Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

lijiayangCS
/
DiTFuse

Image-to-Image
Transformers
Safetensors
English
ditfuse
image-fusion
infrared-visible-fusion
multi-focus-fusion
multi-exposure-fusion
diffusion
transformer
multimodal
text-guided
Model card Files Files and versions
xet
Community

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
DiTFuse / V1
75.5 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 1 commit
lijiayangCS's picture
lijiayangCS
Upload DiTFuse weights and config
9298334 verified 6 months ago
  • README.md
    5.06 kB
    Upload DiTFuse weights and config 6 months ago
  • adapter_config.json
    773 Bytes
    Upload DiTFuse weights and config 6 months ago
  • adapter_model.safetensors
    75.5 MB
    xet
    Upload DiTFuse weights and config 6 months ago