ddim for Animatediff webui

ddim-animatediff.patch

diff --git a/ldm/models/diffusion/ddim.py b/ldm/models/diffusion/ddim.py
index c6cfd57..2569ea1 100644
--- a/ldm/models/diffusion/ddim.py
+++ b/ldm/models/diffusion/ddim.py
@@ -25,12 +25,24 @@ class DDIMSampler(object):
         self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
                                                   num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
         alphas_cumprod = self.model.alphas_cumprod
+
+        beta_start = 0.00085
+        beta_end = 0.012
+        # beta_schedule = "linear"
+        # num_train_timesteps = 1000  # default
+
+        betas = torch.linspace(beta_start, beta_end, self.ddpm_num_timesteps, dtype=torch.float32)
+
+        alphas = 1.0 - betas
+        alphas_cumprod = torch.cumprod(alphas, dim=0)
+        alphas_cumprod_prev = torch.cat((torch.tensor([1.0], dtype=torch.float32), alphas_cumprod[:-1]))
+
         assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
         to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
 
-        self.register_buffer('betas', to_torch(self.model.betas))
+        self.register_buffer('betas', to_torch(betas))
         self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
+        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
 
         # calculations for diffusion q(x_t | x_{t-1}) and others
         self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))