Fix logspam and live previews

1 files changed, 15 insertions, 5 deletions

diff --git a/modules/models/diffusion/uni_pc/sampler.py b/modules/models/diffusion/uni_pc/sampler.py
index 7cccd8a2..219e9862 100644
--- a/modules/models/diffusion/uni_pc/sampler.py
+++ b/modules/models/diffusion/uni_pc/sampler.py
@@ -19,9 +19,10 @@ class UniPCSampler(object):
                 attr = attr.to(torch.device("cuda"))
         setattr(self, name, attr)
 
-    def set_hooks(self, before, after):
-        self.before_sample = before
-        self.after_sample = after
+    def set_hooks(self, before_sample, after_sample, after_update):
+        self.before_sample = before_sample
+        self.after_sample = after_sample
+        self.after_update = after_update
 
     @torch.no_grad()
     def sample(self,
@@ -50,9 +51,17 @@ class UniPCSampler(object):
                ):
         if conditioning is not None:
             if isinstance(conditioning, dict):
-                cbs = conditioning[list(conditioning.keys())[0]].shape[0]
+                ctmp = conditioning[list(conditioning.keys())[0]]
+                while isinstance(ctmp, list): ctmp = ctmp[0]
+                cbs = ctmp.shape[0]
                 if cbs != batch_size:
                     print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+
+            elif isinstance(conditioning, list):
+                for ctmp in conditioning:
+                    if ctmp.shape[0] != batch_size:
+                        print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+
             else:
                 if conditioning.shape[0] != batch_size:
                     print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
@@ -60,6 +69,7 @@ class UniPCSampler(object):
         # sampling
         C, H, W = shape
         size = (batch_size, C, H, W)
+        print(f'Data shape for UniPC sampling is {size}, eta {eta}')
 
         device = self.model.betas.device
         if x_T is None:
@@ -79,7 +89,7 @@ class UniPCSampler(object):
             guidance_scale=unconditional_guidance_scale,
         )
 
-        uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, condition=conditioning, unconditional_condition=unconditional_conditioning, before_sample=self.before_sample, after_sample=self.after_sample)
+        uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, condition=conditioning, unconditional_condition=unconditional_conditioning, before_sample=self.before_sample, after_sample=self.after_sample, after_update=self.after_update)
         x = uni_pc.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=3, lower_order_final=True)
 
         return x.to(device), None