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| import spaces | |
| import os | |
| 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) as app: | |
| gr.Markdown("# 🧩 (Ivan) DiffuseCraft") | |
| 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="models/animaPencilXL_v500.safetensors" 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", | |
| show_label=False, | |
| elem_id="gallery", | |
| columns=[2], | |
| rows=[2], | |
| object_fit="contain", | |
| # height="auto", | |
| interactive=False, | |
| preview=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, | |
| ) | |