import gradio as gr import numpy as np from options import Banner, Video from huggingface_hub import login import os login(token=os.getenv("TOKEN")) MAX_SEED = np.iinfo(np.int32).max MAX_IMAGE_SIZE = 2048 with gr.Blocks() as demo: gr.Markdown("# Create your own Advertisement") with gr.Tab("Banner"): gr.Markdown("# Take your banner to the next LEVEL!") with gr.TabItem("Create your Banner"): textInput = gr.Textbox(label="Enter the text to get a good start") with gr.Accordion("Advanced Settings", open=False): with gr.Row(): width = gr.Slider( label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=8, value=1024, ) height = gr.Slider( label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024, ) guidance_scale = gr.Slider( label="Guidance Scale", minimum=1, maximum=15, step=0.1, value=3.5, ) num_inference_steps = gr.Slider( label="Number of Inference Steps", minimum=1, maximum=50, step=1, value=28, ) submit = gr.Button("Submit") submit.click( fn=Banner.TextImage, inputs=[textInput, width, height, guidance_scale, num_inference_steps], outputs=gr.Image() ) with gr.TabItem("Edit your Banner"): with gr.Row(): with gr.Column(): input_image_editor_component = gr.ImageEditor( label='Image', type='pil', sources=["upload", "webcam"], image_mode='RGB', layers=False, brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed")) with gr.Row(): input_text_component = gr.Text( label="Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt", container=False, ) submit_button_component = gr.Button( value='Submit', variant='primary', scale=0) with gr.Accordion("Advanced Settings", open=False): seed_slicer_component = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=42, ) randomize_seed_checkbox_component = gr.Checkbox( label="Randomize seed", value=True) with gr.Row(): strength_slider_component = gr.Slider( label="Strength", info="Indicates extent to transform the reference `image`. " "Must be between 0 and 1. `image` is used as a starting " "point and more noise is added the higher the `strength`.", minimum=0, maximum=1, step=0.01, value=0.85, ) num_inference_steps_slider_component = gr.Slider( label="Number of inference steps", info="The number of denoising steps. More denoising steps " "usually lead to a higher quality image at the", minimum=1, maximum=50, step=1, value=20, ) with gr.Column(): output_image_component = gr.Image( type='pil', image_mode='RGB', label='Generated image', format="png") with gr.Accordion("Debug", open=False): output_mask_component = gr.Image( type='pil', image_mode='RGB', label='Input mask', format="png") with gr.Row(): submit_button_component.click( fn=Banner.Image2Image, inputs=[ input_image_editor_component, input_text_component, seed_slicer_component, randomize_seed_checkbox_component, strength_slider_component, num_inference_steps_slider_component ], outputs=[ output_image_component, output_mask_component ] ) with gr.TabItem("Upgrade your Banner"): img = gr.Image() prompt = gr.Textbox(label="Enter the text to get a good start") btn = gr.Button() with gr.Accordion("Advanced options", open=False): size = gr.Slider(label="Size", minimum=256, maximum=MAX_IMAGE_SIZE, step=8, value=1024) num_inference_steps = gr.Slider(label="num_inference_steps", minimum=1, maximum=100,step=1, value=20) guidance_scale=gr.Slider(label="guidance_scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1) out_img = gr.Image() btn.click(Banner.Image2Image_2, [prompt, img,size,num_inference_steps,guidance_scale], out_img) with gr.Tab("Video"): gr.Markdown("# Create your own Video") img=gr.Image() btn = gr.Button() video=gr.Video() btn.click(Video.Video, img, video) demo.launch()