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Running
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| task_stablepy = { | |
| 'txt2img': 'txt2img', | |
| 'img2img': 'img2img', | |
| 'inpaint': 'inpaint', | |
| 'sdxl_canny T2I Adapter': 'sdxl_canny', | |
| 'sdxl_sketch T2I Adapter': 'sdxl_sketch', | |
| 'sdxl_lineart T2I Adapter': 'sdxl_lineart', | |
| 'sdxl_depth-midas T2I Adapter': 'sdxl_depth-midas', | |
| 'sdxl_openpose T2I Adapter': 'sdxl_openpose', | |
| 'sd_openpose ControlNet': 'openpose', | |
| 'sd_canny ControlNet': 'canny', | |
| 'sd_mlsd ControlNet': 'mlsd', | |
| 'sd_scribble ControlNet': 'scribble', | |
| 'sd_softedge ControlNet': 'softedge', | |
| 'sd_segmentation ControlNet': 'segmentation', | |
| 'sd_depth ControlNet': 'depth', | |
| 'sd_normalbae ControlNet': 'normalbae', | |
| 'sd_lineart ControlNet': 'lineart', | |
| 'sd_lineart_anime ControlNet': 'lineart_anime', | |
| 'sd_shuffle ControlNet': 'shuffle', | |
| 'sd_ip2p ControlNet': 'ip2p', | |
| } | |
| task_model_list = list(task_stablepy.keys()) | |
| ####################### | |
| # UTILS | |
| ####################### | |
| import spaces | |
| import os | |
| from stablepy import Model_Diffusers | |
| from stablepy.diffusers_vanilla.model import scheduler_names | |
| from stablepy.diffusers_vanilla.style_prompt_config import STYLE_NAMES | |
| import torch | |
| import re | |
| preprocessor_controlnet = { | |
| "openpose": [ | |
| "Openpose", | |
| "None", | |
| ], | |
| "scribble": [ | |
| "HED", | |
| "Pidinet", | |
| "None", | |
| ], | |
| "softedge": [ | |
| "Pidinet", | |
| "HED", | |
| "HED safe", | |
| "Pidinet safe", | |
| "None", | |
| ], | |
| "segmentation": [ | |
| "UPerNet", | |
| "None", | |
| ], | |
| "depth": [ | |
| "DPT", | |
| "Midas", | |
| "None", | |
| ], | |
| "normalbae": [ | |
| "NormalBae", | |
| "None", | |
| ], | |
| "lineart": [ | |
| "Lineart", | |
| "Lineart coarse", | |
| "LineartAnime", | |
| "None", | |
| "None (anime)", | |
| ], | |
| "shuffle": [ | |
| "ContentShuffle", | |
| "None", | |
| ], | |
| "canny": [ | |
| "Canny" | |
| ], | |
| "mlsd": [ | |
| "MLSD" | |
| ], | |
| "ip2p": [ | |
| "ip2p" | |
| ] | |
| } | |
| def download_things(directory, url, hf_token="", civitai_api_key=""): | |
| url = url.strip() | |
| if "drive.google.com" in url: | |
| original_dir = os.getcwd() | |
| os.chdir(directory) | |
| os.system(f"gdown --fuzzy {url}") | |
| os.chdir(original_dir) | |
| elif "huggingface.co" in url: | |
| url = url.replace("?download=true", "") | |
| if "/blob/" in url: | |
| url = url.replace("/blob/", "/resolve/") | |
| user_header = f'"Authorization: Bearer {hf_token}"' | |
| if hf_token: | |
| os.system(f"aria2c --console-log-level=error --summary-interval=10 --header={user_header} -c -x 16 -k 1M -s 16 {url} -d {directory} -o {url.split('/')[-1]}") | |
| else: | |
| os.system (f"aria2c --optimize-concurrent-downloads --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 {url} -d {directory} -o {url.split('/')[-1]}") | |
| elif "civitai.com" in url: | |
| if "?" in url: | |
| url = url.split("?")[0] | |
| if civitai_api_key: | |
| url = url + f"?token={civitai_api_key}" | |
| os.system(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}") | |
| else: | |
| print("\033[91mYou need an API key to download Civitai models.\033[0m") | |
| else: | |
| os.system(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}") | |
| def get_model_list(directory_path): | |
| model_list = [] | |
| valid_extensions = {'.ckpt' , '.pt', '.pth', '.safetensors', '.bin'} | |
| for filename in os.listdir(directory_path): | |
| if os.path.splitext(filename)[1] in valid_extensions: | |
| name_without_extension = os.path.splitext(filename)[0] | |
| file_path = os.path.join(directory_path, filename) | |
| # model_list.append((name_without_extension, file_path)) | |
| model_list.append(file_path) | |
| print('\033[34mFILE: ' + file_path + '\033[0m') | |
| return model_list | |
| def process_string(input_string): | |
| parts = input_string.split('/') | |
| if len(parts) == 2: | |
| first_element = parts[1] | |
| complete_string = input_string | |
| result = (first_element, complete_string) | |
| return result | |
| else: | |
| return None | |
| directory_models = 'models' | |
| os.makedirs(directory_models, exist_ok=True) | |
| directory_loras = 'loras' | |
| os.makedirs(directory_loras, exist_ok=True) | |
| directory_vaes = 'vaes' | |
| os.makedirs(directory_vaes, exist_ok=True) | |
| # - **Download SD 1.5 Models** | |
| download_model = "https://huggingface.co/frankjoshua/toonyou_beta6/resolve/main/toonyou_beta6.safetensors" | |
| # - **Download VAEs** | |
| download_vae = "https://huggingface.co/fp16-guy/anything_kl-f8-anime2_vae-ft-mse-840000-ema-pruned_blessed_clearvae_fp16_cleaned/resolve/main/anything_fp16.safetensors" | |
| # - **Download LoRAs** | |
| download_lora = "https://civitai.com/api/download/models/97655, https://civitai.com/api/download/models/124358" | |
| load_diffusers_format_model = ['stabilityai/stable-diffusion-xl-base-1.0', 'runwayml/stable-diffusion-v1-5'] | |
| CIVITAI_API_KEY = "" | |
| hf_token = "" | |
| # Download stuffs | |
| for url in [url.strip() for url in download_model.split(',')]: | |
| if not os.path.exists(f"./models/{url.split('/')[-1]}"): | |
| download_things(directory_models, url, hf_token, CIVITAI_API_KEY) | |
| for url in [url.strip() for url in download_vae.split(',')]: | |
| if not os.path.exists(f"./vaes/{url.split('/')[-1]}"): | |
| download_things(directory_vaes, url, hf_token, CIVITAI_API_KEY) | |
| for url in [url.strip() for url in download_lora.split(',')]: | |
| if not os.path.exists(f"./loras/{url.split('/')[-1]}"): | |
| download_things(directory_loras, url, hf_token, CIVITAI_API_KEY) | |
| # Download Embeddings | |
| directory_embeds = 'embedings' | |
| os.makedirs(directory_embeds, exist_ok=True) | |
| download_embeds = [ | |
| 'https://huggingface.co/datasets/Nerfgun3/bad_prompt/resolve/main/bad_prompt.pt', | |
| 'https://huggingface.co/datasets/Nerfgun3/bad_prompt/blob/main/bad_prompt_version2.pt', | |
| 'https://huggingface.co/embed/EasyNegative/resolve/main/EasyNegative.safetensors', | |
| 'https://huggingface.co/embed/negative/resolve/main/EasyNegativeV2.safetensors', | |
| 'https://huggingface.co/embed/negative/resolve/main/bad-hands-5.pt', | |
| 'https://huggingface.co/embed/negative/resolve/main/bad-artist.pt', | |
| 'https://huggingface.co/embed/negative/resolve/main/ng_deepnegative_v1_75t.pt', | |
| 'https://huggingface.co/embed/negative/resolve/main/bad-artist-anime.pt', | |
| 'https://huggingface.co/embed/negative/resolve/main/bad-image-v2-39000.pt', | |
| 'https://huggingface.co/embed/negative/resolve/main/verybadimagenegative_v1.3.pt', | |
| ] | |
| for url_embed in download_embeds: | |
| if not os.path.exists(f"./embedings/{url_embed.split('/')[-1]}"): | |
| download_things(directory_embeds, url_embed, hf_token, CIVITAI_API_KEY) | |
| # Build list models | |
| embed_list = get_model_list(directory_embeds) | |
| model_list = get_model_list(directory_models) | |
| model_list = load_diffusers_format_model + model_list | |
| lora_model_list = get_model_list(directory_loras) | |
| lora_model_list.insert(0, "None") | |
| vae_model_list = get_model_list(directory_vaes) | |
| vae_model_list.insert(0, "None") | |
| print('\033[33m🏁 Download and listing of valid models completed.\033[0m') | |
| upscaler_dict_gui = { | |
| None : None, | |
| "Lanczos" : "Lanczos", | |
| "Nearest" : "Nearest", | |
| "RealESRGAN_x4plus" : "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth", | |
| "RealESRNet_x4plus" : "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth", | |
| "RealESRGAN_x4plus_anime_6B": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth", | |
| "RealESRGAN_x2plus": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth", | |
| "realesr-animevideov3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth", | |
| "realesr-general-x4v3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth", | |
| "realesr-general-wdn-x4v3" : "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth", | |
| "4x-UltraSharp" : "https://huggingface.co/Shandypur/ESRGAN-4x-UltraSharp/resolve/main/4x-UltraSharp.pth", | |
| "4x_foolhardy_Remacri" : "https://huggingface.co/FacehugmanIII/4x_foolhardy_Remacri/resolve/main/4x_foolhardy_Remacri.pth", | |
| "Remacri4xExtraSmoother" : "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/Remacri%204x%20ExtraSmoother.pth", | |
| "AnimeSharp4x" : "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/AnimeSharp%204x.pth", | |
| "lollypop" : "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/lollypop.pth", | |
| "RealisticRescaler4x" : "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/RealisticRescaler%204x.pth", | |
| "NickelbackFS4x" : "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/NickelbackFS%204x.pth" | |
| } | |
| def extract_parameters(input_string): | |
| parameters = {} | |
| input_string = input_string.replace("\n", "") | |
| if not "Negative prompt:" in input_string: | |
| print("Negative prompt not detected") | |
| parameters["prompt"] = input_string | |
| return parameters | |
| parm = input_string.split("Negative prompt:") | |
| parameters["prompt"] = parm[0] | |
| if not "Steps:" in parm[1]: | |
| print("Steps not detected") | |
| parameters["neg_prompt"] = parm[1] | |
| return parameters | |
| parm = parm[1].split("Steps:") | |
| parameters["neg_prompt"] = parm[0] | |
| input_string = "Steps:" + parm[1] | |
| # Extracting Steps | |
| steps_match = re.search(r'Steps: (\d+)', input_string) | |
| if steps_match: | |
| parameters['Steps'] = int(steps_match.group(1)) | |
| # Extracting Size | |
| size_match = re.search(r'Size: (\d+x\d+)', input_string) | |
| if size_match: | |
| parameters['Size'] = size_match.group(1) | |
| width, height = map(int, parameters['Size'].split('x')) | |
| parameters['width'] = width | |
| parameters['height'] = height | |
| # Extracting other parameters | |
| other_parameters = re.findall(r'(\w+): (.*?)(?=, \w+|$)', input_string) | |
| for param in other_parameters: | |
| parameters[param[0]] = param[1].strip('"') | |
| return parameters | |
| ####################### | |
| # GUI | |
| ####################### | |
| import spaces | |
| import gradio as gr | |
| from PIL import Image | |
| import IPython.display | |
| import time, json | |
| from IPython.utils import capture | |
| import logging | |
| logging.getLogger("diffusers").setLevel(logging.ERROR) | |
| import diffusers | |
| diffusers.utils.logging.set_verbosity(40) | |
| import warnings | |
| warnings.filterwarnings(action="ignore", category=FutureWarning, module="diffusers") | |
| warnings.filterwarnings(action="ignore", category=UserWarning, module="diffusers") | |
| warnings.filterwarnings(action="ignore", category=FutureWarning, module="transformers") | |
| from stablepy import logger | |
| logger.setLevel(logging.DEBUG) | |
| class GuiSD: | |
| def __init__(self): | |
| self.model = None | |
| def infer(self, model, pipe_params): | |
| images, image_list = model(**pipe_params) | |
| return images | |
| # @spaces.GPU | |
| def generate_pipeline( | |
| self, | |
| prompt, | |
| neg_prompt, | |
| num_images, | |
| steps, | |
| cfg, | |
| clip_skip, | |
| seed, | |
| lora1, | |
| lora_scale1, | |
| lora2, | |
| lora_scale2, | |
| lora3, | |
| lora_scale3, | |
| lora4, | |
| lora_scale4, | |
| lora5, | |
| lora_scale5, | |
| sampler, | |
| img_height, | |
| img_width, | |
| model_name, | |
| vae_model, | |
| task, | |
| image_control, | |
| preprocessor_name, | |
| preprocess_resolution, | |
| image_resolution, | |
| style_prompt, # list [] | |
| style_json_file, | |
| image_mask, | |
| strength, | |
| low_threshold, | |
| high_threshold, | |
| value_threshold, | |
| distance_threshold, | |
| controlnet_output_scaling_in_unet, | |
| controlnet_start_threshold, | |
| controlnet_stop_threshold, | |
| textual_inversion, | |
| syntax_weights, | |
| loop_generation, | |
| leave_progress_bar, | |
| disable_progress_bar, | |
| image_previews, | |
| display_images, | |
| save_generated_images, | |
| image_storage_location, | |
| retain_compel_previous_load, | |
| retain_detailfix_model_previous_load, | |
| retain_hires_model_previous_load, | |
| t2i_adapter_preprocessor, | |
| t2i_adapter_conditioning_scale, | |
| t2i_adapter_conditioning_factor, | |
| upscaler_model_path, | |
| upscaler_increases_size, | |
| esrgan_tile, | |
| esrgan_tile_overlap, | |
| hires_steps, | |
| hires_denoising_strength, | |
| hires_sampler, | |
| hires_prompt, | |
| hires_negative_prompt, | |
| hires_before_adetailer, | |
| hires_after_adetailer, | |
| xformers_memory_efficient_attention, | |
| freeu, | |
| generator_in_cpu, | |
| adetailer_inpaint_only, | |
| adetailer_verbose, | |
| adetailer_sampler, | |
| adetailer_active_a, | |
| prompt_ad_a, | |
| negative_prompt_ad_a, | |
| strength_ad_a, | |
| face_detector_ad_a, | |
| person_detector_ad_a, | |
| hand_detector_ad_a, | |
| mask_dilation_a, | |
| mask_blur_a, | |
| mask_padding_a, | |
| adetailer_active_b, | |
| prompt_ad_b, | |
| negative_prompt_ad_b, | |
| strength_ad_b, | |
| face_detector_ad_b, | |
| person_detector_ad_b, | |
| hand_detector_ad_b, | |
| mask_dilation_b, | |
| mask_blur_b, | |
| mask_padding_b, | |
| ): | |
| task = task_stablepy[task] | |
| # First load | |
| model_precision = torch.float16 | |
| if not self.model: | |
| from stablepy import Model_Diffusers | |
| print("Loading model...") | |
| self.model = Model_Diffusers( | |
| base_model_id=model_name, | |
| task_name=task, | |
| vae_model=vae_model if vae_model != "None" else None, | |
| type_model_precision=model_precision | |
| ) | |
| self.model.load_pipe( | |
| model_name, | |
| task_name=task, | |
| vae_model=vae_model if vae_model != "None" else None, | |
| type_model_precision=model_precision | |
| ) | |
| if task != "txt2img" and not image_control: | |
| raise ValueError("No control image found: To use this function, you have to upload an image in 'Image ControlNet/Inpaint/Img2img'") | |
| if task == "inpaint" and not image_mask: | |
| raise ValueError("No mask image found: Specify one in 'Image Mask'") | |
| if upscaler_model_path in [None, "Lanczos", "Nearest"]: | |
| upscaler_model = upscaler_model_path | |
| else: | |
| directory_upscalers = 'upscalers' | |
| os.makedirs(directory_upscalers, exist_ok=True) | |
| url_upscaler = upscaler_dict_gui[upscaler_model_path] | |
| if not os.path.exists(f"./upscalers/{url_upscaler.split('/')[-1]}"): | |
| download_things(directory_upscalers, url_upscaler, hf_token) | |
| upscaler_model = f"./upscalers/{url_upscaler.split('/')[-1]}" | |
| if textual_inversion and self.model.class_name == "StableDiffusionXLPipeline": | |
| print("No Textual inversion for SDXL") | |
| logging.getLogger("ultralytics").setLevel(logging.INFO if adetailer_verbose else logging.ERROR) | |
| adetailer_params_A = { | |
| "face_detector_ad" : face_detector_ad_a, | |
| "person_detector_ad" : person_detector_ad_a, | |
| "hand_detector_ad" : hand_detector_ad_a, | |
| "prompt": prompt_ad_a, | |
| "negative_prompt" : negative_prompt_ad_a, | |
| "strength" : strength_ad_a, | |
| # "image_list_task" : None, | |
| "mask_dilation" : mask_dilation_a, | |
| "mask_blur" : mask_blur_a, | |
| "mask_padding" : mask_padding_a, | |
| "inpaint_only" : adetailer_inpaint_only, | |
| "sampler" : adetailer_sampler, | |
| } | |
| adetailer_params_B = { | |
| "face_detector_ad" : face_detector_ad_b, | |
| "person_detector_ad" : person_detector_ad_b, | |
| "hand_detector_ad" : hand_detector_ad_b, | |
| "prompt": prompt_ad_b, | |
| "negative_prompt" : negative_prompt_ad_b, | |
| "strength" : strength_ad_b, | |
| # "image_list_task" : None, | |
| "mask_dilation" : mask_dilation_b, | |
| "mask_blur" : mask_blur_b, | |
| "mask_padding" : mask_padding_b, | |
| } | |
| pipe_params = { | |
| "prompt": prompt, | |
| "negative_prompt": neg_prompt, | |
| "img_height": img_height, | |
| "img_width": img_width, | |
| "num_images": num_images, | |
| "num_steps": steps, | |
| "guidance_scale": cfg, | |
| "clip_skip": clip_skip, | |
| "seed": seed, | |
| "image": image_control, | |
| "preprocessor_name": preprocessor_name, | |
| "preprocess_resolution": preprocess_resolution, | |
| "image_resolution": image_resolution, | |
| "style_prompt": style_prompt if style_prompt else "", | |
| "style_json_file": "", | |
| "image_mask": image_mask, # only for Inpaint | |
| "strength": strength, # only for Inpaint or ... | |
| "low_threshold": low_threshold, | |
| "high_threshold": high_threshold, | |
| "value_threshold": value_threshold, | |
| "distance_threshold": distance_threshold, | |
| "lora_A": lora1 if lora1 != "None" else None, | |
| "lora_scale_A": lora_scale1, | |
| "lora_B": lora2 if lora2 != "None" else None, | |
| "lora_scale_B": lora_scale2, | |
| "lora_C": lora3 if lora3 != "None" else None, | |
| "lora_scale_C": lora_scale3, | |
| "lora_D": lora4 if lora4 != "None" else None, | |
| "lora_scale_D": lora_scale4, | |
| "lora_E": lora5 if lora5 != "None" else None, | |
| "lora_scale_E": lora_scale5, | |
| "textual_inversion": embed_list if textual_inversion and self.model.class_name != "StableDiffusionXLPipeline" else [], | |
| "syntax_weights": syntax_weights, # "Classic" | |
| "sampler": sampler, | |
| "xformers_memory_efficient_attention": xformers_memory_efficient_attention, | |
| "gui_active": True, | |
| "loop_generation": loop_generation, | |
| "controlnet_conditioning_scale": float(controlnet_output_scaling_in_unet), | |
| "control_guidance_start": float(controlnet_start_threshold), | |
| "control_guidance_end": float(controlnet_stop_threshold), | |
| "generator_in_cpu": generator_in_cpu, | |
| "FreeU": freeu, | |
| "adetailer_A": adetailer_active_a, | |
| "adetailer_A_params": adetailer_params_A, | |
| "adetailer_B": adetailer_active_b, | |
| "adetailer_B_params": adetailer_params_B, | |
| "leave_progress_bar": leave_progress_bar, | |
| "disable_progress_bar": disable_progress_bar, | |
| "image_previews": image_previews, | |
| "display_images": display_images, | |
| "save_generated_images": save_generated_images, | |
| "image_storage_location": image_storage_location, | |
| "retain_compel_previous_load": retain_compel_previous_load, | |
| "retain_detailfix_model_previous_load": retain_detailfix_model_previous_load, | |
| "retain_hires_model_previous_load": retain_hires_model_previous_load, | |
| "t2i_adapter_preprocessor": t2i_adapter_preprocessor, | |
| "t2i_adapter_conditioning_scale": float(t2i_adapter_conditioning_scale), | |
| "t2i_adapter_conditioning_factor": float(t2i_adapter_conditioning_factor), | |
| "upscaler_model_path": upscaler_model, | |
| "upscaler_increases_size": upscaler_increases_size, | |
| "esrgan_tile": esrgan_tile, | |
| "esrgan_tile_overlap": esrgan_tile_overlap, | |
| "hires_steps": hires_steps, | |
| "hires_denoising_strength": hires_denoising_strength, | |
| "hires_prompt": hires_prompt, | |
| "hires_negative_prompt": hires_negative_prompt, | |
| "hires_sampler": hires_sampler, | |
| "hires_before_adetailer": hires_before_adetailer, | |
| "hires_after_adetailer": hires_after_adetailer | |
| } | |
| # print(pipe_params) | |
| return self.infer(self.model, pipe_params) | |
| sd_gen = GuiSD() | |
| title_tab_one = "<h2 style='color: #2C5F2D;'>SD Interactive</h2>" | |
| title_tab_adetailer = "<h2 style='color: #97BC62;'>Adetailer</h2>" | |
| title_tab_hires = "<h2 style='color: #97BC62;'>High-resolution</h2>" | |
| title_tab_settings = "<h2 style='color: #97BC62;'>Settings</h2>" | |
| CSS =""" | |
| .contain { display: flex; flex-direction: column; } | |
| #component-0 { height: 100%; } | |
| #gallery { flex-grow: 1; } | |
| """ | |
| with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app: | |
| gr.Markdown("# 🧩 DiffuseCraft") | |
| gr.Markdown( | |
| f""" | |
| ### This demo uses [diffusers](https://github.com/huggingface/diffusers) to perform different tasks in image generation. | |
| """ | |
| ) | |
| with gr.Tab("Generation"): | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| task_gui = gr.Dropdown(label="Task", choices=task_model_list, value=task_model_list[0]) | |
| model_name_gui = gr.Dropdown(label="Model", choices=model_list, value=model_list[0], allow_custom_value=True) | |
| prompt_gui = gr.Textbox(lines=5, placeholder="Enter prompt") | |
| neg_prompt_gui = gr.Textbox(lines=3, placeholder="Enter Neg prompt") | |
| generate_button = gr.Button(value="GENERATE", variant="primary") | |
| result_images = gr.Gallery( | |
| label="Generated images", | |
| show_label=False, | |
| elem_id="gallery", | |
| columns=[2], | |
| rows=[3], | |
| object_fit="contain", | |
| # height="auto", | |
| interactive=False, | |
| preview=True, | |
| selected_index=50, | |
| ) | |
| with gr.Column(scale=1): | |
| steps_gui = gr.Slider(minimum=1, maximum=100, step=1, value=30, label="Steps") | |
| cfg_gui = gr.Slider(minimum=0, maximum=30, step=0.5, value=7.5, label="CFG") | |
| sampler_gui = gr.Dropdown(label="Sampler", choices=scheduler_names, value="Euler a") | |
| img_height_gui = gr.Slider(minimum=64, maximum=4096, step=8, value=1024, label="Img Height") | |
| img_width_gui = gr.Slider(minimum=64, maximum=4096, step=8, value=1024, label="Img Width") | |
| clip_skip_gui = gr.Checkbox(value=True, label="Layer 2 Clip Skip") | |
| free_u_gui = gr.Checkbox(value=True, label="FreeU") | |
| seed_gui = gr.Number(minimum=-1, maximum=9999999999, value=-1, label="Seed") | |
| num_images_gui = gr.Slider(minimum=1, maximum=8, step=1, value=1, label="Images") | |
| prompt_s_options = [("Compel (default) format: (word)weight", "Compel"), ("Classic (sd1.5 long prompts) format: (word:weight)", "Classic")] | |
| prompt_syntax_gui = gr.Dropdown(label="Prompt Syntax", choices=prompt_s_options, value=prompt_s_options[0][1]) | |
| vae_model_gui = gr.Dropdown(label="VAE Model", choices=vae_model_list) | |
| with gr.Accordion("ControlNet / Img2img / Inpaint", open=False, visible=True): | |
| image_control = gr.Image(label="Image ControlNet/Inpaint/Img2img", type="filepath") | |
| image_mask_gui = gr.Image(label="Image Mask", type="filepath") | |
| strength_gui = gr.Slider(minimum=0.01, maximum=1.0, step=0.01, value=0.35, label="Strength") | |
| image_resolution_gui = gr.Slider(minimum=64, maximum=2048, step=64, value=1024, label="Image Resolution") | |
| preprocessor_name_gui = gr.Dropdown(label="Preprocessor Name", choices=preprocessor_controlnet["canny"]) | |
| def change_preprocessor_choices(task): | |
| task = task_stablepy[task] | |
| if task in preprocessor_controlnet.keys(): | |
| choices_task = preprocessor_controlnet[task] | |
| else: | |
| choices_task = preprocessor_controlnet["canny"] | |
| return gr.update(choices=choices_task, value=choices_task[0]) | |
| task_gui.change( | |
| change_preprocessor_choices, | |
| [task_gui], | |
| [preprocessor_name_gui], | |
| ) | |
| preprocess_resolution_gui = gr.Slider(minimum=64, maximum=2048, step=64, value=512, label="Preprocess Resolution") | |
| low_threshold_gui = gr.Slider(minimum=1, maximum=255, step=1, value=100, label="Canny low threshold") | |
| high_threshold_gui = gr.Slider(minimum=1, maximum=255, step=1, value=200, label="Canny high threshold") | |
| value_threshold_gui = gr.Slider(minimum=1, maximum=2.0, step=0.01, value=0.1, label="Hough value threshold (MLSD)") | |
| distance_threshold_gui = gr.Slider(minimum=1, maximum=20.0, step=0.01, value=0.1, label="Hough distance threshold (MLSD)") | |
| control_net_output_scaling_gui = gr.Slider(minimum=0, maximum=5.0, step=0.1, value=1, label="ControlNet Output Scaling in UNet") | |
| control_net_start_threshold_gui = gr.Slider(minimum=0, maximum=1, step=0.01, value=0, label="ControlNet Start Threshold (%)") | |
| control_net_stop_threshold_gui = gr.Slider(minimum=0, maximum=1, step=0.01, value=1, label="ControlNet Stop Threshold (%)") | |
| with gr.Accordion("T2I adapter", open=False, visible=True): | |
| t2i_adapter_preprocessor_gui = gr.Checkbox(value=True, label="T2i Adapter Preprocessor") | |
| adapter_conditioning_scale_gui = gr.Slider(minimum=0, maximum=5., step=0.1, value=1, label="Adapter Conditioning Scale") | |
| adapter_conditioning_factor_gui = gr.Slider(minimum=0, maximum=1., step=0.01, value=0.55, label="Adapter Conditioning Factor (%)") | |
| with gr.Accordion("LoRA", open=False, visible=False): | |
| lora1_gui = gr.Dropdown(label="Lora1", choices=lora_model_list) | |
| lora_scale_1_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1, label="Lora Scale 1") | |
| lora2_gui = gr.Dropdown(label="Lora2", choices=lora_model_list) | |
| lora_scale_2_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1, label="Lora Scale 2") | |
| lora3_gui = gr.Dropdown(label="Lora3", choices=lora_model_list) | |
| lora_scale_3_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1, label="Lora Scale 3") | |
| lora4_gui = gr.Dropdown(label="Lora4", choices=lora_model_list) | |
| lora_scale_4_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1, label="Lora Scale 4") | |
| lora5_gui = gr.Dropdown(label="Lora5", choices=lora_model_list) | |
| lora_scale_5_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1, label="Lora Scale 5") | |
| with gr.Accordion("Styles", open=False, visible=True): | |
| try: | |
| style_names_found = sd_gen.model.STYLE_NAMES | |
| except: | |
| style_names_found = STYLE_NAMES | |
| style_prompt_gui = gr.Dropdown( | |
| style_names_found, | |
| multiselect=True, | |
| value=None, | |
| label="Style Prompt", | |
| interactive=True, | |
| ) | |
| style_json_gui = gr.File(label="Style JSON File") | |
| style_button = gr.Button("Load styles") | |
| def load_json_style_file(json): | |
| if not sd_gen.model: | |
| gr.Info("First load the model") | |
| return gr.update(value=None, choices=STYLE_NAMES) | |
| sd_gen.model.load_style_file(json) | |
| gr.Info(f"{len(sd_gen.model.STYLE_NAMES)} styles loaded") | |
| return gr.update(value=None, choices=sd_gen.model.STYLE_NAMES) | |
| style_button.click(load_json_style_file, [style_json_gui], [style_prompt_gui]) | |
| with gr.Accordion("Textual inversion", open=False, visible=False): | |
| active_textual_inversion_gui = gr.Checkbox(value=False, label="Active Textual Inversion in prompt") | |
| with gr.Accordion("Hires fix", open=False, visible=True): | |
| upscaler_keys = list(upscaler_dict_gui.keys()) | |
| upscaler_model_path_gui = gr.Dropdown(label="Upscaler", choices=upscaler_keys, value=upscaler_keys[0]) | |
| upscaler_increases_size_gui = gr.Slider(minimum=1.1, maximum=6., step=0.1, value=1.5, label="Upscale by") | |
| esrgan_tile_gui = gr.Slider(minimum=0, value=100, maximum=500, step=1, label="ESRGAN Tile") | |
| esrgan_tile_overlap_gui = gr.Slider(minimum=1, maximum=200, step=1, value=10, label="ESRGAN Tile Overlap") | |
| hires_steps_gui = gr.Slider(minimum=0, value=30, maximum=100, step=1, label="Hires Steps") | |
| hires_denoising_strength_gui = gr.Slider(minimum=0.1, maximum=1.0, step=0.01, value=0.55, label="Hires Denoising Strength") | |
| hires_sampler_gui = gr.Dropdown(label="Hires Sampler", choices=["Use same sampler"] + scheduler_names[:-1], value="Use same sampler") | |
| hires_prompt_gui = gr.Textbox(label="Hires Prompt", placeholder="Main prompt will be use", lines=3) | |
| hires_negative_prompt_gui = gr.Textbox(label="Hires Negative Prompt", placeholder="Main negative prompt will be use", lines=3) | |
| with gr.Accordion("Detailfix", open=False, visible=False): | |
| # Adetailer Inpaint Only | |
| adetailer_inpaint_only_gui = gr.Checkbox(label="Inpaint only", value=True) | |
| # Adetailer Verbose | |
| adetailer_verbose_gui = gr.Checkbox(label="Verbose", value=False) | |
| # Adetailer Sampler | |
| adetailer_sampler_options = ["Use same sampler"] + scheduler_names[:-1] | |
| adetailer_sampler_gui = gr.Dropdown(label="Adetailer sampler:", choices=adetailer_sampler_options, value="Use same sampler") | |
| with gr.Accordion("Detailfix A", open=False, visible=True): | |
| # Adetailer A | |
| adetailer_active_a_gui = gr.Checkbox(label="Enable Adetailer A", value=False) | |
| prompt_ad_a_gui = gr.Textbox(label="Main prompt", placeholder="Main prompt will be use", lines=3) | |
| negative_prompt_ad_a_gui = gr.Textbox(label="Negative prompt", placeholder="Main negative prompt will be use", lines=3) | |
| strength_ad_a_gui = gr.Number(label="Strength:", value=0.35, step=0.01, minimum=0.01, maximum=1.0) | |
| face_detector_ad_a_gui = gr.Checkbox(label="Face detector", value=True) | |
| person_detector_ad_a_gui = gr.Checkbox(label="Person detector", value=True) | |
| hand_detector_ad_a_gui = gr.Checkbox(label="Hand detector", value=False) | |
| mask_dilation_a_gui = gr.Number(label="Mask dilation:", value=4, minimum=1) | |
| mask_blur_a_gui = gr.Number(label="Mask blur:", value=4, minimum=1) | |
| mask_padding_a_gui = gr.Number(label="Mask padding:", value=32, minimum=1) | |
| with gr.Accordion("Detailfix B", open=False, visible=True): | |
| # Adetailer B | |
| adetailer_active_b_gui = gr.Checkbox(label="Enable Adetailer B", value=False) | |
| prompt_ad_b_gui = gr.Textbox(label="Main prompt", placeholder="Main prompt will be use", lines=3) | |
| negative_prompt_ad_b_gui = gr.Textbox(label="Negative prompt", placeholder="Main negative prompt will be use", lines=3) | |
| strength_ad_b_gui = gr.Number(label="Strength:", value=0.35, step=0.01, minimum=0.01, maximum=1.0) | |
| face_detector_ad_b_gui = gr.Checkbox(label="Face detector", value=True) | |
| person_detector_ad_b_gui = gr.Checkbox(label="Person detector", value=True) | |
| hand_detector_ad_b_gui = gr.Checkbox(label="Hand detector", value=False) | |
| mask_dilation_b_gui = gr.Number(label="Mask dilation:", value=4, minimum=1) | |
| mask_blur_b_gui = gr.Number(label="Mask blur:", value=4, minimum=1) | |
| mask_padding_b_gui = gr.Number(label="Mask padding:", value=32, minimum=1) | |
| with gr.Accordion("Other settings", open=False, visible=False): | |
| hires_before_adetailer_gui = gr.Checkbox(value=False, label="Hires Before Adetailer") | |
| hires_after_adetailer_gui = gr.Checkbox(value=True, label="Hires After Adetailer") | |
| loop_generation_gui = gr.Slider(minimum=1, value=1, label="Loop Generation") | |
| leave_progress_bar_gui = gr.Checkbox(value=True, label="Leave Progress Bar") | |
| disable_progress_bar_gui = gr.Checkbox(value=False, label="Disable Progress Bar") | |
| image_previews_gui = gr.Checkbox(value=False, label="Image Previews") | |
| display_images_gui = gr.Checkbox(value=False, label="Display Images") | |
| save_generated_images_gui = gr.Checkbox(value=False, label="Save Generated Images") | |
| image_storage_location_gui = gr.Textbox(value="./images", label="Image Storage Location") | |
| retain_compel_previous_load_gui = gr.Checkbox(value=False, label="Retain Compel Previous Load") | |
| retain_detailfix_model_previous_load_gui = gr.Checkbox(value=False, label="Retain Detailfix Model Previous Load") | |
| retain_hires_model_previous_load_gui = gr.Checkbox(value=False, label="Retain Hires Model Previous Load") | |
| xformers_memory_efficient_attention_gui = gr.Checkbox(value=False, label="Xformers Memory Efficient Attention") | |
| generator_in_cpu_gui = gr.Checkbox(value=False, label="Generator in CPU") | |
| with gr.Tab("Inpaint mask maker", render=True): | |
| def create_mask_now(img, invert): | |
| import numpy as np | |
| import time | |
| time.sleep(0.5) | |
| transparent_image = img["layers"][0] | |
| # Extract the alpha channel | |
| alpha_channel = np.array(transparent_image)[:, :, 3] | |
| # Create a binary mask by thresholding the alpha channel | |
| binary_mask = alpha_channel > 1 | |
| if invert: | |
| print("Invert") | |
| # Invert the binary mask so that the drawn shape is white and the rest is black | |
| binary_mask = np.invert(binary_mask) | |
| # Convert the binary mask to a 3-channel RGB mask | |
| rgb_mask = np.stack((binary_mask,) * 3, axis=-1) | |
| # Convert the mask to uint8 | |
| rgb_mask = rgb_mask.astype(np.uint8) * 255 | |
| return img["background"], rgb_mask | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| # image_base = gr.ImageEditor(label="Base image", show_label=True, brush=gr.Brush(colors=["#000000"])) | |
| image_base = gr.ImageEditor( | |
| sources=["upload", "clipboard"], | |
| # crop_size="1:1", | |
| # enable crop (or disable it) | |
| # transforms=["crop"], | |
| brush=gr.Brush( | |
| default_size="16", # or leave it as 'auto' | |
| color_mode="fixed", # 'fixed' hides the user swatches and colorpicker, 'defaults' shows it | |
| #default_color="black", # html names are supported | |
| colors=[ | |
| "rgba(0, 0, 0, 1)", # rgb(a) | |
| "rgba(0, 0, 0, 0.1)", | |
| "rgba(255, 255, 255, 0.1)", | |
| # "hsl(360, 120, 120)" # in fact any valid colorstring | |
| ] | |
| ), | |
| eraser=gr.Eraser(default_size="16") | |
| ) | |
| invert_mask = gr.Checkbox(value=False, label="Invert mask") | |
| btn = gr.Button("Create mask") | |
| with gr.Column(scale=1): | |
| img_source = gr.Image(interactive=False) | |
| img_result = gr.Image(label="Mask image", show_label=True, interactive=False) | |
| btn_send = gr.Button("Send to the first tab") | |
| btn.click(create_mask_now, [image_base, invert_mask], [img_source, img_result]) | |
| def send_img(img_source, img_result): | |
| return img_source, img_result | |
| btn_send.click(send_img, [img_source, img_result], [image_control, image_mask_gui]) | |
| generate_button.click( | |
| fn=sd_gen.generate_pipeline, | |
| inputs=[ | |
| prompt_gui, | |
| neg_prompt_gui, | |
| num_images_gui, | |
| steps_gui, | |
| cfg_gui, | |
| clip_skip_gui, | |
| seed_gui, | |
| lora1_gui, | |
| lora_scale_1_gui, | |
| lora2_gui, | |
| lora_scale_2_gui, | |
| lora3_gui, | |
| lora_scale_3_gui, | |
| lora4_gui, | |
| lora_scale_4_gui, | |
| lora5_gui, | |
| lora_scale_5_gui, | |
| sampler_gui, | |
| img_height_gui, | |
| img_width_gui, | |
| model_name_gui, | |
| vae_model_gui, | |
| task_gui, | |
| image_control, | |
| preprocessor_name_gui, | |
| preprocess_resolution_gui, | |
| image_resolution_gui, | |
| style_prompt_gui, | |
| style_json_gui, | |
| image_mask_gui, | |
| strength_gui, | |
| low_threshold_gui, | |
| high_threshold_gui, | |
| value_threshold_gui, | |
| distance_threshold_gui, | |
| control_net_output_scaling_gui, | |
| control_net_start_threshold_gui, | |
| control_net_stop_threshold_gui, | |
| active_textual_inversion_gui, | |
| prompt_syntax_gui, | |
| loop_generation_gui, | |
| leave_progress_bar_gui, | |
| disable_progress_bar_gui, | |
| image_previews_gui, | |
| display_images_gui, | |
| save_generated_images_gui, | |
| image_storage_location_gui, | |
| retain_compel_previous_load_gui, | |
| retain_detailfix_model_previous_load_gui, | |
| retain_hires_model_previous_load_gui, | |
| t2i_adapter_preprocessor_gui, | |
| adapter_conditioning_scale_gui, | |
| adapter_conditioning_factor_gui, | |
| upscaler_model_path_gui, | |
| upscaler_increases_size_gui, | |
| esrgan_tile_gui, | |
| esrgan_tile_overlap_gui, | |
| hires_steps_gui, | |
| hires_denoising_strength_gui, | |
| hires_sampler_gui, | |
| hires_prompt_gui, | |
| hires_negative_prompt_gui, | |
| hires_before_adetailer_gui, | |
| hires_after_adetailer_gui, | |
| xformers_memory_efficient_attention_gui, | |
| free_u_gui, | |
| generator_in_cpu_gui, | |
| adetailer_inpaint_only_gui, | |
| adetailer_verbose_gui, | |
| adetailer_sampler_gui, | |
| adetailer_active_a_gui, | |
| prompt_ad_a_gui, | |
| negative_prompt_ad_a_gui, | |
| strength_ad_a_gui, | |
| face_detector_ad_a_gui, | |
| person_detector_ad_a_gui, | |
| hand_detector_ad_a_gui, | |
| mask_dilation_a_gui, | |
| mask_blur_a_gui, | |
| mask_padding_a_gui, | |
| adetailer_active_b_gui, | |
| prompt_ad_b_gui, | |
| negative_prompt_ad_b_gui, | |
| strength_ad_b_gui, | |
| face_detector_ad_b_gui, | |
| person_detector_ad_b_gui, | |
| hand_detector_ad_b_gui, | |
| mask_dilation_b_gui, | |
| mask_blur_b_gui, | |
| mask_padding_b_gui, | |
| ], | |
| outputs=[result_images], | |
| queue=True, | |
| ) | |
| app.queue() # default_concurrency_limit=40 | |
| app.launch( | |
| # max_threads=40, | |
| # share=False, | |
| show_error=True, | |
| # quiet=False, | |
| debug=True, | |
| # allowed_paths=["./assets/"], | |
| ) | |