mirror of
https://github.com/openvinotoolkit/stable-diffusion-webui.git
synced 2024-12-14 22:53:25 +03:00
Merge branch 'shared-hires-prompt-raw' into shared-hires-prompt-test
This commit is contained in:
Robert Barron
commit
863613293e
@ -326,12 +326,14 @@ class StableDiffusionProcessing:
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self.main_prompt = self.all_prompts[0]
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self.main_negative_prompt = self.all_negative_prompts[0]
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def cached_params(self, required_prompts, steps, extra_network_data):
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def cached_params(self, required_prompts, steps, hires_steps, extra_network_data, use_old_scheduling):
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"""Returns parameters that invalidate the cond cache if changed"""
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return (
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required_prompts,
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steps,
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hires_steps,
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use_old_scheduling,
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opts.CLIP_stop_at_last_layers,
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shared.sd_model.sd_checkpoint_info,
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extra_network_data,
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@ -341,7 +343,7 @@ class StableDiffusionProcessing:
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self.height,
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)
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def get_conds_with_caching(self, function, required_prompts, steps, caches, extra_network_data):
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def get_conds_with_caching(self, function, required_prompts, steps, hires_steps, caches, extra_network_data):
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"""
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Returns the result of calling function(shared.sd_model, required_prompts, steps)
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using a cache to store the result if the same arguments have been used before.
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@ -354,7 +356,7 @@ class StableDiffusionProcessing:
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caches is a list with items described above.
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"""
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cached_params = self.cached_params(required_prompts, steps, extra_network_data)
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cached_params = self.cached_params(required_prompts, steps, hires_steps, extra_network_data, shared.opts.use_old_scheduling)
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for cache in caches:
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if cache[0] is not None and cached_params == cache[0]:
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@ -363,7 +365,7 @@ class StableDiffusionProcessing:
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cache = caches[0]
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with devices.autocast():
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cache[1] = function(shared.sd_model, required_prompts, steps)
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cache[1] = function(shared.sd_model, required_prompts, steps, hires_steps, shared.opts.use_old_scheduling)
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cache[0] = cached_params
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return cache[1]
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@ -374,8 +376,9 @@ class StableDiffusionProcessing:
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sampler_config = sd_samplers.find_sampler_config(self.sampler_name)
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self.step_multiplier = 2 if sampler_config and sampler_config.options.get("second_order", False) else 1
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self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, self.steps * self.step_multiplier, [self.cached_uc], self.extra_network_data)
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self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, self.steps * self.step_multiplier, [self.cached_c], self.extra_network_data)
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self.firstpass_steps = self.steps * self.step_multiplier
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self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, self.firstpass_steps, None, [self.cached_uc], self.extra_network_data)
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self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, self.firstpass_steps, None, [self.cached_c], self.extra_network_data)
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def get_conds(self):
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return self.c, self.uc
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@ -1188,8 +1191,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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hr_prompts = prompt_parser.SdConditioning(self.hr_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y)
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hr_negative_prompts = prompt_parser.SdConditioning(self.hr_negative_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y, is_negative_prompt=True)
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self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, self.steps * self.step_multiplier, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data)
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self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, self.steps * self.step_multiplier, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)
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hires_steps = (self.hr_second_pass_steps or self.steps) * self.step_multiplier
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self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, self.firstpass_steps, hires_steps, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data)
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self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, self.firstpass_steps, hires_steps, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)
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def setup_conds(self):
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super().setup_conds()
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@ -26,7 +26,7 @@ plain: /([^\\\[\]():|]|\\.)+/
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%import common.SIGNED_NUMBER -> NUMBER
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""")
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def get_learned_conditioning_prompt_schedules(prompts, steps):
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def get_learned_conditioning_prompt_schedules(prompts, base_steps, hires_steps=None, use_old_scheduling=False):
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"""
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>>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10)[0]
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>>> g("test")
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@ -57,18 +57,39 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
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[[1, 'female'], [2, 'male'], [3, 'female'], [4, 'male'], [5, 'female'], [6, 'male'], [7, 'female'], [8, 'male'], [9, 'female'], [10, 'male']]
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>>> g("[fe|||]male")
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[[1, 'female'], [2, 'male'], [3, 'male'], [4, 'male'], [5, 'female'], [6, 'male'], [7, 'male'], [8, 'male'], [9, 'female'], [10, 'male']]
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>>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10, 10)[0]
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>>> g("a [b:.5] c")
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[[10, 'a b c']]
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>>> g("a [b:1.5] c")
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[[5, 'a c'], [10, 'a b c']]
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"""
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if hires_steps is None or use_old_scheduling:
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int_offset = 0
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flt_offset = 0
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steps = base_steps
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else:
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int_offset = base_steps
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flt_offset = 1.0
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steps = hires_steps
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def collect_steps(steps, tree):
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res = [steps]
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class CollectSteps(lark.Visitor):
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def scheduled(self, tree):
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tree.children[-2] = float(tree.children[-2])
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if tree.children[-2] < 1:
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tree.children[-2] *= steps
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tree.children[-2] = min(steps, int(tree.children[-2]))
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res.append(tree.children[-2])
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s = tree.children[-2]
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v = float(s)
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if use_old_scheduling:
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v = v*steps if v<1 else v
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else:
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if "." in s:
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v = (v - flt_offset) * steps
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else:
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v = (v - int_offset)
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tree.children[-2] = min(steps, int(v))
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if tree.children[-2] >= 1:
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res.append(tree.children[-2])
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def alternate(self, tree):
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res.extend(range(1, steps+1))
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@ -134,7 +155,7 @@ class SdConditioning(list):
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def get_learned_conditioning(model, prompts: SdConditioning | list[str], steps):
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def get_learned_conditioning(model, prompts: SdConditioning | list[str], steps, hires_steps=None, use_old_scheduling=False):
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"""converts a list of prompts into a list of prompt schedules - each schedule is a list of ScheduledPromptConditioning, specifying the comdition (cond),
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and the sampling step at which this condition is to be replaced by the next one.
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@ -154,7 +175,7 @@ def get_learned_conditioning(model, prompts: SdConditioning | list[str], steps):
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"""
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res = []
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prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps)
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prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps, hires_steps, use_old_scheduling)
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cache = {}
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for prompt, prompt_schedule in zip(prompts, prompt_schedules):
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@ -229,7 +250,7 @@ class MulticondLearnedConditioning:
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self.batch: List[List[ComposableScheduledPromptConditioning]] = batch
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def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning:
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def get_multicond_learned_conditioning(model, prompts, steps, hires_steps=None, use_old_scheduling=False) -> MulticondLearnedConditioning:
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"""same as get_learned_conditioning, but returns a list of ScheduledPromptConditioning along with the weight objects for each prompt.
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For each prompt, the list is obtained by splitting the prompt using the AND separator.
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@ -238,7 +259,7 @@ def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearne
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res_indexes, prompt_flat_list, prompt_indexes = get_multicond_prompt_list(prompts)
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learned_conditioning = get_learned_conditioning(model, prompt_flat_list, steps)
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learned_conditioning = get_learned_conditioning(model, prompt_flat_list, steps, hires_steps, use_old_scheduling)
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res = []
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for indexes in res_indexes:
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@ -1,52 +1,839 @@
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import datetime
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import json
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import os
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import re
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import sys
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import threading
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import time
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import logging
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import gradio as gr
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import torch
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import tqdm
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from modules import shared_cmd_options, shared_gradio_themes, options, shared_items
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import launch
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import modules.interrogate
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import modules.memmon
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import modules.styles
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import modules.devices as devices
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from modules import localization, script_loading, errors, ui_components, shared_items, cmd_args
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from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir # noqa: F401
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from ldm.models.diffusion.ddpm import LatentDiffusion
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from modules import util
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from typing import Optional
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cmd_opts = shared_cmd_options.cmd_opts
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parser = shared_cmd_options.parser
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batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
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parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
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styles_filename = cmd_opts.styles_file
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config_filename = cmd_opts.ui_settings_file
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hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config}
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log = logging.getLogger(__name__)
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demo = None
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device = None
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parser = cmd_args.parser
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weight_load_location = None
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script_loading.preload_extensions(extensions_dir, parser, extension_list=launch.list_extensions(launch.args.ui_settings_file))
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script_loading.preload_extensions(extensions_builtin_dir, parser)
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if os.environ.get('IGNORE_CMD_ARGS_ERRORS', None) is None:
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cmd_opts = parser.parse_args()
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else:
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cmd_opts, _ = parser.parse_known_args()
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restricted_opts = {
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"samples_filename_pattern",
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"directories_filename_pattern",
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"outdir_samples",
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"outdir_txt2img_samples",
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"outdir_img2img_samples",
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"outdir_extras_samples",
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"outdir_grids",
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"outdir_txt2img_grids",
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"outdir_save",
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"outdir_init_images"
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}
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# https://huggingface.co/datasets/freddyaboulton/gradio-theme-subdomains/resolve/main/subdomains.json
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gradio_hf_hub_themes = [
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"gradio/base",
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"gradio/glass",
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"gradio/monochrome",
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"gradio/seafoam",
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"gradio/soft",
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"gradio/dracula_test",
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"abidlabs/dracula_test",
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"abidlabs/Lime",
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"abidlabs/pakistan",
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"Ama434/neutral-barlow",
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"dawood/microsoft_windows",
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"finlaymacklon/smooth_slate",
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"Franklisi/darkmode",
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"freddyaboulton/dracula_revamped",
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"freddyaboulton/test-blue",
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"gstaff/xkcd",
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"Insuz/Mocha",
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"Insuz/SimpleIndigo",
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"JohnSmith9982/small_and_pretty",
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"nota-ai/theme",
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"nuttea/Softblue",
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"ParityError/Anime",
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"reilnuud/polite",
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"remilia/Ghostly",
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"rottenlittlecreature/Moon_Goblin",
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"step-3-profit/Midnight-Deep",
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"Taithrah/Minimal",
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"ysharma/huggingface",
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"ysharma/steampunk"
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]
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cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access
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devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
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(devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'esrgan', 'codeformer'])
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devices.dtype = torch.float32 if cmd_opts.no_half else torch.float16
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devices.dtype_vae = torch.float32 if cmd_opts.no_half or cmd_opts.no_half_vae else torch.float16
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device = devices.device
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weight_load_location = None if cmd_opts.lowram else "cpu"
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batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
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parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
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xformers_available = False
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config_filename = cmd_opts.ui_settings_file
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os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
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hypernetworks = {}
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loaded_hypernetworks = []
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state = None
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prompt_styles = None
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def reload_hypernetworks():
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from modules.hypernetworks import hypernetwork
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global hypernetworks
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interrogator = None
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hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
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class State:
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skipped = False
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interrupted = False
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job = ""
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job_no = 0
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job_count = 0
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processing_has_refined_job_count = False
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job_timestamp = '0'
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sampling_step = 0
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sampling_steps = 0
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current_latent = None
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current_image = None
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current_image_sampling_step = 0
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id_live_preview = 0
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textinfo = None
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time_start = None
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server_start = None
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_server_command_signal = threading.Event()
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_server_command: Optional[str] = None
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@property
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def need_restart(self) -> bool:
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# Compatibility getter for need_restart.
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return self.server_command == "restart"
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@need_restart.setter
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def need_restart(self, value: bool) -> None:
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# Compatibility setter for need_restart.
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if value:
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self.server_command = "restart"
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@property
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def server_command(self):
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return self._server_command
|
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|
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@server_command.setter
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def server_command(self, value: Optional[str]) -> None:
|
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"""
|
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Set the server command to `value` and signal that it's been set.
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"""
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self._server_command = value
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self._server_command_signal.set()
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|
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def wait_for_server_command(self, timeout: Optional[float] = None) -> Optional[str]:
|
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"""
|
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Wait for server command to get set; return and clear the value and signal.
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"""
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if self._server_command_signal.wait(timeout):
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self._server_command_signal.clear()
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req = self._server_command
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self._server_command = None
|
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return req
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return None
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|
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def request_restart(self) -> None:
|
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self.interrupt()
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self.server_command = "restart"
|
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log.info("Received restart request")
|
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|
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def skip(self):
|
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self.skipped = True
|
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log.info("Received skip request")
|
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|
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def interrupt(self):
|
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self.interrupted = True
|
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log.info("Received interrupt request")
|
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|
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def nextjob(self):
|
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if opts.live_previews_enable and opts.show_progress_every_n_steps == -1:
|
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self.do_set_current_image()
|
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|
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self.job_no += 1
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self.sampling_step = 0
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self.current_image_sampling_step = 0
|
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|
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def dict(self):
|
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obj = {
|
||||
"skipped": self.skipped,
|
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"interrupted": self.interrupted,
|
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"job": self.job,
|
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"job_count": self.job_count,
|
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"job_timestamp": self.job_timestamp,
|
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"job_no": self.job_no,
|
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"sampling_step": self.sampling_step,
|
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"sampling_steps": self.sampling_steps,
|
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}
|
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|
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return obj
|
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|
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def begin(self, job: str = "(unknown)"):
|
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self.sampling_step = 0
|
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self.job_count = -1
|
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self.processing_has_refined_job_count = False
|
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self.job_no = 0
|
||||
self.job_timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
|
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self.current_latent = None
|
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self.current_image = None
|
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self.current_image_sampling_step = 0
|
||||
self.id_live_preview = 0
|
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self.skipped = False
|
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self.interrupted = False
|
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self.textinfo = None
|
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self.time_start = time.time()
|
||||
self.job = job
|
||||
devices.torch_gc()
|
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log.info("Starting job %s", job)
|
||||
|
||||
def end(self):
|
||||
duration = time.time() - self.time_start
|
||||
log.info("Ending job %s (%.2f seconds)", self.job, duration)
|
||||
self.job = ""
|
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self.job_count = 0
|
||||
|
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devices.torch_gc()
|
||||
|
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def set_current_image(self):
|
||||
"""sets self.current_image from self.current_latent if enough sampling steps have been made after the last call to this"""
|
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if not parallel_processing_allowed:
|
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return
|
||||
|
||||
if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and opts.live_previews_enable and opts.show_progress_every_n_steps != -1:
|
||||
self.do_set_current_image()
|
||||
|
||||
def do_set_current_image(self):
|
||||
if self.current_latent is None:
|
||||
return
|
||||
|
||||
import modules.sd_samplers
|
||||
|
||||
try:
|
||||
if opts.show_progress_grid:
|
||||
self.assign_current_image(modules.sd_samplers.samples_to_image_grid(self.current_latent))
|
||||
else:
|
||||
self.assign_current_image(modules.sd_samplers.sample_to_image(self.current_latent))
|
||||
|
||||
self.current_image_sampling_step = self.sampling_step
|
||||
|
||||
except Exception:
|
||||
# when switching models during genration, VAE would be on CPU, so creating an image will fail.
|
||||
# we silently ignore this error
|
||||
errors.record_exception()
|
||||
|
||||
def assign_current_image(self, image):
|
||||
self.current_image = image
|
||||
self.id_live_preview += 1
|
||||
|
||||
|
||||
state = State()
|
||||
state.server_start = time.time()
|
||||
|
||||
styles_filename = cmd_opts.styles_file
|
||||
prompt_styles = modules.styles.StyleDatabase(styles_filename)
|
||||
|
||||
interrogator = modules.interrogate.InterrogateModels("interrogate")
|
||||
|
||||
face_restorers = []
|
||||
|
||||
options_templates = None
|
||||
opts = None
|
||||
restricted_opts = None
|
||||
|
||||
sd_model: LatentDiffusion = None
|
||||
class OptionInfo:
|
||||
def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None, comment_before='', comment_after=''):
|
||||
self.default = default
|
||||
self.label = label
|
||||
self.component = component
|
||||
self.component_args = component_args
|
||||
self.onchange = onchange
|
||||
self.section = section
|
||||
self.refresh = refresh
|
||||
self.do_not_save = False
|
||||
|
||||
self.comment_before = comment_before
|
||||
"""HTML text that will be added after label in UI"""
|
||||
|
||||
self.comment_after = comment_after
|
||||
"""HTML text that will be added before label in UI"""
|
||||
|
||||
def link(self, label, url):
|
||||
self.comment_before += f"[<a href='{url}' target='_blank'>{label}</a>]"
|
||||
return self
|
||||
|
||||
def js(self, label, js_func):
|
||||
self.comment_before += f"[<a onclick='{js_func}(); return false'>{label}</a>]"
|
||||
return self
|
||||
|
||||
def info(self, info):
|
||||
self.comment_after += f"<span class='info'>({info})</span>"
|
||||
return self
|
||||
|
||||
def html(self, html):
|
||||
self.comment_after += html
|
||||
return self
|
||||
|
||||
def needs_restart(self):
|
||||
self.comment_after += " <span class='info'>(requires restart)</span>"
|
||||
return self
|
||||
|
||||
def needs_reload_ui(self):
|
||||
self.comment_after += " <span class='info'>(requires Reload UI)</span>"
|
||||
return self
|
||||
|
||||
|
||||
class OptionHTML(OptionInfo):
|
||||
def __init__(self, text):
|
||||
super().__init__(str(text).strip(), label='', component=lambda **kwargs: gr.HTML(elem_classes="settings-info", **kwargs))
|
||||
|
||||
self.do_not_save = True
|
||||
|
||||
|
||||
def options_section(section_identifier, options_dict):
|
||||
for v in options_dict.values():
|
||||
v.section = section_identifier
|
||||
|
||||
return options_dict
|
||||
|
||||
|
||||
def list_checkpoint_tiles():
|
||||
import modules.sd_models
|
||||
return modules.sd_models.checkpoint_tiles()
|
||||
|
||||
|
||||
def refresh_checkpoints():
|
||||
import modules.sd_models
|
||||
return modules.sd_models.list_models()
|
||||
|
||||
|
||||
def list_samplers():
|
||||
import modules.sd_samplers
|
||||
return modules.sd_samplers.all_samplers
|
||||
|
||||
|
||||
hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config}
|
||||
tab_names = []
|
||||
|
||||
options_templates = {}
|
||||
|
||||
options_templates.update(options_section(('saving-images', "Saving images/grids"), {
|
||||
"samples_save": OptionInfo(True, "Always save all generated images"),
|
||||
"samples_format": OptionInfo('png', 'File format for images'),
|
||||
"samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
|
||||
"save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs),
|
||||
|
||||
"grid_save": OptionInfo(True, "Always save all generated image grids"),
|
||||
"grid_format": OptionInfo('png', 'File format for grids'),
|
||||
"grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
|
||||
"grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
|
||||
"grid_prevent_empty_spots": OptionInfo(False, "Prevent empty spots in grid (when set to autodetect)"),
|
||||
"grid_zip_filename_pattern": OptionInfo("", "Archive filename pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
|
||||
"n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
|
||||
"font": OptionInfo("", "Font for image grids that have text"),
|
||||
"grid_text_active_color": OptionInfo("#000000", "Text color for image grids", ui_components.FormColorPicker, {}),
|
||||
"grid_text_inactive_color": OptionInfo("#999999", "Inactive text color for image grids", ui_components.FormColorPicker, {}),
|
||||
"grid_background_color": OptionInfo("#ffffff", "Background color for image grids", ui_components.FormColorPicker, {}),
|
||||
|
||||
"enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
|
||||
"save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
|
||||
"save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
|
||||
"save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."),
|
||||
"save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
|
||||
"save_mask": OptionInfo(False, "For inpainting, save a copy of the greyscale mask"),
|
||||
"save_mask_composite": OptionInfo(False, "For inpainting, save a masked composite"),
|
||||
"jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
|
||||
"webp_lossless": OptionInfo(False, "Use lossless compression for webp images"),
|
||||
"export_for_4chan": OptionInfo(True, "Save copy of large images as JPG").info("if the file size is above the limit, or either width or height are above the limit"),
|
||||
"img_downscale_threshold": OptionInfo(4.0, "File size limit for the above option, MB", gr.Number),
|
||||
"target_side_length": OptionInfo(4000, "Width/height limit for the above option, in pixels", gr.Number),
|
||||
"img_max_size_mp": OptionInfo(200, "Maximum image size", gr.Number).info("in megapixels"),
|
||||
|
||||
"use_original_name_batch": OptionInfo(True, "Use original name for output filename during batch process in extras tab"),
|
||||
"use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"),
|
||||
"save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
|
||||
"save_init_img": OptionInfo(False, "Save init images when using img2img"),
|
||||
|
||||
"temp_dir": OptionInfo("", "Directory for temporary images; leave empty for default"),
|
||||
"clean_temp_dir_at_start": OptionInfo(False, "Cleanup non-default temporary directory when starting webui"),
|
||||
|
||||
"save_incomplete_images": OptionInfo(False, "Save incomplete images").info("save images that has been interrupted in mid-generation; even if not saved, they will still show up in webui output."),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('saving-paths', "Paths for saving"), {
|
||||
"outdir_samples": OptionInfo("", "Output directory for images; if empty, defaults to three directories below", component_args=hide_dirs),
|
||||
"outdir_txt2img_samples": OptionInfo("outputs/txt2img-images", 'Output directory for txt2img images', component_args=hide_dirs),
|
||||
"outdir_img2img_samples": OptionInfo("outputs/img2img-images", 'Output directory for img2img images', component_args=hide_dirs),
|
||||
"outdir_extras_samples": OptionInfo("outputs/extras-images", 'Output directory for images from extras tab', component_args=hide_dirs),
|
||||
"outdir_grids": OptionInfo("", "Output directory for grids; if empty, defaults to two directories below", component_args=hide_dirs),
|
||||
"outdir_txt2img_grids": OptionInfo("outputs/txt2img-grids", 'Output directory for txt2img grids', component_args=hide_dirs),
|
||||
"outdir_img2img_grids": OptionInfo("outputs/img2img-grids", 'Output directory for img2img grids', component_args=hide_dirs),
|
||||
"outdir_save": OptionInfo("log/images", "Directory for saving images using the Save button", component_args=hide_dirs),
|
||||
"outdir_init_images": OptionInfo("outputs/init-images", "Directory for saving init images when using img2img", component_args=hide_dirs),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
|
||||
"save_to_dirs": OptionInfo(True, "Save images to a subdirectory"),
|
||||
"grid_save_to_dirs": OptionInfo(True, "Save grids to a subdirectory"),
|
||||
"use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
|
||||
"directories_filename_pattern": OptionInfo("[date]", "Directory name pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
|
||||
"directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1, **hide_dirs}),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('upscaling', "Upscaling"), {
|
||||
"ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}).info("0 = no tiling"),
|
||||
"ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap for ESRGAN upscalers.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}).info("Low values = visible seam"),
|
||||
"realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI.", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
|
||||
"upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('face-restoration', "Face restoration"), {
|
||||
"face_restoration_model": OptionInfo("CodeFormer", "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
|
||||
"code_former_weight": OptionInfo(0.5, "CodeFormer weight", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}).info("0 = maximum effect; 1 = minimum effect"),
|
||||
"face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('system', "System"), {
|
||||
"show_warnings": OptionInfo(False, "Show warnings in console.").needs_reload_ui(),
|
||||
"show_gradio_deprecation_warnings": OptionInfo(True, "Show gradio deprecation warnings in console.").needs_reload_ui(),
|
||||
"memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}).info("0 = disable"),
|
||||
"samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),
|
||||
"multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
|
||||
"print_hypernet_extra": OptionInfo(False, "Print extra hypernetwork information to console."),
|
||||
"list_hidden_files": OptionInfo(True, "Load models/files in hidden directories").info("directory is hidden if its name starts with \".\""),
|
||||
"disable_mmap_load_safetensors": OptionInfo(False, "Disable memmapping for loading .safetensors files.").info("fixes very slow loading speed in some cases"),
|
||||
"hide_ldm_prints": OptionInfo(True, "Prevent Stability-AI's ldm/sgm modules from printing noise to console."),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('training', "Training"), {
|
||||
"unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
|
||||
"pin_memory": OptionInfo(False, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."),
|
||||
"save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training of embedding or HN can be resumed with the matching optim file."),
|
||||
"save_training_settings_to_txt": OptionInfo(True, "Save textual inversion and hypernet settings to a text file whenever training starts."),
|
||||
"dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
|
||||
"dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
|
||||
"training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
|
||||
"training_write_csv_every": OptionInfo(500, "Save an csv containing the loss to log directory every N steps, 0 to disable"),
|
||||
"training_xattention_optimizations": OptionInfo(False, "Use cross attention optimizations while training"),
|
||||
"training_enable_tensorboard": OptionInfo(False, "Enable tensorboard logging."),
|
||||
"training_tensorboard_save_images": OptionInfo(False, "Save generated images within tensorboard."),
|
||||
"training_tensorboard_flush_every": OptionInfo(120, "How often, in seconds, to flush the pending tensorboard events and summaries to disk."),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('sd', "Stable Diffusion"), {
|
||||
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
|
||||
"sd_checkpoints_limit": OptionInfo(1, "Maximum number of checkpoints loaded at the same time", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1}),
|
||||
"sd_checkpoints_keep_in_cpu": OptionInfo(True, "Only keep one model on device").info("will keep models other than the currently used one in RAM rather than VRAM"),
|
||||
"sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}).info("obsolete; set to 0 and use the two settings above instead"),
|
||||
"sd_unet": OptionInfo("Automatic", "SD Unet", gr.Dropdown, lambda: {"choices": shared_items.sd_unet_items()}, refresh=shared_items.refresh_unet_list).info("choose Unet model: Automatic = use one with same filename as checkpoint; None = use Unet from checkpoint"),
|
||||
"enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds").needs_reload_ui(),
|
||||
"enable_emphasis": OptionInfo(True, "Enable emphasis").info("use (text) to make model pay more attention to text and [text] to make it pay less attention"),
|
||||
"enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
|
||||
"comma_padding_backtrack": OptionInfo(20, "Prompt word wrap length limit", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1}).info("in tokens - for texts shorter than specified, if they don't fit into 75 token limit, move them to the next 75 token chunk"),
|
||||
"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer"),
|
||||
"upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
|
||||
"randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU", "NV"]}).info("changes seeds drastically; use CPU to produce the same picture across different videocard vendors; use NV to produce same picture as on NVidia videocards"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('sdxl', "Stable Diffusion XL"), {
|
||||
"sdxl_crop_top": OptionInfo(0, "crop top coordinate"),
|
||||
"sdxl_crop_left": OptionInfo(0, "crop left coordinate"),
|
||||
"sdxl_refiner_low_aesthetic_score": OptionInfo(2.5, "SDXL low aesthetic score", gr.Number).info("used for refiner model negative prompt"),
|
||||
"sdxl_refiner_high_aesthetic_score": OptionInfo(6.0, "SDXL high aesthetic score", gr.Number).info("used for refiner model prompt"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('vae', "VAE"), {
|
||||
"sd_vae_explanation": OptionHTML("""
|
||||
<abbr title='Variational autoencoder'>VAE</abbr> is a neural network that transforms a standard <abbr title='red/green/blue'>RGB</abbr>
|
||||
image into latent space representation and back. Latent space representation is what stable diffusion is working on during sampling
|
||||
(i.e. when the progress bar is between empty and full). For txt2img, VAE is used to create a resulting image after the sampling is finished.
|
||||
For img2img, VAE is used to process user's input image before the sampling, and to create an image after sampling.
|
||||
"""),
|
||||
"sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
|
||||
"sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list).info("choose VAE model: Automatic = use one with same filename as checkpoint; None = use VAE from checkpoint"),
|
||||
"sd_vae_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
|
||||
"auto_vae_precision": OptionInfo(True, "Automaticlly revert VAE to 32-bit floats").info("triggers when a tensor with NaNs is produced in VAE; disabling the option in this case will result in a black square image"),
|
||||
"sd_vae_encode_method": OptionInfo("Full", "VAE type for encode", gr.Radio, {"choices": ["Full", "TAESD"]}).info("method to encode image to latent (use in img2img, hires-fix or inpaint mask)"),
|
||||
"sd_vae_decode_method": OptionInfo("Full", "VAE type for decode", gr.Radio, {"choices": ["Full", "TAESD"]}).info("method to decode latent to image"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('img2img', "img2img"), {
|
||||
"inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01}),
|
||||
"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
|
||||
"img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies.").info("normally you'd do less with less denoising"),
|
||||
"img2img_background_color": OptionInfo("#ffffff", "With img2img, fill transparent parts of the input image with this color.", ui_components.FormColorPicker, {}),
|
||||
"img2img_editor_height": OptionInfo(720, "Height of the image editor", gr.Slider, {"minimum": 80, "maximum": 1600, "step": 1}).info("in pixels").needs_reload_ui(),
|
||||
"img2img_sketch_default_brush_color": OptionInfo("#ffffff", "Sketch initial brush color", ui_components.FormColorPicker, {}).info("default brush color of img2img sketch").needs_reload_ui(),
|
||||
"img2img_inpaint_mask_brush_color": OptionInfo("#ffffff", "Inpaint mask brush color", ui_components.FormColorPicker, {}).info("brush color of inpaint mask").needs_reload_ui(),
|
||||
"img2img_inpaint_sketch_default_brush_color": OptionInfo("#ffffff", "Inpaint sketch initial brush color", ui_components.FormColorPicker, {}).info("default brush color of img2img inpaint sketch").needs_reload_ui(),
|
||||
"return_mask": OptionInfo(False, "For inpainting, include the greyscale mask in results for web"),
|
||||
"return_mask_composite": OptionInfo(False, "For inpainting, include masked composite in results for web"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('optimizations', "Optimizations"), {
|
||||
"cross_attention_optimization": OptionInfo("Automatic", "Cross attention optimization", gr.Dropdown, lambda: {"choices": shared_items.cross_attention_optimizations()}),
|
||||
"s_min_uncond": OptionInfo(0.0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 15.0, "step": 0.01}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
|
||||
"token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9256").info("0=disable, higher=faster"),
|
||||
"token_merging_ratio_img2img": OptionInfo(0.0, "Token merging ratio for img2img", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
|
||||
"token_merging_ratio_hr": OptionInfo(0.0, "Token merging ratio for high-res pass", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
|
||||
"pad_cond_uncond": OptionInfo(False, "Pad prompt/negative prompt to be same length").info("improves performance when prompt and negative prompt have different lengths; changes seeds"),
|
||||
"persistent_cond_cache": OptionInfo(True, "Persistent cond cache").info("Do not recalculate conds from prompts if prompts have not changed since previous calculation"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('compatibility', "Compatibility"), {
|
||||
"use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
|
||||
"use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),
|
||||
"no_dpmpp_sde_batch_determinism": OptionInfo(False, "Do not make DPM++ SDE deterministic across different batch sizes."),
|
||||
"use_old_hires_fix_width_height": OptionInfo(False, "For hires fix, use width/height sliders to set final resolution rather than first pass (disables Upscale by, Resize width/height to)."),
|
||||
"dont_fix_second_order_samplers_schedule": OptionInfo(False, "Do not fix prompt schedule for second order samplers."),
|
||||
"hires_fix_use_firstpass_conds": OptionInfo(False, "For hires fix, calculate conds of second pass using extra networks of first pass."),
|
||||
"use_old_scheduling": OptionInfo(False, "Use old prompt where first pass and hires both used the same timeline, and < 1 meant relative and >= 1 meant absolute"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('interrogate', "Interrogate"), {
|
||||
"interrogate_keep_models_in_memory": OptionInfo(False, "Keep models in VRAM"),
|
||||
"interrogate_return_ranks": OptionInfo(False, "Include ranks of model tags matches in results.").info("booru only"),
|
||||
"interrogate_clip_num_beams": OptionInfo(1, "BLIP: num_beams", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
|
||||
"interrogate_clip_min_length": OptionInfo(24, "BLIP: minimum description length", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
|
||||
"interrogate_clip_max_length": OptionInfo(48, "BLIP: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
|
||||
"interrogate_clip_dict_limit": OptionInfo(1500, "CLIP: maximum number of lines in text file").info("0 = No limit"),
|
||||
"interrogate_clip_skip_categories": OptionInfo([], "CLIP: skip inquire categories", gr.CheckboxGroup, lambda: {"choices": modules.interrogate.category_types()}, refresh=modules.interrogate.category_types),
|
||||
"interrogate_deepbooru_score_threshold": OptionInfo(0.5, "deepbooru: score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
|
||||
"deepbooru_sort_alpha": OptionInfo(True, "deepbooru: sort tags alphabetically").info("if not: sort by score"),
|
||||
"deepbooru_use_spaces": OptionInfo(True, "deepbooru: use spaces in tags").info("if not: use underscores"),
|
||||
"deepbooru_escape": OptionInfo(True, "deepbooru: escape (\\) brackets").info("so they are used as literal brackets and not for emphasis"),
|
||||
"deepbooru_filter_tags": OptionInfo("", "deepbooru: filter out those tags").info("separate by comma"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('extra_networks', "Extra Networks"), {
|
||||
"extra_networks_show_hidden_directories": OptionInfo(True, "Show hidden directories").info("directory is hidden if its name starts with \".\"."),
|
||||
"extra_networks_hidden_models": OptionInfo("When searched", "Show cards for models in hidden directories", gr.Radio, {"choices": ["Always", "When searched", "Never"]}).info('"When searched" option will only show the item when the search string has 4 characters or more'),
|
||||
"extra_networks_default_multiplier": OptionInfo(1.0, "Default multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 2.0, "step": 0.01}),
|
||||
"extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks").info("in pixels"),
|
||||
"extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks").info("in pixels"),
|
||||
"extra_networks_card_text_scale": OptionInfo(1.0, "Card text scale", gr.Slider, {"minimum": 0.0, "maximum": 2.0, "step": 0.01}).info("1 = original size"),
|
||||
"extra_networks_card_show_desc": OptionInfo(True, "Show description on card"),
|
||||
"extra_networks_add_text_separator": OptionInfo(" ", "Extra networks separator").info("extra text to add before <...> when adding extra network to prompt"),
|
||||
"ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order").needs_reload_ui(),
|
||||
"textual_inversion_print_at_load": OptionInfo(False, "Print a list of Textual Inversion embeddings when loading model"),
|
||||
"textual_inversion_add_hashes_to_infotext": OptionInfo(True, "Add Textual Inversion hashes to infotext"),
|
||||
"sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None", *hypernetworks]}, refresh=reload_hypernetworks),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('ui', "User interface"), {
|
||||
"localization": OptionInfo("None", "Localization", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)).needs_reload_ui(),
|
||||
"gradio_theme": OptionInfo("Default", "Gradio theme", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + gradio_hf_hub_themes}).info("you can also manually enter any of themes from the <a href='https://huggingface.co/spaces/gradio/theme-gallery'>gallery</a>.").needs_reload_ui(),
|
||||
"gradio_themes_cache": OptionInfo(True, "Cache gradio themes locally").info("disable to update the selected Gradio theme"),
|
||||
"return_grid": OptionInfo(True, "Show grid in results for web"),
|
||||
"do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
|
||||
"send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
|
||||
"send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
|
||||
"js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
|
||||
"js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
|
||||
"js_modal_lightbox_gamepad": OptionInfo(False, "Navigate image viewer with gamepad"),
|
||||
"js_modal_lightbox_gamepad_repeat": OptionInfo(250, "Gamepad repeat period, in milliseconds"),
|
||||
"show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
|
||||
"samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group").needs_reload_ui(),
|
||||
"dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row").needs_reload_ui(),
|
||||
"keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
|
||||
"keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
|
||||
"keyedit_delimiters": OptionInfo(".,\\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"),
|
||||
"keyedit_move": OptionInfo(True, "Alt+left/right moves prompt elements"),
|
||||
"quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that appear at the top of page rather than in settings tab").needs_reload_ui(),
|
||||
"ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_reload_ui(),
|
||||
"hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_reload_ui(),
|
||||
"ui_reorder_list": OptionInfo([], "txt2img/img2img UI item order", ui_components.DropdownMulti, lambda: {"choices": list(shared_items.ui_reorder_categories())}).info("selected items appear first").needs_reload_ui(),
|
||||
"hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires checkpoint and sampler selection").needs_reload_ui(),
|
||||
"hires_fix_show_prompts": OptionInfo(False, "Hires fix: show hires prompt and negative prompt").needs_reload_ui(),
|
||||
"disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_reload_ui(),
|
||||
}))
|
||||
|
||||
|
||||
options_templates.update(options_section(('infotext', "Infotext"), {
|
||||
"add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
|
||||
"add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
|
||||
"add_user_name_to_info": OptionInfo(False, "Add user name to generation information when authenticated"),
|
||||
"add_version_to_infotext": OptionInfo(True, "Add program version to generation information"),
|
||||
"disable_weights_auto_swap": OptionInfo(True, "Disregard checkpoint information from pasted infotext").info("when reading generation parameters from text into UI"),
|
||||
"infotext_styles": OptionInfo("Apply if any", "Infer styles from prompts of pasted infotext", gr.Radio, {"choices": ["Ignore", "Apply", "Discard", "Apply if any"]}).info("when reading generation parameters from text into UI)").html("""<ul style='margin-left: 1.5em'>
|
||||
<li>Ignore: keep prompt and styles dropdown as it is.</li>
|
||||
<li>Apply: remove style text from prompt, always replace styles dropdown value with found styles (even if none are found).</li>
|
||||
<li>Discard: remove style text from prompt, keep styles dropdown as it is.</li>
|
||||
<li>Apply if any: remove style text from prompt; if any styles are found in prompt, put them into styles dropdown, otherwise keep it as it is.</li>
|
||||
</ul>"""),
|
||||
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('ui', "Live previews"), {
|
||||
"show_progressbar": OptionInfo(True, "Show progressbar"),
|
||||
"live_previews_enable": OptionInfo(True, "Show live previews of the created image"),
|
||||
"live_previews_image_format": OptionInfo("png", "Live preview file format", gr.Radio, {"choices": ["jpeg", "png", "webp"]}),
|
||||
"show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
|
||||
"show_progress_every_n_steps": OptionInfo(10, "Live preview display period", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}).info("in sampling steps - show new live preview image every N sampling steps; -1 = only show after completion of batch"),
|
||||
"show_progress_type": OptionInfo("Approx NN", "Live preview method", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap", "TAESD"]}).info("Full = slow but pretty; Approx NN and TAESD = fast but low quality; Approx cheap = super fast but terrible otherwise"),
|
||||
"live_preview_content": OptionInfo("Prompt", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"]}),
|
||||
"live_preview_refresh_period": OptionInfo(1000, "Progressbar and preview update period").info("in milliseconds"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
|
||||
"hide_samplers": OptionInfo([], "Hide samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}).needs_reload_ui(),
|
||||
"eta_ddim": OptionInfo(0.0, "Eta for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}).info("noise multiplier; higher = more unperdictable results"),
|
||||
"eta_ancestral": OptionInfo(1.0, "Eta for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}).info("noise multiplier; applies to Euler a and other samplers that have a in them"),
|
||||
"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
|
||||
's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 100.0, "step": 0.01}),
|
||||
's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
's_tmax': OptionInfo(0.0, "sigma tmax", gr.Slider, {"minimum": 0.0, "maximum": 999.0, "step": 0.01}).info("0 = inf"),
|
||||
's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
'k_sched_type': OptionInfo("Automatic", "scheduler type", gr.Dropdown, {"choices": ["Automatic", "karras", "exponential", "polyexponential"]}).info("lets you override the noise schedule for k-diffusion samplers; choosing Automatic disables the three parameters below"),
|
||||
'sigma_min': OptionInfo(0.0, "sigma min", gr.Number).info("0 = default (~0.03); minimum noise strength for k-diffusion noise scheduler"),
|
||||
'sigma_max': OptionInfo(0.0, "sigma max", gr.Number).info("0 = default (~14.6); maximum noise strength for k-diffusion noise schedule"),
|
||||
'rho': OptionInfo(0.0, "rho", gr.Number).info("0 = default (7 for karras, 1 for polyexponential); higher values result in a more steep noise schedule (decreases faster)"),
|
||||
'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}).info("ENSD; does not improve anything, just produces different results for ancestral samplers - only useful for reproducing images"),
|
||||
'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma").link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/6044"),
|
||||
'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}),
|
||||
'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}),
|
||||
'uni_pc_order': OptionInfo(3, "UniPC order", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}).info("must be < sampling steps"),
|
||||
'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('postprocessing', "Postprocessing"), {
|
||||
'postprocessing_enable_in_main_ui': OptionInfo([], "Enable postprocessing operations in txt2img and img2img tabs", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
|
||||
'postprocessing_operation_order': OptionInfo([], "Postprocessing operation order", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
|
||||
'upscaling_max_images_in_cache': OptionInfo(5, "Maximum number of images in upscaling cache", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section((None, "Hidden options"), {
|
||||
"disabled_extensions": OptionInfo([], "Disable these extensions"),
|
||||
"disable_all_extensions": OptionInfo("none", "Disable all extensions (preserves the list of disabled extensions)", gr.Radio, {"choices": ["none", "extra", "all"]}),
|
||||
"restore_config_state_file": OptionInfo("", "Config state file to restore from, under 'config-states/' folder"),
|
||||
"sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
|
||||
}))
|
||||
|
||||
|
||||
options_templates.update()
|
||||
|
||||
|
||||
class Options:
|
||||
data = None
|
||||
data_labels = options_templates
|
||||
typemap = {int: float}
|
||||
|
||||
def __init__(self):
|
||||
self.data = {k: v.default for k, v in self.data_labels.items()}
|
||||
|
||||
def __setattr__(self, key, value):
|
||||
if self.data is not None:
|
||||
if key in self.data or key in self.data_labels:
|
||||
assert not cmd_opts.freeze_settings, "changing settings is disabled"
|
||||
|
||||
info = opts.data_labels.get(key, None)
|
||||
if info.do_not_save:
|
||||
return
|
||||
|
||||
comp_args = info.component_args if info else None
|
||||
if isinstance(comp_args, dict) and comp_args.get('visible', True) is False:
|
||||
raise RuntimeError(f"not possible to set {key} because it is restricted")
|
||||
|
||||
if cmd_opts.hide_ui_dir_config and key in restricted_opts:
|
||||
raise RuntimeError(f"not possible to set {key} because it is restricted")
|
||||
|
||||
self.data[key] = value
|
||||
return
|
||||
|
||||
return super(Options, self).__setattr__(key, value)
|
||||
|
||||
def __getattr__(self, item):
|
||||
if self.data is not None:
|
||||
if item in self.data:
|
||||
return self.data[item]
|
||||
|
||||
if item in self.data_labels:
|
||||
return self.data_labels[item].default
|
||||
|
||||
return super(Options, self).__getattribute__(item)
|
||||
|
||||
def set(self, key, value):
|
||||
"""sets an option and calls its onchange callback, returning True if the option changed and False otherwise"""
|
||||
|
||||
oldval = self.data.get(key, None)
|
||||
if oldval == value:
|
||||
return False
|
||||
|
||||
if self.data_labels[key].do_not_save:
|
||||
return False
|
||||
|
||||
try:
|
||||
setattr(self, key, value)
|
||||
except RuntimeError:
|
||||
return False
|
||||
|
||||
if self.data_labels[key].onchange is not None:
|
||||
try:
|
||||
self.data_labels[key].onchange()
|
||||
except Exception as e:
|
||||
errors.display(e, f"changing setting {key} to {value}")
|
||||
setattr(self, key, oldval)
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def get_default(self, key):
|
||||
"""returns the default value for the key"""
|
||||
|
||||
data_label = self.data_labels.get(key)
|
||||
if data_label is None:
|
||||
return None
|
||||
|
||||
return data_label.default
|
||||
|
||||
def save(self, filename):
|
||||
assert not cmd_opts.freeze_settings, "saving settings is disabled"
|
||||
|
||||
with open(filename, "w", encoding="utf8") as file:
|
||||
json.dump(self.data, file, indent=4)
|
||||
|
||||
def same_type(self, x, y):
|
||||
if x is None or y is None:
|
||||
return True
|
||||
|
||||
type_x = self.typemap.get(type(x), type(x))
|
||||
type_y = self.typemap.get(type(y), type(y))
|
||||
|
||||
return type_x == type_y
|
||||
|
||||
def load(self, filename):
|
||||
with open(filename, "r", encoding="utf8") as file:
|
||||
self.data = json.load(file)
|
||||
|
||||
# 1.1.1 quicksettings list migration
|
||||
if self.data.get('quicksettings') is not None and self.data.get('quicksettings_list') is None:
|
||||
self.data['quicksettings_list'] = [i.strip() for i in self.data.get('quicksettings').split(',')]
|
||||
|
||||
# 1.4.0 ui_reorder
|
||||
if isinstance(self.data.get('ui_reorder'), str) and self.data.get('ui_reorder') and "ui_reorder_list" not in self.data:
|
||||
self.data['ui_reorder_list'] = [i.strip() for i in self.data.get('ui_reorder').split(',')]
|
||||
|
||||
bad_settings = 0
|
||||
for k, v in self.data.items():
|
||||
info = self.data_labels.get(k, None)
|
||||
if info is not None and not self.same_type(info.default, v):
|
||||
print(f"Warning: bad setting value: {k}: {v} ({type(v).__name__}; expected {type(info.default).__name__})", file=sys.stderr)
|
||||
bad_settings += 1
|
||||
|
||||
if bad_settings > 0:
|
||||
print(f"The program is likely to not work with bad settings.\nSettings file: {filename}\nEither fix the file, or delete it and restart.", file=sys.stderr)
|
||||
|
||||
def onchange(self, key, func, call=True):
|
||||
item = self.data_labels.get(key)
|
||||
item.onchange = func
|
||||
|
||||
if call:
|
||||
func()
|
||||
|
||||
def dumpjson(self):
|
||||
d = {k: self.data.get(k, v.default) for k, v in self.data_labels.items()}
|
||||
d["_comments_before"] = {k: v.comment_before for k, v in self.data_labels.items() if v.comment_before is not None}
|
||||
d["_comments_after"] = {k: v.comment_after for k, v in self.data_labels.items() if v.comment_after is not None}
|
||||
return json.dumps(d)
|
||||
|
||||
def add_option(self, key, info):
|
||||
self.data_labels[key] = info
|
||||
|
||||
def reorder(self):
|
||||
"""reorder settings so that all items related to section always go together"""
|
||||
|
||||
section_ids = {}
|
||||
settings_items = self.data_labels.items()
|
||||
for _, item in settings_items:
|
||||
if item.section not in section_ids:
|
||||
section_ids[item.section] = len(section_ids)
|
||||
|
||||
self.data_labels = dict(sorted(settings_items, key=lambda x: section_ids[x[1].section]))
|
||||
|
||||
def cast_value(self, key, value):
|
||||
"""casts an arbitrary to the same type as this setting's value with key
|
||||
Example: cast_value("eta_noise_seed_delta", "12") -> returns 12 (an int rather than str)
|
||||
"""
|
||||
|
||||
if value is None:
|
||||
return None
|
||||
|
||||
default_value = self.data_labels[key].default
|
||||
if default_value is None:
|
||||
default_value = getattr(self, key, None)
|
||||
if default_value is None:
|
||||
return None
|
||||
|
||||
expected_type = type(default_value)
|
||||
if expected_type == bool and value == "False":
|
||||
value = False
|
||||
else:
|
||||
value = expected_type(value)
|
||||
|
||||
return value
|
||||
|
||||
|
||||
opts = Options()
|
||||
if os.path.exists(config_filename):
|
||||
opts.load(config_filename)
|
||||
|
||||
|
||||
class Shared(sys.modules[__name__].__class__):
|
||||
"""
|
||||
this class is here to provide sd_model field as a property, so that it can be created and loaded on demand rather than
|
||||
at program startup.
|
||||
"""
|
||||
|
||||
sd_model_val = None
|
||||
|
||||
@property
|
||||
def sd_model(self):
|
||||
import modules.sd_models
|
||||
|
||||
return modules.sd_models.model_data.get_sd_model()
|
||||
|
||||
@sd_model.setter
|
||||
def sd_model(self, value):
|
||||
import modules.sd_models
|
||||
|
||||
modules.sd_models.model_data.set_sd_model(value)
|
||||
|
||||
|
||||
sd_model: LatentDiffusion = None # this var is here just for IDE's type checking; it cannot be accessed because the class field above will be accessed instead
|
||||
sys.modules[__name__].__class__ = Shared
|
||||
|
||||
settings_components = None
|
||||
"""assinged from ui.py, a mapping on setting names to gradio components repsponsible for those settings"""
|
||||
|
||||
tab_names = []
|
||||
|
||||
latent_upscale_default_mode = "Latent"
|
||||
latent_upscale_modes = {
|
||||
"Latent": {"mode": "bilinear", "antialias": False},
|
||||
@ -65,24 +852,121 @@ progress_print_out = sys.stdout
|
||||
|
||||
gradio_theme = gr.themes.Base()
|
||||
|
||||
total_tqdm = None
|
||||
|
||||
mem_mon = None
|
||||
def reload_gradio_theme(theme_name=None):
|
||||
global gradio_theme
|
||||
if not theme_name:
|
||||
theme_name = opts.gradio_theme
|
||||
|
||||
options_section = options.options_section
|
||||
OptionInfo = options.OptionInfo
|
||||
OptionHTML = options.OptionHTML
|
||||
default_theme_args = dict(
|
||||
font=["Source Sans Pro", 'ui-sans-serif', 'system-ui', 'sans-serif'],
|
||||
font_mono=['IBM Plex Mono', 'ui-monospace', 'Consolas', 'monospace'],
|
||||
)
|
||||
|
||||
natural_sort_key = util.natural_sort_key
|
||||
listfiles = util.listfiles
|
||||
html_path = util.html_path
|
||||
html = util.html
|
||||
walk_files = util.walk_files
|
||||
ldm_print = util.ldm_print
|
||||
if theme_name == "Default":
|
||||
gradio_theme = gr.themes.Default(**default_theme_args)
|
||||
else:
|
||||
try:
|
||||
theme_cache_dir = os.path.join(script_path, 'tmp', 'gradio_themes')
|
||||
theme_cache_path = os.path.join(theme_cache_dir, f'{theme_name.replace("/", "_")}.json')
|
||||
if opts.gradio_themes_cache and os.path.exists(theme_cache_path):
|
||||
gradio_theme = gr.themes.ThemeClass.load(theme_cache_path)
|
||||
else:
|
||||
os.makedirs(theme_cache_dir, exist_ok=True)
|
||||
gradio_theme = gr.themes.ThemeClass.from_hub(theme_name)
|
||||
gradio_theme.dump(theme_cache_path)
|
||||
except Exception as e:
|
||||
errors.display(e, "changing gradio theme")
|
||||
gradio_theme = gr.themes.Default(**default_theme_args)
|
||||
|
||||
reload_gradio_theme = shared_gradio_themes.reload_gradio_theme
|
||||
|
||||
list_checkpoint_tiles = shared_items.list_checkpoint_tiles
|
||||
refresh_checkpoints = shared_items.refresh_checkpoints
|
||||
list_samplers = shared_items.list_samplers
|
||||
reload_hypernetworks = shared_items.reload_hypernetworks
|
||||
class TotalTQDM:
|
||||
def __init__(self):
|
||||
self._tqdm = None
|
||||
|
||||
def reset(self):
|
||||
self._tqdm = tqdm.tqdm(
|
||||
desc="Total progress",
|
||||
total=state.job_count * state.sampling_steps,
|
||||
position=1,
|
||||
file=progress_print_out
|
||||
)
|
||||
|
||||
def update(self):
|
||||
if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:
|
||||
return
|
||||
if self._tqdm is None:
|
||||
self.reset()
|
||||
self._tqdm.update()
|
||||
|
||||
def updateTotal(self, new_total):
|
||||
if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:
|
||||
return
|
||||
if self._tqdm is None:
|
||||
self.reset()
|
||||
self._tqdm.total = new_total
|
||||
|
||||
def clear(self):
|
||||
if self._tqdm is not None:
|
||||
self._tqdm.refresh()
|
||||
self._tqdm.close()
|
||||
self._tqdm = None
|
||||
|
||||
|
||||
total_tqdm = TotalTQDM()
|
||||
|
||||
mem_mon = modules.memmon.MemUsageMonitor("MemMon", device, opts)
|
||||
mem_mon.start()
|
||||
|
||||
|
||||
def natural_sort_key(s, regex=re.compile('([0-9]+)')):
|
||||
return [int(text) if text.isdigit() else text.lower() for text in regex.split(s)]
|
||||
|
||||
|
||||
def listfiles(dirname):
|
||||
filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname), key=natural_sort_key) if not x.startswith(".")]
|
||||
return [file for file in filenames if os.path.isfile(file)]
|
||||
|
||||
|
||||
def html_path(filename):
|
||||
return os.path.join(script_path, "html", filename)
|
||||
|
||||
|
||||
def html(filename):
|
||||
path = html_path(filename)
|
||||
|
||||
if os.path.exists(path):
|
||||
with open(path, encoding="utf8") as file:
|
||||
return file.read()
|
||||
|
||||
return ""
|
||||
|
||||
|
||||
def walk_files(path, allowed_extensions=None):
|
||||
if not os.path.exists(path):
|
||||
return
|
||||
|
||||
if allowed_extensions is not None:
|
||||
allowed_extensions = set(allowed_extensions)
|
||||
|
||||
items = list(os.walk(path, followlinks=True))
|
||||
items = sorted(items, key=lambda x: natural_sort_key(x[0]))
|
||||
|
||||
for root, _, files in items:
|
||||
for filename in sorted(files, key=natural_sort_key):
|
||||
if allowed_extensions is not None:
|
||||
_, ext = os.path.splitext(filename)
|
||||
if ext not in allowed_extensions:
|
||||
continue
|
||||
|
||||
if not opts.list_hidden_files and ("/." in root or "\\." in root):
|
||||
continue
|
||||
|
||||
yield os.path.join(root, filename)
|
||||
|
||||
|
||||
def ldm_print(*args, **kwargs):
|
||||
if opts.hide_ldm_prints:
|
||||
return
|
||||
|
||||
print(*args, **kwargs)
|
||||
|
||||
@ -197,6 +197,7 @@ options_templates.update(options_section(('compatibility', "Compatibility"), {
|
||||
"use_old_hires_fix_width_height": OptionInfo(False, "For hires fix, use width/height sliders to set final resolution rather than first pass (disables Upscale by, Resize width/height to)."),
|
||||
"dont_fix_second_order_samplers_schedule": OptionInfo(False, "Do not fix prompt schedule for second order samplers."),
|
||||
"hires_fix_use_firstpass_conds": OptionInfo(False, "For hires fix, calculate conds of second pass using extra networks of first pass."),
|
||||
"use_old_scheduling": OptionInfo(False, "Use old prompt where first pass and hires both used the same timeline, and < 1 meant relative and >= 1 meant absolute"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('interrogate', "Interrogate"), {
|
||||
|
||||
Loading…
Reference in New Issue
Block a user