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modules/textual_inversion/image_embedding.py
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@ -2,122 +2,134 @@ import base64
import json
import numpy as np
import zlib
from PIL import Image,PngImagePlugin,ImageDraw,ImageFont
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 {'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)
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)
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
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)
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]))
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) )
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):
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_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
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)))
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_low = data_np_low.reshape((h, -1, d))
data_np_high.resize(next_size)
data_np_high = data_np_high.reshape((h,-1,d))
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)
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')
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))
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 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)
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 = 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)
return json.loads(data, cls=EmbeddingDecoder)
def caption_image_overlay(srcimage,title,footerLeft,footerMid,footerRight,textfont=None):
def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, textfont=None):
from math import cos
image = srcimage.copy()
@ -130,11 +142,11 @@ def caption_image_overlay(srcimage,title,footerLeft,footerMid,footerRight,textfo
textfont = Roboto
factor = 1.5
gradient = Image.new('RGBA', (1,image.size[1]), color=(0,0,0,0))
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)))
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)
@ -142,41 +154,41 @@ def caption_image_overlay(srcimage,title,footerLeft,footerMid,footerRight,textfo
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)
_, _, 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), 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)
_, _, 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))
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))
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__':
testEmbed = Image.open('test_embedding.png')
data = extract_image_data_embed(testEmbed)
assert data is not None
data = embedding_from_b64(testEmbed.text['sd-ti-embedding'])
assert data is not None
image = Image.new('RGBA',(512,512),(255,255,200,255))
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)))}}
test_embed = {'string_to_param': {'*': torch.from_numpy(np.random.random((2, 4096)))}}
embedded_image = insert_image_data_embed(cap_image, test_embed)
@ -191,16 +203,16 @@ if __name__ == '__main__':
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,
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
assert shared_random == reference_random
hunna_kay_random_sum = sum(np.array([next(g) for _ in range(100000)]).astype(np.uint8).tolist())