I tested a GPT Image 2.0 × Seedance 2.5 workflow for a 29-second mother-daughter dialogue scene on Atlas Cloud.

The main thing I learned: if you want convincing AI acting, don’t treat dialogue, facial expressions, and camera as separate pieces.

How to keep characters consistent in Seedance 2.5?

I prepared 3 direct reference images:

• daughter character turnaround
• mother character turnaround
• scene reference for blocking, lighting, and environment

I also made a fourth image just to map the mother’s emotional progression.

Prompt idea:

lock identity, age, body proportions, hairstyle, clothing, lighting, and spatial relationship to the reference images throughout the scene.

How to make AI acting feel more natural

Give each character a clear personality and emotional history before writing the scene.

For this one:

the daughter is cautious but still believes love will be enough.
the mother is haunted by her past and terrified her daughter will repeat it.

Then I mapped specific dialogue beats to emotional changes.

How to structure a Seedance 2.5 acting prompt?

I used 6 blocks:

references → characters → scene → dialogue → camera → performance timeline

The performance timeline was the most important part.

Example:

“your father” triggers a brief flash of memory.
“money ran out” becomes the first emotional break.
“ENOUGH” is the peak, followed by an immediate cut to black.

Instead of just saying “she gets angry,” I defined exactly when the emotion changes and what line causes it.

How do you use camera movement to support AI acting?

I mostly used 50mm and 85mm framing, then gradually tightened the shots as the argument became more intense.

The mother also moves closer while the daughter is pushed back toward the wall, so the blocking itself reinforces the emotional pressure.

My takeaway:

references + character history + timed acting beats + camera progression

seems to work much better than just writing a long “cinematic” prompt.

The Qwen-Image 3.0 series is now available on Atlas Cloud, with both Qwen-Image 3.0 and Qwen-Image 3.0 Pro.

The series combines image generation and multi-image editing in a single model family, while the Pro tier delivers higher image quality and stronger consistency for more demanding production use cases.

What Qwen-Image 3.0 ships with

• Support for up to 3 reference images for editing and subject-driven generation
• Strong instruction following for complex prompts
• Native 1K / 2K generation
• Consistency-preserving edits for outfit changes, background replacement, and local repainting
• Qwen-Image 3.0 Pro further improves detail, color, and consistency in complex scenes

Pricing

Pay-per-image, with failed generations not charged.

Qwen-Image 3.0

• Generation: $0.03 / image
• Reference image input: $0.003 / image

Qwen-Image 3.0 Pro

• 1K generation: $0.04 / image
• 2K generation: $0.075 / image
• Reference image input: $0.003 / image

Billing is based on the actual number of generated images returned, along with the corresponding 1K / 2K output tier.

Try it on Atlas Cloud

Qwen-Image 3.0
https://www.atlascloud.ai/models/qwen-image-3.0/text-to-image

Qwen-Image 3.0 Pro
https://www.atlascloud.ai/models/qwen-image-3.0-pro/text-to-image

Both endpoints are live on Atlas Cloud now, with Playground and API access available.

I wanted to share the prompt I used for this set with GPT image 2 on Atlas Cloud, but the face seems to be impossible to reproduce now

the overall style still works, but the character’s face keeps changing. maybe the original result was just a lucky generation.

Prompt below 👇

Photorealistic smartphone photography with a real adult woman and natural lighting.

A beautiful adult woman with a soft oval face, cute rounded features, black medium-length straight hair, and a bright warm ivory skin tone. Slight pink tones on the tip of her nose and cheeks. Large, bright almond-shaped eyes, realistic fine skin texture, wearing a loose light-gray puffer jacket.

A gentle winter breeze lifts a few strands of her hair. Tiny snowflakes rest on her hair and clothing. She wears a fluffy earmuff hat covering the top of her head and both ears.

Outdoor winter snow scene. Soft, diffused daylight evenly illuminates her face. Use the reflected light from the snow to keep the shadows bright and prevent the skin from becoming too dark.

Her pose and expression should feel cute and slightly playful, like an adult woman taking a deliberately cute photo for social media.

Background: a snowy park with a playground slide. Keep the background softly blurred, around 10% out of focus.

If anyone figures out how to lock or reproduce this exact face, please let me know 👀

saw someone run this concept and now i can't unsee it: take an anime empress-type character and put her through a full ninja-warrior water obstacle course, thirty seconds, one continuous take, quad steps, spinning logs, the big curved wall, all of it.

the reason it's not really doable yet is it needs three hard things at once. the character has to stay the exact same person through every slip, fall, and climb. the physics have to actually react, foot hits a spinning step and it turns under her weight. and it needs live crowd audio and splashes or the whole thing feels dead.

that's basically the wan 3.0 spec sheet: 30-second clips, omni-reference holding one character across every shot, real motion physics, native audio baked in. the api opens later this month and it lands on atlas at launch, so this exact kind of long continuous clip is finally in reach.

straight about it though, 3.0 is the standard model, it moderates. so a spicier take on the concept still runs on the uncensored wan 2.x, which is already on atlas too. wan model page.

who's running their character through the course the second the 30s model goes live?

I always want to make a fantasy concept art prompt built more around scale and atmosphere than packing the frame with detail. The scene was pretty simple: one traveler in a red cloak facing a huge rock formation cut through by multiple waterfalls, almost like the whole landscape was set up as the real subject and the traveler was just there to give it scale.

The background ended up being the most important. There’s an old Nepal-style village tucked into the landscape, snowy peaks pushed far enough into the distance that they make the world feel bigger without turning the frame into a mess, and a bright clear sky that gives the eye a place to rest before everything drops down into the cliffs and water.

A lot of the look came from the style language as much as the subject itself. I pushed it toward ultra-detailed concept art, but with more of a magical realism feel than a pure high-fantasy illustration. The haze, god rays, and slight hallation helped a lot, mostly because the light started shaping the image instead of just lighting it.

I ran it in Seedream 5.0 Pro on Atlas Cloud, and what held up best was that the atmosphere carried more weight than the character did. The traveler stays small and mostly still, while the mountain, waterfalls, and light end up doing almost all of the emotional work in the frame.

A new collaboration built on Seedance 2.5 is working with Michael Owen to revisit some of the defining moments of his career, the signature moves, the goals, the instincts that made him one of the most recognizable strikers of his generation. From those moments on the pitch to what else becomes possible once a model can work with a real career instead of a single clip, it's built around unlocking what used to be impossible.

It's also a good preview of where Seedance 2.5 is headed more broadly, and the direction is a real jump forward. Original generations are set to run up to 30 seconds, the longest single generation the line has offered so far, enough room to hold an actual narrative arc instead of a quick clip. It's also shaping up to be the first video model in the lineup released at 720p resolution, a real step up in fidelity for a line that keeps pushing further with each release.

Character consistency is getting a real push too, holding the same face and identity recognizable across an entire sequence of different moments instead of drifting scene to scene, which is exactly what a piece built around one real person's career actually demands. On top of that, the generations are shaping up to be more editable after the fact, adjusting a shot without having to regenerate the whole sequence from scratch.

Classic moments, now within reach, and just an early look at what the model can do once it's treating an entire career as material to work with.

This is honestly terrifying. How are we supposed to tell what’s real anymore?

I’ve been experimenting with Seedance-style travel vlog prompts, and I found that the best results come from writing everything as a timeline-based, shot-by-shot storyboard.

The key is to lock the character, styling, camera language, and scene continuity from the very beginning. Also: no other people should appear anywhere in the video.

Here’s the prompt formula I’ve been using 👇. I tried it with Seedance 2.5 on Atlas Cloud.

Generate a [total duration]-second, [aspect ratio] travel vlog set in [location/theme], consisting of [number of shots] shots.

Feature only one character throughout the entire video:
[character description]

Do not include any other people, faces, silhouettes, reflections, crowds, background figures, or passersby.

Character Lock:
Keep the same face, hairstyle, body type, skin tone, and makeup consistent across every shot.

Fixed Styling:
Keep the same clothing, shoes, bag, and accessories throughout the video.

Conditional Props:
Include [props] only in [specified scene].

Unified Aesthetic:
Maintain consistent lighting, color grading, phone/camera texture, and realism level.

Editing Rhythm:
Use fast cuts. Each shot should contain one deliberate action followed by one natural reaction.

[Shot 01 | Start Time–End Time | Shot Purpose]

Scene:
[Location and essential environmental details]

Action:
[One deliberate action → one natural reaction]

Camera:
[Shot scale, camera position, and one camera movement only]

End State:
[Character’s position, facing direction, props in hand, and emotional state]

[Continue all subsequent shots using the same format]

Audio:
[Environmental sounds, action sounds, and music corresponding to each scene]

Prohibited:
Face changes, costume changes, additional people, background characters, crowds, reflections of other people, unexplained disappearing props, excessive skin retouching, studio-shot visuals, or an obvious CGI look.

Final Frame:
[Character’s final action, expression, props, and background]

honestly, i think AI travel vlogs are going to become 🔥

one thing I've been using as a benchmark for video models is deliberately mundane footage, since flashy action scenes are easy to fake convincingly but ordinary physical motion is where most models fall apart.

for this test I generated a 15-second clip simulating the technical characteristics of an early-2000s consumer camcorder with soft ambient lighting, and natural fabric physics during a simple folding motion, plus casual unscripted-sounding dialogue rather than a scripted line delivery.

what stood out was how well Minimax H3 handled the small stuff that usually gives AI video away: the autofocus actually looked like it was hunting and settling rather than staying artificially locked, the fabric had believable weight and drape as it moved, and the handheld motion had the right kind of irregular micro-jitter instead of a smoothed-out gimbal feel.

my takeaway: low-stakes, everyday physical motion is honestly a better stress test for a video model than big dramatic scenes. If a model can nail folding laundry without it looking like "AI smooth," that says a lot more about its motion coherence than an explosion or a chase scene would.

curious if others have found similarly "boring" test scenarios that reveal model quality better than flashy prompts do.

Seedance 2.5 is now live on the Atlas Cloud API. What is new over 2.0:

- A true 30 second single take, no stitching

- Up to 50 multimodal reference inputs across image, video, and audio in one generation

- New 3D camera occlusion control for depth-aware motion

- Native 4K with synced audio, and stronger prompt adherence

Same API key and the same endpoint as the rest of the Seedance line. You point at the new model id and go: bytedance/seedance-2.5/text-to-video (also image-to-video and reference-to-video).

Model page and docs: https://www.atlascloud.ai/models/seedance-2.5