fix min snr logic, thanks to @justinlovelace

Author: lucidrains

denoising_diffusion_pytorch/classifier_free_guidance.py

@@ -562,7 +562,7 @@ def __init__(
 
         maybe_clipped_snr = snr.clone()
         if min_snr_loss_weight:
-            maybe_clipped_snr.clamp_(min = min_snr_gamma)
+            maybe_clipped_snr.clamp_(max = min_snr_gamma)
 
         if objective == 'pred_noise':
             loss_weight = maybe_clipped_snr / snr

denoising_diffusion_pytorch/denoising_diffusion_pytorch.py

@@ -538,7 +538,7 @@ def __init__(
 
         maybe_clipped_snr = snr.clone()
         if min_snr_loss_weight:
-            maybe_clipped_snr.clamp_(min = min_snr_gamma)
+            maybe_clipped_snr.clamp_(max = min_snr_gamma)
 
         if objective == 'pred_noise':
             register_buffer('loss_weight', maybe_clipped_snr / snr)

denoising_diffusion_pytorch/guided_diffusion.py

@@ -532,7 +532,7 @@ def __init__(
 
         maybe_clipped_snr = snr.clone()
         if min_snr_loss_weight:
-            maybe_clipped_snr.clamp_(min = min_snr_gamma)
+            maybe_clipped_snr.clamp_(max = min_snr_gamma)
 
         if objective == 'pred_noise':
             loss_weight = maybe_clipped_snr / snr

denoising_diffusion_pytorch/simple_diffusion.py

@@ -690,7 +690,7 @@ def p_losses(self, x_start, times, noise = None):
 
         maybe_clip_snr = snr.clone()
         if self.min_snr_loss_weight:
-            maybe_clip_snr.clamp_(min = self.min_snr_gamma)
+            maybe_clip_snr.clamp_(max = self.min_snr_gamma)
 
         if self.pred_objective == 'v':
             loss_weight = maybe_clip_snr / (snr + 1)

denoising_diffusion_pytorch/version.py

@@ -1 +1 @@
-__version__ = '1.5.2'
+__version__ = '1.5.3'