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import torch
import PIL
from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
class InstructPix2Pix():
"""
A wrapper around the StableDiffusionInstructPix2PixPipeline for guided image transformation.
This class uses the Pix2Pix pipeline to transform an image based on an instruction prompt.
Reference: https://huggingface.co/docs/diffusers/api/pipelines/pix2pix
"""
def __init__(self, device="cuda", weight="timbrooks/instruct-pix2pix"):
"""
Attributes:
pipe (StableDiffusionInstructPix2PixPipeline): The Pix2Pix pipeline for image transformation.
Args:
device (str, optional): Device on which the pipeline runs. Defaults to "cuda".
weight (str, optional): Pretrained weights for the model. Defaults to "timbrooks/instruct-pix2pix".
"""
self.pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
weight,
torch_dtype=torch.float16,
safety_checker=None,
).to(device)
self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(
self.pipe.scheduler.config)
def infer_one_image(self, src_image: PIL.Image.Image = None, src_prompt: str = None, target_prompt: str = None, instruct_prompt: str = None, seed: int = 42, negative_prompt=""):
"""
Modifies the source image based on the provided instruction prompt.
Args:
src_image (PIL.Image.Image): Source image in RGB format.
instruct_prompt (str): Caption for editing the image.
seed (int, optional): Seed for random generator. Defaults to 42.
Returns:
PIL.Image.Image: The transformed image.
"""
src_image = src_image.convert('RGB') # force it to RGB format
generator = torch.manual_seed(seed)
# configs from https://github.com/timothybrooks/instruct-pix2pix/blob/main/edit_cli.py
image = self.pipe(instruct_prompt, image=src_image,
num_inference_steps=100,
image_guidance_scale=1.5,
guidance_scale=7.5,
negative_prompt=negative_prompt,
generator=generator
).images[0]
return image
class MagicBrush(InstructPix2Pix):
def __init__(self, device="cuda", weight="vinesmsuic/magicbrush-jul7"):
"""
A class for MagicBrush.
Args:
device (str, optional): The device on which the model should run. Default is "cuda".
weight (str, optional): The pretrained model weights for MagicBrush. Default is "vinesmsuic/magicbrush-jul7".
"""
super().__init__(device=device, weight=weight)
def infer_one_image(self, src_image: PIL.Image.Image = None, src_prompt: str = None, target_prompt: str = None, instruct_prompt: str = None, seed: int = 42, negative_prompt=""):
return super().infer_one_image(src_image, src_prompt, target_prompt, instruct_prompt, seed, negative_prompt) |