maisi: occuring NaN during the diffusion model training

Hi,

When I train the diffusion model with the trained VAE autoencoder weights, I encounter the issue of NaN loss. The following is part log:

lr: [0.0001]
lr: [0.0001]
Epoch 201 train_vae_loss 0.039707845827617515: {'recons_loss': 0.015235490621573968, 'kl_loss': 85897.05040993346, 'p_loss': 0.05294216721683401}.
Save trained autoencoder to ./models/vae/pretrain/20241128190854/autoencoder.pt
Save trained discriminator to ./models/vae/pretrain/20241128190854/discriminator.pt
lr: [0.0001]
lr: [0.0001]
Epoch 202 train_vae_loss 0.036837538356057416: {'recons_loss': 0.013532256549082612, 'kl_loss': 84689.69741415161, 'p_loss': 0.049454373551865494}.
Save trained autoencoder to ./models/vae/pretrain/20241128190854/autoencoder.pt
Save trained discriminator to ./models/vae/pretrain/20241128190854/discriminator.pt
lr: [0.0001]
Epoch 203 train_vae_loss 0.04066830881296869: {'recons_loss': 0.01579887273158354, 'kl_loss': 86930.33920508555, 'p_loss': 0.053921340536255344}.
lr: [0.0001]
Save trained autoencoder to ./models/vae/pretrain/20241128190854/autoencoder.pt
Save trained discriminator to ./models/vae/pretrain/20241128190854/discriminator.pt
lr: [0.0001]
Epoch 204 train_vae_loss 0.04466726511873636: {'recons_loss': 0.017411270744553255, 'kl_loss': 86347.42582580798, 'p_loss': 0.06207083930534102}.
lr: [0.0001]
Save trained autoencoder to ./models/vae/pretrain/20241128190854/autoencoder.pt
Save trained discriminator to ./models/vae/pretrain/20241128190854/discriminator.pt
lr: [0.0001]
Epoch 205 train_vae_loss 0.039213172850076874: {'recons_loss': 0.014744859089642877, 'kl_loss': 85096.7134475998, 'p_loss': 0.05319547471891338}.
lr: [0.0001]
Save trained autoencoder to ./models/vae/pretrain/20241128190854/autoencoder.pt
Save trained discriminator to ./models/vae/pretrain/20241128190854/discriminator.pt
lr: [0.0001]
lr: [0.0001]
Epoch 206 train_vae_loss 0.038383807665236705: {'recons_loss': 0.014200327562047841, 'kl_loss': 85760.27710610742, 'p_loss': 0.05202484130859375}.
Save trained autoencoder to ./models/vae/pretrain/20241128190854/autoencoder.pt
Save trained discriminator to ./models/vae/pretrain/20241128190854/discriminator.pt
lr: [0.0001]
Epoch 207 train_vae_loss 0.03932591601622308: {'recons_loss': 0.014606543448346422, 'kl_loss': 87033.87854978612, 'p_loss': 0.05338661570966017}.
lr: [0.0001]
Save trained autoencoder to ./models/vae/pretrain/20241128190854/autoencoder.pt
Save trained discriminator to ./models/vae/pretrain/20241128190854/discriminator.pt
lr: [0.0001]
lr: [0.0001]
Epoch 208 train_vae_loss 0.058224454522186636: {'recons_loss': 0.022603081671181118, 'kl_loss': 143004.20642229088, 'p_loss': 0.07106984069592145}.
Save trained autoencoder to ./models/vae/pretrain/20241128190854/autoencoder.pt
Save trained discriminator to ./models/vae/pretrain/20241128190854/discriminator.pt
lr: [0.0001]
Epoch 209 train_vae_loss 0.04279889678179953: {'recons_loss': 0.016451959535408723, 'kl_loss': 91421.0720725404, 'p_loss': 0.057349433463789214}.
lr: [0.0001]
Save trained autoencoder to ./models/vae/pretrain/20241128190854/autoencoder.pt
Save trained discriminator to ./models/vae/pretrain/20241128190854/discriminator.pt
lr: [0.0001]
Epoch 210 train_vae_loss 0.04419510093541189: {'recons_loss': 0.017602585414566184, 'kl_loss': 88753.12170270912, 'p_loss': 0.05905734450191599}.
Save trained autoencoder to ./models/vae/pretrain/20241128190854/autoencoder.pt
Save trained discriminator to ./models/vae/pretrain/20241128190854/discriminator.pt
lr: [0.0001]
Epoch 210 val_vae_loss nan: {'recons_loss': nan, 'kl_loss': nan, 'p_loss': nan}.
lr: [0.0001]
Epoch 211 train_vae_loss 0.047102449947657665: {'recons_loss': 0.018618881483757056, 'kl_loss': 99892.8082075808, 'p_loss': 0.061647625477141754}.
lr: [0.0001]
Save trained autoencoder to ./models/vae/pretrain/20241128190854/autoencoder.pt
Save trained discriminator to ./models/vae/pretrain/20241128190854/discriminator.pt
lr: [0.0001]
Epoch 212 train_vae_loss 0.04109727745322826: {'recons_loss': 0.015783639634453017, 'kl_loss': 88632.56267823195, 'p_loss': 0.054834605169840185}.
lr: [0.0001]
Save trained autoencoder to ./models/vae/pretrain/20241128190854/autoencoder.pt
Save trained discriminator to ./models/vae/pretrain/20241128190854/discriminator.pt
lr: [0.0001]
Epoch 213 train_vae_loss 0.04030142836448789: {'recons_loss': 0.015340764416353387, 'kl_loss': 87118.63764704135, 'p_loss': 0.054162667278101234}.
lr: [0.0001]
Save trained autoencoder to ./models/vae/pretrain/20241128190854/autoencoder.pt
Save trained discriminator to ./models/vae/pretrain/20241128190854/discriminator.pt
lr: [0.0001]
Epoch 214 train_vae_loss 0.040564939826983476: {'recons_loss': 0.015572800811826333, 'kl_loss': 89774.16198312737, 'p_loss': 0.05338240938948134}.
lr: [0.0001]
Save trained autoencoder to ./models/vae/pretrain/20241128190854/autoencoder.pt
Save trained discriminator to ./models/vae/pretrain/20241128190854/discriminator.pt
lr: [0.0001]
Epoch 215 train_vae_loss 0.04143597853471239: {'recons_loss': 0.01541382184043215, 'kl_loss': 96709.1665948788, 'p_loss': 0.054504133449307865}.
lr: [0.0001]
Save trained autoencoder to ./models/vae/pretrain/20241128190854/autoencoder.pt
Save trained discriminator to ./models/vae/pretrain/20241128190854/discriminator.pt
lr: [0.0001]
Epoch 216 train_vae_loss 0.04318358794278379: {'recons_loss': 0.015925193886261985, 'kl_loss': 91295.16699590067, 'p_loss': 0.06042959118977246}.
lr: [0.0001]
Save trained autoencoder to ./models/vae/pretrain/20241128190854/autoencoder.pt
Save trained discriminator to ./models/vae/pretrain/20241128190854/discriminator.pt
lr: [0.0001]
Epoch 217 train_vae_loss 0.05705382756280092: {'recons_loss': 0.020152890289860986, 'kl_loss': 109472.0783923479, 'p_loss': 0.08651243144568381}.
lr: [0.0001]
Save trained autoencoder to ./models/vae/pretrain/20241128190854/autoencoder.pt
Save trained discriminator to ./models/vae/pretrain/20241128190854/discriminator.pt
lr: [0.0001]
lr: [0.0001]
Epoch 218 train_vae_loss nan: {'recons_loss': nan, 'kl_loss': nan, 'p_loss': nan}.
Save trained autoencoder to ./models/vae/pretrain/20241128190854/autoencoder.pt
Save trained discriminator to ./models/vae/pretrain/20241128190854/discriminator.pt
lr: [0.0001]
Epoch 219 train_vae_loss nan: {'recons_loss': nan, 'kl_loss': nan, 'p_loss': nan}.
lr: [0.0001]
Save trained autoencoder to ./models/vae/pretrain/20241128190854/autoencoder.pt
Save trained discriminator to ./models/vae/pretrain/20241128190854/discriminator.pt
lr: [0.0001]
Epoch 220 train_vae_loss nan: {'recons_loss': nan, 'kl_loss': nan, 'p_loss': nan}.
Save trained autoencoder to ./models/vae/pretrain/20241128190854/autoencoder.pt
Save trained discriminator to ./models/vae/pretrain/20241128190854/discriminator.pt
lr: [0.0001]

Thanks a lot.

Beginne mit dem Trainingsworkflow des Maisi-Diffusionsmodells unter Verwendung der trainierten VAE-Autoencoder-Gewichte und vergleiche die Trainings- und Validierungs-Logs rund um die Epochen 210–220. Untersuche den Übergang von endlichen Loss-Werten zu NaN-Werten und dokumentiere die Bedingungen, unter denen er reproduziert werden kann. Erledigt bedeutet, eine reproduzierbare Ursache zu identifizieren oder die Ursache auf die Trainingskonfiguration oder die Daten einzugrenzen.