aljaz/DALLE2-pytorch
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cleanup

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This commit is contained in:
Phil Wang
2022-06-07 17:31:38 -07:00
parent bee5bf3815
commit 0f31980362
1 changed files with 5 additions and 11 deletions
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dalle2_pytorch/dalle2_pytorch.py
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@@ -1,7 +1,6 @@
import math
import random
from tqdm import tqdm
from inspect import isfunction
from functools import partial, wraps
from contextlib import contextmanager
from collections import namedtuple
@@ -57,7 +56,7 @@ def maybe(fn):
def default(val, d):
if exists(val):
return val
return d() if isfunction(d) else d
return d() if callable(d) else d
def cast_tuple(val, length = 1):
if isinstance(val, list):
@@ -314,11 +313,6 @@ def extract(a, t, x_shape):
out = a.gather(-1, t)
return out.reshape(b, *((1,) * (len(x_shape) - 1)))
def noise_like(shape, device, repeat=False):
repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
noise = lambda: torch.randn(shape, device=device)
return repeat_noise() if repeat else noise()
def meanflat(x):
return x.mean(dim = tuple(range(1, len(x.shape))))
@@ -946,10 +940,10 @@ class DiffusionPrior(BaseGaussianDiffusion):
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, t, text_cond = None, clip_denoised = True, repeat_noise = False, cond_scale = 1.):
def p_sample(self, x, t, text_cond = None, clip_denoised = True, cond_scale = 1.):
b, *_, device = *x.shape, x.device
model_mean, _, model_log_variance = self.p_mean_variance(x = x, t = t, text_cond = text_cond, clip_denoised = clip_denoised, cond_scale = cond_scale)
noise = noise_like(x.shape, device, repeat_noise)
noise = torch.randn_like(x)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
@@ -1956,10 +1950,10 @@ class Decoder(BaseGaussianDiffusion):
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, unet, x, t, image_embed, text_encodings = None, text_mask = None, cond_scale = 1., lowres_cond_img = None, predict_x_start = False, learned_variance = False, clip_denoised = True, repeat_noise = False):
def p_sample(self, unet, x, t, image_embed, text_encodings = None, text_mask = None, cond_scale = 1., lowres_cond_img = None, predict_x_start = False, learned_variance = False, clip_denoised = True):
b, *_, device = *x.shape, x.device
model_mean, _, model_log_variance = self.p_mean_variance(unet, x = x, t = t, image_embed = image_embed, text_encodings = text_encodings, text_mask = text_mask, cond_scale = cond_scale, lowres_cond_img = lowres_cond_img, clip_denoised = clip_denoised, predict_x_start = predict_x_start, learned_variance = learned_variance)
noise = noise_like(x.shape, device, repeat_noise)
noise = torch.randn_like(x)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise