
# KerasCV Stable Diffusion in Diffusers 🧨🤗

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).
It can be used by modifying the `instance_prompt`: **a drawing of drawbayc monkey**

## Description

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.).
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!

## Examples

> A drawing of drawbayc monkey dressed as an astronaut

![a drawing of drawbayc monkey dressed as an astronaut](https://huggingface.co/nielsgl/dreambooth-bored-ape/resolve/main/examples/astronaut.jpg)

> A drawing of drawbayc monkey dressed as the pope

![> A drawing of drawbayc monkey dressed as an astronaut](https://huggingface.co/nielsgl/dreambooth-bored-ape/resolve/main/examples/pope.jpg)


## Usage

```python
from diffusers import StableDiffusionPipeline

pipeline = StableDiffusionPipeline.from_pretrained('nielsgl/dreambooth-bored-ape')
image = pipeline().images[0]
image
```

## Training hyperparameters

The following hyperparameters were used during training:

| Hyperparameters | Value |
| :-- | :-- |
| name | RMSprop |
| weight_decay | None |
| clipnorm | None |
| global_clipnorm | None |
| clipvalue | None |
| use_ema | False |
| ema_momentum | 0.99 |
| ema_overwrite_frequency | 100 |
| jit_compile | True |
| is_legacy_optimizer | False |
| learning_rate | 0.0010000000474974513 |
| rho | 0.9 |
| momentum | 0.0 |
| epsilon | 1e-07 |
| centered | False |
| training_precision | float32 |

{"license": "creativeml-openrail-m", "tags": ["keras", "diffusers", "stable-diffusion", "text-to-image", "diffusion-models-class", "keras-sprint", "keras-dreambooth", "scifi"], "inference": true, "widget": [{"text": "a drawing of drawbayc monkey as a turtle"}]}
diffusers keras stable-diffusion text-to-image diffusion-models-class keras-sprint keras-dreambooth scifi license:creativeml-openrail-m endpoints_compatible has_space diffusers:StableDiffusionPipeline region:us
TAGS
#diffusers #keras #stable-diffusion #text-to-image #diffusion-models-class #keras-sprint #keras-dreambooth #scifi #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us 

KerasCV Stable Diffusion in Diffusers
=====================================


DreamBooth model for the 'drawbayc monkey' concept trained by nielsgl on the 'nielsgl/bayc-tiny' dataset, images from this Kaggle dataset.
It can be used by modifying the 'instance\_prompt': a drawing of drawbayc monkey


Description
-----------


The pipeline contained in this repository was created using a modified version of this Space for StableDiffusionV2 from KerasCV. The purpose is to convert the KerasCV Stable Diffusion weights in a way that is compatible with 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, fast attention, etc.).
This model was created as part of the Keras DreamBooth Sprint . Visit the organisation page for instructions on how to take part!


Examples
--------



> 
> A drawing of drawbayc monkey dressed as an astronaut
> 
> 
> 


!a drawing of drawbayc monkey dressed as an astronaut



> 
> A drawing of drawbayc monkey dressed as the pope
> 
> 
> 


!> A drawing of drawbayc monkey dressed as an astronaut


Usage
-----


Training hyperparameters
------------------------


The following hyperparameters were used during training:




TAGS
#diffusers #keras #stable-diffusion #text-to-image #diffusion-models-class #keras-sprint #keras-dreambooth #scifi #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us 

TAGS
#diffusers #keras #stable-diffusion #text-to-image #diffusion-models-class #keras-sprint #keras-dreambooth #scifi #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us 
