with \(\alpha_t := 1 - \beta_t\) and \(\bar{\alpha}t := \Pi{s=1}^{t} \alpha_s\). Let's refer to this equation as the "nice property". This means we can sample Gaussian noise and scale it appropriatly and add it to \(\mathbf{x}_0\) to get \(\m...
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`Optional[torch.FloatTensor]`, defaults to `None`): Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image generation. Can...
[2] Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution(一种真实图像超分辨率的局部判别学习方法) paper:https://arxiv.org/abs/2203.09195 code:https://github.com/csjliang/LDL [...
def create_gaussian_diffusion( *, steps=1000, learn_sigma=False, sigma_small=False, noise_schedule="linear", use_kl=False, predict_xstart=False, rescale_timesteps=False, rescale_learned_sigmas=False, timestep_respacing="", linear_start=0.0001, linear_end=0.02, ): betas = get_named_beta...
"However, either the image or the noise timestep has not been provided." )if return_image_latents or (latents is None and not is_strength_max): image = image.to(device=device, dtype=dtype)if image.shape[1] == 4: image_latents = image ...
Alternative noising processes are also possible in continuous diffusion models, but as noted in the paper, only additive Gaussian noise has received significant attention. Autoregressive Models It's perhaps more interesting to compare VQ-Diffusion to AR models as they more frequently feature ...
dict(type='ImageContentJitter', ), ], ), dict( type='RandomApply', prob=0.4, transforms=[ dict( type='ImgAugWrapper', args=[dict(cls='AdditiveGaussianNoise', scale=0.1**0.5)]), ], ), dict( type='RandomApply', prob=0.4, transforms=[ dict(type='ReversePixels', ), ], ), dict...
136 + # Gaussian random 137 + seed_val = np.random.randint(10000) 138 + rng = default_rng(seed=seed_val) 139 + 140 + # create random noise image 141 + noise = rng.integers(0, 255, (self.resize[0], self.resize[1]), np.uint8, True) 142 + 143 + # blur the noi...
gaussian_gray_noise_prob=0.4, poisson_scale=[0.05, 2.5], poisson_gray_noise_prob=0.4, gaussian_sigma_step=0.1, poisson_scale_step=0.005), keys=['lq'], ), dict( type='RandomJPEGCompression', params=dict(quality=[30, 95], quality_step=3), keys=['lq'], ), dict( type='Degradation...
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. ...