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/lcm.md from d-wang26/wd: direct link, hf CLI and curl.
- Browser
- Download file 1.09 kB
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https://huggingface.co/d-wang26/wd/resolve/main/diffusers/docs/source/en/api/schedulers/lcm.md
- Command line
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hf download hf://d-wang26/wd/diffusers/docs/source/en/api/schedulers/lcm.md
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curl -L -o lcm.md https://huggingface.co/d-wang26/wd/resolve/main/diffusers/docs/source/en/api/schedulers/lcm.md
1.09 kB
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| Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with | |
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| # Latent Consistency Model Multistep Scheduler | |
| ## Overview | |
| Multistep and onestep scheduler (Algorithm 3) introduced alongside latent consistency models in the paper [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://arxiv.org/abs/2310.04378) by Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao. | |
| This scheduler should be able to generate good samples from [`LatentConsistencyModelPipeline`] in 1-8 steps. | |
| ## LCMScheduler | |
| [[autodoc]] LCMScheduler | |