# -*- coding: utf-8 -*- import gradio as gr import os import sys import random import time import uuid 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 StableDiffusionXLPipeline #import spaces import tensorflow as tf print(tf.__version__) print("GPU available:", len(tf.config.list_physical_devices('GPU')) > 0) def is_tensor(x): return tf.is_tensor(x) os.environ['KERAS_BACKEND'] = 'tensorflow' 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']=True 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 로드 translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en", device=0 if torch.cuda.is_available() else -1, framework="pt") # 이미지 생성 모델 로드 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") pipe = StableDiffusionXLPipeline.from_pretrained( "SG161222/RealVisXL_V4.0", torch_dtype=torch.float32, use_safetensors=True, add_watermarker=False ).to(device) def generate_image(prompt: str): # 한글 입력 감지 및 번역 if any('\uac00' <= char <= '\ud7a3' for char in prompt): translated = translator(prompt, max_length=512) prompt = translated[0]['translation_text'] # Hi-res와 3840x2160 스타일 적용 prompt = f"hyper-realistic 8K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic" # 고정된 설정값 negative_prompt = "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly, (deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, disgusting, amputation" width = 1024 height = 576 guidance_scale = 6 num_inference_steps = 100 seed = random.randint(0, 2**32 - 1) generator = torch.Generator().manual_seed(seed) image = pipe( prompt=prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, generator=generator, ).images[0] unique_name = str(uuid.uuid4()) + ".png" image.save(unique_name) return unique_name # @spaces.GPU(duration=300, gpu_type="l40s") def infer(prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123, frames=64): try: # 이미지 생성 image_path = generate_image(prompt) image = torchvision.io.read_image(image_path).float() / 255.0 # 한글 입력 확인 및 번역 if any('\u3131' <= char <= '\u318E' or '\uAC00' <= char <= '\uD7A3' for char in prompt): translated = translator(prompt, max_length=512) prompt = translated[0]['translation_text'] resolution = (576, 1024) save_fps = 8 seed_everything(seed) transform = transforms.Compose([ transforms.Resize(resolution, antialias=True), ]) 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 h, w = resolution[0] // 8, resolution[1] // 8 noise_shape = [batch_size, channels, frames, h, w] with torch.no_grad(), torch.cuda.amp.autocast(): text_emb = model.get_learned_conditioning([prompt]) img_tensor = image.to(model.device) img_tensor = (img_tensor - 0.5) * 2 image_tensor_resized = transform(img_tensor) videos = image_tensor_resized.unsqueeze(0) z = get_latent_z(model, videos.unsqueeze(2)) 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)) 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]} 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) # 메모리 정리 del text_emb, img_tensor, image_tensor_resized, videos, z, img_tensor_repeat, cond_images, img_emb, imtext_cond, cond, batch_samples torch.cuda.empty_cache() return video_path except Exception as e: print(f"Error occurred: {e}") return None finally: torch.cuda.empty_cache() i2v_examples = [ ['우주인 복장으로 기타를 치는 남자', 30, 7.5, 1.0, 6, 123, 64], ['time-lapse of a blooming flower with leaves and a stem', 30, 7.5, 1.0, 10, 123, 64], ] css = """#output_vid {max-width: 1024px; max-height: 576px}""" with gr.Blocks(analytics_enabled=False, css=css) as dynamicrafter_iface: with gr.Tab(label='ImageAnimation_576x1024'): with gr.Column(): with gr.Row(): with gr.Column(): with gr.Row(): i2v_input_text = gr.Textbox(label='Prompts (한글 입력 가능)') 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=50) i2v_motion = gr.Slider(minimum=5, maximum=20, step=1, elem_id="i2v_motion", label="FPS", value=10) i2v_frames = gr.Slider(minimum=16, maximum=128, step=16, elem_id="i2v_frames", label="Number of frames", value=64) 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_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_motion, i2v_seed, i2v_frames], outputs=[i2v_output_video], fn = infer, cache_examples=False ) i2v_end_btn.click(inputs=[i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_motion, i2v_seed, i2v_frames], outputs=[i2v_output_video], fn = infer ) # dynamicrafter_iface.launch(server_port=7930, server_name="0.0.0.0", share=True) dynamicrafter_iface.launch()