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#!/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)
# surprisingly, CLIPModel doesnt use, or want, this key!?!
# have to remove it.
if "text_model.embeddings.position_ids" in sd:
print("Removing key text_model.embeddings.position_ids")
sd.pop("text_model.embeddings.position_ids")
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()
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