shift embedding logic out of textual_inversion
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DepFA
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@ -7,124 +7,11 @@ import tqdm
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import html
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import datetime
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from PIL import Image,PngImagePlugin,ImageDraw
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from ..images import captionImageOverlay
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import numpy as np
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import base64
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import json
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import zlib
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from PIL import Image,PngImagePlugin
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from modules import shared, devices, sd_hijack, processing, sd_models
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import modules.textual_inversion.dataset
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class EmbeddingEncoder(json.JSONEncoder):
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def default(self, obj):
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if isinstance(obj, torch.Tensor):
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return {'TORCHTENSOR':obj.cpu().detach().numpy().tolist()}
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return json.JSONEncoder.default(self, obj)
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class EmbeddingDecoder(json.JSONDecoder):
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def __init__(self, *args, **kwargs):
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json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
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def object_hook(self, d):
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if 'TORCHTENSOR' in d:
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return torch.from_numpy(np.array(d['TORCHTENSOR']))
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return d
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def embeddingToB64(data):
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d = json.dumps(data,cls=EmbeddingEncoder)
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return base64.b64encode(d.encode())
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def embeddingFromB64(data):
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d = base64.b64decode(data)
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return json.loads(d,cls=EmbeddingDecoder)
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def lcg(m=2**32, a=1664525, c=1013904223, seed=0):
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while True:
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seed = (a * seed + c) % m
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yield seed
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def xorBlock(block):
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g = lcg()
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randblock = np.array([next(g) for _ in range(np.product(block.shape))]).astype(np.uint8).reshape(block.shape)
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return np.bitwise_xor(block.astype(np.uint8),randblock & 0x0F)
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def styleBlock(block,sequence):
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im = Image.new('RGB',(block.shape[1],block.shape[0]))
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draw = ImageDraw.Draw(im)
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i=0
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for x in range(-6,im.size[0],8):
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for yi,y in enumerate(range(-6,im.size[1],8)):
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offset=0
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if yi%2==0:
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offset=4
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shade = sequence[i%len(sequence)]
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i+=1
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draw.ellipse((x+offset, y, x+6+offset, y+6), fill =(shade,shade,shade) )
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fg = np.array(im).astype(np.uint8) & 0xF0
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return block ^ fg
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def insertImageDataEmbed(image,data):
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d = 3
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data_compressed = zlib.compress( json.dumps(data,cls=EmbeddingEncoder).encode(),level=9)
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dnp = np.frombuffer(data_compressed,np.uint8).copy()
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dnphigh = dnp >> 4
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dnplow = dnp & 0x0F
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h = image.size[1]
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next_size = dnplow.shape[0] + (h-(dnplow.shape[0]%h))
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next_size = next_size + ((h*d)-(next_size%(h*d)))
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dnplow.resize(next_size)
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dnplow = dnplow.reshape((h,-1,d))
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dnphigh.resize(next_size)
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dnphigh = dnphigh.reshape((h,-1,d))
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edgeStyleWeights = list(data['string_to_param'].values())[0].cpu().detach().numpy().tolist()[0][:1024]
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edgeStyleWeights = (np.abs(edgeStyleWeights)/np.max(np.abs(edgeStyleWeights))*255).astype(np.uint8)
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dnplow = styleBlock(dnplow,sequence=edgeStyleWeights)
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dnplow = xorBlock(dnplow)
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dnphigh = styleBlock(dnphigh,sequence=edgeStyleWeights[::-1])
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dnphigh = xorBlock(dnphigh)
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imlow = Image.fromarray(dnplow,mode='RGB')
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imhigh = Image.fromarray(dnphigh,mode='RGB')
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background = Image.new('RGB',(image.size[0]+imlow.size[0]+imhigh.size[0]+2,image.size[1]),(0,0,0))
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background.paste(imlow,(0,0))
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background.paste(image,(imlow.size[0]+1,0))
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background.paste(imhigh,(imlow.size[0]+1+image.size[0]+1,0))
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return background
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def crop_black(img,tol=0):
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mask = (img>tol).all(2)
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mask0,mask1 = mask.any(0),mask.any(1)
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col_start,col_end = mask0.argmax(),mask.shape[1]-mask0[::-1].argmax()
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row_start,row_end = mask1.argmax(),mask.shape[0]-mask1[::-1].argmax()
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return img[row_start:row_end,col_start:col_end]
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def extractImageDataEmbed(image):
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d=3
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outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) ) & 0x0F
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blackCols = np.where( np.sum(outarr, axis=(0,2))==0)
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if blackCols[0].shape[0] < 2:
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print('No Image data blocks found.')
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return None
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dataBlocklower = outarr[:,:blackCols[0].min(),:].astype(np.uint8)
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dataBlockupper = outarr[:,blackCols[0].max()+1:,:].astype(np.uint8)
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dataBlocklower = xorBlock(dataBlocklower)
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dataBlockupper = xorBlock(dataBlockupper)
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dataBlock = (dataBlockupper << 4) | (dataBlocklower)
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dataBlock = dataBlock.flatten().tobytes()
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data = zlib.decompress(dataBlock)
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return json.loads(data,cls=EmbeddingDecoder)
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class Embedding:
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def __init__(self, vec, name, step=None):
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@ -199,10 +86,10 @@ class EmbeddingDatabase:
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if filename.upper().endswith('.PNG'):
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embed_image = Image.open(path)
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if 'sd-ti-embedding' in embed_image.text:
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data = embeddingFromB64(embed_image.text['sd-ti-embedding'])
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data = embedding_from_b64(embed_image.text['sd-ti-embedding'])
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name = data.get('name',name)
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else:
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data = extractImageDataEmbed(embed_image)
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data = extract_image_data_embed(embed_image)
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name = data.get('name',name)
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else:
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data = torch.load(path, map_location="cpu")
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@ -393,7 +280,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
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info = PngImagePlugin.PngInfo()
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data = torch.load(last_saved_file)
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info.add_text("sd-ti-embedding", embeddingToB64(data))
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info.add_text("sd-ti-embedding", embedding_to_b64(data))
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title = "<{}>".format(data.get('name','???'))
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checkpoint = sd_models.select_checkpoint()
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@ -401,8 +288,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
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footer_mid = '[{}]'.format(checkpoint.hash)
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footer_right = '{}'.format(embedding.step)
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captioned_image = captionImageOverlay(image,title,footer_left,footer_mid,footer_right)
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captioned_image = insertImageDataEmbed(captioned_image,data)
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captioned_image = caption_image_overlay(image,title,footer_left,footer_mid,footer_right)
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captioned_image = insert_image_data_embed(captioned_image,data)
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captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
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