MediaPipe Solution (you are using)
MediaPipe Image Generator / Image Generation API
Programming language
Android Java (with MediaPipe Tasks / TensorFlow Lite backend)
Are you willing to contribute it
Yes
Describe the feature and the current behaviour/state
Current image generation conditioning can guide the generated result, but it does not always preserve important details from the reference image. The output may change original objects, background details, material appearance, textures, colors, and the identity of people or subjects. It would be valuable to have stronger image-to-image style conditioning that preserves the original scene while still allowing controlled generation changes. Similar capabilities in other diffusion systems use reference-image conditioning, appearance adapters, or feature-map injection to maintain details such as object identity, background structure, and material properties. A stronger conditioning mechanism or optional plugin/API support for high-fidelity reference image preservation would make MediaPipe Image Generation more useful for editing workflows.
Will this change the current API? How?
Ideally this could be added as an optional conditioning mode or additional plugin/adapter input rather than changing the existing API. For example, users could provide a reference image or appearance feature input when they need higher fidelity preservation. Existing generation workflows could remain unchanged.
Who will benefit with this feature?
Developers building mobile AI image editing applications, creative tools, AR experiences, and offline/on-device generative AI applications would benefit. Users would benefit from more realistic image editing where the generated result maintains the original subject and environment.
Please specify the use cases for this feature
Please specify the use cases for this feature Image-to-image editing where the original scene should remain recognizable Removing or changing parts of an image while keeping the rest unchanged Preserving people, pets, products, and objects during generation Maintaining materials, textures, lighting, and backgrounds Mobile photo editing applications requiring reliable reference-image fidelity Creative effects where users expect controlled transformations rather than a completely new image
Any Other info
Must be 16kb page size and implementation("com.google.mediapipe:tasks-vision-image-generator:x.xx.xx") { exclude(group = "com.google.protobuf", module = "protobuf-javalite") } implementation("com.google.protobuf:protobuf-java:4.32.0")
MediaPipe Solution (you are using)
MediaPipe Image Generator / Image Generation API
Programming language
Android Java (with MediaPipe Tasks / TensorFlow Lite backend)
Are you willing to contribute it
Yes
Describe the feature and the current behaviour/state
Current image generation conditioning can guide the generated result, but it does not always preserve important details from the reference image. The output may change original objects, background details, material appearance, textures, colors, and the identity of people or subjects. It would be valuable to have stronger image-to-image style conditioning that preserves the original scene while still allowing controlled generation changes. Similar capabilities in other diffusion systems use reference-image conditioning, appearance adapters, or feature-map injection to maintain details such as object identity, background structure, and material properties. A stronger conditioning mechanism or optional plugin/API support for high-fidelity reference image preservation would make MediaPipe Image Generation more useful for editing workflows.
Will this change the current API? How?
Ideally this could be added as an optional conditioning mode or additional plugin/adapter input rather than changing the existing API. For example, users could provide a reference image or appearance feature input when they need higher fidelity preservation. Existing generation workflows could remain unchanged.
Who will benefit with this feature?
Developers building mobile AI image editing applications, creative tools, AR experiences, and offline/on-device generative AI applications would benefit. Users would benefit from more realistic image editing where the generated result maintains the original subject and environment.
Please specify the use cases for this feature
Please specify the use cases for this feature Image-to-image editing where the original scene should remain recognizable Removing or changing parts of an image while keeping the rest unchanged Preserving people, pets, products, and objects during generation Maintaining materials, textures, lighting, and backgrounds Mobile photo editing applications requiring reliable reference-image fidelity Creative effects where users expect controlled transformations rather than a completely new image
Any Other info
Must be 16kb page size and implementation("com.google.mediapipe:tasks-vision-image-generator:x.xx.xx") { exclude(group = "com.google.protobuf", module = "protobuf-javalite") } implementation("com.google.protobuf:protobuf-java:4.32.0")