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original_image (torch.FloatTensor) — |
the original image to inpaint on. |
mask (torch.FloatTensor) — |
the mask where 0.0 values define which part of the original image to inpaint (change). |
generator (torch.Generator, optional) — random number generator. |
return_dict (bool) — option for returning tuple rather than |
DDPMSchedulerOutput class |
Returns |
~schedulers.scheduling_utils.RePaintSchedulerOutput or tuple |
~schedulers.scheduling_utils.RePaintSchedulerOutput if return_dict is True, otherwise a tuple. When |
returning a tuple, the first element is the sample tensor. |
Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion |
process from the learned model outputs (most often the predicted noise). |
Stochastic Karras VE |
Overview |
Elucidating the Design Space of Diffusion-Based Generative Models by Tero Karras, Miika Aittala, Timo Aila and Samuli Laine. |
The abstract of the paper is the following: |
We argue that the theory and practice of diffusion-based generative models are currently unnecessarily convoluted and seek to remedy the situation by presenting a design space that clearly separates the concrete design choices. This lets us identify several changes to both the sampling and training processes, as well a... |
This pipeline implements the Stochastic sampling tailored to the Variance-Expanding (VE) models. |
Available Pipelines: |
Pipeline |
Tasks |
Colab |
pipeline_stochastic_karras_ve.py |
Unconditional Image Generation |
- |
KarrasVePipeline |
class diffusers.KarrasVePipeline |
< |
source |
> |
( |
unet: UNet2DModel |
scheduler: KarrasVeScheduler |
) |
Parameters |
unet (UNet2DModel) — U-Net architecture to denoise the encoded image. |
scheduler (KarrasVeScheduler) — |
Scheduler for the diffusion process to be used in combination with unet to denoise the encoded image. |
Stochastic sampling from Karras et al. [1] tailored to the Variance-Expanding (VE) models [2]. Use Algorithm 2 and |
the VE column of Table 1 from [1] for reference. |
[1] Karras, Tero, et al. “Elucidating the Design Space of Diffusion-Based Generative Models.” |
https://arxiv.org/abs/2206.00364 [2] Song, Yang, et al. “Score-based generative modeling through stochastic |
differential equations.” https://arxiv.org/abs/2011.13456 |
__call__ |
< |
source |
> |
( |
batch_size: int = 1 |
num_inference_steps: int = 50 |
generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None |
output_type: typing.Optional[str] = 'pil' |
return_dict: bool = True |
**kwargs |
) |
→ |
ImagePipelineOutput or tuple |
Parameters |
batch_size (int, optional, defaults to 1) — |
The number of images to generate. |
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