--- ## 🔑 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 ```bash pip install torch diffusers safetensors ```` --- ### 2. Load the FP8 Model ```python 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} } ```