Instructions to use JosephusCheung/RuminationDiffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JosephusCheung/RuminationDiffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("JosephusCheung/RuminationDiffusion", dtype=torch.bfloat16, device_map="cuda") prompt = "masterpiece, best quality, anime, 1girl, brown hair, green eyes, colorful, autumn, cumulonimbus clouds, lighting, blue sky, garden, looking at viewer" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download unet/diffusion_pytorch_model.bin from JosephusCheung/RuminationDiffusion: direct link, hf CLI and curl.
- Browser
- Download file 3.46 GB
-
https://huggingface.co/JosephusCheung/RuminationDiffusion/resolve/49f00a7db73b34feaeeb15a5fcb664a67891b2aa/unet/diffusion_pytorch_model.bin
- Command line
-
hf download hf://JosephusCheung/RuminationDiffusion@49f00a7db73b34feaeeb15a5fcb664a67891b2aa/unet/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/JosephusCheung/RuminationDiffusion/resolve/49f00a7db73b34feaeeb15a5fcb664a67891b2aa/unet/diffusion_pytorch_model.bin
3.46 GB
- Xet hash:
- 38ddbccb943997a128f5dd89d1bf621b285c4a22ca44cda3476f0b25e0e1a3a1
- Size of remote file:
- 3.46 GB
- SHA256:
- 5792f9075beacbbfcb88cb75a6227c416bcdba8d49f4e9ba1ad5b3779577b72f
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