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
@JAV_Pomoe Personally, on my end and from what I see in all the guides, I get better results by training either with kohya_ss dreambooth or onetrainer and then extracting the lora from the finetuned model with koyha_ss. The resulting extracted lora seems better to me than one training directly trained as such. Plus, you also have the finetuned model you can keep too It's crucial to use the good settings for extracting the lora and finetuning the model though. For extracting the LORA, the settings can be found in an image at the very bottom of this patreon post However, do note that in the image, the Clamp Quantile and Minimum difference don't have red rectangels around them, but they do have different values than the default and you have to use those precisely.
Technique consisting in a fine-tuned AI model [WAN 2.1 / txt2vid]. Hundreds of hours of training and testing. Still far from good, but hopefully getting somewhere. I'm a huge fan of this technique.
Music by @klsr-av
You can access these new WAN 2.1 LORAs, through my Patreon profile.
looking for some advice on this regularization workflow for training this flux concept. my concept im training is faces pressed against glass. So i have 75 images of faces like the above. Then I have captioned each one without any mention of pressing against glass, and have generated regularization images with those captions so that each training image has a corresponding regularization image to help isolate the concept. With this workflow, do i want to keep repeats on both training and reg images at 1?
Honestly I'm always wondering how to know the number of epochs and such. Should we just aim for a certain # of total steps and play with the # of epochs and repeats?
For example, with 15 images 200-300 epochs but how many images repeats? Or is it better 1 epoch and tons of repeats?
Ahh I see. Then for sdxl is it better to use onetrainer instead in your opinion? Considering I have only 8 portrait images and the 5000 reg images of your patreon. Also unsure if I should use captions or not
Let's say I have portrait images that are mediocre quality and not 1024x1024. Which of the following is the best workflow:
Workflow A: joycaption,supir 5x upscale with caption,outpaint image sides,downscale to 1024x1024 Workflow B: joycaption,supir 5x upscale with caption,downscale to 1024x1024 with transparent sides Workflow C: joycaption,supir 5x upscale with caption,downscale to 1024x1024 with black or white sides
And for having square images, for my current dataset, I had used Fooocus to outpaint the sides since the original images were portrait, but would it be acceptable or good to just resize to 1024x1024 and have the sides be black,white or transparent..? Or will that confuse the training?
No I mean I'm currently using the 48go config with an L40S and getting around4.8 sec. I have a deadline and would need the training to be twice faster, so I wondered if that was achievable with an H100 SXM. Sorry if I explained myself wrong