Instructions to use lylogummy/BunnyMint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lylogummy/BunnyMint with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lylogummy/BunnyMint", 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
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BunnyMint is a Pony based on two finetunes to the Pony model. The first finetune was trained on 8k randomly selected artist images with score:>99 from danbooru for 10 epochs to improve overall aesthetic. The second finetune was trained again on 20k hand picked NAIv3 images for 20 epochs. Personally trained Blue Archive LoRA was then merged on top to make style more alligned with personal preferences.
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## Donate
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**Finetuning models on personal hardware is pretty costly, so if anyone likes what I do and feels like it, here's my gumroad:
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https://lylogummy.gumroad.com/l/avhut
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## Usage
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BunnyMint is a Pony based on two finetunes to the Pony model. The first finetune was trained on 8k randomly selected artist images with score:>99 from danbooru for 10 epochs to improve overall aesthetic. The second finetune was trained again on 20k hand picked NAIv3 images for 20 epochs. Personally trained Blue Archive LoRA was then merged on top to make style more alligned with personal preferences.
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## Donate
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**Finetuning models on personal hardware is pretty costly, so if anyone likes what I do and feels like it, here's my gumroad:**
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**https://lylogummy.gumroad.com/l/avhut**
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## Usage
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