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

text stringlengths 0 5.54k
prompt=prompt,
image=init_image,
mask_image=mask_image,
generator=generator,
num_inference_steps=4,
guidance_scale=4,
).images[0]
make_image_grid([init_image, mask_image, image], rows=1, cols=3) AnimateDiff AnimateDiff allows you to animate images using Stable Diffusion models. To get good results, we need to generate multiple frames (16-24), and doing this with standard SD models can be very slow.
LCM-LoRA can be used to speed up the process significantly, as you just need to do 4-8 steps for each frame. Let’s look at how we can perform animation with LCM-LoRA and AnimateDiff. Copied import torch
from diffusers import MotionAdapter, AnimateDiffPipeline, DDIMScheduler, LCMScheduler
from diffusers.utils import export_to_gif

adapter = MotionAdapter.from_pretrained("diffusers/animatediff-motion-adapter-v1-5")
pipe = AnimateDiffPipeline.from_pretrained(
"frankjoshua/toonyou_beta6",
motion_adapter=adapter,
).to("cuda")

# set scheduler
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)

# load LCM-LoRA
pipe.load_lora_weights("latent-consistency/lcm-lora-sdv1-5", adapter_name="lcm")
pipe.load_lora_weights("guoyww/animatediff-motion-lora-zoom-in", weight_name="diffusion_pytorch_model.safetensors", adapter_name="motion-lora")

pipe.set_adapters(["lcm", "motion-lora"], adapter_weights=[0.55, 1.2])

prompt = "best quality, masterpiece, 1girl, looking at viewer, blurry background, upper body, contemporary, dress"
generator = torch.manual_seed(0)
frames = pipe(
prompt=prompt,
num_inference_steps=5,
guidance_scale=1.25,
cross_attention_kwargs={"scale": 1},
num_frames=24,
generator=generator
).frames[0]
export_to_gif(frames, "animation.gif")
RePaintScheduler RePaintScheduler is a DDPM-based inpainting scheduler for unsupervised inpainting with extreme masks. It is designed to be used with the RePaintPipeline, and it is based on the paper RePaint: Inpainting using Denoising Diffusion Probabilistic Models by Andreas Lugmayr et al. The abstract from the paper is: Free-form inpainting is the task of adding new content to an image in the regions specified by an arbitrary binary mask. Most existing approaches train for a certain distribution of masks, which limits their generalization capabilities to unseen mask types. Furthermore, training with pixel-wise and perceptual losses often leads to simple textural extensions towards the missing areas instead of semantically meaningful generation. In this work, we propose RePaint: A Denoising Diffusion Probabilistic Model (DDPM) based inpainting approach that is applicable to even extreme masks. We employ a pretrained unconditional DDPM as the generative prior. To condition the generation process, we only alter the reverse diffusion iterations by sampling the unmasked regions using the given image information. Since this technique does not modify or condition the original DDPM network itself, the model produces high-quality and diverse output images for any inpainting form. We validate our method for both faces and general-purpose image inpainting using standard and extreme masks. RePaint outperforms state-of-the-art Autoregressive, and GAN approaches for at least five out of six mask distributions. GitHub Repository: this http URL. The original implementation can be found at andreas128/RePaint. RePaintScheduler class diffusers.RePaintScheduler < source > ( num_train_timesteps: int = 1000 beta_start: float = 0.0001 beta_end: float = 0.02 beta_schedule: str = 'linear' eta: float = 0.0 trained_betas: Optional = None clip_sample: bool = True ) 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, scaled_linear, squaredcos_cap_v2, or sigmoid. eta (float) —
The weight of noise for added noise in diffusion step. If its value is between 0.0 and 1.0 it corresponds
to the DDIM scheduler, and if its value is between -0.0 and 1.0 it corresponds to the DDPM scheduler. trained_betas (np.ndarray, optional) —
Pass an array of betas directly to the constructor to bypass beta_start and beta_end. clip_sample (bool, defaults to True) —
Clip the predicted sample between -1 and 1 for numerical stability. RePaintScheduler is a scheduler for DDPM inpainting inside a given mask. 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: Optional = None ) → 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_timesteps < source > ( num_inference_steps: int jump_length: int = 10 jump_n_sample: int = 10 device: Union = None ) Parameters num_inference_steps (int) —
The number of diffusion steps used when generating samples with a pre-trained model. If used,
timesteps must be None. jump_length (int, defaults to 10) —
The number of steps taken forward in time before going backward in time for a single jump (“j” in
RePaint paper). Take a look at Figure 9 and 10 in the paper. jump_n_sample (int, defaults to 10) —
The number of times to make a forward time jump for a given chosen time sample. Take a look at Figure 9
and 10 in the paper. 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: FloatTensor timestep: int sample: FloatTensor original_image: FloatTensor mask: FloatTensor generator: Optional = None return_dict: bool = True ) → RePaintSchedulerOutput or tuple Parameters model_output (torch.FloatTensor) —
The direct output from learned diffusion model. timestep (int) —
The current discrete timestep in the diffusion chain. sample (torch.FloatTensor) —
A current instance of a sample created by the diffusion process. original_image (torch.FloatTensor) —
The original image to inpaint on. mask (torch.FloatTensor) —
The mask where a value of 0.0 indicates which part of the original image to inpaint. generator (torch.Generator, optional) —
A random number generator. return_dict (bool, optional, defaults to True) —
Whether or not to return a RePaintSchedulerOutput or tuple. Returns
RePaintSchedulerOutput or tuple

If return_dict is True, RePaintSchedulerOutput 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). RePaintSchedulerOutput class diffusers.schedulers.scheduling_repaint.RePaintSchedulerOutput < source > ( prev_sample: FloatTensor pred_original_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. pred_original_sample (torch.FloatTensor of shape (batch_size, num_channels, height, width) for images) —
The predicted denoised sample (x_{0}) based on the model output from
the current timestep. pred_original_sample can be used to preview progress or for guidance. Output class for the scheduler’s step function output.
ScoreSdeVeScheduler ScoreSdeVeScheduler is a variance exploding stochastic differential equation (SDE) scheduler. It was introduced in the Score-Based Generative Modeling through Stochastic Differential Equations paper by Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole. The abstract from the paper is: 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. ScoreSdeVeScheduler class diffusers.ScoreSdeVeScheduler < source > ( num_train_timesteps: int = 2000 snr: float = 0.15 sigma_min: float = 0.01 sigma_max: float = 1348.0 sampling_eps: float = 1e-05 correct_steps: int = 1 ) Parameters num_train_timesteps (int, defaults to 1000) —
The number of diffusion steps to train the model. snr (float, defaults to 0.15) —
A coefficient weighting the step from the model_output sample (from the network) to the random noise. sigma_min (float, defaults to 0.01) —
The initial noise scale for the sigma sequence in the sampling procedure. The minimum sigma should mirror
the distribution of the data. sigma_max (float, defaults to 1348.0) —
The maximum value used for the range of continuous timesteps passed into the model. sampling_eps (float, defaults to 1e-5) —
The end value of sampling where timesteps decrease progressively from 1 to epsilon. correct_steps (int, defaults to 1) —
The number of correction steps performed on a produced sample. ScoreSdeVeScheduler is a variance exploding stochastic differential equation (SDE) scheduler. 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: Optional = None ) → 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_sigmas < source > ( num_inference_steps: int sigma_min: float = None sigma_max: float = None sampling_eps: float = None ) Parameters num_inference_steps (int) —
The number of diffusion steps used when generating samples with a pre-trained model. sigma_min (float, optional) —
The initial noise scale value (overrides value given during scheduler instantiation). sigma_max (float, optional) —
The final noise scale value (overrides value given during scheduler instantiation). sampling_eps (float, optional) —
The final timestep value (overrides value given during scheduler instantiation). Sets the noise scales used for the diffusion chain (to be run before inference). The sigmas control the weight

prompt=prompt,

image=init_image,

mask_image=mask_image,

generator=generator,

num_inference_steps=4,

guidance_scale=4,

).images[0]

make_image_grid([init_image, mask_image, image], rows=1, cols=3) AnimateDiff AnimateDiff allows you to animate images using Stable Diffusion models. To get good results, we need to generate multiple frames (16-24), and doing this with standard SD models can be very slow.

LCM-LoRA can be used to speed up the process significantly, as you just need to do 4-8 steps for each frame. Let’s look at how we can perform animation with LCM-LoRA and AnimateDiff. Copied import torch

from diffusers import MotionAdapter, AnimateDiffPipeline, DDIMScheduler, LCMScheduler

from diffusers.utils import export_to_gif

adapter = MotionAdapter.from_pretrained("diffusers/animatediff-motion-adapter-v1-5")

pipe = AnimateDiffPipeline.from_pretrained(

"frankjoshua/toonyou_beta6",

motion_adapter=adapter,

).to("cuda")

# set scheduler

pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)

# load LCM-LoRA

pipe.load_lora_weights("latent-consistency/lcm-lora-sdv1-5", adapter_name="lcm")

pipe.load_lora_weights("guoyww/animatediff-motion-lora-zoom-in", weight_name="diffusion_pytorch_model.safetensors", adapter_name="motion-lora")

pipe.set_adapters(["lcm", "motion-lora"], adapter_weights=[0.55, 1.2])

prompt = "best quality, masterpiece, 1girl, looking at viewer, blurry background, upper body, contemporary, dress"

generator = torch.manual_seed(0)

frames = pipe(

prompt=prompt,

num_inference_steps=5,

guidance_scale=1.25,

cross_attention_kwargs={"scale": 1},

num_frames=24,

generator=generator

).frames[0]

export_to_gif(frames, "animation.gif")

RePaintScheduler RePaintScheduler is a DDPM-based inpainting scheduler for unsupervised inpainting with extreme masks. It is designed to be used with the RePaintPipeline, and it is based on the paper RePaint: Inpainting using Denoising Diffusion Probabilistic Models by Andreas Lugmayr et al. The abstract from the paper is: Free-form inpainting is the task of adding new content to an image in the regions specified by an arbitrary binary mask. Most existing approaches train for a certain distribution of masks, which limits their generalization capabilities to unseen mask types. Furthermore, training with pixel-wise and perceptual losses often leads to simple textural extensions towards the missing areas instead of semantically meaningful generation. In this work, we propose RePaint: A Denoising Diffusion Probabilistic Model (DDPM) based inpainting approach that is applicable to even extreme masks. We employ a pretrained unconditional DDPM as the generative prior. To condition the generation process, we only alter the reverse diffusion iterations by sampling the unmasked regions using the given image information. Since this technique does not modify or condition the original DDPM network itself, the model produces high-quality and diverse output images for any inpainting form. We validate our method for both faces and general-purpose image inpainting using standard and extreme masks. RePaint outperforms state-of-the-art Autoregressive, and GAN approaches for at least five out of six mask distributions. GitHub Repository: this http URL. The original implementation can be found at andreas128/RePaint. RePaintScheduler class diffusers.RePaintScheduler < source > ( num_train_timesteps: int = 1000 beta_start: float = 0.0001 beta_end: float = 0.02 beta_schedule: str = 'linear' eta: float = 0.0 trained_betas: Optional = None clip_sample: bool = True ) 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, scaled_linear, squaredcos_cap_v2, or sigmoid. eta (float) —

The weight of noise for added noise in diffusion step. If its value is between 0.0 and 1.0 it corresponds

to the DDIM scheduler, and if its value is between -0.0 and 1.0 it corresponds to the DDPM scheduler. trained_betas (np.ndarray, optional) —

Pass an array of betas directly to the constructor to bypass beta_start and beta_end. clip_sample (bool, defaults to True) —

Clip the predicted sample between -1 and 1 for numerical stability. RePaintScheduler is a scheduler for DDPM inpainting inside a given mask. 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: Optional = None ) → 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_timesteps < source > ( num_inference_steps: int jump_length: int = 10 jump_n_sample: int = 10 device: Union = None ) Parameters num_inference_steps (int) —

The number of diffusion steps used when generating samples with a pre-trained model. If used,

timesteps must be None. jump_length (int, defaults to 10) —

The number of steps taken forward in time before going backward in time for a single jump (“j” in

RePaint paper). Take a look at Figure 9 and 10 in the paper. jump_n_sample (int, defaults to 10) —

The number of times to make a forward time jump for a given chosen time sample. Take a look at Figure 9

and 10 in the paper. 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: FloatTensor timestep: int sample: FloatTensor original_image: FloatTensor mask: FloatTensor generator: Optional = None return_dict: bool = True ) → RePaintSchedulerOutput or tuple Parameters model_output (torch.FloatTensor) —

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

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

A current instance of a sample created by the diffusion process. original_image (torch.FloatTensor) —

The original image to inpaint on. mask (torch.FloatTensor) —

The mask where a value of 0.0 indicates which part of the original image to inpaint. generator (torch.Generator, optional) —

A random number generator. return_dict (bool, optional, defaults to True) —

Whether or not to return a RePaintSchedulerOutput or tuple. Returns

RePaintSchedulerOutput or tuple

If return_dict is True, RePaintSchedulerOutput 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). RePaintSchedulerOutput class diffusers.schedulers.scheduling_repaint.RePaintSchedulerOutput < source > ( prev_sample: FloatTensor pred_original_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. pred_original_sample (torch.FloatTensor of shape (batch_size, num_channels, height, width) for images) —

The predicted denoised sample (x_{0}) based on the model output from

the current timestep. pred_original_sample can be used to preview progress or for guidance. Output class for the scheduler’s step function output.

ScoreSdeVeScheduler ScoreSdeVeScheduler is a variance exploding stochastic differential equation (SDE) scheduler. It was introduced in the Score-Based Generative Modeling through Stochastic Differential Equations paper by Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole. The abstract from the paper is: 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. ScoreSdeVeScheduler class diffusers.ScoreSdeVeScheduler < source > ( num_train_timesteps: int = 2000 snr: float = 0.15 sigma_min: float = 0.01 sigma_max: float = 1348.0 sampling_eps: float = 1e-05 correct_steps: int = 1 ) Parameters num_train_timesteps (int, defaults to 1000) —

The number of diffusion steps to train the model. snr (float, defaults to 0.15) —

A coefficient weighting the step from the model_output sample (from the network) to the random noise. sigma_min (float, defaults to 0.01) —

The initial noise scale for the sigma sequence in the sampling procedure. The minimum sigma should mirror

the distribution of the data. sigma_max (float, defaults to 1348.0) —

The maximum value used for the range of continuous timesteps passed into the model. sampling_eps (float, defaults to 1e-5) —

The end value of sampling where timesteps decrease progressively from 1 to epsilon. correct_steps (int, defaults to 1) —

The number of correction steps performed on a produced sample. ScoreSdeVeScheduler is a variance exploding stochastic differential equation (SDE) scheduler. 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: Optional = None ) → 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_sigmas < source > ( num_inference_steps: int sigma_min: float = None sigma_max: float = None sampling_eps: float = None ) Parameters num_inference_steps (int) —

The number of diffusion steps used when generating samples with a pre-trained model. sigma_min (float, optional) —

The initial noise scale value (overrides value given during scheduler instantiation). sigma_max (float, optional) —

The final noise scale value (overrides value given during scheduler instantiation). sampling_eps (float, optional) —

The final timestep value (overrides value given during scheduler instantiation). Sets the noise scales used for the diffusion chain (to be run before inference). The sigmas control the weight