Instructions to use SidXXD/rl-HAAD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SidXXD/rl-HAAD 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/rl-HAAD", dtype=torch.bfloat16, device_map="cuda") prompt = "photo of a sks person" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download pytorch_custom_diffusion_weights.bin from SidXXD/rl-HAAD: direct link, hf CLI and curl.
- Browser
- Download file 76.7 MB
-
https://huggingface.co/SidXXD/rl-HAAD/resolve/main/pytorch_custom_diffusion_weights.bin
- Command line
-
hf download hf://SidXXD/rl-HAAD/pytorch_custom_diffusion_weights.bin
-
curl -L -o pytorch_custom_diffusion_weights.bin https://huggingface.co/SidXXD/rl-HAAD/resolve/main/pytorch_custom_diffusion_weights.bin
76.7 MB
- Xet hash:
- 50ce2e6ba27418d1ad9b686eb5174156f4acc8b5c82b0d408c63eea62c1a655c
- Size of remote file:
- 76.7 MB
- SHA256:
- 1c6f8895579897e6a5818b5a9e516efb2cd695d3e8de85e47cf75dd3bc9db4eb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.