Instructions to use spooncats/mk-walkcycle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use spooncats/mk-walkcycle with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("spooncats/mk-walkcycle", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download text_encoder/pytorch_model.bin from spooncats/mk-walkcycle: direct link, hf CLI and curl.
- Browser
- Download file 492 MB
-
https://huggingface.co/spooncats/mk-walkcycle/resolve/refs%2Fpr%2F3/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://spooncats/mk-walkcycle@refs/pr/3/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/spooncats/mk-walkcycle/resolve/refs%2Fpr%2F3/text_encoder/pytorch_model.bin
492 MB
- Xet hash:
- 2a61f5b7e2c89473b0cc3737fc1e5f160afb053ad22b02bb70ea25bc09f57aa0
- Size of remote file:
- 492 MB
- SHA256:
- 805e97567aee48281ab59acc801936003bb0fe4d6420b5dd3af8a686ed49e290
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.