1038lab / Qwen-Image-Edit-2511-FP8

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.

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🔑 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.).

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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:

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🚀 Quick Start

1. Install Dependencies

pip install torch diffusers safetensors

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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")

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⚖️ License

This repository follows the Apache-2.0 license , consistent with the original Qwen model.

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📚 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}
}