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| from gradio_client import Client, handle_file | |
| import gradio as gr | |
| # Initialize Gradio Client | |
| client = Client("JeffreyXiang/TRELLIS") # Replace with your Hugging Face Space | |
| # Helper Functions for API Calls | |
| def start_session(): | |
| result = client.predict(api_name="/start_session") | |
| return result | |
| def preprocess_image(image): | |
| result = client.predict( | |
| image=handle_file(image), | |
| api_name="/preprocess_image" | |
| ) | |
| return result | |
| def preprocess_images(images): | |
| processed_images = [ | |
| {"image": handle_file(img), "caption": None} for img in images | |
| ] | |
| result = client.predict( | |
| images=processed_images, | |
| api_name="/preprocess_images" | |
| ) | |
| return result | |
| def get_seed(randomize_seed, seed): | |
| result = client.predict( | |
| randomize_seed=randomize_seed, | |
| seed=seed, | |
| api_name="/get_seed" | |
| ) | |
| return result | |
| def image_to_3d(image, seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps, multiimage_algo): | |
| result = client.predict( | |
| image=handle_file(image), | |
| multiimages=[], | |
| seed=seed, | |
| ss_guidance_strength=ss_guidance_strength, | |
| ss_sampling_steps=ss_sampling_steps, | |
| slat_guidance_strength=slat_guidance_strength, | |
| slat_sampling_steps=slat_sampling_steps, | |
| multiimage_algo=multiimage_algo, | |
| api_name="/image_to_3d" | |
| ) | |
| return result["video"] | |
| def extract_glb(mesh_simplify, texture_size): | |
| result = client.predict( | |
| mesh_simplify=mesh_simplify, | |
| texture_size=texture_size, | |
| api_name="/extract_glb" | |
| ) | |
| return result[1] # Return the GLB file path for download | |
| def extract_gaussian(): | |
| result = client.predict(api_name="/extract_gaussian") | |
| return result[1] # Return the Gaussian file path for download | |
| # Define Gradio UI | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Image to 3D Model with TRELLIS API") | |
| with gr.Row(): | |
| with gr.Column(): | |
| image = gr.Image(type="filepath", label="Upload Image") | |
| seed = gr.Slider(0, 100, value=0, step=1, label="Seed") | |
| ss_guidance_strength = gr.Slider(0.0, 10.0, value=7.5, step=0.1, label="SS Guidance Strength") | |
| ss_sampling_steps = gr.Slider(1, 50, value=12, step=1, label="SS Sampling Steps") | |
| slat_guidance_strength = gr.Slider(0.0, 10.0, value=3.0, step=0.1, label="SLAT Guidance Strength") | |
| slat_sampling_steps = gr.Slider(1, 50, value=12, step=1, label="SLAT Sampling Steps") | |
| multiimage_algo = gr.Radio(["stochastic", "multidiffusion"], value="stochastic", label="Multi-image Algorithm") | |
| generate_btn = gr.Button("Generate 3D Model") | |
| with gr.Column(): | |
| video_output = gr.Video(label="3D Model Preview") | |
| download_glb_btn = gr.Button("Download GLB") | |
| download_gaussian_btn = gr.Button("Download Gaussian") | |
| glb_file = gr.File(label="GLB File") | |
| gaussian_file = gr.File(label="Gaussian File") | |
| # Define Actions | |
| generate_btn.click( | |
| fn=image_to_3d, | |
| inputs=[image, seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps, multiimage_algo], | |
| outputs=[video_output] | |
| ) | |
| # Download Buttons and Actions | |
| download_glb_btn.click( | |
| fn=lambda: extract_glb(mesh_simplify=0.95, texture_size=1024), # Static values | |
| inputs=[], # No dynamic inputs | |
| outputs=[glb_file] # Output file for GLB | |
| ) | |
| download_gaussian_btn.click( | |
| fn=extract_gaussian, # Direct function call | |
| inputs=[], # No dynamic inputs | |
| outputs=[gaussian_file] # Output file for Gaussian | |
| ) | |
| # Launch Gradio App | |
| if __name__ == "__main__": | |
| demo.launch(show_error=True) |