├── AutoChar.py ├── LICENSE ├── README.md ├── face_detection_yunet_2022mar.onnx └── face_detection_yunet_2023mar.onnx /AutoChar.py: -------------------------------------------------------------------------------- 1 | import modules.scripts as scripts 2 | import gradio as gr 3 | import os 4 | 5 | from modules import images 6 | from modules.processing import process_images, Processed 7 | from modules.processing import Processed 8 | from modules.shared import opts, cmd_opts, state 9 | from modules.shared_state import State 10 | from modules.ui_components import FormRow 11 | from modules.shared_cmd_options import cmd_opts 12 | from modules.shared_options import options_templates 13 | from modules import scripts_postprocessing, shared 14 | from modules.processing import StableDiffusionProcessingImg2Img 15 | from modules.ui_common import create_refresh_button 16 | from modules import processing, shared, sd_samplers, images, devices,shared_items 17 | from modules import sd_models, sd_vae 18 | from modules import styles 19 | 20 | # Check OpenCV version and update if necessary 21 | import cv2 22 | import subprocess 23 | import pkg_resources 24 | 25 | def update_opencv(): 26 | subprocess.check_call(["python", '-m', 'pip', 'install', '--upgrade', 'opencv-python']) 27 | 28 | # Get the current OpenCV version 29 | current_version = cv2.__version__ 30 | latest_version = pkg_resources.get_distribution("opencv-python").version 31 | 32 | # If the current version is not the latest, update OpenCV 33 | if current_version != latest_version: 34 | print(f"Updating OpenCV from version {current_version} to {latest_version}") 35 | update_opencv() 36 | print("Update complete.") 37 | 38 | 39 | #print(sd_models.checkpoints_list) 40 | #print(sd_vae.vae_dict) 41 | 42 | class Script(scripts.Script): 43 | 44 | # The title of the script. This is what will be displayed in the dropdown menu. 45 | def title(self): 46 | 47 | return "AutoChar 0.9.5" 48 | 49 | def ui(self, is_img2img): 50 | gr.Markdown( 51 | """ 52 | ## Welcome to AutoChar! 53 | ### If you're new to it, feel free to take a look at the official guide for this version: https://www.youtube.com/watch?v=jNUMHtH1U6E 54 | """) 55 | 56 | scale_factor = gr.Slider(minimum=1.0, maximum=5, step=0.1, value=2, 57 | info= "Desired output image scale relative to resolution chosen in main interface", 58 | label="Final scale factor") 59 | 60 | with FormRow(variant="compact",equal_height=True): 61 | options = gr.CheckboxGroup(label="Options", choices=['Automatic face inpaint', 'Automatic eyes inpaint', 62 | 'Display info in terminal'], 63 | value=['Automatic face inpaint', 'Automatic eyes inpaint'], 64 | info= "Choose options for AutoChar algorithm", 65 | elem_id=self.elem_id("options")) 66 | mode = gr.Radio(label="Operating mode", choices=['Txt2Img', 'Img2Img'], 67 | value="Txt2Img", 68 | info= "Choose operating mode. Txt2Img will use full pipeline, Img2Img will skip first steps before SD Upscale", 69 | elem_id=self.elem_id("mode")) 70 | 71 | with FormRow(variant="compact",equal_height=True): 72 | ui_upscaler_1 = gr.Dropdown( 73 | choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], 74 | label="Upscaler 1 (High-Res Fix)", 75 | value="Latent (bicubic antialiased)", 76 | info= "Latents are great for adding details, for consistency with basic generation use any -GAN or 4x-UltraSharp", 77 | elem_id=self.elem_id("ui_upscaler_1") 78 | ) 79 | 80 | 81 | ui_upscaler_2 = gr.Dropdown( 82 | [x.name for x in shared.sd_upscalers], label="Upscaler 2 (SD Upscale)", 83 | value="R-ESRGAN 4x+", 84 | info= "Highly recommended to download and use 4x-UltraSharp for general purposes", 85 | type="index", 86 | elem_id=self.elem_id("ui_upscaler_2") 87 | ) 88 | 89 | 90 | with gr.Accordion('Advanced options', open=False): 91 | gr.Markdown( 92 | """### Algorithm additional functions """) 93 | with gr.Row(): 94 | with gr.Column(): 95 | gr.Markdown( 96 | """ ##### Quality functions """) 97 | filtering = gr.Checkbox(True, label="Filtering function") 98 | 99 | biggest_face = gr.Checkbox(True, label="Inpaint only the biggest face on the image") 100 | 101 | lower_lora_param = gr.Checkbox(True, 102 | label="Lower LoRA strength for face inpaint. Helps avoid burnout with strong LORAs") 103 | use_ddim = gr.Checkbox(True, label="Use DDIM sampler for better inpaint. Will use chosen in interface otherwise") 104 | 105 | lower_cfg = gr.Checkbox(False, label="Lower CFG for face inpaint. Helps avoid burning with multiple LoRAs") 106 | 107 | with gr.Column(): 108 | gr.Markdown( 109 | """ ##### Algorithm-alterting functions """) 110 | inpaint_hair = gr.Checkbox(False, label="Make face inpaint box larger to inpaint hair along with the face") 111 | 112 | inpaint_hair_and_face = gr.Checkbox(False, label="Do face inpaint after hair inpaint") 113 | 114 | mid_inpainting = gr.Checkbox(False, label="Attempt mid-uspcale inpainting with chosen options") 115 | 116 | use_img2img = gr.Checkbox(False, label="Use plain Image2Image instead of SD Upscale") 117 | 118 | do_not_sd_upscale = gr.Checkbox(False, label="Don't use SD upscale and inpaint HRfix result. Great for weak GPUs") 119 | gr.Markdown( 120 | """ 121 | #### Regulate denoise for each step 122 | """) 123 | with FormRow(variant="compact"): 124 | first_denoise = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, value=0.5, 125 | label="High-Res Fix denoising strength", info="0.45-0.6 for Latents, 0.2-0.4 for all others") 126 | face_denoise = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, value=0.3, 127 | label="Face inpainting denoising strength", info="Higher for smaller faces, lower for bigger. Also, lower for anime styles") 128 | 129 | with FormRow(variant="compact"): 130 | second_denoise = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, value=0.3, 131 | label="SD Upscale denoising strength") 132 | eyes_denoise = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, value=0.3, 133 | label="Eyes inpainting denoising strength") 134 | 135 | gr.Markdown( 136 | """ 137 | #### Sliders for parameters 138 | """) 139 | 140 | scale_factor0 = gr.Slider(minimum=1.0, maximum=3, step=0.05, value=1.25, 141 | label="High-Res Fix scale factor", info="For bigger scales Latents will go wild and produce artifacts and new limbs, consider it before increasing") 142 | 143 | with FormRow(variant="compact"): 144 | 145 | strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, value=0.3, 146 | label="Strength of Filtering") 147 | lora_lowering = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, value=0.35, 148 | label="Multiplier for LoRA strength lowering") 149 | 150 | face_confidence_threshold = gr.Slider(label="Face Recognition minimum confidence", minimum=0.0, maximum=1.0, step=0.05, value=0.7) 151 | 152 | with gr.Accordion('REALLY advanced options (Brain Damage alert)', open=False): 153 | with FormRow(variant="compact"): 154 | overlap = gr.Slider(label="Tile Overlap parameter for SD Upscale", minimum=0, maximum=256, step=16, value=64, elem_id=self.elem_id("overlap"), info="Can be helpful to regulate time and VRAM consumption on SD upscale step") 155 | face_resolution_scale_slider = gr.Slider(minimum=1.0, maximum=4, step=0.1, value=2.5, 156 | label="Scaling factor for face inpainting", info="Higher = sharper and crispier face, but higher time and VRAM consumption") 157 | eyes_resolution_scale_slider = gr.Slider(minimum=1.0, maximum=3, step=0.1, value=1.5,label="Scaling factor for eyes inpainting", 158 | info="Note that it uses enlarged face as base, so the modifer is lower") 159 | gr.Markdown( 160 | """ 161 | #### High-Res Fix settings 162 | """) 163 | with FormRow(variant="compact"): 164 | hr_model = gr.Dropdown( choices=["Use same checkpoint"] + sd_models.checkpoint_tiles(use_short=True), value="Use same checkpoint", label='Checkpoint for High-Res Fix') 165 | create_refresh_button(hr_model, sd_models.list_models, lambda: {"choices": ["Use same checkpoint"] + sd_models.checkpoint_tiles(use_short=True)}, "hr_checkpoint_refresh") 166 | 167 | hr_sampler = gr.Dropdown( 168 | [x.name for x in shared_items.list_samplers()], label="High-Res Fix sampler", 169 | value="DPM++ 2M Karras", 170 | elem_id=self.elem_id("hr_sampler") 171 | ) 172 | first_steps = gr.Slider(minimum=0, maximum=100, step=1, value=12, 173 | label="High-Res Fix steps") 174 | 175 | #hr_upscale_vae = gr.Dropdown(choices=['None', *sd_vae.vae_dict.keys()], label='VAE for High-Res Fix', value='None') 176 | with FormRow(variant="compact"): 177 | highres_prompt = gr.Textbox(label="High-Res Fix prompt", lines=3, placeholder="Prompt for High-Res Fix pass.\nLeave empty to use the same prompt as in main textbox.",scale=2) 178 | highres_negprompt = gr.Textbox(label="High-Res Fix negative prompt", lines=3, placeholder="Negative Prompt for High-Res Fix pass.\nLeave empty to use the same prompt as in main textbox." ) 179 | 180 | gr.Markdown( 181 | """ 182 | #### SD Upscale settings 183 | """) 184 | with FormRow(variant="compact"): 185 | sd_upscale_model = gr.Dropdown( choices=["Use same checkpoint"] + sd_models.checkpoint_tiles(use_short=True), value="Use same checkpoint", label='Checkpoint for SD Upscale', scale=2) 186 | create_refresh_button(sd_upscale_model, sd_models.list_models, lambda: {"choices": ["Use same checkpoint"] + sd_models.checkpoint_tiles(use_short=True)}, "sd_upscale_checkpoint_refresh") 187 | 188 | sd_upscale_sampler = gr.Dropdown( 189 | [x.name for x in shared_items.list_samplers()], label="SD Upscale sampler", 190 | value="DPM++ 2M Karras", 191 | elem_id=self.elem_id("sd_upscale_sampler"), scale=2 192 | ) 193 | second_clip = gr.Slider(minimum=1, maximum=12, step=1, value=1, 194 | label="Clip Skip") 195 | second_steps = gr.Slider(minimum=0, maximum=100, step=1, value=12, scale=2, 196 | label="SD Upscale steps") 197 | 198 | #sd_upscale_vae = gr.Dropdown(choices=['None', *sd_vae.vae_dict.keys()], label='VAE for upscale', value='None') 199 | with FormRow(variant="compact"): 200 | sd_upscale_prompt = gr.Textbox(label="SD Upscale prompt", lines=3, placeholder="Prompt for SD Upscale pass.\nLeave empty to use the same prompt as in main textbox.", scale=2) 201 | sd_upscale_negprompt = gr.Textbox(label="SD Upscale negative prompt", lines=3, placeholder="Negative Prompt for SD Upscale pass.\nLeave empty to use the same prompt as in main textbox.") 202 | 203 | gr.Markdown( 204 | """ 205 | #### Inpaint settings 206 | """) 207 | with FormRow(variant="compact"): 208 | face_model = gr.Dropdown( choices=["Use same checkpoint"] + sd_models.checkpoint_tiles(use_short=True), value="Use same checkpoint", label='Checkpoint for inpaint', scale=2) 209 | create_refresh_button(face_model, sd_models.list_models, lambda: {"choices": ["Use same checkpoint"] + sd_models.checkpoint_tiles(use_short=True)}, "face_checkpoint_refresh") 210 | 211 | face_sampler = gr.Dropdown( 212 | [x.name for x in shared_items.list_samplers()], label="Inpaint sampler", 213 | value="DPM++ 2M Karras", 214 | elem_id=self.elem_id("face_sampler"), scale=2 215 | ) 216 | face_clip = gr.Slider(minimum=1, maximum=12, step=1, value=1, 217 | label="Clip Skip") 218 | face_steps = gr.Slider(minimum=0, maximum=100, step=1, value=12, 219 | label="Inpaint steps", scale=2) 220 | 221 | with FormRow(variant="compact"): 222 | inpaint_prompt = gr.Textbox(label="Inpaint prompt", lines=3, placeholder="Prompt for Inpaint passes.\nLeave empty to use the same prompt as in main textbox.",scale=2 ) 223 | inpaint_negprompt = gr.Textbox(label="Inpaint negative prompt", lines=3, placeholder="Negative Prompt for Inpaint passes.\nLeave empty to use the same prompt as in main textbox.") 224 | #face_vae = gr.Dropdown(choices=['None', *sd_vae.vae_dict.keys()], label='VAE for inpaint', value='None') 225 | gr.Markdown( 226 | """ 227 | ### In case of questions or troubles, visit https://civitai.com/models/95923 228 | """) 229 | 230 | return [filtering, strength, ui_upscaler_1, first_denoise, second_denoise, face_denoise, eyes_denoise, options,mode, 231 | mid_inpainting, scale_factor, scale_factor0, lower_cfg,lower_lora_param,biggest_face,ui_upscaler_2,overlap,use_img2img,lora_lowering, 232 | face_confidence_threshold,inpaint_hair, inpaint_hair_and_face,use_ddim, do_not_sd_upscale, 233 | inpaint_prompt,inpaint_negprompt,highres_prompt,highres_negprompt, sd_upscale_prompt,sd_upscale_negprompt,first_steps, hr_sampler, second_steps,sd_upscale_sampler,face_steps, face_sampler, 234 | hr_model,sd_upscale_model,face_model,face_resolution_scale_slider,eyes_resolution_scale_slider, second_clip,face_clip] 235 | 236 | def run(self, p, filtering, strength, ui_upscaler_1, first_denoise, second_denoise, face_denoise, eyes_denoise,options, mode, 237 | mid_inpainting, scale_factor, scale_factor0, lower_cfg,lower_lora_param,biggest_face,ui_upscaler_2,overlap,use_img2img,lora_lowering,face_confidence_threshold, 238 | inpaint_hair,inpaint_hair_and_face,use_ddim, do_not_sd_upscale,inpaint_prompt,inpaint_negprompt,highres_prompt,highres_negprompt, sd_upscale_prompt,sd_upscale_negprompt, 239 | first_steps, hr_sampler, second_steps,sd_upscale_sampler,face_steps, face_sampler, 240 | hr_model,sd_upscale_model,face_model,face_resolution_scale_slider,eyes_resolution_scale_slider, second_clip,face_clip): 241 | 242 | 243 | initial_seed_and_info = [None, None, None] 244 | face_inpaint_flag = True if "Automatic face inpaint" in options else False 245 | eyes_inpaint_flag = True if "Automatic eyes inpaint" in options else False 246 | info_flag = True if "Display info in terminal" in options else False 247 | if "Img2Img" in mode: 248 | filtering = False 249 | i2i_only_flag = True if "Img2Img" in mode else False 250 | mid_face_flag = None 251 | mid_eyes_flag = None 252 | inpaint_hair_flag = None 253 | inpaint_hair_and_face_flag = None 254 | use_ddim_flag = None 255 | 256 | if info_flag: 257 | print(options) 258 | print('filtering', filtering, 'strength', strength, 'ui_upscaler_1', ui_upscaler_1, 'face_inpaint_flag', 259 | face_inpaint_flag, 'eyes_inpaint_flag', eyes_inpaint_flag, 'scale_factor', scale_factor) 260 | 261 | if mid_inpainting: 262 | mid_face_flag = face_inpaint_flag 263 | mid_eyes_flag = eyes_inpaint_flag 264 | 265 | if inpaint_hair: 266 | inpaint_hair_flag = True 267 | 268 | if inpaint_hair_and_face: 269 | inpaint_hair_and_face_flag = True 270 | 271 | if use_ddim: 272 | use_ddim_flag = True 273 | 274 | 275 | upscaler = ui_upscaler_1 276 | 277 | from PIL import Image 278 | 279 | import cv2 280 | import numpy as np 281 | import torch 282 | import math 283 | import re 284 | import random 285 | 286 | initial_prompt = p.prompt 287 | initial_seed = None 288 | initial_info = None 289 | 290 | is_last = False 291 | 292 | all_images = [] 293 | pos = 0 294 | batch_count = p.n_iter 295 | p.n_iter = 1 296 | State.job_count = batch_count 297 | 298 | instance_img2img = StableDiffusionProcessingImg2Img() 299 | instance_img2img.outpath_samples = opts.outdir_img2img_samples 300 | instance_inpaint = StableDiffusionProcessingImg2Img() 301 | instance_inpaint.outpath_samples = opts.outdir_img2img_samples 302 | instance_sd_upscale = StableDiffusionProcessingImg2Img() 303 | instance_sd_upscale.outpath_samples = opts.outdir_img2img_samples 304 | 305 | #styles.StyleDatabase.apply_styles_to_prompt(p.prompt, styles) 306 | 307 | # Function for rounding resolution 308 | def closest(value, divider): 309 | return min((i for i in range(value - divider + 1, value + divider - 1) 310 | if i % divider == 0), 311 | key=lambda x: abs(x - value)) 312 | 313 | 314 | # Function for scaling small eye boxes 315 | def proportional_scaling(width, height, factor, threshold): 316 | 317 | step = 8 # 8 or 64 318 | # Calculate the aspect ratio of the image 319 | aspect_ratio = width / height 320 | 321 | # Scale the width and height by the given factor 322 | width = width * factor 323 | height = height * factor 324 | 325 | # Ensure that the resulting width and height are within the given threshold 326 | if width > threshold: 327 | # print(f" Proportional Scaling hit the limit: width {width} is larger than {threshold}") 328 | width = threshold 329 | height = width / aspect_ratio 330 | 331 | if height > threshold: 332 | # print(f" Proportional Scaling hit the limit: height {height} is larger than {threshold}") 333 | height = threshold 334 | width = height * aspect_ratio 335 | 336 | width = int(width) 337 | height = int(height) 338 | 339 | # Output height and width suitable for generation 340 | height = int(math.ceil(float(height) / float(step))) * step 341 | width = int(math.ceil(float(width) / float(step))) * step 342 | 343 | return (width, height) 344 | 345 | # Function for filtering of images 346 | def enhance_image(image, strength): 347 | import numpy as np 348 | import cv2 349 | # Parameters: 350 | # image_path - path to image 351 | # strength - desired strength of filter, from 0 to 1, default = 1 352 | # np_frame = np.array(image.images[0].convert("RGB")) 353 | image = np.array(image.images[0]) 354 | # Detail enhance 355 | dst = cv2.detailEnhance(image, sigma_s=10, sigma_r=0.15) 356 | # sigma_s controls how much the image is smoothed - the larger its value, 357 | # the more smoothed the image gets, but it's also slower to compute. 358 | # sigma_r is important if you want to preserve edges while smoothing the image. 359 | # Small sigma_r results in only very similar colors to be averaged (i.e. smoothed), while colors that differ much will stay intact. 360 | # Sharpening kernel init 361 | kernel_sharpening = np.array([[-1, -1, -1], 362 | [-1, 9, -1], 363 | [-1, -1, -1]]) 364 | # Sharpening 365 | dst2 = cv2.filter2D(image, -1, kernel_sharpening) 366 | # Blending detailed and sharpened images 367 | blended = cv2.addWeighted(dst, 0.6, dst2, 0.4, 0) 368 | # Denoising 369 | denoised = cv2.fastNlMeansDenoisingColored(blended, None, 5, 5, 7, 14) 370 | # Blending with the original 371 | denoised_blended = cv2.addWeighted(image, 1 - strength, denoised, strength, 0) 372 | if info_flag: 373 | print('Filtering complete') 374 | return Image.fromarray(denoised_blended) 375 | 376 | # Function for face detection and mask creation 377 | def mask_create(image,hair_inpaint_flag_param): 378 | 379 | # Regulating parameters 380 | face_resolution_scale = face_resolution_scale_slider 381 | resolution_scale = eyes_resolution_scale_slider 382 | if hair_inpaint_flag_param: 383 | face_resolution_scale = face_resolution_scale_slider*1.5 384 | mask_dilation = 1.5 385 | facebox_size_multiplier = 1.25 386 | face_found = True 387 | rotate = False 388 | directory = os.path.dirname(__file__) 389 | image = np.array(image) 390 | 391 | # Load the model 392 | 393 | weights = os.path.join(directory, "face_detection_yunet_2023mar.onnx") 394 | if os.path.isfile(weights) == False: 395 | weights = os.path.join(directory, "face_detection_yunet_2022mar.onnx") 396 | 397 | face_detector = cv2.FaceDetectorYN_create(weights, "", (0, 0), face_confidence_threshold) 398 | 399 | # Face detection 400 | 401 | channels = 1 if len(image.shape) == 2 else image.shape[2] 402 | if channels == 1: 403 | image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR) 404 | if channels == 4: 405 | image = cv2.cvtColor(image, cv2.COLOR_BGRA2BGR) 406 | 407 | height, width, _ = image.shape 408 | face_detector.setInputSize((width, height)) 409 | face_height_multiplier = 1 410 | 411 | _, faces = face_detector.detect(image) 412 | faces = faces if faces is not None else [] 413 | 414 | faces_quantity = len (faces) 415 | # Checking if the face was found 416 | if faces_quantity == 0: 417 | print("Face detection failed! Try more realistic picture.") 418 | face_found = False 419 | 420 | 421 | if biggest_face and faces_quantity > 1: 422 | # Find the biggest face in the array of faces 423 | # Size is determined by face[2] - width and face[3] - height of face box 424 | biggest_face_size = 0 425 | biggest_face_index = 0 426 | for i, face in enumerate(faces): 427 | face_size = face[2] * face[3] 428 | if face_size > biggest_face_size: 429 | biggest_face_size = face_size 430 | biggest_face_index = i 431 | # Keep only the biggest face 432 | faces = [faces[biggest_face_index]] 433 | faces_quantity = 1 434 | 435 | 436 | # Checking if the face box is horizontal 437 | if faces_quantity == 1: 438 | face1 = faces[0] 439 | if face1[2] > face1[3]: 440 | face_height_multiplier = 1.2 441 | image = cv2.rotate(image, cv2.ROTATE_90_CLOCKWISE) 442 | rotate = True 443 | height, width, _ = image.shape 444 | face_detector.setInputSize((width, height)) 445 | _, horizontalFaces = face_detector.detect(image) 446 | if horizontalFaces is not None: 447 | faces = horizontalFaces 448 | print(faces) 449 | 450 | results = [] 451 | if faces is not None: 452 | for face in faces: 453 | face_width = face[2] 454 | 455 | 456 | # finding higher eye to measure eye y-coordinate difference 457 | if face[5] > face[7]: 458 | higher_eye = (face[4], face[5]) 459 | lower_eye = (face[6], face[7]) 460 | else: 461 | higher_eye = (face[6], face[7]) 462 | lower_eye = (face[4], face[5]) 463 | eye_y_dif = abs(int(higher_eye[1] / lower_eye[1])) 464 | eye_x_distance = abs(int(higher_eye[0] - lower_eye[0])) 465 | # if eye_x_distance >= 0.4 * face_width: 466 | if abs(face[4] - face[8]) >= 0.15 * face_width and abs(face[6] - face[8]) >= 0.15 * face_width: 467 | # front or 3/4 view 468 | eye_box_corner = ( 469 | int(face[4] - eye_x_distance * 0.55), 470 | int(higher_eye[1] - eye_x_distance * 0.25 * (1.5 * eye_y_dif))) 471 | face_height_multiplier = face_height_multiplier * 1.2 472 | eye_y_dif_multiplier = 1.25 473 | else: 474 | # profile view 475 | eye_box_corner = ( 476 | int(face[4] - eye_x_distance), 477 | int(higher_eye[1] - eye_x_distance * 0.45 * (1.4 * eye_y_dif))) 478 | face_height_multiplier = face_height_multiplier * 1.3 479 | eye_y_dif_multiplier = 1.5 480 | if (face[4]) < (face[0] + 0.5 * face_width): # left eye profile view 481 | eye_box_corner = ( 482 | int(face[4] - eye_x_distance * 0.1), 483 | int(higher_eye[1] - eye_x_distance * 0.45 * (1.65 * eye_y_dif))) 484 | eye_x_distance = eye_x_distance * 1.2 485 | box = list(map(int, face[:4])) 486 | 487 | eye_box = [eye_box_corner[0], eye_box_corner[1], int(eye_x_distance * 2), 488 | int((eye_x_distance * 0.8) * (eye_y_dif_multiplier * eye_y_dif))] 489 | color = (0, 0, 255) 490 | 491 | thickness = 2 492 | cv2.rectangle(image, box, color, thickness, cv2.LINE_AA) 493 | cv2.rectangle(image, eye_box, (0, 255, 255), thickness, cv2.LINE_AA) 494 | cv2.circle(image, eye_box_corner, 5, (0, 255, 255), -1, cv2.LINE_AA) 495 | 496 | if rotate: 497 | image = cv2.rotate(image, cv2.ROTATE_90_COUNTERCLOCKWISE) 498 | 499 | mask1 = np.zeros(image.shape[:2], dtype="uint8") 500 | mask2 = np.zeros(image.shape[:2], dtype="uint8") 501 | mask3 = np.zeros(image.shape[:2], dtype="uint8") 502 | 503 | hair_box = box.copy() 504 | hair_box[0] -= int(box[0]*0.5) 505 | hair_box[1] -= int(box[1]*0.5) 506 | 507 | box = [box[0], box[1], closest(int(box[2] * 1.25), 4), 508 | closest(int(box[3] * face_height_multiplier), 4)] 509 | 510 | hair_box = [hair_box[0], hair_box[1], closest(int(hair_box[2] * 4.25), 4), 511 | closest(int(hair_box[3] * face_height_multiplier *1.5), 4)] 512 | 513 | cv2.rectangle(mask1, box, 255, -1) 514 | cv2.rectangle(mask2, eye_box, 255, -1) 515 | cv2.rectangle(mask3, hair_box, 255, -1) 516 | 517 | inpaint_face_size = [closest(int(box[2] * face_resolution_scale), 4), 518 | closest(int(box[3] * face_resolution_scale), 4)] 519 | inpaint_eye_size = list( 520 | [closest(int(eye_box[2] * resolution_scale), 4), closest(int(eye_box[3] * resolution_scale), 4)]) 521 | 522 | if info_flag and (inpaint_face_size[0] < 384 or inpaint_face_size[1] < 384): 523 | print('Face box too small! Initiating proportional_scaling') 524 | while inpaint_face_size[0] < 384 or inpaint_face_size[1] < 384: 525 | inpaint_face_size = list( 526 | proportional_scaling(inpaint_face_size[0], inpaint_face_size[1], 1.1, 2048)) 527 | if info_flag and (inpaint_eye_size[0] < 384 or inpaint_eye_size[1] < 384): 528 | print('Eye box too small! Initiating proportional_scaling') 529 | while inpaint_eye_size[0] < 384 or inpaint_eye_size[1] < 384: 530 | inpaint_eye_size = list(proportional_scaling(inpaint_eye_size[0], inpaint_eye_size[1], 1.2, 2048)) 531 | if info_flag: 532 | print("Resolution for face inpaint:\n" + str(inpaint_face_size), 'type:', type(inpaint_face_size)) 533 | print("Resolution for eye inpaint:\n" + str(inpaint_eye_size), 'type:', type(inpaint_eye_size)) 534 | results.append( 535 | (Image.fromarray(mask1), Image.fromarray(mask2),Image.fromarray(mask3), inpaint_face_size, inpaint_eye_size, face_found, faces_quantity)) 536 | 537 | 538 | return results 539 | 540 | # Function for lowering LORA strength 541 | def lower_lora(current_prompt): 542 | loras = re.findall(r'(]*(:(\d*\.?\d*)>))', current_prompt)# find all LoRAs and save them into capture groups. [0] full LoRA = "", [1] suffix only = ":0.5>", [2] strength only = "0.5" 543 | new_prompt = current_prompt 544 | if info_flag: 545 | print("found LoRAs:", loras) 546 | for lora in loras: 547 | lora_strength = round(float(lora[2]) * lora_lowering,3) 548 | lora_suffix = ':'+str(lora_strength)+'>' # extra step to prevent potentially breaking LoRAs that have numbers in them 549 | new_lora = lora[0].replace(lora[1], lora_suffix) 550 | new_prompt = new_prompt.replace(lora[0], new_lora) 551 | return new_prompt 552 | 553 | # Custom wrapper for process_images(p) to start txt2img+hrfix 554 | def text2img_hr(upscaler, scale_factor0): 555 | 556 | # print the input 557 | if info_flag: 558 | print('txt2img+hr fix \n upscaler:', upscaler) 559 | 560 | # Change parameters 561 | p.enable_hr = True 562 | p.denoising_strength = first_denoise 563 | p.hr_scale = scale_factor0 564 | p.hr_upscaler = upscaler 565 | p.hr_second_pass_steps = first_steps 566 | p.sampler_name = hr_sampler 567 | if highres_prompt: 568 | p.hr_prompt = highres_prompt 569 | if highres_negprompt: 570 | p.hr_negative_prompt = highres_negprompt 571 | if hr_model == 'Use same checkpoint': 572 | p.hr_checkpoint_name = None 573 | else: p.hr_checkpoint_name = hr_model 574 | 575 | if i2i_only_flag: 576 | p.steps = 1 577 | p.enable_hr = False 578 | # Start generation with high-res fix 579 | hr_output = process_images(p) 580 | 581 | # Write down the seed info for reproducibility 582 | initial_seed_and_info[0] = hr_output.seed 583 | initial_seed_and_info[1] = hr_output.info 584 | initial_seed_and_info[2] = hr_output.prompt 585 | 586 | 587 | # Print confirmation 588 | if info_flag: 589 | print('HR fix complete') 590 | 591 | # Clear cache 592 | torch.cuda.empty_cache() 593 | 594 | return hr_output 595 | 596 | # Custom wrapper for process_images(p) to start img2img 597 | def img2img(init_image, scale_factor, is_last_img2img): 598 | 599 | # Print the input 600 | if info_flag: 601 | print(f"Input size for img2img: {init_image.images[0].size}") 602 | 603 | if sd_upscale_model != 'Use same checkpoint': 604 | instance_img2img.refiner_checkpoint = sd_upscale_model 605 | instance_img2img.refiner_switch_at = 0.01 606 | # Clear cache 607 | torch.cuda.empty_cache() 608 | 609 | # Check if it's our last step 610 | if is_last_img2img: 611 | instance_img2img.outpath_samples = opts.outdir_txt2img_samples 612 | 613 | # Check for changes in prompt (DYNAMIC PROMPTS PROBLEM) 614 | #print(instance_img2img.prompt) 615 | if prompt_temp != initial_prompt: 616 | 617 | print('Prompt is different, assigning Wildcard result') 618 | 619 | instance_img2img.prompt = prompt_temp 620 | #print(type(instance_img2img.prompt)) 621 | 622 | else: print ('Prompt is the same') 623 | #print(instance_img2img.prompt) 624 | # Change parameters 625 | 626 | #print(instance_img2img.prompt) 627 | 628 | instance_img2img.negative_prompt = init_image.negative_prompt 629 | if sd_upscale_prompt: 630 | instance_img2img.prompt = sd_upscale_prompt 631 | if sd_upscale_negprompt: 632 | instance_img2img.negative_prompt = sd_upscale_negprompt 633 | instance_img2img.seed = init_image.seed 634 | instance_img2img.init_images = [init_image.images[0]] 635 | instance_sd_upscale.clip_skip = second_clip 636 | instance_img2img.denoising_strength = second_denoise 637 | instance_img2img.steps = second_steps 638 | 639 | # Change resolution so that it's surely dividable by 4 640 | instance_img2img.width = closest(int(scale_factor * p.width), 4) 641 | instance_img2img.height = closest(int(scale_factor * p.height), 4) 642 | 643 | # Print new resolution 644 | if info_flag: 645 | print('img2img resolution: ', instance_img2img.width, instance_img2img.height) 646 | 647 | # Run img2img 648 | img2img_output = process_images(instance_img2img) 649 | 650 | # Print confirmation 651 | if info_flag: 652 | print('img2mg finished!') 653 | 654 | # Clear cache 655 | torch.cuda.empty_cache() 656 | 657 | # Reset saving path 658 | if is_last_img2img: 659 | instance_img2img.outpath_samples = opts.outdir_img2img_samples 660 | 661 | return img2img_output 662 | 663 | # Custom wrapper for SD uspcale 664 | def sd_upscale(init_image, scale_factor,is_last_sd_upscale,overlap_func, ui_upscaler_2_func): 665 | # Print the input 666 | if info_flag: 667 | print(f"Input size for sd_upscale: {init_image.images[0].size}") 668 | 669 | from scripts.sd_upscale import Script as sd_up_run 670 | 671 | if sd_upscale_model != 'Use same checkpoint': 672 | instance_sd_upscale.refiner_checkpoint = sd_upscale_model 673 | instance_sd_upscale.refiner_switch_at = 0.01 674 | 675 | # Clear cache 676 | torch.cuda.empty_cache() 677 | 678 | # Check if it's our last step 679 | if is_last_sd_upscale: 680 | instance_sd_upscale.outpath_samples = opts.outdir_txt2img_samples 681 | 682 | 683 | # Check for changes in prompt (DYNAMIC PROMPTS PROBLEM) 684 | if prompt_temp != initial_prompt: 685 | instance_sd_upscale.prompt = prompt_temp 686 | # Change parameters 687 | instance_sd_upscale.negative_prompt = init_image.negative_prompt 688 | if sd_upscale_prompt: 689 | instance_sd_upscale.prompt = sd_upscale_prompt 690 | if sd_upscale_negprompt: 691 | instance_sd_upscale.negative_prompt = sd_upscale_negprompt 692 | 693 | instance_sd_upscale.seed = init_image.seed 694 | instance_sd_upscale.init_images = [init_image.images[0]] 695 | instance_sd_upscale.extra_generation_params["SD upscale overlap"] = 64 696 | instance_sd_upscale.extra_generation_params["SD upscale upscaler"] = shared.sd_upscalers[ui_upscaler_2_func].name 697 | instance_sd_upscale.clip_skip = second_clip 698 | instance_sd_upscale.steps = second_steps 699 | instance_sd_upscale.denoising_strength = second_denoise 700 | instance_sd_upscale.do_not_save_grid = True 701 | instance_sd_upscale.do_not_save_samples = True 702 | instance_sd_upscale.sampler_name = sd_upscale_sampler 703 | 704 | 705 | # Change resolution so that it's surely dividable by 4 706 | #instance_sd_upscale.width = closest(int(scale_factor * p.width), 4) 707 | #instance_sd_upscale.height = closest(int(scale_factor * p.height), 4) 708 | #instance_sd_upscale.width, instance_sd_upscale.height = closest_ratio_preserving(int(scale_factor * p.width),int(scale_factor * p.height), 4) 709 | 710 | sd_upscale_scale_factor = round(scale_factor/ scale_factor0, 3) 711 | 712 | # Print new resolution 713 | if info_flag: 714 | print('SD upscale resolution: ', sd_upscale_scale_factor) 715 | 716 | 717 | # Run sd_upscale 718 | #sd_upscale_output = sd_up_run.run(instance_sd_upscale) 719 | sd_upscale_output = sd_up_run.run(self, instance_sd_upscale, None , overlap_func, ui_upscaler_2_func, sd_upscale_scale_factor) 720 | 721 | 722 | # Print confirmation 723 | if info_flag: 724 | print('SD upscale finished!') 725 | 726 | # Clear cache 727 | torch.cuda.empty_cache() 728 | 729 | # Reset saving path 730 | if is_last_sd_upscale: 731 | instance_sd_upscale.outpath_samples = opts.outdir_img2img_samples 732 | 733 | return sd_upscale_output 734 | 735 | # Custom wrapper for process_images(p) to start inpaint 736 | def inpaint(init_image, p_mask, w, h, denoise,is_last_inpaint, rewrite_seed=False): 737 | 738 | 739 | if face_model != 'Use same checkpoint': 740 | instance_inpaint.refiner_checkpoint = face_model 741 | instance_inpaint.refiner_switch_at = 0.01 742 | 743 | print("Inpaint debug flag") 744 | # Change parameters 745 | if not rewrite_seed: 746 | instance_inpaint.seed = init_image.seed 747 | else: 748 | instance_inpaint.seed = init_image.seed + 10000 749 | 750 | 751 | 752 | # Check for changes in prompt (DYNAMIC PROMPTS PROBLEM) 753 | if prompt_temp != initial_prompt: 754 | instance_inpaint.prompt = prompt_temp 755 | 756 | 757 | instance_inpaint.negative_prompt = init_image.negative_prompt 758 | 759 | if inpaint_prompt: 760 | instance_inpaint.prompt = inpaint_prompt 761 | if inpaint_negprompt: 762 | instance_inpaint.negative_prompt = inpaint_negprompt 763 | 764 | if lower_cfg: 765 | if instance_inpaint.cfg_scale != p.cfg_scale: 766 | instance_inpaint.cfg_scale = p.cfg_scale 767 | instance_inpaint.cfg_scale = int(instance_inpaint.cfg_scale * 0.66) 768 | if info_flag: 769 | print('Lowering CFG for inpaint \n new CFG:', instance_inpaint.cfg_scale) 770 | 771 | if lower_lora_param: 772 | new_prompt = lower_lora(instance_inpaint.prompt) 773 | instance_inpaint.prompt = new_prompt 774 | #print(instance_inpaint.prompt) 775 | if info_flag: 776 | print('Lowering LORA strength for inpaint \n new LORA strengths:', new_prompt) 777 | 778 | # Check if it's our last step 779 | 780 | if is_last_inpaint: 781 | instance_inpaint.outpath_samples = opts.outdir_txt2img_samples 782 | if i2i_only_flag: 783 | instance_inpaint.outpath_samples = opts.outdir_img2img_samples 784 | 785 | instance_inpaint.clip_skip = face_clip 786 | instance_inpaint.seed = init_image.seed 787 | instance_inpaint.init_images = [init_image.images[0]] 788 | instance_inpaint.image_mask = p_mask 789 | instance_inpaint.mask_blur = int(w * 0.01) 790 | instance_inpaint.inpainting_fill = 1 791 | instance_inpaint.inpaint_full_res = True 792 | instance_inpaint.inpaint_full_res_padding = int(w * 0.04) 793 | instance_inpaint.denoising_strength = denoise 794 | instance_inpaint.sampler_name = face_sampler 795 | instance_inpaint.steps = face_steps 796 | instance_inpaint.width = w 797 | instance_inpaint.height = h 798 | 799 | if use_ddim_flag: 800 | instance_inpaint.sampler_name = 'DDIM' 801 | if instance_inpaint.steps<20: 802 | instance_inpaint.steps = 20 803 | 804 | 805 | # Print inpaint parameters 806 | if info_flag: 807 | print('Inpaint parameters: ', instance_inpaint.sampler_name, instance_inpaint.steps, instance_inpaint.width, instance_inpaint.height) 808 | 809 | # Run inpaint 810 | inpaint_output = process_images(instance_inpaint) 811 | 812 | 813 | # Clear cache 814 | torch.cuda.empty_cache() 815 | 816 | # Reset saving path 817 | 818 | if is_last_inpaint: 819 | instance_inpaint.outpath_samples = opts.outdir_img2img_samples 820 | 821 | return inpaint_output 822 | 823 | # the job itself 824 | for n in range(batch_count): 825 | # Reset to original init image at the start of each batch 826 | State.job = f"batch {n} of {batch_count} batches \n" 827 | #State.sampling_steps = p.steps + second_steps + (face_steps*2) 828 | print(State.job) 829 | print(p.sampler_name) 830 | last_image_batch = text2img_hr(upscaler, scale_factor0) 831 | #hf_fix_output_str = ' '.join([str(elem) for i, elem in enumerate(hr_fix_output.info)]) 832 | #prompt_temp = hr_fix_output.info 833 | # prompt_temp = hr_fix_output.info.split(('egative prompt:')[0]) 834 | prompt_temp = re.split('Negative prompt: ',last_image_batch.info)[0] 835 | #print(prompt_temp) 836 | #print (initial_prompt) 837 | if i2i_only_flag: 838 | last_image_batch.images = p.init_images 839 | if filtering: 840 | last_image_batch.images[0] = enhance_image(last_image_batch, strength) 841 | 842 | 843 | 844 | if mid_face_flag: 845 | for (mask_face, mask_eyes, mask_hair, inpaint_face_size, inpaint_eye_size, face_found, faces_quantity) in mask_create( 846 | last_image_batch.images[0],inpaint_hair_flag): 847 | if face_found: 848 | if info_flag: 849 | print('Mid-uspcale face inpaint started: ') 850 | 851 | mid_image_face_inpaint = inpaint(last_image_batch, mask_face, inpaint_face_size[0], 852 | inpaint_face_size[1], 853 | face_denoise,is_last, True) 854 | 855 | if mid_eyes_flag: 856 | if info_flag: 857 | print('Mid-uspcale eyes inpaint started: ') 858 | mid_image_eyes_inpaint = inpaint(mid_image_face_inpaint, mask_eyes, inpaint_eye_size[0], 859 | inpaint_eye_size[1], eyes_denoise,is_last, True) 860 | last_image_batch.images[0] = mid_image_eyes_inpaint.images[0] 861 | else: 862 | last_image_batch.images[0] = mid_image_face_inpaint.images[0] 863 | 864 | 865 | if not face_inpaint_flag: 866 | is_last = True 867 | 868 | if face_inpaint_flag: 869 | is_last = True 870 | for (mask_face, mask_eyes, mask_hair, inpaint_face_size, inpaint_eye_size, face_found, faces_quantity) in mask_create( 871 | last_image_batch.images[0],inpaint_hair_flag): 872 | if face_found: 873 | is_last = False 874 | break 875 | 876 | if info_flag: 877 | if is_last: 878 | print('NO Face found in pre upscale check. Marking Upscale as last Image') 879 | else: 880 | print('Face found in pre upscale check') 881 | 882 | if not do_not_sd_upscale: 883 | if not use_img2img: 884 | last_image_batch = sd_upscale(last_image_batch, scale_factor, is_last,overlap, ui_upscaler_2) 885 | else: last_image_batch = img2img(last_image_batch, scale_factor, is_last) 886 | 887 | is_last = False 888 | 889 | #print("islast",is_last) 890 | 891 | if face_inpaint_flag: 892 | for (mask_face, mask_eyes, mask_hair, inpaint_face_size, inpaint_eye_size, face_found, faces_quantity) in mask_create( 893 | last_image_batch.images[0],inpaint_hair_flag): 894 | if face_found: 895 | pos+=1 896 | 897 | if inpaint_hair_flag: 898 | if info_flag: 899 | print('Hair inpaint started: ') 900 | if not eyes_inpaint_flag and pos == faces_quantity: 901 | is_last = True 902 | print("islast", is_last) 903 | last_image_batch = inpaint(last_image_batch, mask_hair, inpaint_face_size[0], 904 | inpaint_face_size[1], 905 | face_denoise,is_last, True) 906 | #last_image_batch = image_face_inpaint 907 | 908 | if inpaint_hair_and_face_flag: 909 | 910 | if info_flag: 911 | print('Face inpaint started: ') 912 | 913 | if not eyes_inpaint_flag and pos == faces_quantity: 914 | is_last = True 915 | print("islast", is_last) 916 | last_image_batch = inpaint(last_image_batch, mask_face, inpaint_face_size[0], 917 | inpaint_face_size[1], 918 | face_denoise,is_last, True) 919 | 920 | else: 921 | if info_flag: 922 | print('Face inpaint started: ') 923 | 924 | if not eyes_inpaint_flag and pos == faces_quantity: 925 | is_last = True 926 | print("islast", is_last) 927 | last_image_batch = inpaint(last_image_batch, mask_face, inpaint_face_size[0], 928 | inpaint_face_size[1], 929 | face_denoise,is_last, True) 930 | 931 | if eyes_inpaint_flag: 932 | if info_flag: 933 | print('Eye inpaint started: ') 934 | if pos == faces_quantity: 935 | is_last = True 936 | print("islast", is_last) 937 | last_image_batch = inpaint(last_image_batch, mask_eyes, inpaint_eye_size[0], 938 | inpaint_eye_size[1], eyes_denoise,is_last, True) 939 | #last_image_batch = image_eyes_inpaint 940 | #else: 941 | #last_image_batch = image_face_inpaint 942 | 943 | #last_image_batch.prompt = initial_seed_and_info[2] 944 | if i2i_only_flag: 945 | last_image_batch.outpath_samples = opts.outdir_img2img_samples 946 | else: 947 | last_image_batch.outpath_samples = opts.outdir_txt2img_samples 948 | last_image = last_image_batch.images[0] 949 | #last_info = last_image_batch.info 950 | 951 | if initial_seed is None: 952 | initial_seed = last_image_batch.seed 953 | initial_info = last_image_batch.info 954 | 955 | all_images.append(last_image) 956 | #all_infos.append(last_info) 957 | 958 | p.seed = last_image_batch.seed + 1 959 | p.prompt = initial_prompt 960 | is_last = False 961 | pos = 0 962 | 963 | processed = Processed(p, all_images, initial_seed, initial_info) 964 | return processed 965 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU AFFERO GENERAL PUBLIC LICENSE 2 | Version 3, 19 November 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU Affero General Public License is a free, copyleft license for 11 | software and other kinds of works, specifically designed to ensure 12 | cooperation with the community in the case of network server software. 13 | 14 | The licenses for most software and other practical works are designed 15 | to take away your freedom to share and change the works. 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It is safest 628 | to attach them to the start of each source file to most effectively 629 | state the exclusion of warranty; and each file should have at least 630 | the "copyright" line and a pointer to where the full notice is found. 631 | 632 | 633 | Copyright (C) 634 | 635 | This program is free software: you can redistribute it and/or modify 636 | it under the terms of the GNU Affero General Public License as published 637 | by the Free Software Foundation, either version 3 of the License, or 638 | (at your option) any later version. 639 | 640 | This program is distributed in the hope that it will be useful, 641 | but WITHOUT ANY WARRANTY; without even the implied warranty of 642 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 643 | GNU Affero General Public License for more details. 644 | 645 | You should have received a copy of the GNU Affero General Public License 646 | along with this program. If not, see . 647 | 648 | Also add information on how to contact you by electronic and paper mail. 649 | 650 | If your software can interact with users remotely through a computer 651 | network, you should also make sure that it provides a way for users to 652 | get its source. For example, if your program is a web application, its 653 | interface could display a "Source" link that leads users to an archive 654 | of the code. There are many ways you could offer source, and different 655 | solutions will be better for different programs; see section 13 for the 656 | specific requirements. 657 | 658 | You should also get your employer (if you work as a programmer) or school, 659 | if any, to sign a "copyright disclaimer" for the program, if necessary. 660 | For more information on this, and how to apply and follow the GNU AGPL, see 661 | . 662 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # AutoChar - Version 0.9.5 is here! 2 | https://civitai.com/models/95923/autochar-easy-character-art-creation-with-face-auto-inpaint 3 | 4 | AutoChar Control Panel is a custom script for Stable Diffusion WebUI by Automatic1111 (1.6.0+) made to help newbies and enthusiasts alike achieve great pictures with less effort. 5 | Basically, it's automation of my basic SD workflow for illustrations (check 'em here: https://t.me/dreamforge_tg) and also my Bachelor graduation work, for which I got an A. I've put decent emphasis in code readability and comments for it, so I hope it will help future contributors and developers of other scripts. 6 | 7 | Please check my new guide for it on YouTube that explain all basic functions and pipeline: https://www.youtube.com/watch?v=jNUMHtH1U6E 8 | For text description of scripts' basic idea check 0.9 version tab on CivitAI page. 9 | 10 | ## Installation 11 | **Just put script and .onnx face recognition model in your stable-diffusion-webui/scripts folder 12 | PLEASE, don't try to install via URL, it's not an extension, it won't be visible this way! 13 | Also I highly recommend to download 4x-UltraSharp Upscaler (https://mega.nz/folder/qZRBmaIY#nIG8KyWFcGNTuMX_XNbJ_g) and put in /modes/ESRGAN folder** 14 | 15 | 16 | ## How to use, in short 17 | 1. Go to your txt2img tab 18 | 2. Write prompt, select basic parameters as usual (you don't need highres fix, since it's included in the algorithm) 19 | 3. Select "AutoChar Beta 0.9" in dropdown menu Scripts in the lower part of page 20 | 4. Click "Generate" and enjoy 21 | ![image](https://github.com/alexv0iceh/AutoChar/assets/74978526/16919ca6-1de3-4052-a2fd-6729c9e890e5) 22 | 23 | ### 0.9.5 changes: 24 | - Fully revamped interface: 25 | - Info added for all crucial parameters, containing tips for usage and clarification for not-so-obvious functions 26 | - Upscaler choosing changed from check panels to dropdowns to reduce distraction 27 | - Function and slider groups divided to different blocks with clarification headers 28 | - True img2img mode: edit existing pictures with SD upscale and automatic face&eyes inpaint 29 | - Additional **Advanced options**! 30 | - Brand new **Really Advanced options** tab for brave enthusiasts willing to take complete control of AutoChar's generation pipeline and maximize their creativity 31 | - Various fixes: 32 | - Fixed infamous bug with OpenCV on inpaint step (If you STILL have it, do this: https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/11727#issuecomment-1632550894 ) 33 | - Fixed inpaint only masked padding, drastically improving results on some artstyles and checkpoints 34 | - Fixed High-Res Fix upscalers' list, now it shows all available upscalers as it should 35 | - Styles from Styles Menu are now working properly 36 | - Many small fixes in code's logic and parameters 37 | 38 | ![image](https://github.com/alexv0iceh/AutoChar/assets/74978526/9eccfbaa-8a09-41c7-ab97-856e6b16c7d4) 39 | 40 | ### Comprehensive description of Advanced and Really Advanced options and tips for their usage: 41 | - **Advanced options:** 42 | - Quality functions: 43 | - **Filtering function:** sharpens and applies denoising filter to image after High-Res Fix to improve quality and reduce the number of necessary 44 | img2img steps. May negatively impact desired result on "noisy" and blurry artstyles. _On by default_ 45 | - **Inpaint only the biggest face on the image:** does what it says, can be great to prevent undesired face detection and inpaint of background or body parts. May cause problems on images with small character head (full-height pictures and landscapes). In this case, either increase Face Recognition minimum confidence or disable this options. Also disable for pictures with two or more characters._On by default_ 46 | - **Lower LoRA strength for face inpaint. Helps avoid burnout with strong LORAs:** does what it says._On by default_ 47 | - **Use DDIM sampler for better inpaint. Will use chosen in interface otherwise:** better for detailed faces. Note that from SD WebUi's version 1.6.0+ denoising strength works differently for DMP++ samplers, so if you're disabling this option because of possible mask residue issues, consider increasing denoising strength for inpaint steps. _On by default_ 48 | - **Lower CFG for face inpaint. Helps avoid burning with multiple LoRAs:** does what it says. _Off by default_ 49 | - Algorithm-alterting functions: 50 | - **Make face inpaint box larger to inpaint hair along with the face:** does what it says. It can become quite VRAM heavy, so consider lowering Scaling factor for face inpainting if you're running into issues with it. _Off by default_ 51 | - **Do face inpaint after hair inpaint:** does what it says. _Off by default_ 52 | - **Attempt mid-uspcale inpainting with chosen options:** does what it says. Can be helpful for adding an additional level of detail. Off by default 53 | - **Use plain Image2Image instead of SD Upscale:** does what it says. _Off by default_ 54 | - **Don't use SD upscale and inpaint HRfix result. Great for weak GPUs:** besides stated reason to use it, it can be useful to people accustomed to High-Res Fix-only pipeline._Off by default_ 55 | - Regulate denoise for each step: 56 | - All needed info is already in UI, but i would like to add that rom SD WebUi's version 1.6.0+ necessary denoise for DPM++ samplers is like x2 from DDIM denoise up to 0.5; E.g. 0.2 on DDIM is roughly the same as 0.4 on DPM++ 2M Karras 57 | - Sliders for parameters: 58 | - **High-Res Fix scale factor:** all info in UI 59 | - **Strength of Filtering:** intensity of Filtering function's effect. 0.3-0.5 works best, higher is tricky, but can be helpful for some artstyles 60 | - **Multiplier for LoRA strength lowering:** does what it says. Increase if you want to preserve more of artstyle from your LoRAs 61 | - **Face Recognition minimum confidence:** increase for stricter face detection, decrease if having problems on more anime-like artstyles 62 | - **Really advanced options:** 63 | - Tile Overlap parameter for SD Upscale, Scaling factor for face inpainting, Scaling factor for eyes inpainting: all info in UI 64 | - Algorithm's steps' settings: 65 | - **Checkpoint:** allows you to choose different one of your checkpoints to be used on this step. Great for mixing artstyles and combining best qualities of each checkpoint! 66 | - **Sampler:** obvious 67 | - **Clip Skip:** my use case is to generate base image on Сlip Skip 2 but work with it on later steps on Clip Skip 1 for better realism 68 | - **Steps:** obvious 69 | - **Prompt & Negative prompt:** allows you to use different prompts and LoRAs for each step. Like, using object or content LoRAs and exclude them from later steps, replacing with LoRAs that have great style, but negatively impact image's content if used in txt2img generation 70 | 71 | ![image](https://github.com/alexv0iceh/AutoChar/assets/74978526/cd6f7365-e4fd-43dc-94c9-fa535c8cc249) 72 | 73 | 74 | 75 | ## _Algorithm itself:_ 76 | 1. Txt2img generation in chosen resolution 77 | 2. High-res fix to boost details and gain sharpness 78 | 3. [Optional, "on" by default] Filtering with chosen strength 79 | 4. [Optional, "off" by default] Automatic inpainting of face and eyes with chosen parameters 80 | 5. SD Upscale 81 | 6. Automatic inpainting of face and eyes with chosen parameters 82 | 83 | ![image](https://github.com/alexv0iceh/AutoChar/assets/74978526/38c0bed6-84b0-43c5-adf7-14a169b4caf6) 84 | 85 | 86 | 87 | ## _Coming in 1.0:_ 88 | - Release as full extension. 89 | - ControlNet integration. 90 | - More face recognition models (including anime-friendly) 91 | ![image](https://github.com/alexv0iceh/AutoChar/assets/74978526/798a92e9-0105-4b39-85b6-5b89048a108e) 92 | 93 | ![image](https://github.com/alexv0iceh/AutoChar/assets/74978526/2b60ba4f-86af-4c53-a4f3-2d85d3f03e10) 94 | 95 | ![image](https://github.com/alexv0iceh/AutoChar/assets/74978526/4da581ed-3e00-4abc-88e3-f41710f37cee) 96 | -------------------------------------------------------------------------------- /face_detection_yunet_2022mar.onnx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/alexv0iceh/AutoChar/d6ac3761aef63c0da58ef4baf8cc171543062895/face_detection_yunet_2022mar.onnx -------------------------------------------------------------------------------- /face_detection_yunet_2023mar.onnx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/alexv0iceh/AutoChar/d6ac3761aef63c0da58ef4baf8cc171543062895/face_detection_yunet_2023mar.onnx --------------------------------------------------------------------------------