Instructions to use tangjs/uv-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tangjs/uv-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("SG161222/Realistic_Vision_V5.1_noVAE", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("tangjs/uv-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-2000/optimizer.bin from tangjs/uv-lora: direct link, hf CLI and curl.
- Browser
- Download file 12.9 MB
-
https://huggingface.co/tangjs/uv-lora/resolve/main/checkpoint-2000/optimizer.bin
- Command line
-
hf download hf://tangjs/uv-lora/checkpoint-2000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/tangjs/uv-lora/resolve/main/checkpoint-2000/optimizer.bin
12.9 MB
- Xet hash:
- 9dfe2e89b68f5552229d7a667f009217f43ef7bdf537a9acb58fa5ffe9329eea
- Size of remote file:
- 12.9 MB
- SHA256:
- 8b1ca698c436d082508af2c45293db18d00a9377713621c032406234bf935936
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