diffusers/main/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]]

• 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).

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 the configured shift transformation to a sigma tensor.

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.

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

Linearly interpolate sample toward noise according to the normalized timestep.

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.

• 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. 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.

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

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.