diff --git a/scripts/sample_score/sample_score.jl b/scripts/sample_score/sample_score.jl
index 9f1829fc..4f32289c 100644
--- a/scripts/sample_score/sample_score.jl
+++ b/scripts/sample_score/sample_score.jl
@@ -95,3 +95,4 @@ for f in mfiles
 	# compute and save the score
 	save_sample_score(f, data, seed, ac)
 end
+@info "DONE"
\ No newline at end of file
diff --git a/scripts/sample_score/sample_score_latent.jl b/scripts/sample_score/sample_score_latent.jl
new file mode 100644
index 00000000..706f7947
--- /dev/null
+++ b/scripts/sample_score/sample_score_latent.jl
@@ -0,0 +1,98 @@
+using DrWatson
+@quickactivate
+using ArgParse
+using GenerativeAD
+import StatsBase: fit!, predict
+using StatsBase
+using BSON, FileIO
+using Flux
+using GenerativeModels
+using DistributionsAD
+using ValueHistories
+
+s = ArgParseSettings()
+@add_arg_table! s begin
+	"modelname"
+		default = "vae"
+		arg_type = String
+		help = "model name"
+	"datatype"
+		default = "tabular"
+		arg_type = String
+		help = "tabular or image"
+	"dataset"
+		default = "iris"
+		arg_type = String
+		help = "dataset"
+	"--seed"
+		default = nothing
+		help = "if specified, only results for a given seed will be recomputed"
+	"--anomaly_class"
+		default = nothing
+		help = "if specified, only results for a given anomaly class will be recomputed"
+end
+parsed_args = parse_args(ARGS, s)
+@unpack dataset, datatype, modelname, seed, anomaly_class = parsed_args
+
+masterpath = datadir("experiments/$(datatype)/$(modelname)/$(dataset)")
+files = GenerativeAD.Evaluation.collect_files(masterpath)
+mfiles = filter(f->occursin("model", f), files)
+if seed != nothing
+	filter!(x->occursin("/seed=$seed/", x), mfiles)
+end
+if anomaly_class != nothing
+	filter!(x->occursin("/ac=$(anomaly_class)/", x), mfiles)
+end
+
+sample_score_batched(m,x,L,batchsize) = 
+	vcat(map(y-> Base.invokelatest(GenerativeAD.Models.latent_score, m, y, L), Flux.Data.DataLoader(x, batchsize=batchsize))...)
+sample_score_batched_gpu(m,x,L,batchsize) = 
+	vcat(map(y-> cpu(Base.invokelatest(GenerativeAD.Models.latent_score, m, gpu(Array(y)), L)), Flux.Data.DataLoader(x, batchsize=batchsize))...)
+
+function save_sample_score(f::String, data, seed::Int, ac=nothing)
+	# get model
+	savepath = dirname(f)
+	mdata = load(f)
+	model = mdata["model"]
+	L = 100
+
+	# setup entries to be saved
+	save_entries = (
+		modelname = modelname,
+		fit_t = mdata["fit_t"],
+		history = mdata["history"],
+		dataset = dataset,
+		npars = sum(map(p->length(p), Flux.params(model))),
+		model = nothing,
+		seed = seed
+		)
+	save_entries = (ac == nothing) ? save_entries : merge(save_entries, (ac=ac,))
+	if ac == nothing
+		result = (x -> sample_score_batched(model, x, L, 512), 
+			merge(mdata["parameters"], (L = L, score = "latent-sampled"))) 
+	else
+		result = (x -> sample_score_batched_gpu(gpu(model), x, L, 256), 
+			merge(mdata["parameters"], (L = L, score = "latent-sampled"))) 
+	end
+
+	# if the file does not exist already, compute the scores
+	savef = joinpath(savepath, savename(result[2], "bson", digits=5))
+	if !isfile(savef)
+		@info "computing sample score for $f"
+		GenerativeAD.experiment(result..., data, savepath; save_entries...)
+	end
+end
+
+for f in mfiles
+	# get data
+	savepath = dirname(f)
+	local seed = parse(Int, replace(basename(savepath), "seed=" => ""))
+	ac = occursin("ac=", savepath) ? parse(Int, replace(basename(dirname(savepath)), "ac=" => "")) : nothing
+	data = (ac == nothing) ? 
+		GenerativeAD.load_data(dataset, seed=seed) : 
+		GenerativeAD.load_data(dataset, seed=seed, anomaly_class_ind=ac)
+		
+	# compute and save the score
+	save_sample_score(f, data, seed, ac)
+end
+@info "DONE"
diff --git a/scripts/sample_score/sample_score_latent_gpu_run.sh b/scripts/sample_score/sample_score_latent_gpu_run.sh
new file mode 100755
index 00000000..8adba9e0
--- /dev/null
+++ b/scripts/sample_score/sample_score_latent_gpu_run.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+#SBATCH --time=24:00:00
+#SBATCH --nodes=1 --ntasks-per-node=2 --cpus-per-task=2
+#SBATCH --gres=gpu:1
+#SBATCH --partition=gpu
+#SBATCH --mem=80G
+
+MODEL=$1
+DATATYPE=$2
+DATASET=$3
+SEED=$4
+AC=$5
+
+module load Julia/1.5.1-linux-x86_64
+module load Python/3.8.2-GCCcore-9.3.0
+
+julia ./sample_score_latent.jl $MODEL $DATATYPE $DATASET $SEED $AC
diff --git a/scripts/sample_score/sample_score_latent_run.sh b/scripts/sample_score/sample_score_latent_run.sh
new file mode 100755
index 00000000..c86208c3
--- /dev/null
+++ b/scripts/sample_score/sample_score_latent_run.sh
@@ -0,0 +1,16 @@
+#!/bin/bash
+#SBATCH --time=24:00:00
+#SBATCH --nodes=1 --ntasks-per-node=2 --cpus-per-task=1
+#SBATCH --mem=20G
+
+MODEL=$1
+DATATYPE=$2
+DATASET=$3
+SEED=$4
+AC=$5
+
+module load Julia/1.5.1-linux-x86_64
+module load Python/3.8.2-GCCcore-9.3.0
+
+julia ./sample_score_latent.jl $MODEL $DATATYPE $DATASET $SEED $AC
+
diff --git a/scripts/sample_score/sample_score_latent_run_parallel.sh b/scripts/sample_score/sample_score_latent_run_parallel.sh
new file mode 100755
index 00000000..5be5316d
--- /dev/null
+++ b/scripts/sample_score/sample_score_latent_run_parallel.sh
@@ -0,0 +1,30 @@
+#!/bin/bash
+# This runs jacodeco for a certain model over a set of datasets.
+# USAGE EXAMPLE
+# 	./jacodeco_run_parallel.sh vae tabular datasets_tabular.txt
+MODEL=$1 		# which model to run
+DATATYPE=$2			# tabular/image
+DATASET_FILE=$3	# file with dataset list
+SEED=$4
+AC=$5
+
+LOG_DIR="${HOME}/logs/sample_score"
+
+if [ ! -d "$LOG_DIR" ]; then
+	mkdir $LOG_DIR
+fi
+
+
+while read d; do
+	if [ $d = "MNIST" ] || [ $d = "FashionMNIST" ] || [ $d = "CIFAR10" ] || [ $d = "SVHN2" ]
+	then
+		RUNSCRIPT="./sample_score_latent_gpu_run.sh"
+	else
+		RUNSCRIPT="./sample_score_latent_run.sh"
+	fi
+
+	# submit to slurm
+    sbatch \
+    --output="${LOG_DIR}/${DATATYPE}_${MODEL}_${d}-%A.out" \
+    $RUNSCRIPT $MODEL $DATATYPE $d $SEED $AC
+done < ${DATASET_FILE}