Datasets:
sayakpaul
/
diffusers-qa-chatbot-artifacts

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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 as preconditioning of the score networks. Together, our improvements yield new state-of-the-art FID of 1.79 for CIFAR-10 in a class-conditional setting and 1.97 in an unconditional setting, with much faster sampling (35 network evaluations per image) than prior designs. To further demonstrate their modular nature, we show that our design changes dramatically improve both the efficiency and quality obtainable with pre-trained score networks from previous work, including improving the FID of an existing ImageNet-64 model from 2.07 to near-SOTA 1.55.
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.