💡 During the style training process unevenly cropped images will get resized to the default size required by the training. Therefore it's ideal to upload images suggested training size by default. If you have bigger images that's also OK for AI learning, it's just would be important to at least have ALL images uploaded in the same size so that they can be resized the same amount at least to make sure proportions of the assets. Alternatively you can use the cropping tool to ensure consistency. 

💡 Let's say you're training a set of furnitures which have the same proportions if assets upload are not centered the same way for ex: some are zoomed in and some are zoomed out AI might have hard time generating outputs in the fixed proportions. Here is a good example of how to format the assets paying attention to assets positions:

💡 As mentioned in the first point, AI resizes the images to aspect ratio 1:1. 512x512 or 1024x1024 depending on the default size required by the style training. If any of these aspect ratios is not identical to the others, Layer completes the images to square before resizing (and this resizing is not content aware), this might mean loss of certain important pixels or addition of new pixels which might cause distortion in certain areas. and this resizing is not content-aware) therefore it’s important to make sure assets going into training have the same aspect ratio. This artifact is particularly obvious when the image contains some objects, we humans are very familiar with, e.g., faces, human bodies. For ex: if you are training backgrounds as long as you upload images as all portraits, they will be completed to squares the same amount therefore training quality may not be affected significant enough to bother you.

💡 default learning happens with 512x512 so for top results upload images in this resolution, second best thing to do is for all images to at least have the same resolution.

💡 When low quality images such as blurry or low resolution are uploaded as training images, AI will learn those artifacts as a part of the target style. Accordingly, this will then generate suboptimal results.

💡 Upscaling helps add new pixels to the AI generated image which might mean distortions or blurriness in the images get fixed.

💡 AI learns more effectively from variation and diversity. For example, if you are training a set of characters, you need to have enough variety of characters in the training otherwise AI will think the characters features are constant. Here is an example showing how characters with different ages, gender, facial expressions are used for a rich training dataset.

💡 If one attribute/accessory of a character is always present in the training set, it’s easily recognized as an inseparable part of the target style. For example, if only smiling faces of a character are provided, smiling could be learned as your character’s default facial expression; sad faces later generated for your character can be different from what you wanted. Similarly, if the training set contains multiple similar/identical images, that dominant mode can be learned as a feature of your style while the remaining training samples’ impact gets suppressed.

💡 We recommend uploading up to 25 images if you have them. More images allow a richer learning experience for AI and uploading images respecting upper limits is more ideal.

💡 The rationale is that prompt words used to describe the subject will all be translated into some 'codes' in a magical high dimensional space for NN to use to guide the image generation process. In this translation process, words close together are more likely associated. So, if the subject is mentioned in the beginning of the prompt, which usually is the case, important attributes should also be mentioned earlier than later.

💡 AI painting inherently carries randomness. We encourage you to take advantage of your unlimited plans on Layer.ai to generate many different variations and select the best one!

[O] A kitchen with a dining table and a few chairs.

[O] A restaurant sitting at the corner of a busy street.

[X] A restaurant sitting at the corner of a busy street on a sunny day. There is a dining table and a few chairs in its kitchen, where the table has 4 legs, and each leg has a flower pattern near the bottom.

💡 Why AI can draw many items at the same time in one single image; It can also draw many details of one specific item; it’s just challenging to do both at once! To create a complex image with great details, one can try [top down]: generate the first image with rough layout, enlarge it, and edit each component one by one; or [bottom up]: generate asset one by one in separate images, each of them with great detail, and compose them in a big canvas. Note, some information is simply hard to describe in language. Consider using a reference image to guide your forge. Please see below.

💡 While text prompt is handy to describe an image in your mind, finer guidance may need to be specified by reference, because other modalities can more easily carry information in some cases. One prominent example is handling human pose. Try letting the reference skeleton guide the character pose, even if you don’t know how to describe some tricky posture in language. Another example is AI is not good at counting and physics. If you want exactly 8 flowers, not 7, nor 9, on the ground, a reference sketch will give you a higher chance to get it.