Image-to-Image
Transformers
Safetensors
English
ditfuse
image-fusion
infrared-visible-fusion
multi-focus-fusion
multi-exposure-fusion
diffusion
transformer
multimodal
text-guided
Instructions to use lijiayangCS/DiTFuse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lijiayangCS/DiTFuse with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-to-image", model="lijiayangCS/DiTFuse")# Load model directly from transformers import DiTFuse model = DiTFuse.from_pretrained("lijiayangCS/DiTFuse", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7b89c79a98da1a70efcc7f07e7a8bd05252d825286d35bc3e43b8bf54eb96a8c
- Size of remote file:
- 75.5 MB
- SHA256:
- 25c5b63e404c0b68fbdede24adb508a0081d13c984ae8fd779f7003da7309e50
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