app.py

import torch
from diffusers import DiffusionPipeline, AutoencoderKL
import gradio as gr
from pathlib import Path
from slugify import slugify

# Function to load the model.
def load_model():
    pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
    pipeline.load_lora_weights("Meaning-Machine/old_mike_stasny_LoRA")
    
    vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
    pipe = DiffusionPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0",
        vae=vae,
        torch_dtype=torch.float16,
        variant="fp16",
        use_safetensors=True
    )
    pipe.load_lora_weights("Meaning-Machine/old_mike_stasny_LoRa")
    _ = pipe.to("cuda")
    return pipe

def generate_image(prompt, num_inference_steps):
    pipe = load_model()
    if not isinstance(prompt, str):
        raise ValueError("The prompt should be a string.")
    if not isinstance(num_inference_steps, str):
        raise ValueError("The number of inference steps should be a string.")
    
    # Convert num_inference_steps to an integer for the model call
    num_inference_steps_int = int(num_inference_steps)
    
    image = pipe(prompt, num_inference_steps=num_inference_steps_int).images[0]

    # save the generated image
    DIR_NAME="./images/"
    dirpath = Path(DIR_NAME)
    # create parent dir if doesn't exist
    dirpath.mkdir(parents=True, exist_ok=True)
    # create filename for image based on prompt
    image_name = f'{slugify(prompt)}.jpg'
    image_path = dirpath / image_name
    image.save(image_path)
    print(image_name)
    
    return image

iface = gr.Interface(
    fn=generate_image,
    inputs=[
        gr.Textbox(label="Enter a prompt for the image"),
        gr.Textbox(label="Number of Inference Steps min=10 or max=50")
    ],
    outputs="image",
    title="Stable Diffusion XL Text2Image Finetune Dreambooth",
    description="Generate images in the style of Mike Stasny from text prompts using Fine-Tuned Stable Diffusion XL."
)
# Launch the Gradio app
iface.launch(share=True)