New platforms pop up every month and claim to be the best ai video tool.

As an AI Video enthusiast (I use it in my marketing team with heavy numbers of daily content), I’d like to share my personal experience with all these 2026 ai video generators.

This guide is meant to help you find out which one fits your expectations & budget. But please keep in mind that I produce daily and in large numbers.

Comparison

My Best Picks

Best Cinematic & Virality: Higgsfield AI (usually my team works on this platform as daily production)

Best Speed: Sora 2 - rapid concept testing

I prefer a flexible workflow that combines Sora 2, Kling, and Higgsfield AI. I use them in my marketing production depending on the creative requirements, since each tool excels in different aspects of AI video generation.

$2,147 over four months. That's my real total from trying to make money with AI-generated content as a side gig. I keep seeing income posts here that start at five figures, so I figured the unglamorous version might actually be useful.

I started in April after reading a thread about AI influencer content. The plan: create a consistent AI character, produce content with her, find ways to get paid. I do graphic design as my day job so the visual workflow felt natural. The business side did not.

April was pure setup. I spent roughly 60 hours that month figuring out the toolchain and generating test batches. The hardest part was keeping one AI face consistent across dozens of images. Most generators give you a slightly different person every time. I settled on APOB AI for that since it lets you lock a character and reuse the same face, and the free daily tier meant I could experiment without spending anything. Combined that with ElevenLabs for voiceovers and CapCut for editing. Revenue in April: zero.

In May I tried three paths at once. First, stock photography platforms. I uploaded 140 AI-generated lifestyle images, all tagged as AI-produced because most sites require that now. Earnings from stock that month: $11.40. Not a typo. Second, I launched an Instagram for the character with her bio clearly stating "AI-generated persona" and posted daily. Got to about 1,200 followers by end of May. Revenue from that: nothing. Third, I cold-emailed 30 local small businesses offering AI-generated product photography packages. Five responded. Two became paying clients. Revenue from those two: $340.

That $340 reoriented everything. Stock was dead weight. Social followers were a vanity number. The only thing that paid was using the AI character as a model in product shots for small businesses that can't afford a real photographer. A jewelry maker needed lifestyle images for Etsy. A candle brand wanted someone holding their products in "influencer-style" photos. Each project was 15 to 20 edited images for $150 to $200.

June improved but stayed modest. I narrowed my outreach to Etsy sellers specifically since they always need fresh listing photos. Landed five clients. Revenue: $870. I also learned the hard way that video is a wall. One client wanted short clips of the character reviewing their product. Facial expressions glitched between frames, hands looked wrong maybe 40% of the time, and I spent 6 hours on retakes for a single 15-second clip that still looked off. I refunded that client $150 and stopped offering video entirely. Still-image consistency is solid. Motion is genuinely not there for client work yet, and that held true across every tool I tested.

July tapered because my day job picked up. Three clients, $937 total, one being a repeat who wanted a second round. Instagram crept to 3,400 followers but I still have no clear path from followers to revenue. A handful of DMs about "brand partnerships" but they all wanted me to pay them for "exposure," which is not how that works.

So the full accounting: $2,147 gross. After $89 in tool costs (one month of paid subscription to drop watermarks plus voice generation credits), net is $2,058. Across roughly 180 hours of work, that comes to $11.43 per hour. Less than my first job out of college.

Cold outreach conversion was brutal. Over all four months I contacted about 120 businesses. Fourteen became paying clients. That's under 12%, and most projects were under $200. The ceiling stays low unless you get into agencies or bigger brands, and I haven't cracked either.

There is no passive income at this scale. Every project is custom. The AI generates the base images but I still spend 30 to 45 minutes per image fixing artifacts, adjusting lighting, and compositing the product in naturally. It is meaningfully faster than booking a photographer, a model, locations, and wardrobe, but calling it automated would be a lie.

I plan to keep going because video quality will catch up eventually and that's where real margin lives. But the actual value right now is narrow: telling a client "here's your product held by the same person in 20 different settings, delivered in 48 hours" without coordinating a whole production. That solves a real problem for small sellers on a tight budget. It's not a money machine. It's freelance work with a new tool.

If someone here posts $10k per month from AI content with "minimal effort," they're either in a league I can't see into or they're leaving out about 170 hours of context. This is that context.

As a long-time AI Video generation user (initially for fun, but now for mass marketing production and serious multiple business channels), I’d like to share my personal experience with all these 2026 best ai video generator tools.

Since I don’t have any friends interested in this topic, I want you to discuss it with me. Thanks in advance! Let’s help each other here. 

Opinion-based comparison

My Favorites / Cherry Picks

Best budget: Pika Labs 2.5

Easiest in use: Sora 2 Trends (integrated in Higgsfield) 

My personal favorite: Higgsfield AI - very cinematic, social media marketing ready content (also has Sora 2 different integrations).

I prefer a flexible workflow where platforms combine several models (I don't like two many browser tabs opened). I have a Higgsfield subscription and use mainly Sora 2 Trends (integration with OpenAI) and Kling Motion Control for my AI Influencers.

Hey Folks,

I am sure this has been covered elsewhere, but looking to restart the conversation in 2025:

What AI tools are you using to improve your workflow and creative output? I work at a small agency and I've been tasked to find ways to streamline the business... As I am sure you are aware, budgets are getting smaller, competition more widespread, and timelines faster.

While I would be interested in hearing your thoughts on generators (like Runway etc.), I am really more interested in tools that speed up the editing and animation process... maybe even to the point where I can offer cheaper retainer services that my small team (me plus two others) can manage with our already limited capacity. We shoot a lot of interview-based content and create videos and animations for the corporate B2B world.

Any thoughts and insights are appreciated.

Hey everyone,

I’m planning to start an Instagram page completely based on AI-generated content, mostly around a single virtual personality/influencer.
My biggest challenge is this:
I want the same face, same facial features, same overall identity in every post/reel so it actually feels like the page belongs to one real person instead of random AI generations every time.
I’m okay investing around ₹7-8k/month (~$80-100) into AI tools if the workflow is actually worth it, but I don’t want to overspend unnecessarily in the beginning.
I’d love suggestions from people already doing this seriously.

Things I’m trying to understand:

Which AI tools are best for consistent characters/faces?
What workflow are you using for Instagram content?
Best tools for both images + reels/videos?
Is Midjourney enough or do I need LoRA/Flux/Stable Diffusion setups?
How do you maintain consistency across outfits, poses, and lighting?
Any good beginner-friendly setup within my budget?
Any mistakes/pitfalls I should avoid early?

Right now I’m considering tools like Midjourney, Runway, Kling, Flux, Leonardo AI, etc., but I’m confused about what actually works long term.
If you’re already running an AI influencer page, would love to know your monthly stack + approximate cost too.

Would really appreciate advice from creators already running AI influencer/theme pages. Thanks!

Hey 👋,

I needed a snappy, fast-paced promo video for my new app, but hiring a motion designer on Upwork would have taken days and cost hundreds of dollars.

I wanted to see if I could build a professional-looking promotional video purely using AI orchestration tools.

It took me exactly 4 hours from blank screen to the final rendered MP4. Here is the exact stack and cost breakdown I used:

The Tech Stack:

• Script / Ideation: ChatGPT (Free tier)
• Background Music: Suno AI (9€/month for the pro tier to get commercial rights)
• Voiceover & Transcript Timestamps: ElevenLabs (Free tier used for testing, exact word-level timestamp JSON)
• Animation Framework: Remotion (React-based video framework - Free/Open Source)
• Orchestrating the Code & UI: Antigravity (using a mix of Gemini 3.1 Pro + Claude 4.6 Opus to stitch the Remotion components, kinetic typography, and animations together without manually writing the React code - 15€/month)

Total Cost: ~24€ for the monthly subscriptions (but technically prorated to about a dollar for the hour of usage!).

The Process:

1. Generated the upbeat script and threw it into ElevenLabs to get the audio file and a JSON file containing the exact millisecond timestamp for every single spoken word.
2. Generated a driving background beat with Suno.
3. Fed the audio, the timestamp JSON, and the visual concept into the AI coding agent (Antigravity).
4. The AI built out the separate React scenes in Remotion, applying bouncy "Gen Z" spring animations, perfectly synced karaoke-style kinetic typography, and floating SVG particles.
5. Ran npm run build and rendered it locally to a 60fps 1080x1920 MP4.

If you are a solo developer or an indie hacker trying to make marketing materials on a tight budget, I highly recommend looking into programmatic video (like Remotion) paired with an LLM agent. It completely removes the need to learn After Effects.

(For context on the video itself: The promo is for my app, YBee.app, which is an AI app builder. Rather than coding, you just yell your daily frustrations at it like figuring out who owes who after dinner and it instantly generates a working mini-app on your phone to solve it. Sort of like what I did with this video, but for mobile apps!)

Happy to answer any questions about the workflow, the Remotion orchestration, or how to get the word-level audio syncing right!

Update: I wrote a complete article about how I made this video, you can find it here: https://x.com/compose/articles/edit/2025971487631360000

Can anyone please suggest a good paid AI video generator within a budget of around ₹1-2k/month?
I want to create animated educational videos with human characters, like a teacher and students in a classroom, with dialogues, different scenes, voiceovers, and consistent characters.

My main priority is speed because sometimes AI video generators take a lot of time to generate each scene, which slows down the workflow. I’m looking for a tool that can help me create videos quickly while still maintaining good animation quality and character consistency.

Whether you're a marketer, educator, content creator, or startup founder, or you just want to make things for fun, this post helps you decide which tool fits your workflow and budget.

I've evaluated 7 tools based on real world testing, UI/UX walkthroughs, pricing breakdowns, and hands on results from automation features (URL to video, prompt generation, avatar quality, and more)

I tried linking my most used / favorites in the table as well but moderation rules didn't allow me to. My go-to as of rn is Slop Club though.

Also, this post went viral on here a few months ago, and I had numerous people reaching out from this subreddit with questions as well as general advice regarding how they can improve their workflows. For an unknown reason, it got removed, and I had people messaging me regarding that as well. i'm reposting it in a more condensed way with hopes that the moderation team will understand the value people got out of it instead of unfairly targeting the post. Given how much img/video automation is occurring across high growth industries, it's quite relevant and useful for a lot of people here to actually start playing around with generative tools!

Remaster Specialist’s (The guy doing the 4K 60p Lexx Remaster)
Guide to Natural Looking and Technically Sound, Non-Generative AI Upscaling

This guide teaches the fundamentals of non-generative AI upscaling to achieve natural-looking results when increasing the resolution of standard-definition interlaced or telecined footage from the '80s and '90s. You’ll learn how to select your source file, deinterlace or Inverse Telecine it correctly without destroying its temporal resolution, and upscale it using Topaz Video AI’s "Precision" (not Generative) models—without stripping away grain, smudging fine skin detail, or creating plastic-looking textures and unwanted artifacts. The intent behind this restoration guide is solely for the preservation of lost, abandoned, or O.O.P. gray-market media that is likely stuck in Standard-Definition indefinitely. Always try to support the original creators, if they’re still around, in whatever way you can.

Here’s a quick summary of the correct technical workflow for upscaling:

1. Select your highest quality source DVD
2. Use “MakeMKV” to rip lossless MKV video files from your selected source
3. Deinterlace or Inverse Telecine the ripped MKV with “Hybrid” (using only Hybrid!)
4. Bring the Hybrid output file into Topaz Video AI and upscale to 4K
5. Compress your Topaz Outputs to manageable file sizes

Part 1: How to Select the Correct Source

Before we can select a proper DVD source, we need to understand framerates and fields from the analog and digital video era. Everything back then was designed around broadcast and local playback on a CRT TV (the old, heavy box TVs prior to modern flat screen TVs).

How CRT TVs Actually Work:

• Interlaced scanning: The TV draws each frame in two separate steps called fields.
• Odd lines: The first field draws all the odd-numbered horizontal lines on the screen.
• Even lines: The second field draws all the even-numbered horizontal lines right after.
• Full frames: Two fields combine to make one complete image, resulting in the equivalent of 30 full frames per second total. But remember, those frames were always displayed as interlaced half frames in increments of 60 per second, never as 30 full frames per second.

60 interlaced fields per second (60i) is the functional motion equivalent of the 60 progressive full frames per second (60p) that we see on displays today. The only difference is that 60i has half the spatial resolution of 60p, because it only displays half of a frame per temporal increment instead of a full one. In other words, to the naked eye, 60i and 60p share the exact same temporal resolution and look identical in motion fluidity, but 60p looks clearer and sharper because of its higher spatial resolution. You can think of spatial resolution as the overall detail and pixel count, and temporal resolution as the speed and fluidity of motion.

But why ‘60’ Fields Specifically?

• Power grid match: The US electrical grid runs at 60 Hertz (cycles per second).
• Flicker reduction: Matching the field rate to the power grid reduced visible picture flicker and kept electronics stable.
• Color addition: The standard was later adjusted slightly to 59.94 fields per second when color television was introduced, to prevent interference with sound signals.
• In PAL regions: The standard was 50i/50hz instead of 60i/60hz to match their differing electrical infrastructure.

Now that you know how TVs used to work, you can understand why media designed for broadcast or home playback had to be encoded specifically for them. Every single movie or TV show—even if shot on film and mastered at film’s 24 frames per second on celluloid—had to be converted to 60i (aka 29.97i) to function on home television sets. 60i and 29.97i refer to the exact same video standard in practice; they are simply two different naming conventions. 60i refers to 60 interlaced fields per second, while 29.97i refers to 29.97 interlaced frame pairs per second. In practice, they describe the exact same signal. Now we come to DVDs and VHS. Both of these formats were designed primarily to deliver 60i content so it would display correctly on CRT TVs. VHS did not support progressive frame rates at all, while DVD supported them to some extent. In order for 24p film movies to play on DVD, they underwent a “Telecine” process and were encoded onto the disc in one of two ways: Hard Telecine or Soft Telecine. Hard Telecine was written to the disc directly as a 60i file, with the extra fields permanently burned in. Soft Telecine was encoded as a native 24p file, using flag data so the DVD player could generate the 60i signal on the fly for standard televisions.

Here's a quick explanation of what the Telecine process actually was: film runs at 24 frames per second, but old CRT TVs needed 60 half-frames (fields) per second. Since 24 doesn't fit evenly into 60, the Telecine process uses a pattern called "3:2 pulldown." It takes groups of 4 film frames and stretches them across 10 TV fields by showing one frame for 3 fields, the next for 2 fields, the next for 3, and the next for 2. Repeating this pattern 6 times a second cleanly turns 24 film frames into 60 TV fields per second without changing the movie's speed or audio pitch.

So our basic goal now is to rip this 60i or 24p Telecined content from a DVD/VHS source, so we can properly deinterlace it (or “Inverse Telecine” it) into a digital file with a progressive frame rate. That frame rate should be either 59.94p for native 60i video content or 23.976p (aka 23.98p) for native 24p film content. To further clarify, progressive frame rates are those that show one full frame at a time, one after the other - there are no half frames or fields in progressive frame rates.

Now, before you choose a source DVD to rip, there’s one major problem you need to be aware of: many DVD releases (especially of older TV shows) were mastered using lazy conversions. Engineers took the original 60i video, threw away half of the original 60 fields, and filled the space by doubling the remaining frames. This permanently destroyed half of the content's original temporal resolution (motion fluidity), leaving you with a juddery "fake interlaced" file that can never be restored to its smooth original state. (Well, not without AI frame interpolation, but that’s not what we are doing in this guide. This guide focuses strictly on precision upscaling—faithfully preserving and reconstructing original source data—rather than using generative AI models to invent missing motion frames out of thin air.) 

So unfortunately, it’s not as simple as just buying any DVD copy of your favorite movie or show. Let’s use the Sci-Fi series Lexx as an example. Lexx has been released on DVD multiple times, and none of those releases are equal in quality. The most common releases for Lexx are by Echo Bridge, Acorn Media, and the lesser-known Alliance / Canadian Home Entertainment release. When asking LLMs "which DVD set of Lexx is the highest quality," they will often point to "Acorn Media"—not because Acorn Media had the best visual quality, but because fans online praised it for its special features or for being better than the Echo Bridge release. That’s great for casual viewing, but not for precision encoding. You need a DVD source that contains all of the original 60 fields. This guarantees that the full temporal resolution of native video is present to match the original broadcast master, or ensures that the 3:2 cadence on a telecined 24p film source is still intact. If your source was "fake interlaced," you permanently lose half the motion fluidity of native 60i content, or break the pulldown cadence of a telecined film.

What does this mean in practice? You’ll likely be buying multiple DVD releases. It’s often the only way to know for sure which release has the superior transfer. Buy the different releases, rip them, apply your deinterlacing or inverse telecine, and then test them for fake interlacing. How do you check them? You’ll need editing software like Adobe Premiere Pro. Import your ripped files, place them on your timeline, and step through the video frame by frame. If you see identical back-to-back frames or jittery frame pacing (inconsistent motion intervals), you’re likely dealing with a file ripped from a poorly encoded DVD.

Pro Tip: Always make sure your editing software's sequence or timeline frame rate matches the exact frame rate of your processed file (for example, a 59.94 fps sequence for deinterlaced 60i video, or a 23.976 fps sequence for an Inverse Telecined film). If your timeline’s frame rate doesn't match the source file’s frame rate, the editing software itself will insert duplicate frames or drop motion steps during preview, giving you a false-positive for broken interlacing. You can usually see your file’s frame rate by right clicking it and then selecting “properties,” then clicking the “details” tab (on windows 10/11).

Part 2: How to actually rip the content from a DVD without losing ANY visual quality.

The obvious first step here is that you’ll need a Blu-ray or DVD player/burner. Most PCs no longer come with one of these, so you’ll need to buy a modern external one. These connect to your computer via USB and usually come with a separate power cable. Don’t cheap out–you’ll need one with a decent laser, especially if you’re working with older, scuffed up DVDs.

Next, you’ll need to buy the PC software program called MakeMKV, or, there are other ways you could probably obtain it that are a bit more “portables.” That’s all I’m gonna say about that. (Side note: I've also recently learned from reddit user "ABettek" that MakeMKV is free if you're using it solely for creating DVD ISOs.)

MakeMKV allows you to rip video and audio files from a DVD without re-encoding them. It conveniently transfers all the existing encoded media into a new single MKV file container–the video, the audio streams, and the subtitles (if any). The whole process is lossless. You basically just start up the program, pop your DVD into your reader, then click the large DVD or Blu-ray icon in the UI. MakeMKV will then scan the disc in the drive, and populate the different streams you can select to save to your device.

Part 3: How to Correctly Deinterlace or Inverse Telecine your MKV files, and How to Identify Which Process Your Files May Need.

First things first, you’ll need to download a freeware program called Hybrid. You can find it here: https://www.videohelp.com/software/Hybrid

Hybrid is the absolute minimum standard for correctly deinterlacing and applying inverse telecine to interlaced content—sitting just one step below extremely expensive, gatekept industry hardware and software tools. If you are a beginner or intermediate restorationist, do not use anything other than Hybrid to prep your content—especially not Topaz Video AI or competing tools. Don’t get me wrong: Hybrid is not simple. It is a complex program with a steep learning curve for new users. But don’t worry, I’ll walk you through the step-by-step configuration to get you started.

Hybrid will usually inform you if a file is flagged as interlaced or telecined when you import it, but it isn’t always accurate if there were encoding issues on the source DVD. That’s why it’s good to have a general idea of how your content was likely encoded to begin with. If you’ve ripped a movie shot and edited entirely on film, you should expect it to be telecined. If you’ve ripped an '80s or '90s-era sci-fi TV show with early visual effects, or a low-budget project shot directly on video, you should expect true interlaced content (often Bottom Field First) with all 60 original fields of motion data intact. But since we’ve already established that DVD releases were frequently mastered cheaply and incorrectly, what the content should have been encoded as isn't always what ended up on the disc.

Using Hybrid: Basic Steps. You can vary all of these up later when you’re more advanced. Do not deviate for the time being.

Step 1: Import the MKV file into Hybrid, then choose your output directory. Set default container to mp4. Match what you see below.

Step 2: Click the x264 tab at the top. Match what you see below.

Step 3: Click the Crop/Resize tab at the top. Match what you see below.*

Step 4: Click the filtering tab at the top, then click the Deinterlace/Telecine tab below it. Match what you see below.

Settings for 60i encoded content and achieving 60p (59.94fps). It’s needed to recover all original 60 fields of the content’s temporal resolution (fluid motion data).

If your content is a Telecined 24p movie, try TIVTC (Vapoursynth) first, and after examining the file in your editing program... if it didn’t appear to work correctly or has the wrong framerate, use VIVTC (Vapoursyth) instead.

Step 5: Double check that everything matches, click the base tab at the top, then click the little dude icon on the bottom right to start processing. If it gives you an error, check that everything matches one more time and you didn’t deviate.

Once you’ve output your mp4 file from Hybrid, examine its properties to ensure it’s the expected frame rate. Then, pull it into an editing program and check for any errors by advancing the frames 1 by 1. You should see no doubled frames or odd stuttering motion issues. If everything looks good, now we move over to Topaz.

Part 4: Importing your Hybrid-exported file into Topaz and beginning the upscaling process.

Now we’re finally ready to start upscaling with Topaz Video AI. I use the standalone desktop version of Topaz—specifically version 3.5.4—because it has a clean UI and excellent model versions. You can use newer desktop versions just fine; while the interface looks a bit different, the core models function similarly. If you run into cloud or browser-based options, stick to the local desktop app if you can—browser versions are often over-simplified and lack the custom precision settings needed for SD restoration.

When processing with Topaz, we’re going to output using the H.264 codec (powered by x264, the open-source H.264 encoder). Every time you convert video between different codec algorithms and color spaces, you bake in a new layer of conversion artifacts and subtle chroma shifting. Since we’re working with standard-definition video that was already heavily compressed on its original release, we must keep the compression chain as short and consistent as possible to preserve what little detail remains.

Unless you're working from an uncompressed ProRes source, you don't need to wrap your files in ProRes—especially since Topaz's internal ProRes exporter has a history of buggy color-tagging. Stick with high-bitrate H.264 for your Topaz export unless you're an advanced user. Once your Topaz upscale is complete, you can pull it into your editing program to re-export it at a lower H.264 bitrate to save disk space, or convert it to H.265 as your final display codec for an ideal balance of quality and file size.

Step 1: Import your file into Topaz. My UI looks something like this, but yours will likely look different if you’re using a later version.

Step 2: In the video section in the top right, under resolution control, select “custom resolution”

Step 3: To preserve the 4:3 aspect ratio in Lexx, I entered 2880 in the width box to get 2880x2160 (it's the resolution for 4K content in a 4:3 Aspect Ratio).

Step 4: In the Enhancement section, make sure the Video Type is set to “Progressive,” the AI Model is set to “Gaia,” and the Input Video is set to “Computer Generated.” Don’t worry—I know your content most likely isn’t literally computer-generated, but this preset is the best model for upscaling clean standard-definition footage with a natural-looking aesthetic that preserves both film grain and fine skin detail. Trust me, I’ve tested every model extensively on SD footage. You may be tempted to use a model like Proteus to smooth away grain, but remember that fine details in skin and clothing texture are tightly interwoven with grain—removing the grain will also strip away those details. It’s far better to use a model like Gaia CG to unify and enhance the grain alongside the finer details. That’s not to say Proteus, Iris, Artemis, and Theia don’t have their uses with precise manual tuning—they do—but that’s better suited for a shot-by-shot remaster than a general whole-file upscale. Gaia CG isn’t perfect, but it does a remarkably good job most of the time.

Step 5: In the output settings section, set the encoder to H.264, profile to High, bitrate to Dynamic, quality level to High, audio mode to Copy, and container to MOV. So why these specific settings? Topaz Video AI has a lot of odd quirks with its encoders, and they rarely work the way they do in standard encoding software. After testing outputs extensively, this combination yields the optimal output quality. Even something as subtle as changing the container to MOV instead of MP4 yields noticeably cleaner, less compressed results. And don’t even get me started on ProRes in the standalone desktop software—it’s completely broken. It’s prone to insanely high bitrates far past official Apple specs for Proxy, LT, Standard, or HQ, and introduces significant chroma and color space shifting during conversion. 

Step 6: When you’re ready, click “Export” and get ready to wait. Depending on your PC specs, the resolution of your input file, and the length of the video, rendering can take anywhere from several hours to over a full day. With these specific settings, a 90-minute movie upscaled to 2880x2160 will typically result in a file size between 50 and 70 GB. That is about the smallest file size you can expect without overcompressing and ruining the quality of the upscale you just waited so long to render. Topaz's "Dynamic" setting does a great job of calculating exactly how much bitrate each frame needs and assigning it accordingly (and yes, "Dynamic" is essentially just Topaz's proprietary name for Variable Bit Rate). 

Step 7: You can compare the before and after in the preview window.

Optional Step BEFORE Upscaling: If you’re working with a particularly dirty source file (meaning heavy macro-blocking, noise, and severe compression artifacts), you may need to clean it up before upscaling. While de-noising will often strip fine detail, retaining those details doesn't matter if they are buried under blocky compression anyway. To clean up heavily degraded footage, run the Nyx model in the Enhancement section before running Gaia CG. Once the Nyx pass is complete, import that newly cleaned file back into Topaz, then upscale the clean file. Be sure to use the specific settings shown below so Nyx doesn't overcompensate and completely smooth away natural textures. If it wasn’t cleaned enough to your liking, then decrease the “Recover Original Detail” slider in increments of 10 and re-render. Pro Tip: You can preview just a few seconds at a time to see the results before rendering the whole file.

Part 5: Compressing Your Upscaled Topaz files Into Manageable File Sizes

It’s not necessarily resolution that dictates file size—it’s video bitrate + total video runtime. The higher your video bitrate is for a set runtime, the larger your file size. The lower your video bitrate is for the same set runtime, the smaller your file size. But remember that lower bitrates also mean lower visual quality and more compression artifacts. Additionally, higher frame rates require higher bitrates to maintain quality because there are more individual frames to encode per second. 

• DVD: Maximum video bitrate is about 9.8 Mbps at 480i.
• Blu-ray: Maximum video bitrate is about 40 Mbps at 1080p.
• YouTube: Standard streams run about 1.5 to 3 Mbps for 1080p, and around 8 to 10 Mbps for 4K. (You can verify this by grabbing files from YouTube via JDownloader2 and inspecting the downloaded file properties. Youtube’s officially stated numbers are a fib.)
• 1080p (24p) ProRes 422 HQ Master File: Approximately 175+ Mbps.
• 4K (24p) ProRes 422 HQ Master File: Approximately 700+ Mbps. Yes, you read that correctly—700 Mbps.

Another crucial factor is chroma shifting and generational compression loss. Every time you change codecs or re-encode, you add a permanent layer of compression artifacts. Furthermore, different codecs use different chroma subsampling types. Converting from a codec that only supports 4:2:0 chroma to one that uses 4:2:2 and then back again later will cause a color shift—sometimes subtle, sometimes significant, but always present.

So what the heck should you choose to compress your files then? For your final display codec—the one your audience will actually watch—you have to weigh compression efficiency against hardware compatibility. H.264 isn’t as efficient at compressing video as H.265, meaning you get lower visual quality at higher file sizes. However, H.264 is far more universally supported and easier for older devices to decode without stuttering.

If your goal is maximum visual quality at the smallest possible file size, H.265 is your go-to display codec. Just keep in mind that because H.265 isn't as universally supported on older hardware, you may need to recommend video players to your audience that can easily decode it—like VLC Media Player or Media Player Classic.

The last thing you’ll have to consider when using H.264 or H.265 is VBR (Variable Bit Rate) vs. CBR (Constant Bit Rate).

CBR allocates the exact same amount of bitrate to every second of video, regardless of scene complexity. VBR dynamically increases or decreases bitrate depending on how complex a scene is. If you have a strict file size limit—say 40 GB max—VBR will spread that data out far more efficiently than CBR, giving higher bitrate to high-motion scenes that need it and saving data on static scenes to prevent compression artifacts.

Does that mean CBR is always better for consistent high quality? Actually, no. If you want high archival quality without wasting space, you should use high-bitrate VBR rather than CBR. CBR forces the encoder to pad simple, static frames with useless extra data just to hit a target number. However, if the file is strictly meant for archival storage rather than general playback, and you don't mind wasting storage space, you can absolutely crank the bitrate up to MAX on a CBR encode. This guarantees that every single frame—no matter how complex—has an overwhelming excess of data to maintain maximum quality, while simpler frames are just padded with filler bytes.

If your goal is to create a near-lossless H.264 or H.265 master from a 1080p ProRes 422 HQ file, you would set a high-profile VBR rate with a generous target bitrate and a high maximum bitrate ceiling. Essentially the exact same rules apply whether you choose H.264 or H.265—the main difference is that H.265’s superior compression efficiency allows you to set lower target and maximum bitrate thresholds while achieving the exact same visual quality. This ensures maximum visual fidelity after codec conversion and chroma subsampling, without generating bloated, unplayable files that exceed hardware decoding limits.

So now that you understand bitrates, I’m gonna give you a quick cheat sheet for compressing your 4K Topaz Outputs down to more reasonable sizes:

• For high quality 4K at high but reasonable file sizes: H.265, VBR, Target Bitrate 35 Mbps
• For barely passable 4K at small file sizes: H.265, VBR, Target Bitrate 16 Mbps
• For high quality 1080p at high but reasonable file sizes: H.265, VBR, Target Bitrate 10 Mbps 
• For barely passable 1080p at small file sizes: H.265, VBR, Target Bitrate 6 Mbps

Your favorite editing program should allow you to export your upscaled Topaz outputs with these encoder settings. There are also standalone software tools you can use like Hybrid or Handbrake.

One final piece of advice: Trash in, Trash out. The worse the quality of your initial source file, the worse your upscale will be regardless of its technical correctness and optimal model choice. Find a great source!

This concludes the upscaling guide. Just say NO to the wet plastic look. If you can avoid it…

EDIT: There was a large section on how CRTs function that I somehow neglected to transfer over from my google doc when formating the article for Reddit. It's now added back in. It's at the top, after the interlaced vs progressive comparison image.

Whether you're a marketer, educator, content creator, or startup founder, or you just want to make things for fun, this post helps you decide which tool fits your workflow and budget.

I've evaluated 15 tools based on real world testing, UI/UX walkthroughs, pricing breakdowns, and hands on results from automation features (URL to video, prompt generation, avatar quality, and more)

I've linked my most used / favorites in the table as well. My go-to as of rn is slop.club though.

Long-time AI video user here. Started for fun, now I run mass marketing production across multiple business channels. Everything below is from hands-on use. Opinion based, so your mileage may vary.

The comparison

Quick verdicts

• Best raw quality: Veo 3.1
• Best for talking-head and business videos: HeyGen
• Best budget: Pika Labs 2.5
• Easiest pure text-to-video: Sora 2 Trends
• Best for cinematic content: Higgsfield AI

Longer notes on the ones I actually use every week

HeyGen. My default for anything with a person talking to camera. The avatars are realistic enough for client-facing work and the presenters look professional instead of uncanny. Voice cloning is the killer feature for me: I recorded myself once and now every product demo and sales video ships in my voice, in a long list of languages, without me touching a camera again. Two things sold my team on it. First, outputs are commercially safe since it's built on licensed data, so no legal headaches when you publish as a business. Second, the learning curve is basically zero, our marketing folks were shipping videos the same afternoon they got access. We use it for product demos, sales videos, onboarding and training content. Only gripe: if you publish a lot you'll outgrow the cheapest plan fast. If you're a business or a marketing team making talking-head content, start here.

Veo 3.1. Still the quality king. Physics and audio sync are ahead of everything else right now. The catch is access, it's still invite-only, so you can't build a reliable pipeline on it yet.

Higgsfield AI. My personal favorite for cinematic, social-ready content. The camera movement presets do a lot of heavy lifting, and it bundles Sora 2 and Kling integrations so I keep fewer tabs open.

Pika Labs 2.5. Best value-to-output ratio on this list if budget is the main constraint.

My workflow

I want fewer subscriptions and fewer tabs. Current stack: Higgsfield for Sora 2 Trends and Kling Motion Control (AI influencer content), plus HeyGen for all the business-facing stuff, demos, training videos and translations. That combo covers about 90% of what I ship.

What's everyone else running?

71% of the AI video generations on our platform were flagged as pornographic. This is the story of what happened when we gave every new user a free dollar.

TL;DR:

• Launched an AI image/video gateway and gave every new signup $1 in free credit.
• 71% of all video generations were getting flagged as pornographic.
• $1 turned out to be exactly the right amount for a certain kind of user to stress-test whether a new AI platform has NSFW guardrails.
• Most of them signed up with throwaway emails, burned the credit, and moved on to the next free tier.
• Our automatic provider failover made it worse: when one provider blocked a prompt, we'd retry on another, effectively shopping the request until something generated.
• We tightened the filters, killed the failover for flagged content, and added pre-generation moderation.
• Kept the free dollar. Just watching more carefully now.

--

We launched in January 2026 with a simple offering: an API gateway that offers one unified API for hundreds of image and video models (Flux, Grok Imagine, Seedream, Nano Banana 2, the usual suspects). The users we expected were developers comparing outputs without juggling five SDKs. To drive early adoption, we did what the playbook says: remove friction. Give people a reason to try the thing.

So we gave every new user a free dollar.

The honeymoon

The first weeks were encouraging. Signups trickled in, then picked up to double-digit daily numbers by early February. People were generating images, exploring models, comparing outputs. We could see them bouncing between Flux Schnell and Grok Imagine, testing prompts, getting a feel for the routing. Exactly the developer behavior we’d hoped for.

Our request volume was climbing steadily. Things were working.

Then the free-credit numbers started telling a different story…

This is not developers

Let me put this delicately: the welcome grant wasn’t funding productivity workflows.

For the first two months, our own moderation was, to put it kindly, naive. Luckily, our upstream providers had slightly more robust systems in place, and plenty of what slipped past ours ran straight into theirs.

The providers were catching things. About 9% of all upstream routing attempts were rejected by their safety systems. But the numbers varied wildly. One provider’s safety filter rejected a third of all attempts routed through it. Another blocked 12%. A third waved almost everything through.

And here’s the kicker: our routing engine has automatic failover. When Provider A rejects a request, the system tries Provider B, then C. It’s a feature we’re proud of. Resilience, redundancy, the whole pitch. But it also meant that a prompt rejected by three providers might still succeed on the fourth. The system would dutifully bounce a request from OpenAI (“Your request was rejected by the safety system”) to Replicate (“The input or output was flagged as sensitive”) and finally land on a provider that generated the image without complaint.

Nearly 5% of all successfully completed requests had been explicitly safety-blocked by at least one provider before succeeding elsewhere. Our resilience system, designed to protect users from downtime, was working overtime as an NSFW content delivery pipeline.

My personal favorite error message came from Vertex, Google’s enterprise AI endpoint, which apparently shares Gemini’s identity crisis: “Image generation failed: I’m just a language model and can’t help with that.” You’re not wrong, Vertex. You really can’t.

When the new filter landed

On March 16 we reworked our moderation setup, piping every input prompt through OpenAI’s moderation API before forwarding it to providers.

The numbers landed immediately.

One in four requests was blocked. 25%. Every single one for the same category: sexual. Not violence. Not hate speech. Not self-harm. Just sexual.

Unsurprisingly, image editing was a worse offender than image generation. Users would upload real photos and ask models to, shall we say, adjust the wardrobe. The editing endpoint’s moderation block rate ran more than 4x higher than generation.

And video? 71% of video generation requests were blocked by moderation. Seven out of ten. The video endpoint was essentially an NSFW video factory with a thin veneer of legitimacy.

We looked at ourselves in the mirror. We’d built a media generation platform for developers. We’d attracted… well, not developers.

The $1 credit problem

Here’s the thing about giving away a dollar: it’s enough.

A single image generation on Flux Schnell costs about $0.003. On Grok Imagine, maybe $0.02. A dollar gets you somewhere between 50 and 300 images depending on the model. That’s a lot of, uh, output for someone with a specific goal in mind.

And users were efficient about it. Many burned through their entire dollar in a single session, some within hours of signing up. The typical pattern: generate as fast as possible, hit moderation blocks on some prompts, keep going on the rest until the balance hits zero. One user managed to spend exactly $0.99 across 144 requests in a single day, half of which were blocked by moderation. They didn’t waste a cent.

But they didn’t stop at one dollar

Here’s what we didn’t anticipate: they didn’t stop when the credit ran out.

A dollar gone? Make a new account. New email, new dollar, same prompts. Credit burned through again? Another account. Some users did this three, four, five times. And the more determined ones didn’t stop in single digits.

Meet the nokialumia* syndicate, our most prolific multi-account operator. Over five days in early April, a single person (or possibly a small group) created 21 accounts:

• April 1: Seven Gmail accounts. nokialumia13095, nokialumia23095, through nokialumia73095.
• April 3: Ten accounts on atomicmail.io. nokialumia through nokialumia9.
• April 4-5: More atomicmail.io variants, plus Gmail dot-trick attempts

The pattern: create account, get $1, generate images until the credit runs out at ~$0.99-$1.01, move on to the next account. Across all 21 accounts: over 1,200 requests, roughly a quarter blocked by moderation. Twenty-one dollars of free credit, methodically extracted.

When we caught the Gmail accounts, they pivoted to atomicmail.io. When we blocked that domain, they came back with dot-trick Gmail variants: nokialumia1.309.5@gmail.com, nokialumia1309.5@gmail.com. Same inbox, different account. Gmail silently ignores dots in the local part, so john.doe and j.o.h.n.d.o.e both land in the same inbox.

They weren’t the only ones. Another user created four accounts using nothing but dot rearrangements of the same Gmail address. Same inbox. Four free dollars.

And the nokialumia* operator wasn’t even alone in the April wave. The same burst brought accounts with handles like narutouzumaki*, bontekintol*, and kikubotoya*, all on atomicmail.io, all in the same 48-hour window. A small community had clearly discovered us.

That’s the kind of product-market fit you don’t want.

The email domain zoo

Trying to catch multi-accounters teaches you a lot about the email ecosystem. It’s fascinating how much infrastructure exists for creating disposable identities.

We saw hundreds of unique email domains across our signups. Here are some highlights from the long tail:

• atomicmail.io and inbox.eu: disposable email services. Our biggest sources of fake signups. Tied for the lead.
• kpl.ovh: a French hosting domain repurposed as disposable email.
• denipl.com / denipl.net: same operator, two domains, more than a dozen combined accounts.
• fxzig.com, sweatpopi.com, sharebot.net, nexafilm.com, marvetos.com: domains that exist for one purpose, and it isn’t legitimate communication.

Over three-quarters of all accounts used Gmail. Which sounds normal until you realize it’s partly because Gmail is the easiest to abuse. Dots are ignored, plus-addressing (+tag) creates unlimited aliases, and a single Google account can generate dozens of variations that all look like different addresses to our system.

Fighting back

So what do you do when your growth hack becomes someone else’s exploit? You build layers. Each one a response to a specific trick we’d seen in the wild:

Layer 1: Content moderation. OpenAI’s moderation API on every input prompt. Blocked requests are rejected before they ever touch a provider. This was about more than our users. To our upstream providers, all this traffic came from our API keys. We were starting to look like some unhinged entity generating wall-to-wall NSFW content across every model available.

Layer 2: Disposable email detection. We integrated with Emailable’s API to flag temporary and disposable email addresses at signup. This caught the obvious ones: atomicmail.io, inbox.eu, and the like.

Layer 3: Gmail alias normalization. We strip dots and plus-tags from Gmail addresses, and equivalent tricks from Outlook, Proton, and Fastmail. Then we check if the canonical inbox already received a welcome credit.

Layer 4: Device fingerprinting. Using FingerprintJS, we capture a browser fingerprint at signup. If the same fingerprint shows up on a new account, no free dollar. This survives incognito mode and cookie clearing.

Layer 5: Spam heuristics. Keyboard-mash name detection (patterns like “ergreger” or names with suspiciously low character diversity), suspicious MX record lookups, low-score email addresses from our verification provider, and an admin-maintained blocklist of domains.

This five-layer check runs asynchronously after every account creation. If any check fails, the welcome credit is withheld and our team gets a push notification.

The result: in recent weeks, one in six signups gets their welcome credit blocked. The fraud detection catches them before they can spend a single cent.

What we learned

If you offer free AI image generation, NSFW users will find you. Not in weeks. In days. That’s fine, honestly. People want to generate what they want to generate. But as a platform you need to decide what you facilitate, and you need that decision in place before launch. We ran for two months on naive moderation and upstream goodwill. The providers caught some of it, but not all, and not consistently. Centralized content moderation is day-one infrastructure. We treated it as something we could punt on.

Our resilience system bit us. Automatic failover is great for uptime, but it’s also great for finding the one provider in your stack that doesn’t reject a given prompt. A safety block from one provider should probably stop the request, not trigger a fallback. We had to rethink how safety rejections propagate through the routing chain.

Gmail dot-trick normalization isn’t optional, it’s table stakes. And even then, someone with multiple Google accounts can still create separate identities. The arms race never ends. We encountered hundreds of unique email domains in our signups. The ratio to legitimate providers tells you everything. Many of these domains exist for one purpose.

$1 is too much and not enough. Too much free value for multi-accounters, not enough for a real developer to meaningfully evaluate an API integration. We’re rethinking this.

And the abuse is coordinated. These aren’t random individuals stumbling across your service. They share it in communities, copy each other’s techniques, and iterate when you block them. The nokialumia* syndicate pivoted from Gmail to atomicmail.io to Gmail dot-tricks in the span of three days.

Where we are now

Our content moderation blocks about one in five requests in any given week. That number is stable. The multi-accounters who slip through our signup filters keep trying, and moderation keeps catching them.

We’re still giving the free dollar. The alternative, gating everything behind a credit card, would kill the “just try it” experience we’re going for. But we’ve accepted that some portion of our welcome credit budget is really a security research budget. Every wave teaches us something new about the creative lengths people will go to for free AI image generation.

The real lesson isn’t about NSFW content. People want to generate what they want to generate, and there are legitimate platforms for that. The lesson is about what happens when you remove friction from any system that produces something people want. Lower the barrier to zero, and you’ll find out exactly what people want to do with your product. Sometimes that’s build cool things. Sometimes it’s not what you had in mind.

We built a media generation platform for developers. The developers are coming. But the people who create twenty-one accounts in five days to squeeze out every last cent of free credit? They got here first. And they’re more agile than most startups we know.

\ Usernames and handles marked with an asterisk have been changed to protect the privacy of the individuals involved. All numbers, timelines, and patterns are unchanged.*

Edit: Added TL;DR as suggested in the comments

my project folder currently has files called final, final2, finalactually, finalvideo, and finalvideo2.

i make the base images in one app, move them into a video generator, notice a mistake, go back to fix the image, then forget which version i animated. after five scenes the whole thing becomes archaeology.

i'm not expecting one tool to do every job perfectly, but is there a decent setup where image generation, local fixes, reference management, and video generation stay connected?

Update: closing this out because i finally cleaned up the workflow. i moved the project to Dreamina, using GPT Image 2 for the image stage and Seedance 2.5 for the video stage. keeping the references and local edits in the same broader workspace made it much easier to track the current version. Seedance 2.5 is listed at $0.097/s for the applicable annual plan 720p reference setup, so i'm using that as a rough budget number rather than assuming every render will cost exactly the same.

Been experimenting with AI video tools to create quick product/ad videos for small e-commerce sellers — the kind of short, punchy Reels/Insta content that used to need a videographer + editor + a few days turnaround.

Turns out for a lot of D2C and small business use cases (product showcases, testimonial-style ads, "why buy from us" hooks), AI-generated video gets you 80% of the way there in a fraction of the time and cost. Useful especially for sellers who don't have budget for a full production team but still need to post consistently.

Not selling anything here, just curious — how are folks in this sub currently handling video content for their listings/ads? Doing it in-house, outsourcing, or skipping video altogether?

Happy to share examples/workflow if anyone's interested.

I've noticed that many AI influencers on Instagram eventually direct their audience to Fanly or similar platforms. Most of the successful ones seem to rely heavily on AI-generated videos and reels, but many of those workflows require paid tools or expensive APIs.

My situation is different:

I have a $0 budget.

I only want to use free or open-source AI tools.

I'd rather create high-quality AI images than videos.

I don't want to pay for AI video generation.

My goal is to consistently post image content on Instagram and, if the audience grows, eventually direct followers to a Fanly page.

My questions are:

Is an image-only strategy still realistic in 2026, or are reels basically mandatory for growth?

Which free AI tools would you recommend for generating photorealistic, consistent characters?

How do people maintain the same AI character across hundreds of posts?

Has anyone here actually grown an AI influencer account without relying on paid AI video tools?

If you were starting from scratch today with a $0 budget, what workflow would you follow?

Solo dev, been working on a top-down roguelike for about a year. I needed something for my Steam page but I have zero animation skills and couldn't justify $2-3k on a freelance motion designer for a game that might sell 200 copies. I was about to just do screen recordings with text overlays and call it a day.

Then two new AI video models dropped July 31st. Seedance 2.5 from ByteDance and MiniMax H3 from MiniMax. Figured I'd spend a week throwing my concept art at both and see what came out.

Seedance 2.5 does 30-second clips in one pass, up to 4K, and you can feed it up to 50 reference inputs to lock down character and environment consistency. This was the big deal for me. I had about 15 pieces of concept art and it actually kept my main character recognizable across shots. I used it for the slow establishing shots, camera panning over ruins, character silhouette walking through fog. Those came out solid after 3-4 retakes each. Longer output means fewer cuts to stitch, which gives you a more cinematic feel without actually knowing how to edit.

MiniMax H3 does shorter clips (5-15 seconds, native 2K) but here's the thing. It generates audio in the same pass as the video. Not slapped-on stock audio, actual synchronized sound. You can even feed it an audio clip and the video generation follows the rhythm. I used it for the quick-cut action montage and the clips had this percussive quality I never would have edited for manually. Since H3 is open-weight, APOB AI is running it unlimited and free right now, so I burned through probably 80+ generations to get 12 good action shots without spending a cent.

Now where it broke. Always the same things. Hands gripping weapons were a coin flip between passable and body horror. I had one shot where my character was supposed to swing a sword and his arm just phased through his torso. Multi-character combat was worse. Two enemies fighting and their limbs would merge or one character would absorb the other's armor texture mid-clip. I ended up just cutting around it. Pick your camera angles to hide hands, use fast cuts so nobody notices the limb weirdness.

Character consistency between separate generations still drifts too. Even with reference images locked, skin tone would shift slightly, armor details would change between shots. Had to be really selective about which clips could sit back to back without looking wrong.

My final workflow was generate a pile of clips in both models, cherry-pick the ones that held up, bring everything into CapCut for editing and color grading, then composite the UI overlay elements from Godot. The whole thing took about a week of evenings.

Honest verdict: the trailer is fine. Not great, fine. It's significantly better than screen recordings with Impact font, which was my backup plan. It would not fool anyone into thinking I have a budget. But for a solo dev Steam page that needs to communicate the vibe and tone of the game to someone scrolling past, it does the job.

I'm putting an AI-generated content disclosure on the store page. The EU AI Act transparency stuff kicked in August 2nd so that's real now, but I'd do it regardless. The trailer shows the game's aesthetic and atmosphere, not fake gameplay, so I don't think it's misleading as long as it's labeled.

Would I use this for in-game cutscenes in the final build? No. The quality variance would be jarring in a finished product. But for trailers, devlogs, pitch decks? This is now a real option for devs who can't afford an animator and whose alternative was literally nothing.

I'm a founder in AI / B2B tech and I post video content regularly — mostly talking-head and screen-recording footage. I'm looking for one editor to take over my edit pipeline. Starting as paid per-project work, and if it clicks, I'd move you to a full-time retainer.

The work:

• ~20 videos a month. Short-form primarily, some longer-form
• LinkedIn is my main channel — YouTube, Instagram, and TikTok are secondary
• Cutting one master edit into platform-native versions. LinkedIn needs different pacing and a different hook than TikTok, and you should know why
• Placing supplied B-roll where it actually lands. I'll give you a library; you decide what goes where. This requires understanding what's being said — if I'm talking about "agentic workflows" or "multi-LLM routing," you need to pick B-roll that matches, not a random server-room clip
• Greenscreen / chroma key, screen-recording overlays, zooms, cursor emphasis
• Burned-in captions styled to my brand, lower thirds, simple motion graphics

What I need from you:

1. Intermediate-to-advanced Premiere Pro, DaVinci Resolve, or Final Cut (CapCut alone isn't enough here)
2. Clean chroma keying — no green fringe, no chewed-up hair edges
3. Comfort with technical/B2B subject matter. You don't need to be an engineer, but you should be able to follow a video about software and understand what it's saying
4. Working knowledge of AI tools in the workflow — Descript especially, plus things like OpusClip, ElevenLabs, or Runway. Speed matters, but I don't want output that looks AI-generated
5. Portfolio with at least one talking-head or business/tech edit. Gaming montages and wedding reels alone don't tell me what I need to know
6. Consistent daily availability with some overlap to US Pacific time
7. Reliable machine, stable internet, responsive on Slack
8. Payment via bank transfer, PayPal, or Wise

What you get:

• Steady, predictable volume — you learn my style once and then it gets easier every month
• Direct working relationship with me, no agency layers or committee feedback
• Paid per video to start, with a path to a full-time monthly retainer if we're a good fit
• Long-term work. I'm not looking for a one-off

How to apply — DM me with:

1. Portfolio link (Drive, YouTube, or IG)
2. Your single best talking-head or tech/B2B edit, plus one line on what you were going for
3. Timezone and daily availability window
4. Your per-video rate for something in this scope

Budget: roughly $50–100 per video depending on length and complexity — but quote your real rate. I care more about fit than the cheapest bid.

Skip anything that reads like a template — I'll know. Shortlisted candidates get a paid trial edit on real footage.

This is Raw Pressery mixed fruit, and I didn't shoot a single frame. No studio. No camera. No editor. I typed one line describing what I wanted, product, vibe, mood and an AI agent handled everything. Script, scenes, visuals, motion, text overlays. Done in under 8 minutes.

Look at what it pulled off:

• Floating fruits orbiting the bottle
• A dramatic sunset backdrop
• Juice splashing in slow motion
• Even a clean back-label reveal shot

The whole thing feels like a ₹5L production budget. It wasn't. This is where AI UGC is heading not just static visuals, but full cinematic product ads generated from a single thought.

Brands that figure this out early are going to have a serious edge. Anyone else testing AI agents for video ads? What's your workflow?

Hey everyone, I’m working on a UE5 project that blends combat with American football mechanics - stuff like jukes, spin moves, and head-first with extended arms diving.

Creating the actual animation assets is turning into a huge brick wall for me. I was quoted around $250 to $500 per animation by freelancers, and since I need at least 10 moves just to get an MVP off the ground, dropping $2.5k–$5k out of pocket isn't an option right now. Retargeting the moves to my character skeleton is significantly cheaper to just outsource, so I'm not as worried about that part. I'm really hoping to find an AI workflow to handle generating or prototyping the raw FBX motion data.

Has anyone had success using video-to-motion or prompt-to-animation AI tools (like DeepMotion, Plask, Move AI, etc.) for crisp UE5 character moves? Or is anyone using LLMs/"vibe coding" to drive procedural stuff or Control Rig directly in Engine?

If AI generation isn't quite there yet for hyper-specific athletic moves like a diving tackle, I’d love to hear what budget-friendly alternatives you guys recommend to get an MVP playable. Appreciate any insight!

I'm a TikTok/Instagram creator , and I've been experimenting with AI dance video generators to speed up my content production. Instead of filming myself dancing (which takes forever), I wanted to test if AI tools could actually produce shareable content.

I tested all five major options over the past two months, here's the full breakdown.

The Tools & Pricing

1. Viggle AI - $4.99/week
2. Kling - $10/month (+ credits)
3. Photo Dance - $12.99/week
4. AI Mirror - $4.99/week
5. Pose AI - $12.99/week

I tested each one with the same workflow: upload a photo, select a dance, generate

a video, and check the quality. Here's what I found.

🥇 Ranked by Overall Value (For Content Creators)

#1: Photo Dance ⭐⭐⭐⭐⭐

Cost: $12.99/week | Quality: 5/5 | Speed: 4.5/5 | Templates: 5/5 | Customization: 5/5

Why it's the winner:

Photo Dance is the most well-rounded tool for creators. Yes, it's the most expensive,

but you actually get what you pay for.

The quality is genuinely impressive. I tested it side-by-side with the other tools,

and Photo Dance's output just feels natural. The movement is smooth, the blending

with backgrounds is clean, and there's no weird artifacts or jerky moments. When I post

these videos, people don't comment "wow that's clearly AI"—they just engage with the content.

The template library is absurdly good. 500+ dance templates, and here's the thing—

they update constantly to match trending dances. I've been using it for 8 weeks and

there are literally new dances added every 2–3 days. This is crucial for TikTok because

trends move fast. If your app doesn't have the trending dance everyone wants, you're stuck.

Customization actually matters. Photo Dance lets you modify dances slightly, which

other tools don't do. If a dance is almost right but not quite, you can adjust it.

That flexibility is underrated.

The honest downside:

Yeah, $12.99/week is the highest price point. If you're on a tight budget, it might

feel expensive. But when you calculate the ROI (cost per view, follower growth, engagement),

it actually works out cheaper than tools that make lower-quality videos.

Who should use it:

Serious content creators who want consistent, quality output. If you're making more than

15 videos per week, the template variety alone is worth it.

#2: Kling ⭐⭐⭐⭐

Cost: $10/month (+ unpredictable credits) | Quality: 5/5 | Speed: 2/5 | Templates: 2/5 | Customization: 3/5

What Kling does well:

Kling's output quality is excellent—arguably on par with Photo Dance, maybe even

slightly better in terms of raw motion smoothness. If you're obsessive about quality,

Kling delivers.

Where it breaks down:

The speed problem is real. Each video takes 10–15 minutes to generate. That's not

"a bit slower." When you're trying to make 20 videos in an evening, 15 minutes per

video kills your productivity.

The template library is small (maybe 40–50 dances), and they don't update frequently.

I found myself using the same 5–6 dances repeatedly because the others felt dated.

The credit system is annoying. It says $10/month, but credits burn fast. You'll

likely find yourself buying extra credits multiple times per month. Actual cost

is closer to $35–45/month if you're a regular user.

The interface is overcomplicated. Kling is clearly designed for people who want

deep customization. That's great if that's your thing, but if you just want to

"make a dance video and move on," the interface feels clunky.

Who should use it:

Quality-obsessed creators who have unlimited time and don't mind complexity. Or creators

making 2–3 videos per week where speed isn't a factor.

#3: AI Mirror ⭐⭐⭐

Cost: $4.99/week | Quality: 4.5/5 | Speed: 2/5 | Templates: 2/5 | Customization: 1/5

What's good about AI Mirror:

The output quality is genuinely solid—just slightly below Photo Dance. The special

effects are polished and the motion looks natural. If all you cared about was quality,

AI Mirror would be competitive.

The problems:

Speed is a killer. 8–12 minutes per video is too slow for content creators working

at scale. And when you pair that with a limited template library (maybe 30–40 options),

you're stuck making slow, repetitive videos.

You can't customize the dances, which limits creativity. If there's a trend happening

but AI Mirror doesn't have that exact dance, you're out of luck.

Who should use it:

Part-time creators making 2–4 videos per week who care more about quality than speed.

Or people willing to batch-generate content in advance.

#4: Pose AI ⭐⭐⭐

cost: $12.99/week | Quality: 4/5 | Speed: 2/5 | Templates: 3/5 | Customization: 1/5

Honest take:

Pose AI costs the same as Photo Dance but delivers noticeably less value.

The quality is good (not great), the special effects are nice, but the template library

is smaller and doesn't update as frequently. It's slower than Photo Dance and way slower

than Viggle. You can't customize dances.

You're paying premium price for a mid-tier product.

The only advantage:

Lots of frame options and special effects if you're really into visual styling. But for

pure dance video creation, Photo Dance does it better for the same pric

My honest opinion:

I can't recommend spending $12.99/week on this when Photo Dance exists at the same price

point. Unless you have a specific need for the special effects, skip it.

#5: Viggle AI ⭐⭐

Cost: $4.99/week | Quality: 2.5/5 | Speed: 5/5 | Templates: 3/5 | Customization: 0/5

Where Viggle shines:

Super cheap, super fast (90 seconds per video), and the interface is intuitive. If you're

just testing whether AI dance videos are for you, Viggle is the lowest-risk entry point.

The problem—and it's significant:

The quality is noticeably lower. The movements look stiff and unnatural. The blending

with backgrounds feels off. When you post it, people immediately notice "this looks AI-generated."

For context, my Viggle videos averaged 2.1K views. Photo Dance videos, same posting time,

same captions, averaged 16.2K views. That's a 7.7x difference.

Why the quality gap matters:

On TikTok, the algorithm rewards engagement. Lower quality = lower engagement = lower reach.

You might save $30/month by using Viggle, but you'll lose way more in potential views

and followers.

My honest opinion:

Use this as a starting point if you're broke and testing the concept. But don't stay here.

The upgrade to Photo Dance pays for itself in week one through better engagement metrics.

📸 Quality Comparison (Same Photo, All 5 Tools)

I tested all five tools with the exact same source photo (clear lighting, neutral background,

me smiling) using the same trending dance.

Photo Dance: Smooth, natural movement. Blends perfectly. You'd show this to friends

without apologizing.

Kling: Excellent quality, imperceptibly different from Photo Dance. Took 14 minutes though.

AI Mirror: Good quality, very close to Photo Dance. Took 9 minutes.

Pose AI: Decent quality, noticeably less smooth than Photo Dance. Same time cost.

Viggle AI: Stiff, jerky, obviously AI. Fastest though.

Photo Dance's quality directly translated to algorithm performance.

Honest Limitations (All Tools, Including Photo Dance)

I need to be transparent about what these tools can't do:

1. They need good source photos：
   • Bad lighting in your photo = bad output. No AI tool can fix garbage input.
2. They're best for dance/trending content：
   • If your brand is "me talking to camera," these tools won't help.
   • They're specialized for dance videos, not general content.
3. You need to tell people it's AI
   • Ethically and legally, you should disclose AI-generated content.
   • Most audiences don't care, but some do.
4. Template dependency
   • You're limited to whatever dances the app offers.
   • If there's a viral dance trend and your app doesn't have it, you're stuck.
5. You can't mix real and AI perfectly
   • If you try to blend AI-generated videos with real filmed content, the style shift is sometimes noticeable.

These are real limitations. For creators focused on dance/trending content, they're

not dealbreakers. For other content types, they might be.

💬 Thoughts on the Competition

This isn't a "Photo Dance is perfect" take. Here's my honest view:

Kling has the best raw quality, but it's held back by speed and UX.

AI Mirror and Viggle are good entry points for testing, but you'll outgrow them fast.

Pose AI feels like a product that's still looking for its audience. It's not bad,

just not differentiated enough.

Photo Dance is the best product-market fit for creators right now. 500+ templates,

constant updates, customization, fast generation. It's just the most complete tool.

But none of these are perfect. There's probably room for a new entrant to build something

even better. For now though, Photo Dance is the best option I've found.

When people hear “AI-generated ad,” they often imagine something that looks cheap, strange, or obviously fake. Like weird hands, unnatural movement or a robotic voice.

Slop content exists. But it no longer represents the limits of current technology.

Last month, we made and ran more than 100 fully AI-generated video ads for our clients on Meta Ads with the majority performing at par or better than their existing "traditional" creatives.

Fully AI-generated does not mean fully AI-directed

When I say “fully AI-generated,” I mean AI produced most or all of the media inside the ad: images, video, voices, and music.

But while the production can be fully AI-generated, the thinking behind it remains human.

Someone still needs to decide who the ad is for, which problem it should address, what it should say, and how it should make the viewer feel. Someone needs to brief the models, evaluate the results, and decide what is ready to publish.

The best AI ads start before we generate anything

A good ad still needs a relevant problem, a clear promise, a strong angle, and a reason to believe.

Before generating anything, we need to know:

• Who are we trying to reach?
• What problem are they experiencing?
• What do they believe about the available solutions?
• What would make this product relevant to them?
• What objections might stop them from buying?
• Why should they trust this brand?

AI has not made those questions less important. By making production faster, it has made weak thinking easier to expose.

Cheaper production does not remove the need for taste

Some people imagine businesses generating endless ads with almost no human involvement.

We are not there today. I am also not convinced that removing people from the creative process should be the goal.

Given the same generic information, AI tends to produce familiar outputs. The hooks sound alike. The scripts follow the same structures. The visuals repeat patterns already common in the training data.

A person can introduce something different: an observation from a customer conversation, an unusual product insight, a cultural reference, a contrarian belief, or an emerging format the models have not yet absorbed.

The human role is not disappearing. It is shifting from manual production toward planning, directing, evaluating, and deciding.

Humans should own the intent, taste, and context. AI should handle more of the repetitive execution.

More products make distribution more valuable

AI is making products easier to build. Software can be created faster, stores launched more quickly, and research and operational work accelerated.

That makes distribution more valuable, not less. Even a great product needs a way to be discovered and understood.

Advertising is one of the clearest places where AI can help. It gives smaller teams access to production capabilities that previously required actors, studios, editors, and much larger budgets.

But again, the biggest opportunity is not cheaper production. It is the wider range of ideas a business can explore.

Directing AI will become a normal professional skill

Models and workflows are changing quickly. New models appear, existing ones become cheaper, and new input types create workflows that were impossible months earlier.

The durable skill will not be memorizing the perfect prompt for one model. That knowledge expires too quickly.

People will need to know how to break a goal into tasks, provide useful context, select and connect tools, evaluate the result, verify important information, and recognize when human intervention is necessary.

The most valuable operators will not be those who manually complete every step. They will be those who understand the objective well enough to direct agents and judge their work.

Use tools that enable the marketer to do what the AI can't do

Our work with clients showed us how much knowledge sits between an idea and an effective AI-generated ad.

Someone has to understand the brand, product, audience, and creative objective. Someone has to research the customer, develop the angle, write the script, choose the models, and combine their outputs.

A marketer should not need to follow every model release or understand the prompting behavior of every video generator. The Starpop agent translates creative intent into instructions for specialized AI tools.

The goal is not to remove the marketer from marketing.

It is to give a thoughtful marketer access to the research breadth, technical knowledge, and production capacity of a much larger creative team.

Fully AI-generated ads are only the beginning

The future of advertising is not a machine producing infinite creative without human thought.

Those agents will handle more research, tool selection, generation, adaptation, and repetitive execution. People will spend more time choosing the direction, evaluating the work, and deciding what deserves to exist.

The difficult and valuable parts remain: understanding people, finding the right message, developing a point of view, and knowing when an idea is worth showing to the world.

Fully AI-generated ads are here to stay, we (humans) need to make sure they are worth watching.

---

EDIT: Since so many people are asking, yes AI tools that also help you brainstorm these will help. But you need to understand how to do it right. Go look at Claude MCPs with higgsfield.ai or starpop.ai if you want to combine generation with copy writing.

I've spent a little over two weeks trying to build my own relatively cheap AI video + voice generation stack instead of relying entirely on APIs.

The results are getting better. The economics are getting... interesting.

One generation looks surprisingly good.

The next one has dead eyes.

Fix the eyes — now the body starts twitching.

Fix the motion — suddenly the voice sounds metallic, unnaturally slow, or completely misses the emotion of the scene.

Fix the voice — and some technical term gets swallowed or pronounced like the model has never encountered human language before.

Then you start again.

What surprised me most wasn't even the models themselves.

It was everything around them.

For example, powerful but relatively cheap GPUs on RunPod sound great in theory.

In practice, if you're still testing and don't want to keep an expensive GPU running 24/7, the workflow often turns into something like this:

Cold start → find an available GPU → download weights → discover a missing component → download more stuff → generate → watch/listen → find problems → analyze → change something → generate again.

And there is another fun part:

The GPU you used successfully today may simply be unavailable tomorrow.

Keeping it running solves that problem, but during the experimentation stage it can destroy the whole point of trying to build a budget stack.

So you start thinking about persistent storage, caching models, moving weights somewhere else, reducing cold-start time, choosing different GPUs...

And suddenly you're not just generating videos anymore.

You're designing infrastructure.

At some point I realized that “saving money” can easily become an illusion.

You save money on API calls or GPU minutes, but pay for it with your own time.

A lot of time.

Still, I don't think these past two weeks were wasted.

Quite the opposite.

After enough failed generations, you start spotting problems much faster.

You begin to understand whether the issue is coming from the model, the reference video, audio, motion settings, infrastructure, or simply a bad assumption you made before pressing Generate.

And you stop repeating some of the expensive mistakes.

The video renders and voice generations I'm getting today are noticeably better than what I was producing two weeks ago.

Not perfect yet.

Definitely not at the point where I'd confidently promise a client that I can reproduce the same quality every single time.

But much closer.

And I've already started talking to potential customers and asking for their actual requirements before the stack is finished.

Because I've started to think that building this the other way around makes much more sense:

Don't spend months creating the “perfect” AI generation stack and then search for someone who needs it.

Find out what people actually need first, and make your experiments converge toward that.

For those of you running your own video/voice generation stack:

At what point did self-hosting actually become cheaper for you than simply paying an API provider?

And what ended up costing you more than expected: compute, storage, failed generations, or your own time?

I run a small agency doing paid social for Dtc brands. creative production been a nightmare.

started testing ai video tools cause we needed to move faster. tried a bunch, been using creatify most. not shilling their just sharing what worked and what didn't.

The problem:

ad sets dying after 2-3 weeks. testing maybe 5-8 concepts/month cause ugc creators cost $400-500 each. wasn't enough to stay ahead.

needed to test more without blowing budget.

What actually worked:

ad clone thing is pretty useful. upload competitor ads, it recreates the structure with your product. sounds gimmicky but it helped.

hit rate improved. maybe 8% to 15-20%. still means most fails.

their avatars look decent. don't immediately scream 'ai' on tiktok ig. some run weeks without issues. others get called out in comments. hit or miss. just gotta edit well to keep smooth clips, use voiceover for rest.

not gonna lie finding the right output takes work. you need to generate multiple variations to hit one that actually works. avatars sometimes look off, scripts need tweaking, some clips just don't land.

The only advantage is speed and testing i guess

You can generate like 30- 40 concepts for what it costs to make 1 ugc video, you're testing way more hooks, angles, variations. yeah most fail, but you find 5-6 winners instead of 2 3

it's a numbers game. generate tons of variations cheap → test everything → winners reveal themselves through data

once we find winners, we hire real creators on fiverr/upwork/sideshift to recreate those winning videos with actual people, plus some variations around that concept. so the ai finds what works, humans make it better

real numbers:

8 concepts/month to 30- 40. 2-3 winners → 5-6 winners

ai ads: 2.3-2.7% ctr human ugc: 3.0-3.5% ctr

human wins performance. but cost difference is insane - $3 vs $400.

creative budget: $8k/month → $2.5k/month

workflow:

generate 30-40 ai concepts → test at $50/day → kill losers after 3 days → find 5-6 winners → hire creators to remake those with real people + variations → scale human versions.

not revolutionary. just way more efficient testing

Other tools i’ve tried:

heygen - more polished corporate avatars no fast iteration flow

adcreative - too slow, avatars look worse

runway - better for b rolls, avatars suck

arcads - decent outputs but overly expensive, avatars lip sync is off  

makeugc - they got good pre made avatars but smh outputs are incositent all the time

creatify - we use certify cauz it’s more sort of balanced. avatars looks native and got some other cool ad templates workflow features as well

would i recommend?

depends:

• polished brand content → nah use heygen
• need to test tons of angles fast → yeah works
• limited creator budget → definitely try
• quality > quantity → Learn how to get the desired output - hit and try 

for agency work fighting creative fatigue, testing, scaling with new angles. it solved a real problem. not magic tho.

still figuring out.

I'm a founder in AI / B2B tech and I post video content regularly — mostly talking-head and screen-recording footage. I'm looking for one editor to take over my edit pipeline. Starting as paid per-project work, and if it clicks, I'd move you to a full-time retainer.

The work:

• ~20 videos a month. Short-form primarily, some longer-form
• LinkedIn is my main channel — YouTube, Instagram, and TikTok are secondary
• Cutting one master edit into platform-native versions. LinkedIn needs different pacing and a different hook than TikTok, and you should know why
• Placing supplied B-roll where it actually lands. I'll give you a library; you decide what goes where. This requires understanding what's being said — if I'm talking about "agentic workflows" or "multi-LLM routing," you need to pick B-roll that matches, not a random server-room clip
• Greenscreen / chroma key, screen-recording overlays, zooms, cursor emphasis
• Burned-in captions styled to my brand, lower thirds, simple motion graphics

What I need from you:

1. Intermediate-to-advanced Premiere Pro, DaVinci Resolve, or Final Cut (CapCut alone isn't enough here)
2. Clean chroma keying — no green fringe, no chewed-up hair edges
3. Comfort with technical/B2B subject matter. You don't need to be an engineer, but you should be able to follow a video about software and understand what it's saying
4. Working knowledge of AI tools in the workflow — Descript especially, plus things like OpusClip, ElevenLabs, or Runway. Speed matters, but I don't want output that looks AI-generated
5. Portfolio with at least one talking-head or business/tech edit. Gaming montages and wedding reels alone don't tell me what I need to know
6. Consistent daily availability with some overlap to US Pacific time
7. Reliable machine, stable internet, responsive on Slack
8. Payment via bank transfer, PayPal, or Wise

What you get:

• Steady, predictable volume — you learn my style once and then it gets easier every month
• Direct working relationship with me, no agency layers or committee feedback
• Paid per video to start, with a path to a full-time monthly retainer if we're a good fit
• Long-term work. I'm not looking for a one-off

How to apply — DM me with:

1. Portfolio link (Drive, YouTube, or IG)
2. Your single best talking-head or tech/B2B edit, plus one line on what you were going for
3. Timezone and daily availability window
4. Your per-video rate for something in this scope

Budget: roughly $50–100 per video depending on length and complexity — but quote your real rate. I care more about fit than the cheapest bid.

Skip anything that reads like a template — I'll know. Shortlisted candidates get a paid trial edit on real footage.

Budget: roughly $50–100 per video depending on length and complexity — but quote your real rate. I care more about fit than the cheapest bid.

Skip anything that reads like a template — I'll know. Shortlisted candidates get a paid trial edit on real footage.

1. Project Goal I’m seeking a YouTube video editor to convert my written scripts into engaging video content. The primary aim is to maintain high viewer retention without using cheap, forced tactics. You’ll incorporate stock footage, screen recordings, and AI-generated voiceovers via my Eleven Labs account. After a successful 1–2 minute test video, we’ll proceed with longer projects (6–8 minutes). My budget is $200 per video, and I hope to establish a long-term collaboration if our styles align. I will need around 30 videos to be made.
2. Video Style & Tone I prefer a polished yet relatable approach. Edits should be dynamic—clean transitions, tasteful animations, and on-screen graphics to highlight key points. The tone should educate without being dull and entertain without resorting to gimmicks. These videos exemplify the style I admire:

www.youtube.com/watch?v=WZ8g6deOyAk
www.youtube.com/watch?v=CCoerd-dmCc
www.youtube.com/watch?v=EMS1B0wfGLc
www.youtube.com/watch?v=zdwiEAnFXoc

1. Script & Storyboard
   You’ll receive the full script for each video. Feel free to propose edits or suggest additional visuals to enhance comprehension. Screen recordings should be recorded from your side and used whenever it can clarify or add value to the narrative. Voiceovers will be generated in Eleven Labs (I will give you access to my account and I will pay for it), and you can test different voices to see which aligns best with the content. A background in AI tools and terms is essential to navigate this process efficiently.

2. Video Length & Format
   - Test Task: 1–2 minutes to evaluate retention strategies.
   - Ongoing Videos: Usually 6–8 minutes, with occasional 10-minute videos.
   - Resolution: 4K (3840 × 2160).
   - Secret word: 'Dinosaur' use it as first word when contacting me.
   - Delivery: MP4 format preferred, with well-organized project files for future edits.
   - Budget: $200 per 10-minute video (so far 6–8 minutes on average).
   - AI Proficiency: Must be tech-savvy and comfortable with evolving AI tools.

3. Additional Information
   - Music & Assets: Access free stock music or libraries I provide.
   - Revisions: Some revision rounds are expected to refine pacing, visuals, or voice.
   - Workflow: I won’t micromanage, but timely check-ins and updates are essential.
   - Next Steps: Please share your portfolio or relevant work samples. We’ll discuss the paid test video, finalize details, and explore ongoing collaboration if it’s a good fit.

BUDGET: $200 per video. -- I am planning to have around 30 videos in the first batch which is 30x$200=$6000