app.py

import os

import gradio as gr
from modelscope.pipelines import pipeline
from modelscope.outputs import OutputKeys

image_to_video_pipe = pipeline(task="image-to-video", model='damo/i2vgen-xl', model_revision='v1.1.4', device='cuda:0')

# Get the current working directory
current_directory = os.path.dirname(os.path.realpath(__file__))
print("", current_directory)

def upload_file(file):
    return file.name

def image_to_video(image_in, text_in):
    if image_in is None:
        raise gr.Error('Please upload the image or wait for the image upload to complete')
    print(image_in)
    output_video_path = image_to_video_pipe(image_in, caption=text_in)[OutputKeys.OUTPUT_VIDEO]
    print(output_video_path)
    return output_video_path


with gr.Blocks() as demo:
    gr.Markdown(
        """<center><font size=7>COLIPIC.AI-XLv2</center>
        <left><font size=3>SRI LANKA</left>

        <left><font size=3>COLIPIC.AI-XLv2 can generate videos with similar contents and semantics based on user input static images and text. The generated videos have characteristics such as high-definition (1280 * 720), widescreen (16:9), coherent timing, and good texture.</left>
        """
    )
    with gr.Box():
        gr.Markdown(
        """<left><font size=3></left>

        <left><font size=3>Please choose the image to upload (we recommend the image size be 1280 * 720), provide the English text description of the video you wish to create, and then click on "Generate Video" to receive the generated video.</left>"""
        )
        with gr.Row():
            with gr.Column():
                text_in = gr.Textbox(label="Text description", lines=2, elem_id="text-in")
                image_in = gr.Image(label="Image Input", type="filepath", interactive=False, elem_id="image-in", height=300)
                with gr.Row():
                    upload_image = gr.UploadButton("Upload Image", file_types=["image"], file_count="single")
                    image_submit = gr.Button("Generate video")
            with gr.Column():
                video_out_1 = gr.Video(label='Generated Video', elem_id='video-out_1', interactive=False, height=300)
    gr.Markdown("""<left><font size=2></left>
    
        <left><font size=2>Note: If the generated video cannot be played, please try to upgrade your browser or use the Chrome browser.</left>"""
               )

    upload_image.upload(upload_file, upload_image, image_in, queue=False)
    image_submit.click(fn=image_to_video, inputs=[image_in, text_in], outputs=[video_out_1])

    with gr.Row(variant="panel"):
        gr.Examples(examples=[
                    [os.path.join(current_directory, 'example_images/sample2.png'), 'A girl with yellow hair and black clothes stood in front of the camera'],
                    [os.path.join(current_directory, 'example_images/sample13.png'), 'A girl in hat and sunglasses'],
                    [os.path.join(current_directory, 'example_images/sample6.png'), 'Papers were floating in the air on a table in the library'],
                    [os.path.join(current_directory, 'example_images/sample12.jpg'),'Night sky lit with milky way galaxy'],
                    [os.path.join(current_directory, 'example_images/sample1.png'), 'A blonde girl in jeans'],
                    [os.path.join(current_directory, 'example_images/sample11.jpg'),'A wet road between the woods'],
                    [os.path.join(current_directory, 'example_images/sample5.png'), 'A painting of a city street with a giant monster'],
                    [os.path.join(current_directory, 'example_images/sample7.png'), 'A red woodcut bird'],
                    [os.path.join(current_directory, 'example_images/sample8.jpg'), 'A green frog floats on the surface of the water on green lotus leaves, with several pink lotus flowers, in a Chinese painting style.']
                    ],
                    inputs=[image_in, text_in],
                    outputs=[video_out_1],
                    fn=image_to_video,
                    cache_examples=True,
                    examples_per_page=5,
                    label='Examples',
                   )
        
demo.queue(status_update_rate=1, api_open=False).launch(share=False, show_error=True)