# BLIP-2[[blip-2]]

## 개요[[overview]]
BLIP-2 모델은 Junnan Li, Dongxu Li, Silvio Savarese, Steven Hoi의 [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://huggingface.co/papers/2301.12597) 논문에서 제안되었습니다. BLIP-2는 동결된 사전 학습 이미지 인코더와 대규모 언어 모델(LLM)을 연결하는 12층의 경량 Transformer 인코더를 학습시켜, 여러 비전-언어 작업에서 SOTA(현재 최고의 성능)을 달성했습니다. 특히, BLIP-2는 800억 개의 파라미터를 가진 Flamingo 모델보다 제로샷 VQAv2에서 8.7% 더 높은 성능을 기록했으며, 학습 가능한 파라미터 수는 Flamingo보다 54배 적습니다.

논문의 초록은 다음과 같습니다:

*비전-언어 사전 학습의 비용은 대규모 모델의 엔드-투-엔드 학습으로 인해 점점 더 부담스러워지고 있습니다. 본 논문은 사전 학습된 이미지 인코더와 대규모 언어 모델을 활용하여 비전-언어 사전 학습을 부트스트래핑하는 일반적이고 효율적인 사전 학습 전략인 BLIP-2를 제안합니다. BLIP-2는 경량화된 Querying Transformer를 통해 모달리티 간의 차이를 연결하며, 두 단계로 사전 학습됩니다. 첫 번째 단계는 동결된 이미지 인코더로부터 비전-언어 표현 학습을 부트스트래핑하고, 두 번째 단계는 동결된 언어 모델로부터 비전-언어 생성 학습을 부트스트래핑합니다. BLIP-2는 기존 방법들에 비해 훨씬 적은 학습 가능한 파라미터로 다양한 비전-언어 작업에서 최첨단 성능을 달성합니다. 예를 들어, 우리 모델은 제로샷 VQAv2에서 Flamingo80B보다 8.7% 높은 성능을 기록하며, 학습 가능한 파라미터 수는 54배 적습니다. 우리는 또한 자연어 명령을 따를 수 있는 제로샷 이미지-텍스트 생성의 새로운 기능을 입증했습니다.*

<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/blip2_architecture.jpg"
alt="drawing" width="600"/> 

 BLIP-2 구조. 원본 논문 에서 발췌. 

이 모델은 [nielsr](https://huggingface.co/nielsr)가 기여했습니다. 원본 코드는 [여기](https://github.com/salesforce/LAVIS/tree/5ee63d688ba4cebff63acee04adaef2dee9af207)에서 확인할 수 있습니다.

## 사용 팁[[usage-tips]]

- BLIP-2는 이미지와 조건에 따라 텍스트 프롬프트를 입력받아 조건부 텍스트를 생성합니다. 추론 시 `generate` 메소드를 사용하는 것이 권장됩니다.
- [Blip2Processor](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Processor)를 사용하여 모델에 이미지를 준비하고, 예측된 토큰 ID를 텍스트로 디코딩할 수 있습니다.

## 자료[[resources]]

BLIP-2를 시작하는 데 도움이 되는 공식 Hugging Face 및 커뮤니티(🌎 표시) 자료 목록입니다.

- 이미지 캡셔닝, 시각 질문 응답(VQA), 채팅과 같은 대화형 작업을 위한 BLIP-2 데모 노트북은 [여기](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/BLIP-2)에서 찾을 수 있습니다.

리소스를 제출하여 여기에 포함하고 싶다면 언제든지 풀 리퀘스트를 열어주세요! 리소스는 기존 리소스를 복제하지 않고 새로운 내용이어야 합니다.

## Blip2Config[[transformers.Blip2Config]][[transformers.Blip2Config]]

#### transformers.Blip2Config[[transformers.Blip2Config]]

[Source](https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/blip_2/configuration_blip_2.py#L107)

This is the configuration class to store the configuration of a Blip 2Model. It is used to instantiate a Blip 2
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the [Salesforce/blip2-opt-2.7b](https://huggingface.co/Salesforce/blip2-opt-2.7b)

Configuration objects inherit from [PreTrainedConfig](/docs/transformers/v5.8.0/ko/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the
documentation from [PreTrainedConfig](/docs/transformers/v5.8.0/ko/main_classes/configuration#transformers.PreTrainedConfig) for more information.

Example:

```python
>>> from transformers import (
...     Blip2VisionConfig,
...     Blip2QFormerConfig,
...     OPTConfig,
...     Blip2Config,
...     Blip2ForConditionalGeneration,
... )

>>> # Initializing a Blip2Config with Salesforce/blip2-opt-2.7b style configuration
>>> configuration = Blip2Config()

>>> # Initializing a Blip2ForConditionalGeneration (with random weights) from the Salesforce/blip2-opt-2.7b style configuration
>>> model = Blip2ForConditionalGeneration(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config

>>> # We can also initialize a Blip2Config from a Blip2VisionConfig, Blip2QFormerConfig and any PreTrainedConfig

>>> # Initializing BLIP-2 vision, BLIP-2 Q-Former and language model configurations
>>> vision_config = Blip2VisionConfig()
>>> qformer_config = Blip2QFormerConfig()
>>> text_config = OPTConfig()

>>> config = Blip2Config(vision_config=vision_config, qformer_config=qformer_config, text_config=text_config)
```

**Parameters:**

vision_config (`Union[dict, ~configuration_utils.PreTrainedConfig]`, *optional*) : The config object or dictionary of the vision backbone.

qformer_config (`dict`, *optional*) : Dictionary of configuration options used to initialize [Blip2QFormerConfig](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2QFormerConfig).

text_config (`Union[dict, ~configuration_utils.PreTrainedConfig]`, *optional*) : The config object or dictionary of the text backbone.

num_query_tokens (`int`, *optional*, defaults to 32) : The number of query tokens passed through the Transformer.

image_text_hidden_size (`int`, *optional*, defaults to 256) : Dimensionality of the hidden state of the image-text fusion layer.

image_token_index (`int`, *optional*) : The image token index used as a placeholder for input images.

initializer_factor (`float`, *optional*, defaults to `1.0`) : A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing).

initializer_range (`float`, *optional*, defaults to `0.02`) : The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

## Blip2VisionConfig[[transformers.Blip2VisionConfig]][[transformers.Blip2VisionConfig]]

#### transformers.Blip2VisionConfig[[transformers.Blip2VisionConfig]]

[Source](https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/blip_2/configuration_blip_2.py#L29)

This is the configuration class to store the configuration of a Blip 2Model. It is used to instantiate a Blip 2
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the [Salesforce/blip2-opt-2.7b](https://huggingface.co/Salesforce/blip2-opt-2.7b)

Configuration objects inherit from [PreTrainedConfig](/docs/transformers/v5.8.0/ko/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the
documentation from [PreTrainedConfig](/docs/transformers/v5.8.0/ko/main_classes/configuration#transformers.PreTrainedConfig) for more information.

Example:

```python
>>> from transformers import Blip2VisionConfig, Blip2VisionModel

>>> # Initializing a Blip2VisionConfig with Salesforce/blip2-opt-2.7b style configuration
>>> configuration = Blip2VisionConfig()

>>> # Initializing a Blip2VisionModel (with random weights) from the Salesforce/blip2-opt-2.7b style configuration
>>> model = Blip2VisionModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```

**Parameters:**

hidden_size (`int`, *optional*, defaults to `1408`) : Dimension of the hidden representations.

intermediate_size (`int`, *optional*, defaults to `6144`) : Dimension of the MLP representations.

num_hidden_layers (`int`, *optional*, defaults to `39`) : Number of hidden layers in the Transformer decoder.

num_attention_heads (`int`, *optional*, defaults to `16`) : Number of attention heads for each attention layer in the Transformer decoder.

image_size (`Union[int, list[int], tuple[int, int]]`, *optional*, defaults to `224`) : The size (resolution) of each image.

patch_size (`Union[int, list[int], tuple[int, int]]`, *optional*, defaults to `14`) : The size (resolution) of each patch.

hidden_act (`str`, *optional*, defaults to `gelu`) : The non-linear activation function (function or string) in the decoder. For example, `"gelu"`, `"relu"`, `"silu"`, etc.

layer_norm_eps (`float`, *optional*, defaults to `1e-06`) : The epsilon used by the layer normalization layers.

attention_dropout (`Union[float, int]`, *optional*, defaults to `0.0`) : The dropout ratio for the attention probabilities.

initializer_range (`float`, *optional*, defaults to `1e-10`) : The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

qkv_bias (`bool`, *optional*, defaults to `True`) : Whether to add a bias to the queries, keys and values.

## Blip2QFormerConfig[[transformers.Blip2QFormerConfig]][[transformers.Blip2QFormerConfig]]

#### transformers.Blip2QFormerConfig[[transformers.Blip2QFormerConfig]]

[Source](https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/blip_2/configuration_blip_2.py#L64)

This is the configuration class to store the configuration of a Blip 2Model. It is used to instantiate a Blip 2
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the [Salesforce/blip2-opt-2.7b](https://huggingface.co/Salesforce/blip2-opt-2.7b)

Configuration objects inherit from [PreTrainedConfig](/docs/transformers/v5.8.0/ko/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the
documentation from [PreTrainedConfig](/docs/transformers/v5.8.0/ko/main_classes/configuration#transformers.PreTrainedConfig) for more information.

Examples:

```python
>>> from transformers import Blip2QFormerConfig, Blip2QFormerModel

>>> # Initializing a BLIP-2 Salesforce/blip2-opt-2.7b style configuration
>>> configuration = Blip2QFormerConfig()

>>> # Initializing a model (with random weights) from the Salesforce/blip2-opt-2.7b style configuration
>>> model = Blip2QFormerModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```

**Parameters:**

vocab_size (`int`, *optional*, defaults to `30522`) : Vocabulary size of the model. Defines the number of different tokens that can be represented by the `input_ids`.

hidden_size (`int`, *optional*, defaults to `768`) : Dimension of the hidden representations.

num_hidden_layers (`int`, *optional*, defaults to `12`) : Number of hidden layers in the Transformer decoder.

num_attention_heads (`int`, *optional*, defaults to `12`) : Number of attention heads for each attention layer in the Transformer decoder.

intermediate_size (`int`, *optional*, defaults to `3072`) : Dimension of the MLP representations.

hidden_act (`str`, *optional*, defaults to `gelu`) : The non-linear activation function (function or string) in the decoder. For example, `"gelu"`, `"relu"`, `"silu"`, etc.

hidden_dropout_prob (`Union[float, int]`, *optional*, defaults to `0.1`) : The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.

attention_probs_dropout_prob (`Union[float, int]`, *optional*, defaults to `0.1`) : The dropout ratio for the attention probabilities.

max_position_embeddings (`int`, *optional*, defaults to `512`) : The maximum sequence length that this model might ever be used with.

initializer_range (`float`, *optional*, defaults to `0.02`) : The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

layer_norm_eps (`float`, *optional*, defaults to `1e-12`) : The epsilon used by the layer normalization layers.

pad_token_id (`int`, *optional*, defaults to `0`) : Token id used for padding in the vocabulary.

cross_attention_frequency (`int`, *optional*, defaults to 2) : The frequency of adding cross-attention to the Transformer layers.

encoder_hidden_size (`int`, *optional*, defaults to `1408`) : Dimension of the hidden representations.

use_qformer_text_input (`bool`, *optional*, defaults to `False`) : Whether to use BERT-style embeddings.

## Blip2Processor[[transformers.Blip2Processor]][[transformers.Blip2Processor]]

#### transformers.Blip2Processor[[transformers.Blip2Processor]]

[Source](https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/blip_2/processing_blip_2.py#L45)

Constructs a Blip2Processor which wraps a image processor and a tokenizer into a single processor.

[Blip2Processor](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Processor) offers all the functionalities of [BlipImageProcessor](/docs/transformers/v5.8.0/ko/model_doc/blip#transformers.BlipImageProcessor) and [GPT2Tokenizer](/docs/transformers/v5.8.0/ko/model_doc/gpt2#transformers.GPT2Tokenizer). See the
[~BlipImageProcessor](/docs/transformers/v5.8.0/ko/model_doc/blip#transformers.BlipImageProcessor) and [~GPT2Tokenizer](/docs/transformers/v5.8.0/ko/model_doc/gpt2#transformers.GPT2Tokenizer) for more information.

**Parameters:**

image_processor (`BlipImageProcessor`) : The image processor is a required input.

tokenizer (`GPT2Tokenizer`) : The tokenizer is a required input.

num_query_tokens (`int`, *optional*) : Number of tokens used by the Qformer as queries, should be same as in model's config.

## Blip2VisionModel[[transformers.Blip2VisionModel]][[transformers.Blip2VisionModel]]

#### transformers.Blip2VisionModel[[transformers.Blip2VisionModel]]

[Source](https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/blip_2/modeling_blip_2.py#L483)

The bare Blip 2 Model outputting raw hidden-states without any specific head on top.

This model inherits from [PreTrainedModel](/docs/transformers/v5.8.0/ko/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.

forwardtransformers.Blip2VisionModel.forwardhttps://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/blip_2/modeling_blip_2.py#L503[{"name": "pixel_values", "val": ": torch.FloatTensor | None = None"}, {"name": "interpolate_pos_encoding", "val": ": bool = False"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, *optional*) --
  The tensors corresponding to the input images. Pixel values can be obtained using
  [BlipImageProcessor](/docs/transformers/v5.8.0/ko/model_doc/blip#transformers.BlipImageProcessor). See `BlipImageProcessor.__call__()` for details ([Blip2Processor](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Processor) uses
  [BlipImageProcessor](/docs/transformers/v5.8.0/ko/model_doc/blip#transformers.BlipImageProcessor) for processing images).
- **interpolate_pos_encoding** (`bool`, *optional*, defaults to `False`) --
  Whether to interpolate the pre-trained position encodings.0[BaseModelOutputWithPooling](/docs/transformers/v5.8.0/ko/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`A [BaseModelOutputWithPooling](/docs/transformers/v5.8.0/ko/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Blip2Config](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Config)) and inputs.
The [Blip2VisionModel](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2VisionModel) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) after further processing
  through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
  the classification token after processing through a linear layer and a tanh activation function. The linear
  layer weights are trained from the next sentence prediction (classification) objective during pretraining.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.

**Parameters:**

config ([Blip2VisionConfig](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2VisionConfig)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/v5.8.0/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

**Returns:**

`[BaseModelOutputWithPooling](/docs/transformers/v5.8.0/ko/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)``

A [BaseModelOutputWithPooling](/docs/transformers/v5.8.0/ko/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Blip2Config](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Config)) and inputs.

## Blip2QFormerModel[[transformers.Blip2QFormerModel]][[transformers.Blip2QFormerModel]]

#### transformers.Blip2QFormerModel[[transformers.Blip2QFormerModel]]

[Source](https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/blip_2/modeling_blip_2.py#L879)

BLIP-2 Querying Transformer (Q-Former).

This model inherits from [PreTrainedModel](/docs/transformers/v5.8.0/ko/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.

forwardtransformers.Blip2QFormerModel.forwardhttps://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/blip_2/modeling_blip_2.py#L959[{"name": "query_embeds", "val": ": FloatTensor"}, {"name": "query_length", "val": ": int | None = None"}, {"name": "attention_mask", "val": ": torch.FloatTensor | None = None"}, {"name": "encoder_hidden_states", "val": ": torch.FloatTensor | None = None"}, {"name": "encoder_attention_mask", "val": ": torch.FloatTensor | None = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- **query_embeds** (`torch.FloatTensor`  of shape `(batch_size, sequence_length, hidden_size)`) --
  Hidden states to be used in the attention computation. If cross-attention,
  will be used for the query (i.e., key and value will use the encoder_hidden_states).
- **query_length** (`int`, *optional*) --
  Length of the query, usually based on the number of query tokens.
  If no value is provided, query_length will be inferred by the query_embeds.
- **attention_mask** (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **encoder_hidden_states** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) --
  Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
  if the model is configured as a decoder.
- **encoder_attention_mask** (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
  the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.0[BaseModelOutputWithPoolingAndCrossAttentions](/docs/transformers/v5.8.0/ko/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions) or `tuple(torch.FloatTensor)`A [BaseModelOutputWithPoolingAndCrossAttentions](/docs/transformers/v5.8.0/ko/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Blip2Config](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Config)) and inputs.
The [Blip2QFormerModel](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2QFormerModel) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) after further processing
  through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
  the classification token after processing through a linear layer and a tanh activation function. The linear
  layer weights are trained from the next sentence prediction (classification) objective during pretraining.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.
- **cross_attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
  weighted average in the cross-attention heads.
- **past_key_values** (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a [Cache](/docs/transformers/v5.8.0/ko/internal/generation_utils#transformers.Cache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).

  Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
  `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
  input) to speed up sequential decoding.

**Parameters:**

config ([Blip2QFormerConfig](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2QFormerConfig)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/v5.8.0/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

**Returns:**

`[BaseModelOutputWithPoolingAndCrossAttentions](/docs/transformers/v5.8.0/ko/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions) or `tuple(torch.FloatTensor)``

A [BaseModelOutputWithPoolingAndCrossAttentions](/docs/transformers/v5.8.0/ko/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Blip2Config](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Config)) and inputs.

## Blip2Model[[transformers.Blip2Model]][[transformers.Blip2Model]]

#### transformers.Blip2Model[[transformers.Blip2Model]]

[Source](https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/blip_2/modeling_blip_2.py#L1045)

BLIP-2 Model for generating text and image features. The model consists of a vision encoder, Querying Transformer
(Q-Former) and a language model.

This model inherits from [PreTrainedModel](/docs/transformers/v5.8.0/ko/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.

forwardtransformers.Blip2Model.forwardhttps://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/blip_2/modeling_blip_2.py#L1246[{"name": "pixel_values", "val": ": FloatTensor"}, {"name": "input_ids", "val": ": FloatTensor"}, {"name": "attention_mask", "val": ": torch.LongTensor | None = None"}, {"name": "decoder_input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "decoder_attention_mask", "val": ": torch.LongTensor | None = None"}, {"name": "labels", "val": ": torch.LongTensor | None = None"}, {"name": "interpolate_pos_encoding", "val": ": bool = False"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`) --
  The tensors corresponding to the input images. Pixel values can be obtained using
  [BlipImageProcessor](/docs/transformers/v5.8.0/ko/model_doc/blip#transformers.BlipImageProcessor). See `BlipImageProcessor.__call__()` for details ([Blip2Processor](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Processor) uses
  [BlipImageProcessor](/docs/transformers/v5.8.0/ko/model_doc/blip#transformers.BlipImageProcessor) for processing images).
- **input_ids** (`torch.FloatTensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained using [AutoTokenizer](/docs/transformers/v5.8.0/ko/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.8.0/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/v5.8.0/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **decoder_input_ids** (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Indices of decoder input sequence tokens in the vocabulary.

  Indices can be obtained using [AutoTokenizer](/docs/transformers/v5.8.0/ko/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.8.0/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/v5.8.0/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are decoder input IDs?](../glossary#decoder-input-ids)
- **decoder_attention_mask** (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
  be used by default.

  Only relevant in case an encoder-decoder language model (like T5) is used.
- **labels** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
  config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
  (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
- **interpolate_pos_encoding** (`bool`, *optional*, defaults to `False`) --
  Whether to interpolate the pre-trained position encodings.0`Blip2ForConditionalGenerationModelOutput` or `tuple(torch.FloatTensor)`A `Blip2ForConditionalGenerationModelOutput` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Blip2Config](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Config)) and inputs.
The [Blip2Model](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Model) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

- **loss** (`torch.FloatTensor`, *optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`) -- Language modeling loss from the language model.
- **logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) -- Prediction scores of the language modeling head of the language model.
- **vision_outputs** (`~modeling_outputs.BaseModelOutputWithPooling`, *optional*, defaults to `None`) -- Outputs of the vision encoder.
- **qformer_outputs** (`~modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions`, *optional*, defaults to `None`) -- Outputs of the Q-Former (Querying Transformer).
- **language_model_outputs** (`CausalLMOutputWithPast` or `Seq2SeqLMOutput`) -- Outputs of the language model.

Examples:

```python
>>> from PIL import Image
>>> import httpx
>>> from io import BytesIO
>>> from transformers import Blip2Processor, Blip2Model
>>> import torch

>>> device = "cuda" if torch.cuda.is_available() else "cpu"

>>> processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
>>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b", dtype=torch.float16)
>>> model.to(device)
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> with httpx.stream("GET", url) as response:
...     image = Image.open(BytesIO(response.read()))

>>> prompt = "Question: how many cats are there? Answer:"
>>> inputs = processor(images=image, text=prompt, return_tensors="pt").to(device, torch.float16)

>>> outputs = model(**inputs)
```

**Parameters:**

config ([Blip2Config](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Config)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/v5.8.0/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

**Returns:**

``Blip2ForConditionalGenerationModelOutput` or `tuple(torch.FloatTensor)``

A `Blip2ForConditionalGenerationModelOutput` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Blip2Config](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Config)) and inputs.
#### get_text_features[[transformers.Blip2Model.get_text_features]]

[Source](https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/blip_2/modeling_blip_2.py#L1088)

- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) after further processing
  through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
  the classification token after processing through a linear layer and a tanh activation function. The linear
  layer weights are trained from the next sentence prediction (classification) objective during pretraining.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.

Examples:
```python
>>> import torch
>>> from transformers import AutoTokenizer, Blip2Model

>>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b")
>>> tokenizer = AutoTokenizer.from_pretrained("Salesforce/blip2-opt-2.7b")

>>> inputs = tokenizer(["a photo of a cat"], padding=True, return_tensors="pt")
>>> with torch.inference_mode():
...     text_features = model.get_text_features(**inputs)
```

**Parameters:**

input_ids (`torch.Tensor` of shape `(batch_size, sequence_length)`) : Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.  Indices can be obtained using [AutoTokenizer](/docs/transformers/v5.8.0/ko/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.8.0/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/v5.8.0/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.  [What are input IDs?](../glossary#input-ids)

attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) : Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:  - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**.  [What are attention masks?](../glossary#attention-mask)

decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*) : Indices of decoder input sequence tokens in the vocabulary.  Indices can be obtained using [AutoTokenizer](/docs/transformers/v5.8.0/ko/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.8.0/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/v5.8.0/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.  [What are decoder input IDs?](../glossary#decoder-input-ids)  T5 uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).  To know more on how to prepare `decoder_input_ids` for pretraining take a look at [T5 Training](./t5#training).

decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*) : Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default.

labels (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) : Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

**Returns:**

`[BaseModelOutputWithPooling](/docs/transformers/v5.8.0/ko/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)``

A [BaseModelOutputWithPooling](/docs/transformers/v5.8.0/ko/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Blip2Config](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Config)) and inputs.
#### get_image_features[[transformers.Blip2Model.get_image_features]]

[Source](https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/blip_2/modeling_blip_2.py#L1150)

- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) after further processing
  through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
  the classification token after processing through a linear layer and a tanh activation function. The linear
  layer weights are trained from the next sentence prediction (classification) objective during pretraining.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.

Examples:
```python
>>> import torch
>>> from transformers import AutoProcessor, Blip2Model
>>> from transformers.image_utils import load_image

>>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b")

>>> processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = load_image(url)

>>> inputs = processor(images=image, return_tensors="pt")
>>> with torch.inference_mode():
...     image_outputs = model.get_image_features(**inputs)
```

**Parameters:**

pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`) : The tensors corresponding to the input images. Pixel values can be obtained using [BlipImageProcessor](/docs/transformers/v5.8.0/ko/model_doc/blip#transformers.BlipImageProcessor). See `BlipImageProcessor.__call__()` for details ([Blip2Processor](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Processor) uses [BlipImageProcessor](/docs/transformers/v5.8.0/ko/model_doc/blip#transformers.BlipImageProcessor) for processing images).

interpolate_pos_encoding (`bool`, *optional*, defaults to `False`) : Whether to interpolate the pre-trained position encodings.

**Returns:**

`[BaseModelOutputWithPooling](/docs/transformers/v5.8.0/ko/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)``

A [BaseModelOutputWithPooling](/docs/transformers/v5.8.0/ko/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Blip2Config](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Config)) and inputs.
#### get_qformer_features[[transformers.Blip2Model.get_qformer_features]]

[Source](https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/blip_2/modeling_blip_2.py#L1181)

Examples:

```python
>>> import torch
>>> from transformers import AutoProcessor, Blip2Model
>>> from transformers.image_utils import load_image

>>> processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
>>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b")

>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = load_image(url)

>>> inputs = processor(images=image, return_tensors="pt")
>>> with torch.inference_mode():
...     qformer_outputs = model.get_qformer_features(**inputs)
```

**Parameters:**

pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`) : The tensors corresponding to the input images. Pixel values can be obtained using [BlipImageProcessor](/docs/transformers/v5.8.0/ko/model_doc/blip#transformers.BlipImageProcessor). See `BlipImageProcessor.__call__()` for details ([Blip2Processor](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Processor) uses [BlipImageProcessor](/docs/transformers/v5.8.0/ko/model_doc/blip#transformers.BlipImageProcessor) for processing images).

interpolate_pos_encoding (`bool`, *optional*, defaults to `False`) : Whether to interpolate the pre-trained position encodings.

**Returns:**

`qformer_outputs (`torch.FloatTensor`)`

The Q-Former model's last layer hidden states.

## Blip2ForConditionalGeneration[[transformers.Blip2ForConditionalGeneration]][[transformers.Blip2ForConditionalGeneration]]

#### transformers.Blip2ForConditionalGeneration[[transformers.Blip2ForConditionalGeneration]]

[Source](https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/blip_2/modeling_blip_2.py#L1559)

BLIP-2 Model for generating text given an image and an optional text prompt. The model consists of a vision
encoder, Querying Transformer (Q-Former) and a language model.

One can optionally pass `input_ids` to the model, which serve as a text prompt, to make the language model continue
the prompt. Otherwise, the language model starts generating text from the [BOS] (beginning-of-sequence) token.

Note that Flan-T5 checkpoints cannot be cast to float16. They are pre-trained using bfloat16.

This model inherits from [PreTrainedModel](/docs/transformers/v5.8.0/ko/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.

forwardtransformers.Blip2ForConditionalGeneration.forwardhttps://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/blip_2/modeling_blip_2.py#L1692[{"name": "pixel_values", "val": ": FloatTensor"}, {"name": "input_ids", "val": ": LongTensor"}, {"name": "attention_mask", "val": ": torch.LongTensor | None = None"}, {"name": "decoder_input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "decoder_attention_mask", "val": ": torch.LongTensor | None = None"}, {"name": "inputs_embeds", "val": ": torch.FloatTensor | None = None"}, {"name": "labels", "val": ": torch.LongTensor | None = None"}, {"name": "interpolate_pos_encoding", "val": ": bool = False"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`) --
  The tensors corresponding to the input images. Pixel values can be obtained using
  [BlipImageProcessor](/docs/transformers/v5.8.0/ko/model_doc/blip#transformers.BlipImageProcessor). See `BlipImageProcessor.__call__()` for details ([Blip2Processor](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Processor) uses
  [BlipImageProcessor](/docs/transformers/v5.8.0/ko/model_doc/blip#transformers.BlipImageProcessor) for processing images).
- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of input sequence tokens in the vocabulary of the language model. Input tokens can optionally be
  provided to serve as text prompt, which the language model can continue.

  Indices can be obtained using [Blip2Processor](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Processor). See `Blip2Processor.__call__()` for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **decoder_input_ids** (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Indices of decoder input sequence tokens in the vocabulary.

  Indices can be obtained using [AutoTokenizer](/docs/transformers/v5.8.0/ko/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.8.0/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/v5.8.0/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are decoder input IDs?](../glossary#decoder-input-ids)
- **decoder_attention_mask** (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
  be used by default.

  Only relevant in case an encoder-decoder language model (like T5) is used.
- **inputs_embeds** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) --
  Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
  is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
  model's internal embedding lookup matrix.
- **labels** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
  config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
  (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
- **interpolate_pos_encoding** (`bool`, *optional*, defaults to `False`) --
  Whether to interpolate the pre-trained position encodings.0`Blip2ForConditionalGenerationModelOutput` or `tuple(torch.FloatTensor)`A `Blip2ForConditionalGenerationModelOutput` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Blip2Config](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Config)) and inputs.
The [Blip2ForConditionalGeneration](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2ForConditionalGeneration) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

- **loss** (`torch.FloatTensor`, *optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`) -- Language modeling loss from the language model.
- **logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) -- Prediction scores of the language modeling head of the language model.
- **vision_outputs** (`~modeling_outputs.BaseModelOutputWithPooling`, *optional*, defaults to `None`) -- Outputs of the vision encoder.
- **qformer_outputs** (`~modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions`, *optional*, defaults to `None`) -- Outputs of the Q-Former (Querying Transformer).
- **language_model_outputs** (`CausalLMOutputWithPast` or `Seq2SeqLMOutput`) -- Outputs of the language model.

Examples:

Prepare processor, model and image input

```python
>>> from PIL import Image
>>> import httpx
>>> from io import BytesIO
>>> from transformers import Blip2Processor, Blip2ForConditionalGeneration
>>> import torch

>>> device = "cuda" if torch.cuda.is_available() else "cpu"

>>> processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
>>> model = Blip2ForConditionalGeneration.from_pretrained(
...     "Salesforce/blip2-opt-2.7b", load_in_8bit=True, device_map={"": 0}, dtype=torch.float16
... )  # doctest: +IGNORE_RESULT

>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> with httpx.stream("GET", url) as response:
...     image = Image.open(BytesIO(response.read()))
```

Image captioning (without providing a text prompt):

```python
>>> inputs = processor(images=image, return_tensors="pt").to(device, torch.float16)

>>> generated_ids = model.generate(**inputs)
>>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
>>> print(generated_text)
two cats laying on a couch
```

Visual question answering (prompt = question):

```python
>>> prompt = "Question: how many cats are there? Answer:"
>>> inputs = processor(images=image, text=prompt, return_tensors="pt").to(device="cuda", dtype=torch.float16)

>>> generated_ids = model.generate(**inputs)
>>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
>>> print(generated_text)
two
```

Note that int8 inference is also supported through [bitsandbytes](https://github.com/TimDettmers/bitsandbytes).
This greatly reduces the amount of memory used by the model while maintaining the same performance.

```python
>>> model = Blip2ForConditionalGeneration.from_pretrained(
...     "Salesforce/blip2-opt-2.7b", load_in_8bit=True, device_map={"": 0}, dtype=torch.bfloat16
... )  # doctest: +IGNORE_RESULT

>>> inputs = processor(images=image, text=prompt, return_tensors="pt").to(device="cuda", dtype=torch.bfloat16)

>>> generated_ids = model.generate(**inputs)
>>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
>>> print(generated_text)
two
```

**Parameters:**

config ([Blip2Config](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Config)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/v5.8.0/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

**Returns:**

``Blip2ForConditionalGenerationModelOutput` or `tuple(torch.FloatTensor)``

A `Blip2ForConditionalGenerationModelOutput` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Blip2Config](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Config)) and inputs.
#### generate[[transformers.Blip2ForConditionalGeneration.generate]]

[Source](https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/blip_2/modeling_blip_2.py#L1842)

Overrides `generate` function to be able to use the model as a conditional generator.

**Parameters:**

pixel_values (`torch.FloatTensor` of shape (batch_size, num_channels, height, width)) : Input images to be processed.

input_ids (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*) : The sequence used as a prompt for the generation.

attention_mask (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*) : Mask to avoid performing attention on padding token indices

inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) : Embedded representation of the inputs. Should be float, not int tokens.

interpolate_pos_encoding (`bool`, *optional*, defaults to `False`) : Whether to interpolate the positional encoding of the image embeddings.

**Returns:**

`captions (list)`

A list of strings of length batch_size * num_captions.

## Blip2ForImageTextRetrieval[[transformers.Blip2ForImageTextRetrieval]][[transformers.Blip2ForImageTextRetrieval]]

#### transformers.Blip2ForImageTextRetrieval[[transformers.Blip2ForImageTextRetrieval]]

[Source](https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/blip_2/modeling_blip_2.py#L1936)

BLIP-2 Model with a vision and text projector, and a classification head on top. The model is used in the context
of image-text retrieval. Given an image and a text, the model returns the probability of the text being relevant to
the image.

This model inherits from [PreTrainedModel](/docs/transformers/v5.8.0/ko/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.

forwardtransformers.Blip2ForImageTextRetrieval.forwardhttps://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/blip_2/modeling_blip_2.py#L1970[{"name": "pixel_values", "val": ": FloatTensor"}, {"name": "input_ids", "val": ": LongTensor"}, {"name": "attention_mask", "val": ": torch.LongTensor | None = None"}, {"name": "use_image_text_matching_head", "val": ": bool | None = False"}, {"name": "output_attentions", "val": ": bool | None = None"}, {"name": "output_hidden_states", "val": ": bool | None = None"}, {"name": "return_dict", "val": ": bool | None = None"}, {"name": "**kwargs", "val": ""}]- **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`) --
  The tensors corresponding to the input images. Pixel values can be obtained using
  [BlipImageProcessor](/docs/transformers/v5.8.0/ko/model_doc/blip#transformers.BlipImageProcessor). See `BlipImageProcessor.__call__()` for details ([Blip2Processor](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Processor) uses
  [BlipImageProcessor](/docs/transformers/v5.8.0/ko/model_doc/blip#transformers.BlipImageProcessor) for processing images).
- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of input sequence tokens in the vocabulary of the language model. Input tokens can optionally be
  provided to serve as text prompt, which the language model can continue.

  Indices can be obtained using [Blip2Processor](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Processor). See `Blip2Processor.__call__()` for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **use_image_text_matching_head** (`bool`, *optional*) --
  Whether to return the Image-Text Matching or Contrastive scores.
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/v5.8.0/ko/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.0`Blip2ImageTextMatchingModelOutput` or `tuple(torch.FloatTensor)`A `Blip2ImageTextMatchingModelOutput` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Blip2Config](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Config)) and inputs.
The [Blip2ForImageTextRetrieval](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2ForImageTextRetrieval) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`) -- Contrastive loss for image-text similarity.
- **logits_per_image** (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`) -- The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
  similarity scores.
- **logits_per_text** (`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`) -- The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
  similarity scores.
- **text_embeds** (`torch.FloatTensor` of shape `(batch_size, output_dim`) -- The text embeddings obtained by applying the projection layer to the pooled output.
- **image_embeds** (`torch.FloatTensor` of shape `(batch_size, output_dim`) -- The image embeddings obtained by applying the projection layer to the pooled output.
- **text_model_output** (`~modeling_outputs.BaseModelOutputWithPooling`, defaults to `None`) -- The output of the [Blip2QFormerModel](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2QFormerModel).
- **vision_model_output** (`~modeling_outputs.BaseModelOutputWithPooling`, defaults to `None`) -- The output of the [Blip2VisionModel](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2VisionModel).

Examples:

```python
>>> import torch
>>> from PIL import Image
>>> import httpx
>>> from io import BytesIO
>>> from transformers import AutoProcessor, Blip2ForImageTextRetrieval

>>> device = "cuda" if torch.cuda.is_available() else "cpu"

>>> model = Blip2ForImageTextRetrieval.from_pretrained("Salesforce/blip2-itm-vit-g", dtype=torch.float16)
>>> processor = AutoProcessor.from_pretrained("Salesforce/blip2-itm-vit-g")

>>> model.to(device)
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> with httpx.stream("GET", url) as response:
...     image = Image.open(BytesIO(response.read()))
>>> text = "two cats laying on a pink blanket"

>>> inputs = processor(images=image, text=text, return_tensors="pt").to(device, torch.float16)
>>> itm_out = model(**inputs, use_image_text_matching_head=True)
>>> logits_per_image = torch.nn.functional.softmax(itm_out.logits_per_image, dim=1)
>>> probs = logits_per_image.softmax(dim=1)  # we can take the softmax to get the label probabilities

>>> print(f"{probs[0][0]:.1%} that image 0 is not '{text}'")
26.9% that image 0 is not 'two cats laying on a pink blanket'

>>> print(f"{probs[0][1]:.1%} that image 0 is '{text}'")
73.0% that image 0 is 'two cats laying on a pink blanket'

>>> texts = ["a photo of a cat", "a photo of a dog"]

>>> inputs = processor(images=image, text=texts, return_tensors="pt").to(device, torch.float16)
>>> itc_out = model(**inputs, use_image_text_matching_head=False)
>>> logits_per_image = itc_out.logits_per_image  # this is the image-text similarity score
>>> probs = logits_per_image.softmax(dim=1)  # we can take the softmax to get the label probabilities

>>> print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'")
55.3% that image 0 is 'a photo of a cat'

>>> print(f"{probs[0][1]:.1%} that image 0 is '{texts[1]}'")
44.7% that image 0 is 'a photo of a dog'
```

**Parameters:**

config ([Blip2Config](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Config)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/v5.8.0/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

**Returns:**

``Blip2ImageTextMatchingModelOutput` or `tuple(torch.FloatTensor)``

A `Blip2ImageTextMatchingModelOutput` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Blip2Config](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Config)) and inputs.

## Blip2TextModelWithProjection[[transformers.Blip2TextModelWithProjection]][[transformers.Blip2TextModelWithProjection]]

#### transformers.Blip2TextModelWithProjection[[transformers.Blip2TextModelWithProjection]]

[Source](https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/blip_2/modeling_blip_2.py#L1373)

The Blip 2 Model with a projection layer on top (a linear layer on top of the pooled output).

This model inherits from [PreTrainedModel](/docs/transformers/v5.8.0/ko/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.

forwardtransformers.Blip2TextModelWithProjection.forwardhttps://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/blip_2/modeling_blip_2.py#L1397[{"name": "input_ids", "val": ": torch.Tensor | None = None"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "position_ids", "val": ": torch.Tensor | None = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- **input_ids** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained using [AutoTokenizer](/docs/transformers/v5.8.0/ko/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.8.0/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/v5.8.0/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **position_ids** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`.

  [What are position IDs?](../glossary#position-ids)0`Blip2TextModelOutput` or `tuple(torch.FloatTensor)`A `Blip2TextModelOutput` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Blip2Config](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Config)) and inputs.
The [Blip2TextModelWithProjection](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2TextModelWithProjection) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

- **text_embeds** (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`) -- The text embeddings obtained by applying the projection layer to the pooler_output.
- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*, defaults to `None`) -- Sequence of hidden-states at the output of the last layer of the model.
- **hidden_states** (`tuple[torch.FloatTensor, ...]`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple[torch.FloatTensor, ...]`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.

Examples:

```python
>>> import torch
>>> from transformers import AutoProcessor, Blip2TextModelWithProjection

>>> device = "cuda" if torch.cuda.is_available() else "cpu"

>>> model = Blip2TextModelWithProjection.from_pretrained(
...     "Salesforce/blip2-itm-vit-g", dtype=torch.float16
... )

>>> model.to(device)
>>> processor = AutoProcessor.from_pretrained("Salesforce/blip2-itm-vit-g")

>>> inputs = processor(text=["a photo of a cat", "a photo of a dog"], return_tensors="pt").to(device)

>>> outputs = model(**inputs)
>>> text_embeds = outputs.text_embeds
>>> print(text_embeds.shape)
torch.Size([2, 7, 256])
```

**Parameters:**

config ([Blip2Config](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Config)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/v5.8.0/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

**Returns:**

``Blip2TextModelOutput` or `tuple(torch.FloatTensor)``

A `Blip2TextModelOutput` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Blip2Config](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Config)) and inputs.

## Blip2VisionModelWithProjection[[transformers.Blip2VisionModelWithProjection]][[transformers.Blip2VisionModelWithProjection]]

#### transformers.Blip2VisionModelWithProjection[[transformers.Blip2VisionModelWithProjection]]

[Source](https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/blip_2/modeling_blip_2.py#L1458)

The Blip 2 Model with a projection layer on top (a linear layer on top of the pooled output).

This model inherits from [PreTrainedModel](/docs/transformers/v5.8.0/ko/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.

forwardtransformers.Blip2VisionModelWithProjection.forwardhttps://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/blip_2/modeling_blip_2.py#L1481[{"name": "pixel_values", "val": ": torch.FloatTensor | None = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, *optional*) --
  The tensors corresponding to the input images. Pixel values can be obtained using
  [BlipImageProcessor](/docs/transformers/v5.8.0/ko/model_doc/blip#transformers.BlipImageProcessor). See `BlipImageProcessor.__call__()` for details ([Blip2Processor](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Processor) uses
  [BlipImageProcessor](/docs/transformers/v5.8.0/ko/model_doc/blip#transformers.BlipImageProcessor) for processing images).0`Blip2VisionModelOutput` or `tuple(torch.FloatTensor)`A `Blip2VisionModelOutput` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Blip2Config](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Config)) and inputs.
The [Blip2VisionModelWithProjection](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2VisionModelWithProjection) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

- **image_embeds** (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`) -- The image embeddings obtained by applying the projection layer to the pooler_output.
- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*, defaults to `None`) -- Sequence of hidden-states at the output of the last layer of the model.
- **hidden_states** (`tuple[torch.FloatTensor, ...]`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple[torch.FloatTensor, ...]`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.

Examples:

```python
>>> import torch
>>> from transformers import AutoProcessor, Blip2VisionModelWithProjection
>>> from transformers.image_utils import load_image

>>> device = "cuda" if torch.cuda.is_available() else "cpu"

>>> processor = AutoProcessor.from_pretrained("Salesforce/blip2-itm-vit-g")
>>> model = Blip2VisionModelWithProjection.from_pretrained(
...     "Salesforce/blip2-itm-vit-g", dtype=torch.float16
... )
>>> model.to(device)
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = load_image(url)

>>> inputs = processor(images=image, return_tensors="pt").to(device, torch.float16)

>>> with torch.inference_mode():
...     outputs = model(**inputs)
>>> image_embeds = outputs.image_embeds
>>> print(image_embeds.shape)
torch.Size([1, 32, 256])
```

**Parameters:**

config ([Blip2Config](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Config)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/v5.8.0/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

**Returns:**

``Blip2VisionModelOutput` or `tuple(torch.FloatTensor)``

A `Blip2VisionModelOutput` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Blip2Config](/docs/transformers/v5.8.0/ko/model_doc/blip-2#transformers.Blip2Config)) and inputs.