Instructions to use zigg-ai/kemar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zigg-ai/kemar with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("zigg-ai/kemar", dtype=torch.bfloat16, device_map="cuda") prompt = "sdcid" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download text_encoder/pytorch_model.bin from zigg-ai/kemar: direct link, hf CLI and curl.
- Browser
- Download file 492 MB
-
https://huggingface.co/zigg-ai/kemar/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://zigg-ai/kemar/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/zigg-ai/kemar/resolve/main/text_encoder/pytorch_model.bin
492 MB
- Xet hash:
- 242da4e70ff680b38599efa4099a790b8d601e287e4cf1fbf8d877fd18a8e0fa
- Size of remote file:
- 492 MB
- SHA256:
- e282dcb52237ea138646a6bc8f8b8dfaf652efc0beeb8173ce2bb1ebf6be8e70
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