##!/usr/bin/python3
# -*- coding: utf-8 -*-
import os
print("Installing correct gradio version...")
os.system("pip uninstall -y gradio")
os.system("pip install gradio==3.50.0")
print("Installing Finished!")
import cv2
from PIL import Image
import numpy as np
from segment_anything import SamPredictor, sam_model_registry
import torch
from diffusers import StableDiffusionBrushNetPipeline, BrushNetModel, UniPCMultistepScheduler
import random
import gradio as gr
mobile_sam = sam_model_registry['vit_h'](checkpoint='data/ckpt/sam_vit_h_4b8939.pth')
mobile_sam.eval()
mobile_predictor = SamPredictor(mobile_sam)
colors = [(255, 0, 0), (0, 255, 0)]
markers = [1, 5]
# - - - - - examples - - - - - #
image_examples = [
["examples/brushnet/src/test_image.jpg", "A beautiful cake on the table", "examples/brushnet/src/test_mask.jpg", 0, [], [Image.open("examples/brushnet/src/test_result.png")]],
["examples/brushnet/src/example_1.jpg", "A man in Chinese traditional clothes", "examples/brushnet/src/example_1_mask.jpg", 1, [], [Image.open("examples/brushnet/src/example_1_result.png")]],
["examples/brushnet/src/example_2.jpg", "a charming woman with dress standing by the sea", "examples/brushnet/src/example_2_mask.jpg", 2, [], [Image.open("examples/brushnet/src/example_2_result.png")]],
["examples/brushnet/src/example_3.jpg", "a cut toy on the table", "examples/brushnet/src/example_3_mask.jpg", 3, [], [Image.open("examples/brushnet/src/example_3_result.png")]],
["examples/brushnet/src/example_4.jpeg", "a car driving in the wild", "examples/brushnet/src/example_4_mask.jpg", 4, [], [Image.open("examples/brushnet/src/example_4_result.png")]],
["examples/brushnet/src/example_5.jpg", "a charming woman wearing dress standing in the dark forest", "examples/brushnet/src/example_5_mask.jpg", 5, [], [Image.open("examples/brushnet/src/example_5_result.png")]],
]
# choose the base model here
base_model_path = "data/ckpt/realisticVisionV60B1_v51VAE"
# base_model_path = "runwayml/stable-diffusion-v1-5"
# input brushnet ckpt path
brushnet_path = "data/ckpt/segmentation_mask_brushnet_ckpt"
# input source image / mask image path and the text prompt
image_path="examples/brushnet/src/test_image.jpg"
mask_path="examples/brushnet/src/test_mask.jpg"
caption="A cake on the table."
# conditioning scale
paintingnet_conditioning_scale=1.0
brushnet = BrushNetModel.from_pretrained(brushnet_path, torch_dtype=torch.float16)
pipe = StableDiffusionBrushNetPipeline.from_pretrained(
base_model_path, brushnet=brushnet, torch_dtype=torch.float16, low_cpu_mem_usage=False
)
# speed up diffusion process with faster scheduler and memory optimization
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
# remove following line if xformers is not installed or when using Torch 2.0.
# pipe.enable_xformers_memory_efficient_attention()
# memory optimization.
# pipe.enable_model_cpu_offload()
def resize_image(input_image, resolution):
H, W, C = input_image.shape
H = float(H)
W = float(W)
k = float(resolution) / min(H, W)
H *= k
W *= k
H = int(np.round(H / 64.0)) * 64
W = int(np.round(W / 64.0)) * 64
img = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA)
return img
def process(input_image,
original_image,
original_mask,
input_mask,
selected_points,
prompt,
negative_prompt,
blended,
invert_mask,
control_strength,
seed,
randomize_seed,
guidance_scale,
num_inference_steps):
if original_image is None:
raise gr.Error('Please upload the input image')
if (original_mask is None or len(selected_points)==0) and input_mask is None:
raise gr.Error("Please click the region where you hope unchanged/changed, or upload a white-black Mask image")
# load example image
if isinstance(original_image, int):
image_name = image_examples[original_image][0]
original_image = cv2.imread(image_name)
original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
if input_mask is not None:
H,W=original_image.shape[:2]
original_mask = cv2.resize(input_mask, (W, H))
else:
original_mask = np.clip(255 - original_mask, 0, 255).astype(np.uint8)
if invert_mask:
original_mask=255-original_mask
mask = 1.*(original_mask.sum(-1)>255)[:,:,np.newaxis]
masked_image = original_image * (1-mask)
init_image = Image.fromarray(masked_image.astype(np.uint8)).convert("RGB")
mask_image = Image.fromarray(original_mask.astype(np.uint8)).convert("RGB")
generator = torch.Generator().manual_seed(random.randint(0,2147483647) if randomize_seed else seed)
image = pipe(
[prompt]*2,
init_image,
mask_image,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
generator=generator,
brushnet_conditioning_scale=float(control_strength),
negative_prompt=[negative_prompt]*2,
).images
if blended:
if control_strength<1.0:
raise gr.Error('Using blurred blending with control strength less than 1.0 is not allowed')
blended_image=[]
# blur, you can adjust the parameters for better performance
mask_blurred = cv2.GaussianBlur(mask*255, (21, 21), 0)/255
mask_blurred = mask_blurred[:,:,np.newaxis]
mask = 1-(1-mask) * (1-mask_blurred)
for image_i in image:
image_np=np.array(image_i)
image_pasted=original_image * (1-mask) + image_np*mask
image_pasted=image_pasted.astype(image_np.dtype)
blended_image.append(Image.fromarray(image_pasted))
image=blended_image
return image
block = gr.Blocks(
theme=gr.themes.Soft(
radius_size=gr.themes.sizes.radius_none,
text_size=gr.themes.sizes.text_md
)
).queue()
with block:
with gr.Row():
with gr.Column():
gr.HTML(f"""
BrushNet: A Plug-and-Play Image Inpainting Model with Decomposed Dual-Branch Diffusion
""")
with gr.Accordion(label="🧭 Instructions:", open=True, elem_id="accordion"):
with gr.Row(equal_height=True):
gr.Markdown("""
- ⭐️ step1: Upload or select one image from Example
- ⭐️ step2: Click on Input-image to select the object to be retained (or upload a white-black Mask image, in which white color indicates the region you want to keep unchanged). You can tick the 'Invert Mask' box to switch region unchanged and change.
- ⭐️ step3: Input prompt for generating new contents
- ⭐️ step4: Click Run button
""")
with gr.Row():
with gr.Column():
with gr.Column(elem_id="Input"):
with gr.Row():
with gr.Tabs(elem_classes=["feedback"]):
with gr.TabItem("Input Image"):
input_image = gr.Image(type="numpy", label="input",scale=2, height=640)
original_image = gr.State(value=None,label="index")
original_mask = gr.State(value=None)
selected_points = gr.State([],label="select points")
with gr.Row(elem_id="Seg"):
radio = gr.Radio(['foreground', 'background'], label='Click to seg: ', value='foreground',scale=2)
undo_button = gr.Button('Undo seg', elem_id="btnSEG",scale=1)
prompt = gr.Textbox(label="Prompt", placeholder="Please input your prompt",value='',lines=1)
negative_prompt = gr.Text(
label="Negative Prompt",
max_lines=5,
placeholder="Please input your negative prompt",
value='ugly, low quality',lines=1
)
with gr.Group():
with gr.Row():
blending = gr.Checkbox(label="Blurred Blending", value=False)
invert_mask = gr.Checkbox(label="Invert Mask", value=True)
run_button = gr.Button("Run",elem_id="btn")
with gr.Accordion("More input params (highly-recommended)", open=False, elem_id="accordion1"):
control_strength = gr.Slider(
label="Control Strength: ", show_label=True, minimum=0, maximum=1.1, value=1, step=0.01
)
with gr.Group():
seed = gr.Slider(
label="Seed: ", minimum=0, maximum=2147483647, step=1, value=551793204
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
with gr.Group():
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=1,
maximum=12,
step=0.1,
value=12,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=50,
)
with gr.Row(elem_id="Image"):
with gr.Tabs(elem_classes=["feedback1"]):
with gr.TabItem("User-specified Mask Image (Optional)"):
input_mask = gr.Image(type="numpy", label="Mask Image", height=640)
with gr.Column():
with gr.Tabs(elem_classes=["feedback"]):
with gr.TabItem("Outputs"):
result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery", preview=True)
with gr.Row():
def process_example(input_image, prompt, input_mask, original_image, selected_points,result_gallery): #
return input_image, prompt, input_mask, original_image, [], result_gallery
example = gr.Examples(
label="Input Example",
examples=image_examples,
inputs=[input_image, prompt, input_mask, original_image, selected_points,result_gallery],
outputs=[input_image, prompt, input_mask, original_image, selected_points],
fn=process_example,
run_on_click=True,
examples_per_page=10
)
# once user upload an image, the original image is stored in `original_image`
def store_img(img):
# image upload is too slow
if min(img.shape[0], img.shape[1]) > 512:
img = resize_image(img, 512)
if max(img.shape[0], img.shape[1])*1.0/min(img.shape[0], img.shape[1])>2.0:
raise gr.Error('image aspect ratio cannot be larger than 2.0')
return img, img, [], None # when new image is uploaded, `selected_points` should be empty
input_image.upload(
store_img,
[input_image],
[input_image, original_image, selected_points]
)
# user click the image to get points, and show the points on the image
def segmentation(img, sel_pix):
# online show seg mask
points = []
labels = []
for p, l in sel_pix:
points.append(p)
labels.append(l)
mobile_predictor=mobile_predictor.to("cuda")
mobile_predictor.set_image(img if isinstance(img, np.ndarray) else np.array(img))
with torch.no_grad():
masks, _, _ = mobile_predictor.predict(point_coords=np.array(points), point_labels=np.array(labels), multimask_output=False)
output_mask = np.ones((masks.shape[1], masks.shape[2], 3))*255
for i in range(3):
output_mask[masks[0] == True, i] = 0.0
mask_all = np.ones((masks.shape[1], masks.shape[2], 3))
color_mask = np.random.random((1, 3)).tolist()[0]
for i in range(3):
mask_all[masks[0] == True, i] = color_mask[i]
masked_img = img / 255 * 0.3 + mask_all * 0.7
masked_img = masked_img*255
## draw points
for point, label in sel_pix:
cv2.drawMarker(masked_img, point, colors[label], markerType=markers[label], markerSize=20, thickness=5)
return masked_img, output_mask
def get_point(img, sel_pix, point_type, evt: gr.SelectData):
if point_type == 'foreground':
sel_pix.append((evt.index, 1)) # append the foreground_point
elif point_type == 'background':
sel_pix.append((evt.index, 0)) # append the background_point
else:
sel_pix.append((evt.index, 1)) # default foreground_point
if isinstance(img, int):
image_name = image_examples[img][0]
img = cv2.imread(image_name)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# online show seg mask
masked_img, output_mask = segmentation(img, sel_pix)
return masked_img.astype(np.uint8), output_mask
input_image.select(
get_point,
[original_image, selected_points, radio],
[input_image, original_mask],
)
# undo the selected point
def undo_points(orig_img, sel_pix):
# draw points
output_mask = None
if len(sel_pix) != 0:
if isinstance(orig_img, int): # if orig_img is int, the image if select from examples
temp = cv2.imread(image_examples[orig_img][0])
temp = cv2.cvtColor(temp, cv2.COLOR_BGR2RGB)
else:
temp = orig_img.copy()
sel_pix.pop()
# online show seg mask
if len(sel_pix) !=0:
temp, output_mask = segmentation(temp, sel_pix)
return temp.astype(np.uint8), output_mask
else:
gr.Error("Nothing to Undo")
undo_button.click(
undo_points,
[original_image, selected_points],
[input_image, original_mask]
)
ips=[input_image, original_image, original_mask, input_mask, selected_points, prompt, negative_prompt, blending, invert_mask, control_strength, seed, randomize_seed, guidance_scale, num_inference_steps]
run_button.click(fn=process, inputs=ips, outputs=[result_gallery])
block.launch()
