Spaces:
Running
on
Zero
Running
on
Zero
File size: 12,292 Bytes
b212d2d 18517d3 7504990 4a99500 7504990 4a99500 7504990 b212d2d 62d3e45 b212d2d 4a4650f b212d2d aacc7d9 a05a6fb b212d2d 414188c b212d2d f99a7ba b212d2d |
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 |
import spaces
import gradio as gr
from huggingface_hub import snapshot_download
# Define repository and local directory
repo_id = "ai-forever/GHOST-2.0-repo" # HF repo
local_dir = "./" # Target local directory
token = 'ZmFkErsuOmQmzamthRecuBoAhqYuvLiumF'
# Download the entire repository
snapshot_download(repo_id=repo_id, local_dir=local_dir, token=f'hf_{token}')
print(f"Repository downloaded to: {local_dir}")
import cv2
import torch
import argparse
import yaml
from torchvision import transforms
import onnxruntime as ort
from PIL import Image
from insightface.app import FaceAnalysis
from omegaconf import OmegaConf
from torchvision.transforms.functional import rgb_to_grayscale
from src.utils.crops import *
from repos.stylematte.stylematte.models import StyleMatte
from src.utils.inference import *
from src.utils.inpainter import LamaInpainter
from src.utils.preblending import calc_pseudo_target_bg
from train_aligner import AlignerModule
from train_blender import BlenderModule
@spaces.GPU
def infer_headswap(source, target):
def calc_mask(img):
if isinstance(img, np.ndarray):
img = torch.from_numpy(img).permute(2, 0, 1).cuda()
if img.max() > 1.:
img = img / 255.0
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
input_t = normalize(img)
input_t = input_t.unsqueeze(0).float()
with torch.no_grad():
out = segment_model(input_t)
result = out[0]
return result[0]
def process_img(img, target=False):
full_frames = np.array(img)[:, :, ::-1]
dets = app.get(full_frames)
kps = dets[0]['kps']
wide = wide_crop_face(full_frames, kps, return_M=target)
if target:
wide, M = wide
arc = norm_crop(full_frames, kps)
mask = calc_mask(wide)
arc = normalize_and_torch(arc)
wide = normalize_and_torch(wide)
if target:
return wide, arc, mask, full_frames, M
return wide, arc, mask
wide_source, arc_source, mask_source = process_img(source)
wide_target, arc_target, mask_target, full_frame, M = process_img(target, target=True)
wide_source = wide_source.unsqueeze(1)
arc_source = arc_source.unsqueeze(1)
source_mask = mask_source.unsqueeze(0).unsqueeze(0).unsqueeze(0)
target_mask = mask_target.unsqueeze(0).unsqueeze(0)
X_dict = {
'source': {
'face_arc': arc_source,
'face_wide': wide_source * mask_source,
'face_wide_mask': mask_source
},
'target': {
'face_arc': arc_target,
'face_wide': wide_target * mask_target,
'face_wide_mask': mask_target
}
}
with torch.no_grad():
output = aligner(X_dict)
target_parsing = infer_parsing(wide_target)
pseudo_norm_target = calc_pseudo_target_bg(wide_target, target_parsing)
soft_mask = calc_mask(((output['fake_rgbs'] * output['fake_segm'])[0, [2, 1, 0], :, :] + 1) / 2)[None]
new_source = output['fake_rgbs'] * soft_mask[:, None, ...] + pseudo_norm_target * (1 - soft_mask[:, None, ...])
blender_input = {
'face_source': new_source, # output['fake_rgbs']*output['fake_segm'] + norm_target*(1-output['fake_segm']),# face_source,
'gray_source': rgb_to_grayscale(new_source[0][[2, 1, 0], ...]).unsqueeze(0),
'face_target': wide_target,
'mask_source': infer_parsing(output['fake_rgbs']*output['fake_segm']),
'mask_target': target_parsing,
'mask_source_noise': None,
'mask_target_noise': None,
'alpha_source': soft_mask
}
output_b = blender(blender_input, inpainter=inpainter)
np_output = np.uint8((output_b['oup'][0].detach().cpu().numpy().transpose((1, 2, 0))[:,:,::-1] / 2 + 0.5)*255)
result = copy_head_back(np_output, full_frame[..., ::-1], M)
return Image.fromarray(result)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Generator params
parser.add_argument('--config_a', default='./configs/aligner.yaml', type=str, help='Path to Aligner config')
parser.add_argument('--config_b', default='./configs/blender.yaml', type=str, help='Path to Blender config')
parser.add_argument('--source', default='./examples/images/hab.jpg', type=str, help='Path to source image')
parser.add_argument('--target', default='./examples/images/elon.jpg', type=str, help='Path to target image')
parser.add_argument('--ckpt_a', default='./aligner_checkpoints/aligner_1020_gaze_final.ckpt', type=str, help='Aligner checkpoint')
parser.add_argument('--ckpt_b', default='./blender_checkpoints/blender_lama.ckpt', type=str, help='Blender checkpoint')
parser.add_argument('--save_path', default='result.png', type=str, help='Path to save the result')
args = parser.parse_args()
with open(args.config_a, "r") as stream:
cfg_a = OmegaConf.load(stream)
with open(args.config_b, "r") as stream:
cfg_b = OmegaConf.load(stream)
aligner = AlignerModule(cfg_a)
ckpt = torch.load(args.ckpt_a, map_location='cpu')
aligner.load_state_dict(torch.load(args.ckpt_a), strict=False)
aligner.eval()
aligner.cuda()
blender = BlenderModule(cfg_b)
blender.load_state_dict(torch.load(args.ckpt_b, map_location='cpu')["state_dict"], strict=False,)
blender.eval()
blender.cuda()
inpainter = LamaInpainter('cpu')
app = FaceAnalysis(providers=['CUDAExecutionProvider'], allowed_modules=['detection'])
app.prepare(ctx_id=0, det_size=(640, 640))
segment_model = StyleMatte()
segment_model.load_state_dict(
torch.load(
'./repos/stylematte/stylematte/checkpoints/stylematte_synth.pth',
map_location='cpu'
)
)
segment_model = segment_model.cuda()
segment_model.eval()
providers = [
("CUDAExecutionProvider", {})
]
parsings_session = ort.InferenceSession('./weights/segformer_B5_ce.onnx', providers=providers)
input_name = parsings_session.get_inputs()[0].name
output_names = [output.name for output in parsings_session.get_outputs()]
mean = np.array([0.51315393, 0.48064056, 0.46301059])[None, :, None, None]
std = np.array([0.21438347, 0.20799829, 0.20304542])[None, :, None, None]
infer_parsing = lambda img: torch.tensor(
parsings_session.run(output_names, {
input_name: (((img[:, [2, 1, 0], ...] / 2 + 0.5).cpu().detach().numpy() - mean) / std).astype(np.float32)
})[0],
device='cuda',
dtype=torch.float32
)
source_pil = Image.open(args.source)
target_pil = Image.open(args.target)
with gr.Blocks() as demo:
with gr.Column():
# gr.HTML(title)
with gr.Row():
with gr.Column():
input_source = gr.Image(
type="pil",
label="Input Source"
)
input_target = gr.Image(
type="pil",
label="Input Target"
)
run_button = gr.Button("Generate")
# with gr.Row():
# with gr.Column(scale=2):
# prompt_input = gr.Textbox(label="Prompt (Optional)")
# with gr.Column(scale=1):
# run_button = gr.Button("Generate")
# with gr.Row():
# target_ratio = gr.Radio(
# label="Expected Ratio",
# choices=["9:16", "16:9", "1:1", "Custom"],
# value="9:16",
# scale=2
# )
# alignment_dropdown = gr.Dropdown(
# choices=["Middle", "Left", "Right", "Top", "Bottom"],
# value="Middle",
# label="Alignment"
# )
# with gr.Accordion(label="Advanced settings", open=False) as settings_panel:
# with gr.Column():
# with gr.Row():
# width_slider = gr.Slider(
# label="Target Width",
# minimum=720,
# maximum=1536,
# step=8,
# value=720, # Set a default value
# )
# height_slider = gr.Slider(
# label="Target Height",
# minimum=720,
# maximum=1536,
# step=8,
# value=1280, # Set a default value
# )
# num_inference_steps = gr.Slider(label="Steps", minimum=4, maximum=12, step=1, value=8)
# with gr.Group():
# overlap_percentage = gr.Slider(
# label="Mask overlap (%)",
# minimum=1,
# maximum=50,
# value=10,
# step=1
# )
# with gr.Row():
# overlap_top = gr.Checkbox(label="Overlap Top", value=True)
# overlap_right = gr.Checkbox(label="Overlap Right", value=True)
# with gr.Row():
# overlap_left = gr.Checkbox(label="Overlap Left", value=True)
# overlap_bottom = gr.Checkbox(label="Overlap Bottom", value=True)
# with gr.Row():
# resize_option = gr.Radio(
# label="Resize input image",
# choices=["Full", "50%", "33%", "25%", "Custom"],
# value="Full"
# )
# custom_resize_percentage = gr.Slider(
# label="Custom resize (%)",
# minimum=1,
# maximum=100,
# step=1,
# value=50,
# visible=False
# )
# with gr.Column():
# preview_button = gr.Button("Preview alignment and mask")
# gr.Examples(
# examples=[
# ["./examples/example_1.webp", 1280, 720, "Middle"],
# ["./examples/example_2.jpg", 1440, 810, "Left"],
# ["./examples/example_3.jpg", 1024, 1024, "Top"],
# ["./examples/example_3.jpg", 1024, 1024, "Bottom"],
# ],
# inputs=[input_image, width_slider, height_slider, alignment_dropdown],
# )
with gr.Column():
result = gr.Image(type='pil', label='Image Output')
# use_as_input_button = gr.Button("Use as Input Image", visible=False)
# history_gallery = gr.Gallery(label="History", columns=6, object_fit="contain", interactive=False)
# preview_image = gr.Image(label="Preview")
run_button.click(
fn=infer_headswap,
inputs=[input_source, input_target],
outputs=[result]
)
demo.launch() |