Zero-Shot Conditional Generation

• Zero-shot conditional generation is the process of synthesizing data for unseen classes or conditions using class-level or attribute-based side information.
• It leverages diverse conditional generative models—such as VAEs, GANs, normalizing flows, and diffusion techniques—to achieve high fidelity, semantic alignment, and diversity in sample synthesis.
• Empirical evidence and theoretical analysis demonstrate that these methods outperform traditional embedding approaches, offering scalable solutions for applications like personalized content creation, data-free model compression, and multimodal synthesis.

Zero-shot conditional generation (ZSCG) refers to the synthesis of data instances from classes or conditional settings for which no direct exemplars exist in the training data, using only class-level or conditional side information. ZSCG frameworks are core to zero-shot learning (ZSL), generalized zero-shot learning (GZSL), and a broad family of data-free generative tasks spanning modality- and condition-agnostic content generation. Approaches for ZSCG integrate methods such as conditional generative models, plug-and-play guidance, probabilistic formulations, and multi-modal or multi-attribute conditioning, with the goals of high sample fidelity, semantic alignment, and generalization to unseen conditions.

1. Foundations and Problem Formulation

In ZSCG, the generator $G$ is tasked with producing data $x$ that satisfy a structured conditioning variable $c$ (such as a class embedding, textual prompt, or logical constraint) despite the absence of labeled exemplars for some $(x, c)$ pairs during training. The canonical ZSL scenario assumes that classes $\mathcal{Y}_u$ (unseen during training) are associated with semantic side information (attributes, word embeddings), while only the seen class set $\mathcal{Y}_s$ has instance-level data. The generative model $G$ aims to learn $p(x|c)$ on $\mathcal{Y}_s$ and generalize to $\mathcal{Y}_u$ by synthesizing instances for unseen contexts.

Traditional embedding-based ZSL approaches, such as compatibility score learning $S(x, y; W) = \theta(x)^T W \phi(y)$, are limited in expressivity and hampered in settings where discriminative classifiers are required or when both seen and unseen classes must be accommodated at test time (the GZSL problem) (Bucher et al., 2017). A core innovation in ZSCG is the explicit use of a conditional generative model that "hallucinates" features or data for unseen or unobserved conditional regimes, thus reducing ZS or GZSL to a standard supervised learning problem (Bucher et al., 2017).

2. Conditional Generative Architectures

Several paradigms of conditional generative models underpin ZSCG methods:

• Conditional VAEs and Class-Conditioned VAEs: Models such as CVAE (Mishra et al., 2017) and class-conditioned deep VAEs (Wang et al., 2017, Yu et al., 2019) concatenate semantic embeddings with instance features in the encoder/decoder or define priors over latent codes as functions of class attributes, optimizing a variational lower bound. Discriminative classifiers or matching criteria in the latent space (e.g., margin-based loss) are then used for classification of generated and real features.
• Conditional Generative Adversarial Networks (GANs): AC-GANs or related conditional GAN architectures (Bucher et al., 2017) produce synthetic features or data from semantic descriptions, employing discriminators augmented with auxiliary classification heads to align semantic and visual domains.
• Conditional Generative Flows/Normalizing Flows: Invertible flows with class-conditional or attribute-conditional latent priors (Gu et al., 2020, Chen et al., 2022) provide exact likelihood-based training and facilitate sampling under flexible conditional regimes.
• Decoupled and Plug-and-Play Models: Some models decouple the generative process into separate unconditional and conditional generators, e.g., DecGAN (Marmoreo et al., 2021), or apply "plug-and-play" modularity to transform unconditional generators into conditional ones via auxiliary networks or Langevin updates (TR0N) (Liu et al., 2023).
• Score-Based Diffusion and Plug-and-Play Guidance: Diffusion-based frameworks extend ZSCG by manipulating learned score fields or applying classifier/classifier-free guidance to steer reverse diffusion towards satisfying arbitrary conditions (Scassola et al., 2023, Liang et al., 17 Oct 2024). Neuro-symbolic soft constraints enable diffusion models to satisfy logical or combinatorial conditioning objectives (Scassola et al., 2023).

3. Mathematical Principles and Sequential Conditioning

ZSCG for high-dimensional or multi-attribute conditioning challenges the assumption of conditional independence among attributes or prompt elements. Z-Magic reformulates multi-attribute conditioning using conditional probability theory, showing that the joint conditional probability does not factor as $\prod_{i=1}^n p(\mathbf{c}_i|\mathbf{x}_t)$, but instead as a chain $$p(\mathbf{c}_1|\mathbf{x}_t)p(\mathbf{c}_2|\mathbf{c}_1,\mathbf{x}_t)\cdots$$, with sequential gradient-based conditioning $$\nabla_{\mathbf{x}_t}\log p(\mathbf{c}_1,\dots,\mathbf{c}_n|\mathbf{x}_t) = \sum_{i} \nabla_{\mathbf{x}_t}\log p(\mathbf{c}_i|\mathbf{c}_1,\dots,\mathbf{c}_{i-1},\mathbf{x}_t)$$ (Deng et al., 15 Mar 2025). This principle enables context-aware diffusion guidance and mitigates nearly orthogonal gradients when aggregating multiple independent attribute losses, resulting in more coherent sample synthesis when dealing with dependencies among attribute or prompt conditions.

4. Training-Free and Data-Free Zero-Shot Conditional Generation

Several ZSCG approaches eliminate the need for direct access to training data under the target distribution:

• Data-Free Quantization: ZS-CGAN utilizes a pre-trained classifier ("teacher") to supervise a conditional generator via cross-entropy on synthetic labels and by matching internal batch normalization statistics, providing class-discriminative synthetic samples for data-free network quantization (Choi et al., 2022).
• Plug-and-Play Conditional Generation: TR0N leverages a fixed unconditional generator and a pre-trained auxiliary model to train a lightweight translator network that samples from a condition-to-latent distribution, further refined by Langevin dynamics to match an energy-based condition (Liu et al., 2023).
• Zero-Shot Diffusion Conditioning: Zero-shot score-based diffusion techniques directly modify the score function during reverse diffusion by a constraint-derived gradient term, effectively steering the generative process toward arbitrary constraints at sample time without additional training (Scassola et al., 2023, Liang et al., 17 Oct 2024).
• Retrieval-Augmented Generation: AudioBox TTA-RAG augments flow-matching audio generation with cross-modal retrieval, conditioning generation on retrieved audio samples in addition to the text prompt, enhancing few-shot and zero-shot audio synthesis (Yang et al., 7 Nov 2024).

5. Handling Attribute Dependencies and Multi-Task Conditioning

Explicit modeling of conditional dependencies between multiple attributes or tasks is central in achieving coherent zero-shot generation in complex semantic spaces:

• Sequential Guidance: Z-Magic explicitly models the multivariate conditional distribution for multiple attribute settings, computing the gradient updates in sequence with each attribute's context depending on previous ones, utilizing chain rule differentiation and Jacobian corrections (Deng et al., 15 Mar 2025).
• Multi-Task Optimization: The connection to multi-task learning is operationalized by viewing each attribute-pair or attribute-set as defining a loss component. Using techniques such as conflict-averse gradient descent (CAGrad), Z-Magic finds a shared update direction minimizing the sum of all pairwise and higher-order attribute losses, thereby efficiently sharing computation across conditionals while reducing conflicts in the gradient space (Deng et al., 15 Mar 2025).
• Conditional Attribute Embeddings: Enhanced generalization is achieved by learning dynamic, conditional embeddings for attributes as functions of recognized objects and image context, using hypernetworks and base learners. Such representations enable flexible composition and generalization to unseen attribute-object pairs, as validated for compositional ZSL tasks (Wang et al., 2023).

6. Empirical Performance and Theoretical Guarantees

Empirical results across a spectrum of datasets (AwA, CUB, SUN, ImageNet, C-GQA, COCO, AudioSet) consistently show that explicit conditional generation methods:

• Outperform compatibility function-based embedding approaches in zero-shot and generalized zero-shot classification by flattening the seen-unseen bias and enabling discriminative classifier training on augmented synthetic samples (Bucher et al., 2017, Mishra et al., 2017, Wang et al., 2017, Gu et al., 2020).
• Accurately synthesize visual, auditory, or multimodal samples under complex attribute dependencies, demonstrating state-of-the-art FID, CLAP, mIoU, and harmonic mean accuracy metrics in their respective domains (Chen et al., 2022, Couairon et al., 2023, Yang et al., 7 Nov 2024, Deng et al., 15 Mar 2025).
• Achieve improved sample diversity and semantic alignment, as shown by retrieval-based and multi-branch approaches, especially under novel or composite conditioning (Yang et al., 7 Nov 2024, Kimura et al., 2021).

Theoretical analysis has rigorously established that score-mismatched diffusion models for zero-shot conditioning incur an unavoidable, non-vanishing asymptotic bias in KL divergence, proportional to the mismatch between conditional and unconditional scores. This bias can be minimized in linear conditional models with bias-optimal samplers, for which dimension- and noise-dependent convergence rates have been derived (Liang et al., 17 Oct 2024).

7. Applications and Prospects

ZSCG has immediate impact across personalized content creation, data-efficient model training, creative design, model compression, data-free privacy-preserving deployment, audio and video synthesis, and generalized zero-shot and few-shot learning. It enables scalable, real-time, and dynamically customizable generative systems that synthesize plausible data for classes or attributes never explicitly observed during training.

Active research directions include further handling of high-dimensional conditioning, robust attribute disentanglement in complex domains (e.g., video or multimodal generative tasks), plug-and-play semantic guidance under diverse logical or neuro-symbolic constraints, improving retrieval and cross-modal composition, and deepening the theoretical understanding of convergence and bias in conditional generative models.

ZSCG frameworks continue to expand the practical and theoretical limits of conditional data generation, bridging supervised, unsupervised, and few-shot paradigms by leveraging conditional structures, efficient guidance, and the compositionality of deep generative models.