StableDiffusionImageVariationPipeline lets you generate variations from an input image using Stable Diffusion. It uses a fine-tuned version of Stable Diffusion model, trained by Justin Pinkney (@Buntworthy) at Lambda
The original codebase can be found here: Stable Diffusion Image Variations
Available Checkpoints are:
- sd-image-variations-diffusers: lambdalabs/sd-image-variations-diffusers
class diffusers.StableDiffusionImageVariationPipeline< source >
( vae: AutoencoderKL image_encoder: CLIPVisionModelWithProjection unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers safety_checker: StableDiffusionSafetyChecker feature_extractor: CLIPFeatureExtractor requires_safety_checker: bool = True )
- vae (AutoencoderKL) — Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
CLIPVisionModelWithProjection) — Frozen CLIP image-encoder. Stable Diffusion Image Variation uses the vision portion of CLIP, specifically the clip-vit-large-patch14 variant.
- unet (UNet2DConditionModel) — Conditional U-Net architecture to denoise the encoded image latents.
scheduler (SchedulerMixin) —
A scheduler to be used in combination with
unetto denoise the encoded image latents. Can be one of DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler.
StableDiffusionSafetyChecker) — Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the model card for details.
CLIPFeatureExtractor) — Model that extracts features from generated images to be used as inputs for the
Pipeline to generate variations from an input image using Stable Diffusion.
This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
__call__< source >
image: typing.Union[PIL.Image.Image, typing.List[PIL.Image.Image], torch.FloatTensor]
height: typing.Optional[int] = None
width: typing.Optional[int] = None
num_inference_steps: int = 50
guidance_scale: float = 7.5
num_images_per_prompt: typing.Optional[int] = 1
eta: float = 0.0
generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None
latents: typing.Optional[torch.FloatTensor] = None
output_type: typing.Optional[str] = 'pil'
return_dict: bool = True
callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None
callback_steps: int = 1
torch.FloatTensor) — The image or images to guide the image generation. If you provide a tensor, it needs to comply with the configuration of this
int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The height in pixels of the generated image.
int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The width in pixels of the generated image.
int, optional, defaults to 50) — The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.
float, optional, defaults to 7.5) — Guidance scale as defined in Classifier-Free Diffusion Guidance.
guidance_scaleis defined as
wof equation 2. of Imagen Paper. Guidance scale is enabled by setting
guidance_scale > 1. Higher guidance scale encourages to generate images that are closely linked to the text
prompt, usually at the expense of lower image quality.
int, optional, defaults to 1) — The number of images to generate per prompt.
float, optional, defaults to 0.0) — Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to schedulers.DDIMScheduler, will be ignored for others.
torch.Generator, optional) — One or a list of torch generator(s) to make generation deterministic.
torch.FloatTensor, optional) — Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random
str, optional, defaults to
"pil") — The output format of the generate image. Choose between PIL:
bool, optional, defaults to
True) — Whether or not to return a StableDiffusionPipelineOutput instead of a plain tuple.
Callable, optional) — A function that will be called every
callback_stepssteps during inference. The function will be called with the following arguments:
callback(step: int, timestep: int, latents: torch.FloatTensor).
int, optional, defaults to 1) — The frequency at which the
callbackfunction will be called. If not specified, the callback will be called at every step.
return_dict is True, otherwise a
tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of bool
s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the safety_checker`.
Function invoked when calling the pipeline for generation.
enable_attention_slicing< source >
( slice_size: typing.Union[str, int, NoneType] = 'auto' )
int, optional, defaults to
"auto") — When
"auto", halves the input to the attention heads, so attention will be computed in two steps. If
"max", maxium amount of memory will be saved by running only one slice at a time. If a number is provided, uses as many slices as
attention_head_dim // slice_size. In this case,
attention_head_dimmust be a multiple of
Enable sliced attention computation.
When this option is enabled, the attention module will split the input tensor in slices, to compute attention in several steps. This is useful to save some memory in exchange for a small speed decrease.
disable_attention_slicing< source >
Disable sliced attention computation. If
enable_attention_slicing was previously invoked, this method will go
back to computing attention in one step.
enable_xformers_memory_efficient_attention< source >
( attention_op: typing.Optional[typing.Callable] = None )
Callable, optional) — Override the default
Noneoperator for use as
opargument to the
memory_efficient_attention()function of xFormers.
Enable memory efficient attention as implemented in xformers.
When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference time. Speed up at training time is not guaranteed.
Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention is used.
import torch from diffusers import DiffusionPipeline from xformers.ops import MemoryEfficientAttentionFlashAttentionOp pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16) pipe = pipe.to("cuda") pipe.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp) # Workaround for not accepting attention shape using VAE for Flash Attention pipe.vae.enable_xformers_memory_efficient_attention(attention_op=None)
disable_xformers_memory_efficient_attention< source >
Disable memory efficient attention as implemented in xformers.
enable_sequential_cpu_offload< source >
( gpu_id = 0 )
Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
torch.device('meta') and loaded to GPU only when their specific submodule has its forward` method called.