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/using-diffusers/pipeline_overview.md from d-wang26/wd: direct link, hf CLI and curl.
- Browser
- Download file 1.44 kB
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https://huggingface.co/d-wang26/wd/resolve/main/diffusers/docs/source/en/using-diffusers/pipeline_overview.md
- Command line
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hf download hf://d-wang26/wd/diffusers/docs/source/en/using-diffusers/pipeline_overview.md
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curl -L -o pipeline_overview.md https://huggingface.co/d-wang26/wd/resolve/main/diffusers/docs/source/en/using-diffusers/pipeline_overview.md
1.44 kB
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| # Overview | |
| A pipeline is an end-to-end class that provides a quick and easy way to use a diffusion system for inference by bundling independently trained models and schedulers together. Certain combinations of models and schedulers define specific pipeline types, like [`StableDiffusionXLPipeline`] or [`StableDiffusionControlNetPipeline`], with specific capabilities. All pipeline types inherit from the base [`DiffusionPipeline`] class; pass it any checkpoint, and it'll automatically detect the pipeline type and load the necessary components. | |
| This section demonstrates how to use specific pipelines such as Stable Diffusion XL, ControlNet, and DiffEdit. You'll also learn how to use a distilled version of the Stable Diffusion model to speed up inference, how to create reproducible pipelines, and how to use and contribute community pipelines. | |