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# Depth-to-image

<div class="flex flex-wrap space-x-1">
  <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
</div>

The Stable Diffusion model can also infer depth based on an image using [MiDaS](https://github.com/isl-org/MiDaS). This allows you to pass a text prompt and an initial image to condition the generation of new images as well as a `depth_map` to preserve the image structure.

> [!TIP]
> Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
>
> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!

## StableDiffusionDepth2ImgPipeline

[[autodoc]] StableDiffusionDepth2ImgPipeline
	- all
	- __call__
	- enable_attention_slicing
	- disable_attention_slicing
	- enable_xformers_memory_efficient_attention
	- disable_xformers_memory_efficient_attention
	- load_textual_inversion
	- load_lora_weights
	- save_lora_weights

## StableDiffusionPipelineOutput

[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput
