Customized Diffusion Models

• Customized diffusion models are adaptive generative systems that fine-tune network parameters and conditioning signals to produce user-specific visual, audio, or video content.
• They employ techniques such as neuron-, token-, and module-level adaptations to integrate new concepts, style transfers, and secure watermarking for diverse applications.
• Advanced adaptations like LoRA modules and concept neurons enable precise control and robust protection against adversarial attacks while maintaining generative fidelity.

Customized diffusion models are adaptive generative modeling systems where core parameters or conditioning signals are modified to yield images, audio, or video content aligned with user-specific, fine-grained requirements. These modifications range from neuron-level or module-level adaptation (e.g., “concept neurons,” LoRA modules, multimodal prompt fusion) to pipeline changes for robust concept transfer, text customization, style transfer, video motion decoupling, or security protection. Unlike conventional pre-trained diffusion models, customized diffusion models are explicitly optimized, fine-tuned, or engineered to instantiate new visual, auditory, or compositional concepts while preserving fidelity to both user input and overall generative performance.

1. Fundamental Principles and Taxonomy

Customized diffusion models generally fall into several paradigms based on the specific mechanisms of adaptation:

• Neuron-, Layer-, or Module-level Adaptation: Direct modification or identification of critical units (e.g., “concept neurons” in key–value attention layers (Liu et al., 2023)) whereby only a sparse subset of the network is required for encoding a new visual subject or attribute.
• Token and Embedding-based Customization: Text-driven personalization, such as optimizing “rare tokens” for new visual concepts (e.g., Textual Inversion, Custom Diffusion (Choi et al., 2023)).
• Cross-modal Reference-guided Generation: Incorporation of user-provided examples (images, audio, video) as conditional information (e.g., reference-based image or audio customization (Yuan et al., 7 Sep 2025)).
• Multi-level, Multi-domain Customization: Simultaneous adaptation for style, text, or multi-concept synthesis, often using novel control or guidance modules (e.g., Style Adapter in HiCAST (Wang et al., 11 Jan 2024), mixed prompt modules for image restoration (Ren et al., 15 Jul 2024)).
• Security and Forensics-driven Adaptation: Watermarking, adversarial perturbation, and prompt-agnostic defense mechanisms to protect against unauthorized use, privacy violations, or model misuse (e.g., AquaLoRA (Feng et al., 18 May 2024), CAAT (Xu et al., 23 Apr 2024), PAP (Wan et al., 20 Aug 2024)).

This diversity has enabled customized diffusion models to serve in arbitrary style transfer, logo insertion, attribute disentanglement, concept decomposition, motion transfer, customizable manga generation, and text-to-audio synthesis, among other applications.

2. Parameter, Concept, and Layer Customization

A major branch of customization targets the network’s parameters and (latent) space representations.

• Concept Neurons: Empirical analysis demonstrates that subject-specific “concept neurons” can be identified in a pre-trained diffusion model by analyzing the products θ·(∂L/∂θ) for each parameter θ in attention layers, where L is the concept-implanting loss ((Liu et al., 2023), Equation 1). If this value is positive, the neuron meaningfully contributes to the subject. Customization thus becomes a sparse index mapping, providing up to 90% reduction in storage over baseline subject-driven generation.
• Weight Space as Latent Space: Surveying the LoRA-updated (low-rank) weight deltas across >60,000 fine-tuned models reveals a low-dimensional “weights2weights” (w2w) manifold where principal components encode meaningful attributes (gender, beard, etc.). Linear interpolation or traversal in this space enables efficient sampling of novel identities, attribute editing, and inversion for both in-distribution and out-of-distribution images (Dravid et al., 13 Jun 2024).
• Minimally Invasive Watermarking: Customized watermark LoRA modules embed watermark information via a scaling matrix S, constructed from a binary string, so that even white-box attempts to remove the watermark degrade model performance. Prior Preserving Fine-Tuning (PPFT) ensures the watermark induces only a fixed offset to the intrinsic generative distribution (Feng et al., 18 May 2024).

3. Token-, Prompt-, and Embedding-level Customization

Customization at the conditioning input (token, prompt, or embedding) level is central to many methods:

• Language-relevant Parameter Fine-tuning: Customization may directly alter only the cross-attention keys/values and prompt tokens (“rare token” V*) to enforce strong reference similarity while preserving source image structure (Choi et al., 2023). Augmented prompts and prior preservation losses are used for balancing language drift.
• Contrastive Fine-tuning for Non-Confusing Concepts: CLIF (Lin et al., 11 May 2024) addresses visual confusion in multi-concept composition by directly fine-tuning the CLIP text encoder to enforce non-overlapping, contrastive representations for distinct concepts through over-segmented data augmentation. The resulting textual embeddings are decoupled in the latent space, enabling clear cross-attention foci for each concept and reducing identity loss and attribute leakage.
• Custom Text Rendering: CustomText (Paliwal et al., 21 May 2024) fuses prompt structures (bounding boxes, formatting attributes) and integrates a ControlNet-based consistency decoder for control over font color, type, and small font rendering, supporting attribute-preserving and layout-specific text synthesis.

4. Advanced Architectural and Multi-domain Customization

Several customization schemes introduce architectural innovations enabling fine-grained control over more complex content:

• Adapter and Control Modules: HiCAST (Wang et al., 11 Jan 2024) uses a Style Adapter for multi-scale, weighted fusion of control maps (edges, depth, segmentation), allowing explicit semantic-level control in image/video style transfer tasks. MoE-DiffIR (Ren et al., 15 Jul 2024) uses a Mixture-of-Experts prompt pool with noisy top-K routing, dynamically selecting task-specialized prompts for robust compressed image restoration.
• Logo and Visual Element Insertion: LogoSticker (Zhu et al., 18 Jul 2024) integrates an actor-critic pre-training regime (for spatial relation learning, using CLIP as the critic for adaptive resampling) and a decoupled identity learning scheme (Textual Inversion on isolated logo backgrounds, followed by contextual fine-tuning) for precise, context-aware logo generation.
• Customized Manga and Story Generation: DiffSensei (Wu et al., 10 Dec 2024) employs masked cross-attention and multimodal LLM-based adapters so multi-character features can be learned from reference images, synchronized with narrative text, and used for dynamic manga panel composition. Explicit dialog region embedding further facilitates downstream post-editing.
• Decomposition and Open-World Concept Control: CusConcept (Xu et al., 1 Oct 2024) decouples visual concepts in a two-stage process by first constructing attribute/object vocabulary axes using LLM guidance and weighted sum centroids, then joint refinement of token embeddings via multi-token Textual Inversion to disentangle, control, and semantically recombine objects and attributes in generated images.
• Video and Motion Customization: MoTrans (Li et al., 2 Dec 2024) extends customization to video diffusion by decoupling appearance from motion using a multi-stage pipeline: MLLM-based recaptioner enriches textual appearance descriptions, an appearance injection module fuses reference image encodings into temporal transformer blocks, and motion-specific embeddings focus the model on verb-centric action patterns.

5. Customization for Robustness, Security, and Privacy

Customization exposes models to new security and privacy threats, driving research in adversarial and protective techniques:

• Adversarial Attacks on Attention Mechanisms: CAAT (Xu et al., 23 Apr 2024) efficiently disrupts customized diffusion models by adversarially perturbing input images; the perturbations optimally target the highly sensitive cross-attention key/value projections, transferring downstream to fine-tuned models and degrading text-image correspondence with imperceptible perturbations.
• Prompt-Agnostic Adversarial Protection: PAP (Wan et al., 20 Aug 2024) leverages Laplace approximations of the prompt embedding posterior, sampling adversarial perturbations over a distribution of plausible prompts rather than a fixed one, substantially improving robustness in face privacy and artistic style protection.
• Flexible, Robust Watermarking: AquaLoRA (Feng et al., 18 May 2024) achieves white-box protection by embedding secrets directly into LoRA-wrapped weights of Stable Diffusion’s U-Net, where the watermark can be updated dynamically without retraining, and fidelity is maintained by minimizing the perturbation to the model’s pretrained distribution.

6. Evaluation, Benchmarks, and Applications

Performance is commonly established through:

• Qualitative Measures: Visual or auditory inspection for fidelity, identity preservation, style, and controllability (e.g., user studies and side-by-side comparisons with DreamBooth, Custom Diffusion, DALLE-3, etc.).
• Quantitative Measures: Image/text/audio alignment (CLIP, CLAP), Fréchet Inception Distance (FID), perceptual similarity (LPIPS), user-oriented metrics (Quality Score, dialog region F1), and task-specific scores (e.g., Motion Fidelity, small-font OCR).
• Benchmarks: Custom datasets such as MangaZero (Wu et al., 10 Dec 2024), domain-specific mixed/overlayed audio sets (Yuan et al., 7 Sep 2025), synthetic grids for self-distillation (Cai et al., 27 Nov 2024), and compositional zero-shot learning adaptations (Xu et al., 1 Oct 2024).

Practical applications span branding (logo/logo-sticker insertion), advertising, digital art, e-commerce (virtual try-on, product customization), story and manga generation, AR/VR scene adaptation, robust image/video restoration, privacy preservation, and forensic tracking via watermarking.

7. Open Directions and Future Challenges

Multiple works highlight future research challenges:

• Scalability and Diversity: Expanding datasets and domain breadth (e.g., w2w spaces for non-human concepts (Dravid et al., 13 Jun 2024), open-world attribute axes (Xu et al., 1 Oct 2024)) and improved robustness to bias and edge cases.
• Integrative Defenses: Combining prompt-agnostic adversarial methods with purification/post-protection techniques to resist attacks (Wan et al., 20 Aug 2024), and designing cross-module redundancies for enhanced security (Xu et al., 23 Apr 2024).
• Multimodal and Multi-task Generalization: Extending concepts such as multi-reference customization (DreamAudio (Yuan et al., 7 Sep 2025)) and multimodal adapters (DiffSensei (Wu et al., 10 Dec 2024), MoTrans (Li et al., 2 Dec 2024)) for richer, controllable synthesis beyond single modality or concept.
• Interpretable and Modular Control: Exploiting linear subspaces, composable editing, and interpretable neuron or token spaces for more transparent and granular control over generative processes (Dravid et al., 13 Jun 2024, Liu et al., 2023).
• Optimal Loss and Process Customization: Leveraging flexible distributions for diffusion increments (non-normal diffusion (Li, 10 Dec 2024)), thereby enabling L₁-, L₂-, or hybrid losses for task-appropriate optimization routines and sample characteristics.

These directions underscore the ongoing evolution of customized diffusion models beyond naive personalization toward robust, secure, multi-domain, and semantically disentangled generative models.