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Add memory cache for VAE weights

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hitomi 2022-12-25 20:49:25 +08:00
parent c6f347b81f
commit 893933e05a
2 changed files with 26 additions and 6 deletions
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31
modules/sd_vae.py
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@ -1,5 +1,6 @@
import torch import torch
import os import os
import collections
from collections import namedtuple from collections import namedtuple
from modules import shared, devices, script_callbacks from modules import shared, devices, script_callbacks
from modules.paths import models_path from modules.paths import models_path
@ -30,6 +31,7 @@ base_vae = None
loaded_vae_file = None loaded_vae_file = None
checkpoint_info = None checkpoint_info = None
checkpoints_loaded = collections.OrderedDict()
def get_base_vae(model): def get_base_vae(model):
if base_vae is not None and checkpoint_info == model.sd_checkpoint_info and model: if base_vae is not None and checkpoint_info == model.sd_checkpoint_info and model:
@ -149,13 +151,30 @@ def load_vae(model, vae_file=None):
global first_load, vae_dict, vae_list, loaded_vae_file global first_load, vae_dict, vae_list, loaded_vae_file
# save_settings = False # save_settings = False
cache_enabled = shared.opts.sd_vae_checkpoint_cache > 0
if vae_file: if vae_file:
assert os.path.isfile(vae_file), f"VAE file doesn't exist: {vae_file}" if cache_enabled and vae_file in checkpoints_loaded:
print(f"Loading VAE weights from: {vae_file}") # use vae checkpoint cache
store_base_vae(model) print(f"Loading VAE weights [{get_filename(vae_file)}] from cache")
vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location) store_base_vae(model)
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} _load_vae_dict(model, checkpoints_loaded[vae_file])
_load_vae_dict(model, vae_dict_1) else:
assert os.path.isfile(vae_file), f"VAE file doesn't exist: {vae_file}"
print(f"Loading VAE weights from: {vae_file}")
store_base_vae(model)
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}
_load_vae_dict(model, vae_dict_1)
if cache_enabled:
# cache newly loaded vae
checkpoints_loaded[vae_file] = vae_dict_1.copy()
# clean up cache if limit is reached
if cache_enabled:
while len(checkpoints_loaded) > shared.opts.sd_vae_checkpoint_cache + 1: # we need to count the current model
checkpoints_loaded.popitem(last=False) # LRU
# If vae used is not in dict, update it # If vae used is not in dict, update it
# It will be removed on refresh though # It will be removed on refresh though

1
modules/shared.py
View File

@ -356,6 +356,7 @@ options_templates.update(options_section(('training', "Training"), {
options_templates.update(options_section(('sd', "Stable Diffusion"), { options_templates.update(options_section(('sd', "Stable Diffusion"), {
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints), "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
"sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}), "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
"sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
"sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": sd_vae.vae_list}, refresh=sd_vae.refresh_vae_list), "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": sd_vae.vae_list}, refresh=sd_vae.refresh_vae_list),
"sd_vae_as_default": OptionInfo(False, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"), "sd_vae_as_default": OptionInfo(False, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
"sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks), "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),