diff --git a/R/grid_helpers.R b/R/grid_helpers.R
index 68af5791..6fcad549 100644
--- a/R/grid_helpers.R
+++ b/R/grid_helpers.R
@@ -316,19 +316,52 @@ compute_grid_info <- function(workflow, grid) {
   any_parameters_model <- nrow(parameters_model) > 0
   any_parameters_preprocessor <- nrow(parameters_preprocessor) > 0
 
-  if (any_parameters_model) {
-    if (any_parameters_preprocessor) {
-      compute_grid_info_model_and_preprocessor(workflow, grid, parameters_model)
-    } else {
-      compute_grid_info_model(workflow, grid, parameters_model)
-    }
+  res <- min_grid(extract_spec_parsnip(workflow), grid)
+
+  if (any_parameters_preprocessor) {
+    res$.iter_preprocessor <- seq_len(nrow(res))
   } else {
-    if (any_parameters_preprocessor) {
-      compute_grid_info_preprocessor(workflow, grid, parameters_model)
-    } else {
-      rlang::abort("Internal error: `workflow` should have some tunable parameters if `grid` is not `NULL`.")
-    }
+    res$.iter_preprocessor <- 1L
+  }
+
+  res$.msg_preprocessor <-
+    new_msgs_preprocessor(
+      seq_len(max(res$.iter_preprocessor)),
+      max(res$.iter_preprocessor)
+    )
+
+  if (nrow(res) != nrow(grid) ||
+      (any_parameters_model && !any_parameters_preprocessor)) {
+    res$.iter_model <- seq_len(dplyr::n_distinct(res[parameters_model$id]))
+  } else {
+    res$.iter_model <- 1L
+  }
+
+  res$.iter_config <- list(list())
+  for (row in seq_len(nrow(res))) {
+    res$.iter_config[row] <- list(iter_config(res[row, ]))
   }
+
+  res$.msg_model <-
+    new_msgs_model(i = res$.iter_model, n = max(res$.iter_model), res$.msg_preprocessor)
+
+  res
+}
+
+iter_config <- function(res_row) {
+  submodels <- res_row$.submodels[[1]]
+  if (identical(submodels, list())) {
+    models <- res_row$.iter_model
+  } else {
+    models <- seq_len(length(submodels[[1]]) + 1)
+  }
+
+  paste0(
+    "Preprocessor",
+    res_row$.iter_preprocessor,
+    "_Model",
+    format_with_padding(models)
+  )
 }
 
 # This generates a "dummy" grid_info object that has the same
@@ -360,217 +393,6 @@ new_grid_info_resamples <- function() {
   out
 }
 
-compute_grid_info_preprocessor <- function(workflow,
-                                           grid,
-                                           parameters_model) {
-  out <- grid
-
-  n_preprocessors <- nrow(out)
-  seq_preprocessors <- seq_len(n_preprocessors)
-
-  # Preprocessor<i>_Model1
-  ids <- format_with_padding(seq_preprocessors)
-  iter_configs <- paste0("Preprocessor", ids, "_Model1")
-  iter_configs <- as.list(iter_configs)
-
-  # preprocessor <i>/<n>
-  msgs_preprocessor <- new_msgs_preprocessor(
-    i = seq_preprocessors,
-    n = n_preprocessors
-  )
-
-  # preprocessor <i>/<n>, model 1/1
-  msgs_model <- new_msgs_model(
-    i = 1L,
-    n = 1L,
-    msgs_preprocessor = msgs_preprocessor
-  )
-
-  # Manually add .submodels column, which will always have empty lists
-  submodels <- rep_len(list(list()), n_preprocessors)
-
-  out <- tibble::add_column(
-    .data = out,
-    .iter_preprocessor = seq_preprocessors,
-    .before = 1L
-  )
-
-  out <- tibble::add_column(
-    .data = out,
-    .msg_preprocessor = msgs_preprocessor,
-    .after = ".iter_preprocessor"
-  )
-
-  # Add at the end
-  out <- tibble::add_column(
-    .data = out,
-    .iter_model = 1L,
-    .after = NULL
-  )
-
-  out <- tibble::add_column(
-    .data = out,
-    .iter_config = iter_configs,
-    .after = ".iter_model"
-  )
-
-  out <- tibble::add_column(
-    .data = out,
-    .msg_model = msgs_model,
-    .after = ".iter_config"
-  )
-
-  out <- tibble::add_column(
-    .data = out,
-    .submodels = submodels,
-    .after = ".msg_model"
-  )
-
-  out
-}
-
-compute_grid_info_model <- function(workflow,
-                                    grid,
-                                    parameters_model) {
-  spec <- extract_spec_parsnip(workflow)
-  out <- min_grid(spec, grid)
-
-  n_fit_models <- nrow(out)
-  seq_fit_models <- seq_len(n_fit_models)
-
-  # preprocessor 1/1
-  msgs_preprocessor <- new_msgs_preprocessor(i = 1L, n = 1L)
-  msgs_preprocessor <- rep(msgs_preprocessor, times = n_fit_models)
-
-  # preprocessor 1/1, model <i_fit>/<n_fit>
-  msgs_model <- new_msgs_model(
-    i = seq_fit_models,
-    n = n_fit_models,
-    msgs_preprocessor = msgs_preprocessor
-  )
-
-  # Preprocessor1_Model<i>
-  iter_configs <- compute_config_ids(out, "Preprocessor1")
-
-  out <- tibble::add_column(
-    .data = out,
-    .iter_preprocessor = 1L,
-    .before = 1L
-  )
-
-  out <- tibble::add_column(
-    .data = out,
-    .msg_preprocessor = msgs_preprocessor,
-    .after = ".iter_preprocessor"
-  )
-
-  out <- tibble::add_column(
-    .data = out,
-    .iter_model = seq_fit_models,
-    .after = ".msg_preprocessor"
-  )
-
-  out <- tibble::add_column(
-    .data = out,
-    .iter_config = iter_configs,
-    .after = ".iter_model"
-  )
-
-  out <- tibble::add_column(
-    .data = out,
-    .msg_model = msgs_model,
-    .after = ".iter_config"
-  )
-
-  out
-}
-
-compute_grid_info_model_and_preprocessor <- function(workflow,
-                                                     grid,
-                                                     parameters_model) {
-  parameter_names_model <- parameters_model[["id"]]
-
-  # Nest model parameters, keep preprocessor parameters outside
-  out <- tidyr::nest(grid, data = dplyr::all_of(parameter_names_model))
-
-  n_preprocessors <- nrow(out)
-  seq_preprocessors <- seq_len(n_preprocessors)
-
-  # preprocessor <i_pre>/<n_pre>
-  msgs_preprocessor <- new_msgs_preprocessor(
-    i = seq_preprocessors,
-    n = n_preprocessors
-  )
-
-  out <- tibble::add_column(
-    .data = out,
-    .iter_preprocessor = seq_preprocessors,
-    .before = 1L
-  )
-
-  out <- tibble::add_column(
-    .data = out,
-    .msg_preprocessor = msgs_preprocessor,
-    .after = ".iter_preprocessor"
-  )
-
-  spec <- extract_spec_parsnip(workflow)
-
-  ids_preprocessor <- format_with_padding(seq_preprocessors)
-  ids_preprocessor <- paste0("Preprocessor", ids_preprocessor)
-
-  model_grids <- out[["data"]]
-
-  for (i in seq_preprocessors) {
-    model_grid <- model_grids[[i]]
-
-    model_grid <- min_grid(spec, model_grid)
-
-    n_fit_models <- nrow(model_grid)
-    seq_fit_models <- seq_len(n_fit_models)
-
-    msg_preprocessor <- msgs_preprocessor[[i]]
-    id_preprocessor <- ids_preprocessor[[i]]
-
-    # preprocessor <i_pre>/<n_pre>, model <i_mod>/<n_mod>
-    msgs_model <- new_msgs_model(
-      i = seq_fit_models,
-      n = n_fit_models,
-      msgs_preprocessor = msg_preprocessor
-    )
-
-    # Preprocessor<i_pre>_Model<i>
-    iter_configs <- compute_config_ids(model_grid, id_preprocessor)
-
-    model_grid <- tibble::add_column(
-      .data = model_grid,
-      .iter_model = seq_fit_models,
-      .before = 1L
-    )
-
-    model_grid <- tibble::add_column(
-      .data = model_grid,
-      .iter_config = iter_configs,
-      .after = ".iter_model"
-    )
-
-    model_grid <- tibble::add_column(
-      .data = model_grid,
-      .msg_model = msgs_model,
-      .after = ".iter_config"
-    )
-
-    model_grids[[i]] <- model_grid
-  }
-
-  out[["data"]] <- model_grids
-
-  # Unnest to match other grid-info generators
-  out <- tidyr::unnest(out, data)
-
-  out
-}
-
 new_msgs_preprocessor <- function(i, n) {
   paste0("preprocessor ", i, "/", n)
 }
@@ -583,35 +405,6 @@ format_with_padding <- function(x) {
   gsub(" ", "0", format(x))
 }
 
-compute_config_ids <- function(data, id_preprocessor) {
-  submodels <- unnest(data, .submodels, keep_empty = TRUE)
-  submodels <- pull(submodels, .submodels)
-
-  # Current model that actually is fit is not included in the submodel count
-  # so we add 1
-  model_sizes <- lengths(submodels) + 1L
-
-  n_total_models <- sum(model_sizes)
-
-  ids <- format_with_padding(seq_len(n_total_models))
-  ids <- paste0(id_preprocessor, "_Model", ids)
-
-  n_fit_models <- nrow(data)
-
-  out <- vector("list", length = n_fit_models)
-
-  start <- 1L
-
-  for (i in seq_len(n_fit_models)) {
-    size <- model_sizes[[i]]
-    stop <- start + size - 1L
-    out[[i]] <- ids[rlang::seq2(start, stop)]
-    start <- stop + 1L
-  }
-
-  out
-}
-
 # ------------------------------------------------------------------------------
 
 has_preprocessor <- function(workflow) {