Duplicate from keras-dreambooth/traditional-furniture-demo

Co-authored-by: Kadir Nar <kadirnar@users.noreply.huggingface.co>

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README.md

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+---
+title: Traditional Furniture Demo
+emoji: 👀
+colorFrom: indigo
+colorTo: pink
+sdk: gradio
+sdk_version: 3.20.1
+app_file: app.py
+pinned: false
+license: apache-2.0
+tags:
+- keras-dreambooth
+- wildcard
+duplicated_from: keras-dreambooth/traditional-furniture-demo
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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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/keras-diffusion-traditional-furniture")
+dreambooth_model._diffusion_model = loaded_diffusion_model
+
+
+def generate_images(prompt: str, negative_prompt:str, num_imgs_to_gen: int, num_steps: int):
+    """
+    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.
+        num_imgs_to_gen (int): The number of images to be generated using given prompt.
+        num_steps (int): The number of denoising steps
+    Returns:
+        generated_img (List): List of images that were generated using the model
+    """
+    generated_img = dreambooth_model.text_to_image(
+        prompt, 
+        negative_prompt=negative_prompt,
+        batch_size=num_imgs_to_gen,
+        num_steps=num_steps,
+    )
+   
+    return generated_img
+    
+with gr.Blocks() as demo:
+    gr.HTML("<h2 style=\"font-size: 2em; font-weight: bold\" align=\"center\">Keras Dreambooth - Traditional Furniture Demo</h2>")    
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(lines=1, value="sks traditional furniture", label="Base Prompt")
+            negative_prompt = gr.Textbox(lines=1, value="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)
+            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], outputs=gallery)
+    
+    gr.Examples([["photo of traditional furniture","deformed", 1, 50]],
+                [prompt,negative_prompt, samples,num_steps], gallery, generate_images)
+    gr.Markdown('\n Demo created by: <a href=\"https://huggingface.co/kadirnar/\">Kadir Nar</a>')
+
+demo.launch(debug=True)

requirements.txt

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+keras_cv
+tensorflow

utils_app.py

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+from huggingface_hub import from_pretrained_keras
+from keras_cv import models
+from tensorflow import keras
+import tensorflow as tf
+import gradio as gr
+
+
+keras.mixed_precision.set_global_policy("mixed_float16")
+
+keras_model_list = [
+    "kadirnar/dreambooth_diffusion_model_v5",
+    "kadirnar/dreambooth_diffusion_model_v3"
+]
+
+stable_prompt_list = [
+        "a photo of sks traditional furniture",
+    ]
+
+stable_negative_prompt_list = [
+        "bad, ugly",
+        "deformed"
+    ]
+
+def keras_stable_diffusion(
+    model_path:str,
+    prompt:str,
+    negative_prompt:str,
+    guidance_scale:int,
+    num_inference_step:int,
+    height:int,
+    width:int,
+    ):
+        
+    sd_dreambooth_model = models.StableDiffusion(
+        img_width=height, 
+        img_height=width
+        )
+    
+    db_diffusion_model = from_pretrained_keras(model_path)
+    sd_dreambooth_model._diffusion_model = db_diffusion_model
+
+    generated_images = sd_dreambooth_model.text_to_image(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        num_steps=num_inference_step,
+        unconditional_guidance_scale=guidance_scale
+    )
+    tf.keras.backend.clear_session()
+
+
+    return generated_images
+
+def keras_stable_diffusion_app():
+    with gr.Blocks():
+        with gr.Row():
+            with gr.Column():
+                keras_text2image_model_path = gr.Dropdown(
+                    choices=keras_model_list, 
+                    value=keras_model_list[0], 
+                    label='Text-Image Model Id'
+                )
+
+                keras_text2image_prompt = gr.Textbox(
+                    lines=1, 
+                    value=stable_prompt_list[0], 
+                    label='Prompt'
+                )
+
+                keras_text2image_negative_prompt = gr.Textbox(
+                    lines=1, 
+                    value=stable_negative_prompt_list[0], 
+                    label='Negative Prompt'
+                )
+
+                with gr.Accordion("Advanced Options", open=False):
+                    keras_text2image_guidance_scale = gr.Slider(
+                        minimum=0.1, 
+                        maximum=15, 
+                        step=0.1, 
+                        value=7.5, 
+                        label='Guidance Scale'
+                    )
+
+                    keras_text2image_num_inference_step = gr.Slider(
+                        minimum=1, 
+                        maximum=100, 
+                        step=1, 
+                        value=50, 
+                        label='Num Inference Step'
+                    )
+
+                    keras_text2image_height = gr.Slider(
+                        minimum=128, 
+                        maximum=1280, 
+                        step=32, 
+                        value=512, 
+                        label='Image Height'
+                    )
+
+                    keras_text2image_width = gr.Slider(
+                        minimum=128, 
+                        maximum=1280, 
+                        step=32, 
+                        value=512, 
+                        label='Image Height'
+                    )
+
+                keras_text2image_predict = gr.Button(value='Generator')
+    
+            with gr.Column():
+                output_image = gr.Gallery(label='Output')
+                        
+        keras_text2image_predict.click(
+            fn=keras_stable_diffusion,
+            inputs=[
+                keras_text2image_model_path,
+                keras_text2image_prompt,
+                keras_text2image_negative_prompt,
+                keras_text2image_guidance_scale,
+                keras_text2image_num_inference_step,
+                keras_text2image_height,
+                keras_text2image_width
+            ],
+            outputs=output_image
+        )