Instructions to use fusing/rdm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use fusing/rdm with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fusing/rdm", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Download vae/diffusion_pytorch_model.bin from fusing/rdm: direct link, hf CLI and curl.
- Browser
- Download file 279 MB
-
https://huggingface.co/fusing/rdm/resolve/main/vae/diffusion_pytorch_model.bin
- Command line
-
hf download hf://fusing/rdm/vae/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/fusing/rdm/resolve/main/vae/diffusion_pytorch_model.bin
279 MB
- Xet hash:
- 6f2ff9f252cbdde13ef48c9746394f60d0244b72b873f0a5b29418fadb309d48
- Size of remote file:
- 279 MB
- SHA256:
- 6497e7c67b820698b0811d0098175d59314bda177e4fb18bd3debaf4b3bb5027
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