I actually generated more than 200 buildings (and even some vehicles), from which I picked a smaller dataset, keeping enough variability within a certain consistent style.
I will keep the remaining data for new trainings in the coming days :)
Once the model was trained, I tried a few simple prompts to evaluate which type of isometric buildings it could generate.
Such as... a nuclear plant (not really in the original training dataset)
Or a factory
A soviet bunker.
A radar dome.
A garage...
A refinery.
And finally... the bunker. An easy example, in the style of a "pillbox" in Command & Conquer.
This board was generated using #img2img, from one initial image (to the right, from the original training dataset).
I tried other "shapes" for a bunker, such as the ones below (taking the βfactoryβ image as the input data to img2img)
It worked too, but maybe not as well as the one above.
So I re-trained the model with two differences > reducing the dataset to 12 images (to increase the consistency, at the risk of lowering the variability). I also set the text encoder at 50% (vs. 100%)
And it worked much better. Here's a first bunker (tower-shaped)
Back to the "pillbox" shape.
The pillbox looked good, so I customized it as if it was a soviet bunker.
"isometric bunker, realistic, soviet flag, red, video game".
Boom done.
Of course, there has to be the "allied" counterpart.
"isometric bunker, realistic, USA flag, blue, allied, video game"
I changed the original image to generate bunker with a wider angle (and some structures around).
"isometric bunker, realistic, video game".
That's it. 3 words, a good fine-tune, a curated image (for img2img) and the possibilities are just infinite.
Close-up views
"Dataset engineering" >>> "prompt engineering"
Another img2img, another shape, another style... but the same "Command and Conquer-like" universe.
And another style of bunker or "command tower".
For this img2img batch, I used an ATC tower as an input (right)
The #AI transformed the control tower into a vertical bunker (and kept some of the original visual features)
I even explored other buildings, such as a #lighthouse, always in the same style (generated from the fine-tune)
This is just infinitely powerful. ESPECIALLY for artists with all the creativity, knowledge, and culture of gaming art.
Once you master different features (training finetunes, prompts, img2img, inpainting...), then the possibilities are just endless.
I predict game #studios will end up managing hundreds (if not thousands) of finetuned models, which will undergo some validation process before being used in production by various teams (artists, developers, designers, marketers...)
And if you still doubt it, this is a quick example of the SAME concept and methodology, but this time applied to the "Fallout" video game (post-apocalyptic #RPG)
A radar dome.
A radar dish/antenna.
A decaying building, etc etc.
If you like this concept, RT this thread, give us a follow (@Scenario_gg), or get on the waitlist (scenario.gg) π
We start rolling out in 10-15 days and after.
And let us know what you'd like to see next!
π€
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Here are the key steps to creating stunning turnaround, using #Scenario ()
1/ Train or pick a character model (A).
2/ Optionaly>, pick a style model (B). Use it to create training images for (A), or you can merge both (A + B = C) for example.
3/ Utilize the custom model (A or C) to generate consistent characters. Then select a reference image to produce initial character turnarounds in your desired poses.
4/ Refine these initial outputs using Sketching and image2image.
5/ Select the best result and refine details in the Canvas for maximum consistency.
6/ Finally, upscale your final image (up to 8K resolution.)
@ClaireSilver12 - (I hope you don't mind me RT this for a broader reach and to share it with more users.)
Here's an advanced use case for the IP Adapter. You can adjust or remove the steps depending on the desired output/goal. Bear with me; it's actually quite straightforward.
1 - Train a LoRA on a specific subject (e.g., character).
2 - Blend the LoRA to perfectly capture the style (e.g., comic, cartoon, oil painting, 3D...).
3 - Run inference on that "blended" model.
4 - Select an image that stands out and use it as a reference with the IP Adapter.
5 - Modify the prompt to create variations of the subject.
Let's get started ππ
1/ The first step is to train one (or more LoRA) models on a specific subject (e.g. character or object), or also a style.
The process is straightforward. I'll use the example of the "girl with pink hair" (ππ« ) that I shared before (12 training images)
Simply select "New Model - Train" on . I use 9 images of the model, showcasing various angles and zoom levels, accompanied by concise captions (details below).
This could be the best model I've ever created for generating isometric buildings, on Scenario.
Output consistently match the style I wanted, and the model responds perfectly to (short) prompts, without any reference images needed.
It's a LoRA composition. More below.
Process: it's pretty simple.
I created a LoRA composition from 4β£ distinct LoRA.
(i) - My own "Fantasy Buildings" LoRA
(ii) - Three LoRAs available on #Scenario: "Isometric Storybook", "Stylized Fantasy Iconic Imagery" and "Belgian School Comics".
The influence of each LoRA is below.
My prompt structure was dead simple... less than 10 words!
(type of building/scene), solid color background, highly detailed, centered.