Instructions to use ethers/sd-loral-cat-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ethers/sd-loral-cat-model with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ethers/sd-loral-cat-model") 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 checkpoint-3000/pytorch_model.bin from ethers/sd-loral-cat-model: direct link, hf CLI and curl.
- Browser
- Download file 3.29 MB
-
https://huggingface.co/ethers/sd-loral-cat-model/resolve/main/checkpoint-3000/pytorch_model.bin
- Command line
-
hf download hf://ethers/sd-loral-cat-model/checkpoint-3000/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ethers/sd-loral-cat-model/resolve/main/checkpoint-3000/pytorch_model.bin
3.29 MB
- Xet hash:
- d8824c47c55b9a8dfc9591a665a99924cf656925a3a121013e8d12efb54f6e1c
- Size of remote file:
- 3.29 MB
- SHA256:
- d791531d185631706e6ce2a91169f4cc9170ec604fbbfe07e7ffdee0f05d7800
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