import spaces import os os.system("python -m pip uninstall -y stablepy && python -m pip install -r requirements.txt") from stablepy.diffusers_vanilla.style_prompt_config import STYLE_NAMES import re from stablepy import ( CONTROLNET_MODEL_IDS, VALID_TASKS, T2I_PREPROCESSOR_NAME, FLASH_LORA, SCHEDULER_CONFIG_MAP, scheduler_names, IP_ADAPTER_MODELS, IP_ADAPTERS_SD, IP_ADAPTERS_SDXL, REPO_IMAGE_ENCODER, ALL_PROMPT_WEIGHT_OPTIONS, SD15_TASKS, SDXL_TASKS, ) from config import ( DEFAULT_STEPS, DEFAULT_CFG, MINIMUM_IMAGE_NUMBER, MAXIMUM_IMAGE_NUMBER, DEFAULT_NEGATIVE_PROMPT, DEFAULT_POSITIVE_PROMPT, task_stablepy ) from models.vae import VAE_LIST as download_vae from models.checkpoints import CHECKPOINT_LIST as download_model from models.loras import LORA_LIST as download_lora from models.format_models import FORMAT_MODELS as load_diffusers_format_model from models.upscaler import upscaler_dict_gui from models.controlnet import preprocessor_controlnet from models.embeds import download_embeds from examples.examples import example_prompts from utils.download_utils import download_things from utils.model_utils import get_model_list import gradio as gr import logging from utils.string_utils import extract_parameters from stablepy import logger import diffusers import warnings from gui import GuiSD # LOAD ALL ENV TOKEN CIVITAI_API_KEY: str = os.environ.get("CIVITAI_API_KEY") hf_token: str = os.environ.get("HF_TOKEN") task_model_list = list(task_stablepy.keys()) # [Create directories] directory_models: str = 'models' os.makedirs( directory_models, exist_ok=True ) directory_loras: str = 'loras' os.makedirs( directory_loras, exist_ok=True ) directory_vaes: str = 'vaes' os.makedirs( directory_vaes, exist_ok=True ) directory_embeds: str = 'embedings' os.makedirs( directory_embeds, exist_ok=True ) # 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 ) 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: list = get_model_list(directory_embeds) model_list: list = get_model_list(directory_models) model_list: list = load_diffusers_format_model + model_list lora_model_list: list = get_model_list(directory_loras) lora_model_list.insert(0, "None") vae_model_list: list = get_model_list(directory_vaes) vae_model_list.insert(0, "None") def get_my_lora(link_url) -> tuple: for __url in [_url.strip() for _url in link_url.split(',')]: if not os.path.exists(f"./loras/{__url.split('/')[-1]}"): download_things( directory_loras, __url, hf_token, CIVITAI_API_KEY ) new_lora_model_list: list = get_model_list(directory_loras) new_lora_model_list.insert(0, "None") return gr.update( choices=new_lora_model_list ), gr.update( choices=new_lora_model_list ), gr.update( choices=new_lora_model_list ), gr.update( choices=new_lora_model_list ), gr.update( choices=new_lora_model_list ) print('\033[33m🏁 Download and listing of valid models completed.\033[0m') ####################### # GUI ####################### logging.getLogger("diffusers").setLevel(logging.ERROR) diffusers.utils.logging.set_verbosity(40) warnings.filterwarnings( action="ignore", category=FutureWarning, module="diffusers" ) warnings.filterwarnings( action="ignore", category=UserWarning, module="diffusers" ) warnings.filterwarnings( action="ignore", category=FutureWarning, module="transformers" ) logger.setLevel(logging.DEBUG) # init GuiSD SD_GEN: GuiSD = GuiSD( model_list=model_list, task_stablepy=task_stablepy, lora_model_list=lora_model_list, embed_list=embed_list, ) with open("app.css", "r") as f: CSS: str = f.read() sdxl_task = [k for k, v in task_stablepy.items() if v in SDXL_TASKS] sd_task = [k for k, v in task_stablepy.items() if v in SD15_TASKS] def update_task_options( model_name: str, task_name: str): """ :param model_name: :param task_name: :return: """ if model_name in model_list: if "xl" in model_name.lower(): new_choices = sdxl_task else: new_choices = sd_task if task_name not in new_choices: task_name = "txt2img" return gr.update( value=task_name, choices=new_choices ) else: return gr.update( value=task_name, choices=task_model_list ) # APP with gr.Blocks(css=CSS, delete_cache=(86400, 86400)) as app: gr.Markdown("# 🐠 AnyDiffuse") with gr.Tab("Generation"): with gr.Row(): with gr.Column(scale=2): task_gui = gr.Dropdown( label="Task", choices=sdxl_task, value=task_model_list[0], ) model_name_gui = gr.Dropdown( label="Model", choices=model_list, value="eienmojiki/Anything-XL" or model_list[0], allow_custom_value=True ) prompt_gui = gr.Textbox( lines=5, placeholder="Enter Positive prompt", label="Positive Prompt", value=DEFAULT_POSITIVE_PROMPT ) neg_prompt_gui = gr.Textbox( lines=3, placeholder="Enter Negative prompt", label="Negative prompt", value=DEFAULT_NEGATIVE_PROMPT ) with gr.Row(equal_height=False): set_params_gui = gr.Button(value="↙️") clear_prompt_gui = gr.Button(value="🗑️") set_random_seed = gr.Button(value="🎲") generate_button = gr.Button( value="GENERATE", variant="primary" ) model_name_gui.change( update_task_options, [model_name_gui, task_gui], [task_gui], ) load_model_gui = gr.HTML() result_images = gr.Gallery( label="Generated images", format="png", type="pil", show_label=False, elem_id="gallery", columns=[2], rows=[2], object_fit="contain", # height="auto", interactive=False, selected_index=50, ) actual_task_info = gr.HTML() with gr.Column(scale=1): steps_gui = gr.Slider( minimum=1, maximum=100, step=1, value=DEFAULT_STEPS, label="Steps" ) cfg_gui = gr.Slider( minimum=0, maximum=30, step=0.5, value=DEFAULT_CFG, label="CFG" ) sampler_gui = gr.Dropdown( label="Sampler", choices=scheduler_names, value="DPM++ 2M Karras" ) img_width_gui = gr.Slider( minimum=64, maximum=4096, step=8, value=1024, label="Img Width" ) img_height_gui = gr.Slider( minimum=64, maximum=4096, step=8, value=1024, label="Img Height" ) seed_gui = gr.Number( minimum=-1, maximum=9999999999, value=-1, label="Seed" ) with gr.Row(): clip_skip_gui = gr.Checkbox( value=False, label="Layer 2 Clip Skip" ) free_u_gui = gr.Checkbox( value=True, label="FreeU" ) with gr.Row(equal_height=False): def run_set_params_gui(base_prompt): valid_receptors: dict = { # default values "prompt": gr.update(value=base_prompt), "neg_prompt": gr.update(value=""), "Steps": gr.update(value=30), "width": gr.update(value=1024), "height": gr.update(value=1024), "Seed": gr.update(value=-1), "Sampler": gr.update(value="Euler a"), "scale": gr.update(value=7.5), # cfg "skip": gr.update(value=True), } valid_keys = list(valid_receptors.keys()) parameters: dict = extract_parameters(base_prompt) for key, val in parameters.items(): if key in valid_keys: if key == "Sampler": if val not in scheduler_names: continue elif key == "skip": if int(val) >= 2: val = True if key == "prompt": if ">" in val and "<" in val: val = re.sub(r'<[^>]+>', '', val) print("Removed LoRA written in the prompt") if key in ["prompt", "neg_prompt"]: val = val.strip() if key in ["Steps", "width", "height", "Seed"]: val = int(val) if key == "scale": val = float(val) if key == "Seed": continue valid_receptors[key] = gr.update(value=val) return [value for value in valid_receptors.values()] set_params_gui.click( run_set_params_gui, [prompt_gui], [ prompt_gui, neg_prompt_gui, steps_gui, img_width_gui, img_height_gui, seed_gui, sampler_gui, cfg_gui, clip_skip_gui, ], ) def run_clear_prompt_gui(): return gr.update(value=""), gr.update(value="") clear_prompt_gui.click( run_clear_prompt_gui, [], [prompt_gui, neg_prompt_gui] ) def run_set_random_seed(): return -1 set_random_seed.click( run_set_random_seed, [], seed_gui ) num_images_gui = gr.Slider( minimum=MINIMUM_IMAGE_NUMBER, maximum=MAXIMUM_IMAGE_NUMBER, step=1, value=1, label="Images" ) prompt_s_options = [ ("Classic format: (word:weight)", "Classic"), ("Compel format: (word)weight", "Compel"), ("Classic-original format: (word:weight)", "Classic-original"), ("Classic-no_norm format: (word:weight)", "Classic-no_norm"), ("Classic-ignore", "Classic-ignore"), ("None", "None"), ] 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, value=vae_model_list[1] ) 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=1.4, 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("LoRA", open=False, visible=True): lora1_gui = gr.Dropdown( label="Lora1", choices=lora_model_list ) lora_scale_1_gui = gr.Slider( minimum=-2, maximum=2, step=0.01, value=0.33, 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=0.33, 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=0.33, 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=0.33, 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=0.33, label="Lora Scale 5" ) with gr.Accordion( "From URL", open=False, visible=True): text_lora = gr.Textbox( label="URL", placeholder="http://...my_lora_url.safetensors", lines=1 ) button_lora = gr.Button("Get and update lists of LoRAs") button_lora.click( get_my_lora, [text_lora], [ lora1_gui, lora2_gui, lora3_gui, lora4_gui, lora5_gui ] ) with gr.Accordion( "IP-Adapter", open=False, visible=True): # IP-Adapter IP_MODELS = sorted( list( set( IP_ADAPTERS_SD + IP_ADAPTERS_SDXL ) ) ) MODE_IP_OPTIONS = [ "original", "style", "layout", "style+layout" ] with gr.Accordion("IP-Adapter 1", open=False, visible=True): image_ip1 = gr.Image( label="IP Image", type="filepath" ) mask_ip1 = gr.Image( label="IP Mask", type="filepath" ) model_ip1 = gr.Dropdown( value="plus_face", label="Model", choices=IP_MODELS ) mode_ip1 = gr.Dropdown( value="original", label="Mode", choices=MODE_IP_OPTIONS ) scale_ip1 = gr.Slider( minimum=0., maximum=2., step=0.01, value=0.7, label="Scale" ) with gr.Accordion("IP-Adapter 2", open=False, visible=True): image_ip2 = gr.Image( label="IP Image", type="filepath" ) mask_ip2 = gr.Image( label="IP Mask (optional)", type="filepath" ) model_ip2 = gr.Dropdown( value="base", label="Model", choices=IP_MODELS ) mode_ip2 = gr.Dropdown( value="style", label="Mode", choices=MODE_IP_OPTIONS ) scale_ip2 = gr.Slider( minimum=0., maximum=2., step=0.01, value=0.7, label="Scale" ) 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=0.55, label="Strength", info="This option adjusts the level of changes for img2img and inpainting." ) 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( "Styles", open=False, visible=True): # noinspection PyBroadException try: style_names_found = SD_GEN.model.STYLE_NAMES except Exception as e: 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( "Detailfix", open=False, visible=True): # 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=True): image_previews_gui = gr.Checkbox( value=True, label="Image Previews" ) hires_before_adetailer_gui = gr.Checkbox( value=False, label="Hires Before Adetailer" ) hires_after_adetailer_gui = gr.Checkbox( value=True, label="Hires After Adetailer" ) generator_in_cpu_gui = gr.Checkbox( value=False, label="Generator in CPU" ) with gr.Accordion( "More settings", open=False, visible=False): loop_generation_gui = gr.Slider( minimum=1, value=1, label="Loop Generation" ) retain_task_cache_gui = gr.Checkbox( value=False, label="Retain task model in cache" ) leave_progress_bar_gui = gr.Checkbox( value=True, label="Leave Progress Bar" ) disable_progress_bar_gui = gr.Checkbox( value=False, label="Disable Progress Bar" ) display_images_gui = gr.Checkbox( value=True, 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_detail_fix_model_previous_load_gui = gr.Checkbox( value=False, label="Retain Detail fix 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" ) # example and Help Section with gr.Accordion( "Examples and help", open=False, visible=True): gr.Markdown( """### Help: - The current space runs on a ZERO GPU which is assigned for approximately 60 seconds; Therefore, \ if you submit expensive tasks, the operation may be canceled upon reaching the \ maximum allowed time with 'GPU TASK ABORTED'. - Distorted or strange images often result from high prompt weights, \ so it's best to use low weights and scales, and consider using Classic variants like 'Classic-original'. - For better results with Pony Diffusion, \ try using sampler DPM++ 1s or DPM2 with Compel or Classic prompt weights. """ ) gr.Markdown( """### The following examples perform specific tasks: 1. Generation with SDXL and upscale 2. Generation with SDXL 3. ControlNet Canny SDXL 4. Optical pattern (Optical illusion) SDXL 5. Convert an image to a coloring drawing 6. ControlNet OpenPose SD 1.5 - Different tasks can be performed, such as img2img or using the IP adapter, \ to preserve a person's appearance or a specific style based on an image. """ ) """ gr.Examples( examples=example_prompts, 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, upscaler_model_path_gui, ], outputs=[result_images], cache_examples=False, ) """ 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) -> tuple: 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.load_new_model, inputs=[ model_name_gui, vae_model_gui, task_gui ], outputs=[load_model_gui], queue=True, show_progress="minimal", ).success( 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, 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, 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_detail_fix_model_previous_load_gui, retain_hires_model_previous_load_gui, t2i_adapter_preprocessor_gui, adapter_conditioning_scale_gui, adapter_conditioning_factor_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, retain_task_cache_gui, image_ip1, mask_ip1, model_ip1, mode_ip1, scale_ip1, image_ip2, mask_ip2, model_ip2, mode_ip2, scale_ip2, ], outputs=[ result_images, actual_task_info ], queue=True, show_progress="minimal", ) app.queue() app.launch( show_error=True, debug=True, )