Instructions to use d-wang26/wd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use d-wang26/wd with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("d-wang26/wd", 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
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Download diffusers/docs/source/en/api/schedulers/edm_euler.md from d-wang26/wd: direct link, hf CLI and curl.
- Browser
- Download file 1.28 kB
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https://huggingface.co/d-wang26/wd/resolve/main/diffusers/docs/source/en/api/schedulers/edm_euler.md
- Command line
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hf download hf://d-wang26/wd/diffusers/docs/source/en/api/schedulers/edm_euler.md
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curl -L -o edm_euler.md https://huggingface.co/d-wang26/wd/resolve/main/diffusers/docs/source/en/api/schedulers/edm_euler.md
1.28 kB
EDMEulerScheduler
The Karras formulation of the Euler scheduler (Algorithm 2) from the Elucidating the Design Space of Diffusion-Based Generative Models paper by Karras et al. This is a fast scheduler which can often generate good outputs in 20-30 steps. The scheduler is based on the original k-diffusion implementation by Katherine Crowson.
EDMEulerScheduler
[[autodoc]] EDMEulerScheduler
EDMEulerSchedulerOutput
[[autodoc]] schedulers.scheduling_edm_euler.EDMEulerSchedulerOutput