The precise domain of the generator $A$ of an Itō diffusion on a Hilbert space $H$ (assume $H=\mathbb R^d$, if that's easier for you to work with) can usually not be determined explicitly$^1$. Usually, we can only show that $C_c^2(H)$ is contained in $\mathcal D(A)$.
Now, we know that if $\mu$ is a measure on $H$ with a density $p$ with respect to a reference measure $\lambda$ on $H$, then $\mu$ is invariant with respect to the diffusion process iff $$A^\ast p=0\tag2,$$ where $A^\ast$ denotes the adjoint of $A$ with respect to the bracket $$\langle\;\cdot\;,\;\cdot\;\rangle:\mathcal B_b(H)\times\mathcal L^1(\lambda)\;,\;\;\;(f,g)\mapsto\int fg\:{\rm d}\lambda\tag3,$$ where $\mathcal B_b(H)$ denotes the space of bounded Borel measurable real-valued functions on $H$.
(If you want to think of an example: Take standard Brownian motion; then $A=\frac12\Delta$ and $\mathcal D(A)$ is the closure of $C_c^\infty(\mathbb R^d)$ with respect to the graph norm of the Laplacian. This operator is self-adjoint; so $A=A^\ast$.)
Question: From this result, it seems like there is no chance to construct a diffusion process for a given $\mu$ with a non-differentiable or even non-continuous density $p$. Is that really the whole story? Nothing we can do about?
EDIT: In case it is not clear what I'm asking: Say we have a probability measure $\mu$ with non-differentiable density $p$ with respect to the Lebesgue measure on $\mathbb R^d$. Can we choose the drift and diffusion coefficients $b$ and $\sigma$ of an SDE $${\rm d}X_t=b(X_t){\rm d}t+\sigma(X_t){\rm d}W_t\tag4$$ such that $\mu$ is invariant with respect to the transition kernel of $(X_t)_{t\ge0}$?
Remark: The reason for this question is the following observation: No matter how smooth $p$ is, a Metropolis-Hastings kernel $\kappa$ with target distribution $\mu$ will always be $\mu$-reversible and hence $\mu$ will always be $\kappa$-invariant. Assume that we are using a Gaussian proposal kernel (this is usually called "Random Walk Metropolis"). Now, by specifying a constant holding rate, we can easily embed $\kappa$ into continuous-time. The resulting process, doesn't seem to be far from a (discretized) Brownian motion. Though, clearly, there is acceptance-rejection occurring. In that sense, we maybe can do something similar by running a suitably modified simulation of a diffusion process.
$^1$ Please correct me, if that's wrong, but it won't be important for this question.