app.py

import gradio as gr
import numpy as np
from infer import infer, CONTROLNET_MODE

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024

examples = [
    "The image of a cartoonish mouse eating from a red bowl of yellow triangle chips, her cheeks are full. The mouse is gray with big pink ears, small white eyes and a black pointed nose. It has a simple design, the background color is white. The style of the image is reminiscent of a sticker or a digital illustration.",
    "The image of a cartoonish mouse with red hearts instead of eyes meaning that the mouse is in love with something. The mouse is gray with big pink ears and a black pointed nose. It has a simple design, the background color is white. The style of the image is reminiscent of a sticker or a digital illustration.",
    "The image of a cartoonish mouse with sunglasses and smiling. The mouse is gray with big pink ears and a black pointed nose. It has a simple design, the background color is white. The style of the image is reminiscent of a sticker or a digital illustration.",
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 640px;
}
"""

def on_checkbox_change(use_advanced):
    visible = use_advanced
    return (gr.update(visible=visible, interactive=visible),
            gr.update(visible=visible, interactive=visible),
            gr.update(visible=visible, interactive=visible))

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(" # Maria Lashina T2I Rat Stickers Generation App")

        MODEL_LIST = [
            "CompVis/stable-diffusion-v1-4",
            "stable-diffusion-v1-5/stable-diffusion-v1-5",
            "Maria_Lashina_LoRA"
        ]
        with gr.Row():
            model_id = gr.Dropdown(
                label="Model",
                choices=MODEL_LIST
            )

        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )

            run_button = gr.Button("Run", scale=0, variant="primary")

        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                visible=True,
            )

            with gr.Row():
                delete_background = gr.Checkbox(label="Delete background?")

            use_controlnet = gr.Checkbox(label="Use ControlNet")
            control_strength = gr.Slider(
                label="ControlNet strength",
                minimum=0,
                maximum=1,
                step=0.1,
                value=0.8,
                visible=False
            )
            controlnet_mode = gr.Dropdown(CONTROLNET_MODE.keys(), label="ControlNet mode", visible=False)
            controlnet_image = gr.Image(label="ControlNet image", visible=False)
            use_controlnet.change(on_checkbox_change, use_controlnet, [control_strength, controlnet_mode, controlnet_image])

            use_ip_adapter = gr.Checkbox(label="Use IPAdapter")
            ip_adapter_scale = gr.Slider(
                label="IPAdapter scale",
                minimum=0,
                maximum=1,
                step=0.1,
                value=0.8,
                visible=False
            )
            ip_adapter_image = gr.Image(label="IPAdapter image", visible=False)
            use_ip_adapter.change(on_checkbox_change, use_ip_adapter, [ip_adapter_scale, ip_adapter_image])

            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=42,
            )

            randomize_seed = gr.Checkbox(label="Randomize seed", value=False)

            with gr.Row():
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,  # Replace with defaults that work for your model
                )

                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,  # Replace with defaults that work for your model
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=15.0,
                    step=0.5,
                    value=8.0,  # Replace with defaults that work for your model
                )

                lora_scale = gr.Slider(
                    label="LoRA scale",
                    minimum=0.0,
                    maximum=1.0,
                    step=0.1,
                    value=0.8,  # Replace with defaults that work for your model
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=40,  # Replace with defaults that work for your model
                )

        gr.Examples(examples=examples, inputs=[prompt])
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            model_id,
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            lora_scale,
            num_inference_steps,
            use_controlnet,
            control_strength,
            controlnet_mode,
            controlnet_image,
            use_ip_adapter,
            ip_adapter_scale,
            ip_adapter_image,
            delete_background
        ],
        outputs=[result, seed],
    )

if __name__ == "__main__":
    demo.launch(share=True, debug=True)