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anton-l 2022-06-14 11:33:31 +02:00
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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 ..pipeline_utils import DiffusionPipeline
class PNDM(DiffusionPipeline):
def __init__(self, unet, noise_scheduler):
super().__init__()
noise_scheduler = noise_scheduler.set_format("pt")
self.register_modules(unet=unet, noise_scheduler=noise_scheduler)
def __call__(self, batch_size=1, generator=None, torch_device=None, 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.timesteps
inference_step_times = range(0, num_trained_timesteps, num_trained_timesteps // num_inference_steps)
self.unet.to(torch_device)
# Sample gaussian noise to begin loop
image = torch.randn(
(batch_size, self.unet.in_channels, self.unet.resolution, self.unet.resolution),
generator=generator,
)
image = image.to(torch_device)
seq = list(inference_step_times)
seq_next = [-1] + list(seq[:-1])
model = self.unet
warmup_steps = [len(seq) - (i // 4 + 1) for i in range(3 * 4)]
ets = []
prev_image = image
for i, step_idx in enumerate(warmup_steps):
i = seq[step_idx]
j = seq_next[step_idx]
t = (torch.ones(image.shape[0]) * i)
t_next = (torch.ones(image.shape[0]) * j)
residual = model(image.to("cuda"), t.to("cuda"))
residual = residual.to("cpu")
image = image.to("cpu")
image = self.noise_scheduler.transfer(prev_image.to("cpu"), t_list[0], t_list[1], residual)
if i % 4 == 0:
ets.append(residual)
prev_image = image
for
ets = []
step_idx = len(seq) - 1
while step_idx >= 0:
i = seq[step_idx]
j = seq_next[step_idx]
t = (torch.ones(image.shape[0]) * i)
t_next = (torch.ones(image.shape[0]) * j)
residual = model(image.to("cuda"), t.to("cuda"))
residual = residual.to("cpu")
t_list = [t, (t+t_next)/2, t_next]
ets.append(residual)
if len(ets) <= 3:
image = image.to("cpu")
x_2 = self.noise_scheduler.transfer(image.to("cpu"), t_list[0], t_list[1], residual)
e_2 = model(x_2.to("cuda"), t_list[1].to("cuda")).to("cpu")
x_3 = self.noise_scheduler.transfer(image, t_list[0], t_list[1], e_2)
e_3 = model(x_3.to("cuda"), t_list[1].to("cuda")).to("cpu")
x_4 = self.noise_scheduler.transfer(image, t_list[0], t_list[2], e_3)
e_4 = model(x_4.to("cuda"), t_list[2].to("cuda")).to("cpu")
residual = (1 / 6) * (residual + 2 * e_2 + 2 * e_3 + e_4)
else:
residual = (1 / 24) * (55 * ets[-1] - 59 * ets[-2] + 37 * ets[-3] - 9 * ets[-4])
img_next = self.noise_scheduler.transfer(image.to("cpu"), t, t_next, residual)
image = img_next
step_idx = step_idx - 1
# if len(prev_noises) in [1, 2]:
# t = (t + t_next) / 2
# elif len(prev_noises) == 3:
# t = t_next / 2
# if len(prev_noises) == 0:
# ets.append(residual)
#
# if len(ets) > 3:
# residual = (1 / 24) * (55 * ets[-1] - 59 * ets[-2] + 37 * ets[-3] - 9 * ets[-4])
# step_idx = step_idx - 1
# elif len(ets) <= 3 and len(prev_noises) == 3:
# residual = (1 / 6) * (prev_noises[-3] + 2 * prev_noises[-2] + 2 * prev_noises[-1] + residual)
# prev_noises = []
# step_idx = step_idx - 1
# elif len(ets) <= 3 and len(prev_noises) < 3:
# prev_noises.append(residual)
# if len(prev_noises) < 2:
# t_next = (t + t_next) / 2
#
# image = self.noise_scheduler.transfer(image.to("cpu"), t, t_next, residual)
return image
# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
# Ideally, read DDIM paper in-detail understanding
# Notation (<variable name> -> <name in paper>
# - pred_noise_t -> e_theta(x_t, t)
# - pred_original_image -> f_theta(x_t, t) or x_0
# - std_dev_t -> sigma_t
# - eta -> η
# - pred_image_direction -> "direction pointingc to x_t"
# - pred_prev_image -> "x_t-1"
# for t in tqdm.tqdm(reversed(range(num_inference_steps)), total=num_inference_steps):
# 1. predict noise residual
# with torch.no_grad():
# residual = self.unet(image, inference_step_times[t])
#
# 2. predict previous mean of image x_t-1
# pred_prev_image = self.noise_scheduler.step(residual, image, t, num_inference_steps, eta)
#
# 3. optionally sample variance
# variance = 0
# if eta > 0:
# noise = torch.randn(image.shape, generator=generator).to(image.device)
# variance = self.noise_scheduler.get_variance(t, num_inference_steps).sqrt() * eta * noise
#
# 4. set current image to prev_image: x_t -> x_t-1
# image = pred_prev_image + variance

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src/diffusers/pipelines/pipeline_pndm.py
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@ -44,67 +44,55 @@ class PNDM(DiffusionPipeline):
)
image = image.to(torch_device)
seq = inference_step_times
seq = list(inference_step_times)
seq_next = [-1] + list(seq[:-1])
model = self.unet
warmup_time_steps = list(reversed([(t + 5) // 10 * 10 for t in range(seq[-4], seq[-1], 5)]))
cur_residual = 0
prev_image = image
ets = []
for i, j in zip(reversed(seq), reversed(seq_next)):
for i in range(len(warmup_time_steps)):
t = warmup_time_steps[i] * torch.ones(image.shape[0])
t_next = (warmup_time_steps[i + 1] if i < len(warmup_time_steps) - 1 else warmup_time_steps[-1]) * torch.ones(image.shape[0])
residual = model(image.to("cuda"), t.to("cuda"))
residual = residual.to("cpu")
if i % 4 == 0:
cur_residual += 1 / 6 * residual
ets.append(residual)
prev_image = image
elif (i - 1) % 4 == 0:
cur_residual += 1 / 3 * residual
elif (i - 2) % 4 == 0:
cur_residual += 1 / 3 * residual
elif (i - 3) % 4 == 0:
cur_residual += 1 / 6 * residual
residual = cur_residual
cur_residual = 0
image = image.to("cpu")
t_2 = warmup_time_steps[4 * (i // 4)] * torch.ones(image.shape[0])
image = self.noise_scheduler.transfer(prev_image.to("cpu"), t_2, t_next, residual)
step_idx = len(seq) - 4
while step_idx >= 0:
i = seq[step_idx]
j = seq_next[step_idx]
t = (torch.ones(image.shape[0]) * i)
t_next = (torch.ones(image.shape[0]) * j)
residual = model(image.to("cuda"), t.to("cuda"))
residual = residual.to("cpu")
t_list = [t, (t+t_next)/2, t_next]
if len(ets) <= 2:
ets.append(residual)
image = image.to("cpu")
x_2 = self.noise_scheduler.transfer(image, t_list[0], t_list[1], residual)
e_2 = model(x_2.to("cuda"), t_list[1].to("cuda")).to("cpu")
x_3 = self.noise_scheduler.transfer(image, t_list[0], t_list[1], e_2)
e_3 = model(x_3.to("cuda"), t_list[1].to("cuda")).to("cpu")
x_4 = self.noise_scheduler.transfer(image, t_list[0], t_list[2], e_3)
e_4 = model(x_4.to("cuda"), t_list[2].to("cuda")).to("cpu")
residual = (1 / 6) * (residual + 2 * e_2 + 2 * e_3 + e_4)
else:
ets.append(residual)
residual = (1 / 24) * (55 * ets[-1] - 59 * ets[-2] + 37 * ets[-3] - 9 * ets[-4])
ets.append(residual)
residual = (1 / 24) * (55 * ets[-1] - 59 * ets[-2] + 37 * ets[-3] - 9 * ets[-4])
img_next = self.noise_scheduler.transfer(image.to("cpu"), t, t_next, residual)
# with torch.no_grad():
# t_start, t_end = t_next, t
# img_next, ets = self.noise_scheduler.step(image, t_start, t_end, model, ets)
image = img_next
step_idx = step_idx - 1
return image
# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
# Ideally, read DDIM paper in-detail understanding
# Notation (<variable name> -> <name in paper>
# - pred_noise_t -> e_theta(x_t, t)
# - pred_original_image -> f_theta(x_t, t) or x_0
# - std_dev_t -> sigma_t
# - eta -> η
# - pred_image_direction -> "direction pointingc to x_t"
# - pred_prev_image -> "x_t-1"
# for t in tqdm.tqdm(reversed(range(num_inference_steps)), total=num_inference_steps):
# 1. predict noise residual
# with torch.no_grad():
# residual = self.unet(image, inference_step_times[t])
#
# 2. predict previous mean of image x_t-1
# pred_prev_image = self.noise_scheduler.step(residual, image, t, num_inference_steps, eta)
#
# 3. optionally sample variance
# variance = 0
# if eta > 0:
# noise = torch.randn(image.shape, generator=generator).to(image.device)
# variance = self.noise_scheduler.get_variance(t, num_inference_steps).sqrt() * eta * noise
#
# 4. set current image to prev_image: x_t -> x_t-1
# image = pred_prev_image + variance