Instructions to use dinushiTJ/lpg_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dinushiTJ/lpg_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("stabilityai/stable-diffusion-2-1-base", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("dinushiTJ/lpg_lora") prompt = "A <LPG> aerial view" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-2000/optimizer.bin from dinushiTJ/lpg_lora: direct link, hf CLI and curl.
- Browser
- Download file 13 MB
-
https://huggingface.co/dinushiTJ/lpg_lora/resolve/main/checkpoint-2000/optimizer.bin
- Command line
-
hf download hf://dinushiTJ/lpg_lora/checkpoint-2000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/dinushiTJ/lpg_lora/resolve/main/checkpoint-2000/optimizer.bin
13 MB
- Xet hash:
- a322e9899ed3c7d50ea0f11e11bb44ebeaff30f6d63ede5989671309e59f55c9
- Size of remote file:
- 13 MB
- SHA256:
- 7a153c0b8683ecea2565a95d13eb3b3c549539da2ae4701aea5e61f45fb93947
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