thu-ml/prolificdreamer

Health v2 · maintenance only

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1564 stars · 43 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official PyTorch implementation of ProlificDreamer, a NeurIPS 2023 method for high-fidelity text-to-3D generation using Variational Score Distillation (VSD) with pretrained Stable Diffusion. It generates NeRF scenes in a multi-stage pipeline and refines them into detailed, textured meshes via DMTet.

Use cases

• generate 3d models from text prompts
• create textured meshes from a text description
• train a nerf from text using stable diffusion guidance
• research variational score distillation for text-to-3d
• compare sds vs vsd for 3d generation
• produce high-resolution 3d assets for games or rendering

When to choose

• you need state-of-the-art text-to-3d quality with detailed meshes
• you are researching score distillation methods like VSD vs SDS
• you have a high-memory GPU (20-27GB+) and want to reproduce paper results

When to avoid

• you need fast or real-time 3d generation (training takes many GPU-hours)
• you lack a large-VRAM GPU
• you need production-ready tooling rather than research code
• you cannot tolerate the multi-face Janus artifact common to SDS/VSD methods

Facets

library · maturity maintenance

machine-learning deep-learning graphics simulation artificial-intelligence computer-vision graphics deep-learning python text-to-3d nerf diffusion-models stable-diffusion vsd score-distillation dmtet research-code neurips-2023 linux gpu

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