---

name: schedulers description: "Select, configure, swap, serialize, and troubleshoot Diffusers schedulers, timesteps, sigmas, and noise schedules for inference and tests." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Schedulers

Use this sub-skill when a task mentions Diffusers schedulers, samplers, DDIM, DDPM, Euler, DPM-Solver, DPM++, Karras, AYS, timestep spacing, custom timesteps, custom sigmas, prediction_type, rescale_betas_zero_snr, set_timesteps, scheduler.step, or scheduler config round-trips.

Route first

• Full pipeline loading, prompt execution, model downloads, device maps, and image generation belong to the pipelines/inference skill; return here for scheduler choice and configuration.
• Training loop use of schedulers and model training arguments belong to training recipes; this skill covers inference and scheduler-focused tests.
• Model architecture changes and prediction heads are not scheduler tasks; only set prediction_type here when it is known to match the checkpoint.
• Packaging a whole pipeline belongs elsewhere, but this skill owns scheduler-only from_config, from_pretrained, and save_pretrained behavior.

Default workflow

1. Inspect the current scheduler with type(pipe.scheduler).__name__, dict(pipe.scheduler.config), and important fields such as prediction_type, timestep_spacing, num_train_timesteps, and sigma options.
2. Pick the scheduler for the sampling goal: DPM-Solver multistep for fast high-quality inference, Euler/Euler ancestral for Euler-style sampler parity, DDIM for deterministic DDIM loops, DDPM for DDPM/noise-addition parity, or model-specific schedulers for FlowMatch, LCM, TCD, video, or distilled checkpoints.
3. Swap an already loaded pipeline with NewScheduler.from_config(pipe.scheduler.config, explicit_overrides...); load a scheduler config from a model directory or Hub repo with NewScheduler.from_pretrained(model_id_or_dir, subfolder="scheduler").
4. Always pass explicit overrides for behavior the user cares about, especially prediction_type, timestep_spacing, rescale_betas_zero_snr, solver options, and sigma conversion flags.
5. Before manual stepping, call scheduler.set_timesteps(...); in denoising loops call scheduler.scale_model_input(sample, timestep) before the model and consume scheduler.step(...).prev_sample.
6. Validate a swap with a tiny deterministic CPU tensor, finite-output checks, and a config printout before running full inference.

Quick choices

• Replace DDIM with Euler while preserving compatible config:

from diffusers import EulerDiscreteScheduler

pipe.scheduler = EulerDiscreteScheduler.from_config(
    pipe.scheduler.config,
    prediction_type=pipe.scheduler.config.prediction_type,
    timestep_spacing="trailing",
)

• Use DPM++ 2M SDE Karras-style inference:

from diffusers import DPMSolverMultistepScheduler

pipe.scheduler = DPMSolverMultistepScheduler.from_config(
    pipe.scheduler.config,
    algorithm_type="sde-dpmsolver++",
    solver_order=2,
    use_karras_sigmas=True,
)

• Use zero terminal SNR only for compatible models, commonly v_prediction checkpoints:

pipe.scheduler = type(pipe.scheduler).from_config(
    pipe.scheduler.config,
    prediction_type="v_prediction",
    rescale_betas_zero_snr=True,
    timestep_spacing="trailing",
)
image = pipe(prompt, guidance_rescale=0.7).images[0]

References

• Scheduler API and workflow details: references/scheduler-reference.md
• Failure diagnosis and fixes: references/troubleshooting.md
• Local validation helper: scripts/scheduler_smoke.py

Safe validation

Run the bundled helper after installing Diffusers and Torch:

python path/to/sub-skills/schedulers/scripts/scheduler_smoke.py --help
python path/to/sub-skills/schedulers/scripts/scheduler_smoke.py

Expected signals: common scheduler imports succeed; constructor and step signatures print; short set_timesteps schedules are produced; tiny CPU step checks return finite tensors; and common misuse checks raise the expected errors.