1455 lines
75 KiB
Python
1455 lines
75 KiB
Python
import threading
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from extras.inpaint_mask import generate_mask_from_image, SAMOptions
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from modules.patch import PatchSettings, patch_settings, patch_all
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import modules.config
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patch_all()
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class AsyncTask:
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def __init__(self, args):
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from modules.flags import Performance, MetadataScheme, ip_list, controlnet_image_count
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from modules.util import get_enabled_loras
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from modules.config import default_max_lora_number
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import args_manager
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self.args = args.copy()
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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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self.performance_loras = []
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if len(args) == 0:
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return
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args.reverse()
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self.generate_image_grid = args.pop()
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self.prompt = args.pop()
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self.negative_prompt = args.pop()
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self.style_selections = args.pop()
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self.performance_selection = Performance(args.pop())
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self.steps = self.performance_selection.steps()
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self.original_steps = self.steps
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self.aspect_ratios_selection = args.pop()
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self.image_number = args.pop()
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self.output_format = args.pop()
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self.seed = int(args.pop())
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self.read_wildcards_in_order = args.pop()
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self.sharpness = args.pop()
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self.cfg_scale = args.pop()
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self.base_model_name = args.pop()
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self.refiner_model_name = args.pop()
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self.refiner_switch = args.pop()
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self.loras = get_enabled_loras([(bool(args.pop()), str(args.pop()), float(args.pop())) for _ in
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range(default_max_lora_number)])
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self.input_image_checkbox = args.pop()
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self.current_tab = args.pop()
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self.uov_method = args.pop()
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self.uov_input_image = args.pop()
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self.outpaint_selections = args.pop()
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self.inpaint_input_image = args.pop()
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self.inpaint_additional_prompt = args.pop()
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self.inpaint_mask_image_upload = args.pop()
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self.disable_preview = args.pop()
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self.disable_intermediate_results = args.pop()
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self.disable_seed_increment = args.pop()
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self.black_out_nsfw = args.pop()
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self.adm_scaler_positive = args.pop()
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self.adm_scaler_negative = args.pop()
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self.adm_scaler_end = args.pop()
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self.adaptive_cfg = args.pop()
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self.clip_skip = args.pop()
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self.sampler_name = args.pop()
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self.scheduler_name = args.pop()
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self.vae_name = args.pop()
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self.overwrite_step = args.pop()
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self.overwrite_switch = args.pop()
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self.overwrite_width = args.pop()
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self.overwrite_height = args.pop()
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self.overwrite_vary_strength = args.pop()
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self.overwrite_upscale_strength = args.pop()
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self.mixing_image_prompt_and_vary_upscale = args.pop()
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self.mixing_image_prompt_and_inpaint = args.pop()
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self.debugging_cn_preprocessor = args.pop()
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self.skipping_cn_preprocessor = args.pop()
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self.canny_low_threshold = args.pop()
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self.canny_high_threshold = args.pop()
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self.refiner_swap_method = args.pop()
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self.controlnet_softness = args.pop()
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self.freeu_enabled = args.pop()
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self.freeu_b1 = args.pop()
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self.freeu_b2 = args.pop()
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self.freeu_s1 = args.pop()
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self.freeu_s2 = args.pop()
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self.debugging_inpaint_preprocessor = args.pop()
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self.inpaint_disable_initial_latent = args.pop()
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self.inpaint_engine = args.pop()
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self.inpaint_strength = args.pop()
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self.inpaint_respective_field = args.pop()
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self.inpaint_advanced_masking_checkbox = args.pop()
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self.invert_mask_checkbox = args.pop()
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self.inpaint_erode_or_dilate = args.pop()
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self.save_metadata_to_images = args.pop() if not args_manager.args.disable_metadata else False
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self.metadata_scheme = MetadataScheme(
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args.pop()) if not args_manager.args.disable_metadata else MetadataScheme.FOOOCUS
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self.cn_tasks = {x: [] for x in ip_list}
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for _ in range(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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cn_type = args.pop()
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if cn_img is not None:
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self.cn_tasks[cn_type].append([cn_img, cn_stop, cn_weight])
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self.debugging_dino = args.pop()
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self.dino_erode_or_dilate = args.pop()
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self.debugging_enhance_masks_checkbox = args.pop()
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self.enhance_input_image = args.pop()
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self.enhance_checkbox = args.pop()
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self.enhance_uov_method = args.pop()
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self.enhance_uov_processing_order = args.pop()
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self.enhance_uov_prompt_type = args.pop()
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self.enhance_ctrls = []
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for _ in range(modules.config.default_enhance_tabs):
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enhance_enabled = args.pop()
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enhance_mask_dino_prompt_text = args.pop()
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enhance_prompt = args.pop()
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enhance_negative_prompt = args.pop()
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enhance_mask_model = args.pop()
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enhance_mask_cloth_category = args.pop()
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enhance_mask_sam_model = args.pop()
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enhance_mask_text_threshold = args.pop()
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enhance_mask_box_threshold = args.pop()
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enhance_mask_sam_max_detections = args.pop()
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enhance_inpaint_disable_initial_latent = args.pop()
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enhance_inpaint_engine = args.pop()
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enhance_inpaint_strength = args.pop()
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enhance_inpaint_respective_field = args.pop()
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enhance_inpaint_erode_or_dilate = args.pop()
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enhance_mask_invert = args.pop()
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if enhance_enabled:
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self.enhance_ctrls.append([
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enhance_mask_dino_prompt_text,
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enhance_prompt,
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enhance_negative_prompt,
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enhance_mask_model,
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enhance_mask_cloth_category,
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enhance_mask_sam_model,
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enhance_mask_text_threshold,
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enhance_mask_box_threshold,
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enhance_mask_sam_max_detections,
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enhance_inpaint_disable_initial_latent,
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enhance_inpaint_engine,
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enhance_inpaint_strength,
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enhance_inpaint_respective_field,
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enhance_inpaint_erode_or_dilate,
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enhance_mask_invert
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])
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async_tasks = []
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class EarlyReturnException:
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pass
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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 torch
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import time
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import shared
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import random
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import copy
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import cv2
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import modules.default_pipeline as pipeline
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import modules.core as core
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import modules.flags as flags
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import modules.patch
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import ldm_patched.modules.model_management
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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 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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from extras.censor import default_censor
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from modules.sdxl_styles import apply_style, get_random_style, fooocus_expansion, apply_arrays, random_style_name
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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, get_image_shape_ceil, set_image_shape_ceil,
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get_shape_ceil, resample_image, erode_or_dilate, parse_lora_references_from_prompt,
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apply_wildcards)
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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
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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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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)}'''
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if async_gradio_app.share:
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flag += f''' or {async_gradio_app.share_url}'''
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print(flag)
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except Exception as e:
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print(e)
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def progressbar(async_task, number, text):
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print(f'[Fooocus] {text}')
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async_task.yields.append(['preview', (number, text, None)])
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def yield_result(async_task, imgs, progressbar_index, black_out_nsfw, censor=True, do_not_show_finished_images=False):
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if not isinstance(imgs, list):
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imgs = [imgs]
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if censor and (modules.config.default_black_out_nsfw or black_out_nsfw):
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progressbar(async_task, progressbar_index, 'Checking for NSFW content ...')
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imgs = default_censor(imgs)
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async_task.results = async_task.results + imgs
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if do_not_show_finished_images:
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return
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async_task.yields.append(['results', async_task.results])
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return
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def build_image_wall(async_task):
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results = []
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if len(async_task.results) < 2:
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return
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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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for img in results:
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Hn, Wn, Cn = img.shape
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if H != Hn:
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return
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if W != Wn:
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return
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if C != Cn:
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return
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cols = float(len(results)) ** 0.5
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cols = int(math.ceil(cols))
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rows = float(len(results)) / float(cols)
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rows = int(math.ceil(rows))
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wall = np.zeros(shape=(H * rows, W * cols, C), dtype=np.uint8)
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for y in range(rows):
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for x in range(cols):
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if y * cols + x < len(results):
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img = results[y * cols + x]
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wall[y * H:y * H + H, x * W:x * W + W, :] = img
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# must use deep copy otherwise gradio is super laggy. Do not use list.append() .
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async_task.results = async_task.results + [wall]
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return
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def process_task(all_steps, async_task, callback, controlnet_canny_path, controlnet_cpds_path, current_task_id,
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denoising_strength, final_scheduler_name, goals, initial_latent, steps, switch, positive_cond,
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negative_cond, task, loras, tiled, use_expansion, width, height, base_progress, preparation_steps,
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total_count, show_intermediate_results):
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if async_task.last_stop is not False:
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ldm_patched.modules.model_management.interrupt_current_processing()
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if 'cn' in goals:
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for cn_flag, cn_path in [
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(flags.cn_canny, controlnet_canny_path),
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(flags.cn_cpds, controlnet_cpds_path)
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]:
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for cn_img, cn_stop, cn_weight in async_task.cn_tasks[cn_flag]:
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positive_cond, negative_cond = core.apply_controlnet(
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positive_cond, negative_cond,
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pipeline.loaded_ControlNets[cn_path], cn_img, cn_weight, 0, cn_stop)
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imgs = pipeline.process_diffusion(
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positive_cond=positive_cond,
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negative_cond=negative_cond,
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steps=steps,
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switch=switch,
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width=width,
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height=height,
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image_seed=task['task_seed'],
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callback=callback,
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sampler_name=async_task.sampler_name,
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scheduler_name=final_scheduler_name,
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latent=initial_latent,
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denoise=denoising_strength,
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tiled=tiled,
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cfg_scale=async_task.cfg_scale,
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refiner_swap_method=async_task.refiner_swap_method,
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disable_preview=async_task.disable_preview
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)
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del positive_cond, negative_cond # Save memory
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if inpaint_worker.current_task is not None:
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imgs = [inpaint_worker.current_task.post_process(x) for x in imgs]
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current_progress = int(base_progress + (100 - preparation_steps) / float(all_steps) * steps)
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if modules.config.default_black_out_nsfw or async_task.black_out_nsfw:
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progressbar(async_task, current_progress, 'Checking for NSFW content ...')
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imgs = default_censor(imgs)
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progressbar(async_task, current_progress,
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f'Saving image {current_task_id + 1}/{total_count} to system ...')
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img_paths = save_and_log(async_task, height, imgs, task, use_expansion, width, loras)
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yield_result(async_task, img_paths, current_progress, async_task.black_out_nsfw, False,
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do_not_show_finished_images=not show_intermediate_results or async_task.disable_intermediate_results)
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return imgs, img_paths, current_progress
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def apply_patch_settings(async_task):
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patch_settings[pid] = PatchSettings(
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async_task.sharpness,
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async_task.adm_scaler_end,
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async_task.adm_scaler_positive,
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async_task.adm_scaler_negative,
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async_task.controlnet_softness,
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async_task.adaptive_cfg
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)
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def save_and_log(async_task, height, imgs, task, use_expansion, width, loras) -> list:
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img_paths = []
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for x in imgs:
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d = [('Prompt', 'prompt', task['log_positive_prompt']),
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('Negative Prompt', 'negative_prompt', task['log_negative_prompt']),
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('Fooocus V2 Expansion', 'prompt_expansion', task['expansion']),
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('Styles', 'styles',
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str(task['styles'] if not use_expansion else [fooocus_expansion] + task['styles'])),
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('Performance', 'performance', async_task.performance_selection.value),
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('Steps', 'steps', async_task.steps),
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('Resolution', 'resolution', str((width, height))),
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('Guidance Scale', 'guidance_scale', async_task.cfg_scale),
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('Sharpness', 'sharpness', async_task.sharpness),
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('ADM Guidance', 'adm_guidance', str((
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modules.patch.patch_settings[pid].positive_adm_scale,
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modules.patch.patch_settings[pid].negative_adm_scale,
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modules.patch.patch_settings[pid].adm_scaler_end))),
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('Base Model', 'base_model', async_task.base_model_name),
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('Refiner Model', 'refiner_model', async_task.refiner_model_name),
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('Refiner Switch', 'refiner_switch', async_task.refiner_switch)]
|
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if async_task.refiner_model_name != 'None':
|
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if async_task.overwrite_switch > 0:
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d.append(('Overwrite Switch', 'overwrite_switch', async_task.overwrite_switch))
|
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if async_task.refiner_swap_method != flags.refiner_swap_method:
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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:
|
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d.append(
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('CFG Mimicking from TSNR', 'adaptive_cfg', modules.patch.patch_settings[pid].adaptive_cfg))
|
||
|
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if async_task.clip_skip > 1:
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d.append(('CLIP Skip', 'clip_skip', async_task.clip_skip))
|
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d.append(('Sampler', 'sampler', async_task.sampler_name))
|
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d.append(('Scheduler', 'scheduler', async_task.scheduler_name))
|
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d.append(('VAE', 'vae', async_task.vae_name))
|
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d.append(('Seed', 'seed', str(task['task_seed'])))
|
||
|
||
if async_task.freeu_enabled:
|
||
d.append(('FreeU', 'freeu',
|
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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))
|
||
|
||
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:
|
||
for j, s in enumerate(task_styles):
|
||
if s == random_style_name:
|
||
s = get_random_style(task_rng)
|
||
task_styles[j] = s
|
||
p, n = apply_style(s, positive=task_prompt)
|
||
positive_basic_workloads = positive_basic_workloads + p
|
||
negative_basic_workloads = negative_basic_workloads + n
|
||
else:
|
||
positive_basic_workloads.append(task_prompt)
|
||
|
||
negative_basic_workloads.append(task_negative_prompt) # Always use independent workload for negative.
|
||
|
||
positive_basic_workloads = positive_basic_workloads + task_extra_positive_prompts
|
||
negative_basic_workloads = negative_basic_workloads + task_extra_negative_prompts
|
||
|
||
positive_basic_workloads = remove_empty_str(positive_basic_workloads, default=task_prompt)
|
||
negative_basic_workloads = remove_empty_str(negative_basic_workloads, default=task_negative_prompt)
|
||
|
||
tasks.append(dict(
|
||
task_seed=task_seed,
|
||
task_prompt=task_prompt,
|
||
task_negative_prompt=task_negative_prompt,
|
||
positive=positive_basic_workloads,
|
||
negative=negative_basic_workloads,
|
||
expansion='',
|
||
c=None,
|
||
uc=None,
|
||
positive_top_k=len(positive_basic_workloads),
|
||
negative_top_k=len(negative_basic_workloads),
|
||
log_positive_prompt='\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):
|
||
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)
|
||
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)
|
||
|
||
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):
|
||
# 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)
|
||
|
||
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 ...')
|
||
|
||
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
|
||
|
||
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)])
|
||
|
||
should_enhance = async_task.enhance_checkbox and (async_task.enhance_uov_method != flags.disabled.casefold() or len(async_task.enhance_ctrls) > 0)
|
||
show_intermediate_results = len(tasks) > 1 or should_enhance
|
||
|
||
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)
|
||
|
||
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 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()
|
||
if should_process_enhance_uov:
|
||
active_enhance_tabs += 1
|
||
total_count = len(images_to_enhance) * active_enhance_tabs
|
||
|
||
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 img in images_to_enhance:
|
||
enhancement_image_start_time = time.perf_counter()
|
||
|
||
last_enhance_prompt = async_task.prompt
|
||
last_enhance_negative_prompt = async_task.negative_prompt
|
||
|
||
if should_process_enhance_uov and async_task.enhance_uov_processing_order == flags.enhancement_uov_before:
|
||
current_task_id += 1
|
||
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)
|
||
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()
|
||
|
||
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)
|
||
|
||
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)
|
||
|
||
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 should_process_enhance_uov and async_task.enhance_uov_processing_order == flags.enhancement_uov_after:
|
||
current_task_id += 1
|
||
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)
|
||
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()
|