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

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List of denoised PIL images of length batch_size or NumPy array of shape (batch_size, height, width, num_channels). nsfw_content_detected (List[bool]) —
List indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content or
None if safety checking could not be performed. Output class for Stable Diffusion pipelines.
Overview 🤗 Diffusers provides a collection of training scripts for you to train your own diffusion models. You can find all of our training scripts in diffusers/examples. Each training script is: Self-contained: the training script does not depend on any local files, and all packages required to run the script are installed from the requirements.txt file. Easy-to-tweak: the training scripts are an example of how to train a diffusion model for a specific task and won’t work out-of-the-box for every training scenario. You’ll likely need to adapt the training script for your specific use-case. To help you with that, we’ve fully exposed the data preprocessing code and the training loop so you can modify it for your own use. Beginner-friendly: the training scripts are designed to be beginner-friendly and easy to understand, rather than including the latest state-of-the-art methods to get the best and most competitive results. Any training methods we consider too complex are purposefully left out. Single-purpose: each training script is expressly designed for only one task to keep it readable and understandable. Our current collection of training scripts include: Training SDXL-support LoRA-support Flax-support unconditional image generation text-to-image 👍 👍 👍 textual inversion 👍 DreamBooth 👍 👍 👍 ControlNet 👍 👍 InstructPix2Pix 👍 Custom Diffusion T2I-Adapters 👍 Kandinsky 2.2 👍 Wuerstchen 👍 These examples are actively maintained, so please feel free to open an issue if they aren’t working as expected. If you feel like another training example should be included, you’re more than welcome to start a Feature Request to discuss your feature idea with us and whether it meets our criteria of being self-contained, easy-to-tweak, beginner-friendly, and single-purpose. Install Make sure you can successfully run the latest versions of the example scripts by installing the library from source in a new virtual environment: Copied git clone https://github.com/huggingface/diffusers
cd diffusers
pip install . Then navigate to the folder of the training script (for example, DreamBooth) and install the requirements.txt file. Some training scripts have a specific requirement file for SDXL, LoRA or Flax. If you’re using one of these scripts, make sure you install its corresponding requirements file. Copied cd examples/dreambooth
pip install -r requirements.txt
# to train SDXL with DreamBooth
pip install -r requirements_sdxl.txt To speedup training and reduce memory-usage, we recommend: using PyTorch 2.0 or higher to automatically use scaled dot product attention during training (you don’t need to make any changes to the training code) installing xFormers to enable memory-efficient attention
DPMSolverMultistepScheduler DPMSolverMultistep is a multistep scheduler from DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps and DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. DPMSolver (and the improved version DPMSolver++) is a fast dedicated high-order solver for diffusion ODEs with convergence order guarantee. Empirically, DPMSolver sampling with only 20 steps can generate high-quality
samples, and it can generate quite good samples even in 10 steps. Tips It is recommended to set solver_order to 2 for guide sampling, and solver_order=3 for unconditional sampling. Dynamic thresholding from Imagen is supported, and for pixel-space
diffusion models, you can set both algorithm_type="dpmsolver++" and thresholding=True to use the dynamic
thresholding. This thresholding method is unsuitable for latent-space diffusion models such as
Stable Diffusion. The SDE variant of DPMSolver and DPM-Solver++ is also supported, but only for the first and second-order solvers. This is a fast SDE solver for the reverse diffusion SDE. It is recommended to use the second-order sde-dpmsolver++. DPMSolverMultistepScheduler class diffusers.DPMSolverMultistepScheduler < source > ( num_train_timesteps: int = 1000 beta_start: float = 0.0001 beta_end: float = 0.02 beta_schedule: str = 'linear' trained_betas: Union = None solver_order: int = 2 prediction_type: str = 'epsilon' thresholding: bool = False dynamic_thresholding_ratio: float = 0.995 sample_max_value: float = 1.0 algorithm_type: str = 'dpmsolver++' solver_type: str = 'midpoint' lower_order_final: bool = True euler_at_final: bool = False use_karras_sigmas: Optional = False use_lu_lambdas: Optional = False lambda_min_clipped: float = -inf variance_type: Optional = None 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, 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. solver_order (int, defaults to 2) —
The DPMSolver order which can be 1 or 2 or 3. It is recommended to use solver_order=2 for guided
sampling, and solver_order=3 for unconditional sampling. 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 and
algorithm_type="dpmsolver++". algorithm_type (str, defaults to dpmsolver++) —
Algorithm type for the solver; can be dpmsolver, dpmsolver++, sde-dpmsolver or sde-dpmsolver++. The
dpmsolver type implements the algorithms in the DPMSolver
paper, and the dpmsolver++ type implements the algorithms in the
DPMSolver++ paper. It is recommended to use dpmsolver++ or
sde-dpmsolver++ with solver_order=2 for guided sampling like in Stable Diffusion. solver_type (str, defaults to midpoint) —
Solver type for the second-order solver; can be midpoint or heun. The solver type slightly affects the
sample quality, especially for a small number of steps. It is recommended to use midpoint solvers. lower_order_final (bool, defaults to True) —
Whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. This can
stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10. euler_at_final (bool, defaults to False) —
Whether to use Euler’s method in the final step. It is a trade-off between numerical stability and detail
richness. This can stabilize the sampling of the SDE variant of DPMSolver for small number of inference
steps, but sometimes may result in blurring. 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}. use_lu_lambdas (bool, optional, defaults to False) —
Whether to use the uniform-logSNR for step sizes proposed by Lu’s DPM-Solver in the noise schedule during
the sampling process. If True, the sigmas and time steps are determined according to a sequence of
lambda(t). lambda_min_clipped (float, defaults to -inf) —
Clipping threshold for the minimum value of lambda(t) for numerical stability. This is critical for the
cosine (squaredcos_cap_v2) noise schedule. variance_type (str, optional) —
Set to “learned” or “learned_range” for diffusion models that predict variance. If set, the model’s output
contains the predicted Gaussian variance. 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. DPMSolverMultistepScheduler is a fast dedicated high-order solver for diffusion ODEs. 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. convert_model_output < source > ( model_output: FloatTensor args sample: FloatTensor = None *kwargs ) → torch.FloatTensor Parameters model_output (torch.FloatTensor) —
The direct output from the learned diffusion model. sample (torch.FloatTensor) —
A current instance of a sample created by the diffusion process. Returns
torch.FloatTensor

The converted model output.
Convert the model output to the corresponding type the DPMSolver/DPMSolver++ algorithm needs. DPM-Solver is
designed to discretize an integral of the noise prediction model, and DPM-Solver++ is designed to discretize an
integral of the data prediction model. The algorithm and model type are decoupled. You can use either DPMSolver or DPMSolver++ for both noise
prediction and data prediction models. dpm_solver_first_order_update < source > ( model_output: FloatTensor args sample: FloatTensor = None noise: Optional = None *kwargs ) → torch.FloatTensor Parameters model_output (torch.FloatTensor) —
The direct output from the learned diffusion model. sample (torch.FloatTensor) —
A current instance of a sample created by the diffusion process. Returns
torch.FloatTensor

The sample tensor at the previous timestep.
One step for the first-order DPMSolver (equivalent to DDIM). multistep_dpm_solver_second_order_update < source > ( model_output_list: List args sample: FloatTensor = None noise: Optional = None *kwargs ) → torch.FloatTensor Parameters model_output_list (List[torch.FloatTensor]) —
The direct outputs from learned diffusion model at current and latter timesteps. sample (torch.FloatTensor) —
A current instance of a sample created by the diffusion process. Returns
torch.FloatTensor

The sample tensor at the previous timestep.
One step for the second-order multistep DPMSolver. multistep_dpm_solver_third_order_update < source > ( model_output_list: List args sample: FloatTensor = None *kwargs ) → torch.FloatTensor Parameters model_output_list (List[torch.FloatTensor]) —
The direct outputs from learned diffusion model at current and latter timesteps. sample (torch.FloatTensor) —
A current instance of a sample created by diffusion process. Returns
torch.FloatTensor

The sample tensor at the previous timestep.
One step for the third-order multistep DPMSolver. scale_model_input < source > ( sample: FloatTensor args *kwargs ) → torch.FloatTensor Parameters sample (torch.FloatTensor) —
The input sample. 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 = None device: Union = 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: FloatTensor timestep: int sample: FloatTensor generator = None return_dict: bool = True ) → SchedulerOutput 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. generator (torch.Generator, optional) —
A random number generator. 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

List of denoised PIL images of length batch_size or NumPy array of shape (batch_size, height, width, num_channels). nsfw_content_detected (List[bool]) —

List indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content or

None if safety checking could not be performed. Output class for Stable Diffusion pipelines.

Overview 🤗 Diffusers provides a collection of training scripts for you to train your own diffusion models. You can find all of our training scripts in diffusers/examples. Each training script is: Self-contained: the training script does not depend on any local files, and all packages required to run the script are installed from the requirements.txt file. Easy-to-tweak: the training scripts are an example of how to train a diffusion model for a specific task and won’t work out-of-the-box for every training scenario. You’ll likely need to adapt the training script for your specific use-case. To help you with that, we’ve fully exposed the data preprocessing code and the training loop so you can modify it for your own use. Beginner-friendly: the training scripts are designed to be beginner-friendly and easy to understand, rather than including the latest state-of-the-art methods to get the best and most competitive results. Any training methods we consider too complex are purposefully left out. Single-purpose: each training script is expressly designed for only one task to keep it readable and understandable. Our current collection of training scripts include: Training SDXL-support LoRA-support Flax-support unconditional image generation text-to-image 👍 👍 👍 textual inversion 👍 DreamBooth 👍 👍 👍 ControlNet 👍 👍 InstructPix2Pix 👍 Custom Diffusion T2I-Adapters 👍 Kandinsky 2.2 👍 Wuerstchen 👍 These examples are actively maintained, so please feel free to open an issue if they aren’t working as expected. If you feel like another training example should be included, you’re more than welcome to start a Feature Request to discuss your feature idea with us and whether it meets our criteria of being self-contained, easy-to-tweak, beginner-friendly, and single-purpose. Install Make sure you can successfully run the latest versions of the example scripts by installing the library from source in a new virtual environment: Copied git clone https://github.com/huggingface/diffusers

cd diffusers

pip install . Then navigate to the folder of the training script (for example, DreamBooth) and install the requirements.txt file. Some training scripts have a specific requirement file for SDXL, LoRA or Flax. If you’re using one of these scripts, make sure you install its corresponding requirements file. Copied cd examples/dreambooth

pip install -r requirements.txt

# to train SDXL with DreamBooth

pip install -r requirements_sdxl.txt To speedup training and reduce memory-usage, we recommend: using PyTorch 2.0 or higher to automatically use scaled dot product attention during training (you don’t need to make any changes to the training code) installing xFormers to enable memory-efficient attention

DPMSolverMultistepScheduler DPMSolverMultistep is a multistep scheduler from DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps and DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. DPMSolver (and the improved version DPMSolver++) is a fast dedicated high-order solver for diffusion ODEs with convergence order guarantee. Empirically, DPMSolver sampling with only 20 steps can generate high-quality

samples, and it can generate quite good samples even in 10 steps. Tips It is recommended to set solver_order to 2 for guide sampling, and solver_order=3 for unconditional sampling. Dynamic thresholding from Imagen is supported, and for pixel-space

diffusion models, you can set both algorithm_type="dpmsolver++" and thresholding=True to use the dynamic

thresholding. This thresholding method is unsuitable for latent-space diffusion models such as

Stable Diffusion. The SDE variant of DPMSolver and DPM-Solver++ is also supported, but only for the first and second-order solvers. This is a fast SDE solver for the reverse diffusion SDE. It is recommended to use the second-order sde-dpmsolver++. DPMSolverMultistepScheduler class diffusers.DPMSolverMultistepScheduler < source > ( num_train_timesteps: int = 1000 beta_start: float = 0.0001 beta_end: float = 0.02 beta_schedule: str = 'linear' trained_betas: Union = None solver_order: int = 2 prediction_type: str = 'epsilon' thresholding: bool = False dynamic_thresholding_ratio: float = 0.995 sample_max_value: float = 1.0 algorithm_type: str = 'dpmsolver++' solver_type: str = 'midpoint' lower_order_final: bool = True euler_at_final: bool = False use_karras_sigmas: Optional = False use_lu_lambdas: Optional = False lambda_min_clipped: float = -inf variance_type: Optional = None 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, 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. solver_order (int, defaults to 2) —

The DPMSolver order which can be 1 or 2 or 3. It is recommended to use solver_order=2 for guided

sampling, and solver_order=3 for unconditional sampling. 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 and

algorithm_type="dpmsolver++". algorithm_type (str, defaults to dpmsolver++) —

Algorithm type for the solver; can be dpmsolver, dpmsolver++, sde-dpmsolver or sde-dpmsolver++. The

dpmsolver type implements the algorithms in the DPMSolver

paper, and the dpmsolver++ type implements the algorithms in the

DPMSolver++ paper. It is recommended to use dpmsolver++ or

sde-dpmsolver++ with solver_order=2 for guided sampling like in Stable Diffusion. solver_type (str, defaults to midpoint) —

Solver type for the second-order solver; can be midpoint or heun. The solver type slightly affects the

sample quality, especially for a small number of steps. It is recommended to use midpoint solvers. lower_order_final (bool, defaults to True) —

Whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. This can

stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10. euler_at_final (bool, defaults to False) —

Whether to use Euler’s method in the final step. It is a trade-off between numerical stability and detail

richness. This can stabilize the sampling of the SDE variant of DPMSolver for small number of inference

steps, but sometimes may result in blurring. 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}. use_lu_lambdas (bool, optional, defaults to False) —

Whether to use the uniform-logSNR for step sizes proposed by Lu’s DPM-Solver in the noise schedule during

the sampling process. If True, the sigmas and time steps are determined according to a sequence of

lambda(t). lambda_min_clipped (float, defaults to -inf) —

Clipping threshold for the minimum value of lambda(t) for numerical stability. This is critical for the

cosine (squaredcos_cap_v2) noise schedule. variance_type (str, optional) —

Set to “learned” or “learned_range” for diffusion models that predict variance. If set, the model’s output

contains the predicted Gaussian variance. 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. DPMSolverMultistepScheduler is a fast dedicated high-order solver for diffusion ODEs. 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. convert_model_output < source > ( model_output: FloatTensor *args sample: FloatTensor = None **kwargs ) → torch.FloatTensor Parameters model_output (torch.FloatTensor) —

The direct output from the learned diffusion model. sample (torch.FloatTensor) —

A current instance of a sample created by the diffusion process. Returns

torch.FloatTensor

The converted model output.

Convert the model output to the corresponding type the DPMSolver/DPMSolver++ algorithm needs. DPM-Solver is

designed to discretize an integral of the noise prediction model, and DPM-Solver++ is designed to discretize an

integral of the data prediction model. The algorithm and model type are decoupled. You can use either DPMSolver or DPMSolver++ for both noise

prediction and data prediction models. dpm_solver_first_order_update < source > ( model_output: FloatTensor *args sample: FloatTensor = None noise: Optional = None **kwargs ) → torch.FloatTensor Parameters model_output (torch.FloatTensor) —

The direct output from the learned diffusion model. sample (torch.FloatTensor) —

A current instance of a sample created by the diffusion process. Returns

torch.FloatTensor

The sample tensor at the previous timestep.

One step for the first-order DPMSolver (equivalent to DDIM). multistep_dpm_solver_second_order_update < source > ( model_output_list: List *args sample: FloatTensor = None noise: Optional = None **kwargs ) → torch.FloatTensor Parameters model_output_list (List[torch.FloatTensor]) —

The direct outputs from learned diffusion model at current and latter timesteps. sample (torch.FloatTensor) —

A current instance of a sample created by the diffusion process. Returns

torch.FloatTensor

The sample tensor at the previous timestep.

One step for the second-order multistep DPMSolver. multistep_dpm_solver_third_order_update < source > ( model_output_list: List *args sample: FloatTensor = None **kwargs ) → torch.FloatTensor Parameters model_output_list (List[torch.FloatTensor]) —

The direct outputs from learned diffusion model at current and latter timesteps. sample (torch.FloatTensor) —

A current instance of a sample created by diffusion process. Returns

torch.FloatTensor

The sample tensor at the previous timestep.

One step for the third-order multistep DPMSolver. scale_model_input < source > ( sample: FloatTensor *args **kwargs ) → torch.FloatTensor Parameters sample (torch.FloatTensor) —

The input sample. 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 = None device: Union = 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: FloatTensor timestep: int sample: FloatTensor generator = None return_dict: bool = True ) → SchedulerOutput 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. generator (torch.Generator, optional) —

A random number generator. 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