Minta K
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Congrats :D
It will focus on dataset curation through training on a pre-determined style to give a better insight on my process.
Curious what are some questions you might have that I can try to answer in it?
Midsommar Cartoon
A playful cartoon style featuring bold colors and a retro aesthetic. Personal favorite at the moment.
alvdansen/midsommarcartoon
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Wood Block XL
I've started training public domain styles to create some interesting datasets. In this case I found a group of images taken from really beautiful and colorful Japanese Blockprints.
alvdansen/wood-block-xl
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Dimension W
For this model I did actually end up working on an SD 1.5 model as well as an SDXL. I prefer the SDXL version, and I am still looking for parameters I am really happy with for SD 1.5. That said, both have their merits. I trained this with the short film I am working on in mind.
alvdansen/dimension-w
alvdansen/dimension-w-sd15
I typically use Kohya, but I also test a lot of platform services for the right one because I am a creature of comfort :)
I need to double check the train_text_encoder_frac as I typically don't mess with that. For rank I'm usually at 32.
Here I take a somewhat strong stance and am petitioning to revisit the default training parameters on the Diffusers LoRA page.
In my opinion and after observing and testing may training pipelines shared by startups and resources, I have found that many of them exhibit the same types of issues. Upon discussing with some of these founders and creators, the common theme has been working backwards from the Diffusers LoRA page.
In this article, I explain why the defaults in the Diffuser LoRA code produce some positive results, which can be initially misleading, and a suggestion on how that could be improved.
https://huggingface.co/blog/alvdansen/revisit-diffusers-default-params
No - I change them however itβs very case by case. I am trying to emphasize elements other than hyperparameters, because in my experience these concepts apply to a variety of hyperparameters.
Here is fine also, I will check later
Are you on X? You can contact me there @araminta_k
I have added further observations here:
https://huggingface.co/blog/alvdansen/enhancing-lora-training-through-effective-captions
π
I will need to take a look at what the exact backend of face to all is. What is the result youβre getting ?
I've been asked a lot of share more on how I train LoRAs. The truth is I don't think my advice is very helpful without also including more contextual, theoretical commentary on how I **think** about training LoRAs for SDXL and other models.
I wrote a first article here about it - let me know what you think.
https://huggingface.co/blog/alvdansen/thoughts-on-lora-training-1
Edit: Also people kept asking where to start so I made a list of possible resources:
https://huggingface.co/blog/alvdansen/thoughts-on-lora-training-pt-2-training-services
Thank you!
I intend to start writing more fully on the thought process behind my approach to curating and training style and subject finetuning, beginning this next week.
Thank you for reading this post! You can find the models on my page and I'll drop a few previews here.
thank you <3
π Thank you so much for sharing! I will be sharing a training workflow in the coming week :D