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
Halftone artefacts
Hi, this is very impressive. What do you think about the viability of pre-processing the training data and removing scanning artefacts (halftone/rosette)?
It shows up in the results of this model. A partial solution is to apply a halftone-removing patch after image generation, but maybe the altered data set would yield better results?
You can delete most of it adding "(Grain)" to the negative prompt or using a secondary model for the last touches. β¨
While that's true it doesn't grant the best results. Instead I trained a custom model on a similar dataset but without the artefacts here https://huggingface.co/rullaf/magic-diffusion, and I also trained a post-processing upscaling model that removes the rosetta in https://huggingface.co/rullaf/RealESRGAN_MtG