import threading
class AsyncTask:
def __init__(self, args):
self.args = args
self.yields = []
self.results = []
async_tasks = []
def worker():
global async_tasks
import traceback
import math
import numpy as np
import torch
import time
import shared
import random
import copy
import modules.default_pipeline as pipeline
import modules.core as core
import modules.flags as flags
import modules.config
import modules.patch
import fcbh.model_management
import fooocus_extras.preprocessors as preprocessors
import modules.inpaint_worker as inpaint_worker
import modules.constants as constants
import modules.advanced_parameters as advanced_parameters
import fooocus_extras.ip_adapter as ip_adapter
import fooocus_extras.face_crop
from modules.sdxl_styles import apply_style, apply_wildcards, fooocus_expansion
from modules.private_logger import log
from modules.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
from modules.upscaler import perform_upscale
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(async_task, number, text):
print(f'[Fooocus] {text}')
async_task.yields.append(['preview', (number, text, None)])
def yield_result(async_task, imgs, do_not_show_finished_images=False):
if not isinstance(imgs, list):
imgs = [imgs]
async_task.results = async_task.results + imgs
if do_not_show_finished_images:
return
async_task.yields.append(['results', async_task.results])
return
def build_image_wall(async_task):
if not advanced_parameters.generate_image_grid:
return
results = async_task.results
if len(results) < 2:
return
for img in results:
if not isinstance(img, np.ndarray):
return
if img.ndim != 3:
return
H, W, C = results[0].shape
for img in results:
Hn, Wn, Cn = img.shape
if H != Hn:
return
if W != Wn:
return
if C != Cn:
return
cols = float(len(results)) ** 0.5
cols = int(math.ceil(cols))
rows = float(len(results)) / float(cols)
rows = int(math.ceil(rows))
wall = np.zeros(shape=(H * rows, W * cols, C), dtype=np.uint8)
for y in range(rows):
for x in range(cols):
if y * cols + x < len(results):
img = results[y * cols + x]
wall[y * H:y * H + H, x * W:x * W + W, :] = img
# must use deep copy otherwise gradio is super laggy. Do not use list.append() .
async_task.results = async_task.results + [wall]
return
@torch.no_grad()
@torch.inference_mode()
def handler(async_task):
execution_start_time = time.perf_counter()
args = async_task.args
args.reverse()
prompt = args.pop()
negative_prompt = args.pop()
style_selections = args.pop()
performance_selection = args.pop()
aspect_ratios_selection = args.pop()
image_number = 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(5)]
input_image_checkbox = args.pop()
current_tab = args.pop()
uov_method = args.pop()
uov_input_image = args.pop()
outpaint_selections = args.pop()
inpaint_input_image = args.pop()
inpaint_additional_prompt = args.pop()
cn_tasks = {x: [] for x in flags.ip_list}
for _ in range(4):
cn_img = args.pop()
cn_stop = args.pop()
cn_weight = args.pop()
cn_type = args.pop()
if cn_img is not None:
cn_tasks[cn_type].append([cn_img, cn_stop, cn_weight])
outpaint_selections = [o.lower() for o in outpaint_selections]
base_model_additional_loras = []
raw_style_selections = copy.deepcopy(style_selections)
uov_method = uov_method.lower()
if fooocus_expansion in style_selections:
use_expansion = True
style_selections.remove(fooocus_expansion)
else:
use_expansion = False
use_style = len(style_selections) > 0
if base_model_name == refiner_model_name:
print(f'Refiner disabled because base model and refiner are same.')
refiner_model_name = 'None'
assert performance_selection in ['Speed', 'Quality', 'Extreme Speed']
steps = 30
if performance_selection == 'Speed':
steps = 30
if performance_selection == 'Quality':
steps = 60
if performance_selection == 'Extreme Speed':
print('Enter LCM mode.')
progressbar(async_task, 1, 'Downloading LCM components ...')
loras += [(modules.config.downloading_sdxl_lcm_lora(), 1.0)]
if refiner_model_name != 'None':
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
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
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] ADM Scale = '
f'{modules.patch.positive_adm_scale} : '
f'{modules.patch.negative_adm_scale} : '
f'{modules.patch.adm_scaler_end}')
cfg_scale = float(guidance_scale)
print(f'[Parameters] CFG = {cfg_scale}')
initial_latent = None
denoising_strength = 1.0
tiled = False
width, height = aspect_ratios_selection.replace('×', ' ').split(' ')[:2]
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_image = None
inpaint_mask = None
inpaint_head_model_path = None
use_synthetic_refiner = False
controlnet_canny_path = None
controlnet_cpds_path = None
clip_vision_path, ip_negative_path, ip_adapter_path, ip_adapter_face_path = None, None, None, None
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)) \
and uov_method != flags.disabled and uov_input_image is not None:
uov_input_image = HWC3(uov_input_image)
if 'vary' in uov_method:
goals.append('vary')
elif 'upscale' in uov_method:
goals.append('upscale')
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
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)) \
and isinstance(inpaint_input_image, dict):
inpaint_image = inpaint_input_image['image']
inpaint_mask = inpaint_input_image['mask'][:, :, 0]
inpaint_image = HWC3(inpaint_image)
if isinstance(inpaint_image, np.ndarray) and isinstance(inpaint_mask, np.ndarray) \
and (np.any(inpaint_mask > 127) or len(outpaint_selections) > 0):
if inpaint_parameterized:
progressbar(async_task, 1, 'Downloading inpainter ...')
modules.config.downloading_upscale_model()
inpaint_head_model_path, inpaint_patch_model_path = modules.config.downloading_inpaint_models(
advanced_parameters.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':
use_synthetic_refiner = True
refiner_switch = 0.5
else:
inpaint_head_model_path, inpaint_patch_model_path = None, None
print(f'[Inpaint] Parameterized inpaint is disabled.')
if inpaint_additional_prompt != '':
if prompt == '':
prompt = inpaint_additional_prompt
else:
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:
goals.append('cn')
progressbar(async_task, 1, 'Downloading control models ...')
if len(cn_tasks[flags.cn_canny]) > 0:
controlnet_canny_path = modules.config.downloading_controlnet_canny()
if len(cn_tasks[flags.cn_cpds]) > 0:
controlnet_cpds_path = modules.config.downloading_controlnet_cpds()
if len(cn_tasks[flags.cn_ip]) > 0:
clip_vision_path, ip_negative_path, ip_adapter_path = modules.config.downloading_ip_adapters('ip')
if len(cn_tasks[flags.cn_ip_face]) > 0:
clip_vision_path, ip_negative_path, ip_adapter_face_path = modules.config.downloading_ip_adapters(
'face')
progressbar(async_task, 1, 'Loading control models ...')
# Load or unload CNs
pipeline.refresh_controlnets([controlnet_canny_path, controlnet_cpds_path])
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)
switch = int(round(steps * refiner_switch))
if advanced_parameters.overwrite_step > 0:
steps = advanced_parameters.overwrite_step
if advanced_parameters.overwrite_switch > 0:
switch = advanced_parameters.overwrite_switch
if advanced_parameters.overwrite_width > 0:
width = advanced_parameters.overwrite_width
if advanced_parameters.overwrite_height > 0:
height = advanced_parameters.overwrite_height
print(f'[Parameters] Sampler = {sampler_name} - {scheduler_name}')
print(f'[Parameters] Steps = {steps} - {switch}')
progressbar(async_task, 1, 'Initializing ...')
if not skip_prompt_processing:
prompts = remove_empty_str([safe_str(p) for p in prompt.splitlines()], default='')
negative_prompts = remove_empty_str([safe_str(p) for p in negative_prompt.splitlines()], default='')
prompt = prompts[0]
negative_prompt = negative_prompts[0]
if prompt == '':
# disable expansion when empty since it is not meaningful and influences image prompt
use_expansion = False
extra_positive_prompts = prompts[1:] if len(prompts) > 1 else []
extra_negative_prompts = negative_prompts[1:] if len(negative_prompts) > 1 else []
progressbar(async_task, 3, 'Loading models ...')
pipeline.refresh_everything(refiner_model_name=refiner_model_name, base_model_name=base_model_name,
loras=loras, base_model_additional_loras=base_model_additional_loras,
use_synthetic_refiner=use_synthetic_refiner)
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
task_prompt = apply_wildcards(prompt, task_rng)
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]
positive_basic_workloads = []
negative_basic_workloads = []
if use_style:
for s in style_selections:
p, n = apply_style(s, positive=task_prompt)
positive_basic_workloads = positive_basic_workloads + p
negative_basic_workloads = negative_basic_workloads + n
else:
positive_basic_workloads.append(task_prompt)
negative_basic_workloads.append(task_negative_prompt) # Always use independent workload for negative.
positive_basic_workloads = positive_basic_workloads + task_extra_positive_prompts
negative_basic_workloads = negative_basic_workloads + task_extra_negative_prompts
positive_basic_workloads = remove_empty_str(positive_basic_workloads, default=task_prompt)
negative_basic_workloads = remove_empty_str(negative_basic_workloads, default=task_negative_prompt)
tasks.append(dict(
task_seed=task_seed,
task_prompt=task_prompt,
task_negative_prompt=task_negative_prompt,
positive=positive_basic_workloads,
negative=negative_basic_workloads,
expansion='',
c=None,
uc=None,
positive_top_k=len(positive_basic_workloads),
negative_top_k=len(negative_basic_workloads),
log_positive_prompt='; '.join([task_prompt] + task_extra_positive_prompts),
log_negative_prompt='; '.join([task_negative_prompt] + task_extra_negative_prompts),
))
if use_expansion:
for i, t in enumerate(tasks):
progressbar(async_task, 5, f'Preparing Fooocus text #{i + 1} ...')
expansion = pipeline.final_expansion(t['task_prompt'], t['task_seed'])
print(f'[Prompt Expansion] {expansion}')
t['expansion'] = expansion
t['positive'] = copy.deepcopy(t['positive']) + [expansion] # Deep copy.
for i, t in enumerate(tasks):
progressbar(async_task, 7, f'Encoding positive #{i + 1} ...')
t['c'] = pipeline.clip_encode(texts=t['positive'], pool_top_k=t['positive_top_k'])
for i, t in enumerate(tasks):
if abs(float(cfg_scale) - 1.0) < 1e-4:
t['uc'] = pipeline.clone_cond(t['c'])
else:
progressbar(async_task, 10, f'Encoding negative #{i + 1} ...')
t['uc'] = pipeline.clip_encode(texts=t['negative'], pool_top_k=t['negative_top_k'])
if len(goals) > 0:
progressbar(async_task, 13, 'Image processing ...')
if 'vary' in goals:
if 'subtle' in uov_method:
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
shape_ceil = get_image_shape_ceil(uov_input_image)
if shape_ceil < 1024:
print(f'[Vary] Image is resized because it is too small.')
shape_ceil = 1024
elif shape_ceil > 2048:
print(f'[Vary] Image is resized because it is too big.')
shape_ceil = 2048
uov_input_image = set_image_shape_ceil(uov_input_image, shape_ceil)
initial_pixels = core.numpy_to_pytorch(uov_input_image)
progressbar(async_task, 13, 'VAE encoding ...')
candidate_vae, _ = pipeline.get_candidate_vae(
steps=steps,
switch=switch,
denoise=denoising_strength,
refiner_swap_method=refiner_swap_method
)
initial_latent = core.encode_vae(vae=candidate_vae, pixels=initial_pixels)
B, C, H, W = initial_latent['samples'].shape
width = W * 8
height = H * 8
print(f'Final resolution is {str((height, width))}.')
if 'upscale' in goals:
H, W, C = uov_input_image.shape
progressbar(async_task, 13, f'Upscaling image from {str((H, W))} ...')
uov_input_image = perform_upscale(uov_input_image)
print(f'Image upscaled.')
if '1.5x' in uov_method:
f = 1.5
elif '2x' in uov_method:
f = 2.0
else:
f = 1.0
shape_ceil = get_shape_ceil(H * f, W * f)
if shape_ceil < 1024:
print(f'[Upscale] Image is resized because it is too small.')
uov_input_image = set_image_shape_ceil(uov_input_image, 1024)
shape_ceil = 1024
else:
uov_input_image = resample_image(uov_input_image, width=W * f, height=H * f)
image_is_super_large = shape_ceil > 2800
if 'fast' in uov_method:
direct_return = True
elif image_is_super_large:
print('Image is too large. Directly returned the SR image. '
'Usually directly return SR image at 4K resolution '
'yields better results than SDXL diffusion.')
direct_return = True
else:
direct_return = False
if direct_return:
d = [('Upscale (Fast)', '2x')]
log(uov_input_image, d, single_line_number=1)
yield_result(async_task, uov_input_image, 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
initial_pixels = core.numpy_to_pytorch(uov_input_image)
progressbar(async_task, 13, 'VAE encoding ...')
candidate_vae, _ = pipeline.get_candidate_vae(
steps=steps,
switch=switch,
denoise=denoising_strength,
refiner_swap_method=refiner_swap_method
)
initial_latent = core.encode_vae(
vae=candidate_vae,
pixels=initial_pixels, tiled=True)
B, C, H, W = initial_latent['samples'].shape
width = W * 8
height = H * 8
print(f'Final resolution is {str((height, width))}.')
if 'inpaint' in goals:
if len(outpaint_selections) > 0:
H, W, C = inpaint_image.shape
if 'top' in outpaint_selections:
inpaint_image = np.pad(inpaint_image, [[int(H * 0.3), 0], [0, 0], [0, 0]], mode='edge')
inpaint_mask = np.pad(inpaint_mask, [[int(H * 0.3), 0], [0, 0]], mode='constant',
constant_values=255)
if 'bottom' in outpaint_selections:
inpaint_image = np.pad(inpaint_image, [[0, int(H * 0.3)], [0, 0], [0, 0]], mode='edge')
inpaint_mask = np.pad(inpaint_mask, [[0, int(H * 0.3)], [0, 0]], mode='constant',
constant_values=255)
H, W, C = inpaint_image.shape
if 'left' in outpaint_selections:
inpaint_image = np.pad(inpaint_image, [[0, 0], [int(H * 0.3), 0], [0, 0]], mode='edge')
inpaint_mask = np.pad(inpaint_mask, [[0, 0], [int(H * 0.3), 0]], mode='constant',
constant_values=255)
if 'right' in outpaint_selections:
inpaint_image = np.pad(inpaint_image, [[0, 0], [0, int(H * 0.3)], [0, 0]], mode='edge')
inpaint_mask = np.pad(inpaint_mask, [[0, 0], [0, int(H * 0.3)]], mode='constant',
constant_values=255)
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
denoising_strength = advanced_parameters.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
)
if advanced_parameters.debugging_inpaint_preprocessor:
yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(),
do_not_show_finished_images=True)
return
progressbar(async_task, 13, 'VAE Inpaint encoding ...')
inpaint_pixel_fill = core.numpy_to_pytorch(inpaint_worker.current_task.interested_fill)
inpaint_pixel_image = core.numpy_to_pytorch(inpaint_worker.current_task.interested_image)
inpaint_pixel_mask = core.numpy_to_pytorch(inpaint_worker.current_task.interested_mask)
candidate_vae, candidate_vae_swap = pipeline.get_candidate_vae(
steps=steps,
switch=switch,
denoise=denoising_strength,
refiner_swap_method=refiner_swap_method
)
latent_inpaint, latent_mask = core.encode_vae_inpaint(
mask=inpaint_pixel_mask,
vae=candidate_vae,
pixels=inpaint_pixel_image)
latent_swap = None
if candidate_vae_swap is not None:
progressbar(async_task, 13, 'VAE SD15 encoding ...')
latent_swap = core.encode_vae(
vae=candidate_vae_swap,
pixels=inpaint_pixel_fill)['samples']
progressbar(async_task, 13, 'VAE encoding ...')
latent_fill = core.encode_vae(
vae=candidate_vae,
pixels=inpaint_pixel_fill)['samples']
inpaint_worker.current_task.load_latent(
latent_fill=latent_fill, latent_mask=latent_mask, latent_swap=latent_swap)
if inpaint_parameterized:
pipeline.final_unet = inpaint_worker.current_task.patch(
inpaint_head_model_path=inpaint_head_model_path,
inpaint_latent=latent_inpaint,
inpaint_latent_mask=latent_mask,
model=pipeline.final_unet
)
if not advanced_parameters.inpaint_disable_initial_latent:
initial_latent = {'samples': latent_fill}
B, C, H, W = latent_fill.shape
height, width = H * 8, W * 8
final_height, final_width = inpaint_worker.current_task.image.shape[:2]
print(f'Final resolution is {str((final_height, final_width))}, latent is {str((height, width))}.')
if 'cn' in goals:
for task in cn_tasks[flags.cn_canny]:
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)
cn_img = HWC3(cn_img)
task[0] = core.numpy_to_pytorch(cn_img)
if advanced_parameters.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:
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:
yield_result(async_task, cn_img, do_not_show_finished_images=True)
return
for task in cn_tasks[flags.cn_ip]:
cn_img, cn_stop, cn_weight = task
cn_img = HWC3(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_path)
if advanced_parameters.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:
cn_img = fooocus_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:
yield_result(async_task, cn_img, do_not_show_finished_images=True)
return
all_ip_tasks = cn_tasks[flags.cn_ip] + cn_tasks[flags.cn_ip_face]
if len(all_ip_tasks) > 0:
pipeline.final_unet = ip_adapter.patch_model(pipeline.final_unet, all_ip_tasks)
if advanced_parameters.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
)
all_steps = steps * image_number
print(f'[Parameters] Denoising Strength = {denoising_strength}')
if isinstance(initial_latent, dict) and 'samples' in initial_latent:
log_shape = initial_latent['samples'].shape
else:
log_shape = f'Image Space {(height, width)}'
print(f'[Parameters] Initial Latent shape: {log_shape}')
preparation_time = time.perf_counter() - execution_start_time
print(f'Preparation time: {preparation_time:.2f} seconds')
final_sampler_name = sampler_name
final_scheduler_name = scheduler_name
if scheduler_name == 'lcm':
final_scheduler_name = 'sgm_uniform'
if pipeline.final_unet is not None:
pipeline.final_unet = core.opModelSamplingDiscrete.patch(
pipeline.final_unet,
sampling='lcm',
zsnr=False)[0]
if pipeline.final_refiner_unet is not None:
pipeline.final_refiner_unet = core.opModelSamplingDiscrete.patch(
pipeline.final_refiner_unet,
sampling='lcm',
zsnr=False)[0]
print('Using lcm scheduler.')
async_task.yields.append(['preview', (13, 'Moving model to GPU ...', None)])
def callback(step, x0, x, total_steps, y):
done_steps = current_task_id * steps + step
async_task.yields.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)])
for current_task_id, task in enumerate(tasks):
execution_start_time = time.perf_counter()
try:
positive_cond, negative_cond = task['c'], task['uc']
if 'cn' in goals:
for cn_flag, cn_path in [
(flags.cn_canny, controlnet_canny_path),
(flags.cn_cpds, controlnet_cpds_path)
]:
for cn_img, cn_stop, cn_weight in cn_tasks[cn_flag]:
positive_cond, negative_cond = core.apply_controlnet(
positive_cond, negative_cond,
pipeline.loaded_ControlNets[cn_path], cn_img, cn_weight, 0, cn_stop)
imgs = pipeline.process_diffusion(
positive_cond=positive_cond,
negative_cond=negative_cond,
steps=steps,
switch=switch,
width=width,
height=height,
image_seed=task['task_seed'],
callback=callback,
sampler_name=final_sampler_name,
scheduler_name=final_scheduler_name,
latent=initial_latent,
denoise=denoising_strength,
tiled=tiled,
cfg_scale=cfg_scale,
refiner_swap_method=refiner_swap_method
)
del task['c'], task['uc'], positive_cond, negative_cond # Save memory
if inpaint_worker.current_task is not None:
imgs = [inpaint_worker.current_task.post_process(x) for x in imgs]
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'])
]
for n, w in loras:
if n != 'None':
d.append((f'LoRA [{n}] weight', w))
log(x, d, single_line_number=3)
yield_result(async_task, imgs, do_not_show_finished_images=len(tasks) == 1)
except fcbh.model_management.InterruptProcessingException as e:
if shared.last_stop == 'skip':
print('User skipped')
continue
else:
print('User stopped')
break
execution_time = time.perf_counter() - execution_start_time
print(f'Generating and saving time: {execution_time:.2f} seconds')
return
while True:
time.sleep(0.01)
if len(async_tasks) > 0:
task = async_tasks.pop(0)
try:
handler(task)
except:
traceback.print_exc()
finally:
build_image_wall(task)
task.yields.append(['finish', task.results])
pipeline.prepare_text_encoder(async_call=True)
pass
threading.Thread(target=worker, daemon=True).start()