| import gradio as gr |
| from diffusers import StableDiffusionPipeline |
| import torch |
|
|
| |
| device = "cuda" if torch.cuda.is_available() else "cpu" |
|
|
| |
| pipe = StableDiffusionPipeline.from_pretrained( |
| "stabilityai/stable-diffusion-2-1", |
| torch_dtype=torch.float16 if device == "cuda" else torch.float32, |
| revision="fp16" if device == "cuda" else "main" |
| ).to(device) |
|
|
| |
| def generate_muse(prompt, output_type): |
| if output_type == "Image": |
| image = pipe(prompt).images[0] |
| return image |
| else: |
| return "⚠️ 3D Model generation coming soon. Please select 'Image'." |
|
|
| |
| with gr.Blocks() as demo: |
| gr.Markdown("## 🎨 Welcome to **MUSE**") |
| gr.Markdown("Turn your architectural, artistic, or design ideas into stunning visual concepts.\n\n🧠 Just describe your **muse**, pick a format, and let the AI do the rest.") |
|
|
| with gr.Row(): |
| with gr.Column(): |
| prompt = gr.Textbox( |
| label="🖋️ Describe your muse idea", |
| placeholder="e.g. a floating bamboo pavilion shaped like a jellyfish with fabric sails", |
| lines=3 |
| ) |
| output_type = gr.Radio(["Image", "3D Model"], label="📌 Choose output type", value="Image") |
| generate_button = gr.Button("✨ Generate Muse") |
|
|
| with gr.Column(): |
| image_output = gr.Image(label="🎨 Your Muse Output") |
|
|
| generate_button.click(fn=generate_muse, inputs=[prompt, output_type], outputs=image_output) |
|
|
| demo.launch() |
|
|
|
|
