DFloat11 / Qwen-Image-Edit-2509-DF11

DFloat11 Compressed Model: Qwen/Qwen-Image-Edit-2509

This is a DFloat11 losslessly compressed version of the original Qwen/Qwen-Image-Edit-2509 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-2509 can now run on a single 32GB GPU , or on a single 24GB GPU with CPU offloading , while maintaining full model quality. 🔥🔥🔥

📊 Performance Comparison

🔧 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]
   

2. Install or upgrade diffusers:

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

3. Save the following code to a Python file qwen_image_edit.py :

   import os
   import torch
   import argparse
   from diffusers import QwenImageEditPlusPipeline
   from diffusers.utils import load_image
   from dfloat11 import DFloat11Model
   
   parser = argparse.ArgumentParser(description="Qwen Image Edit with DFloat11")
   parser.add_argument("--image", default="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png", help="Image URL or path")
   parser.add_argument("--prompt", default="Make this cat an astronaut gazing at planet earth from space", help="Edit prompt")
   parser.add_argument("--output", default="qwen_image_edit_output.png", help="Output image path")
   parser.add_argument("--steps", type=int, default=40, help="Number of inference steps")
   parser.add_argument("--seed", type=int, default=42, help="Random seed")
   parser.add_argument("--true_cfg_scale", type=float, default=4.0, help="True CFG scale")
   parser.add_argument("--negative_prompt", default=" ", help="Negative prompt")
   parser.add_argument("--guidance_scale", type=float, default=1.0, help="Guidance scale")
   parser.add_argument("--cpu_offload", action="store_true", help="Enable CPU offloading")
   parser.add_argument("--cpu_offload_blocks", type=int, default=20, help="Number of blocks to offload to CPU for block swapping")
   parser.add_argument("--cpu_offload_no_pin_memory", action="store_true", help="Disable memory pinning for CPU offloading")
   args = parser.parse_args()
   
   pipeline = QwenImageEditPlusPipeline.from_pretrained("Qwen/Qwen-Image-Edit-2509", torch_dtype=torch.bfloat16)
   DFloat11Model.from_pretrained(
       "DFloat11/Qwen-Image-Edit-2509-DF11",
       bfloat16_model=pipeline.transformer,
       device="cpu",
       cpu_offload=args.cpu_offload,
       cpu_offload_blocks=args.cpu_offload_blocks,
       pin_memory=not args.cpu_offload_no_pin_memory,
   )
   pipeline.enable_model_cpu_offload()
   
   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.steps,
       "guidance_scale": args.guidance_scale,
       "num_images_per_prompt": 1,
   }
   with torch.inference_mode():
       output = pipeline(**inputs)
       output_image = output.images[0]
       output_image.save(args.output)
       print("Image saved at", os.path.abspath(args.output))
   
   max_memory = torch.cuda.max_memory_allocated()
   print(f"Max memory: {max_memory / (1000 ** 3):.2f} GB")
   

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

   python qwen_image_edit.py
   

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

   python qwen_image_edit.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_edit.py --cpu_offload --cpu_offload_blocks 16
   
   # Disable memory-pinning (the most memory efficient way, but could be slower):
   python qwen_image_edit.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