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Semantic Guidance

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Semantic Guidance

Semantic Guidance for Diffusion Models was proposed in SEGA: Instructing Text-to-Image Models using Semantic Guidance and provides strong semantic control over image generation. Small changes to the text prompt usually result in entirely different output images. However, with SEGA a variety of changes to the image are enabled that can be controlled easily and intuitively, while staying true to the original image composition.

The abstract from the paper is:

Text-to-image diffusion models have recently received a lot of interest for their astonishing ability to produce high-fidelity images from text only. However, achieving one-shot generation that aligns with the user’s intent is nearly impossible, yet small changes to the input prompt often result in very different images. This leaves the user with little semantic control. To put the user in control, we show how to interact with the diffusion process to flexibly steer it along semantic directions. This semantic guidance (SEGA) generalizes to any generative architecture using classifier-free guidance. More importantly, it allows for subtle and extensive edits, changes in composition and style, as well as optimizing the overall artistic conception. We demonstrate SEGA’s effectiveness on both latent and pixel-based diffusion models such as Stable Diffusion, Paella, and DeepFloyd-IF using a variety of tasks, thus providing strong evidence for its versatility, flexibility, and improvements over existing methods.

Make sure to check out the Schedulers guide to learn how to explore the tradeoff between scheduler speed and quality, and see the reuse components across pipelines section to learn how to efficiently load the same components into multiple pipelines.

SemanticStableDiffusionPipeline

class diffusers.SemanticStableDiffusionPipeline

< >

( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers safety_checker: StableDiffusionSafetyChecker feature_extractor: CLIPImageProcessor requires_safety_checker: bool = True )

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 scheduler to be used in combination with unet to denoise the encoded image latents. Can be one of DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler.
  • safety_checker (Q16SafetyChecker) — Classification module that estimates whether generated images could be considered offensive or harmful. Please refer to the model card for more details about a model’s potential harms.
  • feature_extractor (CLIPImageProcessor) — A CLIPImageProcessor to extract features from generated images; used as inputs to the safety_checker.

Pipeline for text-to-image generation using Stable Diffusion with latent editing.

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

__call__

< >

( prompt: typing.Union[str, typing.List[str]] height: typing.Optional[int] = None width: typing.Optional[int] = None num_inference_steps: int = 50 guidance_scale: float = 7.5 negative_prompt: typing.Union[str, typing.List[str], NoneType] = None num_images_per_prompt: 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 editing_prompt: typing.Union[str, typing.List[str], NoneType] = None editing_prompt_embeddings: typing.Optional[torch.Tensor] = None reverse_editing_direction: typing.Union[bool, typing.List[bool], NoneType] = False edit_guidance_scale: typing.Union[float, typing.List[float], NoneType] = 5 edit_warmup_steps: typing.Union[int, typing.List[int], NoneType] = 10 edit_cooldown_steps: typing.Union[int, typing.List[int], NoneType] = None edit_threshold: typing.Union[float, typing.List[float], NoneType] = 0.9 edit_momentum_scale: typing.Optional[float] = 0.1 edit_mom_beta: typing.Optional[float] = 0.4 edit_weights: typing.Optional[typing.List[float]] = None sem_guidance: typing.Optional[typing.List[torch.Tensor]] = None ) → ~pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput or tuple

Parameters

  • prompt (str or List[str]) — The prompt or prompts to guide image generation.
  • height (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The height in pixels of the generated image.
  • width (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The width in pixels of the generated image.
  • 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 < 1).
  • num_images_per_prompt (int, optional, defaults to 1) — The number of images to generate per prompt.
  • 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.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 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.FloatTensor).
  • 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.
  • editing_prompt (str or List[str], optional) — The prompt or prompts to use for semantic guidance. Semantic guidance is disabled by setting editing_prompt = None. Guidance direction of prompt should be specified via reverse_editing_direction.
  • editing_prompt_embeddings (torch.Tensor, optional) — Pre-computed embeddings to use for semantic guidance. Guidance direction of embedding should be specified via reverse_editing_direction.
  • reverse_editing_direction (bool or List[bool], optional, defaults to False) — Whether the corresponding prompt in editing_prompt should be increased or decreased.
  • edit_guidance_scale (float or List[float], optional, defaults to 5) — Guidance scale for semantic guidance. If provided as a list, values should correspond to editing_prompt.
  • edit_warmup_steps (float or List[float], optional, defaults to 10) — Number of diffusion steps (for each prompt) for which semantic guidance is not applied. Momentum is calculated for those steps and applied once all warmup periods are over.
  • edit_cooldown_steps (float or List[float], optional, defaults to None) — Number of diffusion steps (for each prompt) after which semantic guidance is longer applied.
  • edit_threshold (float or List[float], optional, defaults to 0.9) — Threshold of semantic guidance.
  • edit_momentum_scale (float, optional, defaults to 0.1) — Scale of the momentum to be added to the semantic guidance at each diffusion step. If set to 0.0, momentum is disabled. Momentum is already built up during warmup (for diffusion steps smaller than sld_warmup_steps). Momentum is only added to latent guidance once all warmup periods are finished.
  • edit_mom_beta (float, optional, defaults to 0.4) — Defines how semantic guidance momentum builds up. edit_mom_beta indicates how much of the previous momentum is kept. Momentum is already built up during warmup (for diffusion steps smaller than edit_warmup_steps).
  • edit_weights (List[float], optional, defaults to None) — Indicates how much each individual concept should influence the overall guidance. If no weights are provided all concepts are applied equally.
  • sem_guidance (List[torch.Tensor], optional) — List of pre-generated guidance vectors to be applied at generation. Length of the list has to correspond to num_inference_steps.

Returns

~pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput or tuple

If return_dict is True, ~pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput is returned, otherwise a tuple is returned where the first element is a list with the generated images and the second element is a list of bools indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content.

The call function to the pipeline for generation.

Examples:

>>> import torch
>>> from diffusers import SemanticStableDiffusionPipeline

>>> pipe = SemanticStableDiffusionPipeline.from_pretrained(
...     "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")

>>> out = pipe(
...     prompt="a photo of the face of a woman",
...     num_images_per_prompt=1,
...     guidance_scale=7,
...     editing_prompt=[
...         "smiling, smile",  # Concepts to apply
...         "glasses, wearing glasses",
...         "curls, wavy hair, curly hair",
...         "beard, full beard, mustache",
...     ],
...     reverse_editing_direction=[
...         False,
...         False,
...         False,
...         False,
...     ],  # Direction of guidance i.e. increase all concepts
...     edit_warmup_steps=[10, 10, 10, 10],  # Warmup period for each concept
...     edit_guidance_scale=[4, 5, 5, 5.4],  # Guidance scale for each concept
...     edit_threshold=[
...         0.99,
...         0.975,
...         0.925,
...         0.96,
...     ],  # Threshold for each concept. Threshold equals the percentile of the latent space that will be discarded. I.e. threshold=0.99 uses 1% of the latent dimensions
...     edit_momentum_scale=0.3,  # Momentum scale that will be added to the latent guidance
...     edit_mom_beta=0.6,  # Momentum beta
...     edit_weights=[1, 1, 1, 1, 1],  # Weights of the individual concepts against each other
... )
>>> image = out.images[0]

StableDiffusionSafePipelineOutput

class diffusers.pipelines.semantic_stable_diffusion.pipeline_output.SemanticStableDiffusionPipelineOutput

< >

( 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 or None if safety checking could not be performed.

Output class for Stable Diffusion pipelines.

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