approachingalmost
Approaching (Almost) Any Machine Learning Problem (by abhishekkrthakur)
diffusion-models-class
Materials for the Hugging Face Diffusion Models Course (by huggingface)
| approachingalmost | diffusion-models-class | |
|---|---|---|
| 7 | 22 | |
| 6,389 | 3,245 | |
| - | 2.7% | |
| 0.0 | 6.3 | |
| about 1 year ago | about 1 month ago | |
| Jupyter Notebook | ||
| - | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
approachingalmost
Posts with mentions or reviews of approachingalmost.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-06-03.
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Ai Coding roadmap
Check out this report and this book if you are interesting in ML competitions.
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[D] Anyone else having issues with this book?
book: approachingalmost/AAAMLP.pdf at master · abhishekkrthakur/approachingalmost (github.com)
- approachingalmost: Approaching (Almost) Any Machine Learning Problem
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13 July 2021- Daily Chat Thread
Here is one: https://github.com/abhishekkrthakur/approachingalmost
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[D] Machine Learning Roadmap: Done feeling like an imposter
Approaching (Almost) Any Machine Learning Problem https://github.com/abhishekkrthakur/approachingalmost/blob/master/AAAMLP.pdf
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What books are you currently reading?
Approaching almost any machine learning problem by Abhishek Thakur. Get it Here
- [Complete Book] Approaching (Almost) Any Machine Learning Problem
diffusion-models-class
Posts with mentions or reviews of diffusion-models-class.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-12-04.
- diffusion low level question
- Here's a learning resource
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[R] Classifier-Free Guidance can be applied to LLMs too. It generally gives results of a model twice the size you apply it to. New SotA on LAMBADA with LLaMA-7B over PaLM-540B and plenty other experimental results.
When you use stable diffusion, you can adjust the classifier free guidance scale to control how much it follows the input prompt. From what I understand(check https://github.com/huggingface/diffusion-models-class/tree/main/unit3), what cfg does is that it generates an unconditional image and an image conditional on the text prompt, and then scale up the difference.
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Ai Coding roadmap
https://huggingface.co/learn/nlp-course/ https://huggingface.co/docs/transformers (go through the task guide) https://github.com/huggingface/diffusion-models-class http://d2l.ai/ https://www.youtube.com/watch?v=VMj-3S1tku0&list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ
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How does stable diffusion work from a technical perspective?
I couldn't understand the original paper(havent done meth in a long time). This blog post and short course help me to understand.
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Using SD programatically with APIs
The Diffusion Models Course is another good resource to learn more technical details.
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I made a generative 3D game and took a walk in the streets of Paris. Playback speed 30x
Next, you need to become familiar with the diffusion model. I recommend this huggingface's course(https://github.com/huggingface/diffusion-models-class) because it is very high quality and you will learn while using diffusers. At first glance, it may not seem directly related to this game, but in my case, knowing what is happening in diffusers helped me in many ways: trial and error, inspiration for ideas, etc. I had no knowledge of pytorch (the deep learning library used for diffusers), so I also took this course (https://www.udacity.com/course/deep-learning-pytorch--ud188) which was in the prerequisites for that huggingface's course. It was also very good.
- Sunt AI Research Scientist, AMA
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Dreambooth Hackaton: How can we use a text-to-image model to explore the cinematographic appeal of Torres del Paine 🇨🇱?
Hugging Face Dreambooth Hackaton details
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[N] Personalise Stable Diffusion models in DreamBooth Hackathon
Details: https://github.com/huggingface/diffusion-models-class/blob/main/hackathon/README.md
What are some alternatives?
When comparing approachingalmost and diffusion-models-class you can also consider the following projects:
origin - Origin is a browser extension that uses LLMs to redefine the browser experience.
deforum-stable-diffusion
UnstableFusion - A Stable Diffusion desktop frontend with inpainting, img2img and more!
tutorials - AI-related tutorials. Access any of them for free → https://towardsai.net/editorial
pml-book - "Probabilistic Machine Learning" - a book series by Kevin Murphy
deforumed-walk - Take a walk in the generated world.
sd-webui-controlnet - WebUI extension for ControlNet
pml2-book - Probabilistic Machine Learning: Advanced Topics
sd_dreambooth_extension
civitai - A repository of models, textual inversions, and more
stable-diffusion-webui - Stable Diffusion web UI
approachingalmost vs origin
diffusion-models-class vs deforum-stable-diffusion
diffusion-models-class vs UnstableFusion
diffusion-models-class vs tutorials
diffusion-models-class vs pml-book
diffusion-models-class vs deforumed-walk
diffusion-models-class vs sd-webui-controlnet
diffusion-models-class vs origin
diffusion-models-class vs pml2-book
diffusion-models-class vs sd_dreambooth_extension
diffusion-models-class vs civitai
diffusion-models-class vs stable-diffusion-webui