From 707a431100362645e914042bb344d08439f48ac8 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Mon, 10 Oct 2022 15:34:49 +0100 Subject: [PATCH] add pixel data footer --- .../textual_inversion/textual_inversion.py | 48 ++++++++++++++++++- 1 file changed, 46 insertions(+), 2 deletions(-) diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 7a24192e4..6fb64691c 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -12,6 +12,7 @@ from ..images import captionImageOverlay import numpy as np import base64 import json +import zlib from modules import shared, devices, sd_hijack, processing, sd_models import modules.textual_inversion.dataset @@ -20,7 +21,7 @@ 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, o) + return json.JSONEncoder.default(self, obj) class EmbeddingDecoder(json.JSONDecoder): def __init__(self, *args, **kwargs): @@ -38,6 +39,45 @@ def embeddingFromB64(data): d = base64.b64decode(data) return json.loads(d,cls=EmbeddingDecoder) +def appendImageDataFooter(image,data): + d = 3 + data_compressed = zlib.compress( json.dumps(data,cls=EmbeddingEncoder).encode(),level=9) + dnp = np.frombuffer(data_compressed,np.uint8).copy() + w = image.size[0] + next_size = dnp.shape[0] + (w-(dnp.shape[0]%w)) + next_size = next_size + ((w*d)-(next_size%(w*d))) + dnp.resize(next_size) + dnp = dnp.reshape((-1,w,d)) + print(dnp.shape) + im = Image.fromarray(dnp,mode='RGB') + background = Image.new('RGB',(image.size[0],image.size[1]+im.size[1]+1),(0,0,0)) + background.paste(image,(0,0)) + background.paste(im,(0,image.size[1]+1)) + 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 extractImageDataFooter(image): + d=3 + outarr = crop_black(np.array(image.getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) ) + lastRow = np.where( np.sum(outarr, axis=(1,2))==0) + if lastRow[0].shape[0] == 0: + print('Image data block not found.') + return None + lastRow = lastRow[0] + + lastRow = lastRow.max() + + dataBlock = outarr[lastRow+1::].astype(np.uint8).flatten().tobytes() + print(lastRow) + data = zlib.decompress(dataBlock) + return json.loads(data,cls=EmbeddingDecoder) + class Embedding: def __init__(self, vec, name, step=None): self.vec = vec @@ -113,6 +153,9 @@ class EmbeddingDatabase: if 'sd-ti-embedding' in embed_image.text: data = embeddingFromB64(embed_image.text['sd-ti-embedding']) name = data.get('name',name) + else: + data = extractImageDataFooter(embed_image) + name = data.get('name',name) else: data = torch.load(path, map_location="cpu") @@ -190,7 +233,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'): return fn -def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file): +def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding): assert embedding_name, 'embedding not selected' shared.state.textinfo = "Initializing textual inversion training..." @@ -308,6 +351,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini footer_right = '{}'.format(embedding.step) captioned_image = captionImageOverlay(image,title,footer_left,footer_mid,footer_right) + captioned_image = appendImageDataFooter(captioned_image,data) captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)