Qwen-Image-Edit-2511-FP8
This repository contains the FP8 quantized version of the Qwen-Image-Edit-2511 model.
It is designed for efficient inference while maintaining high-quality image editing capabilities.
🔑 Features
- FP8 quantization for reduced memory usage and faster inference.
- Supports single-model image editing workflows.
- Compatible with standard pipelines (Diffusers, custom ComfyUI nodes, etc.).
Showcase
Qwen-Image-Edit-2511 Enhances Character Consistency In Qwen-Image-Edit-2511, character consistency has been significantly improved. The model can perform imaginative edits based on an input portrait while preserving the identity and visual characteristics of the subject.
Improved Multi-Person Consistency
While Qwen-Image-Edit-2509 already improved consistency for single-subject editing, Qwen-Image-Edit-2511 further enhances consistency in multi-person group photos—enabling high-fidelity fusion of two separate person images into a coherent group shot:
Built-in Support for Community-Created LoRAs Since Qwen-Image-Edit’s release, the community has developed many creative and high-quality LoRAs—greatly expanding its expressive potential. Qwen-Image-Edit-2511 integrates selected popular LoRAs directly into the base model, unlocking their effects without extra tuning.
For example, Lighting Enhancement LoRA
Realistic lighting control is now achievable out-of-the-box:
Another example, generating new viewpoints can now be done directly with the base model:
Industrial Design Applications
We’ve paid special attention to practical engineering scenarios—for instance, batch industrial product design:
…and material replacement for industrial components:
Enhanced Geometric Reasoning Qwen-Image-Edit-2511 introduces stronger geometric reasoning capability—e.g., directly generating auxiliary construction lines for design or annotation purposes:
🚀 Quick Start
1. Install Dependencies
pip install torch diffusers safetensors
2. Load the FP8 Model
import torch
from diffusers import QwenImageEditPipeline # or your compatible pipeline
model_path = "./Qwen-Image-Edit-2511-FP8"
pipe = QwenImageEditPipeline.from_pretrained(
model_path,
torch_dtype=torch.bfloat16
)
pipe.to("cuda")
# Example usage
# outputs = pipe(image=input_image, prompt="Your edit prompt")
⚖️ License
This repository follows the Apache-2.0 license, consistent with the original Qwen model.
📚 Citation
@misc{wu2025qwenimagetechnicalreport,
title={Qwen-Image Technical Report},
author={Wu et al.},
year={2025},
eprint={2508.02324},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2508.02324}
}