wyh6666/CoCoEdit
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NEO-unify: Building Native Multimodal Unified Models End to End Organizations
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NEO-unify: Building Native Multimodal Unified Models End to End
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Introducing FLUX.2-Klein-LoRA-Studio, a demo for image editing using specialized LoRA adapters built for the FLUX.2-Klein-Distilled model. It features an edit-style gallery for multi-style image editing, including de-light, face swap, mannequin, and more. Try the demo below.
🤗Demo: prithivMLmods/FLUX.2-Klein-LoRA-Studio
🤗Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
🤗GitHub: https://github.com/PRITHIVSAKTHIUR/FLUX.2-Klein-LoRA-Studio
To learn more, visit the app page or the respective model pages.
🤗Demo: prithivMLmods/FLUX.2-Klein-LoRA-Studio
🤗Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
🤗GitHub: https://github.com/PRITHIVSAKTHIUR/FLUX.2-Klein-LoRA-Studio
To learn more, visit the app page or the respective model pages.
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🚀 Introducing VideoCoF: Unified Video Editing with a Temporal Reasoner (Chain-of-Frames)!
We’re excited to introduce VideoCoF, a unified framework for instruction-based video editing that enables temporal reasoning and ~4× video length extrapolation, trained with only 50k video pairs. 🔥
🔍 What makes VideoCoF different?
🧠 Chain-of-Frames reasoning , mimic human thinking process like Seeing → Reasoning → Editing to apply edits accurately over time without external masks, ensuring physically plausible results.
📈 Strong length generalization — trained on 33-frame clips, yet supports multi-shot editing and long-video extrapolation (~4×).
🎯 Unified fine-grained editing — Object Removal, Addition, Swap, and Local Style Transfer, with instance-level & part-level, spatial-aware control.
⚡ Fast inference update
🚀 H100: ~20s / video with 4-step inference, making high-quality video editing far more practical for real-world use.
🔗 Links
📄 Paper: https://arxiv.org/abs/2512.07469
💻 Code: https://github.com/knightyxp/VideoCoF
🤗 Demo: XiangpengYang/VideoCoF
🧩 Models: XiangpengYang/VideoCoF
🌐 Project Page: https://videocof.github.io/
#VideoEditing #DiffusionModels #GenerativeAI #ComputerVision #AI
We’re excited to introduce VideoCoF, a unified framework for instruction-based video editing that enables temporal reasoning and ~4× video length extrapolation, trained with only 50k video pairs. 🔥
🔍 What makes VideoCoF different?
🧠 Chain-of-Frames reasoning , mimic human thinking process like Seeing → Reasoning → Editing to apply edits accurately over time without external masks, ensuring physically plausible results.
📈 Strong length generalization — trained on 33-frame clips, yet supports multi-shot editing and long-video extrapolation (~4×).
🎯 Unified fine-grained editing — Object Removal, Addition, Swap, and Local Style Transfer, with instance-level & part-level, spatial-aware control.
⚡ Fast inference update
🚀 H100: ~20s / video with 4-step inference, making high-quality video editing far more practical for real-world use.
🔗 Links
📄 Paper: https://arxiv.org/abs/2512.07469
💻 Code: https://github.com/knightyxp/VideoCoF
🤗 Demo: XiangpengYang/VideoCoF
🧩 Models: XiangpengYang/VideoCoF
🌐 Project Page: https://videocof.github.io/
#VideoEditing #DiffusionModels #GenerativeAI #ComputerVision #AI
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update 3 months ago
Post
3566
🚀 Introducing VideoCoF: Unified Video Editing with a Temporal Reasoner (Chain-of-Frames)!
We’re excited to introduce VideoCoF, a unified framework for instruction-based video editing that enables temporal reasoning and ~4× video length extrapolation, trained with only 50k video pairs. 🔥
🔍 What makes VideoCoF different?
🧠 Chain-of-Frames reasoning , mimic human thinking process like Seeing → Reasoning → Editing to apply edits accurately over time without external masks, ensuring physically plausible results.
📈 Strong length generalization — trained on 33-frame clips, yet supports multi-shot editing and long-video extrapolation (~4×).
🎯 Unified fine-grained editing — Object Removal, Addition, Swap, and Local Style Transfer, with instance-level & part-level, spatial-aware control.
⚡ Fast inference update
🚀 H100: ~20s / video with 4-step inference, making high-quality video editing far more practical for real-world use.
🔗 Links
📄 Paper: https://arxiv.org/abs/2512.07469
💻 Code: https://github.com/knightyxp/VideoCoF
🤗 Demo: XiangpengYang/VideoCoF
🧩 Models: XiangpengYang/VideoCoF
🌐 Project Page: https://videocof.github.io/
#VideoEditing #DiffusionModels #GenerativeAI #ComputerVision #AI
We’re excited to introduce VideoCoF, a unified framework for instruction-based video editing that enables temporal reasoning and ~4× video length extrapolation, trained with only 50k video pairs. 🔥
🔍 What makes VideoCoF different?
🧠 Chain-of-Frames reasoning , mimic human thinking process like Seeing → Reasoning → Editing to apply edits accurately over time without external masks, ensuring physically plausible results.
📈 Strong length generalization — trained on 33-frame clips, yet supports multi-shot editing and long-video extrapolation (~4×).
🎯 Unified fine-grained editing — Object Removal, Addition, Swap, and Local Style Transfer, with instance-level & part-level, spatial-aware control.
⚡ Fast inference update
🚀 H100: ~20s / video with 4-step inference, making high-quality video editing far more practical for real-world use.
🔗 Links
📄 Paper: https://arxiv.org/abs/2512.07469
💻 Code: https://github.com/knightyxp/VideoCoF
🤗 Demo: XiangpengYang/VideoCoF
🧩 Models: XiangpengYang/VideoCoF
🌐 Project Page: https://videocof.github.io/
#VideoEditing #DiffusionModels #GenerativeAI #ComputerVision #AI
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reacted to XiangpengYang's post with 🔥 3 months ago
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🚀 Introducing VideoCoF: Unified Video Editing with a Temporal Reasoner (Chain-of-Frames)!
We’re excited to introduce VideoCoF, a unified framework for instruction-based video editing that enables temporal reasoning and ~4× video length extrapolation, trained with only 50k video pairs. 🔥
🔍 What makes VideoCoF different?
🧠 Chain-of-Frames reasoning , mimic human thinking process like Seeing → Reasoning → Editing to apply edits accurately over time without external masks, ensuring physically plausible results.
📈 Strong length generalization — trained on 33-frame clips, yet supports multi-shot editing and long-video extrapolation (~4×).
🎯 Unified fine-grained editing — Object Removal, Addition, Swap, and Local Style Transfer, with instance-level & part-level, spatial-aware control.
⚡ Fast inference update
🚀 H100: ~20s / video with 4-step inference, making high-quality video editing far more practical for real-world use.
🔗 Links
📄 Paper: https://arxiv.org/abs/2512.07469
💻 Code: https://github.com/knightyxp/VideoCoF
🤗 Demo: XiangpengYang/VideoCoF
🧩 Models: XiangpengYang/VideoCoF
🌐 Project Page: https://videocof.github.io/
#VideoEditing #DiffusionModels #GenerativeAI #ComputerVision #AI
We’re excited to introduce VideoCoF, a unified framework for instruction-based video editing that enables temporal reasoning and ~4× video length extrapolation, trained with only 50k video pairs. 🔥
🔍 What makes VideoCoF different?
🧠 Chain-of-Frames reasoning , mimic human thinking process like Seeing → Reasoning → Editing to apply edits accurately over time without external masks, ensuring physically plausible results.
📈 Strong length generalization — trained on 33-frame clips, yet supports multi-shot editing and long-video extrapolation (~4×).
🎯 Unified fine-grained editing — Object Removal, Addition, Swap, and Local Style Transfer, with instance-level & part-level, spatial-aware control.
⚡ Fast inference update
🚀 H100: ~20s / video with 4-step inference, making high-quality video editing far more practical for real-world use.
🔗 Links
📄 Paper: https://arxiv.org/abs/2512.07469
💻 Code: https://github.com/knightyxp/VideoCoF
🤗 Demo: XiangpengYang/VideoCoF
🧩 Models: XiangpengYang/VideoCoF
🌐 Project Page: https://videocof.github.io/
#VideoEditing #DiffusionModels #GenerativeAI #ComputerVision #AI
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