Merge remote-tracking branch 'upstream/master' into sub-quad_attn_opt

        return;

    }

    const tmpFile = files[0];

    imgWrap.querySelector('.modify-upload button + button, .touch-none + div button + button')?.click();

    const callback = () => {

        const fileInput = imgWrap.querySelector('input[type="file"]');

        if ( fileInput ) {

            if ( files.length === 0 ) {

                files = new DataTransfer();

                files.items.add(tmpFile);

                fileInput.files = files.files;

            } else {

                fileInput.files = files;

            }

            fileInput.dispatchEvent(new Event('change'));   

        }

    };

    "vram": "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%).",

    "Highres. fix": "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition",

    "Scale latent": "Uscale the image in latent space. Alternative is to produce the full image from latent representation, upscale that, and then move it back to latent space.",

    "Eta noise seed delta": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",

    "Do not add watermark to images": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.",

    "Clip skip": "Early stopping parameter for CLIP model; 1 is stop at last layer as usual, 2 is stop at penultimate layer, etc.",

    "Approx NN": "Cheap neural network approximation. Very fast compared to VAE, but produces pictures with 4 times smaller horizontal/vertical resoluton and lower quality.",

    "Approx cheap": "Very cheap approximation. Very fast compared to VAE, but produces pictures with 8 times smaller horizontal/vertical resoluton and extremely low quality."

    "Approx cheap": "Very cheap approximation. Very fast compared to VAE, but produces pictures with 8 times smaller horizontal/vertical resoluton and extremely low quality.",

    "Hires. fix": "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition",

    "Hires steps": "Number of sampling steps for upscaled picture. If 0, uses same as for original.",

    "Upscale by": "Adjusts the size of the image by multiplying the original width and height by the selected value. Ignored if either Resize width to or Resize height to are non-zero.",

    "Resize width to": "Resizes image to this width. If 0, width is inferred from either of two nearby sliders.",

    "Resize height to": "Resizes image to this height. If 0, height is inferred from either of two nearby sliders."

}

                if(e && e.parentElement.tagName == 'DIV'){

                    e.style.cursor='pointer'

                    e.style.userSelect='none'

                    e.addEventListener('click', function (evt) {

                        if(!opts.js_modal_lightbox) return;

                    e.addEventListener('mousedown', function (evt) {

                        if(!opts.js_modal_lightbox || evt.button != 0) return;

                        modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed)

                        showModal(evt)

                    }, true);

// various functions for interation with ui.py not large enough to warrant putting them in separate files

// various functions for interaction with ui.py not large enough to warrant putting them in separate files

function set_theme(theme){

    gradioURL = window.location.href

import base64

import io

import time

import datetime

import uvicorn

from threading import Lock

from io import BytesIO

from gradio.processing_utils import decode_base64_to_file

from fastapi import APIRouter, Depends, FastAPI, HTTPException

from fastapi import APIRouter, Depends, FastAPI, HTTPException, Request, Response

from fastapi.security import HTTPBasic, HTTPBasicCredentials

from secrets import compare_digest

from modules.textual_inversion.preprocess import preprocess

from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork

from PIL import PngImagePlugin,Image

from modules.sd_models import checkpoints_list

from modules.sd_models import checkpoints_list, find_checkpoint_config

from modules.realesrgan_model import get_realesrgan_models

from modules import devices

from typing import List

        bytes_data = output_bytes.getvalue()

    return base64.b64encode(bytes_data)

def api_middleware(app: FastAPI):

    @app.middleware("http")

    async def log_and_time(req: Request, call_next):

        ts = time.time()

        res: Response = await call_next(req)

        duration = str(round(time.time() - ts, 4))

        res.headers["X-Process-Time"] = duration

        endpoint = req.scope.get('path', 'err')

        if shared.cmd_opts.api_log and endpoint.startswith('/sdapi'):

            print('API {t} {code} {prot}/{ver} {method} {endpoint} {cli} {duration}'.format(

                t = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f"),

                code = res.status_code,

                ver = req.scope.get('http_version', '0.0'),

                cli = req.scope.get('client', ('0:0.0.0', 0))[0],

                prot = req.scope.get('scheme', 'err'),

                method = req.scope.get('method', 'err'),

                endpoint = endpoint,

                duration = duration,

            ))

        return res

class Api:

    def __init__(self, app: FastAPI, queue_lock: Lock):

        self.router = APIRouter()

        self.app = app

        self.queue_lock = queue_lock

        api_middleware(self.app)

        self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)

        self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)

        self.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)

        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()]

        return [{"title":x.title, "model_name":x.model_name, "hash":x.hash, "filename": x.filename, "config": find_checkpoint_config(x)} for x in checkpoints_list.values()]

    def get_hypernetworks(self):

        return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks]