Text-to-image model editing

Text-to-image model editing

Editing Implicit Assumptions in Text-to-Image Diffusion Models is by Hadas Orgad, Bahjat Kawar, and Yonatan Belinkov. This pipeline enables editing diffusion model weights, such that its assumptions of a given concept are changed. The resulting change is expected to take effect in all prompt generations related to the edited concept.

The abstract from the paper is:

Text-to-image diffusion models often make implicit assumptions about the world when generating images. While some assumptions are useful (e.g., the sky is blue), they can also be outdated, incorrect, or reflective of social biases present in the training data. Thus, there is a need to control these assumptions without requiring explicit user input or costly re-training. In this work, we aim to edit a given implicit assumption in a pre-trained diffusion model. Our Text-to-Image Model Editing method, TIME for short, receives a pair of inputs: a “source” under-specified prompt for which the model makes an implicit assumption (e.g., “a pack of roses”), and a “destination” prompt that describes the same setting, but with a specified desired attribute (e.g., “a pack of blue roses”). TIME then updates the model’s cross-attention layers, as these layers assign visual meaning to textual tokens. We edit the projection matrices in these layers such that the source prompt is projected close to the destination prompt. Our method is highly efficient, as it modifies a mere 2.2% of the model’s parameters in under one second. To evaluate model editing approaches, we introduce TIMED (TIME Dataset), containing 147 source and destination prompt pairs from various domains. Our experiments (using Stable Diffusion) show that TIME is successful in model editing, generalizes well for related prompts unseen during editing, and imposes minimal effect on unrelated generations.

You can find additional information about model editing on the project page, original codebase, and try it out in a demo.

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.

StableDiffusionModelEditingPipeline

class diffusers.StableDiffusionModelEditingPipeline

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( vae: AutoencoderKLtext_encoder: CLIPTextModeltokenizer: CLIPTokenizerunet: UNet2DConditionModelscheduler: SchedulerMixinsafety_checker: StableDiffusionSafetyCheckerfeature_extractor: CLIPFeatureExtractorrequires_safety_checker: bool = Truewith_to_k: bool = Truewith_augs: list = ['A photo of ', 'An image of ', 'A picture of '] )

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 (StableDiffusionSafetyChecker) — 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 (CLIPFeatureExtractor) — A CLIPFeatureExtractor to extract features from generated images; used as inputs to the safety_checker.

• with_to_k (bool) — Whether to edit the key projection matrices along with the value projection matrices.

• with_augs (list) — Textual augmentations to apply while editing the text-to-image model. Set to [] for no augmentations.

Pipeline for text-to-image model editing.

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.).

__call__

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( prompt: typing.Union[str, typing.List[str]] = Noneheight: typing.Optional[int] = Nonewidth: typing.Optional[int] = Nonenum_inference_steps: int = 50guidance_scale: float = 7.5negative_prompt: typing.Union[str, typing.List[str], NoneType] = Nonenum_images_per_prompt: typing.Optional[int] = 1eta: float = 0.0generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = Nonelatents: typing.Optional[torch.FloatTensor] = Noneprompt_embeds: typing.Optional[torch.FloatTensor] = Nonenegative_prompt_embeds: typing.Optional[torch.FloatTensor] = Noneoutput_type: typing.Optional[str] = 'pil'return_dict: bool = Truecallback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = Nonecallback_steps: int = 1cross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None ) → StableDiffusionPipelineOutput or tuple

Parameters

• prompt (str or List[str], optional) — The prompt or prompts to guide image generation. If not defined, you need to pass prompt_embeds.

• 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.

• prompt_embeds (torch.FloatTensor, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, text embeddings are generated from the prompt input argument.

• negative_prompt_embeds (torch.FloatTensor, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, negative_prompt_embeds are generated from the negative_prompt input argument.

• 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.

• cross_attention_kwargs (dict, optional) — A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined in self.processor.

Returns

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 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:

Copied

>>> import torch
>>> from diffusers import StableDiffusionModelEditingPipeline

>>> model_ckpt = "CompVis/stable-diffusion-v1-4"
>>> pipe = StableDiffusionModelEditingPipeline.from_pretrained(model_ckpt)

>>> pipe = pipe.to("cuda")

>>> source_prompt = "A pack of roses"
>>> destination_prompt = "A pack of blue roses"
>>> pipe.edit_model(source_prompt, destination_prompt)

>>> prompt = "A field of roses"
>>> image = pipe(prompt).images[0]

disable_vae_slicing

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( )

Disable sliced VAE decoding. If enable_vae_slicing was previously enabled, this method will go back to computing decoding in one step.

edit_model

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( source_prompt: strdestination_prompt: strlamb: float = 0.1restart_params: bool = True )

Parameters

• source_prompt (str) — The source prompt containing the concept to be edited.

• destination_prompt (str) — The destination prompt. Must contain all words from source_prompt with additional ones to specify the target edit.

• lamb (float, optional, defaults to 0.1) — The lambda parameter specifying the regularization intesity. Smaller values increase the editing power.

• restart_params (bool, optional, defaults to True) — Restart the model parameters to their pre-trained version before editing. This is done to avoid edit compounding. When it is False, edits accumulate.

Apply model editing via closed-form solution (see Eq. 5 in the TIME paper).

enable_vae_slicing

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( )

Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.

encode_prompt

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( promptdevicenum_images_per_promptdo_classifier_free_guidancenegative_prompt = Noneprompt_embeds: typing.Optional[torch.FloatTensor] = Nonenegative_prompt_embeds: typing.Optional[torch.FloatTensor] = Nonelora_scale: typing.Optional[float] = None )

Parameters

• prompt (str or List[str], optional) — prompt to be encoded device — (torch.device): torch device

• num_images_per_prompt (int) — number of images that should be generated per prompt

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

• negative_prompt (str or List[str], optional) — The prompt or prompts not to guide the image generation. If not defined, one has to pass negative_prompt_embeds instead. 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.

• lora_scale (float, optional) — A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.

Encodes the prompt into text encoder hidden states.

StableDiffusionPipelineOutput

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 or None if safety checking could not be performed.

Output class for Stable Diffusion pipelines.