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[Major Update] Fooocus 2.0.0 (#346)

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[Major Update] Fooocus 2.0.0 (#346)
This commit is contained in:
lllyasviel
2023-09-11 23:10:45 -07:00
committed by GitHub
parent 25fed6a4fe
commit 47876aaf99
9 changed files with 191 additions and 96 deletions
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modules/async_worker.py
+93 -60
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@@ -11,14 +11,15 @@ def worker():
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_negative, apply_style_positive, aspect_ratios
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
from modules.util import join_prompts, remove_empty_str
try:
async_gradio_app = shared.gradio_root
@@ -29,20 +30,42 @@ def worker():
except Exception as e:
print(e)
def progressbar(number, text):
outputs.append(['preview', (number, text, None)])
def handler(task):
prompt, negative_prompt, style_selction, performance_selction, \
aspect_ratios_selction, image_number, image_seed, sharpness, raw_mode, \
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
outputs.append(['preview', (1, 'Initializing ...', None)])
progressbar(1, 'Initializing ...')
prompt = safe_str(prompt)
negative_prompt = safe_str(negative_prompt)
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)
@@ -52,63 +75,74 @@ def worker():
seed = - seed
seed = seed % max_seed
outputs.append(['preview', (3, 'Load models ...', None)])
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()
tasks = []
if raw_mode:
outputs.append(['preview', (5, 'Encoding negative text ...', None)])
n_txt = apply_style_negative(style_selction, negative_prompt)
n_cond = pipeline.process_prompt(n_txt)
outputs.append(['preview', (9, 'Encoding positive text ...', None)])
p_txt = apply_style_positive(style_selction, prompt)
p_cond = pipeline.process_prompt(p_txt)
progressbar(3, 'Processing prompts ...')
for i in range(image_number):
tasks.append(dict(
prompt=prompt,
negative_prompt=negative_prompt,
seed=seed + i,
n_cond=n_cond,
p_cond=p_cond,
real_positive_prompt=p_txt,
real_negative_prompt=n_txt
))
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:
for i in range(image_number):
outputs.append(['preview', (5, f'Preparing positive text #{i + 1} ...', None)])
current_seed = seed + i
positive_basic_workloads.append(prompt)
expansion_weight = 0.1
negative_basic_workloads.append(negative_prompt) # Always use independent workload for negative.
suffix = pipeline.expansion(prompt, current_seed)
suffix = f'({suffix}:{expansion_weight})'
print(f'[Prompt Expansion] New suffix: {suffix}')
positive_basic_workloads = positive_basic_workloads + extra_positive_prompts
negative_basic_workloads = negative_basic_workloads + extra_negative_prompts
p_txt = apply_style_positive(style_selction, prompt)
p_txt = safe_str(p_txt)
positive_basic_workloads = remove_empty_str(positive_basic_workloads, default=prompt)
negative_basic_workloads = remove_empty_str(negative_basic_workloads, default=negative_prompt)
p_txt = join_prompts(p_txt, suffix)
positive_top_k = len(positive_basic_workloads)
negative_top_k = len(negative_basic_workloads)
tasks.append(dict(
prompt=prompt,
negative_prompt=negative_prompt,
seed=current_seed,
real_positive_prompt=p_txt,
))
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)]
outputs.append(['preview', (9, 'Encoding negative text ...', None)])
n_txt = apply_style_negative(style_selction, negative_prompt)
n_cond = pipeline.process_prompt(n_txt)
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):
outputs.append(['preview', (12, f'Encoding positive text #{i + 1} ...', None)])
t['p_cond'] = pipeline.process_prompt(t['real_positive_prompt'])
t['real_negative_prompt'] = n_txt
t['n_cond'] = n_cond
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
@@ -117,6 +151,7 @@ def worker():
steps = 60
switch = 40
pipeline.clear_all_caches() # save memory
width, height = aspect_ratios[aspect_ratios_selction]
results = []
@@ -132,34 +167,32 @@ def worker():
outputs.append(['preview', (13, 'Starting tasks ...', None)])
for current_task_id, task in enumerate(tasks):
imgs = pipeline.process_diffusion(
positive_cond=task['p_cond'],
negative_cond=task['n_cond'],
positive_cond=task['c'],
negative_cond=task['uc'],
steps=steps,
switch=switch,
width=width,
height=height,
image_seed=task['seed'],
image_seed=task['task_seed'],
callback=callback)
for x in imgs:
d = [
('Prompt', task['prompt']),
('Negative Prompt', task['negative_prompt']),
('Real Positive Prompt', task['real_positive_prompt']),
('Real Negative Prompt', task['real_negative_prompt']),
('Raw Mode', str(raw_mode)),
('Style', style_selction),
('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['seed'])
('Seed', task['task_seed'])
]
for n, w in loras:
if n != 'None':
d.append((f'LoRA [{n}] weight', w))
log(x, d)
log(x, d, single_line_number=3)
results += imgs