followed the steps and im only getting 1 .txt file even though I have many images. In the CMD prompt it describes all my images but only saves 1 .txt file that describes 1 random image .. any idea?
oh really? This AI toolkit resize them? Well, I got used to, just one of the practical steps that I did in SD and SDXL Lora. update: yes, the dataset prep section did mentioned : github.com/ostris/ai-toolkit?tab=readme-ov-file#dataset-preparation
I think this line is critical: Images with different dimensions will be trained for different aspect ratios. As we know, the assignment is based on one of the longest sides of the image. I believe that different dimensions will result in a different final outcome. (More combinations without overtraining) For example, a 768x1024 resolution might produce one result, while 896x1024 might yield another. This is because the model is trained on a different ratio, potentially resulting in better quality.
That's why in new IPAdapter, they mentioned 50k steps with 512, and 25k steps with 1024. Maybe if they don't prep dataset like the old way, it will might become better.
@@wereldeconomie1233 lol, you cant burn your gpu just because you dont have enough vram, why you are so "smart" i cant understand script will fail with oom, thats all
I did LORa training for FLUX, when I put more than one person in the image, it duplicates the LORA faces in all the characters that appear in the image, what can I do to avoid duplicating the faces of the LORA training?
Some say they don't do captioning and just submit images to the trainer. But I guess that will take longer time to train. I just like to prepare everything nicely for the dataset before train. Also resize image , in other comment, i know the trainer do resizing. But again it take longer time in the whole process.
@@FJKMIsotryFitiavanaSiteWeb Puedes editar el archivo .yaml para especificar otras rutas en las están los modelos. Por otro lado, si estas en Windows, también podrías utilizar el comando mklink para crear un vínculo entre distintas carpetas.
when i start running the training process, i get this error :( "ImportError: cannot import name 'apply_rope' from 'diffusers.models.attention_processor' (C:\Users\Frozen\AppData\Local\Programs\Python\Python312\Lib\site-packages\diffusers\models\attention_processor.py) I've reinstalled everything, but same error
The absolute minimum is 16 GB of VRAM on a gpu, integrated ones are out of question and is not possible whatsoever. I'm guessing some gpu with 24 GB of VRAM, and 64 GB of RAM with NVME storage is a necessity do train lora on flux, otherwise it's going to be a pain with lower requirements or literally impossible.
I'm getting error mesage 'No module nemed dotenv' There is not a .env file in the ai-toolkit directory, instead a folder called 'venv' which doesn't appear to be in your directory in the example
Learn how to use it : ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-EQZWyn9eCFE.html I put this link in the description and mentioned it already in the video.
Thanks I've been wanting to try a flux lora locally after trying for weeks to train a character lora in sdxl without much success. In sdxl when I use the loras each picture has elements of the original reference images but doesn't really look like the character. And this is even at 10000+ steps and prodigy set to LR of 1 😔
More like capability. Controlnet and ipadapter requires you to copy an image, lora lets you generate completely novel images. You can use LORAs to do what control net does but you can't use control net to do what a LORA can do.
@@daetojekf5973 for Flux sometimes you need to turn the lora weight really high, when you think it's too high keep going. Some of the ones I've downloaded from civit don't seem to have any effect until 2,3 or even 4 weight. And also maybe a dumb suggestion but make sure you're using the correct base model eg dev for a Dev lora, schnell for well you get the picture 🍀
i find it impossible to download the models. it starts downloading the model at 30mb/s and then it goes down to just a few Kbytes and stays at 99%. i have tried with different hugginface tokens (write, read finegrain....). i also leave the .yaml by default except the path where i indicate the directory of my dataset. by the way i have a 14900k 4090 and 128ram and windows 11
@@TheFutureThinker I mean, finetune model. Just like with the SD1.5 'realism' models, where they create mixes from each other. Some people focus on mixing, while others create their own models using a large number of images. Or this still not possible?
Why should I train "private" and LOCAL on my PC ... if Huggingface/BlackForestLabs get's my data anyways and knows everything.... And I think, I must be online for training (to start). ?!
But How can BlackForestLabs access your data if you train locally? The access token in the environment is only for accessing and pulling the model to your pc and ensuring it's not used for commercial purposes.
No chance. It's already a struggle to generate images on 8GB of VRAM (if even possible) but i don't think lora training will be possible on 8GB of VRAM, unless community does some insane optimization, it's a big model. I'm still struggling to find a way to train flux lora with 12 GB of VRAM, let alone 8GB.
Me resulta imposible descargar los modelos. comienza a descargar el modelo a 30 mb/s y luego baja a solo unos pocos Kbytes y se queda en el 99%. He probado con diferentes tokens de Hugginface (escribir, leer finegrain....). También dejo el .yaml por defecto excepto la ruta donde indico el directorio de mi conjunto de datos. Por cierto, tengo un 14900K, 4090 y 128RAM y Windows 11
Please Kohya next, they 've got it having good results with 500 steps. All my loras have been good (way better than sdxl and pony) with default settings using ai-toolkit but they take hours to go to 2000-3000 steps. As someone mentioned below fiddle with high weights, 1.5-2 have worked for me. Also comment out most of those samples when training, they take unnecessarily long. I want to bump up learning rate but not sure how high to go; I read somewhere that people have been using 4-e4 but I tried 1-e3 and it wen crazy after 500 steps so I was thinking even that would be too high.
