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| from huggingface_hub import from_pretrained_keras | |
| import keras_cv | |
| import gradio as gr | |
| from tensorflow import keras | |
| keras.mixed_precision.set_global_policy("mixed_float16") | |
| # load keras model | |
| resolution = 512 | |
| dreambooth_model = keras_cv.models.StableDiffusion( | |
| img_width=resolution, img_height=resolution, jit_compile=True, | |
| ) | |
| loaded_diffusion_model = from_pretrained_keras("keras-dreambooth/ignatius") | |
| dreambooth_model._diffusion_model = loaded_diffusion_model | |
| # generate images | |
| def generate_images(prompt, negative_prompt, num_imgs_to_gen, num_steps, guidance_scale): | |
| """ | |
| This function is used to generate images using our fine-tuned keras dreambooth stable diffusion model. | |
| Args: | |
| prompt (str): The text input given by the user based on which images will be generated. | |
| negative_prompt (srt): The text to eliminate from the generation some concepts. | |
| num_imgs_to_gen (int): The number of images to be generated using given prompt. | |
| num_steps (int): The number of denoising steps | |
| guidance_scale (double): Increasing guidance makes generation follow more closely to the prompt. | |
| Returns: | |
| generated_img (List): List of images that were generated using the model | |
| """ | |
| 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 | |
| with gr.Blocks() as demo: | |
| gr.HTML("<h2 style=\"font-size: 2em; font-weight: bold\" align=\"center\">Ignatius Farray - The cavern of the muffled scream</h2>") | |
| with gr.Row(): | |
| with gr.Column(): | |
| prompt = gr.Textbox(lines=1, value="ignatius in a standup comedy spectacle", label="Base Prompt") | |
| negative_prompt = gr.Textbox(lines=1, value="bad anatomy, blurry, ugly, deformed", label="Negative Prompt") | |
| samples = gr.Slider(minimum=1, maximum=10, default=1, step=1, label="Number of Image") | |
| num_steps = gr.Slider(label="Inference Steps", value=50) | |
| guidance_scale = gr.Number(label="Guidance scale", value=7.5) | |
| run = gr.Button(value="Run") | |
| with gr.Column(): | |
| gallery = gr.Gallery(label="Outputs").style(grid=(1,2)) | |
| run.click(generate_images, inputs=[prompt, negative_prompt, samples, num_steps, guidance_scale], outputs=gallery) | |
| gr.Examples([["ignatius on the moon","bad anatomy, blurry, ugly", 2, 150, 15], | |
| ["A photo of ignatius person inside a box","bad anatomy, blurry, ugly", 2, 150, 15], | |
| ["A closeup portrait of ignatius, highly detailed, high qulity","bad anatomy, blurry, ugly", 2, 150, 15]], | |
| [prompt, negative_prompt, samples, num_steps, guidance_scale], gallery, generate_images) | |
| gr.Markdown('\n Demo created by: <a href=\"https://huggingface.co/matallanas/\">Eduardo Matallanas</a>') | |
| demo.launch(debug=True) |