Update app.py

app.py

@@ -1,277 +1,355 @@
-import os
-import random
-from pathlib import Path
-import numpy as np
-import torch
-from diffusers import AutoencoderKL, DDIMScheduler
-from PIL import Image
-from src.models.unet_2d_condition import UNet2DConditionModel
-from src.models.unet_3d_emo import EMOUNet3DConditionModel
-from src.models.whisper.audio2feature import load_audio_model
-from src.pipelines.pipeline_echomimicv2 import EchoMimicV2Pipeline
-from src.utils.util import save_videos_grid
-from src.models.pose_encoder import PoseEncoder
-from src.utils.dwpose_util import draw_pose_select_v2
-from moviepy.editor import VideoFileClip, AudioFileClip
-
-import gradio as gr
-from datetime import datetime
-from torchao.quantization import quantize_, int8_weight_only
-import gc
-
-total_vram_in_gb = torch.cuda.get_device_properties(0).total_memory / 1073741824
-print(f'\033[32mCUDA版本：{torch.version.cuda}\033[0m')
-print(f'\033[32mPytorch版本：{torch.__version__}\033[0m')
-print(f'\033[32m显卡型号：{torch.cuda.get_device_name()}\033[0m')
-print(f'\033[32m显存大小：{total_vram_in_gb:.2f}GB\033[0m')
-print(f'\033[32m精度：float16\033[0m')
-dtype = torch.float16
-if torch.cuda.is_available():
-        device = "cuda"
-else:
-    print("cuda not available, using cpu")
-    device = "cpu"
-
-ffmpeg_path = os.getenv('FFMPEG_PATH')
-if ffmpeg_path is None:
-    print("please download ffmpeg-static and export to FFMPEG_PATH. \nFor example: export FFMPEG_PATH=./ffmpeg-4.4-amd64-static")
-elif ffmpeg_path not in os.getenv('PATH'):
-    print("add ffmpeg to path")
-    os.environ["PATH"] = f"{ffmpeg_path}:{os.environ['PATH']}"
-
-
-def generate(image_input, audio_input, pose_input, width, height, length, steps, sample_rate, cfg, fps, context_frames, context_overlap, quantization_input, seed):
-    gc.collect()
-    torch.cuda.empty_cache()
-    torch.cuda.ipc_collect()
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    save_dir = Path("outputs")
-    save_dir.mkdir(exist_ok=True, parents=True)
-
-    ############# model_init started #############
-    ## vae init
-    vae = AutoencoderKL.from_pretrained("./pretrained_weights/sd-vae-ft-mse").to(device, dtype=dtype)
-    if quantization_input:
-        quantize_(vae, int8_weight_only())
-        print("使用int8量化")
-
-    ## reference net init
-    reference_unet = UNet2DConditionModel.from_pretrained("./pretrained_weights/sd-image-variations-diffusers", subfolder="unet", use_safetensors=False).to(dtype=dtype, device=device)
-    reference_unet.load_state_dict(torch.load("./pretrained_weights/reference_unet.pth", weights_only=True))
-    if quantization_input:
-        quantize_(reference_unet, int8_weight_only())
-
-    ## denoising net init
-    if os.path.exists("./pretrained_weights/motion_module.pth"):
-        print('using motion module')
-    else:
-        exit("motion module not found")
-        ### stage1 + stage2
-    denoising_unet = EMOUNet3DConditionModel.from_pretrained_2d(
-        "./pretrained_weights/sd-image-variations-diffusers",
-        "./pretrained_weights/motion_module.pth",
-        subfolder="unet",
-        unet_additional_kwargs = {
-            "use_inflated_groupnorm": True,
-            "unet_use_cross_frame_attention": False,
-            "unet_use_temporal_attention": False,
-            "use_motion_module": True,
-            "cross_attention_dim": 384,
-            "motion_module_resolutions": [
-                1,
-                2,
-                4,
-                8
-            ],
-            "motion_module_mid_block": True ,
-            "motion_module_decoder_only": False,
-            "motion_module_type": "Vanilla",
-            "motion_module_kwargs":{
-                "num_attention_heads": 8,
-                "num_transformer_block": 1,
-                "attention_block_types": [
-                    'Temporal_Self',
-                    'Temporal_Self'
-                ],
-                "temporal_position_encoding": True,
-                "temporal_position_encoding_max_len": 32,
-                "temporal_attention_dim_div": 1,
-            }
-        },
-    ).to(dtype=dtype, device=device)
-    denoising_unet.load_state_dict(torch.load("./pretrained_weights/denoising_unet.pth", weights_only=True),strict=False)
-
-    # pose net init
-    pose_net = PoseEncoder(320, conditioning_channels=3, block_out_channels=(16, 32, 96, 256)).to(dtype=dtype, device=device)
-    pose_net.load_state_dict(torch.load("./pretrained_weights/pose_encoder.pth", weights_only=True))
-
-    ### load audio processor params
-    audio_processor = load_audio_model(model_path="./pretrained_weights/audio_processor/tiny.pt", device=device)
-   
-    ############# model_init finished #############
-    sched_kwargs = {
-        "beta_start": 0.00085,
-        "beta_end": 0.012,
-        "beta_schedule": "linear",
-        "clip_sample": False,
-        "steps_offset": 1,
-        "prediction_type": "v_prediction",
-        "rescale_betas_zero_snr": True,
-        "timestep_spacing": "trailing"
-    }
-    scheduler = DDIMScheduler(**sched_kwargs)
-
-    pipe = EchoMimicV2Pipeline(
-        vae=vae,
-        reference_unet=reference_unet,
-        denoising_unet=denoising_unet,
-        audio_guider=audio_processor,
-        pose_encoder=pose_net,
-        scheduler=scheduler,
-    )
-
-    pipe = pipe.to(device, dtype=dtype)
-
-    if seed is not None and seed > -1:
-        generator = torch.manual_seed(seed)
-    else:
-        seed = random.randint(100, 1000000)
-        generator = torch.manual_seed(seed)
-
-    inputs_dict = {
-        "refimg": image_input,
-        "audio": audio_input,
-        "pose": pose_input,
-    }
-
-    print('Pose:', inputs_dict['pose'])
-    print('Reference:', inputs_dict['refimg'])
-    print('Audio:', inputs_dict['audio'])
-
-    save_name = f"{save_dir}/{timestamp}"
-    
-    ref_image_pil = Image.open(inputs_dict['refimg']).resize((width, height))
-    audio_clip = AudioFileClip(inputs_dict['audio'])
-    
-    length = min(length, int(audio_clip.duration * fps), len(os.listdir(inputs_dict['pose'])))
-
-    start_idx = 0
-
-    pose_list = []
-    for index in range(start_idx, start_idx + length):
-        tgt_musk = np.zeros((width, height, 3)).astype('uint8')
-        tgt_musk_path = os.path.join(inputs_dict['pose'], "{}.npy".format(index))
-        detected_pose = np.load(tgt_musk_path, allow_pickle=True).tolist()
-        imh_new, imw_new, rb, re, cb, ce = detected_pose['draw_pose_params']
-        im = draw_pose_select_v2(detected_pose, imh_new, imw_new, ref_w=800)
-        im = np.transpose(np.array(im),(1, 2, 0))
-        tgt_musk[rb:re,cb:ce,:] = im
-
-        tgt_musk_pil = Image.fromarray(np.array(tgt_musk)).convert('RGB')
-        pose_list.append(torch.Tensor(np.array(tgt_musk_pil)).to(dtype=dtype, device=device).permute(2,0,1) / 255.0)
-    
-    poses_tensor = torch.stack(pose_list, dim=1).unsqueeze(0)
-    audio_clip = AudioFileClip(inputs_dict['audio'])
-    
-    audio_clip = audio_clip.set_duration(length / fps)
-    video = pipe(
-        ref_image_pil,
-        inputs_dict['audio'],
-        poses_tensor[:,:,:length,...],
-        width,
-        height,
-        length,
-        steps,
-        cfg,
-        generator=generator,
-        audio_sample_rate=sample_rate,
-        context_frames=context_frames,
-        fps=fps,
-        context_overlap=context_overlap,
-        start_idx=start_idx,
-    ).videos 
-    
-    final_length = min(video.shape[2], poses_tensor.shape[2], length)
-    video_sig = video[:, :, :final_length, :, :]
-    
-    save_videos_grid(
-        video_sig,
-        save_name + "_woa_sig.mp4",
-        n_rows=1,
-        fps=fps,
-    )
-
-    video_clip_sig = VideoFileClip(save_name + "_woa_sig.mp4",)
-    video_clip_sig = video_clip_sig.set_audio(audio_clip)
-    video_clip_sig.write_videofile(save_name + "_sig.mp4", codec="libx264", audio_codec="aac", threads=2)
-    video_output = save_name + "_sig.mp4"
-    seed_text = gr.update(visible=True, value=seed)
-    return video_output, seed_text
-
-
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("""
-            <div>
-                <h2 style="font-size: 30px;text-align: center;">EchoMimicV2</h2>
-            </div>
-            <div style="text-align: center;">
-                <a href="https://github.com/antgroup/echomimic_v2">🌐 Github</a> |
-                <a href="https://arxiv.org/abs/2411.10061">📜 arXiv </a>
-            </div>
-            <div style="text-align: center; font-weight: bold; color: red;">
-                ⚠️ 该演示仅供学术研究和体验使用。
-            </div>
-            
-            """)
-    with gr.Column():
-        with gr.Row():
-            with gr.Column():
-                with gr.Group():
-                    image_input = gr.Image(label="图像输入（自动缩放）", type="filepath")
-                    audio_input = gr.Audio(label="音频输入", type="filepath")
-                    pose_input = gr.Textbox(label="姿态输入（目录地址）", placeholder="请输入姿态数据的目录地址", value="assets/halfbody_demo/pose/01")
-                with gr.Group():
-                    with gr.Row():
-                        width = gr.Number(label="宽度（16的倍数，推荐768）", value=768)
-                        height = gr.Number(label="高度（16的倍数，推荐768）", value=768)
-                        length = gr.Number(label="视频长度，推荐240）", value=240)
-                    with gr.Row():
-                        steps = gr.Number(label="步骤（推荐30）", value=20)
-                        sample_rate = gr.Number(label="采样率（推荐16000）", value=16000)
-                        cfg = gr.Number(label="cfg（推荐2.5）", value=2.5, step=0.1)
-                    with gr.Row():
-                        fps = gr.Number(label="帧率（推荐24）", value=24)
-                        context_frames = gr.Number(label="上下文框架（推荐12）", value=12)
-                        context_overlap = gr.Number(label="上下文重叠（推荐3）", value=3)
-                    with gr.Row():
-                        quantization_input = gr.Checkbox(label="int8量化（推荐显存12G的用户开启，并使用不超过5秒的音频）", value=False)
-                        seed = gr.Number(label="种子(-1为随机)", value=-1)
-                generate_button = gr.Button("🎬 生成视频")
-            with gr.Column():
-                video_output = gr.Video(label="输出视频")
-                seed_text = gr.Textbox(label="种子", interactive=False, visible=False)
-        gr.Examples(
-            examples=[
-                ["EMTD_dataset/ref_imgs_by_FLUX/man/0001.png", "assets/halfbody_demo/audio/chinese/echomimicv2_man.wav"],
-                ["EMTD_dataset/ref_imgs_by_FLUX/woman/0077.png", "assets/halfbody_demo/audio/chinese/echomimicv2_woman.wav"],
-                ["EMTD_dataset/ref_imgs_by_FLUX/man/0003.png", "assets/halfbody_demo/audio/chinese/fighting.wav"],
-                ["EMTD_dataset/ref_imgs_by_FLUX/woman/0033.png", "assets/halfbody_demo/audio/chinese/good.wav"],
-                ["EMTD_dataset/ref_imgs_by_FLUX/man/0010.png", "assets/halfbody_demo/audio/chinese/news.wav"],
-                ["EMTD_dataset/ref_imgs_by_FLUX/man/1168.png", "assets/halfbody_demo/audio/chinese/no_smoking.wav"],
-                ["EMTD_dataset/ref_imgs_by_FLUX/woman/0057.png", "assets/halfbody_demo/audio/chinese/ultraman.wav"]
-            ],
-            inputs=[image_input, audio_input],  
-            label="预设人物及音频",
-        )
-
-    generate_button.click(
-        generate,
-        inputs=[image_input, audio_input, pose_input, width, height, length, steps, sample_rate, cfg, fps, context_frames, context_overlap, quantization_input, seed],
-        outputs=[video_output, seed_text],
-    )
-
-
-
-if __name__ == "__main__":
-    demo.queue()
-    demo.launch(inbrowser=True)
+import os
+import random
+from pathlib import Path
+import numpy as np
+import torch
+from diffusers import AutoencoderKL, DDIMScheduler
+from PIL import Image
+from src.models.unet_2d_condition import UNet2DConditionModel
+from src.models.unet_3d_emo import EMOUNet3DConditionModel
+from src.models.whisper.audio2feature import load_audio_model
+from src.pipelines.pipeline_echomimicv2 import EchoMimicV2Pipeline
+from src.utils.util import save_videos_grid
+from src.models.pose_encoder import PoseEncoder
+from src.utils.dwpose_util import draw_pose_select_v2
+from moviepy.editor import VideoFileClip, AudioFileClip
+
+import gradio as gr
+from datetime import datetime
+from torchao.quantization import quantize_, int8_weight_only
+import gc
+
+import requests
+import tarfile
+
+def download_and_setup_ffmpeg():
+    url = "https://www.johnvansickle.com/ffmpeg/old-releases/ffmpeg-4.4-amd64-static.tar.xz"
+    download_path = "ffmpeg-4.4-amd64-static.tar.xz"
+    extract_dir = "ffmpeg-4.4-amd64-static"
+    
+    try:
+        # Download the file
+        response = requests.get(url, stream=True)
+        response.raise_for_status()  # Check for HTTP request errors
+        with open(download_path, "wb") as file:
+            for chunk in response.iter_content(chunk_size=8192):
+                file.write(chunk)
+
+        # Extract the tar.xz file
+        with tarfile.open(download_path, "r:xz") as tar:
+            tar.extractall(path=extract_dir)
+        
+        # Set the FFMPEG_PATH environment variable
+        ffmpeg_binary_path = os.path.join(extract_dir, "ffmpeg-4.4-amd64-static", "ffmpeg")
+        os.environ["FFMPEG_PATH"] = ffmpeg_binary_path
+        
+        return f"FFmpeg downloaded and setup successfully! Path: {ffmpeg_binary_path}"
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
+
+download_and_setup_ffmpeg()
+
+from huggingface_hub import snapshot_download
+
+# Create the main "pretrained_weights" folder
+os.makedirs("pretrained_weights", exist_ok=True)
+
+# List of subdirectories to create inside "pretrained_weights"
+subfolders = [
+    "sd-vae-ft-mse",
+    "sd-image-variations-diffusers",
+    "audio_processor"
+]
+
+# Create each subdirectory
+for subfolder in subfolders:
+    os.makedirs(os.path.join("pretrained_weights", subfolder), exist_ok=True)
+    
+snapshot_download(
+    repo_id = "BadToBest/EchoMimicV2",
+    local_dir="./pretrained_weights"
+)
+snapshot_download(
+    repo_id = "stabilityai/sd-vae-ft-mse",
+    local_dir="./pretrained_weights/sd-vae-ft-mse"
+)
+snapshot_download(
+    repo_id = "lambdalabs/sd-image-variations-diffusers",
+    local_dir="./pretrained_weights/sd-image-variations-diffusers"
+)
+
+# Download and place the Whisper model in the "audio_processor" folder
+def download_whisper_model():
+    url = "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt"
+    save_path = os.path.join("pretrained_weights", "audio_processor", "tiny.pt")
+    
+    try:
+        # Download the file
+        response = requests.get(url, stream=True)
+        response.raise_for_status()  # Check for HTTP request errors
+        with open(save_path, "wb") as file:
+            for chunk in response.iter_content(chunk_size=8192):
+                file.write(chunk)
+        print(f"Whisper model downloaded and saved to {save_path}")
+    except Exception as e:
+        print(f"An error occurred while downloading the model: {str(e)}")
+
+# Download the Whisper model
+download_whisper_model()
+
+total_vram_in_gb = torch.cuda.get_device_properties(0).total_memory / 1073741824
+print(f'\033[32mCUDA版本：{torch.version.cuda}\033[0m')
+print(f'\033[32mPytorch版本：{torch.__version__}\033[0m')
+print(f'\033[32m显卡型号：{torch.cuda.get_device_name()}\033[0m')
+print(f'\033[32m显存大小：{total_vram_in_gb:.2f}GB\033[0m')
+print(f'\033[32m精度：float16\033[0m')
+dtype = torch.float16
+if torch.cuda.is_available():
+        device = "cuda"
+else:
+    print("cuda not available, using cpu")
+    device = "cpu"
+
+ffmpeg_path = os.getenv('FFMPEG_PATH')
+if ffmpeg_path is None:
+    print("please download ffmpeg-static and export to FFMPEG_PATH. \nFor example: export FFMPEG_PATH=./ffmpeg-4.4-amd64-static")
+elif ffmpeg_path not in os.getenv('PATH'):
+    print("add ffmpeg to path")
+    os.environ["PATH"] = f"{ffmpeg_path}:{os.environ['PATH']}"
+
+
+def generate(image_input, audio_input, pose_input, width, height, length, steps, sample_rate, cfg, fps, context_frames, context_overlap, quantization_input, seed):
+    gc.collect()
+    torch.cuda.empty_cache()
+    torch.cuda.ipc_collect()
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    save_dir = Path("outputs")
+    save_dir.mkdir(exist_ok=True, parents=True)
+
+    ############# model_init started #############
+    ## vae init
+    vae = AutoencoderKL.from_pretrained("./pretrained_weights/sd-vae-ft-mse").to(device, dtype=dtype)
+    if quantization_input:
+        quantize_(vae, int8_weight_only())
+        print("使用int8量化")
+
+    ## reference net init
+    reference_unet = UNet2DConditionModel.from_pretrained("./pretrained_weights/sd-image-variations-diffusers", subfolder="unet", use_safetensors=False).to(dtype=dtype, device=device)
+    reference_unet.load_state_dict(torch.load("./pretrained_weights/reference_unet.pth", weights_only=True))
+    if quantization_input:
+        quantize_(reference_unet, int8_weight_only())
+
+    ## denoising net init
+    if os.path.exists("./pretrained_weights/motion_module.pth"):
+        print('using motion module')
+    else:
+        exit("motion module not found")
+        ### stage1 + stage2
+    denoising_unet = EMOUNet3DConditionModel.from_pretrained_2d(
+        "./pretrained_weights/sd-image-variations-diffusers",
+        "./pretrained_weights/motion_module.pth",
+        subfolder="unet",
+        unet_additional_kwargs = {
+            "use_inflated_groupnorm": True,
+            "unet_use_cross_frame_attention": False,
+            "unet_use_temporal_attention": False,
+            "use_motion_module": True,
+            "cross_attention_dim": 384,
+            "motion_module_resolutions": [
+                1,
+                2,
+                4,
+                8
+            ],
+            "motion_module_mid_block": True ,
+            "motion_module_decoder_only": False,
+            "motion_module_type": "Vanilla",
+            "motion_module_kwargs":{
+                "num_attention_heads": 8,
+                "num_transformer_block": 1,
+                "attention_block_types": [
+                    'Temporal_Self',
+                    'Temporal_Self'
+                ],
+                "temporal_position_encoding": True,
+                "temporal_position_encoding_max_len": 32,
+                "temporal_attention_dim_div": 1,
+            }
+        },
+    ).to(dtype=dtype, device=device)
+    denoising_unet.load_state_dict(torch.load("./pretrained_weights/denoising_unet.pth", weights_only=True),strict=False)
+
+    # pose net init
+    pose_net = PoseEncoder(320, conditioning_channels=3, block_out_channels=(16, 32, 96, 256)).to(dtype=dtype, device=device)
+    pose_net.load_state_dict(torch.load("./pretrained_weights/pose_encoder.pth", weights_only=True))
+
+    ### load audio processor params
+    audio_processor = load_audio_model(model_path="./pretrained_weights/audio_processor/tiny.pt", device=device)
+   
+    ############# model_init finished #############
+    sched_kwargs = {
+        "beta_start": 0.00085,
+        "beta_end": 0.012,
+        "beta_schedule": "linear",
+        "clip_sample": False,
+        "steps_offset": 1,
+        "prediction_type": "v_prediction",
+        "rescale_betas_zero_snr": True,
+        "timestep_spacing": "trailing"
+    }
+    scheduler = DDIMScheduler(**sched_kwargs)
+
+    pipe = EchoMimicV2Pipeline(
+        vae=vae,
+        reference_unet=reference_unet,
+        denoising_unet=denoising_unet,
+        audio_guider=audio_processor,
+        pose_encoder=pose_net,
+        scheduler=scheduler,
+    )
+
+    pipe = pipe.to(device, dtype=dtype)
+
+    if seed is not None and seed > -1:
+        generator = torch.manual_seed(seed)
+    else:
+        seed = random.randint(100, 1000000)
+        generator = torch.manual_seed(seed)
+
+    inputs_dict = {
+        "refimg": image_input,
+        "audio": audio_input,
+        "pose": pose_input,
+    }
+
+    print('Pose:', inputs_dict['pose'])
+    print('Reference:', inputs_dict['refimg'])
+    print('Audio:', inputs_dict['audio'])
+
+    save_name = f"{save_dir}/{timestamp}"
+    
+    ref_image_pil = Image.open(inputs_dict['refimg']).resize((width, height))
+    audio_clip = AudioFileClip(inputs_dict['audio'])
+    
+    length = min(length, int(audio_clip.duration * fps), len(os.listdir(inputs_dict['pose'])))
+
+    start_idx = 0
+
+    pose_list = []
+    for index in range(start_idx, start_idx + length):
+        tgt_musk = np.zeros((width, height, 3)).astype('uint8')
+        tgt_musk_path = os.path.join(inputs_dict['pose'], "{}.npy".format(index))
+        detected_pose = np.load(tgt_musk_path, allow_pickle=True).tolist()
+        imh_new, imw_new, rb, re, cb, ce = detected_pose['draw_pose_params']
+        im = draw_pose_select_v2(detected_pose, imh_new, imw_new, ref_w=800)
+        im = np.transpose(np.array(im),(1, 2, 0))
+        tgt_musk[rb:re,cb:ce,:] = im
+
+        tgt_musk_pil = Image.fromarray(np.array(tgt_musk)).convert('RGB')
+        pose_list.append(torch.Tensor(np.array(tgt_musk_pil)).to(dtype=dtype, device=device).permute(2,0,1) / 255.0)
+    
+    poses_tensor = torch.stack(pose_list, dim=1).unsqueeze(0)
+    audio_clip = AudioFileClip(inputs_dict['audio'])
+    
+    audio_clip = audio_clip.set_duration(length / fps)
+    video = pipe(
+        ref_image_pil,
+        inputs_dict['audio'],
+        poses_tensor[:,:,:length,...],
+        width,
+        height,
+        length,
+        steps,
+        cfg,
+        generator=generator,
+        audio_sample_rate=sample_rate,
+        context_frames=context_frames,
+        fps=fps,
+        context_overlap=context_overlap,
+        start_idx=start_idx,
+    ).videos 
+    
+    final_length = min(video.shape[2], poses_tensor.shape[2], length)
+    video_sig = video[:, :, :final_length, :, :]
+    
+    save_videos_grid(
+        video_sig,
+        save_name + "_woa_sig.mp4",
+        n_rows=1,
+        fps=fps,
+    )
+
+    video_clip_sig = VideoFileClip(save_name + "_woa_sig.mp4",)
+    video_clip_sig = video_clip_sig.set_audio(audio_clip)
+    video_clip_sig.write_videofile(save_name + "_sig.mp4", codec="libx264", audio_codec="aac", threads=2)
+    video_output = save_name + "_sig.mp4"
+    seed_text = gr.update(visible=True, value=seed)
+    return video_output, seed_text
+
+
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+            <div>
+                <h2 style="font-size: 30px;text-align: center;">EchoMimicV2</h2>
+            </div>
+            <div style="text-align: center;">
+                <a href="https://github.com/antgroup/echomimic_v2">🌐 Github</a> |
+                <a href="https://arxiv.org/abs/2411.10061">📜 arXiv </a>
+            </div>
+            <div style="text-align: center; font-weight: bold; color: red;">
+                ⚠️ 该演示仅供学术研究和体验使用。
+            </div>
+            
+            """)
+    with gr.Column():
+        with gr.Row():
+            with gr.Column():
+                with gr.Group():
+                    image_input = gr.Image(label="图像输入（自动缩放）", type="filepath")
+                    audio_input = gr.Audio(label="音频输入", type="filepath")
+                    pose_input = gr.Textbox(label="姿态输入（目录地址）", placeholder="请输入姿态数据的目录地址", value="assets/halfbody_demo/pose/01")
+                with gr.Group():
+                    with gr.Row():
+                        width = gr.Number(label="宽度（16的倍数，推荐768）", value=768)
+                        height = gr.Number(label="高度（16的倍数，推荐768）", value=768)
+                        length = gr.Number(label="视频长度，推荐240）", value=240)
+                    with gr.Row():
+                        steps = gr.Number(label="步骤（推荐30）", value=20)
+                        sample_rate = gr.Number(label="采样率（推荐16000）", value=16000)
+                        cfg = gr.Number(label="cfg（推荐2.5）", value=2.5, step=0.1)
+                    with gr.Row():
+                        fps = gr.Number(label="帧率（推荐24）", value=24)
+                        context_frames = gr.Number(label="上下文框架（推荐12）", value=12)
+                        context_overlap = gr.Number(label="上下文重叠（推荐3）", value=3)
+                    with gr.Row():
+                        quantization_input = gr.Checkbox(label="int8量化（推荐显存12G的用户开启，并使用不超过5秒的音频）", value=False)
+                        seed = gr.Number(label="种子(-1为随机)", value=-1)
+                generate_button = gr.Button("🎬 生成视频")
+            with gr.Column():
+                video_output = gr.Video(label="输出视频")
+                seed_text = gr.Textbox(label="种子", interactive=False, visible=False)
+        gr.Examples(
+            examples=[
+                ["EMTD_dataset/ref_imgs_by_FLUX/man/0001.png", "assets/halfbody_demo/audio/chinese/echomimicv2_man.wav"],
+                ["EMTD_dataset/ref_imgs_by_FLUX/woman/0077.png", "assets/halfbody_demo/audio/chinese/echomimicv2_woman.wav"],
+                ["EMTD_dataset/ref_imgs_by_FLUX/man/0003.png", "assets/halfbody_demo/audio/chinese/fighting.wav"],
+                ["EMTD_dataset/ref_imgs_by_FLUX/woman/0033.png", "assets/halfbody_demo/audio/chinese/good.wav"],
+                ["EMTD_dataset/ref_imgs_by_FLUX/man/0010.png", "assets/halfbody_demo/audio/chinese/news.wav"],
+                ["EMTD_dataset/ref_imgs_by_FLUX/man/1168.png", "assets/halfbody_demo/audio/chinese/no_smoking.wav"],
+                ["EMTD_dataset/ref_imgs_by_FLUX/woman/0057.png", "assets/halfbody_demo/audio/chinese/ultraman.wav"]
+            ],
+            inputs=[image_input, audio_input],  
+            label="预设人物及音频",
+        )
+
+    generate_button.click(
+        generate,
+        inputs=[image_input, audio_input, pose_input, width, height, length, steps, sample_rate, cfg, fps, context_frames, context_overlap, quantization_input, seed],
+        outputs=[video_output, seed_text],
+    )
+
+
+
+if __name__ == "__main__":
+    demo.queue()
+    demo.launch(inbrowser=True)