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| from huggingface_hub import from_pretrained_keras | |
| from keras_cv import models | |
| import gradio as gr | |
| from tensorflow import keras | |
| keras.mixed_precision.set_global_policy("mixed_float16") | |
| # prepare model | |
| resolution = 512 | |
| sd_dreambooth_model = models.StableDiffusion( | |
| img_width=resolution, img_height=resolution | |
| ) | |
| db_diffusion_model = from_pretrained_keras("kfahn/dreambooth-mandelbulb") | |
| sd_dreambooth_model._diffusion_model = db_diffusion_model | |
| # generate images | |
| def infer(prompt): | |
| generated_images = sd_dreambooth_model.text_to_image( | |
| prompt, batch_size=2 | |
| ) | |
| return generated_images | |
| output = gr.Gallery(label="Outputs").style(grid=(1,2)) | |
| # customize interface | |
| title = "Dreambooth Mandelbulb flower" | |
| description = "This is a dreambooth model fine-tuned on mandelbulb images. To try it, input the concept with {sks a hydrangea floweret shaped like a mandelbulb}." | |
| examples=[["sks a hydrangea floweret shaped like a mandelbulb on a bush"]] | |
| gr.Interface(infer, inputs=["text"], outputs=[output], title=title, description=description, examples=examples).queue().launch() | |