AI Researcher Trains Custom Lynda Carter Model, Raises Deepfake Ethics

By an Investigative Journalist

In a quiet corner of the internet, a significant milestone in personal artificial intelligence has been reached, raising profound questions about identity, copyright, and the future of digital media. A researcher operating under the pseudonym "latentbroadcasting" has successfully trained a custom AI model, known as a LoRA (Low-Rank Adaptation), to generate highly accurate images of Lynda Carter's portrayal of Wonder Woman from the 1975-1979 television series. This project, while presented as a technical learning exercise, illuminates the powerful and democratized tools now available to create synthetic likenesses of public figures.

The Technical Breakthrough

According to the researcher's detailed post on a popular AI forum, the model was trained on a dataset of 642 high-quality images using a new base model called "Wan 2.2." The creator noted being "impressed by the quality and likeness" achieved compared to previous attempts with other models like "Flux." The training was conducted using a publicly available AI-Toolkit with default settings, suggesting a lowering barrier to entry for such sophisticated digital recreations.

"It was trained on 642 high-quality images... using AI-Toolkit with default settings," the researcher stated, framing the work as a baseline for future experiments. The project's success has spurred plans to create more style and concept-based models, with the creator soliciting public input on future directions. This collaborative aspect underscores the community-driven nature of much open-source AI development.

A Deliberate Withholding and Its Implications

Perhaps the most telling aspect of this announcement is the creator's explicit decision not to publicly release the trained model. "Since this is for research and learning only, I won't be uploading the model," the post clarifies. This restraint is not merely a personal choice but a microcosm of a larger, simmering ethical debate within the AI community. The ability to generate convincing likenesses of living celebrities—without their consent and outside the bounds of traditional licensing—presents a legal and ethical minefield.

The technical process hinges on precise data curation. As discussed in language learning contexts, the specificity of the input defines the output. A question about a broad domain versus regarding a specific object changes the nature of the inquiry. Similarly, the AI's training on hundreds of specific images of Lynda Carter creates a model that understands her likeness as a distinct, replicable concept, not just a generic "superhero." This precision is what makes the technology both powerful and potentially problematic.

The Broader Context: A World of Synthetic Selves

This incident is not isolated. It reflects a burgeoning ecosystem where tools once confined to well-funded studios or academic labs are now accessible to individual enthusiasts. The ethical frameworks, however, have not evolved at the same pace. The researcher's cautious approach—learning, testing, but not distributing—suggests a personal awareness of these stakes, a line that others may not choose to respect.

Furthermore, the linguistic precision noted in discussions about English usage mirrors the precision required in AI training. Just as the difference between "first" and "firstly" can alter the tone and structure of a sentence, the difference between a model trained on licensed, consented images and one trained on scraped web data can alter the legal and moral standing of the output. The creator's mention of this being their "first" Wan 2.2 LoRA subtly positions it within a sequence of learning, a beginning rather than an end point.

Legal Gray Zones and the Future of Persona

Lynda Carter's Wonder Woman is a culturally iconic figure, but Lynda Carter herself is a living person with rights to her own image. Current copyright and publicity rights laws are notoriously ill-equipped to handle the nuances of AI-generated content. Does training a model on publicly available photographs constitute fair use for research? Does generating a new, synthetic image that never existed violate a celebrity's right of publicity? These are the unresolved questions that projects like this one bring to the fore.

The researcher's work, while seemingly innocuous, sits at the intersection of fandom, technology, and ethics. It demonstrates that the capability to recreate and reimagine famous personas is already here, residing on personal computers. The decision of what to do with that capability—to share, to sell, or to keep private—will define the next chapter of digital media.

Conclusion: A Canary in the Coalmine

The successful training of a custom Lynda Carter AI model is a canary in the coalmine for the entertainment industry, legal systems, and society at large. It is a testament to remarkable technological progress and a harbinger of complex ethical dilemmas. As the researcher moves on to "style and concept LoRAs," the foundational issue remains: in an age where anyone can synthesize a convincing digital double, where do we draw the line between homage, art, and infringement? The answer, much like the AI models themselves, is still being trained.

This report synthesizes information from a primary source detailing the AI training project and contextual analysis informed by discussions on technical precision and specificity in language and data processing.