Instructions to use HuggingVince/B4gged_Krea2_V3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use HuggingVince/B4gged_Krea2_V3 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("HuggingVince/B4gged_Krea2_V3") prompt = "{" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
B4gged

- Prompt
- {
Model description
Version 3 of my consensual breath-play Lora Just use natural language, the new Lora training is 500 times better than my last method there seems to be no more bleed where the Lora damages the quality of the default model, i think this may be the final version as it does exactly as i expected, it was captioned with a large amount of relevant words to associate them, i have yet to spend the time to work out what is the best epoch so i just uploaded them all try from 15 onwards but the final epoch seems awesome even at 1.0 strength, for any wondering the new training method is now using the RAW model and using https://github.com/shootthesound/Fizgig.
- after testing will do a v4 removing watermarks
Trigger words
You should use B4gged but just use natural language to trigger the image generation.
Download model
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Model tree for HuggingVince/B4gged_Krea2_V3
Base model
krea/Krea-2-Raw