●Change8

Migrating to Transformers v4.49.0-SigLIP-2

Version v4.49.0-SigLIP-2 introduces 4 breaking changes. This guide details how to update your code.

Released: 2/21/2025

4
Breaking Changes
5
Migration Steps
5
Affected Symbols

⚠️ Check Your Code

If you use any of these symbols, you need to read this guide:

Siglip2ModelSiglip2ProcessorSiglip2VisionModelSiglip2TextModelGemmaTokenizerFast

Breaking Changes

●Issue #1

When using SigLIP2 models, the output logits should be processed using the sigmoid activation function instead of softmax, as the training loss differs from standard CLIP models.

✓Solution

Replace any usage of softmax on the output logits of SigLIP2 models with torch.sigmoid().

●Issue #2

If using the standalone [GemmaTokenizerFast] for text preprocessing with SigLIP2, specific padding arguments are required to match training configuration.

✓Solution

When using GemmaTokenizerFast, ensure you pass padding="max_length" and max_length=64 during tokenization.

●Issue #3

The model was trained with lowercased text, meaning input text labels must match this format for optimal performance.

✓Solution

Ensure all text labels used for classification or retrieval tasks are converted to lowercase before processing.

●Issue #4

To achieve results consistent with the provided examples (especially for zero-shot classification), a specific prompt template must be used.

✓Solution

Use the prompt template "this is a photo of {label}" when formatting text inputs for classification tasks.

Migration Steps

  1. 1
    If you are installing this new version via Git, update your installation command to use the specific tag: `pip install git+https://github.com/huggingface/transformers@v4.49.0-SigLIP-2`.
  2. 2
    If you are using SigLIP2 models, modify your inference code to apply torch.sigmoid() to the raw output logits instead of torch.softmax().
  3. 3
    If you are manually tokenizing text inputs for SigLIP2 using GemmaTokenizerFast, ensure you set `padding="max_length"` and `max_length=64`.
  4. 4
    Verify that all text inputs (labels or prompts) passed to the SigLIP2 model are lowercased.
  5. 5
    If using the model for zero-shot classification, adopt the recommended prompt template: "this is a photo of {label}".

Release Summary

This release introduces SigLIP-2, a new family of multilingual vision-language encoders featuring improved semantic understanding and support for native aspect ratio image processing via the NaFlex variant.

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View the full release notes and all changes for Transformers v4.49.0-SigLIP-2.

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