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README.md

@@ -9,14 +9,22 @@ Primary tools are:
 *  "graph-embeddings.py": plots graph of full values of two embeddings
 
 
-## calculate-distances.py
+## (clipmodel,cliptextmodel)-calculate-distances.py
 
 Loads the generated embeddings, reads in a word, calculates "distance" to every
 embedding, and then shows the closest "neighbours".
 
-To run this requires the files "embeddings.safetensors" and "dictionary"
+To run this requires the files "embeddings.safetensors" and "dictionary",
+in matching format
 
-You will need to rename or copy appropriate files for this as mentioned below
+You will need to rename or copy appropriate files for this as mentioned below.
+
+Note that SD models use cliptextmodel, NOT clipmodel
+
+## graph-textmodels.py
+
+Shows the difference between the same word, embedded by CLIPTextModel
+vs CLIPModel
 
 ## graph-embeddings.py
 

graph-textmodels.py

@@ -0,0 +1,113 @@
+#!/bin/env python
+
+"""
+    Input a single word, and it will graph it, 
+    as embedded by CLIPModel vs CLIPTextModel
+
+    It will then print out the "distance" between the two,
+    and then show you a coordinate graph
+
+    You will want to zoom in to actually see the differences, usually
+
+"""
+
+
+import sys
+import json
+import torch
+from transformers import CLIPProcessor,CLIPModel,CLIPTextModel
+import logging
+# Turn off  stupid mesages from CLIPModel.load
+logging.disable(logging.WARNING)
+
+import PyQt5
+import matplotlib
+matplotlib.use('QT5Agg')  # Set the backend to TkAgg
+
+import matplotlib.pyplot as plt
+
+
+clipsrc="openai/clip-vit-large-patch14"
+
+overlaymodel="text_encoder.bin"
+overlaymodel2="text_encoder2.bin"
+
+processor=None
+clipmodel=None
+cliptextmodel=None
+
+device=torch.device("cuda")
+
+print("loading processor from "+clipsrc,file=sys.stderr)
+processor = CLIPProcessor.from_pretrained(clipsrc)
+print("done",file=sys.stderr)
+
+def clipmodel_one_time(text):
+    global clipmodel
+    if clipmodel == None:
+        print("loading CLIPModel from "+clipsrc,file=sys.stderr)
+        clipmodel = CLIPModel.from_pretrained(clipsrc)
+        clipmodel = clipmodel.to(device)
+        print("done",file=sys.stderr)
+
+    inputs = processor(text=text, return_tensors="pt")
+    inputs.to(device)
+    with torch.no_grad():
+        text_features = clipmodel.get_text_features(**inputs)
+    return text_features
+    #shape = (1,768)
+
+
+def cliptextmodel_one_time(text):
+    global cliptextmodel
+    if cliptextmodel == None:
+        print("loading CLIPTextModel from "+clipsrc,file=sys.stderr)
+        cliptextmodel = CLIPTextModel.from_pretrained(clipsrc)
+        cliptextmodel = cliptextmodel.to(device)
+        print("done",file=sys.stderr)
+    inputs = processor(text=text, return_tensors="pt")
+    inputs.to(device)
+    with torch.no_grad():
+        outputs = cliptextmodel(**inputs)
+    embeddings = outputs.pooler_output
+    return embeddings
+    # shape is (1,768)
+
+def print_distance(emb1,emb2):
+    targetdistance = torch.norm( emb1 - emb2)
+    print("DISTANCE:",targetdistance)
+
+
+
+def prompt_for_word():
+    fig, ax = plt.subplots()
+
+    text1 = input("Word or prompt: ")
+    if text1 == "q":
+        exit(0)
+
+    print("generating embeddings for each now")
+
+    emb1 = clipmodel_one_time(text1)[0]
+    graph1=emb1.tolist()
+    ax.plot(graph1, label="clipmodel")
+
+    emb2 = cliptextmodel_one_time(text1)[0]
+    graph2=emb2.tolist()
+    ax.plot(graph2, label="cliptextmodel")
+
+    print_distance(emb1,emb2)
+
+    # Add labels, title, and legend
+    #ax.set_xlabel('Index')
+    ax.set_ylabel('Values')
+    ax.set_title('Graph embedding from std libs')
+    ax.legend()
+
+    # Display the graph
+    print("Pulling up the graph. To calculate more distances, close graph")
+    plt.show()
+    # Dont know why plt.show only works once !
+
+while True:
+    prompt_for_word()