feat(genai): Add new image generation samples #13320
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Summary of Changes
This pull request introduces new code samples for Imagen Customization using the Google Gen AI SDK for Python. It includes examples for canny edge detection, scribble customization, style customization, style transfer, and subject customization. Each sample demonstrates how to use the edit_image method with different configurations to achieve various image editing effects.
Highlights
- New Samples: Adds five new code samples demonstrating different Imagen customization techniques: canny edge, scribble, style, style transfer, and subject customization.
- Image Editing: Each sample uses the
edit_imagemethod of the Google Gen AI SDK to modify images based on prompts and reference images. - Configuration: The samples utilize various configuration options such as
EditImageConfig,ControlReferenceConfig,StyleReferenceConfig, andSubjectReferenceConfigto control the image editing process.
Changelog
Click here to see the changelog
- genai/image_generation/img_customization_canny_edge.py
- Introduces a new sample demonstrating image customization using canny edge detection.
- Uses
ControlReferenceImageandControlReferenceConfigto specify the canny edge control type. - The sample takes an image of a car with canny edges and generates a watercolor painting of a red car driving on a road, using the canny edges as a guide.
- genai/image_generation/img_customization_scribble.py
- Introduces a new sample demonstrating image customization using scribble.
- Uses
ControlReferenceImageandControlReferenceConfigto specify the scribble control type. - The sample takes an image of a car with scribbles and generates an oil painting showing the side of a red car, using the scribbles as a guide.
- genai/image_generation/img_customization_style.py
- Introduces a new sample demonstrating image customization using style reference.
- Uses
StyleReferenceImageandStyleReferenceConfigto specify the style description. - The sample takes an image of a neon sign and generates an image of a neon sign with the words 'have a great day', using the style of the reference image.
- genai/image_generation/img_customization_style_transfer.py
- Introduces a new sample demonstrating image customization using style transfer.
- Uses
RawReferenceImageto specify the style image. - The sample takes an image of a teacup and transforms it so that the teacup is made entirely out of chocolate, using the style of the reference image.
- genai/image_generation/img_customization_subject.py
- Introduces a new sample demonstrating image customization using subject reference and face mesh control.
- Uses
SubjectReferenceImage,SubjectReferenceConfig,ControlReferenceImage, andControlReferenceConfigto specify the subject and control type. - The sample takes a headshot of a woman and generates a portrait of a woman in the pose of the control image in a watercolor style.
- genai/image_generation/noxfile_config.py
- Adds a default TEST_CONFIG_OVERRIDE for python repos.
- Specifies ignored Python versions and enforces type hints.
- genai/image_generation/requirements-test.txt
- Adds pytest as a test dependency.
- genai/image_generation/requirements.txt
- Adds google-genai as a dependency.
- genai/image_generation/test_img_customization.py
- Introduces new tests for the image customization samples.
- Sets environment variables for using Vertex AI.
- Each test asserts that the length of the generated image bytes is greater than 1000.
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With Imagen's artful hand,
Images transform at command.
From scribble's trace,
A new image we embrace,
AI's magic, across the land.
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Code Review
This pull request introduces new code samples for Imagen Customization using the Google Gen AI SDK for Python. The samples cover various customization techniques like Canny edge, scribble, style, style transfer, and subject customization. Overall, the code is well-structured and easy to understand. However, there are a few areas that could be improved for better clarity and maintainability.
Summary of Findings
- Missing Error Handling: The code samples lack explicit error handling. It's crucial to handle potential exceptions, such as network errors or API failures, to ensure the robustness of the samples.
- Inconsistent Variable Naming: There are some inconsistencies in variable naming across the different code samples. Maintaining a consistent naming convention improves readability and reduces cognitive load.
- Hardcoded Values: The code uses hardcoded values for parameters like model name and GCS URIs. Consider making these configurable or using constants to improve flexibility and maintainability.
Merge Readiness
The pull request introduces valuable code samples for Imagen Customization. However, addressing the identified issues, particularly the missing error handling, is crucial before merging. I am unable to directly approve this pull request, and recommend that others review and approve this code before merging. At a minimum, the high severity issues should be addressed before merging.
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LGTM, deferring to @msampathkumar on Sample Region Tags/File names
Description
Add new code samples for Imagen Customization using the Google Gen AI SDK for Python