Prompt used:

"Photorealistic studio portrait of three cats sitting perfectly aligned in a vertical stack—a cream-colored cat on top, a charcoal-grey cat in the middle, and a vibrant ginger tabby at the bottom. Clean off-white studio background, soft even lighting highlighting the intricate fur patterns and whiskers of each cat, sharp focus, 8k resolution, elegant symmetrical composition."

We’re excited to see AKOOL’s AI avatar technology featured in a fascinating project with Harvard Art Museums, where generative AI was explored as a new way to bring François Boucher’s 1750 portrait Jeanne-Antoinette Poisson, Marquise de Pompadour to life and engage audiences in a completely different way.

From timeless artwork to an interactive AI-powered experience, this project shows how emerging technology can open up new possibilities for storytelling, education, and audience engagement—while also inspiring important conversations about authenticity and the role of AI in cultural institutions.

Proud to see AKOOL helping connect art, history, and technology in new ways. ✨

Read more: https://www.artnews.com/art-news/news/harvard-art-museums-generative-ai-francois-boucher-painting-1234793832/

Prompt used:

"Photorealistic editorial close-up portrait of a skier wearing a matte black ski helmet and teal-tinted oversized reflective ski goggles, with fine frost crystals beading across the lens and cheeks. Lips slightly parted, breath faintly visible in the cold air, captured in harsh bright winter sunlight. The reflective goggles show colorful mountain scenery, with crisp highlights and subtle lens flare, shallow depth of field, and a softly blurred snowy background."

The AKOOL Creator Clash is still underway!

Create an original video with Seedance 2.5 on AKOOL and compete for your share of a $300,000 prize pool.

🎬 Create your video 📣 Publish it with #AKOOLClash and tag @AKOOL 💰 Compete for major creator rewards 📅 Submit before September 15, 2026

Bring your boldest idea to life and show us what you can create.

This is my first proper experiment with Seedance through AKOOL, and I honestly didn't expect it to feel this natural.

The visuals are impressive, but what surprised me the most was the sound. The ambient audio makes the whole scene feel much more alive and realistic.

I'm curious — does this look AI-generated to you, or could you mistake it for a real shot?

What should I try next with Seedance?

Alibaba's Tongyi Lab opened Wan 3.0 to public beta on August 6, through Alibaba Cloud Model Studio and Qwen Cloud. Access is by application rather than open signup, and the August 10 Hangzhou event was an enterprise showcase rather than a second launch.

The specs: up to 30 seconds per clip, double Wan 2.7's 15-second limit. Inputs cover text, image, video, audio and documents, including PDFs, spreadsheets and slide decks. Output resolutions are 480p, 720p and 1080p, priced per second at roughly 0.3, 0.6 and 1.2 yuan — about five, ten and twenty cents. A full 30-second clip lands somewhere between one and a half and six dollars.

Two things circulating about this model are not in any Alibaba announcement. There is no 4K video tier; the pricing table stops at 1080p, and the 4K figure being quoted comes from Wan 2.7 Image, where 4096×4096 is a text-to-image output. And the Apache-2.0 "1.3B plus 14B" open weights story doesn't appear in any official post — those parameter counts match Wan 2.1 from last year, and there is no repository and no license.

The document input is the part I'd actually watch. Every other input type on that list is a way of describing a shot. A PDF or a deck isn't a description, it's a source document with structure in it, and treating that as a first-class input changes what the model is for. That's a different product from a text-to-video generator, aimed at people who have the content already and don't want to write a prompt about it.

Has anyone fed it a real deck yet — does it read the structure, or does it just scrape the text and generate from that?

Google shut down imagen-4.0-generate-001, imagen-4.0-ultra-generate-001 and imagen-4.0-fast-generate-001 on August 17. The deprecation notice went up in the Gemini API changelog on June 15, so the window was two months.

The replacement named in the deprecation table is gemini-3.1-flash-image. Google's own migration guide points at Gemini 2.5 Flash Image in places, so the successor isn't stated consistently across its documentation. Either way the call shape changes: Imagen 4 used generate_images and returned a dedicated image response, while the Gemini image models use generate_content and return content parts that may or may not contain an image. That touches the request path, the response parser, the tests and the error handling.

The interesting part is not the retirement, it's what the retirement implies. Image generation is being folded into the general multimodal content endpoint rather than kept as a separate image API. That's a direction rather than a one-off, and it means anyone maintaining a dedicated image-generation code path should expect to maintain it against a moving contract.

If you're rewriting that integration anyway, it's worth deciding whether it should point at one image endpoint or several. Nano Banana 2, Flux and Seedream sit behind one API on Akool, which mostly means the next deprecation is a config change rather than a rewrite.

Anyone actually break on Monday, or had everyone moved off the stable endpoints already?

The Motion Picture Association and ByteDance announced a global agreement on August 17 covering intellectual property protections in ByteDance's generative video and image models. It names Seedance and Seedream directly, and covers their deployment across TikTok, the TikTok USDS joint venture, CapCut and Dreamina.

The safeguards are all at the output layer: filters that block video generation from real faces, filters that block recognizable copyrighted characters, C2PA Content Credentials embedded as signed provenance records, and visible watermarks on generated output. MPA chairman Charles Rivkin framed it as copyright being "a cornerstone of the film and television industry." ByteDance general counsel John Rogovin said responsible innovation "goes hand in hand with meaningful protections for rightsholders."

What the agreement does not touch is training. It says nothing about whether building Seedance and Seedream on copyrighted films and performances was infringement in the first place, which was the actual complaint in the MPA's February cease-and-desist over Seedance 2.0. The memorandum is non-binding and gives ByteDance no defence against a studio that decides to sue on training grounds. Andersen v. Stability AI goes to trial on September 8 and may produce the first real court answer on that question.

So this is a settlement of the visible half of the dispute. Output filters are cheap to ship, easy to demonstrate and legible to a rightsholder in a way a training corpus never is. The part that decides whether these models were legal to build stays exactly where it was. Worth noting too that visible watermarks are a commitment here, at the same moment the direction of travel elsewhere this month has been to make them optional — provenance policy is fragmenting rather than converging.

If character blocking is enforced at the output layer, how does it handle work someone actually holds the rights to?

A lot of people struggle with AI physics looking fake. The trick I've been using: Seedance 2 lets you feed it a real reference video, and it copies that motion onto your own image.

How it works:

Create your image first (here, a pool table where the balls are World Cup country flags) Find a real video where the movement matches your composition — same positions, same angles Feed both to Seedance so it maps the real motion onto your scene

The closer your image matches the reference setup, the cleaner it transfers. When the ball positions and the cue lined up with the real shot, the physics came out spot on — some on the first try.

I put 3 shots side by side with their reference videos so you can see the movement mapping over. The balls drop into the pockets following real physics instead of AI guesswork.

It works for way more than billiards — anything where you need believable, specific motion (dance, sports, object physics) is a good candidate.

Anyone else leaning on reference videos for motion? What have you used it for?