deviandice/DL-Art-School
1
0
Fork 0
forked from mrq/DL-Art-School
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Fix ts=0 with new formulation

Browse Source
This commit is contained in:
James Betker 2022-07-20 12:12:33 -06:00
parent 82bd62019f
commit dbebe18602
2 changed files with 13 additions and 6 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

9
codes/models/audio/music/transformer_diffusion13.py
View File

@ -192,8 +192,9 @@ class TransformerDiffusion(nn.Module):
# Now diffuse the prior randomly between the x timestep and 0. # Now diffuse the prior randomly between the x timestep and 0.
adv = torch.rand_like(ts.float()) adv = torch.rand_like(ts.float())
t_prior = (adv * ts).long() t_prior = (adv * ts).long() - 1
s_prior_diffused = diffuser.q_sample(s_prior, t_prior, torch.randn_like(s_prior)) # The t_prior-1 below is an important detail: it forces s_prior to be unmodified for ts=0. It also means that t_prior is not on the same timescale as ts (instead it is shifted by 1).
s_prior_diffused = diffuser.q_sample(s_prior, t_prior-1, torch.randn_like(s_prior), allow_negatives=True)
self.preprocessed = (s_prior_diffused, t_prior, torch.tensor([resolution] * x.shape[0], dtype=torch.long, device=x.device)) self.preprocessed = (s_prior_diffused, t_prior, torch.tensor([resolution] * x.shape[0], dtype=torch.long, device=x.device))
return s return s
@ -226,7 +227,7 @@ class TransformerDiffusion(nn.Module):
else: else:
assert x.shape[-1] > x_prior.shape[-1] * 3.9, f'{x.shape} {x_prior.shape}' assert x.shape[-1] > x_prior.shape[-1] * 3.9, f'{x.shape} {x_prior.shape}'
x_prior = F.interpolate(x_prior, size=(x.shape[-1],), mode='linear', align_corners=True) x_prior = F.interpolate(x_prior, size=(x.shape[-1],), mode='linear', align_corners=True)
assert torch.all(timesteps - prior_timesteps > 0), f'Prior timesteps should always be lower (more resolved) than input timesteps. {timesteps}, {prior_timesteps}' assert torch.all(timesteps - prior_timesteps >= 0), f'Prior timesteps should always be lower (more resolved) than input timesteps. {timesteps}, {prior_timesteps}'
if conditioning_free: if conditioning_free:
code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, 1) code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, 1)
@ -284,7 +285,7 @@ def test_tfd():
betas=get_named_beta_schedule('linear', 4000)) betas=get_named_beta_schedule('linear', 4000))
clip = torch.randn(2,256,10336) clip = torch.randn(2,256,10336)
cond = torch.randn(2,256,10336) cond = torch.randn(2,256,10336)
ts = torch.LongTensor([600, 600]) ts = torch.LongTensor([0, 0])
model = TransformerDiffusion(in_channels=256, model_channels=1024, contraction_dim=512, model = TransformerDiffusion(in_channels=256, model_channels=1024, contraction_dim=512,
num_heads=512//64, input_vec_dim=256, num_layers=12, dropout=.1, num_heads=512//64, input_vec_dim=256, num_layers=12, dropout=.1,
unconditioned_percentage=.6) unconditioned_percentage=.6)

10
codes/models/diffusion/gaussian_diffusion.py
View File

@ -234,7 +234,7 @@ class GaussianDiffusion:
) )
return mean, variance, log_variance return mean, variance, log_variance
def q_sample(self, x_start, t, noise=None): def q_sample(self, x_start, t, noise=None, allow_negatives=False):
""" """
Diffuse the data for a given number of diffusion steps. Diffuse the data for a given number of diffusion steps.
@ -248,11 +248,17 @@ class GaussianDiffusion:
if noise is None: if noise is None:
noise = th.randn_like(x_start) noise = th.randn_like(x_start)
assert noise.shape == x_start.shape assert noise.shape == x_start.shape
return ( if allow_negatives:
mask = (t < 0)
t[mask] = 0
result = (
_extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+ _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) + _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)
* noise * noise
) )
if allow_negatives:
result[mask] = x_start[mask]
return result
def q_posterior_mean_variance(self, x_start, x_t, t): def q_posterior_mean_variance(self, x_start, x_t, t):
""" """