You can change this to render_mode = 'stf' to render the 3D models with Style Transfer Function (STF) rendering mode. Here we will render the 3D models, using render_mode = 'nerf' Neural Radiance Fields (NeRF) to render the 3D models. For me it took around 22 seconds to generate 3D models with batch_size=1. It will take some time to generate the 3D models, based on your batch_size higher batch_size will take longer time to generate the 3D models. Model_kwargs = dict (texts = * batch_size ) , Prompt = "a donut" # this is the prompt, you can change this to anything you want. Guidance_scale = 15.0 # this is the scale of the guidance, higher values make the model look more like the prompt. Now, we need to clone Shap-E repository to our Google Colab Notebook.īatch_size = 1 # this is the size of the models, higher values take longer to generate. Creating new Notebook in Google Colab Step 2 - Clone the repository Go to Google Colab and create new Notebook. Let's start by creating new Notebook in Google Colab. Let's start! Step 1 - Setting up the project Go to and download Blender Studio compatible with your operating system. In this tutorial we will learn how to create a Notebook in Google Colab, set up and use OpenAI's Shape-E model to generate 3D models and customize them with Blender Studio. With its unique capabilities, Shap-E is redefining the way we approach text-to-3D and image-to-3D generation and what creative possibilities for AI applications can arise from it. What makes Shap-E truly remarkable is its versatility: the generated 3D objects can be opened in software like Microsoft Paint 3D or even converted into STL files for 3D printing. This extraordinary technology is accessible for free on GitHub, allowing users to run it seamlessly on their computers without requiring an OpenAI API key or an internet connection. Shap-E, a groundbreaking model developed by OpenAI, produces an array of 3D objects using text or images as input, transforming the realm of 3D applications with its innovative approach.
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