Localized Concept Erasure in Text-to-Image Diffusion Models via High-Level Representation Misdirection | ScienceToStartup | ScienceToStartup

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References (46)

[1]

FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space

2025Black Forest Labs, Stephen Batifol et al.

[2]

When Are Concepts Erased From Diffusion Models?

2025Kevin Lu, Nicky Kriplani et al.

[3]

SPEED: Scalable, Precise, and Efficient Concept Erasure for Diffusion Models

Founder's Pitch

"HiRM allows for precise concept removal in text-to-image diffusion models with minimal impact on unrelated concepts, enhancing content moderation."

Generative AI•Score: 5•View PDF ↗

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Localized Concept Erasure in Text-to-Image Diffusion Models via High-Level Representation Misdirection

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References (46)

Founder's Pitch

"HiRM allows for precise concept removal in text-to-image diffusion models with minimal impact on unrelated concepts, enhancing content moderation."

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Localized Concept Erasure in Text-to-Image Diffusion Models via High-Level Representation Misdirection

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References (46)

Founder's Pitch

"HiRM allows for precise concept removal in text-to-image diffusion models with minimal impact on unrelated concepts, enhancing content moderation."

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