AMD-SHARK-Studio/web/index.py

# from models.resnet50 import resnet_inf
# from models.albert_maskfill import albert_maskfill_inf
from models.stable_diffusion.main import stable_diff_inf

# from models.diffusion.v_diffusion import vdiff_inf
import gradio as gr
from PIL import Image
import json
import os


def debug_event(debug):
    return gr.Textbox.update(visible=debug)


prompt_examples = []
prompt_loc = "./prompts.json"
if os.path.exists(prompt_loc):
    with open("./prompts.json", encoding="utf-8") as fopen:
        prompt_examples = json.load(fopen)


demo_css = """
.gradio-container {background-color: black}
.container {background-color: black !important; padding-top:20px !important; }
#ui_title {padding: 10px !important; }
#top_logo {background-color: transparent; border-radius: 0 !important; border: 0; } 
#demo_title {background-color: black; border-radius: 0 !important; border: 0; padding-top: 50px; padding-bottom: 0px; width: 460px !important;} 

#demo_title_outer  {border-radius: 0; } 
#prompt_box_outer div:first-child  {border-radius: 0 !important}
#prompt_box textarea  {background-color:#1d1d1d !important}
#prompt_examples {margin:0 !important}
#prompt_examples svg {display: none !important;}

.gr-sample-textbox { border-radius: 1rem !important; border-color: rgb(31,41,55) !important; border-width:2px !important; }
#ui_body {background-color: #111111 !important; padding: 10px !important; border-radius: 0.5em !important;}

#img_result+div {display: none !important;}

footer {display: none !important;}
"""

with gr.Blocks(css=demo_css) as shark_web:
    # load prompt examples.

    with gr.Row(elem_id="ui_title"):
        with gr.Column(scale=1, elem_id="demo_title_outer"):
            logo2 = Image.open("./logos/sd-demo-logo.png")
            gr.Image(
                value=logo2,
                show_label=False,
                interactive=False,
                elem_id="demo_title",
            ).style(width=230)
            # with gr.Column(scale=1):
            #    gr.Label(value="Ultra fast Stable Diffusion")

    with gr.Row(elem_id="ui_body"):
        prompt = (
            scheduler
        ) = (
            iters_count
        ) = (
            batch_size
        ) = (
            steps
        ) = (
            guidance
        ) = (
            height
        ) = (
            width
        ) = (
            seed
        ) = (
            precision
        ) = (
            device
        ) = (
            cache
        ) = (
            iree_vulkan_target_triple
        ) = (
            live_preview
        ) = (
            debug
        ) = save_img = stable_diffusion = generated_img = std_output = None
        # load prompts.

        with gr.Row():
            with gr.Column(scale=1, min_width=600):
                with gr.Group(elem_id="prompt_box_outer"):
                    prompt = gr.Textbox(
                        label="Prompt",
                        value="A photograph of an astronaut riding a horse",
                        lines=1,
                        elem_id="prompt_box",
                    )
                with gr.Group():
                    ex = gr.Examples(
                        label="Examples",
                        examples=prompt_examples,
                        inputs=prompt,
                        cache_examples=False,
                        elem_id="prompt_examples",
                    )
                with gr.Row():
                    steps = gr.Slider(1, 100, value=50, step=1, label="Steps")
                    guidance = gr.Slider(
                        0,
                        50,
                        value=7.5,
                        step=0.1,
                        label="Guidance Scale",
                        interactive=False,
                    )
                with gr.Row():
                    height = gr.Slider(
                        384,
                        768,
                        value=512,
                        step=64,
                        label="Height",
                        interactive=False,
                    )
                    width = gr.Slider(
                        384,
                        768,
                        value=512,
                        step=64,
                        label="Width",
                        interactive=False,
                    )
                with gr.Row():
                    precision = gr.Radio(
                        label="Precision",
                        value="fp16",
                        choices=["fp16", "fp32"],
                    )
                    seed = gr.Textbox(value="42", max_lines=1, label="Seed")
                with gr.Row():
                    cache = gr.Checkbox(label="Cache", value=True)
                    # debug = gr.Checkbox(label="DEBUG", value=False)
                    save_img = gr.Checkbox(label="Save Image", value=False)
                    live_preview = gr.Checkbox(
                        label="Live Preview", value=False
                    )
                    # Hidden Items.
                    scheduler = gr.Radio(
                        label="Scheduler",
                        value="LMS",
                        choices=["PNDM", "LMS", "DDIM"],
                        interactive=False,
                        visible=False,
                    )
                    device = gr.Radio(
                        label="Device",
                        value="vulkan",
                        choices=["cpu", "cuda", "vulkan"],
                        interactive=False,
                        visible=False,
                        elem_id="ugly_line",
                    )
                    iters_count = gr.Slider(
                        1,
                        24,
                        value=1,
                        step=1,
                        label="Iteration Count",
                        visible=False,
                    )
                    batch_size = gr.Slider(
                        1,
                        4,
                        value=1,
                        step=1,
                        label="Batch Size",
                        visible=False,
                    )
                    iree_vulkan_target_triple = gr.Textbox(
                        value="",
                        max_lines=1,
                        label="IREE VULKAN TARGET TRIPLE",
                        visible=False,
                        elem_id="ugly_line",
                    )
                stable_diffusion = gr.Button("Generate Image")
                # logo
                nod_logo = Image.open("./logos/amd-nod-logo.png")
                gr.Image(
                    value=nod_logo,
                    show_label=False,
                    interactive=False,
                    elem_id="top_logo",
                ).style(width=230)
            with gr.Column(scale=1, min_width=600):
                generated_img = gr.Image(
                    type="pil", elem_id="img_result", interactive=False
                ).style(height=768, width=768)
                std_output = gr.Textbox(
                    label="Std Output",
                    value="Nothing.",
                    lines=5,
                    visible=False,
                    elem_id="ugly_line",
                )
        """
        debug.change(
            debug_event,
            inputs=[debug],
            outputs=[std_output],
            show_progress=False,
        )
        """

        stable_diffusion.click(
            stable_diff_inf,
            inputs=[
                prompt,
                scheduler,
                iters_count,
                batch_size,
                steps,
                guidance,
                height,
                width,
                seed,
                precision,
                device,
                cache,
                iree_vulkan_target_triple,
                live_preview,
                save_img,
            ],
            outputs=[generated_img, std_output],
            show_progress=False,
        )

shark_web.queue()
shark_web.launch(server_name="0.0.0.0", server_port=8080, enable_queue=True)