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

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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 sample with
the multistep DPMSolver. 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.
DDIMScheduler Denoising Diffusion Implicit Models (DDIM) by Jiaming Song, Chenlin Meng and Stefano Ermon. The abstract from the paper is: Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample.
To accelerate sampling, we present denoising diffusion implicit models (DDIMs), a more efficient class of iterative implicit probabilistic models
with the same training procedure as DDPMs. In DDPMs, the generative process is defined as the reverse of a Markovian diffusion process.
We construct a class of non-Markovian diffusion processes that lead to the same training objective, but whose reverse process can be much faster to sample from.
We empirically demonstrate that DDIMs can produce high quality samples 10× to 50× faster in terms of wall-clock time compared to DDPMs, allow us to trade off computation for sample quality, and can perform semantically meaningful image interpolation directly in the latent space. The original codebase of this paper can be found at ermongroup/ddim, and you can contact the author on tsong.me. Tips The paper Common Diffusion Noise Schedules and Sample Steps are Flawed claims that a mismatch between the training and inference settings leads to suboptimal inference generation results for Stable Diffusion. To fix this, the authors propose: 🧪 This is an experimental feature! rescale the noise schedule to enforce zero terminal signal-to-noise ratio (SNR) Copied pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config, rescale_betas_zero_snr=True) train a model with v_prediction (add the following argument to the train_text_to_image.py or train_text_to_image_lora.py scripts) Copied --prediction_type="v_prediction" change the sampler to always start from the last timestep Copied pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing") rescale classifier-free guidance to prevent over-exposure Copied image = pipe(prompt, guidance_rescale=0.7).images[0] For example: Copied from diffusers import DiffusionPipeline, DDIMScheduler
import torch

pipe = DiffusionPipeline.from_pretrained("ptx0/pseudo-journey-v2", torch_dtype=torch.float16)
pipe.scheduler = DDIMScheduler.from_config(
pipe.scheduler.config, rescale_betas_zero_snr=True, timestep_spacing="trailing"
)
pipe.to("cuda")

prompt = "A lion in galaxies, spirals, nebulae, stars, smoke, iridescent, intricate detail, octane render, 8k"
image = pipe(prompt, guidance_rescale=0.7).images[0]
image DDIMScheduler class diffusers.DDIMScheduler < source > ( num_train_timesteps: int = 1000 beta_start: float = 0.0001 beta_end: float = 0.02 beta_schedule: str = 'linear' trained_betas: Union = None clip_sample: bool = True set_alpha_to_one: bool = True steps_offset: int = 0 prediction_type: str = 'epsilon' thresholding: bool = False dynamic_thresholding_ratio: float = 0.995 clip_sample_range: float = 1.0 sample_max_value: float = 1.0 timestep_spacing: str = 'leading' rescale_betas_zero_snr: bool = False ) 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, or squaredcos_cap_v2. 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 for numerical stability. clip_sample_range (float, defaults to 1.0) —
The maximum magnitude for sample clipping. Valid only when clip_sample=True. set_alpha_to_one (bool, defaults to True) —
Each diffusion step uses the alphas product value at that step and at the previous one. For the final step
there is no previous alpha. When this option is True the previous alpha product is fixed to 1,
otherwise it uses the alpha value at step 0. 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. 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). thresholding (bool, defaults to False) —
Whether to use the “dynamic thresholding” method. This is unsuitable for latent-space diffusion models such
as Stable Diffusion. dynamic_thresholding_ratio (float, defaults to 0.995) —
The ratio for the dynamic thresholding method. Valid only when thresholding=True. sample_max_value (float, defaults to 1.0) —
The threshold value for dynamic thresholding. Valid only when thresholding=True. timestep_spacing (str, defaults to "leading") —
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. rescale_betas_zero_snr (bool, defaults to False) —
Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and
dark samples instead of limiting it to samples with medium brightness. Loosely related to
--offset_noise. DDIMScheduler extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with
non-Markovian guidance. 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 device: Union = None ) Parameters num_inference_steps (int) —
The number of diffusion steps used when generating samples with a pre-trained model. Sets the discrete timesteps used for the diffusion chain (to be run before inference). step < source > ( model_output: FloatTensor timestep: int sample: FloatTensor eta: float = 0.0 use_clipped_model_output: bool = False generator = None variance_noise: Optional = None return_dict: bool = True ) → ~schedulers.scheduling_utils.DDIMSchedulerOutput 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. eta (float) —
The weight of noise for added noise in diffusion step. use_clipped_model_output (bool, defaults to False) —
If True, computes “corrected” model_output from the clipped predicted original sample. Necessary
because predicted original sample is clipped to [-1, 1] when self.config.clip_sample is True. If no
clipping has happened, “corrected” model_output would coincide with the one provided as input and
use_clipped_model_output has no effect. generator (torch.Generator, optional) —
A random number generator. variance_noise (torch.FloatTensor) —
Alternative to generating noise with generator by directly providing the noise for the variance
itself. Useful for methods such as CycleDiffusion. return_dict (bool, optional, defaults to True) —
Whether or not to return a DDIMSchedulerOutput or tuple. Returns
~schedulers.scheduling_utils.DDIMSchedulerOutput or tuple

If return_dict is True, DDIMSchedulerOutput 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). DDIMSchedulerOutput class diffusers.schedulers.scheduling_ddim.DDIMSchedulerOutput < source > ( prev_sample: FloatTensor pred_original_sample: Optional = None ) 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.
Text-to-Video Generation with AnimateDiff Overview AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning by Yuwei Guo, Ceyuan Yang, Anyi Rao, Yaohui Wang, Yu Qiao, Dahua Lin, Bo Dai. The abstract of the paper is the following: With the advance of text-to-image models (e.g., Stable Diffusion) and corresponding personalization techniques such as DreamBooth and LoRA, everyone can manifest their imagination into high-quality images at an affordable cost. Subsequently, there is a great demand for image animation techniques to further combine generated static images with motion dynamics. In this report, we propose a practical framework to animate most of the existing personalized text-to-image models once and for all, saving efforts in model-specific tuning. At the core of the proposed framework is to insert a newly initialized motion modeling module into the frozen text-to-image model and train it on video clips to distill reasonable motion priors. Once trained, by simply injecting this motion modeling module, all personalized versions derived from the same base T2I readily become text-driven models that produce diverse and personalized animated images. We conduct our evaluation on several public representative personalized text-to-image models across anime pictures and realistic photographs, and demonstrate that our proposed framework helps these models generate temporally smooth animation clips while preserving the domain and diversity of their outputs. Code and pre-trained weights will be publicly available at this https URL. Available Pipelines Pipeline Tasks Demo AnimateDiffPipeline Text-to-Video Generation with AnimateDiff Available checkpoints Motion Adapter checkpoints can be found under guoyww. These checkpoints are meant to work with any model based on Stable Diffusion 1.4/1.5. Usage example AnimateDiff works with a MotionAdapter checkpoint and a Stable Diffusion model checkpoint. The MotionAdapter is a collection of Motion Modules that are responsible for adding coherent motion across image frames. These modules are applied after the Resnet and Attention blocks in Stable Diffusion UNet. The following example demonstrates how to use a MotionAdapter checkpoint with Diffusers for inference based on StableDiffusion-1.4/1.5. Copied import torch
from diffusers import AnimateDiffPipeline, DDIMScheduler, MotionAdapter
from diffusers.utils import export_to_gif

# Load the motion adapter
adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16)
# load SD 1.5 based finetuned model
model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16)
scheduler = DDIMScheduler.from_pretrained(
model_id,
subfolder="scheduler",
clip_sample=False,
timestep_spacing="linspace",
beta_schedule="linear",
steps_offset=1,
)
pipe.scheduler = scheduler

# enable memory savings
pipe.enable_vae_slicing()

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 sample with

the multistep DPMSolver. 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.

DDIMScheduler Denoising Diffusion Implicit Models (DDIM) by Jiaming Song, Chenlin Meng and Stefano Ermon. The abstract from the paper is: Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample.

To accelerate sampling, we present denoising diffusion implicit models (DDIMs), a more efficient class of iterative implicit probabilistic models

with the same training procedure as DDPMs. In DDPMs, the generative process is defined as the reverse of a Markovian diffusion process.

We construct a class of non-Markovian diffusion processes that lead to the same training objective, but whose reverse process can be much faster to sample from.

We empirically demonstrate that DDIMs can produce high quality samples 10× to 50× faster in terms of wall-clock time compared to DDPMs, allow us to trade off computation for sample quality, and can perform semantically meaningful image interpolation directly in the latent space. The original codebase of this paper can be found at ermongroup/ddim, and you can contact the author on tsong.me. Tips The paper Common Diffusion Noise Schedules and Sample Steps are Flawed claims that a mismatch between the training and inference settings leads to suboptimal inference generation results for Stable Diffusion. To fix this, the authors propose: 🧪 This is an experimental feature! rescale the noise schedule to enforce zero terminal signal-to-noise ratio (SNR) Copied pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config, rescale_betas_zero_snr=True) train a model with v_prediction (add the following argument to the train_text_to_image.py or train_text_to_image_lora.py scripts) Copied --prediction_type="v_prediction" change the sampler to always start from the last timestep Copied pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing") rescale classifier-free guidance to prevent over-exposure Copied image = pipe(prompt, guidance_rescale=0.7).images[0] For example: Copied from diffusers import DiffusionPipeline, DDIMScheduler

import torch

pipe = DiffusionPipeline.from_pretrained("ptx0/pseudo-journey-v2", torch_dtype=torch.float16)

pipe.scheduler = DDIMScheduler.from_config(

pipe.scheduler.config, rescale_betas_zero_snr=True, timestep_spacing="trailing"

)

pipe.to("cuda")

prompt = "A lion in galaxies, spirals, nebulae, stars, smoke, iridescent, intricate detail, octane render, 8k"

image = pipe(prompt, guidance_rescale=0.7).images[0]

image DDIMScheduler class diffusers.DDIMScheduler < source > ( num_train_timesteps: int = 1000 beta_start: float = 0.0001 beta_end: float = 0.02 beta_schedule: str = 'linear' trained_betas: Union = None clip_sample: bool = True set_alpha_to_one: bool = True steps_offset: int = 0 prediction_type: str = 'epsilon' thresholding: bool = False dynamic_thresholding_ratio: float = 0.995 clip_sample_range: float = 1.0 sample_max_value: float = 1.0 timestep_spacing: str = 'leading' rescale_betas_zero_snr: bool = False ) 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, or squaredcos_cap_v2. 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 for numerical stability. clip_sample_range (float, defaults to 1.0) —

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

Each diffusion step uses the alphas product value at that step and at the previous one. For the final step

there is no previous alpha. When this option is True the previous alpha product is fixed to 1,

otherwise it uses the alpha value at step 0. 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. 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). thresholding (bool, defaults to False) —

Whether to use the “dynamic thresholding” method. This is unsuitable for latent-space diffusion models such

as Stable Diffusion. dynamic_thresholding_ratio (float, defaults to 0.995) —

The ratio for the dynamic thresholding method. Valid only when thresholding=True. sample_max_value (float, defaults to 1.0) —

The threshold value for dynamic thresholding. Valid only when thresholding=True. timestep_spacing (str, defaults to "leading") —

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. rescale_betas_zero_snr (bool, defaults to False) —

Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and

dark samples instead of limiting it to samples with medium brightness. Loosely related to

--offset_noise. DDIMScheduler extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with

non-Markovian guidance. 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 device: Union = None ) Parameters num_inference_steps (int) —

The number of diffusion steps used when generating samples with a pre-trained model. Sets the discrete timesteps used for the diffusion chain (to be run before inference). step < source > ( model_output: FloatTensor timestep: int sample: FloatTensor eta: float = 0.0 use_clipped_model_output: bool = False generator = None variance_noise: Optional = None return_dict: bool = True ) → ~schedulers.scheduling_utils.DDIMSchedulerOutput 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. eta (float) —

The weight of noise for added noise in diffusion step. use_clipped_model_output (bool, defaults to False) —

If True, computes “corrected” model_output from the clipped predicted original sample. Necessary

because predicted original sample is clipped to [-1, 1] when self.config.clip_sample is True. If no

clipping has happened, “corrected” model_output would coincide with the one provided as input and

use_clipped_model_output has no effect. generator (torch.Generator, optional) —

A random number generator. variance_noise (torch.FloatTensor) —

Alternative to generating noise with generator by directly providing the noise for the variance

itself. Useful for methods such as CycleDiffusion. return_dict (bool, optional, defaults to True) —

Whether or not to return a DDIMSchedulerOutput or tuple. Returns

~schedulers.scheduling_utils.DDIMSchedulerOutput or tuple

If return_dict is True, DDIMSchedulerOutput 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). DDIMSchedulerOutput class diffusers.schedulers.scheduling_ddim.DDIMSchedulerOutput < source > ( prev_sample: FloatTensor pred_original_sample: Optional = None ) 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.

Text-to-Video Generation with AnimateDiff Overview AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning by Yuwei Guo, Ceyuan Yang, Anyi Rao, Yaohui Wang, Yu Qiao, Dahua Lin, Bo Dai. The abstract of the paper is the following: With the advance of text-to-image models (e.g., Stable Diffusion) and corresponding personalization techniques such as DreamBooth and LoRA, everyone can manifest their imagination into high-quality images at an affordable cost. Subsequently, there is a great demand for image animation techniques to further combine generated static images with motion dynamics. In this report, we propose a practical framework to animate most of the existing personalized text-to-image models once and for all, saving efforts in model-specific tuning. At the core of the proposed framework is to insert a newly initialized motion modeling module into the frozen text-to-image model and train it on video clips to distill reasonable motion priors. Once trained, by simply injecting this motion modeling module, all personalized versions derived from the same base T2I readily become text-driven models that produce diverse and personalized animated images. We conduct our evaluation on several public representative personalized text-to-image models across anime pictures and realistic photographs, and demonstrate that our proposed framework helps these models generate temporally smooth animation clips while preserving the domain and diversity of their outputs. Code and pre-trained weights will be publicly available at this https URL. Available Pipelines Pipeline Tasks Demo AnimateDiffPipeline Text-to-Video Generation with AnimateDiff Available checkpoints Motion Adapter checkpoints can be found under guoyww. These checkpoints are meant to work with any model based on Stable Diffusion 1.4/1.5. Usage example AnimateDiff works with a MotionAdapter checkpoint and a Stable Diffusion model checkpoint. The MotionAdapter is a collection of Motion Modules that are responsible for adding coherent motion across image frames. These modules are applied after the Resnet and Attention blocks in Stable Diffusion UNet. The following example demonstrates how to use a MotionAdapter checkpoint with Diffusers for inference based on StableDiffusion-1.4/1.5. Copied import torch

from diffusers import AnimateDiffPipeline, DDIMScheduler, MotionAdapter

from diffusers.utils import export_to_gif

# Load the motion adapter

adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16)

# load SD 1.5 based finetuned model

model_id = "SG161222/Realistic_Vision_V5.1_noVAE"

pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16)

scheduler = DDIMScheduler.from_pretrained(

model_id,

subfolder="scheduler",

clip_sample=False,

timestep_spacing="linspace",

beta_schedule="linear",

steps_offset=1,

)

pipe.scheduler = scheduler

# enable memory savings

pipe.enable_vae_slicing()