chroma-DF11",
       bfloat16_model=transformer,
       device="cpu",
   )
   
   pipe = ChromaPipeline.from_pretrained("lodestones/Chroma", transformer=transformer, torch_dtype=torch.bfloat16)
   pipe.enable_model_cpu_offload()
   
   prompt = [
       "A high-fashion close-up portrait of a blonde woman in clear sunglasses. The image uses a bold teal and red color split for dramatic lighting. The background is a simple teal-green. The photo is sharp and well-composed, and is designed for viewing with anaglyph 3D glasses for optimal effect. It looks professionally done."
   ]
   negative_prompt =  ["low quality, ugly, unfinished, out of focus, deformed, disfigure, blurry, smudged, restricted palette, flat colors"]
   
   image = pipe(
       prompt=prompt,
       negative_prompt=negative_prompt,
       generator=torch.Generator("cpu").manual_seed(433),
       num_inference_steps=40,
       guidance_scale=3.0,
   ).images[0]
   
   image.save("chroma-output.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.

📄 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