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| # Overview |
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| 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 [`StableDiffusionPipeline`] 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. |
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| This section introduces you to some of the tasks supported by our pipelines such as unconditional image generation and different techniques and variations of text-to-image generation. You'll also learn how to gain more control over the generation process by setting a seed for reproducibility and weighting prompts to adjust the influence certain words in the prompt has over the output. Finally, you'll see how you can create a community pipeline for a custom task like generating images from speech. |
