import base64
import hashlib
import html
import io
import itertools
import json
import threading
import time
import subprocess
import re
from pathlib import Path

import imageio.v3 as iio
import requests
import torch
import torch.nn.functional as NNF
import transformers
import numpy as np
import mss
from PIL import Image, ImageDraw
from aiohttp import web
from aiohttp.web_exceptions import HTTPForbidden, HTTPError

import server
import folder_paths
import comfy
from nodes import CheckpointLoaderSimple, VAELoader

import sys
sys.path.append("ComfyUI/custom_nodes")

try:
    from llama_cpp import Llama  # llama-cpp-python
except:
    Llama = None

try:
    rembg = __import__("comfyui-inspyrenet-rembg")
except:
    rembg = None

try:
    florence2 = __import__("comfyui-florence2")
except:
    florence2 = None

try:
    film = __import__("comfyui-frame-interpolation.vfi_models.film").vfi_models.film
except:
    film = None

try:
    from comfyui_tensorrt import TensorRTLoader
except:
    TensorRTLoader = None

def btoa_utf8(value):
    return base64.b64encode(value.encode("utf-8")).decode()

def atob_utf8(value):
    if value is None:
        return None
    if value == "":
        return ""
    return base64.b64decode(value.encode()).decode("utf-8")

STREAM_COMPRESSION = 1
UPDATE_DELAY = 1.0
allowed_ips = ["127.0.0.1"]
refresh_updating = 0
refresh_data = {}
refresh_param = {}
default_param = {"lora": "", "_capture_offsetX": 0, "_capture_offsetY": 0, "_capture_scale": 100}
param = default_param.copy()
state = {"presetTitle": time.strftime("%Y%m%d-%H%M"), "presetFolder": "", "presetFile": "", "loraRate": "1", "loraRank": "0", "loraMode": "", "loraFolder": "", "loraFile": "", "loraTagOptions": "[]", "loraTag": "", "loraLinkHref": "", "loraPreviewSrc": "", "darker": 0.0, "lastElapsed": 0}
frame_buffer = []
frame_mtime = 0
frame_fps = 16
setframe_mtime = 0
setframe_buffer = []

class AnyType(str):
    def __ne__(self, __value: object) -> bool:
        return False

anytype = AnyType("*")


HEAD=r"""
<head>
<style type="text/css">

html {
    background-color: black;
}

body {
    color: lightgray;
    margin: 4px;
    padding: 0px;
}

textarea {
    color: lightgray;
    background-color: black;
    width: 100%;
    font-size: 100%;
}

input {
    color: lightgray;
    background-color: black;
    font-size: 100%;
}

button {
    color: black;
    background-color: dimgray;
    font-size: 100%;
    border-radius: 4px;
    padding: 0px;
    min-width: 2em;
}

button.tags {
    padding: 2px;
    min-width: 2em;
}

hr {
    background-color: dimgray;
    border: none;
    height: 3px;
    padding: 0px;
    margin: 1px;    
}

button.disabled {
    opacity: 0.5;
}

select {
    color: lightgray;
    background-color: black;
    font-size: 100%;
}

div.row {
    display: flex;
    position: relative;
}

div#presetXorkeyInputDiv {
    display: none;
    position: relative;
}

.FlipStreamSlider {
    width: 50%;
}

.FlipStreamInputBox {
    width: 100%;
}

.FlipStreamSelectBox,
.FlipStreamFolderSelect,
.FlipStreamSizeSelect,
.FlipStreamFileSelect,
.FlipStreamMoveFileSelect {
    width: 100%;
}

.FlipStreamMoveFileSelect {
    display: none;
}

.FlipStreamPasteBox {
    border: 1px dimgray;
    max-width: 100%;
    min-height: 32px;
    max-height: 160px;
    object-fit: scale-down;
}

.FlipStreamPreviewBox {
    border: 1px dimgray;
    max-width: 100%;
    min-height: 32px;
    max-height: 160px;
    object-fit: scale-down;
}

.FlipStreamPreviewRoi {
    position: absolute;
    top: 0;
    left: 0;
}

#leftPanel,
#rightPanel,
#captureLeftPanel,
#captureRightPanel,
#tagLeftPanel,
#tagRightPanel,
#presetLeftPanel,
#presetRightPanel {
    width: 12%;
    background: rgba(0, 0, 0, 0.8);
}

#centerPanel,
#captureCenterPanel,
#tagCenterPanel,
#presetCenterPanel {
    width: 76%;
    text-align: center;
}

#captureCenterPanel,
#tagCenterPanel {
    background: rgba(0, 0, 0, 0.8);
}

#mainDialog, #captureDialog, #tagDialog, #presetDialog {
    position: fixed;
    top: 0;
    left: 0;
    width: 100%;
    height: 100%;
}

#mainDialog {
    display: flex;
    background-position: center;
    background-repeat: no-repeat;
    background-blend-mode: overlay;
}

#captureDialog, #tagDialog, #presetDialog {
    display: none;
    z-index: 999;
}

#presetFolderSelect,
#movePresetSelect,
#loraModeSelect,
#loraFolderSelect,
#moveLoraSelect {
    width: 100%;
}

#movePresetSelect,
#moveLoraSelect {
    display: none;
}

#presetXorkey {
    display: none;
}

#presetFileSelect,
#presetTitleInput,
#loraFileSelect,
#loraTagSelect {
    width: 80%;
}

#darkerRange {
    width: 50%;
}

#messageBox {
    width: 100%;
    height: 100%;
    border: none;
    padding: 10px;
    background: transparent;
    text-align: left;
    user-select: none;
    font-size: 1rem;
    text-shadow: 
    black 2px 0px,  black -2px 0px,
    black 0px -2px, black 0px 2px,
    black 2px 2px , black -2px 2px,
    black 2px -2px, black -2px -2px,
    black 1px 2px,  black -1px 2px,
    black 1px -2px, black -1px -2px,
    black 2px 1px,  black -2px 1px,
    black 2px -1px, black -2px -1px;
}

#loraLink img {
    max-width: 100%;
    max-height: 160px;
    width: auto;
    height: auto;
    margin: auto;
    display: block;
}

#offsetXRange,
#offsetYRange,
#scaleRange {
    width: 80%;
}

</style>
<title>FlipStreamViewer</title>
</head>
"""

SCRIPT_PARAM=r"""
function btoa_utf8(str) {
    return btoa(unescape(encodeURIComponent(str)));
}

function atob_utf8(str) {
    if (!str) {
        return "";
    }
    return decodeURIComponent(escape(atob(str)));
}

function getStateAsJson(force_state={}) {
    const presetTitle = document.getElementById("presetTitleInput").value;
    const presetFolder = document.getElementById("presetFolderSelect").value;
    const presetFile = document.getElementById("presetFileSelect").value;
    const loraMode = document.getElementById("loraModeSelect").value;
    const loraFolder = document.getElementById("loraFolderSelect").value;
    const loraFile = document.getElementById("loraFileSelect").value;
    const loraTagSelect = document.getElementById("loraTagSelect");
    const loraTagOptions = JSON.stringify([...loraTagSelect.options].map(o => ({ value: o.value, text: o.text })));
    const loraTag = document.getElementById("loraTagSelect").value;
    const loraRate = document.getElementById("loraRate").value;
    const loraRank = document.getElementById("loraRank").value;
    const loraLinkHref = document.getElementById("loraLink").getAttribute("href");
    const loraPreviewSrc = document.getElementById("loraPreview").getAttribute("src");
    const darker = parseFloat(document.getElementById("darkerRange").value);
    const lastElapsed = document.getElementById("statusLastElapsed").value;
    var res = { presetTitle: presetTitle, presetFolder: presetFolder, presetFile: presetFile, loraRate: loraRate, loraRank: loraRank, loraMode: loraMode, loraFolder: loraFolder, loraFile: loraFile, loraTagOptions: loraTagOptions, loraTag: loraTag, loraLinkHref: loraLinkHref, loraPreviewSrc: loraPreviewSrc, loraTagOptions: loraTagOptions, darker: darker, lastElapsed: lastElapsed };
    document.querySelectorAll('.FlipStreamFolderSelect').forEach(x => res[x.name] = x.value);
    res = Object.assign(res, force_state);
    return res;
}

function getParamAsJson(force_param={}) {
    const lora = btoa_utf8(document.getElementById("loraInput").value.trim());
    const _capture_offsetX = parseInt(document.getElementById("offsetXRange").value);
    const _capture_offsetY = parseInt(document.getElementById("offsetYRange").value);
    const _capture_scale = parseInt(document.getElementById("scaleRange").value);

    var res = { lora: lora, _capture_offsetX: _capture_offsetX, _capture_offsetY: _capture_offsetY, _capture_scale: _capture_scale }
    document.querySelectorAll('.FlipStreamSlider').forEach(x => res[x.name] = x.value);
    document.querySelectorAll('.FlipStreamTextBox').forEach(x => res[x.name] = btoa_utf8(x.value));
    document.querySelectorAll('.FlipStreamInputBox').forEach(x => res[x.name] = x.value);
    document.querySelectorAll('.FlipStreamSelectBox').forEach(x => res[x.name] = x.value);
    document.querySelectorAll('.FlipStreamSizeSelect').forEach(x => res[x.name] = x.value);
    document.querySelectorAll('.FlipStreamFileSelect').forEach(x => res[x.name] = x.value);
    res = Object.assign(res, force_param);
    return res;
}

function setParam(param) {
    document.getElementById("loraInput").value = param.lora || "";
    document.getElementById("offsetXRange").value = param.offsetX || 0;
    document.getElementById("offsetYRange").value = param.offsetY || 0;
    document.getElementById("scaleRange").value = param.scale || 100;
    for (let key in param) {
        const elem = document.querySelector(`input[name=${key}]`);
        elem.value = param[key] || elem.value;
    }
}

function updateParam(reload=false, run_once=false, force_state={}, force_param={}, search="") {
    fetch("/flipstreamviewer/update_param", {
        method: "POST",
        body: JSON.stringify([getStateAsJson(force_state), getParamAsJson(force_param)]),
        headers: {"Content-Type": "application/json"}         
    }).then(response => {
        if (response.ok) {
            if (run_once) {
                fetch('/flipstreamviewer/run_once');
            }
            if (reload) {
                reloadPage(search);
            }
        } else {
            alert("Failed to update parameter.");
        }
    }).catch(error => {
        alert("An error occurred while updating parameter.");
    });
}

function setupPreviewRoi() {
    document.querySelectorAll('.FlipStreamPreviewRoi').forEach(canvas => {
        const ctx = canvas.getContext('2d');
        const img = canvas.parentElement.querySelector('img');
        const label = canvas.id.replace("PreviewRoi", "");
        img.onload = () => {
            canvas.width = img.width;
            canvas.height = img.height;
        };

        let drawing = false, x, y;
        canvas.onmousedown = e => { drawing = true; [x, y] = [e.offsetX, e.offsetY]; };
        canvas.onmousemove = e => { if (drawing) { canvas.width = canvas.width; ctx.strokeStyle = 'red'; ctx.strokeRect(x, y, e.offsetX - x, e.offsetY - y); } };
        canvas.onmouseup = e => {
            if (!drawing) return;
            drawing = false;
            var sx = x / canvas.width;
            var sy = y / canvas.height;
            var ex = e.offsetX / canvas.width;
            var ey = e.offsetY / canvas.height;
            if (ex <= sx || ey <= sy) {
                sx = 0;
                sy = 0;
                ex = 1;
                ey = 1;
            }
            fetch('/flipstreamviewer/preview_setroi', {
                method: 'POST',
                headers: { 'Content-Type': 'application/json' },
                body: JSON.stringify({
                    label: label,
                    sx: sx,
                    sy: sy,
                    ex: ex,
                    ey: ey
                })
            });
        };
    });
}
setupPreviewRoi();
"""

SCRIPT_PRESET=r"""
function loadPreset(loraPromptOnly=false, force_state={}, search="") {
    const xorkey = document.getElementById("presetXorkeyInput").value;
    fetch("/flipstreamviewer/load_preset", {
        method: "POST",
        body: JSON.stringify([getStateAsJson(force_state), loraPromptOnly, xorkey]),
        headers: {"Content-Type": "application/json"}
    }).then(response => response.json()).then(json => {
        if (loraPromptOnly) {
            document.getElementById("loraInput").value = atob_utf8(json.lora);
            document.getElementById("presetTitleInput").value = json.presetTitle;
        }
        else {
            fetch('/flipstreamviewer/run_once');
            reloadPage(search);
        }
    }).catch(error => {
        alert("An error occurred while loading preset: " + error);
    });
}

function movePreset() {
    document.getElementById("movePresetSelect").style.display = "block";
    document.getElementById("movePresetSelect").value = "";
}

function movePresetFile() {
    const moveFile = document.getElementById("presetFileSelect").value;
    const moveTo = document.getElementById("movePresetSelect").value;
    if (!moveFile || !moveTo) {
        return;
    }
    fetch("/flipstreamviewer/move_presetfile", {
        method: "POST",
        body: JSON.stringify([getStateAsJson(), moveTo]),
        headers: {"Content-Type": "application/json"}
    }).then(response => {
        if (response.ok) {
            alert("Preset moved successfully!");
        } else {
            alert("Failed to move preset.");
        }
    }).catch(error => {
        alert("An error occurred while moving preset.");
    });
}

function savePreset() {
    const title = document.getElementById("presetTitleInput").value;
    const xorkey = document.getElementById("presetXorkeyInput").value;
    if (title == "") {
        alert("Please enter a title to save.");
        return;
    }
    if (!/^[a-zA-Z0-9-_]+$/.test(title)) {
        alert("Title can only contain [a-zA-Z0-9-_] characters.");
        return;
    }
    fetch("/flipstreamviewer/save_preset", {
        method: "POST",
        body: JSON.stringify([getStateAsJson(), getParamAsJson(), xorkey]),
        headers: {"Content-Type": "application/json"}
    }).then(response => {
        if (response.ok) {
            alert("Preset saved successfully!");
        } else {
            alert("Failed to save preset.");
        }
    }).catch(error => {
        alert("An error occurred while saving preset.");
    });
}

function showPresetDialog() {
    document.getElementById("mainDialog").style.display = "none";
    document.getElementById("presetDialog").style.display = "flex";
}

function closePresetDialog() {
    document.getElementById("mainDialog").style.display = "flex";
    document.getElementById("presetDialog").style.display = "none";
}
"""

SCRIPT_CKPT=r"""
function showMoveFileSelect(obj) {
    obj.style.display="block";
    obj.value="";
}

function moveFile(folder_path, mode, label) {
    const moveFrom = document.getElementById(label + "FileSelect").value;
    const moveTo = document.getElementById(label + "MoveFileSelect").value;
    if (!moveFrom || !moveTo) {
        return;
    }
    fetch("/flipstreamviewer/move_file", {
        method: "POST",
        body: JSON.stringify([getStateAsJson(), folder_path, mode, label, moveFrom, moveTo]),
        headers: {"Content-Type": "application/json"}
    }).then(response => {
        if (response.ok) {
            alert("Moved successfully!");
        } else {
            alert("Failed to move.");
        }
        reloadPage();
    }).catch(error => {
        alert("An error occurred while moving.");
    });
}
"""

SCRIPT_LORA=r"""
function selectLoraFile() {
    const loraFileSelect = document.getElementById("loraFileSelect");
    const loraFile = loraFileSelect.value;
    const loraFolder = document.getElementById("loraFolderSelect").value;
    
    if (loraFile) toggleLora();

    fetch("/flipstreamviewer/get_lorainfo", {
        method: "POST",
        body: JSON.stringify({loraFolder: loraFolder, loraFile: loraFile}),
        headers: {"Content-Type": "application/json"}
    }).then(response => response.json()).then(json => {
        const tagSelect = document.getElementById("loraTagSelect");
        tagSelect.innerHTML = '<option value="">tags</option>';
        if (json.ss_tag_frequency) {
            const tags = {}, datasets = JSON.parse(json.ss_tag_frequency);
            for (const set of Object.values(datasets)) {
                for (const [t, v] of Object.entries(set)) tags[t] = (tags[t] || 0) + v;
            }
            Object.entries(tags).sort((a, b) => b[1] - a[1]).forEach(([t, v]) => {
                tagSelect.add(new Option(`${t}:${v}`, t));
            });
        }
        document.getElementById("loraLink").href = json._lorapreview_href || "";
        document.getElementById("loraPreview").src = json._lorapreview_src || "";
    });
}

function toggleLora() {
    const loraMode = document.getElementById("loraModeSelect").value;
    const loraFileSelect = document.getElementById("loraFileSelect");
    const loraName = loraFileSelect.options[loraFileSelect.selectedIndex].text;
    const loraRate = document.getElementById("loraRate").value;
    const loraInput = document.getElementById("loraInput");
    
    const re = new RegExp("[\\n]?<lora:" + loraName + ":[-]?[0-9.]+>", "g");
    const match = re.exec(loraInput.value);

    if (match) {
        loraInput.setRangeText("", match.index, match.index + match[0].length, 'preserve');
    } else {
        const loraTag = `<lora:${loraName}:${loraRate}>`;
        const container = `@${loraMode}{`;
        const containerIdx = loraMode ? loraInput.value.indexOf(container) : -1;

        if (containerIdx !== -1) {
            const pos = containerIdx + container.length;
            loraInput.setRangeText("\n" + loraTag, pos, pos, 'end');
        } else {
            const prefix = loraInput.value.trim() ? "\n" : "";
            const text = loraMode ? `${prefix}@${loraMode}{\n${loraTag}\n}` : `${prefix}${loraTag}`;
            loraInput.setRangeText(text, loraInput.value.length, loraInput.value.length, 'end');
            if (loraMode) loraInput.setSelectionRange(loraInput.value.length - 2, loraInput.value.length - 2);
        }
    }
    loraInput.focus();
}


function moveLora() {
    document.getElementById("moveLoraSelect").style.display = "block";
    document.getElementById("moveLoraSelect").value = "";
}

function moveLoraFile() {
    const moveFile = document.getElementById("loraFileSelect").value;
    const moveTo = document.getElementById("moveLoraSelect").value;
    if (!moveFile || !moveTo) {
        return;
    }
    fetch("/flipstreamviewer/move_lorafile", {
        method: "POST",
        body: JSON.stringify([getStateAsJson(), moveTo]),
        headers: {"Content-Type": "application/json"}
    }).then(response => {
        if (response.ok) {
            alert("Lora moved successfully!");
        } else {
            alert("Failed to move lora.");
        }
    }).catch(error => {
        alert("An error occurred while moving lora.");
    });
}
"""

SCRIPT_TAG=r"""
function toggleTag() {
    const el = document.getElementById("loraInput"), s = document.getElementById("loraTagSelect");
    const tag = s.value || Array.from(s.options).slice(1, parseInt(document.getElementById("loraRank").value) + 1).map(o => o.value).join(", ");
    const re = new RegExp(`(\\s*,\\s*)?${tag}(\\s*,\\s*)?`, "g");

    if (el.value.match(re)) {
        const match = re.exec(el.value);
        el.focus();
        el.setRangeText("", match.index, match.index + match[0].length, "end");
    } else {
        const pre = (el.selectionStart > 0 && !/[,\s\n]$/.test(el.value.slice(0, el.selectionStart))) ? ", " : "";
        el.focus();
        el.setRangeText(pre + tag, el.selectionStart, el.selectionEnd, 'end');
    }
}

function randomTag() {
    const options = document.getElementById("loraTagSelect").options;
    const loraInput = document.getElementById("loraInput");
    const loraTag = options[Math.floor(Math.random() * options.length)].value;
    if (loraTag) {
        const re = new RegExp("^\\s*" + loraTag + "\\s*(,\\s*|\n|$)|,\\s*" + loraTag + "\\s*(,|\n|$)", "g");
        if (loraInput.value.search(re) < 0) {
            if (loraInput.value) {
                loraInput.value += ", " + loraTag;
            } else {
                loraInput.value = loraTag;
            }
        }
    }
}

function clearLoraInput() {
    document.getElementById("loraInput").value = "";
}

function showTagDialog() {
    const lora = document.getElementById("loraInput").value;
    const tags = lora.replace(/\n/g, ',').split(',').map(value => value.trim());
    const tagCenterPanel = document.getElementById("tagCenterPanel");
    const count = tagCenterPanel.children.length;

    for (const child of Array.from(tagCenterPanel.children)) {
        if (!child.classList.contains("disabled")) {
            child.classList.add("disabled");
        }
    }

    for (let i = 0; i < tags.length; i++) {
        if (!tags[i])
            continue;

        const child = Array.from(tagCenterPanel.children).find(child => child.value == tags[i]);
        if (child)
        {
            child.classList.remove("disabled");
            continue;
        }

        if (tags[i] == "----") {
            if (count == 0) {
                const hr = document.createElement("hr");
                hr.classList.add("tags");
                hr.classList.add("separator");
                tagCenterPanel.appendChild(hr);
            }
        } else {
            const button = document.createElement("button");
            button.value = tags[i];
            button.textContent = tags[i];
            button.classList.add("tags");
            button.addEventListener("click", function() {
                if (button.classList.contains("disabled")) {
                    button.classList.remove("disabled");
                } else {
                    button.classList.add("disabled");
                }
            });
            tagCenterPanel.appendChild(button);
        }
    }

    document.getElementById("tagDialog").style.display = "flex";
}

function getRandomInt(min, max) {
    return Math.floor(Math.random() * (max - min + 1) + min);
}

function tagCSel() {
    const tagCenterPanel = document.getElementById("tagCenterPanel");
    Array.from(tagCenterPanel.children).forEach(child => child.classList.contains("disabled") || child.classList.add("disabled"));
}

function tagRSel() {
    const tagCenterPanel = document.getElementById("tagCenterPanel");
    var group = [];
    Array.from(tagCenterPanel.children).forEach(child => {
        if (child.classList.contains("separator") && group.length > 0) {
            const r = getRandomInt(0, group.length);
            if (r != group.length) {
                group[r].classList.remove("disabled");
            }
            group = [];
        } else {
            group.push(child);
        }
    });
    const r = getRandomInt(0, group.length);
    if (r != group.length) {
        group[r].classList.remove("disabled");
    }
}

function tagOK() {
    const tagCenterPanel = document.getElementById("tagCenterPanel");
    var selectedTags = [];
    Array.from(tagCenterPanel.children).forEach(child => {
        if (!child.classList.contains("separator") && !child.classList.contains("disabled")) {
            selectedTags.push(child.value);
        }
    });
    loraInput.value = selectedTags.join(", ");
    closeTagDialog();
}

function closeTagDialog() {
    document.getElementById("tagDialog").style.display = "none";
}
"""

SCRIPT_VIEW=r"""
var toggleViewFlag = true;
var streamViewFlag = true;
var streamInfo = { mtime: 0, fps: 8, count: 0 };
var streamMTime = 0;
var streamCache = [];
var streamIndex = 0;

function detailsState() {
    const ds = document.querySelectorAll('details');
    ds.forEach((d, i) => d.open = sessionStorage.getItem(`details_state-${i}`) === 'open');
    document.addEventListener('toggle', e => {
        sessionStorage.setItem(`details_state-${Array.from(ds).indexOf(e.target)}`, e.target.open ? 'open' : 'closed');
    }, true);
}
detailsState();

function onPasteBox() {
    document.querySelectorAll('.FlipStreamPasteBox').forEach(i => {
        i.tabIndex = 0;
        i.onclick = () => i.focus();
        i.onpaste = e => {
            const f = e.clipboardData.files[0];
            if (f?.type[0] == 'i') fetch(`/flipstreamviewer/paste_upload?label=${i.name}`, {method:'POST', body:f});
        };
        i.onkeydown = e => {
            if (e.key === 'Delete' || e.key === 'Backspace') {
                fetch(`/flipstreamviewer/paste_remove?label=${i.name}`, {method:'POST'});
            }
        };
    });
}
onPasteBox();

function toggleView() {
    const leftPanel = document.getElementById("leftPanel");
    const rightPanel = document.getElementById("rightPanel");
    const presetLeftPanel = document.getElementById("presetLeftPanel");
    const presetRightPanel = document.getElementById("presetRightPanel");
    const messageBox = document.getElementById("messageBox");
    if (toggleViewFlag) {
        leftPanel.style.visibility = "hidden";
        rightPanel.style.visibility = "hidden";
        presetLeftPanel.style.visibility = "hidden";
        presetRightPanel.style.visibility = "hidden";
        messageBox.style.visibility = "hidden";
        toggleViewFlag = false;
    } else {
        leftPanel.style.visibility = "visible";
        rightPanel.style.visibility = "visible";
        presetLeftPanel.style.visibility = "visible";
        presetRightPanel.style.visibility = "visible";
        messageBox.style.visibility = "visible";
        toggleViewFlag = true;
    }
}

function hideView() {
    const leftPanel = document.getElementById("leftPanel");
    const centerPanel = document.getElementById("centerPanel");
    const rightPanel = document.getElementById("rightPanel");
    const presetLeftPanel = document.getElementById("presetLeftPanel");
    const presetRightPanel = document.getElementById("presetRightPanel");
    const messageBox = document.getElementById("messageBox");
    closeCaptureDialog();
    messageBox.style.visibility = "hidden";
    document.getElementById("loraPreview").src = "";
    document.querySelectorAll('.FlipStreamPreviewBox').forEach(x => x.src = "");
    document.querySelectorAll('.FlipStreamPasteBox').forEach(x => x.src = "");
    leftPanel.style.visibility = "hidden";
    centerPanel.style.visibility = "hidden";
    rightPanel.style.visibility = "hidden";
    presetLeftPanel.style.visibility = "hidden";
    presetRightPanel.style.visibility = "hidden";
    toggleViewFlag = false;
    streamViewFlag = false;
}
setTimeout(hideView, 300 * 1000);

async function updateStreamView() {
    const container = document.getElementById('mainDialog');
    if (streamViewFlag && streamCache.length > 0) {
        const img = streamCache[streamIndex];
        container.style.backgroundImage = `url(${img.src})`;
        streamIndex = (streamIndex + 1) % streamCache.length;
    } else {
        container.style.backgroundImage = 'none';
    }
}
var updateStreamInterval = setInterval(updateStreamView, 1000 / streamInfo.fps);

async function refreshView() {
    const data = await fetch("/flipstreamviewer/refresh_view")
        .then(r => r.json()).catch(e => ({ status: "Fails to refresh view: " + e }));
    if (data.status_elapsed == 0 && document.getElementById("statusElapsed").value != 0) {
        document.getElementById("statusLastElapsed").value = document.getElementById("statusElapsed").value;
    }
    document.getElementById("statusElapsed").value = data.status_elapsed || 0;
    document.getElementById("statusInfo").textContent = data.status_info || "Empty";
    document.getElementById("messageBox").innerText = atob_utf8(data.message) || "";
    document.getElementById("messageBox").style.fontSize = data.message_fontsize || "1rem";
    for (const k in data.param) {
        const el = document.querySelector(`[name="${k}"]`);
        if (el) {
            if (el.classList.contains('FlipStreamTextBox')) {
                el.value = atob_utf8(data.param[k]);
            } else if (el.type === 'range') {
                el.value = parseFloat(data.param[k]);
            } else {
                el.value = data.param[k];
            }
        }
    }
    document.querySelectorAll('.FlipStreamLogBox').forEach(async x => {
        if (x.id in data.log) {
            x.value = atob_utf8(data.log[x.id]) || "";
        } else {
            x.value = "";
        }
    });
    if (streamViewFlag) {
        darker = document.getElementById("darkerRange").value;
        document.querySelectorAll('.FlipStreamPreviewBox').forEach(async x => {
            if (x.src != "") {
                x.src = `/flipstreamviewer/preview?label=${x.name}&mtime=${data.preview_mtime[x.id] || 0}`;
            }
        });
        document.querySelectorAll('.FlipStreamPasteBox').forEach(async x => {
            if (x.src != "") {
                x.src = `/flipstreamviewer/paste?label=${x.name}&mtime=${data.mtime[x.name+'_mtime'] || 0}`;
            }
        });

        const response = await fetch('/flipstreamviewer/stream/info');
        streamInfo = await response.json();
        if (streamInfo.mtime != streamMTime) {
            streamMTime = streamInfo.mtime;
            clearInterval(updateStreamInterval);
            streamIndex = 0;
            streamCache = await Promise.all(
                Array.from({ length: streamInfo.count }, (_, j) => 
                    new Promise(resolve => {
                        const img = new Image();
                        img.onload = () => resolve(img);
                        img.onerror = () => resolve(img);
                        img.src = `/flipstreamviewer/stream/${j}.png?mtime=${streamInfo.mtime}`;
                    })
                )
            );
            updateStreamInterval = setInterval(updateStreamView, 1000 / streamInfo.fps);
        }
    }
}
setInterval(refreshView, 1000);
"""

SCRIPT_CAPTURE=r"""
let capturedCanvas = document.createElement("canvas");
async function capture() {
    const video = document.createElement("video");
    video.srcObject = await navigator.mediaDevices.getDisplayMedia({ video: true });
    video.style.display = "none";
    video.play();
    video.addEventListener("loadedmetadata", () => {
        const { videoWidth, videoHeight } = video;
        capturedCtx = capturedCanvas.getContext("2d");
        capturedCtx.clearRect(0, 0, capturedCanvas.width, capturedCanvas.height);
        capturedCanvas.width = videoWidth;
        capturedCanvas.height = videoHeight;
        capturedCtx.drawImage(video, 0, 0);
        video.srcObject.getTracks().forEach(track => track.stop());
        video.srcObject = null;
        updateCanvas();
    });
}

function updateCanvas() {
    document.getElementById("captureDialog").style.display = "flex";
    const canvas = document.getElementById("canvas");
    const ctx = canvas.getContext("2d");
    const _capture_offsetX = parseInt(document.getElementById("offsetXRange").value, 10);
    const _capture_offsetY = parseInt(document.getElementById("offsetYRange").value, 10);
    const _capture_scale = parseInt(document.getElementById("scaleRange").value, 10) / 100;
    ctx.clearRect(0, 0, canvas.width, canvas.height);
    ctx.save();
    ctx.scale(_capture_scale, _capture_scale);
    ctx.translate(_capture_offsetX, _capture_offsetY);
    ctx.drawImage(capturedCanvas, 0, 0);
    ctx.restore();
}

function closeCaptureDialog() {
    document.getElementById("captureDialog").style.display = "none";
}

function resetPos() {
    document.getElementById("offsetXRange").value = 0;
    document.getElementById("offsetYRange").value = 0;
    document.getElementById("scaleRange").value = 100;
    updateCanvas();
}

async function setFrame() {
    const canvas = document.getElementById("canvas");
    await fetch("/flipstreamviewer/set_frame", {
        method: "POST",
        body: canvas.toDataURL("image/png"),
        headers: { "Content-Type": "image/png" }
    });
    closeCaptureDialog();
}
"""

SCRIPT_QUERY=r"""
function onInputDarker(value=-1) {
    value = parseFloat(value);
    if (value < 0) {
        value = document.getElementById("darkerRange").value;
    } else {
        document.getElementById("darkerRange").value = value;
    }
    document.getElementById("darkerValue").innerText = value; 
    document.getElementById('mainDialog').style.backgroundColor = 'rgba(0,0,0,' + value + ')';
    document.getElementById('loraPreview').style.filter = `brightness(${1 - value})`;
    document.querySelectorAll('.FlipStreamPreviewBox').forEach(async x => {
        x.style.filter = `brightness(${1 - value})`;
    });
    document.querySelectorAll('.FlipStreamPasteBox').forEach(async x => {
        x.style.filter = `brightness(${1 - value})`;
    });
}
onInputDarker();

function reloadPage(search="") {
    if (search) {
        location.replace(location.pathname + "?" + search);
    } else {
        location.reload();
    }
}

function parseQueryParam() {
    const p = new URLSearchParams(location.search)
    if (p.has("px")) {
        document.getElementById("presetXorkeyInputDiv").style.display = "block";
    }
    if (p.has("darker")) {
        onInputDarker(p.get("darker"));
    }
    if (p.has("toggleView")) {
        toggleView();
    }
    if (p.has("showPresetDialog")) {
        showPresetDialog();
    }
    if (p.has("presetFolder")) {
        const presetFolder = document.getElementById("presetFolderSelect").value;
        if (presetFolder != p.get("presetFolder")) {
            updateParam(true, false, { presetFolder: p.get("presetFolder") }, {}, p.toString());
            return;
        }
    }
    if (p.has("presetFile")) {
        const presetFile = document.getElementById("presetFileSelect").value;
        if (presetFile != p.get("presetFile")) {
            loadPreset(false, { presetFile: p.get("presetFile") }, p.toString());
            return;
        }
    }
}
parseQueryParam();
"""


@server.PromptServer.instance.routes.get("/flipstreamviewer/run_once")
async def run_once(request):
    server.PromptServer.instance.send_sync("FlipStreamViewer_run_once", {})
    global refresh_updating
    refresh_updating = 0
    return web.Response()


@server.PromptServer.instance.routes.get("/flipstreamviewer/stream/info")
async def stream_count(request):
    if request.remote not in allowed_ips:
        raise HTTPForbidden()

    return web.json_response({"mtime": frame_mtime, "fps": frame_fps, "count": len(frame_buffer)})


@server.PromptServer.instance.routes.get("/flipstreamviewer/stream/{frame_id:\\d+}.png")
async def stream_frame(request):
    if request.remote not in allowed_ips:
        raise HTTPForbidden()

    try:
        frame_id = int(request.match_info["frame_id"])
    except (KeyError, ValueError):
        raise web.HTTPBadRequest(text="Invalid frame ID")

    if not (0 <= frame_id < len(frame_buffer)):
        raise web.HTTPNotFound(text="Frame not found")
    
    return web.Response(body=frame_buffer[frame_id], headers={"Content-Type": "image/png"})


@server.PromptServer.instance.routes.get("/flipstreamviewer/preview")
async def preview(request):
    if request.remote not in allowed_ips:
        raise HTTPForbidden()

    label = request.query.get('label')
    if not label:
        return web.Response(status=400, text="Label required")

    key = label + "PreviewBox"
    if key in state:
        data = state[key][1]
    else:
        data = b""
    return web.Response(body=data, headers={"Content-Type": "image/png"})


@server.PromptServer.instance.routes.post("/flipstreamviewer/preview_setroi")
async def preview_setroi(request):
    if request.remote not in allowed_ips:
        raise HTTPForbidden()
    
    data = await request.json()
    param[data['label'] + "PreviewRoi"] = data
    return web.Response()


@server.PromptServer.instance.routes.get("/flipstreamviewer/paste")
async def paste(request):
    if request.remote not in allowed_ips:
        raise HTTPForbidden()

    label = request.query.get('label')
    if not label:
        return web.Response(status=400, text="Label required")

    if label + "_thumbnail" in state:
        data = state[label + "_thumbnail"]
    else:
        data = b""
    return web.Response(body=data, headers={"Content-Type": "image/png"})


@server.PromptServer.instance.routes.post("/flipstreamviewer/paste_upload")
async def paste_upload(request):
    if request.remote not in allowed_ips:
        raise HTTPForbidden()

    label = request.query.get('label')
    if not label:
        return web.Response(status=400, text="Label required")

    data = await request.read()
    img = Image.open(io.BytesIO(data))
    image = torch.from_numpy(np.array(img)).float()[None, :] / 255.0
    if image.shape[3] == 4:
        image = image[:,:,:,:3] * image[:,:,:,3:4]
    state[label] = image
    state[label + "_thumbnail"] = data
    state[label + "_mtime"] = time.time()
    return web.Response()


@server.PromptServer.instance.routes.post("/flipstreamviewer/paste_remove")
async def paste_upload(request):
    if request.remote not in allowed_ips:
        raise HTTPForbidden()
    
    label = request.query.get('label')
    if not label:
        return web.Response(status=400, text="Label required")

    if label in state:
        del state[label]
        del state[label + "_thumbnail"]
        del state[label + "_mtime"]
    return web.Response()


@server.PromptServer.instance.routes.get("/flipstreamviewer")
async def viewer(request):
    if request.remote not in allowed_ips:
        print(request.remote)
        raise HTTPForbidden()

    block = {}

    def add_section(title, section, **_):
        block[f"{title}_{section}"] = f"""
          </details>
          <details>
            <summary><i>{section}</i></summary>"""

    def add_button(title, run, capture, **_):
        block[f"{title}"] = f"""
            <div class="row">"""
        if run:
            block[f"{title}"] += f"""
                <button class="willreload" onclick="updateParam(true, true)">Run</button>"""
        if capture:
            block[f"{title}"] += f"""
                <button onclick="capture()">Capture</button>"""
        block[f"{title}"] += f"""
            </div>"""

    def add_slider(title, label, default, min, max, step, **_):
        if not label.isidentifier():
            raise RuntimeError(f"{title}: label must contain only valid identifier characters.")
        param.setdefault(label, default)
        block[f"{title}_{label}"] = f"""
            <div class="row" style="color: lightslategray;">
                <input class="FlipStreamSlider" id="{label}Slider" type="range" min="{min}" max="{max}" step="{step}" name="{label}" value="{float(param[label]):g}" oninput="{label}Value.innerText = this.value;" />
                <span id="{label}Value">{float(param[label]):g}</span>{label}
            </div>"""

    def add_textbox(title, label, default, rows, **_):
        if not label.isidentifier():
            raise RuntimeError(f"{title}: label must contain only valid identifier characters.")
        param.setdefault(label, btoa_utf8(default))
        block[f"{title}_{label}"] = f"""
            <textarea class="FlipStreamTextBox" id="{label}TextBox" style="color: lightslategray;" placeholder="{label}" rows="{rows}" name="{label}">{atob_utf8(param[label])}</textarea>"""

    def add_inputbox(title, label, default, boxtype, **_):
        if not label.isidentifier():
            raise RuntimeError(f"{title}: label must contain only valid identifier characters.")
        param.setdefault(label, default)
        if boxtype == "seed":
            block[f"{title}_{label}"] = f"""
            <div class="row" style="color: lightslategray;">
                {label}: <input class="FlipStreamInputBox" id="{label}InputBox" style="color: lightslategray;" placeholder="{label}" type="number" name="{label}" value="{param[label]}" />
                <button onclick="{label}InputBox.value=Math.floor(Math.random()*1e7); updateParam(true, true)">R</button>
            </div>"""
        elif boxtype == "r4d":
            block[f"{title}_{label}"] = f"""
            <div class="row" style="color: lightslategray;">
                {label}: <input class="FlipStreamInputBox" id="{label}InputBox" style="color: lightslategray;" placeholder="{label}" type="number" name="{label}" value="{param[label]}" />
                <button onclick="{label}InputBox.value=Math.floor(Math.random()*1e4); updateParam(true, true)">R</button>
            </div>"""
        else:
            block[f"{title}_{label}"] = f"""
            <div class="row" style="color: lightslategray;">
                {label}: <input class="FlipStreamInputBox" id="{label}InputBox" style="color: lightslategray;" placeholder="{label}" type="{boxtype}" name="{label}" value="{param[label]}" />
                <button onclick="updateParam(true, true)">U</button>
            </div>"""

    def add_selectbox(title, label, listitems, **_):
        listitems = listitems.split(",")
        if not label.isidentifier():
            raise RuntimeError(f"{title}: label must contain only valid identifier characters.")
        if not all(item == html.escape(item) for item in listitems):
            raise RuntimeError(f"{title}: listitems must contain only HTML-acceptable characters.")
        param.setdefault(label, "")
        text_html = f"""
            <select class="FlipStreamSelectBox" id="{label}SelectBox" name="{label}">
                <option value="">{label}</option>"""
        for item in listitems:
            text_html += f"""
                <option value="{item}"{" selected" if param[label] == item else ""}>{item}</option>"""
        text_html += f"""
            </select>"""
        block[f"{title}_{label}"] = text_html

    def add_sizeselect(title, label, listitems, **_):
        listitems = listitems.split(",")
        if not label.isidentifier():
            raise RuntimeError(f"{title}: label must contain only valid identifier characters.")
        if not all(item == html.escape(item) for item in listitems):
            raise RuntimeError(f"{title}: listitems must contain only HTML-acceptable characters.")
        param.setdefault(label, "")
        text_html = f"""
            <select class="FlipStreamSizeSelect" id="{label}SizeSelect" name="{label}">
                <option value="">{label}</option>"""
        for item in listitems:
            text_html += f"""
                <option value="{item}"{" selected" if param[label] == item else ""}>{item}</option>"""
        text_html += f"""
            </select>"""
        block[f"{title}_{label}"] = text_html

    def add_fileselect(title, label, folder_name, folder_path, mode, use_sub, use_move, **_):
        if not label.isidentifier():
            raise RuntimeError(f"{title}: label must contain only valid identifier characters.")
        if not (mode == "" or mode.isidentifier()):
            raise RuntimeError(f"{title}: mode must contain only valid identifier characters.")
        param.setdefault(label, "")
        state.setdefault(f"{label}Folder", "")
        text_html = ""
        if use_sub:
            text_html += f"""
            <select class="FlipStreamFolderSelect willreload" id="{label}FolderSelect" name="{label}Folder" onchange="updateParam(true)">
                <option value="">{label} folder</option>"""
            for dir in sorted(Path(folder_path, mode).glob("*/")):
                selected = " selected" if state[f"{label}Folder"] == dir.name else ""
                text_html += f"""
                <option value="{dir.name}"{selected}>{dir.name}</option>"""
            text_html += f"""
            </select>"""
        text_html += f"""
            <div class="row">
                <select class="FlipStreamFileSelect" id="{label}FileSelect" name="{label}">
                    <option value="">{label}</option>"""
        if param[label]:
            text_html += f"""
                    <option value="{param[label]}" selected>*{Path(param[label]).stem}</option>"""
        if use_sub:
            files = sorted(Path(folder_path, mode, state[f"{label}Folder"]).glob("*.*"))
            files = [Path(file).relative_to(folder_path) for file in files]
        else:
            files = [Path(file) for file in FlipStreamFileSelect.get_filelist(folder_name, folder_path, mode)]
        
        png_files = [file for file in files if file.suffix.lower() == '.png']
        other_files = [file for file in files if file.suffix.lower() != '.png']
        
        for file in other_files:
            if file != param[label]:
                text_html += f"""
                    <option value="{file}">{file.stem}</option>"""
                png_file = file.with_suffix('.png')
                if png_file in png_files:
                    png_files.remove(png_file)
        for file in png_files:
            if file != param[label]:
                text_html += f"""
                    <option value="{file}">{file.stem}</option>"""
        text_html += f"""
                </select>"""
        if use_move:
            text_html += f"""
                <button class="willreload" id="{label}MoveButton" onClick="showMoveFileSelect({label}MoveFileSelect)">M</button>"""
        text_html += f"""
            </div>"""
        if use_move:
            text_html += f"""
            <select class="FlipStreamMoveFileSelect" id="{label}MoveFileSelect" onchange="moveFile(String.raw`{folder_path}`, '{mode}', '{label}')">
                <option value="" disabled selected>move to</option>"""
            for dir in Path(folder_path, mode).glob("*/"):
                text_html += f"""
                <option value="{dir.name}">{dir.name}</option>"""
            text_html += f"""
            </select>"""
        block[f"{title}_{label}"] = text_html

    def add_previewbox(title, label, **_):
        if not label.isidentifier():
            raise RuntimeError(f"{title}: label must contain only valid identifier characters.")
        if (label + "PreviewBox") in state:
            block[f"{title}_{label}"] = f"""
            <div class="row" style="color: lightslategray;">
                <img class="FlipStreamPreviewBox" id="{label}PreviewBox" name="{label}" src="/flipstreamviewer/preview?label={label}" alt onerror="this.onerror = null; this.src='';" />
                <canvas class="FlipStreamPreviewRoi" id="{label}PreviewRoi"></canvas>
            </div>"""
    
    def add_pastebox(title, label, **_):
        if not label.isidentifier():
            raise RuntimeError(f"{title}: label must contain only valid identifier characters.")
        block[f"{title}_{label}"] = f"""
            <div class="row" style="color: lightslategray;">
                <img class="FlipStreamPasteBox" id="{label}PasteBox" name="{label}" src="/flipstreamviewer/paste?label={label}" alt="{label}" />
            </div>"""

    def add_logbox(title, label, rows, **_):
        if not label.isidentifier():
            raise RuntimeError(f"{title}: label must contain only valid identifier characters.")
        if (label + "LogBox") in state:
            block[f"{title}_{label}"] = f"""
            <textarea class="FlipStreamLogBox" id="{label}LogBox" style="color: lightslategray;" placeholder="{label}" rows="{rows}" name="{label}" readonly></textarea>"""

    info = server.PromptServer.instance.prompt_queue.get_history(max_items=1, map_function=lambda p: p["prompt"][2])
    nodelist = next(iter(info.values())).values() if info else []
    if nodelist:
        for node in nodelist:
            class_type = node["class_type"]
            title = node["_meta"]["title"]
            inputs = node["inputs"]
            if class_type == "FlipStreamSection":
                add_section(title, **inputs)
            if class_type == "FlipStreamButton":
                add_button(title, **inputs)
            if class_type == "FlipStreamSlider":
                add_slider(title, **inputs)
            if class_type == "FlipStreamTextBox":
                add_textbox(title, **inputs)
            if class_type == "FlipStreamInputBox":
                add_inputbox(title, **inputs)
            if class_type.startswith("FlipStreamSelectBox"):
                add_selectbox(title, **inputs)
            if class_type.startswith("FlipStreamSizeSelect"):
                add_sizeselect(title, **inputs)
            if class_type.startswith("FlipStreamFileSelect"):
                add_fileselect(title, **inputs)
            if class_type == "FlipStreamPreviewBox":
                add_previewbox(title, **inputs)
            if class_type == "FlipStreamPasteBox":
                add_pastebox(title, **inputs)
            if class_type == "FlipStreamLogBox":
                add_logbox(title, **inputs)

    text_html = f"""<html>{HEAD}<body>
    <div id="mainDialog">
        <div id="leftPanel">
            <div class="row">
                <button class="willreload" onclick="updateParam(true, true)">Run</button>
            </div>
          <details>
            <summary><i>Input</i></summary>
            {"".join([x[1] for x in sorted(block.items())])}
          </details>
        </div>
        <div id="centerPanel" onclick="toggleView()">
            <div id="messageBox"></div>
        </div>
        <div id="rightPanel">
            <progress id="statusLastElapsed" max="120" value="{state["lastElapsed"]}" style="width: 100%;"></progress>
            <progress id="statusElapsed" max="120" value="0" style="width: 100%;"></progress>
          <details>
            <summary><i>Status</i></summary>
            <textarea id="statusInfo" style="color: lightslategray;" rows="5"></textarea>
            <div class="row">
                <input id="darkerRange" type="range" min="0" max="1" step="0.01" value="{state["darker"]}" oninput="onInputDarker();" />
                <span id="darkerValue">{state["darker"]}</span>drk
            </div>
          </details>
          <details>
            <summary><i>Preset</i></summary>
            <select id="presetFolderSelect" class="willreload" onchange="updateParam(true)">
                <option value="" selected>preset folder</option>
                {"".join([f'<option value="{dir.name}"{" selected" if state["presetFolder"] == dir.name else ""}>{dir.name}</option>' for dir in Path("preset").glob("*/")])}
            </select>
            <div class="row">
                <select id="presetFileSelect">
                    <option value="" disabled selected>preset</option>
                    {"".join([f'<option value="{file.name}"{" selected" if state["presetFile"] == file.name else ""}>{file.stem}</option>' for file in Path("preset", state["presetFolder"]).glob("*.json")])}
                </select>
                <button onclick="movePreset()">M</button>
            </div>
            <select id="movePresetSelect" onchange="movePresetFile()">
                <option value="" disabled selected>move to</option>
                {"".join([f'<option value="{dir.name}">{dir.name}</option>' for dir in Path("preset").glob("*/")])}
            </select>
            <div class="row">
                <input id="presetTitleInput" placeholder="preset title" value="{state["presetTitle"]}" />
                <button onclick="savePreset()">Save</button>
            </div>
            <div id="presetXorkeyInputDiv">
                <input id="presetXorkeyInput" type="password" placeholder="xorkey" />
            </div>
            <div class="row">
                <button onclick="showPresetDialog()">Choose</button>
                <button class="willreload" onclick="loadPreset()">Load</button>
                <button class="willreload" onclick="loadPreset(true)">LoraOnly</button>
            </div>
          </details>
          <details>
            <summary><i>Lora</i></summary>
            <select id="loraModeSelect" class="willreload" onchange="updateParam(true)">
                <option value="" selected>lora mode</option>
                {"".join([f'<option value="{dir.name}"{" selected" if state["loraMode"] == dir.name else ""}>{dir.name}</option>' for dir in Path("ComfyUI/models/loras").glob("*/")])}
            </select>
            <select id="loraFolderSelect" class="willreload" onchange="updateParam(true)">
                <option value="" selected>lora folder</option>
                {"".join([f'<option value="{dir.name}"{" selected" if state["loraFolder"] == dir.name else ""}>{dir.name}</option>' for dir in Path("ComfyUI/models/loras", state["loraMode"]).glob("*/")])}
            </select>
            <div class="row">
                <select id="loraFileSelect" onchange="selectLoraFile()">
                    <option value="" disabled selected>lora file</option>
                    {"".join([f'<option value="{file.name}"{" selected" if state["loraFile"] == file.name else ""}>{file.stem}</option>' for file in Path("ComfyUI/models/loras", state["loraMode"], state["loraFolder"]).glob("*.safetensors")])}
                </select>
                <button onclick="toggleLora()">T</button>
                <button onClick="moveLora()">M</button>
            </div>
            <select id="moveLoraSelect" onchange="moveLoraFile()">
                <option value="" disabled selected>move to</option>
                {"".join([f'<option value="{dir.name}">{dir.name}</option>' for dir in Path("ComfyUI/models/loras", state["loraMode"]).glob("*/")])}
            </select>
            <div class="row">
                <select id="loraTagSelect" onchange="toggleTag()">
                    <option value="">tags</option>
                    {"".join([f'<option value="{opt["value"]}"{" selected" if state["loraTag"] == opt["value"] else ""}>{opt["text"]}</option>' if opt["value"] else "" for opt in json.loads(state["loraTagOptions"])])}
                </select>
                <button onclick="toggleTag()">T</button>
                <button onclick="randomTag()">R</button>
            </div>
            Rate: <select id="loraRate">
                <option value="8" {"selected" if state["loraRate"] == "8" else ""}>8</option>
                <option value="7" {"selected" if state["loraRate"] == "7" else ""}>7</option>
                <option value="6" {"selected" if state["loraRate"] == "6" else ""}>6</option>
                <option value="5" {"selected" if state["loraRate"] == "5" else ""}>5</option>
                <option value="4" {"selected" if state["loraRate"] == "4" else ""}>4</option>
                <option value="3" {"selected" if state["loraRate"] == "3" else ""}>3</option>
                <option value="2" {"selected" if state["loraRate"] == "2" else ""}>2</option>
                <option value="1" {"selected" if state["loraRate"] == "1" else ""}>1</option>
                <option value="0.9" {"selected" if state["loraRate"] == "0.9" else ""}>0.9</option>
                <option value="0.8" {"selected" if state["loraRate"] == "0.8" else ""}>0.8</option>
                <option value="0.7" {"selected" if state["loraRate"] == "0.7" else ""}>0.7</option>
                <option value="0.6" {"selected" if state["loraRate"] == "0.6" else ""}>0.6</option>
                <option value="0.5" {"selected" if state["loraRate"] == "0.5" else ""}>0.5</option>
                <option value="0.4" {"selected" if state["loraRate"] == "0.4" else ""}>0.4</option>
                <option value="0.3" {"selected" if state["loraRate"] == "0.3" else ""}>0.3</option>
                <option value="0.2" {"selected" if state["loraRate"] == "0.2" else ""}>0.2</option>
                <option value="0.1" {"selected" if state["loraRate"] == "0.1" else ""}>0.1</option>
                <option value="0" {"selected" if state["loraRate"] == "0" else ""}>0</option>
                <option value="-1" {"selected" if state["loraRate"] == "-1" else ""}>-1</option>
                <option value="-2" {"selected" if state["loraRate"] == "-2" else ""}>-2</option>
                <option value="-3" {"selected" if state["loraRate"] == "-3" else ""}>-3</option>
                <option value="-4" {"selected" if state["loraRate"] == "-4" else ""}>-4</option>
                <option value="-5" {"selected" if state["loraRate"] == "-5" else ""}>-5</option>
            </select>
            <select id="loraRank">
                <option value="0" {"selected" if state["loraRank"] == "0" else ""}>0</option>
                <option value="10" {"selected" if state["loraRank"] == "10" else ""}>10</option>
                <option value="20" {"selected" if state["loraRank"] == "20" else ""}>20</option>
                <option value="30" {"selected" if state["loraRank"] == "30" else ""}>30</option>
                <option value="40" {"selected" if state["loraRank"] == "40" else ""}>40</option>
                <option value="50" {"selected" if state["loraRank"] == "50" else ""}>50</option>
            </select>
            <div class="row">
                <button onclick="showTagDialog()">Choose</button>
                <button onclick="updateParam(true, true)">Run</button>
                <button onclick="clearLoraInput()">Clr</button>
                <button onclick="showTagDialog();tagCSel();tagRSel();tagOK();updateParam(true, true)">R</button>
            </div>
            <textarea id="loraInput" placeholder="Enter lora" rows="12">{atob_utf8(param["lora"])}</textarea>
            <div class="row">
                <a id="loraLink" href="{state["loraLinkHref"] or "javascript:void(0)"}" target="_blank">
                    <img id="loraPreview" src="{state["loraPreviewSrc"]}" alt onerror="this.onerror = null; this.src='';" />
                </a>
            </div>
          </details>
        </div>
    </div>
    <div id="captureDialog">
        <div id="captureLeftPanel">
        </div>
        <div id="captureCenterPanel">
            <canvas id="canvas" width="960" height="600" />
        </div>
        <div id="captureRightPanel">
            <div class="row">
                <button onclick="resetPos()">Reset</button>
            </div>
            <div class="row">
                X <input id="offsetXRange" type="range" min="-960" max="960" value="{param["_capture_offsetX"]}" onchange="updateCanvas()">
            </div>
            <div class="row">
                Y <input id="offsetYRange" type="range" min="-600" max="600" value="{param["_capture_offsetY"]}" onchange="updateCanvas()">
            </div>
            <div class="row">
                S <input id="scaleRange" type="range" min="50" max="400" value="{param["_capture_scale"]}" onchange="updateCanvas()">
            </div>
            <div class="row">
                <button onclick="setFrame()">SetFrame</button>
                <button onclick="closeCaptureDialog()">Close</button>
            </div>
        </div>
    </div>
    <div id="tagDialog">
        <div id="tagLeftPanel">
        </div>
        <div id="tagCenterPanel">
        </div>
        <div id="tagRightPanel">
            <div class="row">
                <button onclick="tagCSel()">CSel</button>
                <button onclick="tagRSel()">RSel</button>
            </div>
            <div class="row">
                <button onclick="tagOK()">OK</button>
                <button onclick="closeTagDialog()">Cancel</button>
            </div>
        </div>
    </div>
    <div id="presetDialog">
        <div id="presetLeftPanel">
        </div>
        <div id="presetCenterPanel" onclick="toggleView()">
            <div id="messageBox"></div>
        </div>
        <div id="presetRightPanel">
            <div class="row">
                <button onclick="closePresetDialog()">Close</button>
            </div>
            <div class="row"><i>Status</i></div>
            <div id="statusInfo"></div>
            <div class="row"><i>Darker</i></div>
            <div class="row">
                <input id="darkerRange" type="range" min="0" max="1" step="0.01" value="{state["darker"]}" oninput="onInputDarker(this.value);" />
                <span id="darkerValue">{state["darker"]}</span>drk
            </div>
            <div class="row"><i>Preset</i></div>
            {"".join([f'''
            <button onclick="loadPreset(false, {{ presetFile: '{file.name}' }}, 'showPresetDialog')">{file.stem}</button>
            ''' for file in Path("preset", state["presetFolder"]).glob("*.json")])}
        </div>
    </div>
    <script>
    {SCRIPT_PARAM}
    {SCRIPT_PRESET}
    {SCRIPT_CKPT}
    {SCRIPT_LORA}
    {SCRIPT_TAG}
    {SCRIPT_VIEW}
    {SCRIPT_CAPTURE}
    {SCRIPT_QUERY}
    </script>
    </body></html>"""
    return web.Response(text=text_html, content_type="text/html")


@server.PromptServer.instance.routes.get("/flipstreamviewer/refresh_view")
async def get_status(request):
    if request.remote not in allowed_ips:
        raise HTTPForbidden()

    status_elapsed = 0
    status_info = []
    
    remain = server.PromptServer.instance.prompt_queue.get_tasks_remaining()
    global refresh_updating
    if remain == 0:
        refresh_updating = 0
    elif refresh_updating == 0:
        refresh_updating = time.time()

    if refresh_updating:
        status_elapsed = int(time.time() - refresh_updating)
        status_info.append(f"{status_elapsed}s")

    if "current" in state:
        status_info.append(state["current"]) 

    status_info.append(f"q{remain}")

    info = server.PromptServer.instance.prompt_queue.get_history(max_items=1, map_function=lambda p: p["status"])
    status = next(iter(info.values())) if info else []
    if status:
        status_info.append(status["status_str"])
        errinfo = status["messages"][2][1]
        status_info += [errinfo[key] for key in ["node_id", "node_type", "exception_message", "exception_type"] if key in errinfo]

    data = refresh_data.copy()
    data["param"] = refresh_param.copy()
    param.update(refresh_param)
    refresh_param.clear()
    data["status_elapsed"] = status_elapsed
    data["status_info"] = status_info
    data["preview_mtime"] = {key: state[key][0] for key in state if key.endswith("PreviewBox")}
    data["mtime"] = {key: state[key] for key in state if key.endswith("_mtime")}
    data["log"] = {key: state[key] for key in state if key.endswith("LogBox")}
    return web.json_response(data)


@server.PromptServer.instance.routes.post("/flipstreamviewer/update_param")
async def update_param(request):
    if request.remote not in allowed_ips:
        raise HTTPForbidden()

    stt, prm = await request.json()
    state.update(stt)
    param.update(prm)
    time.sleep(UPDATE_DELAY)
    return web.Response()


@server.PromptServer.instance.routes.post("/flipstreamviewer/get_lorainfo")
async def get_lorainfo(request):
    if request.remote not in allowed_ips:
        raise HTTPForbidden()

    req = await request.json()
    loraPath = Path("ComfyUI/models/loras", state["loraMode"], req["loraFolder"], req["loraFile"])
    hash = None
    with open(loraPath, "rb") as file:
        header_size = int.from_bytes(file.read(8), "little", signed=False)
        if header_size <= 0:
            raise HTTPError("get_lorainfo: Invalid header size")

        header = file.read(header_size)
        if header_size <= 0:
            raise HTTPError("get_lorainfo: Invalid header")
        
        file.seek(0)
        hash = hashlib.sha256(file.read()).hexdigest()

    header_json = json.loads(header)
    lorainfo = header_json["__metadata__"] if "__metadata__" in header_json else {}

    if hash:
        res = requests.get("https://civitai.com/api/v1/model-versions/by-hash/" + hash).json()
        lorainfo["_lorapreview_href"] = "https://civitai.com/models/" + str(res["modelId"])
        lorainfo["_lorapreview_src"] = res["images"][0]["url"]

    return web.json_response(lorainfo)


@server.PromptServer.instance.routes.post("/flipstreamviewer/set_frame")
async def set_frame(request):
    if request.remote not in allowed_ips:
        raise HTTPForbidden()

    frame = base64.b64decode((await request.text()).split(',', 1)[1])
    global frame_buffer
    global frame_mtime
    global setframe_mtime
    global setframe_buffer
    frame_buffer = [frame]
    frame_mtime = time.time()
    setframe_buffer = [frame]
    setframe_mtime = frame_mtime
    return web.Response()


@server.PromptServer.instance.routes.post("/flipstreamviewer/move_file")
async def move_file(request):
    if request.remote not in allowed_ips:
        raise HTTPForbidden()

    stt, folder_path, mode, label, moveFrom, moveTo = await request.json()
    state.update(stt)
    pathFrom = Path(folder_path, mode, state[label + "Folder"], Path(moveFrom).name)
    pathTo = Path(folder_path, mode, moveTo, pathFrom.name)
    Path(pathFrom).rename(pathTo)
    state[label + "Folder"] = moveTo
    return web.Response()


@server.PromptServer.instance.routes.post("/flipstreamviewer/move_presetfile")
async def move_presetfile(request):
    if request.remote not in allowed_ips:
        raise HTTPForbidden()

    stt, moveTo = await request.json()
    state.update(stt)
    pathFrom = Path("preset", state["presetFolder"], state["presetFile"])
    pathTo = Path("preset", moveTo, pathFrom.name)
    Path(pathFrom).rename(pathTo)
    state["presetFolder"] = moveTo
    return web.Response()


@server.PromptServer.instance.routes.post("/flipstreamviewer/move_lorafile")
async def move_lorafile(request):
    if request.remote not in allowed_ips:
        raise HTTPForbidden()

    stt, moveTo = await request.json()
    state.update(stt)
    pathFrom = Path("ComfyUI/models/loras", state["loraMode"], state["loraFolder"], state["loraFile"])
    pathTo = Path("ComfyUI/models/loras", state["loraMode"], moveTo, pathFrom.name)
    Path(pathFrom).rename(pathTo)
    state["loraFolder"] = moveTo
    return web.Response()


def xor_crypt(data_str, key_str, crypt=True):
    xorall = lambda data, key: bytes([b1 ^ b2 for b1, b2 in zip(data, itertools.cycle(key))])
    key_bytes = key_str.encode('utf-8')
    input_bytes = data_str.encode('utf-8')
    if crypt:
        encrypted_bytes = xorall(input_bytes, key_bytes)
        return base64.b64encode(encrypted_bytes).decode('utf-8')
    else:
        decoded_bytes = base64.b64decode(input_bytes)
        return xorall(decoded_bytes, key_bytes).decode('utf-8')


@server.PromptServer.instance.routes.post("/flipstreamviewer/load_preset")
async def load_preset(request):
    if request.remote not in allowed_ips:
        raise HTTPForbidden()

    stt, loraPromptOnly, xorkey = await request.json()
    filename = stt["presetFile"]
    path = Path("preset", stt["presetFolder"], filename)
    with open(path, "r") as file:
        if xorkey:
            buf = json.loads(xor_crypt(file.read(), xorkey + filename, False))
        else:
            buf = json.load(file)
        if loraPromptOnly:
            buf = {"lora": buf.get("lora", "")}

    state.update(stt)
    param.clear()
    param.update(default_param)
    param.update(buf)
    state["presetTitle"] = Path(state["presetFile"]).stem
    time.sleep(UPDATE_DELAY)
    return web.json_response({"lora": param["lora"], "presetTitle": state["presetTitle"]})


@server.PromptServer.instance.routes.post("/flipstreamviewer/save_preset")
async def save_preset(request):
    if request.remote not in allowed_ips:
        raise HTTPForbidden()

    stt, prm, xorkey = await request.json()
    state.update(stt)
    param.update(prm)
    time.sleep(UPDATE_DELAY)

    filename = state["presetTitle"] + ".json"
    path = Path("preset", state["presetFolder"], filename)
    with open(path, "w") as file:
        if xorkey:
            file.write(xor_crypt(json.dumps(param), xorkey + filename))
        else:
            json.dump(param, file)
    return web.Response()


class FlipStreamSection:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "section": ("STRING", {"default": "Section"}),
            },
            "optional": {
                "hook": (anytype,),
            }
        }

    RETURN_TYPES = (anytype,)
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, hook=True, **_):
        return (hook,)


class FlipStreamButton:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "run": ("BOOLEAN", {"default": True}),
                "capture": ("BOOLEAN", {"default": True}),
            },
            "optional": {
                "hook": (anytype,),
            }
        }

    RETURN_TYPES = (anytype,)
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, hook=True, **_):
        return (hook,)


class FlipStreamSlider:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "label": ("STRING", {"default": "empty"}),
                "default": ("FLOAT", {"default": 0, "step": 0.01, "round": 0.01}),
                "min": ("FLOAT", {"default": 0, "step": 0.01, "round": 0.01}),
                "max": ("FLOAT", {"default": 0, "step": 0.01, "round": 0.01}),
                "step": ("FLOAT", {"default": 0.01, "min": 0.00, "step": 0.01, "round": 0.01}),
            }
        }

    RETURN_TYPES = ("FLOAT", "INT", "BOOLEAN")
    RETURN_NAMES = ("float", "int", "enable")
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    @classmethod
    def IS_CHANGED(cls, label, default, **_):
        param.setdefault(label, default)
        return hash(param[label])
    
    def run(self, label, default, **_):
        param.setdefault(label, default)
        return (float(param[label]), int(float(param[label])), bool(float(param[label])))


class FlipStreamTextBox:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "label": ("STRING", {"default": "empty"}),
                "default": ("STRING", {"default": "", "multiline": True}),
                "rows": ("INT", {"default": 3}),
            }
        }

    RETURN_TYPES = ("STRING", "BOOLEAN")
    RETURN_NAMES = ("text", "enable")
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    @classmethod
    def IS_CHANGED(cls, label, default, **_):
        param.setdefault(label, btoa_utf8(default))
        return hash(param[label])
   
    def run(self, label, default, **_):
        param.setdefault(label, btoa_utf8(default))
        text = atob_utf8(param[label])
        return (text, bool(text))


class FlipStreamInputBox:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "label": ("STRING", {"default": "empty"}),
                "default": ("STRING", {"default": ""}),
                "boxtype": (["text", "number", "seed", "r4d"],),
            }
        }

    RETURN_TYPES = ("STRING", "FLOAT", "INT", "BOOLEAN")
    RETURN_NAMES = ("text", "float", "int", "enable")
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    @classmethod
    def IS_CHANGED(cls, label, default, **_):
        return hash(param.get(label))
    
    def run(self, label, default, boxtype, **_):
        param.setdefault(label, default)
        text = param[label]
        enable = bool(text)
        try:
            num = float(text)
            enable = bool(num)
        except:
            num = 0
        return (text, num, int(num), enable)


class FlipStreamSelectBox:
    LISTITEMS = ["a", "b", "c"]

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "label": ("STRING", {"default": "empty"}),
                "default": (s.LISTITEMS,),
                "listitems": ("STRING", {"default": ",".join(s.LISTITEMS)})
            },
        }

    RETURN_TYPES = (anytype, "BOOLEAN",)
    RETURN_NAMES = ("item", "enable",)
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    @classmethod
    def IS_CHANGED(cls, label, **_):
        param.setdefault(label, "")
        return hash(param[label])

    def run(self, label, default, **_):
        param.setdefault(label, "")
        item = param[label] if param[label] else default
        return (item, param[label] != "")


class FlipStreamSelectBox_Samplers(FlipStreamSelectBox):
    LISTITEMS = comfy.samplers.KSampler.SAMPLERS


class FlipStreamSelectBox_Scheduler(FlipStreamSelectBox):
    LISTITEMS = comfy.samplers.KSampler.SCHEDULERS


class FlipStreamSizeSelect:
    SIZEDICT = {
        "sdxl": {
            "1:1": (1024, 1024),
            "16:9": (1344, 768),
            "9:16": (768, 1344),
            "4:3": (1152, 896),
            "3:4": (896, 1152),
            "3:2": (1216, 832),
            "2:3": (832, 1216),
        },
        "qwen": {
            "1:1": (1328, 1328),
            "16:9": (1664, 928),
            "9:16": (928, 1664),
            "4:3": (1472, 1104),
            "3:4": (1104, 1472),
            "3:2": (1584, 1056),
            "2:3": (1056, 1584),
        },
        "wan": {
            "1:1": (512, 512),
            "16:9": (832, 480),
            "9:16": (480, 832),
            "4:3": (832, 480),
            "3:4": (480, 832),
            "3:2": (832, 480),
            "2:3": (480, 832),
        }
    }

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "label": ("STRING", {"default": "empty"}),
                "default": (list(s.SIZEDICT["sdxl"].keys()),),
                "modeltype": (["sdxl", "qwen", "wan"],),
                "listitems": ("STRING", {"default": ",".join(s.SIZEDICT["sdxl"].keys())})
            },
        }

    RETURN_TYPES = ("INT", "INT", "BOOLEAN")
    RETURN_NAMES = ("width", "height", "enable")
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    @classmethod
    def IS_CHANGED(cls, label, default, **_):
        param.setdefault(label, default)
        return hash(param[label])

    def run(self, label, default, modeltype, **_):
        param.setdefault(label, default)
        width, height = self.SIZEDICT[modeltype][param.get(label)]
        return (width, height, param[label] != "")


class FlipStreamGetSize(FlipStreamSizeSelect):
    pass


class FlipStreamFileSelect:
    FOLDER_NAME = ""
    FOLDER_PATH = ""

    @staticmethod
    def get_filelist(folder_name, folder_path, mode):
        if folder_name == "checkpoints" and not mode:
            return CheckpointLoaderSimple.INPUT_TYPES()["required"]["ckpt_name"][0]
        elif folder_name == "vae":
            return VAELoader.INPUT_TYPES()["required"]["vae_name"][0]
        elif folder_name == "controlnet":
            return folder_paths.get_filename_list("controlnet")
        elif folder_name == "tensorrt":
            if folder_path.startswith("_error_"):
                return [folder_path]
            if TensorRTLoader is None:
                return ["_error_ ComfyUI_TensorRT is not installed"]
            return TensorRTLoader.INPUT_TYPES()["required"]["unet_name"][0]
        elif folder_name == "animatediff_models":
            if folder_path.startswith("_error_"):
                return [folder_path]
            return folder_paths.get_filename_list(folder_name)
        else:
            return [str(p.relative_to(folder_path)) for p in Path(folder_path, mode).glob("*.*")]

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "label": ("STRING", {"default": "empty"}),
                "default": ([""] + s.get_filelist(s.FOLDER_NAME, s.FOLDER_PATH, ""),),
                "folder_name": ([s.FOLDER_NAME],),
                "folder_path": ([s.FOLDER_PATH],),
                "mode": ("STRING", {"default": ""}),
                "use_sub": ("BOOLEAN", {"defalut": False}),
                "use_move": ("BOOLEAN", {"defalut": False}),
            }
        }

    RETURN_TYPES = (anytype, anytype, "BOOLEAN",)
    RETURN_NAMES = ("file", "path", "enable",)
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    @classmethod
    def IS_CHANGED(cls, label, **_):
        param.setdefault(label, "")
        return hash(param[label])

    def run(self, label, default, folder_path, **_):
        param.setdefault(label, "")
        file = param[label] if param[label] else default
        return (file, str(Path(folder_path, file)), param[label] != "")


class FlipStreamFileSelect_Checkpoints(FlipStreamFileSelect):
    FOLDER_NAME = "checkpoints"
    FOLDER_PATH = Path(folder_paths.get_folder_paths(FOLDER_NAME)[0]).relative_to(Path.cwd()).as_posix()


class FlipStreamFileSelect_Loras(FlipStreamFileSelect):
    FOLDER_NAME = "loras"
    FOLDER_PATH = Path(folder_paths.get_folder_paths(FOLDER_NAME)[0]).relative_to(Path.cwd()).as_posix()


class FlipStreamFileSelect_VAE(FlipStreamFileSelect):
    FOLDER_NAME = "vae"
    FOLDER_PATH = Path(folder_paths.get_folder_paths(FOLDER_NAME)[0]).relative_to(Path.cwd()).as_posix()


class FlipStreamFileSelect_LLM(FlipStreamFileSelect):
    FOLDER_NAME = "LLM"
    FOLDER_PATH = Path(folder_paths.models_dir, FOLDER_NAME).relative_to(Path.cwd()).as_posix()


class FlipStreamFileSelect_ControlNetModel(FlipStreamFileSelect):
    FOLDER_NAME = "controlnet"
    FOLDER_PATH = Path(folder_paths.get_folder_paths(FOLDER_NAME)[0]).relative_to(Path.cwd()).as_posix()


class FlipStreamFileSelect_TensorRT(FlipStreamFileSelect):
    FOLDER_NAME = "tensorrt"
    try:
        FOLDER_PATH = Path(folder_paths.get_folder_paths(FOLDER_NAME)[0]).relative_to(Path.cwd()).as_posix()
    except:
        FOLDER_PATH = "_error_ tensorrt folder is not found"


class FlipStreamFileSelect_AnimateDiffModel(FlipStreamFileSelect):
    FOLDER_NAME = "animatediff_models"
    try:
        FOLDER_PATH = Path(folder_paths.get_folder_paths(FOLDER_NAME)[0]).relative_to(Path.cwd()).as_posix()
    except:
        FOLDER_PATH = "_error_ animatediff_models folder is not found"


class FlipStreamFileSelect_Input(FlipStreamFileSelect):
    FOLDER_NAME = "input"
    FOLDER_PATH = Path(folder_paths.input_directory).relative_to(Path.cwd()).as_posix()


class FlipStreamFileSelect_Output(FlipStreamFileSelect):
    FOLDER_NAME = "output"
    FOLDER_PATH = Path(folder_paths.output_directory).relative_to(Path.cwd()).as_posix()


class FlipStreamPreviewBox:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "label": ("STRING", {"default": "empty"}),
                "tensor": ("IMAGE",),
            }
        }

    RETURN_TYPES = ()
    OUTPUT_NODE = True
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, label, tensor, **_):
        if tensor is None:
            tensor = torch.zeros((1, 8, 8, 3))
        buf = np.array(tensor[0].cpu().numpy() * 255, dtype=np.uint8)
        image = Image.fromarray(buf)
        image.thumbnail((256, 256))
        with io.BytesIO() as output:
            image.save(output, format="PNG", compress_level=STREAM_COMPRESSION)
            state[label + "PreviewBox"] = (time.time(), output.getvalue())
        return ()


class FlipStreamPasteBox:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "label": ("STRING", {"default": "empty"}),
            }
        }

    RETURN_TYPES = ("IMAGE", "BOOLEAN")
    RETURN_NAMES = ("image", "enable")
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    @classmethod
    def IS_CHANGED(cls, label):
        return hash(state.get(label + "_mtime"))
    
    def run(self, label, **_):
        if label not in state:
            return (None, False)
        return (state[label], True)
    

class FlipStreamLogBox:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "label": ("STRING", {"default": "empty"}),
                "log": ("STRING",),
                "rows": ("INT", {"default": 3}),
            }
        }

    RETURN_TYPES = ()
    OUTPUT_NODE = True
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, label, log, **_):
        state[label + "LogBox"] = btoa_utf8(log)
        return ()


class FlipStreamRunOnce:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
            },
            "optional": {
                "hook": (anytype,),
            }
        }

    RETURN_TYPES = (anytype,)
    OUTPUT_NODE = True
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, hook=True, **_):
        server.PromptServer.instance.send_sync("FlipStreamViewer_run_once", {})
        global refresh_updating
        refresh_updating = 0
        return (hook,)


class FlipStreamSetMessage:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "message": ("STRING", {"default": "", "multiline": True}),
                "fontsize": ("FLOAT", {"default": 1.0, "min": 0.5, "max": 2.0, "step": 0.1}),
            },
            "optional": {
                "hook": (anytype,),
            }
        }

    RETURN_TYPES = (anytype,)
    OUTPUT_NODE = True
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, message, fontsize, hook=True, **_):
        refresh_data["message"] = btoa_utf8(message)
        refresh_data["message_fontsize"] = f"{fontsize}rem"
        return (hook,)


class FlipStreamAnd:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {},
            "optional": {f"v{i}": (anytype,) for i in range(10)}
        }

    RETURN_TYPES = (anytype,)
    OUTPUT_NODE = True
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, **kwargs):
        res = None
        for res in kwargs.values():
            if not res: break
        return (res,)


class FlipStreamOr:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {},
            "optional": {f"v{i}": (anytype,) for i in range(10)}
        }

    RETURN_TYPES = (anytype,)
    OUTPUT_NODE = True
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, **kwargs):
        res = None
        for res in kwargs.values():
            if res: break
        return (res,)


class FlipStreamSetState:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "label": ("STRING", {"default": "empty"}),
                "replace": ("BOOLEAN", {"default": False}),
                "thumbnail": ("BOOLEAN", {"default": False}),
            },
            "optional": {
                "hook": (anytype,),
                "value": (anytype,),
            }
        }

    RETURN_TYPES = (anytype,)
    OUTPUT_NODE = True
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, label, replace, thumbnail, hook=True, value=None, **_):
        if replace or label not in state:
            if thumbnail and value is not None:
                buf = np.array(value[0].cpu().numpy() * 255, dtype=np.uint8)
                image = Image.fromarray(buf)
                image.thumbnail((256, 256))
                with io.BytesIO() as output:
                    image.save(output, format="PNG", compress_level=STREAM_COMPRESSION)
                    state[label + "_thumbnail"] = output.getvalue()
                    state[label + "_mtime"] = time.time()
            else:
                if label + "_thumbnail" in state:
                    del state[label + "_thumbnail"]
                    del state[label + "_mtime"]
            state[label] = value
        return (hook,)


class FlipStreamGetState:
    CACHED_LASTGET = {}

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "label": ("STRING", {"default": "empty"}),
            },
            "optional": {
                "hook": (anytype,),
            },
            "hidden": {
                "unique_id": "UNIQUE_ID"
            }
        }

    RETURN_TYPES = (anytype, "BOOLEAN", "BOOLEAN")
    RETURN_NAMES = ("value", "enable", "changed")
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    @classmethod
    def IS_CHANGED(cls, label, **_):
        value = state.get(label)
        return hash(value[:1024] if hasattr(value, '__getitem__') else value)

    def run(self, label, unique_id, **_):
        enable = label in param
        value = state.get(label)

        changed = False
        h = hash(str(value[:1024]) if hasattr(value, '__getitem__') else value)
        cache = FlipStreamGetState.CACHED_LASTGET.setdefault(unique_id, {})
        if cache.get(label, h) != h:
            changed = True
        cache[label] = h
        return (value, enable, changed)


class FlipStreamSetParam:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "label": ("STRING", {"default": "empty"}),
                "value": ("STRING", {"default": "", "multiline": True}),
                "replace": ("BOOLEAN", {"default": False}),
                "b64enc": ("BOOLEAN", {"default": False}),
            },
            "optional": {
                "hook": (anytype,),
            }
        }

    RETURN_TYPES = (anytype,)
    OUTPUT_NODE = True
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, label, value, replace, b64enc, hook=True, **_):
        empty = ""
        if b64enc:
            value = btoa_utf8(value)
            empty = btoa_utf8(empty)
        if replace or label not in param or param[label] == empty:
            param[label] = value
            refresh_param[label] = value
        return (hook,)


class FlipStreamGetParam:
    CACHED_LASTGET = {}

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "label": ("STRING", {"default": "empty"}),
                "default": ("STRING", {"default": ""}),
                "b64dec": ("BOOLEAN", {"default": False}),
            },
            "optional": {
                "hook": (anytype,),
            },
            "hidden": {
                "unique_id": "UNIQUE_ID"
            }
        }

    RETURN_TYPES = (anytype, "FLOAT", "INT", "BOOLEAN", "BOOLEAN")
    RETURN_NAMES = ("text", "float", "int", "enable", "changed")
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    @classmethod
    def IS_CHANGED(cls, label, **_):
        return hash(param.get(label))

    def __init__(self):
        self.lastget = {}
    
    def run(self, label, default, b64dec, unique_id, **_):
        enable = label in param
        text = param.get(label, default)
        if enable and b64dec:
            text = atob_utf8(text)
        enable = bool(text)
        try:
            num = float(text)
            enable = bool(num)
        except:
            num = 0

        changed = False
        h = hash(text)
        cache = FlipStreamGetParam.CACHED_LASTGET.setdefault(unique_id, {})
        if cache.get(label, h) != h:
            changed = True
        cache[label] = h
        return (text, num, int(num), enable, changed)


class FlipStreamGet:
    CACHED_LASTGET = {}

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {},
            "optional": {
                "hook": (anytype,),
                **{f"label{i}": ("STRING", {"default": ""}) for i in range(20)}
            },
            "hidden": {
                "unique_id": "UNIQUE_ID"
            }
        }

    RETURN_TYPES = (anytype, "BOOLEAN") + (anytype,) * 20
    RETURN_NAMES = ("hook", "changed") + tuple(f"value{i}" for i in range(20))
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def get_value(self, label, unique_id):
        def auto(v):
            try:
                return atob_utf8(v)
            except:
                return v

        convert_map = {
            "": auto,
            "text": str,
            "bool": bool,
            "int": int,
            "float": float,
            "b64dec": atob_utf8,
        }

        convert = ""
        source = param
        label, _, convert = label.partition("->")
        label, sep, default = label.partition("|")
        if not sep: default = None
        if label.startswith("state:"):
            _, _, label = label.partition(":")
            source = state

        value = None
        changed = False
        if label:
            value = convert_map[convert](source.get(label, default))
            cache = FlipStreamGet.CACHED_LASTGET.setdefault(unique_id, {})
            h = hash(str(value[:1024]) if hasattr(value, '__getitem__') else value)
            if cache.get(label, h) != h:
                changed = True
            cache[label] = h
            
        return value, changed

    def run(self, unique_id, hook=True, **kwargs):
        vlist = []
        changed = False
        for k in kwargs:
            if k.startswith("label"):
                val, c = self.get_value(kwargs[k], unique_id)
                vlist.append(val)
                changed |= c
        return (hook, changed, *vlist)


class FlipStreamGetPreviewRoi:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "label": ("STRING", {"default": "empty"}),
                "width": ("INT", {"default": 512, "min": 256, "max": 2048, "step": 32}),
                "height": ("INT", {"default": 512, "min": 256, "max": 2048, "step": 32}),
                "default_left": ("INT", {"default": 0}),
                "default_top": ("INT", {"default": 0}),
                "default_right": ("INT", {"default": 0}),
                "default_bottom": ("INT", {"default": 0}),
            },
        }

    RETURN_TYPES = ("INT", "INT", "INT", "INT", "INT", "INT")
    RETURN_NAMES = ("left", "top", "right", "bottom", "inner_width", "inner_height")
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    @classmethod
    def IS_CHANGED(cls, label, **_):
        roi_data = frozenset(param.get(label + "PreviewRoi", {}).items())
        return hash(roi_data)

    def run(self, label, default_left, default_top, default_right, default_bottom, width, height):
        roi_data = param.get(label + "PreviewRoi", {})
        left = int(roi_data['sx'] * width) if 'sx' in roi_data else default_left
        top = int(roi_data['sy'] * height) if 'sy' in roi_data else default_top
        right = int(width - roi_data['ex'] * width) if 'ex' in roi_data else default_right
        bottom = int(height - roi_data['ey'] * height) if 'ey' in roi_data else default_bottom
        inner_width = width - left - right
        inner_height = height - top - bottom
        return (left, top, right, bottom, inner_width, inner_height)


class FlipStreamImageSize:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "tensor": ("IMAGE",),
            }
        }

    RETURN_TYPES = ("IMAGE","INT","INT","INT")
    RETURN_NAMES = ("tensor","width","height","batchsize")
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, tensor):
        (batchsize, height, width) = tensor.shape[0:3]
        return (tensor, width, height, batchsize)


class FlipStreamTextReplace:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "text": ("STRING", {"default": "", "multiline": True}),
                "find": ("STRING", {"default": ""}),
                "replace": ("STRING", {"default": ""}),
            },
            "optional": {
                "value": (anytype,),
            }
        }

    RETURN_TYPES = ("STRING",)
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, text, find, replace, value=None):
        for word in find.split(","):
            word = word.strip()
            if not word:
                continue
            text = text.replace(word, replace.format(value))
        return (text,)


class FlipStreamTextConcat:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "joinstr": ("STRING", {"default": "", "multiline": True}),
                "enable": ("BOOLEAN", {"default": True})
            },
            "optional": {
                **{f"text{i}": ("STRING", {"default": ""}) for i in range(20)}
            }
        }

    RETURN_TYPES = ("STRING",)
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, joinstr, enable, **kwargs):
        if not enable:
            return ("",)
        
        text = []
        for k in kwargs:
            if k.startswith("text"):
                val = kwargs[k]
                text.append(val)

        return (str(joinstr).join(map(str, filter(None, text))),)


class FlipStreamGetFrame:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "index": ("INT", {"default": 0, "min": 0}),
                "frames": ("INT", {"default": 1, "min": 1}),
                "capture_only": ("BOOLEAN", {"default": True}),
                "enable": ("BOOLEAN", {"default": True}),
            }
        }

    RETURN_TYPES = ("IMAGE", "BOOLEAN")
    RETURN_NAMES = ("image", "enable")
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    @classmethod
    def IS_CHANGED(cls, capture_only, **_):
        if capture_only:
            return setframe_mtime
        else:
            return frame_mtime
    
    def run(self, index, frames, capture_only, enable, **_):
        if not enable:
            return (None, False)            
        buf = setframe_buffer if capture_only else frame_buffer
        images_list = [
            np.array(Image.open(io.BytesIO(buf[i]))) 
            for i in range(index, min(index + frames, len(buf)))
        ]        
        if not images_list:
            return (None, False)
        image = torch.from_numpy(np.stack(images_list)).float() / 255.0
        if image is not None and image.shape[3] == 4:
            image = image[:,:,:,:3] * image[:,:,:,3:4]
        return (image, True)


class FlipStreamScreenGrabber:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "left": ("INT", {"default": 0, "min": 0, "step": 32}),
                "top": ("INT", {"default": 0, "min": 0, "step": 32}),
                "width": ("INT", {"default": 512, "min": 256, "max": 2048, "step": 32}),
                "height": ("INT", {"default": 512, "min": 256, "max": 2048, "step": 32}),
                "frames": ("INT", {"default": 8, "min": 1, "max": 100}),
                "fps": ("INT", {"default": 8, "min": 1, "max": 30}),
                "delay_sec": ("FLOAT", {"default": 3, "min": 0.0, "max": 30.0, "step": 0.1}),
                "enable": ("BOOLEAN", {"default": True}),
            }
        }

    RETURN_TYPES = ("IMAGE", "BOOLEAN")
    RETURN_NAMES = ("image", "enable")
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def __init__(self):
        self.grabber_thread = None
        self.flag_stop_grabber = False
        self.last_args = None
        self.grabbed_frames = []

    def grabber(self, area, frames, fps):
        with mss.mss() as sct:
            while True:
                self.grabbed_frames.append(sct.grab(area))  # BGRA format
                self.grabbed_frames = self.grabbed_frames[-frames:]
                time.sleep(1 / fps)
                if self.flag_stop_grabber:
                    break

    def start_grabber(self, area, frames, fps):
        if self.grabber_thread is None or not self.grabber_thread.is_alive():
            self.flag_stop_grabber = False
            self.grabber_thread = threading.Thread(target=self.grabber, args=(area, frames, fps), daemon=True)
            self.grabber_thread.start()

    def stop_grabber(self):
        if self.grabber_thread is not None and self.grabber_thread.is_alive():
            self.flag_stop_grabber = True
            self.grabber_thread.join()

    def __del__(self):
        self.stop_grabber()

    def run(self, top, left, width, height, frames, fps, delay_sec, enable):
        image = None
        if enable:
            enable = False
            area = {"left": left, "top": top, "width": width, "height": height}
            if self.last_args is None or self.last_args != (area, frames, fps):
                self.last_args = (area, frames, fps)
                self.stop_grabber()
            self.start_grabber(area, frames, fps)
            time.sleep(delay_sec)
            buf = self.grabbed_frames[-frames:]
            if len(buf) == frames:
                image = torch.tensor(np.array(buf)[:, :, :, (2, 1, 0)] / 255, dtype=torch.float32)
                enable = True
        else:
            self.stop_grabber()
        return (image, enable)


class FlipStreamVideoInput:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "path": ("STRING", {"default": ""}),
                "first": ("INT", {"default": 0, "min": 0}),
                "step": ("INT", {"default": 1, "min": 1}),
                "frames": ("INT", {"default": 1, "min": 1}),
            },
        }

    RETURN_TYPES = ("IMAGE", "BOOLEAN")
    RETURN_NAMES = ("image", "enable")
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, path, first, step, frames):
        if not path or not Path(path).is_file():
            return (torch.zeros((1, 64, 64, 3)), False)
        with iio.imopen(path, "r") as file:
            buf = [file.read(index=i) for i in range(first, first+(frames-1)*step+1, step)]
        buf = np.stack(buf).astype(np.float32) / 255
        enable = buf.shape[0] == frames
        image = torch.from_numpy(buf) if enable else None
        return (image, enable)


class FlipStreamSource:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "width": ("INT", {"default": 0, "min": 0, "max": 2048, "step": 32}),
                "height": ("INT", {"default": 0, "min": 0, "max": 2048, "step": 32}),
                "frames": ("INT", {"default": 0, "min": 0}),
                "strim": ("INT", {"default": 0, "min": 0}),
                "etrim": ("INT", {"default": 0, "min": 0}),
            },
            "optional": {
                "image": ("IMAGE",),
                "vae": ("VAE",),
            }
        }

    RETURN_TYPES = ("IMAGE", "LATENT", "BOOLEAN")
    RETURN_NAMES = ("image", "latent", "enable")
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, width, height, frames, strim, etrim, image=None, vae=None):
        latent = None
        if image is not None and not torch.any(image):
            image = None
        if image is not None:
            if image.shape[3] == 4:
                image = image[:,:,:,:3] * image[:,:,:,3:4]
            if height and not width:
                width = int(image.shape[2] * height / image.shape[1] // 32 * 32)
            if width and not height:
                height = int(image.shape[1] * width / image.shape[2] // 32 * 32)
            if not width and not height:
                width = image.shape[2]
                height = image.shape[1]
            if not frames:
                frames = image.shape[0]
        if image is not None and image.shape[0] >= frames and frames > strim + etrim:
            buf = image[strim:frames-etrim]
            buf = buf.movedim(-1,1)
            buf = comfy.utils.common_upscale(buf, width, height, "lanczos", "center")
            image = buf.movedim(1,-1)
            if vae:
                latent = {"samples": vae.encode(image)}
            else:
                latent = {"samples": torch.zeros([frames, 4, height // 8, width // 8], device=comfy.model_management.intermediate_device())}
            return (image, latent, True)
        else:
            if not frames:
                frames = 1
            if not width:
                width = 64
            if not height:
                height = 64
            image = torch.zeros([frames, height, width, 3])
            latent = {"samples": torch.zeros([frames, 4, height // 8, width // 8], device=comfy.model_management.intermediate_device())}
            return (image, latent, False)


class FlipStreamSwitch:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "value": (anytype,),
            },
            "optional": {
                "value_enable": (anytype,),
                "enable": ("BOOLEAN",),
            }
        }

    RETURN_TYPES = (anytype,)
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, value=None, value_enable=None, enable=None):
        if enable:
            return (value_enable,)
        return (value,)


class FlipStreamSwitchImage:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "image": ("IMAGE",),
            },
            "optional": {
                "image_enable": ("IMAGE",),
                "enable": ("BOOLEAN",),
            }
        }

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, image=None, image_enable=None, enable=None):
        if enable:
            return (image_enable,)
        return (image,)


class FlipStreamSwitchLatent:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "latent": ("LATENT",),
            },
            "optional": {
                "latent_enable": ("LATENT",),
                "enable": ("BOOLEAN",),
            }
        }

    RETURN_TYPES = ("LATENT",)
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, latent, latent_enable=None, enable=None):
        if enable:
            return (latent_enable,)
        return (latent,)


class FlipStreamGate:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "model": ("MODEL",),
                "pos": ("CONDITIONING",),
                "neg": ("CONDITIONING",),
                "latent": ("LATENT",),
            },
            "optional": {
                "a": (anytype,),
                "b": (anytype,),
                "c": (anytype,),
                "d": (anytype,),
                "e": (anytype,),
                "f": (anytype,),
                "g": (anytype,),
                "h": (anytype,)
            }
        }

    RETURN_TYPES = ("MODEL", "CONDITIONING", "CONDITIONING", "LATENT", anytype, anytype, anytype, anytype, anytype, anytype, anytype, anytype)
    RETURN_NAMES = ("model", "pos", "neg", "latent", "a", "b", "c", "d", "e", "f", "g", "h")
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, model, pos, neg, latent, a=None, b=None, c=None, d=None, e=None, f=None, g=None, h=None):
        return (model, pos, neg, latent, a, b, c, d, e, f, g, h)


class FlipStreamRembg:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "image": ("IMAGE",),
            },
        }

    RETURN_TYPES = ("IMAGE", "MASK")
    FUNCTION = "run"
    CATEGORY = "image"

    def __init__(self):
        if rembg is None:
            raise RuntimeError("FlipStreamRembg: ComfyUI-Inspyrenet-Rembg must be installed to use this function.")

        self.rembg = rembg.InspyrenetRembg()

    def run(self, image):
        img, mask = self.rembg.remove_background(image, "default")
        return (img[..., :3] * img[..., 3:4], mask)


class FlipStreamSegMask:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "tensor": ("IMAGE",),
                "target": ("STRING", {"default": "", "multiline": True}),
            },
        }

    RETURN_TYPES = ("IMAGE", "MASK",)
    RETURN_NAMES =("preview", "mask",) 
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    model = None
    processor = None

    def run(self, tensor, target):
        if florence2 is None:
            raise RuntimeError("FlipStreamSegMask: ComfyUI-Florence2 must be installed to use this function.")

        # Create mask
        _, h, w = tensor.shape[0:3]
        mask_image = Image.new('RGB', (w, h), 'black')

        # Skip if empty target
        if target.strip() == "":
            mask_tensor = torch.from_numpy(np.array(mask_image).astype(np.float32) / 255.0).unsqueeze(0)
            return (mask_tensor, mask_tensor[:,:,:,0])

        # Download model if it not found
        model_id = 'microsoft/Florence-2-large'
        if FlipStreamSegMask.model is None:
            model_dir = Path(folder_paths.models_dir, "LLM")
            model_dir.mkdir(exist_ok=True)
            model_name = model_id.rsplit('/', 1)[-1]
            model_path = Path(model_dir, model_name)
            
            if not model_path.exists():
                print(f"Downloading Florence2 model to: {model_path}")
                from huggingface_hub import snapshot_download
                snapshot_download(repo_id=model_id, local_dir=str(model_path), local_dir_use_symlinks=False)
            
        # Load model
        if FlipStreamSegMask.model is None:
            FlipStreamSegMask.model = transformers.Florence2ForConditionalGeneration.from_pretrained(
                model_id,
                trust_remote_code=True,
                attn_implementation='sdpa',
                device_map=comfy.model_management.get_torch_device(),
                torch_dtype=torch.bfloat16
            )
            FlipStreamSegMask.processor = transformers.AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
        model = FlipStreamSegMask.model
        processor = FlipStreamSegMask.processor
        model.to(comfy.model_management.get_torch_device())

        # Process prompt for each target
        task_prompt = '<REFERRING_EXPRESSION_SEGMENTATION>'
        image = Image.fromarray(np.clip(255. * tensor[0,:,:,:].cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
        mask_draw = ImageDraw.Draw(mask_image)
        for item in target.split(','):
            prompt = task_prompt + item
            inputs = processor(text=prompt, images=image, return_tensors="pt", do_rescale=False).to('cuda', torch.bfloat16)
            generated_ids = model.generate(
                input_ids=inputs["input_ids"].cuda(),
                pixel_values=inputs["pixel_values"].cuda(),
                max_new_tokens=1024,
                early_stopping=False,
                do_sample=False,
                num_beams=1,
            )
            generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
            parsed_answer = processor.post_process_generation(generated_text, task=task_prompt, image_size=(w, h))

            # Draw mask
            predictions = parsed_answer[task_prompt]
            for polygons in predictions['polygons']:
                for p in polygons:
                    p = np.array(p).reshape(-1, 2)
                    p = np.clip(p, [0, 0], [w - 1, h - 1])
                    if len(p) < 3:  
                        print('Invalid polygon:', p)
                        continue

                    p = p.reshape(-1).tolist()
                    mask_draw.polygon(p, outline="white", fill="white")

        # Offload model
        model.to(torch.device("cpu"))

        mask_tensor = torch.from_numpy(np.array(mask_image).astype(np.float32) / 255.0).unsqueeze(0)
        return (mask_tensor, mask_tensor[:,:,:,0])


class FlipStreamChat:
    CACHED_OUTPUT = {}

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "model_file": ([path.name for path in Path(folder_paths.models_dir, "LLM").glob("*.gguf")],),
                "n_ctx": ("INT", {"default": 2048, "min": 0, "max": 8192}),
                "n_gpu_layers": ("INT", {"default": -1, "min": -1}),
                "unload_other_models": ("BOOLEAN", {"default": False}),
                "close_after_use": ("BOOLEAN", {"default": False}),
                "system": ("STRING", {"default": "", "multiline": True}),
                "user": ("STRING", {"default": "", "multiline": True}),
                "instant": ("BOOLEAN", {"default": False}),
                "max_history": ("INT", {"default": 10, "min": -1}),
                "stop": ("STRING", {"default": "[,<"}),
                "temperature": ("FLOAT", {"default": 0.2, "min": 0}),
                "top_p": ("FLOAT", {"default": 0.95, "min": 0}),
                "seed": ("INT", {"default": -1}),
                "max_tokens": ("INT", {"default": 1024, "min": 0, "max": 8192}),
                "presence_penalty": ("FLOAT", {"default": 0, "min": 0}),
                "frequency_penalty": ("FLOAT", {"default": 0.5, "min": 0}),
                "repeat_penalty": ("FLOAT", {"default": 1.0, "min": 0}),
                "response_format": ("STRING", {"default": "", "multiline": True}),
                "enable": ("BOOLEAN", {"default": True}),
            },
            "optional": {
                "chat_model": ("CHAT_MODEL",),
                "messages": ("MESSAGES",)
            },
            "hidden": {
                "unique_id": "UNIQUE_ID"
            }
        }

    RETURN_TYPES = ("CHAT_MODEL", "STRING", "MESSAGES")
    RETURN_NAMES = ("chat_model", "response", "messages")
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def __init__(self):
        self.model = None
        self.system = None
        self.messages = []

    def load_model(self, model_file, n_ctx, n_gpu_layers, unload_other_models):
        h = hash((model_file, n_ctx, n_gpu_layers))
        if self.model is None or self.model._FlipStreamChat_is_closed or self.model._FlipStreamChat_last_hash != h:
            model_path = Path(folder_paths.models_dir, "LLM", model_file)
            if not model_path.exists():
                raise RuntimeError(f"FlipStreamChat: {model_path} not found.")
            if Llama is None:
                raise RuntimeError("FlipStreamChat: llama-cpp-python required.")
            if unload_other_models:
                try:
                    comfy.model_management.unload_all_models()
                    comfy.model_management.soft_empty_cache(True)
                    comfy.gc.collect()
                    torch.cuda.empty_cache()
                    torch.cuda.ipc_collect()
                except:
                    pass
            self.model = Llama(str(model_path), chat_format="llama-2", n_ctx=n_ctx, n_gpu_layers=n_gpu_layers, verbose=False)
            self.model._FlipStreamChat_is_closed = False
            self.model._FlipStreamChat_last_hash = h

    def close_model(self):
        if self.model:
            self.model.close()
            self.model._FlipStreamChat_is_closed = True

    def chat(self, system, user, stop, messages, response_format, **kwargs):
        if system != self.system:
            self.system = system
            messages.clear()
        if system and len(messages) == 0:
            messages.append(dict(role="system", content=system))
        if system and messages[0]["role"] != "system":
            messages[0] = dict(role="system", content=system)
        if user:
            messages.append(dict(role="user", content=user))
        if response_format:
            response_format = json.loads(response_format)
        else:
            response_format = None
        return self.model.create_chat_completion(messages, stop=list(filter(str.strip, stop.split(","))), response_format=response_format, **kwargs)["choices"][0]["message"]

    def run(self, model_file, n_ctx, n_gpu_layers, unload_other_models, close_after_use, system, user, instant, max_history, stop, enable, unique_id, chat_model=None, messages=None, **kwargs):
        if chat_model is not None:
            self.model = chat_model
        if messages is None:
            messages = self.messages
        if not enable:
            if close_after_use:
                self.close_model()
            return (self.model, None, messages)
        
        if max_history == 0:
            messages.clear()
        elif max_history >= 1:
            messages[:] = messages[:max_history]
        self.load_model(model_file, n_ctx, n_gpu_layers, unload_other_models)
        cache = FlipStreamChat.CACHED_OUTPUT
        if instant:
            res = self.chat(system, user, stop, messages.copy(), **kwargs)
            cache[unique_id] = res["content"]
        else:
            res = self.chat(system, user, stop, messages, **kwargs)
            cache[unique_id] = res["content"]
            if res["role"] == "assistant":
                messages.append(dict(role="assistant", content=cache[unique_id]))
        self.messages = messages
        if close_after_use:
            self.close_model()
        return (self.model, cache.get(unique_id), messages)


class FlipStreamParseJson:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "json_input": ("STRING", {"default": "", "multiline": True}),
                "keys": ("STRING", {"default": "", "multiline": True}),
                "joinstr": ("STRING", {"default": ",", "multiline": True}),
                "ignore_error": ("BOOLEAN", {"default": True}),
                "enable": ("BOOLEAN", {"default": True}),
            }
         }

    RETURN_TYPES = ("STRING",)
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, json_input, keys, joinstr, ignore_error, enable):
        if not enable:
            return ("",)

        value = []
        try:
            for key in keys.split("\n"):
                value.append(json.loads(json_input, strict=False)[key.strip()])
        except Exception as e:
            if not ignore_error:
                raise RuntimeError(f"FlipStreamParseJsonItem: Invalid JSON input: {e}: {json_input}")
        return (joinstr.join(value),)


class FlipStreamChatJson(FlipStreamChat):
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "model_file": ([path.name for path in Path(folder_paths.models_dir, "LLM").glob("*.gguf")],),
                "unload_other_models": ("BOOLEAN", {"default": False}),
                "close_after_use": ("BOOLEAN", {"default": False}),
                "system": ("STRING", {"default": "", "multiline": True}),
                "user": ("STRING", {"default": "", "multiline": True}),
                "seed": ("INT", {"default": -1}),
                "n_ctx": ("INT", {"default": 2048, "min": 0, "max": 8192}),
                "enable": ("BOOLEAN", {"default": True})
            },
            "optional": {
                "chat_model": ("CHAT_MODEL",),
                **{f"label{i}": ("STRING", {"default": ""}) for i in range(20)}
            },
            "hidden": {
                "unique_id": "UNIQUE_ID"
            }
        }

    RETURN_TYPES = ("CHAT_MODEL", "STRING", "MESSAGES") + (anytype,) * 20
    RETURN_NAMES = ("chat_model", "response", "messages") + tuple(f"value{i}" for i in range(20))
    FUNCTION = "run2"
    CATEGORY = "FlipStreamViewer"

    def run2(self, **kwargs):
        kwargs.update({
            "n_gpu_layers": -1,
            "instant": False,
            "max_history": 0,
            "stop": "",
            "temperature":0.2,
            "top_p": 0.95,
            "max_tokens": kwargs["n_ctx"] - 512,
            "presence_penalty": 0,
            "frequency_penalty": 0.5,
            "repeat_penalty": 1.0
        })

        label = {}
        for k in list(kwargs):
            if k.startswith('label'):
                if kwargs[k]:
                    label[k] = kwargs[k]
                del kwargs[k]
        
        response_format = {
            "type": "json_object",
            "schema": {
                "type": "object",
                "properties": {k: {"type": "string"} for k in label.values()},
                "required": list(label.values())
            }
        }
        kwargs["response_format"] = json.dumps(response_format, ensure_ascii=False, indent=4)
        chat_model, response, messages = self.run(**kwargs)
        value = {f"label{i}": "" for i in range(20)}
        if response:
            data = json.loads(response, strict=False)
            value.update({k: data[v] for k, v in label.items()})
        return (chat_model, response, messages, *value.values())


class FlipStreamBatchPrompt:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "prompt": ("STRING", {"default": "", "multiline": True}),
                "clip": ("CLIP",),
                "frames": ("INT",),
            }
        }

    RETURN_TYPES = ("CONDITIONING",)
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, prompt, clip, frames):
        cond_buf = []
        pooled_buf = []

        buf = prompt.split("----")
        prompt = buf[0].strip()
        batchPrompt = buf[1].strip() if len(buf) > 1 else ""
        appPrompt = buf[2].strip() if len(buf) > 2 else ""
        count = len(batchPrompt.split("\n"))
        batchPrompt = ",\n".join([f'"{int(frames * n / count)}":"{item.lstrip("-").strip()}"' for n, item in enumerate(batchPrompt.split("\n"))])

        tokens = clip.tokenize(prompt)
        cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
        s = json.loads("{" + batchPrompt + "}")
        for i in range(frames):
            if str(i) in s:
                tokens = clip.tokenize(" ".join([prompt, s[str(i)], appPrompt]).strip())
                cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
            cond_buf.append(cond)
            pooled_buf.append(pooled)
        
        max_length = max([t.size(1) for t in cond_buf])
        cond_buf = [
            NNF.pad(t, (0, 0, 0, max_length - t.size(1))) if t.size(1) < max_length else t[:, :max_length, :]
            for t in cond_buf
        ]
        return ([[torch.cat(cond_buf, dim=0), {"pooled_output":torch.cat(pooled_buf, dim=0)}]],)


class FlipStreamFilmVfi:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "frames": ("IMAGE",),
                "multiplier": ("INT", {"default": 1, "min": 1, "max": 16}),
                "clear_cache_after_n_frames": ("INT", {"default": 10, "min": 1, "max": 1000}),
            },
        }
    
    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"        

    model = None

    def run(self, frames, multiplier, clear_cache_after_n_frames):
        if frames.shape[0] < 2 or multiplier < 2:
            return (frames,)
        
        if film is None:
            raise RuntimeError("FlipStreamFilmVfi: ComfyUI-Frame-Interpolation must be installed to use this function.")
        
        if FlipStreamFilmVfi.model is None:
            model_path = film.load_file_from_github_release("film", "film_net_fp32.pt")
            model = torch.jit.load(model_path, map_location="cpu")
            model.eval()
            FlipStreamFilmVfi.model = model
        model = FlipStreamFilmVfi.model
        model = model.to(comfy.model_management.get_torch_device())
        dtype = torch.float32

        frames = film.preprocess_frames(frames)
        number_of_frames_processed_since_last_cleared_cuda_cache = 0
        output_frames = []
        
        for frame_itr in range(len(frames) - 1):
            frame_0 = frames[frame_itr:frame_itr+1].to(comfy.model_management.get_torch_device()).float()
            frame_1 = frames[frame_itr+1:frame_itr+2].to(comfy.model_management.get_torch_device()).float()
            relust = film.inference(model, frame_0, frame_1, multiplier - 1)
            output_frames.extend([frame.detach().cpu().to(dtype=dtype) for frame in relust[:-1]])

            number_of_frames_processed_since_last_cleared_cuda_cache += 1
            if number_of_frames_processed_since_last_cleared_cuda_cache >= clear_cache_after_n_frames:
                film.soft_empty_cache()
                number_of_frames_processed_since_last_cleared_cuda_cache = 0

        output_frames.append(frames[-1:].to(dtype=dtype))
        output_frames = [frame.cpu() for frame in output_frames]
        out = torch.cat(output_frames, dim=0)
        film.soft_empty_cache()
        model.to(torch.device("cpu"))
        return (film.postprocess_frames(out),)


class FlipStreamViewer:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "tensor": ("IMAGE",),
                "allowip": ("STRING", {"default": ""}),
                "idle": ("FLOAT", {"default": 1.0, "min": 0.0}),
                "fps": ("INT", {"default": 16, "min": 1, "max": 30}),
                "loramode": ("STRING", {"default": ""}),
                "pingpong": ("BOOLEAN", {"default": True}),
            },
        }

    @classmethod
    def IS_CHANGED(cls, allowip, idle, loramode, **_):
        global allowed_ips
        allowed_ips = ["127.0.0.1"] + list(map(str.strip, allowip.split(",")))
        state["loraMode"] = loramode
        time.sleep(idle)
        return None

    RETURN_TYPES = ()
    OUTPUT_NODE = True
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, allowip, tensor, fps, loramode, pingpong, **_):
        global allowed_ips
        allowed_ips = ["127.0.0.1"] + list(map(str.strip, allowip.split(",")))
        state["loraMode"] = loramode

        fb = []
        buf = (tensor.detach().cpu().numpy() * 255).astype(np.uint8)
        if tensor.shape[0] != 1 and pingpong:
            buf = np.concatenate([buf, np.flip(buf, axis=0)])
        for image in buf:
            with io.BytesIO() as output:
                img = Image.fromarray(image)
                img.save(output, format="PNG", compress_level=STREAM_COMPRESSION)
                fb.append(output.getvalue())
        global frame_buffer
        global frame_mtime
        global frame_fps
        frame_buffer = fb
        frame_mtime = time.time()
        frame_fps = fps
        return ()


class FlipStreamViewerSimple:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "tensor": ("IMAGE",),
                "idle": ("FLOAT", {"default": 0, "min": 0.0}),
                "fps": ("INT", {"default": 16, "min": 1, "max": 30}),
                "pingpong": ("BOOLEAN", {"default": True}),
            },
        }

    @classmethod
    def IS_CHANGED(cls, idle, **_):
        time.sleep(idle)
        return None

    RETURN_TYPES = ()
    OUTPUT_NODE = True
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, tensor, fps, pingpong, **_):
        fb = []
        buf = (tensor.detach().cpu().numpy() * 255).astype(np.uint8)
        if tensor.shape[0] != 1 and pingpong:
            buf = np.concatenate([buf, np.flip(buf, axis=0)])
        for image in buf:
            with io.BytesIO() as output:
                img = Image.fromarray(image)
                img.save(output, format="PNG", compress_level=STREAM_COMPRESSION)
                fb.append(output.getvalue())
        global frame_buffer
        global frame_mtime
        global frame_fps
        frame_buffer = fb
        frame_mtime = time.time()
        frame_fps = fps
        return ()


class FlipStreamAllowIp:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "allowip": ("STRING", {"default": ""}),
            },
            "optional": {
                "hook": (anytype,),
            },
        }

    RETURN_TYPES = (anytype,)
    OUTPUT_NODE = True
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, allowip, hook=True, **_):
        global allowed_ips
        allowed_ips = ["127.0.0.1"] + list(map(str.strip, allowip.split(",")))
        return (hook,)


class FlipStreamCurrent:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "current": ("STRING", {"default": ""}),
            },
            "optional": {
                "hook": (anytype,),
            },
        }

    RETURN_TYPES = (anytype,)
    OUTPUT_NODE = True
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, current, hook=True, **_):
        state["current"] = current
        return (hook,)


class FlipStreamLoraMode:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "loraMode": ("STRING", {"default": ""}),
            },
            "optional": {
                "hook": (anytype,),
            },
        }

    RETURN_TYPES = (anytype,)
    OUTPUT_NODE = True
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, loraMode, hook=True, **_):
        state["loraMode"] = loraMode
        return (hook,)


class FlipStreamLoadLora:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "model": ("MODEL",),
                "clip": ("CLIP",),
                "mode": ("STRING", {"default": ""}),
            },
            "optional": {
                "hook": (anytype,),
            },
        }

    RETURN_TYPES = ("MODEL", "CLIP", "STRING")
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    @classmethod
    def IS_CHANGED(cls, mode, **_):
        text = re.sub(rf"@(\w+)\{{(.*?)\}}", lambda m: m.group(2) if m.group(1) == mode else "", atob_utf8(param.get("lora", "")), flags=re.S)
        return hash(text.strip())

    def run(self, model, clip, mode, hook=True):
        text = re.sub(rf"@(\w+)\{{(.*?)\}}", lambda m: m.group(2) if m.group(1) == mode else "", atob_utf8(param.get("lora", "")), flags=re.S)

        def apply(m):
            nonlocal model, clip
            name, w = m.group(1), float(m.group(2))
            for file in folder_paths.get_filename_list("loras"):
                if Path(file).name.startswith(name):
                    if path := folder_paths.get_full_path("loras", file):
                        model, clip = comfy.sd.load_lora_for_models(model, clip, comfy.utils.load_torch_file(path), w, w)
                        print(f"FlipStreamLoadLora: loaded: {name} -> {path}")
                        break
            else:
                print(f"FlipStreamLoadLora: LoRA not found: {name}")
            return ""
        
        text = re.sub(r"\<lora:(.*?):(.*?)\>", apply, text).strip()
        return (model, clip, text)


class FlipStreamSaveApiWorkflow:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "savepath": ("STRING", {"default": ""}),
                "override": ("BOOLEAN", {"default": False}),
            },
            "optional": {
                "hook": (anytype,),
            },
            "hidden": {
                "prompt": "PROMPT"
            }
        }

    RETURN_TYPES = (anytype,)
    OUTPUT_NODE = True
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, savepath, override, hook=True, prompt=None):
        p = Path(savepath)
        if prompt and (override or not p.exists()):
            p.write_text(json.dumps(prompt))
            print(f"FlipStreamSaveApiWorkflow: saved {savepath}")
        return (hook,)


class FlipStreamRunApiWorkflow:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "path": ("STRING", {"default": ""}),
                "enable": ("BOOLEAN", {"default": True}),
            },
            "optional": {
                "hook": (anytype,),
            }
        }

    RETURN_TYPES = (anytype,)
    OUTPUT_NODE = True
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, enable, path, hook=None):
        if enable:
            prompt = json.loads(Path(path).read_text())
            print(f"FlipStreamRunApiWorkflow: queue {path}")
            requests.post(f"http://127.0.0.1:{server.PromptServer.instance.port}/prompt", json={"prompt": prompt})
        return (hook,)


class FlipStreamFree:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "unload_models": ("BOOLEAN", {"default": True}),
                "free_memory": ("BOOLEAN", {"default": True}),
            },
            "optional": {
                "hook": (anytype,),
            }
        }

    RETURN_TYPES = (anytype,)
    OUTPUT_NODE = True
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, unload_models, free_memory, hook=None):
        requests.post(f"http://127.0.0.1:{server.PromptServer.instance.port}/free", json={"unload_models": unload_models, "free_memory": free_memory})
        return (hook,)


class FlipStreamShutdown:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "minutes": ("INT", {"default": 30, "min": 1}),
            },
            "optional": {
                "hook": (anytype,),
            }
        }

    RETURN_TYPES = (anytype,)
    OUTPUT_NODE = True
    FUNCTION = "run"
    CATEGORY = "FlipStreamViewer"

    def run(self, minutes, hook=True, **_):
        subprocess.run(["shutdown", "/a"], stderr=subprocess.DEVNULL)
        subprocess.run(["shutdown", "/s", "/t", str(minutes * 60)])
        return (hook,)


WEB_DIRECTORY = "./web"

NODE_CLASS_MAPPINGS = {
    "FlipStreamSection": FlipStreamSection,
    "FlipStreamButton": FlipStreamButton,
    "FlipStreamSlider": FlipStreamSlider,
    "FlipStreamTextBox": FlipStreamTextBox,
    "FlipStreamInputBox": FlipStreamInputBox,
    "FlipStreamSelectBox_Samplers": FlipStreamSelectBox_Samplers,
    "FlipStreamSelectBox_Scheduler": FlipStreamSelectBox_Scheduler,
    "FlipStreamSizeSelect": FlipStreamSizeSelect,
    "FlipStreamGetSize": FlipStreamGetSize,
    "FlipStreamFileSelect_Checkpoints": FlipStreamFileSelect_Checkpoints,
    "FlipStreamFileSelect_Loras": FlipStreamFileSelect_Loras,
    "FlipStreamFileSelect_VAE": FlipStreamFileSelect_VAE,
    "FlipStreamFileSelect_LLM": FlipStreamFileSelect_LLM,
    "FlipStreamFileSelect_ControlNetModel": FlipStreamFileSelect_ControlNetModel,
    "FlipStreamFileSelect_TensorRT": FlipStreamFileSelect_TensorRT,
    "FlipStreamFileSelect_AnimateDiffModel": FlipStreamFileSelect_AnimateDiffModel,
    "FlipStreamFileSelect_Input": FlipStreamFileSelect_Input,
    "FlipStreamFileSelect_Output": FlipStreamFileSelect_Output,
    "FlipStreamPreviewBox": FlipStreamPreviewBox,
    "FlipStreamPasteBox": FlipStreamPasteBox,
    "FlipStreamLogBox": FlipStreamLogBox,
    "FlipStreamRunOnce": FlipStreamRunOnce,
    "FlipStreamSetMessage": FlipStreamSetMessage,
    "FlipStreamAnd": FlipStreamAnd,
    "FlipStreamOr": FlipStreamOr,
    "FlipStreamSetState": FlipStreamSetState,
    "FlipStreamGetState": FlipStreamGetState,
    "FlipStreamSetParam": FlipStreamSetParam,
    "FlipStreamGetParam": FlipStreamGetParam,
    "FlipStreamGet": FlipStreamGet,
    "FlipStreamGetFrame": FlipStreamGetFrame,
    "FlipStreamGetPreviewRoi": FlipStreamGetPreviewRoi,
    "FlipStreamImageSize": FlipStreamImageSize,
    "FlipStreamTextReplace": FlipStreamTextReplace,
    "FlipStreamTextConcat": FlipStreamTextConcat,
    "FlipStreamScreenGrabber": FlipStreamScreenGrabber,
    "FlipStreamVideoInput": FlipStreamVideoInput,
    "FlipStreamSource": FlipStreamSource,
    "FlipStreamSwitch": FlipStreamSwitch,
    "FlipStreamSwitchImage": FlipStreamSwitchImage,
    "FlipStreamSwitchLatent": FlipStreamSwitchLatent,
    "FlipStreamGate": FlipStreamGate,
    "FlipStreamRembg": FlipStreamRembg,
    "FlipStreamSegMask": FlipStreamSegMask,
    "FlipStreamChat": FlipStreamChat,
    "FlipStreamParseJson": FlipStreamParseJson,
    "FlipStreamChatJson": FlipStreamChatJson,
    "FlipStreamBatchPrompt": FlipStreamBatchPrompt,
    "FlipStreamFilmVfi": FlipStreamFilmVfi,
    "FlipStreamViewer": FlipStreamViewer,
    "FlipStreamViewerSimple": FlipStreamViewerSimple,
    "FlipStreamAllowIp": FlipStreamAllowIp,
    "FlipStreamCurrent": FlipStreamCurrent,
    "FlipStreamLoraMode": FlipStreamLoraMode,
    "FlipStreamLoadLora": FlipStreamLoadLora,
    "FlipStreamSaveApiWorkflow": FlipStreamSaveApiWorkflow,
    "FlipStreamRunApiWorkflow": FlipStreamRunApiWorkflow,
    "FlipStreamFree": FlipStreamFree,
    "FlipStreamShutdown": FlipStreamShutdown,
}

NODE_DISPLAY_NAME_MAPPINGS = {
    "FlipStreamSection": "FlipStreamSection",
    "FlipStreamButton": "FlipStreamButton",
    "FlipStreamSlider": "FlipStreamSlider",
    "FlipStreamTextBox": "FlipStreamTextBox",
    "FlipStreamInputBox": "FlipStreamInputBox",
    "FlipStreamSelectBox_Samplers": "FlipStreamSelectBox_Samplers",
    "FlipStreamSelectBox_Scheduler": "FlipStreamSelectBox_Scheduler",
    "FlipStreamSizeSelect": "FlipStreamSizeSelect",
    "FlipStreamGetSize": "FlipStreamGetSize",
    "FlipStreamFileSelect_Checkpoints": "FlipStreamFileSelect_Checkpoints",
    "FlipStreamFileSelect_Loras": "FlipStreamFileSelect_Loras",
    "FlipStreamFileSelect_VAE": "FlipStreamFileSelect_VAE",
    "FlipStreamFileSelect_LLM": "FlipStreamFileSelect_LLM",
    "FlipStreamFileSelect_ControlNetModel": "FlipStreamFileSelect_ControlNetModel",
    "FlipStreamFileSelect_TensorRT": "FlipStreamFileSelect_TensorRT",
    "FlipStreamFileSelect_AnimateDiffModel": "FlipStreamFileSelect_AnimateDiffModel",
    "FlipStreamFileSelect_Input": "FlipStreamFileSelect_Input",
    "FlipStreamFileSelect_Output": "FlipStreamFileSelect_Output",
    "FlipStreamPreviewBox": "FlipStreamPreviewBox",
    "FlipStreamPasteBox": "FlipStreamPasteBox",
    "FlipStreamLogBox": "FlipStreamLogBox",
    "FlipStreamRunOnce": "FlipStreamRunOnce",
    "FlipStreamSetMessage": "FlipStreamSetMessage",
    "FlipStreamAnd": "FlipStreamAnd(experimental)",
    "FlipStreamOr": "FlipStreamOr(experimental)",
    "FlipStreamSetState": "FlipStreamSetState",
    "FlipStreamGetState": "FlipStreamGetState",
    "FlipStreamSetParam": "FlipStreamSetParam",
    "FlipStreamGetParam": "FlipStreamGetParam",
    "FlipStreamGet": "FlipStreamGet(experimental)",
    "FlipStreamGetFrame": "FlipStreamGetFrame",
    "FlipStreamGetPreviewRoi": "FlipStreamGetPreviewRoi",
    "FlipStreamImageSize": "FlipStreamImageSize",
    "FlipStreamTextReplace": "FlipStreamTextReplace",
    "FlipStreamTextConcat": "FlipStreamTextConcat",
    "FlipStreamScreenGrabber": "FlipStreamScreenGrabber",
    "FlipStreamVideoInput": "FlipStreamVideoInput",
    "FlipStreamSource": "FlipStreamSource",
    "FlipStreamSwitch": "FlipStreamSwitch",
    "FlipStreamSwitchImage": "FlipStreamSwitchImage",
    "FlipStreamSwitchLatent": "FlipStreamSwitchLatent",
    "FlipStreamGate": "FlipStreamGate(deprecated)",
    "FlipStreamRembg": "FlipStreamRembg",
    "FlipStreamSegMask": "FlipStreamSegMask(deprecated)",
    "FlipStreamChat": "FlipStreamChat",
    "FlipStreamParseJson": "FlipStreamParseJson",
    "FlipStreamChatJson": "FlipStreamChatJson(experimental)",
    "FlipStreamBatchPrompt": "FlipStreamBatchPrompt",
    "FlipStreamFilmVfi": "FlipStreamFilmVfi",
    "FlipStreamViewer": "FlipStreamViewer",
    "FlipStreamViewerSimple": "FlipStreamViewerSimple",
    "FlipStreamAllowIp": "FlipStreamAllowIp",
    "FlipStreamCurrent": "FlipStreamCurrent",
    "FlipStreamLoraMode": "FlipStreamLoraMode",
    "FlipStreamLoadLora": "FlipStreamLoadLora",
    "FlipStreamSaveApiWorkflow": "FlipStreamSaveApiWorkflow",
    "FlipStreamRunApiWorkflow": "FlipStreamRunApiWorkflow",
    "FlipStreamFree": "FlipStreamFree",
    "FlipStreamShutdown": "FlipStreamShutdown",
}