content.raw: 🚀 Excited to Share Our Latest Work: In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer～
content.type: text
content.value: 🚀 Excited to Share Our Latest Work: In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer～
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content.raw: 🎨 Daily Paper:
content.type: text
content.value: 🎨 Daily Paper:
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content.raw: In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer (2504.20690)
content.type: text
content.value: In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer (2504.20690)
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content.raw: 🔓 Code is now open source!
content.type: text
content.value: 🔓 Code is now open source!
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content.raw: 🔥 Huggingface DEMO: 
content.type: text
content.value: 🔥 Huggingface DEMO: 
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content.raw: RiverZ/ICEdit
content.type: text
content.value: RiverZ/ICEdit
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content.raw: 🌐 Project Website: 
content.type: text
content.value: 🌐 Project Website: 
content.href: https://river-zhang.github.io/ICEdit-gh-pages/
content.raw: https://river-zhang.github.io/ICEdit-gh-pages/
content.type: link
content.type: new_line
content.raw: 🏠 GitHub Repository: 
content.type: text
content.value: 🏠 GitHub Repository: 
content.href: https://github.com/River-Zhang/ICEdit/blob/main/scripts/gradio_demo.py
content.raw: https://github.com/River-Zhang/ICEdit/blob/main/scripts/gradio_demo.py
content.type: link
content.type: new_line
content.raw: 🤗 Huggingface: 
content.type: text
content.value: 🤗 Huggingface: 
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content.raw: sanaka87/ICEdit-MoE-LoRA
content.type: text
content.value: sanaka87/ICEdit-MoE-LoRA
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content.raw: 📄 arxiv Paper: 
content.type: text
content.value: 📄 arxiv Paper: 
content.type: new_line
content.raw: In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer (2504.20690)
content.type: text
content.value: In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer (2504.20690)
content.type: new_line
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content.raw: 🔥 Why it’s cool:
content.type: text
content.value: 🔥 Why it’s cool:
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content.raw: - Achieves high-quality, multi-task image editing.
content.type: text
content.value: - Achieves high-quality, multi-task image editing.
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content.raw: - Uses only 1% of the training parameters and 0.1% of the training data compared to existing methods — extremely efficient
content.type: text
content.value: - Uses only 1% of the training parameters and 0.1% of the training data compared to existing methods — extremely efficient
content.type: new_line
content.raw: - Beats several commercial models on background preservation, ID control, and consistency
content.type: text
content.value: - Beats several commercial models on background preservation, ID control, and consistency
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content.raw: - Open-source, low-cost, faster, and stronger — think of it as the “DeepSeek of image editing” 👀
content.type: text
content.value: - Open-source, low-cost, faster, and stronger — think of it as the “DeepSeek of image editing” 👀
content.type: new_line
content.type: new_line
content.raw: We also implemented a Gradio demo app, available directly in our GitHub repo! And we made a flashy demo video — happy to send it your way!
content.type: text
content.value: We also implemented a Gradio demo app, available directly in our GitHub repo! And we made a flashy demo video — happy to send it your way!
rawContent: 🚀 Excited to Share Our Latest Work: In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer～
  
  🎨 Daily Paper:
  In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer (2504.20690)
  
  
  🔓 Code is now open source!
  🔥 Huggingface DEMO: 
  RiverZ/ICEdit
  
  🌐 Project Website: https://river-zhang.github.io/ICEdit-gh-pages/
  🏠 GitHub Repository: https://github.com/River-Zhang/ICEdit/blob/main/scripts/gradio_demo.py
  🤗 Huggingface: 
  sanaka87/ICEdit-MoE-LoRA
  
  📄 arxiv Paper: 
  In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer (2504.20690)
  
  
  🔥 Why it’s cool:
  - Achieves high-quality, multi-task image editing.
  - Uses only 1% of the training parameters and 0.1% of the training data compared to existing methods — extremely efficient
  - Beats several commercial models on background preservation, ID control, and consistency
  - Open-source, low-cost, faster, and stronger — think of it as the “DeepSeek of image editing” 👀
  
  We also implemented a Gradio demo app, available directly in our GitHub repo! And we made a flashy demo video — happy to send it your way!
reactions.reaction: 🤗
reactions.users: RiverZ sanaka87 John6666 abidlabs victor
reactions.reaction: 🔥
reactions.users: RiverZ sanaka87 abidlabs linoyts
reactions.reaction: 👀
reactions.users: jgitsolutions
reactions.reaction: ❤️
reactions.users: MrDevolver