Instructions to use d-wang26/mk1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use d-wang26/mk1 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/mk1", 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 diffusers/docs/source/en/api/schedulers/euler_ancestral.md from d-wang26/mk1: direct link, hf CLI and curl.
- Browser
- Download file 1.23 kB
-
https://huggingface.co/d-wang26/mk1/resolve/main/diffusers/docs/source/en/api/schedulers/euler_ancestral.md
- Command line
-
hf download hf://d-wang26/mk1/diffusers/docs/source/en/api/schedulers/euler_ancestral.md
-
curl -L -o euler_ancestral.md https://huggingface.co/d-wang26/mk1/resolve/main/diffusers/docs/source/en/api/schedulers/euler_ancestral.md
1.23 kB
EulerAncestralDiscreteScheduler
A scheduler that uses ancestral sampling with Euler method steps. 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.
EulerAncestralDiscreteScheduler
[[autodoc]] EulerAncestralDiscreteScheduler
EulerAncestralDiscreteSchedulerOutput
[[autodoc]] schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteSchedulerOutput