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-rw-r--r--.eslintrc.js4
-rw-r--r--javascript/extraNetworks.js29
-rw-r--r--javascript/localStorage.js26
-rw-r--r--javascript/ui.js24
-rw-r--r--modules/generation_parameters_copypaste.py8
-rw-r--r--modules/processing.py19
-rw-r--r--modules/sd_samplers_common.py44
-rw-r--r--modules/sd_vae_approx.py2
-rw-r--r--modules/sd_vae_taesd.py52
-rw-r--r--modules/shared.py2
-rw-r--r--modules/ui_common.py2
-rw-r--r--modules/ui_extra_networks.py5
-rw-r--r--style.css5
13 files changed, 175 insertions, 47 deletions
diff --git a/.eslintrc.js b/.eslintrc.js
index f33aca09..e3b4fb76 100644
--- a/.eslintrc.js
+++ b/.eslintrc.js
@@ -87,5 +87,9 @@ module.exports = {
modalNextImage: "readonly",
// token-counters.js
setupTokenCounters: "readonly",
+ // localStorage.js
+ localSet: "readonly",
+ localGet: "readonly",
+ localRemove: "readonly"
}
};
diff --git a/javascript/extraNetworks.js b/javascript/extraNetworks.js
index 44d02349..897ebeba 100644
--- a/javascript/extraNetworks.js
+++ b/javascript/extraNetworks.js
@@ -1,3 +1,20 @@
+function toggleCss(key, css, enable) {
+ var style = document.getElementById(key);
+ if (enable && !style) {
+ style = document.createElement('style');
+ style.id = key;
+ style.type = 'text/css';
+ document.head.appendChild(style);
+ }
+ if (style && !enable) {
+ document.head.removeChild(style);
+ }
+ if (style) {
+ style.innerHTML == '';
+ style.appendChild(document.createTextNode(css));
+ }
+}
+
function setupExtraNetworksForTab(tabname) {
gradioApp().querySelector('#' + tabname + '_extra_tabs').classList.add('extra-networks');
@@ -7,12 +24,15 @@ function setupExtraNetworksForTab(tabname) {
var sort = gradioApp().getElementById(tabname + '_extra_sort');
var sortOrder = gradioApp().getElementById(tabname + '_extra_sortorder');
var refresh = gradioApp().getElementById(tabname + '_extra_refresh');
+ var showDirsDiv = gradioApp().getElementById(tabname + '_extra_show_dirs');
+ var showDirs = gradioApp().querySelector('#' + tabname + '_extra_show_dirs input');
sort.dataset.sortkey = 'sortDefault';
tabs.appendChild(searchDiv);
tabs.appendChild(sort);
tabs.appendChild(sortOrder);
tabs.appendChild(refresh);
+ tabs.appendChild(showDirsDiv);
var applyFilter = function() {
var searchTerm = search.value.toLowerCase();
@@ -78,6 +98,15 @@ function setupExtraNetworksForTab(tabname) {
});
extraNetworksApplyFilter[tabname] = applyFilter;
+
+ var showDirsUpdate = function() {
+ var css = '#' + tabname + '_extra_tabs .extra-network-subdirs { display: none; }';
+ toggleCss(tabname + '_extra_show_dirs_style', css, !showDirs.checked);
+ localSet('extra-networks-show-dirs', showDirs.checked ? 1 : 0);
+ };
+ showDirs.checked = localGet('extra-networks-show-dirs', 1) == 1;
+ showDirs.addEventListener("change", showDirsUpdate);
+ showDirsUpdate();
}
function applyExtraNetworkFilter(tabname) {
diff --git a/javascript/localStorage.js b/javascript/localStorage.js
new file mode 100644
index 00000000..dc1a36c3
--- /dev/null
+++ b/javascript/localStorage.js
@@ -0,0 +1,26 @@
+
+function localSet(k, v) {
+ try {
+ localStorage.setItem(k, v);
+ } catch (e) {
+ console.warn(`Failed to save ${k} to localStorage: ${e}`);
+ }
+}
+
+function localGet(k, def) {
+ try {
+ return localStorage.getItem(k);
+ } catch (e) {
+ console.warn(`Failed to load ${k} from localStorage: ${e}`);
+ }
+
+ return def;
+}
+
+function localRemove(k) {
+ try {
+ return localStorage.removeItem(k);
+ } catch (e) {
+ console.warn(`Failed to remove ${k} from localStorage: ${e}`);
+ }
+}
diff --git a/javascript/ui.js b/javascript/ui.js
index abf23a78..bade3089 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -152,15 +152,11 @@ function submit() {
showSubmitButtons('txt2img', false);
var id = randomId();
- try {
- localStorage.setItem("txt2img_task_id", id);
- } catch (e) {
- console.warn(`Failed to save txt2img task id to localStorage: ${e}`);
- }
+ localSet("txt2img_task_id", id);
requestProgress(id, gradioApp().getElementById('txt2img_gallery_container'), gradioApp().getElementById('txt2img_gallery'), function() {
showSubmitButtons('txt2img', true);
- localStorage.removeItem("txt2img_task_id");
+ localRemove("txt2img_task_id");
showRestoreProgressButton('txt2img', false);
});
@@ -175,15 +171,11 @@ function submit_img2img() {
showSubmitButtons('img2img', false);
var id = randomId();
- try {
- localStorage.setItem("img2img_task_id", id);
- } catch (e) {
- console.warn(`Failed to save img2img task id to localStorage: ${e}`);
- }
+ localSet("img2img_task_id", id);
requestProgress(id, gradioApp().getElementById('img2img_gallery_container'), gradioApp().getElementById('img2img_gallery'), function() {
showSubmitButtons('img2img', true);
- localStorage.removeItem("img2img_task_id");
+ localRemove("img2img_task_id");
showRestoreProgressButton('img2img', false);
});
@@ -197,7 +189,7 @@ function submit_img2img() {
function restoreProgressTxt2img() {
showRestoreProgressButton("txt2img", false);
- var id = localStorage.getItem("txt2img_task_id");
+ var id = localGet("txt2img_task_id");
if (id) {
requestProgress(id, gradioApp().getElementById('txt2img_gallery_container'), gradioApp().getElementById('txt2img_gallery'), function() {
@@ -211,7 +203,7 @@ function restoreProgressTxt2img() {
function restoreProgressImg2img() {
showRestoreProgressButton("img2img", false);
- var id = localStorage.getItem("img2img_task_id");
+ var id = localGet("img2img_task_id");
if (id) {
requestProgress(id, gradioApp().getElementById('img2img_gallery_container'), gradioApp().getElementById('img2img_gallery'), function() {
@@ -224,8 +216,8 @@ function restoreProgressImg2img() {
onUiLoaded(function() {
- showRestoreProgressButton('txt2img', localStorage.getItem("txt2img_task_id"));
- showRestoreProgressButton('img2img', localStorage.getItem("img2img_task_id"));
+ showRestoreProgressButton('txt2img', localGet("txt2img_task_id"));
+ showRestoreProgressButton('img2img', localGet("img2img_task_id"));
});
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 4e286558..593abfef 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -307,6 +307,12 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
if "Schedule rho" not in res:
res["Schedule rho"] = 0
+ if "VAE Encoder" not in res:
+ res["VAE Encoder"] = "Full"
+
+ if "VAE Decoder" not in res:
+ res["VAE Decoder"] = "Full"
+
return res
@@ -332,6 +338,8 @@ infotext_to_setting_name_mapping = [
('RNG', 'randn_source'),
('NGMS', 's_min_uncond'),
('Pad conds', 'pad_cond_uncond'),
+ ('VAE Encoder', 'sd_vae_encode_method'),
+ ('VAE Decoder', 'sd_vae_decode_method'),
]
diff --git a/modules/processing.py b/modules/processing.py
index ae58b108..43cb763f 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -16,6 +16,7 @@ from typing import Any, Dict, List
import modules.sd_hijack
from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors
from modules.sd_hijack import model_hijack
+from modules.sd_samplers_common import images_tensor_to_samples, decode_first_stage, approximation_indexes
from modules.shared import opts, cmd_opts, state
import modules.shared as shared
import modules.paths as paths
@@ -30,7 +31,6 @@ from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
from einops import repeat, rearrange
from blendmodes.blend import blendLayers, BlendType
-decode_first_stage = sd_samplers_common.decode_first_stage
# some of those options should not be changed at all because they would break the model, so I removed them from options.
opt_C = 4
@@ -84,7 +84,7 @@ def txt2img_image_conditioning(sd_model, x, width, height):
# The "masked-image" in this case will just be all zeros since the entire image is masked.
image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
- image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
+ image_conditioning = images_tensor_to_samples(image_conditioning, approximation_indexes.get(opts.sd_vae_encode_method))
# Add the fake full 1s mask to the first dimension.
image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
@@ -203,7 +203,7 @@ class StableDiffusionProcessing:
midas_in = torch.from_numpy(transformed["midas_in"][None, ...]).to(device=shared.device)
midas_in = repeat(midas_in, "1 ... -> n ...", n=self.batch_size)
- conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image))
+ conditioning_image = images_tensor_to_samples(source_image*0.5+0.5, approximation_indexes.get(opts.sd_vae_encode_method))
conditioning = torch.nn.functional.interpolate(
self.sd_model.depth_model(midas_in),
size=conditioning_image.shape[2:],
@@ -216,7 +216,7 @@ class StableDiffusionProcessing:
return conditioning
def edit_image_conditioning(self, source_image):
- conditioning_image = self.sd_model.encode_first_stage(source_image).mode()
+ conditioning_image = images_tensor_to_samples(source_image*0.5+0.5, approximation_indexes.get(opts.sd_vae_encode_method))
return conditioning_image
@@ -795,6 +795,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if getattr(samples_ddim, 'already_decoded', False):
x_samples_ddim = samples_ddim
else:
+ p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method
x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
x_samples_ddim = torch.stack(x_samples_ddim).float()
@@ -1135,11 +1136,10 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
batch_images.append(image)
decoded_samples = torch.from_numpy(np.array(batch_images))
- decoded_samples = decoded_samples.to(shared.device)
- decoded_samples = 2. * decoded_samples - 1.
decoded_samples = decoded_samples.to(shared.device, dtype=devices.dtype_vae)
- samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
+ self.extra_generation_params['VAE Encoder'] = opts.sd_vae_encode_method
+ samples = images_tensor_to_samples(decoded_samples, approximation_indexes.get(opts.sd_vae_encode_method))
image_conditioning = self.img2img_image_conditioning(decoded_samples, samples)
@@ -1374,10 +1374,9 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
raise RuntimeError(f"bad number of images passed: {len(imgs)}; expecting {self.batch_size} or less")
image = torch.from_numpy(batch_images)
- image = 2. * image - 1.
image = image.to(shared.device, dtype=devices.dtype_vae)
-
- self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
+ self.extra_generation_params['VAE Encoder'] = opts.sd_vae_encode_method
+ self.init_latent = images_tensor_to_samples(image, approximation_indexes.get(opts.sd_vae_encode_method), self.sd_model)
devices.torch_gc()
if self.resize_mode == 3:
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py
index b3d344e7..42a29fc9 100644
--- a/modules/sd_samplers_common.py
+++ b/modules/sd_samplers_common.py
@@ -23,19 +23,29 @@ def setup_img2img_steps(p, steps=None):
approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2, "TAESD": 3}
-def single_sample_to_image(sample, approximation=None):
+def samples_to_images_tensor(sample, approximation=None, model=None):
+ '''latents -> images [-1, 1]'''
if approximation is None:
approximation = approximation_indexes.get(opts.show_progress_type, 0)
if approximation == 2:
- x_sample = sd_vae_approx.cheap_approximation(sample) * 0.5 + 0.5
+ x_sample = sd_vae_approx.cheap_approximation(sample)
elif approximation == 1:
- x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach() * 0.5 + 0.5
+ x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype)).detach()
elif approximation == 3:
x_sample = sample * 1.5
- x_sample = sd_vae_taesd.model()(x_sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
+ x_sample = sd_vae_taesd.decoder_model()(x_sample.to(devices.device, devices.dtype)).detach()
+ x_sample = x_sample * 2 - 1
else:
- x_sample = decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0] * 0.5 + 0.5
+ if model is None:
+ model = shared.sd_model
+ x_sample = model.decode_first_stage(sample)
+
+ return x_sample
+
+
+def single_sample_to_image(sample, approximation=None):
+ x_sample = samples_to_images_tensor(sample.unsqueeze(0), approximation)[0] * 0.5 + 0.5
x_sample = torch.clamp(x_sample, min=0.0, max=1.0)
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
@@ -45,9 +55,9 @@ def single_sample_to_image(sample, approximation=None):
def decode_first_stage(model, x):
- x = model.decode_first_stage(x.to(devices.dtype_vae))
-
- return x
+ x = x.to(devices.dtype_vae)
+ approx_index = approximation_indexes.get(opts.sd_vae_decode_method, 0)
+ return samples_to_images_tensor(x, approx_index, model)
def sample_to_image(samples, index=0, approximation=None):
@@ -58,6 +68,24 @@ def samples_to_image_grid(samples, approximation=None):
return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples])
+def images_tensor_to_samples(image, approximation=None, model=None):
+ '''image[0, 1] -> latent'''
+ if approximation is None:
+ approximation = approximation_indexes.get(opts.sd_vae_encode_method, 0)
+
+ if approximation == 3:
+ image = image.to(devices.device, devices.dtype)
+ x_latent = sd_vae_taesd.encoder_model()(image)
+ else:
+ if model is None:
+ model = shared.sd_model
+ image = image.to(shared.device, dtype=devices.dtype_vae)
+ image = image * 2 - 1
+ x_latent = model.get_first_stage_encoding(model.encode_first_stage(image))
+
+ return x_latent
+
+
def store_latent(decoded):
state.current_latent = decoded
diff --git a/modules/sd_vae_approx.py b/modules/sd_vae_approx.py
index 86bd658a..3965e223 100644
--- a/modules/sd_vae_approx.py
+++ b/modules/sd_vae_approx.py
@@ -81,6 +81,6 @@ def cheap_approximation(sample):
coefs = torch.tensor(coeffs).to(sample.device)
- x_sample = torch.einsum("lxy,lr -> rxy", sample, coefs)
+ x_sample = torch.einsum("...lxy,lr -> ...rxy", sample, coefs)
return x_sample
diff --git a/modules/sd_vae_taesd.py b/modules/sd_vae_taesd.py
index 5bf7c76e..808eb362 100644
--- a/modules/sd_vae_taesd.py
+++ b/modules/sd_vae_taesd.py
@@ -44,7 +44,17 @@ def decoder():
)
-class TAESD(nn.Module):
+def encoder():
+ return nn.Sequential(
+ conv(3, 64), Block(64, 64),
+ conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
+ conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
+ conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
+ conv(64, 4),
+ )
+
+
+class TAESDDecoder(nn.Module):
latent_magnitude = 3
latent_shift = 0.5
@@ -55,21 +65,28 @@ class TAESD(nn.Module):
self.decoder.load_state_dict(
torch.load(decoder_path, map_location='cpu' if devices.device.type != 'cuda' else None))
- @staticmethod
- def unscale_latents(x):
- """[0, 1] -> raw latents"""
- return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude)
+
+class TAESDEncoder(nn.Module):
+ latent_magnitude = 3
+ latent_shift = 0.5
+
+ def __init__(self, encoder_path="taesd_encoder.pth"):
+ """Initialize pretrained TAESD on the given device from the given checkpoints."""
+ super().__init__()
+ self.encoder = encoder()
+ self.encoder.load_state_dict(
+ torch.load(encoder_path, map_location='cpu' if devices.device.type != 'cuda' else None))
def download_model(model_path, model_url):
if not os.path.exists(model_path):
os.makedirs(os.path.dirname(model_path), exist_ok=True)
- print(f'Downloading TAESD decoder to: {model_path}')
+ print(f'Downloading TAESD model to: {model_path}')
torch.hub.download_url_to_file(model_url, model_path)
-def model():
+def decoder_model():
model_name = "taesdxl_decoder.pth" if getattr(shared.sd_model, 'is_sdxl', False) else "taesd_decoder.pth"
loaded_model = sd_vae_taesd_models.get(model_name)
@@ -78,7 +95,7 @@ def model():
download_model(model_path, 'https://github.com/madebyollin/taesd/raw/main/' + model_name)
if os.path.exists(model_path):
- loaded_model = TAESD(model_path)
+ loaded_model = TAESDDecoder(model_path)
loaded_model.eval()
loaded_model.to(devices.device, devices.dtype)
sd_vae_taesd_models[model_name] = loaded_model
@@ -86,3 +103,22 @@ def model():
raise FileNotFoundError('TAESD model not found')
return loaded_model.decoder
+
+
+def encoder_model():
+ model_name = "taesdxl_encoder.pth" if getattr(shared.sd_model, 'is_sdxl', False) else "taesd_encoder.pth"
+ loaded_model = sd_vae_taesd_models.get(model_name)
+
+ if loaded_model is None:
+ model_path = os.path.join(paths_internal.models_path, "VAE-taesd", model_name)
+ download_model(model_path, 'https://github.com/madebyollin/taesd/raw/main/' + model_name)
+
+ if os.path.exists(model_path):
+ loaded_model = TAESDEncoder(model_path)
+ loaded_model.eval()
+ loaded_model.to(devices.device, devices.dtype)
+ sd_vae_taesd_models[model_name] = loaded_model
+ else:
+ raise FileNotFoundError('TAESD model not found')
+
+ return loaded_model.encoder
diff --git a/modules/shared.py b/modules/shared.py
index fb317972..367f815e 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -437,6 +437,8 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
"upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
"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"),
"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"),
+ "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(('sdxl', "Stable Diffusion XL"), {
diff --git a/modules/ui_common.py b/modules/ui_common.py
index 1dda1627..303af9cd 100644
--- a/modules/ui_common.py
+++ b/modules/ui_common.py
@@ -239,7 +239,7 @@ def create_refresh_button(refresh_component, refresh_method, refreshed_args, ele
for comp in refresh_components:
setattr(comp, k, v)
- return (gr.update(**(args or {})) for _ in refresh_components) if len(refresh_components) > 1 else gr.update(**(args or {}))
+ return [gr.update(**(args or {})) for _ in refresh_components] if len(refresh_components) > 1 else gr.update(**(args or {}))
refresh_button = ToolButton(value=refresh_symbol, elem_id=elem_id, tooltip=f"{label}: refresh" if label else "Refresh")
refresh_button.click(
diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py
index 3a73c89e..e0b932b9 100644
--- a/modules/ui_extra_networks.py
+++ b/modules/ui_extra_networks.py
@@ -375,15 +375,16 @@ def create_ui(interface: gr.Blocks, unrelated_tabs, tabname):
dropdown_sort = gr.Dropdown(choices=['Default Sort', 'Date Created', 'Date Modified', 'Name'], value='Default Sort', elem_id=tabname+"_extra_sort", elem_classes="sort", multiselect=False, visible=False, show_label=False, interactive=True, label=tabname+"_extra_sort_order")
button_sortorder = ToolButton(up_down_symbol, elem_id=tabname+"_extra_sortorder", elem_classes="sortorder", visible=False)
button_refresh = gr.Button('Refresh', elem_id=tabname+"_extra_refresh", visible=False)
+ checkbox_show_dirs = gr.Checkbox(True, label='Show dirs', elem_id=tabname+"_extra_show_dirs", elem_classes="show-dirs", visible=False)
ui.button_save_preview = gr.Button('Save preview', elem_id=tabname+"_save_preview", visible=False)
ui.preview_target_filename = gr.Textbox('Preview save filename', elem_id=tabname+"_preview_filename", visible=False)
for tab in unrelated_tabs:
- tab.select(fn=lambda: [gr.update(visible=False) for _ in range(5)], inputs=[], outputs=[edit_search, edit_search, dropdown_sort, button_sortorder, button_refresh], show_progress=False)
+ tab.select(fn=lambda: [gr.update(visible=False) for _ in range(5)], inputs=[], outputs=[edit_search, dropdown_sort, button_sortorder, button_refresh, checkbox_show_dirs], show_progress=False)
for tab in related_tabs:
- tab.select(fn=lambda: [gr.update(visible=True) for _ in range(5)], inputs=[], outputs=[edit_search, edit_search, dropdown_sort, button_sortorder, button_refresh], show_progress=False)
+ tab.select(fn=lambda: [gr.update(visible=True) for _ in range(5)], inputs=[], outputs=[edit_search, dropdown_sort, button_sortorder, button_refresh, checkbox_show_dirs], show_progress=False)
def pages_html():
if not ui.pages_contents:
diff --git a/style.css b/style.css
index 52919f71..dc4d37b9 100644
--- a/style.css
+++ b/style.css
@@ -801,9 +801,12 @@ footer {
margin: 0 0.15em;
}
.extra-networks .tab-nav .search,
-.extra-networks .tab-nav .sort{
+.extra-networks .tab-nav .sort,
+.extra-networks .tab-nav .show-dirs
+{
margin: 0.3em;
align-self: center;
+ width: auto;
}
.extra-networks .tab-nav .search {
generated by cgit v1.2.3 (git 2.25.1) at 2025-06-14 16:20:10 +0000