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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 the pope |
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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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