Merge branch 'master' into test_resolve_conflicts
This commit is contained in:
MalumaDev
GitHub
commit
c2765c9bcd
@ -523,7 +523,6 @@ Affandi,0.7170285,nudity
|
||||
Diane Arbus,0.655138,digipa-high-impact
|
||||
Joseph Ducreux,0.65247905,digipa-high-impact
|
||||
Berthe Morisot,0.7165984,fineart
|
||||
Hilma AF Klint,0.71643853,scribbles
|
||||
Hilma af Klint,0.71643853,scribbles
|
||||
Filippino Lippi,0.7163017,fineart
|
||||
Leonid Afremov,0.7163005,fineart
|
||||
@ -738,14 +737,12 @@ Abraham Mignon,0.60605425,fineart
|
||||
Albert Bloch,0.69573116,nudity
|
||||
Charles Dana Gibson,0.67155975,fineart
|
||||
Alexandre-Évariste Fragonard,0.6507174,fineart
|
||||
Alexandre-Évariste Fragonard,0.6507174,fineart
|
||||
Ernst Fuchs,0.6953538,nudity
|
||||
Alfredo Jaar,0.6952965,digipa-high-impact
|
||||
Judy Chicago,0.6952246,weird
|
||||
Frans van Mieris the Younger,0.6951849,fineart
|
||||
Aertgen van Leyden,0.6951305,fineart
|
||||
Emily Carr,0.69512105,fineart
|
||||
Frances Macdonald,0.6950408,scribbles
|
||||
Frances MacDonald,0.6950408,scribbles
|
||||
Hannah Höch,0.69495845,scribbles
|
||||
Gillis Rombouts,0.58770025,fineart
|
||||
@ -895,7 +892,6 @@ Richard McGuire,0.6820089,scribbles
|
||||
Anni Albers,0.65708244,digipa-high-impact
|
||||
Aleksey Savrasov,0.65207493,fineart
|
||||
Wayne Barlowe,0.6537874,fineart
|
||||
Giorgio De Chirico,0.6815907,fineart
|
||||
Giorgio de Chirico,0.6815907,fineart
|
||||
Ernest Procter,0.6815795,fineart
|
||||
Adriaen Brouwer,0.6815058,fineart
|
||||
@ -1241,7 +1237,6 @@ Betty Churcher,0.65387225,fineart
|
||||
Claes Corneliszoon Moeyaert,0.65386075,fineart
|
||||
David Bomberg,0.6537477,fineart
|
||||
Abraham Bosschaert,0.6535562,fineart
|
||||
Giuseppe De Nittis,0.65354455,fineart
|
||||
Giuseppe de Nittis,0.65354455,fineart
|
||||
John La Farge,0.65342575,fineart
|
||||
Frits Thaulow,0.65341854,fineart
|
||||
@ -1522,7 +1517,6 @@ Gertrude Harvey,0.5903887,fineart
|
||||
Grant Wood,0.6266253,fineart
|
||||
Fyodor Vasilyev,0.5234919,digipa-med-impact
|
||||
Cagnaccio di San Pietro,0.6261671,fineart
|
||||
Cagnaccio Di San Pietro,0.6261671,fineart
|
||||
Doris Boulton-Maude,0.62593174,fineart
|
||||
Adolf Hirémy-Hirschl,0.5946784,fineart
|
||||
Harold von Schmidt,0.6256755,fineart
|
||||
@ -2411,7 +2405,6 @@ Hermann Feierabend,0.5346168,digipa-high-impact
|
||||
Antonio Donghi,0.4610982,digipa-low-impact
|
||||
Adonna Khare,0.4858036,digipa-med-impact
|
||||
James Stokoe,0.5015107,digipa-med-impact
|
||||
Art & Language,0.5341332,digipa-high-impact
|
||||
Agustín Fernández,0.53403986,fineart
|
||||
Germán Londoño,0.5338712,fineart
|
||||
Emmanuelle Moureaux,0.5335641,digipa-high-impact
|
||||
|
||||
|
@ -9,9 +9,38 @@ addEventListener('keydown', (event) => {
|
||||
let minus = "ArrowDown"
|
||||
if (event.key != plus && event.key != minus) return;
|
||||
|
||||
selectionStart = target.selectionStart;
|
||||
selectionEnd = target.selectionEnd;
|
||||
if(selectionStart == selectionEnd) return;
|
||||
let selectionStart = target.selectionStart;
|
||||
let selectionEnd = target.selectionEnd;
|
||||
// If the user hasn't selected anything, let's select their current parenthesis block
|
||||
if (selectionStart === selectionEnd) {
|
||||
// Find opening parenthesis around current cursor
|
||||
const before = target.value.substring(0, selectionStart);
|
||||
let beforeParen = before.lastIndexOf("(");
|
||||
if (beforeParen == -1) return;
|
||||
let beforeParenClose = before.lastIndexOf(")");
|
||||
while (beforeParenClose !== -1 && beforeParenClose > beforeParen) {
|
||||
beforeParen = before.lastIndexOf("(", beforeParen - 1);
|
||||
beforeParenClose = before.lastIndexOf(")", beforeParenClose - 1);
|
||||
}
|
||||
|
||||
// Find closing parenthesis around current cursor
|
||||
const after = target.value.substring(selectionStart);
|
||||
let afterParen = after.indexOf(")");
|
||||
if (afterParen == -1) return;
|
||||
let afterParenOpen = after.indexOf("(");
|
||||
while (afterParenOpen !== -1 && afterParen > afterParenOpen) {
|
||||
afterParen = after.indexOf(")", afterParen + 1);
|
||||
afterParenOpen = after.indexOf("(", afterParenOpen + 1);
|
||||
}
|
||||
if (beforeParen === -1 || afterParen === -1) return;
|
||||
|
||||
// Set the selection to the text between the parenthesis
|
||||
const parenContent = target.value.substring(beforeParen + 1, selectionStart + afterParen);
|
||||
const lastColon = parenContent.lastIndexOf(":");
|
||||
selectionStart = beforeParen + 1;
|
||||
selectionEnd = selectionStart + lastColon;
|
||||
target.setSelectionRange(selectionStart, selectionEnd);
|
||||
}
|
||||
|
||||
event.preventDefault();
|
||||
|
||||
|
||||
@ -1,5 +1,12 @@
|
||||
// various functions for interation with ui.py not large enough to warrant putting them in separate files
|
||||
|
||||
function set_theme(theme){
|
||||
gradioURL = window.location.href
|
||||
if (!gradioURL.includes('?__theme=')) {
|
||||
window.location.replace(gradioURL + '?__theme=' + theme);
|
||||
}
|
||||
}
|
||||
|
||||
function selected_gallery_index(){
|
||||
var buttons = gradioApp().querySelectorAll('[style="display: block;"].tabitem .gallery-item')
|
||||
var button = gradioApp().querySelector('[style="display: block;"].tabitem .gallery-item.\\!ring-2')
|
||||
|
||||
35
launch.py
35
launch.py
@ -87,6 +87,23 @@ def git_clone(url, dir, name, commithash=None):
|
||||
run(f'"{git}" -C {dir} checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}")
|
||||
|
||||
|
||||
def version_check(commit):
|
||||
try:
|
||||
import requests
|
||||
commits = requests.get('https://api.github.com/repos/AUTOMATIC1111/stable-diffusion-webui/branches/master').json()
|
||||
if commit != "<none>" and commits['commit']['sha'] != commit:
|
||||
print("--------------------------------------------------------")
|
||||
print("| You are not up to date with the most recent release. |")
|
||||
print("| Consider running `git pull` to update. |")
|
||||
print("--------------------------------------------------------")
|
||||
elif commits['commit']['sha'] == commit:
|
||||
print("You are up to date with the most recent release.")
|
||||
else:
|
||||
print("Not a git clone, can't perform version check.")
|
||||
except Exception as e:
|
||||
print("versipm check failed",e)
|
||||
|
||||
|
||||
def prepare_enviroment():
|
||||
torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113")
|
||||
requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
|
||||
@ -110,13 +127,14 @@ def prepare_enviroment():
|
||||
codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
|
||||
blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
|
||||
|
||||
args = shlex.split(commandline_args)
|
||||
sys.argv += shlex.split(commandline_args)
|
||||
|
||||
args, skip_torch_cuda_test = extract_arg(args, '--skip-torch-cuda-test')
|
||||
args, reinstall_xformers = extract_arg(args, '--reinstall-xformers')
|
||||
xformers = '--xformers' in args
|
||||
deepdanbooru = '--deepdanbooru' in args
|
||||
ngrok = '--ngrok' in args
|
||||
sys.argv, skip_torch_cuda_test = extract_arg(sys.argv, '--skip-torch-cuda-test')
|
||||
sys.argv, reinstall_xformers = extract_arg(sys.argv, '--reinstall-xformers')
|
||||
sys.argv, update_check = extract_arg(sys.argv, '--update-check')
|
||||
xformers = '--xformers' in sys.argv
|
||||
deepdanbooru = '--deepdanbooru' in sys.argv
|
||||
ngrok = '--ngrok' in sys.argv
|
||||
|
||||
try:
|
||||
commit = run(f"{git} rev-parse HEAD").strip()
|
||||
@ -163,9 +181,10 @@ def prepare_enviroment():
|
||||
|
||||
run_pip(f"install -r {requirements_file}", "requirements for Web UI")
|
||||
|
||||
sys.argv += args
|
||||
if update_check:
|
||||
version_check(commit)
|
||||
|
||||
if "--exit" in args:
|
||||
if "--exit" in sys.argv:
|
||||
print("Exiting because of --exit argument")
|
||||
exit(0)
|
||||
|
||||
|
||||
@ -157,8 +157,7 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_o
|
||||
# sort by reverse by likelihood and normal for alpha, and format tag text as requested
|
||||
unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort))
|
||||
for weight, tag in unsorted_tags_in_theshold:
|
||||
# note: tag_outformat will still have a colon if include_ranks is True
|
||||
tag_outformat = tag.replace(':', ' ')
|
||||
tag_outformat = tag
|
||||
if use_spaces:
|
||||
tag_outformat = tag_outformat.replace('_', ' ')
|
||||
if use_escape:
|
||||
|
||||
@ -216,8 +216,11 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
|
||||
if theta_func1:
|
||||
for key in tqdm.tqdm(theta_1.keys()):
|
||||
if 'model' in key:
|
||||
t2 = theta_2.get(key, torch.zeros_like(theta_1[key]))
|
||||
theta_1[key] = theta_func1(theta_1[key], t2)
|
||||
if key in theta_2:
|
||||
t2 = theta_2.get(key, torch.zeros_like(theta_1[key]))
|
||||
theta_1[key] = theta_func1(theta_1[key], t2)
|
||||
else:
|
||||
theta_1[key] = torch.zeros_like(theta_1[key])
|
||||
del theta_2, teritary_model
|
||||
|
||||
for key in tqdm.tqdm(theta_0.keys()):
|
||||
|
||||
@ -419,11 +419,6 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
|
||||
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds,
|
||||
subseed_strength=p.subseed_strength)
|
||||
|
||||
if state.interrupted or state.skipped:
|
||||
# if we are interrupted, sample returns just noise
|
||||
# use the image collected previously in sampler loop
|
||||
samples_ddim = shared.state.current_latent
|
||||
|
||||
samples_ddim = samples_ddim.to(devices.dtype_vae)
|
||||
x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim)
|
||||
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
|
||||
|
||||
@ -96,6 +96,7 @@ def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
|
||||
class ScriptRunner:
|
||||
def __init__(self):
|
||||
self.scripts = []
|
||||
self.titles = []
|
||||
|
||||
def setup_ui(self, is_img2img):
|
||||
for script_class, path in scripts_data:
|
||||
@ -107,9 +108,10 @@ class ScriptRunner:
|
||||
|
||||
self.scripts.append(script)
|
||||
|
||||
titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.scripts]
|
||||
self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.scripts]
|
||||
|
||||
dropdown = gr.Dropdown(label="Script", choices=["None"] + titles, value="None", type="index")
|
||||
dropdown = gr.Dropdown(label="Script", choices=["None"] + self.titles, value="None", type="index")
|
||||
dropdown.save_to_config = True
|
||||
inputs = [dropdown]
|
||||
|
||||
for script in self.scripts:
|
||||
@ -139,6 +141,15 @@ class ScriptRunner:
|
||||
|
||||
return [ui.gr_show(True if i == 0 else args_from <= i < args_to) for i in range(len(inputs))]
|
||||
|
||||
def init_field(title):
|
||||
if title == 'None':
|
||||
return
|
||||
script_index = self.titles.index(title)
|
||||
script = self.scripts[script_index]
|
||||
for i in range(script.args_from, script.args_to):
|
||||
inputs[i].visible = True
|
||||
|
||||
dropdown.init_field = init_field
|
||||
dropdown.change(
|
||||
fn=select_script,
|
||||
inputs=[dropdown],
|
||||
|
||||
@ -296,10 +296,16 @@ def xformers_attnblock_forward(self, x):
|
||||
try:
|
||||
h_ = x
|
||||
h_ = self.norm(h_)
|
||||
q1 = self.q(h_).contiguous()
|
||||
k1 = self.k(h_).contiguous()
|
||||
v = self.v(h_).contiguous()
|
||||
out = xformers.ops.memory_efficient_attention(q1, k1, v)
|
||||
q = self.q(h_)
|
||||
k = self.k(h_)
|
||||
v = self.v(h_)
|
||||
b, c, h, w = q.shape
|
||||
q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
|
||||
q = q.contiguous()
|
||||
k = k.contiguous()
|
||||
v = v.contiguous()
|
||||
out = xformers.ops.memory_efficient_attention(q, k, v)
|
||||
out = rearrange(out, 'b (h w) c -> b c h w', h=h)
|
||||
out = self.proj_out(out)
|
||||
return x + out
|
||||
except NotImplementedError:
|
||||
|
||||
@ -98,25 +98,8 @@ def store_latent(decoded):
|
||||
shared.state.current_image = sample_to_image(decoded)
|
||||
|
||||
|
||||
|
||||
def extended_tdqm(sequence, *args, desc=None, **kwargs):
|
||||
state.sampling_steps = len(sequence)
|
||||
state.sampling_step = 0
|
||||
|
||||
seq = sequence if cmd_opts.disable_console_progressbars else tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
|
||||
|
||||
for x in seq:
|
||||
if state.interrupted or state.skipped:
|
||||
break
|
||||
|
||||
yield x
|
||||
|
||||
state.sampling_step += 1
|
||||
shared.total_tqdm.update()
|
||||
|
||||
|
||||
ldm.models.diffusion.ddim.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs)
|
||||
ldm.models.diffusion.plms.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs)
|
||||
class InterruptedException(BaseException):
|
||||
pass
|
||||
|
||||
|
||||
class VanillaStableDiffusionSampler:
|
||||
@ -128,14 +111,32 @@ class VanillaStableDiffusionSampler:
|
||||
self.init_latent = None
|
||||
self.sampler_noises = None
|
||||
self.step = 0
|
||||
self.stop_at = None
|
||||
self.eta = None
|
||||
self.default_eta = 0.0
|
||||
self.config = None
|
||||
self.last_latent = None
|
||||
|
||||
def number_of_needed_noises(self, p):
|
||||
return 0
|
||||
|
||||
def launch_sampling(self, steps, func):
|
||||
state.sampling_steps = steps
|
||||
state.sampling_step = 0
|
||||
|
||||
try:
|
||||
return func()
|
||||
except InterruptedException:
|
||||
return self.last_latent
|
||||
|
||||
def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
|
||||
if state.interrupted or state.skipped:
|
||||
raise InterruptedException
|
||||
|
||||
if self.stop_at is not None and self.step > self.stop_at:
|
||||
raise InterruptedException
|
||||
|
||||
|
||||
conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
|
||||
unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
|
||||
|
||||
@ -159,11 +160,16 @@ class VanillaStableDiffusionSampler:
|
||||
res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)
|
||||
|
||||
if self.mask is not None:
|
||||
store_latent(self.init_latent * self.mask + self.nmask * res[1])
|
||||
self.last_latent = self.init_latent * self.mask + self.nmask * res[1]
|
||||
else:
|
||||
store_latent(res[1])
|
||||
self.last_latent = res[1]
|
||||
|
||||
store_latent(self.last_latent)
|
||||
|
||||
self.step += 1
|
||||
state.sampling_step = self.step
|
||||
shared.total_tqdm.update()
|
||||
|
||||
return res
|
||||
|
||||
def initialize(self, p):
|
||||
@ -192,7 +198,7 @@ class VanillaStableDiffusionSampler:
|
||||
self.init_latent = x
|
||||
self.step = 0
|
||||
|
||||
samples = self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning)
|
||||
samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
|
||||
|
||||
return samples
|
||||
|
||||
@ -206,9 +212,9 @@ class VanillaStableDiffusionSampler:
|
||||
|
||||
# existing code fails with certain step counts, like 9
|
||||
try:
|
||||
samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)
|
||||
samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
|
||||
except Exception:
|
||||
samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)
|
||||
samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
|
||||
|
||||
return samples_ddim
|
||||
|
||||
@ -223,6 +229,9 @@ class CFGDenoiser(torch.nn.Module):
|
||||
self.step = 0
|
||||
|
||||
def forward(self, x, sigma, uncond, cond, cond_scale):
|
||||
if state.interrupted or state.skipped:
|
||||
raise InterruptedException
|
||||
|
||||
conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
|
||||
uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
|
||||
|
||||
@ -268,25 +277,6 @@ class CFGDenoiser(torch.nn.Module):
|
||||
return denoised
|
||||
|
||||
|
||||
def extended_trange(sampler, count, *args, **kwargs):
|
||||
state.sampling_steps = count
|
||||
state.sampling_step = 0
|
||||
|
||||
seq = range(count) if cmd_opts.disable_console_progressbars else tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
|
||||
|
||||
for x in seq:
|
||||
if state.interrupted or state.skipped:
|
||||
break
|
||||
|
||||
if sampler.stop_at is not None and x > sampler.stop_at:
|
||||
break
|
||||
|
||||
yield x
|
||||
|
||||
state.sampling_step += 1
|
||||
shared.total_tqdm.update()
|
||||
|
||||
|
||||
class TorchHijack:
|
||||
def __init__(self, kdiff_sampler):
|
||||
self.kdiff_sampler = kdiff_sampler
|
||||
@ -314,9 +304,28 @@ class KDiffusionSampler:
|
||||
self.eta = None
|
||||
self.default_eta = 1.0
|
||||
self.config = None
|
||||
self.last_latent = None
|
||||
|
||||
def callback_state(self, d):
|
||||
store_latent(d["denoised"])
|
||||
step = d['i']
|
||||
latent = d["denoised"]
|
||||
store_latent(latent)
|
||||
self.last_latent = latent
|
||||
|
||||
if self.stop_at is not None and step > self.stop_at:
|
||||
raise InterruptedException
|
||||
|
||||
state.sampling_step = step
|
||||
shared.total_tqdm.update()
|
||||
|
||||
def launch_sampling(self, steps, func):
|
||||
state.sampling_steps = steps
|
||||
state.sampling_step = 0
|
||||
|
||||
try:
|
||||
return func()
|
||||
except InterruptedException:
|
||||
return self.last_latent
|
||||
|
||||
def number_of_needed_noises(self, p):
|
||||
return p.steps
|
||||
@ -339,9 +348,6 @@ class KDiffusionSampler:
|
||||
self.sampler_noise_index = 0
|
||||
self.eta = p.eta or opts.eta_ancestral
|
||||
|
||||
if hasattr(k_diffusion.sampling, 'trange'):
|
||||
k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs)
|
||||
|
||||
if self.sampler_noises is not None:
|
||||
k_diffusion.sampling.torch = TorchHijack(self)
|
||||
|
||||
@ -383,8 +389,9 @@ class KDiffusionSampler:
|
||||
|
||||
self.model_wrap_cfg.init_latent = x
|
||||
|
||||
return self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
|
||||
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
|
||||
|
||||
return samples
|
||||
|
||||
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
|
||||
steps = steps or p.steps
|
||||
@ -406,6 +413,8 @@ class KDiffusionSampler:
|
||||
extra_params_kwargs['n'] = steps
|
||||
else:
|
||||
extra_params_kwargs['sigmas'] = sigmas
|
||||
samples = self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
|
||||
|
||||
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
|
||||
|
||||
return samples
|
||||
|
||||
|
||||
@ -73,13 +73,13 @@ parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image upload
|
||||
parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
|
||||
parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
|
||||
parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
|
||||
parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None)
|
||||
parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
|
||||
parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
|
||||
parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
|
||||
parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
|
||||
parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
|
||||
|
||||
|
||||
cmd_opts = parser.parse_args()
|
||||
restricted_opts = [
|
||||
"samples_filename_pattern",
|
||||
@ -308,6 +308,7 @@ options_templates.update(options_section(('ui', "User interface"), {
|
||||
"do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
|
||||
"add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
|
||||
"add_model_name_to_info": OptionInfo(False, "Add model name to generation information"),
|
||||
"disable_weights_auto_swap": OptionInfo(False, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
|
||||
"font": OptionInfo("", "Font for image grids that have text"),
|
||||
"js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
|
||||
"js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
|
||||
|
||||
@ -45,7 +45,7 @@ class StyleDatabase:
|
||||
if not os.path.exists(path):
|
||||
return
|
||||
|
||||
with open(path, "r", encoding="utf8", newline='') as file:
|
||||
with open(path, "r", encoding="utf-8-sig", newline='') as file:
|
||||
reader = csv.DictReader(file)
|
||||
for row in reader:
|
||||
# Support loading old CSV format with "name, text"-columns
|
||||
@ -79,7 +79,7 @@ class StyleDatabase:
|
||||
def save_styles(self, path: str) -> None:
|
||||
# Write to temporary file first, so we don't nuke the file if something goes wrong
|
||||
fd, temp_path = tempfile.mkstemp(".csv")
|
||||
with os.fdopen(fd, "w", encoding="utf8", newline='') as file:
|
||||
with os.fdopen(fd, "w", encoding="utf-8-sig", newline='') as file:
|
||||
# _fields is actually part of the public API: typing.NamedTuple is a replacement for collections.NamedTuple,
|
||||
# and collections.NamedTuple has explicit documentation for accessing _fields. Same goes for _asdict()
|
||||
writer = csv.DictWriter(file, fieldnames=PromptStyle._fields)
|
||||
|
||||
@ -547,6 +547,10 @@ def apply_setting(key, value):
|
||||
if value is None:
|
||||
return gr.update()
|
||||
|
||||
# dont allow model to be swapped when model hash exists in prompt
|
||||
if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
|
||||
return gr.update()
|
||||
|
||||
if key == "sd_model_checkpoint":
|
||||
ckpt_info = sd_models.get_closet_checkpoint_match(value)
|
||||
|
||||
@ -1809,7 +1813,7 @@ Requested path was: {f}
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
|
||||
def loadsave(path, x):
|
||||
def apply_field(obj, field, condition=None):
|
||||
def apply_field(obj, field, condition=None, init_field=None):
|
||||
key = path + "/" + field
|
||||
|
||||
if getattr(obj,'custom_script_source',None) is not None:
|
||||
@ -1825,6 +1829,8 @@ Requested path was: {f}
|
||||
print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
|
||||
else:
|
||||
setattr(obj, field, saved_value)
|
||||
if init_field is not None:
|
||||
init_field(saved_value)
|
||||
|
||||
if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible:
|
||||
apply_field(x, 'visible')
|
||||
@ -1850,7 +1856,8 @@ Requested path was: {f}
|
||||
# Since there are many dropdowns that shouldn't be saved,
|
||||
# we only mark dropdowns that should be saved.
|
||||
if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False):
|
||||
apply_field(x, 'value', lambda val: val in x.choices)
|
||||
apply_field(x, 'value', lambda val: val in x.choices, getattr(x, 'init_field', None))
|
||||
apply_field(x, 'visible')
|
||||
|
||||
visit(txt2img_interface, loadsave, "txt2img")
|
||||
visit(img2img_interface, loadsave, "img2img")
|
||||
@ -1872,6 +1879,9 @@ for filename in sorted(os.listdir(jsdir)):
|
||||
with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile:
|
||||
javascript += f"\n<script>{jsfile.read()}</script>"
|
||||
|
||||
if cmd_opts.theme is not None:
|
||||
javascript += f"\n<script>set_theme('{cmd_opts.theme}');</script>\n"
|
||||
|
||||
javascript += f"\n<script>{localization.localization_js(shared.opts.localization)}</script>"
|
||||
|
||||
if 'gradio_routes_templates_response' not in globals():
|
||||
|
||||
@ -33,7 +33,7 @@ goto :launch
|
||||
:skip_venv
|
||||
|
||||
:launch
|
||||
%PYTHON% launch.py
|
||||
%PYTHON% launch.py %*
|
||||
pause
|
||||
exit /b
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user