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| from gradio_client import Client, handle_file | |
| import time | |
| import os | |
| from PIL import Image | |
| # Use the specific space URL if the name redirects | |
| client = Client("JeffreyXiang/TRELLIS") | |
| # Create a robust dummy image | |
| image_path = "test_vehicle.png" | |
| img = Image.new('RGB', (512, 512), color = 'red') | |
| img.save(image_path) | |
| print(f"Created dummy image at {os.path.abspath(image_path)}") | |
| print("1. Uploading and generating 3D asset...") | |
| try: | |
| result_video = client.predict( | |
| image=handle_file(image_path), | |
| multiimages=[], | |
| seed=0, | |
| ss_guidance_strength=7.5, | |
| ss_sampling_steps=12, | |
| slat_guidance_strength=3.0, | |
| slat_sampling_steps=12, | |
| multiimage_algo="stochastic", | |
| api_name="/image_to_3d" | |
| ) | |
| print(f"Generation complete. Result: {result_video}") | |
| print("2. Extracting GLB...") | |
| result_glb = client.predict( | |
| mesh_simplify=0.95, | |
| texture_size=1024, | |
| api_name="/extract_glb" | |
| ) | |
| print(f"Extraction complete. Result: {result_glb}") | |
| except Exception as e: | |
| print(f"Error: {e}") | |