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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
## 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
css='''
.container {max-width: 1150px;margin: auto;padding-top: 1.5rem}
.image_upload{min-height:500px}
.image_upload [data-testid="image"], .image_upload [data-testid="image"] > div{min-height: 500px}
.image_upload [data-testid="sketch"], .image_upload [data-testid="sketch"] > div{min-height: 500px}
.image_upload .touch-none{display: flex}
#output_image{min-height:500px;max-height=500px;}
'''
def get_guide(image):
return hed(image,scribble=True)
def create_demo():
# Global Storage
CURRENT_IMAGE={'image': None,
'mask': None,
'guide': None
}
HEIGHT, WIDTH=512,512
with gr.Blocks(theme=gr.themes.Default(font=[gr.themes.GoogleFont("IBM Plex Mono"), "ui-monospace","monospace"],
primary_hue="lime",
secondary_hue="emerald",
neutral_hue="slate",
), css=css) as demo:
gr.Markdown('# Cut and Sketch ✂️▶️✏️')
with gr.Accordion('Instructions', open=False):
gr.Markdown('## Cut ✂️')
gr.Markdown('1. Upload your image below')
gr.Markdown('2. **Draw the mask** for the region you want changed (Cut ✂️)')
gr.Markdown('3. Click `Set Mask` when it is ready!')
gr.Markdown('## Sketch ✏️')
gr.Markdown('4. Now, you can **sketch a replacement** object! (Sketch ✏️)')
gr.Markdown('5. (You can also provide a **text prompt** if you want)')
gr.Markdown('6. 🔮 Click `Generate` when ready! ')
example_button=gr.Button(label='example',value='Try example image!').style(full_width=False, size='sm')
with gr.Group():
with gr.Box():
with gr.Column():
with gr.Row() as main_blocks:
with gr.Column() as step_1:
gr.Markdown('### Mask Input')
image = gr.Image(source='upload',
shape=[HEIGHT,WIDTH],
type='pil',#numpy',
elem_classes="image_upload",
label='Mask Draw (Cut!)',
tool='sketch',
brush_radius=60).style(height=500)
input_image=image
mask_button = gr.Button(label='Set Mask', value='Set Mask')
with gr.Column(visible=False) as step_2:
gr.Markdown('### Sketch Input')
sketch = gr.Image(source='upload',
shape=[HEIGHT,WIDTH],
type='pil',#'numpy',
elem_classes="image_upload",
label='Fill Draw (Sketch!)',
tool='sketch',
brush_radius=10).style(height=500)
sketch_image=sketch
run_button = gr.Button(label='Generate', value='Generate', variant="primary")
prompt = gr.Textbox(label='Prompt')
with gr.Column() as output_step:
gr.Markdown('### Output')
output_image = gr.Gallery(
label="Generated images",
show_label=False,
elem_id="output_image",
).style(height=500,containter=True)
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)
with gr.Accordion('More Info', open=False):
gr.Markdown('This demo was created by Mikolaj Czerkawski [@mikonvergence](https://twitter.com/mikonvergence) based on the 🌱 open-source implementation of [ControlNetInpaint](https://github.com/mikonvergence/ControlNetInpaint) (diffusers-friendly!).')
gr.Markdown('The tool currently only works with image resolution of 512px.')
gr.Markdown('💡 To learn more about diffusion with interactive code, check out my open-source ⏩[DiffusionFastForward](https://github.com/mikonvergence/DiffusionFastForward) course. It contains example code, executable notebooks, videos, notes, and a few use cases for training from scratch!')
inputs = [
sketch_image,
prompt,
num_steps,
text_scale,
sketch_scale,
seed
]
# STEP 1: Set Mask
def set_mask(content):
if content is None:
gr.Error("You must upload an image first.")
return {input_image : None,
sketch_image : None,
step_1: gr.update(visible=True),
step_2: gr.update(visible=False)
}
background=np.array(content["image"].convert("RGB").resize((512, 512))) # note: direct numpy seemed buggy
mask=np.array(content["mask"].convert("RGB").resize((512, 512)))
if (mask==0).all():
gr.Error("You must draw a mask for the cut out first.")
return {input_image : content['image'],
sketch_image : None,
step_1: gr.update(visible=True),
step_2: gr.update(visible=False)
}
mask=1*(mask>0)
# save vars
CURRENT_IMAGE['image']=background
CURRENT_IMAGE['mask']=mask
guide=get_guide(background)
CURRENT_IMAGE['guide']=np.array(guide)
guide=255-np.asarray(guide)
seg_img = guide*(1-mask) + mask*192
preview = background * (seg_img==255)
vis_image=(preview/2).astype(seg_img.dtype) + seg_img * (seg_img!=255)
return {input_image : content["image"],
sketch_image : vis_image,
step_1: gr.update(visible=False),
step_2: gr.update(visible=True)
}
# STEP 2: Generate
def generate(content,
prompt,
num_steps,
text_scale,
sketch_scale,
seed):
sketch=np.array(content["mask"].convert("RGB").resize((512, 512)))
sketch=(255*(sketch>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 {output_image : new_image,
step_1: gr.update(visible=True),
step_2: gr.update(visible=False)
}
def example_fill():
return Image.open('data/xp-love.jpg')
example_button.click(fn=example_fill, outputs=[input_image])
mask_button.click(fn=set_mask, inputs=[input_image], outputs=[input_image, sketch_image, step_1,step_2])
run_button.click(fn=generate, inputs=inputs, outputs=[output_image, step_1,step_2])
return demo
if __name__ == '__main__':
demo = create_demo()
demo.queue().launch() |