使用Dino+SAM+Stable diffusion 自动进行图片的修改

SD指南1年前 (2023)发布 一起用AI
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SAM 是Mata发布的“Segment Anything Model”可以准确识别和提取图像中的对象。 它可以分割任何的图片,但是如果需要分割特定的物体,则需要需要点、框的特定提示才能准确分割图像。 所以本文将介绍一种称为 Grounding Dino 的技术来自动生成 SAM 进行分割所需的框。

使用Dino+SAM+Stable diffusion 自动进行图片的修改

除了分割以外,我们还可以通过将 SAM 与 Grounding Dino 和 Stable Diffusion 相结合,获得高度准确图像分割结果,并且对分割后的图像进行细微的更改。

下面就是我们需要的所有的包:

`%cd /content

!git clone https://github.com/IDEA-Research/Grounded-Segment-Anything

%cd /content/Grounded-Segment-Anything

!pip install -q -r requirements.txt

%cd /content/Grounded-Segment-Anything/GroundingDINO

!pip install -q .

%cd /content/Grounded-Segment-Anything/segment_anything

!pip install -q .

%cd /content/Grounded-Segment-Anything

导入必要的包:

import os, sys

sys.path.append(os.path.join(os.getcwd(), “GroundingDINO”))

import argparse

import copy

from IPython.display import display

from PIL import Image, ImageDraw, ImageFont

from torchvision.ops import box_convert

# Grounding DINO

import GroundingDINO.groundingdino.datasets.transforms as T

from GroundingDINO.groundingdino.models import build_model

from GroundingDINO.groundingdino.util import box_ops

from GroundingDINO.groundingdino.util.slconfig import SLConfig

from GroundingDINO.groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap

from GroundingDINO.groundingdino.util.inference import annotate, load_image, predict

import supervision as sv

# segment anything

from segment_anything import build_sam, SamPredictor

import cv2

import numpy as np

import matplotlib.pyplot as plt

# diffusers

import PIL

import requests

import torch

from io import BytesIO

from diffusers import StableDiffusionInpaintPipeline

from huggingface_hub import hf_hub_download

然后我们设置处理的设备:

device = torch.device(cuda if torch.cuda.is_available() else cpu)

然后我们创建一个 GroundingDino 模型的实例。

def load_model_hf(repo_id, filename, ckpt_config_filename, device=cpu):

cache_config_file = hf_hub_download(repo_id=repo_id, filename=ckpt_config_filename)

args = SLConfig.fromfile(cache_config_file)

args.device = device

model = build_model(args)

cache_file = hf_hub_download(repo_id=repo_id, filename=filename)

checkpoint = torch.load(cache_file, map_location=device)

log = model.load_state_dict(clean_state_dict(checkpoint[model]), strict=False)

print(“Model loaded from {} \n => {}”.format(cache_file, log))

_ = model.eval()

return model

ckpt_repo_id = “ShilongLiu/GroundingDINO”

ckpt_filenmae = “groundingdino_swinb_cogcoor.pth”

ckpt_config_filename = “GroundingDINO_SwinB.cfg.py”

groundingdino_model = load_model_hf(ckpt_repo_id, ckpt_filenmae, ckpt_config_filename, device)

下面开始创建SAM 模型,定义模型并创建一个实例。

! wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth

sam_checkpoint = sam_vit_h_4b8939.pth

sam_predictor = SamPredictor(build_sam(checkpoint=sam_checkpoint).to(device))

这里我们使用与训练的 vit_h 模型,下面就是扩散模型了:

sd_pipe = StableDiffusionInpaintPipeline.from_pretrained(

“stabilityai/stable-diffusion-2-inpainting”,

torch_dtype=torch.float16,

).to(device)

然后我们开始测试:

# Load image

def download_image(url, image_file_path):

r = requests.get(url, timeout=4.0)

if r.status_code != requests.codes.ok:

assert False, Status code error: {}..format(r.status_code)

with Image.open(BytesIO(r.content)) as im:

im.save(image_file_path)

print(Image downloaded from url: {} and saved to: {}..format(url, image_file_path))

local_image_path = “assets/inpaint_demo.jpg”

image_url = “https://images.rawpixel.com/image_800/cHJpdmF0ZS9sci9pbWFnZXMvd2Vic2l0ZS8yMDIyLTA1L3Vwd2s2MTc3Nzk0MS13aWtpbWVkaWEtaW1hZ2Uta293YnN1MHYuanBn.jpg”

download_image(image_url, local_image_path)

image_source, image = load_image(local_image_path)

Image.fromarray(image_source)

先使用Grounding Dino 进行检测:

# detect object using grounding DINO

def detect(image, text_prompt, model, box_threshold = 0.3, text_threshold = 0.25):

boxes, logits, phrases = predict(

model=model,

image=image,

caption=text_prompt,

box_threshold=box_threshold,

text_threshold=text_threshold

)

annotated_frame = annotate(image_source=image_source, boxes=boxes, logits=logits, phrases=phrases)

annotated_frame = annotated_frame[…,::-1] # BGR to RGB

return annotated_frame, boxes

annotated_frame, detected_boxes = detect(image, text_prompt=”bench”, model=groundingdino_model)

Image.fromarray(annotated_frame)

让我们看看结果:

使用Dino+SAM+Stable diffusion 自动进行图片的修改

然后使用 SAM 分割这个狐狸:

def segment(image, sam_model, boxes):

sam_model.set_image(image)

H, W, _ = image.shape

boxes_xyxy = box_ops.box_cxcywh_to_xyxy(boxes) * torch.Tensor([W, H, W, H])

transformed_boxes = sam_model.transform.apply_boxes_torch(boxes_xyxy.to(device), image.shape[:2])

masks, _, _ = sam_model.predict_torch(

point_coords = None,

point_labels = None,

boxes = transformed_boxes,

multimask_output = False,

)

return masks.cpu()

def draw_mask(mask, image, random_color=True):

if random_color:

color = np.concatenate([np.random.random(3), np.array([0.8])], axis=0)

else:

color = np.array([30/255, 144/255, 255/255, 0.6])

h, w = mask.shape[-2:]

mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)

annotated_frame_pil = Image.fromarray(image).convert(“RGBA”)

mask_image_pil = Image.fromarray((mask_image.cpu().numpy() * 255).astype(np.uint8)).convert(“RGBA”)

return np.array(Image.alpha_composite(annotated_frame_pil, mask_image_pil))

segmented_frame_masks = segment(image_source, sam_predictor, boxes=detected_boxes)

annotated_frame_with_mask = draw_mask(segmented_frame_masks[0][0], annotated_frame)

Image.fromarray(annotated_frame_with_mask)

这样就可以通过上面的分割结果为的扩散模型生成掩码:

# create mask images

mask = segmented_frame_masks[0][0].cpu().numpy()

inverted_mask = ((1 – mask) * 255).astype(np.uint8)

image_source_pil = Image.fromarray(image_source)

image_mask_pil = Image.fromarray(mask)

inverted_image_mask_pil = Image.fromarray(inverted_mask)

display(*[image_source_pil, image_mask_pil, inverted_image_mask_pil])

使用Dino+SAM+Stable diffusion 自动进行图片的修改

绘时我们还需要一个背景的掩码,这个就是上面掩码的反操作

def generate_image(image, mask, prompt, negative_prompt, pipe, seed):

# resize for inpainting

w, h = image.size

in_image = image.resize((512, 512))

in_mask = mask.resize((512, 512))

generator = torch.Generator(device).manual_seed(seed)

result = pipe(image=in_image, mask_image=in_mask, prompt=prompt, negative_prompt=negative_prompt, generator=generator)

result = result.images[0]

return result.resize((w, h))

然后我们可以开始改图,输入一个提示:

prompt=” a brown bulldog”

negative_prompt=”low resolution, ugly”

seed = -1 # for reproducibility

generated_image = generate_image(image=image_source_pil, mask=image_mask_pil, prompt=prompt, negative_prompt=negative_prompt, pipe=sd_pipe, seed=seed)

generated_image

使用Dino+SAM+Stable diffusion 自动进行图片的修改

或者用上面的背景掩码来修改背景:

prompt=”a hill with grasses ,weak sunlight “

negative_prompt=”people, low resolution, ugly”

seed = 32 # for reproducibility

generated_image = generate_image(image_source_pil, inverted_image_mask_pil, prompt, negative_prompt, sd_pipe, seed)

generated_image

使用Dino+SAM+Stable diffusion 自动进行图片的修改

可以看到效果还是很好的

SAM、Grounding Dino 和 Stable Diffusion 的组合为我们提供了强大的工具。这些技术为探索令人兴奋的图像处理世界提供了坚实的基础 并为艺术家和开发者提供巨大的创造潜力。

如果你想在线测试,这里有完整的源代码:

https://avoid.overfit.cn/post/e9e083807a434935910c8116c85c8375

作者:Amir Shakiba

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