mirror of
https://github.com/lllyasviel/Fooocus.git
synced 2026-08-16 13:13:16 +02:00
Merge branch 'main_upstream' into feature/add-nsfw-filter
# Conflicts: # modules/advanced_parameters.py # modules/async_worker.py # modules/config.py # webui.py
This commit is contained in:
Manuel Schmid
+237
-144
@@ -1,11 +1,16 @@
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import threading
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import re
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from modules.patch import PatchSettings, patch_settings, patch_all
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patch_all()
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class AsyncTask:
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def __init__(self, args):
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self.args = args
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self.yields = []
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self.results = []
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self.last_stop = False
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self.processing = False
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async_tasks = []
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@@ -14,9 +19,11 @@ async_tasks = []
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def worker():
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global async_tasks
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import os
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import traceback
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import math
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import numpy as np
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import cv2
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import torch
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import time
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import shared
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@@ -31,18 +38,23 @@ def worker():
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import extras.preprocessors as preprocessors
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import modules.inpaint_worker as inpaint_worker
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import modules.constants as constants
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import modules.advanced_parameters as advanced_parameters
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import extras.ip_adapter as ip_adapter
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import extras.face_crop
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import fooocus_version
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import args_manager
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from modules.censor import censor_batch
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from modules.sdxl_styles import apply_style, apply_wildcards, fooocus_expansion
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from modules.sdxl_styles import apply_style, apply_wildcards, fooocus_expansion, apply_arrays
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from modules.private_logger import log
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from extras.expansion import safe_str
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from modules.util import remove_empty_str, HWC3, resize_image, \
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get_image_shape_ceil, set_image_shape_ceil, get_shape_ceil, resample_image, erode_or_dilate
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from modules.util import remove_empty_str, HWC3, resize_image, get_image_shape_ceil, set_image_shape_ceil, \
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get_shape_ceil, resample_image, erode_or_dilate, ordinal_suffix, get_enabled_loras
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from modules.upscaler import perform_upscale
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from modules.flags import Performance
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from modules.meta_parser import get_metadata_parser, MetadataScheme
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pid = os.getpid()
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print(f'Started worker with PID {pid}')
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try:
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async_gradio_app = shared.gradio_root
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@@ -74,19 +86,20 @@ def worker():
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return
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def build_image_wall(async_task):
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if not advanced_parameters.generate_image_grid:
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results = []
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if len(async_task.results) < 2:
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return
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results = async_task.results
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if len(results) < 2:
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return
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for img in results:
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for img in async_task.results:
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if isinstance(img, str) and os.path.exists(img):
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img = cv2.imread(img)
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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if not isinstance(img, np.ndarray):
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return
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if img.ndim != 3:
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return
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results.append(img)
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H, W, C = results[0].shape
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@@ -120,6 +133,7 @@ def worker():
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@torch.inference_mode()
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def handler(async_task):
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execution_start_time = time.perf_counter()
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async_task.processing = True
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args = async_task.args
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args.reverse()
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@@ -127,16 +141,18 @@ def worker():
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prompt = args.pop()
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negative_prompt = args.pop()
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style_selections = args.pop()
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performance_selection = args.pop()
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performance_selection = Performance(args.pop())
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aspect_ratios_selection = args.pop()
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image_number = args.pop()
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output_format = args.pop()
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image_seed = args.pop()
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read_wildcards_in_order = args.pop()
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sharpness = args.pop()
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guidance_scale = args.pop()
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base_model_name = args.pop()
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refiner_model_name = args.pop()
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refiner_switch = args.pop()
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loras = [[str(args.pop()), float(args.pop())] for _ in range(5)]
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loras = get_enabled_loras([[bool(args.pop()), str(args.pop()), float(args.pop())] for _ in range(modules.config.default_max_lora_number)])
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input_image_checkbox = args.pop()
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current_tab = args.pop()
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uov_method = args.pop()
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@@ -146,8 +162,48 @@ def worker():
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inpaint_additional_prompt = args.pop()
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inpaint_mask_image_upload = args.pop()
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disable_preview = args.pop()
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disable_intermediate_results = args.pop()
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disable_seed_increment = args.pop()
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adm_scaler_positive = args.pop()
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adm_scaler_negative = args.pop()
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adm_scaler_end = args.pop()
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adaptive_cfg = args.pop()
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sampler_name = args.pop()
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scheduler_name = args.pop()
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overwrite_step = args.pop()
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overwrite_switch = args.pop()
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overwrite_width = args.pop()
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overwrite_height = args.pop()
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overwrite_vary_strength = args.pop()
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overwrite_upscale_strength = args.pop()
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mixing_image_prompt_and_vary_upscale = args.pop()
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mixing_image_prompt_and_inpaint = args.pop()
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debugging_cn_preprocessor = args.pop()
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skipping_cn_preprocessor = args.pop()
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canny_low_threshold = args.pop()
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canny_high_threshold = args.pop()
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refiner_swap_method = args.pop()
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controlnet_softness = args.pop()
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freeu_enabled = args.pop()
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freeu_b1 = args.pop()
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freeu_b2 = args.pop()
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freeu_s1 = args.pop()
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freeu_s2 = args.pop()
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debugging_inpaint_preprocessor = args.pop()
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inpaint_disable_initial_latent = args.pop()
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inpaint_engine = args.pop()
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inpaint_strength = args.pop()
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inpaint_respective_field = args.pop()
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inpaint_mask_upload_checkbox = args.pop()
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invert_mask_checkbox = args.pop()
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inpaint_erode_or_dilate = args.pop()
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save_metadata_to_images = args.pop() if not args_manager.args.disable_metadata else False
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metadata_scheme = MetadataScheme(args.pop()) if not args_manager.args.disable_metadata else MetadataScheme.FOOOCUS
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cn_tasks = {x: [] for x in flags.ip_list}
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for _ in range(4):
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for _ in range(flags.controlnet_image_count):
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cn_img = args.pop()
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cn_stop = args.pop()
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cn_weight = args.pop()
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@@ -172,17 +228,9 @@ def worker():
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print(f'Refiner disabled because base model and refiner are same.')
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refiner_model_name = 'None'
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assert performance_selection in ['Speed', 'Quality', 'Extreme Speed']
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steps = performance_selection.steps()
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steps = 30
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if performance_selection == 'Speed':
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steps = 30
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if performance_selection == 'Quality':
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steps = 60
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if performance_selection == 'Extreme Speed':
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if performance_selection == Performance.EXTREME_SPEED:
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print('Enter LCM mode.')
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progressbar(async_task, 1, 'Downloading LCM components ...')
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loras += [(modules.config.downloading_sdxl_lcm_lora(), 1.0)]
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@@ -191,30 +239,51 @@ def worker():
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print(f'Refiner disabled in LCM mode.')
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refiner_model_name = 'None'
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sampler_name = advanced_parameters.sampler_name = 'lcm'
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scheduler_name = advanced_parameters.scheduler_name = 'lcm'
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modules.patch.sharpness = sharpness = 0.0
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cfg_scale = guidance_scale = 1.0
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modules.patch.adaptive_cfg = advanced_parameters.adaptive_cfg = 1.0
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sampler_name = 'lcm'
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scheduler_name = 'lcm'
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sharpness = 0.0
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guidance_scale = 1.0
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adaptive_cfg = 1.0
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refiner_switch = 1.0
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modules.patch.positive_adm_scale = advanced_parameters.adm_scaler_positive = 1.0
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modules.patch.negative_adm_scale = advanced_parameters.adm_scaler_negative = 1.0
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modules.patch.adm_scaler_end = advanced_parameters.adm_scaler_end = 0.0
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steps = 8
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adm_scaler_positive = 1.0
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adm_scaler_negative = 1.0
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adm_scaler_end = 0.0
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modules.patch.adaptive_cfg = advanced_parameters.adaptive_cfg
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print(f'[Parameters] Adaptive CFG = {modules.patch.adaptive_cfg}')
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elif performance_selection == Performance.LIGHTNING:
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print('Enter Lightning mode.')
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progressbar(async_task, 1, 'Downloading Lightning components ...')
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loras += [(modules.config.downloading_sdxl_lightning_lora(), 1.0)]
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modules.patch.sharpness = sharpness
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print(f'[Parameters] Sharpness = {modules.patch.sharpness}')
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if refiner_model_name != 'None':
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print(f'Refiner disabled in Lightning mode.')
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modules.patch.positive_adm_scale = advanced_parameters.adm_scaler_positive
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modules.patch.negative_adm_scale = advanced_parameters.adm_scaler_negative
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modules.patch.adm_scaler_end = advanced_parameters.adm_scaler_end
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refiner_model_name = 'None'
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sampler_name = 'euler'
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scheduler_name = 'sgm_uniform'
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sharpness = 0.0
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guidance_scale = 1.0
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adaptive_cfg = 1.0
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refiner_switch = 1.0
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adm_scaler_positive = 1.0
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adm_scaler_negative = 1.0
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adm_scaler_end = 0.0
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print(f'[Parameters] Adaptive CFG = {adaptive_cfg}')
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print(f'[Parameters] Sharpness = {sharpness}')
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print(f'[Parameters] ControlNet Softness = {controlnet_softness}')
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print(f'[Parameters] ADM Scale = '
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f'{modules.patch.positive_adm_scale} : '
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f'{modules.patch.negative_adm_scale} : '
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f'{modules.patch.adm_scaler_end}')
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f'{adm_scaler_positive} : '
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f'{adm_scaler_negative} : '
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f'{adm_scaler_end}')
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patch_settings[pid] = PatchSettings(
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sharpness,
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adm_scaler_end,
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adm_scaler_positive,
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adm_scaler_negative,
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controlnet_softness,
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adaptive_cfg
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)
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cfg_scale = float(guidance_scale)
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print(f'[Parameters] CFG = {cfg_scale}')
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@@ -227,10 +296,9 @@ def worker():
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width, height = int(width), int(height)
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skip_prompt_processing = False
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refiner_swap_method = advanced_parameters.refiner_swap_method
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inpaint_worker.current_task = None
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inpaint_parameterized = advanced_parameters.inpaint_engine != 'None'
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inpaint_parameterized = inpaint_engine != 'None'
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inpaint_image = None
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inpaint_mask = None
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inpaint_head_model_path = None
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@@ -244,15 +312,12 @@ def worker():
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seed = int(image_seed)
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print(f'[Parameters] Seed = {seed}')
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sampler_name = advanced_parameters.sampler_name
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scheduler_name = advanced_parameters.scheduler_name
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goals = []
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tasks = []
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if input_image_checkbox:
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if (current_tab == 'uov' or (
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current_tab == 'ip' and advanced_parameters.mixing_image_prompt_and_vary_upscale)) \
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current_tab == 'ip' and mixing_image_prompt_and_vary_upscale)) \
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and uov_method != flags.disabled and uov_input_image is not None:
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uov_input_image = HWC3(uov_input_image)
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if 'vary' in uov_method:
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@@ -262,26 +327,17 @@ def worker():
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if 'fast' in uov_method:
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skip_prompt_processing = True
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else:
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steps = 18
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if performance_selection == 'Speed':
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steps = 18
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if performance_selection == 'Quality':
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steps = 36
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if performance_selection == 'Extreme Speed':
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steps = 8
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steps = performance_selection.steps_uov()
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progressbar(async_task, 1, 'Downloading upscale models ...')
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modules.config.downloading_upscale_model()
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if (current_tab == 'inpaint' or (
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current_tab == 'ip' and advanced_parameters.mixing_image_prompt_and_inpaint)) \
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current_tab == 'ip' and mixing_image_prompt_and_inpaint)) \
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and isinstance(inpaint_input_image, dict):
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inpaint_image = inpaint_input_image['image']
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inpaint_mask = inpaint_input_image['mask'][:, :, 0]
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if advanced_parameters.inpaint_mask_upload_checkbox:
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if inpaint_mask_upload_checkbox:
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if isinstance(inpaint_mask_image_upload, np.ndarray):
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if inpaint_mask_image_upload.ndim == 3:
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H, W, C = inpaint_image.shape
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@@ -290,10 +346,10 @@ def worker():
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inpaint_mask_image_upload = (inpaint_mask_image_upload > 127).astype(np.uint8) * 255
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inpaint_mask = np.maximum(inpaint_mask, inpaint_mask_image_upload)
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if int(advanced_parameters.inpaint_erode_or_dilate) != 0:
|
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inpaint_mask = erode_or_dilate(inpaint_mask, advanced_parameters.inpaint_erode_or_dilate)
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if int(inpaint_erode_or_dilate) != 0:
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inpaint_mask = erode_or_dilate(inpaint_mask, inpaint_erode_or_dilate)
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if advanced_parameters.invert_mask_checkbox:
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if invert_mask_checkbox:
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inpaint_mask = 255 - inpaint_mask
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inpaint_image = HWC3(inpaint_image)
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@@ -304,12 +360,12 @@ def worker():
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if inpaint_parameterized:
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progressbar(async_task, 1, 'Downloading inpainter ...')
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inpaint_head_model_path, inpaint_patch_model_path = modules.config.downloading_inpaint_models(
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advanced_parameters.inpaint_engine)
|
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inpaint_engine)
|
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base_model_additional_loras += [(inpaint_patch_model_path, 1.0)]
|
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print(f'[Inpaint] Current inpaint model is {inpaint_patch_model_path}')
|
||||
if refiner_model_name == 'None':
|
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use_synthetic_refiner = True
|
||||
refiner_switch = 0.5
|
||||
refiner_switch = 0.8
|
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else:
|
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inpaint_head_model_path, inpaint_patch_model_path = None, None
|
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print(f'[Inpaint] Parameterized inpaint is disabled.')
|
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@@ -320,8 +376,8 @@ def worker():
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prompt = inpaint_additional_prompt + '\n' + prompt
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goals.append('inpaint')
|
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if current_tab == 'ip' or \
|
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advanced_parameters.mixing_image_prompt_and_inpaint or \
|
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advanced_parameters.mixing_image_prompt_and_vary_upscale:
|
||||
mixing_image_prompt_and_vary_upscale or \
|
||||
mixing_image_prompt_and_inpaint:
|
||||
goals.append('cn')
|
||||
progressbar(async_task, 1, 'Downloading control models ...')
|
||||
if len(cn_tasks[flags.cn_canny]) > 0:
|
||||
@@ -340,19 +396,19 @@ def worker():
|
||||
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)
|
||||
|
||||
if overwrite_step > 0:
|
||||
steps = overwrite_step
|
||||
|
||||
switch = int(round(steps * refiner_switch))
|
||||
|
||||
if advanced_parameters.overwrite_step > 0:
|
||||
steps = advanced_parameters.overwrite_step
|
||||
if overwrite_switch > 0:
|
||||
switch = overwrite_switch
|
||||
|
||||
if advanced_parameters.overwrite_switch > 0:
|
||||
switch = advanced_parameters.overwrite_switch
|
||||
if overwrite_width > 0:
|
||||
width = overwrite_width
|
||||
|
||||
if advanced_parameters.overwrite_width > 0:
|
||||
width = advanced_parameters.overwrite_width
|
||||
|
||||
if advanced_parameters.overwrite_height > 0:
|
||||
height = advanced_parameters.overwrite_height
|
||||
if overwrite_height > 0:
|
||||
height = overwrite_height
|
||||
|
||||
print(f'[Parameters] Sampler = {sampler_name} - {scheduler_name}')
|
||||
print(f'[Parameters] Steps = {steps} - {switch}')
|
||||
@@ -381,14 +437,19 @@ def worker():
|
||||
|
||||
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
|
||||
if disable_seed_increment:
|
||||
task_seed = seed % (constants.MAX_SEED + 1)
|
||||
else:
|
||||
task_seed = (seed + i) % (constants.MAX_SEED + 1) # randint is inclusive, % is not
|
||||
|
||||
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]
|
||||
task_rng = random.Random(task_seed) # may bind to inpaint noise in the future
|
||||
task_prompt = apply_wildcards(prompt, task_rng, i, read_wildcards_in_order)
|
||||
task_prompt = apply_arrays(task_prompt, i)
|
||||
task_negative_prompt = apply_wildcards(negative_prompt, task_rng, i, read_wildcards_in_order)
|
||||
task_extra_positive_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in extra_positive_prompts]
|
||||
task_extra_negative_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in extra_negative_prompts]
|
||||
|
||||
positive_basic_workloads = []
|
||||
negative_basic_workloads = []
|
||||
@@ -451,8 +512,8 @@ def worker():
|
||||
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
|
||||
if overwrite_vary_strength > 0:
|
||||
denoising_strength = overwrite_vary_strength
|
||||
|
||||
shape_ceil = get_image_shape_ceil(uov_input_image)
|
||||
if shape_ceil < 1024:
|
||||
@@ -515,16 +576,16 @@ def worker():
|
||||
direct_return = False
|
||||
|
||||
if direct_return:
|
||||
d = [('Upscale (Fast)', '2x')]
|
||||
log(uov_input_image, d)
|
||||
yield_result(async_task, uov_input_image, do_not_show_finished_images=True)
|
||||
d = [('Upscale (Fast)', 'upscale_fast', '2x')]
|
||||
uov_input_image_path = log(uov_input_image, d, output_format=output_format)
|
||||
yield_result(async_task, uov_input_image_path, 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
|
||||
if overwrite_upscale_strength > 0:
|
||||
denoising_strength = overwrite_upscale_strength
|
||||
|
||||
initial_pixels = core.numpy_to_pytorch(uov_input_image)
|
||||
progressbar(async_task, 13, 'VAE encoding ...')
|
||||
@@ -558,29 +619,29 @@ def worker():
|
||||
|
||||
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',
|
||||
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 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',
|
||||
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())
|
||||
advanced_parameters.inpaint_strength = 1.0
|
||||
advanced_parameters.inpaint_respective_field = 1.0
|
||||
inpaint_strength = 1.0
|
||||
inpaint_respective_field = 1.0
|
||||
|
||||
denoising_strength = advanced_parameters.inpaint_strength
|
||||
denoising_strength = 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
|
||||
k=inpaint_respective_field
|
||||
)
|
||||
|
||||
if advanced_parameters.debugging_inpaint_preprocessor:
|
||||
if debugging_inpaint_preprocessor:
|
||||
yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(),
|
||||
do_not_show_finished_images=True)
|
||||
return
|
||||
@@ -626,7 +687,7 @@ def worker():
|
||||
model=pipeline.final_unet
|
||||
)
|
||||
|
||||
if not advanced_parameters.inpaint_disable_initial_latent:
|
||||
if not inpaint_disable_initial_latent:
|
||||
initial_latent = {'samples': latent_fill}
|
||||
|
||||
B, C, H, W = latent_fill.shape
|
||||
@@ -639,24 +700,24 @@ def worker():
|
||||
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)
|
||||
if not skipping_cn_preprocessor:
|
||||
cn_img = preprocessors.canny_pyramid(cn_img, canny_low_threshold, canny_high_threshold)
|
||||
|
||||
cn_img = HWC3(cn_img)
|
||||
task[0] = core.numpy_to_pytorch(cn_img)
|
||||
if advanced_parameters.debugging_cn_preprocessor:
|
||||
if 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:
|
||||
if not 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:
|
||||
if debugging_cn_preprocessor:
|
||||
yield_result(async_task, cn_img, do_not_show_finished_images=True)
|
||||
return
|
||||
for task in cn_tasks[flags.cn_ip]:
|
||||
@@ -667,21 +728,21 @@ def worker():
|
||||
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:
|
||||
if 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:
|
||||
if not 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 advanced_parameters.debugging_cn_preprocessor:
|
||||
if debugging_cn_preprocessor:
|
||||
yield_result(async_task, cn_img, do_not_show_finished_images=True)
|
||||
return
|
||||
|
||||
@@ -690,14 +751,14 @@ def worker():
|
||||
if len(all_ip_tasks) > 0:
|
||||
pipeline.final_unet = ip_adapter.patch_model(pipeline.final_unet, all_ip_tasks)
|
||||
|
||||
if advanced_parameters.freeu_enabled:
|
||||
if 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
|
||||
freeu_b1,
|
||||
freeu_b2,
|
||||
freeu_s1,
|
||||
freeu_s2
|
||||
)
|
||||
|
||||
all_steps = steps * image_number
|
||||
@@ -737,13 +798,14 @@ def worker():
|
||||
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)])
|
||||
f'Step {step}/{total_steps} in the {current_task_id + 1}{ordinal_suffix(current_task_id + 1)} Sampling', y)])
|
||||
|
||||
for current_task_id, task in enumerate(tasks):
|
||||
execution_start_time = time.perf_counter()
|
||||
|
||||
try:
|
||||
if async_task.last_stop is not False:
|
||||
ldm_patched.modules.model_management.interrupt_current_processing()
|
||||
positive_cond, negative_cond = task['c'], task['uc']
|
||||
|
||||
if 'cn' in goals:
|
||||
@@ -771,7 +833,8 @@ def worker():
|
||||
denoise=denoising_strength,
|
||||
tiled=tiled,
|
||||
cfg_scale=cfg_scale,
|
||||
refiner_swap_method=refiner_swap_method
|
||||
refiner_swap_method=refiner_swap_method,
|
||||
disable_preview=disable_preview
|
||||
)
|
||||
|
||||
del task['c'], task['uc'], positive_cond, negative_cond # Save memory
|
||||
@@ -779,37 +842,61 @@ def worker():
|
||||
if inpaint_worker.current_task is not None:
|
||||
imgs = [inpaint_worker.current_task.post_process(x) for x in imgs]
|
||||
|
||||
img_paths = []
|
||||
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']),
|
||||
]
|
||||
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(raw_style_selections)),
|
||||
('Performance', 'performance', performance_selection.value)]
|
||||
|
||||
if performance_selection.steps() != steps:
|
||||
d.append(('Steps', 'steps', steps))
|
||||
|
||||
d += [('Resolution', 'resolution', str((width, height))),
|
||||
('Guidance Scale', 'guidance_scale', guidance_scale),
|
||||
('Sharpness', 'sharpness', 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', base_model_name),
|
||||
('Refiner Model', 'refiner_model', refiner_model_name),
|
||||
('Refiner Switch', 'refiner_switch', refiner_switch)]
|
||||
|
||||
if refiner_model_name != 'None':
|
||||
if overwrite_switch > 0:
|
||||
d.append(('Overwrite Switch', 'overwrite_switch', overwrite_switch))
|
||||
if refiner_swap_method != flags.refiner_swap_method:
|
||||
d.append(('Refiner Swap Method', 'refiner_swap_method', 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))
|
||||
|
||||
d.append(('Sampler', 'sampler', sampler_name))
|
||||
d.append(('Scheduler', 'scheduler', scheduler_name))
|
||||
d.append(('Seed', 'seed', str(task['task_seed'])))
|
||||
|
||||
if freeu_enabled:
|
||||
d.append(('FreeU', 'freeu', str((freeu_b1, freeu_b2, freeu_s1, freeu_s2))))
|
||||
|
||||
for li, (n, w) in enumerate(loras):
|
||||
if n != 'None':
|
||||
d.append((f'LoRA {li + 1}', f'{n} : {w}'))
|
||||
d.append(('Version', 'v' + fooocus_version.version))
|
||||
log(x, d)
|
||||
d.append((f'LoRA {li + 1}', f'lora_combined_{li + 1}', f'{n} : {w}'))
|
||||
|
||||
yield_result(async_task, imgs, do_not_show_finished_images=len(tasks) == 1, progressbar_index=int(15.0 + 85.0 * float((current_task_id + 1) * steps) / float(all_steps)))
|
||||
metadata_parser = None
|
||||
if save_metadata_to_images:
|
||||
metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme)
|
||||
metadata_parser.set_data(task['log_positive_prompt'], task['positive'],
|
||||
task['log_negative_prompt'], task['negative'],
|
||||
steps, base_model_name, refiner_model_name, loras)
|
||||
d.append(('Metadata Scheme', 'metadata_scheme', metadata_scheme.value if save_metadata_to_images else save_metadata_to_images))
|
||||
d.append(('Version', 'version', 'Fooocus v' + fooocus_version.version))
|
||||
img_paths.append(log(x, d, metadata_parser, output_format))
|
||||
yield_result(async_task, img_paths, do_not_show_finished_images=len(tasks) == 1 or disable_intermediate_results, progressbar_index=int(15.0 + 85.0 * float((current_task_id + 1) * steps) / float(all_steps)))
|
||||
except ldm_patched.modules.model_management.InterruptProcessingException as e:
|
||||
if shared.last_stop == 'skip':
|
||||
if async_task.last_stop == 'skip':
|
||||
print('User skipped')
|
||||
async_task.last_stop = False
|
||||
continue
|
||||
else:
|
||||
print('User stopped')
|
||||
@@ -817,21 +904,27 @@ def worker():
|
||||
|
||||
execution_time = time.perf_counter() - execution_start_time
|
||||
print(f'Generating and saving time: {execution_time:.2f} seconds')
|
||||
|
||||
async_task.processing = False
|
||||
return
|
||||
|
||||
while True:
|
||||
time.sleep(0.01)
|
||||
if len(async_tasks) > 0:
|
||||
task = async_tasks.pop(0)
|
||||
generate_image_grid = task.args.pop(0)
|
||||
|
||||
try:
|
||||
handler(task)
|
||||
build_image_wall(task)
|
||||
if 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
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user