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---
base_model:
- lodestones/Chroma
base_model_relation: quantized
pipeline_tag: text-to-image
tags:
- dfloat11
- df11
- lossless compression
- 70% size, 100% accuracy
---
# 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<br>(1024×1024 image generation) | 18.33 GB | 13.26 GB |
| Generation Time<br>(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)*:
```bash
pip install -U dfloat11[cuda12]
# or if you have CUDA version 11:
# pip install -U dfloat11[cuda11]
```
2. Install or upgrade the diffusers library.
```bash
pip install -U diffusers
```
3. To use the DFloat11 model, run the following example code in Python:
```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](https://arxiv.org/abs/2504.11651).
### 📄 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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