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Running
on
Zero
| """SAMPLING ONLY.""" | |
| import torch | |
| import numpy as np | |
| from diffusion.model.sa_solver import NoiseScheduleVP, model_wrapper, SASolver | |
| from .model import gaussian_diffusion as gd | |
| class SASolverSampler(object): | |
| def __init__(self, model, | |
| noise_schedule="linear", | |
| diffusion_steps=1000, | |
| device='cpu', | |
| ): | |
| super().__init__() | |
| self.model = model | |
| self.device = device | |
| to_torch = lambda x: x.clone().detach().to(torch.float32).to(device) | |
| betas = torch.tensor(gd.get_named_beta_schedule(noise_schedule, diffusion_steps)) | |
| alphas = 1.0 - betas | |
| self.register_buffer('alphas_cumprod', to_torch(np.cumprod(alphas, axis=0))) | |
| def register_buffer(self, name, attr): | |
| if type(attr) == torch.Tensor: | |
| if attr.device != torch.device("cuda"): | |
| attr = attr.to(torch.device("cuda")) | |
| setattr(self, name, attr) | |
| def sample(self, | |
| S, | |
| batch_size, | |
| shape, | |
| conditioning=None, | |
| callback=None, | |
| normals_sequence=None, | |
| img_callback=None, | |
| quantize_x0=False, | |
| eta=0., | |
| mask=None, | |
| x0=None, | |
| temperature=1., | |
| noise_dropout=0., | |
| score_corrector=None, | |
| corrector_kwargs=None, | |
| verbose=True, | |
| x_T=None, | |
| log_every_t=100, | |
| unconditional_guidance_scale=1., | |
| unconditional_conditioning=None, | |
| model_kwargs={}, | |
| # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... | |
| **kwargs | |
| ): | |
| if conditioning is not None: | |
| if isinstance(conditioning, dict): | |
| cbs = conditioning[list(conditioning.keys())[0]].shape[0] | |
| if cbs != batch_size: | |
| print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") | |
| else: | |
| if conditioning.shape[0] != batch_size: | |
| print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") | |
| # sampling | |
| C, H, W = shape | |
| size = (batch_size, C, H, W) | |
| device = self.device | |
| if x_T is None: | |
| img = torch.randn(size, device=device) | |
| else: | |
| img = x_T | |
| ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod) | |
| model_fn = model_wrapper( | |
| self.model, | |
| ns, | |
| model_type="noise", | |
| guidance_type="classifier-free", | |
| condition=conditioning, | |
| unconditional_condition=unconditional_conditioning, | |
| guidance_scale=unconditional_guidance_scale, | |
| model_kwargs=model_kwargs, | |
| ) | |
| sasolver = SASolver(model_fn, ns, algorithm_type="data_prediction") | |
| tau_t = lambda t: eta if 0.2 <= t <= 0.8 else 0 | |
| x = sasolver.sample(mode='few_steps', x=img, tau=tau_t, steps=S, skip_type='time', skip_order=1, predictor_order=2, corrector_order=2, pc_mode='PEC', return_intermediate=False) | |
| return x.to(device), None |