Instructions to use anshudaur/cat_model_small_lr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use anshudaur/cat_model_small_lr with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("anshudaur/cat_model_small_lr", dtype=torch.bfloat16, device_map="cuda") prompt = "photo of a <new1> cat" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- dc63ce24d9140b084aa6e329a701a0220b0eb4a4bf397d8f640a56f2b68edd2d
- Size of remote file:
- 457 MB
- SHA256:
- 1ec3e2c5ac11ae315ac1db195408b0654ff9e66dd999585313450d34baa6147e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.