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* add Blip2ForImageTextRetrieval * use one line and remove unnecessary space in tests Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * use value from the config, rather than hardcoded * change order of params in Blip2QFormerModel.forward * update docstring * fix style * update test_inference_opt * move embeddings out of Blip2QFormerModel * remove from_vision_qformer_configs * remove autocast float16 in Blip2QFormerModel * rename fiels into vision_projection,text_projection,use_image_text_matching_head * use CLIPOutput for Blip2ImageTextMatchingModelOutput * remove past_key_values_length from Blip2TextEmbeddings * fix small typo in the CLIPOutput docstring * add Blip2ForImageTextRetrieval to Zero Shot Image Classification mapping * update docstring and add require_torch_fp16 * rollback test_inference_opt * use use_image_text_matching_head=True in convert * skip test_model_get_set_embeddings * fix create_rename_keys error on new itm fields * revert to do scale after dot product between "query" and "key" * fix ValueError on convert script for blip2-opt-2.7b * update org of paths to Salesforce * add is_pipeline_test_to_skip for VisualQuestionAnsweringPipelineTests * [run_slow] blip_2 * removed Blip2ForImageTextRetrieval from IGNORE_NON_AUTO_CONFIGURED * fix docstring of Blip2ImageTextMatchingModelOutput * [run_slow] blip_2 * fix multi-gpu tests * [run_slow] blip_2 * [run_slow] blip_2 --------- Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
104 lines
4.6 KiB
Markdown
104 lines
4.6 KiB
Markdown
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
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# BLIP-2
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## Overview
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The BLIP-2 model was proposed in [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597) by
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Junnan Li, Dongxu Li, Silvio Savarese, Steven Hoi. BLIP-2 leverages frozen pre-trained image encoders and large language models (LLMs) by training a lightweight, 12-layer Transformer
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encoder in between them, achieving state-of-the-art performance on various vision-language tasks. Most notably, BLIP-2 improves upon [Flamingo](https://arxiv.org/abs/2204.14198), an 80 billion parameter model, by 8.7%
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on zero-shot VQAv2 with 54x fewer trainable parameters.
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The abstract from the paper is the following:
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*The cost of vision-and-language pre-training has become increasingly prohibitive due to end-to-end training of large-scale models. This paper proposes BLIP-2, a generic and efficient pre-training strategy that bootstraps vision-language pre-training from off-the-shelf frozen pre-trained image encoders and frozen large language models. BLIP-2 bridges the modality gap with a lightweight Querying Transformer, which is pre-trained in two stages. The first stage bootstraps vision-language representation learning from a frozen image encoder. The second stage bootstraps vision-to-language generative learning from a frozen language model. BLIP-2 achieves state-of-the-art performance on various vision-language tasks, despite having significantly fewer trainable parameters than existing methods. For example, our model outperforms Flamingo80B by 8.7% on zero-shot VQAv2 with 54x fewer trainable parameters. We also demonstrate the model's emerging capabilities of zero-shot image-to-text generation that can follow natural language instructions.*
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/blip2_architecture.jpg"
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alt="drawing" width="600"/>
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<small> BLIP-2 architecture. Taken from the <a href="https://arxiv.org/abs/2301.12597">original paper.</a> </small>
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This model was contributed by [nielsr](https://huggingface.co/nielsr).
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The original code can be found [here](https://github.com/salesforce/LAVIS/tree/5ee63d688ba4cebff63acee04adaef2dee9af207).
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## Usage tips
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- BLIP-2 can be used for conditional text generation given an image and an optional text prompt. At inference time, it's recommended to use the [`generate`] method.
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- One can use [`Blip2Processor`] to prepare images for the model, and decode the predicted tokens ID's back to text.
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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 BLIP-2.
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- Demo notebooks for BLIP-2 for image captioning, visual question answering (VQA) and chat-like conversations can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/BLIP-2).
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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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## Blip2Config
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[[autodoc]] Blip2Config
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- from_vision_qformer_text_configs
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## Blip2VisionConfig
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[[autodoc]] Blip2VisionConfig
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## Blip2QFormerConfig
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[[autodoc]] Blip2QFormerConfig
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## Blip2Processor
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[[autodoc]] Blip2Processor
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## Blip2VisionModel
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[[autodoc]] Blip2VisionModel
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- forward
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## Blip2QFormerModel
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[[autodoc]] Blip2QFormerModel
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- forward
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## Blip2Model
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[[autodoc]] Blip2Model
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- forward
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- get_text_features
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- get_image_features
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- get_qformer_features
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## Blip2ForConditionalGeneration
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[[autodoc]] Blip2ForConditionalGeneration
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- forward
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- generate
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## Blip2ForImageTextRetrieval
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[[autodoc]] Blip2ForImageTextRetrieval
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- forward
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## Blip2TextModelWithProjection
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[[autodoc]] Blip2TextModelWithProjection
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## Blip2VisionModelWithProjection
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[[autodoc]] Blip2VisionModelWithProjection
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