app-backup2.py

import spaces
import gradio as gr
import os
import sys
import argparse
import random
import time
import numpy as np
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 DiffusionPipeline

# 상수 정의
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048

# DynamiCrafter 모델 설정
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)

# 모델 다운로드 실행
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")


import torch
from diffusers import DiffusionPipeline

# FLUX 모델 설정
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if torch.cuda.is_available() else torch.float32
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.float16)
pipe.enable_model_cpu_offload()


# 메모리 최적화 (GPU 사용 시에만 적용)
if torch.cuda.is_available():
    pipe.enable_attention_slicing()

@spaces.GPU(duration=300)
def infer_t2i(prompt, seed=42, randomize_seed=False, width=1024, height=576, guidance_scale=5.0, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
    # 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}")

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator(device=device).manual_seed(seed)
    
    with torch.no_grad():
        image = pipe(
            prompt=prompt, 
            width=width,
            height=height,
            num_inference_steps=num_inference_steps, 
            generator=generator,
            guidance_scale=guidance_scale
        ).images[0]
    
    torch.cuda.empty_cache()
    return image, seed, prompt  # 번역된 프롬프트도 반환

    
@spaces.GPU(duration=300)
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]

    # text cond
    with torch.no_grad(), torch.cuda.amp.autocast():
        text_emb = model.get_learned_conditioning([prompt])
    
        # img cond
        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) #3,256,256
        videos = image_tensor_resized.unsqueeze(0) # bchw
        
        z = get_latent_z(model, videos.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]}
        
        ## inference
        batch_samples = batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=steps, ddim_eta=eta, cfg_scale=cfg_scale)
        ## b,samples,c,t,h,w
    
        video_path = './output.mp4'
        save_videos(batch_samples, './', filenames=['output'], fps=save_fps)
    return video_path

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


css = """
.tab-nav {
    border-bottom: 2px solid #ddd;
    padding: 0;
    margin-bottom: 20px;
}
.tab-nav button {
    background-color: #f8f8f8;
    border: none;
    outline: none;
    cursor: pointer;
    padding: 10px 20px;
    transition: 0.3s;
    font-size: 16px;
    border-radius: 10px 10px 0 0;
    margin-right: 5px;
}
.tab-nav button:hover {
    background-color: #ddd;
}
.tab-nav button.selected {
    background-color: #fff;
    border: 2px solid #ddd;
    border-bottom: 2px solid #fff;
    font-weight: bold;
}
.tab-content {
    padding: 20px;
    border: 2px solid #ddd;
    border-radius: 0 10px 10px 10px;
}
/* 탭별 색상 */
.tab-nav button:nth-child(1) { border-top: 3px solid #ff6b6b; }
.tab-nav button:nth-child(2) { border-top: 3px solid #4ecdc4; }
.tab-nav button:nth-child(3) { border-top: 3px solid #f7b731; }
"""


with gr.Blocks(analytics_enabled=False, css=css) as dynamicrafter_iface:
    gr.Markdown("이미지로 영상 생성 테스트 (한글 프롬프트 지원)")
    with gr.Tab(label='Image+Text to Video'):
        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=3.5, 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 Image'):
        with gr.Column():
            with gr.Row():
                t2i_input_text = gr.Text(label='Prompt')
            with gr.Row():
                t2i_seed = gr.Slider(label='Seed', minimum=0, maximum=MAX_SEED, step=1, value=42)
                t2i_randomize_seed = gr.Checkbox(label='Randomize seed', value=False)
            with gr.Row():
                t2i_width = gr.Slider(label='Width', minimum=256, maximum=MAX_IMAGE_SIZE, step=64, value=1024)
                t2i_height = gr.Slider(label='Height', minimum=256, maximum=MAX_IMAGE_SIZE, step=64, value=576)
            with gr.Row():
                t2i_guidance_scale = gr.Slider(label='Guidance Scale', minimum=1.0, maximum=20.0, step=0.1, value=5.0)
                t2i_num_inference_steps = gr.Slider(label='Inference Steps', minimum=1, maximum=100, step=1, value=28)
#            t2i_generate_btn = gr.Button("Generate")
#            t2i_output_image = gr.Image(label="Generated Image", elem_id="t2i_output_img")
#            t2i_output_seed = gr.Number(label="Used Seed", elem_id="t2i_output_seed")

        t2i_generate_btn = gr.Button("Generate")
        t2i_output_image = gr.Image(label="Generated Image", elem_id="t2i_output_img")
        t2i_output_seed = gr.Number(label="Used Seed", elem_id="t2i_output_seed")
        t2i_translated_prompt = gr.Text(label="Translated Prompt (if applicable)", elem_id="t2i_translated_prompt")

    t2i_generate_btn.click(
        fn=infer_t2i,
        inputs=[t2i_input_text, t2i_seed, t2i_randomize_seed, t2i_width, t2i_height, t2i_guidance_scale, t2i_num_inference_steps],
        outputs=[t2i_output_image, t2i_output_seed, t2i_translated_prompt]
    )
    
dynamicrafter_iface.queue(max_size=12).launch(show_api=True)