Text-to-Image
Diffusers
Safetensors
English
StableDiffusionPipeline
stable-diffusion
image-to-image
art
magic-the-gathering
mtg
Instructions to use volrath50/fantasy-card-diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use volrath50/fantasy-card-diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("volrath50/fantasy-card-diffusion", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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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). 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. The result is a comprehensive model that has a good understanding of MTG artists, sets, planes, card types, creature types, years, colors, and more. If you had ever wondered what a Merfolk, drawn by Ron Spencer, would have looked like on Tarkir, as part of the Mardu clan, with dash, haste, and trample - this model can deliver what you want. Prior information has been preserved well, allowing you to incorporate non-MTG artists, people, characters, places, etc. A few examples are below. I've done only limited model testing - this was origina…d9c444f -
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