Upload graph-embeddings-frommodel.py

graph-embeddings-frommodel.py

@@ -0,0 +1,110 @@
+#!/bin/env python
+
+""" Demo source that explores difference between embeddings from 
+    stock CLIPModel data, vs one embedded in a full SD model.
+    Input a single word, and it will graph each version.
+
+    You will want to zoom in to actually see the differences, usually
+
+Required data file: "text_encoder.bin"
+
+    Find the "diffusers format" version of the model you are interested in,
+    and steal from that.
+    eg: grab
+    stablediffusionapi/ghostmix/text_encoder/pytorch_model.bin
+
+    and download it, renamed to 
+        "text_encoder.bin"
+
+"""
+
+
+import sys
+import json
+import torch
+from transformers import CLIPProcessor,CLIPModel
+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"
+
+processor=None
+model=None
+
+device=torch.device("cuda")
+
+
+def init():
+    global processor
+    global model
+    # Load the processor and model
+    print("loading processor from "+clipsrc,file=sys.stderr)
+    processor = CLIPProcessor.from_pretrained(clipsrc)
+    print("done",file=sys.stderr)
+    print("loading model from "+clipsrc,file=sys.stderr)
+    model = CLIPModel.from_pretrained(clipsrc)
+    print("done",file=sys.stderr)
+    model = model.to(device)
+
+def load_overlay():
+    global model
+    print("loading overlay",overlaymodel)
+    overlay=torch.load(overlaymodel)
+    if "state_dict" in overlay:
+        print("dereferencing state_dict")
+        overlay=overlay["state_dict"]
+
+    print("Attempting to update old from new")
+    sd=model.state_dict()
+    sd.update(overlay)
+    print("Reloading merged data")
+    model.load_state_dict(sd)
+    model = model.to(device)
+
+# Expect SINGLE WORD ONLY
+def standard_embed_calc(text):
+    inputs = processor(text=text, return_tensors="pt")
+    inputs.to(device)
+    with torch.no_grad():
+        text_features = model.get_text_features(**inputs)
+    embedding = text_features[0]
+    return embedding
+
+
+init()
+
+fig, ax = plt.subplots()
+
+
+text1 = input("First word or prompt: ")
+
+
+print("generating embeddings for each now")
+emb1 = standard_embed_calc(text1)
+graph1=emb1.tolist()
+ax.plot(graph1, label=text1[:20])
+
+load_overlay()
+emb2 = standard_embed_calc(text1)
+graph2=emb2.tolist()
+ax.plot(graph2, label="overlay data")
+
+# Add labels, title, and legend
+#ax.set_xlabel('Index')
+ax.set_ylabel('Values')
+ax.set_title('Graph embedding from standard vs MERGED dict')
+ax.legend()
+
+# Display the graph
+print("Pulling up the graph")
+plt.show()