some cleanup

model_loading.py

@@ -60,7 +60,7 @@ class CogVideoLoraSelect:
         cog_loras_list.append(cog_lora)
         print(cog_loras_list)
         return (cog_loras_list,)
-
+#region DownloadAndLoadCogVideoModel
 class DownloadAndLoadCogVideoModel:
     @classmethod
     def INPUT_TYPES(s):
@@ -259,12 +259,9 @@ class DownloadAndLoadCogVideoModel:
                 if fuse:
                     pipe.fuse_lora(lora_scale=1 / lora_rank, components=["transformer"])
         
-        
-
         if enable_sequential_cpu_offload:
             pipe.enable_sequential_cpu_offload()
             
-        
         # compilation
         if compile == "torch":
             pipe.transformer.to(memory_format=torch.channels_last)
@@ -277,8 +274,6 @@ class DownloadAndLoadCogVideoModel:
                 for i, block in enumerate(pipe.transformer.transformer_blocks):
                     if "CogVideoXBlock" in str(block):
                         pipe.transformer.transformer_blocks[i] = torch.compile(block, fullgraph=False, dynamic=False, backend="inductor")
-  
-      
             
         elif compile == "onediff":
             from onediffx import compile_pipe
@@ -303,7 +298,7 @@ class DownloadAndLoadCogVideoModel:
         }
 
         return (pipeline,)
-
+#region GGUF
 class DownloadAndLoadCogVideoGGUFModel:
     @classmethod
     def INPUT_TYPES(s):
@@ -483,7 +478,7 @@ class DownloadAndLoadCogVideoGGUFModel:
         }
 
         return (pipeline,)
-
+#region Tora
 class DownloadAndLoadToraModel:
     @classmethod
     def INPUT_TYPES(s):
@@ -591,7 +586,7 @@ class DownloadAndLoadToraModel:
         }
 
         return (toramodel,)
-
+#region controlnet
 class DownloadAndLoadCogVideoControlNet:
     @classmethod
     def INPUT_TYPES(s):

nodes.py

@@ -816,7 +816,12 @@ class CogVideoSampler:
         base_path = pipeline["base_path"]
 
         assert "fun" not in base_path.lower(), "'Fun' models not supported in 'CogVideoSampler', use the 'CogVideoXFunSampler'"
-        assert ("I2V" not in pipeline.get("model_name","") or num_frames == 49 or context_options is not None), "I2V model can only do 49 frames"
+        assert (
+            "I2V" not in pipeline.get("model_name", "") or
+            "1.5" in pipeline.get("model_name", "") or
+            num_frames == 49 or
+            context_options is not None
+        ), "1.0 I2V model can only do 49 frames"
 
         device = mm.get_torch_device()
         offload_device = mm.unet_offload_device()

pipeline_cogvideox.py

@@ -317,18 +317,17 @@ class CogVideoXPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
         width: int,
         num_frames: int,
         device: torch.device,
-        start_frame: int = None,
-        end_frame: int = None,
         ) -> 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)
+
         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
         )
@@ -336,19 +335,8 @@ class CogVideoXPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
             embed_dim=self.transformer.config.attention_head_dim,
             crops_coords=grid_crops_coords,
             grid_size=(grid_height, grid_width),
-            temporal_size=base_num_frames,
-            use_real=True,
+            temporal_size=base_num_frames
         )
-        
-        if start_frame is not None:
-            freqs_cos = freqs_cos.view(num_frames, grid_height * grid_width, -1)
-            freqs_sin = freqs_sin.view(num_frames, grid_height * grid_width, -1)
-
-            freqs_cos = freqs_cos[start_frame:end_frame]
-            freqs_sin = freqs_sin[start_frame:end_frame]
-
-            freqs_cos = freqs_cos.view(-1, freqs_cos.shape[-1])
-            freqs_sin = freqs_sin.view(-1, freqs_sin.shape[-1])
 
         freqs_cos = freqs_cos.to(device=device)
         freqs_sin = freqs_sin.to(device=device)
@@ -535,13 +523,6 @@ class CogVideoXPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
         # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
-        # 6.5. Create rotary embeds if required
-        image_rotary_emb = (
-            self._prepare_rotary_positional_embeddings(height, width, latents.size(1), device)
-            if self.transformer.config.use_rotary_positional_embeddings
-            else None
-        )
-
         # masks
         if self.original_mask is not None:
             mask = self.original_mask.to(device)
@@ -579,7 +560,7 @@ class CogVideoXPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
             use_temporal_tiling = False
             use_context_schedule = False
             logger.info("Temporal tiling and context schedule disabled")
-            # 7. Create rotary embeds if required
+            # 8.5. Create rotary embeds if required
             image_rotary_emb = (
                 self._prepare_rotary_positional_embeddings(height, width, latents.size(1), device)
                 if self.transformer.config.use_rotary_positional_embeddings
@@ -882,7 +863,6 @@ class CogVideoXPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
                         controlnet_states=controlnet_states,
                         controlnet_weights=control_weights,
                         video_flow_features=video_flow_features if (tora is not None and tora["start_percent"] <= current_step_percentage <= tora["end_percent"]) else None,
-
                     )[0]
                     noise_pred = noise_pred.float()