Merge branch 'AUTOMATIC1111:master' into kr-localization (563fb0aa) · Commits · github_fork / Stable Diffusion Webui

javascript/localization.js

+21 −0

Original line number	Diff line number	Diff line
		@@ -108,6 +108,9 @@ function processNode(node){

		function dumpTranslations(){
		dumped = {}
		if (localization.rtl) {
		dumped.rtl = true
		}

		Object.keys(original_lines).forEach(function(text){
		if(dumped[text] !== undefined) return
		@@ -129,6 +132,24 @@ onUiUpdate(function(m){

		document.addEventListener("DOMContentLoaded", function() {
		processNode(gradioApp())

		if (localization.rtl) { // if the language is from right to left,
		(new MutationObserver((mutations, observer) => { // wait for the style to load
		mutations.forEach(mutation => {
		mutation.addedNodes.forEach(node => {
		if (node.tagName === 'STYLE') {
		observer.disconnect();

		for (const x of node.sheet.rules) { // find all rtl media rules
		if (Array.from(x.media \|\| []).includes('rtl')) {
		x.media.appendMedium('all'); // enable them
		}
		}
		}
		})
		});
		})).observe(gradioApp(), { childList: true });
		}
		})

		function download_localization() {

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		@@ -45,7 +45,7 @@ def enable_tf32():

		errors.run(enable_tf32, "Enabling TF32")

		device = device_interrogate = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = None
		device = device_interrogate = device_gfpgan = device_swinir = device_esrgan = device_scunet = device_codeformer = None
		dtype = torch.float16
		dtype_vae = torch.float16

		@@ -81,3 +81,7 @@ def autocast(disable=False):
		return contextlib.nullcontext()

		return torch.autocast("cuda")

		# MPS workaround for https://github.com/pytorch/pytorch/issues/79383
		def mps_contiguous(input_tensor, device): return input_tensor.contiguous() if device.type == 'mps' else input_tensor
		def mps_contiguous_to(input_tensor, device): return mps_contiguous(input_tensor, device).to(device)

+1 −1

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		@@ -190,7 +190,7 @@ def upscale_without_tiling(model, img):
		img = img[:, :, ::-1]
		img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
		img = torch.from_numpy(img).float()
		img = img.unsqueeze(0).to(devices.device_esrgan)
		img = devices.mps_contiguous_to(img.unsqueeze(0), devices.device_esrgan)
		with torch.no_grad():
		output = model(img)
		output = output.squeeze().float().cpu().clamp_(0, 1).numpy()