The Granite Vision model is a variant of LLaVA-NeXT, leveraging a Granite language model alongside a SigLIP visual encoder. It utilizes multiple concatenated vision hidden states as its image features, similar to VipLlava. It also uses a larger set of image grid pinpoints than the original LlaVa-NeXT models to support additional aspect ratios.
Tips:
This model is loaded into Transformers as an instance of LlaVA-Next. The usage and tips from LLaVA-NeXT apply to this model as well.
You can apply the chat template on the tokenizer / processor in the same way as well. Example chat format:
"<|user|>\nWhat’s shown in this image?\n<|assistant|>\nThis image shows a red stop sign.<|end_of_text|><|user|>\nDescribe the image in more details.\n<|assistant|>\n"
Sample inference:
from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration
model_path = "ibm-granite/granite-vision-3.1-2b-preview"
processor = LlavaNextProcessor.from_pretrained(model_path)
model = LlavaNextForConditionalGeneration.from_pretrained(model_path).to("cuda")
url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"
conversation = [
{
"role": "user",
"content": [
{"type": "image", "url": url},
{"type": "text", "text": "What is shown in this image?"},
],
},
]
inputs = processor.apply_chat_template(
conversation,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt"
).to("cuda")
output = model.generate(**inputs, max_new_tokens=100)
print(processor.decode(output[0], skip_special_tokens=True))
This model was contributed by Alexander Brooks.
LlavaNextConfig class transformers.LlavaNextConfig < source >( vision_config = None text_config = None image_token_index = 32000 projector_hidden_act = 'gelu' vision_feature_select_strategy = 'default' vision_feature_layer = -2 image_grid_pinpoints = None tie_word_embeddings = False image_seq_length = 576 multimodal_projector_bias = True **kwargs )
Parameters
Union[AutoConfig, dict], optional, defaults to CLIPVisionConfig) — The config object or dictionary of the vision backbone. Union[AutoConfig, dict], optional, defaults to LlamaConfig) — The config object or dictionary of the text backbone. int, optional, defaults to 32000) — The image token index to encode the image prompt. str, optional, defaults to "gelu") — The activation function used by the multimodal projector. str, optional, defaults to "default") — The feature selection strategy used to select the vision feature from the vision backbone. Can be one of "default" or "full". If "default", the CLS token is removed from the vision features. If "full", the full vision features are used. Union[int, List[int]], optional, defaults to -2) — The index of the layer to select the vision feature. If multiple indices are provided, the vision feature of the corresponding indices will be concatenated to form the vision features. List, optional, defaults to [[336, 672], [672, 336], [672, 672], [1008, 336], [336, 1008]]) — A list of possible resolutions to use for processing high resolution images. Each item in the list should be a tuple or list of the form (height, width). bool, optional, defaults to False) — Whether the model’s input and output word embeddings should be tied. int, optional, defaults to 576) — Sequence length of one image embedding. bool, optional, defaults to True) — Whether to use bias in the multimodal projector. This is the configuration class to store the configuration of a LlavaNextForConditionalGeneration. It is used to instantiate an Llava-NeXT model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the llava-hf/llava-v1.6-mistral-7b-hf model.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.
Example:
>>> from transformers import LlavaNextForConditionalGeneration, LlavaNextConfig, CLIPVisionConfig, LlamaConfig >>> >>> vision_config = CLIPVisionConfig() >>> >>> text_config = LlamaConfig() >>> >>> configuration = LlavaNextConfig(vision_config, text_config) >>> >>> model = LlavaNextForConditionalGeneration(configuration) >>> >>> configuration = model.configLlavaNextImageProcessor class transformers.LlavaNextImageProcessor < source >
( do_resize: bool = True size: typing.Dict[str, int] = None image_grid_pinpoints: typing.List = None resample: Resampling = <Resampling.BICUBIC: 3> do_center_crop: bool = True crop_size: typing.Dict[str, int] = None do_rescale: bool = True rescale_factor: typing.Union[int, float] = 0.00392156862745098 do_normalize: bool = True image_mean: typing.Union[float, typing.List[float], NoneType] = None image_std: typing.Union[float, typing.List[float], NoneType] = None do_pad: typing.Optional[bool] = True do_convert_rgb: bool = True **kwargs )
Parameters
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. 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. List optional, defaults to [[672, 336], [336, 672], [672, 672], [336, 1008], [1008, 336]]) — A list of possible resolutions to use for processing high resolution images. The best resolution is selected based on the original size of the image. Can be overridden by image_grid_pinpoints in the preprocess method. PILImageResampling, optional, defaults to Resampling.BICUBIC) — Resampling filter to use if resizing the image. Can be overridden by resample in the preprocess method. 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. 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. 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. 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. bool, optional, defaults to True) — Whether to normalize the image. Can be overridden by do_normalize in the preprocess method. 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. 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. bool, optional, defaults to True) — Whether to pad the image. If True, will pad the patch dimension of the images in the batch to the largest number of patches in the batch. Padding will be applied to the bottom and right with zeros. bool, optional, defaults to True) — Whether to convert the image to RGB. Constructs a LLaVa-NeXT image processor. Based on CLIPImageProcessor with incorporation of additional techniques for processing high resolution images as explained in the LLaVa paper.
preprocess < source >( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']] do_resize: typing.Optional[bool] = None size: typing.Dict[str, int] = None image_grid_pinpoints: typing.List = None resample: Resampling = None do_center_crop: typing.Optional[bool] = None crop_size: typing.Optional[int] = None do_rescale: typing.Optional[bool] = None rescale_factor: typing.Optional[float] = None do_normalize: typing.Optional[bool] = None image_mean: typing.Union[float, typing.List[float], NoneType] = None image_std: typing.Union[float, typing.List[float], NoneType] = None do_pad: typing.Optional[bool] = None do_convert_rgb: typing.Optional[bool] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None data_format: typing.Optional[transformers.image_utils.ChannelDimension] = <ChannelDimension.FIRST: 'channels_first'> input_data_format: typing.Union[str, transformers.image_utils.ChannelDimension, NoneType] = None )
Parameters
ImageInput) — 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. bool, optional, defaults to self.do_resize) — Whether to resize the image. 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. List optional, defaults to self.image_grid_pinpoints) — A list of possible resolutions to use for processing high resolution images. The best resolution is selected based on the original size of the image. 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. bool, optional, defaults to self.do_center_crop) — Whether to center crop the image. 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. bool, optional, defaults to self.do_rescale) — Whether to rescale the image. float, optional, defaults to self.rescale_factor) — Rescale factor to rescale the image by if do_rescale is set to True. bool, optional, defaults to self.do_normalize) — Whether to normalize the image. 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. 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. bool, optional, defaults to self.do_pad) — Whether to pad the image. If True, will pad the patch dimension of the images in the batch to the largest number of patches in the batch. Padding will be applied to the bottom and right with zeros. bool, optional, defaults to self.do_convert_rgb) — Whether to convert the image to RGB. str or TensorType, optional) — The type of tensors to return. Can be one 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.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.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.( image_processor = None tokenizer = None patch_size = None vision_feature_select_strategy = None chat_template = None image_token = '<image>' num_additional_image_tokens = 0 **kwargs )
Parameters
int, optional) — Patch size from the vision tower. str, optional) — The feature selection strategy used to select the vision feature from the vision backbone. Shoudl be same as in model’s config str, optional) — A Jinja template which will be used to convert lists of messages in a chat into a tokenizable string. str, optional, defaults to "<image>") — Special token used to denote image location. int, optional, defaults to 0) — Number of additional tokens added to the image embeddings, such as CLS (+1). If the backbone has no CLS or other extra tokens appended, no need to set this arg. Constructs a LLaVa-NeXT processor which wraps a LLaVa-NeXT image processor and a LLaMa tokenizer into a single processor.
LlavaNextProcessor offers all the functionalities of LlavaNextImageProcessor and LlamaTokenizerFast. See the __call__() and decode() for more information.
This method forwards all its arguments to LlamaTokenizerFast’s batch_decode(). Please refer to the docstring of this method for more information.
This method forwards all its arguments to LlamaTokenizerFast’s decode(). Please refer to the docstring of this method for more information.
LlavaNextForConditionalGeneration class transformers.LlavaNextForConditionalGeneration < source >( config: LlavaNextConfig )
Parameters
LlavaNextVisionConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights. The LLAVA-NeXT model which consists of a vision backbone and a language model. This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward < source >( input_ids: typing.Optional[torch.LongTensor] = None pixel_values: typing.Optional[torch.FloatTensor] = None image_sizes: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.LongTensor] = None past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None vision_feature_layer: typing.Union[int, typing.List[int], NoneType] = None vision_feature_select_strategy: typing.Optional[str] = None labels: typing.Optional[torch.LongTensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None cache_position: typing.Optional[torch.LongTensor] = None logits_to_keep: typing.Union[int, torch.Tensor] = 0 **lm_kwargs ) → transformers.models.llava_next.modeling_llava_next.LlavaNextCausalLMOutputWithPast or tuple(torch.FloatTensor)
Parameters
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.
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. See LlavaNextImageProcessor.call() for details. LlavaProcessor uses LlavaNextImageProcessor for processing images. torch.LongTensor of shape (batch_size, 2), optional) — The sizes of the images in the batch, being (height, width) for each image. torch.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
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.
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? 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).
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. Union[int, List[int]], *optional*, defaults to -2) — The index of the layer to select the vision feature. If multiple indices are provided, the vision feature of the corresponding indices will be concatenated to form the vision features. str, optional, defaults to "default") — The feature selection strategy used to select the vision feature from the vision backbone. Can be one of "default" or "full". If "default", the CLS token is removed from the vision features. If "full", the full vision features are used. 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). bool, optional) — Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail. bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail. bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple. torch.LongTensor of shape (sequence_length), optional) — Indices depicting the position of the input sequence tokens in the sequence. Contrarily to position_ids, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length. 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]. int or torch.Tensor, optional) — If an int, compute logits for the last logits_to_keep tokens. If 0, calculate logits for all input_ids (special case). Only last token logits are needed for generation, and calculating them only for that token can save memory, which becomes pretty significant for long sequences or large vocabulary size. If a torch.Tensor, must be 1D corresponding to the indices to keep in the sequence length dimension. This is useful when using packed tensor format (single dimension for batch and sequence length). Returns
transformers.models.llava_next.modeling_llava_next.LlavaNextCausalLMOutputWithPast or tuple(torch.FloatTensor)
A transformers.models.llava_next.modeling_llava_next.LlavaNextCausalLMOutputWithPast 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 (LlavaNextConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss (for next-token prediction).
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape (batch_size, num_heads, sequence_length, embed_size_per_head))
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
image_hidden_states (torch.FloatTensor, optional) — A torch.FloatTensor of size (batch_size * num_patches, num_images, sequence_length, hidden_size)`. image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
The LlavaNextForConditionalGeneration 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
>>> from transformers import AutoProcessor, LlavaNextForConditionalGeneration
>>> model = LlavaNextForConditionalGeneration.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
>>> processor = AutoProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
>>> prompt = "[INST] <image>\nWhat is shown in this image? [/INST]"
>>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, text=prompt, return_tensors="pt")
>>>
>>> generate_ids = model.generate(**inputs, max_length=30)
>>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"[INST] \nWhat is shown in this image? [/INST] The image appears to be a radar chart, which is a type of multi-dimensional plot (...)"< > Update on GitHub
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