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---
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datasets:
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- allenai/objaverse
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tags:
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- 3d
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extra_gated_fields:
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Name: text
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Email: text
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Country: text
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Organization or Affiliation: text
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I ALLOW Stability AI to email me about new model releases: checkbox
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license: mit
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pipeline_tag: image-to-3d
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---
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# TripoSR
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![](figures/input800.mp4)
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TripoSR is a fast and feed-forward 3D generative model developed in collaboration between Stability AI and Tripo AI.
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## Model Details
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### Model Description
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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).
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* **Developed by**: [Stability AI](https://stability.ai/), [Tripo AI](https://tripo3d.ai/)
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* **Model type**: Feed-forward 3D reconstruction from a single image
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* **License**: MIT
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* **Hardware**: We train `TripoSR` for 5 days on 22 GPU nodes each with 8 A100 40GB GPUs
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### Model Sources
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* **Repository**: https://github.com/VAST-AI-Research/TripoSR
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* **Tech report**: https://arxiv.org/abs/2403.02151
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* **Demo**: https://huggingface.co/spaces/stabilityai/TripoSR
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### Training Dataset
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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.
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## Usage
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* For usage instructions, please refer to our [TripoSR GitHub repository](https://github.com/VAST-AI-Research/TripoSR)
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* You can also try it in [our gradio demo](https://huggingface.co/spaces/stabilityai/TripoSR)
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### Misuse, Malicious Use, and Out-of-Scope Use
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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.
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cond_image_size: 512
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image_tokenizer_cls: tsr.models.tokenizers.image.DINOSingleImageTokenizer
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image_tokenizer:
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pretrained_model_name_or_path: "facebook/dino-vitb16"
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tokenizer_cls: tsr.models.tokenizers.triplane.Triplane1DTokenizer
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tokenizer:
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plane_size: 32
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num_channels: 1024
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backbone_cls: tsr.models.transformer.transformer_1d.Transformer1D
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backbone:
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in_channels: ${tokenizer.num_channels}
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num_attention_heads: 16
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attention_head_dim: 64
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num_layers: 16
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cross_attention_dim: 768
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post_processor_cls: tsr.models.network_utils.TriplaneUpsampleNetwork
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post_processor:
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in_channels: 1024
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out_channels: 40
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decoder_cls: tsr.models.network_utils.NeRFMLP
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decoder:
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in_channels: 120 # 3 * 40
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n_neurons: 64
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n_hidden_layers: 9
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activation: silu
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renderer_cls: tsr.models.nerf_renderer.TriplaneNeRFRenderer
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renderer:
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radius: 0.87 # slightly larger than 0.5 * sqrt(3)
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feature_reduction: concat
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density_activation: exp
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density_bias: -1.0
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num_samples_per_ray: 128
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