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import os
from huggingface_hub import login
import requests
import base64
from PIL import Image
import io
# Login to Hugging Face
if os.getenv("HF_TOKEN"):
login(token=os.getenv("HF_TOKEN"))
def generate_image_mlx(prompt, negative_prompt, num_inference_steps, guidance_scale, width, height, seed):
"""Generate image using MLX via Hugging Face Inference API"""
try:
print(f"Generating image with prompt: {prompt}")
# Use Hugging Face Inference API with MLX
api_url = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0"
headers = {"Authorization": f"Bearer {os.getenv('HF_TOKEN')}"}
payload = {
"inputs": prompt,
"parameters": {
"negative_prompt": negative_prompt,
"num_inference_steps": num_inference_steps,
"guidance_scale": guidance_scale,
"width": width,
"height": height,
"seed": seed if seed else None
}
}
response = requests.post(api_url, headers=headers, json=payload)
if response.status_code == 200:
# Convert response to PIL Image
image = Image.open(io.BytesIO(response.content))
print("Image generated successfully with MLX!")
return image
else:
print(f"API request failed: {response.status_code} - {response.text}")
return None
except Exception as e:
print(f"Error generating image: {str(e)}")
return None
# Create Gradio interface
with gr.Blocks(title="Silicon Photonics Image Generator") as demo:
gr.Markdown("# 🔬 Silicon Photonics Image Generator")
gr.Markdown("Generate technical diagrams and visualizations for silicon photonics using MLX-powered inference.")
gr.Markdown("**🚀 Powered by MLX for ultra-fast Apple Silicon performance!**")
with gr.Row():
with gr.Column():
prompt = gr.Textbox(
label="Prompt",
value="A detailed technical diagram of a silicon waveguide showing light propagation",
lines=3
)
negative_prompt = gr.Textbox(
label="Negative Prompt",
value="blurry, low quality, cartoon, artistic, hand-drawn, sketchy, unprofessional, low resolution",
lines=2
)
with gr.Row():
num_inference_steps = gr.Slider(1, 50, value=20, label="Inference Steps")
guidance_scale = gr.Slider(1.0, 20.0, value=7.0, label="Guidance Scale")
with gr.Row():
width = gr.Slider(512, 1024, value=1024, step=64, label="Width")
height = gr.Slider(512, 1024, value=1024, step=64, label="Height")
seed = gr.Number(value=42, label="Seed (0 for random)")
generate_btn = gr.Button("🎨 Generate Image", variant="primary")
with gr.Column():
output_image = gr.Image(label="Generated Image", type="pil")
# Example prompts
gr.Markdown("### 💡 Example Prompts")
examples = [
"A detailed technical diagram of a silicon waveguide showing light propagation",
"Cross-section view of a silicon photonic integrated circuit with waveguides and couplers",
"3D visualization of a silicon ring resonator for optical filtering",
"Schematic diagram of a silicon photonic modulator with electrodes",
"Top view of a silicon photonic array with multiple waveguides and couplers"
]
gr.Examples(
examples=[[ex] for ex in examples],
inputs=[prompt]
)
# Event handlers
generate_btn.click(
fn=generate_image_mlx,
inputs=[prompt, negative_prompt, num_inference_steps, guidance_scale, width, height, seed],
outputs=[output_image]
)
if __name__ == "__main__":
demo.launch()
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