Merge pull request #11720 from akx/closing
Use closing() with processing classes everywhere
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commit
910d4f61e5
@ -3,6 +3,7 @@ import glob
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import html
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import os
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import inspect
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from contextlib import closing
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import modules.textual_inversion.dataset
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import torch
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@ -711,8 +712,9 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
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preview_text = p.prompt
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processed = processing.process_images(p)
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image = processed.images[0] if len(processed.images) > 0 else None
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with closing(p):
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processed = processing.process_images(p)
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image = processed.images[0] if len(processed.images) > 0 else None
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if unload:
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shared.sd_model.cond_stage_model.to(devices.cpu)
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@ -1,4 +1,5 @@
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import os
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from contextlib import closing
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from pathlib import Path
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import numpy as np
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@ -217,18 +218,17 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
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if mask:
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p.extra_generation_params["Mask blur"] = mask_blur
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if is_batch:
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assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
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with closing(p):
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if is_batch:
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assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
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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)
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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)
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processed = Processed(p, [], p.seed, "")
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else:
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processed = modules.scripts.scripts_img2img.run(p, *args)
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if processed is None:
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processed = process_images(p)
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p.close()
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processed = Processed(p, [], p.seed, "")
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else:
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processed = modules.scripts.scripts_img2img.run(p, *args)
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if processed is None:
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processed = process_images(p)
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shared.total_tqdm.clear()
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@ -1,5 +1,6 @@
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import os
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from collections import namedtuple
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from contextlib import closing
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import torch
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import tqdm
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@ -584,8 +585,9 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
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preview_text = p.prompt
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processed = processing.process_images(p)
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image = processed.images[0] if len(processed.images) > 0 else None
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with closing(p):
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processed = processing.process_images(p)
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image = processed.images[0] if len(processed.images) > 0 else None
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if unload:
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shared.sd_model.first_stage_model.to(devices.cpu)
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@ -1,3 +1,5 @@
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from contextlib import closing
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import modules.scripts
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from modules import sd_samplers, processing
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from modules.generation_parameters_copypaste import create_override_settings_dict
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@ -53,12 +55,11 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step
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if cmd_opts.enable_console_prompts:
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print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
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processed = modules.scripts.scripts_txt2img.run(p, *args)
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with closing(p):
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processed = modules.scripts.scripts_txt2img.run(p, *args)
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if processed is None:
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processed = processing.process_images(p)
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p.close()
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if processed is None:
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processed = processing.process_images(p)
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shared.total_tqdm.clear()
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