Upload generate-embeddingXL.py

generate-embeddingXL.py

@@ -0,0 +1,99 @@
+#!/bin/env python
+
+""" Work in progress
+
+Similar to generate-embedding.py, but outputs in the format
+that SDXL models expect. I hope.
+
+Also tries to load the SDXL base text encoder specifically.
+Requires you populate the two paths mentioned immediately below this comment section.
+
+You can get them from:
+https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/tree/main/text_encoder_2
+
+(rename diffusion_pytorch_model.safetensors to text_encoder_xl.safetensors)
+
+
+Plan:
+   Take input for a single word or phrase.
+   Save out calculations, to "generatedXL.safetensors"
+
+   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"
+
+import sys
+import torch
+from transformers import CLIPProcessor, CLIPTextModel, CLIPTextModelWithProjection
+from safetensors.torch import save_file
+
+# 1. Load the pretrained model
+# Note that it doesnt like a leading "/" in the name!!
+
+
+model=None
+processor=None
+
+device=torch.device("cuda")
+
+# Note the default, required 2 pathnames
+def initXLCLIPmodel():
+    global model
+    print("loading",model_path)
+    model = CLIPTextModelWithProjection.from_pretrained(model_path,config=model_config,local_files_only=True,use_safetensors=True)
+    model.to(device)
+
+# a bit wierd, but SDXL seems to still use this tokeninzer
+def initCLIPprocessor():
+    global processor
+    CLIPname= "openai/clip-vit-large-patch14"
+    print("getting processor from",CLIPname)
+    processor = CLIPProcessor.from_pretrained(CLIPname)
+
+def embed_from_text(text):
+    global processor,model
+    if processor == None:
+        initCLIPprocessor()
+        initXLCLIPmodel()
+    print("getting tokens")
+    inputs = processor(text=text, return_tensors="pt")
+    inputs.to(device)
+
+    print("getting embeddings?")
+    outputs = model(**inputs)
+    print("finalizing")
+    embeddings = outputs.text_embeds
+    return embeddings
+
+
+
+##########################################
+
+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
+
+print("Shape of result = ",embs.shape)
+# Note that programs like shapes such as
+#  torch.Size([1, 768])
+
+output = "generatedXL.safetensors"
+# if single word used, then rename output file
+if all(char.isalpha() for char in word):
+    output=f"{word}XL.safetensors"
+print(f"Saving to {output}...")
+save_file({"clip_g": embs}, 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])
+
+