# Cosmos3 Edge

[Cosmos3 Edge](https://huggingface.co/nvidia/Cosmos3-Edge) is NVIDIA's multimodal reasoning model from the Cosmos3
family. Transformers integrates the **Reasoner** tower only; the checkpoint's diffusion Generator, VAE, scheduler,
and other generation components remain Diffusers components.

The reasoner uses a dense, Llama-compatible language tower with 28 decoder blocks, each containing attention and an
MLP. Its SigLIP2 vision encoder accepts packed variable-resolution patches, uses sequence boundaries to keep images
and video frames independent during vision attention, groups patches spatially in 2×2 blocks, and projects them into
the language model. Image and video inputs use multimodal rotary position IDs; video prompts are expanded into one
timestamped vision span per sampled frame.

## Usage

```python
from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "nvidia/Cosmos3-Edge"
model = AutoModelForImageTextToText.from_pretrained(model_id, device_map="auto")
processor = AutoProcessor.from_pretrained(model_id)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg",
            },
            {"type": "text", "text": "Describe this image."},
        ],
    }
]

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids = [output_ids[len(input_ids) :] for input_ids, output_ids in zip(inputs.input_ids, generated_ids)]
print(processor.batch_decode(generated_ids, skip_special_tokens=True))
```

## Cosmos3EdgeConfig[[transformers.Cosmos3EdgeConfig]]

- **text_config** (`Union[~models.cosmos3_edge.configuration_cosmos3_edge.Cosmos3EdgeTextConfig, dict]`, *optional*) --
  The config object or dictionary of the text backbone.
- **vision_config** (`Union[~models.cosmos3_edge.configuration_cosmos3_edge.Cosmos3EdgeVisionConfig, dict]`, *optional*) --
  The config object or dictionary of the vision backbone.
- **projector_hidden_size** (`int`, *optional*, defaults to 11520) --
  Intermediate hidden size of the vision-to-language projector MLP.
- **image_token_id** (`int`, *optional*, defaults to `19`) --
  The image token index used as a placeholder for input images.
- **video_token_id** (`int`, *optional*, defaults to `18`) --
  The video token index used as a placeholder for input videos.
- **vision_start_token_id** (`int`, *optional*, defaults to `20`) --
  Token ID that marks the start of a visual segment in the multimodal input sequence.
- **vision_end_token_id** (`int`, *optional*, defaults to `21`) --
  Token ID that marks the end of a visual segment in the multimodal input sequence.
- **tie_word_embeddings** (`bool`, *optional*, defaults to `False`) --
  Whether to tie weight embeddings according to model's `tied_weights_keys` mapping.

This is the configuration class to store the configuration of a Cosmos3EdgeModel. It is used to instantiate a Cosmos3 Edge
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 [nvidia/Cosmos3-Edge-Reasoner](https://huggingface.co/nvidia/Cosmos3-Edge-Reasoner)

Configuration objects inherit from [PreTrainedConfig](/docs/transformers/main/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the
documentation from [PreTrainedConfig](/docs/transformers/main/en/main_classes/configuration#transformers.PreTrainedConfig) for more information.

Example:

```python
>>> from transformers import Cosmos3EdgeConfig, Cosmos3EdgeForConditionalGeneration

>>> configuration = Cosmos3EdgeConfig()
>>> model = Cosmos3EdgeForConditionalGeneration(configuration)
```

## Cosmos3EdgeTextConfig[[transformers.Cosmos3EdgeTextConfig]]

- **vocab_size** (`int`, *optional*, defaults to `131072`) --
  Vocabulary size of the model. Defines the number of different tokens that can be represented by the `input_ids`.
- **hidden_size** (`int`, *optional*, defaults to `2048`) --
  Dimension of the hidden representations.
- **intermediate_size** (`int`, *optional*, defaults to `9216`) --
  Dimension of the MLP representations.
- **num_hidden_layers** (`int`, *optional*, defaults to `28`) --
  Number of hidden layers in the Transformer decoder.
- **num_attention_heads** (`int`, *optional*, defaults to `16`) --
  Number of attention heads for each attention layer in the Transformer decoder.
- **num_key_value_heads** (`int`, *optional*, defaults to `8`) --
  This is the number of key_value heads that should be used to implement Grouped Query Attention. If
  `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
  `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
  converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
  by meanpooling all the original heads within that group. For more details, check out [this
  paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
  `num_attention_heads`.
- **hidden_act** (`str`, *optional*, defaults to `relu2`) --
  The non-linear activation function (function or string) in the decoder. For example, `"gelu"`,
  `"relu"`, `"silu"`, etc.
- **max_position_embeddings** (`int`, *optional*, defaults to `131072`) --
  The maximum sequence length that this model might ever be used with.
- **initializer_range** (`float`, *optional*, defaults to `0.02`) --
  The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
- **rms_norm_eps** (`float`, *optional*, defaults to `1e-05`) --
  The epsilon used by the rms normalization layers.
- **use_cache** (`bool`, *optional*, defaults to `True`) --
  Whether or not the model should return the last key/values attentions (not used by all models). Only
  relevant if `config.is_decoder=True` or when the model is a decoder-only generative model.
- **pad_token_id** (`int`, *optional*, defaults to `0`) --
  Token id used for padding in the vocabulary.
- **bos_token_id** (`int`, *optional*, defaults to `1`) --
  Token id used for beginning-of-stream in the vocabulary.
- **eos_token_id** (`Union[int, list[int]]`, *optional*, defaults to `11`) --
  Token id used for end-of-stream in the vocabulary.
- **pretraining_tp** (`int`, *optional*, defaults to `1`) --
  Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
  document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to
  understand more about it. This value is necessary to ensure exact reproducibility of the pretraining
  results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232).
- **tie_word_embeddings** (`bool`, *optional*, defaults to `False`) --
  Whether to tie weight embeddings according to model's `tied_weights_keys` mapping.
- **rope_parameters** (`dict`, *optional*) --
  Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain
  a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
  with longer `max_position_embeddings`.
- **attention_bias** (`bool`, *optional*, defaults to `False`) --
  Whether to use a bias in the query, key, value and output projection layers during self-attention.
- **attention_dropout** (`Union[float, int]`, *optional*, defaults to `0.0`) --
  The dropout ratio for the attention probabilities.
- **mlp_bias** (`bool`, *optional*, defaults to `False`) --
  Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
- **head_dim** (`int`, *optional*, defaults to `128`) --
  The attention head dimension. If None, it will default to hidden_size // num_attention_heads

This is the configuration class to store the configuration of a Cosmos3EdgeModel. It is used to instantiate a Cosmos3 Edge
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 [nvidia/Cosmos3-Edge-Reasoner](https://huggingface.co/nvidia/Cosmos3-Edge-Reasoner)

Configuration objects inherit from [PreTrainedConfig](/docs/transformers/main/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the
documentation from [PreTrainedConfig](/docs/transformers/main/en/main_classes/configuration#transformers.PreTrainedConfig) for more information.

```python
>>> from transformers import Cosmos3EdgeTextModel, Cosmos3EdgeTextConfig

>>> # Initializing a Cosmos3EdgeText cosmos3_edge_text-7b style configuration
>>> configuration = Cosmos3EdgeTextConfig()

>>> # Initializing a model from the cosmos3_edge_text-7b style configuration
>>> model = Cosmos3EdgeTextModel(configuration)

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

## Cosmos3EdgeVisionConfig[[transformers.Cosmos3EdgeVisionConfig]]

- **hidden_size** (`int`, *optional*, defaults to `1152`) --
  Dimension of the hidden representations.
- **intermediate_size** (`int`, *optional*, defaults to `4304`) --
  Dimension of the MLP representations.
- **num_hidden_layers** (`int`, *optional*, defaults to `27`) --
  Number of hidden layers in the Transformer decoder.
- **num_attention_heads** (`int`, *optional*, defaults to `16`) --
  Number of attention heads for each attention layer in the Transformer decoder.
- **num_channels** (`int`, *optional*, defaults to `3`) --
  The number of input channels.
- **patch_size** (`Union[int, list[int], tuple[int, int]]`, *optional*, defaults to `16`) --
  The size (resolution) of each patch.
- **hidden_act** (`str`, *optional*, defaults to `gelu_pytorch_tanh`) --
  The non-linear activation function (function or string) in the decoder. For example, `"gelu"`,
  `"relu"`, `"silu"`, etc.
- **layer_norm_eps** (`float`, *optional*, defaults to `1e-06`) --
  The epsilon used by the layer normalization layers.
- **attention_dropout** (`Union[float, int]`, *optional*, defaults to `0.0`) --
  The dropout ratio for the attention probabilities.
- **num_patches** (`int`, *optional*, defaults to 256) --
  Number of patches in the learned reference positional-embedding grid.
- **spatial_merge_size** (`int`, *optional*, defaults to `2`) --
  The size of the spatial merge window used to reduce the number of visual tokens by merging neighboring patches.

This is the configuration class to store the configuration of a Cosmos3EdgeModel. It is used to instantiate a Cosmos3 Edge
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 [nvidia/Cosmos3-Edge-Reasoner](https://huggingface.co/nvidia/Cosmos3-Edge-Reasoner)

Configuration objects inherit from [PreTrainedConfig](/docs/transformers/main/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the
documentation from [PreTrainedConfig](/docs/transformers/main/en/main_classes/configuration#transformers.PreTrainedConfig) for more information.

## Cosmos3EdgeProcessor[[transformers.Cosmos3EdgeProcessor]]

- **image_processor** (`Cosmos3EdgeImageProcessor`) --
  The image processor is a required input.
- **tokenizer** (`tokenizer_class`) --
  The tokenizer is a required input.
- **video_processor** (`Cosmos3EdgeVideoProcessor`) --
  The video processor is a required input.
- **chat_template** (`str`) --
  A Jinja template to convert lists of messages in a chat into a tokenizable string.
Constructs a Cosmos3EdgeProcessor which wraps a image processor, a tokenizer, and a video processor into a single processor.

[Cosmos3EdgeProcessor](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeProcessor) offers all the functionalities of [Cosmos3EdgeImageProcessor](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeImageProcessor), `tokenizer_class`, and [Cosmos3EdgeVideoProcessor](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeVideoProcessor). See the
[~Cosmos3EdgeImageProcessor](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeImageProcessor), `~tokenizer_class`, and [~Cosmos3EdgeVideoProcessor](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeVideoProcessor) for more information.

- **images** (`Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]]`, *optional*) --
  Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
  passing in images with pixel values between 0 and 1, set `do_rescale=False`.
- **text** (`Union[str, list[str], list[list[str]]]`, *optional*) --
  The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
  (pretokenized string). If you pass a pretokenized input, set `is_split_into_words=True` to avoid ambiguity with batched inputs.
- **videos** (`Union[list[PIL.Image.Image], numpy.ndarray, torch.Tensor, list[numpy.ndarray], list[torch.Tensor], list[list[PIL.Image.Image]], list[list[numpy.ndarray]], list[list[torch.Tensor]], ~video_utils.URL, list[~video_utils.URL], list[list[~video_utils.URL]], ~video_utils.Path, list[~video_utils.Path], list[list[~video_utils.Path]]]`, *optional*) --
  Video to preprocess. Expects a single or batch of videos with pixel values ranging from 0 to 255. If
  passing in videos with pixel values between 0 and 1, set `do_rescale=False`.
- **audio** (`Union[numpy.ndarray, torch.Tensor, collections.abc.Sequence[numpy.ndarray], collections.abc.Sequence[torch.Tensor]]`, *optional*) --
  The audio or batch of audios to be prepared. Each audio can be a NumPy array or PyTorch tensor.
  In case of a NumPy array/PyTorch tensor, each audio should be of shape (C, T), where C is a number of channels,
  and T is the sample length of the audio.
- **return_tensors** (`str` or [TensorType](/docs/transformers/main/en/internal/file_utils#transformers.TensorType), *optional*) --
  If set, will return tensors of a particular framework. Acceptable values are:

  - `'pt'`: Return PyTorch `torch.Tensor` objects.
  - `'np'`: Return NumPy `np.ndarray` objects.
- ****kwargs** ([ProcessingKwargs](/docs/transformers/main/en/main_classes/processors#transformers.ProcessingKwargs), *optional*) --
  Additional processing options for each modality (text, images, videos, audio). Model-specific parameters
  are listed above; see the TypedDict class for the complete list of supported arguments.

- **conversation** (`Union[list[Dict, [str, str]], list[list[dict[str, str]]]]`) --
  The conversation to format.
- **chat_template** (`Optional[str]`, *optional*) --
  The Jinja template to use for formatting the conversation. If not provided, the tokenizer's
  chat template is used.

Similar to the `apply_chat_template` method on tokenizers, this method applies a Jinja template to input
conversations to turn them into a single tokenizable string.

The input is expected to be in the following format, where each message content is a list consisting of text and
optionally image or video inputs. One can also provide an image, video, URL or local path which will be used to form
`pixel_values` when `return_dict=True`. If not provided, one will get only the formatted text, optionally tokenized text.

conversation = [
{
"role": "user",
"content": [
{"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"},
{"type": "text", "text": "Please describe this image in detail."},
],
},
]

## Cosmos3EdgeImageProcessor[[transformers.Cosmos3EdgeImageProcessor]]

- **patch_size** (`int`, *kwargs*, *optional*, defaults to `16`) --
  Spatial patch size of the vision encoder.
- **merge_size** (`int`, *kwargs*, *optional*, defaults to `2`) --
  Number of adjacent patches merged along each spatial axis by the projector.
- ****kwargs** ([ImagesKwargs](/docs/transformers/main/en/main_classes/processors#transformers.ImagesKwargs), *optional*) --
  Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class
  for the complete list of supported arguments.
Constructs a Cosmos3EdgeImageProcessor image processor.

- **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`.
- **patch_size** (`int`, *kwargs*, *optional*, defaults to `16`) --
  Spatial patch size of the vision encoder.
- **merge_size** (`int`, *kwargs*, *optional*, defaults to `2`) --
  Number of adjacent patches merged along each spatial axis by the projector.
- **return_tensors** (`str` or [TensorType](/docs/transformers/main/en/internal/file_utils#transformers.TensorType), *optional*) --
  Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors.
- ****kwargs** ([ImagesKwargs](/docs/transformers/main/en/main_classes/processors#transformers.ImagesKwargs), *optional*) --
  Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class
  for the complete list of supported arguments.`~image_processing_base.BatchFeature`- **data** (`dict`) -- Dictionary of lists/arrays/tensors returned by the __call__ method ('pixel_values', etc.).
- **tensor_type** (`Union[None, str, TensorType]`, *optional*) -- You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at
  initialization.

## Cosmos3EdgeImageProcessorPil[[transformers.Cosmos3EdgeImageProcessorPil]]

Dynamically resize images and return packed, unpadded SigLIP2 patches.

patch_size (`int`, *optional*, defaults to `16`):
Spatial patch size of the vision encoder.
merge_size (`int`, *optional*, defaults to `2`):
Number of adjacent patches merged along each spatial axis by the projector.

## Cosmos3EdgeVideoProcessor[[transformers.Cosmos3EdgeVideoProcessor]]

- **do_resize** (`bool`, *optional*, defaults to `self.do_resize`) --
  Whether to resize the video's (height, width) dimensions to the specified `size`. Can be overridden by the
  `do_resize` parameter in the `preprocess` method.
- **size** (`dict`, *optional*, defaults to `self.size`) --
  Size of the output video after resizing. Can be overridden by the `size` parameter in the `preprocess`
  method.
- **size_divisor** (`int`, *optional*, defaults to `self.size_divisor`) --
  The size by which to make sure both the height and width can be divided.
- **default_to_square** (`bool`, *optional*, defaults to `self.default_to_square`) --
  Whether to default to a square video when resizing, if size is an int.
- **resample** (`PILImageResampling`, *optional*, defaults to `self.resample`) --
  Resampling filter to use if resizing the video. Only has an effect if `do_resize` is set to `True`. Can be
  overridden by the `resample` parameter in the `preprocess` method.
- **do_center_crop** (`bool`, *optional*, defaults to `self.do_center_crop`) --
  Whether to center crop the video to the specified `crop_size`. Can be overridden by `do_center_crop` in the
  `preprocess` method.
- **crop_size** (`dict[str, int]` *optional*, defaults to `self.crop_size`) --
  Size of the output video after applying `center_crop`. Can be overridden by `crop_size` in the `preprocess`
  method.
- **do_rescale** (`bool`, *optional*, defaults to `self.do_rescale`) --
  Whether to rescale the video by the specified scale `rescale_factor`. Can be overridden by the
  `do_rescale` parameter in the `preprocess` method.
- **rescale_factor** (`int` or `float`, *optional*, defaults to `self.rescale_factor`) --
  Scale factor to use if rescaling the video. Only has an effect if `do_rescale` is set to `True`. Can be
  overridden by the `rescale_factor` parameter in the `preprocess` method.
- **do_normalize** (`bool`, *optional*, defaults to `self.do_normalize`) --
  Whether to normalize the video. Can be overridden by the `do_normalize` parameter in the `preprocess`
  method. Can be overridden by the `do_normalize` parameter in the `preprocess` method.
- **image_mean** (`float` or `list[float]`, *optional*, defaults to `self.image_mean`) --
  Mean to use if normalizing the video. This is a float or list of floats the length of the number of
  channels in the video. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be
  overridden by the `image_mean` parameter in the `preprocess` method.
- **image_std** (`float` or `list[float]`, *optional*, defaults to `self.image_std`) --
  Standard deviation to use if normalizing the video. This is a float or list of floats the length of the
  number of channels in the video. 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 `self.image_std`) --
  Whether to convert the video to RGB.
- **video_metadata** (`VideoMetadata`, *optional*) --
  Metadata of the video containing information about total duration, fps and total number of frames.
- **do_sample_frames** (`int`, *optional*, defaults to `self.do_sample_frames`) --
  Whether to sample frames from the video before processing or to process the whole video.
- **num_frames** (`int`, *optional*, defaults to `self.num_frames`) --
  Maximum number of frames to sample when `do_sample_frames=True`.
- **fps** (`int` or `float`, *optional*, defaults to `self.fps`) --
  Target frames to sample per second when `do_sample_frames=True`.
- **return_tensors** (`str` or `TensorType`, *optional*) --
  Returns stacked tensors if set to `pt, otherwise returns a list of tensors.
- **data_format** (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`) --
  The channel dimension format for the output video. Can be one of:
  - `"channels_first"` or `ChannelDimension.FIRST`: video in (num_channels, height, width) format.
  - `"channels_last"` or `ChannelDimension.LAST`: video in (height, width, num_channels) format.
  - Unset: Use the channel dimension format of the input video.
- **input_data_format** (`ChannelDimension` or `str`, *optional*) --
  The channel dimension format for the input video. If unset, the channel dimension format is inferred
  from the input video. Can be one of:
  - `"channels_first"` or `ChannelDimension.FIRST`: video in (num_channels, height, width) format.
  - `"channels_last"` or `ChannelDimension.LAST`: video in (height, width, num_channels) format.
  - `"none"` or `ChannelDimension.NONE`: video in (height, width) format.
- **device** (`torch.device`, *optional*) --
  The device to process the videos on. If unset, the device is inferred from the input videos.
- **return_metadata** (`bool`, *optional*) --
  Whether to return video metadata or not.

- **patch_size** (`int`, *optional*, defaults to 16) --
  Spatial patch size of the vision encoder.
- **temporal_patch_size** (`int`, *optional*, defaults to 1) --
  Temporal patch size of the vision encoder. Cosmos3 Edge processes each sampled frame independently.
- **merge_size** (`int`, *optional*, defaults to 2) --
  Spatial merge size applied by the vision projector.
Constructs a video processor that dynamically resizes and packs Cosmos3 Edge video frames.

- **do_resize** (`bool`, *optional*, defaults to `self.do_resize`) --
  Whether to resize the video's (height, width) dimensions to the specified `size`. Can be overridden by the
  `do_resize` parameter in the `preprocess` method.
- **size** (`dict`, *optional*, defaults to `self.size`) --
  Size of the output video after resizing. Can be overridden by the `size` parameter in the `preprocess`
  method.
- **size_divisor** (`int`, *optional*, defaults to `self.size_divisor`) --
  The size by which to make sure both the height and width can be divided.
- **default_to_square** (`bool`, *optional*, defaults to `self.default_to_square`) --
  Whether to default to a square video when resizing, if size is an int.
- **resample** (`PILImageResampling`, *optional*, defaults to `self.resample`) --
  Resampling filter to use if resizing the video. Only has an effect if `do_resize` is set to `True`. Can be
  overridden by the `resample` parameter in the `preprocess` method.
- **do_center_crop** (`bool`, *optional*, defaults to `self.do_center_crop`) --
  Whether to center crop the video to the specified `crop_size`. Can be overridden by `do_center_crop` in the
  `preprocess` method.
- **crop_size** (`dict[str, int]` *optional*, defaults to `self.crop_size`) --
  Size of the output video after applying `center_crop`. Can be overridden by `crop_size` in the `preprocess`
  method.
- **do_rescale** (`bool`, *optional*, defaults to `self.do_rescale`) --
  Whether to rescale the video by the specified scale `rescale_factor`. Can be overridden by the
  `do_rescale` parameter in the `preprocess` method.
- **rescale_factor** (`int` or `float`, *optional*, defaults to `self.rescale_factor`) --
  Scale factor to use if rescaling the video. Only has an effect if `do_rescale` is set to `True`. Can be
  overridden by the `rescale_factor` parameter in the `preprocess` method.
- **do_normalize** (`bool`, *optional*, defaults to `self.do_normalize`) --
  Whether to normalize the video. Can be overridden by the `do_normalize` parameter in the `preprocess`
  method. Can be overridden by the `do_normalize` parameter in the `preprocess` method.
- **image_mean** (`float` or `list[float]`, *optional*, defaults to `self.image_mean`) --
  Mean to use if normalizing the video. This is a float or list of floats the length of the number of
  channels in the video. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be
  overridden by the `image_mean` parameter in the `preprocess` method.
- **image_std** (`float` or `list[float]`, *optional*, defaults to `self.image_std`) --
  Standard deviation to use if normalizing the video. This is a float or list of floats the length of the
  number of channels in the video. 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 `self.image_std`) --
  Whether to convert the video to RGB.
- **video_metadata** (`VideoMetadata`, *optional*) --
  Metadata of the video containing information about total duration, fps and total number of frames.
- **do_sample_frames** (`int`, *optional*, defaults to `self.do_sample_frames`) --
  Whether to sample frames from the video before processing or to process the whole video.
- **num_frames** (`int`, *optional*, defaults to `self.num_frames`) --
  Maximum number of frames to sample when `do_sample_frames=True`.
- **fps** (`int` or `float`, *optional*, defaults to `self.fps`) --
  Target frames to sample per second when `do_sample_frames=True`.
- **return_tensors** (`str` or `TensorType`, *optional*) --
  Returns stacked tensors if set to `pt, otherwise returns a list of tensors.
- **data_format** (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`) --
  The channel dimension format for the output video. Can be one of:
  - `"channels_first"` or `ChannelDimension.FIRST`: video in (num_channels, height, width) format.
  - `"channels_last"` or `ChannelDimension.LAST`: video in (height, width, num_channels) format.
  - Unset: Use the channel dimension format of the input video.
- **input_data_format** (`ChannelDimension` or `str`, *optional*) --
  The channel dimension format for the input video. If unset, the channel dimension format is inferred
  from the input video. Can be one of:
  - `"channels_first"` or `ChannelDimension.FIRST`: video in (num_channels, height, width) format.
  - `"channels_last"` or `ChannelDimension.LAST`: video in (height, width, num_channels) format.
  - `"none"` or `ChannelDimension.NONE`: video in (height, width) format.
- **device** (`torch.device`, *optional*) --
  The device to process the videos on. If unset, the device is inferred from the input videos.
- **return_metadata** (`bool`, *optional*) --
  Whether to return video metadata or not.

## Cosmos3EdgeModel[[transformers.Cosmos3EdgeModel]]

- **config** ([Cosmos3EdgeConfig](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeConfig)) --
  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()](/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

The bare Cosmos3 Edge Model outputting raw hidden-states without any specific head on top.

This model inherits from [PreTrainedModel](/docs/transformers/main/en/main_classes/model#transformers.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](https://pytorch.org/docs/stable/nn.html#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.

- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained using [AutoTokenizer](/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **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?](../glossary#position-ids)
- **past_key_values** (`~cache_utils.Cache`, *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`.

  Only [Cache](/docs/transformers/main/en/internal/generation_utils#transformers.Cache) instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
  If no `past_key_values` are passed, [DynamicCache](/docs/transformers/main/en/internal/generation_utils#transformers.DynamicCache) will be initialized by default.

  The model will output the same cache format that is fed as input.

  If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't
  have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` 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.
- **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`).
- **pixel_values** (`torch.Tensor` of shape `(batch_size, num_channels, image_size, image_size)`, *optional*) --
  The tensors corresponding to the input images. Pixel values can be obtained using
  [Cosmos3EdgeImageProcessor](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeImageProcessor). See `Cosmos3EdgeImageProcessor.__call__()` for details ([Cosmos3EdgeProcessor](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeProcessor) uses
  [Cosmos3EdgeImageProcessor](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeImageProcessor) for processing images).
- **pixel_values_videos** (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, frame_size, frame_size)`, *optional*) --
  The tensors corresponding to the input video. Pixel values for videos can be obtained using
  [Cosmos3EdgeVideoProcessor](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeVideoProcessor). See `Cosmos3EdgeVideoProcessor.__call__()` for details ([Cosmos3EdgeProcessor](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeProcessor) uses
  [Cosmos3EdgeVideoProcessor](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeVideoProcessor) for processing videos).
- **image_grid_thw** (`torch.LongTensor` of shape `(num_images, 3)`, *optional*) --
  The temporal, height, and width of the feature grid for each image.
- **video_grid_thw** (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*) --
  The temporal, height, and width of the feature grid for each video.
- **mm_token_type_ids** (`torch.IntTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of input sequence tokens matching each modality. For example text (0), image (1), video (2).
  Multimodal token type ids can be obtained using [AutoProcessor](/docs/transformers/main/en/model_doc/auto#transformers.AutoProcessor). See [ProcessorMixin.__call__()](/docs/transformers/main/en/main_classes/processors#transformers.ProcessorMixin.__call__) for details.[BaseModelOutputWithPast](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast) or `tuple(torch.FloatTensor)`A [BaseModelOutputWithPast](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast) 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 ([Cosmos3EdgeConfig](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeConfig)) and inputs.
The [Cosmos3EdgeModel](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeModel) 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.

- **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.

  If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
  hidden_size)` is output.
- **past_key_values** (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a [Cache](/docs/transformers/main/en/internal/generation_utils#transformers.Cache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).

  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.
- **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.

- **pixel_values** (`torch.FloatTensor` of shape `(num_patches, num_channels * patch_size * patch_size)`) --
  Packed image patches.
- **image_grid_thw** (`torch.LongTensor` of shape `(num_images, 3)`, *optional*) --
  The temporal, height, and width dimensions of every packed image patch grid.[BaseModelOutputWithPooling](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`A [BaseModelOutputWithPooling](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Cosmos3EdgeConfig](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeConfig)) and inputs.

- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) after further processing
  through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
  the classification token after processing through a linear layer and a tanh activation function. The linear
  layer weights are trained from the next sentence prediction (classification) objective during pretraining.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

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

- **pixel_values_videos** (`torch.FloatTensor` of shape `(num_patches, num_channels * patch_size * patch_size)`) --
  Packed video-frame patches.
- **video_grid_thw** (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*) --
  The temporal, height, and width dimensions of every packed video patch grids.[BaseModelOutputWithPooling](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`A [BaseModelOutputWithPooling](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Cosmos3EdgeConfig](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeConfig)) and inputs.

- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) after further processing
  through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
  the classification token after processing through a linear layer and a tanh activation function. The linear
  layer weights are trained from the next sentence prediction (classification) objective during pretraining.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

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

## Cosmos3EdgeTextModel[[transformers.Cosmos3EdgeTextModel]]

- **config** ([Cosmos3EdgeTextConfig](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeTextConfig)) --
  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()](/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

The bare Cosmos3 Edge Text Model outputting raw hidden-states without any specific head on to.

This model inherits from [PreTrainedModel](/docs/transformers/main/en/main_classes/model#transformers.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](https://pytorch.org/docs/stable/nn.html#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.

- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained using [AutoTokenizer](/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **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?](../glossary#position-ids)
- **past_key_values** (`~cache_utils.Cache`, *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`.

  Only [Cache](/docs/transformers/main/en/internal/generation_utils#transformers.Cache) instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
  If no `past_key_values` are passed, [DynamicCache](/docs/transformers/main/en/internal/generation_utils#transformers.DynamicCache) will be initialized by default.

  The model will output the same cache format that is fed as input.

  If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't
  have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` 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.
- **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`).[BaseModelOutputWithPast](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast) or `tuple(torch.FloatTensor)`A [BaseModelOutputWithPast](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast) 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 ([Cosmos3EdgeConfig](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeConfig)) and inputs.
The [Cosmos3EdgeTextModel](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeTextModel) 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.

- **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.

  If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
  hidden_size)` is output.
- **past_key_values** (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a [Cache](/docs/transformers/main/en/internal/generation_utils#transformers.Cache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).

  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.
- **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.

## Cosmos3EdgeVisionModel[[transformers.Cosmos3EdgeVisionModel]]

Packed variable-resolution SigLIP2 vision tower used by Cosmos3 Edge.

- **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
  [Cosmos3EdgeImageProcessor](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeImageProcessor). See `Cosmos3EdgeImageProcessor.__call__()` for details ([Cosmos3EdgeProcessor](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeProcessor) uses
  [Cosmos3EdgeImageProcessor](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeImageProcessor) for processing images).
- **grid_thw** (`torch.LongTensor` of shape `(num_images_or_videos, 3)`) --
  The temporal, height, and width patch-grid dimensions for each packed image or video.[BaseModelOutputWithPooling](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`A [BaseModelOutputWithPooling](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Cosmos3EdgeConfig](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeConfig)) and inputs.
The [Cosmos3EdgeVisionModel](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeVisionModel) 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.

- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) after further processing
  through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
  the classification token after processing through a linear layer and a tanh activation function. The linear
  layer weights are trained from the next sentence prediction (classification) objective during pretraining.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

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

## Cosmos3EdgeForConditionalGeneration[[transformers.Cosmos3EdgeForConditionalGeneration]]

- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained using [AutoTokenizer](/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **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?](../glossary#position-ids)
- **past_key_values** (`~cache_utils.Cache`, *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`.

  Only [Cache](/docs/transformers/main/en/internal/generation_utils#transformers.Cache) instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
  If no `past_key_values` are passed, [DynamicCache](/docs/transformers/main/en/internal/generation_utils#transformers.DynamicCache) will be initialized by default.

  The model will output the same cache format that is fed as input.

  If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't
  have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` 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.
- **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), and the loss is only computed for 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`).
- **pixel_values** (`torch.Tensor` of shape `(batch_size, num_channels, image_size, image_size)`, *optional*) --
  The tensors corresponding to the input images. Pixel values can be obtained using
  [Cosmos3EdgeImageProcessor](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeImageProcessor). See `Cosmos3EdgeImageProcessor.__call__()` for details ([Cosmos3EdgeProcessor](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeProcessor) uses
  [Cosmos3EdgeImageProcessor](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeImageProcessor) for processing images).
- **pixel_values_videos** (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, frame_size, frame_size)`, *optional*) --
  The tensors corresponding to the input video. Pixel values for videos can be obtained using
  [Cosmos3EdgeVideoProcessor](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeVideoProcessor). See `Cosmos3EdgeVideoProcessor.__call__()` for details ([Cosmos3EdgeProcessor](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeProcessor) uses
  [Cosmos3EdgeVideoProcessor](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeVideoProcessor) for processing videos).
- **image_grid_thw** (`torch.LongTensor` of shape `(num_images, 3)`, *optional*) --
  The temporal, height, and width of the feature grid for each image.
- **video_grid_thw** (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*) --
  The temporal, height, and width of the feature grid for each video.
- **mm_token_type_ids** (`torch.IntTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of input sequence tokens matching each modality. For example text (0), image (1), video (2).
  Multimodal token type ids can be obtained using [AutoProcessor](/docs/transformers/main/en/model_doc/auto#transformers.AutoProcessor). See [ProcessorMixin.__call__()](/docs/transformers/main/en/main_classes/processors#transformers.ProcessorMixin.__call__) for details.

- **logits_to_keep** (`Union[int, torch.Tensor]`, *optional*, 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).[CausalLMOutputWithPast](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithPast) or `tuple(torch.FloatTensor)`A [CausalLMOutputWithPast](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithPast) 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 ([Cosmos3EdgeConfig](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeConfig)) and inputs.
The [Cosmos3EdgeForConditionalGeneration](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeForConditionalGeneration) forward method, overrides the `__call__` special method.

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

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `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** (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a [Cache](/docs/transformers/main/en/internal/generation_utils#transformers.Cache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).

  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.

Example:

```python
>>> from transformers import AutoProcessor, Cosmos3EdgeForConditionalGeneration

>>> model = Cosmos3EdgeForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
>>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")

>>> messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg",
            },
            {"type": "text", "text": "Describe the image."},
        ],
    }
]

>>> inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt"
)

>>> # Generate
>>> generated_ids = model.generate(**inputs, max_new_tokens=1024)
>>> generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
>>> output_text = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
>>> print(output_text)
```

- **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`) --
  The tensors corresponding to the input images.
- **image_grid_thw** (`torch.LongTensor` of shape `(num_images, 3)`, *optional*) --
  The temporal, height and width of feature shape of each image in LLM.[BaseModelOutputWithPooling](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`A [BaseModelOutputWithPooling](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Cosmos3EdgeConfig](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeConfig)) and inputs.

- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) after further processing
  through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
  the classification token after processing through a linear layer and a tanh activation function. The linear
  layer weights are trained from the next sentence prediction (classification) objective during pretraining.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

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

Example:

```python
>>> from PIL import Image
>>> from transformers import AutoProcessor, Cosmos3EdgeForConditionalGeneration

>>> model = Cosmos3EdgeForConditionalGeneration.from_pretrained("nvidia/Cosmos3-Edge-Reasoner")
>>> processor = AutoProcessor.from_pretrained("nvidia/Cosmos3-Edge-Reasoner")

>>> messages = [
...     {
...         "role": "user", "content": [
...             {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"},
...             {"type": "text", "text": "Where is the cat standing?"},
...         ]
...     },
... ]

>>> inputs = processor.apply_chat_template(
...     messages,
...     tokenize=True,
...     return_dict=True,
...     return_tensors="pt",
...     add_generation_prompt=True
... )
>>> # Generate
>>> generate_ids = model.generate(**inputs)
>>> processor.batch_decode(generate_ids, skip_special_tokens=True)[0]
```

- **pixel_values_videos** (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`) --
  The tensors corresponding to the input videos.
- **video_grid_thw** (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*) --
  The temporal, height and width of feature shape of each video in LLM.[BaseModelOutputWithPooling](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`A [BaseModelOutputWithPooling](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Cosmos3EdgeConfig](/docs/transformers/main/en/model_doc/cosmos3_edge#transformers.Cosmos3EdgeConfig)) and inputs.

- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) after further processing
  through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
  the classification token after processing through a linear layer and a tanh activation function. The linear
  layer weights are trained from the next sentence prediction (classification) objective during pretraining.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

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

Example:

```python
>>> from PIL import Image
>>> from transformers import AutoProcessor, Cosmos3EdgeForConditionalGeneration

>>> model = Cosmos3EdgeForConditionalGeneration.from_pretrained("nvidia/Cosmos3-Edge-Reasoner")
>>> processor = AutoProcessor.from_pretrained("nvidia/Cosmos3-Edge-Reasoner")

>>> messages = [
...     {
...         "role": "user", "content": [
...             {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"},
...             {"type": "text", "text": "Where is the cat standing?"},
...         ]
...     },
... ]

>>> inputs = processor.apply_chat_template(
...     messages,
...     tokenize=True,
...     return_dict=True,
...     return_tensors="pt",
...     add_generation_prompt=True
... )
>>> # Generate
>>> generate_ids = model.generate(**inputs)
>>> processor.batch_decode(generate_ids, skip_special_tokens=True)[0]
```