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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README.md
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- Draw real-world or non-MtG characters, in the style of MTG art
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- Mix and match all of the above
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## Using the Model
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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.
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- Draw real-world or non-MtG characters, in the style of MTG art
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- Mix and match all of the above
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## Updates
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- 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.
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## Using the Model
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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.
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