diffusion-models-class
Materials for the Hugging Face Diffusion Models Course (by huggingface)
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diffusion-models-class | sd_dreambooth_extension | |
---|---|---|
22 | 115 | |
3,221 | 1,824 | |
5.5% | - | |
6.3 | 8.7 | |
19 days ago | about 1 month ago | |
Jupyter Notebook | Python | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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.
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
sd_dreambooth_extension
Posts with mentions or reviews of sd_dreambooth_extension.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-07-06.
- SDXL Training for Auto1111 is now Working on a 24GB Card
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(Requesting Help)
I am trying to use StableDiffusion via AUTOMATIC1111 with the Dreambooth extension
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it will be an absolute madness when sdxl becomes standard model and we start getting other models from it
When I first attempted SD training, I was very frustrated. It wasn't until I found this obscure forum thread on Github that I actually started producing great results with Dreambooth. Because I have such satisfactory results, I'm very reluctant to beat my brains against LoRa and its related training techniques. I gave up trying to train TI embeddings a long time ago. And I never figured out how to train or how to use hypernetworks. I've only been able to get good results with Dreambooth directly because of that thread I linked above. I make LoRas by extracting them from Dreambooth-trained checkpoints. And I have no idea if I'm doing the extractions the right way or not.
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"Exception training model: ' Some tensors share memory" with Dreambooth on Vladmatic
Getting the same with automatic1111 and sd_dreambooth extension. Check out more here in the issues log: https://github.com/d8ahazard/sd_dreambooth_extension/issues/1266
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Yo, DreamBooth gatekeepers, SHARE YOUR HYPERPARAMETERS, please.
It's several moths old and many things have changed. But the spreadsheet available through this thread on Github has been indispensable for me when I train Dreambooth models. I'm astounded no one talks about it. I bring it up all the time. The research presented there should be continued. I'd love to see similar research done for SD v2.1.
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What is the BEST solution for hyper realistic person training?
Training rate is paramount. Read this Github thread.
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How do you train your LoRAs, 1 Epoch or >1 Epoch (same # of steps)?
https://github.com/d8ahazard/sd_dreambooth_extension/discussions/547/ (in depth training principles understanding)
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Struggling to install Dreambooth
sd_dreambooth_extension https://github.com/d8ahazard/sd_dreambooth_extension.git main 926ae204 Fri Mar 31 15:12:45 2023 unknown
- Attempting to train a lora with RTX 2060 6 GB vRAM, how to go about this?
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SD just released an open source version of their GUI called StableStudio
also the Dreambooth extension supports API (https://github.com/d8ahazard/sd_dreambooth_extension/blob/main/scripts/api.py) so i'm not sure where do you get those news :/
What are some alternatives?
When comparing diffusion-models-class and sd_dreambooth_extension you can also consider the following projects:
deforum-stable-diffusion
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
UnstableFusion - A Stable Diffusion desktop frontend with inpainting, img2img and more!
kohya_ss
tutorials - AI-related tutorials. Access any of them for free → https://towardsai.net/editorial
kohya-trainer - Adapted from https://note.com/kohya_ss/n/nbf7ce8d80f29 for easier cloning
approachingalmost - Approaching (Almost) Any Machine Learning Problem
stable-diffusion-webui-wd14-tagger - Labeling extension for Automatic1111's Web UI
pml-book - "Probabilistic Machine Learning" - a book series by Kevin Murphy
dreambooth-training-guide
deforumed-walk - Take a walk in the generated world.
sd-scripts
diffusion-models-class vs deforum-stable-diffusion
sd_dreambooth_extension vs lora
diffusion-models-class vs UnstableFusion
sd_dreambooth_extension vs kohya_ss
diffusion-models-class vs tutorials
sd_dreambooth_extension vs kohya-trainer
diffusion-models-class vs approachingalmost
sd_dreambooth_extension vs stable-diffusion-webui-wd14-tagger
diffusion-models-class vs pml-book
sd_dreambooth_extension vs dreambooth-training-guide
diffusion-models-class vs deforumed-walk
sd_dreambooth_extension vs sd-scripts