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Source channel @githubtrending · Post #14643 · Apr 28

#python#3d#3d_aigc#3d_generation#diffusion_models#hunyuan3d#image_to_3d#shape#shape_generation#text_to_3d#texture_generation Hunyuan3D 2.0 is a powerful tool that creates detailed 3D models with textures in two steps: first building the shape, then adding colors and materials. It works efficiently on standard computers (as low as 5GB VRAM for basic models) and offers multiple ways to use it, like coding, Blender plugins, or online demos, making it accessible for creating game-ready 3D assets, VR/AR content, or custom designs without needing advanced hardware. https://github.com/Tencent/Hunyuan3D-2

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#python #3d #3d_aigc #3d_generation #diffusion_models #hunyuan3d #image_to_3d #shape #shape_generation #text_to_3d #texture_generation

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djangoproject

@djangoproject · Post #274 · 03/18/2017, 01:48 AM

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https://github.com/riga/tfdeploy Google's TensorFlow framework is taking off big-time now that it's at a full 1.0 release. One common question about it: How can I make use of the models I train in TensorFlow without using TensorFlow itself? #Tfdeploy is a partial answer to that question. It exports a trained TensorFlow model to "a simple #NumPy-based callable," meaning the model can be used in Python with Tfdeploy and the the NumPy math-and-stats library as the only dependencies. Most of the operations you can perform in TensorFlow can also be performed in Tfdeploy, and you can extend the behaviors of the library by way of standard Python metaphors (such as overloading a class). Now the bad news: Tfdeploy doesn't support GPU acceleration, if only because NumPy doesn't do that. Tfdeploy's creator suggests using the gNumPy project as a possible replacement. #Machine_learning

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