Text-to-Image
Diffusers
Safetensors
StableDiffusionPipeline
dreambooth
diffusers-training
stable-diffusion
stable-diffusion-diffusers
Instructions to use camgitblame/the_shining with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use camgitblame/the_shining with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("camgitblame/the_shining", dtype=torch.bfloat16, device_map="cuda") prompt = "a photo of sks theshining" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-500/optimizer.bin from camgitblame/the_shining: direct link, hf CLI and curl.
- Browser
- Download file 7.86 GB
-
https://huggingface.co/camgitblame/the_shining/resolve/main/checkpoint-500/optimizer.bin
- Command line
-
hf download hf://camgitblame/the_shining/checkpoint-500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/camgitblame/the_shining/resolve/main/checkpoint-500/optimizer.bin
7.86 GB
- Xet hash:
- 3ac57203d39a2e0fa09535f6612c74cdd5c0c433db0cc507a2ea9af2494575c8
- Size of remote file:
- 7.86 GB
- SHA256:
- 772c6dc524f8d5a01558a7e0d2ccff15f870a52dfb023b76fe4abcc49a3530d7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.