[SigLIP] Don't pad by default (#28578)

First draft

docs/source/en/model_doc/siglip.md

@@ -28,7 +28,7 @@ The abstract from the paper is the following:
 
 - Usage of SigLIP is similar to [CLIP](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](https://github.com/mlfoundations/open_clip/blob/73ad04ae7fb93ede1c02dc9040a828634cb1edf1/src/open_clip/loss.py#L307), which leverages various `torch.distributed` utilities.
-- When using the standalone [`SiglipTokenizer`], make sure to pass `padding="max_length"` as that's how the model was trained. The multimodal [`SiglipProcessor`] takes care of this behind the scenes.
+- When using the standalone [`SiglipTokenizer`] or [`SiglipProcessor`], make sure to pass `padding="max_length"` as that's how the model was trained.
 
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/siglip_table.jpeg"
 alt="drawing" width="600"/>
@@ -82,7 +82,8 @@ If you want to do the pre- and postprocessing yourself, here's how to do that:
 >>> image = Image.open(requests.get(url, stream=True).raw)
 
 >>> texts = ["a photo of 2 cats", "a photo of 2 dogs"]
->>> inputs = processor(text=texts, images=image, return_tensors="pt")
+>>> # 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")
 
 >>> with torch.no_grad():
 ...     outputs = model(**inputs)