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# --------------------------------------------------------
# X-Decoder -- Generalized Decoding for Pixel, Image, and Language
# Copyright (c) 2022 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Jianwei Yang (jianwyan@microsoft.com), Xueyan Zou (xueyan@cs.wisc.edu)
# --------------------------------------------------------
import torch
import numpy as np
from PIL import Image
from utils.inpainting import pad_image
from torchvision import transforms
from utils.visualizer import Visualizer
from diffusers import StableDiffusionInpaintPipeline
from detectron2.utils.colormap import random_color
from detectron2.data import MetadataCatalog
from scipy import ndimage
t = []
t.append(transforms.Resize(512, interpolation=Image.BICUBIC))
transform = transforms.Compose(t)
metadata = MetadataCatalog.get('ade20k_panoptic_train')
pipe = StableDiffusionInpaintPipeline.from_pretrained(
# "stabilityai/stable-diffusion-2-inpainting",
"runwayml/stable-diffusion-inpainting",
revision="fp16",
torch_dtype=torch.float16,
).to("cuda")
def crop_image(input_image):
crop_w, crop_h = np.floor(np.array(input_image.size) / 64).astype(int) * 64
im_cropped = Image.fromarray(np.array(input_image)[:crop_h, :crop_w])
return im_cropped
def referring_inpainting(model, image, texts, inpainting_text, *args, **kwargs):
model.model.metadata = metadata
texts = [[texts if texts.strip().endswith('.') else (texts.strip() + '.')]]
image_ori = crop_image(transform(image))
with torch.no_grad():
width = image_ori.size[0]
height = image_ori.size[1]
image = np.asarray(image_ori)
image_ori_np = np.asarray(image_ori)
images = torch.from_numpy(image.copy()).permute(2,0,1).cuda()
batch_inputs = [{'image': images, 'height': height, 'width': width, 'groundings': {'texts': texts}}]
outputs = model.model.evaluate_grounding(batch_inputs, None)
visual = Visualizer(image_ori_np, metadata=metadata)
grd_mask = (outputs[0]['grounding_mask'] > 0).float().cpu().numpy()
for idx, mask in enumerate(grd_mask):
color = random_color(rgb=True, maximum=1).astype(np.int32).tolist()
demo = visual.draw_binary_mask(mask, color=color, text=texts[idx])
res = demo.get_image()
if inpainting_text not in ['no', '']:
# if we want to do inpainting
image_crop = image_ori
struct2 = ndimage.generate_binary_structure(2, 2)
mask_dilated = ndimage.binary_dilation(grd_mask[0], structure=struct2, iterations=3).astype(grd_mask[0].dtype)
mask = Image.fromarray(mask_dilated * 255).convert('RGB')
image_and_mask = {
"image": image_crop,
"mask": mask,
}
width = image_crop.size[0]; height = image_crop.size[1]
images_inpainting = pipe(prompt = inpainting_text.strip(), image=image_and_mask['image'], mask_image=image_and_mask['mask'], height=height, width=width).images[0]
# put images_inpainting back to original image
# image_ori.paste(images_inpainting)
torch.cuda.empty_cache()
return Image.fromarray(res) ,'' , images_inpainting
else:
torch.cuda.empty_cache()
return image_ori, 'text', Image.fromarray(res) |