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| # Copyright 2024 NVIDIA CORPORATION & AFFILIATES | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # | |
| # SPDX-License-Identifier: Apache-2.0 | |
| """SAMPLING ONLY.""" | |
| import numpy as np | |
| import torch | |
| from diffusion.model.sa_solver import NoiseScheduleVP, SASolver, model_wrapper | |
| from .model import gaussian_diffusion as gd | |
| class SASolverSampler: | |
| 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=0.0, | |
| mask=None, | |
| x0=None, | |
| temperature=1.0, | |
| noise_dropout=0.0, | |
| score_corrector=None, | |
| corrector_kwargs=None, | |
| verbose=True, | |
| x_T=None, | |
| log_every_t=100, | |
| unconditional_guidance_scale=1.0, | |
| 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 | |