Correction, 2026-10-01: we said you can run the same prompt across Veo 3.1, Kling v3 and Seedance 2.0 on 8frame. Kling v3 there is image-to-video only and needs a start image, so a fair comparison gives all three the same still, or uses Kling 2.6 Pro for a text-only run. We also described how Veo 3.1, Kling v3 and Seedance 2.0 behave (stable crowd scenes, better hand gestures than most models at the price, less blur on action) without having measured it, and pointed to temporal consistency scores that do not exist. Those lines now give each model's settings on 8frame and what to check, and the page has an FAQ.
Correction, 2026-09-30: We said Veo 3.1 makes 4K clips on 8frame in about 90 seconds; it renders at 1080p, and we have no measured timing for it.
Video diffusion is an AI architecture that generates video by iteratively denoising a sequence of frames, using time as a conditioning dimension so that motion stays coherent from the first frame to the last.
It's the technology under every major AI video model released since 2024. When you type a prompt into Veo 3.1 and get back a six-second cinematic clip, that clip was produced by a diffusion process that ran across both space (pixels within a frame) and time (how those pixels change between frames). The spatial quality is what you see in a single freeze-frame. The temporal quality is what separates a convincing walk cycle from a jittery, morphing mess.
How video diffusion works
Standard image diffusion works in two phases: a forward pass that adds noise to a training image until it becomes pure noise, and a reverse pass where the model learns to reconstruct the original image from that noise. Video diffusion extends this idea to a stack of frames at once.
During training, the model learns that pixels at frame 10 should be causally related to pixels at frame 9, not just spatially plausible on their own. This is called temporal denoising. The model isn't predicting each frame independently. It's predicting a motion trajectory across the whole clip.
At inference time, the process runs in reverse: start from a random noise volume (think of it as a 3D tensor of noise with width, height, and time as dimensions), then iteratively denoise it toward a coherent video conditioned on your text prompt or reference image. Each denoising step refines both what things look like and how they move.
The result is a model that can produce physically plausible motion, including water ripples, hair in wind, or a figure walking, without ever accessing real physics. It learned the appearance of physics from video data.
When you encounter video diffusion
Every time you use a text-to-video or image-to-video model, you're running a video diffusion model. You don't configure the diffusion process directly. What you control are the inputs that condition it:
- Prompt. Your text description shifts which region of the model's learned distribution gets denoised.
- Reference frame. Some models accept a starting image, which anchors the first frame and constrains the rest.
- Duration and resolution. Longer clips mean more temporal frames to keep consistent, which is why high-quality 60-second generations are harder than 4-second ones.
- Motion intensity settings. Several models expose a "motion strength" slider that effectively changes how much temporal variation the denoising allows.
Quality gaps between models come from differences in training data volume, architecture choices (how the model attends to temporal context), and how well the post-training alignment was tuned. That's why two models given the same prompt can produce clips that feel completely different in motion style.
Examples
Veo 3.1 is Google's video model, with sound generated alongside the picture. On 8frame it renders 4, 6 or 8 second clips at 1080p in 16:9 or 9:16 (Veo 3.1 Lite at 720p). Slow camera pushes and lighting changes are good tests of temporal consistency: watch whether edges and light stay steady through the move.
Kling v3 is Kuaishou's video model and the default of the Kling tool on 8frame, where it animates a start image for 3 to 15 seconds: Kling v3 Standard is 57 credits for 5 silent seconds, 86 with audio, at 720p. Hands and upper-body gestures are where temporal errors show first, so check fingers frame by frame before you scale a batch.
Seedance 2.0 is ByteDance's video model. On 8frame it runs 4 to 15 seconds at 480p or 720p, 48 or 108 credits for 5 seconds. On fast-moving subjects and action, check each fast move for blur or distortion before you commit to longer takes.
All three run on 8frame's canvas, so you can compare temporal quality directly before committing to a generation budget. Kling v3 needs a start image there, so give all three the same still and prompt, or switch Kling to Kling 2.6 Pro for a text-only comparison.
Related concepts
- For how the major video models compare by job, see best AI video generator 2026.
- For a direct breakdown of how Veo 3.1, Sora 2, and Kling v3 differ in practice, see Veo 3 vs Sora 2 vs Kling v3.
- Text-to-video AI is the generation mode built on top of video diffusion. The diffusion architecture is the how; text-to-video describes the interface (prompt in, clip out).
- Temporal consistency is the specific quality metric video diffusion is designed to solve: keeping objects, lighting, and motion physically plausible across every frame.
FAQ
Do I control the diffusion process when I generate a video?
No. You control what conditions it: the prompt, a start image, the length and, where the model offers one, the resolution. On 8frame, Kling v3 needs a start image; Veo 3.1 and Seedance 2.0 also run from text alone.
Why do two models give different motion from the same prompt?
They differ in training data, in how they attend to neighbouring frames and in post-training tuning. The practical answer is to test: give each model the same still and the same prompt and compare the motion.
What does a three-model comparison cost on 8frame?
About 373 credits: Veo 3.1 Fast for 8 seconds with audio (168, the Veo tool's default), Kling v3 Standard for 5 seconds with audio (86) from a Nano Banana 2 still at 1K (11), and Seedance 2.0 for 5 seconds at 720p (108). The same still can serve all three.
Sources
- Kling v3 start-image requirement on 8frame: 8frame video generation code, read 2026-10-01
- Veo 3.1, Kling v3 Standard, Seedance 2.0 and Nano Banana 2 prices, lengths and resolutions: 8frame tool registry (calculateVeo31Cost, calculateKlingV3Cost, calculateSeedance2Cost, Nano Banana 2 pricing) and content fact sheet, read 2026-10-01
Want to compare video diffusion models on your own prompt? Give Veo 3.1, Kling v3 and Seedance 2.0 the same still on the 8frame canvas, or read best AI video generator 2026 for how they compare by job.
Try it without signing up: the 8frame canvas opens on a real board. The canvas is free and unlimited; generation is paid from $19/month.