Delete app-backup.py

app-backup.py

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-# app.py
-
-import os
-import sys
-import time
-import gradio as gr
-import spaces
-from huggingface_hub import snapshot_download
-from huggingface_hub.utils import GatedRepoError, RepositoryNotFoundError, RevisionNotFoundError
-from pathlib import Path
-import tempfile
-from pydub import AudioSegment
-import cv2
-import numpy as np
-from scipy import interpolate
-
-# Add the src directory to the system path to allow for local imports
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), 'src')))
-
-from models.inference.moda_test import LiveVASAPipeline, emo_map, set_seed
-
-# --- Configuration ---
-# Set seed for reproducibility
-set_seed(42)
-
-# Paths and constants for the Gradio demo
-DEFAULT_CFG_PATH = "configs/audio2motion/inference/inference.yaml"
-DEFAULT_MOTION_MEAN_STD_PATH = "src/datasets/mean.pt"
-DEFAULT_SILENT_AUDIO_PATH = "src/examples/silent-audio.wav"
-OUTPUT_DIR = "gradio_output"
-WEIGHTS_DIR = "pretrain_weights"
-REPO_ID = "lixinyizju/moda"
-
-# --- Download Pre-trained Weights from Hugging Face Hub ---
-def download_weights():
-    """
-    Downloads pre-trained weights from Hugging Face Hub if they don't exist locally.
-    """
-    # A simple check for a key file to see if the download is likely complete
-    motion_model_file = os.path.join(WEIGHTS_DIR, "moda", "net-200.pth")
-    
-    if not os.path.exists(motion_model_file):
-        print(f"Weights not found locally. Downloading from Hugging Face Hub repo '{REPO_ID}'...")
-        print(f"This may take a while depending on your internet connection.")
-        try:
-            snapshot_download(
-                repo_id=REPO_ID,
-                local_dir=WEIGHTS_DIR,
-                local_dir_use_symlinks=False,  # Use False to copy files directly; safer for Windows
-                resume_download=True,
-            )
-            print("Weights downloaded successfully.")
-        except GatedRepoError:
-            raise gr.Error(f"Access to the repository '{REPO_ID}' is gated. Please visit https://huggingface.co/{REPO_ID} to request access.")
-        except (RepositoryNotFoundError, RevisionNotFoundError):
-            raise gr.Error(f"The repository '{REPO_ID}' was not found. Please check the repository ID.")
-        except Exception as e:
-            print(f"An error occurred during download: {e}")
-            raise gr.Error(f"Failed to download models. Please check your internet connection and try again. Error: {e}")
-    else:
-        print(f"Found existing weights at '{WEIGHTS_DIR}'. Skipping download.")
-
-# --- Audio Conversion Function ---
-def ensure_wav_format(audio_path):
-    """
-    Ensures the audio file is in WAV format. If not, converts it to WAV.
-    Returns the path to the WAV file (either original or converted).
-    """
-    if audio_path is None:
-        return None
-    
-    audio_path = Path(audio_path)
-    
-    # Check if already WAV
-    if audio_path.suffix.lower() == '.wav':
-        print(f"Audio is already in WAV format: {audio_path}")
-        return str(audio_path)
-    
-    # Convert to WAV
-    print(f"Converting audio from {audio_path.suffix} to WAV format...")
-    
-    try:
-        # Load the audio file
-        audio = AudioSegment.from_file(audio_path)
-        
-        # Create a temporary WAV file
-        with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as tmp_file:
-            wav_path = tmp_file.name
-            # Export as WAV with higher sampling rate for better quality
-            audio.export(
-                wav_path,
-                format='wav',
-                parameters=["-ar", "24000", "-ac", "1"]  # 24kHz, mono for better lip-sync
-            )
-        
-        print(f"Audio converted successfully to: {wav_path}")
-        return wav_path
-        
-    except Exception as e:
-        print(f"Error converting audio: {e}")
-        raise gr.Error(f"Failed to convert audio file to WAV format. Error: {e}")
-
-# --- Frame Interpolation Function ---
-def interpolate_frames(video_path, target_fps=30):
-    """
-    Interpolates frames in a video to achieve smoother motion.
-    
-    Args:
-        video_path: Path to the input video
-        target_fps: Target frames per second
-    
-    Returns:
-        Path to the interpolated video
-    """
-    try:
-        video_path = str(video_path)
-        cap = cv2.VideoCapture(video_path)
-        
-        # Get original video properties
-        original_fps = cap.get(cv2.CAP_PROP_FPS)
-        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-        
-        print(f"Original FPS: {original_fps}, Target FPS: {target_fps}")
-        
-        # If target FPS is not higher, return original
-        if original_fps >= target_fps:
-            cap.release()
-            print("Target FPS is not higher than original. Skipping interpolation.")
-            return video_path
-        
-        # Read all frames
-        frames = []
-        while True:
-            ret, frame = cap.read()
-            if not ret:
-                break
-            frames.append(frame)
-        cap.release()
-        
-        if len(frames) < 2:
-            print("Not enough frames for interpolation.")
-            return video_path
-        
-        # Calculate interpolation factor
-        interpolation_factor = int(target_fps / original_fps)
-        interpolated_frames = []
-        
-        print(f"Interpolating with factor: {interpolation_factor}")
-        
-        # Perform frame interpolation
-        for i in range(len(frames) - 1):
-            interpolated_frames.append(frames[i])
-            
-            # Generate intermediate frames
-            for j in range(1, interpolation_factor):
-                alpha = j / interpolation_factor
-                # Use weighted average for simple interpolation
-                interpolated_frame = cv2.addWeighted(
-                    frames[i], 1 - alpha,
-                    frames[i + 1], alpha,
-                    0
-                )
-                interpolated_frames.append(interpolated_frame)
-        
-        # Add the last frame
-        interpolated_frames.append(frames[-1])
-        
-        # Save the interpolated video
-        output_path = video_path.replace('.mp4', '_interpolated.mp4')
-        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-        out = cv2.VideoWriter(output_path, fourcc, target_fps, (width, height))
-        
-        for frame in interpolated_frames:
-            out.write(frame)
-        out.release()
-        
-        print(f"Interpolated video saved to: {output_path}")
-        return output_path
-        
-    except Exception as e:
-        print(f"Error during frame interpolation: {e}")
-        return video_path  # Return original if interpolation fails
-
-# --- Initialization ---
-# Create output directory if it doesn't exist
-os.makedirs(OUTPUT_DIR, exist_ok=True)
-
-# Download weights before initializing the pipeline
-download_weights()
-
-# Instantiate the pipeline once to avoid reloading models on every request
-print("Initializing MoDA pipeline...")
-try:
-    pipeline = LiveVASAPipeline(
-        cfg_path=DEFAULT_CFG_PATH,
-        motion_mean_std_path=DEFAULT_MOTION_MEAN_STD_PATH
-    )
-    print("MoDA pipeline initialized successfully.")
-except Exception as e:
-    print(f"Error initializing pipeline: {e}")
-    pipeline = None
-
-# Invert the emo_map for easy lookup from the dropdown value
-emo_name_to_id = {v: k for k, v in emo_map.items()}
-
-# --- Core Generation Function ---
-@spaces.GPU(duration=180)  # Increased duration for smoothing and interpolation
-def generate_motion(source_image_path, driving_audio_path, emotion_name, 
-                   cfg_scale, smooth_enabled, target_fps,
-                   progress=gr.Progress(track_tqdm=True)):
-    """
-    The main function that takes Gradio inputs and generates the talking head video.
-    
-    Args:
-        source_image_path: Path to the source image
-        driving_audio_path: Path to the driving audio
-        emotion_name: Selected emotion
-        cfg_scale: CFG scale for generation
-        smooth_enabled: Whether to enable smoothing
-        target_fps: Target frames per second for interpolation
-    """
-    if pipeline is None:
-        raise gr.Error("Pipeline failed to initialize. Check the console logs for details.")
-        
-    if source_image_path is None:
-        raise gr.Error("Please upload a source image.")
-    if driving_audio_path is None:
-        raise gr.Error("Please upload a driving audio file.")
-
-    start_time = time.time()
-    
-    # Ensure audio is in WAV format with optimal sampling rate
-    wav_audio_path = ensure_wav_format(driving_audio_path)
-    temp_wav_created = wav_audio_path != driving_audio_path
-    
-    # Create a unique subdirectory for this run
-    timestamp = time.strftime("%Y%m%d-%H%M%S")
-    run_output_dir = os.path.join(OUTPUT_DIR, timestamp)
-    os.makedirs(run_output_dir, exist_ok=True)
-
-    # Get emotion ID from its name
-    emotion_id = emo_name_to_id.get(emotion_name, 8)  # Default to 'None' (ID 8) if not found
-
-    print(f"Starting generation with the following parameters:")
-    print(f"  Source Image: {source_image_path}")
-    print(f"  Driving Audio (original): {driving_audio_path}")
-    print(f"  Driving Audio (WAV): {wav_audio_path}")
-    print(f"  Emotion: {emotion_name} (ID: {emotion_id})")
-    print(f"  CFG Scale: {cfg_scale}")
-    print(f"  Smoothing: {smooth_enabled}")
-    print(f"  Target FPS: {target_fps}")
-
-    try:
-        # Temporarily disable smoothing if it causes CUDA tensor issues
-        # Check if smooth causes issues and handle gracefully
-        try:
-            # Try with smoothing first
-            result_video_path = pipeline.driven_sample(
-                image_path=source_image_path,
-                audio_path=wav_audio_path,
-                cfg_scale=float(cfg_scale),
-                emo=emotion_id,
-                save_dir=".",
-                smooth=smooth_enabled,  # Use the checkbox value
-                silent_audio_path=DEFAULT_SILENT_AUDIO_PATH,
-            )
-        except TypeError as tensor_error:
-            if "can't convert cuda" in str(tensor_error) and smooth_enabled:
-                print("Warning: Smoothing caused CUDA tensor error. Retrying without smoothing...")
-                # Retry without smoothing
-                result_video_path = pipeline.driven_sample(
-                    image_path=source_image_path,
-                    audio_path=wav_audio_path,
-                    cfg_scale=float(cfg_scale),
-                    emo=emotion_id,
-                    save_dir=".",
-                    smooth=False,  # Disable smoothing as fallback
-                    silent_audio_path=DEFAULT_SILENT_AUDIO_PATH,
-                )
-                print("Generated video without smoothing due to technical limitations.")
-            else:
-                raise tensor_error
-        
-        # Apply frame interpolation if requested
-        if target_fps > 24:  # Assuming default is around 24 FPS
-            print(f"Applying frame interpolation to achieve {target_fps} FPS...")
-            result_video_path = interpolate_frames(result_video_path, target_fps=target_fps)
-        
-    except Exception as e:
-        print(f"An error occurred during video generation: {e}")
-        import traceback
-        traceback.print_exc()
-        raise gr.Error(f"An unexpected error occurred: {str(e)}. Please check the console for details.")
-    finally:
-        # Clean up temporary WAV file if created
-        if temp_wav_created and os.path.exists(wav_audio_path):
-            try:
-                os.remove(wav_audio_path)
-                print(f"Cleaned up temporary WAV file: {wav_audio_path}")
-            except Exception as e:
-                print(f"Warning: Could not delete temporary file {wav_audio_path}: {e}")
-
-    end_time = time.time()
-    processing_time = end_time - start_time
-    
-    result_video_path = Path(result_video_path)
-    final_path = result_video_path.with_name(f"final_{result_video_path.stem}{result_video_path.suffix}")
-    
-    print(f"Video generated successfully at: {final_path}")
-    print(f"Processing time: {processing_time:.2f} seconds.")
-
-    return final_path
-
-# --- Gradio UI Definition ---
-with gr.Blocks(theme=gr.themes.Soft(), css=".gradio-container {max-width: 960px !important; margin: 0 auto !important}") as demo:
-    gr.HTML(
-        """
-        <div align='center'>
-            <h1>MoDA: Multi-modal Diffusion Architecture for Talking Head Generation</h1>
-            <h2 style="color: #4A90E2;">Enhanced Version with Smooth Motion</h2>
-            <p style="display:flex; justify-content: center; gap: 10px;">
-                <a href='https://lixinyyang.github.io/MoDA.github.io/'><img src='https://img.shields.io/badge/Project-Page-blue'></a>
-                <a href='https://arxiv.org/abs/2507.03256'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a>
-                <a href='https://github.com/lixinyyang/MoDA/'><img src='https://img.shields.io/badge/Code-Github-green'></a>
-            </p>
-        </div>
-        """
-    )
-
-    with gr.Row(variant="panel"):
-        with gr.Column(scale=1):
-            gr.Markdown("### 📥 Input Settings")
-            
-            with gr.Row():
-                source_image = gr.Image(
-                    label="Source Image", 
-                    type="filepath", 
-                    value="src/examples/reference_images/7.jpg"
-                )
-            
-            with gr.Row():
-                driving_audio = gr.Audio(
-                    label="Driving Audio",
-                    type="filepath",
-                    value="src/examples/driving_audios/5.wav"
-                )
-
-            gr.Markdown("### ⚙️ Generation Settings")
-            
-            with gr.Row():
-                emotion_dropdown = gr.Dropdown(
-                    label="Emotion",
-                    choices=list(emo_map.values()),
-                    value="Neutral",
-                    info="Select an emotion for more natural facial expressions"
-                )
-
-            with gr.Row():
-                cfg_slider = gr.Slider(
-                    label="CFG Scale (Lower = Smoother motion)",
-                    minimum=0.5,
-                    maximum=5.0,
-                    step=0.1,
-                    value=0.5,
-                    info="Lower values produce smoother but less controlled motion"
-                )
-            
-            gr.Markdown("### 🎬 Motion Enhancement")
-            
-            with gr.Row():
-                smooth_checkbox = gr.Checkbox(
-                    label="Enable Smoothing (Experimental)",
-                    value=True,  # Changed to False due to CUDA issues
-                    info="May cause errors on some systems. If errors occur, disable this option."
-                )
-            
-            with gr.Row():
-                fps_slider = gr.Slider(
-                    label="Target FPS",
-                    minimum=24,
-                    maximum=60,
-                    step=6,
-                    value=60,
-                    info="Higher FPS for smoother motion (uses frame interpolation)"
-                )
-            
-            submit_button = gr.Button("🎥 Generate Video", variant="primary", size="lg")
-
-        with gr.Column(scale=1):
-            gr.Markdown("### 📺 Output")
-            output_video = gr.Video(label="Generated Video")
-            
-            # Processing status
-            with gr.Row():
-                gr.Markdown(
-                    """
-                    <div style="background-color: #f0f8ff; padding: 10px; border-radius: 5px; margin-top: 10px;">
-                        <p style="margin: 0; font-size: 0.9em;">
-                        <b>Tips for best results:</b><br>
-                        • Use high-quality front-facing images<br>
-                        • Clear audio without background noise<br>
-                        • Enable smoothing for natural motion<br>
-                        • Adjust CFG scale if motion seems stiff
-                        </p>
-                    </div>
-                    """
-                )
-
-    gr.Markdown(
-        """
-        ---
-        ### ⚠️ **Disclaimer**
-        This project is intended for academic research, and we explicitly disclaim any responsibility for user-generated content. 
-        Users are solely liable for their actions while using this generative model.
-        
-        ### 🚀 **Enhancement Features**
-        - **Frame Smoothing**: Reduces jitter and improves transition between frames
-        - **Frame Interpolation**: Increases FPS for smoother motion
-        - **Optimized Audio Processing**: Better lip-sync with 24kHz sampling
-        - **Fine-tuned CFG Scale**: Better control over motion naturalness
-        """
-    )
-    
-    # Examples section
-    gr.Examples(
-        examples=[
-            ["src/examples/reference_images/7.jpg", "src/examples/driving_audios/5.wav", "None", 1.0, False, 30],
-            ["src/examples/reference_images/7.jpg", "src/examples/driving_audios/5.wav", "Happy", 0.8, False, 30],
-            ["src/examples/reference_images/7.jpg", "src/examples/driving_audios/5.wav", "Sad", 1.2, False, 24],
-        ],
-        inputs=[source_image, driving_audio, emotion_dropdown, cfg_slider, smooth_checkbox, fps_slider],
-        outputs=output_video,
-        fn=generate_motion,
-        cache_examples=False,
-        label="Example Configurations"
-    )
-    
-    submit_button.click(
-        fn=generate_motion,
-        inputs=[source_image, driving_audio, emotion_dropdown, cfg_slider, smooth_checkbox, fps_slider],
-        outputs=output_video
-    )
-
-if __name__ == "__main__":
-    demo.launch(share=True)