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*This model was published in HF papers on 2023-03-27 and contributed to Hugging Face Transformers on 2024-01-08.*

<div style="float: right;">
    <div class="flex flex-wrap space-x-1">
                <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
            <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
    </div>
</div>

# SigLIP

[SigLIP](https://huggingface.co/papers/2303.15343) is a multimodal image-text model similar to [CLIP](clip). It uses separate image and text encoders to generate representations for both modalities.

Unlike CLIP, SigLIP employs a pairwise sigmoid loss on image-text pairs during training. This training loss eliminates the need for a global view of all pairwise similarities between images and texts within a batch. Consequently, it enables more efficient scaling to larger batch sizes while also delivering superior performance with smaller batch sizes.

You can find all the original SigLIP checkpoints under the [SigLIP](https://huggingface.co/collections/google/siglip-659d5e62f0ae1a57ae0e83ba) collection.

> [!TIP]
> Click on the SigLIP models in the right sidebar for more examples of how to apply SigLIP to different image and text tasks.

The example below demonstrates how to generate similarity scores between texts and image(s) with [`Pipeline`] or the [`AutoModel`] class.

<hfoptions id="usage">
<hfoption id="Pipeline">

```python
from transformers import pipeline


image = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
candidate_labels = ["a Pallas cat", "a lion", "a Siberian tiger"]

pipeline = pipeline(task="zero-shot-image-classification", model="google/siglip-base-patch16-224", device=0)
pipeline(image, candidate_labels=candidate_labels)
```

</hfoption>
<hfoption id="AutoModel">

```python
import requests
import torch
from PIL import Image

from transformers import AutoModel, AutoProcessor


model = AutoModel.from_pretrained("google/siglip-base-patch16-224", device_map="auto", attn_implementation="sdpa")
processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224")

url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
image = Image.open(requests.get(url, stream=True).raw)
candidate_labels = ["a Pallas cat", "a lion", "a Siberian tiger"]
texts = [f'This is a photo of {label}.' for label in candidate_labels]
inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt").to(model.device)

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

logits_per_image = outputs.logits_per_image
probs = torch.sigmoid(logits_per_image)
print(f"{probs[0][0]:.1%} that image 0 is '{candidate_labels[0]}'")
```

</hfoption>
</hfoptions>

Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.

The example below uses [bitsandbytes](../quantization/bitsandbytes) to only quantize the weights to int4.

```python
import requests
import torch
from PIL import Image

from transformers import AutoModel, AutoProcessor, BitsAndBytesConfig


bnb_config = BitsAndBytesConfig(load_in_4bit=True)
model = AutoModel.from_pretrained("google/siglip-base-patch16-224", quantization_config=bnb_config, device_map="auto", attn_implementation="sdpa")
processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224")

url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
image = Image.open(requests.get(url, stream=True).raw)
candidate_labels = ["a Pallas cat", "a lion", "a Siberian tiger"]
texts = [f'This is a photo of {label}.' for label in candidate_labels]
inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt").to(model.device)

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

logits_per_image = outputs.logits_per_image
probs = torch.sigmoid(logits_per_image)
print(f"{probs[0][0]:.1%} that image 0 is '{candidate_labels[0]}'")
```

## Notes

- Training is supported for DDP and FSDP on single-node multi-GPU setups. However, it does not use [torch.distributed](https://pytorch.org/tutorials/beginner/dist_overview.html) utilities which may limit the scalability of batch size.
- When using the standalone [`SiglipTokenizer`] or [`SiglipProcessor`], make sure to pass `padding="max_length"` because that is how the model was trained.
- To get the same results as the [`Pipeline`], a prompt template of `"This is a photo of {label}."` should be passed to the processor.
- Toggle the `attn_implementation` parameter to either `"sdpa"` or `"flash_attention_2"` to use a more memory-efficient attention.

    ```py
    # pip install -U flash-attn --no-build-isolation

    from transformers import SiglipModel

    model = SiglipModel.from_pretrained(
        "google/siglip-so400m-patch14-384",
        attn_implementation="flash_attention_2",
        device_map="auto",
    )
    ```

## SiglipConfig

[[autodoc]] SiglipConfig

## SiglipTextConfig

[[autodoc]] SiglipTextConfig

## SiglipVisionConfig

[[autodoc]] SiglipVisionConfig

## SiglipTokenizer

[[autodoc]] SiglipTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary

## SiglipImageProcessor

[[autodoc]] SiglipImageProcessor
    - preprocess

## SiglipImageProcessorPil

[[autodoc]] SiglipImageProcessorPil
    - preprocess

## SiglipProcessor

[[autodoc]] SiglipProcessor
    - __call__

## SiglipModel

[[autodoc]] SiglipModel
    - forward
    - get_text_features
    - get_image_features

## SiglipTextModel

[[autodoc]] SiglipTextModel
    - forward

## SiglipVisionModel

[[autodoc]] SiglipVisionModel
    - forward

## SiglipForImageClassification

[[autodoc]] SiglipForImageClassification
    - forward
