del

app.py

@@ -468,8 +468,8 @@ def worker(input_image, end_image, image_position, end_stillness, prompts, n_pro
             return [start_latent, image_encoder_last_hidden_state]
             
         [start_latent, image_encoder_last_hidden_state] = get_start_latent(input_image, height, width, vae, gpu, image_encoder, high_vram)
-        input_image = None
-        end_image = None
+        del input_image
+        del end_image
 
         # Dtype
 
@@ -565,7 +565,7 @@ def worker(input_image, end_image, image_position, end_stillness, prompts, n_pro
             [llama_vec, clip_l_pooler, llama_vec_n, clip_l_pooler_n, llama_attention_mask, llama_attention_mask_n] = prompt_parameters[prompt_index]
             
             if prompt_index < len(prompt_parameters) - 1 or (prompt_index == total_latent_sections - 1):
-                prompt_parameters[prompt_index] = None
+                del prompt_parameters[prompt_index]
 
             if not high_vram:
                 unload_complete_models()
@@ -613,6 +613,13 @@ def worker(input_image, end_image, image_position, end_stillness, prompts, n_pro
                 clean_latent_4x_indices=clean_latent_4x_indices,
                 callback=callback,
             )
+            del clean_latents
+            del clean_latents_2x
+            del clean_latents_4x
+            del latent_indices
+            del clean_latent_indices
+            del clean_latent_2x_indices
+            del clean_latent_4x_indices
 
             [total_generated_latent_frames, history_latents, history_pixels] = post_process(forward, generated_latents, total_generated_latent_frames, history_latents, high_vram, transformer, gpu, vae, history_pixels, latent_window_size, enable_preview, section_index, total_latent_sections, outputs_folder, mp4_crf, stream)
             
@@ -626,7 +633,8 @@ def worker(input_image, end_image, image_position, end_stillness, prompts, n_pro
                     real_history_latents = history_latents[:, :, :total_generated_latent_frames, :, :]
                     zero_latents = history_latents[:, :, total_generated_latent_frames:, :, :]
                     history_latents = torch.cat([zero_latents, real_history_latents], dim=2)
-                    real_history_latents = zero_latents = None
+                    del real_history_latents
+                    del zero_latents
                     
                     forward = True
                     section_index = first_section_index
@@ -754,8 +762,8 @@ def worker_start_end(input_image, end_image, image_position, end_stillness, prom
             return [start_latent, end_latent, image_encoder_last_hidden_state]
             
         [start_latent, end_latent, image_encoder_last_hidden_state] = get_start_latent(input_image, has_end_image, end_image, height, width, vae, gpu, image_encoder, high_vram)
-        input_image = None
-        end_image = None
+        del input_image
+        del end_image
 
         # Dtype
         image_encoder_last_hidden_state = image_encoder_last_hidden_state.to(transformer.dtype)
@@ -905,6 +913,13 @@ def worker_start_end(input_image, end_image, image_position, end_stillness, prom
                 clean_latent_4x_indices=clean_latent_4x_indices,
                 callback=callback,
             )
+            del clean_latents
+            del clean_latents_2x
+            del clean_latents_4x
+            del latent_indices
+            del clean_latent_indices
+            del clean_latent_2x_indices
+            del clean_latent_4x_indices
 
             [total_generated_latent_frames, history_latents, history_pixels] = post_process(job_id, start_latent, generated_latents, total_generated_latent_frames, history_latents, high_vram, transformer, gpu, vae, history_pixels, latent_window_size, enable_preview, outputs_folder, mp4_crf, stream, is_last_section)
 
@@ -949,7 +964,7 @@ def worker_video(input_video, end_frame, end_stillness, prompts, n_prompt, seed,
 
         # 20250506 pftq: Encode video
         start_latent, input_image_np, video_latents, fps, height, width = video_encode(input_video, resolution, no_resize, vae, vae_batch_size=vae_batch, device=gpu)
-        input_video = None
+        del input_video
         start_latent = start_latent.to(dtype=torch.float32, device=cpu)
         video_latents = video_latents.cpu()
 
@@ -987,7 +1002,7 @@ def worker_video(input_video, end_frame, end_stillness, prompts, n_prompt, seed,
             load_model_as_complete(image_encoder, target_device=gpu)
 
         image_encoder_output = hf_clip_vision_encode(input_image_np, feature_extractor, image_encoder)
-        input_image_np = None
+        del input_image_np
 
         # 20250507 pftq: Process end frame if provided
         if end_frame is not None:
@@ -999,7 +1014,7 @@ def worker_video(input_video, end_frame, end_stillness, prompts, n_prompt, seed,
                 end_frame, target_width=width, target_height=height, vae=vae,
                 image_encoder=image_encoder, feature_extractor=feature_extractor, device=gpu
             )[0]
-            end_frame = None
+            del end_frame
             end_latent = end_latent.to(dtype=torch.float32, device=cpu)
         else:
             end_latent = None
@@ -1009,7 +1024,7 @@ def worker_video(input_video, end_frame, end_stillness, prompts, n_prompt, seed,
             unload_complete_models(image_encoder, vae)
 
         image_encoder_last_hidden_state = image_encoder_output.last_hidden_state
-        image_encoder_output = None
+        del image_encoder_output
 
         # Dtype
         image_encoder_last_hidden_state = image_encoder_last_hidden_state.to(transformer.dtype)
@@ -1119,8 +1134,7 @@ def worker_video(input_video, end_frame, end_stillness, prompts, n_prompt, seed,
             history_latents = video_latents
             total_generated_latent_frames = history_latents.shape[2]
             # 20250506 pftq: Initialize history_pixels to fix UnboundLocalError
-            history_pixels = None
-            previous_video = None
+            history_pixels = previous_video = None
 
             # 20250509 Generate backwards with end frame for better end frame anchoring
             if total_latent_sections > 4:
@@ -1181,13 +1195,13 @@ def worker_video(input_video, end_frame, end_stillness, prompts, n_prompt, seed,
                     clean_latent_4x_indices=clean_latent_4x_indices,
                     callback=callback,
                 )
-                clean_latents = None
-                clean_latents_2x = None
-                clean_latents_4x = None
-                latent_indices = None
-                clean_latent_indices = None
-                clean_latent_2x_indices = None
-                clean_latent_4x_indices = None
+                del clean_latents
+                del clean_latents_2x
+                del clean_latents_4x
+                del latent_indices
+                del clean_latent_indices
+                del clean_latent_2x_indices
+                del clean_latent_4x_indices
 
                 total_generated_latent_frames += int(generated_latents.shape[2])
                 history_latents = torch.cat([history_latents, generated_latents.to(history_latents)], dim=2)