#
ALIGN

ALIGN is pretrained on a noisy 1.8 billion alt‑text and image pair dataset to show that scale can make up for the noise. It uses a dual‑encoder architecture, EfficientNet for images and BERT for text, and a contrastive loss to align similar image–text embeddings together while pushing different embeddings apart. Once trained, ALIGN can encode any image and candidate captions into a shared vector space for zero‑shot retrieval or classification without requiring extra labels. This scale‑first approach reduces dataset curation costs and powers state‑of‑the‑art image–text retrieval and zero‑shot ImageNet classification.

You can find all the original ALIGN checkpoints under the Kakao Brain organization.

Click on the ALIGN models in the right sidebar for more examples of how to apply ALIGN to different vision and text related tasks.

The example below demonstrates zero-shot image classification with Pipeline or the AutoModel class.

Pipeline
AutoModel

    import torch
    from transformers import pipeline

    pipeline = pipeline(
        task="zero-shot-image-classification",
        model="kakaobrain/align-base",
        device=0,
        torch_dtype=torch.bfloat16
    )

    candidate_labels = [
        "a photo of a dog",
        "a photo of a cat",
        "a photo of a person"
    ]

    pipeline("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg", candidate_labels=candidate_labels)

##
Notes

- ALIGN projects the text and visual features into latent space and the dot product between the projected image and text features is used as the similarity score. The example below demonstrates how to calculate the image-text similarity score with AlignProcessor and AlignModel.

      # Example of using ALIGN for image-text similarity
      from transformers import AlignProcessor, AlignModel
      import torch
      from PIL import Image
      import requests
      from io import BytesIO

      # Load processor and model
      processor = AlignProcessor.from_pretrained("kakaobrain/align-base")
      model = AlignModel.from_pretrained("kakaobrain/align-base")

      # Download image from URL
      url = "https://huggingface.co/roschmid/dog-races/resolve/main/images/Golden_Retriever.jpg"
      response = requests.get(url)
      image = Image.open(BytesIO(response.content))  # Convert the downloaded bytes to a PIL Image

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

      # Process image and text inputs
      inputs = processor(images=image, text=texts, return_tensors="pt")

      # Get the embeddings
      with torch.no_grad():
          outputs = model(**inputs)

      image_embeds = outputs.image_embeds
      text_embeds = outputs.text_embeds

      # Normalize embeddings for cosine similarity
      image_embeds = image_embeds / image_embeds.norm(dim=1, keepdim=True)
      text_embeds = text_embeds / text_embeds.norm(dim=1, keepdim=True)

      # Calculate similarity scores
      similarity_scores = torch.matmul(text_embeds, image_embeds.T)

      # Print raw scores
      print("Similarity scores:", similarity_scores)

      # Convert to probabilities
      probs = torch.nn.functional.softmax(similarity_scores, dim=0)
      print("Probabilities:", probs)

      # Get the most similar text
      most_similar_idx = similarity_scores.argmax().item()
      print(f"Most similar text: '{texts[most_similar_idx]}'")

##
Resources

- Refer to the Kakao Brain’s Open Source ViT, ALIGN, and the New COYO Text-Image Dataset blog post for more details.

##
AlignConfig

###
class transformers.AlignConfig

< source >

( text_config = None vision_config = None projection_dim = 640 temperature_init_value = 1.0 initializer_range = 0.02 **kwargs )

Parameters

- text_config (`dict`, optional) — Dictionary of configuration options used to initialize AlignTextConfig.
- vision_config (`dict`, optional) — Dictionary of configuration options used to initialize AlignVisionConfig.
- projection_dim (`int`, optional, defaults to 640) — Dimensionality of text and vision projection layers.
- temperature_init_value (`float`, optional, defaults to 1.0) — The initial value of the temperature parameter. Default is used as per the original ALIGN implementation.
- initializer_range (`float`, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
- kwargs (optional) — Dictionary of keyword arguments.

AlignConfig is the configuration class to store the configuration of a AlignModel. It is used to instantiate a ALIGN model according to the specified arguments, defining the text model and vision model configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the ALIGN kakaobrain/align-base architecture.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Example:

    >>> from transformers import AlignConfig, AlignModel

    >>> # Initializing a AlignConfig with kakaobrain/align-base style configuration
    >>> configuration = AlignConfig()

    >>> # Initializing a AlignModel (with random weights) from the kakaobrain/align-base style configuration
    >>> model = AlignModel(configuration)

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

    >>> # We can also initialize a AlignConfig from a AlignTextConfig and a AlignVisionConfig
    >>> from transformers import AlignTextConfig, AlignVisionConfig

    >>> # Initializing ALIGN Text and Vision configurations
    >>> config_text = AlignTextConfig()
    >>> config_vision = AlignVisionConfig()

    >>> config = AlignConfig.from_text_vision_configs(config_text, config_vision)

####
from_text_vision_configs

< source >

( text_config: AlignTextConfig vision_config: AlignVisionConfig **kwargs ) →
AlignConfig

Returns

AlignConfig

An instance of a configuration object

Instantiate a AlignConfig (or a derived class) from align text model configuration and align vision model configuration.

##
AlignTextConfig

###
class transformers.AlignTextConfig

< source >

( vocab_size = 30522 hidden_size = 768 num_hidden_layers = 12 num_attention_heads = 12 intermediate_size = 3072 hidden_act = 'gelu' hidden_dropout_prob = 0.1 attention_probs_dropout_prob = 0.1 max_position_embeddings = 512 type_vocab_size = 2 initializer_range = 0.02 layer_norm_eps = 1e-12 pad_token_id = 0 position_embedding_type = 'absolute' use_cache = True **kwargs )

Parameters

- vocab_size (`int`, optional, defaults to 30522) — Vocabulary size of the Align Text model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling AlignTextModel.
- hidden_size (`int`, optional, defaults to 768) — Dimensionality of the encoder layers and the pooler layer.
- num_hidden_layers (`int`, optional, defaults to 12) — Number of hidden layers in the Transformer encoder.
- num_attention_heads (`int`, optional, defaults to 12) — Number of attention heads for each attention layer in the Transformer encoder.
- intermediate_size (`int`, optional, defaults to 3072) — Dimensionality of the “intermediate” (often named feed-forward) layer in the Transformer encoder.
- hidden_act (`str` or `Callable`, optional, defaults to `"gelu"`) — The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported.
- hidden_dropout_prob (`float`, optional, defaults to 0.1) — The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
- attention_probs_dropout_prob (`float`, 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. Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
- type_vocab_size (`int`, optional, defaults to 2) — The vocabulary size of the `token_type_ids` passed when calling AlignTextModel.
- 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) — Padding token id.
- position_embedding_type (`str`, optional, defaults to `"absolute"`) — Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to Self-Attention with Relative Position Representations (Shaw et al.). For more information on `"relative_key_query"`, please refer to Method 4 in Improve Transformer Models with Better Relative Position Embeddings (Huang et al.).
- use_cache (`bool`, optional, defaults to `True`) — Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`.

This is the configuration class to store the configuration of a AlignTextModel. It is used to instantiate a ALIGN text encoder according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the text encoder of the ALIGN kakaobrain/align-base architecture. The default values here are copied from BERT.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Example:

    >>> from transformers import AlignTextConfig, AlignTextModel

    >>> # Initializing a AlignTextConfig with kakaobrain/align-base style configuration
    >>> configuration = AlignTextConfig()

    >>> # Initializing a AlignTextModel (with random weights) from the kakaobrain/align-base style configuration
    >>> model = AlignTextModel(configuration)

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

##
AlignVisionConfig

###
class transformers.AlignVisionConfig

< source >

( num_channels: int = 3 image_size: int = 600 width_coefficient: float = 2.0 depth_coefficient: float = 3.1 depth_divisor: int = 8 kernel_sizes: typing.List[int] = [3, 3, 5, 3, 5, 5, 3] in_channels: typing.List[int] = [32, 16, 24, 40, 80, 112, 192] out_channels: typing.List[int] = [16, 24, 40, 80, 112, 192, 320] depthwise_padding: typing.List[int] = [] strides: typing.List[int] = [1, 2, 2, 2, 1, 2, 1] num_block_repeats: typing.List[int] = [1, 2, 2, 3, 3, 4, 1] expand_ratios: typing.List[int] = [1, 6, 6, 6, 6, 6, 6] squeeze_expansion_ratio: float = 0.25 hidden_act: str = 'swish' hidden_dim: int = 2560 pooling_type: str = 'mean' initializer_range: float = 0.02 batch_norm_eps: float = 0.001 batch_norm_momentum: float = 0.99 drop_connect_rate: float = 0.2 **kwargs )

Parameters

- num_channels (`int`, optional, defaults to 3) — The number of input channels.
- image_size (`int`, optional, defaults to 600) — The input image size.
- width_coefficient (`float`, optional, defaults to 2.0) — Scaling coefficient for network width at each stage.
- depth_coefficient (`float`, optional, defaults to 3.1) — Scaling coefficient for network depth at each stage.
- depth_divisor `int`, optional, defaults to 8) — A unit of network width.
- kernel_sizes (`List[int]`, optional, defaults to `[3, 3, 5, 3, 5, 5, 3]`) — List of kernel sizes to be used in each block.
- in_channels (`List[int]`, optional, defaults to `[32, 16, 24, 40, 80, 112, 192]`) — List of input channel sizes to be used in each block for convolutional layers.
- out_channels (`List[int]`, optional, defaults to `[16, 24, 40, 80, 112, 192, 320]`) — List of output channel sizes to be used in each block for convolutional layers.
- depthwise_padding (`List[int]`, optional, defaults to `[]`) — List of block indices with square padding.
- strides (`List[int]`, optional, defaults to `[1, 2, 2, 2, 1, 2, 1]`) — List of stride sizes to be used in each block for convolutional layers.
- num_block_repeats (`List[int]`, optional, defaults to `[1, 2, 2, 3, 3, 4, 1]`) — List of the number of times each block is to repeated.
- expand_ratios (`List[int]`, optional, defaults to `[1, 6, 6, 6, 6, 6, 6]`) — List of scaling coefficient of each block.
- squeeze_expansion_ratio (`float`, optional, defaults to 0.25) — Squeeze expansion ratio.
- hidden_act (`str` or `function`, optional, defaults to `"silu"`) — The non-linear activation function (function or string) in each block. If string, `"gelu"`, `"relu"`, `"selu",` “gelu_new”`,` “silu”`and`“mish”` are supported.
- hidden_dim (`int`, optional, defaults to 1280) — The hidden dimension of the layer before the classification head.
- pooling_type (`str` or `function`, optional, defaults to `"mean"`) — Type of final pooling to be applied before the dense classification head. Available options are [`"mean"`, `"max"`]
- initializer_range (`float`, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
- batch_norm_eps (`float`, optional, defaults to 1e-3) — The epsilon used by the batch normalization layers.
- batch_norm_momentum (`float`, optional, defaults to 0.99) — The momentum used by the batch normalization layers.
- drop_connect_rate (`float`, optional, defaults to 0.2) — The drop rate for skip connections.

This is the configuration class to store the configuration of a AlignVisionModel. It is used to instantiate a ALIGN vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the vision encoder of the ALIGN kakaobrain/align-base architecture. The default values are copied from EfficientNet (efficientnet-b7)

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Example:

    >>> from transformers import AlignVisionConfig, AlignVisionModel

    >>> # Initializing a AlignVisionConfig with kakaobrain/align-base style configuration
    >>> configuration = AlignVisionConfig()

    >>> # Initializing a AlignVisionModel (with random weights) from the kakaobrain/align-base style configuration
    >>> model = AlignVisionModel(configuration)

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

##
AlignProcessor

###
class transformers.AlignProcessor

< source >

( image_processor tokenizer )

Parameters

- image_processor (EfficientNetImageProcessor) — The image processor is a required input.
- tokenizer ([`BertTokenizer`, `BertTokenizerFast`]) — The tokenizer is a required input.

Constructs an ALIGN processor which wraps EfficientNetImageProcessor and BertTokenizer/BertTokenizerFast into a single processor that inherits both the image processor and tokenizer functionalities. See the `__call__()` and `decode()` for more information. The preferred way of passing kwargs is as a dictionary per modality, see usage example below.

    from transformers import AlignProcessor
    from PIL import Image
    model_id = "kakaobrain/align-base"
    processor = AlignProcessor.from_pretrained(model_id)

    processor(
        images=your_pil_image,
        text=["What is that?"],
        images_kwargs = {"crop_size": {"height": 224, "width": 224}},
        text_kwargs = {"padding": "do_not_pad"},
        common_kwargs = {"return_tensors": "pt"},
    )

####
batch_decode

< source >

( *args **kwargs )

This method forwards all its arguments to BertTokenizerFast’s batch_decode(). Please refer to the docstring of this method for more information.

####
decode

< source >

( *args **kwargs )

This method forwards all its arguments to BertTokenizerFast’s decode(). Please refer to the docstring of this method for more information.

##
AlignModel

###
class transformers.AlignModel

< source >

( config: AlignConfig )

Parameters

- config (AlignConfig) — 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() method to load the model weights.

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

This model inherits from 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 subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( input_ids: typing.Optional[torch.LongTensor] = None pixel_values: typing.Optional[torch.FloatTensor] = None attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None head_mask: typing.Optional[torch.Tensor] = None inputs_embeds: typing.Optional[torch.Tensor] = None return_loss: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) →
`transformers.models.align.modeling_align.AlignOutput` or `tuple(torch.FloatTensor)`

Parameters

- input_ids (`torch.LongTensor` 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. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- 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 `{image_processor_class}`. See `{image_processor_class}.__call__` for details (`{processor_class}` uses `{image_processor_class}` for processing images).
- 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?

- token_type_ids (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
  - 0 corresponds to a sentence A token,
  - 1 corresponds to a sentence B token.

  What are token type IDs?

- 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?

- head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- inputs_embeds (`torch.Tensor` 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.
- return_loss (`bool`, optional) — Whether or not to return the contrastive loss.
- 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 instead of a plain tuple.

Returns

`transformers.models.align.modeling_align.AlignOutput` or `tuple(torch.FloatTensor)`

A `transformers.models.align.modeling_align.AlignOutput` 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 (AlignConfig) and inputs.

- 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 of AlignTextModel.
- image_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`) — The output of AlignVisionModel.
- text_model_output(`BaseModelOutputWithPoolingAndCrossAttentions`): The output of the AlignTextModel.
- vision_model_output(`BaseModelOutputWithPoolingAndNoAttention`): The output of the AlignVisionModel.

The AlignModel 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.

Examples:

    >>> from PIL import Image
    >>> import requests
    >>> from transformers import AutoProcessor, AlignModel

    >>> model = AlignModel.from_pretrained("kakaobrain/align-base")
    >>> processor = AutoProcessor.from_pretrained("kakaobrain/align-base")

    >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    >>> image = Image.open(requests.get(url, stream=True).raw)

    >>> inputs = processor(
    ...     images=image, text=["a photo of a cat", "a photo of a dog"], return_tensors="pt", padding=True
    ... )

    >>> outputs = model(**inputs)
    >>> logits_per_image = outputs.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

####
get_text_features

< source >

( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None head_mask: typing.Optional[torch.Tensor] = None inputs_embeds: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) →
text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`)

Parameters

- 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. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are 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?

- token_type_ids (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
  - 0 corresponds to a sentence A token,
  - 1 corresponds to a sentence B token.

  What are token type IDs?

- 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?

- head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- inputs_embeds (`torch.Tensor` 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.
- 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 instead of a plain tuple.

Returns

text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`)

The text embeddings obtained by applying the projection layer to the pooled output of AlignTextModel.

Examples:

    >>> from transformers import AutoTokenizer, AlignModel

    >>> model = AlignModel.from_pretrained("kakaobrain/align-base")
    >>> tokenizer = AutoTokenizer.from_pretrained("kakaobrain/align-base")

    >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
    >>> text_features = model.get_text_features(**inputs)

####
get_image_features

< source >

( pixel_values: typing.Optional[torch.FloatTensor] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) →
image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`)

Parameters

- 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 `{image_processor_class}`. See `{image_processor_class}.__call__` for details (`{processor_class}` uses `{image_processor_class}` for processing images).
- 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 instead of a plain tuple.

Returns

image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`)

The image embeddings obtained by applying the projection layer to the pooled output of AlignVisionModel.

Examples:

    >>> from PIL import Image
    >>> import requests
    >>> from transformers import AutoProcessor, AlignModel

    >>> model = AlignModel.from_pretrained("kakaobrain/align-base")
    >>> processor = AutoProcessor.from_pretrained("kakaobrain/align-base")

    >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    >>> image = Image.open(requests.get(url, stream=True).raw)

    >>> inputs = processor(images=image, return_tensors="pt")

    >>> image_features = model.get_image_features(**inputs)

##
AlignTextModel

###
class transformers.AlignTextModel

< source >

( config: AlignTextConfig add_pooling_layer: bool = True )

Parameters

- config (AlignTextConfig) — 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() method to load the model weights.
- add_pooling_layer (`bool`, optional, defaults to `True`) — Whether to add a pooling layer

The text model from ALIGN without any head or projection on top.

This model inherits from 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 subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None head_mask: typing.Optional[torch.Tensor] = None inputs_embeds: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) →
transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or `tuple(torch.FloatTensor)`

Parameters

- 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. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are 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?

- token_type_ids (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
  - 0 corresponds to a sentence A token,
  - 1 corresponds to a sentence B token.

  What are token type IDs?

- 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?

- head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- inputs_embeds (`torch.Tensor` 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.
- 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 instead of a plain tuple.

Returns

transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or `tuple(torch.FloatTensor)`

A 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 (AlignConfig) and inputs.

- 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 instance. For more details, see our kv cache guide.

  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.

The AlignTextModel 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.

Examples:

    >>> from transformers import AutoTokenizer, AlignTextModel

    >>> model = AlignTextModel.from_pretrained("kakaobrain/align-base")
    >>> tokenizer = AutoTokenizer.from_pretrained("kakaobrain/align-base")

    >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")

    >>> outputs = model(**inputs)
    >>> last_hidden_state = outputs.last_hidden_state
    >>> pooled_output = outputs.pooler_output  # pooled (EOS token) states

##
AlignVisionModel

###
class transformers.AlignVisionModel

< source >

( config: AlignVisionConfig )

Parameters

- config (AlignVisionConfig) — 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() method to load the model weights.

The vision model from ALIGN without any head or projection on top.

This model inherits from 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 subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( pixel_values: typing.Optional[torch.FloatTensor] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) →
`transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention` or `tuple(torch.FloatTensor)`

Parameters

- 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 `{image_processor_class}`. See `{image_processor_class}.__call__` for details (`{processor_class}` uses `{image_processor_class}` for processing images).
- 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 instead of a plain tuple.

Returns

`transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention` or `tuple(torch.FloatTensor)`

A `transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention` 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 (AlignConfig) and inputs.

- last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`) — 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 after a pooling operation on the spatial dimensions.

- 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, num_channels, height, width)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

The AlignVisionModel 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.

Examples:

    >>> from PIL import Image
    >>> import requests
    >>> from transformers import AutoProcessor, AlignVisionModel

    >>> model = AlignVisionModel.from_pretrained("kakaobrain/align-base")
    >>> processor = AutoProcessor.from_pretrained("kakaobrain/align-base")

    >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    >>> image = Image.open(requests.get(url, stream=True).raw)

    >>> inputs = processor(images=image, return_tensors="pt")

    >>> outputs = model(**inputs)
    >>> last_hidden_state = outputs.last_hidden_state
    >>> pooled_output = outputs.pooler_output  # pooled CLS states

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