mirror of
https://github.com/storytold/storyteller-ml.git
synced 2026-10-16 00:13:07 +00:00
Brandon Thomas
5ab5b1c036
@ d7814fa44a0a1154524b92fce0e3133a2604d333 https://github.com/ali-vilab/UniAnimate
499 lines
17 KiB
Python
499 lines
17 KiB
Python
"""
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GaussianDiffusion wraps operators for denoising diffusion models, including the
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diffusion and denoising processes, as well as the loss evaluation.
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"""
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import torch
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import torchsde
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import random
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from tqdm.auto import trange
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__all__ = ['GaussianDiffusion']
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def _i(tensor, t, x):
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"""
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Index tensor using t and format the output according to x.
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"""
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shape = (x.size(0), ) + (1, ) * (x.ndim - 1)
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return tensor[t.to(tensor.device)].view(shape).to(x.device)
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class BatchedBrownianTree:
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"""
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A wrapper around torchsde.BrownianTree that enables batches of entropy.
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"""
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def __init__(self, x, t0, t1, seed=None, **kwargs):
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t0, t1, self.sign = self.sort(t0, t1)
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w0 = kwargs.get('w0', torch.zeros_like(x))
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if seed is None:
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seed = torch.randint(0, 2 ** 63 - 1, []).item()
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self.batched = True
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try:
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assert len(seed) == x.shape[0]
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w0 = w0[0]
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except TypeError:
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seed = [seed]
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self.batched = False
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self.trees = [torchsde.BrownianTree(
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t0, w0, t1, entropy=s, **kwargs
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) for s in seed]
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@staticmethod
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def sort(a, b):
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return (a, b, 1) if a < b else (b, a, -1)
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def __call__(self, t0, t1):
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t0, t1, sign = self.sort(t0, t1)
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w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign)
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return w if self.batched else w[0]
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class BrownianTreeNoiseSampler:
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"""
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A noise sampler backed by a torchsde.BrownianTree.
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Args:
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x (Tensor): The tensor whose shape, device and dtype to use to generate
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random samples.
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sigma_min (float): The low end of the valid interval.
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sigma_max (float): The high end of the valid interval.
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seed (int or List[int]): The random seed. If a list of seeds is
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supplied instead of a single integer, then the noise sampler will
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use one BrownianTree per batch item, each with its own seed.
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transform (callable): A function that maps sigma to the sampler's
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internal timestep.
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"""
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def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x):
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self.transform = transform
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t0 = self.transform(torch.as_tensor(sigma_min))
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t1 = self.transform(torch.as_tensor(sigma_max))
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self.tree = BatchedBrownianTree(x, t0, t1, seed)
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def __call__(self, sigma, sigma_next):
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t0 = self.transform(torch.as_tensor(sigma))
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t1 = self.transform(torch.as_tensor(sigma_next))
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return self.tree(t0, t1) / (t1 - t0).abs().sqrt()
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def get_scalings(sigma):
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c_out = -sigma
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c_in = 1 / (sigma ** 2 + 1. ** 2) ** 0.5
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return c_out, c_in
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@torch.no_grad()
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def sample_dpmpp_2m_sde(
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noise,
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model,
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sigmas,
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eta=1.,
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s_noise=1.,
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solver_type='midpoint',
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show_progress=True
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):
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"""
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DPM-Solver++ (2M) SDE.
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"""
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assert solver_type in {'heun', 'midpoint'}
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x = noise * sigmas[0]
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sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas[sigmas < float('inf')].max()
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noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max)
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old_denoised = None
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h_last = None
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for i in trange(len(sigmas) - 1, disable=not show_progress):
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if sigmas[i] == float('inf'):
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# Euler method
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denoised = model(noise, sigmas[i])
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x = denoised + sigmas[i + 1] * noise
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else:
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_, c_in = get_scalings(sigmas[i])
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denoised = model(x * c_in, sigmas[i])
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if sigmas[i + 1] == 0:
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# Denoising step
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x = denoised
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else:
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# DPM-Solver++(2M) SDE
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t, s = -sigmas[i].log(), -sigmas[i + 1].log()
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h = s - t
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eta_h = eta * h
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x = sigmas[i + 1] / sigmas[i] * (-eta_h).exp() * x + \
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(-h - eta_h).expm1().neg() * denoised
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if old_denoised is not None:
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r = h_last / h
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if solver_type == 'heun':
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x = x + ((-h - eta_h).expm1().neg() / (-h - eta_h) + 1) * \
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(1 / r) * (denoised - old_denoised)
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elif solver_type == 'midpoint':
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x = x + 0.5 * (-h - eta_h).expm1().neg() * \
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(1 / r) * (denoised - old_denoised)
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x = x + noise_sampler(
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sigmas[i],
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sigmas[i + 1]
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) * sigmas[i + 1] * (-2 * eta_h).expm1().neg().sqrt() * s_noise
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old_denoised = denoised
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h_last = h
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return x
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class GaussianDiffusion(object):
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def __init__(self, sigmas, prediction_type='eps'):
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assert prediction_type in {'x0', 'eps', 'v'}
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self.sigmas = sigmas.float() # noise coefficients
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self.alphas = torch.sqrt(1 - sigmas ** 2).float() # signal coefficients
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self.num_timesteps = len(sigmas)
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self.prediction_type = prediction_type
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def diffuse(self, x0, t, noise=None):
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"""
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Add Gaussian noise to signal x0 according to:
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q(x_t | x_0) = N(x_t | alpha_t x_0, sigma_t^2 I).
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"""
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noise = torch.randn_like(x0) if noise is None else noise
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xt = _i(self.alphas, t, x0) * x0 + _i(self.sigmas, t, x0) * noise
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return xt
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def denoise(
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self,
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xt,
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t,
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s,
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model,
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model_kwargs={},
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guide_scale=None,
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guide_rescale=None,
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clamp=None,
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percentile=None
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):
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"""
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Apply one step of denoising from the posterior distribution q(x_s | x_t, x0).
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Since x0 is not available, estimate the denoising results using the learned
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distribution p(x_s | x_t, \hat{x}_0 == f(x_t)).
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"""
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s = t - 1 if s is None else s
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# hyperparams
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sigmas = _i(self.sigmas, t, xt)
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alphas = _i(self.alphas, t, xt)
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alphas_s = _i(self.alphas, s.clamp(0), xt)
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alphas_s[s < 0] = 1.
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sigmas_s = torch.sqrt(1 - alphas_s ** 2)
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# precompute variables
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betas = 1 - (alphas / alphas_s) ** 2
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coef1 = betas * alphas_s / sigmas ** 2
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coef2 = (alphas * sigmas_s ** 2) / (alphas_s * sigmas ** 2)
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var = betas * (sigmas_s / sigmas) ** 2
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log_var = torch.log(var).clamp_(-20, 20)
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# prediction
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if guide_scale is None:
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assert isinstance(model_kwargs, dict)
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out = model(xt, t=t, **model_kwargs)
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else:
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# classifier-free guidance (arXiv:2207.12598)
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# model_kwargs[0]: conditional kwargs
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# model_kwargs[1]: non-conditional kwargs
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assert isinstance(model_kwargs, list) and len(model_kwargs) == 2
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y_out = model(xt, t=t, **model_kwargs[0])
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if guide_scale == 1.:
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out = y_out
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else:
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u_out = model(xt, t=t, **model_kwargs[1])
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out = u_out + guide_scale * (y_out - u_out)
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# rescale the output according to arXiv:2305.08891
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if guide_rescale is not None:
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assert guide_rescale >= 0 and guide_rescale <= 1
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ratio = (y_out.flatten(1).std(dim=1) / (
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out.flatten(1).std(dim=1) + 1e-12
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)).view((-1, ) + (1, ) * (y_out.ndim - 1))
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out *= guide_rescale * ratio + (1 - guide_rescale) * 1.0
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# compute x0
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if self.prediction_type == 'x0':
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x0 = out
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elif self.prediction_type == 'eps':
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x0 = (xt - sigmas * out) / alphas
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elif self.prediction_type == 'v':
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x0 = alphas * xt - sigmas * out
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else:
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raise NotImplementedError(
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f'prediction_type {self.prediction_type} not implemented'
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)
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# restrict the range of x0
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if percentile is not None:
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# NOTE: percentile should only be used when data is within range [-1, 1]
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assert percentile > 0 and percentile <= 1
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s = torch.quantile(x0.flatten(1).abs(), percentile, dim=1)
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s = s.clamp_(1.0).view((-1, ) + (1, ) * (xt.ndim - 1))
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x0 = torch.min(s, torch.max(-s, x0)) / s
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elif clamp is not None:
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x0 = x0.clamp(-clamp, clamp)
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# recompute eps using the restricted x0
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eps = (xt - alphas * x0) / sigmas
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# compute mu (mean of posterior distribution) using the restricted x0
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mu = coef1 * x0 + coef2 * xt
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return mu, var, log_var, x0, eps
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@torch.no_grad()
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def sample(
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self,
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noise,
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model,
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model_kwargs={},
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condition_fn=None,
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guide_scale=None,
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guide_rescale=None,
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clamp=None,
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percentile=None,
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solver='euler_a',
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steps=20,
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t_max=None,
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t_min=None,
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discretization=None,
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discard_penultimate_step=None,
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return_intermediate=None,
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show_progress=False,
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seed=-1,
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**kwargs
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):
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# sanity check
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assert isinstance(steps, (int, torch.LongTensor))
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assert t_max is None or (t_max > 0 and t_max <= self.num_timesteps - 1)
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assert t_min is None or (t_min >= 0 and t_min < self.num_timesteps - 1)
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assert discretization in (None, 'leading', 'linspace', 'trailing')
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assert discard_penultimate_step in (None, True, False)
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assert return_intermediate in (None, 'x0', 'xt')
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# function of diffusion solver
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solver_fn = {
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# 'heun': sample_heun,
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'dpmpp_2m_sde': sample_dpmpp_2m_sde
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}[solver]
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# options
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schedule = 'karras' if 'karras' in solver else None
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discretization = discretization or 'linspace'
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seed = seed if seed >= 0 else random.randint(0, 2 ** 31)
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if isinstance(steps, torch.LongTensor):
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discard_penultimate_step = False
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if discard_penultimate_step is None:
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discard_penultimate_step = True if solver in (
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'dpm2',
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'dpm2_ancestral',
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'dpmpp_2m_sde',
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'dpm2_karras',
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'dpm2_ancestral_karras',
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'dpmpp_2m_sde_karras'
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) else False
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# function for denoising xt to get x0
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intermediates = []
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def model_fn(xt, sigma):
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# denoising
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t = self._sigma_to_t(sigma).repeat(len(xt)).round().long()
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x0 = self.denoise(
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xt, t, None, model, model_kwargs, guide_scale, guide_rescale, clamp,
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percentile
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)[-2]
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# collect intermediate outputs
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if return_intermediate == 'xt':
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intermediates.append(xt)
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elif return_intermediate == 'x0':
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intermediates.append(x0)
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return x0
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# get timesteps
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if isinstance(steps, int):
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steps += 1 if discard_penultimate_step else 0
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t_max = self.num_timesteps - 1 if t_max is None else t_max
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t_min = 0 if t_min is None else t_min
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# discretize timesteps
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if discretization == 'leading':
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steps = torch.arange(
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t_min, t_max + 1, (t_max - t_min + 1) / steps
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).flip(0)
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elif discretization == 'linspace':
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steps = torch.linspace(t_max, t_min, steps)
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elif discretization == 'trailing':
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steps = torch.arange(t_max, t_min - 1, -((t_max - t_min + 1) / steps))
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else:
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raise NotImplementedError(
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f'{discretization} discretization not implemented'
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)
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steps = steps.clamp_(t_min, t_max)
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steps = torch.as_tensor(steps, dtype=torch.float32, device=noise.device)
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# get sigmas
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sigmas = self._t_to_sigma(steps)
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sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])
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if schedule == 'karras':
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if sigmas[0] == float('inf'):
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sigmas = karras_schedule(
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n=len(steps) - 1,
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sigma_min=sigmas[sigmas > 0].min().item(),
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sigma_max=sigmas[sigmas < float('inf')].max().item(),
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rho=7.
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).to(sigmas)
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sigmas = torch.cat([
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sigmas.new_tensor([float('inf')]), sigmas, sigmas.new_zeros([1])
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])
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else:
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sigmas = karras_schedule(
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n=len(steps),
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sigma_min=sigmas[sigmas > 0].min().item(),
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sigma_max=sigmas.max().item(),
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rho=7.
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).to(sigmas)
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sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])
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if discard_penultimate_step:
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sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
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# sampling
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x0 = solver_fn(
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noise,
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model_fn,
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sigmas,
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show_progress=show_progress,
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**kwargs
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)
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return (x0, intermediates) if return_intermediate is not None else x0
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@torch.no_grad()
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def ddim_reverse_sample(
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self,
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xt,
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t,
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model,
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model_kwargs={},
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clamp=None,
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percentile=None,
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guide_scale=None,
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guide_rescale=None,
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ddim_timesteps=20,
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reverse_steps=600
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):
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r"""Sample from p(x_{t+1} | x_t) using DDIM reverse ODE (deterministic).
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"""
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stride = reverse_steps // ddim_timesteps
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# predict distribution of p(x_{t-1} | x_t)
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_, _, _, x0, eps = self.denoise(
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xt, t, None, model, model_kwargs, guide_scale, guide_rescale, clamp,
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percentile
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)
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# derive variables
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s = (t + stride).clamp(0, reverse_steps-1)
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# hyperparams
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sigmas = _i(self.sigmas, t, xt)
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alphas = _i(self.alphas, t, xt)
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alphas_s = _i(self.alphas, s.clamp(0), xt)
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alphas_s[s < 0] = 1.
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sigmas_s = torch.sqrt(1 - alphas_s ** 2)
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# reverse sample
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mu = alphas_s * x0 + sigmas_s * eps
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return mu, x0
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@torch.no_grad()
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def ddim_reverse_sample_loop(
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self,
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x0,
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model,
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model_kwargs={},
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clamp=None,
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percentile=None,
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guide_scale=None,
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guide_rescale=None,
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ddim_timesteps=20,
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reverse_steps=600
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):
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# prepare input
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b = x0.size(0)
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xt = x0
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# reconstruction steps
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steps = torch.arange(0, reverse_steps, reverse_steps // ddim_timesteps)
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for step in steps:
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t = torch.full((b, ), step, dtype=torch.long, device=xt.device)
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xt, _ = self.ddim_reverse_sample(xt, t, model, model_kwargs, clamp, percentile, guide_scale, guide_rescale, ddim_timesteps, reverse_steps)
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return xt
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def _sigma_to_t(self, sigma):
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if sigma == float('inf'):
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t = torch.full_like(sigma, len(self.sigmas) - 1)
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else:
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log_sigmas = torch.sqrt(
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self.sigmas ** 2 / (1 - self.sigmas ** 2)
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).log().to(sigma)
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log_sigma = sigma.log()
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dists = log_sigma - log_sigmas[:, None]
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low_idx = dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(
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max=log_sigmas.shape[0] - 2
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)
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high_idx = low_idx + 1
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low, high = log_sigmas[low_idx], log_sigmas[high_idx]
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w = (low - log_sigma) / (low - high)
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w = w.clamp(0, 1)
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t = (1 - w) * low_idx + w * high_idx
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t = t.view(sigma.shape)
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if t.ndim == 0:
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t = t.unsqueeze(0)
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return t
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def _t_to_sigma(self, t):
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t = t.float()
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low_idx, high_idx, w = t.floor().long(), t.ceil().long(), t.frac()
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log_sigmas = torch.sqrt(self.sigmas ** 2 / (1 - self.sigmas ** 2)).log().to(t)
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log_sigma = (1 - w) * log_sigmas[low_idx] + w * log_sigmas[high_idx]
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log_sigma[torch.isnan(log_sigma) | torch.isinf(log_sigma)] = float('inf')
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return log_sigma.exp()
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def prev_step(self, model_out, t, xt, inference_steps=50):
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prev_t = t - self.num_timesteps // inference_steps
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sigmas = _i(self.sigmas, t, xt)
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alphas = _i(self.alphas, t, xt)
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alphas_prev = _i(self.alphas, prev_t.clamp(0), xt)
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alphas_prev[prev_t < 0] = 1.
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sigmas_prev = torch.sqrt(1 - alphas_prev ** 2)
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|
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x0 = alphas * xt - sigmas * model_out
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eps = (xt - alphas * x0) / sigmas
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prev_sample = alphas_prev * x0 + sigmas_prev * eps
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return prev_sample
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def next_step(self, model_out, t, xt, inference_steps=50):
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t, next_t = min(t - self.num_timesteps // inference_steps, 999), t
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|
|
|
sigmas = _i(self.sigmas, t, xt)
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alphas = _i(self.alphas, t, xt)
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|
alphas_next = _i(self.alphas, next_t.clamp(0), xt)
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|
alphas_next[next_t < 0] = 1.
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|
sigmas_next = torch.sqrt(1 - alphas_next ** 2)
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|
|
|
x0 = alphas * xt - sigmas * model_out
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|
eps = (xt - alphas * x0) / sigmas
|
|
next_sample = alphas_next * x0 + sigmas_next * eps
|
|
return next_sample
|
|
|
|
def get_noise_pred_single(self, xt, t, model, model_kwargs):
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|
assert isinstance(model_kwargs, dict)
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|
out = model(xt, t=t, **model_kwargs)
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|
return out
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