Refactor so that zsnr can be set in the sampling_settings.

Author: comfyanonymous

comfy/model_sampling.py

@@ -2,6 +2,25 @@
 from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule
 import math
 
+def rescale_zero_terminal_snr_sigmas(sigmas):
+    alphas_cumprod = 1 / ((sigmas * sigmas) + 1)
+    alphas_bar_sqrt = alphas_cumprod.sqrt()
+
+    # Store old values.
+    alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
+    alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
+
+    # Shift so the last timestep is zero.
+    alphas_bar_sqrt -= (alphas_bar_sqrt_T)
+
+    # Scale so the first timestep is back to the old value.
+    alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
+
+    # Convert alphas_bar_sqrt to betas
+    alphas_bar = alphas_bar_sqrt**2  # Revert sqrt
+    alphas_bar[-1] = 4.8973451890853435e-08
+    return ((1 - alphas_bar) / alphas_bar) ** 0.5
+
 class EPS:
     def calculate_input(self, sigma, noise):
         sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
@@ -48,7 +67,7 @@ def inverse_noise_scaling(self, sigma, latent):
         return latent / (1.0 - sigma)
 
 class ModelSamplingDiscrete(torch.nn.Module):
-    def __init__(self, model_config=None):
+    def __init__(self, model_config=None, zsnr=None):
         super().__init__()
 
         if model_config is not None:
@@ -61,11 +80,14 @@ def __init__(self, model_config=None):
         linear_end = sampling_settings.get("linear_end", 0.012)
         timesteps = sampling_settings.get("timesteps", 1000)
 
-        self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=8e-3)
+        if zsnr is None:
+            zsnr = sampling_settings.get("zsnr", False)
+
+        self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=8e-3, zsnr=zsnr)
         self.sigma_data = 1.0
 
     def _register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
-                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3, zsnr=False):
         if given_betas is not None:
             betas = given_betas
         else:
@@ -83,6 +105,9 @@ def _register_schedule(self, given_betas=None, beta_schedule="linear", timesteps
         # self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32))
 
         sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5
+        if zsnr:
+            sigmas = rescale_zero_terminal_snr_sigmas(sigmas)
+
         self.set_sigmas(sigmas)
 
     def set_sigmas(self, sigmas):

comfy_extras/nodes_model_advanced.py

@@ -51,25 +51,6 @@ def sigma(self, timestep):
         return log_sigma.exp().to(timestep.device)
 
 
-def rescale_zero_terminal_snr_sigmas(sigmas):
-    alphas_cumprod = 1 / ((sigmas * sigmas) + 1)
-    alphas_bar_sqrt = alphas_cumprod.sqrt()
-
-    # Store old values.
-    alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
-    alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
-
-    # Shift so the last timestep is zero.
-    alphas_bar_sqrt -= (alphas_bar_sqrt_T)
-
-    # Scale so the first timestep is back to the old value.
-    alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
-
-    # Convert alphas_bar_sqrt to betas
-    alphas_bar = alphas_bar_sqrt**2  # Revert sqrt
-    alphas_bar[-1] = 4.8973451890853435e-08
-    return ((1 - alphas_bar) / alphas_bar) ** 0.5
-
 class ModelSamplingDiscrete:
     @classmethod
     def INPUT_TYPES(s):
@@ -100,9 +81,7 @@ def patch(self, model, sampling, zsnr):
         class ModelSamplingAdvanced(sampling_base, sampling_type):
             pass
 
-        model_sampling = ModelSamplingAdvanced(model.model.model_config)
-        if zsnr:
-            model_sampling.set_sigmas(rescale_zero_terminal_snr_sigmas(model_sampling.sigmas))
+        model_sampling = ModelSamplingAdvanced(model.model.model_config, zsnr=zsnr)
 
         m.add_object_patch("model_sampling", model_sampling)
         return (m, )