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Merge branch 'main_upstream'

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# Conflicts:
#	css/style.css
#	fooocus_colab.ipynb
#	fooocus_version.py
#	launch.py
#	modules/async_worker.py
#	modules/config.py
#	modules/flags.py
#	modules/meta_parser.py
#	webui.py
This commit is contained in:
Manuel Schmid
2024-03-18 21:27:56 +01:00
parent aadc75f845 a1bda88aa3
commit 679c02a09f
33 changed files with 513 additions and 273 deletions
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modules/async_worker.py
+13 -19
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@@ -1,5 +1,6 @@
import threading
import os
import re
from modules.patch import PatchSettings, patch_settings, patch_all
patch_all()
@@ -49,8 +50,8 @@ def worker():
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 remove_empty_str, HWC3, resize_image, \
get_image_shape_ceil, set_image_shape_ceil, get_shape_ceil, resample_image, erode_or_dilate
from modules.util import remove_empty_str, HWC3, resize_image, get_image_shape_ceil, set_image_shape_ceil, \
get_shape_ceil, resample_image, erode_or_dilate, get_enabled_loras
from modules.upscaler import perform_upscale
from modules.flags import Performance
from modules.meta_parser import get_metadata_parser, MetadataScheme
@@ -131,14 +132,6 @@ 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):
@@ -157,12 +150,13 @@ def worker():
image_number = args.pop()
output_format = args.pop()
image_seed = args.pop()
read_wildcards_in_order = args.pop()
sharpness = args.pop()
guidance_scale = args.pop()
base_model_name = args.pop()
refiner_model_name = args.pop()
refiner_switch = args.pop()
loras = apply_enabled_loras([[bool(args.pop()), str(args.pop()), float(args.pop()), ] for _ in range(modules.config.default_max_lora_number)])
loras = get_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()
@@ -385,7 +379,7 @@ def worker():
print(f'[Inpaint] Current inpaint model is {inpaint_patch_model_path}')
if refiner_model_name == 'None':
use_synthetic_refiner = True
refiner_switch = 0.5
refiner_switch = 0.8
else:
inpaint_head_model_path, inpaint_patch_model_path = None, None
print(f'[Inpaint] Parameterized inpaint is disabled.')
@@ -460,16 +454,16 @@ def worker():
for i in range(image_number):
if disable_seed_increment:
task_seed = seed
task_seed = seed % (constants.MAX_SEED + 1)
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_wildcards(prompt, task_rng, i, read_wildcards_in_order)
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]
task_negative_prompt = apply_wildcards(negative_prompt, task_rng, i, read_wildcards_in_order)
task_extra_positive_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in extra_positive_prompts]
task_extra_negative_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in extra_negative_prompts]
positive_basic_workloads = []
negative_basic_workloads = []
@@ -827,7 +821,7 @@ def worker():
try:
if async_task.last_stop is not False:
ldm_patched.model_management.interrupt_current_processing()
ldm_patched.modules.model_management.interrupt_current_processing()
positive_cond, negative_cond = task['c'], task['uc']
if 'cn' in goals:
@@ -902,7 +896,7 @@ def worker():
d.append(('Sampler', 'sampler', sampler_name))
d.append(('Scheduler', 'scheduler', scheduler_name))
d.append(('Seed', 'seed', task['task_seed']))
d.append(('Seed', 'seed', str(task['task_seed'])))
if freeu_enabled:
d.append(('FreeU', 'freeu', str((freeu_b1, freeu_b2, freeu_s1, freeu_s2))))