From f741a98baccae100fcfb40c017b5c35c5cba1b0c Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 10 May 2023 08:43:42 +0300
Subject: imports cleanup for ruff
---
extensions-builtin/ScuNET/scripts/scunet_model.py | 1 -
1 file changed, 1 deletion(-)
(limited to 'extensions-builtin/ScuNET/scripts/scunet_model.py')
diff --git a/extensions-builtin/ScuNET/scripts/scunet_model.py b/extensions-builtin/ScuNET/scripts/scunet_model.py
index c7fd5739..aa2fdb3a 100644
--- a/extensions-builtin/ScuNET/scripts/scunet_model.py
+++ b/extensions-builtin/ScuNET/scripts/scunet_model.py
@@ -13,7 +13,6 @@ import modules.upscaler
from modules import devices, modelloader
from scunet_model_arch import SCUNet as net
from modules.shared import opts
-from modules import images
class UpscalerScuNET(modules.upscaler.Upscaler):
--
cgit v1.2.3
From a5121e7a0623db328a9462d340d389ed6737374a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 10 May 2023 11:37:18 +0300
Subject: fixes for B007
---
extensions-builtin/LDSR/ldsr_model_arch.py | 2 +-
extensions-builtin/Lora/lora.py | 2 +-
extensions-builtin/ScuNET/scripts/scunet_model.py | 2 +-
extensions-builtin/SwinIR/swinir_model_arch.py | 2 +-
extensions-builtin/SwinIR/swinir_model_arch_v2.py | 2 +-
modules/codeformer_model.py | 2 +-
modules/esrgan_model.py | 8 ++------
modules/extra_networks.py | 2 +-
modules/generation_parameters_copypaste.py | 2 +-
modules/hypernetworks/hypernetwork.py | 12 ++++++------
modules/images.py | 2 +-
modules/interrogate.py | 4 ++--
modules/prompt_parser.py | 14 +++++++-------
modules/safe.py | 4 ++--
modules/scripts.py | 10 +++++-----
modules/scripts_postprocessing.py | 8 ++++----
modules/sd_hijack_clip.py | 2 +-
modules/shared.py | 6 +++---
modules/textual_inversion/learn_schedule.py | 2 +-
modules/textual_inversion/textual_inversion.py | 10 +++++-----
modules/ui.py | 6 +++---
modules/ui_extra_networks.py | 2 +-
modules/ui_tempdir.py | 2 +-
modules/upscaler.py | 2 +-
pyproject.toml | 1 -
scripts/prompts_from_file.py | 2 +-
scripts/sd_upscale.py | 4 ++--
scripts/xyz_grid.py | 2 +-
28 files changed, 57 insertions(+), 62 deletions(-)
(limited to 'extensions-builtin/ScuNET/scripts/scunet_model.py')
diff --git a/extensions-builtin/LDSR/ldsr_model_arch.py b/extensions-builtin/LDSR/ldsr_model_arch.py
index a5fb8907..27e38549 100644
--- a/extensions-builtin/LDSR/ldsr_model_arch.py
+++ b/extensions-builtin/LDSR/ldsr_model_arch.py
@@ -88,7 +88,7 @@ class LDSR:
x_t = None
logs = None
- for n in range(n_runs):
+ for _ in range(n_runs):
if custom_shape is not None:
x_t = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)
x_t = repeat(x_t, '1 c h w -> b c h w', b=custom_shape[0])
diff --git a/extensions-builtin/Lora/lora.py b/extensions-builtin/Lora/lora.py
index 9795540f..7b56136f 100644
--- a/extensions-builtin/Lora/lora.py
+++ b/extensions-builtin/Lora/lora.py
@@ -418,7 +418,7 @@ def infotext_pasted(infotext, params):
added = []
- for k, v in params.items():
+ for k in params:
if not k.startswith("AddNet Model "):
continue
diff --git a/extensions-builtin/ScuNET/scripts/scunet_model.py b/extensions-builtin/ScuNET/scripts/scunet_model.py
index aa2fdb3a..1f5ea0d3 100644
--- a/extensions-builtin/ScuNET/scripts/scunet_model.py
+++ b/extensions-builtin/ScuNET/scripts/scunet_model.py
@@ -132,7 +132,7 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
model.load_state_dict(torch.load(filename), strict=True)
model.eval()
- for k, v in model.named_parameters():
+ for _, v in model.named_parameters():
v.requires_grad = False
model = model.to(device)
diff --git a/extensions-builtin/SwinIR/swinir_model_arch.py b/extensions-builtin/SwinIR/swinir_model_arch.py
index 75f7bedc..de195d9b 100644
--- a/extensions-builtin/SwinIR/swinir_model_arch.py
+++ b/extensions-builtin/SwinIR/swinir_model_arch.py
@@ -848,7 +848,7 @@ class SwinIR(nn.Module):
H, W = self.patches_resolution
flops += H * W * 3 * self.embed_dim * 9
flops += self.patch_embed.flops()
- for i, layer in enumerate(self.layers):
+ for layer in self.layers:
flops += layer.flops()
flops += H * W * 3 * self.embed_dim * self.embed_dim
flops += self.upsample.flops()
diff --git a/extensions-builtin/SwinIR/swinir_model_arch_v2.py b/extensions-builtin/SwinIR/swinir_model_arch_v2.py
index d4c0b0da..15777af9 100644
--- a/extensions-builtin/SwinIR/swinir_model_arch_v2.py
+++ b/extensions-builtin/SwinIR/swinir_model_arch_v2.py
@@ -1001,7 +1001,7 @@ class Swin2SR(nn.Module):
H, W = self.patches_resolution
flops += H * W * 3 * self.embed_dim * 9
flops += self.patch_embed.flops()
- for i, layer in enumerate(self.layers):
+ for layer in self.layers:
flops += layer.flops()
flops += H * W * 3 * self.embed_dim * self.embed_dim
flops += self.upsample.flops()
diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py
index 8e56cb89..ececdbae 100644
--- a/modules/codeformer_model.py
+++ b/modules/codeformer_model.py
@@ -94,7 +94,7 @@ def setup_model(dirname):
self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
self.face_helper.align_warp_face()
- for idx, cropped_face in enumerate(self.face_helper.cropped_faces):
+ for cropped_face in self.face_helper.cropped_faces:
cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer)
diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index 85aa6934..a009eb42 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -16,9 +16,7 @@ def mod2normal(state_dict):
# this code is copied from https://github.com/victorca25/iNNfer
if 'conv_first.weight' in state_dict:
crt_net = {}
- items = []
- for k, v in state_dict.items():
- items.append(k)
+ items = list(state_dict)
crt_net['model.0.weight'] = state_dict['conv_first.weight']
crt_net['model.0.bias'] = state_dict['conv_first.bias']
@@ -52,9 +50,7 @@ def resrgan2normal(state_dict, nb=23):
if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:
re8x = 0
crt_net = {}
- items = []
- for k, v in state_dict.items():
- items.append(k)
+ items = list(state_dict)
crt_net['model.0.weight'] = state_dict['conv_first.weight']
crt_net['model.0.bias'] = state_dict['conv_first.bias']
diff --git a/modules/extra_networks.py b/modules/extra_networks.py
index 1978673d..f9db41bc 100644
--- a/modules/extra_networks.py
+++ b/modules/extra_networks.py
@@ -91,7 +91,7 @@ def deactivate(p, extra_network_data):
"""call deactivate for extra networks in extra_network_data in specified order, then call
deactivate for all remaining registered networks"""
- for extra_network_name, extra_network_args in extra_network_data.items():
+ for extra_network_name in extra_network_data:
extra_network = extra_network_registry.get(extra_network_name, None)
if extra_network is None:
continue
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 7fbbe707..b0e945a1 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -247,7 +247,7 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
lines.append(lastline)
lastline = ''
- for i, line in enumerate(lines):
+ for line in lines:
line = line.strip()
if line.startswith("Negative prompt:"):
done_with_prompt = True
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 6ef0bfdf..38ef074f 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -177,34 +177,34 @@ class Hypernetwork:
def weights(self):
res = []
- for k, layers in self.layers.items():
+ for layers in self.layers.values():
for layer in layers:
res += layer.parameters()
return res
def train(self, mode=True):
- for k, layers in self.layers.items():
+ for layers in self.layers.values():
for layer in layers:
layer.train(mode=mode)
for param in layer.parameters():
param.requires_grad = mode
def to(self, device):
- for k, layers in self.layers.items():
+ for layers in self.layers.values():
for layer in layers:
layer.to(device)
return self
def set_multiplier(self, multiplier):
- for k, layers in self.layers.items():
+ for layers in self.layers.values():
for layer in layers:
layer.multiplier = multiplier
return self
def eval(self):
- for k, layers in self.layers.items():
+ for layers in self.layers.values():
for layer in layers:
layer.eval()
for param in layer.parameters():
@@ -619,7 +619,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
try:
sd_hijack_checkpoint.add()
- for i in range((steps-initial_step) * gradient_step):
+ for _ in range((steps-initial_step) * gradient_step):
if scheduler.finished:
break
if shared.state.interrupted:
diff --git a/modules/images.py b/modules/images.py
index 7392cb8b..c4e98c75 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -149,7 +149,7 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0):
return ImageFont.truetype(Roboto, fontsize)
def draw_texts(drawing, draw_x, draw_y, lines, initial_fnt, initial_fontsize):
- for i, line in enumerate(lines):
+ for line in lines:
fnt = initial_fnt
fontsize = initial_fontsize
while drawing.multiline_textsize(line.text, font=fnt)[0] > line.allowed_width and fontsize > 0:
diff --git a/modules/interrogate.py b/modules/interrogate.py
index a1c8e537..111b1322 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -207,8 +207,8 @@ class InterrogateModels:
image_features /= image_features.norm(dim=-1, keepdim=True)
- for name, topn, items in self.categories():
- matches = self.rank(image_features, items, top_count=topn)
+ for cat in self.categories():
+ matches = self.rank(image_features, cat.items, top_count=cat.topn)
for match, score in matches:
if shared.opts.interrogate_return_ranks:
res += f", ({match}:{score/100:.3f})"
diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index 3a720721..b4aff704 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -143,7 +143,7 @@ def get_learned_conditioning(model, prompts, steps):
conds = model.get_learned_conditioning(texts)
cond_schedule = []
- for i, (end_at_step, text) in enumerate(prompt_schedule):
+ for i, (end_at_step, _) in enumerate(prompt_schedule):
cond_schedule.append(ScheduledPromptConditioning(end_at_step, conds[i]))
cache[prompt] = cond_schedule
@@ -219,8 +219,8 @@ def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_s
res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
for i, cond_schedule in enumerate(c):
target_index = 0
- for current, (end_at, cond) in enumerate(cond_schedule):
- if current_step <= end_at:
+ for current, entry in enumerate(cond_schedule):
+ if current_step <= entry.end_at_step:
target_index = current
break
res[i] = cond_schedule[target_index].cond
@@ -234,13 +234,13 @@ def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):
tensors = []
conds_list = []
- for batch_no, composable_prompts in enumerate(c.batch):
+ for composable_prompts in c.batch:
conds_for_batch = []
- for cond_index, composable_prompt in enumerate(composable_prompts):
+ for composable_prompt in composable_prompts:
target_index = 0
- for current, (end_at, cond) in enumerate(composable_prompt.schedules):
- if current_step <= end_at:
+ for current, entry in enumerate(composable_prompt.schedules):
+ if current_step <= entry.end_at_step:
target_index = current
break
diff --git a/modules/safe.py b/modules/safe.py
index 2d5b972f..1e791c5b 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -95,11 +95,11 @@ def check_pt(filename, extra_handler):
except zipfile.BadZipfile:
- # if it's not a zip file, it's an olf pytorch format, with five objects written to pickle
+ # if it's not a zip file, it's an old pytorch format, with five objects written to pickle
with open(filename, "rb") as file:
unpickler = RestrictedUnpickler(file)
unpickler.extra_handler = extra_handler
- for i in range(5):
+ for _ in range(5):
unpickler.load()
diff --git a/modules/scripts.py b/modules/scripts.py
index d945b89f..0c12ebd5 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -231,7 +231,7 @@ def load_scripts():
syspath = sys.path
def register_scripts_from_module(module):
- for key, script_class in module.__dict__.items():
+ for script_class in module.__dict__.values():
if type(script_class) != type:
continue
@@ -295,9 +295,9 @@ class ScriptRunner:
auto_processing_scripts = scripts_auto_postprocessing.create_auto_preprocessing_script_data()
- for script_class, path, basedir, script_module in auto_processing_scripts + scripts_data:
- script = script_class()
- script.filename = path
+ for script_data in auto_processing_scripts + scripts_data:
+ script = script_data.script_class()
+ script.filename = script_data.path
script.is_txt2img = not is_img2img
script.is_img2img = is_img2img
@@ -492,7 +492,7 @@ class ScriptRunner:
module = script_loading.load_module(script.filename)
cache[filename] = module
- for key, script_class in module.__dict__.items():
+ for script_class in module.__dict__.values():
if type(script_class) == type and issubclass(script_class, Script):
self.scripts[si] = script_class()
self.scripts[si].filename = filename
diff --git a/modules/scripts_postprocessing.py b/modules/scripts_postprocessing.py
index b11568c0..6751406c 100644
--- a/modules/scripts_postprocessing.py
+++ b/modules/scripts_postprocessing.py
@@ -66,9 +66,9 @@ class ScriptPostprocessingRunner:
def initialize_scripts(self, scripts_data):
self.scripts = []
- for script_class, path, basedir, script_module in scripts_data:
- script: ScriptPostprocessing = script_class()
- script.filename = path
+ for script_data in scripts_data:
+ script: ScriptPostprocessing = script_data.script_class()
+ script.filename = script_data.path
if script.name == "Simple Upscale":
continue
@@ -124,7 +124,7 @@ class ScriptPostprocessingRunner:
script_args = args[script.args_from:script.args_to]
process_args = {}
- for (name, component), value in zip(script.controls.items(), script_args):
+ for (name, component), value in zip(script.controls.items(), script_args): # noqa B007
process_args[name] = value
script.process(pp, **process_args)
diff --git a/modules/sd_hijack_clip.py b/modules/sd_hijack_clip.py
index 9fa5c5c5..c0c350f6 100644
--- a/modules/sd_hijack_clip.py
+++ b/modules/sd_hijack_clip.py
@@ -223,7 +223,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
self.hijack.fixes = [x.fixes for x in batch_chunk]
for fixes in self.hijack.fixes:
- for position, embedding in fixes:
+ for position, embedding in fixes: # noqa: B007
used_embeddings[embedding.name] = embedding
z = self.process_tokens(tokens, multipliers)
diff --git a/modules/shared.py b/modules/shared.py
index e2691585..913c9e63 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -211,7 +211,7 @@ class OptionInfo:
def options_section(section_identifier, options_dict):
- for k, v in options_dict.items():
+ for v in options_dict.values():
v.section = section_identifier
return options_dict
@@ -579,7 +579,7 @@ class Options:
section_ids = {}
settings_items = self.data_labels.items()
- for k, item in settings_items:
+ for _, item in settings_items:
if item.section not in section_ids:
section_ids[item.section] = len(section_ids)
@@ -740,7 +740,7 @@ def walk_files(path, allowed_extensions=None):
if allowed_extensions is not None:
allowed_extensions = set(allowed_extensions)
- for root, dirs, files in os.walk(path):
+ for root, _, files in os.walk(path):
for filename in files:
if allowed_extensions is not None:
_, ext = os.path.splitext(filename)
diff --git a/modules/textual_inversion/learn_schedule.py b/modules/textual_inversion/learn_schedule.py
index fda58898..c56bea45 100644
--- a/modules/textual_inversion/learn_schedule.py
+++ b/modules/textual_inversion/learn_schedule.py
@@ -12,7 +12,7 @@ class LearnScheduleIterator:
self.it = 0
self.maxit = 0
try:
- for i, pair in enumerate(pairs):
+ for pair in pairs:
if not pair.strip():
continue
tmp = pair.split(':')
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index c37bb2ad..47035332 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -29,7 +29,7 @@ textual_inversion_templates = {}
def list_textual_inversion_templates():
textual_inversion_templates.clear()
- for root, dirs, fns in os.walk(shared.cmd_opts.textual_inversion_templates_dir):
+ for root, _, fns in os.walk(shared.cmd_opts.textual_inversion_templates_dir):
for fn in fns:
path = os.path.join(root, fn)
@@ -198,7 +198,7 @@ class EmbeddingDatabase:
if not os.path.isdir(embdir.path):
return
- for root, dirs, fns in os.walk(embdir.path, followlinks=True):
+ for root, _, fns in os.walk(embdir.path, followlinks=True):
for fn in fns:
try:
fullfn = os.path.join(root, fn)
@@ -215,7 +215,7 @@ class EmbeddingDatabase:
def load_textual_inversion_embeddings(self, force_reload=False):
if not force_reload:
need_reload = False
- for path, embdir in self.embedding_dirs.items():
+ for embdir in self.embedding_dirs.values():
if embdir.has_changed():
need_reload = True
break
@@ -228,7 +228,7 @@ class EmbeddingDatabase:
self.skipped_embeddings.clear()
self.expected_shape = self.get_expected_shape()
- for path, embdir in self.embedding_dirs.items():
+ for embdir in self.embedding_dirs.values():
self.load_from_dir(embdir)
embdir.update()
@@ -469,7 +469,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
try:
sd_hijack_checkpoint.add()
- for i in range((steps-initial_step) * gradient_step):
+ for _ in range((steps-initial_step) * gradient_step):
if scheduler.finished:
break
if shared.state.interrupted:
diff --git a/modules/ui.py b/modules/ui.py
index 84d661b2..83bfb7d8 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -416,7 +416,7 @@ def create_sampler_and_steps_selection(choices, tabname):
def ordered_ui_categories():
user_order = {x.strip(): i * 2 + 1 for i, x in enumerate(shared.opts.ui_reorder.split(","))}
- for i, category in sorted(enumerate(shared.ui_reorder_categories), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)):
+ for _, category in sorted(enumerate(shared.ui_reorder_categories), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)):
yield category
@@ -1646,7 +1646,7 @@ def create_ui():
with gr.Blocks(theme=shared.gradio_theme, analytics_enabled=False, title="Stable Diffusion") as demo:
with gr.Row(elem_id="quicksettings", variant="compact"):
- for i, k, item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])):
+ for _i, k, _item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])):
component = create_setting_component(k, is_quicksettings=True)
component_dict[k] = component
@@ -1673,7 +1673,7 @@ def create_ui():
outputs=[text_settings, result],
)
- for i, k, item in quicksettings_list:
+ for _i, k, _item in quicksettings_list:
component = component_dict[k]
info = opts.data_labels[k]
diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py
index ab585917..2fd82e8e 100644
--- a/modules/ui_extra_networks.py
+++ b/modules/ui_extra_networks.py
@@ -90,7 +90,7 @@ class ExtraNetworksPage:
subdirs = {}
for parentdir in [os.path.abspath(x) for x in self.allowed_directories_for_previews()]:
- for root, dirs, files in os.walk(parentdir):
+ for root, dirs, _ in os.walk(parentdir):
for dirname in dirs:
x = os.path.join(root, dirname)
diff --git a/modules/ui_tempdir.py b/modules/ui_tempdir.py
index cac73c51..f05049e1 100644
--- a/modules/ui_tempdir.py
+++ b/modules/ui_tempdir.py
@@ -72,7 +72,7 @@ def cleanup_tmpdr():
if temp_dir == "" or not os.path.isdir(temp_dir):
return
- for root, dirs, files in os.walk(temp_dir, topdown=False):
+ for root, _, files in os.walk(temp_dir, topdown=False):
for name in files:
_, extension = os.path.splitext(name)
if extension != ".png":
diff --git a/modules/upscaler.py b/modules/upscaler.py
index e145be30..8acb6e96 100644
--- a/modules/upscaler.py
+++ b/modules/upscaler.py
@@ -55,7 +55,7 @@ class Upscaler:
dest_w = int(img.width * scale)
dest_h = int(img.height * scale)
- for i in range(3):
+ for _ in range(3):
shape = (img.width, img.height)
img = self.do_upscale(img, selected_model)
diff --git a/pyproject.toml b/pyproject.toml
index 346a0cde..c88907be 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -20,7 +20,6 @@ ignore = [
"I001", # Import block is un-sorted or un-formatted
"C901", # Function is too complex
"C408", # Rewrite as a literal
- "B007", # Loop control variable not used within loop body
]
diff --git a/scripts/prompts_from_file.py b/scripts/prompts_from_file.py
index 149bc85f..27af5ff6 100644
--- a/scripts/prompts_from_file.py
+++ b/scripts/prompts_from_file.py
@@ -156,7 +156,7 @@ class Script(scripts.Script):
images = []
all_prompts = []
infotexts = []
- for n, args in enumerate(jobs):
+ for args in jobs:
state.job = f"{state.job_no + 1} out of {state.job_count}"
copy_p = copy.copy(p)
diff --git a/scripts/sd_upscale.py b/scripts/sd_upscale.py
index d873a09c..0b1d3096 100644
--- a/scripts/sd_upscale.py
+++ b/scripts/sd_upscale.py
@@ -56,7 +56,7 @@ class Script(scripts.Script):
work = []
- for y, h, row in grid.tiles:
+ for _y, _h, row in grid.tiles:
for tiledata in row:
work.append(tiledata[2])
@@ -85,7 +85,7 @@ class Script(scripts.Script):
work_results += processed.images
image_index = 0
- for y, h, row in grid.tiles:
+ for _y, _h, row in grid.tiles:
for tiledata in row:
tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (p.width, p.height))
image_index += 1
diff --git a/scripts/xyz_grid.py b/scripts/xyz_grid.py
index 332e0ecd..38a20381 100644
--- a/scripts/xyz_grid.py
+++ b/scripts/xyz_grid.py
@@ -704,7 +704,7 @@ class Script(scripts.Script):
if not include_sub_grids:
# Done with sub-grids, drop all related information:
- for sg in range(z_count):
+ for _ in range(z_count):
del processed.images[1]
del processed.all_prompts[1]
del processed.all_seeds[1]
--
cgit v1.2.3
From a00e42556ffbc1b757fda5ba3f85a9e11c931441 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 14 May 2023 11:04:21 +0300
Subject: add a bunch of descriptions and reword a lot of settings (sorry,
localizers)
---
extensions-builtin/ScuNET/scripts/scunet_model.py | 13 +++-
javascript/ui_settings_hints.js | 3 +-
modules/shared.py | 94 ++++++++++++-----------
style.css | 4 +-
4 files changed, 65 insertions(+), 49 deletions(-)
(limited to 'extensions-builtin/ScuNET/scripts/scunet_model.py')
diff --git a/extensions-builtin/ScuNET/scripts/scunet_model.py b/extensions-builtin/ScuNET/scripts/scunet_model.py
index 1f5ea0d3..cc2cbc6a 100644
--- a/extensions-builtin/ScuNET/scripts/scunet_model.py
+++ b/extensions-builtin/ScuNET/scripts/scunet_model.py
@@ -10,7 +10,7 @@ from tqdm import tqdm
from basicsr.utils.download_util import load_file_from_url
import modules.upscaler
-from modules import devices, modelloader
+from modules import devices, modelloader, script_callbacks
from scunet_model_arch import SCUNet as net
from modules.shared import opts
@@ -137,3 +137,14 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
model = model.to(device)
return model
+
+
+def on_ui_settings():
+ import gradio as gr
+ from modules import shared
+
+ shared.opts.add_option("SCUNET_tile", shared.OptionInfo(256, "Tile size for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")).info("0 = no tiling"))
+ shared.opts.add_option("SCUNET_tile_overlap", shared.OptionInfo(8, "Tile overlap for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, section=('upscaling', "Upscaling")).info("Low values = visible seam"))
+
+
+script_callbacks.on_ui_settings(on_ui_settings)
diff --git a/javascript/ui_settings_hints.js b/javascript/ui_settings_hints.js
index 9251fd71..6d1933dc 100644
--- a/javascript/ui_settings_hints.js
+++ b/javascript/ui_settings_hints.js
@@ -15,7 +15,8 @@ onOptionsChanged(function(){
var span = null
if(div.classList.contains('gradio-checkbox')) span = div.querySelector('label span')
- else if(div.classList.contains('gradio-checkboxgroup')) span = div.querySelector('span')
+ else if(div.classList.contains('gradio-checkboxgroup')) span = div.querySelector('span').firstChild
+ else if(div.classList.contains('gradio-radio')) span = div.querySelector('span').firstChild
else span = div.querySelector('label span').firstChild
if(!span) return
diff --git a/modules/shared.py b/modules/shared.py
index 24fdcd59..a0577644 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -228,6 +228,12 @@ class OptionInfo:
self.comment_after += f"({info})"
return self
+ def needs_restart(self):
+ self.comment_after += " (requires restart)"
+ return self
+
+
+
def options_section(section_identifier, options_dict):
for v in options_dict.values():
@@ -278,10 +284,10 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
"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, "If the saved image file size is above the limit, or its either width or height are above the limit, save a downscaled copy as JPG"),
+ "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, in megapixels", 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"),
@@ -314,23 +320,21 @@ options_templates.update(options_section(('saving-to-dirs', "Saving to a directo
}))
options_templates.update(options_section(('upscaling', "Upscaling"), {
- "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
- "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
- "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
+ "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]}),
- "SCUNET_tile": OptionInfo(256, "Tile size for SCUNET upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
- "SCUNET_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SCUNET upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}),
}))
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 parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
+ "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."),
- "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}),
+ "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."),
@@ -355,20 +359,20 @@ 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_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
"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),
+ "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"),
"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 (normally you'd do less with less denoising)."),
+ "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 image's transparent parts with this color.", ui_components.FormColorPicker, {}),
"enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
- "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
+ "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, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
- "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
+ "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 nrtwork; 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. Changes seeds drastically. Use CPU to produce the same picture across different vidocard vendors.", gr.Radio, {"choices": ["GPU", "CPU"]}),
+ "randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU"]}).info("changes seeds drastically; use CPU to produce the same picture across different vidocard vendors"),
"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_hr": OptionInfo(0.0, "Togen merging ratio for high-res pass", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}),
}))
@@ -382,30 +386,32 @@ options_templates.update(options_section(('compatibility', "Compatibility"), {
}))
options_templates.update(options_section(('interrogate', "Interrogate Options"), {
- "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
- "interrogate_return_ranks": OptionInfo(False, "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators)."),
- "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
- "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
- "interrogate_clip_max_length": OptionInfo(48, "Interrogate: 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 (0 = No limit)"),
+ "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, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
- "deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"),
- "deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"),
- "deepbooru_escape": OptionInfo(True, "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)"),
- "deepbooru_filter_tags": OptionInfo("", "filter out those tags from deepbooru output (separated by comma)"),
+ "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_default_view": OptionInfo("cards", "Default view for Extra Networks", gr.Dropdown, {"choices": ["cards", "thumbs"]}),
"extra_networks_default_multiplier": OptionInfo(1.0, "Multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
- "extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks (px)"),
- "extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks (px)"),
- "extra_networks_add_text_separator": OptionInfo(" ", "Extra text to add before <...> when adding extra network to prompt"),
+ "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_add_text_separator": OptionInfo(" ", "Extra networks separator").info("extra text to add before <...> when adding extra network to prompt"),
"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_restart(),
+ "gradio_theme": OptionInfo("Default", "Gradio theme", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + gradio_hf_hub_themes}).needs_restart(),
"return_grid": OptionInfo(True, "Show grid in results for web"),
"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"),
@@ -418,17 +424,15 @@ options_templates.update(options_section(('ui', "User interface"), {
"js_modal_lightbox_gamepad": OptionInfo(True, "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"),
- "dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row"),
+ "samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group").needs_restart(),
+ "dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row").needs_restart(),
"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 ", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
"keyedit_delimiters": OptionInfo(".,\\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"),
- "quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings"),
- "hidden_tabs": OptionInfo([], "Hidden UI tabs (requires restart)", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}),
+ "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_restart(),
+ "hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(),
"ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),
- "ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order"),
- "localization": OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
- "gradio_theme": OptionInfo("Default", "Gradio theme (requires restart)", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + gradio_hf_hub_themes})
+ "ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order").needs_restart(),
}))
options_templates.update(options_section(('infotext', "Infotext"), {
@@ -443,26 +447,26 @@ options_templates.update(options_section(('ui', "Live previews"), {
"live_previews_enable": OptionInfo(True, "Show live previews of the created image"),
"live_previews_format": OptionInfo("auto", "Live preview file format", gr.Radio, {"choices": ["auto", "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, "Show new live preview image every N sampling steps. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
- "show_progress_type": OptionInfo("Approx NN", "Image creation progress preview mode", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap"]}),
+ "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"]}).info("Full = slow but pretty; Approx NN = 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/preview update period, in milliseconds")
+ "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 (requires restart)", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}),
- "eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
- "eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+ "hide_samplers": OptionInfo([], "Hide samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}).needs_restart(),
+ "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": 1.0, "step": 0.01}),
's_min_uncond': OptionInfo(0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}),
's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
- 'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
- 'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma"),
+ '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 (must be < sampling steps)", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}),
+ '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"),
}))
diff --git a/style.css b/style.css
index 1e978592..0c2f453c 100644
--- a/style.css
+++ b/style.css
@@ -425,11 +425,11 @@ table.settings-value-table td{
color: var(--body-text-color);
}
-#settings .gradio-textbox, #settings .gradio-slider, #settings .gradio-number, #settings .gradio-dropdown, #settings .gradio-checkboxgroup{
+#settings .gradio-textbox, #settings .gradio-slider, #settings .gradio-number, #settings .gradio-dropdown, #settings .gradio-checkboxgroup, #settings .gradio-radio{
margin-top: 0.75em;
}
-.gradio-textbox .settings-comment, .gradio-slider .settings-comment, .gradio-number .settings-comment, .gradio-dropdown .settings-comment, .gradio-checkboxgroup .settings-comment {
+#settings span .settings-comment {
display: inline
}
--
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