"
+ embedding_yet_to_be_embedded = False
+
+ is_training_inpainting_model = shared.sd_model.model.conditioning_key in {'hybrid', 'concat'}
+ img_c = None
+
+ pbar = tqdm.tqdm(total=steps - initial_step)
+ try:
+ sd_hijack_checkpoint.add()
+
+ for i in range((steps-initial_step) * gradient_step):
+ if scheduler.finished:
+ break
+ if shared.state.interrupted:
+ break
+ for j, batch in enumerate(dl):
+ # works as a drop_last=True for gradient accumulation
+ if j == max_steps_per_epoch:
+ break
+ scheduler.apply(optimizer, embedding.step)
+ if scheduler.finished:
+ break
+ if shared.state.interrupted:
+ break
+
+ if clip_grad:
+ clip_grad_sched.step(embedding.step)
+
+ with devices.autocast():
+ x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
+ if use_weight:
+ w = batch.weight.to(devices.device, non_blocking=pin_memory)
+ c = shared.sd_model.cond_stage_model(batch.cond_text)
+
+ if is_training_inpainting_model:
+ if img_c is None:
+ img_c = processing.txt2img_image_conditioning(shared.sd_model, c, training_width, training_height)
+
+ cond = {"c_concat": [img_c], "c_crossattn": [c]}
+ else:
+ cond = c
+
+ if use_weight:
+ loss = shared.sd_model.weighted_forward(x, cond, w)[0] / gradient_step
+ del w
+ else:
+ loss = shared.sd_model.forward(x, cond)[0] / gradient_step
+ del x
+
+ _loss_step += loss.item()
+ scaler.scale(loss).backward()
+
+ # go back until we reach gradient accumulation steps
+ if (j + 1) % gradient_step != 0:
+ continue
+
+ if clip_grad:
+ clip_grad(embedding.vec, clip_grad_sched.learn_rate)
+
+ scaler.step(optimizer)
+ scaler.update()
+ embedding.step += 1
+ pbar.update()
+ optimizer.zero_grad(set_to_none=True)
+ loss_step = _loss_step
+ _loss_step = 0
+
+ steps_done = embedding.step + 1
+
+ epoch_num = embedding.step // steps_per_epoch
+ epoch_step = embedding.step % steps_per_epoch
+
+ description = f"Training textual inversion [Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}] loss: {loss_step:.7f}"
+ pbar.set_description(description)
+ if embedding_dir is not None and steps_done % save_embedding_every == 0:
+ # Before saving, change name to match current checkpoint.
+ embedding_name_every = f'{embedding_name}-{steps_done}'
+ last_saved_file = os.path.join(embedding_dir, f'{embedding_name_every}.pt')
+ save_embedding(embedding, optimizer, checkpoint, embedding_name_every, last_saved_file, remove_cached_checksum=True)
+ embedding_yet_to_be_embedded = True
+
+ write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, steps_per_epoch, {
+ "loss": f"{loss_step:.7f}",
+ "learn_rate": scheduler.learn_rate
+ })
+
+ if images_dir is not None and steps_done % create_image_every == 0:
+ forced_filename = f'{embedding_name}-{steps_done}'
+ last_saved_image = os.path.join(images_dir, forced_filename)
+
+ shared.sd_model.first_stage_model.to(devices.device)
+
+ p = processing.StableDiffusionProcessingTxt2Img(
+ sd_model=shared.sd_model,
+ do_not_save_grid=True,
+ do_not_save_samples=True,
+ do_not_reload_embeddings=True,
+ )
+
+ if preview_from_txt2img:
+ p.prompt = preview_prompt
+ p.negative_prompt = preview_negative_prompt
+ p.steps = preview_steps
+ p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
+ p.cfg_scale = preview_cfg_scale
+ p.seed = preview_seed
+ p.width = preview_width
+ p.height = preview_height
+ else:
+ p.prompt = batch.cond_text[0]
+ p.steps = 20
+ p.width = training_width
+ p.height = training_height
+
+ preview_text = p.prompt
+
+ processed = processing.process_images(p)
+ image = processed.images[0] if len(processed.images) > 0 else None
+
+ if unload:
+ shared.sd_model.first_stage_model.to(devices.cpu)
+
+ if image is not None:
+ shared.state.assign_current_image(image)
+
+ last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
+ last_saved_image += f", prompt: {preview_text}"
+
+ if shared.opts.training_enable_tensorboard and shared.opts.training_tensorboard_save_images:
+ tensorboard_add_image(tensorboard_writer, f"Validation at epoch {epoch_num}", image, embedding.step)
+
+ if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
+
+ last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')
+
+ info = PngImagePlugin.PngInfo()
+ data = torch.load(last_saved_file)
+ info.add_text("sd-ti-embedding", embedding_to_b64(data))
+
+ title = "<{}>".format(data.get('name', '???'))
+
+ try:
+ vectorSize = list(data['string_to_param'].values())[0].shape[0]
+ except Exception as e:
+ vectorSize = '?'
+
+ checkpoint = sd_models.select_checkpoint()
+ footer_left = checkpoint.model_name
+ footer_mid = '[{}]'.format(checkpoint.shorthash)
+ footer_right = '{}v {}s'.format(vectorSize, steps_done)
+
+ captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
+ captioned_image = insert_image_data_embed(captioned_image, data)
+
+ captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
+ embedding_yet_to_be_embedded = False
+
+ last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
+ last_saved_image += f", prompt: {preview_text}"
+
+ shared.state.job_no = embedding.step
+
+ shared.state.textinfo = f"""
+
+Loss: {loss_step:.7f}
+Step: {steps_done}
+Last prompt: {html.escape(batch.cond_text[0])}
+Last saved embedding: {html.escape(last_saved_file)}
+Last saved image: {html.escape(last_saved_image)}
+
+"""
+ filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
+ save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, remove_cached_checksum=True)
+ except Exception:
+ print(traceback.format_exc(), file=sys.stderr)
+ pass
+ finally:
+ pbar.leave = False
+ pbar.close()
+ shared.sd_model.first_stage_model.to(devices.device)
+ shared.parallel_processing_allowed = old_parallel_processing_allowed
+ sd_hijack_checkpoint.remove()
+
+ return embedding, filename
+
+
+def save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, remove_cached_checksum=True):
+ old_embedding_name = embedding.name
+ old_sd_checkpoint = embedding.sd_checkpoint if hasattr(embedding, "sd_checkpoint") else None
+ old_sd_checkpoint_name = embedding.sd_checkpoint_name if hasattr(embedding, "sd_checkpoint_name") else None
+ old_cached_checksum = embedding.cached_checksum if hasattr(embedding, "cached_checksum") else None
+ try:
+ embedding.sd_checkpoint = checkpoint.shorthash
+ embedding.sd_checkpoint_name = checkpoint.model_name
+ if remove_cached_checksum:
+ embedding.cached_checksum = None
+ embedding.name = embedding_name
+ embedding.optimizer_state_dict = optimizer.state_dict()
+ embedding.save(filename)
+ except:
+ embedding.sd_checkpoint = old_sd_checkpoint
+ embedding.sd_checkpoint_name = old_sd_checkpoint_name
+ embedding.name = old_embedding_name
+ embedding.cached_checksum = old_cached_checksum
+ raise
diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
new file mode 100644
index 0000000000000000000000000000000000000000..35c4feeff455b6bd2b699dd72b27f852932c78c2
--- /dev/null
+++ b/modules/textual_inversion/ui.py
@@ -0,0 +1,45 @@
+import html
+
+import gradio as gr
+
+import modules.textual_inversion.textual_inversion
+import modules.textual_inversion.preprocess
+from modules import sd_hijack, shared
+
+
+def create_embedding(name, initialization_text, nvpt, overwrite_old):
+ filename = modules.textual_inversion.textual_inversion.create_embedding(name, nvpt, overwrite_old, init_text=initialization_text)
+
+ sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
+
+ return gr.Dropdown.update(choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())), f"Created: {filename}", ""
+
+
+def preprocess(*args):
+ modules.textual_inversion.preprocess.preprocess(*args)
+
+ return f"Preprocessing {'interrupted' if shared.state.interrupted else 'finished'}.", ""
+
+
+def train_embedding(*args):
+
+ assert not shared.cmd_opts.lowvram, 'Training models with lowvram not possible'
+
+ apply_optimizations = shared.opts.training_xattention_optimizations
+ try:
+ if not apply_optimizations:
+ sd_hijack.undo_optimizations()
+
+ embedding, filename = modules.textual_inversion.textual_inversion.train_embedding(*args)
+
+ res = f"""
+Training {'interrupted' if shared.state.interrupted else 'finished'} at {embedding.step} steps.
+Embedding saved to {html.escape(filename)}
+"""
+ return res, ""
+ except Exception:
+ raise
+ finally:
+ if not apply_optimizations:
+ sd_hijack.apply_optimizations()
+
diff --git a/modules/timer.py b/modules/timer.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba92be336cdb21eef875b8b82cb30b788ceac32a
--- /dev/null
+++ b/modules/timer.py
@@ -0,0 +1,38 @@
+import time
+
+
+class Timer:
+ def __init__(self):
+ self.start = time.time()
+ self.records = {}
+ self.total = 0
+
+ def elapsed(self):
+ end = time.time()
+ res = end - self.start
+ self.start = end
+ return res
+
+ def record(self, category, extra_time=0):
+ e = self.elapsed()
+ if category not in self.records:
+ self.records[category] = 0
+
+ self.records[category] += e + extra_time
+ self.total += e + extra_time
+
+ def summary(self):
+ res = f"{self.total:.1f}s"
+
+ additions = [x for x in self.records.items() if x[1] >= 0.1]
+ if not additions:
+ return res
+
+ res += " ("
+ res += ", ".join([f"{category}: {time_taken:.1f}s" for category, time_taken in additions])
+ res += ")"
+
+ return res
+
+ def reset(self):
+ self.__init__()
diff --git a/modules/txt2img.py b/modules/txt2img.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5981c57a4031323d263c7deb0a71ab5fccd0b1a
--- /dev/null
+++ b/modules/txt2img.py
@@ -0,0 +1,108 @@
+import modules.scripts
+from modules import sd_samplers
+from modules.generation_parameters_copypaste import create_override_settings_dict
+from modules.processing import (
+ StableDiffusionProcessing,
+ Processed,
+ StableDiffusionProcessingTxt2Img,
+ StableDiffusionProcessingImg2Img,
+ process_images,
+)
+from modules.shared import opts, cmd_opts
+import modules.shared as shared
+import modules.processing as processing
+from modules.ui import plaintext_to_html
+
+
+def txt2img(
+ id_task: str,
+ prompt: str,
+ negative_prompt: str,
+ prompt_styles,
+ steps: int,
+ sampler_index: int,
+ restore_faces: bool,
+ tiling: bool,
+ n_iter: int,
+ batch_size: int,
+ cfg_scale: float,
+ seed: int,
+ subseed: int,
+ subseed_strength: float,
+ seed_resize_from_h: int,
+ seed_resize_from_w: int,
+ seed_enable_extras: bool,
+ height: int,
+ width: int,
+ enable_hr: bool,
+ denoising_strength: float,
+ hr_scale: float,
+ hr_upscaler: str,
+ hr_second_pass_steps: int,
+ hr_resize_x: int,
+ hr_resize_y: int,
+ override_settings_texts,
+ *args,
+):
+ override_settings = create_override_settings_dict(override_settings_texts)
+
+ p = StableDiffusionProcessingTxt2Img(
+ sd_model=shared.sd_model,
+ outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
+ outpath_grids=opts.outdir_grids or opts.outdir_txt2img_grids,
+ prompt=prompt,
+ styles=prompt_styles,
+ negative_prompt=negative_prompt,
+ seed=seed,
+ subseed=subseed,
+ subseed_strength=subseed_strength,
+ seed_resize_from_h=seed_resize_from_h,
+ seed_resize_from_w=seed_resize_from_w,
+ seed_enable_extras=seed_enable_extras,
+ sampler_name=sd_samplers.samplers[sampler_index].name,
+ batch_size=batch_size,
+ n_iter=n_iter,
+ steps=steps,
+ cfg_scale=cfg_scale,
+ width=width,
+ height=height,
+ restore_faces=restore_faces,
+ tiling=tiling,
+ enable_hr=enable_hr,
+ denoising_strength=denoising_strength if enable_hr else None,
+ hr_scale=hr_scale,
+ hr_upscaler=hr_upscaler,
+ hr_second_pass_steps=hr_second_pass_steps,
+ hr_resize_x=hr_resize_x,
+ hr_resize_y=hr_resize_y,
+ override_settings=override_settings,
+ )
+
+ p.scripts = modules.scripts.scripts_txt2img
+ p.script_args = args
+
+ if cmd_opts.enable_console_prompts:
+ print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
+
+ processed = modules.scripts.scripts_txt2img.run(p, *args)
+
+ if processed is None:
+ processed = process_images(p)
+
+ p.close()
+
+ shared.total_tqdm.clear()
+
+ generation_info_js = processed.js()
+ if opts.samples_log_stdout:
+ print(generation_info_js)
+
+ if opts.do_not_show_images:
+ processed.images = []
+
+ return (
+ processed.images,
+ generation_info_js,
+ plaintext_to_html(processed.info),
+ plaintext_to_html(processed.comments),
+ )
diff --git a/modules/ui.py b/modules/ui.py
new file mode 100644
index 0000000000000000000000000000000000000000..627fbe0b5f3aec0a0f3c0c98f15f08270abc7ea8
--- /dev/null
+++ b/modules/ui.py
@@ -0,0 +1,1844 @@
+import html
+import json
+import math
+import mimetypes
+import os
+import platform
+import random
+import sys
+import tempfile
+import time
+import traceback
+from functools import partial, reduce
+import warnings
+
+import gradio as gr
+import gradio.routes
+import gradio.utils
+import numpy as np
+from PIL import Image, PngImagePlugin
+from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
+
+from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, postprocessing, ui_components, ui_common, ui_postprocessing
+from modules.ui_components import FormRow, FormColumn, FormGroup, ToolButton, FormHTML
+from modules.paths import script_path, data_path
+
+from modules.shared import opts, cmd_opts, restricted_opts
+
+import modules.codeformer_model
+import modules.generation_parameters_copypaste as parameters_copypaste
+import modules.gfpgan_model
+import modules.hypernetworks.ui
+import modules.scripts
+import modules.shared as shared
+import modules.styles
+import modules.textual_inversion.ui
+from modules import prompt_parser
+from modules.images import save_image
+from modules.sd_hijack import model_hijack
+from modules.sd_samplers import samplers, samplers_for_img2img
+from modules.textual_inversion import textual_inversion
+import modules.hypernetworks.ui
+from modules.generation_parameters_copypaste import image_from_url_text
+import modules.extras
+
+warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning)
+
+# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
+mimetypes.init()
+mimetypes.add_type('application/javascript', '.js')
+
+if not cmd_opts.share and not cmd_opts.listen:
+ # fix gradio phoning home
+ gradio.utils.version_check = lambda: None
+ gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
+
+if cmd_opts.ngrok is not None:
+ import modules.ngrok as ngrok
+ print('ngrok authtoken detected, trying to connect...')
+ ngrok.connect(
+ cmd_opts.ngrok,
+ cmd_opts.port if cmd_opts.port is not None else 7860,
+ cmd_opts.ngrok_region
+ )
+
+
+def gr_show(visible=True):
+ return {"visible": visible, "__type__": "update"}
+
+
+sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
+sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
+
+# Using constants for these since the variation selector isn't visible.
+# Important that they exactly match script.js for tooltip to work.
+random_symbol = '\U0001f3b2\ufe0f' # 🎲️
+reuse_symbol = '\u267b\ufe0f' # ♻️
+paste_symbol = '\u2199\ufe0f' # ↙
+refresh_symbol = '\U0001f504' # 🔄
+save_style_symbol = '\U0001f4be' # 💾
+apply_style_symbol = '\U0001f4cb' # 📋
+clear_prompt_symbol = '\U0001f5d1\ufe0f' # 🗑️
+extra_networks_symbol = '\U0001F3B4' # 🎴
+switch_values_symbol = '\U000021C5' # ⇅
+
+
+def plaintext_to_html(text):
+ return ui_common.plaintext_to_html(text)
+
+
+def send_gradio_gallery_to_image(x):
+ if len(x) == 0:
+ return None
+ return image_from_url_text(x[0])
+
+def visit(x, func, path=""):
+ if hasattr(x, 'children'):
+ for c in x.children:
+ visit(c, func, path)
+ elif x.label is not None:
+ func(path + "/" + str(x.label), x)
+
+
+def add_style(name: str, prompt: str, negative_prompt: str):
+ if name is None:
+ return [gr_show() for x in range(4)]
+
+ style = modules.styles.PromptStyle(name, prompt, negative_prompt)
+ shared.prompt_styles.styles[style.name] = style
+ # Save all loaded prompt styles: this allows us to update the storage format in the future more easily, because we
+ # reserialize all styles every time we save them
+ shared.prompt_styles.save_styles(shared.styles_filename)
+
+ return [gr.Dropdown.update(visible=True, choices=list(shared.prompt_styles.styles)) for _ in range(2)]
+
+
+def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y):
+ from modules import processing, devices
+
+ if not enable:
+ return ""
+
+ p = processing.StableDiffusionProcessingTxt2Img(width=width, height=height, enable_hr=True, hr_scale=hr_scale, hr_resize_x=hr_resize_x, hr_resize_y=hr_resize_y)
+
+ with devices.autocast():
+ p.init([""], [0], [0])
+
+ return f"resize: from {p.width}x{p.height} to {p.hr_resize_x or p.hr_upscale_to_x}x{p.hr_resize_y or p.hr_upscale_to_y}"
+
+
+def apply_styles(prompt, prompt_neg, styles):
+ prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, styles)
+ prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, styles)
+
+ return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value=[])]
+
+
+def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_dir, *ii_singles):
+ if mode in {0, 1, 3, 4}:
+ return [interrogation_function(ii_singles[mode]), None]
+ elif mode == 2:
+ return [interrogation_function(ii_singles[mode]["image"]), None]
+ elif mode == 5:
+ assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
+ images = shared.listfiles(ii_input_dir)
+ print(f"Will process {len(images)} images.")
+ if ii_output_dir != "":
+ os.makedirs(ii_output_dir, exist_ok=True)
+ else:
+ ii_output_dir = ii_input_dir
+
+ for image in images:
+ img = Image.open(image)
+ filename = os.path.basename(image)
+ left, _ = os.path.splitext(filename)
+ print(interrogation_function(img), file=open(os.path.join(ii_output_dir, left + ".txt"), 'a'))
+
+ return [gr.update(), None]
+
+
+def interrogate(image):
+ prompt = shared.interrogator.interrogate(image.convert("RGB"))
+ return gr.update() if prompt is None else prompt
+
+
+def interrogate_deepbooru(image):
+ prompt = deepbooru.model.tag(image)
+ return gr.update() if prompt is None else prompt
+
+
+def create_seed_inputs(target_interface):
+ with FormRow(elem_id=target_interface + '_seed_row', variant="compact"):
+ seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=target_interface + '_seed')
+ seed.style(container=False)
+ random_seed = ToolButton(random_symbol, elem_id=target_interface + '_random_seed')
+ reuse_seed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_seed')
+
+ seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False)
+
+ # Components to show/hide based on the 'Extra' checkbox
+ seed_extras = []
+
+ with FormRow(visible=False, elem_id=target_interface + '_subseed_row') as seed_extra_row_1:
+ seed_extras.append(seed_extra_row_1)
+ subseed = gr.Number(label='Variation seed', value=-1, elem_id=target_interface + '_subseed')
+ subseed.style(container=False)
+ random_subseed = ToolButton(random_symbol, elem_id=target_interface + '_random_subseed')
+ reuse_subseed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_subseed')
+ subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=target_interface + '_subseed_strength')
+
+ with FormRow(visible=False) as seed_extra_row_2:
+ seed_extras.append(seed_extra_row_2)
+ seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=target_interface + '_seed_resize_from_w')
+ seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=target_interface + '_seed_resize_from_h')
+
+ random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed])
+ random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed])
+
+ def change_visibility(show):
+ return {comp: gr_show(show) for comp in seed_extras}
+
+ seed_checkbox.change(change_visibility, show_progress=False, inputs=[seed_checkbox], outputs=seed_extras)
+
+ return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
+
+
+
+def connect_clear_prompt(button):
+ """Given clear button, prompt, and token_counter objects, setup clear prompt button click event"""
+ button.click(
+ _js="clear_prompt",
+ fn=None,
+ inputs=[],
+ outputs=[],
+ )
+
+
+def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed):
+ """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
+ (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
+ was 0, i.e. no variation seed was used, it copies the normal seed value instead."""
+ def copy_seed(gen_info_string: str, index):
+ res = -1
+
+ try:
+ gen_info = json.loads(gen_info_string)
+ index -= gen_info.get('index_of_first_image', 0)
+
+ if is_subseed and gen_info.get('subseed_strength', 0) > 0:
+ all_subseeds = gen_info.get('all_subseeds', [-1])
+ res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
+ else:
+ all_seeds = gen_info.get('all_seeds', [-1])
+ res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
+
+ except json.decoder.JSONDecodeError as e:
+ if gen_info_string != '':
+ print("Error parsing JSON generation info:", file=sys.stderr)
+ print(gen_info_string, file=sys.stderr)
+
+ return [res, gr_show(False)]
+
+ reuse_seed.click(
+ fn=copy_seed,
+ _js="(x, y) => [x, selected_gallery_index()]",
+ show_progress=False,
+ inputs=[generation_info, dummy_component],
+ outputs=[seed, dummy_component]
+ )
+
+
+def update_token_counter(text, steps):
+ try:
+ text, _ = extra_networks.parse_prompt(text)
+
+ _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
+ prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)
+
+ except Exception:
+ # a parsing error can happen here during typing, and we don't want to bother the user with
+ # messages related to it in console
+ prompt_schedules = [[[steps, text]]]
+
+ flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
+ prompts = [prompt_text for step, prompt_text in flat_prompts]
+ token_count, max_length = max([model_hijack.get_prompt_lengths(prompt) for prompt in prompts], key=lambda args: args[0])
+ return f"{token_count}/{max_length}"
+
+
+def create_toprow(is_img2img):
+ id_part = "img2img" if is_img2img else "txt2img"
+
+ with gr.Row(elem_id=f"{id_part}_toprow", variant="compact"):
+ with gr.Column(elem_id=f"{id_part}_prompt_container", scale=6):
+ with gr.Row():
+ with gr.Column(scale=80):
+ with gr.Row():
+ prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)")
+
+ with gr.Row():
+ with gr.Column(scale=80):
+ with gr.Row():
+ negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)")
+
+ button_interrogate = None
+ button_deepbooru = None
+ if is_img2img:
+ with gr.Column(scale=1, elem_classes="interrogate-col"):
+ button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
+ button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
+
+ with gr.Column(scale=1, elem_id=f"{id_part}_actions_column"):
+ with gr.Row(elem_id=f"{id_part}_generate_box", elem_classes="generate-box"):
+ interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt", elem_classes="generate-box-interrupt")
+ skip = gr.Button('Skip', elem_id=f"{id_part}_skip", elem_classes="generate-box-skip")
+ submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
+
+ skip.click(
+ fn=lambda: shared.state.skip(),
+ inputs=[],
+ outputs=[],
+ )
+
+ interrupt.click(
+ fn=lambda: shared.state.interrupt(),
+ inputs=[],
+ outputs=[],
+ )
+
+ with gr.Row(elem_id=f"{id_part}_tools"):
+ paste = ToolButton(value=paste_symbol, elem_id="paste")
+ clear_prompt_button = ToolButton(value=clear_prompt_symbol, elem_id=f"{id_part}_clear_prompt")
+ extra_networks_button = ToolButton(value=extra_networks_symbol, elem_id=f"{id_part}_extra_networks")
+ prompt_style_apply = ToolButton(value=apply_style_symbol, elem_id=f"{id_part}_style_apply")
+ save_style = ToolButton(value=save_style_symbol, elem_id=f"{id_part}_style_create")
+
+ token_counter = gr.HTML(value="0/75", elem_id=f"{id_part}_token_counter", elem_classes=["token-counter"])
+ token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
+ negative_token_counter = gr.HTML(value="0/75", elem_id=f"{id_part}_negative_token_counter", elem_classes=["token-counter"])
+ negative_token_button = gr.Button(visible=False, elem_id=f"{id_part}_negative_token_button")
+
+ clear_prompt_button.click(
+ fn=lambda *x: x,
+ _js="confirm_clear_prompt",
+ inputs=[prompt, negative_prompt],
+ outputs=[prompt, negative_prompt],
+ )
+
+ with gr.Row(elem_id=f"{id_part}_styles_row"):
+ prompt_styles = gr.Dropdown(label="Styles", elem_id=f"{id_part}_styles", choices=[k for k, v in shared.prompt_styles.styles.items()], value=[], multiselect=True)
+ create_refresh_button(prompt_styles, shared.prompt_styles.reload, lambda: {"choices": [k for k, v in shared.prompt_styles.styles.items()]}, f"refresh_{id_part}_styles")
+
+ return prompt, prompt_styles, negative_prompt, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button
+
+
+def setup_progressbar(*args, **kwargs):
+ pass
+
+
+def apply_setting(key, value):
+ if value is None:
+ return gr.update()
+
+ if shared.cmd_opts.freeze_settings:
+ return gr.update()
+
+ # dont allow model to be swapped when model hash exists in prompt
+ if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
+ return gr.update()
+
+ if key == "sd_model_checkpoint":
+ ckpt_info = sd_models.get_closet_checkpoint_match(value)
+
+ if ckpt_info is not None:
+ value = ckpt_info.title
+ else:
+ return gr.update()
+
+ comp_args = opts.data_labels[key].component_args
+ if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
+ return
+
+ valtype = type(opts.data_labels[key].default)
+ oldval = opts.data.get(key, None)
+ opts.data[key] = valtype(value) if valtype != type(None) else value
+ if oldval != value and opts.data_labels[key].onchange is not None:
+ opts.data_labels[key].onchange()
+
+ opts.save(shared.config_filename)
+ return getattr(opts, key)
+
+
+def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
+ def refresh():
+ refresh_method()
+ args = refreshed_args() if callable(refreshed_args) else refreshed_args
+
+ for k, v in args.items():
+ setattr(refresh_component, k, v)
+
+ return gr.update(**(args or {}))
+
+ refresh_button = ToolButton(value=refresh_symbol, elem_id=elem_id)
+ refresh_button.click(
+ fn=refresh,
+ inputs=[],
+ outputs=[refresh_component]
+ )
+ return refresh_button
+
+
+def create_output_panel(tabname, outdir):
+ return ui_common.create_output_panel(tabname, outdir)
+
+
+def create_sampler_and_steps_selection(choices, tabname):
+ if opts.samplers_in_dropdown:
+ with FormRow(elem_id=f"sampler_selection_{tabname}"):
+ sampler_index = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
+ steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
+ else:
+ with FormGroup(elem_id=f"sampler_selection_{tabname}"):
+ steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
+ sampler_index = gr.Radio(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
+
+ return steps, sampler_index
+
+
+def ordered_ui_categories():
+ user_order = {x.strip(): i * 2 + 1 for i, x in enumerate(shared.opts.ui_reorder.split(","))}
+
+ for i, category in sorted(enumerate(shared.ui_reorder_categories), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)):
+ yield category
+
+
+def get_value_for_setting(key):
+ value = getattr(opts, key)
+
+ info = opts.data_labels[key]
+ args = info.component_args() if callable(info.component_args) else info.component_args or {}
+ args = {k: v for k, v in args.items() if k not in {'precision'}}
+
+ return gr.update(value=value, **args)
+
+
+def create_override_settings_dropdown(tabname, row):
+ dropdown = gr.Dropdown([], label="Override settings", visible=False, elem_id=f"{tabname}_override_settings", multiselect=True)
+
+ dropdown.change(
+ fn=lambda x: gr.Dropdown.update(visible=len(x) > 0),
+ inputs=[dropdown],
+ outputs=[dropdown],
+ )
+
+ return dropdown
+
+
+def create_ui():
+ import modules.img2img
+ import modules.txt2img
+
+ reload_javascript()
+
+ parameters_copypaste.reset()
+
+ modules.scripts.scripts_current = modules.scripts.scripts_txt2img
+ modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
+
+ with gr.Blocks(analytics_enabled=False) as txt2img_interface:
+ txt2img_prompt, txt2img_prompt_styles, txt2img_negative_prompt, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button = create_toprow(is_img2img=False)
+
+ dummy_component = gr.Label(visible=False)
+ txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="binary", visible=False)
+
+ with FormRow(variant='compact', elem_id="txt2img_extra_networks", visible=False) as extra_networks:
+ from modules import ui_extra_networks
+ extra_networks_ui = ui_extra_networks.create_ui(extra_networks, extra_networks_button, 'txt2img')
+
+ with gr.Row().style(equal_height=False):
+ with gr.Column(variant='compact', elem_id="txt2img_settings"):
+ for category in ordered_ui_categories():
+ if category == "sampler":
+ steps, sampler_index = create_sampler_and_steps_selection(samplers, "txt2img")
+
+ elif category == "dimensions":
+ with FormRow():
+ with gr.Column(elem_id="txt2img_column_size", scale=4):
+ width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
+ height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
+
+ 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")
+
+ if opts.dimensions_and_batch_together:
+ with gr.Column(elem_id="txt2img_column_batch"):
+ batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
+ batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
+
+ elif category == "cfg":
+ cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale")
+
+ elif category == "seed":
+ seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('txt2img')
+
+ elif category == "checkboxes":
+ with FormRow(elem_classes="checkboxes-row", variant="compact"):
+ restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces")
+ tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling")
+ enable_hr = gr.Checkbox(label='Hires. fix', value=False, elem_id="txt2img_enable_hr")
+ hr_final_resolution = FormHTML(value="", elem_id="txtimg_hr_finalres", label="Upscaled resolution", interactive=False)
+
+ elif category == "hires_fix":
+ with FormGroup(visible=False, elem_id="txt2img_hires_fix") as hr_options:
+ with FormRow(elem_id="txt2img_hires_fix_row1", variant="compact"):
+ hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode)
+ hr_second_pass_steps = gr.Slider(minimum=0, maximum=150, step=1, label='Hires steps', value=0, elem_id="txt2img_hires_steps")
+ denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")
+
+ with FormRow(elem_id="txt2img_hires_fix_row2", variant="compact"):
+ hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale")
+ hr_resize_x = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize width to", value=0, elem_id="txt2img_hr_resize_x")
+ hr_resize_y = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize height to", value=0, elem_id="txt2img_hr_resize_y")
+
+ elif category == "batch":
+ if not opts.dimensions_and_batch_together:
+ with FormRow(elem_id="txt2img_column_batch"):
+ batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
+ batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
+
+ elif category == "override_settings":
+ with FormRow(elem_id="txt2img_override_settings_row") as row:
+ override_settings = create_override_settings_dropdown('txt2img', row)
+
+ elif category == "scripts":
+ with FormGroup(elem_id="txt2img_script_container"):
+ custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
+
+ hr_resolution_preview_inputs = [enable_hr, width, height, hr_scale, hr_resize_x, hr_resize_y]
+ for input in hr_resolution_preview_inputs:
+ input.change(
+ fn=calc_resolution_hires,
+ inputs=hr_resolution_preview_inputs,
+ outputs=[hr_final_resolution],
+ show_progress=False,
+ )
+ input.change(
+ None,
+ _js="onCalcResolutionHires",
+ inputs=hr_resolution_preview_inputs,
+ outputs=[],
+ show_progress=False,
+ )
+
+ txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)
+
+ connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
+ connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
+
+ txt2img_args = dict(
+ fn=wrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs=[None, '', '']),
+ _js="submit",
+ inputs=[
+ dummy_component,
+ txt2img_prompt,
+ txt2img_negative_prompt,
+ txt2img_prompt_styles,
+ steps,
+ sampler_index,
+ restore_faces,
+ tiling,
+ batch_count,
+ batch_size,
+ cfg_scale,
+ seed,
+ subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
+ height,
+ width,
+ enable_hr,
+ denoising_strength,
+ hr_scale,
+ hr_upscaler,
+ hr_second_pass_steps,
+ hr_resize_x,
+ hr_resize_y,
+ override_settings,
+ ] + custom_inputs,
+
+ outputs=[
+ txt2img_gallery,
+ generation_info,
+ html_info,
+ html_log,
+ ],
+ show_progress=False,
+ )
+
+ txt2img_prompt.submit(**txt2img_args)
+ submit.click(**txt2img_args)
+
+ res_switch_btn.click(lambda w, h: (h, w), inputs=[width, height], outputs=[width, height], show_progress=False)
+
+ txt_prompt_img.change(
+ fn=modules.images.image_data,
+ inputs=[
+ txt_prompt_img
+ ],
+ outputs=[
+ txt2img_prompt,
+ txt_prompt_img
+ ]
+ )
+
+ enable_hr.change(
+ fn=lambda x: gr_show(x),
+ inputs=[enable_hr],
+ outputs=[hr_options],
+ show_progress = False,
+ )
+
+ txt2img_paste_fields = [
+ (txt2img_prompt, "Prompt"),
+ (txt2img_negative_prompt, "Negative prompt"),
+ (steps, "Steps"),
+ (sampler_index, "Sampler"),
+ (restore_faces, "Face restoration"),
+ (cfg_scale, "CFG scale"),
+ (seed, "Seed"),
+ (width, "Size-1"),
+ (height, "Size-2"),
+ (batch_size, "Batch size"),
+ (subseed, "Variation seed"),
+ (subseed_strength, "Variation seed strength"),
+ (seed_resize_from_w, "Seed resize from-1"),
+ (seed_resize_from_h, "Seed resize from-2"),
+ (denoising_strength, "Denoising strength"),
+ (enable_hr, lambda d: "Denoising strength" in d),
+ (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
+ (hr_scale, "Hires upscale"),
+ (hr_upscaler, "Hires upscaler"),
+ (hr_second_pass_steps, "Hires steps"),
+ (hr_resize_x, "Hires resize-1"),
+ (hr_resize_y, "Hires resize-2"),
+ *modules.scripts.scripts_txt2img.infotext_fields
+ ]
+ parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields, override_settings)
+ parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+ paste_button=txt2img_paste, tabname="txt2img", source_text_component=txt2img_prompt, source_image_component=None,
+ ))
+
+ txt2img_preview_params = [
+ txt2img_prompt,
+ txt2img_negative_prompt,
+ steps,
+ sampler_index,
+ cfg_scale,
+ seed,
+ width,
+ height,
+ ]
+
+ token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_prompt, steps], outputs=[token_counter])
+ negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_negative_prompt, steps], outputs=[negative_token_counter])
+
+ ui_extra_networks.setup_ui(extra_networks_ui, txt2img_gallery)
+
+ modules.scripts.scripts_current = modules.scripts.scripts_img2img
+ modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)
+
+ with gr.Blocks(analytics_enabled=False) as img2img_interface:
+ img2img_prompt, img2img_prompt_styles, img2img_negative_prompt, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button = create_toprow(is_img2img=True)
+
+ img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="binary", visible=False)
+
+ with FormRow(variant='compact', elem_id="img2img_extra_networks", visible=False) as extra_networks:
+ from modules import ui_extra_networks
+ extra_networks_ui_img2img = ui_extra_networks.create_ui(extra_networks, extra_networks_button, 'img2img')
+
+ with FormRow().style(equal_height=False):
+ with gr.Column(variant='compact', elem_id="img2img_settings"):
+ copy_image_buttons = []
+ copy_image_destinations = {}
+
+ def add_copy_image_controls(tab_name, elem):
+ with gr.Row(variant="compact", elem_id=f"img2img_copy_to_{tab_name}"):
+ gr.HTML("Copy image to: ", elem_id=f"img2img_label_copy_to_{tab_name}")
+
+ for title, name in zip(['img2img', 'sketch', 'inpaint', 'inpaint sketch'], ['img2img', 'sketch', 'inpaint', 'inpaint_sketch']):
+ if name == tab_name:
+ gr.Button(title, interactive=False)
+ copy_image_destinations[name] = elem
+ continue
+
+ button = gr.Button(title)
+ copy_image_buttons.append((button, name, elem))
+
+ with gr.Tabs(elem_id="mode_img2img"):
+ with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img:
+ init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA").style(height=480)
+ add_copy_image_controls('img2img', init_img)
+
+ with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch:
+ sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=480)
+ add_copy_image_controls('sketch', sketch)
+
+ with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab") as tab_inpaint:
+ init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=480)
+ add_copy_image_controls('inpaint', init_img_with_mask)
+
+ with gr.TabItem('Inpaint sketch', id='inpaint_sketch', elem_id="img2img_inpaint_sketch_tab") as tab_inpaint_color:
+ inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=480)
+ inpaint_color_sketch_orig = gr.State(None)
+ add_copy_image_controls('inpaint_sketch', inpaint_color_sketch)
+
+ def update_orig(image, state):
+ if image is not None:
+ same_size = state is not None and state.size == image.size
+ has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
+ edited = same_size and has_exact_match
+ return image if not edited or state is None else state
+
+ inpaint_color_sketch.change(update_orig, [inpaint_color_sketch, inpaint_color_sketch_orig], inpaint_color_sketch_orig)
+
+ with gr.TabItem('Inpaint upload', id='inpaint_upload', elem_id="img2img_inpaint_upload_tab") as tab_inpaint_upload:
+ init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", elem_id="img_inpaint_base")
+ init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", elem_id="img_inpaint_mask")
+
+ with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch:
+ hidden = '
Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
+ gr.HTML(
+ f"Process images in a directory on the same machine where the server is running." +
+ f"
Use an empty output directory to save pictures normally instead of writing to the output directory." +
+ f"
Add inpaint batch mask directory to enable inpaint batch processing."
+ f"{hidden}
"
+ )
+ img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
+ img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
+ img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
+
+ def copy_image(img):
+ if isinstance(img, dict) and 'image' in img:
+ return img['image']
+
+ return img
+
+ for button, name, elem in copy_image_buttons:
+ button.click(
+ fn=copy_image,
+ inputs=[elem],
+ outputs=[copy_image_destinations[name]],
+ )
+ button.click(
+ fn=lambda: None,
+ _js="switch_to_"+name.replace(" ", "_"),
+ inputs=[],
+ outputs=[],
+ )
+
+ with FormRow():
+ resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
+
+ for category in ordered_ui_categories():
+ if category == "sampler":
+ steps, sampler_index = create_sampler_and_steps_selection(samplers_for_img2img, "img2img")
+
+ elif category == "dimensions":
+ with FormRow():
+ with gr.Column(elem_id="img2img_column_size", scale=4):
+ width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
+ height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
+
+ 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")
+
+ if opts.dimensions_and_batch_together:
+ with gr.Column(elem_id="img2img_column_batch"):
+ batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
+ batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
+
+ elif category == "cfg":
+ with FormGroup():
+ with FormRow():
+ cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
+ image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit")
+ denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
+
+ elif category == "seed":
+ seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img')
+
+ elif category == "checkboxes":
+ with FormRow(elem_classes="checkboxes-row", variant="compact"):
+ restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
+ tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")
+
+ elif category == "batch":
+ if not opts.dimensions_and_batch_together:
+ with FormRow(elem_id="img2img_column_batch"):
+ batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
+ batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
+
+ elif category == "override_settings":
+ with FormRow(elem_id="img2img_override_settings_row") as row:
+ override_settings = create_override_settings_dropdown('img2img', row)
+
+ elif category == "scripts":
+ with FormGroup(elem_id="img2img_script_container"):
+ custom_inputs = modules.scripts.scripts_img2img.setup_ui()
+
+ elif category == "inpaint":
+ with FormGroup(elem_id="inpaint_controls", visible=False) as inpaint_controls:
+ with FormRow():
+ mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur")
+ mask_alpha = gr.Slider(label="Mask transparency", visible=False, elem_id="img2img_mask_alpha")
+
+ with FormRow():
+ inpainting_mask_invert = gr.Radio(label='Mask mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode")
+
+ with FormRow():
+ inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill")
+
+ with FormRow():
+ with gr.Column():
+ inpaint_full_res = gr.Radio(label="Inpaint area", choices=["Whole picture", "Only masked"], type="index", value="Whole picture", elem_id="img2img_inpaint_full_res")
+
+ with gr.Column(scale=4):
+ inpaint_full_res_padding = gr.Slider(label='Only masked padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")
+
+ def select_img2img_tab(tab):
+ return gr.update(visible=tab in [2, 3, 4]), gr.update(visible=tab == 3),
+
+ for i, elem in enumerate([tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]):
+ elem.select(
+ fn=lambda tab=i: select_img2img_tab(tab),
+ inputs=[],
+ outputs=[inpaint_controls, mask_alpha],
+ )
+
+ img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
+
+ connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
+ connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
+
+ img2img_prompt_img.change(
+ fn=modules.images.image_data,
+ inputs=[
+ img2img_prompt_img
+ ],
+ outputs=[
+ img2img_prompt,
+ img2img_prompt_img
+ ]
+ )
+
+ img2img_args = dict(
+ fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
+ _js="submit_img2img",
+ inputs=[
+ dummy_component,
+ dummy_component,
+ img2img_prompt,
+ img2img_negative_prompt,
+ img2img_prompt_styles,
+ init_img,
+ sketch,
+ init_img_with_mask,
+ inpaint_color_sketch,
+ inpaint_color_sketch_orig,
+ init_img_inpaint,
+ init_mask_inpaint,
+ steps,
+ sampler_index,
+ mask_blur,
+ mask_alpha,
+ inpainting_fill,
+ restore_faces,
+ tiling,
+ batch_count,
+ batch_size,
+ cfg_scale,
+ image_cfg_scale,
+ denoising_strength,
+ seed,
+ subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
+ height,
+ width,
+ resize_mode,
+ inpaint_full_res,
+ inpaint_full_res_padding,
+ inpainting_mask_invert,
+ img2img_batch_input_dir,
+ img2img_batch_output_dir,
+ img2img_batch_inpaint_mask_dir,
+ override_settings,
+ ] + custom_inputs,
+ outputs=[
+ img2img_gallery,
+ generation_info,
+ html_info,
+ html_log,
+ ],
+ show_progress=False,
+ )
+
+ interrogate_args = dict(
+ _js="get_img2img_tab_index",
+ inputs=[
+ dummy_component,
+ img2img_batch_input_dir,
+ img2img_batch_output_dir,
+ init_img,
+ sketch,
+ init_img_with_mask,
+ inpaint_color_sketch,
+ init_img_inpaint,
+ ],
+ outputs=[img2img_prompt, dummy_component],
+ )
+
+ img2img_prompt.submit(**img2img_args)
+ submit.click(**img2img_args)
+ res_switch_btn.click(lambda w, h: (h, w), inputs=[width, height], outputs=[width, height], show_progress=False)
+
+ img2img_interrogate.click(
+ fn=lambda *args: process_interrogate(interrogate, *args),
+ **interrogate_args,
+ )
+
+ img2img_deepbooru.click(
+ fn=lambda *args: process_interrogate(interrogate_deepbooru, *args),
+ **interrogate_args,
+ )
+
+ prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
+ style_dropdowns = [txt2img_prompt_styles, img2img_prompt_styles]
+ style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"]
+
+ for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts):
+ button.click(
+ fn=add_style,
+ _js="ask_for_style_name",
+ # Have to pass empty dummy component here, because the JavaScript and Python function have to accept
+ # the same number of parameters, but we only know the style-name after the JavaScript prompt
+ inputs=[dummy_component, prompt, negative_prompt],
+ outputs=[txt2img_prompt_styles, img2img_prompt_styles],
+ )
+
+ for button, (prompt, negative_prompt), styles, js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs):
+ button.click(
+ fn=apply_styles,
+ _js=js_func,
+ inputs=[prompt, negative_prompt, styles],
+ outputs=[prompt, negative_prompt, styles],
+ )
+
+ token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
+ negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[img2img_negative_prompt, steps], outputs=[negative_token_counter])
+
+ ui_extra_networks.setup_ui(extra_networks_ui_img2img, img2img_gallery)
+
+ img2img_paste_fields = [
+ (img2img_prompt, "Prompt"),
+ (img2img_negative_prompt, "Negative prompt"),
+ (steps, "Steps"),
+ (sampler_index, "Sampler"),
+ (restore_faces, "Face restoration"),
+ (cfg_scale, "CFG scale"),
+ (image_cfg_scale, "Image CFG scale"),
+ (seed, "Seed"),
+ (width, "Size-1"),
+ (height, "Size-2"),
+ (batch_size, "Batch size"),
+ (subseed, "Variation seed"),
+ (subseed_strength, "Variation seed strength"),
+ (seed_resize_from_w, "Seed resize from-1"),
+ (seed_resize_from_h, "Seed resize from-2"),
+ (denoising_strength, "Denoising strength"),
+ (mask_blur, "Mask blur"),
+ *modules.scripts.scripts_img2img.infotext_fields
+ ]
+ parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields, override_settings)
+ parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields, override_settings)
+ parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+ paste_button=img2img_paste, tabname="img2img", source_text_component=img2img_prompt, source_image_component=None,
+ ))
+
+ modules.scripts.scripts_current = None
+
+ with gr.Blocks(analytics_enabled=False) as extras_interface:
+ ui_postprocessing.create_ui()
+
+ with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
+ with gr.Row().style(equal_height=False):
+ with gr.Column(variant='panel'):
+ image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil")
+
+ with gr.Column(variant='panel'):
+ html = gr.HTML()
+ generation_info = gr.Textbox(visible=False, elem_id="pnginfo_generation_info")
+ html2 = gr.HTML()
+ with gr.Row():
+ buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
+
+ for tabname, button in buttons.items():
+ parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+ paste_button=button, tabname=tabname, source_text_component=generation_info, source_image_component=image,
+ ))
+
+ image.change(
+ fn=wrap_gradio_call(modules.extras.run_pnginfo),
+ inputs=[image],
+ outputs=[html, generation_info, html2],
+ )
+
+ def update_interp_description(value):
+ interp_description_css = "{}
"
+ interp_descriptions = {
+ "No interpolation": interp_description_css.format("No interpolation will be used. Requires one model; A. Allows for format conversion and VAE baking."),
+ "Weighted sum": interp_description_css.format("A weighted sum will be used for interpolation. Requires two models; A and B. The result is calculated as A * (1 - M) + B * M"),
+ "Add difference": interp_description_css.format("The difference between the last two models will be added to the first. Requires three models; A, B and C. The result is calculated as A + (B - C) * M")
+ }
+ return interp_descriptions[value]
+
+ with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
+ with gr.Row().style(equal_height=False):
+ with gr.Column(variant='compact'):
+ interp_description = gr.HTML(value=update_interp_description("Weighted sum"), elem_id="modelmerger_interp_description")
+
+ with FormRow(elem_id="modelmerger_models"):
+ primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
+ create_refresh_button(primary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_A")
+
+ secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
+ create_refresh_button(secondary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_B")
+
+ tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
+ create_refresh_button(tertiary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_C")
+
+ custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name")
+ interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount")
+ interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method")
+ interp_method.change(fn=update_interp_description, inputs=[interp_method], outputs=[interp_description])
+
+ with FormRow():
+ checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="ckpt", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
+ save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half")
+
+ with FormRow():
+ with gr.Column():
+ config_source = gr.Radio(choices=["A, B or C", "B", "C", "Don't"], value="A, B or C", label="Copy config from", type="index", elem_id="modelmerger_config_method")
+
+ with gr.Column():
+ with FormRow():
+ bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE", elem_id="modelmerger_bake_in_vae")
+ create_refresh_button(bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "modelmerger_refresh_bake_in_vae")
+
+ with FormRow():
+ discard_weights = gr.Textbox(value="", label="Discard weights with matching name", elem_id="modelmerger_discard_weights")
+
+ with gr.Row():
+ modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary')
+
+ with gr.Column(variant='compact', elem_id="modelmerger_results_container"):
+ with gr.Group(elem_id="modelmerger_results_panel"):
+ modelmerger_result = gr.HTML(elem_id="modelmerger_result", show_label=False)
+
+ with gr.Blocks(analytics_enabled=False) as train_interface:
+ with gr.Row().style(equal_height=False):
+ gr.HTML(value="See wiki for detailed explanation.
")
+
+ with gr.Row(variant="compact").style(equal_height=False):
+ with gr.Tabs(elem_id="train_tabs"):
+
+ with gr.Tab(label="Create embedding"):
+ new_embedding_name = gr.Textbox(label="Name", elem_id="train_new_embedding_name")
+ initialization_text = gr.Textbox(label="Initialization text", value="*", elem_id="train_initialization_text")
+ nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1, elem_id="train_nvpt")
+ overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding", elem_id="train_overwrite_old_embedding")
+
+ with gr.Row():
+ with gr.Column(scale=3):
+ gr.HTML(value="")
+
+ with gr.Column():
+ create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding")
+
+ with gr.Tab(label="Create hypernetwork"):
+ new_hypernetwork_name = gr.Textbox(label="Name", elem_id="train_new_hypernetwork_name")
+ new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"], elem_id="train_new_hypernetwork_sizes")
+ new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'", elem_id="train_new_hypernetwork_layer_structure")
+ new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys, elem_id="train_new_hypernetwork_activation_func")
+ new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"], elem_id="train_new_hypernetwork_initialization_option")
+ new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization", elem_id="train_new_hypernetwork_add_layer_norm")
+ new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout", elem_id="train_new_hypernetwork_use_dropout")
+ new_hypernetwork_dropout_structure = gr.Textbox("0, 0, 0", label="Enter hypernetwork Dropout structure (or empty). Recommended : 0~0.35 incrementing sequence: 0, 0.05, 0.15", placeholder="1st and last digit must be 0 and values should be between 0 and 1. ex:'0, 0.01, 0'")
+ overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork", elem_id="train_overwrite_old_hypernetwork")
+
+ with gr.Row():
+ with gr.Column(scale=3):
+ gr.HTML(value="")
+
+ with gr.Column():
+ create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork")
+
+ with gr.Tab(label="Preprocess images"):
+ process_src = gr.Textbox(label='Source directory', elem_id="train_process_src")
+ process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst")
+ process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width")
+ process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_process_height")
+ preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action")
+
+ with gr.Row():
+ process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip")
+ process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split")
+ process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop")
+ process_multicrop = gr.Checkbox(label='Auto-sized crop', elem_id="train_process_multicrop")
+ process_caption = gr.Checkbox(label='Use BLIP for caption', elem_id="train_process_caption")
+ process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True, elem_id="train_process_caption_deepbooru")
+
+ with gr.Row(visible=False) as process_split_extra_row:
+ process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_split_threshold")
+ process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="train_process_overlap_ratio")
+
+ with gr.Row(visible=False) as process_focal_crop_row:
+ process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_face_weight")
+ process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight")
+ process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight")
+ process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
+
+ with gr.Column(visible=False) as process_multicrop_col:
+ gr.Markdown('Each image is center-cropped with an automatically chosen width and height.')
+ with gr.Row():
+ process_multicrop_mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id="train_process_multicrop_mindim")
+ process_multicrop_maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="train_process_multicrop_maxdim")
+ with gr.Row():
+ process_multicrop_minarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area lower bound", value=64*64, elem_id="train_process_multicrop_minarea")
+ process_multicrop_maxarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area upper bound", value=640*640, elem_id="train_process_multicrop_maxarea")
+ with gr.Row():
+ process_multicrop_objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="train_process_multicrop_objective")
+ process_multicrop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="train_process_multicrop_threshold")
+
+ with gr.Row():
+ with gr.Column(scale=3):
+ gr.HTML(value="")
+
+ with gr.Column():
+ with gr.Row():
+ interrupt_preprocessing = gr.Button("Interrupt", elem_id="train_interrupt_preprocessing")
+ run_preprocess = gr.Button(value="Preprocess", variant='primary', elem_id="train_run_preprocess")
+
+ process_split.change(
+ fn=lambda show: gr_show(show),
+ inputs=[process_split],
+ outputs=[process_split_extra_row],
+ )
+
+ process_focal_crop.change(
+ fn=lambda show: gr_show(show),
+ inputs=[process_focal_crop],
+ outputs=[process_focal_crop_row],
+ )
+
+ process_multicrop.change(
+ fn=lambda show: gr_show(show),
+ inputs=[process_multicrop],
+ outputs=[process_multicrop_col],
+ )
+
+ def get_textual_inversion_template_names():
+ return sorted([x for x in textual_inversion.textual_inversion_templates])
+
+ with gr.Tab(label="Train"):
+ gr.HTML(value="Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images [wiki]
")
+ with FormRow():
+ train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
+ create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
+
+ train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()])
+ create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")
+
+ with FormRow():
+ embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate")
+ hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001", elem_id="train_hypernetwork_learn_rate")
+
+ with FormRow():
+ clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
+ clip_grad_value = gr.Textbox(placeholder="Gradient clip value", value="0.1", show_label=False)
+
+ with FormRow():
+ batch_size = gr.Number(label='Batch size', value=1, precision=0, elem_id="train_batch_size")
+ gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0, elem_id="train_gradient_step")
+
+ dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images", elem_id="train_dataset_directory")
+ log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion", elem_id="train_log_directory")
+
+ with FormRow():
+ template_file = gr.Dropdown(label='Prompt template', value="style_filewords.txt", elem_id="train_template_file", choices=get_textual_inversion_template_names())
+ create_refresh_button(template_file, textual_inversion.list_textual_inversion_templates, lambda: {"choices": get_textual_inversion_template_names()}, "refrsh_train_template_file")
+
+ training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width")
+ training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height")
+ varsize = gr.Checkbox(label="Do not resize images", value=False, elem_id="train_varsize")
+ steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps")
+
+ with FormRow():
+ create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every")
+ save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every")
+
+ use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False, elem_id="use_weight")
+
+ save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True, elem_id="train_save_image_with_stored_embedding")
+ preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False, elem_id="train_preview_from_txt2img")
+
+ shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False, elem_id="train_shuffle_tags")
+ tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0, elem_id="train_tag_drop_out")
+
+ latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'], elem_id="train_latent_sampling_method")
+
+ with gr.Row():
+ train_embedding = gr.Button(value="Train Embedding", variant='primary', elem_id="train_train_embedding")
+ interrupt_training = gr.Button(value="Interrupt", elem_id="train_interrupt_training")
+ train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary', elem_id="train_train_hypernetwork")
+
+ params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
+
+ script_callbacks.ui_train_tabs_callback(params)
+
+ with gr.Column(elem_id='ti_gallery_container'):
+ ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
+ ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
+ ti_progress = gr.HTML(elem_id="ti_progress", value="")
+ ti_outcome = gr.HTML(elem_id="ti_error", value="")
+
+ create_embedding.click(
+ fn=modules.textual_inversion.ui.create_embedding,
+ inputs=[
+ new_embedding_name,
+ initialization_text,
+ nvpt,
+ overwrite_old_embedding,
+ ],
+ outputs=[
+ train_embedding_name,
+ ti_output,
+ ti_outcome,
+ ]
+ )
+
+ create_hypernetwork.click(
+ fn=modules.hypernetworks.ui.create_hypernetwork,
+ inputs=[
+ new_hypernetwork_name,
+ new_hypernetwork_sizes,
+ overwrite_old_hypernetwork,
+ new_hypernetwork_layer_structure,
+ new_hypernetwork_activation_func,
+ new_hypernetwork_initialization_option,
+ new_hypernetwork_add_layer_norm,
+ new_hypernetwork_use_dropout,
+ new_hypernetwork_dropout_structure
+ ],
+ outputs=[
+ train_hypernetwork_name,
+ ti_output,
+ ti_outcome,
+ ]
+ )
+
+ run_preprocess.click(
+ fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
+ _js="start_training_textual_inversion",
+ inputs=[
+ dummy_component,
+ process_src,
+ process_dst,
+ process_width,
+ process_height,
+ preprocess_txt_action,
+ process_flip,
+ process_split,
+ process_caption,
+ process_caption_deepbooru,
+ process_split_threshold,
+ process_overlap_ratio,
+ process_focal_crop,
+ process_focal_crop_face_weight,
+ process_focal_crop_entropy_weight,
+ process_focal_crop_edges_weight,
+ process_focal_crop_debug,
+ process_multicrop,
+ process_multicrop_mindim,
+ process_multicrop_maxdim,
+ process_multicrop_minarea,
+ process_multicrop_maxarea,
+ process_multicrop_objective,
+ process_multicrop_threshold,
+ ],
+ outputs=[
+ ti_output,
+ ti_outcome,
+ ],
+ )
+
+ train_embedding.click(
+ fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
+ _js="start_training_textual_inversion",
+ inputs=[
+ dummy_component,
+ train_embedding_name,
+ embedding_learn_rate,
+ batch_size,
+ gradient_step,
+ dataset_directory,
+ log_directory,
+ training_width,
+ training_height,
+ varsize,
+ steps,
+ clip_grad_mode,
+ clip_grad_value,
+ shuffle_tags,
+ tag_drop_out,
+ latent_sampling_method,
+ use_weight,
+ create_image_every,
+ save_embedding_every,
+ template_file,
+ save_image_with_stored_embedding,
+ preview_from_txt2img,
+ *txt2img_preview_params,
+ ],
+ outputs=[
+ ti_output,
+ ti_outcome,
+ ]
+ )
+
+ train_hypernetwork.click(
+ fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),
+ _js="start_training_textual_inversion",
+ inputs=[
+ dummy_component,
+ train_hypernetwork_name,
+ hypernetwork_learn_rate,
+ batch_size,
+ gradient_step,
+ dataset_directory,
+ log_directory,
+ training_width,
+ training_height,
+ varsize,
+ steps,
+ clip_grad_mode,
+ clip_grad_value,
+ shuffle_tags,
+ tag_drop_out,
+ latent_sampling_method,
+ use_weight,
+ create_image_every,
+ save_embedding_every,
+ template_file,
+ preview_from_txt2img,
+ *txt2img_preview_params,
+ ],
+ outputs=[
+ ti_output,
+ ti_outcome,
+ ]
+ )
+
+ interrupt_training.click(
+ fn=lambda: shared.state.interrupt(),
+ inputs=[],
+ outputs=[],
+ )
+
+ interrupt_preprocessing.click(
+ fn=lambda: shared.state.interrupt(),
+ inputs=[],
+ outputs=[],
+ )
+
+ def create_setting_component(key, is_quicksettings=False):
+ def fun():
+ return opts.data[key] if key in opts.data else opts.data_labels[key].default
+
+ info = opts.data_labels[key]
+ t = type(info.default)
+
+ args = info.component_args() if callable(info.component_args) else info.component_args
+
+ if info.component is not None:
+ comp = info.component
+ elif t == str:
+ comp = gr.Textbox
+ elif t == int:
+ comp = gr.Number
+ elif t == bool:
+ comp = gr.Checkbox
+ else:
+ raise Exception(f'bad options item type: {str(t)} for key {key}')
+
+ elem_id = "setting_"+key
+
+ if info.refresh is not None:
+ if is_quicksettings:
+ res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+ create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
+ else:
+ with FormRow():
+ res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+ create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
+ else:
+ res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+
+ return res
+
+ components = []
+ component_dict = {}
+ shared.settings_components = component_dict
+
+ script_callbacks.ui_settings_callback()
+ opts.reorder()
+
+ def run_settings(*args):
+ changed = []
+
+ for key, value, comp in zip(opts.data_labels.keys(), args, components):
+ assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
+
+ for key, value, comp in zip(opts.data_labels.keys(), args, components):
+ if comp == dummy_component:
+ continue
+
+ if opts.set(key, value):
+ changed.append(key)
+
+ try:
+ opts.save(shared.config_filename)
+ except RuntimeError:
+ return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'
+ return opts.dumpjson(), f'{len(changed)} settings changed{": " if len(changed) > 0 else ""}{", ".join(changed)}.'
+
+ def run_settings_single(value, key):
+ if not opts.same_type(value, opts.data_labels[key].default):
+ return gr.update(visible=True), opts.dumpjson()
+
+ if not opts.set(key, value):
+ return gr.update(value=getattr(opts, key)), opts.dumpjson()
+
+ opts.save(shared.config_filename)
+
+ return get_value_for_setting(key), opts.dumpjson()
+
+ with gr.Blocks(analytics_enabled=False) as settings_interface:
+ with gr.Row():
+ with gr.Column(scale=6):
+ settings_submit = gr.Button(value="Apply settings", variant='primary', elem_id="settings_submit")
+ with gr.Column():
+ restart_gradio = gr.Button(value='Reload UI', variant='primary', elem_id="settings_restart_gradio")
+
+ result = gr.HTML(elem_id="settings_result")
+
+ quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")]
+ quicksettings_names = {x: i for i, x in enumerate(quicksettings_names) if x != 'quicksettings'}
+
+ quicksettings_list = []
+
+ previous_section = None
+ current_tab = None
+ current_row = None
+ with gr.Tabs(elem_id="settings"):
+ for i, (k, item) in enumerate(opts.data_labels.items()):
+ section_must_be_skipped = item.section[0] is None
+
+ if previous_section != item.section and not section_must_be_skipped:
+ elem_id, text = item.section
+
+ if current_tab is not None:
+ current_row.__exit__()
+ current_tab.__exit__()
+
+ gr.Group()
+ current_tab = gr.TabItem(elem_id="settings_{}".format(elem_id), label=text)
+ current_tab.__enter__()
+ current_row = gr.Column(variant='compact')
+ current_row.__enter__()
+
+ previous_section = item.section
+
+ if k in quicksettings_names and not shared.cmd_opts.freeze_settings:
+ quicksettings_list.append((i, k, item))
+ components.append(dummy_component)
+ elif section_must_be_skipped:
+ components.append(dummy_component)
+ else:
+ component = create_setting_component(k)
+ component_dict[k] = component
+ components.append(component)
+
+ if current_tab is not None:
+ current_row.__exit__()
+ current_tab.__exit__()
+
+ with gr.TabItem("Actions"):
+ request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
+ download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
+ reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
+ with gr.Row():
+ unload_sd_model = gr.Button(value='Unload SD checkpoint to free VRAM', elem_id="sett_unload_sd_model")
+ reload_sd_model = gr.Button(value='Reload the last SD checkpoint back into VRAM', elem_id="sett_reload_sd_model")
+
+ with gr.TabItem("Licenses"):
+ gr.HTML(shared.html("licenses.html"), elem_id="licenses")
+
+ gr.Button(value="Show all pages", elem_id="settings_show_all_pages")
+
+
+ def unload_sd_weights():
+ modules.sd_models.unload_model_weights()
+
+ def reload_sd_weights():
+ modules.sd_models.reload_model_weights()
+
+ unload_sd_model.click(
+ fn=unload_sd_weights,
+ inputs=[],
+ outputs=[]
+ )
+
+ reload_sd_model.click(
+ fn=reload_sd_weights,
+ inputs=[],
+ outputs=[]
+ )
+
+ request_notifications.click(
+ fn=lambda: None,
+ inputs=[],
+ outputs=[],
+ _js='function(){}'
+ )
+
+ download_localization.click(
+ fn=lambda: None,
+ inputs=[],
+ outputs=[],
+ _js='download_localization'
+ )
+
+ def reload_scripts():
+ modules.scripts.reload_script_body_only()
+ reload_javascript() # need to refresh the html page
+
+ reload_script_bodies.click(
+ fn=reload_scripts,
+ inputs=[],
+ outputs=[]
+ )
+
+ def request_restart():
+ shared.state.interrupt()
+ shared.state.need_restart = True
+
+ restart_gradio.click(
+ fn=request_restart,
+ _js='restart_reload',
+ inputs=[],
+ outputs=[],
+ )
+
+ interfaces = [
+ (txt2img_interface, "txt2img", "txt2img"),
+ (img2img_interface, "img2img", "img2img"),
+ (extras_interface, "Extras", "extras"),
+ (pnginfo_interface, "PNG Info", "pnginfo"),
+ (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
+ (train_interface, "Train", "ti"),
+ ]
+
+ 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(analytics_enabled=False, title="Stable Diffusion") as demo:
+ with gr.Row(elem_id="quicksettings", variant="compact"):
+ for i, k, item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])):
+ component = create_setting_component(k, is_quicksettings=True)
+ component_dict[k] = component
+
+ parameters_copypaste.connect_paste_params_buttons()
+
+ with gr.Tabs(elem_id="tabs") as tabs:
+ for interface, label, ifid in interfaces:
+ if label in shared.opts.hidden_tabs:
+ continue
+ with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
+ interface.render()
+
+ if os.path.exists(os.path.join(script_path, "notification.mp3")):
+ audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
+
+ footer = shared.html("footer.html")
+ footer = footer.format(versions=versions_html())
+ gr.HTML(footer, elem_id="footer")
+
+ text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
+ settings_submit.click(
+ fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),
+ inputs=components,
+ outputs=[text_settings, result],
+ )
+
+ for i, k, item in quicksettings_list:
+ component = component_dict[k]
+ info = opts.data_labels[k]
+
+ component.change(
+ fn=lambda value, k=k: run_settings_single(value, key=k),
+ inputs=[component],
+ outputs=[component, text_settings],
+ show_progress=info.refresh is not None,
+ )
+
+ text_settings.change(
+ fn=lambda: gr.update(visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit"),
+ inputs=[],
+ outputs=[image_cfg_scale],
+ )
+
+ button_set_checkpoint = gr.Button('Change checkpoint', elem_id='change_checkpoint', visible=False)
+ button_set_checkpoint.click(
+ fn=lambda value, _: run_settings_single(value, key='sd_model_checkpoint'),
+ _js="function(v){ var res = desiredCheckpointName; desiredCheckpointName = ''; return [res || v, null]; }",
+ inputs=[component_dict['sd_model_checkpoint'], dummy_component],
+ outputs=[component_dict['sd_model_checkpoint'], text_settings],
+ )
+
+ component_keys = [k for k in opts.data_labels.keys() if k in component_dict]
+
+ def get_settings_values():
+ return [get_value_for_setting(key) for key in component_keys]
+
+ demo.load(
+ fn=get_settings_values,
+ inputs=[],
+ outputs=[component_dict[k] for k in component_keys],
+ queue=False,
+ )
+
+ def modelmerger(*args):
+ try:
+ results = modules.extras.run_modelmerger(*args)
+ except Exception as e:
+ print("Error loading/saving model file:", file=sys.stderr)
+ print(traceback.format_exc(), file=sys.stderr)
+ modules.sd_models.list_models() # to remove the potentially missing models from the list
+ return [*[gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(4)], f"Error merging checkpoints: {e}"]
+ return results
+
+ modelmerger_merge.click(fn=lambda: '', inputs=[], outputs=[modelmerger_result])
+ modelmerger_merge.click(
+ fn=wrap_gradio_gpu_call(modelmerger, extra_outputs=lambda: [gr.update() for _ in range(4)]),
+ _js='modelmerger',
+ inputs=[
+ dummy_component,
+ primary_model_name,
+ secondary_model_name,
+ tertiary_model_name,
+ interp_method,
+ interp_amount,
+ save_as_half,
+ custom_name,
+ checkpoint_format,
+ config_source,
+ bake_in_vae,
+ discard_weights,
+ ],
+ outputs=[
+ primary_model_name,
+ secondary_model_name,
+ tertiary_model_name,
+ component_dict['sd_model_checkpoint'],
+ modelmerger_result,
+ ]
+ )
+
+ ui_config_file = cmd_opts.ui_config_file
+ ui_settings = {}
+ settings_count = len(ui_settings)
+ error_loading = False
+
+ try:
+ if os.path.exists(ui_config_file):
+ with open(ui_config_file, "r", encoding="utf8") as file:
+ ui_settings = json.load(file)
+ except Exception:
+ error_loading = True
+ print("Error loading settings:", file=sys.stderr)
+ print(traceback.format_exc(), file=sys.stderr)
+
+ def loadsave(path, x):
+ def apply_field(obj, field, condition=None, init_field=None):
+ key = path + "/" + field
+
+ if getattr(obj, 'custom_script_source', None) is not None:
+ key = 'customscript/' + obj.custom_script_source + '/' + key
+
+ if getattr(obj, 'do_not_save_to_config', False):
+ return
+
+ saved_value = ui_settings.get(key, None)
+ if saved_value is None:
+ ui_settings[key] = getattr(obj, field)
+ elif condition and not condition(saved_value):
+ pass
+
+ # this warning is generally not useful;
+ # print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
+ else:
+ setattr(obj, field, saved_value)
+ if init_field is not None:
+ init_field(saved_value)
+
+ if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown] and x.visible:
+ apply_field(x, 'visible')
+
+ if type(x) == gr.Slider:
+ apply_field(x, 'value')
+ apply_field(x, 'minimum')
+ apply_field(x, 'maximum')
+ apply_field(x, 'step')
+
+ if type(x) == gr.Radio:
+ apply_field(x, 'value', lambda val: val in x.choices)
+
+ if type(x) == gr.Checkbox:
+ apply_field(x, 'value')
+
+ if type(x) == gr.Textbox:
+ apply_field(x, 'value')
+
+ if type(x) == gr.Number:
+ apply_field(x, 'value')
+
+ if type(x) == gr.Dropdown:
+ def check_dropdown(val):
+ if getattr(x, 'multiselect', False):
+ return all([value in x.choices for value in val])
+ else:
+ return val in x.choices
+
+ apply_field(x, 'value', check_dropdown, getattr(x, 'init_field', None))
+
+ visit(txt2img_interface, loadsave, "txt2img")
+ visit(img2img_interface, loadsave, "img2img")
+ visit(extras_interface, loadsave, "extras")
+ visit(modelmerger_interface, loadsave, "modelmerger")
+ visit(train_interface, loadsave, "train")
+
+ if not error_loading and (not os.path.exists(ui_config_file) or settings_count != len(ui_settings)):
+ with open(ui_config_file, "w", encoding="utf8") as file:
+ json.dump(ui_settings, file, indent=4)
+
+ # Required as a workaround for change() event not triggering when loading values from ui-config.json
+ interp_description.value = update_interp_description(interp_method.value)
+
+ return demo
+
+
+def webpath(fn):
+ if fn.startswith(script_path):
+ web_path = os.path.relpath(fn, script_path).replace('\\', '/')
+ else:
+ web_path = os.path.abspath(fn)
+
+ return f'file={web_path}?{os.path.getmtime(fn)}'
+
+
+def javascript_html():
+ script_js = os.path.join(script_path, "script.js")
+ head = f'\n'
+
+ inline = f"{localization.localization_js(shared.opts.localization)};"
+ if cmd_opts.theme is not None:
+ inline += f"set_theme('{cmd_opts.theme}');"
+
+ for script in modules.scripts.list_scripts("javascript", ".js"):
+ head += f'\n'
+
+ for script in modules.scripts.list_scripts("javascript", ".mjs"):
+ head += f'\n'
+
+ head += f'\n'
+
+ return head
+
+
+def css_html():
+ head = ""
+
+ def stylesheet(fn):
+ return f''
+
+ for cssfile in modules.scripts.list_files_with_name("style.css"):
+ if not os.path.isfile(cssfile):
+ continue
+
+ head += stylesheet(cssfile)
+
+ if os.path.exists(os.path.join(data_path, "user.css")):
+ head += stylesheet(os.path.join(data_path, "user.css"))
+
+ return head
+
+
+def reload_javascript():
+ js = javascript_html()
+ css = css_html()
+
+ def template_response(*args, **kwargs):
+ res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
+ res.body = res.body.replace(b'', f'{js}'.encode("utf8"))
+ res.body = res.body.replace(b'