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* upadte * Update src/transformers/models/paligemma/processing_paligemma.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * update docs * better example in tests * support image tokens * read token * Update tests/models/paligemma/test_processing_paligemma.py Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com> * nit: naming * Update docs/source/en/model_doc/paligemma.md Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * conflicts after rebasing --------- Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com>
107 lines
5.4 KiB
Markdown
107 lines
5.4 KiB
Markdown
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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# PaliGemma
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## Overview
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The PaliGemma model was proposed in [PaliGemma – Google's Cutting-Edge Open Vision Language Model](https://huggingface.co/blog/paligemma) by Google. It is a 3B vision-language model composed by a [SigLIP](siglip) vision encoder and a [Gemma](gemma) language decoder linked by a multimodal linear projection. It cuts an image into a fixed number of VIT tokens and prepends it to an optional prompt. One particularity is that the model uses full block attention on all the image tokens plus the input text tokens. It comes in 3 resolutions, 224x224, 448x448 and 896x896 with 3 base models, with 55 fine-tuned versions for different tasks, and 2 mix models.
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/paligemma/paligemma_arch.png"
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alt="drawing" width="600"/>
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<small> PaliGemma architecture. Taken from the <a href="https://huggingface.co/blog/paligemma">blog post.</a> </small>
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This model was contributed by [Molbap](https://huggingface.co/Molbap).
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## Usage tips
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- PaliGemma is not meant for conversational use, and it works best when fine-tuning to a specific use case. Some downstream tasks on which PaliGemma can be fine-tuned include image captioning, visual question answering (VQA), object detection, referring expression segmentation and document understanding.
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- One can use `PaliGemmaProcessor` to prepare images, text and optional labels for the model. When fine-tuning a PaliGemma model, the `suffix` argument can be passed to the processor which creates the `labels` for the model:
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```python
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prompt = "What is on the flower?"
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answer = "a bee"
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inputs = processor(images=raw_image, text=prompt, suffix=answer, return_tensors="pt")
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```
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## Usage Example
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The model can accept a single or multiple images. According to the [paper](https://arxiv.org/abs/2407.07726v1), the checkpoint PaliGemma can transfer to tasks which take multiple images as input. NLVR2 is one such task, which asks one question about two images, and requires looking at both to give the correct answer. Here's an example code for single and multi image inference.
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### Single-image Inference
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```python
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from transformers import AutoProcessor, PaliGemmaForConditionalGeneration
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model_id = "google/paligemma-3b-mix-224"
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model = PaliGemmaForConditionalGeneration.from_pretrained(model_id)
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processor = AutoProcessor.from_pretrained(model_id)
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prompt = "What is on the flower?"
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image_file = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg?download=true"
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raw_image = Image.open(requests.get(image_file, stream=True).raw)
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inputs = processor(raw_image, prompt, return_tensors="pt")
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output = model.generate(**inputs, max_new_tokens=20)
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print(processor.decode(output[0], skip_special_tokens=True)[inputs.input_ids.shape[1]: ])
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```
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### Multi-image Inference
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```python
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model_id = "google/paligemma-3b-ft-nlvr2-448" # checkpoint tuned for multiple images
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model = PaliGemmaForConditionalGeneration.from_pretrained(model_id)
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processor = PaliGemmaProcessor.from_pretrained(model_id)
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prompt = "answer en Which of the two pictures shows a snowman, first or second?"
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stop_sign_image = Image.open(
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requests.get("https://www.ilankelman.org/stopsigns/australia.jpg", stream=True).raw
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)
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snow_image = Image.open(
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requests.get(
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"https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.jpg", stream=True
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).raw
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)
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inputs = processor(images=[[snow_image, stop_sign_image]], text=prompt, return_tensors="pt")
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output = model.generate(**inputs, max_new_tokens=20)
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print(processor.decode(output[0], skip_special_tokens=True)[inputs.input_ids.shape[1]: ])
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```
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## Resources
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A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with PaliGemma. 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.
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- A blog post introducing all the features of PaliGemma can be found [here](https://huggingface.co/blog/paligemma).
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- Demo notebooks on how to fine-tune PaliGemma for VQA with the Trainer API along with inference can be found [here](https://github.com/huggingface/notebooks/tree/main/examples/paligemma).
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- Demo notebooks on how to fine-tune PaliGemma on a custom dataset (receipt image -> JSON) along with inference can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/PaliGemma). 🌎
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## PaliGemmaConfig
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[[autodoc]] PaliGemmaConfig
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## PaliGemmaProcessor
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[[autodoc]] PaliGemmaProcessor
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## PaliGemmaForConditionalGeneration
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[[autodoc]] PaliGemmaForConditionalGeneration
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- forward
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