Fooocus/modules/async_worker.py
lllyasviel e0f97b6fef
just no grad everything to prevent mistakes (#359)
* just no grad everything to prevent mistakes

* just no grad everything to prevent mistakes
2023-09-13 03:07:47 -07:00

213 lines
7.6 KiB
Python

import threading
import torch
buffer = []
outputs = []
def worker():
global buffer, outputs
import time
import shared
import random
import copy
import modules.default_pipeline as pipeline
import modules.path
import modules.patch
from modules.sdxl_styles import apply_style, aspect_ratios, fooocus_expansion
from modules.private_logger import log
from modules.expansion import safe_str
from modules.util import join_prompts, remove_empty_str
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 progressbar(number, text):
outputs.append(['preview', (number, text, None)])
@torch.no_grad()
def handler(task):
prompt, negative_prompt, style_selections, 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)]
raw_style_selections = copy.deepcopy(style_selections)
if fooocus_expansion in style_selections:
use_expansion = True
style_selections.remove(fooocus_expansion)
else:
use_expansion = False
use_style = len(style_selections) > 0
modules.patch.sharpness = sharpness
progressbar(1, 'Initializing ...')
raw_prompt = prompt
raw_negative_prompt = negative_prompt
prompts = remove_empty_str([safe_str(p) for p in prompt.split('\n')], default='')
negative_prompts = remove_empty_str([safe_str(p) for p in negative_prompt.split('\n')], default='')
prompt = prompts[0]
negative_prompt = negative_prompts[0]
extra_positive_prompts = prompts[1:] if len(prompts) > 1 else []
extra_negative_prompts = negative_prompts[1:] if len(negative_prompts) > 1 else []
seed = image_seed
max_seed = int(1024 * 1024 * 1024)
if not isinstance(seed, int):
seed = random.randint(1, max_seed)
if seed < 0:
seed = - seed
seed = seed % max_seed
progressbar(3, 'Loading models ...')
pipeline.refresh_base_model(base_model_name)
pipeline.refresh_refiner_model(refiner_model_name)
pipeline.refresh_loras(loras)
pipeline.clear_all_caches()
progressbar(3, 'Processing prompts ...')
positive_basic_workloads = []
negative_basic_workloads = []
if use_style:
for s in style_selections:
p, n = apply_style(s, positive=prompt)
positive_basic_workloads.append(p)
negative_basic_workloads.append(n)
else:
positive_basic_workloads.append(prompt)
negative_basic_workloads.append(negative_prompt) # Always use independent workload for negative.
positive_basic_workloads = positive_basic_workloads + extra_positive_prompts
negative_basic_workloads = negative_basic_workloads + extra_negative_prompts
positive_basic_workloads = remove_empty_str(positive_basic_workloads, default=prompt)
negative_basic_workloads = remove_empty_str(negative_basic_workloads, default=negative_prompt)
positive_top_k = len(positive_basic_workloads)
negative_top_k = len(negative_basic_workloads)
tasks = [dict(
task_seed=seed + i,
positive=positive_basic_workloads,
negative=negative_basic_workloads,
expansion='',
c=[None, None],
uc=[None, None],
) for i in range(image_number)]
if use_expansion:
for i, t in enumerate(tasks):
progressbar(5, f'Preparing Fooocus text #{i + 1} ...')
expansion = pipeline.expansion(prompt, t['task_seed'])
print(f'[Prompt Expansion] New suffix: {expansion}')
t['expansion'] = expansion
t['positive'] = copy.deepcopy(t['positive']) + [join_prompts(prompt, expansion)] # Deep copy.
for i, t in enumerate(tasks):
progressbar(7, f'Encoding base positive #{i + 1} ...')
t['c'][0] = pipeline.clip_encode(sd=pipeline.xl_base_patched, texts=t['positive'],
pool_top_k=positive_top_k)
for i, t in enumerate(tasks):
progressbar(9, f'Encoding base negative #{i + 1} ...')
t['uc'][0] = pipeline.clip_encode(sd=pipeline.xl_base_patched, texts=t['negative'],
pool_top_k=negative_top_k)
if pipeline.xl_refiner is not None:
for i, t in enumerate(tasks):
progressbar(11, f'Encoding refiner positive #{i + 1} ...')
t['c'][1] = pipeline.clip_encode(sd=pipeline.xl_refiner, texts=t['positive'],
pool_top_k=positive_top_k)
for i, t in enumerate(tasks):
progressbar(13, f'Encoding refiner negative #{i + 1} ...')
t['uc'][1] = pipeline.clip_encode(sd=pipeline.xl_refiner, texts=t['negative'],
pool_top_k=negative_top_k)
if performance_selction == 'Speed':
steps = 30
switch = 20
else:
steps = 60
switch = 40
pipeline.clear_all_caches() # save memory
width, height = aspect_ratios[aspect_ratios_selction]
results = []
all_steps = steps * image_number
def callback(step, x0, x, total_steps, y):
done_steps = current_task_id * steps + step
outputs.append(['preview', (
int(15.0 + 85.0 * float(done_steps) / float(all_steps)),
f'Step {step}/{total_steps} in the {current_task_id + 1}-th Sampling',
y)])
outputs.append(['preview', (13, 'Starting tasks ...', None)])
for current_task_id, task in enumerate(tasks):
imgs = pipeline.process_diffusion(
positive_cond=task['c'],
negative_cond=task['uc'],
steps=steps,
switch=switch,
width=width,
height=height,
image_seed=task['task_seed'],
callback=callback)
for x in imgs:
d = [
('Prompt', raw_prompt),
('Negative Prompt', raw_negative_prompt),
('Fooocus V2 Expansion', task['expansion']),
('Styles', str(raw_style_selections)),
('Performance', performance_selction),
('Resolution', str((width, height))),
('Sharpness', sharpness),
('Base Model', base_model_name),
('Refiner Model', refiner_model_name),
('Seed', task['task_seed'])
]
for n, w in loras:
if n != 'None':
d.append((f'LoRA [{n}] weight', w))
log(x, d, single_line_number=3)
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()