When I first heard about “multimodal input,” it sounded intimidating. Images, videos, audio, text – all together in a single video generation? I wasn’t sure how this actually worked in practice or if I even needed all of these features.
But as I started experimenting with Seedance 2.0, I realized that the multimodal capability wasn’t a complicated luxury feature; It was actually the easiest way to create better videos.
Let me walk you through my first real project with multimodal input and what I learned along the way.
What I imagined would be a multimodal input
Before I actually tried it, I had some misconceptions. I imagined that this would require some technical skill – like some kind of advanced prompt engineering, where I would have to specify exactly how each file interacts with every other file. I thought I needed to understand the “rules” for combining images with audio or know the exact syntax for referencing multiple inputs.
The reality was much simpler.
Multimodal input just means you can throw different types of files into Seedance 2.0 and tell the model what to do with them. That’s it. You don’t have to switch between different tools or learn a special command language. They just give the model more information to work with.
My first project: A short brand story video
I was approached by a local coffee roaster who wanted a 10 second promotional video. They had given me:
- Three high-quality product photos of their different types of beans
- A 5 second video clip of someone pouring coffee into a cup (he shot it himself)
- A 3 second audio clip of coffee brewing sounds
- A brief description of the desired mood: “warm, inviting, craft-oriented”
Normally in post-production I would have had to choose between using the images OR the video OR the sound. I would create an asset and try to make it work while leaving other materials unused.
With Seedance 2.0’s multimodal capability, I was able to use everything at once.
How I actually set it up
Step one: Gather the assets
The coffee roaster provided me with three product photos, a pouring video and brewing sounds. I organized these before uploading, although honestly I could have just uploaded them randomly – the point is that Seedance 2.0 can handle everything at once.
Step two: upload everything
Seedance 2.0 allows you to upload:
- Up to 9 images
- Up to 3 videos (total duration ≤15 seconds)
- Up to 3 audio files (total duration ≤15 seconds)
- Text descriptions of unlimited length
For my project I uploaded all three product photos, the pouring video and the brewing clay. The platform accepted everything without complaint.
Step three: Write a description in natural language
That was the crucial part that surprised me. I didn’t have to learn any special syntax. I just described what I wanted by referencing the files by number or type.
My prompt looked something like this:
“Create a 10-second promotional video. Start with a close-up of @image1 (the espresso beans), with the coffee brewing sounds of @audio1 playing underneath. Transition smoothly to @video1 (the pouring shot), with the warm, artistic aesthetic of @image2 in the background. End with a final shot of @image3 (the roasting beans close-up), with the brewing sounds fading out. The overall mood should be warm and inviting, like a specialty cafe experience.”
That was it. Natural language. No special operators or complex syntax.
What happened when I generated
I honestly wasn’t sure what to expect. Would it use all files? Would it ignore some of them? Would it misunderstand my descriptions?
The first generation was surprisingly good. The video opened with the espresso beans from my first image, the audio played continuously, and the shot of the pouring appeared in the middle. The transition between still image and video felt natural and not jarring. The final product felt cohesive in a way that would have been difficult to achieve with traditional video editing.
Was it perfect? No. There were a few things I would adjust on the second try. But the point is that all of my various media assets – photos, videos and audio – were merged into a single cohesive video without me having to manually edit them together.
Why this is important for my workflow
Before I understood multimodal input, I was used to this process:
- Choose a primary asset (usually videos or images)
- Create additional graphics or transitions in editing software
- Add audio in the post
- Export the final video
It was time consuming and resulted in a patchwork feel – pieces put together rather than something that felt naturally integrated.
With multimodal input:
- Collect all assets (images, video, audio, description)
- Upload everything to Seedance 2.0
- Describe what I want
- Get a generated video with all elements integrated
- Make minor changes if necessary
The second workflow is faster and produces more coherent results because the model puts everything together from the start, rather than me trying to glue individual pieces together after the fact.
Practical examples of multimodal combinations
Since that first project, I’ve experimented with different combinations:
Educational videos
I used reference images of diagrams, a short video clip showing a concept in action, and a voice-over audio track explaining what’s happening. The model generates a video that contains the visual information, dynamic demonstration and audio explanation at the same time. Students receive a more complete learning experience than if I had chosen just one format.
E-commerce product demonstrations
Multiple product photos + a video showing the product in action + background music = a more engaging product video than I could create with a single asset type alone. The images show what the product looks like, the video shows how it works, and the audio creates the right emotional tone.
Social media clips
For Instagram Reels, I combined a still image of the caption text I want to display, a short motion video that fits the content, and an upbeat tone. The multimodal approach ensures that all elements appear in the final video without me having to assemble them manually.
The learning curve
Honestly, there wasn’t much of it. The most important thing I had to learn was to be more specific about which asset I wanted to reference and where. In my first attempts, I was vague—like, “Use the images throughout the video”—and the results were less predictable.
Once I started being explicit – “start with frame1, transition to video1, end with frame3” – the model understood my intent better. Specificity significantly improved results.
The other lesson was that quality varies depending on the type of investment. My higher resolution images worked better than lower resolution ones. My stable video clips worked better than shaky handheld shots. That’s not surprising, but it’s worth noting: even with AI, incorrect input still results in less impressive output.
Limitations I encountered
Multimodal input is powerful but has limitations. If I upload too many assets and ask the model to fit them all into a short 5-second video, the result seems rushed or confusing. There is an appropriate ratio of content to output duration.
Additionally, if the audio I provide has specific timing – like a voiceover with precise pauses – the model doesn’t always match the visual content to those exact timestamps. It’s close, but not perfect. For critical applications like lip syncing, I may need to make adjustments after the fact.
Complex interactions between assets can also be unpredictable. If I upload a video where the subject is wearing a blue shirt and a photo where they are wearing a red shirt, the model may have consistency issues. It works better when reference materials are conceptually compatible.
Why I am now a multimodal believer
The practical advantage is that I can incorporate more creative assets into my videos without having to manually edit the videos. This means shorter lead times and more sophisticated end products. This means I can use all the reference material a client gives me and don’t have to decide which piece to prioritize.
This is really valuable for freelancers and small teams. It eliminates a technical bottleneck in the production process.
Go forward
I’m still exploring what multimodal input makes possible. I started experimenting with edge cases – like uploading multiple audio tracks to see how the model combines them, or using reference images and videos with very different aesthetics to see if the model can stitch them together into something cohesive.
The feature is not a magical solution to poor planning or low-quality assets. However, if you gather good reference material and think clearly about what you want to create, Seedance 2.0’s multimodal capability can really simplify your creative process.
For anyone used to assembling videos from disparate parts in post-production, this approach feels like a sensible step forward. You describe your vision uniquely and clearly, and the model generates something that incorporates all of your reference materials from the start. This is the true power of multimodal input.
