add txt2imglora pipeline

app-txt2imglora.py

@@ -1,254 +0,0 @@
-import asyncio
-import json
-import logging
-import traceback
-from pydantic import BaseModel
-
-from fastapi import FastAPI, WebSocket, HTTPException, WebSocketDisconnect
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import (
-    StreamingResponse,
-    JSONResponse,
-    HTMLResponse,
-    FileResponse,
-)
-
-from diffusers import DiffusionPipeline, LCMScheduler, AutoencoderTiny
-from compel import Compel
-import torch
-
-try:
-    import intel_extension_for_pytorch as ipex
-except:
-    pass
-from PIL import Image
-import numpy as np
-import gradio as gr
-import io
-import uuid
-import os
-import time
-import psutil
-
-
-MAX_QUEUE_SIZE = int(os.environ.get("MAX_QUEUE_SIZE", 0))
-TIMEOUT = float(os.environ.get("TIMEOUT", 0))
-SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", None)
-TORCH_COMPILE = os.environ.get("TORCH_COMPILE", None)
-
-WIDTH = 512
-HEIGHT = 512
-
-# check if MPS is available OSX only M1/M2/M3 chips
-mps_available = hasattr(torch.backends, "mps") and torch.backends.mps.is_available()
-xpu_available = hasattr(torch, "xpu") and torch.xpu.is_available()
-device = torch.device(
-    "cuda" if torch.cuda.is_available() else "xpu" if xpu_available else "cpu"
-)
-torch_device = device
-# change to torch.float16 to save GPU memory
-torch_dtype = torch.float
-
-print(f"TIMEOUT: {TIMEOUT}")
-print(f"SAFETY_CHECKER: {SAFETY_CHECKER}")
-print(f"MAX_QUEUE_SIZE: {MAX_QUEUE_SIZE}")
-print(f"device: {device}")
-
-if mps_available:
-    device = torch.device("mps")
-    torch_device = "cpu"
-    torch_dtype = torch.float32
-
-model_id = "wavymulder/Analog-Diffusion"
-lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5"
-
-if SAFETY_CHECKER == "True":
-    pipe = DiffusionPipeline.from_pretrained(model_id)
-else:
-    pipe = DiffusionPipeline.from_pretrained(model_id, safety_checker=None)
-
-
-pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
-pipe.set_progress_bar_config(disable=True)
-pipe.to(device=torch_device, dtype=torch_dtype).to(device)
-pipe.unet.to(memory_format=torch.channels_last)
-
-# check if computer has less than 64GB of RAM using sys or os
-if psutil.virtual_memory().total < 64 * 1024**3:
-    pipe.enable_attention_slicing()
-
-if TORCH_COMPILE:
-    pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
-    pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead", fullgraph=True)
-
-    pipe(prompt="warmup", num_inference_steps=1, guidance_scale=8.0)
-
-# Load LCM LoRA
-pipe.load_lora_weights(
-    lcm_lora_id,
-    adapter_name="lcm"
-)
-
-compel_proc = Compel(
-    tokenizer=pipe.tokenizer,
-    text_encoder=pipe.text_encoder,
-    truncate_long_prompts=False,
-)
-user_queue_map = {}
-
-
-class InputParams(BaseModel):
-    seed: int = 2159232
-    prompt: str
-    guidance_scale: float = 0.5
-    strength: float = 0.5
-    steps: int = 4
-    lcm_steps: int = 50
-    width: int = WIDTH
-    height: int = HEIGHT
-
-
-def predict(params: InputParams):
-    generator = torch.manual_seed(params.seed)
-    prompt_embeds = compel_proc(params.prompt)
-    results = pipe(
-        prompt_embeds=prompt_embeds,
-        generator=generator,
-        num_inference_steps=params.steps,
-        guidance_scale=params.guidance_scale,
-        width=params.width,
-        height=params.height,
-        output_type="pil",
-    )
-    nsfw_content_detected = (
-        results.nsfw_content_detected[0]
-        if "nsfw_content_detected" in results
-        else False
-    )
-    if nsfw_content_detected:
-        return None
-    return results.images[0]
-
-
-app = FastAPI()
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-
-@app.websocket("/ws")
-async def websocket_endpoint(websocket: WebSocket):
-    await websocket.accept()
-    if MAX_QUEUE_SIZE > 0 and len(user_queue_map) >= MAX_QUEUE_SIZE:
-        print("Server is full")
-        await websocket.send_json({"status": "error", "message": "Server is full"})
-        await websocket.close()
-        return
-
-    try:
-        uid = str(uuid.uuid4())
-        print(f"New user connected: {uid}")
-        await websocket.send_json(
-            {"status": "success", "message": "Connected", "userId": uid}
-        )
-        user_queue_map[uid] = {
-            "queue": asyncio.Queue(),
-        }
-        await websocket.send_json(
-            {"status": "start", "message": "Start Streaming", "userId": uid}
-        )
-        await handle_websocket_data(websocket, uid)
-    except WebSocketDisconnect as e:
-        logging.error(f"WebSocket Error: {e}, {uid}")
-        traceback.print_exc()
-    finally:
-        print(f"User disconnected: {uid}")
-        queue_value = user_queue_map.pop(uid, None)
-        queue = queue_value.get("queue", None)
-        if queue:
-            while not queue.empty():
-                try:
-                    queue.get_nowait()
-                except asyncio.QueueEmpty:
-                    continue
-
-
-@app.get("/queue_size")
-async def get_queue_size():
-    queue_size = len(user_queue_map)
-    return JSONResponse({"queue_size": queue_size})
-
-
-@app.get("/stream/{user_id}")
-async def stream(user_id: uuid.UUID):
-    uid = str(user_id)
-    try:
-        user_queue = user_queue_map[uid]
-        queue = user_queue["queue"]
-
-        async def generate():
-            while True:
-                params = await queue.get()
-                if params is None:
-                    continue
-
-                image = predict(params)
-                if image is None:
-                    continue
-                frame_data = io.BytesIO()
-                image.save(frame_data, format="JPEG")
-                frame_data = frame_data.getvalue()
-                if frame_data is not None and len(frame_data) > 0:
-                    yield b"--frame\r\nContent-Type: image/jpeg\r\n\r\n" + frame_data + b"\r\n"
-
-                await asyncio.sleep(1.0 / 120.0)
-
-        return StreamingResponse(
-            generate(), media_type="multipart/x-mixed-replace;boundary=frame"
-        )
-    except Exception as e:
-        logging.error(f"Streaming Error: {e}, {user_queue_map}")
-        traceback.print_exc()
-        return HTTPException(status_code=404, detail="User not found")
-
-
-async def handle_websocket_data(websocket: WebSocket, user_id: uuid.UUID):
-    uid = str(user_id)
-    user_queue = user_queue_map[uid]
-    queue = user_queue["queue"]
-    if not queue:
-        return HTTPException(status_code=404, detail="User not found")
-    last_time = time.time()
-    try:
-        while True:
-            params = await websocket.receive_json()
-            params = InputParams(**params)
-            while not queue.empty():
-                try:
-                    queue.get_nowait()
-                except asyncio.QueueEmpty:
-                    continue
-            await queue.put(params)
-            if TIMEOUT > 0 and time.time() - last_time > TIMEOUT:
-                await websocket.send_json(
-                    {
-                        "status": "timeout",
-                        "message": "Your session has ended",
-                        "userId": uid,
-                    }
-                )
-                await websocket.close()
-                return
-
-    except Exception as e:
-        logging.error(f"Error: {e}")
-        traceback.print_exc()
-
-
-@app.get("/", response_class=HTMLResponse)
-async def root():
-    return FileResponse("./static/txt2imglora.html")

frontend/src/lib/components/Checkbox.svelte

@@ -1,7 +1,11 @@
 <script lang="ts">
   import type { FieldProps } from '$lib/types';
+  import { onMount } from 'svelte';
   export let value = false;
   export let params: FieldProps;
+  onMount(() => {
+    value = Boolean(params?.default) ?? 8.0;
+  });
 </script>
 
 <div class="grid max-w-md grid-cols-4 items-center justify-items-start gap-3">

frontend/src/lib/components/PipelineOptions.svelte

@@ -20,7 +20,7 @@
       {#if params.field === FieldType.RANGE}
         <InputRange {params} bind:value={$pipelineValues[params.id]}></InputRange>
       {:else if params.field === FieldType.SEED}
-        <SeedInput bind:value={$pipelineValues[params.id]}></SeedInput>
+        <SeedInput {params} bind:value={$pipelineValues[params.id]}></SeedInput>
       {:else if params.field === FieldType.TEXTAREA}
         <TextArea {params} bind:value={$pipelineValues[params.id]}></TextArea>
       {:else if params.field === FieldType.CHECKBOX}
@@ -30,7 +30,7 @@
   {/if}
 </div>
 
-<details open>
+<details>
   <summary class="cursor-pointer font-medium">Advanced Options</summary>
   <div
     class="grid grid-cols-1 items-center gap-3 {pipelineParams.length > 5 ? 'sm:grid-cols-2' : ''}"
@@ -40,7 +40,7 @@
         {#if params.field === FieldType.RANGE}
           <InputRange {params} bind:value={$pipelineValues[params.id]}></InputRange>
         {:else if params.field === FieldType.SEED}
-          <SeedInput bind:value={$pipelineValues[params.id]}></SeedInput>
+          <SeedInput {params} bind:value={$pipelineValues[params.id]}></SeedInput>
         {:else if params.field === FieldType.TEXTAREA}
           <TextArea {params} bind:value={$pipelineValues[params.id]}></TextArea>
         {:else if params.field === FieldType.CHECKBOX}

frontend/src/lib/components/SeedInput.svelte

@@ -1,7 +1,13 @@
 <script lang="ts">
+  import type { FieldProps } from '$lib/types';
+  import { onMount } from 'svelte';
   import Button from './Button.svelte';
   export let value = 299792458;
+  export let params: FieldProps;
 
+  onMount(() => {
+    value = Number(params?.default ?? '');
+  });
   function randomize() {
     value = Math.floor(Math.random() * Number.MAX_SAFE_INTEGER);
   }

frontend/src/lib/types.ts

@@ -22,6 +22,9 @@ export interface FieldProps {
     id: string;
 }
 export interface PipelineInfo {
+    title: {
+        default: string;
+    }
     name: string;
     description: string;
     input_mode: {

frontend/src/routes/+page.svelte

@@ -78,6 +78,9 @@
 <main class="container mx-auto flex max-w-4xl flex-col gap-3 px-4 py-4">
   <article class="flex- mx-auto max-w-xl text-center">
     <h1 class="text-3xl font-bold">Real-Time Latent Consistency Model</h1>
+    {#if pipelineInfo?.title?.default}
+      <h3 class="text-xl font-bold">{pipelineInfo?.title?.default}</h3>
+    {/if}
     <p class="py-2 text-sm">
       This demo showcases
       <a

pipelines/txt2img.py

@@ -21,6 +21,7 @@ default_prompt = "Portrait of The Terminator with , glare pose, detailed, intric
 class Pipeline:
     class Info(BaseModel):
         name: str = "txt2img"
+        title: str = "txt2img"
         description: str = "Generates an image from a text prompt"
         input_mode: str = "text"
 

pipelines/txt2imglora.py

@@ -1,4 +1,4 @@
-from diffusers import DiffusionPipeline, AutoencoderTiny
+from diffusers import DiffusionPipeline, AutoencoderTiny, LCMScheduler
 from compel import Compel
 import torch
 
@@ -9,85 +9,108 @@ except:
 
 import psutil
 from config import Args
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 from PIL import Image
-from typing import Callable
 
-base_model = "SimianLuo/LCM_Dreamshaper_v7"
-WIDTH = 512
-HEIGHT = 512
-
-model_id = "wavymulder/Analog-Diffusion"
+base_model = "wavymulder/Analog-Diffusion"
 lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5"
+taesd_model = "madebyollin/taesd"
+
+default_prompt = "Analog style photograph of young Harrison Ford as Han Solo, star wars behind the scenes"
 
 
 class Pipeline:
+    class Info(BaseModel):
+        name: str = "txt2imglora"
+        title: str = "txt2imglora"
+        description: str = "Generates an image from a text prompt"
+        input_mode: str = "text"
+
     class InputParams(BaseModel):
-        seed: int = 2159232
-        prompt: str
-        guidance_scale: float = 8.0
-        strength: float = 0.5
-        steps: int = 4
-        lcm_steps: int = 50
-        width: int = WIDTH
-        height: int = HEIGHT
+        prompt: str = Field(
+            default_prompt,
+            title="Prompt",
+            field="textarea",
+            id="prompt",
+        )
+        seed: int = Field(
+            8638236174640251, min=0, title="Seed", field="seed", hide=True, id="seed"
+        )
+        steps: int = Field(
+            4, min=2, max=15, title="Steps", field="range", hide=True, id="steps"
+        )
+        width: int = Field(
+            512, min=2, max=15, title="Width", disabled=True, hide=True, id="width"
+        )
+        height: int = Field(
+            512, min=2, max=15, title="Height", disabled=True, hide=True, id="height"
+        )
+        guidance_scale: float = Field(
+            0.2,
+            min=0,
+            max=4,
+            step=0.001,
+            title="Guidance Scale",
+            field="range",
+            hide=True,
+            id="guidance_scale",
+        )
 
-    @staticmethod
-    def create_pipeline(
-        args: Args, device: torch.device, torch_dtype: torch.dtype
-    ) -> Callable[["Pipeline.InputParams"], Image.Image]:
+    def __init__(self, args: Args, device: torch.device, torch_dtype: torch.dtype):
         if args.safety_checker:
-            pipe = DiffusionPipeline.from_pretrained(base_model)
+            self.pipe = DiffusionPipeline.from_pretrained(base_model)
         else:
-            pipe = DiffusionPipeline.from_pretrained(base_model, safety_checker=None)
+            self.pipe = DiffusionPipeline.from_pretrained(
+                base_model, safety_checker=None
+            )
         if args.use_taesd:
-            pipe.vae = AutoencoderTiny.from_pretrained(
-                "madebyollin/taesd", torch_dtype=torch_dtype, use_safetensors=True
+            self.pipe.vae = AutoencoderTiny.from_pretrained(
+                taesd_model, torch_dtype=torch_dtype, use_safetensors=True
             )
+        self.pipe.scheduler = LCMScheduler.from_config(self.pipe.scheduler.config)
+        self.pipe.set_progress_bar_config(disable=True)
+        self.pipe.to(device=device, dtype=torch_dtype)
+        self.pipe.unet.to(memory_format=torch.channels_last)
 
-        pipe.set_progress_bar_config(disable=True)
-        pipe.to(device=device, dtype=torch_dtype)
-        pipe.unet.to(memory_format=torch.channels_last)
-
-        # Load LCM LoRA
-        pipe.load_lora_weights(lcm_lora_id, adapter_name="lcm")
         # check if computer has less than 64GB of RAM using sys or os
         if psutil.virtual_memory().total < 64 * 1024**3:
-            pipe.enable_attention_slicing()
+            self.pipe.enable_attention_slicing()
 
         if args.torch_compile:
-            pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
-            pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead", fullgraph=True)
+            self.pipe.unet = torch.compile(
+                self.pipe.unet, mode="reduce-overhead", fullgraph=True
+            )
+            self.pipe.vae = torch.compile(
+                self.pipe.vae, mode="reduce-overhead", fullgraph=True
+            )
 
-            pipe(prompt="warmup", num_inference_steps=1, guidance_scale=8.0)
+            self.pipe(prompt="warmup", num_inference_steps=1, guidance_scale=8.0)
 
-        compel_proc = Compel(
-            tokenizer=pipe.tokenizer,
-            text_encoder=pipe.text_encoder,
+        self.pipe.load_lora_weights(lcm_lora_id, adapter_name="lcm")
+
+        self.compel_proc = Compel(
+            tokenizer=self.pipe.tokenizer,
+            text_encoder=self.pipe.text_encoder,
             truncate_long_prompts=False,
         )
 
-        def predict(params: "Pipeline.InputParams") -> Image.Image:
-            generator = torch.manual_seed(params.seed)
-            prompt_embeds = compel_proc(params.prompt)
-            # Can be set to 1~50 steps. LCM support fast inference even <= 4 steps. Recommend: 1~8 steps.
-            results = pipe(
-                prompt_embeds=prompt_embeds,
-                generator=generator,
-                num_inference_steps=params.steps,
-                guidance_scale=params.guidance_scale,
-                width=params.width,
-                height=params.height,
-                original_inference_steps=params.lcm_steps,
-                output_type="pil",
-            )
-            nsfw_content_detected = (
-                results.nsfw_content_detected[0]
-                if "nsfw_content_detected" in results
-                else False
-            )
-            if nsfw_content_detected:
-                return None
-            return results.images[0]
-
-        return predict
+    def predict(self, params: "Pipeline.InputParams") -> Image.Image:
+        generator = torch.manual_seed(params.seed)
+        prompt_embeds = self.compel_proc(params.prompt)
+        results = self.pipe(
+            prompt_embeds=prompt_embeds,
+            generator=generator,
+            num_inference_steps=params.steps,
+            guidance_scale=params.guidance_scale,
+            width=params.width,
+            height=params.height,
+            output_type="pil",
+        )
+        nsfw_content_detected = (
+            results.nsfw_content_detected[0]
+            if "nsfw_content_detected" in results
+            else False
+        )
+        if nsfw_content_detected:
+            return None
+        return results.images[0]

static/txt2imglora.html

@@ -1,279 +0,0 @@
-<!doctype html>
-<html>
-
-<head>
-    <meta charset="UTF-8">
-    <title>Real-Time Latent Consistency Model</title>
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <script
-        src="https://cdnjs.cloudflare.com/ajax/libs/iframe-resizer/4.3.1/iframeResizer.contentWindow.min.js"></script>
-    <script src="https://cdn.jsdelivr.net/npm/[email protected]/piexif.min.js"></script>
-    <script src="https://cdn.tailwindcss.com"></script>
-    <style type="text/tailwindcss">
-        .button {
-          @apply bg-gray-700 hover:bg-gray-800 text-white font-normal p-2 rounded disabled:bg-gray-300 dark:disabled:bg-gray-700 disabled:cursor-not-allowed dark:disabled:text-black
-        }
-    </style>
-    <script type="module">
-        const getValue = (id) => {
-            const el = document.querySelector(`${id}`)
-            if (el.type === "checkbox")
-                return el.checked;
-            return el.value;
-        }
-        const startBtn = document.querySelector("#start");
-        const stopBtn = document.querySelector("#stop");
-        const videoEl = document.querySelector("#webcam");
-        const imageEl = document.querySelector("#player");
-        const queueSizeEl = document.querySelector("#queue_size");
-        const errorEl = document.querySelector("#error");
-        const snapBtn = document.querySelector("#snap");
-        const paramsEl = document.querySelector("#params");
-        const promptEl = document.querySelector("#prompt");
-        paramsEl.addEventListener("submit", (e) => e.preventDefault());
-        function LCMLive(promptEl, paramsEl, liveImage) {
-            let websocket;
-
-            async function start() {
-                return new Promise((resolve, reject) => {
-                    const websocketURL = `${window.location.protocol === "https:" ? "wss" : "ws"
-                        }:${window.location.host}/ws`;
-
-                    const socket = new WebSocket(websocketURL);
-                    socket.onopen = () => {
-                        console.log("Connected to websocket");
-                    };
-                    socket.onclose = () => {
-                        console.log("Disconnected from websocket");
-                        stop();
-                        resolve({ "status": "disconnected" });
-                    };
-                    socket.onerror = (err) => {
-                        console.error(err);
-                        reject(err);
-                    };
-                    socket.onmessage = (event) => {
-                        const data = JSON.parse(event.data);
-                        switch (data.status) {
-                            case "success":
-                                break;
-                            case "start":
-                                const userId = data.userId;
-                                initPromptStream(userId);
-                                break;
-                            case "timeout":
-                                stop();
-                                resolve({ "status": "timeout" });
-                            case "error":
-                                stop();
-                                reject(data.message);
-                        }
-                    };
-                    websocket = socket;
-                })
-            }
-
-            async function promptUpdateStream(e) {
-                const [WIDTH, HEIGHT] = [512, 512];
-                websocket.send(JSON.stringify({
-                    "seed": getValue("#seed"),
-                    "prompt": getValue("#prompt"),
-                    "guidance_scale": getValue("#guidance-scale"),
-                    "steps": getValue("#steps"),
-                    "width": WIDTH,
-                    "height": HEIGHT,
-                }));
-            }
-            function debouceInput(fn, delay) {
-                let timer;
-                return function (...args) {
-                    clearTimeout(timer);
-                    timer = setTimeout(() => {
-                        fn(...args);
-                    }, delay);
-                }
-            }
-            const debouncedInput = debouceInput(promptUpdateStream, 200);
-            function initPromptStream(userId) {
-                liveImage.src = `/stream/${userId}`;
-                paramsEl.addEventListener("change", debouncedInput);
-                promptEl.addEventListener("input", debouncedInput);
-            }
-
-            async function stop() {
-                websocket.close();
-                paramsEl.removeEventListener("change", debouncedInput);
-                promptEl.removeEventListener("input", debouncedInput);
-            }
-            return {
-                start,
-                stop
-            }
-        }
-        function toggleMessage(type) {
-            errorEl.hidden = false;
-            errorEl.scrollIntoView();
-            switch (type) {
-                case "error":
-                    errorEl.innerText = "To many users are using the same GPU, please try again later.";
-                    errorEl.classList.toggle("bg-red-300", "text-red-900");
-                    break;
-                case "success":
-                    errorEl.innerText = "Your session has ended, please start a new one.";
-                    errorEl.classList.toggle("bg-green-300", "text-green-900");
-                    break;
-            }
-            setTimeout(() => {
-                errorEl.hidden = true;
-            }, 2000);
-        }
-        function snapImage() {
-            try {
-                const zeroth = {};
-                const exif = {};
-                const gps = {};
-                zeroth[piexif.ImageIFD.Make] = "LCM Text-to-Image";
-                zeroth[piexif.ImageIFD.ImageDescription] = `prompt: ${getValue("#prompt")} | seed: ${getValue("#seed")} | guidance_scale: ${getValue("#guidance-scale")}  | steps: ${getValue("#steps")}`;
-                zeroth[piexif.ImageIFD.Software] = "https://github.com/radames/Real-Time-Latent-Consistency-Model";
-
-                exif[piexif.ExifIFD.DateTimeOriginal] = new Date().toISOString();
-
-                const exifObj = { "0th": zeroth, "Exif": exif, "GPS": gps };
-                const exifBytes = piexif.dump(exifObj);
-
-                const canvas = document.createElement("canvas");
-                canvas.width = imageEl.naturalWidth;
-                canvas.height = imageEl.naturalHeight;
-                const ctx = canvas.getContext("2d");
-                ctx.drawImage(imageEl, 0, 0);
-                const dataURL = canvas.toDataURL("image/jpeg");
-                const withExif = piexif.insert(exifBytes, dataURL);
-
-                const a = document.createElement("a");
-                a.href = withExif;
-                a.download = `lcm_txt_2_img${Date.now()}.png`;
-                a.click();
-            } catch (err) {
-                console.log(err);
-            }
-        }
-
-
-        const lcmLive = LCMLive(promptEl, paramsEl, imageEl);
-        startBtn.addEventListener("click", async () => {
-            try {
-                startBtn.disabled = true;
-                snapBtn.disabled = false;
-                const res = await lcmLive.start();
-                startBtn.disabled = false;
-                if (res.status === "timeout")
-                    toggleMessage("success")
-            } catch (err) {
-                console.log(err);
-                toggleMessage("error")
-                startBtn.disabled = false;
-            }
-        });
-        stopBtn.addEventListener("click", () => {
-            lcmLive.stop();
-        });
-        window.addEventListener("beforeunload", () => {
-            lcmLive.stop();
-        });
-        snapBtn.addEventListener("click", snapImage);
-        setInterval(() =>
-            fetch("/queue_size")
-                .then((res) => res.json())
-                .then((data) => {
-                    queueSizeEl.innerText = data.queue_size;
-                })
-                .catch((err) => {
-                    console.log(err);
-                })
-            , 5000);
-    </script>
-</head>
-
-<body class="text-black dark:bg-gray-900 dark:text-white">
-    <div class="fixed right-2 top-2 p-4 font-bold text-sm rounded-lg max-w-xs text-center" id="error">
-    </div>  
-    <main class="container mx-auto px-4 py-4 max-w-4xl flex flex-col gap-4">
-        <article class="text-center max-w-xl mx-auto">
-            <h1 class="text-3xl font-bold">Real-Time Latent Consistency Model</h1>
-            <h2 class="text-2xl font-bold mb-4">Text to Image Lora</h2>
-            <p class="text-sm">
-                This demo showcases
-                <a href="https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7" target="_blank"
-                    class="text-blue-500 underline hover:no-underline">LCM</a> Text to Image model
-                using
-                <a href="https://github.com/huggingface/diffusers/tree/main/examples/community#latent-consistency-pipeline"
-                    target="_blank" class="text-blue-500 underline hover:no-underline">Diffusers</a> with a MJPEG
-                stream server. Featuring <a href="https://huggingface.co/wavymulder/Analog-Diffusion" target="_blank"
-                    class="text-blue-500 underline hover:no-underline">Analog Diffusion</a> Model.
-            </p>
-            <p class="text-sm">
-                There are <span id="queue_size" class="font-bold">0</span> user(s) sharing the same GPU, affecting
-                real-time performance.
-            </p>
-        </article>
-        <div>
-            <h2 class="font-medium">Prompt</h2>
-            <p class="text-sm text-gray-500 dark:text-gray-400">
-                Start your session and type your prompt here, accepts
-                <a href="https://github.com/damian0815/compel/blob/main/doc/syntax.md" target="_blank"
-                    class="text-blue-500 underline hover:no-underline">Compel</a> syntax.
-            </p>
-            <div class="flex text-normal px-1 py-1 border border-gray-700 rounded-md items-center">
-                <textarea type="text" id="prompt" class="font-light w-full px-3 py-2 mx-1  outline-none dark:text-black"
-                    title=" Start your session and type your prompt here, you can see the result in real-time."
-                    placeholder="Add your prompt here...">Analog style photograph of young Harrison Ford as Han Solo, star wars behind the scenes</textarea>
-            </div>
-
-        </div>
-        <div class="">
-            <details>
-                <summary class="font-medium cursor-pointer">Advanced Options</summary>
-                <form class="grid grid-cols-3 items-center gap-3 py-3" id="params" action="">
-                    <label class="text-sm font-medium " for="steps">Inference Steps
-                    </label>
-                    <input type="range" id="steps" name="steps" min="2" max="10" value="4"
-                        oninput="this.nextElementSibling.value = Number(this.value)">
-                    <output class="text-xs w-[50px] text-center font-light px-1 py-1 border border-gray-700 rounded-md">
-                        4</output>
-                    <label class="text-sm font-medium" for="guidance-scale">Guidance Scale
-                    </label>
-                    <input type="range" id="guidance-scale" name="guidance-scale" min="0" max="5" step="0.0001"
-                        value="0.8" oninput="this.nextElementSibling.value = Number(this.value).toFixed(2)">
-                    <output class="text-xs w-[50px] text-center font-light px-1 py-1 border border-gray-700 rounded-md">
-                        0.8</output>
-                    <!--  -->
-                    <label class="text-sm font-medium" for="seed">Seed</label>
-                    <input type="number" id="seed" name="seed" value="299792458"
-                        class="font-light border border-gray-700 text-right rounded-md p-2 dark:text-black">
-                    <button class="button"
-                        onclick="document.querySelector('#seed').value = Math.floor(Math.random() * 1000000000); document.querySelector('#params').dispatchEvent(new Event('change'))">
-                        Rand
-                    </button>
-                    <!--  -->
-                </form>
-            </details>
-        </div>
-        <div class="flex gap-3">
-            <button id="start" class="button">
-                Start
-            </button>
-            <button id="stop" class="button">
-                Stop
-            </button>
-            <button id="snap" disabled class="button ml-auto">
-                Snapshot
-            </button>
-        </div>
-        <div class="relative rounded-lg border border-slate-300 overflow-hidden">
-            <img id="player" class="w-full aspect-square rounded-lg"
-                src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNkYAAAAAYAAjCB0C8AAAAASUVORK5CYII=">
-        </div>
-    </main>
-</body>
-
-</html>