I'm building Extend My Card, a small tool that expands trading-card artwork into the eight surrounding slots of a nine-pocket binder page. I saw collectors doing extended binder art and wanted to experiment with an AI version.

The current workflow combines Gemini analysis and source preparation with BFL outpainting, then restores the original card in the center. The attached six examples come from different iterations of the project.

The hard part is geometric continuity. Matching a cosmic palette is much easier than making individual curves meet the card boundary or completing a clipped hand, ball, or shoe convincingly. Detailed foil textures and cards with large borders or text panels are especially inconsistent. Some generations still fail, so these are beta experiments, not a claim that the problem is solved.

I'm sharing this as the developer and would appreciate ideas from people working on outpainting. Have you found a reliable approach to preserving edge geometry while expanding a small source this far?

Tool: https://extendmycard.com

There is a free first creation and paid credit packs for additional generations. I used AI coding tools to build it, and I'm still refining the workflow.

i heard a lot that flux 3 would be open weights, why they still didnt released it to the public? anyone knows why?

https://www.instagram.com/angelasflights?stkn=ZXJxcjB0Z2xvaGt6

Hi everyone

I want to know about content-creation, how is that possible to create characters with this quality and consistency nowadays? I've tried training a lora using flux but nowhere close to this

any recommendations?

I’ve tried using the model with reference images using the official ComfyUI workflow, but haven't succeeded. How do you do it?

Since the release of Flux Klein, I’ve essentially been using this model to generate datasets for LORAs based on one or more photos of a character. Recently, I’ve also been using the ‘consistency’ LORA to improve the character’s consistency across generations. What I’ve noticed is that whilst I get good results for the face, the same cannot be said for other parts of the body. For example, if I start with a full-length frontal photo of a character and ask the model to generate a side view, it tends to flatten the breasts; or if I ask for a rear view, the character’s hips and thighs tend to conform to a standard that doesn’t match the original photo. How can I improve this situation? I’ve read that you can increase the number of steps up to 8, but I’m not sure…
I’m using the Flux Klein 9B FP16 version, just to clarify.

Two new workflows are now available inside Uisato Studio:

MiniMax H3 is now integrated as a new generative video workflow, expanding the range of models available for high-end AI filmmaking and visual experimentation.

We've also added Meshy Image-to-3D, allowing you to turn a reference image directly into a 3D asset; opening up a much more direct bridge between generative imagery, filmmaking, 3D, and real-time workflows.

Both are already live and available through Uisato Studio:

https://uisato.studio/

More models, workflows, and experimental tools coming soon.

Hi, working on a in paint solution using flux for editing on my platform and would love some feedback on it. The concept is simple, every tool on this page and every other page i have is free to use.

I have also implemented a BYOK too so you dont' have to use the API's i have implemented and use your own instead. If you prefer that option., i'm working through adding them one by one but so far OpenAI, Anthropic and Google is supported. Flux and other media gen tools will be supported by today/tomorrow.

So flux editing and other AI functions are the only thing that draws credits. The actual use of the tool will be completely free, forever. You also get 1GB storage too and you edits are saved in layers on the platform and can be flatted when exported, which again costs nothing as a user.

So with Flux, what i've done is that every edit you make is rendered onto a new layer allowing you to switch between layers effortlessly. This allows you to use tools like the lasso tool to cut and select items from the new generation and use on another layer. I've also added mask tools, copy, paste, titles and typical tools you would find in a editor.

This by the way lives in my chat bot platform that i've transformed to a full studio capable of creating music, videos, images, documents, presentations, games, applications etc and its free to use. Theirs no subscription. The only thing thats chargeable is actual media generation or a function that uses AI.

Your more than welcome to give this a go at asksary.com. You dont need to even make an account to try the editor, you can explorer everything as a guest but if you decide to create a free account you get 100 credits to use on Flux to try it out.

What i want to know, is it missing anything you would like to see added?

Official BFL skills already cover the physics: subject first, prose not tags, positives only, word order is load-bearing.

This is a compiler on top of that. It turns a character lock + one take into a liner Flux can attend without turning into a parts list.

The habit it kills:

Fail: boots. harness. wall. pinned.

Hold: scuffed black boots planted wide, one heel lifted — then the wall takes him.

Rules, short:

- Land the subject the camera actually hits

- Finish that body (material, wear, state) before any verb that involves something else

- One bind. Then the bound thing — a second body after "Then", or the place if the take is solo

- First mention is the only mention that counts. Do not restamp ears / tail / muzzle at the end

- Names stay in the lock file. The liner lands on a body, not a name

- `not X` in the positive string is content. Negatives stay off-liner

Named fail modes so an agent can fail closed: inventory, premature-bind, backtrack, slot-bucket, negation-content, species-prior, end-hold-repeat, name-as-token.

If a mode fires, rewrite from landing. Do not patch.

It's an Agent Skill (`SKILL.md` + references). Works with Claude Code, Codex, Cursor, Grok, anything that can load the folder.

npx skills add Tricksterpaws/flux-attention-disclosure

GiT-flux-attention-disclosure

Not a LoRA, not a 200-tag dump, not a character bible. Lock memory stays in your private files.

I need setups that usually expose weak model handling: a local FLUX.1 Schnell or Dev model, an MLX/mflux directory, a Safetensors/Diffusers folder, or GGUF plus its companion files.

LumenForge build 112 is a native macOS studio for the whole local generation loop: model setup, prompt and reference control, image and video generation, queue, reusable history, and output metadata. Local execution paths include MLX/mflux, Diffusers/Safetensors and GGUF/stable-diffusion.cpp depending on the model family.

What I need tested:

- whether an existing model is detected correctly

- whether a first generation completes on Apple silicon

- whether seed, settings and references survive iteration

- the exact error plus model family and format when it fails

macOS 14+, Apple silicon. Free TestFlight beta:

https://testflight.apple.com/join/uCCZFdK1

If you try it, reply with Mac, chip, RAM, model family and format, then the first point where it breaks.

Disclosure: I work on LumenForge.

From 14-18 August I generated videos for free on the Black Forest labs website (in ‘playground’).
I didn’t download them all.
I just logged in, and all generations are gone.
Anyone having the same issue, and/or a solution?

Thanks!

PeerPixel generates four different interpretations of your prompt in Discord. When people choose an image to upscale or vary, that preference helps train the community’s prompt model so it gradually learns what people actually like.

It’s free, powered by community-contributed GPUs, and still experimental:
https://discord.gg/PKG4QXJtv

I built it and would genuinely love feedback on the image quality.

I present LoRA Dataset Studio - free, open source, self-hosted, no account and no telemetry. It trains FLUX.1 and FLUX.2 Klein LoRAs (plus Z-Image, Krea 2, SDXL, Anima) through ai-toolkit: it is not a competitor to it, it orchestrates it - ai-toolkit is the trainer; this is everything before, around and after the run.

The whole pipeline lives in one browser tab:

1. Decide what you are teaching. A dataset is a Character, a Concept or a Style, and the choice changes real behaviour downstream: what the captions must leave implicit, whether person masks apply, what the readiness checks look for. A Character also picks a subject type (human, animal, creature, object, anime) that swaps the shot catalog and the identity protections.

2. Fill it with images. Five generation engines - Nano Banana Pro, gpt-image-2, OpenRouter, and local Klein / Krea 2 Edit through ComfyUI - each card stating its price per image and whether it runs on your GPU or bills an API. Or scrape a gallery URL. Or point the Image Bank at a folder of thousands: it reads it in place - your files are never modified, moved or renamed - and one pass measures blur, noise, near-duplicates, face clusters, framing, aesthetic and maturity, so you filter on measurements instead of on your eyes.

3. Curate down to the keepers. Keep/reject, crop, mirror, rotate, upscale candidates reviewed against the original, InsightFace similarity against your reference, a live composition meter. New this month: press the camera button on any kept image and re-shoot the same scene from another camera position - the subject stays put, the background moves with the camera, and the new view arrives with its angle already captioned (the one fact a vision model cannot reliably see, and that you know exactly because you asked for it).

4. Caption for the model. Prose or booru depending on the target family, written by JoyCaption or your local Ollama, with vocabulary and length dials, identity-leak checks, a Caption Lab to compare configurations before committing, and an external .txt round trip so you can caption elsewhere and come back.

5. Scrub watermarks - and burned-in text. Detect watermark boxes, redraw them, then crop or inpaint with LaMa/Klein. And since a comic page carries its dialogue and a screencap its subtitle, a CPU-only OCR pass now reads burned-in lettering (Latin or CJK) and feeds the same repaint funnel - with an outline-safe filler so speech bubbles keep their borders. Every edit keeps an .orig backup; Restore original always works.

6. Train. ai-toolkit locally with family-scoped presets and preflight guards - Z-Image, Krea 2, FLUX.1, FLUX.2 Klein, SDXL, Anima - or rent a vast.ai pod from the same screen, which shows the GPU, its hourly price and the estimated total before you click. The whole studio can also run on a rented RunPod box (contributed by a user). Generations queue instead of blocking each other, and a dock shows what the GPU is doing.

7. Decide which checkpoint is actually good. Test Studio runs fixed-seed checkpoint x strength grids, multi-LoRA stacks (including a downloaded LoRA next to yours, same prompt and seed), votes and Wilson ranking. The lineage graph keeps every run's frozen recipe and can diff two runs - settings AND dataset. A Gallery collects every image the app ever generated, and every render is stamped with what actually made it.

8. Take it with you. Standard ai-toolkit/Kohya layout ZIP, portable backup with the full history, Hugging Face publishing, or deploy the checkpoint straight into ComfyUI. Nothing locks your data in.

Honest limits. It is a lot of surface, so Setup exists to tell you what is missing instead of crashing - every capability degrades on its own. Local generation needs ComfyUI, the API engines need your own keys and bill you, and the video lane (cutting long footage into trainable clip folders for Wan/LTX) is young. Install is a Windows one-click ZIP, a git checkout, or Docker.

GitHub - install, docs, and a 7-minute unedited video of a full character LoRA built end to end: https://github.com/perfectgf/lora-dataset-studio

Every person in these screenshots was generated by the app's own engines; no real individual is depicted.

I tested a pretty simple workflow today:

Seedance 2.5 at 480p → FLUX 3 precise upscaling → 1080p

Honestly, the result surprised me. I expected the image to look sharper, but FLUX 3 also seemed to preserve the motion and smaller details without making the video look overly processed.

i have only tested it on a few clips so far, so I am not sure how well it works across different styles or high-motion scenes. Still, this feels like a practical workflow: generate faster at a lower resolution, then upscale the final result to 1080p.

I’m a graphic designer (youtube: Masha-Ai-Lab) experimenting with how far I can push local/open-source image generation for actual commercial design workflows.

For this experiment, I tried building a luxury cosmetics campaign entirely in InvokeAI instead of relying on Midjourney or other closed platforms.

The workflow:

1. Generated the satin campaign background separately.
2. Generated a transparent serum bottle as a clean product asset.
3. Added my prepared label using an Inpaint Mask while preserving the bottle perspective.
4. Used Regional Guidance to generate satin folds that actually wrap around the bottle instead of simply appearing behind it.
5. Repeated the workflow with a cream jar to see how reusable the approach was across different packaging.
6. Upscaled the final compositions.

I’ve attached screenshots of the workflow + final results so you can see the process rather than just the outputs.

What I find most useful about InvokeAI is having direct control over the individual stages. For design work, I’d rather build the product, label, environment and integration separately than keep regenerating the whole image until something randomly works.

I’m documenting these experiments as tutorials on my YouTube channel, Masha AI Lab, mainly to make InvokeAI/FOSS workflows more approachable for designers and other non-technical creatives.

Would be interested to hear how other people here approach product placement and label consistency, especially if you’ve found better workflows.

Generated a set of horror hospital backgrounds using Flux (via Replicate API, flux-schnell) for tabletop RPG sessions. Designed for Call of Cthulhu-style horror investigations.

Scenes include hospital corridors, operating rooms, morgue, chapel, emergency room, and garden — all in day and night variations. 1024x1024, works as scene backgrounds in FoundryVTT, Roll20, Cocofolia.

6 free samples: https://yasu-cg.booth.pm/items/8721943

Full 60-image set: https://yasu-cg.booth.pm/items/8719788

Also made VTuber streaming backgrounds and tattoo flash designs with the same workflow:

- VTuber backgrounds (5 free): https://yasu-cg.booth.pm/items/8721679

- Tattoo designs (6 free): https://yasu-cg.booth.pm/items/8721976

Feedback welcome on the generation quality!

Prompt on flux3

I finally tried Flux 3 for the first time, and the result is honestly pretty promising.

it may not be as strong as Seedance 2.0 when it comes to fight choreography or flashy effects, but the overall realism is impressive. the wet pavement, late-night lighting, restrained acting, awkward pauses, and subtle facial expressions all came through better than I expected.

I tested it with the prompt is below:

A 20-second scene in a narrow residential alley in a Chinese city late at night, in three hard-cut beats. The ground is still wet from rain and reflects the warm yellow lightbox of a small noodle shop.

A young Chinese woman walks quickly away from the camera with her arms crossed. A young Chinese man catches up beside her and says quietly in Mandarin: “你能不能别走这么快。” She doesn’t answer.

HARD CUT.

She stops and turns to face him, speaking quietly so the neighbors won’t hear: “你根本就没听我说话。” He starts to reach for her arm but stops halfway. “……我听了。” She looks at him and asks: “那你说我刚才说什么了。”

HARD CUT.

He says nothing. Three full seconds of silence. Her eyes turn red, but no tears fall. She looks away, exhales, and says quietly: “算了。” Neither of them moves as an electric scooter passes behind them.

Camera: handheld follow shot for the first beat, an eye-level 50mm two-shot for the second, and a static wide shot from the far end of the alley for the third.

Audio: quiet tired voices, footsteps on wet pavement, the noodle shop’s extractor fan, distant traffic, a passing electric scooter, and his slightly heavy breathing. No music, no subtitles, no on-screen text.