Kohya trainer reddit Hi all, So I notice that in realistic Lora training, when you caption a realistic image, it usually has "woman" and if you caption an illustration or anime image, it usually has "1girl", that is if there is female in the image of course Looking for some advices how to speed up my LORA training (SDXL 1. TLDR: - If you have one concept/one folder in kohya, set your repeats to one (1_) and control your steps with epochs, not repeats. I am able to train 4000+ steps in about 6 hours. But there's a caveat, its a slow training scheduler because its doing that heavy lifting for you. ss_session_id: "2321401183", ss_shuffle_caption: "False", After a multiple tries, I wasn't able to get training down to a reasonable speed. Duplicate the data set, do one in square aspect ratio 512x512, and the other in portrait, 512x768. Oh another Caveat, I prefer OneTrainer's implementation of prodigy versus kohya_ss, for kohya i always used the manual schedulers as they gave better In Kohya_SS, set training precision to BF16 and select "full BF16 training" I don't have a 12 GB card here to test it on, but using ADAFACTOR optimizer and batch size of 1, it is only using 11. I wanted to try out SDXL 1. Hi my laptop specs are: i7-8750H - 6 cores (x2 threads). Settings that use more VRAM than you have can cause the trainer to start using RAM, which is significantly slower. So far I used the trainer with SDXL basemodel, but i'd like to train new Loras using Ponydiffusion. I recently discovered that you can create your own LoRas locally if you have enough GPU power. Although this time I heard a little bit of my video card fan raising the volume in the first minute of training. I really learned a lot from the first part (dataset preparation)! I have never had good luck training a subject LORA while not describing the subject, either with natural language or booru tags ( depending on what the model uses). During this time, I’ve trained dozens of character LORAs with kohya and achieved decent results. We would like to show you a description here but the site won’t allow us. I'm on Arch linux and the SD WebUI worked without any additional packages, but the trainer won't use the GPU. Also, as this was my first attempt at LoRA training so at the time I was using Aitrepeneur's horrible settings from his old low VRAM training video, which nobody should use. In the Kohya_ss folder open "requirements_windows_torch2. Kohya would usually take around 35 mins to train 1500 steps of a 768x lora while one trainer does it in 10 mins. Training will continue without caption for these images". 5, I used the SD 1. Of course there are settings that are depended on the the model you are training on, Like the resolution (1024,1024 on SDXL) I suggest to set a very long training time and test the lora meanwhile you are still training, when it starts to become overtrain stop the training and test the different versions to pick the best one for your needs. I would try network dimensi Well I don't think for now something much better than kohya, at least idk better (or working) aproch in comfy. To actually use multiple gpu's for training you need to use accelerate scripts manually and do things without a UI. Any idea? 7 days till release. 99 driver is more than 2x times slower on my 3060 Eagle OC 12 gb!!! I wish ee could just switch deiver version now… course i really like higher Vram from new driver for image gen with SD XL but Kohya training is horrible… Anyways check your driver version… PS makes me wonder if on DRR5 Ram speed is diferent from my DDR4 ram As i said that’s how kohya outputs it. 0 strength and I couldn’t believe my eyes how much improved the merged lora was. The possible caveat to that is any settings which changes over time, LR warmup, Stop text encoder training, etc. 00005 Unet LR 0. Let me know if it worked. people say onetrainer, but I've been using kohya_ss. Tensorboard just provides logging capabilities. I can get batch size 8 using adafactor on my 4090. (Like many, utterly new to this. I find it helpful to include regularization images at a 1:4 ratio to training images*. By the time you have complied your training set, configured kohya, and performed the training, you'll have a lora/finetune that is valid for 6 days. Did something change or happen that caused Kohya to no longer be relevant? Training is basically just showing a computer some pictures, and telling it what is in the image (using text). It turns out that Kohya scripts were not training Text Encoder for a long time for SD 1. If you resumed training and stopped it once it reaches the original number of training steps minus the steps completed during the first training, you'll have a model that was trained the same amount of steps as one run. . It is possibly a venv issue - remove the venv folder and allow Kohya to rebuild it. txt Mixed Precision = fp16 Save Precision = fp16 I'm going to assume you know how to do the basic steps like training a LORA in the below - this is an outline only - for the sake of protecting my job and not breaking an NDA. and even reducing the number of images for the program to train with. Last I used Kohya it didn't support turning off the TENC l, if that's still the case don't train the TENC at all. Make sure to crop your images to fit your training resolution. 09s/it. What's happening right now is that the interface for DB training in the AUTO1111 GUI is totally unfamiliar to me now. If someone is training a particular person, you are showing the computer images of that person so it will learn that person. I don't see in the gui how to actually use it though. 0 in the setup (not sure if this is crucial, cause now stable diffusion webui isnt functioning (needs torch 2. With Kohya if you don’t start your image folder with a number and underscore it breaks. /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper moderation, and exclude blind users from the site. I have to reinstall kohya_ss or that bytesandbits software, it's so annoying. 0, I used the common AdamW training with constant sheduler and a LR / UNET / TE of 3e-05 ( training is in full B16 with NO optimations like shuffle captions, random crop, color AUG etc I've been playing with Kohya_ss gui Lora trainer and it seems like it takes around 2-7 hours to train one model. So I collected about 50 images of manly parts from various sources to start the training. The faster you drive the quicker you'll get there. Your still best off (imho) using kohya's script to do SDXL dreambooth training. Think of the training rate like the speed you are driving your car. But now when I open gui-user. I thought I was doing something wrong so I kept all the same settings but changed the source model to 1. I know it's better to have PNG files because they're lossless but unfortunately, I couldn't find those same pics with this format. Does anyone know of a good beginner's guide in any format that is recent and not exclusively concerned with SDXL? This will not be noticeable until the UNET catches up so training often looks like it's going good. My preferred method is my RTX A4500 with Kohya. Settings used for . Apr 11, 2025 · I’m training my first LoRA (with my face) and after reading a lot about the right/best way to do it, I have some questions and I’m hoping to find answers here. 99 08/08/23, not tested on older drivers. When you boot up OneTrainer, select the SD1. In fact, don't use the captions at all - just use the folder name "1_[something]" where [something] is what you want to prompt. This might be different if you do not need to use split_mode with kohya or if you have a lot faster PCIe and RAM than I have (which is stressed by split_mode as far as I can tell). Aug 4, 2024 · I get 200s/it with the kohya flux lora while I get 1. When doing OneTrainer training, I added a second concept and used it as a regularization images. I've been training with Dreambooth in Colab for months which has worked well, but I want to also try LoRA. Needless to say that the caption TXT. Instead, I recommend studying this video. That’s how it is, you can’t make everyone happy. I have a couple of questions regarding the relationship between the tags used as part of training set directory names, and the text prompts associated with each training image. Other Lora's work fine in SD. It also has built in masking and captioning tools to make your workflow easier. Although that may be true and it can be ignored, it does cutdown on training time. Share and showcase results, tips, resources, ideas, and more. As far as I am aware. This current config is set to 512x512 so you'll need to reduce the batch size if your image size is larger. 5 I'm playing around with Kohya just to test out things and what it would mean to train a LoRA. Accelerate is. I am doing some training of Lora models, using the Kohya GUI. When training, kohya only generates blank images. 2 (seems helpful with data streaming "suspect resize bar and/or GPUDirect Storage" implamentation currently unknown). 11 votes, 28 comments. and, btw, I can't understand 10 epochs 52 hours, 3 epochs 32. I recommend learning dreambooth training with the extension only because the user interface is just much better. bat and then upgrade. Much will change with 1. I've tried leaving stable diffusion open in the background, closed. The workflow is : prepare data -> prepare training -> train -> generate Lots of factors here , resolution of training images, how disprportional is some camera pictures, tags, picture lighting, disliked looks by the owner, prompting, generation resolution etc etc After that, it's a case of having good training images. here is a link to a really good tutorial that explains the settings, for training on a person at least, but it will still give you an idea on how to train with kohya which in my experience has been the best way to train a good lora Some quick infos about the training : base model is the SDXL base model 1. 2 again. When installing Kohya, should I pick torch 1 or 2? Also fp16,bf166 or option no. As for success, someone said they managed to train a LoRA this way, there are even some on Civitai already, but as it's written on the Github page, training is highly unpredictable. Also keep in mind that aspect ratio matters a lot so generate using the training AR or else make sure you get a variety of AR in the training set (even including duplicates with various cropping). 28 hours I was instructed to use the same seed for each image and use that seed as the seed listed in the Kohya GUI for training. For example, it’s much easier to see a loss graph, learning rate curve, sample outputs, and pause training. There are lots of branches a training could go like genetic selective algorithm. PARAMETERS - TRAINING PARAMETERS. It looks like Automatic1111 seems to have training tools built into it now? I'm trying to train LoRAs on my RX6700XT using the scripts by derrian-distro which use the Kohya trainer, just make it simpler. txt" in a text editor. But also, the faster you drive the more likely it is that you'll miss your parking lot. Let's say I had 16 training images, then 8 steps at batch size 2 would cover all the images and that would be 1 epoch. It is what helped me train my first SDXL LoRA with Kohya. You really think it's worth the effort for 6 days? After training 100s model with dreambooth or Loras, i am now ready to try actual Finetuning with kohya or everydream trainer2. I know what a VAE is but why would you want to "replace for training" it? Keep n tokens, what does that do? Max Token Length, 75, 150 or 225, what's better? Most seem to use 75. Although it does lose the (overfit) exact style of specific training images a bit, that is for the most part a good thing, as it makes way more detailed and diverse results. Are some parameters different for training a LoRA on Pony? Most of my parameters are Kohya default: Images: 42 Repeats 20 Epoch: 3 Batch 2 LR 0. For best efficiency When… Trying to balance some new parameters out with kohya_ss, results are so so. Then I tried cutting down my dataset size, training steps per image, and only used 1 epoch. I'm retraining using as many of the configs I can copy over from the collab LoRA TOML config. it took 13 hours to complete 6000 steps! One step took around 7 seconds to complete I tried every possible settings, optimizers. i was getting 47s/it now im getting 3. Redownload xformers seemed to have work for other users in the guide version 1. But still very slow. its all depends on dataset quality, subject type and latent weights compatibility. Love it I have noticed that one of the first messages in the command window after starting the training is ""No caption file found for xx images. Does anyone have any recommendations for other LoRA model training tools? The second tutorial is about training the LoRA model itself. com/@yushantripleseven/dreambooth-training-sdxl-using-kohya-ss-windows-7d2491460608 I've seen a small improvement in training speed after forcing a kohya webui upgrade. I've tried training a LORA locally with my RTX 3090 Nothing fancy 20 pictures/ 600 regularization images 1024 resolution following the only tutorial I've found on SECourse. With batch size 2, you should be getting about 4 seconds / iteration. And kohya implements some of Accelerate. I wanted to check if Colab is any faster and there I'm… I'm new to training LORA's, but have been getting some decent results in Kohya_ss, up to the point I'm quite satisfied with the results that I'm getting in the preview images that are generated during training. Training a full character LoRA takes about 15-20 minutes. After opening py file click Raw button to right of page on gith 2 things converted me from Kohya to one trainer: masked training and the speed. Is the slow inference possibly due to insufficient VRAM or RAM? You could try training LoRA with a lower dim value, or consider using a machine with more VRAM and RAM for inference. Supposedly, this method (custom regularization images) produces better results than using generic images. You can see how to use these Kohya configurations at this video: https://youtu. Nothing seems to work. Watched many tutorials on YouTube, after watching I notice there are many mixed and opposite practises and guides. true. Still can't get the training below 30 hours. After training 100s model with dreambooth or Loras, i am now ready to try actual Finetuning with kohya or everydream trainer2. You can train SDXL LoRAs with 12 GB. I'm trying training a LORA with Kohya with a pc mounting a 4070 super (12GB Vram) for the training, it needs 8000 steps and more than 48 hours it crashes after 27 hours 10 epochs What can I reduce or fix? I've tried training with OneTrainer but the result isn't that good. I think the training seed is only to get the training samples keeping the same seed, but correct me if I'm wrong. So for me I found the choose folder method of one trainer to be way way more intuitive. 9 won't be valid. Apr 4, 2024 · When training with kohya, train images repeating was 150 and trained for 1 epoch. For example, you can log your loss and accuracy while training. everything else looks fine too, except you should keep buckets enabled. I read many papers on how to do actual finetuning, but i was wondering how do you guys do it? Dataset preparation, number of images, captions, parameters? There are probably other people that do a lot more training than me and can give better advice, but I go with the standard 100 epochs and since I use batch size 2 I always make sure I have an even number of training images. Captions are hugely important, if you are using a photorealistic model you will probably want to manually caption for such a small training set, BLIP is simply not good at auto captioning. So the new 536. Training with a constant factor is like driving with a constant speed: /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper moderation, and exclude blind users We would like to show you a description here but the site won’t allow us. 16 GB RAM. I posted this in other thread but diffusers added training support so you can test it out now. And if they decided this is the best way to “explain” the speed, that’s what I’ll use. It has some features that Kohya_ss doesn't (like masked training so you can avoid training backgrounds), and has a much easier to understand interface. Let me explain epochs in Kohya and why they're helpful. Restrict it to 20-30% only. The batch size was tweaked until I filled my VRAM. which is in kohya_ss folder itself. I had downloaded the kohya_ss or whatever tools and attempted to run them. As for the regularization images, they are intended to be generated by the model you are training against, and representing the class. 0001 All are default parameters. 200 images of "a photo of a man". I have a preset where i just run all the time. So if you don't mind longer training times, Prodigy is a great. 5 based versions Moreover it never trained Text Encoder However even though Text Encoder were never trained still it is a beast with only rare token + class token Auto adjust means the learning rate will start to ratchet down. You cannot avoid overfit training it for the amount of steps the UNET needs to train properly. 19s/it after a few checks, repairs and installs, im using the latest nvidia gpu drivers 536. I apologise if these are obvious, or well documented. lets say: high LR = low epoch (good for when SD model knows the subject or style, like a common face or a car or style, but this requires constant checks for if epoch is overshoot or under, so even 1 or 2 epochs can overshoot easily) For a long time, I had no idea what the various options on Kohya did and searching Google didn't get me much either for many of them. so about 1500 steps, which is usually a good number. The Kohya SS GUI seemed to be the way people were doing it. Also anyone who is training in kohya or dreambooth or whatever knows this. Hi, I use Linaqruf's Colab Kohya trainer XL for SDXL (… /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper moderation, and exclude blind users from the site. kohya_ss GUI's UI as been updated a bit since the tutorial was made, but not so much that it is problematic. 0 LoRA training and found out that my RTX 3060 gets only 21. But the times are ridiculous, anything between 6-11 days or roughly 4-7 minutes So I am new to training LORA using Dreambooth, and what I see throughout multiple variations of settings the loss doesn't go down, instead it keeps oscillating around some average, but the samples that I check look better the more steps I am in. Got the Kohya GUI working Selected 20+ sample training images of a subject (person from various angle and clothing) (for now I didn't put any regulation images) Selected LoRA tab and used GUI to set up the folders (img, log, model) I wish there was a rock-solid formula for LoRA training like I found in that spreadsheet for Dreambooth training. Huge time saver on that front. To train my own model, and if it’s any good publish it. Hi, i had the same issue, win 11, 12700k, 3060ti 8gb, 32gb ddr4, 2tb m. My understanding is that Kohya takes this folder name and uses the first part for the number of repeats and the second part for my instance prompt. Y u all buying "gaming" cards? You are training and using AI, not gaming. Since the model I'm training my LoRA on is SD 1. Tick or untick the box for "train text encoder. There is a possibility that your dataset or captions are messed up, but let's put that aside for a second. SDXL LoRA, 30min training time, far more versatile than SD1. txt" and save. the config TOML file wasnt exactly the same but the learning rate was also off. Once you have a dataset and Kohya installed, it will take you less than 10 minutes to start a LORA each time. And because of masked training, the results are better. Go to the "LORA -> TRAINING -> PARAMETERS -> BASIC" tab and fill the fields as stated below (I'm not listing ALL the fields, only the ones you'll need to change): Train Batch Size = 1 Epoch = 10 Save Every N epochs = 1 Caption extension = . However, I’m still interested in finding better settings to improve my training speed and likeness. Sep 17, 2024 · On my 3060 using 512 as resolution gives me 3,5-3,7 s/it with OneTrainer while i got 9,5 s/it with the ComfyUI Flux Trainer (which is a kohya wrapper). The creator of the tutorial discusses the relevant settings in kohya_ss GUI, which is quite helpful. bat, a CMD window opens and closes at the same time. And was added to kohya ss gui and original kohya ss scripts. if you don't have training images of different sizes/dimensions, it simply won't change anything. Network dimension of 256 seems pretty high, but 256 for network alpha seems to be WAY, WAY too high. It's official Windows GUI for Kohya scripts, nothing unsafe there. However, if you are training with captions or tags much different than what SDXL knows, you may need to train it. Training about 65-100 images 5 passes, 10 epochs, save every other epoch then pick whichever one seems best. I tried running the Kohya LoRA colab but there are so many settings I have no clue what to do, none of it makes sense to me. I tried unet only, no buckets, 768 resolution, and experimenting with different optimizers. The kohya ss gui dev baltamis mentions it's technically just a lora parameter. Anyone having trouble with really slow training Lora Sdxl in kohya on 4090? When i say slow i mean it. Since I have a fairly powerful workstation, I can train my own Dreambooth checkpoints and then extract a LoRA from them. In Kohya, the training images folder has the structure "5_myconcept". Over the past many years, I've subscribed to various pretty girl style subreddits, and when I see a pretty I'll just start by saying i have a 3090 ti. If i were to translate into it/s for example i would confuse someone else. etc Vram usage immediately goes up to 24gb and it stays like that during whole training. 5 as this goes very quickly. py file too. Also, there are no solutions that can Thank you, I think I understand better now. files are in the training folder because they were copied with the "Prepare training data" button in the GUI. But - it looks like there is lots of web content talking about it right up to about 8 months ago. I’m trying to learn LoRA training (via kohya-ss) by doing some character LoRAs, and I'm struggling with aspects of a character that aren’t always consistent. I've trained some LORAs using Kohya-ss but wasn't very satisfied with my results, so I'm interested in training full dreambooth models using Kohya-ss at this point. You can observe this by plotting the learning rate vs the loss in kohya_ss. Persistent data loader, what's that? V Pred like loss, what's that? incase you are using the user based LoRa trainer and having a similar issue^ switching to torch 2. Fine tuning process with kohya is similar to training a LoRA, except you have 1 folder with images and you set how many repeats in the parameters. You really think it's worth the effort for 6 days? But it took Kohya 2 tabs and about 7 collapsable bars to embrace all the details of the lora training process. This is better than using several training folders with various repeats # and better than training with the high quality images only. In your case, it doesn't say it's out of memory. Training the text encoder will increase VRAM usage. With the same data set and similar settings (33 images, 10 repeats, PRODIGY optimizer, 10 epochs, about 3000 steps, Batch 2 - 1024x1024) it took about 55(!) hours to train the LoRA for SDXL!! That's insane more time!! I also found some information on how to supposedly train a LORA on my own machine (I only have an RTX2060 with 8gb of VRAM though) but so far when I attempted that I got some strange python errors. While searching through the GitHub Repo for what "VAE batch size" was, I finally found the trove that is the LoRA Options Documentation page: yeah, do 22 images, 40 steps, 2 epochs, when you set your steps, it creates a folder inside Lora folder on left side where file explorer is, dig into it, youll find a folder named 40_nameofyourthingy , you have to put images in there, pick model 1. 6 GB of VRAM, so it should be able to work on a 12 GB graphics card. " For large finetunes, it is most common to NOT train the text encoder. Welcome to the official subreddit of the PC Master Race / PCMR! All PC-related content is welcome, including build help, tech support, and any doubt one might have about PC ownership. ) Link: Linaqruf/kohya-trainer: I only see it always got errors with : ERROR: pip's dependency resolver does not currently take into account all the… Skip to main content Open menu Open navigation Go to Reddit Home Training is a very involved process with a lot of moving parts and philosophies so it would take a book to talk about everything, but this should get you started in the right path. Training and Samples: For the sample images during training that it spits out. 0 and 1. VAE (Optional) path to checkpoint of vae to replace for training. DreamBooth is a method by Google AI that has been notably implemented into models like Stable Diffusion. 5 checkpoint and DDIM sampling method. Nevertheless, I'm interested in training LoRA models directly. 0. I'm getting decent speeds finally training LORA models in Kohya_ss. I've always gotten much more usable, and easier to use results from having a very descriptive training set for the subject. Add the following line before "-r requirements. Become A Master Of SDXL Training With Kohya SS LoRAs - Combine Power Of Automatic1111 & SDXL LoRAs - 85 Minutes - Fully Edited And Chaptered - 73 Chapters - Manually Corrected - Subtitles youtube upvote · comment We would like to show you a description here but the site won’t allow us. It cannot tell you how long each CUDA kernel takes to execute. I'm using kohya-LoRA-trainer-XL for colab in order to train SD Lora. I watched this video where he takes just 6 minutes! I'm obviously using an older card (I'm on a 1080ti with 12gb vram) but surely it shouldn't be THAT much slower? Currently I am training LoRAs for SD 1. 1 at this current time with build I have), turns out I wasnt checking the Unet Learning Rate or TE Learning Rate box) Problem with opening Kohya for training LORA I have just installed Kohya, following the instructions on its GitHub page, using the git command, opening setup. bat. when you have training images of different sizes/dimensions, it will process them together instead of separately. 0 using kohya ss). It's installed as part of the kohya-trainer folder, try deleting it and running 1. be/EEV8RPohsbw. To create py files, just open the py link from github page from koyha_ss github main page. so it's win/win leaving it We would like to show you a description here but the site won’t allow us. As for the other issues you're facing with training in Kohya, I don't know what the problem might be. SD is working fine, but the moment I tell it to use the custom Lora it only generates blank images. this is actually recommended, cause its hard to find /rare that your training data is good, i generate headshots,medium shots and train again with these, so i dont have any training images with hands close to head etc which happens often with human made art, this improves training a lot, or you can try to fix and inpaint first training set but its harder if you dont have that style already I've tried recently to create an embedding file and a Lora file with some images but, of course, my GPU couldn't carry on even when trying to minimize the resources used (minimal parameters, using CPU, 448x448 training images). 1070 8GB dedicated + 8GB shared. Notable changes that got me better performance - plus, please make the same thing, train_db . We are working with Kohya who is doing incredible work optimizing their trainer so that everyone can train their own works into XL soon on consumer hardware To clarify, Kohya SS isn't letting you set multi-GPU. 0001 Dim 8 Alpha 1 FP16 Adambitw8 Text encoder LR 0. Not new, but I would like to get way much better results way more often. Why? Why would you make the folder name a part of the training parameters. If I had to guess, there are probably some concepts that would still require captions and training the text encoder(s), but for most of us we can get away with a lot simpler training data. I am afraid, comfyui cannot satisfy picky users that want to have a full control over the training process. I don't know anything about . One tip is that you can upweight the training images you like most (or which are highest quality) by using multiple folders with a different number of steps. But I am still a little bit confused, all my training images are random resolution, 1000X2000, 1440X740etc. After a bit of tweaking, I finally got Kohya SS running for lora training on 11 images. Especially not the settings files he links in the video, which for some reason cause compatibility issues with Kohya now, but he hasn't bothered to take them down. 5 it will get it for you, I’ve been using SD for 4 months and SDXL since beta. Seen a couple of posts about triton and most people mention it's not needed for training with Kohya. I'm just starting out training LoRA on a 3090, never having done it before, and am getting lost in a plethora of out of date guides. But dreambooth/lora training methods cause the model to think that ALL people look like the person you are I tried training a lora with 12gb vram, it worked fine but took 5 hours for 1900 steps, 11 or 12 seconds per iteration. Before doing any of this - consider changing sleep timer on your computer to something large. The second tutorial is about training the LoRA model itself. First, understand epochs are arbitrary and not necessary. You absolutely won't need an a100 to start training this model. I have a bunch of JPEG images in my directory that I want to train with Kohya_ss. Ie. It seems to work well so far. Do any of you guys know a way to use KOHYA with an AMD GPU or maybe an alternative trainer that would? The Nvidia GPUs are really expensive and I dunno how long it will take me to save for one so I really don't want to wait if I don't have to even if it means doing a bunch of steps. Since then - silence. But yea kohya_ss breaking 2-3 times everytime another exenstion from sd or comfyui breaks the version. Can you please provide some guidance? pit wont work if you dont have original images it was trained iwth, it will just forget them and train on your new images, one time i accidentally replaced images and it just learned them instead of keep training old subject, well it is logical , so no, you wont be able to unless you can generate some images with that lora and include them in training set. (Usually 10, but have had a few interesting ones where 8 or 6 actually have better results. I make sure they are set to 1024x1024 in Kohya by adding --w 1024 --h 1024 --l 7 --s 20 to sample prompt section, the default of 512x512 size can't be trusted at lower res in SDXL so you should be good to go there with my cfg. A few months ago, Nvidia released a driver update allowing applications that consume all available VRAM to start using RAM to avoid crashes. The other difference is fine tuning is very slow in learning. Much of the training advice online is supremely terrible. Also no update to kohya_ss as well. However, I have done my research) My wife owns a small business and it is outside her abilities to go get new professional photos taken right now and she could desperately use some new pictures for her business website and social media postings. What I mean is they are simply a way to divide your training into chunks, so that you can output incremental models to check for over fitting. because I want to train a STYLE for both portrait and landscape, can I just throw all these random resolution images into khoya_ss without any cropping once I enable buckets? I want to train a lora on about 1000 images consisting of about 20 unrelated concepts. This is a great tutorial (posted it below also, but yea it'll get you going): https://medium. A couple of days ago I tried using kohya_ss on my 3070 locally, and it pretty much froze up my system to the point where I had to hard reset. However, tensorboard does not provide kernel-level timing data. To answer your question. 5 and suddenly I was getting 2 iterations per second and it was going to take less than 30 minutes. But some tools is existing, maybe not for training, but more flexible use (merging, some fine-tune etc) Hi! I just watched your video, but I quickly found I couldn't follow it because the kohya's trainer was updated and the interface has changed a lot. ipynb files but I'm guessing it's some sort of preset for Kohya? If it is, don't use it. 8s/it using other flux lora from civitai. :( I wonder if you have plans to make an updated video. Switch to the advanced sub tab. If I remember right that number at the beginning is supposed to indicate the number of epochs? I used kohya ss to merge them at 1. It seems it may give much better results than Lora. Prepare a Lora training data set for training your subject, TWICE. Oh also use One trainer over kohya_ss trainer, kohya is a shit trainer. This was bucketed training at 1mp resolution (same as the base model). 5 / SDXL LoRA training preset at the top. Does anyone have any recommendations for other LoRA model training tools? I am trying to train a LORA using the kohya v3 training guide on this sub, but it doesn't mention how to set a class name and a few other things that are apparently super important for getting good results. That is way too slow. Iconic “Accessories” - Some characters have iconic items or companions that frequently appear alongside them. ncxqjxwxvdvntkzipkfbattqeawpgygxfkjujofcdmngtmxnoorcgs