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| from huggingface_hub import from_pretrained_keras | |
| from keras_cv import models | |
| import gradio as gr | |
| from tensorflow import keras | |
| from diffusers import StableDiffusionPipeline | |
| 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("keras-dreambooth/dreambooth_diffusion_model") | |
| # sd_dreambooth_model._diffusion_model = db_diffusion_model | |
| # checkpoint of the converted Stable Diffusion from KerasCV | |
| model_ckpt = "nielsgl/dreambooth-keras-pug-ace-sd2.1" | |
| pipeline = StableDiffusionPipeline.from_pretrained(model_ckpt) | |
| pipeline.to("cuda") | |
| unique_id = "puggieace" | |
| class_label = "dog" | |
| prompt = f"A photo of {unique_id} {class_label} on the beach" | |
| image = pipeline(prompt, num_inference_steps=50).images[0] | |
| # generate images | |
| def infer(prompt, negative_prompt, guidance_scale=10, num_inference_steps=50): | |
| neg = negative_prompt if negative_prompt else None | |
| imgs = [] | |
| while len(imgs) != 2: | |
| next_prompt = pipeline(prompt, negative_prompt=neg, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, num_images_per_prompt=5) | |
| for img, is_neg in zip(next_prompt.images, next_prompt.nsfw_content_detected): | |
| if not is_neg: | |
| imgs.append(img) | |
| if len(imgs) == 2: | |
| break | |
| return imgs | |
| output = gr.Gallery(label="Outputs").style(grid=(1,2)) | |
| # customize interface | |
| title = "KerasCV Stable Diffusion Demo on images of Ace." | |
| description = "This is a dreambooth model fine-tuned on images of my pug named Ace. To try it, input the concept with `puggieace dog`." | |
| examples=[ | |
| ["Portrait photo of puggieace dog on a beachtowel wearing sunglasses on the beach, sunset in background, golden hour", "", 12, 50], | |
| ["A photo of a cute puggieace dog getting a haircut in a barbershop, ultra realistic, intricate details, highly detailed, photorealistic, octane render, 8 k, unreal engine. art by artgerm and greg rutkowski and charlie bowater and magali villeneuve and alphonse mucha", "", 12, 75], | |
| ["Portrait of puggieace dog as a Roman Emperor, city in background, ultra realistic, intricate details, eerie, highly detailed, photorealistic, octane render, 8 k, unreal engine. art by artgerm and greg rutkowski and charlie bowater and magali villeneuve and alphonse mucha", "", 15, 75], | |
| ["Photo of cute puggieace dog as an astronaut, space and planet in background, ultra realistic, concept art, intricate details, highly detailed, photorealistic, octane render, 8 k, unreal engine. trending on artstation", "", 15, 75], | |
| ["Photo of cute puggieace dog as super hero, futuristic city in background, cinematic light, high dynamic range, insane intricate details, stunning cinema effects, aesthetic, masterpiece, trending on artstation, cartoon art", "", 12, 75], | |
| ] | |
| base_14 = "https://huggingface.co/nielsgl/dreambooth-pug-ace-sd1.4/resolve/main/" | |
| base_21 = "https://huggingface.co/nielsgl/dreambooth-keras-pug-ace-sd2.1/resolve/main/" | |
| model_card_1 = f""" | |
| # KerasCV Stable Diffusion in Diffusers 🧨🤗 | |
| DreamBooth model for the `puggieace` concept trained by nielsgl on the [nielsgl/dreambooth-ace](https://huggingface.co/datasets/nielsgl/dreambooth-ace) dataset. | |
| It can be used by modifying the `instance_prompt`: **a photo of puggieace**. | |
| 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`). | |
| ## Description | |
| 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.). | |
| 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! | |
| ## Demo | |
| """ | |
| model_card_2 = f""" | |
| ## Examples | |
| ### Stable Diffusion V1.4 | |
| > Portrait of puggieace dog as a Roman Emperor, city in background | |
|  | |
| > Photo of puggieace dog wearing sunglasses on the beach, sunset in background, golden hour | |
|  | |
| > Photo of cute puggieace dog as an astronaut, planet and spaceship in background | |
|  | |
| ### Stable Diffusion V2.1 | |
| > Portrait painting of a cute puggieace dog as a samurai | |
|  | |
| > Photo of cute puggieace dog as an astronaut, space and planet in background | |
|  | |
| > A photo of a cute puggieace dog getting a haircut in a barbershop | |
|  | |
| > Portrait photo of puggieace dog in New York | |
|  | |
| > Portrait of puggieace dog as a Roman Emperor, city in background | |
|  | |
| ## Usage with Stable Diffusion V1.4 | |
| ```python | |
| from huggingface_hub import from_pretrained_keras | |
| import keras_cv | |
| import matplotlib.pyplot as plt | |
| model = keras_cv.models.StableDiffusion(img_width=512, img_height=512, jit_compile=True) | |
| model._diffusion_model = from_pretrained_keras("nielsgl/dreambooth-pug-ace") | |
| model._text_encoder = from_pretrained_keras("nielsgl/dreambooth-pug-ace-text-encoder") | |
| images = model.text_to_image("a photo of puggieace dog on the beach", batch_size=3) | |
| plt.imshow(image[0]) | |
| ``` | |
| ## Usage with Stable Diffusion V2.1 | |
| ```python | |
| from diffusers import StableDiffusionPipeline | |
| pipeline = StableDiffusionPipeline.from_pretrained('nielsgl/dreambooth-keras-pug-ace-sd2.1') | |
| image = pipeline().images[0] | |
| image | |
| ``` | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training for Stable Diffusion v1.4: | |
| | 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 | | |
| """ | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| gr.Markdown(model_card_1) | |
| with gr.Row(): | |
| with gr.Column(): | |
| prompt_pos = gr.Textbox(label="Positive Prompt", value="a photo of puggieace dog getting a haircut") | |
| prompt_neg = gr.Textbox(label="Negative Prompt", value="bad anatomy, blurry") | |
| # gr.Slider(label='Number of gen image', minimum=1, maximum=4, value=2, step=1), | |
| prompt_gs = gr.Number(label='Guidance scale', value=12) | |
| prompt_steps = gr.Slider(label="Inference Steps",value=50) | |
| prompt_btn = gr.Button("Generate") | |
| with gr.Column(): | |
| output = gr.Gallery(label="Outputs").style(grid=(1,2)) | |
| prompt_btn.click(infer, inputs=[prompt_pos, prompt_neg, prompt_gs, prompt_steps], outputs=[output]) | |
| with gr.Row(): | |
| gr.Examples(examples, inputs=[prompt_pos, prompt_neg, prompt_gs, prompt_steps], outputs=output, fn=infer, cache_examples=True) | |
| # gr.Interface(infer, inputs=[gr.Textbox(label="Positive Prompt", value="a photo of puggieace dog getting a haircut"), | |
| # 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.Number(label='Guidance scale', value=12), | |
| # gr.Slider(label="Inference Steps",value=50), | |
| # ], outputs=[output], title=title, description=description, examples=examples).queue() | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown(model_card_2) | |
| with gr.Column(): | |
| gr.Markdown(" ") | |
| demo.queue().launch() | |
| # with gr.Blocks() as card_interface: | |
| # gr.Markdown(model_card) | |
| # demo_interface = gr.Interface(infer, inputs=[gr.Textbox(label="Positive Prompt", value="a photo of puggieace dog getting a haircut"), | |
| # 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.Number(label='Guidance scale', value=12), | |
| # gr.Slider(label="Inference Steps",value=50), | |
| # ], outputs=[output], title=title, description=description, examples=examples).queue() | |
| # gr.TabbedInterface([card_interface, demo_interface], ["Model Card", "Demo 🤗"]).launch() | |