diffusers/pr_11739/en/api/schedulers/flow_match_euler_discrete.md

FlowMatchEulerDiscreteScheduler

FlowMatchEulerDiscreteScheduler is based on the flow-matching sampling introduced in Stable Diffusion 3.

FlowMatchEulerDiscreteScheduler[[diffusers.FlowMatchEulerDiscreteScheduler]]

diffusers.FlowMatchEulerDiscreteScheduler[[diffusers.FlowMatchEulerDiscreteScheduler]]

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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_noisediffusers.FlowMatchEulerDiscreteScheduler.scale_noisehttps://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py#L171[{"name": "sample", "val": ": FloatTensor"}, {"name": "timestep", "val": ": typing.Union[float, torch.FloatTensor]"}, {"name": "noise", "val": ": typing.Optional[torch.FloatTensor] = None"}]- sample (torch.FloatTensor) -- The input sample.

• timestep (int, optional) -- The current timestep in the diffusion chain.0torch.FloatTensorA scaled input sample.

Forward process in flow-matching

Parameters:

num_train_timesteps (int, defaults to 1000) : The number of diffusion steps to train the model.

shift (float, defaults to 1.0) : The shift value for the timestep schedule.

use_dynamic_shifting (bool, defaults to False) : Whether to apply timestep shifting on-the-fly based on the image resolution.

base_shift (float, defaults to 0.5) : Value to stabilize image generation. Increasing base_shift reduces variation and image is more consistent with desired output.

max_shift (float, defaults to 1.15) : Value change allowed to latent vectors. Increasing max_shift encourages more variation and image may be more exaggerated or stylized.

base_image_seq_len (int, defaults to 256) : The base image sequence length.

max_image_seq_len (int, defaults to 4096) : The maximum image sequence length.

invert_sigmas (bool, defaults to False) : Whether to invert the sigmas.

shift_terminal (float, defaults to None) : The end value of the shifted timestep schedule.

use_karras_sigmas (bool, defaults to False) : Whether to use Karras sigmas for step sizes in the noise schedule during sampling.

use_exponential_sigmas (bool, defaults to False) : Whether to use exponential sigmas for step sizes in the noise schedule during sampling.

use_beta_sigmas (bool, defaults to False) : Whether to use beta sigmas for step sizes in the noise schedule during sampling.

time_shift_type (str, defaults to "exponential") : The type of dynamic resolution-dependent timestep shifting to apply. Either "exponential" or "linear".

stochastic_sampling (bool, defaults to False) : Whether to use stochastic sampling.

Returns:

torch.FloatTensor

A scaled input sample.

set_begin_index[[diffusers.FlowMatchEulerDiscreteScheduler.set_begin_index]]

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Sets the begin index for the scheduler. This function should be run from pipeline before the inference.

Parameters:

begin_index (int, defaults to 0) : The begin index for the scheduler.

set_timesteps[[diffusers.FlowMatchEulerDiscreteScheduler.set_timesteps]]

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Sets the discrete timesteps used for the diffusion chain (to be run before inference).

Parameters:

num_inference_steps (int, optional) : 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.

sigmas (List[float], optional) : Custom values for sigmas to be used for each diffusion step. If None, the sigmas are computed automatically.

mu (float, optional) : Determines the amount of shifting applied to sigmas when performing resolution-dependent timestep shifting.

timesteps (List[float], optional) : Custom values for timesteps to be used for each diffusion step. If None, the timesteps are computed automatically.

step[[diffusers.FlowMatchEulerDiscreteScheduler.step]]

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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).

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.

per_token_timesteps (torch.Tensor, optional) : The timesteps for each token in the sample.

return_dict (bool) : Whether or not to return a FlowMatchEulerDiscreteSchedulerOutput or tuple.

Returns:

FlowMatchEulerDiscreteSchedulerOutput` or `tuple

If return_dict is True, FlowMatchEulerDiscreteSchedulerOutput is returned, otherwise a tuple is returned where the first element is the sample tensor.

stretch_shift_to_terminal[[diffusers.FlowMatchEulerDiscreteScheduler.stretch_shift_to_terminal]]

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Stretches and shifts the timestep schedule to ensure it terminates at the configured shift_terminal config value.

Reference: https://github.com/Lightricks/LTX-Video/blob/a01a171f8fe3d99dce2728d60a73fecf4d4238ae/ltx_video/schedulers/rf.py#L51

Parameters:

t (torch.Tensor) : A tensor of timesteps to be stretched and shifted.

Returns:

torch.Tensor

A tensor of adjusted timesteps such that the final value equals self.config.shift_terminal.