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SigLIP2

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SigLIP2

Overview

The SigLIP2 model was proposed in SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features by Michael Tschannen, Alexey Gritsenko, Xiao Wang, Muhammad Ferjad Naeem, Ibrahim Alabdulmohsin, Nikhil Parthasarathy, Talfan Evans, Lucas Beyer, Ye Xia, Basil Mustafa, Olivier Hénaff, Jeremiah Harmsen, Andreas Steiner and Xiaohua Zhai.

The model comes in two variants

1) FixRes - model works with fixed resolution images (backward compatible with SigLIP v1) 2) NaFlex - model works with variable image aspect ratios and resolutions (SigLIP2 in transformers)

The abstract from the paper is the following:

We introduce SigLIP 2, a family of new multilingual vision-language encoders that build on the success of the original SigLIP. In this second iteration, we extend the original image-text training objective with several prior, independently developed techniques into a unified recipe—this includes decoder-based pretraining, self-supervised losses (self-distillation, masked prediction) and online data curation. With these changes, SigLIP 2 models outperform their SigLIP counterparts at all model scales in core capabilities, including zero-shot classification (best SigLIP 2 ViT-g/16 achieves 85.0% ImageNet zero-shot accuracy), image-text retrieval, and transfer performance when extracting visual representations for Vision-Language Models (VLMs). Furthermore, the new training recipe leads to significant improvements on localization and dense prediction tasks. We also train variants which support multiple resolutions and preserve the input’s native aspect ratio. Finally, we train on a more diverse data-mixture that includes de-biasing techniques, leading to much better multilingual understanding and improved fair- ness. To provide users with the ability to trade-off inference cost with performance, we release model checkpoints at four sizes (ViT-B/86M, L/303M, So400m/400M, and g/1B).

Usage tips

Usage of SigLIP2 is similar to SigLIP and CLIP. The main difference from CLIP 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 supported but does not use torch.distributed utilities which may limit the scalability of batch size. However, DDP and FDSP works on single-node multi-gpu setup.
When using the standalone GemmaTokenizerFast make sure to pass padding="max_length" and max_length=64 as that’s how the model was trained.
Model was trained with lowercased text, make sure you make the same preprocessing for your text labels.
To get the same results as the pipeline, a prompt template of “this is a photo of {label}” should be used.
The NaFlex variant supports processing images at higher resolutions by adjusting the max_num_patches parameter in the Processor. The default value is max_num_patches=256. Increasing max_num_patches to 1024 (4x) will approximately double processed image height and width, while preserving the aspect ratio.

This model was contributed by qubvel. The original code can be found here.

Usage example

There are 2 main ways to use SigLIP2: either using the pipeline API, which abstracts away all the complexity for you, or by using the Siglip2Model class yourself.

FixRes variant

Pipeline API

The pipeline allows to use the model in a few lines of code:

>>> from transformers import pipeline
>>> from PIL import Image
>>> import requests

>>> # load pipe
>>> image_classifier = pipeline(
...     task="zero-shot-image-classification",
...     model="google/siglip2-base-patch16-224",
... )

>>> # load image
>>> url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
>>> image = Image.open(requests.get(url, stream=True).raw)

>>> # inference
>>> candidate_labels = ["2 cats", "a plane", "a remote"]
>>> outputs = image_classifier(image, candidate_labels=candidate_labels)
>>> outputs = [{"score": round(output["score"], 4), "label": output["label"] } for output in outputs]
>>> print(outputs)
[{'score': 0.1499, 'label': '2 cats'}, {'score': 0.0008, 'label': 'a remote'}, {'score': 0.0, 'label': 'a plane'}]

Using the model yourself

If you want to do the pre- and postprocessing yourself, here’s how to do that:

>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, AutoModel
>>> import torch

>>> model = AutoModel.from_pretrained("google/siglip2-base-patch16-224")
>>> processor = AutoProcessor.from_pretrained("google/siglip2-base-patch16-224")

>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)

>>> candidate_labels = ["2 cats", "2 dogs"]
# follows the pipeline prompt template to get same results
>>> texts = [f"This is a photo of {label}." for label in candidate_labels]

# IMPORTANT: we pass `padding=max_length` and `max_length=64` since the model was trained with this
>>> inputs = processor(text=texts, images=image, padding="max_length", max_length=64, return_tensors="pt")

>>> with torch.no_grad():
...     outputs = model(**inputs)

>>> logits_per_image = outputs.logits_per_image
>>> probs = torch.sigmoid(logits_per_image) # these are the probabilities
>>> print(f"{probs[0][0]:.1%} that image 0 is '{candidate_labels[0]}'")
15.0% that image 0 is '2 cats'

NaFlex variant

NaFlex combines ideas from FlexiViT, i.e. supporting multiple, predefined sequence lengths with a single ViT model, and NaViT, namely processing images at their native aspect ratio. This enables processing different types of images at appropriate resolution, e.g. using a larger resolution to process document images, while at the same time minimizing the impact of aspect ratio distortion on certain inference tasks, e.g. on OCR.

Given a patch size and target sequence length, NaFlex preprocesses the data by first resizing the input image such that the height and width after resizing are multiples of the patch size, while

keeping the aspect ratio distortion as small as possible
producing a sequence length of at most the desired target sequence length (max_num_patches)

The resulting distortion in width and height is at most (patch_size - 1) / width and (patch_size - 1) / height, respectively, which tends to be small for common resolutions and aspect ratios. After resizing, the image is split into a sequence of patches, and a mask with padding information is added.

>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, AutoModel
>>> import torch

>>> model = AutoModel.from_pretrained("google/siglip2-base-patch16-naflex")
>>> processor = AutoProcessor.from_pretrained("google/siglip2-base-patch16-naflex")

>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)

>>> candidate_labels = ["2 cats", "2 dogs"]
# follows the pipeline prompt template to get same results
>>> texts = [f"This is a photo of {label}." for label in candidate_labels]

# default value for `max_num_patches` is 256, but you can increase resulted image resolution providing
# higher values e.g. `max_num_patches=512`
>>> inputs = processor(text=texts, images=image, max_num_patches=256, return_tensors="pt")

>>> with torch.no_grad():
...     outputs = model(**inputs)

>>> logits_per_image = outputs.logits_per_image
>>> probs = torch.sigmoid(logits_per_image) # these are the probabilities
>>> print(f"{probs[0][0]:.1%} that image 0 is '{candidate_labels[0]}'")
21.1% that image 0 is '2 cats'

Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with SigLIP2.

Zero-shot image classification task guide
Demo notebook for SigLIP2 can be found here. 🌎

If you’re interested in submitting a resource to be included here, please feel free to open a Pull Request and we’ll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

Combining SigLIP2 and Flash Attention 2

First, make sure to install the latest version of Flash Attention 2.

pip install -U flash-attn --no-build-isolation

Make also sure that you have a hardware that is compatible with Flash-Attention 2. Read more about it in the official documentation of flash-attn repository. Make also sure to load your model in half-precision (e.g. `torch.float16“)

To load and run a model using Flash Attention 2, refer to the snippet below:

>>> import torch
>>> import requests
>>> from PIL import Image
>>> from transformers import AutoProcessor, AutoModel
>>> device = "cuda" # the device to load the model onto

>>> model = AutoModel.from_pretrained(
...     "google/siglip2-so400m-patch14-384",
...     attn_implementation="flash_attention_2",
...     torch_dtype=torch.float16,
...     device_map=device,
... )
>>> processor = AutoProcessor.from_pretrained("google/siglip2-so400m-patch14-384")

>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)

>>> candidate_labels = ["2 cats", "2 dogs"]
# follows the pipeline prompt template to get same results
>>> texts = [f'This is a photo of {label}.' for label in candidate_labels]
# important: we pass `padding=max_length` since the model was trained with this
>>> inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt").to(device)

>>> with torch.no_grad():
...     with torch.autocast(device):
...         outputs = model(**inputs)

>>> logits_per_image = outputs.logits_per_image
>>> probs = torch.sigmoid(logits_per_image) # these are the probabilities
>>> print(f"{probs[0][0]:.1%} that image 0 is '{candidate_labels[0]}'")
19.8% that image 0 is '2 cats'

Transformers

SigLIP2

Overview

Usage tips

Usage example

FixRes variant

NaFlex variant

Resources

Combining SigLIP2 and Flash Attention 2

Siglip2Config

class transformers.Siglip2Config

from_text_vision_configs

Siglip2TextConfig

class transformers.Siglip2TextConfig

Siglip2VisionConfig

class transformers.Siglip2VisionConfig

Siglip2ImageProcessor

class transformers.Siglip2ImageProcessor

preprocess

Siglip2ImageProcessorFast

class transformers.Siglip2ImageProcessorFast

preprocess

Siglip2Processor

class transformers.Siglip2Processor

batch_decode

decode

Siglip2Model

class transformers.Siglip2Model

forward

get_text_features

get_image_features

Siglip2TextModel

class transformers.Siglip2TextModel

forward

Siglip2VisionModel

class transformers.Siglip2VisionModel

forward

Siglip2ForImageClassification

class transformers.Siglip2ForImageClassification

forward