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CLIP

CLIP is a is a multimodal vision and language model motivated by overcoming the fixed number of object categories when training a computer vision model. CLIP learns about images directly from raw text by jointly training on 400M (image, text) pairs. Pretraining on this scale enables zero-shot transfer to downstream tasks. CLIP uses an image encoder and text encoder to get visual features and text features. Both features are projected to a latent space with the same number of dimensions and their dot product gives a similarity score.

You can find all the original CLIP checkpoints under the OpenAI organization.

Click on the CLIP models in the right sidebar for more examples of how to apply CLIP to different image and language tasks.

The example below demonstrates how to calculate similarity scores between multiple text descriptions and an image with Pipeline or the AutoModel class.

from transformers import pipeline

clip = pipeline(
   task="zero-shot-image-classification",
   model="openai/clip-vit-base-patch32",
   device=0
)
labels = ["a photo of a cat", "a photo of a dog", "a photo of a car"]
clip("http://images.cocodataset.org/val2017/000000039769.jpg", candidate_labels=labels)

import requests
from PIL import Image

from transformers import AutoModel, AutoProcessor

model = AutoModel.from_pretrained("openai/clip-vit-base-patch32", attn_implementation="sdpa", device_map="auto")
processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
labels = ["a photo of a cat", "a photo of a dog", "a photo of a car"]

inputs = processor(text=labels, images=image, return_tensors="pt", padding=True).to(model.device)

outputs = model(**inputs)
logits_per_image = outputs.logits_per_image
probs = logits_per_image.softmax(dim=1)
most_likely_idx = probs.argmax(dim=1).item()
most_likely_label = labels[most_likely_idx]
print(f"Most likely label: {most_likely_label} with probability: {probs[0][most_likely_idx].item():.3f}")

Notes

Use CLIPImageProcessor to resize (or rescale) and normalizes images for the model.

CLIPConfig[[transformers.CLIPConfig]]

transformers.CLIPConfig[[transformers.CLIPConfig]]

Source

This is the configuration class to store the configuration of a CLIPModel. It is used to instantiate a Clip 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 openai/clip-vit-base-patch32

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 CLIPConfig, CLIPModel

>>> # Initializing a CLIPConfig with openai/clip-vit-base-patch32 style configuration
>>> configuration = CLIPConfig()

>>> # Initializing a CLIPModel (with random weights) from the openai/clip-vit-base-patch32 style configuration
>>> model = CLIPModel(configuration)

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

>>> # We can also initialize a CLIPConfig from a CLIPTextConfig and a CLIPVisionConfig
>>> from transformers import CLIPTextConfig, CLIPVisionConfig

>>> # Initializing a CLIPText and CLIPVision configuration
>>> config_text = CLIPTextConfig()
>>> config_vision = CLIPVisionConfig()

>>> config = CLIPConfig(text_config=config_text, vision_config=config_vision)

Parameters:

text_config (dict, optional) : Dictionary of configuration options used to initialize CLIPTextConfig.

vision_config (dict, optional) : Dictionary of configuration options used to initialize CLIPVisionConfig.

projection_dim (int, optional, defaults to 512) : Dimensionality of text and vision projection layers.

logit_scale_init_value (float | int, optional, defaults to 2.6592) : The initial value of the logit_scale parameter. Default is used as per the original CLIP implementation.

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).

CLIPTextConfig[[transformers.CLIPTextConfig]]

transformers.CLIPTextConfig[[transformers.CLIPTextConfig]]

Source

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 CLIPTextConfig, CLIPTextModel

>>> # Initializing a CLIPTextConfig with openai/clip-vit-base-patch32 style configuration
>>> configuration = CLIPTextConfig()

>>> # Initializing a CLIPTextModel (with random weights) from the openai/clip-vit-base-patch32 style configuration
>>> model = CLIPTextModel(configuration)

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

Parameters:

vocab_size (int, optional, defaults to 49408) : 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 512) : Dimension of the hidden representations.

intermediate_size (int, optional, defaults to 2048) : Dimension of the MLP representations.

projection_dim (int, optional, defaults to 512) : Dimensionality of text and vision projection layers.

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

num_attention_heads (int, optional, defaults to 8) : Number of attention heads for each attention layer in the Transformer decoder.

max_position_embeddings (int, optional, defaults to 77) : The maximum sequence length that this model might ever be used with.

hidden_act (str, optional, defaults to quick_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-05) : The epsilon used by the layer normalization layers.

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

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

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).

pad_token_id (int, optional, defaults to 1) : Token id used for padding in the vocabulary.

bos_token_id (int, optional, defaults to 49406) : Token id used for beginning-of-stream in the vocabulary.

eos_token_id (Union[int, list[int]], optional, defaults to 49407) : Token id used for end-of-stream in the vocabulary.

CLIPVisionConfig[[transformers.CLIPVisionConfig]]

transformers.CLIPVisionConfig[[transformers.CLIPVisionConfig]]

Source

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 CLIPVisionConfig, CLIPVisionModel

>>> # Initializing a CLIPVisionConfig with openai/clip-vit-base-patch32 style configuration
>>> configuration = CLIPVisionConfig()

>>> # Initializing a CLIPVisionModel (with random weights) from the openai/clip-vit-base-patch32 style configuration
>>> model = CLIPVisionModel(configuration)

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

Parameters:

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

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

projection_dim (int, optional, defaults to 512) : Dimensionality of text and vision projection layers.

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.

num_channels (int, optional, defaults to 3) : The number of input channels.

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 32) : The size (resolution) of each patch.

hidden_act (str, optional, defaults to quick_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-05) : The epsilon used by the layer normalization layers.

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

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

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).

CLIPTokenizer[[transformers.CLIPTokenizer]]

transformers.CLIPTokenizer[[transformers.CLIPTokenizer]]

Source

Construct a CLIP tokenizer (backed by HuggingFace's tokenizers library). Based on byte-level Byte-Pair-Encoding.

This tokenizer inherits from TokenizersBackend which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

get_special_tokens_masktransformers.CLIPTokenizer.get_special_tokens_maskhttps://github.com/huggingface/transformers/blob/vr_43265/src/transformers/tokenization_utils_base.py#L1322[{"name": "token_ids_0", "val": ": list[int]"}, {"name": "token_ids_1", "val": ": list[int] | None = None"}, {"name": "already_has_special_tokens", "val": ": bool = False"}]- token_ids_0 -- List of IDs for the (possibly already formatted) sequence.

token_ids_1 -- Unused when already_has_special_tokens=True. Must be None in that case.
already_has_special_tokens -- Whether the sequence is already formatted with special tokens.0A list of integers in the range [0, 1]1 for a special token, 0 for a sequence token.

Retrieve sequence ids from a token list that has no special tokens added.

For fast tokenizers, data collators call this with already_has_special_tokens=True to build a mask over an already-formatted sequence. In that case, we compute the mask by checking membership in all_special_ids.

Parameters:

vocab (str, dict or list, optional) : Vocabulary dict to use for the tokenizer.

merges (str or list, optional) : Merges list to use for the BPE tokenizer.

unk_token (str, optional, defaults to "<|endoftext|>") : The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.

bos_token (str, optional, defaults to "<|startoftext|>") : The beginning of sequence token.

eos_token (str, optional, defaults to "<|endoftext|>") : The end of sequence token.

pad_token (str, optional, defaults to "<|endoftext|>") : The token used for padding, for example when batching sequences of different lengths.

Returns:

A list of integers in the range [0, 1]

1 for a special token, 0 for a sequence token.

save_vocabulary[[transformers.CLIPTokenizer.save_vocabulary]]

Source

CLIPTokenizerFast[[transformers.CLIPTokenizer]]

transformers.CLIPTokenizer[[transformers.CLIPTokenizer]]

Source

Construct a CLIP tokenizer (backed by HuggingFace's tokenizers library). Based on byte-level Byte-Pair-Encoding.

This tokenizer inherits from TokenizersBackend which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

Parameters:

vocab (str, dict or list, optional) : Vocabulary dict to use for the tokenizer.

merges (str or list, optional) : Merges list to use for the BPE tokenizer.

unk_token (str, optional, defaults to "<|endoftext|>") : The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.

bos_token (str, optional, defaults to "<|startoftext|>") : The beginning of sequence token.

eos_token (str, optional, defaults to "<|endoftext|>") : The end of sequence token.

pad_token (str, optional, defaults to "<|endoftext|>") : The token used for padding, for example when batching sequences of different lengths.

CLIPImageProcessor[[transformers.CLIPImageProcessor]]

transformers.CLIPImageProcessor[[transformers.CLIPImageProcessor]]

Source

Constructs a CLIPImageProcessor image processor.

preprocesstransformers.CLIPImageProcessor.preprocesshttps://github.com/huggingface/transformers/blob/vr_43265/src/transformers/image_processing_utils.py#L382[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]"}, {"name": "*args", "val": ""}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.processing_utils.ImagesKwargs]"}]- images (Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]]) -- Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set do_rescale=False.

return_tensors (str or TensorType, optional) -- Returns stacked tensors if set to 'pt', otherwise returns a list of tensors.
**kwargs (ImagesKwargs, optional) -- Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.0~image_processing_base.BatchFeature- data (dict) -- Dictionary of lists/arrays/tensors returned by the call method ('pixel_values', etc.).
tensor_type (Union[None, str, TensorType], optional) -- You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization.

Parameters:

**kwargs (ImagesKwargs, optional) : Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.

Returns:

~image_processing_base.BatchFeature

data (dict) -- Dictionary of lists/arrays/tensors returned by the call method ('pixel_values', etc.).
tensor_type (Union[None, str, TensorType], optional) -- You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization.

CLIPImageProcessorPil[[transformers.CLIPImageProcessorPil]]

transformers.CLIPImageProcessorPil[[transformers.CLIPImageProcessorPil]]

Source

Constructs a CLIPImageProcessor image processor.

preprocesstransformers.CLIPImageProcessorPil.preprocesshttps://github.com/huggingface/transformers/blob/vr_43265/src/transformers/image_processing_utils.py#L382[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]"}, {"name": "*args", "val": ""}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.processing_utils.ImagesKwargs]"}]- images (Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]]) -- Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set do_rescale=False.

return_tensors (str or TensorType, optional) -- Returns stacked tensors if set to 'pt', otherwise returns a list of tensors.
**kwargs (ImagesKwargs, optional) -- Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.0~image_processing_base.BatchFeature- data (dict) -- Dictionary of lists/arrays/tensors returned by the call method ('pixel_values', etc.).
tensor_type (Union[None, str, TensorType], optional) -- You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization.

Parameters:

**kwargs (ImagesKwargs, optional) : Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.

Returns:

~image_processing_base.BatchFeature

data (dict) -- Dictionary of lists/arrays/tensors returned by the call method ('pixel_values', etc.).
tensor_type (Union[None, str, TensorType], optional) -- You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization.

CLIPProcessor[[transformers.CLIPProcessor]]

transformers.CLIPProcessor[[transformers.CLIPProcessor]]

Source

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

CLIPProcessor offers all the functionalities of CLIPImageProcessor and CLIPTokenizer. See the ~CLIPImageProcessor and ~CLIPTokenizer for more information.

__call__transformers.CLIPProcessor.__call__https://github.com/huggingface/transformers/blob/vr_43265/src/transformers/processing_utils.py#L643[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor'], NoneType] = None"}, {"name": "text", "val": ": str | list[str] | list[list[str]] | None = None"}, {"name": "videos", "val": ": typing.Union[list['PIL.Image.Image'], numpy.ndarray, ForwardRef('torch.Tensor'), list[numpy.ndarray], list['torch.Tensor'], list[list['PIL.Image.Image']], list[list[numpy.ndarray]], list[list['torch.Tensor']], transformers.video_utils.URL, list[transformers.video_utils.URL], list[list[transformers.video_utils.URL]], transformers.video_utils.Path, list[transformers.video_utils.Path], list[list[transformers.video_utils.Path]], NoneType] = None"}, {"name": "audio", "val": ": typing.Union[numpy.ndarray, ForwardRef('torch.Tensor'), collections.abc.Sequence[numpy.ndarray], collections.abc.Sequence['torch.Tensor'], NoneType] = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.processing_utils.ProcessingKwargs]"}]- images (Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]], optional) -- Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set do_rescale=False.

text (Union[str, list[str], list[list[str]]], optional) -- The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If you pass a pretokenized input, set is_split_into_words=True to avoid ambiguity with batched inputs.
videos (Union[list[PIL.Image.Image], numpy.ndarray, torch.Tensor, list[numpy.ndarray], list[torch.Tensor], list[list[PIL.Image.Image]], list[list[numpy.ndarray]], list[list[torch.Tensor]], ~video_utils.URL, list[~video_utils.URL], list[list[~video_utils.URL]], ~video_utils.Path, list[~video_utils.Path], list[list[~video_utils.Path]]], optional) -- Video to preprocess. Expects a single or batch of videos with pixel values ranging from 0 to 255. If passing in videos with pixel values between 0 and 1, set do_rescale=False.
audio (Union[numpy.ndarray, torch.Tensor, collections.abc.Sequence[numpy.ndarray], collections.abc.Sequence[torch.Tensor]], optional) -- The audio or batch of audios to be prepared. Each audio can be a NumPy array or PyTorch tensor. In case of a NumPy array/PyTorch tensor, each audio should be of shape (C, T), where C is a number of channels, and T is the sample length of the audio.
return_tensors (str or TensorType, optional) -- If set, will return tensors of a particular framework. Acceptable values are:
- 'pt': Return PyTorch torch.Tensor objects.
- 'np': Return NumPy np.ndarray objects.
**kwargs (ProcessingKwargs, optional) -- Additional processing options for each modality (text, images, videos, audio). Model-specific parameters are listed above; see the TypedDict class for the complete list of supported arguments.0

Parameters:

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

tokenizer (CLIPTokenizer) : The tokenizer is a required input.

CLIPModel[[transformers.CLIPModel]]

transformers.CLIPModel[[transformers.CLIPModel]]

Source

The bare Clip 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.

forwardtransformers.CLIPModel.forwardhttps://github.com/huggingface/transformers/blob/vr_43265/src/transformers/models/clip/modeling_clip.py#L762[{"name": "input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "pixel_values", "val": ": torch.FloatTensor | None = None"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "position_ids", "val": ": torch.LongTensor | None = None"}, {"name": "return_loss", "val": ": bool | None = None"}, {"name": "interpolate_pos_encoding", "val": ": bool = False"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- 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 CLIPImageProcessor. See CLIPImageProcessor.__call__() for details (CLIPProcessor uses CLIPImageProcessor 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?
position_ids (torch.LongTensor 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?
return_loss (bool, optional) -- Whether or not to return the contrastive loss.
interpolate_pos_encoding (bool, optional, defaults to False) -- Whether to interpolate the pre-trained position encodings.0CLIPOutput or tuple(torch.FloatTensor)A CLIPOutput 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 (CLIPConfig) and inputs. The CLIPModel 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 of CLIPTextModel.
image_embeds (torch.FloatTensor of shape (batch_size, output_dim) -- The image embeddings obtained by applying the projection layer to the pooled output of CLIPVisionModel.
text_model_output (~modeling_outputs.BaseModelOutputWithPooling, optional) -- The output of the CLIPTextModel.
vision_model_output (~modeling_outputs.BaseModelOutputWithPooling, optional) -- The output of the CLIPVisionModel.

Examples:

>>> import torch
>>> from transformers import AutoProcessor, CLIPModel
>>> from transformers.image_utils import load_image

>>> model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
>>> processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")

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

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

>>> with torch.inference_mode():
...     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

Parameters:

config (CLIPConfig) : 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.

Returns:

CLIPOutput` or `tuple(torch.FloatTensor)

A CLIPOutput 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 (CLIPConfig) and inputs.

get_text_features[[transformers.CLIPModel.get_text_features]]

Source

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:

>>> import torch
>>> from transformers import AutoTokenizer, CLIPModel

>>> model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
>>> tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32")

>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], 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. 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?

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?

Returns:

[BaseModelOutputWithPooling](/docs/transformers/pr_43265/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or tuple(torch.FloatTensor)``

A 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 (CLIPConfig) and inputs.

get_image_features[[transformers.CLIPModel.get_image_features]]

Source

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:

>>> import torch
>>> from transformers import AutoProcessor, CLIPModel
>>> from transformers.image_utils import load_image

>>> model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
>>> processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")

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

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

>>> with torch.inference_mode():
...     image_features = 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 CLIPImageProcessor. See CLIPImageProcessor.__call__() for details (CLIPProcessor uses CLIPImageProcessor for processing images).

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

Returns:

[BaseModelOutputWithPooling](/docs/transformers/pr_43265/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or tuple(torch.FloatTensor)``

CLIPTextModel[[transformers.CLIPTextModel]]

transformers.CLIPTextModel[[transformers.CLIPTextModel]]

Source

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

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.

forwardtransformers.CLIPTextModel.forwardhttps://github.com/huggingface/transformers/blob/vr_43265/src/transformers/models/clip/modeling_clip.py#L517[{"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. 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?
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?0BaseModelOutputWithPooling or tuple(torch.FloatTensor)A 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 (CLIPConfig) and inputs. The CLIPTextModel forward method, overrides the __call__ special method.

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:

>>> from transformers import AutoTokenizer, CLIPTextModel

>>> model = CLIPTextModel.from_pretrained("openai/clip-vit-base-patch32")
>>> tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32")

>>> 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

Parameters:

config (CLIPTextConfig) : 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.

Returns:

[BaseModelOutputWithPooling](/docs/transformers/pr_43265/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or tuple(torch.FloatTensor)``

CLIPTextModelWithProjection[[transformers.CLIPTextModelWithProjection]]

transformers.CLIPTextModelWithProjection[[transformers.CLIPTextModelWithProjection]]

Source

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

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.

forwardtransformers.CLIPTextModelWithProjection.forwardhttps://github.com/huggingface/transformers/blob/vr_43265/src/transformers/models/clip/modeling_clip.py#L861[{"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. 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?
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?0CLIPTextModelOutput or tuple(torch.FloatTensor)A CLIPTextModelOutput 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 (CLIPConfig) and inputs. The CLIPTextModelWithProjection forward method, overrides the __call__ special method.

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) -- 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:

>>> import torch
>>> from transformers import AutoTokenizer, CLIPTextModelWithProjection

>>> model = CLIPTextModelWithProjection.from_pretrained("openai/clip-vit-base-patch32")
>>> tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32")

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

>>> with torch.inference_mode():
...     outputs = model(**inputs)
>>> text_embeds = outputs.text_embeds

Parameters:

Returns:

CLIPTextModelOutput` or `tuple(torch.FloatTensor)

A CLIPTextModelOutput 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 (CLIPConfig) and inputs.

CLIPVisionModelWithProjection[[transformers.CLIPVisionModelWithProjection]]

transformers.CLIPVisionModelWithProjection[[transformers.CLIPVisionModelWithProjection]]

Source

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

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.

forwardtransformers.CLIPVisionModelWithProjection.forwardhttps://github.com/huggingface/transformers/blob/vr_43265/src/transformers/models/clip/modeling_clip.py#L922[{"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 CLIPImageProcessor. See CLIPImageProcessor.__call__() for details (CLIPProcessor uses CLIPImageProcessor for processing images).

interpolate_pos_encoding (bool, optional, defaults to False) -- Whether to interpolate the pre-trained position encodings.0CLIPVisionModelOutput or tuple(torch.FloatTensor)A CLIPVisionModelOutput 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 (CLIPConfig) and inputs. The CLIPVisionModelWithProjection forward method, overrides the __call__ special method.

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) -- 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:

>>> import torch
>>> from transformers import AutoProcessor, CLIPVisionModelWithProjection
>>> from transformers.image_utils import load_image

>>> model = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-base-patch32")
>>> processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")

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

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

>>> with torch.inference_mode():
...     outputs = model(**inputs)
>>> image_embeds = outputs.image_embeds

Parameters:

config (CLIPVisionConfig) : 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.

Returns:

CLIPVisionModelOutput` or `tuple(torch.FloatTensor)

A CLIPVisionModelOutput 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 (CLIPConfig) and inputs.

CLIPVisionModel[[transformers.CLIPVisionModel]]

transformers.CLIPVisionModel[[transformers.CLIPVisionModel]]

Source

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

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.

forwardtransformers.CLIPVisionModel.forwardhttps://github.com/huggingface/transformers/blob/vr_43265/src/transformers/models/clip/modeling_clip.py#L617[{"name": "pixel_values", "val": ": torch.FloatTensor | None = None"}, {"name": "interpolate_pos_encoding", "val": ": bool | None = 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 CLIPImageProcessor. See CLIPImageProcessor.__call__() for details (CLIPProcessor uses CLIPImageProcessor for processing images).

interpolate_pos_encoding (bool, optional, defaults to False) -- Whether to interpolate the pre-trained position encodings.0BaseModelOutputWithPooling or tuple(torch.FloatTensor)A 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 (CLIPConfig) and inputs. The CLIPVisionModel forward method, overrides the __call__ special method.

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.

Example:

>>> from PIL import Image
>>> import httpx
>>> from io import BytesIO
>>> from transformers import AutoProcessor, CLIPVisionModel

>>> model = CLIPVisionModel.from_pretrained("openai/clip-vit-base-patch32")
>>> processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")

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

>>> 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

Parameters:

Returns:

[BaseModelOutputWithPooling](/docs/transformers/pr_43265/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or tuple(torch.FloatTensor)``

CLIPForImageClassification[[transformers.CLIPForImageClassification]]

transformers.CLIPForImageClassification[[transformers.CLIPForImageClassification]]

Source

CLIP vision encoder with an image classification head on top (a linear layer on top of the pooled final hidden states of the patch tokens) e.g. for ImageNet.

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.

forwardtransformers.CLIPForImageClassification.forwardhttps://github.com/huggingface/transformers/blob/vr_43265/src/transformers/models/clip/modeling_clip.py#L991[{"name": "pixel_values", "val": ": torch.Tensor | None = None"}, {"name": "labels", "val": ": torch.Tensor | None = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- pixel_values (torch.Tensor of shape (batch_size, num_channels, image_size, image_size), optional) -- The tensors corresponding to the input images. Pixel values can be obtained using CLIPImageProcessor. See CLIPImageProcessor.__call__() for details (CLIPProcessor uses CLIPImageProcessor for processing images).

labels (torch.LongTensor of shape (batch_size,), optional) -- Labels for computing the image classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), If config.num_labels > 1 a classification loss is computed (Cross-Entropy).0ImageClassifierOutput or tuple(torch.FloatTensor)A ImageClassifierOutput 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 (CLIPConfig) and inputs. The CLIPForImageClassification forward method, overrides the __call__ special method.

loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) -- Classification (or regression if config.num_labels==1) loss.
logits (torch.FloatTensor of shape (batch_size, config.num_labels)) -- Classification (or regression if config.num_labels==1) scores (before SoftMax).
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 stage) of shape (batch_size, sequence_length, hidden_size). Hidden-states (also called feature maps) of the model at the output of each stage.
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, patch_size, sequence_length).

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

Example:

>>> from transformers import AutoImageProcessor, CLIPForImageClassification
>>> import torch
>>> from datasets import load_dataset

>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]

>>> image_processor = AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch32")
>>> model = CLIPForImageClassification.from_pretrained("openai/clip-vit-base-patch32")

>>> inputs = image_processor(image, return_tensors="pt")

>>> with torch.no_grad():
...     logits = model(**inputs).logits

>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
...

Parameters:

Returns:

[ImageClassifierOutput](/docs/transformers/pr_43265/en/main_classes/output#transformers.modeling_outputs.ImageClassifierOutput) or tuple(torch.FloatTensor)``

A ImageClassifierOutput 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 (CLIPConfig) and inputs.

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