Merge pull request #3986 from R-N/vae-picker

VAE Selector

6 files changed, 233 insertions, 28 deletions

diff --git a/models/VAE/Put VAE here.txt b/models/VAE/Put VAE here.txt
new file mode 100644
index 00000000..e69de29b
--- /dev/null
+++ b/models/VAE/Put VAE here.txt
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 90007da3..883639d1 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -9,7 +9,7 @@ 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

 

@@ -159,14 +159,15 @@ 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):

+def load_model_weights(model, checkpoint_info, vae_file="auto"):

     checkpoint_file = checkpoint_info.filename

     sd_model_hash = checkpoint_info.hash

 

-    if checkpoint_info not in checkpoints_loaded:

+    vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)

+

+    checkpoint_key = checkpoint_info

+

+    if checkpoint_key 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)

@@ -187,32 +188,24 @@ 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)

-

-        model.first_stage_model.to(devices.dtype_vae)

-

         if shared.opts.sd_checkpoint_cache > 0:

-            checkpoints_loaded[checkpoint_info] = model.state_dict().copy()

+            # 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:

-        print(f"Loading weights [{sd_model_hash}] from cache")

-        checkpoints_loaded.move_to_end(checkpoint_info)

-        model.load_state_dict(checkpoints_loaded[checkpoint_info])

+        vae_name = sd_vae.get_filename(vae_file)

+        print(f"Loading weights [{sd_model_hash}] with {vae_name} VAE from cache")

+        checkpoints_loaded.move_to_end(checkpoint_key)

+        model.load_state_dict(checkpoints_loaded[checkpoint_key])

 

     model.sd_model_hash = sd_model_hash

     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

@@ -263,7 +256,7 @@ def load_model(checkpoint_info=None):
 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

 

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
new file mode 100644
index 00000000..71e7a6e6
--- /dev/null
+++ b/modules/sd_vae.py
@@ -0,0 +1,207 @@
+import torch
+import os
+from collections import namedtuple
+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 = {}
+    candidates = [
+        *glob.iglob(os.path.join(model_path, '**/*.vae.ckpt'), recursive=True),
+        *glob.iglob(os.path.join(model_path, '**/*.vae.pt'), recursive=True),
+        *glob.iglob(os.path.join(vae_path, '**/*.ckpt'), recursive=True),
+        *glob.iglob(os.path.join(vae_path, '**/*.pt'), 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(res)
+    vae_dict.update(default_vae_dict)
+    return vae_list
+
+
+def resolve_vae(checkpoint_file, vae_file="auto"):
+    global first_load, vae_dict, vae_list
+
+    # if vae_file argument is provided, it takes priority, but not saved
+    if vae_file and vae_file not in default_vae_list:
+        if not os.path.isfile(vae_file):
+            vae_file = "auto"
+            print("VAE provided as function argument doesn't exist")
+    # for the first load, if vae-path is provided, it takes priority, saved, 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
+            shared.opts.data['sd_vae'] = get_filename(vae_file)
+        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
+
+    return vae_file
+
+
+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}
+        load_vae_dict(model, vae_dict_1)
+
+        # 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:
+        if vae_file is None:
+            vae_opt = "None"
+
+        # shared.opts.sd_vae = vae_opt
+    """
+
+    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/modules/shared.py b/modules/shared.py
index 01f47e38..d8e99f85 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -15,7 +15,7 @@ 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

 

@@ -319,6 +319,7 @@ 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}),

@@ -437,11 +438,12 @@ 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()}

diff --git a/style.css b/style.css
index e53079e0..a0382a8c 100644
--- a/style.css
+++ b/style.css
@@ -501,7 +501,7 @@ input[type="range"]{
     padding: 0;

 }

 

-#refresh_sd_model_checkpoint, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization{

+#refresh_sd_model_checkpoint, #refresh_sd_vae, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization{

     max-width: 2.5em;

     min-width: 2.5em;

     height: 2.4em;

diff --git a/webui.py b/webui.py
index dc4223dc..3b21c071 100644
--- a/webui.py
+++ b/webui.py
@@ -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

 import modules.script_callbacks

@@ -77,8 +78,10 @@ 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.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)