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ARXIV:2603.25074 · GENERATIVE IMAGE SAFETY · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.25074GENERATIVE IMAGE SAFETYSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALENanxiang Jiang · Zhaoxin Fan · Baisen Wang · Daiheng Gao · Junhang Cheng · Jifeng Guo · +5 at arXiv
A novel framework for stable concept erasure in single-stream text-to-image diffusion models, overcoming generation collapse and achieving state-of-the-art performance.
Opportunity summary
Pain A novel framework for stable concept erasure in single-stream text-to-image diffusion models, overcoming generation collapse and achieving state-of-the-art performance.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A novel framework for stable concept erasure in single-stream text-to-image diffusion models, overcoming generation collapse and achieving state-of-the-art performance. While extensively studied in U-Net and dual-stream architectures (e.g., Flux), this task remains under-explored in…
Concept erasure serves as a vital safety mechanism for removing unwanted concepts from text-to-image (T2I) models. While extensively studied in U-Net and dual-stream architectures (e.g., Flux), this task remains under-explored in the recent emerging…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. To guarantee stable image generation, Z-Erase first proposes a Stream Disentangled Concept Erasure Framework that decouples updates and enables existing methods on single-stream models.…
Generative Image Safety moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A novel framework for stable concept erasure in single-stream text-to-image diffusion models, overcoming generation collapse and achieving state-of-the-art performance.
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10.48550/arXiv.2603.25074A novel framework for stable concept erasure in single-stream text-to-image diffusion models, overcoming generation collapse and achieving state-of-the-art performance.
Abstract
Concept erasure serves as a vital safety mechanism for removing unwanted concepts from text-to-image (T2I) models. While extensively studied in U-Net and dual-stream architectures (e.g., Flux), this task remains under-explored in the recent emerging paradigm of single-stream diffusion transformers (e.g., Z-Image). In this new paradigm, text and image tokens are processed as a single unified sequence via shared parameters. Consequently, directly applying prior erasure methods typically leads to generation collapse. To bridge this gap, we introduce Z-Erase, the first concept erasure method tailored for single-stream T2I models. To guarantee stable image generation, Z-Erase first proposes a Stream Disentangled Concept Erasure Framework that decouples updates and enables existing methods on single-stream models. Subsequently, within this framework, we introduce Lagrangian-Guided Adaptive Erasure Modulation, a constrained algorithm that further balances the sensitive erasure-preservation trade-off. Moreover, we provide a rigorous convergence analysis proving that Z-Erase can converge to a Pareto stationary point. Experiments demonstrate that Z-Erase successfully overcomes the generation collapse issue, achieving state-of-the-art performance across a wide range of tasks.
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PROBLEM
A novel framework for stable concept erasure in single-stream text-to-image diffusion models, overcoming generation collapse and achieving state-of-the-art performance. While extensively studied in U-Net and dual-stream architectures (e.g., Flux), this task remains under-explore...
METHOD
Concept erasure serves as a vital safety mechanism for removing unwanted concepts from text-to-image (T2I) models. While extensively studied in U-Net and dual-stream architectures (e.g., Flux), this task remains under-explored in the recent emerging paradigm of single-stream dif...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. To guarantee stable image generation, Z-Erase first proposes a Stream Disentangled Concept Erasure Framework that decouples updates and enables existing methods on single-stream models. Code availability...
WHY NOW
Generative Image Safety moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A novel framework for stable concept erasure in single-stream text-to-image diffusion models, overcoming generation collapse and achieving state-of-the-art performance. While extensively studied in U-Net and dual-stream architectures (e.g., Flux), this task remains under-explored in the recent emerging paradigm of single-stream diffusion transformers (e.g., Z-Image).
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Concept erasure serves as a vital safety mechanism for removing unwanted concepts from text-to-image (T2I) models. While extensively studied in U-Net and dual-stream architectures (e.g., Flux), this task remains under-explored in the recent emerging paradigm of single-stream diffusion transformers (e.g., Z-Image).
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. To guarantee stable image generation, Z-Erase first proposes a Stream Disentangled Concept Erasure Framework that decouples updates and enables existing methods on single-stream models. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Generative Image Safety moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A novel framework for stable concept erasure in single-stream text-to-image diffusion models, overcoming generation collapse and achieving state-of-the-art performance.
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Generative Image Safety
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