wehub-resource-sync
125 lines
3.8 KiB
Python
Executable File
125 lines
3.8 KiB
Python
Executable File
# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# SPDX-License-Identifier: Apache-2.0
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"""SAMPLING ONLY."""
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import numpy as np
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import torch
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from diffusion.model import gaussian_diffusion as gd
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from diffusion.model.sa_solver import NoiseScheduleVP, SASolver, model_wrapper
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class SASolverSampler:
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def __init__(
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self,
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model,
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noise_schedule="linear",
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diffusion_steps=1000,
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device="cpu",
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):
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super().__init__()
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self.model = model
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self.device = device
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to_torch = lambda x: x.clone().detach().to(torch.float32).to(device)
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betas = torch.tensor(gd.get_named_beta_schedule(noise_schedule, diffusion_steps))
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alphas = 1.0 - betas
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self.register_buffer("alphas_cumprod", to_torch(np.cumprod(alphas, axis=0)))
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def register_buffer(self, name, attr):
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if type(attr) == torch.Tensor:
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if attr.device != torch.device("cuda"):
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attr = attr.to(torch.device("cuda"))
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setattr(self, name, attr)
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@torch.no_grad()
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def sample(
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self,
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S,
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batch_size,
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shape,
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conditioning=None,
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callback=None,
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normals_sequence=None,
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img_callback=None,
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quantize_x0=False,
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eta=0.0,
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mask=None,
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x0=None,
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temperature=1.0,
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noise_dropout=0.0,
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score_corrector=None,
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corrector_kwargs=None,
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verbose=True,
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x_T=None,
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log_every_t=100,
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unconditional_guidance_scale=1.0,
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unconditional_conditioning=None,
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model_kwargs={},
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# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
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**kwargs,
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):
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if conditioning is not None:
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if isinstance(conditioning, dict):
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cbs = conditioning[list(conditioning.keys())[0]].shape[0]
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if cbs != batch_size:
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print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
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else:
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if conditioning.shape[0] != batch_size:
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print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
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# sampling
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C, H, W = shape
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size = (batch_size, C, H, W)
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device = self.device
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if x_T is None:
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img = torch.randn(size, device=device)
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else:
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img = x_T
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ns = NoiseScheduleVP("discrete", alphas_cumprod=self.alphas_cumprod)
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model_fn = model_wrapper(
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self.model,
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ns,
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model_type="noise",
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guidance_type="classifier-free",
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condition=conditioning,
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unconditional_condition=unconditional_conditioning,
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guidance_scale=unconditional_guidance_scale,
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model_kwargs=model_kwargs,
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)
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sasolver = SASolver(model_fn, ns, algorithm_type="data_prediction")
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tau_t = lambda t: eta if 0.2 <= t <= 0.8 else 0
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x = sasolver.sample(
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mode="few_steps",
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x=img,
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tau=tau_t,
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steps=S,
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skip_type="time",
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skip_order=1,
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predictor_order=2,
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corrector_order=2,
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pc_mode="PEC",
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return_intermediate=False,
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)
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return x.to(device), None
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