index.mdx

import GenerationConfigurationWorkflow from '@site/docs/use-cases/_shared/_generation_configuration_workflow.mdx';

Additional Usage Options

:::tip Check out Python, C++, and JavaScript image generation samples. :::

Use Different Generation Parameters

Image Generation Configuration

You can adjust several parameters to control the image generation process, including dimensions and the number of inference steps:

```python import openvino_genai as ov_genai from PIL import Image

    pipe = ov_genai.Text2ImagePipeline(model_path, "CPU")
    image_tensor = pipe.generate(
      prompt,
      # highlight-start
      width=512,
      height=512,
      num_images_per_prompt=1,
      num_inference_steps=30,
      guidance_scale=7.5
      # highlight-end
    )

    image = Image.fromarray(image_tensor.data[0])
    image.save("image.bmp")
    ```
</TabItemPython>
<TabItemCpp>
    ```cpp
    #include "openvino/genai/image_generation/text2image_pipeline.hpp"
    #include "imwrite.hpp"

    int main(int argc, char* argv[]) {
        const std::string models_path = argv[1], prompt = argv[2];

        ov::genai::Text2ImagePipeline pipe(models_path, "CPU");
        ov::Tensor image = pipe.generate(
            prompt,
            // highlight-start
            ov::genai::width(512),
            ov::genai::height(512),
            ov::genai::num_images_per_prompt(1),
            ov::genai::num_inference_steps(30),
            ov::genai::guidance_scale(7.5f)
            // highlight-end
        );

        imwrite("image.bmp", image, true);
    }
    ```
</TabItemCpp>
<TabItemJS>
    ```javascript
    import { Text2ImagePipeline } from 'openvino-genai-node';

    const pipeline = await Text2ImagePipeline(modelPath, 'CPU');
    const result = await pipeline.generate(prompt, {
        // highlight-start
        width: 512,
        height: 512,
        num_images_per_prompt: 1,
        num_inference_steps: 30,
        guidance_scale: 7.5,
        // highlight-end
    });
    ```
</TabItemJS>

:::info Understanding Image Generation Parameters

• width: The width of resulting image(s).
• height: The height of resulting image(s).
• num_images_per_prompt: Specifies how many image variations to generate in a single request for the same prompt.
• num_inference_steps: Defines denoising iteration count. Higher values increase quality and generation time, lower values generate faster with less detail.
• guidance_scale: Balances prompt adherence vs. creativity. Higher values follow prompt more strictly, lower values allow more creative freedom.
• rng_seed: Controls randomness for reproducible results. Same seed produces identical images across runs.

For the full list of generation parameters, refer to the Image Generation Config API.

:::

Working with LoRA Adapters

For image generation models like Stable Diffusion, LoRA adapters can modify the generation process to produce images with specific artistic styles, content types, or quality enhancements.

Refer to the LoRA Adapters for more details on working with LoRA adapters.