diff --git a/libavfilter/dnn_backend_native.c b/libavfilter/dnn_backend_native.c
index fe43116..18735c0 100644
--- a/libavfilter/dnn_backend_native.c
+++ b/libavfilter/dnn_backend_native.c
@@ -25,7 +25,7 @@
 
 #include "dnn_backend_native.h"
 
-static DNNReturnType set_input_output_native(void *model, DNNData *input, const char *input_name, DNNData *output, const char *output_name)
+static DNNReturnType set_input_output_native(void *model, DNNData *input, const char *input_name, const char *output_name)
 {
     ConvolutionalNetwork *network = (ConvolutionalNetwork *)model;
     InputParams *input_params;
@@ -81,11 +81,6 @@ static DNNReturnType set_input_output_native(void *model, DNNData *input, const
         }
     }
 
-    output->data = network->layers[network->layers_num - 1].output;
-    output->height = cur_height;
-    output->width = cur_width;
-    output->channels = cur_channels;
-
     return DNN_SUCCESS;
 }
 
@@ -280,7 +275,7 @@ static void depth_to_space(const float *input, float *output, int block_size, in
     }
 }
 
-DNNReturnType ff_dnn_execute_model_native(const DNNModel *model)
+DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData *output)
 {
     ConvolutionalNetwork *network = (ConvolutionalNetwork *)model->model;
     int cur_width, cur_height, cur_channels;
@@ -322,6 +317,11 @@ DNNReturnType ff_dnn_execute_model_native(const DNNModel *model)
         }
     }
 
+    output->data = network->layers[network->layers_num - 1].output;
+    output->height = cur_height;
+    output->width = cur_width;
+    output->channels = cur_channels;
+
     return DNN_SUCCESS;
 }
 
diff --git a/libavfilter/dnn_backend_native.h b/libavfilter/dnn_backend_native.h
index 51d4cac..adaf4a7 100644
--- a/libavfilter/dnn_backend_native.h
+++ b/libavfilter/dnn_backend_native.h
@@ -63,7 +63,7 @@ typedef struct ConvolutionalNetwork{
 
 DNNModel *ff_dnn_load_model_native(const char *model_filename);
 
-DNNReturnType ff_dnn_execute_model_native(const DNNModel *model);
+DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData *output);
 
 void ff_dnn_free_model_native(DNNModel **model);
 
diff --git a/libavfilter/dnn_backend_tf.c b/libavfilter/dnn_backend_tf.c
index a838907..7bee45c 100644
--- a/libavfilter/dnn_backend_tf.c
+++ b/libavfilter/dnn_backend_tf.c
@@ -35,7 +35,6 @@ typedef struct TFModel{
     TF_Status *status;
     TF_Output input, output;
     TF_Tensor *input_tensor;
-    DNNData *output_data;
 } TFModel;
 
 static void free_buffer(void *data, size_t length)
@@ -76,13 +75,12 @@ static TF_Buffer *read_graph(const char *model_filename)
     return graph_buf;
 }
 
-static DNNReturnType set_input_output_tf(void *model, DNNData *input, const char *input_name, DNNData *output, const char *output_name)
+static DNNReturnType set_input_output_tf(void *model, DNNData *input, const char *input_name, const char *output_name)
 {
     TFModel *tf_model = (TFModel *)model;
     int64_t input_dims[] = {1, input->height, input->width, input->channels};
     TF_SessionOptions *sess_opts;
     const TF_Operation *init_op = TF_GraphOperationByName(tf_model->graph, "init");
-    TF_Tensor *output_tensor;
 
     // Input operation
     tf_model->input.oper = TF_GraphOperationByName(tf_model->graph, input_name);
@@ -132,26 +130,6 @@ static DNNReturnType set_input_output_tf(void *model, DNNData *input, const char
         }
     }
 
-    // Execute network to get output height, width and number of channels
-    TF_SessionRun(tf_model->session, NULL,
-                  &tf_model->input, &tf_model->input_tensor, 1,
-                  &tf_model->output, &output_tensor, 1,
-                  NULL, 0, NULL, tf_model->status);
-    if (TF_GetCode(tf_model->status) != TF_OK){
-        return DNN_ERROR;
-    }
-    else{
-        output->height = TF_Dim(output_tensor, 1);
-        output->width = TF_Dim(output_tensor, 2);
-        output->channels = TF_Dim(output_tensor, 3);
-        output->data = av_malloc(output->height * output->width * output->channels * sizeof(float));
-        if (!output->data){
-            return DNN_ERROR;
-        }
-        tf_model->output_data = output;
-        TF_DeleteTensor(output_tensor);
-    }
-
     return DNN_SUCCESS;
 }
 
@@ -489,7 +467,6 @@ DNNModel *ff_dnn_load_model_tf(const char *model_filename)
     }
     tf_model->session = NULL;
     tf_model->input_tensor = NULL;
-    tf_model->output_data = NULL;
 
     if (load_tf_model(tf_model, model_filename) != DNN_SUCCESS){
         if (load_native_model(tf_model, model_filename) != DNN_SUCCESS){
@@ -508,10 +485,12 @@ DNNModel *ff_dnn_load_model_tf(const char *model_filename)
 
 
 
-DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model)
+DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model, DNNData *output)
 {
     TFModel *tf_model = (TFModel *)model->model;
     TF_Tensor *output_tensor;
+    uint64_t count;
+    uint64_t old_count = output->height * output->width * output->channels * sizeof(float);
 
     TF_SessionRun(tf_model->session, NULL,
                   &tf_model->input, &tf_model->input_tensor, 1,
@@ -521,14 +500,26 @@ DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model)
     if (TF_GetCode(tf_model->status) != TF_OK){
         return DNN_ERROR;
     }
-    else{
-        memcpy(tf_model->output_data->data, TF_TensorData(output_tensor),
-               tf_model->output_data->height * tf_model->output_data->width *
-               tf_model->output_data->channels * sizeof(float));
-        TF_DeleteTensor(output_tensor);
 
-        return DNN_SUCCESS;
+    output->height = TF_Dim(output_tensor, 1);
+    output->width = TF_Dim(output_tensor, 2);
+    output->channels = TF_Dim(output_tensor, 3);
+    count = output->height * output->width * output->channels * sizeof(float);
+    if (output->data) {
+        if (count > old_count) {
+            av_freep(&output->data);
+        }
+    }
+    if (!output->data) {
+        output->data = av_malloc(count);
+        if (!output->data){
+            return DNN_ERROR;
+        }
     }
+    memcpy(output->data, TF_TensorData(output_tensor), count);
+    TF_DeleteTensor(output_tensor);
+
+    return DNN_SUCCESS;
 }
 
 void ff_dnn_free_model_tf(DNNModel **model)
@@ -550,9 +541,6 @@ void ff_dnn_free_model_tf(DNNModel **model)
         if (tf_model->input_tensor){
             TF_DeleteTensor(tf_model->input_tensor);
         }
-        if (tf_model->output_data){
-            av_freep(&tf_model->output_data->data);
-        }
         av_freep(&tf_model);
         av_freep(model);
     }
diff --git a/libavfilter/dnn_backend_tf.h b/libavfilter/dnn_backend_tf.h
index 7ba84f4..47a24ec 100644
--- a/libavfilter/dnn_backend_tf.h
+++ b/libavfilter/dnn_backend_tf.h
@@ -31,7 +31,7 @@
 
 DNNModel *ff_dnn_load_model_tf(const char *model_filename);
 
-DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model);
+DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model, DNNData *output);
 
 void ff_dnn_free_model_tf(DNNModel **model);
 
diff --git a/libavfilter/dnn_interface.h b/libavfilter/dnn_interface.h
index 0390e39..822f6e5 100644
--- a/libavfilter/dnn_interface.h
+++ b/libavfilter/dnn_interface.h
@@ -38,9 +38,9 @@ typedef struct DNNData{
 typedef struct DNNModel{
     // Stores model that can be different for different backends.
     void *model;
-    // Sets model input and output, while allocating additional memory for intermediate calculations.
+    // Sets model input and output.
     // Should be called at least once before model execution.
-    DNNReturnType (*set_input_output)(void *model, DNNData *input, const char *input_name, DNNData *output, const char *output_name);
+    DNNReturnType (*set_input_output)(void *model, DNNData *input, const char *input_name, const char *output_name);
 } DNNModel;
 
 // Stores pointers to functions for loading, executing, freeing DNN models for one of the backends.
@@ -48,7 +48,7 @@ typedef struct DNNModule{
     // Loads model and parameters from given file. Returns NULL if it is not possible.
     DNNModel *(*load_model)(const char *model_filename);
     // Executes model with specified input and output. Returns DNN_ERROR otherwise.
-    DNNReturnType (*execute_model)(const DNNModel *model);
+    DNNReturnType (*execute_model)(const DNNModel *model, DNNData *output);
     // Frees memory allocated for model.
     void (*free_model)(DNNModel **model);
 } DNNModule;
diff --git a/libavfilter/vf_sr.c b/libavfilter/vf_sr.c
index 085ac19..7c92730 100644
--- a/libavfilter/vf_sr.c
+++ b/libavfilter/vf_sr.c
@@ -122,20 +122,31 @@ static int config_props(AVFilterLink *inlink)
     sr_context->input.height = inlink->h * sr_context->scale_factor;
     sr_context->input.channels = 1;
 
-    result = (sr_context->model->set_input_output)(sr_context->model->model, &sr_context->input, "x", &sr_context->output, "y");
+    result = (sr_context->model->set_input_output)(sr_context->model->model, &sr_context->input, "x", "y");
     if (result != DNN_SUCCESS){
         av_log(context, AV_LOG_ERROR, "could not set input and output for the model\n");
         return AVERROR(EIO);
     }
 
+    result = (sr_context->dnn_module->execute_model)(sr_context->model, &sr_context->output);
+    if (result != DNN_SUCCESS){
+        av_log(context, AV_LOG_ERROR, "failed to execute loaded model\n");
+        return AVERROR(EIO);
+    }
+
     if (sr_context->input.height != sr_context->output.height || sr_context->input.width != sr_context->output.width){
         sr_context->input.width = inlink->w;
         sr_context->input.height = inlink->h;
-        result = (sr_context->model->set_input_output)(sr_context->model->model, &sr_context->input, "x", &sr_context->output, "y");
+        result = (sr_context->model->set_input_output)(sr_context->model->model, &sr_context->input, "x", "y");
         if (result != DNN_SUCCESS){
             av_log(context, AV_LOG_ERROR, "could not set input and output for the model\n");
             return AVERROR(EIO);
         }
+        result = (sr_context->dnn_module->execute_model)(sr_context->model, &sr_context->output);
+        if (result != DNN_SUCCESS){
+            av_log(context, AV_LOG_ERROR, "failed to execute loaded model\n");
+            return AVERROR(EIO);
+        }
         sr_context->scale_factor = 0;
     }
     outlink->h = sr_context->output.height;
@@ -248,7 +259,7 @@ static int filter_frame(AVFilterLink *inlink, AVFrame *in)
     }
     av_frame_free(&in);
 
-    dnn_result = (sr_context->dnn_module->execute_model)(sr_context->model);
+    dnn_result = (sr_context->dnn_module->execute_model)(sr_context->model, &sr_context->output);
     if (dnn_result != DNN_SUCCESS){
         av_log(context, AV_LOG_ERROR, "failed to execute loaded model\n");
         return AVERROR(EIO);
@@ -266,6 +277,9 @@ static av_cold void uninit(AVFilterContext *context)
     int i;
     SRContext *sr_context = context->priv;
 
+    if (sr_context->backend_type == DNN_TF)
+        av_freep(&sr_context->output.data);
+
     if (sr_context->dnn_module){
         (sr_context->dnn_module->free_model)(&sr_context->model);
         av_freep(&sr_context->dnn_module);
