import os
import numpy as np
import torch
from loguru import logger
from .base import InpaintModel
from .ddim_sampler import DDIMSampler
from .plms_sampler import PLMSSampler
from iopaint.schema import InpaintRequest, LDMSampler
torch.manual_seed(42)
import torch.nn as nn
from iopaint.helper import (
download_model,
norm_img,
get_cache_path_by_url,
load_jit_model,
)
from .utils import (
make_beta_schedule,
timestep_embedding,
)
LDM_ENCODE_MODEL_URL = os.environ.get(
"LDM_ENCODE_MODEL_URL",
"https://github.com/Sanster/models/releases/download/add_ldm/cond_stage_model_encode.pt",
)
LDM_ENCODE_MODEL_MD5 = os.environ.get(
"LDM_ENCODE_MODEL_MD5", "23239fc9081956a3e70de56472b3f296"
)
LDM_DECODE_MODEL_URL = os.environ.get(
"LDM_DECODE_MODEL_URL",
"https://github.com/Sanster/models/releases/download/add_ldm/cond_stage_model_decode.pt",
)
LDM_DECODE_MODEL_MD5 = os.environ.get(
"LDM_DECODE_MODEL_MD5", "fe419cd15a750d37a4733589d0d3585c"
)
LDM_DIFFUSION_MODEL_URL = os.environ.get(
"LDM_DIFFUSION_MODEL_URL",
"https://github.com/Sanster/models/releases/download/add_ldm/diffusion.pt",
)
LDM_DIFFUSION_MODEL_MD5 = os.environ.get(
"LDM_DIFFUSION_MODEL_MD5", "b0afda12bf790c03aba2a7431f11d22d"
)
class DDPM(nn.Module):
# classic DDPM with Gaussian diffusion, in image space
def __init__(
self,
device,
timesteps=1000,
beta_schedule="linear",
linear_start=0.0015,
linear_end=0.0205,
cosine_s=0.008,
original_elbo_weight=0.0,
v_posterior=0.0, # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
l_simple_weight=1.0,
parameterization="eps", # all assuming fixed variance schedules
use_positional_encodings=False,
):
super().__init__()
self.device = device
self.parameterization = parameterization
self.use_positional_encodings = use_positional_encodings
self.v_posterior = v_posterior
self.original_elbo_weight = original_elbo_weight
self.l_simple_weight = l_simple_weight
self.register_schedule(
beta_schedule=beta_schedule,
timesteps=timesteps,
linear_start=linear_start,
linear_end=linear_end,
cosine_s=cosine_s,
)
def register_schedule(
self,
given_betas=None,
beta_schedule="linear",
timesteps=1000,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
):
betas = make_beta_schedule(
self.device,
beta_schedule,
timesteps,
linear_start=linear_start,
linear_end=linear_end,
cosine_s=cosine_s,
)
alphas = 1.0 - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1])
(timesteps,) = betas.shape
self.num_timesteps = int(timesteps)
self.linear_start = linear_start
self.linear_end = linear_end
assert (
alphas_cumprod.shape[0] == self.num_timesteps
), "alphas have to be defined for each timestep"
to_torch = lambda x: torch.tensor(x, dtype=torch.float32).to(self.device)
self.register_buffer("betas", to_torch(betas))
self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
self.register_buffer("alphas_cumprod_prev", to_torch(alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer("sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod)))
self.register_buffer(
"sqrt_one_minus_alphas_cumprod", to_torch(np.sqrt(1.0 - alphas_cumprod))
)
self.register_buffer(
"log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod))
)
self.register_buffer(
"sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod))
)
self.register_buffer(
"sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod - 1))
)
# calculations for posterior q(x_{t-1} | x_t, x_0)
posterior_variance = (1 - self.v_posterior) * betas * (
1.0 - alphas_cumprod_prev
) / (1.0 - alphas_cumprod) + self.v_posterior * betas
# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
self.register_buffer("posterior_variance", to_torch(posterior_variance))
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
self.register_buffer(
"posterior_log_variance_clipped",
to_torch(np.log(np.maximum(posterior_variance, 1e-20))),
)
self.register_buffer(
"posterior_mean_coef1",
to_torch(betas * np.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)),
)
self.register_buffer(
"posterior_mean_coef2",
to_torch(
(1.0 - alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - alphas_cumprod)
),
)
if self.parameterization == "eps":
lvlb_weights = self.betas**2 / (
2
* self.posterior_variance
* to_torch(alphas)
* (1 - self.alphas_cumprod)
)
elif self.parameterization == "x0":
lvlb_weights = (
0.5
* np.sqrt(torch.Tensor(alphas_cumprod))
/ (2.0 * 1 - torch.Tensor(alphas_cumprod))
)
else:
raise NotImplementedError("mu not supported")
# TODO how to choose this term
lvlb_weights[0] = lvlb_weights[1]
self.register_buffer("lvlb_weights", lvlb_weights, persistent=False)
assert not torch.isnan(self.lvlb_weights).all()
class LatentDiffusion(DDPM):
def __init__(
self,
diffusion_model,
device,
cond_stage_key="image",
cond_stage_trainable=False,
concat_mode=True,
scale_factor=1.0,
scale_by_std=False,
*args,
**kwargs,
):
self.num_timesteps_cond = 1
self.scale_by_std = scale_by_std
super().__init__(device, *args, **kwargs)
self.diffusion_model = diffusion_model
self.concat_mode = concat_mode
self.cond_stage_trainable = cond_stage_trainable
self.cond_stage_key = cond_stage_key
self.num_downs = 2
self.scale_factor = scale_factor
def make_cond_schedule(
self,
):
self.cond_ids = torch.full(
size=(self.num_timesteps,),
fill_value=self.num_timesteps - 1,
dtype=torch.long,
)
ids = torch.round(
torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)
).long()
self.cond_ids[: self.num_timesteps_cond] = ids
def register_schedule(
self,
given_betas=None,
beta_schedule="linear",
timesteps=1000,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
):
super().register_schedule(
given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s
)
self.shorten_cond_schedule = self.num_timesteps_cond > 1
if self.shorten_cond_schedule:
self.make_cond_schedule()
def apply_model(self, x_noisy, t, cond):
# x_recon = self.model(x_noisy, t, cond['c_concat'][0]) # cond['c_concat'][0].shape 1,4,128,128
t_emb = timestep_embedding(x_noisy.device, t, 256, repeat_only=False)
x_recon = self.diffusion_model(x_noisy, t_emb, cond)
return x_recon
class LDM(InpaintModel):
name = "ldm"
pad_mod = 32
is_erase_model = True
def __init__(self, device, fp16: bool = True, **kwargs):
self.fp16 = fp16
super().__init__(device)
self.device = device
def init_model(self, device, **kwargs):
self.diffusion_model = load_jit_model(
LDM_DIFFUSION_MODEL_URL, device, LDM_DIFFUSION_MODEL_MD5
)
self.cond_stage_model_decode = load_jit_model(
LDM_DECODE_MODEL_URL, device, LDM_DECODE_MODEL_MD5
)
self.cond_stage_model_encode = load_jit_model(
LDM_ENCODE_MODEL_URL, device, LDM_ENCODE_MODEL_MD5
)
if self.fp16 and "cuda" in str(device):
self.diffusion_model = self.diffusion_model.half()
self.cond_stage_model_decode = self.cond_stage_model_decode.half()
self.cond_stage_model_encode = self.cond_stage_model_encode.half()
self.model = LatentDiffusion(self.diffusion_model, device)
@staticmethod
def download():
download_model(LDM_DIFFUSION_MODEL_URL, LDM_DIFFUSION_MODEL_MD5)
download_model(LDM_DECODE_MODEL_URL, LDM_DECODE_MODEL_MD5)
download_model(LDM_ENCODE_MODEL_URL, LDM_ENCODE_MODEL_MD5)
@staticmethod
def is_downloaded() -> bool:
model_paths = [
get_cache_path_by_url(LDM_DIFFUSION_MODEL_URL),
get_cache_path_by_url(LDM_DECODE_MODEL_URL),
get_cache_path_by_url(LDM_ENCODE_MODEL_URL),
]
return all([os.path.exists(it) for it in model_paths])
@torch.cuda.amp.autocast()
def forward(self, image, mask, config: InpaintRequest):
"""
image: [H, W, C] RGB
mask: [H, W, 1]
return: BGR IMAGE
"""
# image [1,3,512,512] float32
# mask: [1,1,512,512] float32
# masked_image: [1,3,512,512] float32
if config.ldm_sampler == LDMSampler.ddim:
sampler = DDIMSampler(self.model)
elif config.ldm_sampler == LDMSampler.plms:
sampler = PLMSSampler(self.model)
else:
raise ValueError()
steps = config.ldm_steps
image = norm_img(image)
mask = norm_img(mask)
mask[mask < 0.5] = 0
mask[mask >= 0.5] = 1
image = torch.from_numpy(image).unsqueeze(0).to(self.device)
mask = torch.from_numpy(mask).unsqueeze(0).to(self.device)
masked_image = (1 - mask) * image
mask = self._norm(mask)
masked_image = self._norm(masked_image)
c = self.cond_stage_model_encode(masked_image)
torch.cuda.empty_cache()
cc = torch.nn.functional.interpolate(mask, size=c.shape[-2:]) # 1,1,128,128
c = torch.cat((c, cc), dim=1) # 1,4,128,128
shape = (c.shape[1] - 1,) + c.shape[2:]
samples_ddim = sampler.sample(
steps=steps, conditioning=c, batch_size=c.shape[0], shape=shape
)
torch.cuda.empty_cache()
x_samples_ddim = self.cond_stage_model_decode(
samples_ddim
) # samples_ddim: 1, 3, 128, 128 float32
torch.cuda.empty_cache()
# image = torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)
# mask = torch.clamp((mask + 1.0) / 2.0, min=0.0, max=1.0)
inpainted_image = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
# inpainted = (1 - mask) * image + mask * predicted_image
inpainted_image = inpainted_image.cpu().numpy().transpose(0, 2, 3, 1)[0] * 255
inpainted_image = inpainted_image.astype(np.uint8)[:, :, ::-1]
return inpainted_image
def _norm(self, tensor):
return tensor * 2.0 - 1.0