Instructions to use quarterturn/krea2-danbooru-1024-40k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use quarterturn/krea2-danbooru-1024-40k with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("krea/Krea-2-Raw", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("quarterturn/krea2-danbooru-1024-40k") 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
A krea2 bf16 rank 128 style LoRA for making Danbooru 1024 explicit and questionable images.
Trained on a 40K subset of my Danbooru 1024 dataset https://huggingface.co/datasets/quarterturn/danbooru-1024-eq-captioned. The dataset was further filtered to exclude any images tagged as having censorship or English text.
The dataset was character tagged in conjunction with the Danbooru-2026-clean tag database, feeding artist and subject information into the captioning process for each image.
The resulting lora should assist krea2 with genning anime characters by name only, or emulating an artist style, though results will vary based on how well the artist or subject is represented in the subset of the dataset used for training.
Donations
Donations in any amount are gratefully accepted to support future datasets and research costs.
Ethereum (ETH): 0x73a3fB9EB1fa259b7395F8cBBa3C1025453530d6
- Downloads last month
- 421
Model tree for quarterturn/krea2-danbooru-1024-40k
Base model
krea/Krea-2-Raw