app.py

import os
os.environ["GRADIO_DISABLE_OAUTH"] = "1"
import gradio as gr
import numpy as np
import random
import torch
from diffusers import DiffusionPipeline
from PIL import Image

# ---------------------------------------------------------
# Device
# ---------------------------------------------------------
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if device == "cuda" else torch.float32

# ---------------------------------------------------------
# Models
# ---------------------------------------------------------
BASE_MODEL = "stabilityai/stable-diffusion-2-1"
LORA_PATH = "bayndrysf/dreambooth-project-style"

# ---------------------------------------------------------
# Load pipeline
# ---------------------------------------------------------
pipe = DiffusionPipeline.from_pretrained(
    BASE_MODEL,
    torch_dtype=dtype,
    use_auth_token=True
)

if device == "cuda":
    pipe.enable_xformers_memory_efficient_attention()

pipe.to(device)

pipe.load_lora_weights(
    LORA_PATH,
    weight_name="pytorch_lora_weights.safetensors"
)

# ---------------------------------------------------------
# Constants
# ---------------------------------------------------------
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024

# ---------------------------------------------------------
# Inference
# ---------------------------------------------------------
def infer(
    prompt,
    negative_prompt,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps
):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

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

    image = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt if negative_prompt else None,
        width=width,
        height=height,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        generator=generator,
        num_images_per_prompt=1
    ).images[0]

    return image

# ---------------------------------------------------------
# Examples
# ---------------------------------------------------------
examples = [
    "A whirling dervish performing in a historic Istanbul courtyard, captured in the iconic style of Ara Güler.",
    "An elderly man sipping tea at a street café in Istanbul, captured in the iconic style of Ara Güler.",
    "A group of friends enjoying a ferry ride on the Bosphorus, captured in the iconic style of Ara Güler.",
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 520px;
}
"""

power_device = "GPU" if device == "cuda" else "CPU"

# ---------------------------------------------------------
# Gradio UI
# ---------------------------------------------------------
with gr.Blocks(css=css) as demo:

    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""
        # Ara Güler's Istanbul: Image Generation with Stable Diffusion
        Currently running on **{power_device}**
        """)

        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)

        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",
            )

            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,
                )

                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=1.0,
                    maximum=10.0,
                    step=0.1,
                    value=7.5,
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=10,
                    maximum=50,
                    step=1,
                    value=25,
                )

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

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

demo.queue().launch()