app.py

import os
import gradio as gr
import torch
from diffusers import DiffusionPipeline, AutoencoderTiny
import random
import spaces

# Initialize the base model
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
base_model = "black-forest-labs/FLUX.1-dev"

taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1).to(device)

MAX_SEED = 2**32-1

# Hidden variables (you would set these based on your specific LoRA)
LORA_PATH = "SebastianBodza/Flux_Aquarell_Watercolor_v2"
TRIGGER_WORD = "AQUACOLTOK"

# Load LoRA weights (do this once at startup)
pipe.load_lora_weights(LORA_PATH)

@spaces.GPU(duration=70)
def generate_image(prompt, width, height):
    # Combine prompt with trigger word
    full_prompt = f"{TRIGGER_WORD} {prompt}"
    
    # Set up generation parameters
    seed = random.randint(0, MAX_SEED)
    generator = torch.Generator(device="cuda").manual_seed(seed)
    
    # Generate image
    image = pipe(
        prompt=full_prompt,
        num_inference_steps=28,
        guidance_scale=3.5,
        width=width,
        height=height,
        generator=generator,
    ).images[0]
    
    return image

def run_lora(prompt, width, height):
    return generate_image(prompt, width, height)

# Set up the Gradio interface
with gr.Blocks() as app:
    gr.Markdown("# LoRA Image Generator")
    
    with gr.Row():
        prompt = gr.Textbox(label="Prompt", lines=3, placeholder="Enter your prompt here")
    
    with gr.Row():
        width = gr.Slider(label="Width", minimum=256, maximum=1024, step=64, value=512)
        height = gr.Slider(label="Height", minimum=256, maximum=1024, step=64, value=512)
    
    generate_button = gr.Button("Generate Image")
    
    output_image = gr.Image(label="Generated Image")
    
    generate_button.click(
        fn=run_lora,
        inputs=[prompt, width, height],
        outputs=[output_image]
    )

if __name__ == "__main__":
    app.queue()
    app.launch()