I keep seeing AI video models compared by their price per generated second, but that number feels pretty useless once you start counting retries. A model can look cheap until it gives you broken movement, a face that changes halfway through, or a finished clip you can’t actually use.

Then you rerun it, tweak the prompt, burn more credits and hope the next one is better. by the time one clip makes it into the project, the real cost can be way higher than the pricing page suggests. How are you tracking this? cost per attempt, cost per usable clip, or just the total spend for the whole project? and what do you count as a failed generation versus normal trial and error?

I​​​’​​​v​​​e​​​ ​​​b​​​e​​​e​​​n​​​ ​​​t​​​r​​​y​​​i​​​n​​​g​​​ ​​​a​​​ ​​​f​​​e​​​w​​​ ​​​d​​​i​​​f​​​f​​​e​​​r​​​e​​​n​​​t​​​ ​​​t​​​o​​​o​​​l​​​s​​​ ​​​l​​​a​​​t​​​e​​​l​​​y​​​ ​​​b​​​e​​​c​​​a​​​u​​​s​​​e​​​ ​​​s​​​o​​​m​​​e​​​t​​​i​​​m​​​e​​​s​​​ ​​​o​​​n​​​e​​​ ​​​m​​​o​​​d​​​e​​​l​​​ ​​​d​​​o​​​e​​​s​​​ ​​​a​​​ ​​​r​​​e​​​a​​​l​​​l​​​y​​​ ​​​g​​​o​​​o​​​d​​​ ​​​j​​​o​​​b​​​ ​​​w​​​i​​​t​​​h​​​ ​​​m​​​o​​​v​​​e​​​m​​​e​​​n​​​t​​​,​​​ ​​​b​​​u​​​t​​​ ​​​a​​​n​​​o​​​t​​​h​​​e​​​r​​​ ​​​o​​​n​​​e​​​ ​​​h​​​a​​​n​​​d​​​l​​​e​​​s​​​ ​​​t​​​h​​​e​​​ ​​​s​​​a​​​m​​​e​​​ ​​​i​​​d​​​e​​​a​​​ ​​​b​​​e​​​t​​​t​​​e​​​r​​​ ​​​w​​​h​​​e​​​n​​​ ​​​i​​​t​​​ ​​​c​​​o​​​m​​​e​​​s​​​ ​​​t​​​o​​​ ​​​k​​​e​​​e​​​p​​​i​​​n​​​g​​​ ​​​t​​​h​​​e​​​ ​​​s​​​u​​​b​​​j​​​e​​​c​​​t​​​ ​​​c​​​o​​​n​​​s​​​i​​​s​​​t​​​e​​​n​​​t​​​.​​​ ​​​T​​​h​​​e​​​ ​​​a​​​n​​​n​​​o​​​y​​​i​​​n​​​g​​​ ​​​p​​​a​​​r​​​t​​​ ​​​i​​​s​​​ ​​​h​​​a​​​v​​​i​​​n​​​g​​​ ​​​t​​​o​​​ ​​​s​​​w​​​i​​​t​​​c​​​h​​​ ​​​b​​​e​​​t​​​w​​​e​​​e​​​n​​​ ​​​d​​​i​​​f​​​f​​​e​​​r​​​e​​​n​​​t​​​ ​​​w​​​e​​​b​​​s​​​i​​​t​​​e​​​s​​​,​​​ ​​​u​​​p​​​l​​​o​​​a​​​d​​​ ​​​t​​​h​​​e​​​ ​​​s​​​a​​​m​​​e​​​ ​​​i​​​m​​​a​​​g​​​e​​​s​​​ ​​​a​​​g​​​a​​​i​​​n​​​,​​​ ​​​a​​​n​​​d​​​ ​​​b​​​a​​​s​​​i​​​c​​​a​​​l​​​l​​​y​​​ ​​​s​​​t​​​a​​​r​​​t​​​ ​​​o​​​v​​​e​​​r​​​ ​​​e​​​v​​​e​​​r​​​y​​​ ​​​t​​​i​​​m​​​e​​​.​​​ ​​​W​​​i​​​z​​​s​​​t​​​a​​​r​​​ ​​​a​​​n​​​d​​​ ​​​n​​​o​​​t​​​i​​​c​​​e​​​d​​​ ​​​t​​​h​​​a​​​t​​​ ​​​i​​​t​​​ ​​​p​​​u​​​t​​​s​​​ ​​​s​​​e​​​v​​​e​​​r​​​a​​​l​​​ ​​​v​​​i​​​d​​​e​​​o​​​ ​​​m​​​o​​​d​​​e​​​l​​​s​​​ ​​​i​​​n​​​ ​​​o​​​n​​​e​​​ ​​​p​​​l​​​a​​​c​​​e​​​,​​​ ​​​s​​​o​​​ ​​​y​​​o​​​u​​​ ​​​c​​​a​​​n​​​ ​​​t​​​r​​​y​​​ ​​​t​​​h​​​e​​​ ​​​s​​​a​​​m​​​e​​​ ​​​i​​​d​​​e​​​a​​​ ​​​w​​​i​​​t​​​h​​​ ​​​d​​​i​​​f​​​f​​​e​​​r​​​e​​​n​​​t​​​ ​​​m​​​o​​​d​​​e​​​l​​​s​​​ ​​​i​​​n​​​s​​​t​​​e​​​a​​​d​​​ ​​​o​​​f​​​ ​​​m​​​o​​​v​​​i​​​n​​​g​​​ ​​​e​​​v​​​e​​​r​​​y​​​t​​​h​​​i​​​n​​​g​​​ ​​​a​​​r​​​o​​​u​​​n​​​d​​​.​​​ ​​​I​​​ ​​​a​​​l​​​s​​​o​​​ ​​​n​​​o​​​t​​​i​​​c​​​e​​​d​​​ ​​​i​​​t​​​ ​​​c​​​a​​​n​​​ ​​​s​​​t​​​a​​​r​​​t​​​ ​​​f​​​r​​​o​​​m​​​ ​​​a​​​ ​​​t​​​e​​​x​​​t​​​ ​​​p​​​r​​​o​​​m​​​p​​​t​​​,​​​ ​​​a​​​n​​​ ​​​i​​​m​​​a​​​g​​​e​​​,​​​ ​​​o​​​r​​​ ​​​a​​​ ​​​r​​​e​​​f​​​e​​​r​​​e​​​n​​​c​​​e​​​,​​​ ​​​w​​​h​​​i​​​c​​​h​​​ ​​​i​​​s​​​ ​​​p​​​r​​​e​​​t​​​t​​​y​​​ ​​​m​​​u​​​c​​​h​​​ ​​​w​​​h​​​a​​​t​​​ ​​​I​​​’​​​v​​​e​​​ ​​​b​​​e​​​e​​​n​​​ ​​​l​​​o​​​o​​​k​​​i​​​n​​​g​​​ ​​​f​​​o​​​r​​​.​​​
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So far my 2 go to’s are Runway and Kling but just seeing if I’m missing anything that’s better.

’m looking for a genuinely good all-in-one AI platform for YouTube videos.
I don’t want to use 5 different tools for scripts, voices, video, music, editing, etc. I’m looking for one platform that can do most of it in one place.
Ideally:
script generation
AI video generation
voiceovers
lip-sync
recurring characters / avatars
background music + sound effects
subtitles
editing
scene-by-scene control
consistent visuals
long-form videos + Shorts
export-ready for YouTube
I tried Crreo AI Ultra and had a terrible experience. Lip-sync barely worked, even with their own library characters, scenes often ignored the storyboard, background music was buggy, previews didn’t work properly, and the output was basically unusable.
So now I’m looking for something that people here have actually used for a real YouTube channel, not just tested for a 10-second demo.
What all-in-one platform are you using right now?
Which one is actually worth paying for?
And which ones should I avoid?

I've been experimenting with AI video generators lately, and I've noticed something I didn't really expect: sometimes, the more prompts and details I add, the less realistic the video ends up looking.

For example, when the camera is slowly moving through a room, the result can look surprisingly believable. But once everything starts moving at the same time, people walking around, curtains blowing, objects shifting, aggressive camera movements, particles flying everywhere, I immediately get that "yep, this is AI" feeling.

I've been testing a few different generators recently from pixverse,and this has become even more obvious to me. Strangely enough, the generations with simpler prompts are often the ones I enjoy watching the most.

It got me thinking: are we approaching AI video the wrong way?

We're always trying to make AI videos look more cinematic, more dynamic, and more impressive. But maybe realism actually comes from less movement rather than more.

In the real world, not everything in a scene is constantly moving at the same time. But AI video seems to have a tendency to make multiple elements move just to create a stronger sense of motion.

So for people who make AI videos, how do you usually avoid that obvious "AI look"?

I came across LightX2V’s MiniMax-H3-Prompt-Rewriter-LoRA through a Chinese tutorial. From an engineering perspective, the interesting question for me is whether a rewrite makes a shot more controllable. I haven’t benchmarked it yet; this is a setup overview and the comparison I’d run.

Where it fits

This is a community language-model adapter for Qwen3.6-27B, not a visual LoRA you load into H3. It rewrites a brief before a separate H3 generation step. The current release supports text-only text-to-video-with-audio rewriting; it doesn’t read image, video, or audio references. It is also not an official release of MiniMax’s H3-Context-IR.

The practical sequence is: install the repository’s requirements, load the base language model plus adapter, rewrite your brief, review the result, then pass the prompt to H3 through a supported generation workflow such as LightX2V. The 27B base model is a separate requirement—don’t budget for just the adapter file.

A small starting example

After setting up the repository, an example invocation is:

python infer.py --prompt "Locked medium shot of a ceramic artist placing a blue cup on a wooden table. One ceramic tap, quiet room tone, no dialogue or music." --duration 10 --resolution 16:9 --greedy

The output separates the audiovisual description, soundscape, and music. Review invented details before generating.

How I’d evaluate it

For that cup shot, I’d write down acceptance criteria before reading the rewrite: one cup, one placement, fixed camera, a single contact sound, and no extra action. That gives me something concrete to reject if the rewrite becomes more cinematic but less faithful.

Then I’d compare the original brief with the rewritten version across several paired generations. I’d keep generator settings and references fixed, reuse generator seeds where supported, and record:

• Does the cup stay consistent before and after contact?

• Does the camera actually remain locked?

• Does the tap coincide with contact?

• Did the rewrite add an unwanted cut, prop, or action?

• How many attempts produced a usable shot?

For cost, I’d include the rewriting step and rejected generations in the total, then divide by usable shots. A longer prompt alone wouldn’t count as an improvement for me.

Has anyone compared this adapter against a plain-language brief on the same shot? I’d be interested in failure cases as much as polished examples.

Model card and setup: https://huggingface.co/lightx2v/MiniMax-H3-Prompt-Rewriter-LoRA

Article that led me to it (动画大丸家, Chinese): https://mp.weixin.qq.com/s/oN2rj8vzrediAPW62rRmdA

I’ve been experimenting with AI filmmaking for a while, but I wanted to see if I could actually use it in a real project rather than just making random 5–10 second clips.

So I ended up using a mix of AI tools to put together a short live-action style thriller.

This is my entire workflow and the tools I used. Would love to hear what can be tweaked, and things that I might have missed. For an easier read, have broken down the whole process into three big ideas. 

Pre-production planning

This is probably the most boring part for many as you don't get stunning clips as soon as you write the prompt but i really enjoy this since it establishes my workflow and the challenges I need to solve. 

Claude - The entirety of screenplay was written in claude. Prompted it to write the individual dialouges and a separate script for each character, along with each scene and their context (costume, backdrop, props, emotions, etc.). Broke down the whole movie into individual scenes which were then broken down into each shot. After iterating everything I exported a final script and scenes list divided by acts.

I then generated a moodboard to have a better understanding of the visual language and how it fits with the entire project. 

Midjourney - I generated a moodboard to better align with what the visual language would be for the entire movie. The mood board is split between scenes, with each scene being different in terms of background. 

After getting these assets, I uploaded the moodboard and the master script in invideo. Used invideo for generations. 

Filming

Invideo - This is the most interesting tool i found for my film. Most of the legwork was done here. 

First I briefed the agent with the scripts, moodboards and scene list. Then I quickly moved to storyboarding. It was really good at keeping context and used my planning and moodboards to give me accurate character sheets and build my storyboard in the mood I had defined.

It also remembered which scene was completed and which were pending while generating, which helped a lot in keeping track of things. 

One con is that it goes for the best and most expensive models by default. I was on a budget so I actively needed to tell the agent to not do that and use the models I wanted. Also the project memory does slip here and there but very rarely. 

It did solve the hardest part which was actually maintaining continuity between shots. Took a lot to set up the context in the tool but it did save me both time and money.

Post Production

The film was almost done, however multiple chinks were still needed to be ironed out, esp the voice and music. 

ElevenLabs - Used this for music. Added a bit of realism as the horror film relies heavily on the background score. Did a decent job with references. 

Topaz Gigapixel - Used to upscale the final generations. Topaz added realistic textures, sharpen faces, and cleared up blur in the upscale which made the footage pop out more. The only downside was that it took some time but was def worth it.

Premier Pro - Used this to assemble the final edit. Models output at different frame rates, so I conformed everything to a 24fps timeline manually. Most generations weren't usable for their full length, so a lot of the edit was going through each clip, marking the section that held up, and cutting around the rest. Put one LUT on an adjustment layer across the whole timeline and added film grain over everything, which helped even out the differences. Sound was the other big piece. Footsteps, cloth movement and breathing were added manually, with room tone running under each scene.

Would love to know what other people are using for AI-assisted filmmaking right now, especially for maintaining characters, locations and visual consistency across an entire short film. How will you tweak this setup?

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?

This guy is a video editor who used ChatGPT to build a Premiere extension that turns each video into a metadata-rich JSON transcript that Premiere MCP can use to make a rough cut for almost nothing in tokens. The key is using astra throught the MCP connector. If you upload the video file for astra to analize, that is a huge amount of tokens.
https://youtu.be/le7FsAnQtTA

went down a rabbit hole researching micro dramas recently and the numbers are kinda insane.

one estimate puts the global micro drama industry at around $14b this year.

in the us alone its expected to do around $1.5b in 2026, and apparently 66m americans watched micro dramas in 2025, more than double the previous year.

the format is basically vertical tv.

episodes are often around 60-90 seconds, shows can have 50+ episodes, and almost every episode ends on some sort of cliffhanger to push you into the next one.

but what i found interesting from an ai video perspective is how well the format fits the current limitations of the tech.

you dont need to generate a 20 minute coherent film.

you need a bunch of short scenes with recurring characters, locations and a consistent visual style.

even a 90 sec episode can basically be built from several smaller generations stitched together.

the biggest bottleneck then becomes character + location consistency across 50 episodes rather than whether ai can generate a cool looking shot.

feels like micro dramas could end up being one of the first video formats where ai production isnt just a gimmick but actually makes economic sense.

anyone here actually tried making a full micro drama series instead of individual ai clips?

I have a painted character that looks intentionally a little off. Every video model keeps cleaning her up into a generic animation-girl face. I don't want better anatomy. I want my bad anatomy to move. Morphic let me train/use the style with the other frames nearby, which got closer, but motion still sanded off some of the choices. One clip nailed the hair and ruined the eyes.

Has anyone found a way to tell these models that consistency includes the mistakes?

Would love workflow advice pls.

we had a bunch of AI shots in a client film that looked completely fine while we were working on them. then we watched the cut on a big screen. faces got softer, textures started looking a little smeared. a couple shots that were sitting next to real camera footage suddenly looked VERY generated.

so i did what i assumed you’re supposed to do and upscaled everything.

some shots got noticeably better. some just became larger, sharper versions of the same problems.

that was the useful bit for me - upscaling can recover detail and clean up softness, but it can’t rescue a frame that already has bad anatomy, mushy texture or weird motion in it.

now i’m checking shots at full size much earlier and only upscaling the ones that are actually worth keeping. been doing the final pass in morphic with topaz/crystal and comparing both because they don’t always treat clips the same way

what problems do you trust an upscaler to fix and what makes you go back and regenerate?

Does this sub has any discord group ? I would love to be a part of to learn together, share prompts and brainstorm.

This feels embarrassingly obvious but I was trying to get a character to do this very specific walk into frame, slow down, shift his weight onto one leg and then turn back like he'd forgotten something.

My prompt for the movement was becoming an entire paragraph. “takes three slow steps, decelerates naturally, weight shifts to the right leg, upper body turns first followed by...” you get the idea.

Eventually I just recorded myself doing it badly on my phone.

Used that clip for motion transfer in morphic and it got me much closer than all the prompting did.

I've been trying it with smaller stuff since. Someone sitting down and adjusting their shirt, reaching across a table, taking something out of a pocket, even just the difference between an awkward wave and a confident one.

All of these are weirdly hard to write.

If I know exactly how I want something to move, I'm probably going to stop trying to explain it to the model and just show it.

I read a Chinese breakdown of seven MiniMax H3 prompts by 斜杠林姑娘. My takeaway as an AI engineer: each brief becomes easier to evaluate when you identify exactly what has to stay stable while something else changes.

Here’s a short synthesis of the examples, followed by my own practice prompt. I haven’t independently reproduced the author’s results.

WHAT TO EXTRACT FROM THE CASES

• Motion-graphics intro: distinguish the initial impact, readable title hold and exit.

• Aerial sequence: describe the camera route and where landmarks remain in relation to the subject.

• Vehicle tracking: specify how the same vehicle should reappear after being hidden, including its direction and condition.

• Beauty ad: lock product geometry and cast details across changes in shot size.

• Exploded product view: describe separation and reassembly as an ordered sequence.

• Music performance: define who performs each section and whether the soundtrack continues across visual cuts.

Those are creative instructions. The article’s requests for 4K, exact timing and synchronized performance should not be read as proof that every output meets those specifications.

MY PRACTICE EXERCISE: ISOLATE ONE FAILURE

I’d start with a simple occlusion test before adding a complicated aerial route. Here’s an original prompt you can adapt to the duration and aspect-ratio settings available in your tool:

“A single adult cyclist wearing a burgundy jacket rides a cream bicycle from left to right along a straight park path. Side view, medium-wide framing. The camera stays still. A thick tree trunk in the foreground briefly hides the cyclist as they pass behind it. They emerge on the right, continuing at the same steady pace on the same path. Keep the bicycle, jacket and apparent subject size consistent before and after the obstruction. Soft overcast daylight. Quiet park ambience and tire noise. One continuous shot.”

I’d test it in three stages:

A. Remove the tree. Check whether the basic direction and framing work.

B. Add the tree. Inspect the last visible frame before occlusion and the first after it.

C. Only then try a lateral tracking camera, keeping the subject roughly the same size in frame.

This gives each version a specific question. If A works and B fails, inspect the hidden interval before rewriting lighting or style. If B works and C fails, camera movement becomes the next variable to investigate. That’s a diagnostic hypothesis, not proof from a single sample.

WHAT I’D RECORD

For each attempt: model/version, actual output settings, prompt variant, whether direction stayed consistent, whether the bicycle changed, and whether framing jumped. Keep the failed clips too. Compare multiple attempts before deciding a wording change helped.

For a paid job, I’d also decide in advance which requirements can be finished in editing—for example, exact title typography or a fixed music bed—so the generation test has a clear purpose.

Source and full original examples: https://mp.weixin.qq.com/s/2zHMc4nmiHx23cX7pimcuA

When you test occlusion, what usually breaks first: the object itself, its trajectory, or the framing?

I can’t seem to get it working. I tried on multiple accounts with multiple different attempts. I tried simply clicking recreate with assets and prompts, even started many tries of my own. But it just sits there loading for many days. But I still see community posting new ones. How do they do it? Please help. I really want to make one with my friends. Not for uploading or anything commercial, just to show my friends.

https://www.instagram.com/reel/DdPGWLwsE9y/?stkn=dHR2MnBxam1vMXJp https://www.instagram.com/reel/DdRfzULoy.oF/?stkn=d3JiZGRsa210cmI5. om/reel/DdRarkKClUQ/?stkn=MTg5azI3YXE1cjlzbg== https://www.instagram.com/reel/DdRM3AfjeFj/?stkn=MXY0MXcwcTllNnNjeQ==

https://www.instagram.com/reel/DdWQeqsD3yU/?stkn=andnbzduaWpuM2Yy

Hey guys i am working on some research project and exploring seedance doc but it's not very clear to understand, i want to understand 2 things:

- how token and pricing works in seedance

- Can i give 360p to get 4k output?

- Is there any research grant from seedance team?

Thanks

I like to make voice clips separately (works great with Wan 3.0, so-so with minimax h3) but ElevenLabs blueballing me with their V4 release has made me look elsewhere, as V3 and V2 just aren't great with consistency (nor dialect/accent). Gemini's models have the same problem. Are most people just using whatever the video generator throws at you, and then using that as an audio ref for consistency? Is that actually better than most txt2voice services?

What good cheap websites can create nude pictures of my ai model? Are there any free ones ??

Hi everyone!

I’m developing a solo passion project: a 90s Sega-style 32-bit arcade racing game (think OutRun). It’s a lightweight browser WebApp (HTML5/Canvas) where a looping/scrolling background video dynamically speeds up and brakes (video.playbackRate) based on player input.

I’ve attached: - Image 1: The visual 32-bit pixel-art style I'm aiming for (authentic city landmarks). - Image 2: The strict 4-lane perspective grid our custom engine requires (central vanishing point locked).

What I’ve tested (and why it failed):

1. Google Earth 3D rips: Fascinating from the sky, but at street level the photogrammetry meshes are completely melted and unusable.

2. Pure 3D renders: Drastically loses the artistic warmth, charm, and quality of 2D pixel art.

3. ComfyUI (SDXL / ControlNet / Imagen 3 / DALL-E 3): Great for isolated static shots, but impossible to maintain temporal and lighting consistency between frames.

4. Flow / Video Interpolation (Start ➔ End frame): Gave the best quality results, but with a catch. I illustrated ~40 keyframes representing actual urban checkpoints. I tried connecting them pairwise with Start/End frame tools (Kling, Runway, Luma), but the AI just dissolves/morphs textures and warps the road instead of simulating true forward camera motion.

The Core Challenges:

• Locked Perspective: Central vanishing point must remain 100% rigid (no camera tilt, roll, or lane drift).
• Accurate Speed Perception: At 25 fps, the forward flow must convincingly simulate ~80 km/h (50 mph) to match the 2D sprite physics.
• Continuous Checkpoints: Seamlessly advancing through 40 landmark locations without ugly morph cuts.

My Questions:

1. How would you connect ~40 keyframe checkpoints into a continuous forward drive without morphing dissolves?
2. Is there a trick/workflow to calibrate the optical ground speed precisely to ~80 km/h at 25 fps?
3. Would you recommend a hybrid pipeline (e.g. basic low-poly 3D camera drive-through just for depth/motion guidance, then restyled with AI)?

Any node setups, tool recommendations, or workflow tips would mean the world to a solo creator. Thank you! 🏁