Hello everyone. I am Dr. Furkan Gözükara. PhD Computer Engineer. SECourses is a dedicated YouTube channel for the following topics : Tech, AI, News, Science, Robotics, Singularity, ComfyUI, SwarmUI, ML, Artificial Intelligence, Humanoid Robots, Wan 2.2, FLUX, Krea, Qwen Image, VLMs, Stable Diffusion
Hi, Doc. There is a typo error in Line-4: "Fine Tuning / DreamBooth: 256 Training Images & Batch Size is 7 : Best Epoch is 140 = 256 x 40 / 7 = 1480 steps : Duration is 11 hours 56 minutes = around 4 USD cost." It should say either '40 epochs' OR '256 x 140 / 7 = 5,120 steps'. But, I think you meant 40 epochs?
I was following the tutorial and ran a finetune on massed compute, unfortunately it stopped with "low disk space on root" and threw an error. What did I do wrong?
Do not skip any part of this tutorial to master how to use Stable Diffusion 3 (SD3) with the most advanced generative AI open source APP SwarmUI. Automatic1111 SD Web UI or Fooocus are not supporting the #SD3 yet. Therefore, I am starting to make tutorials for SwarmUI as well. #StableSwarmUI is officially developed by the StabilityAI and your m...
FLUX is the first time every an open source txt2img is able to truly surpass and produce better quality and better prompt following images than #Midjourney, Adobe Firefly, Leonardo Ai, Playground Ai, Stable Diffusion, SDXL, SD3 and Dall E3. #FLUX is developed Black Forest Labs and its team is mainly composed by the original authors of #StableDi...
another short noob question. I now have a flux model finetuned to a person. I can generate images with that person in it. However as soon as I want another person in there (lets say finetuned person shaking queen elizabeths hand) the finetuned person is not in the image but a random person.
Is that a limiation of the model/finetuning or am I doing something wrong?
I had surmised earlier that maybe flux_dev fails to learn multiple poses at the same time, so I tried training flux_dev_de_distill, but that did not work either.
Right now, I am lora training a single pose, E.g., 'ohwx pose'. 15 images x 320 epochs (Rank1 settings & all images captioned as ohwx pose). If that does not work either, I'm not sure what will.
Doc, had a question regarding dataset preparation! What if one artificially manipulated the background on the dataset to make it more varied (E.g., using photoshop or lightroom? Will the trainining pick up edge artifacts?