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)