sayakpaul/diffusers-qa-chatbot-artifacts · Datasets at Hugging Face

text stringlengths 0 5.54k
... caption,
... source_embeds=source_embeds,
... target_embeds=target_embeds,
... num_inference_steps=50,
... cross_attention_guidance_amount=0.15,
... generator=generator,
... latents=inv_latents,
... negative_prompt=caption,
... ).images[0]
>>> image.save("edited_image.png")
HeunDiscreteScheduler The Heun scheduler (Algorithm 1) is from the Elucidating the Design Space of Diffusion-Based Generative Models paper by Karras et al. The scheduler is ported from the k-diffusion library and created by Katherine Crowson. HeunDiscreteScheduler class diffusers.HeunDiscreteScheduler < source > ( num_train_timesteps: int = 1000 beta_start: float = 0.00085 beta_end: float = 0.012 beta_schedule: str = 'linear' trained_betas: Union = None prediction_type: str = 'epsilon' use_karras_sigmas: Optional = False clip_sample: Optional = False clip_sample_range: float = 1.0 timestep_spacing: str = 'linspace' steps_offset: int = 0 ) Parameters num_train_timesteps (int, defaults to 1000) —
The number of diffusion steps to train the model. beta_start (float, defaults to 0.0001) —
The starting beta value of inference. beta_end (float, defaults to 0.02) —
The final beta value. beta_schedule (str, defaults to "linear") —
The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
linear or scaled_linear. trained_betas (np.ndarray, optional) —
Pass an array of betas directly to the constructor to bypass beta_start and beta_end. prediction_type (str, defaults to epsilon, optional) —
Prediction type of the scheduler function; can be epsilon (predicts the noise of the diffusion process),
sample (directly predicts the noisy sample) or v_prediction` (see section 2.4 of Imagen
Video paper). clip_sample (bool, defaults to True) —
Clip the predicted sample for numerical stability. clip_sample_range (float, defaults to 1.0) —
The maximum magnitude for sample clipping. Valid only when clip_sample=True. use_karras_sigmas (bool, optional, defaults to False) —
Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If True,
the sigmas are determined according to a sequence of noise levels {σi}. timestep_spacing (str, defaults to "linspace") —
The way the timesteps should be scaled. Refer to Table 2 of the Common Diffusion Noise Schedules and
Sample Steps are Flawed for more information. steps_offset (int, defaults to 0) —
An offset added to the inference steps. You can use a combination of offset=1 and
set_alpha_to_one=False to make the last step use step 0 for the previous alpha product like in Stable
Diffusion. Scheduler with Heun steps for discrete beta schedules. This model inherits from SchedulerMixin and ConfigMixin. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving. scale_model_input < source > ( sample: FloatTensor timestep: Union ) → torch.FloatTensor Parameters sample (torch.FloatTensor) —
The input sample. timestep (int, optional) —
The current timestep in the diffusion chain. Returns
torch.FloatTensor

A scaled input sample.
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
current timestep. set_begin_index < source > ( begin_index: int = 0 ) Parameters begin_index (int) —
The begin index for the scheduler. Sets the begin index for the scheduler. This function should be run from pipeline before the inference. set_timesteps < source > ( num_inference_steps: int device: Union = None num_train_timesteps: Optional = None ) Parameters num_inference_steps (int) —
The number of diffusion steps used when generating samples with a pre-trained model. device (str or torch.device, optional) —
The device to which the timesteps should be moved to. If None, the timesteps are not moved. Sets the discrete timesteps used for the diffusion chain (to be run before inference). step < source > ( model_output: Union timestep: Union sample: Union return_dict: bool = True ) → SchedulerOutput or tuple Parameters model_output (torch.FloatTensor) —
The direct output from learned diffusion model. timestep (float) —
The current discrete timestep in the diffusion chain. sample (torch.FloatTensor) —
A current instance of a sample created by the diffusion process. return_dict (bool) —
Whether or not to return a SchedulerOutput or tuple. Returns
SchedulerOutput or tuple

If return_dict is True, SchedulerOutput is returned, otherwise a
tuple is returned where the first element is the sample tensor.
Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
process from the learned model outputs (most often the predicted noise). SchedulerOutput class diffusers.schedulers.scheduling_utils.SchedulerOutput < source > ( prev_sample: FloatTensor ) Parameters prev_sample (torch.FloatTensor of shape (batch_size, num_channels, height, width) for images) —
Computed sample (x_{t-1}) of previous timestep. prev_sample should be used as next model input in the
denoising loop. Base class for the output of a scheduler’s step function.
Score SDE VE


Overview

Score-Based Generative Modeling through Stochastic Differential Equations (Score SDE) by Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon and Ben Poole.
The abstract of the paper is the following:
Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially, the reverse-time SDE depends only on the time-dependent gradient field (\aka, score) of the perturbed data distribution. By leveraging advances in score-based generative modeling, we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples. We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling, allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE. We also derive an equivalent neural ODE that samples from the same distribution as the SDE, but additionally enables exact likelihood computation, and improved sampling efficiency. In addition, we provide a new way to solve inverse problems with score-based models, as demonstrated with experiments on class-conditional generation, image inpainting, and colorization. Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9.89 and FID of 2.20, a competitive likelihood of 2.99 bits/dim, and demonstrate high fidelity generation of 1024 x 1024 images for the first time from a score-based generative model.
The original codebase can be found here.
This pipeline implements the Variance Expanding (VE) variant of the method.

Available Pipelines:

Pipeline
Tasks
Colab
pipeline_score_sde_ve.py
Unconditional Image Generation
-

ScoreSdeVePipeline


class diffusers.ScoreSdeVePipeline

<
source
>
(
unet: UNet2DModel
scheduler: DiffusionPipeline

)


Parameters

This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods the —


library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) —
unet (UNet2DModel): U-Net architecture to denoise the encoded image. scheduler (SchedulerMixin):
The ScoreSdeVeScheduler scheduler to be used in combination with unet to denoise the encoded image.




__call__

... caption,

... source_embeds=source_embeds,

... target_embeds=target_embeds,

... num_inference_steps=50,

... cross_attention_guidance_amount=0.15,

... generator=generator,

... latents=inv_latents,

... negative_prompt=caption,

... ).images[0]

>>> image.save("edited_image.png")

HeunDiscreteScheduler The Heun scheduler (Algorithm 1) is from the Elucidating the Design Space of Diffusion-Based Generative Models paper by Karras et al. The scheduler is ported from the k-diffusion library and created by Katherine Crowson. HeunDiscreteScheduler class diffusers.HeunDiscreteScheduler < source > ( num_train_timesteps: int = 1000 beta_start: float = 0.00085 beta_end: float = 0.012 beta_schedule: str = 'linear' trained_betas: Union = None prediction_type: str = 'epsilon' use_karras_sigmas: Optional = False clip_sample: Optional = False clip_sample_range: float = 1.0 timestep_spacing: str = 'linspace' steps_offset: int = 0 ) Parameters num_train_timesteps (int, defaults to 1000) —

The number of diffusion steps to train the model. beta_start (float, defaults to 0.0001) —

The starting beta value of inference. beta_end (float, defaults to 0.02) —

The final beta value. beta_schedule (str, defaults to "linear") —

The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from

linear or scaled_linear. trained_betas (np.ndarray, optional) —

Pass an array of betas directly to the constructor to bypass beta_start and beta_end. prediction_type (str, defaults to epsilon, optional) —

Prediction type of the scheduler function; can be epsilon (predicts the noise of the diffusion process),

sample (directly predicts the noisy sample) or v_prediction` (see section 2.4 of Imagen

Video paper). clip_sample (bool, defaults to True) —

Clip the predicted sample for numerical stability. clip_sample_range (float, defaults to 1.0) —

The maximum magnitude for sample clipping. Valid only when clip_sample=True. use_karras_sigmas (bool, optional, defaults to False) —

Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If True,

the sigmas are determined according to a sequence of noise levels {σi}. timestep_spacing (str, defaults to "linspace") —

The way the timesteps should be scaled. Refer to Table 2 of the Common Diffusion Noise Schedules and

Sample Steps are Flawed for more information. steps_offset (int, defaults to 0) —

An offset added to the inference steps. You can use a combination of offset=1 and

set_alpha_to_one=False to make the last step use step 0 for the previous alpha product like in Stable

Diffusion. Scheduler with Heun steps for discrete beta schedules. This model inherits from SchedulerMixin and ConfigMixin. Check the superclass documentation for the generic

methods the library implements for all schedulers such as loading and saving. scale_model_input < source > ( sample: FloatTensor timestep: Union ) → torch.FloatTensor Parameters sample (torch.FloatTensor) —

The input sample. timestep (int, optional) —

The current timestep in the diffusion chain. Returns

torch.FloatTensor

A scaled input sample.

Ensures interchangeability with schedulers that need to scale the denoising model input depending on the

current timestep. set_begin_index < source > ( begin_index: int = 0 ) Parameters begin_index (int) —

The begin index for the scheduler. Sets the begin index for the scheduler. This function should be run from pipeline before the inference. set_timesteps < source > ( num_inference_steps: int device: Union = None num_train_timesteps: Optional = None ) Parameters num_inference_steps (int) —

The number of diffusion steps used when generating samples with a pre-trained model. device (str or torch.device, optional) —

The device to which the timesteps should be moved to. If None, the timesteps are not moved. Sets the discrete timesteps used for the diffusion chain (to be run before inference). step < source > ( model_output: Union timestep: Union sample: Union return_dict: bool = True ) → SchedulerOutput or tuple Parameters model_output (torch.FloatTensor) —

The direct output from learned diffusion model. timestep (float) —

The current discrete timestep in the diffusion chain. sample (torch.FloatTensor) —

A current instance of a sample created by the diffusion process. return_dict (bool) —

Whether or not to return a SchedulerOutput or tuple. Returns

SchedulerOutput or tuple

If return_dict is True, SchedulerOutput is returned, otherwise a

tuple is returned where the first element is the sample tensor.

Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion

process from the learned model outputs (most often the predicted noise). SchedulerOutput class diffusers.schedulers.scheduling_utils.SchedulerOutput < source > ( prev_sample: FloatTensor ) Parameters prev_sample (torch.FloatTensor of shape (batch_size, num_channels, height, width) for images) —

Computed sample (x_{t-1}) of previous timestep. prev_sample should be used as next model input in the

denoising loop. Base class for the output of a scheduler’s step function.

Score SDE VE

Overview

Score-Based Generative Modeling through Stochastic Differential Equations (Score SDE) by Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon and Ben Poole.

The abstract of the paper is the following:

Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially, the reverse-time SDE depends only on the time-dependent gradient field (\aka, score) of the perturbed data distribution. By leveraging advances in score-based generative modeling, we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples. We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling, allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE. We also derive an equivalent neural ODE that samples from the same distribution as the SDE, but additionally enables exact likelihood computation, and improved sampling efficiency. In addition, we provide a new way to solve inverse problems with score-based models, as demonstrated with experiments on class-conditional generation, image inpainting, and colorization. Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9.89 and FID of 2.20, a competitive likelihood of 2.99 bits/dim, and demonstrate high fidelity generation of 1024 x 1024 images for the first time from a score-based generative model.

The original codebase can be found here.

This pipeline implements the Variance Expanding (VE) variant of the method.

Available Pipelines:

Pipeline

Tasks

Colab

pipeline_score_sde_ve.py

Unconditional Image Generation

-

ScoreSdeVePipeline

class diffusers.ScoreSdeVePipeline

<

source

>

(

unet: UNet2DModel

scheduler: DiffusionPipeline

)

Parameters

This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods the —

library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) —

unet (UNet2DModel): U-Net architecture to denoise the encoded image. scheduler (SchedulerMixin):

The ScoreSdeVeScheduler scheduler to be used in combination with unet to denoise the encoded image.

__call__