Transformers documentation
MiniCPM-V
This model was published in HF papers on 2025-09-16 and contributed to Hugging Face Transformers on 2026-04-28.
MiniCPM-V
MiniCPM-V is a series of efficient multimodal large language models developed by OpenBMB. The MiniCPM-V 4.6 architecture uses a SigLIP vision encoder with a window-attention merger and a Qwen3.5 language model backbone, supporting both 4x and 16x visual downsampling modes.
This model was contributed by OpenBMB. The original code can be found here.
Usage example
Inference with Pipeline
from transformers import pipeline
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg",
},
{"type": "text", "text": "Describe this image."},
],
},
]
pipe = pipeline("image-text-to-text", model="openbmb/MiniCPM-V-4_6")
outputs = pipe(text=messages, max_new_tokens=50, return_full_text=False)
outputs[0]["generated_text"]Inference on a single image
The model has been trained with a specific prompt format for chatting. Use
processor.apply_chat_template(my_conversation_dict)to correctly format your prompts.
from transformers import AutoProcessor, AutoModelForImageTextToText
model_checkpoint = "openbmb/MiniCPM-V-4_6"
processor = AutoProcessor.from_pretrained(model_checkpoint)
model = AutoModelForImageTextToText.from_pretrained(model_checkpoint, device_map="auto")
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, add_generation_prompt=True, tokenize=True,
return_dict=True, return_tensors="pt",
).to(model.device, dtype=model.dtype)
output = model.generate(**inputs, max_new_tokens=100)
decoded_output = processor.decode(output[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(decoded_output)Downsampling mode
MiniCPM-V 4.6 supports two visual downsampling modes:
- 16x (default): More aggressive downsampling, fewer visual tokens, faster inference.
- 4x: Less downsampling, more visual tokens, better for detail-rich tasks.
You can change the downsampling mode at runtime by passing downsample_mode via processor_kwargs and to model.generate:
inputs = processor.apply_chat_template(
messages, add_generation_prompt=True, tokenize=True,
return_dict=True, return_tensors="pt",
processor_kwargs={"downsample_mode": "4x"},
).to(model.device, dtype=model.dtype)
output = model.generate(**inputs, max_new_tokens=100, downsample_mode="4x")Thinking mode
The model supports a thinking mode controlled by enable_thinking in the chat template. When enabled, the model generates internal reasoning before providing the final answer:
inputs = processor.apply_chat_template(
messages, add_generation_prompt=True, tokenize=True,
return_dict=True, return_tensors="pt",
enable_thinking=True,
).to(model.device, dtype=model.dtype)
output = model.generate(**inputs, max_new_tokens=1024)To disable thinking (default for evaluation):
inputs = processor.apply_chat_template(
messages, add_generation_prompt=True, tokenize=True,
return_dict=True, return_tensors="pt",
enable_thinking=False,
).to(model.device, dtype=model.dtype)Image processing backend
MiniCPM-V 4.6 provides two image processing backends:
- torchvision (default): Uses
torchvision.transformsfor image resizing. - pil: Uses
PIL.Image.resize, matching the original implementation.
To use the PIL backend:
from transformers import AutoProcessor, AutoImageProcessor
processor = AutoProcessor.from_pretrained(model_checkpoint)
processor.image_processor = AutoImageProcessor.from_pretrained(model_checkpoint, backend="pil")Video inference
MiniCPM-V 4.6 supports video understanding.
messages = [
{
"role": "user",
"content": [
{"type": "video", "video": "path/to/video.mp4"},
{"type": "text", "text": "Describe what happens in this video."},
],
}
]
inputs = processor.apply_chat_template(
messages, add_generation_prompt=True, tokenize=True,
return_dict=True, return_tensors="pt",
).to(model.device, dtype=model.dtype)
output = model.generate(**inputs, max_new_tokens=200)
decoded_output = processor.decode(output[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(decoded_output)If you already have the rendered prompt string, you can call processor(text=..., videos=[...]) directly instead.
MiniCPMV4_6Config
class transformers.MiniCPMV4_6Config
< 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']] = Nonetext_config: dict | transformers.configuration_utils.PreTrainedConfig | None = Nonevision_config: dict | transformers.configuration_utils.PreTrainedConfig | None = Noneinsert_layer_id: int = 6image_size: int = 448drop_vision_last_layer: bool = Falseimage_token_id: int | None = Nonevideo_token_id: int | None = Nonetie_word_embeddings: bool = Falsedownsample_mode: str = '16x'merge_kernel_size: tuple[int, int] | list[int] = (2, 2)merger_times: int = 1 )
Parameters
- text_config (
Union[dict, ~configuration_utils.PreTrainedConfig], optional) — The config object or dictionary of the text backbone. - vision_config (
Union[dict, ~configuration_utils.PreTrainedConfig], optional) — The config object or dictionary of the vision backbone. - insert_layer_id (
int, optional, defaults to 6) — Vision encoder layer index after which the window-attention merger is applied. - image_size (
int, optional, defaults to 448) — Base resolution for image preprocessing. - drop_vision_last_layer (
bool, optional, defaults toFalse) — Whether to drop the last layer of the vision encoder. - image_token_id (
int, optional) — Token id used as the image placeholder. - video_token_id (
int, optional) — Token id used as the video placeholder. - tie_word_embeddings (
bool, optional, defaults toFalse) — Whether to tie weight embeddings according to model’stied_weights_keysmapping. - downsample_mode (
str, optional, defaults to"16x") — Visual token downsampling ratio."4x"keeps 4× more tokens. - merge_kernel_size (
tuple[int, int], optional, defaults to(2, 2)) — Kernel size(h, w)for merging adjacent visual patches in the Merger. - merger_times (
int, optional, defaults to 1) — Number of iterative merge rounds in the Merger.
This is the configuration class to store the configuration of a MiniCPMV4_6Model. It is used to instantiate a Minicpmv4 6 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 openbmb/MiniCPM-V-4.6
Configuration objects inherit from PreTrainedConfig and can be used to control the model outputs. Read the documentation from PreTrainedConfig for more information.
MiniCPMV4_6VisionConfig
class transformers.MiniCPMV4_6VisionConfig
< 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 = 768intermediate_size: int = 3072num_hidden_layers: int = 12num_attention_heads: int = 12num_channels: int = 3image_size: int | list[int] | tuple[int, int] = 224patch_size: int | list[int] | tuple[int, int] = 16hidden_act: str = 'gelu_pytorch_tanh'layer_norm_eps: float = 1e-06attention_dropout: float | int = 0.0insert_layer_id: int = 6window_kernel_size: tuple[int, int] | list[int] = (2, 2) )
Parameters
- hidden_size (
int, optional, defaults to768) — 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 to12) — 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 to16) — 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. - insert_layer_id (
int, optional, defaults to 6) — Vision encoder layer index after which the window-attention merger is applied. - window_kernel_size (
tuple[int, int], optional, defaults to(2, 2)) — Window size(h, w)for the intermediate window-attention merger.
This is the configuration class to store the configuration of a MiniCPMV4_6Model. It is used to instantiate a Minicpmv4 6 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 openbmb/MiniCPM-V-4.6
Configuration objects inherit from PreTrainedConfig and can be used to control the model outputs. Read the documentation from PreTrainedConfig for more information.
MiniCPMV4_6Model
class transformers.MiniCPMV4_6Model
< source >( config: MiniCPMV4_6Config )
Parameters
- config (MiniCPMV4_6Config) — 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 MiniCPMV4_6 model which consists of a vision backbone and a language model, without a language modeling head.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward
< source >( input_ids: typing.Optional[torch.LongTensor] = Nonepixel_values: typing.Optional[torch.FloatTensor] = Nonetarget_sizes: typing.Optional[torch.IntTensor] = Nonepixel_values_videos: typing.Optional[torch.FloatTensor] = Nonetarget_sizes_videos: typing.Optional[torch.IntTensor] = Noneattention_mask: typing.Optional[torch.Tensor] = Noneposition_ids: typing.Optional[torch.LongTensor] = Nonepast_key_values: list[torch.FloatTensor] | None = Noneinputs_embeds: typing.Optional[torch.FloatTensor] = Noneuse_cache: bool | None = Nonedownsample_mode: str | None = None**kwargs: Unpack ) → BaseModelOutputWithPast 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.
- pixel_values (
torch.FloatTensor, optional) — Pixel value patches for images, NaViT-packed. - target_sizes (
torch.IntTensor, optional) — Height and width (in patches) for each image. - pixel_values_videos (
torch.FloatTensor, optional) — Pixel value patches for video frames, NaViT-packed. - target_sizes_videos (
torch.IntTensor, optional) — Height and width (in patches) for each video frame. - 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 (
list[torch.FloatTensor], optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in 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. - 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). - downsample_mode (
str, optional) —"4x"keeps 4x more visual tokens; default"16x"applies full merge.
Returns
BaseModelOutputWithPast or tuple(torch.FloatTensor)
A 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 (MiniCPMV4_6Config) and inputs.
The MiniCPMV4_6Model 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.
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.If
past_key_valuesis used only the last hidden-state of the sequences of shape(batch_size, 1, hidden_size)is output.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 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.
get_image_features
< source >( pixel_values: FloatTensortarget_sizes: IntTensordownsample_mode: str | None = None ) → 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 values can be obtained using MiniCPMV4_6ImageProcessor. SeeMiniCPMV4_6ImageProcessor.__call__()for details (MiniCPMV4_6Processor uses MiniCPMV4_6ImageProcessor for processing images). - target_sizes (
torch.IntTensorof shape(num_images, 2)) — Height and width (in patches) of each image. - downsample_mode (
str, optional) — When set to"4x"the intermediatevit_mergeris skipped so that each image keeps4×more visual tokens. Default"16x"mode applies the full merge pipeline.
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 (MiniCPMV4_6Config) and inputs.
Extract image features: vision encoder, insert merger, then MLP merger.
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.
MiniCPMV4_6ForConditionalGeneration
forward
< source >( input_ids: typing.Optional[torch.LongTensor] = Nonepixel_values: typing.Optional[torch.FloatTensor] = Nonetarget_sizes: typing.Optional[torch.IntTensor] = Nonepixel_values_videos: typing.Optional[torch.FloatTensor] = Nonetarget_sizes_videos: typing.Optional[torch.IntTensor] = Noneattention_mask: typing.Optional[torch.Tensor] = Noneposition_ids: typing.Optional[torch.LongTensor] = Nonepast_key_values: list[torch.FloatTensor] | None = Noneinputs_embeds: typing.Optional[torch.FloatTensor] = Nonelabels: typing.Optional[torch.LongTensor] = Noneuse_cache: bool | None = Nonedownsample_mode: str | None = None**kwargs: Unpack ) → CausalLMOutputWithPast 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.
- pixel_values (
torch.FloatTensor, optional) — Pixel value patches for images, NaViT-packed. - target_sizes (
torch.IntTensor, optional) — Height and width (in patches) for each image. - pixel_values_videos (
torch.FloatTensor, optional) — Pixel value patches for video frames, NaViT-packed. - target_sizes_videos (
torch.IntTensor, optional) — Height and width (in patches) for each video frame. - 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 (
list[torch.FloatTensor], optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in 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. - 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]or -100 (seeinput_idsdocstring). Tokens with indices set to-100are 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). - downsample_mode (
str, optional) —"4x"keeps 4x more visual tokens; default"16x"applies full merge.
Returns
CausalLMOutputWithPast or tuple(torch.FloatTensor)
A 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 (MiniCPMV4_6Config) and inputs.
The MiniCPMV4_6ForConditionalGeneration 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.
Example:
>>> from PIL import Image
>>> from transformers import AutoProcessor, MiniCPMV4_6ForConditionalGeneration
>>> model = MiniCPMV4_6ForConditionalGeneration.from_pretrained("openbmb/MiniCPM-V-4.6")
>>> processor = AutoProcessor.from_pretrained("openbmb/MiniCPM-V-4.6")
>>> 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]get_image_features
< source >( *args**kwargs ) → BaseModelOutputWithPooling or tuple(torch.FloatTensor)
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 (MiniCPMV4_6Config) and inputs.
Extract image features: vision encoder, insert merger, then MLP merger.
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, MiniCPMV4_6ForConditionalGeneration
>>> model = MiniCPMV4_6ForConditionalGeneration.from_pretrained("openbmb/MiniCPM-V-4.6")
>>> processor = AutoProcessor.from_pretrained("openbmb/MiniCPM-V-4.6")
>>> 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]MiniCPMV4_6Processor
class transformers.MiniCPMV4_6Processor
< source >( image_processor = Nonevideo_processor = Nonetokenizer = Nonechat_template = None**kwargs )
Parameters
- image_processor (
MiniCPMV4_6ImageProcessor) — The image processor is a required input. - video_processor (
MiniCPMV4_6VideoProcessor) — The video processor is a required input. - tokenizer (
TokenizersBackend) — The tokenizer is a required input. - chat_template (
str) — A Jinja template to convert lists of messages in a chat into a tokenizable string.
Constructs a MiniCPMV4_6Processor which wraps a image processor, a video processor, and a tokenizer into a single processor.
MiniCPMV4_6Processor offers all the functionalities of MiniCPMV4_6ImageProcessor, MiniCPMV4_6VideoProcessor, and TokenizersBackend. See the ~MiniCPMV4_6ImageProcessor, ~MiniCPMV4_6VideoProcessor, and ~TokenizersBackend 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] = 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. - 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.
MiniCPMV4_6ImageProcessor
class transformers.MiniCPMV4_6ImageProcessor
< source >( **kwargs: Unpack )
Parameters
- do_convert_rgb (bool, kwargs, optional, defaults to True) — Whether to convert the image to RGB.
- do_resize (bool, kwargs, optional, defaults to True) — 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, defaults to True) — 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 applying center_crop.
- resample (Annotated[Union[int, PILImageResampling, NoneType], None], kwargs, defaults to Resampling.BICUBIC) — Resampling filter to use if resizing the image. This can be one of the enum PILImageResampling. Only has an effect if do_resize is set to True.
- do_rescale (bool, kwargs, optional, defaults to True) — Whether to rescale the image.
- rescale_factor (float, kwargs, optional, defaults to 0.00392156862745098) — Rescale factor to rescale the image by if do_rescale is set to True.
- do_normalize (bool, kwargs, optional, defaults to True) — 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 if do_normalize is set to True.
- 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 if do_normalize is set to True.
- 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. If pad_size is not provided, images will be padded to the largest height and width in the batch. Applied only when do_pad=True.
- do_center_crop (bool, kwargs, optional) — Whether to center crop the image.
- data_format (Union[str, ~image_utils.ChannelDimension], kwargs, optional) — Only ChannelDimension.FIRST is 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” or ChannelDimension.FIRST: image in (num_channels, height, width) format.
- “channels_last” or ChannelDimension.LAST: image in (height, width, num_channels) format.
- “none” or ChannelDimension.NONE: image in (height, width) format.
- device (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.
- max_slice_nums (int, kwargs, optional, defaults to 9) — Maximum number of slices when splitting a high-resolution image.
- scale_resolution (int, kwargs, optional, defaults to 448) — Target resolution for individual slices.
- patch_size (int, kwargs, optional, defaults to 14) — Spatial patch size of the vision encoder.
- slice_mode (bool, kwargs, optional, defaults to True) — Whether to split images into multiple slices for higher resolution.
- downsample_mode (str, kwargs, optional, defaults to “16x”) — Visual token downsampling mode. “16x” applies full merge; “4x” keeps 4x more tokens.
- use_image_id (bool, kwargs, optional, defaults to True) —
Whether to prepend an image-id tag (
<image_id>N</image_id>) before each image placeholder. Consumed by the Processor for placeholder generation, not by the image processing pipeline itself.
Constructs a MiniCPMV4_6ImageProcessor 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, set do_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 applying center_crop.
- resample (Annotated[Union[int, PILImageResampling, NoneType], None], kwargs) — Resampling filter to use if resizing the image. This can be one of the enum PILImageResampling. Only has an effect if do_resize is set to True.
- do_rescale (bool, kwargs, optional) — Whether to rescale the image.
- rescale_factor (float, kwargs, optional) — Rescale factor to rescale the image by if do_rescale is set to True.
- 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 if do_normalize is set to True.
- image_std (Union[float, list[float], tuple[float, …]], kwargs, optional) — Image standard deviation to use for normalization. Only has an effect if do_normalize is set to True.
- 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. If pad_size is not provided, images will be padded to the largest height and width in the batch. Applied only when do_pad=True.
- do_center_crop (bool, kwargs, optional) — Whether to center crop the image.
- data_format (Union[str, ~image_utils.ChannelDimension], kwargs, optional) — Only ChannelDimension.FIRST is 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” or ChannelDimension.FIRST: image in (num_channels, height, width) format.
- “channels_last” or ChannelDimension.LAST: image in (height, width, num_channels) format.
- “none” or ChannelDimension.NONE: image in (height, width) format.
- device (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.
- max_slice_nums (int, kwargs, optional, defaults to 9) — Maximum number of slices when splitting a high-resolution image.
- scale_resolution (int, kwargs, optional, defaults to 448) — Target resolution for individual slices.
- patch_size (int, kwargs, optional, defaults to 14) — Spatial patch size of the vision encoder.
- slice_mode (bool, kwargs, optional, defaults to True) — Whether to split images into multiple slices for higher resolution.
- downsample_mode (str, kwargs, optional, defaults to “16x”) — Visual token downsampling mode. “16x” applies full merge; “4x” keeps 4x more tokens.
- use_image_id (bool, kwargs, optional, defaults to True) —
Whether to prepend an image-id tag (
<image_id>N</image_id>) before each image placeholder. Consumed by the Processor for placeholder generation, not by the image processing pipeline itself.
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.
MiniCPMV4_6ImageProcessorPil
class transformers.MiniCPMV4_6ImageProcessorPil
< source >( **kwargs: Unpack )
Constructs a MiniCPMV4_6ImageProcessor 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. - return_tensors (
stror TensorType, optional) — Returns stacked tensors if set to'pt', otherwise returns a list of tensors. - **kwargs (
MiniCPMV4_6ImageProcessorPilKwargs, optional) — Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.
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.
MiniCPMV4_6VideoProcessor
class transformers.MiniCPMV4_6VideoProcessor
< source >( **kwargs: Unpack )
Parameters
- do_convert_rgb (bool, kwargs, optional, defaults to True) — Whether to convert the image to RGB.
- do_resize (bool, kwargs, optional, defaults to True) — Whether to resize the image.
- size (Annotated[int | list[int] | tuple[int, …] | dict[str, int] | None, None], kwargs, defaults to None) — Describes the maximum input dimensions to the model.
- default_to_square (bool, kwargs, optional, defaults to True) — Whether to default to a square image when resizing, if size is an int.
- resample (Annotated[Union[int, PILImageResampling, NoneType], None], kwargs, defaults to Resampling.BICUBIC) — Resampling filter to use if resizing the image. This can be one of the enum PILImageResampling. Only has an effect if do_resize is set to True.
- do_rescale (bool, kwargs, optional, defaults to True) — Whether to rescale the image.
- rescale_factor (float, kwargs, optional, defaults to 0.00392156862745098) — Rescale factor to rescale the image by if do_rescale is set to True.
- do_normalize (bool, kwargs, optional, defaults to True) — 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 if do_normalize is set to True.
- 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 if do_normalize is set to True.
- do_center_crop (bool, kwargs, optional, defaults to None) — 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, defaults to None) — Size of the output image after applying center_crop.
- data_format (Union[str, ~image_utils.ChannelDimension], kwargs, optional) — Only ChannelDimension.FIRST is 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” or ChannelDimension.FIRST: image in (num_channels, height, width) format.
- “channels_last” or ChannelDimension.LAST: image in (height, width, num_channels) format.
- “none” or ChannelDimension.NONE: image in (height, width) format.
- device (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, defaults to True) — 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, defaults to None) — 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, defaults to None) — Target frames to sample per second when do_sample_frames=True.
- num_frames (Annotated[int | None, None], kwargs, defaults to None) — Maximum number of frames to sample when do_sample_frames=True.
- return_metadata (bool, kwargs, optional, defaults to False) — 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.
- max_num_frames (int, kwargs, optional, defaults to 128) — Maximum number of main frames to sample per video.
- stack_frames (int, kwargs, optional, defaults to 1) —
Sub-frames per second to stack.
1disables stacking. - max_slice_nums (int, kwargs, optional, defaults to 9) — Maximum number of slices when splitting a high-resolution image.
- scale_resolution (int, kwargs, optional, defaults to 448) — Target resolution for individual slices.
- patch_size (int, kwargs, optional, defaults to 14) — Spatial patch size of the vision encoder.
- slice_mode (bool, kwargs, optional, defaults to True) — Whether to split images into multiple slices for higher resolution.
- downsample_mode (str, kwargs, optional, defaults to “16x”) — Visual token downsampling mode. “16x” applies full merge; “4x” keeps 4x more tokens.
- use_image_id (bool, kwargs, optional, defaults to True) —
Whether to prepend an image-id tag (
<image_id>N</image_id>) before each image placeholder. Consumed by the Processor for placeholder generation, not by the image processing pipeline itself.
Constructs a MiniCPMV4_6VideoProcessor video processor.
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.