416 lines
18 KiB
Python
416 lines
18 KiB
Python
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import os
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import numpy as np
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import torch
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torch.manual_seed(42)
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import torch.nn as nn
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from tqdm import tqdm
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import cv2
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from lama_cleaner.helper import pad_img_to_modulo, download_model
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from lama_cleaner.ldm.utils import make_beta_schedule, make_ddim_timesteps, make_ddim_sampling_parameters, noise_like, \
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timestep_embedding
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LDM_ENCODE_MODEL_URL = os.environ.get(
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"LDM_ENCODE_MODEL_URL",
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"https://github.com/Sanster/models/releases/download/add_ldm/cond_stage_model_encode.pt",
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)
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LDM_DECODE_MODEL_URL = os.environ.get(
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"LDM_DECODE_MODEL_URL",
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"https://github.com/Sanster/models/releases/download/add_ldm/cond_stage_model_decode.pt",
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)
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LDM_DIFFUSION_MODEL_URL = os.environ.get(
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"LDM_DIFFUSION_MODEL_URL",
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"https://github.com/Sanster/models/releases/download/add_ldm/diffusion.pt",
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)
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class DDPM(nn.Module):
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# classic DDPM with Gaussian diffusion, in image space
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def __init__(self,
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device,
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timesteps=1000,
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beta_schedule="linear",
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linear_start=0.0015,
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linear_end=0.0205,
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cosine_s=0.008,
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original_elbo_weight=0.,
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v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
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l_simple_weight=1.,
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parameterization="eps", # all assuming fixed variance schedules
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use_positional_encodings=False):
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super().__init__()
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self.device = device
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self.parameterization = parameterization
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self.use_positional_encodings = use_positional_encodings
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self.v_posterior = v_posterior
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self.original_elbo_weight = original_elbo_weight
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self.l_simple_weight = l_simple_weight
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self.register_schedule(beta_schedule=beta_schedule, timesteps=timesteps,
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linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
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def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
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linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
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betas = make_beta_schedule(self.device, beta_schedule, timesteps, linear_start=linear_start,
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linear_end=linear_end,
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cosine_s=cosine_s)
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alphas = 1. - betas
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alphas_cumprod = np.cumprod(alphas, axis=0)
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alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
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timesteps, = betas.shape
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self.num_timesteps = int(timesteps)
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self.linear_start = linear_start
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self.linear_end = linear_end
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assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
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to_torch = lambda x: torch.tensor(x, dtype=torch.float32).to(self.device)
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self.register_buffer('betas', to_torch(betas))
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self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
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self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
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# calculations for diffusion q(x_t | x_{t-1}) and others
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self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
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self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
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self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
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self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
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self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
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# calculations for posterior q(x_{t-1} | x_t, x_0)
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posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
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1. - alphas_cumprod) + self.v_posterior * betas
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# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
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self.register_buffer('posterior_variance', to_torch(posterior_variance))
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# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
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self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
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self.register_buffer('posterior_mean_coef1', to_torch(
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betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
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self.register_buffer('posterior_mean_coef2', to_torch(
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(1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
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if self.parameterization == "eps":
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lvlb_weights = self.betas ** 2 / (
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2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
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elif self.parameterization == "x0":
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lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
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else:
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raise NotImplementedError("mu not supported")
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# TODO how to choose this term
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lvlb_weights[0] = lvlb_weights[1]
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self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
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assert not torch.isnan(self.lvlb_weights).all()
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class LatentDiffusion(DDPM):
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def __init__(self,
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diffusion_model,
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device,
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cond_stage_key="image",
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cond_stage_trainable=False,
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concat_mode=True,
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scale_factor=1.0,
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scale_by_std=False,
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*args, **kwargs):
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self.num_timesteps_cond = 1
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self.scale_by_std = scale_by_std
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super().__init__(device, *args, **kwargs)
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self.diffusion_model = diffusion_model
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self.concat_mode = concat_mode
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self.cond_stage_trainable = cond_stage_trainable
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self.cond_stage_key = cond_stage_key
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self.num_downs = 2
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self.scale_factor = scale_factor
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def make_cond_schedule(self, ):
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self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
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ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
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self.cond_ids[:self.num_timesteps_cond] = ids
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def register_schedule(self,
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given_betas=None, beta_schedule="linear", timesteps=1000,
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linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
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super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
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self.shorten_cond_schedule = self.num_timesteps_cond > 1
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if self.shorten_cond_schedule:
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self.make_cond_schedule()
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def apply_model(self, x_noisy, t, cond):
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# x_recon = self.model(x_noisy, t, cond['c_concat'][0]) # cond['c_concat'][0].shape 1,4,128,128
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t_emb = timestep_embedding(x_noisy.device, t, 256, repeat_only=False)
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x_recon = self.diffusion_model(x_noisy, t_emb, cond)
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return x_recon
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class DDIMSampler(object):
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def __init__(self, model, schedule="linear"):
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super().__init__()
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self.model = model
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self.ddpm_num_timesteps = model.num_timesteps
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self.schedule = schedule
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def register_buffer(self, name, attr):
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setattr(self, name, attr)
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def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
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self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
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# array([1])
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num_ddpm_timesteps=self.ddpm_num_timesteps, verbose=verbose)
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alphas_cumprod = self.model.alphas_cumprod # torch.Size([1000])
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assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
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to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
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self.register_buffer('betas', to_torch(self.model.betas))
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self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
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self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
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# calculations for diffusion q(x_t | x_{t-1}) and others
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self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
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self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
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self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
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self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
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self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
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# ddim sampling parameters
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ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
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ddim_timesteps=self.ddim_timesteps,
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eta=ddim_eta, verbose=verbose)
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self.register_buffer('ddim_sigmas', ddim_sigmas)
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self.register_buffer('ddim_alphas', ddim_alphas)
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self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
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self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
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sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
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(1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
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1 - self.alphas_cumprod / self.alphas_cumprod_prev))
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self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
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@torch.no_grad()
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def sample(self, steps, conditioning, batch_size, shape):
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self.make_schedule(ddim_num_steps=steps, ddim_eta=0, verbose=False)
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# sampling
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C, H, W = shape
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size = (batch_size, C, H, W)
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# samples: 1,3,128,128
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return self.ddim_sampling(conditioning,
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size,
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quantize_denoised=False,
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ddim_use_original_steps=False,
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noise_dropout=0,
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temperature=1.,
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)
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@torch.no_grad()
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def ddim_sampling(self, cond, shape,
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ddim_use_original_steps=False,
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quantize_denoised=False,
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temperature=1., noise_dropout=0.):
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device = self.model.betas.device
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b = shape[0]
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img = torch.randn(shape, device=device) # 用了
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timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps # 用了
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time_range = reversed(range(0, timesteps)) if ddim_use_original_steps else np.flip(timesteps)
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total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
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print(f"Running DDIM Sampling with {total_steps} timesteps")
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iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
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for i, step in enumerate(iterator):
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index = total_steps - i - 1
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ts = torch.full((b,), step, device=device, dtype=torch.long)
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outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
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quantize_denoised=quantize_denoised, temperature=temperature,
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noise_dropout=noise_dropout)
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img, _ = outs
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return img
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@torch.no_grad()
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def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
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temperature=1., noise_dropout=0.):
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b, *_, device = *x.shape, x.device
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e_t = self.model.apply_model(x, t, c)
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alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
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alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
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sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
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sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
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# select parameters corresponding to the currently considered timestep
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a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
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a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
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sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
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sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device)
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# current prediction for x_0
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pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
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if quantize_denoised: # 没用
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pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
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# direction pointing to x_t
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dir_xt = (1. - a_prev - sigma_t ** 2).sqrt() * e_t
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noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
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if noise_dropout > 0.: # 没用
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noise = torch.nn.functional.dropout(noise, p=noise_dropout)
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x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
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return x_prev, pred_x0
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def load_jit_model(url, device):
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model_path = download_model(url)
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model = torch.jit.load(model_path).to(device)
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model.eval()
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return model
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class LDM:
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def __init__(self, device, steps=50):
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self.device = device
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self.diffusion_model = load_jit_model(LDM_DIFFUSION_MODEL_URL, device)
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self.cond_stage_model_decode = load_jit_model(LDM_DECODE_MODEL_URL, device)
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self.cond_stage_model_encode = load_jit_model(LDM_ENCODE_MODEL_URL, device)
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model = LatentDiffusion(self.diffusion_model, device)
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self.sampler = DDIMSampler(model)
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self.steps = steps
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def _norm(self, tensor):
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return tensor * 2.0 - 1.0
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@torch.no_grad()
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def __call__(self, image, mask):
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"""
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image: [C, H, W] RGB
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mask: [1, H, W]
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return: BGR IMAGE
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"""
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# image [1,3,512,512] float32
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# mask: [1,1,512,512] float32
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# masked_image: [1,3,512,512] float32
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||
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|
origin_height, origin_width = image.shape[1:]
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image = pad_img_to_modulo(image, mod=32)
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|
mask = pad_img_to_modulo(mask, mod=32)
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|
|
padded_height, padded_width = image.shape[1:]
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mask[mask < 0.5] = 0
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|
mask[mask >= 0.5] = 1
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||
|
|
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||
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|
# crop 512 x 512
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||
|
|
if padded_width <= 512 or padded_height <= 512:
|
||
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|
np_img = self._forward(image, mask, self.device)
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|
else:
|
||
|
|
print("Try to zoom in")
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||
|
|
# zoom in
|
||
|
|
# x,y,w,h
|
||
|
|
# box = self.box_from_bitmap(mask)
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||
|
|
box = self.find_main_content(mask)
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||
|
|
if box is None:
|
||
|
|
print("No bbox found")
|
||
|
|
np_img = self._forward(image, mask, self.device)
|
||
|
|
else:
|
||
|
|
print(f"box: {box}")
|
||
|
|
box_x, box_y, box_w, box_h = box
|
||
|
|
cx = box_x + box_w // 2
|
||
|
|
cy = box_y + box_h // 2
|
||
|
|
|
||
|
|
w = max(512, box_w)
|
||
|
|
h = max(512, box_h)
|
||
|
|
|
||
|
|
left = max(cx - w // 2, 0)
|
||
|
|
top = max(cy - h // 2, 0)
|
||
|
|
right = min(cx + w // 2, origin_width)
|
||
|
|
bottom = min(cy + h // 2, origin_height)
|
||
|
|
|
||
|
|
x = left
|
||
|
|
y = top
|
||
|
|
w = right - left
|
||
|
|
h = bottom - top
|
||
|
|
|
||
|
|
crop_img = image[:, int(y):int(y + h), int(x):int(x + w)]
|
||
|
|
crop_mask = mask[:, int(y):int(y + h), int(x):int(x + w)]
|
||
|
|
|
||
|
|
print(f"Apply zoom in size width x height: {crop_img.shape}")
|
||
|
|
|
||
|
|
crop_img_height, crop_img_width = crop_img.shape[1:]
|
||
|
|
|
||
|
|
crop_img = pad_img_to_modulo(crop_img, mod=32)
|
||
|
|
crop_mask = pad_img_to_modulo(crop_mask, mod=32)
|
||
|
|
# RGB
|
||
|
|
np_img = self._forward(crop_img, crop_mask, self.device)
|
||
|
|
|
||
|
|
image = (image.transpose(1, 2, 0) * 255).astype(np.uint8)
|
||
|
|
image[int(y): int(y + h), int(x): int(x + w), :] = np_img[0:crop_img_height, 0:crop_img_width, :]
|
||
|
|
np_img = image
|
||
|
|
# BGR to RGB
|
||
|
|
# np_img = image[:, :, ::-1]
|
||
|
|
|
||
|
|
np_img = np_img[0:origin_height, 0:origin_width, :]
|
||
|
|
np_img = np_img[:, :, ::-1]
|
||
|
|
|
||
|
|
return np_img
|
||
|
|
|
||
|
|
def _forward(self, image, mask, device):
|
||
|
|
image = torch.from_numpy(image).unsqueeze(0).to(device)
|
||
|
|
mask = torch.from_numpy(mask).unsqueeze(0).to(device)
|
||
|
|
masked_image = (1 - mask) * image
|
||
|
|
|
||
|
|
image = self._norm(image)
|
||
|
|
mask = self._norm(mask)
|
||
|
|
masked_image = self._norm(masked_image)
|
||
|
|
|
||
|
|
c = self.cond_stage_model_encode(masked_image)
|
||
|
|
|
||
|
|
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 = self.sampler.sample(steps=self.steps,
|
||
|
|
conditioning=c,
|
||
|
|
batch_size=c.shape[0],
|
||
|
|
shape=shape)
|
||
|
|
x_samples_ddim = self.cond_stage_model_decode(samples_ddim) # samples_ddim: 1, 3, 128, 128 float32
|
||
|
|
|
||
|
|
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)
|
||
|
|
predicted_image = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
|
||
|
|
|
||
|
|
inpainted = (1 - mask) * image + mask * predicted_image
|
||
|
|
inpainted = inpainted.cpu().numpy().transpose(0, 2, 3, 1)[0] * 255
|
||
|
|
np_img = inpainted.astype(np.uint8)
|
||
|
|
return np_img
|
||
|
|
|
||
|
|
def find_main_content(self, bitmap: np.ndarray):
|
||
|
|
th2 = bitmap[0].astype(np.uint8)
|
||
|
|
row_sum = th2.sum(1)
|
||
|
|
col_sum = th2.sum(0)
|
||
|
|
xmin = max(0, np.argwhere(col_sum != 0).min() - 20)
|
||
|
|
xmax = min(np.argwhere(col_sum != 0).max() + 20, th2.shape[1])
|
||
|
|
ymin = max(0, np.argwhere(row_sum != 0).min() - 20)
|
||
|
|
ymax = min(np.argwhere(row_sum != 0).max() + 20, th2.shape[0])
|
||
|
|
|
||
|
|
left, top, right, bottom = int(xmin), int(ymin), int(xmax), int(ymax)
|
||
|
|
return left, top, right - left, bottom - top
|
||
|
|
|
||
|
|
def box_from_bitmap(self, bitmap):
|
||
|
|
"""
|
||
|
|
bitmap: single map with shape (NUM_CLASSES, H, W),
|
||
|
|
whose values are binarized as {0, 1}
|
||
|
|
"""
|
||
|
|
contours, _ = cv2.findContours(
|
||
|
|
(bitmap[0] * 255).astype(np.uint8), cv2.RETR_FLOODFILL, cv2.CHAIN_APPROX_NONE
|
||
|
|
)
|
||
|
|
|
||
|
|
contours = sorted(contours, key=lambda x: cv2.contourArea(x), reverse=True)
|
||
|
|
num_contours = len(contours)
|
||
|
|
print(f"contours size: {num_contours}")
|
||
|
|
if num_contours != 1:
|
||
|
|
return None
|
||
|
|
|
||
|
|
# x,y,w,h
|
||
|
|
return cv2.boundingRect(contours[0])
|