Changed the model for Linux pipeline

.github/workflows/stable_diffusion_1_5_cpp.yml

@@ -62,12 +62,12 @@ jobs:
       - name: Download and convert models and tokenizer
         run: |
           source openvino_sd_cpp/bin/activate
-          optimum-cli export openvino --model botp/stable-diffusion-v1-5 --weight-format fp16 --task stable-diffusion models/stable_diffusion_v1_5_ov/FP16
+          optimum-cli export openvino --model dreamlike-art/dreamlike-anime-1.0 --weight-format fp16 --task stable-diffusion models/dreamlike-art-dreamlike-anime-1.0/FP16
 
       - name: Run app
         run: |
           source ${{ env.OV_INSTALL_DIR }}/setupvars.sh
-          ./build/samples/cpp/stable_diffusion/stable_diffusion ./models/stable_diffusion_v1_5_ov/FP16 "cyberpunk cityscape like Tokyo New York  with tall buildings at dusk golden hour cinematic lighting"
+          ./build/samples/cpp/stable_diffusion/stable_diffusion ./models/dreamlike-art-dreamlike-anime-1.0/FP16 "cyberpunk cityscape like Tokyo New York  with tall buildings at dusk golden hour cinematic lighting"
 
   stable_diffusion_1_5_cpp-windows:
       runs-on: windows-latest

samples/cpp/stable_diffusion/CMakeLists.txt

@@ -25,4 +25,4 @@ set_target_properties(stable_diffusion PROPERTIES
 install(TARGETS stable_diffusion
         RUNTIME DESTINATION samples_bin/
         COMPONENT samples_bin
-        EXCLUDE_FROM_ALL)
+        EXCLUDE_FROM_ALL)

samples/cpp/stable_diffusion/README.md

@@ -1,6 +1,6 @@
 # Stable Diffusion C++ Image Generation Pipeline
 
-This example showcases inference of text to image models like Stable Diffusion 1.x, 2.x, LCM. The application doesn't have many configuration options to encourage the reader to explore and modify the source code. For example, change the device for inference to GPU. The sample features `ov::genai::Text2ImagePipeline` and uses a text prompt as input source.
+This example showcases inference of text to image models like Stable Diffusion 1.5, 2.1, LCM. The application doesn't have many configuration options to encourage the reader to explore and modify the source code. For example, change the device for inference to GPU. The sample features `ov::genai::Text2ImagePipeline` and uses a text prompt as input source.
 
 Users can change the sample code and play with the following generation parameters:
 
@@ -18,16 +18,16 @@ It's not required to install [../../requirements.txt](../../requirements.txt) fo
 
 ```sh
 pip install --upgrade-strategy eager -r ../../requirements.txt
-optimum-cli export openvino --model dreamlike-art/dreamlike-anime-1.0 --task stable-diffusion --weight-format fp16 dreamlike_anime_1_0_ov/FP16`
+optimum-cli export openvino --model dreamlike-art/dreamlike-anime-1.0 --task stable-diffusion --weight-format fp16 dreamlike_anime_1_0_ov/FP16
 ```
 
 ## Run
 
-`stable_diffusion ./dreamlike_anime_1_0_ov/FP16 'cyberpunk cityscape like Tokyo New York  with tall buildings at dusk golden hour cinematic lighting'`
+`stable_diffusion ./dreamlike_anime_1_0_ov/FP16 'cyberpunk cityscape like Tokyo New York with tall buildings at dusk golden hour cinematic lighting'`
 
 ### Examples
 
-Prompt: `cyberpunk cityscape like Tokyo New York  with tall buildings at dusk golden hour cinematic lighting`
+Prompt: `cyberpunk cityscape like Tokyo New York with tall buildings at dusk golden hour cinematic lighting`
 
    ![](./512x512.bmp)
 
@@ -39,8 +39,8 @@ Models can be downloaded from [OpenAI HiggingFace](https://huggingface.co/openai
 - [dreamlike-art/dreamlike-anime-1.0](https://huggingface.co/dreamlike-art/dreamlike-anime-1.0)
 - [SimianLuo/LCM_Dreamshaper_v7](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7)
 
-## Troubleshooting
+## Note
 
 - Image generated with HuggingFace / Optimum Intel is not the same generated by this C++ sample:
 
-C++ random generation with MT19937 results differ from `numpy.random.randn()` and `diffusers.utils.randn_tensor`. So, it's expected that image generated by Python and C++ versions provide different images, because latent images are initialize differently. Users can implement its own random generator derived from `ov::genai::Generator` and pass it to `Text2ImagePipeline::generate` method.
+C++ random generation with MT19937 results differ from `numpy.random.randn()` and `diffusers.utils.randn_tensor`. So, it's expected that image generated by Python and C++ versions provide different images, because latent images are initialize differently. Users can implement their own random generator derived from `ov::genai::Generator` and pass it to `Text2ImagePipeline::generate` method.