While we're here... I'm not a flower specialist. So I asked #ChatGPT to provide me with some ideas of well-known flower species.
Prompt: "list the names of 20 of the most beautiful flowers"
Done.
"Iris flower, pixel art, 8-bit, sRGB, icon"
"Peony flower, pixel art, 8-bit, sRGB, icon"
"Lavender flower, pixel art, 8-bit, sRGB, icon"
Note: it looks like the AI took "lavender" as the color of the flower, not precisely its type (genus/species).
"Hibiscus flower, pixel art, 8-bit, sRGB, icon"
"Chrysanthemum, pixel art, 8-bit, sRGB, icon"
"Clematis flower, pixel art, 8-bit, sRGB, icon
"Poppy flower, pixel art, 8-bit, sRGB, icon"
"Carnation flower, pixel art, 8-bit, sRGB, icon"
"Passion flower, pixel art, 8-bit, sRGB, icon"
I can access all my AI-generated images under the "Images" tab and also via the "Generator" icon itself.
I can easily see the prompts I used, compare batches, or download individual images (more filtering/sorting features are coming shortly).
While not all output images are perfect (some prompts could have been more precise, and some flowers could have looked better), it's still an excellent example of how to use Stable Diffusion finetunes (with @Scenario_gg) to explore a specific graphic direction, consistently.
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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 👇👇
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" (😊🫠) 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 4⃣ 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.