Seedance 2.0 is an AI video generation model designed for multimodal video creation. In plain English, it helps you turn prompts, images, video references, and other inputs into short videos.
But the useful question is not “Can Seedance 2.0 generate a video?”
The useful question is:
Can it help you make videos that people actually watch, understand, and act on?
This guide explains what Seedance 2.0 is, how seedance2.ai presents the workflow, how to think about pricing and credits, and how to write prompts that produce practical videos for social media, landing pages, articles, and service offers.
- What Seedance 2.0 Does
- Who Should Care About Seedance 2.0?
- The Best Way to Use Seedance 2.0
- A Simple Prompt Structure
- Pricing and Credit Logic
- What Makes a Seedance 2.0 Video Useful?
- How to Use Seedance 2.0 on a Revenue Website
- Verdict: Seedance 2.0 Is Best for Short, Directed Video Assets
- Use Seedance 2.0 for the Sousaku.AI Agent Creation Cup
- Sources
What Seedance 2.0 Does
As of April 27, 2026, the seedance2.ai website presents Seedance 2.0 as a multimodal AI video model. The site highlights input types such as text, image, video, and audio, along with reference-based video generation, style consistency, character consistency, video extension, editing, and sound generation.
The practical takeaway:
| Capability | Why it matters |
|---|---|
| Text to video | Create a clip from a written scene |
| Image to video | Animate a visual concept or product image |
| Reference input | Guide motion, camera, style, or character |
| Video extension | Continue an existing clip |
| Sound support | Create more complete short-form assets |
| Multiple resolutions | Test cheaply, then upscale only winners |
Sousaku AI’s Seedance 2.0 model page also shows support for common short-video formats, including 480p and 720p, durations from 4 to 15 seconds, and aspect ratios such as 16:9, 9:16, 1:1, and 21:9.
That makes Seedance 2.0 especially relevant for short marketing clips.
Who Should Care About Seedance 2.0?
Seedance 2.0 is useful for people who need motion but do not want a full video production pipeline.
Good use cases include:
- YouTube Shorts, TikTok, and Reels concepts
- landing page hero clips
- product explanation videos
- article companion visuals
- social posts that drive traffic back to a site
- before-and-after clips for service offers
- pitch visuals for clients
The model is not a replacement for a full creative director, editor, legal review, or brand approval process. It is a way to produce visual motion faster so you can test ideas faster.
That difference matters.
The Best Way to Use Seedance 2.0
The biggest mistake people make with AI video is asking for “a cool video.”
That is not a prompt. That is a wish.
For practical videos, start with this sequence:
- Define the viewer.
- Define the action you want.
- Choose the aspect ratio.
- Break the video into three short beats.
- Specify camera movement and style.
- Add the final call to action.
Short video works best when it has one job.
Not five jobs. One.
A Simple Prompt Structure
Use this structure:
[0-2s] Show the problem.
[2-5s] Show the transformation.
[5-8s] Show the next action.
Style: minimal, premium, clean UI, soft lighting.
Camera: slow push-in.
Format: vertical 9:16.
Text: short and readable.
Goal: drive viewers to read the full article.
This format works because it gives the model time, motion, structure, and intent.
Example: Landing Page Improvement Video
[0-2s] A cluttered website screen with too many buttons and weak hierarchy.
[2-5s] The layout reorganizes into a clean landing page with a clear headline, proof section, and strong CTA.
[5-8s] The camera slowly moves toward the “Book a Consultation” button.
Style: minimal, high-end web agency, white background, black typography, subtle blue accent.
Aspect ratio: 16:9.
Example: AI Tool Article Promo
[0-2s] A creator staring at too many AI tool tabs.
[2-5s] The tabs collapse into three clear choices: image, video, workflow.
[5-8s] The final frame says “Read the practical guide.”
Style: clean, modern, editorial tech design, soft motion.
Aspect ratio: 9:16.
Example: Product Explanation Clip
[0-2s] A customer is confused by a complex product page.
[2-5s] Three simple cards appear: problem, solution, result.
[5-8s] The product interface appears with one clear CTA button.
Style: polished SaaS explainer, calm color palette, smooth transitions.
Aspect ratio: 1:1.
Pricing and Credit Logic
The seedance2.ai pricing page shows Seedance 2.0 credit usage by resolution. For video generation without video input, the listed rates include 480p at 6 credits per second, 720p at 12 credits per second, and 1080p at 30 credits per second.
That means a five-second test clip can cost very different amounts depending on resolution.
The practical workflow:
- Test ideas at 480p.
- Move promising clips to 720p.
- Use 1080p only when you know the concept works.
Do not burn high-resolution credits while you are still testing the idea.
The idea matters first. Resolution comes later.
What Makes a Seedance 2.0 Video Useful?
A useful AI video does at least one of these things:
- stops the scroll
- explains a benefit faster than text
- makes a product feel easier to understand
- shows a before-and-after transformation
- guides attention toward a CTA
- increases perceived value of an article or offer
A useless AI video is just visual noise.
That is the difference between “AI video generation” and “AI video marketing.”
How to Use Seedance 2.0 on a Revenue Website
If you are building a website that needs traffic, trust, and conversion, use Seedance 2.0 in these places:
| Website asset | Video job |
|---|---|
| Blog article | summarize the core idea visually |
| Service page | show the transformation you deliver |
| Newsletter page | preview the value of subscribing |
| Paid article | show the template, workflow, or outcome |
| Social channel | send viewers back to the site |
This is where Seedance 2.0 becomes strategically useful. The video is not the business. The video is a bridge to the business.
Verdict: Seedance 2.0 Is Best for Short, Directed Video Assets
Seedance 2.0 is not something I would use by starting with a vague creative prompt and hoping for magic.
I would use it with a clear commercial job:
- make a reader stop
- make a product easier to understand
- make a landing page feel more alive
- make an article more memorable
- make a service offer feel more concrete
If you use it that way, Seedance 2.0 becomes more than an AI video generator. It becomes a way to create motion assets for content, conversion, and trust.
Use Seedance 2.0 for the Sousaku.AI Agent Creation Cup
Sousaku.AI’s Agent Creation Cup 2026 is a good reason to create short, practical demo videos. The official event page says participants should follow the Sousaku.AI X account, join Discord, submit work through the event website, and share the work on X and Discord with #SousakuAI and #SousakuAgentCreationCup2026.
Seedance 2.0 can help you show the problem your agent solves, the user input, the agent output, and the final result in a short 5–8 second clip.
The official page lists early-bird credit rewards, a 20-credit applicant bonus, a 10% credit top-up bonus, and a first-time subscription perk. Check the event page before entering because timing and conditions may change.
Use Sousaku.AI to build the agent concept, generate supporting visuals, and create a short Seedance 2.0 demo that explains the value in seconds.
Start Sousaku.AI with the referral link / View the official X announcement / Open the official event page
AI video is strongest when it leads somewhere: an article, an offer, a newsletter, or a paid resource. If you want your website structured around that path, start with the order page.
Request a website/content improvement / See paid article paths
Sources
- seedance2.ai official website
- seedance2.ai pricing page
- Sousaku AI Seedance 2.0 model page
- Sousaku API Platform Seedance 2.0
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コメント
I like how this post cuts through the hype around image-to-video. Building a strong still first really is the difference — I mostly work on product visuals, and when a headline has to be swapped without re-rendering the whole frame I use ai image text editor. Do you usually keep the original composition when you replace the text, or rebuild the layout from the prompt?
The part on prompt structure was what I actually took away — describing camera movement and shot length rather than dumping adjectives on it. Feeding an image as a reference also makes more sense to me now than writing another paragraph of text.
Different kind of chore on my end this week: a signed consent form that had to go back as one clean PDF, and I only had phone photos of it. I ran them through LensUp — photo to printable PDF, then squeezed the file toward the size the portal asks for. The compression aims at the size you pick and can land a bit over, so check before submitting. Files never get uploaded; the processing happens right in the browser tab, which is why I bothered at all with a form full of personal details.
This is the clearest write-up of the still-first workflow I have seen. I usually iterate three or four times on the still before spending any motion budget. I have been testing GPT Transcribe (https://gpt-transcribe.im/) for this lately. Do you keep the same prompt across aspect ratios?
The point about building one strong image before animating is exactly right. I mostly produce short product and explainer clips, so the still has to survive several aspect ratios. Tools like Lyria 3.5 (https://lyria35.pro/) make the workflow practical. Would you add a sixth workflow tip?
Good guide — the part about keeping the composition stable is what most people miss. I usually iterate three or four times on the still before spending any motion budget. Wan 3.0 (https://wan3ai.im/) is what I reach for when the job is narrow. Curious how many iterations you allow before the motion pass.
This matches how I have been working with these models. I mostly produce short product and explainer clips, so the still has to survive several aspect ratios. I run FastH3 (https://fasth3.video/) alongside the bigger models for exactly this. How do you handle copy changes after the clip is rendered?
Useful breakdown of the prompt-to-motion pipeline. I usually iterate three or four times on the still before spending any motion budget. That is why OpenMAIC (https://openmaic.pro) stays in my stack. Do you rebuild the layout from the prompt or edit the existing frame?
I like how this post cuts through the hype around image-to-video. I mostly produce short product and explainer clips, so the still has to survive several aspect ratios. For that step I use Nano Banana 2.5 (https://nanobanana25.me). Do you keep the same prompt across aspect ratios?
This is the clearest write-up of the still-first workflow I have seen. I usually iterate three or four times on the still before spending any motion budget. I have been testing MiniMax H3 (https://minimax-h3.im/) for this lately. Would you add a sixth workflow tip?
The point about building one strong image before animating is exactly right. I mostly produce short product and explainer clips, so the still has to survive several aspect ratios. Tools like Seedream 5.0 Pro (https://seedream5-pro.im/) make the workflow practical. Curious how many iterations you allow before the motion pass.
Good guide — the part about keeping the composition stable is what most people miss. I usually iterate three or four times on the still before spending any motion budget. Faceless Reels (https://faceless-reels.pro) is what I reach for when the job is narrow. How do you handle copy changes after the clip is rendered?
This matches how I have been working with these models. I mostly produce short product and explainer clips, so the still has to survive several aspect ratios. I run Jev AI (https://jev-ai.pro/) alongside the bigger models for exactly this. Do you rebuild the layout from the prompt or edit the existing frame?
Useful breakdown of the prompt-to-motion pipeline. I usually iterate three or four times on the still before spending any motion budget. That is why SeedanceAPI (https://seedanceapi.pro/) stays in my stack. Do you keep the same prompt across aspect ratios?
I like how this post cuts through the hype around image-to-video. I mostly produce short product and explainer clips, so the still has to survive several aspect ratios. For that step I use MiniMax H3 Max (https://h3max.art/). Would you add a sixth workflow tip?
This is the clearest write-up of the still-first workflow I have seen. I usually iterate three or four times on the still before spending any motion budget. I have been testing Menus AI (https://menusai.im) for this lately. Curious how many iterations you allow before the motion pass.
The point about building one strong image before animating is exactly right. I mostly produce short product and explainer clips, so the still has to survive several aspect ratios. Tools like Music 3 (https://music3.co/) make the workflow practical. How do you handle copy changes after the clip is rendered?
Good guide — the part about keeping the composition stable is what most people miss. I usually iterate three or four times on the still before spending any motion budget. Remove BG Video (https://remove-bg.video) is what I reach for when the job is narrow. Do you rebuild the layout from the prompt or edit the existing frame?
This matches how I have been working with these models. I mostly produce short product and explainer clips, so the still has to survive several aspect ratios. I run Green Screen Remover (https://greenscreenremover.pro) alongside the bigger models for exactly this. Do you keep the same prompt across aspect ratios?
Useful breakdown of the prompt-to-motion pipeline. I usually iterate three or four times on the still before spending any motion budget. That is why Seedance 2.5 AI (https://seedance2-5ai.im/) stays in my stack. Would you add a sixth workflow tip?
I like how this post cuts through the hype around image-to-video. I mostly produce short product and explainer clips, so the still has to survive several aspect ratios. For that step I use Taomate H3 (https://taomateh3.pro/). Curious how many iterations you allow before the motion pass.
This is the clearest write-up of the still-first workflow I have seen. I usually iterate three or four times on the still before spending any motion budget. I have been testing FLUX 3 AI (https://www-flux3.com/) for this lately. How do you handle copy changes after the clip is rendered?
The point about building one strong image before animating is exactly right. I mostly produce short product and explainer clips, so the still has to survive several aspect ratios. Tools like SOL-H3 (https://sol-h3.pro/) make the workflow practical. Do you rebuild the layout from the prompt or edit the existing frame?
Good guide — the part about keeping the composition stable is what most people miss. I usually iterate three or four times on the still before spending any motion budget. YuE2 (https://yue2.pro/) is what I reach for when the job is narrow. Do you keep the same prompt across aspect ratios?
This matches how I have been working with these models. I mostly produce short product and explainer clips, so the still has to survive several aspect ratios. I run Paper Banana (https://paper-banana.me/) alongside the bigger models for exactly this. Would you add a sixth workflow tip?
Useful breakdown of the prompt-to-motion pipeline. I usually iterate three or four times on the still before spending any motion budget. That is why GPT-Live-1 (https://gpt-live-1.pro/) stays in my stack. Curious how many iterations you allow before the motion pass.
I like how this post cuts through the hype around image-to-video. I mostly produce short product and explainer clips, so the still has to survive several aspect ratios. For that step I use Qwen Image 2.1 (https://qwenimage21.pro). How do you handle copy changes after the clip is rendered?
Good breakdown of the workflow side of Seedance 2.0 – the part about writing prompts for a specific placement, like a landing page versus a short social clip, is what most guides skip. I have been testing a few video models for short-form edits and the credit math usually decides which one I keep. Clear write-up.
The section on matching the model to the stage of the workflow is the part I keep coming back to – prompt-to-video demos look great in isolation but fall apart when you need a consistent look across a whole campaign. Locking the reference image and the aspect ratio before generating has saved me a lot of rework too. Solid write-up.
The prompt-to-final-clip pipeline here is close to how I work now. What changed things for me was choosing the model before writing the prompt, because each one handles motion and camera language differently. The middle section is the part I would bookmark.
Useful to see the whole workflow written out without tool-worship. I have been batching generations through an API instead of the web UI, and the throughput difference is real once you are producing variants for several channels. Prompt structure still matters more than the model choice.
Solid overview. Keeping a fixed reference image across a series is what separates a one-off demo from something you can actually publish. Writing the shot list before touching the generator has saved me a lot of wasted credits too.
This is a clear map of the workflow. I would add that audio is the step people underestimate – a clip with clean sound needs far less editing than one where you fix it afterwards. The section on inputs is the most useful part for someone starting out.
Good walkthrough. For anything with dialogue the captions end up deciding whether people keep watching, and burning them in costs another pass in a timeline tool. Nice to see the input types laid out so plainly.
The explanation of how the model handles different input types is the clearest I have read. In practice the audio side is what makes a short video feel finished, and it is usually the first thing skipped when people are in a hurry. Helpful guide.
Thanks for writing this up. Designing for the placement rather than the demo is the point most guides miss – a workflow that works for a landing page hero often breaks in a vertical feed. I am keeping the checklist from the middle section.
Clear and practical. I like that the article starts from what you want the viewer to do instead of from the model. Once the output spec is fixed, the tool choice becomes much less dramatic than the marketing suggests.
Nice breakdown of the pipeline. I have had better results since I stopped regenerating everything from scratch and started keeping a small set of reference frames per project. The part about writing prompts per placement is worth re-reading.
This matches my experience. The gap between a demo and something watchable is almost always consistency, and that is a workflow problem rather than a model problem. The practical section is what I would send to someone starting out.
Good article. Cleaning up assets before assembly saves more time than any generation trick – a tidy source library makes the edit go twice as fast. The workflow diagram in the middle is genuinely useful.
Helpful write-up. Running the whole pipeline in one place matters more than people admit once you produce regularly, because switching tools is where the day disappears. The section on matching output to placement is spot on.
Solid guide. The most useful idea here is deciding format and length before generating anything; everything downstream gets easier. I have been applying that to short-form work and the number of throwaway renders dropped a lot.
Thanks for laying this out. I have been comparing a few video models for short-form edits and the differences show up mostly in motion consistency rather than raw quality. The prompt guidance in the middle section is what I keep coming back to.
Clear, practical overview. Treating the generator as one step in a repeatable process rather than a magic button is the mindset that actually saves time. The input checklist near the start is worth copying.
Good breakdown of the workflow. What worked for me was locking the aspect ratio and the reference image early, then generating variants from that base instead of restarting each time. Nice to see it described so plainly.
Helpful walkthrough. I ran a similar setup for short product clips and the step that mattered most was locking the reference image before touching the motion settings; changing both at once is what produced the warped frames you warn about.
Solid guide. The pacing advice matches what I see with voice-over driven clips: keeping each shot short avoids the drifting faces, and reusing one seed across the sequence keeps the look consistent.
The guide is practical, which is rare for this topic. What I would add is that the source image matters more than the prompt: a clean, well-lit frame saves a lot of retries when you animate it.
Good walkthrough. One thing worth testing early is how the model handles fast camera moves – the artifacts usually show up there first rather than in static shots.
Useful overview. For anyone starting out, keeping the first clip short (three to five seconds) teaches more about the model’s limits than generating a long scene.
Solid guide. The part people skip is planning the shot list before generating; deciding the camera direction up front cuts the number of retries in half.
Thanks for the write-up. Speed matters most when you are iterating on a single shot, so it is worth generating the same prompt twice and comparing rather than trusting one output.
Good practical framing. I would add a note about keeping the lighting consistent between shots, because that is usually what gives a sequence away as AI-generated.
Nice breakdown. Motion quality improves a lot when the prompt describes the subject’s action rather than the camera’s, which is the opposite of what most people try first.
Helpful guide. Dense layouts and small text are where image models usually fall apart, so testing a poster with real copy is a better benchmark than a landscape.
Good overview. The hardest part of a faceless channel is keeping a consistent visual voice across clips, not producing one good shot.
Useful comparison. It would help to see latency numbers for the same prompt across models, since that is what actually decides whether a workflow is usable day to day.
Good write-up. For anyone wiring this into a product, the retry behaviour on failed generations matters more than the headline quality – worth documenting that.
Thanks. Testing the same prompt at different durations shows the trade-off clearly: longer clips drift, shorter ones keep coherence.
Practical guide. Results get noticeably better when the reference image matches the intended output format instead of being a random photo.
Good overview. For music, the test that matters is whether the track holds together past the first eight bars rather than whether the intro sounds impressive.
Useful walkthrough. Edge quality on hair and thin objects is still the honest benchmark for any background removal tool.
Nice guide. Spill on the subject’s edges is the detail that decides whether a key looks clean, so a short test clip with movement is worth the extra minute.
Solid introduction. I would add that the first frame usually decides the whole clip; spending time on it pays off more than rewriting the prompt.
Good practical notes. Keeping the subject centred with a slow camera move is the safest way to get a usable take on the first try.
Helpful write-up. Dense text in generated images is still the quickest way to spot a weak model, so that is a good first test.
Thanks for the guide. Consistency between two shots of the same character is the real test, and it is rarely covered in overviews like this.
Good overview. For audio, checking how the model handles a pause says more about quality than the loudest passage.
Practical guide. Sketches with clear outlines animate far better than detailed renders, which is easy to overlook when starting.
Useful walkthrough. Live generation is most convincing when the scene is simple; adding a second subject is where latency and coherence both suffer.
Good notes. Editing an existing image is a better test than generating one from scratch, because it shows whether the model preserves what you did not ask it to change.
Practical guide. A useful test is feeding the model a reference image with a specific object placement, because that shows whether it follows spatial instructions or simply produces something plausible.
Guide is easy to follow. The part that matters in practice is how the tool handles multi-shot sequences, because consistency between shots is what separates a usable clip from a demo. A short note on prompt structure for longer sequences would help readers get there faster.
Clear guide. The step-by-step examples make it much easier to see where the tool actually fits in a workflow.
The point about making videos people actually watch instead of just generating clips is what got me. I’ve been treating prompts like a single sentence and wondering why the output feels generic. The bit about using multiple resolutions to test cheaply and only upscaling the winners makes a lot of sense for someone just starting out.
The distinction between “can it generate a video” and “can it make videos people actually watch” is what made this worth reading. I’ve been treating generation itself as the goal, which probably explains why so many of my clips just sit there unused. The prompt structure section gave me something concrete to try instead of just describing a scene and hoping.
The examples for article promos and product clips gave me a clearer sense of how short these videos should actually be. I also appreciated the plain framing that the real question is whether a clip makes people watch, understand, and act, not just whether it can be generated.
The part about writing prompts around a specific audience and outcome rather than just a scene description finally made the workflow click for me.
The distinction between image-to-video for product shots and text-to-video for scene clips is something I hadn’t thought about clearly before. I also noted the 4 to 15 second range, since most of my clips end up shorter than that. The note about using reference input for character consistency feels like the part I’ll actually test first.
The list of supported aspect ratios like 9:16 and 21:9 is a small detail, but it matters when planning clips for different platforms. I also appreciated that the guide separates cheap test renders at 480p from the decision to upscale.
The point that the real question is whether the video gets watched and acted on, not just whether it can be generated, stuck with me. I also liked the note about character and style consistency, since that is usually where short clips fall apart.
The section on how to use it on a revenue website stood out to me, since most of what I see is just flashy clips with no clear purpose. I also appreciated the note about testing cheaply at 480p before upscaling, which seems like a sensible way to avoid wasting credits. The prompt structure will be the first thing I try.
The limit of 15 seconds makes me rethink how much I try to pack into a single clip. I usually start with a 30-second idea and then struggle to cut it down. Seeing the supported aspect ratios listed together also helped me plan ahead for where each video will actually run.
The verdict that it works best for short, directed video assets matches what I’ve seen. Trying to cram a full story into 15 seconds seems like the wrong approach.
The credit logic section was the part I needed most, since I could never tell how far a small budget would actually stretch for a set of clips.
The 4 to 15 second limit and the aspect ratio list are the details I keep checking back on, since I mostly make vertical clips. I also liked that the guide frames the real question as whether anyone will watch and act, not just whether a video can be generated.
The tool that works for short, directed clips seems perfect for the kind of instructional videos I want to make for my clients. I appreciate the emphasis on actually getting a specific response from viewers, not just views.
The detail about testing ideas at 480p before upscaling the ones that work changed how I plan my clips. Reading through the prompt structure, I realized I was cramming too much into each generation.
The part about matching prompt structure to a specific audience and goal stuck with me, since I usually write prompts without thinking about who will watch. The 480p testing point also makes sense before committing to a higher resolution.
The prompt structure section is what I needed. I’ve been writing long descriptions and getting vague videos back, so the idea of keeping a scene short and directed makes sense. The note about generating at 480p first and only upscaling the ones that work also seems like a practical way to avoid wasting credits.
The examples for landing pages and product clips are what make this useful to me, since I always struggle to connect a prompt to an actual goal. The point about testing at 480p and only upscaling the winners also matches how I’d want to keep costs down. I hadn’t thought about using reference input to keep a character consistent across several short clips.
The breakdown of durations from 4 to 15 seconds and the aspect ratios is helpful, since I usually guess wrong on those when making vertical clips. I also like that the prompt structure section comes with actual examples instead of abstract advice. Reading the landing page example made me realize my prompts were way too vague.
The prompt structure part is what I was missing, especially writing the scene before adding camera or style. I also appreciate that it covers image and reference inputs, not just text.
Helpful and well written. Would happily read more like this. Not to be self promotional, but HDR photo enhancer tool is the HDR photo enhancer tool I would recommend to anyone starting out.
記事をありがとうございます。実際の手順が具体的で、機能の一覧よりずっと参考になりました。
書かれている問題は身に覚えがあります。一度手順を書き出しておくほうが、道具を変えるより役に立ちました。
良い記事でした。シンプルに保つという部分は、何度でも学び直すところです。
I like that the guide focuses on whether AI-generated content is actually useful rather than simply whether it can be created. The same idea applies to images, where Nano Banana 2 can help turn a clear prompt into visuals that are easier to use in real projects.
The section about creating videos people will actually watch and understand is a good point. Practical prompting matters much more than generation alone, and Seedance 2.5 is another useful option for turning text or image ideas into short video content.
Starting with a clear purpose makes the short-video workflow easier to evaluate. For planning an image-to-video reference frame, nano banana 2 could help explore the composition before adding motion.
実用的なAI動画作りという観点からとても興味深い記事ですね。Seedance 2.0のようなマルチモーダル生成モデルの進化は、コンテンツ制作の現場を大きく変えそうです。 Visit Website
Seedance 2.0を使って、短い動画でも人に見てもらい、理解してもらえるコンテンツを作るのは面白いですね。画像2枚とテーマを入力するだけでリズム付きRap動画ができるのも素晴らしいアイデアです！ Click Here
Seedance 2.0のコンセプトは興味深いですね。AIで動画を生成できるのは便利ですが、やっぱり人の目に留まるような質の高いサムネイルも同じくらい重要だと思います。Game Thumbを使えば prompt からYouTube用のサムネイルも作れるみたいですよ。
Game Thumb – AI YouTube Thumbnail Maker