File size: 17,507 Bytes
2d0e22d 8ba5cfe 2d0e22d 61ae0cb 2d0e22d 61ae0cb 2d0e22d 61ae0cb 2d0e22d 61ae0cb 2d0e22d 61ae0cb 2d0e22d 61ae0cb 2d0e22d 61ae0cb 2d0e22d b7b1d93 61ae0cb 2d0e22d 61ae0cb 2d0e22d db29364 61ae0cb 2d0e22d 9a50f62 2d0e22d f0d0ec8 e767711 2d0e22d b7b1d93 2d0e22d b7b1d93 2d0e22d b7b1d93 2d0e22d b7b1d93 2d0e22d b7b1d93 2d0e22d 0f00e60 2d0e22d b7b1d93 2d0e22d e767711 f0d0ec8 e767711 2d0e22d f0d0ec8 2d0e22d f0d0ec8 2d0e22d 61ae0cb 2d0e22d 61ae0cb 2d0e22d 61ae0cb 2d0e22d 61ae0cb 2d0e22d e767711 2d0e22d e767711 f0d0ec8 e767711 249da71 e767711 2d0e22d f0d0ec8 2d0e22d e767711 2d0e22d 61ae0cb 2d0e22d 1bec3ee 61ae0cb 2d0e22d f0d0ec8 e767711 2d0e22d 61ae0cb 2d0e22d f0d0ec8 2d0e22d 61ae0cb bc59621 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 |
from __future__ import annotations
import spaces
import math
import random
import sys
from argparse import ArgumentParser
from tqdm.auto import trange
import einops
import gradio as gr
import k_diffusion as K
import numpy as np
import torch
import torch.nn as nn
from einops import rearrange
from omegaconf import OmegaConf
from PIL import Image, ImageOps, ImageFilter
from torch import autocast
import cv2
import imageio
sys.path.append("./stable_diffusion")
from stable_diffusion.ldm.util import instantiate_from_config
class CFGDenoiser(nn.Module):
def __init__(self, model):
super().__init__()
self.inner_model = model
def forward(self, z_0, z_1, sigma, cond, uncond, text_cfg_scale, image_cfg_scale):
cfg_z_0 = einops.repeat(z_0, "1 ... -> n ...", n=3)
cfg_z_1 = einops.repeat(z_1, "1 ... -> n ...", n=3)
cfg_sigma = einops.repeat(sigma, "1 ... -> n ...", n=3)
cfg_cond = {
"c_crossattn": [torch.cat([cond["c_crossattn"][0], uncond["c_crossattn"][0], uncond["c_crossattn"][0]])],
"c_concat": [torch.cat([cond["c_concat"][0], cond["c_concat"][0], uncond["c_concat"][0]])],
}
output_0, output_1 = self.inner_model(cfg_z_0, cfg_z_1, cfg_sigma, cond=cfg_cond)
out_cond_0, out_img_cond_0, out_uncond_0 = output_0.chunk(3)
out_cond_1, _, _ = output_1.chunk(3)
return out_uncond_0 + text_cfg_scale * (out_cond_0 - out_img_cond_0) + image_cfg_scale * (out_img_cond_0 - out_uncond_0), \
out_cond_1
def load_model_from_config(config, ckpt, vae_ckpt=None, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
if vae_ckpt is not None:
print(f"Loading VAE from {vae_ckpt}")
vae_sd = torch.load(vae_ckpt, map_location="cpu")["state_dict"]
sd = {
k: vae_sd[k[len("first_stage_model.") :]] if k.startswith("first_stage_model.") else v
for k, v in sd.items()
}
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=True)
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
return model
def append_dims(x, target_dims):
"""Appends dimensions to the end of a tensor until it has target_dims dimensions."""
dims_to_append = target_dims - x.ndim
if dims_to_append < 0:
raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
return x[(...,) + (None,) * dims_to_append]
class CompVisDenoiser(K.external.CompVisDenoiser):
def __init__(self, model, quantize=False, device='cpu'):
super().__init__(model, quantize, device)
def get_eps(self, *args, **kwargs):
return self.inner_model.apply_model(*args, **kwargs)
def forward(self, input_0, input_1, sigma, **kwargs):
c_out, c_in = [append_dims(x, input_0.ndim) for x in self.get_scalings(sigma)]
# eps_0, eps_1 = self.get_eps(input_0 * c_in, input_1 * c_in, self.sigma_to_t(sigma), **kwargs)
eps_0, eps_1 = self.get_eps(input_0 * c_in, self.sigma_to_t(sigma.cpu().float()).cuda(), **kwargs)
return input_0 + eps_0 * c_out, eps_1
def to_d(x, sigma, denoised):
"""Converts a denoiser output to a Karras ODE derivative."""
return (x - denoised) / append_dims(sigma, x.ndim)
def default_noise_sampler(x):
return lambda sigma, sigma_next: torch.randn_like(x)
def get_ancestral_step(sigma_from, sigma_to, eta=1.):
"""Calculates the noise level (sigma_down) to step down to and the amount
of noise to add (sigma_up) when doing an ancestral sampling step."""
if not eta:
return sigma_to, 0.
sigma_up = min(sigma_to, eta * (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5)
sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5
return sigma_down, sigma_up
def decode_mask(mask, height = 256, width = 256):
mask = nn.functional.interpolate(mask, size=(height, width), mode="bilinear", align_corners=False)
mask = torch.where(mask > 0, 1, -1) # Thresholding step
mask = torch.clamp((mask + 1.0) / 2.0, min=0.0, max=1.0)
mask = 255.0 * rearrange(mask, "1 c h w -> h w c")
mask = torch.cat([mask, mask, mask], dim=-1)
mask = mask.type(torch.uint8).cpu().numpy()
return mask
def sample_euler_ancestral(model, x_0, x_1, sigmas, height, width, extra_args=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
"""Ancestral sampling with Euler method steps."""
extra_args = {} if extra_args is None else extra_args
noise_sampler = default_noise_sampler(x_0) if noise_sampler is None else noise_sampler
s_in = x_0.new_ones([x_0.shape[0]])
mask_list = []
image_list = []
for i in trange(len(sigmas) - 1, disable=disable):
denoised_0, denoised_1 = model(x_0, x_1, sigmas[i] * s_in, **extra_args)
image_list.append(denoised_0)
sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
d_0 = to_d(x_0, sigmas[i], denoised_0)
# Euler method
dt = sigma_down - sigmas[i]
x_0 = x_0 + d_0 * dt
if sigmas[i + 1] > 0:
x_0 = x_0 + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
x_1 = denoised_1
mask_list.append(decode_mask(x_1, height, width))
image_list = torch.cat(image_list, dim=0)
return x_0, x_1, image_list, mask_list
parser = ArgumentParser()
parser.add_argument("--resolution", default=512, type=int)
parser.add_argument("--config", default="configs/generate_diffree.yaml", type=str)
parser.add_argument("--ckpt", default="checkpoints/epoch=000041-step=000010999.ckpt", type=str)
parser.add_argument("--vae-ckpt", default=None, type=str)
args = parser.parse_args()
config = OmegaConf.load(args.config)
model = load_model_from_config(config, args.ckpt, args.vae_ckpt)
model.eval().cuda()
model_wrap = CompVisDenoiser(model)
model_wrap_cfg = CFGDenoiser(model_wrap)
null_token = model.get_learned_conditioning([""])
@spaces.GPU(duration=30)
def generate(
input_image: Image.Image,
instruction: str,
steps: int,
randomize_seed: bool,
seed: int,
randomize_cfg: bool,
text_cfg_scale: float,
image_cfg_scale: float,
weather_close_video: bool,
decode_image_batch: int
):
seed = random.randint(0, 100000) if randomize_seed else seed
text_cfg_scale = round(random.uniform(6.0, 9.0), ndigits=2) if randomize_cfg else text_cfg_scale
image_cfg_scale = round(random.uniform(1.2, 1.8), ndigits=2) if randomize_cfg else image_cfg_scale
width, height = input_image.size
factor = args.resolution / max(width, height)
factor = math.ceil(min(width, height) * factor / 64) * 64 / min(width, height)
width = int((width * factor) // 64) * 64
height = int((height * factor) // 64) * 64
input_image = ImageOps.fit(input_image, (width, height), method=Image.Resampling.LANCZOS)
input_image_copy = input_image.convert("RGB")
if instruction == "":
return [input_image, seed]
model.cuda()
with torch.no_grad(), autocast("cuda"), model.ema_scope():
cond = {}
cond["c_crossattn"] = [model.get_learned_conditioning([instruction]).to(model.device)]
input_image = 2 * torch.tensor(np.array(input_image)).float() / 255 - 1
input_image = rearrange(input_image, "h w c -> 1 c h w").to(model.device)
cond["c_concat"] = [model.encode_first_stage(input_image).mode().to(model.device)]
uncond = {}
uncond["c_crossattn"] = [null_token.to(model.device)]
uncond["c_concat"] = [torch.zeros_like(cond["c_concat"][0])]
sigmas = model_wrap.get_sigmas(steps).to(model.device)
extra_args = {
"cond": cond,
"uncond": uncond,
"text_cfg_scale": text_cfg_scale,
"image_cfg_scale": image_cfg_scale,
}
torch.manual_seed(seed)
z_0 = torch.randn_like(cond["c_concat"][0]).to(model.device) * sigmas[0]
z_1 = torch.randn_like(cond["c_concat"][0]).to(model.device) * sigmas[0]
z_0, z_1, image_list, mask_list = sample_euler_ancestral(model_wrap_cfg, z_0, z_1, sigmas, height, width, extra_args=extra_args)
x_0 = model.decode_first_stage(z_0)
if model.first_stage_downsample:
x_1 = nn.functional.interpolate(z_1, size=(height, width), mode="bilinear", align_corners=False)
x_1 = torch.where(x_1 > 0, 1, -1) # Thresholding step
else:
x_1 = model.decode_first_stage(z_1)
x_0 = torch.clamp((x_0 + 1.0) / 2.0, min=0.0, max=1.0)
x_1 = torch.clamp((x_1 + 1.0) / 2.0, min=0.0, max=1.0)
x_0 = 255.0 * rearrange(x_0, "1 c h w -> h w c")
x_1 = 255.0 * rearrange(x_1, "1 c h w -> h w c")
x_1 = torch.cat([x_1, x_1, x_1], dim=-1)
edited_image = Image.fromarray(x_0.type(torch.uint8).cpu().numpy())
edited_mask = Image.fromarray(x_1.type(torch.uint8).cpu().numpy())
image_video_path = None
if not weather_close_video:
image_video = []
for i in range(0, len(image_list), decode_image_batch):
if i + decode_image_batch < len(image_list):
tmp_image_list = image_list[i:i+decode_image_batch]
else:
tmp_image_list = image_list[i:]
tmp_image_list = model.decode_first_stage(tmp_image_list)
tmp_image_list = torch.clamp((tmp_image_list + 1.0) / 2.0, min=0.0, max=1.0)
tmp_image_list = 255.0 * rearrange(tmp_image_list, "b c h w -> b h w c")
tmp_image_list = tmp_image_list.type(torch.uint8).cpu().numpy()
# image list to image
for image in tmp_image_list:
image_video.append(image)
image_video_path = "image.mp4"
fps = 30
with imageio.get_writer(image_video_path, fps=fps) as video:
for image in image_video:
video.append_data(image)
# 对edited_mask做膨胀
edited_mask_copy = edited_mask.copy()
kernel = np.ones((3, 3), np.uint8)
edited_mask = cv2.dilate(np.array(edited_mask), kernel, iterations=3)
edited_mask = Image.fromarray(edited_mask)
m_img = edited_mask.filter(ImageFilter.GaussianBlur(radius=3))
m_img = np.asarray(m_img).astype('float') / 255.0
img_np = np.asarray(input_image_copy).astype('float') / 255.0
ours_np = np.asarray(edited_image).astype('float') / 255.0
mix_image_np = m_img * ours_np + (1 - m_img) * img_np
mix_image = Image.fromarray((mix_image_np * 255).astype(np.uint8)).convert('RGB')
red = np.array(mix_image).astype('float') * 1
red[:, :, 0] = 180.0
red[:, :, 2] = 0
red[:, :, 1] = 0
mix_result_with_red_mask = np.array(mix_image)
mix_result_with_red_mask = Image.fromarray(
(mix_result_with_red_mask.astype('float') * (1 - m_img.astype('float') / 2.0) +
m_img.astype('float') / 2.0 * red).astype('uint8'))
mask_video_path = "mask.mp4"
fps = 30
with imageio.get_writer(mask_video_path, fps=fps) as video:
for image in mask_list:
video.append_data(image)
return [int(seed), text_cfg_scale, image_cfg_scale, edited_image, mix_image, edited_mask_copy, mask_video_path, image_video_path, input_image_copy, mix_result_with_red_mask]
def reset():
return [100, "Randomize Seed", 1372, "Fix CFG", 7.5, 1.5, None, None, None, None, None, None, None, "Close Image Video", 10]
def get_example():
return [
["example_images/dufu.png", "black and white suit", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5],
["example_images/girl.jpeg", "reflective sunglasses", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5],
["example_images/road_sign.png", "stop sign", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5],
["example_images/dufu.png", "blue medical mask", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5],
["example_images/people_standing.png", "dark green pleated skirt", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5],
["example_images/girl.jpeg", "shiny golden crown", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5],
["example_images/dufu.png", "sunglasses", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5],
["example_images/girl.jpeg", "diamond necklace", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5],
["example_images/iron_man.jpg", "sunglasses", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5],
["example_images/girl.jpeg", "the queen's crown", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5],
["example_images/girl.jpeg", "gorgeous yellow gown", 100, "Fix Seed", 1372, "Fix CFG", 7.5, 1.5],
]
with gr.Blocks(css="footer {visibility: hidden}") as demo:
with gr.Row():
gr.Markdown(
"<div align='center'><font size='14'>Diffree: Text-Guided Shape Free Object Inpainting with Diffusion Model</font></div>" # noqa
)
with gr.Row():
with gr.Column(scale=1, min_width=100):
with gr.Row():
input_image = gr.Image(label="Input Image", type="pil", interactive=True)
with gr.Row():
instruction = gr.Textbox(lines=1, label="Object description", interactive=True)
with gr.Row():
steps = gr.Number(value=100, precision=0, label="Steps", interactive=True)
randomize_seed = gr.Radio(
["Fix Seed", "Randomize Seed"],
value="Randomize Seed",
type="index",
label="Seed Selection",
show_label=False,
interactive=True,
)
seed = gr.Number(value=1372, precision=0, label="Seed", interactive=True)
randomize_cfg = gr.Radio(
["Fix CFG", "Randomize CFG"],
value="Fix CFG",
type="index",
label="CFG Selection",
show_label=False,
interactive=True,
)
text_cfg_scale = gr.Number(value=7.5, label=f"Text CFG", interactive=True)
image_cfg_scale = gr.Number(value=1.5, label=f"Image CFG", interactive=True)
with gr.Row():
reset_button = gr.Button("Reset")
generate_button = gr.Button("Generate")
with gr.Column(scale=1, min_width=100):
with gr.Column():
mix_image = gr.Image(label=f"Mix Image", type="pil", interactive=False)
with gr.Column():
edited_mask = gr.Image(label=f"Output Mask", type="pil", interactive=False)
with gr.Accordion('More outputs', open=False):
with gr.Row():
weather_close_video = gr.Radio(
["Show Image Video", "Close Image Video"],
value="Close Image Video",
type="index",
label="Image Generation Process Selection (close for faster generation)",
interactive=True,
)
decode_image_batch = gr.Number(value=10, precision=0, label="Decode Image Batch (<steps)", interactive=True)
with gr.Row():
image_video = gr.Video(label="Image Video of Generation Process")
mask_video = gr.Video(label="Mask Video of Generation Process")
with gr.Row():
original_image = gr.Image(label=f"Original Image", type="pil", interactive=False)
edited_image = gr.Image(label=f"Output Image", type="pil", interactive=False)
mix_result_with_red_mask = gr.Image(label=f"Mix Image With Red Mask", type="pil", interactive=False)
with gr.Row():
gr.Examples(
examples=get_example(),
fn=generate,
inputs=[input_image, instruction, steps, randomize_seed, seed, randomize_cfg, text_cfg_scale, image_cfg_scale],
outputs=[seed, text_cfg_scale, image_cfg_scale, edited_image, mix_image, edited_mask, mask_video, image_video, original_image, mix_result_with_red_mask],
cache_examples=False,
)
generate_button.click(
fn=generate,
inputs=[
input_image,
instruction,
steps,
randomize_seed,
seed,
randomize_cfg,
text_cfg_scale,
image_cfg_scale,
weather_close_video,
decode_image_batch
],
outputs=[seed, text_cfg_scale, image_cfg_scale, edited_image, mix_image, edited_mask, mask_video, image_video, original_image, mix_result_with_red_mask],
)
reset_button.click(
fn=reset,
inputs=[],
outputs=[steps, randomize_seed, seed, randomize_cfg, text_cfg_scale, image_cfg_scale, edited_image, mix_image, edited_mask, mask_video, image_video, original_image, mix_result_with_red_mask, weather_close_video, decode_image_batch],
)
# demo.queue(concurrency_count=1)
# demo.launch(share=True)
demo.queue().launch()
|