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import threading
buffer = []
outputs = []
def worker():
global buffer, outputs
import time
import shared
import random
import modules.default_pipeline as pipeline
import modules.path
import modules.patch
from modules.sdxl_styles import apply_style, aspect_ratios
from modules.private_logger import log
try:
async_gradio_app = shared.gradio_root
flag = f'''App started successful. Use the app with {str(async_gradio_app.local_url)} or {str(async_gradio_app.server_name)}:{str(async_gradio_app.server_port)}'''
if async_gradio_app.share:
flag += f''' or {async_gradio_app.share_url}'''
print(flag)
except Exception as e:
print(e)
def handler(task):
prompt, negative_prompt, style_selction, performance_selction, \
aspect_ratios_selction, image_number, image_seed, sharpness, base_model_name, refiner_model_name, \
l1, w1, l2, w2, l3, w3, l4, w4, l5, w5 = task
loras = [(l1, w1), (l2, w2), (l3, w3), (l4, w4), (l5, w5)]
modules.patch.sharpness = sharpness
pipeline.refresh_base_model(base_model_name)
pipeline.refresh_refiner_model(refiner_model_name)
pipeline.refresh_loras(loras)
pipeline.clean_prompt_cond_caches()
p_txt, n_txt = apply_style(style_selction, prompt, negative_prompt)
if performance_selction == 'Speed':
steps = 30
switch = 20
else:
steps = 60
switch = 40
width, height = aspect_ratios[aspect_ratios_selction]
results = []
seed = image_seed
if not isinstance(seed, int) or seed < 0 or seed > 1024*1024*1024:
seed = random.randint(1, 1024*1024*1024)
all_steps = steps * image_number
def callback(step, x0, x, total_steps, y):
done_steps = i * steps + step
outputs.append(['preview', (
int(100.0 * float(done_steps) / float(all_steps)),
f'Step {step}/{total_steps} in the {i}-th Sampling',
y)])
for i in range(image_number):
imgs = pipeline.process(p_txt, n_txt, steps, switch, width, height, seed, callback=callback)
for x in imgs:
d = [
('Prompt', prompt),
('Negative Prompt', negative_prompt),
('Style', style_selction),
('Performance', performance_selction),
('Resolution', str((width, height))),
('Sharpness', sharpness),
('Base Model', base_model_name),
('Refiner Model', refiner_model_name),
('Seed', seed)
]
for n, w in loras:
if n != 'None':
d.append((f'LoRA [{n}] weight', w))
log(x, d)
seed += 1
results += imgs
outputs.append(['results', results])
return
while True:
time.sleep(0.01)
if len(buffer) > 0:
task = buffer.pop(0)
handler(task)
pass
threading.Thread(target=worker, daemon=True).start()
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