Okay, so stats.probplot has left me a little confused. The documentation clearly states that:
probplot generates a probability plot, which should not be confused with
a Q-Q or a P-P plot.
Yet all the sources I can find state that a probability plot refers to either a Q-Q plot or P-P plot. Go figure.
Anyway, as far as I'm concerned, what you've generated is a Q-Q plot.
It also seems to me that the option fit=False of stats.probplot is ignored, and a regression line is always added to the data.
Anyway, to get what you desire, we can explicitly create a matplotlib axes instance, and use the get_lines method to remove the unwanted regression lines and change the marker colours.
import scipy.stats as stats
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('seaborn')
x = numpy.random.beta(2, 3, size=100)
fig, ax = plt.subplots(1, 1, figsize=(6, 4))
stats.probplot(x, dist=stats.beta, sparams=(2,3), plot=plt, fit=False)
stats.probplot(x, dist=stats.beta, sparams=(1,2), plot=plt, fit=False)
stats.probplot(x, dist=stats.beta, sparams=(1,4), plot=plt, fit=False)
# Remove the regression lines
ax.get_lines()[1].remove()
ax.get_lines()[2].remove()
ax.get_lines()[3].remove()
# Change colour of scatter
ax.get_lines()[0].set_markerfacecolor('C0')
ax.get_lines()[1].set_markerfacecolor('C1')
ax.get_lines()[2].set_markerfacecolor('C2')
# Add on y=x line
ax.plot([0, 1], [0, 1], c='C3')
This gave me the following, which I think this time really is what you desired:

matplotlib.pyplot as pltand running the above I get a single QQ-plot with multiple lines. Isn't that what you want? Here is the plot I generated