diff options
Diffstat (limited to 'modules/sd_models.py')
| -rw-r--r-- | modules/sd_models.py | 54 |
1 files changed, 32 insertions, 22 deletions
diff --git a/modules/sd_models.py b/modules/sd_models.py index 0a55b4c3..f3660d8d 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -1,4 +1,4 @@ -import glob
+import collections
import os.path
import sys
from collections import namedtuple
@@ -15,6 +15,7 @@ model_path = os.path.abspath(os.path.join(models_path, model_dir)) CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config'])
checkpoints_list = {}
+checkpoints_loaded = collections.OrderedDict()
try:
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
@@ -132,38 +133,46 @@ def load_model_weights(model, checkpoint_info): checkpoint_file = checkpoint_info.filename
sd_model_hash = checkpoint_info.hash
- print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
+ if checkpoint_info not in checkpoints_loaded:
+ print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
- pl_sd = torch.load(checkpoint_file, map_location="cpu")
- if "global_step" in pl_sd:
- print(f"Global Step: {pl_sd['global_step']}")
+ pl_sd = torch.load(checkpoint_file, map_location="cpu")
+ if "global_step" in pl_sd:
+ print(f"Global Step: {pl_sd['global_step']}")
- sd = get_state_dict_from_checkpoint(pl_sd)
+ sd = get_state_dict_from_checkpoint(pl_sd)
+ model.load_state_dict(sd, strict=False)
- model.load_state_dict(sd, strict=False)
+ if shared.cmd_opts.opt_channelslast:
+ model.to(memory_format=torch.channels_last)
- if shared.cmd_opts.opt_channelslast:
- model.to(memory_format=torch.channels_last)
+ if not shared.cmd_opts.no_half:
+ model.half()
- if not shared.cmd_opts.no_half:
- model.half()
+ 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
- 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"
- 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 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="cpu")
+ vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
- if os.path.exists(vae_file):
- print(f"Loading VAE weights from: {vae_file}")
- vae_ckpt = torch.load(vae_file, map_location="cpu")
- vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
+ model.first_stage_model.load_state_dict(vae_dict)
- model.first_stage_model.load_state_dict(vae_dict)
+ model.first_stage_model.to(devices.dtype_vae)
- model.first_stage_model.to(devices.dtype_vae)
+ 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)
+ model.load_state_dict(checkpoints_loaded[checkpoint_info])
model.sd_model_hash = sd_model_hash
model.sd_model_checkpoint = checkpoint_file
@@ -202,6 +211,7 @@ def reload_model_weights(sd_model, info=None): return
if sd_model.sd_checkpoint_info.config != checkpoint_info.config:
+ checkpoints_loaded.clear()
shared.sd_model = load_model()
return shared.sd_model
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