Merge branch 'master' into notification-sound

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48DESIGN 2022-09-27 08:05:19 +02:00 committed by GitHub
commit e4145c8453
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7 changed files with 104 additions and 20 deletions

1
.gitignore vendored
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@ -21,3 +21,4 @@ __pycache__
/user.css
/.idea
notification.mp3
/SwinIR

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@ -68,13 +68,19 @@ window.addEventListener('paste', e => {
if ( ! isValidImageList( files ) ) {
return;
}
[...gradioApp().querySelectorAll('input[type=file][accept="image/x-png,image/gif,image/jpeg"]')]
.filter(input => !input.matches('.\\!hidden input[type=file]'))
.forEach(input => {
input.files = files;
input.dispatchEvent(new Event('change'))
});
[...gradioApp().querySelectorAll('[data-testid="image"]')]
.filter(imgWrap => !imgWrap.closest('.\\!hidden'))
.forEach(imgWrap => dropReplaceImage( imgWrap, files ));
const visibleImageFields = [...gradioApp().querySelectorAll('[data-testid="image"]')]
.filter(el => uiElementIsVisible(el));
if ( ! visibleImageFields.length ) {
return;
}
const firstFreeImageField = visibleImageFields
.filter(el => el.querySelector('input[type=file]'))?.[0];
dropReplaceImage(
firstFreeImageField ?
firstFreeImageField :
visibleImageFields[visibleImageFields.length - 1]
, files );
});

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@ -1,9 +1,8 @@
// various functions for interation with ui.py not large enough to warrant putting them in separate files
function selected_gallery_index(){
var gr = gradioApp()
var buttons = gradioApp().querySelectorAll(".gallery-item")
var button = gr.querySelector(".gallery-item.\\!ring-2")
var buttons = gradioApp().querySelectorAll('[style="display: block;"].tabitem .gallery-item')
var button = gradioApp().querySelector('[style="display: block;"].tabitem .gallery-item.\\!ring-2')
var result = -1
buttons.forEach(function(v, i){ if(v==button) { result = i } })

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@ -406,7 +406,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
index_of_first_image = 1
if opts.grid_save:
images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p)
images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
devices.torch_gc()
return Processed(p, output_images, all_seeds[0], infotext(), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image)

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@ -66,7 +66,7 @@ class State:
job = ""
job_no = 0
job_count = 0
job_timestamp = 0
job_timestamp = '0'
sampling_step = 0
sampling_steps = 0
current_latent = None
@ -80,6 +80,7 @@ class State:
self.job_no += 1
self.sampling_step = 0
self.current_image_sampling_step = 0
def get_job_timestamp(self):
return datetime.datetime.now().strftime("%Y%m%d%H%M%S")

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@ -39,3 +39,24 @@ document.addEventListener("DOMContentLoaded", function() {
});
mutationObserver.observe( gradioApp(), { childList:true, subtree:true })
});
/**
* checks that a UI element is not in another hidden element or tab content
*/
function uiElementIsVisible(el) {
let isVisible = !el.closest('.\\!hidden');
if ( ! isVisible ) {
return false;
}
while( isVisible = el.closest('.tabitem')?.style.display !== 'none' ) {
if ( ! isVisible ) {
return false;
} else if ( el.parentElement ) {
el = el.parentElement
} else {
break;
}
}
return isVisible;
}

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@ -59,7 +59,55 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
return x / x.std()
Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "original_prompt", "original_negative_prompt"])
Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "original_prompt", "original_negative_prompt", "sigma_adjustment"])
# Based on changes suggested by briansemrau in https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/736
def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
x = p.init_latent
s_in = x.new_ones([x.shape[0]])
dnw = K.external.CompVisDenoiser(shared.sd_model)
sigmas = dnw.get_sigmas(steps).flip(0)
shared.state.sampling_steps = steps
for i in trange(1, len(sigmas)):
shared.state.sampling_step += 1
x_in = torch.cat([x] * 2)
sigma_in = torch.cat([sigmas[i - 1] * s_in] * 2)
cond_in = torch.cat([uncond, cond])
c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)]
if i == 1:
t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2))
else:
t = dnw.sigma_to_t(sigma_in)
eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)
denoised_uncond, denoised_cond = (x_in + eps * c_out).chunk(2)
denoised = denoised_uncond + (denoised_cond - denoised_uncond) * cfg_scale
if i == 1:
d = (x - denoised) / (2 * sigmas[i])
else:
d = (x - denoised) / sigmas[i - 1]
dt = sigmas[i] - sigmas[i - 1]
x = x + d * dt
sd_samplers.store_latent(x)
# This shouldn't be necessary, but solved some VRAM issues
del x_in, sigma_in, cond_in, c_out, c_in, t,
del eps, denoised_uncond, denoised_cond, denoised, d, dt
shared.state.nextjob()
return x / sigmas[-1]
class Script(scripts.Script):
@ -78,9 +126,10 @@ class Script(scripts.Script):
cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0)
st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50)
randomness = gr.Slider(label="Randomness", minimum=0.0, maximum=1.0, step=0.01, value=0.0)
return [original_prompt, original_negative_prompt, cfg, st, randomness]
sigma_adjustment = gr.Checkbox(label="Sigma adjustment for finding noise for image", value=False)
return [original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment]
def run(self, p, original_prompt, original_negative_prompt, cfg, st, randomness):
def run(self, p, original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment):
p.batch_size = 1
p.batch_count = 1
@ -88,7 +137,10 @@ class Script(scripts.Script):
def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
lat = (p.init_latent.cpu().numpy() * 10).astype(int)
same_params = self.cache is not None and self.cache.cfg_scale == cfg and self.cache.steps == st and self.cache.original_prompt == original_prompt and self.cache.original_negative_prompt == original_negative_prompt
same_params = self.cache is not None and self.cache.cfg_scale == cfg and self.cache.steps == st \
and self.cache.original_prompt == original_prompt \
and self.cache.original_negative_prompt == original_negative_prompt \
and self.cache.sigma_adjustment == sigma_adjustment
same_everything = same_params and self.cache.latent.shape == lat.shape and np.abs(self.cache.latent-lat).sum() < 100
if same_everything:
@ -97,8 +149,11 @@ class Script(scripts.Script):
shared.state.job_count += 1
cond = p.sd_model.get_learned_conditioning(p.batch_size * [original_prompt])
uncond = p.sd_model.get_learned_conditioning(p.batch_size * [original_negative_prompt])
rec_noise = find_noise_for_image(p, cond, uncond, cfg, st)
self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt)
if sigma_adjustment:
rec_noise = find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg, st)
else:
rec_noise = find_noise_for_image(p, cond, uncond, cfg, st)
self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt, sigma_adjustment)
rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], [p.seed + x + 1 for x in range(p.init_latent.shape[0])])
@ -121,6 +176,7 @@ class Script(scripts.Script):
p.extra_generation_params["Decode CFG scale"] = cfg
p.extra_generation_params["Decode steps"] = st
p.extra_generation_params["Randomness"] = randomness
p.extra_generation_params["Sigma Adjustment"] = sigma_adjustment
processed = processing.process_images(p)