Spaces:
Running
on
L4
Running
on
L4
File size: 13,821 Bytes
5bf7c30 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 |
##!/usr/bin/python3
# -*- coding: utf-8 -*-
import gradio as gr
import os
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
mobile_sam = sam_model_registry['vit_h'](checkpoint='data/ckpt/sam_vit_h_4b8939.pth').to("cuda")
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, []],
]
# 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("cuda").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 = cv2.GaussianBlur(mask*255, (21, 21), 0)/255
mask = mask[:,:,np.newaxis]
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"""
<div style="text-align: center;">
<h1>BrushNet: A Plug-and-Play Image Inpainting Model with Decomposed Dual-Branch Diffusion</h1>
<div style="display: flex; justify-content: center; align-items: center; text-align: center;">
<a href=""></a>
<a href='https://tencentarc.github.io/BrushNet/'><img src='https://img.shields.io/badge/Project_Page-BrushNet-green' alt='Project Page'></a>
<a href='https://arxiv.org/abs/2403.06976'><img src='https://img.shields.io/badge/Paper-Arxiv-blue'></a>
</div>
</br>
</div>
""")
with gr.Accordion(label="🧭 Instructions:", open=True, elem_id="accordion"):
with gr.Row(equal_height=True):
gr.Markdown("""
- ⭐️ <b>step1: </b>Upload or select one image from Example
- ⭐️ <b>step2: </b>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.
- ⭐️ <b>step3: </b>Input prompt for generating new contents
- ⭐️ <b>step4: </b>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): #
return input_image, prompt, input_mask, original_image, []
example = gr.Examples(
label="Input Example",
examples=image_examples,
inputs=[input_image, prompt, input_mask, original_image, selected_points],
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.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() |