192 lines
7.8 KiB
Python
192 lines
7.8 KiB
Python
# Copyright 2022 UC Berkely Team and The HuggingFace Team. All rights reserved.
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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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# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
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import math
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from typing import Union
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import numpy as np
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import torch
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from ..configuration_utils import ConfigMixin, register_to_config
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from .scheduling_utils import SchedulerMixin
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def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999):
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"""
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Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
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(1-beta) over time from t = [0,1].
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:param num_diffusion_timesteps: the number of betas to produce. :param alpha_bar: a lambda that takes an argument t
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from 0 to 1 and
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produces the cumulative product of (1-beta) up to that part of the diffusion process.
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:param max_beta: the maximum beta to use; use values lower than 1 to
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prevent singularities.
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"""
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def alpha_bar(time_step):
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return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2
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betas = []
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for i in range(num_diffusion_timesteps):
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t1 = i / num_diffusion_timesteps
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t2 = (i + 1) / num_diffusion_timesteps
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betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
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return np.array(betas, dtype=np.float32)
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class DDPMScheduler(SchedulerMixin, ConfigMixin):
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@register_to_config
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def __init__(
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self,
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num_train_timesteps=1000,
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beta_start=0.0001,
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beta_end=0.02,
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beta_schedule="linear",
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trained_betas=None,
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variance_type="fixed_small",
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clip_sample=True,
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tensor_format="pt",
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):
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if trained_betas is not None:
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self.betas = np.asarray(trained_betas)
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elif beta_schedule == "linear":
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self.betas = np.linspace(beta_start, beta_end, num_train_timesteps, dtype=np.float32)
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elif beta_schedule == "scaled_linear":
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# this schedule is very specific to the latent diffusion model.
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self.betas = np.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=np.float32) ** 2
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elif beta_schedule == "squaredcos_cap_v2":
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# Glide cosine schedule
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self.betas = betas_for_alpha_bar(num_train_timesteps)
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else:
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raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
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self.alphas = 1.0 - self.betas
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self.alphas_cumprod = np.cumprod(self.alphas, axis=0)
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self.one = np.array(1.0)
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# setable values
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self.num_inference_steps = None
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self.timesteps = np.arange(0, num_train_timesteps)[::-1].copy()
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self.tensor_format = tensor_format
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self.set_format(tensor_format=tensor_format)
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self.variance_type = variance_type
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def set_timesteps(self, num_inference_steps):
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num_inference_steps = min(self.config.num_train_timesteps, num_inference_steps)
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self.num_inference_steps = num_inference_steps
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self.timesteps = np.arange(
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0, self.config.num_train_timesteps, self.config.num_train_timesteps // self.num_inference_steps
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)[::-1].copy()
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self.set_format(tensor_format=self.tensor_format)
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def _get_variance(self, t, predicted_variance=None, variance_type=None):
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alpha_prod_t = self.alphas_cumprod[t]
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alpha_prod_t_prev = self.alphas_cumprod[t - 1] if t > 0 else self.one
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# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
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# and sample from it to get previous sample
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# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
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variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
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if variance_type is None:
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variance_type = self.config.variance_type
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# hacks - were probs added for training stability
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if variance_type == "fixed_small":
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variance = self.clip(variance, min_value=1e-20)
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# for rl-diffuser https://arxiv.org/abs/2205.09991
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elif variance_type == "fixed_small_log":
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variance = self.log(self.clip(variance, min_value=1e-20))
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elif variance_type == "fixed_large":
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variance = self.betas[t]
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elif variance_type == "fixed_large_log":
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# Glide max_log
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variance = self.log(self.betas[t])
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elif variance_type == "learned":
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return predicted_variance
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elif variance_type == "learned_range":
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min_log = variance
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max_log = self.betas[t]
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frac = (predicted_variance + 1) / 2
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variance = frac * max_log + (1 - frac) * min_log
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return variance
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def step(
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self,
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model_output: Union[torch.FloatTensor, np.ndarray],
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timestep: int,
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sample: Union[torch.FloatTensor, np.ndarray],
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predict_epsilon=True,
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generator=None,
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):
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t = timestep
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if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
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model_output, predicted_variance = torch.split(model_output, sample.shape[1], dim=1)
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else:
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predicted_variance = None
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# 1. compute alphas, betas
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alpha_prod_t = self.alphas_cumprod[t]
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alpha_prod_t_prev = self.alphas_cumprod[t - 1] if t > 0 else self.one
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beta_prod_t = 1 - alpha_prod_t
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beta_prod_t_prev = 1 - alpha_prod_t_prev
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# 2. compute predicted original sample from predicted noise also called
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# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
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if predict_epsilon:
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pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
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else:
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pred_original_sample = model_output
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# 3. Clip "predicted x_0"
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if self.config.clip_sample:
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pred_original_sample = self.clip(pred_original_sample, -1, 1)
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# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
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# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
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pred_original_sample_coeff = (alpha_prod_t_prev ** (0.5) * self.betas[t]) / beta_prod_t
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current_sample_coeff = self.alphas[t] ** (0.5) * beta_prod_t_prev / beta_prod_t
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# 5. Compute predicted previous sample µ_t
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# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
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pred_prev_sample = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
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# 6. Add noise
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variance = 0
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if t > 0:
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noise = self.randn_like(model_output, generator=generator)
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variance = (self._get_variance(t, predicted_variance=predicted_variance) ** 0.5) * noise
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pred_prev_sample = pred_prev_sample + variance
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return {"prev_sample": pred_prev_sample}
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def add_noise(self, original_samples, noise, timesteps):
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sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
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sqrt_alpha_prod = self.match_shape(sqrt_alpha_prod, original_samples)
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sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[timesteps]) ** 0.5
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sqrt_one_minus_alpha_prod = self.match_shape(sqrt_one_minus_alpha_prod, original_samples)
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noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
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return noisy_samples
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def __len__(self):
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return self.config.num_train_timesteps
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