Instructions to use backnotprop/np_cr_model3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use backnotprop/np_cr_model3 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_model3") prompt = "spiral wave flower by <s0><s1>,minimalism,abstract,photoshop generated abstract colorful object mesh" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- 7ef7f30dd95449ae7d3d427aaca77ec8e352de39eaed1be54f23f64a070d731a
- Size of remote file:
- 4.59 GB
- SHA256:
- cc2f4d0778e35d8383ad19d0f23b2fcfd875d9369bfb3af7a64484098369d9f0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.