Allow image_only_indicator to be None.

comfy/ldm/modules/diffusionmodules/openaimodel.py

@@ -484,7 +484,6 @@ class UNetModel(nn.Module):
         self.predict_codebook_ids = n_embed is not None
 
         self.default_num_video_frames = None
-        self.default_image_only_indicator = None
 
         time_embed_dim = model_channels * 4
         self.time_embed = nn.Sequential(
@@ -830,7 +829,7 @@ class UNetModel(nn.Module):
         transformer_patches = transformer_options.get("patches", {})
 
         num_video_frames = kwargs.get("num_video_frames", self.default_num_video_frames)
-        image_only_indicator = kwargs.get("image_only_indicator", self.default_image_only_indicator)
+        image_only_indicator = kwargs.get("image_only_indicator", None)
         time_context = kwargs.get("time_context", None)
 
         assert (y is not None) == (

comfy/ldm/modules/diffusionmodules/util.py

@@ -46,23 +46,25 @@ class AlphaBlender(nn.Module):
         else:
             raise ValueError(f"unknown merge strategy {self.merge_strategy}")
 
-    def get_alpha(self, image_only_indicator: torch.Tensor) -> torch.Tensor:
+    def get_alpha(self, image_only_indicator: torch.Tensor, device) -> torch.Tensor:
         # skip_time_mix = rearrange(repeat(skip_time_mix, 'b -> (b t) () () ()', t=t), '(b t) 1 ... -> b 1 t ...', t=t)
         if self.merge_strategy == "fixed":
             # make shape compatible
             # alpha = repeat(self.mix_factor, '1 -> b () t  () ()', t=t, b=bs)
-            alpha = self.mix_factor.to(image_only_indicator.device)
+            alpha = self.mix_factor.to(device)
         elif self.merge_strategy == "learned":
-            alpha = torch.sigmoid(self.mix_factor.to(image_only_indicator.device))
+            alpha = torch.sigmoid(self.mix_factor.to(device))
             # make shape compatible
             # alpha = repeat(alpha, '1 -> s () ()', s = t * bs)
         elif self.merge_strategy == "learned_with_images":
-            assert image_only_indicator is not None, "need image_only_indicator ..."
-            alpha = torch.where(
-                image_only_indicator.bool(),
-                torch.ones(1, 1, device=image_only_indicator.device),
-                rearrange(torch.sigmoid(self.mix_factor.to(image_only_indicator.device)), "... -> ... 1"),
-            )
+            if image_only_indicator is None:
+                alpha = rearrange(torch.sigmoid(self.mix_factor.to(device)), "... -> ... 1")
+            else:
+                alpha = torch.where(
+                    image_only_indicator.bool(),
+                    torch.ones(1, 1, device=image_only_indicator.device),
+                    rearrange(torch.sigmoid(self.mix_factor.to(image_only_indicator.device)), "... -> ... 1"),
+                )
             alpha = rearrange(alpha, self.rearrange_pattern)
             # make shape compatible
             # alpha = repeat(alpha, '1 -> s () ()', s = t * bs)
@@ -76,7 +78,7 @@ class AlphaBlender(nn.Module):
         x_temporal,
         image_only_indicator=None,
     ) -> torch.Tensor:
-        alpha = self.get_alpha(image_only_indicator)
+        alpha = self.get_alpha(image_only_indicator, x_spatial.device)
         x = (
             alpha.to(x_spatial.dtype) * x_spatial
             + (1.0 - alpha).to(x_spatial.dtype) * x_temporal

comfy/model_base.py

@@ -372,7 +372,6 @@ class SVD_img2vid(BaseModel):
         if "time_conditioning" in kwargs:
             out["time_context"] = comfy.conds.CONDCrossAttn(kwargs["time_conditioning"])
 
-        out['image_only_indicator'] = comfy.conds.CONDConstant(torch.zeros((1,), device=device))
         out['num_video_frames'] = comfy.conds.CONDConstant(noise.shape[0])
         return out