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--- |
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language: |
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- en |
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tags: |
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- image-fusion |
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- infrared-visible-fusion |
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- multi-focus-fusion |
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- multi-exposure-fusion |
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- diffusion |
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- transformer |
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- multimodal |
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- text-guided |
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pipeline_tag: image-to-image |
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library_name: transformers |
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license: mit |
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datasets: |
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- custom |
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model_name: DiTFuse |
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--- |
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# **DiTFuse: Towards Unified Semantic and Controllable Image Fusion: A Diffusion Transformer Approach (Official Weights)** |
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This repository provides the **official pretrained weights** for **DiTFuse**. |
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The project code is available on GitHub: |
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👉 **GitHub:** [https://github.com/Henry-Lee-real/DiTFuse](https://github.com/Henry-Lee-real/DiTFuse) |
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DiTFuse supports multiple fusion tasks—including **infrared–visible fusion, multi-focus fusion, multi-exposure fusion**, and **instruction-driven controllable fusion / segmentation**—all within a single unified model. |
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--- |
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## **📌 Available Model Versions** |
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### **🔹 V1 — Stronger Zero-Shot Generalization** |
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* Designed with better **zero-shot fusion capability**. |
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* Performs robustly on unseen fusion scenarios. |
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* Recommended if your use case emphasizes **cross-dataset generalization**. |
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### **🔹 V2 — Full Capability Version (Paper Model)** |
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* This is the **main model used in the DiTFuse paper**. |
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* Provides the **most comprehensive capabilities**: |
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* Full instruction-following control |
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* Joint fusion + segmentation |
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* Better fidelity and controllability |
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* Stronger alignment with text prompts |
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* Recommended for **research reproduction**, **benchmarking**, and **controllable image fusion tasks**. |
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