app.py

import spaces
import gradio as gr
import os
import sys
import argparse
import random
import time
from omegaconf import OmegaConf
import torch
import torchvision
from pytorch_lightning import seed_everything
from huggingface_hub import hf_hub_download
from einops import repeat
import torchvision.transforms as transforms
from utils.utils import instantiate_from_config
sys.path.insert(0, "scripts/evaluation")
from funcs import (
    batch_ddim_sampling,
    load_model_checkpoint,
    get_latent_z,
    save_videos
)
from transformers import pipeline
from diffusers import FluxPipeline
from PIL import Image
import numpy as np
from huggingface_hub import login

# Hugging Face 토큰 설정 및 로그인
hf_token = os.getenv("HF_TOKEN")
if hf_token:
    login(token=hf_token)
else:
    print("Warning: HF_TOKEN not found in environment variables. You may encounter authentication issues.")


def download_model():
    REPO_ID = 'Doubiiu/DynamiCrafter_1024'
    filename_list = ['model.ckpt']
    if not os.path.exists('./checkpoints/dynamicrafter_1024_v1/'):
        os.makedirs('./checkpoints/dynamicrafter_1024_v1/')
    for filename in filename_list:
        local_file = os.path.join('./checkpoints/dynamicrafter_1024_v1/', filename)
        if not os.path.exists(local_file):
            hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/dynamicrafter_1024_v1/', force_download=True)

download_model()
ckpt_path='checkpoints/dynamicrafter_1024_v1/model.ckpt'
config_file='configs/inference_1024_v1.0.yaml'
config = OmegaConf.load(config_file)
model_config = config.pop("model", OmegaConf.create())
model_config['params']['unet_config']['params']['use_checkpoint']=False   
model = instantiate_from_config(model_config)
assert os.path.exists(ckpt_path), "Error: checkpoint Not Found!"
model = load_model_checkpoint(model, ckpt_path)
model.eval()
model = model.cuda()

# 번역 모델 초기화
translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en", device=0)  # GPU 사용 설정

# FLUX 파이프라인 초기화 부분 수정
flux_pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    torch_dtype=torch.bfloat16,
    use_auth_token=hf_token  # 토큰을 사용하여 인증
)
flux_pipe.enable_model_cpu_offload()


def generate_image_from_text(prompt, seed=0):
    generator = torch.Generator("cpu").manual_seed(seed)
    image = flux_pipe(
        prompt,
        height=576,
        width=1024,
        guidance_scale=3.5,
        num_inference_steps=50,
        max_sequence_length=512,
        generator=generator
    ).images[0]
    return image

@spaces.GPU(duration=600)
def infer(image, prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123, video_length=2):
    # 한글 입력 감지 및 번역
    if any('\u3131' <= char <= '\u318E' or '\uAC00' <= char <= '\uD7A3' for char in prompt):
        translated = translator(prompt, max_length=512)[0]['translation_text']
        prompt = translated
        print(f"Translated prompt: {prompt}")

    resolution = (576, 1024)
    save_fps = 8
    seed_everything(seed)
    transform = transforms.Compose([
        transforms.Resize(min(resolution)),
        transforms.CenterCrop(resolution),
        ])
    torch.cuda.empty_cache()
    print('Start:', prompt, time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time())))
    start = time.time()
    if steps > 60:
        steps = 60

    batch_size = 1
    channels = model.model.diffusion_model.out_channels
    frames = int(video_length * save_fps)  # 비디오 길이에 따른 프레임 수 계산
    h, w = resolution[0] // 8, resolution[1] // 8
    noise_shape = [batch_size, channels, frames, h, w]

    # 텍스트 조건 설정
    with torch.no_grad(), torch.cuda.amp.autocast():
        text_emb = model.get_learned_conditioning([prompt])
        img_tensor = torch.from_numpy(image).permute(2, 0, 1).float().to(model.device)
        img_tensor = (img_tensor / 255. - 0.5) * 2
        image_tensor_resized = transform(img_tensor).unsqueeze(0)  # bchw
        
        z = get_latent_z(model, image_tensor_resized.unsqueeze(2)) #bc,1,hw
        img_tensor_repeat = repeat(z, 'b c t h w -> b c (repeat t) h w', repeat=frames)
        cond_images = model.embedder(img_tensor.unsqueeze(0)) # blc
        img_emb = model.image_proj_model(cond_images)
        imtext_cond = torch.cat([text_emb, img_emb], dim=1)
        
        fs = torch.tensor([fs], dtype=torch.long, device=model.device)
        cond = {"c_crossattn": [imtext_cond], "fs": fs, "c_concat": [img_tensor_repeat]}
        
        # 추론 실행
        batch_samples = batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=steps, ddim_eta=eta, cfg_scale=cfg_scale)
        
        video_path = './output.mp4'
        save_videos(batch_samples, './', filenames=['output'], fps=save_fps)
    return video_path


@spaces.GPU(duration=300)
def infer_t2v(prompt, video_prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123, video_length=2):
    # 이미지 생성
    image = generate_image_from_text(prompt, seed)
    
    # 이미지를 numpy 배열로 변환
    image_np = np.array(image)
    
    # 비디오 생성을 위해 기존 infer 함수 호출
    return infer(image_np, video_prompt, steps, cfg_scale, eta, fs, seed, video_length)

i2v_examples = [
    ['prompts/1024/astronaut04.png', 'a man in an astronaut suit playing a guitar', 30, 7.5, 1.0, 6, 123, 2],
]

css = """#input_img {max-width: 1024px !important} #output_vid {max-width: 1024px; max-height: 576px}"""

def generate_only_image(prompt, seed=123):
    # 이미지 생성
    image = generate_image_from_text(prompt, seed)

    # PIL 이미지로 변환 후 반환
    return Image.fromarray(np.array(image))

with gr.Blocks(analytics_enabled=False, css=css) as dynamicrafter_iface:
    gr.Markdown("kAI 무비 스튜디오")


    with gr.Tab(label='Image(+Text) Generation'):
        with gr.Column():
            with gr.Row():
                with gr.Column():
                    img_input_text = gr.Text(label='Image Generation Prompt')
                    img_seed = gr.Slider(label='Random Seed', minimum=0, maximum=10000, step=1, value=123)
                    img_generate_btn = gr.Button("Generate Image")
                with gr.Row():
                    img_output_image = gr.Image(label="Generated Image")

            img_generate_btn.click(
                inputs=[img_input_text, img_seed],
                outputs=[img_output_image],
                fn=generate_only_image
        ) 


    with gr.Tab(label='Image to Video Generation'):
        with gr.Column():
            with gr.Row():
                with gr.Column():
                    with gr.Row():
                        i2v_input_image = gr.Image(label="Input Image",elem_id="input_img")
                    with gr.Row():
                        i2v_input_text = gr.Text(label='Prompts')
                    with gr.Row():
                        i2v_seed = gr.Slider(label='Random Seed', minimum=0, maximum=10000, step=1, value=123)
                        i2v_eta = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='ETA', value=1.0, elem_id="i2v_eta")
                        i2v_cfg_scale = gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='CFG Scale', value=7.5, elem_id="i2v_cfg_scale")
                    with gr.Row():
                        i2v_steps = gr.Slider(minimum=1, maximum=50, step=1, elem_id="i2v_steps", label="Sampling steps", value=30)
                        i2v_motion = gr.Slider(minimum=5, maximum=20, step=1, elem_id="i2v_motion", label="FPS", value=8)
                    with gr.Row():
                        i2v_video_length = gr.Slider(minimum=2, maximum=8, step=1, elem_id="i2v_video_length", label="Video Length (seconds)", value=2)
                    i2v_end_btn = gr.Button("Generate")
                with gr.Row():
                    i2v_output_video = gr.Video(label="Generated Video",elem_id="output_vid",autoplay=True,show_share_button=True)

            gr.Examples(examples=i2v_examples,
                        inputs=[i2v_input_image, i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_motion, i2v_seed, i2v_video_length],
                        outputs=[i2v_output_video],
                        fn = infer,
                        cache_examples=True,
            )
        i2v_end_btn.click(inputs=[i2v_input_image, i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_motion, i2v_seed, i2v_video_length],
                        outputs=[i2v_output_video],
                        fn = infer
        )
    
    with gr.Tab(label='Text to Video Generation'):
        with gr.Column():
            with gr.Row():
                with gr.Column():
                    with gr.Row():
                        t2v_video_prompt = gr.Text(label='Video Generation Prompt')
                    with gr.Row():
                        t2v_seed = gr.Slider(label='Random Seed', minimum=0, maximum=10000, step=1, value=123)
                        t2v_eta = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='ETA', value=1.0)
                        t2v_cfg_scale = gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='CFG Scale', value=7.5)
                    with gr.Row():
                        t2v_steps = gr.Slider(minimum=1, maximum=50, step=1, label="Sampling steps", value=30)
                        t2v_motion = gr.Slider(minimum=5, maximum=20, step=1, label="FPS", value=8)
                    with gr.Row():
                        t2v_video_length = gr.Slider(minimum=2, maximum=8, step=1, label="Video Length (seconds)", value=2)
                    t2v_end_btn = gr.Button("Generate")
                with gr.Row():
                    t2v_output_video = gr.Video(label="Generated Video", autoplay=True, show_share_button=True)

        t2v_end_btn.click(
            inputs=[t2v_input_text, t2v_video_prompt, t2v_steps, t2v_cfg_scale, t2v_eta, t2v_motion, t2v_seed, t2v_video_length],
            outputs=[t2v_output_video],
            fn=infer_t2v
        )
    




dynamicrafter_iface.queue(max_size=12).launch(show_api=True)