#
Stable Diffusion Latent Upscaler

##
StableDiffusionLatentUpscalePipeline

The Stable Diffusion Latent Upscaler model was created by Katherine Crowson in collaboration with Stability AI. It can be used on top of any StableDiffusionUpscalePipeline checkpoint to enhance its output image resolution by a factor of 2.

A notebook that demonstrates the original implementation can be found here:

- Stable Diffusion Upscaler Demo

Available Checkpoints are:

- stabilityai/latent-upscaler: stabilityai/sd-x2-latent-upscaler

###
class diffusers.StableDiffusionLatentUpscalePipeline

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( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel scheduler: EulerDiscreteScheduler )

Parameters

- vae (AutoencoderKL) — Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
- text_encoder (`CLIPTextModel`) — Frozen text-encoder. Stable Diffusion uses the text portion of CLIP, specifically the clip-vit-large-patch14 variant.
- tokenizer (`CLIPTokenizer`) — Tokenizer of class CLIPTokenizer.
- unet (UNet2DConditionModel) — Conditional U-Net architecture to denoise the encoded image latents.
- scheduler (SchedulerMixin) — A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of EulerDiscreteScheduler.

Pipeline to upscale the resolution of Stable Diffusion output images by a factor of 2.

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.)

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__call__

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( prompt: typing.Union[str, typing.List[str]] image: typing.Union[torch.FloatTensor, PIL.Image.Image, typing.List[PIL.Image.Image]] num_inference_steps: int = 75 guidance_scale: float = 9.0 negative_prompt: typing.Union[str, typing.List[str], NoneType] = None 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 ) → StableDiffusionPipelineOutput or `tuple`

Parameters

- prompt (`str` or `List[str]`) — The prompt or prompts to guide the image upscaling.
- image (`PIL.Image.Image` or List`PIL.Image.Image` or `torch.FloatTensor`) — `Image`, or tensor representing an image batch which will be upscaled. If it’s a tensor, it can be either a latent output from a stable diffusion model, or an image tensor in the range `[-1, 1]`. It will be considered a `latent` if `image.shape[1]` is `4`; otherwise, it will be considered to be an image representation and encoded using this pipeline’s `vae` encoder.
- num_inference_steps (`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.
- guidance_scale (`float`, optional, defaults to 7.5) — Guidance scale as defined in Classifier-Free Diffusion Guidance. `guidance_scale` is defined as `w` of 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.
- negative_prompt (`str` or `List[str]`, optional) — The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
- eta (`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.
- generator (`torch.Generator`, optional) — One or a list of torch generator(s) to make generation deterministic.
- latents (`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 `generator`.
- output_type (`str`, optional, defaults to `"pil"`) — The output format of the generate image. Choose between PIL: `PIL.Image.Image` or `np.array`.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a StableDiffusionPipelineOutput instead of a plain tuple.
- callback (`Callable`, optional) — A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
- callback_steps (`int`, optional, defaults to 1) — The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step.

Returns

StableDiffusionPipelineOutput or `tuple`

StableDiffusionPipelineOutput if `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.

Examples:

    >>> from diffusers import StableDiffusionLatentUpscalePipeline, StableDiffusionPipeline
    >>> import torch

    >>> pipeline = StableDiffusionPipeline.from_pretrained(
    ...     "CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16
    ... )
    >>> pipeline.to("cuda")

    >>> model_id = "stabilityai/sd-x2-latent-upscaler"
    >>> upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
    >>> upscaler.to("cuda")

    >>> prompt = "a photo of an astronaut high resolution, unreal engine, ultra realistic"
    >>> generator = torch.manual_seed(33)

    >>> low_res_latents = pipeline(prompt, generator=generator, output_type="latent").images

    >>> with torch.no_grad():
    ...     image = pipeline.decode_latents(low_res_latents)
    >>> image = pipeline.numpy_to_pil(image)[0]

    >>> image.save("../images/a1.png")

    >>> upscaled_image = upscaler(
    ...     prompt=prompt,
    ...     image=low_res_latents,
    ...     num_inference_steps=20,
    ...     guidance_scale=0,
    ...     generator=generator,
    ... ).images[0]

    >>> upscaled_image.save("../images/a2.png")

####
enable_sequential_cpu_offload

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( 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.

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enable_attention_slicing

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( slice_size: typing.Union[str, int, NoneType] = 'auto' )

Parameters

- slice_size (`str` or `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_dim` must be a multiple of `slice_size`.

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.

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disable_attention_slicing

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( )

Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go back to computing attention in one step.

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enable_xformers_memory_efficient_attention

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( attention_op: typing.Optional[typing.Callable] = None )

Parameters

- attention_op (`Callable`, optional) — Override the default `None` operator for use as `op` argument 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.

Examples:

    >>> 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)

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disable_xformers_memory_efficient_attention

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( )

Disable memory efficient attention as implemented in xformers.