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docs: improve docstring scheduling_dpmsolver_multistep_inverse.py #13083

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Merged
stevhliu merged 1 commit into from
Feb 4, 2026
Merged

docs: improve docstring scheduling_dpmsolver_multistep_inverse.py #13083

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4 changes: 3 additions & 1 deletion src/diffusers/schedulers/scheduling_cosine_dpmsolver_multistep.py
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Original file line number Diff line number Diff line change
Expand Up @@ -545,7 +545,9 @@ def multistep_dpm_solver_second_order_update(

# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.index_for_timestep
def index_for_timestep(
self, timestep: Union[int, torch.Tensor], schedule_timesteps: Optional[torch.Tensor] = None
self,
timestep: Union[int, torch.Tensor],
schedule_timesteps: Optional[torch.Tensor] = None,
) -> int:
"""
Find the index for a given timestep in the schedule.
Expand Down
4 changes: 3 additions & 1 deletion src/diffusers/schedulers/scheduling_deis_multistep.py
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Original file line number Diff line number Diff line change
Expand Up @@ -867,7 +867,9 @@ def ind_fn(t, b, c, d):

# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.index_for_timestep
def index_for_timestep(
self, timestep: Union[int, torch.Tensor], schedule_timesteps: Optional[torch.Tensor] = None
self,
timestep: Union[int, torch.Tensor],
schedule_timesteps: Optional[torch.Tensor] = None,
) -> int:
"""
Find the index for a given timestep in the schedule.
Expand Down
68 changes: 53 additions & 15 deletions src/diffusers/schedulers/scheduling_dpmsolver_multistep.py
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Original file line number Diff line number Diff line change
Expand Up @@ -245,21 +245,42 @@ def __init__(
):
if self.config.use_beta_sigmas and not is_scipy_available():
raise ImportError("Make sure to install scipy if you want to use beta sigmas.")
if sum([self.config.use_beta_sigmas, self.config.use_exponential_sigmas, self.config.use_karras_sigmas]) > 1:
if (
sum(
[
self.config.use_beta_sigmas,
self.config.use_exponential_sigmas,
self.config.use_karras_sigmas,
]
)
> 1
):
raise ValueError(
"Only one of `config.use_beta_sigmas`, `config.use_exponential_sigmas`, `config.use_karras_sigmas` can be used."
)
if algorithm_type in ["dpmsolver", "sde-dpmsolver"]:
deprecation_message = f"algorithm_type {algorithm_type} is deprecated and will be removed in a future version. Choose from `dpmsolver++` or `sde-dpmsolver++` instead"
deprecate("algorithm_types dpmsolver and sde-dpmsolver", "1.0.0", deprecation_message)
deprecate(
"algorithm_types dpmsolver and sde-dpmsolver",
"1.0.0",
deprecation_message,
)

if trained_betas is not None:
self.betas = torch.tensor(trained_betas, dtype=torch.float32)
elif beta_schedule == "linear":
self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
self.betas = (
torch.linspace(
beta_start**0.5,
beta_end**0.5,
num_train_timesteps,
dtype=torch.float32,
)
** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
self.betas = betas_for_alpha_bar(num_train_timesteps)
Expand Down Expand Up @@ -287,7 +308,12 @@ def __init__(
self.init_noise_sigma = 1.0

# settings for DPM-Solver
if algorithm_type not in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
if algorithm_type not in [
"dpmsolver",
"dpmsolver++",
"sde-dpmsolver",
"sde-dpmsolver++",
]:
if algorithm_type == "deis":
self.register_to_config(algorithm_type="dpmsolver++")
else:
Expand Down Expand Up @@ -724,7 +750,7 @@ def convert_model_output(
self,
model_output: torch.Tensor,
*args,
sample: torch.Tensor = None,
sample: Optional[torch.Tensor] = None,
**kwargs,
) -> torch.Tensor:
"""
Expand All @@ -738,7 +764,7 @@ def convert_model_output(
Args:
model_output (`torch.Tensor`):
The direct output from the learned diffusion model.
sample (`torch.Tensor`):
sample (`torch.Tensor`, *optional*):
A current instance of a sample created by the diffusion process.

Returns:
Expand Down Expand Up @@ -822,7 +848,7 @@ def dpm_solver_first_order_update(
self,
model_output: torch.Tensor,
*args,
sample: torch.Tensor = None,
sample: Optional[torch.Tensor] = None,
noise: Optional[torch.Tensor] = None,
**kwargs,
) -> torch.Tensor:
Expand All @@ -832,8 +858,10 @@ def dpm_solver_first_order_update(
Args:
model_output (`torch.Tensor`):
The direct output from the learned diffusion model.
sample (`torch.Tensor`):
sample (`torch.Tensor`, *optional*):
A current instance of a sample created by the diffusion process.
noise (`torch.Tensor`, *optional*):
The noise tensor.

Returns:
`torch.Tensor`:
Expand All @@ -860,7 +888,10 @@ def dpm_solver_first_order_update(
"Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`",
)

sigma_t, sigma_s = self.sigmas[self.step_index + 1], self.sigmas[self.step_index]
sigma_t, sigma_s = (
self.sigmas[self.step_index + 1],
self.sigmas[self.step_index],
)
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)
alpha_s, sigma_s = self._sigma_to_alpha_sigma_t(sigma_s)
lambda_t = torch.log(alpha_t) - torch.log(sigma_t)
Expand Down Expand Up @@ -891,7 +922,7 @@ def multistep_dpm_solver_second_order_update(
self,
model_output_list: List[torch.Tensor],
*args,
sample: torch.Tensor = None,
sample: Optional[torch.Tensor] = None,
noise: Optional[torch.Tensor] = None,
**kwargs,
) -> torch.Tensor:
Expand All @@ -901,7 +932,7 @@ def multistep_dpm_solver_second_order_update(
Args:
model_output_list (`List[torch.Tensor]`):
The direct outputs from learned diffusion model at current and latter timesteps.
sample (`torch.Tensor`):
sample (`torch.Tensor`, *optional*):
A current instance of a sample created by the diffusion process.

Returns:
Expand Down Expand Up @@ -1014,7 +1045,7 @@ def multistep_dpm_solver_third_order_update(
self,
model_output_list: List[torch.Tensor],
*args,
sample: torch.Tensor = None,
sample: Optional[torch.Tensor] = None,
noise: Optional[torch.Tensor] = None,
**kwargs,
) -> torch.Tensor:
Expand All @@ -1024,8 +1055,10 @@ def multistep_dpm_solver_third_order_update(
Args:
model_output_list (`List[torch.Tensor]`):
The direct outputs from learned diffusion model at current and latter timesteps.
sample (`torch.Tensor`):
sample (`torch.Tensor`, *optional*):
A current instance of a sample created by diffusion process.
noise (`torch.Tensor`, *optional*):
The noise tensor.

Returns:
`torch.Tensor`:
Expand Down Expand Up @@ -1106,7 +1139,9 @@ def multistep_dpm_solver_third_order_update(
return x_t

def index_for_timestep(
self, timestep: Union[int, torch.Tensor], schedule_timesteps: Optional[torch.Tensor] = None
self,
timestep: Union[int, torch.Tensor],
schedule_timesteps: Optional[torch.Tensor] = None,
) -> int:
"""
Find the index for a given timestep in the schedule.
Expand Down Expand Up @@ -1216,7 +1251,10 @@ def step(
sample = sample.to(torch.float32)
if self.config.algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"] and variance_noise is None:
noise = randn_tensor(
model_output.shape, generator=generator, device=model_output.device, dtype=torch.float32
model_output.shape,
generator=generator,
device=model_output.device,
dtype=torch.float32,
)
elif self.config.algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
noise = variance_noise.to(device=model_output.device, dtype=torch.float32)
Expand Down
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