AnimateDiff, based on this research paper by Yuwei Guo, Ceyuan Yang, Anyi Rao, Yaohui Wang, Yu Qiao, Dahua Lin, and Bo Dai, is a way to add limited motion to Stable Diffusion generations. Still is a lot. You signed in with another tab or window. 0! In addition to that, we will also learn how to generate. $234. It can generate novel images from text descriptions and produces. To start running SDXL on a 6GB VRAM system using Comfy UI, follow these steps: How to install and use ComfyUI - Stable Diffusion. Lecture 18: How Use Stable Diffusion, SDXL, ControlNet, LoRAs For FREE Without A GPU On Kaggle Like Google Colab. Schedule (times subject to change): Thursday,. It's possible to train XL lora on 8gb in reasonable time. (UPDATED) Please note that if you are using the Rapid machine on ThinkDiffusion, then the training batch size should be set to 1 as it has lower vRam; 2. 2023. Fine-tuning Stable Diffusion XL with DreamBooth and LoRA on a free-tier Colab Notebook 🧨. 5, SD 2. OneTrainer is a one-stop solution for all your stable diffusion training needs. Your image will open in the img2img tab, which you will automatically navigate to. Just tried with the exact settings on your video using the gui which was much more conservative than mine. It needs at least 15-20 seconds to complete 1 single step, so it is impossible to train. DeepSpeed is a deep learning framework for optimizing extremely big (up to 1T parameter) networks that can offload some variable from GPU VRAM to CPU RAM. It. Checked out the last april 25th green bar commit. How to install #Kohya SS GUI trainer and do #LoRA training with Stable Diffusion XL (#SDXL) this is the video you are looking for. Training scripts for SDXL. Finally had some breakthroughs in SDXL training. 23. Augmentations. Do you have any use for someone like me? I can assist in user guides or with captioning conventions. Next as usual and start with param: withwebui --backend diffusers. For instance, SDXL produces high-quality images, displays better photorealism, and provides more Vram usage. SDXL Support for Inpainting and Outpainting on the Unified Canvas. copy your weights file to modelsldmstable-diffusion-v1model. Cosine: starts off fast and slows down as it gets closer to finishing. For those purposes, you. Constant: same rate throughout training. SDXL parameter count is 2. I think the key here is that it'll work with a 4GB card, but you need the system RAM to get you across the finish line. 5 based checkpoints see here . Well dang I guess. I heard of people training them on as little as 6GB, so I set the size to 64x64, thinking it'd work then, but. Finally had some breakthroughs in SDXL training. The results were okay'ish, not good, not bad, but also not satisfying. I'm using AUTOMATIC1111. 41:45 How to manually edit generated Kohya training command and execute it. MASSIVE SDXL ARTIST COMPARISON: I tried out 208 different artist names with the same subject prompt for SDXL. (For my previous LoRA for 1. I was expecting performance to be poorer, but not by. Used torch. Even less VRAM usage - Less than 2 GB for 512x512 images on ‘low’ VRAM usage setting (SD 1. You're asked to pick which image you like better of the two. do you mean training a dreambooth checkpoint or a lora? there aren't very good hyper realistic checkpoints for sdxl yet like epic realism, photogasm, etc. 0 is 768 X 768 and have problems with low end cards. This allows us to qualitatively check if the training is progressing as expected. My VRAM usage is super close to full (23. You can specify the dimension of the conditioning image embedding with --cond_emb_dim. Will investigate training only unet without text encoder. The rank of the LoRA-like module is also 64. but from these numbers I'm guessing that the minimum VRAM required for SDXL will still end up being about. There's no point. On average, VRAM utilization was 83. How To Do SDXL LoRA Training On RunPod With Kohya SS GUI Trainer & Use LoRAs With Automatic1111 UI. r/StableDiffusion. It's important that you don't exceed your vram, otherwise it will use system ram and get extremly slow. py, but it also supports DreamBooth dataset. I was impressed with SDXL so did a fresh install of the newest kohya_ss model in order to try training SDXL models, but when I tried it's super slow and runs out of memory. It is the successor to the popular v1. Automatic 1111 launcher used in the video: line arguments list: SDXL is Vram hungry, it’s going to require a lot more horsepower for the community to train models…(?) When can we expect multi-gpu training options? I have a quad 3090 setup which isn’t being used to its full potential. 46:31 How much VRAM is SDXL LoRA training using with Network Rank (Dimension) 32 47:15 SDXL LoRA training speed of RTX 3060 47:25 How to fix image file is truncated error[Tutorial] How To Use Stable Diffusion SDXL Locally And Also In Google Colab On Google Colab . 5 (especially for finetuning dreambooth and Lora), and SDXL probably wont even run on consumer hardware. And if you're rich with 48 GB you're set but I don't have that luck, lol. I have shown how to install Kohya from scratch. Around 7 seconds per iteration. 5 models and remembered they, too, were more flexible than mere loras. Here are my results on a 1060 6GB: pure pytorch. Reload to refresh your session. -Works on 16GB RAM + 12GB VRAM and can render 1920x1920. I tried the official codes from Stability without much modifications, and also tried to reduce the VRAM consumption using all my knowledges. Stable Diffusion is a latent diffusion model, a kind of deep generative artificial neural network. Each lora cost me 5 credits (for the time I spend on the A100). At the moment I experimenting with lora trainig on 3070. Generated enough heat to cook an egg on. 6 billion, compared with 0. I think the minimum. 0 and 2. 0 came out, I've been messing with various settings in kohya_ss to train LoRAs, as well as create my own fine tuned checkpoints. Also, as counterintuitive as it might seem, don't generate low resolution images, test it with 1024x1024 at. The LoRA training can be done with 12GB GPU memory. I tried recreating my regular Dreambooth style training method, using 12 training images with very varied content but similar aesthetics. Next Vlad with SDXL 0. The batch size determines how many images the model processes simultaneously. I uploaded that model to my dropbox and run the following command in a jupyter cell to upload it to the GPU (you may do the same): import urllib. The largest consumer GPU has 24 GB of VRAM. i miss my fast 1. Currently training SDXL using kohya on runpod. sh: The next time you launch the web ui it should use xFormers for image generation. AUTOMATIC1111 has fixed high VRAM issue in Pre-release version 1. Thank you so much. Dreambooth on Windows with LOW VRAM! Yes, it's that brand new one with even LOWER VRAM requirements! Also much faster thanks to xformers. 5, one image at a time and takes less than 45 seconds per image, But, for other things, or for generating more than one image in batch, I have to lower the image resolution to 480 px x 480 px or to 384 px x 384 px. radianart • 4 mo. For LoRA, 2-3 epochs of learning is sufficient. 6. And even having Gradient Checkpointing on (decreasing quality). We can adjust the learning rate as needed to improve learning over longer or shorter training processes, within limitation. Full tutorial for python and git. Despite its powerful output and advanced architecture, SDXL 0. Got down to 4s/it but still if you got 2. Based on a local experiment with GeForce RTX 4090 GPU (24GB), the VRAM consumption is as follows: 512 resolution — 11GB for training, 19GB when saving checkpoint; 1024 resolution — 17GB for training, 19GB when saving checkpoint; Let’s proceed to the next section for the installation process. Modified date: March 10, 2023. Supported models: Stable Diffusion 1. Don't forget your FULL MODELS on SDXL are 6. 5 is about 262,000 total pixels, that means it's training four times as a many pixels per step as 512x512 1 batch in sd 1. although your results with base sdxl dreambooth look fantastic so far!It is if you have less then 16GB and are using ComfyUI because it aggressively offloads stuff to RAM from VRAM as you gen to save on memory. The generation is fast and takes about 20 seconds per 1024×1024 image with the refiner. Model downloaded. 5. As for the RAM part, I guess it's because the size of. You must be using cpu mode, on my rtx 3090, SDXL custom models take just over 8. We can adjust the learning rate as needed to improve learning over longer or shorter training processes, within limitation. 0. 5, 2. com はじめに今回の学習は「DreamBooth fine-tuning of the SDXL UNet via LoRA」として紹介されています。いわゆる通常のLoRAとは異なるようです。16GBで動かせるということはGoogle Colabで動かせるという事だと思います。自分は宝の持ち腐れのRTX 4090をここぞとばかりに使いました。 touch-sp. Low VRAM Usage: Create a. I found that is easier to train in SDXL and is probably due the base is way better than 1. Successfully merging a pull request may close this issue. Batch Size 4. You want to use Stable Diffusion, use image generative AI models for free, but you can't pay online services or you don't have a strong computer. I was playing around with training loras using kohya-ss. 11. 動作が速い. PyTorch 2 seems to use slightly less GPU memory than PyTorch 1. 8GB of system RAM usage and 10661/12288MB of VRAM usage on my 3080 Ti 12GB. Cause as you can see you got only 1. 5 I could generate an image in a dozen seconds. probably even default settings works. 0, the various. If you remember SDv1, the early training for that took over 40GiB of VRAM - now you can train it on a potato, thanks to mass community-driven optimization. 7. json workflows) and a bunch of "CUDA out of memory" errors on Vlad (even with the. 0. First Ever SDXL Training With Kohya LoRA - Stable Diffusion XL Training Will Replace Older Models - Full Tutorial. Takes around 34 seconds per 1024 x 1024 image on an 8GB 3060TI and 32 GB system ram. Reply reply42. 6 GB of VRAM, so it should be able to work on a 12 GB graphics card. . I can train lora model in b32abdd version using rtx3050 4g laptop with --xformers --shuffle_caption --use_8bit_adam --network_train_unet_only --mixed_precision="fp16" but when I update to 82713e9 version (which is lastest) I just out of m. VRAM spends 77G. AdamW and AdamW8bit are the most commonly used optimizers for LoRA training. 7 GB out of 24 GB) but doesn't dip into "shared GPU memory usage" (using regular RAM). 3a. This reduces VRAM usage A LOT!!! Almost half. 99. Applying ControlNet for SDXL on Auto1111 would definitely speed up some of my workflows. download the model through web UI interface -do not use . Simplest solution is to just switch to ComfyUI. New comments cannot be posted. 0 is exceptionally well-tuned for vibrant and accurate colors, boasting enhanced contrast, lighting, and shadows compared to its predecessor, all in a native 1024x1024 resolution. 92GB during training. 9 Models (Base + Refiner) around 6GB each. 98. py script shows how to implement the training procedure and adapt it for Stable Diffusion XL. StableDiffusion XL is designed to generate high-quality images with shorter prompts. 32 DIM should be your ABSOLUTE MINIMUM for SDXL at the current moment. Since I've been on a roll lately with some really unpopular opinions, let see if I can garner some more downvotes. Alternatively, use 🤗 Accelerate to gain full control over the training loop. py training script. CANUCKS ANNOUNCE 2023 TRAINING CAMP IN VICTORIA. IXL is here to help you grow, with immersive learning, insights into progress, and targeted recommendations for next steps. Prediction: SDXL has the same strictures as SD 2. The SDXL base model performs significantly better than the previous variants, and the model combined with the refinement module achieves the best overall performance. The following is a list of the common parameters that should be modified based on your use cases: pretrained_model_name_or_path — Path to pretrained model or model identifier from. Then I did a Linux environment and the same thing happened. 9 can be run on a modern consumer GPU. Email : [email protected]. 1 to gather feedback from developers so we can build a robust base to support the extension ecosystem in the long run. Stable Diffusion web UI. @echo off set PYTHON= set GIT= set VENV_DIR= set COMMANDLINE_ARGS=--medvram-sdxl --xformers call webui. Tried that now, definitely faster. Cannot be used with --lowvram/Sequential CPU offloading. 0 will be out in a few weeks with optimized training scripts that Kohya and Stability collaborated on. As i know 6 Gb of VRam are minimal system requirements. I can train lora model in b32abdd version using rtx3050 4g laptop with --xformers --shuffle_caption --use_8bit_adam --network_train_unet_only --mixed_precision="fp16" but when I update to 82713e9 version (which is lastest) I just out of m. 24GB GPU, Full training with unet and both text encoders. 5% of the original average usage when sampling was occuring. The SDXL base model performs significantly better than the previous variants, and the model combined with the refinement module achieves the best overall performance. 4070 solely for the Ada architecture. 46:31 How much VRAM is SDXL LoRA training using with Network Rank (Dimension) 32 47:15 SDXL LoRA training speed of RTX 3060 47:25 How to fix image file is truncated error [Tutorial] How To Use Stable Diffusion SDXL Locally And Also In Google Colab On Google Colab . . bat as outlined above and prepped a set of images for 384p and voila. This guide provides information about adding a virtual infrastructure workload domain with NSX-T. -Pruned SDXL 0. SD Version 2. DreamBooth is a training technique that updates the entire diffusion model by training on just a few images of a subject or style. 5 to get their lora's working again, sometimes requiring the models to be retrained from scratch. Next. In this notebook, we show how to fine-tune Stable Diffusion XL (SDXL) with DreamBooth and LoRA on a T4 GPU. 5). I run it following their docs and the sample validation images look great but I’m struggling to use it outside of the diffusers code. train_batch_size: This is the size of the training batch to fit the GPU. 0. 直接使用EasyPhoto训练出的SDXL的Lora模型，用于SDWebUI文生图效果优秀 ，提示词 (easyphoto_face, easyphoto, 1person) + LoRA EasyPhoto 推理对比 I was looking at that figuring out all the argparse commands. It is the most advanced version of Stability AI’s main text-to-image algorithm and has been evaluated against several other models. 3. Shop for the AORUS Radeon™ RX 7900 XTX ELITE Edition w/ 24GB GDDR6 VRAM, Dual DisplayPort v2. ) Local - PC - Free. 🧨 DiffusersStability AI released SDXL model 1. WORKFLOW. I get errors using kohya-ss which don't specify it being vram related but I assume it is. Superfast SDXL inference with TPU-v5e and JAX. So, this is great. Fooocus is a rethinking of Stable Diffusion and Midjourney’s designs: Learned from. With Stable Diffusion XL 1. I’ve trained a few already myself. For speed it is just a little slower than my RTX 3090 (mobile version 8gb vram) when doing a batch size of 8. SDXL+ Controlnet on 6GB VRAM GPU : any success? I tried on ComfyUI to apply an open pose SD XL controlnet to no avail with my 6GB graphic card. 10 seems good, unless your training image set is very large, then you might just try 5. Similarly, someone somewhere was talking about killing their web browser to save VRAM, but I think that the VRAM used by the GPU for stuff like browser and desktop windows comes from "shared". Preview. Even after spending an entire day trying to make SDXL 0. Share Sort by: Best. This exciting development paves the way for seamless stable diffusion and Lora training in the world of AI art. Most ppl use ComfyUI which is supposed to be more optimized than A1111 but for some reason, for me, A1111 is more faster, and I love the external network browser to organize my Loras. And I'm running the dev branch with the latest updates. 0-RC , its taking only 7. 0:00 Introduction to easy tutorial of using RunPod. I did try using SDXL 1. Reload to refresh your session. (Be sure to always set the image dimensions in multiples of 16 to avoid errors) I have installed. With 3090 and 1500 steps with my settings 2-3 hours. Used batch size 4 though. batter159. Most ppl use ComfyUI which is supposed to be more optimized than A1111 but for some reason, for me, A1111 is more faster, and I love the external network browser to organize my Loras. Version could work much faster with --xformers --medvram. How much VRAM is required, recommended, and the best amount to have for training to make SDXL 1. One of the reasons SDXL (and SD 2. Using the Pick-a-Pic dataset of 851K crowdsourced pairwise preferences, we fine-tune the base model of the state-of-the-art Stable Diffusion XL (SDXL)-1. Some limitations in training but can still get it work at reduced resolutions. In this notebook, we show how to fine-tune Stable Diffusion XL (SDXL) with DreamBooth and LoRA on a T4 GPU. TRAINING TEXTUAL INVERSION USING 6GB VRAM. . You know need a Compliance. By design, the extension should clear all prior VRAM usage before training, and then restore SD back to "normal" when training is complete. Switch to the 'Dreambooth TI' tab. 5 and output is somewhat plain and the waiting time is 4. There's no official write-up either because all info related to it comes from the NovelAI leak. At the very least, SDXL 0. SDXL LoRA training question. since LoRA files are not that large, I removed the hf. Find the 🤗 Accelerate example further down in this guide. (5) SDXL cannot really seem to do wireframe views of 3d models that one would get in any 3D production software. 512x1024 same settings - 14-17 seconds. 5 on 3070 that’s still incredibly slow for a. check this post for a tutorial. This is the ultimate LORA step-by-step training guide, and I have to say this b. ) Google Colab — Gradio — Free. 7:42 How to set classification images and use which images as regularization images 536. 46:31 How much VRAM is SDXL LoRA training using with Network Rank (Dimension) 32 47:15 SDXL LoRA training speed of RTX 3060 47:25 How to fix image file is truncated errorAs the title says, training lora for sdxl on 4090 is painfully slow. Undi95 opened this issue Jul 28, 2023 · 5 comments. 5 Models > Generate Studio Quality Realistic Photos By Kohya LoRA Stable Diffusion Training - Full TutorialI'm not an expert but since is 1024 X 1024, I doubt It will work in a 4gb vram card. Introducing our latest YouTube video, where we unveil the official SDXL support for Automatic1111. Click to see where Colab generated images will be saved . 0, which is more advanced than its predecessor, 0. Four-day Training Camp to take place from September 21-24. ago. SDXL Lora training with 8GB VRAM. Which is normal. 1 = Skyrim AE. You may use Google collab Also you may try to close all programs including chrome. SDXL 1. May be even lowering desktop resolution and switch off 2nd monitor if you have it. While for smaller datasets like lambdalabs/pokemon-blip-captions, it might not be a problem, it can definitely lead to memory problems when the script is used on a larger dataset. 4 participants. finally , AUTOMATIC1111 has fixed high VRAM issue in Pre-release version 1. For the second command, if you don't use the option --cache_text_encoder_outputs, Text Encoders are on VRAM, and it uses a lot of VRAM. 12GB VRAM – this is the recommended VRAM for working with SDXL. 1. Folder structure used for this training, including the cropped training images is in the attachments. 5 and 30 steps, and 6-20 minutes (it varies wildly) with SDXL. DeepSpeed integration allowing for training SDXL on 12G of VRAM - although, incidentally, DeepSpeed stage 1 is required for SimpleTuner to work on 24G of VRAM as well. you can use SDNext and set the diffusers to use sequential CPU offloading, it loads the part of the model its using while it generates the image, because of that you only end up using around 1-2GB of vram. 0 is weeks away. In this case, 1 epoch is 50x10 = 500 trainings. Funny, I've been running 892x1156 native renders in A1111 with SDXL for the last few days. This will increase speed and lessen VRAM usage at almost no quality loss. But you can compare a 3060 12GB with a 4060 TI 16GB. For example 40 images, 15 epoch, 10-20 repeats and with minimal tweakings on rate works. With swinlr to upscale 1024x1024 up to 4-8 times. . I don't believe there is any way to process stable diffusion images with the ram memory installed in your PC. 2 GB and pruning has not been a thing yet. 5 locally on my RTX 3080 ti Windows 10, I've gotten good results and it only takes me a couple hours. and it works extremely well. If you wish to perform just the textual inversion, you can set lora_lr to 0. Stable Diffusion XL (SDXL) was proposed in SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis by Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Stable Diffusion XL（SDXL. VRAM settings. 4. 5 and 2. A very similar process can be applied to Google Colab (you must manually upload the SDXL model to Google Drive). IXL is here to help you grow, with immersive learning, insights into progress, and targeted recommendations for next steps. Launch a new Anaconda/Miniconda terminal window. SDXL 1. The training is based on image-caption pairs datasets using SDXL 1. Hi u/Jc_105, the guide I linked contains instructions on setting up bitsnbytes and xformers for Windows without the use of WSL (Windows Subsystem for Linux. This video shows you how to get it works on Microsoft Windows so now everyone with a 12GB 3060 can train at home too :)SDXL is a new version of SD. It's using around 23-24GBs of RAM when generating images. So my question is, would CPU and RAM affect training tasks this much? I thought graphics card was the only determining factor here, but it looks like a monster CPU and RAM would also contribute a lot. Higher rank will use more VRAM and slow things down a bit, or a lot if you're close to the VRAM limit and there's lots of swapping to regular RAM, so maybe try training ranks in the 16-64 range. . . Object training: 4e-6 for about 150-300 epochs or 1e-6 for about 600 epochs. conf and set nvidia modesetting=0 kernel parameter). Stable Diffusion --> Stable diffusion backend, even when I start with --backend diffusers, it was for me set to original. 10-20 images are enough to inject the concept into the model. From the testing above, it’s easy to see how the RTX 4060 Ti 16GB is the best-value graphics card for AI image generation you can buy right now. Click to see where Colab generated images will be saved . Fooocus is an image generating software (based on Gradio ). ) Automatic1111 Web UI - PC - FreeThis might seem like a dumb question, but I've started trying to run SDXL locally to see what my computer was able to achieve. In the database, the LCM task status will show as. It provides step-by-step deployment instructions for Dell EMC OS10 Enterprise. The chart above evaluates user preference for SDXL (with and without refinement) over SDXL 0. Finally got around to finishing up/releasing SDXL training on Auto1111/SD. Considering that the training resolution is 1024x1024 (a bit more than 1 million total pixels) and that 512x512 training resolution for SD 1. It's definitely possible. However, please disable sample generations during training when fp16. Tried SDNext as its bumf said it supports AMD/Windows and built to run SDXL. For running it after install run below command and use 3001 connect button on MyPods interface ; If it doesn't start at the first time execute againSDXL TRAINING CONTEST TIME!. No need for batching, gradient and batch were set to 1. Res 1024X1024. 0004 lr instead of 0. It'll stop the generation and throw "cuda not. the A1111 took forever to generate an image without refiner the UI was very laggy I did remove all the extensions but nothing really change so the image always stocked on 98% I don't know why. 4. safetensors. probably even default settings works. The usage is almost the same as fine_tune. Master SDXL training with Kohya SS LoRAs in this 1-2 hour tutorial by SE Courses. Is it possible? Question | Help Have somebody managed to train a lora on SDXL with only 8gb of VRAM? This PR of sd-scripts states that it is now possible, though i did not manage to start the training without running OOM immediately: Sort by: Open comment sort options The actual model training will also take time, but it's something you can have running in the background. Default is 1. after i run the above code on colab and finish lora training,then execute the following python code: from huggingface_hub. Training and inference will be done using the StableDiffusionPipeline class directly. If you’re training on a GPU with limited vRAM, you should try enabling the gradient_checkpointing and mixed_precision parameters in the. The base models work fine; sometimes custom models will work better. I have 6GB Nvidia GPU and I can generate SDXL images up to 1536x1536 within ComfyUI with that. 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. ControlNet. train_batch_size x Epoch x Repeats가 총 스텝수이다. There are two ways to use the refiner: use the base and refiner model together to produce a refined image; use the base model to produce an image, and subsequently use the refiner model to add more. • 3 mo. By using DeepSpeed it's possible to offload some tensors from VRAM to either CPU or NVME allowing to train with less VRAM. Currently training a LoRA on SDXL with just 512x512 and 768x768 images, and if the preview samples are anything to go by, it's going pretty horribly at epoch 8. I can generate images without problem if I use medVram or lowVram, but I wanted to try and train an embedding, but no matter how low I set the settings it just threw out of VRAM errors. Don't forget to change how many images are stored in memory to 1. Generate images of anything you can imagine using Stable Diffusion 1. i'm running on 6gb vram, i've switched from a1111 to comfyui for sdxl for a 1024x1024 base + refiner takes around 2m. All you need is a Windows 10 or 11, or Linux operating system, with 16GB RAM, an Nvidia GeForce RTX 20 graphics card (or equivalent with a higher standard) equipped with a minimum of 8GB. Next, you’ll need to add a commandline parameter to enable xformers the next time you start the web ui, like in this line from my webui-user. 0 and updating could break your Civitai lora's which has happened to lora's updating to SD 2. Future models might need more RAM (for instance google uses T5 language model for their Imagen). Set classifier free guidance (CFG) to zero after 8 steps. r/StableDiffusion. 4. With 3090 and 1500 steps with my settings 2-3 hours. I use. The next step for Stable Diffusion has to be fixing prompt engineering and applying multimodality. Note that by default we will be using LoRA for training, and if you instead want to use Dreambooth you can set is_lora to false. Max resolution – 1024,1024 (or use 768,768 to save on Vram, but it will produce lower-quality images). But after training sdxl loras here I'm not really digging it more than dreambooth training. Without its batch size of 1. If these predictions are right then how many people think vanilla SDXL doesn't just. Takes around 34 seconds per 1024 x 1024 image on an 8GB 3060TI. Learn to install Automatic1111 Web UI, use LoRAs, and train models with minimal VRAM. 9 doesn't seem to work with less than 1024×1024, and so it uses around 8-10 gb vram even at the bare minimum for 1 image batch due to the model being loaded itself as well The max I can do on 24gb vram is 6 image batch of 1024×1024. This requires minumum 12 GB VRAM. 36+ working on your system.