diff options
Diffstat (limited to 'modules')
| -rw-r--r-- | modules/sd_models.py | 31 | ||||
| -rw-r--r-- | modules/sd_vae.py | 121 | ||||
| -rw-r--r-- | modules/shared.py | 8 |
3 files changed, 136 insertions, 24 deletions
diff --git a/modules/sd_models.py b/modules/sd_models.py index f86dc3ed..91ad4b5e 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -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)
diff --git a/modules/sd_vae.py b/modules/sd_vae.py new file mode 100644 index 00000000..82764e55 --- /dev/null +++ b/modules/sd_vae.py @@ -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) diff --git a/modules/shared.py b/modules/shared.py index e4f163c1..06440ac4 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -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()}
|