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Merge branch 'main' of https://github.com/lllyasviel/Fooocus into lora-reference-parsing

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cantor-set
2024-03-03 19:19:12 -05:00
parent 9e60a8c3f8 4ea3baff50
commit 10bca50984
38 changed files with 1878 additions and 479 deletions
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modules/async_worker.py
+213 -131
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@@ -1,11 +1,15 @@
import threading
from modules.patch import PatchSettings, patch_settings, patch_all
patch_all()
class AsyncTask:
def __init__(self, args):
self.args = args
self.yields = []
self.results = []
self.last_stop = False
self.processing = False
async_tasks = []
@@ -14,9 +18,11 @@ async_tasks = []
def worker():
global async_tasks
import os
import traceback
import math
import numpy as np
import cv2
import torch
import time
import shared
@@ -31,12 +37,12 @@ def worker():
import extras.preprocessors as preprocessors
import modules.inpaint_worker as inpaint_worker
import modules.constants as constants
import modules.advanced_parameters as advanced_parameters
import extras.ip_adapter as ip_adapter
import extras.face_crop
import fooocus_version
import args_manager
from modules.sdxl_styles import apply_style, apply_wildcards, fooocus_expansion
from modules.sdxl_styles import apply_style, apply_wildcards, fooocus_expansion, apply_arrays
from modules.private_logger import log
from extras.expansion import safe_str
from modules.util import (
@@ -51,6 +57,11 @@ def worker():
parse_lora_references_from_prompt
)
from modules.upscaler import perform_upscale
from modules.flags import Performance
from modules.meta_parser import get_metadata_parser, MetadataScheme
pid = os.getpid()
print(f'Started worker with PID {pid}')
MAX_LORAS = 5
@@ -80,19 +91,20 @@ def worker():
return
def build_image_wall(async_task):
if not advanced_parameters.generate_image_grid:
results = []
if len(async_task.results) < 2:
return
results = async_task.results
if len(results) < 2:
return
for img in results:
for img in async_task.results:
if isinstance(img, str) and os.path.exists(img):
img = cv2.imread(img)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
if not isinstance(img, np.ndarray):
return
if img.ndim != 3:
return
results.append(img)
H, W, C = results[0].shape
@@ -122,10 +134,19 @@ def worker():
async_task.results = async_task.results + [wall]
return
def apply_enabled_loras(loras):
enabled_loras = []
for lora_enabled, lora_model, lora_weight in loras:
if lora_enabled:
enabled_loras.append([lora_model, lora_weight])
return enabled_loras
@torch.no_grad()
@torch.inference_mode()
def handler(async_task):
execution_start_time = time.perf_counter()
async_task.processing = True
args = async_task.args
args.reverse()
@@ -133,16 +154,21 @@ def worker():
prompt = args.pop()
negative_prompt = args.pop()
style_selections = args.pop()
performance_selection = args.pop()
performance_selection = Performance(args.pop())
aspect_ratios_selection = args.pop()
image_number = args.pop()
output_format = args.pop()
image_seed = args.pop()
sharpness = args.pop()
guidance_scale = args.pop()
base_model_name = args.pop()
refiner_model_name = args.pop()
refiner_switch = args.pop()
loras = [[str(args.pop()), float(args.pop())] for _ in range(MAX_LORAS)]
loras = apply_enabled_loras([[bool(args.pop()), str(args.pop()), float(args.pop()), ] for _ in range(modules.config.default_max_lora_number)])
input_image_checkbox = args.pop()
current_tab = args.pop()
uov_method = args.pop()
@@ -152,8 +178,48 @@ def worker():
inpaint_additional_prompt = args.pop()
inpaint_mask_image_upload = args.pop()
disable_preview = args.pop()
disable_intermediate_results = args.pop()
disable_seed_increment = args.pop()
adm_scaler_positive = args.pop()
adm_scaler_negative = args.pop()
adm_scaler_end = args.pop()
adaptive_cfg = args.pop()
sampler_name = args.pop()
scheduler_name = args.pop()
overwrite_step = args.pop()
overwrite_switch = args.pop()
overwrite_width = args.pop()
overwrite_height = args.pop()
overwrite_vary_strength = args.pop()
overwrite_upscale_strength = args.pop()
mixing_image_prompt_and_vary_upscale = args.pop()
mixing_image_prompt_and_inpaint = args.pop()
debugging_cn_preprocessor = args.pop()
skipping_cn_preprocessor = args.pop()
canny_low_threshold = args.pop()
canny_high_threshold = args.pop()
refiner_swap_method = args.pop()
controlnet_softness = args.pop()
freeu_enabled = args.pop()
freeu_b1 = args.pop()
freeu_b2 = args.pop()
freeu_s1 = args.pop()
freeu_s2 = args.pop()
debugging_inpaint_preprocessor = args.pop()
inpaint_disable_initial_latent = args.pop()
inpaint_engine = args.pop()
inpaint_strength = args.pop()
inpaint_respective_field = args.pop()
inpaint_mask_upload_checkbox = args.pop()
invert_mask_checkbox = args.pop()
inpaint_erode_or_dilate = args.pop()
save_metadata_to_images = args.pop() if not args_manager.args.disable_metadata else False
metadata_scheme = MetadataScheme(args.pop()) if not args_manager.args.disable_metadata else MetadataScheme.FOOOCUS
cn_tasks = {x: [] for x in flags.ip_list}
for _ in range(4):
for _ in range(flags.controlnet_image_count):
cn_img = args.pop()
cn_stop = args.pop()
cn_weight = args.pop()
@@ -178,17 +244,9 @@ def worker():
print(f'Refiner disabled because base model and refiner are same.')
refiner_model_name = 'None'
assert performance_selection in ['Speed', 'Quality', 'Extreme Speed']
steps = performance_selection.steps()
steps = 30
if performance_selection == 'Speed':
steps = 30
if performance_selection == 'Quality':
steps = 60
if performance_selection == 'Extreme Speed':
if performance_selection == Performance.EXTREME_SPEED:
print('Enter LCM mode.')
progressbar(async_task, 1, 'Downloading LCM components ...')
loras += [(modules.config.downloading_sdxl_lcm_lora(), 1.0)]
@@ -197,30 +255,32 @@ def worker():
print(f'Refiner disabled in LCM mode.')
refiner_model_name = 'None'
sampler_name = advanced_parameters.sampler_name = 'lcm'
scheduler_name = advanced_parameters.scheduler_name = 'lcm'
modules.patch.sharpness = sharpness = 0.0
cfg_scale = guidance_scale = 1.0
modules.patch.adaptive_cfg = advanced_parameters.adaptive_cfg = 1.0
sampler_name = 'lcm'
scheduler_name = 'lcm'
sharpness = 0.0
guidance_scale = 1.0
adaptive_cfg = 1.0
refiner_switch = 1.0
modules.patch.positive_adm_scale = advanced_parameters.adm_scaler_positive = 1.0
modules.patch.negative_adm_scale = advanced_parameters.adm_scaler_negative = 1.0
modules.patch.adm_scaler_end = advanced_parameters.adm_scaler_end = 0.0
steps = 8
adm_scaler_positive = 1.0
adm_scaler_negative = 1.0
adm_scaler_end = 0.0
modules.patch.adaptive_cfg = advanced_parameters.adaptive_cfg
print(f'[Parameters] Adaptive CFG = {modules.patch.adaptive_cfg}')
modules.patch.sharpness = sharpness
print(f'[Parameters] Sharpness = {modules.patch.sharpness}')
modules.patch.positive_adm_scale = advanced_parameters.adm_scaler_positive
modules.patch.negative_adm_scale = advanced_parameters.adm_scaler_negative
modules.patch.adm_scaler_end = advanced_parameters.adm_scaler_end
print(f'[Parameters] Adaptive CFG = {adaptive_cfg}')
print(f'[Parameters] Sharpness = {sharpness}')
print(f'[Parameters] ControlNet Softness = {controlnet_softness}')
print(f'[Parameters] ADM Scale = '
f'{modules.patch.positive_adm_scale} : '
f'{modules.patch.negative_adm_scale} : '
f'{modules.patch.adm_scaler_end}')
f'{adm_scaler_positive} : '
f'{adm_scaler_negative} : '
f'{adm_scaler_end}')
patch_settings[pid] = PatchSettings(
sharpness,
adm_scaler_end,
adm_scaler_positive,
adm_scaler_negative,
controlnet_softness,
adaptive_cfg
)
cfg_scale = float(guidance_scale)
print(f'[Parameters] CFG = {cfg_scale}')
@@ -233,10 +293,9 @@ def worker():
width, height = int(width), int(height)
skip_prompt_processing = False
refiner_swap_method = advanced_parameters.refiner_swap_method
inpaint_worker.current_task = None
inpaint_parameterized = advanced_parameters.inpaint_engine != 'None'
inpaint_parameterized = inpaint_engine != 'None'
inpaint_image = None
inpaint_mask = None
inpaint_head_model_path = None
@@ -250,15 +309,12 @@ def worker():
seed = int(image_seed)
print(f'[Parameters] Seed = {seed}')
sampler_name = advanced_parameters.sampler_name
scheduler_name = advanced_parameters.scheduler_name
goals = []
tasks = []
if input_image_checkbox:
if (current_tab == 'uov' or (
current_tab == 'ip' and advanced_parameters.mixing_image_prompt_and_vary_upscale)) \
current_tab == 'ip' and mixing_image_prompt_and_vary_upscale)) \
and uov_method != flags.disabled and uov_input_image is not None:
uov_input_image = HWC3(uov_input_image)
if 'vary' in uov_method:
@@ -268,26 +324,17 @@ def worker():
if 'fast' in uov_method:
skip_prompt_processing = True
else:
steps = 18
if performance_selection == 'Speed':
steps = 18
if performance_selection == 'Quality':
steps = 36
if performance_selection == 'Extreme Speed':
steps = 8
steps = performance_selection.steps_uov()
progressbar(async_task, 1, 'Downloading upscale models ...')
modules.config.downloading_upscale_model()
if (current_tab == 'inpaint' or (
current_tab == 'ip' and advanced_parameters.mixing_image_prompt_and_inpaint)) \
current_tab == 'ip' and mixing_image_prompt_and_inpaint)) \
and isinstance(inpaint_input_image, dict):
inpaint_image = inpaint_input_image['image']
inpaint_mask = inpaint_input_image['mask'][:, :, 0]
if advanced_parameters.inpaint_mask_upload_checkbox:
if inpaint_mask_upload_checkbox:
if isinstance(inpaint_mask_image_upload, np.ndarray):
if inpaint_mask_image_upload.ndim == 3:
H, W, C = inpaint_image.shape
@@ -296,10 +343,10 @@ def worker():
inpaint_mask_image_upload = (inpaint_mask_image_upload > 127).astype(np.uint8) * 255
inpaint_mask = np.maximum(inpaint_mask, inpaint_mask_image_upload)
if int(advanced_parameters.inpaint_erode_or_dilate) != 0:
inpaint_mask = erode_or_dilate(inpaint_mask, advanced_parameters.inpaint_erode_or_dilate)
if int(inpaint_erode_or_dilate) != 0:
inpaint_mask = erode_or_dilate(inpaint_mask, inpaint_erode_or_dilate)
if advanced_parameters.invert_mask_checkbox:
if invert_mask_checkbox:
inpaint_mask = 255 - inpaint_mask
inpaint_image = HWC3(inpaint_image)
@@ -310,7 +357,7 @@ def worker():
if inpaint_parameterized:
progressbar(async_task, 1, 'Downloading inpainter ...')
inpaint_head_model_path, inpaint_patch_model_path = modules.config.downloading_inpaint_models(
advanced_parameters.inpaint_engine)
inpaint_engine)
base_model_additional_loras += [(inpaint_patch_model_path, 1.0)]
print(f'[Inpaint] Current inpaint model is {inpaint_patch_model_path}')
if refiner_model_name == 'None':
@@ -326,8 +373,8 @@ def worker():
prompt = inpaint_additional_prompt + '\n' + prompt
goals.append('inpaint')
if current_tab == 'ip' or \
advanced_parameters.mixing_image_prompt_and_inpaint or \
advanced_parameters.mixing_image_prompt_and_vary_upscale:
mixing_image_prompt_and_vary_upscale or \
mixing_image_prompt_and_inpaint:
goals.append('cn')
progressbar(async_task, 1, 'Downloading control models ...')
if len(cn_tasks[flags.cn_canny]) > 0:
@@ -346,19 +393,19 @@ def worker():
ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_path)
ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_face_path)
if advanced_parameters.overwrite_step > 0:
steps = advanced_parameters.overwrite_step
if overwrite_step > 0:
steps = overwrite_step
switch = int(round(steps * refiner_switch))
if advanced_parameters.overwrite_switch > 0:
switch = advanced_parameters.overwrite_switch
if overwrite_switch > 0:
switch = overwrite_switch
if advanced_parameters.overwrite_width > 0:
width = advanced_parameters.overwrite_width
if overwrite_width > 0:
width = overwrite_width
if advanced_parameters.overwrite_height > 0:
height = advanced_parameters.overwrite_height
if overwrite_height > 0:
height = overwrite_height
print(f'[Parameters] Sampler = {sampler_name} - {scheduler_name}')
print(f'[Parameters] Steps = {steps} - {switch}')
@@ -391,11 +438,16 @@ def worker():
progressbar(async_task, 3, 'Processing prompts ...')
tasks = []
for i in range(image_number):
task_seed = (seed + i) % (constants.MAX_SEED + 1) # randint is inclusive, % is not
task_rng = random.Random(task_seed) # may bind to inpaint noise in the future
if disable_seed_increment:
task_seed = seed
else:
task_seed = (seed + i) % (constants.MAX_SEED + 1) # randint is inclusive, % is not
task_rng = random.Random(task_seed) # may bind to inpaint noise in the future
task_prompt = apply_wildcards(prompt, task_rng)
task_prompt = apply_arrays(task_prompt, i)
task_negative_prompt = apply_wildcards(negative_prompt, task_rng)
task_extra_positive_prompts = [apply_wildcards(pmt, task_rng) for pmt in extra_positive_prompts]
task_extra_negative_prompts = [apply_wildcards(pmt, task_rng) for pmt in extra_negative_prompts]
@@ -461,8 +513,8 @@ def worker():
denoising_strength = 0.5
if 'strong' in uov_method:
denoising_strength = 0.85
if advanced_parameters.overwrite_vary_strength > 0:
denoising_strength = advanced_parameters.overwrite_vary_strength
if overwrite_vary_strength > 0:
denoising_strength = overwrite_vary_strength
shape_ceil = get_image_shape_ceil(uov_input_image)
if shape_ceil < 1024:
@@ -525,16 +577,16 @@ def worker():
direct_return = False
if direct_return:
d = [('Upscale (Fast)', '2x')]
log(uov_input_image, d)
yield_result(async_task, uov_input_image, do_not_show_finished_images=True)
d = [('Upscale (Fast)', 'upscale_fast', '2x')]
uov_input_image_path = log(uov_input_image, d, output_format=output_format)
yield_result(async_task, uov_input_image_path, do_not_show_finished_images=True)
return
tiled = True
denoising_strength = 0.382
if advanced_parameters.overwrite_upscale_strength > 0:
denoising_strength = advanced_parameters.overwrite_upscale_strength
if overwrite_upscale_strength > 0:
denoising_strength = overwrite_upscale_strength
initial_pixels = core.numpy_to_pytorch(uov_input_image)
progressbar(async_task, 13, 'VAE encoding ...')
@@ -578,19 +630,19 @@ def worker():
inpaint_image = np.ascontiguousarray(inpaint_image.copy())
inpaint_mask = np.ascontiguousarray(inpaint_mask.copy())
advanced_parameters.inpaint_strength = 1.0
advanced_parameters.inpaint_respective_field = 1.0
inpaint_strength = 1.0
inpaint_respective_field = 1.0
denoising_strength = advanced_parameters.inpaint_strength
denoising_strength = inpaint_strength
inpaint_worker.current_task = inpaint_worker.InpaintWorker(
image=inpaint_image,
mask=inpaint_mask,
use_fill=denoising_strength > 0.99,
k=advanced_parameters.inpaint_respective_field
k=inpaint_respective_field
)
if advanced_parameters.debugging_inpaint_preprocessor:
if debugging_inpaint_preprocessor:
yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(),
do_not_show_finished_images=True)
return
@@ -636,7 +688,7 @@ def worker():
model=pipeline.final_unet
)
if not advanced_parameters.inpaint_disable_initial_latent:
if not inpaint_disable_initial_latent:
initial_latent = {'samples': latent_fill}
B, C, H, W = latent_fill.shape
@@ -649,24 +701,24 @@ def worker():
cn_img, cn_stop, cn_weight = task
cn_img = resize_image(HWC3(cn_img), width=width, height=height)
if not advanced_parameters.skipping_cn_preprocessor:
cn_img = preprocessors.canny_pyramid(cn_img)
if not skipping_cn_preprocessor:
cn_img = preprocessors.canny_pyramid(cn_img, canny_low_threshold, canny_high_threshold)
cn_img = HWC3(cn_img)
task[0] = core.numpy_to_pytorch(cn_img)
if advanced_parameters.debugging_cn_preprocessor:
if debugging_cn_preprocessor:
yield_result(async_task, cn_img, do_not_show_finished_images=True)
return
for task in cn_tasks[flags.cn_cpds]:
cn_img, cn_stop, cn_weight = task
cn_img = resize_image(HWC3(cn_img), width=width, height=height)
if not advanced_parameters.skipping_cn_preprocessor:
if not skipping_cn_preprocessor:
cn_img = preprocessors.cpds(cn_img)
cn_img = HWC3(cn_img)
task[0] = core.numpy_to_pytorch(cn_img)
if advanced_parameters.debugging_cn_preprocessor:
if debugging_cn_preprocessor:
yield_result(async_task, cn_img, do_not_show_finished_images=True)
return
for task in cn_tasks[flags.cn_ip]:
@@ -677,21 +729,21 @@ def worker():
cn_img = resize_image(cn_img, width=224, height=224, resize_mode=0)
task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_path)
if advanced_parameters.debugging_cn_preprocessor:
if debugging_cn_preprocessor:
yield_result(async_task, cn_img, do_not_show_finished_images=True)
return
for task in cn_tasks[flags.cn_ip_face]:
cn_img, cn_stop, cn_weight = task
cn_img = HWC3(cn_img)
if not advanced_parameters.skipping_cn_preprocessor:
if not skipping_cn_preprocessor:
cn_img = extras.face_crop.crop_image(cn_img)
# https://github.com/tencent-ailab/IP-Adapter/blob/d580c50a291566bbf9fc7ac0f760506607297e6d/README.md?plain=1#L75
cn_img = resize_image(cn_img, width=224, height=224, resize_mode=0)
task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_face_path)
if advanced_parameters.debugging_cn_preprocessor:
if debugging_cn_preprocessor:
yield_result(async_task, cn_img, do_not_show_finished_images=True)
return
@@ -700,14 +752,14 @@ def worker():
if len(all_ip_tasks) > 0:
pipeline.final_unet = ip_adapter.patch_model(pipeline.final_unet, all_ip_tasks)
if advanced_parameters.freeu_enabled:
if freeu_enabled:
print(f'FreeU is enabled!')
pipeline.final_unet = core.apply_freeu(
pipeline.final_unet,
advanced_parameters.freeu_b1,
advanced_parameters.freeu_b2,
advanced_parameters.freeu_s1,
advanced_parameters.freeu_s2
freeu_b1,
freeu_b2,
freeu_s1,
freeu_s2
)
all_steps = steps * image_number
@@ -753,6 +805,8 @@ def worker():
execution_start_time = time.perf_counter()
try:
if async_task.last_stop is not False:
ldm_patched.model_management.interrupt_current_processing()
positive_cond, negative_cond = task['c'], task['uc']
if 'cn' in goals:
@@ -780,7 +834,8 @@ def worker():
denoise=denoising_strength,
tiled=tiled,
cfg_scale=cfg_scale,
refiner_swap_method=refiner_swap_method
refiner_swap_method=refiner_swap_method,
disable_preview=disable_preview
)
del task['c'], task['uc'], positive_cond, negative_cond # Save memory
@@ -788,37 +843,58 @@ def worker():
if inpaint_worker.current_task is not None:
imgs = [inpaint_worker.current_task.post_process(x) for x in imgs]
img_paths = []
for x in imgs:
d = [
('Prompt', task['log_positive_prompt']),
('Negative Prompt', task['log_negative_prompt']),
('Fooocus V2 Expansion', task['expansion']),
('Styles', str(raw_style_selections)),
('Performance', performance_selection),
('Resolution', str((width, height))),
('Sharpness', sharpness),
('Guidance Scale', guidance_scale),
('ADM Guidance', str((
modules.patch.positive_adm_scale,
modules.patch.negative_adm_scale,
modules.patch.adm_scaler_end))),
('Base Model', base_model_name),
('Refiner Model', refiner_model_name),
('Refiner Switch', refiner_switch),
('Sampler', sampler_name),
('Scheduler', scheduler_name),
('Seed', task['task_seed']),
]
d = [('Prompt', 'prompt', task['log_positive_prompt']),
('Negative Prompt', 'negative_prompt', task['log_negative_prompt']),
('Fooocus V2 Expansion', 'prompt_expansion', task['expansion']),
('Styles', 'styles', str(raw_style_selections)),
('Performance', 'performance', performance_selection.value),
('Resolution', 'resolution', str((width, height))),
('Guidance Scale', 'guidance_scale', guidance_scale),
('Sharpness', 'sharpness', sharpness),
('ADM Guidance', 'adm_guidance', str((
modules.patch.patch_settings[pid].positive_adm_scale,
modules.patch.patch_settings[pid].negative_adm_scale,
modules.patch.patch_settings[pid].adm_scaler_end))),
('Base Model', 'base_model', base_model_name),
('Refiner Model', 'refiner_model', refiner_model_name),
('Refiner Switch', 'refiner_switch', refiner_switch)]
if refiner_model_name != 'None':
if overwrite_switch > 0:
d.append(('Overwrite Switch', 'overwrite_switch', overwrite_switch))
if refiner_swap_method != flags.refiner_swap_method:
d.append(('Refiner Swap Method', 'refiner_swap_method', refiner_swap_method))
if modules.patch.patch_settings[pid].adaptive_cfg != modules.config.default_cfg_tsnr:
d.append(('CFG Mimicking from TSNR', 'adaptive_cfg', modules.patch.patch_settings[pid].adaptive_cfg))
d.append(('Sampler', 'sampler', sampler_name))
d.append(('Scheduler', 'scheduler', scheduler_name))
d.append(('Seed', 'seed', task['task_seed']))
if freeu_enabled:
d.append(('FreeU', 'freeu', str((freeu_b1, freeu_b2, freeu_s1, freeu_s2))))
metadata_parser = None
if save_metadata_to_images:
metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme)
metadata_parser.set_data(task['log_positive_prompt'], task['positive'],
task['log_negative_prompt'], task['negative'],
steps, base_model_name, refiner_model_name, loras)
for li, (n, w) in enumerate(loras):
if n != 'None':
d.append((f'LoRA {li + 1}', f'{n} : {w}'))
d.append(('Version', 'v' + fooocus_version.version))
log(x, d)
d.append((f'LoRA {li + 1}', f'lora_combined_{li + 1}', f'{n} : {w}'))
yield_result(async_task, imgs, do_not_show_finished_images=len(tasks) == 1)
d.append(('Version', 'version', 'Fooocus v' + fooocus_version.version))
img_paths.append(log(x, d, metadata_parser, output_format))
yield_result(async_task, img_paths, do_not_show_finished_images=len(tasks) == 1 or disable_intermediate_results)
except ldm_patched.modules.model_management.InterruptProcessingException as e:
if shared.last_stop == 'skip':
if async_task.last_stop == 'skip':
print('User skipped')
async_task.last_stop = False
continue
else:
print('User stopped')
@@ -826,21 +902,27 @@ def worker():
execution_time = time.perf_counter() - execution_start_time
print(f'Generating and saving time: {execution_time:.2f} seconds')
async_task.processing = False
return
while True:
time.sleep(0.01)
if len(async_tasks) > 0:
task = async_tasks.pop(0)
generate_image_grid = task.args.pop(0)
try:
handler(task)
build_image_wall(task)
if generate_image_grid:
build_image_wall(task)
task.yields.append(['finish', task.results])
pipeline.prepare_text_encoder(async_call=True)
except:
traceback.print_exc()
task.yields.append(['finish', task.results])
finally:
if pid in modules.patch.patch_settings:
del modules.patch.patch_settings[pid]
pass