("cuda")### LoRA Related Code ###transformer.update_lora_params("mit-han-lab/svdquant-lora-collection/svdq-int4-flux.1-dev-ghibsky.safetensors")# Path to your converted LoRA safetensors, can also be a remote HuggingFace pathtransformer.set_lora_strength(1)# Your LoRA strength here##...
CUDA/cuDNN version: -- (using CPU) GPU models and configuration: -- GCC version (if compiling from source): -- CMake version: -- Versions of any other relevant libraries: MKL latest (i hope) cc@jianyuh@nikitaved@pearu@mruberry@heitorschueroff@walterddr@IvanYashchuk@VitalyFedyunin@ngime...
github (recommend to read this): https://github.com/lx9t01/cuda_SVD Project Description The core idea of using matrix factorization in recommender System is to map user rating space to item space, via several implicit multi-dimensional singular value decomposition factor matrices to describe the ...
to("cuda") pipeline("An image of a squirrel in Picasso style").images[0] You can also dig into the models and schedulers toolbox to build your own diffusion system: from diffusers import DDPMScheduler, UNet2DModel from PIL import Image import torch scheduler = DDPMScheduler.from_pretrained...
OS: OS Platform and Distribution (e.g., Linux Ubuntu 16.04): CentOS release 7.4.1708 TensorFlow installed from (source or binary): From source Python version: 2.7.13 Bazel version: 0.6.1 CUDA/cuDNN version: CUDA 8.0/cuDNN 6.0.21 GPU mode...
decorators=[skipCUDAIfNoMagma, skipCPUIfNoLapack]), OpInfo('pinverse', op=torch.pinverse, dtypes=floating_and_complex_types(), test_inplace_grad=False, supports_tensor_out=False, sample_inputs_func=sample_inputs_pinverse, decorators=[skipCUDAIfNoMagma, skipCPUIfNoLapack]), ] if TEST_SCIPY...