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vae/config.json

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+model:
+  base_learning_rate: 1.5e-7
+  target: ldm.models.autoencoder.AutoencoderKL
+  params:
+    monitor: "val/rec_loss"
+    embed_dim: 4
+    lossconfig:
+      target: ldm.modules.losses.LPIPSWithDiscriminator
+      params:
+        disc_start: 50001
+        kl_weight: 0.000001
+        disc_weight: 0.5
+
+    ddconfig:
+      double_z: True
+      z_channels: 4
+      resolution: 256
+      in_channels: 3
+      out_ch: 3
+      ch: 128
+      ch_mult: [ 1,2,4,4 ]  # num_down = len(ch_mult)-1
+      num_res_blocks: 2
+      attn_resolutions: [ ]
+      dropout: 0.0

vae/kl-f8-anime.ckpt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2f11c4a99ddc28d0ad8bce0acc38bed310b45d38a3fe4bb367dc30f3ef1a4868
+size 404657953

vae/kl-f8-anime2.ckpt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:df3c506e51b7ee1d7b5a6a2bb7142d47d488743c96aa778afb0f53a2cdc2d38d
+size 404662241

vae/kl-f8-anime2.vae.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:df3c506e51b7ee1d7b5a6a2bb7142d47d488743c96aa778afb0f53a2cdc2d38d
+size 404662241

vae/pruner.py

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+import torch
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--input', '-I', type=str, help='Input file to prune', required = True)
+args = parser.parse_args()
+file = args.input
+
+checkpoint = torch.load(file)
+new_sd = dict()
+for k in checkpoint.keys():
+    if k != 'optimizer_states':
+        new_sd[k] = checkpoint[k]
+
+torch.save(new_sd, f'pruned-{file}')