From 6770b784a6d676d08171cf59ca6ad1850c046f9e Mon Sep 17 00:00:00 2001 From: Ashen Date: Mon, 6 Mar 2023 20:48:27 -0800 Subject: [PATCH] =?UTF-8?q?=E2=9C=A8Adding=20after=5Fui=5Fcallback=20for?= =?UTF-8?q?=20easier=20gradio=20component=20access=20for=20extensions,=20c?= =?UTF-8?q?rlf?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- modules/script_callbacks.py | 734 +++---- modules/ui.py | 3599 ++++++++++++++++++----------------- 2 files changed, 2176 insertions(+), 2157 deletions(-) diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py index edd0e2a7259..b8d4fc48c06 100644 --- a/modules/script_callbacks.py +++ b/modules/script_callbacks.py @@ -1,359 +1,375 @@ -import sys -import traceback -from collections import namedtuple -import inspect -from typing import Optional, Dict, Any - -from fastapi import FastAPI -from gradio import Blocks - - -def report_exception(c, job): - print(f"Error executing callback {job} for {c.script}", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) - - -class ImageSaveParams: - def __init__(self, image, p, filename, pnginfo): - self.image = image - """the PIL image itself""" - - self.p = p - """p object with processing parameters; either StableDiffusionProcessing or an object with same fields""" - - self.filename = filename - """name of file that the image would be saved to""" - - self.pnginfo = pnginfo - """dictionary with parameters for image's PNG info data; infotext will have the key 'parameters'""" - - -class CFGDenoiserParams: - def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps): - self.x = x - """Latent image representation in the process of being denoised""" - - self.image_cond = image_cond - """Conditioning image""" - - self.sigma = sigma - """Current sigma noise step value""" - - self.sampling_step = sampling_step - """Current Sampling step number""" - - self.total_sampling_steps = total_sampling_steps - """Total number of sampling steps planned""" - - -class CFGDenoisedParams: - def __init__(self, x, sampling_step, total_sampling_steps): - self.x = x - """Latent image representation in the process of being denoised""" - - self.sampling_step = sampling_step - """Current Sampling step number""" - - self.total_sampling_steps = total_sampling_steps - """Total number of sampling steps planned""" - - -class UiTrainTabParams: - def __init__(self, txt2img_preview_params): - self.txt2img_preview_params = txt2img_preview_params - - -class ImageGridLoopParams: - def __init__(self, imgs, cols, rows): - self.imgs = imgs - self.cols = cols - self.rows = rows - - -ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"]) -callback_map = dict( - callbacks_app_started=[], - callbacks_model_loaded=[], - callbacks_ui_tabs=[], - callbacks_ui_train_tabs=[], - callbacks_ui_settings=[], - callbacks_before_image_saved=[], - callbacks_image_saved=[], - callbacks_cfg_denoiser=[], - callbacks_cfg_denoised=[], - callbacks_before_component=[], - callbacks_after_component=[], - callbacks_image_grid=[], - callbacks_infotext_pasted=[], - callbacks_script_unloaded=[], - callbacks_before_ui=[], -) - - -def clear_callbacks(): - for callback_list in callback_map.values(): - callback_list.clear() - - -def app_started_callback(demo: Optional[Blocks], app: FastAPI): - for c in callback_map['callbacks_app_started']: - try: - c.callback(demo, app) - except Exception: - report_exception(c, 'app_started_callback') - - -def model_loaded_callback(sd_model): - for c in callback_map['callbacks_model_loaded']: - try: - c.callback(sd_model) - except Exception: - report_exception(c, 'model_loaded_callback') - - -def ui_tabs_callback(): - res = [] - - for c in callback_map['callbacks_ui_tabs']: - try: - res += c.callback() or [] - except Exception: - report_exception(c, 'ui_tabs_callback') - - return res - - -def ui_train_tabs_callback(params: UiTrainTabParams): - for c in callback_map['callbacks_ui_train_tabs']: - try: - c.callback(params) - except Exception: - report_exception(c, 'callbacks_ui_train_tabs') - - -def ui_settings_callback(): - for c in callback_map['callbacks_ui_settings']: - try: - c.callback() - except Exception: - report_exception(c, 'ui_settings_callback') - - -def before_image_saved_callback(params: ImageSaveParams): - for c in callback_map['callbacks_before_image_saved']: - try: - c.callback(params) - except Exception: - report_exception(c, 'before_image_saved_callback') - - -def image_saved_callback(params: ImageSaveParams): - for c in callback_map['callbacks_image_saved']: - try: - c.callback(params) - except Exception: - report_exception(c, 'image_saved_callback') - - -def cfg_denoiser_callback(params: CFGDenoiserParams): - for c in callback_map['callbacks_cfg_denoiser']: - try: - c.callback(params) - except Exception: - report_exception(c, 'cfg_denoiser_callback') - - -def cfg_denoised_callback(params: CFGDenoisedParams): - for c in callback_map['callbacks_cfg_denoised']: - try: - c.callback(params) - except Exception: - report_exception(c, 'cfg_denoised_callback') - - -def before_component_callback(component, **kwargs): - for c in callback_map['callbacks_before_component']: - try: - c.callback(component, **kwargs) - except Exception: - report_exception(c, 'before_component_callback') - - -def after_component_callback(component, **kwargs): - for c in callback_map['callbacks_after_component']: - try: - c.callback(component, **kwargs) - except Exception: - report_exception(c, 'after_component_callback') - - -def image_grid_callback(params: ImageGridLoopParams): - for c in callback_map['callbacks_image_grid']: - try: - c.callback(params) - except Exception: - report_exception(c, 'image_grid') - - -def infotext_pasted_callback(infotext: str, params: Dict[str, Any]): - for c in callback_map['callbacks_infotext_pasted']: - try: - c.callback(infotext, params) - except Exception: - report_exception(c, 'infotext_pasted') - - -def script_unloaded_callback(): - for c in reversed(callback_map['callbacks_script_unloaded']): - try: - c.callback() - except Exception: - report_exception(c, 'script_unloaded') - - -def before_ui_callback(): - for c in reversed(callback_map['callbacks_before_ui']): - try: - c.callback() - except Exception: - report_exception(c, 'before_ui') - - -def add_callback(callbacks, fun): - stack = [x for x in inspect.stack() if x.filename != __file__] - filename = stack[0].filename if len(stack) > 0 else 'unknown file' - - callbacks.append(ScriptCallback(filename, fun)) - - -def remove_current_script_callbacks(): - stack = [x for x in inspect.stack() if x.filename != __file__] - filename = stack[0].filename if len(stack) > 0 else 'unknown file' - if filename == 'unknown file': - return - for callback_list in callback_map.values(): - for callback_to_remove in [cb for cb in callback_list if cb.script == filename]: - callback_list.remove(callback_to_remove) - - -def remove_callbacks_for_function(callback_func): - for callback_list in callback_map.values(): - for callback_to_remove in [cb for cb in callback_list if cb.callback == callback_func]: - callback_list.remove(callback_to_remove) - - -def on_app_started(callback): - """register a function to be called when the webui started, the gradio `Block` component and - fastapi `FastAPI` object are passed as the arguments""" - add_callback(callback_map['callbacks_app_started'], callback) - - -def on_model_loaded(callback): - """register a function to be called when the stable diffusion model is created; the model is - passed as an argument; this function is also called when the script is reloaded. """ - add_callback(callback_map['callbacks_model_loaded'], callback) - - -def on_ui_tabs(callback): - """register a function to be called when the UI is creating new tabs. - The function must either return a None, which means no new tabs to be added, or a list, where - each element is a tuple: - (gradio_component, title, elem_id) - - gradio_component is a gradio component to be used for contents of the tab (usually gr.Blocks) - title is tab text displayed to user in the UI - elem_id is HTML id for the tab - """ - add_callback(callback_map['callbacks_ui_tabs'], callback) - - -def on_ui_train_tabs(callback): - """register a function to be called when the UI is creating new tabs for the train tab. - Create your new tabs with gr.Tab. - """ - add_callback(callback_map['callbacks_ui_train_tabs'], callback) - - -def on_ui_settings(callback): - """register a function to be called before UI settings are populated; add your settings - by using shared.opts.add_option(shared.OptionInfo(...)) """ - add_callback(callback_map['callbacks_ui_settings'], callback) - - -def on_before_image_saved(callback): - """register a function to be called before an image is saved to a file. - The callback is called with one argument: - - params: ImageSaveParams - parameters the image is to be saved with. You can change fields in this object. - """ - add_callback(callback_map['callbacks_before_image_saved'], callback) - - -def on_image_saved(callback): - """register a function to be called after an image is saved to a file. - The callback is called with one argument: - - params: ImageSaveParams - parameters the image was saved with. Changing fields in this object does nothing. - """ - add_callback(callback_map['callbacks_image_saved'], callback) - - -def on_cfg_denoiser(callback): - """register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs. - The callback is called with one argument: - - params: CFGDenoiserParams - parameters to be passed to the inner model and sampling state details. - """ - add_callback(callback_map['callbacks_cfg_denoiser'], callback) - - -def on_cfg_denoised(callback): - """register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs. - The callback is called with one argument: - - params: CFGDenoisedParams - parameters to be passed to the inner model and sampling state details. - """ - add_callback(callback_map['callbacks_cfg_denoised'], callback) - - -def on_before_component(callback): - """register a function to be called before a component is created. - The callback is called with arguments: - - component - gradio component that is about to be created. - - **kwargs - args to gradio.components.IOComponent.__init__ function - - Use elem_id/label fields of kwargs to figure out which component it is. - This can be useful to inject your own components somewhere in the middle of vanilla UI. - """ - add_callback(callback_map['callbacks_before_component'], callback) - - -def on_after_component(callback): - """register a function to be called after a component is created. See on_before_component for more.""" - add_callback(callback_map['callbacks_after_component'], callback) - - -def on_image_grid(callback): - """register a function to be called before making an image grid. - The callback is called with one argument: - - params: ImageGridLoopParams - parameters to be used for grid creation. Can be modified. - """ - add_callback(callback_map['callbacks_image_grid'], callback) - - -def on_infotext_pasted(callback): - """register a function to be called before applying an infotext. - The callback is called with two arguments: - - infotext: str - raw infotext. - - result: Dict[str, any] - parsed infotext parameters. - """ - add_callback(callback_map['callbacks_infotext_pasted'], callback) - - -def on_script_unloaded(callback): - """register a function to be called before the script is unloaded. Any hooks/hijacks/monkeying about that - the script did should be reverted here""" - - add_callback(callback_map['callbacks_script_unloaded'], callback) - - -def on_before_ui(callback): - """register a function to be called before the UI is created.""" - - add_callback(callback_map['callbacks_before_ui'], callback) +import sys +import traceback +from collections import namedtuple +import inspect +from typing import Optional, Dict, Any + +from fastapi import FastAPI +from gradio import Blocks + + +def report_exception(c, job): + print(f"Error executing callback {job} for {c.script}", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) + + +class ImageSaveParams: + def __init__(self, image, p, filename, pnginfo): + self.image = image + """the PIL image itself""" + + self.p = p + """p object with processing parameters; either StableDiffusionProcessing or an object with same fields""" + + self.filename = filename + """name of file that the image would be saved to""" + + self.pnginfo = pnginfo + """dictionary with parameters for image's PNG info data; infotext will have the key 'parameters'""" + + +class CFGDenoiserParams: + def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps): + self.x = x + """Latent image representation in the process of being denoised""" + + self.image_cond = image_cond + """Conditioning image""" + + self.sigma = sigma + """Current sigma noise step value""" + + self.sampling_step = sampling_step + """Current Sampling step number""" + + self.total_sampling_steps = total_sampling_steps + """Total number of sampling steps planned""" + + +class CFGDenoisedParams: + def __init__(self, x, sampling_step, total_sampling_steps): + self.x = x + """Latent image representation in the process of being denoised""" + + self.sampling_step = sampling_step + """Current Sampling step number""" + + self.total_sampling_steps = total_sampling_steps + """Total number of sampling steps planned""" + + +class UiTrainTabParams: + def __init__(self, txt2img_preview_params): + self.txt2img_preview_params = txt2img_preview_params + + +class ImageGridLoopParams: + def __init__(self, imgs, cols, rows): + self.imgs = imgs + self.cols = cols + self.rows = rows + + +ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"]) +callback_map = dict( + callbacks_app_started=[], + callbacks_model_loaded=[], + callbacks_ui_tabs=[], + callbacks_ui_train_tabs=[], + callbacks_ui_settings=[], + callbacks_before_image_saved=[], + callbacks_image_saved=[], + callbacks_cfg_denoiser=[], + callbacks_cfg_denoised=[], + callbacks_before_component=[], + callbacks_after_component=[], + callbacks_image_grid=[], + callbacks_infotext_pasted=[], + callbacks_script_unloaded=[], + callbacks_before_ui=[], + callbacks_after_ui=[], +) + + +def clear_callbacks(): + for callback_list in callback_map.values(): + callback_list.clear() + + +def app_started_callback(demo: Optional[Blocks], app: FastAPI): + for c in callback_map['callbacks_app_started']: + try: + c.callback(demo, app) + except Exception: + report_exception(c, 'app_started_callback') + + +def model_loaded_callback(sd_model): + for c in callback_map['callbacks_model_loaded']: + try: + c.callback(sd_model) + except Exception: + report_exception(c, 'model_loaded_callback') + + +def ui_tabs_callback(): + res = [] + + for c in callback_map['callbacks_ui_tabs']: + try: + res += c.callback() or [] + except Exception: + report_exception(c, 'ui_tabs_callback') + + return res + + +def ui_train_tabs_callback(params: UiTrainTabParams): + for c in callback_map['callbacks_ui_train_tabs']: + try: + c.callback(params) + except Exception: + report_exception(c, 'callbacks_ui_train_tabs') + + +def ui_settings_callback(): + for c in callback_map['callbacks_ui_settings']: + try: + c.callback() + except Exception: + report_exception(c, 'ui_settings_callback') + + +def before_image_saved_callback(params: ImageSaveParams): + for c in callback_map['callbacks_before_image_saved']: + try: + c.callback(params) + except Exception: + report_exception(c, 'before_image_saved_callback') + + +def image_saved_callback(params: ImageSaveParams): + for c in callback_map['callbacks_image_saved']: + try: + c.callback(params) + except Exception: + report_exception(c, 'image_saved_callback') + + +def cfg_denoiser_callback(params: CFGDenoiserParams): + for c in callback_map['callbacks_cfg_denoiser']: + try: + c.callback(params) + except Exception: + report_exception(c, 'cfg_denoiser_callback') + + +def cfg_denoised_callback(params: CFGDenoisedParams): + for c in callback_map['callbacks_cfg_denoised']: + try: + c.callback(params) + except Exception: + report_exception(c, 'cfg_denoised_callback') + + +def before_component_callback(component, **kwargs): + for c in callback_map['callbacks_before_component']: + try: + c.callback(component, **kwargs) + except Exception: + report_exception(c, 'before_component_callback') + + +def after_component_callback(component, **kwargs): + for c in callback_map['callbacks_after_component']: + try: + c.callback(component, **kwargs) + except Exception: + report_exception(c, 'after_component_callback') + + +def image_grid_callback(params: ImageGridLoopParams): + for c in callback_map['callbacks_image_grid']: + try: + c.callback(params) + except Exception: + report_exception(c, 'image_grid') + + +def infotext_pasted_callback(infotext: str, params: Dict[str, Any]): + for c in callback_map['callbacks_infotext_pasted']: + try: + c.callback(infotext, params) + except Exception: + report_exception(c, 'infotext_pasted') + + +def script_unloaded_callback(): + for c in reversed(callback_map['callbacks_script_unloaded']): + try: + c.callback() + except Exception: + report_exception(c, 'script_unloaded') + + +def before_ui_callback(): + for c in reversed(callback_map['callbacks_before_ui']): + try: + c.callback() + except Exception: + report_exception(c, 'before_ui') + + +def after_ui_callback(): + for c in reversed(callback_map['callbacks_after_ui']): + try: + c.callback() + except Exception: + report_exception(c, 'after_ui') + + +def add_callback(callbacks, fun): + stack = [x for x in inspect.stack() if x.filename != __file__] + filename = stack[0].filename if len(stack) > 0 else 'unknown file' + + callbacks.append(ScriptCallback(filename, fun)) + + +def remove_current_script_callbacks(): + stack = [x for x in inspect.stack() if x.filename != __file__] + filename = stack[0].filename if len(stack) > 0 else 'unknown file' + if filename == 'unknown file': + return + for callback_list in callback_map.values(): + for callback_to_remove in [cb for cb in callback_list if cb.script == filename]: + callback_list.remove(callback_to_remove) + + +def remove_callbacks_for_function(callback_func): + for callback_list in callback_map.values(): + for callback_to_remove in [cb for cb in callback_list if cb.callback == callback_func]: + callback_list.remove(callback_to_remove) + + +def on_app_started(callback): + """register a function to be called when the webui started, the gradio `Block` component and + fastapi `FastAPI` object are passed as the arguments""" + add_callback(callback_map['callbacks_app_started'], callback) + + +def on_model_loaded(callback): + """register a function to be called when the stable diffusion model is created; the model is + passed as an argument; this function is also called when the script is reloaded. """ + add_callback(callback_map['callbacks_model_loaded'], callback) + + +def on_ui_tabs(callback): + """register a function to be called when the UI is creating new tabs. + The function must either return a None, which means no new tabs to be added, or a list, where + each element is a tuple: + (gradio_component, title, elem_id) + + gradio_component is a gradio component to be used for contents of the tab (usually gr.Blocks) + title is tab text displayed to user in the UI + elem_id is HTML id for the tab + """ + add_callback(callback_map['callbacks_ui_tabs'], callback) + + +def on_ui_train_tabs(callback): + """register a function to be called when the UI is creating new tabs for the train tab. + Create your new tabs with gr.Tab. + """ + add_callback(callback_map['callbacks_ui_train_tabs'], callback) + + +def on_ui_settings(callback): + """register a function to be called before UI settings are populated; add your settings + by using shared.opts.add_option(shared.OptionInfo(...)) """ + add_callback(callback_map['callbacks_ui_settings'], callback) + + +def on_before_image_saved(callback): + """register a function to be called before an image is saved to a file. + The callback is called with one argument: + - params: ImageSaveParams - parameters the image is to be saved with. You can change fields in this object. + """ + add_callback(callback_map['callbacks_before_image_saved'], callback) + + +def on_image_saved(callback): + """register a function to be called after an image is saved to a file. + The callback is called with one argument: + - params: ImageSaveParams - parameters the image was saved with. Changing fields in this object does nothing. + """ + add_callback(callback_map['callbacks_image_saved'], callback) + + +def on_cfg_denoiser(callback): + """register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs. + The callback is called with one argument: + - params: CFGDenoiserParams - parameters to be passed to the inner model and sampling state details. + """ + add_callback(callback_map['callbacks_cfg_denoiser'], callback) + + +def on_cfg_denoised(callback): + """register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs. + The callback is called with one argument: + - params: CFGDenoisedParams - parameters to be passed to the inner model and sampling state details. + """ + add_callback(callback_map['callbacks_cfg_denoised'], callback) + + +def on_before_component(callback): + """register a function to be called before a component is created. + The callback is called with arguments: + - component - gradio component that is about to be created. + - **kwargs - args to gradio.components.IOComponent.__init__ function + + Use elem_id/label fields of kwargs to figure out which component it is. + This can be useful to inject your own components somewhere in the middle of vanilla UI. + """ + add_callback(callback_map['callbacks_before_component'], callback) + + +def on_after_component(callback): + """register a function to be called after a component is created. See on_before_component for more.""" + add_callback(callback_map['callbacks_after_component'], callback) + + +def on_image_grid(callback): + """register a function to be called before making an image grid. + The callback is called with one argument: + - params: ImageGridLoopParams - parameters to be used for grid creation. Can be modified. + """ + add_callback(callback_map['callbacks_image_grid'], callback) + + +def on_infotext_pasted(callback): + """register a function to be called before applying an infotext. + The callback is called with two arguments: + - infotext: str - raw infotext. + - result: Dict[str, any] - parsed infotext parameters. + """ + add_callback(callback_map['callbacks_infotext_pasted'], callback) + + +def on_script_unloaded(callback): + """register a function to be called before the script is unloaded. Any hooks/hijacks/monkeying about that + the script did should be reverted here""" + + add_callback(callback_map['callbacks_script_unloaded'], callback) + + +def on_before_ui(callback): + """register a function to be called before the UI is created.""" + + add_callback(callback_map['callbacks_before_ui'], callback) + + +def on_after_ui(callback): + """register a function to be called after the UI is created.""" + + add_callback(callback_map['callbacks_after_ui'], callback) + diff --git a/modules/ui.py b/modules/ui.py index 0516c643608..7e8a459dd54 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1,1798 +1,1801 @@ -import html -import json -import math -import mimetypes -import os -import platform -import random -import sys -import tempfile -import time -import traceback -from functools import partial, reduce -import warnings - -import gradio as gr -import gradio.routes -import gradio.utils -import numpy as np -from PIL import Image, PngImagePlugin -from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call - -from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, postprocessing, ui_components, ui_common, ui_postprocessing -from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML -from modules.paths import script_path, data_path - -from modules.shared import opts, cmd_opts, restricted_opts - -import modules.codeformer_model -import modules.generation_parameters_copypaste as parameters_copypaste -import modules.gfpgan_model -import modules.hypernetworks.ui -import modules.scripts -import modules.shared as shared -import modules.styles -import modules.textual_inversion.ui -from modules import prompt_parser -from modules.images import save_image -from modules.sd_hijack import model_hijack -from modules.sd_samplers import samplers, samplers_for_img2img -from modules.textual_inversion import textual_inversion -import modules.hypernetworks.ui -from modules.generation_parameters_copypaste import image_from_url_text -import modules.extras - -warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning) - -# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI -mimetypes.init() -mimetypes.add_type('application/javascript', '.js') - -if not cmd_opts.share and not cmd_opts.listen: - # fix gradio phoning home - gradio.utils.version_check = lambda: None - gradio.utils.get_local_ip_address = lambda: '127.0.0.1' - -if cmd_opts.ngrok is not None: - import modules.ngrok as ngrok - print('ngrok authtoken detected, trying to connect...') - ngrok.connect( - cmd_opts.ngrok, - cmd_opts.port if cmd_opts.port is not None else 7860, - cmd_opts.ngrok_region - ) - - -def gr_show(visible=True): - return {"visible": visible, "__type__": "update"} - - -sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg" -sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None - -css_hide_progressbar = """ -.wrap .m-12 svg { display:none!important; } -.wrap .m-12::before { content:"Loading..." } -.wrap .z-20 svg { display:none!important; } -.wrap .z-20::before { content:"Loading..." } -.wrap.cover-bg .z-20::before { content:"" } -.progress-bar { display:none!important; } -.meta-text { display:none!important; } -.meta-text-center { display:none!important; } -""" - -# Using constants for these since the variation selector isn't visible. -# Important that they exactly match script.js for tooltip to work. -random_symbol = '\U0001f3b2\ufe0f' # 🎲️ -reuse_symbol = '\u267b\ufe0f' # ♻️ -paste_symbol = '\u2199\ufe0f' # ↙ -refresh_symbol = '\U0001f504' # 🔄 -save_style_symbol = '\U0001f4be' # 💾 -apply_style_symbol = '\U0001f4cb' # 📋 -clear_prompt_symbol = '\U0001F5D1' # 🗑️ -extra_networks_symbol = '\U0001F3B4' # 🎴 -switch_values_symbol = '\U000021C5' # ⇅ - - -def plaintext_to_html(text): - return ui_common.plaintext_to_html(text) - - -def send_gradio_gallery_to_image(x): - if len(x) == 0: - return None - return image_from_url_text(x[0]) - -def visit(x, func, path=""): - if hasattr(x, 'children'): - for c in x.children: - visit(c, func, path) - elif x.label is not None: - func(path + "/" + str(x.label), x) - - -def add_style(name: str, prompt: str, negative_prompt: str): - if name is None: - return [gr_show() for x in range(4)] - - style = modules.styles.PromptStyle(name, prompt, negative_prompt) - shared.prompt_styles.styles[style.name] = style - # Save all loaded prompt styles: this allows us to update the storage format in the future more easily, because we - # reserialize all styles every time we save them - shared.prompt_styles.save_styles(shared.styles_filename) - - return [gr.Dropdown.update(visible=True, choices=list(shared.prompt_styles.styles)) for _ in range(2)] - - -def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y): - from modules import processing, devices - - if not enable: - return "" - - p = processing.StableDiffusionProcessingTxt2Img(width=width, height=height, enable_hr=True, hr_scale=hr_scale, hr_resize_x=hr_resize_x, hr_resize_y=hr_resize_y) - - with devices.autocast(): - p.init([""], [0], [0]) - - return f"resize: from {p.width}x{p.height} to {p.hr_resize_x or p.hr_upscale_to_x}x{p.hr_resize_y or p.hr_upscale_to_y}" - - -def apply_styles(prompt, prompt_neg, styles): - prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, styles) - prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, styles) - - return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value=[])] - - -def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_dir, *ii_singles): - if mode in {0, 1, 3, 4}: - return [interrogation_function(ii_singles[mode]), None] - elif mode == 2: - return [interrogation_function(ii_singles[mode]["image"]), None] - elif mode == 5: - assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled" - images = shared.listfiles(ii_input_dir) - print(f"Will process {len(images)} images.") - if ii_output_dir != "": - os.makedirs(ii_output_dir, exist_ok=True) - else: - ii_output_dir = ii_input_dir - - for image in images: - img = Image.open(image) - filename = os.path.basename(image) - left, _ = os.path.splitext(filename) - print(interrogation_function(img), file=open(os.path.join(ii_output_dir, left + ".txt"), 'a')) - - return [gr.update(), None] - - -def interrogate(image): - prompt = shared.interrogator.interrogate(image.convert("RGB")) - return gr.update() if prompt is None else prompt - - -def interrogate_deepbooru(image): - prompt = deepbooru.model.tag(image) - return gr.update() if prompt is None else prompt - - -def create_seed_inputs(target_interface): - with FormRow(elem_id=target_interface + '_seed_row'): - seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=target_interface + '_seed') - seed.style(container=False) - random_seed = gr.Button(random_symbol, elem_id=target_interface + '_random_seed') - reuse_seed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_seed') - - with gr.Group(elem_id=target_interface + '_subseed_show_box'): - seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False) - - # Components to show/hide based on the 'Extra' checkbox - seed_extras = [] - - with FormRow(visible=False, elem_id=target_interface + '_subseed_row') as seed_extra_row_1: - seed_extras.append(seed_extra_row_1) - subseed = gr.Number(label='Variation seed', value=-1, elem_id=target_interface + '_subseed') - subseed.style(container=False) - random_subseed = gr.Button(random_symbol, elem_id=target_interface + '_random_subseed') - reuse_subseed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_subseed') - subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=target_interface + '_subseed_strength') - - with FormRow(visible=False) as seed_extra_row_2: - seed_extras.append(seed_extra_row_2) - seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=target_interface + '_seed_resize_from_w') - seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=target_interface + '_seed_resize_from_h') - - random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed]) - random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed]) - - def change_visibility(show): - return {comp: gr_show(show) for comp in seed_extras} - - seed_checkbox.change(change_visibility, show_progress=False, inputs=[seed_checkbox], outputs=seed_extras) - - return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox - - - -def connect_clear_prompt(button): - """Given clear button, prompt, and token_counter objects, setup clear prompt button click event""" - button.click( - _js="clear_prompt", - fn=None, - inputs=[], - outputs=[], - ) - - -def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed): - """ Connects a 'reuse (sub)seed' button's click event so that it copies last used - (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength - was 0, i.e. no variation seed was used, it copies the normal seed value instead.""" - def copy_seed(gen_info_string: str, index): - res = -1 - - try: - gen_info = json.loads(gen_info_string) - index -= gen_info.get('index_of_first_image', 0) - - if is_subseed and gen_info.get('subseed_strength', 0) > 0: - all_subseeds = gen_info.get('all_subseeds', [-1]) - res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0] - else: - all_seeds = gen_info.get('all_seeds', [-1]) - res = all_seeds[index if 0 <= index < len(all_seeds) else 0] - - except json.decoder.JSONDecodeError as e: - if gen_info_string != '': - print("Error parsing JSON generation info:", file=sys.stderr) - print(gen_info_string, file=sys.stderr) - - return [res, gr_show(False)] - - reuse_seed.click( - fn=copy_seed, - _js="(x, y) => [x, selected_gallery_index()]", - show_progress=False, - inputs=[generation_info, dummy_component], - outputs=[seed, dummy_component] - ) - - -def update_token_counter(text, steps): - try: - text, _ = extra_networks.parse_prompt(text) - - _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text]) - prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps) - - except Exception: - # a parsing error can happen here during typing, and we don't want to bother the user with - # messages related to it in console - prompt_schedules = [[[steps, text]]] - - flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules) - prompts = [prompt_text for step, prompt_text in flat_prompts] - token_count, max_length = max([model_hijack.get_prompt_lengths(prompt) for prompt in prompts], key=lambda args: args[0]) - return f"{token_count}/{max_length}" - - -def create_toprow(is_img2img): - id_part = "img2img" if is_img2img else "txt2img" - - with gr.Row(elem_id=f"{id_part}_toprow", variant="compact"): - with gr.Column(elem_id=f"{id_part}_prompt_container", scale=6): - with gr.Row(): - with gr.Column(scale=80): - with gr.Row(): - prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)") - - with gr.Row(): - with gr.Column(scale=80): - with gr.Row(): - negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)") - - button_interrogate = None - button_deepbooru = None - if is_img2img: - with gr.Column(scale=1, elem_id="interrogate_col"): - button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate") - button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru") - - with gr.Column(scale=1, elem_id=f"{id_part}_actions_column"): - with gr.Row(elem_id=f"{id_part}_generate_box"): - interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt") - skip = gr.Button('Skip', elem_id=f"{id_part}_skip") - submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary') - - skip.click( - fn=lambda: shared.state.skip(), - inputs=[], - outputs=[], - ) - - interrupt.click( - fn=lambda: shared.state.interrupt(), - inputs=[], - outputs=[], - ) - - with gr.Row(elem_id=f"{id_part}_tools"): - paste = ToolButton(value=paste_symbol, elem_id="paste") - clear_prompt_button = ToolButton(value=clear_prompt_symbol, elem_id=f"{id_part}_clear_prompt") - extra_networks_button = ToolButton(value=extra_networks_symbol, elem_id=f"{id_part}_extra_networks") - prompt_style_apply = ToolButton(value=apply_style_symbol, elem_id=f"{id_part}_style_apply") - save_style = ToolButton(value=save_style_symbol, elem_id=f"{id_part}_style_create") - - token_counter = gr.HTML(value="", elem_id=f"{id_part}_token_counter") - token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button") - negative_token_counter = gr.HTML(value="", elem_id=f"{id_part}_negative_token_counter") - negative_token_button = gr.Button(visible=False, elem_id=f"{id_part}_negative_token_button") - - clear_prompt_button.click( - fn=lambda *x: x, - _js="confirm_clear_prompt", - inputs=[prompt, negative_prompt], - outputs=[prompt, negative_prompt], - ) - - with gr.Row(elem_id=f"{id_part}_styles_row"): - prompt_styles = gr.Dropdown(label="Styles", elem_id=f"{id_part}_styles", choices=[k for k, v in shared.prompt_styles.styles.items()], value=[], multiselect=True) - create_refresh_button(prompt_styles, shared.prompt_styles.reload, lambda: {"choices": [k for k, v in shared.prompt_styles.styles.items()]}, f"refresh_{id_part}_styles") - - return prompt, prompt_styles, negative_prompt, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button - - -def setup_progressbar(*args, **kwargs): - pass - - -def apply_setting(key, value): - if value is None: - return gr.update() - - if shared.cmd_opts.freeze_settings: - return gr.update() - - # dont allow model to be swapped when model hash exists in prompt - if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap: - return gr.update() - - if key == "sd_model_checkpoint": - ckpt_info = sd_models.get_closet_checkpoint_match(value) - - if ckpt_info is not None: - value = ckpt_info.title - else: - return gr.update() - - comp_args = opts.data_labels[key].component_args - if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False: - return - - valtype = type(opts.data_labels[key].default) - oldval = opts.data.get(key, None) - opts.data[key] = valtype(value) if valtype != type(None) else value - if oldval != value and opts.data_labels[key].onchange is not None: - opts.data_labels[key].onchange() - - opts.save(shared.config_filename) - return getattr(opts, key) - - -def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id): - def refresh(): - refresh_method() - args = refreshed_args() if callable(refreshed_args) else refreshed_args - - for k, v in args.items(): - setattr(refresh_component, k, v) - - return gr.update(**(args or {})) - - refresh_button = ToolButton(value=refresh_symbol, elem_id=elem_id) - refresh_button.click( - fn=refresh, - inputs=[], - outputs=[refresh_component] - ) - return refresh_button - - -def create_output_panel(tabname, outdir): - return ui_common.create_output_panel(tabname, outdir) - - -def create_sampler_and_steps_selection(choices, tabname): - if opts.samplers_in_dropdown: - with FormRow(elem_id=f"sampler_selection_{tabname}"): - sampler_index = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index") - steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20) - else: - with FormGroup(elem_id=f"sampler_selection_{tabname}"): - steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20) - sampler_index = gr.Radio(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index") - - return steps, sampler_index - - -def ordered_ui_categories(): - user_order = {x.strip(): i * 2 + 1 for i, x in enumerate(shared.opts.ui_reorder.split(","))} - - for i, category in sorted(enumerate(shared.ui_reorder_categories), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)): - yield category - - -def get_value_for_setting(key): - value = getattr(opts, key) - - info = opts.data_labels[key] - args = info.component_args() if callable(info.component_args) else info.component_args or {} - args = {k: v for k, v in args.items() if k not in {'precision'}} - - return gr.update(value=value, **args) - - -def create_override_settings_dropdown(tabname, row): - dropdown = gr.Dropdown([], label="Override settings", visible=False, elem_id=f"{tabname}_override_settings", multiselect=True) - - dropdown.change( - fn=lambda x: gr.Dropdown.update(visible=len(x) > 0), - inputs=[dropdown], - outputs=[dropdown], - ) - - return dropdown - - -def create_ui(): - import modules.img2img - import modules.txt2img - - reload_javascript() - - parameters_copypaste.reset() - - modules.scripts.scripts_current = modules.scripts.scripts_txt2img - modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False) - - with gr.Blocks(analytics_enabled=False) as txt2img_interface: - txt2img_prompt, txt2img_prompt_styles, txt2img_negative_prompt, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button = create_toprow(is_img2img=False) - - dummy_component = gr.Label(visible=False) - txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="binary", visible=False) - - with FormRow(variant='compact', elem_id="txt2img_extra_networks", visible=False) as extra_networks: - from modules import ui_extra_networks - extra_networks_ui = ui_extra_networks.create_ui(extra_networks, extra_networks_button, 'txt2img') - - with gr.Row().style(equal_height=False): - with gr.Column(variant='compact', elem_id="txt2img_settings"): - for category in ordered_ui_categories(): - if category == "sampler": - steps, sampler_index = create_sampler_and_steps_selection(samplers, "txt2img") - - elif category == "dimensions": - with FormRow(): - with gr.Column(elem_id="txt2img_column_size", scale=4): - width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width") - height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height") - - res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn") - if opts.dimensions_and_batch_together: - with gr.Column(elem_id="txt2img_column_batch"): - batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count") - batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size") - - elif category == "cfg": - cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale") - - elif category == "seed": - seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('txt2img') - - elif category == "checkboxes": - with FormRow(elem_id="txt2img_checkboxes", variant="compact"): - restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces") - tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling") - enable_hr = gr.Checkbox(label='Hires. fix', value=False, elem_id="txt2img_enable_hr") - hr_final_resolution = FormHTML(value="", elem_id="txtimg_hr_finalres", label="Upscaled resolution", interactive=False) - - elif category == "hires_fix": - with FormGroup(visible=False, elem_id="txt2img_hires_fix") as hr_options: - with FormRow(elem_id="txt2img_hires_fix_row1", variant="compact"): - hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode) - hr_second_pass_steps = gr.Slider(minimum=0, maximum=150, step=1, label='Hires steps', value=0, elem_id="txt2img_hires_steps") - denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength") - - with FormRow(elem_id="txt2img_hires_fix_row2", variant="compact"): - hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale") - hr_resize_x = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize width to", value=0, elem_id="txt2img_hr_resize_x") - hr_resize_y = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize height to", value=0, elem_id="txt2img_hr_resize_y") - - elif category == "batch": - if not opts.dimensions_and_batch_together: - with FormRow(elem_id="txt2img_column_batch"): - batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count") - batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size") - - elif category == "override_settings": - with FormRow(elem_id="txt2img_override_settings_row") as row: - override_settings = create_override_settings_dropdown('txt2img', row) - - elif category == "scripts": - with FormGroup(elem_id="txt2img_script_container"): - custom_inputs = modules.scripts.scripts_txt2img.setup_ui() - - hr_resolution_preview_inputs = [enable_hr, width, height, hr_scale, hr_resize_x, hr_resize_y] - for input in hr_resolution_preview_inputs: - input.change( - fn=calc_resolution_hires, - inputs=hr_resolution_preview_inputs, - outputs=[hr_final_resolution], - show_progress=False, - ) - input.change( - None, - _js="onCalcResolutionHires", - inputs=hr_resolution_preview_inputs, - outputs=[], - show_progress=False, - ) - - txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples) - - connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False) - connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True) - - txt2img_args = dict( - fn=wrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs=[None, '', '']), - _js="submit", - inputs=[ - dummy_component, - txt2img_prompt, - txt2img_negative_prompt, - txt2img_prompt_styles, - steps, - sampler_index, - restore_faces, - tiling, - batch_count, - batch_size, - cfg_scale, - seed, - subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox, - height, - width, - enable_hr, - denoising_strength, - hr_scale, - hr_upscaler, - hr_second_pass_steps, - hr_resize_x, - hr_resize_y, - override_settings, - ] + custom_inputs, - - outputs=[ - txt2img_gallery, - generation_info, - html_info, - html_log, - ], - show_progress=False, - ) - - txt2img_prompt.submit(**txt2img_args) - submit.click(**txt2img_args) - - res_switch_btn.click(lambda w, h: (h, w), inputs=[width, height], outputs=[width, height]) - - txt_prompt_img.change( - fn=modules.images.image_data, - inputs=[ - txt_prompt_img - ], - outputs=[ - txt2img_prompt, - txt_prompt_img - ] - ) - - enable_hr.change( - fn=lambda x: gr_show(x), - inputs=[enable_hr], - outputs=[hr_options], - show_progress = False, - ) - - txt2img_paste_fields = [ - (txt2img_prompt, "Prompt"), - (txt2img_negative_prompt, "Negative prompt"), - (steps, "Steps"), - (sampler_index, "Sampler"), - (restore_faces, "Face restoration"), - (cfg_scale, "CFG scale"), - (seed, "Seed"), - (width, "Size-1"), - (height, "Size-2"), - (batch_size, "Batch size"), - (subseed, "Variation seed"), - (subseed_strength, "Variation seed strength"), - (seed_resize_from_w, "Seed resize from-1"), - (seed_resize_from_h, "Seed resize from-2"), - (denoising_strength, "Denoising strength"), - (enable_hr, lambda d: "Denoising strength" in d), - (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)), - (hr_scale, "Hires upscale"), - (hr_upscaler, "Hires upscaler"), - (hr_second_pass_steps, "Hires steps"), - (hr_resize_x, "Hires resize-1"), - (hr_resize_y, "Hires resize-2"), - *modules.scripts.scripts_txt2img.infotext_fields - ] - parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields, override_settings) - parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding( - paste_button=txt2img_paste, tabname="txt2img", source_text_component=txt2img_prompt, source_image_component=None, - )) - - txt2img_preview_params = [ - txt2img_prompt, - txt2img_negative_prompt, - steps, - sampler_index, - cfg_scale, - seed, - width, - height, - ] - - token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_prompt, steps], outputs=[token_counter]) - negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_negative_prompt, steps], outputs=[negative_token_counter]) - - ui_extra_networks.setup_ui(extra_networks_ui, txt2img_gallery) - - modules.scripts.scripts_current = modules.scripts.scripts_img2img - modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True) - - with gr.Blocks(analytics_enabled=False) as img2img_interface: - img2img_prompt, img2img_prompt_styles, img2img_negative_prompt, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button = create_toprow(is_img2img=True) - - img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="binary", visible=False) - - with FormRow(variant='compact', elem_id="img2img_extra_networks", visible=False) as extra_networks: - from modules import ui_extra_networks - extra_networks_ui_img2img = ui_extra_networks.create_ui(extra_networks, extra_networks_button, 'img2img') - - with FormRow().style(equal_height=False): - with gr.Column(variant='compact', elem_id="img2img_settings"): - copy_image_buttons = [] - copy_image_destinations = {} - - def add_copy_image_controls(tab_name, elem): - with gr.Row(variant="compact", elem_id=f"img2img_copy_to_{tab_name}"): - gr.HTML("Copy image to: ", elem_id=f"img2img_label_copy_to_{tab_name}") - - for title, name in zip(['img2img', 'sketch', 'inpaint', 'inpaint sketch'], ['img2img', 'sketch', 'inpaint', 'inpaint_sketch']): - if name == tab_name: - gr.Button(title, interactive=False) - copy_image_destinations[name] = elem - continue - - button = gr.Button(title) - copy_image_buttons.append((button, name, elem)) - - with gr.Tabs(elem_id="mode_img2img"): - with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img: - init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA").style(height=480) - add_copy_image_controls('img2img', init_img) - - with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch: - sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=480) - add_copy_image_controls('sketch', sketch) - - with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab") as tab_inpaint: - init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=480) - add_copy_image_controls('inpaint', init_img_with_mask) - - with gr.TabItem('Inpaint sketch', id='inpaint_sketch', elem_id="img2img_inpaint_sketch_tab") as tab_inpaint_color: - inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=480) - inpaint_color_sketch_orig = gr.State(None) - add_copy_image_controls('inpaint_sketch', inpaint_color_sketch) - - def update_orig(image, state): - if image is not None: - same_size = state is not None and state.size == image.size - has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1)) - edited = same_size and has_exact_match - return image if not edited or state is None else state - - inpaint_color_sketch.change(update_orig, [inpaint_color_sketch, inpaint_color_sketch_orig], inpaint_color_sketch_orig) - - with gr.TabItem('Inpaint upload', id='inpaint_upload', elem_id="img2img_inpaint_upload_tab") as tab_inpaint_upload: - init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", elem_id="img_inpaint_base") - init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", elem_id="img_inpaint_mask") - - with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch: - hidden = '
Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else '' - gr.HTML( - f"

Process images in a directory on the same machine where the server is running." + - f"
Use an empty output directory to save pictures normally instead of writing to the output directory." + - f"
Add inpaint batch mask directory to enable inpaint batch processing." - f"{hidden}

" - ) - img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir") - img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir") - img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir") - - def copy_image(img): - if isinstance(img, dict) and 'image' in img: - return img['image'] - - return img - - for button, name, elem in copy_image_buttons: - button.click( - fn=copy_image, - inputs=[elem], - outputs=[copy_image_destinations[name]], - ) - button.click( - fn=lambda: None, - _js="switch_to_"+name.replace(" ", "_"), - inputs=[], - outputs=[], - ) - - with FormRow(): - resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize") - - for category in ordered_ui_categories(): - if category == "sampler": - steps, sampler_index = create_sampler_and_steps_selection(samplers_for_img2img, "img2img") - - elif category == "dimensions": - with FormRow(): - with gr.Column(elem_id="img2img_column_size", scale=4): - width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width") - height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height") - - res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn") - if opts.dimensions_and_batch_together: - with gr.Column(elem_id="img2img_column_batch"): - batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count") - batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size") - - elif category == "cfg": - with FormGroup(): - with FormRow(): - cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale") - image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit") - denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength") - - elif category == "seed": - seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img') - - elif category == "checkboxes": - with FormRow(elem_id="img2img_checkboxes", variant="compact"): - restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces") - tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling") - - elif category == "batch": - if not opts.dimensions_and_batch_together: - with FormRow(elem_id="img2img_column_batch"): - batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count") - batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size") - - elif category == "override_settings": - with FormRow(elem_id="img2img_override_settings_row") as row: - override_settings = create_override_settings_dropdown('img2img', row) - - elif category == "scripts": - with FormGroup(elem_id="img2img_script_container"): - custom_inputs = modules.scripts.scripts_img2img.setup_ui() - - elif category == "inpaint": - with FormGroup(elem_id="inpaint_controls", visible=False) as inpaint_controls: - with FormRow(): - mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur") - mask_alpha = gr.Slider(label="Mask transparency", visible=False, elem_id="img2img_mask_alpha") - - with FormRow(): - inpainting_mask_invert = gr.Radio(label='Mask mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode") - - with FormRow(): - inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill") - - with FormRow(): - with gr.Column(): - inpaint_full_res = gr.Radio(label="Inpaint area", choices=["Whole picture", "Only masked"], type="index", value="Whole picture", elem_id="img2img_inpaint_full_res") - - with gr.Column(scale=4): - inpaint_full_res_padding = gr.Slider(label='Only masked padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding") - - def select_img2img_tab(tab): - return gr.update(visible=tab in [2, 3, 4]), gr.update(visible=tab == 3), - - for i, elem in enumerate([tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]): - elem.select( - fn=lambda tab=i: select_img2img_tab(tab), - inputs=[], - outputs=[inpaint_controls, mask_alpha], - ) - - img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples) - - connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False) - connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True) - - img2img_prompt_img.change( - fn=modules.images.image_data, - inputs=[ - img2img_prompt_img - ], - outputs=[ - img2img_prompt, - img2img_prompt_img - ] - ) - - img2img_args = dict( - fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']), - _js="submit_img2img", - inputs=[ - dummy_component, - dummy_component, - img2img_prompt, - img2img_negative_prompt, - img2img_prompt_styles, - init_img, - sketch, - init_img_with_mask, - inpaint_color_sketch, - inpaint_color_sketch_orig, - init_img_inpaint, - init_mask_inpaint, - steps, - sampler_index, - mask_blur, - mask_alpha, - inpainting_fill, - restore_faces, - tiling, - batch_count, - batch_size, - cfg_scale, - image_cfg_scale, - denoising_strength, - seed, - subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox, - height, - width, - resize_mode, - inpaint_full_res, - inpaint_full_res_padding, - inpainting_mask_invert, - img2img_batch_input_dir, - img2img_batch_output_dir, - img2img_batch_inpaint_mask_dir, - override_settings, - ] + custom_inputs, - outputs=[ - img2img_gallery, - generation_info, - html_info, - html_log, - ], - show_progress=False, - ) - - interrogate_args = dict( - _js="get_img2img_tab_index", - inputs=[ - dummy_component, - img2img_batch_input_dir, - img2img_batch_output_dir, - init_img, - sketch, - init_img_with_mask, - inpaint_color_sketch, - init_img_inpaint, - ], - outputs=[img2img_prompt, dummy_component], - ) - - img2img_prompt.submit(**img2img_args) - submit.click(**img2img_args) - res_switch_btn.click(lambda w, h: (h, w), inputs=[width, height], outputs=[width, height]) - - img2img_interrogate.click( - fn=lambda *args: process_interrogate(interrogate, *args), - **interrogate_args, - ) - - img2img_deepbooru.click( - fn=lambda *args: process_interrogate(interrogate_deepbooru, *args), - **interrogate_args, - ) - - prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)] - style_dropdowns = [txt2img_prompt_styles, img2img_prompt_styles] - style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"] - - for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts): - button.click( - fn=add_style, - _js="ask_for_style_name", - # Have to pass empty dummy component here, because the JavaScript and Python function have to accept - # the same number of parameters, but we only know the style-name after the JavaScript prompt - inputs=[dummy_component, prompt, negative_prompt], - outputs=[txt2img_prompt_styles, img2img_prompt_styles], - ) - - for button, (prompt, negative_prompt), styles, js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs): - button.click( - fn=apply_styles, - _js=js_func, - inputs=[prompt, negative_prompt, styles], - outputs=[prompt, negative_prompt, styles], - ) - - token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter]) - negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_negative_prompt, steps], outputs=[negative_token_counter]) - - ui_extra_networks.setup_ui(extra_networks_ui_img2img, img2img_gallery) - - img2img_paste_fields = [ - (img2img_prompt, "Prompt"), - (img2img_negative_prompt, "Negative prompt"), - (steps, "Steps"), - (sampler_index, "Sampler"), - (restore_faces, "Face restoration"), - (cfg_scale, "CFG scale"), - (image_cfg_scale, "Image CFG scale"), - (seed, "Seed"), - (width, "Size-1"), - (height, "Size-2"), - (batch_size, "Batch size"), - (subseed, "Variation seed"), - (subseed_strength, "Variation seed strength"), - (seed_resize_from_w, "Seed resize from-1"), - (seed_resize_from_h, "Seed resize from-2"), - (denoising_strength, "Denoising strength"), - (mask_blur, "Mask blur"), - *modules.scripts.scripts_img2img.infotext_fields - ] - parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields, override_settings) - parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields, override_settings) - parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding( - paste_button=img2img_paste, tabname="img2img", source_text_component=img2img_prompt, source_image_component=None, - )) - - modules.scripts.scripts_current = None - - with gr.Blocks(analytics_enabled=False) as extras_interface: - ui_postprocessing.create_ui() - - with gr.Blocks(analytics_enabled=False) as pnginfo_interface: - with gr.Row().style(equal_height=False): - with gr.Column(variant='panel'): - image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil") - - with gr.Column(variant='panel'): - html = gr.HTML() - generation_info = gr.Textbox(visible=False, elem_id="pnginfo_generation_info") - html2 = gr.HTML() - with gr.Row(): - buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"]) - - for tabname, button in buttons.items(): - parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding( - paste_button=button, tabname=tabname, source_text_component=generation_info, source_image_component=image, - )) - - image.change( - fn=wrap_gradio_call(modules.extras.run_pnginfo), - inputs=[image], - outputs=[html, generation_info, html2], - ) - - def update_interp_description(value): - interp_description_css = "

{}

" - interp_descriptions = { - "No interpolation": interp_description_css.format("No interpolation will be used. Requires one model; A. Allows for format conversion and VAE baking."), - "Weighted sum": interp_description_css.format("A weighted sum will be used for interpolation. Requires two models; A and B. The result is calculated as A * (1 - M) + B * M"), - "Add difference": interp_description_css.format("The difference between the last two models will be added to the first. Requires three models; A, B and C. The result is calculated as A + (B - C) * M") - } - return interp_descriptions[value] - - with gr.Blocks(analytics_enabled=False) as modelmerger_interface: - with gr.Row().style(equal_height=False): - with gr.Column(variant='compact'): - interp_description = gr.HTML(value=update_interp_description("Weighted sum"), elem_id="modelmerger_interp_description") - - with FormRow(elem_id="modelmerger_models"): - primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)") - create_refresh_button(primary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_A") - - secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)") - create_refresh_button(secondary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_B") - - tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)") - create_refresh_button(tertiary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_C") - - custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name") - interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount") - interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method") - interp_method.change(fn=update_interp_description, inputs=[interp_method], outputs=[interp_description]) - - with FormRow(): - checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="ckpt", label="Checkpoint format", elem_id="modelmerger_checkpoint_format") - save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half") - - with FormRow(): - with gr.Column(): - config_source = gr.Radio(choices=["A, B or C", "B", "C", "Don't"], value="A, B or C", label="Copy config from", type="index", elem_id="modelmerger_config_method") - - with gr.Column(): - with FormRow(): - bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE", elem_id="modelmerger_bake_in_vae") - create_refresh_button(bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "modelmerger_refresh_bake_in_vae") - - with FormRow(): - discard_weights = gr.Textbox(value="", label="Discard weights with matching name", elem_id="modelmerger_discard_weights") - - with gr.Row(): - modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary') - - with gr.Column(variant='compact', elem_id="modelmerger_results_container"): - with gr.Group(elem_id="modelmerger_results_panel"): - modelmerger_result = gr.HTML(elem_id="modelmerger_result", show_label=False) - - with gr.Blocks(analytics_enabled=False) as train_interface: - with gr.Row().style(equal_height=False): - gr.HTML(value="

See wiki for detailed explanation.

") - - with gr.Row(variant="compact").style(equal_height=False): - with gr.Tabs(elem_id="train_tabs"): - - with gr.Tab(label="Create embedding"): - new_embedding_name = gr.Textbox(label="Name", elem_id="train_new_embedding_name") - initialization_text = gr.Textbox(label="Initialization text", value="*", elem_id="train_initialization_text") - nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1, elem_id="train_nvpt") - overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding", elem_id="train_overwrite_old_embedding") - - with gr.Row(): - with gr.Column(scale=3): - gr.HTML(value="") - - with gr.Column(): - create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding") - - with gr.Tab(label="Create hypernetwork"): - new_hypernetwork_name = gr.Textbox(label="Name", elem_id="train_new_hypernetwork_name") - new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"], elem_id="train_new_hypernetwork_sizes") - new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'", elem_id="train_new_hypernetwork_layer_structure") - new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys, elem_id="train_new_hypernetwork_activation_func") - new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"], elem_id="train_new_hypernetwork_initialization_option") - new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization", elem_id="train_new_hypernetwork_add_layer_norm") - new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout", elem_id="train_new_hypernetwork_use_dropout") - new_hypernetwork_dropout_structure = gr.Textbox("0, 0, 0", label="Enter hypernetwork Dropout structure (or empty). Recommended : 0~0.35 incrementing sequence: 0, 0.05, 0.15", placeholder="1st and last digit must be 0 and values should be between 0 and 1. ex:'0, 0.01, 0'") - overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork", elem_id="train_overwrite_old_hypernetwork") - - with gr.Row(): - with gr.Column(scale=3): - gr.HTML(value="") - - with gr.Column(): - create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork") - - with gr.Tab(label="Preprocess images"): - process_src = gr.Textbox(label='Source directory', elem_id="train_process_src") - process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst") - process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width") - process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_process_height") - preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action") - - with gr.Row(): - process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip") - process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split") - process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop") - process_multicrop = gr.Checkbox(label='Auto-sized crop', elem_id="train_process_multicrop") - process_caption = gr.Checkbox(label='Use BLIP for caption', elem_id="train_process_caption") - process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True, elem_id="train_process_caption_deepbooru") - - with gr.Row(visible=False) as process_split_extra_row: - process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_split_threshold") - process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="train_process_overlap_ratio") - - with gr.Row(visible=False) as process_focal_crop_row: - process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_face_weight") - process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight") - process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight") - process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug") - - with gr.Column(visible=False) as process_multicrop_col: - gr.Markdown('Each image is center-cropped with an automatically chosen width and height.') - with gr.Row(): - process_multicrop_mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id="train_process_multicrop_mindim") - process_multicrop_maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="train_process_multicrop_maxdim") - with gr.Row(): - process_multicrop_minarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area lower bound", value=64*64, elem_id="train_process_multicrop_minarea") - process_multicrop_maxarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area upper bound", value=640*640, elem_id="train_process_multicrop_maxarea") - with gr.Row(): - process_multicrop_objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="train_process_multicrop_objective") - process_multicrop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="train_process_multicrop_threshold") - - with gr.Row(): - with gr.Column(scale=3): - gr.HTML(value="") - - with gr.Column(): - with gr.Row(): - interrupt_preprocessing = gr.Button("Interrupt", elem_id="train_interrupt_preprocessing") - run_preprocess = gr.Button(value="Preprocess", variant='primary', elem_id="train_run_preprocess") - - process_split.change( - fn=lambda show: gr_show(show), - inputs=[process_split], - outputs=[process_split_extra_row], - ) - - process_focal_crop.change( - fn=lambda show: gr_show(show), - inputs=[process_focal_crop], - outputs=[process_focal_crop_row], - ) - - process_multicrop.change( - fn=lambda show: gr_show(show), - inputs=[process_multicrop], - outputs=[process_multicrop_col], - ) - - def get_textual_inversion_template_names(): - return sorted([x for x in textual_inversion.textual_inversion_templates]) - - with gr.Tab(label="Train"): - gr.HTML(value="

Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images [wiki]

") - with FormRow(): - train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())) - create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name") - - train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()]) - create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name") - - with FormRow(): - embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate") - hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001", elem_id="train_hypernetwork_learn_rate") - - with FormRow(): - clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"]) - clip_grad_value = gr.Textbox(placeholder="Gradient clip value", value="0.1", show_label=False) - - with FormRow(): - batch_size = gr.Number(label='Batch size', value=1, precision=0, elem_id="train_batch_size") - gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0, elem_id="train_gradient_step") - - dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images", elem_id="train_dataset_directory") - log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion", elem_id="train_log_directory") - - with FormRow(): - template_file = gr.Dropdown(label='Prompt template', value="style_filewords.txt", elem_id="train_template_file", choices=get_textual_inversion_template_names()) - create_refresh_button(template_file, textual_inversion.list_textual_inversion_templates, lambda: {"choices": get_textual_inversion_template_names()}, "refrsh_train_template_file") - - training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width") - training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height") - varsize = gr.Checkbox(label="Do not resize images", value=False, elem_id="train_varsize") - steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps") - - with FormRow(): - create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every") - save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every") - - use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False, elem_id="use_weight") - - save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True, elem_id="train_save_image_with_stored_embedding") - preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False, elem_id="train_preview_from_txt2img") - - shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False, elem_id="train_shuffle_tags") - tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0, elem_id="train_tag_drop_out") - - latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'], elem_id="train_latent_sampling_method") - - with gr.Row(): - train_embedding = gr.Button(value="Train Embedding", variant='primary', elem_id="train_train_embedding") - interrupt_training = gr.Button(value="Interrupt", elem_id="train_interrupt_training") - train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary', elem_id="train_train_hypernetwork") - - params = script_callbacks.UiTrainTabParams(txt2img_preview_params) - - script_callbacks.ui_train_tabs_callback(params) - - with gr.Column(elem_id='ti_gallery_container'): - ti_output = gr.Text(elem_id="ti_output", value="", show_label=False) - ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4) - ti_progress = gr.HTML(elem_id="ti_progress", value="") - ti_outcome = gr.HTML(elem_id="ti_error", value="") - - create_embedding.click( - fn=modules.textual_inversion.ui.create_embedding, - inputs=[ - new_embedding_name, - initialization_text, - nvpt, - overwrite_old_embedding, - ], - outputs=[ - train_embedding_name, - ti_output, - ti_outcome, - ] - ) - - create_hypernetwork.click( - fn=modules.hypernetworks.ui.create_hypernetwork, - inputs=[ - new_hypernetwork_name, - new_hypernetwork_sizes, - overwrite_old_hypernetwork, - new_hypernetwork_layer_structure, - new_hypernetwork_activation_func, - new_hypernetwork_initialization_option, - new_hypernetwork_add_layer_norm, - new_hypernetwork_use_dropout, - new_hypernetwork_dropout_structure - ], - outputs=[ - train_hypernetwork_name, - ti_output, - ti_outcome, - ] - ) - - run_preprocess.click( - fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]), - _js="start_training_textual_inversion", - inputs=[ - dummy_component, - process_src, - process_dst, - process_width, - process_height, - preprocess_txt_action, - process_flip, - process_split, - process_caption, - process_caption_deepbooru, - process_split_threshold, - process_overlap_ratio, - process_focal_crop, - process_focal_crop_face_weight, - process_focal_crop_entropy_weight, - process_focal_crop_edges_weight, - process_focal_crop_debug, - process_multicrop, - process_multicrop_mindim, - process_multicrop_maxdim, - process_multicrop_minarea, - process_multicrop_maxarea, - process_multicrop_objective, - process_multicrop_threshold, - ], - outputs=[ - ti_output, - ti_outcome, - ], - ) - - train_embedding.click( - fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]), - _js="start_training_textual_inversion", - inputs=[ - dummy_component, - train_embedding_name, - embedding_learn_rate, - batch_size, - gradient_step, - dataset_directory, - log_directory, - training_width, - training_height, - varsize, - steps, - clip_grad_mode, - clip_grad_value, - shuffle_tags, - tag_drop_out, - latent_sampling_method, - use_weight, - create_image_every, - save_embedding_every, - template_file, - save_image_with_stored_embedding, - preview_from_txt2img, - *txt2img_preview_params, - ], - outputs=[ - ti_output, - ti_outcome, - ] - ) - - train_hypernetwork.click( - fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]), - _js="start_training_textual_inversion", - inputs=[ - dummy_component, - train_hypernetwork_name, - hypernetwork_learn_rate, - batch_size, - gradient_step, - dataset_directory, - log_directory, - training_width, - training_height, - varsize, - steps, - clip_grad_mode, - clip_grad_value, - shuffle_tags, - tag_drop_out, - latent_sampling_method, - use_weight, - create_image_every, - save_embedding_every, - template_file, - preview_from_txt2img, - *txt2img_preview_params, - ], - outputs=[ - ti_output, - ti_outcome, - ] - ) - - interrupt_training.click( - fn=lambda: shared.state.interrupt(), - inputs=[], - outputs=[], - ) - - interrupt_preprocessing.click( - fn=lambda: shared.state.interrupt(), - inputs=[], - outputs=[], - ) - - def create_setting_component(key, is_quicksettings=False): - def fun(): - return opts.data[key] if key in opts.data else opts.data_labels[key].default - - info = opts.data_labels[key] - t = type(info.default) - - args = info.component_args() if callable(info.component_args) else info.component_args - - if info.component is not None: - comp = info.component - elif t == str: - comp = gr.Textbox - elif t == int: - comp = gr.Number - elif t == bool: - comp = gr.Checkbox - else: - raise Exception(f'bad options item type: {str(t)} for key {key}') - - elem_id = "setting_"+key - - if info.refresh is not None: - if is_quicksettings: - res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {})) - create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key) - else: - with FormRow(): - res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {})) - create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key) - else: - res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {})) - - return res - - components = [] - component_dict = {} - shared.settings_components = component_dict - - script_callbacks.ui_settings_callback() - opts.reorder() - - def run_settings(*args): - changed = [] - - for key, value, comp in zip(opts.data_labels.keys(), args, components): - assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}" - - for key, value, comp in zip(opts.data_labels.keys(), args, components): - if comp == dummy_component: - continue - - if opts.set(key, value): - changed.append(key) - - try: - opts.save(shared.config_filename) - except RuntimeError: - return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.' - return opts.dumpjson(), f'{len(changed)} settings changed{": " if len(changed) > 0 else ""}{", ".join(changed)}.' - - def run_settings_single(value, key): - if not opts.same_type(value, opts.data_labels[key].default): - return gr.update(visible=True), opts.dumpjson() - - if not opts.set(key, value): - return gr.update(value=getattr(opts, key)), opts.dumpjson() - - opts.save(shared.config_filename) - - return get_value_for_setting(key), opts.dumpjson() - - with gr.Blocks(analytics_enabled=False) as settings_interface: - with gr.Row(): - with gr.Column(scale=6): - settings_submit = gr.Button(value="Apply settings", variant='primary', elem_id="settings_submit") - with gr.Column(): - restart_gradio = gr.Button(value='Reload UI', variant='primary', elem_id="settings_restart_gradio") - - result = gr.HTML(elem_id="settings_result") - - quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")] - quicksettings_names = {x: i for i, x in enumerate(quicksettings_names) if x != 'quicksettings'} - - quicksettings_list = [] - - previous_section = None - current_tab = None - current_row = None - with gr.Tabs(elem_id="settings"): - for i, (k, item) in enumerate(opts.data_labels.items()): - section_must_be_skipped = item.section[0] is None - - if previous_section != item.section and not section_must_be_skipped: - elem_id, text = item.section - - if current_tab is not None: - current_row.__exit__() - current_tab.__exit__() - - gr.Group() - current_tab = gr.TabItem(elem_id="settings_{}".format(elem_id), label=text) - current_tab.__enter__() - current_row = gr.Column(variant='compact') - current_row.__enter__() - - previous_section = item.section - - if k in quicksettings_names and not shared.cmd_opts.freeze_settings: - quicksettings_list.append((i, k, item)) - components.append(dummy_component) - elif section_must_be_skipped: - components.append(dummy_component) - else: - component = create_setting_component(k) - component_dict[k] = component - components.append(component) - - if current_tab is not None: - current_row.__exit__() - current_tab.__exit__() - - with gr.TabItem("Actions"): - request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications") - download_localization = gr.Button(value='Download localization template', elem_id="download_localization") - reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies") - - with gr.TabItem("Licenses"): - gr.HTML(shared.html("licenses.html"), elem_id="licenses") - - gr.Button(value="Show all pages", elem_id="settings_show_all_pages") - - request_notifications.click( - fn=lambda: None, - inputs=[], - outputs=[], - _js='function(){}' - ) - - download_localization.click( - fn=lambda: None, - inputs=[], - outputs=[], - _js='download_localization' - ) - - def reload_scripts(): - modules.scripts.reload_script_body_only() - reload_javascript() # need to refresh the html page - - reload_script_bodies.click( - fn=reload_scripts, - inputs=[], - outputs=[] - ) - - def request_restart(): - shared.state.interrupt() - shared.state.need_restart = True - - restart_gradio.click( - fn=request_restart, - _js='restart_reload', - inputs=[], - outputs=[], - ) - - interfaces = [ - (txt2img_interface, "txt2img", "txt2img"), - (img2img_interface, "img2img", "img2img"), - (extras_interface, "Extras", "extras"), - (pnginfo_interface, "PNG Info", "pnginfo"), - (modelmerger_interface, "Checkpoint Merger", "modelmerger"), - (train_interface, "Train", "ti"), - ] - - css = "" - - for cssfile in modules.scripts.list_files_with_name("style.css"): - if not os.path.isfile(cssfile): - continue - - with open(cssfile, "r", encoding="utf8") as file: - css += file.read() + "\n" - - if os.path.exists(os.path.join(data_path, "user.css")): - with open(os.path.join(data_path, "user.css"), "r", encoding="utf8") as file: - css += file.read() + "\n" - - if not cmd_opts.no_progressbar_hiding: - css += css_hide_progressbar - - interfaces += script_callbacks.ui_tabs_callback() - interfaces += [(settings_interface, "Settings", "settings")] - - extensions_interface = ui_extensions.create_ui() - interfaces += [(extensions_interface, "Extensions", "extensions")] - - with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo: - with gr.Row(elem_id="quicksettings", variant="compact"): - for i, k, item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])): - component = create_setting_component(k, is_quicksettings=True) - component_dict[k] = component - - parameters_copypaste.connect_paste_params_buttons() - - with gr.Tabs(elem_id="tabs") as tabs: - for interface, label, ifid in interfaces: - with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid): - interface.render() - - if os.path.exists(os.path.join(script_path, "notification.mp3")): - audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False) - - footer = shared.html("footer.html") - footer = footer.format(versions=versions_html()) - gr.HTML(footer, elem_id="footer") - - text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False) - settings_submit.click( - fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]), - inputs=components, - outputs=[text_settings, result], - ) - - for i, k, item in quicksettings_list: - component = component_dict[k] - - component.change( - fn=lambda value, k=k: run_settings_single(value, key=k), - inputs=[component], - outputs=[component, text_settings], - ) - - text_settings.change( - fn=lambda: gr.update(visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit"), - inputs=[], - outputs=[image_cfg_scale], - ) - - button_set_checkpoint = gr.Button('Change checkpoint', elem_id='change_checkpoint', visible=False) - button_set_checkpoint.click( - fn=lambda value, _: run_settings_single(value, key='sd_model_checkpoint'), - _js="function(v){ var res = desiredCheckpointName; desiredCheckpointName = ''; return [res || v, null]; }", - inputs=[component_dict['sd_model_checkpoint'], dummy_component], - outputs=[component_dict['sd_model_checkpoint'], text_settings], - ) - - component_keys = [k for k in opts.data_labels.keys() if k in component_dict] - - def get_settings_values(): - return [get_value_for_setting(key) for key in component_keys] - - demo.load( - fn=get_settings_values, - inputs=[], - outputs=[component_dict[k] for k in component_keys], - ) - - def modelmerger(*args): - try: - results = modules.extras.run_modelmerger(*args) - except Exception as e: - print("Error loading/saving model file:", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) - modules.sd_models.list_models() # to remove the potentially missing models from the list - return [*[gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(4)], f"Error merging checkpoints: {e}"] - return results - - modelmerger_merge.click(fn=lambda: '', inputs=[], outputs=[modelmerger_result]) - modelmerger_merge.click( - fn=wrap_gradio_gpu_call(modelmerger, extra_outputs=lambda: [gr.update() for _ in range(4)]), - _js='modelmerger', - inputs=[ - dummy_component, - primary_model_name, - secondary_model_name, - tertiary_model_name, - interp_method, - interp_amount, - save_as_half, - custom_name, - checkpoint_format, - config_source, - bake_in_vae, - discard_weights, - ], - outputs=[ - primary_model_name, - secondary_model_name, - tertiary_model_name, - component_dict['sd_model_checkpoint'], - modelmerger_result, - ] - ) - - ui_config_file = cmd_opts.ui_config_file - ui_settings = {} - settings_count = len(ui_settings) - error_loading = False - - try: - if os.path.exists(ui_config_file): - with open(ui_config_file, "r", encoding="utf8") as file: - ui_settings = json.load(file) - except Exception: - error_loading = True - print("Error loading settings:", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) - - def loadsave(path, x): - def apply_field(obj, field, condition=None, init_field=None): - key = path + "/" + field - - if getattr(obj, 'custom_script_source', None) is not None: - key = 'customscript/' + obj.custom_script_source + '/' + key - - if getattr(obj, 'do_not_save_to_config', False): - return - - saved_value = ui_settings.get(key, None) - if saved_value is None: - ui_settings[key] = getattr(obj, field) - elif condition and not condition(saved_value): - pass - - # this warning is generally not useful; - # print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.') - else: - setattr(obj, field, saved_value) - if init_field is not None: - init_field(saved_value) - - if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown] and x.visible: - apply_field(x, 'visible') - - if type(x) == gr.Slider: - apply_field(x, 'value') - apply_field(x, 'minimum') - apply_field(x, 'maximum') - apply_field(x, 'step') - - if type(x) == gr.Radio: - apply_field(x, 'value', lambda val: val in x.choices) - - if type(x) == gr.Checkbox: - apply_field(x, 'value') - - if type(x) == gr.Textbox: - apply_field(x, 'value') - - if type(x) == gr.Number: - apply_field(x, 'value') - - if type(x) == gr.Dropdown: - def check_dropdown(val): - if getattr(x, 'multiselect', False): - return all([value in x.choices for value in val]) - else: - return val in x.choices - - apply_field(x, 'value', check_dropdown, getattr(x, 'init_field', None)) - - visit(txt2img_interface, loadsave, "txt2img") - visit(img2img_interface, loadsave, "img2img") - visit(extras_interface, loadsave, "extras") - visit(modelmerger_interface, loadsave, "modelmerger") - visit(train_interface, loadsave, "train") - - if not error_loading and (not os.path.exists(ui_config_file) or settings_count != len(ui_settings)): - with open(ui_config_file, "w", encoding="utf8") as file: - json.dump(ui_settings, file, indent=4) - - # Required as a workaround for change() event not triggering when loading values from ui-config.json - interp_description.value = update_interp_description(interp_method.value) - - return demo - - -def reload_javascript(): - head = f'\n' - - inline = f"{localization.localization_js(shared.opts.localization)};" - if cmd_opts.theme is not None: - inline += f"set_theme('{cmd_opts.theme}');" - - for script in modules.scripts.list_scripts("javascript", ".js"): - head += f'\n' - - head += f'\n' - - def template_response(*args, **kwargs): - res = shared.GradioTemplateResponseOriginal(*args, **kwargs) - res.body = res.body.replace(b'', f'{head}'.encode("utf8")) - res.init_headers() - return res - - gradio.routes.templates.TemplateResponse = template_response - - -if not hasattr(shared, 'GradioTemplateResponseOriginal'): - shared.GradioTemplateResponseOriginal = gradio.routes.templates.TemplateResponse - - -def versions_html(): - import torch - import launch - - python_version = ".".join([str(x) for x in sys.version_info[0:3]]) - commit = launch.commit_hash() - short_commit = commit[0:8] - - if shared.xformers_available: - import xformers - xformers_version = xformers.__version__ - else: - xformers_version = "N/A" - - return f""" -python: {python_version} - •  -torch: {getattr(torch, '__long_version__',torch.__version__)} - •  -xformers: {xformers_version} - •  -gradio: {gr.__version__} - •  -commit: {short_commit} - •  -checkpoint: N/A -""" +import html +import json +import math +import mimetypes +import os +import platform +import random +import sys +import tempfile +import time +import traceback +from functools import partial, reduce +import warnings + +import gradio as gr +import gradio.routes +import gradio.utils +import numpy as np +from PIL import Image, PngImagePlugin +from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call + +from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, postprocessing, ui_components, ui_common, ui_postprocessing +from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML +from modules.paths import script_path, data_path + +from modules.shared import opts, cmd_opts, restricted_opts + +import modules.codeformer_model +import modules.generation_parameters_copypaste as parameters_copypaste +import modules.gfpgan_model +import modules.hypernetworks.ui +import modules.scripts +import modules.shared as shared +import modules.styles +import modules.textual_inversion.ui +from modules import prompt_parser +from modules.images import save_image +from modules.sd_hijack import model_hijack +from modules.sd_samplers import samplers, samplers_for_img2img +from modules.textual_inversion import textual_inversion +import modules.hypernetworks.ui +from modules.generation_parameters_copypaste import image_from_url_text +import modules.extras + +warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning) + +# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI +mimetypes.init() +mimetypes.add_type('application/javascript', '.js') + +if not cmd_opts.share and not cmd_opts.listen: + # fix gradio phoning home + gradio.utils.version_check = lambda: None + gradio.utils.get_local_ip_address = lambda: '127.0.0.1' + +if cmd_opts.ngrok is not None: + import modules.ngrok as ngrok + print('ngrok authtoken detected, trying to connect...') + ngrok.connect( + cmd_opts.ngrok, + cmd_opts.port if cmd_opts.port is not None else 7860, + cmd_opts.ngrok_region + ) + + +def gr_show(visible=True): + return {"visible": visible, "__type__": "update"} + + +sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg" +sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None + +css_hide_progressbar = """ +.wrap .m-12 svg { display:none!important; } +.wrap .m-12::before { content:"Loading..." } +.wrap .z-20 svg { display:none!important; } +.wrap .z-20::before { content:"Loading..." } +.wrap.cover-bg .z-20::before { content:"" } +.progress-bar { display:none!important; } +.meta-text { display:none!important; } +.meta-text-center { display:none!important; } +""" + +# Using constants for these since the variation selector isn't visible. +# Important that they exactly match script.js for tooltip to work. +random_symbol = '\U0001f3b2\ufe0f' # 🎲️ +reuse_symbol = '\u267b\ufe0f' # ♻️ +paste_symbol = '\u2199\ufe0f' # ↙ +refresh_symbol = '\U0001f504' # 🔄 +save_style_symbol = '\U0001f4be' # 💾 +apply_style_symbol = '\U0001f4cb' # 📋 +clear_prompt_symbol = '\U0001F5D1' # 🗑️ +extra_networks_symbol = '\U0001F3B4' # 🎴 +switch_values_symbol = '\U000021C5' # ⇅ + + +def plaintext_to_html(text): + return ui_common.plaintext_to_html(text) + + +def send_gradio_gallery_to_image(x): + if len(x) == 0: + return None + return image_from_url_text(x[0]) + +def visit(x, func, path=""): + if hasattr(x, 'children'): + for c in x.children: + visit(c, func, path) + elif x.label is not None: + func(path + "/" + str(x.label), x) + + +def add_style(name: str, prompt: str, negative_prompt: str): + if name is None: + return [gr_show() for x in range(4)] + + style = modules.styles.PromptStyle(name, prompt, negative_prompt) + shared.prompt_styles.styles[style.name] = style + # Save all loaded prompt styles: this allows us to update the storage format in the future more easily, because we + # reserialize all styles every time we save them + shared.prompt_styles.save_styles(shared.styles_filename) + + return [gr.Dropdown.update(visible=True, choices=list(shared.prompt_styles.styles)) for _ in range(2)] + + +def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y): + from modules import processing, devices + + if not enable: + return "" + + p = processing.StableDiffusionProcessingTxt2Img(width=width, height=height, enable_hr=True, hr_scale=hr_scale, hr_resize_x=hr_resize_x, hr_resize_y=hr_resize_y) + + with devices.autocast(): + p.init([""], [0], [0]) + + return f"resize: from {p.width}x{p.height} to {p.hr_resize_x or p.hr_upscale_to_x}x{p.hr_resize_y or p.hr_upscale_to_y}" + + +def apply_styles(prompt, prompt_neg, styles): + prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, styles) + prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, styles) + + return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value=[])] + + +def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_dir, *ii_singles): + if mode in {0, 1, 3, 4}: + return [interrogation_function(ii_singles[mode]), None] + elif mode == 2: + return [interrogation_function(ii_singles[mode]["image"]), None] + elif mode == 5: + assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled" + images = shared.listfiles(ii_input_dir) + print(f"Will process {len(images)} images.") + if ii_output_dir != "": + os.makedirs(ii_output_dir, exist_ok=True) + else: + ii_output_dir = ii_input_dir + + for image in images: + img = Image.open(image) + filename = os.path.basename(image) + left, _ = os.path.splitext(filename) + print(interrogation_function(img), file=open(os.path.join(ii_output_dir, left + ".txt"), 'a')) + + return [gr.update(), None] + + +def interrogate(image): + prompt = shared.interrogator.interrogate(image.convert("RGB")) + return gr.update() if prompt is None else prompt + + +def interrogate_deepbooru(image): + prompt = deepbooru.model.tag(image) + return gr.update() if prompt is None else prompt + + +def create_seed_inputs(target_interface): + with FormRow(elem_id=target_interface + '_seed_row'): + seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=target_interface + '_seed') + seed.style(container=False) + random_seed = gr.Button(random_symbol, elem_id=target_interface + '_random_seed') + reuse_seed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_seed') + + with gr.Group(elem_id=target_interface + '_subseed_show_box'): + seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False) + + # Components to show/hide based on the 'Extra' checkbox + seed_extras = [] + + with FormRow(visible=False, elem_id=target_interface + '_subseed_row') as seed_extra_row_1: + seed_extras.append(seed_extra_row_1) + subseed = gr.Number(label='Variation seed', value=-1, elem_id=target_interface + '_subseed') + subseed.style(container=False) + random_subseed = gr.Button(random_symbol, elem_id=target_interface + '_random_subseed') + reuse_subseed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_subseed') + subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=target_interface + '_subseed_strength') + + with FormRow(visible=False) as seed_extra_row_2: + seed_extras.append(seed_extra_row_2) + seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=target_interface + '_seed_resize_from_w') + seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=target_interface + '_seed_resize_from_h') + + random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed]) + random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed]) + + def change_visibility(show): + return {comp: gr_show(show) for comp in seed_extras} + + seed_checkbox.change(change_visibility, show_progress=False, inputs=[seed_checkbox], outputs=seed_extras) + + return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox + + + +def connect_clear_prompt(button): + """Given clear button, prompt, and token_counter objects, setup clear prompt button click event""" + button.click( + _js="clear_prompt", + fn=None, + inputs=[], + outputs=[], + ) + + +def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed): + """ Connects a 'reuse (sub)seed' button's click event so that it copies last used + (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength + was 0, i.e. no variation seed was used, it copies the normal seed value instead.""" + def copy_seed(gen_info_string: str, index): + res = -1 + + try: + gen_info = json.loads(gen_info_string) + index -= gen_info.get('index_of_first_image', 0) + + if is_subseed and gen_info.get('subseed_strength', 0) > 0: + all_subseeds = gen_info.get('all_subseeds', [-1]) + res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0] + else: + all_seeds = gen_info.get('all_seeds', [-1]) + res = all_seeds[index if 0 <= index < len(all_seeds) else 0] + + except json.decoder.JSONDecodeError as e: + if gen_info_string != '': + print("Error parsing JSON generation info:", file=sys.stderr) + print(gen_info_string, file=sys.stderr) + + return [res, gr_show(False)] + + reuse_seed.click( + fn=copy_seed, + _js="(x, y) => [x, selected_gallery_index()]", + show_progress=False, + inputs=[generation_info, dummy_component], + outputs=[seed, dummy_component] + ) + + +def update_token_counter(text, steps): + try: + text, _ = extra_networks.parse_prompt(text) + + _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text]) + prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps) + + except Exception: + # a parsing error can happen here during typing, and we don't want to bother the user with + # messages related to it in console + prompt_schedules = [[[steps, text]]] + + flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules) + prompts = [prompt_text for step, prompt_text in flat_prompts] + token_count, max_length = max([model_hijack.get_prompt_lengths(prompt) for prompt in prompts], key=lambda args: args[0]) + return f"{token_count}/{max_length}" + + +def create_toprow(is_img2img): + id_part = "img2img" if is_img2img else "txt2img" + + with gr.Row(elem_id=f"{id_part}_toprow", variant="compact"): + with gr.Column(elem_id=f"{id_part}_prompt_container", scale=6): + with gr.Row(): + with gr.Column(scale=80): + with gr.Row(): + prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)") + + with gr.Row(): + with gr.Column(scale=80): + with gr.Row(): + negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)") + + button_interrogate = None + button_deepbooru = None + if is_img2img: + with gr.Column(scale=1, elem_id="interrogate_col"): + button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate") + button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru") + + with gr.Column(scale=1, elem_id=f"{id_part}_actions_column"): + with gr.Row(elem_id=f"{id_part}_generate_box"): + interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt") + skip = gr.Button('Skip', elem_id=f"{id_part}_skip") + submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary') + + skip.click( + fn=lambda: shared.state.skip(), + inputs=[], + outputs=[], + ) + + interrupt.click( + fn=lambda: shared.state.interrupt(), + inputs=[], + outputs=[], + ) + + with gr.Row(elem_id=f"{id_part}_tools"): + paste = ToolButton(value=paste_symbol, elem_id="paste") + clear_prompt_button = ToolButton(value=clear_prompt_symbol, elem_id=f"{id_part}_clear_prompt") + extra_networks_button = ToolButton(value=extra_networks_symbol, elem_id=f"{id_part}_extra_networks") + prompt_style_apply = ToolButton(value=apply_style_symbol, elem_id=f"{id_part}_style_apply") + save_style = ToolButton(value=save_style_symbol, elem_id=f"{id_part}_style_create") + + token_counter = gr.HTML(value="", elem_id=f"{id_part}_token_counter") + token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button") + negative_token_counter = gr.HTML(value="", elem_id=f"{id_part}_negative_token_counter") + negative_token_button = gr.Button(visible=False, elem_id=f"{id_part}_negative_token_button") + + clear_prompt_button.click( + fn=lambda *x: x, + _js="confirm_clear_prompt", + inputs=[prompt, negative_prompt], + outputs=[prompt, negative_prompt], + ) + + with gr.Row(elem_id=f"{id_part}_styles_row"): + prompt_styles = gr.Dropdown(label="Styles", elem_id=f"{id_part}_styles", choices=[k for k, v in shared.prompt_styles.styles.items()], value=[], multiselect=True) + create_refresh_button(prompt_styles, shared.prompt_styles.reload, lambda: {"choices": [k for k, v in shared.prompt_styles.styles.items()]}, f"refresh_{id_part}_styles") + + return prompt, prompt_styles, negative_prompt, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button + + +def setup_progressbar(*args, **kwargs): + pass + + +def apply_setting(key, value): + if value is None: + return gr.update() + + if shared.cmd_opts.freeze_settings: + return gr.update() + + # dont allow model to be swapped when model hash exists in prompt + if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap: + return gr.update() + + if key == "sd_model_checkpoint": + ckpt_info = sd_models.get_closet_checkpoint_match(value) + + if ckpt_info is not None: + value = ckpt_info.title + else: + return gr.update() + + comp_args = opts.data_labels[key].component_args + if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False: + return + + valtype = type(opts.data_labels[key].default) + oldval = opts.data.get(key, None) + opts.data[key] = valtype(value) if valtype != type(None) else value + if oldval != value and opts.data_labels[key].onchange is not None: + opts.data_labels[key].onchange() + + opts.save(shared.config_filename) + return getattr(opts, key) + + +def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id): + def refresh(): + refresh_method() + args = refreshed_args() if callable(refreshed_args) else refreshed_args + + for k, v in args.items(): + setattr(refresh_component, k, v) + + return gr.update(**(args or {})) + + refresh_button = ToolButton(value=refresh_symbol, elem_id=elem_id) + refresh_button.click( + fn=refresh, + inputs=[], + outputs=[refresh_component] + ) + return refresh_button + + +def create_output_panel(tabname, outdir): + return ui_common.create_output_panel(tabname, outdir) + + +def create_sampler_and_steps_selection(choices, tabname): + if opts.samplers_in_dropdown: + with FormRow(elem_id=f"sampler_selection_{tabname}"): + sampler_index = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index") + steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20) + else: + with FormGroup(elem_id=f"sampler_selection_{tabname}"): + steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20) + sampler_index = gr.Radio(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index") + + return steps, sampler_index + + +def ordered_ui_categories(): + user_order = {x.strip(): i * 2 + 1 for i, x in enumerate(shared.opts.ui_reorder.split(","))} + + for i, category in sorted(enumerate(shared.ui_reorder_categories), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)): + yield category + + +def get_value_for_setting(key): + value = getattr(opts, key) + + info = opts.data_labels[key] + args = info.component_args() if callable(info.component_args) else info.component_args or {} + args = {k: v for k, v in args.items() if k not in {'precision'}} + + return gr.update(value=value, **args) + + +def create_override_settings_dropdown(tabname, row): + dropdown = gr.Dropdown([], label="Override settings", visible=False, elem_id=f"{tabname}_override_settings", multiselect=True) + + dropdown.change( + fn=lambda x: gr.Dropdown.update(visible=len(x) > 0), + inputs=[dropdown], + outputs=[dropdown], + ) + + return dropdown + + +def create_ui(): + import modules.img2img + import modules.txt2img + + reload_javascript() + + parameters_copypaste.reset() + + modules.scripts.scripts_current = modules.scripts.scripts_txt2img + modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False) + + with gr.Blocks(analytics_enabled=False) as txt2img_interface: + txt2img_prompt, txt2img_prompt_styles, txt2img_negative_prompt, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button = create_toprow(is_img2img=False) + + dummy_component = gr.Label(visible=False) + txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="binary", visible=False) + + with FormRow(variant='compact', elem_id="txt2img_extra_networks", visible=False) as extra_networks: + from modules import ui_extra_networks + extra_networks_ui = ui_extra_networks.create_ui(extra_networks, extra_networks_button, 'txt2img') + + with gr.Row().style(equal_height=False): + with gr.Column(variant='compact', elem_id="txt2img_settings"): + for category in ordered_ui_categories(): + if category == "sampler": + steps, sampler_index = create_sampler_and_steps_selection(samplers, "txt2img") + + elif category == "dimensions": + with FormRow(): + with gr.Column(elem_id="txt2img_column_size", scale=4): + width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width") + height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height") + + res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn") + if opts.dimensions_and_batch_together: + with gr.Column(elem_id="txt2img_column_batch"): + batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count") + batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size") + + elif category == "cfg": + cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale") + + elif category == "seed": + seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('txt2img') + + elif category == "checkboxes": + with FormRow(elem_id="txt2img_checkboxes", variant="compact"): + restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces") + tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling") + enable_hr = gr.Checkbox(label='Hires. fix', value=False, elem_id="txt2img_enable_hr") + hr_final_resolution = FormHTML(value="", elem_id="txtimg_hr_finalres", label="Upscaled resolution", interactive=False) + + elif category == "hires_fix": + with FormGroup(visible=False, elem_id="txt2img_hires_fix") as hr_options: + with FormRow(elem_id="txt2img_hires_fix_row1", variant="compact"): + hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode) + hr_second_pass_steps = gr.Slider(minimum=0, maximum=150, step=1, label='Hires steps', value=0, elem_id="txt2img_hires_steps") + denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength") + + with FormRow(elem_id="txt2img_hires_fix_row2", variant="compact"): + hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale") + hr_resize_x = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize width to", value=0, elem_id="txt2img_hr_resize_x") + hr_resize_y = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize height to", value=0, elem_id="txt2img_hr_resize_y") + + elif category == "batch": + if not opts.dimensions_and_batch_together: + with FormRow(elem_id="txt2img_column_batch"): + batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count") + batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size") + + elif category == "override_settings": + with FormRow(elem_id="txt2img_override_settings_row") as row: + override_settings = create_override_settings_dropdown('txt2img', row) + + elif category == "scripts": + with FormGroup(elem_id="txt2img_script_container"): + custom_inputs = modules.scripts.scripts_txt2img.setup_ui() + + hr_resolution_preview_inputs = [enable_hr, width, height, hr_scale, hr_resize_x, hr_resize_y] + for input in hr_resolution_preview_inputs: + input.change( + fn=calc_resolution_hires, + inputs=hr_resolution_preview_inputs, + outputs=[hr_final_resolution], + show_progress=False, + ) + input.change( + None, + _js="onCalcResolutionHires", + inputs=hr_resolution_preview_inputs, + outputs=[], + show_progress=False, + ) + + txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples) + + connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False) + connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True) + + txt2img_args = dict( + fn=wrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs=[None, '', '']), + _js="submit", + inputs=[ + dummy_component, + txt2img_prompt, + txt2img_negative_prompt, + txt2img_prompt_styles, + steps, + sampler_index, + restore_faces, + tiling, + batch_count, + batch_size, + cfg_scale, + seed, + subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox, + height, + width, + enable_hr, + denoising_strength, + hr_scale, + hr_upscaler, + hr_second_pass_steps, + hr_resize_x, + hr_resize_y, + override_settings, + ] + custom_inputs, + + outputs=[ + txt2img_gallery, + generation_info, + html_info, + html_log, + ], + show_progress=False, + ) + + txt2img_prompt.submit(**txt2img_args) + submit.click(**txt2img_args) + + res_switch_btn.click(lambda w, h: (h, w), inputs=[width, height], outputs=[width, height]) + + txt_prompt_img.change( + fn=modules.images.image_data, + inputs=[ + txt_prompt_img + ], + outputs=[ + txt2img_prompt, + txt_prompt_img + ] + ) + + enable_hr.change( + fn=lambda x: gr_show(x), + inputs=[enable_hr], + outputs=[hr_options], + show_progress = False, + ) + + txt2img_paste_fields = [ + (txt2img_prompt, "Prompt"), + (txt2img_negative_prompt, "Negative prompt"), + (steps, "Steps"), + (sampler_index, "Sampler"), + (restore_faces, "Face restoration"), + (cfg_scale, "CFG scale"), + (seed, "Seed"), + (width, "Size-1"), + (height, "Size-2"), + (batch_size, "Batch size"), + (subseed, "Variation seed"), + (subseed_strength, "Variation seed strength"), + (seed_resize_from_w, "Seed resize from-1"), + (seed_resize_from_h, "Seed resize from-2"), + (denoising_strength, "Denoising strength"), + (enable_hr, lambda d: "Denoising strength" in d), + (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)), + (hr_scale, "Hires upscale"), + (hr_upscaler, "Hires upscaler"), + (hr_second_pass_steps, "Hires steps"), + (hr_resize_x, "Hires resize-1"), + (hr_resize_y, "Hires resize-2"), + *modules.scripts.scripts_txt2img.infotext_fields + ] + parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields, override_settings) + parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding( + paste_button=txt2img_paste, tabname="txt2img", source_text_component=txt2img_prompt, source_image_component=None, + )) + + txt2img_preview_params = [ + txt2img_prompt, + txt2img_negative_prompt, + steps, + sampler_index, + cfg_scale, + seed, + width, + height, + ] + + token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_prompt, steps], outputs=[token_counter]) + negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_negative_prompt, steps], outputs=[negative_token_counter]) + + ui_extra_networks.setup_ui(extra_networks_ui, txt2img_gallery) + modules.script_callbacks.after_ui_callback() + + modules.scripts.scripts_current = modules.scripts.scripts_img2img + modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True) + + with gr.Blocks(analytics_enabled=False) as img2img_interface: + img2img_prompt, img2img_prompt_styles, img2img_negative_prompt, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button = create_toprow(is_img2img=True) + + img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="binary", visible=False) + + with FormRow(variant='compact', elem_id="img2img_extra_networks", visible=False) as extra_networks: + from modules import ui_extra_networks + extra_networks_ui_img2img = ui_extra_networks.create_ui(extra_networks, extra_networks_button, 'img2img') + + with FormRow().style(equal_height=False): + with gr.Column(variant='compact', elem_id="img2img_settings"): + copy_image_buttons = [] + copy_image_destinations = {} + + def add_copy_image_controls(tab_name, elem): + with gr.Row(variant="compact", elem_id=f"img2img_copy_to_{tab_name}"): + gr.HTML("Copy image to: ", elem_id=f"img2img_label_copy_to_{tab_name}") + + for title, name in zip(['img2img', 'sketch', 'inpaint', 'inpaint sketch'], ['img2img', 'sketch', 'inpaint', 'inpaint_sketch']): + if name == tab_name: + gr.Button(title, interactive=False) + copy_image_destinations[name] = elem + continue + + button = gr.Button(title) + copy_image_buttons.append((button, name, elem)) + + with gr.Tabs(elem_id="mode_img2img"): + with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img: + init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA").style(height=480) + add_copy_image_controls('img2img', init_img) + + with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch: + sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=480) + add_copy_image_controls('sketch', sketch) + + with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab") as tab_inpaint: + init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=480) + add_copy_image_controls('inpaint', init_img_with_mask) + + with gr.TabItem('Inpaint sketch', id='inpaint_sketch', elem_id="img2img_inpaint_sketch_tab") as tab_inpaint_color: + inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=480) + inpaint_color_sketch_orig = gr.State(None) + add_copy_image_controls('inpaint_sketch', inpaint_color_sketch) + + def update_orig(image, state): + if image is not None: + same_size = state is not None and state.size == image.size + has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1)) + edited = same_size and has_exact_match + return image if not edited or state is None else state + + inpaint_color_sketch.change(update_orig, [inpaint_color_sketch, inpaint_color_sketch_orig], inpaint_color_sketch_orig) + + with gr.TabItem('Inpaint upload', id='inpaint_upload', elem_id="img2img_inpaint_upload_tab") as tab_inpaint_upload: + init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", elem_id="img_inpaint_base") + init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", elem_id="img_inpaint_mask") + + with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch: + hidden = '
Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else '' + gr.HTML( + f"

Process images in a directory on the same machine where the server is running." + + f"
Use an empty output directory to save pictures normally instead of writing to the output directory." + + f"
Add inpaint batch mask directory to enable inpaint batch processing." + f"{hidden}

" + ) + img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir") + img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir") + img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir") + + def copy_image(img): + if isinstance(img, dict) and 'image' in img: + return img['image'] + + return img + + for button, name, elem in copy_image_buttons: + button.click( + fn=copy_image, + inputs=[elem], + outputs=[copy_image_destinations[name]], + ) + button.click( + fn=lambda: None, + _js="switch_to_"+name.replace(" ", "_"), + inputs=[], + outputs=[], + ) + + with FormRow(): + resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize") + + for category in ordered_ui_categories(): + if category == "sampler": + steps, sampler_index = create_sampler_and_steps_selection(samplers_for_img2img, "img2img") + + elif category == "dimensions": + with FormRow(): + with gr.Column(elem_id="img2img_column_size", scale=4): + width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width") + height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height") + + res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn") + if opts.dimensions_and_batch_together: + with gr.Column(elem_id="img2img_column_batch"): + batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count") + batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size") + + elif category == "cfg": + with FormGroup(): + with FormRow(): + cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale") + image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit") + denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength") + + elif category == "seed": + seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img') + + elif category == "checkboxes": + with FormRow(elem_id="img2img_checkboxes", variant="compact"): + restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces") + tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling") + + elif category == "batch": + if not opts.dimensions_and_batch_together: + with FormRow(elem_id="img2img_column_batch"): + batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count") + batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size") + + elif category == "override_settings": + with FormRow(elem_id="img2img_override_settings_row") as row: + override_settings = create_override_settings_dropdown('img2img', row) + + elif category == "scripts": + with FormGroup(elem_id="img2img_script_container"): + custom_inputs = modules.scripts.scripts_img2img.setup_ui() + + elif category == "inpaint": + with FormGroup(elem_id="inpaint_controls", visible=False) as inpaint_controls: + with FormRow(): + mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur") + mask_alpha = gr.Slider(label="Mask transparency", visible=False, elem_id="img2img_mask_alpha") + + with FormRow(): + inpainting_mask_invert = gr.Radio(label='Mask mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode") + + with FormRow(): + inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill") + + with FormRow(): + with gr.Column(): + inpaint_full_res = gr.Radio(label="Inpaint area", choices=["Whole picture", "Only masked"], type="index", value="Whole picture", elem_id="img2img_inpaint_full_res") + + with gr.Column(scale=4): + inpaint_full_res_padding = gr.Slider(label='Only masked padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding") + + def select_img2img_tab(tab): + return gr.update(visible=tab in [2, 3, 4]), gr.update(visible=tab == 3), + + for i, elem in enumerate([tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]): + elem.select( + fn=lambda tab=i: select_img2img_tab(tab), + inputs=[], + outputs=[inpaint_controls, mask_alpha], + ) + + img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples) + + connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False) + connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True) + + img2img_prompt_img.change( + fn=modules.images.image_data, + inputs=[ + img2img_prompt_img + ], + outputs=[ + img2img_prompt, + img2img_prompt_img + ] + ) + + img2img_args = dict( + fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']), + _js="submit_img2img", + inputs=[ + dummy_component, + dummy_component, + img2img_prompt, + img2img_negative_prompt, + img2img_prompt_styles, + init_img, + sketch, + init_img_with_mask, + inpaint_color_sketch, + inpaint_color_sketch_orig, + init_img_inpaint, + init_mask_inpaint, + steps, + sampler_index, + mask_blur, + mask_alpha, + inpainting_fill, + restore_faces, + tiling, + batch_count, + batch_size, + cfg_scale, + image_cfg_scale, + denoising_strength, + seed, + subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox, + height, + width, + resize_mode, + inpaint_full_res, + inpaint_full_res_padding, + inpainting_mask_invert, + img2img_batch_input_dir, + img2img_batch_output_dir, + img2img_batch_inpaint_mask_dir, + override_settings, + ] + custom_inputs, + outputs=[ + img2img_gallery, + generation_info, + html_info, + html_log, + ], + show_progress=False, + ) + + interrogate_args = dict( + _js="get_img2img_tab_index", + inputs=[ + dummy_component, + img2img_batch_input_dir, + img2img_batch_output_dir, + init_img, + sketch, + init_img_with_mask, + inpaint_color_sketch, + init_img_inpaint, + ], + outputs=[img2img_prompt, dummy_component], + ) + + img2img_prompt.submit(**img2img_args) + submit.click(**img2img_args) + res_switch_btn.click(lambda w, h: (h, w), inputs=[width, height], outputs=[width, height]) + + img2img_interrogate.click( + fn=lambda *args: process_interrogate(interrogate, *args), + **interrogate_args, + ) + + img2img_deepbooru.click( + fn=lambda *args: process_interrogate(interrogate_deepbooru, *args), + **interrogate_args, + ) + + prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)] + style_dropdowns = [txt2img_prompt_styles, img2img_prompt_styles] + style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"] + + for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts): + button.click( + fn=add_style, + _js="ask_for_style_name", + # Have to pass empty dummy component here, because the JavaScript and Python function have to accept + # the same number of parameters, but we only know the style-name after the JavaScript prompt + inputs=[dummy_component, prompt, negative_prompt], + outputs=[txt2img_prompt_styles, img2img_prompt_styles], + ) + + for button, (prompt, negative_prompt), styles, js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs): + button.click( + fn=apply_styles, + _js=js_func, + inputs=[prompt, negative_prompt, styles], + outputs=[prompt, negative_prompt, styles], + ) + + token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter]) + negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_negative_prompt, steps], outputs=[negative_token_counter]) + + ui_extra_networks.setup_ui(extra_networks_ui_img2img, img2img_gallery) + + img2img_paste_fields = [ + (img2img_prompt, "Prompt"), + (img2img_negative_prompt, "Negative prompt"), + (steps, "Steps"), + (sampler_index, "Sampler"), + (restore_faces, "Face restoration"), + (cfg_scale, "CFG scale"), + (image_cfg_scale, "Image CFG scale"), + (seed, "Seed"), + (width, "Size-1"), + (height, "Size-2"), + (batch_size, "Batch size"), + (subseed, "Variation seed"), + (subseed_strength, "Variation seed strength"), + (seed_resize_from_w, "Seed resize from-1"), + (seed_resize_from_h, "Seed resize from-2"), + (denoising_strength, "Denoising strength"), + (mask_blur, "Mask blur"), + *modules.scripts.scripts_img2img.infotext_fields + ] + parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields, override_settings) + parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields, override_settings) + parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding( + paste_button=img2img_paste, tabname="img2img", source_text_component=img2img_prompt, source_image_component=None, + )) + + modules.script_callbacks.after_ui_callback() + + modules.scripts.scripts_current = None + + with gr.Blocks(analytics_enabled=False) as extras_interface: + ui_postprocessing.create_ui() + + with gr.Blocks(analytics_enabled=False) as pnginfo_interface: + with gr.Row().style(equal_height=False): + with gr.Column(variant='panel'): + image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil") + + with gr.Column(variant='panel'): + html = gr.HTML() + generation_info = gr.Textbox(visible=False, elem_id="pnginfo_generation_info") + html2 = gr.HTML() + with gr.Row(): + buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"]) + + for tabname, button in buttons.items(): + parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding( + paste_button=button, tabname=tabname, source_text_component=generation_info, source_image_component=image, + )) + + image.change( + fn=wrap_gradio_call(modules.extras.run_pnginfo), + inputs=[image], + outputs=[html, generation_info, html2], + ) + + def update_interp_description(value): + interp_description_css = "

{}

" + interp_descriptions = { + "No interpolation": interp_description_css.format("No interpolation will be used. Requires one model; A. Allows for format conversion and VAE baking."), + "Weighted sum": interp_description_css.format("A weighted sum will be used for interpolation. Requires two models; A and B. The result is calculated as A * (1 - M) + B * M"), + "Add difference": interp_description_css.format("The difference between the last two models will be added to the first. Requires three models; A, B and C. The result is calculated as A + (B - C) * M") + } + return interp_descriptions[value] + + with gr.Blocks(analytics_enabled=False) as modelmerger_interface: + with gr.Row().style(equal_height=False): + with gr.Column(variant='compact'): + interp_description = gr.HTML(value=update_interp_description("Weighted sum"), elem_id="modelmerger_interp_description") + + with FormRow(elem_id="modelmerger_models"): + primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)") + create_refresh_button(primary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_A") + + secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)") + create_refresh_button(secondary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_B") + + tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)") + create_refresh_button(tertiary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_C") + + custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name") + interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount") + interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method") + interp_method.change(fn=update_interp_description, inputs=[interp_method], outputs=[interp_description]) + + with FormRow(): + checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="ckpt", label="Checkpoint format", elem_id="modelmerger_checkpoint_format") + save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half") + + with FormRow(): + with gr.Column(): + config_source = gr.Radio(choices=["A, B or C", "B", "C", "Don't"], value="A, B or C", label="Copy config from", type="index", elem_id="modelmerger_config_method") + + with gr.Column(): + with FormRow(): + bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE", elem_id="modelmerger_bake_in_vae") + create_refresh_button(bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "modelmerger_refresh_bake_in_vae") + + with FormRow(): + discard_weights = gr.Textbox(value="", label="Discard weights with matching name", elem_id="modelmerger_discard_weights") + + with gr.Row(): + modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary') + + with gr.Column(variant='compact', elem_id="modelmerger_results_container"): + with gr.Group(elem_id="modelmerger_results_panel"): + modelmerger_result = gr.HTML(elem_id="modelmerger_result", show_label=False) + + with gr.Blocks(analytics_enabled=False) as train_interface: + with gr.Row().style(equal_height=False): + gr.HTML(value="

See wiki for detailed explanation.

") + + with gr.Row(variant="compact").style(equal_height=False): + with gr.Tabs(elem_id="train_tabs"): + + with gr.Tab(label="Create embedding"): + new_embedding_name = gr.Textbox(label="Name", elem_id="train_new_embedding_name") + initialization_text = gr.Textbox(label="Initialization text", value="*", elem_id="train_initialization_text") + nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1, elem_id="train_nvpt") + overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding", elem_id="train_overwrite_old_embedding") + + with gr.Row(): + with gr.Column(scale=3): + gr.HTML(value="") + + with gr.Column(): + create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding") + + with gr.Tab(label="Create hypernetwork"): + new_hypernetwork_name = gr.Textbox(label="Name", elem_id="train_new_hypernetwork_name") + new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"], elem_id="train_new_hypernetwork_sizes") + new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'", elem_id="train_new_hypernetwork_layer_structure") + new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys, elem_id="train_new_hypernetwork_activation_func") + new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"], elem_id="train_new_hypernetwork_initialization_option") + new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization", elem_id="train_new_hypernetwork_add_layer_norm") + new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout", elem_id="train_new_hypernetwork_use_dropout") + new_hypernetwork_dropout_structure = gr.Textbox("0, 0, 0", label="Enter hypernetwork Dropout structure (or empty). Recommended : 0~0.35 incrementing sequence: 0, 0.05, 0.15", placeholder="1st and last digit must be 0 and values should be between 0 and 1. ex:'0, 0.01, 0'") + overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork", elem_id="train_overwrite_old_hypernetwork") + + with gr.Row(): + with gr.Column(scale=3): + gr.HTML(value="") + + with gr.Column(): + create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork") + + with gr.Tab(label="Preprocess images"): + process_src = gr.Textbox(label='Source directory', elem_id="train_process_src") + process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst") + process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width") + process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_process_height") + preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action") + + with gr.Row(): + process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip") + process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split") + process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop") + process_multicrop = gr.Checkbox(label='Auto-sized crop', elem_id="train_process_multicrop") + process_caption = gr.Checkbox(label='Use BLIP for caption', elem_id="train_process_caption") + process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True, elem_id="train_process_caption_deepbooru") + + with gr.Row(visible=False) as process_split_extra_row: + process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_split_threshold") + process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="train_process_overlap_ratio") + + with gr.Row(visible=False) as process_focal_crop_row: + process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_face_weight") + process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight") + process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight") + process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug") + + with gr.Column(visible=False) as process_multicrop_col: + gr.Markdown('Each image is center-cropped with an automatically chosen width and height.') + with gr.Row(): + process_multicrop_mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id="train_process_multicrop_mindim") + process_multicrop_maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="train_process_multicrop_maxdim") + with gr.Row(): + process_multicrop_minarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area lower bound", value=64*64, elem_id="train_process_multicrop_minarea") + process_multicrop_maxarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area upper bound", value=640*640, elem_id="train_process_multicrop_maxarea") + with gr.Row(): + process_multicrop_objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="train_process_multicrop_objective") + process_multicrop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="train_process_multicrop_threshold") + + with gr.Row(): + with gr.Column(scale=3): + gr.HTML(value="") + + with gr.Column(): + with gr.Row(): + interrupt_preprocessing = gr.Button("Interrupt", elem_id="train_interrupt_preprocessing") + run_preprocess = gr.Button(value="Preprocess", variant='primary', elem_id="train_run_preprocess") + + process_split.change( + fn=lambda show: gr_show(show), + inputs=[process_split], + outputs=[process_split_extra_row], + ) + + process_focal_crop.change( + fn=lambda show: gr_show(show), + inputs=[process_focal_crop], + outputs=[process_focal_crop_row], + ) + + process_multicrop.change( + fn=lambda show: gr_show(show), + inputs=[process_multicrop], + outputs=[process_multicrop_col], + ) + + def get_textual_inversion_template_names(): + return sorted([x for x in textual_inversion.textual_inversion_templates]) + + with gr.Tab(label="Train"): + gr.HTML(value="

Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images [wiki]

") + with FormRow(): + train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())) + create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name") + + train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()]) + create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name") + + with FormRow(): + embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate") + hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001", elem_id="train_hypernetwork_learn_rate") + + with FormRow(): + clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"]) + clip_grad_value = gr.Textbox(placeholder="Gradient clip value", value="0.1", show_label=False) + + with FormRow(): + batch_size = gr.Number(label='Batch size', value=1, precision=0, elem_id="train_batch_size") + gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0, elem_id="train_gradient_step") + + dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images", elem_id="train_dataset_directory") + log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion", elem_id="train_log_directory") + + with FormRow(): + template_file = gr.Dropdown(label='Prompt template', value="style_filewords.txt", elem_id="train_template_file", choices=get_textual_inversion_template_names()) + create_refresh_button(template_file, textual_inversion.list_textual_inversion_templates, lambda: {"choices": get_textual_inversion_template_names()}, "refrsh_train_template_file") + + training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width") + training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height") + varsize = gr.Checkbox(label="Do not resize images", value=False, elem_id="train_varsize") + steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps") + + with FormRow(): + create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every") + save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every") + + use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False, elem_id="use_weight") + + save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True, elem_id="train_save_image_with_stored_embedding") + preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False, elem_id="train_preview_from_txt2img") + + shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False, elem_id="train_shuffle_tags") + tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0, elem_id="train_tag_drop_out") + + latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'], elem_id="train_latent_sampling_method") + + with gr.Row(): + train_embedding = gr.Button(value="Train Embedding", variant='primary', elem_id="train_train_embedding") + interrupt_training = gr.Button(value="Interrupt", elem_id="train_interrupt_training") + train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary', elem_id="train_train_hypernetwork") + + params = script_callbacks.UiTrainTabParams(txt2img_preview_params) + + script_callbacks.ui_train_tabs_callback(params) + + with gr.Column(elem_id='ti_gallery_container'): + ti_output = gr.Text(elem_id="ti_output", value="", show_label=False) + ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4) + ti_progress = gr.HTML(elem_id="ti_progress", value="") + ti_outcome = gr.HTML(elem_id="ti_error", value="") + + create_embedding.click( + fn=modules.textual_inversion.ui.create_embedding, + inputs=[ + new_embedding_name, + initialization_text, + nvpt, + overwrite_old_embedding, + ], + outputs=[ + train_embedding_name, + ti_output, + ti_outcome, + ] + ) + + create_hypernetwork.click( + fn=modules.hypernetworks.ui.create_hypernetwork, + inputs=[ + new_hypernetwork_name, + new_hypernetwork_sizes, + overwrite_old_hypernetwork, + new_hypernetwork_layer_structure, + new_hypernetwork_activation_func, + new_hypernetwork_initialization_option, + new_hypernetwork_add_layer_norm, + new_hypernetwork_use_dropout, + new_hypernetwork_dropout_structure + ], + outputs=[ + train_hypernetwork_name, + ti_output, + ti_outcome, + ] + ) + + run_preprocess.click( + fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]), + _js="start_training_textual_inversion", + inputs=[ + dummy_component, + process_src, + process_dst, + process_width, + process_height, + preprocess_txt_action, + process_flip, + process_split, + process_caption, + process_caption_deepbooru, + process_split_threshold, + process_overlap_ratio, + process_focal_crop, + process_focal_crop_face_weight, + process_focal_crop_entropy_weight, + process_focal_crop_edges_weight, + process_focal_crop_debug, + process_multicrop, + process_multicrop_mindim, + process_multicrop_maxdim, + process_multicrop_minarea, + process_multicrop_maxarea, + process_multicrop_objective, + process_multicrop_threshold, + ], + outputs=[ + ti_output, + ti_outcome, + ], + ) + + train_embedding.click( + fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]), + _js="start_training_textual_inversion", + inputs=[ + dummy_component, + train_embedding_name, + embedding_learn_rate, + batch_size, + gradient_step, + dataset_directory, + log_directory, + training_width, + training_height, + varsize, + steps, + clip_grad_mode, + clip_grad_value, + shuffle_tags, + tag_drop_out, + latent_sampling_method, + use_weight, + create_image_every, + save_embedding_every, + template_file, + save_image_with_stored_embedding, + preview_from_txt2img, + *txt2img_preview_params, + ], + outputs=[ + ti_output, + ti_outcome, + ] + ) + + train_hypernetwork.click( + fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]), + _js="start_training_textual_inversion", + inputs=[ + dummy_component, + train_hypernetwork_name, + hypernetwork_learn_rate, + batch_size, + gradient_step, + dataset_directory, + log_directory, + training_width, + training_height, + varsize, + steps, + clip_grad_mode, + clip_grad_value, + shuffle_tags, + tag_drop_out, + latent_sampling_method, + use_weight, + create_image_every, + save_embedding_every, + template_file, + preview_from_txt2img, + *txt2img_preview_params, + ], + outputs=[ + ti_output, + ti_outcome, + ] + ) + + interrupt_training.click( + fn=lambda: shared.state.interrupt(), + inputs=[], + outputs=[], + ) + + interrupt_preprocessing.click( + fn=lambda: shared.state.interrupt(), + inputs=[], + outputs=[], + ) + + def create_setting_component(key, is_quicksettings=False): + def fun(): + return opts.data[key] if key in opts.data else opts.data_labels[key].default + + info = opts.data_labels[key] + t = type(info.default) + + args = info.component_args() if callable(info.component_args) else info.component_args + + if info.component is not None: + comp = info.component + elif t == str: + comp = gr.Textbox + elif t == int: + comp = gr.Number + elif t == bool: + comp = gr.Checkbox + else: + raise Exception(f'bad options item type: {str(t)} for key {key}') + + elem_id = "setting_"+key + + if info.refresh is not None: + if is_quicksettings: + res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {})) + create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key) + else: + with FormRow(): + res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {})) + create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key) + else: + res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {})) + + return res + + components = [] + component_dict = {} + shared.settings_components = component_dict + + script_callbacks.ui_settings_callback() + opts.reorder() + + def run_settings(*args): + changed = [] + + for key, value, comp in zip(opts.data_labels.keys(), args, components): + assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}" + + for key, value, comp in zip(opts.data_labels.keys(), args, components): + if comp == dummy_component: + continue + + if opts.set(key, value): + changed.append(key) + + try: + opts.save(shared.config_filename) + except RuntimeError: + return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.' + return opts.dumpjson(), f'{len(changed)} settings changed{": " if len(changed) > 0 else ""}{", ".join(changed)}.' + + def run_settings_single(value, key): + if not opts.same_type(value, opts.data_labels[key].default): + return gr.update(visible=True), opts.dumpjson() + + if not opts.set(key, value): + return gr.update(value=getattr(opts, key)), opts.dumpjson() + + opts.save(shared.config_filename) + + return get_value_for_setting(key), opts.dumpjson() + + with gr.Blocks(analytics_enabled=False) as settings_interface: + with gr.Row(): + with gr.Column(scale=6): + settings_submit = gr.Button(value="Apply settings", variant='primary', elem_id="settings_submit") + with gr.Column(): + restart_gradio = gr.Button(value='Reload UI', variant='primary', elem_id="settings_restart_gradio") + + result = gr.HTML(elem_id="settings_result") + + quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")] + quicksettings_names = {x: i for i, x in enumerate(quicksettings_names) if x != 'quicksettings'} + + quicksettings_list = [] + + previous_section = None + current_tab = None + current_row = None + with gr.Tabs(elem_id="settings"): + for i, (k, item) in enumerate(opts.data_labels.items()): + section_must_be_skipped = item.section[0] is None + + if previous_section != item.section and not section_must_be_skipped: + elem_id, text = item.section + + if current_tab is not None: + current_row.__exit__() + current_tab.__exit__() + + gr.Group() + current_tab = gr.TabItem(elem_id="settings_{}".format(elem_id), label=text) + current_tab.__enter__() + current_row = gr.Column(variant='compact') + current_row.__enter__() + + previous_section = item.section + + if k in quicksettings_names and not shared.cmd_opts.freeze_settings: + quicksettings_list.append((i, k, item)) + components.append(dummy_component) + elif section_must_be_skipped: + components.append(dummy_component) + else: + component = create_setting_component(k) + component_dict[k] = component + components.append(component) + + if current_tab is not None: + current_row.__exit__() + current_tab.__exit__() + + with gr.TabItem("Actions"): + request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications") + download_localization = gr.Button(value='Download localization template', elem_id="download_localization") + reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies") + + with gr.TabItem("Licenses"): + gr.HTML(shared.html("licenses.html"), elem_id="licenses") + + gr.Button(value="Show all pages", elem_id="settings_show_all_pages") + + request_notifications.click( + fn=lambda: None, + inputs=[], + outputs=[], + _js='function(){}' + ) + + download_localization.click( + fn=lambda: None, + inputs=[], + outputs=[], + _js='download_localization' + ) + + def reload_scripts(): + modules.scripts.reload_script_body_only() + reload_javascript() # need to refresh the html page + + reload_script_bodies.click( + fn=reload_scripts, + inputs=[], + outputs=[] + ) + + def request_restart(): + shared.state.interrupt() + shared.state.need_restart = True + + restart_gradio.click( + fn=request_restart, + _js='restart_reload', + inputs=[], + outputs=[], + ) + + interfaces = [ + (txt2img_interface, "txt2img", "txt2img"), + (img2img_interface, "img2img", "img2img"), + (extras_interface, "Extras", "extras"), + (pnginfo_interface, "PNG Info", "pnginfo"), + (modelmerger_interface, "Checkpoint Merger", "modelmerger"), + (train_interface, "Train", "ti"), + ] + + css = "" + + for cssfile in modules.scripts.list_files_with_name("style.css"): + if not os.path.isfile(cssfile): + continue + + with open(cssfile, "r", encoding="utf8") as file: + css += file.read() + "\n" + + if os.path.exists(os.path.join(data_path, "user.css")): + with open(os.path.join(data_path, "user.css"), "r", encoding="utf8") as file: + css += file.read() + "\n" + + if not cmd_opts.no_progressbar_hiding: + css += css_hide_progressbar + + interfaces += script_callbacks.ui_tabs_callback() + interfaces += [(settings_interface, "Settings", "settings")] + + extensions_interface = ui_extensions.create_ui() + interfaces += [(extensions_interface, "Extensions", "extensions")] + + with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo: + with gr.Row(elem_id="quicksettings", variant="compact"): + for i, k, item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])): + component = create_setting_component(k, is_quicksettings=True) + component_dict[k] = component + + parameters_copypaste.connect_paste_params_buttons() + + with gr.Tabs(elem_id="tabs") as tabs: + for interface, label, ifid in interfaces: + with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid): + interface.render() + + if os.path.exists(os.path.join(script_path, "notification.mp3")): + audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False) + + footer = shared.html("footer.html") + footer = footer.format(versions=versions_html()) + gr.HTML(footer, elem_id="footer") + + text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False) + settings_submit.click( + fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]), + inputs=components, + outputs=[text_settings, result], + ) + + for i, k, item in quicksettings_list: + component = component_dict[k] + + component.change( + fn=lambda value, k=k: run_settings_single(value, key=k), + inputs=[component], + outputs=[component, text_settings], + ) + + text_settings.change( + fn=lambda: gr.update(visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit"), + inputs=[], + outputs=[image_cfg_scale], + ) + + button_set_checkpoint = gr.Button('Change checkpoint', elem_id='change_checkpoint', visible=False) + button_set_checkpoint.click( + fn=lambda value, _: run_settings_single(value, key='sd_model_checkpoint'), + _js="function(v){ var res = desiredCheckpointName; desiredCheckpointName = ''; return [res || v, null]; }", + inputs=[component_dict['sd_model_checkpoint'], dummy_component], + outputs=[component_dict['sd_model_checkpoint'], text_settings], + ) + + component_keys = [k for k in opts.data_labels.keys() if k in component_dict] + + def get_settings_values(): + return [get_value_for_setting(key) for key in component_keys] + + demo.load( + fn=get_settings_values, + inputs=[], + outputs=[component_dict[k] for k in component_keys], + ) + + def modelmerger(*args): + try: + results = modules.extras.run_modelmerger(*args) + except Exception as e: + print("Error loading/saving model file:", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) + modules.sd_models.list_models() # to remove the potentially missing models from the list + return [*[gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(4)], f"Error merging checkpoints: {e}"] + return results + + modelmerger_merge.click(fn=lambda: '', inputs=[], outputs=[modelmerger_result]) + modelmerger_merge.click( + fn=wrap_gradio_gpu_call(modelmerger, extra_outputs=lambda: [gr.update() for _ in range(4)]), + _js='modelmerger', + inputs=[ + dummy_component, + primary_model_name, + secondary_model_name, + tertiary_model_name, + interp_method, + interp_amount, + save_as_half, + custom_name, + checkpoint_format, + config_source, + bake_in_vae, + discard_weights, + ], + outputs=[ + primary_model_name, + secondary_model_name, + tertiary_model_name, + component_dict['sd_model_checkpoint'], + modelmerger_result, + ] + ) + + ui_config_file = cmd_opts.ui_config_file + ui_settings = {} + settings_count = len(ui_settings) + error_loading = False + + try: + if os.path.exists(ui_config_file): + with open(ui_config_file, "r", encoding="utf8") as file: + ui_settings = json.load(file) + except Exception: + error_loading = True + print("Error loading settings:", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) + + def loadsave(path, x): + def apply_field(obj, field, condition=None, init_field=None): + key = path + "/" + field + + if getattr(obj, 'custom_script_source', None) is not None: + key = 'customscript/' + obj.custom_script_source + '/' + key + + if getattr(obj, 'do_not_save_to_config', False): + return + + saved_value = ui_settings.get(key, None) + if saved_value is None: + ui_settings[key] = getattr(obj, field) + elif condition and not condition(saved_value): + pass + + # this warning is generally not useful; + # print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.') + else: + setattr(obj, field, saved_value) + if init_field is not None: + init_field(saved_value) + + if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown] and x.visible: + apply_field(x, 'visible') + + if type(x) == gr.Slider: + apply_field(x, 'value') + apply_field(x, 'minimum') + apply_field(x, 'maximum') + apply_field(x, 'step') + + if type(x) == gr.Radio: + apply_field(x, 'value', lambda val: val in x.choices) + + if type(x) == gr.Checkbox: + apply_field(x, 'value') + + if type(x) == gr.Textbox: + apply_field(x, 'value') + + if type(x) == gr.Number: + apply_field(x, 'value') + + if type(x) == gr.Dropdown: + def check_dropdown(val): + if getattr(x, 'multiselect', False): + return all([value in x.choices for value in val]) + else: + return val in x.choices + + apply_field(x, 'value', check_dropdown, getattr(x, 'init_field', None)) + + visit(txt2img_interface, loadsave, "txt2img") + visit(img2img_interface, loadsave, "img2img") + visit(extras_interface, loadsave, "extras") + visit(modelmerger_interface, loadsave, "modelmerger") + visit(train_interface, loadsave, "train") + + if not error_loading and (not os.path.exists(ui_config_file) or settings_count != len(ui_settings)): + with open(ui_config_file, "w", encoding="utf8") as file: + json.dump(ui_settings, file, indent=4) + + # Required as a workaround for change() event not triggering when loading values from ui-config.json + interp_description.value = update_interp_description(interp_method.value) + + return demo + + +def reload_javascript(): + head = f'\n' + + inline = f"{localization.localization_js(shared.opts.localization)};" + if cmd_opts.theme is not None: + inline += f"set_theme('{cmd_opts.theme}');" + + for script in modules.scripts.list_scripts("javascript", ".js"): + head += f'\n' + + head += f'\n' + + def template_response(*args, **kwargs): + res = shared.GradioTemplateResponseOriginal(*args, **kwargs) + res.body = res.body.replace(b'', f'{head}'.encode("utf8")) + res.init_headers() + return res + + gradio.routes.templates.TemplateResponse = template_response + + +if not hasattr(shared, 'GradioTemplateResponseOriginal'): + shared.GradioTemplateResponseOriginal = gradio.routes.templates.TemplateResponse + + +def versions_html(): + import torch + import launch + + python_version = ".".join([str(x) for x in sys.version_info[0:3]]) + commit = launch.commit_hash() + short_commit = commit[0:8] + + if shared.xformers_available: + import xformers + xformers_version = xformers.__version__ + else: + xformers_version = "N/A" + + return f""" +python: {python_version} + •  +torch: {getattr(torch, '__long_version__',torch.__version__)} + •  +xformers: {xformers_version} + •  +gradio: {gr.__version__} + •  +commit: {short_commit} + •  +checkpoint: N/A +"""