Optimize some unneeded if conditions in the sampling code.

comfy/k_diffusion/sampling.py

@@ -129,8 +129,13 @@ def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None,
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
     for i in trange(len(sigmas) - 1, disable=disable):
-        gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
-        sigma_hat = sigmas[i] * (gamma + 1)
+        if s_churn > 0:
+            gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
+            sigma_hat = sigmas[i] * (gamma + 1)
+        else:
+            gamma = 0
+            sigma_hat = sigmas[i]
+
         if gamma > 0:
             eps = torch.randn_like(x) * s_noise
             x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
@@ -170,7 +175,13 @@ def sample_heun(model, x, sigmas, extra_args=None, callback=None, disable=None,
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
     for i in trange(len(sigmas) - 1, disable=disable):
-        gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
+        if s_churn > 0:
+            gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
+            sigma_hat = sigmas[i] * (gamma + 1)
+        else:
+            gamma = 0
+            sigma_hat = sigmas[i]
+
         sigma_hat = sigmas[i] * (gamma + 1)
         if gamma > 0:
             eps = torch.randn_like(x) * s_noise
@@ -199,8 +210,13 @@ def sample_dpm_2(model, x, sigmas, extra_args=None, callback=None, disable=None,
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
     for i in trange(len(sigmas) - 1, disable=disable):
-        gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
-        sigma_hat = sigmas[i] * (gamma + 1)
+        if s_churn > 0:
+            gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
+            sigma_hat = sigmas[i] * (gamma + 1)
+        else:
+            gamma = 0
+            sigma_hat = sigmas[i]
+
         if gamma > 0:
             eps = torch.randn_like(x) * s_noise
             x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5