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9f818c5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | # SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: OpenMDW-1.1
"""
Impl of multistep methods to solve the ODE in the diffusion model.
"""
from typing import Callable, List, Tuple
import torch
from cosmos_framework.utils.functional.runge_kutta import reg_x0_euler_step, res_x0_rk2_step
def order2_fn(
x_s: torch.Tensor, s: torch.Tensor, t: torch.Tensor, x0_s: torch.Tensor, x0_preds: torch.Tensor
) -> Tuple[torch.Tensor, List[torch.Tensor]]:
"""
impl the second order multistep method in https://arxiv.org/pdf/2308.02157
Adams Bashforth approach!
"""
if x0_preds:
x0_s1, s1 = x0_preds[0]
x_t = res_x0_rk2_step(x_s, t, s, x0_s, s1, x0_s1)
else:
x_t = reg_x0_euler_step(x_s, s, t, x0_s)[0]
return x_t, [(x0_s, s)]
# key: method name, value: method function
# key: order + algorithm name
MULTISTEP_FNs = {
"2ab": order2_fn,
}
def get_multi_step_fn(name: str) -> Callable:
if name in MULTISTEP_FNs:
return MULTISTEP_FNs[name]
methods = "\n\t".join(MULTISTEP_FNs.keys())
raise RuntimeError("Only support multistep method\n" + methods)
def is_multi_step_fn_supported(name: str) -> bool:
"""
Check if the multistep method is supported.
"""
return name in MULTISTEP_FNs
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