Refactor LCM to support more model types.

comfy_extras/nodes_model_advanced.py

@@ -17,41 +17,19 @@ class LCM(comfy.model_sampling.EPS):
 
         return c_out * x0 + c_skip * model_input
 
-class ModelSamplingDiscreteDistilled(torch.nn.Module):
+class ModelSamplingDiscreteDistilled(comfy.model_sampling.ModelSamplingDiscrete):
     original_timesteps = 50
 
-    def __init__(self):
-        super().__init__()
-        self.sigma_data = 1.0
-        timesteps = 1000
-        beta_start = 0.00085
-        beta_end = 0.012
+    def __init__(self, model_config=None):
+        super().__init__(model_config)
 
-        betas = torch.linspace(beta_start**0.5, beta_end**0.5, timesteps, dtype=torch.float32) ** 2
-        alphas = 1.0 - betas
-        alphas_cumprod = torch.cumprod(alphas, dim=0)
+        self.skip_steps = self.num_timesteps // self.original_timesteps
 
-        self.skip_steps = timesteps // self.original_timesteps
-
-
-        alphas_cumprod_valid = torch.zeros((self.original_timesteps), dtype=torch.float32)
+        sigmas_valid = torch.zeros((self.original_timesteps), dtype=torch.float32)
         for x in range(self.original_timesteps):
-            alphas_cumprod_valid[self.original_timesteps - 1 - x] = alphas_cumprod[timesteps - 1 - x * self.skip_steps]
-
-        sigmas = ((1 - alphas_cumprod_valid) / alphas_cumprod_valid) ** 0.5
-        self.set_sigmas(sigmas)
-
-    def set_sigmas(self, sigmas):
-        self.register_buffer('sigmas', sigmas)
-        self.register_buffer('log_sigmas', sigmas.log())
+            sigmas_valid[self.original_timesteps - 1 - x] = self.sigmas[self.num_timesteps - 1 - x * self.skip_steps]
 
-    @property
-    def sigma_min(self):
-        return self.sigmas[0]
-
-    @property
-    def sigma_max(self):
-        return self.sigmas[-1]
+        self.set_sigmas(sigmas_valid)
 
     def timestep(self, sigma):
         log_sigma = sigma.log()
@@ -66,14 +44,6 @@ class ModelSamplingDiscreteDistilled(torch.nn.Module):
         log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx]
         return log_sigma.exp().to(timestep.device)
 
-    def percent_to_sigma(self, percent):
-        if percent <= 0.0:
-            return 999999999.9
-        if percent >= 1.0:
-            return 0.0
-        percent = 1.0 - percent
-        return self.sigma(torch.tensor(percent * 999.0)).item()
-
 
 def rescale_zero_terminal_snr_sigmas(sigmas):
     alphas_cumprod = 1 / ((sigmas * sigmas) + 1)
@@ -154,7 +124,7 @@ class ModelSamplingContinuousEDM:
         class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingContinuousEDM, sampling_type):
             pass
 
-        model_sampling = ModelSamplingAdvanced()
+        model_sampling = ModelSamplingAdvanced(model.model.model_config)
         model_sampling.set_sigma_range(sigma_min, sigma_max)
         m.add_object_patch("model_sampling", model_sampling)
         return (m, )