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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)
if torch.cuda.is_available():
# 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 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#[0]
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"])) as demo:
gr.Markdown('## Cut and Sketch ✂️▶️✏️')
with gr.Column() as step_1:
gr.Markdown('**Start Here**')
gr.Markdown('1. Upload your image below')
gr.Markdown('2. **Draw the mask** for the region you want changed (Cut ✂️)')
input_image = gr.Image(source='upload',
shape=[HEIGHT,WIDTH],
type='numpy',
label='Mask Draw (Cut!)',
tool='sketch',
brush_radius=80)
gr.Markdown('3. Click `Set Mask` when it is ready!')
mask_button = gr.Button(label='Set Mask', value='Set Mask')
with gr.Column(visible=False) as step_2:
gr.Markdown('4. Now, you can **sketch a replacement** object! (Sketch ✏️)')
sketch_image = gr.Image(source='upload',
shape=[HEIGHT,WIDTH],
type='numpy',
label='Fill Draw (Sketch!)',
tool='sketch',
brush_radius=20)
gr.Markdown('5. (You can also provide a **text prompt** if you want)')
prompt = gr.Textbox(label='Prompt')
gr.Markdown('6. 🔮 Click `Generate` when ready! ')
run_button = gr.Button(label='Generate', value='Generate')
output_image = gr.Gallery(
label="Generated images",
visible=False,
show_label=False,
elem_id="gallery",
)#.style(grid=(1, 2))
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
]
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 {input_image : image['image'], sketch_image : vis_image, step_2: gr.update(visible=True)}
mask_button.click(fn=set_mask, inputs=[input_image], outputs=[input_image, sketch_image,step_2])
run_button.click(fn=generate, inputs=inputs, outputs=output_image)
return demo
if __name__ == '__main__':
demo = create_demo()
demo.queue().launch() |