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import gradio as gr
from diffusers import StableDiffusionPipeline
import torch
# ----------------------------------------
# Load PosterCraft model from Hugging Face
# ----------------------------------------
# If your Space uses a free CPU runtime, you can leave torch_dtype=None.
# On GPU, float16 is faster and uses less memory.
model_id = "PosterCraft/PosterCraft-v1_RL"
try:
pipe = StableDiffusionPipeline.from_pretrained(
model_id,
torch_dtype=torch.float16 if torch.cuda.is_available() else None
)
except Exception as e:
raise RuntimeError(f"Error loading model {model_id}: {e}")
# Select device
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe.to(device)
# ----------------------------------------
# Generation function
# ----------------------------------------
def generate_poster(prompt: str):
"""
Generate a poster/flyer based on the user's text prompt.
"""
if not prompt.strip():
return None
image = pipe(prompt).images[0]
return image
# ----------------------------------------
# Gradio UI
# ----------------------------------------
demo = gr.Interface(
fn=generate_poster,
inputs=gr.Textbox(
label="Enter your poster prompt",
placeholder="e.g. Modern music festival flyer with bold typography and neon lights"
),
outputs=gr.Image(type="pil", label="Generated Poster"),
title="AI Poster Generator",
description="Enter a description and get an AI-designed poster or flyer."
)
# Launch the app
if __name__ == "__main__":
demo.launch()
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