Instructions to use prushton/logo-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use prushton/logo-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("prushton/logo-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-5000/optimizer.bin from prushton/logo-lora: direct link, hf CLI and curl.
- Browser
- Download file 6.59 MB
-
https://huggingface.co/prushton/logo-lora/resolve/main/checkpoint-5000/optimizer.bin
- Command line
-
hf download hf://prushton/logo-lora/checkpoint-5000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/prushton/logo-lora/resolve/main/checkpoint-5000/optimizer.bin
6.59 MB
- Xet hash:
- 72dfd917d9a020e99d26b21f5a87aed6bc988c0a7008e660bd3135fd88e270ec
- Size of remote file:
- 6.59 MB
- SHA256:
- 780bc4004d3271bdb8c144c7cff56f9537cb74cc732f9e0d76ff08997f58b85b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.