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diffusers-qa-chatbot-artifacts

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A dictionary of all the parameters stored in a JSON configuration file.
Load a model or scheduler configuration. from_config < source > ( config: Union = None return_unused_kwargs = False **kwargs ) β†’ ModelMixin or SchedulerMixin Parameters config (Dict[str, Any]) β€”
A config dictionary from which the Python class is instantiated. Make sure to only load configuration
files of compatible classes. return_unused_kwargs (bool, optional, defaults to False) β€”
Whether kwargs that are not consumed by the Python class should be returned or not. kwargs (remaining dictionary of keyword arguments, optional) β€”
Can be used to update the configuration object (after it is loaded) and initiate the Python class.
**kwargs are passed directly to the underlying scheduler/model’s __init__ method and eventually
overwrite the same named arguments in config. Returns
ModelMixin or SchedulerMixin
A model or scheduler object instantiated from a config dictionary.
Instantiate a Python class from a config dictionary. Examples: Copied >>> from diffusers import DDPMScheduler, DDIMScheduler, PNDMScheduler
>>> # Download scheduler from huggingface.co and cache.
>>> scheduler = DDPMScheduler.from_pretrained("google/ddpm-cifar10-32")
>>> # Instantiate DDIM scheduler class with same config as DDPM
>>> scheduler = DDIMScheduler.from_config(scheduler.config)
>>> # Instantiate PNDM scheduler class with same config as DDPM
>>> scheduler = PNDMScheduler.from_config(scheduler.config) save_config < source > ( save_directory: Union push_to_hub: bool = False **kwargs ) Parameters save_directory (str or os.PathLike) β€”
Directory where the configuration JSON file is saved (will be created if it does not exist). push_to_hub (bool, optional, defaults to False) β€”
Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the
repository you want to push to with repo_id (will default to the name of save_directory in your
namespace). kwargs (Dict[str, Any], optional) β€”
Additional keyword arguments passed along to the push_to_hub() method. Save a configuration object to the directory specified in save_directory so that it can be reloaded using the
from_config() class method. to_json_file < source > ( json_file_path: Union ) Parameters json_file_path (str or os.PathLike) β€”
Path to the JSON file to save a configuration instance’s parameters. Save the configuration instance’s parameters to a JSON file. to_json_string < source > ( ) β†’ str Returns
str
String containing all the attributes that make up the configuration instance in JSON format.
Serializes the configuration instance to a JSON string.
LMSDiscreteScheduler LMSDiscreteScheduler is a linear multistep scheduler for discrete beta schedules. The scheduler is ported from and created by Katherine Crowson, and the original implementation can be found at crowsonkb/k-diffusion. LMSDiscreteScheduler class diffusers.LMSDiscreteScheduler < source > ( num_train...
The number of diffusion steps to train the model. beta_start (float, defaults to 0.0001) β€”
The starting beta value of inference. beta_end (float, defaults to 0.02) β€”
The final beta value. beta_schedule (str, defaults to "linear") β€”
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) β€”
Pass an array of betas directly to the constructor to bypass beta_start and beta_end. use_karras_sigmas (bool, optional, defaults to False) β€”
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 {Οƒi}. prediction_type (str, defaults to epsilon, optional) β€”
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). timestep_spacing (str, defaults to "linspace") β€”
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) β€”
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. A linear multistep scheduler 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. get_lms_coefficient < source > ( order t current_order ) Parameters order () β€” t () β€” current_order () β€” Compute the linear multistep coefficient. scale_model_input < source > ( sample: FloatTensor timestep: Union ) β†’ torch.F...
The input sample. timestep (float or torch.FloatTensor) β€”
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) β€”
The number of diffusion steps used when generating samples with a pre-trained model. device (str or torch.device, optional) β€”
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: Union sample: FloatTensor order: int = 4 return_dict: bool = True ) β†’ SchedulerOu...
The direct output from learned diffusion model. timestep (float or torch.FloatTensor) β€”
The current discrete timestep in the diffusion chain. sample (torch.FloatTensor) β€”
A current instance of a sample created by the diffusion process. order (int, defaults to 4) β€”
The order of the linear multistep method. return_dict (bool, optional, defaults to True) β€”
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). LMSDiscreteSchedulerOutput class diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteSchedulerOutput < source > ( prev_sample: FloatTensor pred_original_sample: Optional = None ) Parameters prev_sample (torch.FloatTensor of shape (batc...
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) β€”
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.
KDPM2AncestralDiscreteScheduler The KDPM2DiscreteScheduler with ancestral sampling is inspired by the Elucidating the Design Space of Diffusion-Based Generative Models paper, and the scheduler is ported from and created by Katherine Crowson. The original codebase can be found at crowsonkb/k-diffusion. KDPM2AncestralDi...
The number of diffusion steps to train the model. beta_start (float, defaults to 0.00085) β€”
The starting beta value of inference. beta_end (float, defaults to 0.012) β€”
The final beta value. beta_schedule (str, defaults to "linear") β€”
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) β€”
Pass an array of betas directly to the constructor to bypass beta_start and beta_end. use_karras_sigmas (bool, optional, defaults to False) β€”
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 {Οƒi}. prediction_type (str, defaults to epsilon, optional) β€”
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). timestep_spacing (str, defaults to "linspace") β€”
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) β€”
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. KDPM2DiscreteScheduler with ancestral sampling is inspired by the DPMSolver2 and Algorithm 2 from the Elucidating
the Design Space of Diffusion-Based Generative Models paper. 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 ) β†’ torch.FloatTensor Parameters sample (torch.FloatTensor) β€”
The input sample. timestep (int, optional) β€”
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