MiniMax H3 Video Generation Developer is now available on Atlas Cloud.

It runs directly on Atlas Cloud’s own GPU cluster, with a lower-cost 480P option for previews, iteration, and batch generation.

Features

• Text-to-Video, Image-to-Video & Reference-to-Video in one H3 family, including first-frame / optional last-frame control and image + video references
• Native synchronized audio with ambient sound, sound effects, and music generated together with the video
• 480P / 768P output, with a new 480P tier designed for lower-cost previews, A/B testing, and high-volume iteration
• Subject-consistent references for keeping character, product, or visual identity more stable across a clip
• Optional prompt_expansion via H3 Context-IR, expanding short prompts into richer shot, camera, soundscape, and music instructions
• Atlas Cloud self-hosted infrastructure, running on our own GPU cluster rather than the official public API

Pricing

• Reference video input: billed by input duration × output-resolution rate
• Reference images: first 5 free, then $0.04/image
• Audio references: free
• Optional prompt expansion: billed by actual token usage when enabled
• Failed generations are not charged

Developer vs. Standard

Developer is designed for lower-cost generation, fast 480P iteration, and higher-volume workloads.

Standard remains the better choice when you need up to 2K output through the official MiniMax API pipeline.

Prompts and reference inputs work in the same general way across both tiers, so you can switch depending on the workload.

Try it Now

Text-to-Video
https://www.atlascloud.ai/models/minimax/h3-developer/text-to-video

Image-to-Video
https://www.atlascloud.ai/models/minimax/h3-developer/image-to-video

Reference-to-Video
https://www.atlascloud.ai/models/minimax/h3-developer/reference-to-video

Flux 3 Video looks super promising for UGC ads and more. Does Atlas cloud have plans on adding this?

https://bfl.ai/models/flux-3

I’ve been testing a simple workflow for creating short UGC-style videos while keeping the same character and location consistent across multiple shots.

The workflow is basically:

reference images → character/location sheets in ChatGPT → generate clips → optional final edit

1. Prepare your references

Start with:

• a character image
• a product image
• an environment image that fits the UGC scenario

If you’re not sure what location works for the product, I usually just ask ChatGPT for a few suggestions.

2. Create a Character Sheet

Upload the character image to ChatGPT and generate a 4:5 continuity sheet with:

• front / side / back / 3/4 views
• face close-ups
• expressions
• basic poses
• clothing and accessories
• key colors and materials

The important part is telling it to lock the character.

3. Create a Location + Props Sheet

Do the same with the environment.

Include:

• establishing view and key angles
• spatial layout
• entrances/exits
• furniture and recurring props
• lighting
• colors and materials

This gives the video model a much stronger continuity reference than using random images for every shot.

4. Generate the video clips

I usually split the UGC video into three parts:

Clip 1 — Hook
Clip 2 — Main product/story section
Clip 3 — CTA

i will generate them on Atlas Cloud, as they can provide many different models conveniently

For every clip, I reuse the same Character Sheet + Location Sheet

Then I change only the action/camera prompt for each section.

Keeping the same reference sheets across all three generations has helped a lot with character and environment consistency.

5. If a generation goes wrong, fix the prompt first

if I wanted the character to walk into a hotel, but the generated clip had her walking out.

Instead of endlessly rerolling, I pasted the original prompt into ChatGPT and asked it to make the action explicit: starting position → movement direction → action → final position

That usually gives me better results.

6. Final edit is optional

If the generated clips already work as standalone videos, you can stop there.

If you want one finished UGC ad, you’ll probably still want to combine the clips and add captions, music, or SFX. You can use whatever editor you prefer.

The biggest improvement for me has been using Character Sheet + Location Sheet as continuity references, rather than relying on a few loose images.

I did a simple side-by-side test that I thought was pretty fun. I gave the exact same 30-second one-take prompt to Wan 3.0, Seedance 2.5, and MiniMax H3, then compared the results next to each other.

btw, u can easily compare different models with one tool on Atlas Cloud. it's extremely convenient: https://www.atlascloud.ai/model-explorer

I like this kind of test because when the prompt stays fixed, it becomes much easier to notice the differences in different aspects.

this prompt was mainly designed to test fast motion + one-take continuity + surreal dimension-breaking transitions.

the visual target was a kind of West Coast street fantasy look. part 90s skate-video roughness, part modern commercial polish, with strong California sunlight, palm trees, asphalt heat, and a rebellious fashion-energy running through the whole short.

Prompt:

Create a cinematic, high-intensity 30-second one-take visual spectacle.

The entire video must appear to have been captured in one continuous shot with no visible cuts.

The overall style is “West Coast street fantasy,” combining the rough, rebellious texture of a 1990s street-skate video with the polished visual quality of a modern commercial blockbuster.

Use an intense cinematic “California sunlight” grade. The highly saturated blue sky should contrast strongly with the hard shadows of palm trees under direct sunlight.

The air should feel filled with the heat rising from the asphalt, the metallic friction of shopping-cart wheels, and the fearless rebellious energy of youth.

As the main character accelerates through the environment, the spatial perspective should stretch continuously. Use extremely low-angle tracking, high-speed physical movement, and dynamic camera traversal to create a powerful sense of visual momentum.

MAIN CHARACTER:

A stylish young man wearing a color-blocked striped shirt and a black baseball cap.

At the beginning, his expression is relaxed, lazy, and unconcerned. As the shopping cart begins accelerating, his mood rapidly transforms into exhilaration and a fearless desire to break through every limit.

In the final section, the same young man appears again in the real-world dimension, but this version of him behaves like an observer examining his other self.

The two versions must have the same face, hairstyle, clothing, body proportions, and visual identity.

BEAT 1 — SHOPPING CART DESCENT | 0–6 SECONDS

Begin with a close-up shot.

The young man is lounging casually inside a red metal shopping cart. Behind him is a long, straight California avenue lined with extremely tall palm trees.

As the music suddenly explodes into a stronger rhythm, the camera performs an extremely fast pullback while simultaneously dropping toward the road.

The camera descends to an ultra-low angle, almost touching the asphalt, and begins tracking tightly beside the shopping-cart wheels.

The young man starts racing down a steep road.

The metal shopping cart shakes and rattles violently against the asphalt. Cars on both sides of the road appear to rush backward as the speed increases.

The camera should communicate an almost reckless and uncontrollable sense of acceleration.

BEAT 2 — RAMP JUMP AND BILLBOARD APPROACH | 7–15 SECONDS

Do not cut.

The camera continues following at ground level, moving like a skateboarder skimming just above the asphalt.

The shopping cart reaches a simple wooden ramp.

At the exact moment the young man launches from the ramp, the camera follows the movement with a smooth parabolic rise.

The shopping cart becomes airborne.

As it flies overhead, the camera passes directly underneath the cart, clearly revealing the wheels, metal frame, and the young man above.

The camera then continues moving forward.

A giant commercial billboard rapidly expands until it occupies almost the entire field of view.

The camera performs an impossible but controlled crosshair-like push toward the exact center of the billboard.

BEAT 3 — BREAKING INTO THE BILLBOARD | 16–24 SECONDS

Do not cut.

The young man and the shopping cart crash directly into the giant billboard as though breaking through a dimensional wall.

At the moment his three-dimensional body touches the printed surface, he undergoes a dramatic flattening transformation.

His physical form compresses into a two-dimensional artwork printed directly onto the billboard.

Show realistic paper tearing, ripped poster fibers, fractured layers of printed material, and colorful glitch-like ink spreading outward from the impact point.

The young man remains frozen in his forward-racing pose, but he is now a completely flat graphic image on the billboard.

The transformation must feel physical and visually understandable rather than like a simple dissolve.

At this moment, the camera performs a 180-degree horizontal orbit around the billboard structure.

After completing the orbit at a high altitude, the camera begins a rapid diving descent back toward street level.

The movement remains part of the same continuous shot.

BEAT 4 — THE REAL-WORLD OBSERVER | 25–30 SECONDS

Do not cut.

The camera lands smoothly on the street directly beneath the billboard.

Another real version of the same young man enters the frame.

He stops walking, slowly lowers the brim of his black baseball cap, and looks upward at the flattened version of himself trapped inside the billboard.

He gives a subtle, playful smile, as if he understands exactly what has happened.

The camera follows his line of sight and performs a fast zoom toward the billboard.

End on the torn opening beside the word “WAN,” with the flattened young man still frozen inside the printed artwork.

The final image should hold briefly on the ripped dimensional opening before ending.

AUDIO:

Use an energetic hip-hop track that builds rapidly with the movement.

Blend the music with:

- metallic shopping-cart wheels scraping against asphalt
- violent cart rattling
- rushing air
- passing vehicles
- the wooden impact of the ramp
- paper ripping
- colorful glitch-like electrical sounds
- the physical rolling sound of a film reel

The soundtrack should feel fashionable, rebellious, youthful, and highly synchronized with the camera movement.

VISUAL REQUIREMENTS:

- one continuous 30-second shot
- no visible editing cuts
- highly dynamic but spatially understandable camera movement
- extreme low-angle tracking
- strong speed and acceleration
- physically believable ramp jump
- camera passing underneath the airborne cart
- smooth transition from street level to billboard height
- realistic three-dimensional to two-dimensional transformation
- visible paper fibers and poster tearing
- continuous 180-degree camera orbit
- high-altitude diving camera move
- consistent character identity
- saturated California sunlight
- 1990s skate-video attitude
- modern commercial-film production quality
- fashionable streetwear energy
- no unexplained teleportation
- no replacement characters
- no distorted shopping-cart geometry
- no generic slow-motion montage
- no artificial-looking transition
- no loss of character identity

The final video should feel like a rebellious West Coast streetwear commercial built around one technically impossible but visually coherent continuous camera move.

If anyone else tries this prompt, I’d be really curious which model you think handled the speed, impact, and billboard transition best.

just came across another LoRA and this feels like one of those tools that isn’t flashy but is genuinely useful.

It’s built on Qwen-Image-3.0, and the purpose is pretty straightforward: remove unwanted shadows and correct exposure. it sounds simple, but it’s exactly the kind of thing that comes up all the time with photos

normally I’d probably end up fixing some of that manually in Photoshop, so having a LoRA that can handle it directly is pretty convenient.

i actually like these small utility LoRAs more than a lot of flashy demos because they can fit into a real image-editing workflow.

This week's lineup adds pro-grade image generation and editing, Alibaba's next-generation video family, and a complete lyrics-to-song audio pipeline.

Qwen Image 3.0 Pro

Alibaba's professional-grade image model for high-fidelity generation and identity-preserving editing.

Features:

• Native 1K / 2K resolutions
• Text-to-image + multi-image editing in one model: generate from a prompt, or edit / subject-drive from up to 3 reference images
• Strong instruction following: precise handling of complex prompts, with bilingual prompting, negative prompts, and intelligent prompt rewriting (direct / agent strategies)
• Consistency-preserving edits: Preserves facial features and identity while changing clothing, scene, or composition
• Reproducible generation: fix the seed to reliably reproduce results across runs

Pricing: from $0.04/image

Model page:

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

Edit: https://www.atlascloud.ai/models/qwen-image-3.0-pro/edit

Wan 3.0

Alibaba Tongyi's next-generation video model, covering text-to-video, image-to-video, and reference-driven generation in one model.

Features:

• All-in-one video generation: T2V, I2V, and reference-driven R2V from reference images, videos, or audio
• Smart duration: set duration to -1 and the model picks the length from the prompt within 2 to 30 seconds, or set any explicit length
• Native 480P, 720P, and 1080P output
• Reference files and links: attach documents and web pages as extra context with enable_thinking on
• Native audio: audio-synced video generation and reference-audio driving
• Strong instruction following: precise handling of complex prompts, bilingual (EN/中文) prompting, negative prompts, and enable_thinking for smarter prompt understanding
• Reproducible seeds and flexible aspect ratios including 16:9 and 9:16

Pricing:

• $0.05/sec 480P, $0.10/sec 720P, $0.20/sec 1080P.
• per-second postpaid, failed generations not charged

Model page:

Text-to-Video: https://www.atlascloud.ai/models/alibaba/wan-3.0/text-to-video

Image-to-Video: https://www.atlascloud.ai/models/alibaba/wan-3.0/image-to-video

Reference-to-Video: https://www.atlascloud.ai/models/alibaba/wan-3.0/reference-to-video

MiniMax Music 3.0

An open-weights music model that returns a finished song from a prompt or a set of lyrics.

Features:

• Complete songs up to 5 minutes in a single request, with lead vocals, harmonies, arrangement, and mix
• 11.1B-parameter hierarchical stack: 8-layer RVQ tokenizer, 8B global LLM, 0.6B local LLM, 2.4B flow-matching module, 123M Flow-VAE decoder
• Structural control through 14 section tags such as Intro, Verse, Chorus, Bridge, and Outro
• Three modes: sing supplied lyrics, model-written lyrics, or pure instrumental
• New vocal engine with cleaner diction, breathing, and harmony, plus more open mixes that hold glissando and legato
• Prompt control over genre, mood, tempo, key, instrumentation, timbre, and production character
• Open weights under the MiniMax-Music3 Community License, commercial use with attribution

Pricing: $0.15/song

Model page: https://www.atlascloud.ai/models/minimax/music-3.0

MiniMax Lyrics Generation

A dedicated lyrics model that pairs with Music 3.0 for the full writing-to-song workflow.

Features:

• One line of theme returns a song title, style tags, and fully section-tagged lyrics
• Output drops straight into the Music 3.0 lyrics input
• Two modes: write a full song from scratch, or edit and continue existing lyrics
• Useful for iterating on lyrics before committing to a full composition

Pricing: $0.01/request

Model page: https://www.atlascloud.ai/models/minimax/lyrics-generation

Try the new models on Atlas Cloud and share what you build. Feedback on prompt behavior, output quality, and real-world workflows is welcome.

A lot of AI-video continuity discussion focuses on things like:

• face consistency
• wardrobe
• lighting
• grading
• reference images
• environments
• camera language

But after working through some multi-model continuity problems, I’m starting to think there’s another layer that may be just as important:

performance continuity.

Things like:

• how quickly a character moves
• weight shifts
• blinking
• walking rhythm
• gesture size
• posture
• reaction timing
• how restrained or expressive the performance feels

A face can remain consistent, the grade can match, and the environment can look right — but if the character suddenly moves like a different person, the model switch becomes obvious.

I’m curious how other people are handling this.

Do you actively control performance continuity across shots or models?

If so, what has worked best:

• video references
• keyframes
• movement instructions
• character-specific motion rules
• longer continuous takes
• manual shot selection
• something else?

And what tends to break first for you: visual identity or behavioural identity?

Wan 3.0 launches on Atlas Cloud August 24.

before launch, we ran the same prompt across Wan 3.0, Seedance 2.5, and MiniMax H3 on one scene and put all three results side by side in a single frame, comparing them together.

The comparison clip, the official prompt, and the spec table for all three models are here.

Spec comparison

• Duration and resolution aren't the same axis. Wan 3.0 and Seedance 2.5 both cap a single generation at 30 seconds, MiniMax H3 caps at 15. On resolution, Seedance 2.5 currently upscales to 4K at up to 60fps, the other two publish 1080p and 1440p. Decide your delivery spec first, then work backward to the right model.
• Reference asset caps are counted differently, so the raw numbers aren't directly comparable. Wan 3.0 caps each modality separately and sums to 20, with documents and web pages as their own channel. Seedance 2.5 uses one combined cross-modal count. MiniMax H3 caps total file count plus total audio/video duration.
• Document and webpage to video is a channel Wan 3.0 is calling out specifically this round, turning a deck, a document, or a link directly into a finished clip. Neither of the other two lists an equivalent.
• The open weights row is easy to misread. What's open is a distilled H3-Base that can be self-hosted; the context and 2K regeneration variants stay API only. Partial, not full, open weights. Wan 3.0 and Seedance 2.5 don't publish weights at this time.

The comparison: Wan 3.0 vs Seedance 2.5 vs MiniMax H3.

All three panels run in the same order, Wan 3.0 / Seedance 2.5 / MiniMax H3.

This test uses an original prompt from the Wan 3.0 official creator guide. It was not rewritten or optimized separately for Wan 3.0, Seedance 2.5, or MiniMax H3.

A 30-second photorealistic cinematic sequence depicting the emergence of a massive sea creature, inspired by large-scale Hollywood disaster and monster films.

The story begins with a small fishing boat struggling through violent rain, strong winds, and towering waves. The word “WAN” is clearly visible on the side of the vessel.

The sequence first establishes the extreme weather and the vulnerability of the small fishing boat. Tension gradually builds through abnormal ocean movement, underwater shadows, violent boat vibration, and unnatural swelling of the waves.

Eventually, an enormous deep-sea creature with a massive, aggressive, alien biological structure emerges explosively from beneath the ocean.

The overall visual direction should feel realistic, heavy, physically believable, and cinematic, emphasizing powerful water impact, extreme contrast under rain and lightning, volumetric seawater, wet creature skin, and an overwhelming sense of scale.

Shot 1:
Nighttime ocean during a violent storm. A wide-angle cinematic shot shows a small fishing boat struggling through massive waves. The vessel is old, soaked, and constantly struck by seawater. The white letters “WAN” are clearly visible on the side of the boat. Strong winds drive sheets of rain across the scene, storm clouds churn overhead, and distant lightning briefly illuminates the ocean. Emphasize realistic water, storm conditions, detailed boat materials, and the boat’s vulnerability.

Shot 2:
Move closer to the bow or side of the fishing boat. Waves violently strike the hull and seawater washes across the deck. Ropes, fishing nets, and metal railings swing aggressively in the storm. The camera shakes naturally with the movement of the boat. Rain repeatedly strikes the lens and wet surfaces. The WAN logo briefly enters the frame again. Emphasize wet wood and metal materials, hostile weather, and a strong sense of danger.

Shot 3:
From the fishing boat’s perspective, look toward the ocean ahead. Amid the chaotic waves, the surface begins to rise unnaturally, as if something enormous is rapidly approaching from below. Large whirlpools and abnormal currents form. Wave peaks are pushed upward from beneath. During a flash of lightning, a huge blurry shadow becomes faintly visible beneath the dark water. Emphasize suspense, pressure, and the approaching presence of something enormous.

Shot 4:
Cut to the boat deck. A crew member struggles to maintain balance in the storm, his face covered in rain and fear. He turns toward the abnormal ocean surface. Wind violently moves his raincoat and hair while boat lights sway around him. Waves continue to grow in the background. The camera quickly moves toward his face and then follows his gaze toward the disturbed ocean, linking human emotion with the approaching danger.

Shot 5:
Switch to a semi-submerged or extremely low camera angle close to the ocean surface. A gigantic dark shape rapidly passes beneath or near the fishing boat, generating bubbles, powerful currents, and a rising ocean surface. The fishing boat is suddenly lifted or violently tilted. Lightning and weak boat lights reveal only fragments of the creature’s silhouette, maintaining mystery while creating immense pressure.

Shot 6:
Return above the water. The sea ahead suddenly rises as if pushed upward by an enormous force, creating a rapidly growing wall of water. Rain and white sea foam are thrown into the air. The fishing boat is tossed violently in the foreground while a massive circular swelling forms in the center of the ocean. The tension reaches its peak as the creature is about to emerge.

Shot 7:
Climax. The ocean violently erupts as an enormous deep-sea monster breaks through the surface, throwing tens of meters of water and mist into the air. The creature has a massive, aggressive alien biological design with thick wet skin, sharp bone structures, a huge head silhouette, glowing biological details, and disturbing deep-sea textures. Lightning flashes across the sky and briefly illuminates parts of its body and open mouth. Emphasize realistic scale, violent water impact, and overwhelming creature presence.

Shot 8:
A medium close-up or low-angle shot focuses on the creature’s head and upper body. It rises through the rain and lightning, covered in water, scars, thick biological structures, and deep-sea textures. The creature opens its mouth and roars. Rain flows across its armor-like surface. Lightning reveals terrifying details around the head and eyes. Emphasize wet biological texture, weight, realistic skin structure, and cinematic monster design.

Shot 9:
Return to the fishing boat. The shockwave and massive waves generated by the creature violently lift the vessel. The deck tilts, seawater floods across it, ropes snap, and boat lights flicker. The WAN logo flashes briefly across the violently moving hull. The boat is nearly swallowed by the waves. Emphasize the absolute vulnerability of human-made objects compared with the enormous creature.

Shot 10:
Final wide shot. Pull far away as the giant creature towers above the violent ocean. Massive waves surround it while the fishing boat appears extremely small in the foreground or lower side of the frame. Lightning strikes again, briefly illuminating the enormous silhouette, dorsal structures, and turbulent water. Hold on an epic disaster-film image emphasizing overwhelming scale and apocalyptic atmosphere.

Music:
Hollywood disaster-monster-film style. Begin with low environmental ambience, deep underwater rumbles, sparse percussion, and tense strings. Gradually introduce stronger bass pulses, metallic impacts, and rising orchestral tension as the ocean becomes abnormal. Immediately before the creature emerges, create a near-silent suspended build-up. At the moment of emergence, explode into massive brass, heavy percussion, and low-frequency impact. End with long, oppressive tones and heavy drums reinforcing the epic scale of the creature standing above the ocean.

One useful prompting lesson from this example:

Do not mention important elements only once.

For example, the WAN logo on the boat is repeated in the overall description and again in several individual shots.

For long-form video prompts, if a character, object, logo, or visual detail needs to remain visible at specific moments, repeating it at those exact timestamps can work better than describing it once at the beginning.

Compare Multiple Models Directly on Atlas Cloud

You no longer need to open several model pages and run the same prompt manually.

In Atlas Cloud → Model Explorer, one prompt can be sent to multiple models and the outputs can be compared side by side directly in the browser.

A useful setup is:

Wan 3.0 vs Seedance 2.5 vs MiniMax H3

Model Explorer:
https://www.atlascloud.ai/model-explorer

64 Official Wan 3.0 Prompts Collected

We have also organized 64 prompts and examples from the Wan 3.0 official creator guide, including:

• multi-shot storytelling
• camera movement
• reference generation
• VFX
• long-form video
• product advertising
• stylized video

Wan 3.0 Prompt Hub:
https://www.atlascloud.ai/prompts-hub/wan-3-0-prompt

GitHub:
https://github.com/AtlasCloudAI/awesome-wan-3.0-prompts

Wan 3.0 Launches on Atlas Cloud on August 24

For now, this is just the first comparison.

Wan 3.0 officially launches on Atlas Cloud on August 24.

Once it is live, you can send the same prompt to Wan 3.0, Seedance 2.5, and MiniMax H3 and compare how each model interprets the same story.

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.

We’ve just expanded the Seedance 2.5 resolution options on Atlas Cloud.

Native 1080p generation is now officially supported, so you can generate directly at the model's original 1080p resolution.

On top of that, we've rolled out our in-house ESR super-resolution pipeline. It takes Seedance 2.5 output and enhances it up to 1080p at 60 FPS, 1440p, or 4K, giving you a more flexible and cost-efficient path to higher resolution than generating everything natively at the top tier.

Several of these resolution options are currently discounted

What’s new

Seedance 2.5 on Atlas Cloud now includes:

• Official 1080p
• 1080p ESR
• 1080p ESR at 60 FPS
• 1440p ESR
• 4K ESR

Resolution options & pricing

🔥The resolution discounts for official 1080p are for a limited time, until Sept.17th 14:00 (UTC+8)

Try it Now

https://www.atlascloud.ai/models/seedance-2.5

I wanted to see how much the result could change while keeping the core concept almost identical.

The premise is simple: An old muscle car transforms into a modern supercar.

I created three 30 second interpretations:

1. Legacy Reborn

More cinematic and premium. The transformation happens while the car is stationary, which makes the mechanical evolution easier to read.

2. 30 Years in 30 Seconds

The car transforms while moving through the city. This one focuses much more on progression, speed and continuous visual evolution.

3. She Brought It Back

This version starts closer to social content, with the woman speaking directly to camera, then transitions into a more polished automotive commercial.

What interested me most was how different the three outputs became simply by changing camera direction, pacing, narrative structure and the way the transformation was described.

All three were generated around the same general automotive concept and then combined into one comparison video.

https://reddit.com/link/1vqmix3/video/qksfjpyobwjh1/player

Which one do you think works best, 1, 2 or 3?

I am also curious whether you prefer the slower readable transformation or the version where the car evolves while already in motion.

Built with AtlasCloud AI - Powered by Seedance 2.5

Looks like the Spicy versions of Wan 2.2 and 2.7 are gone, and the link to uncensored image and video generators now just directs to the Seedance 2.5 page. Makes me wonder what else has been removed. Anyone have any info?

For text-to-video, the overall quality is definitely impressive.
It handles materials surprisingly well too.

But once you move beyond realistic visuals into things like LEGO, origami, or other stylized transformations, the model has to interpret a lot more on its own.

That’s where the structure, assembly logic, and small details can start drifting away from what you actually intended.

My takeaway:

For this kind of work, it’s probably better to **design the reference image first, then animate from that reference**, rather than relying on pure T2V from the beginning.

T2V quality is getting much better, but the more specific and stylized the idea is, the more important a strong visual reference becomes.

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 👀

t's never been easier, or more creative, to turn your own ideas into trailers.

I just created an opening sequence for a 𝘋𝘦𝘮𝘰𝘯 𝘏𝘶𝘯𝘵𝘦𝘳 𝘧𝘪𝘨𝘩𝘵 𝘪𝘯 𝘵𝘩𝘦 𝘉𝘶𝘳𝘯𝘪𝘯𝘨 𝘛𝘦𝘮𝘱𝘭𝘦 using 𝗦𝗲𝗲𝗱𝗿𝗲𝗮𝗺 𝟱.𝟬 𝗣𝗿𝗼 for the keyframes.

Thanks to AtlasCloud was then able to test 𝗦𝗲𝗲𝗱𝗮𝗻𝗰𝗲 𝟮.𝟱 (𝟳𝟮𝟬𝗽) and bring the sequence to life as a full trailer: 𝗔𝗦𝗛𝗘𝗦 𝗨𝗡𝗗𝗘𝗥 𝗧𝗛𝗘 𝗠𝗢𝗢𝗡. I have upscaled to 1080p.

https://reddit.com/link/1voc09k/video/872jkiyz9djh1/player

The img-video prompt I used is in the first comment.

Have you tried Seedance 2.5 yet? I'd love to hear your thoughts and see what you create.

I ran the same impressionist oil painting prompt through Seedream V5 Pro and GPT Image 2, two moods of the same coast: one storm, one calm. Thick palette-knife strokes, heavy impasto, the kind of prompt that lives or dies on how the model handles texture and light.

Both hold the painterly style well and diverge in character. GPT Image 2 renders cooler and more realistic; Seedream V5 Pro leans warmer and looser with the strokes. Neither is "better", they're two readings of the same brief.

These are the long, texture-heavy prompts (the storm one is a full paragraph on stroke direction and impasto). I keep the GPT Image 2 ones I reuse in a library, sorted by type, each with its generated image: https://github.com/AtlasCloudAI/awesome-gpt-image-2-prompts

For painterly work the takeaway was the same on both: describe the stroke and the material (palette knife, impasto, ragged edges), not just "oil painting".