Instructions to use Benjaminwfriedman/dlr_demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Benjaminwfriedman/dlr_demo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Benjaminwfriedman/dlr_demo", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
Download vae/diffusion_pytorch_model.bin from Benjaminwfriedman/dlr_demo: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/Benjaminwfriedman/dlr_demo/resolve/main/vae/diffusion_pytorch_model.bin
- Command line
-
hf download hf://Benjaminwfriedman/dlr_demo/vae/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/Benjaminwfriedman/dlr_demo/resolve/main/vae/diffusion_pytorch_model.bin
167 MB
- Xet hash:
- abda4979bcc234c39799f62952062185adff6b3789fb39d4e546ad0e382d24e7
- Size of remote file:
- 167 MB
- SHA256:
- aa8c1b74b3e2781e4347b9b350203597674d8860a4338b46431de760c3a5dd22
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