Transformers documentation
SmolVLM
This model was published in HF papers on 2025-04-07 and contributed to Hugging Face Transformers on 2025-02-20.
SmolVLM
Overview
SmolVLM2 (blog post) is an adaptation of the Idefics3 model with two main differences:
- It uses SmolLM2 for the text model.
- It supports multi-image and video inputs
Usage tips
Input images are processed either by upsampling (if resizing is enabled) or at their original resolution. The resizing behavior depends on two parameters: do_resize and size.
Videos should not be upsampled.
If do_resize is set to True, the model resizes images so that the longest edge is 4512 pixels by default.
The default resizing behavior can be customized by passing a dictionary to the size parameter. For example, `{“longest_edge”: 4 512}` is the default, but you can change it to a different value if needed.
Here’s how to control resizing and set a custom size:
image_processor = SmolVLMImageProcessor(do_resize=True, size={"longest_edge": 2 * 512}, max_image_size=512)Additionally, the max_image_size parameter, which controls the size of each square patch the image is decomposed into, is set to 512 by default but can be adjusted as needed. After resizing (if applicable), the image processor decomposes the images into square patches based on the max_image_size parameter.
This model was contributed by orrzohar.
Usage example
Single Media inference
The model can accept both images and videos as input, but you should use only one of the modalities at a time. Here’s an example code for that.
from transformers import AutoModelForImageTextToText, AutoProcessor
processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM2-256M-Video-Instruct")
model = AutoModelForImageTextToText.from_pretrained(
"HuggingFaceTB/SmolVLM2-256M-Video-Instruct",
device_map="auto"
)
conversation = [
{
"role": "user",
"content":[
{"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
{"type": "text", "text": "Describe this image."}
]
}
]
inputs = processor.apply_chat_template(
conversation,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
output_ids = model.generate(**inputs, max_new_tokens=128)
generated_texts = processor.batch_decode(output_ids, skip_special_tokens=True)
print(generated_texts)
# Video
conversation = [
{
"role": "user",
"content": [
{"type": "video", "path": "/path/to/video.mp4"},
{"type": "text", "text": "Describe this video in detail"}
]
},
]
inputs = processor.apply_chat_template(
conversation,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
generated_ids = model.generate(**inputs, do_sample=False, max_new_tokens=100)
generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)
print(generated_texts[0])Batch Mixed Media Inference
The model can batch inputs composed of several images/videos and text. Here is an example.
from transformers import AutoModelForImageTextToText, AutoProcessor
processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM2-256M-Video-Instruct")
model = AutoModelForImageTextToText.from_pretrained(
"HuggingFaceTB/SmolVLM2-256M-Video-Instruct",
device_map="auto"
)
# Conversation for the first image
conversation1 = [
{
"role": "user",
"content": [
{"type": "image", "path": "/path/to/image.jpg"},
{"type": "text", "text": "Describe this image."}
]
}
]
# Conversation with two images
conversation2 = [
{
"role": "user",
"content": [
{"type": "image", "path": "/path/to/image.jpg"},
{"type": "image", "path": "/path/to/image.jpg"},
{"type": "text", "text": "What is written in the pictures?"}
]
}
]
# Conversation with pure text
conversation3 = [
{"role": "user","content": "who are you?"}
]
conversations = [conversation1, conversation2, conversation3]
inputs = processor.apply_chat_template(
conversations,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
generated_ids = model.generate(**inputs, do_sample=False, max_new_tokens=100)
generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)
print(generated_texts[0])SmolVLMConfig
class transformers.SmolVLMConfig
< source >( transformers_version: str | None = Nonearchitectures: list[str] | None = Noneoutput_hidden_states: bool | None = Falsereturn_dict: bool | None = Truedtype: typing.Union[str, ForwardRef('torch.dtype'), NoneType] = Nonechunk_size_feed_forward: int = 0is_encoder_decoder: bool = Falseid2label: dict[int, str] | dict[str, str] | None = Nonelabel2id: dict[str, int] | dict[str, str] | None = Noneproblem_type: typing.Optional[typing.Literal['regression', 'single_label_classification', 'multi_label_classification']] = Noneuse_cache: bool = Trueimage_token_id: int = 128257tie_word_embeddings: bool = Falsevision_config: dict | transformers.configuration_utils.PreTrainedConfig | None = Nonetext_config: dict | transformers.configuration_utils.PreTrainedConfig | None = Nonescale_factor: int = 2pad_token_id: int | None = 128002 )
Parameters
- use_cache (
bool, optional, defaults toTrue) — Whether or not the model should return the last key/values attentions (not used by all models). Only relevant ifconfig.is_decoder=Trueor when the model is a decoder-only generative model. - image_token_id (
int, optional, defaults to128257) — The image token index used as a placeholder for input images. - tie_word_embeddings (
bool, optional, defaults toFalse) — Whether to tie weight embeddings according to model’stied_weights_keysmapping. - vision_config (
Union[dict, ~configuration_utils.PreTrainedConfig], optional) — The config object or dictionary of the vision backbone. - text_config (
Union[dict, ~configuration_utils.PreTrainedConfig], optional) — The config object or dictionary of the text backbone. - scale_factor (
int, optional, defaults to 2) — The scale factor for the image encoder. - pad_token_id (
int, optional, defaults to128002) — Token id used for padding in the vocabulary.
This is the configuration class to store the configuration of a SmolVLMModel. It is used to instantiate a Smolvlm 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 HuggingFaceTB/SmolVLM2-2.2B-Instruct
Configuration objects inherit from PreTrainedConfig and can be used to control the model outputs. Read the documentation from PreTrainedConfig for more information.
SmolVLMVisionConfig
class transformers.SmolVLMVisionConfig
< source >( transformers_version: str | None = Nonearchitectures: list[str] | None = Noneoutput_hidden_states: bool | None = Falsereturn_dict: bool | None = Truedtype: typing.Union[str, ForwardRef('torch.dtype'), NoneType] = Nonechunk_size_feed_forward: int = 0is_encoder_decoder: bool = Falseid2label: dict[int, str] | dict[str, str] | None = Nonelabel2id: dict[str, int] | dict[str, str] | None = Noneproblem_type: typing.Optional[typing.Literal['regression', 'single_label_classification', 'multi_label_classification']] = Nonehidden_size: int = 1152intermediate_size: int = 3072num_hidden_layers: int = 12num_attention_heads: int = 16num_channels: int = 3image_size: int | list[int] | tuple[int, int] = 224patch_size: int | list[int] | tuple[int, int] = 32hidden_act: str = 'gelu_pytorch_tanh'layer_norm_eps: float = 1e-06attention_dropout: float | int = 0.0initializer_range: float = 0.02 )
Parameters
- hidden_size (
int, optional, defaults to1152) — Dimension of the hidden representations. - intermediate_size (
int, optional, defaults to3072) — Dimension of the MLP representations. - num_hidden_layers (
int, optional, defaults to12) — Number of hidden layers in the Transformer decoder. - num_attention_heads (
int, optional, defaults to16) — Number of attention heads for each attention layer in the Transformer decoder. - num_channels (
int, optional, defaults to3) — The number of input channels. - image_size (
Union[int, list[int], tuple[int, int]], optional, defaults to224) — The size (resolution) of each image. - patch_size (
Union[int, list[int], tuple[int, int]], optional, defaults to32) — The size (resolution) of each patch. - hidden_act (
str, optional, defaults togelu_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 to1e-06) — The epsilon used by the layer normalization layers. - attention_dropout (
Union[float, int], optional, defaults to0.0) — The dropout ratio for the attention probabilities. - initializer_range (
float, optional, defaults to0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
This is the configuration class to store the configuration of a SmolVLMModel. It is used to instantiate a Smolvlm 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 HuggingFaceTB/SmolVLM2-2.2B-Instruct
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.models.smolvlm.modeling_smolvlm import SmolVLMVisionTransformer
>>> from transformers.models.smolvlm.configuration_smolvlm import SmolVLMVisionConfig
>>> # Initializing a SmolVLMVisionConfig with google/siglip-so400m-patch14-384 style configuration
>>> configuration = SmolVLMVisionConfig()
>>> # Initializing a SmolVLMVisionTransformer (with random weights) from the google/siglip-so400m-patch14-384 style configuration
>>> model = SmolVLMVisionTransformer(configuration)
>>> # Accessing the model configuration
>>> configuration = model.configIdefics3VisionTransformer
class transformers.SmolVLMVisionTransformer
< source >( config: SmolVLMVisionConfig )
Parameters
- config (SmolVLMVisionConfig) — 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 SmolVLM Vision Transformer Model outputting raw image embedding.
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.
SmolVLMModel
class transformers.SmolVLMModel
< source >( config: SmolVLMConfig )
Parameters
- config (SmolVLMConfig) — 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.
SmolVLM model consisting of a SIGLIP vision encoder and Llama3 language decoder
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] = Noneattention_mask: typing.Optional[torch.Tensor] = Noneposition_ids: typing.Optional[torch.LongTensor] = Nonepast_key_values: transformers.cache_utils.Cache | None = Noneinputs_embeds: typing.Optional[torch.FloatTensor] = Nonepixel_values: typing.Optional[torch.FloatTensor] = Nonepixel_attention_mask: typing.Optional[torch.BoolTensor] = Noneimage_hidden_states: typing.Optional[torch.FloatTensor] = Noneuse_cache: bool | None = None**kwargs: Unpack ) → SmolVLMBaseModelOutputWithPast or tuple(torch.FloatTensor)
Parameters
- input_ids (
torch.LongTensorof 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. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
- attention_mask (
torch.Tensorof 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.
- position_ids (
torch.LongTensorof 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]. - 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 thepast_key_valuesreturned by the model at a previous stage of decoding, whenuse_cache=Trueorconfig.use_cache=True.Only Cache instance is allowed as input, see our kv cache guide. If no
past_key_valuesare passed, DynamicCache will be initialized by default.The model will output the same cache format that is fed as input.
If
past_key_valuesare used, the user is expected to input only unprocessedinput_ids(those that don’t have their past key value states given to this model) of shape(batch_size, unprocessed_length)instead of allinput_idsof shape(batch_size, sequence_length). - inputs_embeds (
torch.FloatTensorof shape(batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_idsindices into associated vectors than the model’s internal embedding lookup matrix. - pixel_values (
torch.FloatTensorof shape(batch_size, num_channels, image_size, image_size), optional) — The tensors corresponding to the input images. Pixel values can be obtained using SmolVLMImageProcessor. SeeSmolVLMImageProcessor.__call__()for details (SmolVLMProcessor uses SmolVLMImageProcessor for processing images). - pixel_attention_mask (
torch.Tensorof shape(batch_size, image_size, image_size), optional) — Mask to avoid performing attention on padding pixel indices. - image_hidden_states (
torch.FloatTensorof shape(batch_size, num_channels, image_size, image_size)) — The hidden states of the image encoder after modality projection. - use_cache (
bool, optional) — If set toTrue,past_key_valueskey value states are returned and can be used to speed up decoding (seepast_key_values).
Returns
SmolVLMBaseModelOutputWithPast or tuple(torch.FloatTensor)
A SmolVLMBaseModelOutputWithPast 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 (SmolVLMConfig) and inputs.
Inputs fed to the model can have an arbitrary number of images. To account for this, pixel_values fed to the model have image padding -> (batch_size, max_num_images, 3, max_heights, max_widths) where max_num_images is the maximum number of images among the batch_size samples in the batch. Padding images are not needed beyond padding the pixel_values at the entrance of the model. For efficiency, we only pass through the vision_model’s forward the real images by discarding the padding images i.e. pixel_values of size (image_batch_size, 3, height, width) where image_batch_size would be 7 when num_images_per_sample=[1, 3, 1, 2] and max_num_images would be 3.
last_hidden_state (
torch.FloatTensorof shape(batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model. Ifpast_key_valuesis used only the last hidden-state of the sequences of shape(batch_size, 1, hidden_size)is output.past_key_values (
~cache_utils.Cache, optional, returned whenuse_cache=Trueis passed or whenconfig.use_cache=True) — It is a Cache instance. For more details, see our kv cache guide.Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
config.is_encoder_decoder=Truein the cross-attention blocks) that can be used (seepast_key_valuesinput) to speed up sequential decoding.hidden_states (
tuple[torch.FloatTensor], optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.FloatTensor(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length).Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
image_hidden_states (
tuple(torch.FloatTensor), optional) — Tuple oftorch.FloatTensor(one for the output of the image embeddings,(batch_size, num_images, sequence_length, hidden_size). image_hidden_states of the model produced by the vision encoder
get_image_features
< source >( pixel_values: FloatTensorpixel_attention_mask: typing.Optional[torch.LongTensor] = None**kwargs: Unpack ) → BaseModelOutputWithPooling or tuple(torch.FloatTensor)
Parameters
- pixel_values (
torch.FloatTensorof shape(batch_size, num_channels, image_size, image_size)) — The tensors corresponding to the input images. - pixel_attention_mask (
torch.LongTensor, optional) — The attention mask indicating padded regions in the image.
Returns
BaseModelOutputWithPooling or tuple(torch.FloatTensor)
A 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 (SmolVLMConfig) and inputs.
Encodes images into continuous embeddings that can be forwarded to the language model.
last_hidden_state (
torch.FloatTensorof shape(batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.pooler_output (
torch.FloatTensorof 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 whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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.
SmolVLMForConditionalGeneration
class transformers.SmolVLMForConditionalGeneration
< source >( config )
Parameters
- config (SmolVLMForConditionalGeneration) — 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 SmolVLM Model with a language modeling head. It is made up a SigLIP vision encoder, with a language modeling head on top.
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] = Noneattention_mask: typing.Optional[torch.Tensor] = Noneposition_ids: typing.Optional[torch.LongTensor] = Nonepast_key_values: transformers.cache_utils.Cache | None = Noneinputs_embeds: typing.Optional[torch.FloatTensor] = Nonepixel_values: typing.Optional[torch.FloatTensor] = Nonepixel_attention_mask: typing.Optional[torch.BoolTensor] = Noneimage_hidden_states: typing.Optional[torch.FloatTensor] = Nonelabels: typing.Optional[torch.LongTensor] = Noneuse_cache: bool | None = Nonelogits_to_keep: typing.Union[int, torch.Tensor] = 0**kwargs: Unpack ) → SmolVLMCausalLMOutputWithPast or tuple(torch.FloatTensor)
Parameters
- input_ids (
torch.LongTensorof 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. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
- attention_mask (
torch.Tensorof 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.
- position_ids (
torch.LongTensorof 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]. - 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 thepast_key_valuesreturned by the model at a previous stage of decoding, whenuse_cache=Trueorconfig.use_cache=True.Only Cache instance is allowed as input, see our kv cache guide. If no
past_key_valuesare passed, DynamicCache will be initialized by default.The model will output the same cache format that is fed as input.
If
past_key_valuesare used, the user is expected to input only unprocessedinput_ids(those that don’t have their past key value states given to this model) of shape(batch_size, unprocessed_length)instead of allinput_idsof shape(batch_size, sequence_length). - inputs_embeds (
torch.FloatTensorof shape(batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_idsindices into associated vectors than the model’s internal embedding lookup matrix. - pixel_values (
torch.FloatTensorof shape(batch_size, num_channels, image_size, image_size), optional) — The tensors corresponding to the input images. Pixel values can be obtained using SmolVLMImageProcessor. SeeSmolVLMImageProcessor.__call__()for details (SmolVLMProcessor uses SmolVLMImageProcessor for processing images). - pixel_attention_mask (
torch.Tensorof shape(batch_size, image_size, image_size), optional) — Mask to avoid performing attention on padding pixel indices. - image_hidden_states (
torch.FloatTensorof shape(batch_size, num_channels, image_size, image_size)) — The hidden states of the image encoder after modality projection. - labels (
torch.LongTensorof shape(batch_size, sequence_length), optional) — Labels for computing the masked language modeling loss. Indices should either be in[0, ..., config.vocab_size]ormodel.image_token_id. Tokens with indices set tomodel.image_token_idare ignored (masked), the loss is only computed for the tokens with labels in[0, ..., config.vocab_size]. - use_cache (
bool, optional) — If set toTrue,past_key_valueskey value states are returned and can be used to speed up decoding (seepast_key_values). - logits_to_keep (
Union[int, torch.Tensor], optional, defaults to0) — If anint, compute logits for the lastlogits_to_keeptokens. If0, calculate logits for allinput_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 atorch.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
SmolVLMCausalLMOutputWithPast or tuple(torch.FloatTensor)
A SmolVLMCausalLMOutputWithPast 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 (SmolVLMConfig) and inputs.
The SmolVLMForConditionalGeneration forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the
Moduleinstance 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.FloatTensorof shape(1,), optional, returned whenlabelsis provided) — Language modeling loss (for next-token prediction).logits (
torch.FloatTensorof 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 whenuse_cache=Trueis passed or whenconfig.use_cache=True) — It is a Cache instance. For more details, see our kv cache guide.Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
past_key_valuesinput) to speed up sequential decoding.hidden_states (
tuple[torch.FloatTensor], optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.FloatTensor(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length).Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
image_hidden_states (
tuple(torch.FloatTensor), optional) — Tuple oftorch.FloatTensor(one for the output of the image embeddings,(batch_size, num_images, sequence_length, hidden_size). image_hidden_states of the model produced by the vision encoder
Example:
>>> import httpx
>>> from io import BytesIO
>>> import torch
>>> from PIL import Image
>>> from io import BytesIO
>>> from transformers import AutoProcessor, AutoModelForImageTextToText
>>> from transformers.image_utils import load_image
>>> # Note that passing the image urls (instead of the actual pil images) to the processor is also possible
>>> image1 = load_image("https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg")
>>> image2 = load_image("https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg")
>>> image3 = load_image("https://cdn.britannica.com/68/170868-050-8DDE8263/Golden-Gate-Bridge-San-Francisco.jpg")
>>> processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM2-2.2B-Instruct")
>>> model = AutoModelForImageTextToText.from_pretrained("HuggingFaceTB/SmolVLM2-2.2B-Instruct", dtype=torch.bfloat16, device_map="auto")
>>> # Create inputs
>>> messages = [
... {
... "role": "user",
... "content": [
... {"type": "video", "path": path/to/video},
... {"type": "text", "text": "What is happening in this video?"},
... ]
... }
... ]
>>> inputs = processor.apply_chat_template([messages], add_generation_prompt=True)
>>> # Generate
>>> generated_ids = model.generate(**inputs, max_new_tokens=256)
>>> generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)
>>> print(generated_texts)get_image_features
< source >( pixel_values: FloatTensorpixel_attention_mask: typing.Optional[torch.LongTensor] = None**kwargs: Unpack ) → BaseModelOutputWithPooling or tuple(torch.FloatTensor)
Parameters
- pixel_values (
torch.FloatTensorof shape(batch_size, num_channels, image_size, image_size)) — The tensors corresponding to the input images. - pixel_attention_mask (
torch.LongTensor, optional) — The attention mask indicating padded regions in the image.
Returns
BaseModelOutputWithPooling or tuple(torch.FloatTensor)
A 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 (SmolVLMConfig) and inputs.
last_hidden_state (
torch.FloatTensorof shape(batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.pooler_output (
torch.FloatTensorof 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 whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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:
>>> from PIL import Image
>>> from transformers import AutoProcessor, SmolVLMForConditionalGeneration
>>> model = SmolVLMForConditionalGeneration.from_pretrained("HuggingFaceTB/SmolVLM2-2.2B-Instruct")
>>> processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM2-2.2B-Instruct")
>>> 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]SmolVLMImageProcessor
class transformers.SmolVLMImageProcessor
< source >( **kwargs: Unpack )
Parameters
- do_convert_rgb (
bool, kwargs, optional, defaults toTrue) — Whether to convert the image to RGB. - do_resize (
bool, kwargs, optional, defaults toTrue) — Whether to resize the image. - size (
Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs, defaults to{'longest_edge' -- 1456}): Describes the maximum input dimensions to the model. - default_to_square (
bool, kwargs, optional, defaults toTrue) — Whether to default to a square image when resizing, if size is an int. - crop_size (
Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — Size of the output image after applyingcenter_crop. - resample (
Annotated[Union[int, PILImageResampling, NoneType], None], kwargs, defaults toResampling.LANCZOS) — Resampling filter to use if resizing the image. This can be one of the enumPILImageResampling. Only has an effect ifdo_resizeis set toTrue. - do_rescale (
bool, kwargs, optional, defaults toTrue) — Whether to rescale the image. - rescale_factor (
float, kwargs, optional, defaults to0.00392156862745098) — Rescale factor to rescale the image by ifdo_rescaleis set toTrue. - do_normalize (
bool, kwargs, optional, defaults toTrue) — Whether to normalize the image. - image_mean (
Union[float, list[float], tuple[float, ...]], kwargs, optional, defaults to[0.5, 0.5, 0.5]) — Image mean to use for normalization. Only has an effect ifdo_normalizeis set toTrue. - image_std (
Union[float, list[float], tuple[float, ...]], kwargs, optional, defaults to[0.5, 0.5, 0.5]) — Image standard deviation to use for normalization. Only has an effect ifdo_normalizeis set toTrue. - do_pad (
bool, kwargs, optional, defaults toTrue) — Whether to pad the image. Padding is done either to the largest size in the batch or to a fixed square size per image. The exact padding strategy depends on the model. - pad_size (
Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — The size in{"height": int, "width" int}to pad the images to. Must be larger than any image size provided for preprocessing. Ifpad_sizeis not provided, images will be padded to the largest height and width in the batch. Applied only whendo_pad=True. - do_center_crop (
bool, kwargs, optional) — Whether to center crop the image. - data_format (
Union[str, ~image_utils.ChannelDimension], kwargs, optional) — OnlyChannelDimension.FIRSTis supported. Added for compatibility with slow processors. - input_data_format (
Union[str, ~image_utils.ChannelDimension], kwargs, 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"orChannelDimension.FIRST: image in (num_channels, height, width) format."channels_last"orChannelDimension.LAST: image in (height, width, num_channels) format."none"orChannelDimension.NONE: image in (height, width) format.
- device (
Annotated[Union[str, torch.device, NoneType], None], kwargs) — The device to process the videos on. If unset, the device is inferred from the input videos. - return_tensors (
Annotated[str | ~utils.generic.TensorType | None, None], kwargs) — Returns stacked tensors if set to'pt', otherwise returns a list of tensors. - disable_grouping (
bool, kwargs, optional) — Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157 - image_seq_length (
int, kwargs, optional) — The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. - do_image_splitting (
bool, kwargs, optional, defaults toTrue) — Whether to split the image into sub-images concatenated with the original image. They are split into patches such that each patch has a size ofmax_image_size["height"]xmax_image_size["width"]. - max_image_size (
Dict, kwargs, optional, defaults to{"longest_edge" -- 364}): Maximum resolution of the patches of images accepted by the model. This is a dictionary containing the key “longest_edge”. - return_row_col_info (
bool, kwargs, optional, defaults toFalse) — Whether to return the row and column information of the images.
Constructs a SmolVLMImageProcessor image processor.
preprocess
< source >( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]**kwargs: Unpack ) → ~image_processing_base.BatchFeature
Parameters
- images (
Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]]) — Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, setdo_rescale=False. - do_convert_rgb (
bool, kwargs, optional) — Whether to convert the image to RGB. - do_resize (
bool, kwargs, optional) — Whether to resize the image. - size (
Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — Describes the maximum input dimensions to the model. - default_to_square (
bool, kwargs, optional) — Whether to default to a square image when resizing, if size is an int. - crop_size (
Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — Size of the output image after applyingcenter_crop. - resample (
Annotated[Union[int, PILImageResampling, NoneType], None], kwargs) — Resampling filter to use if resizing the image. This can be one of the enumPILImageResampling. Only has an effect ifdo_resizeis set toTrue. - do_rescale (
bool, kwargs, optional) — Whether to rescale the image. - rescale_factor (
float, kwargs, optional) — Rescale factor to rescale the image by ifdo_rescaleis set toTrue. - do_normalize (
bool, kwargs, optional) — Whether to normalize the image. - image_mean (
Union[float, list[float], tuple[float, ...]], kwargs, optional) — Image mean to use for normalization. Only has an effect ifdo_normalizeis set toTrue. - image_std (
Union[float, list[float], tuple[float, ...]], kwargs, optional) — Image standard deviation to use for normalization. Only has an effect ifdo_normalizeis set toTrue. - do_pad (
bool, kwargs, optional) — Whether to pad the image. Padding is done either to the largest size in the batch or to a fixed square size per image. The exact padding strategy depends on the model. - pad_size (
Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — The size in{"height": int, "width" int}to pad the images to. Must be larger than any image size provided for preprocessing. Ifpad_sizeis not provided, images will be padded to the largest height and width in the batch. Applied only whendo_pad=True. - do_center_crop (
bool, kwargs, optional) — Whether to center crop the image. - data_format (
Union[str, ~image_utils.ChannelDimension], kwargs, optional) — OnlyChannelDimension.FIRSTis supported. Added for compatibility with slow processors. - input_data_format (
Union[str, ~image_utils.ChannelDimension], kwargs, 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"orChannelDimension.FIRST: image in (num_channels, height, width) format."channels_last"orChannelDimension.LAST: image in (height, width, num_channels) format."none"orChannelDimension.NONE: image in (height, width) format.
- device (
Annotated[Union[str, torch.device, NoneType], None], kwargs) — The device to process the videos on. If unset, the device is inferred from the input videos. - return_tensors (
Annotated[str | ~utils.generic.TensorType | None, None], kwargs) — Returns stacked tensors if set to'pt', otherwise returns a list of tensors. - disable_grouping (
bool, kwargs, optional) — Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157 - image_seq_length (
int, kwargs, optional) — The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. - do_image_splitting (
bool, kwargs, optional, defaults toTrue) — Whether to split the image into sub-images concatenated with the original image. They are split into patches such that each patch has a size ofmax_image_size["height"]xmax_image_size["width"]. - max_image_size (
Dict, kwargs, optional, defaults to{"longest_edge" -- 364}): Maximum resolution of the patches of images accepted by the model. This is a dictionary containing the key “longest_edge”. - return_row_col_info (
bool, kwargs, optional, defaults toFalse) — Whether to return the row and column information of the images.
Returns
~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.
SmolVLMImageProcessorPil
class transformers.SmolVLMImageProcessorPil
< source >( **kwargs: Unpack )
Parameters
- do_convert_rgb (
bool, kwargs, optional, defaults toTrue) — Whether to convert the image to RGB. - do_resize (
bool, kwargs, optional, defaults toTrue) — Whether to resize the image. - size (
Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs, defaults to{'longest_edge' -- 1456}): Describes the maximum input dimensions to the model. - default_to_square (
bool, kwargs, optional, defaults toTrue) — Whether to default to a square image when resizing, if size is an int. - crop_size (
Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — Size of the output image after applyingcenter_crop. - resample (
Annotated[Union[int, PILImageResampling, NoneType], None], kwargs, defaults toResampling.LANCZOS) — Resampling filter to use if resizing the image. This can be one of the enumPILImageResampling. Only has an effect ifdo_resizeis set toTrue. - do_rescale (
bool, kwargs, optional, defaults toTrue) — Whether to rescale the image. - rescale_factor (
float, kwargs, optional, defaults to0.00392156862745098) — Rescale factor to rescale the image by ifdo_rescaleis set toTrue. - do_normalize (
bool, kwargs, optional, defaults toTrue) — Whether to normalize the image. - image_mean (
Union[float, list[float], tuple[float, ...]], kwargs, optional, defaults to[0.5, 0.5, 0.5]) — Image mean to use for normalization. Only has an effect ifdo_normalizeis set toTrue. - image_std (
Union[float, list[float], tuple[float, ...]], kwargs, optional, defaults to[0.5, 0.5, 0.5]) — Image standard deviation to use for normalization. Only has an effect ifdo_normalizeis set toTrue. - do_pad (
bool, kwargs, optional, defaults toTrue) — Whether to pad the image. Padding is done either to the largest size in the batch or to a fixed square size per image. The exact padding strategy depends on the model. - pad_size (
Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — The size in{"height": int, "width" int}to pad the images to. Must be larger than any image size provided for preprocessing. Ifpad_sizeis not provided, images will be padded to the largest height and width in the batch. Applied only whendo_pad=True. - do_center_crop (
bool, kwargs, optional) — Whether to center crop the image. - data_format (
Union[str, ~image_utils.ChannelDimension], kwargs, optional) — OnlyChannelDimension.FIRSTis supported. Added for compatibility with slow processors. - input_data_format (
Union[str, ~image_utils.ChannelDimension], kwargs, 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"orChannelDimension.FIRST: image in (num_channels, height, width) format."channels_last"orChannelDimension.LAST: image in (height, width, num_channels) format."none"orChannelDimension.NONE: image in (height, width) format.
- device (
Annotated[Union[str, torch.device, NoneType], None], kwargs) — The device to process the videos on. If unset, the device is inferred from the input videos. - return_tensors (
Annotated[str | ~utils.generic.TensorType | None, None], kwargs) — Returns stacked tensors if set to'pt', otherwise returns a list of tensors. - disable_grouping (
bool, kwargs, optional) — Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157 - image_seq_length (
int, kwargs, optional) — The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. - do_image_splitting (
bool, kwargs, optional, defaults toTrue) — Whether to split the image into sub-images concatenated with the original image. They are split into patches such that each patch has a size ofmax_image_size["height"]xmax_image_size["width"]. - max_image_size (
Dict, kwargs, optional, defaults to{"longest_edge" -- 364}): Maximum resolution of the patches of images accepted by the model. This is a dictionary containing the key “longest_edge”. - return_row_col_info (
bool, kwargs, optional, defaults toFalse) — Whether to return the row and column information of the images.
Constructs a SmolVLMImageProcessor image processor.
preprocess
< source >( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]**kwargs: Unpack ) → ~image_processing_base.BatchFeature
Parameters
- images (
Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]]) — Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, setdo_rescale=False. - do_convert_rgb (
bool, kwargs, optional) — Whether to convert the image to RGB. - do_resize (
bool, kwargs, optional) — Whether to resize the image. - size (
Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — Describes the maximum input dimensions to the model. - default_to_square (
bool, kwargs, optional) — Whether to default to a square image when resizing, if size is an int. - crop_size (
Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — Size of the output image after applyingcenter_crop. - resample (
Annotated[Union[int, PILImageResampling, NoneType], None], kwargs) — Resampling filter to use if resizing the image. This can be one of the enumPILImageResampling. Only has an effect ifdo_resizeis set toTrue. - do_rescale (
bool, kwargs, optional) — Whether to rescale the image. - rescale_factor (
float, kwargs, optional) — Rescale factor to rescale the image by ifdo_rescaleis set toTrue. - do_normalize (
bool, kwargs, optional) — Whether to normalize the image. - image_mean (
Union[float, list[float], tuple[float, ...]], kwargs, optional) — Image mean to use for normalization. Only has an effect ifdo_normalizeis set toTrue. - image_std (
Union[float, list[float], tuple[float, ...]], kwargs, optional) — Image standard deviation to use for normalization. Only has an effect ifdo_normalizeis set toTrue. - do_pad (
bool, kwargs, optional) — Whether to pad the image. Padding is done either to the largest size in the batch or to a fixed square size per image. The exact padding strategy depends on the model. - pad_size (
Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — The size in{"height": int, "width" int}to pad the images to. Must be larger than any image size provided for preprocessing. Ifpad_sizeis not provided, images will be padded to the largest height and width in the batch. Applied only whendo_pad=True. - do_center_crop (
bool, kwargs, optional) — Whether to center crop the image. - data_format (
Union[str, ~image_utils.ChannelDimension], kwargs, optional) — OnlyChannelDimension.FIRSTis supported. Added for compatibility with slow processors. - input_data_format (
Union[str, ~image_utils.ChannelDimension], kwargs, 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"orChannelDimension.FIRST: image in (num_channels, height, width) format."channels_last"orChannelDimension.LAST: image in (height, width, num_channels) format."none"orChannelDimension.NONE: image in (height, width) format.
- device (
Annotated[Union[str, torch.device, NoneType], None], kwargs) — The device to process the videos on. If unset, the device is inferred from the input videos. - return_tensors (
Annotated[str | ~utils.generic.TensorType | None, None], kwargs) — Returns stacked tensors if set to'pt', otherwise returns a list of tensors. - disable_grouping (
bool, kwargs, optional) — Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157 - image_seq_length (
int, kwargs, optional) — The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. - do_image_splitting (
bool, kwargs, optional, defaults toTrue) — Whether to split the image into sub-images concatenated with the original image. They are split into patches such that each patch has a size ofmax_image_size["height"]xmax_image_size["width"]. - max_image_size (
Dict, kwargs, optional, defaults to{"longest_edge" -- 364}): Maximum resolution of the patches of images accepted by the model. This is a dictionary containing the key “longest_edge”. - return_row_col_info (
bool, kwargs, optional, defaults toFalse) — Whether to return the row and column information of the images.
Returns
~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.
SmolVLMVideoProcessor
preprocess
< source >( videos: typing.Union[list['PIL.Image.Image'], numpy.ndarray, ForwardRef('torch.Tensor'), list[numpy.ndarray], list['torch.Tensor'], list[list['PIL.Image.Image']], list[list[numpy.ndarray]], list[list['torch.Tensor']], transformers.video_utils.URL, list[transformers.video_utils.URL], list[list[transformers.video_utils.URL]], transformers.video_utils.Path, list[transformers.video_utils.Path], list[list[transformers.video_utils.Path]]]**kwargs: Unpack ) → ~image_processing_base.BatchFeature
Parameters
- 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]]]) — 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, setdo_rescale=False. - do_convert_rgb (
bool, kwargs, optional) — Whether to convert the image to RGB. - do_resize (
bool, kwargs, optional) — Whether to resize the image. - size (
Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — Describes the maximum input dimensions to the model. - default_to_square (
bool, kwargs, optional) — Whether to default to a square image when resizing, if size is an int. - resample (
Annotated[Union[int, PILImageResampling, NoneType], None], kwargs) — Resampling filter to use if resizing the image. This can be one of the enumPILImageResampling. Only has an effect ifdo_resizeis set toTrue. - do_rescale (
bool, kwargs, optional) — Whether to rescale the image. - rescale_factor (
float, kwargs, optional) — Rescale factor to rescale the image by ifdo_rescaleis set toTrue. - do_normalize (
bool, kwargs, optional) — Whether to normalize the image. - image_mean (
Union[float, list[float], tuple[float, ...]], kwargs, optional) — Image mean to use for normalization. Only has an effect ifdo_normalizeis set toTrue. - image_std (
Union[float, list[float], tuple[float, ...]], kwargs, optional) — Image standard deviation to use for normalization. Only has an effect ifdo_normalizeis set toTrue. - do_center_crop (
bool, kwargs, optional) — Whether to center crop the image. - do_pad (
bool, kwargs, optional) — Whether to pad the image. Padding is done either to the largest size in the batch or to a fixed square size per image. The exact padding strategy depends on the model. - crop_size (
Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — Size of the output image after applyingcenter_crop. - data_format (
Union[str, ~image_utils.ChannelDimension], kwargs, optional) — OnlyChannelDimension.FIRSTis supported. Added for compatibility with slow processors. - input_data_format (
Union[str, ~image_utils.ChannelDimension], kwargs, 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"orChannelDimension.FIRST: image in (num_channels, height, width) format."channels_last"orChannelDimension.LAST: image in (height, width, num_channels) format."none"orChannelDimension.NONE: image in (height, width) format.
- device (
Annotated[Union[str, torch.device, NoneType], None], kwargs) — The device to process the videos on. If unset, the device is inferred from the input videos. - do_sample_frames (
bool, kwargs, optional) — Whether to sample frames from the video before processing or to process the whole video. - video_metadata (
Annotated[~video_utils.VideoMetadata | dict | list[dict | ~video_utils.VideoMetadata] | list[list[dict | ~video_utils.VideoMetadata]] | None, None], kwargs) — Metadata of the video containing information about total duration, fps and total number of frames. It will be used to sample frames from video or compute timestamps. Don’t pass any metadata unless you are trying to decode the video manually before processing - fps (
Annotated[int | float | None, None], kwargs) — Target frames to sample per second whendo_sample_frames=True. - num_frames (
Annotated[int | None, None], kwargs) — Maximum number of frames to sample whendo_sample_frames=True. - return_metadata (
bool, kwargs, optional) — Whether to return video metadata or not. Video metadats is an object containing info about video duration, fps, decoding backend, etc. - return_tensors (
Annotated[str | ~utils.generic.TensorType | None, None], kwargs) — Returns stacked tensors if set to'pt', otherwise returns a list of tensors.
Returns
~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.
SmolVLMProcessor
class transformers.SmolVLMProcessor
< source >( image_processortokenizervideo_processorimage_seq_len: int = 169chat_template: str | None = None**kwargs )
Parameters
- image_processor (
SmolVLMImageProcessor) — The image processor is a required input. - tokenizer (
TokenizersBackend) — The tokenizer is a required input. - video_processor (
SmolVLMVideoProcessor) — The video processor is a required input. - image_seq_len (
int, optional, defaults to 169) — The length of the image sequence i.e. the number oftokens per image in the input. This parameter is used to build the string from the input prompt and image tokens and should match the value the model used. It is computed as: image_seq_len = int(((image_size // patch_size) 2) / (scale_factor2)) - chat_template (
str, optional) — A Jinja template to convert lists of messages in a chat into a tokenizable string.
Constructs a SmolVLMProcessor which wraps a image processor, a tokenizer, and a video processor into a single processor.
SmolVLMProcessor offers all the functionalities of SmolVLMImageProcessor, TokenizersBackend, and SmolVLMVideoProcessor. See the ~SmolVLMImageProcessor, ~TokenizersBackend, and ~SmolVLMVideoProcessor for more information.
__call__
< source >( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor'], NoneType] = Nonetext: str | list[str] | list[list[str]] | None = Nonevideos: typing.Union[list['PIL.Image.Image'], numpy.ndarray, ForwardRef('torch.Tensor'), list[numpy.ndarray], list['torch.Tensor'], list[list['PIL.Image.Image']], list[list[numpy.ndarray]], list[list['torch.Tensor']], transformers.video_utils.URL, list[transformers.video_utils.URL], list[list[transformers.video_utils.URL]], transformers.video_utils.Path, list[transformers.video_utils.Path], list[list[transformers.video_utils.Path]], NoneType] = Noneaudio: typing.Union[numpy.ndarray, ForwardRef('torch.Tensor'), collections.abc.Sequence[numpy.ndarray], collections.abc.Sequence['torch.Tensor'], NoneType] = None**kwargs: Unpack )
Parameters
- 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, setdo_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, setis_split_into_words=Trueto 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, setdo_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 (
stror TensorType, optional) — If set, will return tensors of a particular framework. Acceptable values are:'pt': Return PyTorchtorch.Tensorobjects.'np': Return NumPynp.ndarrayobjects.
- **kwargs (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.