#
Video-LLaVA

##
Overview

Video-LLaVa is an open-source multimodal LLM trained by fine-tuning LlamA/Vicuna on multimodal instruction-following data generated by Llava1.5 and VideChat. It is an auto-regressive language model, based on the transformer architecture. Video-LLaVa unifies visual representations to the language feature space, and enables an LLM to perform visual reasoning capabilities on both images and videos simultaneously.

The Video-LLaVA model was proposed in Video-LLaVA: Learning United Visual Representation by Alignment Before Projection by Bin Lin, Yang Ye, Bin Zhu, Jiaxi Cui, Munang Ning, Peng Jin, Li Yuan.

The abstract from the paper is the following:

The Large Vision-Language Model (LVLM) has enhanced the performance of various downstream tasks in visual-language understanding. Most existing approaches encode images and videos into separate feature spaces, which are then fed as inputs to large language models. However, due to the lack of unified tokenization for images and videos, namely misalignment before projection, it becomes challenging for a Large Language Model (LLM) to learn multi-modal interactions from several poor projection layers. In this work, we unify visual representation into the language feature space to advance the foundational LLM towards a unified LVLM. As a result, we establish a simple but robust LVLM baseline, Video-LLaVA, which learns from a mixed dataset of images and videos, mutually enhancing each other. Video-LLaVA achieves superior performances on a broad range of 9 image benchmarks across 5 image question-answering datasets and 4 image benchmark toolkits. Additionally, our Video-LLaVA also outperforms Video-ChatGPT by 5.8%, 9.9%, 18.6%, and 10.1% on MSRVTT, MSVD, TGIF, and ActivityNet, respectively. Notably, extensive experiments demonstrate that Video-LLaVA mutually benefits images and videos within a unified visual representation, outperforming models designed specifically for images or videos. We aim for this work to provide modest insights into the multi-modal inputs for the LLM

##
Usage 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.

- Note the model has not been explicitly trained to process multiple images/videos in the same prompt, although this is technically possible, you may experience inaccurate results.

- Note that the video inputs should have exactly 8 frames at the input, since the models were trained in that setting.

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

##
Usage example

###
Single Media Mode

The model can accept both images and videos as input. Here’s an example code for inference in half-precision (`torch.float16`):

    import av
    import torch
    import numpy as np
    from transformers import VideoLlavaForConditionalGeneration, VideoLlavaProcessor

    def read_video_pyav(container, indices):
        '''
        Decode the video with PyAV decoder.
        Args:
            container (`av.container.input.InputContainer`): PyAV container.
            indices (`List[int]`): List of frame indices to decode.
        Returns:
            result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).
        '''
        frames = []
        container.seek(0)
        start_index = indices[0]
        end_index = indices[-1]
        for i, frame in enumerate(container.decode(video=0)):
            if i > end_index:
                break
            if i >= start_index and i in indices:
                frames.append(frame)
        return np.stack([x.to_ndarray(format="rgb24") for x in frames])

    # Load the model in half-precision
    model = VideoLlavaForConditionalGeneration.from_pretrained("LanguageBind/Video-LLaVA-7B-hf", torch_dtype=torch.float16, device_map="auto")
    processor = VideoLlavaProcessor.from_pretrained("LanguageBind/Video-LLaVA-7B-hf")

    # Load the video as an np.arrau, sampling uniformly 8 frames
    video_path = hf_hub_download(repo_id="raushan-testing-hf/videos-test", filename="sample_demo_1.mp4", repo_type="dataset")
    container = av.open(video_path)
    total_frames = container.streams.video[0].frames
    indices = np.arange(0, total_frames, total_frames / 8).astype(int)
    video = read_video_pyav(container, indices)

    # For better results, we recommend to prompt the model in the following format
    prompt = "USER: <video>\nWhy is this funny? ASSISTANT:"
    inputs = processor(text=prompt, videos=video, return_tensors="pt")

    out = model.generate(**inputs, max_new_tokens=60)
    processor.batch_decode(out, skip_special_tokens=True, clean_up_tokenization_spaces=True)

For multiple turns conversation change the prompt format to:

    "USER: <video>\nWhat do you see in this video? ASSISTANT: A baby reading a book. USER: Why is the it funny? ASSISTANT:"

###
Mixed Media Mode

The model can also generate from an interleaved image-video inputs. However note, that it was not trained in interleaved image-video setting which might affect the performance. Below is an example usage for mixed media input, add the following lines to the above code snippet:

    from PIL import Image
    import requests

    # Generate from image and video mixed inputs
    # Load and image and write a new prompt
    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    image = Image.open(requests.get(url, stream=True).raw)
    prompt = "USER: <image>\nHow many cats are there in the image? ASSISTANT: There are two cats. USER: <video>\nWhy is this video funny? ASSISTANT:"

    inputs = processor(text=prompt, images=image, videos=clip, padding=True, return_tensors="pt")

    # Generate
    generate_ids = model.generate(**inputs, max_length=50)
    processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)

##
Model optimization

###
Quantization using Bitsandbytes for memory efficiency

The model can be loaded in lower bits, significantly reducing memory burden while maintaining the performance of the original model. his allows for efficient deployment on resource-constrained cases.

First make sure to install bitsandbytes by running `pip install bitsandbytes` and to have access to a CUDA compatible GPU device. Load the quantized model by simply adding `BitsAndBytesConfig` as shown below:

    from transformers import VideoLlavaForConditionalGeneration, 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 = VideoLlavaForConditionalGeneration.from_pretrained("LanguageBind/Video-LLaVA-7B-hf", quantization_config=quantization_config, device_map="auto")

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

Additionally, we can greatly speed-up model inference by using Flash Attention, which is a faster implementation of the attention mechanism used inside the model.

First, make sure to install the latest version of Flash Attention 2:

    pip install -U flash-attn --no-build-isolation

Also, you should have a hardware that is compatible with Flash-Attention 2. Read more about it in the official documentation of the flash attention repository. FlashAttention-2 can only be used when a model is loaded in `torch.float16` or `torch.bfloat16`.

To load and run a model using Flash Attention-2, simply add `attn_implementation="flash_attention_2"` when loading the model as follows:

    from transformers import VideoLlavaForConditionalGeneration

    model = VideoLlavaForConditionalGeneration.from_pretrained(
        "LanguageBind/Video-LLaVA-7B-hf",
        torch_dtype=torch.float16,
        attn_implementation="flash_attention_2",
    ).to(0)

##
VideoLlavaConfig

###
class transformers.VideoLlavaConfig

< source >

( vision_config = None text_config = None ignore_index = -100 image_token_index = 32000 video_token_index = 32001 projector_hidden_act = 'gelu' vision_feature_select_strategy = 'default' vision_feature_layer = -2 **kwargs )

Parameters

- vision_config (`VideoLlavaVisionConfig`, optional) — Custom vision config or dict. Defaults to `CLIPVisionConfig` if not indicated.
- text_config (`Union[AutoConfig, dict]`, optional) — The config object of the text backbone. Can be any of `LlamaConfig` or `MistralConfig`. Defaults to `LlamaConfig` if not indicated.
- ignore_index (`int`, optional, defaults to -100) — The ignore index for the loss function.
- image_token_index (`int`, optional, defaults to 32000) — The image token index to encode the image prompt.
- video_token_index (`int`, optional, defaults to 32001) — The video token index to encode the image 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 `"default"`) — The feature selection strategy used to select the vision feature from the CLIP backbone. Can be either “full” to select all features or “default” to select features without `CLS`.
- vision_feature_layer (`int`, optional, defaults to -2) — The index of the layer to select the vision feature.

This is the configuration class to store the configuration of a VideoLlavaForConditionalGeneration. It is used to instantiate an VideoLlava 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 like LanguageBind/Video-LLaVA-7B-hf.

e.g. LanguageBind/Video-LLaVA-7B-hf

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 VideoLlavaForConditionalGeneration, VideoLlavaConfig, CLIPVisionConfig, LlamaConfig

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

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

    >>> # Initializing a VideoLlava video_llava-1.5-7b style configuration
    >>> configuration = VideoLlavaConfig(vision_config, text_config)

    >>> # Initializing a model from the video_llava-1.5-7b style configuration
    >>> model = VideoLlavaForConditionalGeneration(configuration)

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

##
VideoLlavaImageProcessor

###
class transformers.VideoLlavaImageProcessor

< source >

( do_resize: bool = True size: Dict = None resample: Resampling = <Resampling.BICUBIC: 3> do_center_crop: bool = True crop_size: Dict = None do_rescale: bool = True rescale_factor: Union = 0.00392156862745098 do_normalize: bool = True image_mean: Union = None image_std: Union = 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 `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.
- 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_center_crop (`bool`, optional, defaults to `True`) — Whether to center crop the image to the specified `crop_size`. Can be overridden by `do_center_crop` in the `preprocess` method.
- crop_size (`Dict[str, int]` optional, defaults to 224) — Size of the output image after applying `center_crop`. Can be overridden by `crop_size` 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_convert_rgb (`bool`, optional, defaults to `True`) — Whether to convert the image to RGB.

Constructs a CLIP image processor.

####
preprocess

< source >

( images: List = None videos: List = None do_resize: bool = None size: Dict = None resample: Resampling = None do_center_crop: bool = None crop_size: int = None do_rescale: bool = None rescale_factor: float = None do_normalize: bool = None image_mean: Union = None image_std: Union = None do_convert_rgb: bool = None return_tensors: Union = None data_format: Optional = <ChannelDimension.FIRST: 'channels_first'> input_data_format: Union = None )

Parameters

- images (`ImageInput`, optional) — List of images 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`.
- videos (`VideoInput`, optional) — List of videos to preprocess. Expects a single or batch of videos with pixel values ranging from 0 to 255. If passing in videos with pixel values between 0 and 1, set `do_rescale=False`.
- 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.
- 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_center_crop (`bool`, optional, defaults to `self.do_center_crop`) — Whether to center crop the image.
- crop_size (`Dict[str, int]`, optional, defaults to `self.crop_size`) — Size of the center crop. Only has an effect if `do_center_crop` 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_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.

Preprocess an image or batch of images.

####
resize

< source >

( image: ndarray size: Dict resample: Resampling = <Resampling.BICUBIC: 3> data_format: Union = None input_data_format: Union = None **kwargs )

Parameters

- image (`np.ndarray`) — Image to resize.
- size (`Dict[str, int]`) — Size of the output image.
- resample (`PILImageResampling`, optional, defaults to `PILImageResampling.BICUBIC`) — Resampling filter to use when resiizing the image.
- data_format (`str` or `ChannelDimension`, optional) — The channel dimension format of the image. If not provided, it will be the same as the input image.
- input_data_format (`ChannelDimension` or `str`, optional) — The channel dimension format of the input image. If not provided, it will be inferred.

Resize an image. The shortest edge of the image is resized to size[“shortest_edge”], with the longest edge resized to keep the input aspect ratio.

##
VideoLlavaProcessor

###
class transformers.VideoLlavaProcessor

< source >

( image_processor = None tokenizer = None chat_template = None **kwargs )

Parameters

- image_processor (VideoLlavaImageProcessor, optional) — The image processor is a required input.
- tokenizer (LlamaTokenizerFast, optional) — The tokenizer is a required input.
- chat_template (`str`, optional) — A Jinja template which will be used to convert lists of messages in a chat into a tokenizable string.

Constructs a VideoLlava processor which wraps a VideoLlava image processor and a Llava tokenizer into a single processor.

VideoLlavaProcessor offers all the functionalities of VideoLlavaImageProcessor and LlamaTokenizerFast. See the `__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.

##
VideoLlavaForConditionalGeneration

###
class transformers.VideoLlavaForConditionalGeneration

< source >

( config: VideoLlavaConfig )

Parameters

- config (VideoLlavaConfig or `VideoLlavaVisionConfig`) — 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 VideoLlava 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_images: FloatTensor = None pixel_values_videos: FloatTensor = None attention_mask: Optional = None position_ids: Optional = None past_key_values: Optional = None inputs_embeds: Optional = None vision_feature_layer: Optional = None vision_feature_select_strategy: Optional = None labels: Optional = None use_cache: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None return_dict: Optional = None ) →
`transformers.models.video_llava.modeling_video_llava.VideoLlavaCausalLMOutputWithPast` 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?

- pixel_values_images (`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 [AutoImageProcessor](/docs/transformers/pr_30674/en/model_doc/auto#transformers.AutoImageProcessor). See [VideoLlavaImageProcessor.__call__()](/docs/transformers/pr_30674/en/model_doc/layoutlmv2#transformers.LayoutLMv2FeatureExtractor.__call__) for details ([]`LlavaProcessor`] uses VideoLlavaImageProcessor for processing images).
- pixel_values_videos (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, image_size, image_size)) -- The tensors corresponding to the input video. Pixel values can be obtained using [AutoImageProcessor](/docs/transformers/pr_30674/en/model_doc/auto#transformers.AutoImageProcessor). See [VideoLlavaImageProcessor.__call__()](/docs/transformers/pr_30674/en/model_doc/layoutlmv2#transformers.LayoutLMv2FeatureExtractor.__call__) for details ([]`LlavaProcessor`] uses VideoLlavaImageProcessor for processing videos).
- 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?

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

  If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).

  If you want to change padding behavior, you should read `modeling_opt._prepare_decoder_attention_mask` and modify to your needs. See diagram 1 in the paper for more information on the default strategy.

  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- 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 (`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 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 in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential 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)`.

- 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 (`int`, optional, defaults to -2) — The index of the layer to select the vision feature.
- 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"`
- 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.

  Args — 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]`.

Returns

`transformers.models.video_llava.modeling_video_llava.VideoLlavaCausalLMOutputWithPast` or `tuple(torch.FloatTensor)`

A `transformers.models.video_llava.modeling_video_llava.VideoLlavaCausalLMOutputWithPast` 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 (VideoLlavaConfig) 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 (`tuple(torch.FloatTensor)`, optional) — Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images, sequence_length, hidden_size)`.

  image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver

The VideoLlavaForConditionalGeneration 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 numpy as np
    >>> import av
    >>> from huggingface_hub import hf_hub_download
    >>> from transformers import VideoLlavaProcessor, VideoLlavaForConditionalGeneration

    >>> def read_video_pyav(container, indices):
    ...     '''
    ...     Decode the video with PyAV decoder.
    ...     Args:
    ...         container (`av.container.input.InputContainer`): PyAV container.
    ...         indices (`List[int]`): List of frame indices to decode.
    ...     Returns:
    ...         result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).
    ...     '''
    ...     frames = []
    ...     container.seek(0)
    ...     start_index = indices[0]
    ...     end_index = indices[-1]
    ...     for i, frame in enumerate(container.decode(video=0)):
    ...         if i > end_index:
    ...             break
    ...         if i >= start_index and i in indices:
    ...             frames.append(frame)
    ...     return np.stack([x.to_ndarray(format="rgb24") for x in frames])

    >>> model = VideoLlavaForConditionalGeneration.from_pretrained("LanguageBind/Video-LLaVA-7B-hf")
    >>> processor = VideoLlavaProcessor.from_pretrained("LanguageBind/Video-LLaVA-7B-hf")

    >>> prompt = "USER: <video>\nWhy is this video funny? ASSISTANT:"
    >>> video_path = hf_hub_download(repo_id="raushan-testing-hf/videos-test", filename="sample_demo_1.mp4", repo_type="dataset")
    >>> container = av.open(video_path)

    >>> # sample uniformly 8 frames from the video
    >>> total_frames = container.streams.video[0].frames
    >>> indices = np.arange(0, total_frames, total_frames / 8).astype(int)
    >>> clip = read_video_pyav(container, indices)

    >>> inputs = processor(text=prompt, videos=clip, return_tensors="pt")

    >>> # Generate
    >>> generate_ids = model.generate(**inputs, max_length=80)
    >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
    "USER:  Why is this video funny? ASSISTANT: The video is funny because the baby is playing with a Wii remote while sitting on the floor, and the baby is wearing glasses.Ъ. The baby's actions are amusing because it is a young child trying to interact with a video game, which is not a typical activity for a"

    >>> # to generate from image and video mix
    >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    >>> image = Image.open(requests.get(url, stream=True).raw)
    >>> prompt = [
    ...     "USER: <image>\nHow many cats do you see? ASSISTANT:",
    ...     "USER: <video>\nWhy is this video funny? ASSISTANT:"
    ... ]
    >>> inputs = processor(text=prompt, images=image, videos=clip, padding=True, return_tensors="pt")

    >>> # Generate
    >>> generate_ids = model.generate(**inputs, max_length=50)
    >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
    ['USER:   How many cats do you see? ASSISTANT: There are two cats visible in the image. (or three, if you count the one in the background).', 'USER:  Why is this video funny? ASSISTANT: The video is funny because it shows a baby sitting on a bed and playing with a Wii remote.Ъ. The baby is holding the remote']

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