diffusers/pr_13331/en/api/pipelines/pix2pix.md

InstructPix2Pix

InstructPix2Pix: Learning to Follow Image Editing Instructions is by Tim Brooks, Aleksander Holynski and Alexei A. Efros.

The abstract from the paper is:

We propose a method for editing images from human instructions: given an input image and a written instruction that tells the model what to do, our model follows these instructions to edit the image. To obtain training data for this problem, we combine the knowledge of two large pretrained models -- a language model (GPT-3) and a text-to-image model (Stable Diffusion) -- to generate a large dataset of image editing examples. Our conditional diffusion model, InstructPix2Pix, is trained on our generated data, and generalizes to real images and user-written instructions at inference time. Since it performs edits in the forward pass and does not require per example fine-tuning or inversion, our model edits images quickly, in a matter of seconds. We show compelling editing results for a diverse collection of input images and written instructions.

You can find additional information about InstructPix2Pix 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.

StableDiffusionInstructPix2PixPipeline[[diffusers.StableDiffusionInstructPix2PixPipeline]]

diffusers.StableDiffusionInstructPix2PixPipeline[[diffusers.StableDiffusionInstructPix2PixPipeline]]

Source

Pipeline for pixel-level image editing by following text instructions (based on Stable Diffusion).

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:

• load_textual_inversion() for loading textual inversion embeddings
• load_lora_weights() for loading LoRA weights
• save_lora_weights() for saving LoRA weights
• load_ip_adapter() for loading IP Adapters

__call__diffusers.StableDiffusionInstructPix2PixPipeline.__call__https://github.com/huggingface/diffusers/blob/vr_13331/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py#L172[{"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 = 100"}, {"name": "guidance_scale", "val": ": float = 7.5"}, {"name": "image_guidance_scale", "val": ": float = 1.5"}, {"name": "negative_prompt", "val": ": str | list[str] | None = None"}, {"name": "num_images_per_prompt", "val": ": int | None = 1"}, {"name": "eta", "val": ": float = 0.0"}, {"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": "ip_adapter_image", "val": ": PIL.Image.Image | numpy.ndarray | torch.Tensor | list[PIL.Image.Image] | list[numpy.ndarray] | list[torch.Tensor] | None = None"}, {"name": "ip_adapter_image_embeds", "val": ": list[torch.Tensor] | None = None"}, {"name": "output_type", "val": ": str | None = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "callback_on_step_end", "val": ": typing.Union[typing.Callable[[int, int], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": list = ['latents']"}, {"name": "cross_attention_kwargs", "val": ": dict[str, typing.Any] | None = None"}, {"name": "**kwargs", "val": ""}]- prompt (str or list[str], optional) -- The prompt or prompts to guide image generation. If not defined, you need to pass prompt_embeds.

• image (torch.Tensor np.ndarray, PIL.Image.Image, list[torch.Tensor], list[PIL.Image.Image], or list[np.ndarray]) -- Image or tensor representing an image batch to be repainted according to prompt. Can also accept image latents as image, but if passing latents directly it is not encoded again.
• num_inference_steps (int, optional, defaults to 100) -- 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.
• image_guidance_scale (float, optional, defaults to 1.5) -- Push the generated image towards the initial image. Image guidance scale is enabled by setting image_guidance_scale > 1. Higher image guidance scale encourages generated images that are closely linked to the source image, usually at the expense of lower image quality. This pipeline requires a value of at least 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_13331/en/api/pipelines/stable_diffusion/inpaint#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput) or tupleIf return_dictisTrue, [StableDiffusionPipelineOutput](/docs/diffusers/pr_13331/en/api/pipelines/stable_diffusion/inpaint#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput) is returned, otherwise a tupleis returned where the first element is a list with the generated images and the second element is a list ofbool`s indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content.

The call function to the pipeline for generation.

Examples:

>>> import PIL
>>> import requests
>>> import torch
>>> from io import BytesIO

>>> from diffusers import StableDiffusionInstructPix2PixPipeline

>>> def download_image(url):
...     response = requests.get(url)
...     return PIL.Image.open(BytesIO(response.content)).convert("RGB")

>>> img_url = "https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png"

>>> image = download_image(img_url).resize((512, 512))

>>> pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
...     "timbrooks/instruct-pix2pix", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")

>>> prompt = "make the mountains snowy"
>>> image = pipe(prompt=prompt, image=image).images[0]

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 (CLIPImageProcessor) : A CLIPImageProcessor to extract features from generated images; used as inputs to the safety_checker.

Returns:

[StableDiffusionPipelineOutput](/docs/diffusers/pr_13331/en/api/pipelines/stable_diffusion/inpaint#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 and the second element is a list of bools indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content.

load_textual_inversion[[diffusers.StableDiffusionInstructPix2PixPipeline.load_textual_inversion]]

Source

Load Textual Inversion embeddings into the text encoder of StableDiffusionPipeline (both 🤗 Diffusers and Automatic1111 formats are supported).

Example:

To load a Textual Inversion embedding vector in 🤗 Diffusers format:

from diffusers import StableDiffusionPipeline
import torch

model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")

pipe.load_textual_inversion("sd-concepts-library/cat-toy")

prompt = "A  backpack"

image = pipe(prompt, num_inference_steps=50).images[0]
image.save("cat-backpack.png")

To load a Textual Inversion embedding vector in Automatic1111 format, make sure to download the vector first (for example from civitAI) and then load the vector

locally:

from diffusers import StableDiffusionPipeline
import torch

model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")

pipe.load_textual_inversion("./charturnerv2.pt", token="charturnerv2")

prompt = "charturnerv2, multiple views of the same character in the same outfit, a character turnaround of a woman wearing a black jacket and red shirt, best quality, intricate details."

image = pipe(prompt, num_inference_steps=50).images[0]
image.save("character.png")

Parameters:

pretrained_model_name_or_path (str or os.PathLike or list[str or os.PathLike] or Dict or list[Dict]) : Can be either one of the following or a list of them: - A string, the model id (for example sd-concepts-library/low-poly-hd-logos-icons) of a pretrained model hosted on the Hub. - A path to a directory (for example ./my_text_inversion_directory/) containing the textual inversion weights. - A path to a file (for example ./my_text_inversions.pt) containing textual inversion weights. - A torch state dict.

token (str or list[str], optional) : Override the token to use for the textual inversion weights. If pretrained_model_name_or_path is a list, then token must also be a list of equal length.

text_encoder (CLIPTextModel, optional) : Frozen text-encoder (clip-vit-large-patch14). If not specified, function will take self.tokenizer.

tokenizer (CLIPTokenizer, optional) : A CLIPTokenizer to tokenize text. If not specified, function will take self.tokenizer.

weight_name (str, optional) : Name of a custom weight file. This should be used when: - The saved textual inversion file is in 🤗 Diffusers format, but was saved under a specific weight name such as text_inv.bin. - The saved textual inversion file is in the Automatic1111 format.

cache_dir (str | os.PathLike, optional) : Path to a directory where a downloaded pretrained model configuration is cached if the standard cache is not used.

force_download (bool, optional, defaults to False) : Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.

proxies (dict[str, str], optional) : A dictionary of proxy servers to use by protocol or endpoint, for example, {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request.

local_files_only (bool, optional, defaults to False) : Whether to only load local model weights and configuration files or not. If set to True, the model won't be downloaded from the Hub.

hf_token (str or bool, optional) : The token to use as HTTP bearer authorization for remote files. If True, the token generated from diffusers-cli login (stored in ~/.huggingface) is used.

revision (str, optional, defaults to "main") : The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier allowed by Git.

subfolder (str, optional, defaults to "") : The subfolder location of a model file within a larger model repository on the Hub or locally.

mirror (str, optional) : Mirror source to resolve accessibility issues if you're downloading a model in China. We do not guarantee the timeliness or safety of the source, and you should refer to the mirror site for more information.

load_lora_weights[[diffusers.StableDiffusionInstructPix2PixPipeline.load_lora_weights]]

Source

Load LoRA weights specified in pretrained_model_name_or_path_or_dict into self.unet and self.text_encoder.

All kwargs are forwarded to self.lora_state_dict.

See lora_state_dict() for more details on how the state dict is loaded.

See load_lora_into_unet() for more details on how the state dict is loaded into self.unet.

See load_lora_into_text_encoder() for more details on how the state dict is loaded into self.text_encoder.

Parameters:

pretrained_model_name_or_path_or_dict (str or os.PathLike or dict) : See lora_state_dict().

adapter_name (str, optional) : Adapter name to be used for referencing the loaded adapter model. If not specified, it will use default_{i} where i is the total number of adapters being loaded.

low_cpu_mem_usage (bool, optional) : Speed up model loading by only loading the pretrained LoRA weights and not initializing the random weights.

hotswap (bool, optional) : Defaults to False. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter in-place. This means that, instead of loading an additional adapter, this will take the existing adapter weights and replace them with the weights of the new adapter. This can be faster and more memory efficient. However, the main advantage of hotswapping is that when the model is compiled with torch.compile, loading the new adapter does not require recompilation of the model. When using hotswapping, the passed adapter_name should be the name of an already loaded adapter. If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need to call an additional method before loading the adapter: py pipeline = ... # load diffusers pipeline max_rank = ... # the highest rank among all LoRAs that you want to load # call *before* compiling and loading the LoRA adapter pipeline.enable_lora_hotswap(target_rank=max_rank) pipeline.load_lora_weights(file_name) # optionally compile the model now Note that hotswapping adapters of the text encoder is not yet supported. There are some further limitations to this technique, which are documented here: https://huggingface.co/docs/peft/main/en/package_reference/hotswap

kwargs (dict, optional) : See lora_state_dict().

save_lora_weights[[diffusers.StableDiffusionInstructPix2PixPipeline.save_lora_weights]]

Source

Save the LoRA parameters corresponding to the UNet and text encoder.

Parameters:

save_directory (str or os.PathLike) : Directory to save LoRA parameters to. Will be created if it doesn't exist.

unet_lora_layers (dict[str, torch.nn.Module] or dict[str, torch.Tensor]) : State dict of the LoRA layers corresponding to the unet.

text_encoder_lora_layers (dict[str, torch.nn.Module] or dict[str, torch.Tensor]) : State dict of the LoRA layers corresponding to the text_encoder. Must explicitly pass the text encoder LoRA state dict because it comes from 🤗 Transformers.

is_main_process (bool, optional, defaults to True) : Whether the process calling this is the main process or not. Useful during distributed training and you need to call this function on all processes. In this case, set is_main_process=True only on the main process to avoid race conditions.

save_function (Callable) : The function to use to save the state dictionary. Useful during distributed training when you need to replace torch.save with another method. Can be configured with the environment variable DIFFUSERS_SAVE_MODE.

safe_serialization (bool, optional, defaults to True) : Whether to save the model using safetensors or the traditional PyTorch way with pickle.

unet_lora_adapter_metadata : LoRA adapter metadata associated with the unet to be serialized with the state dict.

text_encoder_lora_adapter_metadata : LoRA adapter metadata associated with the text encoder to be serialized with the state dict.

StableDiffusionXLInstructPix2PixPipeline[[diffusers.StableDiffusionXLInstructPix2PixPipeline]]

diffusers.StableDiffusionXLInstructPix2PixPipeline[[diffusers.StableDiffusionXLInstructPix2PixPipeline]]

Source

Pipeline for pixel-level image editing by following text instructions. Based on Stable Diffusion XL.

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.)

The pipeline also inherits the following loading methods:

• load_textual_inversion() for loading textual inversion embeddings
• from_single_file() for loading .ckpt files
• load_lora_weights() for loading LoRA weights
• save_lora_weights() for saving LoRA weights

__call__diffusers.StableDiffusionXLInstructPix2PixPipeline.__call__https://github.com/huggingface/diffusers/blob/vr_13331/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_instruct_pix2pix.py#L595[{"name": "prompt", "val": ": str | list[str] = None"}, {"name": "prompt_2", "val": ": str | list[str] | None = None"}, {"name": "image", "val": ": PIL.Image.Image | numpy.ndarray | torch.Tensor | list[PIL.Image.Image] | list[numpy.ndarray] | list[torch.Tensor] = None"}, {"name": "height", "val": ": int | None = None"}, {"name": "width", "val": ": int | None = None"}, {"name": "num_inference_steps", "val": ": int = 100"}, {"name": "denoising_end", "val": ": float | None = None"}, {"name": "guidance_scale", "val": ": float = 5.0"}, {"name": "image_guidance_scale", "val": ": float = 1.5"}, {"name": "negative_prompt", "val": ": str | list[str] | None = None"}, {"name": "negative_prompt_2", "val": ": str | list[str] | None = None"}, {"name": "num_images_per_prompt", "val": ": int | None = 1"}, {"name": "eta", "val": ": float = 0.0"}, {"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"}, {"name": "cross_attention_kwargs", "val": ": dict[str, typing.Any] | None = None"}, {"name": "guidance_rescale", "val": ": float = 0.0"}, {"name": "original_size", "val": ": tuple = None"}, {"name": "crops_coords_top_left", "val": ": tuple = (0, 0)"}, {"name": "target_size", "val": ": tuple = None"}]- prompt (str or list[str], optional) -- The prompt or prompts to guide the image generation. If not defined, one has to pass prompt_embeds. instead.

• prompt_2 (str or list[str], optional) -- The prompt or prompts to be sent to the tokenizer_2 and text_encoder_2. If not defined, prompt is used in both text-encoders
• image (torch.Tensor or PIL.Image.Image or np.ndarray or list[torch.Tensor] or list[PIL.Image.Image] or list[np.ndarray]) -- The image(s) to modify with the pipeline.
• 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.
• denoising_end (float, optional) -- When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be completed before it is intentionally prematurely terminated. As a result, the returned sample will still retain a substantial amount of noise as determined by the discrete timesteps selected by the scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a "Mixture of Denoisers" multi-pipeline setup, as elaborated in Refining the Image Output
• guidance_scale (float, optional, defaults to 5.0) -- 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.
• image_guidance_scale (float, optional, defaults to 1.5) -- Image guidance scale is to push the generated image towards the initial image image. Image guidance scale is enabled by setting image_guidance_scale > 1. Higher image guidance scale encourages to generate images that are closely linked to the source image image, usually at the expense of lower image quality. This pipeline requires a value of at least 1.
• 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).
• negative_prompt_2 (str or list[str], optional) -- The prompt or prompts not to guide the image generation to be sent to tokenizer_2 and text_encoder_2. If not defined, negative_prompt is used in both text-encoders.
• 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://huggingface.co/papers/2010.02502. Only applies to schedulers.DDIMScheduler, will be ignored for others.
• generator (torch.Generator or list[torch.Generator], optional) -- One or a list of torch generator(s) 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 will be generated by sampling using the supplied random generator.
• prompt_embeds (torch.Tensor, 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.Tensor, 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.
• 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 ~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput 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.Tensor).
• 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.
• cross_attention_kwargs (dict, optional) -- A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under self.processor in diffusers.models.attention_processor.
• guidance_rescale (float, optional, defaults to 0.0) -- Guidance rescale factor proposed by Common Diffusion Noise Schedules and Sample Steps are Flawed guidance_scale is defined as φ in equation 16. of Common Diffusion Noise Schedules and Sample Steps are Flawed. Guidance rescale factor should fix overexposure when using zero terminal SNR.
• original_size (tuple[int], optional, defaults to (1024, 1024)) -- If original_size is not the same as target_size the image will appear to be down- or upsampled. original_size defaults to (height, width) if not specified. Part of SDXL's micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952.
• crops_coords_top_left (tuple[int], optional, defaults to (0, 0)) -- crops_coords_top_left can be used to generate an image that appears to be "cropped" from the position crops_coords_top_left downwards. Favorable, well-centered images are usually achieved by setting crops_coords_top_left to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952.
• target_size (tuple[int], optional, defaults to (1024, 1024)) -- For most cases, target_size should be set to the desired height and width of the generated image. If not specified it will default to (height, width). Part of SDXL's micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952.
• aesthetic_score (float, optional, defaults to 6.0) -- Used to simulate an aesthetic score of the generated image by influencing the positive text condition. Part of SDXL's micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952.
• negative_aesthetic_score (float, optional, defaults to 2.5) -- Part of SDXL's micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952. Can be used to simulate an aesthetic score of the generated image by influencing the negative text condition.0~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput or tuple``~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput if return_dict is True, otherwise a tuple. When returning a tuple, the first element is a list with the generated images.

Function invoked when calling the pipeline for generation.

Examples:

>>> import torch
>>> from diffusers import StableDiffusionXLInstructPix2PixPipeline
>>> from diffusers.utils import load_image

>>> resolution = 768
>>> image = load_image(
...     "https://hf.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png"
... ).resize((resolution, resolution))
>>> edit_instruction = "Turn sky into a cloudy one"

>>> pipe = StableDiffusionXLInstructPix2PixPipeline.from_pretrained(
...     "diffusers/sdxl-instructpix2pix-768", torch_dtype=torch.float16
... ).to("cuda")

>>> edited_image = pipe(
...     prompt=edit_instruction,
...     image=image,
...     height=resolution,
...     width=resolution,
...     guidance_scale=3.0,
...     image_guidance_scale=1.5,
...     num_inference_steps=30,
... ).images[0]
>>> edited_image

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 XL uses the text portion of CLIP, specifically the clip-vit-large-patch14 variant.

text_encoder_2 ( CLIPTextModelWithProjection) : Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of CLIP, specifically the laion/CLIP-ViT-bigG-14-laion2B-39B-b160k variant.

tokenizer (CLIPTokenizer) : Tokenizer of class CLIPTokenizer.

tokenizer_2 (CLIPTokenizer) : Second 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 latents. Can be one of DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler.

requires_aesthetics_score (bool, optional, defaults to "False") : Whether the unet requires a aesthetic_score condition to be passed during inference. Also see the config of stabilityai/stable-diffusion-xl-refiner-1-0.

force_zeros_for_empty_prompt (bool, optional, defaults to "True") : Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of stabilityai/stable-diffusion-xl-base-1-0.

add_watermarker (bool, optional) : Whether to use the invisible_watermark library to watermark output images. If not defined, it will default to True if the package is installed, otherwise no watermarker will be used.

is_cosxl_edit (bool, optional) : When set the image latents are scaled.

Returns:

~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput` or `tuple

~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput if return_dict is True, otherwise a tuple. When returning a tuple, the first element is a list with the generated images.

encode_prompt[[diffusers.StableDiffusionXLInstructPix2PixPipeline.encode_prompt]]

Source

Encodes the prompt into text encoder hidden states.

Parameters:

prompt (str or list[str], optional) : prompt to be encoded

prompt_2 (str or list[str], optional) : The prompt or prompts to be sent to the tokenizer_2 and text_encoder_2. If not defined, prompt is used in both text-encoders

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).

negative_prompt_2 (str or list[str], optional) : The prompt or prompts not to guide the image generation to be sent to tokenizer_2 and text_encoder_2. If not defined, negative_prompt is used in both text-encoders

prompt_embeds (torch.Tensor, 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.Tensor, 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.

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