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Fix some documentation in ./src/diffusers/models/adapter.py (#9591)
* Fix some documentation in ./src/diffusers/models/adapter.py * Update src/diffusers/models/adapter.py * Update src/diffusers/models/adapter.py * Update src/diffusers/models/adapter.py * Update src/diffusers/models/adapter.py * Update src/diffusers/models/adapter.py * Update src/diffusers/models/adapter.py * Update src/diffusers/models/adapter.py * Update src/diffusers/models/adapter.py * Update src/diffusers/models/adapter.py * Update src/diffusers/models/adapter.py * Update src/diffusers/models/adapter.py * Update src/diffusers/models/adapter.py * Update src/diffusers/models/adapter.py * Update src/diffusers/models/adapter.py * Update src/diffusers/models/adapter.py * Update src/diffusers/models/adapter.py * Update src/diffusers/models/adapter.py * Update src/diffusers/models/adapter.py Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * Update src/diffusers/models/adapter.py Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * Update src/diffusers/models/adapter.py Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * Update src/diffusers/models/adapter.py Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * Update src/diffusers/models/adapter.py Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * Update src/diffusers/models/adapter.py Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * Update src/diffusers/models/adapter.py Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * run make style * make style & fix * make style : 0.1.5 version ruff * revert changes to examples --------- Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> Co-authored-by: Aryan <aryan@huggingface.co>
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@@ -30,10 +30,10 @@ class MultiAdapter(ModelMixin):
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MultiAdapter is a wrapper model that contains multiple adapter models and merges their outputs according to
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user-assigned weighting.
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This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library
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implements for all the model (such as downloading or saving, etc.)
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This model inherits from [`ModelMixin`]. Check the superclass documentation for common methods such as downloading
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or saving.
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Parameters:
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Args:
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adapters (`List[T2IAdapter]`, *optional*, defaults to None):
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A list of `T2IAdapter` model instances.
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"""
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@@ -77,11 +77,13 @@ class MultiAdapter(ModelMixin):
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r"""
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Args:
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xs (`torch.Tensor`):
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(batch, channel, height, width) input images for multiple adapter models concated along dimension 1,
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`channel` should equal to `num_adapter` * "number of channel of image".
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A tensor of shape (batch, channel, height, width) representing input images for multiple adapter
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models, concatenated along dimension 1(channel dimension). The `channel` dimension should be equal to
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`num_adapter` * number of channel per image.
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adapter_weights (`List[float]`, *optional*, defaults to None):
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List of floats representing the weight which will be multiply to each adapter's output before adding
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them together.
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A list of floats representing the weights which will be multiplied by each adapter's output before
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summing them together. If `None`, equal weights will be used for all adapters.
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"""
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if adapter_weights is None:
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adapter_weights = torch.tensor([1 / self.num_adapter] * self.num_adapter)
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@@ -109,24 +111,24 @@ class MultiAdapter(ModelMixin):
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variant: Optional[str] = None,
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):
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"""
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Save a model and its configuration file to a directory, so that it can be re-loaded using the
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Save a model and its configuration file to a specified directory, allowing it to be re-loaded with the
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`[`~models.adapter.MultiAdapter.from_pretrained`]` class method.
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Arguments:
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Args:
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save_directory (`str` or `os.PathLike`):
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Directory to which to save. Will be created if it doesn't exist.
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is_main_process (`bool`, *optional*, defaults to `True`):
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Whether the process calling this is the main process or not. Useful when in distributed training like
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TPUs and need to call this function on all processes. In this case, set `is_main_process=True` only on
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the main process to avoid race conditions.
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The directory where the model will be saved. If the directory does not exist, it will be created.
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is_main_process (`bool`, optional, defaults=True):
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Indicates whether current process is the main process or not. Useful for distributed training (e.g.,
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TPUs) and need to call this function on all processes. In this case, set `is_main_process=True` only
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for the main process to avoid race conditions.
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save_function (`Callable`):
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The function to use to save the state dictionary. Useful on distributed training like TPUs when one
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need to replace `torch.save` by another method. Can be configured with the environment variable
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`DIFFUSERS_SAVE_MODE`.
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safe_serialization (`bool`, *optional*, defaults to `True`):
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Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
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Function used to save the state dictionary. Useful for distributed training (e.g., TPUs) to replace
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`torch.save` with another method. Can also be configured using`DIFFUSERS_SAVE_MODE` environment
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variable.
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safe_serialization (`bool`, optional, defaults=True):
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If `True`, save the model using `safetensors`. If `False`, save the model with `pickle`.
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variant (`str`, *optional*):
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If specified, weights are saved in the format pytorch_model.<variant>.bin.
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If specified, weights are saved in the format `pytorch_model.<variant>.bin`.
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"""
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idx = 0
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model_path_to_save = save_directory
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@@ -145,19 +147,17 @@ class MultiAdapter(ModelMixin):
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@classmethod
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def from_pretrained(cls, pretrained_model_path: Optional[Union[str, os.PathLike]], **kwargs):
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r"""
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Instantiate a pretrained MultiAdapter model from multiple pre-trained adapter models.
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Instantiate a pretrained `MultiAdapter` model from multiple pre-trained adapter models.
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The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train
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the model, you should first set it back in training mode with `model.train()`.
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the model, set it back to training mode using `model.train()`.
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The warning *Weights from XXX not initialized from pretrained model* means that the weights of XXX do not come
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pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning
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task.
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Warnings:
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*Weights from XXX not initialized from pretrained model* means that the weights of XXX are not pretrained
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with the rest of the model. It is up to you to train those weights with a downstream fine-tuning. *Weights
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from XXX not used in YYY* means that the layer XXX is not used by YYY, so those weights are discarded.
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The warning *Weights from XXX not used in YYY* means that the layer XXX is not used by YYY, therefore those
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weights are discarded.
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Parameters:
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Args:
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pretrained_model_path (`os.PathLike`):
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A path to a *directory* containing model weights saved using
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[`~diffusers.models.adapter.MultiAdapter.save_pretrained`], e.g., `./my_model_directory/adapter`.
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@@ -175,20 +175,20 @@ class MultiAdapter(ModelMixin):
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more information about each option see [designing a device
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map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
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max_memory (`Dict`, *optional*):
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A dictionary device identifier to maximum memory. Will default to the maximum memory available for each
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GPU and the available CPU RAM if unset.
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A dictionary mapping device identifiers to their maximum memory. Default to the maximum memory
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available for each GPU and the available CPU RAM if unset.
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low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
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Speed up model loading by not initializing the weights and only loading the pre-trained weights. This
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also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the
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model. This is only supported when torch version >= 1.9.0. If you are using an older version of torch,
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setting this argument to `True` will raise an error.
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variant (`str`, *optional*):
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If specified load weights from `variant` filename, *e.g.* pytorch_model.<variant>.bin. `variant` is
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ignored when using `from_flax`.
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If specified, load weights from a `variant` file (*e.g.* pytorch_model.<variant>.bin). `variant` will
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be ignored when using `from_flax`.
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use_safetensors (`bool`, *optional*, defaults to `None`):
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If set to `None`, the `safetensors` weights will be downloaded if they're available **and** if the
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`safetensors` library is installed. If set to `True`, the model will be forcibly loaded from
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`safetensors` weights. If set to `False`, loading will *not* use `safetensors`.
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If `None`, the `safetensors` weights will be downloaded if available **and** if`safetensors` library is
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installed. If `True`, the model will be forcibly loaded from`safetensors` weights. If `False`,
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`safetensors` is not used.
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"""
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idx = 0
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adapters = []
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@@ -223,22 +223,22 @@ class T2IAdapter(ModelMixin, ConfigMixin):
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and
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[AdapterLight](https://github.com/TencentARC/T2I-Adapter/blob/686de4681515662c0ac2ffa07bf5dda83af1038a/ldm/modules/encoders/adapter.py#L235).
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This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library
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implements for all the model (such as downloading or saving, etc.)
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This model inherits from [`ModelMixin`]. Check the superclass documentation for the common methods, such as
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downloading or saving.
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Parameters:
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in_channels (`int`, *optional*, defaults to 3):
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Number of channels of Aapter's input(*control image*). Set this parameter to 1 if you're using gray scale
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image as *control image*.
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Args:
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in_channels (`int`, *optional*, defaults to `3`):
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The number of channels in the adapter's input (*control image*). Set it to 1 if you're using a gray scale
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image.
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channels (`List[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
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The number of channel of each downsample block's output hidden state. The `len(block_out_channels)` will
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also determine the number of downsample blocks in the Adapter.
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num_res_blocks (`int`, *optional*, defaults to 2):
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The number of channels in each downsample block's output hidden state. The `len(block_out_channels)`
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determines the number of downsample blocks in the adapter.
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num_res_blocks (`int`, *optional*, defaults to `2`):
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Number of ResNet blocks in each downsample block.
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downscale_factor (`int`, *optional*, defaults to 8):
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downscale_factor (`int`, *optional*, defaults to `8`):
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A factor that determines the total downscale factor of the Adapter.
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adapter_type (`str`, *optional*, defaults to `full_adapter`):
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The type of Adapter to use. Choose either `full_adapter` or `full_adapter_xl` or `light_adapter`.
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Adapter type (`full_adapter` or `full_adapter_xl` or `light_adapter`) to use.
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"""
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@register_to_config
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@@ -393,7 +393,7 @@ class AdapterBlock(nn.Module):
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An AdapterBlock is a helper model that contains multiple ResNet-like blocks. It is used in the `FullAdapter` and
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`FullAdapterXL` models.
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Parameters:
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Args:
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in_channels (`int`):
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Number of channels of AdapterBlock's input.
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out_channels (`int`):
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@@ -401,7 +401,7 @@ class AdapterBlock(nn.Module):
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num_res_blocks (`int`):
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Number of ResNet blocks in the AdapterBlock.
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down (`bool`, *optional*, defaults to `False`):
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Whether to perform downsampling on AdapterBlock's input.
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If `True`, perform downsampling on AdapterBlock's input.
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"""
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def __init__(self, in_channels: int, out_channels: int, num_res_blocks: int, down: bool = False):
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@@ -440,7 +440,7 @@ class AdapterResnetBlock(nn.Module):
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r"""
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An `AdapterResnetBlock` is a helper model that implements a ResNet-like block.
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Parameters:
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Args:
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channels (`int`):
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Number of channels of AdapterResnetBlock's input and output.
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"""
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@@ -518,7 +518,7 @@ class LightAdapterBlock(nn.Module):
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A `LightAdapterBlock` is a helper model that contains multiple `LightAdapterResnetBlocks`. It is used in the
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`LightAdapter` model.
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Parameters:
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Args:
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in_channels (`int`):
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Number of channels of LightAdapterBlock's input.
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out_channels (`int`):
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@@ -526,7 +526,7 @@ class LightAdapterBlock(nn.Module):
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num_res_blocks (`int`):
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Number of LightAdapterResnetBlocks in the LightAdapterBlock.
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down (`bool`, *optional*, defaults to `False`):
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Whether to perform downsampling on LightAdapterBlock's input.
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If `True`, perform downsampling on LightAdapterBlock's input.
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"""
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def __init__(self, in_channels: int, out_channels: int, num_res_blocks: int, down: bool = False):
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@@ -561,7 +561,7 @@ class LightAdapterResnetBlock(nn.Module):
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A `LightAdapterResnetBlock` is a helper model that implements a ResNet-like block with a slightly different
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architecture than `AdapterResnetBlock`.
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Parameters:
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Args:
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channels (`int`):
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Number of channels of LightAdapterResnetBlock's input and output.
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"""
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