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KOSMOS-2.5

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KOSMOS-2.5

The Kosmos-2.5 model was proposed in KOSMOS-2.5: A Multimodal Literate Model by Microsoft.

The abstract from the paper is the following:

We present Kosmos-2.5, a multimodal literate model for machine reading of text-intensive images. Pre-trained on large-scale text-intensive images, Kosmos-2.5 excels in two distinct yet cooperative transcription tasks: (1) generating spatially-aware text blocks, where each block of text is assigned its spatial coordinates within the image, and (2) producing structured text output that captures styles and structures into the markdown format. This unified multimodal literate capability is achieved through a shared Transformer architecture, task-specific prompts, and flexible text representations. We evaluate Kosmos-2.5 on end-to-end document-level text recognition and image-to-markdown text generation. Furthermore, the model can be readily adapted for any text-intensive image understanding task with different prompts through supervised fine-tuning, making it a general-purpose tool for real-world applications involving text-rich images. This work also paves the way for the future scaling of multimodal large language models.

Overview of tasks that KOSMOS-2.5 can handle. Taken from the original paper.

The examples below demonstrates how to generate with AutoModel, for both Markdown and OCR tasks.

AutoModel - Markdown Task

AutoModel - OCR Task

Example

Markdown Task: For usage instructions, please refer to md.py.

OCR Task: For usage instructions, please refer to ocr.py.

Kosmos2_5Config

class transformers.Kosmos2_5Config

< source >

( text_config = None vision_config = None latent_query_num = 2048 **kwargs )

Parameters

text_config (dict, optional) — Dictionary of configuration options used to initialize Kosmos2_5TextConfig.
vision_config (dict, optional) — Dictionary of configuration options used to initialize Kosmos2_5VisionConfig.
latent_query_num (int, optional, defaults to 2048) — The number of latent query tokens that represent the image features used in the text decoder component.
kwargs (optional) — Dictionary of keyword arguments.

This is the configuration class to store the configuration of a Kosmos2_5Model. It is used to instantiate a KOSMOS-2.5 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 KOSMOS-2.5 microsoft/kosmos-2.5 architecture.

Kosmos2_5ImageProcessor

class transformers.Kosmos2_5ImageProcessor

< source >

( do_convert_rgb: bool = True do_normalize: bool = True patch_size: typing.Optional[dict[str, int]] = None max_patches: int = 4096 **kwargs )

Parameters

do_convert_rgb (bool, optional, defaults to True) — Whether to convert the image to RGB.
do_normalize (bool, optional, defaults to True) — Whether to normalize the image. Can be overridden by the do_normalize parameter in the preprocess method. According to Kosmos2_5 paper and code, the image is normalized with its own mean and standard deviation.
patch_size (Dict[str, int], optional, defaults to {"height" -- 16, "width": 16}): The patch size to use for the image. According to Kosmos2_5 paper and code, the patch size is 16x16.
max_patches (int, optional, defaults to 4096) — The maximum number of patches to extract from the image as per the KOSMOS 2.5 paper.

Constructs a Kosmos2_5 image processor.

preprocess

< source >

( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']] do_convert_rgb: typing.Optional[bool] = None do_normalize: typing.Optional[bool] = None max_patches: typing.Optional[int] = None patch_size: typing.Optional[dict[str, int]] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None data_format: ChannelDimension = <ChannelDimension.FIRST: 'channels_first'> input_data_format: typing.Union[str, transformers.image_utils.ChannelDimension, NoneType] = None **kwargs )

Parameters

images (ImageInput) — Image to preprocess. Expects a single or batch of images.
do_convert_rgb (bool, optional, defaults to self.do_convert_rgb) — Whether to convert the image to RGB.
do_normalize (bool, optional, defaults to self.do_normalize) — Whether to normalize the image.
max_patches (int, optional, defaults to self.max_patches) — Maximum number of patches to extract.
patch_size (dict, optional, defaults to self.patch_size) — Dictionary containing the patch height and width.
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:
- "channels_first" or ChannelDimension.FIRST: image in (num_channels, height, width) format.
- "channels_last" or ChannelDimension.LAST: image in (height, width, num_channels) format.
- Unset: Use the channel dimension format of the input image.
input_data_format (ChannelDimension or str, optional) — The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of:
- "channels_first" or ChannelDimension.FIRST: image in (num_channels, height, width) format.
- "channels_last" or ChannelDimension.LAST: image in (height, width, num_channels) format.
- "none" or ChannelDimension.NONE: image in (height, width) format.

Preprocess an image or batch of images. The processor first computes the maximum possible number of aspect-ratio preserving patches of size patch_size that can be extracted from the image. It then pads the image with zeros to make the image respect the constraint of max_patches. Before extracting the patches the images are standardized following the tensorflow implementation of per_image_standardization (https://www.tensorflow.org/api_docs/python/tf/image/per_image_standardization).

Kosmos2_5ImageProcessorFast

class transformers.Kosmos2_5ImageProcessorFast

< source >

( **kwargs: typing_extensions.Unpack[transformers.models.kosmos2_5.image_processing_kosmos2_5_fast.Kosmos2_5FastImageProcessorKwargs] )

Constructs a fast Kosmos2 5 image processor.

preprocess

< source >

( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']] **kwargs: typing_extensions.Unpack[transformers.models.kosmos2_5.image_processing_kosmos2_5_fast.Kosmos2_5FastImageProcessorKwargs] ) → <class 'transformers.image_processing_base.BatchFeature'>

Parameters

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.
do_resize (bool, optional) — Whether to resize the image.
size (dict[str, int], optional) — Describes the maximum input dimensions to the model.
default_to_square (bool, optional) — Whether to default to a square image when resizing, if size is an int.
resample (Union[PILImageResampling, F.InterpolationMode, NoneType]) — Resampling filter to use if resizing the image. This can be one of the enum PILImageResampling. Only has an effect if do_resize is set to True.
do_center_crop (bool, optional) — Whether to center crop the image.
crop_size (dict[str, int], optional) — Size of the output image after applying center_crop.
do_rescale (bool, optional) — Whether to rescale the image.
rescale_factor (Union[int, float, NoneType]) — Rescale factor to rescale the image by if do_rescale is set to True.
do_normalize (bool, optional) — Whether to normalize the image.
image_mean (Union[float, list[float], NoneType]) — Image mean to use for normalization. Only has an effect if do_normalize is set to True.
image_std (Union[float, list[float], NoneType]) — Image standard deviation to use for normalization. Only has an effect if do_normalize is set to True.
do_convert_rgb (bool, optional) — Whether to convert the image to RGB.
return_tensors (Union[str, ~utils.generic.TensorType, NoneType]) — Returns stacked tensors if set to `pt, otherwise returns a list of tensors.
data_format (~image_utils.ChannelDimension, optional) — Only ChannelDimension.FIRST is supported. Added for compatibility with slow processors.
input_data_format (Union[str, ~image_utils.ChannelDimension, NoneType]) — The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of:
- "channels_first" or ChannelDimension.FIRST: image in (num_channels, height, width) format.
- "channels_last" or ChannelDimension.LAST: image in (height, width, num_channels) format.
- "none" or ChannelDimension.NONE: image in (height, width) format.
device (torch.device, optional) — The device to process the images on. If unset, the device is inferred from the input images.
disable_grouping (bool, optional) — Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157
patch_size (Dict[str, int], optional, defaults to {"height" -- 16, "width": 16}): The patch size to use for the image. According to Kosmos2_5 paper and code, the patch size is 16x16.
max_patches (int, optional, defaults to 4096) — The maximum number of patches to extract from the image as per the KOSMOS 2.5 paper.

Returns

<class 'transformers.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/TensorFlow/Numpy Tensors at initialization.

Kosmos2_5Processor

class transformers.Kosmos2_5Processor

< source >

( image_processor tokenizer )

Parameters

image_processor (Kosmos2_5ImageProcessor) — An instance of Kosmos2_5ImageProcessor. The image processor is a required input.
tokenizer (Union[T5TokenizerFast, T5Tokenizer]) — An instance of [‘T5TokenizerFast`] or [‘T5Tokenizer`]. The tokenizer is a required input.

Constructs a Kosmos2_5 processor which wraps a PreTrainedTokenizerFast and Kosmos2_5 image processor into a single processor.

Kosmos2_5Processor offers all the functionalities of Kosmos2_5ImageProcessor and PreTrainedTokenizerFast. See the docstring of __call__() and decode() for more information.

batch_decode

< source >

( *args **kwargs )

This method forwards all its arguments to Kosmos2_5TokenizerFast’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 Kosmos2_5TokenizerFast’s decode(). Please refer to the docstring of this method for more information.

Kosmos2_5Model

class transformers.Kosmos2_5Model

< source >

( config: Kosmos2_5Config )

Parameters

config (Kosmos2_5Config) — 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.

KOSMOS-2.5 Model for generating text and image features. The model consists of a vision encoder and a language model.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

< source >

( input_ids: typing.Optional[torch.Tensor] = None flattened_patches: typing.Optional[torch.Tensor] = None width: typing.Optional[torch.Tensor] = None height: typing.Optional[torch.Tensor] = None image_embeds_position_mask: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None past_key_values: typing.Union[transformers.cache_utils.Cache, list[torch.FloatTensor], NoneType] = None image_embeds: typing.Optional[torch.Tensor] = None inputs_embeds: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None cache_position: typing.Optional[torch.LongTensor] = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → transformers.models.kosmos2_5.modeling_kosmos2_5.Kosmos2_5ModelOutput 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 AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

What are input IDs?
flattened_patches (torch.FloatTensor of shape (batch_size, max_patches, 2 + patch_height * patch_width * image_channels)) — Flattened patches of the images. flattened_patches can be obtained using AutoImageProcessor. See Kosmos2_5ImageProcessor.call() for details.
width (torch.FloatTensor of shape (batch_size,)) — The original width (before resizing) of each image in the batch. This can be obtained using AutoImageProcessor. See Kosmos2_5ImageProcessor.call() for details.
height (torch.FloatTensor of shape (batch_size,)) — The original height (before resizing) of each image in the batch. This can be obtained using AutoImageProcessor. See Kosmos2_5ImageProcessor.call() for details.
image_embeds_position_mask (torch.Tensor of shape (batch_size, sequence_length), optional) — Mask to indicate the location in a sequence to insert the image features . Mask values selected in [0, 1]:
- 1 for places where to put the image features,
- 0 for places that are not for image features (i.e. for text tokens).
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?
past_key_values (tuple(tuple(torch.FloatTensor)) of length config.n_layers with each tuple having 4 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)) — 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).
image_embeds — (torch.FloatTensor of shape (batch_size, latent_query_num, hidden_size), optional): Sequence of hidden-states at the output of Kosmos2ImageToTextProjection.
inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
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?
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).
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.

Returns

transformers.models.kosmos2_5.modeling_kosmos2_5.Kosmos2_5ModelOutput or tuple(torch.FloatTensor)

A transformers.models.kosmos2_5.modeling_kosmos2_5.Kosmos2_5ModelOutput 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.kosmos2_5.configuration_kosmos2_5.Kosmos2_5Config'>) 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.
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.
width (torch.FloatTensor of shape (batch_size,)) — The original width (before resizing) of each image in the batch.
height (torch.FloatTensor of shape (batch_size,)) — The original height (before resizing) of each image in the batch.
image_embeds (torch.FloatTensor of shape (batch_size, latent_query_num, hidden_size), optional) — Sequence of hidden-states at the output of Kosmos2ImageToTextProjection.
projection_attentions (tuple(torch.FloatTensor), optional) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

Attentions weights given by Kosmos2ImageToTextProjection, after the attention softmax, used to compute the weighted average in the self-attention heads.
vision_model_output(BaseModelOutputWithPooling, optional) — The output of the Kosmos2VisionModel.
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape (batch_size, num_heads, sequence_length, embed_size_per_head)) and optionally if config.is_encoder_decoder=True 2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head).

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

The Kosmos2_5Model forward method, overrides the __call__ special method.

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

Examples:

>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, Kosmos2_5Model

>>> model = Kosmos2_5Model.from_pretrained("microsoft/kosmos2.5")
>>> processor = AutoProcessor.from_pretrained("microsoft/kosmos2.5")

>>> url = "https://huggingface.co/microsoft/kosmos2.5/resolve/main/snowman.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)

>>> text = (
...     "<grounding> An image of<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863>"
...     "</object> warming himself by<phrase> a fire</phrase><object><patch_index_0005><patch_index_0911>"
...     "</object>"
... )

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

>>> last_hidden_state = model(
...     pixel_values=inputs["pixel_values"],
...     input_ids=inputs["input_ids"],
...     attention_mask=inputs["attention_mask"],
...     image_embeds_position_mask=inputs["image_embeds_position_mask"],
... ).last_hidden_state
>>> list(last_hidden_state.shape)
[1, 91, 2048]

Kosmos2_5ForConditionalGeneration

class transformers.Kosmos2_5ForConditionalGeneration

< source >

( config: Kosmos2_5Config )

Parameters

config (Kosmos2_5Config) — 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.

KOSMOS-2.5 Model for generating text and bounding boxes given an image. The model consists of a vision encoder and a language model.

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

< source >

( input_ids: typing.Optional[torch.Tensor] = None flattened_patches: typing.Optional[torch.Tensor] = None width: typing.Optional[torch.Tensor] = None height: typing.Optional[torch.Tensor] = None image_embeds_position_mask: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None past_key_values: typing.Union[transformers.cache_utils.Cache, list[torch.FloatTensor], NoneType] = None image_embeds: typing.Optional[torch.Tensor] = None inputs_embeds: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None labels: typing.Optional[torch.LongTensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → transformers.models.kosmos2_5.modeling_kosmos2_5.Kosmos2_5ForConditionalGenerationModelOutput 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 AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

What are input IDs?
flattened_patches (torch.FloatTensor of shape (batch_size, max_patches, 2 + patch_height * patch_width * image_channels)) — Flattened patches of the images. flattened_patches can be obtained using AutoImageProcessor. See Kosmos2_5ImageProcessor.call() for details.
width (torch.FloatTensor of shape (batch_size,)) — The original width (before resizing) of each image in the batch. This can be obtained using AutoImageProcessor. See Kosmos2_5ImageProcessor.call() for details.
height (torch.FloatTensor of shape (batch_size,)) — The original height (before resizing) of each image in the batch. This can be obtained using AutoImageProcessor. See Kosmos2_5ImageProcessor.call() for details.
image_embeds_position_mask (torch.Tensor of shape (batch_size, sequence_length), optional) — Mask to indicate the location in a sequence to insert the image features . Mask values selected in [0, 1]:
- 1 for places where to put the image features,
- 0 for places that are not for image features (i.e. for text tokens).
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?
past_key_values (tuple(tuple(torch.FloatTensor)) of length config.n_layers with each tuple having 4 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)) — 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).
image_embeds — (torch.FloatTensor of shape (batch_size, latent_query_num, hidden_size), optional): Sequence of hidden-states at the output of Kosmos2ImageToTextProjection.
inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
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?
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).
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.
labels (torch.LongTensor of shape (batch_size, sequence_length), optional) — Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in [-100, 0, ..., config.vocab_size] (see input_ids docstring) Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size]

Returns

transformers.models.kosmos2_5.modeling_kosmos2_5.Kosmos2_5ForConditionalGenerationModelOutput or tuple(torch.FloatTensor)

A transformers.models.kosmos2_5.modeling_kosmos2_5.Kosmos2_5ForConditionalGenerationModelOutput 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.kosmos2_5.configuration_kosmos2_5.Kosmos2_5Config'>) and inputs.

loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss (for next-token prediction).
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token 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 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.
width (torch.FloatTensor of shape (batch_size,)) — The original width (before resizing) of each image in the batch.
height (torch.FloatTensor of shape (batch_size,)) — The original height (before resizing) of each image in the batch.
image_embeds (torch.FloatTensor of shape (batch_size, latent_query_num, hidden_size), optional) — Sequence of hidden-states at the output of Kosmos2ImageToTextProjection.
projection_attentions (tuple(torch.FloatTensor), optional) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

Attentions weights given by Kosmos2ImageToTextProjection, after the attention softmax, used to compute the weighted average in the self-attention heads.
vision_model_output(BaseModelOutputWithPooling, optional) — The output of the Kosmos2VisionModel.
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape (batch_size, num_heads, sequence_length, embed_size_per_head)) and optionally if config.is_encoder_decoder=True 2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head).

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

The Kosmos2_5ForConditionalGeneration forward method, overrides the __call__ special method.

Examples:

>>> from PIL import Image
>>> import requests
>>> import torch
>>> from transformers import AutoProcessor, Kosmos2_5ForConditionalGeneration

>>> repo = "ydshieh/kosmos-2.5"
>>> device = "cuda:0"
>>> dtype = torch.bfloat16 # torch.float16
>>> model = Kosmos2_5ForConditionalGeneration.from_pretrained(repo, device_map=device, torch_dtype=dtype)
>>> processor = AutoProcessor.from_pretrained(repo)

>>> url = "https://huggingface.co/ydshieh/kosmos-2.5/resolve/main/receipt_00008.png"

>>> image = Image.open(requests.get(url, stream=True).raw)

>>> prompt = "<ocr>" # <md>

>>> inputs = processor(text=prompt, images=image, return_tensors="pt")
>>> height, width = inputs.pop("height"), inputs.pop("width")
>>> inputs = {k: v.to(device) if v is not None else None for k, v in inputs.items()}
>>> inputs["flattened_patches"] = inputs["flattened_patches"].to(dtype)

>>> generated_ids = model.generate(**inputs,max_new_tokens=1024)
>>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
>>> generated_text
'<ocr><bbox><x_53><y_573><x_69><y_606></bbox>1\n<bbox><x_79><y_573><x_464><y_612></bbox>[REG] BLACK SAKURA\n<bbox><x_690><y_569><x_810><y_606></bbox>45,455\n<bbox><x_53><y_614><x_69><y_648></bbox>1\n<bbox><x_79><y_614><x_468><y_650></bbox>COOKIE DOH SAUCES\n<bbox><x_788><y_609><x_812><y_644></bbox>0\n<bbox><x_50><y_658><x_69><y_693></bbox>1\n<bbox><x_79><y_658><x_358><y_693></bbox>NATA DE COCO\n<bbox><x_790><y_652><x_814><y_687></bbox>0\n<bbox><x_31><y_742><x_820><y_781></bbox>Sub Total 45,455\n<bbox><x_27><y_781><x_822><y_827></bbox>PB1 (10%) 4,545\n<bbox><x_27><y_826><x_824><y_872></bbox>Rounding 0\n<bbox><x_24><y_872><x_827><y_921></bbox>Total 50,000\n<bbox><x_17><y_1056><x_836><y_1108></bbox>Card Payment 50,000\n'

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