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-1000/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-1000/optimizer.bin
- Command line
-
hf download hf://camgitblame/the_shining/checkpoint-1000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/camgitblame/the_shining/resolve/main/checkpoint-1000/optimizer.bin
7.86 GB
- Xet hash:
- 995f39b970371021fd0987ebdc84002901e705d5c214b4f23ead63b9ef7e2239
- Size of remote file:
- 7.86 GB
- SHA256:
- 5500a8536c21956e005bbdfd7c4df674c43a7f0a0f56e716b2273a4508e06bbe
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