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fix copy inconsistencies #11

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lawrence-cj merged 1 commit into from
Jan 15, 2024
Merged

fix copy inconsistencies #11

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26 changes: 6 additions & 20 deletions src/diffusers/schedulers/scheduling_sasolver.py
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Original file line number Diff line number Diff line change
Expand Up @@ -328,9 +328,7 @@ def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
batch_size, channels, *remaining_dims = sample.shape

if dtype not in (torch.float32, torch.float64):
sample = (
sample.float()
) # upcast for quantile calculation, and clamp not implemented for cpu half
sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half

# Flatten sample for doing quantile calculation along each image
sample = sample.reshape(batch_size, channels * np.prod(remaining_dims))
Expand All @@ -342,9 +340,7 @@ def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
s, min=1, max=self.config.sample_max_value
) # When clamped to min=1, equivalent to standard clipping to [-1, 1]
s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0
sample = (
torch.clamp(sample, -s, s) / s
) # "we threshold xt0 to the range [-s, s] and then divide by s"
sample = torch.clamp(sample, -s, s) / s # "we threshold xt0 to the range [-s, s] and then divide by s"

sample = sample.reshape(batch_size, channels, *remaining_dims)
sample = sample.to(dtype)
Expand All @@ -360,11 +356,7 @@ def _sigma_to_t(self, sigma, log_sigmas):
dists = log_sigma - log_sigmas[:, np.newaxis]

# get sigmas range
low_idx = (
np.cumsum((dists >= 0), axis=0)
.argmax(axis=0)
.clip(max=log_sigmas.shape[0] - 2)
)
low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)
high_idx = low_idx + 1

low = log_sigmas[low_idx]
Expand All @@ -387,9 +379,7 @@ def _sigma_to_alpha_sigma_t(self, sigma):
return alpha_t, sigma_t

# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras
def _convert_to_karras(
self, in_sigmas: torch.FloatTensor, num_inference_steps
) -> torch.FloatTensor:
def _convert_to_karras(self, in_sigmas: torch.FloatTensor, num_inference_steps) -> torch.FloatTensor:
"""Constructs the noise schedule of Karras et al. (2022)."""

# Hack to make sure that other schedulers which copy this function don't break
Expand Down Expand Up @@ -1200,9 +1190,7 @@ def add_noise(
timesteps: torch.IntTensor,
) -> torch.FloatTensor:
# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
alphas_cumprod = self.alphas_cumprod.to(
device=original_samples.device, dtype=original_samples.dtype
)
alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)
timesteps = timesteps.to(original_samples.device)

sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
Expand All @@ -1215,9 +1203,7 @@ def add_noise(
while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)

noisy_samples = (
sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
)
noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples

def __len__(self):
Expand Down