Merge remote-tracking branch 'upstream/master' into ModelLoader (5756d517) · Commits · github_fork / Stable Diffusion Webui

.gitignore

+2 −0

Original line number	Diff line number	Diff line
		@@ -21,3 +21,5 @@ __pycache__
		/interrogate
		/user.css
		/.idea
		notification.mp3
		/SwinIR

javascript/dragdrop.js

+15 −9

Original line number	Diff line number	Diff line
		@@ -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 );
		});

javascript/notification.js

+3 −0

Original line number	Diff line number	Diff line
		@@ -25,6 +25,9 @@ onUiUpdate(function(){

		lastHeadImg = headImg;

		// play notification sound if available
		gradioApp().querySelector('#audio_notification audio')?.play();

		if (document.hasFocus()) return;

		// Multiple copies of the images are in the DOM when one is selected. Dedup with a Set to get the real number generated.

javascript/ui.js

+2 −3

Original line number	Diff line number	Diff line
		// 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 } })

modules/extras.py

+57 −0

Original line number	Diff line number	Diff line
		@@ -3,6 +3,9 @@ import os
		import numpy as np
		from PIL import Image

		import torch
		import tqdm

		from modules import processing, shared, images, devices
		from modules.shared import opts
		import modules.gfpgan_model
		@@ -137,3 +140,57 @@ def run_pnginfo(image):
		info = f"<div><p>{message}<p></div>"

		return '', geninfo, info


		def run_modelmerger(modelname_0, modelname_1, interp_method, interp_amount):
		# Linear interpolation (https://en.wikipedia.org/wiki/Linear_interpolation)
		def weighted_sum(theta0, theta1, alpha):
		return ((1 - alpha) * theta0) + (alpha * theta1)

		# Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
		def sigmoid(theta0, theta1, alpha):
		alpha = alpha * alpha * (3 - (2 * alpha))
		return theta0 + ((theta1 - theta0) * alpha)

		if os.path.exists(modelname_0):
		model0_filename = modelname_0
		modelname_0 = os.path.splitext(os.path.basename(modelname_0))[0]
		else:
		model0_filename = 'models/' + modelname_0 + '.ckpt'

		if os.path.exists(modelname_1):
		model1_filename = modelname_1
		modelname_1 = os.path.splitext(os.path.basename(modelname_1))[0]
		else:
		model1_filename = 'models/' + modelname_1 + '.ckpt'

		print(f"Loading {model0_filename}...")
		model_0 = torch.load(model0_filename, map_location='cpu')

		print(f"Loading {model1_filename}...")
		model_1 = torch.load(model1_filename, map_location='cpu')

		theta_0 = model_0['state_dict']
		theta_1 = model_1['state_dict']

		theta_funcs = {
		"Weighted Sum": weighted_sum,
		"Sigmoid": sigmoid,
		}
		theta_func = theta_funcs[interp_method]

		print(f"Merging...")
		for key in tqdm.tqdm(theta_0.keys()):
		if 'model' in key and key in theta_1:
		theta_0[key] = theta_func(theta_0[key], theta_1[key], interp_amount)

		for key in theta_1.keys():
		if 'model' in key and key not in theta_0:
		theta_0[key] = theta_1[key]

		output_modelname = 'models/' + modelname_0 + '-' + modelname_1 + '-merged.ckpt'
		print(f"Saving to {output_modelname}...")
		torch.save(model_0, output_modelname)

		print(f"Checkpoint saved.")
		return "Checkpoint saved to " + output_modelname

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