Diffusion Story Continuation

• Diffusion-based story continuation utilizes autoregressive latent diffusion models to condition image generation on accumulated visual and textual context, ensuring narrative coherence.
• Advanced memory, attention, and adaptive context mechanisms enable models such as Make-A-Story and Causal-Story to maintain consistent character, background, and plot details across frames.
• Robust multimodal integration and fine-tuning strategies, including text encoders and plugin-based adaptations, support scalable, interactive, and zero-shot editing of story sequences.

Diffusion-based story continuation encompasses a set of techniques for generating temporally and semantically consistent image sequences corresponding to narrative text, leveraging the underlying denoising diffusion probabilistic model (DDPM) or its latent-space variants. Unlike approaches that generate each caption–image pair independently, diffusion-based story continuation methods explicitly model dependencies among preceding visuals and narrative context, enabling the coherent extension of stories and the preservation of character, background, and plot consistency across multiple panels or frames.

1. Sequential Conditioning in Latent Diffusion

Diffusion-based story continuation centers on latent diffusion models (LDMs) conditioned not only on the current narrative descriptor but also on historical visual and textual context. Autoregressive LDMs such as AR-LDM formalize the joint story distribution for a sequence of frames as

$$P_{AR}(X|C) = \prod_{j=1}^L P(x_j | \widetilde{x}_{<j}, C),$$

where $X = [x_1, \ldots, x_L]$ are diffusion-generated images and $C = [c_1, \ldots, c_L]$ are captions. The model relies on conditioning the generation of frame $j$ on all captions up to time $j$ and all visuals generated to date. Historical conditioning is implemented via a multimodal encoder $\tau_\theta(\widetilde{x}_{<j}, c_{\leq j})$ that outputs a context code $\phi_j$ guiding the denoising process:

$$p_\theta(z_0^{[j]} | \phi_j), \quad \phi_j = \tau_\theta(\widetilde{x}_{<j}, c_{\leq j})$$

This ensures that salient visual details—character appearance, background layout, and scene attributes—are propagated through the sequence, mitigating drift or inconsistency.

2. Visual Memory, Attention, and Adaptive Context

Several methods introduce explicit memory and attention mechanisms to align historical relevance with current narrative demands. Make-A-Story implements a visual memory module storing intermediate latent representations (as well as sentences), employing sentence-conditioned soft attention:

$$\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{Q K^\top}{\sqrt{d}}\right) V,$$

where $Q$ comes from the current sentence, $K$ from previous sentences, and $V$ from the associated latents. This enables the model to resolve references in ambiguous text (e.g., pronouns) and maintain actor/background continuity. Further developments refactor historical aggregation by employing causal or adaptive attention. For instance, Causal-Story leverages local causal attention where each step's attention is masked to attend only to valid predecessor tokens, encoded as

$$\text{Attention}_i(Q_i, K, V) = \sum_{j \leq i} \frac{\exp{(Q_i \cdot K_j/\sqrt{d})}}{\sum_{k \leq i} \exp{(Q_i \cdot K_k/\sqrt{d})}} V_j,$$

ensuring each frame reflects relevant prior context. Adaptive Context Modeling and Adaptive Visual Conditioning (AVC) further refine this by selecting which prior frames to condition on, based on similarity (e.g., via CLIP cosine similarity), and restrict visual memories in the diffusion process to relevant timesteps if semantic misalignment is detected. AVC uses a similarity scheduling function $m(s)$ tuned to the semantic match score $s$ computed as

$$S_j = \frac{1}{2}(\tilde{s}_{\text{text}}(x_t, x_j) + \tilde{s}_{\text{image}}(x_t, I_j)),$$

selecting the memory $j^* = \arg\max_j S_j$ and adaptively injecting its influence up to timestep $m(s)$ based on predefined thresholds.

3. Integration of Textual and Visual Signals

Central to advanced story continuation systems is the robust integration of multimodal cues. Conditioning networks typically employ dedicated encoders:

• A CLIP-based text encoder to process the current or all relevant captions.
• A BLIP or similar encoder to embed combinations of historical image-caption pairs.
• Additional type and temporal embeddings to inform the model of sequence position and modality.

In plugin-based frameworks (e.g., CogCartoon), compact character plugins are plugged into the text encoder at inference, replacing token embeddings for character names with those from the plugin, enabling both efficient extension to new characters and low data/storage overhead.

Furthermore, systems such as DreamStory introduce Masked Mutual Self-Attention (MMSA) and Masked Mutual Cross-Attention (MMCA) modules. MMSA incorporates appearance features of reference portraits for each subject, whereas MMCA selectively injects reference text embedding, both controlled via spatial masks generated from segmentation.

4. Training, Inference, and Editing Strategies

Training of diffusion-based story continuation models is performed by minimizing a denoising error objective:

$$\mathcal{L} = \mathbb{E}_{z_0, \epsilon \sim \mathcal{N}(0, I), t} \left[\| \epsilon - \epsilon_\theta(z_t, t, \phi) \|^2\right],$$

where $z_t$ is the noised latent at time $t$ and $\phi$ is the conditioning code. Classifier-free guidance and context-dependent scaling or gating may be used at inference to bias generation toward textual, visual, or combination cues.

Models are also designed to facilitate practical post-hoc interaction (e.g., Plot’n Polish), allowing selective multi-frame editing via grid-based latent blending and segmentation-driven mask control. This makes fine-grained, zero-shot editing and refinement feasible across an entire narrative sequence.

5. Adaptation and Customization for New Characters

A persistent challenge is the integration of new or previously unseen characters. Methods derived from textual inversion and DreamBooth are adapted: a new token (e.g., <char>) is introduced and its embedding is initialized from a relevant base, then fine-tuned with a small set of character images. Compact adapter-based approaches (e.g., CharCom) encapsulate each character in separate LoRA adapters, which are composed at inference based on prompt-aware similarity between the narrative prompt and the stored adapter tokens. This decoupling of identity from the base model prevents interference and scales efficiently to narratives with many characters.

EpicEvo advances this further by applying adversarial character alignment within the diffusion process. A discriminator is trained to distinguish whether sampled latents correspond to the correct reference character, while knowledge distillation aligns the outputs of the adapted model with those of the pre-trained one, preserving background and previously existing characters.

6. Evaluation, Metrics, and Benchmarks

Standard quantitative metrics include:

• Fréchet Inception Distance (FID): Lower FID indicates greater similarity between generated and ground-truth image distributions.
• Character Accuracy and F1 Score: Assesses the correct depiction of characters across frames, employing pretrained classifiers for detection.
• CLIP-Based Scores (CLIP-I and CLIP-T): Measure image–text and text–text semantic similarity.
• Consistency Indices: Metrics such as DINO similarity, LPIPS, or bespoke integrated scores trace consistency across narrative sequences.

Benchmarks such as PororoSV, FlintstonesSV, VIST, MUGEN (with extended referencing and co-references), NewEpisode (for new character integration), and DS-500 (for open-domain, multi-subject consistency) are commonly employed, supporting both controlled and open-domain evaluations.

7. Scalability, Continual Generation, and Future Directions

Emerging directions address scalability and continual generative capability. Continual Consistency Diffusion (CCD) introduces a formal paradigm for lifelong diffusion-based generation, integrating three hierarchical loss terms—inter-task, unconditional, and label knowledge consistency—to prevent catastrophic forgetting and preserve both low- and high-level story coherence over evolving narratives. StateSpaceDiffuser brings state-space model features into the generative pipeline, ensuring long-range context retention, critical for stories spanning many panels or complex world models.

Architectural modifications such as Consistent Self-Attention (StoryDiffusion), plugin-guided and layout-guided inference (CogCartoon), and agentic audit-and-repair loops (Audit & Repair) further enhance both the consistency and interactivity of story continuation. New frameworks support zero-shot, multi-frame editing, composability, and user-driven refinement at scale, laying the groundwork for broader application in comic creation, animation, interactive storytelling, and adaptive content generation.

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In sum, diffusion-based story continuation has evolved into a robust, multifaceted field that combines autoregressive latent diffusion, semantic memory selection, modular adaptation, and multi-modal conditioning. Leading models formally factorize dependencies, leverage advanced attention mechanisms, and employ principled evaluation, providing a strong foundation for consistent, semantically rich, and practically editable visual narratives.