minimal version test

.gitmodules

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+[submodule "src/ControlNetInpaint"]
+	path = src/ControlNetInpaint
+	url = https://github.com/mikonvergence/ControlNetInpaint.git

app.py

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+# this code is largely inspired by https://huggingface.co/spaces/hysts/ControlNet-with-Anything-v4/blob/main/app_scribble_interactive.py
+# Thank you, hysts!
+
+import sys
+sys.path.append('./src/ControlNetInpaint/')
+# functionality based on https://github.com/mikonvergence/ControlNetInpaint
+
+import gradio as gr
+#import torch
+#from torch import autocast // only for GPU
+
+from PIL import Image
+import numpy as np
+from io import BytesIO
+import os
+
+# Usage
+# 1. Upload image or fill with white
+# 2. Sketch the mask (image->[image,mask]
+# 3. Sketch the content of the mask
+
+# Global Storage
+CURRENT_IMAGE={'image' : None,
+               'mask' : None,
+               'guide' : None
+            }
+
+HEIGHT,WIDTH=512,512
+
+## SETUP PIPE
+
+from diffusers import StableDiffusionInpaintPipeline, ControlNetModel, UniPCMultistepScheduler
+from src.pipeline_stable_diffusion_controlnet_inpaint import *
+from diffusers.utils import load_image
+from controlnet_aux import HEDdetector
+
+hed = HEDdetector.from_pretrained('lllyasviel/ControlNet')
+
+controlnet = ControlNetModel.from_pretrained(
+    "fusing/stable-diffusion-v1-5-controlnet-scribble", torch_dtype=torch.float16
+)
+pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
+     "runwayml/stable-diffusion-inpainting", controlnet=controlnet, torch_dtype=torch.float16
+ )
+
+pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+
+# Remove if you do not have xformers installed
+# see https://huggingface.co/docs/diffusers/v0.13.0/en/optimization/xformers#installing-xformers
+# for installation instructions
+pipe.enable_xformers_memory_efficient_attention()
+
+pipe.to('cuda')
+
+# Functions
+
+def get_guide(image):  
+    return hed(image,scribble=True)
+
+def set_mask(image):
+    img=image['image'][...,:3]
+    mask=1*(image['mask'][...,:3]>0)
+    # save vars
+    CURRENT_IMAGE['image']=img
+    CURRENT_IMAGE['mask']=mask
+
+    guide=get_guide(img)
+    CURRENT_IMAGE['guide']=np.array(guide)
+    guide=255-np.asarray(guide)  
+
+    seg_img = guide*(1-mask) + mask*192
+    preview = img * (seg_img==255)
+
+    vis_image=(preview/2).astype(seg_img.dtype) + seg_img * (seg_img!=255)
+
+    return vis_image
+
+def generate(image,
+             prompt,
+             num_steps,
+             text_scale,
+             sketch_scale,
+             seed):
+
+    sketch=(255*(image['mask'][...,:3]>0)).astype(CURRENT_IMAGE['image'].dtype)
+    mask=CURRENT_IMAGE['mask']
+
+    CURRENT_IMAGE['guide']=(CURRENT_IMAGE['guide']*(mask==0) + sketch*(mask!=0)).astype(CURRENT_IMAGE['image'].dtype)
+
+    mask_img=255*CURRENT_IMAGE['mask'].astype(CURRENT_IMAGE['image'].dtype)
+
+    new_image = pipe(
+      prompt,
+      num_inference_steps=num_steps,
+      guidance_scale=text_scale,
+      generator=torch.manual_seed(seed),
+      image=Image.fromarray(CURRENT_IMAGE['image']),
+      control_image=Image.fromarray(CURRENT_IMAGE['guide']),
+      controlnet_conditioning_scale=sketch_scale,
+      mask_image=Image.fromarray(mask_img)
+    ).images
+
+    return new_image
+
+def create_demo(max_images=12, default_num_images=3):
+  
+    with gr.Blocks(theme=gr.themes.Default(font=[gr.themes.GoogleFont("IBM Plex Mono"), "ui-monospace","monospace"]),
+                   css=".gradio-container {background-color: #f2faf2}"
+                  ) as demo:
+        gr.Markdown('## Cut and Sketch ✂️▶️✏️')
+        
+        prompt = gr.Textbox(label='Prompt')
+
+        with gr.Row():
+            with gr.Column():
+                with gr.Row():
+                    input_image = gr.Image(source='upload',
+                                            shape=[HEIGHT,WIDTH],
+                                            type='numpy',
+                                          label='Mask Draw',
+                                            tool='sketch',
+                                            brush_radius=70)
+                    sketch_image = gr.Image(source='upload',
+                                            shape=[HEIGHT,WIDTH],
+                                            type='numpy',
+                                          label='Fill Draw',
+                                            tool='sketch',
+                                            brush_radius=15)
+                with gr.Row():
+                    mask_button = gr.Button(label='Set Mask', value='Set Mask')
+                    run_button = gr.Button(label='Generate', value='Generate')
+            output_image = gr.Gallery(
+                        label="Generated images",
+                        show_label=False,
+                        elem_id="gallery",
+                    )
+
+        with gr.Accordion('Advanced options', open=False):
+            num_steps = gr.Slider(label='Steps',
+                                minimum=1,
+                                maximum=100,
+                                value=20,
+                                step=1)
+            text_scale = gr.Slider(label='Text Guidance Scale',
+                                      minimum=0.1,
+                                      maximum=30.0,
+                                      value=7.5,
+                                      step=0.1)
+            seed = gr.Slider(label='Seed',
+                            minimum=-1,
+                            maximum=2147483647,
+                            step=1,
+                            randomize=True)  
+
+            sketch_scale = gr.Slider(label='Sketch Guidance Scale',
+                                      minimum=0.0,
+                                      maximum=1.0,
+                                      value=1.0,
+                                      step=0.05)                  
+
+        inputs = [
+          sketch_image,
+          prompt,
+          num_steps,
+          text_scale,
+          sketch_scale,
+          seed
+        ]
+
+        mask_button.click(fn=set_mask, inputs=input_image, outputs=sketch_image)     
+        run_button.click(fn=generate, inputs=inputs, outputs=output_image)
+        return demo
+
+if __name__ == '__main__':
+    demo = create_demo()
+    demo.queue().launch()

requirements.txt

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+diffusers
+xformers
+transformers
+scipy
+ftfy
+accelerate
+controlnet_aux

src/ControlNetInpaint

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+Subproject commit f33c386fe81d226dc50d4344c509288a1bcca7a2