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| from huggingface_hub import from_pretrained_keras | |
| from keras_cv import models | |
| import gradio as gr | |
| dreambooth_model = models.StableDiffusion(img_width=256, img_height=256) | |
| diffusion_model = from_pretrained_keras("moizsajid/dreambooth-markhor") | |
| dreambooth_model._diffusion_model = diffusion_model | |
| # generate images | |
| def infer(prompt: str, negative_prompt: str, num_imgs_to_gen: int, num_steps: int, guidance_scale: float): | |
| generated_images = dreambooth_model.text_to_image( | |
| prompt, | |
| negative_prompt=negative_prompt, | |
| batch_size=num_imgs_to_gen, | |
| num_steps=num_steps, | |
| unconditional_guidance_scale=guidance_scale | |
| ) | |
| return generated_images | |
| # pass function, input type for prompt, the output for multiple images | |
| gr.Interface( | |
| infer, [ | |
| gr.Textbox(label="Positive Prompt", value="a markhor in space"), | |
| gr.Textbox(label="Negative Prompt", value="bad anatomy, blurry"), | |
| gr.Slider(label='Number of gen image', minimum=1, maximum=4, value=2, step=1), | |
| gr.Slider(label="Inference Steps",value=100), | |
| gr.Number(label='Guidance scale', value=10), | |
| ], [ | |
| gr.Gallery(show_label=False), | |
| ], | |
| title="Dreambooth Markhor Demo", | |
| description = "This model is fine-tuned on images of Markhor from the internet (iStock). To use the demo, please add {markhor} to the input string.", | |
| examples = [["a picture of markhor upside down", "", 4, 100, 10]], | |
| ).launch() | |