File size: 11,341 Bytes
447ff7c |
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 |
import cv2
import einops
import numpy as np
import torch
import random
import gradio as gr
import os
import albumentations as A
from PIL import Image
import torchvision.transforms as T
from datasets.data_utils import *
from cldm.model import create_model, load_state_dict
from cldm.ddim_hacked import DDIMSampler
from omegaconf import OmegaConf
from cldm.hack import disable_verbosity, enable_sliced_attention
cv2.setNumThreads(0)
cv2.ocl.setUseOpenCL(False)
save_memory = False
disable_verbosity()
if save_memory:
enable_sliced_attention()
config = OmegaConf.load('./configs/demo.yaml')
model_ckpt = config.pretrained_model
model_config = config.config_file
model = create_model(model_config ).cpu()
model.load_state_dict(load_state_dict(model_ckpt, location='cuda'))
model = model.cuda()
ddim_sampler = DDIMSampler(model)
def crop_back( pred, tar_image, extra_sizes, tar_box_yyxx_crop):
H1, W1, H2, W2 = extra_sizes
y1,y2,x1,x2 = tar_box_yyxx_crop
pred = cv2.resize(pred, (W2, H2))
m = 3 # maigin_pixel
if W1 == H1:
tar_image[y1+m :y2-m, x1+m:x2-m, :] = pred[m:-m, m:-m]
return tar_image
if W1 < W2:
pad1 = int((W2 - W1) / 2)
pad2 = W2 - W1 - pad1
pred = pred[:,pad1: -pad2, :]
else:
pad1 = int((H2 - H1) / 2)
pad2 = H2 - H1 - pad1
pred = pred[pad1: -pad2, :, :]
tar_image[y1+m :y2-m, x1+m:x2-m, :] = pred[m:-m, m:-m]
return tar_image
def inference_single_image(ref_image,
ref_mask,
tar_image,
tar_mask,
num_samples,
strength,
ddim_steps,
scale,
seed,
):
item = process_pairs(ref_image, ref_mask, tar_image, tar_mask)
ref = item['ref']
hint = item['hint']
num_samples = 1
control = torch.from_numpy(hint.copy()).float().cuda()
control = torch.stack([control for _ in range(num_samples)], dim=0)
control = einops.rearrange(control, 'b h w c -> b c h w').clone()
clip_input = torch.from_numpy(ref.copy()).float().cuda()
clip_input = torch.stack([clip_input for _ in range(num_samples)], dim=0)
clip_input = einops.rearrange(clip_input, 'b h w c -> b c h w').clone()
H,W = 512,512
cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning( clip_input )]}
un_cond = {"c_concat": [control],
"c_crossattn": [model.get_learned_conditioning([torch.zeros((1,3,224,224))] * num_samples)]}
shape = (4, H // 8, W // 8)
if save_memory:
model.low_vram_shift(is_diffusing=True)
model.control_scales = ([strength] * 13)
samples, _ = ddim_sampler.sample(ddim_steps, num_samples,
shape, cond, verbose=False, eta=0,
unconditional_guidance_scale=scale,
unconditional_conditioning=un_cond)
if save_memory:
model.low_vram_shift(is_diffusing=False)
x_samples = model.decode_first_stage(samples)
x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy()
result = x_samples[0][:,:,::-1]
result = np.clip(result,0,255)
pred = x_samples[0]
pred = np.clip(pred,0,255)[1:,:,:]
sizes = item['extra_sizes']
tar_box_yyxx_crop = item['tar_box_yyxx_crop']
tar_image = crop_back(pred, tar_image, sizes, tar_box_yyxx_crop)
return tar_image
def process_pairs(ref_image, ref_mask, tar_image, tar_mask, max_ratio = 0.8):
# ========= Reference ===========
# ref expand
ref_box_yyxx = get_bbox_from_mask(ref_mask)
# ref filter mask
ref_mask_3 = np.stack([ref_mask,ref_mask,ref_mask],-1)
masked_ref_image = ref_image * ref_mask_3 + np.ones_like(ref_image) * 255 * (1-ref_mask_3)
y1,y2,x1,x2 = ref_box_yyxx
masked_ref_image = masked_ref_image[y1:y2,x1:x2,:]
ref_mask = ref_mask[y1:y2,x1:x2]
ratio = np.random.randint(11, 15) / 10 #11,13
masked_ref_image, ref_mask = expand_image_mask(masked_ref_image, ref_mask, ratio=ratio)
ref_mask_3 = np.stack([ref_mask,ref_mask,ref_mask],-1)
# to square and resize
masked_ref_image = pad_to_square(masked_ref_image, pad_value = 255, random = False)
masked_ref_image = cv2.resize(masked_ref_image.astype(np.uint8), (224,224) ).astype(np.uint8)
ref_mask_3 = pad_to_square(ref_mask_3 * 255, pad_value = 0, random = False)
ref_mask_3 = cv2.resize(ref_mask_3.astype(np.uint8), (224,224) ).astype(np.uint8)
ref_mask = ref_mask_3[:,:,0]
# collage aug
masked_ref_image_compose, ref_mask_compose = masked_ref_image, ref_mask
ref_mask_3 = np.stack([ref_mask_compose,ref_mask_compose,ref_mask_compose],-1)
ref_image_collage = sobel(masked_ref_image_compose, ref_mask_compose/255)
# ========= Target ===========
tar_box_yyxx = get_bbox_from_mask(tar_mask)
tar_box_yyxx = expand_bbox(tar_mask, tar_box_yyxx, ratio=[1.1,1.2]) #1.1 1.3
# crop
tar_box_yyxx_crop = expand_bbox(tar_image, tar_box_yyxx, ratio=[1.3, 3.0])
tar_box_yyxx_crop = box2squre(tar_image, tar_box_yyxx_crop) # crop box
y1,y2,x1,x2 = tar_box_yyxx_crop
cropped_target_image = tar_image[y1:y2,x1:x2,:]
cropped_tar_mask = tar_mask[y1:y2,x1:x2]
tar_box_yyxx = box_in_box(tar_box_yyxx, tar_box_yyxx_crop)
y1,y2,x1,x2 = tar_box_yyxx
# collage
ref_image_collage = cv2.resize(ref_image_collage.astype(np.uint8), (x2-x1, y2-y1))
ref_mask_compose = cv2.resize(ref_mask_compose.astype(np.uint8), (x2-x1, y2-y1))
ref_mask_compose = (ref_mask_compose > 128).astype(np.uint8)
collage = cropped_target_image.copy()
collage[y1:y2,x1:x2,:] = ref_image_collage
collage_mask = cropped_target_image.copy() * 0.0
collage_mask[y1:y2,x1:x2,:] = 1.0
collage_mask = np.stack([cropped_tar_mask,cropped_tar_mask,cropped_tar_mask],-1)
# the size before pad
H1, W1 = collage.shape[0], collage.shape[1]
cropped_target_image = pad_to_square(cropped_target_image, pad_value = 0, random = False).astype(np.uint8)
collage = pad_to_square(collage, pad_value = 0, random = False).astype(np.uint8)
collage_mask = pad_to_square(collage_mask, pad_value = 0, random = False).astype(np.uint8)
# the size after pad
H2, W2 = collage.shape[0], collage.shape[1]
cropped_target_image = cv2.resize(cropped_target_image.astype(np.uint8), (512,512)).astype(np.float32)
collage = cv2.resize(collage.astype(np.uint8), (512,512)).astype(np.float32)
collage_mask = (cv2.resize(collage_mask.astype(np.uint8), (512,512)).astype(np.float32) > 0.5).astype(np.float32)
masked_ref_image = masked_ref_image / 255
cropped_target_image = cropped_target_image / 127.5 - 1.0
collage = collage / 127.5 - 1.0
collage = np.concatenate([collage, collage_mask[:,:,:1] ] , -1)
item = dict(ref=masked_ref_image.copy(), jpg=cropped_target_image.copy(), hint=collage.copy(), extra_sizes=np.array([H1, W1, H2, W2]), tar_box_yyxx_crop=np.array( tar_box_yyxx_crop ) )
return item
ref_dir='./examples/Gradio/FG'
image_dir='./examples/Gradio/BG'
ref_list=[os.path.join(ref_dir,file) for file in os.listdir(ref_dir) if '.jpg' in file or '.png' in file or '.jpeg' in file ]
ref_list.sort()
image_list=[os.path.join(image_dir,file) for file in os.listdir(image_dir) if '.jpg' in file or '.png' in file or '.jpeg' in file]
image_list.sort()
def mask_image(image, mask):
blanc = np.ones_like(image) * 255
mask = np.stack([mask,mask,mask],-1) / 255
masked_image = mask * ( 0.5 * blanc + 0.5 * image) + (1-mask) * image
return masked_image.astype(np.uint8)
def run_local(base,
ref,
*args):
image = base["image"].convert("RGB")
mask = base["mask"].convert("L")
ref_image = ref["image"].convert("RGB")
ref_mask = ref["mask"].convert("L")
image = np.asarray(image)
mask = np.asarray(mask)
mask = np.where(mask > 128, 255, 0).astype(np.uint8)
ref_image = np.asarray(ref_image)
ref_mask = np.asarray(ref_mask)
ref_mask = np.where(ref_mask > 128, 1, 0).astype(np.uint8)
processed_item = process_pairs(ref_image.copy(), ref_mask.copy(), image.copy(), mask.copy(), max_ratio = 0.8)
masked_ref = (processed_item['ref']*255)
mased_image = mask_image(image, mask)
#synthesis = image
synthesis = inference_single_image(ref_image.copy(), ref_mask.copy(), image.copy(), mask.copy(), *args)
synthesis = torch.from_numpy(synthesis).permute(2, 0, 1)
synthesis = synthesis.permute(1, 2, 0).numpy()
masked_ref = cv2.resize(masked_ref.astype(np.uint8), (512,512))
return [synthesis]
with gr.Blocks() as demo:
with gr.Column():
gr.Markdown("# Play with AnyDoor to Teleport your Target Objects! ")
with gr.Row():
baseline_gallery = gr.Gallery(label='Output', show_label=True, elem_id="gallery", columns=1, height=768)
with gr.Accordion("Advanced Option", open=True):
num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01)
ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=30, step=1)
scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=3.0, step=0.1)
seed = gr.Slider(label="Seed", minimum=-1, maximum=999999999, step=1, value=-1)
gr.Markdown(" Higher guidance-scale makes higher fidelity, while lower guidance-scale leads to more harmonized blending.")
gr.Markdown("# Upload / Select Images for the Background (left) and Reference Object (right)")
gr.Markdown("### Your could draw coarse masks on the background to indicate the desired location and shape.")
gr.Markdown("### <u>Do not forget</u> to annotate the target object on the reference image.")
with gr.Row():
base = gr.Image(label="Background", source="upload", tool="sketch", type="pil", height=512, brush_color='#FFFFFF', mask_opacity=0.5)
ref = gr.Image(label="Reference", source="upload", tool="sketch", type="pil", height=512, brush_color='#FFFFFF', mask_opacity=0.5)
run_local_button = gr.Button(label="Generate", value="Run")
with gr.Row():
with gr.Column():
gr.Examples(image_list, inputs=[base],label="Examples - Background Image",examples_per_page=16)
with gr.Column():
gr.Examples(ref_list, inputs=[ref],label="Examples - Reference Object",examples_per_page=16)
run_local_button.click(fn=run_local,
inputs=[base,
ref,
num_samples,
strength,
ddim_steps,
scale,
seed,
],
outputs=[baseline_gallery]
)
demo.launch(server_name="0.0.0.0") |