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
|
Download diffusers/docs/source/en/api/schedulers/dpm_sde.md from d-wang26/wd: direct link, hf CLI and curl.
- Browser
- Download file 1.04 kB
-
https://huggingface.co/d-wang26/wd/resolve/main/diffusers/docs/source/en/api/schedulers/dpm_sde.md
- Command line
-
hf download hf://d-wang26/wd/diffusers/docs/source/en/api/schedulers/dpm_sde.md
-
curl -L -o dpm_sde.md https://huggingface.co/d-wang26/wd/resolve/main/diffusers/docs/source/en/api/schedulers/dpm_sde.md
1.04 kB
DPMSolverSDEScheduler
The DPMSolverSDEScheduler is inspired by the stochastic sampler from the Elucidating the Design Space of Diffusion-Based Generative Models paper, and the scheduler is ported from and created by Katherine Crowson.
DPMSolverSDEScheduler
[[autodoc]] DPMSolverSDEScheduler
SchedulerOutput
[[autodoc]] schedulers.scheduling_utils.SchedulerOutput