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Fantasy Card Diffusion

7758°
Fantasy Card Diffusion
Creator notes
Originally posted to 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) to 140,000 steps, using Stable Diffusion v1.5 as a base model. 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 belowFeatures - Incorporate the styles of artists you know and love from Magic: the Gathering - Produce art that looks like it is from a given MtG plane, set or year - Create fantasy creatures in the style as they exist in Magic the Gathering - Draw fantasy creature types that are unique to MtG (like Eldrazi) - Use well known MTG characters (such as the planeswalkers) - Draw real-world or non-MtG characters, in the style of MTG art - 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, To briefly explain some of the entries: Every card art is tagged at the start with "MTG card art". Usually you want to use this. It does generalize the image a bit, however. Experiment with using it and not using it. Sometimes, if you are having trouble making something look distinctly "Tarkir" or something, taking off this tag can help de-generalize the art. In a similar fashion, the more general the tag is (ie, rarity, the word "legendary", etc.), the more of a generalizing effect it has on the image. Play around and find out. Artist: Every artist name is preceded in the training data with the word "by", as in "by Mark Tedin". The model has a really good understanding of the styles of MtG artists - that's actually how this project started. My exposure to art, frankly, is mostly through Magic: the Gathering, and back in August, was finding that the base Stable Diffusion model just did not have a great understanding of a lot of the artists I was trying to draw from, with some exceptions (Greg Rutkowski, of course, and Rebbecca Guay are well represented in the base Stable Diffusion model.) Even if not trying to create MtG-style art, this model should be great for using the art styles of MtG artists. It also works really well to mix artist styles. See the "Innistrad Moon Goddess" example below, where I used six different artist styles with varying weights to create the look I was going for. Set type: this is usually "expansion". Other possibilities are "core", "funny", and some other. You can check the Scryfall API documents for more information. Security stamp: I translated some of these for ease of use. The main two of note are "acorn" and "universes beyond". There are a few other rare stamps, like one for the My Little Pony cards. Story Spotlight: cards that are a story spotlight are tagged as such. This wasn't really worth including, and I'll probably take it out of a future version of the model. Pretty much every tag from normal Stable Diffusion still works as expected (ie, extremely detailed, intricate details). I've found adding "beautiful composition" tends to make things look nice, but I'm sure everyone has their own set of personal tags they like to use - they should work with this model. I like to write my prompts like an art description - you can see in the examples I made up below. Example Images and Prompts This model is trained on so many things, I'm just scratching the surface of figuring out what it can do. I thought it would be helpful to show a gallery of the sort of things I've been able to create with it. Full generation parameters, seeds, etc, should be in the images. All these examples were made with Automatic1111's UI, fantasycarddiffusion-140000.ckpt, and the "DPM++2S a Karras" sampler. CFG varies - I find around 11 works as a good baseline. Most of these were done with around 40-50 steps - probably overkill. Ascended Eldrazi (an Eldrazi that has somehow made his way to Theros, chilled out, and attained godhood) MTG card art, ascended eldrazi, (by eric deschamps:1.1), (legendary enchantment creature - god:1.2) (eldrazi:1.2), colorless, theros, ths, jou, bng, thb, mythic, indestructible, annihilator, trample, a wise eldrazi titan emerging from the horizon, ascended to godhood, now looking serene, calm, divine, powerful, beautiful composition, emrakul, kozilek, ulamog, (sense of scale:1.2), sense of wonder, overwhelming, extremely detailed, intricate details Negative prompt: weak, angry, scary, underwhelming, powerless Speedy Sliver (a Mardu sliver that gives dash, on Tarkir) MTG card art, speedy sliver, by John avon, Creature - (sliver:1.3), white, black, red, wbr, (Mardu:1.1), Khans of tarkir, ktk, dash, a fast sliver is speeding through the Mardu (steppe:1.1) landscape, beautiful composition Negative prompt: human, humanoid, m14 Taylor Swift, Wandering Bard (self explanatory, Taylor Swift, as a bard, on Eldraine. Future Secret Lair?) mtg card art, (Taylor Swift:1.2), wandering bard, legendary creature - human (bard:1.2), white, red, green, wrg, throne of eldraine, eld, by chris rahn, by volkan baga, by zoltan boros, armored bard tayl
Trigger words
MTG card art
972 downloads · 124 likes on Civitai
SHA256
55E74E95128F50033D0C1306FE8768CF319FC8D3436398B86B7E852AB58FCAC6
Ranking badge
Updated Oct 22, 2026
Fantasy Card Diffusion on ModelTrends.ai: #67 Hottest Stable Diffusion 1.5 base model
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Fantasy Card Diffusion Popularity Trend
Heat score, last 12 months
Heat score
7,758°
This week
n/a
Wasn't released/tracked
This month
n/a
Wasn't released/tracked
Past year
n/a
Wasn't released/tracked
Fantasy Card Diffusion: Heat score, last 12 months7,000°7,383°7,766°8,148°8,531°Oct 2025Dec 2025Mar 2026May 2026Aug 2026Oct 20267,758°ModelTrends.ai
7,000°
Floor
The scale runs from 0° to 9,999°. Everything below 7,000° is kept for models no source reports on yet, which carry a stand-in number there so lists stay complete. 7,000° is where measured data begins: a model with real data but almost no downloads sits at the floor, and the measurements fill the degrees above it. The floor says nothing about the model; only the degrees above it do, and they are what separates one model from another.
+899°
Civitai downloads
972 downloads of this exact version on Civitai. Downloads are the heaviest ingredient and are counted on a log scale: ten times the downloads adds the same 300°, so a model with a million downloads is not a thousand times hotter than one with a thousand. The six download sources share that one term, split between them in proportion to their counts.
+315°
Likes
124 likes and thumbs up across the sources. A like weighs half a download, on the same log scale.
+3°
Adoption rate
About 0 downloads a day, measured between two readings of the sources, never guessed from a lifetime average. Speed weighs a quarter of the download term, so a fresh surge shows without letting a one-day spike outrank years of use.
+150°
Full model
+150° because this is a full model: a checkpoint, the weights themselves. A LoRA or an embedding is an add-on to someone else's weights and does not outrank a checkpoint of equal popularity.
−609°
Age
−609° because the SD15 weights came out in 2022. Every model inherits the age of the weights it is built on: 150° a year, up to 750°, and it keeps growing until then. Without it the top of the list would be 2022 and 2023 checkpoints on the strength of downloads that had years to pile up.
Show detailed breakdown
Every number on this site can be checked. This model's heat score is 7,758°, and here it is built one ingredient at a time, with the running total after each: the floor, the degrees each source adds, the bonus for the kind of model and what the age of its weights takes away. The colors are the bands of the chart above. The glossary at the end defines every term.
  1. 1
    Start at the floor.
    Why 7,000° and not zero: the scale runs from 0° to 9,999°, and the part below 7,000° is reserved for models no source reports on yet, which carry a stand-in number there so the lists stay complete and never outrank a model with data. 7,000° is where measured data begins. A model with real data but almost no downloads sits here; everything that follows is measurement, and the room above the floor holds ten tenfold steps in downloads at 300° each. The floor is the same for every model, so it decides nothing; the degrees above it do.
    7,000°
    running total
  2. 2
    Add the downloads.
    972 download-equivalents in all, from Civitai downloads. Ten times the downloads adds the same 300°, so size matters but never runs away with the score.
    +899°
    7,899°
    running total
  3. 3
    Add the likes.
    124 likes and thumbs up, at half the weight of a download.
    +315°
    8,214°
    running total
  4. 4
    Add the adoption rate.
    About 0 downloads a day right now, measured between two readings, at a quarter of the weight.
    +3°
    8,217°
    running total
  5. 5
    Bonus for a full model.
    A checkpoint carries the weights themselves; add-ons do not outrank one of equal popularity.
    +150°
    8,367°
    running total
  6. 6
    Take off the age.
    The SD15 weights came out in 2022: 150° a year, up to 750°, growing until then.
    −609°
    7,758°
    running total
  7. =
    The heat score.
    Where this model stands on every list of this site. Nothing else adjusts it.
    7,758°
Glossary: the terms used here
Heat score
One number for how much a model is used and wanted right now, from 0° to 9,999°. Every list on this site that says hottest is sorted by it and nothing else.
Floor
The 7,000° every model with real data starts from. The scale runs from 0° to 9,999°; the part below the floor is reserved for stand-ins (models without data), so that a model with data always ranks above one without. The floor is the same for everyone and decides nothing; the degrees above it are the measurement.
Download-equivalents
Everything the score treats as a download, added up: downloads themselves, each torrent seeder as 1,000, each AI Horde image and each run on a generation site as one.
Log scale
Counting by tens, not by ones: going from 1,000 to 10,000 downloads adds the same degrees as going from 10,000 to 100,000. It keeps giants from crowding out everything else.
Likes
Thumbs up and likes from every source, worth half a download each on the log scale.
Adoption rate
How many downloads a day a model gets right now, from the difference between two readings of a source. A quarter of the weight of the download term.
Measured and estimated
Measured means two readings of a source exist and the difference is real. Estimated means only today's total is known and the past is spread over the model's life; the site says which one it is showing.
Seeder
Someone who keeps a copy of the weights online for others to download over a torrent, or a hosted mirror doing the same. Each one counts as 1,000 downloads.
AI Horde
A volunteer network that generates images for free on donated computers. The number of images it has made with a model is public, and each counts as one download.
Generation sites
Sites that host their own copies of a model and let people generate with it, such as PixAI, SeaArt and Yodayo. Their downloads count like Civitai's and each generation run as one download.
OpenRouter tokens
A text model served through an API, such as Claude or GPT, has no weights to download, so its usage is measured in tokens: the pieces of text it read and wrote through OpenRouter, a gateway many apps use, over the last 30 days. Every 10,000,000 tokens count as one download. Source: OpenRouter (openrouter.ai/rankings), CC BY 4.0.
Full model (checkpoint)
The weights themselves, the file that generates on its own. It gets a bonus because add-ons depend on it.
LoRA and embedding
Small add-on files that steer someone else's weights toward a style, a character or a concept. They cannot run alone.
Family and weights
The base a model is built on, such as SD 1.5, SDXL, Flux or Wan. Every model inherits the release date of its family's weights.
Age
Degrees taken off for how long ago the family's weights came out: 150° a year, up to 750°. It keeps old downloads that had years to pile up from freezing the top of the list.
Family boost
A small, bounded nudge for LoRAs of a family this site is pushing forward right now, always listed in the breakdown.
Stand-in score
A stable number below the floor given to a model no source reports on yet, so lists stay complete. It never ranks above a model with data.
New! (grace period)
A model less than 7 days old shows New! instead of a score and holds no place in the rankings. Its score is computed from day one, but a model one day old grows faster than anything, so without the wait the week's #1 would always be whatever came out yesterday rather than what held up for a week.
Trend
How a model's growth over a period compares with the typical model's growth over the same period. Faster than typical reads as a rise, slower as a fall.
Typical model
The middle of the catalog: half the models with a figure grew faster over the period, half slower.
How this model's popularity has moved against the rest of the catalog: its growth in downloads, likes and usage over each period, measured against the typical model's growth over the same period, with its heat score at the time. Above the typical growth reads as a rise, below it as a fall. The past is reconstructed from today's totals, the model's first day and its measured download speed where two readings exist; a model that did not exist at the start of a period shows n/a.
Example renders
Prompts and settings shared by the people who made these renders on Civitai. Pick a render to see how it was made.
No prompt was shared with this render.
Render by volrath50 on
Civitai
·
Prompt on BunchaPrompts
Flag
Settings
Sampler
DPM++ 2S a Karras
Steps
35
Guidance
14
Seed
3410912099
Size
512x512
Prompt
mtg card art, (emrakul:1.2), (compleated:1.1) doom, (by seb mckinnon:1.1), legendary creature - (phyrexian:1.1) (eldrazi:1.2) (horror:1.1), black, (strixhaven, arcivos:1.2), annihilator, (infect:1.2), 15/15, a (phyrexianized:1.1), compleated Emrakul, attacking (strixhaven school, university campus:1.2), stx, beautiful composition, detailed painting, (sense of scale:1.2), horror, dark, terrifying, eldritch horror, new phyrexia, nph, rise of the eldrazi, roe, extremely detailed, intricate details, masterpiece, best quality, emrakul, the aeons torn, emrakul, the promised end
Negative prompt
zendikar, water, ocean, funny, happy, optimistic, bright, tentacles, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, octopus, spikes, urchin, tentacles, arms, hands, legs
Render by volrath50 on
Civitai
·
Prompt on BunchaPrompts
Flag
Settings
Sampler
DPM++ 2S a Karras
Steps
50
Guidance
11
Seed
2172298641
Size
512x512
Prompt
MTG card art, ascended eldrazi, (by eric deschamps:1.1), (legendary enchantment creature - god:1.2) (eldrazi:1.2), colorless, theros, ths, jou, bng, thb, mythic, indestructible, annihilator, trample, a wise eldrazi titan emerging from the horizon, ascended to godhood, now looking serene, calm, divine, powerful, beautiful composition, emrakul, kozilek, ulamog, (sense of scale:1.2), sense of wonder, overwhelming, extremely detailed, intricate details
Negative prompt
weak, angry, scary, underwhelming, powerless
Render by volrath50 on
Civitai
·
Prompt on BunchaPrompts
Flag
Settings
Sampler
DPM++ 2S a Karras
Steps
50
Guidance
11.5
Seed
2916557879
Size
512x512
Prompt
mtg card art, (toronto:1.2), (basic land - island:1.1), new phyrexia, nph, by adam paquette, (toronto skyline:1.2), (phyrexian:1.1), dark, horror, cn tower, rogers centre, extremely detailed, intricate details, masterpiece, best quality
Render by NanashiAnon on
Civitai
·
Prompt on BunchaPrompts
Flag
Settings
Sampler
DPM++ 2M Karras
Steps
25
Guidance
7
Seed
2
Size
512x768
Clip skip
2
Prompt
no humans, AlienCorridor, darkness, Angkor ruins,
Negative prompt
sunlight, lamp, light fixture, sunlight
Render by volrath50 on
Civitai
·
Prompt on BunchaPrompts
Flag
Settings
Sampler
DPM++ 2S a Karras
Steps
30
Guidance
10
Seed
83061463
Size
512x512
Prompt
mtg card art, neltharion, (deathwing:1.2), (by edward beard, jr:1.1), 1994, legendary creature - (elder dragon:1.1), black, red, br, legends, leg, flying, trample, (world of warcraft cataclysm:1.2), large Firey flaming black dragon perched on stormwind castle rampart, roaring, breathing fire, flames, destruction, beautiful composition, extremely detailed, intricate details, masterpiece, best quality, terrifying, epic, cinematic
Negative prompt
lowres, text, error, cropped, worst quality, low quality, normal quality, jpeg artifacts, watermark, blurry, human, humanoid, deformed, mutant, (ugly:1.3)
No prompt was shared with this render.
No prompt was shared with this render.
Run Locally
fantasyCardDiffusion_140k.ckpt
3.82 GB · PickleTensor · fp16 · full
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