FlowMatchEulerDiscreteScheduler
FlowMatchEulerDiscreteScheduler is based on the flow-matching sampling introduced in Stable Diffusion 3.
FlowMatchEulerDiscreteScheduler
class diffusers.FlowMatchEulerDiscreteScheduler
< source >( num_train_timesteps: int = 1000 shift: float = 1.0 use_dynamic_shifting = False base_shift: typing.Optional[float] = 0.5 max_shift: typing.Optional[float] = 1.15 base_image_seq_len: typing.Optional[int] = 256 max_image_seq_len: typing.Optional[int] = 4096 invert_sigmas: bool = False shift_terminal: typing.Optional[float] = None use_karras_sigmas: typing.Optional[bool] = False use_exponential_sigmas: typing.Optional[bool] = False use_beta_sigmas: typing.Optional[bool] = False )
Parameters
- num_train_timesteps (
int, defaults to 1000) — The number of diffusion steps to train the model. - 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. - shift (
float, defaults to 1.0) — The shift value for the timestep schedule.
Euler 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_noise
< source >( sample: FloatTensor timestep: typing.Union[float, torch.FloatTensor] noise: typing.Optional[torch.FloatTensor] = None ) → torch.FloatTensor
Forward process in flow-matching
set_begin_index
< source >( begin_index: int = 0 )
Sets the begin index for the scheduler. This function should be run from pipeline before the inference.
set_timesteps
< source >( num_inference_steps: int = None device: typing.Union[str, torch.device] = None sigmas: typing.Optional[typing.List[float]] = None mu: typing.Optional[float] = None )
Sets the discrete timesteps used for the diffusion chain (to be run before inference).
step
< source >( model_output: FloatTensor timestep: typing.Union[float, torch.FloatTensor] sample: FloatTensor s_churn: float = 0.0 s_tmin: float = 0.0 s_tmax: float = inf s_noise: float = 1.0 generator: typing.Optional[torch._C.Generator] = None return_dict: bool = True ) → EulerDiscreteSchedulerOutput 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. - s_churn (
float) — - s_tmin (
float) — - s_tmax (
float) — - s_noise (
float, defaults to 1.0) — Scaling factor for noise added to the sample. - generator (
torch.Generator, optional) — A random number generator. - return_dict (
bool) — Whether or not to return a EulerDiscreteSchedulerOutput or tuple.
Returns
EulerDiscreteSchedulerOutput or tuple
If return_dict is True, EulerDiscreteSchedulerOutput 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).
stretch_shift_to_terminal
< source >( t: Tensor ) → torch.Tensor
Stretches and shifts the timestep schedule to ensure it terminates at the configured shift_terminal config
value.