Text-to-Image
Diffusers
Safetensors
English
StableDiffusionPipeline
stable-diffusion
finetune
icons
art
Instructions to use proxima/fantassified_icons with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use proxima/fantassified_icons with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("proxima/fantassified_icons", 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
- Draw Things
- DiffusionBee
Question about training
#2
by ymzlygw - opened
Hi, thanks for your good model, the result is surprising!
My question is about :
- Training way
It looks you train it under dreambooth. But as I think your dataset is very large, it has potion, shield, sword and so on.
How you set the instance prompt and class prompt? And did you train with a prior-preserving loss?
sorry, somehow really missed this question. in case this still helps anyone:
I used Dreambooth (JoePenna repo). Dataset was 118 images. instance token is just "fantassified", class is "icon". I did use prior-preservation but I don't remember anymore how many reg images were used. probably 1000.
Amazing work! Does 118 images were all potion?