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> Try our new model: SF3D with several improvements such as faster generation and more game-ready assets. > > The model is available here and we also have a demo.
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2 extraitsWe closely follow LRM network architecture for the model design, where TripoSR incorporates a series of technical advancements over the LRM model in terms of both data curation as well as model and training improvements. For more technical details and evaluations, please refer to our tech report.
TripoSR for 5 days on 22 GPU nodes each with 8 A100 40GB GPUsWe use renders from the Objaverse dataset, utilizing our enhanced rendering method that more closely replicate the distribution of images found in the real world, significantly improving our model’s ability to generalize. We selected a carefully curated subset of the Objaverse dataset for the training data, which is available under the CC-BY license.
For usage instructions, please refer to our TripoSR GitHub repository
You can also try it in our gradio demo
The model should not be used to intentionally create or disseminate 3D models that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
--- datasets: - allenai/objaverse tags: - 3d extra_gated_fields: Name: text Email: text Country: text Organization or Affiliation: text I ALLOW Stability AI to email me about new model releases: checkbox license: mit pipeline_tag: image-to-3d --- > Try our new model: **SF3D** with several improvements such as faster generation and more game-ready assets. > > The model is available [here](https://huggingface.co/stabilityai/stable-fast-3d) and we also have a [demo](https://huggingface.co/spaces/stabilityai/stable-fast-3d). # TripoSR  TripoSR is a fast and feed-forward 3D generative model developed in collaboration between Stability AI and Tripo AI. ## Model Details ### Model Description We closely follow [LRM](https://arxiv.org/abs/2311.04400) network architecture for the model design, where TripoSR incorporates a series of technical advancements over the LRM model in terms of both data curation as well as model and training improvements. For more technical details and evaluations, please refer to [our tech report](https://arxiv.org/abs/2403.02151). * **Developed by**: [Stability AI](https://stability.ai/), [Tripo AI](https://tripo3d.ai/) * **Model type**: Feed-forward 3D reconstruction from a single image * **License**: MIT * **Hardware**: We train `TripoSR` for 5 days on 22 GPU nodes each with 8 A100 40GB GPUs ### Model Sources * **Repository**: https://github.com/VAST-AI-Research/TripoSR * **Tech report**: https://arxiv.org/abs/2403.02151 * **Demo**: https://huggingface.co/spaces/stabilityai/TripoSR ### Training Dataset We use renders from the [Objaverse](https://objaverse.allenai.org/objaverse-1.0) dataset, utilizing our enhanced rendering method that more closely replicate the distribution of images found in the real world, significantly improving our model’s ability to generalize. We selected a carefully curated subset of the Objaverse dataset for the training data, which is available under the CC-BY license. ## Usage * For usage instructions, please refer to our [TripoSR GitHub repository](https://github.com/VAST-AI-Research/TripoSR) * You can also try it in [our gradio demo](https://huggingface.co/spaces/stabilityai/TripoSR) ### Misuse, Malicious Use, and Out-of-Scope Use The model should not be used to intentionally create or disseminate 3D models that people would foreseeably find disturbing, distressin...
Source context: 191765 downloads · 651 likes · Pipeline image-to-3d · Repo stabilityai/TripoSR