neuralleap/CogVideoX-5B-API-V2

1"""2THis is the main file for the gradio web demo. It uses the CogVideoX-5B model to generate videos gradio web demo.3set environment variable OPENAI_API_KEY to use the OpenAI API to enhance the prompt.4 5Usage:6    OpenAI_API_KEY=your_openai_api_key OPENAI_BASE_URL=https://api.openai.com/v1 python inference/gradio_web_demo.py7"""8 9import math10import os11import random12import threading13import time14 15import cv216import tempfile17import imageio_ffmpeg18import gradio as gr19import torch20from PIL import Image21from diffusers import (22    CogVideoXPipeline,23    CogVideoXDPMScheduler,24    CogVideoXVideoToVideoPipeline,25    CogVideoXImageToVideoPipeline,26    CogVideoXTransformer3DModel,27)28from diffusers.utils import load_video, load_image29from datetime import datetime, timedelta30 31from diffusers.image_processor import VaeImageProcessor32from openai import OpenAI33import moviepy.editor as mp34import utils35from rife_model import load_rife_model, rife_inference_with_latents36from huggingface_hub import hf_hub_download, snapshot_download37import gc38 39from fastapi import FastAPI, HTTPException40from fastapi.middleware.cors import CORSMiddleware41from fastapi.responses import FileResponse42from huggingface_hub import hf_hub_download, snapshot_download43 44from data_class.GenerateResponse import GenerateResponse45from data_class.GenerateRequest import GenerateRequest46 47app = FastAPI()48 49# CORS Configuration50app.add_middleware(51    CORSMiddleware,52    allow_origins=["*"],53    allow_credentials=True,54    allow_methods=["*"],55    allow_headers=["*"],56)57 58@app.get("/info")59def get_root():60    return {"message": "TestInfo"}61    62device = "cuda" if torch.cuda.is_available() else "cpu"63 64hf_hub_download(repo_id="ai-forever/Real-ESRGAN", filename="RealESRGAN_x4.pth", local_dir="model_real_esran")65snapshot_download(repo_id="AlexWortega/RIFE", local_dir="model_rife")66 67pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-5b", torch_dtype=torch.bfloat16).to("cpu")68pipe.scheduler = CogVideoXDPMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")69 70i2v_transformer = CogVideoXTransformer3DModel.from_pretrained(71    "THUDM/CogVideoX-5b-I2V", subfolder="transformer", torch_dtype=torch.bfloat1672)73 74# pipe.transformer.to(memory_format=torch.channels_last)75# pipe.transformer = torch.compile(pipe.transformer, mode="max-autotune", fullgraph=True)76# pipe_image.transformer.to(memory_format=torch.channels_last)77# pipe_image.transformer = torch.compile(pipe_image.transformer, mode="max-autotune", fullgraph=True)78 79os.makedirs("./output", exist_ok=True)80os.makedirs("./gradio_tmp", exist_ok=True)81 82upscale_model = utils.load_sd_upscale("model_real_esran/RealESRGAN_x4.pth", device)83frame_interpolation_model = load_rife_model("model_rife")84 85sys_prompt = """You are part of a team of bots that creates videos. You work with an assistant bot that will draw anything you say in square brackets.86 87For example , outputting " a beautiful morning in the woods with the sun peaking through the trees " will trigger your partner bot to output an video of a forest morning , as described. You will be prompted by people looking to create detailed , amazing videos. The way to accomplish this is to take their short prompts and make them extremely detailed and descriptive.88There are a few rules to follow:89 90You will only ever output a single video description per user request.91 92When modifications are requested , you should not simply make the description longer . You should refactor the entire description to integrate the suggestions.93Other times the user will not want modifications , but instead want a new image . In this case , you should ignore your previous conversation with the user.94 95Video descriptions must have the same num of words as examples below. Extra words will be ignored.96"""97 98 99def resize_if_unfit(input_video, progress=gr.Progress(track_tqdm=True)):100    width, height = get_video_dimensions(input_video)101 102    if width == 720 and height == 480:103        processed_video = input_video104    else:105        processed_video = center_crop_resize(input_video)106    return processed_video107 108 109def get_video_dimensions(input_video_path):110    reader = imageio_ffmpeg.read_frames(input_video_path)111    metadata = next(reader)112    return metadata["size"]113 114 115def center_crop_resize(input_video_path, target_width=720, target_height=480):116    cap = cv2.VideoCapture(input_video_path)117 118    orig_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))119    orig_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))120    orig_fps = cap.get(cv2.CAP_PROP_FPS)121    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))122 123    width_factor = target_width / orig_width124    height_factor = target_height / orig_height125    resize_factor = max(width_factor, height_factor)126 127    inter_width = int(orig_width * resize_factor)128    inter_height = int(orig_height * resize_factor)129 130    target_fps = 8131    ideal_skip = max(0, math.ceil(orig_fps / target_fps) - 1)132    skip = min(5, ideal_skip)  # Cap at 5133 134    while (total_frames / (skip + 1)) < 49 and skip > 0:135        skip -= 1136 137    processed_frames = []138    frame_count = 0139    total_read = 0140 141    while frame_count < 49 and total_read < total_frames:142        ret, frame = cap.read()143        if not ret:144            break145 146        if total_read % (skip + 1) == 0:147            resized = cv2.resize(frame, (inter_width, inter_height), interpolation=cv2.INTER_AREA)148 149            start_x = (inter_width - target_width) // 2150            start_y = (inter_height - target_height) // 2151            cropped = resized[start_y : start_y + target_height, start_x : start_x + target_width]152 153            processed_frames.append(cropped)154            frame_count += 1155 156        total_read += 1157 158    cap.release()159 160    with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as temp_file:161        temp_video_path = temp_file.name162        fourcc = cv2.VideoWriter_fourcc(*"mp4v")163        out = cv2.VideoWriter(temp_video_path, fourcc, target_fps, (target_width, target_height))164 165        for frame in processed_frames:166            out.write(frame)167 168        out.release()169 170    return temp_video_path171 172 173def convert_prompt(prompt: str, retry_times: int = 3) -> str:174    if not os.environ.get("OPENAI_API_KEY"):175        return prompt176    client = OpenAI()177    text = prompt.strip()178 179    for i in range(retry_times):180        response = client.chat.completions.create(181            messages=[182                {"role": "system", "content": sys_prompt},183                {184                    "role": "user",185                    "content": 'Create an imaginative video descriptive caption or modify an earlier caption for the user input : "a girl is on the beach"',186                },187                {188                    "role": "assistant",189                    "content": "A radiant woman stands on a deserted beach, arms outstretched, wearing a beige trench coat, white blouse, light blue jeans, and chic boots, against a backdrop of soft sky and sea. Moments later, she is seen mid-twirl, arms exuberant, with the lighting suggesting dawn or dusk. Then, she runs along the beach, her attire complemented by an off-white scarf and black ankle boots, the tranquil sea behind her. Finally, she holds a paper airplane, her pose reflecting joy and freedom, with the ocean's gentle waves and the sky's soft pastel hues enhancing the serene ambiance.",190                },191                {192                    "role": "user",193                    "content": 'Create an imaginative video descriptive caption or modify an earlier caption for the user input : "A man jogging on a football field"',194                },195                {196                    "role": "assistant",197                    "content": "A determined man in athletic attire, including a blue long-sleeve shirt, black shorts, and blue socks, jogs around a snow-covered soccer field, showcasing his solitary exercise in a quiet, overcast setting. His long dreadlocks, focused expression, and the serene winter backdrop highlight his dedication to fitness. As he moves, his attire, consisting of a blue sports sweatshirt, black athletic pants, gloves, and sneakers, grips the snowy ground. He is seen running past a chain-link fence enclosing the playground area, with a basketball hoop and children's slide, suggesting a moment of solitary exercise amidst the empty field.",198                },199                {200                    "role": "user",201                    "content": 'Create an imaginative video descriptive caption or modify an earlier caption for the user input : " A woman is dancing, HD footage, close-up"',202                },203                {204                    "role": "assistant",205                    "content": "A young woman with her hair in an updo and wearing a teal hoodie stands against a light backdrop, initially looking over her shoulder with a contemplative expression. She then confidently makes a subtle dance move, suggesting rhythm and movement. Next, she appears poised and focused, looking directly at the camera. Her expression shifts to one of introspection as she gazes downward slightly. Finally, she dances with confidence, her left hand over her heart, symbolizing a poignant moment, all while dressed in the same teal hoodie against a plain, light-colored background.",206                },207                {208                    "role": "user",209                    "content": f'Create an imaginative video descriptive caption or modify an earlier caption in ENGLISH for the user input: "{text}"',210                },211            ],212            model="glm-4-plus",213            temperature=0.01,214            top_p=0.7,215            stream=False,216            max_tokens=200,217        )218        if response.choices:219            return response.choices[0].message.content220    return prompt221 222 223def infer(224    prompt: str,225    image_input: str,226    video_input: str,227    video_strenght: float,228    num_inference_steps: int,229    guidance_scale: float,230    seed: int = -1,231    progress=gr.Progress(track_tqdm=True),232):233    if seed == -1:234        seed = random.randint(0, 2**8 - 1)235 236    if video_input is not None:237        video = load_video(video_input)[:49]  # Limit to 49 frames238        pipe_video = CogVideoXVideoToVideoPipeline.from_pretrained(239            "THUDM/CogVideoX-5b",240            transformer=pipe.transformer,241            vae=pipe.vae,242            scheduler=pipe.scheduler,243            tokenizer=pipe.tokenizer,244            text_encoder=pipe.text_encoder,245            torch_dtype=torch.bfloat16,246        ).to(device)247        video_pt = pipe_video(248            video=video,249            prompt=prompt,250            num_inference_steps=num_inference_steps,251            num_videos_per_prompt=1,252            strength=video_strenght,253            use_dynamic_cfg=True,254            output_type="pt",255            guidance_scale=guidance_scale,256            generator=torch.Generator(device="cpu").manual_seed(seed),257        ).frames258        pipe_video.to("cpu")259        del pipe_video260        gc.collect()261        torch.cuda.empty_cache()262    elif image_input is not None:263        pipe_image = CogVideoXImageToVideoPipeline.from_pretrained(264            "THUDM/CogVideoX-5b-I2V",265            transformer=i2v_transformer,266            vae=pipe.vae,267            scheduler=pipe.scheduler,268            tokenizer=pipe.tokenizer,269            text_encoder=pipe.text_encoder,270            torch_dtype=torch.bfloat16,271        ).to(device)272        image_input = Image.fromarray(image_input).resize(size=(720, 480))  # Convert to PIL273        image = load_image(image_input)274        video_pt = pipe_image(275            image=image,276            prompt=prompt,277            num_inference_steps=num_inference_steps,278            num_videos_per_prompt=1,279            use_dynamic_cfg=True,280            output_type="pt",281            guidance_scale=guidance_scale,282            generator=torch.Generator(device="cpu").manual_seed(seed),283        ).frames284        pipe_image.to("cpu")285        del pipe_image286        gc.collect()287        torch.cuda.empty_cache()288    else:289        pipe.to(device)290        video_pt = pipe(291            prompt=prompt,292            num_videos_per_prompt=1,293            num_inference_steps=num_inference_steps,294            num_frames=49,295            use_dynamic_cfg=True,296            output_type="pt",297            guidance_scale=guidance_scale,298            generator=torch.Generator(device="cpu").manual_seed(seed),299        ).frames300        pipe.to("cpu")301        gc.collect()302    return (video_pt, seed)303 304 305def convert_to_gif(video_path):306    clip = mp.VideoFileClip(video_path)307    clip = clip.set_fps(8)308    clip = clip.resize(height=240)309    gif_path = video_path.replace(".mp4", ".gif")310    clip.write_gif(gif_path, fps=8)311    return gif_path312 313 314def delete_old_files():315    while True:316        now = datetime.now()317        cutoff = now - timedelta(minutes=10)318        directories = ["./output", "./gradio_tmp"]319 320        for directory in directories:321            for filename in os.listdir(directory):322                file_path = os.path.join(directory, filename)323                if os.path.isfile(file_path):324                    file_mtime = datetime.fromtimestamp(os.path.getmtime(file_path))325                    if file_mtime < cutoff:326                        os.remove(file_path)327        time.sleep(600)328 329 330threading.Thread(target=delete_old_files, daemon=True).start()331examples_videos = [["example_videos/horse.mp4"], ["example_videos/kitten.mp4"], ["example_videos/train_running.mp4"]]332examples_images = [["example_images/beach.png"], ["example_images/street.png"], ["example_images/camping.png"]]333 334with gr.Blocks() as demo:335    gr.Markdown("""336           <div style="text-align: center; font-size: 32px; font-weight: bold; margin-bottom: 20px;">337               CogVideoX-5B Huggingface Space🤗338           </div>339           <div style="text-align: center;">340               <a href="https://huggingface.co/THUDM/CogVideoX-5B">🤗 5B(T2V) Model Hub</a> |341               <a href="https://huggingface.co/THUDM/CogVideoX-5B-I2V">🤗 5B(I2V) Model Hub</a> |342               <a href="https://github.com/THUDM/CogVideo">🌐 Github</a> |343               <a href="https://arxiv.org/pdf/2408.06072">📜 arxiv </a>344           </div>345           <div style="text-align: center;display: flex;justify-content: center;align-items: center;margin-top: 1em;margin-bottom: .5em;">346              <span>If the Space is too busy, duplicate it to use privately</span>347              <a href="https://huggingface.co/spaces/THUDM/CogVideoX-5B-Space?duplicate=true"><img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-lg.svg" width="160" style="348                margin-left: .75em;349            "></a>350           </div>351           <div style="text-align: center; font-size: 15px; font-weight: bold; color: red; margin-bottom: 20px;">352            ⚠️ This demo is for academic research and experiential use only. 353            </div>354           """)355    with gr.Row():356        with gr.Column():357            with gr.Accordion("I2V: Image Input (cannot be used simultaneously with video input)", open=False):358                image_input = gr.Image(label="Input Image (will be cropped to 720 * 480)")359                examples_component_images = gr.Examples(examples_images, inputs=[image_input], cache_examples=False)360            with gr.Accordion("V2V: Video Input (cannot be used simultaneously with image input)", open=False):361                video_input = gr.Video(label="Input Video (will be cropped to 49 frames, 6 seconds at 8fps)")362                strength = gr.Slider(0.1, 1.0, value=0.8, step=0.01, label="Strength")363                examples_component_videos = gr.Examples(examples_videos, inputs=[video_input], cache_examples=False)364            prompt = gr.Textbox(label="Prompt (Less than 200 Words)", placeholder="Enter your prompt here", lines=5)365 366            with gr.Row():367                gr.Markdown(368                    "✨Upon pressing the enhanced prompt button, we will use [GLM-4 Model](https://github.com/THUDM/GLM-4) to polish the prompt and overwrite the original one."369                )370                enhance_button = gr.Button("✨ Enhance Prompt(Optional)")371            with gr.Group():372                with gr.Column():373                    with gr.Row():374                        seed_param = gr.Number(375                            label="Inference Seed (Enter a positive number, -1 for random)", value=-1376                        )377                    with gr.Row():378                        enable_scale = gr.Checkbox(label="Super-Resolution (720 × 480 -> 2880 × 1920)", value=False)379                        enable_rife = gr.Checkbox(label="Frame Interpolation (8fps -> 16fps)", value=False)380                    gr.Markdown(381                        "✨In this demo, we use [RIFE](https://github.com/hzwer/ECCV2022-RIFE) for frame interpolation and [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) for upscaling(Super-Resolution).<br>&nbsp;&nbsp;&nbsp;&nbsp;The entire process is based on open-source solutions."382                    )383 384            generate_button = gr.Button("🎬 Generate Video")385 386        with gr.Column():387            video_output = gr.Video(label="CogVideoX Generate Video", width=720, height=480)388            with gr.Row():389                download_video_button = gr.File(label="📥 Download Video", visible=False)390                download_gif_button = gr.File(label="📥 Download GIF", visible=False)391                seed_text = gr.Number(label="Seed Used for Video Generation", visible=False)392 393    gr.Markdown("""394    <table border="0" style="width: 100%; text-align: left; margin-top: 20px;">395        <div style="text-align: center; font-size: 32px; font-weight: bold; margin-bottom: 20px;">396            🎥 Video Gallery397        </div>398        <tr>399            <td style="width: 25%; vertical-align: top; font-size: 0.9em;">400                <p>A garden comes to life as a kaleidoscope of butterflies flutters amidst the blossoms, their delicate wings casting shadows on the petals below. In the background, a grand fountain cascades water with a gentle splendor, its rhythmic sound providing a soothing backdrop. Beneath the cool shade of a mature tree, a solitary wooden chair invites solitude and reflection, its smooth surface worn by the touch of countless visitors seeking a moment of tranquility in nature's embrace.</p>401            </td>402            <td style="width: 25%; vertical-align: top;">403                <video src="https://github.com/user-attachments/assets/cf5953ea-96d3-48fd-9907-c4708752c714" width="100%" controls autoplay loop></video>404            </td>405            <td style="width: 25%; vertical-align: top; font-size: 0.9em;">406                <p>A small boy, head bowed and determination etched on his face, sprints through the torrential downpour as lightning crackles and thunder rumbles in the distance. The relentless rain pounds the ground, creating a chaotic dance of water droplets that mirror the dramatic sky's anger. In the far background, the silhouette of a cozy home beckons, a faint beacon of safety and warmth amidst the fierce weather. The scene is one of perseverance and the unyielding spirit of a child braving the elements.</p>407            </td>408            <td style="width: 25%; vertical-align: top;">409                <video src="https://github.com/user-attachments/assets/fe0a78e6-b669-4800-8cf0-b5f9b5145b52" width="100%" controls autoplay loop></video>410            </td>411        </tr>412        <tr>413            <td style="width: 25%; vertical-align: top; font-size: 0.9em;">414                <p>A suited astronaut, with the red dust of Mars clinging to their boots, reaches out to shake hands with an alien being, their skin a shimmering blue, under the pink-tinged sky of the fourth planet. In the background, a sleek silver rocket, a beacon of human ingenuity, stands tall, its engines powered down, as the two representatives of different worlds exchange a historic greeting amidst the desolate beauty of the Martian landscape.</p>415            </td>416            <td style="width: 25%; vertical-align: top;">417                <video src="https://github.com/user-attachments/assets/c182f606-8f8c-421d-b414-8487070fcfcb" width="100%" controls autoplay loop></video>418            </td>419            <td style="width: 25%; vertical-align: top; font-size: 0.9em;">420                <p>An elderly gentleman, with a serene expression, sits at the water's edge, a steaming cup of tea by his side. He is engrossed in his artwork, brush in hand, as he renders an oil painting on a canvas that's propped up against a small, weathered table. The sea breeze whispers through his silver hair, gently billowing his loose-fitting white shirt, while the salty air adds an intangible element to his masterpiece in progress. The scene is one of tranquility and inspiration, with the artist's canvas capturing the vibrant hues of the setting sun reflecting off the tranquil sea.</p>421            </td>422            <td style="width: 25%; vertical-align: top;">423                <video src="https://github.com/user-attachments/assets/7db2bbce-194d-434d-a605-350254b6c298" width="100%" controls autoplay loop></video>424            </td>425        </tr>426        <tr>427            <td style="width: 25%; vertical-align: top; font-size: 0.9em;">428                <p>In a dimly lit bar, purplish light bathes the face of a mature man, his eyes blinking thoughtfully as he ponders in close-up, the background artfully blurred to focus on his introspective expression, the ambiance of the bar a mere suggestion of shadows and soft lighting.</p>429            </td>430            <td style="width: 25%; vertical-align: top;">431                <video src="https://github.com/user-attachments/assets/62b01046-8cab-44cc-bd45-4d965bb615ec" width="100%" controls autoplay loop></video>432            </td>433            <td style="width: 25%; vertical-align: top; font-size: 0.9em;">434                <p>A golden retriever, sporting sleek black sunglasses, with its lengthy fur flowing in the breeze, sprints playfully across a rooftop terrace, recently refreshed by a light rain. The scene unfolds from a distance, the dog's energetic bounds growing larger as it approaches the camera, its tail wagging with unrestrained joy, while droplets of water glisten on the concrete behind it. The overcast sky provides a dramatic backdrop, emphasizing the vibrant golden coat of the canine as it dashes towards the viewer.</p>435            </td>436            <td style="width: 25%; vertical-align: top;">437                <video src="https://github.com/user-attachments/assets/d78e552a-4b3f-4b81-ac3f-3898079554f6" width="100%" controls autoplay loop></video>438            </td>439        </tr>440        <tr>441            <td style="width: 25%; vertical-align: top; font-size: 0.9em;">442                <p>On a brilliant sunny day, the lakeshore is lined with an array of willow trees, their slender branches swaying gently in the soft breeze. The tranquil surface of the lake reflects the clear blue sky, while several elegant swans glide gracefully through the still water, leaving behind delicate ripples that disturb the mirror-like quality of the lake. The scene is one of serene beauty, with the willows' greenery providing a picturesque frame for the peaceful avian visitors.</p>443            </td>444            <td style="width: 25%; vertical-align: top;">445                <video src="https://github.com/user-attachments/assets/30894f12-c741-44a2-9e6e-ddcacc231e5b" width="100%" controls autoplay loop></video>446            </td>447            <td style="width: 25%; vertical-align: top; font-size: 0.9em;">448                <p>A Chinese mother, draped in a soft, pastel-colored robe, gently rocks back and forth in a cozy rocking chair positioned in the tranquil setting of a nursery. The dimly lit bedroom is adorned with whimsical mobiles dangling from the ceiling, casting shadows that dance on the walls. Her baby, swaddled in a delicate, patterned blanket, rests against her chest, the child's earlier cries now replaced by contented coos as the mother's soothing voice lulls the little one to sleep. The scent of lavender fills the air, adding to the serene atmosphere, while a warm, orange glow from a nearby nightlight illuminates the scene with a gentle hue, capturing a moment of tender love and comfort.</p>449            </td>450            <td style="width: 25%; vertical-align: top;">451                <video src="https://github.com/user-attachments/assets/926575ca-7150-435b-a0ff-4900a963297b" width="100%" controls autoplay loop></video>452            </td>453        </tr>454    </table>455        """)456 457    def generate(458        prompt,459        image_input,460        video_input,461        video_strength,462        seed_value,463        scale_status,464        rife_status,465        progress=gr.Progress(track_tqdm=True)466    ):467        latents, seed = infer(468            prompt,469            image_input,470            video_input,471            video_strength,472            num_inference_steps=50,  # NOT Changed473            guidance_scale=7.0,  # NOT Changed474            seed=seed_value,475            progress=progress,476        )477        if scale_status:478            latents = utils.upscale_batch_and_concatenate(upscale_model, latents, device)479        if rife_status:480            latents = rife_inference_with_latents(frame_interpolation_model, latents)481 482        batch_size = latents.shape[0]483        batch_video_frames = []484        for batch_idx in range(batch_size):485            pt_image = latents[batch_idx]486            pt_image = torch.stack([pt_image[i] for i in range(pt_image.shape[0])])487 488            image_np = VaeImageProcessor.pt_to_numpy(pt_image)489            image_pil = VaeImageProcessor.numpy_to_pil(image_np)490            batch_video_frames.append(image_pil)491 492        video_path = utils.save_video(batch_video_frames[0], fps=math.ceil((len(batch_video_frames[0]) - 1) / 6))493        video_update = gr.update(visible=True, value=video_path)494        gif_path = convert_to_gif(video_path)495        gif_update = gr.update(visible=True, value=gif_path)496        seed_update = gr.update(visible=True, value=seed)497 498        return video_path, video_update, gif_update, seed_update499 500    def enhance_prompt_func(prompt):501        return convert_prompt(prompt, retry_times=1)502 503    generate_button.click(504        generate,505        inputs=[prompt, image_input, video_input, strength, seed_param, enable_scale, enable_rife],506        outputs=[video_output, download_video_button, download_gif_button, seed_text],507    )508 509    enhance_button.click(enhance_prompt_func, inputs=[prompt], outputs=[prompt])510    video_input.upload(resize_if_unfit, inputs=[video_input], outputs=[video_input])511 512@app.get("/health")513def health_check():514    """Simple health check endpoint"""515    return {"status": "OK"}516 517 518if __name__ == "__main__":519    demo.queue(max_size=15)520    demo.launch()521