Stable diffusion pipelines
Stable Diffusion is a text-to-image latent diffusion model created by the researchers and engineers from CompVis, Stability AI and LAION. It’s 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, Björn Ommer. You can learn more details about it in the specific pipeline for latent diffusion that is part of 🤗 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:
- To tweak your prompts on a specific result you liked, you can generate your own latents, as demonstrated in the following notebook:
Overview:
Tips
How to load and use different schedulers.
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)How to convert all use cases with multiple or single pipeline
If you want to use all possible use cases in a single DiffusionPipeline you can either:
- Make use of the Stable Diffusion Mega Pipeline or
- Make use of the
componentsfunctionality 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 pipelineStableDiffusionPipelineOutput
class diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput
< source >( images: typing.Union[typing.List[PIL.Image.Image], numpy.ndarray] nsfw_content_detected: typing.Optional[typing.List[bool]] )
Parameters
-
images (
List[PIL.Image.Image]ornp.ndarray) — List of denoised PIL images of lengthbatch_sizeor numpy array of shape(batch_size, height, width, num_channels). PIL images or numpy array present the denoised images of the diffusion pipeline. -
nsfw_content_detected (
List[bool]) — List of flags denoting whether the corresponding generated image likely represents “not-safe-for-work” (nsfw) content, orNoneif safety checking could not be performed.
Output class for Stable Diffusion pipelines.