

Usage of SigLIP is similar to CLIP. The main difference is the training loss, which does not require a global view of all the pairwise similarities of images and texts within a batch. One needs to apply the sigmoid activation function to the logits, rather than the softmax.
Training is not yet supported. If you want to fine-tune SigLIP or train from scratch, refer to the loss function from OpenCLIP, which leverages various torch.distributed utilities.
When using the standalone [SiglipTokenizer] or [SiglipProcessor], make sure to pass padding="max_length" as that's how the model was trained.