Allow image resolutions multiple of 8 instead of 64 in SVD pipeline

src/diffusers/models/unets/unet_3d_blocks.py

@@ -1375,6 +1375,7 @@ def forward(
         res_hidden_states_tuple: Tuple[torch.Tensor, ...],
         temb: Optional[torch.Tensor] = None,
         image_only_indicator: Optional[torch.Tensor] = None,
+        upsample_size: Optional[int] = None,
     ) -> torch.Tensor:
         for resnet in self.resnets:
             # pop res hidden states
@@ -1415,7 +1416,7 @@ def custom_forward(*inputs):
 
         if self.upsamplers is not None:
             for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states)
+                hidden_states = upsampler(hidden_states, upsample_size)
 
         return hidden_states
 
@@ -1485,6 +1486,7 @@ def forward(
         temb: Optional[torch.Tensor] = None,
         encoder_hidden_states: Optional[torch.Tensor] = None,
         image_only_indicator: Optional[torch.Tensor] = None,
+        upsample_size: Optional[int] = None,
     ) -> torch.Tensor:
         for resnet, attn in zip(self.resnets, self.attentions):
             # pop res hidden states
@@ -1533,6 +1535,6 @@ def custom_forward(*inputs):
 
         if self.upsamplers is not None:
             for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states)
+                hidden_states = upsampler(hidden_states, upsample_size)
 
         return hidden_states

src/diffusers/models/unets/unet_spatio_temporal_condition.py

@@ -382,6 +382,20 @@ def forward(
                 If `return_dict` is True, an [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] is
                 returned, otherwise a `tuple` is returned where the first element is the sample tensor.
         """
+        # By default samples have to be AT least a multiple of the overall upsampling factor.
+        # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).
+        # However, the upsampling interpolation output size can be forced to fit any upsampling size
+        # on the fly if necessary.
+        default_overall_up_factor = 2**self.num_upsamplers
+
+        # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
+        forward_upsample_size = False
+        upsample_size = None
+
+        if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
+            logger.info("Forward upsample size to force interpolation output size.")
+            forward_upsample_size = True
+
         # 1. time
         timesteps = timestep
         if not torch.is_tensor(timesteps):
@@ -457,22 +471,31 @@ def forward(
 
         # 5. up
         for i, upsample_block in enumerate(self.up_blocks):
+            is_final_block = i == len(self.up_blocks) - 1
+
             res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
             down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
 
+            # if we have not reached the final block and need to forward the
+            # upsample size, we do it here
+            if not is_final_block and forward_upsample_size:
+                upsample_size = down_block_res_samples[-1].shape[2:]
+
             if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
                 sample = upsample_block(
                     hidden_states=sample,
                     temb=emb,
                     res_hidden_states_tuple=res_samples,
                     encoder_hidden_states=encoder_hidden_states,
+                    upsample_size=upsample_size,
                     image_only_indicator=image_only_indicator,
                 )
             else:
                 sample = upsample_block(
                     hidden_states=sample,
                     temb=emb,
                     res_hidden_states_tuple=res_samples,
+                    upsample_size=upsample_size,
                     image_only_indicator=image_only_indicator,
                 )