diff --git a/ldm/models/diffusion/ddpm.py b/ldm/models/diffusion/ddpm.py
index 0b9bc98..aa8b4ff 100644
--- a/ldm/models/diffusion/ddpm.py
+++ b/ldm/models/diffusion/ddpm.py
@@ -1473,9 +1473,10 @@ class LatentUpscaleDiffusion(LatentDiffusion):
         x_low = x_low.to(memory_format=torch.contiguous_format).float()
         zx, noise_level = self.low_scale_model(x_low)
         all_conds = {"c_concat": [zx], "c_crossattn": [c], "c_adm": noise_level}
+        #import pudb; pu.db
         if log_mode:
             # TODO: maybe disable if too expensive
-            interpretability = True
+            interpretability = False
             if interpretability:
                 zx = zx[:, :, ::2, ::2]
             x_low_rec = self.low_scale_model.decode(zx)
@@ -1553,13 +1554,13 @@ class LatentUpscaleDiffusion(LatentDiffusion):
                 if k == "c_crossattn":
                     assert isinstance(c[k], list) and len(c[k]) == 1
                     uc[k] = [uc_tmp]
-                elif k == "c_adm":
+                elif k == "c_adm":  # todo: only run with text-based guidance?
                     assert isinstance(c[k], torch.Tensor)
-                    uc[k] = torch.ones_like(c[k]) * (self.low_scale_model.max_noise_level-1)
+                    uc[k] = torch.ones_like(c[k]) * self.low_scale_model.max_noise_level
                 elif isinstance(c[k], list):
-                    uc[k] = [torch.zeros_like(c[k][i]) for i in range(len(c[k]))]
+                    uc[k] = [c[k][i] for i in range(len(c[k]))]
                 else:
-                    uc[k] = torch.zeros_like(c[k])
+                    uc[k] = c[k]
 
             with ema_scope("Sampling with classifier-free guidance"):
                 samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
@@ -1628,8 +1629,7 @@ class LatentInpaintDiffusion(LatentDiffusion):
                 new_entry[:, :self.keep_dims, ...] = sd[k]
                 sd[k] = new_entry
 
-        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
-            sd, strict=False)
+        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(sd, strict=False)
         print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
         if len(missing) > 0:
             print(f"Missing Keys: {missing}")
diff --git a/ldm/modules/encoders/modules.py b/ldm/modules/encoders/modules.py
index 21ba5b4..84e5604 100644
--- a/ldm/modules/encoders/modules.py
+++ b/ldm/modules/encoders/modules.py
@@ -255,8 +255,9 @@ class LowScaleEncoder(nn.Module):
         z = z * self.scale_factor
         noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long()
         z = self.q_sample(z, noise_level)
-        #z = torch.nn.functional.interpolate(z, size=self.out_size, mode="nearest")  # TODO: experiment with mode
-        z = z.repeat_interleave(2, -2).repeat_interleave(2, -1)
+        if self.out_size is not None:
+            z = torch.nn.functional.interpolate(z, size=self.out_size, mode="nearest")  # TODO: experiment with mode
+        # z = z.repeat_interleave(2, -2).repeat_interleave(2, -1)
         return z, noise_level
 
     def decode(self, z):