98 lines
6.3 KiB
Plaintext
98 lines
6.3 KiB
Plaintext
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
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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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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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-->
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# Stable diffusion pipelines
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Stable Diffusion is a text-to-image _latent diffusion_ model created by the researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/) and [LAION](https://laion.ai/). It's trained on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) dataset. This model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With its 860M UNet and 123M text encoder, the model is relatively lightweight and can run on consumer GPUs.
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Latent diffusion is the research on top of which Stable Diffusion was built. It was proposed in [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer. You can learn more details about it in the [specific pipeline for latent diffusion](pipelines/latent_diffusion) that is part of 🤗 Diffusers.
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For more details about how Stable Diffusion works and how it differs from the base latent diffusion model, please refer to the official [launch announcement post](https://stability.ai/blog/stable-diffusion-announcement) and [this section of our own blog post](https://huggingface.co/blog/stable_diffusion#how-does-stable-diffusion-work).
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*Tips*:
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- To tweak your prompts on a specific result you liked, you can generate your own latents, as demonstrated in the following notebook: [](https://colab.research.google.com/github/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb)
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*Overview*:
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| Pipeline | Tasks | Colab | Demo
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|---|---|:---:|:---:|
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| [pipeline_stable_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py) | *Text-to-Image Generation* | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_diffusion.ipynb) | [🤗 Stable Diffusion](https://huggingface.co/spaces/stabilityai/stable-diffusion)
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| [pipeline_stable_diffusion_img2img.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py) | *Image-to-Image Text-Guided Generation* | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb) | [🤗 Diffuse the Rest](https://huggingface.co/spaces/huggingface/diffuse-the-rest)
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| [pipeline_stable_diffusion_inpaint.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py) | **Experimental** – *Text-Guided Image Inpainting* | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb) | Coming soon
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## Tips
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### How to load and use different schedulers.
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The stable diffusion pipeline uses [`PNDMScheduler`] scheduler by default. But `diffusers` provides many other schedulers that can be used with the stable diffusion pipeline such as [`DDIMScheduler`], [`LMSDiscreteScheduler`], [`EulerDiscreteScheduler`], [`EulerAncestralDiscreteScheduler`] etc.
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To use a different scheduler, you can either change it via the [`ConfigMixin.from_config`] method or pass the `scheduler` argument to the `from_pretrained` method of the pipeline. For example, to use the [`EulerDiscreteScheduler`], you can do the following:
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```python
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>>> from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler
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>>> pipeline = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
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>>> pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
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>>> # or
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>>> euler_scheduler = EulerDiscreteScheduler.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
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>>> pipeline = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", scheduler=euler_scheduler)
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```
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### How to conver all use cases with multiple or single pipeline
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If you want to use all possible use cases in a single `DiffusionPipeline` you can either:
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- Make use of the [Stable Diffusion Mega Pipeline](https://github.com/huggingface/diffusers/tree/main/examples/community#stable-diffusion-mega) or
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- Make use of the `components` functionality to instantiate all components in the most memory-efficient way:
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```python
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>>> from diffusers import (
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... StableDiffusionPipeline,
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... StableDiffusionImg2ImgPipeline,
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... StableDiffusionInpaintPipeline,
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... )
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>>> text2img = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
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>>> img2img = StableDiffusionImg2ImgPipeline(**text2img.components)
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>>> inpaint = StableDiffusionInpaintPipeline(**text2img.components)
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>>> # now you can use text2img(...), img2img(...), inpaint(...) just like the call methods of each respective pipeline
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```
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## StableDiffusionPipelineOutput
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[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput
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## StableDiffusionPipeline
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[[autodoc]] StableDiffusionPipeline
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- __call__
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- enable_attention_slicing
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- disable_attention_slicing
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## StableDiffusionImg2ImgPipeline
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[[autodoc]] StableDiffusionImg2ImgPipeline
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- __call__
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- enable_attention_slicing
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- disable_attention_slicing
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## StableDiffusionInpaintPipeline
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[[autodoc]] StableDiffusionInpaintPipeline
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- __call__
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- enable_attention_slicing
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- disable_attention_slicing
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## StableDiffusionImageVariationPipeline
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[[autodoc]] StableDiffusionImageVariationPipeline
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- __call__
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- enable_attention_slicing
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- disable_attention_slicing
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