Instructions to use RenderFormer/renderformer-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RenderFormer/renderformer-v2 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("RenderFormer/renderformer-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add pipeline tag and links to paper and project page
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by nielsr HF Staff - opened
README.md
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license: mit
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library_name: renderformer-studio
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tags:
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- neural-rendering
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- rendering
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instructions, scene conversion, and inference examples live in
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[RenderFormer Studio](https://github.com/iamNCJ/RenderFormer-Studio).
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If this repository is access-protected during release staging, authenticate
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with Hugging Face before loading it. One repository revision selects every
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component below as an atomic, compatible set.
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## Citation
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Please cite both RenderFormer papers when using this bundle. Full BibTeX
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entries are provided in the RenderFormer Studio README.
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---
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library_name: renderformer-studio
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license: mit
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pipeline_tag: other
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tags:
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- neural-rendering
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- rendering
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instructions, scene conversion, and inference examples live in
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[RenderFormer Studio](https://github.com/iamNCJ/RenderFormer-Studio).
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The model was presented in the paper [RenderFormer-V2: Neural Rendering with Heterogeneous Scene Primitives](https://huggingface.co/papers/2609.05738). See the [project page](https://renderformer.github.io/v2/) for additional results and details.
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If this repository is access-protected during release staging, authenticate
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with Hugging Face before loading it. One repository revision selects every
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component below as an atomic, compatible set.
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## Citation
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Please cite both RenderFormer papers when using this bundle. Full BibTeX
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entries are provided in the RenderFormer Studio README.
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