Don't use numpy for calculating sigmas.

comfy/ldm/modules/diffusionmodules/util.py

@@ -98,7 +98,7 @@ def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2,
         alphas = torch.cos(alphas).pow(2)
         alphas = alphas / alphas[0]
         betas = 1 - alphas[1:] / alphas[:-1]
-        betas = np.clip(betas, a_min=0, a_max=0.999)
+        betas = torch.clamp(betas, min=0, max=0.999)
 
     elif schedule == "squaredcos_cap_v2":  # used for karlo prior
         # return early
@@ -113,7 +113,7 @@ def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2,
         betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
     else:
         raise ValueError(f"schedule '{schedule}' unknown.")
-    return betas.numpy()
+    return betas
 
 
 def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):

comfy/model_sampling.py

 import torch
-import numpy as np
 from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule
 import math
 
@@ -42,8 +41,7 @@ class ModelSamplingDiscrete(torch.nn.Module):
         else:
             betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
         alphas = 1. - betas
-        alphas_cumprod = torch.tensor(np.cumprod(alphas, axis=0), dtype=torch.float32)
-        # alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
+        alphas_cumprod = torch.cumprod(alphas, dim=0)
 
         timesteps, = betas.shape
         self.num_timesteps = int(timesteps)
@@ -58,8 +56,8 @@ class ModelSamplingDiscrete(torch.nn.Module):
         self.set_sigmas(sigmas)
 
     def set_sigmas(self, sigmas):
-        self.register_buffer('sigmas', sigmas)
-        self.register_buffer('log_sigmas', sigmas.log())
+        self.register_buffer('sigmas', sigmas.float())
+        self.register_buffer('log_sigmas', sigmas.log().float())
 
     @property
     def sigma_min(self):