# FlowMatchHeunDiscreteScheduler

`FlowMatchHeunDiscreteScheduler` is based on the flow-matching sampling introduced in [EDM](https://huggingface.co/papers/2403.03206).

## FlowMatchHeunDiscreteScheduler[[diffusers.FlowMatchHeunDiscreteScheduler]]
#### diffusers.FlowMatchHeunDiscreteScheduler[[diffusers.FlowMatchHeunDiscreteScheduler]]

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py#L43)

Heun scheduler.

This model inherits from [SchedulerMixin](/docs/diffusers/main/en/api/schedulers/overview#diffusers.SchedulerMixin) and [ConfigMixin](/docs/diffusers/main/en/api/configuration#diffusers.ConfigMixin). Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving.

index_for_timestepdiffusers.FlowMatchHeunDiscreteScheduler.index_for_timestephttps://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py#L179[{"name": "timestep", "val": ": float | torch.FloatTensor"}, {"name": "schedule_timesteps", "val": ": torch.FloatTensor | None = None"}]- **timestep** (`float` or `torch.FloatTensor`) --
  The timestep value to find in the schedule.
- **schedule_timesteps** (`torch.FloatTensor`, *optional*) --
  The timestep schedule to search in. If `None`, uses `self.timesteps`.0`int`The index of the timestep in the schedule.

Find the index of a given timestep in the timestep schedule.

**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.

**Returns:**

``int``

The index of the timestep in the schedule.
#### scale_noise[[diffusers.FlowMatchHeunDiscreteScheduler.scale_noise]]

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py#L106)

Forward process in flow-matching

**Parameters:**

sample (`torch.FloatTensor`) : The input sample.

timestep (`float` or `torch.FloatTensor`) : The current timestep in the diffusion chain.

noise (`torch.FloatTensor`) : The noise tensor.

**Returns:**

``torch.FloatTensor``

A scaled input sample.
#### set_begin_index[[diffusers.FlowMatchHeunDiscreteScheduler.set_begin_index]]

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py#L96)

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.FlowMatchHeunDiscreteScheduler.set_timesteps]]

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py#L139)

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

**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.
#### step[[diffusers.FlowMatchHeunDiscreteScheduler.step]]

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py#L225)

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` or `torch.FloatTensor`) : 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`) : Stochasticity parameter that controls the amount of noise added during sampling. Higher values increase randomness.

s_tmin (`float`) : Minimum timestep threshold for applying stochasticity. Only timesteps above this value will have noise added.

s_tmax (`float`) : Maximum timestep threshold for applying stochasticity. Only timesteps below this value will have noise added.

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 `FlowMatchHeunDiscreteSchedulerOutput` tuple.

**Returns:**

``FlowMatchHeunDiscreteSchedulerOutput` or `tuple``

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