Provenance
Keep the transformation attached to the artifact. Follow the decisions behind output manifests, model identifiers, configuration records, and the separation of generated artifacts from original observations. This collection is for teams that need to review, reproduce, or approve an output after the initial job completes.
Use these guides to decide which context belongs with a delivered dataset, image, or audio file. Keep artifact approval separate from technical completion and retain checksums where exact files matter. A seed alone is not a complete history, and a successful export is not a universal statement of suitability.
Super Resolution: Better-Looking Is Not Always More Faithful
Evaluate upscaling with honest baselines, realistic inputs, artifact review, and a clear distinction between inferred and observed detail.
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Text-to-Speech GAN APIs: Where the Vocoder Fits
Connect text processing, acoustic modeling, and waveform synthesis without confusing a GAN vocoder with a complete speech system.
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Image Generation APIs: Seeds, Latent Controls, and Reproducibility
Expose controls that match the checkpoint, preserve artifact provenance, and evaluate diversity alongside visual quality.
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Designing a GAN API: From Checkpoint to Reliable Interface
Turn a GAN checkpoint into a clear, versioned interface with useful job states, output provenance, and realistic evaluation.
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