Upload 3 files

app.py

@@ -9,14 +9,20 @@ from diffusion_webui.controlnet.controlnet_seg import stable_diffusion_controlne
 from diffusion_webui.stable_diffusion.text2img_app import stable_diffusion_text2img_app, stable_diffusion_text2img
 from diffusion_webui.stable_diffusion.img2img_app import stable_diffusion_img2img_app, stable_diffusion_img2img
 from diffusion_webui.stable_diffusion.inpaint_app import stable_diffusion_inpaint_app, stable_diffusion_inpaint
+from diffusion_webui.stable_diffusion.keras_txt2img import keras_stable_diffusion, keras_stable_diffusion_app
 
 
 import gradio as gr
 
-
 app = gr.Blocks()
 with app:
-    gr.Markdown("# **<h1 align='center'>Stable Diffusion + ControlNet WebUI<h1>**")
+    gr.HTML(
+        """
+        <h1 style='text-align: center'>
+        Stable Diffusion + ControlNet WebUI
+        </h1>
+        """
+    )
     gr.Markdown(
         """
         <h4 style='text-align: center'>
@@ -39,7 +45,8 @@ with app:
                 controlnet_pose_app = stable_diffusion_controlnet_pose_app()
                 controlnet_scribble_app = stable_diffusion_controlnet_scribble_app()
                 controlnet_seg_app = stable_diffusion_controlnet_seg_app()
-
+            
+            keras_diffusion_app = keras_stable_diffusion_app()
 
         with gr.Tab('Output'):
             with gr.Column():
@@ -175,5 +182,20 @@ with app:
         ],
         outputs = [output_image],
     )
+    
+    keras_diffusion_app['predict'].click(
+        fn = keras_stable_diffusion,
+        inputs = [
+            keras_diffusion_app['model_path'],
+            keras_diffusion_app['prompt'],
+            keras_diffusion_app['negative_prompt'],
+            keras_diffusion_app['guidance_scale'],
+            keras_diffusion_app['num_inference_step'],
+            keras_diffusion_app['height'],
+            keras_diffusion_app['width'],
+        ],
+        outputs = [output_image],
+    )
+
 
 app.launch(debug=True)   

keras_txt2img.py

@@ -0,0 +1,116 @@
+from huggingface_hub import from_pretrained_keras
+from keras_cv import models
+from tensorflow import keras
+
+import gradio as gr
+
+stable_model_list = [
+    "keras-dreambooth/dreambooth_diffusion_model"
+]
+
+stable_prompt_list = [
+        "a photo of a man.",
+        "a photo of a girl."
+    ]
+
+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,
+    ):
+
+    keras.mixed_precision.set_global_policy("mixed_float16")
+
+    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
+    )
+
+    return generated_images
+
+def keras_stable_diffusion_app():
+    with gr.Tab('Keras Diffusion'):
+        keras_text2image_model_path = gr.Dropdown(
+            choices=stable_model_list, 
+            value=stable_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=768, 
+                label='Image Height'
+            )
+
+        keras_text2image_predict = gr.Button(value='Generator')
+    
+    variables = {
+        "model_path": keras_text2image_model_path,
+        "prompt": keras_text2image_prompt,
+        "negative_prompt": keras_text2image_negative_prompt,
+        "guidance_scale": keras_text2image_guidance_scale,
+        "num_inference_step": keras_text2image_num_inference_step,
+        "height": keras_text2image_height,
+        "width": keras_text2image_width,
+        "predict": keras_text2image_predict
+    }
+
+    return variables 

requirements.txt

@@ -5,4 +5,8 @@ controlnet_aux
 diffusers
 imageio
 gradio
-triton
+triton
+tensorflow
+huggingface-hub
+keras-cv
+pycocotools