
 
BLIP
	
Overview
	
The BLIP model was proposed in BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation by Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi.
BLIP is a model that is able to perform various multi-modal tasks including
Visual Question Answering 
Image-Text retrieval (Image-text matching)
Image Captioning
The abstract from the paper is the following:
Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks.
However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to videolanguage tasks in a zero-shot manner. Code, models, and datasets are released.
This model was contributed by ybelkada.
The original code can be found here.
Resources
	
Jupyter notebook on how to fine-tune BLIP for image captioning on a custom dataset
BlipConfig
	
class transformers.BlipConfig
<
source
>
(
text_config = None
vision_config = None
projection_dim = 512
logit_scale_init_value = 2.6592
image_text_hidden_size = 256
**kwargs
)
Parameters 
text_config (dict, optional) —
Dictionary of configuration options used to initialize BlipTextConfig.
vision_config (dict, optional) —
Dictionary of configuration options used to initialize BlipVisionConfig.
projection_dim (int, optional, defaults to 512) —
Dimentionality of text and vision projection layers.
logit_scale_init_value (float, optional, defaults to 2.6592) —
The inital value of the logit_scale paramter. Default is used as per the original BLIP implementation.
image_text_hidden_size (int, optional, defaults to 768) —
Dimentionality of the hidden state of the image-text fusion layer.
kwargs (optional) —
Dictionary of keyword arguments.
BlipConfig is the configuration class to store the configuration of a BlipModel. It is used to instantiate
a BLIP 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 BLIP-base
Salesforce/blip-vqa-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:
	Copied
 from transformers import BlipConfig, BlipModel
 # Initializing a BlipConfig with Salesforce/blip-vqa-base style configuration
 configuration = BlipConfig()
 # Initializing a BlipPModel (with random weights) from the Salesforce/blip-vqa-base style configuration
 model = BlipModel(configuration)
 # Accessing the model configuration
 configuration = model.config
 # We can also initialize a BlipConfig from a BlipTextConfig and a BlipVisionConfig
 # Initializing a BLIPText and BLIPVision configuration
 config_text = BlipTextConfig()
 config_vision = BlipVisionConfig()
 config = BlipConfig.from_text_vision_configs(config_text, config_vision)
from_text_vision_configs
<
source
>
(
text_config: BlipTextConfig
vision_config: BlipVisionConfig
**kwargs
)
→
BlipConfig
Returns
BlipConfig
An instance of a configuration object
Instantiate a BlipConfig (or a derived class) from blip text model configuration and blip vision model
configuration.
BlipTextConfig
	
class transformers.BlipTextConfig
<
source
>
(
vocab_size = 30524
hidden_size = 768
encoder_hidden_size = 768
intermediate_size = 3072
projection_dim = 768
num_hidden_layers = 12
num_attention_heads = 8
max_position_embeddings = 512
hidden_act = 'gelu'
layer_norm_eps = 1e-12
hidden_dropout_prob = 0.0
attention_probs_dropout_prob = 0.0
initializer_range = 0.02
bos_token_id = 30522
eos_token_id = 2
pad_token_id = 0
sep_token_id = 102
is_decoder = True
use_cache = True
**kwargs
)
Parameters 
vocab_size (int, optional, defaults to 30522) —
Vocabulary size of the Blip text model. Defines the number of different tokens that can be represented by
the inputs_ids passed when calling BlipModel.
hidden_size (int, optional, defaults to 768) —
Dimensionality of the encoder layers and the pooler layer.
encoder_hidden_size (int, optional, defaults to 768) —
Dimensionality of the encoder layers from the vision model.
intermediate_size (int, optional, defaults to 3072) —
Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.
num_hidden_layers (int, optional, defaults to 12) —
Number of hidden layers in the Transformer encoder.
num_attention_heads (int, optional, defaults to 8) —
Number of attention heads for each attention layer in the Transformer encoder.
max_position_embeddings (int, optional, defaults to 77) —
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).
hidden_act (str or function, optional, defaults to "gelu") —
The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu",
"relu", "selu" and "gelu_new" `"gelu" are supported.
layer_norm_eps (float, optional, defaults to 1e-12) —
The epsilon used by the layer normalization layers.
hidden_dropout_prob (float, optional, defaults to 0.0) —
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (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.
bos_token_id (int, optional, defaults to 30522) —
The id of the beginning-of-sequence token.
eos_token_id (int, optional, defaults to 2) —
The id of the end-of-sequence token.
pad_token_id (int, optional, defaults to 0) —
The id of the padding token.
sep_token_id (int, optional, defaults to 102) —
The id of the separator token.
is_decoder (bool, optional, defaults to False) —
Whether the model is used as a decoder.
use_cache (bool, optional, defaults to True) —
Whether or not the model should return the last key/values attentions (not used by all models).
This is the configuration class to store the configuration of a BlipTextModel. It is used to instantiate a BLIP
text 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 BlipText used by the base
architectures.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.
Example:
	Copied
 from transformers import BlipTextConfig, BlipTextModel
 # Initializing a BlipTextConfig with Salesforce/blip-vqa-base style configuration
 configuration = BlipTextConfig()
 # Initializing a BlipTextModel (with random weights) from the Salesforce/blip-vqa-base style configuration
 model = BlipTextModel(configuration)
 # Accessing the model configuration
 configuration = model.config
BlipVisionConfig
	
class transformers.BlipVisionConfig
<
source
>
(
hidden_size = 768
intermediate_size = 3072
projection_dim = 512
num_hidden_layers = 12
num_attention_heads = 12
image_size = 384
patch_size = 16
hidden_act = 'gelu'
layer_norm_eps = 1e-05
attention_dropout = 0.0
initializer_range = 1e-10
**kwargs
)
Parameters 
hidden_size (int, optional, defaults to 768) —
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (int, optional, defaults to 3072) —
Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.
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.
image_size (int, optional, defaults to 224) —
The size (resolution) of each image.
patch_size (int, optional, defaults to 32) —
The size (resolution) of each patch.
hidden_act (str or function, optional, defaults to "gelu") —
The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu",
"relu", "selu" and "gelu_new" `"gelu" are supported.
layer_norm_eps (float, optional, defaults to 1e-5) —
The epsilon used by the layer normalization layers.
attention_dropout (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.
This is the configuration class to store the configuration of a BlipVisionModel. It is used to instantiate a
BLIP vision model according to the specified arguments, defining the model architecture. Instantiating a
configuration defaults will yield a similar configuration to that of the Blip-base
Salesforce/blip-vqa-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:
	Copied
 from transformers import BlipVisionConfig, BlipVisionModel
 # Initializing a BlipVisionConfig with Salesforce/blip-vqa-base style configuration
 configuration = BlipVisionConfig()
 # Initializing a BlipVisionModel (with random weights) from the Salesforce/blip-vqa-base style configuration
 model = BlipVisionModel(configuration)
 # Accessing the model configuration
 configuration = model.config
BlipProcessor
	
class transformers.BlipProcessor
<
source
>
(
image_processor
tokenizer
)
Parameters 
image_processor (BlipImageProcessor) —
An instance of BlipImageProcessor. The image processor is a required input.
tokenizer (BertTokenizerFast) —
An instance of [‘BertTokenizerFast`]. The tokenizer is a required input.
Constructs a BLIP processor which wraps a BERT tokenizer and BLIP image processor into a single processor.
BlipProcessor offers all the functionalities of BlipImageProcessor and BertTokenizerFast. See the
docstring of __call__() and decode() for more information.
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.
BlipImageProcessor
	
class transformers.BlipImageProcessor
<
source
>
(
do_resize: bool = True
size: typing.Dict[str, int] = None
resample: Resampling = <Resampling.BICUBIC: 3>
do_rescale: bool = True
rescale_factor: typing.Union[int, float] = 0.00392156862745098
do_normalize: bool = True
image_mean: typing.Union[float, typing.List[float], NoneType] = None
image_std: typing.Union[float, typing.List[float], NoneType] = None
do_convert_rgb: bool = True
**kwargs
)
Parameters 
do_resize (bool, optional, defaults to True) —
Whether to resize the image’s (height, width) dimensions to the specified size. Can be overridden by the
do_resize parameter in the preprocess method.
size (dict, optional, defaults to {"height" -- 384, "width": 384}):
Size of the output image after resizing. Can be overridden by the size parameter in the preprocess
method.
resample (PILImageResampling, optional, defaults to PILImageResampling.BICUBIC) —
Resampling filter to use if resizing the image. Only has an effect if do_resize is set to True. Can be
overridden by the resample parameter in the preprocess method.
do_rescale (bool, optional, defaults to True) —
Wwhether to rescale the image by the specified scale rescale_factor. Can be overridden by the
do_rescale parameter in the preprocess method.
rescale_factor (int or float, optional, defaults to 1/255) —
Scale factor to use if rescaling the image. Only has an effect if do_rescale is set to True. Can be
overridden by the rescale_factor parameter in the preprocess method.
do_normalize (bool, optional, defaults to True) —
Whether to normalize the image. Can be overridden by the do_normalize parameter in the preprocess
method. Can be overridden by the do_normalize parameter in the preprocess method.
image_mean (float or List[float], optional, defaults to IMAGENET_STANDARD_MEAN) —
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the image_mean parameter in the preprocess method. Can be
overridden by the image_mean parameter in the preprocess method.
image_std (float or List[float], optional, defaults to IMAGENET_STANDARD_STD) —
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the image_std parameter in the preprocess method.
Can be overridden by the image_std parameter in the preprocess method.
do_convert_rgb (bool, optional, defaults to True) —
Whether to convert the image to RGB.
Constructs a BLIP image processor.
preprocess
<
source
>
(
images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), typing.List[ForwardRef('PIL.Image.Image')], typing.List[numpy.ndarray], typing.List[ForwardRef('torch.Tensor')]]
do_resize: typing.Optional[bool] = None
size: typing.Union[typing.Dict[str, int], NoneType] = None
resample: Resampling = None
do_rescale: typing.Optional[bool] = None
rescale_factor: typing.Optional[float] = None
do_normalize: typing.Optional[bool] = None
image_mean: typing.Union[float, typing.List[float], NoneType] = None
image_std: typing.Union[float, typing.List[float], NoneType] = None
return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None
do_convert_rgb: bool = None
data_format: ChannelDimension = <ChannelDimension.FIRST: 'channels_first'>
**kwargs
)
Parameters 
images (ImageInput) —
Image to preprocess.
do_resize (bool, optional, defaults to self.do_resize) —
Whether to resize the image.
size (Dict[str, int], optional, defaults to self.size) —
Controls the size of the image after resize. The shortest edge of the image is resized to
size["shortest_edge"] whilst preserving the aspect ratio. If the longest edge of this resized image
is > int(size["shortest_edge"] * (1333 / 800)), then the image is resized again to make the longest
edge equal to int(size["shortest_edge"] * (1333 / 800)).
resample (PILImageResampling, optional, defaults to self.resample) —
Resampling filter to use if resizing the image. Only has an effect if do_resize is set to True.
do_rescale (bool, optional, defaults to self.do_rescale) —
Whether to rescale the image values between [0 - 1].
rescale_factor (float, optional, defaults to self.rescale_factor) —
Rescale factor to rescale the image by if do_rescale is set to True.
do_normalize (bool, optional, defaults to self.do_normalize) —
Whether to normalize the image.
image_mean (float or List[float], optional, defaults to self.image_mean) —
Image mean to normalize the image by if do_normalize is set to True.
image_std (float or List[float], optional, defaults to self.image_std) —
Image standard deviation to normalize the image by if do_normalize is set to True.
do_convert_rgb (bool, optional, defaults to self.do_convert_rgb) —
Whether to convert the image to RGB.
return_tensors (str or TensorType, optional) —
The type of tensors to return. Can be one of:
Unset: Return a list of np.ndarray.
TensorType.TENSORFLOW or 'tf': Return a batch of type tf.Tensor.
TensorType.PYTORCH or 'pt': Return a batch of type torch.Tensor.
TensorType.NUMPY or 'np': Return a batch of type np.ndarray.
TensorType.JAX or 'jax': Return a batch of type jax.numpy.ndarray.
data_format (ChannelDimension or str, optional, defaults to ChannelDimension.FIRST) —
The channel dimension format for the output image. Can be one of:
ChannelDimension.FIRST: image in (num_channels, height, width) format.
ChannelDimension.LAST: image in (height, width, num_channels) format.
Preprocess an image or batch of images.
BlipModel
	
class transformers.BlipModel
<
source
>
(
config: BlipConfig
)
Parameters 
config (BlipConfig) — 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.
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
position_ids: typing.Optional[torch.LongTensor] = 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.blip.modeling_blip.BlipOutput or tuple(torch.FloatTensor)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using AutoProcessor. See BlipProcessor.__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.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.max_position_embeddings - 1].
What are position IDs?
pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
BlipImageProcessor. See BlipImageProcessor.call() for details.
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.blip.modeling_blip.BlipOutput or tuple(torch.FloatTensor)
A transformers.models.blip.modeling_blip.BlipOutput 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 (<class 'transformers.models.blip.configuration_blip.BlipConfig'>) 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 BlipTextModel.
image_embeds(torch.FloatTensor of shape (batch_size, output_dim) — The image embeddings obtained by applying the projection layer to the pooled output of BlipVisionModel.
text_model_output(BaseModelOutputWithPooling):
The output of the BlipTextModel.
vision_model_output(BaseModelOutputWithPooling):
The output of the BlipVisionModel.
The BlipModel 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:
	Copied
 from PIL import Image
 import requests
 from transformers import AutoProcessor, BlipModel
 model = BlipModel.from_pretrained("Salesforce/blip-image-captioning-base")
 processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 inputs = processor(
...     text=["a photo of a cat", "a photo of a dog"], images=image, 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
position_ids: typing.Optional[torch.Tensor] = None
return_dict: typing.Optional[bool] = None
)
→
text_features (torch.FloatTensor of shape (batch_size, output_dim)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using AutoProcessor. See BlipProcessor.__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.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.max_position_embeddings - 1].
What are position IDs?
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 BlipTextModel.
The BlipModel 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:
	Copied
 from transformers import AutoProcessor, BlipModel
 model = BlipModel.from_pretrained("Salesforce/blip-image-captioning-base")
 processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
 inputs = processor(text=["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
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, height, width)) —
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
BlipImageProcessor. See BlipImageProcessor.call() for details.
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
image_features (torch.FloatTensor of shape (batch_size, output_dim)
The image embeddings obtained by
applying the projection layer to the pooled output of BlipVisionModel.
The BlipModel 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:
	Copied
 from PIL import Image
 import requests
 from transformers import AutoProcessor, BlipModel
 model = BlipModel.from_pretrained("Salesforce/blip-image-captioning-base")
 processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-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)
BlipTextModel
	
class transformers.BlipTextModel
<
source
>
(
config
add_pooling_layer = True
)
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in Attention is
all you need by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. argument and is_decoder set to True; an
encoder_hidden_states is then expected as an input to the forward pass.
forward
<
source
>
(
input_ids: typing.Optional[torch.Tensor] = None
attention_mask: 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
encoder_embeds: typing.Optional[torch.Tensor] = None
encoder_hidden_states: typing.Optional[torch.Tensor] = None
encoder_attention_mask: typing.Optional[torch.Tensor] = None
past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None
use_cache: typing.Optional[bool] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
is_decoder: typing.Optional[bool] = False
)
encoder_hidden_states  (torch.FloatTensor, 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, 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.
past_key_values (tuple(tuple(torch.FloatTensor)), optional):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that
don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all
decoder_input_ids of shape (batch_size, sequence_length).
use_cache (bool, optional):
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_key_values).
BlipVisionModel
	
class transformers.BlipVisionModel
<
source
>
(
config: BlipVisionConfig
)
forward
<
source
>
(
pixel_values: typing.Optional[torch.FloatTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.BaseModelOutputWithPooling or tuple(torch.FloatTensor)
Parameters 
pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
BlipImageProcessor. See BlipImageProcessor.call() for details.
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.BaseModelOutputWithPooling or tuple(torch.FloatTensor)
A 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 (<class 'transformers.models.blip.configuration_blip.BlipVisionConfig'>) 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.
The BlipVisionModel 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.
BlipForConditionalGeneration
	
class transformers.BlipForConditionalGeneration
<
source
>
(
config: BlipConfig
)
Parameters 
config (BlipConfig) — 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.
BLIP Model for image captioning. The model consists of a vision encoder and a text decoder. One can optionally pass
input_ids to the model, which serve as a text prompt, to make the text decoder continue the prompt. Otherwise,
the decoder starts generating text from the [BOS] (beginning-of-sequence) token. will start generating the caption
from the text input. If no text input is provided, the decoder will start with the [BOS] token only.
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: FloatTensor
input_ids: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.LongTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
labels: typing.Optional[torch.LongTensor] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.models.blip.modeling_blip.BlipForConditionalGenerationModelOutput or tuple(torch.FloatTensor)
Parameters 
pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
BlipImageProcessor. See BlipImageProcessor.call() for details.
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.blip.modeling_blip.BlipForConditionalGenerationModelOutput or tuple(torch.FloatTensor)
A transformers.models.blip.modeling_blip.BlipForConditionalGenerationModelOutput 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 (<class 'transformers.models.blip.configuration_blip.BlipVisionConfig'>) and inputs.
loss (torch.FloatTensor, optional, returned when labels is provided, torch.FloatTensor of shape (1,)) — Languge modeling loss from the text decoder.
decoder_logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size), optional) — Prediction scores of the language modeling head of the text decoder model.
image_embeds (torch.FloatTensor of shape (batch_size, output_dim), optional) — The image embeddings obtained after applying the Vision Transformer model to the input image.
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) — 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) — 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.
The BlipForConditionalGeneration 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:
	Copied
 from PIL import Image
 import requests
 from transformers import AutoProcessor, BlipForConditionalGeneration
 processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
 model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 text = "A picture of"
 inputs = processor(images=image, text=text, return_tensors="pt")
 outputs = model(**inputs)
BlipForImageTextRetrieval
	
class transformers.BlipForImageTextRetrieval
<
source
>
(
config: BlipConfig
)
Parameters 
config (BlipConfig) — 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.
BLIP 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. 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: LongTensor
pixel_values: FloatTensor
use_itm_head: typing.Optional[bool] = True
attention_mask: typing.Optional[torch.LongTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.models.blip.modeling_blip.BlipTextVisionModelOutput or tuple(torch.FloatTensor)
Parameters 
pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
BlipImageProcessor. See BlipImageProcessor.call() for details.
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.blip.modeling_blip.BlipTextVisionModelOutput or tuple(torch.FloatTensor)
A transformers.models.blip.modeling_blip.BlipTextVisionModelOutput 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 (<class 'transformers.models.blip.configuration_blip.BlipVisionConfig'>) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Languge modeling loss from the text decoder.
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)) — 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.
The BlipForImageTextRetrieval 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:
	Copied
 from PIL import Image
 import requests
 from transformers import AutoProcessor, BlipForImageTextRetrieval
 model = BlipForImageTextRetrieval.from_pretrained("Salesforce/blip-itm-base-coco")
 processor = AutoProcessor.from_pretrained("Salesforce/blip-itm-base-coco")
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 text = "an image of a cat"
 inputs = processor(images=image, text=text, return_tensors="pt")
 outputs = model(**inputs)
BlipForQuestionAnswering
	
class transformers.BlipForQuestionAnswering
<
source
>
(
config: BlipConfig
)
Parameters 
config (BlipConfig) — 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.
BLIP Model for visual question answering. The model consists of a vision encoder, a text encoder as well as a text
decoder. The vision encoder will encode the input image, the text encoder will encode the input question together
with the encoding of the image, and the text decoder will output the answer to the question.
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: LongTensor
pixel_values: FloatTensor
decoder_input_ids: typing.Optional[torch.LongTensor] = None
decoder_attention_mask: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.LongTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
labels: typing.Optional[torch.LongTensor] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.models.blip.modeling_blip.BlipTextVisionModelOutput or tuple(torch.FloatTensor)
Parameters 
pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
BlipImageProcessor. See BlipImageProcessor.call() for details.
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.blip.modeling_blip.BlipTextVisionModelOutput or tuple(torch.FloatTensor)
A transformers.models.blip.modeling_blip.BlipTextVisionModelOutput 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 (<class 'transformers.models.blip.configuration_blip.BlipVisionConfig'>) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Languge modeling loss from the text decoder.
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)) — 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.
The BlipForQuestionAnswering 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:
	Copied
 from PIL import Image
 import requests
 from transformers import AutoProcessor, BlipForQuestionAnswering
 model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base")
 processor = AutoProcessor.from_pretrained("Salesforce/blip-vqa-base")
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 # training
 text = "How many cats are in the picture?"
 label = "2"
 inputs = processor(images=image, text=text, return_tensors="pt")
 labels = processor(text=label, return_tensors="pt").input_ids
 inputs["labels"] = labels
 outputs = model(**inputs)
 loss = outputs.loss
 loss.backward()
 # inference
 text = "How many cats are in the picture?"
 inputs = processor(images=image, text=text, return_tensors="pt")
 outputs = model.generate(**inputs)
 print(processor.decode(outputs[0], skip_special_tokens=True))
2
TFBlipModel
	
class transformers.TFBlipModel
<
source
>
(
*args
**kwargs
)
call
<
source
>
(
input_ids: tf.Tensor | None = None
pixel_values: tf.Tensor | None = None
attention_mask: tf.Tensor | None = None
position_ids: tf.Tensor | None = None
return_loss: Optional[bool] = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
training: Optional[bool] = None
)
→
transformers.models.blip.modeling_tf_blip.TFBlipOutput or tuple(tf.Tensor)
Parameters 
input_ids (tf.Tensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using AutoProcessor. See BlipProcessor.__call__() for details.
What are input IDs?
attention_mask (tf.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 (tf.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.max_position_embeddings - 1].
What are position IDs?
pixel_values (tf.Tensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
BlipImageProcessor. See BlipImageProcessor.call() for details.
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.blip.modeling_tf_blip.TFBlipOutput or tuple(tf.Tensor)
A transformers.models.blip.modeling_tf_blip.TFBlipOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (<class 'transformers.models.blip.configuration_blip.BlipConfig'>) and inputs.
loss (tf.Tensor of shape (1,), optional, returned when return_loss is True) — Contrastive loss for image-text similarity.
logits_per_image:(tf.Tensor 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:(tf.Tensor 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(tf.Tensor of shape (batch_size, output_dim) — The text embeddings obtained by applying the projection layer to the pooled output of BlipTextModel.
image_embeds(tf.Tensor of shape (batch_size, output_dim) — The image embeddings obtained by applying the projection layer to the pooled output of BlipVisionModel.
text_model_output(BaseModelOutputWithPooling):
The output of the BlipTextModel.
vision_model_output(BaseModelOutputWithPooling):
The output of the BlipVisionModel.
The TFBlipModel 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:
	Copied
 from PIL import Image
 import requests
 from transformers import AutoProcessor, TFBlipModel
 model = TFBlipModel.from_pretrained("Salesforce/blip-image-captioning-base")
 processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 inputs = processor(
...     text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="tf", padding=True
... )
 outputs = model(**inputs)
 logits_per_image = outputs.logits_per_image  # this is the image-text similarity score
 probs = tf.nn.softmax(logits_per_image, axis=1)  # we can take the softmax to get the label probabilities
get_text_features
<
source
>
(
input_ids: tf.Tensor | None = None
attention_mask: tf.Tensor | None = None
position_ids: tf.Tensor | None = None
return_dict: Optional[bool] = None
)
→
text_features (tf.Tensor of shape (batch_size, output_dim)
Parameters 
input_ids (tf.Tensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using AutoProcessor. See BlipProcessor.__call__() for details.
What are input IDs?
attention_mask (tf.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 (tf.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.max_position_embeddings - 1].
What are position IDs?
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 (tf.Tensor of shape (batch_size, output_dim)
The text embeddings obtained by applying
the projection layer to the pooled output of TFBlipTextModel.
The TFBlipModel 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:
	Copied
 from transformers import AutoProcessor, TFBlipModel
 model = TFBlipModel.from_pretrained("Salesforce/blip-image-captioning-base")
 processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
 inputs = processor(text=["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="tf")
 text_features = model.get_text_features(**inputs)
get_image_features
<
source
>
(
pixel_values: tf.Tensor | None = None
return_dict: Optional[bool] = None
)
→
image_features (tf.Tensor of shape (batch_size, output_dim)
Parameters 
pixel_values (tf.Tensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
BlipImageProcessor. See BlipImageProcessor.call() for details.
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
image_features (tf.Tensor of shape (batch_size, output_dim)
The image embeddings obtained by applying
the projection layer to the pooled output of TFBlipVisionModel.
The TFBlipModel 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:
	Copied
 from PIL import Image
 import requests
 from transformers import AutoProcessor, TFBlipModel
 model = TFBlipModel.from_pretrained("Salesforce/blip-image-captioning-base")
 processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 inputs = processor(images=image, return_tensors="tf")
 image_features = model.get_image_features(**inputs)
TFBlipTextModel
	
class transformers.TFBlipTextModel
<
source
>
(
*args
**kwargs
)
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in Attention is
all you need by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. argument and is_decoder set to True; an
encoder_hidden_states is then expected as an input to the forward pass.
call
<
source
>
(
input_ids = None
attention_mask = None
position_ids = None
head_mask = None
inputs_embeds = None
encoder_embeds = None
encoder_hidden_states = None
encoder_attention_mask = None
past_key_values = None
use_cache = None
output_attentions = None
output_hidden_states = None
return_dict = None
is_decoder = False
training = None
)
Parameters 
input_ids (tf.Tensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using AutoProcessor. See BlipProcessor.__call__() for details.
What are input IDs?
attention_mask (tf.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 (tf.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.max_position_embeddings - 1].
What are position IDs?
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.
encoder_hidden_states  (tf.Tensor, 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 (tf.Tensor, 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.
past_key_values (tuple(tuple(tf.Tensor)), optional) —
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that
don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all
decoder_input_ids of shape (batch_size, sequence_length).
use_cache (bool, optional) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_key_values).
The TFBlipTextModel 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.
TFBlipVisionModel
	
class transformers.TFBlipVisionModel
<
source
>
(
*args
**kwargs
)
call
<
source
>
(
pixel_values: tf.Tensor | None = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
training: Optional[bool] = None
)
→
transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling or tuple(tf.Tensor)
Parameters 
pixel_values (tf.Tensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
BlipImageProcessor. See BlipImageProcessor.call() for details.
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_tf_outputs.TFBaseModelOutputWithPooling or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (<class 'transformers.models.blip.configuration_blip.BlipVisionConfig'>) and inputs.
last_hidden_state (tf.Tensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.
pooler_output (tf.Tensor of shape (batch_size, hidden_size)) — Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.
This output is usually not a good summary of the semantic content of the input, you’re often better with
averaging or pooling the sequence of hidden-states for the whole input sequence.
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + 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 initial embedding outputs.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (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.
The TFBlipVisionModel 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.
TFBlipForConditionalGeneration
	
class transformers.TFBlipForConditionalGeneration
<
source
>
(
*args
**kwargs
)
Parameters 
config (BlipConfig) — 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.
BLIP Model for image captioning. The model consists of a vision encoder and a text decoder. One can optionally pass
input_ids to the model, which serve as a text prompt, to make the text decoder continue the prompt. Otherwise,
the decoder starts generating text from the [BOS] (beginning-of-sequence) token. will start generating the caption
from the text input. If no text input is provided, the decoder will start with the [BOS] token only.
This model inherits from TFPreTrainedModel. 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 tf.keras.Model subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
call
<
source
>
(
pixel_values: tf.Tensor
input_ids: tf.Tensor | None = None
attention_mask: tf.Tensor | None = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
labels: tf.Tensor | None = None
return_dict: Optional[bool] = None
training: Optional[bool] = None
)
→
transformers.models.blip.modeling_tf_blip.TFBlipForConditionalGenerationModelOutput or tuple(tf.Tensor)
Parameters 
pixel_values (tf.Tensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
BlipImageProcessor. See BlipImageProcessor.call() for details.
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.blip.modeling_tf_blip.TFBlipForConditionalGenerationModelOutput or tuple(tf.Tensor)
A transformers.models.blip.modeling_tf_blip.TFBlipForConditionalGenerationModelOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (<class 'transformers.models.blip.configuration_blip.BlipConfig'>) and inputs.
loss (tf.Tensor, optional, returned when labels is provided, tf.Tensor of shape (1,)) — Languge modeling loss from the text decoder.
decoder_logits (tf.Tensor of shape (batch_size, sequence_length, config.vocab_size), optional) — Prediction scores of the language modeling head of the text decoder model.
image_embeds (tf.Tensor of shape (batch_size, output_dim), optional) — The image embeddings obtained after applying the Vision Transformer model to the input image.
last_hidden_state (tf.Tensor 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(tf.Tensor), optional, returned when output_hidden_states=True) — Tuple of tf.Tensor (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(tf.Tensor), optional, returned when output_attentions=True is passed) — Tuple of tf.Tensor (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.`
The TFBlipForConditionalGeneration 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:
	Copied
 from PIL import Image
 import requests
 from transformers import AutoProcessor, TFBlipForConditionalGeneration
 processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
 model = TFBlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 text = "A picture of"
 inputs = processor(images=image, text=text, return_tensors="tf")
 outputs = model(**inputs)
TFBlipForImageTextRetrieval
	
class transformers.TFBlipForImageTextRetrieval
<
source
>
(
*args
**kwargs
)
Parameters 
config (BlipConfig) — 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.
BLIP 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 TFPreTrainedModel. 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 tf.keras.Model subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
call
<
source
>
(
input_ids: tf.Tensor
pixel_values: tf.Tensor | None = None
use_itm_head: Optional[bool] = True
attention_mask: tf.Tensor | None = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
training: Optional[bool] = None
)
→
transformers.models.blip.modeling_tf_blip.TFBlipImageTextMatchingModelOutput or tuple(tf.Tensor)
Parameters 
pixel_values (tf.Tensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
BlipImageProcessor. See BlipImageProcessor.call() for details.
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.blip.modeling_tf_blip.TFBlipImageTextMatchingModelOutput or tuple(tf.Tensor)
A transformers.models.blip.modeling_tf_blip.TFBlipImageTextMatchingModelOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (<class 'transformers.models.blip.configuration_blip.BlipVisionConfig'>) and inputs.
itm_score (tf.Tensor) — The image-text similarity scores.
loss (tf.Tensor of shape (1,), optional, returned when labels is provided) — Languge modeling loss from the text decoder.
image_embeds (tf.Tensor 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 (tf.Tensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (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.
vision_pooler_output (tf.Tensor of shape (batch_size, hidden_size), optional) — Last layer hidden-state of the vision of the vision-only branch of the model.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (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.
question_embeds (tf.Tensor) — The question embeddings obtained by the text projection layer.
The TFBlipForImageTextRetrieval 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:
	Copied
 from PIL import Image
 import requests
 from transformers import AutoProcessor, TFBlipForImageTextRetrieval
 model = TFBlipForImageTextRetrieval.from_pretrained("Salesforce/blip-itm-base-coco")
 processor = AutoProcessor.from_pretrained("Salesforce/blip-itm-base-coco")
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 text = "an image of a cat"
 inputs = processor(images=image, text=text, return_tensors="tf")
 outputs = model(**inputs)
TFBlipForQuestionAnswering
	
class transformers.TFBlipForQuestionAnswering
<
source
>
(
*args
**kwargs
)
Parameters 
config (BlipConfig) — 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.
BLIP Model for visual question answering. The model consists of a vision encoder, a text encoder as well as a text
decoder. The vision encoder will encode the input image, the text encoder will encode the input question together
with the encoding of the image, and the text decoder will output the answer to the question.
This model inherits from TFPreTrainedModel. 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 tf.keras.Model subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
call
<
source
>
(
input_ids: tf.Tensor
pixel_values: tf.Tensor | None = None
decoder_input_ids: tf.Tensor | None = None
decoder_attention_mask: tf.Tensor | None = None
attention_mask: tf.Tensor | None = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
labels: tf.Tensor | None = None
return_dict: Optional[bool] = None
training: Optional[bool] = None
)
→
transformers.models.blip.modeling_tf_blip.TFBlipTextVisionModelOutput or tuple(tf.Tensor)
Parameters 
pixel_values (tf.Tensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
BlipImageProcessor. See BlipImageProcessor.call() for details.
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.blip.modeling_tf_blip.TFBlipTextVisionModelOutput or tuple(tf.Tensor)
A transformers.models.blip.modeling_tf_blip.TFBlipTextVisionModelOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (<class 'transformers.models.blip.configuration_blip.BlipVisionConfig'>) and inputs.
loss (tf.Tensor of shape (1,), optional, returned when labels is provided) — Languge modeling loss from the text decoder.
image_embeds (tf.Tensor 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 (tf.Tensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (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(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (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.
The TFBlipForQuestionAnswering 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:
	Copied
 from PIL import Image
 import requests
 from transformers import AutoProcessor, TFBlipForQuestionAnswering
 model = TFBlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base")
 processor = AutoProcessor.from_pretrained("Salesforce/blip-vqa-base")
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 # training
 text = "How many cats are in the picture?"
 label = "2"
 inputs = processor(images=image, text=text, return_tensors="tf")
 labels = processor(text=label, return_tensors="tf").input_ids
 inputs["labels"] = labels
 outputs = model(**inputs)
 loss = outputs.loss
 # inference
 text = "How many cats are in the picture?"
 inputs = processor(images=image, text=text, return_tensors="tf")
 outputs = model.generate(**inputs)
 print(processor.decode(outputs[0], skip_special_tokens=True))
2
←AltCLIP
BLIP-2→
BLIP
Overview
Resources
BlipConfig
BlipTextConfig
BlipVisionConfig
BlipProcessor
BlipImageProcessor
BlipModel
BlipTextModel
BlipVisionModel
BlipForConditionalGeneration
BlipForImageTextRetrieval
BlipForQuestionAnswering
TFBlipModel
TFBlipTextModel
TFBlipVisionModel
TFBlipForConditionalGeneration
TFBlipForImageTextRetrieval
TFBlipForQuestionAnswering
