test_flux_specific.py

import os
from dotenv import load_dotenv
import google.generativeai as genai

load_dotenv()

def generate_flux_optimized():
    api_key = os.getenv('GOOGLE_API_KEY')
    genai.configure(api_key=api_key)
    model = genai.GenerativeModel('gemini-2.5-flash-preview-05-20')
    
    prompt = """
Generate optimized Python code for running FLUX.1-schnell diffusion model on Apple Silicon (MPS) hardware.

Requirements:
- Use FluxPipeline from diffusers library
- Model: "black-forest-labs/FLUX.1-schnell"
- Target device: MPS (Apple Silicon)
- Image size: 768x1360
- Inference steps: 4
- Prompt: "A cat holding a sign that says hello world"

Apply these Apple Silicon optimizations:
1. Use torch.bfloat16 (better than float16 for MPS)
2. Enable attention slicing and VAE slicing for memory efficiency
3. Use guidance_scale=0.0 for FLUX.1-schnell
4. Add max_sequence_length=256 for memory optimization
5. Include proper error handling
6. Add torch.inference_mode() for speed

Generate ONLY Python code without markdown formatting.
"""
    
    try:
        response = model.generate_content(prompt)
        code = response.text.strip()
        
        # Clean up any markdown formatting
        if code.startswith('```python'):
            code = code[9:]
        if code.endswith('```'):
            code = code[:-3]
        
        print("FLUX-Optimized Code for Apple Silicon:")
        print("=" * 50)
        print(code)
        print("=" * 50)
        
    except Exception as e:
        print(f"Error: {e}")

if __name__ == "__main__":
    generate_flux_optimized()