Choosing Between Image-to-Video Models: Route Yourself by Task

You have a still image and you need it to move. The solo filmmaker wants a slow dolly shot across a landscape. The social-media creator needs a product shot to pulse for six seconds without the subject morphing. The open-source tinkerer has a 24 GB GPU and wants to run inference locally, no API bill. The legal reviewer needs to know whether the output carries a copyright claim. The educator needs to show students exactly where the model breaks. Each asks the same thing: how to choose an image-to-video model. But the answer is different for every one of them. This page routes you to the right comparison by what you are actually trying to do. It does not rank models. It hands you the decision criteria that matter for your task and sends you to the guide that answers the specific problem you brought.

Every recommendation here carries its date. Model capabilities, access paths, licence terms, and platform labelling policies change. The facts in this page were checked on 26 September 2026 and the documentation dates for each model are noted in their entries. Verify commercial terms and version numbers before you commit a workflow to any single model.

• Models In Scope: 11: Veo, Kling, Runway, Sora, Luma, Pika, Seedance, Hailuo, Wan, LTX-Video, Stable Video Diffusion
• What Image-to-Video Does: Produces a 2-10 second moving clip from a single still image. No persistent 3D world model exists; the model predicts pixel motion statistically.
• Key Decision Criteria: Motion coherence, source-image fidelity, camera-motion control, VRAM requirement, commercial-use licence, watermarking, and clip length.
• Documentation Check Date: 2026-09-26. Model versions and terms marked are revised yearly.

Apply these attributes in the order below. It is not alphabetical. It follows the evidence.

Motion Coherence

This matters most. Motion coherence measures how often limbs swap, objects morph, or backgrounds slide independently of the foreground. A model with poor coherence is unusable for filmmaking, no matter its resolution. Claimed clip lengths are 40-60% longer than the duration over which motion coherence stays acceptable. A model advertising 10 seconds of output may produce visible subject drift after four seconds. Test the usable duration, not the advertised one.

Source-Image Fidelity at Clip End

How recognisable is the original subject after the full clip? Many models drift. The solo filmmaker who needs a face to remain the same face over a five-second shot cannot use a model that allows identity shift. Runway Gen-4 and Veo 2 score well here. Models with weaker temporal attention, like early versions of Stable Video Diffusion, score poorly.

Camera-Motion Control Granularity

A single motion-strength slider is not enough for controlled filmmaking. Look for distinct controllable axes: pan, tilt, zoom, dolly, crane, and roll. Runway Gen-4 offers the most axes with its director-mode controls, including dolly and crane presets, as of its 2026 documentation. Pika 2.0 adds region-mask support, allowing selective animation within the frame while keeping the rest static. Kling 1.6 offers pan, tilt, zoom, and roll presets. If your task requires a specific camera move, choose the model that exposes that axis natively.

VRAM Requirement at 720p

For local generation, this constraint eliminates most consumer hardware. Documented minimum VRAM figures reflect ideal batch-size-1 inference. Real usage with the full ComfyUI pipeline, including VAE decode and upscaling, can exceed the stated figure by 2-4 GB. LTX-Video requires the least VRAM among open-weights models. Stable Video Diffusion is the next-lowest entry point. Wan 2.1 comes in multiple parameter sizes; the 14B variant will not run on a 24 GB card without quantisation.

Commercial-Use Licence

If you sell the output or use it in client work, the terms are non-negotiable. Runway, Veo, Kling, Sora, Luma, Pika, Seedance, Hailuo, and Wan permit commercial use under their terms of service as of the 2026 documentation check. LTX-Video is Apache 2.0, which explicitly permits commercial use. Stable Video Diffusion 1.1 uses a non-commercial licence. Version 1.0 allows commercial use under the Stability AI Community Licence. The open-weights versus open-source distinction matters: a model may release weights but not training data or code. That does not affect your right to use the output commercially. It does affect reproducibility and the ability to fine-tune.

Watermarking

Visible watermarks affect aesthetics. Metadata-only watermarks affect platform compliance without visual impact. A critical gap exists: platform detection systems may not read a model's specific metadata format, leaving the uploader exposed to rejection or demonetisation. YouTube, TikTok, and Meta use their own detection systems. A model claiming compliance with platform disclosure rules may still produce output that a platform flags. For any distribution, test the output against the target platform's detection system before committing to a workflow.

Each route names the problem it answers and points to the guide where that problem is answered in detail. The guides own the depth; this page owns the map.

The Solo Filmmaker or Video Artist

Which model gives the most controllable camera movement and the fewest artefacts on your source image? Start with camera-motion control granularity. Runway Gen-4 gives you pan, tilt, zoom, dolly, crane, and roll presets, plus region-mask support via Motion Brush. Veo 2 offers pan, tilt, and zoom presets with motion strength up to 10. Both generate audio alongside video, which saves a sync step. Kling 1.6 supports start-and-end-frame specification. That is essential for precise shot transitions without seam artefacts. The guide for solo filmmakers answers at the level of a specific shot: how to choose between Runway's director mode and Veo's motion-strength slider when your source image is a moody landscape at dusk.

The Social-Media Content Creator

Which model produces the fastest output with the least watermarking impact on your platform? Speed and watermarking policy are the primary criteria. Luma Dream Machine generates a five-second clip quickly through a web interface, but its native resolution is 1360×752. You will need to upscale for most platforms. Runway and Pika both output up to 1080p and generate audio. Sora offers a 20-second native clip length, reducing the number of concatenation steps for longer sequences. Platform labelling policies matter: YouTube requires creator self-disclosure for altered or synthetic content; TikTok applies an automatic 'AI-generated' label; Meta applies its 'Made with AI' label via automated detection, all as of the 2026 check date. The guide for social-media creators answers which model's watermarking and resolution profile best matches a given platform's detection and display requirements.

The Open-Source Tinkerer Running Locally

Which model runs on your GPU and integrates with your existing ComfyUI setup? VRAM requirement is the gate. LTX-Video is the lowest entry point, runs on consumer GPUs, and is Apache 2.0 licensed. Stable Video Diffusion is next, but check the licence: version 1.1 is non-commercial. Wan 2.1 offers parameter sizes from 1.3B to 14B; the smaller variant runs on 24 GB cards with quantisation. All three accept image input and work with ComfyUI node graphs. Documented VRAM figures are minimums for inference alone. Real peak usage including VAE decode can add 2-4 GB. The guide for tinkerers answers which open-weights model to download first based on your exact GPU and whether you need commercial rights.

The Legal or Compliance Reviewer

What disclosure obligations apply to this output, and does it carry a copyright claim? The US Copyright Office's position, stated in its 2023 guidance and reaffirmed in its 2025 report, is that pure AI-generated output is not registrable. Output combining human authorship with AI generation may be registrable if the human contribution is sufficient and the AI-generated portions are disclaimed. The EU AI Act's Article 50 imposes transparency obligations on deployers: outputs must be marked in machine-readable format and disclosed as artificially generated. That obligation applies from 2 August 2026 for general-purpose AI systems. Platform labelling policies from YouTube, TikTok, and Meta each have their own thresholds and enforcement mechanisms. The guide for legal reviewers answers how to document the provenance of a generated clip and which jurisdiction's rules apply based on where the clip will be distributed.

The Educator or Researcher

Where does the model fail, and what benchmarks exist to measure that failure? Documented failure modes include subject drift, limb mutation, background sliding, identity shift, flicker, and motion collapse. The solo filmmaker avoids these. The educator demonstrates them. Models differ in how well they expose these failures. The guide for educators answers which model to use for teaching temporal attention failures and which evaluation benchmarks have been published for a given model.

The five decision criteria above narrow the field fast. Here is how they interact for a specific project type.

A solo filmmaker shooting a 16-second establishing shot with a slow dolly move needs high motion coherence, good source fidelity, and granular camera control. Runway Gen-4 and Veo 2 are the candidates. Runway offers more camera axes and region masks. Veo offers a longer native clip length. Both generate audio. The decision comes down to whether you need the dolly axis and region-mask support, which Runway has and Veo does not.

A social-media creator producing 50 short clips a week for TikTok needs speed and platform compliance above all. Luma Dream Machine is fast but low resolution. Pika 2.0 outputs 1080p and generates audio, saving a soundtrack step. Sora's 20-second clip length means fewer concatenation artefacts. The decision comes down to whether your platform penalises upscaled footage and whether you need audio generation.

A tinkerer with a 24 GB GPU and a ComfyUI workflow has one model that guarantees inference without VRAM exhaustion: LTX-Video. Its Apache 2.0 licence means you can sell the output. If you need higher resolution, Wan 2.1's 1.3B variant is the next step up, but budget for the additional 2-4 GB peak VRAM during decode.

The legal reviewer starts with the licence and jurisdiction, not the model capabilities. For output distributed in the EU, the EU AI Act's transparency obligation applies from 2 August 2026. For US distribution, the Copyright Office's human-authorship requirement determines registrability. Platform labelling policies add another layer. The model choice matters only after these constraints are satisfied.

Identify your primary use case from the five routes: solo filmmaker, social-media creator, open-source tinkerer, legal reviewer, or educator. Open the guide that answers the problem you brought. That guide owns the depth. This page has given you the decision criteria and the map. The rest is in the comparison.