wassname/denoising-diffusion-pytorch
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update with new and improved cosine noise scheduler

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Phil Wang committed 2020-10-09 21:21:02 -07:00
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commit ff451f697e
3 files changed
+25 -17
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README.md
+4 -8
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@@ -27,10 +27,8 @@ model = Unet(
diffusion = GaussianDiffusion(
model,
beta_start = 0.0001,
beta_end = 0.02,
num_diffusion_timesteps = 1000, # number of steps
loss_type = 'l1' # L1 or L2 (wavegrad paper claims l1 is better?)
timesteps = 1000, # number of steps
loss_type = 'l1' # L1 or L2
)
training_images = torch.randn(8, 3, 128, 128)
@@ -54,10 +52,8 @@ model = Unet(
diffusion = GaussianDiffusion(
model,
beta_start = 0.0001,
beta_end = 0.02,
num_diffusion_timesteps = 1000, # number of steps
loss_type = 'l1' # L1 or L2
timesteps = 1000, # number of steps
loss_type = 'l1' # L1 or L2
).cuda()
trainer = Trainer(