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|
|
| # Depth-to-image |
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| 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. |
|
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| <Tip> |
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| 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! |
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| 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! |
|
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| </Tip> |
|
|
| ## StableDiffusionDepth2ImgPipeline |
|
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| [[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 |
