metadata

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

DFloat11 Compressed Model: `Qwen/Qwen-Image-Edit`

This is a DFloat11 losslessly compressed version of the original Qwen/Qwen-Image-Edit 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-Edit can now run on a single 32GB GPU, or on a single 24GB GPU with CPU offloading, while maintaining full model quality. 🔥🔥🔥

📊 Performance Comparison

Model	Model Size	Peak GPU Memory	Generation Time (A100 GPU)
Qwen-Image-Edit (BFloat16)	~41 GB	OOM	-
Qwen-Image-Edit (DFloat11)	28.43 GB	30.11 GB	280 seconds
Qwen-Image-Edit (DFloat11 + CPU Offloading)	28.43 GB	22.71 GB	570 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_edit.py:

import argparse
import torch
from diffusers.utils import load_image
from diffusers import QwenImageTransformer2DModel, QwenImageEditPipeline
from transformers.modeling_utils import no_init_weights
from dfloat11 import DFloat11Model

def parse_args():
    parser = argparse.ArgumentParser(description='Edit images using Qwen-Image-Edit model')
    parser.add_argument('--cpu_offload', action='store_true', help='Enable CPU offloading')
    parser.add_argument('--cpu_offload_blocks', type=int, default=16, 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('--image', type=str, default="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png",
                        help='Path to input image or URL')
    parser.add_argument('--prompt', type=str, default='Add a hat to the cat.',
                        help='Text prompt for image editing')
    parser.add_argument('--negative_prompt', type=str, default=' ',
                        help='Negative prompt for image editing')
    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='qwen_image_edit.png',
                        help='Output image path')
    return parser.parse_args()

args = parse_args()
model_id = "Qwen/Qwen-Image-Edit"

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

DFloat11Model.from_pretrained(
    "DFloat11/Qwen-Image-Edit-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,
)

pipeline = QwenImageEditPipeline.from_pretrained(
    model_id, transformer=transformer, torch_dtype=torch.bfloat16,
)
pipeline.enable_model_cpu_offload()
pipeline.set_progress_bar_config(disable=None)

image = load_image(args.image)
inputs = {
    "image": image,
    "prompt": args.prompt,
    "generator": torch.manual_seed(args.seed),
    "true_cfg_scale": args.true_cfg_scale,
    "negative_prompt": args.negative_prompt,
    "num_inference_steps": args.num_inference_steps,
}

with torch.inference_mode():
    output = pipeline(**inputs)
    output_image = output.images[0]
    output_image.save(args.output)

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

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

python qwen_image_edit.py

To run with CPU offloading (24GB VRAM required, 50GB CPU RAM required):

python qwen_image_edit.py --cpu_offload

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

# Offload only 12 blocks (offloading more blocks uses less GPU memory and more CPU memory; offloading less blocks is faster):
python qwen_image_edit.py --cpu_offload --cpu_offload_blocks 12

# Disable memory-pinning (the most memory efficient way, but could be slower):
python qwen_image_edit.py --cpu_offload --cpu_offload_blocks 60 --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-Edit

📊 Performance Comparison

🔧 How to Use

🔍 How It Works

📄 Learn More

DFloat11 Compressed Model: `Qwen/Qwen-Image-Edit`