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
5090 would be 17% faster at fp16 flux dev inference compared to 4090 according to current reports Not a deal breaker but nice incremental upgrade, plus 32GB vram for faster prompt switch
@Dr. Furkan Gözükara I had tried 'couple trainings' with images of them together in the input dataset. I captioned images using joycaption. Tbh, the model always learnt one person better than the other. What could be the reason for this? I used the same settings as your one -character dreambooths.
I've written python scripts with chatGPT that will process the high resolution images for training by cutting them into 1024x1024 pieces with a bit of overlap.
0 runs, 0 stars, 0 downloads. This fine tuned checkpoint is based on Flux dev de-distilled thus requires a special comfyUI workflow and won't work very well ...
not really the higher the resolution the longer it takes to render obviously - and you get better results but also diminishing returns eventually - this was resized to 1080p i believe took 5min to render
i sent the workflow above - input video - play with the prompt and setting, make sure to match the skip steps with the normal scheduler step to equal 20 step of genning anything under that breaks teh genning
I already test some top Flux dev de-distilled model, quality quite dissapointed, most of them only good on realistic image, for the style only the same or even worse than Flux dev although time for render is double due to CFG >1. Are there any de-distilled models you guys can recommend and maybe can be used to train lora and finetuning ?