ICEdit-MoE-LoRA: In-Context Instructional Image Editing

"used with Flux-dev-fill"

workflow

https://civitai.com/models/1534861?modelVersionId=1736??0

try this

https://civitai.com/models/1108146/fluxfill-devgguf

"here i update some thing useful for me and share it for u

1- read any video and generate the prompe as u want in perfect way

2- do excellent random prompt using best key words through last 3 years

https://civitai.com/models/1533911"

ICEdit-MoE-LoRA is a lightweight Low-Rank Adaptation (LoRA) module designed to enable powerful, instruction-based image editing within large diffusion transformers. By fine-tuning only 1% of the parameters and using just 0.5% of the original training data, it achieves state-of-the-art editing capabilities—matching or even surpassing heavier commercial solutions.

🔑 Key Features

• Minimal Footprint
  Only 1% of parameters vs. prior SOTA, making it fast to load and apply.

• Instruction-Driven Editing
  Follow natural-language instructions to add, change, or remove objects, adjust colors, and perform style transfers.

• High Precision & Flexibility
  Supports multi-turn editing sessions with precise control, as well as diverse single-turn edits across realistic imagery.

• Compatibility
  Built atop the FLUX base model; integrates seamlessly into existing diffusion pipelines via a simple LoRA injection.

⚖️ Comparison with Commercial Models

ICEdit-MoE-LoRA demonstrates performance on par with—and in some metrics, superior to—closed-source giants like Gemini and GPT-4o, especially in:

• Character Identity Preservation

• Instruction Fidelity

• Speed (~9 s per image)

• Cost Efficiency

• Open-Source Accessibility

🙏 Special Thanks
A heartfelt thank you to sanaka87 for releasing the ICEdit-MoE-LoRA checkpoint and accompanying demos on Hugging Face. Your open-source commitment accelerates innovation across the community!

Model & Demo → https://huggingface.co/sanaka87/ICEdit-MoE-LoRA