Merge pull request #11720 from akx/closing

Use closing() with processing classes everywhere

modules/hypernetworks/hypernetwork.py

@@ -3,6 +3,7 @@ import glob
 import html
 import os
 import inspect
+from contextlib import closing
 
 import modules.textual_inversion.dataset
 import torch
@@ -711,8 +712,9 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
 
                     preview_text = p.prompt
 
-                    processed = processing.process_images(p)
-                    image = processed.images[0] if len(processed.images) > 0 else None
+                    with closing(p):
+                        processed = processing.process_images(p)
+                        image = processed.images[0] if len(processed.images) > 0 else None
 
                     if unload:
                         shared.sd_model.cond_stage_model.to(devices.cpu)

modules/img2img.py

 import os
+from contextlib import closing
 from pathlib import Path
 
 import numpy as np
@@ -217,18 +218,17 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
     if mask:
         p.extra_generation_params["Mask blur"] = mask_blur
 
-    if is_batch:
-        assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
+    with closing(p):
+        if is_batch:
+            assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
 
-        process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by, use_png_info=img2img_batch_use_png_info, png_info_props=img2img_batch_png_info_props, png_info_dir=img2img_batch_png_info_dir)
+            process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by, use_png_info=img2img_batch_use_png_info, png_info_props=img2img_batch_png_info_props, png_info_dir=img2img_batch_png_info_dir)
 
-        processed = Processed(p, [], p.seed, "")
-    else:
-        processed = modules.scripts.scripts_img2img.run(p, *args)
-        if processed is None:
-            processed = process_images(p)
-
-    p.close()
+            processed = Processed(p, [], p.seed, "")
+        else:
+            processed = modules.scripts.scripts_img2img.run(p, *args)
+            if processed is None:
+                processed = process_images(p)
 
     shared.total_tqdm.clear()
 

modules/textual_inversion/textual_inversion.py

 import os
 from collections import namedtuple
+from contextlib import closing
 
 import torch
 import tqdm
@@ -584,8 +585,9 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
 
                     preview_text = p.prompt
 
-                    processed = processing.process_images(p)
-                    image = processed.images[0] if len(processed.images) > 0 else None
+                    with closing(p):
+                        processed = processing.process_images(p)
+                        image = processed.images[0] if len(processed.images) > 0 else None
 
                     if unload:
                         shared.sd_model.first_stage_model.to(devices.cpu)

modules/txt2img.py

+from contextlib import closing
+
 import modules.scripts
 from modules import sd_samplers, processing
 from modules.generation_parameters_copypaste import create_override_settings_dict
@@ -53,12 +55,11 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step
     if cmd_opts.enable_console_prompts:
         print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
 
-    processed = modules.scripts.scripts_txt2img.run(p, *args)
-
-    if processed is None:
-        processed = processing.process_images(p)
+    with closing(p):
+        processed = modules.scripts.scripts_txt2img.run(p, *args)
 
-    p.close()
+        if processed is None:
+            processed = processing.process_images(p)
 
     shared.total_tqdm.clear()