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
🔧 How to Use
- 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]- Install or upgrade the diffusers library.
pip install -U diffusers- 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.