u/In_finite_line

Ovaj post prikazuje eksperiment o tome kako se može pristupiti izazovu za tačnu imitaciju specifičnog neuobičajenog oblika putem AI alata.
Isprobana su tri načina za integrisanje objekata specifičnih geometrijskih oblika u enterijer. Metode su testirane na Ton-Merano stolici koja ima nezgodnu geometriju. Na različite načine su kombinovani: SDXL LoRA model radi rekreiranja geometrijskih karakteristika (Lora model je kreiran za ovaj eksperiment) i noviji (Flux 2 Klein) modeli za realističniji prikaz.

• Prva slika – Cela slika enterijera je kreirana pomoću SDXL modela i LoRA modela stolice. Zatim su generisane različite varijacije izabrane slike korišćenjem ControlNeta (depth map, i dalje sa SDXL+LoRA). Na kraju, Flux 2 je upotrebljen za poboljšanje realizma.
• Druga slika – Samo krupni kadar stolice je kreiran pomoću SDXL modela i LoRA modela stolice. Vizuelni prikaz je unapređen korišćenjem Flux 2 modela, a zatim je korišćen outpainting (opet Flux) kako bi se generisao ostatak enterijera.
• Treća slika – Početna slika je kreirana korišćenjem Flux 2 modela sa tri referentne slike stolice. Slika je zatim obrađena pomoću SDXL+LoRA za stolicu i ControlNet Depth-a kako bi se geometrija stolice donekle korigovala. Slika je vraćena u Flux 2 (img2img) i dorađena da izgleda realističnije.

U svim primerima i dalje postoje određena odstupanja u proporcijama, pa mislim da se ovo može dodatno unaprediti. Fokus je bio na testiranju različitih pristupa. Inpainting i Photoshop su takođe korišćeni kod sve tri metode tamo gde je trebalo napraviti manje korekcije.

Najbrže je dobijen dobar rezultat korišćenjem treće verzije, ali to je možda zato što je to prvi pokušaj pravljenja LoRA modela, pa je bilo potrebno mnogo truda da se dobije dobra slika u SDXL-u.

Koja metoda vam deluje najbolje? Imate li još neku drugu ideju ili savet kako generisati sliku koja verno predstavlja oblik elementa specifičnog dizajna?

This post showcases three ways to integrate objects of a specific geometric shape into an interior. The methods were tested on a Ton-Merano-styled chair. I combined an SDXL LoRA model that I created for the chair to reinterpret the geometric characteristics of the chair with newer models for a more realistic output.

First image – The entire interior image was created using the SDXL model + the chair LoRA. Then, various variations of the selected image were generated using a depth map (still with SDXL+LoRA). Finally, Flux 2 was used to enhance realism.

Second image – Only a close-up shot of the chair was created using the SDXL model + the chair LoRA. The visual was enhanced using the Flux 2 model, and then outpainting was done with the same model to generate the rest of the interior.

Third image – The initial image was created using Flux 2 with three reference images of the chair. The image was then processed with SDXL+LoRA for the chair and ControlNet Depth to improve chair geometry a bit. The image was passed back into Flux 2 (img2img) and refined to look more realistic.

There are still some inconsistency in proportions in all examples, so I think it could be further improved. I was more focused to test different approaches.

Inpainting and Photoshop were also used across all three methods where it was easier to make minor corrections. I achieved a good result fastest with the third version, but that might be because I am not an expert at creating a good enough LoRA model, so it took a lot of effort to get a good image in SDXL.

Which method seems best to you? Do you have any other ideas or advice on how to generate an image that faithfully represents the shape of a specific specific design element?

These are the results of the workshop for architects to apply ComfyUI for architectural visualization.

In architectural visualization, it is essential to preserve the proportions and geometric characteristics of objects. It is also crucial to reduce randomness, meaning guiding the AI to generate images exactly as the architect envisioned regarding materials and ambiance. Although there is a widespread belief among architects that AI tools produce random results and that everything comes down to prompt writing, the participants managed to get visuals that exactly match the pre-defined project program.

Each group was assigned a task that required starting from a simple input image (e.g., a wireframe, SketchUp screenshot, drawing, clay render, or photograph). Everyone specified which part of the visual to keep and which to change, as well as the desired ambiance, style, and appearance of people in the visual.

The workflow used consists of three main iterative stages: (1) initial image generation using ControlNet-based conditioning, (2) addition of elements and local refinement using inpainting, and (3) global enhancement using image-to-image techniques and/or upscaling. A combination of SDXL, Flux, and ZImage models was used.

How do you like the results? How do you imagine the future development of this field?

U ovom postu su prikazani rezultati trodnevne radionice o metodama upotrebe AI alata u arhitektonskoj vizualizaciji primenom ConfyUI u lokalu, što omogućava visok nivo kontrole i besplatno je. U arhitektonskoj vizualizaciji je veoma bitno smanjiti nasumičnost i navesti AI da generiše slike kakve je arhitetka zamislio po pitanju materijala i ambijenta, a posebno je značajno da proporcije i geometrijske karakteristike objekata ostanu očuvane.

Iako je medju arhitektama rasprostranjeno mišljenje da AI alati daju nasumične rezultate i da se sve svodi na pisanje promptova, polaznici su uspeli da dobiju vizuale koji tačno odgovaraju unapred osmišljenom projektnom programu.

Ulazni materijal bila je jednostavna slika (fotografija, crtež, wireframe, SketchUp screenshot ili clay render), a zadatak je bio da se određeni prostorni elementi sačuvaju, da se dodaju novi i da se kreira vizual koji oponaša određeni arhitektonski stil i ambijent.

Kao i mnogi iz oblasti arhitekture, polaznici na početku nisu bili svesni koliko se proces generisanja slike može kontrolisati, odnosno kako AI može postati precizan alat u procesu dizajna i vizuelizacije, a ne nasumični generator. Nakon radionice svi polaznici promenili mišljenje o mogućnostima AI alata u arhitektonskoj vizualizaciji i izrazili želju da nastave da istražuju ovu oblast.

Kako vam se čine rezultati? Kako zamišljate razvoj ove oblasti?