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| import os | |
| import json | |
| import gradio as gr | |
| from PIL import Image | |
| import torch | |
| from huggingface_hub import hf_hub_download | |
| import tempfile | |
| # Constants | |
| MODEL_ID = "MeiGen-AI/MeiGen-MultiTalk" | |
| DEVICE = "cuda" if torch.cuda.is_available() else "cpu" | |
| def load_models(): | |
| """Load required models""" | |
| # Here we'll add model loading logic | |
| pass | |
| def process_video( | |
| image, | |
| audio_files, | |
| prompt, | |
| resolution="480p", | |
| audio_cfg=4.0, | |
| cfg=7.5, | |
| seed=42, | |
| max_duration=15 | |
| ): | |
| """Process video generation""" | |
| try: | |
| # Create temporary directory for processing | |
| with tempfile.TemporaryDirectory() as temp_dir: | |
| # Save uploaded image | |
| image_path = os.path.join(temp_dir, "reference.jpg") | |
| image.save(image_path) | |
| # Save uploaded audio files | |
| audio_paths = [] | |
| for audio in audio_files: | |
| audio_path = os.path.join(temp_dir, f"audio_{len(audio_paths)}.wav") | |
| audio_paths.append(audio_path) | |
| # Save audio file | |
| with open(audio_path, "wb") as f: | |
| f.write(audio) | |
| # Create configuration | |
| config = { | |
| "image": image_path, | |
| "audio": audio_paths[0] if len(audio_paths) == 1 else audio_paths, | |
| "prompt": prompt, | |
| "resolution": resolution, | |
| "audio_cfg": float(audio_cfg), | |
| "cfg": float(cfg), | |
| "seed": int(seed), | |
| "max_duration": int(max_duration) | |
| } | |
| # Save configuration | |
| config_path = os.path.join(temp_dir, "config.json") | |
| with open(config_path, "w") as f: | |
| json.dump(config, f, indent=2) | |
| # Here we'll add video generation logic | |
| # For now, return a message | |
| return "Video generation will be implemented here" | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| # Create Gradio interface | |
| with gr.Blocks(title="MeiGen-MultiTalk Demo") as demo: | |
| gr.Markdown(""" | |
| # MeiGen-MultiTalk Demo | |
| Generate talking head videos from images and audio files. | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| image_input = gr.Image(label="Reference Image", type="pil") | |
| audio_input = gr.Audio(label="Audio File(s)", type="binary", multiple=True) | |
| prompt_input = gr.Textbox(label="Prompt", placeholder="Describe the desired video...") | |
| with gr.Row(): | |
| resolution_input = gr.Dropdown( | |
| choices=["480p", "720p"], | |
| value="480p", | |
| label="Resolution" | |
| ) | |
| audio_cfg_input = gr.Slider( | |
| minimum=1.0, | |
| maximum=10.0, | |
| value=4.0, | |
| step=0.1, | |
| label="Audio CFG" | |
| ) | |
| with gr.Row(): | |
| cfg_input = gr.Slider( | |
| minimum=1.0, | |
| maximum=15.0, | |
| value=7.5, | |
| step=0.1, | |
| label="Guidance Scale" | |
| ) | |
| seed_input = gr.Number( | |
| value=42, | |
| label="Random Seed", | |
| precision=0 | |
| ) | |
| max_duration_input = gr.Slider( | |
| minimum=1, | |
| maximum=15, | |
| value=10, | |
| step=1, | |
| label="Max Duration (seconds)" | |
| ) | |
| generate_btn = gr.Button("Generate Video") | |
| with gr.Column(): | |
| output = gr.Video(label="Generated Video") | |
| generate_btn.click( | |
| fn=process_video, | |
| inputs=[ | |
| image_input, | |
| audio_input, | |
| prompt_input, | |
| resolution_input, | |
| audio_cfg_input, | |
| cfg_input, | |
| seed_input, | |
| max_duration_input | |
| ], | |
| outputs=output | |
| ) | |
| # Launch locally if running directly | |
| if __name__ == "__main__": | |
| demo.launch() |