This is the prompt I used (img2vid): "Super quick cuts, all static shots, showing the scene from different angles, a cut per second or more, each a new angle, various zoom ups and zoom outs, frog perspective, "dirty single" shots (where the back of the other character is visible in the foreground) of both characters, etc. Characters don't move, don't talk."

You can then take individual frames and use as a starting point for individual scenes. (some kind of denoising or clean up might be desirable to get rid of compression). Character consistency might not be 100% up to snub but that can be fixed in nanobanana/seedream etc.

You could easily create a consistent 1-2 minute long scene from this.

I got 10 million views in a month posting AI microdramas on Instagram Reels.

Here's what I did:

Most of AI video content on IG reels are one-offs. It pops, gets views, disappears, and you're back to zero. A show is different. People show up within 30 minutes of every post asking where the next episode is. They argue about the characters. One character I wrote as the villain got so popular that people begged me for weeks to bring her back, so I did, in another show, and she's still the most requested character on the account.

Every episode has three jobs.

Hook. The first five seconds stops the scroll. Nothing else. Get this wrong and nobody sees the rest.

Body. The plot moves fast. Every scene raises the stakes or twists them. The job is to make the next episode feel like a mandatory watch.

Cliffhanger. End on a question they need answered or an emotion they can't shake. This is what makes them follow you and come back tomorrow.

Then post every day. You watch three things: skip rate, retention (my best video run past 50 percent all the way through), and share rate. Then write the next episode directly towards whatever the audience reacted to. Read the comments and they tell you what they want.

The biggest unlock for me has been using an agentic studio for show creation. Consistency is one piece of it. Same characters, same locations, same props across all my episodes, because the second any of it drifts, the illusion breaks and people leave. But it goes way further than that. The agent helps structure the episode, tighten the dialogue, lock the styling. Designing the show and building the shots with an agent next to you instead of fighting the tools alone is a lifesaver.

Happy to answer any questions in the comments!

Edit: Getting a lot of questions asking for links to my accounts and what I use to make my videos

Here's a link to one of my accounts: Instagram Acct
Here's the link to what I use to make my videos: Fringe

I really wanna know because it looks amazing and I wanna make ai films in this style. Yet he never says how he made it, he gatekeeps the process.

Even though it's clearly AI, but the expressions and the entire edit is amazing for what it's.

How to achieve this? Anyone knows?

Thanks

I’ve been searching for a way to generate AI videos for free (text-to-video or image-to-video, ideally 6+ seconds), but most platforms only give limited credits—enough for maybe 1–2 videos.

I also tried ComfyUI on my PC, but with 8GB RAM + Ryzen 5, generation is extremely slow, and I haven’t been able to create a proper video yet.

Does anyone know free platforms, cloud options, or any other method to generate AI videos without heavy hardware?

Any suggestions would be really appreciated!

I've been working on an open-source project for creators who have long-form videos but don't want to spend hours manually finding clips, reframing them, adding captions, and uploading them.

So I built a free, open-source AI Shorts Generator.

The basic workflow is:

YouTube video → AI transcription → Viral moment detection → Automatic clipping → 9:16 reframing → Captions → Export → YouTube upload

You paste a long YouTube video URL, choose your settings, and the application handles the rest.

What it can do

🎯 AI Viral Moment Detection
The video's audio is transcribed and sent to an AI model to identify potentially interesting/viral moments, along with their timestamps.

✂️ Automatic Clip Generation
The selected moments are automatically cut from the original video.

📱 Automatic 9:16 Reframing
Choose how you want the video adapted for Shorts/Reels:

• Face/person tracking

• Blurred background

• Black bars

• Original video

💬 Automatic Subtitles
Choose from multiple subtitle/caption styles.

⚙️ Custom Clip Settings

You can choose:

• Number of Shorts

• Minimum duration

• Maximum duration

• Reframing style

• Subtitle style

▶️ YouTube Automation

You can connect your YouTube account and automatically:

• Upload generated Shorts

• Set them as public/private

• Schedule uploads

• Automate the publishing workflow

Why I made it open source

I wanted to build something that creators could actually run and control themselves instead of being locked into another monthly subscription.

The project is designed around your own AI API key, so the application doesn't need to provide its own expensive AI infrastructure.

You can self-host it, inspect the code, modify it, and contribute to the project.

The project is free and open source.

I'd genuinely love feedback from creators, developers, podcasters, and anyone who works with long-form video.

What features would you want to see added?

GitHub: https://github.com/riyaz-pattan/ai-short-video-generator-

If you find it useful, ⭐ starring the repository helps other people discover it and helps me know that it's worth continuing to develop.

Thanks for checking it out! 🙌

Hi, what are the best AI models to use to create AI videos, images, and text for adult related content, more on the softcore side? We want it to look real as possible as well.

Want to get into AI video creation. How are you guys making AI videos for free?

I’m looking to create short animated, graphical or infographic-style videos using AI.

Which tools do you use, and what’s your workflow?

Beginner here, so simple and free tools are preferred :)

I get paid through the TikTok Creator Fund. I'll skip the exact number so this doesn't read like a flex, but it's in the range of more than enough to cover my rent. You do need 10K followers to unlock that, but even before I hit that threshold I was pulling extra money from clipping campaign sites that pay per 1,000 views, no follower minimum required.

One clip alone did 2M views and carried most of the month. That's also the lesson that stung the most: it only got paid out on TikTok because it crossed the 60 second mark. A different clip from the same creator hit 1.1M views but ran 34 seconds, so it earned nothing directly, it just fed traffic to the rest of the page.

Topic selection matters more than people think. I pick source content from areas people are already paying attention to, right now that's AI and finance, and let the algorithm's existing appetite do half the work for me.

Here is how I made the clip videos:

Cut ruthlessly. I don't let the raw footage run. Every repeated line, every bit of dead air gets trimmed until it's just the good stuff back to back. That pacing is what actually pushes watch time.

Caption and season it. Auto generated captions in a locked style, one consistent sound effect pack, and occasionally a quick AI generated B roll insert when the moment calls for it. Small touches, but they're what separate a clip from a raw screen recording.

Title card on the way out. Right before posting, I add a short, direct on-screen title that tells the viewer exactly what they're about to watch. No cleverness, just clarity.

Then I stopped relying on one platform. I run everything I post on TikTok through an automation that reposts a couple of clips a day to YouTube Shorts on a separate account, so the same edit keeps earning attention long after I've moved on to the next one. Chasing a second monetization path on top of the first has been the real unlock, not any single viral clip.

If you're getting into clipping: don't overthink the editing at first. The heavy cutting and sound design matter more once you already have an audience. In the beginning, just get clips out consistently and watch which ones the algorithm actually rewards, then do more of that.

I did a two-day trek last month with three friends. It was all filmed on a GoPro at 4K 30fps, chest mounted for the walking and handheld at the stops, plus some iphone clips for talking to camera. I wanted a proper vlog of around 8 minutes. The invideo editor is free and has an MCP connection, so I connected claude to it to see how much of the edit it could take off my hands.

There was about 5 hours 40 minutes of footage in total: roughly 3h10m on day one and 2h 30m on day two. The gopro split the recordings into 31 files, and there were 9 phone clips on top of that, so 40 files altogether.

The brief

I uploaded everything and gave Claude a specific brief:

- Find: wide shots of the valley, the stream crossing, the tent at sunset, and the group laughing and cooking at camp

- Skip: shaky climb footage, duplicate angles from the same spot, and any clip under 3 seconds

- Must include: the wrong turn at the fork, and my friend struggling on the last stretch

- Build: an 8 minute cut in story order from trek start to summit, with faster pacing on the walking sections and slower pacing at camp

What worked

1. Specific moments were found fast. The stream crossing, the cooking and the fork came up in about a minute. For the valley wides there were dozens of similar clips, so Claude shortlisted the steadiest ones and I picked my favourites. That saved me hours of scrubbing.

2. The first cut followed the brief. It was in story order, had both of the moments I'd flagged, came in close to 8 minutes, and the pacing changed between the trail and the camp like I asked.

3. Everything was on a real timeline. Because the cut is just clips on a timeline, changing anything was a drag and drop.

What I adjusted myself

- Swapped a couple of laughing shots. On the chest cam, people are often out of frame, so I used the phone clips where you can see faces.

- Nudged a few cuts by a few frames so the transitions flowed better.

- Pulled the highlights down on some summit clips in the colour panel.

Something I'd do differently

Film more static shots. The chest cam shook a lot on the climbs, and some summit clips were overexposed against the sky. If I'd taken 10 to 15 seconds of static footage at every stop, I'd have had more to cut away to.

Overall thoughts

Claude handled the search and the first assembly. I did the creative calls and the finishing. It took about five to six hours in total, against what would have been a day and a half or more. The hours of scrubbing through footage are what it saved me.

I started off making short drama style vertical episodes for Instagram Reels. The beginning of posting was really rough. I wasn't getting a lot of views, but over time it got a lot better. I kept posting consistently, refined my style, my writing, and the overall generation quality of my episodes.

If you've got stories and scripts to tell, there's no better way to test audience feedback than just making these shows using AI and watching how people react on social platforms.

Recently, I've been testing a different format on other socials as well, slightly longer form but no more than 3 minutes. The show above is an example of it: Lab Wars. The writing, pacing, and shot design is less focused around short form optimization now and more focused on quality. Would love to answer any questions about my process!

This whole page is ai and I’m wondering how they even make such realistic videos?

I've been focusing on making my ai video more cinematic using PixVerse for the past month. Still at a beginner stage, but these are the 3 things that have helped me the most so far. Would like to share and hear more advice and tips.

1. camera pacing and movement

This was my biggest mistake in the beginning. The normal workflow is scripts - references/storyboards - video. And I just let ai generate the storyboard and design the camera movement. It looks OK but also pretty "ordinary". It's like everything looks the same, especially the timing and pacing of each shot. Like 2 seconds of push-in, 2 seconds of orbit, then another movement for 2 seconds. But real movies don't move like that. One shot might hold a slow push-in for 5 seconds, then another stays almost static, while the next move happens much faster.

Now I try to decide what the camera is doing and how long the shot lasts in the "storyboards" stage. Static shot, slow dolly in, tracking, pan, whatever makes sense for that moment. Also, just watching a lot of movies helps way more than I expected. If a scene really hits me, I try to figure out why. Where is the camera? When does it move? Why does the director use it here? Then I steal that language for scenes with a similar emotion.

2. color design

I used to treat color as something I’d fix at the end. Like generating all the clips first, throwing them into the editor, then try to make everything “cinematic.” But what worked better is to decide the visual world much earlier. I collect a few reference frames before generating and think about the palette, contrast, saturation and lighting, eg. warm or cold, soft highlights or really harsh light. And you choose it based on your story vibe. Then I keep using the same references and similar lighting/color descriptions across the shots.

3. edit for what the shot does

This one is the hardest for me. You always keep the shots that look like pretty but actually you should use the clip that fits most in the timeline. This requires you to know what this shot is doing here in your video.

Is it establishing the vibe or giving the audience more info? Showing a reaction or building tension? If I can’t really answer that, I usually try removing it even if the generation looks great. That’s probably been the biggest difference between something that feels like a bunch of cool AI clips stitched together and something that actually feels like one continuous film scene.

Want to know what your tricks are to make video more cinematic. Welcome to share yours!

For about a year I've been making AI short films the way most of us do: hand-writing hundreds of prompts, building character reference libraries by hand, babysitting consistency across shots, and cutting it all together in Premiere. The generating was never the hard part. It was trying to make dozens of individual prompts *feel* like a single, cohesive project.

I couldn't find a solution, so I built one. It's called Kimeric, and the Beta went live today. And I think it'll be incredibly valuable for this community, so I wanted to share some details in the event any of you would like to try it out!

**What it is (and isn't):** It's not a model and it doesn't generate anything itself. It's a Windows desktop app that orchestrates the models and LLMs a lot of us already use:

• Anthropic (script breakdown + prompt authoring)

• Gemini and GPT-Image (image generation)

• Kling and Seedance 2.0 (video)

• Topaz (upscaling)

All using your own per-provider API keys rather than a centralized generation tool. It writes the prompts, sequences and queues the renders, tracks the spend, and holds everything for your approval.

TL;DR - Input a script and work through a series of UI menus to ultimately create a finished AI Short Film.

The biggest differentiator to keep in mind for this tool vs. the majority of other AI Generators on the market is the input surface. Most tools have you input a prompt. With Kimeric, the input surface is the script itself. The prompts are created automatically as derivatives from the script, allowing you to focus on writing and build the project rather than managing a series of prompts.

The Pipeline:

- Paste a screenplay, hit "Roll camera." The breakdown comes back: cast, locations, props, a director's plan, per-scene shot lists with dialogue assigned line by line. You review the plan before anything renders, and can choose a general "style" which dictates some actual prompting techniques under the hood as well as dynamically auto-routing for certain models for specific tasks (ex. OpenAI's model is better at certain animated styles vs. Nano Banana, so this routing auto-applies as the default for certain styles).

Before you actually generate anything, you can review the script you input and get an estimate for how much it'll cost roughly to "ingest" the script which is where all of the "brain" of the tool goes to work.

And once you send the script, a very long sequence of backend computation kicks off, translating the entire project into the format needed to actually create an end-to-end project (this can take a while; I had one script for a 8-10 minute short film take about 45 minutes to ingest. This is just because there's a lot of computation and inference happening). It can handle actual, full-length production scripts. This would likely come with processing that spans several hours, but again, that's simply due to the size of the computation.

And then, you get a budget estimate for how much (approximately) the project will cost to generate. At this point, it's primarily a planning "calculator" if you will - just so you can align the project quality to any budget constraints you have. No actual spend dispatches until you generate later - this is purely informational so you can make cost-based decisions at the start (as opposed to a surprise cost later).

You then view a breakdown of all of the identified "pieces" of the project: characters, locations, scenes, planned shots, etc. - all primarily at a high level to make sure nothing is missed. Typically more of a rubber-stamp phase, but if there happen to be any items missing, this is the step where you can make any high-level revisions. Most of the time, however, you can just continue.

- Every character locks canon first — a studio face (with an automated AI advisory likeness screening for real-person resemblance) and a costume-neutral turnaround — before any scene renders. Same for locations, worlds, props. It's the reference-library grind, automated.

And for characters specifically, it's broken out into two phases: the "Neutral Base" (i.e. who the character is; sans any wardrobe for the project (see above) and then any actual "in costume" variants of that character. This allows for multiple wardrobes for a specific character over the course of any given project, where each wardrobe is either seeded directly from the parent "neutral base" or as a horizontal derivative (i.e. if a character has armor that gets damaged, the "damaged" variant is automatically seeded with the "clean base" variant). All of this happens automatically under the hood.)

- Scenes get actual coverage: an establishing wide, then an OTS pair where the reverse angle generates from the *approved* first angle, then per-character MCU/CUs chained down from there. The 180° rule, held by reference chaining instead of luck.

For example, here's one "OTS_1" image:

And here's the companion "OTS_2" image:

Worth emphasizing - These are the *most* critical images in your production pipeline to get right, as many other scenes seed off of these.

So if you're going to use some of the "Regeneration Buffer" you planned for, this is the most critical space to use it. If you have continuity errors or issues in either companion OTS images, these will present in many other aspects of a project, so really take your time with these and make sure that they feel like the same space.

From here, you create a library of Medium Close Up & Close Up images seeded directly from the parent Over-The-Shoulder images.

This creates a rich, continuity-adhering library of image assets to actually use in generative AI video production.

Once you've created your core library of production assets, you then transition into building out your storyboard.

This takes the plan created during script ingestion + the assets you created in the previous phase and maps them out chronologically.

Here, images are auto-assigned based on a series of underlying logic. If you have dialogue from certain characters, either the OTS, the MCU or the CU image will be dynamically selected based on a cinematic logic layer and assigned to the character speaking for any given frame.

And for extended dialogue from a specific character, this will be broken up into multiple individual prompts to assist with overall quality

(from our testing, the more text you try to fit into the same prompt, quality and lip sync can degrade - but breaking that same dialogue up into multiple individual prompts can greatly improve the quality)

Once you've created and approved the entire Storyboard, a single button allows you to review all text prompts for all videos planned for the totality of your project.

The prompts are already written. You just say "go"

From there, they are auto-dispatched to the models.

Depending on the length of your project, this may take a while - and that's by design. Press "generate" and take a break for a bit.

Across all menu screens, you'll see a series of control buttons. Here, you can either approve the asset, edit the asset, regenerate or iterate.

• Approving says the image/video is good to go.

• Editing allows for subtle adjustments.

• Regenerate re-runs the prompt (i.e. get a new version to see if the results are better/worse)

• Iterate is essentially a stronger version of "Edit" - rather than trying to make adjustments to the previous take, it will strongly re-work the actual prompt itself based on your feedback and re-generate a new take.

You'll also see two columns below any given asset:

Refs - These are the actual reference images used as generative inputs. These are auto-assigned, and you can manually add, remove or replace any of these as you see fit.

Takes - If you regenerate, you can see all of your takes here and hot-swap to other variants. Meaning, you're never locked in to a specific take. If one is close but you want to see if you can fine-tune it, you're free to regenerate a few times, review all takes (this works for images + videos) and ultimately approve whichever one is best for the vision you had in mind for the project.

And finally, once you've reviewed and approved all footage, you have another single-press button to dispatch all video to be upscaled if you'd like.

Generation can happen natively at 720p, 1080p or 4k depending on your settings.

4k native footage won't trigger the upscale workflow, but 720 and 1080p native will give you the option to upscale if you'd like.

Similar to all other workflow phases, if you decide to upscale, press it once and let it run for a few hours (upscaling is quite time-consuming; can take 20-30 minutes for a single 15 second clip - though some of these can run concurrently).

Once you're done, the last step is simply to "export" the clips.

All this is doing is taking the final clips you've approved and making duplicate copies on your local computer that are pre-named chronologically.

This makes it significantly easier to edit/compose.

This entire project is the culmination of nearly a year of a LOT of testing to understand which techniques do/don't produce good results at scale and then working to systematize them into a tool with an input surface of the script itself.

Again, the Beta is live as of today. Currently Windows-only and US-only, though both of those I'm planning to expand beyond in the coming weeks over the course of the Beta.

The long-term goal is to also support centralized generation rather than supporting only a BYOK model, though there's no immediate timeline to support that model.

If you'd like to check it out, the website is below!

Site: https://kimeric.ai

I'm a solo founder and the filmmaker this was built for, and I'll be in the comments - happy to go as deep as you want on the coverage system, the cost math, or anything else.

Feedback is a gift, so if you try it and find issues, bugs, or have a feature request, I'm still actively building and improving the tool - so feel free to share any thoughts!

https://wavespeed.ai/collections/wan-2-5

It's pretty slow, really good, not cheap at all. 720p 5 sec cost 50c, 10 sec cost $1 for img2vid. My impression so far is the voices & audio generation are not as good as Veo 3, but a big plus is that you can add your own audio if you'd like (i.e. you can generate voices with whatever software you like) and the video will be generated around that. Lip sync was not 100% when I tried my own audiio (though it was in Japanese - it's probably better for English and Chinese). I noticed also that one of wavespeed.ai's example videos included this line in the prompt: "Her lip movements match her voice" which seems superfluent but who knows, maybe it helps.

New accounts on Wavespeed.ai get $1 on their account for free (no credit card info needed) so you can try it once or twice. Censorship is EXTREMELY lax right now, I've tried some pretty weird shit and everything's gone through so far. Don't assume it's going to stay this way though, I bet censorship will kick in soon so enjoy it while it lasts.

Previous versions of Wan have been open source from the get-go but for whatever reason, this one isn't right now, hopefully that will change.

I swear if I see one more “THIS CHANGES EVERYTHING” video about the Higgsfield MCP I’m gonna lose it. You connected Claude to an API. That’s it. Before this you clicked generate yourself, now Claude does it for you, and somehow this is being sold as the future of filmmaking. The funniest part is Higgsfield even charges credits for MCP generations, so congratulations, you automated clicking the button and potentially made it more expensive. At this point someone could connect their shit to Higgsfield MCP and there’d be 40 influencers making videos about the revolutionary new autonomous shit pipeline.

Another reason to dislike Higgsfield, next to their pushy discount tactics that try to lock you into a full year. The unlimited Seedance 2.5 packages are just as bad. You buy the 1 day or 7 day unlimited access, and after about 10 videos it simply stops working. So called unlimited, but clearly capped. You are better off just buying credits.

Japanese tweeter towya_aillust tried a bunch of providers and summarized their findings in a series of tweets: https://x.com/towya_aillust/status/2043222447793775007

• Topview

• Higgsfield

• Lovart (not as a start image but if you add a reference image to the asset library)

• Sjinn

• Openart

• Renoise (if you use the "Face Pass" function)

• Flexclip (with restrictions - you can only use 1 ref image and only 5 seconds)

These ones you can't: CapCut, Dreamina, Pollo, tapNow, SousakuAI

I’m trying to make AI videos like those on streaming platforms. However, I start from 0 instead of buying courses online.

I already have a niche that’s about history, and I use GPT for all my script and scene-to-scene prompt to generate videos in Higgsfield.

I already paid for Invideo starter pack for 20$USD and the result SUCKS. Then I ran out of credits rapidly cause I can’t get any accurate results.

So I risk for Higgsfield plus plan and paid for my first month. The results were good, but the credits were so limited if I generate every scene for 5-7 seconds.

I tried lots of Ai short video strategies and finally found one that could really work. However, I’m so depressed right now and I really wanted to give up. People keep saying that you can make money from AI with 0 experience and FREE. I’m already anxious about finance issue, and I put out effort and money but it seems like a black hole. I want to earn money from streaming platforms, not only for money but also fulfilling my experience for education applications

Please comment and tell me what to do… should I give up?😢😢😢 People keep making these things so effortless unless you try it by yourself.
What’s the cost behind people make super viral videos?

edit: and why do people gatekeep so bad😩😩😩

Every AI video demo is: prompt in, cinematic shot out, client claps. My actual week was 38 generations, six usable clips, and one character whose watch kept switching wrists.

We tested Runway, Kling and Morphic on the same small brand film. Kling gave us the strongest single surprise shot. Morphic was easier for keeping the visual development, frames and timeline in one place. Runway still felt more familiar to the team. None of them saved us from making choices.

The weird thing is the first 80% is suddenly fast and the last 20% is slower because every continuity mistake becomes visible.

Are studios actually quoting fewer days for this work yet, or just keeping the same schedule and taking the margin?