diff options
Diffstat (limited to 'modules')
| -rw-r--r-- | modules/deepbooru.py | 3 | ||||
| -rw-r--r-- | modules/extras.py | 10 | ||||
| -rw-r--r-- | modules/localization.py | 31 | ||||
| -rw-r--r-- | modules/processing.py | 6 | ||||
| -rw-r--r-- | modules/scripts.py | 15 | ||||
| -rw-r--r-- | modules/sd_hijack_optimizations.py | 14 | ||||
| -rw-r--r-- | modules/sd_models.py | 28 | ||||
| -rw-r--r-- | modules/sd_samplers.py | 107 | ||||
| -rw-r--r-- | modules/shared.py | 9 | ||||
| -rw-r--r-- | modules/styles.py | 4 | ||||
| -rw-r--r-- | modules/ui.py | 48 |
11 files changed, 190 insertions, 85 deletions
diff --git a/modules/deepbooru.py b/modules/deepbooru.py index 4ad334a1..8914662d 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -157,8 +157,7 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_o # sort by reverse by likelihood and normal for alpha, and format tag text as requested unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort)) for weight, tag in unsorted_tags_in_theshold: - # note: tag_outformat will still have a colon if include_ranks is True - tag_outformat = tag.replace(':', ' ') + tag_outformat = tag if use_spaces: tag_outformat = tag_outformat.replace('_', ' ') if use_escape: diff --git a/modules/extras.py b/modules/extras.py index 8dbab240..b853fa5b 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -91,7 +91,8 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
pixels = tuple(np.array(small).flatten().tolist())
- key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight) + pixels
+ key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight,
+ resize_mode, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop) + pixels
c = cached_images.get(key)
if c is None:
@@ -215,8 +216,11 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam if theta_func1:
for key in tqdm.tqdm(theta_1.keys()):
if 'model' in key:
- t2 = theta_2.get(key, torch.zeros_like(theta_1[key]))
- theta_1[key] = theta_func1(theta_1[key], t2)
+ if key in theta_2:
+ t2 = theta_2.get(key, torch.zeros_like(theta_1[key]))
+ theta_1[key] = theta_func1(theta_1[key], t2)
+ else:
+ theta_1[key] = torch.zeros_like(theta_1[key])
del theta_2, teritary_model
for key in tqdm.tqdm(theta_0.keys()):
diff --git a/modules/localization.py b/modules/localization.py new file mode 100644 index 00000000..b1810cda --- /dev/null +++ b/modules/localization.py @@ -0,0 +1,31 @@ +import json
+import os
+import sys
+import traceback
+
+localizations = {}
+
+
+def list_localizations(dirname):
+ localizations.clear()
+
+ for file in os.listdir(dirname):
+ fn, ext = os.path.splitext(file)
+ if ext.lower() != ".json":
+ continue
+
+ localizations[fn] = os.path.join(dirname, file)
+
+
+def localization_js(current_localization_name):
+ fn = localizations.get(current_localization_name, None)
+ data = {}
+ if fn is not None:
+ try:
+ with open(fn, "r", encoding="utf8") as file:
+ data = json.load(file)
+ except Exception:
+ print(f"Error loading localization from {fn}:", file=sys.stderr)
+ print(traceback.format_exc(), file=sys.stderr)
+
+ return f"var localization = {json.dumps(data)}\n"
diff --git a/modules/processing.py b/modules/processing.py index 024a4fc3..ea926fc3 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -410,12 +410,6 @@ def process_images(p: StableDiffusionProcessing) -> Processed: with devices.autocast():
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
- if state.interrupted or state.skipped:
-
- # if we are interrupted, sample returns just noise
- # use the image collected previously in sampler loop
- samples_ddim = shared.state.current_latent
-
samples_ddim = samples_ddim.to(devices.dtype_vae)
x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
diff --git a/modules/scripts.py b/modules/scripts.py index ac66d448..1039fa9c 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -96,6 +96,7 @@ def wrap_call(func, filename, funcname, *args, default=None, **kwargs): class ScriptRunner:
def __init__(self):
self.scripts = []
+ self.titles = []
def setup_ui(self, is_img2img):
for script_class, path in scripts_data:
@@ -107,9 +108,10 @@ class ScriptRunner: self.scripts.append(script)
- titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.scripts]
+ self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.scripts]
- dropdown = gr.Dropdown(label="Script", choices=["None"] + titles, value="None", type="index")
+ dropdown = gr.Dropdown(label="Script", choices=["None"] + self.titles, value="None", type="index")
+ dropdown.save_to_config = True
inputs = [dropdown]
for script in self.scripts:
@@ -139,6 +141,15 @@ class ScriptRunner: return [ui.gr_show(True if i == 0 else args_from <= i < args_to) for i in range(len(inputs))]
+ def init_field(title):
+ if title == 'None':
+ return
+ script_index = self.titles.index(title)
+ script = self.scripts[script_index]
+ for i in range(script.args_from, script.args_to):
+ inputs[i].visible = True
+
+ dropdown.init_field = init_field
dropdown.change(
fn=select_script,
inputs=[dropdown],
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index 79405525..a3345bb9 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -296,10 +296,16 @@ def xformers_attnblock_forward(self, x): try:
h_ = x
h_ = self.norm(h_)
- q1 = self.q(h_).contiguous()
- k1 = self.k(h_).contiguous()
- v = self.v(h_).contiguous()
- out = xformers.ops.memory_efficient_attention(q1, k1, v)
+ q = self.q(h_)
+ k = self.k(h_)
+ v = self.v(h_)
+ b, c, h, w = q.shape
+ q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
+ q = q.contiguous()
+ k = k.contiguous()
+ v = v.contiguous()
+ out = xformers.ops.memory_efficient_attention(q, k, v)
+ out = rearrange(out, 'b (h w) c -> b c h w', h=h)
out = self.proj_out(out)
return x + out
except NotImplementedError:
diff --git a/modules/sd_models.py b/modules/sd_models.py index 3aa21ec1..7ad6d474 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -122,11 +122,33 @@ def select_checkpoint(): return checkpoint_info
+chckpoint_dict_replacements = {
+ 'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
+ 'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
+ 'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
+}
+
+
+def transform_checkpoint_dict_key(k):
+ for text, replacement in chckpoint_dict_replacements.items():
+ if k.startswith(text):
+ k = replacement + k[len(text):]
+
+ return k
+
+
def get_state_dict_from_checkpoint(pl_sd):
if "state_dict" in pl_sd:
- return pl_sd["state_dict"]
+ pl_sd = pl_sd["state_dict"]
+
+ sd = {}
+ for k, v in pl_sd.items():
+ new_key = transform_checkpoint_dict_key(k)
+
+ if new_key is not None:
+ sd[new_key] = v
- return pl_sd
+ return sd
def load_model_weights(model, checkpoint_info):
@@ -141,7 +163,7 @@ def load_model_weights(model, checkpoint_info): print(f"Global Step: {pl_sd['global_step']}")
sd = get_state_dict_from_checkpoint(pl_sd)
- model.load_state_dict(sd, strict=False)
+ missing, extra = model.load_state_dict(sd, strict=False)
if shared.cmd_opts.opt_channelslast:
model.to(memory_format=torch.channels_last)
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 20309e06..b58e810b 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -98,25 +98,8 @@ def store_latent(decoded): shared.state.current_image = sample_to_image(decoded)
-
-def extended_tdqm(sequence, *args, desc=None, **kwargs):
- state.sampling_steps = len(sequence)
- state.sampling_step = 0
-
- seq = sequence if cmd_opts.disable_console_progressbars else tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
-
- for x in seq:
- if state.interrupted or state.skipped:
- break
-
- yield x
-
- state.sampling_step += 1
- shared.total_tqdm.update()
-
-
-ldm.models.diffusion.ddim.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs)
-ldm.models.diffusion.plms.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs)
+class InterruptedException(BaseException):
+ pass
class VanillaStableDiffusionSampler:
@@ -128,14 +111,32 @@ class VanillaStableDiffusionSampler: self.init_latent = None
self.sampler_noises = None
self.step = 0
+ self.stop_at = None
self.eta = None
self.default_eta = 0.0
self.config = None
+ self.last_latent = None
def number_of_needed_noises(self, p):
return 0
+ def launch_sampling(self, steps, func):
+ state.sampling_steps = steps
+ state.sampling_step = 0
+
+ try:
+ return func()
+ except InterruptedException:
+ return self.last_latent
+
def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
+ if state.interrupted or state.skipped:
+ raise InterruptedException
+
+ if self.stop_at is not None and self.step > self.stop_at:
+ raise InterruptedException
+
+
conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
@@ -159,11 +160,16 @@ class VanillaStableDiffusionSampler: res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)
if self.mask is not None:
- store_latent(self.init_latent * self.mask + self.nmask * res[1])
+ self.last_latent = self.init_latent * self.mask + self.nmask * res[1]
else:
- store_latent(res[1])
+ self.last_latent = res[1]
+
+ store_latent(self.last_latent)
self.step += 1
+ state.sampling_step = self.step
+ shared.total_tqdm.update()
+
return res
def initialize(self, p):
@@ -192,7 +198,7 @@ class VanillaStableDiffusionSampler: self.init_latent = x
self.step = 0
- samples = self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning)
+ samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
return samples
@@ -206,9 +212,9 @@ class VanillaStableDiffusionSampler: # existing code fails with certain step counts, like 9
try:
- samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)
+ samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
except Exception:
- samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)
+ samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
return samples_ddim
@@ -223,6 +229,9 @@ class CFGDenoiser(torch.nn.Module): self.step = 0
def forward(self, x, sigma, uncond, cond, cond_scale):
+ if state.interrupted or state.skipped:
+ raise InterruptedException
+
conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
@@ -268,25 +277,6 @@ class CFGDenoiser(torch.nn.Module): return denoised
-def extended_trange(sampler, count, *args, **kwargs):
- state.sampling_steps = count
- state.sampling_step = 0
-
- seq = range(count) if cmd_opts.disable_console_progressbars else tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
-
- for x in seq:
- if state.interrupted or state.skipped:
- break
-
- if sampler.stop_at is not None and x > sampler.stop_at:
- break
-
- yield x
-
- state.sampling_step += 1
- shared.total_tqdm.update()
-
-
class TorchHijack:
def __init__(self, kdiff_sampler):
self.kdiff_sampler = kdiff_sampler
@@ -314,9 +304,28 @@ class KDiffusionSampler: self.eta = None
self.default_eta = 1.0
self.config = None
+ self.last_latent = None
def callback_state(self, d):
- store_latent(d["denoised"])
+ step = d['i']
+ latent = d["denoised"]
+ store_latent(latent)
+ self.last_latent = latent
+
+ if self.stop_at is not None and step > self.stop_at:
+ raise InterruptedException
+
+ state.sampling_step = step
+ shared.total_tqdm.update()
+
+ def launch_sampling(self, steps, func):
+ state.sampling_steps = steps
+ state.sampling_step = 0
+
+ try:
+ return func()
+ except InterruptedException:
+ return self.last_latent
def number_of_needed_noises(self, p):
return p.steps
@@ -339,9 +348,6 @@ class KDiffusionSampler: self.sampler_noise_index = 0
self.eta = p.eta or opts.eta_ancestral
- if hasattr(k_diffusion.sampling, 'trange'):
- k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs)
-
if self.sampler_noises is not None:
k_diffusion.sampling.torch = TorchHijack(self)
@@ -383,8 +389,9 @@ class KDiffusionSampler: self.model_wrap_cfg.init_latent = x
- return self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
+ samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
+ return samples
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
steps = steps or p.steps
@@ -406,6 +413,8 @@ class KDiffusionSampler: extra_params_kwargs['n'] = steps
else:
extra_params_kwargs['sigmas'] = sigmas
- samples = self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
+
+ samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
+
return samples
diff --git a/modules/shared.py b/modules/shared.py index 8b436970..f7d66870 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -13,7 +13,7 @@ import modules.memmon import modules.sd_models
import modules.styles
import modules.devices as devices
-from modules import sd_samplers, sd_models
+from modules import sd_samplers, sd_models, localization
from modules.hypernetworks import hypernetwork
from modules.paths import models_path, script_path, sd_path
@@ -31,6 +31,7 @@ parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
+parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory")
parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
@@ -69,6 +70,7 @@ parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image upload parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
+parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None)
parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
@@ -104,7 +106,6 @@ os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True) hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
loaded_hypernetwork = None
-
def reload_hypernetworks():
global hypernetworks
@@ -152,6 +153,8 @@ interrogator = modules.interrogate.InterrogateModels("interrogate") face_restorers = []
+localization.list_localizations(cmd_opts.localizations_dir)
+
def realesrgan_models_names():
import modules.realesrgan_model
@@ -292,11 +295,13 @@ options_templates.update(options_section(('ui', "User interface"), { "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
"add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
"add_model_name_to_info": OptionInfo(False, "Add model name to generation information"),
+ "disable_weights_auto_swap": OptionInfo(False, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
"font": OptionInfo("", "Font for image grids that have text"),
"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"),
"show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
+ 'localization': OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
}))
options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
diff --git a/modules/styles.py b/modules/styles.py index d44dfc1a..3bf5c5b6 100644 --- a/modules/styles.py +++ b/modules/styles.py @@ -45,7 +45,7 @@ class StyleDatabase: if not os.path.exists(path):
return
- with open(path, "r", encoding="utf8", newline='') as file:
+ with open(path, "r", encoding="utf-8-sig", newline='') as file:
reader = csv.DictReader(file)
for row in reader:
# Support loading old CSV format with "name, text"-columns
@@ -79,7 +79,7 @@ class StyleDatabase: def save_styles(self, path: str) -> None:
# Write to temporary file first, so we don't nuke the file if something goes wrong
fd, temp_path = tempfile.mkstemp(".csv")
- with os.fdopen(fd, "w", encoding="utf8", newline='') as file:
+ with os.fdopen(fd, "w", encoding="utf-8-sig", newline='') as file:
# _fields is actually part of the public API: typing.NamedTuple is a replacement for collections.NamedTuple,
# and collections.NamedTuple has explicit documentation for accessing _fields. Same goes for _asdict()
writer = csv.DictWriter(file, fieldnames=PromptStyle._fields)
diff --git a/modules/ui.py b/modules/ui.py index 43dc88fc..fb6eb5a0 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -23,7 +23,7 @@ import gradio as gr import gradio.utils
import gradio.routes
-from modules import sd_hijack, sd_models
+from modules import sd_hijack, sd_models, localization
from modules.paths import script_path
from modules.shared import opts, cmd_opts, restricted_opts
if cmd_opts.deepdanbooru:
@@ -542,6 +542,10 @@ def apply_setting(key, value): if value is None:
return gr.update()
+ # dont allow model to be swapped when model hash exists in prompt
+ if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
+ return gr.update()
+
if key == "sd_model_checkpoint":
ckpt_info = sd_models.get_closet_checkpoint_match(value)
@@ -1056,10 +1060,10 @@ def create_ui(wrap_gradio_gpu_call): upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
with gr.Group():
- extras_upscaler_1 = gr.Radio(label='Upscaler 1', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
+ extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
with gr.Group():
- extras_upscaler_2 = gr.Radio(label='Upscaler 2', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
+ extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1)
with gr.Group():
@@ -1224,10 +1228,10 @@ def create_ui(wrap_gradio_gpu_call): with gr.Tab(label="Train"):
gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")
with gr.Row():
- train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
+ train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
with gr.Row():
- train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])
+ train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()])
create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")
learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005")
batch_size = gr.Number(label='Batch size', value=1, precision=0)
@@ -1376,16 +1380,18 @@ def create_ui(wrap_gradio_gpu_call): else:
raise Exception(f'bad options item type: {str(t)} for key {key}')
+ elem_id = "setting_"+key
+
if info.refresh is not None:
if is_quicksettings:
- res = comp(label=info.label, value=fun, **(args or {}))
- refresh_button = create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
+ res = comp(label=info.label, value=fun, elem_id=elem_id, **(args or {}))
+ create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
else:
with gr.Row(variant="compact"):
- res = comp(label=info.label, value=fun, **(args or {}))
- refresh_button = create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
+ res = comp(label=info.label, value=fun, elem_id=elem_id, **(args or {}))
+ create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
else:
- res = comp(label=info.label, value=fun, **(args or {}))
+ res = comp(label=info.label, value=fun, elem_id=elem_id, **(args or {}))
return res
@@ -1509,6 +1515,9 @@ Requested path was: {f} with gr.Row():
request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
+ download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
+
+ with gr.Row():
reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
@@ -1519,6 +1528,13 @@ Requested path was: {f} _js='function(){}'
)
+ download_localization.click(
+ fn=lambda: None,
+ inputs=[],
+ outputs=[],
+ _js='download_localization'
+ )
+
def reload_scripts():
modules.scripts.reload_script_body_only()
@@ -1721,7 +1737,7 @@ Requested path was: {f} print(traceback.format_exc(), file=sys.stderr)
def loadsave(path, x):
- def apply_field(obj, field, condition=None):
+ def apply_field(obj, field, condition=None, init_field=None):
key = path + "/" + field
if getattr(obj,'custom_script_source',None) is not None:
@@ -1737,6 +1753,8 @@ Requested path was: {f} print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
else:
setattr(obj, field, saved_value)
+ if init_field is not None:
+ init_field(saved_value)
if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible:
apply_field(x, 'visible')
@@ -1762,11 +1780,13 @@ Requested path was: {f} # Since there are many dropdowns that shouldn't be saved,
# we only mark dropdowns that should be saved.
if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False):
- apply_field(x, 'value', lambda val: val in x.choices)
+ apply_field(x, 'value', lambda val: val in x.choices, getattr(x, 'init_field', None))
+ apply_field(x, 'visible')
visit(txt2img_interface, loadsave, "txt2img")
visit(img2img_interface, loadsave, "img2img")
visit(extras_interface, loadsave, "extras")
+ visit(modelmerger_interface, loadsave, "modelmerger")
if not error_loading and (not os.path.exists(ui_config_file) or settings_count != len(ui_settings)):
with open(ui_config_file, "w", encoding="utf8") as file:
@@ -1783,6 +1803,10 @@ for filename in sorted(os.listdir(jsdir)): with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile:
javascript += f"\n<script>{jsfile.read()}</script>"
+if cmd_opts.theme is not None:
+ javascript += f"\n<script>set_theme('{cmd_opts.theme}');</script>\n"
+
+javascript += f"\n<script>{localization.localization_js(shared.opts.localization)}</script>"
if 'gradio_routes_templates_response' not in globals():
def template_response(*args, **kwargs):
|