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# 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.
import torch
import tqdm
from diffusers import DiffusionPipeline
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 corresponds to η in paper and should be between [0, 1]
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()
# model forward
with torch.no_grad():
noise_residual = self.unet(image, train_step)
# predict mean of prev image
pred_mean = alpha_prod_t_rsqrt * (image - beta_prod_t_sqrt * noise_residual)
pred_mean = torch.clamp(pred_mean, -1, 1)
pred_mean = (1 / alpha_prod_t_prev_rsqrt) * pred_mean + coeff_2 * noise_residual
# if eta > 0.0 add noise. Note eta = 1.0 essentially corresponds to DDPM
if eta > 0.0:
noise = self.noise_scheduler.sample_noise(image.shape, device=image.device, generator=generator)
image = pred_mean + coeff_1 * noise
else:
image = pred_mean
return image
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