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Fix and enforce no trailing whitespace.

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comfyanonymous
2024-12-31 03:16:37 -05:00
parent a90aafafc1
commit b7572b2f87
20 changed files with 51 additions and 50 deletions
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18
comfy/extra_samplers/uni_pc.py
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@@ -226,7 +226,7 @@ def model_wrapper(
The input `model` has the following format:
``
model(x, t_input, **model_kwargs) -> noise | x_start | v | score
``
``
The input `classifier_fn` has the following format:
``
@@ -240,7 +240,7 @@ def model_wrapper(
The input `model` has the following format:
``
model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score
``
``
And if cond == `unconditional_condition`, the model output is the unconditional DPM output.
[4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance."
@@ -254,7 +254,7 @@ def model_wrapper(
``
def model_fn(x, t_continuous) -> noise:
t_input = get_model_input_time(t_continuous)
return noise_pred(model, x, t_input, **model_kwargs)
return noise_pred(model, x, t_input, **model_kwargs)
``
where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver.
@@ -359,7 +359,7 @@ class UniPC:
max_val=1.,
variant='bh1',
):
"""Construct a UniPC.
"""Construct a UniPC.
We support both data_prediction and noise_prediction.
"""
@@ -372,7 +372,7 @@ class UniPC:
def dynamic_thresholding_fn(self, x0, t=None):
"""
The dynamic thresholding method.
The dynamic thresholding method.
"""
dims = x0.dim()
p = self.dynamic_thresholding_ratio
@@ -404,7 +404,7 @@ class UniPC:
def model_fn(self, x, t):
"""
Convert the model to the noise prediction model or the data prediction model.
Convert the model to the noise prediction model or the data prediction model.
"""
if self.predict_x0:
return self.data_prediction_fn(x, t)
@@ -461,7 +461,7 @@ class UniPC:
def denoise_to_zero_fn(self, x, s):
"""
Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization.
Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization.
"""
return self.data_prediction_fn(x, s)
@@ -510,7 +510,7 @@ class UniPC:
col = torch.ones_like(rks)
for k in range(1, K + 1):
C.append(col)
col = col * rks / (k + 1)
col = col * rks / (k + 1)
C = torch.stack(C, dim=1)
if len(D1s) > 0:
@@ -626,7 +626,7 @@ class UniPC:
R.append(torch.pow(rks, i - 1))
b.append(h_phi_k * factorial_i / B_h)
factorial_i *= (i + 1)
h_phi_k = h_phi_k / hh - 1 / factorial_i
h_phi_k = h_phi_k / hh - 1 / factorial_i
R = torch.stack(R)
b = torch.tensor(b, device=x.device)