Instructions to use tzvc/sd-tzvc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tzvc/sd-tzvc with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("tzvc/sd-tzvc", dtype=torch.bfloat16, device_map="cuda") prompt = "sdtzvc" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download text_encoder/pytorch_model.bin from tzvc/sd-tzvc: direct link, hf CLI and curl.
- Browser
- Download file 492 MB
-
https://huggingface.co/tzvc/sd-tzvc/resolve/refs%2Fpr%2F3/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://tzvc/sd-tzvc@refs/pr/3/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tzvc/sd-tzvc/resolve/refs%2Fpr%2F3/text_encoder/pytorch_model.bin
492 MB
- Xet hash:
- 08e87d5317f0a9b4d353013a0685ffb67eb3abe209c7503259e7481a5407f6f5
- Size of remote file:
- 492 MB
- SHA256:
- 96cad37fccabd5e29c478dd29baabb285eb93b5233f8d86b5e2ae25878f12e4c
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