## APIs:
<details>
<summary>We offer handy APIs for our pipeline and its components.</summary>

```python
from gradio_client import Client
client = Client("https://one-2-3-45-one-2-3-45.hf.space/")
input_img_path = "https://huggingface.co/spaces/One-2-3-45/One-2-3-45/resolve/main/demo_examples/01_wild_hydrant.png"

### Single image to 3D mesh
generated_mesh_filepath = client.predict(
	input_img_path,	
	True,		# image preprocessing
	api_name="/generate_mesh"
)

### Elevation estimation 
# DON'T TO ASK USERS TO ESTIMATE ELEVATION! This OFF-THE-SHELF algorithm is ALL YOU NEED!
elevation_angle_deg = client.predict(
	input_img_path,
	True,		# image preprocessing
	api_name="/estimate_elevation"
)

### Image preprocessing: segment, rescale, and recenter
segmented_img_filepath = client.predict(
	input_img_path,	
	api_name="/preprocess"
)
```
</details>

## Tuning Tips:

1. The multi-view prediction module (Zero123) operates probabilistically. If some of the predicted views are not satisfactory, you may select and regenerate them.

2. In “advanced options”, you can tune two parameters as in other common diffusion models:
  - Diffusion Guidance Scale determines how much you want the model to respect the input information (input image + viewpoints). Increasing the scale typically results in better adherence, less diversity, and also higher image distortion.
  
  - Number of diffusion inference steps controls the number of diffusion steps applied to generate each image. Generally, a higher value yields better results but with diminishing returns.

Enjoy creating your 3D asset!