#
LLaVA-OneVision

##
Overview

The LLaVA-OneVision model was proposed in LLaVA-OneVision: Easy Visual Task Transfer by <Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, Chunyuan Li

LLaVA-OneVision is a Vision-Language Model that can generate text conditioned on one or several images/videos. The model consists of SigLIP vision encoder and a Qwen2 language backbone. The images are processed with anyres-9 technique where the image is split into 9 patches to better process high resolution images and capture as much details as possible. However, videos are pooled to a total sequence length of 196 tokens each frame for more memory efficient computation. LLaVA-OneVision is available in three sizes: 0.5B, 7B and 72B and achieves remarkable performance on benchmark evaluations.

The abstract from the paper is the following:

We present LLaVA-OneVision, a family of open large multimodal models (LMMs) developed by consolidating our insights into data, models, and visual representations in the LLaVA-NeXT blog series. Our experimental results demonstrate that LLaVA-OneVision is the first single model that can simultaneously push the performance boundaries of open LMMs in three important computer vision scenarios: single-image, multi-image, and video scenarios. Importantly, the design of LLaVAOneVision allows strong transfer learning across different modalities/scenarios, yielding new emerging capabilities. In particular, strong video understanding and cross-scenario capabilities are demonstrated through task transfer from images to videos.

LLaVA-OneVision architecture. Taken from the original paper.

Tips:

- We advise users to use `padding_side="left"` when computing batched generation as it leads to more accurate results. Simply make sure to call `processor.tokenizer.padding_side = "left"` before generating.

- Llava-OneVision uses different number of patches for images and thus has to pad the inputs inside modeling code, aside from the padding done when processing the inputs. The default setting is “left-padding” if model is in `eval()` mode, otherwise “right-padding”.

###
Formatting Prompts with Chat Templates

Each checkpoint is trained with a specific prompt format, depending on the underlying large language model backbone. To ensure correct formatting, use the processor’s `apply_chat_template` method.

Important:

- You must construct a conversation history — passing a plain string won’t work.
- Each message should be a dictionary with `"role"` and `"content"` keys.
- The `"content"` should be a list of dictionaries for different modalities like `"text"` and `"image"`.

Here’s an example of how to structure your input. We will use llava-onevision-qwen2-7b-si-hf and a conversation history of text and image. Each content field has to be a list of dicts, as follows:

    from transformers import AutoProcessor

    processor = AutoProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-7b-si-hf")

    conversation = [
        {
            "role": "user",
            "content": [
                {"type": "image"},
                {"type": "text", "text": "What’s shown in this image?"},
            ],
        },
        {
            "role": "assistant",
            "content": [{"type": "text", "text": "This image shows a red stop sign."},]
        },
        {

            "role": "user",
            "content": [
                {"type": "text", "text": "Describe the image in more details."},
            ],
        },
    ]

    text_prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)

    # Note that the template simply formats your prompt, you still have to tokenize it and obtain pixel values for your images
    print(text_prompt)
    '<|im_start|>user\n<image>What is shown in this image?<|im_end|>\n<|im_start|>assistant\nPage showing the list of options.<|im_end|>'

🚀 Bonus: If you’re using `transformers>=4.49.0`, you can also get a vectorized output from `apply_chat_template`. See the Usage Examples below for more details on how to use it.

This model was contributed by RaushanTurganbay. The original code can be found here.

##
Usage example

###
Single image inference

Here’s how to load the model and perform inference in half-precision (`torch.float16`):

    from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration
    import torch

    processor = AutoProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf")
    model = LlavaOnevisionForConditionalGeneration.from_pretrained(
        "llava-hf/llava-onevision-qwen2-7b-ov-hf",
        torch_dtype=torch.float16,
        low_cpu_mem_usage=True,
        device_map="cuda:0"
    )

    # prepare image and text prompt, using the appropriate prompt template
    url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"
    conversation = [
        {
            "role": "user",
            "content": [
                {"type": "image", "url": url},
                {"type": "text", "text": "What is shown in this image?"},
            ],
        },
    ]
    inputs = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt")
    inputs = inputs.to("cuda:0", torch.float16)

    # autoregressively complete prompt
    output = model.generate(**inputs, max_new_tokens=100)
    print(processor.decode(output[0], skip_special_tokens=True))
    'user\n\nWhat is shown in this image?\nassistant\nThe image shows a radar chart, also known as a spider chart or a star chart, which is used to compare multiple quantitative variables. Each axis represents a different variable, and the chart is filled with'

###
Multi image inference

LLaVa-OneVision can perform inference with multiple images as input, where images either belong to the same prompt or different prompts (in batched inference). For that you have to use checkpoints with an “ov” suffix. Here is how you can do it:

    import requests
    from PIL import Image
    import torch
    from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration

    # Load the model in half-precision
    model = LlavaOnevisionForConditionalGeneration.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf", torch_dtype=torch.float16, device_map="auto")
    processor = AutoProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf")

    # Prepare a batch of two prompts, where the first one is a multi-turn conversation and the second is not
    conversation_1 = [
        {
            "role": "user",
            "content": [
                {"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"},
                {"type": "text", "text": "What is shown in this image?"},
                ],
        },
        {
            "role": "assistant",
            "content": [
                {"type": "text", "text": "There is a red stop sign in the image."},
                ],
        },
        {
            "role": "user",
            "content": [
                {"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
                {"type": "text", "text": "What about this image? How many cats do you see?"},
                ],
        },
    ]

    conversation_2 = [
        {
            "role": "user",
            "content": [
                {"type": "image", "url": "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.jpg"},
                {"type": "text", "text": "What is shown in this image?"},
                ],
        },
    ]

    inputs = processor.apply_chat_template(
        [conversation_1, conversation_2],
        add_generation_prompt=True,
        tokenize=True,
        return_dict=True,
        padding=True,
        return_tensors="pt"
    ).to(model.device, torch.float16)

    # Generate
    generate_ids = model.generate(**inputs, max_new_tokens=30)
    processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
    ['user\n\nWhat is shown in this image?\nassistant\nThere is a red stop sign in the image.\nuser\n\nWhat about this image? How many cats do you see?\nassistant\ntwo', 'user\n\nWhat is shown in this image?\nassistant\n']

###
Video inference

LLaVa-OneVision also can perform inference with videos as input, where video frames are treated as multiple images. Here is how you can do it:

    from huggingface_hub import hf_hub_download
    import torch
    from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration

    # Load the model in half-precision
    model = LlavaOnevisionForConditionalGeneration.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf", torch_dtype=torch.float16, device_map="auto")
    processor = AutoProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf")

    video_path = hf_hub_download(repo_id="raushan-testing-hf/videos-test", filename="sample_demo_1.mp4", repo_type="dataset")
    conversation = [
        {

            "role": "user",
            "content": [
                {"type": "video", "path": video_path},
                {"type": "text", "text": "Why is this video funny?"},
                ],
        },
    ]

    inputs = processor.apply_chat_template(
        conversation,
        num_frames=8
        add_generation_prompt=True,
        tokenize=True,
        return_dict=True,
        return_tensors="pt"
    ).to(model.device, torch.float16)

    out = model.generate(**inputs, max_new_tokens=60)
    processor.batch_decode(out, skip_special_tokens=True, clean_up_tokenization_spaces=True)
    ["user\n\nWhy is this video funny?\nassistant\nThe video appears to be humorous because it shows a young child, who is wearing glasses and holding a book, seemingly reading with a serious and focused expression. The child's glasses are a bit oversized for their face, which adds a comical touch, as it's a common trope to see children wearing"]

##
Model optimization

###
Quantization using bitsandbytes

The model can be loaded in 8 or 4 bits, greatly reducing the memory requirements while maintaining the performance of the original model. First make sure to install bitsandbytes, `pip install bitsandbytes` and make sure to have access to a GPU/accelerator that is supported by the library.

bitsandbytes is being refactored to support multiple backends beyond CUDA. Currently, ROCm (AMD GPU) and Intel CPU implementations are mature, with Intel XPU in progress and Apple Silicon support expected by Q4/Q1. For installation instructions and the latest backend updates, visit this link.

We value your feedback to help identify bugs before the full release! Check out these docs for more details and feedback links.

Simply change the snippet above with:

    from transformers import LlavaOnevisionForConditionalGeneration, BitsAndBytesConfig

    # specify how to quantize the model
    quantization_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_quant_type="nf4",
        bnb_4bit_compute_dtype=torch.float16,
    )

    model = LlavaOnevisionForConditionalGeneration.from_pretrained(model_id, quantization_config=quantization_config, device_map="auto")

###
Use Flash-Attention 2 to further speed-up generation

First make sure to install flash-attn. Refer to the original repository of Flash Attention regarding that package installation. Simply change the snippet above with:

    from transformers import LlavaOnevisionForConditionalGeneration

    model = LlavaOnevisionForConditionalGeneration.from_pretrained(
        model_id,
        torch_dtype=torch.float16,
        low_cpu_mem_usage=True,
        use_flash_attention_2=True
    ).to(0)

##
LlavaOnevisionConfig

###
class transformers.LlavaOnevisionConfig

< source >

( vision_config = None text_config = None image_token_index = 151646 video_token_index = 151647 projector_hidden_act = 'gelu' vision_feature_select_strategy = 'full' vision_feature_layer = -1 vision_aspect_ratio = 'anyres_max_9' image_grid_pinpoints = None tie_word_embeddings = False multimodal_projector_bias = True **kwargs )

Parameters

- vision_config (`Union[AutoConfig, dict]`, optional, defaults to `SiglipVisionConfig`) — The config object or dictionary of the vision backbone.
- text_config (`Union[AutoConfig, dict]`, optional, defaults to `Qwen2Config`) — The config object or dictionary of the text backbone.
- image_token_index (`int`, optional, defaults to 151646) — The image token index to encode the image prompt.
- video_token_index (`int`, optional, defaults to 151647) — The video token index to encode the video prompt.
- projector_hidden_act (`str`, optional, defaults to `"gelu"`) — The activation function used by the multimodal projector.
- vision_feature_select_strategy (`str`, optional, defaults to `"full"`) — The feature selection strategy used to select the vision feature from the vision backbone. Can be one of `"default"` or `"full"`. If `"default"`, the CLS token is removed from the vision features. If `"full"`, the full vision features are used.
- vision_feature_layer (`Union[int, List[int]]`, optional, defaults to -1) — The index of the layer to select the vision feature. If multiple indices are provided, the vision feature of the corresponding indices will be concatenated to form the vision features.
- vision_aspect_ratio (`str`, optional, defaults to `"anyres_max_9"`) — Aspect ratio used when processong image features. The default value is “anyres_max_9”.
- image_grid_pinpoints (`List`, optional) — A list of possible resolutions to use for processing high resolution images. Each item in the list should be a tuple or list of the form `(height, width)`.
- tie_word_embeddings (`bool`, optional, defaults to `False`) — Whether the model’s input and output word embeddings should be tied.
- multimodal_projector_bias (`bool`, optional, defaults to `True`) — Whether to use bias in the multimodal projector.

This is the configuration class to store the configuration of a LlavaOnevisionForConditionalGeneration. It is used to instantiate an Llava-NeXT 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 llava-hf/llava-onevision-qwen2-7b-ov-hf model.

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

Example:

    >>> from transformers import LlavaOnevisionForConditionalGeneration, LlavaOnevisionConfig, SiglipVisionConfig, Qwen2Config

    >>> # Initializing a CLIP-vision config
    >>> vision_config = SiglipVisionConfig()

    >>> # Initializing a Llama config
    >>> text_config = Qwen2Config()

    >>> # Initializing a Llava-Next llava-hf/llava-onevision-qwen2-7b-ov-hf style configuration
    >>> configuration = LlavaOnevisionConfig(vision_config, text_config)

    >>> # Initializing a model from the llava-hf/llava-onevision-qwen2-7b-ov-hf style configuration
    >>> model = LlavaOnevisionForConditionalGeneration(configuration)

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

##
LlavaOnevisionProcessor

###
class transformers.LlavaOnevisionProcessor

< source >

( image_processor = None tokenizer = None video_processor = None num_image_tokens = None vision_feature_select_strategy = None chat_template = None image_token = '<image>' video_token = '<video>' vision_aspect_ratio = 'anyres_max_9' **kwargs )

Parameters

- image_processor (LlavaOnevisionImageProcessor, optional) — The image processor is a required input.
- tokenizer (LlamaTokenizerFast, optional) — The tokenizer is a required input.
- video_processor (LlavaOnevisionVideoProcessor, optional) — The video processor is a required input.
- num_image_tokens (`int`, optional) — Number of image tokens for one imagethat will be returned by vision tower.
- vision_feature_select_strategy (`str`, optional) — The feature selection strategy used to select the vision feature from the vision backbone. Should be same as in model’s config
- chat_template (`str`, optional) — A Jinja template which will be used to convert lists of messages in a chat into a tokenizable string.
- image_token (`str`, optional, defaults to `"<image>"`) — Special token used to denote image location.
- video_token (`str`, optional, defaults to `"<video>"`) — Special token used to denote video location.
- vision_aspect_ratio (`str`, optional, defaults to `"anyres_max_9"`) — Aspect ratio used when processong image features. The default value is “anyres_max_9”.

Constructs a LLaVa-Onevision processor which wraps a LLaVa-Onevision video processor, LLaVa-NeXT image processor and a LLaMa tokenizer into a single processor.

LlavaNextProcessor offers all the functionalities of LlavaOnevisionVideoProcessor, LlavaOnevisionImageProcessor and LlamaTokenizerFast. See the `__call__()`, `__call__()` and decode() for more information.

####
batch_decode

< source >

( *args **kwargs )

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

##
LlavaOnevisionImageProcessor

###
class transformers.LlavaOnevisionImageProcessor

< source >

( do_resize: bool = True size: typing.Optional[typing.Dict[str, int]] = None image_grid_pinpoints: typing.Optional[typing.List] = 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_pad: typing.Optional[bool] = True 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 `do_resize` in the `preprocess` method.
- size (`Dict[str, int]` optional, defaults to `{"shortest_edge" -- 224}`): Size of the image after resizing. The shortest edge of the image is resized to size[“shortest_edge”], with the longest edge resized to keep the input aspect ratio. Can be overridden by `size` in the `preprocess` method.
- image_grid_pinpoints (`List` optional, defaults to `[[672, 336], [336, 672], [672, 672], [336, 1008], [1008, 336]]`) — A list of possible resolutions to use for processing high resolution images. The best resolution is selected based on the original size of the image. Can be overridden by `image_grid_pinpoints` in the `preprocess` method. Not used for processing videos.
- resample (`PILImageResampling`, optional, defaults to `Resampling.BICUBIC`) — Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method.
- do_rescale (`bool`, optional, defaults to `True`) — Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by `do_rescale` in the `preprocess` method.
- rescale_factor (`int` or `float`, optional, defaults to `1/255`) — Scale factor to use if rescaling the image. Can be overridden by `rescale_factor` in the `preprocess` method.
- do_normalize (`bool`, optional, defaults to `True`) — Whether to normalize the image. Can be overridden by `do_normalize` in the `preprocess` method.
- image_mean (`float` or `List[float]`, optional, defaults to `[0.48145466, 0.4578275, 0.40821073]`) — 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.
- image_std (`float` or `List[float]`, optional, defaults to `[0.26862954, 0.26130258, 0.27577711]`) — 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_pad (`bool`, optional, defaults to `True`) — Whether to pad the image. If `True`, will pad the patch dimension of the images in the batch to the largest number of patches in the batch. Padding will be applied to the bottom and right with zeros.
- do_convert_rgb (`bool`, optional, defaults to `True`) — Whether to convert the image to RGB.

Constructs a LLaVa-Onevision image processor. Based on SiglipImageProcessor with incorporation of processing each video frame.

####
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_resize: typing.Optional[bool] = None size: typing.Optional[typing.Dict[str, int]] = None image_grid_pinpoints: typing.Optional[typing.List] = 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 do_pad: typing.Optional[bool] = None do_convert_rgb: typing.Optional[bool] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None data_format: typing.Optional[transformers.image_utils.ChannelDimension] = <ChannelDimension.FIRST: 'channels_first'> input_data_format: typing.Union[str, transformers.image_utils.ChannelDimension, NoneType] = None )

Parameters

- images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`) — The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch tensor. Both channels-first and channels-last formats are supported.
- do_resize (`bool`, optional, defaults to `self.do_resize`) — Whether to resize the image.
- size (`Dict[str, int]`, optional, defaults to `self.size`) — Size of the image after resizing. Shortest edge of the image is resized to size[“shortest_edge”], with the longest edge resized to keep the input aspect ratio.
- image_grid_pinpoints (`List` optional, defaults to `self.image_grid_pinpoints`) — A list of possible resolutions to use for processing high resolution images. The best resolution is selected based on the original size of the image.
- resample (`int`, optional, defaults to `self.resample`) — 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_rescale (`bool`, optional, defaults to `self.do_rescale`) — Whether to rescale the image.
- 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 use for normalization. Only has an effect if `do_normalize` is set to `True`.
- image_std (`float` or `List[float]`, optional, defaults to `self.image_std`) — Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to `True`.
- do_pad (`bool`, optional, defaults to `self.do_pad`) — Whether to pad the image. If `True`, will pad the patch dimension of the images in the batch to the largest number of patches in the batch. Padding will be applied to the bottom and right with zeros.
- 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:
  - `"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.

##
LlavaOnevisionImageProcessorFast

###
class transformers.LlavaOnevisionImageProcessorFast

< source >

( **kwargs: typing_extensions.Unpack[transformers.models.llava_onevision.image_processing_llava_onevision_fast.LlavaOnevisionFastImageProcessorKwargs] )

Parameters

- do_resize (`bool`, optional, defaults to `True`) — Whether to resize the image.
- size (`dict[str, int]`, optional, defaults to `{'height' -- 384, 'width': 384}`): Describes the maximum input dimensions to the model.
- default_to_square (`bool`, optional, defaults to `False`) — Whether to default to a square image when resizing, if size is an int.
- resample (`Union[PILImageResampling, F.InterpolationMode, NoneType]`, defaults to `Resampling.BICUBIC`) — 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, defaults to `None`) — Whether to center crop the image.
- crop_size (`dict[str, int]`, optional, defaults to `None`) — Size of the output image after applying `center_crop`.
- do_rescale (`bool`, optional, defaults to `True`) — Whether to rescale the image.
- rescale_factor (`Union[int, float, NoneType]`, defaults to `0.00392156862745098`) — Rescale factor to rescale the image by if `do_rescale` is set to `True`.
- do_normalize (`bool`, optional, defaults to `True`) — Whether to normalize the image.
- image_mean (`Union[float, list[float], NoneType]`, defaults to `[0.48145466, 0.4578275, 0.40821073]`) — Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
- image_std (`Union[float, list[float], NoneType]`, defaults to `[0.26862954, 0.26130258, 0.27577711]`) — Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to `True`.
- do_convert_rgb (`bool`, optional, defaults to `True`) — Whether to convert the image to RGB.
- return_tensors (`Union[str, ~utils.generic.TensorType, NoneType]`, defaults to `None`) — Returns stacked tensors if set to `pt, otherwise returns a list of tensors.
- data_format (`~image_utils.ChannelDimension`, optional, defaults to `ChannelDimension.FIRST`) — Only `ChannelDimension.FIRST` is supported. Added for compatibility with slow processors.
- input_data_format (`Union[~image_utils.ChannelDimension, str, NoneType]`, defaults to `None`) — 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, defaults to `None`) — The device to process the images on. If unset, the device is inferred from the input images.
- image_grid_pinpoints (`List[List[int]]`, optional) — A list of possible resolutions to use for processing high resolution images. The best resolution is selected based on the original size of the image. Can be overridden by `image_grid_pinpoints` in the `preprocess` method.
- do_pad (`bool`, optional) — Whether to pad the image. If `True`, will pad the patch dimension of the images in the batch to the largest number of patches in the batch. Padding will be applied to the bottom and right with zeros.

Constructs a fast Llava Onevision 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.llava_onevision.image_processing_llava_onevision_fast.LlavaOnevisionFastImageProcessorKwargs] ) →
`<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[~image_utils.ChannelDimension, str, 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.
- image_grid_pinpoints (`List[List[int]]`, optional) — A list of possible resolutions to use for processing high resolution images. The best resolution is selected based on the original size of the image. Can be overridden by `image_grid_pinpoints` in the `preprocess` method.
- do_pad (`bool`, optional) — Whether to pad the image. If `True`, will pad the patch dimension of the images in the batch to the largest number of patches in the batch. Padding will be applied to the bottom and right with zeros.

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.

##
LlavaOnevisionVideoProcessor

###
class transformers.LlavaOnevisionVideoProcessor

< source >

( **kwargs: typing_extensions.Unpack[transformers.models.llava_onevision.video_processing_llava_onevision.LlavaOnevisionFastVideoProcessorInitKwargs] )

##
LlavaOnevisionVideoProcessor

###
class transformers.LlavaOnevisionVideoProcessor

< source >

( **kwargs: typing_extensions.Unpack[transformers.models.llava_onevision.video_processing_llava_onevision.LlavaOnevisionFastVideoProcessorInitKwargs] )

##
LlavaOnevisionModel

###
class transformers.LlavaOnevisionModel

< source >

( config )

Parameters

- config (LlavaOnevisionModel) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

The Llava-Next model which consists of a vision backbone and a language model without language modeling head.

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 = None pixel_values: FloatTensor = None image_sizes: typing.Optional[torch.LongTensor] = None pixel_values_videos: FloatTensor = None image_sizes_videos: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.LongTensor] = None past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None vision_feature_layer: typing.Union[int, typing.List[int], NoneType] = None vision_feature_select_strategy: typing.Optional[str] = None vision_aspect_ratio: typing.Optional[str] = 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 cache_position: typing.Optional[torch.LongTensor] = None **kwargs: typing_extensions.Unpack[transformers.modeling_flash_attention_utils.FlashAttentionKwargs] ) →
`transformers.models.llava_onevision.modeling_llava_onevision.LlavaOnevisionModelOutputWithPast` 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.

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`) — The tensors corresponding to the input images. Pixel values can be obtained using `{image_processor_class}`. See `{image_processor_class}.__call__` for details (`{processor_class}` uses `{image_processor_class}` for processing images).
- image_sizes (`torch.LongTensor` of shape `(batch_size, 2)`, optional) — The sizes of the images in the batch, being (height, width) for each image.
- pixel_values_videos (`torch.FloatTensor` of shape `(batch_size, frames, num_channels, image_size, image_size)) -- The tensors corresponding to the input videos. Pixel values can be obtained using [LlavaNextVideoProcessor](/docs/transformers/main/en/model_doc/llava_next_video#transformers.LlavaNextVideoProcessor). See` LlavaNextVideoProcessor.call()` for details. LlavaProcessor uses LlavaNextVideoProcessor for processing videos.
- image_sizes_videos (`torch.LongTensor` of shape `(batch_size, frames, 2)`, optional) — The sizes of the videos in the batch, being (height, width) for each frame in the video.
- attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`.

  What are position IDs?

- past_key_values (`List[torch.FloatTensor]`, optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.

  Two formats are allowed:

  - a Cache instance, see our kv cache guide;
  - 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)`). This is also known as the legacy cache format.

  The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the legacy cache format will be returned.

  If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don’t have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` of shape `(batch_size, sequence_length)`.

- 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.
- vision_feature_layer (`Union[int, List[int], NoneType]`) — The index of the layer to select the vision feature. If multiple indices are provided, the vision feature of the corresponding indices will be concatenated to form the vision features.
- vision_feature_select_strategy (`str`, optional, defaults to `"default"`) — The feature selection strategy used to select the vision feature from the vision backbone. Can be one of `"default"` or `"full"`. If `"default"`, the CLS token is removed from the vision features. If `"full"`, the full vision features are used.
- vision_aspect_ratio (`str`, optional, defaults to `"anyres_max_9"`) — Aspect ratio used when processong image features. The default value is “anyres_max_9”.
- 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.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
- cache_position (`torch.LongTensor` of shape `(sequence_length)`, optional) — Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length.

Returns

`transformers.models.llava_onevision.modeling_llava_onevision.LlavaOnevisionModelOutputWithPast` or `tuple(torch.FloatTensor)`

A `transformers.models.llava_onevision.modeling_llava_onevision.LlavaOnevisionModelOutputWithPast` 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 (LlavaOnevisionConfig) 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.

- 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)`)

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

- 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.

- image_hidden_states (`torch.FloatTensor`, optional) — A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`. image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.

- video_hidden_states (`torch.FloatTensor`, optional) — A `torch.FloatTensor` of size `(batch_size * num_frames, num_videos, sequence_length, hidden_size)`. video_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.

The LlavaOnevisionModel 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.

####
get_image_features

< source >

( pixel_values: FloatTensor image_sizes: Tensor vision_feature_layer: typing.Union[int, typing.List[int]] vision_feature_select_strategy: str ) →
image_features (List`torch.Tensor`)

Parameters

- pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_patches, channels, height, width)`) — The tensors corresponding to the input images.
- image_sizes (`torch.Tensor` of shape `(num_images, 2)`) — Actual image size of each images (H, W).
- vision_feature_layer (`Union[int, List[int]]`) — The index of the layer to select the vision feature. If multiple indices are provided, the vision feature of the corresponding indices will be concatenated to form the vision features.
- vision_feature_select_strategy (`str`) — The feature selection strategy used to select the vision feature from the vision backbone. Can be one of `"default"` or `"full"`

Returns

image_features (List`torch.Tensor`)

List of image feature tensor, each contains all the visual feature of all patches and are of shape `(num_patches, image_length, embed_dim)`).

Obtains image last hidden states from the vision tower and apply multimodal projection.

####
get_video_features

< source >

( pixel_values: FloatTensor vision_feature_layer: typing.Union[int, typing.List[int]] vision_feature_select_strategy: str ) →
video_features (List`torch.Tensor`)

Parameters

- pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_frames, channels, height, width)`) — The tensors corresponding to the input video.
- vision_feature_layer (`Union[int, List[int]], *optional*, defaults to -2`) — The index of the layer to select the vision feature. If multiple indices are provided, the vision feature of the corresponding indices will be concatenated to form the vision features.
- vision_feature_select_strategy (`str`) — The feature selection strategy used to select the vision feature from the vision backbone. Can be one of `"default"` or `"full"`

Returns

video_features (List`torch.Tensor`)

List of video feature tensor, each contains all the visual feature of all patches and are of shape `(num_videos, video_length, embed_dim)`).

Obtains video last hidden states from the vision tower, apply multimodal projection and pooling.

####
pack_image_features

< source >

( image_features image_sizes image_newline = None vision_aspect_ratio = 'anyres_max_9' )

Parameters

- image_features (`List[torch.Tensor]` of length num_images, each of shape `(num_patches, image_length, embed_dim)`) — List of image feature tensor, each contains all the visual feature of all patches.
- image_sizes (`torch.Tensor` of shape `(num_images, 2)`) — Actual image size of each images (H, W).
- image_newline (`torch.Tensor` of shape `(embed_dim)`) — New line embedding vector.
- vision_aspect_ratio (`str`, optional, “anyres_max_9”) — Aspect ratio used when processong image features. The default value is “anyres_max_9”.

Reshape, unpad and then pack each image_feature into a single image_features tensor containing all visual vectors.

##
LlavaOnevisionForConditionalGeneration

###
class transformers.LlavaOnevisionForConditionalGeneration

< source >

( config: LlavaOnevisionConfig )

Parameters

- config (LlavaOnevisionConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

The LLAVA-NeXT model which consists of a vision backbone 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: LongTensor = None pixel_values: FloatTensor = None image_sizes: typing.Optional[torch.LongTensor] = None pixel_values_videos: FloatTensor = None image_sizes_videos: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.LongTensor] = None past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None vision_feature_layer: typing.Union[int, typing.List[int], NoneType] = None vision_feature_select_strategy: typing.Optional[str] = None vision_aspect_ratio: typing.Optional[str] = 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 return_dict: typing.Optional[bool] = None cache_position: typing.Optional[torch.LongTensor] = None logits_to_keep: typing.Union[int, torch.Tensor] = 0 **kwargs: typing_extensions.Unpack[transformers.models.llava_onevision.modeling_llava_onevision.KwargsForCausalLM] ) →
`transformers.models.llava_onevision.modeling_llava_onevision.LlavaOnevisionCausalLMOutputWithPast` 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.

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`) — The tensors corresponding to the input images. Pixel values can be obtained using `{image_processor_class}`. See `{image_processor_class}.__call__` for details (`{processor_class}` uses `{image_processor_class}` for processing images).
- image_sizes (`torch.LongTensor` of shape `(batch_size, 2)`, optional) — The sizes of the images in the batch, being (height, width) for each image.
- pixel_values_videos (`torch.FloatTensor` of shape `(batch_size, frames, num_channels, image_size, image_size)) -- The tensors corresponding to the input videos. Pixel values can be obtained using [LlavaNextVideoProcessor](/docs/transformers/main/en/model_doc/llava_next_video#transformers.LlavaNextVideoProcessor). See` LlavaNextVideoProcessor.call()` for details. LlavaProcessor uses LlavaNextVideoProcessor for processing videos.
- image_sizes_videos (`torch.LongTensor` of shape `(batch_size, frames, 2)`, optional) — The sizes of the videos in the batch, being (height, width) for each frame in the video.
- attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`.

  What are position IDs?

- past_key_values (`List[torch.FloatTensor]`, optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.

  Two formats are allowed:

  - a Cache instance, see our kv cache guide;
  - 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)`). This is also known as the legacy cache format.

  The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the legacy cache format will be returned.

  If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don’t have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` of shape `(batch_size, sequence_length)`.

- 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.
- vision_feature_layer (`Union[int, List[int], NoneType]`) — The index of the layer to select the vision feature. If multiple indices are provided, the vision feature of the corresponding indices will be concatenated to form the vision features.
- vision_feature_select_strategy (`str`, optional, defaults to `"default"`) — The feature selection strategy used to select the vision feature from the vision backbone. Can be one of `"default"` or `"full"`. If `"default"`, the CLS token is removed from the vision features. If `"full"`, the full vision features are used.
- vision_aspect_ratio (`str`, optional, defaults to `"anyres_max_9"`) — Aspect ratio used when processong image features. The default value is “anyres_max_9”.
- labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
- 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.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
- cache_position (`torch.LongTensor` of shape `(sequence_length)`, optional) — Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length.
- logits_to_keep (`Union[int, torch.Tensor]`, defaults to `0`) — If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that token can save memory, which becomes pretty significant for long sequences or large vocabulary size. If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension. This is useful when using packed tensor format (single dimension for batch and sequence length).

Returns

`transformers.models.llava_onevision.modeling_llava_onevision.LlavaOnevisionCausalLMOutputWithPast` or `tuple(torch.FloatTensor)`

A `transformers.models.llava_onevision.modeling_llava_onevision.LlavaOnevisionCausalLMOutputWithPast` 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 (LlavaOnevisionConfig) 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).

- 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)`)

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

- 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.

- image_hidden_states (`torch.FloatTensor`, optional) — A `torch.FloatTensor` of size (batch_size * num_patches, num_images, sequence_length, hidden_size)`. image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.

- video_hidden_states (`torch.FloatTensor`, optional) — A `torch.FloatTensor` of size `(batch_size * num_frames, num_videos, sequence_length, hidden_size)`. video_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.

The LlavaOnevisionForConditionalGeneration 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.

Example:

    >>> from PIL import Image
    >>> import requests
    >>> import torch
    >>> from transformers import LlavaOnevisionProcessor, LlavaOnevisionForConditionalGeneration

    >>> model = LlavaOnevisionForConditionalGeneration.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf", torch_dtype="float16", device_map="cuda:0")
    >>> processor = LlavaOnevisionProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf")

    >>> conversation = [
    ...     {
    ...       "role": "user",
    ...       "content": [
    ...           {"type": "text", "text": "What is shown in this image?"},
    ...           {"type": "image"},
    ...         ],
    ...     },
    ... ]
    >>> prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)

    >>> image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"
    >>> raw_image = Image.open(requests.get(image_file, stream=True).raw)
    >>> inputs = processor(text=prompt, images=raw_image, return_tensors='pt').to(0, torch.float16)

    >>> output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
    >>> processor.batch_decode(output, skip_special_tokens=True)[0]
    "user\n\nWhat is shown in this image?\nassistant\ncat"

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