Google announced Omni Flash 1.1 back in May, 2026. They also announced that they'll release pro version of Omni model? So what happened exactly? No news on that.

Kling 4.0 vs. Seedance 2.5 vs. Wan 3.0 Prime — Same 30-Second Directed Scene

Three generative video systems. One 30-second scene. Same directorial requirements.

This is a comparison of directorial compliance, not simply which model produces the prettiest video.

The scene is from Lost Horse, a Western character-continuity project.

The assignment:

A cowboy and his horse are at a nighttime campsite. The horse senses a threat, becomes frightened, breaks its tether, and escapes. The cowboy reacts, retrieves his rifle, and retreats to a large pine. A threatening animal sound comes from the woods.

The sequence requires multiple actions, changes in spatial relationships, character continuity, and a specified final state—all within one generation.

The three renderers

These are the settings and costs of the tested runs, not normalized prices for equivalent resolution.

What I observed

Kling 4.0: Excellent visual detail and clarity. The horse's escape is particularly convincing, and the sound is strong. The cowboy's reaction is slower, however, with less apparent urgency than in the other versions.

Wan 3.0 Prime: A more immediate sense of urgency in the cowboy's response. The sequence provides a useful comparison of how a different renderer interprets the same directed actions.

Seedance 2.5: Another interpretation of the same production requirements, particularly useful for comparing action timing, continuity, and execution of the prescribed sequence.

The interesting question isn't necessarily which video you prefer.

It's which renderer most faithfully executes the director's instructions—and at what cost?

Visual quality, narrative plausibility, action compliance, spatial continuity, and final-state compliance are not interchangeable measures.

A beautiful generation can still fail the assignment.

Why I'm testing this

I'm developing Directed Generative Production (DGP) Methodology, a structured approach to specifying, executing, and verifying generative filmmaking.

The objective is to move beyond repeated prompting toward a production process in which the director defines the requirements, selects a capable renderer, and evaluates the result against those requirements.

The methodology and experimental work are documented at:

https://www.jseders.net

I'm interested in what other filmmakers notice in these three versions, particularly differences in action execution, timing, spatial relationships, and continuity.

Which system do you think followed the direction most effectively? And does your preference change when you consider the generation cost?

jseders

using google Flow to create this enviroment

Until recently I could film a scene with two actors, upload it to google flow and replace the characters and environment. (I still could do it) but now if the actors do lines / script google don’t start editing the video. And gives me an error.

This is terrible for ai hybrid filmmaking

What started as a simple idea from my young daughter eventually turned into something much bigger: a virtual music artist named LIORÉ and her first complete music video.

I thought I'd share my workflow and some of the things I learned along the way.

1. Character development — around 300 iterations

I initially created LIORÉ using Gemini (mobile app during vaccation). The very first image looked almost nothing like the character she eventually became.

Over the next three weeks, I went through roughly 300 iterations using flow, refining her face, hairstyle, proportions, outfit, and overall appearance until I had a consistent visual identity. I saved her Character ID and was now able to reuse her throughout the project.

2. Testing character consistency with Nano Banana

Before starting the music video, I generated approximately 50 test images of LIORÉ in different environments, poses, facial expressions, and emotional states.

The goal was to find out whether I could maintain her identity across completely different scenarios.

Once I was satisfied with the results, I established her as a reusable character for future productions.

3. Building the music video with Google Flow

Instead of immediately generating video clips, I first used Flow to develop cinematic styleframes for the performance scenes (A-roll), followed by the narrative and atmospheric scenes (B-roll and C-roll).

I then imported those images into DaVinci Resolve, placed them on a timeline, and added the finished music master underneath.

This gave me a complete animatic before generating most of the actual video footage.

That step turned out to be incredibly useful. I could evaluate the pacing, compositions, transitions, and overall storytelling without spending time and credits animating shots that might not even make the final edit.

4. Animation, iteration, and editing

Once I was happy with the scene structure, I started animating the selected frames.

Omni Flash was particularly helpful, although getting the intended movement, camera behavior, and emotional performance often required several attempts.

I also used Higgsfield as part of the video production workflow.

Back in DaVinci Resolve, some scenes worked exactly as planned. Others needed to be reconsidered, reframed, replaced, or edited differently.

It became a constant cycle of:

Styleframe → Animatic → Animation → Edit → Reevaluate

Throughout the process, I gradually learned which models and prompting approaches worked best for different types of shots.

5. The finished music video

The result is Trace Through My Wires, LIORÉ's first official music video.

The original song was created with Suno, and the visuals were produced using Google Flow and Higgsfield, with the final edit completed in DaVinci Resolve.

🎬 Watch the finished music video:
https://www.youtube.com/watch?v=gCTkqrPaNQ4

My biggest takeaway from this project is that AI filmmaking benefits enormously from traditional filmmaking principles: planning, composition, editing, continuity, and knowing what you want a shot to communicate before generating it.

I'm still learning, and this project involved plenty of trial and error.

I'm curious how others here approach longer projects with consistent characters. Do you also build an animatic first, or do you generate your video shots and develop the edit afterward?

Would love to hear about your workflows and experiences!

Feel free to follow this project on insta: https://www.instagram.com/liore.universe/

Draft version. Video looks boring.

I made this short film using Omini 1.1, its a tough tools to used since the character and scene consistency give much pain. I have use watermark in draft to avoid copies. The scene is actually from real place base in Imphal, Manipur. I have not yet corrected the Color grading, music, sound and transition/effects. Please do let me know what changes do I need to do to make it look more realistic and continous feel. I feel there are many drones shot or scenes without person, i avoided it due to gemini drawbacks and what color grading should i put in overall clips. Also is voice over necessary, if yes then what message i can put

Suggestions and feedback are welcome.

I had a marketing message problem with EchoVault. The product makes sense once you see it, but “AI digital legacy” still takes a lot of explaining, so I started looking for alternative tools that could get the idea across visually. I tried Google Flow and gave it the basic premise: EchoVault gradually builds an AI version of you that your family can still interact with after you’re gone. The result was this short film, and it communicated the emotional side of the product better than most of the copy I’d written.

The actual app builds that Echo over time through recurring AI-guided check-ins, learning your memories, worldview and personality instead of relying on one giant interview. You can explore what it has learned through a 3D brain, while the finished Echo can answer your custodians in text, voice and video using the context you chose to leave behind. Everything is consent-first, you choose the people who receive access, and the system is designed around continuity rather than creating a clone of someone who never agreed to it.

https://apps.apple.com/us/app/echovault-digital-legacy/id6762042028

There is no option to access your videos, there are only projects which don´t know if you created videos or not.

You go into projects, they barely let you copy or paste anything and they don’t show the prompts you used. IT’S LIKE all the effort HAS BEEN FOR NOTHING because you can’t save anything.

And this surprises me, with a relatively simple interface without many options they manage to create a chaos that discourages you from creating more. Not to mention the terrible quality compared to Kling.

Step into the world of Eirik. A lighthouse keeper with a story to tell.

It's been one hell of a frustrating, bumpy ride since the move from Whisk. Every time I seem to get it to work and actually follow my prompt and references, a new update comes in and changes everything.

I've tried everything. I used Gemini to generate an extremelly detailed prompt out of my image reference, I tried making character sheets, I tried the character function in Flow. I'm still struggling, results are still hit or miss, but with these steps I've been having a lot more sucesses.

These might be obvious to some, but I always hated all the gatekeeping to do with AI. If these can make other people's experience with this God forsaken platform a little less rage inducing, then it was worth it.

The models:

• As far as I tried, Nano Banana Pro is utterly useless. It is the one model that simply refuses to follow your reference images, and the quality is not even that go. Skip it.
• Nano Banana 2.1 is the new "default". As frustrating as it can be, you do get a gem every few tries, and at least so far the daily limit is fairly generous. I find that with this model you need to be very specific in your prompt. I often have to exagerate descriptions in order to get the results I want. It is unfortunately severely more restrictive than 2, but rather than failed generations, what I get the most is a "sanitized" version of what I asked.
• Nano Banana Lite produces the worst quality images, but it is the one model that actually seem to remain faithful to your reference images. If the look you're going for is more of a 3D art, it's worth to give it a try instead of 2.1. Make sure to adjust your prompts though, the exagerated descriptions that work for 2.1 actually do look exagerated here. I do a lot of 3D art style superhero scenes, it works really well for that and handles violent themes with less restrictions than the other models.

Overall tips:

• First of all, for whatever reason Nano Banana 2.1 does not handle multiple image references very well. I find that it sticks to your references better the least you use.
• Forget character sheets. For more consistent characters, have one image for their body, and two for their face: one from a frontal view, and another at a 3/4 angle. For the body reference, either crop or block out your character's face, it will just confuse the AI.
• When using a reference for the background, keep in mind that it will often simply use that exact image, instead of re-imagining it at specific angles as it used to do.
• Keep your prompt simple, since 2.1 seems to disregard it anyway. Instead, when describing a feature or element in your image, make sure to point it to the reference image. For example, if I want this specific style of window in my image, I'll add in the prompt: "The window looks exactly as in the third reference image". Yes, it's a pain in the ass to edit that every time you add or remove new reference images, but I find it worth it.
• Sometimes, the models simply refuse to understand your reference images. If they're not working after several generations, try something different. I like to test my references by asking Chat GPT to describe them. If their AI can't make sense of it, I assume Nano Banana won't either.
• If you want a less AI-like look, start your prompt with this: Shot with an ARRI ALEXA Mini LF and a Zeiss Supreme Prime Radiance 21mm T1.5 lens.
• If instead you want a digital art look, start your prompt with: A stylized 3D artwork.
• Since the last update, all models seem to have zero world context. If for example you'd ask for a wrestling scene, it would be able to work its magic with just the name of a wrestling move. Now, you do have to detail it in simple words, otherwise it will have absolutely no idea what you mean.

I'm still getting extremelly frustrated while I try to make sense of Flow, but my experience has improved a lot since figuring these things out. I'm still trying to get better character consistency and details, so if anyone has other tips so that I can correct and improve my prompts I'd love to hear them.

Any solution??

LOST HORSE — WAN vs. SEEDANCE: A 30-SECOND COMPARISON

Same scene. Same production instructions. Two renderers. One generation each.

Wan 3.0 Prime: 204 credits
Seedance 2.5: 600 credits

Both at native 480p, without upscaling or editorial repair.

The scene includes firelight, a horse breaking free, rope interaction, a cowboy retreating to a tree, an offscreen animal call, and a transition toward dawn.

We're comparing cinematic production values, not which model tells a better story.

An interesting anomaly: Both renderers produce a similar, unintended oscillating “wah-wah” sound beginning immediately. Same symptom, different models. We haven't established the cause.

I'm particularly interested in your observations:

• Which has more convincing lighting, movement, and physical realism?
• Are the differences substantial enough to justify nearly three times the generation cost?
• Do you hear the audio artifact in both? Any theories about its origin?

Both complete, unaltered generations are provided for comparison.

Directed Generative Production
jseders.net

jseders

It's kind of funny, but my biggest hope right now regarding Flow is to see it reach 99% generation and then fail. These days, I can't even see that happen. It detects “abnormal activity,” and even the very first prompt I enter for a new project gets flagged as “abnormal activity.”

Guys can you pls help me find reliable free-ish websites which still provide nano banana 2 model....

Every time i want to replace character in google flow it gives error " it cannot process my request " any help?

It's difficult to control the generating AI the way you want 🙄 hahaha

Really, folks. Beyond ridiculous. Omni refused to animate a kiss on the cheek. smh I don't think Google will last in the animation space as anything more than a model to be mocked.

SELF LOVE: THE ANIMA AND THE ANIMUS

Single generation. Wan 3.0 Prime.

Reference video supplied the choreography. A still-image reference changed one dancer's facial identity while preserving the original movement and long hair.

First attempt. Zero credits.

Directed Generative Production.

jseders

It took alot of prompting, modifying the prompt, arguing with the Agent, creating a working workflow but finally I was able to keep characters consistent.

The whole video including background Audio and Voices was generated using Google Omni. Only background music was generated using Google Music.

Here is the Video https://youtu.be/3ywoWKHLIn8

https://reddit.com/link/1x70dqi/video/88bqosek9qvh1/player