webgui.py

#!/usr/bin/env python
# -*- coding: UTF-8 -*-
'''
webui
'''

import os
import random
from datetime import datetime
from pathlib import Path

import cv2
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler
from omegaconf import OmegaConf
from PIL import Image
from src.models.unet_2d_condition import UNet2DConditionModel
from src.models.unet_3d_echo import EchoUNet3DConditionModel
from src.models.whisper.audio2feature import load_audio_model
from src.pipelines.pipeline_echo_mimic import Audio2VideoPipeline
from src.utils.util import save_videos_grid, crop_and_pad
from src.models.face_locator import FaceLocator
from moviepy.editor import VideoFileClip, AudioFileClip
from facenet_pytorch import MTCNN
import argparse

import gradio as gr

default_values = {
    "width": 512,
    "height": 512,
    "length": 1200,
    "seed": 420,
    "facemask_dilation_ratio": 0.1,
    "facecrop_dilation_ratio": 0.5,
    "context_frames": 12,
    "context_overlap": 3,
    "cfg": 2.5,
    "steps": 30,
    "sample_rate": 16000,
    "fps": 24,
    "device": "cuda"
}

ffmpeg_path = os.getenv('FFMPEG_PATH')
if ffmpeg_path is None:
    print("please download ffmpeg-static and export to FFMPEG_PATH. \nFor example: export FFMPEG_PATH=/musetalk/ffmpeg-4.4-amd64-static")
elif ffmpeg_path not in os.getenv('PATH'):
    print("add ffmpeg to path")
    os.environ["PATH"] = f"{ffmpeg_path}:{os.environ['PATH']}"


config_path = "./configs/prompts/animation.yaml"
config = OmegaConf.load(config_path)
if config.weight_dtype == "fp16":
    weight_dtype = torch.float16
else:
    weight_dtype = torch.float32

device = "cuda"
if not torch.cuda.is_available():
    device = "cpu"

inference_config_path = config.inference_config
infer_config = OmegaConf.load(inference_config_path)

############# model_init started #############
## vae init
vae = AutoencoderKL.from_pretrained(config.pretrained_vae_path).to("cuda", dtype=weight_dtype)

## reference net init
reference_unet = UNet2DConditionModel.from_pretrained(
    config.pretrained_base_model_path,
    subfolder="unet",
).to(dtype=weight_dtype, device=device)
reference_unet.load_state_dict(torch.load(config.reference_unet_path, map_location="cpu"))

## denoising net init
if os.path.exists(config.motion_module_path):
    ### stage1 + stage2
    denoising_unet = EchoUNet3DConditionModel.from_pretrained_2d(
        config.pretrained_base_model_path,
        config.motion_module_path,
        subfolder="unet",
        unet_additional_kwargs=infer_config.unet_additional_kwargs,
    ).to(dtype=weight_dtype, device=device)
else:
    ### only stage1
    denoising_unet = EchoUNet3DConditionModel.from_pretrained_2d(
        config.pretrained_base_model_path,
        "",
        subfolder="unet",
        unet_additional_kwargs={
            "use_motion_module": False,
            "unet_use_temporal_attention": False,
            "cross_attention_dim": infer_config.unet_additional_kwargs.cross_attention_dim
        }
    ).to(dtype=weight_dtype, device=device)

denoising_unet.load_state_dict(torch.load(config.denoising_unet_path, map_location="cpu"), strict=False)

## face locator init
face_locator = FaceLocator(320, conditioning_channels=1, block_out_channels=(16, 32, 96, 256)).to(dtype=weight_dtype, device="cuda")
face_locator.load_state_dict(torch.load(config.face_locator_path))

## load audio processor params
audio_processor = load_audio_model(model_path=config.audio_model_path, device=device)

## load face detector params
face_detector = MTCNN(image_size=320, margin=0, min_face_size=20, thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=True, device=device)

############# model_init finished #############

sched_kwargs = OmegaConf.to_container(infer_config.noise_scheduler_kwargs)
scheduler = DDIMScheduler(**sched_kwargs)

pipe = Audio2VideoPipeline(
    vae=vae,
    reference_unet=reference_unet,
    denoising_unet=denoising_unet,
    audio_guider=audio_processor,
    face_locator=face_locator,
    scheduler=scheduler,
).to("cuda", dtype=weight_dtype)

def select_face(det_bboxes, probs):
    ## max face from faces that the prob is above 0.8
    ## box: xyxy
    if det_bboxes is None or probs is None:
        return None
    filtered_bboxes = []
    for bbox_i in range(len(det_bboxes)):
        if probs[bbox_i] > 0.8:
            filtered_bboxes.append(det_bboxes[bbox_i])
    if len(filtered_bboxes) == 0:
        return None
    sorted_bboxes = sorted(filtered_bboxes, key=lambda x:(x[3]-x[1]) * (x[2] - x[0]), reverse=True)
    return sorted_bboxes[0]

def process_video(uploaded_img, uploaded_audio, width, height, length, seed, facemask_dilation_ratio, facecrop_dilation_ratio, context_frames, context_overlap, cfg, steps, sample_rate, fps, device):

    if seed is not None and seed > -1:
        generator = torch.manual_seed(seed)
    else:
        generator = torch.manual_seed(random.randint(100, 1000000))

    #### face musk prepare
    face_img = cv2.imread(uploaded_img)
    face_mask = np.zeros((face_img.shape[0], face_img.shape[1])).astype('uint8')
    det_bboxes, probs = face_detector.detect(face_img)
    select_bbox = select_face(det_bboxes, probs)
    if select_bbox is None:
        face_mask[:, :] = 255
    else:
        xyxy = select_bbox[:4]
        xyxy = np.round(xyxy).astype('int')
        rb, re, cb, ce = xyxy[1], xyxy[3], xyxy[0], xyxy[2]
        r_pad = int((re - rb) * facemask_dilation_ratio)
        c_pad = int((ce - cb) * facemask_dilation_ratio)
        face_mask[rb - r_pad : re + r_pad, cb - c_pad : ce + c_pad] = 255
        
        #### face crop
        r_pad_crop = int((re - rb) * facecrop_dilation_ratio)
        c_pad_crop = int((ce - cb) * facecrop_dilation_ratio)
        crop_rect = [max(0, cb - c_pad_crop), max(0, rb - r_pad_crop), min(ce + c_pad_crop, face_img.shape[1]), min(re + r_pad_crop, face_img.shape[0])]
        face_img = crop_and_pad(face_img, crop_rect)
        face_mask = crop_and_pad(face_mask, crop_rect)
        face_img = cv2.resize(face_img, (width, height))
        face_mask = cv2.resize(face_mask, (width, height))

    ref_image_pil = Image.fromarray(face_img[:, :, [2, 1, 0]])
    face_mask_tensor = torch.Tensor(face_mask).to(dtype=weight_dtype, device="cuda").unsqueeze(0).unsqueeze(0).unsqueeze(0) / 255.0
    
    video = pipe(
        ref_image_pil,
        uploaded_audio,
        face_mask_tensor,
        width,
        height,
        length,
        steps,
        cfg,
        generator=generator,
        audio_sample_rate=sample_rate,
        context_frames=context_frames,
        fps=fps,
        context_overlap=context_overlap
    ).videos

    save_dir = Path("output/tmp")
    save_dir.mkdir(exist_ok=True, parents=True)
    output_video_path = save_dir / "output_video.mp4"
    save_videos_grid(video, str(output_video_path), n_rows=1, fps=fps)

    video_clip = VideoFileClip(str(output_video_path))
    audio_clip = AudioFileClip(uploaded_audio)
    final_output_path = save_dir / "output_video_with_audio.mp4"
    video_clip = video_clip.set_audio(audio_clip)
    video_clip.write_videofile(str(final_output_path), codec="libx264", audio_codec="aac")

    return final_output_path
  
with gr.Blocks() as demo:
    gr.Markdown('# EchoMimic')
    gr.Markdown('![]()')
    with gr.Row():
        with gr.Column():
            uploaded_img = gr.Image(type="filepath", label="Reference Image")
            uploaded_audio = gr.Audio(type="filepath", label="Input Audio")
        with gr.Column():
            output_video = gr.Video()

    with gr.Accordion("Configuration", open=False):
        width = gr.Slider(label="Width", minimum=128, maximum=1024, value=default_values["width"])
        height = gr.Slider(label="Height", minimum=128, maximum=1024, value=default_values["height"])
        length = gr.Slider(label="Length", minimum=100, maximum=5000, value=default_values["length"])
        seed = gr.Slider(label="Seed", minimum=0, maximum=10000, value=default_values["seed"])
        facemask_dilation_ratio = gr.Slider(label="Facemask Dilation Ratio", minimum=0.0, maximum=1.0, step=0.01, value=default_values["facemask_dilation_ratio"])
        facecrop_dilation_ratio = gr.Slider(label="Facecrop Dilation Ratio", minimum=0.0, maximum=1.0, step=0.01, value=default_values["facecrop_dilation_ratio"])
        context_frames = gr.Slider(label="Context Frames", minimum=0, maximum=50, step=1, value=default_values["context_frames"])
        context_overlap = gr.Slider(label="Context Overlap", minimum=0, maximum=10, step=1, value=default_values["context_overlap"])
        cfg = gr.Slider(label="CFG", minimum=0.0, maximum=10.0, step=0.1, value=default_values["cfg"])
        steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=default_values["steps"])
        sample_rate = gr.Slider(label="Sample Rate", minimum=8000, maximum=48000, step=1000, value=default_values["sample_rate"])
        fps = gr.Slider(label="FPS", minimum=1, maximum=60, step=1, value=default_values["fps"])
        device = gr.Radio(label="Device", choices=["cuda", "cpu"], value=default_values["device"])

    generate_button = gr.Button("Generate Video")

    def generate_video(uploaded_img, uploaded_audio, width, height, length, seed, facemask_dilation_ratio, facecrop_dilation_ratio, context_frames, context_overlap, cfg, steps, sample_rate, fps, device):

        final_output_path = process_video(
            uploaded_img, uploaded_audio, width, height, length, seed, facemask_dilation_ratio, facecrop_dilation_ratio, context_frames, context_overlap, cfg, steps, sample_rate, fps, device
        )        
        output_video= final_output_path
        return final_output_path

    generate_button.click(
        generate_video,
        inputs=[
            uploaded_img,
            uploaded_audio,
            width,
            height,
            length,
            seed,
            facemask_dilation_ratio,
            facecrop_dilation_ratio,
            context_frames,
            context_overlap,
            cfg,
            steps,
            sample_rate,
            fps,
            device
        ],
        outputs=output_video
    )
parser = argparse.ArgumentParser(description='EchoMimic')
parser.add_argument('--server_name', type=str, default='0.0.0.0', help='Server name')
parser.add_argument('--server_port', type=int, default=7680, help='Server port')
args = parser.parse_args()

# demo.launch(server_name=args.server_name, server_port=args.server_port, inbrowser=True)

if __name__ == '__main__':
    #demo.launch(server_name='0.0.0.0')
    demo.launch(server_name=args.server_name, server_port=args.server_port, inbrowser=True)