Instructions to use jtlowell/gentzy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jtlowell/gentzy with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jtlowell/gentzy", dtype=torch.bfloat16, device_map="cuda") prompt = "gentzface" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download text_encoder/pytorch_model.bin from jtlowell/gentzy: direct link, hf CLI and curl.
- Browser
- Download file 1.36 GB
-
https://huggingface.co/jtlowell/gentzy/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://jtlowell/gentzy/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jtlowell/gentzy/resolve/main/text_encoder/pytorch_model.bin
1.36 GB
- Xet hash:
- 4622c3a4cf24fd193bc83267bc28526e8bfcaf2aa8f66d84be855f3703967231
- Size of remote file:
- 1.36 GB
- SHA256:
- 28c21476d233d4d0dac5db6cb28e3ae36df6bd023521a9024c936314908b4149
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