Upload graph-embeddings-XL.py

graph-embeddings-XL.py

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+#!/bin/env python
+
+"""
+ (Similar to graph-embeddings, but for SDXL)
+
+ This program requires two files as arguments: 
+   A text encoder model (SDXL style), and matching config.json
+
+ You can get the fancy SDXL "vit-bigg" based text encoding model and config, from
+ https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/tree/main/text_encoder_2
+ Take the config.json and one of the .safetensors files
+
+ The sd1.5 encoding model resides at
+ https://huggingface.co/runwayml/stable-diffusion-v1-5/tree/main/text_encoder
+
+ Once it has read those files in, it asks for 1-2 text prompts, and then graphs them.
+ (and pops up a prog to display the output)
+
+"""
+import sys
+import torch
+from transformers import CLIPProcessor, CLIPTextModel
+
+if len(sys.argv) <3:
+    print("Error: require clipmodel file and config file as arguments")
+    exit(1)
+
+# 1. Load the pretrained model
+# Note that it doesnt like a leading "/" in the name!!
+#
+model_path = sys.argv[1]
+model_config = sys.argv[2]
+print("loading",model_path)
+model = CLIPTextModel.from_pretrained( 
+        model_path,config=model_config,local_files_only=True,use_safetensors=True)
+
+
+# This is the tokenizer for sd1 and sdxl
+CLIPname = "openai/clip-vit-large-patch14"
+print("getting processor",CLIPname)
+processor = CLIPProcessor.from_pretrained(CLIPname)
+
+def embed_from_text(text):
+    print("getting tokens for",text)
+    inputs = processor(text=text, return_tensors="pt")
+    outputs = model(**inputs)
+    embeddings = outputs.pooler_output
+    return embeddings
+
+
+import PyQt5
+import matplotlib
+matplotlib.use('QT5Agg')  # Set the backend to TkAgg
+
+import matplotlib.pyplot as plt
+
+
+fig, ax = plt.subplots()
+
+
+text1 = input("First prompt: ")
+text2 = input("Second prompt(or leave blank): ")
+
+
+emb1 = embed_from_text(text1)
+print("shape of emb1:",emb1.shape)
+
+graph1=emb1[0].tolist()
+ax.plot(graph1, label=text1[:20])
+
+if len(text2) >0:
+    emb2 = embed_from_text(text2)
+    graph2=emb2[0].tolist()
+    ax.plot(graph2, label=text2[:20])
+
+# Add labels, title, and legend
+#ax.set_xlabel('Index')
+ax.set_ylabel('Values')
+ax.set_title(f"Graph of Embeddings in {model_path}")
+ax.legend()
+
+# Display the graph
+print("Pulling up the graph")
+plt.show()