import gradio as gr from text_to_video import model_t2v_fun,setup_seed from omegaconf import OmegaConf import torch import imageio import os import cv2 import pandas as pd import torchvision import random from huggingface_hub import snapshot_download config_path = "./base/configs/sample.yaml" args = OmegaConf.load("./base/configs/sample.yaml") device = "cuda" if torch.cuda.is_available() else "cpu" # ------- get model --------------- # model_t2V = model_t2v_fun(args) # model_t2V.to(device) # if device == "cuda": # model_t2V.enable_xformers_memory_efficient_attention() css = """ h1 { text-align: center; } #component-0 { max-width: 730px; margin: auto; } """ sd_path = args.pretrained_path + "/stable-diffusion-v1-4" unet = get_models(args, sd_path).to(device, dtype=torch.float16) state_dict = find_model("./pretrained_models/lavie_base.pt") unet.load_state_dict(state_dict) vae = AutoencoderKL.from_pretrained(sd_path, subfolder="vae", torch_dtype=torch.float16).to(device) tokenizer_one = CLIPTokenizer.from_pretrained(sd_path, subfolder="tokenizer") text_encoder_one = CLIPTextModel.from_pretrained(sd_path, subfolder="text_encoder", torch_dtype=torch.float16).to(device) # huge unet.eval() vae.eval() text_encoder_one.eval() def infer(prompt, seed_inp, ddim_steps,cfg, infer_type): if seed_inp!=-1: setup_seed(seed_inp) else: seed_inp = random.choice(range(10000000)) setup_seed(seed_inp) if infer_type == 'ddim': scheduler = DDIMScheduler.from_pretrained(sd_path, subfolder="scheduler", beta_start=args.beta_start, beta_end=args.beta_end, beta_schedule=args.beta_schedule) elif infer_type == 'eulerdiscrete': scheduler = EulerDiscreteScheduler.from_pretrained(sd_path, subfolder="scheduler", beta_start=args.beta_start, beta_end=args.beta_end, beta_schedule=args.beta_schedule) elif infer_type == 'ddpm': scheduler = DDPMScheduler.from_pretrained(sd_path, subfolder="scheduler", beta_start=args.beta_start, beta_end=args.beta_end, beta_schedule=args.beta_schedule) model = VideoGenPipeline(vae=vae, text_encoder=text_encoder_one, tokenizer=tokenizer_one, scheduler=scheduler, unet=unet) model.to(device) if device == "cuda": model.enable_xformers_memory_efficient_attention() videos = model(prompt, video_length=16, height = 320, width= 512, num_inference_steps=ddim_steps, guidance_scale=cfg).video print(videos[0].shape) if not os.path.exists(args.output_folder): os.mkdir(args.output_folder) torchvision.io.write_video(args.output_folder + prompt[0:30].replace(' ', '_') + '-'+str(seed_inp)+'-'+str(ddim_steps)+'-'+str(cfg)+ '-.mp4', videos[0], fps=8) return args.output_folder + prompt[0:30].replace(' ', '_') + '-'+str(seed_inp)+'-'+str(ddim_steps)+'-'+str(cfg)+ '-.mp4' # def clean(): # return gr.Video.update(value=None) title = """

Intern·Vchitect (Text-to-Video)

Apply Intern·Vchitect to generate a video

""" with gr.Blocks(css='style.css') as demo: gr.Markdown("
LaVie: Text-to-Video generation
") with gr.Column(): with gr.Row(elem_id="col-container"): # inputs = [prompt, seed_inp, ddim_steps] # outputs = [video_out] with gr.Column(): prompt = gr.Textbox(value="a corgi walking in the park at sunrise, oil painting style", label="Prompt", placeholder="enter prompt", show_label=True, elem_id="prompt-in", min_width=200, lines=2) infer_type = gr.Dropdown(['ddpm','ddim','eulerdiscrete'], label='infer_type',value='ddim') ddim_steps = gr.Slider(label='Steps', minimum=50, maximum=300, value=50, step=1) seed_inp = gr.Slider(value=-1,label="seed (for random generation, use -1)",show_label=True,minimum=-1,maximum=2147483647) cfg = gr.Number(label="guidance_scale",value=7.5) # seed_inp = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, value=400, elem_id="seed-in") with gr.Column(): submit_btn = gr.Button("Generate video") # clean_btn = gr.Button("Clean video") video_out = gr.Video(label="Video result", elem_id="video-output") inputs = [prompt, seed_inp, ddim_steps, cfg, infer_type] outputs = [video_out] ex = gr.Examples( examples = [['a corgi walking in the park at sunrise, oil painting style',400,50,7,'ddim'], ['a cut teddy bear reading a book in the park, oil painting style, high quality',700,50,7,'ddim'], ['an epic tornado attacking above a glowing city at night, the tornado is made of smoke, highly detailed',230,50,7,'ddim'], ['a jar filled with fire, 4K video, 3D rendered, well-rendered',400,50,7,'ddim'], ['a teddy bear walking in the park, oil painting style, high quality',400,50,7,'ddim'], ['a teddy bear walking on the street, 2k, high quality',100,50,7,'ddim'], ['a panda taking a selfie, 2k, high quality',400,50,7,'ddim'], ['a polar bear playing drum kit in NYC Times Square, 4k, high resolution',400,50,7,'ddim'], ['jungle river at sunset, ultra quality',400,50,7,'ddim'], ['a shark swimming in clear Carribean ocean, 2k, high quality',400,50,7,'ddim'], ['A steam train moving on a mountainside by Vincent van Gogh',230,50,7,'ddim'], ['a confused grizzly bear in calculus class',1000,50,7,'ddim']], fn = infer, inputs=[prompt, seed_inp, ddim_steps,cfg,infer_type], outputs=[video_out], cache_examples=False, ) ex.dataset.headers = [""] clean_btn.click(clean, inputs=[], outputs=[video_out], queue=False) submit_btn.click(infer, inputs, outputs) # share_button.click(None, [], [], _js=share_js) demo.queue(max_size=12).launch()