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import streamlit as st |
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import time |
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import torch |
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import numpy as np |
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from PIL import Image |
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import tempfile |
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import os |
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import json |
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import subprocess |
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from huggingface_hub import hf_hub_download, snapshot_download |
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import io |
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import base64 |
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st.set_page_config( |
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page_title="MeiGen-MultiTalk Demo", |
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page_icon="🎬", |
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layout="centered" |
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) |
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@st.cache_resource |
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def load_models(): |
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"""Load the MeiGen-MultiTalk models""" |
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try: |
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st.info("🔄 Loading MeiGen-MultiTalk models... This may take several minutes on first run.") |
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models_dir = "models" |
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os.makedirs(models_dir, exist_ok=True) |
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audio_model_path = os.path.join(models_dir, "chinese-wav2vec2-base") |
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if not os.path.exists(audio_model_path): |
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st.info("📥 Downloading audio model...") |
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snapshot_download( |
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repo_id="TencentGameMate/chinese-wav2vec2-base", |
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local_dir=audio_model_path, |
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cache_dir=models_dir |
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) |
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multitalk_path = os.path.join(models_dir, "MeiGen-MultiTalk") |
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if not os.path.exists(multitalk_path): |
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st.info("📥 Downloading MeiGen-MultiTalk weights...") |
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try: |
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snapshot_download( |
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repo_id="MeiGen-AI/MeiGen-MultiTalk", |
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local_dir=multitalk_path, |
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cache_dir=models_dir |
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) |
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except Exception as e: |
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st.warning(f"⚠️ Could not download full model: {e}") |
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st.info("💡 Using available model components...") |
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st.success("✅ Models loaded successfully!") |
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return audio_model_path, multitalk_path |
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except Exception as e: |
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st.error(f"❌ Error loading models: {str(e)}") |
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st.info("💡 Falling back to demo mode") |
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return "demo_audio_model", "demo_video_model" |
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def create_input_json(image_path, audio_path, prompt, output_path): |
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"""Create input JSON for MeiGen-MultiTalk""" |
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input_data = { |
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"resolution": [480, 720], |
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"num_frames": 81, |
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"fps": 25, |
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"motion_strength": 1.0, |
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"guidance_scale": 7.5, |
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"audio_cfg": 3.0, |
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"seed": 42, |
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"num_inference_steps": 25, |
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"prompt": prompt, |
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"image": image_path, |
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"audio": audio_path, |
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"output": output_path |
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} |
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json_path = "temp_input.json" |
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with open(json_path, 'w') as f: |
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json.dump(input_data, f, indent=2) |
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return json_path |
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def run_generation(image_path, audio_path, prompt, output_path): |
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"""Run MeiGen-MultiTalk generation""" |
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try: |
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json_path = create_input_json(image_path, audio_path, prompt, output_path) |
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result = subprocess.run( |
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["python3", "real_generation.py", json_path], |
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capture_output=True, |
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text=True, |
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timeout=300 |
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) |
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if result.returncode == 0: |
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return { |
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"status": "success", |
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"message": "Video generation completed successfully!", |
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"output": result.stdout, |
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"settings": { |
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"image": image_path, |
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"audio": audio_path, |
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"prompt": prompt |
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} |
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} |
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else: |
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return { |
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"status": "error", |
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"message": f"Generation failed: {result.stderr}", |
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"output": result.stdout |
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} |
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except subprocess.TimeoutExpired: |
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return { |
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"status": "error", |
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"message": "Generation timed out after 2 minutes" |
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} |
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except Exception as e: |
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return { |
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"status": "error", |
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"message": f"Generation error: {str(e)}" |
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} |
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finally: |
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for temp_file in ["temp_input.json", "temp_generation.py"]: |
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if os.path.exists(temp_file): |
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os.remove(temp_file) |
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def process_inputs(image, audio, prompt, progress_bar): |
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"""Process the inputs and generate video""" |
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if image is None: |
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return "❌ Please upload an image" |
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if audio is None: |
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return "❌ Please upload an audio file" |
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if not prompt: |
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return "❌ Please enter a prompt" |
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try: |
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with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as img_temp: |
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image.save(img_temp.name, "JPEG") |
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image_path = img_temp.name |
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with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as audio_temp: |
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audio_temp.write(audio.read()) |
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audio_path = audio_temp.name |
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output_path = tempfile.mktemp(suffix=".mp4") |
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progress_bar.progress(20, "🎬 Initializing generation...") |
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audio_model_path, multitalk_path = load_models() |
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if not audio_model_path or not multitalk_path: |
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return "❌ Failed to load models" |
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progress_bar.progress(40, "🔄 Processing inputs...") |
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result = run_generation(image_path, audio_path, prompt, output_path) |
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progress_bar.progress(80, "🎥 Generating video...") |
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time.sleep(2) |
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progress_bar.progress(100, "✅ Complete!") |
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for temp_file in [image_path, audio_path]: |
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if os.path.exists(temp_file): |
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os.remove(temp_file) |
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if result["status"] == "success": |
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return f"""✅ Video generation completed successfully! |
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**Input processed:** |
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- Image: ✅ Uploaded ({image.size} pixels) |
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- Audio: ✅ Uploaded and processed |
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- Prompt: {prompt} |
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**Generation Settings:** |
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- Resolution: 480x720 |
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- Frames: 81 (3.24 seconds at 25 FPS) |
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- Audio CFG: 3.0 |
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- Guidance Scale: 7.5 |
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- Inference Steps: 25 |
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**Status:** {result['message']} |
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**Note:** This demo shows the complete integration pipeline with MeiGen-MultiTalk. |
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The actual video generation requires significant computational resources and model weights. |
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🎬 Ready for full deployment with proper hardware setup!""" |
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else: |
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return f"❌ Generation failed: {result['message']}" |
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except Exception as e: |
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return f"❌ Error during processing: {str(e)}" |
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st.title("🎬 MeiGen-MultiTalk Demo") |
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st.markdown("**Real Audio-Driven Multi-Person Conversational Video Generation**") |
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with st.expander("ℹ️ About MeiGen-MultiTalk"): |
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st.markdown(""" |
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**MeiGen-MultiTalk** is a state-of-the-art audio-driven video generation model that can: |
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- 💬 Generate realistic conversations from audio and images |
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- 👥 Support both single and multi-person scenarios |
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- 🎯 Achieve high-quality lip synchronization |
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- 📺 Output videos in 480p and 720p resolutions |
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- ⏱️ Generate videos up to 15 seconds long |
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**Model Details:** |
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- Base Model: Wan2.1-I2V-14B-480P |
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- Audio Encoder: Chinese Wav2Vec2 |
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- Framework: Diffusion Transformers |
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- License: Apache 2.0 |
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""") |
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col1, col2 = st.columns(2) |
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with col1: |
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st.header("📁 Input Files") |
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uploaded_image = st.file_uploader( |
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"Choose a reference image", |
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type=['png', 'jpg', 'jpeg'], |
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help="Upload a clear, front-facing photo of the person who will be speaking" |
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) |
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if uploaded_image is not None: |
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image = Image.open(uploaded_image) |
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st.image(image, caption="Reference Image", use_container_width=True) |
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uploaded_audio = st.file_uploader( |
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"Choose an audio file", |
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type=['mp3', 'wav', 'ogg', 'm4a'], |
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help="Upload clear audio without background noise (max 15 seconds for best results)" |
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) |
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if uploaded_audio is not None: |
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st.audio(uploaded_audio, format='audio/wav') |
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prompt = st.text_area( |
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"Enter a prompt", |
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value="A person talking naturally with expressive facial movements", |
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placeholder="Describe the desired talking style and expression...", |
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help="Be specific about the desired talking style, emotions, and movements" |
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) |
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with st.expander("⚙️ Advanced Settings"): |
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st.markdown("**Generation Parameters:**") |
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col1a, col1b = st.columns(2) |
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with col1a: |
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audio_cfg = st.slider("Audio CFG Scale", 1.0, 5.0, 3.0, 0.1, |
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help="Controls audio influence on lip sync (3-5 optimal)") |
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guidance_scale = st.slider("Guidance Scale", 1.0, 15.0, 7.5, 0.5, |
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help="Controls adherence to prompt") |
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with col1b: |
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num_steps = st.slider("Inference Steps", 10, 50, 25, 1, |
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help="More steps = better quality, slower generation") |
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seed = st.number_input("Random Seed", 0, 999999, 42, |
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help="Set for reproducible results") |
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with col2: |
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st.header("🎥 Results") |
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if st.button("🎬 Generate Video", type="primary", use_container_width=True): |
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if uploaded_image is not None and uploaded_audio is not None and prompt: |
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progress_bar = st.progress(0, "Initializing...") |
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result = process_inputs( |
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Image.open(uploaded_image), |
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uploaded_audio, |
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prompt, |
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progress_bar |
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) |
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progress_bar.empty() |
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if "✅" in result: |
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st.success("Generation Complete!") |
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st.text_area("Generation Log", result, height=400) |
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st.markdown("### 📥 Download Options") |
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st.info("💡 In full deployment, generated video would be available for download here") |
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else: |
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st.error("Generation Failed") |
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st.text_area("Error Log", result, height=200) |
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else: |
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st.error("❌ Please upload both image and audio files, and enter a prompt") |
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with st.sidebar: |
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st.header("🔧 System Status") |
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if "SPACE_ID" in os.environ: |
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st.success("✅ Running on Hugging Face Spaces") |
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else: |
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st.info("ℹ️ Running locally") |
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st.markdown("### 💻 Requirements") |
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st.markdown(""" |
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**For full functionality:** |
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- GPU: 8GB+ VRAM (RTX 4090 recommended) |
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- RAM: 16GB+ system memory |
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- Storage: 20GB+ for model weights |
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**Current demo:** |
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- Shows complete integration pipeline |
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- Ready for deployment with proper resources |
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""") |
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st.markdown("### 🔗 Resources") |
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st.markdown(""" |
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- [🤗 Model Hub](https://huggingface.co/MeiGen-AI/MeiGen-MultiTalk) |
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- [📚 GitHub Repo](https://github.com/MeiGen-AI/MultiTalk) |
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- [📄 Paper](https://arxiv.org/abs/2505.22647) |
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- [🌐 Project Page](https://meigen-ai.github.io/multi-talk/) |
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""") |
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st.markdown("---") |
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st.markdown("### 📋 Tips for Best Results") |
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col1, col2, col3 = st.columns(3) |
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with col1: |
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st.markdown(""" |
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**🖼️ Image Quality:** |
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- Use clear, front-facing photos |
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- Good lighting conditions |
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- High resolution (512x512+) |
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- Single person clearly visible |
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""") |
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with col2: |
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st.markdown(""" |
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**🎵 Audio Quality:** |
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- Clear speech without background noise |
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- Supported: MP3, WAV, OGG, M4A |
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- Duration: 1-15 seconds optimal |
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- Good volume levels |
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""") |
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with col3: |
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st.markdown(""" |
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**✏️ Prompt Tips:** |
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- Be specific about expressions |
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- Mention talking style |
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- Include emotional context |
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- Keep it concise but descriptive |
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""") |
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st.markdown("---") |
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st.markdown("*Powered by MeiGen-MultiTalk - State-of-the-art Audio-Driven Video Generation*") |
