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