LION

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LION is an open-source AI tool for 3D shape generation, specifically using latent point diffusion models. It provides code and resources for researchers and developers working on 3D shape generation.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is LION?

LION (Latent Point Diffusion Models for 3D Shape Generation) is an open-source project presented at NeurIPS 2022, offering a robust framework for generating 3D shapes. This tool leverages advanced diffusion models to create 3D point clouds, enabling researchers and developers to explore and innovate in the field of 3D content creation. It includes functionalities for training VAE and diffusion prior models, with options for conditioning inputs like CLIP image embeddings for tasks such as single-view reconstruction or text-to-shape generation. The project provides detailed installation instructions, demo scripts, and evaluation tools, making it a valuable resource for those working with 3D shape synthesis and analysis.

Best used for

Ideal for researchers and developers who need to generate complex 3D shapes, train advanced diffusion models, and explore text-to-shape generation. Especially valuable for academic research and experimental projects in 3D content creation.

Common actions

generate 3D shapes

train diffusion models

research 3D generation

workflowsautomated workflowlow-code/no-codeopen-sourcegithub copilotdeepfakecollaborationface swapping"AI Agents"

Capabilities

Key features

3D shape generation
Latent point diffusion models
VAE training
Diffusion prior training
CLIP-conditioned generation

Target Audience

professor

Integrations

comet-mlwandbtensorboard

Pricing & Plans

Open Source

Free

FAQs

What are the primary dependencies for running LION?

LION primarily requires CUDA 11.6 and can be set up using a conda environment from the provided `env.yaml` file. Additional packages like OpenAI's CLIP library also need to be installed via pip. Docker is also an option for environment setup.

Can LION generate 3D shapes from text descriptions?

Yes, LION supports text-to-shape generation. This is achieved by training a diffusion prior with CLIP feature conditioning, where the model takes CLIP image embeddings as input to guide the shape generation process.

How can I monitor experiments when training models with LION?

LION supports experiment monitoring through several platforms. Users can integrate with Comet-ML by providing an API key, or use Weights & Biases (wandb) and TensorBoard by setting specific environment variables before training.

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Research & Education › Academic Research Coding & Development › Open Source & Models

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