diffusers/pr_13745/en/api/schedulers/flow_map_euler_discrete.md

FlowMapEulerDiscreteScheduler

FlowMapEulerDiscreteScheduler is an Euler-style sampler designed for flow-map-distilled diffusion models. Flow-map models learn arbitrary-interval transitions $\mathbf{z}_t \to \mathbf{z}_r$ rather than the fixed $\mathbf{z}_t \to \mathbf{z}_0$ mapping of consistency models. Both endpoints of the step are caller-provided, which is what enables any-step sampling: a single distilled checkpoint can be evaluated at 1, 2, 4, 8, 16... NFE without retraining.

The scheduler was introduced in AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation and ships with the AnyFlowPipeline and AnyFlowFARPipeline integrations, but it is not AnyFlow-specific — any flow-map-distilled checkpoint can use it.

FlowMapEulerDiscreteScheduler[[diffusers.FlowMapEulerDiscreteScheduler]]

diffusers.FlowMapEulerDiscreteScheduler[[diffusers.FlowMapEulerDiscreteScheduler]]

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Euler-style sampler for flow-map-distilled diffusion models.

Flow-map models learn arbitrary-interval transitions $z_t\to z_{r}z\_t\; \backslash to\; z\_r$ rather than the fixed $z_t\to z_{0}z\_t\; \backslash to\; z\_0$ mapping of consistency models, so a single distilled checkpoint can be evaluated at 1, 2, 4, 8, ... NFE without retraining. The step method advances the sample from timestep to r_timestep along the predicted velocity.

Introduced in AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation by Yuchao Gu, Guian Fang et al.

This scheduler inherits from [SchedulerMixin] and [ConfigMixin]. Check the superclass documentation for the generic methods implemented for all schedulers (loading, saving, etc.).

apply_shiftdiffusers.FlowMapEulerDiscreteScheduler.apply_shifthttps://github.com/huggingface/diffusers/blob/vr_13745/src/diffusers/schedulers/scheduling_flow_map_euler_discrete.py#L117[{"name": "sigmas", "val": ": Tensor"}] Apply the configured shift transformation to a sigma tensor.

Parameters:

num_train_timesteps (int, defaults to 1000) : The number of diffusion steps used to train the underlying flow-map model.

shift (float, defaults to 1.0) : Multiplicative timestep shift applied to the inference schedule. shift=1.0 is the identity; values greater than 1.0 push the schedule toward more denoising at later steps (e.g., shift=5 matches the Wan2.1 default).

index_for_timestep[[diffusers.FlowMapEulerDiscreteScheduler.index_for_timestep]]

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Return the index of timestep on the current schedule, or None if off-schedule.

Lookup is done against self.timesteps with a small fp tolerance. Used to recover the corresponding sigma without assuming the linear timesteps = sigmas * num_train_timesteps relationship — that way a custom schedule (e.g. non-linear shift, manually-set timesteps) still resolves correctly.

scale_model_input[[diffusers.FlowMapEulerDiscreteScheduler.scale_model_input]]

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No-op identity scaling. Provided for API compatibility with other Diffusers schedulers.

scale_noise[[diffusers.FlowMapEulerDiscreteScheduler.scale_noise]]

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Linearly interpolate sample toward noise according to the normalized timestep.

set_begin_index[[diffusers.FlowMapEulerDiscreteScheduler.set_begin_index]]

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Set the begin index for the scheduler. Pipelines that start mid-schedule (e.g. image-to-image) call this between set_timesteps and the first step to anchor the rollout.

set_timesteps[[diffusers.FlowMapEulerDiscreteScheduler.set_timesteps]]

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Build the inference timestep schedule.

Internally tracks self.sigmas of length num_inference_steps + 1 (the configured shift applied to a linspace from 1.0 to 0.0 by default); self.timesteps exposes the first num_inference_steps sigmas scaled by num_train_timesteps — i.e. one timestep per inference step, matching FlowMatchEulerDiscreteScheduler. The final sigma (0) is the implicit r-endpoint of the last step and is appended automatically when sigmas / timesteps are user-provided.

Parameters:

num_inference_steps (int, optional) : Number of inference steps. If None, must pass sigmas or timesteps.

device (str or torch.device, optional) : Target device for self.sigmas / self.timesteps.

sigmas (List[float], optional) : Custom sigma schedule of length num_inference_steps. The terminal 0 sigma is appended automatically. The configured shift is applied on top.

timesteps (List[float], optional) : Custom timestep schedule of length num_inference_steps, in the same units as self.timesteps (i.e. scaled by num_train_timesteps). Converted to sigmas internally. If both sigmas and timesteps are passed, their lengths must match.

step[[diffusers.FlowMapEulerDiscreteScheduler.step]]

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Advance sample from timestep to r_timestep using the model-predicted velocity.

Unlike a standard Euler scheduler, both endpoints of the interval can be caller-provided so that any-step sampling is possible: a single model call can step from t to any chosen target r (including r=0 for a one-shot generation). When r_timestep is omitted, it defaults to the next timestep on the schedule (matching FlowMatchEulerDiscreteScheduler semantics).

Internally the source and target sigmas are recovered by indexing self.sigmas via index_for_timestep rather than by dividing the input timesteps by num_train_timesteps, so any schedule whose timestep / sigma relationship is non-linear (for example a custom shift) stays correct. For an off-schedule r_timestep, the scheduler falls back to r_timestep / num_train_timesteps so any-step sampling outside the schedule remains supported.

Parameters:

model_output (torch.Tensor) : Direct output from the flow-map model (predicted mean velocity).

timestep (float or torch.Tensor) : Source timestep t in the same units as self.timesteps.

sample (torch.Tensor) : Current sample $z_{t}z\_t$.

r_timestep (float or torch.Tensor, optional) : Target timestep r. Defaults to the next timestep on the schedule when None; pass an explicit value for any-step sampling. r_timestep == timestep is a no-op.

return_dict (bool, defaults to True) : Whether to return a [FlowMapEulerDiscreteSchedulerOutput] (the default) or a plain tuple.

Returns:

[*FlowMapEulerDiscreteSchedulerOutput*] or *tuple*

When return_dict=True, returns a [FlowMapEulerDiscreteSchedulerOutput] whose prev_sample is $z_{r}z\_r$. Otherwise returns a 1-tuple (prev_sample,).