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fix: DDPMScheduler.set_timesteps() #1912

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Jan 4, 2023
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fix: DDPMScheduler.set_timesteps() #1912

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8 changes: 8 additions & 0 deletions src/diffusers/schedulers/scheduling_ddim.py
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Original file line number Diff line number Diff line change
Expand Up @@ -201,6 +201,14 @@ def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.devic
num_inference_steps (`int`):
the number of diffusion steps used when generating samples with a pre-trained model.
"""

if num_inference_steps > self.config.num_train_timesteps:
raise ValueError(
f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
f" maximal {self.config.num_train_timesteps} timesteps."
)

self.num_inference_steps = num_inference_steps
step_ratio = self.config.num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
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15 changes: 11 additions & 4 deletions src/diffusers/schedulers/scheduling_ddpm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -184,11 +184,18 @@ def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.devic
num_inference_steps (`int`):
the number of diffusion steps used when generating samples with a pre-trained model.
"""
num_inference_steps = min(self.config.num_train_timesteps, num_inference_steps)

if num_inference_steps > self.config.num_train_timesteps:
raise ValueError(
f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
f" maximal {self.config.num_train_timesteps} timesteps."
)

self.num_inference_steps = num_inference_steps
timesteps = np.arange(
0, self.config.num_train_timesteps, self.config.num_train_timesteps // self.num_inference_steps
)[::-1].copy()

step_ratio = self.config.num_train_timesteps // self.num_inference_steps
timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.int64)
self.timesteps = torch.from_numpy(timesteps).to(device)

def _get_variance(self, t, predicted_variance=None, variance_type=None):
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