app.py

import gradio as gr
import numpy as np
import random

# import spaces #[uncomment to use ZeroGPU]
from peft import PeftModel
from diffusers import DiffusionPipeline, StableDiffusionPipeline
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "alexanz/SD14_lora_pusheen"  # Replace to the model you would like to use

if torch.cuda.is_available():
    torch_dtype = torch.float16
else:
    torch_dtype = torch.float32

pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
pipe = pipe.to(device)
pipe.unet = PeftModel.from_pretrained(pipe.unet, "alexanz/SD14_lora_pusheen")
pipe.safety_checker = None
pipe.requires_safety_checker = False

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 512


# @spaces.GPU #[uncomment to use ZeroGPU]
def load_model(model_id, lora_strength):
    global pipe

    if pipe is not None:
        del pipe
        torch.cuda.empty_cache()

    try:
        if model_id == "CompVis/stable-diffusion-v1-4":
            pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype)
            pipe = pipe.to(device)
        if model_id == "alexanz/SD14_lora_pusheen":
            pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
            pipe = pipe.to(device)
            pipe.unet = PeftModel.from_pretrained(pipe.unet, "alexanz/SD14_lora_pusheen", scaling=lora_strength)

        pipe.safety_checker = None
        pipe.requires_safety_checker = False

        return f"Model {model_id} loaded successfully!"
    except Exception as e:
        return f"Error loading model {model_id}: {str(e)}"


def infer(
        prompt,
        negative_prompt,
        seed,
        randomize_seed,
        width,
        height,
        lora_strength,
        guidance_scale,
        num_inference_steps,
        progress=gr.Progress(track_tqdm=True),
):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    generator = torch.Generator().manual_seed(seed)

    image = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        width=width,
        height=height,
        generator=generator,
    ).images[0]

    return image, seed


examples = [
    "Sticker of Pusheen. Cartoon image of a gray cat with cap of tea.",
    "Sticker of Pusheen. Gray cat holding a guitar, sitting under a disco ball, with colorful lights and a happy face.",
    "Sticker of Pusheen. A cute cartoon fluffy cat.",
]

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

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(" # Text-to-Image Gradio Template")
        model_dropdown = gr.Dropdown(label="Model ID",
                                     choices=["alexanz/SD14_lora_pusheen", "CompVis/stable-diffusion-v1-4"],
                                     value="alexanz/SD14_lora_pusheen")
        model_status = gr.Textbox(label="Model Status", interactive=False)

        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",
            )

            lora_strength = gr.Slider(
                label="Lora strength",
                minimum=0.0,
                maximum=1.0,
                step=0.1,
                value=1.0,
            )

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

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

            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=10.0,
                    step=0.1,
                    value=7.5,  # 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=20,  # Replace with defaults that work for your model
                )

        gr.Examples(examples=examples, inputs=[prompt])

    model_dropdown.change(
        fn=load_model,
        inputs=[model_dropdown, lora_strength],
        outputs=model_status,
    )

    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            lora_strength,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result, seed],
    )

if __name__ == "__main__":
    demo.launch()