Instructions to use johnowhitaker/lora_pn03_036sim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use johnowhitaker/lora_pn03_036sim with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("playgroundai/playground-v2-1024px-aesthetic", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("johnowhitaker/lora_pn03_036sim") 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/scheduler.bin from johnowhitaker/lora_pn03_036sim: direct link, hf CLI and curl.
- Browser
- Download file 1 kB
-
https://huggingface.co/johnowhitaker/lora_pn03_036sim/resolve/main/checkpoint-2000/scheduler.bin
- Command line
-
hf download hf://johnowhitaker/lora_pn03_036sim/checkpoint-2000/scheduler.bin
-
curl -L -o scheduler.bin https://huggingface.co/johnowhitaker/lora_pn03_036sim/resolve/main/checkpoint-2000/scheduler.bin
1 kB
- Xet hash:
- 6b0ff5fffac406cae6b776fdd9370b90c50069584ba6a8dd6f5d3f722d4a35ec
- Size of remote file:
- 1 kB
- SHA256:
- 58b1244a63b890a4ff3cf1a883daec4d142a2f0a3f6da815695e7d742cf6e8cf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.