Update README.md

README.md

@@ -2,11 +2,15 @@
 language:
 - en
 license: creativeml-openrail-m
-thumbnail: "https://huggingface.co/volrath50/fantasy-card-diffusion/resolve/main/collage_sd_jpg.jpg"
+thumbnail: >-
+  https://huggingface.co/volrath50/fantasy-card-diffusion/resolve/main/collage_sd_jpg.jpg
 tags:
 - stable-diffusion
 - text-to-image
 - image-to-image
+- art
+- magic-the-gathering
+- mtg
 ---
 #fantasy-card-diffusion
 
@@ -30,6 +34,7 @@ tags:
 - Mix and match all of the above
 
 ## Updates
+- 14 May 2024: There should be a safetensors version of this model, finally. Get it here: [https://huggingface.co/volrath50/fantasy-card-diffusion/blob/main/fantasycarddiffusion_140000.safetensors]. I'd been meaning to convert the ancient (in AI terms) .ckpt file to safetensors for over a year, and finally a robot did it for me. With regards to an updated version of the model, I've trained two more versions, one on 2.1, in DEC 2022 and again on 1.5 in APR 2023, but never released them. This is partially due to neither of them turning out strictly better than my NOV 2022 model (they did some things better, but a lot of things worse; I think I mostly got lucky that the NOV 2022 model turned out as good as it is), but probably moreso due to work, children, and having ADHD. I had wanted to try training onto SDXL, but never got around to even starting that.
 - 13 Dec 2022: I am currently training v2 of this model on top of Stable Diffusion 2.1 (512), using the Stable Tuner trainer. This has solved the cropping issue v1 had, and has allowed me to train on the full resolution, uncropped art from Scryfall. I expect to release v2 within the next few days, once I determine a good stopping point, and create new example images. v2 is currently at 25 Epochs (about 87,500 steps), and still showing good improvement each epoch.
 
 ## Using the Model
@@ -218,4 +223,4 @@ Cropping was done with ImageMagick (see below, under issues).
 - Cropping: MTG art is rectangular. I initially tried to use a trainer that could handle different aspect ratios, but after a couple failed tries, I just did a quick mass cropping job with ImageMagick, resizing and cropping everything to 512x512, so I could get training running. I forget what exactly I did, but it appears it focused on the left side of the card, universally cutting off the right side. You'll see this in lots of images, that tend to have everything on the right as a result
 - Plane information was only added around step 70,000, so it may be less trained than other information - basically, I wanted a way to group sets together by plane, as I was finding how well it knew the look of a set depended on whether WotC had incorporated the name of the plane into the set itself - ie: using "Theros" would only get you "Theros" and "Theros: Beyond Death" and not "Born of the Gods" or "Journey into Nyx"
 - 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"
+- 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"