Zero
Zero
I don't use Ollama at all, don't need that if i use LMS.
> @ZeroCool22 is this from the catalog on the home page or from search results? Search. 
> Facing exactly the same issue. Any news on that yet? Chaning onnx version didn't help, tried all kinds of combinations. Version: v1.10.1 Python: 3.10.6 CUDA: 12.1 > > Current...
> I answered this on reddit. Would close but I can't. But that is true, it could cause degradation i mean, the GPU is really in risk?
> maybe try to choose the "Diffusion in Low Bits" on top not automatic but 16bit ! It's already selected, check the image again.
 @lllyasviel Why the patching is so damn slow? Isn't supposed if we use **Automatic (fp16 LoRA)** the patching must be **almost instantly**?
> 2 weeks ago for me worked if i reduce in top line the "GPU weight" -> 7000 MB GGUF and LORA isnt yet very well programmed .... BE PATIENT...
> reduce in top line the "GPU weight" -> 7000 MB GGUF But doing that, you are not taking the benefit of all your VRAM (dunno what GPU you have).
> 16GB RTX and if you read the CMD lines it tryes to free/clean the vram ... for what ever ... and if that fails it lora-patch was slow ......