Shap-E The Shap-E model was proposed in Shap-E: Generating Conditional 3D Implicit Functions by Alex Nichol and Heewoo Jun from OpenAI. The abstract from the paper is: 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. The original codebase can be found at openai/shap-e. See the reuse components across pipelines section to learn how to efficiently load the same components into multiple pipelines.  ShapEPipeline  class diffusers.ShapEPipeline  < source > ( prior: PriorTransformer text_encoder: CLIPTextModelWithProjection tokenizer: CLIPTokenizer scheduler: HeunDiscreteScheduler shap_e_renderer: ShapERenderer  )   Parameters   prior (PriorTransformer) —