fn_registry/python/functions/ml/diffusers_set_scheduler.md

name, kind, lang, domain, version, purity, signature, description, tags, uses_functions, uses_types, returns, returns_optional, error_type, imports, params, output, tested, tests, test_file_path, file_path

name	kind	lang	domain	version	purity	signature	description	tags	uses_functions	uses_types	returns	returns_optional	error_type	imports	params	output	tested	tests	test_file_path	file_path
diffusers_set_scheduler	function	py	ml	1.0.0	impure	def diffusers_set_scheduler(pipe: Any, sampler: str) -> Any	Reemplaza el scheduler de un pipeline diffusers por la clase correspondiente al sampler solicitado. Usa from_config para heredar configuracion base del modelo.	diffusers ml scheduler sampler image-generation pendiente-usar				false	error_go_core	diffusers	name desc pipe Pipeline diffusers cargado con atributo pipe.scheduler y pipe.scheduler.config. name desc sampler Nombre del sampler: euler, euler_a, dpm++2m, dpm++2m_v2, heun, dpm2, lcm.	El mismo pipe con pipe.scheduler reemplazado. Modificacion in-place, retorna pipe para composicion.	true	euler cambia scheduler a EulerDiscreteScheduler sampler invalido lanza ValueError	python/functions/ml/tests/test_diffusers_backend.py	python/functions/ml/diffusers_set_scheduler.py

Ejemplo

from diffusers_load_pipeline import diffusers_load_pipeline
from diffusers_set_scheduler import diffusers_set_scheduler
from model_ref import ModelRef

model = ModelRef(name="sd-turbo", model_type="sd15", path="/path/to/model")
pipe = diffusers_load_pipeline(model)
pipe = diffusers_set_scheduler(pipe, "euler_a")
# type(pipe.scheduler).__name__ == "EulerAncestralDiscreteScheduler"

Mapping de samplers

sampler	clase diffusers	kwargs extra
euler	EulerDiscreteScheduler	—
euler_a	EulerAncestralDiscreteScheduler	—
dpm++2m	DPMSolverMultistepScheduler	algorithm_type="dpmsolver++"
dpm++2m_v2	DPMSolverMultistepScheduler	algorithm_type="dpmsolver++", solver_order=2
heun	HeunDiscreteScheduler	—
dpm2	KDPM2DiscreteScheduler	—
lcm	LCMScheduler	—

Notas

Usa SchedulerCls.from_config(pipe.scheduler.config, **extra_kwargs) para heredar beta_start, beta_end, clip_sample, etc. del modelo base.

Import lazy de diffusers — ImportError descriptivo si no instalado.