Create 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)