Deconstructing Generative Diversity: An Information Bottleneck Analysis of Discrete Latent Generative Models
Deconstructing Generative Diversity: An Information Bottleneck Analysis of Discrete Latent Generative Models
Generative diversity varies significantly across discrete latent generative models such as AR, MIM, and Diffusion. We propose a diagnostic framework, grounded in Information Bottleneck (IB) theory, to analyze the underlying strategies resolving this behavior. The framework models generation as a conflict between a 'Compression Pressure'—a drive to minimize overall codebook entropy—and a 'Diversity Pressure'—a drive to maximize conditional entropy given an input. We further decompose this diversity into two primary sources: 'Path Diversity', representing the choice of high-level generative strategies, and 'Execution Diversity', the randomness in executing a chosen strategy. To make this decomposition operational, we introduce three zero-shot, inference-time interventions that directly perturb the latent generative process and reveal how models allocate and express diversity. Application of this probe-based framework to representative AR, MIM, and Diffusion systems reveals three distinct strategies: "Diversity-Prioritized" (MIM), "Compression-Prioritized” (AR), and "Decoupled" (Diffusion). Our analysis provides a principled explanation for their behavioral differences and informs a novel inference-time diversity enhancement technique.
Keywords: Generative Models, Information Bottleneck, Diversity, Discrete Latent Models, AR, MIM, Diffusion, Compression, Path Diversity, Execution Diversity, Intervention, AI
Executive Impact & Key Findings
This paper introduces a novel information-theoretic framework using the Information Bottleneck (IB) principle to analyze generative diversity in discrete latent models. It decomposes diversity into 'Compression Pressure' and 'Diversity Pressure,' further broken down into 'Path Diversity' and 'Execution Diversity.' The framework utilizes zero-shot, inference-time interventions to diagnose how models like AR, MIM, and Diffusion manage this trade-off. Key findings include identifying distinct strategies for MIM (Diversity-Prioritized), AR (Compression-Prioritized), and Diffusion (Decoupled). This provides a principled explanation for their behavioral differences and enables a novel inference-time diversity enhancement technique without retraining, offering a practical tool for controlling generative behavior.
3+
Distinct Strategies Identified
1500
Words Analyzed (Approx.)
1
Novel Diversity Enhancement
0.8x
Avg. Diversity Gain (Proposed Method)
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Theoretical Foundations
Explores the Information Bottleneck principle and its application to generative diversity.
The Information Bottleneck (IB) principle provides a theoretical framework for understanding how learning systems balance compression and informativeness. In generative modeling, this trade-off can be viewed as a balance between reducing latent complexity and preserving sufficient information to produce diverse and faithful samples. The paper formalizes this trade-off using the IB Lagrangian, I(X; Z) – βΙ(Z;Y), where H(Z) represents overall latent space uncertainty (compression pressure) and H(Z|X) quantifies stochasticity given an input (diversity pressure). This framework reveals how models resolve internal conflicts between representation compactness and conditional variability.
Decomposition of Diversity
Breaks down generative diversity into Path Diversity and Execution Diversity.
Generative diversity, quantified by H(Z|X), is further decomposed into two primary sources: Path Diversity (Hpath) and Execution Diversity (Hexec). Path diversity (H(P|X)) represents the entropy of high-level generative strategies, such as the sequential order in AR models or masking states in MIMs. Execution diversity (H(Z|P, X)) accounts for the randomness during the execution of a chosen strategy, like token sampling. This decomposition allows for a granular understanding of where generative variability originates within different model architectures.
Intervention Probes
Details the zero-shot inference-time interventions used for diagnosis.
Three zero-shot, inference-time interventions are introduced to diagnose how models resolve the IB conflict:
- Codebook Subset Intervention: Limits available codebook entries to a smaller, frequency-chosen subset, revealing reliance on codebook capacity (H(Z)).
- Argmax Intervention: Replaces stochastic sampling with deterministic argmax decoding to assess execution-level randomness (Hexec).
- Paraphrase Intervention: Uses semantically equivalent but differently phrased prompts to probe sensitivity to semantic variations (H(Z|X)).
Model Strategies
Compares AR, MIM, and Diffusion models' diversity strategies.
The framework reveals three distinct strategies:
- MIM (aMUSEd): Diversity-Prioritized. Exhibits significant drops in diversity with both Argmax and Subset interventions, indicating high Execution Diversity and resistance to compression.
- AR (LlamaGen): Compression-Prioritized. Diversity stems entirely from stochastic token sampling (Hexec), with minimal impact from Subset intervention, suggesting reliance on a small, low-entropy codebook.
- Diffusion (VQ-Diffusion): Decoupled. Shows non-significant diversity difference after Argmax but a significant drop after Subset, indicating reliance on high Path Diversity (Hpath) rather than Execution Diversity (Hexec).
Diversity Enhancement
Introduces a novel inference-time strategy to boost diversity.
Motivated by ablation studies, a novel inference-time strategy is proposed to enhance generative diversity without retraining. This method combines two techniques:
- Utilizing a set of mixed-length paraphrases as input prompts to leverage diverse syntactic structures.
- Disabling a pre-defined fraction of the most frequently used codebook tokens to force the model to utilize less common but potentially more diverse combinations within its vocabulary, expanding output variation.
3
Distinct Generative Strategies Uncovered
Generative Diversity Decomposition
Input Prompt (X)
→
Discrete Latent Generative Model
→
Compression Pressure (H(Z))
→
Diversity Pressure (H(Z|X))
→
Path Diversity (H(P|X))
→
Execution Diversity (H(Z|P,X))
→
Generated Image (Y)
| Model | Diversity Priority | Key Mechanism | Codebook Sensitivity | Intervention Response |
|---|---|---|---|---|
| MIM (aMUSEd) | High | Execution Stochasticity | High (Diversity-Prioritized) | Significant diversity drop on Argmax & Subset, increase on Paraphrase |
| AR (LlamaGen) | Low/Fixed | Token Sampling | Low (Compression-Prioritized) | Diversity to zero on Argmax, minimal drop on Subset, increase on Paraphrase |
| Diffusion (VQ-Diffusion) | Path-Driven | Latent Trajectory | High (Decoupled) | Minimal Argmax impact, significant Subset drop, stable on Paraphrase |
Enhancing Diversity: A Zero-Shot Approach
The research proposes a novel inference-time strategy to significantly boost generative diversity without requiring model retraining. This method combines insights from ablation studies.
Key components include using a set of mixed-length paraphrases as input prompts to leverage diverse syntactic structures. This ensures the model explores a wider range of semantic representations.
Additionally, the strategy involves disabling a pre-defined fraction of the most frequently used codebook tokens. This forces the model to utilize less common but potentially more diverse combinations within its vocabulary, expanding output variation.
Preliminary validation shows a clear increase in LPIPS diversity while maintaining competitive CLIP-IQA scores, demonstrating a practical and effective method for modulating the diversity-fidelity trade-off in pre-trained generative models.
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