app.py

import json
import random

import gradio as gr
import numpy as np
import spaces
import torch
from diffusers import DiffusionPipeline, LCMScheduler

DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
model_id = "stabilityai/stable-diffusion-xl-base-1.0"

pipe = DiffusionPipeline.from_pretrained(model_id, variant="fp16")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.load_lora_weights("jasperai/flash-sdxl", adapter_name="lora")
pipe.load_lora_weights("JacobLinCool/sdxl-lora-gdsc-1", adapter_name="gdsc")
pipe.set_adapters(["lora", "gdsc"], adapter_weights=[1.0, 1.0])
pipe.to(device=DEVICE, dtype=torch.float16)


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

@spaces.GPU
def infer(
    pre_prompt,
    prompt,
    seed,
    randomize_seed,
    num_inference_steps,
    negative_prompt,
    guidance_scale,
    progress=gr.Progress(track_tqdm=True),
):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

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

    if pre_prompt != "":
        prompt = f"{pre_prompt} {prompt}"

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

    return image


css = """

h1 {
    text-align: center;
    display:block;
}

p {
    text-align: justify;
    display:block;
}

"""

if torch.cuda.is_available():
    power_device = "GPU"
else:
    power_device = "CPU"

with gr.Blocks(css=css) as demo:
    with gr.Row():
        with gr.Column():
            with gr.Row():
                prompt = gr.Text(
                    label="Prompt",
                    show_label=False,
                    max_lines=1,
                    placeholder="Enter your prompt",
                    container=False,
                    scale=5,
                )

                run_button = gr.Button("Run", scale=1)

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

            with gr.Accordion("Advanced Settings", open=False):

                pre_prompt = gr.Text(
                    label="Pre-Prompt",
                    show_label=True,
                    max_lines=1,
                    placeholder="Pre Prompt from the LoRA config",
                    container=True,
                    scale=5,
                )

                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():

                    num_inference_steps = gr.Slider(
                        label="Number of inference steps",
                        minimum=4,
                        maximum=8,
                        step=1,
                        value=4,
                    )

                with gr.Row():

                    guidance_scale = gr.Slider(
                        label="Guidance Scale",
                        minimum=1,
                        maximum=6,
                        step=0.5,
                        value=1,
                    )

                negative_prompt = gr.Text(
                    label="Negative Prompt",
                    show_label=False,
                    max_lines=1,
                    placeholder="Enter a negative Prompt",
                    container=False,
                )

    run_button.click(
        fn=infer,
        inputs=[
            pre_prompt,
            prompt,
            seed,
            randomize_seed,
            num_inference_steps,
            negative_prompt,
            guidance_scale,
        ],
        outputs=[result],
    )


demo.queue().launch()