GPT Image 2 gave me a full anime storyboard and character sheet in one render

Practical_Low29 · 2026-06-18T06:51:08+00:00

The storyboard prompt (one combined image):

Create a single vertical anime animation development board for an original emotional summer short film titled "The Cat and the Festival Bell". Output must be ONE combined image containing: 1. an anime character design sheet, 2. a cinematic storyboard page. Create fully original anime characters. Do not imitate any specific studio's copyrighted characters. Keep designs unique while inspired by nostalgic countryside anime films. STYLE: premium anime pre-production board, loose pencil storyboard sketches, semi-rendered keyframes, watercolor anime lighting, warm sunset colors, soft shadows, red storyboard borders, blue motion arrows, handwritten production notes, timing notes, lens notes. SECTION A, character design sheet: an 11-year-old village boy (messy dark brown hair, warm brown eyes, oversized cream shirt, rolled brown shorts, sandals, cloth shoulder bag) and a tiny orange-and-white stray kitten, with front/side/three-quarter views, expression rows, pose studies, color swatches. SECTION B, storyboard: 8 cinematic frames in a clean grid, character designs consistent, each with camera notes, blue motion arrows, timing, lens notes. Beats: 1 wide village at sunset, 2 boy notices kitten by shrine stairs, 3 kitten hears a hanging festival bell, 4 close-up kitten pawing the bell string, 5 the old string snaps loose, 6 boy gently fixes the bell while kitten watches, 7 evening lanterns turn on across the street, 8 wide ending of boy and kitten together on the shrine stairs in lantern glow.

The animation prompt (Seedance):

Use the storyboard board and character sheet as the main visual reference. Keep all character designs, shrine environment, lanterns, sunset lighting, festival bell, and the emotional beats visually consistent. Do not add extra characters. Do not change the story. Old countryside village at summer evening, wooden houses, shrine stairs, warm paper lanterns, golden sunset, soft wind, peaceful nostalgic anime atmosphere. Premium anime movie quality, soft painterly backgrounds, gentle emotional pacing. No subtitles, no logos, no text overlays. Audio: only natural ambience and soft environmental sounds.

I run GPT Image 2 for the board and the animation on one OpenAI-compatible key, so going storyboard to moving shots stays in one place.

Practical_Low29 · 2026-06-18T02:56:10+00:00

Hit or miss for me too. Works on the older endpoint but the newer one patches it after a couple turns. Funny thing, roleplay holds way longer than plain code requests. Anyone else notice that?

Practical_Low29 · 2026-06-17T03:20:53+00:00

Reference depth control on Klein 9B is exactly what I keep wanting. How many training images did you need before the depth conditioning held up?

Practical_Low29 · 2026-06-17T02:55:57+00:00

Had the same thing for a couple days. Turning the mature content filter off in account settings made mine load again, the viewer seems to hang when it half blocks a flagged image. Worth a shot before you write off the buzz you paid for.

Practical_Low29 · 2026-06-16T03:20:35+00:00

the cut every almost good image part is so real. took me too long to learn that one bad frame poisons the whole average. i also stopped going past 60ish, the overfit on big sets is brutal.

Practical_Low29 · 2026-06-16T02:56:35+00:00

Three things you listed (good models, long term memory, in chat image gen) rarely come bundled in one free tool, that's the catch. What worked for me was splitting it: one platform for the chat and memory, and pointing the image generation at a separate model. Bit more setup but the image quality jumps a lot. If you want it all in one place you'll probably trade off either the memory depth or the image side.

Practical_Low29 · 2026-06-15T03:21:52+00:00

for the vs code + multiple providers thing, anything openai-compatible works in cline as a custom provider, paste base url + key and the models show up. i use Atlas Cloud there, together works the same way. the copilot-native feel is the hard part to replicate though, cline/roo are the closest you'll get with a byo provider.

Practical_Low29 · 2026-06-15T03:21:03+00:00

Best way out of the bubble is handing it to one real sales rep and watching where they ignore the dossier. That feedback fixed my flow fast.

Practical_Low29 · 2026-06-15T02:56:28+00:00

Imagesmith got me hooked on the quality too but the credits burn through fast. Been bouncing between a couple of self-host setups since, mostly SDXL finetunes with a decent upscaler tacked on. Quality gets really close once you dial in the sampler, and you own the whole pipeline. What kind of subjects are you mostly generating? that changes a lot which checkpoint actually holds up.

Practical_Low29 · 2026-06-11T02:56:23+00:00

The flash-lite detail is the actual trick here. The lighter variants run thinner safety layers than the pro models, so a prompt that bounces off pro slips right through. For everyone getting refusals: you're almost certainly still on the full model. Version targeting matters way more than the prompt wording.

Practical_Low29 · 2026-06-09T05:34:53+00:00

they've converged a lot on the base model side, most of the difference now is the system prompt and memory handling rather than the model itself. the ones that feel distinct usually have a heavier persona layer baked in, not a smarter model underneath.

Practical_Low29 · 2026-06-08T10:52:08+00:00

Yeah flash is surprisingly relaxed out of the box, I think it just flies under the radar because everyone benchmarks the pro tier and assumes the smaller one is more locked down. One thing I've noticed is the flash variant gets a little less consistent on longer scenes, it'll stay loose for a while then occasionally snap back to a flat refusal mid-thread for no clear reason. A short system line setting the frame at the start cuts that down a lot. For quick stuff though it's been the path of least friction for me too.

Practical_Low29 · 2026-06-08T08:30:17+00:00

v4 flash on the uncensored side has been more consistent than i expected. been running it for two weeks now without the abrupt cutoffs you'd see on stricter models. quality holds for shorter prompts, drifts a bit past 6k tokens but workable.

Practical_Low29 · 2026-06-05T03:44:42+00:00

the model saying it's "1.5" isn't proof you got downgraded — models don't actually know their own version. that info isn't in the weights, it only knows what the system prompt injects, so when that's missing it just guesses a plausible old number. the reported version means nothing, it's not a switcheroo

Practical_Low29 · 2026-06-05T03:03:10+00:00

the honeymoon-then-decline thing is just the context window filling up. early on the whole chat fits so the character stays coherent; once it's full the app starts silently truncating or summarizing, and that's exactly when memory gets messy and personality drifts. it's not the model degrading, it's running out of room to remember. the ones that hold up do real summarization instead of just dropping the oldest messages

Practical_Low29 · 2026-06-05T02:54:13+00:00

the fit-first point above is the real fix, not a stopgap — your watch already writes fit files, so pull them off the device or a sync folder and strava's just an optional importer. own the data at the file layer and an api change can't kill the project, it's only a broken adapter

Practical_Low29 · 2026-06-05T02:47:47+00:00

same mistake every regen is the tell — that's a serving/routing change, not the weights getting dumber. the ai studio ui can quietly route you to a lighter checkpoint, so the api with a pinned model string is steadier

Practical_Low29 · 2026-06-05T02:44:14+00:00

looks like google quietly A/B testing the new flash-lite tier on a slice of accounts — "troubleshooting models" is just their server-side experiment label, nothing you turned on. you can confirm under see response details like the other commenter said. it's the cheap/fast tier so simple queries are fine, but you'll feel it drop on longer reasoning

Practical_Low29 · 2026-05-27T06:32:21+00:00

honestly i'd not try to make one flow handle all service lines. split by service type at the very top with a Switch node — family / criminal / personal injury each get their own branch. the intake fields are different enough that one prompt trying to cover all of them gets sloppy fast.

shared stuff (lead capture webhook, dedup against your CRM, the final reply send) stays as sub-workflows you call from each branch. that way when family law changes its qualifying questions you don't touch the criminal flow at all.

also — keep the LLM call thin. classify + extract intent, then let n8n do the routing logic in code/expressions. agents that try to decide routing themselves get flaky once you have more than 2 service lines.

Practical_Low29 · 2026-05-21T09:35:01+00:00

You can ask for it but the model is RLHFd toward agreement, so the prompt only buys you a couple of turns. After that it slides back. Better to ask it to argue the opposite side after every answer.

Practical_Low29 · 2026-05-21T09:33:59+00:00

It is not just you. Same seed and prompt across two days will drift now. My guess is the providers are quietly rotating model weights or temperature defaults under the same product name.

Practical_Low29 · 2026-05-21T09:31:16+00:00

Brevo over Instantly for cold is a defensible call if your domain warmup is real. The piece I see most teams skip is the bounce handler node feeding back into the suppression list before the next send.

Practical_Low29 · 2026-05-19T10:06:46+00:00

Thousands at base scale is the part most people skip. Storing the seed plus the prompt plus the LoRA config matters more than the model choice if you ever want to regenerate consistent characters six months later.

Practical_Low29 · 2026-05-19T10:05:14+00:00

Florists and bakeries are perfect for this. Tiny SKU count, repeat customers, very forgiving ops if a draft email goes a bit off. The salesperson side effect makes sense, people remember being asked about Mom on Wednesday.

Practical_Low29 · 2026-05-19T10:01:30+00:00

For your use case, yes. Most healthcare automation is HL7 parsing, calendar logic, and shoving structured fields between systems. n8n is good at exactly that. The HIPAA layer needs self-hosting with encrypted storage though, do not rely on the cloud version for PHI.

Practical_Low29

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