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author | Volpeon <git@volpeon.ink> | 2022-11-14 17:09:58 +0100 |
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committer | Volpeon <git@volpeon.ink> | 2022-11-14 17:09:58 +0100 |
commit | 2ad46871e2ead985445da2848a4eb7072b6e48aa (patch) | |
tree | 3137923e2c00fe1d3cd37ddcc93c8a847b0c0762 /schedulers | |
parent | Update (diff) | |
download | textual-inversion-diff-2ad46871e2ead985445da2848a4eb7072b6e48aa.tar.gz textual-inversion-diff-2ad46871e2ead985445da2848a4eb7072b6e48aa.tar.bz2 textual-inversion-diff-2ad46871e2ead985445da2848a4eb7072b6e48aa.zip |
Update
Diffstat (limited to 'schedulers')
-rw-r--r-- | schedulers/scheduling_euler_ancestral_discrete.py | 261 |
1 files changed, 0 insertions, 261 deletions
diff --git a/schedulers/scheduling_euler_ancestral_discrete.py b/schedulers/scheduling_euler_ancestral_discrete.py deleted file mode 100644 index cef50fe..0000000 --- a/schedulers/scheduling_euler_ancestral_discrete.py +++ /dev/null | |||
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1 | # Copyright 2022 Katherine Crowson and The HuggingFace Team. All rights reserved. | ||
2 | # | ||
3 | # Licensed under the Apache License, Version 2.0 (the "License"); | ||
4 | # you may not use this file except in compliance with the License. | ||
5 | # You may obtain a copy of the License at | ||
6 | # | ||
7 | # http://www.apache.org/licenses/LICENSE-2.0 | ||
8 | # | ||
9 | # Unless required by applicable law or agreed to in writing, software | ||
10 | # distributed under the License is distributed on an "AS IS" BASIS, | ||
11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
12 | # See the License for the specific language governing permissions and | ||
13 | # limitations under the License. | ||
14 | |||
15 | from dataclasses import dataclass | ||
16 | from typing import Optional, Tuple, Union | ||
17 | |||
18 | import numpy as np | ||
19 | import torch | ||
20 | |||
21 | from diffusers.configuration_utils import ConfigMixin, register_to_config | ||
22 | from diffusers.utils import BaseOutput, deprecate, logging | ||
23 | from diffusers.schedulers.scheduling_utils import SchedulerMixin | ||
24 | |||
25 | |||
26 | logger = logging.get_logger(__name__) # pylint: disable=invalid-name | ||
27 | |||
28 | |||
29 | @dataclass | ||
30 | # Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete | ||
31 | class EulerAncestralDiscreteSchedulerOutput(BaseOutput): | ||
32 | """ | ||
33 | Output class for the scheduler's step function output. | ||
34 | |||
35 | Args: | ||
36 | prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): | ||
37 | Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the | ||
38 | denoising loop. | ||
39 | pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): | ||
40 | The predicted denoised sample (x_{0}) based on the model output from the current timestep. | ||
41 | `pred_original_sample` can be used to preview progress or for guidance. | ||
42 | """ | ||
43 | |||
44 | prev_sample: torch.FloatTensor | ||
45 | pred_original_sample: Optional[torch.FloatTensor] = None | ||
46 | |||
47 | |||
48 | class EulerAncestralDiscreteScheduler(SchedulerMixin, ConfigMixin): | ||
49 | """ | ||
50 | Ancestral sampling with Euler method steps. Based on the original k-diffusion implementation by Katherine Crowson: | ||
51 | https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L72 | ||
52 | |||
53 | [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` | ||
54 | function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. | ||
55 | [`~ConfigMixin`] also provides general loading and saving functionality via the [`~ConfigMixin.save_config`] and | ||
56 | [`~ConfigMixin.from_config`] functions. | ||
57 | |||
58 | Args: | ||
59 | num_train_timesteps (`int`): number of diffusion steps used to train the model. | ||
60 | beta_start (`float`): the starting `beta` value of inference. | ||
61 | beta_end (`float`): the final `beta` value. | ||
62 | beta_schedule (`str`): | ||
63 | the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from | ||
64 | `linear` or `scaled_linear`. | ||
65 | trained_betas (`np.ndarray`, optional): | ||
66 | option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. | ||
67 | |||
68 | """ | ||
69 | |||
70 | @register_to_config | ||
71 | def __init__( | ||
72 | self, | ||
73 | num_train_timesteps: int = 1000, | ||
74 | beta_start: float = 0.0001, | ||
75 | beta_end: float = 0.02, | ||
76 | beta_schedule: str = "linear", | ||
77 | trained_betas: Optional[np.ndarray] = None, | ||
78 | ): | ||
79 | if trained_betas is not None: | ||
80 | self.betas = torch.from_numpy(trained_betas) | ||
81 | elif beta_schedule == "linear": | ||
82 | self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) | ||
83 | elif beta_schedule == "scaled_linear": | ||
84 | # this schedule is very specific to the latent diffusion model. | ||
85 | self.betas = ( | ||
86 | torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2 | ||
87 | ) | ||
88 | else: | ||
89 | raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}") | ||
90 | |||
91 | self.alphas = 1.0 - self.betas | ||
92 | self.alphas_cumprod = torch.cumprod(self.alphas, dim=0) | ||
93 | |||
94 | sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5) | ||
95 | sigmas = np.concatenate([sigmas[::-1], [0.0]]).astype(np.float32) | ||
96 | self.sigmas = torch.from_numpy(sigmas) | ||
97 | |||
98 | # standard deviation of the initial noise distribution | ||
99 | self.init_noise_sigma = self.sigmas.max() | ||
100 | |||
101 | # setable values | ||
102 | self.num_inference_steps = None | ||
103 | timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=float)[::-1].copy() | ||
104 | self.timesteps = torch.from_numpy(timesteps) | ||
105 | self.is_scale_input_called = False | ||
106 | |||
107 | def scale_model_input( | ||
108 | self, sample: torch.FloatTensor, timestep: Union[float, torch.FloatTensor] | ||
109 | ) -> torch.FloatTensor: | ||
110 | """ | ||
111 | Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the Euler algorithm. | ||
112 | |||
113 | Args: | ||
114 | sample (`torch.FloatTensor`): input sample | ||
115 | timestep (`float` or `torch.FloatTensor`): the current timestep in the diffusion chain | ||
116 | |||
117 | Returns: | ||
118 | `torch.FloatTensor`: scaled input sample | ||
119 | """ | ||
120 | if isinstance(timestep, torch.Tensor): | ||
121 | timestep = timestep.to(self.timesteps.device) | ||
122 | step_index = (self.timesteps == timestep).nonzero().item() | ||
123 | sigma = self.sigmas[step_index] | ||
124 | sample = sample / ((sigma**2 + 1) ** 0.5) | ||
125 | self.is_scale_input_called = True | ||
126 | return sample | ||
127 | |||
128 | def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None): | ||
129 | """ | ||
130 | Sets the timesteps used for the diffusion chain. Supporting function to be run before inference. | ||
131 | |||
132 | Args: | ||
133 | num_inference_steps (`int`): | ||
134 | the number of diffusion steps used when generating samples with a pre-trained model. | ||
135 | device (`str` or `torch.device`, optional): | ||
136 | the device to which the timesteps should be moved to. If `None`, the timesteps are not moved. | ||
137 | """ | ||
138 | self.num_inference_steps = num_inference_steps | ||
139 | |||
140 | timesteps = np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps, dtype=float)[::-1].copy() | ||
141 | sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5) | ||
142 | sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas) | ||
143 | sigmas = np.concatenate([sigmas, [0.0]]).astype(np.float32) | ||
144 | self.sigmas = torch.from_numpy(sigmas).to(device=device) | ||
145 | self.timesteps = torch.from_numpy(timesteps).to(device=device) | ||
146 | |||
147 | def step( | ||
148 | self, | ||
149 | model_output: torch.FloatTensor, | ||
150 | timestep: Union[float, torch.FloatTensor], | ||
151 | sample: torch.FloatTensor, | ||
152 | generator: Optional[torch.Generator] = None, | ||
153 | return_dict: bool = True, | ||
154 | ) -> Union[EulerAncestralDiscreteSchedulerOutput, Tuple]: | ||
155 | """ | ||
156 | Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion | ||
157 | process from the learned model outputs (most often the predicted noise). | ||
158 | |||
159 | Args: | ||
160 | model_output (`torch.FloatTensor`): direct output from learned diffusion model. | ||
161 | timestep (`float`): current timestep in the diffusion chain. | ||
162 | sample (`torch.FloatTensor`): | ||
163 | current instance of sample being created by diffusion process. | ||
164 | generator (`torch.Generator`, optional): Random number generator. | ||
165 | return_dict (`bool`): option for returning tuple rather than EulerAncestralDiscreteSchedulerOutput class | ||
166 | |||
167 | Returns: | ||
168 | [`~schedulers.scheduling_utils.EulerAncestralDiscreteSchedulerOutput`] or `tuple`: | ||
169 | [`~schedulers.scheduling_utils.EulerAncestralDiscreteSchedulerOutput`] if `return_dict` is True, otherwise | ||
170 | a `tuple`. When returning a tuple, the first element is the sample tensor. | ||
171 | |||
172 | """ | ||
173 | |||
174 | if ( | ||
175 | isinstance(timestep, int) | ||
176 | or isinstance(timestep, torch.IntTensor) | ||
177 | or isinstance(timestep, torch.LongTensor) | ||
178 | ): | ||
179 | raise ValueError( | ||
180 | "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to" | ||
181 | " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass" | ||
182 | " one of the `scheduler.timesteps` as a timestep.", | ||
183 | ) | ||
184 | |||
185 | if not self.is_scale_input_called: | ||
186 | logger.warn( | ||
187 | "The `scale_model_input` function should be called before `step` to ensure correct denoising. " | ||
188 | "See `StableDiffusionPipeline` for a usage example." | ||
189 | ) | ||
190 | |||
191 | if isinstance(timestep, torch.Tensor): | ||
192 | timestep = timestep.to(self.timesteps.device) | ||
193 | |||
194 | step_index = (self.timesteps == timestep).nonzero().item() | ||
195 | sigma = self.sigmas[step_index] | ||
196 | |||
197 | # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise | ||
198 | pred_original_sample = sample - sigma * model_output | ||
199 | sigma_from = self.sigmas[step_index] | ||
200 | sigma_to = self.sigmas[step_index + 1] | ||
201 | sigma_up = (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5 | ||
202 | sigma_down = (sigma_to**2 - sigma_up**2) ** 0.5 | ||
203 | |||
204 | # 2. Convert to an ODE derivative | ||
205 | derivative = (sample - pred_original_sample) / sigma | ||
206 | |||
207 | dt = sigma_down - sigma | ||
208 | |||
209 | prev_sample = sample + derivative * dt | ||
210 | |||
211 | device = model_output.device if torch.is_tensor(model_output) else "cpu" | ||
212 | noise = torch.randn(model_output.shape, dtype=model_output.dtype, device=device, generator=generator) | ||
213 | prev_sample = prev_sample + noise * sigma_up | ||
214 | |||
215 | if not return_dict: | ||
216 | return (prev_sample,) | ||
217 | |||
218 | return EulerAncestralDiscreteSchedulerOutput( | ||
219 | prev_sample=prev_sample, pred_original_sample=pred_original_sample | ||
220 | ) | ||
221 | |||
222 | def add_noise( | ||
223 | self, | ||
224 | original_samples: torch.FloatTensor, | ||
225 | noise: torch.FloatTensor, | ||
226 | timesteps: torch.FloatTensor, | ||
227 | ) -> torch.FloatTensor: | ||
228 | # Make sure sigmas and timesteps have the same device and dtype as original_samples | ||
229 | self.sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype) | ||
230 | if original_samples.device.type == "mps" and torch.is_floating_point(timesteps): | ||
231 | # mps does not support float64 | ||
232 | self.timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32) | ||
233 | timesteps = timesteps.to(original_samples.device, dtype=torch.float32) | ||
234 | else: | ||
235 | self.timesteps = self.timesteps.to(original_samples.device) | ||
236 | timesteps = timesteps.to(original_samples.device) | ||
237 | |||
238 | schedule_timesteps = self.timesteps | ||
239 | |||
240 | if isinstance(timesteps, torch.IntTensor) or isinstance(timesteps, torch.LongTensor): | ||
241 | deprecate( | ||
242 | "timesteps as indices", | ||
243 | "0.8.0", | ||
244 | "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to" | ||
245 | " `EulerAncestralDiscreteScheduler.add_noise()` will not be supported in future versions. Make sure to" | ||
246 | " pass values from `scheduler.timesteps` as timesteps.", | ||
247 | standard_warn=False, | ||
248 | ) | ||
249 | step_indices = timesteps | ||
250 | else: | ||
251 | step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps] | ||
252 | |||
253 | sigma = self.sigmas[step_indices].flatten() | ||
254 | while len(sigma.shape) < len(original_samples.shape): | ||
255 | sigma = sigma.unsqueeze(-1) | ||
256 | |||
257 | noisy_samples = original_samples + noise * sigma | ||
258 | return noisy_samples | ||
259 | |||
260 | def __len__(self): | ||
261 | return self.config.num_train_timesteps | ||