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import threading
from extras.inpaint_mask import generate_mask_from_image, SAMOptions
from modules.patch import PatchSettings, patch_settings, patch_all
import modules.config
patch_all()
class AsyncTask:
def __init__(self, args):
from modules.flags import Performance, MetadataScheme, ip_list, disabled
from modules.util import get_enabled_loras
from modules.config import default_max_lora_number
import args_manager
self.args = args.copy()
self.yields = []
self.results = []
self.last_stop = False
self.processing = False
self.performance_loras = []
if len(args) == 0:
return
args.reverse()
self.generate_image_grid = args.pop()
self.prompt = args.pop()
self.negative_prompt = args.pop()
self.style_selections = args.pop()
self.performance_selection = Performance(args.pop())
self.steps = self.performance_selection.steps()
self.original_steps = self.steps
self.aspect_ratios_selection = args.pop()
self.image_number = args.pop()
self.output_format = args.pop()
self.seed = int(args.pop())
self.read_wildcards_in_order = args.pop()
self.sharpness = args.pop()
self.cfg_scale = args.pop()
self.base_model_name = args.pop()
self.refiner_model_name = args.pop()
self.refiner_switch = args.pop()
self.loras = get_enabled_loras([(bool(args.pop()), str(args.pop()), float(args.pop())) for _ in
range(default_max_lora_number)])
self.input_image_checkbox = args.pop()
self.current_tab = args.pop()
self.uov_method = args.pop()
self.uov_input_image = args.pop()
self.outpaint_selections = args.pop()
self.inpaint_input_image = args.pop()
self.inpaint_additional_prompt = args.pop()
self.inpaint_mask_image_upload = args.pop()
self.disable_preview = args.pop()
self.disable_intermediate_results = args.pop()
self.disable_seed_increment = args.pop()
self.black_out_nsfw = args.pop()
self.adm_scaler_positive = args.pop()
self.adm_scaler_negative = args.pop()
self.adm_scaler_end = args.pop()
self.adaptive_cfg = args.pop()
self.clip_skip = args.pop()
self.sampler_name = args.pop()
self.scheduler_name = args.pop()
self.vae_name = args.pop()
self.overwrite_step = args.pop()
self.overwrite_switch = args.pop()
self.overwrite_width = args.pop()
self.overwrite_height = args.pop()
self.overwrite_vary_strength = args.pop()
self.overwrite_upscale_strength = args.pop()
self.mixing_image_prompt_and_vary_upscale = args.pop()
self.mixing_image_prompt_and_inpaint = args.pop()
self.debugging_cn_preprocessor = args.pop()
self.skipping_cn_preprocessor = args.pop()
self.canny_low_threshold = args.pop()
self.canny_high_threshold = args.pop()
self.refiner_swap_method = args.pop()
self.controlnet_softness = args.pop()
self.freeu_enabled = args.pop()
self.freeu_b1 = args.pop()
self.freeu_b2 = args.pop()
self.freeu_s1 = args.pop()
self.freeu_s2 = args.pop()
self.debugging_inpaint_preprocessor = args.pop()
self.inpaint_disable_initial_latent = args.pop()
self.inpaint_engine = args.pop()
self.inpaint_strength = args.pop()
self.inpaint_respective_field = args.pop()
self.inpaint_advanced_masking_checkbox = args.pop()
self.invert_mask_checkbox = args.pop()
self.inpaint_erode_or_dilate = args.pop()
self.save_final_enhanced_image_only = args.pop() if not args_manager.args.disable_image_log else False
self.save_metadata_to_images = args.pop() if not args_manager.args.disable_metadata else False
self.metadata_scheme = MetadataScheme(
args.pop()) if not args_manager.args.disable_metadata else MetadataScheme.FOOOCUS
self.cn_tasks = {x: [] for x in ip_list}
for _ in range(modules.config.default_controlnet_image_count):
cn_img = args.pop()
cn_stop = args.pop()
cn_weight = args.pop()
cn_type = args.pop()
if cn_img is not None:
self.cn_tasks[cn_type].append([cn_img, cn_stop, cn_weight])
self.debugging_dino = args.pop()
self.dino_erode_or_dilate = args.pop()
self.debugging_enhance_masks_checkbox = args.pop()
self.enhance_input_image = args.pop()
self.enhance_checkbox = args.pop()
self.enhance_uov_method = args.pop()
self.enhance_uov_processing_order = args.pop()
self.enhance_uov_prompt_type = args.pop()
self.enhance_ctrls = []
for _ in range(modules.config.default_enhance_tabs):
enhance_enabled = args.pop()
enhance_mask_dino_prompt_text = args.pop()
enhance_prompt = args.pop()
enhance_negative_prompt = args.pop()
enhance_mask_model = args.pop()
enhance_mask_cloth_category = args.pop()
enhance_mask_sam_model = args.pop()
enhance_mask_text_threshold = args.pop()
enhance_mask_box_threshold = args.pop()
enhance_mask_sam_max_detections = args.pop()
enhance_inpaint_disable_initial_latent = args.pop()
enhance_inpaint_engine = args.pop()
enhance_inpaint_strength = args.pop()
enhance_inpaint_respective_field = args.pop()
enhance_inpaint_erode_or_dilate = args.pop()
enhance_mask_invert = args.pop()
if enhance_enabled:
self.enhance_ctrls.append([
enhance_mask_dino_prompt_text,
enhance_prompt,
enhance_negative_prompt,
enhance_mask_model,
enhance_mask_cloth_category,
enhance_mask_sam_model,
enhance_mask_text_threshold,
enhance_mask_box_threshold,
enhance_mask_sam_max_detections,
enhance_inpaint_disable_initial_latent,
enhance_inpaint_engine,
enhance_inpaint_strength,
enhance_inpaint_respective_field,
enhance_inpaint_erode_or_dilate,
enhance_mask_invert
])
self.should_enhance = self.enhance_checkbox and (self.enhance_uov_method != disabled.casefold() or len(self.enhance_ctrls) > 0)
self.images_to_enhance_count = 0
self.enhance_stats = {}
async_tasks = []
class EarlyReturnException(BaseException):
pass
def worker():
global async_tasks
import os
import traceback
import math
import numpy as np
import torch
import time
import shared
import random
import copy
import cv2
import modules.default_pipeline as pipeline
import modules.core as core
import modules.flags as flags
import modules.patch
import ldm_patched.modules.model_management
import extras.preprocessors as preprocessors
import modules.inpaint_worker as inpaint_worker
import modules.constants as constants
import extras.ip_adapter as ip_adapter
import extras.face_crop
import fooocus_version
from extras.censor import default_censor
from modules.sdxl_styles import apply_style, get_random_style, fooocus_expansion, apply_arrays, random_style_name
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, parse_lora_references_from_prompt,
apply_wildcards)
from modules.upscaler import perform_upscale
from modules.flags import Performance
from modules.meta_parser import get_metadata_parser
pid = os.getpid()
print(f'Started worker with PID {pid}')
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, progressbar_index, black_out_nsfw, censor=True, do_not_show_finished_images=False):
if not isinstance(imgs, list):
imgs = [imgs]
if censor and (modules.config.default_black_out_nsfw or black_out_nsfw):
progressbar(async_task, progressbar_index, 'Checking for NSFW content ...')
imgs = default_censor(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):
results = []
if len(async_task.results) < 2:
return
for img in async_task.results:
if isinstance(img, str) and os.path.exists(img):
img = cv2.imread(img)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
if not isinstance(img, np.ndarray):
return
if img.ndim != 3:
return
results.append(img)
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
def process_task(all_steps, async_task, callback, controlnet_canny_path, controlnet_cpds_path, current_task_id,
denoising_strength, final_scheduler_name, goals, initial_latent, steps, switch, positive_cond,
negative_cond, task, loras, tiled, use_expansion, width, height, base_progress, preparation_steps,
total_count, show_intermediate_results, persist_image=True):
if async_task.last_stop is not False:
ldm_patched.modules.model_management.interrupt_current_processing()
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 async_task.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=async_task.sampler_name,
scheduler_name=final_scheduler_name,
latent=initial_latent,
denoise=denoising_strength,
tiled=tiled,
cfg_scale=async_task.cfg_scale,
refiner_swap_method=async_task.refiner_swap_method,
disable_preview=async_task.disable_preview
)
del 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]
current_progress = int(base_progress + (100 - preparation_steps) / float(all_steps) * steps)
if modules.config.default_black_out_nsfw or async_task.black_out_nsfw:
progressbar(async_task, current_progress, 'Checking for NSFW content ...')
imgs = default_censor(imgs)
progressbar(async_task, current_progress, f'Saving image {current_task_id + 1}/{total_count} to system ...')
img_paths = save_and_log(async_task, height, imgs, task, use_expansion, width, loras, persist_image)
yield_result(async_task, img_paths, current_progress, async_task.black_out_nsfw, False,
do_not_show_finished_images=not show_intermediate_results or async_task.disable_intermediate_results)
return imgs, img_paths, current_progress
def apply_patch_settings(async_task):
patch_settings[pid] = PatchSettings(
async_task.sharpness,
async_task.adm_scaler_end,
async_task.adm_scaler_positive,
async_task.adm_scaler_negative,
async_task.controlnet_softness,
async_task.adaptive_cfg
)
def save_and_log(async_task, height, imgs, task, use_expansion, width, loras, persist_image=True) -> list:
img_paths = []
for x in imgs:
d = [('Prompt', 'prompt', task['log_positive_prompt']),
('Negative Prompt', 'negative_prompt', task['log_negative_prompt']),
('Fooocus V2 Expansion', 'prompt_expansion', task['expansion']),
('Styles', 'styles',
str(task['styles'] if not use_expansion else [fooocus_expansion] + task['styles'])),
('Performance', 'performance', async_task.performance_selection.value),
('Steps', 'steps', async_task.steps),
('Resolution', 'resolution', str((width, height))),
('Guidance Scale', 'guidance_scale', async_task.cfg_scale),
('Sharpness', 'sharpness', async_task.sharpness),
('ADM Guidance', 'adm_guidance', str((
modules.patch.patch_settings[pid].positive_adm_scale,
modules.patch.patch_settings[pid].negative_adm_scale,
modules.patch.patch_settings[pid].adm_scaler_end))),
('Base Model', 'base_model', async_task.base_model_name),
('Refiner Model', 'refiner_model', async_task.refiner_model_name),
('Refiner Switch', 'refiner_switch', async_task.refiner_switch)]
if async_task.refiner_model_name != 'None':
if async_task.overwrite_switch > 0:
d.append(('Overwrite Switch', 'overwrite_switch', async_task.overwrite_switch))
if async_task.refiner_swap_method != flags.refiner_swap_method:
d.append(('Refiner Swap Method', 'refiner_swap_method', async_task.refiner_swap_method))
if modules.patch.patch_settings[pid].adaptive_cfg != modules.config.default_cfg_tsnr:
d.append(
('CFG Mimicking from TSNR', 'adaptive_cfg', modules.patch.patch_settings[pid].adaptive_cfg))
if async_task.clip_skip > 1:
d.append(('CLIP Skip', 'clip_skip', async_task.clip_skip))
d.append(('Sampler', 'sampler', async_task.sampler_name))
d.append(('Scheduler', 'scheduler', async_task.scheduler_name))
d.append(('VAE', 'vae', async_task.vae_name))
d.append(('Seed', 'seed', str(task['task_seed'])))
if async_task.freeu_enabled:
d.append(('FreeU', 'freeu',
str((async_task.freeu_b1, async_task.freeu_b2, async_task.freeu_s1, async_task.freeu_s2))))
for li, (n, w) in enumerate(loras):
if n != 'None':
d.append((f'LoRA {li + 1}', f'lora_combined_{li + 1}', f'{n} : {w}'))
metadata_parser = None
if async_task.save_metadata_to_images:
metadata_parser = modules.meta_parser.get_metadata_parser(async_task.metadata_scheme)
metadata_parser.set_data(task['log_positive_prompt'], task['positive'],
task['log_negative_prompt'], task['negative'],
async_task.steps, async_task.base_model_name, async_task.refiner_model_name,
loras, async_task.vae_name)
d.append(('Metadata Scheme', 'metadata_scheme',
async_task.metadata_scheme.value if async_task.save_metadata_to_images else async_task.save_metadata_to_images))
d.append(('Version', 'version', 'Fooocus v' + fooocus_version.version))
img_paths.append(log(x, d, metadata_parser, async_task.output_format, task, persist_image))
return img_paths
def apply_control_nets(async_task, height, ip_adapter_face_path, ip_adapter_path, width, current_progress):
for task in async_task.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 async_task.skipping_cn_preprocessor:
cn_img = preprocessors.canny_pyramid(cn_img, async_task.canny_low_threshold,
async_task.canny_high_threshold)
cn_img = HWC3(cn_img)
task[0] = core.numpy_to_pytorch(cn_img)
if async_task.debugging_cn_preprocessor:
yield_result(async_task, cn_img, current_progress, async_task.black_out_nsfw, do_not_show_finished_images=True)
for task in async_task.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 async_task.skipping_cn_preprocessor:
cn_img = preprocessors.cpds(cn_img)
cn_img = HWC3(cn_img)
task[0] = core.numpy_to_pytorch(cn_img)
if async_task.debugging_cn_preprocessor:
yield_result(async_task, cn_img, current_progress, async_task.black_out_nsfw, do_not_show_finished_images=True)
for task in async_task.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 async_task.debugging_cn_preprocessor:
yield_result(async_task, cn_img, current_progress, async_task.black_out_nsfw, do_not_show_finished_images=True)
for task in async_task.cn_tasks[flags.cn_ip_face]:
cn_img, cn_stop, cn_weight = task
cn_img = HWC3(cn_img)
if not async_task.skipping_cn_preprocessor:
cn_img = 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 async_task.debugging_cn_preprocessor:
yield_result(async_task, cn_img, current_progress, async_task.black_out_nsfw, do_not_show_finished_images=True)
all_ip_tasks = async_task.cn_tasks[flags.cn_ip] + async_task.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)
def apply_vary(async_task, uov_method, denoising_strength, uov_input_image, switch, current_progress, advance_progress=False):
if 'subtle' in uov_method:
denoising_strength = 0.5
if 'strong' in uov_method:
denoising_strength = 0.85
if async_task.overwrite_vary_strength > 0:
denoising_strength = async_task.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)
if advance_progress:
current_progress += 1
progressbar(async_task, current_progress, 'VAE encoding ...')
candidate_vae, _ = pipeline.get_candidate_vae(
steps=async_task.steps,
switch=switch,
denoise=denoising_strength,
refiner_swap_method=async_task.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((width, height))}.')
return uov_input_image, denoising_strength, initial_latent, width, height, current_progress
def apply_inpaint(async_task, initial_latent, inpaint_head_model_path, inpaint_image,
inpaint_mask, inpaint_parameterized, denoising_strength, inpaint_respective_field, switch,
inpaint_disable_initial_latent, current_progress, skip_apply_outpaint=False,
advance_progress=False):
if not skip_apply_outpaint:
inpaint_image, inpaint_mask = apply_outpaint(async_task, inpaint_image, inpaint_mask)
inpaint_worker.current_task = inpaint_worker.InpaintWorker(
image=inpaint_image,
mask=inpaint_mask,
use_fill=denoising_strength > 0.99,
k=inpaint_respective_field
)
if async_task.debugging_inpaint_preprocessor:
yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(), 100,
async_task.black_out_nsfw, do_not_show_finished_images=True)
raise EarlyReturnException
if advance_progress:
current_progress += 1
progressbar(async_task, current_progress, '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=async_task.steps,
switch=switch,
denoise=denoising_strength,
refiner_swap_method=async_task.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:
if advance_progress:
current_progress += 1
progressbar(async_task, current_progress, 'VAE SD15 encoding ...')
latent_swap = core.encode_vae(
vae=candidate_vae_swap,
pixels=inpaint_pixel_fill)['samples']
if advance_progress:
current_progress += 1
progressbar(async_task, current_progress, '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 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_width, final_height))}, latent is {str((width, height))}.')
return denoising_strength, initial_latent, width, height, current_progress
def apply_outpaint(async_task, inpaint_image, inpaint_mask):
if len(async_task.outpaint_selections) > 0:
H, W, C = inpaint_image.shape
if 'top' in async_task.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 async_task.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 async_task.outpaint_selections:
inpaint_image = np.pad(inpaint_image, [[0, 0], [int(W * 0.3), 0], [0, 0]], mode='edge')
inpaint_mask = np.pad(inpaint_mask, [[0, 0], [int(W * 0.3), 0]], mode='constant',
constant_values=255)
if 'right' in async_task.outpaint_selections:
inpaint_image = np.pad(inpaint_image, [[0, 0], [0, int(W * 0.3)], [0, 0]], mode='edge')
inpaint_mask = np.pad(inpaint_mask, [[0, 0], [0, int(W * 0.3)]], mode='constant',
constant_values=255)
inpaint_image = np.ascontiguousarray(inpaint_image.copy())
inpaint_mask = np.ascontiguousarray(inpaint_mask.copy())
async_task.inpaint_strength = 1.0
async_task.inpaint_respective_field = 1.0
return inpaint_image, inpaint_mask
def apply_upscale(async_task, uov_input_image, uov_method, switch, current_progress, advance_progress=False):
H, W, C = uov_input_image.shape
if advance_progress:
current_progress += 1
progressbar(async_task, current_progress, f'Upscaling image from {str((W, H))} ...')
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:
return direct_return, uov_input_image, None, None, None, None, None, current_progress
tiled = True
denoising_strength = 0.382
if async_task.overwrite_upscale_strength > 0:
denoising_strength = async_task.overwrite_upscale_strength
initial_pixels = core.numpy_to_pytorch(uov_input_image)
if advance_progress:
current_progress += 1
progressbar(async_task, current_progress, 'VAE encoding ...')
candidate_vae, _ = pipeline.get_candidate_vae(
steps=async_task.steps,
switch=switch,
denoise=denoising_strength,
refiner_swap_method=async_task.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((width, height))}.')
return direct_return, uov_input_image, denoising_strength, initial_latent, tiled, width, height, current_progress
def apply_overrides(async_task, steps, height, width):
if async_task.overwrite_step > 0:
steps = async_task.overwrite_step
switch = int(round(async_task.steps * async_task.refiner_switch))
if async_task.overwrite_switch > 0:
switch = async_task.overwrite_switch
if async_task.overwrite_width > 0:
width = async_task.overwrite_width
if async_task.overwrite_height > 0:
height = async_task.overwrite_height
return steps, switch, width, height
def process_prompt(async_task, prompt, negative_prompt, base_model_additional_loras, image_number, disable_seed_increment, use_expansion, use_style,
use_synthetic_refiner, current_progress, advance_progress=False):
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 []
if advance_progress:
current_progress += 1
progressbar(async_task, current_progress, 'Loading models ...')
lora_filenames = modules.util.remove_performance_lora(modules.config.lora_filenames,
async_task.performance_selection)
loras, prompt = parse_lora_references_from_prompt(prompt, async_task.loras,
modules.config.default_max_lora_number,
lora_filenames=lora_filenames)
loras += async_task.performance_loras
pipeline.refresh_everything(refiner_model_name=async_task.refiner_model_name,
base_model_name=async_task.base_model_name,
loras=loras, base_model_additional_loras=base_model_additional_loras,
use_synthetic_refiner=use_synthetic_refiner, vae_name=async_task.vae_name)
pipeline.set_clip_skip(async_task.clip_skip)
if advance_progress:
current_progress += 1
progressbar(async_task, current_progress, 'Processing prompts ...')
tasks = []
for i in range(image_number):
if disable_seed_increment:
task_seed = async_task.seed % (constants.MAX_SEED + 1)
else:
task_seed = (async_task.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, i, async_task.read_wildcards_in_order)
task_prompt = apply_arrays(task_prompt, i)
task_negative_prompt = apply_wildcards(negative_prompt, task_rng, i, async_task.read_wildcards_in_order)
task_extra_positive_prompts = [apply_wildcards(pmt, task_rng, i, async_task.read_wildcards_in_order) for pmt
in
extra_positive_prompts]
task_extra_negative_prompts = [apply_wildcards(pmt, task_rng, i, async_task.read_wildcards_in_order) for pmt
in
extra_negative_prompts]
positive_basic_workloads = []
negative_basic_workloads = []
task_styles = async_task.style_selections.copy()
if use_style:
placeholder_replaced = False
for j, s in enumerate(task_styles):
if s == random_style_name:
s = get_random_style(task_rng)
task_styles[j] = s
p, n, style_has_placeholder = apply_style(s, positive=task_prompt)
if style_has_placeholder:
placeholder_replaced = True
positive_basic_workloads = positive_basic_workloads + p
negative_basic_workloads = negative_basic_workloads + n
if not placeholder_replaced:
positive_basic_workloads = [task_prompt] + positive_basic_workloads
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='\n'.join([task_prompt] + task_extra_positive_prompts),
log_negative_prompt='\n'.join([task_negative_prompt] + task_extra_negative_prompts),
styles=task_styles
))
if use_expansion:
if advance_progress:
current_progress += 1
for i, t in enumerate(tasks):
progressbar(async_task, current_progress, 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.
if advance_progress:
current_progress += 1
for i, t in enumerate(tasks):
progressbar(async_task, current_progress, f'Encoding positive #{i + 1} ...')
t['c'] = pipeline.clip_encode(texts=t['positive'], pool_top_k=t['positive_top_k'])
if advance_progress:
current_progress += 1
for i, t in enumerate(tasks):
if abs(float(async_task.cfg_scale) - 1.0) < 1e-4:
t['uc'] = pipeline.clone_cond(t['c'])
else:
progressbar(async_task, current_progress, f'Encoding negative #{i + 1} ...')
t['uc'] = pipeline.clip_encode(texts=t['negative'], pool_top_k=t['negative_top_k'])
return tasks, use_expansion, loras, current_progress
def apply_freeu(async_task):
print(f'FreeU is enabled!')
pipeline.final_unet = core.apply_freeu(
pipeline.final_unet,
async_task.freeu_b1,
async_task.freeu_b2,
async_task.freeu_s1,
async_task.freeu_s2
)
def patch_discrete(unet, scheduler_name):
return core.opModelSamplingDiscrete.patch(unet, scheduler_name, False)[0]
def patch_edm(unet, scheduler_name):
return core.opModelSamplingContinuousEDM.patch(unet, scheduler_name, 120.0, 0.002)[0]
def patch_samplers(async_task):
final_scheduler_name = async_task.scheduler_name
if async_task.scheduler_name in ['lcm', 'tcd']:
final_scheduler_name = 'sgm_uniform'
if pipeline.final_unet is not None:
pipeline.final_unet = patch_discrete(pipeline.final_unet, async_task.scheduler_name)
if pipeline.final_refiner_unet is not None:
pipeline.final_refiner_unet = patch_discrete(pipeline.final_refiner_unet, async_task.scheduler_name)
elif async_task.scheduler_name == 'edm_playground_v2.5':
final_scheduler_name = 'karras'
if pipeline.final_unet is not None:
pipeline.final_unet = patch_edm(pipeline.final_unet, async_task.scheduler_name)
if pipeline.final_refiner_unet is not None:
pipeline.final_refiner_unet = patch_edm(pipeline.final_refiner_unet, async_task.scheduler_name)
return final_scheduler_name
def set_hyper_sd_defaults(async_task, current_progress, advance_progress=False):
print('Enter Hyper-SD mode.')
if advance_progress:
current_progress += 1
progressbar(async_task, current_progress, 'Downloading Hyper-SD components ...')
async_task.performance_loras += [(modules.config.downloading_sdxl_hyper_sd_lora(), 0.8)]
if async_task.refiner_model_name != 'None':
print(f'Refiner disabled in Hyper-SD mode.')
async_task.refiner_model_name = 'None'
async_task.sampler_name = 'dpmpp_sde_gpu'
async_task.scheduler_name = 'karras'
async_task.sharpness = 0.0
async_task.cfg_scale = 1.0
async_task.adaptive_cfg = 1.0
async_task.refiner_switch = 1.0
async_task.adm_scaler_positive = 1.0
async_task.adm_scaler_negative = 1.0
async_task.adm_scaler_end = 0.0
return current_progress
def set_lightning_defaults(async_task, current_progress, advance_progress=False):
print('Enter Lightning mode.')
if advance_progress:
current_progress += 1
progressbar(async_task, 1, 'Downloading Lightning components ...')
async_task.performance_loras += [(modules.config.downloading_sdxl_lightning_lora(), 1.0)]
if async_task.refiner_model_name != 'None':
print(f'Refiner disabled in Lightning mode.')
async_task.refiner_model_name = 'None'
async_task.sampler_name = 'euler'
async_task.scheduler_name = 'sgm_uniform'
async_task.sharpness = 0.0
async_task.cfg_scale = 1.0
async_task.adaptive_cfg = 1.0
async_task.refiner_switch = 1.0
async_task.adm_scaler_positive = 1.0
async_task.adm_scaler_negative = 1.0
async_task.adm_scaler_end = 0.0
return current_progress
def set_lcm_defaults(async_task, current_progress, advance_progress=False):
print('Enter LCM mode.')
if advance_progress:
current_progress += 1
progressbar(async_task, 1, 'Downloading LCM components ...')
async_task.performance_loras += [(modules.config.downloading_sdxl_lcm_lora(), 1.0)]
if async_task.refiner_model_name != 'None':
print(f'Refiner disabled in LCM mode.')
async_task.refiner_model_name = 'None'
async_task.sampler_name = 'lcm'
async_task.scheduler_name = 'lcm'
async_task.sharpness = 0.0
async_task.cfg_scale = 1.0
async_task.adaptive_cfg = 1.0
async_task.refiner_switch = 1.0
async_task.adm_scaler_positive = 1.0
async_task.adm_scaler_negative = 1.0
async_task.adm_scaler_end = 0.0
return current_progress
def apply_image_input(async_task, base_model_additional_loras, clip_vision_path, controlnet_canny_path,
controlnet_cpds_path, goals, inpaint_head_model_path, inpaint_image, inpaint_mask,
inpaint_parameterized, ip_adapter_face_path, ip_adapter_path, ip_negative_path,
skip_prompt_processing, use_synthetic_refiner):
if (async_task.current_tab == 'uov' or (
async_task.current_tab == 'ip' and async_task.mixing_image_prompt_and_vary_upscale)) \
and async_task.uov_method != flags.disabled.casefold() and async_task.uov_input_image is not None:
async_task.uov_input_image, skip_prompt_processing, async_task.steps = prepare_upscale(
async_task, goals, async_task.uov_input_image, async_task.uov_method, async_task.performance_selection,
async_task.steps, 1, skip_prompt_processing=skip_prompt_processing)
if (async_task.current_tab == 'inpaint' or (
async_task.current_tab == 'ip' and async_task.mixing_image_prompt_and_inpaint)) \
and isinstance(async_task.inpaint_input_image, dict):
inpaint_image = async_task.inpaint_input_image['image']
inpaint_mask = async_task.inpaint_input_image['mask'][:, :, 0]
if async_task.inpaint_advanced_masking_checkbox:
if isinstance(async_task.inpaint_mask_image_upload, dict):
if (isinstance(async_task.inpaint_mask_image_upload['image'], np.ndarray)
and isinstance(async_task.inpaint_mask_image_upload['mask'], np.ndarray)
and async_task.inpaint_mask_image_upload['image'].ndim == 3):
async_task.inpaint_mask_image_upload = np.maximum(
async_task.inpaint_mask_image_upload['image'],
async_task.inpaint_mask_image_upload['mask'])
if isinstance(async_task.inpaint_mask_image_upload,
np.ndarray) and async_task.inpaint_mask_image_upload.ndim == 3:
H, W, C = inpaint_image.shape
async_task.inpaint_mask_image_upload = resample_image(async_task.inpaint_mask_image_upload,
width=W, height=H)
async_task.inpaint_mask_image_upload = np.mean(async_task.inpaint_mask_image_upload, axis=2)
async_task.inpaint_mask_image_upload = (async_task.inpaint_mask_image_upload > 127).astype(
np.uint8) * 255
inpaint_mask = np.maximum(inpaint_mask, async_task.inpaint_mask_image_upload)
if int(async_task.inpaint_erode_or_dilate) != 0:
inpaint_mask = erode_or_dilate(inpaint_mask, async_task.inpaint_erode_or_dilate)
if async_task.invert_mask_checkbox:
inpaint_mask = 255 - inpaint_mask
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(async_task.outpaint_selections) > 0):
progressbar(async_task, 1, 'Downloading upscale models ...')
modules.config.downloading_upscale_model()
if inpaint_parameterized:
progressbar(async_task, 1, 'Downloading inpainter ...')
inpaint_head_model_path, inpaint_patch_model_path = modules.config.downloading_inpaint_models(
async_task.inpaint_engine)
base_model_additional_loras += [(inpaint_patch_model_path, 1.0)]
print(f'[Inpaint] Current inpaint model is {inpaint_patch_model_path}')
if async_task.refiner_model_name == 'None':
use_synthetic_refiner = True
async_task.refiner_switch = 0.8
else:
inpaint_head_model_path, inpaint_patch_model_path = None, None
print(f'[Inpaint] Parameterized inpaint is disabled.')
if async_task.inpaint_additional_prompt != '':
if async_task.prompt == '':
async_task.prompt = async_task.inpaint_additional_prompt
else:
async_task.prompt = async_task.inpaint_additional_prompt + '\n' + async_task.prompt
goals.append('inpaint')
if async_task.current_tab == 'ip' or \
async_task.mixing_image_prompt_and_vary_upscale or \
async_task.mixing_image_prompt_and_inpaint:
goals.append('cn')
progressbar(async_task, 1, 'Downloading control models ...')
if len(async_task.cn_tasks[flags.cn_canny]) > 0:
controlnet_canny_path = modules.config.downloading_controlnet_canny()
if len(async_task.cn_tasks[flags.cn_cpds]) > 0:
controlnet_cpds_path = modules.config.downloading_controlnet_cpds()
if len(async_task.cn_tasks[flags.cn_ip]) > 0:
clip_vision_path, ip_negative_path, ip_adapter_path = modules.config.downloading_ip_adapters('ip')
if len(async_task.cn_tasks[flags.cn_ip_face]) > 0:
clip_vision_path, ip_negative_path, ip_adapter_face_path = modules.config.downloading_ip_adapters(
'face')
if async_task.current_tab == 'enhance' and async_task.enhance_input_image is not None:
goals.append('enhance')
skip_prompt_processing = True
async_task.enhance_input_image = HWC3(async_task.enhance_input_image)
return base_model_additional_loras, clip_vision_path, controlnet_canny_path, controlnet_cpds_path, inpaint_head_model_path, inpaint_image, inpaint_mask, ip_adapter_face_path, ip_adapter_path, ip_negative_path, skip_prompt_processing, use_synthetic_refiner
def prepare_upscale(async_task, goals, uov_input_image, uov_method, performance, steps, current_progress,
advance_progress=False, skip_prompt_processing=False):
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
steps = 0
else:
steps = performance.steps_uov()
if advance_progress:
current_progress += 1
progressbar(async_task, current_progress, 'Downloading upscale models ...')
modules.config.downloading_upscale_model()
return uov_input_image, skip_prompt_processing, steps
def prepare_enhance_prompt(prompt: str, fallback_prompt: str):
if safe_str(prompt) == '' or len(remove_empty_str([safe_str(p) for p in prompt.splitlines()], default='')) == 0:
prompt = fallback_prompt
return prompt
def stop_processing(async_task, processing_start_time):
async_task.processing = False
processing_time = time.perf_counter() - processing_start_time
print(f'Processing time (total): {processing_time:.2f} seconds')
def process_enhance(all_steps, async_task, callback, controlnet_canny_path, controlnet_cpds_path,
current_progress, current_task_id, denoising_strength, inpaint_disable_initial_latent,
inpaint_engine, inpaint_respective_field, inpaint_strength,
prompt, negative_prompt, final_scheduler_name, goals, height, img, mask,
preparation_steps, steps, switch, tiled, total_count, use_expansion, use_style,
use_synthetic_refiner, width, show_intermediate_results=True, persist_image=True):
base_model_additional_loras = []
inpaint_head_model_path = None
inpaint_parameterized = inpaint_engine != 'None' # inpaint_engine = None, improve detail
initial_latent = None
prompt = prepare_enhance_prompt(prompt, async_task.prompt)
negative_prompt = prepare_enhance_prompt(negative_prompt, async_task.negative_prompt)
if 'vary' in goals:
img, denoising_strength, initial_latent, width, height, current_progress = apply_vary(
async_task, async_task.enhance_uov_method, denoising_strength, img, switch, current_progress)
if 'upscale' in goals:
direct_return, img, denoising_strength, initial_latent, tiled, width, height, current_progress = apply_upscale(
async_task, img, async_task.enhance_uov_method, switch, current_progress)
if direct_return:
d = [('Upscale (Fast)', 'upscale_fast', '2x')]
if modules.config.default_black_out_nsfw or async_task.black_out_nsfw:
progressbar(async_task, current_progress, 'Checking for NSFW content ...')
img = default_censor(img)
progressbar(async_task, current_progress, f'Saving image {current_task_id + 1}/{total_count} to system ...')
uov_image_path = log(img, d, output_format=async_task.output_format, persist_image=persist_image)
yield_result(async_task, uov_image_path, current_progress, async_task.black_out_nsfw, False,
do_not_show_finished_images=not show_intermediate_results or async_task.disable_intermediate_results)
return current_progress, img, prompt, negative_prompt
if 'inpaint' in goals and inpaint_parameterized:
progressbar(async_task, current_progress, 'Downloading inpainter ...')
inpaint_head_model_path, inpaint_patch_model_path = modules.config.downloading_inpaint_models(
inpaint_engine)
if inpaint_patch_model_path not in base_model_additional_loras:
base_model_additional_loras += [(inpaint_patch_model_path, 1.0)]
progressbar(async_task, current_progress, 'Preparing enhance prompts ...')
# positive and negative conditioning aren't available here anymore, process prompt again
tasks_enhance, use_expansion, loras, current_progress = process_prompt(
async_task, prompt, negative_prompt, base_model_additional_loras, 1, True,
use_expansion, use_style, use_synthetic_refiner, current_progress)
task_enhance = tasks_enhance[0]
# TODO could support vary, upscale and CN in the future
# if 'cn' in goals:
# apply_control_nets(async_task, height, ip_adapter_face_path, ip_adapter_path, width)
if async_task.freeu_enabled:
apply_freeu(async_task)
patch_samplers(async_task)
if 'inpaint' in goals:
denoising_strength, initial_latent, width, height, current_progress = apply_inpaint(
async_task, None, inpaint_head_model_path, img, mask,
inpaint_parameterized, inpaint_strength,
inpaint_respective_field, switch, inpaint_disable_initial_latent,
current_progress, True)
imgs, img_paths, current_progress = process_task(all_steps, async_task, callback, controlnet_canny_path,
controlnet_cpds_path, current_task_id, denoising_strength,
final_scheduler_name, goals, initial_latent, steps, switch,
task_enhance['c'], task_enhance['uc'], task_enhance, loras,
tiled, use_expansion, width, height, current_progress,
preparation_steps, total_count, show_intermediate_results,
persist_image)
del task_enhance['c'], task_enhance['uc'] # Save memory
return current_progress, imgs[0], prompt, negative_prompt
def enhance_upscale(all_steps, async_task, base_progress, callback, controlnet_canny_path, controlnet_cpds_path,
current_task_id, denoising_strength, done_steps_inpainting, done_steps_upscaling, enhance_steps,
prompt, negative_prompt, final_scheduler_name, height, img, preparation_steps, switch, tiled,
total_count, use_expansion, use_style, use_synthetic_refiner, width, persist_image=True):
# reset inpaint worker to prevent tensor size issues and not mix upscale and inpainting
inpaint_worker.current_task = None
current_progress = int(base_progress + (100 - preparation_steps) / float(all_steps) * (done_steps_upscaling + done_steps_inpainting))
goals_enhance = []
img, skip_prompt_processing, steps = prepare_upscale(
async_task, goals_enhance, img, async_task.enhance_uov_method, async_task.performance_selection,
enhance_steps, current_progress)
steps, _, _, _ = apply_overrides(async_task, steps, height, width)
exception_result = ''
if len(goals_enhance) > 0:
try:
current_progress, img, prompt, negative_prompt = process_enhance(
all_steps, async_task, callback, controlnet_canny_path,
controlnet_cpds_path, current_progress, current_task_id, denoising_strength, False,
'None', 0.0, 0.0, prompt, negative_prompt, final_scheduler_name,
goals_enhance, height, img, None, preparation_steps, steps, switch, tiled, total_count,
use_expansion, use_style, use_synthetic_refiner, width, persist_image=persist_image)
except ldm_patched.modules.model_management.InterruptProcessingException:
if async_task.last_stop == 'skip':
print('User skipped')
async_task.last_stop = False
# also skip all enhance steps for this image, but add the steps to the progress bar
if async_task.enhance_uov_processing_order == flags.enhancement_uov_before:
done_steps_inpainting += len(async_task.enhance_ctrls) * enhance_steps
exception_result = 'continue'
else:
print('User stopped')
exception_result = 'break'
finally:
done_steps_upscaling += steps
return current_task_id, done_steps_inpainting, done_steps_upscaling, img, exception_result
@torch.no_grad()
@torch.inference_mode()
def handler(async_task: AsyncTask):
preparation_start_time = time.perf_counter()
async_task.processing = True
async_task.outpaint_selections = [o.lower() for o in async_task.outpaint_selections]
base_model_additional_loras = []
async_task.uov_method = async_task.uov_method.casefold()
async_task.enhance_uov_method = async_task.enhance_uov_method.casefold()
if fooocus_expansion in async_task.style_selections:
use_expansion = True
async_task.style_selections.remove(fooocus_expansion)
else:
use_expansion = False
use_style = len(async_task.style_selections) > 0
if async_task.base_model_name == async_task.refiner_model_name:
print(f'Refiner disabled because base model and refiner are same.')
async_task.refiner_model_name = 'None'
current_progress = 0
if async_task.performance_selection == Performance.EXTREME_SPEED:
set_lcm_defaults(async_task, current_progress, advance_progress=True)
elif async_task.performance_selection == Performance.LIGHTNING:
set_lightning_defaults(async_task, current_progress, advance_progress=True)
elif async_task.performance_selection == Performance.HYPER_SD:
set_hyper_sd_defaults(async_task, current_progress, advance_progress=True)
print(f'[Parameters] Adaptive CFG = {async_task.adaptive_cfg}')
print(f'[Parameters] CLIP Skip = {async_task.clip_skip}')
print(f'[Parameters] Sharpness = {async_task.sharpness}')
print(f'[Parameters] ControlNet Softness = {async_task.controlnet_softness}')
print(f'[Parameters] ADM Scale = '
f'{async_task.adm_scaler_positive} : '
f'{async_task.adm_scaler_negative} : '
f'{async_task.adm_scaler_end}')
print(f'[Parameters] Seed = {async_task.seed}')
apply_patch_settings(async_task)
print(f'[Parameters] CFG = {async_task.cfg_scale}')
initial_latent = None
denoising_strength = 1.0
tiled = False
width, height = async_task.aspect_ratios_selection.replace('×', ' ').split(' ')[:2]
width, height = int(width), int(height)
skip_prompt_processing = False
inpaint_worker.current_task = None
inpaint_parameterized = async_task.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
goals = []
tasks = []
current_progress = 1
if async_task.input_image_checkbox:
base_model_additional_loras, clip_vision_path, controlnet_canny_path, controlnet_cpds_path, inpaint_head_model_path, inpaint_image, inpaint_mask, ip_adapter_face_path, ip_adapter_path, ip_negative_path, skip_prompt_processing, use_synthetic_refiner = apply_image_input(
async_task, base_model_additional_loras, clip_vision_path, controlnet_canny_path, controlnet_cpds_path,
goals, inpaint_head_model_path, inpaint_image, inpaint_mask, inpaint_parameterized, ip_adapter_face_path,
ip_adapter_path, ip_negative_path, skip_prompt_processing, use_synthetic_refiner)
# Load or unload CNs
progressbar(async_task, current_progress, 'Loading control models ...')
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)
async_task.steps, switch, width, height = apply_overrides(async_task, async_task.steps, height, width)
print(f'[Parameters] Sampler = {async_task.sampler_name} - {async_task.scheduler_name}')
print(f'[Parameters] Steps = {async_task.steps} - {switch}')
progressbar(async_task, current_progress, 'Initializing ...')
loras = async_task.loras
if not skip_prompt_processing:
tasks, use_expansion, loras, current_progress = process_prompt(async_task, async_task.prompt, async_task.negative_prompt,
base_model_additional_loras, async_task.image_number,
async_task.disable_seed_increment, use_expansion, use_style,
use_synthetic_refiner, current_progress, advance_progress=True)
if len(goals) > 0:
current_progress += 1
progressbar(async_task, current_progress, 'Image processing ...')
should_enhance = async_task.enhance_checkbox and (async_task.enhance_uov_method != flags.disabled.casefold() or len(async_task.enhance_ctrls) > 0)
if 'vary' in goals:
async_task.uov_input_image, denoising_strength, initial_latent, width, height, current_progress = apply_vary(
async_task, async_task.uov_method, denoising_strength, async_task.uov_input_image, switch,
current_progress)
if 'upscale' in goals:
direct_return, async_task.uov_input_image, denoising_strength, initial_latent, tiled, width, height, current_progress = apply_upscale(
async_task, async_task.uov_input_image, async_task.uov_method, switch, current_progress,
advance_progress=True)
if direct_return:
d = [('Upscale (Fast)', 'upscale_fast', '2x')]
if modules.config.default_black_out_nsfw or async_task.black_out_nsfw:
progressbar(async_task, 100, 'Checking for NSFW content ...')
async_task.uov_input_image = default_censor(async_task.uov_input_image)
progressbar(async_task, 100, 'Saving image to system ...')
uov_input_image_path = log(async_task.uov_input_image, d, output_format=async_task.output_format)
yield_result(async_task, uov_input_image_path, 100, async_task.black_out_nsfw, False,
do_not_show_finished_images=True)
return
if 'inpaint' in goals:
try:
denoising_strength, initial_latent, width, height, current_progress = apply_inpaint(async_task,
initial_latent,
inpaint_head_model_path,
inpaint_image,
inpaint_mask,
inpaint_parameterized,
async_task.inpaint_strength,
async_task.inpaint_respective_field,
switch,
async_task.inpaint_disable_initial_latent,
current_progress,
advance_progress=True)
except EarlyReturnException:
return
if 'cn' in goals:
apply_control_nets(async_task, height, ip_adapter_face_path, ip_adapter_path, width, current_progress)
if async_task.debugging_cn_preprocessor:
return
if async_task.freeu_enabled:
apply_freeu(async_task)
# async_task.steps can have value of uov steps here when upscale has been applied
steps, _, _, _ = apply_overrides(async_task, async_task.steps, height, width)
images_to_enhance = []
if 'enhance' in goals:
async_task.image_number = 1
images_to_enhance += [async_task.enhance_input_image]
height, width, _ = async_task.enhance_input_image.shape
# input image already provided, processing is skipped
steps = 0
yield_result(async_task, async_task.enhance_input_image, current_progress, async_task.black_out_nsfw, False,
async_task.disable_intermediate_results)
all_steps = steps * async_task.image_number
if async_task.enhance_checkbox and async_task.enhance_uov_method != flags.disabled.casefold():
enhance_upscale_steps = async_task.performance_selection.steps()
if 'upscale' in async_task.enhance_uov_method:
if 'fast' in async_task.enhance_uov_method:
enhance_upscale_steps = 0
else:
enhance_upscale_steps = async_task.performance_selection.steps_uov()
enhance_upscale_steps, _, _, _ = apply_overrides(async_task, enhance_upscale_steps, height, width)
enhance_upscale_steps_total = async_task.image_number * enhance_upscale_steps
all_steps += enhance_upscale_steps_total
if async_task.enhance_checkbox and len(async_task.enhance_ctrls) != 0:
enhance_steps, _, _, _ = apply_overrides(async_task, async_task.original_steps, height, width)
all_steps += async_task.image_number * len(async_task.enhance_ctrls) * enhance_steps
all_steps = max(all_steps, 1)
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() - preparation_start_time
print(f'Preparation time: {preparation_time:.2f} seconds')
final_scheduler_name = patch_samplers(async_task)
print(f'Using {final_scheduler_name} scheduler.')
async_task.yields.append(['preview', (current_progress, 'Moving model to GPU ...', None)])
processing_start_time = time.perf_counter()
preparation_steps = current_progress
total_count = async_task.image_number
def callback(step, x0, x, total_steps, y):
if step == 0:
async_task.callback_steps = 0
async_task.callback_steps += (100 - preparation_steps) / float(all_steps)
async_task.yields.append(['preview', (
int(current_progress + async_task.callback_steps),
f'Sampling step {step + 1}/{total_steps}, image {current_task_id + 1}/{total_count} ...', y)])
show_intermediate_results = len(tasks) > 1 or async_task.should_enhance
persist_image = not async_task.should_enhance or not async_task.save_final_enhanced_image_only
for current_task_id, task in enumerate(tasks):
progressbar(async_task, current_progress, f'Preparing task {current_task_id + 1}/{async_task.image_number} ...')
execution_start_time = time.perf_counter()
try:
imgs, img_paths, current_progress = process_task(all_steps, async_task, callback, controlnet_canny_path,
controlnet_cpds_path, current_task_id,
denoising_strength, final_scheduler_name, goals,
initial_latent, async_task.steps, switch, task['c'],
task['uc'], task, loras, tiled, use_expansion, width,
height, current_progress, preparation_steps,
async_task.image_number, show_intermediate_results,
persist_image)
current_progress = int(preparation_steps + (100 - preparation_steps) / float(all_steps) * async_task.steps * (current_task_id + 1))
images_to_enhance += imgs
except ldm_patched.modules.model_management.InterruptProcessingException:
if async_task.last_stop == 'skip':
print('User skipped')
async_task.last_stop = False
continue
else:
print('User stopped')
break
del task['c'], task['uc'] # Save memory
execution_time = time.perf_counter() - execution_start_time
print(f'Generating and saving time: {execution_time:.2f} seconds')
if not async_task.should_enhance:
print(f'[Enhance] Skipping, preconditions aren\'t met')
stop_processing(async_task, processing_start_time)
return
progressbar(async_task, current_progress, 'Processing enhance ...')
active_enhance_tabs = len(async_task.enhance_ctrls)
should_process_enhance_uov = async_task.enhance_uov_method != flags.disabled.casefold()
enhance_uov_before = False
enhance_uov_after = False
if should_process_enhance_uov:
active_enhance_tabs += 1
enhance_uov_before = async_task.enhance_uov_processing_order == flags.enhancement_uov_before
enhance_uov_after = async_task.enhance_uov_processing_order == flags.enhancement_uov_after
total_count = len(images_to_enhance) * active_enhance_tabs
async_task.images_to_enhance_count = len(images_to_enhance)
base_progress = current_progress
current_task_id = -1
done_steps_upscaling = 0
done_steps_inpainting = 0
enhance_steps, _, _, _ = apply_overrides(async_task, async_task.original_steps, height, width)
exception_result = None
for index, img in enumerate(images_to_enhance):
async_task.enhance_stats[index] = 0
enhancement_image_start_time = time.perf_counter()
last_enhance_prompt = async_task.prompt
last_enhance_negative_prompt = async_task.negative_prompt
if enhance_uov_before:
current_task_id += 1
persist_image = not async_task.save_final_enhanced_image_only or active_enhance_tabs == 0
current_task_id, done_steps_inpainting, done_steps_upscaling, img, exception_result = enhance_upscale(
all_steps, async_task, base_progress, callback, controlnet_canny_path, controlnet_cpds_path,
current_task_id, denoising_strength, done_steps_inpainting, done_steps_upscaling, enhance_steps,
async_task.prompt, async_task.negative_prompt, final_scheduler_name, height, img, preparation_steps,
switch, tiled, total_count, use_expansion, use_style, use_synthetic_refiner, width, persist_image)
async_task.enhance_stats[index] += 1
if exception_result == 'continue':
continue
elif exception_result == 'break':
break
# inpaint for all other tabs
for enhance_mask_dino_prompt_text, enhance_prompt, enhance_negative_prompt, enhance_mask_model, enhance_mask_cloth_category, enhance_mask_sam_model, enhance_mask_text_threshold, enhance_mask_box_threshold, enhance_mask_sam_max_detections, enhance_inpaint_disable_initial_latent, enhance_inpaint_engine, enhance_inpaint_strength, enhance_inpaint_respective_field, enhance_inpaint_erode_or_dilate, enhance_mask_invert in async_task.enhance_ctrls:
current_task_id += 1
current_progress = int(base_progress + (100 - preparation_steps) / float(all_steps) * (done_steps_upscaling + done_steps_inpainting))
progressbar(async_task, current_progress, f'Preparing enhancement {current_task_id + 1}/{total_count} ...')
enhancement_task_start_time = time.perf_counter()
is_last_enhance_for_image = (current_task_id + 1) % active_enhance_tabs == 0 and not enhance_uov_after
persist_image = not async_task.save_final_enhanced_image_only or is_last_enhance_for_image
extras = {}
if enhance_mask_model == 'sam':
print(f'[Enhance] Searching for "{enhance_mask_dino_prompt_text}"')
elif enhance_mask_model == 'u2net_cloth_seg':
extras['cloth_category'] = enhance_mask_cloth_category
mask, dino_detection_count, sam_detection_count, sam_detection_on_mask_count = generate_mask_from_image(
img, mask_model=enhance_mask_model, extras=extras, sam_options=SAMOptions(
dino_prompt=enhance_mask_dino_prompt_text,
dino_box_threshold=enhance_mask_box_threshold,
dino_text_threshold=enhance_mask_text_threshold,
dino_erode_or_dilate=async_task.dino_erode_or_dilate,
dino_debug=async_task.debugging_dino,
max_detections=enhance_mask_sam_max_detections,
model_type=enhance_mask_sam_model,
))
if len(mask.shape) == 3:
mask = mask[:, :, 0]
if int(enhance_inpaint_erode_or_dilate) != 0:
mask = erode_or_dilate(mask, enhance_inpaint_erode_or_dilate)
if enhance_mask_invert:
mask = 255 - mask
if async_task.debugging_enhance_masks_checkbox:
async_task.yields.append(['preview', (current_progress, 'Loading ...', mask)])
yield_result(async_task, mask, current_progress, async_task.black_out_nsfw, False,
async_task.disable_intermediate_results)
async_task.enhance_stats[index] += 1
print(f'[Enhance] {dino_detection_count} boxes detected')
print(f'[Enhance] {sam_detection_count} segments detected in boxes')
print(f'[Enhance] {sam_detection_on_mask_count} segments applied to mask')
if enhance_mask_model == 'sam' and (dino_detection_count == 0 or not async_task.debugging_dino and sam_detection_on_mask_count == 0):
print(f'[Enhance] No "{enhance_mask_dino_prompt_text}" detected, skipping')
continue
goals_enhance = ['inpaint']
try:
current_progress, img, enhance_prompt_processed, enhance_negative_prompt_processed = process_enhance(
all_steps, async_task, callback, controlnet_canny_path, controlnet_cpds_path,
current_progress, current_task_id, denoising_strength, enhance_inpaint_disable_initial_latent,
enhance_inpaint_engine, enhance_inpaint_respective_field, enhance_inpaint_strength,
enhance_prompt, enhance_negative_prompt, final_scheduler_name, goals_enhance, height, img, mask,
preparation_steps, enhance_steps, switch, tiled, total_count, use_expansion, use_style,
use_synthetic_refiner, width, persist_image=persist_image)
async_task.enhance_stats[index] += 1
if (should_process_enhance_uov and async_task.enhance_uov_processing_order == flags.enhancement_uov_after
and async_task.enhance_uov_prompt_type == flags.enhancement_uov_prompt_type_last_filled):
if enhance_prompt_processed != '':
last_enhance_prompt = enhance_prompt_processed
if enhance_negative_prompt_processed != '':
last_enhance_negative_prompt = enhance_negative_prompt_processed
except ldm_patched.modules.model_management.InterruptProcessingException:
if async_task.last_stop == 'skip':
print('User skipped')
async_task.last_stop = False
continue
else:
print('User stopped')
exception_result = 'break'
break
finally:
done_steps_inpainting += enhance_steps
enhancement_task_time = time.perf_counter() - enhancement_task_start_time
print(f'Enhancement time: {enhancement_task_time:.2f} seconds')
if exception_result == 'break':
break
if enhance_uov_after:
current_task_id += 1
# last step in enhance, always save
persist_image = True
current_task_id, done_steps_inpainting, done_steps_upscaling, img, exception_result = enhance_upscale(
all_steps, async_task, base_progress, callback, controlnet_canny_path, controlnet_cpds_path,
current_task_id, denoising_strength, done_steps_inpainting, done_steps_upscaling, enhance_steps,
last_enhance_prompt, last_enhance_negative_prompt, final_scheduler_name, height, img,
preparation_steps, switch, tiled, total_count, use_expansion, use_style, use_synthetic_refiner,
width, persist_image)
async_task.enhance_stats[index] += 1
if exception_result == 'continue':
continue
elif exception_result == 'break':
break
enhancement_image_time = time.perf_counter() - enhancement_image_start_time
print(f'Enhancement image time: {enhancement_image_time:.2f} seconds')
stop_processing(async_task, processing_start_time)
return
while True:
time.sleep(0.01)
if len(async_tasks) > 0:
task = async_tasks.pop(0)
try:
handler(task)
if task.generate_image_grid:
build_image_wall(task)
task.yields.append(['finish', task.results])
pipeline.prepare_text_encoder(async_call=True)
except:
traceback.print_exc()
task.yields.append(['finish', task.results])
finally:
if pid in modules.patch.patch_settings:
del modules.patch.patch_settings[pid]
pass
threading.Thread(target=worker, daemon=True).start()
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