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
Update README.md
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README.md
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@@ -27,7 +27,9 @@ Just use natural language,
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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,
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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,
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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,
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for any wondering the new training method is now using the RAW model and using
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## Trigger words
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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,
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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,
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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,
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for any wondering the new training method is now using the RAW model and using https://github.com/shootthesound/Fizgig.
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* after testing will do a v4 removing watermarks
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## Trigger words
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