diff options
Diffstat (limited to 'modules/sd_models.py')
| -rw-r--r-- | modules/sd_models.py | 48 |
1 files changed, 25 insertions, 23 deletions
diff --git a/modules/sd_models.py b/modules/sd_models.py index 90007da3..34c57bfa 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -9,7 +9,7 @@ from omegaconf import OmegaConf from ldm.util import instantiate_from_config
-from modules import shared, modelloader, devices, script_callbacks
+from modules import shared, modelloader, devices, script_callbacks, sd_vae
from modules.paths import models_path
from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
@@ -159,13 +159,16 @@ def get_state_dict_from_checkpoint(pl_sd): return pl_sd
-vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
-
-
-def load_model_weights(model, checkpoint_info):
+def load_model_weights(model, checkpoint_info, vae_file="auto"):
checkpoint_file = checkpoint_info.filename
sd_model_hash = checkpoint_info.hash
+ if shared.opts.sd_checkpoint_cache > 0 and hasattr(model, "sd_checkpoint_info"):
+ sd_vae.restore_base_vae(model)
+ checkpoints_loaded[model.sd_checkpoint_info] = model.state_dict().copy()
+
+ vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
+
if checkpoint_info not in checkpoints_loaded:
print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
@@ -182,37 +185,36 @@ def load_model_weights(model, checkpoint_info): model.to(memory_format=torch.channels_last)
if not shared.cmd_opts.no_half:
+ vae = model.first_stage_model
+
+ # with --no-half-vae, remove VAE from model when doing half() to prevent its weights from being converted to float16
+ if shared.cmd_opts.no_half_vae:
+ model.first_stage_model = None
+
model.half()
+ model.first_stage_model = vae
devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
- vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
-
- if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None:
- vae_file = shared.cmd_opts.vae_path
-
- if os.path.exists(vae_file):
- print(f"Loading VAE weights from: {vae_file}")
- vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
- vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
- model.first_stage_model.load_state_dict(vae_dict)
-
model.first_stage_model.to(devices.dtype_vae)
- if shared.opts.sd_checkpoint_cache > 0:
- checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
- while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
- checkpoints_loaded.popitem(last=False) # LRU
else:
- print(f"Loading weights [{sd_model_hash}] from cache")
- checkpoints_loaded.move_to_end(checkpoint_info)
+ 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")
model.load_state_dict(checkpoints_loaded[checkpoint_info])
+ if shared.opts.sd_checkpoint_cache > 0:
+ while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
+ checkpoints_loaded.popitem(last=False) # LRU
+
model.sd_model_hash = sd_model_hash
model.sd_model_checkpoint = checkpoint_file
model.sd_checkpoint_info = checkpoint_info
+ sd_vae.load_vae(model, vae_file)
+
def load_model(checkpoint_info=None):
from modules import lowvram, sd_hijack
@@ -263,7 +265,7 @@ def load_model(checkpoint_info=None): def reload_model_weights(sd_model=None, info=None):
from modules import lowvram, devices, sd_hijack
checkpoint_info = info or select_checkpoint()
-
+
if not sd_model:
sd_model = shared.sd_model
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