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Merge pull request #2573 from raefu/ckpt-cache

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add --ckpt-cache option for faster model switching
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AUTOMATIC1111 2022-10-15 10:35:26 +03:00 committed by GitHub
commit d13ce89e20
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2 changed files with 33 additions and 26 deletions
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58
modules/sd_models.py
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@ -1,4 +1,4 @@
import glob import collections
import os.path import os.path
import sys import sys
from collections import namedtuple 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']) CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config'])
checkpoints_list = {} checkpoints_list = {}
checkpoints_loaded = collections.OrderedDict()
try: try:
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start. # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
@ -132,41 +133,45 @@ def load_model_weights(model, checkpoint_info):
checkpoint_file = checkpoint_info.filename checkpoint_file = checkpoint_info.filename
sd_model_hash = checkpoint_info.hash 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=shared.weight_load_location) pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
if "global_step" in pl_sd: sd = get_state_dict_from_checkpoint(pl_sd)
print(f"Global Step: {pl_sd['global_step']}") model.load_state_dict(sd, strict=False)
sd = get_state_dict_from_checkpoint(pl_sd) if shared.cmd_opts.opt_channelslast:
model.to(memory_format=torch.channels_last)
model.load_state_dict(sd, strict=False) if not shared.cmd_opts.no_half:
model.half()
if shared.cmd_opts.opt_channelslast: devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
model.to(memory_format=torch.channels_last) devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
if not shared.cmd_opts.no_half: vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
model.half()
devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16 if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None:
devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16 vae_file = shared.cmd_opts.vae_path
vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt" 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"}
model.first_stage_model.load_state_dict(vae_dict)
if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None: model.first_stage_model.to(devices.dtype_vae)
vae_file = shared.cmd_opts.vae_path
if os.path.exists(vae_file): checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
print(f"Loading VAE weights from: {vae_file}") while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
checkpoints_loaded.popitem(last=False) # LRU
vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location) else:
print(f"Loading weights [{sd_model_hash}] from cache")
vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"} checkpoints_loaded.move_to_end(checkpoint_info)
model.load_state_dict(checkpoints_loaded[checkpoint_info])
model.first_stage_model.load_state_dict(vae_dict)
model.first_stage_model.to(devices.dtype_vae)
model.sd_model_hash = sd_model_hash model.sd_model_hash = sd_model_hash
model.sd_model_checkpoint = checkpoint_file model.sd_model_checkpoint = checkpoint_file
@ -205,6 +210,7 @@ def reload_model_weights(sd_model, info=None):
return return
if sd_model.sd_checkpoint_info.config != checkpoint_info.config: if sd_model.sd_checkpoint_info.config != checkpoint_info.config:
checkpoints_loaded.clear()
shared.sd_model = load_model() shared.sd_model = load_model()
return shared.sd_model return shared.sd_model

1
modules/shared.py
View file

@ -242,6 +242,7 @@ 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": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models), "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
"sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
"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),
"sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}), "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."), "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),