diffusers
Pytorch
diffusers | Pytorch | |
---|---|---|
105 | 340 | |
1,870 | 78,016 | |
- | 1.4% | |
7.0 | 10.0 | |
11 months ago | 2 days ago | |
Python | Python | |
Apache License 2.0 | BSD 1-Clause License |
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diffusers
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Useful Links
ShivamShrirao's Diffusers Pretrained diffusion models across multiple modalities.
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DreamBooth fine-tuning failing to get the style
Like the title say I'm trying to fine-tune a model to match a style of a popular manhwa. I'm using the ShivamShrirao Google Colab to accomplish this.
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How to resume Dreambooth training?
I am running the DreamBooth_Stable_Diffusion.ipynb notebook from ShivamShrirao locally on my machine. Let's say I have trained for 500 iterations and it hasn't converged yet. How do I make it resume training from that iteration so it can do another 500?
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Non web-ui colab
My understanding, based on messages from an (alleged) representative of colabs, is that the webui is the problem, not SD itself. This also seems to be the consensus in the comments section of other posts. I have not yet seen a link to colab based webui alternatives so here is something I found from a tutorial. I am certain that there are better alternatives. Anyone have a better idea? This will still probably be useful to other people like me who are just messing around.
- [Stablediffusion] Guide pour DreamBooth avec 8 Go de vram sous Windows
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Finally got Dreambooth running without errors... but is it even using the model I trained?
I'm running ShivamShrirao's fork of diffusers; ran into a fp16 issue and had to patch in a fix from the main branch ( #1567 ).
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Shivam Stable Diffusion: Getting same example models repeatedly (SD + Dreambooth)
I am running Shivam Stable Diffusion Jupyter notebook: diffusers/DreamBooth_Stable_Diffusion.ipynb at main · ShivamShrirao/diffusers · GitHub.
- Running Stable Diffusion locally with personalized changes
- Can't create embedding's with dreambooth ckpt
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Weird issue using Shivam's Diffuser notebook
Are you using this one? https://github.com/S
Pytorch
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AI enthusiasm #9 - A multilingual chatbot📣🈸
torch is a package to manage tensors and dynamic neural networks in python (GitHub)
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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
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
fast-stable-diffusion - fast-stable-diffusion + DreamBooth
mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
A1111-Web-UI-Installer - Complete installer for Automatic1111's infamous Stable Diffusion WebUI
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
xformers - Hackable and optimized Transformers building blocks, supporting a composable construction.
flax - Flax is a neural network library for JAX that is designed for flexibility.
efficient-dreambooth - [Moved to: https://github.com/smy20011/dreambooth-docker]
tinygrad - You like pytorch? You like micrograd? You love tinygrad! ❤️ [Moved to: https://github.com/tinygrad/tinygrad]
Dreambooth-Stable-Diffusion - Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) by way of Textual Inversion (https://arxiv.org/abs/2208.01618) for Stable Diffusion (https://arxiv.org/abs/2112.10752). Tweaks focused on training faces, objects, and styles.
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