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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
# Login function for authentication
def custom_auth(username, password):
return password == "aitutor"
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()
# model_t2V.enable_xformers_memory_efficient_attention()
css = """
h1 {
text-align: center;
}
#component-0 {
max-width: 730px;
margin: auto;
}
"""
def infer(prompt, seed_inp, ddim_steps,cfg):
if seed_inp!=-1:
setup_seed(seed_inp)
else:
seed_inp = random.choice(range(10000000))
setup_seed(seed_inp)
videos = model_t2V(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'
print(1)
# def clean():
# return gr.Image.update(value=None, visible=False), gr.Video.update(value=None)
def clean():
return gr.Video.update(value=None)
title = """
<div style="text-align: center; max-width: 700px; margin: 0 auto;">
<div
style="
display: inline-flex;
align-items: center;
gap: 0.8rem;
font-size: 1.75rem;
"
>
<h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;">
Intern·Vchitect (Text-to-Video)
</h1>
</div>
<p style="margin-bottom: 10px; font-size: 94%">
Apply Intern·Vchitect to generate a video
</p>
</div>
"""
with gr.Blocks(css='style.css') as demo:
gr.Markdown("<font color=red size=10><center>Pixio Text-to-Video generation</center></font>")
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)
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]
outputs = [video_out]
ex = gr.Examples(
examples = [['a corgi walking in the park at sunrise, oil painting style',400,50,7],
['a cut teddy bear reading a book in the park, oil painting style, high quality',700,50,7],
['an epic tornado attacking above a glowing city at night, the tornado is made of smoke, highly detailed',230,50,7],
['a jar filled with fire, 4K video, 3D rendered, well-rendered',400,50,7],
['a teddy bear walking in the park, oil painting style, high quality',400,50,7],
['a teddy bear walking on the street, 2k, high quality',100,50,7],
['a panda taking a selfie, 2k, high quality',400,50,7],
['a polar bear playing drum kit in NYC Times Square, 4k, high resolution',400,50,7],
['jungle river at sunset, ultra quality',400,50,7],
['a shark swimming in clear Carribean ocean, 2k, high quality',400,50,7],
['A steam train moving on a mountainside by Vincent van Gogh',230,50,7],
['a confused grizzly bear in calculus class',1000,50,7]],
fn = infer,
inputs=[prompt, seed_inp, ddim_steps,cfg],
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=18).launch(auth=custom_auth)
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