Optimization

app.py

@@ -357,31 +357,36 @@ def worker(input_image, prompts, n_prompt, seed, resolution, total_second_length
 
         H, W, C = input_image.shape
         height, width = find_nearest_bucket(H, W, resolution=resolution)
-        input_image_np = resize_and_center_crop(input_image, target_width=width, target_height=height)
-
-        Image.fromarray(input_image_np).save(os.path.join(outputs_folder, f'{job_id}.png'))
-
-        input_image_pt = torch.from_numpy(input_image_np).float() / 127.5 - 1
-        input_image_pt = input_image_pt.permute(2, 0, 1)[None, :, None]
-
-        # VAE encoding
-
-        stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'VAE encoding ...'))))
-
-        if not high_vram:
-            load_model_as_complete(vae, target_device=gpu)
-
-        start_latent = vae_encode(input_image_pt, vae)
-
-        # CLIP Vision
-
-        stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'CLIP Vision encoding ...'))))
-
-        if not high_vram:
-            load_model_as_complete(image_encoder, target_device=gpu)
+        
+        def get_start_latent(input_image, height, width, vae, gpu, image_encoder, high_vram):
+            input_image_np = resize_and_center_crop(input_image, target_width=width, target_height=height)
+            
+            #Image.fromarray(input_image_np).save(os.path.join(outputs_folder, f'{job_id}.png'))
+            
+            input_image_pt = torch.from_numpy(input_image_np).float() / 127.5 - 1
+            input_image_pt = input_image_pt.permute(2, 0, 1)[None, :, None]
+            
+            # VAE encoding
+            
+            stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'VAE encoding ...'))))
+            
+            if not high_vram:
+                load_model_as_complete(vae, target_device=gpu)
+            
+            start_latent = vae_encode(input_image_pt, vae)
+            
+            # CLIP Vision
+            
+            stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'CLIP Vision encoding ...'))))
+            
+            if not high_vram:
+                load_model_as_complete(image_encoder, target_device=gpu)
+                
+            return start_latent
+            
+        start_latent = get_start_latent(input_image, height, width, vae, gpu, image_encoder, high_vram)
 
-        image_encoder_output = hf_clip_vision_encode(input_image_np, feature_extractor, image_encoder)
-        image_encoder_last_hidden_state = image_encoder_output.last_hidden_state
+        image_encoder_last_hidden_state = hf_clip_vision_encode(input_image_np, feature_extractor, image_encoder).last_hidden_state
 
         # Dtype
 
@@ -573,31 +578,36 @@ def worker_last_frame(input_image, prompts, n_prompt, seed, resolution, total_se
 
         H, W, C = input_image.shape
         height, width = find_nearest_bucket(H, W, resolution=resolution)
-        input_image_np = resize_and_center_crop(input_image, target_width=width, target_height=height)
-
-        Image.fromarray(input_image_np).save(os.path.join(outputs_folder, f'{job_id}.png'))
-
-        input_image_pt = torch.from_numpy(input_image_np).float() / 127.5 - 1
-        input_image_pt = input_image_pt.permute(2, 0, 1)[None, :, None]
-
-        # VAE encoding
-
-        stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'VAE encoding ...'))))
-
-        if not high_vram:
-            load_model_as_complete(vae, target_device=gpu)
-
-        start_latent = vae_encode(input_image_pt, vae)
-
-        # CLIP Vision
-
-        stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'CLIP Vision encoding ...'))))
-
-        if not high_vram:
-            load_model_as_complete(image_encoder, target_device=gpu)
+        
+        def get_start_latent(input_image, height, width, vae, gpu, image_encoder, high_vram):
+            input_image_np = resize_and_center_crop(input_image, target_width=width, target_height=height)
+            
+            #Image.fromarray(input_image_np).save(os.path.join(outputs_folder, f'{job_id}.png'))
+            
+            input_image_pt = torch.from_numpy(input_image_np).float() / 127.5 - 1
+            input_image_pt = input_image_pt.permute(2, 0, 1)[None, :, None]
+            
+            # VAE encoding
+            
+            stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'VAE encoding ...'))))
+            
+            if not high_vram:
+                load_model_as_complete(vae, target_device=gpu)
+            
+            start_latent = vae_encode(input_image_pt, vae)
+            
+            # CLIP Vision
+            
+            stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'CLIP Vision encoding ...'))))
+            
+            if not high_vram:
+                load_model_as_complete(image_encoder, target_device=gpu)
+                
+            return start_latent
+            
+        start_latent = get_start_latent(input_image, height, width, vae, gpu, image_encoder, high_vram)
 
-        image_encoder_output = hf_clip_vision_encode(input_image_np, feature_extractor, image_encoder)
-        image_encoder_last_hidden_state = image_encoder_output.last_hidden_state
+        image_encoder_last_hidden_state = hf_clip_vision_encode(input_image_np, feature_extractor, image_encoder).last_hidden_state
 
         # Dtype