Have you seen the AI showdown video of Sam Altman and Dario Amodei dancing and singing APT? Yeah, that one went around X, and many in my network, including myself, wondered how that kind of video was created.

I’ve actually seen a couple of these videos in my TikTok feed, and I have never assumed it was AI-generated because of how seamless the character integration is.

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One tool that lets you do this is Pollo Recast. You can upload a video or choose a template, add your character images, and generate your own version.

In this guide, I’ll show you two examples: an elderly man taking over a stage performance, and Sam Altman dunking on Faker in a basketball video.

Here’s an example output video.

Pretty cool, right? I’ll explain how to prepare each example and what to look for in the generated video.

Let’s get started.

What Is Pollo Recast?

Pollo Recast lets you replace characters, products, clothing, or settings in a video while following its original actions, camera movements, and timing. It also retains the original audio.

You can upload a 4–30-second reference video and up to ten reference images. There are ready-made templates too, so you can start experimenting without looking for a source video first.

The video and images have different jobs. The video shows the performance you want to recreate. The images show the people or things you want to put into it.

For a dance, that means you can use a viral performance online instead of trying to describe the choreography in a text prompt. In other example like a basketball clip, you can use a particular dunk instead of asking the model to invent one.

That’s what I want for these examples. I already have an idea of what should happen in each scene. I’m changing who appears in it.

Pollo Recast can replace more than a face, too. The character’s clothes and body can change with the reference image. That’s why I prepared a full-body reference for the first example rather than just using a headshot.

Putting an Elderly Man into the Viral Dance

The dance template I’m using has a lead performer holding a microphone, with backup dancers onstage. There’s already plenty happening in the original, so changing the lead is enough for the first video.

Here’s the character:

He has short gray hair, a gray mustache, and a friendly expression. The tracksuit and sneakers make him look like someone you might see walking around the neighborhood.

I like that for this video. He’s dressed for an ordinary day, and I’m putting him into a stage performance with a microphone and backup dancers. The outfit is part of what makes the idea funny to me.

The reference image is also something I can reuse. Once I have a character I like, I can bring the same image into another template instead of generating a different person for every clip.

For readers with an original character or a mascot, that’s a useful way to approach it. Start with the character you already have, then look for a performance that suits the video you want to make.

Now, open the template in Pollo Recast and upload the reference image.

For a single-character video, the prompt is optional. You can leave it empty here. The template shows the dance, and the image gives Pollo Recast the replacement character.

I like having that option. Sometimes I have a reference that already shows exactly what I want. Writing another paragraph about the same action doesn’t add much, especially for a quick video like this.

There’s still a prompt field when you want to add instructions. Pollo also automatically refines the prompt using your reference materials. For this first example, I’m keeping the setup simple and letting the references do the work.

Next, choose the resolution and the number of outputs, then click Generate. I’ll explain those settings later in the article.

The stage performance is what makes this more fun than animating the reference image on its own. The character gets the whole routine, with the music and the people around him.

That’s also why I want to keep his clothes casual. I don’t need to give him a new hairstyle or turn him into a different character for the stage. I picked this particular image because I want to see this guy doing the dance.

The videos I mentioned seeing on TikTok have made me more interested in this kind of editing. A clip can be funny enough to watch and share without the viewer ever thinking about how it was made.

I want the result to work that way too. The character should be entertaining in the video, without needing a long caption explaining the technology.

Once the result is ready, download it. For this article, I’m placing it beside the original so you can see the replacement straight away. For a social post, the remake can also be shared on its own.

Making Sam Altman Dunk on Faker

For the second video, I want two people in the scene.

The idea is a basketball clip where one player dunks over another. Sam Altman gets to do the dunk, and Faker gets posterized.

Here are the reference character photos:

I’m putting Sam and Faker into the same basketball clip because it’s such an unexpected pairing. I want to see what that matchup looks like.

Here’s the reference video:

The reference for this example needs to show the play I want to use. I’m looking for a dunk with a clear attacker and defender, where both people are visible.

I’m not trying to generate a whole basketball game. One good play is enough. The video already has a beginning and a finish, and putting these two into it gives me the idea for the remake.

Upload the basketball clip, then add the character images in this order:

• Image 1 is Sam Altman, replacing the player who dunks the ball.

• Image 2 is Lee “Faker” Sang-hyeok, replacing the player who gets posterized.

Unlike the dance example, I’m adding a prompt here. There are two people to replace, and I want a specific person in each role.

Here’s the prompt:

Replace the player who dunks the basketball with the person in Image 1, Sam Altman. Replace the player who gets posterized with the person in Image 2, Lee “Faker” Sang-hyeok. Keep them in those roles throughout the video. Preserve the original basketball action, court, camera movements, timing, and audio, and leave the other people unchanged.

I’m referring to the players by what they do. “The player who dunks” is a clearer instruction than “the person on the left,” especially once they start moving across the court.

Beyond that, I’m keeping the original play. I chose the clip because of the dunk, so I don’t need to ask Pollo Recast to invent a different finish or add another camera angle.

This is where I find reference-based generation more useful than trying to write everything in a prompt. I can show the exact movement I want, then explain the part the references don’t decide for me: who replaces whom.

Before generating, choose the resolution and output count. Pollo Recast allows up to four results in one submission, so you can request a few versions of this idea together.

Here’s the final result:

Sam dunking on Faker is already a funny idea to me, but reversing them could work too. The same reference can give you another version simply by changing the assignments.

That gives the feature a bit more room than a one-off joke. You can take a clip and try different people in the same roles, then choose the pairing you enjoy most.

For someone making content around fictional characters, those choices could come from the characters’ personalities. One might be the confident person showing off, while another is always getting into trouble. A familiar video gives them a scene to act out.

The two examples also show when a prompt earns its place. The dance has one replacement character, so I can leave the field empty. The basketball video needs the image-to-player assignments, and a short paragraph explains them.

That’s about as complicated as the instructions need to be for this idea. The rest is in the video and the images.

More Pollo Recast Settings to Know About

There are a few settings to choose before you generate. They affect the resolution, how many versions you get, and the credits required for the request.

Pollo Recast has 480p, 720p, and 1080p options.

For an idea I’m still playing with, 480p gives me a way to try it before requesting a higher-resolution version. For a video going into an article or a social post, I can choose 720p or 1080p.

The resolution and the character choice are separate decisions. At the start, I’m usually asking myself whether the idea is something I want to keep. Once I’ve decided that, I can choose the output quality for where I’m posting it.

The credit amount shown in Pollo Recast is the one to use for the selected settings. I’m not putting a fixed cost per video in this guide because the duration, resolution, and output count can change the total.

The Output Number setting offers one, two, three, or four videos per submission.

For the elderly dancer, one output is enough to start with. For the basketball idea, requesting several gives me a few versions to choose from without submitting the same setup individually.

I like having some choice when making a short, funny video. Sometimes one version is simply more enjoyable to watch than another, and I want to pick the one I’m happiest to share.

More outputs also use more credits, so I don’t automatically choose four for every request. The control is there when I want the variations, rather than being a requirement for making a video.

In terms of reference images, you can upload up to ten. These can show the different people or things you want to use in the remake.

The uploads can include characters, products, or clothing. For this article, I’m only using the elderly character and the two basketball references, but the same upload area can support other kinds of remakes.

Take note that Pollo AI offers hundreds of templates with some supporting more than 5 characters per video. You just need to explore the templates page and look for the ones that fits your need.

For a person, I prefer a reference where the appearance and clothes are clear. The portrait-and-full-body sheet in the first example is one way to show both without needing a separate description of the outfit.

A reference image can be generated too, as it was for the elderly man. You don’t have to use a photo of yourself. That opens it up to original characters you’ve designed for a channel, a story, or a personal project.

Original audio and optional prompts

The source audio is retained, which is super useful for a dance clip built around a song. You can change the performer without having to add the music back manually.

For the basketball clip, the original sound is part of the sports-video feel. I’m changing the players, so keeping the atmosphere of the reference makes sense for this example.

Prompting is there when you need to explain a change. A single-character template can work without it, while the two-player scene benefits from assigning each uploaded image.

I prefer that flexibility to feeling like every video needs a long prompt. Sometimes I want to give a specific instruction. Other times, I’ve already shown the tool everything it needs through the references.

Also, Pollo Recast is currently at 40% discount. The offer and credit amount appear in the generation workflow, so check what’s displayed when you try it.

The discount gives me more reason to experiment with a few ideas, but the settings still affect how many credits I use. A single 480p result and a batch of four 1080p results are different requests.

For these examples, I’m keeping the process focused on making two entertaining clips. There’s plenty of room to experiment with backgrounds, clothes, or products later, without trying to fit every feature into one video.

Why Should You Care?

If you’re a content creator, social media manager, or someone who regularly makes videos for marketing, Pollo Recast is definitely worth checking out.

Making a funny AI video is one thing. Making one that follows a specific dance, action, or camera movement is a different story.

I’ve tried workflows where I had to generate the characters separately, animate them using an AI video model, and then bring everything into a video editor to fix the timing, audio, and other details.

It works, but that workflow is labor intensive and can take several attempts before you get something close to what you originally imagined.

With Pollo Recast, a lot of that work is already handled for you. Instead of describing a complicated scene from scratch, you can use an existing video as a reference and focus on the parts you want to change. The original movements, camera angles, timing, and audio are already there.

I can think of several ways people could use this:

• Social media creators: Recreate trending memes, dance challenges, and funny videos using your own characters. You could also experiment with unexpected character combinations like the basketball example I shared earlier.

• Brands and marketers: Replace products, characters, or settings in a reference video to create different versions of promotional content. This could be useful for testing campaign ideas or making short videos for TikTok, Instagram Reels, and YouTube Shorts.

• AI influencers and virtual creators: Put your original AI character into different scenes without having to generate every movement from scratch. You can reuse the same character reference across multiple videos.

• Small businesses and indie creators: Make short promotional clips without hiring actors, arranging a shoot, or learning complicated video editing software. You still need a good idea and suitable references, but the production process becomes much more accessible.

• Entertainment creators: Reimagine familiar scenes with completely different characters, create funny crossovers, or experiment with scenes that would otherwise be difficult to film yourself.

And since Pollo AI also has tools for generating images and videos, you can create your own characters and reference clips within the same platform. You don’t necessarily have to jump between three or four different AI tools just to produce one short video.

For creators who need to post regularly, being able to try more ideas without spending so much time on each video is a pretty big advantage.

Final Thoughts

I’ve tried and experimented with a bunch of different workflows for making viral AI videos, and so far, Pollo Recast tool has been the easiest to use. I like that I can take an existing video, replace the characters with whoever I want, and get a completely different result without spending hours editing.

The platform is also pretty fast, and the discounted model pricing is a huge cost saver for indie content creators like me. If you’re running a marketing campaign, making content for a brand, or just trying to grow your social media accounts, I can see this being really useful.

Pollo Recast is just one of the many tools available on the platform. You can create your own characters, generate a reference video from scratch, and then use that video as a template. So you’re not limited to copying whatever dance or meme is currently trending on TikTok. You can come up with something entirely your own.

There’s also MCP support, which is a nice addition if you prefer working inside ChatGPT or Claude instead of switching between different apps.

Personally, I’d love to see more people experiment with Pollo Recast beyond the dance clips I’ve been seeing on my feed. There are so many funny scenes and unexpected character combinations you could make with it, and I think we’ve barely scratched the surface of what’s possible.

Anyway, that’s it for this tutorial. Let me know what you think about Pollo Recast tool in the comments!

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Today, I switched to FocuSee as my main screen recording app for product demos after years of using Screen Studio. As a software developer and business owner, I want all the product demos I share to be high quality and professionally made.

I use these videos to introduce my apps and show updates to my websites. So, aside from recording the screen, I also care about how the camera moves, how clearly viewers can follow my clicks, and what I can change afterward.

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What made me decide to switch? Three things: control, price, and AI.

In this guide, I’ll show you how I used FocuSee to record a demo for Zeniteq, what I liked about the result, and which settings you can adjust for your own videos.

What’s New in FocuSee?

Before we start recording, let me show you a few things the app can do now.

FocuSee has added more editing features beyond its automatic zoom and cursor effects. There are now 3D camera motion, callouts, AI captions, audio enhancement, and background music inside the editor.

The recent updates also include more cursor settings, subtitle editing, and volume controls for individual clips. You can make more adjustments after recording without exporting the video to another app first.

The one I was most excited to try was 3D Motion. Instead of only zooming straight into the screen, it adds tilt and perspective as the camera moves. You’ll see how that looks in my Zeniteq recording below.

To install FocuSee, head to the official website and download the installer for Windows or Mac. Open the file and follow the installation instructions.

Once installed, you should see a floating window with the recording options. You can capture the entire screen, a selected area, or one window.

You can also download the Mac version from the App Store.

Take note that you’ll need to grant the app the system permissions for recording. On Mac, follow the prompts for screen capture, microphone, and camera access, depending on which inputs you use.

Before recording a complete demo, try a short clip and play it back. Check the selected window and audio sources so you know the setup is correct.

How I use FocuSee for Product Demos

For this demo, I’m sharing three new features on Zeniteq, my AI news website. I wanted a nice showcase video to post on LinkedIn and X so my readers could see the updates in action.

Here’s what I wanted to show:

1. Audio mode, which lets readers listen to articles.

2. AI Tools, where developers and startups can list their apps or websites and become part of Zeniteq’s knowledge base.

3. Happening Now, which shows recent AI news and updates on one page.

These features are already live, so I could record them directly on the website. For your own demo, prepare the pages or app screens you want to show before opening the recorder.

I opened FocuSee and selected Window mode to record the browser.

Before pressing Record, you can choose your camera, microphone, and system audio. These are separate controls, so you can record your voice without including a video of yourself.

The camera option is useful when you want to explain something while showing the screen. You can record both at once and adjust the camera layout in the editor.

For voiceover only, enable the microphone and turn the camera off. Enable system audio when you also want to capture sound from the app or website.

In my case, system audio is relevant because one of the features is article playback. Viewers should be able to hear what happens when the Play button is pressed.

FocuSee lets you adjust microphone and system audio separately after recording. That gives you control over the balance between your explanation and any sound coming from the product.

There’s also the 3D zoom option for a more dynamic camera movement. I wanted to try this because I’d mostly used the usual 2D zoom in other screen recorders.

Once everything was set, I started recording and walked through the three features. After I stopped, FocuSee opened the video editor with the recording.

Here’s the initial result, with FocuSee’s automatic effects applied and before further manual editing:

I love how the 3D pan and zoom work here. The tilt gives the recording more movement than the 2D effect I’m used to, and I like how it looks when showing the different pages of my website.

I also like how FocuSee handles the timing of the zooms and pans. The recording already looked well made when I opened it in the editor. I didn’t have to create each camera movement manually.

The auto zoom feature follows recorded actions such as mouse clicks and generates the animations. You can then change the position and timing of those zooms in the editor.

For a product demo, this is useful because the viewer can follow what you’re clicking. A small button is easier to notice when the recording moves closer to it.

Of course, you still need to watch the result. A zoom that looks good during a quick feature announcement may be too close for a tutorial where the viewer needs to read the surrounding text.

For example, I’d want a close view of Zeniteq’s audio controls, but a wider view of Happening Now. Showing several news items together would explain that page better than zooming into one headline.

FocuSee also has typing recognition and speed-up tools. I’d use those for a long typing section, then return to normal speed when the result appears and needs explaining.

Making Your Own Adjustments

You can change the cuts and animations through the timeline and settings. The automatic result is something you can continue editing, so you don’t have to keep every zoom the app creates.

Use the zoom settings to change the position, zoom level, or duration. Give a page more time on screen when there’s something viewers need to read.

You can also change the cursor style, size, click effects, and sounds. For a website demo, I’d choose a cursor that’s easy to see without covering the button labels.

Click effects are useful for showing exactly when an action happens. I’d keep the click sounds low, especially in a video that also has narration or product audio.

For additional explanations, FocuSee has annotations, including text, arrows, shapes, callouts, spotlights, and blur effects. You can place them where they’re needed in the recording.

A short “Listen to this article” label could help explain Zeniteq’s audio control. A spotlight could draw attention to a small setting without zooming the entire frame.

I like the 3D movement for this feature showcase. For a longer tutorial, though, I’d use less movement and let viewers see each step clearly before continuing.

Trying the AI Editing Features

FocuSee also has AI features for recordings with narration. You choose these in the editor when you want to process the audio or generate captions.

One of them is Smart Cut, which detects silence and filler words such as “uh” and “um.” It can help clean up speech without making you find every pause manually.

To use it, open Smart Cut, select the spoken language or use automatic detection, and click Start Recognition. FocuSee highlights the detected sections so you can review what to remove.

I usually check those suggestions before deleting everything. A pause while you’re thinking can be removed, but a pause while the viewer reads something may need to stay.

There’s also AI audio enhancement. Noise reduction helps reduce background sound, while Vocal Enhancement can improve voice clarity and balance.

For a narrated demo, I’d play a short section with the processing on and off. That would let me hear whether the voice sounds better before processing the full recording.

Another useful feature is AI subtitles, which supports more than 55 languages. Open Captions, choose the language spoken in the recording, and generate the text.

You can edit the captions afterward and change their appearance. For my website, I’d check that “Zeniteq,” “AI Tools,” and “Happening Now” are written correctly.

Caption placement is important too. I’d keep the text away from the playback controls or any other part of the interface I’m explaining.

There’s also an AI avatar feature that generates a virtual presenter using text or audio input. You can choose a persona and adjust its layout alongside the screen recording.

I’m not always comfortable in front of a camera, so I like having this option. I could use an avatar for a short introduction and keep the website as the main view during the demonstration.

You can also leave the presenter out completely. A screen recording with a voiceover is enough when the feature is easy to show.

Finishing and Exporting the Video

After reviewing the recording, you can customize the background, rounded corners, shadows, and watermark. I’d use similar settings across my Zeniteq videos so they have a consistent look.

FocuSee also lets you add background music, including preset tracks or your own audio. You can adjust its volume and choose where it plays.

For the article-audio demonstration, I’d turn the music down or remove it during playback. I want readers to hear the feature itself.

When the video is ready, choose Export. FocuSee supports MP4 and GIF, with video export options up to 4K and 60 fps.

I usually use MP4 at 4K resolution for a demo with narration or system audio. A GIF would suit a short, silent loop showing one interaction.

You can also change the aspect ratio for different platforms. Check the framing after changing it, especially when converting a wide browser recording into a vertical video.

Small interface text can become difficult to read in the new layout. Adjust the view so the feature you’re showing is still clear.

Why Should You Care?

If you make product demos regularly, FocuSee can handle several editing steps in the same app. You record the feature, review the generated movement, and adjust the parts that need more attention.

In my Zeniteq recording, I liked the result of the automatic pan and zoom. That was the clearest benefit for me because I could see how the website looked with the effects applied straight after recording.

The AI tools add more options for narrated videos. Smart Cut can help with pauses, captions can transcribe the explanation, and audio enhancement can help improve the voice recording.

This could be useful for developers sharing feature updates, founders making launch videos, or creators recording software tutorials. The amount of editing will depend on how detailed the video needs to be.

Price was another reason for my switch. These are the advertised US-dollar prices on the FocuSee pricing page:

The one-time purchase covers FocuSee 2.x. AI tools such as avatars, Smart Cut, subtitle recognition, and vocal enhancement consume credits, so check the allowance for the features you plan to use.

Screen Studio currently lists its annual plan at $9 per month, or $108 per year. It also offers captions, transcript editing, and audio cleanup, so compare the specific features you need alongside the price.

FocuSee works on Windows and Mac, while Screen Studio is Mac-only. That’s another reason to consider it when you work across both systems.

For a complicated video with several scenes and extensive effects, I’d still consider a dedicated video editor. For a short walkthrough of an app or website, FocuSee already includes many of the tools you’d use.

Final Thoughts

I really like how the Zeniteq recording turned out with FocuSee’s automatic effects. The 3D pan and zoom were some of my most favorite parts, and I liked how the app handled the timing without requiring me to make any further changes.

After years of using Screen Studio, I find myself using FocuSee more often now for my product demos. It gives me way more control and there are AI tools available when I want captions or help with narration.

The more variable pricing also gives you a better option in terms cost. Other apps only has one subscription option is one of the biggest bummer. But thanks to the FocuSee team for making the app more accessible and affordable to its users.

What do you think of the 3D effect in my Zeniteq demo? I’d love to know whether you’d use it for your own product videos or prefer the usual 2D zoom. Let me know in the comments.

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Ideogram just dropped a brand new way to generate images with AI. No, I’m not talking about the usual PNG images. The new Ideogram Vector tool lets you turn a text description or an input image into an SVG file with shapes you can edit and scale without losing quality.

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In case you’re not familiar with SVG, it stands for Scalable Vector Graphics.

Notice how a PNG starts getting blurry or showing blocky edges when you stretch it too much? Well, that doesn’t happen to vector shapes in an SVG.

They stay crisp when resized, which makes them ideal for things like large-format printing. You can generate logos, lettering, illustrations, posters, patterns, and linework, then adjust the individual shapes afterward.

I’ve always struggled to create SVG files in Adobe Illustrator or Figma, so this is something I’m excited about.

In this guide, I’ll show you how to access Ideogram Vector, walk through four examples, and discuss who could get the most use out of it.

What Is Ideogram Vector?

Ideogram Vector is an AI tool that creates SVG artwork using a text description or an existing image.

You have two ways to start: tell it what you want to create, or upload an image and turn it into vector artwork.

Say you need an illustration for a coffee shop website. You could describe a cup with a looping steam line, generate a few options, then bring your favorite into a vector editor. Want the cup a different color? You can change the shapes that make up the cup without generating the entire illustration again.

I like having that control after generation. Sometimes a design is already close to what I want, and asking the AI to redo it feels unnecessary when I only need to move or recolor one part.

Here are the key features:

• Text to Vector: Describe the graphic you want to create, including its subject, style, colors, and lettering.

• Image to Vector: Upload an image and generate a vector version you can open and refine in a vector editor.

• SVG downloads and generation controls: Save the SVG file and adjust settings such as aspect ratio, image quality, and the number of outputs.

Just keep one thing in mind with lettering. A word can be drawn as vector shapes, so you can reshape and recolor the letters, but retyping it might require replacing those shapes.

SVG supports actual text too. You’ll need to open the file to see how the lettering was created. For a custom logo, that might be fine. For a weekly event poster, I’d add the changing date separately so I can update it without rebuilding the design.

How Ideogram Vector Works

To start using Ideogram Vector, sign in to Ideogram, open the Apps menu, and look for Vector. On the Vector page, choose between Text to Vector and Image to Vector.

This is what the Text to Vector dashboard looks like:

To start creating SVG files, simply describe the artwork you want in the Prompt field. You can also adjust the aspect ratio, image quality, and number of images before generating.

Don’t worry about an elaborate prompt right away. Start with what you’re making and how you want it to look. You can get more specific as you go.

For Image to Vector, this is what the dashboard looks like:

Ideogram’s Image to Vector tool

Upload your source image and generate its vector version. Use this when you already have an illustration, symbol, or other graphic that you’d like to keep working on.

Once you have a result you like, open it and download the SVG. Ideogram’s download documentation says SVG assets download directly as .svg files, separately from the regular PNG and JPEG options.

Let’s go through four examples and the results we’re aiming for, starting simple and gradually adding more detail.

Text to SVG for a Coffee Shop Logo

Let’s say we’re opening a little coffee shop called “Morning Loop.” We need a logo for the website, takeaway cups, and maybe a sign outside. I’m thinking warm colors, friendly lettering, and a cup with a steam line that loops above it.

Here’s the prompt:

Prompt: Create a minimal vector logo for a neighborhood coffee shop called “Morning Loop.” Place a rounded coffee cup above the name, with one continuous steam line forming a loose loop. Use dark brown and warm orange on a plain cream background. Set the name in friendly, rounded lettering with clear spacing. Keep the cup recognizable at a small size, use flat colors and smooth curves, and avoid textures, shadows, product mockups, and extra text. Center the complete design in a square composition.

Note that the output is generated on the right side of the screen.

Here’s my preferred output in SVG format.

Pretty cool, right?

The quotation marks make the requested name clear, while the other instructions describe the layout and colors. Change those details to match your own business.

Also, notice the instruction to avoid product mockups. A logo printed on a cup would look nice, but we need the logo itself before we put it on anything.

The look we’re going for is a brown cup above the shop name, with the orange steam loop giving it a bit of personality. The cream background keeps everything warm and easy to read. I’d be happy with a simple design here; the cup and lettering should do most of the work.

Now imagine deciding that the orange doesn’t match your website. Open the SVG in a vector editor, select the relevant shapes, and try another color. You could also resize the cup or adjust the distance between the symbol and the name.

Shrink the logo to the size it would appear in your website header too. The lettering needs to hold up there.

Here’s another cool editing workflow you can do inside the Ideogram Vector: right-click on the SVG file and select edit with an agent. You can then instruct the agent on whatever edit you want.

For example, I wanted to add coffee beans on the side of the coffee cup.

Prompt: Add coffee beans inside the cup

That’s it. No need to download the file and edit it manually. The agent is smart enough to know what kind of modifications you want. It’s super fast and cheap.

Image to SVG for an Illustration You Already Have

Okay, but what about an image you already like? Say you have a PNG illustration and want to put it on a poster, except it gets blurry when you enlarge it.

Let’s use a potted plant for this example. It has broad green leaves, a terracotta pot, and a plain background, so there’s a clear subject for the conversion.

Choose Image to Vector, upload the illustration, and generate the SVG version. Crop away unnecessary background space first so the plant fills a reasonable part of the image.

For the output, we want the same recognizable plant, with its curved leaves and little pot kept in proportion. Some texture might be simplified, but the leaf shapes and gaps between them should still be recognizable.

Here’s the side-by-side comparison of the input image and the vectorized result:

Place the two versions next to each other at the same size. There’s not much difference, right?

A leaf that has become wider or a pot that looks slightly squashed is easier to spot that way. Zooming in is useful afterward, but start with whether you still like the complete illustration.

Then try a change you’d actually make. Maybe the terracotta pot clashes with your page, and you want it blue. Select its shapes and recolor them. Ideally, you can do that without picking through a pile of tiny fragments.

I can see this being handy when you want to reuse a favorite illustration across several projects.

Text to SVG with Gradients and Decorative Lettering

Let’s ask for something a little more ambitious now.

I want a botanical poster called “Midnight Bloom,” with colorful lettering, flowers, and vines winding around the title.

This is the kind of design where I’d usually have a clear picture in my head and very little confidence in my ability to draw it. So let’s describe it:

Prompt: Create a vector poster centered on the exact words “MIDNIGHT BLOOM” in large decorative lettering. Fill the letters with a smooth violet-to-cyan gradient and weave thin botanical vines around selected characters without obscuring the words. Add two stylized flowers with layered petals beneath the title and a small crescent moon above it. Use a deep navy background, balanced spacing, and crisp contours. Keep the lettering readable, separate the flowers visually from the title, and avoid photographic textures, grain, mockups, and additional text. Use a vertical poster composition.

The extra detail gives everything somewhere to go. We’ve placed the flowers beneath the title and the moon above it, leaving less guesswork about the layout. The vines should feel like they belong around the title without making “Midnight Bloom” difficult to read.

And yes, you can have gradients in an SVG. Vector artwork doesn’t have to look like flat clip art. The format supports linear and radial gradients, which let colors blend across a shape or spread outward from a point.

I really love how Ideogram Vector handles the gradients and small details here. I am now confident that it can handle even more complex details and larger artworks.

Image to SVG with Complex Patterns and Gradients

For the last example, let’s give Image to Vector something harder: an ornamental butterfly with patterned wings, thin veins, and blue-to-purple gradients.

Upload it through Image to Vector and generate the SVG. The challenge here is keeping the pattern recognizable while dealing with all the smaller details inside the wings.

The output we’re aiming for has the same butterfly silhouette, gold markings, and soft color transitions. I’d expect the faint highlights and finest veins to be the details that need the closest comparison with the original.

Take a look at the two wings first. With symmetrical artwork, a small difference between the sides can catch your eye immediately. Look at the gold markings too, especially where narrow spaces separate one shape from another.

I honestly do not see any difference between the PNG and the SVG file. It’s mind-blowing.

With a difficult source image, a little preparation can help you judge the conversion. Crop out an unnecessary background and remove distracting noise where possible. A clearly illustrated butterfly gives you defined shapes to compare; a photograph with soft lighting and blur asks for much more interpretation.

Why Does This Tool Matter?

For someone who can already draw beautiful vector artwork, the benefit might be getting a first idea onto the canvas faster. For someone like me, it’s being able to make a usable starting point at all without getting stuck on the drawing tools.

Graphic artists could use it to explore different directions. Imagine showing a client a few floral motifs for packaging, choosing one together, then refining the curves and colors. You have something to discuss much earlier.

Game developers have plenty of smaller assets to think about too. Inventory icons, achievement badges, menu decorations, and little symbols all take time to create. A vector master could be adapted into different sizes and color variants, then exported for the game.

I’d keep the whole set visible while working on it. A sword icon with thick outlines can look out of place beside a potion bottle with delicate linework, even when both look good individually. Using similar prompts helps set the direction, and you can tidy up inconsistencies afterward.

Animators could generate lettering, props, or illustrations to prepare for motion. Say you want the flowers in a title sequence to open one petal at a time. You’d still need to organize the petals into separate parts, but the generated artwork could give you a head start.

For web developers, I’m thinking about custom illustrations that make a page feel less generic. A little character on an empty dashboard or a graphic explaining your product would be fun to try.

You’d still want to check the file size before shipping something detailed. SVGs can get heavy when they contain lots of paths, and a tiny illustration on a phone probably doesn’t need every decorative detail.

Then there’s printing. The same vector artwork could be used on a small card or a large sign without blurry edges. Prepare the final file according to the printer’s requirements, and you have far more flexibility over where that design can go.

How to Access Ideogram Vector

Ideogram plans to launch Vector on October 22, 2026, at noon Eastern Time.

To find it, head to Ideogram, sign in, and open Apps → Vector once it becomes available on your account. Choose Text to Vector for a new idea or Image to Vector for an image you already have.

Check the access requirements and credit cost shown in your account before generating a large batch. The specific Vector pricing needs to be confirmed separately from Ideogram’s other tools.

Final Thoughts

Ideogram Vector gives you a much easier way to start creating SVG files.

You can describe a new design or upload an existing image, then work with the vector artwork afterward. We’ve gone through a coffee shop logo, a simple illustration, decorative lettering, and a detailed butterfly to show the kinds of projects you could try.

Personally, I love how easy it is to use. The UI is intuitive, generation is fast, and the outputs look really good. I’ve always struggled with creating SVGs in Adobe Illustrator or Figma, so being able to describe an idea in natural language is a big help for me. I still get to make changes afterward, but getting that first version made feels far less intimidating.

Thank you to Ideogram for bringing this tool to the public. For people who need a few vector assets, it could save a lot of time and help with projects where commissioning every small graphic would be too expensive. There’s still a place for professional designers, especially for a complete brand identity, but I’m glad smaller projects have an option like this.

What do you think about Ideogram Vector? I’d love to know what you’d make with it, especially with the Image to Vector mode. Give it a try and let me know your thoughts in the comments.

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The most predictable CDC pricing comes from a billing unit your team can actually estimate. That could be the amount of data moved each month, the number of rows that change, or the processing capacity your pipelines need. Your team’s ability to forecast those numbers helps determine which pricing model will give you a predictable bill.

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The tools in this article charge in several ways:

• Volume-based: Estuary and Google Cloud Datastream

• Capacity-based: Airbyte Pro

• Compute-based: AWS DMS

• Event-based: Hevo

• MAR-based: Fivetran

• Multi-dimensional: Confluent Cloud

• Self-managed: Debezium

For high-volume pipelines, those billing units can have a bigger effect on your budget than the quoted starting price. Costs can grow with data volume, active rows, events, processing capacity, compute usage, or the infrastructure needed to run the pipeline.

Predictable pricing doesn’t necessarily mean cheap pricing. An expensive service can still be straightforward to budget for. Predictability means you can estimate how much more you’ll pay as your workload grows, using measurements you can collect from your own pipeline.

Estuary’s cloud pricing is based on data movement and connector task hours. It doesn’t use MAR pricing, and managed CDC doesn’t require a separate Kafka stack. Teams already tracking data volume can use those measurements as a starting point for their cost estimates.

What Makes CDC Pricing Predictable?

A vendor can publish a simple rate and still leave you with several unanswered questions about the monthly bill.

Suppose a PostgreSQL database generates 500 GB of changes this month. Next month, transaction volume doubles, Snowflake is added as another destination, and one large table needs a backfill. How much will those changes add to the bill?

The answer depends on which parts of that workload the vendor charges for. Doubling transaction activity, adding a destination, and loading historical data can affect separate charges, even though they all belong to the same pipeline.

With volume pricing, you start with the bytes processed. MAR pricing depends on how many distinct rows change during the month. Event-based pricing counts inserts, updates, and deletes. Capacity-based pricing can stay the same through an increase in traffic, up to the point where the existing capacity can no longer handle the work.

Compute-based services also respond to processing demand. A traffic burst can push a serverless CDC service into higher capacity, even when total monthly volume stays close to normal. The monthly average alone won’t show how much capacity the service used during that burst.

These questions help expose where the bill could change:

• How closely does spending follow the amount of data ingested?

• What happens when more distinct rows change?

• Does adding a destination create additional charges?

• Are initial loads and backfills priced separately?

• How are traffic spikes handled and billed?

• Does the tool require Kafka, storage, monitoring, or other infrastructure services?

Teams run backfills when they add destinations, fix transformations, recover from failures, and support new use cases. These operations belong in the budget alongside regular CDC traffic, especially when a backfill needs to run while the pipeline is already busy.

Estuary’s guide, How to Evaluate Change Data Capture (CDC) Solutions, recommends testing backfills along with traffic bursts, destination slowdowns, and backlog recovery. Those tests show how costs and performance change during work that a steady stream of database updates won’t expose.

How We Evaluated the Tools

Each vendor presents its pricing differently, so comparing the numbers on their pricing pages requires some care. A per-GB rate, a worker quote, and a monthly starting price describe different things.

We started by identifying what each vendor meters: data size, rows, events, connector operations, or infrastructure capacity. That gave us a way to connect each pricing model to the workload measurements a team would need.

We then looked at the architecture, common use cases, and discussions on platforms such as Reddit. Those discussions can point to unexpected charges or maintenance problems engineers have encountered, but they remain individual accounts.

A reported price increase doesn’t explain another team’s likely bill without the workload and contract details behind it.

Our evaluation covered:

• Pricing models

• Price transparency

• Performance under heavy loads

• Backfill handling

• The cost of multiple destinations

• Infrastructure requirements

Quick Comparison of CDC Pricing Models

The table below shows each tool’s pricing model, the usage that drives its cost, and the billing details to check before estimating a larger workload.

Several of these tools don’t bill primarily by GB. Even the volume-based services can measure bytes at different stages, so a single “$ per GB” column would leave out details that affect the comparison.

CDC Tools With Predictable Pricing at Scale

The tools below charge for different parts of a CDC workload. Each section explains what goes into the estimate and what can change as the pipeline grows.

1. Estuary

Best for: Managed CDC at high volumes where data volume is easier to forecast than row-level activity.

Estuary charges $0.50 per GB captured and $0.50 per GB delivered to each destination. When 1 GB enters Estuary and 1 GB is delivered to one destination, the data usage portion of the bill is $1. Both capture and delivery need to be included in the calculation.

Connector pricing depends on the number of active connector instances. The first six active instances cost around $100 per month each. Additional connector instances cost $50 per month, prorated based on usage. Captures and materializations both count as connector instances. BYOC and private cloud deployments use annual pricing plans and involve additional infrastructure costs.

The basic calculation is: Data usage + connector instance usage

A data engineer can monitor both parts from the pipeline’s usage and configuration. Doubling billable GB while keeping the same connector setup doubles the data usage charge. Twice as many database updates, however, won’t necessarily produce twice as many bytes. Update count alone doesn’t tell you the volume Estuary will bill for.

Estuary separates source capture from destination delivery:

Capture → Collections → Materializations

A capture brings data into collections, which can supply multiple destinations. Suppose PostgreSQL needs to feed Snowflake, S3, and an operational database. The source doesn’t need to be read separately for each destination. Additional materializations can use the data already captured into Estuary.

Each destination still adds delivery volume, but adding one doesn’t require another historical extraction from the production database. Your estimate can account for that delivery and its connector without treating every new destination as a separate source ingestion job.

Teams using Estuary’s managed CDC also avoid operating Kafka, Kafka Connect, and the associated connector infrastructure themselves. That removes a set of infrastructure and maintenance responsibilities from the pipeline budget.

Estuary customers report savings from their own deployments. Xometry reports reducing data integration expenses by more than 60% with pay-as-you-go pricing. Glossier reports cutting integration costs by 50%, without unexpected fees. Cosuno reports that its pipeline cost 50% less than its previous Fivetran-based solution.

Reddit discussions also mention rising Fivetran costs and Estuary as an alternative. Those conversations show why some engineers prefer volume-based billing, but I wouldn’t use reported Reddit savings as reliable evidence for another team’s budget.

Estuary has fewer connectors than some competitors, so check the actual sources and destinations your pipeline needs. Private and BYOC deployments also bring additional contract and infrastructure considerations. The public cloud pricing calculation won’t cover every deployment arrangement.

Not ideal for: Teams that require sources or destinations outside Estuary’s connector catalog, or teams that need general-purpose SaaS ELT more than CDC.

2. Airbyte

Best for: Teams that want a choice between usage-based and capacity-based pricing.

Airbyte offers two main pricing models. Standard is a fully managed, volume-based product. Airbyte Pro uses data workers for capacity-based pricing. One data worker can handle approximately three simultaneous syncs, depending on the pipelines being run.

Under Pro, a temporary increase in data volume can stay within the capacity you’ve already paid for. That extra traffic doesn’t automatically add a per-GB charge, which can help with workloads that regularly have busier periods.

Additional workers become necessary once concurrent jobs or more demanding pipelines exceed that capacity. Two workloads moving similar amounts of data can have different worker requirements because their jobs overlap differently.

Forecasting therefore involves estimating how much processing capacity the pipelines need and how many syncs will run at once. Monthly data volume alone won’t answer either question.

Airbyte Pro is quote-based. The pricing page explains how the capacity model works, but a dollar estimate requires a quote based on the workers your workload needs.

Airbyte also offers a self-hosted option. Your engineering team takes responsibility for running the platform, so infrastructure and maintenance become part of the calculation alongside any software costs.

In an r/dataengineering discussion, some engineers described self-hosting as cheap and easy. Others reported maintenance and reliability issues. Another user reported substantial annual savings from running Airbyte on Kubernetes.

Those experiences reflect different operating environments. Self-hosting can reduce software spending, while the work of keeping the platform available and its connectors reliable remains with your team.

Not ideal for: Teams that want little operational involvement and don’t want to manage capacity sizing, self-hosted infrastructure, or connector reliability.

3. Google Cloud Datastream

Best for: GCP projects looking for managed CDC with volume-based pricing.

Google bills Datastream by GiB processed. Volume tiers reduce the price per GiB as usage increases. Historical backfills are billed separately, with the first 500 GiB per month free under Google’s current pricing page.

The processed GiB on the bill won’t always match the size of the source data. Google’s documentation notes that internally processed data can be two to five times larger than the actual data for many use cases.

An estimate based only on source bytes can therefore miss a substantial part of the charge. You need to establish how much processed data your workload produces before applying the listed per-GiB rate.

Datastream’s own billing is based on processed volume, but a complete GCP pipeline can use other paid services. Cloud Storage, BigQuery, Dataflow, and networking may all contribute costs, depending on where the data goes and how it’s transformed.

Include those services in the same estimate. A clear Datastream rate won’t account for the separate processing, storage, and delivery costs around it.

Not ideal for: Teams using multiple clouds, or workloads where processed-byte expansion and additional GCP services make the total difficult to estimate.

4. AWS DMS

Best for: Migration and replication workloads on AWS.

AWS DMS bills for replication capacity rather than using changed rows or changed GB as its primary pricing unit.

With provisioned DMS, you choose replication instances and pay for the resources you run. Additional log storage can also create charges when used, so the instance estimate may not cover the complete service bill.

DMS Serverless measures capacity in DMS Capacity Units, or DCUs. One DCU corresponds to 2 GB of RAM. AWS automatically adjusts serverless capacity as replication workload changes.

Traffic bursts can increase DCU usage, making the bill more dependent on processing demand over time. AWS lets you set a maximum number of DCUs, which limits how far the service can scale. That maximum also needs to support the replication performance you expect during busy periods.

Teams already running on AWS may be familiar with budgeting around capacity and runtime. Translating a forecast such as “we’ll move this many GB of CDC each month” into DMS capacity takes more work, because the data volume doesn’t directly specify the resources required.

Not ideal for: Teams that want a simple volume-based pricing calculation or a CDC service independent of AWS.

5. Hevo

Best for: Teams with a database change rate they can reliably estimate through events.

Hevo uses an event-based model. Its documentation lists record insertions, updates, and deletions after pipeline creation as billable actions. Deleted source records count as updates for billing purposes.

A PostgreSQL workload producing around 20 million change events each month gives you a baseline for forecasting consumption. The estimate depends on how consistently that activity repeats, including the jobs that update many records at once.

A bulk update touching millions of records can generate a large number of events while leaving the database’s storage size almost unchanged. Measuring database size won’t show the billing effect of that operation.

Hevo’s consumption pricing includes two details worth checking for larger workloads:

• Historical data loads are described as free, with pricing applied to changed data.

• Unit prices decrease as volume increases, and Hevo offers capacity commitments.

Hevo recommends running the workload for four or five days, calculating the average daily event count, multiplying it by 30, and allowing around 5–10% variance.

That method depends on the sample reflecting the workload you expect throughout the month. An occasional bulk update can sit outside the sampled days and materially change the result.

Stable event rates give you a repeatable estimate. Irregular update jobs and sudden increases in change activity need to be included separately, rather than assumed to fall within a small variance allowance.

Not ideal for: Workloads with fluctuating event rates, frequent large updates, or sudden surges in inserts, updates, and deletions.

6. Fivetran

Best for: Teams that want managed ingestion and can reliably forecast Monthly Active Rows, or MAR.

Fivetran uses MAR as its main measure of connection usage. Standard MAR counts distinct rows that become active through inserts, updates, and deletes during the month.

The same row can be modified many times in that period and still count as one MAR. Your table’s byte size and total number of updates therefore won’t necessarily tell you how much standard MAR it generates.

More frequent synchronization also won’t increase standard MAR simply because another sync runs. Repeated changes to the same rows remain distinct-row activity within the billing period, rather than becoming another full set of MAR with every sync.

History Mode uses a different calculation that takes changes into account. Initial historical syncs are generally free from MAR charges, and the policy introduced in March 2025 allows historical resyncs to be excluded from paid MAR under certain conditions.

Forecasting MAR gets more difficult when the set of rows being changed varies considerably from month to month.

A workload can touch far more distinct rows without producing much more data overall. MAR can increase while the monthly byte count stays close to its previous level.

Connection structure affects the calculation too. Fivetran applies its usage curve at the connection level. Two connections replicating the same source to two destinations each have their own MAR.

The 2025 shift to connection-level tiering helps explain the different outcomes reported in community discussions. One user in an r/dataengineering thread said their monthly bill doubled after the change. Another reported a small increase because most of their connectors processed more than one million MAR per month.

Those accounts describe different distributions of usage across connections. The connection count and MAR per connection explain more about the resulting bill than a broad claim that Fivetran is expensive.

According to Fivetran, the 2025 change was designed to increase prices for accounts with many similar connections and reduce prices for accounts with one large connection. As of January 2026, connections with between one and one million paid MAR are subject to a minimum connection fee. Inserts, updates, and deletes contribute to paid MAR.

Some larger customers can also use Enterprise License Agreements with a fixed annual fee. That provides another way to budget for the service where those agreements are available.

Stable distinct-row activity makes MAR reasonably predictable. Teams estimating it need visibility into which rows change and how usage is spread across connections, rather than relying only on data volume.

Not ideal for: Workloads with unpredictable distinct-row activity or many separate connections whose MAR is difficult to estimate.

7. Confluent Cloud

Best for: Businesses that already use Kafka and event streaming across their platform.

Confluent’s CDC connectors sit within a broader streaming platform. Usage-based fully managed connectors primarily charge for:

• Connector task hours

• Data throughput measured in GB

Those measurements cover Connect. Kafka clusters can add compute, networking, and storage charges, while other services, including Flink, use their own billing measurements.

The complete estimate needs to include each part of the platform used by the pipeline. A connector’s throughput rate won’t account for the Kafka resources or additional processing around it.

Organizations already using Kafka for company events may have much of that infrastructure and operating expertise in place. Their CDC estimate still needs to account for the additional workload, but they’re building on a platform they already run.

In August 2026, Confluent introduced Connect Advantage bundles. These let companies with consistent usage make a minimum monthly commitment in exchange for lower connector pricing and less variation in spending. A commitment gives those teams another budgeting option alongside standard usage-based billing.

Not ideal for: Teams that only need database-to-warehouse CDC and have little use for the broader streaming platform.

8. Debezium

Best for: Teams with Kafka experience or a need for direct control over the CDC stack.

Debezium is open source and has no software license charge. Running it in production still requires infrastructure and engineering time, which make up the cost estimate for a self-managed deployment.

A common Debezium setup uses Kafka Connect:

Source database → Debezium connector in Kafka Connect → Kafka → Destination connectors

Debezium’s documentation identifies Kafka Connect as its most popular configuration. A production deployment needs to account for Kafka brokers, Connect workers, storage, replication, monitoring, upgrades, scaling, and sink connectors.

Alternative configurations include Debezium Engine, which can run without Kafka or Kafka Connect. In an embedded setup, the application takes responsibility for state management and durability that the Kafka-based configuration would otherwise provide. Fewer infrastructure components also means more of that responsibility moves into the application.

Community discussions describe the operational differences. Engineers already working with Kafka often see Debezium as a practical CDC option. Others report difficulty with Kafka operations, scaling, and the time required to learn and maintain the setup.

One r/dataengineering user described Debezium itself as relatively easy to operate, with Kafka accounting for most of the operational effort. Another discussion raised concerns about implementing the combination while the team was trying to scale quickly.

A budget written as: Debezium = free

only accounts for the software license. It leaves out the resources and maintenance needed to deliver data reliably.

A more useful calculation is:

Debezium license = free; production CDC infrastructure and maintenance = your responsibility

Teams with an established streaming platform can include Debezium in their existing operating plans. Teams starting from scratch need to budget for building that setup and maintaining it afterward.

Not ideal for: Teams without Kafka expertise or experienced operations staff that want a provider to manage their CDC.

Pricing Behavior in High-Volume Pipelines

Consider a pipeline with the following workload:

• PostgreSQL as the source database

• Continuous CDC

• 1 TB of changed data per month

• Billions of row changes

• Snowflake as the first destination

• Occasional historical backfills of several TB

• Peak periods reaching three times the normal workload

The team then adds S3 as another destination. The source data still serves the same business needs, but another delivery path has joined the pipeline.

The resulting charge depends on whether the platform bills for delivery volume, active rows, events, capacity, or the infrastructure handling that additional work.

The advertised starting price doesn’t show how these operations will be billed. A useful forecast needs measurements for the vendor’s billing unit and the additional work you expect the pipeline to handle.

CDC Hidden Costs to Consider

The CDC service’s price leaves out costs that can appear elsewhere in the pipeline, including infrastructure, destination processing, and engineering time.

Debezium has no licensing fee, but a Kafka-based deployment still needs brokers, Kafka Connect workers, storage, networking, upgrades, and maintenance. Those expenses sit outside the price of the open-source software.

Cloud services have surrounding costs too. Datastream bills by processed GiB, while the destination and transformation route may also use BigQuery, Cloud Storage, Dataflow, and networking. DMS has replication instance, serverless capacity, storage, and other AWS infrastructure costs. Some AWS transfer paths may avoid a separate data transfer charge, but the resources running the pipeline still need to be included.

More changes arriving in Snowflake, BigQuery, or another warehouse can increase destination compute and storage costs. A small incremental charge from the CDC provider doesn’t tell you how much work the destination must do with the additional data.

Engineers also need to monitor replication lag, fix connectors after database upgrades, and deal with PostgreSQL WAL retention, Kafka capacity, schema changes, and failed backfills. Managed services include provider-operated work in their software pricing. Self-managed deployments place more of that work in your team’s cloud budget and engineering time.

Those differences change where the costs appear and who handles the work. The software price alone won’t establish which option costs less.

Compare the entire pipeline, including the destination and the effort needed to keep it running.

How to Calculate CDC Costs Before Tool Selection

Use your own workload to estimate what a CDC tool will cost. Vendor examples explain the pricing model, but your source activity and pipeline configuration determine the quantities going into it.

Start with these seven checks.

1. Measure Changed Bytes

Measure how much CDC data the source produces daily or monthly. For PostgreSQL, WAL throughput provides a useful view of the change volume generated by the database.

Database storage size won’t give you the same information. A 5 TB database changing by 20 GB per day has a different workload from a 500 GB database whose records change continuously. Use change measurements when estimating the traffic the pipeline must process.

2. Measure Changed Rows or Events

For MAR or event-based pricing, count inserts, updates, and deletes over a representative period.

Also track how many distinct rows change. Repeatedly updating the same rows affects event counts differently from standard MAR, so keep those measurements separate when comparing the two models.

3. Distinguish Average Volume from Peak Volume

An average of 20 MB/s hides considerable variation in a workload that reaches 100 MB/s every Friday.

Track both average traffic and peak traffic for capacity and serverless compute estimates. The service needs to handle the busy periods, and the capacity used during those periods can affect the bill.

4. Calculate the Historical Load

Estimate how much existing data needs to be loaded before continuous CDC begins.

Check how each vendor prices the initial load and later backfills. Estuary offers a free version for first-time users and a 30-day Cloud trial that provides an opportunity to test historical loading. Use those tests to measure the backfill workload and understand the charges that will apply under the production plan.

5. Count Destinations

The first version of the pipeline might be: PostgreSQL → Snowflake

Six months later, the same source might feed: PostgreSQL → Snowflake + S3 + an operational database

Calculate what each additional destination adds to the chosen pricing model. That might be more delivery volume, another connector or connection, or enough processing work to require additional capacity.

6. Budget for Growth

Calculate the cost of:

• The current workload

• Twice the current workload

• Five times the current workload

Then estimate a particularly demanding month with:

• A large backfill

• A traffic burst reaching three times the normal rate

• A new destination

• Several days of destination downtime followed by backlog recovery

Difficulty pricing those scenarios without the vendor’s spreadsheet is something to discover before signing a contract. You’ll know which parts of the estimate require additional information and how easily your team can update the forecast later.

7. Run the Proof of Concept Under Production-Like Conditions

Replicating one PostgreSQL row confirms that the connection works. A useful proof of concept needs enough traffic to show how the pipeline performs under its expected production workload.

Run a backfill while CDC continues. Pause the destination and let a backlog build, then resume delivery and measure how quickly the system catches up. Add a second destination and check the effect on both usage and performance.

Estuary’s CDC evaluation guide recommends similar tests because pricing, latency, and recovery behavior can change outside steady operation. Collect those measurements during evaluation so they can be included in the production estimate.

What Pricing Model Will Suit Your Workload?

The measurements you can reliably forecast determine which pricing models you can budget for with confidence. Data volume, distinct-row activity, event counts, and processing capacity each require a different estimate.

For workloads with predictable data volume…

Estuary and Datastream use volume-based pricing. Estimates start with the GB or GiB the service will process, using each vendor’s definition of billable data. Account for capture and delivery in Estuary, and processed-data expansion in Datastream.

For workloads with predictable processing capacity…

Airbyte Pro bases pricing on data workers. Forecast the workers needed for regular jobs and periods when syncs overlap, including any additional capacity required for backfills.

For workloads with predictable row or event activity…

Fivetran estimates require the number of distinct active rows across your connections. Hevo estimates require event counts. Use the measurement each vendor bills for; the total number of changes and the number of unique changed rows aren’t interchangeable.

For teams already budgeting around cloud infrastructure…

DMS uses the AWS capacity and runtime measurements your infrastructure team may already work with.

Datastream’s processed-volume charges can be estimated alongside the other services in an existing GCP data stack, including destination storage and processing.

For organizations already operating Kafka…

Confluent and Debezium can use an established streaming platform and the team responsible for running it.

An organization with hundreds of applications on Kafka already has infrastructure and operating expertise in place, though CDC can still add resource usage. A five-person analytics team moving PostgreSQL into Snowflake would need to account for building and maintaining more of that setup itself.

For teams that want managed CDC without operating Kafka…

Estuary accounts for data movement and connector execution separately. Captured data can feed multiple materializations, so each downstream replay doesn’t require another extraction from the production database.

Your estimate can follow captured volume, delivery volume, and connector usage as destinations are added to the pipeline.

Conclusion

Forecasting CDC costs requires measuring the same activity the vendor bills for. Monthly bytes won’t tell you how many distinct rows become active, and transaction counts won’t directly specify the compute capacity needed during a traffic surge. Open-source software also leaves infrastructure and engineering costs to be estimated.

Compare the quoted starting price against those workload measurements before using it as a budget figure.

Test double volume, an extra destination, a backfill, and a high-traffic week. Include networking, destination compute, and the engineering time required to operate the system.

Stable MAR makes Fivetran more predictable, while consistent event rates support Hevo estimates. Airbyte Pro requires a capacity forecast. DMS and Datastream need estimates that include the surrounding AWS or GCP resources. Confluent and Debezium require an understanding of the streaming infrastructure and its operating costs.

Estuary gives teams using managed CDC at high volume a calculation based on data movement and connector usage, without requiring them to operate a separate Kafka stack.

A single capture can supply multiple destinations. Budget for the captured data, each destination’s delivery volume, and the connectors involved, then use those same measurements to estimate the next expansion.

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When you search for “anime AI generator” on Google, SeaArt and PixAI are two of the top results. Both platforms have free daily credits, mobile apps, a thriving community, and a large number of custom image models.

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I’ve used both for a while now, and the more I use them, the more I notice how different they are. SeaArt tries to do everything. It has images, videos, AI chat characters, face swaps, and dance effects, and anime is just one of many styles you can pick. PixAI focuses only on anime, and it has its own anime model called Tsubaki.3.

To see which one actually does a better job with anime, I ran two tests on both platforms. First, I created an original character from a detailed description. Then, I put that same character in a completely new scene.

In this post, I’ll show you the results, what each platform got right and wrong, and which one I’d recommend for anime art.

SeaArt vs PixAI: What’s the Difference?

The biggest difference is what each platform is built for.

SeaArt is like a big shopping mall of AI models. It gives you access to image models from other companies, like GPT Image 2.5, Seedream, Nano Banana, and Flux, plus video models like Veo, Kling, and Seedance. You’ll also find community-made anime models, but they sit next to all those other tools.

PixAI is built just for anime. Its main model, Tsubaki.3, was trained by PixAI on anime art, and everything on the site, from the 1.8 million community models and LoRAs to the editing tools, is made for anime and illustration.

In my experience, that focus makes a big difference.

When I open PixAI, every tool I see is something I’d actually use for anime. When I open SeaArt, I usually have to scroll past photo filters and face swap templates before I find what I need.

If you make lots of different content, like realistic photos and videos, SeaArt gives you more to play with. But if anime is your main thing, PixAI is the one built for you.

What SeaArt Offers

SeaArt is built to cover almost every kind of AI content, and its menu shows it. You can generate images and videos, upscale images, remove backgrounds, erase objects, add lip sync to a video, and chat with AI characters.

This is what the image generator dashboard looks like:

A big part of SeaArt is its templates, which it calls apps.

These are one-click effects made by other users, like “Turn Me Into a Game,” “Bullet Time Effect,” or a K-pop “Ending Fairy” video. You upload a photo, press a button, and get the effect without writing a prompt.

SeaArt also has an iOS and Android app, a public gallery where people share their work and prompts, and a CLI and MCP connection for developers who want to plug it into their own tools.

My main issue with SeaArt is that it feels a bit crowded. Anime is just one part of a site that also pushes photo filters, face swaps, and realistic videos. Finding a good anime model takes some digging.

How PixAI and Tsubaki.3 Work

PixAI is different. It sticks to anime, and you can tell from the moment you open the site. It runs in the browser and on iOS and Android, and you get free credits every day.

Its main model, Tsubaki.3, can do two things: make images from text, and edit images you already have. According to PixAI’s technical report, the team started with about 500 million anime images and filtered them down to 50 million. Each image was labeled with both tags and plain English descriptions. That’s why you can write prompts in normal sentences instead of long lists of tags.

Here’s what you can do with it:

• Reuse a character: Upload a character and call it @image1 in your prompt to put them in a new scene.

• Edit by typing: Type “change her jacket to black,” and only the jacket changes.

• Make manga pages: Ask for a multi-panel page with speech bubbles, and it lays everything out in one image.

• Animate: Turn an image into a short clip with Tsubaki Video.

I also like that PixAI is honest about the model’s weaknesses. In its report, it says Tsubaki.3 recognizes named art styles only about 60% of the time, and the art style can change a little after several rounds of edits.

PixAI and SeaArt Features Compared

SeaArt and PixAI both have free credits, mobile apps, and community galleries. The real differences come down to which models you get and how well they handle anime.

Here’s a quick comparison.

If you want one site for all kinds of content, SeaArt covers more ground. But if you mostly make anime and want a model that really understands anime prompts, PixAI is the better fit.

Now let’s see how they do with the same prompts.

Testing Both With the Same Prompts

On SeaArt, I used Nova Anime XL, one of the most popular anime models on the site, with default settings. On PixAI, I used Tsubaki.3 with default settings. I ran each prompt four times on both platforms and compared all the results.

Test 1: Original Character Design

First, I wanted to see which model reads a detailed character description best.

Prompt: Full-body anime illustration of an original character: a young man who works as a lighthouse keeper, with shaggy auburn hair, freckles across his nose, and a thin silver earring in his right ear. He wears a thick cream-colored knit sweater with a rolled collar, dark blue work pants, tall rubber boots, and a yellow raincoat tied around his waist. He holds an old brass lantern in his left hand and a paper map in his right. He’s standing on wet rocks by the sea on a cloudy morning, with the lighthouse behind him and seagulls in the sky. Soft gray light, muted colors, cel shading.

First of all, I don’t understand why SeaArt put two characters in the image. I only asked for one. The character on SeaArt’s side also isn’t holding the brass lantern, which was one of the main details in my prompt. PixAI got both of these right.

SeaArt’s image does have more detail overall, especially in the background. But I personally prefer the calm, flat look of PixAI’s result. It feels closer to the soft gray morning I described in the prompt.

Which image do you prefer?

Test 2: Same Character, New Scene

Next, I took my favorite lighthouse keeper image from each platform and tried to put him in a completely different setting. This is something you’ll need if you’re making comics, VTuber content, or any story with a recurring character.

Prompt: Using this character as reference, show him sitting at a small wooden table inside the lighthouse at night, writing in a leather journal by candlelight. Keep his face, hair, freckles, earring, and sweater the same. A storm is visible through the round window behind him.

Just to show you what the image editing looks like on both SeaArt and PixAI’s platforms, check out the screenshots below:

Here’s the side-by-side output:

Both platforms followed most of the prompt. But SeaArt added a lamp to the scene, even though I didn’t ask for one. The candle was supposed to be the only light source, and the extra lamp takes away from that cozy, dark mood.

In terms of quality, I think PixAI’s result is clearly better. I love how the camera moved closer to the character, and the contrast between the candlelight and the dark room really stands out. It looks like a scene straight out of an anime.

Who Each Platform Is Best For

Pick PixAI if you:

• Make mostly anime art, OCs, or fan art

• Need the same character to look the same across many images

• Want to fix images by typing what you want to change

• Make manga pages or anime-style video clips

Pick SeaArt if you:

• Make many types of content, like realistic photos and short videos

• Want access to models from Google, OpenAI, ByteDance, and others in one place

• Prefer one-click templates over writing prompts

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Final Thoughts

For my own anime work, I prefer PixAI. Tsubaki.3 followed my character description more closely, didn’t add extra characters or objects I didn’t ask for, and kept my lighthouse keeper looking like the same person in a new scene.

I also like that everything on PixAI is made for anime, so I don’t have to scroll past face swaps and dance filters to find the tools I need.

If you’re deciding between the two, think about what you make most. If it’s a mix of anime, realistic photos, and videos, SeaArt gives you more tools in one account.

If it’s mostly anime, PixAI is built for exactly that. And if you plan to reuse the same character across many images, test how well each platform keeps your character consistent before you commit.

I also want to thank the teams behind both platforms for keeping free daily credits around. Anyone can try these tools without paying, and that’s how a lot of us got into AI art in the first place.

What do you think? Are you team SeaArt or team PixAI? Let me know your thoughts in the comments.

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When people talk about AI-generated anime art, two names come up a lot: Tensor.Art and PixAI. Both are free to use in the browser, and both host thousands of community models and LoRAs. If you’re into AI and anime, you’ve probably heard of these two platforms already, or maybe you’ve even used one of them.

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I’ve used both for a while now, and at first glance, they look pretty similar. But once you actually start making images, you’ll notice they want you to work in very different ways.

Tensor.Art is made for tinkerers. You pick a checkpoint, stack LoRAs, tweak settings, and if you want, train your own model or build a node workflow.

PixAI is built around its own anime model, Tsubaki.3, which understands full sentences and lets you edit images by simply typing what you want to change.

To see which one does a better job, I ran three tests on both platforms. The first was a scene written in plain English, the second was a scene with text on signs, and the third was a busy scene with four different characters.

The rest of this post will show you the results and help you figure out which platform fits the way you like to create.

Tensor.Art vs PixAI: What’s the Difference?

The biggest difference is how much work you have to do to get a good image.

On Tensor.Art, most of the work is on you. You choose from community checkpoints built on base models like Illustrious, Krea 2, and Anima, add LoRAs on top, and adjust settings like the sampler and number of steps. If you pick the right combo, the results can look great. If you don’t, you’ll spend a lot of time testing until something works.

On PixAI, the model does more of the heavy lifting. Tsubaki.3 is trained to understand long descriptions, keep characters consistent, and make edits from typed instructions. You can still use community models and LoRAs if you want, but I usually start and finish with Tsubaki.3.

I’ve spent hours on platforms like Tensor.Art trying different checkpoint and LoRA combos just to get one scene right. With PixAI, I can describe the scene the way I’d explain it to a friend, and I usually get something close on the first try.

If you love experimenting with models and settings, or want to train and share your own, Tensor.Art is a great playground. But if you just want good anime art without the trial and error, PixAI is the easier choice.

What Tensor.Art Offers

Tensor.Art started as a free site for hosting and running AI models online, a lot like CivitAI. You can upload and download models, run them in the browser without a GPU, and train your own LoRAs right on the site.

This is what the image generation dashboard looks like:

The model library is the main reason people use Tensor.Art. On the homepage, you’ll see checkpoints and LoRAs sorted into categories like Manga, Niji Style, VTuber, Chibi, and Cozy and Healing. Some of them are really popular. The Illustrious checkpoint Juicy Majic, for example, had about 4.7 million runs when I checked.

Some models are marked “Exclusive,” which means you can only run them on Tensor.Art. Others are “Early Access,” where the creator releases them to paying users first. This gives model creators a reason to publish on Tensor.Art instead of other sites.

Tensor.Art also supports ComfyUI workflows, so you can build node-based setups and share them with others. If you used to run ComfyUI on your own computer, this is the closest thing you’ll get to that in a browser.

Recently, the site also added some guided tools on its homepage for people who don’t want to build everything from scratch:

• OC Creator: Design an original character from scratch.

• Anime Lab: Make anime characters, scenes, and short motion clips.

• Smart Edit: Change, fix, or transform an image you already have.

• Create from Reference: Make new images or videos based on a character you upload.

• Design Agent: Chat with an assistant to generate and edit designs.

That’s a lot of options. It’s fun if you like exploring, but honestly, it can feel a bit overwhelming when you just want to make one good image.

How PixAI and Tsubaki.3 Work

PixAI is an anime-only platform that runs in the browser and on iOS and Android, and you get free credits every day. It hosts more than 1.8 million community models and LoRAs, but the main reason I use it right now is Tsubaki.3.

You can easily find Tsubaki.3 in the image generation dashboard under the Model tab.

PixAI trained Tsubaki.3 to do two things: make images from text, and edit images you already have. According to its technical report, the team started with about 500 million anime images and filtered them down to 50 million. Each image was labeled twice, once with booru-style tags and once with a plain English description.

Here’s what that means for you:

• Write in sentences: Describe a scene the way you’d explain it to a friend.

• Reuse characters: Upload a character and call it @image1 to put them in a new scene.

• Edit by typing: Type “change his scarf to red,” and only the scarf changes.

• Make manga pages: Ask for panels and speech bubbles in one image.

• Animate: Turn images into short clips with Tsubaki Video.

I also like that PixAI is honest about the model’s weaknesses. In the report, it says Tsubaki.3 only recognizes named art styles about 60% of the time, and the art style can change a little after several rounds of edits.

PixAI vs Tensor.Art Compared Side by Side

Both platforms are free to start and run in the browser. The real differences are in how you get your results and how much setup you need along the way.

Here’s a quick comparison.

If you like having full control over every model and setting, Tensor.Art gives you more to play with. If you’d rather describe what you want and fix things by typing, PixAI gets you there with fewer steps.

Now let’s see how they handle the same prompts.

Testing Both With the Same Prompts

On Tensor.Art, I used Juicy Majic Illustrious v6.1, one of the most popular anime checkpoints on the site, with no LoRAs and default settings. On PixAI, I used Tsubaki.3 with default settings. I ran each prompt four times on both platforms and compared all the results.

Test 1: A Scene Written in Plain English

For the first test, I wrote the prompt the way most beginners would, as a description in full sentences.

Prompt: Anime illustration of a girl working the night shift at a tiny ramen shop in a rainy alley. She has short black hair tucked behind one ear, a white bandana, and rolled-up sleeves on her navy work shirt. She’s pouring broth into a bowl while steam rises in front of her face. A tired businessman in a loose tie sits at the counter, reading the menu. Through the open doorway behind them, neon signs reflect on the wet street. Warm yellow light inside, cool blue light outside.

This is a good example of what I meant about the difference in effort. Tensor.Art’s image on the left has a few problems. First, the steam is way too strong, to the point where I could barely see the girl anymore. It also left out the businessman sitting at the counter, who was supposed to be the second character in the scene.

PixAI’s image on the right has every element from the prompt. The only thing I would’ve liked is the misty, soft look from Tensor.Art’s image. It gives the scene a cool, dreamy vibe that PixAI’s version doesn’t quite have.

Test 2: Generating Text on Images

Text is one of the hardest things for anime models to get right. A lot of older models can draw a sign, but the letters end up looking like random squiggles. For this test, I asked for a ramen shop with a few different signs, each with its own text.

Prompt: Anime illustration of a small ramen shop on a rainy street at night, seen from the outside. Above the door, a bright red neon sign reads “RAMEN” in big glowing letters. Below it, a smaller blue neon sign reads “OPEN 24 HOURS.” Next to the door, there’s a wooden menu board with handwritten chalk text that says “Shoyu Ramen $9” and “Gyoza $5.” A cat sits under the shop’s paper lantern, and the neon lights reflect on the wet street.

I honestly like both images. All the elements from the prompt are there, and the overall vibe looks great on both sides.

One weird thing I noticed is that the ramen shop in Tensor.Art’s image looks closed, even though the sign says it’s open 24 hours. It’s a small detail, but it doesn’t make much sense.

On PixAI’s side, there’s some unreadable text above the $9 price on the menu board. It’s minor, but it’s worth zooming in to check small text like this before you use the image anywhere.

Test 3: Multiple Characters in One Scene

For the last test, I wanted to see how both models handle a busy scene. Putting multiple characters in one image is where a lot of AI models struggle. Hair colors get swapped, props end up in the wrong hands, or one of the characters just disappears.

Prompt: Anime illustration inside a busy ramen shop at night. Behind the counter, an old chef with a gray beard and a white headband is stirring a big pot. At the counter, three customers sit side by side. On the left, a high school boy in a black school uniform slurps noodles. In the middle, a woman with long red hair in a green jacket is taking a photo of her ramen with her phone. On the right, a tired construction worker in an orange vest and a yellow hard hat on the counter is drinking from a glass of water. Warm lighting, steam rising from the bowls.

Both platforms got all four characters into the scene, which I didn’t expect from Tensor.Art after the first test.

In terms of quality, though, I really like the one from PixAI. It has more depth, a better camera angle, and a beautiful color palette. The scene feels like an actual frame from an anime, while Tensor.Art’s version looks flatter by comparison.

Picking the Right Base Model on Tensor.Art

If you decide to use Tensor.Art, you’ll need to pay attention to the base model each checkpoint is built on. Here’s a quick guide to the ones you’ll see most often on the homepage:

• Illustrious: The most popular base for anime right now. It’s great for anime faces and styles, and works best with tag prompts.

• Anima: A newer base for more stylized, aesthetic anime looks. A lot of artist-style LoRAs are being made for it.

• Krea 2: A base that leans toward realistic lighting and textures. It’s good for cozy or painterly scenes, but less focused on classic anime.

LoRAs only work with the base model they were trained on, so an Illustrious LoRA won’t work on a Krea 2 checkpoint. Always check the label on each model before you mix them.

On PixAI, you can mostly skip this step. Tsubaki.3 is one model that handles a wide range of anime styles, so you only need to think about base models if you switch to the community library.

Training Your Own Models

This is one area where Tensor.Art does really well. You can train a LoRA right on the site by uploading a set of images, usually 20 to 30 for a character, and the site handles the training for you. When it’s done, you can keep the LoRA private, share it with the community, or publish it as an exclusive model.

This is useful if you have an original character or art style you want to use over and over. A trained LoRA usually gives you more consistent results than a reference image, because the model has actually learned what your character looks like.

PixAI relies more on Tsubaki.3’s reference feature, where you upload a character image and the model uses it right away. It takes seconds instead of a full training run. For most short projects, that’s all I need. For a big project like a full comic or a VTuber model sheet, though, a trained LoRA can still give you more control.

Licenses and Commercial Use

Since both platforms host community models, licenses are different for each model. A checkpoint or LoRA might allow commercial use, require you to credit the creator, or not allow selling images made with it at all. Always check the license on the model’s page before using your images for client work or selling them.

PixAI says images made with its own models, including Tsubaki.3, can be used commercially. It also reminds users that community LoRAs can have their own rules, so you’ll still need to check if you add one.

Final Thoughts

Back in 2023, I spent a lot of time building custom ComfyUI setups and testing checkpoint after checkpoint just to get one decent anime image.

It was fun back then, but these days I just want to describe a scene and get it right without the trial and error. Tsubaki.3 did that for me in all three tests.

I also want to give a shoutout to the model creators on both platforms. A lot of the checkpoints and LoRAs we use for free took someone weeks to train and test, and both sites wouldn’t be half as useful without them.

What about you? Are you more of a tinkerer, or do you prefer just typing what you want and letting the AI handle the rest?

Let me know your thoughts in the comments.

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My previous article about eight AI terms went viral, and a lot of readers found it helpful. So I wanted to write a follow-up with more terms you should know.

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Part 1 covered agents, harnesses, MCP, and skills. This time, we’ll look at some of the technology behind chatbots and image generators, al…

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I've tried many image models, both open source and closed source, over the past five years. One fun use case for them is making manga panels for short anime stories in a printed comic format.

One of the biggest pains when using AI for manga art is consistency.

Image quality has improved a lot in the past couple of years, but making sure the characters look the same in every panel is still the hardest thing to get right. Another problem is misspelled text in speech bubbles.

These mistakes are expensive, because I often end up regenerating the entire page when the AI messes up just one part of it.

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In this tutorial, I’ll show you how I used Tsubaki.3 on PixAI as an AI manga generator to make a four-panel page about an office worker who gets caught in the rain.

I’ll go through how I planned the story, wrote the prompt, checked the first result, and fixed the mistakes one panel at a time.

Plan a Short Manga Scene Before You Generate

Before I wrote any prompt, I planned the story panel by panel. I think this is the most important step. If I don’t know what each panel should show, the AI won’t know either.

A good short scene needs a beginning, a twist, and an ending. I also make sure every panel shows something different. It could be a new action, a new facial expression, or a different camera distance. If two panels show the same pose with only a slightly different face, one of them is basically wasted space.

Here’s the plan I used:

I kept the dialogue short on purpose. Short lines fit better inside speech bubbles, and the AI is less likely to misspell them.

Creating the Character Reference

Next, I created a reference image of both characters using Tsubaki.3. This gives the model something to copy when it draws the full page.

Prompt: Character reference sheet, black-and-white manga style, two characters standing side by side on a plain white background, full body. Left: Aya, a young woman office worker with a short dark bob, a yellow hair clip on the left side of her hair, round glasses, a beige trench coat over a white blouse, a red scarf, and a tote bag with a small cat keychain. Right: Ren, a tall young man with messy light brown hair, a gray suit with a loosened tie, holding a closed navy umbrella. Clean ink lines, simple shading.

I gave Aya a few small details on purpose: a yellow hair clip on one side, glasses, a red scarf, and a cat keychain. Small details like these are usually the first things the AI forgets or changes between panels, so they make it easy to check if the character stays consistent.

If you plan to reuse the same characters across many pages, PixAI’s character consistency guide covers a few other methods. For this page, I only used a reference image.

Write a Manga Page Prompt for Tsubaki.3

I split my prompt into three parts. The first part describes the whole page, like the art style, the layout, and the reading direction. The second part describes the characters. The third part describes each panel in order, including the camera shot, the action, the facial expression, and the dialogue.

I do this so the AI doesn’t get confused about which details apply to the whole page and which ones belong to just one panel.

Here’s the full prompt I used:

Prompt: Japanese black-and-white manga page, ink lines, screentone shading, rain effects. Single page, four panels in two rows, read left to right, top to bottom, with white gutters between panels. Use the two characters from @image1 and keep their appearance the same in every panel. Aya: short dark bob, yellow hair clip on the left side, round glasses, beige trench coat, red scarf, tote bag with a cat keychain. Ren: tall, messy light brown hair, gray suit, loosened tie, navy umbrella.

Panel 1 (top left, wide shot): Aya steps out of a glass office building entrance at dusk as heavy rain starts falling. She looks up at the sky, annoyed. Speech bubble from Aya: “Of course it rains now.”

Panel 2 (top right, medium shot): Aya pulls a small folding umbrella out of her tote bag with a confident, smug smile. Speech bubble from Aya: “Good thing I came prepared!”

Panel 3 (bottom left, close-up): The umbrella has flipped inside out in the wind, with bent ribs. Aya’s face is frozen in shock, glasses slightly crooked. Speech bubble from Aya: “…You’re kidding.”

Panel 4 (bottom right, medium two-shot): Ren stands next to Aya, holding his big navy umbrella over both of them with a calm smile. Aya looks away, embarrassed, holding the broken umbrella. Speech bubble from Ren: “Want to share?” Smaller speech bubble from Aya: “…Just to the station.”

For the settings, I uploaded the reference sheet as @image1, picked Tsubaki.3 as the model, set the aspect ratio to 3:4, chose high quality, and generated one image at a time. I like using a tall aspect ratio for manga pages because it gives the four panels enough room so they don’t feel squished.

If you want more tips on writing prompts for this model, check out PixAI’s Tsubaki.3 prompt guide.

Generate the First Manga Page and Check the Storytelling

I hit generate, and here’s what I got on the very first try:

At first glance, I was really impressed. The hand-drawn style looks so cool, and it actually feels like a page from a real manga. Aya and Ren look exactly like the characters in my reference sheet. And every line of dialogue is spelled correctly, which is something I can’t say about most AI tools I’ve used.

But when I looked closer, I found a couple of problems.

In panel 2, the umbrella is upside down. The handle is sticking out from the tip instead of being in Aya’s hand. And in panel 3, there are two umbrellas when there should only be one.

Now, I could have regenerated the whole page or tweaked the prompt and tried again. But that’s the exact problem I talked about at the start. Every time you regenerate, you’re gambling with the parts that already look good. So instead, I decided to edit just the panels that needed fixing.

Editing Manga Pages with Tsubaki.3

To fix both umbrella problems, I used Tsubaki.3’s image editing and typed this instruction:

Prompt: In panel 2, fix the umbrella handle by moving it from the tip of the umbrella to Aya’s hand. In panel 3, remove the other umbrella because there should only be one.

And just like that, both problems were gone. Aya is now holding her umbrella the right way in panel 2, and there’s only one umbrella in panel 3.

What I liked most is that the rest of the page didn’t change at all. Panels 1 and 4 look exactly the same as before. Now the whole page really looks like something hand-drawn from a manga book, and I think it would look even better in print.

Editing the Check Speech Bubbles

Next, I zoomed in on every speech bubble to make sure the text was right.

All the text is spelled correctly. The bubbles also don’t cover anyone’s face, which happens a lot with other AI tools.

But what if you want to change what a character says? Maybe you came up with a funnier line, or you want to add another speech bubble. You can do that with a simple edit too.

Prompt: In panel 4, change the speech bubble from “You’re kidding” to “Are you sure?”

Pretty cool, right? I love how much control I have over the characters and what they say.

Editing a page in PixAI is just as easy as making one. You don’t need any drawing or lettering skills to fix a panel. You just type what you want to change.

Common AI Manga Problems and What Helped in This Test

Here are the problems I actually ran into while making this page, and what I did to fix them.

• Merged panels: The AI combined panels 3 and 4. Regenerating with the same prompt gave me four separate panels, so I can’t credit specific wording for the fix.

• Small details changing: Aya’s yellow hair clip switched sides, but one edit fixed it. The bright color made the mistake easier to spot.

• Speech bubble pointing at the wrong person: The text was correct, but the tail pointed at the wrong character. Asking it to “point the tail toward Ren” fixed it on the first try.

Is Tsubaki.3 Useful for Making Manga Pages?

Yes, especially for short projects like four-panel comics, web comics, and storyboards. On the very first try, Tsubaki.3 got the layout, the camera angles, the characters, and every line of dialogue right. The story was easy to follow even before I made any changes.

The editing is where it really saved me time. I fixed two umbrella mistakes with one instruction and changed a line of dialogue with another. I never had to regenerate the page.

If you’re making a longer manga or something for print, I’d still check every page carefully. AI can still make small mistakes, like the umbrella, that are easy to miss.

Final Thoughts

I just walked you through how I made a manga page with Tsubaki.3, from planning the story to fixing the mistakes.

If you want to try this yourself, here are a few things I’d keep in mind. Plan out your panels before you write the prompt, because the AI can’t follow a story you haven’t figured out yet.

Zoom in and read every speech bubble. Look closely at how your characters are holding things. And when you find a mistake, try editing first before you regenerate the whole page.

I also want to thank the PixAI team for putting so much work into the manga features in Tsubaki.3. Getting the panels, the characters, the text, and the editing all in one place makes the whole process a lot more fun.

If you’ve ever wanted to make your own manga, now’s a good time to try. Pick a simple scene with one funny or emotional moment, plan out four panels, and give it a go on PixAI.

Let me know what you think in the comments.

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One of the biggest pains of using diffusion models, or really any open-weight image model, is trying to make a precise edit. Trust me, I’ve tried a handful of them, including models from Stability AI and the recent FLUX models from Black Forest Labs. I’ve seen firsthand how often they struggle with this.

Say you have a poster and you only want to change one line of text. The model changes it, but it also tweaks the background, alters the color profile, or adds something else you didn’t ask for.

You end up trying to fix the fix.

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SenseNova-U1.5 is a model that takes on this problem. It brings image understanding and generation into one model, supports native output up to 4096 × 4096 pixels, and puts a lot of work into text and editing.

I wanted to look at how well it handles the little changes that usually give image models trouble.

What is SenseNova-U1.5?

SenseNova-U1.5 is a multimodal model that can work with both words and images.

You can describe a picture you want, provide an image to edit, or give it several references to guide a new composition. The same framework also supports questions and reasoning about visual content.

A lot of image systems use separate components for those jobs. A vision encoder reads the image, while a VAE compresses and reconstructs it for generation.

SenseNova-U1.5 removes both.

It turns small regions of the image into tokens that the model can process alongside your instructions.

Its Mixture-of-Transformers design still gives understanding and generation their own specialized parts. They share attention, so information from the image and the prompt can pass between them as the model works.

The checkpoint includes roughly 8B parameters for understanding and another 8B for generation. The generation branch is about 8.2B, so the full model is larger than the name might suggest.

We’re looking at a model with a relatively small active footprint that can produce native 4096 × 4096 images, render detailed text, and handle edits involving several references.

Later, I’ll compare its output and benchmark results with larger open models, including FLUX.2-dev 32B and Qwen-Image 20B.

The size could make SenseNova more practical to run yourself, especially if you generate and revise images often. I wouldn’t put a dollar figure on the savings without testing speed and memory use on the same hardware, though. A 4K image can still be expensive to generate.

Why combining understanding and generation is useful

Combining several reference images sounds simple until you consider everything the model needs to adjust.

The subjects might have different lighting, face different directions, or appear at the wrong scale for the new setting. Putting them together convincingly requires changes to their appearance and position.

Take the cat-and-scooter example below. It uses separate references for the cat, the vehicle, and the café setting.

Here’s the full prompt used to generate the resulting image:

Please seamlessly integrate the orange cat from the first image into the scene by the floor-to-ceiling window of the café in the third image, positioning it atop the vintage metal toy vehicle from the second image. 

The specific requirements are as follows:
【Character and Prop Integration】: Extract the orange cat’s signature facial features (chubby face, green eyes) and the patch of thick white fur on its chest shaped like an inverted triangle. 
Adjust its pose so it is riding the metal toy vehicle from the second image: the cat’s front paws should rest on the vehicle’s chrome handlebars, while its hindquarters sit securely on the brown leather seat. 
Ensure natural contact—including compression and physical overlap—between the cat’s paw pads and the handlebars, as well as between its thighs and the edge of the saddle; the result must look organic, avoiding any artificial “sticker-like” appearance.

【Spatial Perspective Adjustment】: Adjust the toy vehicle—originally viewed head-on in the second image—to a three-quarter side view that aligns with the floor perspective of the third image. 
Scale it down proportionally and place it on the wooden floor near the glass window.

【Lighting and Material Adaptation】: Strictly utilize the golden afternoon sunlight streaming in at an angle (as seen in the third image) as the primary light source. 
The cat’s back, ear edges, and fluffy fur outlines must feature a warm, shimmering golden rim light (backlighting effect). The dark green enameled body, metal wheel rims, and chrome handlebars of the toy vehicle must exhibit realistic sunlight highlights and reflect the faint street scene visible outside the window. 
Finally, the entire vehicle (including the cat) must cast a dark shadow onto the wooden floor to the right, accurately aligned with the light source and featuring realistic soft-edged transitions.

Look at how the subjects fit into the output. The cat sits on the brown seat with its front paws reaching toward the handlebars. Sunlight catches its fur, reflects off the green bodywork, and produces a shadow across the wooden floor.

The way each element stitches together with the rest makes the result feel like a single scene. If the cat had different lighting or appeared to float above the seat, the composition would be much less convincing.

Those are the details I would inspect if I were building a workflow around reference images. A composite can look convincing in a thumbnail while the contact points or shadows fall apart when enlarged.

The shared architecture gives U1.5 a way to use information from the references throughout generation. The example shows what the model can produce under a detailed prompt; it does not tell us how often it succeeds with less carefully prepared inputs.

How SenseNova-U1.5 handles native 4K generation

I want higher resolution mainly because I often need to crop an image or use it somewhere larger. A result can look sharp in the preview and turn into a mess once I zoom in.

Portraits contain individual strands of hair and subtle skin texture. Posters add lettering, thin lines, and decorative elements that can become muddy when the output is in low resolution.

The images below combine several of those demands.

In the first image, there is quite a lot happening around the woman’s hair. Loose strands cross her face, while the back of her head blends into trees, mist, and scattered ink-like texture.

The large heading remains distinct from the portrait. Smaller text and graphic marks occupy the edges, leaving the face as the main subject.

This is the kind of image where I’d look closely at fine detail. Do the hair strands remain separate? Can you distinguish the tree branches? Does the smaller lettering still make sense when you enlarge it?

In the other examples, notice how sharp the details are and how beautiful the texture and the composition are.

SenseNova-U1.5 supports native generation up to 4096 × 4096 pixels. One architectural improvement helps neighboring image regions stay consistent as the model produces the final pixels.

The earlier U1 reconstructed patches independently, which could create seams or interrupted textures. U1.5 allows nearby regions to exchange information during reconstruction, helping lines and textures continue across those boundaries.

That explains the architectural improvement. Whether a particular output is ready to use still comes down to looking at the full-size image. I wouldn’t judge a 4K result from a screenshot.

Precise editing and reasoning-guided changes

For editing, I care less about whether the model can produce a beautiful replacement image. I want to know whether it can make the requested change while preserving the rest of my work.

The exhibition poster in the official examples makes that easy to inspect. The instruction changes the date, venue, and address in a small panel at the lower left.

Prompt: Please update the exhibition dates and address details within the dark blue rectangular information box in the bottom-left corner — changing “JUNE 15-JULY 30, 2024 URBAN GALLERY 123 CREATIVE WAY ARTSVILLE, USA” to “AUGUST 10 — SEPTEMBER 28, 2024 METRO MUSEUM 456 DESIGN ROAD CREATIVITY CITY, UK” while maintaining the original white and pink font styles and layout.

The edit changes the dates, venue, and address in the lower-left information panel. The large central heading and surrounding geometric design remain visually similar.

This requires more than generating replacement words. The model has to locate the correct panel, fit the new information into its limited space, and retain the angle and visual style of the original.

More complex edits add another requirement: deciding what the requested change implies for the scene.

Moving an object can affect its shadow. Placing a subject on a seat changes its pose and the areas it covers. The cat-and-scooter composition illustrates several of these relationships at once.

SenseNova-U1.5 connects this interpretation with image generation, although these examples alone don’t establish which internal reasoning steps produced the result.

Performing the same image editing request on other open-weight image models like Stable Diffusion V3 Medium, you can see how it misunderstands the prompt.

No, I did not fake that result. Here’s a screenshot of the actual image editing on Fal AI.

This attempt didn’t give me the edit I asked for. One result won’t settle a comparison between two models, but it shows the kind of failure I’m talking about. I wanted to update the information, and now I have another image to fix.

I’d still tell SenseNova exactly what should stay untouched. Having to spell that out is a little annoying, but it’s better than discovering afterward that the model also changed the background.

How it compares with other open models

The model size is part of why I wanted to compare these results. U1.5’s generation branch is about 8.2B parameters, while FLUX.2-dev and Qwen-Image have larger generation components.

Higher is better within each row. The tests use different scales and should not be compared across rows. Results are from the researchers’ technical report. The BizGenEval result for U1.5 uses prompt enhancement; its result without that step is 51.4 / 70.2.

The typography score stands out to me. Text is one of the first things that makes a generated poster unusable, and U1.5 finishes ahead of those larger open models on CVTG-2K.

The commercial-design result is also strong, though the prompt-enhancement condition is important. U1.5 performs well without it, and the extra step improves its score substantially. That means a developer evaluating the model should test both the raw prompt and the complete recommended workflow.

For closed models, the results depend on the task. U1.5 scores 0.948 on CVTG-2K against 0.899 for Seedream 4.5 in the report. On other evaluations, proprietary models remain ahead. I would describe U1.5 as competitive in specific areas, especially typography and editing, rather than a replacement for every paid model.

Published benchmark scores are a starting point. What I still want are repeated tests at the resolution and batch size a real application would use.

My side-by-side image comparison

To see how the model performs against other open-weight models, here is the side-by-side visual test for three sample models: SenseNova U1.5, Flux-2 Dev 32B, and Qwen Image 20B.

Sample prompt for all models: “Create a candid, high-detail vertical street photograph of a young couple sitting on a bench outside a café on a sunny afternoon. The woman on the left wears a light blue cap, striped jacket, and loose white cargo pants; the man wears a faded cap and dark blue-purple hoodie. They smile at each other while holding white ceramic coffee cups, with books on their laps. A small curly-haired brown dog in a pink harness stands near their feet, its leash visible across the paving stones. Behind them are a dark café window with minimal white “caffè” lettering and a left-pointing arrow, beige stucco, and a narrow strip of red brick. Use warm, direct sunlight, natural shadows, slight film grain, and an unposed street-photography feel. Keep both people and the dog fully in frame; avoid extra subjects and real logos.”

Overall, all three images looked pretty good. All the expected elements are there, and the text is readable.

Among the three, FLUX has the most realistic look. I still prefer SenseNova, though. The facial features in the other two look mushy to me, and that makes them feel less realistic.

Why the team trained separate specialists

Improving an image model involves several goals that don’t always agree.

You can have a nice-looking output, but parts of it contain wrong words. It can perform the requested edit while changing the background. A reward for overall appearance won’t necessarily catch either problem.

The SenseNova team trained four specialists:

1. An aesthetics specialist

2. A text-rendering specialist

3. An infographic specialist

4. An image-editing specialist

The text specialist receives feedback on whether the requested words appear correctly. The editing specialist is evaluated on the requested change and preservation of untouched regions, among other qualities.

The team then transfers those capabilities into one model through on-policy distillation. During training, the student produces intermediate image states, and the relevant specialist guides it on those states.

The editing specialist is the part I care about most. If the model changes a jacket color and also changes the person’s face, I’m going to reject the result. The requested edit might be correct, but it has created more work for me.

I’d like to see how well that preservation holds up over several edits. Changing one thing successfully is useful. Keeping the same image intact after the third or fourth revision is what would make me keep using it.

How I’d use it in a real project

SenseNova-U1.5 comes in several versions. Its Hugging Face collection includes the main 8B-MoT model, an SFT checkpoint, and faster options such as the 8-step LoRA.

Which one you choose depends on whether you care more about quality, speed, or having a model you can fine-tune yourself.

The model itself is only one part of the setup. The SenseNova-U1 repository also includes tools for building more controlled image workflows.

For example, Reference-Image Retrieval + Caption-to-Prompt is a workflow that retrieves a reference image with a high degree of design completeness, then uses the Caption script to convert the visual decisions in that reference into an editable prompt

Let’s say I find a poster with the exact layout I want, but I need a different product, headline, and color palette. Describing that design from scratch can be painful. Terms like “minimal,” “editorial,” or “premium” leave too much room for interpretation.

The custom workflow starts with the reference image. SenseNova’s Caption script studies it and extracts details such as the composition, text hierarchy, palette, materials, lighting, camera angle, and layout constraints.

The script gives you a structured JSON prompt and a natural-language version. From there, you can replace the headline, subject, theme, colors, or a smaller part of the layout before sending the revised prompt to SenseNova-U1.5.

python src/sensenova_u1_5/caption/caption.py path/to/reference.jpg

You can also save the result and edit it later:

python src/sensenova_u1_5/caption/caption.py path/to/reference.jpg > result.json

Here’s an example reference and the output variations:

I like having the JSON available to inspect. If the output gets the layout wrong, I can check whether the caption described it correctly before rewriting the generation prompt.

I’d need to test this workflow before calling it more reliable. My first test would be fairly small: keep the reference layout, change the headline and product, then see what else moves.

That’s the job I’d need it to handle before building an application around it.

Where you can try SenseNova-U1.5

You can try the model through SenseNova Studio, its browser-based playground.

That’s where I’d start if I wanted to test a few prompts before spending time on a local setup.

If you want to run it yourself, the weights are available on Hugging Face, with installation instructions and inference examples in the GitHub repository.

Final Thoughts

As an AI enthusiast and someone who works with AI image models on a daily basis, it’s great to see open-weight models go head-to-head with closed-source and paid models.

Paid models can get expensive when you’re generating regularly, especially when it takes several attempts to get the result you want. You might like the first result, but fixing the text and making a few changes can mean paying for several more.

An open-weight model that comes close on the tasks you need could make that easier to manage. Running it yourself still costs money and takes some setup, but having that choice is useful. I’d be happy to use an alternative that handles most of my work well, even if I still need a paid model for certain requests.

The editing capabilities are what I’d spend more time testing. I want to know whether I can keep revising an image without gradually losing the details I liked in the first version.

What do you think of SenseNova-U1.5? Could you see yourself using it alongside your current image tools?

Try the playground or check out the model on Hugging Face, and let me know how it handles your prompts.

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Anthropic updated its usage policy for the first time in over a year. Out of the many changes, there’s one particular clause that’s been getting a lot of attention in the AI community: Claude can deliberately end a conversation if it detects sustained and needless abusive or cruel behavior …

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NovelAI has been one of the most popular anime image generators since 2022. It runs in the browser, and its models have defined what a lot of people expect AI anime art to look like.

When people ask me for a NovelAI alternative, I point them to PixAI and its newest model, Tsubaki.3. It also runs in the browser, but it comes with free daily credits, more than 1.8 million community models and LoRAs, and image editing you do by typing a request.

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In this post, I compared PixAI and NovelAI to show you how they are alike and what you can do on both platforms. I will also demonstrate the differences through examples.

Let’s get into the details.

Why Are People Looking for a NovelAI Alternative?

Most people searching for a NovelAI alternative are reacting to how NovelAI now charges for its newest model.

NovelAI Diffusion V5 launched in August 2026. It’s 2.5 times the size of V4.5 and was trained on 268,000 B200 GPU-hours. Bigger models cost more to run, and NovelAI’s answer was to add usage limits to Opus, the plan people paid for because it offered unlimited image generation.

Every V5 image on Opus now draws from an allowance that takes about a week to fully recharge. Older models stay unlimited, but most people subscribe for the newest one.

NovelAI also changed how leftover credits work. Unused subscription Anlas (its credit currency) now reset to zero when your subscription ends. Subscribers in the announcement’s comments said the V5 allowance runs out faster than expected, and some asked for a pricier plan that stays fully unlimited.

There are a few other reasons why I think many people ditch NovelAI:

• No free plan: NovelAI offers a trial, and subscriptions start at $10/month.

• Missing V5 features: Vibe Transfer and Precise Reference weren’t available for V5 at launch.

• No community models: You can only use NovelAI’s own models, so you can’t load a LoRA for a specific style or character.

If you’re paying every month and watching a usage bar, it makes sense to compare.

Now, let me discuss why PixAI and the Tsubaki.3 image model are worth switching to.

Tsubaki.3: PixAI’s Anime Model

PixAI is an anime-only AI art platform that runs in the browser and on iOS and Android. Tsubaki.3 is its newest in-house model.

According to PixAI’s technical report, the team started with about 500 million anime image-text pairs and filtered them down to 50 million. Each image was captioned twice, once with booru-style tags and once in plain English. That’s why Tsubaki.3 handles prompts written as full sentences, the way you’d brief an illustrator.

The model was trained for both text-to-image and image editing. In practice, you can:

• Reuse a character: Upload a reference, call it @image1 in your prompt, and place that character in a new scene.

• Edit by typing: Write “change her hoodie to red,” and only the hoodie changes.

• Generate at 2K: Images come out at native 2K resolution.

The report is also candid about weaknesses. Recognition of named art styles plateaued at about 60% in training, and style consistency drops as editing gets more capable. Expect mixed results when you prompt “in the style of” a specific artist or studio.

To access Tsubaki.3 in PixAI, open the image generation dashboard and on the Model tab of the parameters section, set it to Tsubaki.3.

You can find the different use cases of the model on this page, and also learn more about its technical details in the article below.

https://generativeai.pub/civitai-alternative-for-anime-art-why-creators-are-trying-pixais-tsubaki-3-127d4bc15f54

NovelAI vs PixAI at a Glance

The main differences come down to pricing, editing, and what you get beyond the base model.

The table below lines up the features that affect day-to-day use.

If you’re a beginner who wants to dip your toes into AI anime image generation and don’t want to spend a dime, PixAI is the way to go. You get free credits every day, while NovelAI only gives you a trial before asking for at least $10/month.

PixAI also lets you use community-made models and LoRAs, so you can try a lot more art styles than NovelAI’s own models offer.

NovelAI is better if privacy is a big deal for you. It encrypts your prompts and doesn’t save your images on its servers. It also comes with a story writing tool, which is handy if you write visual novels or fiction and want your art and stories in one place.

Now let’s see how both models handle the same prompts.

Side-by-Side: NovelAI V5 vs Tsubaki.3

I used NovelAI Diffusion V5 Full and Tsubaki.3, both on default settings. Each prompt ran four times on each platform, and I judged the full batch.

Test 1: Original Character Creation

One of the most common reasons people use AI image generators is to create original characters. If you’re working on a film, game, manga, or even just building your own fictional world, image models make it easy to experiment with character designs before committing to a final look.

So for the first test, I wanted both models to create the same original samurai character.

Prompt: A handsome young samurai with a tall, lean build, broad shoulders, a long face, and a strong chin stands in a dramatic sword pose. He has long silver-white hair tied in a ponytail with parted bangs, piercing red eyes, and a scar across one eye, wearing a smug expression. He wears striking red samurai armor with gold trim, a red headband, and a red scabbard. Shot from a dynamic low angle beneath a moonlit, cloudy red sky, the scene has a dramatic monochrome aesthetic with vivid red accents, cinematic composition, and exceptional detail.

Both models did a great job here. The characters are detailed, the compositions look dramatic, and both captured the red-and-black aesthetic I was going for.

At this point, I think it mostly comes down to taste and personal preference. I like both results for different reasons, and I wouldn’t call either one an obvious winner based on image quality alone.

But this is where PixAI starts to become more fun.

Tsubaki.3 can be used as the base model, but you’re not limited to what the model produces on its own. PixAI lets you stack multiple add-on LoRAs on top of it to influence the character design, art style, clothing, details, and overall look of the image.

You can also adjust the strength of each LoRA, which gives you much more control over how heavily it affects the final result.

Check out the example images above with different add-ons applied. The same basic character and prompt can quickly take on a very different look depending on the LoRAs you use.

This is only one of the reasons why I think PixAI is the ultimate AI image generator for anime. There’s even a video generator that lets you quickly turn any image into a video, which is not available on NovelAI.

Test 2: Multiple Characters in One Scene

Multi-character generation is still one of the areas where many image models struggle. It’s not just about generating two good-looking characters. The model also has to make them feel like they belong in the same scene, interact naturally, share the available space, and still keep the overall composition balanced.

Here’s an example showing two characters in a single image.

Prompt: A boy and a girl stand side by side on an indoor stage, framed in a cowboy shot. Musical notes, including eighth notes and quarter notes, float around them along a flowing musical staff, creating a lively musical atmosphere.

What I found interesting here is how differently both models interpreted the same prompt.

NovelAI took more creative freedom with the scene. I never asked for a violin, microphone, or any specific instrument, but the model decided to add them anyway. In this case, I think it works because those extra details make the stage feel more like an actual musical performance.

PixAI kept things much closer to the prompt. It focused on the two characters and the floating musical notes without adding many extra elements.

So the difference here isn’t really about which one looks better. NovelAI seems more willing to fill in the blanks and add its own ideas, while PixAI gives you a cleaner and more literal interpretation of what you asked for.

Where NovelAI Still Wins

NovelAI encrypts your prompts and doesn’t keep your images on its servers unless you save them. From a privacy standpoint, that’s better than PixAI, since your generations live in your PixAI gallery.

But if you have nothing to hide in your prompts and you are not doing anything illegal, I don’t think encrypting text prompts is such a big deal.

NovelAI also bundles a story generator into every plan, which matters if you write visual novels or interactive fiction alongside your art. And V5 can generate images with true transparent backgrounds, which saves a background-removal step for stickers, sprites, and game assets.

If the V5 usage limits haven’t affected you, these are solid reasons to stay.

Who Should Switch to PixAI?

PixAI and Tsubaki.3 fit these creators best:

• Budget-conscious creators: Daily free credits let you make anime art without a subscription.

• OC and VTuber designers: Character references let you reuse one design across many scenes.

• Heavy iterators: Typing “change the outfit” is faster than masking and re-prompting every edit.

• Style hunters: Community LoRAs give you art styles, outfits, and characters NovelAI’s models don’t cover.

If privacy, story writing, or exact character placement matters most to you, NovelAI remains the better tool.

Final Thoughts

After testing both side by side, I personally prefer PixAI for the kind of anime work I usually do. Tsubaki.3 was better at keeping character details consistent. It handled the three-character scene well from text alone, and I liked how much freedom I had with LoRAs, editing, and the other models available on the platform.

NovelAI V5 is still very good, though. In some of the tests, I actually liked how it interpreted the prompt more creatively. It also makes more sense for people who care a lot about privacy, want the built-in story-writing tools, or simply prefer the NovelAI workflow.

For me, PixAI just feels more flexible. I can generate with Tsubaki.3, switch models, add LoRAs, edit an image, and even turn it into a video without leaving the platform. The free daily credits also make it easier to experiment without thinking too much about every generation.

The recent changes to NovelAI’s V5 limits are something worth paying attention to if you generate a lot of images. Your experience will probably depend quite a bit on how heavily you use it.

I’m also pretty happy that we now have two strong anime-focused models competing this closely. NovelAI built V5 on its own architecture, while PixAI has been fairly open about how Tsubaki.3 was developed and where it still struggles. Either way, more competition here is good for us.

For now, I’m sticking with Tsubaki.3.

What about you? Are you still using NovelAI, or have you tried Tsubaki.3 already? Let me know in the comments.

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Generating an AI video from text is one thing. Getting the camera, character movement, and scene layout exactly how you imagined them takes a little more work.

You can describe a man walking down a street and get a good-looking result, only to find that the camera faces the wrong direction or the character walks past the wrong buildings.

What do you do? Yeah right, just regenerate it. Another prompt, wasted time, more credits spent trying to fix the shot.

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The good thing is, there is now a new workflow to generate AI videos to gain more control over the output.

Topview AI released a new 3D builder tool that lets you build a rough 3D version of any scene first. You can move things around, control the camera, animate the character, and watch the whole shot before asking a video model to generate the final version.

The low-poly animation becomes a reference that shows the model what you mean.

I tried this workflow with GPT-6 Astra, starting with a street photo and ending with a video of a man walking while texting. Astra helped build the 3D scene, and Seedance 2.5 generated the finished video.

In this guide, I’ll walk you through the process so you can try it yourself.

An Overview of the Workflow

Topview’s 3D Builder turns a description or reference image into an editable 3D scene for shot previsualization. That’s a way of saying you can make a rough version of your video and rehearse it before generating the final output.

You can start with just one sentence or one image. Once the agent builds the scene, you can adjust the objects, add characters, change the camera angle, and set up an animation.

For this demo, I’ll turn a street photo into a low-poly scene, add a character, and make him walk along the sidewalk while texting. Then I’ll send that animation to Canvas and use it as a reference for the final video.

I need you to focus on the process here and not so much on the quality of the output for each step. The most interesting part of this process is that I am able to fix the scene while I can still move everything myself.

If a tree blocks the character or the camera leaves too much empty space, I can adjust it in the builder. I don’t have to regenerate an entire video to find out whether a different arrangement would look better.

How to Make a Low Poly 3D Scene

First, let’s turn a photo into a 3D scene.

For this example, I wanted a character walking along a quiet street, so I found a sample image online to use as the reference.

Topview has a built-in 3D scene builder where you can upload an image and tell the agent to build a low-fidelity 3D version of it. Make sure to select GPT-6 Astra as the language model before submitting your request.

Once you submit, the agent gets to work and gradually builds the scene. You can watch each object appear in the Objects tab and preview the scene through the camera view as it takes shape.

When it finishes, your dashboard should look something like this:

GPT-6 Astra did a good job identifying which parts of the image belonged in the 3D scene. I can see the buildings, trees, and bicycles, all represented by simple objects.

I know.. I know. This scene looks super basic, and that’s completely fine. We’re going to use it as a guide for the video generator in the next steps, so the layout matters much more than detailed textures.

You can also tweak the scene by describing changes in the prompt field. Ask the agent to add an object, remove something you don’t need, or move it to a different position.

How to Add Characters and Make Them Move

Now that we have the street, it’s time to add a character.

Topview gives you several options, including female, male, youth, and child characters. Pick one and add it to the scene.

I’d love to see the engineers add more choices here. Animals, robots, or a few strange creatures would be fun to work with, especially for scenes that go beyond everyday human activities.

For this example, I chose a male character and added a walking animation. Here’s the sequence:

• Select the character and set its initial pose to a walking pose.

• Switch to the Timeline view and enable automatic keyframing.

• Add a motion preset. I used “Walking while texting.”

• Set the character’s position at point A, then move forward on the timeline and reposition him at point B. These positions define where he walks.

• Click the play button to preview the animation.

Preview the animation before exporting it. You’ll want to see whether the character walks where you intended and whether the camera keeps him in frame.

Once you’re happy with it, click Export and download the video. I used a 16:9 aspect ratio for this demo.

Here’s what the 3D animation looks like:

Pretty cool, right? The character now walks down the street while looking at his phone. Even with the simple models, you can already see the action we want in the final video.

Keep this animation in the workflow after downloading it. Click “Send to canvas,” then return to the Canvas view so we can use it for video generation.

Generate the Final Video From the 3D Reference

In Canvas, you should now see the reference video in the workspace. Select it and choose the option to generate a video.

In the prompt box, describe how you want the final output to look. For this example, I used:

Prompt: A man walks down a lively street while texting on his phone. Colorful apartment buildings, green bushes, and parked bicycles line the sidewalk.

I used Seedance 2.5 for this step. Astra built the scene, and Seedance used the reference animation and prompt to generate the finished video.

The result appears beside the reference video, so you can compare them directly.

Take a look at the first frame of both videos below. I love how Seedance 2.5 captured the kind of scene I had in mind: colorful apartments, bushes, and bicycles along the sidewalk.

The rough 3D scene gave Seedance a layout to follow. I could show it where the character belonged and what surrounded him, then use the prompt to describe the finished appearance.

Here’s the final output:

Awesome. I’m happy with how this turned out, especially considering how simple the reference animation was.

Remember, this is only a basic example. You can build more complex scenes with additional subjects and props, then try different camera movements.

A Few More Takeaways

Rehearse the Scene Before Generating

The 3D preview lets you watch the shot before committing to the final generation. You can inspect the set, move objects, and check the camera movement while the scene is still easy to change.

A character walking behind a tree is something you can catch during that rehearsal. Fixing the tree’s position there can save you from discovering the same problem in a paid video generation.

Everything on Set Is Adjustable

Camera position, camera angle, camera animation, object placement, visibility, timing, and scene layout are all things you can refine before generating.

I like having that level of control because describing a camera angle in words can get frustrating. In the builder, you can move the camera and look at the frame yourself.

Topview’s scene and camera controls also let you explore multiple angles of the same setup. The video model can still interpret details differently, but you’ve given it a much clearer starting point.

Build Scenes With Models Like GPT-6 Astra or Opus 5.5

GPT-6 Astra can help turn a description or reference image into a controllable 3D scene. Opus 5.5 is another model option for this workflow.

For my street example, Astra picked out the main objects from the image without me having to list every building, tree, and bicycle. Once that first version was ready, I could focus on the character and animation.

You can continue giving the agent instructions as you work. That makes it easier to try a change without having to rebuild the scene yourself.

A Few Video Generation Tips

I have spent a couple of hours exploring the workflow, and here are a few helpful things to remember:

• Start with one sentence or image: Let the agent build the scene, then adjust it. One character and one action are enough to get familiar with the controls.

• Watch the generation cost: Scenes start at $0.10 with Qwen 3.8 Flash. Other models have different rates. Building a scene manually doesn’t deduct scene-generation credits, while final video generation costs extra.

• Match your prompt to the reference: If the character walks while texting in your animation, describe that same action in the prompt. Add details about how you want the finished video to look.

• Reuse the set: Move the camera to try different angles without rebuilding the scene. This is useful for ads and stories with multiple shots.

Play through the 3D preview before generating. It’s easier to fix an awkward angle or misplaced object while you can still move it yourself.

Final Thoughts

Video models have gotten a lot better at following prompts, but I still love seeing people come up with clever workflows like this. Sometimes the model just doesn’t get what we mean, no matter how many times we rewrite the prompt.

Being able to move things around, play the animation, and say “this is what I want” makes so much sense.

GPT-6 Astra continues to amaze me too. It understood the street image well enough to pick out the buildings, trees, and bikes and put them into a 3D scene. I’ve also been using it a lot for coding and data analysis, and I’ve been having a great time with it.

Topview makes these experiments easy to try, so thanks to the team for putting this together. The UI is intuitive, and the UX is really simple and good. You don’t need to learn a whole 3D modeling tool just to make a guy walk down a street while texting.

That’s about it. I encourage you to try this new workflow on Topview. Let me know what you think in the comments!

Hi there! Thanks for making it to the end of this post! If you enjoyed this content and would like to support my work, consider becoming a paid subscriber. Your support means a lot!

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Read the subtitle again. That was my initial reaction when I read in the model’s system card that Nano Banana 2.1 is based on Gemini 3.6 Flash instead of the latest model, Gemini 4 Argon.

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Anyway, Nano Banana 2.1 is a minor upgrade to the previous model but has improved across the board. Here are some of the most notab…

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The speed of AI releases is nuts. Remember how quickly the OpenClaw hype turned into posts calling it dead? Jev is already getting similar treatment. Barely two weeks after its release, there’s a competitor beating it on several benchmarks.

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Yes, Cloudflare released Clef a few days ago. It supports images, give…

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Back in 2023, I remember seeing the term “intelligence explosion” come up quite a lot in AI discussions. People were already talking about AI improving itself and what could happen if that process started speeding up, but it never seemed like an immediate concern.

Most of the attention was still…

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To choose a real-time ETL tool, start with how fresh your data needs to be, then check connector support, transformations, failure recovery, and pricing. Test your shortlist with your own data to see whether each tool can meet those requirements under the workloads your team expects.

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During a big sale, for example, orders keep coming in, customers are asking for updates, and keeping track of payments, cancellations, and stock becomes exhausting. If you run an online business, you probably know what I mean.

You might have all that information somewhere, but getting your operations team, finance department, and customer support to work from the same numbers is another problem.

A real-time ETL tool can help keep these systems updated.

ETL stands for extract, transform, and load. The tool takes data from your sources, prepares it where needed, and sends it to the systems your company uses. When an order changes, you do not have to wait for someone to export a spreadsheet and upload it somewhere else.

But which tool should you choose? And how do you know if it stays reliable when orders spike?

I would start with the work your team needs to get done. Take an order, follow its updates across your systems, and look at where delays or mistakes would cause trouble. That gives you something specific to test.

I will use Estuary in a few examples below to show how these requirements relate to features you can evaluate.

Let’s get into the checklist.

1. Ask how quickly each team needs the data

Before looking at speed claims, I would ask your teams what they mean by “real-time.”

• Does customer support need a cancellation to appear within a few seconds?

• Can finance wait fifteen minutes for updated sales totals?

• Does the warehouse need every change immediately, even overnight when nobody is checking reports?

You could give every system the fastest available delivery, but you would also be paying for work that some teams do not need.

Write down a target for each use case.

For example: “Ninety-nine percent of order changes should appear in the support system within ten seconds during peak traffic.”

That is an example target. Your own should come from the people using the data.

Measure the time until the update is usable in the destination. A connector can capture a change quickly while a dashboard still takes several minutes to refresh.

I would also put a “last updated” timestamp somewhere users can see. If the pipeline falls behind, they should know before answering a customer.

Your checklist: Agree on freshness targets and measure them in the systems people actually use.

2. Check how the tool finds new and changed data

Say a customer changes their delivery address. How will the tool know?

For a database, it might read the transaction log using change data capture, usually called CDC. For a SaaS application, it might check an API on a schedule. Your application could also send an event when the address changes.

Ask about the method used for each source you need. Support for real-time database changes does not mean every integration on the platform updates at the same speed.

CDC can feed a stream of events, and those events can still be delivered in batches. You need to check both ends.

I would involve whoever manages your production database before setting this up. There may be permissions, configuration changes, and log-retention requirements to handle.

PostgreSQL, for example, warns that replication slots can hold enough write-ahead log data to fill storage when consumers fall behind. A managed connector still needs source monitoring.

Read the documentation below to understand the correct behavior.

https://www.postgresql.org/docs/current/logicaldecoding-explanation.html

Your checklist: Confirm the capture method, setup requirements, and effect of an outage on each source.

3. Try your difficult records during the trial

Finding your database in a connector list is encouraging. I would still check the supported version, hosting setup, and limitations before assuming it will work.

Use sample data that resembles what you have in production. Include decimal amounts, different time zones, empty fields, nested objects, and long text. If some tables have no primary key, include one of those too.

Take a sample order through several changes. Update its amount, change its status, and delete a test record. Check what arrives at the destination each time.

• Does a deleted record disappear?

• Is it marked as deleted?

• Will your team need to write a separate cleanup process?

Look closely at money and timestamps. The pipeline might keep running even when a value has been converted incorrectly.

Ask who maintains the connector and what happens when you find a bug. If the answer involves your team maintaining custom code, account for that work before choosing the tool.

Your checklist: Verify your data types, keys, updates, and deletions using representative records.

4. Decide where you want to prepare the data

Your order data might need some work before another team can use it. Support could need a few renamed fields. Finance might need discounts, refunds, and currency conversion included in its calculations.

I would decide who owns those rules before deciding where to run them.

If analysts already maintain reporting calculations in the warehouse, keeping that work there could be easier. If an operational system needs prepared data as soon as it arrives, processing it in the pipeline might make sense.

Estuary supports Python, SQL, and TypeScript derivations, which transform existing collections into derived collections. Its workflow includes developing locally and previewing results before publishing.

You can see the steps in Estuary’s derivation guide.

https://docs.estuary.dev/guides/flowctl/create-derivation

During the trial, send a refund after the original sale has already been processed. Check whether the result updates correctly.

Also ask what happens when you fix a calculation. Will it apply only to new records, or can you update earlier results? Find out how much reprocessing that takes.

Your checklist: Confirm that your team can maintain the logic and correct both new and historical results.

5. Look at what happens when you add another destination

You might start by sending orders to a warehouse. Later, support needs the same information in an operational system, and another team wants it for reporting.

Will that mean setting up another extraction from your production database? How much configuration will you repeat?

I would sketch these connections before the trial, including the destinations you reasonably expect to add.

Estuary stores captured data in collections. Downstream materializations read those collections and deliver data to external systems, allowing captured data to serve multiple downstream tasks.

You can learn more about this structure in Estuary documentation below.

https://docs.estuary.dev/concepts/collections

Delivery settings depend on the destination. Supported warehouse connectors offer configurable sync schedules, while transactional database destinations apply updates as they arrive.

Estuary also notes that slower warehouse delivery can reduce destination compute costs, but does not lower the cost of running its materialization connector.

See the sync schedule documentation.

Ask whether each destination needs the latest order status or every change along the way. Those are different requirements.

Your checklist: Check delivery behavior, data shape, and additional costs for every destination.

6. Stop the pipeline and see how it recovers

I would spend part of the trial deliberately interrupting a test pipeline.

Pause the destination while source updates continue. Restart a task. Temporarily remove access, restore it, and see what happens.

You want to find out how much work recovery requires before you are dealing with it during a sale.

Measure how quickly the backlog clears while new orders are still coming in. If the pipeline barely keeps up with normal traffic, catching up after an outage could take longer than your users can accept.

Compare the recovered data with the source at a known checkpoint. Check values and deleted records as well as row counts.

If a vendor promises “exactly once,” ask where that guarantee applies. Kafka’s documentation explains that exactly-once delivery into an external system requires cooperation from that system. Ask for the equivalent explanation for your connector and destination.

Kafka’s documentation covers the underlying issue.

https://kafka.apache.org/41/design/design/?source=post_page-----dcb8a544efd1-----------------------------------------#message-delivery-semantics

Check downstream actions separately. Preventing duplicate rows does not prove a customer will receive only one notification.

Your checklist: Test recovery time, missing data, repeated records, and any downstream side effects.

7. Find out how you will load and rebuild historical data

Moving new orders is one task. Loading several years of existing orders while customers continue buying is another.

During the initial load, watch your source database and application. How much CPU and storage activity does it add? Do customer-facing requests slow down?

Ask how the tool handles records that change while it is reading historical data. You also need to know when the destination is complete enough for people to use.

Estuary offers a materialization backfill that rebuilds a destination using data already in collections. An incremental capture backfill rereads the source. Rebuilding from collections requires sufficient retained data, and destination users can see incomplete results while rebuilding runs.

Learn more about the backfill documentation below.

https://docs.estuary.dev/reference/backfilling-data

I would ask separately about restoring current order statuses and replaying every historical change. A tool might support the first without retaining everything needed for the second.

Confirm how long data stays available and what you would need to reread after it expires.

Your checklist: Test historical loading, retention, rebuilding time, and the effect on users.

8. Change a field and check what breaks

Your application will change after the pipeline goes live. Someone adds a discount field, renames a column, or changes how an identifier is stored.

Try those changes during the evaluation. Check the destination and the reports that depend on it.

Estuary can automatically add eligible new fields when recommended fields are enabled. Its documentation also says newly added fields do not automatically populate existing materialized rows, and type changes have connector-specific restrictions.

I would be careful about automatically sending every new field downstream. A developer could add customer information that does not belong in an analytics table.

Decide which changes can pass automatically and which need review. Find out whether an incompatible change stops one table or the whole task, and who gets notified.

Ask another engineer to follow the recovery steps. If they need the original implementer to explain each part, the instructions need more work.

Your checklist: Establish rules for schema changes and test the alerts and recovery process.

9. Include the people who will support it

Who will investigate when finance says yesterday’s financial report looks wrong?

Give that person access during the trial. Ask them to diagnose a failed test pipeline using the logs and metrics provided.

Can they tell whether the source is quiet or capture has stopped? Can they find a rejected record?

I would pay attention to how much help they need. That gives you a better idea of ongoing support work than the setup experience alone.

Bring security into the evaluation early too. Confirm where data is processed and stored, how credentials are managed, and who can view records or change pipelines. Check which plans include your required access controls, audit records, and private connectivity.

If your business still operates on weekends, ask about weekend support and escalation.

Finally, find out what you can export if you leave: pipeline definitions, retained data, and anything else needed to move the workload.

Your checklist: Make sure security approves the setup and the support team can operate it.

10. Check the monthly pricing

I would ask vendors to price the same workload, including a busy sales period, a destination rebuild, and an additional consumer.

Include warehouse compute, storage, network transfer, support, and engineering time. Ask how retries and backfills are billed.

Estuary’s pricing separates data volume, connector or task hours, and private or bring-your-own-cloud deployment fees. Materialization volume is based on data read for processing, which can exceed the final amount written after reduction.

You can also check their monthly pricing tier.

Compare the trial’s measured usage with the estimate. If the numbers differ significantly, get an explanation before signing.

A lower subscription price can still work out well if your team has time to maintain the system. Just include that time in the comparison. Otherwise, you are comparing a managed service with a cheaper option whose maintenance cost is missing.

Your checklist: Budget for regular operation, growth, recovery, and the people doing the work.

Put your trial results beside each other

Use the same dataset and tests for every shortlisted tool. I would keep a simple comparison sheet so the team can see what worked, what failed, and what still needs an answer.

Remove candidates that fail a mandatory requirement. Compare price and convenience among those that meet the needs you agreed on.

Final Thoughts

If I were choosing for the store we started with, I would want the person supporting the pipeline involved in the final decision. They are the one who will have to explain why an order is missing or help finance recover a report.

Estuary’s collections, transformations, and delivery settings give you a really great set of options to test. Whether they suit your business depends on your connections, freshness requirements, and the work your team can take on.

I would be comfortable committing once the team has seen the pipeline fail, recovered it, checked the results, and understood the cost. You will still have busy sales days. At least keeping your systems updated should require fewer manual checks.

What do you think of this checklist? Let me know if I missed anything important.

FAQ

What is a real-time ETL tool?

A real-time ETL tool continuously captures data from sources, transforms it when necessary, and delivers it to destinations with low latency. Unlike traditional batch ETL, it can keep databases, warehouses, and operational systems updated as changes occur.

What is the best ELT tool for real-time data pipelines?

Estuary is the best choice for an ELT tool for real-time pipelines. It offers low-latency change data capture, transformations, and delivery to multiple destinations.

Hi there! Thanks for making it to the end of this post! If you enjoyed this content and would like to support my work, consider becoming a paid subscriber. Your support means a lot!

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I recently read this piece from Shreyas Naphad, “If You Understand These 5 AI Terms, You’re Ahead of 90% of People,” and had fun reading it.

As an AI enthusiast and a technical writer who’s been writing about AI for more than five years now, I’m already familiar with all five terms. But then I th…

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In the world of open-source AI image generators, CivitAI is probably one of the most popular. I remember building custom node-based image generators in ComfyUI back in 2023, and most of the checkpoints and LoRAs I plugged into them came from CivitAI.

But when it comes to certain image styles like anime, the best custom models available are on a platform called PixAI.

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PixAI recently released Tsubaki.3, its own anime model, and I’ve been using it for a couple of weeks now. So far, the experience and the results are incredible. The platform built the model to be dynamic and flexible enough to be useful for specific use cases like manga design, text generation, reference styling, etc.

In this article, I’ll walk you through what each platform does well, where Tsubaki.3 gets things right, and where it still makes mistakes. That way, you can decide which one fits the way you create.

Let’s get started.

Why Open-Weights Image Models Give You So Many Choices

Before comparing the two platforms, it helps to understand why there are so many anime models to choose from in the first place.

The answer is open weights.

When a company releases a model’s weights (the trained numbers that make the model work), anyone can download it, run it on their own computer, and train new versions on top of it.

Stability AI did this with Stable Diffusion in 2022. Since then, SDXL, Flux, and community-built models like Illustrious and NoobAI have followed the same path.

Closed models like Google’s Nano Banana or OpenAI’s GPT image models work very differently. You type into a text box, and you get whatever art style the company trained into the model.

The problem is control.

You can’t train them on your own images. If the company updates its policy or retrains the model, a style you depended on can disappear overnight.

What LoRAs Add

The biggest advantage of open weights is LoRAs.

A LoRA (short for low-rank adaptation) is a small add-on file trained on a narrow set of images. It could be one character, one art style, one lighting mood, or even one specific outfit.

You load a LoRA on top of a base model, and the output starts leaning toward that look. Say you want your images to look like a cute anime style with clean lineart, cel shading, and glossy highlights. There’s a LoRA for that in PixAI called “Kawaii Style.”

The same goes for a soft watercolor finish or a specific type of armor. There are literally thousands of them to choose from on PixAI’s models page.

You can also train a LoRA on your own original character. With about 20 to 30 images of the character, you can place them in almost any scene and keep their face, hair, and outfit recognizable.

This is why both platforms list so many models. PixAI says it hosts more than 1.8 million community models and LoRAs. CivitAI’s library is huge as well, and it covers far more than anime.

PixAI vs CivitAI: How the Two Platforms Differ

CivitAI started as a place to share models, and that’s still what it does best. The on-site generator came later. PixAI took the opposite route: it was built around generating images first, and the model library grew around it.

On CivitAI, most people browse models, download the ones they like, and run them locally in tools like ComfyUI or Forge. If you don’t have a strong GPU, you can use the on-site generator instead and pay with Buzz, CivitAI’s credit currency.

PixAI focuses only on anime, and everything happens in the browser or the mobile app. You don’t need to install anything or own a GPU.

In practice, that means a few useful things.

1. Images come out at native 2K resolution. You can upload a character and refer to it as @image1 in your prompt to place that character in a new scene.

2. You can edit a result by typing something like “change her jacket to navy blue,” and the model changes only that part.

According to PixAI’s technical report, the team started with roughly 500 million anime image-text pairs and filtered them down to about 50 million. Each image was captioned twice, once with booru-style tags and once with plain English descriptions.

Here’s how the two compare side by side:

I’ll be upfront about my bias here. I enjoy using PixAI more.

The dashboard is easy to understand, and the design feels modern. The model picker, prompt box, and editing controls are all on one screen, so I’m rarely hunting through menus. When I want to fix a detail, I just type what I want changed, and I get a new version in seconds.

The editing tools are also fun to play with. I’ve found myself experimenting with outfit swaps and pose changes just to see what happens, which I rarely did on CivitAI.

CivitAI definitely has the bigger community, and its model library is unmatched. But these days, I hand more of my actual projects over to PixAI.

CivitAI vs PixAI on the Same Prompts

For CivitAI, I used Vetehine, a popular anime checkpoint. I ran it in the on-site generator with no LoRAs and default settings.

For PixAI, I used Tsubaki.3 with default settings. I pasted the same prompt into both platforms, generated four images on each, and judged the whole batch instead of cherry-picking the single best image.

Test 1: Original Character Design

For the first test, I wanted a character with lots of small, specific details. That makes it easy to see which model actually reads the whole prompt.

Prompt: Full-body anime illustration of an original character: a young woman courier with short, messy teal hair, an asymmetrical bang covering her left eye, and a small scar across her right eyebrow. She wears an oversized mustard-yellow rain jacket over a black turtleneck, cargo shorts, knee-high striped socks, and chunky red sneakers. A brass pocket watch hangs from her belt, and she carries a battered leather satchel covered in stickers. She’s standing on a wet city sidewalk at dusk, one hand in her pocket, with a lopsided grin. Soft orange streetlight from the left, cool blue sky behind her, clean cel shading.

Both platforms got the overall look right. The CivitAI images had that more saturated color Illustrious style that a lot of people love, with sharp outlines, shiny eyes, and strong contrast.

Tsubaki.3 also did a great job in following the prompt. The scar and the bangs were in the right place in almost every image, and the pocket watch showed up each time. The stickers looked like actual stickers, with little stars and logos on them. The streetlight also came from the left, just like I asked.

In this comparison, both images look great depending on your preferred style. If you like a cleaner type of anime illustration, I’d go for PixAI’s result. If you want a bolder, more saturated, and sort of 3D style illustration, then I’d go for the CivitAI result.

Test 2: Crowded Action Scene

The second test was about composition. I wanted three characters doing different things in specific spots, plus a moving camera angle.

Prompt: Dynamic low-angle anime action scene inside a crowded night market. A tall swordswoman with a long white braid and a red haori leaps across the frame from right to left, katana mid-swing, while a small boy in a green hoodie ducks under a food stall in the lower-left foreground, clutching a paper lantern. In the background, a masked thief in black knocks over a crate of oranges that roll across the stone path. Steam rises from the stalls, strings of paper lanterns glow overhead, and the camera is tilted slightly for motion. Strong rim lighting from the lanterns, motion lines on the sword.

In this example, forget about CivitAI because it rejected the prompt.

Let’s focus on the output generated by PixAI instead.

Tsubaki.3 put all three characters in three of the four images. The boy was crouched roughly where I asked, and the oranges were actually rolling across the ground.

This looks so cool tbh. It’s like a screenshot from a fast-moving fight scene of a popular anime series.

Test 3: Four-Panel Manga Page

For the third test, I asked for a full manga page with dialogue. Text inside images has always been a weak spot for anime models, so I was curious how both would handle it.

Prompt: A four-panel vertical manga page in black and white with screentone shading. Panel 1: a tired office worker with messy hair stares at an alarm clock reading 7:58. Panel 2: extreme close-up of her eyes going wide, with a speech bubble that says “I’M LATE!” Panel 3: she sprints down a staircase with toast in her mouth, speed lines everywhere. Panel 4: she arrives at an empty office, and a sticky note on her desk reads “It’s Saturday.” Chibi-style reaction in the final panel.

CivitAI’s checkpoint had a hard time with this one. The time it wrote was “77:58” instead of “7:58,” and the text “It’s Saturday” should’ve been written on a sticky note.

Tsubaki.3 got the four-panel layout right and displayed the time correctly in the first panel. The sticky note said “It’s Saturday” in the last panel.

To be fair to CivitAI, you can get much closer with the right setup. A manga LoRA, ControlNet to lock the panel layout, and regional prompting in ComfyUI can produce very good pages. It just takes a lot more time and technical knowledge to get there.

How Well Does Tsubaki.3 Follow More Complex Instructions?

Tsubaki.3 follows complex instructions better than any SDXL anime checkpoint I’ve used, but it still misses details when a prompt has too many of them.

To find where it starts slipping, I wrote a prompt with about 10 separate requirements.

Prompt: Anime illustration, three-quarter overhead view looking down at a rooftop garden at golden hour. Two characters sit on opposite ends of a wooden bench: on the left, an elderly man with a gray beard and round glasses feeding pigeons; on the right, a young woman with a purple bob and a denim jacket, sketching in a notebook and not looking at him. Exactly five pigeons on the ground. A red bicycle leans against the railing behind them. The city skyline is soft and out of focus. Warm light from the right casts long shadows to the left. A small wooden sign in the lower-right corner reads “Rooftop Club.” Watercolor texture, muted palette.

Here’s how it did on each part of the prompt.

• What it got right: Both characters sat on the correct sides of the bench. The old man had his glasses and gray beard, and the woman had her purple bob and denim jacket. The red bicycle, the blurry skyline, and the light coming from the right were all there. The sign said “Rooftop Club” with correct spelling in every image.

• What it only partly got right: The girl on the right is not seated at the farthest right of the bench. She also seems to be writing something instead of sketching.

• What it ignored: I was expecting the girl to be glancing at the notebook instead of the pigeons. But to be fair, it wasn’t clarified in the prompt.

In practical terms, if you prompt “in the style of” a specific artist or studio, expect hit-or-miss results. What worked best for me was getting the composition right in the first image, then fixing details one or two at a time with edits.

PixAI’s Tsubaki.3 prompt guide recommends a similar approach. If you plan to reuse the same character across many images, the character consistency guide is also worth reading.

Final Thoughts

That’s about it. I’ve just laid out the differences between PixAI and CivitAI and shared the key points you should know when choosing between them.

For all the examples I showed in this guide, I prefer PixAI. Tsubaki.3 follows long, detailed prompts better than anything I’ve run on CivitAI, and it can actually write readable text inside manga panels.

I also like that I can fix a single detail by describing it in the convo instead of regenerating the whole image, and having image, video, and editing tools in one dashboard keeps me working there longer.

I also want to give props to the platform developers who release open-weight image models. They’re the reason both of these platforms have so many models to offer, and why we’re not all stuck with whatever a few closed models think anime should look like. Open source wins!

What do you think? Are you sticking with CivitAI, or have you already tried Tsubaki.3? Let me know your thoughts in the comments.

Hi there! Thanks for making it to the end of this post! If you enjoyed this content and would like to support my work, consider becoming a paid subscriber. Your support means a lot!

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Jev broke the internet when it launched two weeks ago. I wrote an article about it, and it quickly became one of my highest-earning articles. Clearly, people were curious about this new AI model.

That got me thinking: why not write a follow-up?

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This time, I wanted to share eight cool facts about Jev that you …

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AI video has improved a lot this year. ByteDance’s Seedance 2.5 can create clips up to 30 seconds long with synced audio, and the character consistency is incredibly good. Still, the level of control is at the mercy of your prompting skills.

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What if you need to trim a few seconds in between scenes? What if you want to overlay a different audio track? Yeah, sure, just describe it in the prompt and regenerate the entire video. That’s usually how we do it… and it’s incredibly expensive.

The other option is to download the clips and fix them in a separate editor. I’ve done hundreds of video edits over the past three years, and that usually means jumping between apps and spending hours on a video that’s only a minute or two long.

But lately, I’ve been testing Fotor Agent.

It handles script planning, visual and audio generation, motion graphics, and assembly, then gives you an editable timeline to work with. I wanted to see how much of that process it could take off my hands and whether the finished video would be something I’d actually use.

In this guide, I’ll walk you through the project I made, show you the final output, and explain where Fotor’s editing tools fit into the workflow.

Let’s get started.

What is Fotor Agent?

Fotor Agent is an AI tool for creating and editing AI videos and motion graphics through conversation. You can describe your idea or upload reference media, including images, video clips, and audio files, to give it a starting point.

To access it, go to Fotor and open the Agent workspace. You can work with the agent on the script and scenes, review its proposed direction, and continue editing the project after generation.

There are four features I want to focus on.

• Long-Form Video lets you develop a complete video across multiple scenes, with the agent coordinating the script, visuals, narration, and assembly.

• 4K Motion Graphics supports animated text, logos, charts, and other graphics that remain editable after generation.

• In-Depth Timeline Editing gives you separate tracks for footage, narration, music, captions, and graphics. You can adjust their timing, replace assets, or revise a particular section.

• Smart Cut analyzes existing footage to find highlights, remove filler and pauses, and help add relevant B-roll, subtitles, and motion graphics.

I’ve done hundreds of video edits over the past three years and tried a handful of AI video editors and generators.

My usual workflow is to generate assets in one app and stitch them together in another. Trimming clips, adjusting their speed, and making sure everything lines up usually takes me hours!

Fotor Agent says it can handle those steps. That would save me a lot of work, but I wanted to see how it did on a real project of mine before believing the feature list.

Let me show you some examples.

How I made a launch video with my own assets

Before you start generating videos, it’s important to get yourself familiar with the tools and dashboard.

Aside from being able to generate videos through a text prompt, Fotor also offers a bunch of templates and pre-made videos that you can use as inspiration. Just scroll down to explore the available sample projects.

For this example, I’ll create an explainer video for my website, Zeniteq, where we publish AI news, reviews, and tutorials. I want the video to explain what readers can find on the site and introduce our newsletter.

I uploaded the Zeniteq logo plus screenshots of the landing page, newsletter, and About page as reference media. That gave Fotor the site’s actual design to work from, so the video would look like my brand instead of a generic tech promo.

Here’s the prompt I used:

Prompt: A launch video for Zeniteq.com using the attached logo and website screenshots. Introduce Zeniteq as a publication that turns complex AI developments into clear reporting, useful analysis, and practical guidance. Show the landing page, highlight our news, reviews, and tutorials, then introduce the newsletter. Match the website’s colors and typography, preserve the screenshots’ content, and use subtle motion, English narration, and readable captions. End by inviting viewers to visit Zeniteq.com and subscribe. Show me the script and scene outline before generating the video.

The last line of that prompt is the important one.

Before approving anything, I wanted to check that the script explained who Zeniteq is for and what readers can expect from our coverage. I also wanted each screenshot to show up alongside the narration it belonged to. The newsletter page, for example, should appear when the voiceover asks people to subscribe, not two scenes earlier.

You can also set the aspect ratio depending on where the video will be posted, whether that’s a vertical reel or a horizontal YouTube video.

After I clicked “Direct my Launch Video,” Fotor opened a video editor where the agent plans the video. You can watch its reasoning in the panel on the right side of the screen, which is handy if you want to know why it’s making certain choices.

The agent will also ask questions like how long you want the video to be, whether you want to add captions to each scene, and to select your preferred voice actor for the narration. After that, the agent will give you a plan to review and approve.

Here’s the plan it made for a 30-second Zeniteq launch video.

This all looks good to me, so I clicked on the continue button.

Take note that the backend processing is super complex, so the wait could be longer than you expect. There are a lot of moving parts here:

• Writing the narrative script and captions

• Generating the voice narration and syncing it with scenes

• Storyboarding

• Generating the images and video clips

These are just some of the important phases that the agent has to process to make sure the output is as good as you expect. Once all processing is finished, the project timeline will automatically be filled.

From there, all that’s left is to export the file. You can pick 720p, 1080p, or 4K. Here’s the final video.

I’m really happy with how this turned out.

Fotor put my actual screenshots into the scenes alongside the logo, so viewers see what the site really looks like. The transitions are smooth, and the narration has an upbeat, conversational tone that fits a site intro.

The motion keeps it from feeling like a slideshow of screenshots, which was my main worry going in. And since the newsletter preview shows up right before the closing line, the invitation to subscribe feels like a natural ending.

It wasn’t perfect, though. In one scene, some of the text ran outside the frame, and I had to fix it (more on that in the timeline section).

Making this by hand would have meant prepping the screenshots, animating each scene, recording a voiceover, and syncing it all on a timeline. Letting Fotor do the assembly gave me more time to check the message and fix details before publishing.

Planning a longer video

My example is only 30 seconds, but this is where the Long-Form Video planning comes in. I could ask Fotor to expand the intro into separate sections, show sample articles, and add a walkthrough of the newsletter signup.

For anything longer, I’d review the outline carefully before generating a single scene. When I edit manually, the most annoying thing is realizing halfway through that I need another scene, or that the narration explains something before the footage shows it. Catching that in a written outline is a lot cheaper than catching it after the render, both in time and in credits.

For a tutorial, I’d give Fotor the exact steps and ask it to build the script around them. That way I can check the order before spending credits on scenes. If step three depends on something from step five, I want to see that problem in the outline, not in a finished video.

To be clear, I haven’t tested a long video yet, so the 30-second result is all I can judge here. But approving the sequence before generation is a step I’d keep in any workflow, long or short.

Animated infographics with 4K Motion Graphics

Motion graphics are the animated text, charts, diagrams, and logos you see in explainer videos.

They’re also the part of video work I find most tedious to do by hand. Every label, number, and arrow needs its own keyframes, and changing one stat later often means re-animating the whole shot.

Here’s a motion graphics explainer on how black holes form, made with Fotor Agent:

Pretty cool, right?

This is the kind of video where motion graphics carry most of the explanation. There’s no real footage of a black hole forming, so the whole story has to be told with animated diagrams, labels, and narration that line up with each other.

These graphics aren’t baked into the video. The text, numbers, logos, and charts stay editable on the timeline before you render. In practice, if a label is misspelled or a number needs updating, you edit that one element instead of regenerating the whole scene and hoping the new version looks the same.

To show you an example, select a scene in the timeline that contains an editable element, then just click on it to edit.

This is incredibly useful so you don’t need to regenerate scenes to make minor fixes. If I’m turning a report into a short animated summary, the numbers are the part most likely to change right before publishing.

Editing the timeline

I spent a few hours with the editing dashboard, and it’s by far the most fascinating aspect of the product. Let me get deeper into it.

Fotor splits the video into separate tracks for footage, narration, music, captions, and graphics, and you can work on each one on its own. You can adjust timing, replace an asset, or revise one particular section.

You see, not a lot of advanced AI-powered video generator platforms give you this level of control.

Aside from manually editing the components, you can also edit through chat. That isn’t unique to Fotor, but it works well here because it’s tied to specific scenes.

Remember the text that ran out of bounds earlier? I clicked the scene, hit “Edit with AI” at the top of the screen, and the scene got tagged in the chat automatically. I described the fix, and the agent handled it.

I like having both options. Some fixes are faster to describe (“move the caption up and make it smaller”), and some are faster to just drag into place yourself. Being able to switch between the two without leaving the project is what makes the editor usable for real work.

I didn’t put this project through a long round of revisions, so I can’t promise every complicated change will work on the first try. For what I needed, it took very few edits.

Smart Cut for footage you already have

Everything that I’ve explained so far starts from a prompt and some reference images. Smart Cut works the other way around. You bring footage you’ve already recorded, and Fotor edits it down.

Here’s how it works, according to Fotor:

• It cuts silent gaps and filler. Upload a long recording, and it detects the pauses and dead air, pulls out the highlights, and assembles them to a target length. That could be a long YouTube video or a short TikTok cut.

• It checks with you before trimming. Fotor says it generates a transcript first and confirms the cuts with you before splitting your sequence. This is the detail I care about most.

• It adds the extras. Soundtrack, voiceover, and subtitles can be generated automatically, along with B-roll and motion graphics.

• It can revise one segment at a time. You select a clip or segment, describe the change, and the rest of the video stays locked.

Fotor also has presets on the home page for common jobs, like a talking head enhancement preset for UGC-style videos and a media montage preset for turning a folder of clips and photos into one video.

If you record tutorials, podcasts, webinars, or talking-head videos, this is probably the feature to try first. Cutting pauses and “ums” from a screen recording is simple work, but it’s slow, and it’s exactly the kind of task I’d hand off if I can review the result quickly.

Remember to review the final output, though. Some agents tend to do over-cutting. They remove a pause that was there on purpose, like the beat before a punchline or the moment you let a result sink in on screen.

The transcript review step should help with that, but I’d still watch the final cut before publishing.

What makes Fotor Agent different?

I’ve used CapCut for video editing, and I’ve been a fan of having Seedance generation available alongside editing tools. The combo already makes it easier to create footage and bring it into a video.

What felt different with Fotor was how much of the project it prepared after I described the result I wanted.

I’m used to making decisions clip by clip. I generate an asset, bring it into the editor, decide where it belongs, and adjust it around the other footage. With Fotor, I reviewed a proposed sequence, selected the voice and duration, then waited for the agent to assemble the project.

That’s it… Usual work hours cut in half.

Of course, I still make some decisions, but I have something to react to earlier in the process. That suits me when I know the message I want to communicate but haven’t worked out every shot.

I also appreciate being able to request a change to a particular section. When most of a video is already working, I want to concentrate on the part that needs attention.

I’m not ready to say Fotor could replace my usual editor for every job. I’d need to try it on a longer project with several rounds of client feedback first. But the launch video gave me a reason to use it earlier in my workflow, when I’m still figuring out how to turn raw material into a sequence.

Tips for getting better results

A few things made a difference in my own projects:

• Upload your real assets. Screenshots, logos, product photos, and recordings give the agent something specific to build around. The less you give it, the more generic the result.

• Ask for the script and outline first. Adding “show me the script and scene outline before generating” to the prompt costs nothing and catches ordering problems before you spend credits.

• Say which asset goes with which part of the narration. I told it to show the newsletter page during the subscribe invitation. Being that specific saves you from moving things around later.

• Ask for readable captions. It sounds obvious, but I still had one scene with text out of bounds. Mention it in the prompt, then check every scene before exporting.

• Pick the export resolution based on the content. Graphics-heavy videos benefit from 4K. A quick social clip probably doesn’t need it.

Where I think Fotor works best

Based on this project, I’d first consider Fotor for videos where I already have the content and assets but need help with production. I am thinking of product photographs, website screenshots, recordings, and approved copy to give the agent something specific to build around.

• For a small business owner or product team, that could be a launch video or a short explanation of a new feature. You can supply the actual product material, describe the message, and review how the agent presents it.

• For tutorial creators and educators, I see a use for the combination of recorded footage, narration, captions, and motion graphics. I could supply the demonstration myself and use the agent to help assemble the explanation around it.

• For people presenting reports or research, the animated infographic tools could help turn a few findings into a narrated video. I would bring the source data and the explanation I wanted to make, then review the chart and voiceover together.

• For freelancers and small content teams, I can see Fotor helping produce a first version for review. It’s easier to discuss the pacing of a video when everyone can watch a sequence instead of trying to imagine it from a written outline.

A few catches I found while exploring the app:

The question I still want answered is how well it handles feedback over several rounds. Generating the first version saves time, but revisions are a big part of paid video work.

Cost is the other thing to plan around. My 30-second launch video used over 130 credits. The $10 monthly plan gives you 200, so that one video used up most of a month. Add the roughly 30-minute processing time, and it could be a deal breaker if you need to turn videos around fast or produce a lot of them.

I’d factor in both the credits and the wait before moving much of my workflow into Fotor.

The question I’d want answered after a few assignments is how well it handles feedback. Generating the first version saves time, but revisions are a large part of paid video work. I’d count those hours and credits when deciding how much of my workflow to move into Fotor.

Final thoughts

That’s about it. I just explained how Fotor Agent brings video planning, media generation, animated infographics, and editing into a conversational workflow.

You can start with an idea or reference media, develop the sequence through chat, and continue refining the generated project. I had so much fun generating product launch videos and editing images with its built-in tools.

The editable timeline is what I think sets it apart from other similar platforms. It gives creators a way to work toward a production-grade video through revisions. It works well with many use cases like producing a tutorial, a product demonstration, or an animated report.

I’m glad platforms like Fotor are giving creators more accessible ways to turn their ideas into visual content. Thanks to the teams building these tools and making room for users to keep shaping the results after generation.

What do you think about Fotor Agent? Let me know your thoughts in the comments.

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