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README.md
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---
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license: creativeml-openrail-m
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tags:
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- keras
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- diffusers
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- stable-diffusion
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- text-to-image
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- diffusion-models-class
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- keras-sprint
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- keras-dreambooth
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- scifi
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inference: true
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widget:
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- text: a drawing of drawbayc monkey as a turtle
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---
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# KerasCV Stable Diffusion in Diffusers 🧨🤗
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DreamBooth model for the `drawbayc monkey` concept trained by nielsgl on the `nielsgl/bayc-tiny` dataset, images from this [Kaggle dataset](https://www.kaggle.com/datasets/stanleyjzheng/bored-apes-yacht-club).
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It can be used by modifying the `instance_prompt`: **a drawing of drawbayc monkey**
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## Description
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The pipeline contained in this repository was created using a modified version of [this Space](https://huggingface.co/spaces/sayakpaul/convert-kerascv-sd-diffusers) for StableDiffusionV2 from KerasCV. The purpose is to convert the KerasCV Stable Diffusion weights in a way that is compatible with [Diffusers](https://github.com/huggingface/diffusers). This allows users to fine-tune using KerasCV and use the fine-tuned weights in Diffusers taking advantage of its nifty features (like [schedulers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/schedulers), [fast attention](https://huggingface.co/docs/diffusers/optimization/fp16), etc.).
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This model was created as part of the Keras DreamBooth Sprint 🔥. Visit the [organisation page](https://huggingface.co/keras-dreambooth) for instructions on how to take part!
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## Examples
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> A drawing of drawbayc monkey dressed as an astronaut
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![a drawing of drawbayc monkey dressed as an astronaut](https://huggingface.co/nielsgl/dreambooth-bored-ape/resolve/main/examples/astronaut.jpg)
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> A drawing of drawbayc monkey dressed as the pope
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![> A drawing of drawbayc monkey dressed as an astronaut](https://huggingface.co/nielsgl/dreambooth-bored-ape/resolve/main/examples/pope.jpg)
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## Usage
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```python
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from diffusers import StableDiffusionPipeline
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pipeline = StableDiffusionPipeline.from_pretrained('nielsgl/dreambooth-bored-ape')
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image = pipeline().images[0]
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image
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```
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## Training hyperparameters
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The following hyperparameters were used during training:
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| Hyperparameters | Value |
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| :-- | :-- |
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| name | RMSprop |
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| weight_decay | None |
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| clipnorm | None |
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| global_clipnorm | None |
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| clipvalue | None |
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| use_ema | False |
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| ema_momentum | 0.99 |
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| ema_overwrite_frequency | 100 |
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| jit_compile | True |
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| is_legacy_optimizer | False |
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| learning_rate | 0.0010000000474974513 |
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| rho | 0.9 |
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| momentum | 0.0 |
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| epsilon | 1e-07 |
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| centered | False |
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| training_precision | float32 |
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