diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py
index 3b4e9931..dd234435 100644
--- a/comfy/k_diffusion/sampling.py
+++ b/comfy/k_diffusion/sampling.py
@@ -3,7 +3,6 @@ import math
 from scipy import integrate
 import torch
 from torch import nn
-from torchdiffeq import odeint
 import torchsde
 from tqdm.auto import trange, tqdm
 
@@ -287,30 +286,6 @@ def sample_lms(model, x, sigmas, extra_args=None, callback=None, disable=None, o
     return x
 
 
-@torch.no_grad()
-def log_likelihood(model, x, sigma_min, sigma_max, extra_args=None, atol=1e-4, rtol=1e-4):
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-    v = torch.randint_like(x, 2) * 2 - 1
-    fevals = 0
-    def ode_fn(sigma, x):
-        nonlocal fevals
-        with torch.enable_grad():
-            x = x[0].detach().requires_grad_()
-            denoised = model(x, sigma * s_in, **extra_args)
-            d = to_d(x, sigma, denoised)
-            fevals += 1
-            grad = torch.autograd.grad((d * v).sum(), x)[0]
-            d_ll = (v * grad).flatten(1).sum(1)
-        return d.detach(), d_ll
-    x_min = x, x.new_zeros([x.shape[0]])
-    t = x.new_tensor([sigma_min, sigma_max])
-    sol = odeint(ode_fn, x_min, t, atol=atol, rtol=rtol, method='dopri5')
-    latent, delta_ll = sol[0][-1], sol[1][-1]
-    ll_prior = torch.distributions.Normal(0, sigma_max).log_prob(latent).flatten(1).sum(1)
-    return ll_prior + delta_ll, {'fevals': fevals}
-
-
 class PIDStepSizeController:
     """A PID controller for ODE adaptive step size control."""
     def __init__(self, h, pcoeff, icoeff, dcoeff, order=1, accept_safety=0.81, eps=1e-8):
diff --git a/requirements.txt b/requirements.txt
index d632edf7..8ee7b83d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,4 @@
 torch
-torchdiffeq
 torchsde
 einops
 transformers>=4.25.1