add embedding load and save from b64 json

modules/textual_inversion/textual_inversion.py

@@ -7,9 +7,11 @@ import tqdm
 import html
 import datetime
 
-from PIL import Image, PngImagePlugin
+from PIL import Image,PngImagePlugin
+from ..images import captionImge
+import numpy as np
 import base64
-from io import BytesIO
+import json
 
 from modules import shared, devices, sd_hijack, processing, sd_models
 import modules.textual_inversion.dataset
@@ -87,9 +89,9 @@ class EmbeddingDatabase:
 
             if filename.upper().endswith('.PNG'):
                 embed_image = Image.open(path)
-                if 'sd-embedding' in embed_image.text:
-                    embeddingData = base64.b64decode(embed_image.text['sd-embedding'])
-                    data = torch.load(BytesIO(embeddingData), map_location="cpu")
+                if 'sd-ti-embedding' in embed_image.text:
+                    data = embeddingFromB64(embed_image.text['sd-ti-embedding'])
+                    name = data.get('name',name)
             else:
                 data = torch.load(path, map_location="cpu")
 
@@ -258,13 +260,23 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
 
             if save_image_with_stored_embedding:
                 info = PngImagePlugin.PngInfo()
-                info.add_text("sd-embedding", base64.b64encode(open(last_saved_file,'rb').read()))
-                image.save(last_saved_image, "PNG", pnginfo=info)
+                data = torch.load(last_saved_file)
+                info.add_text("sd-ti-embedding", embeddingToB64(data))
+
+                pre_lines = [((255, 207, 175),"<{}>".format(data.get('name','???')))]
+                
+                caption_checkpoint_hash = data.get('sd_checkpoint','UNK')
+                caption_checkpoint_hash = caption_checkpoint_hash.upper() if caption_checkpoint_hash else 'UNK'
+                caption_stepcount = data.get('step',0)
+                caption_stepcount = caption_stepcount if caption_stepcount else 0
+
+                post_lines = [((240, 223, 175),"Trained against checkpoint [{}] for {} steps".format(caption_checkpoint_hash,
+                                                                                                  caption_stepcount))]
+                captioned_image = captionImge(image,prelines=pre_lines,postlines=post_lines)
+                captioned_image.save(last_saved_image, "PNG", pnginfo=info)
             else:
                 image.save(last_saved_image)
 
-
-
             last_saved_image += f", prompt: {text}"
 
         shared.state.job_no = embedding.step