#
Semantic Guidance

Semantic Guidance for Diffusion Models was proposed in SEGA: Instructing Diffusion using Semantic Dimensions and provides strong semantic control over the 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, and stay true to the original image composition.

The abstract of the paper is the following:

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) 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 a variety of tasks and provide evidence for its versatility and flexibility.

Overview:

Pipeline | Tasks | Colab | Demo
--- | --- | --- | ---
pipeline_semantic_stable_diffusion.py | Text-to-Image Generation |  | Coming Soon

##
Tips

- The Semantic Guidance pipeline can be used with any Stable Diffusion checkpoint.

###
Run Semantic Guidance

The interface of SemanticStableDiffusionPipeline provides several additional parameters to influence the image generation. Exemplary usage may look like this:

    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
    )

For more examples check the colab notebook.

##
StableDiffusionSafePipelineOutput

###
class diffusers.pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput

< source >

( 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)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
- nsfw_content_detected (`List[bool]`) — List of flags denoting whether the corresponding generated image likely represents “not-safe-for-work” (nsfw) content, or `None` if safety checking could not be performed.

Output class for Stable Diffusion pipelines.

##
SemanticStableDiffusionPipeline

###
class diffusers.SemanticStableDiffusionPipeline

< source >

( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers safety_checker: StableDiffusionSafetyChecker feature_extractor: CLIPFeatureExtractor 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. 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 latens. 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 details.
- feature_extractor (`CLIPFeatureExtractor`) — Model that extracts features from generated images to be used as inputs for the `safety_checker`.

Pipeline for text-to-image generation with latent editing.

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.)

This model builds on the implementation of [‘StableDiffusionPipeline’]

####
__call__

< source >

( 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 ) → SemanticStableDiffusionPipelineOutput or `tuple`

Parameters

- prompt (`str` or `List[str]`) — The prompt or prompts to guide the 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) — 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`).
- 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) 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.
- 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 list values should correspond to `editing_prompt`. `edit_guidance_scale` is defined as `s_e` of equation 6 of SEGA Paper.
- edit_warmup_steps (`float` or `List[float]`, optional, defaults to 10) — Number of diffusion steps (for each prompt) for which semantic guidance will not be applied. Momentum will still be calculated for those steps and applied once all warmup periods are over. `edit_warmup_steps` is defined as `delta` (δ) of SEGA Paper.
- edit_cooldown_steps (`float` or `List[float]`, optional, defaults to `None`) — Number of diffusion steps (for each prompt) after which semantic guidance will no longer be 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 will be disabled. Momentum is already built up during warmup, i.e. for diffusion steps smaller than `sld_warmup_steps`. Momentum will only be added to latent guidance once all warmup periods are finished. `edit_momentum_scale` is defined as `s_m` of equation 7 of SEGA Paper.
- 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 will be kept. Momentum is already built up during warmup, i.e. for diffusion steps smaller than `edit_warmup_steps`. `edit_mom_beta` is defined as `beta_m` (β) of equation 8 of SEGA Paper.
- 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. `edit_mom_beta` is defined as `g_i` of equation 9 of SEGA Paper.
- 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

SemanticStableDiffusionPipelineOutput or `tuple`

SemanticStableDiffusionPipelineOutput 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.