diff --git a/src/cpp/src/image_generation/models/clip_text_model.cpp b/src/cpp/src/image_generation/models/clip_text_model.cpp
index efbc840d4f..72fdc63082 100644
--- a/src/cpp/src/image_generation/models/clip_text_model.cpp
+++ b/src/cpp/src/image_generation/models/clip_text_model.cpp
@@ -118,13 +118,20 @@ ov::Tensor CLIPTextModel::infer(const std::string& pos_prompt, const std::string
     const size_t text_embedding_batch_size = do_classifier_free_guidance ? 2 : 1;
 
     auto perform_tokenization = [&](const std::string& prompt, ov::Tensor input_ids) {
-        std::fill_n(input_ids.data<int32_t>(), input_ids.get_size(), pad_token_id);
-
         ov::Tensor input_ids_token = m_clip_tokenizer.encode(prompt).input_ids;
-        std::copy_n(input_ids_token.data<std::int64_t>(), input_ids_token.get_size(), input_ids.data<std::int32_t>());
+
+        if (input_ids.get_element_type() == ov::element::i32) {
+            std::fill_n(input_ids.data<int32_t>(), input_ids.get_size(), pad_token_id);
+            std::copy_n(input_ids_token.data<int64_t>(), input_ids_token.get_size(), input_ids.data<int32_t>());
+        } else {
+            std::fill_n(input_ids.data<int64_t>(), input_ids.get_size(), pad_token_id);
+            std::copy_n(input_ids_token.data<int64_t>(), input_ids_token.get_size(), input_ids.data<int64_t>());
+        }
     };
 
-    ov::Tensor input_ids(ov::element::i32, {text_embedding_batch_size, m_config.max_position_embeddings});
+    ov::Tensor input_ids = m_request.get_input_tensor();
+    input_ids.set_shape({text_embedding_batch_size, m_config.max_position_embeddings});
+
     size_t current_batch_idx = 0;
 
     if (do_classifier_free_guidance) {
@@ -141,7 +148,6 @@ ov::Tensor CLIPTextModel::infer(const std::string& pos_prompt, const std::string
                                                {current_batch_idx + 1, m_config.max_position_embeddings}));
 
     // text embeddings
-    m_request.set_tensor("input_ids", input_ids);
     m_request.infer();
 
     return m_request.get_output_tensor(0);
diff --git a/src/cpp/src/image_generation/models/clip_text_model_with_projection.cpp b/src/cpp/src/image_generation/models/clip_text_model_with_projection.cpp
index 982800a701..1160c30b6a 100644
--- a/src/cpp/src/image_generation/models/clip_text_model_with_projection.cpp
+++ b/src/cpp/src/image_generation/models/clip_text_model_with_projection.cpp
@@ -109,13 +109,20 @@ ov::Tensor CLIPTextModelWithProjection::infer(const std::string& pos_prompt, con
     const size_t text_embedding_batch_size = do_classifier_free_guidance ? 2 : 1;
 
     auto perform_tokenization = [&](const std::string& prompt, ov::Tensor input_ids) {
-        std::fill_n(input_ids.data<int64_t>(), input_ids.get_size(), pad_token_id);
-
         ov::Tensor input_ids_token = m_clip_tokenizer.encode(prompt).input_ids;
-        std::copy_n(input_ids_token.data<std::int64_t>(), input_ids_token.get_size(), input_ids.data<std::int64_t>());
+
+        if (input_ids.get_element_type() == ov::element::i32) {
+            std::fill_n(input_ids.data<int32_t>(), input_ids.get_size(), pad_token_id);
+            std::copy_n(input_ids_token.data<int64_t>(), input_ids_token.get_size(), input_ids.data<int32_t>());
+        } else {
+            std::fill_n(input_ids.data<int64_t>(), input_ids.get_size(), pad_token_id);
+            std::copy_n(input_ids_token.data<int64_t>(), input_ids_token.get_size(), input_ids.data<int64_t>());
+        }
     };
 
-    ov::Tensor input_ids(ov::element::i64, {text_embedding_batch_size, m_config.max_position_embeddings});
+    ov::Tensor input_ids = m_request.get_input_tensor();
+    input_ids.set_shape({text_embedding_batch_size, m_config.max_position_embeddings});
+
     size_t current_batch_idx = 0;
 
     if (do_classifier_free_guidance) {
@@ -132,7 +139,6 @@ ov::Tensor CLIPTextModelWithProjection::infer(const std::string& pos_prompt, con
                                                {current_batch_idx + 1, m_config.max_position_embeddings}));
 
     // text embeddings
-    m_request.set_tensor("input_ids", input_ids);
     m_request.infer();
 
     return m_request.get_output_tensor(0);
diff --git a/src/cpp/src/image_generation/models/t5_encoder_model.cpp b/src/cpp/src/image_generation/models/t5_encoder_model.cpp
index 21df456d46..a83697b2e6 100644
--- a/src/cpp/src/image_generation/models/t5_encoder_model.cpp
+++ b/src/cpp/src/image_generation/models/t5_encoder_model.cpp
@@ -80,8 +80,13 @@ ov::Tensor T5EncoderModel::infer(const std::string& pos_prompt, const std::strin
         ov::Tensor input_ids_token = m_tokenizer.encode(prompt).input_ids;
         size_t min_length = std::min(input_ids.get_size(), input_ids_token.get_size());
 
-        std::fill_n(input_ids.data<int32_t>(), input_ids.get_size(), pad_token_id);
-        std::copy_n(input_ids_token.data<std::int64_t>(), min_length, input_ids.data<std::int32_t>());
+        if (input_ids.get_element_type() == ov::element::i32) {
+            std::fill_n(input_ids.data<int32_t>(), input_ids.get_size(), pad_token_id);
+            std::copy_n(input_ids_token.data<int64_t>(), min_length, input_ids.data<int32_t>());
+        } else {
+            std::fill_n(input_ids.data<int64_t>(), input_ids.get_size(), pad_token_id);
+            std::copy_n(input_ids_token.data<int64_t>(), min_length, input_ids.data<int64_t>());
+        }
     };
 
     ov::Tensor input_ids = m_request.get_input_tensor();
@@ -114,7 +119,6 @@ ov::Tensor T5EncoderModel::infer(const std::string& pos_prompt, const std::strin
                                                {current_batch_idx + 1, input_ids.get_shape()[1]}));
 
     // text embeddings
-    m_request.set_tensor("input_ids", input_ids);
     m_request.infer();
 
     return m_request.get_output_tensor(0);
diff --git a/src/cpp/src/image_generation/models/unet_inference_dynamic.hpp b/src/cpp/src/image_generation/models/unet_inference_dynamic.hpp
index 6dc285f76d..914fbcf50b 100644
--- a/src/cpp/src/image_generation/models/unet_inference_dynamic.hpp
+++ b/src/cpp/src/image_generation/models/unet_inference_dynamic.hpp
@@ -12,11 +12,8 @@ namespace genai {
 
 
 class UNet2DConditionModel::UNetInferenceDynamic : public UNet2DConditionModel::UNetInference {
-
 public:
-
-    virtual void compile(std::shared_ptr<ov::Model> model, const std::string& device, const ov::AnyMap& properties) override
-    {
+    virtual void compile(std::shared_ptr<ov::Model> model, const std::string& device, const ov::AnyMap& properties) override {
         ov::Core core = utils::singleton_core();
 
         ov::CompiledModel compiled_model = core.compile_model(model, device, properties);
@@ -24,20 +21,17 @@ class UNet2DConditionModel::UNetInferenceDynamic : public UNet2DConditionModel::
         m_request = compiled_model.create_infer_request();
     }
 
-    virtual void set_hidden_states(const std::string& tensor_name, ov::Tensor encoder_hidden_states) override
-    {
+    virtual void set_hidden_states(const std::string& tensor_name, ov::Tensor encoder_hidden_states) override {
         OPENVINO_ASSERT(m_request, "UNet model must be compiled first");
         m_request.set_tensor(tensor_name, encoder_hidden_states);
     }
 
-    virtual void set_adapters(AdapterController &adapter_controller, const AdapterConfig& adapters) override
-    {
+    virtual void set_adapters(AdapterController &adapter_controller, const AdapterConfig& adapters) override {
         OPENVINO_ASSERT(m_request, "UNet model must be compiled first");
         adapter_controller.apply(m_request, adapters);
     }
 
-    virtual ov::Tensor infer(ov::Tensor sample, ov::Tensor timestep) override
-    {
+    virtual ov::Tensor infer(ov::Tensor sample, ov::Tensor timestep) override {
         OPENVINO_ASSERT(m_request, "UNet model must be compiled first. Cannot infer non-compiled model");
 
         m_request.set_tensor("sample", sample);
@@ -49,10 +43,8 @@ class UNet2DConditionModel::UNetInferenceDynamic : public UNet2DConditionModel::
     }
 
 private:
-
     ov::InferRequest m_request;
 };
 
-
 }  // namespace genai
 }  // namespace ov
\ No newline at end of file
diff --git a/src/cpp/src/image_generation/models/unet_inference_static_bs1.hpp b/src/cpp/src/image_generation/models/unet_inference_static_bs1.hpp
index 7aa6f6301c..f63a8ea237 100644
--- a/src/cpp/src/image_generation/models/unet_inference_static_bs1.hpp
+++ b/src/cpp/src/image_generation/models/unet_inference_static_bs1.hpp
@@ -42,8 +42,7 @@ class UNet2DConditionModel::UNetInferenceStaticBS1 : public UNet2DConditionModel
         ov::CompiledModel compiled_model = core.compile_model(model, device, properties);
         ov::genai::utils::print_compiled_model_properties(compiled_model, "UNet 2D Condition batch-1 model");
 
-        for (int i = 0; i < m_native_batch_size; i++)
-        {
+        for (int i = 0; i < m_native_batch_size; i++) {
             m_requests[i] = compiled_model.create_infer_request();
         }
     }