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author | Muhammad Rizqi Nur <rizqinur2010@gmail.com> | 2022-11-13 03:55:47 +0000 |
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committer | Muhammad Rizqi Nur <rizqinur2010@gmail.com> | 2022-11-19 05:01:41 +0000 |
commit | 2c5ca706a7e624d268545ba3318ba230b7b33477 (patch) | |
tree | ca0041601d18dbd4229ead6fcdaa86da595cac49 | |
parent | e5690d0bf2da1f0719ca581cd2e797e9b74db67e (diff) | |
download | stable-diffusion-webui-gfx803-2c5ca706a7e624d268545ba3318ba230b7b33477.tar.gz stable-diffusion-webui-gfx803-2c5ca706a7e624d268545ba3318ba230b7b33477.tar.bz2 stable-diffusion-webui-gfx803-2c5ca706a7e624d268545ba3318ba230b7b33477.zip |
Remove no longer necessary parts and add vae_file safeguard
-rw-r--r-- | modules/sd_models.py | 10 | ||||
-rw-r--r-- | modules/sd_vae.py | 1 |
2 files changed, 3 insertions, 8 deletions
diff --git a/modules/sd_models.py b/modules/sd_models.py index 80addf03..c59151e0 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -165,16 +165,9 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"): cache_enabled = shared.opts.sd_checkpoint_cache > 0
- if cache_enabled:
- sd_vae.restore_base_vae(model)
-
- vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
-
if cache_enabled and checkpoint_info in checkpoints_loaded:
# use checkpoint cache
- vae_name = sd_vae.get_filename(vae_file) if vae_file else None
- vae_message = f" with {vae_name} VAE" if vae_name else ""
- print(f"Loading weights [{sd_model_hash}]{vae_message} from cache")
+ print(f"Loading weights [{sd_model_hash}] from cache")
model.load_state_dict(checkpoints_loaded[checkpoint_info])
else:
# load from file
@@ -220,6 +213,7 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"): model.sd_model_checkpoint = checkpoint_file
model.sd_checkpoint_info = checkpoint_info
+ vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
sd_vae.load_vae(model, vae_file)
diff --git a/modules/sd_vae.py b/modules/sd_vae.py index 71e7a6e6..8bdb2c17 100644 --- a/modules/sd_vae.py +++ b/modules/sd_vae.py @@ -139,6 +139,7 @@ def load_vae(model, vae_file=None): # save_settings = False if vae_file: + assert os.path.isfile(vae_file), f"VAE file doesn't exist: {vae_file}" print(f"Loading VAE weights from: {vae_file}") vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location) vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys} |