diff --git a/javascript/extensions.js b/javascript/extensions.js
index c593cd2e5..72924a28c 100644
--- a/javascript/extensions.js
+++ b/javascript/extensions.js
@@ -1,5 +1,5 @@
-function extensions_apply(_, _){
+function extensions_apply(_, _, disable_all){
var disable = []
var update = []
@@ -13,10 +13,10 @@ function extensions_apply(_, _){
restart_reload()
- return [JSON.stringify(disable), JSON.stringify(update)]
+ return [JSON.stringify(disable), JSON.stringify(update), disable_all]
}
-function extensions_check(){
+function extensions_check(_, _){
var disable = []
gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
diff --git a/launch.py b/launch.py
index c41ae82d2..68e08114d 100644
--- a/launch.py
+++ b/launch.py
@@ -206,6 +206,10 @@ def list_extensions(settings_file):
print(e, file=sys.stderr)
disabled_extensions = set(settings.get('disabled_extensions', []))
+ disable_all_extensions = settings.get('disable_all_extensions', 'none')
+
+ if disable_all_extensions != 'none':
+ return []
return [x for x in os.listdir(extensions_dir) if x not in disabled_extensions]
@@ -235,7 +239,7 @@ def prepare_environment():
codeformer_repo = os.environ.get('CODEFORMER_REPO', 'https://github.com/sczhou/CodeFormer.git')
blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
- stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "47b6b607fdd31875c9279cd2f4f16b92e4ea958e")
+ stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf")
taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "5b3af030dd83e0297272d861c19477735d0317ec")
codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
diff --git a/models/karlo/ViT-L-14_stats.th b/models/karlo/ViT-L-14_stats.th
new file mode 100644
index 000000000..a6a06e94e
Binary files /dev/null and b/models/karlo/ViT-L-14_stats.th differ
diff --git a/modules/extensions.py b/modules/extensions.py
index f74f40432..2ad7f66e2 100644
--- a/modules/extensions.py
+++ b/modules/extensions.py
@@ -14,7 +14,12 @@ if not os.path.exists(extensions_dir):
def active():
- return [x for x in extensions if x.enabled]
+ if shared.opts.disable_all_extensions == "all":
+ return []
+ elif shared.opts.disable_all_extensions == "extra":
+ return [x for x in extensions if x.enabled and x.is_builtin]
+ else:
+ return [x for x in extensions if x.enabled]
class Extension:
@@ -96,6 +101,11 @@ def list_extensions():
if not os.path.isdir(extensions_dir):
return
+ if shared.opts.disable_all_extensions == "all":
+ print("*** \"Disable all extensions\" option was set, will not load any extensions ***")
+ elif shared.opts.disable_all_extensions == "extra":
+ print("*** \"Disable all extensions\" option was set, will only load built-in extensions ***")
+
extension_paths = []
for dirname in [extensions_dir, extensions_builtin_dir]:
if not os.path.isdir(dirname):
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index b859271d0..84cf4134c 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -311,7 +311,7 @@ class Hypernetwork:
def list_hypernetworks(path):
res = {}
- for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True)):
+ for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True), key=str.lower):
name = os.path.splitext(os.path.basename(filename))[0]
# Prevent a hypothetical "None.pt" from being listed.
if name != "None":
diff --git a/modules/img2img.py b/modules/img2img.py
index d321b1979..83bee95ea 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -156,7 +156,8 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
if shared.cmd_opts.enable_console_prompts:
print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
- p.extra_generation_params["Mask blur"] = mask_blur
+ if mask:
+ p.extra_generation_params["Mask blur"] = mask_blur
if is_batch:
assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
diff --git a/modules/lowvram.py b/modules/lowvram.py
index 042a0254a..e254cc131 100644
--- a/modules/lowvram.py
+++ b/modules/lowvram.py
@@ -55,12 +55,12 @@ def setup_for_low_vram(sd_model, use_medvram):
if hasattr(sd_model.cond_stage_model, 'model'):
sd_model.cond_stage_model.transformer = sd_model.cond_stage_model.model
- # remove four big modules, cond, first_stage, depth (if applicable), and unet from the model and then
+ # remove several big modules: cond, first_stage, depth/embedder (if applicable), and unet from the model and then
# send the model to GPU. Then put modules back. the modules will be in CPU.
- stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, getattr(sd_model, 'depth_model', None), sd_model.model
- sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.model = None, None, None, None
+ stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, getattr(sd_model, 'depth_model', None), getattr(sd_model, 'embedder', None), sd_model.model
+ sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, sd_model.model = None, None, None, None, None
sd_model.to(devices.device)
- sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.model = stored
+ sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, sd_model.model = stored
# register hooks for those the first three models
sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
@@ -69,6 +69,8 @@ def setup_for_low_vram(sd_model, use_medvram):
sd_model.first_stage_model.decode = first_stage_model_decode_wrap
if sd_model.depth_model:
sd_model.depth_model.register_forward_pre_hook(send_me_to_gpu)
+ if sd_model.embedder:
+ sd_model.embedder.register_forward_pre_hook(send_me_to_gpu)
parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
if hasattr(sd_model.cond_stage_model, 'model'):
diff --git a/modules/processing.py b/modules/processing.py
index 345fe59f6..066728c4e 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -78,22 +78,28 @@ def apply_overlay(image, paste_loc, index, overlays):
def txt2img_image_conditioning(sd_model, x, width, height):
- if sd_model.model.conditioning_key not in {'hybrid', 'concat'}:
- # Dummy zero conditioning if we're not using inpainting model.
+ if sd_model.model.conditioning_key in {'hybrid', 'concat'}: # Inpainting models
+
+ # The "masked-image" in this case will just be all zeros since the entire image is masked.
+ image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
+ image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
+
+ # Add the fake full 1s mask to the first dimension.
+ image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
+ image_conditioning = image_conditioning.to(x.dtype)
+
+ return image_conditioning
+
+ elif sd_model.model.conditioning_key == "crossattn-adm": # UnCLIP models
+
+ return x.new_zeros(x.shape[0], 2*sd_model.noise_augmentor.time_embed.dim, dtype=x.dtype, device=x.device)
+
+ else:
+ # Dummy zero conditioning if we're not using inpainting or unclip models.
# Still takes up a bit of memory, but no encoder call.
# Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
- # The "masked-image" in this case will just be all zeros since the entire image is masked.
- image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
- image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
-
- # Add the fake full 1s mask to the first dimension.
- image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
- image_conditioning = image_conditioning.to(x.dtype)
-
- return image_conditioning
-
class StableDiffusionProcessing:
"""
@@ -190,6 +196,14 @@ class StableDiffusionProcessing:
return conditioning_image
+ def unclip_image_conditioning(self, source_image):
+ c_adm = self.sd_model.embedder(source_image)
+ if self.sd_model.noise_augmentor is not None:
+ noise_level = 0 # TODO: Allow other noise levels?
+ c_adm, noise_level_emb = self.sd_model.noise_augmentor(c_adm, noise_level=repeat(torch.tensor([noise_level]).to(c_adm.device), '1 -> b', b=c_adm.shape[0]))
+ c_adm = torch.cat((c_adm, noise_level_emb), 1)
+ return c_adm
+
def inpainting_image_conditioning(self, source_image, latent_image, image_mask=None):
self.is_using_inpainting_conditioning = True
@@ -241,6 +255,9 @@ class StableDiffusionProcessing:
if self.sampler.conditioning_key in {'hybrid', 'concat'}:
return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
+ if self.sampler.conditioning_key == "crossattn-adm":
+ return self.unclip_image_conditioning(source_image)
+
# Dummy zero conditioning if we're not using inpainting or depth model.
return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
diff --git a/modules/sd_models.py b/modules/sd_models.py
index e4c3f5689..1f5f32118 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -125,7 +125,7 @@ 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 model_list:
+ for filename in sorted(model_list, key=str.lower):
checkpoint_info = CheckpointInfo(filename)
checkpoint_info.register()
@@ -389,6 +389,14 @@ def repair_config(sd_config):
elif shared.cmd_opts.upcast_sampling:
sd_config.model.params.unet_config.params.use_fp16 = True
+ if getattr(sd_config.model.params.first_stage_config.params.ddconfig, "attn_type", None) == "vanilla-xformers" and not shared.xformers_available:
+ sd_config.model.params.first_stage_config.params.ddconfig.attn_type = "vanilla"
+
+ # For UnCLIP-L, override the hardcoded karlo directory
+ if hasattr(sd_config.model.params, "noise_aug_config") and hasattr(sd_config.model.params.noise_aug_config.params, "clip_stats_path"):
+ karlo_path = os.path.join(paths.models_path, 'karlo')
+ sd_config.model.params.noise_aug_config.params.clip_stats_path = sd_config.model.params.noise_aug_config.params.clip_stats_path.replace("checkpoints/karlo_models", karlo_path)
+
sd1_clip_weight = 'cond_stage_model.transformer.text_model.embeddings.token_embedding.weight'
sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight'
diff --git a/modules/sd_models_config.py b/modules/sd_models_config.py
index a25263938..bdeb42f42 100644
--- a/modules/sd_models_config.py
+++ b/modules/sd_models_config.py
@@ -14,6 +14,8 @@ config_sd2 = os.path.join(sd_repo_configs_path, "v2-inference.yaml")
config_sd2v = os.path.join(sd_repo_configs_path, "v2-inference-v.yaml")
config_sd2_inpainting = os.path.join(sd_repo_configs_path, "v2-inpainting-inference.yaml")
config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml")
+config_unclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-l-inference.yaml")
+config_unopenclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-h-inference.yaml")
config_inpainting = os.path.join(sd_configs_path, "v1-inpainting-inference.yaml")
config_instruct_pix2pix = os.path.join(sd_configs_path, "instruct-pix2pix.yaml")
config_alt_diffusion = os.path.join(sd_configs_path, "alt-diffusion-inference.yaml")
@@ -67,9 +69,14 @@ def guess_model_config_from_state_dict(sd, filename):
return None
sd2_cond_proj_weight = sd.get('cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight', None)
diffusion_model_input = sd.get('model.diffusion_model.input_blocks.0.0.weight', None)
+ sd2_variations_weight = sd.get('embedder.model.ln_final.weight', None)
if sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None:
return config_depth_model
+ elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 768:
+ return config_unclip
+ elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 1024:
+ return config_unopenclip
if sd2_cond_proj_weight is not None and sd2_cond_proj_weight.shape[1] == 1024:
if diffusion_model_input.shape[1] == 9:
diff --git a/modules/sd_samplers_compvis.py b/modules/sd_samplers_compvis.py
index 083da18ca..bfcc55749 100644
--- a/modules/sd_samplers_compvis.py
+++ b/modules/sd_samplers_compvis.py
@@ -70,8 +70,13 @@ class VanillaStableDiffusionSampler:
# Have to unwrap the inpainting conditioning here to perform pre-processing
image_conditioning = None
+ uc_image_conditioning = None
if isinstance(cond, dict):
- image_conditioning = cond["c_concat"][0]
+ if self.conditioning_key == "crossattn-adm":
+ image_conditioning = cond["c_adm"]
+ uc_image_conditioning = unconditional_conditioning["c_adm"]
+ else:
+ image_conditioning = cond["c_concat"][0]
cond = cond["c_crossattn"][0]
unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]
@@ -98,8 +103,12 @@ class VanillaStableDiffusionSampler:
# Wrap the image conditioning back up since the DDIM code can accept the dict directly.
# Note that they need to be lists because it just concatenates them later.
if image_conditioning is not None:
- cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
- unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+ if self.conditioning_key == "crossattn-adm":
+ cond = {"c_adm": image_conditioning, "c_crossattn": [cond]}
+ unconditional_conditioning = {"c_adm": uc_image_conditioning, "c_crossattn": [unconditional_conditioning]}
+ else:
+ cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
+ unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
return x, ts, cond, unconditional_conditioning
@@ -176,8 +185,12 @@ class VanillaStableDiffusionSampler:
# Wrap the conditioning models with additional image conditioning for inpainting model
if image_conditioning is not None:
- conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
- unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+ if self.conditioning_key == "crossattn-adm":
+ conditioning = {"c_adm": image_conditioning, "c_crossattn": [conditioning]}
+ unconditional_conditioning = {"c_adm": torch.zeros_like(image_conditioning), "c_crossattn": [unconditional_conditioning]}
+ else:
+ conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
+ unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
samples = self.launch_sampling(t_enc + 1, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
@@ -195,8 +208,12 @@ class VanillaStableDiffusionSampler:
# Wrap the conditioning models with additional image conditioning for inpainting model
# dummy_for_plms is needed because PLMS code checks the first item in the dict to have the right shape
if image_conditioning is not None:
- conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]}
- unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]}
+ if self.conditioning_key == "crossattn-adm":
+ conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_adm": image_conditioning}
+ unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_adm": torch.zeros_like(image_conditioning)}
+ else:
+ conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]}
+ unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]}
samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 93f0e55a0..e9f08518f 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -92,14 +92,21 @@ class CFGDenoiser(torch.nn.Module):
batch_size = len(conds_list)
repeats = [len(conds_list[i]) for i in range(batch_size)]
+ if shared.sd_model.model.conditioning_key == "crossattn-adm":
+ image_uncond = torch.zeros_like(image_cond)
+ make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm}
+ else:
+ image_uncond = image_cond
+ make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]}
+
if not is_edit_model:
x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
- image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
+ image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond])
else:
x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
- image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond] + [torch.zeros_like(self.init_latent)])
+ image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)])
denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond)
cfg_denoiser_callback(denoiser_params)
@@ -116,13 +123,13 @@ class CFGDenoiser(torch.nn.Module):
cond_in = torch.cat([tensor, uncond, uncond])
if shared.batch_cond_uncond:
- x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})
+ x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in))
else:
x_out = torch.zeros_like(x_in)
for batch_offset in range(0, x_out.shape[0], batch_size):
a = batch_offset
b = a + batch_size
- x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [cond_in[a:b]], "c_concat": [image_cond_in[a:b]]})
+ x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict([cond_in[a:b]], image_cond_in[a:b]))
else:
x_out = torch.zeros_like(x_in)
batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
@@ -135,9 +142,9 @@ class CFGDenoiser(torch.nn.Module):
else:
c_crossattn = torch.cat([tensor[a:b]], uncond)
- x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": c_crossattn, "c_concat": [image_cond_in[a:b]]})
+ x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
- x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
+ x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:]))
denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps)
cfg_denoised_callback(denoised_params)
diff --git a/modules/shared.py b/modules/shared.py
index 1205702bd..beb1ae690 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -431,7 +431,8 @@ options_templates.update(options_section(('postprocessing', "Postprocessing"), {
}))
options_templates.update(options_section((None, "Hidden options"), {
- "disabled_extensions": OptionInfo([], "Disable those extensions"),
+ "disabled_extensions": OptionInfo([], "Disable these extensions"),
+ "disable_all_extensions": OptionInfo("none", "Disable all extensions (preserves the list of disabled extensions)", gr.Radio, {"choices": ["none", "extra", "all"]}),
"sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
}))
diff --git a/modules/ui.py b/modules/ui.py
index 4241276b8..2c6b08eaa 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -70,17 +70,6 @@ def gr_show(visible=True):
sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
-css_hide_progressbar = """
-.wrap .m-12 svg { display:none!important; }
-.wrap .m-12::before { content:"Loading..." }
-.wrap .z-20 svg { display:none!important; }
-.wrap .z-20::before { content:"Loading..." }
-.wrap.cover-bg .z-20::before { content:"" }
-.progress-bar { display:none!important; }
-.meta-text { display:none!important; }
-.meta-text-center { display:none!important; }
-"""
-
# Using constants for these since the variation selector isn't visible.
# Important that they exactly match script.js for tooltip to work.
random_symbol = '\U0001f3b2\ufe0f' # 🎲️
@@ -1552,22 +1541,6 @@ def create_ui():
(train_interface, "Train", "ti"),
]
- css = ""
-
- for cssfile in modules.scripts.list_files_with_name("style.css"):
- if not os.path.isfile(cssfile):
- continue
-
- with open(cssfile, "r", encoding="utf8") as file:
- css += file.read() + "\n"
-
- if os.path.exists(os.path.join(data_path, "user.css")):
- with open(os.path.join(data_path, "user.css"), "r", encoding="utf8") as file:
- css += file.read() + "\n"
-
- if not cmd_opts.no_progressbar_hiding:
- css += css_hide_progressbar
-
interfaces += script_callbacks.ui_tabs_callback()
interfaces += [(settings_interface, "Settings", "settings")]
@@ -1578,7 +1551,7 @@ def create_ui():
for _interface, label, _ifid in interfaces:
shared.tab_names.append(label)
- with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
+ with gr.Blocks(analytics_enabled=False, title="Stable Diffusion") as demo:
with gr.Row(elem_id="quicksettings", variant="compact"):
for i, k, item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])):
component = create_setting_component(k, is_quicksettings=True)
@@ -1763,25 +1736,60 @@ def create_ui():
return demo
-def reload_javascript():
+def webpath(fn):
+ if fn.startswith(script_path):
+ web_path = os.path.relpath(fn, script_path).replace('\\', '/')
+ else:
+ web_path = os.path.abspath(fn)
+
+ return f'file={web_path}?{os.path.getmtime(fn)}'
+
+
+def javascript_html():
script_js = os.path.join(script_path, "script.js")
- head = f'\n'
+ head = f'\n'
inline = f"{localization.localization_js(shared.opts.localization)};"
if cmd_opts.theme is not None:
inline += f"set_theme('{cmd_opts.theme}');"
for script in modules.scripts.list_scripts("javascript", ".js"):
- head += f'\n'
+ head += f'\n'
for script in modules.scripts.list_scripts("javascript", ".mjs"):
- head += f'\n'
+ head += f'\n'
head += f'\n'
+ return head
+
+
+def css_html():
+ head = ""
+
+ def stylesheet(fn):
+ return f''
+
+ for cssfile in modules.scripts.list_files_with_name("style.css"):
+ if not os.path.isfile(cssfile):
+ continue
+
+ head += stylesheet(cssfile)
+
+ if os.path.exists(os.path.join(data_path, "user.css")):
+ head += stylesheet(os.path.join(data_path, "user.css"))
+
+ return head
+
+
+def reload_javascript():
+ js = javascript_html()
+ css = css_html()
+
def template_response(*args, **kwargs):
res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
- res.body = res.body.replace(b'', f'{head}'.encode("utf8"))
+ res.body = res.body.replace(b'', f'{js}'.encode("utf8"))
+ res.body = res.body.replace(b'', f'{css}'.encode("utf8"))
res.init_headers()
return res
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index 252045cb5..ca92ccee0 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -20,7 +20,7 @@ def check_access():
assert not shared.cmd_opts.disable_extension_access, "extension access disabled because of command line flags"
-def apply_and_restart(disable_list, update_list):
+def apply_and_restart(disable_list, update_list, disable_all):
check_access()
disabled = json.loads(disable_list)
@@ -42,6 +42,7 @@ def apply_and_restart(disable_list, update_list):
shared.exception()
shared.opts.disabled_extensions = disabled
+ shared.opts.disable_all_extensions = disable_all
shared.opts.save(shared.config_filename)
shared.state.interrupt()
@@ -98,9 +99,13 @@ def extension_table():
else:
ext_status = ext.status
+ style = ""
+ if shared.opts.disable_all_extensions == "extra" and not ext.is_builtin or shared.opts.disable_all_extensions == "all":
+ style = ' style="color: var(--primary-400)"'
+
code += f"""