u/Sniper_yoha

Half the posts here are the same ten questions. Answers in one place so people can search before posting. Mods can pin it. I'll edit as things change.

Can I run Seedance 2.0 locally on my own GPU?
No. It's a closed ByteDance model, API only, no weights to download. Anyone selling a "local Seedance install" is selling something else. If local is a hard requirement, you want an open video model like Wan, not Seedance.

What modes does it actually have?
Three. Text to video, image to video (from a first frame, optional last frame), and reference to video. The reference mode also does editing and extending an existing clip. Text to video has an optional web-search step if you want it grounded in something current.

Does it generate sound, or is it silent like most video models?
It generates native audio with the video, in the same pass. That's one of the main reasons to reach for it over the older silent models, so don't go hunting for a separate audio step.

How do I use it outside the web player (API / ComfyUI)?
It's a hosted API. You call it from your own code, or point the OpenAI SDK at a provider that serves it, and in ComfyUI you drive it through an API node instead of a local checkpoint. Send a prompt plus an optional reference image, get back a video. One host I use bills per second and names the model outright, which is the setup you want.

Which site is the "official" one? I keep seeing seedance2.ai and similar.
Seedance is a model, not a store. There's no single official Seedance website selling access, so a lot of the seedance2-dot-whatever sites are resellers wrapping someone else's key with a markup, and some are just skins. If a site hides who's actually serving the model, or turns a per-second model into a monthly subscription, be careful. Use a provider that names the model and bills by usage.

How does it cost out? Any subscription?
Billed per second of output, not a subscription. There's a cheaper Mini tier, a standard tier, and a fast tier in between, so a short Mini test runs very little and a long standard render runs more. Providers run promos on it too, so check current rates wherever you run it.

Mini vs standard vs fast, which should I use?
Mini for drafting prompts and framing, since it's the cheap tier. Fast when you want a quicker turnaround at middling fidelity. Standard for the final. Rendering drafts on the standard tier is how people run up a bill.

Does it do NSFW / uncensored?
It runs content filters, so hard NSFW is blocked at the model level regardless of which provider you go through.

How long can one clip be?
A single generation caps at a short duration, so for longer sequences you generate shots and stitch them. Asking one call for a 60-second continuous scene is the most common reason people say the motion falls apart.

Is the quality good enough for commercial work?
For product shots, b-roll, social clips, and stylized scenes, yes. Still weak on long continuous motion, exact on-screen text, and keeping one face or object identical across separate generations. Plan around those.

When is 2.5 coming?
No official date. Every "2.5 leaked" post so far has been a guess. A real release shows up on providers the same week, not in a screenshot.

That's the recurring set. If a question keeps coming up and isn't here, reply and I'll fold it in.

People writing a car video lock the driver and forget the car, then wonder why it feels off across a sequence. The driver stays consistent while the vehicle quietly mutates, the rims change, a decal moves, the body kit shifts, the color of the underglow drifts. Your eye does not always catch which thing changed, but it reads the shot as fake. The fix is to treat the vehicle as a second character with its own continuity lock.

So I write two continuity blocks, not one. A character block, the same driver, same clothes, hair, tattoos, glasses, every shot. And a separate vehicle block that is just as strict, the exact same modified 1990s coupe, same side decal, same glowing underglow color, same deep-dish rims, same lowered stance, and a hard line that says do not change any logos or text. The car gets described with the same discipline as the person.

That second block is what makes a multi-shot car sequence hold together. Across a downtown drift, a bridge run, a hood-mounted shot and an aerial, the model keeps rebuilding the same machine instead of a slightly different one each cut, because I pinned it as explicitly as I pinned the driver.

If a prop or vehicle appears in more than one shot, it needs its own continuity block. Lock the car like you lock the character, and the sequence stops falling apart.

The reason the exaggerated caricature look is so fun to animate is that it removes the ceiling on expression. On a photoreal face, a big pout or a smug mug or a wild swagger tips straight into uncanny and creepy. On a toy-like caricature with a huge head, chubby rosy cheeks, and oversized eyes, you can push all of that to eleven and it just reads as charming. The exaggeration is not a limitation, it is permission to over-act.

I ran an original caricature footballer to test it. Big blonde mullet, baby face, plain red and white jersey, no crest. He struts out of a dark stadium tunnel onto a sunlit pitch, press cameras flashing, and looks dead into the lens doing comical dramatic pouty faces and a two-finger peace sign. Then a hard cut to a high-energy match, sprinting downfield in shiny golden cleats, tightly controlling the ball, dodging defenders under floodlights with confetti falling. Full swagger, full mugging, zero creepiness, because the caricature face can carry it.

The staging matters as much as the face. The tunnel walkout, the flashing press cameras, the roaring crowd, the floodlights and confetti are all sports-hype shorthand, so the character reads as a superstar before he touches the ball. Exaggerated character plus stadium-hype staging is a very reliable comedy formula.

Push the face past realistic, stage it like a star entrance. The caricature is what lets the performance go big.

The instinct with AI video is to frame the impossible like a movie poster, centered, lit, heroic. I got a stronger result doing the opposite, framing a cosmic event like someone caught it by accident on a normal camera. The scale reads as real precisely because the shot is not trying to be a shot.

The scene is a mundane harbor at dusk, fishing boats, a hazy city skyline, ordinary water and mist. Then something enormous and wrong rises out of the water, a vast structure unfolding like a slow mechanical flower, water sheeting off its arms. What sells it is the framing. The camera is low, slightly off, half-obscured by spray, not tracking the thing cleanly the way a director would. It reads as a bystander who lifted a phone too late and does not fully understand what they are looking at.

That accidental-witness feeling is the whole trick. A clean epic shot invites you to admire the CG. A shaky, badly-composed, ordinary-location shot tricks you into believing it happened, because that is what real footage of a disaster actually looks like. Mundane setting plus bad-on-purpose framing beats spectacle.

Do not stage the impossible. Film it like an accident, in a boring place, and the scale does the rest.

Ran on Seedance 2.0

The look I never trusted models to hold is the warm nostalgic one. Cozy, hand-crafted, a little storybook, the kind of frame that feels like a memory instead of a render. It is easy to get a still that looks the part and then watch it go cold and plasticky the second it moves. So I ran it as image-to-video to see if the warmth survived.

Started from one nostalgic still, a tiny village at night, crooked colorful houses leaning into each other, warm light pouring out of every little window, a deep blue starry sky, stone steps and clutter everywhere. Then I animated it on Gemini Omni Flash. The mood held. Window lights flicker and breathe instead of sitting flat, small things drift in the air, a slow gentle camera push adds parallax between the houses, and the hand-made crooked charm never straightens out into clean CG. It still feels warm and a little imperfect, which is the entire point of the style.

That is the hard part with this aesthetic. Anyone can get a cozy still. Keeping the warmth and the handmade texture alive once there is motion, without the whole thing turning slick and lifeless, is where most models fall apart. This one did not.

One nostalgic still, animated, and the mood came through intact. The cozy look finally moves without losing its soul.

Dumb little gag: give an original anime character a bomb in a blocky voxel sandbox world and see if she thinks the plan through. She does not. She lights it, strikes a proud confident pose, and stays about two steps too close, which is exactly two steps too close.

The comedy is all in the timing and her face. The beat of confidence, the fuse burning down, the tiny dawning realization a half-second before it goes, and the reaction. A cartoon explosion, no real violence, just physics and a very bad risk assessment. Getting the pause-then-boom rhythm and her expression flip to land is the whole joke, and the flat 2D character reacting inside the blocky 3D world is the extra layer.

Confident pose, lit fuse, and a lesson about personal space she is about to learn the hard way. Distance, as it turns out, matters.

Animated on Seedance 2.0, kept on one OpenAI-compatible key so trying it is a model-string swap not another subscription: seedance 2.0

Tried a live-action-quality anime-style duel with fully original characters: two martial artists facing off in a circular arena, one built around blinding speed and taijutsu, the other around slow, overwhelming force, each with their own distinct energy signature. No existing franchise, my own characters and my own power system, on purpose.

The hard part of a fight like this is not the effects, it is the physics and the weight. The fast one has to actually read as fast, with real acceleration and follow-through, not sped-up frames. The heavy one has to move like every step carries mass. The energy has to look like it obeys some consistent rule instead of random glow. And both fighters have to stay on-model through fast, chaotic motion, which is exactly where most AI action falls apart into a blurry mess.

It held better than I expected. Live-action skin and cloth, real impact weight, energy effects that read as a system, and the two characters staying distinct and consistent through the whole exchange.

One honest note: because these are original characters, this does not ride on any franchise recognition, so it will not pull the numbers a familiar-IP version would. What it shows is the actual capability, a coherent, live-action-grade action duel built from scratch.

Made on Seedance 2.0, which I keep on one OpenAI-compatible key with the rest so trying it is a model-string swap not another subscription.

Tried the thing brands normally pay a production house for: a polished, appetizing beverage commercial, made entirely with AI. An original character in a sunny morning kitchen opens the fridge, picks out a cold can of an original drink brand, and the spot does everything a real ad does. Condensation beading on the can, a slow-motion crack and fizz on the open, a pour over ice with fruit dropping in, finishing on the product hero shot.

The hard part of product video is appetite appeal, and it is all in the small stuff. The water droplets sliding down a cold can. The exact way the fizz catches the morning light on the open. The pour reading as cold and crisp instead of syrupy. And the talent feeling like a real person enjoying a drink, not a stiff CGI model holding a prop. Get any of that wrong and it stops looking like an ad and starts looking fake.

Shot it in a handheld, warm-morning, iPhone-cinematic style on purpose, the UGC look brands actually want now, then graded it like a high-end beverage spot. Real-influencer energy, commercial polish, zero film crew.

A full product commercial from a fridge and a prompt. This is the part of advertising that just changed.

Made on Seedance 2.0: https://www.atlascloud.ai/models/bytedance/seedance-2.0

The most reliable way I have found to control performance in AI video is to stop describing the action in words and start performing it with a dummy. You animate a plain mannequin in Blender doing the exact choreography you want, the climb, the crouch, the cautious advance, the way the weight shifts, then the AI character replicates that motion frame for frame instead of inventing its own.

Words are where motion control falls apart. "She climbs up cautiously and advances" can mean a hundred different movements. A dummy performing the move is unambiguous: the timing, the posture, the walking rhythm, the crouch level, the final body orientation are all locked, and the model just reskins the dummy into your character.

The rest is the director's chair. Working from the blockout you decide everything: where the camera looks, where the character enters, what angle follows them, which direction the motion flows, how deep the scene reads, and where the final frame lands. You feed three things to Seedance, the dummy motion as the strict movement reference, a character image as the strict identity reference, and an environment image as the strict location reference, and it fuses them.

Knowing Blender now is not about building 3D scenes. It is about getting to direct the motion instead of describing it and hoping.

Large-scale crowd choreography is the single hardest thing to pull off in AI video, so I built an original piece to push it: dozens of dancers on a dark stage collapsing into one dense sculptural cluster and moving as a single coordinated organism. Not a few figures, a whole stage of them, stacked and interlocked.

Dense crowds are exactly where video models break. Bodies in a tight pile melt into each other, limbs multiply, faces smear, and coordinated motion turns into mush the moment more than a handful of people share the frame. Getting a packed human pyramid where every dancer keeps a distinct body, distinct limbs, and a coherent place in the choreography is the real stress test.

Seedance 2.0 held the pile better than I expected. The bodies stayed separate inside the cluster, the anatomy did not dissolve at the overlaps, and the group read as one deliberate moving sculpture instead of a blob. The stage lighting and the dark drape behind it sold the theater scale.

Original dancers, original choreography, fully generated. A whole ensemble moving as one is the kind of shot that used to be off the table entirely for AI.

Chased a specific look on Seedance 2.0: a future warrior in smooth matte-black armor traced with glowing orange circuit lines, mid-action in a vast dark reflective arena lit with teal accents. The whole appeal of this aesthetic lives or dies on the glow.

The hard part is exactly that glow under motion. Neon edge-lines are the first thing to fall apart when the camera and the subject both move fast, they bloom, smear, or detach from the armor they are supposed to sit on. Here they held: clean bright circuit lines staying locked to the body through a fast dynamic pose, the volumetric light reading like real haze instead of a flat filter, and the reflective floor catching it all.

Matte black, hard orange light, teal arena glow, motion blur that adds speed without dissolving the detail. The restraint of a near-black frame with only the circuit lines and a couple of light sources is what makes it feel cinematic.

The look is all about contrast: a nearly black world, a body drawn entirely in light. Hold that and the rest takes care of itself.

We gave Seedance 2.0 a fun stress test: cut a full retro thriller trailer, the kind of moody 80s teaser with hard shadows, grainy film grade, and a lone figure moving through a dim corridor with that slow-build tension. No real movie behind it, just the trailer.

What we were actually checking was whether the model could hold a trailer's job: consistent character across fast cuts, that specific filmic color and grain, and motion that reads cinematic instead of floaty. It held the look shot to shot, which is the part that usually gives AI video away.

Standard disclaimer: none of the shots in this trailer will appear in the actual short film. There is no actual short film. That is the whole bit.

Pushed Seedance 2.0 on something harder than a pretty clip: a full six-shot animated comedy that has to land a joke. Three sea creatures cracking open an ancient treasure vault, an excitable pufferfish in a tiny explorer's hat, a grumpy skeptical octopus, and a smug seahorse who swears he has a key for everything.

The hard part was never the look. It was holding the same three characters across six separate shots while they act, the pufferfish puffing up and deflating with the mood, all eight octopus tentacles moving independently, the seahorse's fins trailing as he swims. Across cuts they stay on-model and stay in character, which is where most multi-shot attempts fall apart.

And it had to carry comic timing. The whole thing builds to the vault swinging open on a blinding pile of treasure, the trio frozen mid-swim, before the reveal lands and the octopus deadpans his verdict. The payoff is dumb in the best way, and the model sells the stunned-silence beat that makes it work.

Glossy studio-3D look, expressive cartoon performance, six shots, one continuous bit. Full multi-shot prompt is in the comments. The treasure is not what they were hoping for.

Pushed Seedance 2.0 on a 4K cinematic sequence: a lone explorer running through a cave packed floor to ceiling with giant glowing cave pearls, and every pearl they brush ignites with an amber-gold internal light that spreads sphere to sphere like a chain reaction. Seven shots, wide overhead to macro to a final pull-back of the whole chamber lit from within.

What made it work was treating the lighting as the action, not the explorer. Each shot is staged around the activation wave racing ahead of the runner, so the camera always has motion even in the wide, static-looking frames. Writing the chain reaction as a physical wave, not just "glowing pearls," is what kept it coherent across cuts.

4K held the detail where these usually fall apart: the translucent banding inside a single pearl in the macro shot, the specular flares off thousands of wet surfaces in the wides. Full seven-shot prompt is in the comments.

This is the kind of single-location concept piece that used to be a VFX budget and is now an afternoon and a prompt.

A mystery enemy is tearing through the city. The defender mecha built to stop exactly this rises to meet her, except this invader is stronger than anything the defense line has faced. The city is running out of buildings and the defenders are running out of options, so they reach for the one thing they swore they would never use.

I made it as an actual short with a story, not a one-shot showcase, in Seedance 2.0. The hard part was not the explosions. It was keeping two distinct mecha characters consistent across a multi-shot fight while the scale of destruction kept escalating. It held up better than I expected: the dust, the debris, and the weight of the impacts all carried without melting.

This is the kind of sequence that used to mean a VFX house and a schedule. Now it is a long weekend and one key.

Made it on Seedance 2.0, one OpenAI-compatible key for the whole thing.

ByteDance confirmed Seedance 2.5 ships in early July 2026 at the 2026-06-23 Volcano Engine FORCE conference. Specs include 30-second single-shot native output, up to 50 multimodal references per call, and complex multi-shot composition in one generation. For agent-driven video pipelines, this eliminates the stitching middleware layer that currently consumes most of the orchestration complexity.

Confirmed Seedance 2.5 specs (source: ByteDance Volcano Engine FORCE conference, 2026-06-23):

• 30-second single-shot native output, versus 5-second ceiling on 2.0
• Up to 50 multimodal reference inputs per single API call
• Complex multi-shot composition in one generation: scene cuts, spatial transitions, rhythm shifts, thematic resolution
• Second-pass video editing for iterative refinement
• Multi-character ensemble support with stable identity across scenes

Agent workflow implications by pipeline stage:

• Shot planning agent: currently decomposes script into 5-second blocks. After 2.5, decomposes into 30-second scenes instead.
• Generation orchestrator: currently requires 6+ parallel API calls to assemble 30s of content. After 2.5, 1 call per scene.
• Stitching middleware (FFmpeg coordination, transition handling): becomes obsolete.
• Reference management: 1-4 refs per call across 6+ calls today. Consolidates to up to 50 refs in a single call after 2.5.
• Editing agent: currently re-generates full shots for revisions. After 2.5, second-pass editing supports targeted refinement.

Where 2.5 lands against current alternatives:

• Kling 3.0 Turbo: 15s max, native lip-sync, $0.095/sec
• Wan 2.7: 5s max, open-weight, $0.10/sec, multi-shot via stitching
• Seedance 2.5 (early July): 30s native, up to 50 refs, multi-shot in single call

Common questions:

• When does 2.5 launch? Early July 2026, ByteDance confirmed at the 6/23 FORCE conference.
• Will agent pipelines need rewriting? The stitching layer can be removed entirely. Shot-planning logic shifts from "decompose to 5s blocks" to "decompose to 30s scenes". Reference management consolidates from multi-call to single-call.
• Is the 50-reference limit per call hard or soft? ByteDance framed it as "up to 50". Practical attention-cost behavior at high ref counts will need benchmarking post-launch.

For agent devs running multi-model video pipelines, 2.5 shifts orchestration complexity into the model itself. Worth refactoring shot-planning logic ahead of the early July transition.

Tried a storyboard-first workflow for a Japanese-anime-style cooking video, a double cheese burger from raw patty to first bite, and the trick that helped most was making the storyboard deliberately rough.

The workflow:

- Generate an ultra-rough pencil storyboard in an image model: sloppy thumbnail panels, no color, no detail. It is only there to lock composition, camera angles, hand movements, and cooking continuity.

- Feed those panels to Seedance 2.0 as a composition reference, and tell it to convert to a full-color anime cooking video and NOT keep the pencil-sketch look.

- Time the beats to the food: press the finished burger so juices overflow as the hook, then shape, griddle, season, flip, cheese, toast buns, assemble, sauce, and a character takes a bite only at the very end.

Two things mattered. Keeping the storyboard rough stops the model from copying a stiff drawing and frees it to animate. And making the food the star, with the character only at the end, is what gives it that appetite-ASMR feel.

Image model for the storyboard, Seedance 2.0 for the video, both on one OpenAI-compatible key.

Full storyboard and video prompts are in the comments. What dish would you anime-ify?

A character design sheet shows how your AI character looks, but not how it performs. So before committing a character to video, I started auditioning it the way a casting director would, to test voice, emotion, expression, and screen presence first.

The workflow:

- Generate the character, then build a clean character sheet from it: turnarounds, expressions, materials.

- Run a custom audition system prompt that treats the character like a casting agent. It reads the design, suggests roles the face fits, writes a few audition lines, and creates short voice triggers (restrained, dangerous, amused, that kind of thing).

- Feed that into a performance-focused video prompt and generate the audition.

The point is not another consistent-looking still. It is finding out how the character moves, speaks, and reacts before you spend time on real scenes. A face that looks great can audition badly, and you want to know that early.

I keep the sheet, the audition writer, and the video on one OpenAI-compatible key, so the whole loop stays in one place instead of three separate tools.

Full audition system prompt is in the comments. Do you test performance before committing a character, or just lock the look?

Same prompt to both: an 80s teen-movie hallway standoff, two students nose to nose, the crowd closing in. Seedance 2.0 on top, Kling 3.0 below.

Ran both off one key, so it was just a model swap.

Which side sells the tension better?

Ran both on one OpenAI-compatible key here.