app_text_to_video.py

import gradio as gr
from model import Model
import os
from hf_utils import get_model_list

on_huggingspace = os.environ.get("SPACE_AUTHOR_NAME") == "PAIR"

examples = [
    ["an astronaut waving the arm on the moon"],
    ["a sloth surfing on a wakeboard"],
    ["an astronaut walking on a street"],
    ["a cute cat walking on grass"],
    ["a horse is galloping on a street"],
    ["an astronaut is skiing down the hill"],
    ["a gorilla walking alone down the street"],
    ["a gorilla dancing on times square"],
    ["A panda dancing dancing like crazy on Times Square"],
]


def create_demo(model: Model):

    with gr.Blocks() as demo:
        with gr.Row():
            gr.Markdown('## Text2Video-Zero: Video Generation')
        with gr.Row():
            gr.HTML(
                """
                <div style="text-align: left; auto;">
                <h2 style="font-weight: 450; font-size: 1rem; margin: 0rem">
                    Description: Simply input <b>any textual prompt</b> to generate videos right away and unleash your creativity and imagination! You can also select from the examples below. For performance purposes, our current preview release allows to generate up to 16 frames, which can be configured in the Advanced Options.
                </h3>
                </div>
                """)

        with gr.Row():
            with gr.Column():
                model_name = gr.Dropdown(
                    label="Model",
                    choices=get_model_list(),
                    value="dreamlike-art/dreamlike-photoreal-2.0",
                )
                prompt = gr.Textbox(label='Prompt')
                run_button = gr.Button(label='Run')
                with gr.Accordion('Advanced options', open=False):
                    watermark = gr.Radio(["Picsart AI Research", "Text2Video-Zero",
                                         "None"], label="Watermark", value='Picsart AI Research')

                    if on_huggingspace:
                        video_length = gr.Slider(
                            label="Video length", minimum=8, maximum=16, step=1)
                    else:
                        video_length = gr.Number(
                            label="Video length", value=8, precision=0)
                    chunk_size = gr.Slider(
                        label="Chunk size", minimum=2, maximum=16, value=12 if on_huggingspace else 8, step=1, visible=not on_huggingspace)

                    motion_field_strength_x = gr.Slider(
                        label='Global Translation $\delta_{x}$', minimum=-20, maximum=20, value=12, step=1)
                    motion_field_strength_y = gr.Slider(
                        label='Global Translation $\delta_{y}$', minimum=-20, maximum=20, value=12, step=1)

                    t0 = gr.Slider(label="Timestep t0", minimum=0,
                                   maximum=49, value=44, step=1)
                    t1 = gr.Slider(label="Timestep t1", minimum=0,
                                   maximum=49, value=47, step=1)

                    n_prompt = gr.Textbox(
                        label="Optional Negative Prompt", value='')
            with gr.Column():
                result = gr.Video(label="Generated Video")

        inputs = [
            prompt,
            model_name,
            motion_field_strength_x,
            motion_field_strength_y,
            t0,
            t1,
            n_prompt,
            chunk_size,
            video_length,
            watermark,
        ]

        gr.Examples(examples=examples,
                    inputs=inputs,
                    outputs=result,
                    fn=model.process_text2video,
                    run_on_click=False,
                    cache_examples=on_huggingspace,
                    )

        run_button.click(fn=model.process_text2video,
                         inputs=inputs,
                         outputs=result,)
    return demo