update readme

samples/cpp/image_generation/README.md

@@ -8,7 +8,7 @@ There are several sample files:
  - [`heterogeneous_stable_diffusion.cpp`](./heterogeneous_stable_diffusion.cpp) shows how to assemble a heterogeneous txt2image pipeline from individual subcomponents (scheduler, text encoder, unet, vae decoder)
  - [`image2image.cpp`](./image2image.cpp) demonstrates basic usage of the image to image pipeline
  - [`inpainting.cpp`](./inpainting.cpp) demonstrates basic usage of the inpainting pipeline
- - [`benchmark_text2image.cpp](./benchmark_text2image.cpp) demonstrates how to benchmark the text to image pipeline 
+ - [`benchmark_image.cpp](./benchmark_image.cpp) demonstrates how to benchmark the text to image / image to image / inpainting pipeline
 
 Users can change the sample code and play with the following generation parameters:
 

samples/cpp/image_generation/benchmark_image.cpp

@@ -1,4 +1,4 @@
-// Copyright (C) 2023-2024 Intel Corporation
+// Copyright (C) 2023-2025 Intel Corporation
 // SPDX-License-Identifier: Apache-2.0
 
 #include "openvino/genai/image_generation/text2image_pipeline.hpp"

samples/python/image_generation/README.md

@@ -8,7 +8,7 @@ There are several sample files:
  - [`heterogeneous_stable_diffusion.py`](./heterogeneous_stable_diffusion.py) shows how to assemble a heterogeneous text2image pipeline from individual subcomponents (scheduler, text encoder, unet, vae decoder)
  - [`image2image.py`](./image2image.py) demonstrates basic usage of the image to image pipeline
  - [`inpainting.py`](./inpainting.py) demonstrates basic usage of the inpainting pipeline
- - [`benchmark_text2image.py`](./benchmark_text2image.py) demonstrates how to benchmark the text to image pipeline
+ - [`benchmark_image.py`](./benchmark_image.py) demonstrates how to benchmark the text to image / image to image / inpainting pipeline
 
 Users can change the sample code and play with the following generation parameters:
 

samples/python/image_generation/benchmark_image.py

@@ -1,4 +1,4 @@
-# Copyright (C) 2023-2024 Intel Corporation
+# Copyright (C) 2023-2025 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
 import argparse

src/cpp/include/openvino/genai/image_generation/image_generation_perf_metrics.hpp

@@ -1,3 +1,6 @@
+// Copyright (C) 2023-2025 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+
 #pragma once
 
 #include <vector>

src/cpp/src/image_generation/flux_pipeline.hpp

@@ -677,6 +677,7 @@ class FluxPipeline : public DiffusionPipeline {
     std::shared_ptr<IImageProcessor> m_image_processor = nullptr, m_mask_processor_rgb = nullptr, m_mask_processor_gray = nullptr;
     std::shared_ptr<ImageResizer> m_image_resizer = nullptr, m_mask_resizer = nullptr;
     ImageGenerationConfig m_custom_generation_config;
+
     float m_latent_timestep = -1;
 };
 

src/cpp/src/image_generation/image_generation_perf_metrics.cpp

@@ -1,3 +1,6 @@
+// Copyright (C) 2023-2025 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+
 #include <numeric>
 #include <cmath>
 

src/cpp/src/image_generation/stable_diffusion_3_pipeline.hpp

@@ -274,13 +274,8 @@ class StableDiffusion3Pipeline : public DiffusionPipeline {
 
         // text_encoder_1_output - stores positive and negative pooled_prompt_embeds
         auto infer_start = std::chrono::steady_clock::now();
-        ov::Tensor text_encoder_1_output =
-            m_clip_text_encoder_1->infer(positive_prompt,
-                                         negative_prompt_1_str,
-                                         do_classifier_free_guidance(generation_config.guidance_scale));
-        auto infer_duration =
-            std::chrono::duration_cast<std::chrono::milliseconds>(std::chrono::steady_clock::now() - infer_start)
-                .count();
+        ov::Tensor text_encoder_1_output = m_clip_text_encoder_1->infer(positive_prompt, negative_prompt_1_str, do_classifier_free_guidance(generation_config.guidance_scale));
+        auto infer_duration = std::chrono::duration_cast<std::chrono::milliseconds>(std::chrono::steady_clock::now() - infer_start).count();
         m_perf_metrics.encoder_inference_duration["text_encode"] = infer_duration;
 
         // text_encoder_1_hidden_state - stores positive and negative prompt_embeds
@@ -289,13 +284,8 @@ class StableDiffusion3Pipeline : public DiffusionPipeline {
 
         // text_encoder_2_output - stores positive and negative pooled_prompt_2_embeds
         infer_start = std::chrono::steady_clock::now();
-        ov::Tensor text_encoder_2_output =
-            m_clip_text_encoder_2->infer(prompt_2_str,
-                                         negative_prompt_2_str,
-                                         do_classifier_free_guidance(generation_config.guidance_scale));
-        infer_duration =
-            std::chrono::duration_cast<std::chrono::milliseconds>(std::chrono::steady_clock::now() - infer_start)
-                .count();
+        ov::Tensor text_encoder_2_output = m_clip_text_encoder_2->infer(prompt_2_str, negative_prompt_2_str, do_classifier_free_guidance(generation_config.guidance_scale));
+        infer_duration = std::chrono::duration_cast<std::chrono::milliseconds>(std::chrono::steady_clock::now() - infer_start).count();
         m_perf_metrics.encoder_inference_duration["text_encode_2"] = infer_duration;
 
         // text_encoder_2_hidden_state - stores positive and negative prompt_2_embeds

src/cpp/src/image_generation/stable_diffusion_xl_pipeline.hpp

@@ -182,9 +182,7 @@ class StableDiffusionXLPipeline : public StableDiffusionPipeline {
 
         if (compute_negative_prompt) {
             auto infer_start = std::chrono::steady_clock::now();
-            add_text_embeds = m_clip_text_encoder_with_projection->infer(positive_prompt,
-                                                                         negative_prompt_1_str,
-                                                                         batch_size_multiplier > 1);
+            add_text_embeds = m_clip_text_encoder_with_projection->infer(positive_prompt, negative_prompt_1_str, batch_size_multiplier > 1);
             auto infer_duration = std::chrono::duration_cast<std::chrono::milliseconds>(std::chrono::steady_clock::now() - infer_start).count();
             m_perf_metrics.encoder_inference_duration["text_encoder_2"] = infer_duration;
             infer_start = std::chrono::steady_clock::now();
@@ -199,8 +197,7 @@ class StableDiffusionXLPipeline : public StableDiffusionPipeline {
             encoder_hidden_states = numpy_utils::concat(encoder_hidden_states_1, encoder_hidden_states_2, -1);
         } else {
             auto infer_start = std::chrono::steady_clock::now();
-            ov::Tensor add_text_embeds_positive =
-                m_clip_text_encoder_with_projection->infer(positive_prompt, negative_prompt_1_str, false);
+            ov::Tensor add_text_embeds_positive = m_clip_text_encoder_with_projection->infer(positive_prompt, negative_prompt_1_str, false);
             auto infer_duration = std::chrono::duration_cast<std::chrono::milliseconds>(std::chrono::steady_clock::now() - infer_start).count();
             m_perf_metrics.encoder_inference_duration["text_encoder_2"] = infer_duration;
             infer_start = std::chrono::steady_clock::now();