app.py

# coding: utf-8

"""
The entrance of the gradio
"""

import tyro
import gradio as gr
import os.path as osp
from src.utils.helper import load_description
from src.gradio_pipeline import GradioPipeline
from src.config.crop_config import CropConfig
from src.config.argument_config import ArgumentConfig
from src.config.inference_config import InferenceConfig
import spaces
import cv2
import torch


#추가
from elevenlabs_utils import ElevenLabsPipeline
from setup_environment import initialize_environment
from src.utils.video import extract_audio
#from flux_dev import create_flux_tab
from flux_schnell import create_flux_tab
# from diffusers import FluxPipeline

# import gdown
# folder_url = f"https://drive.google.com/drive/folders/1UtKgzKjFAOmZkhNK-OYT0caJ_w2XAnib"
# gdown.download_folder(url=folder_url, output="pretrained_weights", quiet=False)



# #========================= # FLUX 모델 로드 설정
# flux_pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
# flux_pipe.enable_sequential_cpu_offload()
# flux_pipe.vae.enable_slicing()
# flux_pipe.vae.enable_tiling()
# flux_pipe.to(torch.float16)


# @spaces.GPU(duration=120)
# def generate_image(prompt, guidance_scale, width, height):
#     # 이미지를 생성하는 함수
#     output_image = flux_pipe(
#         prompt=prompt,
#         guidance_scale=guidance_scale,
#         height=height,
#         width=width,
#         num_inference_steps=4,
#         max_sequence_length=256,
#     ).images[0]

#     # 결과 폴더 생성
#     result_folder = "/tmp/flux/"
#     os.makedirs(result_folder, exist_ok=True)

#     # 파일 이름 생성
#     timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
#     #filename = f"{prompt.replace(' ', '_')}_{timestamp}.png"
#     filename = f"{'_'.join(prompt.split()[:3])}_{timestamp}.png"
#     output_path = os.path.join(result_folder, filename)

#     # # 이미지를 저장
#     # output_image.save(output_path)

#     return output_image, output_path  # 두 개의 출력 반환

# def flux_tab(): #image_input):  # image_input을 인자로 받습니다.
#     with gr.Tab("FLUX 이미지 생성"):
#         with gr.Row():
#             with gr.Column():
#                 # 사용자 입력 설정
#                 prompt = gr.Textbox(label="Prompt", value="A cat holding a sign that says hello world")
#                 guidance_scale = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=20.0, value=3.5, step=0.1)
#                 width = gr.Slider(label="Width", minimum=256, maximum=2048, value=512, step=64)
#                 height = gr.Slider(label="Height", minimum=256, maximum=2048, value=512, step=64)

#             with gr.Column():
#                 # 출력 이미지와 다운로드 버튼
#                 output_image = gr.Image(type="pil", label="Output")
#                 download_button = gr.File(label="Download")
#                 generate_button = gr.Button("이미지 생성")
#                 #use_in_text2lipsync_button = gr.Button("이 이미지를 Text2Lipsync에서 사용하기")  # 새로운 버튼 추가

#                 # 클릭 이벤트를 정의
#                 generate_button.click(
#                     fn=generate_image,
#                     inputs=[prompt, guidance_scale, width, height],
#                     outputs=[output_image, download_button]
#                 )

#                 # # 새로운 버튼 클릭 이벤트 정의
#                 # use_in_text2lipsync_button.click(
#                 #     fn=lambda img: img,  # 간단한 람다 함수를 사용하여 이미지를 그대로 전달
#                 #     inputs=[output_image],  # 생성된 이미지를 입력으로 사용
#                 #     outputs=[image_input]  # Text to LipSync 탭의 image_input을 업데이트
#                 # )

# #========================= # FLUX 모델 로드 설정
    
initialize_environment()

import sys
sys.path.append('/home/user/.local/lib/python3.10/site-packages')
sys.path.append('/home/user/.local/lib/python3.10/site-packages/stf_alternative/src/stf_alternative')
sys.path.append('/home/user/.local/lib/python3.10/site-packages/stf_tools/src/stf_tools')
sys.path.append('/home/user/app/')
sys.path.append('/home/user/app/stf/')
sys.path.append('/home/user/app/stf/stf_alternative/')
sys.path.append('/home/user/app/stf/stf_alternative/src/stf_alternative')
sys.path.append('/home/user/app/stf/stf_tools')
sys.path.append('/home/user/app/stf/stf_tools/src/stf_tools')


import os
# CUDA 경로를 환경 변수로 설정
os.environ['PATH'] = '/usr/local/cuda/bin:' + os.environ.get('PATH', '')
os.environ['LD_LIBRARY_PATH'] = '/usr/local/cuda/lib64:' + os.environ.get('LD_LIBRARY_PATH', '')
# 확인용 출력
print("PATH:", os.environ['PATH'])
print("LD_LIBRARY_PATH:", os.environ['LD_LIBRARY_PATH'])

from stf_utils import STFPipeline



# audio_path="assets/examples/driving/test_aud.mp3"
#audio_path_component = gr.Textbox(label="Input", value="assets/examples/driving/test_aud.mp3")

# @spaces.GPU(duration=120)
# def gpu_wrapped_stf_pipeline_execute(audio_path):
#     return stf_pipeline.execute(audio_path)

    
# ###### 테스트중 ######
    

# stf_pipeline = STFPipeline()
# driving_video_path=gr.Video()

# # set tyro theme
# tyro.extras.set_accent_color("bright_cyan")
# args = tyro.cli(ArgumentConfig)

# with gr.Blocks(theme=gr.themes.Soft()) as demo:
#     with gr.Row():
#         audio_path_component = gr.Textbox(label="Input", value="assets/examples/driving/test_aud.mp3")
#         stf_button = gr.Button("stf test", variant="primary")
#         stf_button.click(
#                     fn=gpu_wrapped_stf_pipeline_execute,
#                     inputs=[
#                         audio_path_component
#                     ],
#                     outputs=[driving_video_path]
#                 )
#     with gr.Row():
#         driving_video_path.render()

#     with gr.Row():
#         create_flux_tab()  # image_input을 flux_tab에 전달합니다.

# ###### 테스트중 ######


def partial_fields(target_class, kwargs):
    return target_class(**{k: v for k, v in kwargs.items() if hasattr(target_class, k)})

# set tyro theme
tyro.extras.set_accent_color("bright_cyan")
args = tyro.cli(ArgumentConfig)

# specify configs for inference
inference_cfg = partial_fields(InferenceConfig, args.__dict__)  # use attribute of args to initial InferenceConfig
crop_cfg = partial_fields(CropConfig, args.__dict__)  # use attribute of args to initial CropConfig

gradio_pipeline = GradioPipeline(
    inference_cfg=inference_cfg,
    crop_cfg=crop_cfg,
    args=args
)

# 추가 정의
elevenlabs_pipeline = ElevenLabsPipeline()
stf_pipeline = STFPipeline()
driving_video_path=gr.Video()

@spaces.GPU(duration=120)
def gpu_wrapped_stf_pipeline_execute(audio_path):
    return stf_pipeline.execute(audio_path)


@spaces.GPU(duration=200)
def gpu_wrapped_elevenlabs_pipeline_generate_voice(text, voice):
    return elevenlabs_pipeline.generate_voice(text, voice)



@spaces.GPU(duration=240)
def gpu_wrapped_execute_video(*args, **kwargs):
    return gradio_pipeline.execute_video(*args, **kwargs)

@spaces.GPU(duration=240)
def gpu_wrapped_execute_image(*args, **kwargs):
    return gradio_pipeline.execute_image(*args, **kwargs)

def is_square_video(video_path):
    video = cv2.VideoCapture(video_path)

    width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))

    video.release()
    if width != height:
        raise gr.Error("Error: the video does not have a square aspect ratio. We currently only support square videos")

    return gr.update(visible=True)

def txt_to_driving_video(text):
    audio_path = gpu_wrapped_elevenlabs_pipeline_generate_voice(text)
    driving_video_path = gpu_wrapped_stf_pipeline_execute(audio_path)
    return driving_video_path

# assets
title_md = "assets/gradio_title.md"
example_portrait_dir = "assets/examples/source"
example_video_dir = "assets/examples/driving"
data_examples = [
    [osp.join(example_portrait_dir, "s9.jpg"), osp.join(example_video_dir, "d0.mp4"), True, True, True, True],
    [osp.join(example_portrait_dir, "s6.jpg"), osp.join(example_video_dir, "d0.mp4"), True, True, True, True],
    [osp.join(example_portrait_dir, "s10.jpg"), osp.join(example_video_dir, "d0.mp4"), True, True, True, True],
    [osp.join(example_portrait_dir, "s5.jpg"), osp.join(example_video_dir, "d18.mp4"), True, True, True, True],
    [osp.join(example_portrait_dir, "s7.jpg"), osp.join(example_video_dir, "d19.mp4"), True, True, True, True],
    [osp.join(example_portrait_dir, "s22.jpg"), osp.join(example_video_dir, "d0.mp4"), True, True, True, True],
]
#################### interface logic ####################

# Define components first
eye_retargeting_slider = gr.Slider(minimum=0, maximum=0.8, step=0.01, label="target eyes-open ratio")
lip_retargeting_slider = gr.Slider(minimum=0, maximum=0.8, step=0.01, label="target lip-open ratio")
retargeting_input_image = gr.Image(type="filepath")
output_image = gr.Image(type="numpy")
output_image_paste_back = gr.Image(type="numpy")
output_video = gr.Video()
output_video_concat = gr.Video()

video_input = gr.Video()


with gr.Blocks(theme=gr.themes.Soft()) as demo:
    #gr.HTML(load_description(title_md))

    with gr.Tabs():
        with gr.Tab("Text to LipSync"):
            gr.Markdown("# Text to LipSync")
            with gr.Row():
                with gr.Column():
                    script_txt = gr.Text()
                with gr.Column():
                    txt2video_gen_button = gr.Button("txt2video generation", variant="primary")

                # with gr.Column():
                #     audio_gen_button = gr.Button("Audio generation", variant="primary")
            # with gr.Row():
            #         video_input = gr.Audio(label="Generated video", type="filepath")
                
            gr.Markdown(load_description("assets/gradio_description_upload.md"))
            with gr.Row():
                with gr.Accordion(open=True, label="Source Portrait"):
                    image_input = gr.Image(type="filepath")
                    gr.Examples(
                        examples=[
                            [osp.join(example_portrait_dir, "s9.jpg")],
                            [osp.join(example_portrait_dir, "s6.jpg")],
                            [osp.join(example_portrait_dir, "s10.jpg")],
                            [osp.join(example_portrait_dir, "s5.jpg")],
                            [osp.join(example_portrait_dir, "s7.jpg")],
                            [osp.join(example_portrait_dir, "s12.jpg")],
                            [osp.join(example_portrait_dir, "s22.jpg")],
                        ],
                        inputs=[image_input],
                        cache_examples=False,
                    )
                with gr.Accordion(open=True, label="Driving Video"):
                    #video_input = gr.Video()
                    gr.Examples(
                        examples=[
                            [osp.join(example_video_dir, "d0.mp4")],
                            [osp.join(example_video_dir, "d18.mp4")],
                            [osp.join(example_video_dir, "d19.mp4")],
                            [osp.join(example_video_dir, "d14_trim.mp4")],
                            [osp.join(example_video_dir, "d6_trim.mp4")],
                        ],
                        inputs=[video_input],
                        cache_examples=False,
                    )
            with gr.Row():
                with gr.Accordion(open=False, label="Animation Instructions and Options"):
                    gr.Markdown(load_description("assets/gradio_description_animation.md"))
                    with gr.Row():
                        flag_relative_input = gr.Checkbox(value=True, label="relative motion")
                        flag_do_crop_input = gr.Checkbox(value=True, label="do crop")
                        flag_remap_input = gr.Checkbox(value=True, label="paste-back")
            gr.Markdown(load_description("assets/gradio_description_animate_clear.md"))
            with gr.Row():
                with gr.Column():
                    process_button_animation = gr.Button("🚀 Animate", variant="primary")
                with gr.Column():
                    process_button_reset = gr.ClearButton([image_input, video_input, output_video, output_video_concat], value="🧹 Clear")
            with gr.Row():
                with gr.Column():
                    with gr.Accordion(open=True, label="The animated video in the original image space"):
                        output_video.render()
                with gr.Column():
                    with gr.Accordion(open=True, label="The animated video"):
                        output_video_concat.render()
            with gr.Row():
                # Examples
                gr.Markdown("## You could also choose the examples below by one click ⬇️")
            with gr.Row():
                gr.Examples(
                    examples=data_examples,
                    fn=gpu_wrapped_execute_video,
                    inputs=[
                        image_input,
                        video_input,
                        flag_relative_input,
                        flag_do_crop_input,
                        flag_remap_input
                    ],
                    outputs=[output_image, output_image_paste_back],
                    examples_per_page=6,
                    cache_examples=False,
                )
        
            process_button_animation.click(
                fn=gpu_wrapped_execute_video,
                inputs=[
                    image_input,
                    video_input,
                    flag_relative_input,
                    flag_do_crop_input,
                    flag_remap_input
                ],
                outputs=[output_video, output_video_concat],
                show_progress=True
            )
            txt2video_gen_button.click(
                fn=txt_to_driving_video,
                inputs=[
                    script_txt
                ],
                outputs=[video_input],
                show_progress=True
            )


            
            # image_input.change(
            #     fn=gradio_pipeline.prepare_retargeting,
            #     inputs=image_input,
            #     outputs=[eye_retargeting_slider, lip_retargeting_slider, retargeting_input_image]
            # )
            video_input.upload(
                fn=is_square_video,
                inputs=video_input,
                outputs=video_input
            )
        
        # 세 번째 탭: Flux 개발용 탭
        with gr.Tab("FLUX Image"):
            flux_demo = create_flux_tab(image_input)  # Flux 개발용 탭 생성

demo.launch(
    server_port=args.server_port,
    share=args.share,
    server_name=args.server_name
)