diff --git a/javascript/extraNetworks.js b/javascript/extraNetworks.js
index 5582a6e5d..44d02349a 100644
--- a/javascript/extraNetworks.js
+++ b/javascript/extraNetworks.js
@@ -2,16 +2,14 @@ function setupExtraNetworksForTab(tabname) {
gradioApp().querySelector('#' + tabname + '_extra_tabs').classList.add('extra-networks');
var tabs = gradioApp().querySelector('#' + tabname + '_extra_tabs > div');
- var search = gradioApp().querySelector('#' + tabname + '_extra_search textarea');
+ var searchDiv = gradioApp().getElementById(tabname + '_extra_search');
+ var search = searchDiv.querySelector('textarea');
var sort = gradioApp().getElementById(tabname + '_extra_sort');
var sortOrder = gradioApp().getElementById(tabname + '_extra_sortorder');
var refresh = gradioApp().getElementById(tabname + '_extra_refresh');
- search.classList.add('search');
- sort.classList.add('sort');
- sortOrder.classList.add('sortorder');
sort.dataset.sortkey = 'sortDefault';
- tabs.appendChild(search);
+ tabs.appendChild(searchDiv);
tabs.appendChild(sort);
tabs.appendChild(sortOrder);
tabs.appendChild(refresh);
@@ -179,7 +177,7 @@ function saveCardPreview(event, tabname, filename) {
}
function extraNetworksSearchButton(tabs_id, event) {
- var searchTextarea = gradioApp().querySelector("#" + tabs_id + ' > div > textarea');
+ var searchTextarea = gradioApp().querySelector("#" + tabs_id + ' > label > textarea');
var button = event.target;
var text = button.classList.contains("search-all") ? "" : button.textContent.trim();
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 0713dbf0a..593abfef3 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -280,6 +280,9 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
if "Hires sampler" not in res:
res["Hires sampler"] = "Use same sampler"
+ if "Hires checkpoint" not in res:
+ res["Hires checkpoint"] = "Use same checkpoint"
+
if "Hires prompt" not in res:
res["Hires prompt"] = ""
diff --git a/modules/images.py b/modules/images.py
index 38aa933d6..ba3c43a45 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -318,7 +318,7 @@ def resize_image(resize_mode, im, width, height, upscaler_name=None):
return res
-invalid_filename_chars = '<>:"/\\|?*\n'
+invalid_filename_chars = '<>:"/\\|?*\n\r\t'
invalid_filename_prefix = ' '
invalid_filename_postfix = ' .'
re_nonletters = re.compile(r'[\s' + string.punctuation + ']+')
diff --git a/modules/lowvram.py b/modules/lowvram.py
index 3f8306643..96f52b7b4 100644
--- a/modules/lowvram.py
+++ b/modules/lowvram.py
@@ -15,6 +15,9 @@ def send_everything_to_cpu():
def setup_for_low_vram(sd_model, use_medvram):
+ if getattr(sd_model, 'lowvram', False):
+ return
+
sd_model.lowvram = True
parents = {}
diff --git a/modules/processing.py b/modules/processing.py
index a9ee7507e..b9900ded2 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -539,8 +539,12 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
return x
+class DecodedSamples(list):
+ already_decoded = True
+
+
def decode_latent_batch(model, batch, target_device=None, check_for_nans=False):
- samples = []
+ samples = DecodedSamples()
for i in range(batch.shape[0]):
sample = decode_first_stage(model, batch[i:i + 1])[0]
@@ -788,8 +792,12 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
- p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method
- x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
+ if getattr(samples_ddim, 'already_decoded', False):
+ x_samples_ddim = samples_ddim
+ else:
+ p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method
+ x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
+
x_samples_ddim = torch.stack(x_samples_ddim).float()
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
@@ -931,7 +939,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
cached_hr_uc = [None, None]
cached_hr_c = [None, None]
- def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, hr_sampler_name: str = None, hr_prompt: str = '', hr_negative_prompt: str = '', **kwargs):
+ def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, hr_checkpoint_name: str = None, hr_sampler_name: str = None, hr_prompt: str = '', hr_negative_prompt: str = '', **kwargs):
super().__init__(**kwargs)
self.enable_hr = enable_hr
self.denoising_strength = denoising_strength
@@ -942,11 +950,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
self.hr_resize_y = hr_resize_y
self.hr_upscale_to_x = hr_resize_x
self.hr_upscale_to_y = hr_resize_y
+ self.hr_checkpoint_name = hr_checkpoint_name
+ self.hr_checkpoint_info = None
self.hr_sampler_name = hr_sampler_name
self.hr_prompt = hr_prompt
self.hr_negative_prompt = hr_negative_prompt
self.all_hr_prompts = None
self.all_hr_negative_prompts = None
+ self.latent_scale_mode = None
if firstphase_width != 0 or firstphase_height != 0:
self.hr_upscale_to_x = self.width
@@ -969,6 +980,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
def init(self, all_prompts, all_seeds, all_subseeds):
if self.enable_hr:
+ if self.hr_checkpoint_name:
+ self.hr_checkpoint_info = sd_models.get_closet_checkpoint_match(self.hr_checkpoint_name)
+
+ if self.hr_checkpoint_info is None:
+ raise Exception(f'Could not find checkpoint with name {self.hr_checkpoint_name}')
+
+ self.extra_generation_params["Hires checkpoint"] = self.hr_checkpoint_info.short_title
+
if self.hr_sampler_name is not None and self.hr_sampler_name != self.sampler_name:
self.extra_generation_params["Hires sampler"] = self.hr_sampler_name
@@ -978,6 +997,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
if tuple(self.hr_negative_prompt) != tuple(self.negative_prompt):
self.extra_generation_params["Hires negative prompt"] = self.hr_negative_prompt
+ self.latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest")
+ if self.enable_hr and self.latent_scale_mode is None:
+ 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
@@ -1016,14 +1040,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
self.truncate_x = (self.hr_upscale_to_x - target_w) // opt_f
self.truncate_y = (self.hr_upscale_to_y - target_h) // opt_f
- # special case: the user has chosen to do nothing
- if self.hr_upscale_to_x == self.width and self.hr_upscale_to_y == self.height:
- self.enable_hr = False
- self.denoising_strength = None
- self.extra_generation_params.pop("Hires upscale", None)
- self.extra_generation_params.pop("Hires resize", None)
- return
-
if not state.processing_has_refined_job_count:
if state.job_count == -1:
state.job_count = self.n_iter
@@ -1041,17 +1057,32 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
- latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest")
- if self.enable_hr and latent_scale_mode is None:
- 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}")
-
x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
+ del x
if not self.enable_hr:
return samples
+ if self.latent_scale_mode is None:
+ decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)).to(dtype=torch.float32)
+ else:
+ decoded_samples = None
+
+ current = shared.sd_model.sd_checkpoint_info
+ try:
+ if self.hr_checkpoint_info is not None:
+ self.sampler = None
+ sd_models.reload_model_weights(info=self.hr_checkpoint_info)
+ devices.torch_gc()
+
+ return self.sample_hr_pass(samples, decoded_samples, seeds, subseeds, subseed_strength, prompts)
+ finally:
+ self.sampler = None
+ sd_models.reload_model_weights(info=current)
+ devices.torch_gc()
+
+ def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts):
self.is_hr_pass = True
target_width = self.hr_upscale_to_x
@@ -1069,11 +1100,18 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=index)
images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, p=self, suffix="-before-highres-fix")
- if latent_scale_mode is not None:
+ img2img_sampler_name = self.hr_sampler_name or self.sampler_name
+
+ if self.sampler_name in ['PLMS', 'UniPC']: # PLMS/UniPC do not support img2img so we just silently switch to DDIM
+ img2img_sampler_name = 'DDIM'
+
+ self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model)
+
+ if self.latent_scale_mode is not None:
for i in range(samples.shape[0]):
save_intermediate(samples, i)
- samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=latent_scale_mode["mode"], antialias=latent_scale_mode["antialias"])
+ samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=self.latent_scale_mode["mode"], antialias=self.latent_scale_mode["antialias"])
# Avoid making the inpainting conditioning unless necessary as
# this does need some extra compute to decode / encode the image again.
@@ -1082,7 +1120,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
else:
image_conditioning = self.txt2img_image_conditioning(samples)
else:
- decoded_samples = decode_first_stage(self.sd_model, samples)
lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
batch_images = []
@@ -1108,19 +1145,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
shared.state.nextjob()
- img2img_sampler_name = self.hr_sampler_name or self.sampler_name
-
- if self.sampler_name in ['PLMS', 'UniPC']: # PLMS/UniPC do not support img2img so we just silently switch to DDIM
- img2img_sampler_name = 'DDIM'
-
- self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model)
-
samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2]
noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=self)
# GC now before running the next img2img to prevent running out of memory
- x = None
devices.torch_gc()
if not self.disable_extra_networks:
@@ -1139,9 +1168,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio())
+ decoded_samples = decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)
+
self.is_hr_pass = False
- return samples
+ return decoded_samples
def close(self):
super().close()
@@ -1180,8 +1211,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
if self.hr_c is not None:
return
- self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.hr_negative_prompts, self.steps * self.step_multiplier, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data)
- self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.hr_prompts, self.steps * self.step_multiplier, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)
+ hr_prompts = prompt_parser.SdConditioning(self.hr_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y)
+ hr_negative_prompts = prompt_parser.SdConditioning(self.hr_negative_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y, is_negative_prompt=True)
+
+ self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, self.steps * self.step_multiplier, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data)
+ self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, self.steps * self.step_multiplier, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)
def setup_conds(self):
super().setup_conds()
@@ -1189,7 +1223,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
self.hr_uc = None
self.hr_c = None
- if self.enable_hr:
+ if self.enable_hr and self.hr_checkpoint_info is None:
if shared.opts.hires_fix_use_firstpass_conds:
self.calculate_hr_conds()
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 609fd56c4..9ad981998 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -5,7 +5,7 @@ from types import MethodType
from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet
from modules.hypernetworks import hypernetwork
from modules.shared import cmd_opts
-from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
+from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr, sd_hijack_inpainting
import ldm.modules.attention
import ldm.modules.diffusionmodules.model
@@ -29,8 +29,12 @@ ldm.modules.attention.MemoryEfficientCrossAttention = ldm.modules.attention.Cros
ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"] = ldm.modules.attention.CrossAttention
# silence new console spam from SD2
-ldm.modules.attention.print = lambda *args: None
-ldm.modules.diffusionmodules.model.print = lambda *args: None
+ldm.modules.attention.print = shared.ldm_print
+ldm.modules.diffusionmodules.model.print = shared.ldm_print
+ldm.util.print = shared.ldm_print
+ldm.models.diffusion.ddpm.print = shared.ldm_print
+
+sd_hijack_inpainting.do_inpainting_hijack()
optimizers = []
current_optimizer: sd_hijack_optimizations.SdOptimization = None
diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index c1977b194..2d44b8566 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -92,6 +92,4 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F
def do_inpainting_hijack():
- # p_sample_plms is needed because PLMS can't work with dicts as conditionings
-
ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms
diff --git a/modules/sd_models.py b/modules/sd_models.py
index ba15b4518..f60516046 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -15,7 +15,6 @@ import ldm.modules.midas as midas
from ldm.util import instantiate_from_config
from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl, cache
-from modules.sd_hijack_inpainting import do_inpainting_hijack
from modules.timer import Timer
import tomesd
@@ -67,6 +66,7 @@ class CheckpointInfo:
self.shorthash = self.sha256[0:10] if self.sha256 else None
self.title = name if self.shorthash is None else f'{name} [{self.shorthash}]'
+ self.short_title = self.name_for_extra if self.shorthash is None else f'{self.name_for_extra} [{self.shorthash}]'
self.ids = [self.hash, self.model_name, self.title, name, self.name_for_extra, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]'] if self.shorthash else [])
@@ -87,6 +87,7 @@ class CheckpointInfo:
checkpoints_list.pop(self.title, None)
self.title = f'{self.name} [{self.shorthash}]'
+ self.short_title = f'{self.name_for_extra} [{self.shorthash}]'
self.register()
return self.shorthash
@@ -107,14 +108,8 @@ def setup_model():
enable_midas_autodownload()
-def checkpoint_tiles():
- def convert(name):
- return int(name) if name.isdigit() else name.lower()
-
- def alphanumeric_key(key):
- return [convert(c) for c in re.split('([0-9]+)', key)]
-
- return sorted([x.title for x in checkpoints_list.values()], key=alphanumeric_key)
+def checkpoint_tiles(use_short=False):
+ return [x.short_title if use_short else x.title for x in checkpoints_list.values()]
def list_models():
@@ -137,11 +132,14 @@ def list_models():
elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
- for filename in sorted(model_list, key=str.lower):
+ for filename in model_list:
checkpoint_info = CheckpointInfo(filename)
checkpoint_info.register()
+re_strip_checksum = re.compile(r"\s*\[[^]]+]\s*$")
+
+
def get_closet_checkpoint_match(search_string):
checkpoint_info = checkpoint_aliases.get(search_string, None)
if checkpoint_info is not None:
@@ -151,6 +149,11 @@ def get_closet_checkpoint_match(search_string):
if found:
return found[0]
+ search_string_without_checksum = re.sub(re_strip_checksum, '', search_string)
+ found = sorted([info for info in checkpoints_list.values() if search_string_without_checksum in info.title], key=lambda x: len(x.title))
+ if found:
+ return found[0]
+
return None
@@ -430,6 +433,7 @@ sdxl_refiner_clip_weight = 'conditioner.embedders.0.model.ln_final.weight'
class SdModelData:
def __init__(self):
self.sd_model = None
+ self.loaded_sd_models = []
self.was_loaded_at_least_once = False
self.lock = threading.Lock()
@@ -444,6 +448,7 @@ class SdModelData:
try:
load_model()
+
except Exception as e:
errors.display(e, "loading stable diffusion model", full_traceback=True)
print("", file=sys.stderr)
@@ -455,11 +460,24 @@ class SdModelData:
def set_sd_model(self, v):
self.sd_model = v
+ try:
+ self.loaded_sd_models.remove(v)
+ except ValueError:
+ pass
+
+ if v is not None:
+ self.loaded_sd_models.insert(0, v)
+
model_data = SdModelData()
def get_empty_cond(sd_model):
+ from modules import extra_networks, processing
+
+ p = processing.StableDiffusionProcessingTxt2Img()
+ extra_networks.activate(p, {})
+
if hasattr(sd_model, 'conditioner'):
d = sd_model.get_learned_conditioning([""])
return d['crossattn']
@@ -467,19 +485,43 @@ def get_empty_cond(sd_model):
return sd_model.cond_stage_model([""])
+def send_model_to_cpu(m):
+ from modules import lowvram
+
+ if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+ lowvram.send_everything_to_cpu()
+ else:
+ m.to(devices.cpu)
+
+ devices.torch_gc()
+
+
+def send_model_to_device(m):
+ from modules import lowvram
+
+ if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+ lowvram.setup_for_low_vram(m, shared.cmd_opts.medvram)
+ else:
+ m.to(shared.device)
+
+
+def send_model_to_trash(m):
+ m.to(device="meta")
+ devices.torch_gc()
+
+
def load_model(checkpoint_info=None, already_loaded_state_dict=None):
- from modules import lowvram, sd_hijack
+ from modules import sd_hijack
checkpoint_info = checkpoint_info or select_checkpoint()
+ timer = Timer()
+
if model_data.sd_model:
- sd_hijack.model_hijack.undo_hijack(model_data.sd_model)
+ send_model_to_trash(model_data.sd_model)
model_data.sd_model = None
- gc.collect()
devices.torch_gc()
- do_inpainting_hijack()
-
- timer = Timer()
+ timer.record("unload existing model")
if already_loaded_state_dict is not None:
state_dict = already_loaded_state_dict
@@ -519,12 +561,9 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
with sd_disable_initialization.LoadStateDictOnMeta(state_dict, devices.cpu):
load_model_weights(sd_model, checkpoint_info, state_dict, timer)
+ timer.record("load weights from state dict")
- if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
- lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
- else:
- sd_model.to(shared.device)
-
+ send_model_to_device(sd_model)
timer.record("move model to device")
sd_hijack.model_hijack.hijack(sd_model)
@@ -532,7 +571,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
timer.record("hijack")
sd_model.eval()
- model_data.sd_model = sd_model
+ model_data.set_sd_model(sd_model)
model_data.was_loaded_at_least_once = True
sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True) # Reload embeddings after model load as they may or may not fit the model
@@ -553,10 +592,61 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
return sd_model
+def reuse_model_from_already_loaded(sd_model, checkpoint_info, timer):
+ """
+ Checks if the desired checkpoint from checkpoint_info is not already loaded in model_data.loaded_sd_models.
+ If it is loaded, returns that (moving it to GPU if necessary, and moving the currently loadded model to CPU if necessary).
+ If not, returns the model that can be used to load weights from checkpoint_info's file.
+ If no such model exists, returns None.
+ Additionaly deletes loaded models that are over the limit set in settings (sd_checkpoints_limit).
+ """
+
+ already_loaded = None
+ for i in reversed(range(len(model_data.loaded_sd_models))):
+ loaded_model = model_data.loaded_sd_models[i]
+ if loaded_model.sd_checkpoint_info.filename == checkpoint_info.filename:
+ already_loaded = loaded_model
+ continue
+
+ if len(model_data.loaded_sd_models) > shared.opts.sd_checkpoints_limit > 0:
+ print(f"Unloading model {len(model_data.loaded_sd_models)} over the limit of {shared.opts.sd_checkpoints_limit}: {loaded_model.sd_checkpoint_info.title}")
+ model_data.loaded_sd_models.pop()
+ send_model_to_trash(loaded_model)
+ timer.record("send model to trash")
+
+ if shared.opts.sd_checkpoints_keep_in_cpu:
+ send_model_to_cpu(sd_model)
+ timer.record("send model to cpu")
+
+ if already_loaded is not None:
+ send_model_to_device(already_loaded)
+ timer.record("send model to device")
+
+ model_data.set_sd_model(already_loaded)
+ print(f"Using already loaded model {already_loaded.sd_checkpoint_info.title}: done in {timer.summary()}")
+ return model_data.sd_model
+ elif shared.opts.sd_checkpoints_limit > 1 and len(model_data.loaded_sd_models) < shared.opts.sd_checkpoints_limit:
+ print(f"Loading model {checkpoint_info.title} ({len(model_data.loaded_sd_models) + 1} out of {shared.opts.sd_checkpoints_limit})")
+
+ model_data.sd_model = None
+ load_model(checkpoint_info)
+ return model_data.sd_model
+ elif len(model_data.loaded_sd_models) > 0:
+ sd_model = model_data.loaded_sd_models.pop()
+ model_data.sd_model = sd_model
+
+ print(f"Reusing loaded model {sd_model.sd_checkpoint_info.title} to load {checkpoint_info.title}")
+ return sd_model
+ else:
+ return None
+
+
def reload_model_weights(sd_model=None, info=None):
- from modules import lowvram, devices, sd_hijack
+ from modules import devices, sd_hijack
checkpoint_info = info or select_checkpoint()
+ timer = Timer()
+
if not sd_model:
sd_model = model_data.sd_model
@@ -565,19 +655,17 @@ def reload_model_weights(sd_model=None, info=None):
else:
current_checkpoint_info = sd_model.sd_checkpoint_info
if sd_model.sd_model_checkpoint == checkpoint_info.filename:
- return
+ return sd_model
+ sd_model = reuse_model_from_already_loaded(sd_model, checkpoint_info, timer)
+ if sd_model is not None and sd_model.sd_checkpoint_info.filename == checkpoint_info.filename:
+ return sd_model
+
+ if sd_model is not None:
sd_unet.apply_unet("None")
-
- if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
- lowvram.send_everything_to_cpu()
- else:
- sd_model.to(devices.cpu)
-
+ send_model_to_cpu(sd_model)
sd_hijack.model_hijack.undo_hijack(sd_model)
- timer = Timer()
-
state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
@@ -585,7 +673,9 @@ def reload_model_weights(sd_model=None, info=None):
timer.record("find config")
if sd_model is None or checkpoint_config != sd_model.used_config:
- del sd_model
+ if sd_model is not None:
+ send_model_to_trash(sd_model)
+
load_model(checkpoint_info, already_loaded_state_dict=state_dict)
return model_data.sd_model
@@ -608,6 +698,8 @@ def reload_model_weights(sd_model=None, info=None):
print(f"Weights loaded in {timer.summary()}.")
+ model_data.set_sd_model(sd_model)
+
return sd_model
diff --git a/modules/sd_models_xl.py b/modules/sd_models_xl.py
index bc2195087..011233216 100644
--- a/modules/sd_models_xl.py
+++ b/modules/sd_models_xl.py
@@ -98,10 +98,10 @@ def extend_sdxl(model):
model.conditioner.wrapped = torch.nn.Module()
-sgm.modules.attention.print = lambda *args: None
-sgm.modules.diffusionmodules.model.print = lambda *args: None
-sgm.modules.diffusionmodules.openaimodel.print = lambda *args: None
-sgm.modules.encoders.modules.print = lambda *args: None
+sgm.modules.attention.print = shared.ldm_print
+sgm.modules.diffusionmodules.model.print = shared.ldm_print
+sgm.modules.diffusionmodules.openaimodel.print = shared.ldm_print
+sgm.modules.encoders.modules.print = shared.ldm_print
# this gets the code to load the vanilla attention that we override
sgm.modules.attention.SDP_IS_AVAILABLE = True
diff --git a/modules/shared.py b/modules/shared.py
index df454d4a9..516ad7e80 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -220,12 +220,19 @@ class State:
return
import modules.sd_samplers
- if opts.show_progress_grid:
- self.assign_current_image(modules.sd_samplers.samples_to_image_grid(self.current_latent))
- else:
- self.assign_current_image(modules.sd_samplers.sample_to_image(self.current_latent))
- self.current_image_sampling_step = self.sampling_step
+ try:
+ if opts.show_progress_grid:
+ self.assign_current_image(modules.sd_samplers.samples_to_image_grid(self.current_latent))
+ else:
+ self.assign_current_image(modules.sd_samplers.sample_to_image(self.current_latent))
+
+ self.current_image_sampling_step = self.sampling_step
+
+ except Exception:
+ # when switching models during genration, VAE would be on CPU, so creating an image will fail.
+ # we silently ignore this error
+ errors.record_exception()
def assign_current_image(self, image):
self.current_image = image
@@ -393,6 +400,7 @@ options_templates.update(options_section(('system', "System"), {
"print_hypernet_extra": OptionInfo(False, "Print extra hypernetwork information to console."),
"list_hidden_files": OptionInfo(True, "Load models/files in hidden directories").info("directory is hidden if its name starts with \".\""),
"disable_mmap_load_safetensors": OptionInfo(False, "Disable memmapping for loading .safetensors files.").info("fixes very slow loading speed in some cases"),
+ "hide_ldm_prints": OptionInfo(True, "Prevent Stability-AI's ldm/sgm modules from printing noise to console."),
}))
options_templates.update(options_section(('training', "Training"), {
@@ -412,17 +420,14 @@ 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": 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_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"),
"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_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
"sd_unet": OptionInfo("Automatic", "SD Unet", gr.Dropdown, lambda: {"choices": shared_items.sd_unet_items()}, refresh=shared_items.refresh_unet_list).info("choose Unet model: Automatic = use one with same filename as checkpoint; None = use Unet from checkpoint"),
- "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.").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_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds").needs_restart(),
"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"),
@@ -441,6 +446,22 @@ options_templates.update(options_section(('sdxl', "Stable Diffusion XL"), {
"sdxl_refiner_high_aesthetic_score": OptionInfo(6.0, "SDXL high aesthetic score", gr.Number).info("used for refiner model prompt"),
}))
+
+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}),
+ "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, {}),
+ "img2img_editor_height": OptionInfo(720, "Height of the image editor", gr.Slider, {"minimum": 80, "maximum": 1600, "step": 1}).info("in pixels").needs_restart(),
+ "img2img_sketch_default_brush_color": OptionInfo("#ffffff", "Sketch initial brush color", ui_components.FormColorPicker, {}).info("default brush color of img2img sketch").needs_restart(),
+ "img2img_inpaint_mask_brush_color": OptionInfo("#ffffff", "Inpaint mask brush color", ui_components.FormColorPicker, {}).info("brush color of inpaint mask").needs_restart(),
+ "img2img_inpaint_sketch_default_brush_color": OptionInfo("#ffffff", "Inpaint sketch initial brush color", ui_components.FormColorPicker, {}).info("default brush color of img2img inpaint sketch").needs_restart(),
+ "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"),
+}))
+
+
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"),
@@ -460,7 +481,7 @@ options_templates.update(options_section(('compatibility', "Compatibility"), {
"hires_fix_use_firstpass_conds": OptionInfo(False, "For hires fix, calculate conds of second pass using extra networks of first pass."),
}))
-options_templates.update(options_section(('interrogate', "Interrogate Options"), {
+options_templates.update(options_section(('interrogate', "Interrogate"), {
"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}),
@@ -493,10 +514,7 @@ options_templates.update(options_section(('extra_networks', "Extra Networks"), {
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(),
- "img2img_editor_height": OptionInfo(720, "img2img: height of image editor", gr.Slider, {"minimum": 80, "maximum": 1600, "step": 1}).info("in pixels").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"),
"do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
"send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
"send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
@@ -515,11 +533,12 @@ options_templates.update(options_section(('ui', "User interface"), {
"ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(),
"hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(),
"ui_reorder_list": OptionInfo([], "txt2img/img2img UI item order", ui_components.DropdownMulti, lambda: {"choices": list(shared_items.ui_reorder_categories())}).info("selected items appear first").needs_restart(),
- "hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires sampler selection").needs_restart(),
+ "hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires checkpoint and sampler selection").needs_restart(),
"hires_fix_show_prompts": OptionInfo(False, "Hires fix: show hires prompt and negative prompt").needs_restart(),
"disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_restart(),
}))
+
options_templates.update(options_section(('infotext', "Infotext"), {
"add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
"add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
@@ -892,3 +911,10 @@ def walk_files(path, allowed_extensions=None):
continue
yield os.path.join(root, filename)
+
+
+def ldm_print(*args, **kwargs):
+ if opts.hide_ldm_prints:
+ return
+
+ print(*args, **kwargs)
diff --git a/modules/txt2img.py b/modules/txt2img.py
index 29d94e8cb..935ed4181 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_index: int, 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_sampler_index: int, 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_index: int, 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_index: int, 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(
@@ -41,6 +41,7 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step
hr_second_pass_steps=hr_second_pass_steps,
hr_resize_x=hr_resize_x,
hr_resize_y=hr_resize_y,
+ hr_checkpoint_name=None if hr_checkpoint_name == 'Use same checkpoint' else hr_checkpoint_name,
hr_sampler_name=sd_samplers.samplers_for_img2img[hr_sampler_index - 1].name if hr_sampler_index != 0 else None,
hr_prompt=hr_prompt,
hr_negative_prompt=hr_negative_prompt,
diff --git a/modules/ui.py b/modules/ui.py
index 6cf3dff88..1af6b4c88 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -261,7 +261,6 @@ class Toprow:
with gr.Row():
self.negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
-
self.button_interrogate = None
self.button_deepbooru = None
if is_img2img:
@@ -290,7 +289,6 @@ class Toprow:
with gr.Row(elem_id=f"{id_part}_tools"):
self.paste = ToolButton(value=paste_symbol, elem_id="paste")
self.clear_prompt_button = ToolButton(value=clear_prompt_symbol, elem_id=f"{id_part}_clear_prompt")
- self.extra_networks_button = ToolButton(value=extra_networks_symbol, elem_id=f"{id_part}_extra_networks")
self.restore_progress_button = ToolButton(value=restore_progress_symbol, elem_id=f"{id_part}_restore_progress", visible=False)
self.token_counter = gr.HTML(value="0/75", elem_id=f"{id_part}_token_counter", elem_classes=["token-counter"])
@@ -404,11 +402,10 @@ def create_ui():
dummy_component = gr.Label(visible=False)
- with FormRow(variant='compact', elem_id="txt2img_extra_networks", visible=False) as extra_networks:
- from modules import ui_extra_networks
- extra_networks_ui = ui_extra_networks.create_ui(extra_networks, toprow.extra_networks_button, 'txt2img')
+ extra_tabs = gr.Tabs(elem_id="txt2img_extra_tabs")
+ extra_tabs.__enter__()
- with gr.Row(equal_height=False):
+ with gr.Tab("Generation", id="txt2img_generation") as txt2img_generation_tab, gr.Row().style(equal_height=False):
with gr.Column(variant='compact', elem_id="txt2img_settings"):
scripts.scripts_txt2img.prepare_ui()
@@ -456,6 +453,10 @@ def create_ui():
hr_resize_y = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize height to", value=0, elem_id="txt2img_hr_resize_y")
with FormRow(elem_id="txt2img_hires_fix_row3", variant="compact", visible=opts.hires_fix_show_sampler) as hr_sampler_container:
+
+ hr_checkpoint_name = gr.Dropdown(label='Hires checkpoint', elem_id="hr_checkpoint", choices=["Use same checkpoint"] + modules.sd_models.checkpoint_tiles(use_short=True), value="Use same checkpoint")
+ create_refresh_button(hr_checkpoint_name, modules.sd_models.list_models, lambda: {"choices": ["Use same checkpoint"] + modules.sd_models.checkpoint_tiles(use_short=True)}, "hr_checkpoint_refresh")
+
hr_sampler_index = gr.Dropdown(label='Hires sampling method', elem_id="hr_sampler", choices=["Use same sampler"] + [x.name for x in samplers_for_img2img], value="Use same sampler", type="index")
with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact", visible=opts.hires_fix_show_prompts) as hr_prompts_container:
@@ -533,6 +534,7 @@ def create_ui():
hr_second_pass_steps,
hr_resize_x,
hr_resize_y,
+ hr_checkpoint_name,
hr_sampler_index,
hr_prompt,
hr_negative_prompt,
@@ -599,8 +601,9 @@ def create_ui():
(hr_second_pass_steps, "Hires steps"),
(hr_resize_x, "Hires resize-1"),
(hr_resize_y, "Hires resize-2"),
+ (hr_checkpoint_name, "Hires checkpoint"),
(hr_sampler_index, "Hires sampler"),
- (hr_sampler_container, lambda d: gr.update(visible=True) if d.get("Hires sampler", "Use same sampler") != "Use same sampler" else gr.update()),
+ (hr_sampler_container, lambda d: gr.update(visible=True) if d.get("Hires sampler", "Use same sampler") != "Use same sampler" or d.get("Hires checkpoint", "Use same checkpoint") != "Use same checkpoint" else gr.update()),
(hr_prompt, "Hires prompt"),
(hr_negative_prompt, "Hires negative prompt"),
(hr_prompts_container, lambda d: gr.update(visible=True) if d.get("Hires prompt", "") != "" or d.get("Hires negative prompt", "") != "" else gr.update()),
@@ -625,7 +628,11 @@ def create_ui():
toprow.token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps], outputs=[toprow.token_counter])
toprow.negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.negative_prompt, steps], outputs=[toprow.negative_token_counter])
- ui_extra_networks.setup_ui(extra_networks_ui, txt2img_gallery)
+ from modules import ui_extra_networks
+ extra_networks_ui = ui_extra_networks.create_ui(txt2img_interface, [txt2img_generation_tab], 'txt2img')
+ ui_extra_networks.setup_ui(extra_networks_ui, txt2img_gallery)
+
+ extra_tabs.__exit__()
scripts.scripts_current = scripts.scripts_img2img
scripts.scripts_img2img.initialize_scripts(is_img2img=True)
@@ -633,11 +640,10 @@ def create_ui():
with gr.Blocks(analytics_enabled=False) as img2img_interface:
toprow = Toprow(is_img2img=True)
- with FormRow(variant='compact', elem_id="img2img_extra_networks", visible=False) as extra_networks:
- from modules import ui_extra_networks
- extra_networks_ui_img2img = ui_extra_networks.create_ui(extra_networks, toprow.extra_networks_button, 'img2img')
+ extra_tabs = gr.Tabs(elem_id="img2img_extra_tabs")
+ extra_tabs.__enter__()
- with FormRow(equal_height=False):
+ with gr.Tab("Generation", id="img2img_generation") as img2img_generation_tab, FormRow().style(equal_height=False):
with gr.Column(variant='compact', elem_id="img2img_settings"):
copy_image_buttons = []
copy_image_destinations = {}
@@ -663,15 +669,15 @@ def create_ui():
add_copy_image_controls('img2img', init_img)
with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch:
- sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA", height=opts.img2img_editor_height)
+ sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA", height=opts.img2img_editor_height, brush_color=opts.img2img_sketch_default_brush_color)
add_copy_image_controls('sketch', sketch)
with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab") as tab_inpaint:
- init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA", height=opts.img2img_editor_height, brush_color='#ffffff')
+ init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA", height=opts.img2img_editor_height, brush_color=opts.img2img_inpaint_mask_brush_color)
add_copy_image_controls('inpaint', init_img_with_mask)
with gr.TabItem('Inpaint sketch', id='inpaint_sketch', elem_id="img2img_inpaint_sketch_tab") as tab_inpaint_color:
- inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA", height=opts.img2img_editor_height, brush_color='#ffffff')
+ inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA", height=opts.img2img_editor_height, brush_color=opts.img2img_inpaint_sketch_default_brush_color)
inpaint_color_sketch_orig = gr.State(None)
add_copy_image_controls('inpaint_sketch', inpaint_color_sketch)
@@ -959,8 +965,6 @@ def create_ui():
toprow.token_button.click(fn=update_token_counter, inputs=[toprow.prompt, steps], outputs=[toprow.token_counter])
toprow.negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.negative_prompt, steps], outputs=[toprow.negative_token_counter])
- ui_extra_networks.setup_ui(extra_networks_ui_img2img, img2img_gallery)
-
img2img_paste_fields = [
(toprow.prompt, "Prompt"),
(toprow.negative_prompt, "Negative prompt"),
@@ -989,6 +993,12 @@ def create_ui():
paste_button=toprow.paste, tabname="img2img", source_text_component=toprow.prompt, source_image_component=None,
))
+ from modules import ui_extra_networks
+ extra_networks_ui_img2img = ui_extra_networks.create_ui(img2img_interface, [img2img_generation_tab], 'img2img')
+ ui_extra_networks.setup_ui(extra_networks_ui_img2img, img2img_gallery)
+
+ extra_tabs.__exit__()
+
scripts.scripts_current = None
with gr.Blocks(analytics_enabled=False) as extras_interface:
diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py
index c6390db79..3a73c89e8 100644
--- a/modules/ui_extra_networks.py
+++ b/modules/ui_extra_networks.py
@@ -155,7 +155,7 @@ class ExtraNetworksPage:
subdirs = {"": 1, **subdirs}
subdirs_html = "".join([f"""
-