Instructions to use SidXXD/f-1930 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SidXXD/f-1930 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SidXXD/f-1930", dtype=torch.bfloat16, device_map="cuda") prompt = "photo of a <v1*> person" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download pytorch_custom_diffusion_weights.bin from SidXXD/f-1930: direct link, hf CLI and curl.
- Browser
- Download file 76.7 MB
-
https://huggingface.co/SidXXD/f-1930/resolve/main/pytorch_custom_diffusion_weights.bin
- Command line
-
hf download hf://SidXXD/f-1930/pytorch_custom_diffusion_weights.bin
-
curl -L -o pytorch_custom_diffusion_weights.bin https://huggingface.co/SidXXD/f-1930/resolve/main/pytorch_custom_diffusion_weights.bin
76.7 MB
- Xet hash:
- 38eeaa146c1209946b3fdeb39b5ead36132abaa7b5afdaa7ab33f61333e553c5
- Size of remote file:
- 76.7 MB
- SHA256:
- 4392cc47026e696416e9ad36abf1cdc7a99b39710083f21b9d944c08c2672a82
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.