Instructions to use backnotprop/np_cr_model2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use backnotprop/np_cr_model2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("backnotprop/np_cr_model2") prompt = "spiral wave flower by <s0><s1>,minimalism,white_background,abstract,photoshop generated abstract object mesh colorful with white background" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- ac36680368002beff2dd2a2676a3d0f4882130246b135dab15442886b3e41319
- Size of remote file:
- 1.06 kB
- SHA256:
- f69f319474a0f96bcdc98089257a7338a392db6136a936e8466cc8c48e0767f4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.