make fix-copies

Author: a-r-r-o-w

src/diffusers/pipelines/cogvideo/pipeline_cogvideox_fun_control.py

@@ -488,8 +488,13 @@ def _prepare_rotary_positional_embeddings(
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         grid_height = height // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
         grid_width = width // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
-        base_size_width = 720 // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
-        base_size_height = 480 // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
+
+        p = self.transformer.config.patch_size
+        p_t = self.transformer.config.patch_size_t or 1
+
+        base_size_width = self.transformer.config.sample_width // p
+        base_size_height = self.transformer.config.sample_height // p
+        base_num_frames = (num_frames + p_t - 1) // p_t
 
         grid_crops_coords = get_resize_crop_region_for_grid(
             (grid_height, grid_width), base_size_width, base_size_height
@@ -498,7 +503,7 @@ def _prepare_rotary_positional_embeddings(
             embed_dim=self.transformer.config.attention_head_dim,
             crops_coords=grid_crops_coords,
             grid_size=(grid_height, grid_width),
-            temporal_size=num_frames,
+            temporal_size=base_num_frames,
         )
 
         freqs_cos = freqs_cos.to(device=device)

src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py

@@ -522,8 +522,13 @@ def _prepare_rotary_positional_embeddings(
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         grid_height = height // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
         grid_width = width // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
-        base_size_width = 720 // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
-        base_size_height = 480 // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
+
+        p = self.transformer.config.patch_size
+        p_t = self.transformer.config.patch_size_t or 1
+
+        base_size_width = self.transformer.config.sample_width // p
+        base_size_height = self.transformer.config.sample_height // p
+        base_num_frames = (num_frames + p_t - 1) // p_t
 
         grid_crops_coords = get_resize_crop_region_for_grid(
             (grid_height, grid_width), base_size_width, base_size_height
@@ -532,7 +537,7 @@ def _prepare_rotary_positional_embeddings(
             embed_dim=self.transformer.config.attention_head_dim,
             crops_coords=grid_crops_coords,
             grid_size=(grid_height, grid_width),
-            temporal_size=num_frames,
+            temporal_size=base_num_frames,
         )
 
         freqs_cos = freqs_cos.to(device=device)

src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py

@@ -518,8 +518,13 @@ def _prepare_rotary_positional_embeddings(
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         grid_height = height // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
         grid_width = width // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
-        base_size_width = 720 // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
-        base_size_height = 480 // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
+
+        p = self.transformer.config.patch_size
+        p_t = self.transformer.config.patch_size_t or 1
+
+        base_size_width = self.transformer.config.sample_width // p
+        base_size_height = self.transformer.config.sample_height // p
+        base_num_frames = (num_frames + p_t - 1) // p_t
 
         grid_crops_coords = get_resize_crop_region_for_grid(
             (grid_height, grid_width), base_size_width, base_size_height
@@ -528,7 +533,7 @@ def _prepare_rotary_positional_embeddings(
             embed_dim=self.transformer.config.attention_head_dim,
             crops_coords=grid_crops_coords,
             grid_size=(grid_height, grid_width),
-            temporal_size=num_frames,
+            temporal_size=base_num_frames,
         )
 
         freqs_cos = freqs_cos.to(device=device)