enable fp16 for ldm by default
This commit is contained in:
Qing
parent
e369a2f079
commit
e4a6c91f4a
@ -11,8 +11,13 @@ 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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from lama_cleaner.helper import download_model, norm_img, get_cache_path_by_url
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from lama_cleaner.model.utils import make_beta_schedule, make_ddim_timesteps, make_ddim_sampling_parameters, noise_like, \
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timestep_embedding
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from lama_cleaner.model.utils import (
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make_beta_schedule,
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make_ddim_timesteps,
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make_ddim_sampling_parameters,
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noise_like,
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timestep_embedding,
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)
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LDM_ENCODE_MODEL_URL = os.environ.get(
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"LDM_ENCODE_MODEL_URL",
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@ -32,18 +37,20 @@ LDM_DIFFUSION_MODEL_URL = os.environ.get(
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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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def __init__(
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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=0.0,
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v_posterior=0.0, # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
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l_simple_weight=1.0,
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parameterization="eps", # all assuming fixed variance schedules
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use_positional_encodings=False,
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):
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super().__init__()
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self.device = device
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self.parameterization = parameterization
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@ -53,72 +60,120 @@ class DDPM(nn.Module):
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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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self.register_schedule(
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beta_schedule=beta_schedule,
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timesteps=timesteps,
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linear_start=linear_start,
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linear_end=linear_end,
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cosine_s=cosine_s,
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)
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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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def register_schedule(
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self,
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given_betas=None,
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beta_schedule="linear",
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timesteps=1000,
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linear_start=1e-4,
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linear_end=2e-2,
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cosine_s=8e-3,
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):
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betas = make_beta_schedule(
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self.device,
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beta_schedule,
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timesteps,
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linear_start=linear_start,
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linear_end=linear_end,
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cosine_s=cosine_s,
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)
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alphas = 1.0 - 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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alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1])
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timesteps, = betas.shape
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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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assert (
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alphas_cumprod.shape[0] == self.num_timesteps
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), "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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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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self.register_buffer("sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod)))
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self.register_buffer(
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"sqrt_one_minus_alphas_cumprod", to_torch(np.sqrt(1.0 - alphas_cumprod))
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)
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self.register_buffer(
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"log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod))
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)
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self.register_buffer(
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"sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod))
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)
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self.register_buffer(
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"sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod - 1))
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)
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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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posterior_variance = (1 - self.v_posterior) * betas * (
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1.0 - alphas_cumprod_prev
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) / (1.0 - 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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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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self.register_buffer(
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"posterior_log_variance_clipped",
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to_torch(np.log(np.maximum(posterior_variance, 1e-20))),
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)
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self.register_buffer(
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"posterior_mean_coef1",
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to_torch(betas * np.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)),
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)
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self.register_buffer(
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"posterior_mean_coef2",
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to_torch(
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(1.0 - alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - alphas_cumprod)
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),
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)
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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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2
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* self.posterior_variance
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* to_torch(alphas)
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* (1 - self.alphas_cumprod)
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)
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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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lvlb_weights = (
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0.5
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* np.sqrt(torch.Tensor(alphas_cumprod))
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/ (2.0 * 1 - torch.Tensor(alphas_cumprod))
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)
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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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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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def __init__(
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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,
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**kwargs,
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):
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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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@ -129,15 +184,31 @@ class LatentDiffusion(DDPM):
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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 make_cond_schedule(
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self,
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):
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self.cond_ids = torch.full(
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size=(self.num_timesteps,),
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fill_value=self.num_timesteps - 1,
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dtype=torch.long,
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)
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ids = torch.round(
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torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)
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).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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def register_schedule(
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self,
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given_betas=None,
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beta_schedule="linear",
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timesteps=1000,
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linear_start=1e-4,
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linear_end=2e-2,
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cosine_s=8e-3,
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):
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super().register_schedule(
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given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s
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)
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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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@ -160,37 +231,66 @@ class DDIMSampler(object):
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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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def make_schedule(
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self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0.0, verbose=True
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):
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self.ddim_timesteps = make_ddim_timesteps(
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ddim_discr_method=ddim_discretize,
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num_ddim_timesteps=ddim_num_steps,
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# array([1])
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num_ddpm_timesteps=self.ddpm_num_timesteps,
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verbose=verbose,
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)
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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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assert (
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alphas_cumprod.shape[0] == self.ddpm_num_timesteps
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), "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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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(
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"alphas_cumprod_prev", to_torch(self.model.alphas_cumprod_prev)
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)
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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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self.register_buffer(
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"sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod.cpu()))
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)
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self.register_buffer(
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"sqrt_one_minus_alphas_cumprod",
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to_torch(np.sqrt(1.0 - alphas_cumprod.cpu())),
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)
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self.register_buffer(
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"log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod.cpu()))
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)
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self.register_buffer(
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"sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod.cpu()))
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)
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self.register_buffer(
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"sqrt_recipm1_alphas_cumprod",
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to_torch(np.sqrt(1.0 / alphas_cumprod.cpu() - 1)),
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)
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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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ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(
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alphacums=alphas_cumprod.cpu(),
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ddim_timesteps=self.ddim_timesteps,
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eta=ddim_eta,
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verbose=verbose,
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)
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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(1.0 - 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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(1 - self.alphas_cumprod_prev)
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/ (1 - self.alphas_cumprod)
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* (1 - self.alphas_cumprod / self.alphas_cumprod_prev)
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)
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self.register_buffer(
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"ddim_sigmas_for_original_num_steps", sigmas_for_original_sampling_steps
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)
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@torch.no_grad()
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def sample(self, steps, conditioning, batch_size, shape):
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@ -200,65 +300,108 @@ class DDIMSampler(object):
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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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return self.ddim_sampling(
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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=1.0,
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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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def ddim_sampling(
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self,
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cond,
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shape,
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ddim_use_original_steps=False,
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quantize_denoised=False,
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temperature=1.0,
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noise_dropout=0.0,
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):
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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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img = torch.randn(shape, device=device, dtype=cond.dtype)
|
||||
timesteps = (
|
||||
self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
|
||||
)
|
||||
|
||||
time_range = reversed(range(0, timesteps)) if ddim_use_original_steps else np.flip(timesteps)
|
||||
time_range = (
|
||||
reversed(range(0, timesteps))
|
||||
if ddim_use_original_steps
|
||||
else np.flip(timesteps)
|
||||
)
|
||||
total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
|
||||
logger.info(f"Running DDIM Sampling with {total_steps} timesteps")
|
||||
|
||||
iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
|
||||
iterator = tqdm(time_range, desc="DDIM Sampler", total=total_steps)
|
||||
|
||||
for i, step in enumerate(iterator):
|
||||
index = total_steps - i - 1
|
||||
ts = torch.full((b,), step, device=device, dtype=torch.long)
|
||||
|
||||
outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
|
||||
quantize_denoised=quantize_denoised, temperature=temperature,
|
||||
noise_dropout=noise_dropout)
|
||||
outs = self.p_sample_ddim(
|
||||
img,
|
||||
cond,
|
||||
ts,
|
||||
index=index,
|
||||
use_original_steps=ddim_use_original_steps,
|
||||
quantize_denoised=quantize_denoised,
|
||||
temperature=temperature,
|
||||
noise_dropout=noise_dropout,
|
||||
)
|
||||
img, _ = outs
|
||||
|
||||
return img
|
||||
|
||||
@torch.no_grad()
|
||||
def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
|
||||
temperature=1., noise_dropout=0.):
|
||||
def p_sample_ddim(
|
||||
self,
|
||||
x,
|
||||
c,
|
||||
t,
|
||||
index,
|
||||
repeat_noise=False,
|
||||
use_original_steps=False,
|
||||
quantize_denoised=False,
|
||||
temperature=1.0,
|
||||
noise_dropout=0.0,
|
||||
):
|
||||
b, *_, device = *x.shape, x.device
|
||||
e_t = self.model.apply_model(x, t, c)
|
||||
|
||||
alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
|
||||
alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
|
||||
sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
|
||||
sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
|
||||
alphas_prev = (
|
||||
self.model.alphas_cumprod_prev
|
||||
if use_original_steps
|
||||
else self.ddim_alphas_prev
|
||||
)
|
||||
sqrt_one_minus_alphas = (
|
||||
self.model.sqrt_one_minus_alphas_cumprod
|
||||
if use_original_steps
|
||||
else self.ddim_sqrt_one_minus_alphas
|
||||
)
|
||||
sigmas = (
|
||||
self.model.ddim_sigmas_for_original_num_steps
|
||||
if use_original_steps
|
||||
else self.ddim_sigmas
|
||||
)
|
||||
# select parameters corresponding to the currently considered timestep
|
||||
a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
|
||||
a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
|
||||
sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
|
||||
sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device)
|
||||
sqrt_one_minus_at = torch.full(
|
||||
(b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device
|
||||
)
|
||||
|
||||
# current prediction for x_0
|
||||
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
|
||||
if quantize_denoised: # 没用
|
||||
pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
|
||||
# direction pointing to x_t
|
||||
dir_xt = (1. - a_prev - sigma_t ** 2).sqrt() * e_t
|
||||
dir_xt = (1.0 - a_prev - sigma_t ** 2).sqrt() * e_t
|
||||
noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
|
||||
if noise_dropout > 0.: # 没用
|
||||
if noise_dropout > 0.0: # 没用
|
||||
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
|
||||
x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
|
||||
return x_prev, pred_x0
|
||||
@ -275,7 +418,8 @@ def load_jit_model(url, device):
|
||||
class LDM(InpaintModel):
|
||||
pad_mod = 32
|
||||
|
||||
def __init__(self, device):
|
||||
def __init__(self, device, fp16: bool = True):
|
||||
self.fp16 = fp16
|
||||
super().__init__(device)
|
||||
self.device = device
|
||||
|
||||
@ -283,6 +427,10 @@ class LDM(InpaintModel):
|
||||
self.diffusion_model = load_jit_model(LDM_DIFFUSION_MODEL_URL, device)
|
||||
self.cond_stage_model_decode = load_jit_model(LDM_DECODE_MODEL_URL, device)
|
||||
self.cond_stage_model_encode = load_jit_model(LDM_ENCODE_MODEL_URL, device)
|
||||
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()
|
||||
|
||||
model = LatentDiffusion(self.diffusion_model, device)
|
||||
self.sampler = DDIMSampler(model)
|
||||
@ -296,6 +444,7 @@ class LDM(InpaintModel):
|
||||
]
|
||||
return all([os.path.exists(it) for it in model_paths])
|
||||
|
||||
@torch.cuda.amp.autocast()
|
||||
def forward(self, image, mask, config: Config):
|
||||
"""
|
||||
image: [H, W, C] RGB
|
||||
@ -321,16 +470,20 @@ class LDM(InpaintModel):
|
||||
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 = self.sampler.sample(steps=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
|
||||
samples_ddim = self.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)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user