| import gradio as gr | |
| import plotly.graph_objects as go | |
| import torch | |
| from tqdm.auto import tqdm | |
| from model import Model | |
| from settings import CACHE_EXAMPLES, MAX_SEED | |
| from utils import randomize_seed_fn | |
| def inference(prompt): | |
| model = Model() | |
| seed: int = 0 | |
| guidance_scale: float = 15.0 | |
| num_inference_steps: int = 64 | |
| return model.run_text(prompt, seed, guidance_scale, num_inference_steps) | |
| demo = gr.Interface( | |
| fn=inference, | |
| inputs="text", | |
| outputs=gr.Textbox(label = "Text Genereated", lines = 5), | |
| examples=[ | |
| ["a red motorcycle"], | |
| ["a RED pumpkin"], | |
| ["a yellow rubber duck"] | |
| ], | |
| title="Point-E demo: text to 3D", | |
| description="""Generated 3D Point Clouds with [Point-E](https://github.com/openai/point-e/tree/main). This demo uses a small, worse quality text-to-3D model to produce 3D point clouds directly from text descriptions. | |
| Check out the [notebook](https://github.com/openai/point-e/blob/main/point_e/examples/text2pointcloud.ipynb). | |
| """ | |
| ) | |
| demo.queue(max_size=30) | |
| demo.launch(debug=True) |