154 lines
4.6 KiB
Python
Executable File
154 lines
4.6 KiB
Python
Executable File
#!/usr/bin/env python3
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import numpy as np
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import PIL
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import torch
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#from configs.ve import ffhq_ncsnpp_continuous as configs
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# from configs.ve import cifar10_ncsnpp_continuous as configs
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device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
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torch.backends.cuda.matmul.allow_tf32 = False
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torch.manual_seed(0)
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class NewReverseDiffusionPredictor:
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def __init__(self, score_fn, probability_flow=False, sigma_min=0.0, sigma_max=0.0, N=0):
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super().__init__()
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self.sigma_min = sigma_min
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self.sigma_max = sigma_max
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self.N = N
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self.discrete_sigmas = torch.exp(torch.linspace(np.log(self.sigma_min), np.log(self.sigma_max), N))
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self.probability_flow = probability_flow
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self.score_fn = score_fn
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def discretize(self, x, t):
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timestep = (t * (self.N - 1)).long()
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sigma = self.discrete_sigmas.to(t.device)[timestep]
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adjacent_sigma = torch.where(timestep == 0, torch.zeros_like(t),
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self.discrete_sigmas[timestep - 1].to(t.device))
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f = torch.zeros_like(x)
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G = torch.sqrt(sigma ** 2 - adjacent_sigma ** 2)
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labels = self.sigma_min * (self.sigma_max / self.sigma_min) ** t
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result = self.score_fn(x, labels)
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rev_f = f - G[:, None, None, None] ** 2 * result * (0.5 if self.probability_flow else 1.)
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rev_G = torch.zeros_like(G) if self.probability_flow else G
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return rev_f, rev_G
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def update_fn(self, x, t):
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f, G = self.discretize(x, t)
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z = torch.randn_like(x)
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x_mean = x - f
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x = x_mean + G[:, None, None, None] * z
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return x, x_mean
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class NewLangevinCorrector:
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def __init__(self, score_fn, snr, n_steps, sigma_min=0.0, sigma_max=0.0):
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super().__init__()
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self.score_fn = score_fn
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self.snr = snr
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self.n_steps = n_steps
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self.sigma_min = sigma_min
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self.sigma_max = sigma_max
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def update_fn(self, x, t):
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score_fn = self.score_fn
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n_steps = self.n_steps
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target_snr = self.snr
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# if isinstance(sde, VPSDE) or isinstance(sde, subVPSDE):
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# timestep = (t * (sde.N - 1) / sde.T).long()
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# alpha = sde.alphas.to(t.device)[timestep]
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# else:
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alpha = torch.ones_like(t)
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for i in range(n_steps):
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labels = self.sigma_min * (self.sigma_max / self.sigma_min) ** t
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grad = score_fn(x, labels)
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noise = torch.randn_like(x)
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grad_norm = torch.norm(grad.reshape(grad.shape[0], -1), dim=-1).mean()
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noise_norm = torch.norm(noise.reshape(noise.shape[0], -1), dim=-1).mean()
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step_size = (target_snr * noise_norm / grad_norm) ** 2 * 2 * alpha
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x_mean = x + step_size[:, None, None, None] * grad
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x = x_mean + torch.sqrt(step_size * 2)[:, None, None, None] * noise
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return x, x_mean
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def save_image(x):
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image_processed = np.clip(x.permute(0, 2, 3, 1).cpu().numpy() * 255, 0, 255).astype(np.uint8)
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image_pil = PIL.Image.fromarray(image_processed[0])
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image_pil.save("../images/hey.png")
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# ckpt_filename = "exp/ve/cifar10_ncsnpp_continuous/checkpoint_24.pth"
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#ckpt_filename = "exp/ve/ffhq_1024_ncsnpp_continuous/checkpoint_60.pth"
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# Note usually we need to restore ema etc...
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# ema restored checkpoint used from below
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N = 2
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sigma_min = 0.01
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sigma_max = 1348
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sampling_eps = 1e-5
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batch_size = 1
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centered = False
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from diffusers import NCSNpp
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model = NCSNpp.from_pretrained("/home/patrick/ffhq_ncsnpp").to(device)
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model = torch.nn.DataParallel(model)
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img_size = model.module.config.image_size
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channels = model.module.config.num_channels
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shape = (batch_size, channels, img_size, img_size)
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probability_flow = False
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snr = 0.15
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n_steps = 1
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new_corrector = NewLangevinCorrector(score_fn=model, snr=snr, n_steps=n_steps, sigma_min=sigma_min, sigma_max=sigma_max)
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new_predictor = NewReverseDiffusionPredictor(score_fn=model, sigma_min=sigma_min, sigma_max=sigma_max, N=N)
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with torch.no_grad():
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# Initial sample
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x = torch.randn(*shape) * sigma_max
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x = x.to(device)
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timesteps = torch.linspace(1, sampling_eps, N, device=device)
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for i in range(N):
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t = timesteps[i]
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vec_t = torch.ones(shape[0], device=t.device) * t
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x, x_mean = new_corrector.update_fn(x, vec_t)
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x, x_mean = new_predictor.update_fn(x, vec_t)
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x = x_mean
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if centered:
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x = (x + 1.) / 2.
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# save_image(x)
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# for 5 cifar10
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x_sum = 106071.9922
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x_mean = 34.52864456176758
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# for 1000 cifar10
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x_sum = 461.9700
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x_mean = 0.1504
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# for 2 for 1024
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x_sum = 3382810112.0
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x_mean = 1075.366455078125
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def check_x_sum_x_mean(x, x_sum, x_mean):
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assert (x.abs().sum() - x_sum).abs().cpu().item() < 1e-2, f"sum wrong {x.abs().sum()}"
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assert (x.abs().mean() - x_mean).abs().cpu().item() < 1e-4, f"mean wrong {x.abs().mean()}"
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check_x_sum_x_mean(x, x_sum, x_mean)
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