mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-11-27 06:40:10 +08:00
move embedding logic to separate file
This commit is contained in:
DepFA
GitHub
parent
1eaad95533
commit
c080f52cea
234
modules/textual_inversion/image_embedding.py
Normal file
234
modules/textual_inversion/image_embedding.py
Normal file
@ -0,0 +1,234 @@
|
||||
import base64
|
||||
import json
|
||||
import numpy as np
|
||||
import zlib
|
||||
from PIL import Image,PngImagePlugin,ImageDraw,ImageFont
|
||||
from fonts.ttf import Roboto
|
||||
import torch
|
||||
|
||||
class EmbeddingEncoder(json.JSONEncoder):
|
||||
def default(self, obj):
|
||||
if isinstance(obj, torch.Tensor):
|
||||
return {'TORCHTENSOR':obj.cpu().detach().numpy().tolist()}
|
||||
return json.JSONEncoder.default(self, obj)
|
||||
|
||||
class EmbeddingDecoder(json.JSONDecoder):
|
||||
def __init__(self, *args, **kwargs):
|
||||
json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
|
||||
def object_hook(self, d):
|
||||
if 'TORCHTENSOR' in d:
|
||||
return torch.from_numpy(np.array(d['TORCHTENSOR']))
|
||||
return d
|
||||
|
||||
def embedding_to_b64(data):
|
||||
d = json.dumps(data,cls=EmbeddingEncoder)
|
||||
return base64.b64encode(d.encode())
|
||||
|
||||
def embedding_from_b64(data):
|
||||
d = base64.b64decode(data)
|
||||
return json.loads(d,cls=EmbeddingDecoder)
|
||||
|
||||
def lcg(m=2**32, a=1664525, c=1013904223, seed=0):
|
||||
while True:
|
||||
seed = (a * seed + c) % m
|
||||
yield seed%255
|
||||
|
||||
def xor_block(block):
|
||||
g = lcg()
|
||||
randblock = np.array([next(g) for _ in range(np.product(block.shape))]).astype(np.uint8).reshape(block.shape)
|
||||
return np.bitwise_xor(block.astype(np.uint8),randblock & 0x0F)
|
||||
|
||||
def style_block(block,sequence):
|
||||
im = Image.new('RGB',(block.shape[1],block.shape[0]))
|
||||
draw = ImageDraw.Draw(im)
|
||||
i=0
|
||||
for x in range(-6,im.size[0],8):
|
||||
for yi,y in enumerate(range(-6,im.size[1],8)):
|
||||
offset=0
|
||||
if yi%2==0:
|
||||
offset=4
|
||||
shade = sequence[i%len(sequence)]
|
||||
i+=1
|
||||
draw.ellipse((x+offset, y, x+6+offset, y+6), fill =(shade,shade,shade) )
|
||||
|
||||
fg = np.array(im).astype(np.uint8) & 0xF0
|
||||
|
||||
return block ^ fg
|
||||
|
||||
def insert_image_data_embed(image,data):
|
||||
d = 3
|
||||
data_compressed = zlib.compress( json.dumps(data,cls=EmbeddingEncoder).encode(),level=9)
|
||||
data_np_ = np.frombuffer(data_compressed,np.uint8).copy()
|
||||
data_np_high = data_np_ >> 4
|
||||
data_np_low = data_np_ & 0x0F
|
||||
|
||||
h = image.size[1]
|
||||
next_size = data_np_low.shape[0] + (h-(data_np_low.shape[0]%h))
|
||||
next_size = next_size + ((h*d)-(next_size%(h*d)))
|
||||
|
||||
data_np_low.resize(next_size)
|
||||
data_np_low = data_np_low.reshape((h,-1,d))
|
||||
|
||||
data_np_high.resize(next_size)
|
||||
data_np_high = data_np_high.reshape((h,-1,d))
|
||||
|
||||
edge_style = list(data['string_to_param'].values())[0].cpu().detach().numpy().tolist()[0][:1024]
|
||||
edge_style = (np.abs(edge_style)/np.max(np.abs(edge_style))*255).astype(np.uint8)
|
||||
|
||||
data_np_low = style_block(data_np_low,sequence=edge_style)
|
||||
data_np_low = xor_block(data_np_low)
|
||||
data_np_high = style_block(data_np_high,sequence=edge_style[::-1])
|
||||
data_np_high = xor_block(data_np_high)
|
||||
|
||||
im_low = Image.fromarray(data_np_low,mode='RGB')
|
||||
im_high = Image.fromarray(data_np_high,mode='RGB')
|
||||
|
||||
background = Image.new('RGB',(image.size[0]+im_low.size[0]+im_high.size[0]+2,image.size[1]),(0,0,0))
|
||||
background.paste(im_low,(0,0))
|
||||
background.paste(image,(im_low.size[0]+1,0))
|
||||
background.paste(im_high,(im_low.size[0]+1+image.size[0]+1,0))
|
||||
|
||||
return background
|
||||
|
||||
def crop_black(img,tol=0):
|
||||
mask = (img>tol).all(2)
|
||||
mask0,mask1 = mask.any(0),mask.any(1)
|
||||
col_start,col_end = mask0.argmax(),mask.shape[1]-mask0[::-1].argmax()
|
||||
row_start,row_end = mask1.argmax(),mask.shape[0]-mask1[::-1].argmax()
|
||||
return img[row_start:row_end,col_start:col_end]
|
||||
|
||||
def extract_image_data_embed(image):
|
||||
d=3
|
||||
outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) ) & 0x0F
|
||||
black_cols = np.where( np.sum(outarr, axis=(0,2))==0)
|
||||
if black_cols[0].shape[0] < 2:
|
||||
print('No Image data blocks found.')
|
||||
return None
|
||||
|
||||
data_block_lower = outarr[:,:black_cols[0].min(),:].astype(np.uint8)
|
||||
data_block_upper = outarr[:,black_cols[0].max()+1:,:].astype(np.uint8)
|
||||
|
||||
data_block_lower = xor_block(data_block_lower)
|
||||
data_block_upper = xor_block(data_block_upper)
|
||||
|
||||
data_block = (data_block_upper << 4) | (data_block_lower)
|
||||
data_block = data_block.flatten().tobytes()
|
||||
|
||||
data = zlib.decompress(data_block)
|
||||
return json.loads(data,cls=EmbeddingDecoder)
|
||||
|
||||
def addCaptionLines(lines,image,initialx,textfont):
|
||||
draw = ImageDraw.Draw(image)
|
||||
hstart =initialx
|
||||
for fill,line in lines:
|
||||
fontsize = 32
|
||||
font = ImageFont.truetype(textfont, fontsize)
|
||||
_,_,w, h = draw.textbbox((0,0),line,font=font)
|
||||
fontsize = min( int(fontsize * ((image.size[0]-35)/w) ), 28)
|
||||
font = ImageFont.truetype(textfont, fontsize)
|
||||
_,_,w,h = draw.textbbox((0,0),line,font=font)
|
||||
draw.text(((image.size[0]-w)/2,hstart), line, font=font, fill=fill)
|
||||
hstart += h
|
||||
return hstart
|
||||
|
||||
def caption_image(image,prelines,postlines,background=(51, 51, 51),font=None):
|
||||
if font is None:
|
||||
try:
|
||||
font = ImageFont.truetype(opts.font or Roboto, fontsize)
|
||||
font = opts.font or Roboto
|
||||
except Exception:
|
||||
font = Roboto
|
||||
|
||||
sample_image = image
|
||||
background = Image.new("RGBA", (sample_image.size[0],sample_image.size[1]+1024), background)
|
||||
hoffset = addCaptionLines(prelines,background,5,font)+16
|
||||
background.paste(sample_image,(0,hoffset))
|
||||
hoffset = hoffset+sample_image.size[1]+8
|
||||
hoffset = addCaptionLines(postlines,background,hoffset,font)
|
||||
background = background.crop((0,0,sample_image.size[0],hoffset+8))
|
||||
return background
|
||||
|
||||
def caption_image_overlay(srcimage,title,footerLeft,footerMid,footerRight,textfont=None):
|
||||
from math import cos
|
||||
|
||||
image = srcimage.copy()
|
||||
|
||||
if textfont is None:
|
||||
try:
|
||||
textfont = ImageFont.truetype(opts.font or Roboto, fontsize)
|
||||
textfont = opts.font or Roboto
|
||||
except Exception:
|
||||
textfont = Roboto
|
||||
|
||||
factor = 1.5
|
||||
gradient = Image.new('RGBA', (1,image.size[1]), color=(0,0,0,0))
|
||||
for y in range(image.size[1]):
|
||||
mag = 1-cos(y/image.size[1]*factor)
|
||||
mag = max(mag,1-cos((image.size[1]-y)/image.size[1]*factor*1.1))
|
||||
gradient.putpixel((0, y), (0,0,0,int(mag*255)))
|
||||
image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size))
|
||||
|
||||
draw = ImageDraw.Draw(image)
|
||||
fontsize = 32
|
||||
font = ImageFont.truetype(textfont, fontsize)
|
||||
padding = 10
|
||||
|
||||
_,_,w, h = draw.textbbox((0,0),title,font=font)
|
||||
fontsize = min( int(fontsize * (((image.size[0]*0.75)-(padding*4))/w) ), 72)
|
||||
font = ImageFont.truetype(textfont, fontsize)
|
||||
_,_,w,h = draw.textbbox((0,0),title,font=font)
|
||||
draw.text((padding,padding), title, anchor='lt', font=font, fill=(255,255,255,230))
|
||||
|
||||
_,_,w, h = draw.textbbox((0,0),footerLeft,font=font)
|
||||
fontsize_left = min( int(fontsize * (((image.size[0]/3)-(padding))/w) ), 72)
|
||||
_,_,w, h = draw.textbbox((0,0),footerMid,font=font)
|
||||
fontsize_mid = min( int(fontsize * (((image.size[0]/3)-(padding))/w) ), 72)
|
||||
_,_,w, h = draw.textbbox((0,0),footerRight,font=font)
|
||||
fontsize_right = min( int(fontsize * (((image.size[0]/3)-(padding))/w) ), 72)
|
||||
|
||||
font = ImageFont.truetype(textfont, min(fontsize_left,fontsize_mid,fontsize_right))
|
||||
|
||||
draw.text((padding,image.size[1]-padding), footerLeft, anchor='ls', font=font, fill=(255,255,255,230))
|
||||
draw.text((image.size[0]/2,image.size[1]-padding), footerMid, anchor='ms', font=font, fill=(255,255,255,230))
|
||||
draw.text((image.size[0]-padding,image.size[1]-padding), footerRight, anchor='rs', font=font, fill=(255,255,255,230))
|
||||
|
||||
return image
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
image = Image.new('RGBA',(512,512),(255,255,200,255))
|
||||
caption_image(image,[((255,255,255),'line a'),((255,255,255),'line b')],
|
||||
[((255,255,255),'line c'),((255,255,255),'line d')])
|
||||
|
||||
image = Image.new('RGBA',(512,512),(255,255,200,255))
|
||||
cap_image = caption_image_overlay(image, 'title', 'footerLeft', 'footerMid', 'footerRight')
|
||||
|
||||
test_embed = {'string_to_param':{'*':torch.from_numpy(np.random.random((2, 4096)))}}
|
||||
|
||||
embedded_image = insert_image_data_embed(cap_image, test_embed)
|
||||
|
||||
retrived_embed = extract_image_data_embed(embedded_image)
|
||||
|
||||
assert str(retrived_embed) == str(test_embed)
|
||||
|
||||
embedded_image2 = insert_image_data_embed(cap_image, retrived_embed)
|
||||
|
||||
assert embedded_image == embedded_image2
|
||||
|
||||
g = lcg()
|
||||
shared_random = np.array([next(g) for _ in range(100)]).astype(np.uint8).tolist()
|
||||
|
||||
reference_random = [253, 242, 127, 44, 157, 27, 239, 133, 38, 79, 167, 4, 177,
|
||||
95, 130, 79, 78, 14, 52, 215, 220, 194, 126, 28, 240, 179,
|
||||
160, 153, 149, 50, 105, 14, 21, 218, 199, 18, 54, 198, 193,
|
||||
38, 128, 19, 53, 195, 124, 75, 205, 12, 6, 145, 0, 28,
|
||||
30, 148, 8, 45, 218, 171, 55, 249, 97, 166, 12, 35, 0,
|
||||
41, 221, 122, 215, 170, 31, 113, 186, 97, 119, 31, 23, 185,
|
||||
66, 140, 30, 41, 37, 63, 137, 109, 216, 55, 159, 145, 82,
|
||||
204, 86, 73, 222, 44, 198, 118, 240, 97]
|
||||
|
||||
assert shared_random == reference_random
|
||||
|
||||
hunna_kay_random_sum = sum(np.array([next(g) for _ in range(100000)]).astype(np.uint8).tolist())
|
||||
|
||||
assert 12731374 == hunna_kay_random_sum
|
||||
Loading…
Reference in New Issue
Block a user