diffusers/pr_12652/en/api/pipelines/stable_diffusion/latent_upscale.md

Latent upscaler

The Stable Diffusion latent upscaler model was created by Katherine Crowson in collaboration with Stability AI. It is used to enhance the output image resolution by a factor of 2 (see this demo notebook for a demonstration of the original implementation).

Make sure to check out the Stable Diffusion Tips section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!

If you're interested in using one of the official checkpoints for a task, explore the CompVis and Stability AI Hub organizations!

StableDiffusionLatentUpscalePipeline[[diffusers.StableDiffusionLatentUpscalePipeline]]

diffusers.StableDiffusionLatentUpscalePipeline[[diffusers.StableDiffusionLatentUpscalePipeline]]

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Pipeline for upscaling Stable Diffusion output image resolution by a factor of 2.

This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.).

The pipeline also inherits the following loading methods:

• from_single_file() for loading .ckpt files

__call__diffusers.StableDiffusionLatentUpscalePipeline.__call__https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py#L396[{"name": "prompt", "val": ": str | list[str] = None"}, {"name": "image", "val": ": PIL.Image.Image | numpy.ndarray | torch.Tensor | list[PIL.Image.Image] | list[numpy.ndarray] | list[torch.Tensor] = None"}, {"name": "num_inference_steps", "val": ": int = 75"}, {"name": "guidance_scale", "val": ": float = 9.0"}, {"name": "negative_prompt", "val": ": str | list[str] | None = None"}, {"name": "generator", "val": ": torch._C.Generator | list[torch._C.Generator] | None = None"}, {"name": "latents", "val": ": torch.Tensor | None = None"}, {"name": "prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "negative_prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "pooled_prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "negative_pooled_prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "output_type", "val": ": str | None = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "callback", "val": ": typing.Optional[typing.Callable[[int, int, torch.Tensor], NoneType]] = None"}, {"name": "callback_steps", "val": ": int = 1"}]- prompt (str or list[str]) -- The prompt or prompts to guide image upscaling.

• image (torch.Tensor, PIL.Image.Image, np.ndarray, list[torch.Tensor], list[PIL.Image.Image], or list[np.ndarray]) -- Image or tensor representing an image batch to 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 is considered a latent if image.shape[1] is 4; otherwise, it is 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) -- A higher guidance scale value encourages the model to generate images closely linked to the text prompt at the expense of lower image quality. Guidance scale is enabled when guidance_scale > 1.
• negative_prompt (str or list[str], optional) -- The prompt or prompts to guide what to not include in image generation. If not defined, you need to pass negative_prompt_embeds instead. Ignored when not using guidance (guidance_scale 0[StableDiffusionPipelineOutput](/docs/diffusers/pr_12652/en/api/pipelines/stable_diffusion/gligen#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput) or tupleIf return_dictisTrue, [StableDiffusionPipelineOutput](/docs/diffusers/pr_12652/en/api/pipelines/stable_diffusion/gligen#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput) is returned, otherwise a tuple` is returned where the first element is a list with the generated images.

The call function to 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")

Parameters:

vae (AutoencoderKL) : Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.

text_encoder (CLIPTextModel) : Frozen text-encoder (clip-vit-large-patch14).

tokenizer (CLIPTokenizer) : A CLIPTokenizer to tokenize text.

unet (UNet2DConditionModel) : A UNet2DConditionModel to denoise the encoded image latents.

scheduler (SchedulerMixin) : A EulerDiscreteScheduler to be used in combination with unet to denoise the encoded image latents.

Returns:

[StableDiffusionPipelineOutput](/docs/diffusers/pr_12652/en/api/pipelines/stable_diffusion/gligen#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput) or tuple``

If return_dict is True, StableDiffusionPipelineOutput is returned, otherwise a tuple is returned where the first element is a list with the generated images.

enable_sequential_cpu_offload[[diffusers.StableDiffusionLatentUpscalePipeline.enable_sequential_cpu_offload]]

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Offloads all models to CPU using 🤗 Accelerate, significantly reducing memory usage. When called, the state dicts of all torch.nn.Module components (except those in self._exclude_from_cpu_offload) are saved to CPU and then moved to torch.device('meta') and loaded to accelerator only when their specific submodule has its forward method called. Offloading happens on a submodule basis. Memory savings are higher than with enable_model_cpu_offload, but performance is lower.

Parameters:

gpu_id (int, optional) : The ID of the accelerator that shall be used in inference. If not specified, it will default to 0.

device (torch.Device or str, optional, defaults to None) : The PyTorch device type of the accelerator that shall be used in inference. If not specified, it will automatically detect the available accelerator and use.

enable_attention_slicing[[diffusers.StableDiffusionLatentUpscalePipeline.enable_attention_slicing]]

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Enable sliced attention computation. When this option is enabled, the attention module splits the input tensor in slices to compute attention in several steps. For more than one attention head, the computation is performed sequentially over each head. This is useful to save some memory in exchange for a small speed decrease.

> ⚠️ Don't enable attention slicing if you're already using scaled_dot_product_attention (SDPA) from PyTorch > 2.0 or xFormers. These attention computations are already very memory efficient so you won't need to enable > this function. If you enable attention slicing with SDPA or xFormers, it can lead to serious slow downs!

Examples:

>>> import torch
>>> from diffusers import StableDiffusionPipeline

>>> pipe = StableDiffusionPipeline.from_pretrained(
...     "stable-diffusion-v1-5/stable-diffusion-v1-5",
...     torch_dtype=torch.float16,
...     use_safetensors=True,
... )

>>> prompt = "a photo of an astronaut riding a horse on mars"
>>> pipe.enable_attention_slicing()
>>> image = pipe(prompt).images[0]

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", maximum 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.

disable_attention_slicing[[diffusers.StableDiffusionLatentUpscalePipeline.disable_attention_slicing]]

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Disable sliced attention computation. If enable_attention_slicing was previously called, attention is computed in one step.

enable_xformers_memory_efficient_attention[[diffusers.StableDiffusionLatentUpscalePipeline.enable_xformers_memory_efficient_attention]]

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Enable memory efficient attention from xFormers. When this option is enabled, you should observe lower GPU memory usage and a potential speed up during inference. Speed up during training is not guaranteed.

> ⚠️ When memory efficient attention and sliced attention are both enabled, memory efficient attention takes > precedent.

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)

Parameters:

attention_op (Callable, optional) : Override the default None operator for use as op argument to the memory_efficient_attention() function of xFormers.

disable_xformers_memory_efficient_attention[[diffusers.StableDiffusionLatentUpscalePipeline.disable_xformers_memory_efficient_attention]]

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Disable memory efficient attention from xFormers.

encode_prompt[[diffusers.StableDiffusionLatentUpscalePipeline.encode_prompt]]

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Encodes the prompt into text encoder hidden states.

Parameters:

prompt (str or list(int)) : prompt to be encoded

device : (torch.device): torch device

do_classifier_free_guidance (bool) : whether to use classifier free guidance or not

negative_prompt (str or list[str]) : 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).

prompt_embeds (torch.FloatTensor, optional) : Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated from prompt input argument.

negative_prompt_embeds (torch.FloatTensor, optional) : Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, negative_prompt_embeds will be generated from negative_prompt input argument.

pooled_prompt_embeds (torch.Tensor, optional) : Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled text embeddings will be generated from prompt input argument.

negative_pooled_prompt_embeds (torch.Tensor, optional) : Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled negative_prompt_embeds will be generated from negative_prompt input argument.

StableDiffusionPipelineOutput[[diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput]]

diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput[[diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput]]

Source

Output class for Stable Diffusion pipelines.

Parameters:

images (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).

nsfw_content_detected (list[bool]) : list indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content or None if safety checking could not be performed.