app.py

import torch
from diffusers import DiffusionPipeline
import gradio as gr
import os
import spaces

# Load the models outside of the generate_images function
model_list = [model.strip() for model in os.environ.get("MODELS").split(",")]
lora_list = [model.strip() for model in os.environ.get("LORAS").split(",")]

print(f"Detected {len(model_list)} on models and {len(lora_list)} LoRAs.")

models = {}
for model_name in model_list:
    try:
        print(f"\n\nLoading {model_name}...")
        models[model_name] = DiffusionPipeline.from_pretrained(model_name, torch_dtype=torch.float16).to("cuda")
    except Exception as e:
        print(f"Error loading model {model_name}: {e}")

@spaces.GPU
def generate_images(
    model_name,
    prompt,
    negative_prompt,
    num_inference_steps,
    guidance_scale,
    height,
    width,
    num_images=4,
    progress=gr.Progress(track_tqdm=True)
):
    if prompt is not None and prompt.strip() != "":
        pipe = models.get(model_name)
        if pipe is None:
            return []

        print(f"Prompt is: [ {prompt} ]")
        outputs = []

        for _ in range(num_images):
            output = pipe(
                prompt,
                negative_prompt=negative_prompt,
                num_inference_steps=num_inference_steps,
                guidance_scale=guidance_scale,
                height=height,
                width=width
            )["images"][0]
            outputs.append(output)

        

        return outputs
    else:
        gr.Warning("Prompt empty!")

# Create the Gradio blocks
with gr.Blocks() as demo:
    with gr.Row(equal_height=False):
        with gr.Column():
            with gr.Group():
                model_dropdown = gr.Dropdown(choices=list(models.keys()), value=model_list[0] if model_list else None, label="Model")
                prompt = gr.Textbox(label="Prompt")
                generate_btn = gr.Button("Generate Image")
            with gr.Accordion("Advanced", open=False):
                negative_prompt = gr.Textbox(label="Negative Prompt", value="lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]")
                num_inference_steps = gr.Slider(minimum=10, maximum=50, step=1, value=25, label="Number of Inference Steps")
                guidance_scale = gr.Slider(minimum=1, maximum=20, step=0.5, value=7.5, label="Guidance Scale")
                height = gr.Slider(minimum=1024, maximum=2048, step=256, value=1024, label="Height")
                width = gr.Slider(minimum=1024, maximum=2048, step=256, value=1024, label="Width")
                num_images = gr.Slider(minimum=1, maximum=4, step=1, value=4, label="Number of Images")
        with gr.Column():
            output_gallery = gr.Gallery(label="Generated Images", height=480, scale=1)
        
        generate_btn.click(generate_images, inputs=[model_dropdown, prompt, negative_prompt, num_inference_steps, guidance_scale, height, width, num_images], outputs=output_gallery)

demo.launch()