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--- |
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datasets: |
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- nielsgl/dreambooth-ace |
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library_name: tf-keras |
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license: creativeml-openrail-m |
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pipeline_tag: text-to-image |
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tags: |
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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-dreambooth |
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- nature |
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inference: true |
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widget: |
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- text: a photo of puggieace dog on the beach, sunset in background |
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emoji: 🐶 |
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--- |
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# KerasCV Stable Diffusion in Diffusers 🧨🤗 |
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DreamBooth model for the `puggieace` concept trained by nielsgl on the `nielsgl/dreambooth-ace` dataset. |
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It can be used by modifying the `instance_prompt`: **a photo of puggieace**. |
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The examples are from 2 different Keras CV models (`StableDiffusion` and `StableDiffusionV2`, corresponding to Stable Diffusion V1.4 and V2.1, respectively) trained on the same dataset (`nielsgl/dreambooth-ace`). |
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## Description |
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The Stable Diffusion V2 pipeline contained in the corresponding repository (`nielsgl/dreambooth-keras-pug-ace-sd2.1`) 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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### Stable Diffusion V1.4 |
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> Portrait of puggieace dog as a Roman Emperor, city in background |
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> Photo of puggieace dog wearing sunglasses on the beach, sunset in background, golden hour |
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> Photo of cute puggieace dog as an astronaut, planet and spaceship in background |
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### Stable Diffusion V2.1 |
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> Portrait painting of a cute puggieace dog as a samurai |
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> Photo of cute puggieace dog as an astronaut, space and planet in background |
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> A photo of a cute puggieace dog getting a haircut in a barbershop |
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> Portrait photo of puggieace dog in New York |
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> Portrait of puggieace dog as a Roman Emperor, city in background |
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## Usage with Stable Diffusion V1.4 |
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```python |
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from huggingface_hub import from_pretrained_keras |
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import keras_cv |
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import matplotlib.pyplot as plt |
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model = keras_cv.models.StableDiffusion(img_width=512, img_height=512, jit_compile=True) |
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model._diffusion_model = from_pretrained_keras("nielsgl/dreambooth-pug-ace") |
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model._text_encoder = from_pretrained_keras("nielsgl/dreambooth-pug-ace-text-encoder") |
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images = model.text_to_image("a photo of puggieace dog on the beach", batch_size=3) |
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plt.imshow(image[0]) |
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``` |
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## Usage with Stable Diffusion V2.1 |
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```python |
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from diffusers import StableDiffusionPipeline |
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pipeline = StableDiffusionPipeline.from_pretrained('nielsgl/dreambooth-keras-pug-ace-sd2.1') |
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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 for Stable Diffusion v1.4: |
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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 | |
