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-6500/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-6500/optimizer.bin
- Command line
-
hf download hf://ssdxc/RSTP-lora/checkpoint-6500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/ssdxc/RSTP-lora/resolve/main/checkpoint-6500/optimizer.bin
6.59 MB
- Xet hash:
- 9af393edb5fd9ec4823330c823c56a7ff30e81460f2714cc66dd431c97e14613
- Size of remote file:
- 6.59 MB
- SHA256:
- f38ed62598fa91a588ead090d5a40f5cf47f3b5d8262eda37e39fb2dd6efcfa2
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