Update README.md

README.md

@@ -10,9 +10,9 @@ tags:
 ---
 #fantasy-card-diffusion
 
-####A comprehensive Stable Diffusion model for generating fantasy trading card style art, trained on all currently available Magic: the Gathering card art (~35k unique pieces of art). Has a strong understanding of MTG Artists, planes, sets, colors, card types, creature types and much more.
+#### A comprehensive Stable Diffusion model for generating fantasy trading card style art, trained on all currently available Magic: the Gathering card art (~35k unique pieces of art). Has a strong understanding of MTG Artists, planes, sets, colors, card types, creature types and much more.
 
-###For a guide on using the model, scroll down below
+### For a guide on using the model, scroll down below
 
 ![Thumbnail](https://huggingface.co/volrath50/fantasy-card-diffusion/collage.jpg)
 
@@ -26,6 +26,19 @@ tags:
 - Does not, and cannot reproduce the actual card images - at best, you will get an image that looks like an alternate take by the same artist, using the same art description
 - Mix and match all of the above
 
+## Using the Model
+
+The model was trained on MtG card information, not art descriptions. This has the effect of preserving most non-MtG learning intact, allowing you to mix MtG card terms with an art description for great customization.
+
+Each card was trained with card information pulled from Scryfall in the following format:
+
+MTG card art, [Card Name], by [Artist], [year], [colors (words)], [colors (letters)], [card type], [rarity], [set name], [set code], [plane], [set type*], [watermark*], [mana cost], [security stamp*], [power/toughness*], [keywords*], [promo type*], [story spotlight*]
+
+A few examples of actual card data in this format:
+MTG card art, Ayula, Queen Among Bears, by Jesper Ejsing, 2019, Green, G, Legendary Creature - Bear, rare, Modern Horizons, mh1,  draft_innovation,  1G,  None, 2/2, Fight, 
+MTG card art, Force of Will, by Terese Nielsen, 1996, Blue, U, Instant, uncommon, Alliances, all, Dominaria, Terisiare, Ice Age, expansion,  3UU,    
+
+
 ## Training and dataset
 Training was done on a dataset consisting of cropped, 512x512 versions of the art for every MTG card, each of which was tagged using a custom python script, from data pulled from Scryfall. Training was done with the Dreambooth extension for Automatic1111's wonderful UI, to 130,000 steps.
 
@@ -52,7 +65,5 @@ This was intended to be a second test run on the full data set (the first did no
 -- Some artists use special characters in their name. I tried to take away all accents, but I missed at least one, Tom Wänerstrand, who is trained as Tom Wänerstrand, with the umlaut
 -- Greg Rutkowski: Not an issue, but the poster boy for AI art, Greg Rutkowski, is an MTG artist. He uses the Polish form of his name on MTG cards, Grzegorz Rutkowski, and that is what this model was trained with. So you'll get different results using "by Greg Rutkowski" vs "by Grzegorz Rutkowski"
 
-## Using the Model
 
-The model was trained on standardized information pulled from Scryfall. It has no knowledge, specifically, of what is in the art, only what is on the card (beyond the artist) - but because the art tends to correlate with card information, it is pretty good at producing what you want, especially after a bit of playing with it. It doesn't need some secret prompt magic to get good images. Add words like "Ravnica" or "Jace" or "Basic Land - Island" to the prompt, and it will mostly do what you expect. Using weights is a good way to draw out specific tags. However, knowing how the training data works can help to get you what you want.