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This commit is contained in:
Patrick von Platen 2022-06-13 17:34:02 +00:00
parent 809591b7b6
commit 059a6e9d82
1 changed files with 35 additions and 11 deletions
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46
src/diffusers/pipelines/pipeline_pndm.py
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@ -44,12 +44,17 @@ class PNDM(DiffusionPipeline):
) )
image = image.to(torch_device) image = image.to(torch_device)
seq = inference_step_times seq = list(inference_step_times)
seq_next = [-1] + list(seq[:-1]) seq_next = [-1] + list(seq[:-1])
model = self.unet model = self.unet
ets = [] ets = []
for i, j in zip(reversed(seq), reversed(seq_next)): prev_noises = []
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 = (torch.ones(image.shape[0]) * i)
t_next = (torch.ones(image.shape[0]) * j) t_next = (torch.ones(image.shape[0]) * j)
@ -58,10 +63,11 @@ class PNDM(DiffusionPipeline):
t_list = [t, (t+t_next)/2, t_next] t_list = [t, (t+t_next)/2, t_next]
if len(ets) <= 2: ets.append(residual)
ets.append(residual) if len(ets) <= 3:
image = image.to("cpu") image = image.to("cpu")
x_2 = self.noise_scheduler.transfer(image, t_list[0], t_list[1], residual) 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") 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) 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") e_3 = model(x_3.to("cuda"), t_list[1].to("cuda")).to("cpu")
@ -69,17 +75,35 @@ class PNDM(DiffusionPipeline):
e_4 = model(x_4.to("cuda"), t_list[2].to("cuda")).to("cpu") 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) residual = (1 / 6) * (residual + 2 * e_2 + 2 * e_3 + e_4)
else: else:
ets.append(residual)
residual = (1 / 24) * (55 * ets[-1] - 59 * ets[-2] + 37 * ets[-3] - 9 * ets[-4]) 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) 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 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 return image
# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf