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.
Resources:
Pipeline | Tasks | Demo |
---|---|---|
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[0].save("snowy_mountains.png")
( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers safety_checker: StableDiffusionSafetyChecker feature_extractor: CLIPImageProcessor requires_safety_checker: bool = True )
Parameters
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
to 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.
CLIPImageProcessor
) —
Model that extracts features from generated images to be used as inputs for the safety_checker
.
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.)
In addition the pipeline inherits the following loading methods:
as well as the following saving methods:
(
prompt: typing.Union[str, typing.List[str]] = None
image: typing.Union[torch.FloatTensor, PIL.Image.Image, numpy.ndarray, typing.List[torch.FloatTensor], typing.List[PIL.Image.Image], typing.List[numpy.ndarray]] = None
num_inference_steps: int = 100
guidance_scale: float = 7.5
image_guidance_scale: float = 1.5
negative_prompt: typing.Union[typing.List[str], 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
)
→
StableDiffusionPipelineOutput or tuple
Parameters
str
or List[str]
, optional) —
The prompt or prompts to guide the image generation. If not defined, one has to pass prompt_embeds
.
instead.
torch.FloatTensor
np.ndarray
, PIL.Image.Image
, List[torch.FloatTensor]
, List[PIL.Image.Image]
, or List[np.ndarray]
) —
Image
, or tensor representing an image batch which will be repainted according to prompt
. Can also
accpet image latents as image
, if passing latents directly, it will not be encoded again.
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_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. This pipeline requires a value of at least 1
.
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 1
.
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
).
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 generator
.
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.
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.
str
, optional, defaults to "pil"
) —
The output format of the generate image. Choose between
PIL: PIL.Image.Image
or np.array
.
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_steps
steps 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 callback
function will be called. If not specified, the callback will be
called at every step.
Returns
StableDiffusionPipelineOutput or tuple
StableDiffusionPipelineOutput 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
bools 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.
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]
( pretrained_model_name_or_path: typing.Union[str, typing.List[str], typing.Dict[str, torch.Tensor], typing.List[typing.Dict[str, torch.Tensor]]] token: typing.Union[str, typing.List[str], NoneType] = None **kwargs )
Parameters
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:
sd-concepts-library/low-poly-hd-logos-icons
) of a
pretrained model hosted on the Hub../my_text_inversion_directory/
) containing the textual
inversion weights../my_text_inversions.pt
) containing textual inversion weights.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.
str
, optional) —
Name of a custom weight file. This should be used when:
text_inv.bin
.Union[str, os.PathLike]
, optional) —
Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
is not used.
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.
bool
, optional, defaults to False
) —
Whether or not to resume downloading the model weights and configuration files. If set to False
, any
incompletely downloaded files are deleted.
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.
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.
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.
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.
str
, optional, defaults to ""
) —
The subfolder location of a model file within a larger model repository on the Hub or locally.
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 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 = "runwayml/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 <cat-toy> 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 = "runwayml/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")
( pretrained_model_name_or_path_or_dict: typing.Union[str, typing.Dict[str, torch.Tensor]] **kwargs )
Parameters
str
or os.PathLike
or dict
) —
Can be either:
google/ddpm-celebahq-256
) of a pretrained model hosted on
the Hub../my_model_directory
) containing the model weights saved
with ModelMixin.save_pretrained().Union[str, os.PathLike]
, optional) —
Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
is not used.
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.
bool
, optional, defaults to False
) —
Whether or not to resume downloading the model weights and configuration files. If set to False
, any
incompletely downloaded files are deleted.
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.
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.
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.
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.
str
, optional, defaults to ""
) —
The subfolder location of a model file within a larger model repository on the Hub or locally.
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 pretrained LoRA attention processor layers into UNet2DConditionModel and
CLIPTextModel
.
( save_directory: typing.Union[str, os.PathLike] unet_lora_layers: typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None text_encoder_lora_layers: typing.Dict[str, torch.nn.modules.module.Module] = None is_main_process: bool = True weight_name: str = None save_function: typing.Callable = None safe_serialization: bool = False )
Parameters
str
or os.PathLike
) —
Directory to save LoRA parameters to. Will be created if it doesn’t exist.
Dict[str, torch.nn.Module]
or Dict[str, torch.Tensor]
) —
State dict of the LoRA layers corresponding to the UNet.
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 🤗 Transformers.
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.
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
.
Save the LoRA parameters corresponding to the UNet and text encoder.
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to enable_sequential_cpu_offload
, this method moves one whole model at a time to the GPU when its forward
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 unet
.
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
forwardmethod called. Note that offloading happens on a submodule basis. Memory savings are higher than with
enable_model_cpu_offload`, but performance is lower.