Instructions to use duja1/t123rail with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use duja1/t123rail with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("duja1/t123rail", dtype=torch.bfloat16, device_map="cuda") prompt = "t123rail" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download text_encoder/pytorch_model.bin from duja1/t123rail: direct link, hf CLI and curl.
- Browser
- Download file 492 MB
-
https://huggingface.co/duja1/t123rail/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://duja1/t123rail/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/duja1/t123rail/resolve/main/text_encoder/pytorch_model.bin
492 MB
- Xet hash:
- aaf60a53a7d271a45f5342b0d47b24b82fbef5b3386a48fb6f467964faf41fb6
- Size of remote file:
- 492 MB
- SHA256:
- ef75ac69c3fed643e6d75afaaf2cc7df4c5f9102121740175a74af2e1a681734
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