Instructions to use tangjs/tex-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tangjs/tex-lora 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("tangjs/tex-lora") 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-6000/optimizer.bin from tangjs/tex-lora: direct link, hf CLI and curl.
- Browser
- Download file 204 MB
-
https://huggingface.co/tangjs/tex-lora/resolve/main/checkpoint-6000/optimizer.bin
- Command line
-
hf download hf://tangjs/tex-lora/checkpoint-6000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/tangjs/tex-lora/resolve/main/checkpoint-6000/optimizer.bin
204 MB
- Xet hash:
- b39f9d0147c9cd212d52dd9778fdcbd887a324f051c7bb3348b7d012c0d1c95f
- Size of remote file:
- 204 MB
- SHA256:
- 40ca4b57de25d59eab2c958e15cd68fd46eaef111e9b8fca8bb8d1db0ddaae59
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.