Can geom_smooth accept logical variables for glm?

I have a tibble with numerical and logical variables, e.g. like this:

       x     f      y
   <dbl> <int>  <dbl>
 1 -2        1 -0.801
 2 -1.96     0 -2.27 
 3 -1.92     0 -1.75 
 4 -1.88     0 -2.44 
 5 -1.84     1 -0.123
          ...

For reproducibility, it can be generated using:

library(tidyverse)
set.seed(0)
tb1 = tibble(
  x=(-50:50)/25,
  p=plogis(x),
  f=rbinom(p, 1, p),
  y = x+f+rnorm(x, 0, .5)
) %>% select(-p)

I'd like to plot the points and draw regression lines, once taking x as the predictor and f as the outcome (logistic regression), and once taking x and f as predictors and y as the outcome (linear regression). This works well for the logistic regression.

ggplot(tb1, aes(x, f)) +
  geom_point() +
  geom_smooth(method="glm", method.args=list(family="binomial"))

produces:

but:

ggplot(tb1, aes(x, y, colour=f)) +
  geom_point() +
  geom_smooth(method="lm")

produces:

which is wrong. I want f treated as a factor, producing two regression lines, and a discrete instead of the continuous-coloured legend. I can force f manually to a logical value:

tb2 = tb1 %>% mutate(f = f>0)

and obtain the correct linear regression graph:

but now I cannot plot the logistic regression. I get the

Warning message:
Computation failed in stat_smooth():
y values must be 0 <= y <= 1

For some reason, both lm() and glm() have no problems:

summary(glm(f ~ x, binomial, tb1))
summary(lm(y ~ x + f, tb1))
summary(glm(f ~ x, binomial, tb2))
summary(lm(y ~ x + f, tb2))

all produce reasonable results, and the results are identical for tb1 and tb2, as they should be. So is there a way of convincing geom_smooth to accept logical variables, or must I use two redundant variables, with identical values but of a different type, e.g. f.int and f.lgl?