#!/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
from gradio_client import Client, handle_file
from pydub import AudioSegment
import huggingface_hub
huggingface_hub.snapshot_download(
repo_id='BadToBest/EchoMimic',
local_dir='./pretrained_weights',
local_dir_use_symlinks=False,
)
is_shared_ui = True if "fffiloni/EchoMimic" in os.environ['SPACE_ID'] else False
available_property = False if is_shared_ui else True
advanced_settings_label = "Advanced Configuration (only for duplicated spaces)" if is_shared_ui else "Advanced Configuration"
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)
# Get the original dimensions
original_height, original_width = face_img.shape[:2]
# Set the new width to 512 pixels
new_width = 512
# Calculate the new height with the same aspect ratio
new_height = int(original_height * (new_width / original_width))
# Round the new height to the nearest multiple of 8
new_height = round(new_height / 8) * 8
# Resize the image to the calculated dimensions
face_image = cv2.resize(face_img, (new_width, new_height))
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
def get_maskGCT_TTS(prompt_audio_maskGCT, audio_to_clone):
try:
client = Client("amphion/maskgct")
except:
raise gr.Error(f"amphion/maskgct space's api might not be ready, please wait, or upload an audio instead.")
result = client.predict(
prompt_wav = handle_file(audio_to_clone),
target_text = prompt_audio_maskGCT,
target_len=-1,
n_timesteps=25,
api_name="/predict"
)
print(result)
return result, gr.update(value=result, visible=True)
with gr.Blocks() as demo:
gr.Markdown('# EchoMimic')
gr.Markdown('## Lifelike Audio-Driven Portrait Animations through Editable Landmark Conditioning')
gr.Markdown('Inference time: from ~7mins/240frames to ~50s/240frames on V100 GPU')
gr.HTML("""
""")
with gr.Row():
with gr.Column():
uploaded_img = gr.Image(type="filepath", label="Reference Image")
uploaded_audio = gr.Audio(type="filepath", label="Input Audio")
preprocess_audio_file = gr.File(visible=False)
with gr.Accordion(label="Voice cloning with MaskGCT", open=False):
prompt_audio_maskGCT = gr.Textbox(
label = "Text to synthetize",
lines = 2,
max_lines = 2,
elem_id = "text-synth-maskGCT"
)
audio_to_clone_maskGCT = gr.Audio(
label = "Voice to clone",
type = "filepath",
elem_id = "audio-clone-elm-maskGCT"
)
gen_maskGCT_voice_btn = gr.Button("Generate voice clone (optional)")
with gr.Accordion(label=advanced_settings_label, open=False):
with gr.Row():
width = gr.Slider(label="Width", minimum=128, maximum=1024, value=default_values["width"], interactive=available_property)
height = gr.Slider(label="Height", minimum=128, maximum=1024, value=default_values["height"], interactive=available_property)
with gr.Row():
length = gr.Slider(label="Length", minimum=100, maximum=5000, value=default_values["length"], interactive=available_property)
seed = gr.Slider(label="Seed", minimum=0, maximum=10000, value=default_values["seed"], interactive=available_property)
with gr.Row():
facemask_dilation_ratio = gr.Slider(label="Facemask Dilation Ratio", minimum=0.0, maximum=1.0, step=0.01, value=default_values["facemask_dilation_ratio"], interactive=available_property)
facecrop_dilation_ratio = gr.Slider(label="Facecrop Dilation Ratio", minimum=0.0, maximum=1.0, step=0.01, value=default_values["facecrop_dilation_ratio"], interactive=available_property)
with gr.Row():
context_frames = gr.Slider(label="Context Frames", minimum=0, maximum=50, step=1, value=default_values["context_frames"], interactive=available_property)
context_overlap = gr.Slider(label="Context Overlap", minimum=0, maximum=10, step=1, value=default_values["context_overlap"], interactive=available_property)
with gr.Row():
cfg = gr.Slider(label="CFG", minimum=0.0, maximum=10.0, step=0.1, value=default_values["cfg"], interactive=available_property)
steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=default_values["steps"], interactive=available_property)
with gr.Row():
sample_rate = gr.Slider(label="Sample Rate", minimum=8000, maximum=48000, step=1000, value=default_values["sample_rate"], interactive=available_property)
fps = gr.Slider(label="FPS", minimum=1, maximum=60, step=1, value=default_values["fps"], interactive=available_property)
device = gr.Radio(label="Device", choices=["cuda", "cpu"], value=default_values["device"], interactive=available_property)
generate_button = gr.Button("Generate Video")
with gr.Column():
output_video = gr.Video()
gr.Examples(
label = "Portrait examples",
examples = [
['assets/test_imgs/a.png'],
['assets/test_imgs/b.png'],
['assets/test_imgs/c.png'],
['assets/test_imgs/d.png'],
['assets/test_imgs/e.png']
],
inputs = [uploaded_img]
)
gr.Examples(
label = "Audio examples",
examples = [
['assets/test_audios/chunnuanhuakai.wav'],
['assets/test_audios/chunwang.wav'],
['assets/test_audios/echomimic_en_girl.wav'],
['assets/test_audios/echomimic_en.wav'],
['assets/test_audios/echomimic_girl.wav'],
['assets/test_audios/echomimic.wav'],
['assets/test_audios/jane.wav'],
['assets/test_audios/mei.wav'],
['assets/test_audios/walden.wav'],
['assets/test_audios/yun.wav'],
],
inputs = [uploaded_audio]
)
gr.HTML("""
""")
def trim_audio(file_path, output_path, max_duration=10):
# Load the audio file
audio = AudioSegment.from_wav(file_path)
# Convert max duration to milliseconds
max_duration_ms = max_duration * 1000
# Trim the audio if it's longer than max_duration
if len(audio) > max_duration_ms:
audio = audio[:max_duration_ms]
# Export the trimmed audio
audio.export(output_path, format="wav")
print(f"Audio trimmed and saved as {output_path}")
return output_path
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, progress=gr.Progress(track_tqdm=True)):
if is_shared_ui:
gr.Info("Trimming audio to max 10 seconds. Duplicate the space for unlimited audio length.")
uploaded_audio = trim_audio(uploaded_audio, "trimmed_audio.wav")
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
gen_maskGCT_voice_btn.click(
fn = get_maskGCT_TTS,
inputs = [prompt_audio_maskGCT, audio_to_clone_maskGCT],
outputs = [uploaded_audio, preprocess_audio_file],
queue = False,
show_api = False
)
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,
show_api=False
)
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.queue(max_size=3).launch(show_api=False, show_error=True)
#demo.launch(server_name=args.server_name, server_port=args.server_port, inbrowser=True)
