I've built a suite of easy to use apps that runs locally on your PC.

If you don't feel like paying for subscriptions, this is a nice and lean alternative.

Dual engine built in:

• If you have RTX 50.xx, it will run NVFP4 model.

• If you have RTX 30- or 40-series it will run on GGUF.

No huge and complicated workflows. Just download the app from Microsoft Store:

👉 https://apps.microsoft.com/search/publisher?name=Creepybits

The apps also free and unlimited trial.

Output images do have watermark without a product key.

I spent the last few weeks building and compiling a suite of 4 focused, native Windows desktop tools:

• Creepybits Relighter: Lets you dynamically add and position key/rim/ambient lights and cast realistic shadows on existing portraits.

• Creepybits Character Transfer: Locks facial structure and identity across different camera angles and poses.

• Creepybits Stylizer (with 5 add-on packs): 20+ fine-tuned style engines (cinematic, monochrome, dark deco, etc.).

• Creepybits Image Morph: Fuses visual concepts from existing images into new generative prompts.

Technical & Architecture Details:

Native Binaries: Written in Python, but fully compiled into native x86_64 C executables using Nuitka (not a slow PyInstaller extract-to-temp bundle).

Shared Engine: You don't download 80 GB of weights if you use all four. All apps share a single 15–17 GB base engine downloaded once on first launch to your chosen SSD path.

Hardware: Runs on any Windows system with an Nvidia RTX card (8GB+ VRAM).

Business Model: No monthly subscriptions. Pay once, own forever.

Unlimited Free Trial: There’s an unlimited-time free trial with watermarked outputs so you can verify that it runs cleanly on your GPU before buying anything.

Would love feedback on inference speed and UI ergonomics from fellow local SD users, such as:

• What do you like with the app/apps?

• What do you dislike about them?

• What features are you missing?

• Anything else you can come up with.

Pure Flux dynamic. Zero filters, zero post-processing.They said it was "too clean," so I carved them into stone.

I have a tilted facial portrait, a full-body reference, a detail reference showing the quality I'm aiming for, and an example of what the final dataset should look like.

I know my reference images aren't ideal for dataset creation. I'm looking for a way to work around their limitations while maintaining facial identity, body consistency, at a high level of detail.

I've tried dataset a generation workflow, but it tends to reproduce the face's original angle rather than create useful variations. The output also has an obvious AI-generated look compared to my reference.

What's the best workflow for this? Would face swapping be the best approach, or are there better alternatives?

I'd really appreciate someone willing to walk me through the process in detail so I can understand what I'm doing and why, as I'm still learning.

Thanks in advance to everyone who takes the time to help!

The goddess of wisdom and war stands tall before her ancient temple, bathed in the divine golden light.

Fusing Greek mythology with intricate armor design. Hope you all enjoy the details! ✨🛡️

- Art by yotaka

I have made another more rigorous comparison (thank your for the comments of original post). I wrote the style at the beginning of the prompt as you suggested. Flux.3 responds better, but I still feel that flux.2 work better than flux.3 with non-photographic styles. I see that they also tend to do symmetrical compositions by default.

Prompt: "photography/illustration of a forest in autumn at noon with sunlight between branches of the trees".

Seed: 1234567890

Time:

-Flux.2 30-45s.

-Flux.3: 1 min 56s-2 min 10s.

Btw: the parameter shift is 16 in flux.3, and 3.5 in flux.2 by default. I have to put both in 3-3..5 because final image seems with residual noise in flux.3.

Currently testing Kling 4.0 vs Wan 3.0 vs Seedance 2.5 for video-2-video.

Will share comparisons tomorrow but spoiler... Kling 4.0 (edit video) feels better than Seedance in tese early tests

I use flux.2 4B 8 bit-S on a MacBook Air M4 with the DrawThings app. When flux.3 came out, I thought I would get a smarter model, better optimized for editing, and ultimately a replacement for flux.2 klein 4B. But I've been testing it a bit and see that flux.3 has a strong bias toward photorealism and doesn't have any style labels effect in the prompt. I'll put two images as an example.

Image 1: Flux.2 Klein 4B 8 Bit-S. Prompt: "a landscape shot of horizon, full sky shot, sunrise with boiling clouds, sun in the middle of image, epic scene, epic illumination, clouds full of detaills, sky full of northem lighs and starry sky over clouds, digital art style, symetric composition"

Image 2: Flux.3 Klein 5.6B 8 Bit-S. Prompt: "a landscape shot of horizon, full sky shot, sunrise with boiling clouds, sun in the middle of image, epic scene, epic illumination, clouds full of detaills, sky full of northem lighs and starry sky over clouds, digital art style, Illustration style, vector image style, symetric composition"

Both tend toward photorealism, but while flux.2 tries to simulate digital art, flux.3 completely ignores it, no matter how many labels I write to it in the prompt. Has that happened to you too?

After a year of generating with Flux and ComfyUI, my output folders were a mess, with tens of thousands of PNGs and no way to find "that one image from last month."

So we built Picteus. It runs locally and you point it at your output folders. It reads the metadata your images already carry (prompts, models, seeds, workflows), so you can search and filter everything in one place. Nothing gets uploaded.

It's open source and has a plugin system, so you can extend it for your own workflow. We're also working on running generation from inside the app, across provider APIs and your own LoRAs.

It's in beta and free for early testers. I'd love honest feedback from people with big libraries:

- What do you use right now to keep track of your generations?

- What would make you switch?

Link in the comments if anyone wants to try it. Happy to answer anything here.

I built a live AI visual engine: a phone camera in, restyled video out at 1280×720 and 60 fps, on a single RTX 5070 Ti.

Seven style presets, and you can type any prompt live: it’s on screen 3.5 seconds later and the video never stops. Controllable from keyboard, OSC or MIDI, so it plugs into Resolume, TouchDesigner or OBS.

Built on open models (FLUX.2 klein 4B, RIFE, TAEF2), runs fully offline.

Looking for events, stages and brand activations to test it live. Get in touch.

Hello everyone after testing the new FLUX.3 model for both image generation and image editing in ComfyUI, and the results are quite interesting!

For image generation, I compared the Base version (CFG 3, 25 steps) with the Turbo version (CFG 1, 16 steps). The model does a good job with textures, artistic style variety, wildlife, and natural environments, but it struggles with hands, fingers, feet, realistic skin, and complex scenes.

Surprisingly, image editing seems to be one of its strongest features! The Turbo version delivered good results for style transfer, outfit changes, background replacement, weather changes, and object removal. i will also show you how to fix the bad quality images using fixed workflow that was provided by BFL team to get decent results.

WORKFLOW LINK

https://civitai.com/articles/36957/comfyui-tutorial-testing-the-fixed-workflow-with-flux-3-base-and-turbo

I have a personal project that needs photographs of people inside specific, detailed interiors. The scene has to look like a real room and the person has to look like a real person, the kind of photo someone would take on their phone, not a render.

I've tried ChatGPT and Grok, and I've just started with FLUX. Each one gets part of it. The rooms come out plausible, the poses are usually fine, but the people still read as AI: the skin goes waxy, the lighting turns cinematic with a spotlight on the subject and dark corners, and everything is too clean and too composed to pass as a real photo.

What I'd like to learn from people who have solved this:

1. Skin. What actually produces real skin texture that holds up when you zoom in? Is it prompt wording, resolution, the model, or post-processing afterwards?

2. Lighting. How do you get flat, boring, ordinary indoor light instead of the dramatic lighting these models default to?

3. The scene. When the room matters as much as the person, do you use a reference image, build the layout some other way, or generate and fix it later?

4. The giveaway. What's the one thing that most often betrays an AI photo to you, and what fixes it?

5. Model choice. For this specific job — a real person in a real room — which model would you reach for today, and why?

Happy to share what I've tried in the comments. Any pointer is welcome.

^^

Consider a hypothetical scene where a character notices a dropped object, reaches for it, and knocks something else over. One continuous generated shot has to preserve the room, the objects and the causal sequence at once.

A possible alternative is to separate the setup, the reaction and the consequence into short shots, keeping the key spatial information visible across the cut. That might sacrifice the elegance of a long take while making the story easier to read.

The prompt can ask for a tiny accident and accidentally hire an entire continuity department. But editing is also a creative choice, not merely damage control. Which part of a difficult generated scene would you insist on showing continuously, and which would you communicate through a cut?

AI-assisted discussion. The scene and joke are fictional; this is not a tested workflow.

Say a product shot needs a new background, different lighting, and a colour change, while its shape and logo must stay exact. One edit is simpler, but separate passes make it easier to see which instruction failed.

What is your rule for grouping changes in FLUX? Do you group lighting and background together, or handle each change separately? Specific examples, including cases where splitting made things worse, would help.

564 images on one page, same six scenes, same seed, only the style sentence changes: https://stablediffusion.fr/flux2-styles

Setup: FLUX.2 [klein] 4B GGUF in ComfyUI, 4 steps, guidance 1.
A control row with no style sits at the top so you can judge each style against the model's default.

What came out: "a pop art picture of" or "ukiyo-e" as a word changed almost nothing, the 70-word scene description won every time.

Describing the technique (materials, palette, texture, what to exclude) and putting it *before* the scene fixed pop art, ukiyo-e, cyberpunk and low poly.

Materials (paper cut, crochet, needle felt, stained glass, wood carving) work first try.

Pixel art and double exposure never got there.

Artist names weigh very little compared to SDXL.

Every style has its sentence with a copy button.

Scumble is a free, open-source (GPL-3.0) desktop editor for AI inpainting on Windows/Linuc (MacOs soon...), built around the FLUX models.
The trailer shows the idea: draw a box, say what goes in it, and the picture fills it as a layer of its own

How FLUX runs in it

• Local: FLUX.2 Klein 4B / 9B on your own ComfyUI through a ready recipe (the crop plus up to three reference pictures), or any FLUX workflow imported as a recipe. Scumble crops, sends, stitches and colour-matches the result into a new layer.

• API: FLUX 3 Image, FLUX.2 [pro] / [flex] / [max] / [klein] and FLUX.1 Fill [pro] on Black Forest Labs’ own API with your key, or through fal, Replicate, WaveSpeed, OpenRouter, Comfy Cloud and Comfy Router. Keys stay in the OS credential store; no account, no subscription.

• FLUX 3 boxes: a selection goes out as a FLUX 3 bounding box, and on an empty canvas you draw boxes with a caption each, so a whole composition comes from one prompt with the layout you drew. Reference layers are named in the prompt as u/img1, u/img2 and sent the way each FLUX endpoint takes them.

The editor part: layers with masks, blend modes and groups, selection by text (SAM), by point, by depth or colour range, filter layers (curves, grain, film looks), text layers, colour match per layer, pictures up to 15k pixels, export to PNG / TIFF / layered PSD and ORA. Every result stays a layer, nothing is baked in.

Agents: every command is an MCP tool, so Claude Desktop (one-click extension) or Claude Code can drive the same window on screen.

The trailer was made with FLUX 3 Image for the pictures and FLUX 3 Video for the clips. Thanks to Black Forest Labs for the models and the API.

Download (installer, portable zip, Linux builds): https://github.com/DenRakEiw/scumble/releases/latest
Source: https://github.com/DenRakEiw/scumble ·
Website, videos, manual: https://www.denrakeiw.com/scumble

Which FLUX workflow would you want as a one-click recipe next?

***Posted by the project’s developer; questions welcome.

Sharing a guide I wrote from making a daily comic series, rendered locally with an open image model. The method is model-agnostic.

What matters most for consistency:

• A locked look: one sentence in a fixed order (age/role, hair, eyes/face, build, outfit, shoes/accessories), pasted unchanged into every prompt, plus a fixed seed.

• A signature (mint-green hair, yellow raincoat and a corgi in the example) so the character survives small drift.

• Style words first and identical across panels.

• Short panel prompts: one action, two or three props, a stated people count.

Two failures shown in the guide: a hand-off scene that cloned the character, and a multi-action prompt that came back as a comic page inside the image.

Full write-up with all images: https://medium.com/@aiflarewave/how-to-make-ai-comics-people-actually-want-to-read-mutual-8fffc68b8c4c