diff options
Diffstat (limited to 'modules')
| -rw-r--r-- | modules/cache.py | 3 | ||||
| -rw-r--r-- | modules/generation_parameters_copypaste.py | 36 | ||||
| -rw-r--r-- | modules/img2img.py | 4 | ||||
| -rw-r--r-- | modules/options.py | 4 | ||||
| -rw-r--r-- | modules/processing.py | 102 | ||||
| -rw-r--r-- | modules/sd_vae.py | 16 | ||||
| -rw-r--r-- | modules/shared_options.py | 54 | ||||
| -rw-r--r-- | modules/txt2img.py | 4 | ||||
| -rw-r--r-- | modules/ui.py | 47 | ||||
| -rw-r--r-- | modules/ui_components.py | 49 | ||||
| -rw-r--r-- | modules/ui_loadsave.py | 7 |
11 files changed, 194 insertions, 132 deletions
diff --git a/modules/cache.py b/modules/cache.py index 71fe6302..a7cd3aeb 100644 --- a/modules/cache.py +++ b/modules/cache.py @@ -1,11 +1,12 @@ import json
+import os
import os.path
import threading
import time
from modules.paths import data_path, script_path
-cache_filename = os.path.join(data_path, "cache.json")
+cache_filename = os.environ.get('SD_WEBUI_CACHE_FILE', os.path.join(data_path, "cache.json"))
cache_data = None
cache_lock = threading.Lock()
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index d932c67d..386517ac 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -316,34 +316,18 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model infotext_to_setting_name_mapping = [
- ('Clip skip', 'CLIP_stop_at_last_layers', ),
+
+]
+"""Mapping of infotext labels to setting names. Only left for backwards compatibility - use OptionInfo(..., infotext='...') instead.
+Example content:
+
+infotext_to_setting_name_mapping = [
('Conditional mask weight', 'inpainting_mask_weight'),
('Model hash', 'sd_model_checkpoint'),
('ENSD', 'eta_noise_seed_delta'),
('Schedule type', 'k_sched_type'),
- ('Schedule max sigma', 'sigma_max'),
- ('Schedule min sigma', 'sigma_min'),
- ('Schedule rho', 'rho'),
- ('Noise multiplier', 'initial_noise_multiplier'),
- ('Eta', 'eta_ancestral'),
- ('Eta DDIM', 'eta_ddim'),
- ('Sigma churn', 's_churn'),
- ('Sigma tmin', 's_tmin'),
- ('Sigma tmax', 's_tmax'),
- ('Sigma noise', 's_noise'),
- ('Discard penultimate sigma', 'always_discard_next_to_last_sigma'),
- ('UniPC variant', 'uni_pc_variant'),
- ('UniPC skip type', 'uni_pc_skip_type'),
- ('UniPC order', 'uni_pc_order'),
- ('UniPC lower order final', 'uni_pc_lower_order_final'),
- ('Token merging ratio', 'token_merging_ratio'),
- ('Token merging ratio hr', 'token_merging_ratio_hr'),
- ('RNG', 'randn_source'),
- ('NGMS', 's_min_uncond'),
- ('Pad conds', 'pad_cond_uncond'),
- ('VAE Encoder', 'sd_vae_encode_method'),
- ('VAE Decoder', 'sd_vae_decode_method'),
]
+"""
def create_override_settings_dict(text_pairs):
@@ -364,7 +348,8 @@ def create_override_settings_dict(text_pairs): params[k] = v.strip()
- for param_name, setting_name in infotext_to_setting_name_mapping:
+ mapping = [(info.infotext, k) for k, info in shared.opts.data_labels.items() if info.infotext]
+ for param_name, setting_name in mapping + infotext_to_setting_name_mapping:
value = params.get(param_name, None)
if value is None:
@@ -418,7 +403,8 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component, def paste_settings(params):
vals = {}
- for param_name, setting_name in infotext_to_setting_name_mapping:
+ mapping = [(info.infotext, k) for k, info in shared.opts.data_labels.items() if info.infotext]
+ for param_name, setting_name in mapping + infotext_to_setting_name_mapping:
if param_name in already_handled_fields:
continue
diff --git a/modules/img2img.py b/modules/img2img.py index e06ac1d6..c7bbbac8 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -116,7 +116,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal process_images(p)
-def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_name: str, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, request: gr.Request, *args):
+def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_name: str, mask_blur: int, mask_alpha: float, inpainting_fill: int, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, request: gr.Request, *args):
override_settings = create_override_settings_dict(override_settings_texts)
is_batch = mode == 5
@@ -179,8 +179,6 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s cfg_scale=cfg_scale,
width=width,
height=height,
- restore_faces=restore_faces,
- tiling=tiling,
init_images=[image],
mask=mask,
mask_blur=mask_blur,
diff --git a/modules/options.py b/modules/options.py index 59cb75ec..db1fb157 100644 --- a/modules/options.py +++ b/modules/options.py @@ -8,7 +8,7 @@ from modules.shared_cmd_options import cmd_opts class OptionInfo:
- def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None, comment_before='', comment_after=''):
+ def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None, comment_before='', comment_after='', infotext=None):
self.default = default
self.label = label
self.component = component
@@ -24,6 +24,8 @@ class OptionInfo: self.comment_after = comment_after
"""HTML text that will be added before label in UI"""
+ self.infotext = infotext
+
def link(self, label, url):
self.comment_before += f"[<a href='{url}' target='_blank'>{label}</a>]"
return self
diff --git a/modules/processing.py b/modules/processing.py index 6961b7b1..44d47e8c 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -111,7 +111,7 @@ class StableDiffusionProcessing: cached_uc = [None, None]
cached_c = [None, None]
- def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = None, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
+ def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = None, tiling: bool = None, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = None, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
if sampler_index is not None:
print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
@@ -521,7 +521,15 @@ def decode_latent_batch(model, batch, target_device=None, check_for_nans=False): def get_fixed_seed(seed):
- if seed is None or seed == '' or seed == -1:
+ if seed == '' or seed is None:
+ seed = -1
+ elif isinstance(seed, str):
+ try:
+ seed = int(seed)
+ except Exception:
+ seed = -1
+
+ if seed == -1:
return int(random.randrange(4294967294))
return seed
@@ -564,10 +572,12 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter "CFG scale": p.cfg_scale,
"Image CFG scale": getattr(p, 'image_cfg_scale', None),
"Seed": p.all_seeds[0] if use_main_prompt else all_seeds[index],
- "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
+ "Face restoration": opts.face_restoration_model if p.restore_faces else None,
"Size": f"{p.width}x{p.height}",
"Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
"Model": (None if not opts.add_model_name_to_info else shared.sd_model.sd_checkpoint_info.name_for_extra),
+ "VAE hash": sd_vae.get_loaded_vae_hash() if opts.add_model_hash_to_info else None,
+ "VAE": sd_vae.get_loaded_vae_name() if opts.add_model_name_to_info else None,
"Variation seed": (None if p.subseed_strength == 0 else (p.all_subseeds[0] if use_main_prompt else all_subseeds[index])),
"Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
"Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
@@ -580,6 +590,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter "Init image hash": getattr(p, 'init_img_hash', None),
"RNG": opts.randn_source if opts.randn_source != "GPU" and opts.randn_source != "NV" else None,
"NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
+ "Tiling": "True" if p.tiling else None,
**p.extra_generation_params,
"Version": program_version() if opts.add_version_to_infotext else None,
"User": p.user if opts.add_user_name_to_info else None,
@@ -645,6 +656,12 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: seed = get_fixed_seed(p.seed)
subseed = get_fixed_seed(p.subseed)
+ if p.restore_faces is None:
+ p.restore_faces = opts.face_restoration
+
+ if p.tiling is None:
+ p.tiling = opts.tiling
+
modules.sd_hijack.model_hijack.apply_circular(p.tiling)
modules.sd_hijack.model_hijack.clear_comments()
@@ -721,7 +738,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: # strength, which is saved as "Model Strength: 1.0" in the infotext
if n == 0:
with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
- processed = Processed(p, [], p.seed, "")
+ processed = Processed(p, [])
file.write(processed.infotext(p, 0))
p.setup_conds()
@@ -924,6 +941,45 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.hr_c = None
self.hr_uc = None
+ def calculate_target_resolution(self):
+ if opts.use_old_hires_fix_width_height and self.applied_old_hires_behavior_to != (self.width, self.height):
+ self.hr_resize_x = self.width
+ self.hr_resize_y = self.height
+ self.hr_upscale_to_x = self.width
+ self.hr_upscale_to_y = self.height
+
+ self.width, self.height = old_hires_fix_first_pass_dimensions(self.width, self.height)
+ self.applied_old_hires_behavior_to = (self.width, self.height)
+
+ if self.hr_resize_x == 0 and self.hr_resize_y == 0:
+ self.extra_generation_params["Hires upscale"] = self.hr_scale
+ self.hr_upscale_to_x = int(self.width * self.hr_scale)
+ self.hr_upscale_to_y = int(self.height * self.hr_scale)
+ else:
+ self.extra_generation_params["Hires resize"] = f"{self.hr_resize_x}x{self.hr_resize_y}"
+
+ if self.hr_resize_y == 0:
+ self.hr_upscale_to_x = self.hr_resize_x
+ self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width
+ elif self.hr_resize_x == 0:
+ self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height
+ self.hr_upscale_to_y = self.hr_resize_y
+ else:
+ target_w = self.hr_resize_x
+ target_h = self.hr_resize_y
+ src_ratio = self.width / self.height
+ dst_ratio = self.hr_resize_x / self.hr_resize_y
+
+ if src_ratio < dst_ratio:
+ self.hr_upscale_to_x = self.hr_resize_x
+ self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width
+ else:
+ self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height
+ self.hr_upscale_to_y = self.hr_resize_y
+
+ self.truncate_x = (self.hr_upscale_to_x - target_w) // opt_f
+ self.truncate_y = (self.hr_upscale_to_y - target_h) // opt_f
+
def init(self, all_prompts, all_seeds, all_subseeds):
if self.enable_hr:
if self.hr_checkpoint_name:
@@ -948,43 +1004,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): if not any(x.name == self.hr_upscaler for x in shared.sd_upscalers):
raise Exception(f"could not find upscaler named {self.hr_upscaler}")
- if opts.use_old_hires_fix_width_height and self.applied_old_hires_behavior_to != (self.width, self.height):
- self.hr_resize_x = self.width
- self.hr_resize_y = self.height
- self.hr_upscale_to_x = self.width
- self.hr_upscale_to_y = self.height
-
- self.width, self.height = old_hires_fix_first_pass_dimensions(self.width, self.height)
- self.applied_old_hires_behavior_to = (self.width, self.height)
-
- if self.hr_resize_x == 0 and self.hr_resize_y == 0:
- self.extra_generation_params["Hires upscale"] = self.hr_scale
- self.hr_upscale_to_x = int(self.width * self.hr_scale)
- self.hr_upscale_to_y = int(self.height * self.hr_scale)
- else:
- self.extra_generation_params["Hires resize"] = f"{self.hr_resize_x}x{self.hr_resize_y}"
-
- if self.hr_resize_y == 0:
- self.hr_upscale_to_x = self.hr_resize_x
- self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width
- elif self.hr_resize_x == 0:
- self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height
- self.hr_upscale_to_y = self.hr_resize_y
- else:
- target_w = self.hr_resize_x
- target_h = self.hr_resize_y
- src_ratio = self.width / self.height
- dst_ratio = self.hr_resize_x / self.hr_resize_y
-
- if src_ratio < dst_ratio:
- self.hr_upscale_to_x = self.hr_resize_x
- self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width
- else:
- self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height
- self.hr_upscale_to_y = self.hr_resize_y
-
- self.truncate_x = (self.hr_upscale_to_x - target_w) // opt_f
- self.truncate_y = (self.hr_upscale_to_y - target_h) // opt_f
+ self.calculate_target_resolution()
if not state.processing_has_refined_job_count:
if state.job_count == -1:
diff --git a/modules/sd_vae.py b/modules/sd_vae.py index 5ac1ac31..1db01992 100644 --- a/modules/sd_vae.py +++ b/modules/sd_vae.py @@ -2,7 +2,7 @@ import os import collections from dataclasses import dataclass -from modules import paths, shared, devices, script_callbacks, sd_models, extra_networks, lowvram, sd_hijack +from modules import paths, shared, devices, script_callbacks, sd_models, extra_networks, lowvram, sd_hijack, hashes import glob from copy import deepcopy @@ -20,6 +20,20 @@ checkpoint_info = None checkpoints_loaded = collections.OrderedDict() +def get_loaded_vae_name(): + if loaded_vae_file is None: + return None + + return os.path.basename(loaded_vae_file) + + +def get_loaded_vae_hash(): + if loaded_vae_file is None: + return None + + return hashes.sha256(loaded_vae_file, 'vae')[0:10] + + def get_base_vae(model): if base_vae is not None and checkpoint_info == model.sd_checkpoint_info and model: return base_vae diff --git a/modules/shared_options.py b/modules/shared_options.py index 7468bc81..9ae51f18 100644 --- a/modules/shared_options.py +++ b/modules/shared_options.py @@ -92,6 +92,7 @@ options_templates.update(options_section(('upscaling', "Upscaling"), { }))
options_templates.update(options_section(('face-restoration', "Face restoration"), {
+ "face_restoration": OptionInfo(False, "Restore faces", infotext='Face restoration').info("will use a third-party model on generation result to reconstruct faces"),
"face_restoration_model": OptionInfo("CodeFormer", "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in shared.face_restorers]}),
"code_former_weight": OptionInfo(0.5, "CodeFormer weight", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}).info("0 = maximum effect; 1 = minimum effect"),
"face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
@@ -126,7 +127,7 @@ options_templates.update(options_section(('training', "Training"), { }))
options_templates.update(options_section(('sd', "Stable Diffusion"), {
- "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": shared_items.list_checkpoint_tiles()}, refresh=shared_items.refresh_checkpoints),
+ "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": shared_items.list_checkpoint_tiles()}, refresh=shared_items.refresh_checkpoints, infotext='Model hash'),
"sd_checkpoints_limit": OptionInfo(1, "Maximum number of checkpoints loaded at the same time", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1}),
"sd_checkpoints_keep_in_cpu": OptionInfo(True, "Only keep one model on device").info("will keep models other than the currently used one in RAM rather than VRAM"),
"sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}).info("obsolete; set to 0 and use the two settings above instead"),
@@ -135,9 +136,10 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "enable_emphasis": OptionInfo(True, "Enable emphasis").info("use (text) to make model pay more attention to text and [text] to make it pay less attention"),
"enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
"comma_padding_backtrack": OptionInfo(20, "Prompt word wrap length limit", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1}).info("in tokens - for texts shorter than specified, if they don't fit into 75 token limit, move them to the next 75 token chunk"),
- "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer"),
+ "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}, infotext="Clip skip").link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer"),
"upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
"randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU", "NV"]}).info("changes seeds drastically; use CPU to produce the same picture across different videocard vendors; use NV to produce same picture as on NVidia videocards"),
+ "tiling": OptionInfo(False, "Tiling", infotext='Tiling').info("produce a tileable picture"),
}))
options_templates.update(options_section(('sdxl', "Stable Diffusion XL"), {
@@ -155,16 +157,16 @@ image into latent space representation and back. Latent space representation is For img2img, VAE is used to process user's input image before the sampling, and to create an image after sampling.
"""),
"sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
- "sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list).info("choose VAE model: Automatic = use one with same filename as checkpoint; None = use VAE from checkpoint"),
+ "sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list, infotext='VAE').info("choose VAE model: Automatic = use one with same filename as checkpoint; None = use VAE from checkpoint"),
"sd_vae_overrides_per_model_preferences": OptionInfo(True, "Selected VAE overrides per-model preferences").info("you can set per-model VAE either by editing user metadata for checkpoints, or by making the VAE have same name as checkpoint"),
"auto_vae_precision": OptionInfo(True, "Automatically revert VAE to 32-bit floats").info("triggers when a tensor with NaNs is produced in VAE; disabling the option in this case will result in a black square image"),
- "sd_vae_encode_method": OptionInfo("Full", "VAE type for encode", gr.Radio, {"choices": ["Full", "TAESD"]}).info("method to encode image to latent (use in img2img, hires-fix or inpaint mask)"),
- "sd_vae_decode_method": OptionInfo("Full", "VAE type for decode", gr.Radio, {"choices": ["Full", "TAESD"]}).info("method to decode latent to image"),
+ "sd_vae_encode_method": OptionInfo("Full", "VAE type for encode", gr.Radio, {"choices": ["Full", "TAESD"]}, infotext='VAE Encoder').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"]}, infotext='VAE Decoder').info("method to decode latent to image"),
}))
options_templates.update(options_section(('img2img', "img2img"), {
- "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
- "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01}),
+ "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Conditional mask weight'),
+ "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01}, infotext='Noise multiplier'),
"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
"img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies.").info("normally you'd do less with less denoising"),
"img2img_background_color": OptionInfo("#ffffff", "With img2img, fill transparent parts of the input image with this color.", ui_components.FormColorPicker, {}),
@@ -179,10 +181,10 @@ options_templates.update(options_section(('img2img', "img2img"), { options_templates.update(options_section(('optimizations', "Optimizations"), {
"cross_attention_optimization": OptionInfo("Automatic", "Cross attention optimization", gr.Dropdown, lambda: {"choices": shared_items.cross_attention_optimizations()}),
"s_min_uncond": OptionInfo(0.0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 15.0, "step": 0.01}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
- "token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9256").info("0=disable, higher=faster"),
+ "token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}, infotext='Token merging ratio').link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9256").info("0=disable, higher=faster"),
"token_merging_ratio_img2img": OptionInfo(0.0, "Token merging ratio for img2img", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
- "token_merging_ratio_hr": OptionInfo(0.0, "Token merging ratio for high-res pass", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
- "pad_cond_uncond": OptionInfo(False, "Pad prompt/negative prompt to be same length").info("improves performance when prompt and negative prompt have different lengths; changes seeds"),
+ "token_merging_ratio_hr": OptionInfo(0.0, "Token merging ratio for high-res pass", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}, infotext='Token merging ratio hr').info("only applies if non-zero and overrides above"),
+ "pad_cond_uncond": OptionInfo(False, "Pad prompt/negative prompt to be same length", infotext='Pad conds').info("improves performance when prompt and negative prompt have different lengths; changes seeds"),
"persistent_cond_cache": OptionInfo(True, "Persistent cond cache").info("Do not recalculate conds from prompts if prompts have not changed since previous calculation"),
}))
@@ -282,23 +284,23 @@ options_templates.update(options_section(('ui', "Live previews"), { options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
"hide_samplers": OptionInfo([], "Hide samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in shared_items.list_samplers()]}).needs_reload_ui(),
- "eta_ddim": OptionInfo(0.0, "Eta for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}).info("noise multiplier; higher = more unperdictable results"),
- "eta_ancestral": OptionInfo(1.0, "Eta for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}).info("noise multiplier; applies to Euler a and other samplers that have a in them"),
+ "eta_ddim": OptionInfo(0.0, "Eta for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Eta DDIM').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}, infotext='Eta').info("noise multiplier; applies to Euler a and other samplers that have a in them"),
"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
- 's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 100.0, "step": 0.01}).info('amount of stochasticity; only applies to Euler, Heun, and DPM2'),
- 's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 10.0, "step": 0.01}).info('enable stochasticity; start value of the sigma range; only applies to Euler, Heun, and DPM2'),
- 's_tmax': OptionInfo(0.0, "sigma tmax", gr.Slider, {"minimum": 0.0, "maximum": 999.0, "step": 0.01}).info("0 = inf; end value of the sigma range; only applies to Euler, Heun, and DPM2"),
- 's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.1, "step": 0.001}).info('amount of additional noise to counteract loss of detail during sampling; only applies to Euler, Heun, and DPM2'),
- 'k_sched_type': OptionInfo("Automatic", "Scheduler type", gr.Dropdown, {"choices": ["Automatic", "karras", "exponential", "polyexponential"]}).info("lets you override the noise schedule for k-diffusion samplers; choosing Automatic disables the three parameters below"),
- 'sigma_min': OptionInfo(0.0, "sigma min", gr.Number).info("0 = default (~0.03); minimum noise strength for k-diffusion noise scheduler"),
- 'sigma_max': OptionInfo(0.0, "sigma max", gr.Number).info("0 = default (~14.6); maximum noise strength for k-diffusion noise scheduler"),
- 'rho': OptionInfo(0.0, "rho", gr.Number).info("0 = default (7 for karras, 1 for polyexponential); higher values result in a steeper noise schedule (decreases faster)"),
- 'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}).info("ENSD; does not improve anything, just produces different results for ancestral samplers - only useful for reproducing images"),
- 'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma").link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/6044"),
- 'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}),
- 'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}),
- 'uni_pc_order': OptionInfo(3, "UniPC order", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}).info("must be < sampling steps"),
- 'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final"),
+ 's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 100.0, "step": 0.01}, infotext='Sigma churn').info('amount of stochasticity; only applies to Euler, Heun, and DPM2'),
+ 's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 10.0, "step": 0.01}, infotext='Sigma tmin').info('enable stochasticity; start value of the sigma range; only applies to Euler, Heun, and DPM2'),
+ 's_tmax': OptionInfo(0.0, "sigma tmax", gr.Slider, {"minimum": 0.0, "maximum": 999.0, "step": 0.01}, infotext='Sigma tmax').info("0 = inf; end value of the sigma range; only applies to Euler, Heun, and DPM2"),
+ 's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.1, "step": 0.001}, infotext='Sigma noise').info('amount of additional noise to counteract loss of detail during sampling; only applies to Euler, Heun, and DPM2'),
+ 'k_sched_type': OptionInfo("Automatic", "Scheduler type", gr.Dropdown, {"choices": ["Automatic", "karras", "exponential", "polyexponential"]}, infotext='Schedule type').info("lets you override the noise schedule for k-diffusion samplers; choosing Automatic disables the three parameters below"),
+ 'sigma_min': OptionInfo(0.0, "sigma min", gr.Number, infotext='Schedule max sigma').info("0 = default (~0.03); minimum noise strength for k-diffusion noise scheduler"),
+ 'sigma_max': OptionInfo(0.0, "sigma max", gr.Number, infotext='Schedule min sigma').info("0 = default (~14.6); maximum noise strength for k-diffusion noise scheduler"),
+ 'rho': OptionInfo(0.0, "rho", gr.Number, infotext='Schedule rho').info("0 = default (7 for karras, 1 for polyexponential); higher values result in a steeper noise schedule (decreases faster)"),
+ 'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}, infotext='ENSD').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", infotext='Discard penultimate 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"]}, infotext='UniPC variant'),
+ 'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}, infotext='UniPC skip type'),
+ 'uni_pc_order': OptionInfo(3, "UniPC order", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}, infotext='UniPC order').info("must be < sampling steps"),
+ 'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final", infotext='UniPC lower order final'),
}))
options_templates.update(options_section(('postprocessing', "Postprocessing"), {
diff --git a/modules/txt2img.py b/modules/txt2img.py index edad8930..5ea96bba 100644 --- a/modules/txt2img.py +++ b/modules/txt2img.py @@ -9,7 +9,7 @@ from modules.ui import plaintext_to_html import gradio as gr
-def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_name: str, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_checkpoint_name: str, hr_sampler_name: str, hr_prompt: str, hr_negative_prompt, override_settings_texts, request: gr.Request, *args):
+def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_name: str, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_checkpoint_name: str, hr_sampler_name: str, hr_prompt: str, hr_negative_prompt, override_settings_texts, request: gr.Request, *args):
override_settings = create_override_settings_dict(override_settings_texts)
p = processing.StableDiffusionProcessingTxt2Img(
@@ -32,8 +32,6 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step cfg_scale=cfg_scale,
width=width,
height=height,
- restore_faces=restore_faces,
- tiling=tiling,
enable_hr=enable_hr,
denoising_strength=denoising_strength if enable_hr else None,
hr_scale=hr_scale,
diff --git a/modules/ui.py b/modules/ui.py index 4e1daa8d..05292734 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -13,8 +13,8 @@ from PIL import Image, PngImagePlugin # noqa: F401 from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
from modules import gradio_extensons # noqa: F401
-from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, errors, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, ui_prompt_styles, scripts, sd_samplers, processing, devices, ui_extra_networks
-from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
+from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, errors, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, ui_prompt_styles, scripts, sd_samplers, processing, ui_extra_networks
+from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML, InputAccordion
from modules.paths import script_path
from modules.ui_common import create_refresh_button
from modules.ui_gradio_extensions import reload_javascript
@@ -94,11 +94,9 @@ def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resiz return ""
p = processing.StableDiffusionProcessingTxt2Img(width=width, height=height, enable_hr=True, hr_scale=hr_scale, hr_resize_x=hr_resize_x, hr_resize_y=hr_resize_y)
+ p.calculate_target_resolution()
- with devices.autocast():
- p.init([""], [0], [0])
-
- return f"resize: from <span class='resolution'>{p.width}x{p.height}</span> to <span class='resolution'>{p.hr_resize_x or p.hr_upscale_to_x}x{p.hr_resize_y or p.hr_upscale_to_y}</span>"
+ return f"from <span class='resolution'>{p.width}x{p.height}</span> to <span class='resolution'>{p.hr_resize_x or p.hr_upscale_to_x}x{p.hr_resize_y or p.hr_upscale_to_y}</span>"
def resize_from_to_html(width, height, scale_by):
@@ -146,7 +144,11 @@ def interrogate_deepbooru(image): def create_seed_inputs(target_interface):
with FormRow(elem_id=f"{target_interface}_seed_row", variant="compact"):
- seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=f"{target_interface}_seed")
+ if cmd_opts.use_textbox_seed:
+ seed = gr.Textbox(label='Seed', value="", elem_id=f"{target_interface}_seed")
+ else:
+ seed = gr.Number(label='Seed', value=-1, elem_id=f"{target_interface}_seed", precision=0)
+
random_seed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_seed", label='Random seed')
reuse_seed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_seed", label='Reuse seed')
@@ -157,7 +159,7 @@ def create_seed_inputs(target_interface): with FormRow(visible=False, elem_id=f"{target_interface}_subseed_row") as seed_extra_row_1:
seed_extras.append(seed_extra_row_1)
- subseed = gr.Number(label='Variation seed', value=-1, elem_id=f"{target_interface}_subseed")
+ subseed = gr.Number(label='Variation seed', value=-1, elem_id=f"{target_interface}_subseed", precision=0)
random_subseed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_subseed")
reuse_subseed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_subseed")
subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=f"{target_interface}_subseed_strength")
@@ -434,13 +436,13 @@ def create_ui(): elif category == "checkboxes":
with FormRow(elem_classes="checkboxes-row", variant="compact"):
- restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces")
- tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling")
- enable_hr = gr.Checkbox(label='Hires. fix', value=False, elem_id="txt2img_enable_hr")
- hr_final_resolution = FormHTML(value="", elem_id="txtimg_hr_finalres", label="Upscaled resolution", interactive=False)
+ pass
elif category == "hires_fix":
- with FormGroup(visible=False, elem_id="txt2img_hires_fix") as hr_options:
+ with InputAccordion(False, label="Hires. fix") as enable_hr:
+ with enable_hr.extra():
+ hr_final_resolution = FormHTML(value="", elem_id="txtimg_hr_finalres", label="Upscaled resolution", interactive=False, min_width=0)
+
with FormRow(elem_id="txt2img_hires_fix_row1", variant="compact"):
hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode)
hr_second_pass_steps = gr.Slider(minimum=0, maximum=150, step=1, label='Hires steps', value=0, elem_id="txt2img_hires_steps")
@@ -517,8 +519,6 @@ def create_ui(): toprow.ui_styles.dropdown,
steps,
sampler_name,
- restore_faces,
- tiling,
batch_count,
batch_size,
cfg_scale,
@@ -568,19 +568,11 @@ def create_ui(): show_progress=False,
)
- enable_hr.change(
- fn=lambda x: gr_show(x),
- inputs=[enable_hr],
- outputs=[hr_options],
- show_progress = False,
- )
-
txt2img_paste_fields = [
(toprow.prompt, "Prompt"),
(toprow.negative_prompt, "Negative prompt"),
(steps, "Steps"),
(sampler_name, "Sampler"),
- (restore_faces, "Face restoration"),
(cfg_scale, "CFG scale"),
(seed, "Seed"),
(width, "Size-1"),
@@ -594,7 +586,6 @@ def create_ui(): (toprow.ui_styles.dropdown, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
(denoising_strength, "Denoising strength"),
(enable_hr, lambda d: "Denoising strength" in d and ("Hires upscale" in d or "Hires upscaler" in d or "Hires resize-1" in d)),
- (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d and ("Hires upscale" in d or "Hires upscaler" in d or "Hires resize-1" in d))),
(hr_scale, "Hires upscale"),
(hr_upscaler, "Hires upscaler"),
(hr_second_pass_steps, "Hires steps"),
@@ -641,7 +632,7 @@ def create_ui(): extra_tabs = gr.Tabs(elem_id="img2img_extra_tabs")
extra_tabs.__enter__()
- with gr.Tab("Generation", id="img2img_generation") as img2img_generation_tab, FormRow().style(equal_height=False):
+ with gr.Tab("Generation", id="img2img_generation") as img2img_generation_tab, FormRow(equal_height=False):
with gr.Column(variant='compact', elem_id="img2img_settings"):
copy_image_buttons = []
copy_image_destinations = {}
@@ -801,8 +792,7 @@ def create_ui(): elif category == "checkboxes":
with FormRow(elem_classes="checkboxes-row", variant="compact"):
- restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
- tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")
+ pass
elif category == "batch":
if not opts.dimensions_and_batch_together:
@@ -875,8 +865,6 @@ def create_ui(): mask_blur,
mask_alpha,
inpainting_fill,
- restore_faces,
- tiling,
batch_count,
batch_size,
cfg_scale,
@@ -968,7 +956,6 @@ def create_ui(): (toprow.negative_prompt, "Negative prompt"),
(steps, "Steps"),
(sampler_name, "Sampler"),
- (restore_faces, "Face restoration"),
(cfg_scale, "CFG scale"),
(image_cfg_scale, "Image CFG scale"),
(seed, "Seed"),
diff --git a/modules/ui_components.py b/modules/ui_components.py index 8f8a7088..bfe2fbd9 100644 --- a/modules/ui_components.py +++ b/modules/ui_components.py @@ -72,3 +72,52 @@ class DropdownEditable(FormComponent, gr.Dropdown): def get_block_name(self):
return "dropdown"
+
+class InputAccordion(gr.Checkbox):
+ """A gr.Accordion that can be used as an input - returns True if open, False if closed.
+
+ Actaully just a hidden checkbox, but creates an accordion that follows and is followed by the state of the checkbox.
+ """
+
+ global_index = 0
+
+ def __init__(self, value, **kwargs):
+ self.accordion_id = kwargs.get('elem_id')
+ if self.accordion_id is None:
+ self.accordion_id = f"input-accordion-{InputAccordion.global_index}"
+ InputAccordion.global_index += 1
+
+ kwargs['elem_id'] = self.accordion_id + "-checkbox"
+ kwargs['visible'] = False
+ super().__init__(value, **kwargs)
+
+ self.change(fn=None, _js='function(checked){ inputAccordionChecked("' + self.accordion_id + '", checked); }', inputs=[self])
+
+ self.accordion = gr.Accordion(kwargs.get('label', 'Accordion'), open=value, elem_id=self.accordion_id, elem_classes=['input-accordion'])
+
+ def extra(self):
+ """Allows you to put something into the label of the accordion.
+
+ Use it like this:
+
+ ```
+ with InputAccordion(False, label="Accordion") as acc:
+ with acc.extra():
+ FormHTML(value="hello", min_width=0)
+
+ ...
+ ```
+ """
+
+ return gr.Column(elem_id=self.accordion_id + '-extra', elem_classes='input-accordion-extra', min_width=0)
+
+ def __enter__(self):
+ self.accordion.__enter__()
+ return self
+
+ def __exit__(self, exc_type, exc_val, exc_tb):
+ self.accordion.__exit__(exc_type, exc_val, exc_tb)
+
+ def get_block_name(self):
+ return "checkbox"
+
diff --git a/modules/ui_loadsave.py b/modules/ui_loadsave.py index 0052a5cc..ef6b0154 100644 --- a/modules/ui_loadsave.py +++ b/modules/ui_loadsave.py @@ -8,7 +8,7 @@ from modules.ui_components import ToolButton class UiLoadsave:
- """allows saving and restorig default values for gradio components"""
+ """allows saving and restoring default values for gradio components"""
def __init__(self, filename):
self.filename = filename
@@ -48,6 +48,11 @@ class UiLoadsave: elif condition and not condition(saved_value):
pass
else:
+ if isinstance(x, gr.Textbox) and field == 'value': # due to an undersirable behavior of gr.Textbox, if you give it an int value instead of str, everything dies
+ saved_value = str(saved_value)
+ elif isinstance(x, gr.Number) and field == 'value':
+ saved_value = float(saved_value)
+
setattr(obj, field, saved_value)
if init_field is not None:
init_field(saved_value)
|