diff --git a/modules/cmd_args.py b/modules/cmd_args.py
index da93eb2669f..6011cbbc50e 100644
--- a/modules/cmd_args.py
+++ b/modules/cmd_args.py
@@ -98,6 +98,7 @@
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("--api-log", action='store_true', help="use api-log=True to enable logging of all API requests")
parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the API instead of the webui")
+parser.add_argument("--ui-only", action='store_true', help="ui only")
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)
diff --git a/modules/initialize.py b/modules/initialize.py
index ac95fc6f00c..9729ce1de12 100644
--- a/modules/initialize.py
+++ b/modules/initialize.py
@@ -52,6 +52,7 @@ def initialize():
initialize_util.fix_asyncio_event_loop_policy()
initialize_util.validate_tls_options()
initialize_util.configure_sigint_handler()
+ # Theme is initialized here
initialize_util.configure_opts_onchange()
from modules import modelloader
diff --git a/modules/launch_utils.py b/modules/launch_utils.py
index 29506f24965..45262cb08b4 100644
--- a/modules/launch_utils.py
+++ b/modules/launch_utils.py
@@ -458,8 +458,11 @@ def configure_for_tests():
def start():
print(f"Launching {'API server' if '--nowebui' in sys.argv else 'Web UI'} with arguments: {' '.join(sys.argv[1:])}")
import webui
+ import ui_only
if '--nowebui' in sys.argv:
webui.api_only()
+ if '--ui-only' in sys.argv:
+ ui_only.webui()
else:
webui.webui()
diff --git a/modules/processing.py b/modules/processing.py
index 6f01c95f5b6..3d40def694a 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -202,6 +202,52 @@ class StableDiffusionProcessing:
is_api: bool = field(default=False, init=False)
+ def processing_infotext(self):
+ self.setup_prompts()
+
+ return create_infotext(self, self.all_prompts, ['-1'], self.all_subseeds, [], iteration=self.iteration, position_in_batch=0)
+
+ def processing_js(self):
+ # not fully implemented
+ self.setup_prompts()
+
+ obj = {
+ "prompt": self.all_prompts[0],
+ "all_prompts": self.all_prompts,
+ "negative_prompt": self.all_negative_prompts[0],
+ "all_negative_prompts": self.all_negative_prompts,
+ "seed": self.seed,
+ "all_seeds": self.all_seeds or [self.seed],
+ "subseed": self.subseed,
+ "all_subseeds": self.all_subseeds,
+ "subseed_strength": self.subseed_strength,
+ "width": self.width,
+ "height": self.height,
+ "sampler_name": self.sampler_name,
+ "cfg_scale": self.cfg_scale,
+ "steps": self.steps,
+ "batch_size": self.batch_size,
+ "restore_faces": self.restore_faces,
+ "face_restoration_model": self.face_restoration_model if self.restore_faces else None,
+ "sd_model_name": self.sd_model_name,
+ "sd_model_hash": self.sd_model_hash,
+ "sd_vae_name": self.sd_vae_name,
+ "sd_vae_hash": self.sd_vae_hash,
+ "seed_resize_from_w": self.seed_resize_from_w,
+ "seed_resize_from_h": self.seed_resize_from_h,
+ "denoising_strength": self.denoising_strength,
+ "extra_generation_params": self.extra_generation_params,
+ "index_of_first_image": 0,
+ "infotexts": self.infotexts,
+ "styles": self.styles,
+ "job_timestamp": self.job_timestamp,
+ "clip_skip": self.clip_skip,
+ "is_using_inpainting_conditioning": self.is_using_inpainting_conditioning,
+ "version": self.version,
+ }
+
+ return json.dumps(obj)
+
def __post_init__(self):
if self.sampler_index is not None:
print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
diff --git a/modules/txt2img.py b/modules/txt2img.py
index e4e18ceb6dd..52b64a3d4c7 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -45,6 +45,9 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step
p.user = request.username
+ pre_generation_info_text = p.processing_infotext()
+ print(pre_generation_info_text)
+
if shared.opts.enable_console_prompts:
print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
diff --git a/modules/ui_only.py b/modules/ui_only.py
new file mode 100644
index 00000000000..9a3f16885c3
--- /dev/null
+++ b/modules/ui_only.py
@@ -0,0 +1,991 @@
+import datetime
+import mimetypes
+import os
+import sys
+from functools import reduce
+import warnings
+from contextlib import ExitStack
+
+import gradio as gr
+import gradio.utils
+import numpy as np
+from PIL import Image, PngImagePlugin # noqa: F401
+from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
+
+from modules import gradio_extensons # noqa: F401
+from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, scripts, sd_samplers, processing, ui_extra_networks, ui_toprow
+from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML, InputAccordion, ResizeHandleRow
+from modules.paths import script_path
+from modules.ui_common import create_refresh_button
+from modules.ui_gradio_extensions import reload_javascript
+
+from modules.shared import opts, cmd_opts
+
+import modules.generation_parameters_copypaste as parameters_copypaste
+import modules.hypernetworks.ui as hypernetworks_ui
+import modules.textual_inversion.ui as textual_inversion_ui
+import modules.textual_inversion.textual_inversion as textual_inversion
+import modules.shared as shared
+from modules import prompt_parser
+from modules.sd_hijack import model_hijack
+from modules.generation_parameters_copypaste import image_from_url_text
+
+from modules import ui
+ordered_ui_categories = ui.ordered_ui_categories
+create_sampler_and_steps_selection = ui.create_sampler_and_steps_selection
+switch_values_symbol = ui.switch_values_symbol
+create_override_settings_dropdown = ui.create_override_settings_dropdown
+calc_resolution_hires = ui.calc_resolution_hires
+create_output_panel = ui.create_output_panel
+update_token_counter = ui.update_token_counter
+update_negative_prompt_token_counter = ui.update_negative_prompt_token_counter
+resize_from_to_html = ui.resize_from_to_html
+process_interrogate = ui.process_interrogate
+interrogate = ui.interrogate
+interrogate_deepbooru = ui.interrogate_deepbooru
+detect_image_size_symbol = ui.detect_image_size_symbol
+versions_html = ui.versions_html
+
+
+
+
+
+def greet(name, intensity):
+ return "Hello, " + name + "!" * int(intensity)
+
+
+def create_ui():
+ import modules.img2img
+ import modules.txt2img
+
+ reload_javascript()
+
+ parameters_copypaste.reset()
+
+ scripts.scripts_current = scripts.scripts_txt2img
+ scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
+
+ # demo = gr.Interface(
+ # fn=greet,
+ # inputs=["text", "slider"],
+ # outputs=["text"],
+ # )
+
+ # return demo
+ with gr.Blocks(analytics_enabled=False) as txt2img_interface:
+ toprow = ui_toprow.Toprow(is_img2img=False, is_compact=shared.opts.compact_prompt_box)
+
+ dummy_component = gr.Label(visible=False)
+
+ extra_tabs = gr.Tabs(elem_id="txt2img_extra_tabs")
+ extra_tabs.__enter__()
+
+ with gr.Tab("Generation", id="txt2img_generation") as txt2img_generation_tab, ResizeHandleRow(equal_height=False):
+ with ExitStack() as stack:
+ if shared.opts.txt2img_settings_accordion:
+ stack.enter_context(gr.Accordion("Open for Settings", open=False))
+ stack.enter_context(gr.Column(variant='compact', elem_id="txt2img_settings"))
+
+ scripts.scripts_txt2img.prepare_ui()
+
+ for category in ordered_ui_categories():
+ if category == "prompt":
+ toprow.create_inline_toprow_prompts()
+
+ if category == "sampler":
+ steps, sampler_name = create_sampler_and_steps_selection(sd_samplers.visible_sampler_names(), "txt2img")
+
+ elif category == "dimensions":
+ with FormRow():
+ with gr.Column(elem_id="txt2img_column_size", scale=4):
+ width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
+ height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
+
+ with gr.Column(elem_id="txt2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
+ res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", tooltip="Switch width/height")
+
+ if opts.dimensions_and_batch_together:
+ with gr.Column(elem_id="txt2img_column_batch"):
+ batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
+ batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
+
+ elif category == "cfg":
+ with gr.Row():
+ cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale")
+
+ elif category == "checkboxes":
+ with FormRow(elem_classes="checkboxes-row", variant="compact"):
+ pass
+
+ elif category == "accordions":
+ with gr.Row(elem_id="txt2img_accordions", elem_classes="accordions"):
+ with InputAccordion(False, label="Hires. fix", elem_id="txt2img_hr") as enable_hr:
+ with enable_hr.extra():
+ hr_final_resolution = FormHTML(value="", elem_id="txtimg_hr_finalres", label="Upscaled resolution", interactive=False, min_width=0)
+
+ with FormRow(elem_id="txt2img_hires_fix_row1", variant="compact"):
+ hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode)
+ hr_second_pass_steps = gr.Slider(minimum=0, maximum=150, step=1, label='Hires steps', value=0, elem_id="txt2img_hires_steps")
+ denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")
+
+ with FormRow(elem_id="txt2img_hires_fix_row2", variant="compact"):
+ hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale")
+ hr_resize_x = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize width to", value=0, elem_id="txt2img_hr_resize_x")
+ hr_resize_y = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize height to", value=0, elem_id="txt2img_hr_resize_y")
+
+ with FormRow(elem_id="txt2img_hires_fix_row3", variant="compact", visible=opts.hires_fix_show_sampler) as hr_sampler_container:
+
+ hr_checkpoint_name = gr.Dropdown(label='Hires checkpoint', elem_id="hr_checkpoint", choices=["Use same checkpoint"] + modules.sd_models.checkpoint_tiles(use_short=True), value="Use same checkpoint")
+ create_refresh_button(hr_checkpoint_name, modules.sd_models.list_models, lambda: {"choices": ["Use same checkpoint"] + modules.sd_models.checkpoint_tiles(use_short=True)}, "hr_checkpoint_refresh")
+
+ hr_sampler_name = gr.Dropdown(label='Hires sampling method', elem_id="hr_sampler", choices=["Use same sampler"] + sd_samplers.visible_sampler_names(), value="Use same sampler")
+
+ with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact", visible=opts.hires_fix_show_prompts) as hr_prompts_container:
+ with gr.Column(scale=80):
+ with gr.Row():
+ hr_prompt = gr.Textbox(label="Hires prompt", elem_id="hires_prompt", show_label=False, lines=3, placeholder="Prompt for hires fix pass.\nLeave empty to use the same prompt as in first pass.", elem_classes=["prompt"])
+ with gr.Column(scale=80):
+ with gr.Row():
+ hr_negative_prompt = gr.Textbox(label="Hires negative prompt", elem_id="hires_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt for hires fix pass.\nLeave empty to use the same negative prompt as in first pass.", elem_classes=["prompt"])
+
+ scripts.scripts_txt2img.setup_ui_for_section(category)
+
+ elif category == "batch":
+ if not opts.dimensions_and_batch_together:
+ with FormRow(elem_id="txt2img_column_batch"):
+ batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
+ batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
+
+ elif category == "override_settings":
+ with FormRow(elem_id="txt2img_override_settings_row") as row:
+ override_settings = create_override_settings_dropdown('txt2img', row)
+
+ elif category == "scripts":
+ with FormGroup(elem_id="txt2img_script_container"):
+ custom_inputs = scripts.scripts_txt2img.setup_ui()
+
+ if category not in {"accordions"}:
+ scripts.scripts_txt2img.setup_ui_for_section(category)
+
+ hr_resolution_preview_inputs = [enable_hr, width, height, hr_scale, hr_resize_x, hr_resize_y]
+
+ for component in hr_resolution_preview_inputs:
+ event = component.release if isinstance(component, gr.Slider) else component.change
+
+ event(
+ fn=calc_resolution_hires,
+ inputs=hr_resolution_preview_inputs,
+ outputs=[hr_final_resolution],
+ show_progress=False,
+ )
+ event(
+ None,
+ _js="onCalcResolutionHires",
+ inputs=hr_resolution_preview_inputs,
+ outputs=[],
+ show_progress=False,
+ )
+
+ txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples, toprow)
+
+ txt2img_args = dict(
+ fn=wrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs=[None, '', '']),
+ _js="submit",
+ inputs=[
+ dummy_component,
+ toprow.prompt,
+ toprow.negative_prompt,
+ toprow.ui_styles.dropdown,
+ steps,
+ sampler_name,
+ batch_count,
+ batch_size,
+ cfg_scale,
+ height,
+ width,
+ enable_hr,
+ denoising_strength,
+ hr_scale,
+ hr_upscaler,
+ hr_second_pass_steps,
+ hr_resize_x,
+ hr_resize_y,
+ hr_checkpoint_name,
+ hr_sampler_name,
+ hr_prompt,
+ hr_negative_prompt,
+ override_settings,
+
+ ] + custom_inputs,
+
+ outputs=[
+ txt2img_gallery,
+ generation_info,
+ html_info,
+ html_log,
+ ],
+ show_progress=False,
+ )
+
+ toprow.prompt.submit(**txt2img_args)
+ toprow.submit.click(**txt2img_args)
+
+ res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('txt2img')}", inputs=None, outputs=None, show_progress=False)
+
+ toprow.restore_progress_button.click(
+ fn=progress.restore_progress,
+ _js="restoreProgressTxt2img",
+ inputs=[dummy_component],
+ outputs=[
+ txt2img_gallery,
+ generation_info,
+ html_info,
+ html_log,
+ ],
+ show_progress=False,
+ )
+
+ txt2img_paste_fields = [
+ (toprow.prompt, "Prompt"),
+ (toprow.negative_prompt, "Negative prompt"),
+ (steps, "Steps"),
+ (sampler_name, "Sampler"),
+ (cfg_scale, "CFG scale"),
+ (width, "Size-1"),
+ (height, "Size-2"),
+ (batch_size, "Batch size"),
+ (toprow.ui_styles.dropdown, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
+ (denoising_strength, "Denoising strength"),
+ (enable_hr, lambda d: "Denoising strength" in d and ("Hires upscale" in d or "Hires upscaler" in d or "Hires resize-1" in d)),
+ (hr_scale, "Hires upscale"),
+ (hr_upscaler, "Hires upscaler"),
+ (hr_second_pass_steps, "Hires steps"),
+ (hr_resize_x, "Hires resize-1"),
+ (hr_resize_y, "Hires resize-2"),
+ (hr_checkpoint_name, "Hires checkpoint"),
+ (hr_sampler_name, "Hires sampler"),
+ (hr_sampler_container, lambda d: gr.update(visible=True) if d.get("Hires sampler", "Use same sampler") != "Use same sampler" or d.get("Hires checkpoint", "Use same checkpoint") != "Use same checkpoint" else gr.update()),
+ (hr_prompt, "Hires prompt"),
+ (hr_negative_prompt, "Hires negative prompt"),
+ (hr_prompts_container, lambda d: gr.update(visible=True) if d.get("Hires prompt", "") != "" or d.get("Hires negative prompt", "") != "" else gr.update()),
+ *scripts.scripts_txt2img.infotext_fields
+ ]
+ parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields, override_settings)
+ parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+ paste_button=toprow.paste, tabname="txt2img", source_text_component=toprow.prompt, source_image_component=None,
+ ))
+
+ txt2img_preview_params = [
+ toprow.prompt,
+ toprow.negative_prompt,
+ steps,
+ sampler_name,
+ cfg_scale,
+ scripts.scripts_txt2img.script('Seed').seed,
+ width,
+ height,
+ ]
+
+ toprow.token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps], outputs=[toprow.token_counter])
+ toprow.negative_token_button.click(fn=wrap_queued_call(update_negative_prompt_token_counter), inputs=[toprow.negative_prompt, steps], outputs=[toprow.negative_token_counter])
+
+ extra_networks_ui = ui_extra_networks.create_ui(txt2img_interface, [txt2img_generation_tab], 'txt2img')
+ ui_extra_networks.setup_ui(extra_networks_ui, txt2img_gallery)
+
+ extra_tabs.__exit__()
+
+ scripts.scripts_current = scripts.scripts_img2img
+ scripts.scripts_img2img.initialize_scripts(is_img2img=True)
+
+ with gr.Blocks(analytics_enabled=False) as img2img_interface:
+ toprow = ui_toprow.Toprow(is_img2img=True, is_compact=shared.opts.compact_prompt_box)
+
+ extra_tabs = gr.Tabs(elem_id="img2img_extra_tabs")
+ extra_tabs.__enter__()
+
+ with gr.Tab("Generation", id="img2img_generation") as img2img_generation_tab, ResizeHandleRow(equal_height=False):
+ with ExitStack() as stack:
+ if shared.opts.img2img_settings_accordion:
+ stack.enter_context(gr.Accordion("Open for Settings", open=False))
+ stack.enter_context(gr.Column(variant='compact', elem_id="img2img_settings"))
+
+ copy_image_buttons = []
+ copy_image_destinations = {}
+
+ def add_copy_image_controls(tab_name, elem):
+ with gr.Row(variant="compact", elem_id=f"img2img_copy_to_{tab_name}"):
+ gr.HTML("Copy image to: ", elem_id=f"img2img_label_copy_to_{tab_name}")
+
+ for title, name in zip(['img2img', 'sketch', 'inpaint', 'inpaint sketch'], ['img2img', 'sketch', 'inpaint', 'inpaint_sketch']):
+ if name == tab_name:
+ gr.Button(title, interactive=False)
+ copy_image_destinations[name] = elem
+ continue
+
+ button = gr.Button(title)
+ copy_image_buttons.append((button, name, elem))
+
+ scripts.scripts_img2img.prepare_ui()
+
+ for category in ordered_ui_categories():
+ if category == "prompt":
+ toprow.create_inline_toprow_prompts()
+
+ if category == "image":
+ with gr.Tabs(elem_id="mode_img2img"):
+ img2img_selected_tab = gr.State(0)
+
+ with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img:
+ init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA", height=opts.img2img_editor_height)
+ add_copy_image_controls('img2img', init_img)
+
+ with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch:
+ sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGB", height=opts.img2img_editor_height, brush_color=opts.img2img_sketch_default_brush_color)
+ add_copy_image_controls('sketch', sketch)
+
+ with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab") as tab_inpaint:
+ init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA", height=opts.img2img_editor_height, brush_color=opts.img2img_inpaint_mask_brush_color)
+ add_copy_image_controls('inpaint', init_img_with_mask)
+
+ with gr.TabItem('Inpaint sketch', id='inpaint_sketch', elem_id="img2img_inpaint_sketch_tab") as tab_inpaint_color:
+ inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGB", height=opts.img2img_editor_height, brush_color=opts.img2img_inpaint_sketch_default_brush_color)
+ inpaint_color_sketch_orig = gr.State(None)
+ add_copy_image_controls('inpaint_sketch', inpaint_color_sketch)
+
+ def update_orig(image, state):
+ if image is not None:
+ same_size = state is not None and state.size == image.size
+ has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
+ edited = same_size and has_exact_match
+ return image if not edited or state is None else state
+
+ inpaint_color_sketch.change(update_orig, [inpaint_color_sketch, inpaint_color_sketch_orig], inpaint_color_sketch_orig)
+
+ with gr.TabItem('Inpaint upload', id='inpaint_upload', elem_id="img2img_inpaint_upload_tab") as tab_inpaint_upload:
+ init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", elem_id="img_inpaint_base")
+ init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", image_mode="RGBA", elem_id="img_inpaint_mask")
+
+ with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch:
+ hidden = '
Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
+ gr.HTML(
+ "Process images in a directory on the same machine where the server is running." +
+ "
Use an empty output directory to save pictures normally instead of writing to the output directory." +
+ f"
Add inpaint batch mask directory to enable inpaint batch processing."
+ f"{hidden}
"
+ )
+ img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
+ img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
+ img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
+ with gr.Accordion("PNG info", open=False):
+ img2img_batch_use_png_info = gr.Checkbox(label="Append png info to prompts", **shared.hide_dirs, elem_id="img2img_batch_use_png_info")
+ img2img_batch_png_info_dir = gr.Textbox(label="PNG info directory", **shared.hide_dirs, placeholder="Leave empty to use input directory", elem_id="img2img_batch_png_info_dir")
+ img2img_batch_png_info_props = gr.CheckboxGroup(["Prompt", "Negative prompt", "Seed", "CFG scale", "Sampler", "Steps", "Model hash"], label="Parameters to take from png info", info="Prompts from png info will be appended to prompts set in ui.")
+
+ img2img_tabs = [tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]
+
+ for i, tab in enumerate(img2img_tabs):
+ tab.select(fn=lambda tabnum=i: tabnum, inputs=[], outputs=[img2img_selected_tab])
+
+ def copy_image(img):
+ if isinstance(img, dict) and 'image' in img:
+ return img['image']
+
+ return img
+
+ for button, name, elem in copy_image_buttons:
+ button.click(
+ fn=copy_image,
+ inputs=[elem],
+ outputs=[copy_image_destinations[name]],
+ )
+ button.click(
+ fn=lambda: None,
+ _js=f"switch_to_{name.replace(' ', '_')}",
+ inputs=[],
+ outputs=[],
+ )
+
+ with FormRow():
+ resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
+
+ if category == "sampler":
+ steps, sampler_name = create_sampler_and_steps_selection(sd_samplers.visible_sampler_names(), "img2img")
+
+ elif category == "dimensions":
+ with FormRow():
+ with gr.Column(elem_id="img2img_column_size", scale=4):
+ selected_scale_tab = gr.State(value=0)
+
+ with gr.Tabs():
+ with gr.Tab(label="Resize to", elem_id="img2img_tab_resize_to") as tab_scale_to:
+ with FormRow():
+ with gr.Column(elem_id="img2img_column_size", scale=4):
+ width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
+ height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
+ with gr.Column(elem_id="img2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
+ res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn", tooltip="Switch width/height")
+ detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn", tooltip="Auto detect size from img2img")
+
+ with gr.Tab(label="Resize by", elem_id="img2img_tab_resize_by") as tab_scale_by:
+ scale_by = gr.Slider(minimum=0.05, maximum=4.0, step=0.05, label="Scale", value=1.0, elem_id="img2img_scale")
+
+ with FormRow():
+ scale_by_html = FormHTML(resize_from_to_html(0, 0, 0.0), elem_id="img2img_scale_resolution_preview")
+ gr.Slider(label="Unused", elem_id="img2img_unused_scale_by_slider")
+ button_update_resize_to = gr.Button(visible=False, elem_id="img2img_update_resize_to")
+
+ on_change_args = dict(
+ fn=resize_from_to_html,
+ _js="currentImg2imgSourceResolution",
+ inputs=[dummy_component, dummy_component, scale_by],
+ outputs=scale_by_html,
+ show_progress=False,
+ )
+
+ scale_by.release(**on_change_args)
+ button_update_resize_to.click(**on_change_args)
+
+ tab_scale_to.select(fn=lambda: 0, inputs=[], outputs=[selected_scale_tab])
+ tab_scale_by.select(fn=lambda: 1, inputs=[], outputs=[selected_scale_tab])
+
+ if opts.dimensions_and_batch_together:
+ with gr.Column(elem_id="img2img_column_batch"):
+ batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
+ batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
+
+ elif category == "denoising":
+ denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
+
+ elif category == "cfg":
+ with gr.Row():
+ cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
+ image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=False)
+
+ elif category == "checkboxes":
+ with FormRow(elem_classes="checkboxes-row", variant="compact"):
+ pass
+
+ elif category == "accordions":
+ with gr.Row(elem_id="img2img_accordions", elem_classes="accordions"):
+ scripts.scripts_img2img.setup_ui_for_section(category)
+
+ elif category == "batch":
+ if not opts.dimensions_and_batch_together:
+ with FormRow(elem_id="img2img_column_batch"):
+ batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
+ batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
+
+ elif category == "override_settings":
+ with FormRow(elem_id="img2img_override_settings_row") as row:
+ override_settings = create_override_settings_dropdown('img2img', row)
+
+ elif category == "scripts":
+ with FormGroup(elem_id="img2img_script_container"):
+ custom_inputs = scripts.scripts_img2img.setup_ui()
+
+ elif category == "inpaint":
+ with FormGroup(elem_id="inpaint_controls", visible=False) as inpaint_controls:
+ with FormRow():
+ mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur")
+ mask_alpha = gr.Slider(label="Mask transparency", visible=False, elem_id="img2img_mask_alpha")
+
+ with FormRow():
+ inpainting_mask_invert = gr.Radio(label='Mask mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode")
+
+ with FormRow():
+ inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill")
+
+ with FormRow():
+ with gr.Column():
+ inpaint_full_res = gr.Radio(label="Inpaint area", choices=["Whole picture", "Only masked"], type="index", value="Whole picture", elem_id="img2img_inpaint_full_res")
+
+ with gr.Column(scale=4):
+ inpaint_full_res_padding = gr.Slider(label='Only masked padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")
+
+ if category not in {"accordions"}:
+ scripts.scripts_img2img.setup_ui_for_section(category)
+
+ # the code below is meant to update the resolution label after the image in the image selection UI has changed.
+ # as it is now the event keeps firing continuously for inpaint edits, which ruins the page with constant requests.
+ # I assume this must be a gradio bug and for now we'll just do it for non-inpaint inputs.
+ for component in [init_img, sketch]:
+ component.change(fn=lambda: None, _js="updateImg2imgResizeToTextAfterChangingImage", inputs=[], outputs=[], show_progress=False)
+
+ def select_img2img_tab(tab):
+ return gr.update(visible=tab in [2, 3, 4]), gr.update(visible=tab == 3),
+
+ for i, elem in enumerate(img2img_tabs):
+ elem.select(
+ fn=lambda tab=i: select_img2img_tab(tab),
+ inputs=[],
+ outputs=[inpaint_controls, mask_alpha],
+ )
+
+ img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples, toprow)
+
+ img2img_args = dict(
+ fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
+ _js="submit_img2img",
+ inputs=[
+ dummy_component,
+ dummy_component,
+ toprow.prompt,
+ toprow.negative_prompt,
+ toprow.ui_styles.dropdown,
+ init_img,
+ sketch,
+ init_img_with_mask,
+ inpaint_color_sketch,
+ inpaint_color_sketch_orig,
+ init_img_inpaint,
+ init_mask_inpaint,
+ steps,
+ sampler_name,
+ mask_blur,
+ mask_alpha,
+ inpainting_fill,
+ batch_count,
+ batch_size,
+ cfg_scale,
+ image_cfg_scale,
+ denoising_strength,
+ selected_scale_tab,
+ height,
+ width,
+ scale_by,
+ resize_mode,
+ inpaint_full_res,
+ inpaint_full_res_padding,
+ inpainting_mask_invert,
+ img2img_batch_input_dir,
+ img2img_batch_output_dir,
+ img2img_batch_inpaint_mask_dir,
+ override_settings,
+ img2img_batch_use_png_info,
+ img2img_batch_png_info_props,
+ img2img_batch_png_info_dir,
+ ] + custom_inputs,
+ outputs=[
+ img2img_gallery,
+ generation_info,
+ html_info,
+ html_log,
+ ],
+ show_progress=False,
+ )
+
+ interrogate_args = dict(
+ _js="get_img2img_tab_index",
+ inputs=[
+ dummy_component,
+ img2img_batch_input_dir,
+ img2img_batch_output_dir,
+ init_img,
+ sketch,
+ init_img_with_mask,
+ inpaint_color_sketch,
+ init_img_inpaint,
+ ],
+ outputs=[toprow.prompt, dummy_component],
+ )
+
+ toprow.prompt.submit(**img2img_args)
+ toprow.submit.click(**img2img_args)
+
+ res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('img2img')}", inputs=None, outputs=None, show_progress=False)
+
+ detect_image_size_btn.click(
+ fn=lambda w, h, _: (w or gr.update(), h or gr.update()),
+ _js="currentImg2imgSourceResolution",
+ inputs=[dummy_component, dummy_component, dummy_component],
+ outputs=[width, height],
+ show_progress=False,
+ )
+
+ toprow.restore_progress_button.click(
+ fn=progress.restore_progress,
+ _js="restoreProgressImg2img",
+ inputs=[dummy_component],
+ outputs=[
+ img2img_gallery,
+ generation_info,
+ html_info,
+ html_log,
+ ],
+ show_progress=False,
+ )
+
+ toprow.button_interrogate.click(
+ fn=lambda *args: process_interrogate(interrogate, *args),
+ **interrogate_args,
+ )
+
+ toprow.button_deepbooru.click(
+ fn=lambda *args: process_interrogate(interrogate_deepbooru, *args),
+ **interrogate_args,
+ )
+
+ toprow.token_button.click(fn=update_token_counter, inputs=[toprow.prompt, steps], outputs=[toprow.token_counter])
+ toprow.negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.negative_prompt, steps], outputs=[toprow.negative_token_counter])
+
+ img2img_paste_fields = [
+ (toprow.prompt, "Prompt"),
+ (toprow.negative_prompt, "Negative prompt"),
+ (steps, "Steps"),
+ (sampler_name, "Sampler"),
+ (cfg_scale, "CFG scale"),
+ (image_cfg_scale, "Image CFG scale"),
+ (width, "Size-1"),
+ (height, "Size-2"),
+ (batch_size, "Batch size"),
+ (toprow.ui_styles.dropdown, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
+ (denoising_strength, "Denoising strength"),
+ (mask_blur, "Mask blur"),
+ *scripts.scripts_img2img.infotext_fields
+ ]
+ parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields, override_settings)
+ parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields, override_settings)
+ parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+ paste_button=toprow.paste, tabname="img2img", source_text_component=toprow.prompt, source_image_component=None,
+ ))
+
+ extra_networks_ui_img2img = ui_extra_networks.create_ui(img2img_interface, [img2img_generation_tab], 'img2img')
+ ui_extra_networks.setup_ui(extra_networks_ui_img2img, img2img_gallery)
+
+ extra_tabs.__exit__()
+
+ scripts.scripts_current = None
+
+ with gr.Blocks(analytics_enabled=False) as extras_interface:
+ ui_postprocessing.create_ui()
+
+ with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
+ with gr.Row(equal_height=False):
+ with gr.Column(variant='panel'):
+ image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil")
+
+ with gr.Column(variant='panel'):
+ html = gr.HTML()
+ generation_info = gr.Textbox(visible=False, elem_id="pnginfo_generation_info")
+ html2 = gr.HTML()
+ with gr.Row():
+ buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
+
+ for tabname, button in buttons.items():
+ parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+ paste_button=button, tabname=tabname, source_text_component=generation_info, source_image_component=image,
+ ))
+
+ image.change(
+ fn=wrap_gradio_call(modules.extras.run_pnginfo),
+ inputs=[image],
+ outputs=[html, generation_info, html2],
+ )
+
+ modelmerger_ui = ui_checkpoint_merger.UiCheckpointMerger()
+
+ with gr.Blocks(analytics_enabled=False) as train_interface:
+ with gr.Row(equal_height=False):
+ gr.HTML(value="See wiki for detailed explanation.
")
+
+ with gr.Row(variant="compact", equal_height=False):
+ with gr.Tabs(elem_id="train_tabs"):
+
+ with gr.Tab(label="Create embedding", id="create_embedding"):
+ new_embedding_name = gr.Textbox(label="Name", elem_id="train_new_embedding_name")
+ initialization_text = gr.Textbox(label="Initialization text", value="*", elem_id="train_initialization_text")
+ nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1, elem_id="train_nvpt")
+ overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding", elem_id="train_overwrite_old_embedding")
+
+ with gr.Row():
+ with gr.Column(scale=3):
+ gr.HTML(value="")
+
+ with gr.Column():
+ create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding")
+
+ with gr.Tab(label="Create hypernetwork", id="create_hypernetwork"):
+ new_hypernetwork_name = gr.Textbox(label="Name", elem_id="train_new_hypernetwork_name")
+ new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"], elem_id="train_new_hypernetwork_sizes")
+ new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'", elem_id="train_new_hypernetwork_layer_structure")
+ new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=hypernetworks_ui.keys, elem_id="train_new_hypernetwork_activation_func")
+ new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"], elem_id="train_new_hypernetwork_initialization_option")
+ new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization", elem_id="train_new_hypernetwork_add_layer_norm")
+ new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout", elem_id="train_new_hypernetwork_use_dropout")
+ new_hypernetwork_dropout_structure = gr.Textbox("0, 0, 0", label="Enter hypernetwork Dropout structure (or empty). Recommended : 0~0.35 incrementing sequence: 0, 0.05, 0.15", placeholder="1st and last digit must be 0 and values should be between 0 and 1. ex:'0, 0.01, 0'")
+ overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork", elem_id="train_overwrite_old_hypernetwork")
+
+ with gr.Row():
+ with gr.Column(scale=3):
+ gr.HTML(value="")
+
+ with gr.Column():
+ create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork")
+
+ def get_textual_inversion_template_names():
+ return sorted(textual_inversion.textual_inversion_templates)
+
+ with gr.Tab(label="Train", id="train"):
+ gr.HTML(value="Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images [wiki]
")
+ with FormRow():
+ train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
+ create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
+
+ train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=sorted(shared.hypernetworks))
+ create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted(shared.hypernetworks)}, "refresh_train_hypernetwork_name")
+
+ with FormRow():
+ embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate")
+ hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001", elem_id="train_hypernetwork_learn_rate")
+
+ with FormRow():
+ clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
+ clip_grad_value = gr.Textbox(placeholder="Gradient clip value", value="0.1", show_label=False)
+
+ with FormRow():
+ batch_size = gr.Number(label='Batch size', value=1, precision=0, elem_id="train_batch_size")
+ gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0, elem_id="train_gradient_step")
+
+ dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images", elem_id="train_dataset_directory")
+ log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion", elem_id="train_log_directory")
+
+ with FormRow():
+ template_file = gr.Dropdown(label='Prompt template', value="style_filewords.txt", elem_id="train_template_file", choices=get_textual_inversion_template_names())
+ create_refresh_button(template_file, textual_inversion.list_textual_inversion_templates, lambda: {"choices": get_textual_inversion_template_names()}, "refrsh_train_template_file")
+
+ training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width")
+ training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height")
+ varsize = gr.Checkbox(label="Do not resize images", value=False, elem_id="train_varsize")
+ steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps")
+
+ with FormRow():
+ create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every")
+ save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every")
+
+ use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False, elem_id="use_weight")
+
+ save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True, elem_id="train_save_image_with_stored_embedding")
+ preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False, elem_id="train_preview_from_txt2img")
+
+ shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False, elem_id="train_shuffle_tags")
+ tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0, elem_id="train_tag_drop_out")
+
+ latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'], elem_id="train_latent_sampling_method")
+
+ with gr.Row():
+ train_embedding = gr.Button(value="Train Embedding", variant='primary', elem_id="train_train_embedding")
+ interrupt_training = gr.Button(value="Interrupt", elem_id="train_interrupt_training")
+ train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary', elem_id="train_train_hypernetwork")
+
+ params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
+
+ script_callbacks.ui_train_tabs_callback(params)
+
+ with gr.Column(elem_id='ti_gallery_container'):
+ ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
+ gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery', columns=4)
+ gr.HTML(elem_id="ti_progress", value="")
+ ti_outcome = gr.HTML(elem_id="ti_error", value="")
+
+ create_embedding.click(
+ fn=textual_inversion_ui.create_embedding,
+ inputs=[
+ new_embedding_name,
+ initialization_text,
+ nvpt,
+ overwrite_old_embedding,
+ ],
+ outputs=[
+ train_embedding_name,
+ ti_output,
+ ti_outcome,
+ ]
+ )
+
+ create_hypernetwork.click(
+ fn=hypernetworks_ui.create_hypernetwork,
+ inputs=[
+ new_hypernetwork_name,
+ new_hypernetwork_sizes,
+ overwrite_old_hypernetwork,
+ new_hypernetwork_layer_structure,
+ new_hypernetwork_activation_func,
+ new_hypernetwork_initialization_option,
+ new_hypernetwork_add_layer_norm,
+ new_hypernetwork_use_dropout,
+ new_hypernetwork_dropout_structure
+ ],
+ outputs=[
+ train_hypernetwork_name,
+ ti_output,
+ ti_outcome,
+ ]
+ )
+
+ train_embedding.click(
+ fn=wrap_gradio_gpu_call(textual_inversion_ui.train_embedding, extra_outputs=[gr.update()]),
+ _js="start_training_textual_inversion",
+ inputs=[
+ dummy_component,
+ train_embedding_name,
+ embedding_learn_rate,
+ batch_size,
+ gradient_step,
+ dataset_directory,
+ log_directory,
+ training_width,
+ training_height,
+ varsize,
+ steps,
+ clip_grad_mode,
+ clip_grad_value,
+ shuffle_tags,
+ tag_drop_out,
+ latent_sampling_method,
+ use_weight,
+ create_image_every,
+ save_embedding_every,
+ template_file,
+ save_image_with_stored_embedding,
+ preview_from_txt2img,
+ *txt2img_preview_params,
+ ],
+ outputs=[
+ ti_output,
+ ti_outcome,
+ ]
+ )
+
+ train_hypernetwork.click(
+ fn=wrap_gradio_gpu_call(hypernetworks_ui.train_hypernetwork, extra_outputs=[gr.update()]),
+ _js="start_training_textual_inversion",
+ inputs=[
+ dummy_component,
+ train_hypernetwork_name,
+ hypernetwork_learn_rate,
+ batch_size,
+ gradient_step,
+ dataset_directory,
+ log_directory,
+ training_width,
+ training_height,
+ varsize,
+ steps,
+ clip_grad_mode,
+ clip_grad_value,
+ shuffle_tags,
+ tag_drop_out,
+ latent_sampling_method,
+ use_weight,
+ create_image_every,
+ save_embedding_every,
+ template_file,
+ preview_from_txt2img,
+ *txt2img_preview_params,
+ ],
+ outputs=[
+ ti_output,
+ ti_outcome,
+ ]
+ )
+
+ interrupt_training.click(
+ fn=lambda: shared.state.interrupt(),
+ inputs=[],
+ outputs=[],
+ )
+
+ loadsave = ui_loadsave.UiLoadsave(cmd_opts.ui_config_file)
+
+ settings = ui_settings.UiSettings()
+ settings.create_ui(loadsave, dummy_component)
+
+ interfaces = [
+ (txt2img_interface, "txt2img", "txt2img"),
+ (img2img_interface, "img2img", "img2img"),
+ (extras_interface, "Extras", "extras"),
+ (pnginfo_interface, "PNG Info", "pnginfo"),
+ (modelmerger_ui.blocks, "Checkpoint Merger", "modelmerger"),
+ (train_interface, "Train", "train"),
+ ]
+
+ interfaces += script_callbacks.ui_tabs_callback()
+ interfaces += [(settings.interface, "Settings", "settings")]
+
+ extensions_interface = ui_extensions.create_ui()
+ interfaces += [(extensions_interface, "Extensions", "extensions")]
+
+ shared.tab_names = []
+ for _interface, label, _ifid in interfaces:
+ shared.tab_names.append(label)
+
+ with gr.Blocks(theme=shared.gradio_theme, analytics_enabled=False, title="Stable Diffusion") as demo:
+ settings.add_quicksettings()
+
+ parameters_copypaste.connect_paste_params_buttons()
+
+ with gr.Tabs(elem_id="tabs") as tabs:
+ tab_order = {k: i for i, k in enumerate(opts.ui_tab_order)}
+ sorted_interfaces = sorted(interfaces, key=lambda x: tab_order.get(x[1], 9999))
+
+ for interface, label, ifid in sorted_interfaces:
+ if label in shared.opts.hidden_tabs:
+ continue
+ with gr.TabItem(label, id=ifid, elem_id=f"tab_{ifid}"):
+ interface.render()
+
+ if ifid not in ["extensions", "settings"]:
+ loadsave.add_block(interface, ifid)
+
+ loadsave.add_component(f"webui/Tabs@{tabs.elem_id}", tabs)
+
+ loadsave.setup_ui()
+
+ if os.path.exists(os.path.join(script_path, "notification.mp3")) and shared.opts.notification_audio:
+ gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
+
+ footer = shared.html("footer.html")
+ footer = footer.format(versions=versions_html(), api_docs="/docs" if shared.cmd_opts.api else "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/API")
+ gr.HTML(footer, elem_id="footer")
+
+ settings.add_functionality(demo)
+
+ update_image_cfg_scale_visibility = lambda: gr.update(visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit")
+ settings.text_settings.change(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
+ demo.load(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
+
+ modelmerger_ui.setup_ui(dummy_component=dummy_component, sd_model_checkpoint_component=settings.component_dict['sd_model_checkpoint'])
+
+ loadsave.dump_defaults()
+ demo.ui_loadsave = loadsave
+
+ return demo
+
+
+def setup_ui_api(app):
+ from pydantic import BaseModel, Field
+
+ class QuicksettingsHint(BaseModel):
+ name: str = Field(title="Name of the quicksettings field")
+ label: str = Field(title="Label of the quicksettings field")
+
+ def quicksettings_hint():
+ return [QuicksettingsHint(name=k, label=v.label) for k, v in opts.data_labels.items()]
+
+ app.add_api_route("/internal/quicksettings-hint", quicksettings_hint, methods=["GET"], response_model=list[QuicksettingsHint])
+
+ app.add_api_route("/internal/ping", lambda: {}, methods=["GET"])
+
+ app.add_api_route("/internal/profile-startup", lambda: timer.startup_record, methods=["GET"])
+
+ def download_sysinfo(attachment=False):
+ from fastapi.responses import PlainTextResponse
+
+ text = sysinfo.get()
+ filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.json"
+
+ return PlainTextResponse(text, headers={'Content-Disposition': f'{"attachment" if attachment else "inline"}; filename="{filename}"'})
+
+ app.add_api_route("/internal/sysinfo", download_sysinfo, methods=["GET"])
+ app.add_api_route("/internal/sysinfo-download", lambda: download_sysinfo(attachment=True), methods=["GET"])
+
diff --git a/ui_only.py b/ui_only.py
new file mode 100644
index 00000000000..371217718b6
--- /dev/null
+++ b/ui_only.py
@@ -0,0 +1,161 @@
+from __future__ import annotations
+
+import os
+import time
+
+from modules import timer
+from modules import initialize_util
+from modules import initialize
+
+startup_timer = timer.startup_timer
+startup_timer.record("launcher")
+
+initialize.imports()
+
+initialize.check_versions()
+
+
+def create_api(app):
+ from modules.api.api import Api
+ from modules.call_queue import queue_lock
+
+ api = Api(app, queue_lock)
+ return api
+
+
+def api_only():
+ from fastapi import FastAPI
+ from modules.shared_cmd_options import cmd_opts
+
+ initialize.initialize()
+
+ app = FastAPI()
+ initialize_util.setup_middleware(app)
+ api = create_api(app)
+
+ from modules import script_callbacks
+ script_callbacks.before_ui_callback()
+ script_callbacks.app_started_callback(None, app)
+
+ print(f"Startup time: {startup_timer.summary()}.")
+ api.launch(
+ server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1",
+ port=cmd_opts.port if cmd_opts.port else 7861,
+ root_path=f"/{cmd_opts.subpath}" if cmd_opts.subpath else ""
+ )
+
+
+def webui():
+ from modules.shared_cmd_options import cmd_opts
+
+ launch_api = cmd_opts.api
+ initialize.initialize()
+
+ from modules import shared, ui_tempdir, script_callbacks, ui_only, progress, ui_extra_networks
+
+ while 1:
+ if shared.opts.clean_temp_dir_at_start:
+ ui_tempdir.cleanup_tmpdr()
+ startup_timer.record("cleanup temp dir")
+
+ script_callbacks.before_ui_callback()
+ startup_timer.record("scripts before_ui_callback")
+
+ shared.demo = ui_only.create_ui()
+ startup_timer.record("create ui")
+
+ if not cmd_opts.no_gradio_queue:
+ shared.demo.queue(64)
+
+ gradio_auth_creds = list(initialize_util.get_gradio_auth_creds()) or None
+
+ auto_launch_browser = False
+ if os.getenv('SD_WEBUI_RESTARTING') != '1':
+ if shared.opts.auto_launch_browser == "Remote" or cmd_opts.autolaunch:
+ auto_launch_browser = True
+ elif shared.opts.auto_launch_browser == "Local":
+ auto_launch_browser = not cmd_opts.webui_is_non_local
+
+ app, local_url, share_url = shared.demo.launch(
+ server_name=initialize_util.gradio_server_name(),
+ server_port=cmd_opts.port,
+ ssl_keyfile=cmd_opts.tls_keyfile,
+ ssl_certfile=cmd_opts.tls_certfile,
+ ssl_verify=cmd_opts.disable_tls_verify,
+ debug=cmd_opts.gradio_debug,
+ auth=gradio_auth_creds,
+ inbrowser=auto_launch_browser,
+ prevent_thread_lock=True,
+ allowed_paths=cmd_opts.gradio_allowed_path,
+ app_kwargs={
+ "docs_url": "/docs",
+ "redoc_url": "/redoc",
+ },
+ root_path=f"/{cmd_opts.subpath}" if cmd_opts.subpath else "",
+ )
+
+ startup_timer.record("gradio launch")
+
+ # gradio uses a very open CORS policy via app.user_middleware, which makes it possible for
+ # an attacker to trick the user into opening a malicious HTML page, which makes a request to the
+ # running web ui and do whatever the attacker wants, including installing an extension and
+ # running its code. We disable this here. Suggested by RyotaK.
+ app.user_middleware = [x for x in app.user_middleware if x.cls.__name__ != 'CORSMiddleware']
+
+ initialize_util.setup_middleware(app)
+
+ progress.setup_progress_api(app)
+ ui_only.setup_ui_api(app)
+
+ if launch_api:
+ create_api(app)
+
+ ui_extra_networks.add_pages_to_demo(app)
+
+ startup_timer.record("add APIs")
+
+ with startup_timer.subcategory("app_started_callback"):
+ script_callbacks.app_started_callback(shared.demo, app)
+
+ timer.startup_record = startup_timer.dump()
+ print(f"Startup time: {startup_timer.summary()}.")
+
+ try:
+ while True:
+ server_command = shared.state.wait_for_server_command(timeout=5)
+ if server_command:
+ if server_command in ("stop", "restart"):
+ break
+ else:
+ print(f"Unknown server command: {server_command}")
+ except KeyboardInterrupt:
+ print('Caught KeyboardInterrupt, stopping...')
+ server_command = "stop"
+
+ if server_command == "stop":
+ print("Stopping server...")
+ # If we catch a keyboard interrupt, we want to stop the server and exit.
+ shared.demo.close()
+ break
+
+ # disable auto launch webui in browser for subsequent UI Reload
+ os.environ.setdefault('SD_WEBUI_RESTARTING', '1')
+
+ print('Restarting UI...')
+ shared.demo.close()
+ time.sleep(0.5)
+ startup_timer.reset()
+ script_callbacks.app_reload_callback()
+ startup_timer.record("app reload callback")
+ script_callbacks.script_unloaded_callback()
+ startup_timer.record("scripts unloaded callback")
+ initialize.initialize_rest(reload_script_modules=True)
+
+
+if __name__ == "__main__":
+ from modules.shared_cmd_options import cmd_opts
+
+ if cmd_opts.nowebui:
+ api_only()
+ else:
+ webui()
diff --git a/worklog.md b/worklog.md
new file mode 100644
index 00000000000..2b007f342a3
--- /dev/null
+++ b/worklog.md
@@ -0,0 +1,69 @@
+
+
+# Generate a well known ssl cert CA store to be trusted
+https://pypi.org/project/certifi/
+
+pip install certifi
+
+python -m certifi
+
+
+# Check venv ssl cert trust settings
+https://stackoverflow.com/questions/39356413/how-to-add-a-custom-ca-root-certificate-to-the-ca-store-used-by-pip-in-windows
+python -c "import ssl; print(ssl.get_default_verify_paths())"
+
+
+
+# Use the generated truststore and move it to the location being used
+cp /Users/samuelwang/sd/stable-diffusion-webui/venv/lib/python3.10/site-packages/certifi/cacert.pem /Library/Frameworks/Python.framework/Versions/3.10/etc/openssl/cert.pem
+
+
+
+# Prompt Process
+UI inputs provided by users
+users click on the generate button
+call queue wrap inputs and invoke txt2img run process (call_queue.py)
+the result is stored in an Processed instance
+the json inputs are generated from the Processed instance
+
+We do not want to wait until it is processed because we won't process images locally.
+So we need to create a js() method pre process, aka, Processing
+
+
+ def js(self):
+ obj = {
+ "prompt": self.all_prompts[0],
+ "all_prompts": self.all_prompts,
+ "negative_prompt": self.all_negative_prompts[0],
+ "all_negative_prompts": self.all_negative_prompts,
+ "seed": self.seed,
+ "all_seeds": self.all_seeds,
+ "subseed": self.subseed,
+ "all_subseeds": self.all_subseeds,
+ "subseed_strength": self.subseed_strength,
+ "width": self.width,
+ "height": self.height,
+ "sampler_name": self.sampler_name,
+ "cfg_scale": self.cfg_scale,
+ "steps": self.steps,
+ "batch_size": self.batch_size,
+ "restore_faces": self.restore_faces,
+ "face_restoration_model": self.face_restoration_model,
+ "sd_model_name": self.sd_model_name,
+ "sd_model_hash": self.sd_model_hash,
+ "sd_vae_name": self.sd_vae_name,
+ "sd_vae_hash": self.sd_vae_hash,
+ "seed_resize_from_w": self.seed_resize_from_w,
+ "seed_resize_from_h": self.seed_resize_from_h,
+ "denoising_strength": self.denoising_strength,
+ "extra_generation_params": self.extra_generation_params,
+ "index_of_first_image": self.index_of_first_image,
+ "infotexts": self.infotexts,
+ "styles": self.styles,
+ "job_timestamp": self.job_timestamp,
+ "clip_skip": self.clip_skip,
+ "is_using_inpainting_conditioning": self.is_using_inpainting_conditioning,
+ "version": self.version,
+ }
+
+ return json.dumps(obj)
\ No newline at end of file