app.py

import gradio as gr
import spaces
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load the MagicPrompt-Stable-Diffusion model and tokenizer
model_name = "Gustavosta/MagicPrompt-Stable-Diffusion"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name).to("cpu")

@spaces.GPU
def expand_prompt(prompt, num_variants=5, max_length=100):
    """
    Generate expanded prompts using a specialized model fine-tuned for Stable Diffusion.
    """
    # Move model to GPU
    model.to("cuda")
    
    # Tokenize input prompt
    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
    
    # Generate multiple prompt variants
    outputs = model.generate(
        **inputs,
        max_length=max_length,
        do_sample=True,
        num_return_sequences=num_variants,
        pad_token_id=tokenizer.eos_token_id
    )
    
    # Decode generated prompts
    expanded_prompts = [tokenizer.decode(output, skip_special_tokens=True).strip() for output in outputs]
    
    # Move model back to CPU
    model.to("cpu")
    
    return "\n\n".join(expanded_prompts)

# Create a Gradio Interface
iface = gr.Interface(
    fn=expand_prompt,
    inputs=gr.Textbox(lines=2, placeholder="Enter your basic prompt here...", label="Basic Prompt"),
    outputs=gr.Textbox(lines=10, label="Expanded Prompts"),
    title="Prompt Expansion Generator",
    description=(
        "Enter a basic prompt and receive multiple expanded prompt variants optimized for Stable Diffusion. "
        "This tool uses a specialized model fine-tuned on Stable Diffusion prompts. "
        "Simply copy the output for use with your image-generation pipeline."
    )
)

if __name__ == "__main__":
    iface.launch()