NVIDIA Update Solves CUDA error (but very slow) -Train Dreambooth, SDXL LoRA with Low VRAM

#stablediffusion #a1111 #nvidia #update #cuda #cudaerror #lowvram #kohyass #LoRA #dreambooth #tensorRT (Update: while the update is able to solve CUDA memory errors, I have seen it to be very slow with SDXL... it is not very practical to use with low VRAM... works, but slow, hopefully in the next update, we get better performance than the current one, as this is a new feature, and very likely to improve soon) 00:00:00 introduction and note about NVidia drivers 00:00:21 NVidia control panel System memory fall back feature check if the driver update includes System memory fall back features and selecting it 00:01:07 downloading the NVidia driver manually 00:01:47 Testing SDXL training on 8GB VRAM show case sample training in SDXL to show that now it works without CUDA errors 00:03:26 testing dream booth training on 8GB VRAM show case that dreambooth can now work as well without CUDA errors 00:05:05 side note about TensorRT extension for stable diffusion another update that may help double the speed of image generation but with limited usecases Computer Specs: Laptop: Legion 5 Pro Processor :AMD Ryzen 7 5800H , 3201 Mhz System RAM: 16.0 GB Graphics GPU: NVIDIA GeForce RTX 3070 Laptop GPU 8GB This is a new Update for NVidia CARDs which allows anyone to train SDXL LoRAs, Dreambooth with large image sizes without getting CUDA errors even with 6 or 8GB VRAM, possibly with less. the new Feature from NVIDIA Driver update gives System memory fall back features that allows to extend existing VRMA to system memory when no more VRAM is available. This is an amazing update that allows more people to train Dreambooth and SDXL LoRAs on thei PCs even with 8GB VRAM and less. NVIDIA Driver update: choose based on your GPU https://www.nvidia.com/Download/index.aspx LoRA Character Training for SD 1.5 AND SDXL in stable diffusion https://youtu.be/vA2v2IugK6w Style LoRA training guide in stable diffusion https://youtu.be/RT2jj-5t8x8 A1111 https://github.com/AUTOMATIC1111/sta