update

app.py

@@ -1,19 +1,19 @@
-from diffusion_webui.controlnet.controlnet_canny import stable_diffusion_controlnet_canny_app, stable_diffusion_controlnet_canny
-from diffusion_webui.controlnet.controlnet_depth import stable_diffusion_controlnet_depth_app, stable_diffusion_controlnet_depth
-from diffusion_webui.controlnet.controlnet_hed import stable_diffusion_controlnet_hed_app, stable_diffusion_controlnet_hed
-from diffusion_webui.controlnet.controlnet_mlsd import stable_diffusion_controlnet_mlsd_app, stable_diffusion_controlnet_mlsd
-from diffusion_webui.controlnet.controlnet_pose import stable_diffusion_controlnet_pose_app, stable_diffusion_controlnet_pose
-from diffusion_webui.controlnet.controlnet_scribble import stable_diffusion_controlnet_scribble_app, stable_diffusion_controlnet_scribble
-from diffusion_webui.controlnet.controlnet_seg import stable_diffusion_controlnet_seg_app, stable_diffusion_controlnet_seg
-
-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
 
+from diffusion_webui.helpers import (
+    keras_stable_diffusion_app,
+    stable_diffusion_controlnet_canny_app,
+    stable_diffusion_controlnet_depth_app,
+    stable_diffusion_controlnet_hed_app,
+    stable_diffusion_controlnet_mlsd_app,
+    stable_diffusion_controlnet_pose_app,
+    stable_diffusion_controlnet_scribble_app,
+    stable_diffusion_controlnet_seg_app,
+    stable_diffusion_img2img_app,
+    stable_diffusion_inpaint_app,
+    stable_diffusion_text2img_app,
+)
+
 app = gr.Blocks()
 with app:
     gr.HTML(
@@ -33,30 +33,30 @@ with app:
     )
     with gr.Row():
         with gr.Column():
-            with gr.Tab('Text2Img'):
+            with gr.Tab("Text2Img"):
                 stable_diffusion_text2img_app()
-            with gr.Tab('Img2Img'):
+            with gr.Tab("Img2Img"):
                 stable_diffusion_img2img_app()
-            with gr.Tab('Inpaint'): 
+            with gr.Tab("Inpaint"):
                 stable_diffusion_inpaint_app()
-        
-            with gr.Tab('ControlNet'):
-                with gr.Tab('Canny'):
+
+            with gr.Tab("ControlNet"):
+                with gr.Tab("Canny"):
                     stable_diffusion_controlnet_canny_app()
-                with gr.Tab('Depth'):
+                with gr.Tab("Depth"):
                     stable_diffusion_controlnet_depth_app()
-                with gr.Tab('HED'):
+                with gr.Tab("HED"):
                     stable_diffusion_controlnet_hed_app()
-                with gr.Tab('MLSD'):
+                with gr.Tab("MLSD"):
                     stable_diffusion_controlnet_mlsd_app()
-                with gr.Tab('Pose'):
+                with gr.Tab("Pose"):
                     stable_diffusion_controlnet_pose_app()
-                with gr.Tab('Seg'): 
+                with gr.Tab("Seg"):
                     stable_diffusion_controlnet_seg_app()
-                with gr.Tab('Scribble'):
+                with gr.Tab("Scribble"):
                     stable_diffusion_controlnet_scribble_app()
-            
-            with gr.Tab('Keras Diffusion'):       
+
+            with gr.Tab("Keras Diffusion"):
                 keras_diffusion_app = keras_stable_diffusion_app()
 
-app.launch(debug=True)   
+app.launch(debug=True)

data/bash.sh

@@ -0,0 +1,2 @@
+black . --config pyproject.toml
+isort .

diffusion_webui/controlnet/controlnet_canny.py

@@ -1,12 +1,13 @@
-from diffusers import ( StableDiffusionControlNetPipeline, 
-                       ControlNetModel, UniPCMultistepScheduler)
-
-from PIL import Image
+import cv2
 import gradio as gr
 import numpy as np
 import torch
-import cv2
-
+from diffusers import (
+    ControlNetModel,
+    StableDiffusionControlNetPipeline,
+    UniPCMultistepScheduler,
+)
+from PIL import Image
 
 stable_model_list = [
     "runwayml/stable-diffusion-v1-5",
@@ -15,23 +16,22 @@ stable_model_list = [
 
 controlnet_canny_model_list = [
     "lllyasviel/sd-controlnet-canny",
-    "thibaud/controlnet-sd21-canny-diffusers"
+    "thibaud/controlnet-sd21-canny-diffusers",
 ]
 
 
-stable_prompt_list = [
-        "a photo of a man.",
-        "a photo of a girl."
-    ]
+stable_prompt_list = ["a photo of a man.", "a photo of a girl."]
+
+stable_negative_prompt_list = ["bad, ugly", "deformed"]
+
+data_list = [
+    "data/test.png",
+]
 
-stable_negative_prompt_list = [
-        "bad, ugly",
-        "deformed"
-    ]
 
 def controlnet_canny(
-    image_path:str,
-    controlnet_model_path:str,
+    image_path: str,
+    controlnet_model_path: str,
 ):
     image = Image.open(image_path)
     image = np.array(image)
@@ -42,28 +42,29 @@ def controlnet_canny(
     image = Image.fromarray(image)
 
     controlnet = ControlNetModel.from_pretrained(
-        controlnet_model_path,
-        torch_dtype=torch.float16
+        controlnet_model_path, torch_dtype=torch.float16
     )
     return controlnet, image
 
 
 def stable_diffusion_controlnet_canny(
-    image_path:str,
-    stable_model_path:str,
-    controlnet_model_path:str,
-    prompt:str,
-    negative_prompt:str,
-    guidance_scale:int,
-    num_inference_step:int,
-    ):
-
-    controlnet, image = controlnet_canny(image_path, controlnet_model_path)
-    
+    image_path: str,
+    stable_model_path: str,
+    controlnet_model_path: str,
+    prompt: str,
+    negative_prompt: str,
+    guidance_scale: int,
+    num_inference_step: int,
+):
+
+    controlnet, image = controlnet_canny(
+        image_path=image_path, controlnet_model_path=controlnet_model_path
+    )
+
     pipe = StableDiffusionControlNetPipeline.from_pretrained(
-        pretrained_model_name_or_path=stable_model_path, 
-        controlnet=controlnet, 
-        safety_checker=None, 
+        pretrained_model_name_or_path=stable_model_path,
+        controlnet=controlnet,
+        safety_checker=None,
         torch_dtype=torch.float16,
     )
     pipe.to("cuda")
@@ -71,11 +72,11 @@ def stable_diffusion_controlnet_canny(
     pipe.enable_xformers_memory_efficient_attention()
 
     output = pipe(
-        prompt = prompt,
-        image = image,
-        negative_prompt = negative_prompt,
-        num_inference_steps = num_inference_step,
-        guidance_scale = guidance_scale,
+        prompt=prompt,
+        image=image,
+        negative_prompt=negative_prompt,
+        num_inference_steps=num_inference_step,
+        guidance_scale=guidance_scale,
     ).images
 
     return output[0]
@@ -86,56 +87,80 @@ def stable_diffusion_controlnet_canny_app():
         with gr.Row():
             with gr.Column():
                 controlnet_canny_image_file = gr.Image(
-                    type='filepath', 
-                    label='Image'
+                    type="filepath", label="Image"
                 )
 
                 controlnet_canny_stable_model_id = gr.Dropdown(
-                    choices=stable_model_list, 
-                    value=stable_model_list[0], 
-                    label='Stable Model Id'
+                    choices=stable_model_list,
+                    value=stable_model_list[0],
+                    label="Stable Model Id",
                 )
-                
+
                 controlnet_canny_model_id = gr.Dropdown(
                     choices=controlnet_canny_model_list,
                     value=controlnet_canny_model_list[0],
-                    label='Controlnet Model Id'
+                    label="Controlnet Model Id",
                 )
-                    
+
                 controlnet_canny_prompt = gr.Textbox(
-                    lines=1, 
-                    value=stable_prompt_list[0], 
-                    label='Prompt'
+                    lines=1, value=stable_prompt_list[0], label="Prompt"
                 )
 
                 controlnet_canny_negative_prompt = gr.Textbox(
-                    lines=1, 
-                    value=stable_negative_prompt_list[0], 
-                    label='Negative Prompt'
+                    lines=1,
+                    value=stable_negative_prompt_list[0],
+                    label="Negative Prompt",
                 )
 
                 with gr.Accordion("Advanced Options", open=False):
                     controlnet_canny_guidance_scale = gr.Slider(
-                        minimum=0.1, 
-                        maximum=15, 
-                        step=0.1, 
-                        value=7.5, 
-                        label='Guidance Scale'
+                        minimum=0.1,
+                        maximum=15,
+                        step=0.1,
+                        value=7.5,
+                        label="Guidance Scale",
                     )
 
                     controlnet_canny_num_inference_step = gr.Slider(
-                        minimum=1, 
-                        maximum=100, 
-                        step=1, 
-                        value=50, 
-                        label='Num Inference Step'
+                        minimum=1,
+                        maximum=100,
+                        step=1,
+                        value=50,
+                        label="Num Inference Step",
                     )
 
-                controlnet_canny_predict = gr.Button(value='Generator')
-                
+                controlnet_canny_predict = gr.Button(value="Generator")
+
             with gr.Column():
-                output_image = gr.Image(label='Output')
-                    
+                output_image = gr.Image(label="Output")
+
+        gr.Examples(
+            fn=stable_diffusion_controlnet_canny,
+            examples=[
+                [
+                    data_list[0],
+                    stable_model_list[0],
+                    controlnet_canny_model_list[0],
+                    stable_prompt_list[0],
+                    stable_negative_prompt_list[0],
+                    7.5,
+                    50,
+                ]
+            ],
+            inputs=[
+                controlnet_canny_image_file,
+                controlnet_canny_stable_model_id,
+                controlnet_canny_model_id,
+                controlnet_canny_prompt,
+                controlnet_canny_negative_prompt,
+                controlnet_canny_guidance_scale,
+                controlnet_canny_num_inference_step,
+            ],
+            outputs=[output_image],
+            cache_examples=False,
+            label="Controlnet Canny Example",
+        )
+
         controlnet_canny_predict.click(
             fn=stable_diffusion_controlnet_canny,
             inputs=[

diffusion_webui/controlnet/controlnet_depth.py

@@ -1,11 +1,13 @@
-from diffusers import ( StableDiffusionControlNetPipeline, 
-                       ControlNetModel, UniPCMultistepScheduler )
-
-from transformers import pipeline
-from PIL import Image
 import gradio as gr
 import numpy as np
 import torch
+from diffusers import (
+    ControlNetModel,
+    StableDiffusionControlNetPipeline,
+    UniPCMultistepScheduler,
+)
+from PIL import Image
+from transformers import pipeline
 
 stable_model_list = [
     "runwayml/stable-diffusion-v1-5",
@@ -13,27 +15,25 @@ stable_model_list = [
 ]
 
 controlnet_depth_model_list = [
-    "fusing/stable-diffusion-v1-5-controlnet-depth",
-    "thibaud/controlnet-sd21-depth-diffusers"
+    "lllyasviel/sd-controlnet-depth",
+    "thibaud/controlnet-sd21-depth-diffusers",
 ]
 
 
-stable_prompt_list = [
-        "a photo of a man.",
-        "a photo of a girl."
-    ]
+stable_prompt_list = ["a photo of a man.", "a photo of a girl."]
+
+stable_negative_prompt_list = ["bad, ugly", "deformed"]
 
-stable_negative_prompt_list = [
-        "bad, ugly",
-        "deformed"
-    ]
+data_list = [
+    "data/test.png",
+]
 
 
-def controlnet_depth(image_path:str, depth_model_path:str):
-    depth_estimator = pipeline('depth-estimation')
+def controlnet_depth(image_path: str, depth_model_path: str):
+    depth_estimator = pipeline("depth-estimation")
 
     image = Image.open(image_path)
-    image = depth_estimator(image)['depth']
+    image = depth_estimator(image)["depth"]
     image = np.array(image)
     image = image[:, :, None]
     image = np.concatenate([image, image, image], axis=2)
@@ -45,23 +45,26 @@ def controlnet_depth(image_path:str, depth_model_path:str):
 
     return controlnet, image
 
-def stable_diffusion_controlnet_depth(
-    image_path:str,
-    stable_model_path:str,
-    depth_model_path:str,
-    prompt:str,
-    negative_prompt:str,
-    guidance_scale:int,
-    num_inference_step:int,
-    ):
 
-    controlnet, image = controlnet_depth(image_path=image_path, depth_model_path=depth_model_path)
+def stable_diffusion_controlnet_depth(
+    image_path: str,
+    stable_model_path: str,
+    depth_model_path: str,
+    prompt: str,
+    negative_prompt: str,
+    guidance_scale: int,
+    num_inference_step: int,
+):
+
+    controlnet, image = controlnet_depth(
+        image_path=image_path, depth_model_path=depth_model_path
+    )
 
     pipe = StableDiffusionControlNetPipeline.from_pretrained(
-        pretrained_model_name_or_path=stable_model_path, 
-        controlnet=controlnet, 
-        safety_checker=None, 
-        torch_dtype=torch.float16
+        pretrained_model_name_or_path=stable_model_path,
+        controlnet=controlnet,
+        safety_checker=None,
+        torch_dtype=torch.float16,
     )
 
     pipe.to("cuda")
@@ -69,11 +72,11 @@ def stable_diffusion_controlnet_depth(
     pipe.enable_xformers_memory_efficient_attention()
 
     output = pipe(
-        prompt = prompt,
-        image = image,
-        negative_prompt = negative_prompt,
-        num_inference_steps = num_inference_step,
-        guidance_scale = guidance_scale,
+        prompt=prompt,
+        image=image,
+        negative_prompt=negative_prompt,
+        num_inference_steps=num_inference_step,
+        guidance_scale=guidance_scale,
     ).images
 
     return output[0]
@@ -84,56 +87,80 @@ def stable_diffusion_controlnet_depth_app():
         with gr.Row():
             with gr.Column():
                 controlnet_depth_image_file = gr.Image(
-                    type='filepath', 
-                    label='Image'
+                    type="filepath", label="Image"
                 )
 
                 controlnet_depth_stable_model_id = gr.Dropdown(
-                    choices=stable_model_list, 
-                    value=stable_model_list[0], 
-                    label='Stable Model Id'
+                    choices=stable_model_list,
+                    value=stable_model_list[0],
+                    label="Stable Model Id",
                 )
 
                 controlnet_depth_model_id = gr.Dropdown(
                     choices=controlnet_depth_model_list,
                     value=controlnet_depth_model_list[0],
-                    label='ControlNet Model Id'
+                    label="ControlNet Model Id",
                 )
-                
+
                 controlnet_depth_prompt = gr.Textbox(
-                    lines=1, 
-                    value=stable_prompt_list[0], 
-                    label='Prompt'
+                    lines=1, value=stable_prompt_list[0], label="Prompt"
                 )
 
                 controlnet_depth_negative_prompt = gr.Textbox(
-                    lines=1, 
-                    value=stable_negative_prompt_list[0], 
-                    label='Negative Prompt'
+                    lines=1,
+                    value=stable_negative_prompt_list[0],
+                    label="Negative Prompt",
                 )
 
                 with gr.Accordion("Advanced Options", open=False):
                     controlnet_depth_guidance_scale = gr.Slider(
-                        minimum=0.1, 
-                        maximum=15, 
-                        step=0.1, 
-                        value=7.5, 
-                        label='Guidance Scale'
+                        minimum=0.1,
+                        maximum=15,
+                        step=0.1,
+                        value=7.5,
+                        label="Guidance Scale",
                     )
 
                     controlnet_depth_num_inference_step = gr.Slider(
-                        minimum=1, 
-                        maximum=100, 
-                        step=1, 
-                        value=50, 
-                        label='Num Inference Step'
+                        minimum=1,
+                        maximum=100,
+                        step=1,
+                        value=50,
+                        label="Num Inference Step",
                     )
 
-                controlnet_depth_predict = gr.Button(value='Generator')
-            
+                controlnet_depth_predict = gr.Button(value="Generator")
+
             with gr.Column():
-                output_image = gr.Image(label='Output')
-        
+                output_image = gr.Image(label="Output")
+
+        gr.Examples(
+            fn=stable_diffusion_controlnet_depth,
+            examples=[
+                [
+                    data_list[0],
+                    stable_model_list[0],
+                    controlnet_depth_model_list[0],
+                    stable_prompt_list[0],
+                    stable_negative_prompt_list[0],
+                    7.5,
+                    50,
+                ]
+            ],
+            inputs=[
+                controlnet_depth_image_file,
+                controlnet_depth_stable_model_id,
+                controlnet_depth_model_id,
+                controlnet_depth_prompt,
+                controlnet_depth_negative_prompt,
+                controlnet_depth_guidance_scale,
+                controlnet_depth_num_inference_step,
+            ],
+            outputs=[output_image],
+            cache_examples=False,
+            label="ControlNet Depth Example",
+        )
+
         controlnet_depth_predict.click(
             fn=stable_diffusion_controlnet_depth,
             inputs=[
@@ -145,5 +172,5 @@ def stable_diffusion_controlnet_depth_app():
                 controlnet_depth_guidance_scale,
                 controlnet_depth_num_inference_step,
             ],
-            outputs=output_image
+            outputs=output_image,
         )

diffusion_webui/controlnet/controlnet_hed.py

@@ -1,10 +1,12 @@
-from diffusers import ( StableDiffusionControlNetPipeline, 
-                       ControlNetModel, UniPCMultistepScheduler)
-
-from controlnet_aux import HEDdetector
-from PIL import Image
 import gradio as gr
 import torch
+from controlnet_aux import HEDdetector
+from diffusers import (
+    ControlNetModel,
+    StableDiffusionControlNetPipeline,
+    UniPCMultistepScheduler,
+)
+from PIL import Image
 
 stable_model_list = [
     "runwayml/stable-diffusion-v1-5",
@@ -12,51 +14,51 @@ stable_model_list = [
 ]
 
 controlnet_hed_model_list = [
-    "fusing/stable-diffusion-v1-5-controlnet-hed",
-    "thibaud/controlnet-sd21-hed-diffusers"
+    "lllyasviel/sd-controlnet-hed",
+    "thibaud/controlnet-sd21-hed-diffusers",
 ]
 
-stable_prompt_list = [
-        "a photo of a man.",
-        "a photo of a girl."
-    ]
+stable_prompt_list = ["a photo of a man.", "a photo of a girl."]
+
+stable_negative_prompt_list = ["bad, ugly", "deformed"]
 
-stable_negative_prompt_list = [
-        "bad, ugly",
-        "deformed"
-    ]
+data_list = [
+    "data/test.png",
+]
 
 
-def controlnet_hed(image_path:str, controlnet_hed_model_path:str):
-    hed = HEDdetector.from_pretrained('lllyasviel/ControlNet')
+def controlnet_hed(image_path: str, controlnet_hed_model_path: str):
+    hed = HEDdetector.from_pretrained("lllyasviel/ControlNet")
 
     image = Image.open(image_path)
     image = hed(image)
 
     controlnet = ControlNetModel.from_pretrained(
-        controlnet_hed_model_path,
-        torch_dtype=torch.float16
+        controlnet_hed_model_path, torch_dtype=torch.float16
     )
     return controlnet, image
 
 
 def stable_diffusion_controlnet_hed(
-    image_path:str,
-    stable_model_path:str,
-    controlnet_hed_model_path:str,
-    prompt:str,
-    negative_prompt:str,
-    guidance_scale:int,
-    num_inference_step:int,
-    ):
-
-    controlnet, image = controlnet_hed(image_path=image_path, controlnet_hed_model_path=controlnet_hed_model_path)
+    image_path: str,
+    stable_model_path: str,
+    controlnet_hed_model_path: str,
+    prompt: str,
+    negative_prompt: str,
+    guidance_scale: int,
+    num_inference_step: int,
+):
+
+    controlnet, image = controlnet_hed(
+        image_path=image_path,
+        controlnet_hed_model_path=controlnet_hed_model_path,
+    )
 
     pipe = StableDiffusionControlNetPipeline.from_pretrained(
-        pretrained_model_name_or_path=stable_model_path, 
-        controlnet=controlnet, 
-        safety_checker=None, 
-        torch_dtype=torch.float16
+        pretrained_model_name_or_path=stable_model_path,
+        controlnet=controlnet,
+        safety_checker=None,
+        torch_dtype=torch.float16,
     )
 
     pipe.to("cuda")
@@ -64,71 +66,95 @@ def stable_diffusion_controlnet_hed(
     pipe.enable_xformers_memory_efficient_attention()
 
     output = pipe(
-        prompt = prompt,
-        image = image,
-        negative_prompt = negative_prompt,
-        num_inference_steps = num_inference_step,
-        guidance_scale = guidance_scale,
+        prompt=prompt,
+        image=image,
+        negative_prompt=negative_prompt,
+        num_inference_steps=num_inference_step,
+        guidance_scale=guidance_scale,
     ).images
 
     return output[0]
 
+
 def stable_diffusion_controlnet_hed_app():
     with gr.Blocks():
         with gr.Row():
             with gr.Column():
                 controlnet_hed_image_file = gr.Image(
-                    type='filepath', 
-                    label='Image'
+                    type="filepath", label="Image"
                 )
 
                 controlnet_hed_stable_model_id = gr.Dropdown(
-                    choices=stable_model_list, 
-                    value=stable_model_list[0], 
-                    label='Stable Model Id'
+                    choices=stable_model_list,
+                    value=stable_model_list[0],
+                    label="Stable Model Id",
                 )
-                
+
                 controlnet_hed_model_id = gr.Dropdown(
                     choices=stable_model_list,
                     value=stable_model_list[1],
-                    label='ControlNet Model Id'
+                    label="ControlNet Model Id",
                 )
 
                 controlnet_hed_prompt = gr.Textbox(
-                    lines=1, 
-                    value=stable_prompt_list[0], 
-                    label='Prompt'
+                    lines=1, value=stable_prompt_list[0], label="Prompt"
                 )
 
                 controlnet_hed_negative_prompt = gr.Textbox(
-                    lines=1, 
-                    value=stable_negative_prompt_list[0], 
-                    label='Negative Prompt'
+                    lines=1,
+                    value=stable_negative_prompt_list[0],
+                    label="Negative Prompt",
                 )
 
                 with gr.Accordion("Advanced Options", open=False):
                     controlnet_hed_guidance_scale = gr.Slider(
-                        minimum=0.1, 
-                        maximum=15, 
-                        step=0.1, 
-                        value=7.5, 
-                        label='Guidance Scale'
+                        minimum=0.1,
+                        maximum=15,
+                        step=0.1,
+                        value=7.5,
+                        label="Guidance Scale",
                     )
 
                     controlnet_hed_num_inference_step = gr.Slider(
-                        minimum=1, 
-                        maximum=100, 
-                        step=1, 
-                        value=50, 
-                        label='Num Inference Step'
+                        minimum=1,
+                        maximum=100,
+                        step=1,
+                        value=50,
+                        label="Num Inference Step",
                     )
 
-                controlnet_hed_predict = gr.Button(value='Generator')
-            
-        
+                controlnet_hed_predict = gr.Button(value="Generator")
+
             with gr.Column():
-                output_image = gr.Image(label='Output')
-        
+                output_image = gr.Image(label="Output")
+
+        gr.Examples(
+            fn=stable_diffusion_controlnet_hed,
+            examples=[
+                [
+                    data_list[0],
+                    stable_model_list[0],
+                    controlnet_hed_model_list[0],
+                    stable_prompt_list[0],
+                    stable_negative_prompt_list[0],
+                    7.5,
+                    50,
+                ]
+            ],
+            inputs=[
+                controlnet_hed_image_file,
+                controlnet_hed_stable_model_id,
+                controlnet_hed_model_id,
+                controlnet_hed_prompt,
+                controlnet_hed_negative_prompt,
+                controlnet_hed_guidance_scale,
+                controlnet_hed_num_inference_step,
+            ],
+            outputs=[output_image],
+            cache_examples=False,
+            label="ControlNet HED Example",
+        )
+
         controlnet_hed_predict.click(
             fn=stable_diffusion_controlnet_hed,
             inputs=[
@@ -140,6 +166,5 @@ def stable_diffusion_controlnet_hed_app():
                 controlnet_hed_guidance_scale,
                 controlnet_hed_num_inference_step,
             ],
-            outputs=[output_image]
+            outputs=[output_image],
         )
-        

diffusion_webui/controlnet/controlnet_mlsd.py

@@ -1,59 +1,56 @@
-from diffusers import ( StableDiffusionControlNetPipeline, 
-                       ControlNetModel, UniPCMultistepScheduler)
-
-from controlnet_aux import MLSDdetector
-from PIL import Image
 import gradio as gr
 import torch
+from controlnet_aux import MLSDdetector
+from diffusers import (
+    ControlNetModel,
+    StableDiffusionControlNetPipeline,
+    UniPCMultistepScheduler,
+)
+from PIL import Image
 
 stable_model_list = [
     "runwayml/stable-diffusion-v1-5",
-    "stabilityai/stable-diffusion-2",
-    "stabilityai/stable-diffusion-2-base",
-    "stabilityai/stable-diffusion-2-1",
-    "stabilityai/stable-diffusion-2-1-base"
 ]
 
-stable_prompt_list = [
-        "a photo of a man.",
-        "a photo of a girl."
-    ]
+stable_prompt_list = ["a photo of a man.", "a photo of a girl."]
 
-stable_negative_prompt_list = [
-        "bad, ugly",
-        "deformed"
-    ]
+stable_negative_prompt_list = ["bad, ugly", "deformed"]
 
+data_list = [
+    "data/test.png",
+]
 
-def controlnet_mlsd(image_path:str):
-    mlsd = MLSDdetector.from_pretrained('lllyasviel/ControlNet')
+
+def controlnet_mlsd(image_path: str):
+    mlsd = MLSDdetector.from_pretrained("lllyasviel/ControlNet")
 
     image = Image.open(image_path)
     image = mlsd(image)
 
     controlnet = ControlNetModel.from_pretrained(
-        "fusing/stable-diffusion-v1-5-controlnet-mlsd", 
-        torch_dtype=torch.float16
+        "lllyasviel/sd-controlnet-mlsd",
+        torch_dtype=torch.float16,
     )
 
     return controlnet, image
 
+
 def stable_diffusion_controlnet_mlsd(
-    image_path:str,
-    model_path:str,
-    prompt:str,
-    negative_prompt:str,
-    guidance_scale:int,
-    num_inference_step:int,
-    ):
+    image_path: str,
+    model_path: str,
+    prompt: str,
+    negative_prompt: str,
+    guidance_scale: int,
+    num_inference_step: int,
+):
 
     controlnet, image = controlnet_mlsd(image_path=image_path)
 
     pipe = StableDiffusionControlNetPipeline.from_pretrained(
-        pretrained_model_name_or_path=model_path, 
-        controlnet=controlnet, 
-        safety_checker=None, 
-        torch_dtype=torch.float16
+        pretrained_model_name_or_path=model_path,
+        controlnet=controlnet,
+        safety_checker=None,
+        torch_dtype=torch.float16,
     )
 
     pipe.to("cuda")
@@ -61,64 +58,87 @@ def stable_diffusion_controlnet_mlsd(
     pipe.enable_xformers_memory_efficient_attention()
 
     output = pipe(
-        prompt = prompt,
-        image = image,
-        negative_prompt = negative_prompt,
-        num_inference_steps = num_inference_step,
-        guidance_scale = guidance_scale,
+        prompt=prompt,
+        image=image,
+        negative_prompt=negative_prompt,
+        num_inference_steps=num_inference_step,
+        guidance_scale=guidance_scale,
     ).images
 
     return output[0]
 
+
 def stable_diffusion_controlnet_mlsd_app():
     with gr.Blocks():
         with gr.Row():
             with gr.Column():
                 controlnet_mlsd_image_file = gr.Image(
-                    type='filepath', 
-                    label='Image'
+                    type="filepath", label="Image"
                 )
 
                 controlnet_mlsd_model_id = gr.Dropdown(
-                    choices=stable_model_list, 
-                    value=stable_model_list[0], 
-                    label='Stable Model Id'
+                    choices=stable_model_list,
+                    value=stable_model_list[0],
+                    label="Stable Model Id",
                 )
 
                 controlnet_mlsd_prompt = gr.Textbox(
-                    lines=1, 
-                    value=stable_prompt_list[0], 
-                    label='Prompt'
+                    lines=1, value=stable_prompt_list[0], label="Prompt"
                 )
 
                 controlnet_mlsd_negative_prompt = gr.Textbox(
-                    lines=1, 
-                    value=stable_negative_prompt_list[0], 
-                    label='Negative Prompt'
+                    lines=1,
+                    value=stable_negative_prompt_list[0],
+                    label="Negative Prompt",
                 )
 
                 with gr.Accordion("Advanced Options", open=False):
                     controlnet_mlsd_guidance_scale = gr.Slider(
-                        minimum=0.1, 
-                        maximum=15, 
-                        step=0.1, 
-                        value=7.5, 
-                        label='Guidance Scale'
+                        minimum=0.1,
+                        maximum=15,
+                        step=0.1,
+                        value=7.5,
+                        label="Guidance Scale",
                     )
 
                     controlnet_mlsd_num_inference_step = gr.Slider(
-                        minimum=1, 
-                        maximum=100, 
-                        step=1, 
-                        value=50, 
-                        label='Num Inference Step'
+                        minimum=1,
+                        maximum=100,
+                        step=1,
+                        value=50,
+                        label="Num Inference Step",
                     )
 
-                controlnet_mlsd_predict = gr.Button(value='Generator')
+                controlnet_mlsd_predict = gr.Button(value="Generator")
 
             with gr.Column():
-                output_image = gr.Image(label='Output')
-        
+                output_image = gr.Image(label="Output")
+
+        gr.Examples(
+            fn=stable_diffusion_controlnet_mlsd,
+            examples=[
+                [
+                    data_list[0],
+                    stable_model_list[0],
+                    stable_prompt_list[0],
+                    stable_negative_prompt_list[0],
+                    7.5,
+                    50,
+                ]
+            ],
+            inputs=[
+                controlnet_mlsd_image_file,
+                controlnet_mlsd_model_id,
+                controlnet_mlsd_prompt,
+                controlnet_mlsd_negative_prompt,
+                controlnet_mlsd_guidance_scale,
+                controlnet_mlsd_num_inference_step,
+            ],
+            outputs=[output_image],
+            label="ControlNet-MLSD Example",
+            cache_examples=False,
+        )
+
         controlnet_mlsd_predict.click(
             fn=stable_diffusion_controlnet_mlsd,
             inputs=[
@@ -127,7 +147,7 @@ def stable_diffusion_controlnet_mlsd_app():
                 controlnet_mlsd_prompt,
                 controlnet_mlsd_negative_prompt,
                 controlnet_mlsd_guidance_scale,
-                controlnet_mlsd_num_inference_step
+                controlnet_mlsd_num_inference_step,
             ],
-            outputs=output_image
+            outputs=output_image,
         )

diffusion_webui/controlnet/controlnet_pose.py

@@ -1,11 +1,12 @@
-from diffusers import ( StableDiffusionControlNetPipeline, 
-                       ControlNetModel, UniPCMultistepScheduler)
-
-from controlnet_aux import OpenposeDetector
-
-from PIL import Image
 import gradio as gr
 import torch
+from controlnet_aux import OpenposeDetector
+from diffusers import (
+    ControlNetModel,
+    StableDiffusionControlNetPipeline,
+    UniPCMultistepScheduler,
+)
+from PIL import Image
 
 stable_model_list = [
     "runwayml/stable-diffusion-v1-5",
@@ -13,51 +14,52 @@ stable_model_list = [
 ]
 
 controlnet_pose_model_list = [
-    "fusing/stable-diffusion-v1-5-controlnet-openpose",
-    "thibaud/controlnet-sd21-openpose-diffusers"
+    "lllyasviel/sd-controlnet-openpose",
+    "thibaud/controlnet-sd21-openpose-diffusers",
 ]
 
-stable_prompt_list = [
-        "a photo of a man.",
-        "a photo of a girl."
-    ]
+stable_prompt_list = ["a photo of a man.", "a photo of a girl."]
 
-stable_negative_prompt_list = [
-        "bad, ugly",
-        "deformed"
-    ]
+stable_negative_prompt_list = ["bad, ugly", "deformed"]
 
+data_list = [
+    "data/test.png",
+]
 
-def controlnet_pose(image_path:str, controlnet_pose_model_path:str):
-    openpose = OpenposeDetector.from_pretrained('lllyasviel/ControlNet')
+
+def controlnet_pose(image_path: str, controlnet_pose_model_path: str):
+    openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
 
     image = Image.open(image_path)
     image = openpose(image)
 
     controlnet = ControlNetModel.from_pretrained(
-        controlnet_pose_model_path,
-        torch_dtype=torch.float16
+        controlnet_pose_model_path, torch_dtype=torch.float16
     )
 
     return controlnet, image
 
-def stable_diffusion_controlnet_pose(
-    image_path:str,
-    stable_model_path:str,
-    controlnet_pose_model_path:str,
-    prompt:str,
-    negative_prompt:str,
-    guidance_scale:int,
-    num_inference_step:int,
-    ):
 
-    controlnet, image = controlnet_pose(image_path=image_path, controlnet_pose_model_path=controlnet_pose_model_path)
+def stable_diffusion_controlnet_pose(
+    image_path: str,
+    stable_model_path: str,
+    controlnet_pose_model_path: str,
+    prompt: str,
+    negative_prompt: str,
+    guidance_scale: int,
+    num_inference_step: int,
+):
+
+    controlnet, image = controlnet_pose(
+        image_path=image_path,
+        controlnet_pose_model_path=controlnet_pose_model_path,
+    )
 
     pipe = StableDiffusionControlNetPipeline.from_pretrained(
-        pretrained_model_name_or_path=-stable_model_path, 
-        controlnet=controlnet, 
-        safety_checker=None, 
-        torch_dtype=torch.float16
+        pretrained_model_name_or_path=-stable_model_path,
+        controlnet=controlnet,
+        safety_checker=None,
+        torch_dtype=torch.float16,
     )
 
     pipe.to("cuda")
@@ -65,11 +67,11 @@ def stable_diffusion_controlnet_pose(
     pipe.enable_xformers_memory_efficient_attention()
 
     output = pipe(
-        prompt = prompt,
-        image = image,
-        negative_prompt = negative_prompt,
-        num_inference_steps = num_inference_step,
-        guidance_scale = guidance_scale,
+        prompt=prompt,
+        image=image,
+        negative_prompt=negative_prompt,
+        num_inference_steps=num_inference_step,
+        guidance_scale=guidance_scale,
     ).images
 
     return output[0]
@@ -80,57 +82,79 @@ def stable_diffusion_controlnet_pose_app():
         with gr.Row():
             with gr.Column():
                 controlnet_pose_image_file = gr.Image(
-                    type='filepath', 
-                    label='Image'
+                    type="filepath", label="Image"
                 )
 
                 controlnet_pose_stable_model_id = gr.Dropdown(
-                    choices=stable_model_list, 
-                    value=stable_model_list[0], 
-                    label='Stable Model Id'
+                    choices=stable_model_list,
+                    value=stable_model_list[0],
+                    label="Stable Model Id",
                 )
-                
+
                 controlnet_pose_model_id = gr.Dropdown(
                     choices=stable_model_list,
                     value=stable_model_list[1],
-                    label='ControlNet Model Id'
+                    label="ControlNet Model Id",
                 )
-                       
 
                 controlnet_pose_prompt = gr.Textbox(
-                    lines=1, 
-                    value=stable_prompt_list[0], 
-                    label='Prompt'
+                    lines=1, value=stable_prompt_list[0], label="Prompt"
                 )
 
                 controlnet_pose_negative_prompt = gr.Textbox(
-                    lines=1, 
-                    value=stable_negative_prompt_list[0], 
-                    label='Negative Prompt'
+                    lines=1,
+                    value=stable_negative_prompt_list[0],
+                    label="Negative Prompt",
                 )
 
                 with gr.Accordion("Advanced Options", open=False):
                     controlnet_pose_guidance_scale = gr.Slider(
-                        minimum=0.1, 
-                        maximum=15, 
-                        step=0.1, 
-                        value=7.5, 
-                        label='Guidance Scale'
+                        minimum=0.1,
+                        maximum=15,
+                        step=0.1,
+                        value=7.5,
+                        label="Guidance Scale",
                     )
 
                     controlnet_pose_num_inference_step = gr.Slider(
-                        minimum=1, 
-                        maximum=100, 
-                        step=1, 
-                        value=50, 
-                        label='Num Inference Step'
+                        minimum=1,
+                        maximum=100,
+                        step=1,
+                        value=50,
+                        label="Num Inference Step",
                     )
 
-                controlnet_pose_predict = gr.Button(value='Generator')
+                controlnet_pose_predict = gr.Button(value="Generator")
 
             with gr.Column():
-                output_image = gr.Image(label='Output')
-        
+                output_image = gr.Image(label="Output")
+
+        gr.Examples(
+            fn=stable_diffusion_controlnet_pose,
+            examples=[
+                [
+                    data_list[0],
+                    stable_model_list[0],
+                    controlnet_pose_model_list[0],
+                    stable_prompt_list[0],
+                    stable_negative_prompt_list[0],
+                    7.5,
+                    50,
+                ]
+            ],
+            inputs=[
+                controlnet_pose_image_file,
+                controlnet_pose_stable_model_id,
+                controlnet_pose_model_id,
+                controlnet_pose_prompt,
+                controlnet_pose_negative_prompt,
+                controlnet_pose_guidance_scale,
+                controlnet_pose_num_inference_step,
+            ],
+            outputs=[output_image],
+            label="ControlNet Pose Example",
+            cache_examples=False,
+        )
         controlnet_pose_predict.click(
             fn=stable_diffusion_controlnet_pose,
             inputs=[
@@ -142,5 +166,5 @@ def stable_diffusion_controlnet_pose_app():
                 controlnet_pose_guidance_scale,
                 controlnet_pose_num_inference_step,
             ],
-            outputs=output_image
+            outputs=output_image,
         )

diffusion_webui/controlnet/controlnet_scribble.py

@@ -1,11 +1,12 @@
-from diffusers import ( StableDiffusionControlNetPipeline, 
-                       ControlNetModel, UniPCMultistepScheduler)
-
-from controlnet_aux import HEDdetector
-
-from PIL import Image
 import gradio as gr
 import torch
+from controlnet_aux import HEDdetector
+from diffusers import (
+    ControlNetModel,
+    StableDiffusionControlNetPipeline,
+    UniPCMultistepScheduler,
+)
+from PIL import Image
 
 stable_model_list = [
     "runwayml/stable-diffusion-v1-5",
@@ -13,23 +14,21 @@ stable_model_list = [
 ]
 
 controlnet_hed_model_list = [
-    "fusing/stable-diffusion-v1-5-controlnet-hed",
-    "thibaud/controlnet-sd21-scribble-diffusers"
+    "lllyasviel/sd-controlnet-scribble",
+    "thibaud/controlnet-sd21-scribble-diffusers",
 ]
 
-stable_prompt_list = [
-        "a photo of a man.",
-        "a photo of a girl."
-    ]
+stable_prompt_list = ["a photo of a man.", "a photo of a girl."]
+
+stable_negative_prompt_list = ["bad, ugly", "deformed"]
 
-stable_negative_prompt_list = [
-        "bad, ugly",
-        "deformed"
-    ]
+data_list = [
+    "data/test.png",
+]
 
 
-def controlnet_scribble(image_path:str, controlnet_hed_model_path:str):
-    hed = HEDdetector.from_pretrained('lllyasviel/ControlNet')
+def controlnet_scribble(image_path: str, controlnet_hed_model_path: str):
+    hed = HEDdetector.from_pretrained("lllyasviel/ControlNet")
 
     image = Image.open(image_path)
     image = hed(image, scribble=True)
@@ -40,23 +39,27 @@ def controlnet_scribble(image_path:str, controlnet_hed_model_path:str):
 
     return controlnet, image
 
-def stable_diffusion_controlnet_scribble(
-    image_path:str,
-    stable_model_path:str,
-    controlnet_hed_model_path:str,
-    prompt:str,
-    negative_prompt:str,
-    guidance_scale:int,
-    num_inference_step:int,
-    ):
 
-    controlnet, image = controlnet_scribble(image_path=image_path, controlnet_hed_model_path=controlnet_hed_model_path)
+def stable_diffusion_controlnet_scribble(
+    image_path: str,
+    stable_model_path: str,
+    controlnet_hed_model_path: str,
+    prompt: str,
+    negative_prompt: str,
+    guidance_scale: int,
+    num_inference_step: int,
+):
+
+    controlnet, image = controlnet_scribble(
+        image_path=image_path,
+        controlnet_hed_model_path=controlnet_hed_model_path,
+    )
 
     pipe = StableDiffusionControlNetPipeline.from_pretrained(
-        pretrained_model_name_or_path=stable_model_path, 
-        controlnet=controlnet, 
-        safety_checker=None, 
-        torch_dtype=torch.float16
+        pretrained_model_name_or_path=stable_model_path,
+        controlnet=controlnet,
+        safety_checker=None,
+        torch_dtype=torch.float16,
     )
 
     pipe.to("cuda")
@@ -64,70 +67,94 @@ def stable_diffusion_controlnet_scribble(
     pipe.enable_xformers_memory_efficient_attention()
 
     output = pipe(
-        prompt = prompt,
-        image = image,
-        negative_prompt = negative_prompt,
-        num_inference_steps = num_inference_step,
-        guidance_scale = guidance_scale,
+        prompt=prompt,
+        image=image,
+        negative_prompt=negative_prompt,
+        num_inference_steps=num_inference_step,
+        guidance_scale=guidance_scale,
     ).images
 
     return output[0]
 
+
 def stable_diffusion_controlnet_scribble_app():
     with gr.Blocks():
         with gr.Row():
             with gr.Column():
                 controlnet_scribble_image_file = gr.Image(
-                    type='filepath', 
-                    label='Image'
+                    type="filepath", label="Image"
                 )
 
                 controlnet_scribble_stablev1_model_id = gr.Dropdown(
-                    choices=stable_model_list, 
-                    value=stable_model_list[0], 
-                    label='Stable v1.5 Model Id'
+                    choices=stable_model_list,
+                    value=stable_model_list[0],
+                    label="Stable v1.5 Model Id",
                 )
-                
+
                 controlnet_scribble_stablev2_model_id = gr.Dropdown(
                     choices=stable_model_list,
                     value=stable_model_list[1],
-                    label='Stable v2.1 Model Id'
+                    label="Stable v2.1 Model Id",
                 )
 
                 controlnet_scribble_prompt = gr.Textbox(
-                    lines=1, 
-                    value=stable_prompt_list[0], 
-                    label='Prompt'
+                    lines=1, value=stable_prompt_list[0], label="Prompt"
                 )
 
                 controlnet_scribble_negative_prompt = gr.Textbox(
-                    lines=1, 
-                    value=stable_negative_prompt_list[0], 
-                    label='Negative Prompt'
+                    lines=1,
+                    value=stable_negative_prompt_list[0],
+                    label="Negative Prompt",
                 )
 
                 with gr.Accordion("Advanced Options", open=False):
                     controlnet_scribble_guidance_scale = gr.Slider(
-                        minimum=0.1, 
-                        maximum=15, 
-                        step=0.1, 
-                        value=7.5, 
-                        label='Guidance Scale'
+                        minimum=0.1,
+                        maximum=15,
+                        step=0.1,
+                        value=7.5,
+                        label="Guidance Scale",
                     )
 
                     controlnet_scribble_num_inference_step = gr.Slider(
-                        minimum=1, 
-                        maximum=100, 
-                        step=1, 
-                        value=50, 
-                        label='Num Inference Step'
+                        minimum=1,
+                        maximum=100,
+                        step=1,
+                        value=50,
+                        label="Num Inference Step",
                     )
 
-                controlnet_scribble_predict = gr.Button(value='Generator')
+                controlnet_scribble_predict = gr.Button(value="Generator")
 
             with gr.Column():
-                output_image = gr.Image(label='Output')
-        
+                output_image = gr.Image(label="Output")
+
+        gr.Examples(
+            fn=stable_diffusion_controlnet_scribble,
+            examples=[
+                [
+                    data_list[0],
+                    stable_model_list[0],
+                    controlnet_hed_model_list[0],
+                    stable_prompt_list[0],
+                    stable_negative_prompt_list[0],
+                    7.5,
+                    50,
+                ],
+            ],
+            inputs=[
+                controlnet_scribble_image_file,
+                controlnet_scribble_stablev1_model_id,
+                controlnet_scribble_stablev2_model_id,
+                controlnet_scribble_prompt,
+                controlnet_scribble_negative_prompt,
+                controlnet_scribble_guidance_scale,
+                controlnet_scribble_num_inference_step,
+            ],
+            outputs=[output_image],
+            label="ControlNet Scribble Example",
+            cache_examples=False,
+        )
         controlnet_scribble_predict.click(
             fn=stable_diffusion_controlnet_scribble,
             inputs=[
@@ -139,5 +166,5 @@ def stable_diffusion_controlnet_scribble_app():
                 controlnet_scribble_guidance_scale,
                 controlnet_scribble_num_inference_step,
             ],
-            outputs=output_image
+            outputs=output_image,
         )

diffusion_webui/controlnet/controlnet_seg.py

@@ -1,86 +1,200 @@
-from transformers import AutoImageProcessor, UperNetForSemanticSegmentation
-import torch
-from diffusers import (StableDiffusionControlNetPipeline, 
-                       ControlNetModel, UniPCMultistepScheduler)
-
-
-from PIL import Image
 import gradio as gr
 import numpy as np
 import torch
+from diffusers import (
+    ControlNetModel,
+    StableDiffusionControlNetPipeline,
+    UniPCMultistepScheduler,
+)
+from PIL import Image
+from transformers import AutoImageProcessor, UperNetForSemanticSegmentation
 
 stable_model_list = [
     "runwayml/stable-diffusion-v1-5",
-    "stabilityai/stable-diffusion-2",
-    "stabilityai/stable-diffusion-2-base",
-    "stabilityai/stable-diffusion-2-1",
-    "stabilityai/stable-diffusion-2-1-base"
 ]
 
-stable_prompt_list = [
-        "a photo of a man.",
-        "a photo of a girl."
-    ]
+stable_prompt_list = ["a photo of a man.", "a photo of a girl."]
 
-stable_negative_prompt_list = [
-        "bad, ugly",
-        "deformed"
-    ]
+stable_negative_prompt_list = ["bad, ugly", "deformed"]
+
+data_list = [
+    "data/test.png",
+]
 
 
 def ade_palette():
     """ADE20K palette that maps each class to RGB values."""
-    return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
-            [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
-            [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
-            [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
-            [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
-            [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
-            [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
-            [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
-            [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
-            [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
-            [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
-            [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
-            [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
-            [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
-            [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
-            [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
-            [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
-            [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
-            [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
-            [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
-            [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
-            [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
-            [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
-            [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
-            [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
-            [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
-            [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
-            [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
-            [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
-            [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
-            [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
-            [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
-            [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
-            [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
-            [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
-            [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
-            [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
-            [102, 255, 0], [92, 0, 255]]
-
-
-def controlnet_mlsd(image_path:str):
-    image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small")
-    image_segmentor = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small")
-
-    image = Image.open(image_path).convert('RGB')
+    return [
+        [120, 120, 120],
+        [180, 120, 120],
+        [6, 230, 230],
+        [80, 50, 50],
+        [4, 200, 3],
+        [120, 120, 80],
+        [140, 140, 140],
+        [204, 5, 255],
+        [230, 230, 230],
+        [4, 250, 7],
+        [224, 5, 255],
+        [235, 255, 7],
+        [150, 5, 61],
+        [120, 120, 70],
+        [8, 255, 51],
+        [255, 6, 82],
+        [143, 255, 140],
+        [204, 255, 4],
+        [255, 51, 7],
+        [204, 70, 3],
+        [0, 102, 200],
+        [61, 230, 250],
+        [255, 6, 51],
+        [11, 102, 255],
+        [255, 7, 71],
+        [255, 9, 224],
+        [9, 7, 230],
+        [220, 220, 220],
+        [255, 9, 92],
+        [112, 9, 255],
+        [8, 255, 214],
+        [7, 255, 224],
+        [255, 184, 6],
+        [10, 255, 71],
+        [255, 41, 10],
+        [7, 255, 255],
+        [224, 255, 8],
+        [102, 8, 255],
+        [255, 61, 6],
+        [255, 194, 7],
+        [255, 122, 8],
+        [0, 255, 20],
+        [255, 8, 41],
+        [255, 5, 153],
+        [6, 51, 255],
+        [235, 12, 255],
+        [160, 150, 20],
+        [0, 163, 255],
+        [140, 140, 140],
+        [250, 10, 15],
+        [20, 255, 0],
+        [31, 255, 0],
+        [255, 31, 0],
+        [255, 224, 0],
+        [153, 255, 0],
+        [0, 0, 255],
+        [255, 71, 0],
+        [0, 235, 255],
+        [0, 173, 255],
+        [31, 0, 255],
+        [11, 200, 200],
+        [255, 82, 0],
+        [0, 255, 245],
+        [0, 61, 255],
+        [0, 255, 112],
+        [0, 255, 133],
+        [255, 0, 0],
+        [255, 163, 0],
+        [255, 102, 0],
+        [194, 255, 0],
+        [0, 143, 255],
+        [51, 255, 0],
+        [0, 82, 255],
+        [0, 255, 41],
+        [0, 255, 173],
+        [10, 0, 255],
+        [173, 255, 0],
+        [0, 255, 153],
+        [255, 92, 0],
+        [255, 0, 255],
+        [255, 0, 245],
+        [255, 0, 102],
+        [255, 173, 0],
+        [255, 0, 20],
+        [255, 184, 184],
+        [0, 31, 255],
+        [0, 255, 61],
+        [0, 71, 255],
+        [255, 0, 204],
+        [0, 255, 194],
+        [0, 255, 82],
+        [0, 10, 255],
+        [0, 112, 255],
+        [51, 0, 255],
+        [0, 194, 255],
+        [0, 122, 255],
+        [0, 255, 163],
+        [255, 153, 0],
+        [0, 255, 10],
+        [255, 112, 0],
+        [143, 255, 0],
+        [82, 0, 255],
+        [163, 255, 0],
+        [255, 235, 0],
+        [8, 184, 170],
+        [133, 0, 255],
+        [0, 255, 92],
+        [184, 0, 255],
+        [255, 0, 31],
+        [0, 184, 255],
+        [0, 214, 255],
+        [255, 0, 112],
+        [92, 255, 0],
+        [0, 224, 255],
+        [112, 224, 255],
+        [70, 184, 160],
+        [163, 0, 255],
+        [153, 0, 255],
+        [71, 255, 0],
+        [255, 0, 163],
+        [255, 204, 0],
+        [255, 0, 143],
+        [0, 255, 235],
+        [133, 255, 0],
+        [255, 0, 235],
+        [245, 0, 255],
+        [255, 0, 122],
+        [255, 245, 0],
+        [10, 190, 212],
+        [214, 255, 0],
+        [0, 204, 255],
+        [20, 0, 255],
+        [255, 255, 0],
+        [0, 153, 255],
+        [0, 41, 255],
+        [0, 255, 204],
+        [41, 0, 255],
+        [41, 255, 0],
+        [173, 0, 255],
+        [0, 245, 255],
+        [71, 0, 255],
+        [122, 0, 255],
+        [0, 255, 184],
+        [0, 92, 255],
+        [184, 255, 0],
+        [0, 133, 255],
+        [255, 214, 0],
+        [25, 194, 194],
+        [102, 255, 0],
+        [92, 0, 255],
+    ]
+
+
+def controlnet_mlsd(image_path: str):
+    image_processor = AutoImageProcessor.from_pretrained(
+        "openmmlab/upernet-convnext-small"
+    )
+    image_segmentor = UperNetForSemanticSegmentation.from_pretrained(
+        "openmmlab/upernet-convnext-small"
+    )
+
+    image = Image.open(image_path).convert("RGB")
     pixel_values = image_processor(image, return_tensors="pt").pixel_values
 
     with torch.no_grad():
         outputs = image_segmentor(pixel_values)
 
-    seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
+    seg = image_processor.post_process_semantic_segmentation(
+        outputs, target_sizes=[image.size[::-1]]
+    )[0]
 
     color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)
     palette = np.array(ade_palette())
@@ -91,28 +205,28 @@ def controlnet_mlsd(image_path:str):
     color_seg = color_seg.astype(np.uint8)
     image = Image.fromarray(color_seg)
     controlnet = ControlNetModel.from_pretrained(
-        "fusing/stable-diffusion-v1-5-controlnet-seg", torch_dtype=torch.float16
+        "lllyasviel/sd-controlnet-seg", torch_dtype=torch.float16
     )
 
     return controlnet, image
 
 
 def stable_diffusion_controlnet_seg(
-    image_path:str,
-    model_path:str,
-    prompt:str,
-    negative_prompt:str,
-    guidance_scale:int,
-    num_inference_step:int,
-    ):
+    image_path: str,
+    model_path: str,
+    prompt: str,
+    negative_prompt: str,
+    guidance_scale: int,
+    num_inference_step: int,
+):
 
     controlnet, image = controlnet_mlsd(image_path=image_path)
 
     pipe = StableDiffusionControlNetPipeline.from_pretrained(
-        pretrained_model_name_or_path=model_path, 
-        controlnet=controlnet, 
-        safety_checker=None, 
-        torch_dtype=torch.float16
+        pretrained_model_name_or_path=model_path,
+        controlnet=controlnet,
+        safety_checker=None,
+        torch_dtype=torch.float16,
     )
 
     pipe.to("cuda")
@@ -120,64 +234,87 @@ def stable_diffusion_controlnet_seg(
     pipe.enable_xformers_memory_efficient_attention()
 
     output = pipe(
-        prompt = prompt,
-        image = image,
-        negative_prompt = negative_prompt,
-        num_inference_steps = num_inference_step,
-        guidance_scale = guidance_scale,
+        prompt=prompt,
+        image=image,
+        negative_prompt=negative_prompt,
+        num_inference_steps=num_inference_step,
+        guidance_scale=guidance_scale,
     ).images
 
     return output[0]
 
+
 def stable_diffusion_controlnet_seg_app():
     with gr.Blocks():
         with gr.Row():
             with gr.Column():
                 controlnet_seg_image_file = gr.Image(
-                    type='filepath', 
-                    label='Image'
+                    type="filepath", label="Image"
                 )
 
                 controlnet_seg_model_id = gr.Dropdown(
-                    choices=stable_model_list, 
-                    value=stable_model_list[0], 
-                    label='Stable Model Id'
+                    choices=stable_model_list,
+                    value=stable_model_list[0],
+                    label="Stable Model Id",
                 )
 
                 controlnet_seg_prompt = gr.Textbox(
-                    lines=1, 
-                    value=stable_prompt_list[0], 
-                    label='Prompt'
+                    lines=1, value=stable_prompt_list[0], label="Prompt"
                 )
 
                 controlnet_seg_negative_prompt = gr.Textbox(
-                    lines=1, 
-                    value=stable_negative_prompt_list[0], 
-                    label='Negative Prompt'
+                    lines=1,
+                    value=stable_negative_prompt_list[0],
+                    label="Negative Prompt",
                 )
 
                 with gr.Accordion("Advanced Options", open=False):
                     controlnet_seg_guidance_scale = gr.Slider(
-                        minimum=0.1, 
-                        maximum=15, 
-                        step=0.1, 
-                        value=7.5, 
-                        label='Guidance Scale'
+                        minimum=0.1,
+                        maximum=15,
+                        step=0.1,
+                        value=7.5,
+                        label="Guidance Scale",
                     )
 
                     controlnet_seg_num_inference_step = gr.Slider(
-                        minimum=1, 
-                        maximum=100, 
-                        step=1, 
-                        value=50, 
-                        label='Num Inference Step'
+                        minimum=1,
+                        maximum=100,
+                        step=1,
+                        value=50,
+                        label="Num Inference Step",
                     )
 
-                controlnet_seg_predict = gr.Button(value='Generator')
+                controlnet_seg_predict = gr.Button(value="Generator")
 
             with gr.Column():
-                output_image = gr.Image(label='Output')
-        
+                output_image = gr.Image(label="Output")
+
+        gr.Examples(
+            fn=stable_diffusion_controlnet_seg,
+            examples=[
+                [
+                    data_list[0],
+                    stable_model_list[0],
+                    stable_prompt_list[0],
+                    stable_negative_prompt_list[0],
+                    7.5,
+                    50,
+                ],
+            ],
+            inputs=[
+                controlnet_seg_image_file,
+                controlnet_seg_model_id,
+                controlnet_seg_prompt,
+                controlnet_seg_negative_prompt,
+                controlnet_seg_guidance_scale,
+                controlnet_seg_num_inference_step,
+            ],
+            outputs=[output_image],
+            cache_examples=False,
+            label="ControlNet Segmentation Example",
+        )
+
         controlnet_seg_predict.click(
             fn=stable_diffusion_controlnet_seg,
             inputs=[
@@ -190,4 +327,3 @@ def stable_diffusion_controlnet_seg_app():
             ],
             outputs=[output_image],
         )
-            

diffusion_webui/helpers.py

@@ -0,0 +1,33 @@
+from diffusion_webui.controlnet.controlnet_canny import (
+    stable_diffusion_controlnet_canny_app,
+)
+from diffusion_webui.controlnet.controlnet_depth import (
+    stable_diffusion_controlnet_depth_app,
+)
+from diffusion_webui.controlnet.controlnet_hed import (
+    stable_diffusion_controlnet_hed_app,
+)
+from diffusion_webui.controlnet.controlnet_mlsd import (
+    stable_diffusion_controlnet_mlsd_app,
+)
+from diffusion_webui.controlnet.controlnet_pose import (
+    stable_diffusion_controlnet_pose_app,
+)
+from diffusion_webui.controlnet.controlnet_scribble import (
+    stable_diffusion_controlnet_scribble_app,
+)
+from diffusion_webui.controlnet.controlnet_seg import (
+    stable_diffusion_controlnet_seg_app,
+)
+from diffusion_webui.stable_diffusion.img2img_app import (
+    stable_diffusion_img2img_app,
+)
+from diffusion_webui.stable_diffusion.inpaint_app import (
+    stable_diffusion_inpaint_app,
+)
+from diffusion_webui.stable_diffusion.keras_txt2img import (
+    keras_stable_diffusion_app,
+)
+from diffusion_webui.stable_diffusion.text2img_app import (
+    stable_diffusion_text2img_app,
+)

diffusion_webui/stable_diffusion/img2img_app.py

@@ -1,54 +1,46 @@
-from diffusers import StableDiffusionImg2ImgPipeline, DDIMScheduler
-
-from PIL import Image
 import gradio as gr
 import torch
+from diffusers import DDIMScheduler, StableDiffusionImg2ImgPipeline
+from PIL import Image
 
 stable_model_list = [
     "runwayml/stable-diffusion-v1-5",
-    "stabilityai/stable-diffusion-2",
-    "stabilityai/stable-diffusion-2-base",
     "stabilityai/stable-diffusion-2-1",
-    "stabilityai/stable-diffusion-2-1-base"
 ]
 
-stable_prompt_list = [
-        "a photo of a man.",
-        "a photo of a girl."
-    ]
+stable_prompt_list = ["a photo of a man.", "a photo of a girl."]
 
-stable_negative_prompt_list = [
-        "bad, ugly",
-        "deformed"
-    ]
+stable_negative_prompt_list = ["bad, ugly", "deformed"]
+
+data_list = [
+    "data/test.png",
+]
 
 
 def stable_diffusion_img2img(
-    image_path:str,
-    model_path:str,
-    prompt:str,
-    negative_prompt:str,
-    guidance_scale:int,
-    num_inference_step:int,
-    ):
+    image_path: str,
+    model_path: str,
+    prompt: str,
+    negative_prompt: str,
+    guidance_scale: int,
+    num_inference_step: int,
+):
 
     image = Image.open(image_path)
 
     pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-        model_path, 
-        safety_checker=None, 
-        torch_dtype=torch.float16
+        model_path, safety_checker=None, torch_dtype=torch.float16
     )
     pipe.to("cuda")
     pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
     pipe.enable_xformers_memory_efficient_attention()
 
     output = pipe(
-        prompt = prompt,
-        image = image,
-        negative_prompt = negative_prompt,
-        num_inference_steps = num_inference_step,
-        guidance_scale = guidance_scale,
+        prompt=prompt,
+        image=image,
+        negative_prompt=negative_prompt,
+        num_inference_steps=num_inference_step,
+        guidance_scale=guidance_scale,
     ).images
 
     return output[0]
@@ -59,50 +51,72 @@ def stable_diffusion_img2img_app():
         with gr.Row():
             with gr.Column():
                 image2image2_image_file = gr.Image(
-                    type='filepath', 
-                    label='Image'
+                    type="filepath", label="Image"
                 )
 
                 image2image_model_path = gr.Dropdown(
-                    choices=stable_model_list, 
-                    value=stable_model_list[0], 
-                    label='Image-Image Model Id'
+                    choices=stable_model_list,
+                    value=stable_model_list[0],
+                    label="Image-Image Model Id",
                 )
 
                 image2image_prompt = gr.Textbox(
-                    lines=1, 
-                    value=stable_prompt_list[0], 
-                    label='Prompt'
+                    lines=1, value=stable_prompt_list[0], label="Prompt"
                 )
 
                 image2image_negative_prompt = gr.Textbox(
-                    lines=1, 
-                    value=stable_negative_prompt_list[0], 
-                    label='Negative Prompt'
+                    lines=1,
+                    value=stable_negative_prompt_list[0],
+                    label="Negative Prompt",
                 )
 
                 with gr.Accordion("Advanced Options", open=False):
                     image2image_guidance_scale = gr.Slider(
-                        minimum=0.1, 
-                        maximum=15, 
-                        step=0.1, 
-                        value=7.5, 
-                        label='Guidance Scale'
+                        minimum=0.1,
+                        maximum=15,
+                        step=0.1,
+                        value=7.5,
+                        label="Guidance Scale",
                     )
 
                     image2image_num_inference_step = gr.Slider(
-                        minimum=1, 
-                        maximum=100, 
-                        step=1, 
-                        value=50, 
-                        label='Num Inference Step'
+                        minimum=1,
+                        maximum=100,
+                        step=1,
+                        value=50,
+                        label="Num Inference Step",
                     )
 
-                image2image_predict = gr.Button(value='Generator')
+                image2image_predict = gr.Button(value="Generator")
 
             with gr.Column():
-                output_image = gr.Image(label='Output')
-                    
+                output_image = gr.Image(label="Output")
+
+        gr.Examples(
+            fn=stable_diffusion_img2img,
+            examples=[
+                [
+                    data_list[0],
+                    stable_model_list[0],
+                    stable_prompt_list[0],
+                    stable_negative_prompt_list[0],
+                    7.5,
+                    50,
+                ],
+            ],
+            inputs=[
+                image2image2_image_file,
+                image2image_model_path,
+                image2image_prompt,
+                image2image_negative_prompt,
+                image2image_guidance_scale,
+                image2image_num_inference_step,
+            ],
+            outputs=[output_image],
+            cache_examples=False,
+            label="Image-Image Generator",
+        )
+
         image2image_predict.click(
             fn=stable_diffusion_img2img,
             inputs=[
@@ -112,6 +126,6 @@ def stable_diffusion_img2img_app():
                 image2image_negative_prompt,
                 image2image_guidance_scale,
                 image2image_num_inference_step,
-            ],  
+            ],
             outputs=[output_image],
         )

diffusion_webui/stable_diffusion/inpaint_app.py

@@ -1,32 +1,25 @@
-from diffusers import DiffusionPipeline, DDIMScheduler
-import torch
-
 import gradio as gr
+import torch
+from diffusers import DDIMScheduler, DiffusionPipeline
 
 stable_inpiant_model_list = [
     "stabilityai/stable-diffusion-2-inpainting",
-    "runwayml/stable-diffusion-inpainting"
+    "runwayml/stable-diffusion-inpainting",
 ]
 
-stable_prompt_list = [
-        "a photo of a man.",
-        "a photo of a girl."
-    ]
+stable_prompt_list = ["a photo of a man.", "a photo of a girl."]
 
-stable_negative_prompt_list = [
-        "bad, ugly",
-        "deformed"
-    ]
+stable_negative_prompt_list = ["bad, ugly", "deformed"]
 
 
 def stable_diffusion_inpaint(
-    dict:str,
-    model_path:str,
-    prompt:str,
-    negative_prompt:str,
-    guidance_scale:int,
-    num_inference_step:int,
-    ):
+    dict: str,
+    model_path: str,
+    prompt: str,
+    negative_prompt: str,
+    guidance_scale: int,
+    num_inference_step: int,
+):
 
     image = dict["image"].convert("RGB").resize((512, 512))
     mask_image = dict["mask"].convert("RGB").resize((512, 512))
@@ -35,17 +28,17 @@ def stable_diffusion_inpaint(
         revision="fp16",
         torch_dtype=torch.float16,
     )
-    pipe.to('cuda')
+    pipe.to("cuda")
     pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
     pipe.enable_xformers_memory_efficient_attention()
 
     output = pipe(
-        prompt = prompt,
-        image = image,
+        prompt=prompt,
+        image=image,
         mask_image=mask_image,
-        negative_prompt = negative_prompt,
-        num_inference_steps = num_inference_step,
-        guidance_scale = guidance_scale,
+        negative_prompt=negative_prompt,
+        num_inference_steps=num_inference_step,
+        guidance_scale=guidance_scale,
     ).images
 
     return output[0]
@@ -56,54 +49,51 @@ def stable_diffusion_inpaint_app():
         with gr.Row():
             with gr.Column():
                 inpaint_image_file = gr.Image(
-                    source='upload', 
-                    tool='sketch', 
-                    elem_id="image_upload", 
-                    type="pil", 
-                    label="Upload"
+                    source="upload",
+                    tool="sketch",
+                    elem_id="image_upload",
+                    type="pil",
+                    label="Upload",
                 )
 
                 inpaint_model_id = gr.Dropdown(
-                    choices=stable_inpiant_model_list, 
-                    value=stable_inpiant_model_list[0], 
-                    label='Inpaint Model Id'
+                    choices=stable_inpiant_model_list,
+                    value=stable_inpiant_model_list[0],
+                    label="Inpaint Model Id",
                 )
 
                 inpaint_prompt = gr.Textbox(
-                    lines=1, 
-                    value=stable_prompt_list[0], 
-                    label='Prompt'
+                    lines=1, value=stable_prompt_list[0], label="Prompt"
                 )
 
                 inpaint_negative_prompt = gr.Textbox(
-                    lines=1, 
-                    value=stable_negative_prompt_list[0], 
-                    label='Negative Prompt'
+                    lines=1,
+                    value=stable_negative_prompt_list[0],
+                    label="Negative Prompt",
                 )
 
                 with gr.Accordion("Advanced Options", open=False):
                     inpaint_guidance_scale = gr.Slider(
-                        minimum=0.1, 
-                        maximum=15, 
-                        step=0.1, 
-                        value=7.5, 
-                        label='Guidance Scale'
+                        minimum=0.1,
+                        maximum=15,
+                        step=0.1,
+                        value=7.5,
+                        label="Guidance Scale",
                     )
 
                     inpaint_num_inference_step = gr.Slider(
-                        minimum=1, 
-                        maximum=100, 
-                        step=1, 
-                        value=50, 
-                        label='Num Inference Step'
+                        minimum=1,
+                        maximum=100,
+                        step=1,
+                        value=50,
+                        label="Num Inference Step",
                     )
 
-                inpaint_predict = gr.Button(value='Generator')
+                inpaint_predict = gr.Button(value="Generator")
 
-            
             with gr.Column():
                 output_image = gr.Gallery(label="Outputs")
-            
+
         inpaint_predict.click(
             fn=stable_diffusion_inpaint,
             inputs=[
@@ -114,6 +104,5 @@ def stable_diffusion_inpaint_app():
                 inpaint_guidance_scale,
                 inpaint_num_inference_step,
             ],
-            outputs=output_image
+            outputs=output_image,
         )
-                

diffusion_webui/stable_diffusion/keras_txt2img.py

@@ -1,8 +1,8 @@
+import gradio as gr
+import tensorflow as tf
 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_model_list = [
     "keras-dreambooth/keras_diffusion_lowpoly_world",
@@ -11,105 +11,128 @@ keras_model_list = [
 ]
 
 stable_prompt_list = [
-        "a photo of lowpoly_world",
-        "Flower vase inspired by pink floyd division bell"
-    ]
+    "a photo of lowpoly_world",
+    "Flower vase inspired by pink floyd division bell",
+]
+
+stable_negative_prompt_list = ["bad, ugly", "deformed"]
 
-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,
-    ):
-        
-    with tf.device('/GPU:0'):      
+    model_path: str,
+    prompt: str,
+    negative_prompt: str,
+    guidance_scale: int,
+    num_inference_step: int,
+    height: int,
+    width: int,
+):
+
+    with tf.device("/GPU:0"):
         keras.mixed_precision.set_global_policy("mixed_float16")
-    
+
         sd_dreambooth_model = models.StableDiffusion(
-            img_width=height, 
-            img_height=width
-            )
-        
+            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
+            unconditional_guidance_scale=guidance_scale,
         )
 
     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'
+                    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'
+                    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'
+                    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'
+                        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'
+                        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'
+                        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'
+                        minimum=128,
+                        maximum=1280,
+                        step=32,
+                        value=512,
+                        label="Image Height",
                     )
 
-                keras_text2image_predict = gr.Button(value='Generator')
-    
+                keras_text2image_predict = gr.Button(value="Generator")
+
             with gr.Column():
-                output_image = gr.Gallery(label='Output')
-                        
+                output_image = gr.Gallery(label="Output")
+
+        gr.Examples(
+            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],
+            examples=[
+                [
+                    keras_model_list[0],
+                    stable_prompt_list[0],
+                    stable_negative_prompt_list[0],
+                    7.5,
+                    50,
+                    512,
+                    512,
+                ],
+            ],
+            label="Keras Stable Diffusion Example",
+            cache_examples=False,
+        )
+
         keras_text2image_predict.click(
             fn=keras_stable_diffusion,
             inputs=[
@@ -119,7 +142,7 @@ def keras_stable_diffusion_app():
                 keras_text2image_guidance_scale,
                 keras_text2image_num_inference_step,
                 keras_text2image_height,
-                keras_text2image_width
+                keras_text2image_width,
             ],
-            outputs=output_image
+            outputs=output_image,
         )

diffusion_webui/stable_diffusion/text2img_app.py

@@ -1,6 +1,6 @@
-from diffusers import StableDiffusionPipeline, DDIMScheduler
 import gradio as gr
 import torch
+from diffusers import DDIMScheduler, StableDiffusionPipeline
 
 stable_model_list = [
     "runwayml/stable-diffusion-v1-5",
@@ -10,34 +10,26 @@ stable_model_list = [
     "andite/anything-v4.0",
     "Lykon/DreamShaper",
     "nitrosocke/Nitro-Diffusion",
-    "dreamlike-art/dreamlike-diffusion-1.0"
-    
+    "dreamlike-art/dreamlike-diffusion-1.0",
 ]
 
-stable_prompt_list = [
-        "a photo of a man.",
-        "a photo of a girl."
-    ]
+stable_prompt_list = ["a photo of a man.", "a photo of a girl."]
+
+stable_negative_prompt_list = ["bad, ugly", "deformed"]
 
-stable_negative_prompt_list = [
-        "bad, ugly",
-        "deformed"
-    ]
 
 def stable_diffusion_text2img(
-    model_path:str,
-    prompt:str,
-    negative_prompt:str,
-    guidance_scale:int,
-    num_inference_step:int,
-    height:int,
-    width:int,
-    ):
+    model_path: str,
+    prompt: str,
+    negative_prompt: str,
+    guidance_scale: int,
+    num_inference_step: int,
+    height: int,
+    width: int,
+):
 
     pipe = StableDiffusionPipeline.from_pretrained(
-        model_path, 
-        safety_checker=None, 
-        torch_dtype=torch.float16
+        model_path, safety_checker=None, torch_dtype=torch.float16
     ).to("cuda")
 
     pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
@@ -54,67 +46,91 @@ def stable_diffusion_text2img(
 
     return images[0]
 
+
 def stable_diffusion_text2img_app():
     with gr.Blocks():
         with gr.Row():
             with gr.Column():
                 text2image_model_path = gr.Dropdown(
-                    choices=stable_model_list, 
-                    value=stable_model_list[0], 
-                    label='Text-Image Model Id'
+                    choices=stable_model_list,
+                    value=stable_model_list[0],
+                    label="Text-Image Model Id",
                 )
 
                 text2image_prompt = gr.Textbox(
-                    lines=1, 
-                    value=stable_prompt_list[0], 
-                    label='Prompt'
+                    lines=1, value=stable_prompt_list[0], label="Prompt"
                 )
 
                 text2image_negative_prompt = gr.Textbox(
-                    lines=1, 
-                    value=stable_negative_prompt_list[0], 
-                    label='Negative Prompt'
+                    lines=1,
+                    value=stable_negative_prompt_list[0],
+                    label="Negative Prompt",
                 )
 
                 with gr.Accordion("Advanced Options", open=False):
                     text2image_guidance_scale = gr.Slider(
-                        minimum=0.1, 
-                        maximum=15, 
-                        step=0.1, 
-                        value=7.5, 
-                        label='Guidance Scale'
+                        minimum=0.1,
+                        maximum=15,
+                        step=0.1,
+                        value=7.5,
+                        label="Guidance Scale",
                     )
 
                     text2image_num_inference_step = gr.Slider(
-                        minimum=1, 
-                        maximum=100, 
-                        step=1, 
-                        value=50, 
-                        label='Num Inference Step'
+                        minimum=1,
+                        maximum=100,
+                        step=1,
+                        value=50,
+                        label="Num Inference Step",
                     )
 
                     text2image_height = gr.Slider(
-                        minimum=128, 
-                        maximum=1280, 
-                        step=32, 
-                        value=512, 
-                        label='Image Height'
+                        minimum=128,
+                        maximum=1280,
+                        step=32,
+                        value=512,
+                        label="Image Height",
                     )
 
                     text2image_width = gr.Slider(
-                        minimum=128, 
-                        maximum=1280, 
-                        step=32, 
-                        value=768, 
-                        label='Image Height'
+                        minimum=128,
+                        maximum=1280,
+                        step=32,
+                        value=768,
+                        label="Image Width",
                     )
 
-                text2image_predict = gr.Button(value='Generator')
-        
+                text2image_predict = gr.Button(value="Generator")
+
             with gr.Column():
-                output_image = gr.Image(label='Output')
-                    
-        
+                output_image = gr.Image(label="Output")
+
+        gr.Examples(
+            examples=[
+                [
+                    stable_model_list[0],
+                    stable_prompt_list[0],
+                    stable_negative_prompt_list[0],
+                    7.5,
+                    50,
+                    512,
+                    768,
+                ]
+            ],
+            inputs=[
+                text2image_model_path,
+                text2image_prompt,
+                text2image_negative_prompt,
+                text2image_guidance_scale,
+                text2image_num_inference_step,
+                text2image_height,
+                text2image_width,
+            ],
+            outputs=[output_image],
+            cache_examples=False,
+            fn=stable_diffusion_text2img,
+            label="Text2Image Example",
+        )
         text2image_predict.click(
             fn=stable_diffusion_text2img,
             inputs=[
@@ -126,5 +142,5 @@ def stable_diffusion_text2img_app():
                 text2image_height,
                 text2image_width,
             ],
-            outputs=output_image
+            outputs=output_image,
         )

pyproject.toml

@@ -0,0 +1,6 @@
+[tool.black]
+line-length = 80
+
+[tool.isort]
+line_length = 80
+profile = "black"

script/code_formatter.sh

@@ -0,0 +1,2 @@
+black . --config pyproject.toml
+isort .