class MaskedImageModelDescriptor(ModelBase[
Constructor: MaskedImageModelDescriptor(model, state_dict, architecture, purpose, ...)
A model that takes an image and a mask for that image as input and returns an image. See __call__
for more information.
Method | __call__ |
Takes an image tensor and an image mask tensor as input and returns a single image tensor as output. |
Method | __init__ |
Undocumented |
Property | purpose |
The purpose of this model. |
Instance Variable | _call |
Undocumented |
Instance Variable | _purpose |
Undocumented |
Inherited from ModelBase
:
Method | bfloat16 |
Moves the parameters and buffers of the underlying module to bfloat16 precision. |
Method | cpu |
Moves the parameters and buffers of the underlying module to the CPU. |
Method | cuda |
Moves the parameters and buffers of the underlying module to the GPU. |
Method | eval |
Sets the underlying module in evaluation mode. |
Method | float |
Moves the parameters and buffers of the underlying module to single precision (fp32). |
Method | half |
Moves the parameters and buffers of the underlying module to half precision (fp16). |
Method | to |
Moves and casts the parameters and buffers of the underlying module to the given device and data type. |
Method | train |
Sets the underlying module in training mode. |
Instance Variable | input |
The number of input image channels of the model. E.g. 3 for RGB, 1 for grayscale. |
Instance Variable | output |
The number of output image channels of the model. E.g. 3 for RGB, 1 for grayscale. |
Instance Variable | scale |
The output scale of super resolution models. E.g. 4x, 2x, 1x. |
Instance Variable | size |
Size requirements for the input image. E.g. minimum size. |
Instance Variable | supports |
Whether the model supports bfloat16 precision. |
Instance Variable | supports |
Whether the model supports half precision (fp16). |
Instance Variable | tags |
A list of tags for the model, usually describing the size or model parameters. E.g. "64nf" or "large". |
Instance Variable | tiling |
Whether the model supports tiling. |
Property | architecture |
The architecture of the model. |
Property | device |
The device of the underlying module. |
Property | dtype |
The data type of the underlying module. |
Property | model |
The model itself: a torch.nn.Module with weights loaded in. |
Instance Variable | _architecture |
Undocumented |
Instance Variable | _model |
Undocumented |
Takes an image tensor and an image mask tensor as input and returns a single image tensor as output.
The data type (float32, float16, bfloat16) and device of the image
and mask
tensors must be the same as the model.
The image
tensor must be a 4D tensor with shape (1, input_channels, H, W)
. The range of the image
tensor must be [0, 1].
The mask
tensor must be a 4D tensor with shape (1, 1, H, W)
. The width and height must be the same as image
tensor. The values of the mask
tensor must be either 0 (keep) or 1 (inpaint).
The output tensor will be a 4D tensor with shape (1, output_channels, H, W)
. The data type and device of the output tensor will be the same as the image
tensor. The range of the output tensor will be [0, 1].
If the width and height of the image
tensor do not satisfy the size_requirements
of the model, then the image
tensor will be padded to satisfy the requirements. The additional padding will be removed from the output tensor before returning it. If the image already satisfies the requirements, then no padding will be added.
T
, state_dict: StateDict
, architecture: Architecture[ T]
, purpose: Literal[ 'Inpainting']
, tags: list[ str]
, supports_half: bool
, supports_bfloat16: bool
, input_channels: int
, output_channels: int
, size_requirements: SizeRequirements|None
= None, tiling: ModelTiling
= ModelTiling.SUPPORTED, call_fn: Callable[ [ T, Tensor, Tensor], Tensor]|None
= None):
(source)
¶
spandrel.ModelBase.__init__
Undocumented