Merge branch 'AUTOMATIC1111:master' into master
This commit is contained in:
不会画画的中医不是好程序员
GitHub
commit
7c8903367c
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@ -17,6 +17,7 @@ __pycache__
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/webui.settings.bat
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/embeddings
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/styles.csv
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/params.txt
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/styles.csv.bak
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/webui-user.bat
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/webui-user.sh
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|
|
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|
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@ -94,7 +94,7 @@ contextMenuInit = function(){
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}
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gradioApp().addEventListener("click", function(e) {
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let source = e.composedPath()[0]
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if(source.id && source.indexOf('check_progress')>-1){
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if(source.id && source.id.indexOf('check_progress')>-1){
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return
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}
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|
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@ -14,7 +14,7 @@ titles = {
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"\u{1f3b2}\ufe0f": "Set seed to -1, which will cause a new random number to be used every time",
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"\u267b\ufe0f": "Reuse seed from last generation, mostly useful if it was randomed",
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"\u{1f3a8}": "Add a random artist to the prompt.",
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"\u2199\ufe0f": "Read generation parameters from prompt into user interface.",
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"\u2199\ufe0f": "Read generation parameters from prompt or last generation if prompt is empty into user interface.",
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"\u{1f4c2}": "Open images output directory",
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"Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt",
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@ -84,6 +84,8 @@ titles = {
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"Filename word regex": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
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"Filename join string": "This string will be used to hoin split words into a single line if the option above is enabled.",
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"Quicksettings list": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply."
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}
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@ -36,7 +36,7 @@ onUiUpdate(function(){
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const notification = new Notification(
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'Stable Diffusion',
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{
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body: `Generated ${imgs.size > 1 ? imgs.size - 1 : 1} image${imgs.size > 1 ? 's' : ''}`,
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body: `Generated ${imgs.size > 1 ? imgs.size - opts.return_grid : 1} image${imgs.size > 1 ? 's' : ''}`,
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icon: headImg,
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image: headImg,
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}
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@ -33,27 +33,27 @@ function args_to_array(args){
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}
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function switch_to_txt2img(){
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gradioApp().querySelectorAll('button')[0].click();
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gradioApp().querySelector('#tabs').querySelectorAll('button')[0].click();
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return args_to_array(arguments);
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}
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function switch_to_img2img_img2img(){
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gradioApp().querySelectorAll('button')[1].click();
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gradioApp().querySelector('#tabs').querySelectorAll('button')[1].click();
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gradioApp().getElementById('mode_img2img').querySelectorAll('button')[0].click();
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return args_to_array(arguments);
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}
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function switch_to_img2img_inpaint(){
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gradioApp().querySelectorAll('button')[1].click();
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gradioApp().querySelector('#tabs').querySelectorAll('button')[1].click();
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gradioApp().getElementById('mode_img2img').querySelectorAll('button')[1].click();
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return args_to_array(arguments);
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}
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function switch_to_extras(){
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gradioApp().querySelectorAll('button')[2].click();
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gradioApp().querySelector('#tabs').querySelectorAll('button')[2].click();
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return args_to_array(arguments);
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}
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@ -19,6 +19,7 @@ def get_deepbooru_tags(pil_image):
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release_process()
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OPT_INCLUDE_RANKS = "include_ranks"
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def create_deepbooru_opts():
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from modules import shared
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@ -26,6 +27,7 @@ def create_deepbooru_opts():
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"use_spaces": shared.opts.deepbooru_use_spaces,
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"use_escape": shared.opts.deepbooru_escape,
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"alpha_sort": shared.opts.deepbooru_sort_alpha,
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OPT_INCLUDE_RANKS: shared.opts.interrogate_return_ranks,
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}
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@ -113,6 +115,7 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_o
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alpha_sort = deepbooru_opts['alpha_sort']
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use_spaces = deepbooru_opts['use_spaces']
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use_escape = deepbooru_opts['use_escape']
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include_ranks = deepbooru_opts['include_ranks']
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width = model.input_shape[2]
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height = model.input_shape[1]
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@ -151,19 +154,20 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_o
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if alpha_sort:
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sort_ndx = 1
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# sort by reverse by likelihood and normal for alpha
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# sort by reverse by likelihood and normal for alpha, and format tag text as requested
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unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort))
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for weight, tag in unsorted_tags_in_theshold:
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result_tags_out.append(tag)
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# note: tag_outformat will still have a colon if include_ranks is True
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tag_outformat = tag.replace(':', ' ')
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if use_spaces:
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tag_outformat = tag_outformat.replace('_', ' ')
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if use_escape:
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tag_outformat = re.sub(re_special, r'\\\1', tag_outformat)
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if include_ranks:
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tag_outformat = f"({tag_outformat}:{weight:.3f})"
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result_tags_out.append(tag_outformat)
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print('\n'.join(sorted(result_tags_print, reverse=True)))
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tags_text = ', '.join(result_tags_out)
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if use_spaces:
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tags_text = tags_text.replace('_', ' ')
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if use_escape:
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tags_text = re.sub(re_special, r'\\\1', tags_text)
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return tags_text.replace(':', ' ')
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return ', '.join(result_tags_out)
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@ -1,5 +1,8 @@
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import os
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import re
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import gradio as gr
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from modules.shared import script_path
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from modules import shared
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re_param_code = r"\s*([\w ]+):\s*([^,]+)(?:,|$)"
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re_param = re.compile(re_param_code)
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@ -61,6 +64,12 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
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def connect_paste(button, paste_fields, input_comp, js=None):
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def paste_func(prompt):
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if not prompt and not shared.cmd_opts.hide_ui_dir_config:
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filename = os.path.join(script_path, "params.txt")
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if os.path.exists(filename):
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with open(filename, "r", encoding="utf8") as file:
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prompt = file.read()
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params = parse_generation_parameters(prompt)
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res = []
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@ -18,6 +18,8 @@ from modules.textual_inversion.learn_schedule import LearnRateScheduler
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class HypernetworkModule(torch.nn.Module):
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multiplier = 1.0
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def __init__(self, dim, state_dict=None):
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super().__init__()
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@ -36,7 +38,11 @@ class HypernetworkModule(torch.nn.Module):
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self.to(devices.device)
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def forward(self, x):
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return x + (self.linear2(self.linear1(x)))
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return x + (self.linear2(self.linear1(x))) * self.multiplier
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def apply_strength(value=None):
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HypernetworkModule.multiplier = value if value is not None else shared.opts.sd_hypernetwork_strength
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class Hypernetwork:
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@ -123,7 +123,7 @@ class InterrogateModels:
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return caption[0]
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def interrogate(self, pil_image):
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def interrogate(self, pil_image, include_ranks=False):
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res = None
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try:
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@ -156,7 +156,10 @@ class InterrogateModels:
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for name, topn, items in self.categories:
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matches = self.rank(image_features, items, top_count=topn)
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for match, score in matches:
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res += ", " + match
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if include_ranks:
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res += ", " + match
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else:
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res += f", ({match}:{score})"
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except Exception:
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print(f"Error interrogating", file=sys.stderr)
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@ -324,6 +324,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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else:
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assert p.prompt is not None
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with open(os.path.join(shared.script_path, "params.txt"), "w", encoding="utf8") as file:
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processed = Processed(p, [], p.seed, "")
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file.write(processed.infotext(p, 0))
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devices.torch_gc()
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seed = get_fixed_seed(p.seed)
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@ -13,7 +13,7 @@ import modules.memmon
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import modules.sd_models
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import modules.styles
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import modules.devices as devices
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from modules import sd_samplers
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from modules import sd_samplers, sd_models
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from modules.hypernetworks import hypernetwork
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from modules.paths import models_path, script_path, sd_path
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@ -145,14 +145,14 @@ def realesrgan_models_names():
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class OptionInfo:
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def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, show_on_main_page=False):
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def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, show_on_main_page=False, refresh=None):
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self.default = default
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self.label = label
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self.component = component
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self.component_args = component_args
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self.onchange = onchange
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self.section = None
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self.show_on_main_page = show_on_main_page
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self.refresh = refresh
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def options_section(section_identifier, options_dict):
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@ -237,8 +237,9 @@ options_templates.update(options_section(('training', "Training"), {
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}))
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options_templates.update(options_section(('sd', "Stable Diffusion"), {
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"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, show_on_main_page=True),
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"sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}),
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"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
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"sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
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"sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
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"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
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"save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
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"img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
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@ -250,14 +251,17 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
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"filter_nsfw": OptionInfo(False, "Filter NSFW content"),
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'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
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"random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
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'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
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}))
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options_templates.update(options_section(('interrogate', "Interrogate Options"), {
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"interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
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"interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"),
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"interrogate_return_ranks": OptionInfo(False, "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators)."),
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"interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
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"interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
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"interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
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"interrogate_clip_dict_limit": OptionInfo(1500, "CLIP: maximum number of lines in text file (0 = No limit)"),
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"interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
|
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"deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"),
|
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"deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"),
|
||||
|
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@ -345,6 +349,8 @@ class Options:
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item = self.data_labels.get(key)
|
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item.onchange = func
|
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|
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func()
|
||||
|
||||
def dumpjson(self):
|
||||
d = {k: self.data.get(k, self.data_labels.get(k).default) for k in self.data_labels.keys()}
|
||||
return json.dumps(d)
|
||||
|
|
|
|||
|
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@ -17,7 +17,9 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
|
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shared.interrogator.load()
|
||||
|
||||
if process_caption_deepbooru:
|
||||
deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, deepbooru.create_deepbooru_opts())
|
||||
db_opts = deepbooru.create_deepbooru_opts()
|
||||
db_opts[deepbooru.OPT_INCLUDE_RANKS] = False
|
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deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, db_opts)
|
||||
|
||||
preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru)
|
||||
|
||||
|
|
|
|||
|
|
@ -79,6 +79,8 @@ reuse_symbol = '\u267b\ufe0f' # ♻️
|
|||
art_symbol = '\U0001f3a8' # 🎨
|
||||
paste_symbol = '\u2199\ufe0f' # ↙
|
||||
folder_symbol = '\U0001f4c2' # 📂
|
||||
refresh_symbol = '\U0001f504' # 🔄
|
||||
|
||||
|
||||
def plaintext_to_html(text):
|
||||
text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
|
||||
|
|
@ -1218,8 +1220,7 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
outputs=[],
|
||||
)
|
||||
|
||||
|
||||
def create_setting_component(key):
|
||||
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
|
||||
|
||||
|
|
@ -1239,7 +1240,34 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
else:
|
||||
raise Exception(f'bad options item type: {str(t)} for key {key}')
|
||||
|
||||
return comp(label=info.label, value=fun, **(args or {}))
|
||||
if info.refresh is not None:
|
||||
if is_quicksettings:
|
||||
res = comp(label=info.label, value=fun, **(args or {}))
|
||||
refresh_button = gr.Button(value=refresh_symbol, elem_id="refresh_"+key)
|
||||
else:
|
||||
with gr.Row(variant="compact"):
|
||||
res = comp(label=info.label, value=fun, **(args or {}))
|
||||
refresh_button = gr.Button(value=refresh_symbol, elem_id="refresh_" + key)
|
||||
|
||||
def refresh():
|
||||
info.refresh()
|
||||
refreshed_args = info.component_args() if callable(info.component_args) else info.component_args
|
||||
|
||||
for k, v in refreshed_args.items():
|
||||
setattr(res, k, v)
|
||||
|
||||
return gr.update(**(refreshed_args or {}))
|
||||
|
||||
refresh_button.click(
|
||||
fn=refresh,
|
||||
inputs=[],
|
||||
outputs=[res],
|
||||
)
|
||||
else:
|
||||
res = comp(label=info.label, value=fun, **(args or {}))
|
||||
|
||||
|
||||
return res
|
||||
|
||||
components = []
|
||||
component_dict = {}
|
||||
|
|
@ -1313,6 +1341,9 @@ Requested path was: {f}
|
|||
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
|
||||
|
|
@ -1337,7 +1368,7 @@ Requested path was: {f}
|
|||
|
||||
gr.HTML(elem_id="settings_header_text_{}".format(item.section[0]), value='<h1 class="gr-button-lg">{}</h1>'.format(item.section[1]))
|
||||
|
||||
if item.show_on_main_page:
|
||||
if k in quicksettings_names:
|
||||
quicksettings_list.append((i, k, item))
|
||||
components.append(dummy_component)
|
||||
else:
|
||||
|
|
@ -1346,7 +1377,11 @@ Requested path was: {f}
|
|||
components.append(component)
|
||||
items_displayed += 1
|
||||
|
||||
request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
|
||||
with gr.Row():
|
||||
request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
|
||||
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=[],
|
||||
|
|
@ -1354,10 +1389,6 @@ Requested path was: {f}
|
|||
_js='function(){}'
|
||||
)
|
||||
|
||||
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')
|
||||
|
||||
def reload_scripts():
|
||||
modules.scripts.reload_script_body_only()
|
||||
|
||||
|
|
@ -1372,7 +1403,6 @@ Requested path was: {f}
|
|||
shared.state.interrupt()
|
||||
settings_interface.gradio_ref.do_restart = True
|
||||
|
||||
|
||||
restart_gradio.click(
|
||||
fn=request_restart,
|
||||
inputs=[],
|
||||
|
|
@ -1408,12 +1438,12 @@ Requested path was: {f}
|
|||
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)
|
||||
component = create_setting_component(k, is_quicksettings=True)
|
||||
component_dict[k] = component
|
||||
|
||||
settings_interface.gradio_ref = demo
|
||||
|
||||
with gr.Tabs() as tabs:
|
||||
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()
|
||||
|
|
|
|||
|
|
@ -120,15 +120,45 @@ class Script(scripts.Script):
|
|||
return is_img2img
|
||||
|
||||
def ui(self, is_img2img):
|
||||
info = gr.Markdown('''
|
||||
* `CFG Scale` should be 2 or lower.
|
||||
''')
|
||||
|
||||
override_sampler = gr.Checkbox(label="Override `Sampling method` to Euler?(this method is built for it)", value=True)
|
||||
|
||||
override_prompt = gr.Checkbox(label="Override `prompt` to the same value as `original prompt`?(and `negative prompt`)", value=True)
|
||||
original_prompt = gr.Textbox(label="Original prompt", lines=1)
|
||||
original_negative_prompt = gr.Textbox(label="Original negative prompt", lines=1)
|
||||
cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0)
|
||||
|
||||
override_steps = gr.Checkbox(label="Override `Sampling Steps` to the same value as `Decode steps`?", value=True)
|
||||
st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50)
|
||||
|
||||
override_strength = gr.Checkbox(label="Override `Denoising strength` to 1?", value=True)
|
||||
|
||||
cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0)
|
||||
randomness = gr.Slider(label="Randomness", minimum=0.0, maximum=1.0, step=0.01, value=0.0)
|
||||
sigma_adjustment = gr.Checkbox(label="Sigma adjustment for finding noise for image", value=False)
|
||||
return [original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment]
|
||||
|
||||
def run(self, p, original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment):
|
||||
return [
|
||||
info,
|
||||
override_sampler,
|
||||
override_prompt, original_prompt, original_negative_prompt,
|
||||
override_steps, st,
|
||||
override_strength,
|
||||
cfg, randomness, sigma_adjustment,
|
||||
]
|
||||
|
||||
def run(self, p, _, override_sampler, override_prompt, original_prompt, original_negative_prompt, override_steps, st, override_strength, cfg, randomness, sigma_adjustment):
|
||||
# Override
|
||||
if override_sampler:
|
||||
p.sampler_index = [sampler.name for sampler in sd_samplers.samplers].index("Euler")
|
||||
if override_prompt:
|
||||
p.prompt = original_prompt
|
||||
p.negative_prompt = original_negative_prompt
|
||||
if override_steps:
|
||||
p.steps = st
|
||||
if override_strength:
|
||||
p.denoising_strength = 1.0
|
||||
|
||||
|
||||
def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
|
||||
|
|
|
|||
|
|
@ -107,6 +107,10 @@ def apply_hypernetwork(p, x, xs):
|
|||
hypernetwork.load_hypernetwork(name)
|
||||
|
||||
|
||||
def apply_hypernetwork_strength(p, x, xs):
|
||||
hypernetwork.apply_strength(x)
|
||||
|
||||
|
||||
def confirm_hypernetworks(p, xs):
|
||||
for x in xs:
|
||||
if x.lower() in ["", "none"]:
|
||||
|
|
@ -165,6 +169,7 @@ axis_options = [
|
|||
AxisOption("Sampler", str, apply_sampler, format_value, confirm_samplers),
|
||||
AxisOption("Checkpoint name", str, apply_checkpoint, format_value, confirm_checkpoints),
|
||||
AxisOption("Hypernetwork", str, apply_hypernetwork, format_value, confirm_hypernetworks),
|
||||
AxisOption("Hypernet str.", float, apply_hypernetwork_strength, format_value_add_label, None),
|
||||
AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label, None),
|
||||
AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label, None),
|
||||
AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label, None),
|
||||
|
|
@ -175,13 +180,17 @@ axis_options = [
|
|||
]
|
||||
|
||||
|
||||
def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend):
|
||||
res = []
|
||||
|
||||
def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend, include_lone_images):
|
||||
ver_texts = [[images.GridAnnotation(y)] for y in y_labels]
|
||||
hor_texts = [[images.GridAnnotation(x)] for x in x_labels]
|
||||
|
||||
first_processed = None
|
||||
# Temporary list of all the images that are generated to be populated into the grid.
|
||||
# Will be filled with empty images for any individual step that fails to process properly
|
||||
image_cache = []
|
||||
|
||||
processed_result = None
|
||||
cell_mode = "P"
|
||||
cell_size = (1,1)
|
||||
|
||||
state.job_count = len(xs) * len(ys) * p.n_iter
|
||||
|
||||
|
|
@ -189,22 +198,39 @@ def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend):
|
|||
for ix, x in enumerate(xs):
|
||||
state.job = f"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}"
|
||||
|
||||
processed = cell(x, y)
|
||||
if first_processed is None:
|
||||
first_processed = processed
|
||||
|
||||
processed:Processed = cell(x, y)
|
||||
try:
|
||||
res.append(processed.images[0])
|
||||
# this dereference will throw an exception if the image was not processed
|
||||
# (this happens in cases such as if the user stops the process from the UI)
|
||||
processed_image = processed.images[0]
|
||||
|
||||
if processed_result is None:
|
||||
# Use our first valid processed result as a template container to hold our full results
|
||||
processed_result = copy(processed)
|
||||
cell_mode = processed_image.mode
|
||||
cell_size = processed_image.size
|
||||
processed_result.images = [Image.new(cell_mode, cell_size)]
|
||||
|
||||
image_cache.append(processed_image)
|
||||
if include_lone_images:
|
||||
processed_result.images.append(processed_image)
|
||||
processed_result.all_prompts.append(processed.prompt)
|
||||
processed_result.all_seeds.append(processed.seed)
|
||||
processed_result.infotexts.append(processed.infotexts[0])
|
||||
except:
|
||||
res.append(Image.new(res[0].mode, res[0].size))
|
||||
image_cache.append(Image.new(cell_mode, cell_size))
|
||||
|
||||
grid = images.image_grid(res, rows=len(ys))
|
||||
if not processed_result:
|
||||
print("Unexpected error: draw_xy_grid failed to return even a single processed image")
|
||||
return Processed()
|
||||
|
||||
grid = images.image_grid(image_cache, rows=len(ys))
|
||||
if draw_legend:
|
||||
grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)
|
||||
grid = images.draw_grid_annotations(grid, cell_size[0], cell_size[1], hor_texts, ver_texts)
|
||||
|
||||
first_processed.images = [grid]
|
||||
processed_result.images[0] = grid
|
||||
|
||||
return first_processed
|
||||
return processed_result
|
||||
|
||||
|
||||
re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
|
||||
|
|
@ -229,11 +255,12 @@ class Script(scripts.Script):
|
|||
y_values = gr.Textbox(label="Y values", visible=False, lines=1)
|
||||
|
||||
draw_legend = gr.Checkbox(label='Draw legend', value=True)
|
||||
include_lone_images = gr.Checkbox(label='Include Separate Images', value=False)
|
||||
no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False)
|
||||
|
||||
return [x_type, x_values, y_type, y_values, draw_legend, no_fixed_seeds]
|
||||
return [x_type, x_values, y_type, y_values, draw_legend, include_lone_images, no_fixed_seeds]
|
||||
|
||||
def run(self, p, x_type, x_values, y_type, y_values, draw_legend, no_fixed_seeds):
|
||||
def run(self, p, x_type, x_values, y_type, y_values, draw_legend, include_lone_images, no_fixed_seeds):
|
||||
if not no_fixed_seeds:
|
||||
modules.processing.fix_seed(p)
|
||||
|
||||
|
|
@ -344,7 +371,8 @@ class Script(scripts.Script):
|
|||
x_labels=[x_opt.format_value(p, x_opt, x) for x in xs],
|
||||
y_labels=[y_opt.format_value(p, y_opt, y) for y in ys],
|
||||
cell=cell,
|
||||
draw_legend=draw_legend
|
||||
draw_legend=draw_legend,
|
||||
include_lone_images=include_lone_images
|
||||
)
|
||||
|
||||
if opts.grid_save:
|
||||
|
|
@ -354,6 +382,8 @@ class Script(scripts.Script):
|
|||
modules.sd_models.reload_model_weights(shared.sd_model)
|
||||
|
||||
hypernetwork.load_hypernetwork(opts.sd_hypernetwork)
|
||||
hypernetwork.apply_strength()
|
||||
|
||||
|
||||
opts.data["CLIP_stop_at_last_layers"] = CLIP_stop_at_last_layers
|
||||
|
||||
|
|
|
|||
23
style.css
23
style.css
|
|
@ -228,6 +228,8 @@ fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500, label.block s
|
|||
border-top: 1px solid #eee;
|
||||
border-left: 1px solid #eee;
|
||||
border-right: 1px solid #eee;
|
||||
|
||||
z-index: 300;
|
||||
}
|
||||
|
||||
.dark fieldset span.text-gray-500, .dark .gr-block.gr-box span.text-gray-500, .dark label.block span{
|
||||
|
|
@ -480,16 +482,30 @@ input[type="range"]{
|
|||
background: #a55000;
|
||||
}
|
||||
|
||||
#quicksettings {
|
||||
gap: 0.4em;
|
||||
}
|
||||
|
||||
#quicksettings > div{
|
||||
border: none;
|
||||
background: none;
|
||||
flex: unset;
|
||||
gap: 0.5em;
|
||||
}
|
||||
|
||||
#quicksettings > div > div{
|
||||
max-width: 32em;
|
||||
min-width: 24em;
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
#refresh_sd_model_checkpoint, #refresh_sd_hypernetwork{
|
||||
max-width: 2.5em;
|
||||
min-width: 2.5em;
|
||||
height: 2.4em;
|
||||
}
|
||||
|
||||
|
||||
canvas[key="mask"] {
|
||||
z-index: 12 !important;
|
||||
filter: invert();
|
||||
|
|
@ -506,3 +522,10 @@ canvas[key="mask"] {
|
|||
z-index: 200;
|
||||
width: 8em;
|
||||
}
|
||||
#quicksettings .gr-box > div > div > input.gr-text-input {
|
||||
top: -1.12em;
|
||||
}
|
||||
|
||||
.row.gr-compact{
|
||||
overflow: visible;
|
||||
}
|
||||
|
|
|
|||
2
webui.py
2
webui.py
|
|
@ -72,7 +72,6 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
|
|||
|
||||
return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs)
|
||||
|
||||
|
||||
def initialize():
|
||||
modelloader.cleanup_models()
|
||||
modules.sd_models.setup_model()
|
||||
|
|
@ -86,6 +85,7 @@ def initialize():
|
|||
shared.sd_model = modules.sd_models.load_model()
|
||||
shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
|
||||
shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
|
||||
shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)
|
||||
|
||||
|
||||
def webui():
|
||||
|
|
|
|||
Loading…
Reference in New Issue