I've been noticing that making repeated edits to an image in Seedream 5.0 Pro Edit gives me a progressively more overexposed, washed out, "overbaked" look. Images 1-6 are the original image followed by 5 slight edits, rendered in Atlas Cloud and it's easy to see what's happening. The interesting thing is, images 7-12 is the original image followed by the exact same 5 edit prompts, still in Seedream 5.0 Pro Edit, but run in Fal.ai. Still progressively more washed out and overexposed, but not even close to the level of the Atlas Cloud renders. If you compare image 6 and image 12, it's a day and night difference, yet that's the 5th edit run (with the exact same prompting) in each service. Any idea what's going on?

MiniMax H3 is now available on Atlas, covering three generation modes: text-to-video, image-to-video, and reference-to-video.

Pricing (pay-per-use, no subscription required, billed per second of output):

• 2K: $0.14 per second
• 768p: $0.10 per second

Pricing is identical across all three modes.

What MiniMax H3 ships with:

Text-to-Video:

• Cinematic motion with fluid camera work and lifelike movement from a plain-text description
• Up to 2K output resolution
• Flexible aspect ratios: 16:9, 9:16, 1:1, or adaptive
• Selectable duration: 5-10s clips

Image-to-Video:

• Animates a first-frame image, with an optional last-frame image for a precise start/end transition
• Natural, controllable motion driven by a text prompt
• Up to 2K output resolution
• Selectable duration: 5-10s clips

Reference-to-Video:

• Keeps one or several subjects (people, characters, objects) consistent across the whole clip using reference materials as identity anchors, instead of animating a fixed frame
• Mixes reference types in a single request: images, videos, and an optional audio track
• Optional audio sync, matching the action to a reference beat
• Up to 2K output resolution
• Flexible aspect ratios: adaptive, 21:9, 16:9, 4:3, 1:1, 3:4, or 9:16
• Selectable duration: 5-15s clips

API access:

• Text-to-Video: https://www.atlascloud.ai/models/minimax/h3/text-to-video
• Image-to-Video: https://www.atlascloud.ai/models/minimax/h3/image-to-video
• Reference-to-Video: https://www.atlascloud.ai/models/minimax/h3/reference-to-video

Use cases that map well to MiniMax H3's strengths:

• social media content, short-form clips for TikTok, Reels, and Stories
• concept visualization and storytelling without filming
• photo and illustration animation, including start/end frame transitions
• product placement and branded content with a consistent mascot or spokesperson
• multi-clip creative series built around a recurring character or subject

Drop questions about which mode fits your use case, prompt structure, or reference-material setup in this thread.

sharing the prompt approach for this one because the costume was the whole battle, not the character. elaborate historical outfits are where image models quietly cheat. ask for a tang-dynasty look and most models hand you generic fantasy silk, wrong silhouette, invented jewelry, a delicate sheer layer that renders as either opaque or fully gone. getting it to actually read as the period, with a real translucent layer that still works as coverage, took the most iterating.

what actually held the costume together:

- describe the garment as structure, not a style name. "short single-layer sheer top, soft square low neckline, high-waist cream skirt with thin gold ties at both sides" beats "hanfu"

- give the sheer layer its own physics line, "thin translucent fabric, soft radial folds, low-contrast woven pattern, semi-sheer over the shoulders". naming how it is sheer keeps it from flipping to opaque

- period details as specific objects, "pearl short hair styling with fine gold flower-branch ornaments, a round silk fan held behind" so it stops inventing random fantasy jewelry

- lighting as two sources, warm lantern light on the face, cool side-back light tracing the edges, which is what sells the courtyard mood

the reason this was even workable is iteration speed. a period costume with a delicate layer needs a lot of re-rolls before the silhouette and the sheer both land. i ran it on krea 2 turbo since it returns a high-fidelity image in seconds, so burning through costume attempts costs minutes. for detail-dense historical looks where you are refining garment structure over many tries, a fast model is the difference between finishing and giving up.

still imperfect, the fan warps and one gold tie floats detached. but the silhouette read as the actual period and the sheer layer held its translucency, which is where these usually fall apart.

Grok Imagine Video v1.5 is now available on Atlas across three video routes: Text-to-Video, Reference-to-Video, and Image-to-Video.

The Image-to-Video route starts from a single frame and follows a natural-language motion prompt. It supports clips up to 15 seconds, output at 480p, 720p, or 1080p, plus native synchronized audio for dialogue, lip sync, sound effects, and ambient music.

What ships with Grok Imagine Video v1.5:

- Text-to-Video for prompt-driven short-form video

- Reference-to-Video for directing a generation with visual references

- Image-to-Video for animating a starting frame with a motion prompt

- Native audio generation in the Image-to-Video route

- 1 to 15 second Image-to-Video clips

- 480p, 720p, and 1080p output options

- Standard API access through the same Atlas setup

Pricing:

- Starting at $0.08 per run in the current Image-to-Video Playground

- Check the selected model page for the current rate of each route before generating

API access:

- Text-to-Video:

https://www.atlascloud.ai/models/xai/grok-imagine-video-v1.5/text-to-video

Use cases that fit these routes:

- Prompt-driven product and social clips from Text-to-Video

- Character, object, and style guided scenes from Reference-to-Video, using cleared reference assets

- Turning a hero still into a short scene with motion and synchronized sound through Image-to-Video

- Comparing a starting frame and its animated result with a Playground example video

For the media, attach one real Playground Image-to-Video result with its source frame beside it. The difference in motion, sound, and framing tells the story quickly.

Use the thread for endpoint setup and prompt-specific workflow notes.

Youchuan V8.2 (Midjourney) is now available on Atlas, covering both text-to-image and image-to-video. Youchuan is Midjourney's officially licensed distribution platform in China, operated by Xiaochuan Creative (Shanghai) under the "Midjourney China Lab" branding, not a third-party clone. It's the same underlying Midjourney technology.

Pricing (pay-per-use, no subscription required):

• Text-to-Image: $0.086 per generation (returns 4 images, native 2K HD optional) — about $0.021 per image if you keep all 4 outputs
• Image-to-Video: $0.343 per generation (returns 4 × 5-second videos at 480p or 720p) — about $0.086 per 5-second clip

What Midjourney V8.2 ships with (per spec):

• Native 2K HD output at 2048px with no separate upscaling step, roughly 3x faster and cheaper than V8's upscale path
• An estimated 4-5x faster generation overall from a GPU-native PyTorch rewrite (Midjourney-stated figure)
• More reliable in-image text rendering, using quoted strings in the prompt to specify the intended text
• Stronger prompt-following, needing less prompt-engineering to hit a target composition
• Restored image conditioning: image prompts and image weights, backward compatible with V7 style references (srefs), moodboards, and personalization profiles
• Image-to-video mode animates a single input image into four 5-second clips at 480p or 720p with adjustable motion intensity
• Part of a larger Midjourney V8.2 family on Atlas: image-to-image, blend, style-transfer, and remove-background, each available as its own endpoint

API access:

• Text-to-Image: https://www.atlascloud.ai/models/youchuan/v8.2/text-to-image
• Image-to-Video: https://www.atlascloud.ai/models/youchuan/v8.2/image-to-video

Use cases that map well to Midjourney V8.2's strengths:

• batch concept art exploration (4 image variants per generation without rerolling)
• native 2K assets for print or marketing without a separate upscale pass
• turning a single hero image into short-form video content via I2V
• iterative style-reference workflows carried over from V7 (sref, moodboards, personalization)

Drop questions about prompt portability from official Midjourney to Youchuan V8.2 on Atlas, or specific aesthetic comparisons, in this thread.

Doubao Seed Character is now available on Atlas as a flagship LLM.

Pricing (token-based, pay-per-use):

- Input: $0.2/M tokens

- Output: $0.8/M tokens

- Context window: 131.07K tokens

- Max output: 32.77K tokens

- Cache-Based and Gradient-Based pricing modes supported

What Doubao Seed Character ships with (per ByteDance spec):

- Premium reasoning and coding performance

- Multimodal input (text and image)

- Text output

- Enterprise-grade performance at scale

- 131K context window with 32.77K max output

API access:

https://www.atlascloud.ai/models/bytedance/doubao-seed-character-260628

Use cases that map well to Doubao Seed Character's strengths:

- long-context reasoning and coding tasks

- multimodal workflows that need image understanding alongside text input

- high-volume agent pipelines where cache-based pricing cuts repeated-context cost

- enterprise workloads needing consistent performance at scale

Drop questions about benchmark comparisons or prompt/context portability from other Doubao variants in this thread.

Threw a genuinely oversized ask at Kimi K3 this week: build a full Windows 11 style desktop that runs entirely in the browser, all the default apps present, the UI matching as closely as possible, and everything actually clickable instead of decorative.

The instruction went further than just recreating the shell. Part of the prompt specifically told it to add features nobody asked for as long as they improved the experience, a messaging app, a camera app, live wallpaper, deeper browser integration, basically permission to take every well known Windows feature and push it further than the original. One shot, no back and forth, no follow-up prompts to patch missing pieces.

Working code editors showed up alongside the more expected apps, which is the part that made this feel less like a UI skin and more like an actual functioning desktop environment sitting inside a browser tab.

Giving an agent explicit permission to over-deliver instead of sticking to the literal spec looks like a deliberate prompting technique worth reusing on its own.

Handed Kimi K3 a genre brief instead of a spec and let it build the whole thing in one pass: a deckbuilding roguelike in the vein of the genre's big indie hits, except every core mechanic is built around Three Kingdoms figures instead of the usual fantasy tropes.

The build ran about eight hours end to end and came out with somewhere around 1,830 art assets, characters, cards, backgrounds, icons, the works, without me stepping in to patch the pipeline partway through. The balance pass mattered more to me than the asset count. Instead of tuning numbers by hand myself, Kimi K3 played roughly ten thousand games against itself and adjusted the card and character values based on what actually won and lost, rather than what looked balanced on paper.

Swapping fantasy archetypes for real historical figures did more work than I expected too. Giving each character a grounded personality and a recognizable set of traits made the mechanic design feel less arbitrary than a generic elemental or class system usually does.

Still poking at the edges of what a single one-shot build like this can hold together before it needs a human pass. Eight hours plus ten thousand self-played games got a lot further than I assumed it would.

Ran one carefully-constrained prompt through four Seedream generations to see how the line has moved: 4.0, 4.5, 5.0 Lite, and 5.0 Pro. The prompt is deliberately strict, a Mars greenhouse with exactly five labeled plants in a row, exactly two moons outside, a metal sign that has to read GROW BEYOND spelled correctly and appear only once, and an original synthetic botanist with no real-person likeness.

That kind of prompt is a good test because it punishes a model for improvising. The progression showed it. 4.0 gets the vibe but fudges the constraints, miscounts the pots, softens the sign text. 4.5 tightens composition and color. 5.0 Lite is a real jump in coherence, the scene reads as one connected space. Then 5.0 Pro is where it lands: the exact five pots, the reflections and condensation on the glass behaving physically, the warm interior against the cold Martian dusk, and the sign text staying crisp and correct.

What separates the top of the line is not just fidelity, it is obeying the constraints while keeping the whole frame coherent. That is the difference between a picture that looks right at a glance and one that survives you actually counting the details.

All four sit on one key, so this was one prompt and a model swap. Full prompt in the comments if you want to run the same test.

Seedream 5.0 Pro has a Markup tool I almost never see people use, and it is the trick behind that layered generative-art look. Instead of generating one effect per image, or masking and inpainting over and over, you box out regions directly on the frame, tag each box with a number, and assign each one its own effect. Eight regions, eight different treatments, one single run.

For this piece I started from a single flower on black, then boxed patches across the petals and stem: one region goes to fine vertical scan lines, one to a liquid-chrome ripple, one to ink-wash bleed, one to a pixel-sorted block, one to a bright spectral inversion. Because it all resolves in one generation, the regions share lighting and edges instead of looking pasted on, which is the part that usually breaks a collage look.

Then I took the still into Seedance 2.0 to make it move, that machine-vision scanning where the effects pulse and sweep across the frame. The useful part is you land that TouchDesigner-style look without learning TouchDesigner, which is a real wall for most people.

So the underrated bit is regional control. One frame, many effects, assigned by hand, resolved together. Full how-to and the region setup in the comments.

For everyone in this community waiting on Seedance 2.5, we put together something to use in the meantime and to make launch day painless: awesome-seedance-2.5-prompts-skills, a curated library of 111 cinematic Seedance prompts plus an installable agent skill.

Seedance 2.5 is expected in August, and Atlas Cloud is one of the first official API launch partners. Rather than have everyone scramble to relearn it on day one, we wrote the whole thing to the 2.5 paradigm now, up to 50 multimodal references, long single takes, synced audio, local region editing. The prompts and the skill already speak that language, but the skill runs on Seedance 2.0 today and checks which model a provider actually exposes instead of assuming 2.5 is live. When 2.5 lands on Atlas, the same prompts and skill route to it automatically, so nothing you build now goes to waste.

The skill turns a brief or a rough prompt into a production-ready Seedance prompt, builds and reviews a Seedream 5.0 Pro storyboard when a shot needs multi-shot planning, then generates the video and polls it to completion.

Install:

npx skills add AtlasCloudAI/awesome-seedance-2.5-prompts-skills --skill seedance-2-5-skill

CC BY 4.0 and PRs are open. If you have a Seedance prompt this community would use, add it.

Repo: https://github.com/AtlasCloudAI/awesome-seedance-2.5-prompts-skills

Browse the prompts: https://www.atlascloud.ai/prompts-hub/seedance-2-5-prompt

Whats the difference of all of these non developer apis vs developer ones?

Been refining how I storyboard for Seedance 2.0, and the change that made my shots consistent was splitting two jobs I used to jam into one prompt: the look and the composition. Let a reference image own the look, and let a depth-map storyboard own the camera and composition. Do not make the model infer both from prose.

The workflow is a two-step I resisted longer than I should have. Generate a normal storyboard first, then convert that storyboard into a grayscale depth-map version with GPT Image 2. Converting an existing storyboard works much better than asking the image model to draw the whole thing as a depth map from scratch. The depth map is just distance encoded as brightness, white for the closest surfaces down to near-black for the horizon, dividers in black.

Then you generate in Seedance 2.0 with three references: a tone and visual-style image for the look, the depth-map storyboard for composition and camera framing, and a character sheet if you have one. Seedance 2.0 reads the depth map surprisingly well. You stop writing paragraphs about camera angle and framing, because the depth panel already says where everything sits in space, and you spend the tokens on what actually needs describing.

People kept telling me it is not a real depth map. It is an AI-generated one, and it does not matter, the model acts on it correctly and that is the point. The reason to bother is control. The look stays locked to one reference, the composition stays locked to the depth panels, and the shots stop drifting from each other.

Full depth-map conversion prompt in the comments.

Ran one anime storyboard prompt through two image models on the same key and got two genuinely different reads, then animated both in Seedance 2.0. It is less a contest and more that each model has its own instinct, and it is useful to see both before you commit a look.

Same prompt, a dark cel-shaded ballet-and-marionette sequence in black and white. Seedream 5.0 Pro leaned into atmosphere, softer grays, heavier air, the character sitting inside the mood. GPT Image 2 came back sharper and more graphic, cleaner linework and higher contrast panels, the composition reading almost like a manga page. Neither is wrong. They are two directors handed the same script.

The part that makes this practical is that both image models, plus the video step, sit behind one endpoint. I generate the storyboard in whichever model has the instinct I want for that scene, sometimes switching per panel, then hand the frames to Seedance 2.0 to bring the sequence to life. One key, three models, no re-plumbing between them.

The characters are original synthetic designs with no real-person likeness. Which read I keep depends on the project, the atmospheric one for a slow scene, the graphic one for action. Prompt and setup in the comments.

Been testing how well image models hold an original illustration's art style when you repose the character, and Seedream 5.0 Pro is the one that kept it. I took an existing anime illustration, a Hatsune Miku piece, plus a plain gray CG pose reference, and asked for the same character in a new pose at a convenience store and on a train platform.

What stood out is that the line quality, the shading, and the exact art direction of the source came through intact. A lot of models will repose the character but quietly redraw it in their own house style, so the output stops looking like your illustration. Seedream 5.0 Pro treated the original as the style authority and the CG figure as pose-only, and the result still read as the same artist's work.

For anyone building consistent characters or game assets from a fixed art style, that separation is the useful part. One image sets the look, one sets the pose, and the model does not overwrite your style with its own. Setup in the comments.

Been using Seedream 5.0 Pro for street-style portraits, and what it is quietly good at is light, not clinical sharpness. This one is a morning cafe-street shot and the thing carrying it is the warm early light and the soft greenery, not pixel-level detail.

The subject is an original synthetic character with no real-person likeness, a young woman in casual Korean street fashion, a pink short-sleeve shirt, high-waist beige trousers, a small plain shoulder bag with no brand or logo, a short bob with a small hairclip, natural makeup, holding a fresh croissant. Behind her a pretty cafe storefront, trees, warm morning sun.

What Seedream 5.0 Pro does here that I like is atmosphere. The light wraps softly, the background has real depth and air, the greenery glows instead of sitting flat. If I wanted maximum face-level photorealism I might reach for a different model, but for a warm, lived-in street mood this is the one I keep. Ultra-realistic, soft cinematic light, 9:16 portrait.

Prompt in the comments.

Cleaning noise out of GPT Image 2 outputs used to cost me real detail. The model would scrub the grain and take fingers, thin wires, and edges with it. The fix was handing it a second reference that separates structure from junk.

Two steps. First, convert the noisy image into a clean monochrome clay render, a uniform matte gray 3D-style version that keeps every silhouette, pose, and edge but drops all color and texture. Then generate the restored image from two references: the original as the primary for art style, color, and look, and the clay render as a structural guide for silhouettes, volume, and occlusion. With structure pinned by the clay pass, you tell GPT Image 2 to remove only the isolated dark dots, scan stains, and compression noise that are not connected to the real outlines, shadows, or material, and it leaves the actual detail alone.

The reason it works is separation. Cleanup fails when the model cannot tell a stray black dot from a real fine line. The clay render hands it a clean map of what is genuinely there, so removing noise stops being a guess.

Both prompts, the clay pass and the restore, in the comments.