From ac7ecd2d847bf4e3a9503db0f2a291e32b82302c Mon Sep 17 00:00:00 2001
From: Tim Patton <38817597+pattontim@users.noreply.github.com>
Date: Sat, 19 Nov 2022 14:49:22 -0500
Subject: [PATCH 1/6] Label and load SD .safetensors model files
---
README.md | 1 +
modules/modelloader.py | 1 +
modules/sd_models.py | 24 ++++++++++++++++--------
requirements.txt | 1 +
4 files changed, 19 insertions(+), 8 deletions(-)
diff --git a/README.md b/README.md
index 33508f311..ba9f3952f 100644
--- a/README.md
+++ b/README.md
@@ -84,6 +84,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
- API
- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML.
- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
+- Can use safetensors to safely load model files without python pickle
## Where are Aesthetic Gradients?!?!
Aesthetic Gradients are now an extension. You can install it using git:
diff --git a/modules/modelloader.py b/modules/modelloader.py
index e4a6f8acb..7d2f0ade9 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -82,6 +82,7 @@ def cleanup_models():
src_path = models_path
dest_path = os.path.join(models_path, "Stable-diffusion")
move_files(src_path, dest_path, ".ckpt")
+ move_files(src_path, dest_path, ".safetensors")
src_path = os.path.join(root_path, "ESRGAN")
dest_path = os.path.join(models_path, "ESRGAN")
move_files(src_path, dest_path)
diff --git a/modules/sd_models.py b/modules/sd_models.py
index c59151e03..4ccdf30b4 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -4,6 +4,7 @@ import sys
import gc
from collections import namedtuple
import torch
+from safetensors.torch import load_file
import re
from omegaconf import OmegaConf
@@ -16,9 +17,10 @@ from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inp
model_dir = "Stable-diffusion"
model_path = os.path.abspath(os.path.join(models_path, model_dir))
-CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config'])
+CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config', 'exttype'])
checkpoints_list = {}
checkpoints_loaded = collections.OrderedDict()
+checkpoint_types = {'.ckpt':'pickle','.safetensors':'safetensors'}
try:
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
@@ -45,7 +47,7 @@ def checkpoint_tiles():
def list_models():
checkpoints_list.clear()
- model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt"])
+ model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt",".safetensors"])
def modeltitle(path, shorthash):
abspath = os.path.abspath(path)
@@ -60,15 +62,15 @@ def list_models():
if name.startswith("\\") or name.startswith("/"):
name = name[1:]
- shortname = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
+ shortname, ext = os.path.splitext(name.replace("/", "_").replace("\\", "_"))
- return f'{name} [{shorthash}]', shortname
+ return f'{name} [{checkpoint_types[ext]}] [{shorthash}]', shortname
cmd_ckpt = shared.cmd_opts.ckpt
if os.path.exists(cmd_ckpt):
h = model_hash(cmd_ckpt)
title, short_model_name = modeltitle(cmd_ckpt, h)
- checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name, shared.cmd_opts.config)
+ checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name, shared.cmd_opts.config, '')
shared.opts.data['sd_model_checkpoint'] = title
elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
@@ -76,12 +78,12 @@ def list_models():
h = model_hash(filename)
title, short_model_name = modeltitle(filename, h)
- basename, _ = os.path.splitext(filename)
+ basename, ext = os.path.splitext(filename)
config = basename + ".yaml"
if not os.path.exists(config):
config = shared.cmd_opts.config
- checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name, config)
+ checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name, config, ext)
def get_closet_checkpoint_match(searchString):
@@ -173,7 +175,13 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
# load from file
print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
- pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
+ if(checkpoint_types[checkpoint_info.exttype] == 'safetensors'):
+ # safely load weights
+ # TODO: safetensors supports zero copy fast load to gpu, see issue #684
+ pl_sd = load_file(checkpoint_file, device=shared.weight_load_location)
+ else:
+ pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
+
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
diff --git a/requirements.txt b/requirements.txt
index 762db4f34..f7de9f707 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -28,3 +28,4 @@ kornia
lark
inflection
GitPython
+safetensors
From 637815632f9f362c9959e53139d37e88ea9ace6f Mon Sep 17 00:00:00 2001
From: Tim Patton <38817597+pattontim@users.noreply.github.com>
Date: Sun, 20 Nov 2022 13:36:05 -0500
Subject: [PATCH 2/6] Generalize SD torch load/save to implement safetensor
merging compat
---
modules/extras.py | 15 +-
modules/sd_models.py | 25 +-
modules/ui.py | 3626 +++++++++++++++++++++---------------------
3 files changed, 1840 insertions(+), 1826 deletions(-)
diff --git a/modules/extras.py b/modules/extras.py
index 71b93a068..820427de4 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -249,7 +249,7 @@ def run_pnginfo(image):
return '', geninfo, info
-def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, custom_name):
+def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, save_as_safetensors, custom_name):
def weighted_sum(theta0, theta1, alpha):
return ((1 - alpha) * theta0) + (alpha * theta1)
@@ -264,16 +264,16 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
teritary_model_info = sd_models.checkpoints_list.get(teritary_model_name, None)
print(f"Loading {primary_model_info.filename}...")
- primary_model = torch.load(primary_model_info.filename, map_location='cpu')
+ primary_model = sd_models.torch_load(primary_model_info.filename, primary_model_info, map_override='cpu')
theta_0 = sd_models.get_state_dict_from_checkpoint(primary_model)
print(f"Loading {secondary_model_info.filename}...")
- secondary_model = torch.load(secondary_model_info.filename, map_location='cpu')
+ secondary_model = sd_models.torch_load(secondary_model_info.filename, primary_model_info, map_override='cpu')
theta_1 = sd_models.get_state_dict_from_checkpoint(secondary_model)
if teritary_model_info is not None:
print(f"Loading {teritary_model_info.filename}...")
- teritary_model = torch.load(teritary_model_info.filename, map_location='cpu')
+ teritary_model = sd_models.torch_load(teritary_model_info.filename, teritary_model_info, map_override='cpu')
theta_2 = sd_models.get_state_dict_from_checkpoint(teritary_model)
else:
teritary_model = None
@@ -314,12 +314,13 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
- filename = primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt'
- filename = filename if custom_name == '' else (custom_name + '.ckpt')
+ output_exttype = '.safetensors' if save_as_safetensors else '.ckpt'
+ filename = primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + interp_method.replace(" ", "_") + '-merged' + output_exttype
+ filename = filename if custom_name == '' else (custom_name + output_exttype)
output_modelname = os.path.join(ckpt_dir, filename)
print(f"Saving to {output_modelname}...")
- torch.save(primary_model, output_modelname)
+ sd_models.torch_save(primary_model, output_modelname)
sd_models.list_models()
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 4ccdf30b4..2f8c2c48e 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -4,7 +4,7 @@ import sys
import gc
from collections import namedtuple
import torch
-from safetensors.torch import load_file
+from safetensors.torch import load_file, save_file
import re
from omegaconf import OmegaConf
@@ -143,6 +143,22 @@ def transform_checkpoint_dict_key(k):
return k
+def torch_load(model_filename, model_info, map_override=None):
+ map_override=shared.weight_load_location if not map_override else map_override
+ if(checkpoint_types[model_info.exttype] == 'safetensors'):
+ # safely load weights
+ # TODO: safetensors supports zero copy fast load to gpu, see issue #684
+ return load_file(model_filename, device=map_override)
+ else:
+ return torch.load(model_filename, map_location=map_override)
+
+def torch_save(model, output_filename):
+ basename, exttype = os.path.splitext(output_filename)
+ if(checkpoint_types[exttype] == 'safetensors'):
+ # [===== >] Reticulating brines...
+ save_file(model, output_filename, metadata={"format": "pt"})
+ else:
+ torch.save(model, output_filename)
def get_state_dict_from_checkpoint(pl_sd):
if "state_dict" in pl_sd:
@@ -175,12 +191,7 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
# load from file
print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
- if(checkpoint_types[checkpoint_info.exttype] == 'safetensors'):
- # safely load weights
- # TODO: safetensors supports zero copy fast load to gpu, see issue #684
- pl_sd = load_file(checkpoint_file, device=shared.weight_load_location)
- else:
- pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
+ pl_sd = torch_load(checkpoint_file, checkpoint_info)
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
diff --git a/modules/ui.py b/modules/ui.py
index a5953fce5..a2b06aae7 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1,1812 +1,1814 @@
-import html
-import json
-import math
-import mimetypes
-import os
-import platform
-import random
-import subprocess as sp
-import sys
-import tempfile
-import time
-import traceback
-from functools import partial, reduce
-
-import gradio as gr
-import gradio.routes
-import gradio.utils
-import numpy as np
-from PIL import Image, PngImagePlugin
-
-
-from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions
-from modules.paths import script_path
-
-from modules.shared import opts, cmd_opts, restricted_opts
-
-if cmd_opts.deepdanbooru:
- from modules.deepbooru import get_deepbooru_tags
-
-import modules.codeformer_model
-import modules.generation_parameters_copypaste as parameters_copypaste
-import modules.gfpgan_model
-import modules.hypernetworks.ui
-import modules.ldsr_model
-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
-import modules.textual_inversion.ui
-import modules.hypernetworks.ui
-from modules.generation_parameters_copypaste import image_from_url_text
-
-# 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 != 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 != None else 7860, cmd_opts.ngrok_region)
-
-
-def gr_show(visible=True):
- return {"visible": visible, "__type__": "update"}
-
-
-sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
-sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
-
-css_hide_progressbar = """
-.wrap .m-12 svg { display:none!important; }
-.wrap .m-12::before { content:"Loading..." }
-.wrap .z-20 svg { display:none!important; }
-.wrap .z-20::before { content:"Loading..." }
-.progress-bar { display:none!important; }
-.meta-text { display:none!important; }
-.meta-text-center { display:none!important; }
-"""
-
-# Using constants for these since the variation selector isn't visible.
-# Important that they exactly match script.js for tooltip to work.
-random_symbol = '\U0001f3b2\ufe0f' # 🎲️
-reuse_symbol = '\u267b\ufe0f' # ♻️
-art_symbol = '\U0001f3a8' # 🎨
-paste_symbol = '\u2199\ufe0f' # ↙
-folder_symbol = '\U0001f4c2' # 📂
-refresh_symbol = '\U0001f504' # 🔄
-save_style_symbol = '\U0001f4be' # 💾
-apply_style_symbol = '\U0001f4cb' # 📋
-
-
-def plaintext_to_html(text):
- text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
- return text
-
-def send_gradio_gallery_to_image(x):
- if len(x) == 0:
- return None
- return image_from_url_text(x[0])
-
-def save_files(js_data, images, do_make_zip, index):
- import csv
- filenames = []
- fullfns = []
-
- #quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
- class MyObject:
- def __init__(self, d=None):
- if d is not None:
- for key, value in d.items():
- setattr(self, key, value)
-
- data = json.loads(js_data)
-
- p = MyObject(data)
- path = opts.outdir_save
- save_to_dirs = opts.use_save_to_dirs_for_ui
- extension: str = opts.samples_format
- start_index = 0
-
- if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]): # ensures we are looking at a specific non-grid picture, and we have save_selected_only
-
- images = [images[index]]
- start_index = index
-
- os.makedirs(opts.outdir_save, exist_ok=True)
-
- with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
- at_start = file.tell() == 0
- writer = csv.writer(file)
- if at_start:
- writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
-
- for image_index, filedata in enumerate(images, start_index):
- image = image_from_url_text(filedata)
-
- is_grid = image_index < p.index_of_first_image
- i = 0 if is_grid else (image_index - p.index_of_first_image)
-
- fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
-
- filename = os.path.relpath(fullfn, path)
- filenames.append(filename)
- fullfns.append(fullfn)
- if txt_fullfn:
- filenames.append(os.path.basename(txt_fullfn))
- fullfns.append(txt_fullfn)
-
- writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
-
- # Make Zip
- if do_make_zip:
- zip_filepath = os.path.join(path, "images.zip")
-
- from zipfile import ZipFile
- with ZipFile(zip_filepath, "w") as zip_file:
- for i in range(len(fullfns)):
- with open(fullfns[i], mode="rb") as f:
- zip_file.writestr(filenames[i], f.read())
- fullfns.insert(0, zip_filepath)
-
- return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
-
-def save_pil_to_file(pil_image, dir=None):
- use_metadata = False
- metadata = PngImagePlugin.PngInfo()
- for key, value in pil_image.info.items():
- if isinstance(key, str) and isinstance(value, str):
- metadata.add_text(key, value)
- use_metadata = True
-
- file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
- pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
- return file_obj
-
-
-# override save to file function so that it also writes PNG info
-gr.processing_utils.save_pil_to_file = save_pil_to_file
-
-
-def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
- def f(*args, extra_outputs_array=extra_outputs, **kwargs):
- run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
- if run_memmon:
- shared.mem_mon.monitor()
- t = time.perf_counter()
-
- try:
- res = list(func(*args, **kwargs))
- except Exception as e:
- # When printing out our debug argument list, do not print out more than a MB of text
- max_debug_str_len = 131072 # (1024*1024)/8
-
- print("Error completing request", file=sys.stderr)
- argStr = f"Arguments: {str(args)} {str(kwargs)}"
- print(argStr[:max_debug_str_len], file=sys.stderr)
- if len(argStr) > max_debug_str_len:
- print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
-
- print(traceback.format_exc(), file=sys.stderr)
-
- shared.state.job = ""
- shared.state.job_count = 0
-
- if extra_outputs_array is None:
- extra_outputs_array = [None, '']
-
- res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
-
- shared.state.skipped = False
- shared.state.interrupted = False
- shared.state.job_count = 0
-
- if not add_stats:
- return tuple(res)
-
- elapsed = time.perf_counter() - t
- elapsed_m = int(elapsed // 60)
- elapsed_s = elapsed % 60
- elapsed_text = f"{elapsed_s:.2f}s"
- if elapsed_m > 0:
- elapsed_text = f"{elapsed_m}m "+elapsed_text
-
- if run_memmon:
- mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
- active_peak = mem_stats['active_peak']
- reserved_peak = mem_stats['reserved_peak']
- sys_peak = mem_stats['system_peak']
- sys_total = mem_stats['total']
- sys_pct = round(sys_peak/max(sys_total, 1) * 100, 2)
-
- vram_html = f"<p class='vram'>Torch active/reserved: {active_peak}/{reserved_peak} MiB, <wbr>Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)</p>"
- else:
- vram_html = ''
-
- # last item is always HTML
- res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
-
- return tuple(res)
-
- return f
-
-
-def calc_time_left(progress, threshold, label, force_display):
- if progress == 0:
- return ""
- else:
- time_since_start = time.time() - shared.state.time_start
- eta = (time_since_start/progress)
- eta_relative = eta-time_since_start
- if (eta_relative > threshold and progress > 0.02) or force_display:
- if eta_relative > 3600:
- return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))
- elif eta_relative > 60:
- return label + time.strftime('%M:%S', time.gmtime(eta_relative))
- else:
- return label + time.strftime('%Ss', time.gmtime(eta_relative))
- else:
- return ""
-
-
-def check_progress_call(id_part):
- if shared.state.job_count == 0:
- return "", gr_show(False), gr_show(False), gr_show(False)
-
- progress = 0
-
- if shared.state.job_count > 0:
- progress += shared.state.job_no / shared.state.job_count
- if shared.state.sampling_steps > 0:
- progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
-
- time_left = calc_time_left( progress, 1, " ETA: ", shared.state.time_left_force_display )
- if time_left != "":
- shared.state.time_left_force_display = True
-
- progress = min(progress, 1)
-
- progressbar = ""
- if opts.show_progressbar:
- progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:visible;width:{progress * 100}%;white-space:nowrap;">{" " * 2 + str(int(progress*100))+"%" + time_left if progress > 0.01 else ""}</div></div>"""
-
- image = gr_show(False)
- preview_visibility = gr_show(False)
-
- if opts.show_progress_every_n_steps != 0:
- shared.state.set_current_image()
- image = shared.state.current_image
-
- if image is None:
- image = gr.update(value=None)
- else:
- preview_visibility = gr_show(True)
-
- if shared.state.textinfo is not None:
- textinfo_result = gr.HTML.update(value=shared.state.textinfo, visible=True)
- else:
- textinfo_result = gr_show(False)
-
- return f"<span id='{id_part}_progress_span' style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image, textinfo_result
-
-
-def check_progress_call_initial(id_part):
- shared.state.job_count = -1
- shared.state.current_latent = None
- shared.state.current_image = None
- shared.state.textinfo = None
- shared.state.time_start = time.time()
- shared.state.time_left_force_display = False
-
- return check_progress_call(id_part)
-
-
-def roll_artist(prompt):
- allowed_cats = set([x for x in shared.artist_db.categories() if len(opts.random_artist_categories)==0 or x in opts.random_artist_categories])
- artist = random.choice([x for x in shared.artist_db.artists if x.category in allowed_cats])
-
- return prompt + ", " + artist.name if prompt != '' else artist.name
-
-
-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(4)]
-
-
-def apply_styles(prompt, prompt_neg, style1_name, style2_name):
- prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, [style1_name, style2_name])
- prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, [style1_name, style2_name])
-
- return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value="None"), gr.Dropdown.update(value="None")]
-
-
-def interrogate(image):
- prompt = shared.interrogator.interrogate(image)
-
- return gr_show(True) if prompt is None else prompt
-
-
-def interrogate_deepbooru(image):
- prompt = get_deepbooru_tags(image)
- return gr_show(True) if prompt is None else prompt
-
-
-def create_seed_inputs():
- with gr.Row():
- with gr.Box():
- with gr.Row(elem_id='seed_row'):
- seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1)
- seed.style(container=False)
- random_seed = gr.Button(random_symbol, elem_id='random_seed')
- reuse_seed = gr.Button(reuse_symbol, elem_id='reuse_seed')
-
- with gr.Box(elem_id='subseed_show_box'):
- seed_checkbox = gr.Checkbox(label='Extra', elem_id='subseed_show', value=False)
-
- # Components to show/hide based on the 'Extra' checkbox
- seed_extras = []
-
- with gr.Row(visible=False) as seed_extra_row_1:
- seed_extras.append(seed_extra_row_1)
- with gr.Box():
- with gr.Row(elem_id='subseed_row'):
- subseed = gr.Number(label='Variation seed', value=-1)
- subseed.style(container=False)
- random_subseed = gr.Button(random_symbol, elem_id='random_subseed')
- reuse_subseed = gr.Button(reuse_symbol, elem_id='reuse_subseed')
- subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01)
-
- with gr.Row(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=64, label="Resize seed from width", value=0)
- seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from height", value=0)
-
- 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_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:
- _, 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]
- tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1])
- style_class = ' class="red"' if (token_count > max_length) else ""
- return f"<span {style_class}>{token_count}/{max_length}</span>"
-
-
-def create_toprow(is_img2img):
- id_part = "img2img" if is_img2img else "txt2img"
-
- with gr.Row(elem_id="toprow"):
- with gr.Column(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=2,
- placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)"
- )
-
- with gr.Row():
- with gr.Column(scale=80):
- with gr.Row():
- negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2,
- placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)"
- )
-
- with gr.Column(scale=1, elem_id="roll_col"):
- roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
- paste = gr.Button(value=paste_symbol, elem_id="paste")
- save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
- prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
-
- token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
- token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
-
- button_interrogate = None
- button_deepbooru = None
- if is_img2img:
- with gr.Column(scale=1, elem_id="interrogate_col"):
- button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
-
- if cmd_opts.deepdanbooru:
- button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
-
- with gr.Column(scale=1):
- with gr.Row():
- skip = gr.Button('Skip', elem_id=f"{id_part}_skip")
- interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt")
- 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():
- with gr.Column(scale=1, elem_id="style_pos_col"):
- prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
- prompt_style.save_to_config = True
-
- with gr.Column(scale=1, elem_id="style_neg_col"):
- prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
- prompt_style2.save_to_config = True
-
- return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
-
-
-def setup_progressbar(progressbar, preview, id_part, textinfo=None):
- if textinfo is None:
- textinfo = gr.HTML(visible=False)
-
- check_progress = gr.Button('Check progress', elem_id=f"{id_part}_check_progress", visible=False)
- check_progress.click(
- fn=lambda: check_progress_call(id_part),
- show_progress=False,
- inputs=[],
- outputs=[progressbar, preview, preview, textinfo],
- )
-
- check_progress_initial = gr.Button('Check progress (first)', elem_id=f"{id_part}_check_progress_initial", visible=False)
- check_progress_initial.click(
- fn=lambda: check_progress_call_initial(id_part),
- show_progress=False,
- inputs=[],
- outputs=[progressbar, preview, preview, textinfo],
- )
-
-
-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[key]
- 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 value
-
-
-def update_generation_info(args):
- generation_info, html_info, img_index = args
- try:
- generation_info = json.loads(generation_info)
- if img_index < 0 or img_index >= len(generation_info["infotexts"]):
- return html_info
- return plaintext_to_html(generation_info["infotexts"][img_index])
- except Exception:
- pass
- # if the json parse or anything else fails, just return the old html_info
- return html_info
-
-
-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 = gr.Button(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):
- def open_folder(f):
- if not os.path.exists(f):
- print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
- return
- elif not os.path.isdir(f):
- print(f"""
-WARNING
-An open_folder request was made with an argument that is not a folder.
-This could be an error or a malicious attempt to run code on your computer.
-Requested path was: {f}
-""", file=sys.stderr)
- return
-
- if not shared.cmd_opts.hide_ui_dir_config:
- path = os.path.normpath(f)
- if platform.system() == "Windows":
- os.startfile(path)
- elif platform.system() == "Darwin":
- sp.Popen(["open", path])
- else:
- sp.Popen(["xdg-open", path])
-
- with gr.Column(variant='panel'):
- with gr.Group():
- result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
-
- generation_info = None
- with gr.Column():
- with gr.Row():
- if tabname != "extras":
- save = gr.Button('Save', elem_id=f'save_{tabname}')
-
- buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
- button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
- open_folder_button = gr.Button(folder_symbol, elem_id=button_id)
-
- open_folder_button.click(
- fn=lambda: open_folder(opts.outdir_samples or outdir),
- inputs=[],
- outputs=[],
- )
-
- if tabname != "extras":
- with gr.Row():
- do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
-
- with gr.Row():
- download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
-
- with gr.Group():
- html_info = gr.HTML()
- generation_info = gr.Textbox(visible=False)
- if tabname == 'txt2img' or tabname == 'img2img':
- generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
- generation_info_button.click(
- fn=update_generation_info,
- _js="(x, y) => [x, y, selected_gallery_index()]",
- inputs=[generation_info, html_info],
- outputs=[html_info],
- preprocess=False
- )
-
- save.click(
- fn=wrap_gradio_call(save_files),
- _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
- inputs=[
- generation_info,
- result_gallery,
- do_make_zip,
- html_info,
- ],
- outputs=[
- download_files,
- html_info,
- html_info,
- html_info,
- ]
- )
- else:
- html_info_x = gr.HTML()
- html_info = gr.HTML()
- parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)
- return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info
-
-
-def create_ui(wrap_gradio_gpu_call):
- 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, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
- dummy_component = gr.Label(visible=False)
- txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
-
- with gr.Row(elem_id='txt2img_progress_row'):
- with gr.Column(scale=1):
- pass
-
- with gr.Column(scale=1):
- progressbar = gr.HTML(elem_id="txt2img_progressbar")
- txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
- setup_progressbar(progressbar, txt2img_preview, 'txt2img')
-
- with gr.Row().style(equal_height=False):
- with gr.Column(variant='panel'):
- steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
- sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index")
-
- with gr.Group():
- width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
- height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
-
- with gr.Row():
- restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
- tiling = gr.Checkbox(label='Tiling', value=False)
- enable_hr = gr.Checkbox(label='Highres. fix', value=False)
-
- with gr.Row(visible=False) as hr_options:
- firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass width", value=0)
- firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass height", value=0)
- denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
-
- with gr.Row(equal_height=True):
- batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
- batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
-
- cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
-
- seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
-
- with gr.Group():
- custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
-
- txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples)
- parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)
-
- 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),
- _js="submit",
- inputs=[
- txt2img_prompt,
- txt2img_negative_prompt,
- txt2img_prompt_style,
- txt2img_prompt_style2,
- 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,
- firstphase_width,
- firstphase_height,
- ] + custom_inputs,
-
- outputs=[
- txt2img_gallery,
- generation_info,
- html_info
- ],
- show_progress=False,
- )
-
- txt2img_prompt.submit(**txt2img_args)
- submit.click(**txt2img_args)
-
- 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],
- )
-
- roll.click(
- fn=roll_artist,
- _js="update_txt2img_tokens",
- inputs=[
- txt2img_prompt,
- ],
- outputs=[
- txt2img_prompt,
- ]
- )
-
- 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)),
- (firstphase_width, "First pass size-1"),
- (firstphase_height, "First pass size-2"),
- *modules.scripts.scripts_txt2img.infotext_fields
- ]
- parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields)
-
- txt2img_preview_params = [
- txt2img_prompt,
- txt2img_negative_prompt,
- steps,
- sampler_index,
- cfg_scale,
- seed,
- width,
- height,
- ]
-
- token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
-
- 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, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True)
-
- with gr.Row(elem_id='img2img_progress_row'):
- img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
-
- with gr.Column(scale=1):
- pass
-
- with gr.Column(scale=1):
- progressbar = gr.HTML(elem_id="img2img_progressbar")
- img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
- setup_progressbar(progressbar, img2img_preview, 'img2img')
-
- with gr.Row().style(equal_height=False):
- with gr.Column(variant='panel'):
-
- with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
- with gr.TabItem('img2img', id='img2img'):
- init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool).style(height=480)
-
- with gr.TabItem('Inpaint', id='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)
-
- init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
- init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
-
- mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
-
- with gr.Row():
- mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
- inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index")
-
- inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index")
-
- with gr.Row():
- inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False)
- inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32)
-
- with gr.TabItem('Batch img2img', id='batch'):
- hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
- gr.HTML(f"<p class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.<br>Use an empty output directory to save pictures normally instead of writing to the output directory.{hidden}</p>")
- img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs)
- img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs)
-
- with gr.Row():
- resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")
-
- steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
- sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
-
- with gr.Group():
- width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512, elem_id="img2img_width")
- height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512, elem_id="img2img_height")
-
- with gr.Row():
- restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
- tiling = gr.Checkbox(label='Tiling', value=False)
-
- with gr.Row():
- batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
- batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
-
- with gr.Group():
- cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
- denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75)
-
- seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
-
- with gr.Group():
- custom_inputs = modules.scripts.scripts_img2img.setup_ui()
-
- img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples)
- parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt)
-
- 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
- ]
- )
-
- mask_mode.change(
- lambda mode, img: {
- init_img_with_mask: gr_show(mode == 0),
- init_img_inpaint: gr_show(mode == 1),
- init_mask_inpaint: gr_show(mode == 1),
- },
- inputs=[mask_mode, init_img_with_mask],
- outputs=[
- init_img_with_mask,
- init_img_inpaint,
- init_mask_inpaint,
- ],
- )
-
- img2img_args = dict(
- fn=wrap_gradio_gpu_call(modules.img2img.img2img),
- _js="submit_img2img",
- inputs=[
- dummy_component,
- img2img_prompt,
- img2img_negative_prompt,
- img2img_prompt_style,
- img2img_prompt_style2,
- init_img,
- init_img_with_mask,
- init_img_inpaint,
- init_mask_inpaint,
- mask_mode,
- steps,
- sampler_index,
- mask_blur,
- inpainting_fill,
- restore_faces,
- tiling,
- batch_count,
- batch_size,
- 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,
- ] + custom_inputs,
- outputs=[
- img2img_gallery,
- generation_info,
- html_info
- ],
- show_progress=False,
- )
-
- img2img_prompt.submit(**img2img_args)
- submit.click(**img2img_args)
-
- img2img_interrogate.click(
- fn=interrogate,
- inputs=[init_img],
- outputs=[img2img_prompt],
- )
-
- if cmd_opts.deepdanbooru:
- img2img_deepbooru.click(
- fn=interrogate_deepbooru,
- inputs=[init_img],
- outputs=[img2img_prompt],
- )
-
-
- roll.click(
- fn=roll_artist,
- _js="update_img2img_tokens",
- inputs=[
- img2img_prompt,
- ],
- outputs=[
- img2img_prompt,
- ]
- )
-
- prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
- style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)]
- 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_style, img2img_prompt_style, txt2img_prompt_style2, img2img_prompt_style2],
- )
-
- for button, (prompt, negative_prompt), (style1, style2), 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, style1, style2],
- outputs=[prompt, negative_prompt, style1, style2],
- )
-
- token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
-
- img2img_paste_fields = [
- (img2img_prompt, "Prompt"),
- (img2img_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"),
- *modules.scripts.scripts_img2img.infotext_fields
- ]
- parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)
- parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)
-
- modules.scripts.scripts_current = None
-
- with gr.Blocks(analytics_enabled=False) as extras_interface:
- with gr.Row().style(equal_height=False):
- with gr.Column(variant='panel'):
- with gr.Tabs(elem_id="mode_extras"):
- with gr.TabItem('Single Image'):
- extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil")
-
- with gr.TabItem('Batch Process'):
- image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
-
- with gr.TabItem('Batch from Directory'):
- extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.")
- extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.")
- show_extras_results = gr.Checkbox(label='Show result images', value=True)
-
- submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
-
- with gr.Tabs(elem_id="extras_resize_mode"):
- with gr.TabItem('Scale by'):
- upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4)
- with gr.TabItem('Scale to'):
- with gr.Group():
- with gr.Row():
- upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)
- upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)
- upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
-
- with gr.Group():
- extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
-
- with gr.Group():
- extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
- extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1)
-
- with gr.Group():
- gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan)
-
- with gr.Group():
- codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer)
- codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer)
-
- with gr.Group():
- upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False)
-
- result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples)
-
- submit.click(
- fn=wrap_gradio_gpu_call(modules.extras.run_extras),
- _js="get_extras_tab_index",
- inputs=[
- dummy_component,
- dummy_component,
- extras_image,
- image_batch,
- extras_batch_input_dir,
- extras_batch_output_dir,
- show_extras_results,
- gfpgan_visibility,
- codeformer_visibility,
- codeformer_weight,
- upscaling_resize,
- upscaling_resize_w,
- upscaling_resize_h,
- upscaling_crop,
- extras_upscaler_1,
- extras_upscaler_2,
- extras_upscaler_2_visibility,
- upscale_before_face_fix,
- ],
- outputs=[
- result_images,
- html_info_x,
- html_info,
- ]
- )
- parameters_copypaste.add_paste_fields("extras", extras_image, None)
-
- extras_image.change(
- fn=modules.extras.clear_cache,
- inputs=[], outputs=[]
- )
-
- 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)
- html2 = gr.HTML()
- with gr.Row():
- buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
- parameters_copypaste.bind_buttons(buttons, image, generation_info)
-
- image.change(
- fn=wrap_gradio_call(modules.extras.run_pnginfo),
- inputs=[image],
- outputs=[html, generation_info, html2],
- )
-
- with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
- with gr.Row().style(equal_height=False):
- with gr.Column(variant='panel'):
- gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")
-
- with gr.Row():
- primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
- secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
- tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
- custom_name = gr.Textbox(label="Custom Name (Optional)")
- 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)
- interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")
- save_as_half = gr.Checkbox(value=False, label="Save as float16")
- modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
-
- with gr.Column(variant='panel'):
- submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
-
- sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
-
- with gr.Blocks(analytics_enabled=False) as train_interface:
- with gr.Row().style(equal_height=False):
- gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
-
- with gr.Row().style(equal_height=False):
- with gr.Tabs(elem_id="train_tabs"):
-
- with gr.Tab(label="Create embedding"):
- new_embedding_name = gr.Textbox(label="Name")
- initialization_text = gr.Textbox(label="Initialization text", value="*")
- nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
- overwrite_old_embedding = gr.Checkbox(value=False, label="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')
-
- with gr.Tab(label="Create hypernetwork"):
- new_hypernetwork_name = gr.Textbox(label="Name")
- new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
- 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'")
- new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)
- 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"])
- new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
- new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
- overwrite_old_hypernetwork = gr.Checkbox(value=False, label="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')
-
- with gr.Tab(label="Preprocess images"):
- process_src = gr.Textbox(label='Source directory')
- process_dst = gr.Textbox(label='Destination directory')
- process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
- process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
- preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"])
-
- with gr.Row():
- process_flip = gr.Checkbox(label='Create flipped copies')
- process_split = gr.Checkbox(label='Split oversized images')
- process_focal_crop = gr.Checkbox(label='Auto focal point crop')
- process_caption = gr.Checkbox(label='Use BLIP for caption')
- process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)
-
- 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)
- process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05)
-
- 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)
- process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05)
- process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
- process_focal_crop_debug = gr.Checkbox(label='Create debug image')
-
- with gr.Row():
- with gr.Column(scale=3):
- gr.HTML(value="")
-
- with gr.Column():
- with gr.Row():
- interrupt_preprocessing = gr.Button("Interrupt")
- run_preprocess = gr.Button(value="Preprocess", variant='primary')
-
- 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],
- )
-
- with gr.Tab(label="Train"):
- gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
- with gr.Row():
- 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")
- with gr.Row():
- 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 gr.Row():
- embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005")
- hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")
-
- batch_size = gr.Number(label='Batch size', value=1, precision=0)
- dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
- log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
- template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
- training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
- training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
- steps = gr.Number(label='Max steps', value=100000, precision=0)
- create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
- save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
- save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
- preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
-
- with gr.Row():
- interrupt_training = gr.Button(value="Interrupt")
- train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')
- train_embedding = gr.Button(value="Train Embedding", variant='primary')
-
- params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
-
- script_callbacks.ui_train_tabs_callback(params)
-
- with gr.Column():
- progressbar = gr.HTML(elem_id="ti_progressbar")
- 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_preview = gr.Image(elem_id='ti_preview', visible=False)
- ti_progress = gr.HTML(elem_id="ti_progress", value="")
- ti_outcome = gr.HTML(elem_id="ti_error", value="")
- setup_progressbar(progressbar, ti_preview, 'ti', textinfo=ti_progress)
-
- 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
- ],
- 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=[
- 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,
- ],
- 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=[
- train_embedding_name,
- embedding_learn_rate,
- batch_size,
- dataset_directory,
- log_directory,
- training_width,
- training_height,
- steps,
- 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=[
- train_hypernetwork_name,
- hypernetwork_learn_rate,
- batch_size,
- dataset_directory,
- log_directory,
- training_width,
- training_height,
- steps,
- 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 gr.Row(variant="compact"):
- res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
- create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
- else:
- res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
-
- return res
-
- components = []
- component_dict = {}
-
- script_callbacks.ui_settings_callback()
- opts.reorder()
-
- def run_settings(*args):
- changed = []
-
- for key, value, comp in zip(opts.data_labels.keys(), args, components):
- assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
-
- for key, value, comp in zip(opts.data_labels.keys(), args, components):
- if comp == dummy_component:
- continue
-
- if opts.set(key, value):
- changed.append(key)
-
- try:
- opts.save(shared.config_filename)
- except RuntimeError:
- return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'
- return opts.dumpjson(), f'{len(changed)} settings changed: {", ".join(changed)}.'
-
- def run_settings_single(value, key):
- if not opts.same_type(value, opts.data_labels[key].default):
- return gr.update(visible=True), opts.dumpjson()
-
- if not opts.set(key, value):
- return gr.update(value=getattr(opts, key)), opts.dumpjson()
-
- opts.save(shared.config_filename)
-
- return gr.update(value=value), opts.dumpjson()
-
- with gr.Blocks(analytics_enabled=False) as settings_interface:
- settings_submit = gr.Button(value="Apply settings", variant='primary')
- result = gr.HTML()
-
- settings_cols = 3
- items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols)
-
- quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")]
- quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings')
-
- quicksettings_list = []
-
- cols_displayed = 0
- items_displayed = 0
- previous_section = None
- column = None
- with gr.Row(elem_id="settings").style(equal_height=False):
- for i, (k, item) in enumerate(opts.data_labels.items()):
- section_must_be_skipped = item.section[0] is None
-
- if previous_section != item.section and not section_must_be_skipped:
- if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None):
- if column is not None:
- column.__exit__()
-
- column = gr.Column(variant='panel')
- column.__enter__()
-
- items_displayed = 0
- cols_displayed += 1
-
- previous_section = item.section
-
- elem_id, text = item.section
- gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='<h1 class="gr-button-lg">{}</h1>'.format(text))
-
- if k in quicksettings_names and not shared.cmd_opts.freeze_settings:
- quicksettings_list.append((i, k, item))
- components.append(dummy_component)
- elif section_must_be_skipped:
- components.append(dummy_component)
- else:
- component = create_setting_component(k)
- component_dict[k] = component
- components.append(component)
- items_displayed += 1
-
- with gr.Row():
- request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
- download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
-
- with gr.Row():
- reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
- restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
-
- request_notifications.click(
- fn=lambda: None,
- inputs=[],
- outputs=[],
- _js='function(){}'
- )
-
- download_localization.click(
- fn=lambda: None,
- inputs=[],
- outputs=[],
- _js='download_localization'
- )
-
- def reload_scripts():
- modules.scripts.reload_script_body_only()
- reload_javascript() # need to refresh the html page
-
- reload_script_bodies.click(
- fn=reload_scripts,
- inputs=[],
- outputs=[]
- )
-
- def request_restart():
- shared.state.interrupt()
- shared.state.need_restart = True
-
- restart_gradio.click(
- fn=request_restart,
- _js='restart_reload',
- inputs=[],
- outputs=[],
- )
-
- if column is not None:
- column.__exit__()
-
- interfaces = [
- (txt2img_interface, "txt2img", "txt2img"),
- (img2img_interface, "img2img", "img2img"),
- (extras_interface, "Extras", "extras"),
- (pnginfo_interface, "PNG Info", "pnginfo"),
- (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
- (train_interface, "Train", "ti"),
- ]
-
- css = ""
-
- for cssfile in modules.scripts.list_files_with_name("style.css"):
- if not os.path.isfile(cssfile):
- continue
-
- with open(cssfile, "r", encoding="utf8") as file:
- css += file.read() + "\n"
-
- if os.path.exists(os.path.join(script_path, "user.css")):
- with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file:
- css += file.read() + "\n"
-
- if not cmd_opts.no_progressbar_hiding:
- css += css_hide_progressbar
-
- interfaces += script_callbacks.ui_tabs_callback()
- interfaces += [(settings_interface, "Settings", "settings")]
-
- extensions_interface = ui_extensions.create_ui()
- interfaces += [(extensions_interface, "Extensions", "extensions")]
-
- with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
- with gr.Row(elem_id="quicksettings"):
- for i, k, item in quicksettings_list:
- component = create_setting_component(k, is_quicksettings=True)
- component_dict[k] = component
-
- parameters_copypaste.integrate_settings_paste_fields(component_dict)
- parameters_copypaste.run_bind()
-
- with gr.Tabs(elem_id="tabs") as tabs:
- for interface, label, ifid in interfaces:
- with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
- interface.render()
-
- if os.path.exists(os.path.join(script_path, "notification.mp3")):
- audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
-
- text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
- settings_submit.click(
- fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),
- inputs=components,
- outputs=[text_settings, result],
- )
-
- for i, k, item in quicksettings_list:
- component = component_dict[k]
-
- component.change(
- fn=lambda value, k=k: run_settings_single(value, key=k),
- inputs=[component],
- outputs=[component, text_settings],
- )
-
- component_keys = [k for k in opts.data_labels.keys() if k in component_dict]
-
- def get_settings_values():
- return [getattr(opts, key) for key in component_keys]
-
- demo.load(
- fn=get_settings_values,
- inputs=[],
- outputs=[component_dict[k] for k in component_keys],
- )
-
- def modelmerger(*args):
- try:
- results = modules.extras.run_modelmerger(*args)
- except Exception as e:
- print("Error loading/saving model file:", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
- modules.sd_models.list_models() # to remove the potentially missing models from the list
- return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)]
- return results
-
- modelmerger_merge.click(
- fn=modelmerger,
- inputs=[
- primary_model_name,
- secondary_model_name,
- tertiary_model_name,
- interp_method,
- interp_amount,
- save_as_half,
- custom_name,
- ],
- outputs=[
- submit_result,
- primary_model_name,
- secondary_model_name,
- tertiary_model_name,
- component_dict['sd_model_checkpoint'],
- ]
- )
-
- 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):
- 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] 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')
-
- # Since there are many dropdowns that shouldn't be saved,
- # we only mark dropdowns that should be saved.
- if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False):
- apply_field(x, 'value', lambda val: val in x.choices, getattr(x, 'init_field', None))
- apply_field(x, 'visible')
-
- visit(txt2img_interface, loadsave, "txt2img")
- visit(img2img_interface, loadsave, "img2img")
- visit(extras_interface, loadsave, "extras")
- visit(modelmerger_interface, loadsave, "modelmerger")
-
- 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)
-
- return demo
-
-
-def reload_javascript():
- with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:
- javascript = f'<script>{jsfile.read()}</script>'
-
- scripts_list = modules.scripts.list_scripts("javascript", ".js")
-
- for basedir, filename, path in scripts_list:
- with open(path, "r", encoding="utf8") as jsfile:
- javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"
-
- if cmd_opts.theme is not None:
- javascript += f"\n<script>set_theme('{cmd_opts.theme}');</script>\n"
-
- javascript += f"\n<script>{localization.localization_js(shared.opts.localization)}</script>"
-
- def template_response(*args, **kwargs):
- res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
- res.body = res.body.replace(
- b'</head>', f'{javascript}</head>'.encode("utf8"))
- res.init_headers()
- return res
-
- gradio.routes.templates.TemplateResponse = template_response
-
-
-if not hasattr(shared, 'GradioTemplateResponseOriginal'):
- shared.GradioTemplateResponseOriginal = gradio.routes.templates.TemplateResponse
+import html
+import json
+import math
+import mimetypes
+import os
+import platform
+import random
+import subprocess as sp
+import sys
+import tempfile
+import time
+import traceback
+from functools import partial, reduce
+
+import gradio as gr
+import gradio.routes
+import gradio.utils
+import numpy as np
+from PIL import Image, PngImagePlugin
+
+
+from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions
+from modules.paths import script_path
+
+from modules.shared import opts, cmd_opts, restricted_opts
+
+if cmd_opts.deepdanbooru:
+ from modules.deepbooru import get_deepbooru_tags
+
+import modules.codeformer_model
+import modules.generation_parameters_copypaste as parameters_copypaste
+import modules.gfpgan_model
+import modules.hypernetworks.ui
+import modules.ldsr_model
+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
+import modules.textual_inversion.ui
+import modules.hypernetworks.ui
+from modules.generation_parameters_copypaste import image_from_url_text
+
+# 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 != 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 != None else 7860, cmd_opts.ngrok_region)
+
+
+def gr_show(visible=True):
+ return {"visible": visible, "__type__": "update"}
+
+
+sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
+sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
+
+css_hide_progressbar = """
+.wrap .m-12 svg { display:none!important; }
+.wrap .m-12::before { content:"Loading..." }
+.wrap .z-20 svg { display:none!important; }
+.wrap .z-20::before { content:"Loading..." }
+.progress-bar { display:none!important; }
+.meta-text { display:none!important; }
+.meta-text-center { display:none!important; }
+"""
+
+# Using constants for these since the variation selector isn't visible.
+# Important that they exactly match script.js for tooltip to work.
+random_symbol = '\U0001f3b2\ufe0f' # 🎲️
+reuse_symbol = '\u267b\ufe0f' # ♻️
+art_symbol = '\U0001f3a8' # 🎨
+paste_symbol = '\u2199\ufe0f' # ↙
+folder_symbol = '\U0001f4c2' # 📂
+refresh_symbol = '\U0001f504' # 🔄
+save_style_symbol = '\U0001f4be' # 💾
+apply_style_symbol = '\U0001f4cb' # 📋
+
+
+def plaintext_to_html(text):
+ text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
+ return text
+
+def send_gradio_gallery_to_image(x):
+ if len(x) == 0:
+ return None
+ return image_from_url_text(x[0])
+
+def save_files(js_data, images, do_make_zip, index):
+ import csv
+ filenames = []
+ fullfns = []
+
+ #quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
+ class MyObject:
+ def __init__(self, d=None):
+ if d is not None:
+ for key, value in d.items():
+ setattr(self, key, value)
+
+ data = json.loads(js_data)
+
+ p = MyObject(data)
+ path = opts.outdir_save
+ save_to_dirs = opts.use_save_to_dirs_for_ui
+ extension: str = opts.samples_format
+ start_index = 0
+
+ if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]): # ensures we are looking at a specific non-grid picture, and we have save_selected_only
+
+ images = [images[index]]
+ start_index = index
+
+ os.makedirs(opts.outdir_save, exist_ok=True)
+
+ with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
+ at_start = file.tell() == 0
+ writer = csv.writer(file)
+ if at_start:
+ writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
+
+ for image_index, filedata in enumerate(images, start_index):
+ image = image_from_url_text(filedata)
+
+ is_grid = image_index < p.index_of_first_image
+ i = 0 if is_grid else (image_index - p.index_of_first_image)
+
+ fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
+
+ filename = os.path.relpath(fullfn, path)
+ filenames.append(filename)
+ fullfns.append(fullfn)
+ if txt_fullfn:
+ filenames.append(os.path.basename(txt_fullfn))
+ fullfns.append(txt_fullfn)
+
+ writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
+
+ # Make Zip
+ if do_make_zip:
+ zip_filepath = os.path.join(path, "images.zip")
+
+ from zipfile import ZipFile
+ with ZipFile(zip_filepath, "w") as zip_file:
+ for i in range(len(fullfns)):
+ with open(fullfns[i], mode="rb") as f:
+ zip_file.writestr(filenames[i], f.read())
+ fullfns.insert(0, zip_filepath)
+
+ return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
+
+def save_pil_to_file(pil_image, dir=None):
+ use_metadata = False
+ metadata = PngImagePlugin.PngInfo()
+ for key, value in pil_image.info.items():
+ if isinstance(key, str) and isinstance(value, str):
+ metadata.add_text(key, value)
+ use_metadata = True
+
+ file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
+ pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
+ return file_obj
+
+
+# override save to file function so that it also writes PNG info
+gr.processing_utils.save_pil_to_file = save_pil_to_file
+
+
+def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
+ def f(*args, extra_outputs_array=extra_outputs, **kwargs):
+ run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
+ if run_memmon:
+ shared.mem_mon.monitor()
+ t = time.perf_counter()
+
+ try:
+ res = list(func(*args, **kwargs))
+ except Exception as e:
+ # When printing out our debug argument list, do not print out more than a MB of text
+ max_debug_str_len = 131072 # (1024*1024)/8
+
+ print("Error completing request", file=sys.stderr)
+ argStr = f"Arguments: {str(args)} {str(kwargs)}"
+ print(argStr[:max_debug_str_len], file=sys.stderr)
+ if len(argStr) > max_debug_str_len:
+ print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
+
+ print(traceback.format_exc(), file=sys.stderr)
+
+ shared.state.job = ""
+ shared.state.job_count = 0
+
+ if extra_outputs_array is None:
+ extra_outputs_array = [None, '']
+
+ res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
+
+ shared.state.skipped = False
+ shared.state.interrupted = False
+ shared.state.job_count = 0
+
+ if not add_stats:
+ return tuple(res)
+
+ elapsed = time.perf_counter() - t
+ elapsed_m = int(elapsed // 60)
+ elapsed_s = elapsed % 60
+ elapsed_text = f"{elapsed_s:.2f}s"
+ if elapsed_m > 0:
+ elapsed_text = f"{elapsed_m}m "+elapsed_text
+
+ if run_memmon:
+ mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
+ active_peak = mem_stats['active_peak']
+ reserved_peak = mem_stats['reserved_peak']
+ sys_peak = mem_stats['system_peak']
+ sys_total = mem_stats['total']
+ sys_pct = round(sys_peak/max(sys_total, 1) * 100, 2)
+
+ vram_html = f"<p class='vram'>Torch active/reserved: {active_peak}/{reserved_peak} MiB, <wbr>Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)</p>"
+ else:
+ vram_html = ''
+
+ # last item is always HTML
+ res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
+
+ return tuple(res)
+
+ return f
+
+
+def calc_time_left(progress, threshold, label, force_display):
+ if progress == 0:
+ return ""
+ else:
+ time_since_start = time.time() - shared.state.time_start
+ eta = (time_since_start/progress)
+ eta_relative = eta-time_since_start
+ if (eta_relative > threshold and progress > 0.02) or force_display:
+ if eta_relative > 3600:
+ return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))
+ elif eta_relative > 60:
+ return label + time.strftime('%M:%S', time.gmtime(eta_relative))
+ else:
+ return label + time.strftime('%Ss', time.gmtime(eta_relative))
+ else:
+ return ""
+
+
+def check_progress_call(id_part):
+ if shared.state.job_count == 0:
+ return "", gr_show(False), gr_show(False), gr_show(False)
+
+ progress = 0
+
+ if shared.state.job_count > 0:
+ progress += shared.state.job_no / shared.state.job_count
+ if shared.state.sampling_steps > 0:
+ progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
+
+ time_left = calc_time_left( progress, 1, " ETA: ", shared.state.time_left_force_display )
+ if time_left != "":
+ shared.state.time_left_force_display = True
+
+ progress = min(progress, 1)
+
+ progressbar = ""
+ if opts.show_progressbar:
+ progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:visible;width:{progress * 100}%;white-space:nowrap;">{" " * 2 + str(int(progress*100))+"%" + time_left if progress > 0.01 else ""}</div></div>"""
+
+ image = gr_show(False)
+ preview_visibility = gr_show(False)
+
+ if opts.show_progress_every_n_steps != 0:
+ shared.state.set_current_image()
+ image = shared.state.current_image
+
+ if image is None:
+ image = gr.update(value=None)
+ else:
+ preview_visibility = gr_show(True)
+
+ if shared.state.textinfo is not None:
+ textinfo_result = gr.HTML.update(value=shared.state.textinfo, visible=True)
+ else:
+ textinfo_result = gr_show(False)
+
+ return f"<span id='{id_part}_progress_span' style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image, textinfo_result
+
+
+def check_progress_call_initial(id_part):
+ shared.state.job_count = -1
+ shared.state.current_latent = None
+ shared.state.current_image = None
+ shared.state.textinfo = None
+ shared.state.time_start = time.time()
+ shared.state.time_left_force_display = False
+
+ return check_progress_call(id_part)
+
+
+def roll_artist(prompt):
+ allowed_cats = set([x for x in shared.artist_db.categories() if len(opts.random_artist_categories)==0 or x in opts.random_artist_categories])
+ artist = random.choice([x for x in shared.artist_db.artists if x.category in allowed_cats])
+
+ return prompt + ", " + artist.name if prompt != '' else artist.name
+
+
+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(4)]
+
+
+def apply_styles(prompt, prompt_neg, style1_name, style2_name):
+ prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, [style1_name, style2_name])
+ prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, [style1_name, style2_name])
+
+ return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value="None"), gr.Dropdown.update(value="None")]
+
+
+def interrogate(image):
+ prompt = shared.interrogator.interrogate(image)
+
+ return gr_show(True) if prompt is None else prompt
+
+
+def interrogate_deepbooru(image):
+ prompt = get_deepbooru_tags(image)
+ return gr_show(True) if prompt is None else prompt
+
+
+def create_seed_inputs():
+ with gr.Row():
+ with gr.Box():
+ with gr.Row(elem_id='seed_row'):
+ seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1)
+ seed.style(container=False)
+ random_seed = gr.Button(random_symbol, elem_id='random_seed')
+ reuse_seed = gr.Button(reuse_symbol, elem_id='reuse_seed')
+
+ with gr.Box(elem_id='subseed_show_box'):
+ seed_checkbox = gr.Checkbox(label='Extra', elem_id='subseed_show', value=False)
+
+ # Components to show/hide based on the 'Extra' checkbox
+ seed_extras = []
+
+ with gr.Row(visible=False) as seed_extra_row_1:
+ seed_extras.append(seed_extra_row_1)
+ with gr.Box():
+ with gr.Row(elem_id='subseed_row'):
+ subseed = gr.Number(label='Variation seed', value=-1)
+ subseed.style(container=False)
+ random_subseed = gr.Button(random_symbol, elem_id='random_subseed')
+ reuse_subseed = gr.Button(reuse_symbol, elem_id='reuse_subseed')
+ subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01)
+
+ with gr.Row(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=64, label="Resize seed from width", value=0)
+ seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from height", value=0)
+
+ 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_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:
+ _, 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]
+ tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1])
+ style_class = ' class="red"' if (token_count > max_length) else ""
+ return f"<span {style_class}>{token_count}/{max_length}</span>"
+
+
+def create_toprow(is_img2img):
+ id_part = "img2img" if is_img2img else "txt2img"
+
+ with gr.Row(elem_id="toprow"):
+ with gr.Column(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=2,
+ placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)"
+ )
+
+ with gr.Row():
+ with gr.Column(scale=80):
+ with gr.Row():
+ negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2,
+ placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)"
+ )
+
+ with gr.Column(scale=1, elem_id="roll_col"):
+ roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
+ paste = gr.Button(value=paste_symbol, elem_id="paste")
+ save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
+ prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
+
+ token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
+ token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
+
+ button_interrogate = None
+ button_deepbooru = None
+ if is_img2img:
+ with gr.Column(scale=1, elem_id="interrogate_col"):
+ button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
+
+ if cmd_opts.deepdanbooru:
+ button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
+
+ with gr.Column(scale=1):
+ with gr.Row():
+ skip = gr.Button('Skip', elem_id=f"{id_part}_skip")
+ interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt")
+ 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():
+ with gr.Column(scale=1, elem_id="style_pos_col"):
+ prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
+ prompt_style.save_to_config = True
+
+ with gr.Column(scale=1, elem_id="style_neg_col"):
+ prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
+ prompt_style2.save_to_config = True
+
+ return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
+
+
+def setup_progressbar(progressbar, preview, id_part, textinfo=None):
+ if textinfo is None:
+ textinfo = gr.HTML(visible=False)
+
+ check_progress = gr.Button('Check progress', elem_id=f"{id_part}_check_progress", visible=False)
+ check_progress.click(
+ fn=lambda: check_progress_call(id_part),
+ show_progress=False,
+ inputs=[],
+ outputs=[progressbar, preview, preview, textinfo],
+ )
+
+ check_progress_initial = gr.Button('Check progress (first)', elem_id=f"{id_part}_check_progress_initial", visible=False)
+ check_progress_initial.click(
+ fn=lambda: check_progress_call_initial(id_part),
+ show_progress=False,
+ inputs=[],
+ outputs=[progressbar, preview, preview, textinfo],
+ )
+
+
+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[key]
+ 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 value
+
+
+def update_generation_info(args):
+ generation_info, html_info, img_index = args
+ try:
+ generation_info = json.loads(generation_info)
+ if img_index < 0 or img_index >= len(generation_info["infotexts"]):
+ return html_info
+ return plaintext_to_html(generation_info["infotexts"][img_index])
+ except Exception:
+ pass
+ # if the json parse or anything else fails, just return the old html_info
+ return html_info
+
+
+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 = gr.Button(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):
+ def open_folder(f):
+ if not os.path.exists(f):
+ print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
+ return
+ elif not os.path.isdir(f):
+ print(f"""
+WARNING
+An open_folder request was made with an argument that is not a folder.
+This could be an error or a malicious attempt to run code on your computer.
+Requested path was: {f}
+""", file=sys.stderr)
+ return
+
+ if not shared.cmd_opts.hide_ui_dir_config:
+ path = os.path.normpath(f)
+ if platform.system() == "Windows":
+ os.startfile(path)
+ elif platform.system() == "Darwin":
+ sp.Popen(["open", path])
+ else:
+ sp.Popen(["xdg-open", path])
+
+ with gr.Column(variant='panel'):
+ with gr.Group():
+ result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
+
+ generation_info = None
+ with gr.Column():
+ with gr.Row():
+ if tabname != "extras":
+ save = gr.Button('Save', elem_id=f'save_{tabname}')
+
+ buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
+ button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
+ open_folder_button = gr.Button(folder_symbol, elem_id=button_id)
+
+ open_folder_button.click(
+ fn=lambda: open_folder(opts.outdir_samples or outdir),
+ inputs=[],
+ outputs=[],
+ )
+
+ if tabname != "extras":
+ with gr.Row():
+ do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
+
+ with gr.Row():
+ download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
+
+ with gr.Group():
+ html_info = gr.HTML()
+ generation_info = gr.Textbox(visible=False)
+ if tabname == 'txt2img' or tabname == 'img2img':
+ generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
+ generation_info_button.click(
+ fn=update_generation_info,
+ _js="(x, y) => [x, y, selected_gallery_index()]",
+ inputs=[generation_info, html_info],
+ outputs=[html_info],
+ preprocess=False
+ )
+
+ save.click(
+ fn=wrap_gradio_call(save_files),
+ _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
+ inputs=[
+ generation_info,
+ result_gallery,
+ do_make_zip,
+ html_info,
+ ],
+ outputs=[
+ download_files,
+ html_info,
+ html_info,
+ html_info,
+ ]
+ )
+ else:
+ html_info_x = gr.HTML()
+ html_info = gr.HTML()
+ parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)
+ return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info
+
+
+def create_ui(wrap_gradio_gpu_call):
+ 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, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
+ dummy_component = gr.Label(visible=False)
+ txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
+
+ with gr.Row(elem_id='txt2img_progress_row'):
+ with gr.Column(scale=1):
+ pass
+
+ with gr.Column(scale=1):
+ progressbar = gr.HTML(elem_id="txt2img_progressbar")
+ txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
+ setup_progressbar(progressbar, txt2img_preview, 'txt2img')
+
+ with gr.Row().style(equal_height=False):
+ with gr.Column(variant='panel'):
+ steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
+ sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index")
+
+ with gr.Group():
+ width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
+ height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+
+ with gr.Row():
+ restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
+ tiling = gr.Checkbox(label='Tiling', value=False)
+ enable_hr = gr.Checkbox(label='Highres. fix', value=False)
+
+ with gr.Row(visible=False) as hr_options:
+ firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass width", value=0)
+ firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass height", value=0)
+ denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
+
+ with gr.Row(equal_height=True):
+ batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
+ batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
+
+ cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
+
+ seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
+
+ with gr.Group():
+ custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
+
+ txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples)
+ parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)
+
+ 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),
+ _js="submit",
+ inputs=[
+ txt2img_prompt,
+ txt2img_negative_prompt,
+ txt2img_prompt_style,
+ txt2img_prompt_style2,
+ 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,
+ firstphase_width,
+ firstphase_height,
+ ] + custom_inputs,
+
+ outputs=[
+ txt2img_gallery,
+ generation_info,
+ html_info
+ ],
+ show_progress=False,
+ )
+
+ txt2img_prompt.submit(**txt2img_args)
+ submit.click(**txt2img_args)
+
+ 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],
+ )
+
+ roll.click(
+ fn=roll_artist,
+ _js="update_txt2img_tokens",
+ inputs=[
+ txt2img_prompt,
+ ],
+ outputs=[
+ txt2img_prompt,
+ ]
+ )
+
+ 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)),
+ (firstphase_width, "First pass size-1"),
+ (firstphase_height, "First pass size-2"),
+ *modules.scripts.scripts_txt2img.infotext_fields
+ ]
+ parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields)
+
+ txt2img_preview_params = [
+ txt2img_prompt,
+ txt2img_negative_prompt,
+ steps,
+ sampler_index,
+ cfg_scale,
+ seed,
+ width,
+ height,
+ ]
+
+ token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
+
+ 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, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True)
+
+ with gr.Row(elem_id='img2img_progress_row'):
+ img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
+
+ with gr.Column(scale=1):
+ pass
+
+ with gr.Column(scale=1):
+ progressbar = gr.HTML(elem_id="img2img_progressbar")
+ img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
+ setup_progressbar(progressbar, img2img_preview, 'img2img')
+
+ with gr.Row().style(equal_height=False):
+ with gr.Column(variant='panel'):
+
+ with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
+ with gr.TabItem('img2img', id='img2img'):
+ init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool).style(height=480)
+
+ with gr.TabItem('Inpaint', id='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)
+
+ init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
+ init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
+
+ mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
+
+ with gr.Row():
+ mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
+ inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index")
+
+ inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index")
+
+ with gr.Row():
+ inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False)
+ inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32)
+
+ with gr.TabItem('Batch img2img', id='batch'):
+ hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
+ gr.HTML(f"<p class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.<br>Use an empty output directory to save pictures normally instead of writing to the output directory.{hidden}</p>")
+ img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs)
+ img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs)
+
+ with gr.Row():
+ resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")
+
+ steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
+ sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
+
+ with gr.Group():
+ width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512, elem_id="img2img_width")
+ height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512, elem_id="img2img_height")
+
+ with gr.Row():
+ restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
+ tiling = gr.Checkbox(label='Tiling', value=False)
+
+ with gr.Row():
+ batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
+ batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
+
+ with gr.Group():
+ cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
+ denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75)
+
+ seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
+
+ with gr.Group():
+ custom_inputs = modules.scripts.scripts_img2img.setup_ui()
+
+ img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples)
+ parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt)
+
+ 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
+ ]
+ )
+
+ mask_mode.change(
+ lambda mode, img: {
+ init_img_with_mask: gr_show(mode == 0),
+ init_img_inpaint: gr_show(mode == 1),
+ init_mask_inpaint: gr_show(mode == 1),
+ },
+ inputs=[mask_mode, init_img_with_mask],
+ outputs=[
+ init_img_with_mask,
+ init_img_inpaint,
+ init_mask_inpaint,
+ ],
+ )
+
+ img2img_args = dict(
+ fn=wrap_gradio_gpu_call(modules.img2img.img2img),
+ _js="submit_img2img",
+ inputs=[
+ dummy_component,
+ img2img_prompt,
+ img2img_negative_prompt,
+ img2img_prompt_style,
+ img2img_prompt_style2,
+ init_img,
+ init_img_with_mask,
+ init_img_inpaint,
+ init_mask_inpaint,
+ mask_mode,
+ steps,
+ sampler_index,
+ mask_blur,
+ inpainting_fill,
+ restore_faces,
+ tiling,
+ batch_count,
+ batch_size,
+ 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,
+ ] + custom_inputs,
+ outputs=[
+ img2img_gallery,
+ generation_info,
+ html_info
+ ],
+ show_progress=False,
+ )
+
+ img2img_prompt.submit(**img2img_args)
+ submit.click(**img2img_args)
+
+ img2img_interrogate.click(
+ fn=interrogate,
+ inputs=[init_img],
+ outputs=[img2img_prompt],
+ )
+
+ if cmd_opts.deepdanbooru:
+ img2img_deepbooru.click(
+ fn=interrogate_deepbooru,
+ inputs=[init_img],
+ outputs=[img2img_prompt],
+ )
+
+
+ roll.click(
+ fn=roll_artist,
+ _js="update_img2img_tokens",
+ inputs=[
+ img2img_prompt,
+ ],
+ outputs=[
+ img2img_prompt,
+ ]
+ )
+
+ prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
+ style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)]
+ 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_style, img2img_prompt_style, txt2img_prompt_style2, img2img_prompt_style2],
+ )
+
+ for button, (prompt, negative_prompt), (style1, style2), 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, style1, style2],
+ outputs=[prompt, negative_prompt, style1, style2],
+ )
+
+ token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
+
+ img2img_paste_fields = [
+ (img2img_prompt, "Prompt"),
+ (img2img_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"),
+ *modules.scripts.scripts_img2img.infotext_fields
+ ]
+ parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)
+ parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)
+
+ modules.scripts.scripts_current = None
+
+ with gr.Blocks(analytics_enabled=False) as extras_interface:
+ with gr.Row().style(equal_height=False):
+ with gr.Column(variant='panel'):
+ with gr.Tabs(elem_id="mode_extras"):
+ with gr.TabItem('Single Image'):
+ extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil")
+
+ with gr.TabItem('Batch Process'):
+ image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
+
+ with gr.TabItem('Batch from Directory'):
+ extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.")
+ extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.")
+ show_extras_results = gr.Checkbox(label='Show result images', value=True)
+
+ submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
+
+ with gr.Tabs(elem_id="extras_resize_mode"):
+ with gr.TabItem('Scale by'):
+ upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4)
+ with gr.TabItem('Scale to'):
+ with gr.Group():
+ with gr.Row():
+ upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)
+ upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)
+ upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
+
+ with gr.Group():
+ extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
+
+ with gr.Group():
+ extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
+ extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1)
+
+ with gr.Group():
+ gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan)
+
+ with gr.Group():
+ codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer)
+ codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer)
+
+ with gr.Group():
+ upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False)
+
+ result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples)
+
+ submit.click(
+ fn=wrap_gradio_gpu_call(modules.extras.run_extras),
+ _js="get_extras_tab_index",
+ inputs=[
+ dummy_component,
+ dummy_component,
+ extras_image,
+ image_batch,
+ extras_batch_input_dir,
+ extras_batch_output_dir,
+ show_extras_results,
+ gfpgan_visibility,
+ codeformer_visibility,
+ codeformer_weight,
+ upscaling_resize,
+ upscaling_resize_w,
+ upscaling_resize_h,
+ upscaling_crop,
+ extras_upscaler_1,
+ extras_upscaler_2,
+ extras_upscaler_2_visibility,
+ upscale_before_face_fix,
+ ],
+ outputs=[
+ result_images,
+ html_info_x,
+ html_info,
+ ]
+ )
+ parameters_copypaste.add_paste_fields("extras", extras_image, None)
+
+ extras_image.change(
+ fn=modules.extras.clear_cache,
+ inputs=[], outputs=[]
+ )
+
+ 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)
+ html2 = gr.HTML()
+ with gr.Row():
+ buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
+ parameters_copypaste.bind_buttons(buttons, image, generation_info)
+
+ image.change(
+ fn=wrap_gradio_call(modules.extras.run_pnginfo),
+ inputs=[image],
+ outputs=[html, generation_info, html2],
+ )
+
+ with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
+ with gr.Row().style(equal_height=False):
+ with gr.Column(variant='panel'):
+ gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")
+
+ with gr.Row():
+ primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
+ secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
+ tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
+ custom_name = gr.Textbox(label="Custom Name (Optional)")
+ 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)
+ interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")
+ save_as_half = gr.Checkbox(value=False, label="Save as float16")
+ save_as_safetensors = gr.Checkbox(value=False, label="Save as safetensors format")
+ modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
+
+ with gr.Column(variant='panel'):
+ submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
+
+ sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
+
+ with gr.Blocks(analytics_enabled=False) as train_interface:
+ with gr.Row().style(equal_height=False):
+ gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
+
+ with gr.Row().style(equal_height=False):
+ with gr.Tabs(elem_id="train_tabs"):
+
+ with gr.Tab(label="Create embedding"):
+ new_embedding_name = gr.Textbox(label="Name")
+ initialization_text = gr.Textbox(label="Initialization text", value="*")
+ nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
+ overwrite_old_embedding = gr.Checkbox(value=False, label="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')
+
+ with gr.Tab(label="Create hypernetwork"):
+ new_hypernetwork_name = gr.Textbox(label="Name")
+ new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
+ 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'")
+ new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)
+ 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"])
+ new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
+ new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
+ overwrite_old_hypernetwork = gr.Checkbox(value=False, label="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')
+
+ with gr.Tab(label="Preprocess images"):
+ process_src = gr.Textbox(label='Source directory')
+ process_dst = gr.Textbox(label='Destination directory')
+ process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
+ process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+ preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"])
+
+ with gr.Row():
+ process_flip = gr.Checkbox(label='Create flipped copies')
+ process_split = gr.Checkbox(label='Split oversized images')
+ process_focal_crop = gr.Checkbox(label='Auto focal point crop')
+ process_caption = gr.Checkbox(label='Use BLIP for caption')
+ process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)
+
+ 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)
+ process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05)
+
+ 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)
+ process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05)
+ process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
+ process_focal_crop_debug = gr.Checkbox(label='Create debug image')
+
+ with gr.Row():
+ with gr.Column(scale=3):
+ gr.HTML(value="")
+
+ with gr.Column():
+ with gr.Row():
+ interrupt_preprocessing = gr.Button("Interrupt")
+ run_preprocess = gr.Button(value="Preprocess", variant='primary')
+
+ 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],
+ )
+
+ with gr.Tab(label="Train"):
+ gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
+ with gr.Row():
+ 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")
+ with gr.Row():
+ 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 gr.Row():
+ embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005")
+ hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")
+
+ batch_size = gr.Number(label='Batch size', value=1, precision=0)
+ dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
+ log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
+ template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
+ training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
+ training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+ steps = gr.Number(label='Max steps', value=100000, precision=0)
+ create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
+ save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
+ save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
+ preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
+
+ with gr.Row():
+ interrupt_training = gr.Button(value="Interrupt")
+ train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')
+ train_embedding = gr.Button(value="Train Embedding", variant='primary')
+
+ params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
+
+ script_callbacks.ui_train_tabs_callback(params)
+
+ with gr.Column():
+ progressbar = gr.HTML(elem_id="ti_progressbar")
+ 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_preview = gr.Image(elem_id='ti_preview', visible=False)
+ ti_progress = gr.HTML(elem_id="ti_progress", value="")
+ ti_outcome = gr.HTML(elem_id="ti_error", value="")
+ setup_progressbar(progressbar, ti_preview, 'ti', textinfo=ti_progress)
+
+ 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
+ ],
+ 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=[
+ 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,
+ ],
+ 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=[
+ train_embedding_name,
+ embedding_learn_rate,
+ batch_size,
+ dataset_directory,
+ log_directory,
+ training_width,
+ training_height,
+ steps,
+ 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=[
+ train_hypernetwork_name,
+ hypernetwork_learn_rate,
+ batch_size,
+ dataset_directory,
+ log_directory,
+ training_width,
+ training_height,
+ steps,
+ 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 gr.Row(variant="compact"):
+ res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+ create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
+ else:
+ res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+
+ return res
+
+ components = []
+ component_dict = {}
+
+ script_callbacks.ui_settings_callback()
+ opts.reorder()
+
+ def run_settings(*args):
+ changed = []
+
+ for key, value, comp in zip(opts.data_labels.keys(), args, components):
+ assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
+
+ for key, value, comp in zip(opts.data_labels.keys(), args, components):
+ if comp == dummy_component:
+ continue
+
+ if opts.set(key, value):
+ changed.append(key)
+
+ try:
+ opts.save(shared.config_filename)
+ except RuntimeError:
+ return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'
+ return opts.dumpjson(), f'{len(changed)} settings changed: {", ".join(changed)}.'
+
+ def run_settings_single(value, key):
+ if not opts.same_type(value, opts.data_labels[key].default):
+ return gr.update(visible=True), opts.dumpjson()
+
+ if not opts.set(key, value):
+ return gr.update(value=getattr(opts, key)), opts.dumpjson()
+
+ opts.save(shared.config_filename)
+
+ return gr.update(value=value), opts.dumpjson()
+
+ with gr.Blocks(analytics_enabled=False) as settings_interface:
+ settings_submit = gr.Button(value="Apply settings", variant='primary')
+ result = gr.HTML()
+
+ settings_cols = 3
+ items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols)
+
+ quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")]
+ quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings')
+
+ quicksettings_list = []
+
+ cols_displayed = 0
+ items_displayed = 0
+ previous_section = None
+ column = None
+ with gr.Row(elem_id="settings").style(equal_height=False):
+ for i, (k, item) in enumerate(opts.data_labels.items()):
+ section_must_be_skipped = item.section[0] is None
+
+ if previous_section != item.section and not section_must_be_skipped:
+ if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None):
+ if column is not None:
+ column.__exit__()
+
+ column = gr.Column(variant='panel')
+ column.__enter__()
+
+ items_displayed = 0
+ cols_displayed += 1
+
+ previous_section = item.section
+
+ elem_id, text = item.section
+ gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='<h1 class="gr-button-lg">{}</h1>'.format(text))
+
+ if k in quicksettings_names and not shared.cmd_opts.freeze_settings:
+ quicksettings_list.append((i, k, item))
+ components.append(dummy_component)
+ elif section_must_be_skipped:
+ components.append(dummy_component)
+ else:
+ component = create_setting_component(k)
+ component_dict[k] = component
+ components.append(component)
+ items_displayed += 1
+
+ with gr.Row():
+ request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
+ download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
+
+ with gr.Row():
+ reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
+ restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
+
+ request_notifications.click(
+ fn=lambda: None,
+ inputs=[],
+ outputs=[],
+ _js='function(){}'
+ )
+
+ download_localization.click(
+ fn=lambda: None,
+ inputs=[],
+ outputs=[],
+ _js='download_localization'
+ )
+
+ def reload_scripts():
+ modules.scripts.reload_script_body_only()
+ reload_javascript() # need to refresh the html page
+
+ reload_script_bodies.click(
+ fn=reload_scripts,
+ inputs=[],
+ outputs=[]
+ )
+
+ def request_restart():
+ shared.state.interrupt()
+ shared.state.need_restart = True
+
+ restart_gradio.click(
+ fn=request_restart,
+ _js='restart_reload',
+ inputs=[],
+ outputs=[],
+ )
+
+ if column is not None:
+ column.__exit__()
+
+ interfaces = [
+ (txt2img_interface, "txt2img", "txt2img"),
+ (img2img_interface, "img2img", "img2img"),
+ (extras_interface, "Extras", "extras"),
+ (pnginfo_interface, "PNG Info", "pnginfo"),
+ (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
+ (train_interface, "Train", "ti"),
+ ]
+
+ css = ""
+
+ for cssfile in modules.scripts.list_files_with_name("style.css"):
+ if not os.path.isfile(cssfile):
+ continue
+
+ with open(cssfile, "r", encoding="utf8") as file:
+ css += file.read() + "\n"
+
+ if os.path.exists(os.path.join(script_path, "user.css")):
+ with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file:
+ css += file.read() + "\n"
+
+ if not cmd_opts.no_progressbar_hiding:
+ css += css_hide_progressbar
+
+ interfaces += script_callbacks.ui_tabs_callback()
+ interfaces += [(settings_interface, "Settings", "settings")]
+
+ extensions_interface = ui_extensions.create_ui()
+ interfaces += [(extensions_interface, "Extensions", "extensions")]
+
+ with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
+ with gr.Row(elem_id="quicksettings"):
+ for i, k, item in quicksettings_list:
+ component = create_setting_component(k, is_quicksettings=True)
+ component_dict[k] = component
+
+ parameters_copypaste.integrate_settings_paste_fields(component_dict)
+ parameters_copypaste.run_bind()
+
+ with gr.Tabs(elem_id="tabs") as tabs:
+ for interface, label, ifid in interfaces:
+ with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
+ interface.render()
+
+ if os.path.exists(os.path.join(script_path, "notification.mp3")):
+ audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
+
+ text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
+ settings_submit.click(
+ fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),
+ inputs=components,
+ outputs=[text_settings, result],
+ )
+
+ for i, k, item in quicksettings_list:
+ component = component_dict[k]
+
+ component.change(
+ fn=lambda value, k=k: run_settings_single(value, key=k),
+ inputs=[component],
+ outputs=[component, text_settings],
+ )
+
+ component_keys = [k for k in opts.data_labels.keys() if k in component_dict]
+
+ def get_settings_values():
+ return [getattr(opts, key) for key in component_keys]
+
+ demo.load(
+ fn=get_settings_values,
+ inputs=[],
+ outputs=[component_dict[k] for k in component_keys],
+ )
+
+ def modelmerger(*args):
+ try:
+ results = modules.extras.run_modelmerger(*args)
+ except Exception as e:
+ print("Error loading/saving model file:", file=sys.stderr)
+ print(traceback.format_exc(), file=sys.stderr)
+ modules.sd_models.list_models() # to remove the potentially missing models from the list
+ return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)]
+ return results
+
+ modelmerger_merge.click(
+ fn=modelmerger,
+ inputs=[
+ primary_model_name,
+ secondary_model_name,
+ tertiary_model_name,
+ interp_method,
+ interp_amount,
+ save_as_half,
+ save_as_safetensors,
+ custom_name,
+ ],
+ outputs=[
+ submit_result,
+ primary_model_name,
+ secondary_model_name,
+ tertiary_model_name,
+ component_dict['sd_model_checkpoint'],
+ ]
+ )
+
+ 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):
+ 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] 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')
+
+ # Since there are many dropdowns that shouldn't be saved,
+ # we only mark dropdowns that should be saved.
+ if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False):
+ apply_field(x, 'value', lambda val: val in x.choices, getattr(x, 'init_field', None))
+ apply_field(x, 'visible')
+
+ visit(txt2img_interface, loadsave, "txt2img")
+ visit(img2img_interface, loadsave, "img2img")
+ visit(extras_interface, loadsave, "extras")
+ visit(modelmerger_interface, loadsave, "modelmerger")
+
+ 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)
+
+ return demo
+
+
+def reload_javascript():
+ with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:
+ javascript = f'<script>{jsfile.read()}</script>'
+
+ scripts_list = modules.scripts.list_scripts("javascript", ".js")
+
+ for basedir, filename, path in scripts_list:
+ with open(path, "r", encoding="utf8") as jsfile:
+ javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"
+
+ if cmd_opts.theme is not None:
+ javascript += f"\n<script>set_theme('{cmd_opts.theme}');</script>\n"
+
+ javascript += f"\n<script>{localization.localization_js(shared.opts.localization)}</script>"
+
+ def template_response(*args, **kwargs):
+ res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
+ res.body = res.body.replace(
+ b'</head>', f'{javascript}</head>'.encode("utf8"))
+ res.init_headers()
+ return res
+
+ gradio.routes.templates.TemplateResponse = template_response
+
+
+if not hasattr(shared, 'GradioTemplateResponseOriginal'):
+ shared.GradioTemplateResponseOriginal = gradio.routes.templates.TemplateResponse
From 162fef394f8d80b54df7ede9e3b7ba65da23d3c5 Mon Sep 17 00:00:00 2001
From: Tim Patton <38817597+pattontim@users.noreply.github.com>
Date: Mon, 21 Nov 2022 10:50:57 -0500
Subject: [PATCH 3/6] Patch line ui endings
---
modules/ui.py | 3628 ++++++++++++++++++++++++-------------------------
1 file changed, 1814 insertions(+), 1814 deletions(-)
diff --git a/modules/ui.py b/modules/ui.py
index a2b06aae7..54d3293a8 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1,1814 +1,1814 @@
-import html
-import json
-import math
-import mimetypes
-import os
-import platform
-import random
-import subprocess as sp
-import sys
-import tempfile
-import time
-import traceback
-from functools import partial, reduce
-
-import gradio as gr
-import gradio.routes
-import gradio.utils
-import numpy as np
-from PIL import Image, PngImagePlugin
-
-
-from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions
-from modules.paths import script_path
-
-from modules.shared import opts, cmd_opts, restricted_opts
-
-if cmd_opts.deepdanbooru:
- from modules.deepbooru import get_deepbooru_tags
-
-import modules.codeformer_model
-import modules.generation_parameters_copypaste as parameters_copypaste
-import modules.gfpgan_model
-import modules.hypernetworks.ui
-import modules.ldsr_model
-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
-import modules.textual_inversion.ui
-import modules.hypernetworks.ui
-from modules.generation_parameters_copypaste import image_from_url_text
-
-# 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 != 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 != None else 7860, cmd_opts.ngrok_region)
-
-
-def gr_show(visible=True):
- return {"visible": visible, "__type__": "update"}
-
-
-sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
-sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
-
-css_hide_progressbar = """
-.wrap .m-12 svg { display:none!important; }
-.wrap .m-12::before { content:"Loading..." }
-.wrap .z-20 svg { display:none!important; }
-.wrap .z-20::before { content:"Loading..." }
-.progress-bar { display:none!important; }
-.meta-text { display:none!important; }
-.meta-text-center { display:none!important; }
-"""
-
-# Using constants for these since the variation selector isn't visible.
-# Important that they exactly match script.js for tooltip to work.
-random_symbol = '\U0001f3b2\ufe0f' # 🎲️
-reuse_symbol = '\u267b\ufe0f' # ♻️
-art_symbol = '\U0001f3a8' # 🎨
-paste_symbol = '\u2199\ufe0f' # ↙
-folder_symbol = '\U0001f4c2' # 📂
-refresh_symbol = '\U0001f504' # 🔄
-save_style_symbol = '\U0001f4be' # 💾
-apply_style_symbol = '\U0001f4cb' # 📋
-
-
-def plaintext_to_html(text):
- text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
- return text
-
-def send_gradio_gallery_to_image(x):
- if len(x) == 0:
- return None
- return image_from_url_text(x[0])
-
-def save_files(js_data, images, do_make_zip, index):
- import csv
- filenames = []
- fullfns = []
-
- #quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
- class MyObject:
- def __init__(self, d=None):
- if d is not None:
- for key, value in d.items():
- setattr(self, key, value)
-
- data = json.loads(js_data)
-
- p = MyObject(data)
- path = opts.outdir_save
- save_to_dirs = opts.use_save_to_dirs_for_ui
- extension: str = opts.samples_format
- start_index = 0
-
- if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]): # ensures we are looking at a specific non-grid picture, and we have save_selected_only
-
- images = [images[index]]
- start_index = index
-
- os.makedirs(opts.outdir_save, exist_ok=True)
-
- with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
- at_start = file.tell() == 0
- writer = csv.writer(file)
- if at_start:
- writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
-
- for image_index, filedata in enumerate(images, start_index):
- image = image_from_url_text(filedata)
-
- is_grid = image_index < p.index_of_first_image
- i = 0 if is_grid else (image_index - p.index_of_first_image)
-
- fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
-
- filename = os.path.relpath(fullfn, path)
- filenames.append(filename)
- fullfns.append(fullfn)
- if txt_fullfn:
- filenames.append(os.path.basename(txt_fullfn))
- fullfns.append(txt_fullfn)
-
- writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
-
- # Make Zip
- if do_make_zip:
- zip_filepath = os.path.join(path, "images.zip")
-
- from zipfile import ZipFile
- with ZipFile(zip_filepath, "w") as zip_file:
- for i in range(len(fullfns)):
- with open(fullfns[i], mode="rb") as f:
- zip_file.writestr(filenames[i], f.read())
- fullfns.insert(0, zip_filepath)
-
- return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
-
-def save_pil_to_file(pil_image, dir=None):
- use_metadata = False
- metadata = PngImagePlugin.PngInfo()
- for key, value in pil_image.info.items():
- if isinstance(key, str) and isinstance(value, str):
- metadata.add_text(key, value)
- use_metadata = True
-
- file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
- pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
- return file_obj
-
-
-# override save to file function so that it also writes PNG info
-gr.processing_utils.save_pil_to_file = save_pil_to_file
-
-
-def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
- def f(*args, extra_outputs_array=extra_outputs, **kwargs):
- run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
- if run_memmon:
- shared.mem_mon.monitor()
- t = time.perf_counter()
-
- try:
- res = list(func(*args, **kwargs))
- except Exception as e:
- # When printing out our debug argument list, do not print out more than a MB of text
- max_debug_str_len = 131072 # (1024*1024)/8
-
- print("Error completing request", file=sys.stderr)
- argStr = f"Arguments: {str(args)} {str(kwargs)}"
- print(argStr[:max_debug_str_len], file=sys.stderr)
- if len(argStr) > max_debug_str_len:
- print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
-
- print(traceback.format_exc(), file=sys.stderr)
-
- shared.state.job = ""
- shared.state.job_count = 0
-
- if extra_outputs_array is None:
- extra_outputs_array = [None, '']
-
- res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
-
- shared.state.skipped = False
- shared.state.interrupted = False
- shared.state.job_count = 0
-
- if not add_stats:
- return tuple(res)
-
- elapsed = time.perf_counter() - t
- elapsed_m = int(elapsed // 60)
- elapsed_s = elapsed % 60
- elapsed_text = f"{elapsed_s:.2f}s"
- if elapsed_m > 0:
- elapsed_text = f"{elapsed_m}m "+elapsed_text
-
- if run_memmon:
- mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
- active_peak = mem_stats['active_peak']
- reserved_peak = mem_stats['reserved_peak']
- sys_peak = mem_stats['system_peak']
- sys_total = mem_stats['total']
- sys_pct = round(sys_peak/max(sys_total, 1) * 100, 2)
-
- vram_html = f"<p class='vram'>Torch active/reserved: {active_peak}/{reserved_peak} MiB, <wbr>Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)</p>"
- else:
- vram_html = ''
-
- # last item is always HTML
- res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
-
- return tuple(res)
-
- return f
-
-
-def calc_time_left(progress, threshold, label, force_display):
- if progress == 0:
- return ""
- else:
- time_since_start = time.time() - shared.state.time_start
- eta = (time_since_start/progress)
- eta_relative = eta-time_since_start
- if (eta_relative > threshold and progress > 0.02) or force_display:
- if eta_relative > 3600:
- return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))
- elif eta_relative > 60:
- return label + time.strftime('%M:%S', time.gmtime(eta_relative))
- else:
- return label + time.strftime('%Ss', time.gmtime(eta_relative))
- else:
- return ""
-
-
-def check_progress_call(id_part):
- if shared.state.job_count == 0:
- return "", gr_show(False), gr_show(False), gr_show(False)
-
- progress = 0
-
- if shared.state.job_count > 0:
- progress += shared.state.job_no / shared.state.job_count
- if shared.state.sampling_steps > 0:
- progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
-
- time_left = calc_time_left( progress, 1, " ETA: ", shared.state.time_left_force_display )
- if time_left != "":
- shared.state.time_left_force_display = True
-
- progress = min(progress, 1)
-
- progressbar = ""
- if opts.show_progressbar:
- progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:visible;width:{progress * 100}%;white-space:nowrap;">{" " * 2 + str(int(progress*100))+"%" + time_left if progress > 0.01 else ""}</div></div>"""
-
- image = gr_show(False)
- preview_visibility = gr_show(False)
-
- if opts.show_progress_every_n_steps != 0:
- shared.state.set_current_image()
- image = shared.state.current_image
-
- if image is None:
- image = gr.update(value=None)
- else:
- preview_visibility = gr_show(True)
-
- if shared.state.textinfo is not None:
- textinfo_result = gr.HTML.update(value=shared.state.textinfo, visible=True)
- else:
- textinfo_result = gr_show(False)
-
- return f"<span id='{id_part}_progress_span' style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image, textinfo_result
-
-
-def check_progress_call_initial(id_part):
- shared.state.job_count = -1
- shared.state.current_latent = None
- shared.state.current_image = None
- shared.state.textinfo = None
- shared.state.time_start = time.time()
- shared.state.time_left_force_display = False
-
- return check_progress_call(id_part)
-
-
-def roll_artist(prompt):
- allowed_cats = set([x for x in shared.artist_db.categories() if len(opts.random_artist_categories)==0 or x in opts.random_artist_categories])
- artist = random.choice([x for x in shared.artist_db.artists if x.category in allowed_cats])
-
- return prompt + ", " + artist.name if prompt != '' else artist.name
-
-
-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(4)]
-
-
-def apply_styles(prompt, prompt_neg, style1_name, style2_name):
- prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, [style1_name, style2_name])
- prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, [style1_name, style2_name])
-
- return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value="None"), gr.Dropdown.update(value="None")]
-
-
-def interrogate(image):
- prompt = shared.interrogator.interrogate(image)
-
- return gr_show(True) if prompt is None else prompt
-
-
-def interrogate_deepbooru(image):
- prompt = get_deepbooru_tags(image)
- return gr_show(True) if prompt is None else prompt
-
-
-def create_seed_inputs():
- with gr.Row():
- with gr.Box():
- with gr.Row(elem_id='seed_row'):
- seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1)
- seed.style(container=False)
- random_seed = gr.Button(random_symbol, elem_id='random_seed')
- reuse_seed = gr.Button(reuse_symbol, elem_id='reuse_seed')
-
- with gr.Box(elem_id='subseed_show_box'):
- seed_checkbox = gr.Checkbox(label='Extra', elem_id='subseed_show', value=False)
-
- # Components to show/hide based on the 'Extra' checkbox
- seed_extras = []
-
- with gr.Row(visible=False) as seed_extra_row_1:
- seed_extras.append(seed_extra_row_1)
- with gr.Box():
- with gr.Row(elem_id='subseed_row'):
- subseed = gr.Number(label='Variation seed', value=-1)
- subseed.style(container=False)
- random_subseed = gr.Button(random_symbol, elem_id='random_subseed')
- reuse_subseed = gr.Button(reuse_symbol, elem_id='reuse_subseed')
- subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01)
-
- with gr.Row(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=64, label="Resize seed from width", value=0)
- seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from height", value=0)
-
- 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_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:
- _, 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]
- tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1])
- style_class = ' class="red"' if (token_count > max_length) else ""
- return f"<span {style_class}>{token_count}/{max_length}</span>"
-
-
-def create_toprow(is_img2img):
- id_part = "img2img" if is_img2img else "txt2img"
-
- with gr.Row(elem_id="toprow"):
- with gr.Column(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=2,
- placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)"
- )
-
- with gr.Row():
- with gr.Column(scale=80):
- with gr.Row():
- negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2,
- placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)"
- )
-
- with gr.Column(scale=1, elem_id="roll_col"):
- roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
- paste = gr.Button(value=paste_symbol, elem_id="paste")
- save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
- prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
-
- token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
- token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
-
- button_interrogate = None
- button_deepbooru = None
- if is_img2img:
- with gr.Column(scale=1, elem_id="interrogate_col"):
- button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
-
- if cmd_opts.deepdanbooru:
- button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
-
- with gr.Column(scale=1):
- with gr.Row():
- skip = gr.Button('Skip', elem_id=f"{id_part}_skip")
- interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt")
- 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():
- with gr.Column(scale=1, elem_id="style_pos_col"):
- prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
- prompt_style.save_to_config = True
-
- with gr.Column(scale=1, elem_id="style_neg_col"):
- prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
- prompt_style2.save_to_config = True
-
- return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
-
-
-def setup_progressbar(progressbar, preview, id_part, textinfo=None):
- if textinfo is None:
- textinfo = gr.HTML(visible=False)
-
- check_progress = gr.Button('Check progress', elem_id=f"{id_part}_check_progress", visible=False)
- check_progress.click(
- fn=lambda: check_progress_call(id_part),
- show_progress=False,
- inputs=[],
- outputs=[progressbar, preview, preview, textinfo],
- )
-
- check_progress_initial = gr.Button('Check progress (first)', elem_id=f"{id_part}_check_progress_initial", visible=False)
- check_progress_initial.click(
- fn=lambda: check_progress_call_initial(id_part),
- show_progress=False,
- inputs=[],
- outputs=[progressbar, preview, preview, textinfo],
- )
-
-
-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[key]
- 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 value
-
-
-def update_generation_info(args):
- generation_info, html_info, img_index = args
- try:
- generation_info = json.loads(generation_info)
- if img_index < 0 or img_index >= len(generation_info["infotexts"]):
- return html_info
- return plaintext_to_html(generation_info["infotexts"][img_index])
- except Exception:
- pass
- # if the json parse or anything else fails, just return the old html_info
- return html_info
-
-
-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 = gr.Button(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):
- def open_folder(f):
- if not os.path.exists(f):
- print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
- return
- elif not os.path.isdir(f):
- print(f"""
-WARNING
-An open_folder request was made with an argument that is not a folder.
-This could be an error or a malicious attempt to run code on your computer.
-Requested path was: {f}
-""", file=sys.stderr)
- return
-
- if not shared.cmd_opts.hide_ui_dir_config:
- path = os.path.normpath(f)
- if platform.system() == "Windows":
- os.startfile(path)
- elif platform.system() == "Darwin":
- sp.Popen(["open", path])
- else:
- sp.Popen(["xdg-open", path])
-
- with gr.Column(variant='panel'):
- with gr.Group():
- result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
-
- generation_info = None
- with gr.Column():
- with gr.Row():
- if tabname != "extras":
- save = gr.Button('Save', elem_id=f'save_{tabname}')
-
- buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
- button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
- open_folder_button = gr.Button(folder_symbol, elem_id=button_id)
-
- open_folder_button.click(
- fn=lambda: open_folder(opts.outdir_samples or outdir),
- inputs=[],
- outputs=[],
- )
-
- if tabname != "extras":
- with gr.Row():
- do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
-
- with gr.Row():
- download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
-
- with gr.Group():
- html_info = gr.HTML()
- generation_info = gr.Textbox(visible=False)
- if tabname == 'txt2img' or tabname == 'img2img':
- generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
- generation_info_button.click(
- fn=update_generation_info,
- _js="(x, y) => [x, y, selected_gallery_index()]",
- inputs=[generation_info, html_info],
- outputs=[html_info],
- preprocess=False
- )
-
- save.click(
- fn=wrap_gradio_call(save_files),
- _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
- inputs=[
- generation_info,
- result_gallery,
- do_make_zip,
- html_info,
- ],
- outputs=[
- download_files,
- html_info,
- html_info,
- html_info,
- ]
- )
- else:
- html_info_x = gr.HTML()
- html_info = gr.HTML()
- parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)
- return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info
-
-
-def create_ui(wrap_gradio_gpu_call):
- 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, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
- dummy_component = gr.Label(visible=False)
- txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
-
- with gr.Row(elem_id='txt2img_progress_row'):
- with gr.Column(scale=1):
- pass
-
- with gr.Column(scale=1):
- progressbar = gr.HTML(elem_id="txt2img_progressbar")
- txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
- setup_progressbar(progressbar, txt2img_preview, 'txt2img')
-
- with gr.Row().style(equal_height=False):
- with gr.Column(variant='panel'):
- steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
- sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index")
-
- with gr.Group():
- width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
- height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
-
- with gr.Row():
- restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
- tiling = gr.Checkbox(label='Tiling', value=False)
- enable_hr = gr.Checkbox(label='Highres. fix', value=False)
-
- with gr.Row(visible=False) as hr_options:
- firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass width", value=0)
- firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass height", value=0)
- denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
-
- with gr.Row(equal_height=True):
- batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
- batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
-
- cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
-
- seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
-
- with gr.Group():
- custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
-
- txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples)
- parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)
-
- 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),
- _js="submit",
- inputs=[
- txt2img_prompt,
- txt2img_negative_prompt,
- txt2img_prompt_style,
- txt2img_prompt_style2,
- 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,
- firstphase_width,
- firstphase_height,
- ] + custom_inputs,
-
- outputs=[
- txt2img_gallery,
- generation_info,
- html_info
- ],
- show_progress=False,
- )
-
- txt2img_prompt.submit(**txt2img_args)
- submit.click(**txt2img_args)
-
- 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],
- )
-
- roll.click(
- fn=roll_artist,
- _js="update_txt2img_tokens",
- inputs=[
- txt2img_prompt,
- ],
- outputs=[
- txt2img_prompt,
- ]
- )
-
- 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)),
- (firstphase_width, "First pass size-1"),
- (firstphase_height, "First pass size-2"),
- *modules.scripts.scripts_txt2img.infotext_fields
- ]
- parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields)
-
- txt2img_preview_params = [
- txt2img_prompt,
- txt2img_negative_prompt,
- steps,
- sampler_index,
- cfg_scale,
- seed,
- width,
- height,
- ]
-
- token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
-
- 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, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True)
-
- with gr.Row(elem_id='img2img_progress_row'):
- img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
-
- with gr.Column(scale=1):
- pass
-
- with gr.Column(scale=1):
- progressbar = gr.HTML(elem_id="img2img_progressbar")
- img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
- setup_progressbar(progressbar, img2img_preview, 'img2img')
-
- with gr.Row().style(equal_height=False):
- with gr.Column(variant='panel'):
-
- with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
- with gr.TabItem('img2img', id='img2img'):
- init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool).style(height=480)
-
- with gr.TabItem('Inpaint', id='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)
-
- init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
- init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
-
- mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
-
- with gr.Row():
- mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
- inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index")
-
- inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index")
-
- with gr.Row():
- inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False)
- inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32)
-
- with gr.TabItem('Batch img2img', id='batch'):
- hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
- gr.HTML(f"<p class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.<br>Use an empty output directory to save pictures normally instead of writing to the output directory.{hidden}</p>")
- img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs)
- img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs)
-
- with gr.Row():
- resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")
-
- steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
- sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
-
- with gr.Group():
- width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512, elem_id="img2img_width")
- height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512, elem_id="img2img_height")
-
- with gr.Row():
- restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
- tiling = gr.Checkbox(label='Tiling', value=False)
-
- with gr.Row():
- batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
- batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
-
- with gr.Group():
- cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
- denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75)
-
- seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
-
- with gr.Group():
- custom_inputs = modules.scripts.scripts_img2img.setup_ui()
-
- img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples)
- parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt)
-
- 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
- ]
- )
-
- mask_mode.change(
- lambda mode, img: {
- init_img_with_mask: gr_show(mode == 0),
- init_img_inpaint: gr_show(mode == 1),
- init_mask_inpaint: gr_show(mode == 1),
- },
- inputs=[mask_mode, init_img_with_mask],
- outputs=[
- init_img_with_mask,
- init_img_inpaint,
- init_mask_inpaint,
- ],
- )
-
- img2img_args = dict(
- fn=wrap_gradio_gpu_call(modules.img2img.img2img),
- _js="submit_img2img",
- inputs=[
- dummy_component,
- img2img_prompt,
- img2img_negative_prompt,
- img2img_prompt_style,
- img2img_prompt_style2,
- init_img,
- init_img_with_mask,
- init_img_inpaint,
- init_mask_inpaint,
- mask_mode,
- steps,
- sampler_index,
- mask_blur,
- inpainting_fill,
- restore_faces,
- tiling,
- batch_count,
- batch_size,
- 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,
- ] + custom_inputs,
- outputs=[
- img2img_gallery,
- generation_info,
- html_info
- ],
- show_progress=False,
- )
-
- img2img_prompt.submit(**img2img_args)
- submit.click(**img2img_args)
-
- img2img_interrogate.click(
- fn=interrogate,
- inputs=[init_img],
- outputs=[img2img_prompt],
- )
-
- if cmd_opts.deepdanbooru:
- img2img_deepbooru.click(
- fn=interrogate_deepbooru,
- inputs=[init_img],
- outputs=[img2img_prompt],
- )
-
-
- roll.click(
- fn=roll_artist,
- _js="update_img2img_tokens",
- inputs=[
- img2img_prompt,
- ],
- outputs=[
- img2img_prompt,
- ]
- )
-
- prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
- style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)]
- 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_style, img2img_prompt_style, txt2img_prompt_style2, img2img_prompt_style2],
- )
-
- for button, (prompt, negative_prompt), (style1, style2), 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, style1, style2],
- outputs=[prompt, negative_prompt, style1, style2],
- )
-
- token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
-
- img2img_paste_fields = [
- (img2img_prompt, "Prompt"),
- (img2img_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"),
- *modules.scripts.scripts_img2img.infotext_fields
- ]
- parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)
- parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)
-
- modules.scripts.scripts_current = None
-
- with gr.Blocks(analytics_enabled=False) as extras_interface:
- with gr.Row().style(equal_height=False):
- with gr.Column(variant='panel'):
- with gr.Tabs(elem_id="mode_extras"):
- with gr.TabItem('Single Image'):
- extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil")
-
- with gr.TabItem('Batch Process'):
- image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
-
- with gr.TabItem('Batch from Directory'):
- extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.")
- extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.")
- show_extras_results = gr.Checkbox(label='Show result images', value=True)
-
- submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
-
- with gr.Tabs(elem_id="extras_resize_mode"):
- with gr.TabItem('Scale by'):
- upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4)
- with gr.TabItem('Scale to'):
- with gr.Group():
- with gr.Row():
- upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)
- upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)
- upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
-
- with gr.Group():
- extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
-
- with gr.Group():
- extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
- extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1)
-
- with gr.Group():
- gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan)
-
- with gr.Group():
- codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer)
- codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer)
-
- with gr.Group():
- upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False)
-
- result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples)
-
- submit.click(
- fn=wrap_gradio_gpu_call(modules.extras.run_extras),
- _js="get_extras_tab_index",
- inputs=[
- dummy_component,
- dummy_component,
- extras_image,
- image_batch,
- extras_batch_input_dir,
- extras_batch_output_dir,
- show_extras_results,
- gfpgan_visibility,
- codeformer_visibility,
- codeformer_weight,
- upscaling_resize,
- upscaling_resize_w,
- upscaling_resize_h,
- upscaling_crop,
- extras_upscaler_1,
- extras_upscaler_2,
- extras_upscaler_2_visibility,
- upscale_before_face_fix,
- ],
- outputs=[
- result_images,
- html_info_x,
- html_info,
- ]
- )
- parameters_copypaste.add_paste_fields("extras", extras_image, None)
-
- extras_image.change(
- fn=modules.extras.clear_cache,
- inputs=[], outputs=[]
- )
-
- 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)
- html2 = gr.HTML()
- with gr.Row():
- buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
- parameters_copypaste.bind_buttons(buttons, image, generation_info)
-
- image.change(
- fn=wrap_gradio_call(modules.extras.run_pnginfo),
- inputs=[image],
- outputs=[html, generation_info, html2],
- )
-
- with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
- with gr.Row().style(equal_height=False):
- with gr.Column(variant='panel'):
- gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")
-
- with gr.Row():
- primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
- secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
- tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
- custom_name = gr.Textbox(label="Custom Name (Optional)")
- 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)
- interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")
- save_as_half = gr.Checkbox(value=False, label="Save as float16")
- save_as_safetensors = gr.Checkbox(value=False, label="Save as safetensors format")
- modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
-
- with gr.Column(variant='panel'):
- submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
-
- sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
-
- with gr.Blocks(analytics_enabled=False) as train_interface:
- with gr.Row().style(equal_height=False):
- gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
-
- with gr.Row().style(equal_height=False):
- with gr.Tabs(elem_id="train_tabs"):
-
- with gr.Tab(label="Create embedding"):
- new_embedding_name = gr.Textbox(label="Name")
- initialization_text = gr.Textbox(label="Initialization text", value="*")
- nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
- overwrite_old_embedding = gr.Checkbox(value=False, label="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')
-
- with gr.Tab(label="Create hypernetwork"):
- new_hypernetwork_name = gr.Textbox(label="Name")
- new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
- 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'")
- new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)
- 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"])
- new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
- new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
- overwrite_old_hypernetwork = gr.Checkbox(value=False, label="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')
-
- with gr.Tab(label="Preprocess images"):
- process_src = gr.Textbox(label='Source directory')
- process_dst = gr.Textbox(label='Destination directory')
- process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
- process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
- preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"])
-
- with gr.Row():
- process_flip = gr.Checkbox(label='Create flipped copies')
- process_split = gr.Checkbox(label='Split oversized images')
- process_focal_crop = gr.Checkbox(label='Auto focal point crop')
- process_caption = gr.Checkbox(label='Use BLIP for caption')
- process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)
-
- 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)
- process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05)
-
- 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)
- process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05)
- process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
- process_focal_crop_debug = gr.Checkbox(label='Create debug image')
-
- with gr.Row():
- with gr.Column(scale=3):
- gr.HTML(value="")
-
- with gr.Column():
- with gr.Row():
- interrupt_preprocessing = gr.Button("Interrupt")
- run_preprocess = gr.Button(value="Preprocess", variant='primary')
-
- 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],
- )
-
- with gr.Tab(label="Train"):
- gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
- with gr.Row():
- 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")
- with gr.Row():
- 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 gr.Row():
- embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005")
- hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")
-
- batch_size = gr.Number(label='Batch size', value=1, precision=0)
- dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
- log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
- template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
- training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
- training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
- steps = gr.Number(label='Max steps', value=100000, precision=0)
- create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
- save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
- save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
- preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
-
- with gr.Row():
- interrupt_training = gr.Button(value="Interrupt")
- train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')
- train_embedding = gr.Button(value="Train Embedding", variant='primary')
-
- params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
-
- script_callbacks.ui_train_tabs_callback(params)
-
- with gr.Column():
- progressbar = gr.HTML(elem_id="ti_progressbar")
- 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_preview = gr.Image(elem_id='ti_preview', visible=False)
- ti_progress = gr.HTML(elem_id="ti_progress", value="")
- ti_outcome = gr.HTML(elem_id="ti_error", value="")
- setup_progressbar(progressbar, ti_preview, 'ti', textinfo=ti_progress)
-
- 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
- ],
- 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=[
- 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,
- ],
- 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=[
- train_embedding_name,
- embedding_learn_rate,
- batch_size,
- dataset_directory,
- log_directory,
- training_width,
- training_height,
- steps,
- 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=[
- train_hypernetwork_name,
- hypernetwork_learn_rate,
- batch_size,
- dataset_directory,
- log_directory,
- training_width,
- training_height,
- steps,
- 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 gr.Row(variant="compact"):
- res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
- create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
- else:
- res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
-
- return res
-
- components = []
- component_dict = {}
-
- script_callbacks.ui_settings_callback()
- opts.reorder()
-
- def run_settings(*args):
- changed = []
-
- for key, value, comp in zip(opts.data_labels.keys(), args, components):
- assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
-
- for key, value, comp in zip(opts.data_labels.keys(), args, components):
- if comp == dummy_component:
- continue
-
- if opts.set(key, value):
- changed.append(key)
-
- try:
- opts.save(shared.config_filename)
- except RuntimeError:
- return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'
- return opts.dumpjson(), f'{len(changed)} settings changed: {", ".join(changed)}.'
-
- def run_settings_single(value, key):
- if not opts.same_type(value, opts.data_labels[key].default):
- return gr.update(visible=True), opts.dumpjson()
-
- if not opts.set(key, value):
- return gr.update(value=getattr(opts, key)), opts.dumpjson()
-
- opts.save(shared.config_filename)
-
- return gr.update(value=value), opts.dumpjson()
-
- with gr.Blocks(analytics_enabled=False) as settings_interface:
- settings_submit = gr.Button(value="Apply settings", variant='primary')
- result = gr.HTML()
-
- settings_cols = 3
- items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols)
-
- quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")]
- quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings')
-
- quicksettings_list = []
-
- cols_displayed = 0
- items_displayed = 0
- previous_section = None
- column = None
- with gr.Row(elem_id="settings").style(equal_height=False):
- for i, (k, item) in enumerate(opts.data_labels.items()):
- section_must_be_skipped = item.section[0] is None
-
- if previous_section != item.section and not section_must_be_skipped:
- if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None):
- if column is not None:
- column.__exit__()
-
- column = gr.Column(variant='panel')
- column.__enter__()
-
- items_displayed = 0
- cols_displayed += 1
-
- previous_section = item.section
-
- elem_id, text = item.section
- gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='<h1 class="gr-button-lg">{}</h1>'.format(text))
-
- if k in quicksettings_names and not shared.cmd_opts.freeze_settings:
- quicksettings_list.append((i, k, item))
- components.append(dummy_component)
- elif section_must_be_skipped:
- components.append(dummy_component)
- else:
- component = create_setting_component(k)
- component_dict[k] = component
- components.append(component)
- items_displayed += 1
-
- with gr.Row():
- request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
- download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
-
- with gr.Row():
- reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
- restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
-
- request_notifications.click(
- fn=lambda: None,
- inputs=[],
- outputs=[],
- _js='function(){}'
- )
-
- download_localization.click(
- fn=lambda: None,
- inputs=[],
- outputs=[],
- _js='download_localization'
- )
-
- def reload_scripts():
- modules.scripts.reload_script_body_only()
- reload_javascript() # need to refresh the html page
-
- reload_script_bodies.click(
- fn=reload_scripts,
- inputs=[],
- outputs=[]
- )
-
- def request_restart():
- shared.state.interrupt()
- shared.state.need_restart = True
-
- restart_gradio.click(
- fn=request_restart,
- _js='restart_reload',
- inputs=[],
- outputs=[],
- )
-
- if column is not None:
- column.__exit__()
-
- interfaces = [
- (txt2img_interface, "txt2img", "txt2img"),
- (img2img_interface, "img2img", "img2img"),
- (extras_interface, "Extras", "extras"),
- (pnginfo_interface, "PNG Info", "pnginfo"),
- (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
- (train_interface, "Train", "ti"),
- ]
-
- css = ""
-
- for cssfile in modules.scripts.list_files_with_name("style.css"):
- if not os.path.isfile(cssfile):
- continue
-
- with open(cssfile, "r", encoding="utf8") as file:
- css += file.read() + "\n"
-
- if os.path.exists(os.path.join(script_path, "user.css")):
- with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file:
- css += file.read() + "\n"
-
- if not cmd_opts.no_progressbar_hiding:
- css += css_hide_progressbar
-
- interfaces += script_callbacks.ui_tabs_callback()
- interfaces += [(settings_interface, "Settings", "settings")]
-
- extensions_interface = ui_extensions.create_ui()
- interfaces += [(extensions_interface, "Extensions", "extensions")]
-
- with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
- with gr.Row(elem_id="quicksettings"):
- for i, k, item in quicksettings_list:
- component = create_setting_component(k, is_quicksettings=True)
- component_dict[k] = component
-
- parameters_copypaste.integrate_settings_paste_fields(component_dict)
- parameters_copypaste.run_bind()
-
- with gr.Tabs(elem_id="tabs") as tabs:
- for interface, label, ifid in interfaces:
- with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
- interface.render()
-
- if os.path.exists(os.path.join(script_path, "notification.mp3")):
- audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
-
- text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
- settings_submit.click(
- fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),
- inputs=components,
- outputs=[text_settings, result],
- )
-
- for i, k, item in quicksettings_list:
- component = component_dict[k]
-
- component.change(
- fn=lambda value, k=k: run_settings_single(value, key=k),
- inputs=[component],
- outputs=[component, text_settings],
- )
-
- component_keys = [k for k in opts.data_labels.keys() if k in component_dict]
-
- def get_settings_values():
- return [getattr(opts, key) for key in component_keys]
-
- demo.load(
- fn=get_settings_values,
- inputs=[],
- outputs=[component_dict[k] for k in component_keys],
- )
-
- def modelmerger(*args):
- try:
- results = modules.extras.run_modelmerger(*args)
- except Exception as e:
- print("Error loading/saving model file:", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
- modules.sd_models.list_models() # to remove the potentially missing models from the list
- return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)]
- return results
-
- modelmerger_merge.click(
- fn=modelmerger,
- inputs=[
- primary_model_name,
- secondary_model_name,
- tertiary_model_name,
- interp_method,
- interp_amount,
- save_as_half,
- save_as_safetensors,
- custom_name,
- ],
- outputs=[
- submit_result,
- primary_model_name,
- secondary_model_name,
- tertiary_model_name,
- component_dict['sd_model_checkpoint'],
- ]
- )
-
- 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):
- 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] 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')
-
- # Since there are many dropdowns that shouldn't be saved,
- # we only mark dropdowns that should be saved.
- if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False):
- apply_field(x, 'value', lambda val: val in x.choices, getattr(x, 'init_field', None))
- apply_field(x, 'visible')
-
- visit(txt2img_interface, loadsave, "txt2img")
- visit(img2img_interface, loadsave, "img2img")
- visit(extras_interface, loadsave, "extras")
- visit(modelmerger_interface, loadsave, "modelmerger")
-
- 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)
-
- return demo
-
-
-def reload_javascript():
- with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:
- javascript = f'<script>{jsfile.read()}</script>'
-
- scripts_list = modules.scripts.list_scripts("javascript", ".js")
-
- for basedir, filename, path in scripts_list:
- with open(path, "r", encoding="utf8") as jsfile:
- javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"
-
- if cmd_opts.theme is not None:
- javascript += f"\n<script>set_theme('{cmd_opts.theme}');</script>\n"
-
- javascript += f"\n<script>{localization.localization_js(shared.opts.localization)}</script>"
-
- def template_response(*args, **kwargs):
- res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
- res.body = res.body.replace(
- b'</head>', f'{javascript}</head>'.encode("utf8"))
- res.init_headers()
- return res
-
- gradio.routes.templates.TemplateResponse = template_response
-
-
-if not hasattr(shared, 'GradioTemplateResponseOriginal'):
- shared.GradioTemplateResponseOriginal = gradio.routes.templates.TemplateResponse
+import html
+import json
+import math
+import mimetypes
+import os
+import platform
+import random
+import subprocess as sp
+import sys
+import tempfile
+import time
+import traceback
+from functools import partial, reduce
+
+import gradio as gr
+import gradio.routes
+import gradio.utils
+import numpy as np
+from PIL import Image, PngImagePlugin
+
+
+from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions
+from modules.paths import script_path
+
+from modules.shared import opts, cmd_opts, restricted_opts
+
+if cmd_opts.deepdanbooru:
+ from modules.deepbooru import get_deepbooru_tags
+
+import modules.codeformer_model
+import modules.generation_parameters_copypaste as parameters_copypaste
+import modules.gfpgan_model
+import modules.hypernetworks.ui
+import modules.ldsr_model
+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
+import modules.textual_inversion.ui
+import modules.hypernetworks.ui
+from modules.generation_parameters_copypaste import image_from_url_text
+
+# 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 != 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 != None else 7860, cmd_opts.ngrok_region)
+
+
+def gr_show(visible=True):
+ return {"visible": visible, "__type__": "update"}
+
+
+sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
+sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
+
+css_hide_progressbar = """
+.wrap .m-12 svg { display:none!important; }
+.wrap .m-12::before { content:"Loading..." }
+.wrap .z-20 svg { display:none!important; }
+.wrap .z-20::before { content:"Loading..." }
+.progress-bar { display:none!important; }
+.meta-text { display:none!important; }
+.meta-text-center { display:none!important; }
+"""
+
+# Using constants for these since the variation selector isn't visible.
+# Important that they exactly match script.js for tooltip to work.
+random_symbol = '\U0001f3b2\ufe0f' # 🎲️
+reuse_symbol = '\u267b\ufe0f' # ♻️
+art_symbol = '\U0001f3a8' # 🎨
+paste_symbol = '\u2199\ufe0f' # ↙
+folder_symbol = '\U0001f4c2' # 📂
+refresh_symbol = '\U0001f504' # 🔄
+save_style_symbol = '\U0001f4be' # 💾
+apply_style_symbol = '\U0001f4cb' # 📋
+
+
+def plaintext_to_html(text):
+ text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
+ return text
+
+def send_gradio_gallery_to_image(x):
+ if len(x) == 0:
+ return None
+ return image_from_url_text(x[0])
+
+def save_files(js_data, images, do_make_zip, index):
+ import csv
+ filenames = []
+ fullfns = []
+
+ #quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
+ class MyObject:
+ def __init__(self, d=None):
+ if d is not None:
+ for key, value in d.items():
+ setattr(self, key, value)
+
+ data = json.loads(js_data)
+
+ p = MyObject(data)
+ path = opts.outdir_save
+ save_to_dirs = opts.use_save_to_dirs_for_ui
+ extension: str = opts.samples_format
+ start_index = 0
+
+ if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]): # ensures we are looking at a specific non-grid picture, and we have save_selected_only
+
+ images = [images[index]]
+ start_index = index
+
+ os.makedirs(opts.outdir_save, exist_ok=True)
+
+ with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
+ at_start = file.tell() == 0
+ writer = csv.writer(file)
+ if at_start:
+ writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
+
+ for image_index, filedata in enumerate(images, start_index):
+ image = image_from_url_text(filedata)
+
+ is_grid = image_index < p.index_of_first_image
+ i = 0 if is_grid else (image_index - p.index_of_first_image)
+
+ fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
+
+ filename = os.path.relpath(fullfn, path)
+ filenames.append(filename)
+ fullfns.append(fullfn)
+ if txt_fullfn:
+ filenames.append(os.path.basename(txt_fullfn))
+ fullfns.append(txt_fullfn)
+
+ writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
+
+ # Make Zip
+ if do_make_zip:
+ zip_filepath = os.path.join(path, "images.zip")
+
+ from zipfile import ZipFile
+ with ZipFile(zip_filepath, "w") as zip_file:
+ for i in range(len(fullfns)):
+ with open(fullfns[i], mode="rb") as f:
+ zip_file.writestr(filenames[i], f.read())
+ fullfns.insert(0, zip_filepath)
+
+ return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
+
+def save_pil_to_file(pil_image, dir=None):
+ use_metadata = False
+ metadata = PngImagePlugin.PngInfo()
+ for key, value in pil_image.info.items():
+ if isinstance(key, str) and isinstance(value, str):
+ metadata.add_text(key, value)
+ use_metadata = True
+
+ file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
+ pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
+ return file_obj
+
+
+# override save to file function so that it also writes PNG info
+gr.processing_utils.save_pil_to_file = save_pil_to_file
+
+
+def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
+ def f(*args, extra_outputs_array=extra_outputs, **kwargs):
+ run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
+ if run_memmon:
+ shared.mem_mon.monitor()
+ t = time.perf_counter()
+
+ try:
+ res = list(func(*args, **kwargs))
+ except Exception as e:
+ # When printing out our debug argument list, do not print out more than a MB of text
+ max_debug_str_len = 131072 # (1024*1024)/8
+
+ print("Error completing request", file=sys.stderr)
+ argStr = f"Arguments: {str(args)} {str(kwargs)}"
+ print(argStr[:max_debug_str_len], file=sys.stderr)
+ if len(argStr) > max_debug_str_len:
+ print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
+
+ print(traceback.format_exc(), file=sys.stderr)
+
+ shared.state.job = ""
+ shared.state.job_count = 0
+
+ if extra_outputs_array is None:
+ extra_outputs_array = [None, '']
+
+ res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
+
+ shared.state.skipped = False
+ shared.state.interrupted = False
+ shared.state.job_count = 0
+
+ if not add_stats:
+ return tuple(res)
+
+ elapsed = time.perf_counter() - t
+ elapsed_m = int(elapsed // 60)
+ elapsed_s = elapsed % 60
+ elapsed_text = f"{elapsed_s:.2f}s"
+ if elapsed_m > 0:
+ elapsed_text = f"{elapsed_m}m "+elapsed_text
+
+ if run_memmon:
+ mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
+ active_peak = mem_stats['active_peak']
+ reserved_peak = mem_stats['reserved_peak']
+ sys_peak = mem_stats['system_peak']
+ sys_total = mem_stats['total']
+ sys_pct = round(sys_peak/max(sys_total, 1) * 100, 2)
+
+ vram_html = f"<p class='vram'>Torch active/reserved: {active_peak}/{reserved_peak} MiB, <wbr>Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)</p>"
+ else:
+ vram_html = ''
+
+ # last item is always HTML
+ res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
+
+ return tuple(res)
+
+ return f
+
+
+def calc_time_left(progress, threshold, label, force_display):
+ if progress == 0:
+ return ""
+ else:
+ time_since_start = time.time() - shared.state.time_start
+ eta = (time_since_start/progress)
+ eta_relative = eta-time_since_start
+ if (eta_relative > threshold and progress > 0.02) or force_display:
+ if eta_relative > 3600:
+ return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))
+ elif eta_relative > 60:
+ return label + time.strftime('%M:%S', time.gmtime(eta_relative))
+ else:
+ return label + time.strftime('%Ss', time.gmtime(eta_relative))
+ else:
+ return ""
+
+
+def check_progress_call(id_part):
+ if shared.state.job_count == 0:
+ return "", gr_show(False), gr_show(False), gr_show(False)
+
+ progress = 0
+
+ if shared.state.job_count > 0:
+ progress += shared.state.job_no / shared.state.job_count
+ if shared.state.sampling_steps > 0:
+ progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
+
+ time_left = calc_time_left( progress, 1, " ETA: ", shared.state.time_left_force_display )
+ if time_left != "":
+ shared.state.time_left_force_display = True
+
+ progress = min(progress, 1)
+
+ progressbar = ""
+ if opts.show_progressbar:
+ progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:visible;width:{progress * 100}%;white-space:nowrap;">{" " * 2 + str(int(progress*100))+"%" + time_left if progress > 0.01 else ""}</div></div>"""
+
+ image = gr_show(False)
+ preview_visibility = gr_show(False)
+
+ if opts.show_progress_every_n_steps != 0:
+ shared.state.set_current_image()
+ image = shared.state.current_image
+
+ if image is None:
+ image = gr.update(value=None)
+ else:
+ preview_visibility = gr_show(True)
+
+ if shared.state.textinfo is not None:
+ textinfo_result = gr.HTML.update(value=shared.state.textinfo, visible=True)
+ else:
+ textinfo_result = gr_show(False)
+
+ return f"<span id='{id_part}_progress_span' style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image, textinfo_result
+
+
+def check_progress_call_initial(id_part):
+ shared.state.job_count = -1
+ shared.state.current_latent = None
+ shared.state.current_image = None
+ shared.state.textinfo = None
+ shared.state.time_start = time.time()
+ shared.state.time_left_force_display = False
+
+ return check_progress_call(id_part)
+
+
+def roll_artist(prompt):
+ allowed_cats = set([x for x in shared.artist_db.categories() if len(opts.random_artist_categories)==0 or x in opts.random_artist_categories])
+ artist = random.choice([x for x in shared.artist_db.artists if x.category in allowed_cats])
+
+ return prompt + ", " + artist.name if prompt != '' else artist.name
+
+
+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(4)]
+
+
+def apply_styles(prompt, prompt_neg, style1_name, style2_name):
+ prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, [style1_name, style2_name])
+ prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, [style1_name, style2_name])
+
+ return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value="None"), gr.Dropdown.update(value="None")]
+
+
+def interrogate(image):
+ prompt = shared.interrogator.interrogate(image)
+
+ return gr_show(True) if prompt is None else prompt
+
+
+def interrogate_deepbooru(image):
+ prompt = get_deepbooru_tags(image)
+ return gr_show(True) if prompt is None else prompt
+
+
+def create_seed_inputs():
+ with gr.Row():
+ with gr.Box():
+ with gr.Row(elem_id='seed_row'):
+ seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1)
+ seed.style(container=False)
+ random_seed = gr.Button(random_symbol, elem_id='random_seed')
+ reuse_seed = gr.Button(reuse_symbol, elem_id='reuse_seed')
+
+ with gr.Box(elem_id='subseed_show_box'):
+ seed_checkbox = gr.Checkbox(label='Extra', elem_id='subseed_show', value=False)
+
+ # Components to show/hide based on the 'Extra' checkbox
+ seed_extras = []
+
+ with gr.Row(visible=False) as seed_extra_row_1:
+ seed_extras.append(seed_extra_row_1)
+ with gr.Box():
+ with gr.Row(elem_id='subseed_row'):
+ subseed = gr.Number(label='Variation seed', value=-1)
+ subseed.style(container=False)
+ random_subseed = gr.Button(random_symbol, elem_id='random_subseed')
+ reuse_subseed = gr.Button(reuse_symbol, elem_id='reuse_subseed')
+ subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01)
+
+ with gr.Row(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=64, label="Resize seed from width", value=0)
+ seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from height", value=0)
+
+ 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_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:
+ _, 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]
+ tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1])
+ style_class = ' class="red"' if (token_count > max_length) else ""
+ return f"<span {style_class}>{token_count}/{max_length}</span>"
+
+
+def create_toprow(is_img2img):
+ id_part = "img2img" if is_img2img else "txt2img"
+
+ with gr.Row(elem_id="toprow"):
+ with gr.Column(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=2,
+ placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)"
+ )
+
+ with gr.Row():
+ with gr.Column(scale=80):
+ with gr.Row():
+ negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2,
+ placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)"
+ )
+
+ with gr.Column(scale=1, elem_id="roll_col"):
+ roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
+ paste = gr.Button(value=paste_symbol, elem_id="paste")
+ save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
+ prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
+
+ token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
+ token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
+
+ button_interrogate = None
+ button_deepbooru = None
+ if is_img2img:
+ with gr.Column(scale=1, elem_id="interrogate_col"):
+ button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
+
+ if cmd_opts.deepdanbooru:
+ button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
+
+ with gr.Column(scale=1):
+ with gr.Row():
+ skip = gr.Button('Skip', elem_id=f"{id_part}_skip")
+ interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt")
+ 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():
+ with gr.Column(scale=1, elem_id="style_pos_col"):
+ prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
+ prompt_style.save_to_config = True
+
+ with gr.Column(scale=1, elem_id="style_neg_col"):
+ prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
+ prompt_style2.save_to_config = True
+
+ return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
+
+
+def setup_progressbar(progressbar, preview, id_part, textinfo=None):
+ if textinfo is None:
+ textinfo = gr.HTML(visible=False)
+
+ check_progress = gr.Button('Check progress', elem_id=f"{id_part}_check_progress", visible=False)
+ check_progress.click(
+ fn=lambda: check_progress_call(id_part),
+ show_progress=False,
+ inputs=[],
+ outputs=[progressbar, preview, preview, textinfo],
+ )
+
+ check_progress_initial = gr.Button('Check progress (first)', elem_id=f"{id_part}_check_progress_initial", visible=False)
+ check_progress_initial.click(
+ fn=lambda: check_progress_call_initial(id_part),
+ show_progress=False,
+ inputs=[],
+ outputs=[progressbar, preview, preview, textinfo],
+ )
+
+
+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[key]
+ 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 value
+
+
+def update_generation_info(args):
+ generation_info, html_info, img_index = args
+ try:
+ generation_info = json.loads(generation_info)
+ if img_index < 0 or img_index >= len(generation_info["infotexts"]):
+ return html_info
+ return plaintext_to_html(generation_info["infotexts"][img_index])
+ except Exception:
+ pass
+ # if the json parse or anything else fails, just return the old html_info
+ return html_info
+
+
+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 = gr.Button(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):
+ def open_folder(f):
+ if not os.path.exists(f):
+ print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
+ return
+ elif not os.path.isdir(f):
+ print(f"""
+WARNING
+An open_folder request was made with an argument that is not a folder.
+This could be an error or a malicious attempt to run code on your computer.
+Requested path was: {f}
+""", file=sys.stderr)
+ return
+
+ if not shared.cmd_opts.hide_ui_dir_config:
+ path = os.path.normpath(f)
+ if platform.system() == "Windows":
+ os.startfile(path)
+ elif platform.system() == "Darwin":
+ sp.Popen(["open", path])
+ else:
+ sp.Popen(["xdg-open", path])
+
+ with gr.Column(variant='panel'):
+ with gr.Group():
+ result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
+
+ generation_info = None
+ with gr.Column():
+ with gr.Row():
+ if tabname != "extras":
+ save = gr.Button('Save', elem_id=f'save_{tabname}')
+
+ buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
+ button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
+ open_folder_button = gr.Button(folder_symbol, elem_id=button_id)
+
+ open_folder_button.click(
+ fn=lambda: open_folder(opts.outdir_samples or outdir),
+ inputs=[],
+ outputs=[],
+ )
+
+ if tabname != "extras":
+ with gr.Row():
+ do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
+
+ with gr.Row():
+ download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
+
+ with gr.Group():
+ html_info = gr.HTML()
+ generation_info = gr.Textbox(visible=False)
+ if tabname == 'txt2img' or tabname == 'img2img':
+ generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
+ generation_info_button.click(
+ fn=update_generation_info,
+ _js="(x, y) => [x, y, selected_gallery_index()]",
+ inputs=[generation_info, html_info],
+ outputs=[html_info],
+ preprocess=False
+ )
+
+ save.click(
+ fn=wrap_gradio_call(save_files),
+ _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
+ inputs=[
+ generation_info,
+ result_gallery,
+ do_make_zip,
+ html_info,
+ ],
+ outputs=[
+ download_files,
+ html_info,
+ html_info,
+ html_info,
+ ]
+ )
+ else:
+ html_info_x = gr.HTML()
+ html_info = gr.HTML()
+ parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)
+ return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info
+
+
+def create_ui(wrap_gradio_gpu_call):
+ 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, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
+ dummy_component = gr.Label(visible=False)
+ txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
+
+ with gr.Row(elem_id='txt2img_progress_row'):
+ with gr.Column(scale=1):
+ pass
+
+ with gr.Column(scale=1):
+ progressbar = gr.HTML(elem_id="txt2img_progressbar")
+ txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
+ setup_progressbar(progressbar, txt2img_preview, 'txt2img')
+
+ with gr.Row().style(equal_height=False):
+ with gr.Column(variant='panel'):
+ steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
+ sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index")
+
+ with gr.Group():
+ width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
+ height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+
+ with gr.Row():
+ restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
+ tiling = gr.Checkbox(label='Tiling', value=False)
+ enable_hr = gr.Checkbox(label='Highres. fix', value=False)
+
+ with gr.Row(visible=False) as hr_options:
+ firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass width", value=0)
+ firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass height", value=0)
+ denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
+
+ with gr.Row(equal_height=True):
+ batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
+ batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
+
+ cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
+
+ seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
+
+ with gr.Group():
+ custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
+
+ txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples)
+ parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)
+
+ 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),
+ _js="submit",
+ inputs=[
+ txt2img_prompt,
+ txt2img_negative_prompt,
+ txt2img_prompt_style,
+ txt2img_prompt_style2,
+ 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,
+ firstphase_width,
+ firstphase_height,
+ ] + custom_inputs,
+
+ outputs=[
+ txt2img_gallery,
+ generation_info,
+ html_info
+ ],
+ show_progress=False,
+ )
+
+ txt2img_prompt.submit(**txt2img_args)
+ submit.click(**txt2img_args)
+
+ 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],
+ )
+
+ roll.click(
+ fn=roll_artist,
+ _js="update_txt2img_tokens",
+ inputs=[
+ txt2img_prompt,
+ ],
+ outputs=[
+ txt2img_prompt,
+ ]
+ )
+
+ 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)),
+ (firstphase_width, "First pass size-1"),
+ (firstphase_height, "First pass size-2"),
+ *modules.scripts.scripts_txt2img.infotext_fields
+ ]
+ parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields)
+
+ txt2img_preview_params = [
+ txt2img_prompt,
+ txt2img_negative_prompt,
+ steps,
+ sampler_index,
+ cfg_scale,
+ seed,
+ width,
+ height,
+ ]
+
+ token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
+
+ 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, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True)
+
+ with gr.Row(elem_id='img2img_progress_row'):
+ img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
+
+ with gr.Column(scale=1):
+ pass
+
+ with gr.Column(scale=1):
+ progressbar = gr.HTML(elem_id="img2img_progressbar")
+ img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
+ setup_progressbar(progressbar, img2img_preview, 'img2img')
+
+ with gr.Row().style(equal_height=False):
+ with gr.Column(variant='panel'):
+
+ with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
+ with gr.TabItem('img2img', id='img2img'):
+ init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool).style(height=480)
+
+ with gr.TabItem('Inpaint', id='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)
+
+ init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
+ init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
+
+ mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
+
+ with gr.Row():
+ mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
+ inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index")
+
+ inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index")
+
+ with gr.Row():
+ inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False)
+ inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32)
+
+ with gr.TabItem('Batch img2img', id='batch'):
+ hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
+ gr.HTML(f"<p class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.<br>Use an empty output directory to save pictures normally instead of writing to the output directory.{hidden}</p>")
+ img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs)
+ img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs)
+
+ with gr.Row():
+ resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")
+
+ steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
+ sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
+
+ with gr.Group():
+ width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512, elem_id="img2img_width")
+ height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512, elem_id="img2img_height")
+
+ with gr.Row():
+ restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
+ tiling = gr.Checkbox(label='Tiling', value=False)
+
+ with gr.Row():
+ batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
+ batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
+
+ with gr.Group():
+ cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
+ denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75)
+
+ seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
+
+ with gr.Group():
+ custom_inputs = modules.scripts.scripts_img2img.setup_ui()
+
+ img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples)
+ parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt)
+
+ 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
+ ]
+ )
+
+ mask_mode.change(
+ lambda mode, img: {
+ init_img_with_mask: gr_show(mode == 0),
+ init_img_inpaint: gr_show(mode == 1),
+ init_mask_inpaint: gr_show(mode == 1),
+ },
+ inputs=[mask_mode, init_img_with_mask],
+ outputs=[
+ init_img_with_mask,
+ init_img_inpaint,
+ init_mask_inpaint,
+ ],
+ )
+
+ img2img_args = dict(
+ fn=wrap_gradio_gpu_call(modules.img2img.img2img),
+ _js="submit_img2img",
+ inputs=[
+ dummy_component,
+ img2img_prompt,
+ img2img_negative_prompt,
+ img2img_prompt_style,
+ img2img_prompt_style2,
+ init_img,
+ init_img_with_mask,
+ init_img_inpaint,
+ init_mask_inpaint,
+ mask_mode,
+ steps,
+ sampler_index,
+ mask_blur,
+ inpainting_fill,
+ restore_faces,
+ tiling,
+ batch_count,
+ batch_size,
+ 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,
+ ] + custom_inputs,
+ outputs=[
+ img2img_gallery,
+ generation_info,
+ html_info
+ ],
+ show_progress=False,
+ )
+
+ img2img_prompt.submit(**img2img_args)
+ submit.click(**img2img_args)
+
+ img2img_interrogate.click(
+ fn=interrogate,
+ inputs=[init_img],
+ outputs=[img2img_prompt],
+ )
+
+ if cmd_opts.deepdanbooru:
+ img2img_deepbooru.click(
+ fn=interrogate_deepbooru,
+ inputs=[init_img],
+ outputs=[img2img_prompt],
+ )
+
+
+ roll.click(
+ fn=roll_artist,
+ _js="update_img2img_tokens",
+ inputs=[
+ img2img_prompt,
+ ],
+ outputs=[
+ img2img_prompt,
+ ]
+ )
+
+ prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
+ style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)]
+ 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_style, img2img_prompt_style, txt2img_prompt_style2, img2img_prompt_style2],
+ )
+
+ for button, (prompt, negative_prompt), (style1, style2), 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, style1, style2],
+ outputs=[prompt, negative_prompt, style1, style2],
+ )
+
+ token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
+
+ img2img_paste_fields = [
+ (img2img_prompt, "Prompt"),
+ (img2img_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"),
+ *modules.scripts.scripts_img2img.infotext_fields
+ ]
+ parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)
+ parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)
+
+ modules.scripts.scripts_current = None
+
+ with gr.Blocks(analytics_enabled=False) as extras_interface:
+ with gr.Row().style(equal_height=False):
+ with gr.Column(variant='panel'):
+ with gr.Tabs(elem_id="mode_extras"):
+ with gr.TabItem('Single Image'):
+ extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil")
+
+ with gr.TabItem('Batch Process'):
+ image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
+
+ with gr.TabItem('Batch from Directory'):
+ extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.")
+ extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.")
+ show_extras_results = gr.Checkbox(label='Show result images', value=True)
+
+ submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
+
+ with gr.Tabs(elem_id="extras_resize_mode"):
+ with gr.TabItem('Scale by'):
+ upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4)
+ with gr.TabItem('Scale to'):
+ with gr.Group():
+ with gr.Row():
+ upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)
+ upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)
+ upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
+
+ with gr.Group():
+ extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
+
+ with gr.Group():
+ extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
+ extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1)
+
+ with gr.Group():
+ gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan)
+
+ with gr.Group():
+ codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer)
+ codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer)
+
+ with gr.Group():
+ upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False)
+
+ result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples)
+
+ submit.click(
+ fn=wrap_gradio_gpu_call(modules.extras.run_extras),
+ _js="get_extras_tab_index",
+ inputs=[
+ dummy_component,
+ dummy_component,
+ extras_image,
+ image_batch,
+ extras_batch_input_dir,
+ extras_batch_output_dir,
+ show_extras_results,
+ gfpgan_visibility,
+ codeformer_visibility,
+ codeformer_weight,
+ upscaling_resize,
+ upscaling_resize_w,
+ upscaling_resize_h,
+ upscaling_crop,
+ extras_upscaler_1,
+ extras_upscaler_2,
+ extras_upscaler_2_visibility,
+ upscale_before_face_fix,
+ ],
+ outputs=[
+ result_images,
+ html_info_x,
+ html_info,
+ ]
+ )
+ parameters_copypaste.add_paste_fields("extras", extras_image, None)
+
+ extras_image.change(
+ fn=modules.extras.clear_cache,
+ inputs=[], outputs=[]
+ )
+
+ 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)
+ html2 = gr.HTML()
+ with gr.Row():
+ buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
+ parameters_copypaste.bind_buttons(buttons, image, generation_info)
+
+ image.change(
+ fn=wrap_gradio_call(modules.extras.run_pnginfo),
+ inputs=[image],
+ outputs=[html, generation_info, html2],
+ )
+
+ with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
+ with gr.Row().style(equal_height=False):
+ with gr.Column(variant='panel'):
+ gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")
+
+ with gr.Row():
+ primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
+ secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
+ tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
+ custom_name = gr.Textbox(label="Custom Name (Optional)")
+ 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)
+ interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")
+ save_as_half = gr.Checkbox(value=False, label="Save as float16")
+ save_as_safetensors = gr.Checkbox(value=False, label="Save as safetensors format")
+ modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
+
+ with gr.Column(variant='panel'):
+ submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
+
+ sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
+
+ with gr.Blocks(analytics_enabled=False) as train_interface:
+ with gr.Row().style(equal_height=False):
+ gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
+
+ with gr.Row().style(equal_height=False):
+ with gr.Tabs(elem_id="train_tabs"):
+
+ with gr.Tab(label="Create embedding"):
+ new_embedding_name = gr.Textbox(label="Name")
+ initialization_text = gr.Textbox(label="Initialization text", value="*")
+ nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
+ overwrite_old_embedding = gr.Checkbox(value=False, label="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')
+
+ with gr.Tab(label="Create hypernetwork"):
+ new_hypernetwork_name = gr.Textbox(label="Name")
+ new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
+ 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'")
+ new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)
+ 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"])
+ new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
+ new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
+ overwrite_old_hypernetwork = gr.Checkbox(value=False, label="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')
+
+ with gr.Tab(label="Preprocess images"):
+ process_src = gr.Textbox(label='Source directory')
+ process_dst = gr.Textbox(label='Destination directory')
+ process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
+ process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+ preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"])
+
+ with gr.Row():
+ process_flip = gr.Checkbox(label='Create flipped copies')
+ process_split = gr.Checkbox(label='Split oversized images')
+ process_focal_crop = gr.Checkbox(label='Auto focal point crop')
+ process_caption = gr.Checkbox(label='Use BLIP for caption')
+ process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)
+
+ 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)
+ process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05)
+
+ 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)
+ process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05)
+ process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
+ process_focal_crop_debug = gr.Checkbox(label='Create debug image')
+
+ with gr.Row():
+ with gr.Column(scale=3):
+ gr.HTML(value="")
+
+ with gr.Column():
+ with gr.Row():
+ interrupt_preprocessing = gr.Button("Interrupt")
+ run_preprocess = gr.Button(value="Preprocess", variant='primary')
+
+ 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],
+ )
+
+ with gr.Tab(label="Train"):
+ gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
+ with gr.Row():
+ 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")
+ with gr.Row():
+ 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 gr.Row():
+ embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005")
+ hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")
+
+ batch_size = gr.Number(label='Batch size', value=1, precision=0)
+ dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
+ log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
+ template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
+ training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
+ training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+ steps = gr.Number(label='Max steps', value=100000, precision=0)
+ create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
+ save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
+ save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
+ preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
+
+ with gr.Row():
+ interrupt_training = gr.Button(value="Interrupt")
+ train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')
+ train_embedding = gr.Button(value="Train Embedding", variant='primary')
+
+ params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
+
+ script_callbacks.ui_train_tabs_callback(params)
+
+ with gr.Column():
+ progressbar = gr.HTML(elem_id="ti_progressbar")
+ 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_preview = gr.Image(elem_id='ti_preview', visible=False)
+ ti_progress = gr.HTML(elem_id="ti_progress", value="")
+ ti_outcome = gr.HTML(elem_id="ti_error", value="")
+ setup_progressbar(progressbar, ti_preview, 'ti', textinfo=ti_progress)
+
+ 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
+ ],
+ 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=[
+ 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,
+ ],
+ 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=[
+ train_embedding_name,
+ embedding_learn_rate,
+ batch_size,
+ dataset_directory,
+ log_directory,
+ training_width,
+ training_height,
+ steps,
+ 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=[
+ train_hypernetwork_name,
+ hypernetwork_learn_rate,
+ batch_size,
+ dataset_directory,
+ log_directory,
+ training_width,
+ training_height,
+ steps,
+ 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 gr.Row(variant="compact"):
+ res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+ create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
+ else:
+ res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+
+ return res
+
+ components = []
+ component_dict = {}
+
+ script_callbacks.ui_settings_callback()
+ opts.reorder()
+
+ def run_settings(*args):
+ changed = []
+
+ for key, value, comp in zip(opts.data_labels.keys(), args, components):
+ assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
+
+ for key, value, comp in zip(opts.data_labels.keys(), args, components):
+ if comp == dummy_component:
+ continue
+
+ if opts.set(key, value):
+ changed.append(key)
+
+ try:
+ opts.save(shared.config_filename)
+ except RuntimeError:
+ return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'
+ return opts.dumpjson(), f'{len(changed)} settings changed: {", ".join(changed)}.'
+
+ def run_settings_single(value, key):
+ if not opts.same_type(value, opts.data_labels[key].default):
+ return gr.update(visible=True), opts.dumpjson()
+
+ if not opts.set(key, value):
+ return gr.update(value=getattr(opts, key)), opts.dumpjson()
+
+ opts.save(shared.config_filename)
+
+ return gr.update(value=value), opts.dumpjson()
+
+ with gr.Blocks(analytics_enabled=False) as settings_interface:
+ settings_submit = gr.Button(value="Apply settings", variant='primary')
+ result = gr.HTML()
+
+ settings_cols = 3
+ items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols)
+
+ quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")]
+ quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings')
+
+ quicksettings_list = []
+
+ cols_displayed = 0
+ items_displayed = 0
+ previous_section = None
+ column = None
+ with gr.Row(elem_id="settings").style(equal_height=False):
+ for i, (k, item) in enumerate(opts.data_labels.items()):
+ section_must_be_skipped = item.section[0] is None
+
+ if previous_section != item.section and not section_must_be_skipped:
+ if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None):
+ if column is not None:
+ column.__exit__()
+
+ column = gr.Column(variant='panel')
+ column.__enter__()
+
+ items_displayed = 0
+ cols_displayed += 1
+
+ previous_section = item.section
+
+ elem_id, text = item.section
+ gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='<h1 class="gr-button-lg">{}</h1>'.format(text))
+
+ if k in quicksettings_names and not shared.cmd_opts.freeze_settings:
+ quicksettings_list.append((i, k, item))
+ components.append(dummy_component)
+ elif section_must_be_skipped:
+ components.append(dummy_component)
+ else:
+ component = create_setting_component(k)
+ component_dict[k] = component
+ components.append(component)
+ items_displayed += 1
+
+ with gr.Row():
+ request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
+ download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
+
+ with gr.Row():
+ reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
+ restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
+
+ request_notifications.click(
+ fn=lambda: None,
+ inputs=[],
+ outputs=[],
+ _js='function(){}'
+ )
+
+ download_localization.click(
+ fn=lambda: None,
+ inputs=[],
+ outputs=[],
+ _js='download_localization'
+ )
+
+ def reload_scripts():
+ modules.scripts.reload_script_body_only()
+ reload_javascript() # need to refresh the html page
+
+ reload_script_bodies.click(
+ fn=reload_scripts,
+ inputs=[],
+ outputs=[]
+ )
+
+ def request_restart():
+ shared.state.interrupt()
+ shared.state.need_restart = True
+
+ restart_gradio.click(
+ fn=request_restart,
+ _js='restart_reload',
+ inputs=[],
+ outputs=[],
+ )
+
+ if column is not None:
+ column.__exit__()
+
+ interfaces = [
+ (txt2img_interface, "txt2img", "txt2img"),
+ (img2img_interface, "img2img", "img2img"),
+ (extras_interface, "Extras", "extras"),
+ (pnginfo_interface, "PNG Info", "pnginfo"),
+ (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
+ (train_interface, "Train", "ti"),
+ ]
+
+ css = ""
+
+ for cssfile in modules.scripts.list_files_with_name("style.css"):
+ if not os.path.isfile(cssfile):
+ continue
+
+ with open(cssfile, "r", encoding="utf8") as file:
+ css += file.read() + "\n"
+
+ if os.path.exists(os.path.join(script_path, "user.css")):
+ with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file:
+ css += file.read() + "\n"
+
+ if not cmd_opts.no_progressbar_hiding:
+ css += css_hide_progressbar
+
+ interfaces += script_callbacks.ui_tabs_callback()
+ interfaces += [(settings_interface, "Settings", "settings")]
+
+ extensions_interface = ui_extensions.create_ui()
+ interfaces += [(extensions_interface, "Extensions", "extensions")]
+
+ with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
+ with gr.Row(elem_id="quicksettings"):
+ for i, k, item in quicksettings_list:
+ component = create_setting_component(k, is_quicksettings=True)
+ component_dict[k] = component
+
+ parameters_copypaste.integrate_settings_paste_fields(component_dict)
+ parameters_copypaste.run_bind()
+
+ with gr.Tabs(elem_id="tabs") as tabs:
+ for interface, label, ifid in interfaces:
+ with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
+ interface.render()
+
+ if os.path.exists(os.path.join(script_path, "notification.mp3")):
+ audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
+
+ text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
+ settings_submit.click(
+ fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),
+ inputs=components,
+ outputs=[text_settings, result],
+ )
+
+ for i, k, item in quicksettings_list:
+ component = component_dict[k]
+
+ component.change(
+ fn=lambda value, k=k: run_settings_single(value, key=k),
+ inputs=[component],
+ outputs=[component, text_settings],
+ )
+
+ component_keys = [k for k in opts.data_labels.keys() if k in component_dict]
+
+ def get_settings_values():
+ return [getattr(opts, key) for key in component_keys]
+
+ demo.load(
+ fn=get_settings_values,
+ inputs=[],
+ outputs=[component_dict[k] for k in component_keys],
+ )
+
+ def modelmerger(*args):
+ try:
+ results = modules.extras.run_modelmerger(*args)
+ except Exception as e:
+ print("Error loading/saving model file:", file=sys.stderr)
+ print(traceback.format_exc(), file=sys.stderr)
+ modules.sd_models.list_models() # to remove the potentially missing models from the list
+ return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)]
+ return results
+
+ modelmerger_merge.click(
+ fn=modelmerger,
+ inputs=[
+ primary_model_name,
+ secondary_model_name,
+ tertiary_model_name,
+ interp_method,
+ interp_amount,
+ save_as_half,
+ save_as_safetensors,
+ custom_name,
+ ],
+ outputs=[
+ submit_result,
+ primary_model_name,
+ secondary_model_name,
+ tertiary_model_name,
+ component_dict['sd_model_checkpoint'],
+ ]
+ )
+
+ 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):
+ 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] 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')
+
+ # Since there are many dropdowns that shouldn't be saved,
+ # we only mark dropdowns that should be saved.
+ if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False):
+ apply_field(x, 'value', lambda val: val in x.choices, getattr(x, 'init_field', None))
+ apply_field(x, 'visible')
+
+ visit(txt2img_interface, loadsave, "txt2img")
+ visit(img2img_interface, loadsave, "img2img")
+ visit(extras_interface, loadsave, "extras")
+ visit(modelmerger_interface, loadsave, "modelmerger")
+
+ 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)
+
+ return demo
+
+
+def reload_javascript():
+ with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:
+ javascript = f'<script>{jsfile.read()}</script>'
+
+ scripts_list = modules.scripts.list_scripts("javascript", ".js")
+
+ for basedir, filename, path in scripts_list:
+ with open(path, "r", encoding="utf8") as jsfile:
+ javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"
+
+ if cmd_opts.theme is not None:
+ javascript += f"\n<script>set_theme('{cmd_opts.theme}');</script>\n"
+
+ javascript += f"\n<script>{localization.localization_js(shared.opts.localization)}</script>"
+
+ def template_response(*args, **kwargs):
+ res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
+ res.body = res.body.replace(
+ b'</head>', f'{javascript}</head>'.encode("utf8"))
+ res.init_headers()
+ return res
+
+ gradio.routes.templates.TemplateResponse = template_response
+
+
+if not hasattr(shared, 'GradioTemplateResponseOriginal'):
+ shared.GradioTemplateResponseOriginal = gradio.routes.templates.TemplateResponse
From e134b74ce95773789f69d158d23e93b7fe9295dc Mon Sep 17 00:00:00 2001
From: Tim Patton <38817597+pattontim@users.noreply.github.com>
Date: Mon, 21 Nov 2022 10:58:57 -0500
Subject: [PATCH 4/6] Ignore safetensor files
---
.gitignore | 1 +
1 file changed, 1 insertion(+)
diff --git a/.gitignore b/.gitignore
index ee53044c5..21fa26a75 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,6 @@
__pycache__
*.ckpt
+*.safetensors
*.pth
/ESRGAN/*
/SwinIR/*
From 210cb4c128afdd65fa998229a97d0694154983ea Mon Sep 17 00:00:00 2001
From: Tim Patton <38817597+pattontim@users.noreply.github.com>
Date: Mon, 21 Nov 2022 16:40:18 -0500
Subject: [PATCH 5/6] Use GPU for loading safetensors, disable export
---
modules/sd_models.py | 5 +++--
modules/ui.py | 3 ++-
2 files changed, 5 insertions(+), 3 deletions(-)
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 2f8c2c48e..2bbb3bf5a 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -147,8 +147,9 @@ def torch_load(model_filename, model_info, map_override=None):
map_override=shared.weight_load_location if not map_override else map_override
if(checkpoint_types[model_info.exttype] == 'safetensors'):
# safely load weights
- # TODO: safetensors supports zero copy fast load to gpu, see issue #684
- return load_file(model_filename, device=map_override)
+ # TODO: safetensors supports zero copy fast load to gpu, see issue #684.
+ # GPU only for now, see https://github.com/huggingface/safetensors/issues/95
+ return load_file(model_filename, device='cuda')
else:
return torch.load(model_filename, map_location=map_override)
diff --git a/modules/ui.py b/modules/ui.py
index 54d3293a8..c376a59df 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1187,7 +1187,8 @@ def create_ui(wrap_gradio_gpu_call):
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)
interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")
save_as_half = gr.Checkbox(value=False, label="Save as float16")
- save_as_safetensors = gr.Checkbox(value=False, label="Save as safetensors format")
+ # invisible until feature can be verified
+ save_as_safetensors = gr.Checkbox(value=False, label="Save as safetensors format", visible=False)
modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
with gr.Column(variant='panel'):
From ac90cf38c6b55d57d37923aa1fe86c7374e32d0b Mon Sep 17 00:00:00 2001
From: Tim Patton <38817597+pattontim@users.noreply.github.com>
Date: Tue, 22 Nov 2022 10:13:07 -0500
Subject: [PATCH 6/6] safetensors optional for now
---
modules/sd_models.py | 9 ++++++++-
requirements.txt | 1 -
2 files changed, 8 insertions(+), 2 deletions(-)
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 2bbb3bf5a..75f7ab094 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -4,7 +4,6 @@ import sys
import gc
from collections import namedtuple
import torch
-from safetensors.torch import load_file, save_file
import re
from omegaconf import OmegaConf
@@ -149,6 +148,10 @@ def torch_load(model_filename, model_info, map_override=None):
# safely load weights
# TODO: safetensors supports zero copy fast load to gpu, see issue #684.
# GPU only for now, see https://github.com/huggingface/safetensors/issues/95
+ try:
+ from safetensors.torch import load_file
+ except ImportError as e:
+ raise ImportError(f"The model is in safetensors format and it is not installed, use `pip install safetensors`: {e}")
return load_file(model_filename, device='cuda')
else:
return torch.load(model_filename, map_location=map_override)
@@ -157,6 +160,10 @@ def torch_save(model, output_filename):
basename, exttype = os.path.splitext(output_filename)
if(checkpoint_types[exttype] == 'safetensors'):
# [===== >] Reticulating brines...
+ try:
+ from safetensors.torch import save_file
+ except ImportError as e:
+ raise ImportError(f"Export as safetensors selected, yet it is not installed, use `pip install safetensors`: {e}")
save_file(model, output_filename, metadata={"format": "pt"})
else:
torch.save(model, output_filename)
diff --git a/requirements.txt b/requirements.txt
index f7de9f707..762db4f34 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -28,4 +28,3 @@ kornia
lark
inflection
GitPython
-safetensors