modeling_ddim.py

# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# 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.


from diffusers import DiffusionPipeline
import tqdm
import torch


def compute_alpha(beta, t):
    beta = torch.cat([torch.zeros(1).to(beta.device), beta], dim=0)
    a = (1 - beta).cumprod(dim=0).index_select(0, t + 1).view(-1, 1, 1, 1)
    return a


class DDIM(DiffusionPipeline):

    def __init__(self, unet, noise_scheduler):
        super().__init__()
        self.register_modules(unet=unet, noise_scheduler=noise_scheduler)

    def __call__(self, batch_size=1, generator=None, torch_device=None, eta=0.0, num_inference_steps=50):
        # eta is η in paper
        if torch_device is None:
            torch_device = "cuda" if torch.cuda.is_available() else "cpu"

        num_trained_timesteps = self.noise_scheduler.num_timesteps
        inference_step_times = range(0, num_trained_timesteps, num_trained_timesteps // num_inference_steps)

        self.unet.to(torch_device)
        image = self.noise_scheduler.sample_noise((batch_size, self.unet.in_channels, self.unet.resolution, self.unet.resolution), device=torch_device, generator=generator)

        for t in tqdm.tqdm(reversed(range(num_inference_steps)), total=num_inference_steps):
            # get actual t and t-1
            train_step = inference_step_times[t]
            prev_train_step = inference_step_times[t - 1] if t > 0 else - 1

            # compute alphas
            alpha_prod_t = self.noise_scheduler.get_alpha_prod(train_step)
            alpha_prod_t_prev = self.noise_scheduler.get_alpha_prod(prev_train_step)
            alpha_prod_t_rsqrt = 1 / alpha_prod_t.sqrt()
            alpha_prod_t_prev_rsqrt = 1 / alpha_prod_t_prev.sqrt()
            beta_prod_t_sqrt = (1 - alpha_prod_t).sqrt()
            beta_prod_t_prev_sqrt = (1 - alpha_prod_t_prev).sqrt()

            # compute relevant coefficients
            coeff_1 = (alpha_prod_t_prev - alpha_prod_t).sqrt() * alpha_prod_t_prev_rsqrt * beta_prod_t_prev_sqrt / beta_prod_t_sqrt * eta
            coeff_2 = ((1 - alpha_prod_t_prev) - coeff_1 ** 2).sqrt()

            with torch.no_grad():
                noise_residual = self.unet(image, train_step)

            print(train_step)

            pred_mean = (image - noise_residual * beta_prod_t_sqrt) * alpha_prod_t_rsqrt
            xt_next = alpha_prod_t_prev.sqrt() * pred_mean + coeff_1 * torch.randn_like(image) + coeff_2 * noise_residual
#            xt_next = 1 / alpha_prod_t_rsqrt * pred_mean + coeff_1 * torch.randn_like(image) + coeff_2 * noise_residual
            # eta
            image = xt_next

        return image