This is the Linux app named Shap-E whose latest release can be downloaded as shap-esourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named Shap-E with OnWorks for free.
Befolgen Sie diese Anweisungen, um diese App auszuführen:
- 1. Diese Anwendung auf Ihren PC heruntergeladen.
- 2. Geben Sie in unserem Dateimanager https://www.onworks.net/myfiles.php?username=XXXXX den gewünschten Benutzernamen ein.
- 3. Laden Sie diese Anwendung in einem solchen Dateimanager hoch.
- 4. Starten Sie den OnWorks Linux-Online- oder Windows-Online-Emulator oder den MACOS-Online-Emulator von dieser Website.
- 5. Rufen Sie vom gerade gestarteten OnWorks Linux-Betriebssystem aus unseren Dateimanager https://www.onworks.net/myfiles.php?username=XXXXX mit dem gewünschten Benutzernamen auf.
- 6. Laden Sie die Anwendung herunter, installieren Sie sie und führen Sie sie aus.
SCREENSHOTS
Ad
Form
BESCHREIBUNG
The shap-e repository provides the official code and model release for Shap-E, a conditional generative model designed to produce 3D assets (implicit functions, meshes, neural radiance fields) from text or image prompts. The model is built with a two-stage architecture: first an encoder that maps existing 3D assets into parameterizations of implicit functions, and then a conditional diffusion model trained on those parameterizations to generate new assets. Because it works at the level of implicit functions, Shap-E can render output both as textured meshes and NeRF-style volumetric renderings. The repository contains sample notebooks (e.g. sample_text_to_3d.ipynb, sample_image_to_3d.ipynb) so users can try out text → 3D or image → 3D generation. The code is distributed under the MIT license, and includes a “model card” that documents limitations, recommended use, and ethical considerations.
Eigenschaften
- Conditional generation of 3D implicit function models from text or images
- Two-stage model architecture: encoder + diffusion over implicit parameter space
- Output in multiple representations: meshes, NeRF renderings
- Sample notebooks for text23D and image23D use cases
- Model card documenting limitations, biases, and usage guidance
- MIT-licensed code, allowing reuse and extension
Programmiersprache
Python
Kategorien
This is an application that can also be fetched from https://sourceforge.net/projects/shap-e.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
