Mirrors
/
stable-diffusion-webui
mirror of https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Settings to select VAE

This commit is contained in:
Muhammad Rizqi Nur 2022-10-30 21:54:31 +07:00
parent 17a2076f72
commit cb31abcf58
4 changed files with 141 additions and 24 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

31
modules/sd_models.py
View File

@ -8,7 +8,7 @@ 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
@ -160,12 +160,11 @@ def get_state_dict_from_checkpoint(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, force=False):
checkpoint_file = checkpoint_info.filename
sd_model_hash = checkpoint_info.hash
if checkpoint_info not in checkpoints_loaded:
if force or 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)
@ -186,17 +185,7 @@ def load_model_weights(model, checkpoint_info):
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)
sd_vae.load_vae(model, checkpoint_file)
model.first_stage_model.to(devices.dtype_vae)
if shared.opts.sd_checkpoint_cache > 0:
@ -213,7 +202,7 @@ def load_model_weights(model, checkpoint_info):
model.sd_checkpoint_info = checkpoint_info
def load_model(checkpoint_info=None):
def load_model(checkpoint_info=None, force=False):
from modules import lowvram, sd_hijack
checkpoint_info = checkpoint_info or select_checkpoint()
@ -234,7 +223,7 @@ def load_model(checkpoint_info=None):
do_inpainting_hijack()
sd_model = instantiate_from_config(sd_config.model)
load_model_weights(sd_model, checkpoint_info)
load_model_weights(sd_model, checkpoint_info, force=force)
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
@ -252,16 +241,16 @@ def load_model(checkpoint_info=None):
return sd_model
def reload_model_weights(sd_model, info=None):
def reload_model_weights(sd_model, info=None, force=False):
from modules import lowvram, devices, sd_hijack
checkpoint_info = info or select_checkpoint()
if sd_model.sd_model_checkpoint == checkpoint_info.filename:
if sd_model.sd_model_checkpoint == checkpoint_info.filename and not force:
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):
checkpoints_loaded.clear()
load_model(checkpoint_info)
load_model(checkpoint_info, force=force)
return shared.sd_model
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
@ -271,7 +260,7 @@ def reload_model_weights(sd_model, info=None):
sd_hijack.model_hijack.undo_hijack(sd_model)
load_model_weights(sd_model, checkpoint_info)
load_model_weights(sd_model, checkpoint_info, force=force)
sd_hijack.model_hijack.hijack(sd_model)
script_callbacks.model_loaded_callback(sd_model)

121
modules/sd_vae.py Normal file
View File

@ -0,0 +1,121 @@
import torch
import os
from collections import namedtuple
from modules import shared, devices
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
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 = {}
candidates = [
*glob.iglob(os.path.join(model_path, '**/*.vae.pt'), recursive=True),
*glob.iglob(os.path.join(model_path, '**/*.vae.ckpt'), recursive=True),
*glob.iglob(os.path.join(vae_path, '**/*.pt'), recursive=True),
*glob.iglob(os.path.join(vae_path, '**/*.ckpt'), recursive=True)
]
if shared.cmd_opts.vae_path is not None and os.path.isfile(shared.cmd_opts.vae_path):
candidates.append(shared.cmd_opts.vae_path)
for filepath in candidates:
name = get_filename(filepath)
res[name] = filepath
vae_list.clear()
vae_list.extend(default_vae_list)
vae_list.extend(list(res.keys()))
vae_dict.clear()
vae_dict.update(default_vae_dict)
vae_dict.update(res)
return vae_list
def load_vae(model, checkpoint_file, vae_file="auto"):
global first_load, vae_dict, vae_list
# save_settings = False
# if vae_file argument is provided, it takes priority
if vae_file and vae_file not in default_vae_list:
if not os.path.isfile(vae_file):
vae_file = "auto"
# save_settings = True
print("VAE provided as function argument doesn't exist")
# for the first load, if vae-path is provided, it takes priority and failure is reported
if first_load and shared.cmd_opts.vae_path is not None:
if os.path.isfile(shared.cmd_opts.vae_path):
vae_file = shared.cmd_opts.vae_path
# save_settings = True
# print("Using VAE provided as command line argument")
else:
print("VAE provided as command line argument doesn't exist")
# else, we load from settings
if vae_file == "auto" and shared.opts.sd_vae is not None:
# if saved VAE settings isn't recognized, fallback to auto
vae_file = vae_dict.get(shared.opts.sd_vae, "auto")
# if VAE selected but not found, fallback to auto
if vae_file not in default_vae_values and not os.path.isfile(vae_file):
vae_file = "auto"
print("Selected VAE doesn't exist")
# vae-path cmd arg takes priority for auto
if vae_file == "auto" and shared.cmd_opts.vae_path is not None:
if os.path.isfile(shared.cmd_opts.vae_path):
vae_file = shared.cmd_opts.vae_path
print("Using VAE provided as command line argument")
# if still not found, try look for ".vae.pt" beside model
model_path = os.path.splitext(checkpoint_file)[0]
if vae_file == "auto":
vae_file_try = model_path + ".vae.pt"
if os.path.isfile(vae_file_try):
vae_file = vae_file_try
print("Using VAE found beside selected model")
# if still not found, try look for ".vae.ckpt" beside model
if vae_file == "auto":
vae_file_try = model_path + ".vae.ckpt"
if os.path.isfile(vae_file_try):
vae_file = vae_file_try
print("Using VAE found beside selected model")
# No more fallbacks for auto
if vae_file == "auto":
vae_file = None
# Last check, just because
if vae_file and not os.path.exists(vae_file):
vae_file = None
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)
# If vae used is not in dict, update it
# It will be removed on refresh though
if vae_file is not None:
vae_opt = get_filename(vae_file)
if vae_opt not in vae_dict:
vae_dict[vae_opt] = vae_file
vae_list.append(vae_opt)
"""
# Save current VAE to VAE settings, maybe? will it work?
if save_settings:
if vae_file is None:
vae_opt = "None"
# shared.opts.sd_vae = vae_opt
"""
first_load = False
model.first_stage_model.to(devices.dtype_vae)

8
modules/shared.py
View File

@ -14,7 +14,7 @@ import modules.memmon
import modules.sd_models
import modules.styles
import modules.devices as devices
from modules import sd_samplers, sd_models, localization
from modules import sd_samplers, sd_models, localization, sd_vae
from modules.hypernetworks import hypernetwork
from modules.paths import models_path, script_path, sd_path
@ -295,6 +295,7 @@ options_templates.update(options_section(('training', "Training"), {
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_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
"sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": list(sd_vae.vae_list)}, refresh=sd_vae.refresh_vae_list),
"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}),
"inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
@ -407,11 +408,12 @@ class Options:
if bad_settings > 0:
print(f"The program is likely to not work with bad settings.\nSettings file: {filename}\nEither fix the file, or delete it and restart.", file=sys.stderr)
def onchange(self, key, func):
def onchange(self, key, func, call=True):
item = self.data_labels.get(key)
item.onchange = func
func()
if call:
func()
def dumpjson(self):
d = {k: self.data.get(k, self.data_labels.get(k).default) for k in self.data_labels.keys()}

5
webui.py
View File

@ -21,6 +21,7 @@ import modules.paths
import modules.scripts
import modules.sd_hijack
import modules.sd_models
import modules.sd_vae
import modules.shared as shared
import modules.txt2img
@ -74,8 +75,12 @@ def initialize():
modules.scripts.load_scripts()
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(shared.sd_model)))
# 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, and no cache
shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model, force=True)), 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)