Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
textual_inversion
Instructions to use Yorick/sum_cap_dog with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Yorick/sum_cap_dog 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_textual_inversion("Yorick/sum_cap_dog") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-3000/optimizer.bin from Yorick/sum_cap_dog: direct link, hf CLI and curl.
- Browser
- Download file 304 MB
-
https://huggingface.co/Yorick/sum_cap_dog/resolve/main/checkpoint-3000/optimizer.bin
- Command line
-
hf download hf://Yorick/sum_cap_dog/checkpoint-3000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/Yorick/sum_cap_dog/resolve/main/checkpoint-3000/optimizer.bin
304 MB
- Xet hash:
- 2805fb73a02d48b2b3257b375f654a010a196a2abca342c1304454acf4ec1797
- Size of remote file:
- 304 MB
- SHA256:
- 7b8fbeda0ba925760ef94c8c0c34a1d2914f856c877f0e39314a797c4e3921da
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.