repair DDIM/PLMS/UniPC batches

2 files changed, 12 insertions, 11 deletions

diff --git a/modules/sd_samplers_timesteps.py b/modules/sd_samplers_timesteps.py
index 16572c7e..6aed2974 100644
--- a/modules/sd_samplers_timesteps.py
+++ b/modules/sd_samplers_timesteps.py
@@ -51,10 +51,9 @@ class CFGDenoiserTimesteps(CFGDenoiser):
         self.alphas = shared.sd_model.alphas_cumprod

 

     def get_pred_x0(self, x_in, x_out, sigma):

-        ts = int(sigma.item())

+        ts = sigma.to(dtype=int)

 

-        s_in = x_in.new_ones([x_in.shape[0]])

-        a_t = self.alphas[ts].item() * s_in

+        a_t = self.alphas[ts][:, None, None, None]

         sqrt_one_minus_at = (1 - a_t).sqrt()

 

         pred_x0 = (x_in - sqrt_one_minus_at * x_out) / a_t.sqrt()

diff --git a/modules/sd_samplers_timesteps_impl.py b/modules/sd_samplers_timesteps_impl.py
index d32e3521..a72daafd 100644
--- a/modules/sd_samplers_timesteps_impl.py
+++ b/modules/sd_samplers_timesteps_impl.py
@@ -16,16 +16,17 @@ def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=
     sigmas = eta * np.sqrt((1 - alphas_prev.cpu().numpy()) / (1 - alphas.cpu()) * (1 - alphas.cpu() / alphas_prev.cpu().numpy()))

 

     extra_args = {} if extra_args is None else extra_args

-    s_in = x.new_ones([x.shape[0]])

+    s_in = x.new_ones((x.shape[0]))

+    s_x = x.new_ones((x.shape[0], 1, 1, 1))

     for i in tqdm.trange(len(timesteps) - 1, disable=disable):

         index = len(timesteps) - 1 - i

 

         e_t = model(x, timesteps[index].item() * s_in, **extra_args)

 

-        a_t = alphas[index].item() * s_in

-        a_prev = alphas_prev[index].item() * s_in

-        sigma_t = sigmas[index].item() * s_in

-        sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_in

+        a_t = alphas[index].item() * s_x

+        a_prev = alphas_prev[index].item() * s_x

+        sigma_t = sigmas[index].item() * s_x

+        sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_x

 

         pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()

         dir_xt = (1. - a_prev - sigma_t ** 2).sqrt() * e_t

@@ -47,13 +48,14 @@ def plms(model, x, timesteps, extra_args=None, callback=None, disable=None):
 

     extra_args = {} if extra_args is None else extra_args

     s_in = x.new_ones([x.shape[0]])

+    s_x = x.new_ones((x.shape[0], 1, 1, 1))

     old_eps = []

 

     def get_x_prev_and_pred_x0(e_t, index):

         # select parameters corresponding to the currently considered timestep

-        a_t = alphas[index].item() * s_in

-        a_prev = alphas_prev[index].item() * s_in

-        sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_in

+        a_t = alphas[index].item() * s_x

+        a_prev = alphas_prev[index].item() * s_x

+        sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_x

 

         # current prediction for x_0

         pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()