kohya-trainer
Adapted from https://note.com/kohya_ss/n/nbf7ce8d80f29 for easier cloning (by Linaqruf)
latent-diffusion
High-Resolution Image Synthesis with Latent Diffusion Models (by CompVis)
kohya-trainer | latent-diffusion | |
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36 | 70 | |
1,772 | 10,681 | |
- | 3.3% | |
8.3 | 0.0 | |
about 2 months ago | 2 months ago | |
Jupyter Notebook | Jupyter Notebook | |
Apache License 2.0 | MIT License |
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.
kohya-trainer
Posts with mentions or reviews of kohya-trainer.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-08-04.
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Best method for training lora with sdxl
This longer colab notebook: I did use this one (or one of the slight derivatives of it) and got out a safetensors file, but the lora didn't work at all--I'd use it a increase it's weight but I just would see no effect
- Question on SD Finetuning
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Requesting Help: Stable Diffusion with Dreambooth via Automatic1111
It isn't what you are asking for (sry) but I struggled with this thing for way too long until I found out about the Kohya Trainer. https://github.com/Linaqruf/kohya-trainer So much easier with a lot of videos by the various YT folks. Standalone WebUI that just works. Life is good here!
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Do you need a PhD in AI for AI opportunities?
It's seem that he is stable diffusion model creators. In that space, it's less knowing about the code and more experimenting on what would happen in the training. The stable diffusion community has repertoire of fine-tuning tools that is accessible for someone who have no single idea on the code behind it, no different than using application like kohya.
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Am I some kind of idiot? I cant for the life of me get Lora training to work on colab or runpod.
Have you tried out one of the colabs from https://github.com/Linaqruf/kohya-trainer ? The colabs themselves are pretty long, but you just have to read each step and then usually push the button to run that cell, then move on to the next one.
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[Stable Diffusion] Diffusion stable sur Google Colab se bloque toujours!
** https: //github.com/linaqruf/kohya-trainer**
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Lora training steps with large batch sizes?
There are a lot of variables that affect what kind of settings to use, but afaik the best solution to finding the right step count for what your training is still just to save multiple epochs and then run a x/y/z plot comparison. If you can't do that locally because of your 4gb card, you could try using Lora colabs that include inference capabilities.
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Colab Troubles (Addendum)
You seem to be a little confused. You wont find an ipynb of a model. You would reference a model via a content portal like hugginface. If your model is hosted there, you dont have to download it to your computer or gdrive first. You just reference it with the hugginface-style reference, ie runwayml/stable-diffusion-v1-5. Some colabs will let you also reference a URL to pull down the model. Example. https://github.com/Linaqruf/kohya-trainer/blob/main/kohya-LoRA-dreambooth.ipynb. In that case, you can get the direct url to a checkpoint, for example at civit.ai. If you're decent at messing around with code, you can deconstruct that code block to use in a different colab. As for gdrive, it's only a couple dollars to get 100G.
- PNG info not copied from images generated through Kohya.
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Is Colab going to start banning people who use it for Stable Diffusion????
Try this colab to train Lora, it can generate image without the UI too
latent-diffusion
Posts with mentions or reviews of latent-diffusion.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-06-21.
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SDXL: The next generation of Stable Diffusion models for text-to-image synthesis
Stable Diffusion XL (SDXL) is the latest text-to-image generation model developed by Stability AI, based on the latent diffusion techniques. SDXL has the potential to create highly realistic images for media, entertainment, education, and industry domains, opening new ways in practical uses of AI imagery.
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Is it possible to create a checkpoint from scratch?
Here's a link to the early latent-diffusion git, that might be able to create a blank model (I haven't tested it): https://github.com/CompVis/latent-diffusion
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Anything better than pix2pixHD?
Latent diffusion could work for you: https://github.com/CompVis/latent-diffusion (https://arxiv.org/abs/2112.10752)
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Image Upscaler AI
There are a lot but the one implemented as LDSR in most stable guis is this one. https://github.com/CompVis/latent-diffusion
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I've been collecting millions of images of only public domain /cc0 licensing. I'd like to train a stable diffusion model on the collection. Could some one share their knowledge of what this would take? Otherwise, simply enjoy my library.
CompVis/latent-diffusion: High-Resolution Image Synthesis with Latent Diffusion Models (github.com)
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Run Clip on iPhone to Search Photos
The "retrieval based model" refers to https://github.com/CompVis/latent-diffusion#retrieval-augmen..., which uses ScaNN to train a knn embedding searcher.
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Class Action Lawsuit filed against Stable Diffusion and Midjourney.
Stability is basically https://github.com/CompVis/latent-diffusion + training data.
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[D] Influential papers round-up 2022. What are your favorites?
Found relevant code at https://github.com/CompVis/latent-diffusion + all code implementations here
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Can anyone explain differences between sampling methods and their uses to me in simple terms, because all the info I've found so far is either very contradicting or complex and goes over my head
DDIM and PLMS were the original samplers. They were part of Latent Diffusion's repository. They stand for the papers that introduced them, Denoising Diffusion Implicit Models and Pseudo Numerical Methods for Diffusion Models on Manifolds.
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AI art is very dystopian.
yes, https://github.com/CompVis/latent-diffusion
What are some alternatives?
When comparing kohya-trainer and latent-diffusion you can also consider the following projects:
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
disco-diffusion
sd_dreambooth_extension
dalle-mini - DALLĀ·E Mini - Generate images from a text prompt
sd-webui-additional-networks
hent-AI - Automation of censor bar detection
stable-diffusion-webui-colab - stable diffusion webui colab
dalle-2-preview
fast-stable-diffusion - fast-stable-diffusion + DreamBooth
stable-diffusion
EveryDream-trainer - General fine tuning for Stable Diffusion
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
kohya-trainer vs lora
latent-diffusion vs disco-diffusion
kohya-trainer vs sd_dreambooth_extension
latent-diffusion vs dalle-mini
kohya-trainer vs sd-webui-additional-networks
latent-diffusion vs hent-AI
kohya-trainer vs stable-diffusion-webui-colab
latent-diffusion vs dalle-2-preview
kohya-trainer vs fast-stable-diffusion
latent-diffusion vs stable-diffusion
kohya-trainer vs EveryDream-trainer
latent-diffusion vs DALLE2-pytorch