Merge remote-tracking branch 'upstream/master'

This commit is contained in:
Bruno Seoane 2022-11-05 15:56:41 -03:00
commit 59ec427dff
13 changed files with 172 additions and 52 deletions

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@ -142,7 +142,7 @@ def prepare_enviroment():
stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "f4e99857772fc3a126ba886aadf795a332774878")
k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "60e5042ca0da89c14d1dd59d73883280f8fce991")
codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")

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@ -16,6 +16,7 @@
"A merger of the two checkpoints will be generated in your": "체크포인트들이 병합된 결과물이 당신의",
"A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "난수 생성기의 결과물을 지정하는 값 - 동일한 설정값과 동일한 시드를 적용 시, 완전히 똑같은 결과물을 얻게 됩니다.",
"Action": "작업",
"Add a button to convert the prompts used in NovelAI for use in the WebUI. In addition, add a button that allows you to recall a previously used prompt.": "NovelAI에서 사용되는 프롬프트를 WebUI에서 사용할 수 있게 변환하는 버튼을 추가합니다. 덤으로 이전에 사용한 프롬프트를 불러오는 버튼도 추가됩니다.",
"Add a random artist to the prompt.": "프롬프트에 랜덤한 작가 추가",
"Add a second progress bar to the console that shows progress for an entire job.": "콘솔에 전체 작업의 진행도를 보여주는 2번째 프로그레스 바 추가하기",
"Add difference": "차이점 추가",
@ -24,6 +25,7 @@
"Add model hash to generation information": "생성 정보에 모델 해시 추가",
"Add model name to generation information": "생성 정보에 모델 이름 추가",
"Add number to filename when saving": "이미지를 저장할 때 파일명에 숫자 추가하기",
"Adds a tab that lets you preview how CLIP model would tokenize your text.": "CLIP 모델이 텍스트를 어떻게 토큰화할지 미리 보여주는 탭을 추가합니다.",
"Adds a tab to the webui that allows the user to automatically extract keyframes from video, and manually extract 512x512 crops of those frames for use in model training.": "WebUI에 비디오로부터 자동으로 키프레임을 추출하고, 그 키프레임으로부터 모델 훈련에 사용될 512x512 이미지를 잘라낼 수 있는 탭을 추가합니다.",
"Aesthetic Gradients": "스타일 그라디언트",
"Aesthetic Image Scorer": "스타일 이미지 스코어러",
@ -33,6 +35,7 @@
"Aesthetic text for imgs": "스타일 텍스트",
"Aesthetic weight": "스타일 가중치",
"Allowed categories for random artists selection when using the Roll button": "랜덤 버튼을 눌러 무작위 작가를 선택할 때 허용된 카테고리",
"Allows you to include various shortcodes in your prompts. You can pull text from files, set up your own variables, process text through conditional functions, and so much more - it's like wildcards on steroids.": "프롬프트에 다양한 숏코드를 추가할 수 있게 해줍니다. 파일로부터 텍스트 추출, 변수 설정, 조건 함수로 텍스트 처리 등등 - 스테로이드를 맞은 와일드카드라 할 수 있죠.",
"Always print all generation info to standard output": "기본 아웃풋에 모든 생성 정보 항상 출력하기",
"Always save all generated image grids": "생성된 이미지 그리드 항상 저장하기",
"Always save all generated images": "생성된 이미지 항상 저장하기",
@ -54,6 +57,7 @@
"Batch Process": "이미지 여러장 처리",
"Batch size": "배치 크기",
"behind": "최신 아님",
"Booru tag autocompletion": "Booru 태그 자동완성",
"BSRGAN 4x": "BSRGAN 4x",
"built with gradio": "gradio로 제작되었습니다",
"Calculates aesthetic score for generated images using CLIP+MLP Aesthetic Score Predictor based on Chad Scorer": "Chad 스코어러를 기반으로 한 CLIP+MLP 스타일 점수 예측기를 이용해 생성된 이미지의 스타일 점수를 계산합니다.",
@ -114,6 +118,7 @@
"Directory for saving images using the Save button": "저장 버튼을 이용해 저장하는 이미지들의 저장 경로",
"Directory name pattern": "디렉토리명 패턴",
"directory.": "저장 경로에 저장됩니다.",
"Displays autocompletion hints for tags from image booru boards such as Danbooru. Uses local tag CSV files and includes a config for customization.": "Danbooru 같은 이미지 booru 보드의 태그에 대한 자동완성 힌트를 보여줍니다. 로컬 환경에 저장된 CSV 파일을 사용하고 조정 가능한 설정 파일이 포함되어 있습니다.",
"Do not add watermark to images": "이미지에 워터마크 추가하지 않기",
"Do not do anything special": "아무것도 하지 않기",
"Do not save grids consisting of one picture": "이미지가 1개뿐인 그리드는 저장하지 않기",
@ -317,6 +322,7 @@
"None": "없음",
"Nothing": "없음",
"Nothing found in the image.": "Nothing found in the image.",
"novelai-2-local-prompt": "NovelAI 프롬프트 변환기",
"Number of columns on the page": "각 페이지마다 표시할 가로줄 수",
"Number of grids in each row": "각 세로줄마다 표시될 그리드 수",
"number of images to delete consecutively next": "연속적으로 삭제할 이미지 수",
@ -431,6 +437,7 @@
"Save images with embedding in PNG chunks": "PNG 청크로 이미지에 임베딩을 포함시켜 저장",
"Save style": "스타일 저장",
"Save text information about generation parameters as chunks to png files": "이미지 생성 설정값을 PNG 청크에 텍스트로 저장",
"Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file.": "옵티마이저 상태를 별개의 *.optim 파일로 저장하기. 하이퍼네트워크 파일과 일치하는 optim 파일로부터 훈련을 재개할 수 있습니다.",
"Saving images/grids": "이미지/그리드 저장",
"Saving to a directory": "디렉토리에 저장",
"Scale by": "스케일링 배수 지정",
@ -515,6 +522,7 @@
"Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGAN 업스케일러들의 타일 사이즈. 0 = 타일링 없음.",
"Tiling": "타일링",
"Time taken:": "소요 시간 : ",
"tokenizer": "토크나이저",
"Torch active/reserved:": "활성화/예약된 Torch 양 : ",
"Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).": "활성화된 Torch : 생성 도중 캐시된 데이터를 포함해 사용된 VRAM의 최대량\n예약된 Torch : 활성화되고 캐시된 모든 데이터를 포함해 Torch에게 할당된 VRAM의 최대량\n시스템 VRAM : 모든 어플리케이션에 할당된 VRAM 최대량 / 총 GPU VRAM (최고 이용도%)",
"Train": "훈련",

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@ -10,6 +10,7 @@ from modules.api.models import *
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
from modules.sd_samplers import all_samplers
from modules.extras import run_extras, run_pnginfo
from PIL import PngImagePlugin
from modules.sd_models import checkpoints_list
from modules.realesrgan_model import get_realesrgan_models
from typing import List
@ -34,9 +35,21 @@ def setUpscalers(req: dict):
def encode_pil_to_base64(image):
buffer = io.BytesIO()
image.save(buffer, format="png")
return base64.b64encode(buffer.getvalue())
with io.BytesIO() as output_bytes:
# Copy any text-only metadata
use_metadata = False
metadata = PngImagePlugin.PngInfo()
for key, value in image.info.items():
if isinstance(key, str) and isinstance(value, str):
metadata.add_text(key, value)
use_metadata = True
image.save(
output_bytes, "PNG", pnginfo=(metadata if use_metadata else None)
)
bytes_data = output_bytes.getvalue()
return base64.b64encode(bytes_data)
class Api:
@ -205,7 +218,7 @@ class Api:
shared.state.interrupt()
return {}
def get_config(self):
options = {}
for key in shared.opts.data.keys():
@ -214,10 +227,14 @@ class Api:
options.update({key: shared.opts.data.get(key, shared.opts.data_labels.get(key).default)})
else:
options.update({key: shared.opts.data.get(key, None)})
return options
def set_config(self, req: OptionsModel):
# currently req has all options fields even if you send a dict like { "send_seed": false }, which means it will
# overwrite all options with default values.
raise RuntimeError('Setting options via API is not supported')
reqDict = vars(req)
for o in reqDict:
setattr(shared.opts, o, reqDict[o])
@ -233,13 +250,13 @@ class Api:
def get_upscalers(self):
upscalers = []
for upscaler in shared.sd_upscalers:
u = upscaler.scaler
upscalers.append({"name":u.name, "model_name":u.model_name, "model_path":u.model_path, "model_url":u.model_url})
return upscalers
def get_sd_models(self):
return [{"title":x.title, "model_name":x.model_name, "hash":x.hash, "filename": x.filename, "config": x.config} for x in checkpoints_list.values()]
@ -251,11 +268,11 @@ class Api:
def get_realesrgan_models(self):
return [{"name":x.name,"path":x.data_path, "scale":x.scale} for x in get_realesrgan_models(None)]
def get_promp_styles(self):
styleList = []
for k in shared.prompt_styles.styles:
style = shared.prompt_styles.styles[k]
style = shared.prompt_styles.styles[k]
styleList.append({"name":style[0], "prompt": style[1], "negative_prompr": style[2]})
return styleList

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@ -1,11 +1,11 @@
import inspect
from pydantic import BaseModel, Field, create_model
from typing import Any, Optional, Union
from typing import Any, Optional
from typing_extensions import Literal
from inflection import underscore
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
from modules.shared import sd_upscalers, opts, parser
from typing import List
from typing import Dict, List
API_NOT_ALLOWED = [
"self",
@ -185,22 +185,22 @@ _options = vars(parser)['_option_string_actions']
for key in _options:
if(_options[key].dest != 'help'):
flag = _options[key]
_type = str
if(_options[key].default != None): _type = type(_options[key].default)
_type = str
if _options[key].default is not None: _type = type(_options[key].default)
flags.update({flag.dest: (_type,Field(default=flag.default, description=flag.help))})
FlagsModel = create_model("Flags", **flags)
class SamplerItem(BaseModel):
name: str = Field(title="Name")
aliases: list[str] = Field(title="Aliases")
options: dict[str, str] = Field(title="Options")
aliases: List[str] = Field(title="Aliases")
options: Dict[str, str] = Field(title="Options")
class UpscalerItem(BaseModel):
name: str = Field(title="Name")
model_name: str | None = Field(title="Model Name")
model_path: str | None = Field(title="Path")
model_url: str | None = Field(title="URL")
model_name: Optional[str] = Field(title="Model Name")
model_path: Optional[str] = Field(title="Path")
model_url: Optional[str] = Field(title="URL")
class SDModelItem(BaseModel):
title: str = Field(title="Title")
@ -211,23 +211,23 @@ class SDModelItem(BaseModel):
class HypernetworkItem(BaseModel):
name: str = Field(title="Name")
path: str | None = Field(title="Path")
path: Optional[str] = Field(title="Path")
class FaceRestorerItem(BaseModel):
name: str = Field(title="Name")
cmd_dir: str | None = Field(title="Path")
cmd_dir: Optional[str] = Field(title="Path")
class RealesrganItem(BaseModel):
name: str = Field(title="Name")
path: str | None = Field(title="Path")
scale: int | None = Field(title="Scale")
path: Optional[str] = Field(title="Path")
scale: Optional[int] = Field(title="Scale")
class PromptStyleItem(BaseModel):
name: str = Field(title="Name")
prompt: str | None = Field(title="Prompt")
negative_prompt: str | None = Field(title="Negative Prompt")
prompt: Optional[str] = Field(title="Prompt")
negative_prompt: Optional[str] = Field(title="Negative Prompt")
class ArtistItem(BaseModel):
name: str = Field(title="Name")
score: float = Field(title="Score")
category: str = Field(title="Category")
category: str = Field(title="Category")

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@ -34,8 +34,11 @@ class Extension:
if repo is None or repo.bare:
self.remote = None
else:
self.remote = next(repo.remote().urls, None)
self.status = 'unknown'
try:
self.remote = next(repo.remote().urls, None)
self.status = 'unknown'
except Exception:
self.remote = None
def list_files(self, subdir, extension):
from modules import scripts

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@ -22,6 +22,8 @@ from collections import defaultdict, deque
from statistics import stdev, mean
optimizer_dict = {optim_name : cls_obj for optim_name, cls_obj in inspect.getmembers(torch.optim, inspect.isclass) if optim_name != "Optimizer"}
class HypernetworkModule(torch.nn.Module):
multiplier = 1.0
activation_dict = {
@ -142,6 +144,8 @@ class Hypernetwork:
self.use_dropout = use_dropout
self.activate_output = activate_output
self.last_layer_dropout = kwargs['last_layer_dropout'] if 'last_layer_dropout' in kwargs else True
self.optimizer_name = None
self.optimizer_state_dict = None
for size in enable_sizes or []:
self.layers[size] = (
@ -163,6 +167,7 @@ class Hypernetwork:
def save(self, filename):
state_dict = {}
optimizer_saved_dict = {}
for k, v in self.layers.items():
state_dict[k] = (v[0].state_dict(), v[1].state_dict())
@ -178,8 +183,15 @@ class Hypernetwork:
state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
state_dict['activate_output'] = self.activate_output
state_dict['last_layer_dropout'] = self.last_layer_dropout
if self.optimizer_name is not None:
optimizer_saved_dict['optimizer_name'] = self.optimizer_name
torch.save(state_dict, filename)
if shared.opts.save_optimizer_state and self.optimizer_state_dict:
optimizer_saved_dict['hash'] = sd_models.model_hash(filename)
optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict
torch.save(optimizer_saved_dict, filename + '.optim')
def load(self, filename):
self.filename = filename
@ -202,6 +214,18 @@ class Hypernetwork:
print(f"Activate last layer is set to {self.activate_output}")
self.last_layer_dropout = state_dict.get('last_layer_dropout', False)
optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {}
self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW')
print(f"Optimizer name is {self.optimizer_name}")
if sd_models.model_hash(filename) == optimizer_saved_dict.get('hash', None):
self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
else:
self.optimizer_state_dict = None
if self.optimizer_state_dict:
print("Loaded existing optimizer from checkpoint")
else:
print("No saved optimizer exists in checkpoint")
for size, sd in state_dict.items():
if type(size) == int:
self.layers[size] = (
@ -219,11 +243,11 @@ class Hypernetwork:
def list_hypernetworks(path):
res = {}
for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True)):
name = os.path.splitext(os.path.basename(filename))[0]
# Prevent a hypothetical "None.pt" from being listed.
if name != "None":
res[name] = filename
res[name + f"({sd_models.model_hash(filename)})"] = filename
return res
@ -358,6 +382,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
shared.state.textinfo = "Initializing hypernetwork training..."
shared.state.job_count = steps
hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0]
filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name)
@ -404,8 +429,22 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
weights = hypernetwork.weights()
for weight in weights:
weight.requires_grad = True
# if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...
optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
# Here we use optimizer from saved HN, or we can specify as UI option.
if hypernetwork.optimizer_name in optimizer_dict:
optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate)
optimizer_name = hypernetwork.optimizer_name
else:
print(f"Optimizer type {hypernetwork.optimizer_name} is not defined!")
optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)
optimizer_name = 'AdamW'
if hypernetwork.optimizer_state_dict: # This line must be changed if Optimizer type can be different from saved optimizer.
try:
optimizer.load_state_dict(hypernetwork.optimizer_state_dict)
except RuntimeError as e:
print("Cannot resume from saved optimizer!")
print(e)
steps_without_grad = 0
@ -467,7 +506,11 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
# Before saving, change name to match current checkpoint.
hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'
last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt')
hypernetwork.optimizer_name = optimizer_name
if shared.opts.save_optimizer_state:
hypernetwork.optimizer_state_dict = optimizer.state_dict()
save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file)
hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory.
textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
"loss": f"{previous_mean_loss:.7f}",
@ -530,8 +573,12 @@ Last saved image: {html.escape(last_saved_image)}<br/>
report_statistics(loss_dict)
filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
hypernetwork.optimizer_name = optimizer_name
if shared.opts.save_optimizer_state:
hypernetwork.optimizer_state_dict = optimizer.state_dict()
save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename)
del optimizer
hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory.
return hypernetwork, filename
def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):

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@ -9,7 +9,7 @@ from modules import devices, sd_hijack, shared
from modules.hypernetworks import hypernetwork
not_available = ["hardswish", "multiheadattention"]
keys = ["linear"] + list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
keys = list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
# Remove illegal characters from name.

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@ -24,11 +24,15 @@ samplers_k_diffusion = [
('Heun', 'sample_heun', ['k_heun'], {}),
('DPM2', 'sample_dpm_2', ['k_dpm_2'], {}),
('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}),
('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}),
('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),
('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
]
samplers_data_k_diffusion = [

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@ -86,6 +86,10 @@ parser.add_argument("--nowebui", action='store_true', help="use api=True to laun
parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False)
parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origins", default=None)
parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None)
parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
cmd_opts = parser.parse_args()
restricted_opts = {
@ -147,9 +151,9 @@ class State:
self.interrupted = True
def nextjob(self):
if opts.show_progress_every_n_steps == -1:
if opts.show_progress_every_n_steps == -1:
self.do_set_current_image()
self.job_no += 1
self.sampling_step = 0
self.current_image_sampling_step = 0
@ -198,7 +202,7 @@ class State:
return
if self.current_latent is None:
return
if opts.show_progress_grid:
self.current_image = sd_samplers.samples_to_image_grid(self.current_latent)
else:
@ -317,6 +321,7 @@ options_templates.update(options_section(('system', "System"), {
options_templates.update(options_section(('training', "Training"), {
"unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
"save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file."),
"dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
"dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
"training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
@ -406,7 +411,8 @@ class Options:
if key in self.data or key in self.data_labels:
assert not cmd_opts.freeze_settings, "changing settings is disabled"
comp_args = opts.data_labels[key].component_args
info = opts.data_labels.get(key, None)
comp_args = info.component_args if info else None
if isinstance(comp_args, dict) and comp_args.get('visible', True) is False:
raise RuntimeError(f"not possible to set {key} because it is restricted")

View File

@ -1446,17 +1446,19 @@ def create_ui(wrap_gradio_gpu_call):
continue
oldval = opts.data.get(key, None)
setattr(opts, key, value)
try:
setattr(opts, key, value)
except RuntimeError:
continue
if oldval != value:
if opts.data_labels[key].onchange is not None:
opts.data_labels[key].onchange()
changed += 1
opts.save(shared.config_filename)
try:
opts.save(shared.config_filename)
except RuntimeError:
return opts.dumpjson(), f'{changed} settings changed without save.'
return opts.dumpjson(), f'{changed} settings changed.'
def run_settings_single(value, key):

View File

@ -188,7 +188,7 @@ def refresh_available_extensions_from_data():
code += f"""
<tr>
<td><a href="{html.escape(url)}">{html.escape(name)}</a></td>
<td><a href="{html.escape(url)}" target="_blank">{html.escape(name)}</a></td>
<td>{html.escape(description)}</td>
<td>{install_code}</td>
</tr>

View File

@ -57,10 +57,18 @@ class Upscaler:
self.scale = scale
dest_w = img.width * scale
dest_h = img.height * scale
for i in range(3):
if img.width > dest_w and img.height > dest_h:
break
shape = (img.width, img.height)
img = self.do_upscale(img, selected_model)
if shape == (img.width, img.height):
break
if img.width >= dest_w and img.height >= dest_h:
break
if img.width != dest_w or img.height != dest_h:
img = img.resize((int(dest_w), int(dest_h)), resample=LANCZOS)

View File

@ -5,6 +5,7 @@ import importlib
import signal
import threading
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from fastapi.middleware.gzip import GZipMiddleware
from modules.paths import script_path
@ -34,7 +35,7 @@ from modules.shared import cmd_opts
import modules.hypernetworks.hypernetwork
queue_lock = threading.Lock()
server_name = "0.0.0.0" if cmd_opts.listen else cmd_opts.server_name
def wrap_queued_call(func):
def f(*args, **kwargs):
@ -85,6 +86,20 @@ def initialize():
shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)
if cmd_opts.tls_keyfile is not None and cmd_opts.tls_keyfile is not None:
try:
if not os.path.exists(cmd_opts.tls_keyfile):
print("Invalid path to TLS keyfile given")
if not os.path.exists(cmd_opts.tls_certfile):
print(f"Invalid path to TLS certfile: '{cmd_opts.tls_certfile}'")
except TypeError:
cmd_opts.tls_keyfile = cmd_opts.tls_certfile = None
print("TLS setup invalid, running webui without TLS")
else:
print("Running with TLS")
# make the program just exit at ctrl+c without waiting for anything
def sigint_handler(sig, frame):
print(f'Interrupted with signal {sig} in {frame}')
@ -93,6 +108,11 @@ def initialize():
signal.signal(signal.SIGINT, sigint_handler)
def setup_cors(app):
if cmd_opts.cors_allow_origins:
app.add_middleware(CORSMiddleware, allow_origins=cmd_opts.cors_allow_origins.split(','), allow_methods=['*'])
def create_api(app):
from modules.api.api import Api
api = Api(app, queue_lock)
@ -114,6 +134,7 @@ def api_only():
initialize()
app = FastAPI()
setup_cors(app)
app.add_middleware(GZipMiddleware, minimum_size=1000)
api = create_api(app)
@ -131,8 +152,10 @@ def webui():
app, local_url, share_url = demo.launch(
share=cmd_opts.share,
server_name="0.0.0.0" if cmd_opts.listen else None,
server_name=server_name,
server_port=cmd_opts.port,
ssl_keyfile=cmd_opts.tls_keyfile,
ssl_certfile=cmd_opts.tls_certfile,
debug=cmd_opts.gradio_debug,
auth=[tuple(cred.split(':')) for cred in cmd_opts.gradio_auth.strip('"').split(',')] if cmd_opts.gradio_auth else None,
inbrowser=cmd_opts.autolaunch,
@ -147,6 +170,8 @@ def webui():
# runnnig its code. We disable this here. Suggested by RyotaK.
app.user_middleware = [x for x in app.user_middleware if x.cls.__name__ != 'CORSMiddleware']
setup_cors(app)
app.add_middleware(GZipMiddleware, minimum_size=1000)
if launch_api: