10 Common AI Video Generation Errors and How to Fix Them

Intro

Most failures in AI video generation are not mysterious. They fall into a small number of recurring categories, and once the pattern is recognisable the fix is usually quick. What wastes time is treating every failure as novel, or — worse — retrying an identical request that is deterministically going to fail again, spending credits each time.

What follows are the ten I encounter most across Seedance 2.0 and comparable models, in rough order of how often they come up, with what actually resolves each one.

1. Requests fail after a working integration stops working

An integration that ran fine for months starts returning connection errors or authentication failures with no code change. The usual cause is an endpoint that moved.

This happened at scale earlier this year when Seedance2.ai moved to Seevio.ai — the API base URL changed with the domain, so integrations with the host written into the request path stopped resolving while those routing through a single configurable base needed one value updated. Authentication keys themselves were unaffected. The details of what changed and what carried over cover the specifics.

The general lesson is worth taking regardless of provider: hardcoded hosts are a liability, and a base URL held in configuration costs nothing to set up and saves a bad afternoon later.

2. The task never leaves the queue

A submitted job sits in a queued state well past any expected generation time. This is almost never a broken job — it is queue depth, which varies with overall platform load and is the least predictable part of the pipeline.

The fix is patience plus a timeout. Polling loops need a total elapsed limit generous enough to cover the slowest realistic render, after which the job is abandoned rather than held indefinitely. Anything user-facing should communicate the queued state explicitly rather than showing an indefinite spinner, because a user watching a spinner for four minutes assumes the product is broken.

Where queue times are consistently long, the speed-optimised Seedance 2.0 tier is the lever. Drafting work does not need the top tier and clears considerably faster.

3. An unsupported duration value is rejected

A request returns a validation error on the duration parameter despite the value being inside the documented range.

Duration in Seedance 2.0 is not a continuous range. It accepts a fixed set of permitted values, and anything between them is rejected rather than rounded to the nearest allowed option. Passing seven seconds when the accepted set includes six and eight produces an error rather than a six-second clip.

The fix is to constrain the input at the application layer — a dropdown or an enum rather than a free number field — so an invalid value never reaches the API.

4. The prompt is rejected by content filtering

A submission is refused before generation begins. This is usually a content policy match rather than a malformed request.

The common surprises are indirect: descriptions of violence in an obviously fictional context, references to public figures, brand names, and occasionally ordinary phrasing that collides with a filter pattern. Rewriting the specific clause rather than the whole prompt normally resolves it, and identifying which clause is a matter of removing them one at a time.

Worth building in as a distinct error state rather than a generic failure, because retrying an identical rejected prompt will always be rejected again.

5. Faces distort during movement

Output is fine at the first frame and degrades as the subject moves, particularly on turns and expression changes.

Two things fix most of this. The first is asking for less: small movements from a sharp starting frame hold up well, while full turns, walking toward camera, and large expression changes are where distortion appears. The second is supplying reference images rather than describing the person in text. Seedance 2.0 accepts up to nine references in a single generation, and a face supplied as a reference holds far better than a face described in words.

Source image sharpness matters too. A soft or partially obscured face in the input produces a face that dissolves in motion.

6. Motion flickers or jitters

The clip has an unstable quality — surfaces shimmering, fine detail crawling, textures that will not sit still.

This concentrates in fine repeated detail: intricate patterns, small text, dense foliage, hair at certain scales. Simplifying the described subject helps, as does moving up a Seedance 2.0 resolution tier, since the artefact is more visible at lower resolutions.

Contradictory instructions also produce it. A prompt requesting both a static camera and sweeping movement forces a conflict that resolves as instability rather than as either instruction. Prompts are worth reading back for internal consistency before submitting.

7. The reference image is cropped unexpectedly

A supplied first frame appears cut off or reframed in the output.

This is an aspect ratio mismatch. A four-by-three source image generated at nine-by-sixteen has to lose something, and what it loses is decided without input. The fix is to match the source image ratio to the target output ratio before submitting — crop deliberately rather than letting the pipeline crop arbitrarily.

Generating natively at the target ratio rather than producing wide and cropping afterwards avoids the same problem downstream. Seedance 2.0 supports the full range including twenty-one-by-nine, sixteen-by-nine, square, and vertical, so there is rarely a reason to reframe in post.

8. On-screen text comes out as nonsense letterforms

Signage, packaging copy, labels, and captions render as approximations of writing rather than legible words.

This is a known limitation across every current video model rather than a fixable error. Text does not survive generation reliably, and no prompt phrasing changes that.

The workflow answer is to add text in post, where it will be sharper, editable, and localisable. Products with text on the packaging can still be generated from a reference image, but the packaging copy should not be expected to stay legible under motion.

9. Credits were consumed but no usable output arrived

A job reports completion or failure after processing has already begun, and the balance has moved.

Generation failures that occur after processing starts generally consume credits, because the compute was spent regardless of whether the result was usable. This is worth understanding rather than disputing, and it is the strongest argument for the draft-then-final Seedance 2.0 workflow: prompt development happens at the shortest duration and lowest resolution, where a failed attempt costs very little, and only settled prompts run at delivery settings.

Where a balance genuinely does not reconcile — credits deducted with no corresponding task record — the transaction history is the reference point, and support handles the discrepancy from there.

10. The camera instruction is ignored

A specified move does not happen, or something else happens instead.

Three causes account for nearly all of it. Too many instructions at once: a prompt requesting a push in, a pan, and a rack focus within a few seconds produces muddled motion rather than three executed moves. One move per shot is the working rule. Vague terminology: descriptive phrasing performs worse than standard cinematographic terms, and Seedance 2.0 responds precisely to the technical vocabulary while treating loose description as a suggestion. And unmotivated movement: a camera anchored to something in frame — following a subject, revealing a detail — is executed more coherently than an abstract operation.

A general method

The debugging approach that saves the most time is changing one variable per attempt. When a generation disappoints and three things get adjusted at once, the next result carries no information about which change mattered.

Holding the Seedance 2.0 seed parameter constant makes this considerably more effective, since it removes sampling variation and isolates the effect of the prompt edit. Combined with drafting at low resolution, it turns troubleshooting from guesswork into something closer to a controlled test — which is the difference between resolving a problem in four attempts and burning forty credits discovering nothing.