meh

generate-embeddingXL.py

@@ -3,7 +3,7 @@
 """ Work in progress
 
 Similar to generate-embedding.py, but outputs in the format
-that SDXL models expect. I hope.
+that SDXL models expect.
 
 Also tries to load the SDXL base text encoder specifically.
 Requires you populate the two paths mentioned immediately below this comment section.
@@ -20,12 +20,12 @@ Plan:
 
    Note that you can generate an embedding from two words, or even more
    
-   I could also include a "clip_l" key, but..
-   Meh. 
 """
 
-model_path = "text_encoder_xl.safetensors"
-model_config = "text_encoder_2_config.json"
+model_path1 = "text_encoder.safetensors"
+model_config1 = "text_encoder_config.json"
+model_path2 = "text_encoder_2.safetensors"
+model_config2 = "text_encoder_2_config.json"
 
 import sys
 import torch
@@ -36,17 +36,25 @@ from safetensors.torch import save_file
 # Note that it doesnt like a leading "/" in the name!!
 
 
-model=None
+tmodel1=None
+tmodel2=None
 processor=None
 
 device=torch.device("cuda")
 
+def initCLIPmodel(model_path,model_config):
+    global tmodel1
+    print("loading",model_path)
+    tmodel1 = CLIPTextModel.from_pretrained(model_path,config=model_config,local_files_only=True,use_safetensors=True)
+    tmodel1.to(device)
+
+#
 # Note the default, required 2 pathnames
-def initXLCLIPmodel():
-    global model
+def initXLCLIPmodel(model_path,model_config):
+    global tmodel2
     print("loading",model_path)
-    model = CLIPTextModelWithProjection.from_pretrained(model_path,config=model_config,local_files_only=True,use_safetensors=True)
-    model.to(device)
+    tmodel2 = CLIPTextModelWithProjection.from_pretrained(model_path,config=model_config,local_files_only=True,use_safetensors=True)
+    tmodel2.to(device)
 
 # a bit wierd, but SDXL seems to still use this tokeninzer
 def initCLIPprocessor():
@@ -56,17 +64,27 @@ def initCLIPprocessor():
     processor = CLIPProcessor.from_pretrained(CLIPname)
 
 def embed_from_text(text):
-    global processor,model
+    global processor,tmodel1
     if processor == None:
         initCLIPprocessor()
-        initXLCLIPmodel()
-    print("getting tokens")
+        initCLIPmodel(model_path1,model_config1)
+    inputs = processor(text=text, return_tensors="pt")
+    inputs.to(device)
+
+    print("getting embeddings1")
+    outputs = tmodel1(**inputs)
+    embeddings = outputs.pooler_output
+    return embeddings
+
+def embed_from_text2(text):
+    global processor,tmodel2
+    if tmodel2 == None:
+        initXLCLIPmodel(model_path2,model_config2)
     inputs = processor(text=text, return_tensors="pt")
     inputs.to(device)
 
-    print("getting embeddings?")
-    outputs = model(**inputs)
-    print("finalizing")
+    print("getting embeddings2")
+    outputs = tmodel2(**inputs)
     embeddings = outputs.text_embeds
     return embeddings
 
@@ -76,24 +94,17 @@ def embed_from_text(text):
 
 word = input("type a phrase to generate an embedding for: ")
 
-emb = embed_from_text(word)
-#embs=emb.unsqueeze(0) # stupid matrix magic to make it the required shape
-embs=emb
+emb1 = embed_from_text(word)
+emb2 = embed_from_text2(word)
 
-print("Shape of result = ",embs.shape)
-# Note that programs like shapes such as
-#  torch.Size([1, 768])
+print("Shape of results = ",emb1.shape,emb2.shape)
 
-output = "generatedXL.safetensors"
+output = "generated_XL.safetensors"
 # if single word used, then rename output file
 if all(char.isalpha() for char in word):
-    output=f"{word}XL.safetensors"
+    output=f"{word}_XL.safetensors"
 print(f"Saving to {output}...")
-save_file({"clip_g": embs}, output)
+save_file({"clip_g": emb2,"clip_l":emb1}, output)
 
-# technically we are saving a shape ([1][1280])
-# whereas official XL embeddings files, are 
-# (clip_g) shape ([8][1280])
-# (clip_l) shape ([8][768])