Seeing Eye-to-Eye: Reinforcement Learning for Consistent Image Creation

Author: Denis Avetisyan

A new framework leverages pairwise comparisons to train image generation models, dramatically improving visual consistency across multiple outputs.

During PaCo-RL training for Text-to-ImageSet generation, iterative refinement demonstrably improves image quality from a fixed seed, suggesting the algorithm converges towards a more accurate representation of the textual prompt through successive optimization.

PaCo-RL introduces a novel reward model and online algorithm to address challenges in reinforcement learning for consistent image synthesis using diffusion models.

Achieving truly consistent image generation-maintaining identity, style, and coherence across multiple outputs-remains a challenge for supervised learning approaches due to data scarcity and complex perceptual preferences. This paper introduces PaCo-RL: Advancing Reinforcement Learning for Consistent Image Generation with Pairwise Reward Modeling, a novel framework leveraging reinforcement learning to learn these subjective visual criteria in a data-free manner. By combining a specialized consistency reward model, PaCo-Reward, with an efficient online algorithm, PaCo-GRPO, we demonstrate significant improvements in pairwise consistency and training efficiency. Could this approach unlock more scalable and practical solutions for applications demanding visually coherent imagery, such as storytelling and character design?

The Illusion of Coherence: Addressing Visual Inconsistency

Contemporary image generation models, while capable of producing stunning individual visuals, frequently falter when tasked with maintaining consistency across a series of images or during iterative editing processes. This inconsistency manifests as jarring shifts in style, subject appearance, or even fundamental details like the number of fingers on a hand, disrupting the illusion of a cohesive visual narrative. The underlying challenge stems from the models’ reliance on generating each image in isolation, without a robust mechanism for tracking and enforcing visual attributes across multiple outputs. Consequently, details that should remain constant-a character’s hairstyle, the color of a building, the overall lighting conditions-often fluctuate unpredictably, resulting in outputs that lack the visual continuity required for compelling storytelling, immersive virtual experiences, or professional creative workflows.

The demand for visually coherent imagery extends far beyond simple aesthetic preference; it is a fundamental requirement for increasingly immersive and narrative-driven applications. Storytelling, whether through comics, animation, or interactive fiction, relies on consistent character and scene representation to maintain audience engagement and avoid disrupting the flow of the narrative. Similarly, the promise of truly believable virtual reality experiences hinges on the ability to generate scenes where objects and environments remain stable and recognizable across multiple viewpoints and interactions. Creative content creation, encompassing fields like game development and digital art, also benefits immensely from tools capable of producing visually unified assets. These diverse applications collectively necessitate a shift in image synthesis techniques, moving beyond isolated image generation to methods that prioritize and enforce visual consistency across entire sequences or sets of images.

Existing image synthesis techniques frequently stumble when tasked with generating a series of images that demand unwavering consistency, especially when guided by complex textual prompts – a challenge exemplified by the ‘Text-to-ImageSet’ paradigm. Conventional approaches often treat each image generation as an isolated event, failing to establish a cohesive visual thread across multiple outputs. This results in inconsistencies in character appearance, object placement, and overall scene composition, hindering applications that rely on a unified visual narrative. The limitations stem from a lack of mechanisms to enforce long-range dependencies and maintain a consistent ‘visual state’ throughout a sequence, prompting researchers to explore methods that prioritize holistic scene understanding and persistent identity representation for more reliable and controllable image generation.

This comparison demonstrates the relative performance of various methods for generating image sets from text prompts.

Pairwise Consistency: A Reinforcement Learning Approach

Pairwise Consistency Reinforcement Learning (PCRL) is a generative framework utilizing reinforcement learning to produce images by assessing visual coherence between paired examples. The system operates by presenting the agent with image pairs and training it to minimize discrepancies based on a defined consistency metric. Unlike traditional pixel-wise loss functions, PCRL focuses on high-level feature comparisons, enabling the agent to learn representations that prioritize overall visual harmony. The agent iteratively refines its generative process through trial and error, guided by a reward signal derived from the assessed consistency of generated image pairs. This approach allows for the creation of images that exhibit greater structural and semantic consistency, even with limited training data.

Traditional image generation methods often rely on pixel-wise loss functions, such as mean squared error, which can lead to blurry or unrealistic results as they focus on individual pixel accuracy without considering overall structural coherence. Pairwise Consistency Reinforcement Learning addresses this limitation by employing a reinforcement learning framework to refine the generative process. This allows the model to optimize for higher-level visual features and relationships, rather than solely minimizing pixel differences. The agent learns through trial and error, receiving rewards based on the consistency of generated image pairs, effectively moving beyond the constraints of simple pixel-wise comparisons and enabling the creation of more visually plausible and coherent images. This approach facilitates learning complex distributions and capturing subtle image characteristics that are difficult to model with traditional loss functions.

The Reward Model functions as a discriminator within the Pairwise Consistency Reinforcement Learning framework, evaluating the visual consistency between image pairs generated by the agent. This model is trained on a dataset of consistent and inconsistent image pairs to learn a scoring function that quantifies coherence. The output of the Reward Model is a scalar value representing the degree of consistency, which is then used as the reward signal for the reinforcement learning algorithm. Specifically, the agent receives a higher reward for generating pairs that the Reward Model deems more consistent, effectively guiding the generative process towards outputs exhibiting improved visual fidelity and internal coherence. The Reward Model’s architecture typically consists of a convolutional neural network designed to extract features relevant to visual consistency, enabling it to differentiate between plausible and implausible image pairings.

Our PaCo-GRP framework effectively generates image sets from text prompts by leveraging a proposed approach.

PaCo-Reward: Defining Consistency Beyond Pixel Similarity

PaCo-Reward is a reward model specifically designed to evaluate the consistency of visual information between two images, moving beyond assessments based solely on pixel-level or perceptual similarity. This is achieved by training the model to discern whether corresponding elements and relationships within the image pair are logically aligned and maintained. Unlike models focused on general image quality or aesthetic appeal, PaCo-Reward concentrates on the semantic integrity between images, identifying inconsistencies in object states, spatial arrangements, or depicted events. The model’s training process emphasizes the accurate recognition of these relational aspects, enabling it to provide a more nuanced and reliable assessment of visual consistency than traditional methods.

The PaCo-Reward model leverages Vision-Language Models (VLMs) in conjunction with Chain-of-Thought (CoT) reasoning to analyze the semantic relationships present within image pairs, enabling assessment beyond simple pixel-level comparisons. This approach allows the model to identify and evaluate consistency based on understood objects, scenes, and their interactions. To facilitate efficient training, the framework employs Automated Data Synthesis, generating a large and diverse dataset of image pairs and associated consistency labels without manual annotation. This synthesized data is critical for scaling the training process and improving the model’s generalization capabilities to unseen images and scenarios.

PaCo-Reward’s reliability is established through validation against existing benchmarks, specifically ‘ConsistencyRank’ and ‘EditReward-Bench’, and by alignment with human preference data. Quantitative evaluation demonstrates an 8.2% to 15.0% improvement in correlation with human preferences when compared to other reward models. This performance gain indicates a stronger alignment between the automated assessment provided by PaCo-Reward and subjective human judgments of visual consistency, supporting its utility as a robust evaluation metric.

The PaCo-Reward framework proposes a novel approach to reward shaping for improved policy optimization.

PaCo-GRPO: Optimizing Learning Efficiency and Stability

PaCo-GRPO utilizes Resolution Decoupling to accelerate reinforcement learning training by optimizing the generative model at multiple resolutions sequentially, starting from low resolutions and progressively increasing complexity. This approach reduces computational cost and allows for faster convergence. To ensure stable learning, the algorithm employs Log-Tamed Multi-Reward Aggregation, a technique that stabilizes the reward signal by applying a logarithmic transformation and carefully aggregating multiple reward components. This aggregation method mitigates the impact of large reward spikes and facilitates consistent policy updates during the online learning process, improving overall training stability.

PaCo-GRPO extends established group relative policy optimization (GRPO) techniques by incorporating ‘Flow-GRPO’. GRPO methods address the challenge of policy optimization in reinforcement learning by framing the problem as optimizing over a group of policies, enabling more stable and efficient learning. Flow-GRPO specifically introduces a continuous flow formulation within this group optimization framework, allowing for smoother transitions between policies and improved exploration. This approach facilitates online learning by efficiently updating the policy based on incoming data streams, rather than requiring batch processing, and builds upon the theoretical foundations of existing GRPO algorithms to enhance sample efficiency and convergence properties.

The PaCo-GRPO algorithm utilizes Low-Rank Adaptation (LoRA) to efficiently fine-tune the parameters of the underlying FLUX generative model. This approach avoids updating all model parameters during reinforcement learning, significantly reducing computational cost and memory requirements. Empirical results demonstrate that implementing LoRA within PaCo-GRPO yields a 10.3% to 11.7% improvement in consistency metrics when applied to Text-to-ImageSet generation. Furthermore, the algorithm achieves a 10.5% increase in accuracy as measured by the ConsistencyRank benchmark, indicating enhanced performance in maintaining semantic consistency within generated content.

This visualization demonstrates the training progression of the Text-to-ImageSet generation model.

Towards Visually Coherent Digital Experiences

A significant hurdle in crafting truly believable digital environments lies in maintaining visual consistency – ensuring that objects and characters retain their identity across multiple viewpoints and scenes. This work introduces a framework designed to overcome this challenge, paving the way for more immersive experiences in fields like virtual reality and interactive storytelling. By generating a series of images that adhere to a unified aesthetic and accurately represent the same subjects, the system minimizes the jarring discontinuities that often break immersion. This capability extends beyond mere realism; it allows creators to build narratives where visual cues remain reliable, deepening engagement and fostering a stronger connection between the audience and the digital world. Ultimately, consistent visuals are not just about technical fidelity, but about enhancing the emotional and cognitive impact of the experience.

The advent of ‘Text-to-ImageSet’ technology signifies a substantial leap forward in generative art and design workflows. Previously constrained by the stochastic nature of text-to-image models – where even repeating the same prompt yielded visually disparate results – artists can now harness a system capable of producing a cohesive series of images from a single textual description. This consistency unlocks possibilities for visual storytelling, allowing the creation of sequential artwork, graphic novels, or even animated sequences with a unified aesthetic. Designers benefit from the ability to rapidly prototype variations of a concept while maintaining visual harmony, streamlining the creative process and fostering more effective communication of ideas. Ultimately, this technology empowers creators with greater control over the visual narrative, transforming a single textual vision into a fully realized and consistent visual experience.

This research establishes a crucial stepping stone towards more nuanced control over image generation processes. By demonstrating the feasibility of creating cohesive visual sequences from textual descriptions, it unlocks avenues for investigating finer-grained control mechanisms – allowing for not just what is depicted, but also how it is depicted across multiple images. This capability promises to fuel innovation in areas like automated content creation, interactive storytelling, and personalized visual experiences, potentially enabling systems that dynamically adapt visual narratives based on user input or contextual awareness. Ultimately, this work isn’t simply about generating images; it’s about building the foundation for systems that can orchestrate visually compelling content with unprecedented precision and artistry.

The pursuit of consistent image generation, as detailed in this paper, demands a rigorous approach to reward modeling. It echoes Geoffrey Hinton’s sentiment: “The most interesting things are usually the most difficult.” PaCo-RL directly addresses this difficulty by shifting focus from individual image quality to pairwise consistency – a subtle but crucial refinement. The PaCo-Reward model isn’t merely seeking aesthetically pleasing outputs; it’s enforcing a logical coherence between images, ensuring the generated sequence forms a meaningful whole. This aligns with the need for provable correctness, a cornerstone of elegant algorithmic design. The framework’s emphasis on mathematical consistency mirrors a desire for solutions that are not just functional, but demonstrably sound.

What Remains to be Proven?

The pursuit of ‘consistent’ image generation, as exemplified by PaCo-RL, reveals a fundamental tension. The framework demonstrably improves upon existing methods, yet ‘consistency’ itself remains a surprisingly ill-defined metric. A statistically significant improvement on a test suite does not equate to a solution provably free from latent inconsistencies-artifacts imperceptible to current evaluation techniques, but potentially exposed by adversarial perturbations. The reliance on pairwise comparisons, while pragmatic, introduces a combinatorial complexity that will inevitably constrain scalability; a truly elegant solution demands a reward function derivable from first principles, not empirical observation.

Further investigation must address the deterministic reproducibility of results. While the authors likely achieved convergence, the inherent stochasticity of both reinforcement learning and diffusion models necessitates rigorous testing across multiple random seeds and hardware configurations. If the generated images, even with identical parameters, exhibit variation beyond acceptable tolerances, the entire exercise becomes, at best, a statistical approximation-an interesting phenomenon, perhaps, but not a reliable foundation for downstream applications.

Ultimately, the field requires a shift in perspective. The goal should not simply be to mimic visual coherence, but to model the underlying generative process with mathematical precision. Only then can one confidently assert that a generated image is not merely plausible, but logically consistent-a reflection of an immutable, provable reality.

Original article: https://arxiv.org/pdf/2512.04784.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/