Understanding Text-to-Video Models and Their Instruction Decay Challenges

This content reflects the capabilities of text-to-video models as of May 2023. Given that generative video is in its early experimental phase, outputs often contain significant visual artifacts, temporal distortions, and inconsistent character mapping. Furthermore, as safety filters for automated video synthesis are still maturing, users are advised that generative results may vary unpredictably in their adherence to safety guidelines and realistic physics.

Text-to-video models are AI tools that generate short video clips from written descriptions. In practice, the most visible limitation isn’t “can it draw a frame?”—it’s whether the model can keep the same idea stable across time. That stability problem is where instruction decay shows up: the prompt is understood at the beginning, then gradually “leaks” as the clip progresses, producing videos that start on-topic and drift into inconsistencies.

TL;DR

• Text-to-video systems can produce convincing moments, but temporal consistency is the hard part.
• Instruction decay often appears as identity drift (characters morph), flicker (details change frame-to-frame), and frame-ghosting (trails/doubles).
• These issues are closely tied to how models balance 2D-spatial attention (within a frame) against 1D-temporal attention (across frames), under tight compute limits.

How Text-to-Video Models Work

Most state-of-the-art text-to-video systems are diffusion-based. A diffusion model learns how to gradually remove noise from a representation until it becomes a plausible output. For video, the output is not a single image but a sequence of frames that should look like one coherent scene unfolding over time.

At a high level, the workflow looks like this:

• Text conditioning: the prompt is encoded into a representation the model can attend to.
• Latent video generation: the model generates a compact “latent” representation of multiple frames, then decodes it into pixels.
• Cross-attention: the model repeatedly references prompt tokens (objects, style cues, actions) while denoising.

What makes video uniquely difficult is that the model must solve two problems at once: creating a strong image in each frame and maintaining semantic continuity across frames. Many impressive clips succeed at the first problem more often than the second.

Understanding Instruction Decay

In text-to-video, “instruction decay” is less about forgetting the prompt entirely and more about losing constraints over time. A character’s clothing color might shift. A face might subtly change. A background sign might scramble between frames. The prompt is still “in the loop,” but the model’s internal representation struggles to keep every requested detail consistent as motion and new frames introduce fresh uncertainty.

You can think of instruction decay as a symptom of a deeper bottleneck:

Instruction decay is usually temporal consistency failure.

When a model can’t reliably bind “the same entity” across multiple frames, prompt constraints degrade into a sequence of loosely related images rather than a stable scene.

Cross-Frame Attention Bottlenecks

To understand why temporal consistency is hard, it helps to separate two kinds of attention that a model may use.

2D-spatial attention (within a frame)

What it’s good at: building composition inside a single frame—where objects sit, what textures look like, and how style cues spread across the image. When you see a great still frame pulled from a generated video, that’s often spatial attention doing its job.

1D-temporal attention (across frames)

What it’s good at: maintaining continuity—ensuring that “the same character” stays the same character, that motion is physically plausible, and that scene elements don’t randomly mutate. Temporal attention tries to connect representations across time, so frame t knows what frame t-1 established.

Why it’s limited: temporal attention is expensive. Attending across many frames multiplies the amount of information the model must process and store. Under tight compute budgets, models often use constrained temporal windows, approximate temporal modules, or limited cross-frame connections. The result is a common pattern: decent short consistency, then a breakdown after a few seconds.

The Challenge of Semantic Continuity

Humans maintain identity across time almost effortlessly: we recognize the same person from a different angle, under different lighting, mid-motion. For a video model, “identity” is a fragile binding problem: it must keep many attributes locked together across frames—face shape, hair, clothing details, object geometry—while also generating new pixels that reflect motion.

When that binding breaks, you see recognizable artifacts:

• Morphing: a character’s face slowly transforms into a different face.
• Flicker: small details (eyes, jewelry, patterns) pulse or change every frame.
• Frame-ghosting: motion leaves semi-transparent duplicates, like temporal “echoes.”
• Texture swimming: surfaces look alive in the wrong way, shifting as if painted on water.

These are not just aesthetic quirks. They are the visible footprint of the model failing to keep a stable internal “state” of the scene across time.

Early Benchmarks: Runway Gen-2 and ModelScope

Two widely referenced points on the text-to-video landscape are Runway Gen-2 (introduced via research materials and waitlist access) and the open demo experience around ModelScope Text-to-Video-Synthesis. Both are useful for understanding what “state of the art” looks like in practice: short clips that can be strikingly evocative, yet frequently show the temporal artifacts described above.

• Runway Gen-2 (research page)
• ModelScope Text-to-Video-Synthesis (demo)

It’s also helpful to place these tools in the broader research arc. Foundational work like Meta’s Make-A-Video research direction and long-video explorations such as Phenaki illustrate the community’s recognition of the same core hurdle: video generation requires scalable ways to represent time without losing coherence.

Factors Contributing to Instruction Decay

Several technical factors converge into the “instruction decay” experience users notice.

1) Limited temporal receptive field

Problem: the model doesn’t “see” enough of the past to preserve identity and story consistency.

What you observe: the first second looks aligned to the prompt; later seconds drift, as if the model is re-imagining the scene repeatedly.

2) Competing objectives during denoising

Problem: diffusion denoising must satisfy style, composition, motion, and prompt alignment simultaneously. When those constraints conflict, the system may trade away fine-grained identity details first.

What you observe: the scene stays roughly correct (“a person in a forest”) while specifics degrade (face, clothing, props).

3) Ambiguity in language-to-visual binding

Problem: prompts often specify what but not how continuity should be preserved (exact face, exact wardrobe, exact camera path). Without strong constraints, the model fills gaps differently across frames.

What you observe: consistent theme, inconsistent details.

4) Data limitations and coverage gaps

Problem: video-text datasets are smaller and less diverse than image-text datasets, so models may be forced to generalize motion and identity from limited examples.

What you observe: plausible motion in short bursts, then “dream logic” when the model runs out of stable cues.

Considerations for Users of AI Video Tools

If you’re using text-to-video tools, the most reliable way to improve results is to design prompts for stability rather than maximal detail. The goal is to reduce cross-frame ambiguity so the model has fewer opportunities to drift.

Prompting tactics that often help

• Anchor the subject: keep one main character/object and repeat the key identity phrase once (not constantly) to reinforce it.
• Limit simultaneous demands: avoid combining many characters, many actions, and many style changes in one clip.
• Use concrete visuals: clear nouns and adjectives (“red raincoat,” “white helmet,” “neon-lit alley”) often bind better than abstract mood words alone.
• Prefer short clips: treat 3–5 seconds as the sweet spot for coherence; longer clips tend to magnify drift.
• Iterate like casting: generate multiple candidates, pick the best “take,” then refine with small edits rather than rewriting the entire prompt each time.

Also, treat these tools as experimental: avoid using them for sensitive, identity-linked, or high-stakes content without a careful review process. Even when a prompt is safe, outputs can vary unpredictably in what they depict.

Current Limitations and Outlook

Text-to-video models are advancing quickly, but their limitations are still fundamental. Temporal consistency is the gating factor that separates “interesting clip” from “usable scene.” The most promising direction is clear: improve how models allocate attention over time so they can maintain identity, motion logic, and scene structure without exploding compute costs.

FAQ: Tap a question to expand.

▶ What is instruction decay in text-to-video models?

Instruction decay is the tendency for a video model’s output to drift away from detailed prompt constraints as the clip progresses. It most often shows up as temporal inconsistency: the subject or scene starts aligned, then identity and details degrade across frames.

▶ Why is temporal consistency such a difficult problem?

Because the model must preserve the same entities across multiple frames while generating new visual information for motion and changes in viewpoint. Temporal attention helps, but it is compute-intensive, so early systems often use constrained temporal connections that don’t fully stabilize identity over time.

▶ What visual artifacts signal instruction decay?

Common signs include flicker (details changing frame-to-frame), morphing (identity drifting), frame-ghosting (trailing duplicates), and texture swimming (surfaces shifting unnaturally). These artifacts are visible symptoms of weak cross-frame binding.

▶ How can users reduce instruction decay in practice?

Keep clips short, reduce prompt complexity, anchor a single subject, use concrete visual descriptors, and iterate by selecting the best outputs rather than expecting a single prompt to produce a perfectly stable scene.

Conclusion

Text-to-video models open new possibilities for generating motion from language, but instruction decay remains a key challenge because temporal consistency is still fragile. The most productive way to use these tools is as a conceptual storyboard: a fast generator of mood, framing, and visual ideas rather than a final production pipeline.

At the same time, the trajectory of the medium is clear. Instruction decay is not a permanent barrier—it is a technical growing pain of the first generation of video transformers and diffusion systems. The bridge from short, flickering clips to coherent cinematic narrative will be built on solving temporal attention and cross-frame binding, turning the current dream-like quality of AI video into a precise digital craft.