mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
8cd5d95b8a
- this required an update to the invoke-ai fork of gfpgan - simultaneously reverted consolidation of environment and requirements files, as their presence in a directory triggered setup.py to try to install a sub-package.
131 lines
5.1 KiB
Python
131 lines
5.1 KiB
Python
'''Makes available the Txt2Mask class, which assists in the automatic
|
|
assignment of masks via text prompt using clipseg.
|
|
|
|
Here is typical usage:
|
|
|
|
from ldm.invoke.txt2mask import Txt2Mask, SegmentedGrayscale
|
|
from PIL import Image
|
|
|
|
txt2mask = Txt2Mask(self.device)
|
|
segmented = txt2mask.segment(Image.open('/path/to/img.png'),'a bagel')
|
|
|
|
# this will return a grayscale Image of the segmented data
|
|
grayscale = segmented.to_grayscale()
|
|
|
|
# this will return a semi-transparent image in which the
|
|
# selected object(s) are opaque and the rest is at various
|
|
# levels of transparency
|
|
transparent = segmented.to_transparent()
|
|
|
|
# this will return a masked image suitable for use in inpainting:
|
|
mask = segmented.to_mask(threshold=0.5)
|
|
|
|
The threshold used in the call to to_mask() selects pixels for use in
|
|
the mask that exceed the indicated confidence threshold. Values range
|
|
from 0.0 to 1.0. The higher the threshold, the more confident the
|
|
algorithm is. In limited testing, I have found that values around 0.5
|
|
work fine.
|
|
'''
|
|
|
|
import torch
|
|
import numpy as np
|
|
from clipseg.clipseg import CLIPDensePredT
|
|
from einops import rearrange, repeat
|
|
from PIL import Image, ImageOps
|
|
from torchvision import transforms
|
|
|
|
CLIP_VERSION = 'ViT-B/16'
|
|
CLIPSEG_WEIGHTS = 'models/clipseg/clipseg_weights/rd64-uni.pth'
|
|
CLIPSEG_WEIGHTS_REFINED = 'models/clipseg/clipseg_weights/rd64-uni-refined.pth'
|
|
CLIPSEG_SIZE = 352
|
|
|
|
class SegmentedGrayscale(object):
|
|
def __init__(self, image:Image, heatmap:torch.Tensor):
|
|
self.heatmap = heatmap
|
|
self.image = image
|
|
|
|
def to_grayscale(self,invert:bool=False)->Image:
|
|
return self._rescale(Image.fromarray(np.uint8(255 - self.heatmap * 255 if invert else self.heatmap * 255)))
|
|
|
|
def to_mask(self,threshold:float=0.5)->Image:
|
|
discrete_heatmap = self.heatmap.lt(threshold).int()
|
|
return self._rescale(Image.fromarray(np.uint8(discrete_heatmap*255),mode='L'))
|
|
|
|
def to_transparent(self,invert:bool=False)->Image:
|
|
transparent_image = self.image.copy()
|
|
# For img2img, we want the selected regions to be transparent,
|
|
# but to_grayscale() returns the opposite. Thus invert.
|
|
gs = self.to_grayscale(not invert)
|
|
transparent_image.putalpha(gs)
|
|
return transparent_image
|
|
|
|
# unscales and uncrops the 352x352 heatmap so that it matches the image again
|
|
def _rescale(self, heatmap:Image)->Image:
|
|
size = self.image.width if (self.image.width > self.image.height) else self.image.height
|
|
resized_image = heatmap.resize(
|
|
(size,size),
|
|
resample=Image.Resampling.LANCZOS
|
|
)
|
|
return resized_image.crop((0,0,self.image.width,self.image.height))
|
|
|
|
class Txt2Mask(object):
|
|
'''
|
|
Create new Txt2Mask object. The optional device argument can be one of
|
|
'cuda', 'mps' or 'cpu'.
|
|
'''
|
|
def __init__(self,device='cpu',refined=False):
|
|
print('>> Initializing clipseg model for text to mask inference')
|
|
self.device = device
|
|
self.model = CLIPDensePredT(version=CLIP_VERSION, reduce_dim=64, complex_trans_conv=refined)
|
|
self.model.eval()
|
|
# initially we keep everything in cpu to conserve space
|
|
self.model.to('cpu')
|
|
self.model.load_state_dict(torch.load(CLIPSEG_WEIGHTS_REFINED if refined else CLIPSEG_WEIGHTS, map_location=torch.device('cpu')), strict=False)
|
|
|
|
@torch.no_grad()
|
|
def segment(self, image, prompt:str) -> SegmentedGrayscale:
|
|
'''
|
|
Given a prompt string such as "a bagel", tries to identify the object in the
|
|
provided image and returns a SegmentedGrayscale object in which the brighter
|
|
pixels indicate where the object is inferred to be.
|
|
'''
|
|
self._to_device(self.device)
|
|
prompts = [prompt] # right now we operate on just a single prompt at a time
|
|
|
|
transform = transforms.Compose([
|
|
transforms.ToTensor(),
|
|
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
|
|
transforms.Resize((CLIPSEG_SIZE, CLIPSEG_SIZE)), # must be multiple of 64...
|
|
])
|
|
|
|
if type(image) is str:
|
|
image = Image.open(image).convert('RGB')
|
|
|
|
image = ImageOps.exif_transpose(image)
|
|
img = self._scale_and_crop(image)
|
|
img = transform(img).unsqueeze(0)
|
|
|
|
preds = self.model(img.repeat(len(prompts),1,1,1), prompts)[0]
|
|
heatmap = torch.sigmoid(preds[0][0]).cpu()
|
|
self._to_device('cpu')
|
|
return SegmentedGrayscale(image, heatmap)
|
|
|
|
def _to_device(self, device):
|
|
self.model.to(device)
|
|
|
|
def _scale_and_crop(self, image:Image)->Image:
|
|
scaled_image = Image.new('RGB',(CLIPSEG_SIZE,CLIPSEG_SIZE))
|
|
if image.width > image.height: # width is constraint
|
|
scale = CLIPSEG_SIZE / image.width
|
|
else:
|
|
scale = CLIPSEG_SIZE / image.height
|
|
scaled_image.paste(
|
|
image.resize(
|
|
(int(scale * image.width),
|
|
int(scale * image.height)
|
|
),
|
|
resample=Image.Resampling.LANCZOS
|
|
),box=(0,0)
|
|
)
|
|
return scaled_image
|