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35 lines
2.2 KiB
Markdown
35 lines
2.2 KiB
Markdown
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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specific language governing permissions and limitations under the License.
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# Shap-E
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The Shap-E model was proposed in [Shap-E: Generating Conditional 3D Implicit Functions](https://huggingface.co/papers/2305.02463) by Alex Nichol and Heewoo Jun from [OpenAI](https://github.com/openai).
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The abstract from the paper is:
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*We present Shap-E, a conditional generative model for 3D assets. Unlike recent work on 3D generative models which produce a single output representation, Shap-E directly generates the parameters of implicit functions that can be rendered as both textured meshes and neural radiance fields. We train Shap-E in two stages: first, we train an encoder that deterministically maps 3D assets into the parameters of an implicit function; second, we train a conditional diffusion model on outputs of the encoder. When trained on a large dataset of paired 3D and text data, our resulting models are capable of generating complex and diverse 3D assets in a matter of seconds. When compared to Point-E, an explicit generative model over point clouds, Shap-E converges faster and reaches comparable or better sample quality despite modeling a higher-dimensional, multi-representation output space.*
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The original codebase can be found at [openai/shap-e](https://github.com/openai/shap-e).
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> [!TIP]
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> See the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
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## ShapEPipeline
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[[autodoc]] ShapEPipeline
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- all
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- __call__
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## ShapEImg2ImgPipeline
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[[autodoc]] ShapEImg2ImgPipeline
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- all
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- __call__
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## ShapEPipelineOutput
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[[autodoc]] pipelines.shap_e.pipeline_shap_e.ShapEPipelineOutput
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