Latent upscaler (original) (raw)

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, Runway, and Stability AI Hub organizations!

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 (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.

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__

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( prompt: typing.Union[str, typing.List[str]] = None image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None 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.Tensor] = None prompt_embeds: typing.Optional[torch.Tensor] = None negative_prompt_embeds: typing.Optional[torch.Tensor] = None pooled_prompt_embeds: typing.Optional[torch.Tensor] = None negative_pooled_prompt_embeds: typing.Optional[torch.Tensor] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True callback: typing.Optional[typing.Callable[[int, int, torch.Tensor], NoneType]] = None callback_steps: int = 1 ) → StableDiffusionPipelineOutput or tuple

Parameters

• 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 textprompt 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 < 1).
• eta (float, optional, defaults to 0.0) — Corresponds to parameter eta (η) from the DDIM paper. Only applies to the DDIMScheduler, and is ignored in other schedulers.
• generator (torch.Generator or List[torch.Generator], optional) — A torch.Generator to make generation deterministic.
• latents (torch.Tensor, 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 is generated by sampling using the supplied random generator.
• output_type (str, optional, defaults to "pil") — The output format of the generated image. Choose between PIL.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 calls every callback_steps steps during inference. The function is called with the following arguments: callback(step: int, timestep: int, latents: torch.Tensor).
• callback_steps (int, optional, defaults to 1) — The frequency at which the callback function is called. If not specified, the callback is called at every step.

If return_dict is True, 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")

enable_sequential_cpu_offload

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( gpu_id: typing.Optional[int] = None device: typing.Union[torch.device, str] = 'cuda' )

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 “cuda”) — The PyTorch device type of the accelerator that shall be used in inference. If not specified, it will default to “cuda”.

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 GPU only when their specific submodule has its forwardmethod called. Offloading happens on a submodule basis. Memory savings are higher than withenable_model_cpu_offload, but performance is lower.

enable_attention_slicing

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( slice_size: typing.Union[int, str, 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", 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_dimmust be a multiple of slice_size.

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]

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

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 thememory_efficient_attention()function of xFormers.

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)

pipe.vae.enable_xformers_memory_efficient_attention(attention_op=None)

disable_xformers_memory_efficient_attention

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( )

Disable memory efficient attention from xFormers.

encode_prompt

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( prompt device do_classifier_free_guidance negative_prompt = None prompt_embeds: typing.Optional[torch.Tensor] = None negative_prompt_embeds: typing.Optional[torch.Tensor] = None pooled_prompt_embeds: typing.Optional[torch.Tensor] = None negative_pooled_prompt_embeds: typing.Optional[torch.Tensor] = None )

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_promptinput argument.

Encodes the prompt into text encoder hidden states.

class diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput

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( images: typing.Union[typing.List[PIL.Image.Image], numpy.ndarray] nsfw_content_detected: typing.Optional[typing.List[bool]] )

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 orNone if safety checking could not be performed.

Output class for Stable Diffusion pipelines.