Instructions to use ssdxc/RSTP-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ssdxc/RSTP-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("ssdxc/RSTP-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-2500/optimizer.bin from ssdxc/RSTP-lora: direct link, hf CLI and curl.
- Browser
- Download file 6.59 MB
-
https://huggingface.co/ssdxc/RSTP-lora/resolve/main/checkpoint-2500/optimizer.bin
- Command line
-
hf download hf://ssdxc/RSTP-lora/checkpoint-2500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/ssdxc/RSTP-lora/resolve/main/checkpoint-2500/optimizer.bin
6.59 MB
- Xet hash:
- ac8d1704be0e101ba27306ea9cab8f908fe6ec006159985095bc8a5897d863c8
- Size of remote file:
- 6.59 MB
- SHA256:
- 8fef3c546787237b39d30e89644ec791133d565684d142c7eb3d278cea272350
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.