928 lines
40 KiB
Python
928 lines
40 KiB
Python
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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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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import random
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import copy
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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.config
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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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import args_manager
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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, 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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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, 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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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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@torch.no_grad()
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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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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 = 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 = 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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uov_input_image = args.pop()
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outpaint_selections = args.pop()
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inpaint_input_image = args.pop()
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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(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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cn_type = args.pop()
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if cn_img is not None:
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cn_tasks[cn_type].append([cn_img, cn_stop, cn_weight])
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outpaint_selections = [o.lower() for o in outpaint_selections]
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base_model_additional_loras = []
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raw_style_selections = copy.deepcopy(style_selections)
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uov_method = uov_method.lower()
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if fooocus_expansion in style_selections:
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use_expansion = True
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style_selections.remove(fooocus_expansion)
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else:
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use_expansion = False
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use_style = len(style_selections) > 0
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if base_model_name == refiner_model_name:
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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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steps = performance_selection.steps()
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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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if refiner_model_name != 'None':
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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 = '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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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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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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if refiner_model_name != 'None':
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print(f'Refiner disabled in Lightning mode.')
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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'{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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initial_latent = None
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denoising_strength = 1.0
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tiled = False
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width, height = aspect_ratios_selection.replace('×', ' ').split(' ')[:2]
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width, height = int(width), int(height)
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skip_prompt_processing = False
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inpaint_worker.current_task = 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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use_synthetic_refiner = False
|
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controlnet_canny_path = None
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controlnet_cpds_path = None
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clip_vision_path, ip_negative_path, ip_adapter_path, ip_adapter_face_path = None, None, None, None
|
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seed = int(image_seed)
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print(f'[Parameters] Seed = {seed}')
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goals = []
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tasks = []
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||
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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 mixing_image_prompt_and_vary_upscale)) \
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and uov_method != flags.disabled and uov_input_image is not None:
|
||
uov_input_image = HWC3(uov_input_image)
|
||
if 'vary' in uov_method:
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||
goals.append('vary')
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||
elif 'upscale' in uov_method:
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goals.append('upscale')
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||
if 'fast' in uov_method:
|
||
skip_prompt_processing = True
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||
else:
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||
steps = performance_selection.steps_uov()
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||
|
||
progressbar(async_task, 1, 'Downloading upscale models ...')
|
||
modules.config.downloading_upscale_model()
|
||
if (current_tab == 'inpaint' or (
|
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current_tab == 'ip' and mixing_image_prompt_and_inpaint)) \
|
||
and isinstance(inpaint_input_image, dict):
|
||
inpaint_image = inpaint_input_image['image']
|
||
inpaint_mask = inpaint_input_image['mask'][:, :, 0]
|
||
|
||
if inpaint_mask_upload_checkbox:
|
||
if isinstance(inpaint_mask_image_upload, np.ndarray):
|
||
if inpaint_mask_image_upload.ndim == 3:
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H, W, C = inpaint_image.shape
|
||
inpaint_mask_image_upload = resample_image(inpaint_mask_image_upload, width=W, height=H)
|
||
inpaint_mask_image_upload = np.mean(inpaint_mask_image_upload, axis=2)
|
||
inpaint_mask_image_upload = (inpaint_mask_image_upload > 127).astype(np.uint8) * 255
|
||
inpaint_mask = np.maximum(inpaint_mask, inpaint_mask_image_upload)
|
||
|
||
if int(inpaint_erode_or_dilate) != 0:
|
||
inpaint_mask = erode_or_dilate(inpaint_mask, inpaint_erode_or_dilate)
|
||
|
||
if invert_mask_checkbox:
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||
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(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(
|
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inpaint_engine)
|
||
base_model_additional_loras += [(inpaint_patch_model_path, 1.0)]
|
||
print(f'[Inpaint] Current inpaint model is {inpaint_patch_model_path}')
|
||
if refiner_model_name == 'None':
|
||
use_synthetic_refiner = True
|
||
refiner_switch = 0.8
|
||
else:
|
||
inpaint_head_model_path, inpaint_patch_model_path = None, None
|
||
print(f'[Inpaint] Parameterized inpaint is disabled.')
|
||
if inpaint_additional_prompt != '':
|
||
if prompt == '':
|
||
prompt = inpaint_additional_prompt
|
||
else:
|
||
prompt = inpaint_additional_prompt + '\n' + prompt
|
||
goals.append('inpaint')
|
||
if current_tab == 'ip' or \
|
||
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:
|
||
controlnet_canny_path = modules.config.downloading_controlnet_canny()
|
||
if len(cn_tasks[flags.cn_cpds]) > 0:
|
||
controlnet_cpds_path = modules.config.downloading_controlnet_cpds()
|
||
if len(cn_tasks[flags.cn_ip]) > 0:
|
||
clip_vision_path, ip_negative_path, ip_adapter_path = modules.config.downloading_ip_adapters('ip')
|
||
if len(cn_tasks[flags.cn_ip_face]) > 0:
|
||
clip_vision_path, ip_negative_path, ip_adapter_face_path = modules.config.downloading_ip_adapters(
|
||
'face')
|
||
progressbar(async_task, 1, 'Loading control models ...')
|
||
|
||
# Load or unload CNs
|
||
pipeline.refresh_controlnets([controlnet_canny_path, controlnet_cpds_path])
|
||
ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_path)
|
||
ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_face_path)
|
||
|
||
if overwrite_step > 0:
|
||
steps = overwrite_step
|
||
|
||
switch = int(round(steps * refiner_switch))
|
||
|
||
if overwrite_switch > 0:
|
||
switch = overwrite_switch
|
||
|
||
if overwrite_width > 0:
|
||
width = overwrite_width
|
||
|
||
if overwrite_height > 0:
|
||
height = overwrite_height
|
||
|
||
print(f'[Parameters] Sampler = {sampler_name} - {scheduler_name}')
|
||
print(f'[Parameters] Steps = {steps} - {switch}')
|
||
|
||
progressbar(async_task, 1, 'Initializing ...')
|
||
|
||
if not skip_prompt_processing:
|
||
|
||
prompts = remove_empty_str([safe_str(p) for p in prompt.splitlines()], default='')
|
||
negative_prompts = remove_empty_str([safe_str(p) for p in negative_prompt.splitlines()], default='')
|
||
|
||
prompt = prompts[0]
|
||
negative_prompt = negative_prompts[0]
|
||
|
||
if prompt == '':
|
||
# disable expansion when empty since it is not meaningful and influences image prompt
|
||
use_expansion = False
|
||
|
||
extra_positive_prompts = prompts[1:] if len(prompts) > 1 else []
|
||
extra_negative_prompts = negative_prompts[1:] if len(negative_prompts) > 1 else []
|
||
|
||
progressbar(async_task, 3, 'Loading models ...')
|
||
pipeline.refresh_everything(refiner_model_name=refiner_model_name, base_model_name=base_model_name,
|
||
loras=loras, base_model_additional_loras=base_model_additional_loras,
|
||
use_synthetic_refiner=use_synthetic_refiner)
|
||
|
||
progressbar(async_task, 3, 'Processing prompts ...')
|
||
tasks = []
|
||
|
||
for i in range(image_number):
|
||
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_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 = []
|
||
|
||
if use_style:
|
||
for s in style_selections:
|
||
p, n = apply_style(s, positive=task_prompt)
|
||
positive_basic_workloads = positive_basic_workloads + p
|
||
negative_basic_workloads = negative_basic_workloads + n
|
||
else:
|
||
positive_basic_workloads.append(task_prompt)
|
||
|
||
negative_basic_workloads.append(task_negative_prompt) # Always use independent workload for negative.
|
||
|
||
positive_basic_workloads = positive_basic_workloads + task_extra_positive_prompts
|
||
negative_basic_workloads = negative_basic_workloads + task_extra_negative_prompts
|
||
|
||
positive_basic_workloads = remove_empty_str(positive_basic_workloads, default=task_prompt)
|
||
negative_basic_workloads = remove_empty_str(negative_basic_workloads, default=task_negative_prompt)
|
||
|
||
tasks.append(dict(
|
||
task_seed=task_seed,
|
||
task_prompt=task_prompt,
|
||
task_negative_prompt=task_negative_prompt,
|
||
positive=positive_basic_workloads,
|
||
negative=negative_basic_workloads,
|
||
expansion='',
|
||
c=None,
|
||
uc=None,
|
||
positive_top_k=len(positive_basic_workloads),
|
||
negative_top_k=len(negative_basic_workloads),
|
||
log_positive_prompt='\n'.join([task_prompt] + task_extra_positive_prompts),
|
||
log_negative_prompt='\n'.join([task_negative_prompt] + task_extra_negative_prompts),
|
||
))
|
||
|
||
if use_expansion:
|
||
for i, t in enumerate(tasks):
|
||
progressbar(async_task, 5, f'Preparing Fooocus text #{i + 1} ...')
|
||
expansion = pipeline.final_expansion(t['task_prompt'], t['task_seed'])
|
||
print(f'[Prompt Expansion] {expansion}')
|
||
t['expansion'] = expansion
|
||
t['positive'] = copy.deepcopy(t['positive']) + [expansion] # Deep copy.
|
||
|
||
for i, t in enumerate(tasks):
|
||
progressbar(async_task, 7, f'Encoding positive #{i + 1} ...')
|
||
t['c'] = pipeline.clip_encode(texts=t['positive'], pool_top_k=t['positive_top_k'])
|
||
|
||
for i, t in enumerate(tasks):
|
||
if abs(float(cfg_scale) - 1.0) < 1e-4:
|
||
t['uc'] = pipeline.clone_cond(t['c'])
|
||
else:
|
||
progressbar(async_task, 10, f'Encoding negative #{i + 1} ...')
|
||
t['uc'] = pipeline.clip_encode(texts=t['negative'], pool_top_k=t['negative_top_k'])
|
||
|
||
if len(goals) > 0:
|
||
progressbar(async_task, 13, 'Image processing ...')
|
||
|
||
if 'vary' in goals:
|
||
if 'subtle' in uov_method:
|
||
denoising_strength = 0.5
|
||
if 'strong' in uov_method:
|
||
denoising_strength = 0.85
|
||
if overwrite_vary_strength > 0:
|
||
denoising_strength = overwrite_vary_strength
|
||
|
||
shape_ceil = get_image_shape_ceil(uov_input_image)
|
||
if shape_ceil < 1024:
|
||
print(f'[Vary] Image is resized because it is too small.')
|
||
shape_ceil = 1024
|
||
elif shape_ceil > 2048:
|
||
print(f'[Vary] Image is resized because it is too big.')
|
||
shape_ceil = 2048
|
||
|
||
uov_input_image = set_image_shape_ceil(uov_input_image, shape_ceil)
|
||
|
||
initial_pixels = core.numpy_to_pytorch(uov_input_image)
|
||
progressbar(async_task, 13, 'VAE encoding ...')
|
||
|
||
candidate_vae, _ = pipeline.get_candidate_vae(
|
||
steps=steps,
|
||
switch=switch,
|
||
denoise=denoising_strength,
|
||
refiner_swap_method=refiner_swap_method
|
||
)
|
||
|
||
initial_latent = core.encode_vae(vae=candidate_vae, pixels=initial_pixels)
|
||
B, C, H, W = initial_latent['samples'].shape
|
||
width = W * 8
|
||
height = H * 8
|
||
print(f'Final resolution is {str((height, width))}.')
|
||
|
||
if 'upscale' in goals:
|
||
H, W, C = uov_input_image.shape
|
||
progressbar(async_task, 13, f'Upscaling image from {str((H, W))} ...')
|
||
uov_input_image = perform_upscale(uov_input_image)
|
||
print(f'Image upscaled.')
|
||
|
||
if '1.5x' in uov_method:
|
||
f = 1.5
|
||
elif '2x' in uov_method:
|
||
f = 2.0
|
||
else:
|
||
f = 1.0
|
||
|
||
shape_ceil = get_shape_ceil(H * f, W * f)
|
||
|
||
if shape_ceil < 1024:
|
||
print(f'[Upscale] Image is resized because it is too small.')
|
||
uov_input_image = set_image_shape_ceil(uov_input_image, 1024)
|
||
shape_ceil = 1024
|
||
else:
|
||
uov_input_image = resample_image(uov_input_image, width=W * f, height=H * f)
|
||
|
||
image_is_super_large = shape_ceil > 2800
|
||
|
||
if 'fast' in uov_method:
|
||
direct_return = True
|
||
elif image_is_super_large:
|
||
print('Image is too large. Directly returned the SR image. '
|
||
'Usually directly return SR image at 4K resolution '
|
||
'yields better results than SDXL diffusion.')
|
||
direct_return = True
|
||
else:
|
||
direct_return = False
|
||
|
||
if direct_return:
|
||
d = [('Upscale (Fast)', '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 overwrite_upscale_strength > 0:
|
||
denoising_strength = overwrite_upscale_strength
|
||
|
||
initial_pixels = core.numpy_to_pytorch(uov_input_image)
|
||
progressbar(async_task, 13, 'VAE encoding ...')
|
||
|
||
candidate_vae, _ = pipeline.get_candidate_vae(
|
||
steps=steps,
|
||
switch=switch,
|
||
denoise=denoising_strength,
|
||
refiner_swap_method=refiner_swap_method
|
||
)
|
||
|
||
initial_latent = core.encode_vae(
|
||
vae=candidate_vae,
|
||
pixels=initial_pixels, tiled=True)
|
||
B, C, H, W = initial_latent['samples'].shape
|
||
width = W * 8
|
||
height = H * 8
|
||
print(f'Final resolution is {str((height, width))}.')
|
||
|
||
if 'inpaint' in goals:
|
||
if len(outpaint_selections) > 0:
|
||
H, W, C = inpaint_image.shape
|
||
if 'top' in outpaint_selections:
|
||
inpaint_image = np.pad(inpaint_image, [[int(H * 0.3), 0], [0, 0], [0, 0]], mode='edge')
|
||
inpaint_mask = np.pad(inpaint_mask, [[int(H * 0.3), 0], [0, 0]], mode='constant',
|
||
constant_values=255)
|
||
if 'bottom' in outpaint_selections:
|
||
inpaint_image = np.pad(inpaint_image, [[0, int(H * 0.3)], [0, 0], [0, 0]], mode='edge')
|
||
inpaint_mask = np.pad(inpaint_mask, [[0, int(H * 0.3)], [0, 0]], mode='constant',
|
||
constant_values=255)
|
||
|
||
H, W, C = inpaint_image.shape
|
||
if 'left' in outpaint_selections:
|
||
inpaint_image = np.pad(inpaint_image, [[0, 0], [int(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(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())
|
||
inpaint_strength = 1.0
|
||
inpaint_respective_field = 1.0
|
||
|
||
denoising_strength = inpaint_strength
|
||
|
||
inpaint_worker.current_task = inpaint_worker.InpaintWorker(
|
||
image=inpaint_image,
|
||
mask=inpaint_mask,
|
||
use_fill=denoising_strength > 0.99,
|
||
k=inpaint_respective_field
|
||
)
|
||
|
||
if debugging_inpaint_preprocessor:
|
||
yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(),
|
||
do_not_show_finished_images=True)
|
||
return
|
||
|
||
progressbar(async_task, 13, 'VAE Inpaint encoding ...')
|
||
|
||
inpaint_pixel_fill = core.numpy_to_pytorch(inpaint_worker.current_task.interested_fill)
|
||
inpaint_pixel_image = core.numpy_to_pytorch(inpaint_worker.current_task.interested_image)
|
||
inpaint_pixel_mask = core.numpy_to_pytorch(inpaint_worker.current_task.interested_mask)
|
||
|
||
candidate_vae, candidate_vae_swap = pipeline.get_candidate_vae(
|
||
steps=steps,
|
||
switch=switch,
|
||
denoise=denoising_strength,
|
||
refiner_swap_method=refiner_swap_method
|
||
)
|
||
|
||
latent_inpaint, latent_mask = core.encode_vae_inpaint(
|
||
mask=inpaint_pixel_mask,
|
||
vae=candidate_vae,
|
||
pixels=inpaint_pixel_image)
|
||
|
||
latent_swap = None
|
||
if candidate_vae_swap is not None:
|
||
progressbar(async_task, 13, 'VAE SD15 encoding ...')
|
||
latent_swap = core.encode_vae(
|
||
vae=candidate_vae_swap,
|
||
pixels=inpaint_pixel_fill)['samples']
|
||
|
||
progressbar(async_task, 13, 'VAE encoding ...')
|
||
latent_fill = core.encode_vae(
|
||
vae=candidate_vae,
|
||
pixels=inpaint_pixel_fill)['samples']
|
||
|
||
inpaint_worker.current_task.load_latent(
|
||
latent_fill=latent_fill, latent_mask=latent_mask, latent_swap=latent_swap)
|
||
|
||
if inpaint_parameterized:
|
||
pipeline.final_unet = inpaint_worker.current_task.patch(
|
||
inpaint_head_model_path=inpaint_head_model_path,
|
||
inpaint_latent=latent_inpaint,
|
||
inpaint_latent_mask=latent_mask,
|
||
model=pipeline.final_unet
|
||
)
|
||
|
||
if not inpaint_disable_initial_latent:
|
||
initial_latent = {'samples': latent_fill}
|
||
|
||
B, C, H, W = latent_fill.shape
|
||
height, width = H * 8, W * 8
|
||
final_height, final_width = inpaint_worker.current_task.image.shape[:2]
|
||
print(f'Final resolution is {str((final_height, final_width))}, latent is {str((height, width))}.')
|
||
|
||
if 'cn' in goals:
|
||
for task in cn_tasks[flags.cn_canny]:
|
||
cn_img, cn_stop, cn_weight = task
|
||
cn_img = resize_image(HWC3(cn_img), width=width, height=height)
|
||
|
||
if not 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 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 skipping_cn_preprocessor:
|
||
cn_img = preprocessors.cpds(cn_img)
|
||
|
||
cn_img = HWC3(cn_img)
|
||
task[0] = core.numpy_to_pytorch(cn_img)
|
||
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]:
|
||
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 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 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 debugging_cn_preprocessor:
|
||
yield_result(async_task, cn_img, do_not_show_finished_images=True)
|
||
return
|
||
|
||
all_ip_tasks = cn_tasks[flags.cn_ip] + cn_tasks[flags.cn_ip_face]
|
||
|
||
if len(all_ip_tasks) > 0:
|
||
pipeline.final_unet = ip_adapter.patch_model(pipeline.final_unet, all_ip_tasks)
|
||
|
||
if freeu_enabled:
|
||
print(f'FreeU is enabled!')
|
||
pipeline.final_unet = core.apply_freeu(
|
||
pipeline.final_unet,
|
||
freeu_b1,
|
||
freeu_b2,
|
||
freeu_s1,
|
||
freeu_s2
|
||
)
|
||
|
||
all_steps = steps * image_number
|
||
|
||
print(f'[Parameters] Denoising Strength = {denoising_strength}')
|
||
|
||
if isinstance(initial_latent, dict) and 'samples' in initial_latent:
|
||
log_shape = initial_latent['samples'].shape
|
||
else:
|
||
log_shape = f'Image Space {(height, width)}'
|
||
|
||
print(f'[Parameters] Initial Latent shape: {log_shape}')
|
||
|
||
preparation_time = time.perf_counter() - execution_start_time
|
||
print(f'Preparation time: {preparation_time:.2f} seconds')
|
||
|
||
final_sampler_name = sampler_name
|
||
final_scheduler_name = scheduler_name
|
||
|
||
if scheduler_name == 'lcm':
|
||
final_scheduler_name = 'sgm_uniform'
|
||
if pipeline.final_unet is not None:
|
||
pipeline.final_unet = core.opModelSamplingDiscrete.patch(
|
||
pipeline.final_unet,
|
||
sampling='lcm',
|
||
zsnr=False)[0]
|
||
if pipeline.final_refiner_unet is not None:
|
||
pipeline.final_refiner_unet = core.opModelSamplingDiscrete.patch(
|
||
pipeline.final_refiner_unet,
|
||
sampling='lcm',
|
||
zsnr=False)[0]
|
||
print('Using lcm scheduler.')
|
||
|
||
async_task.yields.append(['preview', (13, 'Moving model to GPU ...', None)])
|
||
|
||
def callback(step, x0, x, total_steps, y):
|
||
done_steps = current_task_id * steps + step
|
||
async_task.yields.append(['preview', (
|
||
int(15.0 + 85.0 * float(done_steps) / float(all_steps)),
|
||
f'Step {step}/{total_steps} in the {current_task_id + 1}{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:
|
||
for cn_flag, cn_path in [
|
||
(flags.cn_canny, controlnet_canny_path),
|
||
(flags.cn_cpds, controlnet_cpds_path)
|
||
]:
|
||
for cn_img, cn_stop, cn_weight in cn_tasks[cn_flag]:
|
||
positive_cond, negative_cond = core.apply_controlnet(
|
||
positive_cond, negative_cond,
|
||
pipeline.loaded_ControlNets[cn_path], cn_img, cn_weight, 0, cn_stop)
|
||
|
||
imgs = pipeline.process_diffusion(
|
||
positive_cond=positive_cond,
|
||
negative_cond=negative_cond,
|
||
steps=steps,
|
||
switch=switch,
|
||
width=width,
|
||
height=height,
|
||
image_seed=task['task_seed'],
|
||
callback=callback,
|
||
sampler_name=final_sampler_name,
|
||
scheduler_name=final_scheduler_name,
|
||
latent=initial_latent,
|
||
denoise=denoising_strength,
|
||
tiled=tiled,
|
||
cfg_scale=cfg_scale,
|
||
refiner_swap_method=refiner_swap_method,
|
||
disable_preview=disable_preview
|
||
)
|
||
|
||
del task['c'], task['uc'], positive_cond, negative_cond # Save memory
|
||
|
||
if inpaint_worker.current_task is not None:
|
||
imgs = [inpaint_worker.current_task.post_process(x) for x in imgs]
|
||
|
||
img_paths = []
|
||
for x in imgs:
|
||
d = [('Prompt', 'prompt', task['log_positive_prompt']),
|
||
('Negative Prompt', 'negative_prompt', task['log_negative_prompt']),
|
||
('Fooocus V2 Expansion', 'prompt_expansion', task['expansion']),
|
||
('Styles', 'styles', str(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'lora_combined_{li + 1}', f'{n} : {w}'))
|
||
|
||
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)
|
||
except ldm_patched.modules.model_management.InterruptProcessingException as e:
|
||
if async_task.last_stop == 'skip':
|
||
print('User skipped')
|
||
async_task.last_stop = False
|
||
continue
|
||
else:
|
||
print('User stopped')
|
||
break
|
||
|
||
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)
|
||
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
|
||
|
||
|
||
threading.Thread(target=worker, daemon=True).start()
|