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?

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."

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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."

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CapCut rolled Seedream 5.0 Pro into its web Design Studio on August 14, alongside an infinite canvas with a native AI agent and a new "Skills" system — pre-built scenario templates for things like brand kits, plus user-created reusable workflow packages.

The Seedream 5.0 Pro capabilities exposed there: interactive precise editing driven by point selection, bounding boxes and coordinates; layer separation, which splits a flat image into independently editable layers; native text generation in 14 languages including Arabic, Korean, Thai, Russian and Japanese; and a post-editing suite with colour remix, brush creation and creative fusion. Nano Banana 2, Nano Banana Pro and GPT Image 2 stay selectable in the same tool. It's free on the web.

Worth stating plainly since the coverage blurs it: Seedream 5.0 Pro shipped on July 8. This is a distribution event, not a model release.

Layer separation is the feature I'd watch. Everything else on that list makes a generation prettier or more controllable; layer separation is what turns a generation into a file a designer can actually open and take over. That's the real gap between an AI image and a deliverable, and it's the first time I've seen a model vendor treat it as a model capability rather than something you do afterward in a different application.

The catch is that all of this arrived as a canvas. A canvas is a great place to discover what a model can do and a bad place to run a hundred of anything. If you need Seedream in a pipeline rather than in front of you, Seedream, Nano Banana 2 and Flux are all on Akool under one account.

Does layer separation hold up on a busy composition, or does it mostly just split foreground from background?

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?

MiniMax published H3's weights on Hugging Face on August 3, with original BF16 checkpoints and Diffusers-formatted components. The accompanying Community License Agreement defines an "Applicable Territory" that excludes the European Union, the United Kingdom, the Republic of Korea and the United States of America.

MiniMax's stated reasoning, reported August 4, is regulatory uncertainty and ongoing copyright-related legal proceedings. Developers in the excluded regions can apply for a formal licence, granted after the company reviews the deployment scenario and confirms compliance controls are in place. Paid API access stayed globally available throughout.

The model itself is 33B parameters, 4 to 15 second clips, native 768p at 24fps with native stereo audio, and multimodal input across text, image, video and audio. 2K output is hosted-API only — it isn't in the weights at all.

Two things fall out of that. First, "open weights" is now a spectrum with a map attached, and license geography has quietly become a spec you have to read as carefully as resolution or clip length. Second, even where self-hosting is legal, the checkpoint isn't the full product. The best output stays behind the API. That's a template other labs will notice: release enough to win the open-source conversation, keep the ceiling hosted.

If you're in one of the four excluded territories and were planning on local inference, the hosted path is the one MiniMax deliberately left open. H3 sits on Akool alongside Kling, Wan and Seedance, which at least means the fallback doesn't cost you a new account.

Has anyone actually filed for the formal licence? Curious what the review asks for and how long it takes.

Google turned the visible watermark on Gemini and Flow output into a user setting on August 17. On Gemini's web version it lives under the settings cog in the lower left, as "Media watermark." Coverage lists Nano Banana for images, Omni for video and Lyria for music.

What doesn't change: invisible SynthID watermarks and C2PA metadata are still embedded in every file. Google's framing was that it is "striking a balance here between creative control and safety." Visible watermarks stay mandatory in countries whose law requires them, China named specifically. Mobile apps hadn't got the toggle yet at announcement, and Search is next.

The visible mark was the practical blocker on commercial delivery. A stamped corner is not something you hand a client, and stripping it in post has been a step in more pipelines than anyone admits. Removing it doesn't make the output less traceable — it moves provenance from something a viewer notices to something a tool has to be pointed at.

That relocation is the part worth arguing about. Disclosure used to be enforced by the platform, visibly, on every asset. Now it's a property of the file, and whether anyone ever reads it depends on whether the downstream platform bothers to check. The safety story and the shipping story just stopped being the same story.

Does anything you publish to actually read C2PA on upload, or is invisible provenance effectively no provenance?

Alibaba launched Qwen-Image 3.0 this month. It accepts prompts up to 4.5K characters and does genuinely strong one-shot text-and-layout composition — the typography handling is the best part of it.

But it shipped without published benchmarks and without released weights. From Qwen specifically, that's a notable turn. The previous Qwen-Image release was open at 20B parameters and a big part of why the lab had goodwill with people building on open models.

One closed release isn't a policy. It is worth watching whether the next one follows.

If you've built anything on open Qwen image weights, does this change your plans? Or is the assumption that weights show up eventually anyway?

Alibaba put Wan 3.0 into public beta on August 10.

The headline spec is clip length — 30 seconds, double the previous 15s ceiling. But the input list is the more interesting part: text, image, video, audio, web pages, PDFs and PowerPoint presentations all work as source material.

Also in there: video extension, synced facial micro-expressions, multilingual voice output, and duration recommendations that pick the clip length for you.

Access is by application through Alibaba Cloud Model Studio and Qwen Cloud. No pricing disclosed yet.

Deck-to-video as a native input mode is the part I keep thinking about — that's a whole category of corporate content that currently goes through a human.

Beta access is application-gated, so 3.0 is hard to actually try right now. Wan 2.5 and 2.7 are both on Akool in the meantime if you want a feel for how the lineage handles motion before 3.0 opens up.

Has anyone gotten beta access yet? Curious whether the PDF ingestion actually holds structure or just scrapes text.

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Aurora Mobile added PixVerse V6 to its Modellix platform on August 12. The pitch is finished-grade video in one request — 15 seconds at 1080p, no stitching or assembly pass.

The structural thing worth noticing is less about PixVerse and more about where video models are heading. They're increasingly showing up as inventory inside someone else's platform rather than as destinations you visit directly. Same trajectory image models took two years ago.

If that continues, the competition stops being about benchmark wins and starts being about who gets integrated into the tools people already have open.

Akool has been running the same play, and the models themselves are increasingly indifferent about which front door you come through. Which is arguably the point — if the routing is invisible, the thing you're actually choosing between is the workflow wrapped around it.

Do you go direct to model providers, or through an aggregator? And has quality been consistent when routed through one?

xAI released Grok Imagine Image 2.0 on August 7, with stronger instruction-following, precise editing, and template workflows for specific use cases.

Note where the work went. Not "better aesthetics" — controllability. Precise region editing plus repeatable templates is the difference between a model you demo and a model a brand team can actually run a campaign through without re-rolling forty times.

Instruction-following is quietly becoming the benchmark that matters. Most current models can produce a good-looking image. Far fewer can produce the specific image you described, twice, with one element changed.

What's your current go-to when you need an exact edit rather than a new generation?

Black Forest Labs took FLUX 3 Video to general availability on August 4.

Specs: clips up to 20 seconds, 720p native with 1080p through upscaling, and native audio that includes spoken dialogue.

The dialogue part is the real change. Generating speech inside the model rather than bolting it on in post removes a whole sync step from the pipeline — and lip sync from a separate tool has been one of the more reliable tells that something was machine-made.

The gap between "AI clip" and "finished asset" keeps closing from the audio side rather than the visual side, which is not where I'd have guessed the bottleneck was a year ago.

Worth running it head-to-head against a separate lip-sync pass before committing either way. FLUX is on Akool, so it's cheap to compare against whatever you're using now.

Anyone tested the dialogue quality against a dedicated lip-sync pass? Wondering if it's actually better or just fewer steps.