Before:
After:
Anybody notice the same? Usually it's when it start working on some bigger task, like planning or something. It just changes the font. Not that it break anything, but it's a bit annoying and makes it less readable.
Seedance isn't treating audio as an output layer.
It's a conditioning input, meaning: the model processes your uploaded audio file "during generation", alongside your text and image references.
The temporal branch (the part of the model responsible for reasoning about time, motion, and sequence across frames) uses the sound's structure to decide when cuts happen, how fast camera movements accelerate, where visual energy peaks.
So it's not like post-production sync or something. It's choreography baked into the generation itself.
There are two features that make this real. Most people use neither.
Beat sync: upload a track, get auto-choreographed visuals
Upload an MP3 as `@Audio1`. The model analyzes it across four dimensions simultaneously — beat positions, dynamic contour, timbral texture, and song structure sections.
Then it maps all of that to the visual output. Camera cuts snap to beats. Movement accelerates into the build. Visual energy peaks at the drop.
The prompt structure is three sentences (delete quotes, I had to add them to avoid reddit default formatting when using @):
Use `@Audio1` ( as the rhythmic foundation. Sync camera transitions
to the beat positions. Visual energy should build with the audio
crescendo and peak at the drop.
That's it. Each sentence handles one thing: which file is the rhythm source, which visual element responds to it, how visual energy maps to the audio arc.
You can get more specific if you want different visual elements responding to different audio characteristics:
`@Audio1` drives the visual rhythm. Camera cuts land on the downbeats.
Subject movement accelerates into the build, holds at the peak, releases
on the drop. Colour temperature shifts warmer with the crescendo.
Camera responds to beat position. Movement responds to dynamic contour. Colour responds to the overall energy arc. You're essentially mixing audio-to-visual assignments in the same prompt.
And it stacks with other references. You can run a character reference from `@Image1`, pull camera movement style from `@Video1`, and drive the rhythm from `@Audio1` at the same time. The model processes them all simultaneously:
'@Image1' as character reference. Follow '@Video1' camera movement style.
'@Audio1' as rhythmic foundation — sync all camera transitions to
the beat positions. Character movement should pulse with the music.
The one constraint: `@Video1` camera style and `@Audio1` rhythm have to be compatible. A slow continuous dolly from the video reference fighting an EDM track sends conflicting temporal instructions. Pick references that can coexist.
This is the one that genuinely surprised me. No microphone. No recording session. No post-production audio work. You write a timestamped script inside your text prompt, and Seedance generates the voices, the sound effects, and the lip-sync automatically.
The syntax:
[AUDIO: 0s] sharp inhale
[AUDIO: 2s] sword clash, metallic ring
[AUDIO: 4s] character says "Now you see"
Quoted text inside the marker generates speech with automatic lip-sync. Physical descriptions generate sound effects. Each `[AUDIO: Xs]` is a timestamp in the clip. The model builds the audio and synchronises the character's lip movement to the generated voice waveform.
A more complete example with mixed dialogue and SFX:
[AUDIO: 0s] heavy footsteps on concrete, echoing in a corridor
[AUDIO: 2s] door bursting open, impact bang
[AUDIO: 3s] character says "Nobody move"
[AUDIO: 5s] tense silence, distant traffic
[AUDIO: 7s] character says "Put it down. Slowly."
[AUDIO: 9s] object placed on table, soft thud
One block.
Six audio events.
Two dialogue lines with lip-sync generated at millisecond accuracy.
The model generates the voice first, then maps facial movement to the waveform — so the quality of the lip-sync is mostly determined by how precisely you wrote the dialogue.
Exact quoted text outperforms paraphrase.
A strong character reference in `@Image1` gives the model a consistent mouth structure to animate. Close-up framing produces better lip-sync than wide shots where the face is small.
It works in multiple languages too. Write the dialogue in Spanish, Japanese, French — the model generates speech in that language with appropriate phoneme-level lip-sync.
And you can combine it with beat sync in the same generation:
'@Audio1' as background music. Sync camera transitions to the beats.
[AUDIO: 0s] music from '@Audio1' begins
[AUDIO: 3s] character says "This changes everything"
[AUDIO: 5s] sharp breath — beat drop hits simultaneously
[AUDIO: 8s] character says "Let's go"
Music from the uploaded file as the rhythmic foundation. Dialogue and SFX from the script block as foreground. Camera cuts synced to the beat structure. One generation, complete mixed output.
The audio file limit is 15 seconds.
The model takes the first 15 seconds of whatever you upload.
If you drop in a full 3-minute track and let the model decide what to use, you almost always get the intro — which is low energy, often ambient, no rhythmic drive. Nothing for the model to work with.
The right 15 seconds follow a specific arc: a build followed by a drop.
Rising tension into a peak.
That dynamic gradient is what the model translates into visual structure.
A segment with uniform energy gives the model beats to detect but no arc to map to visual energy shifts — the output is rhythmically synced but dramatically flat.
Where to find the window:
- Pre-chorus into chorus
- Instrumental build into the drop (EDM, electronic, hip-hop)
- Verse climax into a bridge
- The last 15 seconds of an intro that breaks into the first hook
Extract exactly that segment before uploading. 256kbps MP3 or above — lower bitrate degrades beat detection. Don't upload the full track and hope. Pick the window, extract it, upload that.
Flipping the workflow — audio in first, visuals built around it — changes what the model produces at a structural level. It's not a subtle difference.
Go, have fun, try this approach and tell me if that made a difference in your outputs.
Seedance isn't treating audio as an output layer.
It's a conditioning input, meaning: the model processes your uploaded audio file "during generation", alongside your text and image references.
The temporal branch (the part of the model responsible for reasoning about time, motion, and sequence across frames) uses the sound's structure to decide when cuts happen, how fast camera movements accelerate, where visual energy peaks.
So it's not like post-production sync or something. It's choreography baked into the generation itself.
There are two features that make this real. Most people use neither.
Upload an MP3 as `@Audio1`. The model analyzes it across four dimensions simultaneously — beat positions, dynamic contour, timbral texture, and song structure sections.
Then it maps all of that to the visual output. Camera cuts snap to beats. Movement accelerates into the build. Visual energy peaks at the drop.
The prompt structure is three sentences (delete quotes, I had to add them to avoid reddit default formatting when using @):
Use '@Audio1` ( as the rhythmic foundation. Sync camera transitions`
to the beat positions. Visual energy should build with the audio
crescendo and peak at the drop.
That's it. Each sentence handles one thing: which file is the rhythm source, which visual element responds to it, how visual energy maps to the audio arc.
You can get more specific if you want different visual elements responding to different audio characteristics:
'@Audio1` drives the visual rhythm. Camera cuts land on the downbeats.`
Subject movement accelerates into the build, holds at the peak, releases
on the drop. Colour temperature shifts warmer with the crescendo.
Camera responds to beat position. Movement responds to dynamic contour. Colour responds to the overall energy arc. You're essentially mixing audio-to-visual assignments in the same prompt.
And it stacks with other references. You can run a character reference from `@Image1`, pull camera movement style from `@Video1`, and drive the rhythm from `@Audio1` at the same time. The model processes them all simultaneously:
'@Image1' as character reference. Follow '@Video1' camera movement style.
'@Audio1' as rhythmic foundation — sync all camera transitions to
the beat positions. Character movement should pulse with the music.
The one constraint: `@Video1` camera style and `@Audio1` rhythm have to be compatible. A slow continuous dolly from the video reference fighting an EDM track sends conflicting temporal instructions. Pick references that can coexist.
2. The audio script block — dialogue and lip-sync from text alone
This is the one that genuinely surprised me. No microphone. No recording session. No post-production audio work. You write a timestamped script inside your text prompt, and Seedance generates the voices, the sound effects, and the lip-sync automatically.
The syntax:
[AUDIO: 0s] sharp inhale
[AUDIO: 2s] sword clash, metallic ring
[AUDIO: 4s] character says "Now you see"
Quoted text inside the marker generates speech with automatic lip-sync. Physical descriptions generate sound effects. Each `[AUDIO: Xs]` is a timestamp in the clip. The model builds the audio and synchronises the character's lip movement to the generated voice waveform.
A more complete example with mixed dialogue and SFX:
[AUDIO: 0s] heavy footsteps on concrete, echoing in a corridor
[AUDIO: 2s] door bursting open, impact bang
[AUDIO: 3s] character says "Nobody move"
[AUDIO: 5s] tense silence, distant traffic
[AUDIO: 7s] character says "Put it down. Slowly."
[AUDIO: 9s] object placed on table, soft thud
One block.
Six audio events.
Two dialogue lines with lip-sync generated at millisecond accuracy.
The model generates the voice first, then maps facial movement to the waveform — so the quality of the lip-sync is mostly determined by how precisely you wrote the dialogue.
Exact quoted text outperforms paraphrase.
A strong character reference in `@Image1` gives the model a consistent mouth structure to animate. Close-up framing produces better lip-sync than wide shots where the face is small.
It works in multiple languages too. Write the dialogue in Spanish, Japanese, French — the model generates speech in that language with appropriate phoneme-level lip-sync.
And you can combine it with beat sync in the same generation:
'@Audio1' as background music. Sync camera transitions to the beats.
[AUDIO: 0s] music from '@Audio1' begins
[AUDIO: 3s] character says "This changes everything"
[AUDIO: 5s] sharp breath — beat drop hits simultaneously
[AUDIO: 8s] character says "Let's go"
Music from the uploaded file as the rhythmic foundation. Dialogue and SFX from the script block as foreground. Camera cuts synced to the beat structure. One generation, complete mixed output.
3. The 15-second extraction problem
The audio file limit is 15 seconds.
The model takes the first 15 seconds of whatever you upload.
If you drop in a full 3-minute track and let the model decide what to use, you almost always get the intro — which is low energy, often ambient, no rhythmic drive. Nothing for the model to work with.
The right 15 seconds follow a specific arc: a build followed by a drop.
Rising tension into a peak.
That dynamic gradient is what the model translates into visual structure.
A segment with uniform energy gives the model beats to detect but no arc to map to visual energy shifts — the output is rhythmically synced but dramatically flat.
Where to find the window:
- Pre-chorus into chorus
- Instrumental build into the drop (EDM, electronic, hip-hop)
- Verse climax into a bridge
- The last 15 seconds of an intro that breaks into the first hook
Extract exactly that segment before uploading. 256kbps MP3 or above — lower bitrate degrades beat detection. Don't upload the full track and hope. Pick the window, extract it, upload that.
Flipping the workflow — audio in first, visuals built around it — changes what the model produces at a structural level. It's not a subtle difference.
Go, have fun, try this approach and tell me if that made a difference in your outputs.
Seedance isn't treating audio as an output layer.
It's a conditioning input, meaning: the model processes your uploaded audio file "during generation", alongside your text and image references.
The temporal branch (the part of the model responsible for reasoning about time, motion, and sequence across frames) uses the sound's structure to decide when cuts happen, how fast camera movements accelerate, where visual energy peaks.
So it's not like post-production sync or something. It's choreography baked into the generation itself.
There are two features that make this real. Most people use neither.
Upload an MP3 as `@Audio1`. The model analyzes it across four dimensions simultaneously — beat positions, dynamic contour, timbral texture, and song structure sections.
Then it maps all of that to the visual output. Camera cuts snap to beats. Movement accelerates into the build. Visual energy peaks at the drop.
The prompt structure is three sentences (delete quotes, I had to add them to avoid reddit default formatting when using @):
Use \@Audio1` ( as the rhythmic foundation. Sync camera transitions`
to the beat positions. Visual energy should build with the audio
crescendo and peak at the drop.
That's it. Each sentence handles one thing: which file is the rhythm source, which visual element responds to it, how visual energy maps to the audio arc.
You can get more specific if you want different visual elements responding to different audio characteristics:
\@Audio1` drives the visual rhythm. Camera cuts land on the downbeats.`
Subject movement accelerates into the build, holds at the peak, releases
on the drop. Colour temperature shifts warmer with the crescendo.
Camera responds to beat position. Movement responds to dynamic contour. Colour responds to the overall energy arc. You're essentially mixing audio-to-visual assignments in the same prompt.
And it stacks with other references. You can run a character reference from `@Image1`, pull camera movement style from `@Video1`, and drive the rhythm from `@Audio1` at the same time. The model processes them all simultaneously:
'@Image1' as character reference. Follow '@Video1' camera movement style.
'@Audio1' as rhythmic foundation — sync all camera transitions to
the beat positions. Character movement should pulse with the music.
The one constraint: `@Video1` camera style and `@Audio1` rhythm have to be compatible. A slow continuous dolly from the video reference fighting an EDM track sends conflicting temporal instructions. Pick references that can coexist.
2. The audio script block — dialogue and lip-sync from text alone
This is the one that genuinely surprised me. No microphone. No recording session. No post-production audio work. You write a timestamped script inside your text prompt, and Seedance generates the voices, the sound effects, and the lip-sync automatically.
The syntax:
[AUDIO: 0s] sharp inhale
[AUDIO: 2s] sword clash, metallic ring
[AUDIO: 4s] character says "Now you see"
Quoted text inside the marker generates speech with automatic lip-sync. Physical descriptions generate sound effects. Each `[AUDIO: Xs]` is a timestamp in the clip. The model builds the audio and synchronises the character's lip movement to the generated voice waveform.
A more complete example with mixed dialogue and SFX:
[AUDIO: 0s] heavy footsteps on concrete, echoing in a corridor
[AUDIO: 2s] door bursting open, impact bang
[AUDIO: 3s] character says "Nobody move"
[AUDIO: 5s] tense silence, distant traffic
[AUDIO: 7s] character says "Put it down. Slowly."
[AUDIO: 9s] object placed on table, soft thud
One block.
Six audio events.
Two dialogue lines with lip-sync generated at millisecond accuracy.
The model generates the voice first, then maps facial movement to the waveform — so the quality of the lip-sync is mostly determined by how precisely you wrote the dialogue.
Exact quoted text outperforms paraphrase.
A strong character reference in `@Image1` gives the model a consistent mouth structure to animate. Close-up framing produces better lip-sync than wide shots where the face is small.
It works in multiple languages too. Write the dialogue in Spanish, Japanese, French — the model generates speech in that language with appropriate phoneme-level lip-sync.
And you can combine it with beat sync in the same generation:
'@Audio1' as background music. Sync camera transitions to the beats.
[AUDIO: 0s] music from '@Audio1' begins
[AUDIO: 3s] character says "This changes everything"
[AUDIO: 5s] sharp breath — beat drop hits simultaneously
[AUDIO: 8s] character says "Let's go"
Music from the uploaded file as the rhythmic foundation. Dialogue and SFX from the script block as foreground. Camera cuts synced to the beat structure. One generation, complete mixed output.
3. The 15-second extraction problem
The audio file limit is 15 seconds.
The model takes the first 15 seconds of whatever you upload.
If you drop in a full 3-minute track and let the model decide what to use, you almost always get the intro — which is low energy, often ambient, no rhythmic drive. Nothing for the model to work with.
The right 15 seconds follow a specific arc: a build followed by a drop.
Rising tension into a peak.
That dynamic gradient is what the model translates into visual structure.
A segment with uniform energy gives the model beats to detect but no arc to map to visual energy shifts — the output is rhythmically synced but dramatically flat.
Where to find the window:
- Pre-chorus into chorus
- Instrumental build into the drop (EDM, electronic, hip-hop)
- Verse climax into a bridge
- The last 15 seconds of an intro that breaks into the first hook
Extract exactly that segment before uploading. 256kbps MP3 or above — lower bitrate degrades beat detection. Don't upload the full track and hope. Pick the window, extract it, upload that.
Flipping the workflow — audio in first, visuals built around it — changes what the model produces at a structural level. It's not a subtle difference.
Go, have fun, try this approach and tell me if that made a difference in your outputs.
I love Seedance and been heavily using it from 1.5 version, but of course 2.0 is asbolute beast, but you know it already. But it was the first model I really put effort to test, which usually was repeating the same mistakes, or prompt patterns I've used on other models. Here are my thought, I wonder if anyone has the same, or (I hope, that's what this post is for) can add some other tips. Also can't wait for 2.5, it's gonna shake the industry IMHO.
Some of you probably knows that stuff, so maybe it's more for people who just starting out.
I was writing 150–200 word prompts thinking more detail equals more control. It doesn't. Seedance reads left-to-right with diminishing attention weight — your first sentence carries the most influence, and by the third sentence you're well into "detail territory" where coherence per element starts dropping. I tested this directly: a 70-word prompt consistently outperformed a structurally identical 200-word version of the same scene. The model stops treating late-prompt elements as primary instructions and starts sampling them diffusely. The sweet spot I landed on: 50–80 words, structured as subject + action in sentence 1, camera + style in sentence 2, constraints in sentence 3.
I used this word in almost every prompt. It did nothing reliable. The problem is that "cinematic" was attached to an enormous range of footage in training data — dark thrillers, bright rom-coms, nature docs — so the model samples a broad, diffuse distribution when it encounters it. It has no specific meaning to the model. What works instead: name a director or a specific lighting setup. "Wes Anderson symmetry" gives you centered framing and pastel palette. "Kubrick one-point perspective" gives you geometric corridors. "Golden hour backlight, long shadows stretching forward" does what "cinematic lighting" never managed.
"Dolly in while panning left" seems completely reasonable. In Seedance it produces artifact-heavy output every time. The reason: camera movements are spatial vectors, and the model processes them sequentially, not as a unified compound move. Two directional vectors simultaneously means the model tries to execute both in sequence, which produces jitter at the transition. I switched to one primary movement plus one texture modifier at most. "Slow dolly in, slightly handheld" works cleanly. "Dolly in while panning left" doesn't.
Coming from Stable Diffusion, writing "negative: jitter, bent limbs, deformation" felt completely natural to me. It made everything worse. Seedance has no negative embedding architecture — all text is processed as positive instruction. When you write "negative: jitter," the model reads noise it tries to interpret as a scene description, not a constraint. The fix I use now is positive constraint statements:
Instead of this:negative: jitter,negative: bent limbs,negative: flicker,negative: deformation
I use this:Face stable, Limbs anatomically natural,Consistent lighting, no flicker, Body proportions consistent throughout.
So it's like direct declarations of what must be true. That's what the architecture actually responds to.
This one surprised me the most. "Fast" is the single highest-degradation keyword when you combine it with complex action or camera movement. The reason: the temporal branch has to run multiple high-velocity calculations simultaneously when motion elements are layered — and "fast" asks all of them to run at maximum velocity at once. Two competing fast elements produce jitter. Three produce compounding error that's hard to salvage. I stopped using the word entirely. Instead I describe the physics: "feet striking hard, each stride full extension, arms pumping at 90 degrees" generates the perception of speed without triggering the degradation. One element can carry speed — just not all of them simultaneously.
I'd upload a photo of a woman in a red dress and then write "a woman in a red dress standing at a window." The character came back slightly wrong every time. What's happening: when you re-describe the image in text, you give the model two competing inputs for the same subject. The model reconciles them, and reconciliation introduces drift. For image-to-video, I learned to keep the prompt to exactly two things — motion instructions and camera instructions. Everything already visible in the image stays out of the prompt entirely.
"Amazing," "beautiful," "high quality," "epic" — I was loading my prompts with these. You know what I think when I or someone uses these in prompts? That I have no idea what I want to create :). SHortest path to wasted credits and/or slop.
These words are useless because they're high-frequency labels attached to an enormous range of outputs in training data. The model has no idea what "epic" means for your specific use case. The fix: replace every generic adjective with a specific named thing. A director's name. A lighting setup. A lens spec ("anamorphic 2.39:1, lens flare from practical light source"). These sample narrow, well-trained distributions and actually move the output.
Am I missing something? would you add some other stuff?
I love Seedance and been heavily using it from 1.5 version, but of course 2.0 is asbolute beast, but you know it already. But it was the first model I really put effort to test, which usually was repeating the same mistakes, or prompt patterns I've used on other models. Here are my thought, I wonder if anyone has the same, or (I hope, that's what this post is for) can add some other tips. Also can't wait for 2.5, it's gonna shake the industry IMHO.
Some of you probably knows that stuff, so maybe it's more for people who just starting out.
I was writing 150–200 word prompts thinking more detail equals more control. It doesn't. Seedance reads left-to-right with diminishing attention weight — your first sentence carries the most influence, and by the third sentence you're well into "detail territory" where coherence per element starts dropping. I tested this directly: a 70-word prompt consistently outperformed a structurally identical 200-word version of the same scene. The model stops treating late-prompt elements as primary instructions and starts sampling them diffusely. The sweet spot I landed on: 50–80 words, structured as subject + action in sentence 1, camera + style in sentence 2, constraints in sentence 3.
I used this word in almost every prompt. It did nothing reliable. The problem is that "cinematic" was attached to an enormous range of footage in training data — dark thrillers, bright rom-coms, nature docs — so the model samples a broad, diffuse distribution when it encounters it. It has no specific meaning to the model. What works instead: name a director or a specific lighting setup. "Wes Anderson symmetry" gives you centered framing and pastel palette. "Kubrick one-point perspective" gives you geometric corridors. "Golden hour backlight, long shadows stretching forward" does what "cinematic lighting" never managed.
"Dolly in while panning left" seems completely reasonable. In Seedance it produces artifact-heavy output every time. The reason: camera movements are spatial vectors, and the model processes them sequentially, not as a unified compound move. Two directional vectors simultaneously means the model tries to execute both in sequence, which produces jitter at the transition. I switched to one primary movement plus one texture modifier at most. "Slow dolly in, slightly handheld" works cleanly. "Dolly in while panning left" doesn't.
Coming from Stable Diffusion, writing "negative: jitter, bent limbs, deformation" felt completely natural to me. It made everything worse. Seedance has no negative embedding architecture — all text is processed as positive instruction. When you write "negative: jitter," the model reads noise it tries to interpret as a scene description, not a constraint. The fix I use now is positive constraint statements:
Instead of this:negative: jitter,negative: bent limbs,negative: flicker,negative: deformation
I use this:Face stable, Limbs anatomically natural,Consistent lighting, no flicker, Body proportions consistent throughout.
So it's like direct declarations of what must be true. That's what the architecture actually responds to.
This one surprised me the most. "Fast" is the single highest-degradation keyword when you combine it with complex action or camera movement. The reason: the temporal branch has to run multiple high-velocity calculations simultaneously when motion elements are layered — and "fast" asks all of them to run at maximum velocity at once. Two competing fast elements produce jitter. Three produce compounding error that's hard to salvage. I stopped using the word entirely. Instead I describe the physics: "feet striking hard, each stride full extension, arms pumping at 90 degrees" generates the perception of speed without triggering the degradation. One element can carry speed — just not all of them simultaneously.
I'd upload a photo of a woman in a red dress and then write "a woman in a red dress standing at a window." The character came back slightly wrong every time. What's happening: when you re-describe the image in text, you give the model two competing inputs for the same subject. The model reconciles them, and reconciliation introduces drift. For image-to-video, I learned to keep the prompt to exactly two things — motion instructions and camera instructions. Everything already visible in the image stays out of the prompt entirely.
"Amazing," "beautiful," "high quality," "epic" — I was loading my prompts with these. You know what I think when I or someone uses these in prompts? That I have no idea what I want to create :). SHortest path to wasted credits and/or slop.
These words are useless because they're high-frequency labels attached to an enormous range of outputs in training data. The model has no idea what "epic" means for your specific use case. The fix: replace every generic adjective with a specific named thing. A director's name. A lighting setup. A lens spec ("anamorphic 2.39:1, lens flare from practical light source"). These sample narrow, well-trained distributions and actually move the output.
Am I missing something? would you add some other stuff?
I love Seedance and been heavily using it from 1.5 version, but of course 2.0 is asbolute beast, but you know it already. But it was the first model I really put effort to test, which usually was repeating the same mistakes, or prompt patterns I've used on other models. Here are my thought, I wonder if anyone has the same, or (I hope, that's what this post is for) can add some other tips. Also can't wait for 2.5, it's gonna shake the industry IMHO.
Some of you probably knows that stuff, so maybe it's more for people who just starting out.
I was writing 150–200 word prompts thinking more detail equals more control. It doesn't. Seedance reads left-to-right with diminishing attention weight — your first sentence carries the most influence, and by the third sentence you're well into "detail territory" where coherence per element starts dropping. I tested this directly: a 70-word prompt consistently outperformed a structurally identical 200-word version of the same scene. The model stops treating late-prompt elements as primary instructions and starts sampling them diffusely. The sweet spot I landed on: 50–80 words, structured as subject + action in sentence 1, camera + style in sentence 2, constraints in sentence 3.
I used this word in almost every prompt. It did nothing reliable. The problem is that "cinematic" was attached to an enormous range of footage in training data — dark thrillers, bright rom-coms, nature docs — so the model samples a broad, diffuse distribution when it encounters it. It has no specific meaning to the model. What works instead: name a director or a specific lighting setup. "Wes Anderson symmetry" gives you centered framing and pastel palette. "Kubrick one-point perspective" gives you geometric corridors. "Golden hour backlight, long shadows stretching forward" does what "cinematic lighting" never managed.
"Dolly in while panning left" seems completely reasonable. In Seedance it produces artifact-heavy output every time. The reason: camera movements are spatial vectors, and the model processes them sequentially, not as a unified compound move. Two directional vectors simultaneously means the model tries to execute both in sequence, which produces jitter at the transition. I switched to one primary movement plus one texture modifier at most. "Slow dolly in, slightly handheld" works cleanly. "Dolly in while panning left" doesn't.
Coming from Stable Diffusion, writing "negative: jitter, bent limbs, deformation" felt completely natural to me. It made everything worse. Seedance has no negative embedding architecture — all text is processed as positive instruction. When you write "negative: jitter," the model reads noise it tries to interpret as a scene description, not a constraint. The fix I use now is positive constraint statements:
Instead of this:negative: jitter,negative: bent limbs,negative: flicker,negative: deformation
I use this:Face stable, Limbs anatomically natural,Consistent lighting, no flicker, Body proportions consistent throughout.
So it's like direct declarations of what must be true. That's what the architecture actually responds to.
This one surprised me the most. "Fast" is the single highest-degradation keyword when you combine it with complex action or camera movement. The reason: the temporal branch has to run multiple high-velocity calculations simultaneously when motion elements are layered — and "fast" asks all of them to run at maximum velocity at once. Two competing fast elements produce jitter. Three produce compounding error that's hard to salvage. I stopped using the word entirely. Instead I describe the physics: "feet striking hard, each stride full extension, arms pumping at 90 degrees" generates the perception of speed without triggering the degradation. One element can carry speed — just not all of them simultaneously.
I'd upload a photo of a woman in a red dress and then write "a woman in a red dress standing at a window." The character came back slightly wrong every time. What's happening: when you re-describe the image in text, you give the model two competing inputs for the same subject. The model reconciles them, and reconciliation introduces drift. For image-to-video, I learned to keep the prompt to exactly two things — motion instructions and camera instructions. Everything already visible in the image stays out of the prompt entirely.
"Amazing," "beautiful," "high quality," "epic" — I was loading my prompts with these. You know what I think when I or someone uses these in prompts? That I have no idea what I want to create :). SHortest path to wasted credits and/or slop.
These words are useless because they're high-frequency labels attached to an enormous range of outputs in training data. The model has no idea what "epic" means for your specific use case. The fix: replace every generic adjective with a specific named thing. A director's name. A lighting setup. A lens spec ("anamorphic 2.39:1, lens flare from practical light source"). These sample narrow, well-trained distributions and actually move the output.
Am I missing something? would you add some other stuff?
I've been compiling a dataset of small SaaS where the revenue is verified through Stripe read-only integration (not self-reported screenshots). As you can see on the chart, the bottom of the market are startups under $50k MRR, most under a year old. I thought it'd be cool to show numbers and overall 'shape'
Here's a snapshot of 20 from the set:
The MRR is smaller than X (Twitter) would have you believe. Median was about $1,225/mo. Nearly half (9 of 20) sat between $500 and $1,000 MRR. Only one cleared $5k. The "$20k MRR in 3 months" posts are real, but they're the exception, not the floor.
Not surprisingly it's an AI/content world. Just under half (9 of 20) were AI tools or content-creation tools, like AI SEO automation, AI video/demo generators, multi-LLM wrappers, AI fitness coaching. The rest spread across marketing, marketplaces, games, productivity, dev tools.
It's not a US story. Of the ones with a known country, two-thirds were outside the US — UK, Germany, Hong Kong, India, France, Pakistan, Czechia, Belgium. I wonder how the distribution looked like before AI coding agents, and the whole vibe coding era.
They're young. 15 of 20 were founded in 2025 or 2026. This is the freshly-launched cohort, not seasoned products.
WOrth to note that these are 20 startups out of a set of 500+, and these particular 20 were surfaced by sorting for recent growth. So keep in mind that they 'skew' toward the youngest, fastest-moving end.
It's a verified look at the new-launch layer, not a representative draw of the whole market. I'm not claiming it's the average SaaS, but I see it as it's an accurate picture of what freshly-launched, revenue-verified micro-SaaS look like right now.
Two things I'd ignore entirely at this size: growth percentages (a jump from $40 to $500 is "1,100% growth" and means nothing) and any single startup's numbers in isolation.
Happy to break this down further if it's useful, like hwo it looks like by category (meaning: niche), by MRR band, or a cleaner random cut across the full 500+. I find this layer fascinating, it was cool to gather and see through this data:).
If anyone knows any valuable resource to gather more data, or wants to share her/his perspective, feel free to share it in the comments!
I've been compiling a dataset of small SaaS where the revenue is verified through Stripe read-only integration (not self-reported screenshots). As you can see on the chart, the bottom of the market are startups under $50k MRR, most under a year old. I thought it'd be cool to show numbers and overall 'shape'
Here's a snapshot of 20 from the set.
The MRR is smaller than X (Twitter) would have you believe. Median was about $1,225/mo. Nearly half (9 of 20) sat between $500 and $1,000 MRR. Only one cleared $5k. The "$20k MRR in 3 months" posts are real, but they're the exception, not the floor.
Not surprisingly it's an AI/content world. Just under half (9 of 20) were AI tools or content-creation tools, like AI SEO automation, AI video/demo generators, multi-LLM wrappers, AI fitness coaching. The rest spread across marketing, marketplaces, games, productivity, dev tools.
It's not a US story. Of the ones with a known country, two-thirds were outside the US — UK, Germany, Hong Kong, India, France, Pakistan, Czechia, Belgium. I wonder how the distribution looked like before AI coding agents, and the whole vibe coding era.
They're young. 15 of 20 were founded in 2025 or 2026. This is the freshly-launched cohort, not seasoned products.
WOrth to note that these are 20 startups out of a set of 500+, and these particular 20 were surfaced by sorting for recent growth. So keep in mind that they 'skew' toward the youngest, fastest-moving end.
It's a verified look at the new-launch layer, not a representative draw of the whole market. I'm not claiming it's the average SaaS, but I see it as it's an accurate picture of what freshly-launched, revenue-verified micro-SaaS look like right now.
Two things I'd ignore entirely at this size: growth percentages (a jump from $40 to $500 is "1,100% growth" and means nothing) and any single startup's numbers in isolation.
Happy to break this down further if it's useful, like hwo it looks like by category (meaning: niche), by MRR band, or a cleaner random cut across the full 500+. I find this layer fascinating, it was cool to gather and see through this data:).
If anyone knows any valuable resource to gather more data, or wants to share her/his perspective, feel free to share it in the comments!
I've been compiling a dataset of small SaaS where the revenue is verified through Stripe read-only integration (not self-reported screenshots). As you can see on the chart, the bottom of the market are startups under $50k MRR, most under a year old. I thought it'd be cool to show numbers and overall 'shape'
Here's a snapshot of 20 from the set.
The MRR is smaller than X (Twitter) would have you believe. Median was about $1,225/mo. Nearly half (9 of 20) sat between $500 and $1,000 MRR. Only one cleared $5k. The "$20k MRR in 3 months" posts are real, but they're the exception, not the floor.
Not surprisingly it's an AI/content world. Just under half (9 of 20) were AI tools or content-creation tools, like AI SEO automation, AI video/demo generators, multi-LLM wrappers, AI fitness coaching. The rest spread across marketing, marketplaces, games, productivity, dev tools.
It's not a US story. Of the ones with a known country, two-thirds were outside the US — UK, Germany, Hong Kong, India, France, Pakistan, Czechia, Belgium. I wonder how the distribution looked like before AI coding agents, and the whole vibe coding era.
They're young. 15 of 20 were founded in 2025 or 2026. This is the freshly-launched cohort, not seasoned products.
WOrth to note that these are 20 startups out of a set of 500+, and these particular 20 were surfaced by sorting for recent growth. So keep in mind that they 'skew' toward the youngest, fastest-moving end.
It's a verified look at the new-launch layer, not a representative draw of the whole market. I'm not claiming it's the average SaaS, but I see it as it's an accurate picture of what freshly-launched, revenue-verified micro-SaaS look like right now.
Two things I'd ignore entirely at this size: growth percentages (a jump from $40 to $500 is "1,100% growth" and means nothing) and any single startup's numbers in isolation.
Happy to break this down further if it's useful, like hwo it looks like by category (meaning: niche), by MRR band, or a cleaner random cut across the full 500+. I find this layer fascinating, it was cool to gather and see through this data:).
If anyone knows any valuable resource to gather more data, or wants to share her/his perspective, feel free to share it in the comments!
First of all: credits:
I saw this on X, author: aimikoda.
Here's the original post on X.
I suggest you read all of it, see what others do, and adjust it for your needs.
FACS is a visual guide for the Facial Action Coding System. It let's you tell Seedance 2.0 inside prompt, what exact facial expression you want to see. It uses codes which are generated in first step. Disclaimer: remember that this is still AI video generations, not all generations will nail it in first shot. Iterate!:)
Here's step by step mini tutorial:
​
Create a clean educational FACS Action Unit expression grid featuring a realistic adult female character. Use minimal studio lighting, neutral white background, high readability, professional facial anatomy reference sheet aesthetic, realistic skin texture, consistent identity across all panels. COLOR SYSTEM: Use soft pastel color coding for categories while keeping the overall sheet minimal and elegant. Forehead & Brow AUs: soft pastel blue Eye & Eyelid AUs: soft pastel lavender Nose & Cheek AUs: soft pastel peach Lip & Mouth AUs: soft pastel pink Head Movement AUs: soft pastel mint Eye Direction AUs: soft pastel cyan Special / Misc AUs: soft pastel beige Apply the color subtly as: - panel background tint - thin borders - small label accents Keep colors soft, muted and professional. Include these Action Units: GROUPS: FOREHEAD & BROW AU1 Inner Brow Raiser AU2 Outer Brow Raiser AU4 Brow Lowerer AU71 Brow Furrow AU72 Brow Bulge EYE & EYELID AU5 Upper Lid Raiser AU7 Lid Tightener AU41 Lid Droop AU42 Slit Eyes AU43 Eyes Closed AU44 Squint AU45 Blink AU46 Wink NOSE & CHEEK AU6 Cheek Raiser AU9 Nose Wrinkler AU11 Nasolabial Deepener AU82 Nostril Dilator AU83 Nostril Compressor LIP & MOUTH AU10 Upper Lip Raiser AU12 Lip Corner Puller AU13 Sharp Lip Puller AU14 Dimpler AU15 Lip Corner Depressor AU16 Lower Lip Depressor AU17 Chin Raiser AU18 Lip Pucker AU20 Lip Stretcher AU22 Lip Funneler AU23 Lip Tightener AU24 Lip Pressor AU25 Lips Part AU26 Jaw Drop AU27 Mouth Stretch AU28 Lip Suck AU84 Tongue Up AU85 Tongue Out HEAD MOVEMENT AU51 Head Turn Left AU52 Head Turn Right AU53 Head Up AU54 Head Down AU55 Head Tilt Left AU56 Head Tilt Right AU57 Head Forward AU58 Head Back EYE DIRECTION AU61 Eyes Turn Left AU62 Eyes Turn Right AU63 Eyes Up AU64 Eyes Down SPECIAL / MISC AU81 Chewing
And you have your FACS sheet.
2. Use it with Seedance 2.0. Example prompt from aimikoda:
Use the provided character @[image1] as the fixed identity reference.
15s, 1:1, 14 beats, beat-synced, cinematic tight close-up, subtle neutral background, high facial clarity, slow micro push-in, shallow depth of field.
1: AU10
2: AU20
3: AU22
4: AU23
5: AU27
6: AU28
7: AU45
8: AU53
9: AU61
10: AU62
11: AU64
12: AU85
13:AU84
14: AU46
Uneasy, hypnotic, controlled mood. No monster transformation, no gore, no comedy, no text overlay, no watermark.
As you can see, you just prompt the code of specific expression. You can ask your favourite LLM model which code to use to express i.e. anger, etc, it will tell you.
Final thoughts and tips:
Here's the prompt I've used to create top-left video:
Photorealistic 15-second video. 50-year-old Creole woman, face and shoulders only, bare skin no makeup, natural soft diffused light, plain white background, 4K, shallow depth of field.
Timeline: 0–2s: Neutral resting face, eyes forward, relaxed brow and lips. 2–4s: Happy — AU6 (cheek raiser, orbital orbicularis oculi tightens, crow's feet appear) + AU12 (zygomaticus major pulls lip corners up and laterally), Duchenne smile, slight natural eye squint from cheek push. 4–6s: Sad — AU1 (inner brow raise, frontalis medial lifts producing oblique brow) + AU4 (corrugator and procerus knit and lower the brow, grief knot) + AU15 (depressor anguli oris pulls lip corners down), eyes slightly glassy. 6–7s: AU61 — eyes turn left, head stays still, gaze shifts left. 7–8s: AU62 — eyes turn right, head stays still, gaze shifts right. 8–9.5s: AU46 left eye — left orbicularis oculi closes left eye with slight compression, right eye stays open, subtle smirk. 9.5–11s: AU46 right eye — right orbicularis oculi closes right eye with slight compression, left eye stays open. 11–12.5s: AU85 — tongue protrudes straight out from mouth, jaw drops slightly via AU26. 12.5–13.5s: Tongue moves to the left side of the mouth, visible tip extends past left lip corner. 13.5–14.5s: Tongue moves to the right side of the mouth, visible tip extends past right lip corner. 14.5–15s: Returns to neutral, tongue retracts, lips close via AU8, relaxed expression.
I did not include the character's photo for any of the generations used in the video above. There is no difference between using or not using it, of course if you want to have consistency - use image character.
Test different approaches - check what you get if you use codes only, codes with short description. And again - this is still not perfect. Prompts and FACS codes DO NOT guarantee that you'll get what you explicitly told in prompt regarding facial expressions. But the success rate is really high.
I've noticed that the more expressions in one prompt, the less accuracy in output will be, which is absolutely understable. So I'd suggest 3-4 expressions max in one generation.
Of course facial expressions itself are not particularly useful, the purpose is to use them in prompts when creating monologues, dialogs, or other videos where you need specific facial expressions. Here's the example prompt, feel free to test it:
Use the provided character @[image1] as the fixed identity reference. 15s, 16:9, dim interior, single warm lamp, slight low angle, handheld micro-sway, shallow depth of field. Dialogue: "Hey, hey — everything's fine, okay? We're just gonna play a game where we stay really quiet. Can you do that for me?" Beat 1 (0–1s): AU5+AU38 (upper lid raiser + nostril dilator — genuine fear, pre-dialogue) Beat 2 (1–2s): AU45 (blink — forcing reset, composing the mask) Beat 3 (2–4s): AU12+AU6 (Duchenne smile — forced but committed, parental warmth overriding terror) — delivers "Hey, hey — everything's fine" Beat 4 (4–5s): AU1 (inner brow raiser — pleading sincerity leaking through) — delivers "okay?" Beat 5 (5–6s): AU7 (lid tightener — eyes betraying the fear the smile is hiding) Beat 6 (6–8s): AU12+AU2 (smile + outer brow raise — brightening, performing fun) — delivers "We're just gonna play a game" Beat 7 (8–10s): AU4+AU24 (brow lowerer + lip presser — seriousness cracking through for a flash) — delivers "where we stay really quiet" Beat 8 (10–11s): AU45 (blink — catching the slip, resetting to warmth) Beat 9 (11–13s): AU12+AU1 (smile + inner brow raise — tenderness and desperation fused) — delivers "Can you do that" Beat 10 (13–15s): AU6+AU17 (cheek raiser + chin raiser — eyes smiling while chin trembles) — delivers "for me?" Devastating contrast between performed safety and visible terror. The face should never fully commit to either — the audience reads both simultaneously. No action sequences, no visible threat, no sound effects, no text overlay, no watermark.
FACS are being used by professional video animators in movie industry.
I found this resource very helpful to understand the topic, and also started to create my own sheets. Why? Because when you prompt the LLM to generate you a FACS sheet - it's an LLM! It can be wrong. My results improved after studying this resource and free references which available on this website.
PS: 95% of times if you tell not to generate audio, Seedance will listen. Enjoy the remaining 5% from the low left girl :D.
Now go and experiment, and have some fun with it :)
The markdown file I am sharing below is a guide I paste to Claude.
(It is based on Dan Kieft's Youtube videos, feel free to check his channel, it's cool).
Then i describe what the video is about. I found that that Opus 4.6 writes the best prompts.
For this example:
This is what Claude gave me:
===================
FORMAT: 10s / 3 SHOTS / Girl unboxes skincare serum at desk, genuine UGC reaction
SUBJECT: u/Image1. Young woman, light brown hair in a messy bun, fair skin, natural no-makeup look, warm and expressive energy.
WARDROBE u/Image1: Casual oversized hoodie or cozy loungewear top, minimal jewelry.
HERO PROPS: u/Image2 — amber glass dropper bottle, "Skincare Serum" label, 30ml. Small brown shipping box with tissue paper.
ENVIRONMENT: Bedroom desk or vanity area, daytime. Cluttered but cozy — laptop half-closed, a few personal items in background. Soft natural window light from the side.
MOOD: Casual curiosity building to genuine delight.
MUSIC: None. Natural room tone only — rustling tissue paper, box sounds, a soft gasp.
COLOR LOGIC: Warm neutral tones — beige, cream, soft wood. The amber bottle is the color accent.
STYLE: 100% real-life shooting texture. iPhone front-camera selfie footage, vertical framing. Full depth of field — background is sharp, not blurred. No cinematic grain, no lens flare. Slightly overexposed from window light, natural phone-cam color science.
LIGHTING: Soft natural window light from camera-left, filling the face evenly. No dramatic shadows, no rim light. Flat, casual, real.
LOGIC RULE: Only one u/Image1 visible in frame. The product u/Image2 is always the same bottle — no duplicates, no label changes. POV is a fixed iPhone on a desk or propped surface — the phone is never visible in frame.
NEGATIVE PROMPT: Avoid jitter and bent limbs. Avoid identity drift. Avoid shallow depth of field. Avoid cinematic color grading. Avoid studio lighting. Avoid autofocus hunting. No fisheye, no vignette.
---
SHOT 1 — 0:00 to 0:03, MCU, 28mm equivalent, fixed camera.
u/Image1 sits at her desk facing the phone, small brown box in front of her. She smiles and taps the box excitedly with both hands, mouthing "okay let's see what's inside." She opens the top flap and pulls back tissue paper.
SHOT 2 — 0:03 to 0:07, MCU, 28mm equivalent, fixed camera.
She reaches into the box, lifts out u/Image2 with both hands, holds it up to the camera. Her eyes widen and her mouth drops open — a genuine surprised smile. She turns the bottle slowly, reading the label, nodding slightly.
SHOT 3 — 0:07 to 0:10, MCU, 28mm equivalent, fixed camera.
She holds u/Image2 next to her face, tilts her head, grins directly into the camera. She mouths "oh my god I love it" and gives a small happy wiggle. Natural settle — she keeps smiling as the clip ends.
===================
I am quiet happy with the results, but there's one thing I've changed in the guide - there was a 'WARDROBE' part, which caused that Claude described the character's clothes, and when you upload your character's image to the Seedance with particular clothes you wish you wanna see on the video, there will be conflict between prompt and the image, and in my experience, Seedance will choose text above the image. Or mix it, creating crap :(.
That's why I deleted this part from guide.
Feel free to experiment with the guide. It's long. You can use it as it is.
You can paste it to your favourite LLM and try to short it, reverse engineer, or whatever you want.
I am sharing it because it made a huuuge difference in my Seedance 2.0 generations. Of course the UGC was only an example. Go and test it with whatever genre you want.
It's large - I'd recommend read it and then distill shorter guide for specific style - UGC, fight scene, drama, etc.
Just wanted to show some of my older generations, how I made it, and what I did wrong.
Software: Google Flow
I did not generate any image and used it as a starting frame. I've just generated first video with this prompt:
"42 years old woman from Namibia, natural look, no make-up, natural light, ASMR artist, whispers in gently, soothing voice, calmly, these words, with African accent: "hello my friends...this is my first ASMR video...I hope you're doing all...well..."
Then I've just used "extend' opton in Google Flow, and generated videos with the following prompts:
42 years old woman from Namibia, natural look, no make-up, natural light, ASMR artist, whispers in gently, soothing voice, calmly, these words, with African accent, while performing Reiki cleaing movement with her hands: "I want to start with some Reiki, to clean up you aura, and prepare you to deep...relaxing...sleep".
42 years old woman from Namibia, natural look, no make-up, natural light, ASMR artist, whispers in gently, soothing voice, calmly, these words, with African accent, while performing Reiki cleaing movements with her hands: "to.. deep...relaxing...and healing...sleep".
42 years old woman from Namibia, natural look, no make-up, natural light, ASMR artist, performs Reiki cleaing movements with her hands being silent for few seconds. Then she whispers in gently, soothing voice, calmly, these words, with African accent: "Now. Your eyelids...are heavier...and heavier...".
42 years old woman from Namibia, natural look, no make-up, natural light, ASMR artist, performs Reiki cleaing movements with her hands being silent. She gently smiles with compassion.
42 years old woman from Namibia, natural look, no make-up, natural light, ASMR artist, performs Reiki cleaing movements with her hands being silent for few seconds. Then she whispers in gently, soothing voice, calmly, these words, with African accent: "I will now count from three...to...one...".
42 years old woman from Namibia, natural look, no make-up, natural light, ASMR artist, performs Reiki cleaing movements with her hands being silent for few seconds. Then she whispers in gently, soothing voice, calmly, these words, with African accent: "to...one...And when I'll say one...".
Using character description as a start in every prompt was giving me fairly consistent character. But be aware - the longer character description is, the more likely model will start to add some artifacts and loose consistency. From my tests it looks like one sentence is enough to stay on track.
What I don't like:
- sound artifacts: Voice changes are fairly known problem in AI, usually second video will have changed voice. Also some artifacts - birds, mistakes in speech - this is what you get when you're extending the video, that's how Veo is hallucinating.
- light: if you don't explicitly tell the AI specific lighting, you can end with the light like mine. Why is that problematic? Well, AI will eventually start to mess around with light, and as you can see at the end of video, skin pores are exposed because of light change. So light is very important.
Anyway, creating ASMR videos is fun, I hope you like it :)