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/edm_multistep_dpm_solver.md from d-wang26/wd: direct link, hf CLI and curl.
- Browser
- Download file 1.55 kB
-
https://huggingface.co/d-wang26/wd/resolve/main/diffusers/docs/source/en/api/schedulers/edm_multistep_dpm_solver.md
- Command line
-
hf download hf://d-wang26/wd/diffusers/docs/source/en/api/schedulers/edm_multistep_dpm_solver.md
-
curl -L -o edm_multistep_dpm_solver.md https://huggingface.co/d-wang26/wd/resolve/main/diffusers/docs/source/en/api/schedulers/edm_multistep_dpm_solver.md
EDMDPMSolverMultistepScheduler
EDMDPMSolverMultistepScheduler is a Karras formulation of DPMSolverMultistep, a multistep scheduler from DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps and DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu.
DPMSolver (and the improved version DPMSolver++) is a fast dedicated high-order solver for diffusion ODEs with convergence order guarantee. Empirically, DPMSolver sampling with only 20 steps can generate high-quality samples, and it can generate quite good samples even in 10 steps.
EDMDPMSolverMultistepScheduler
[[autodoc]] EDMDPMSolverMultistepScheduler
SchedulerOutput
[[autodoc]] schedulers.scheduling_utils.SchedulerOutput