# PI0 [PI0](https://huggingface.co/papers/2410.24164) is a vision-language-action model for robotics manipulation. It jointly processes visual observations and language instructions to generate robot actions. The abstract from the paper is as follows: *Robot learning holds tremendous promise to unlock the full potential of flexible, general, and dexterous robot systems, as well as to address some of the deepest questions in artificial intelligence. However, bringing robot learning to the level of generality required for effective real-world systems faces major obstacles in terms of data, generalization, and robustness. In this paper, we discuss how generalist robot policies (i.e., robot foundation models) can address these challenges, and how we can design effective generalist robot policies for complex and highly dexterous tasks. We propose a novel flow matching architecture built on top of a pre-trained vision-language model (VLM) to inherit Internet-scale semantic knowledge. We then discuss how this model can be trained on a large and diverse dataset from multiple dexterous robot platforms, including single-arm robots, dual-arm robots, and mobile manipulators. We evaluate our model in terms of its ability to perform tasks in zero shot after pre-training, follow language instructions from people and from a high-level VLM policy, and its ability to acquire new skills via fine-tuning. Our results cover a wide variety of tasks, such as laundry folding, table cleaning, and assembling boxes.* This model was contributed by [Molbap](https://huggingface.co/Molbap) and [RaushanTurganbay](https://huggingface.co/RaushanTurganbay). The original code can be found [here](https://github.com/Physical-Intelligence/openpi). You can find all the checkpoints under the [PI0](https://huggingface.co/collections/lerobot/pi0) collection. ## Usage examples ```python import torch from transformers import PI0ForConditionalGeneration, PI0Processor from transformers.image_utils import load_image model = PI0ForConditionalGeneration.from_pretrained( "lerobot/pi0_base", device_map="auto", attn_implementation="sdpa" ) processor = PI0Processor.from_pretrained("google/paligemma2-3b-mix-224") prompt = "Pick up the object" image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/vla_pi0.jpg") inputs = processor(image, prompt, return_tensors="pt").to(model.device) state = torch.randn(1, 32) # change with actual robot state actions = model.sample_actions(**inputs, state=state, num_steps=3) print(actions) ``` ## PI0Config[[transformers.PI0Config]] #### transformers.PI0Config[[transformers.PI0Config]] [Source](https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/pi0/configuration_pi0.py#L29) This is the configuration class to store the configuration of a PI0Model. It is used to instantiate a Pi0 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 [lerobot/pi0_base](https://huggingface.co/lerobot/pi0_base) Configuration objects inherit from [PreTrainedConfig](/docs/transformers/v5.8.0/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the documentation from [PreTrainedConfig](/docs/transformers/v5.8.0/en/main_classes/configuration#transformers.PreTrainedConfig) for more information. Example: ```python >>> from transformers import PI0ForConditionalGeneration, PI0Config >>> config = PI0Config() >>> model = PI0ForConditionalGeneration(config) ``` **Parameters:** vlm_config (`dict`, *optional*) : Configuration for the vlm backbone (PaliGemmaModel). dit_config (`dict`, *optional*) : Configuration for the DiT backbone. Defaults to a Gemma 300M variant. chunk_size (`int`, *optional*, defaults to 50) : Number of action steps to predict per chunk. max_state_dim (`int`, *optional*, defaults to 32) : Maximum state vector dimension (shorter vectors are zero-padded). max_action_dim (`int`, *optional*, defaults to 32) : Maximum action vector dimension (shorter vectors are zero-padded). num_inference_steps (`int`, *optional*, defaults to 10) : Number of denoising steps during inference. time_sampling_beta_alpha (`float`, *optional*, defaults to 1.5) : Alpha parameter for Beta distribution used to sample diffusion time during training. time_sampling_beta_beta (`float`, *optional*, defaults to 1.0) : Beta parameter for Beta distribution used to sample diffusion time during training. time_sampling_scale (`float`, *optional*, defaults to 0.999) : Scale factor for sampled time values. time_sampling_offset (`float`, *optional*, defaults to 0.001) : Offset added to sampled time values. min_period (`float`, *optional*, defaults to 0.004) : Minimum period for sinusoidal time embedding. max_period (`float`, *optional*, defaults to 4.0) : Maximum period for sinusoidal time embedding. loss_reduction (`str`, *optional*, defaults to `"mean"`) : The reduction to use on MSE loss. ## PI0Processor[[transformers.PI0Processor]] #### transformers.PI0Processor[[transformers.PI0Processor]] [Source](https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/pi0/processing_pi0.py#L53) Constructs a PI0Processor which wraps a image processor and a tokenizer into a single processor. [PI0Processor](/docs/transformers/v5.8.0/en/model_doc/pi0#transformers.PI0Processor) offers all the functionalities of [PI0ImageProcessor](/docs/transformers/v5.8.0/en/model_doc/pi0#transformers.PI0ImageProcessor) and `tokenizer_class`. See the [~PI0ImageProcessor](/docs/transformers/v5.8.0/en/model_doc/pi0#transformers.PI0ImageProcessor) and `~tokenizer_class` for more information. __call__transformers.PI0Processor.__call__https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/pi0/processing_pi0.py#L88[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor'], list[typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]], list[list[typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]]], NoneType]"}, {"name": "text", "val": ": str | list[str] | list[list[str]] | None = None"}, {"name": "actions", "val": ": list | numpy.ndarray | torch.Tensor | None = None"}, {"name": "state", "val": ": list | numpy.ndarray | torch.Tensor | None = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.models.pi0.processing_pi0.PI0ProcessorKwargs]"}]- **images** (`Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor], list[Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]]], list[list[Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]]]]]`, *optional*) -- Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`. - **text** (`Union[str, list[str], list[list[str]]]`, *optional*) -- The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If you pass a pretokenized input, set `is_split_into_words=True` to avoid ambiguity with batched inputs. - **actions** (`list | np.ndarray | torch.Tensor`, *optional*) -- Actions to be predicted by the model. If provided, padding, mean and std normalization will be applied. - **state** (`list | np.ndarray | torch.Tensor`, *optional*) -- Robotic states to be predicted by the model. If provided, padding, mean and std normalization will be applied. - **return_tensors** (`str` or [TensorType](/docs/transformers/v5.8.0/en/internal/file_utils#transformers.TensorType), *optional*) -- If set, will return tensors of a particular framework. Acceptable values are: - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return NumPy `np.ndarray` objects. - ****kwargs** ([ProcessingKwargs](/docs/transformers/v5.8.0/en/main_classes/processors#transformers.ProcessingKwargs), *optional*) -- Additional processing options for each modality (text, images, videos, audio). Model-specific parameters are listed above; see the TypedDict class for the complete list of supported arguments.0[BatchFeature](/docs/transformers/v5.8.0/en/main_classes/feature_extractor#transformers.BatchFeature)A [BatchFeature](/docs/transformers/v5.8.0/en/main_classes/feature_extractor#transformers.BatchFeature) with the following fields: - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. If `suffix` is provided, the `input_ids` will also contain the suffix input ids. - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not `None`). - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. - **pixel_attention_mask** -- Pixel values padding mask to be fed to a model. Returned when `images` is not `None`. - **state** -- Robot state compatible with model if `state` is not None - **actions** -- Label-actions compatible with training if `actions` is not None **Parameters:** image_processor (`PI0ImageProcessor`) : The image processor is a required input. tokenizer (`tokenizer_class`) : The tokenizer is a required input. chat_template (`str`) : A Jinja template to convert lists of messages in a chat into a tokenizable string. **Returns:** `[BatchFeature](/docs/transformers/v5.8.0/en/main_classes/feature_extractor#transformers.BatchFeature)` A [BatchFeature](/docs/transformers/v5.8.0/en/main_classes/feature_extractor#transformers.BatchFeature) with the following fields: - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. If `suffix` is provided, the `input_ids` will also contain the suffix input ids. - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not `None`). - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. - **pixel_attention_mask** -- Pixel values padding mask to be fed to a model. Returned when `images` is not `None`. - **state** -- Robot state compatible with model if `state` is not None - **actions** -- Label-actions compatible with training if `actions` is not None ## PI0ImageProcessor[[transformers.PI0ImageProcessor]] #### transformers.PI0ImageProcessor[[transformers.PI0ImageProcessor]] [Source](https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/pi0/image_processing_pi0.py#L26) Constructs a PI0ImageProcessor image processor. preprocesstransformers.PI0ImageProcessor.preprocesshttps://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/image_processing_utils.py#L382[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]"}, {"name": "*args", "val": ""}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.processing_utils.ImagesKwargs]"}]- **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`. - **return_tensors** (`str` or [TensorType](/docs/transformers/v5.8.0/en/internal/file_utils#transformers.TensorType), *optional*) -- Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors. - ****kwargs** ([ImagesKwargs](/docs/transformers/v5.8.0/en/main_classes/processors#transformers.ImagesKwargs), *optional*) -- Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.0`~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. **Parameters:** - ****kwargs** ([ImagesKwargs](/docs/transformers/v5.8.0/en/main_classes/processors#transformers.ImagesKwargs), *optional*) : Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments. **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. ## PI0Model[[transformers.PI0Model]] #### transformers.PI0Model[[transformers.PI0Model]] [Source](https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/pi0/modeling_pi0.py#L114) The bare Pi0 Model outputting raw hidden-states without any specific head on top. This model inherits from [PreTrainedModel](/docs/transformers/v5.8.0/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. forwardtransformers.PI0Model.forwardhttps://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/pi0/modeling_pi0.py#L152[{"name": "action_embeds", "val": ": Tensor"}, {"name": "input_ids", "val": ": torch.Tensor | None = None"}, {"name": "pixel_values", "val": ": torch.Tensor | None = None"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "pixel_attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "position_ids", "val": ": torch.LongTensor | None = None"}, {"name": "inputs_embeds", "val": ": torch.Tensor | None = None"}, {"name": "past_key_values", "val": ": transformers.cache_utils.Cache | None = None"}, {"name": "**kwargs", "val": ""}]- **action_embeds** (`torch.Tensor`, *optional*) -- The embeddings of input actions and robot states. - **input_ids** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) -- Indices of input sequence tokens in the vocabulary. Padding will be ignored by default. Indices can be obtained using [AutoTokenizer](/docs/transformers/v5.8.0/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.8.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/v5.8.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details. [What are input IDs?](../glossary#input-ids) - **pixel_values** (`torch.Tensor` of shape `(batch_size, num_channels, image_size, image_size)`, *optional*) -- The tensors corresponding to the input images. Pixel values can be obtained using [PI0ImageProcessor](/docs/transformers/v5.8.0/en/model_doc/pi0#transformers.PI0ImageProcessor). See `PI0ImageProcessor.__call__()` for details ([PI0Processor](/docs/transformers/v5.8.0/en/model_doc/pi0#transformers.PI0Processor) uses [PI0ImageProcessor](/docs/transformers/v5.8.0/en/model_doc/pi0#transformers.PI0ImageProcessor) for processing images). - **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) -- Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) - **pixel_attention_mask** (`torch.Tensor`, *optional*) -- The mask indicating padded positions in the input image. - **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) -- Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) - **inputs_embeds** (`torch.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) -- Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. - **past_key_values** (`~cache_utils.Cache`, *optional*) -- Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. Only [Cache](/docs/transformers/v5.8.0/en/internal/generation_utils#transformers.Cache) instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). If no `past_key_values` are passed, [DynamicCache](/docs/transformers/v5.8.0/en/internal/generation_utils#transformers.DynamicCache) will be initialized by default. The model will output the same cache format that is fed as input. If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` instead of all `input_ids` of shape `(batch_size, sequence_length)`.0[BaseModelOutputWithPast](/docs/transformers/v5.8.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast) or `tuple(torch.FloatTensor)`A [BaseModelOutputWithPast](/docs/transformers/v5.8.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast) or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration ([PI0Config](/docs/transformers/v5.8.0/en/model_doc/pi0#transformers.PI0Config)) and inputs. The [PI0Model](/docs/transformers/v5.8.0/en/model_doc/pi0#transformers.PI0Model) forward method, overrides the `__call__` special method. Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them. - **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model. If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output. - **past_key_values** (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a [Cache](/docs/transformers/v5.8.0/en/internal/generation_utils#transformers.Cache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. - **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. - **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. **Parameters:** config ([PI0Config](/docs/transformers/v5.8.0/en/model_doc/pi0#transformers.PI0Config)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/v5.8.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights. **Returns:** `[BaseModelOutputWithPast](/docs/transformers/v5.8.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast) or `tuple(torch.FloatTensor)`` A [BaseModelOutputWithPast](/docs/transformers/v5.8.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast) or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration ([PI0Config](/docs/transformers/v5.8.0/en/model_doc/pi0#transformers.PI0Config)) and inputs. #### embed_prefix[[transformers.PI0Model.embed_prefix]] [Source](https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/pi0/modeling_pi0.py#L127) ## PI0ForConditionalGeneration[[transformers.PI0ForConditionalGeneration]] #### transformers.PI0ForConditionalGeneration[[transformers.PI0ForConditionalGeneration]] [Source](https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/pi0/modeling_pi0.py#L226) PI0 model with action projection heads and flow matching. forwardtransformers.PI0ForConditionalGeneration.forwardhttps://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/pi0/modeling_pi0.py#L239[{"name": "state", "val": ": FloatTensor"}, {"name": "noise", "val": ": torch.FloatTensor | None = None"}, {"name": "timestep", "val": ": torch.FloatTensor | None = None"}, {"name": "input_ids", "val": ": torch.Tensor | None = None"}, {"name": "pixel_values", "val": ": torch.Tensor | None = None"}, {"name": "pixel_attention_mask", "val": ": torch.BoolTensor | None = None"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "position_ids", "val": ": torch.LongTensor | None = None"}, {"name": "inputs_embeds", "val": ": torch.Tensor | None = None"}, {"name": "past_key_values", "val": ": transformers.cache_utils.Cache | None = None"}, {"name": "actions", "val": ": FloatTensor = None"}, {"name": "**kwargs", "val": ""}]- **state** (`torch.Tensor`, *optional*) -- Current robot state. - **noise** (`torch.Tensor`, *optional*) -- Random noise at current timestep that needs to be denoised - **timestep** (`torch.Tensor`, *optional*) -- Current denoising timestep. - **input_ids** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) -- Indices of input sequence tokens in the vocabulary. Padding will be ignored by default. Indices can be obtained using [AutoTokenizer](/docs/transformers/v5.8.0/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.8.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/v5.8.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details. [What are input IDs?](../glossary#input-ids) - **pixel_values** (`torch.Tensor` of shape `(batch_size, num_channels, image_size, image_size)`, *optional*) -- The tensors corresponding to the input images. Pixel values can be obtained using [PI0ImageProcessor](/docs/transformers/v5.8.0/en/model_doc/pi0#transformers.PI0ImageProcessor). See `PI0ImageProcessor.__call__()` for details ([PI0Processor](/docs/transformers/v5.8.0/en/model_doc/pi0#transformers.PI0Processor) uses [PI0ImageProcessor](/docs/transformers/v5.8.0/en/model_doc/pi0#transformers.PI0ImageProcessor) for processing images). - **pixel_attention_mask** (`torch.Tensor`, *optional*) -- The mask indicating padded positions in the input image. - **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) -- Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) - **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) -- Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) - **inputs_embeds** (`torch.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) -- Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. - **past_key_values** (`~cache_utils.Cache`, *optional*) -- Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. Only [Cache](/docs/transformers/v5.8.0/en/internal/generation_utils#transformers.Cache) instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). If no `past_key_values` are passed, [DynamicCache](/docs/transformers/v5.8.0/en/internal/generation_utils#transformers.DynamicCache) will be initialized by default. The model will output the same cache format that is fed as input. If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` instead of all `input_ids` of shape `(batch_size, sequence_length)`. - **actions** (`torch.Tensor`, *optional*) -- Input actions that need to be predicted. Used only when training to compiute loss.0[CausalLMOutputWithPast](/docs/transformers/v5.8.0/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithPast) or `tuple(torch.FloatTensor)`A [CausalLMOutputWithPast](/docs/transformers/v5.8.0/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithPast) or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration ([PI0Config](/docs/transformers/v5.8.0/en/model_doc/pi0#transformers.PI0Config)) and inputs. The [PI0ForConditionalGeneration](/docs/transformers/v5.8.0/en/model_doc/pi0#transformers.PI0ForConditionalGeneration) forward method, overrides the `__call__` special method. Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them. - **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Language modeling loss (for next-token prediction). - **logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) -- Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). - **past_key_values** (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a [Cache](/docs/transformers/v5.8.0/en/internal/generation_utils#transformers.Cache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. - **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. - **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Example: ```python >>> from PIL import Image >>> from transformers import AutoProcessor, PI0ForConditionalGeneration >>> model = PI0ForConditionalGeneration.from_pretrained("lerobot/pi0_base") >>> processor = AutoProcessor.from_pretrained("lerobot/pi0_base") >>> 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] ``` **Parameters:** state (`torch.Tensor`, *optional*) : Current robot state. noise (`torch.Tensor`, *optional*) : Random noise at current timestep that needs to be denoised timestep (`torch.Tensor`, *optional*) : Current denoising timestep. input_ids (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) : Indices of input sequence tokens in the vocabulary. Padding will be ignored by default. Indices can be obtained using [AutoTokenizer](/docs/transformers/v5.8.0/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.8.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/v5.8.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details. [What are input IDs?](../glossary#input-ids) pixel_values (`torch.Tensor` of shape `(batch_size, num_channels, image_size, image_size)`, *optional*) : The tensors corresponding to the input images. Pixel values can be obtained using [PI0ImageProcessor](/docs/transformers/v5.8.0/en/model_doc/pi0#transformers.PI0ImageProcessor). See `PI0ImageProcessor.__call__()` for details ([PI0Processor](/docs/transformers/v5.8.0/en/model_doc/pi0#transformers.PI0Processor) uses [PI0ImageProcessor](/docs/transformers/v5.8.0/en/model_doc/pi0#transformers.PI0ImageProcessor) for processing images). pixel_attention_mask (`torch.Tensor`, *optional*) : The mask indicating padded positions in the input image. attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) : Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) : Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) inputs_embeds (`torch.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) : Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. past_key_values (`~cache_utils.Cache`, *optional*) : Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. Only [Cache](/docs/transformers/v5.8.0/en/internal/generation_utils#transformers.Cache) instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). If no `past_key_values` are passed, [DynamicCache](/docs/transformers/v5.8.0/en/internal/generation_utils#transformers.DynamicCache) will be initialized by default. The model will output the same cache format that is fed as input. If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` instead of all `input_ids` of shape `(batch_size, sequence_length)`. actions (`torch.Tensor`, *optional*) : Input actions that need to be predicted. Used only when training to compiute loss. **Returns:** `[CausalLMOutputWithPast](/docs/transformers/v5.8.0/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithPast) or `tuple(torch.FloatTensor)`` A [CausalLMOutputWithPast](/docs/transformers/v5.8.0/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithPast) or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration ([PI0Config](/docs/transformers/v5.8.0/en/model_doc/pi0#transformers.PI0Config)) and inputs. #### sample_actions[[transformers.PI0ForConditionalGeneration.sample_actions]] [Source](https://github.com/huggingface/transformers/blob/v5.8.0/src/transformers/models/pi0/modeling_pi0.py#L327) Run flow matching inference to generate actions.