Stable Diffusion is a text-to-image latent diffusion model created by the researchers and engineers from CompVis, Stability AI and LAION. Itβs trained on 512x512 images from a subset of the 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.
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 by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, BjΓΆrn Ommer. You can learn more details about it in the specific pipeline for latent diffusion that is part of π€ Diffusers.
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 and this section of our own blog post.
Tips:
Overview:
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.
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:
>>> from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler
>>> pipeline = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
>>> pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
>>> # or
>>> euler_scheduler = EulerDiscreteScheduler.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
>>> pipeline = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", scheduler=euler_scheduler)
If you want to use all possible use cases in a single DiffusionPipeline
you can either:
components
functionality to instantiate all components in the most memory-efficient way:>>> from diffusers import (
... StableDiffusionPipeline,
... StableDiffusionImg2ImgPipeline,
... StableDiffusionInpaintPipeline,
... )
>>> text2img = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
>>> img2img = StableDiffusionImg2ImgPipeline(**text2img.components)
>>> inpaint = StableDiffusionInpaintPipeline(**text2img.components)
>>> # now you can use text2img(...), img2img(...), inpaint(...) just like the call methods of each respective pipeline
( images: typing.Union[typing.List[PIL.Image.Image], numpy.ndarray] nsfw_content_detected: typing.Optional[typing.List[bool]] )
Parameters
List[PIL.Image.Image]
or np.ndarray
) —
List of denoised PIL images of length batch_size
or numpy array of shape (batch_size, height, width, num_channels)
. PIL images or numpy array present the denoised images of the diffusion pipeline.
List[bool]
) —
List of flags denoting whether the corresponding generated image likely represents “not-safe-for-work”
(nsfw) content, or None
if safety checking could not be performed.
Output class for Stable Diffusion pipelines.