kohya-trainer
LoRA
kohya-trainer | LoRA | |
---|---|---|
36 | 34 | |
1,772 | 9,172 | |
- | 4.7% | |
8.3 | 4.7 | |
about 2 months ago | 9 days ago | |
Jupyter Notebook | Python | |
Apache License 2.0 | MIT License |
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kohya-trainer
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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
LoRA
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DECT NR+: A technical dive into non-cellular 5G
This seems to be an order of magnitude better than LoRa (https://lora-alliance.org/ not https://arxiv.org/abs/2106.09685). LoRa doesn't have all the features this one does like OFDM, TDM, FDM, and HARQ. I didn't know there's spectrum dedicated for DECT use.
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Training LLMs Taking Too Much Time? Technique you need to know to train it faster
So to solve this, we tried researching into some optimization techniques and we found LoRA, Which stands for Low-Rank Adaptation of Large Language Models.
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OpenAI employee: GPT-4.5 rumor was a hallucination
> Anyone have any ideas / knowledge on how they deploy little incremental fixes to exploited jailbreaks, etc?
LoRa[1] would be my guess.
For detailed explanation I recommend the paper. But the short explanation is that it is a trick which lets you train a smaller, lower dimensional model which when you add to the original model it gets you the result you want.
1: https://arxiv.org/abs/2106.09685
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Can a LoRa be used on models other than Stable Diffusion?
LoRA was initially developed for large language models, https://arxiv.org/abs/2106.09685 (2021). It was later that people discovered that it worked REALLY well for diffusion models.
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StyleTTS2 – open-source Eleven Labs quality Text To Speech
Curious if we'll see a Civitai-style LoRA[1] marketplace for text-to-speech models.
1 = https://github.com/microsoft/LoRA
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Andreessen Horowitz Invests in Civitai, Which Profits from Nonconsensual AI Porn
From https://arxiv.org/abs/2106.09685:
> LoRA: Low-Rank Adaptation of Large Language Models
> An important paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains. As we pre-train larger models, full fine-tuning, which retrains all model parameters, becomes less feasible. Using GPT-3 175B as an example -- deploying independent instances of fine-tuned models, each with 175B parameters, is prohibitively expensive. We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks. Compared to GPT-3 175B fine-tuned with Adam, LoRA can reduce the number of trainable parameters by 10,000 times and the GPU memory requirement by 3 times. LoRA performs on-par or better than fine-tuning in model quality on RoBERTa, DeBERTa, GPT-2, and GPT-3, despite having fewer trainable parameters, a higher training throughput, and, unlike adapters, no additional inference latency.
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Is supervised learning dead for computer vision?
Yes, your understanding is correct. However, instead of adding a head on top of the network, most fine-tuning is currently done with LoRA (https://github.com/microsoft/LoRA). This introduces low-rank matrices between different layers of your models, those are then trained using your training data while the rest of the models' weights are frozen.
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Run LLMs at home, BitTorrent‑style
Somewhat yes. See "LoRA": https://arxiv.org/abs/2106.09685
They're not composable in the sense that you can take these adaptation layers and arbitrarily combine them, but training different models while sharing a common base of weights is a solved problem.
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New LoRa RF distance record: 1336 km / 830 mi
With all the naive AI zealotry on HN can you really fault me?
They're referring to this:
https://arxiv.org/abs/2106.09685
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Open-source Fine-Tuning on Codebase with Refact
It's possible to fine-tune all parameters (called "full fine-tune"), but recently PEFT methods became popular. PEFT stands for Parameter-Efficient Fine-Tuning. There are several methods available, the most popular so far is LoRA (2106.09685) that can train less than 1% of the original weights. LoRA has one important parameter -- tensor size, called lora_r. It defines how much information LoRA can add to the network. If your codebase is small, the fine-tuning process will see the same data over and over again, many times in a loop. We found that for a smaller codebase small LoRA tensors work best because it won't overfit as much -- the tensors just don't have the capacity to fit the limited training set exactly. As the codebase gets bigger, tensors should become bigger as well. We also unfreeze token embeddings at a certain codebase size. To pick all the parameters automatically, we have developed a heuristic that calculates a score based on the source files it sees. This score is then used to determine the appropriate LoRA size, number of finetuning steps, and other parameters. We have tested this heuristic on several beta test clients, small codebases of several files, and large codebases like the Linux kernel (consisting of about 50,000 useful source files). If the heuristic doesn't work for you for whatever reason, you can set all the parameters yourself.
What are some alternatives?
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
LyCORIS - Lora beYond Conventional methods, Other Rank adaptation Implementations for Stable diffusion.
sd_dreambooth_extension
ComfyUI - The most powerful and modular stable diffusion GUI, api and backend with a graph/nodes interface.
sd-webui-additional-networks
ControlNet - Let us control diffusion models!
stable-diffusion-webui-colab - stable diffusion webui colab
peft - 🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
fast-stable-diffusion - fast-stable-diffusion + DreamBooth
alpaca-lora - Instruct-tune LLaMA on consumer hardware
EveryDream-trainer - General fine tuning for Stable Diffusion
LLaMA-Adapter - [ICLR 2024] Fine-tuning LLaMA to follow Instructions within 1 Hour and 1.2M Parameters