InstructPix2Pix: Learning to Follow Image Editing Instructions by Tim Brooks, Aleksander Holynski and Alexei A. Efros.
The abstract of the paper is the following:
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.
|StableDiffusionInstructPix2PixPipeline||Text-Based Image Editing||🤗 Space|
import PIL import requests import torch from diffusers import StableDiffusionInstructPix2PixPipeline model_id = "timbrooks/instruct-pix2pix" pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda") url = "https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png" def download_image(url): image = PIL.Image.open(requests.get(url, stream=True).raw) image = PIL.ImageOps.exif_transpose(image) image = image.convert("RGB") return image image = download_image(url) prompt = "make the mountains snowy" images = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=1.5, guidance_scale=7).images images.save("snowy_mountains.png")
class diffusers.StableDiffusionInstructPix2PixPipeline< source >
( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers safety_checker: StableDiffusionSafetyChecker feature_extractor: CLIPFeatureExtractor requires_safety_checker: bool = True )
- vae (AutoencoderKL) — Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
CLIPTextModel) — Frozen text-encoder. Stable Diffusion uses the text portion of CLIP, specifically the clip-vit-large-patch14 variant.
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
unetto denoise the encoded image latents. Can be one of DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler.
StableDiffusionSafetyChecker) — Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the model card for details.
CLIPFeatureExtractor) — Model that extracts features from generated images to be used as inputs for the
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 the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
__call__< source >
prompt: typing.Union[str, typing.List[str]] = None
image: typing.Union[torch.FloatTensor, PIL.Image.Image] = None
num_inference_steps: int = 100
guidance_scale: float = 7.5
image_guidance_scale: float = 1.5
negative_prompt: typing.Union[str, typing.List[str], NoneType] = None
num_images_per_prompt: typing.Optional[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
prompt_embeds: typing.Optional[torch.FloatTensor] = None
negative_prompt_embeds: 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
List[str], optional) — The prompt or prompts to guide the image generation. If not defined, one has to pass
Image, or tensor representing an image batch which will be repainted according to
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.
float, optional, defaults to 7.5) — Guidance scale as defined in Classifier-Free Diffusion Guidance.
guidance_scaleis defined as
wof 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. This pipeline requires a value of at least
float, optional, defaults to 1.5) — Image guidance scale is to push the generated image towards the inital 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
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_scaleis less than
int, optional, defaults to 1) — The number of images to generate per prompt.
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.
torch.Generator, optional) — One or a list of torch generator(s) to make generation deterministic.
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
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
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
str, optional, defaults to
"pil") — The output format of the generate image. Choose between PIL:
bool, optional, defaults to
True) — Whether or not to return a StableDiffusionPipelineOutput instead of a plain tuple.
Callable, optional) — A function that will be called every
callback_stepssteps during inference. The function will be called with the following arguments:
callback(step: int, timestep: int, latents: torch.FloatTensor).
int, optional, defaults to 1) — The frequency at which the
callbackfunction will be called. If not specified, the callback will be called at every step.
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.
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
enable_model_cpu_offload< source >
( gpu_id = 0 )
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
enable_sequential_cpu_offload, this method moves one whole model at a time to the GPU when its
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
enable_sequential_cpu_offload, but performance is much better due to the iterative execution of the
enable_sequential_cpu_offload< source >
( gpu_id = 0 )
Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
torch.device('meta') and loaded to GPU only when their specific submodule has its forward
method called. Note that offloading happens on a submodule basis. Memory savings are higher than withenable_model_cpu_offload`, but performance is lower.