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/lms_discrete.md from d-wang26/wd: direct link, hf CLI and curl.
- Browser
- Download file 1.15 kB
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https://huggingface.co/d-wang26/wd/resolve/main/diffusers/docs/source/en/api/schedulers/lms_discrete.md
- Command line
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hf download hf://d-wang26/wd/diffusers/docs/source/en/api/schedulers/lms_discrete.md
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curl -L -o lms_discrete.md https://huggingface.co/d-wang26/wd/resolve/main/diffusers/docs/source/en/api/schedulers/lms_discrete.md
1.15 kB
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| # LMSDiscreteScheduler | |
| `LMSDiscreteScheduler` is a linear multistep scheduler for discrete beta schedules. The scheduler is ported from and created by [Katherine Crowson](https://github.com/crowsonkb/), and the original implementation can be found at [crowsonkb/k-diffusion](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L181). | |
| ## LMSDiscreteScheduler | |
| [[autodoc]] LMSDiscreteScheduler | |
| ## LMSDiscreteSchedulerOutput | |
| [[autodoc]] schedulers.scheduling_lms_discrete.LMSDiscreteSchedulerOutput | |