2

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/demo1_video.mp4 filter=lfs diff=lfs merge=lfs -text
+assets/demo2_video.mp4 filter=lfs diff=lfs merge=lfs -text
+assets/demo3_video.mp4 filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,201 @@
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LatentSync.code-workspace

@@ -0,0 +1,7 @@
+{
+	"folders": [
+		{
+			"path": "."
+		}
+	]
+}

ORIGINAL_README.md

@@ -0,0 +1,158 @@
+# LatentSync: Audio Conditioned Latent Diffusion Models for Lip Sync
+
+<div align="center">
+
+[![arXiv](https://img.shields.io/badge/arXiv_paper-2412.09262-b31b1b)](https://arxiv.org/abs/2412.09262)
+
+</div>
+
+## 📖 Abstract
+
+We present *LatentSync*, an end-to-end lip sync framework based on audio conditioned latent diffusion models without any intermediate motion representation, diverging from previous diffusion-based lip sync methods based on pixel space diffusion or two-stage generation. Our framework can leverage the powerful capabilities of Stable Diffusion to directly model complex audio-visual correlations. Additionally, we found that the diffusion-based lip sync methods exhibit inferior temporal consistency due to the inconsistency in the diffusion process across different frames. We propose *Temporal REPresentation Alignment (TREPA)* to enhance temporal consistency while preserving lip-sync accuracy. TREPA uses temporal representations extracted by large-scale self-supervised video models to align the generated frames with the ground truth frames.
+
+## 🏗️ Framework
+
+<p align="center">
+<img src="assets/framework.png" width=100%>
+<p>
+
+LatentSync uses the Whisper to convert melspectrogram into audio embeddings, which are then integrated into the U-Net via cross-attention layers. The reference and masked frames are channel-wise concatenated with noised latents as the input of U-Net. In the training process, we use one-step method to get estimated clean latents from predicted noises, which are then decoded to obtain the estimated clean frames. The TREPA, LPIPS and SyncNet loss are added in the pixel space.
+
+## 🎬 Demo
+
+<table class="center">
+  <tr style="font-weight: bolder;text-align:center;">
+        <td width="50%"><b>Original video</b></td>
+        <td width="50%"><b>Lip-synced video</b></td>
+  </tr>
+  <tr>
+    <td>
+      <video src=https://github.com/user-attachments/assets/ff3a84da-dc9b-498a-950f-5c54f58dd5c5 controls preload></video>
+    </td>
+    <td>
+      <video src=https://github.com/user-attachments/assets/150e00fd-381e-4421-a478-a9ea3d1212a8 controls preload></video>
+    </td>
+  </tr>
+  <tr>
+    <td>
+      <video src=https://github.com/user-attachments/assets/32c830a9-4d7d-4044-9b33-b184d8e11010 controls preload></video>
+    </td>
+    <td>
+      <video src=https://github.com/user-attachments/assets/84e4fe9d-b108-44a4-8712-13a012348145 controls preload></video>
+    </td>
+  </tr>
+  <tr>
+    <td>
+      <video src=https://github.com/user-attachments/assets/7510a448-255a-44ee-b093-a1b98bd3961d controls preload></video>
+    </td>
+    <td>
+      <video src=https://github.com/user-attachments/assets/6150c453-c559-4ae0-bb00-c565f135ff41 controls preload></video>
+    </td>
+  </tr>
+  <tr>
+    <td width=300px>
+      <video src=https://github.com/user-attachments/assets/0f7f9845-68b2-4165-bd08-c7bbe01a0e52 controls preload></video>
+    </td>
+    <td width=300px>
+      <video src=https://github.com/user-attachments/assets/c34fe89d-0c09-4de3-8601-3d01229a69e3 controls preload></video>
+    </td>
+  </tr>
+  <tr>
+    <td>
+      <video src=https://github.com/user-attachments/assets/7ce04d50-d39f-4154-932a-ec3a590a8f64 controls preload></video>
+    </td>
+    <td>
+      <video src=https://github.com/user-attachments/assets/70bde520-42fa-4a0e-b66c-d3040ae5e065 controls preload></video>
+    </td>
+  </tr>
+</table>
+
+(Photorealistic videos are filmed by contracted models, and anime videos are from [VASA-1](https://www.microsoft.com/en-us/research/project/vasa-1/) and [EMO](https://humanaigc.github.io/emote-portrait-alive/))
+
+## 📑 Open-source Plan
+
+- [x] Inference code and checkpoints
+- [x] Data processing pipeline
+- [x] Training code
+
+## 🔧 Setting up the Environment
+
+Install the required packages and download the checkpoints via:
+
+```bash
+source setup_env.sh
+```
+
+If the download is successful, the checkpoints should appear as follows:
+
+```
+./checkpoints/
+|-- latentsync_unet.pt
+|-- latentsync_syncnet.pt
+|-- whisper
+|   `-- tiny.pt
+|-- auxiliary
+|   |-- 2DFAN4-cd938726ad.zip
+|   |-- i3d_torchscript.pt
+|   |-- koniq_pretrained.pkl
+|   |-- s3fd-619a316812.pth
+|   |-- sfd_face.pth
+|   |-- syncnet_v2.model
+|   |-- vgg16-397923af.pth
+|   `-- vit_g_hybrid_pt_1200e_ssv2_ft.pth
+```
+
+These already include all the checkpoints required for latentsync training and inference. If you just want to try inference, you only need to download `latentsync_unet.pt` and `tiny.pt` from our [HuggingFace repo](https://huggingface.co/chunyu-li/LatentSync)
+
+## 🚀 Inference
+
+Run the script for inference, which requires about 6.5 GB GPU memory.
+
+```bash
+./inference.sh
+```
+
+You can change the parameter `guidance_scale` to 1.5 to improve the lip-sync accuracy.
+
+## 🔄 Data Processing Pipeline
+
+The complete data processing pipeline includes the following steps:
+
+1. Remove the broken video files.
+2. Resample the video FPS to 25, and resample the audio to 16000 Hz.
+3. Scene detect via [PySceneDetect](https://github.com/Breakthrough/PySceneDetect).
+4. Split each video into 5-10 second segments.
+5. Remove videos where the face is smaller than 256 $\times$ 256, as well as videos with more than one face.
+6. Affine transform the faces according to the landmarks detected by [face-alignment](https://github.com/1adrianb/face-alignment), then resize to 256 $\times$ 256.
+7. Remove videos with [sync confidence score](https://www.robots.ox.ac.uk/~vgg/publications/2016/Chung16a/chung16a.pdf) lower than 3, and adjust the audio-visual offset to 0.
+8. Calculate [hyperIQA](https://openaccess.thecvf.com/content_CVPR_2020/papers/Su_Blindly_Assess_Image_Quality_in_the_Wild_Guided_by_a_CVPR_2020_paper.pdf) score, and remove videos with scores lower than 40.
+
+Run the script to execute the data processing pipeline:
+
+```bash
+./data_processing_pipeline.sh
+```
+
+You can change the parameter `input_dir` in the script to specify the data directory to be processed. The processed data will be saved in the same directory. Each step will generate a new directory to prevent the need to redo the entire pipeline in case the process is interrupted by an unexpected error.
+
+## 🏋️‍♂️ Training U-Net
+
+Before training, you must process the data as described above and download all the checkpoints. We released a pretrained SyncNet with 94% accuracy on the VoxCeleb2 dataset for the supervision of U-Net training. Note that this SyncNet is trained on affine transformed videos, so when using or evaluating this SyncNet, you need to perform affine transformation on the video first (the code of affine transformation is included in the data processing pipeline).
+
+If all the preparations are complete, you can train the U-Net with the following script:
+
+```bash
+./train_unet.sh
+```
+
+You should change the parameters in U-Net config file to specify the data directory, checkpoint save path, and other training hyperparameters.
+
+## 🏋️‍♂️ Training SyncNet
+
+In case you want to train SyncNet on your own datasets, you can run the following script. The data processing pipeline for SyncNet is the same as U-Net. 
+
+```bash
+./train_syncnet.sh
+```
+
+After `validations_steps` training, the loss charts will be saved in `train_output_dir`. They contain both the training and validation loss.

README.md

@@ -1,12 +1,13 @@
 ---
 title: LatentSync
-emoji: 😻
-colorFrom: red
-colorTo: purple
+emoji: 👄
+colorFrom: blue
+colorTo: blue
 sdk: gradio
-sdk_version: 5.10.0
+sdk_version: 5.9.1
 app_file: app.py
 pinned: false
+short_description: Audio Conditioned LipSync with Latent Diffusion Models
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,219 @@
+import gradio as gr
+import os
+import sys
+import shutil
+import uuid
+import subprocess
+from glob import glob
+from huggingface_hub import snapshot_download
+
+# Download models
+os.makedirs("checkpoints", exist_ok=True)
+
+snapshot_download(
+    repo_id = "chunyu-li/LatentSync",
+    local_dir = "./checkpoints"  
+)
+
+import tempfile
+from moviepy.editor import VideoFileClip
+from pydub import AudioSegment
+
+def process_video(input_video_path, temp_dir="temp_dir"):
+    """
+    Crop a given MP4 video to a maximum duration of 10 seconds if it is longer than 10 seconds.
+    Save the new video in the specified folder (default is temp_dir).
+    
+    Args:
+        input_video_path (str): Path to the input video file.
+        temp_dir (str): Directory where the processed video will be saved.
+        
+    Returns:
+        str: Path to the cropped video file.
+    """
+    # Ensure the temp_dir exists
+    os.makedirs(temp_dir, exist_ok=True)
+    
+    # Load the video
+    video = VideoFileClip(input_video_path)
+    
+    # Determine the output path
+    input_file_name = os.path.basename(input_video_path)
+    output_video_path = os.path.join(temp_dir, f"cropped_{input_file_name}")
+    
+    # Crop the video to 10 seconds if necessary
+   # if video.duration > 10:
+   #     video = video.subclip(0, 10)
+    
+    # Write the cropped video to the output path
+    video.write_videofile(output_video_path, codec="libx264", audio_codec="aac")
+    
+    # Return the path to the cropped video
+    return output_video_path
+
+def process_audio(file_path, temp_dir):
+    # Load the audio file
+    audio = AudioSegment.from_file(file_path)
+    
+    # Check and cut the audio if longer than 4 seconds
+    max_duration = 8 * 1000  # 4 seconds in milliseconds
+    if len(audio) > max_duration:
+        audio = audio[:max_duration]
+    
+    # Save the processed audio in the temporary directory
+    output_path = os.path.join(temp_dir, "trimmed_audio.wav")
+    audio.export(output_path, format="wav")
+    
+    # Return the path to the trimmed file
+    print(f"Processed audio saved at: {output_path}")
+    return output_path
+
+import argparse
+from omegaconf import OmegaConf
+import torch
+from diffusers import AutoencoderKL, DDIMScheduler
+from latentsync.models.unet import UNet3DConditionModel
+from latentsync.pipelines.lipsync_pipeline import LipsyncPipeline
+from diffusers.utils.import_utils import is_xformers_available
+from accelerate.utils import set_seed
+from latentsync.whisper.audio2feature import Audio2Feature
+
+
+def main(video_path, audio_path, progress=gr.Progress(track_tqdm=True)):
+    inference_ckpt_path = "checkpoints/latentsync_unet.pt"
+    unet_config_path = "configs/unet/second_stage.yaml"
+    config = OmegaConf.load(unet_config_path)
+    
+    print(f"Input video path: {video_path}")
+    print(f"Input audio path: {audio_path}")
+    print(f"Loaded checkpoint path: {inference_ckpt_path}")
+
+    is_shared_ui = True if "fffiloni/LatentSync" in os.environ['SPACE_ID'] else False
+    temp_dir = None
+    if is_shared_ui:
+        temp_dir = tempfile.mkdtemp()
+        cropped_video_path = process_video(video_path)
+        print(f"Cropped video saved to: {cropped_video_path}")
+        video_path=cropped_video_path
+
+        trimmed_audio_path = process_audio(audio_path, temp_dir)
+        print(f"Processed file was stored temporarily at: {trimmed_audio_path}")
+        audio_path=trimmed_audio_path
+
+    scheduler = DDIMScheduler.from_pretrained("configs")
+
+    if config.model.cross_attention_dim == 768:
+        whisper_model_path = "checkpoints/whisper/small.pt"
+    elif config.model.cross_attention_dim == 384:
+        whisper_model_path = "checkpoints/whisper/tiny.pt"
+    else:
+        raise NotImplementedError("cross_attention_dim must be 768 or 384")
+
+    audio_encoder = Audio2Feature(model_path=whisper_model_path, device="cuda", num_frames=config.data.num_frames)
+
+    vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16)
+    vae.config.scaling_factor = 0.18215
+    vae.config.shift_factor = 0
+
+    unet, _ = UNet3DConditionModel.from_pretrained(
+        OmegaConf.to_container(config.model),
+        inference_ckpt_path,  # load checkpoint
+        device="cpu",
+    )
+
+    unet = unet.to(dtype=torch.float16)
+
+    # set xformers
+    if is_xformers_available():
+        unet.enable_xformers_memory_efficient_attention()
+
+    pipeline = LipsyncPipeline(
+        vae=vae,
+        audio_encoder=audio_encoder,
+        unet=unet,
+        scheduler=scheduler,
+    ).to("cuda")
+
+    seed = -1
+    if seed != -1:
+        set_seed(seed)
+    else:
+        torch.seed()
+
+    print(f"Initial seed: {torch.initial_seed()}")
+
+    unique_id = str(uuid.uuid4())
+    video_out_path = f"video_out{unique_id}.mp4"
+
+    pipeline(
+        video_path=video_path,
+        audio_path=audio_path,
+        video_out_path=video_out_path,
+        video_mask_path=video_out_path.replace(".mp4", "_mask.mp4"),
+        num_frames=config.data.num_frames,
+        num_inference_steps=config.run.inference_steps,
+        guidance_scale=1.0,
+        weight_dtype=torch.float16,
+        width=config.data.resolution,
+        height=config.data.resolution,
+    )
+
+    if is_shared_ui:
+        # Clean up the temporary directory
+        if os.path.exists(temp_dir):
+            shutil.rmtree(temp_dir)
+            print(f"Temporary directory {temp_dir} deleted.")
+
+    return video_out_path
+
+
+css="""
+div#col-container{
+    margin: 0 auto;
+    max-width: 982px;
+}
+"""
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown("# LatentSync: Audio Conditioned Latent Diffusion Models for Lip Sync")
+        gr.Markdown("LatentSync, an end-to-end lip sync framework based on audio conditioned latent diffusion models without any intermediate motion representation, diverging from previous diffusion-based lip sync methods based on pixel space diffusion or two-stage generation.")
+        gr.HTML("""
+        <div style="display:flex;column-gap:4px;">
+            <a href="https://github.com/bytedance/LatentSync">
+                <img src='https://img.shields.io/badge/GitHub-Repo-blue'>
+            </a> 
+            <a href="https://arxiv.org/abs/2412.09262">
+                <img src='https://img.shields.io/badge/ArXiv-Paper-red'>
+            </a>
+            <a href="https://huggingface.co/spaces/fffiloni/LatentSync?duplicate=true">
+                <img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-sm.svg" alt="Duplicate this Space">
+            </a>
+            <a href="https://huggingface.co/fffiloni">
+                <img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/follow-me-on-HF-sm-dark.svg" alt="Follow me on HF">
+            </a>
+        </div>
+        """)
+        with gr.Row():
+            with gr.Column():
+                video_input = gr.Video(label="Video Control", format="mp4")
+                audio_input = gr.Audio(label="Audio Input", type="filepath")
+                submit_btn = gr.Button("Submit")
+            with gr.Column():
+                video_result = gr.Video(label="Result")
+
+                gr.Examples(
+                    examples = [
+                        ["assets/demo1_video.mp4", "assets/demo1_audio.wav"],
+                        ["assets/demo2_video.mp4", "assets/demo2_audio.wav"],
+                        ["assets/demo3_video.mp4", "assets/demo3_audio.wav"],
+                    ],
+                    inputs = [video_input, audio_input]
+                )
+
+    submit_btn.click(
+        fn = main,
+        inputs = [video_input, audio_input],
+        outputs = [video_result]
+    )
+
+demo.queue().launch(show_api=False, show_error=True)

data_processing_pipeline.sh

@@ -0,0 +1,9 @@
+#!/bin/bash
+
+python -m preprocess.data_processing_pipeline \
+    --total_num_workers 20 \
+    --per_gpu_num_workers 20 \
+    --resolution 256 \
+    --sync_conf_threshold 3 \
+    --temp_dir temp \
+    --input_dir /mnt/bn/maliva-gen-ai-v2/chunyu.li/VoxCeleb2/raw

inference.sh

@@ -0,0 +1,9 @@
+#!/bin/bash
+
+python -m scripts.inference \
+    --unet_config_path "configs/unet/second_stage.yaml" \
+    --inference_ckpt_path "checkpoints/latentsync_unet.pt" \
+    --guidance_scale 1.0 \
+    --video_path "assets/demo1_video.mp4" \
+    --audio_path "assets/demo1_audio.wav" \
+    --video_out_path "video_out.mp4"

packages.txt

@@ -0,0 +1,2 @@
+ffmpeg
+libgl1

requirements.txt

@@ -0,0 +1,31 @@
+torch==2.2.2
+torchvision==0.17.2
+--extra-index-url https://download.pytorch.org/whl/cu121
+xformers==0.0.26
+triton==2.2.0
+
+diffusers==0.11.1
+transformers==4.38.0
+huggingface-hub==0.25.2
+imageio==2.27.0
+decord==0.6.0
+accelerate==0.26.1
+einops==0.7.0
+omegaconf==2.3.0
+safetensors==0.4.2
+opencv-python==4.9.0.80
+mediapipe==0.10.11
+av==11.0.0
+torch-fidelity==0.3.0
+torchmetrics==1.3.1
+python_speech_features==0.6
+librosa==0.10.1
+scenedetect==0.6.1
+ffmpeg-python==0.2.0
+lpips==0.1.4
+face-alignment==1.4.1
+ninja==1.11.1.1
+pandas==2.0.3
+numpy==1.24.4
+pydub
+moviepy==1.0.3

setup_env.sh

@@ -0,0 +1,23 @@
+#!/bin/bash
+
+# Create a new conda environment
+conda create -y -n latentsync python=3.10.13
+conda activate latentsync
+
+# Install ffmpeg
+conda install -y -c conda-forge ffmpeg
+
+# Python dependencies
+pip install -r requirements.txt
+
+# OpenCV dependencies
+sudo apt -y install libgl1
+
+# Download all the checkpoints from HuggingFace
+huggingface-cli download chunyu-li/LatentSync --local-dir checkpoints --exclude "*.git*" "README.md"
+
+# Soft links for the auxiliary models
+mkdir -p ~/.cache/torch/hub/checkpoints
+ln -s $(pwd)/checkpoints/auxiliary/2DFAN4-cd938726ad.zip ~/.cache/torch/hub/checkpoints/2DFAN4-cd938726ad.zip
+ln -s $(pwd)/checkpoints/auxiliary/s3fd-619a316812.pth ~/.cache/torch/hub/checkpoints/s3fd-619a316812.pth
+ln -s $(pwd)/checkpoints/auxiliary/vgg16-397923af.pth ~/.cache/torch/hub/checkpoints/vgg16-397923af.pth

train_syncnet.sh

@@ -0,0 +1,4 @@
+#!/bin/bash
+
+torchrun --nnodes=1 --nproc_per_node=1 --master_port=25678 -m scripts.train_syncnet \
+    --config_path "configs/syncnet/syncnet_16_pixel.yaml"

train_unet.sh

@@ -0,0 +1,4 @@
+#!/bin/bash
+
+torchrun --nnodes=1 --nproc_per_node=1 --master_port=25678 -m scripts.train_unet \
+    --unet_config_path "configs/unet/first_stage.yaml"