metadata

base_model:
  - Qwen/Qwen-Image
base_model_relation: quantized
tags:
  - dfloat11
  - df11
  - lossless compression
  - 70% size, 100% accuracy

DFloat11 Compressed Model: `Qwen/Qwen-Image`

This is a DFloat11 losslessly compressed version of the original Qwen/Qwen-Image 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, Qwen-Image can now run on a single 32GB GPU, or on a single 16GB GPU with CPU offloading, while maintaining full model quality. 🔥🔥🔥

📊 Performance Comparison

Model	Model Size	Peak GPU Memory (1328x1328 image generation)	Generation Time (A100 GPU)
Qwen-Image (BFloat16)	~41 GB	OOM	-
Qwen-Image (DFloat11)	28.42 GB	29.74 GB	100 seconds
Qwen-Image (DFloat11 + GPU Offloading)	28.42 GB	16.68 GB	260 seconds

🔧 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]
```

Install or upgrade diffusers:

pip install git+https://github.com/huggingface/diffusers

Save the following code to a Python file qwen_image.py:

from diffusers import DiffusionPipeline, QwenImageTransformer2DModel
import torch
from transformers.modeling_utils import no_init_weights
from dfloat11 import DFloat11Model
import argparse

def parse_args():
    parser = argparse.ArgumentParser(description='Generate images using Qwen-Image model')
    parser.add_argument('--cpu_offload', action='store_true', help='Enable CPU offloading')
    parser.add_argument('--cpu_offload_blocks', type=int, default=None, help='Number of transformer blocks to offload to CPU')
    parser.add_argument('--no_pin_memory', action='store_true', help='Disable memory pinning')
    parser.add_argument('--prompt', type=str, default='A coffee shop entrance features a chalkboard sign reading "Qwen Coffee 😊 $2 per cup," with a neon light beside it displaying "通义千问". Next to it hangs a poster showing a beautiful Chinese woman, and beneath the poster is written "π≈3.1415926-53589793-23846264-33832795-02384197".',
                        help='Text prompt for image generation')
    parser.add_argument('--negative_prompt', type=str, default=' ',
                        help='Negative prompt for image generation')
    parser.add_argument('--aspect_ratio', type=str, default='16:9', choices=['1:1', '16:9', '9:16', '4:3', '3:4'],
                        help='Aspect ratio of generated image')
    parser.add_argument('--num_inference_steps', type=int, default=50,
                        help='Number of denoising steps')
    parser.add_argument('--true_cfg_scale', type=float, default=4.0,
                        help='Classifier free guidance scale')
    parser.add_argument('--seed', type=int, default=42,
                        help='Random seed for generation')
    parser.add_argument('--output', type=str, default='example.png',
                        help='Output image path')
    parser.add_argument('--language', type=str, default='en', choices=['en', 'zh'],
                        help='Language for positive magic prompt')
    return parser.parse_args()

args = parse_args()

model_name = "Qwen/Qwen-Image"

with no_init_weights():
    transformer = QwenImageTransformer2DModel.from_config(
        QwenImageTransformer2DModel.load_config(
            model_name, subfolder="transformer",
        ),
    ).to(torch.bfloat16)

DFloat11Model.from_pretrained(
    "DFloat11/Qwen-Image-DF11",
    device="cpu",
    cpu_offload=args.cpu_offload,
    cpu_offload_blocks=args.cpu_offload_blocks,
    pin_memory=not args.no_pin_memory,
    bfloat16_model=transformer,
)

pipe = DiffusionPipeline.from_pretrained(
    model_name,
    transformer=transformer,
    torch_dtype=torch.bfloat16,
)
pipe.enable_model_cpu_offload()

positive_magic = {
    "en": "Ultra HD, 4K, cinematic composition.", # for english prompt,
    "zh": "超清，4K，电影级构图" # for chinese prompt,
}

# Generate with different aspect ratios
aspect_ratios = {
    "1:1": (1328, 1328),
    "16:9": (1664, 928),
    "9:16": (928, 1664),
    "4:3": (1472, 1140),
    "3:4": (1140, 1472),
}

width, height = aspect_ratios[args.aspect_ratio]

image = pipe(
    prompt=args.prompt + positive_magic[args.language],
    negative_prompt=args.negative_prompt,
    width=width,
    height=height,
    num_inference_steps=args.num_inference_steps,
    true_cfg_scale=args.true_cfg_scale,
    generator=torch.Generator(device="cuda").manual_seed(args.seed)
).images[0]

image.save(args.output)

max_memory = torch.cuda.max_memory_allocated()
print(f"Max memory: {max_memory / (1000 ** 3):.2f} GB")

To run without CPU offloading (32GB VRAM required):

python qwen_image.py

To run with CPU offloading (16GB VRAM required):

python qwen_image.py --cpu_offload

If you are getting out-of-CPU-memory errors, try limiting the number of offloaded blocks or disabling memory-pinning:

# Offload only 16 blocks (offloading more blocks uses less GPU memory and more CPU memory; offloading less blocks is faster):
python qwen_image.py --cpu_offload --cpu_offload_blocks 16

# Disable memory-pinning (the most memory efficient way, but could be slower):
python qwen_image.py --cpu_offload --no_pin_memory

🔍 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

DFloat11 Compressed Model: Qwen/Qwen-Image

📊 Performance Comparison

🔧 How to Use

🔍 How It Works

📄 Learn More

DFloat11 Compressed Model: `Qwen/Qwen-Image`