aljaz/DALLE2-pytorch
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fix ddim to use alpha_cumprod

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Phil Wang
2022-08-31 07:40:46 -07:00
parent ba58ae0bf2
commit 6fb7e91343
2 changed files with 8 additions and 3 deletions
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9
dalle2_pytorch/dalle2_pytorch.py
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@@ -1259,7 +1259,7 @@ class DiffusionPrior(nn.Module):
def p_sample_loop_ddim(self, shape, text_cond, *, timesteps, eta = 1., cond_scale = 1.): def p_sample_loop_ddim(self, shape, text_cond, *, timesteps, eta = 1., cond_scale = 1.):
batch, device, alphas, total_timesteps = shape[0], self.device, self.noise_scheduler.alphas_cumprod_prev, self.noise_scheduler.num_timesteps batch, device, alphas, total_timesteps = shape[0], self.device, self.noise_scheduler.alphas_cumprod_prev, self.noise_scheduler.num_timesteps
times = torch.linspace(0., total_timesteps, steps = timesteps + 2)[:-1] times = torch.linspace(-1., total_timesteps, steps = timesteps + 1)[:-1]
times = list(reversed(times.int().tolist())) times = list(reversed(times.int().tolist()))
time_pairs = list(zip(times[:-1], times[1:])) time_pairs = list(zip(times[:-1], times[1:]))
@@ -1294,6 +1294,10 @@ class DiffusionPrior(nn.Module):
if self.predict_x_start and self.sampling_clamp_l2norm: if self.predict_x_start and self.sampling_clamp_l2norm:
x_start = self.l2norm_clamp_embed(x_start) x_start = self.l2norm_clamp_embed(x_start)
if time_next < 0:
image_embed = x_start
continue
c1 = eta * ((1 - alpha / alpha_next) * (1 - alpha_next) / (1 - alpha)).sqrt() c1 = eta * ((1 - alpha / alpha_next) * (1 - alpha_next) / (1 - alpha)).sqrt()
c2 = ((1 - alpha_next) - torch.square(c1)).sqrt() c2 = ((1 - alpha_next) - torch.square(c1)).sqrt()
noise = torch.randn_like(image_embed) if time_next > 0 else 0. noise = torch.randn_like(image_embed) if time_next > 0 else 0.
@@ -2845,12 +2849,13 @@ class Decoder(nn.Module):
inpaint_mask = None, inpaint_mask = None,
inpaint_resample_times = 5 inpaint_resample_times = 5
): ):
batch, device, total_timesteps, alphas, eta = shape[0], self.device, noise_scheduler.num_timesteps, noise_scheduler.alphas_cumprod_prev, self.ddim_sampling_eta batch, device, total_timesteps, alphas, eta = shape[0], self.device, noise_scheduler.num_timesteps, noise_scheduler.alphas_cumprod, self.ddim_sampling_eta
times = torch.linspace(0., total_timesteps, steps = timesteps + 2)[:-1] times = torch.linspace(0., total_timesteps, steps = timesteps + 2)[:-1]
times = list(reversed(times.int().tolist())) times = list(reversed(times.int().tolist()))
time_pairs = list(zip(times[:-1], times[1:])) time_pairs = list(zip(times[:-1], times[1:]))
time_pairs = list(filter(lambda t: t[0] > t[1], time_pairs))
is_inpaint = exists(inpaint_image) is_inpaint = exists(inpaint_image)
resample_times = inpaint_resample_times if is_inpaint else 1 resample_times = inpaint_resample_times if is_inpaint else 1

2
dalle2_pytorch/version.py
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@@ -1 +1 @@
__version__ = '1.10.1' __version__ = '1.10.3'