Instructions to use nyu-visionx/siglip2_decoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nyu-visionx/siglip2_decoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-to-image", model="nyu-visionx/siglip2_decoder")# Load model directly from transformers import AutoImageProcessor, AutoModelForPreTraining processor = AutoImageProcessor.from_pretrained("nyu-visionx/siglip2_decoder") model = AutoModelForPreTraining.from_pretrained("nyu-visionx/siglip2_decoder") - Notebooks
- Google Colab
- Kaggle
Add model card and metadata
#1
by nielsr HF Staff - opened
Hi! I'm Niels from the Hugging Face community science team.
This PR adds a comprehensive model card to the repository. It includes:
- Metadata for
license,library_name, andpipeline_tag. - Links to the paper (Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders), project page, and official GitHub repository.
- A brief description of the model and its findings.
- Sample CLI usage for inference as found in the official documentation.
- Citation information.
This update will make the model more discoverable and provide users with the necessary context and links to the official implementation.
bytetriper changed pull request status to merged