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authorMuhammad Rizqi Nur <rizqinur2010@gmail.com>2022-11-02 12:51:46 +0700
committerMuhammad Rizqi Nur <rizqinur2010@gmail.com>2022-11-02 12:51:46 +0700
commit056f06d3738c267b1014e6e8e1ef5bd97af1fb45 (patch)
treec80870ce44039b839b5c40cbe832794eecdba671
parentf8c6468d42e1202f7aeaeb961ab003aa0a2daf99 (diff)
Reload VAE without reloading sd checkpoint
-rw-r--r--modules/sd_models.py15
-rw-r--r--modules/sd_vae.py97
-rw-r--r--webui.py4
3 files changed, 98 insertions, 18 deletions
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 6ab85b65..883639d1 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -159,15 +159,13 @@ 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, vae_file="auto"):
checkpoint_file = checkpoint_info.filename
sd_model_hash = checkpoint_info.hash
vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
- checkpoint_key = (checkpoint_info, vae_file)
+ checkpoint_key = checkpoint_info
if checkpoint_key not in checkpoints_loaded:
print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
@@ -190,13 +188,12 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
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
- sd_vae.load_vae(model, vae_file)
- model.first_stage_model.to(devices.dtype_vae)
-
if shared.opts.sd_checkpoint_cache > 0:
+ # if PR #4035 were to get merged, restore base VAE first before caching
checkpoints_loaded[checkpoint_key] = model.state_dict().copy()
while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
checkpoints_loaded.popitem(last=False) # LRU
+
else:
vae_name = sd_vae.get_filename(vae_file)
print(f"Loading weights [{sd_model_hash}] with {vae_name} VAE from cache")
@@ -207,6 +204,8 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
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
@@ -254,14 +253,14 @@ def load_model(checkpoint_info=None):
return sd_model
-def reload_model_weights(sd_model=None, info=None, force=False):
+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
- if sd_model.sd_model_checkpoint == checkpoint_info.filename and not force:
+ if sd_model.sd_model_checkpoint == checkpoint_info.filename:
return
if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index e9239326..78e14e8a 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -1,26 +1,65 @@
import torch
import os
from collections import namedtuple
-from modules import shared, devices
+from modules import shared, devices, script_callbacks
from modules.paths import models_path
import glob
+
model_dir = "Stable-diffusion"
model_path = os.path.abspath(os.path.join(models_path, model_dir))
vae_dir = "VAE"
vae_path = os.path.abspath(os.path.join(models_path, vae_dir))
+
vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
+
+
default_vae_dict = {"auto": "auto", "None": "None"}
default_vae_list = ["auto", "None"]
+
+
default_vae_values = [default_vae_dict[x] for x in default_vae_list]
vae_dict = dict(default_vae_dict)
vae_list = list(default_vae_list)
first_load = True
+
+base_vae = None
+loaded_vae_file = None
+checkpoint_info = None
+
+
+def get_base_vae(model):
+ if base_vae is not None and checkpoint_info == model.sd_checkpoint_info and model:
+ return base_vae
+ return None
+
+
+def store_base_vae(model):
+ global base_vae, checkpoint_info
+ if checkpoint_info != model.sd_checkpoint_info:
+ base_vae = model.first_stage_model.state_dict().copy()
+ checkpoint_info = model.sd_checkpoint_info
+
+
+def delete_base_vae():
+ global base_vae, checkpoint_info
+ base_vae = None
+ checkpoint_info = None
+
+
+def restore_base_vae(model):
+ global base_vae, checkpoint_info
+ if base_vae is not None and checkpoint_info == model.sd_checkpoint_info:
+ load_vae_dict(model, base_vae)
+ delete_base_vae()
+
+
def get_filename(filepath):
return os.path.splitext(os.path.basename(filepath))[0]
+
def refresh_vae_list(vae_path=vae_path, model_path=model_path):
global vae_dict, vae_list
res = {}
@@ -43,6 +82,7 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path):
vae_dict.update(res)
return vae_list
+
def resolve_vae(checkpoint_file, vae_file="auto"):
global first_load, vae_dict, vae_list
# save_settings = False
@@ -96,24 +136,26 @@ def resolve_vae(checkpoint_file, vae_file="auto"):
return vae_file
-def load_vae(model, vae_file):
- global first_load, vae_dict, vae_list
+
+def load_vae(model, vae_file=None):
+ global first_load, vae_dict, vae_list, loaded_vae_file
# save_settings = False
if 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}
- model.first_stage_model.load_state_dict(vae_dict_1)
+ load_vae_dict(model, vae_dict_1)
- # If vae used is not in dict, update it
- # It will be removed on refresh though
- if vae_file is not None:
+ # If vae used is not in dict, update it
+ # It will be removed on refresh though
vae_opt = get_filename(vae_file)
if vae_opt not in vae_dict:
vae_dict[vae_opt] = vae_file
vae_list.append(vae_opt)
+ loaded_vae_file = vae_file
+
"""
# Save current VAE to VAE settings, maybe? will it work?
if save_settings:
@@ -124,4 +166,45 @@ def load_vae(model, vae_file):
"""
first_load = False
+
+
+# don't call this from outside
+def load_vae_dict(model, vae_dict_1=None):
+ if vae_dict_1:
+ store_base_vae(model)
+ model.first_stage_model.load_state_dict(vae_dict_1)
+ else:
+ restore_base_vae()
model.first_stage_model.to(devices.dtype_vae)
+
+
+def reload_vae_weights(sd_model=None, vae_file="auto"):
+ from modules import lowvram, devices, sd_hijack
+
+ if not sd_model:
+ sd_model = shared.sd_model
+
+ checkpoint_info = sd_model.sd_checkpoint_info
+ checkpoint_file = checkpoint_info.filename
+ vae_file = resolve_vae(checkpoint_file, vae_file=vae_file)
+
+ if loaded_vae_file == vae_file:
+ return
+
+ if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+ lowvram.send_everything_to_cpu()
+ else:
+ sd_model.to(devices.cpu)
+
+ sd_hijack.model_hijack.undo_hijack(sd_model)
+
+ load_vae(sd_model, vae_file)
+
+ sd_hijack.model_hijack.hijack(sd_model)
+ script_callbacks.model_loaded_callback(sd_model)
+
+ if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
+ sd_model.to(devices.device)
+
+ print(f"VAE Weights loaded.")
+ return sd_model
diff --git a/webui.py b/webui.py
index 7cb4691b..034777a2 100644
--- a/webui.py
+++ b/webui.py
@@ -81,9 +81,7 @@ def initialize():
modules.sd_vae.refresh_vae_list()
modules.sd_models.load_model()
shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights()))
- # I don't know what needs to be done to only reload VAE, with all those hijacks callbacks, and lowvram,
- # so for now this reloads the whole model too
- shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(force=True)), call=False)
+ shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)