diff --git a/config.json b/config.json
deleted file mode 100644
index 1bfd0e0dae3..00000000000
--- a/config.json
+++ /dev/null
@@ -1,116 +0,0 @@
-{
-    "samples_save": true,
-    "samples_format": "png",
-    "samples_filename_pattern": "",
-    "save_images_add_number": true,
-    "grid_save": true,
-    "grid_format": "png",
-    "grid_extended_filename": false,
-    "grid_only_if_multiple": true,
-    "grid_prevent_empty_spots": false,
-    "n_rows": -1,
-    "enable_pnginfo": true,
-    "save_txt": false,
-    "save_images_before_face_restoration": false,
-    "save_images_before_highres_fix": false,
-    "save_images_before_color_correction": false,
-    "jpeg_quality": 80,
-    "export_for_4chan": true,
-    "use_original_name_batch": false,
-    "save_selected_only": true,
-    "do_not_add_watermark": false,
-    "temp_dir": "",
-    "clean_temp_dir_at_start": false,
-    "outdir_samples": "",
-    "outdir_txt2img_samples": "outputs/txt2img-images",
-    "outdir_img2img_samples": "outputs/img2img-images",
-    "outdir_extras_samples": "outputs/extras-images",
-    "outdir_grids": "",
-    "outdir_txt2img_grids": "outputs/txt2img-grids",
-    "outdir_img2img_grids": "outputs/img2img-grids",
-    "outdir_save": "log/images",
-    "save_to_dirs": false,
-    "grid_save_to_dirs": false,
-    "use_save_to_dirs_for_ui": false,
-    "directories_filename_pattern": "",
-    "directories_max_prompt_words": 8,
-    "ESRGAN_tile": 192,
-    "ESRGAN_tile_overlap": 8,
-    "realesrgan_enabled_models": [
-        "R-ESRGAN 4x+",
-        "R-ESRGAN 4x+ Anime6B"
-    ],
-    "SWIN_tile": 192,
-    "SWIN_tile_overlap": 8,
-    "ldsr_steps": 100,
-    "upscaler_for_img2img": null,
-    "use_scale_latent_for_hires_fix": false,
-    "face_restoration_model": null,
-    "code_former_weight": 0.5,
-    "face_restoration_unload": false,
-    "memmon_poll_rate": 8,
-    "samples_log_stdout": false,
-    "multiple_tqdm": true,
-    "unload_models_when_training": false,
-    "pin_memory": false,
-    "save_optimizer_state": false,
-    "dataset_filename_word_regex": "",
-    "dataset_filename_join_string": " ",
-    "training_image_repeats_per_epoch": 1,
-    "training_write_csv_every": 500,
-    "training_xattention_optimizations": false,
-    "sd_model_checkpoint": "",
-    "sd_checkpoint_cache": 0,
-    "sd_vae": "auto",
-    "sd_vae_as_default": false,
-    "sd_hypernetwork": "None",
-    "sd_hypernetwork_strength": 1.0,
-    "inpainting_mask_weight": 1.0,
-    "img2img_color_correction": false,
-    "img2img_fix_steps": false,
-    "enable_quantization": false,
-    "enable_emphasis": true,
-    "use_old_emphasis_implementation": false,
-    "enable_batch_seeds": true,
-    "comma_padding_backtrack": 20,
-    "filter_nsfw": false,
-    "CLIP_stop_at_last_layers": 1,
-    "random_artist_categories": [],
-    "interrogate_keep_models_in_memory": false,
-    "interrogate_use_builtin_artists": true,
-    "interrogate_return_ranks": false,
-    "interrogate_clip_num_beams": 1,
-    "interrogate_clip_min_length": 24,
-    "interrogate_clip_max_length": 48,
-    "interrogate_clip_dict_limit": 1500,
-    "interrogate_deepbooru_score_threshold": 0.5,
-    "deepbooru_sort_alpha": true,
-    "deepbooru_use_spaces": false,
-    "deepbooru_escape": true,
-    "show_progressbar": true,
-    "show_progress_every_n_steps": 0,
-    "show_progress_grid": true,
-    "return_grid": true,
-    "do_not_show_images": false,
-    "add_model_hash_to_info": true,
-    "add_model_name_to_info": false,
-    "disable_weights_auto_swap": false,
-    "send_seed": true,
-    "font": "",
-    "js_modal_lightbox": true,
-    "js_modal_lightbox_initially_zoomed": true,
-    "show_progress_in_title": true,
-    "quicksettings": "",
-    "localization": "zh_CN",
-    "hide_samplers": [],
-    "eta_ddim": 0.0,
-    "eta_ancestral": 1.0,
-    "ddim_discretize": "uniform",
-    "s_churn": 0.0,
-    "s_tmin": 0.0,
-    "s_noise": 1.0,
-    "eta_noise_seed_delta": 0,
-    "disabled_extensions": [],
-    "sagemaker_endpoint": ""
-}
-
diff --git a/modules/call_queue.py b/modules/call_queue.py
index 386d4a6f994..01ef298f0ae 100644
--- a/modules/call_queue.py
+++ b/modules/call_queue.py
@@ -75,7 +75,7 @@ def handle_sagemaker_inference_async(response):
 
         return processed
 
-    def sagemaker_inference(task, infer, *args, **kwargs):
+    def sagemaker_inference(task, infer, username, sagemaker_endpoint, *args, **kwargs):
         infer = 'async'
         if task == 'text-to-image' or task == 'image-to-image':
             if task == 'text-to-image':
@@ -139,7 +139,7 @@ def sagemaker_inference(task, infer, *args, **kwargs):
                 inputs = {
                     'task': task,
                     'txt2img_payload': payload,
-                    'username': shared.username
+                    'username': username
                 }
             else:
                 mode = args[0]
@@ -273,14 +273,13 @@ def sagemaker_inference(task, infer, *args, **kwargs):
                 inputs = {
                     'task': task,
                     'img2img_payload': payload,
-                    'username': shared.username
+                    'username': username
                 }
                 print(sd_samplers.samplers[sampler_index].name)
 
             params = {
-                'endpoint_name': shared.opts.sagemaker_endpoint
+                'endpoint_name': sagemaker_endpoint
             }
-
             response = requests.post(url=f'{shared.api_endpoint}/inference', params=params, json=inputs)
             if infer == 'async':
                 processed = handle_sagemaker_inference_async(response)
@@ -340,7 +339,7 @@ def sagemaker_inference(task, infer, *args, **kwargs):
                 inputs = {
                     'task': task,
                     'extras_single_payload': payload,
-                    'username': shared.username
+                    'username': username
                 }
             else:
                 imageList = []
@@ -372,11 +371,11 @@ def sagemaker_inference(task, infer, *args, **kwargs):
                 inputs = {
                     'task': task,
                     'extras_batch_payload': payload,
-                    'username': shared.username
+                    'username': username
                 }
 
             params = {
-                'endpoint_name': shared.opts.sagemaker_endpoint
+                'endpoint_name': sagemaker_endpoint
             }
             response = requests.post(url=f'{shared.api_endpoint}/inference', params=params, json=inputs)
             if infer == 'async':
@@ -395,11 +394,26 @@ def sagemaker_inference(task, infer, *args, **kwargs):
 
     def f(*args, **kwargs):
         if cmd_opts.pureui and func == modules.txt2img.txt2img:
-            res = sagemaker_inference('text-to-image', 'sync', *args, **kwargs)
+            username = args[len(args) - 2]
+            sagemaker_endpoint = args[len(args) -1]
+            args = args[:-2]
+            print('username:', username)
+            print('sagemaker_endpoint:', sagemaker_endpoint)
+            res = sagemaker_inference('text-to-image', 'sync', username, sagemaker_endpoint, *args, **kwargs)
         elif cmd_opts.pureui and func == modules.img2img.img2img:
-            res = sagemaker_inference('image-to-image', 'sync', *args, **kwargs)
+            username = args[len(args) - 2]
+            sagemaker_endpoint = args[len(args) -1]
+            args = args[:-2]
+            print('username:', username)
+            print('sagemaker_endpoint:', sagemaker_endpoint)
+            res = sagemaker_inference('image-to-image', 'sync', username, sagemaker_endpoint, *args, **kwargs)
         elif cmd_opts.pureui and func == modules.extras.run_extras:
-            res = sagemaker_inference('extras', 'sync', *args, **kwargs)
+            username = args[len(args) - 2]
+            sagemaker_endpoint = args[len(args) -1]
+            args = args[:-2]
+            print('username:', username)
+            print('sagemaker_endpoint:', sagemaker_endpoint)
+            res = sagemaker_inference('extras', 'sync', username, sagemaker_endpoint, *args, **kwargs)
         else:
             shared.state.begin()
             with queue_lock:
diff --git a/modules/sd_models.py b/modules/sd_models.py
index c2f2bad6fd9..8c48904102f 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -56,7 +56,7 @@ def checkpoint_tiles():
     return sorted([x.title for x in checkpoints_list.values()], key = alphanumeric_key)
 
 
-def list_models():
+def list_models(sagemaker_endpoint=None):
     global checkpoints_list
 
     checkpoints_list.clear()
@@ -100,7 +100,7 @@ def modeltitle(path, shorthash):
 
     if shared.cmd_opts.pureui:
         params = {
-            'endpoint_name': shared.opts.sagemaker_endpoint
+            'endpoint_name': sagemaker_endpoint
         }
         response = requests.get(url=f'{api_endpoint}/sd/models', params=params)
         if response.status_code == 200:
diff --git a/modules/shared.py b/modules/shared.py
index 2209c14d5a3..f733f98de35 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -1,646 +1,661 @@
-import argparse
-import datetime
-import json
-import os
-import sys
-import time
-
-import gradio as gr
-import tqdm
-
-import modules.artists
-import modules.interrogate
-import modules.memmon
-import modules.styles
-import modules.devices as devices
-from modules import localization, sd_vae, extensions, script_loading
-from modules.paths import models_path, script_path, sd_path
-import requests
-
-demo = None
-
-sd_model_file = os.path.join(script_path, 'model.ckpt')
-default_sd_model_file = sd_model_file
-parser = argparse.ArgumentParser()
-parser.add_argument("--config", type=str, default=os.path.join(script_path, "v1-inference.yaml"), help="path to config which constructs model",)
-parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
-parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
-parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
-parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
-parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
-parser.add_argument("--no-half-vae", action='store_true', help="do not switch the VAE model to 16-bit floats")
-parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
-parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
-parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
-parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
-parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory")
-parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
-parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
-parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
-parser.add_argument("--lowram", action='store_true', help="load stable diffusion checkpoint weights to VRAM instead of RAM")
-parser.add_argument("--always-batch-cond-uncond", action='store_true', help="disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram")
-parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
-parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
-parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
-parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
-parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us")
-parser.add_argument("--enable-insecure-extension-access", action='store_true', help="enable extensions tab regardless of other options")
-parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
-parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
-parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
-parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN'))
-parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN'))
-parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None)
-parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
-parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
-parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything")
-parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")
-parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
-parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
-parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
-parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
-parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
-parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
-parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
-parser.add_argument("--ui-config-file", type=str, help="filename to use for ui configuration", default=os.path.join(script_path, 'ui-config.json'))
-parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide directory configuration from webui", default=False)
-parser.add_argument("--freeze-settings", action='store_true', help="disable editing settings", default=False)
-parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(script_path, 'config.json'))
-parser.add_argument("--gradio-debug",  action='store_true', help="launch gradio with --debug option")
-parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
-parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image uploader tool: can be either editor for ctopping, or color-sketch for drawing', choices=["color-sketch", "editor"], default="editor")
-parser.add_argument("--gradio-inpaint-tool", type=str, choices=["sketch", "color-sketch"], default="sketch", help="gradio inpainting editor: can be either sketch to only blur/noise the input, or color-sketch to paint over it")
-parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
-parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
-parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
-parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None)
-parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
-parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
-parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
-parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
-parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
-parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)")
-parser.add_argument("--api-auth", type=str, help='Set authentication for API like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
-parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the API instead of the webui")
-parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
-parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
-parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False)
-parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origin(s) in the form of a comma-separated list (no spaces)", default=None)
-parser.add_argument("--cors-allow-origins-regex", type=str, help="Allowed CORS origin(s) in the form of a single regular expression", default=None)
-parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None)
-parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
-parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
-parser.add_argument("--pureui", action='store_true', help="Pure UI without local inference and progress bar", default=False)
-parser.add_argument("--train", action='store_true', help="Train only on SageMaker", default=False)
-parser.add_argument("--train-task", type=str, help='Train task - embedding or hypernetwork', default='embedding')
-parser.add_argument("--train-args", type=str, help='Train args', default='')
-parser.add_argument('--embeddings-s3uri', default='', type=str, help='Embedding S3Uri')
-parser.add_argument('--hypernetwork-s3uri', default='', type=str, help='Hypernetwork S3Uri')
-parser.add_argument('--sd-models-s3uri', default='', type=str, help='SD Models S3Uri')
-parser.add_argument('--db-models-s3uri', default='', type=str, help='DB Models S3Uri')
-parser.add_argument('--region-name', type=str, help='Region Name')
-parser.add_argument('--username', default='', type=str, help='Username')
-parser.add_argument('--api-endpoint', default='', type=str, help='API Endpoint')
-parser.add_argument('--dreambooth-config-id', default='', type=str, help='Dreambooth config ID')
-
-script_loading.preload_extensions(extensions.extensions_dir, parser)
-script_loading.preload_extensions(extensions.extensions_builtin_dir, parser)
-
-cmd_opts = parser.parse_args()
-
-restricted_opts = {
-    "samples_filename_pattern",
-    "directories_filename_pattern",
-    "outdir_samples",
-    "outdir_txt2img_samples",
-    "outdir_img2img_samples",
-    "outdir_extras_samples",
-    "outdir_grids",
-    "outdir_txt2img_grids",
-    "outdir_save",
-}
-
-cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access
-
-devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
-    (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'esrgan', 'codeformer'])
-
-device = devices.device
-weight_load_location = None if cmd_opts.lowram else "cpu"
-
-batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
-parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
-xformers_available = False
-config_filename = cmd_opts.ui_settings_file
-
-os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
-hypernetworks = {}
-loaded_hypernetwork = None
-
-if cmd_opts.pureui:
-    username = ''
-    api_endpoint = os.environ['api_endpoint']
-    industrial_model = ''
-    default_options = {}
-
-def reload_hypernetworks():
-    from modules.hypernetworks import hypernetwork
-    global hypernetworks
-
-    hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
-    hypernetwork.load_hypernetwork(opts.sd_hypernetwork)
-
-class State:
-    skipped = False
-    interrupted = False
-    job = ""
-    job_no = 0
-    job_count = 0
-    job_timestamp = '0'
-    sampling_step = 0
-    sampling_steps = 0
-    current_latent = None
-    current_image = None
-    current_image_sampling_step = 0
-    textinfo = None
-    time_start = None
-    need_restart = False
-
-    def skip(self):
-        self.skipped = True
-
-    def interrupt(self):
-        self.interrupted = True
-
-    def nextjob(self):
-        if opts.show_progress_every_n_steps == -1:
-            self.do_set_current_image()
-
-        self.job_no += 1
-        self.sampling_step = 0
-        self.current_image_sampling_step = 0
-
-    def dict(self):
-        obj = {
-            "skipped": self.skipped,
-            "interrupted": self.skipped,
-            "job": self.job,
-            "job_count": self.job_count,
-            "job_no": self.job_no,
-            "sampling_step": self.sampling_step,
-            "sampling_steps": self.sampling_steps,
-        }
-
-        return obj
-
-    def begin(self):
-        self.sampling_step = 0
-        self.job_count = -1
-        self.job_no = 0
-        self.job_timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
-        self.current_latent = None
-        self.current_image = None
-        self.current_image_sampling_step = 0
-        self.skipped = False
-        self.interrupted = False
-        self.textinfo = None
-        self.time_start = time.time()
-
-        devices.torch_gc()
-
-    def end(self):
-        self.job = ""
-        self.job_count = 0
-
-        devices.torch_gc()
-
-    """sets self.current_image from self.current_latent if enough sampling steps have been made after the last call to this"""
-    def set_current_image(self):
-        if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and opts.show_progress_every_n_steps > 0:
-            self.do_set_current_image()
-
-    def do_set_current_image(self):
-        if not parallel_processing_allowed:
-            return
-        if self.current_latent is None:
-            return
-
-        import modules.sd_samplers
-        if opts.show_progress_grid:
-            self.current_image = modules.sd_samplers.samples_to_image_grid(self.current_latent)
-        else:
-            self.current_image = modules.sd_samplers.sample_to_image(self.current_latent)
-
-        self.current_image_sampling_step = self.sampling_step
-
-state = State()
-
-artist_db = modules.artists.ArtistsDatabase(os.path.join(script_path, 'artists.csv'))
-
-styles_filename = cmd_opts.styles_file
-prompt_styles = modules.styles.StyleDatabase(styles_filename)
-
-interrogator = modules.interrogate.InterrogateModels("interrogate")
-
-face_restorers = []
-
-
-def realesrgan_models_names():
-    import modules.realesrgan_model
-    return [x.name for x in modules.realesrgan_model.get_realesrgan_models(None)]
-
-
-class OptionInfo:
-    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None):
-        self.default = default
-        self.label = label
-        self.component = component
-        self.component_args = component_args
-        self.onchange = onchange
-        self.section = section
-        self.refresh = refresh
-
-
-def options_section(section_identifier, options_dict):
-    for k, v in options_dict.items():
-        v.section = section_identifier
-
-    return options_dict
-
-
-def list_checkpoint_tiles():
-    import modules.sd_models
-    return modules.sd_models.checkpoint_tiles()
-
-
-def refresh_checkpoints():
-    import modules.sd_models
-    return modules.sd_models.list_models()
-
-
-def list_samplers():
-    import modules.sd_samplers
-    return modules.sd_samplers.all_samplers
-
-
-hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config}
-
-options_templates = {}
-
-def refresh_sagemaker_endpoints():
-    global industrial_model, api_endpoint, default_options
-
-    if industrial_model == '':
-        response = requests.get(url=f'{api_endpoint}/sd/industrialmodel')
-        if response.status_code == 200:
-            industrial_model = response.text
-        else:
-            model_name = 'stable-diffusion-webui'
-            model_description = model_name
-            inputs = {
-                'model_algorithm': 'stable-diffusion-webui',
-                'model_name': model_name,
-                'model_description': model_description,
-                'model_extra': '{"visible": "false"}',
-                'model_samples': '',
-                'file_content': {
-                        'data': [(lambda x: int(x))(x) for x in open(os.path.join(script_path, 'logo.ico'), 'rb').read()]
-                }
-            }
-
-            response = requests.post(url=f'{api_endpoint}/industrialmodel', json = inputs)
-            if response.status_code == 200:
-                body = json.loads(response.text)
-                industrial_model = body['id']
-
-            default_options = opts.data
-
-    sagemaker_endpoints = []
-
-    if industrial_model != '':
-        params = {
-            'industrial_model': industrial_model
-        }
-        response = requests.get(url=f'{api_endpoint}/endpoint', params=params)
-        if response.status_code == 200:
-            for endpoint_item in json.loads(response.text):
-                sagemaker_endpoints.append(endpoint_item['EndpointName'])
-
-    return sagemaker_endpoints
-
-options_templates.update(options_section(('sd', "Stable Diffusion"), {
-    "sagemaker_endpoint": OptionInfo(None, "SaegMaker endpoint", gr.Dropdown, lambda: {"choices": refresh_sagemaker_endpoints()}, refresh=refresh_sagemaker_endpoints),
-    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
-    "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
-    "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": sd_vae.vae_list}, refresh=sd_vae.refresh_vae_list),
-    "sd_vae_as_default": OptionInfo(False, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
-    "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
-    "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
-    "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
-    "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
-    "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
-    "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
-    "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
-    "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
-    "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
-    "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
-    'CLIP_stop_at_last_layers': OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
-    "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
-}))
-
-options_templates.update(options_section(('saving-images', "Saving images/grids"), {
-    "samples_save": OptionInfo(True, "Always save all generated images"),
-    "samples_format": OptionInfo('png', 'File format for images'),
-    "samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs),
-    "save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs),
-
-    "grid_save": OptionInfo(True, "Always save all generated image grids"),
-    "grid_format": OptionInfo('png', 'File format for grids'),
-    "grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
-    "grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
-    "grid_prevent_empty_spots": OptionInfo(False, "Prevent empty spots in grid (when set to autodetect)"),
-    "n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
-
-    "enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
-    "save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
-    "save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
-    "save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."),
-    "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
-    "jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
-    "export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
-
-    "use_original_name_batch": OptionInfo(False, "Use original name for output filename during batch process in extras tab"),
-    "save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
-    "do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),
-
-    "temp_dir":  OptionInfo("", "Directory for temporary images; leave empty for default"),
-    "clean_temp_dir_at_start": OptionInfo(False, "Cleanup non-default temporary directory when starting webui"),
-
-}))
-
-options_templates.update(options_section(('saving-paths', "Paths for saving"), {
-    "outdir_samples": OptionInfo("", "Output directory for images; if empty, defaults to three directories below", component_args=hide_dirs),
-    "outdir_txt2img_samples": OptionInfo("outputs/txt2img-images", 'Output directory for txt2img images', component_args=hide_dirs),
-    "outdir_img2img_samples": OptionInfo("outputs/img2img-images", 'Output directory for img2img images', component_args=hide_dirs),
-    "outdir_extras_samples": OptionInfo("outputs/extras-images", 'Output directory for images from extras tab', component_args=hide_dirs),
-    "outdir_grids": OptionInfo("", "Output directory for grids; if empty, defaults to two directories below", component_args=hide_dirs),
-    "outdir_txt2img_grids": OptionInfo("outputs/txt2img-grids", 'Output directory for txt2img grids', component_args=hide_dirs),
-    "outdir_img2img_grids": OptionInfo("outputs/img2img-grids", 'Output directory for img2img grids', component_args=hide_dirs),
-    "outdir_save": OptionInfo("log/images", "Directory for saving images using the Save button", component_args=hide_dirs),
-}))
-
-options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
-    "save_to_dirs": OptionInfo(False, "Save images to a subdirectory"),
-    "grid_save_to_dirs": OptionInfo(False, "Save grids to a subdirectory"),
-    "use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
-    "directories_filename_pattern": OptionInfo("", "Directory name pattern", component_args=hide_dirs),
-    "directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1, **hide_dirs}),
-}))
-
-options_templates.update(options_section(('upscaling', "Upscaling"), {
-    "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
-    "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
-    "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": realesrgan_models_names()}),
-    "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
-    "use_scale_latent_for_hires_fix": OptionInfo(False, "Upscale latent space image when doing hires. fix"),
-}))
-
-options_templates.update(options_section(('face-restoration', "Face restoration"), {
-    "face_restoration_model": OptionInfo(None, "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
-    "code_former_weight": OptionInfo(0.5, "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
-    "face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
-}))
-
-options_templates.update(options_section(('system', "System"), {
-    "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}),
-    "samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),
-    "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
-}))
-
-options_templates.update(options_section(('training', "Training"), {
-    "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
-    "pin_memory": OptionInfo(False, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."),
-    "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file."),
-    "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
-    "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
-    "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
-    "training_write_csv_every": OptionInfo(500, "Save an csv containing the loss to log directory every N steps, 0 to disable"),
-    "training_xattention_optimizations": OptionInfo(False, "Use cross attention optimizations while training"),
-}))
-
-options_templates.update(options_section(('interrogate', "Interrogate Options"), {
-    "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
-    "interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"),
-    "interrogate_return_ranks": OptionInfo(False, "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators)."),
-    "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
-    "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
-    "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
-    "interrogate_clip_dict_limit": OptionInfo(1500, "CLIP: maximum number of lines in text file (0 = No limit)"),
-    "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
-    "deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"),
-    "deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"),
-    "deepbooru_escape": OptionInfo(True, "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)"),
-}))
-
-options_templates.update(options_section(('ui', "User interface"), {
-    "show_progressbar": OptionInfo(True, "Show progressbar"),
-    "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set to 0 to disable. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
-    "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
-    "return_grid": OptionInfo(True, "Show grid in results for web"),
-    "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
-    "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
-    "add_model_name_to_info": OptionInfo(False, "Add model name to generation information"),
-    "disable_weights_auto_swap": OptionInfo(False, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
-    "send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
-    "font": OptionInfo("", "Font for image grids that have text"),
-    "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
-    "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
-    "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
-    'quicksettings': OptionInfo("sagemaker_endpoint", "Quicksettings list"),
-    'localization': OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
-}))
-
-options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
-    "hide_samplers": OptionInfo([], "Hide samplers in user interface (requires restart)", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}),
-    "eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    "eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
-    's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
-}))
-
-options_templates.update(options_section((None, "Hidden options"), {
-    "disabled_extensions": OptionInfo([], "Disable those extensions"),
-}))
-
-options_templates.update()
-
-
-class Options:
-    data = None
-    data_labels = options_templates
-    typemap = {int: float}
-
-    def __init__(self):
-        self.data = {k: v.default for k, v in self.data_labels.items()}
-
-    def __setattr__(self, key, value):
-        if self.data is not None:
-            if key in self.data or key in self.data_labels:
-                assert not cmd_opts.freeze_settings, "changing settings is disabled"
-
-                info = opts.data_labels.get(key, None)
-                comp_args = info.component_args if info else None
-                if isinstance(comp_args, dict) and comp_args.get('visible', True) is False:
-                    raise RuntimeError(f"not possible to set {key} because it is restricted")
-
-                if cmd_opts.hide_ui_dir_config and key in restricted_opts:
-                    raise RuntimeError(f"not possible to set {key} because it is restricted")
-
-                self.data[key] = value
-                return
-
-        return super(Options, self).__setattr__(key, value)
-
-    def __getattr__(self, item):
-        if self.data is not None:
-            if item in self.data:
-                return self.data[item]
-
-        if item in self.data_labels:
-            return self.data_labels[item].default
-
-        return super(Options, self).__getattribute__(item)
-
-    def set(self, key, value):
-        """sets an option and calls its onchange callback, returning True if the option changed and False otherwise"""
-
-        oldval = self.data.get(key, None)
-        if oldval == value:
-            return False
-
-        try:
-            setattr(self, key, value)
-        except RuntimeError:
-            return False
-
-        if self.data_labels[key].onchange is not None:
-            self.data_labels[key].onchange()
-
-        return True
-
-    def save(self, filename):
-        assert not cmd_opts.freeze_settings, "saving settings is disabled"
-
-        with open(filename, "w", encoding="utf8") as file:
-            json.dump(self.data, file, indent=4)
-
-    def same_type(self, x, y):
-        if x is None or y is None:
-            return True
-
-        type_x = self.typemap.get(type(x), type(x))
-        type_y = self.typemap.get(type(y), type(y))
-
-        return type_x == type_y
-
-    def load(self, filename):
-        with open(filename, "r", encoding="utf8") as file:
-            self.data = json.load(file)
-
-        bad_settings = 0
-        for k, v in self.data.items():
-            info = self.data_labels.get(k, None)
-            if info is not None and not self.same_type(info.default, v):
-                print(f"Warning: bad setting value: {k}: {v} ({type(v).__name__}; expected {type(info.default).__name__})", file=sys.stderr)
-                bad_settings += 1
-
-        if bad_settings > 0:
-            print(f"The program is likely to not work with bad settings.\nSettings file: {filename}\nEither fix the file, or delete it and restart.", file=sys.stderr)
-
-    def onchange(self, key, func, call=True):
-        item = self.data_labels.get(key)
-        item.onchange = func
-
-        if call:
-            func()
-
-    def dumpjson(self):
-        d = {k: self.data.get(k, self.data_labels.get(k).default) for k in self.data_labels.keys()}
-        return json.dumps(d)
-
-    def add_option(self, key, info):
-        self.data_labels[key] = info
-
-    def reorder(self):
-        """reorder settings so that all items related to section always go together"""
-
-        section_ids = {}
-        settings_items = self.data_labels.items()
-        for k, item in settings_items:
-            if item.section not in section_ids:
-                section_ids[item.section] = len(section_ids)
-
-        self.data_labels = {k: v for k, v in sorted(settings_items, key=lambda x: section_ids[x[1].section])}
-
-
-opts = Options()
-if os.path.exists(config_filename):
-    opts.load(config_filename)
-    
-if cmd_opts.pureui and opts.localization == None:
-    opts.localization = "zh_CN"
-
-sd_upscalers = []
-
-sd_model = None
-
-clip_model = None
-
-progress_print_out = sys.stdout
-
-
-class TotalTQDM:
-    def __init__(self):
-        self._tqdm = None
-
-    def reset(self):
-        self._tqdm = tqdm.tqdm(
-            desc="Total progress",
-            total=state.job_count * state.sampling_steps,
-            position=1,
-            file=progress_print_out
-        )
-
-    def update(self):
-        if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:
-            return
-        if self._tqdm is None:
-            self.reset()
-        self._tqdm.update()
-
-    def updateTotal(self, new_total):
-        if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:
-            return
-        if self._tqdm is None:
-            self.reset()
-        self._tqdm.total=new_total
-
-    def clear(self):
-        if self._tqdm is not None:
-            self._tqdm.close()
-            self._tqdm = None
-
-
-total_tqdm = TotalTQDM()
-
-mem_mon = modules.memmon.MemUsageMonitor("MemMon", device, opts)
-mem_mon.start()
-
-
-def listfiles(dirname):
-    filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname)) if not x.startswith(".")]
-    return [file for file in filenames if os.path.isfile(file)]
+import argparse
+import datetime
+import json
+import os
+import sys
+import time
+
+import gradio as gr
+import tqdm
+
+import modules.artists
+import modules.interrogate
+import modules.memmon
+import modules.styles
+import modules.devices as devices
+from modules import localization, sd_vae, extensions, script_loading
+from modules.paths import models_path, script_path, sd_path
+import requests
+
+demo = None
+
+sd_model_file = os.path.join(script_path, 'model.ckpt')
+default_sd_model_file = sd_model_file
+parser = argparse.ArgumentParser()
+parser.add_argument("--config", type=str, default=os.path.join(script_path, "v1-inference.yaml"), help="path to config which constructs model",)
+parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
+parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
+parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
+parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
+parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
+parser.add_argument("--no-half-vae", action='store_true', help="do not switch the VAE model to 16-bit floats")
+parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
+parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
+parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
+parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
+parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory")
+parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
+parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
+parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
+parser.add_argument("--lowram", action='store_true', help="load stable diffusion checkpoint weights to VRAM instead of RAM")
+parser.add_argument("--always-batch-cond-uncond", action='store_true', help="disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram")
+parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
+parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
+parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
+parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
+parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us")
+parser.add_argument("--enable-insecure-extension-access", action='store_true', help="enable extensions tab regardless of other options")
+parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
+parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
+parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
+parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN'))
+parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN'))
+parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None)
+parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
+parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
+parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything")
+parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")
+parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
+parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
+parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
+parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
+parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
+parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
+parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
+parser.add_argument("--ui-config-file", type=str, help="filename to use for ui configuration", default=os.path.join(script_path, 'ui-config.json'))
+parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide directory configuration from webui", default=False)
+parser.add_argument("--freeze-settings", action='store_true', help="disable editing settings", default=False)
+parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(script_path, 'config.json'))
+parser.add_argument("--gradio-debug",  action='store_true', help="launch gradio with --debug option")
+parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
+parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image uploader tool: can be either editor for ctopping, or color-sketch for drawing', choices=["color-sketch", "editor"], default="editor")
+parser.add_argument("--gradio-inpaint-tool", type=str, choices=["sketch", "color-sketch"], default="sketch", help="gradio inpainting editor: can be either sketch to only blur/noise the input, or color-sketch to paint over it")
+parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
+parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
+parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
+parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None)
+parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
+parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
+parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
+parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
+parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
+parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)")
+parser.add_argument("--api-auth", type=str, help='Set authentication for API like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
+parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the API instead of the webui")
+parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
+parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
+parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False)
+parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origin(s) in the form of a comma-separated list (no spaces)", default=None)
+parser.add_argument("--cors-allow-origins-regex", type=str, help="Allowed CORS origin(s) in the form of a single regular expression", default=None)
+parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None)
+parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
+parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
+parser.add_argument("--pureui", action='store_true', help="Pure UI without local inference and progress bar", default=False)
+parser.add_argument("--train", action='store_true', help="Train only on SageMaker", default=False)
+parser.add_argument("--train-task", type=str, help='Train task - embedding or hypernetwork', default='embedding')
+parser.add_argument("--train-args", type=str, help='Train args', default='')
+parser.add_argument('--embeddings-s3uri', default='', type=str, help='Embedding S3Uri')
+parser.add_argument('--hypernetwork-s3uri', default='', type=str, help='Hypernetwork S3Uri')
+parser.add_argument('--sd-models-s3uri', default='', type=str, help='SD Models S3Uri')
+parser.add_argument('--db-models-s3uri', default='', type=str, help='DB Models S3Uri')
+parser.add_argument('--region-name', type=str, help='Region Name')
+parser.add_argument('--username', default='', type=str, help='Username')
+parser.add_argument('--api-endpoint', default='', type=str, help='API Endpoint')
+parser.add_argument('--dreambooth-config-id', default='', type=str, help='Dreambooth config ID')
+
+script_loading.preload_extensions(extensions.extensions_dir, parser)
+script_loading.preload_extensions(extensions.extensions_builtin_dir, parser)
+
+cmd_opts = parser.parse_args()
+
+restricted_opts = {
+    "samples_filename_pattern",
+    "directories_filename_pattern",
+    "outdir_samples",
+    "outdir_txt2img_samples",
+    "outdir_img2img_samples",
+    "outdir_extras_samples",
+    "outdir_grids",
+    "outdir_txt2img_grids",
+    "outdir_save",
+}
+
+cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access
+
+devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
+    (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'esrgan', 'codeformer'])
+
+device = devices.device
+weight_load_location = None if cmd_opts.lowram else "cpu"
+
+batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
+parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
+xformers_available = False
+config_filename = cmd_opts.ui_settings_file
+
+os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
+hypernetworks = {}
+loaded_hypernetwork = None
+
+if cmd_opts.pureui:
+    api_endpoint = os.environ['api_endpoint']
+    industrial_model = ''
+    default_options = {}
+    username_state = None
+    sagemaker_endpoint_component = None
+
+def reload_hypernetworks():
+    from modules.hypernetworks import hypernetwork
+    global hypernetworks
+
+    hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
+    hypernetwork.load_hypernetwork(opts.sd_hypernetwork)
+
+class State:
+    skipped = False
+    interrupted = False
+    job = ""
+    job_no = 0
+    job_count = 0
+    job_timestamp = '0'
+    sampling_step = 0
+    sampling_steps = 0
+    current_latent = None
+    current_image = None
+    current_image_sampling_step = 0
+    textinfo = None
+    time_start = None
+    need_restart = False
+
+    def skip(self):
+        self.skipped = True
+
+    def interrupt(self):
+        self.interrupted = True
+
+    def nextjob(self):
+        if opts.show_progress_every_n_steps == -1:
+            self.do_set_current_image()
+
+        self.job_no += 1
+        self.sampling_step = 0
+        self.current_image_sampling_step = 0
+
+    def dict(self):
+        obj = {
+            "skipped": self.skipped,
+            "interrupted": self.skipped,
+            "job": self.job,
+            "job_count": self.job_count,
+            "job_no": self.job_no,
+            "sampling_step": self.sampling_step,
+            "sampling_steps": self.sampling_steps,
+        }
+
+        return obj
+
+    def begin(self):
+        self.sampling_step = 0
+        self.job_count = -1
+        self.job_no = 0
+        self.job_timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
+        self.current_latent = None
+        self.current_image = None
+        self.current_image_sampling_step = 0
+        self.skipped = False
+        self.interrupted = False
+        self.textinfo = None
+        self.time_start = time.time()
+
+        devices.torch_gc()
+
+    def end(self):
+        self.job = ""
+        self.job_count = 0
+
+        devices.torch_gc()
+
+    """sets self.current_image from self.current_latent if enough sampling steps have been made after the last call to this"""
+    def set_current_image(self):
+        if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and opts.show_progress_every_n_steps > 0:
+            self.do_set_current_image()
+
+    def do_set_current_image(self):
+        if not parallel_processing_allowed:
+            return
+        if self.current_latent is None:
+            return
+
+        import modules.sd_samplers
+        if opts.show_progress_grid:
+            self.current_image = modules.sd_samplers.samples_to_image_grid(self.current_latent)
+        else:
+            self.current_image = modules.sd_samplers.sample_to_image(self.current_latent)
+
+        self.current_image_sampling_step = self.sampling_step
+
+state = State()
+
+artist_db = modules.artists.ArtistsDatabase(os.path.join(script_path, 'artists.csv'))
+
+styles_filename = cmd_opts.styles_file
+prompt_styles = modules.styles.StyleDatabase(styles_filename)
+
+interrogator = modules.interrogate.InterrogateModels("interrogate")
+
+face_restorers = []
+
+
+def realesrgan_models_names():
+    import modules.realesrgan_model
+    return [x.name for x in modules.realesrgan_model.get_realesrgan_models(None)]
+
+
+class OptionInfo:
+    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None):
+        self.default = default
+        self.label = label
+        self.component = component
+        self.component_args = component_args
+        self.onchange = onchange
+        self.section = section
+        self.refresh = refresh
+
+
+def options_section(section_identifier, options_dict):
+    for k, v in options_dict.items():
+        v.section = section_identifier
+
+    return options_dict
+
+
+def list_checkpoint_tiles():
+    import modules.sd_models
+    return modules.sd_models.checkpoint_tiles()
+
+
+def refresh_checkpoints(sagemaker_endpoint=None):
+    print('sagemaker_endpoint2:', sagemaker_endpoint)
+
+    import modules.sd_models
+    return modules.sd_models.list_models(sagemaker_endpoint)
+
+
+def list_samplers():
+    import modules.sd_samplers
+    return modules.sd_samplers.all_samplers
+
+
+hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config}
+
+options_templates = {}
+
+sagemaker_endpoints = []
+
+def list_sagemaker_endpoints():
+    global sagemaker_endpoints
+
+    return sagemaker_endpoints
+
+def refresh_sagemaker_endpoints(username=None):
+    global industrial_model, api_endpoint, sagemaker_endpoints
+
+    print('username2:', username)
+
+    sagemaker_endpoints = []
+
+    if not username:
+        return sagemaker_endpoints
+
+    if industrial_model == '':
+        response = requests.get(url=f'{api_endpoint}/sd/industrialmodel')
+        if response.status_code == 200:
+            industrial_model = response.text
+        else:
+            model_name = 'stable-diffusion-webui'
+            model_description = model_name
+            inputs = {
+                'model_algorithm': 'stable-diffusion-webui',
+                'model_name': model_name,
+                'model_description': model_description,
+                'model_extra': '{"visible": "false"}',
+                'model_samples': '',
+                'file_content': {
+                        'data': [(lambda x: int(x))(x) for x in open(os.path.join(script_path, 'logo.ico'), 'rb').read()]
+                }
+            }
+
+            response = requests.post(url=f'{api_endpoint}/industrialmodel', json = inputs)
+            if response.status_code == 200:
+                body = json.loads(response.text)
+                industrial_model = body['id']
+
+    if industrial_model != '':
+        params = {
+            'industrial_model': industrial_model
+        }
+        response = requests.get(url=f'{api_endpoint}/endpoint', params=params)
+        if response.status_code == 200:
+            for endpoint_item in json.loads(response.text):
+                sagemaker_endpoints.append(endpoint_item['EndpointName'])
+
+    return sagemaker_endpoints
+
+options_templates.update(options_section(('sd', "Stable Diffusion"), {
+    "sagemaker_endpoint": OptionInfo(None, "SaegMaker endpoint", gr.Dropdown, lambda: {"choices": list_sagemaker_endpoints()}, refresh=refresh_sagemaker_endpoints),
+    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
+    "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
+    "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": sd_vae.vae_list}, refresh=sd_vae.refresh_vae_list),
+    "sd_vae_as_default": OptionInfo(False, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
+    "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
+    "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
+    "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
+    "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
+    "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
+    "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
+    "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
+    "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
+    "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
+    "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
+    'CLIP_stop_at_last_layers': OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
+    "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
+}))
+
+options_templates.update(options_section(('saving-images', "Saving images/grids"), {
+    "samples_save": OptionInfo(True, "Always save all generated images"),
+    "samples_format": OptionInfo('png', 'File format for images'),
+    "samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs),
+    "save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs),
+
+    "grid_save": OptionInfo(True, "Always save all generated image grids"),
+    "grid_format": OptionInfo('png', 'File format for grids'),
+    "grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
+    "grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
+    "grid_prevent_empty_spots": OptionInfo(False, "Prevent empty spots in grid (when set to autodetect)"),
+    "n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
+
+    "enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
+    "save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
+    "save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
+    "save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."),
+    "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
+    "jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
+    "export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
+
+    "use_original_name_batch": OptionInfo(False, "Use original name for output filename during batch process in extras tab"),
+    "save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
+    "do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),
+
+    "temp_dir":  OptionInfo("", "Directory for temporary images; leave empty for default"),
+    "clean_temp_dir_at_start": OptionInfo(False, "Cleanup non-default temporary directory when starting webui"),
+
+}))
+
+options_templates.update(options_section(('saving-paths', "Paths for saving"), {
+    "outdir_samples": OptionInfo("", "Output directory for images; if empty, defaults to three directories below", component_args=hide_dirs),
+    "outdir_txt2img_samples": OptionInfo("outputs/txt2img-images", 'Output directory for txt2img images', component_args=hide_dirs),
+    "outdir_img2img_samples": OptionInfo("outputs/img2img-images", 'Output directory for img2img images', component_args=hide_dirs),
+    "outdir_extras_samples": OptionInfo("outputs/extras-images", 'Output directory for images from extras tab', component_args=hide_dirs),
+    "outdir_grids": OptionInfo("", "Output directory for grids; if empty, defaults to two directories below", component_args=hide_dirs),
+    "outdir_txt2img_grids": OptionInfo("outputs/txt2img-grids", 'Output directory for txt2img grids', component_args=hide_dirs),
+    "outdir_img2img_grids": OptionInfo("outputs/img2img-grids", 'Output directory for img2img grids', component_args=hide_dirs),
+    "outdir_save": OptionInfo("log/images", "Directory for saving images using the Save button", component_args=hide_dirs),
+}))
+
+options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
+    "save_to_dirs": OptionInfo(False, "Save images to a subdirectory"),
+    "grid_save_to_dirs": OptionInfo(False, "Save grids to a subdirectory"),
+    "use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
+    "directories_filename_pattern": OptionInfo("", "Directory name pattern", component_args=hide_dirs),
+    "directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1, **hide_dirs}),
+}))
+
+options_templates.update(options_section(('upscaling', "Upscaling"), {
+    "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
+    "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
+    "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": realesrgan_models_names()}),
+    "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
+    "use_scale_latent_for_hires_fix": OptionInfo(False, "Upscale latent space image when doing hires. fix"),
+}))
+
+options_templates.update(options_section(('face-restoration', "Face restoration"), {
+    "face_restoration_model": OptionInfo(None, "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
+    "code_former_weight": OptionInfo(0.5, "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
+    "face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
+}))
+
+options_templates.update(options_section(('system', "System"), {
+    "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}),
+    "samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),
+    "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
+}))
+
+options_templates.update(options_section(('training', "Training"), {
+    "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
+    "pin_memory": OptionInfo(False, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."),
+    "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file."),
+    "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
+    "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
+    "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
+    "training_write_csv_every": OptionInfo(500, "Save an csv containing the loss to log directory every N steps, 0 to disable"),
+    "training_xattention_optimizations": OptionInfo(False, "Use cross attention optimizations while training"),
+}))
+
+options_templates.update(options_section(('interrogate', "Interrogate Options"), {
+    "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
+    "interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"),
+    "interrogate_return_ranks": OptionInfo(False, "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators)."),
+    "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
+    "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
+    "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
+    "interrogate_clip_dict_limit": OptionInfo(1500, "CLIP: maximum number of lines in text file (0 = No limit)"),
+    "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
+    "deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"),
+    "deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"),
+    "deepbooru_escape": OptionInfo(True, "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)"),
+}))
+
+options_templates.update(options_section(('ui', "User interface"), {
+    "show_progressbar": OptionInfo(True, "Show progressbar"),
+    "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set to 0 to disable. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
+    "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
+    "return_grid": OptionInfo(True, "Show grid in results for web"),
+    "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
+    "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
+    "add_model_name_to_info": OptionInfo(False, "Add model name to generation information"),
+    "disable_weights_auto_swap": OptionInfo(False, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
+    "send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
+    "font": OptionInfo("", "Font for image grids that have text"),
+    "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
+    "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
+    "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
+    'quicksettings': OptionInfo("", "Quicksettings list"),
+    'localization': OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
+}))
+
+options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
+    "hide_samplers": OptionInfo([], "Hide samplers in user interface (requires restart)", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}),
+    "eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    "eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
+    's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
+}))
+
+options_templates.update(options_section((None, "Hidden options"), {
+    "disabled_extensions": OptionInfo([], "Disable those extensions"),
+}))
+
+options_templates.update()
+
+
+class Options:
+    data = None
+    data_labels = options_templates
+    typemap = {int: float}
+
+    def __init__(self):
+        self.data = {k: v.default for k, v in self.data_labels.items()}
+
+    def __setattr__(self, key, value):
+        if self.data is not None:
+            if key in self.data or key in self.data_labels:
+                assert not cmd_opts.freeze_settings, "changing settings is disabled"
+
+                info = opts.data_labels.get(key, None)
+                comp_args = info.component_args if info else None
+                if isinstance(comp_args, dict) and comp_args.get('visible', True) is False:
+                    raise RuntimeError(f"not possible to set {key} because it is restricted")
+
+                if cmd_opts.hide_ui_dir_config and key in restricted_opts:
+                    raise RuntimeError(f"not possible to set {key} because it is restricted")
+
+                self.data[key] = value
+                return
+
+        return super(Options, self).__setattr__(key, value)
+
+    def __getattr__(self, item):
+        if self.data is not None:
+            if item in self.data:
+                return self.data[item]
+
+        if item in self.data_labels:
+            return self.data_labels[item].default
+
+        return super(Options, self).__getattribute__(item)
+
+    def set(self, key, value):
+        """sets an option and calls its onchange callback, returning True if the option changed and False otherwise"""
+
+        oldval = self.data.get(key, None)
+        if oldval == value:
+            return False
+
+        try:
+            setattr(self, key, value)
+        except RuntimeError:
+            return False
+
+        if self.data_labels[key].onchange is not None:
+            self.data_labels[key].onchange()
+
+        return True
+
+    def save(self, filename):
+        assert not cmd_opts.pureui and not cmd_opts.freeze_settings, "saving settings is disabled"
+
+        with open(filename, "w", encoding="utf8") as file:
+            json.dump(self.data, file, indent=4)
+
+    def same_type(self, x, y):
+        if x is None or y is None:
+            return True
+
+        type_x = self.typemap.get(type(x), type(x))
+        type_y = self.typemap.get(type(y), type(y))
+
+        return type_x == type_y
+
+    def load(self, filename):
+        assert not cmd_opts.pureui
+
+        with open(filename, "r", encoding="utf8") as file:
+            self.data = json.load(file)
+
+        bad_settings = 0
+        for k, v in self.data.items():
+            info = self.data_labels.get(k, None)
+            if info is not None and not self.same_type(info.default, v):
+                print(f"Warning: bad setting value: {k}: {v} ({type(v).__name__}; expected {type(info.default).__name__})", file=sys.stderr)
+                bad_settings += 1
+
+        if bad_settings > 0:
+            print(f"The program is likely to not work with bad settings.\nSettings file: {filename}\nEither fix the file, or delete it and restart.", file=sys.stderr)
+
+    def onchange(self, key, func, call=True):
+        item = self.data_labels.get(key)
+        item.onchange = func
+
+        if call:
+            func()
+
+    def dumpjson(self):
+        d = {k: self.data.get(k, self.data_labels.get(k).default) for k in self.data_labels.keys()}
+        return json.dumps(d)
+
+    def add_option(self, key, info):
+        self.data_labels[key] = info
+
+    def reorder(self):
+        """reorder settings so that all items related to section always go together"""
+
+        section_ids = {}
+        settings_items = self.data_labels.items()
+        for k, item in settings_items:
+            if item.section not in section_ids:
+                section_ids[item.section] = len(section_ids)
+
+        self.data_labels = {k: v for k, v in sorted(settings_items, key=lambda x: section_ids[x[1].section])}
+
+
+opts = Options()
+if os.path.exists(config_filename):
+    opts.load(config_filename)
+
+if cmd_opts.pureui and opts.localization == "None":
+    opts.localization = "zh_CN"
+
+sd_upscalers = []
+
+sd_model = None
+
+clip_model = None
+
+progress_print_out = sys.stdout
+
+
+class TotalTQDM:
+    def __init__(self):
+        self._tqdm = None
+
+    def reset(self):
+        self._tqdm = tqdm.tqdm(
+            desc="Total progress",
+            total=state.job_count * state.sampling_steps,
+            position=1,
+            file=progress_print_out
+        )
+
+    def update(self):
+        if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:
+            return
+        if self._tqdm is None:
+            self.reset()
+        self._tqdm.update()
+
+    def updateTotal(self, new_total):
+        if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:
+            return
+        if self._tqdm is None:
+            self.reset()
+        self._tqdm.total=new_total
+
+    def clear(self):
+        if self._tqdm is not None:
+            self._tqdm.close()
+            self._tqdm = None
+
+
+total_tqdm = TotalTQDM()
+
+mem_mon = modules.memmon.MemUsageMonitor("MemMon", device, opts)
+mem_mon.start()
+
+
+def listfiles(dirname):
+    filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname)) if not x.startswith(".")]
+    return [file for file in filenames if os.path.isfile(file)]
diff --git a/modules/ui.py b/modules/ui.py
index 2ce77ed157d..205a4a81e92 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -56,6 +56,7 @@
         'ml.g4dn.12xlarge',
         'ml.g4dn.16xlarge'
     ]
+    component_dict = {}
 
 # 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()
@@ -501,16 +502,7 @@ def apply_setting(key, value):
     if oldval != value and opts.data_labels[key].onchange is not None:
         opts.data_labels[key].onchange()
 
-    if cmd_opts.pureui:
-        if shared.username != '':
-            inputs = {
-                'action': 'edit',
-                'username': shared.username,
-                'options': json.dumps(opts.data)
-            }
-
-            response = requests.post(url=f'{shared.api_endpoint}/sd/user', json = inputs)
-    else:
+    if not cmd_opts.pureui:
         opts.save(shared.config_filename)
 
     return value
@@ -539,12 +531,45 @@ def refresh():
 
         return gr.update(**(args or {}))
 
+    def refresh_sagemaker_endpoints(username):
+        print('username:', username)
+        refresh_method(username)
+        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 {}))
+
+    def refresh_checkpoints(sagemaker_endpoint):
+        print('sagemaker_endpoint:', sagemaker_endpoint)
+        refresh_method(sagemaker_endpoint)
+        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 = gr.Button(value=refresh_symbol, elem_id=elem_id)
-    refresh_button.click(
-        fn=refresh,
-        inputs=[],
-        outputs=[refresh_component]
-    )
+    if elem_id == 'refresh_sagemaker_endpoint':
+        refresh_button.click(
+            fn=refresh_sagemaker_endpoints,
+            inputs=[shared.username_state],
+            outputs=[refresh_component]
+        )
+    elif elem_id == 'refresh_sd_model_checkpoint':
+        refresh_button.click(
+            fn=refresh_checkpoints,
+            inputs=[shared.sagemaker_endpoint_component],
+            outputs=[refresh_component]
+        )
+    else:
+        refresh_button.click(
+            fn=refresh,
+            inputs=[],
+            outputs=[refresh_component]
+        )
     return refresh_button
 
 
@@ -649,9 +674,216 @@ def create_ui():
     modules.scripts.scripts_current = modules.scripts.scripts_txt2img
     modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
 
+    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 gr.Row(variant="compact"):
+                    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 {}))
+
+        if key == 'sagemaker_endpoint':
+            shared.sagemaker_endpoint_component = res
+
+        return res
+
+    components = []
+    global component_dict
+
+    script_callbacks.ui_settings_callback()
+    opts.reorder()
+
+    def run_settings(*args):
+        assert cmd_opts.pureui
+
+        changed = []
+
+        username = args[len(args) - 1]
+        args = args[:-1]
+        print('username:', username)
+
+        if not username or username == '':
+            return opts.dumpjson(), f'{len(changed)} settings changed: {", ".join(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:
+            inputs = {
+                'action': 'edit',
+                'username': username,
+                'options': opts.dumpjson()
+            }
+
+            response = requests.post(url=f'{shared.api_endpoint}/sd/user', json=inputs)
+            if response.status_code != 200:
+                raise RuntimeError("Settings saved failed")
+        except RuntimeError:
+            return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'
+        return opts.dumpjson(), f'{len(changed)} settings changed: {", ".join(changed)}.'
+
+    def run_settings_single(value, key, username):
+        if username and username != '':
+            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()
+
+            try:
+                if username and username != '':
+                    inputs = {
+                        'action': 'edit',
+                        'username': username,
+                        'options': opts.dumpjson()
+                    }
+
+                    response = requests.post(url=f'{shared.api_endpoint}/sd/user', json=inputs)
+                    if response.status_code != 200:
+                        raise RuntimeError("Settings saved failed")
+            except RuntimeError:
+                return gr.update(visible=True), opts.dumpjson()
+
+            return gr.update(value=value), opts.dumpjson()
+        else:
+            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()
+
+            return gr.update(value=value), opts.dumpjson()
+
+    with gr.Blocks(analytics_enabled=False) as settings_interface:
+        shared.username_state = gr.Text(value='', visible=True)
+        dummy_component = gr.Label(visible=False)
+
+        settings_submit = gr.Button(value="Apply settings", variant='primary')
+        result = gr.HTML()
+
+        settings_cols = 3
+        items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols)
+
+        quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")]
+        quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings')
+
+        quicksettings_list = []
+
+        cols_displayed = 0
+        items_displayed = 0
+        previous_section = None
+        column = None
+        with gr.Row(elem_id="settings").style(equal_height=False):
+            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:
+                    if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None):
+                        if column is not None:
+                            column.__exit__()
+
+                        column = gr.Column(variant='panel')
+                        column.__enter__()
+
+                        items_displayed = 0
+                        cols_displayed += 1
+
+                    previous_section = item.section
+
+                    elem_id, text = item.section
+                    gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='<h1 class="gr-button-lg">{}</h1>'.format(text))
+
+                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)
+                    items_displayed += 1
+
+        with gr.Row():
+            request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
+            download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
+
+        with gr.Row():
+            reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
+            restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
+
+        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=[],
+        )
+
+        if column is not None:
+            column.__exit__()
+
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
         txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, 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="bytes", visible=False)
         txt2img_submit = submit
 
@@ -723,7 +955,7 @@ def create_ui():
                     denoising_strength,
                     firstphase_width,
                     firstphase_height,
-                ] + custom_inputs,
+                ] + custom_inputs + [shared.username_state, shared.sagemaker_endpoint_component],
 
                 outputs=[
                     txt2img_gallery,
@@ -965,7 +1197,7 @@ def update_orig(image, state):
                     inpainting_mask_invert,
                     img2img_batch_input_dir if not cmd_opts.pureui else dummy_component,
                     img2img_batch_output_dir if not cmd_opts.pureui else dummy_component,
-                ] + custom_inputs,
+                ] + custom_inputs + [shared.username_state, shared.sagemaker_endpoint_component],
                 outputs=[
                     img2img_gallery,
                     generation_info,
@@ -1124,7 +1356,7 @@ def update_orig(image, state):
                 extras_upscaler_2,
                 extras_upscaler_2_visibility,
                 upscale_before_face_fix,
-            ],
+            ] + [shared.username_state, shared.sagemaker_endpoint_component],
             outputs=[
                 result_images,
                 html_info_x,
@@ -1614,6 +1846,7 @@ def update_orig(image, state):
                                 create_train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary', visible=False)
 
                 def sagemaker_train_embedding(
+                        username,
                         new_embedding_name,
                         initialization_text,
                         nvpt,
@@ -1697,7 +1930,7 @@ def sagemaker_train_embedding(
                         'train-args': json.dumps(json.dumps(train_args)),
                         'train-task': 'embedding',
                         'ckpt': '/opt/ml/input/data/models/{0}'.format(shared.sd_model.sd_model_name),
-                        'username': shared.username,
+                        'username': username,
                         'api-endpoint': shared.api_endpoint
                     }
                     
@@ -1727,6 +1960,7 @@ def sagemaker_train_embedding(
                         }
                     
                 def sagemaker_train_hypernetwork(
+                        username,
                         new_hypernetwork_name,
                         new_hypernetwork_sizes,
                         new_hypernetwork_layer_structure,
@@ -1766,9 +2000,9 @@ def sagemaker_train_hypernetwork(
                         hypernetwork_preview_from_txt2img,
                         hypernetwork_training_instance_type,
                         hypernetwork_training_instance_count,
-                        *txt2img_preview_params                        
+                        *txt2img_preview_params
                     ):
-                    
+
                     train_args = {
                         'hypernetwork_settings': {
                             'name': new_hypernetwork_name,
@@ -1818,7 +2052,7 @@ def sagemaker_train_hypernetwork(
                         'train-args': json.dumps(json.dumps(train_args)),
                         'train-task': 'hypernetwork',
                         'ckpt': '/opt/ml/input/data/models/{0}'.format(shared.sd_model.sd_model_name),
-                        'username': shared.username,
+                        'username': username,
                         'api-endpoint': shared.api_endpoint
                     }
                     
@@ -1850,6 +2084,7 @@ def sagemaker_train_hypernetwork(
                 create_train_embedding.click(
                     fn=sagemaker_train_embedding,
                     inputs=[
+                        shared.username_state,
                         new_embedding_name,
                         initialization_text,
                         nvpt,
@@ -1893,6 +2128,7 @@ def sagemaker_train_hypernetwork(
                 create_train_hypernetwork.click(
                     fn=sagemaker_train_hypernetwork,
                     inputs=[
+                        shared.username_state,
                         new_hypernetwork_name,
                         new_hypernetwork_sizes,
                         new_hypernetwork_layer_structure,
@@ -1937,182 +2173,6 @@ def sagemaker_train_hypernetwork(
                     outputs=[hypernetwork_output]
                 )
 
-    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 gr.Row(variant="compact"):
-                    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 = {}
-
-    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:
-            if cmd_opts.pureui:
-                if shared.username != '':
-                    inputs = {
-                        'action': 'edit',
-                        'username': shared.username,
-                        'options': json.dumps(opts.data)
-                    }
-
-                    response = requests.post(url=f'{shared.api_endpoint}/sd/user', json=inputs)
-                    if response.status_code != 200:
-                        raise RuntimeError("Settings saved failed")
-            else:
-                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: {", ".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 gr.update(value=value), opts.dumpjson()
-
-    with gr.Blocks(analytics_enabled=False) as settings_interface:
-        settings_submit = gr.Button(value="Apply settings", variant='primary')
-        result = gr.HTML()
-
-        settings_cols = 3
-        items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols)
-
-        quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")]
-        quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings')
-
-        quicksettings_list = []
-
-        cols_displayed = 0
-        items_displayed = 0
-        previous_section = None
-        column = None
-        with gr.Row(elem_id="settings").style(equal_height=False):
-            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:
-                    if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None):
-                        if column is not None:
-                            column.__exit__()
-
-                        column = gr.Column(variant='panel')
-                        column.__enter__()
-
-                        items_displayed = 0
-                        cols_displayed += 1
-
-                    previous_section = item.section
-
-                    elem_id, text = item.section
-                    gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='<h1 class="gr-button-lg">{}</h1>'.format(text))
-
-                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)
-                    items_displayed += 1
-
-        with gr.Row():
-            request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
-            download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
-
-        with gr.Row():
-            reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
-            restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
-
-        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=[],
-        )
-
-        if column is not None:
-            column.__exit__()
-
     with gr.Blocks(analytics_enabled=False) as user_interface:
         def change_sign_options(choice):
             return {
@@ -2120,7 +2180,7 @@ def change_sign_options(choice):
                 signup_column: gr.update(visible=(choice=="Sign Up"))
             }
 
-        with gr.Row(visible=(shared.username!='')) as user_login_row:
+        with gr.Row(visible=False) as user_login_row:
             with gr.Column():
                 login_username = gr.Text(label="Username")
                 login_password = gr.Text(label="Password", type="password")
@@ -2132,7 +2192,7 @@ def change_sign_options(choice):
                 userdelete = gr.Button("Delete")
                 login_output = gr.Label(label="Output")
 
-        with gr.Row(visible=(shared.username=='')) as user_sign_row:
+        with gr.Row(visible=True) as user_sign_row:
             with gr.Column():
                 sign_options = gr.Radio(["Sign In", "Sign Up"], label="Sign Options", value="Sign In", interactive=True)
 
@@ -2160,13 +2220,14 @@ def user_signin(signin_username, signin_password):
 
             response = requests.post(url=f'{shared.api_endpoint}/sd/user', json=inputs)
             if response.status_code == 200:
-                shared.username = json.loads(response.text)['username']
+                username = json.loads(response.text)['username']
                 password = json.loads(response.text)['password']
                 email = json.loads(response.text)['email']
                 options = json.loads(response.text)['options'] if 'options' in json.loads(response.text) else None
 
                 response = {
-                    user_login_row : gr.update(visible=True),
+                    shared.username_state: gr.update(value=username),
+                    user_login_row: gr.update(visible=True),
                     user_sign_row: gr.update(visible=False),
                     login_username: gr.update(value=signin_username),
                     login_password: gr.update(value=password),
@@ -2183,7 +2244,14 @@ def user_signin(signin_username, signin_password):
                     opts.data = json.loads(options)
                     for key in opts.data:
                         if key in component_dict:
-                            response[component_dict[key]] = gr.update(value=opts.data[key])
+                            if key == 'sagemaker_endpoint':
+                                sagemaker_endpoint = opts.data[key]
+                                response[component_dict[key]] = gr.update(value=opts.data[key], choices=shared.refresh_sagemaker_endpoints(username))
+                            elif key == 'sd_model_checkpoint':
+                                shared.refresh_checkpoints(sagemaker_endpoint)
+                                response[component_dict[key]] = gr.update(value=opts.data[key], choices=shared.list_checkpoint_tiles())
+                            else:
+                                response[component_dict[key]] = gr.update(value=opts.data[key])
                     for key in sd_models.checkpoints_list:
                         if sd_models.checkpoints_list[key].title == opts.data['sd_model_checkpoint']:
                             if shared.sd_model:
@@ -2216,10 +2284,11 @@ def user_signup(signup_username, signup_password, signup_email):
             }
 
             response = requests.post(url=f'{shared.api_endpoint}/sd/user', json=inputs)
-            if response.status_code == 200:
-                shared.username = json.loads(response.text)['username']
+            if response.status_code == 200:            
+                username = json.loads(response.text)['username']
 
                 return {
+                    shared.username_state: gr.update(value=username),
                     user_login_row: gr.update(visible=True),
                     user_sign_row: gr.update(visible=False),
                     login_username: gr.update(value=signup_username),
@@ -2238,8 +2307,7 @@ def user_signup(signup_username, signup_password, signup_email):
                 }                    
 
         def user_signout():
-            shared.username=''
-            opts.data = shared.default_options
+            username = ''         
             if 'sd_model_checkpoint' in opts.data:
                 for key in sd_models.checkpoints_list:
                     if sd_models.checkpoints_list[key].title == opts.data['sd_model_checkpoint']:
@@ -2247,7 +2315,8 @@ def user_signout():
                         break            
 
             response = {
-                user_login_row : gr.update(visible=False),
+                shared.username_state: gr.update(value=username),
+                user_login_row: gr.update(visible=False),
                 user_sign_row: gr.update(visible=True),
                 txt2img_submit: gr.update(visible=False),
                 img2img_submit: gr.update(visible=False),
@@ -2258,7 +2327,12 @@ def user_signout():
 
             for key in opts.data:
                 if key in component_dict:
-                    response[component_dict[key]] = gr.update(value=opts.data[key])
+                    if key == 'sagemaker_endpoint':
+                        response[component_dict[key]] = gr.update(value=opts.data[key], choices=[])
+                    elif key == 'sd_model_checkpoint':
+                        response[component_dict[key]] = gr.update(value=opts.data[key], choices=[])
+                    else:
+                        response[component_dict[key]] = gr.update(value=opts.data[key])
 
             return response
 
@@ -2272,8 +2346,6 @@ def user_update(login_username, login_password, login_email):
 
             response = requests.post(url=f'{shared.api_endpoint}/sd/user', json=inputs)
             if response.status_code == 200:
-                shared.username = json.loads(response.text)['username']
-
                 return {
                     login_output: gr.update(value='Update succeed')
                 }
@@ -2291,15 +2363,15 @@ def user_delete(login_username, login_password, login_email):
 
             response = requests.post(url=f'{shared.api_endpoint}/sd/user', json=inputs)
             if response.status_code == 200:
-                shared.username = json.loads(response.text)['username']
-                opts.data = shared.default_options
+                username = ''            
                 for key in sd_models.checkpoints_list:
                     if sd_models.checkpoints_list[key].title == opts.data['sd_model_checkpoint']:
                         shared.sd_model.sd_model_name = sd_models.checkpoints_list[key].model_name
                         break                   
 
                 response = {
-                    user_login_row : gr.update(visible=False),
+                    shared.username_state: gr.update(value=username),
+                    user_login_row: gr.update(visible=False),
                     user_sign_row: gr.update(visible=True),
                     txt2img_submit: gr.update(visible=False),
                     img2img_submit: gr.update(visible=False),
@@ -2311,8 +2383,12 @@ def user_delete(login_username, login_password, login_email):
 
                 for key in opts.data:
                     if key in component_dict:
-                        response[component_dict[key]] = gr.update(value=opts.data[key])
-
+                        if key == 'sagemaker_endpoint':
+                            response[component_dict[key]] = gr.update(value=opts.data[key], choices=[])
+                        elif key == 'sd_model_checkpoint':
+                            response[component_dict[key]] = gr.update(value=opts.data[key], choices=[])
+                        else:
+                            response[component_dict[key]] = gr.update(value=opts.data[key])
                 return response
             else:
                 return {
@@ -2322,19 +2398,19 @@ def user_delete(login_username, login_password, login_email):
         signin.click(
             fn=user_signin,
             inputs=[signin_username, signin_password],
-            outputs=[user_login_row, user_sign_row, login_username, login_password, login_email,txt2img_submit, img2img_submit, extras_submit, create_train_embedding, create_train_hypernetwork, signin_output] + components
+            outputs=[shared.username_state, user_login_row, user_sign_row, login_username, login_password, login_email,txt2img_submit, img2img_submit, extras_submit, create_train_embedding, create_train_hypernetwork, signin_output] + components
         )
 
         signup.click(
             fn=user_signup,
             inputs=[signup_username, signup_password, signup_email],
-            outputs=[user_login_row, user_sign_row, login_username, login_password, login_email, txt2img_submit, img2img_submit, extras_submit, create_train_embedding, create_train_hypernetwork, signup_output]
+            outputs=[shared.username_state, user_login_row, user_sign_row, login_username, login_password, login_email, txt2img_submit, img2img_submit, extras_submit, create_train_embedding, create_train_hypernetwork, signup_output]
         )
 
         signout.click(
             fn=user_signout,
             inputs=[],
-            outputs=[user_login_row, user_sign_row, txt2img_submit, img2img_submit, extras_submit, create_train_embedding, create_train_hypernetwork] + components
+            outputs=[shared.username_state, user_login_row, user_sign_row, txt2img_submit, img2img_submit, extras_submit, create_train_embedding, create_train_hypernetwork] + components
         )
 
         userupdate.click(
@@ -2346,7 +2422,7 @@ def user_delete(login_username, login_password, login_email):
         userdelete.click(
             fn=user_delete,
             inputs=[login_username, login_password, login_email],
-            outputs=[user_login_row, user_sign_row, txt2img_submit, img2img_submit, extras_submit, create_train_embedding, create_train_hypernetwork, login_output] + components
+            outputs=[shared.username_state, user_login_row, user_sign_row, txt2img_submit, img2img_submit, extras_submit, create_train_embedding, create_train_hypernetwork, login_output] + components
         )
 
     if cmd_opts.pureui:
@@ -2410,7 +2486,7 @@ def user_delete(login_username, login_password, login_email):
         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,
+            inputs=components + [shared.username_state],
             outputs=[text_settings, result],
         )
 
@@ -2418,8 +2494,8 @@ def user_delete(login_username, login_password, login_email):
             component = component_dict[k]
 
             component.change(
-                fn=lambda value, k=k: run_settings_single(value, key=k),
-                inputs=[component],
+                fn=lambda value, k=k: run_settings_single(value, key=k, username=shared.username_state),
+                inputs=[component] + [shared.username_state],
                 outputs=[component, text_settings],
             )