dream-textures
Pytorch
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dream-textures | Pytorch | |
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72 | 338 | |
7,599 | 77,783 | |
- | 2.4% | |
5.8 | 10.0 | |
8 days ago | 6 days ago | |
Python | Python | |
GNU General Public License v3.0 only | BSD 1-Clause License |
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dream-textures
- Donut done with Artificial Intelligence and Blender
- Tell HN: The next generation of videogames will be great with midjourney
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After Diffusion, an After Effects Extension Integrating the SD web UI seamlessly.
I'm a long time advanced AE user and would gladly give feedback according to how I envision a nice workflow to be if you want. I recently got into dream textures for blender, which I think is a great reference for the direction things could be heading. It's still not viable for consistent video, but I love how they expose multiple control nets and their weights to be animatable for example. I also suggested them exposed (animatable) prompt weights, which the author now also plans for future release. I see you have such things planned as well for this plugin so big thumbs up!
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Resources for artists interesting in using StableDiffusion as a tool?
Dream Textures (SD for Blender) - https://github.com/carson-katri/dream-textures
- Using AI for 3d Game art
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ControlNet fully integrated with Blender using nodes!
Yes, and it can also automatically bake the texture onto the original UV map instead of the projected UVs. The guide is here: https://github.com/carson-katri/dream-textures/wiki/Texture-Projection
- Using DALL-E 2 to create brick and water textures in Unity.
- 3D animation attempt using Sketchup screenshots and ControlNet
- Blender 3.5
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Master AI Texture Projection for Blender 3
Dream AI latest release: https://github.com/carson-katri/dream-textures/releases
Pytorch
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Einsum in 40 Lines of Python
PyTorch also has some support for them, but it's quite incomplete and has many issues so that it is basically unusable. And its future development is also unclear. https://github.com/pytorch/pytorch/issues/60832
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Library for Machine learning and quantum computing
TensorFlow
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My Favorite DevTools to Build AI/ML Applications!
TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.
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penzai: JAX research toolkit for building, editing, and visualizing neural nets
> does PyTorch have a similar concept
of course https://github.com/pytorch/pytorch/blob/main/torch/utils/_py...
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Tinygrad: Hacked 4090 driver to enable P2P
fyi should work on most 40xx[1]
[1] https://github.com/pytorch/pytorch/issues/119638#issuecommen...
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The Elements of Differentiable Programming
Sure, right here: https://github.com/pytorch/pytorch/blob/main/torch/autograd/...
Here's the documentation: https://pytorch.org/tutorials/intermediate/forward_ad_usage....
> When an input, which we call “primal”, is associated with a “direction” tensor, which we call “tangent”, the resultant new tensor object is called a “dual tensor” for its connection to dual numbers[0].
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Functions and operators for Dot and Matrix multiplication and Element-wise calculation in PyTorch
*My post explains Dot, Matrix and Element-wise multiplication in PyTorch.
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Dot vs Matrix vs Element-wise multiplication in PyTorch
In PyTorch with @, dot() or matmul():
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Building a GPT Model from the Ground Up!
import torch # we use PyTorch: https://pytorch.org data = torch.tensor(encode(text), dtype=torch.long) print(data.shape, data.dtype) print(data[:1000]) # the 1000 characters we looked at earlier will to the GPT look like this
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Open Source Ascendant: The Transformation of Software Development in 2024
AI's Open Embrace Artificial intelligence (AI) and machine learning (ML) are increasingly leveraging open-source frameworks like TensorFlow [https://www.tensorflow.org/] and PyTorch [https://pytorch.org/]. This democratization of AI tools is driving innovation and lowering entry barriers across industries.
What are some alternatives?
stable-diffusion-webui - Stable Diffusion web UI
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
stable-diffusion - This version of CompVis/stable-diffusion features an interactive command-line script that combines text2img and img2img functionality in a "dream bot" style interface, a WebGUI, and multiple features and other enhancements. [Moved to: https://github.com/invoke-ai/InvokeAI]
mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
stable-diffusion - Optimized Stable Diffusion modified to run on lower GPU VRAM
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
stable-diffusion-nvidia-docker - GPU-ready Dockerfile to run Stability.AI stable-diffusion model v2 with a simple web interface. Includes multi-GPUs support.
flax - Flax is a neural network library for JAX that is designed for flexibility.
DeepBump - Normal & height maps generation from single pictures
tinygrad - You like pytorch? You like micrograd? You love tinygrad! ❤️ [Moved to: https://github.com/tinygrad/tinygrad]
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/Sygil-Dev/sygil-webui]
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more