Merge branch 'dev' into efficient-vae-methods (9ac2989e) · Commits · github_fork / Stable Diffusion Webui

javascript/localization.js

+5 −5

Original line number	Diff line number	Diff line
		@@ -11,11 +11,11 @@ var ignore_ids_for_localization = {
		train_hypernetwork: 'OPTION',
		txt2img_styles: 'OPTION',
		img2img_styles: 'OPTION',
		setting_random_artist_categories: 'SPAN',
		setting_face_restoration_model: 'SPAN',
		setting_realesrgan_enabled_models: 'SPAN',
		extras_upscaler_1: 'SPAN',
		extras_upscaler_2: 'SPAN',
		setting_random_artist_categories: 'OPTION',
		setting_face_restoration_model: 'OPTION',
		setting_realesrgan_enabled_models: 'OPTION',
		extras_upscaler_1: 'OPTION',
		extras_upscaler_2: 'OPTION',
		};

		var re_num = /^[.\d]+$/;

+19 −0

Original line number	Diff line number	Diff line
		import json
		import os
		import re
		from collections import defaultdict

		@@ -177,3 +179,20 @@ def parse_prompts(prompts):

		return res, extra_data


		def get_user_metadata(filename):
		if filename is None:
		return {}

		basename, ext = os.path.splitext(filename)
		metadata_filename = basename + '.json'

		metadata = {}
		try:
		if os.path.isfile(metadata_filename):
		with open(metadata_filename, "r", encoding="utf8") as file:
		metadata = json.load(file)
		except Exception as e:
		errors.display(e, f"reading extra network user metadata from {metadata_filename}")

		return metadata

+2 −4

Original line number	Diff line number	Diff line
		@@ -3,7 +3,7 @@ from contextlib import closing
		from pathlib import Path

		import numpy as np
		from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops, UnidentifiedImageError
		from PIL import Image, ImageOps, ImageFilter, ImageEnhance, UnidentifiedImageError
		import gradio as gr

		from modules import sd_samplers, images as imgutil
		@@ -129,9 +129,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
		mask = None
		elif mode == 2: # inpaint
		image, mask = init_img_with_mask["image"], init_img_with_mask["mask"]
		alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
		mask = mask.convert('L').point(lambda x: 255 if x > 128 else 0, mode='1')
		mask = ImageChops.lighter(alpha_mask, mask).convert('L')
		mask = mask.split()[-1].convert("L").point(lambda x: 255 if x > 128 else 0)
		image = image.convert("RGB")
		elif mode == 3: # inpaint sketch
		image = inpaint_color_sketch

+7 −2

Original line number	Diff line number	Diff line
		@@ -20,7 +20,7 @@ prompt: (emphasized \| scheduled \| alternate \| plain \| WHITESPACE)*
		\| "(" prompt ":" prompt ")"
		\| "[" prompt "]"
		scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER [WHITESPACE] "]"
		alternate: "[" prompt ("\|" prompt)+ "]"
		alternate: "[" prompt ("\|" [prompt])+ "]"
		WHITESPACE: /\s+/
		plain: /([^\\\[\]():\|]\|\\.)+/
		%import common.SIGNED_NUMBER -> NUMBER
		@@ -53,6 +53,10 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
		[[3, '((a][:b:c '], [10, '((a][:b:c d']]
		>>> g("[a\|(b:1.1)]")
		[[1, 'a'], [2, '(b:1.1)'], [3, 'a'], [4, '(b:1.1)'], [5, 'a'], [6, '(b:1.1)'], [7, 'a'], [8, '(b:1.1)'], [9, 'a'], [10, '(b:1.1)']]
		>>> g("[fe\|]male")
		[[1, 'female'], [2, 'male'], [3, 'female'], [4, 'male'], [5, 'female'], [6, 'male'], [7, 'female'], [8, 'male'], [9, 'female'], [10, 'male']]
		>>> g("[fe\|\|\|]male")
		[[1, 'female'], [2, 'male'], [3, 'male'], [4, 'male'], [5, 'female'], [6, 'male'], [7, 'male'], [8, 'male'], [9, 'female'], [10, 'male']]
		"""

		def collect_steps(steps, tree):
		@@ -78,7 +82,8 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
		before, after, _, when, _ = args
		yield before or () if step <= when else after
		def alternate(self, args):
		yield next(args[(step - 1)%len(args)])
		args = ["" if not arg else arg for arg in args]
		yield args[(step - 1) % len(args)]
		def start(self, args):
		def flatten(x):
		if type(x) == str:

+3 −2

Original line number	Diff line number	Diff line
		@@ -303,12 +303,13 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
		sd_models_xl.extend_sdxl(model)

		model.load_state_dict(state_dict, strict=False)
		del state_dict
		timer.record("apply weights to model")

		if shared.opts.sd_checkpoint_cache > 0:
		# cache newly loaded model
		checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
		checkpoints_loaded[checkpoint_info] = state_dict

		del state_dict

		if shared.cmd_opts.opt_channelslast:
		model.to(memory_format=torch.channels_last)