From cb31abcf58ea1f64266e6d821937eed058c35f4d Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sun, 30 Oct 2022 21:54:31 +0700
Subject: Settings to select VAE

---
 modules/sd_vae.py | 121 ++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 121 insertions(+)
 create mode 100644 modules/sd_vae.py

(limited to 'modules/sd_vae.py')

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)
-- 
cgit v1.2.3


From e1b2ea6e0012ecc988385fc523d8fb50ea5d6be5 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sun, 30 Oct 2022 22:11:45 +0700
Subject: Change VAE search order and thus priority

---
 modules/sd_vae.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules/sd_vae.py')

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 82764e55..0767b925 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -25,10 +25,10 @@ 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(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)
-- 
cgit v1.2.3


From b96d0c4e9ecec3c856b9b4ec795dbd0d34fcac51 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Mon, 31 Oct 2022 14:42:28 +0700
Subject: Fix typo from previous commit

---
 modules/sd_vae.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules/sd_vae.py')

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 0767b925..2ce44d5f 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -27,8 +27,8 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path):
     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),
+        *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)
-- 
cgit v1.2.3


From 726769da35970f4c100fa7edf11850f9dc059c41 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Mon, 31 Oct 2022 15:19:34 +0700
Subject: Checkpoint cache by combination key of checkpoint and vae

---
 modules/sd_models.py | 27 ++++++++++++++++-----------
 modules/sd_vae.py    |  8 +++++++-
 2 files changed, 23 insertions(+), 12 deletions(-)

(limited to 'modules/sd_vae.py')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 91ad4b5e..850f7b7b 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -160,11 +160,15 @@ 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, force=False):
+def load_model_weights(model, checkpoint_info, vae_file="auto"):
     checkpoint_file = checkpoint_info.filename
     sd_model_hash = checkpoint_info.hash
 
-    if force or checkpoint_info not in checkpoints_loaded:
+    vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
+
+    checkpoint_key = (checkpoint_info, vae_file)
+
+    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)
@@ -185,24 +189,25 @@ def load_model_weights(model, checkpoint_info, force=False):
         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
 
-        sd_vae.load_vae(model, checkpoint_file)
+        sd_vae.load_vae(model, vae_file)
         model.first_stage_model.to(devices.dtype_vae)
 
         if shared.opts.sd_checkpoint_cache > 0:
-            checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
+            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
 
 
-def load_model(checkpoint_info=None, force=False):
+def load_model(checkpoint_info=None):
     from modules import lowvram, sd_hijack
     checkpoint_info = checkpoint_info or select_checkpoint()
 
@@ -223,7 +228,7 @@ def load_model(checkpoint_info=None, force=False):
 
     do_inpainting_hijack()
     sd_model = instantiate_from_config(sd_config.model)
-    load_model_weights(sd_model, checkpoint_info, force=force)
+    load_model_weights(sd_model, checkpoint_info)
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
         lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
@@ -250,7 +255,7 @@ def reload_model_weights(sd_model, info=None, force=False):
 
     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, force=force)
+        load_model(checkpoint_info)
         return shared.sd_model
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
@@ -260,7 +265,7 @@ def reload_model_weights(sd_model, info=None, force=False):
 
     sd_hijack.model_hijack.undo_hijack(sd_model)
 
-    load_model_weights(sd_model, checkpoint_info, force=force)
+    load_model_weights(sd_model, checkpoint_info)
 
     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
index 2ce44d5f..e9239326 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -43,7 +43,7 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path):
     vae_dict.update(res)
     return vae_list
 
-def load_vae(model, checkpoint_file, vae_file="auto"):
+def resolve_vae(checkpoint_file, vae_file="auto"):
     global first_load, vae_dict, vae_list
     # save_settings = False
 
@@ -94,6 +94,12 @@ def load_vae(model, checkpoint_file, vae_file="auto"):
     if vae_file and not os.path.exists(vae_file):
         vae_file = None
 
+    return vae_file
+
+def load_vae(model, vae_file):
+    global first_load, vae_dict, vae_list
+    # 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)
-- 
cgit v1.2.3


From 056f06d3738c267b1014e6e8e1ef5bd97af1fb45 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Wed, 2 Nov 2022 12:51:46 +0700
Subject: Reload VAE without reloading sd checkpoint

---
 modules/sd_models.py | 15 ++++----
 modules/sd_vae.py    | 97 ++++++++++++++++++++++++++++++++++++++++++++++++----
 webui.py             |  4 +--
 3 files changed, 98 insertions(+), 18 deletions(-)

(limited to 'modules/sd_vae.py')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 6ab85b65..883639d1 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -159,15 +159,13 @@ 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, vae_file="auto"):
     checkpoint_file = checkpoint_info.filename
     sd_model_hash = checkpoint_info.hash
 
     vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
 
-    checkpoint_key = (checkpoint_info, vae_file)
+    checkpoint_key = checkpoint_info
 
     if checkpoint_key not in checkpoints_loaded:
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
@@ -190,13 +188,12 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
         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
 
-        sd_vae.load_vae(model, vae_file)
-        model.first_stage_model.to(devices.dtype_vae)
-
         if shared.opts.sd_checkpoint_cache > 0:
+            # 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:
         vae_name = sd_vae.get_filename(vae_file)
         print(f"Loading weights [{sd_model_hash}] with {vae_name} VAE from cache")
@@ -207,6 +204,8 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
     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
@@ -254,14 +253,14 @@ def load_model(checkpoint_info=None):
     return sd_model
 
 
-def reload_model_weights(sd_model=None, info=None, force=False):
+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
 
-    if sd_model.sd_model_checkpoint == checkpoint_info.filename and not force:
+    if sd_model.sd_model_checkpoint == checkpoint_info.filename:
         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):
diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index e9239326..78e14e8a 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -1,26 +1,65 @@
 import torch
 import os
 from collections import namedtuple
-from modules import shared, devices
+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 = {}
@@ -43,6 +82,7 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path):
     vae_dict.update(res)
     return vae_list
 
+
 def resolve_vae(checkpoint_file, vae_file="auto"):
     global first_load, vae_dict, vae_list
     # save_settings = False
@@ -96,24 +136,26 @@ def resolve_vae(checkpoint_file, vae_file="auto"):
 
     return vae_file
 
-def load_vae(model, vae_file):
-    global first_load, vae_dict, vae_list
+
+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}
-        model.first_stage_model.load_state_dict(vae_dict_1)
+        load_vae_dict(model, 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:
+        # 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:
@@ -124,4 +166,45 @@ def load_vae(model, vae_file):
     """
 
     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/webui.py b/webui.py
index 7cb4691b..034777a2 100644
--- a/webui.py
+++ b/webui.py
@@ -81,9 +81,7 @@ def initialize():
     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()))
-    # 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
-    shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(force=True)), call=False)
+    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)
 
-- 
cgit v1.2.3


From a5409a6e4bc3eaa9757a7505d4564ad8e0d899ea Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Wed, 2 Nov 2022 14:37:22 +0700
Subject: Save VAE provided by cmd_opts.vae_path

---
 modules/sd_vae.py | 11 ++++-------
 1 file changed, 4 insertions(+), 7 deletions(-)

(limited to 'modules/sd_vae.py')

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 78e14e8a..71e7a6e6 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -78,27 +78,24 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path):
     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)
+    vae_dict.update(default_vae_dict)
     return vae_list
 
 
 def resolve_vae(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 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"
-            # 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
+    # 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
-            # save_settings = True
-            # print("Using VAE provided as command line argument")
+            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
-- 
cgit v1.2.3


From 8ab4927452b04dcd30847eaf92ea7a9f3b9c74e1 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Wed, 2 Nov 2022 22:54:09 +0700
Subject: Fix model wasn't restored even when choosing "None"

---
 modules/sd_vae.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

(limited to 'modules/sd_vae.py')

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 71e7a6e6..7a79239f 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -50,8 +50,8 @@ def delete_base_vae():
 
 
 def restore_base_vae(model):
-    global base_vae, checkpoint_info
     if base_vae is not None and checkpoint_info == model.sd_checkpoint_info:
+        print("Restoring base VAE")
         load_vae_dict(model, base_vae)
     delete_base_vae()
 
@@ -143,6 +143,7 @@ def load_vae(model, vae_file=None):
         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)
+        store_base_vae(model)
 
         # If vae used is not in dict, update it
         # It will be removed on refresh though
@@ -150,6 +151,9 @@ def load_vae(model, vae_file=None):
         if vae_opt not in vae_dict:
             vae_dict[vae_opt] = vae_file
             vae_list.append(vae_opt)
+            # shared.opts.data['sd_vae'] = vae_opt
+    else:
+        restore_base_vae(model)
 
     loaded_vae_file = vae_file
 
@@ -166,12 +170,8 @@ def load_vae(model, vae_file=None):
 
 
 # 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()
+def load_vae_dict(model, vae_dict_1):
+    model.first_stage_model.load_state_dict(vae_dict_1)
     model.first_stage_model.to(devices.dtype_vae)
 
 
-- 
cgit v1.2.3


From abc1e79a5da24a1ea0f4bceedcdf225f32010aa8 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Thu, 3 Nov 2022 11:10:53 +0700
Subject: Fix base VAE caching was done after loading VAE, also add safeguard

---
 modules/sd_models.py |  1 +
 modules/sd_vae.py    | 19 ++++++++-----------
 2 files changed, 9 insertions(+), 11 deletions(-)

(limited to 'modules/sd_vae.py')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 80addf03..e4dba62c 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -220,6 +220,7 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
     model.sd_model_checkpoint = checkpoint_file
     model.sd_checkpoint_info = checkpoint_info
 
+    sd_vae.clear_loaded_vae()
     sd_vae.load_vae(model, vae_file)
 
 
diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 7a79239f..dd69a5e6 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -15,7 +15,7 @@ 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_dict = {"auto": "auto", "None": None, None: None}
 default_vae_list = ["auto", "None"]
 
 
@@ -39,6 +39,7 @@ def get_base_vae(model):
 def store_base_vae(model):
     global base_vae, checkpoint_info
     if checkpoint_info != model.sd_checkpoint_info:
+        assert not loaded_vae_file, "Trying to store non-base VAE!"
         base_vae = model.first_stage_model.state_dict().copy()
         checkpoint_info = model.sd_checkpoint_info
 
@@ -50,9 +51,11 @@ def delete_base_vae():
 
 
 def restore_base_vae(model):
+    global loaded_vae_file
     if base_vae is not None and checkpoint_info == model.sd_checkpoint_info:
         print("Restoring base VAE")
         load_vae_dict(model, base_vae)
+        loaded_vae_file = None
     delete_base_vae()
 
 
@@ -140,10 +143,10 @@ def load_vae(model, vae_file=None):
 
     if 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)
-        store_base_vae(model)
 
         # If vae used is not in dict, update it
         # It will be removed on refresh though
@@ -157,15 +160,6 @@ def load_vae(model, vae_file=None):
 
     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
 
 
@@ -174,6 +168,9 @@ def load_vae_dict(model, vae_dict_1):
     model.first_stage_model.load_state_dict(vae_dict_1)
     model.first_stage_model.to(devices.dtype_vae)
 
+def clear_loaded_vae():
+    global loaded_vae_file
+    loaded_vae_file = None
 
 def reload_vae_weights(sd_model=None, vae_file="auto"):
     from modules import lowvram, devices, sd_hijack
-- 
cgit v1.2.3


From c7be83bf0240498d9382e2afeaa3f0677d26c7f6 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sun, 13 Nov 2022 11:11:14 +0700
Subject: Misc

Misc
---
 modules/sd_models.py | 1 +
 modules/sd_vae.py    | 3 +--
 modules/shared.py    | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

(limited to 'modules/sd_vae.py')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index e4dba62c..cd7fe37a 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -220,6 +220,7 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
     model.sd_model_checkpoint = checkpoint_file
     model.sd_checkpoint_info = checkpoint_info
 
+    sd_vae.delete_base_vae()
     sd_vae.clear_loaded_vae()
     sd_vae.load_vae(model, vae_file)
 
diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index dd69a5e6..13bf3d31 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -154,8 +154,7 @@ def load_vae(model, vae_file=None):
         if vae_opt not in vae_dict:
             vae_dict[vae_opt] = vae_file
             vae_list.append(vae_opt)
-            # shared.opts.data['sd_vae'] = vae_opt
-    else:
+    elif loaded_vae_file:
         restore_base_vae(model)
 
     loaded_vae_file = vae_file
diff --git a/modules/shared.py b/modules/shared.py
index 17132e42..a9daf800 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -335,7 +335,7 @@ 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_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": 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}),
-- 
cgit v1.2.3


From 9fdc343dcaee70f1a0ff15c0cc668dbd487abc61 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Thu, 17 Nov 2022 18:04:10 +0700
Subject: Fix model caching requiring deepcopy

---
 modules/sd_vae.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

(limited to 'modules/sd_vae.py')

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 13bf3d31..5b4709b5 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -4,6 +4,7 @@ from collections import namedtuple
 from modules import shared, devices, script_callbacks
 from modules.paths import models_path
 import glob
+from copy import deepcopy
 
 
 model_dir = "Stable-diffusion"
@@ -40,7 +41,7 @@ def store_base_vae(model):
     global base_vae, checkpoint_info
     if checkpoint_info != model.sd_checkpoint_info:
         assert not loaded_vae_file, "Trying to store non-base VAE!"
-        base_vae = model.first_stage_model.state_dict().copy()
+        base_vae = deepcopy(model.first_stage_model.state_dict())
         checkpoint_info = model.sd_checkpoint_info
 
 
-- 
cgit v1.2.3


From 028b67b6357b5a00ccbd6ea72d2f244a6664162b Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sat, 19 Nov 2022 01:27:54 +0700
Subject: Use underscore naming for "private" functions in sd_vae

---
 modules/sd_vae.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

(limited to 'modules/sd_vae.py')

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 5b4709b5..d82a7bad 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -55,7 +55,7 @@ def restore_base_vae(model):
     global loaded_vae_file
     if base_vae is not None and checkpoint_info == model.sd_checkpoint_info:
         print("Restoring base VAE")
-        load_vae_dict(model, base_vae)
+        _load_vae_dict(model, base_vae)
         loaded_vae_file = None
     delete_base_vae()
 
@@ -147,7 +147,7 @@ def load_vae(model, vae_file=None):
         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)
+        _load_vae_dict(model, vae_dict_1)
 
         # If vae used is not in dict, update it
         # It will be removed on refresh though
@@ -164,7 +164,7 @@ def load_vae(model, vae_file=None):
 
 
 # don't call this from outside
-def load_vae_dict(model, vae_dict_1):
+def _load_vae_dict(model, vae_dict_1):
     model.first_stage_model.load_state_dict(vae_dict_1)
     model.first_stage_model.to(devices.dtype_vae)
 
-- 
cgit v1.2.3


From 0663706d4405b4f76ce653097f4f8989ee8b8684 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Thu, 3 Nov 2022 13:47:03 +0700
Subject: Option to use selected VAE as default fallback instead of primary
 option

---
 modules/sd_vae.py | 25 ++++++++++++++++---------
 modules/shared.py |  1 +
 webui.py          |  1 +
 3 files changed, 18 insertions(+), 9 deletions(-)

(limited to 'modules/sd_vae.py')

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 71e7a6e6..0b5f0213 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -83,7 +83,19 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path):
     return vae_list
 
 
-def resolve_vae(checkpoint_file, vae_file="auto"):
+def get_vae_from_settings(vae_file="auto"):
+    # else, we load from settings, if not set to be default
+    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")
+    return vae_file
+
+
+def resolve_vae(checkpoint_file=None, vae_file="auto"):
     global first_load, vae_dict, vae_list
 
     # if vae_file argument is provided, it takes priority, but not saved
@@ -98,14 +110,9 @@ def resolve_vae(checkpoint_file, vae_file="auto"):
             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")
+    # fallback to selector in settings, if vae selector not set to act as default fallback
+    if not shared.opts.sd_vae_as_default:
+        vae_file = get_vae_from_settings(vae_file)
     # 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):
diff --git a/modules/shared.py b/modules/shared.py
index 17132e42..b84767f0 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -336,6 +336,7 @@ 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_vae_as_default": OptionInfo(False, "Use selected VAE as default fallback instead"),
     "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}),
diff --git a/webui.py b/webui.py
index f4f1d74d..2cd3bae9 100644
--- a/webui.py
+++ b/webui.py
@@ -82,6 +82,7 @@ def initialize():
     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_vae_as_default", 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)
 
-- 
cgit v1.2.3


From 2c5ca706a7e624d268545ba3318ba230b7b33477 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sun, 13 Nov 2022 10:55:47 +0700
Subject: Remove no longer necessary parts and add vae_file safeguard

---
 modules/sd_models.py | 10 ++--------
 modules/sd_vae.py    |  1 +
 2 files changed, 3 insertions(+), 8 deletions(-)

(limited to 'modules/sd_vae.py')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 80addf03..c59151e0 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -165,16 +165,9 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
 
     cache_enabled = shared.opts.sd_checkpoint_cache > 0
 
-    if cache_enabled:
-        sd_vae.restore_base_vae(model)
-
-    vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
-
     if cache_enabled and checkpoint_info in checkpoints_loaded:
         # use checkpoint cache
-        vae_name = sd_vae.get_filename(vae_file) if vae_file else None
-        vae_message = f" with {vae_name} VAE" if vae_name else ""
-        print(f"Loading weights [{sd_model_hash}]{vae_message} from cache")
+        print(f"Loading weights [{sd_model_hash}] from cache")
         model.load_state_dict(checkpoints_loaded[checkpoint_info])
     else:
         # load from file
@@ -220,6 +213,7 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
     model.sd_model_checkpoint = checkpoint_file
     model.sd_checkpoint_info = checkpoint_info
 
+    vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
     sd_vae.load_vae(model, vae_file)
 
 
diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 71e7a6e6..8bdb2c17 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -139,6 +139,7 @@ def load_vae(model, vae_file=None):
     # save_settings = False
 
     if vae_file:
+        assert os.path.isfile(vae_file), f"VAE file doesn't exist: {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}
-- 
cgit v1.2.3


From 271fd2d700a59e80d9dc9f23ad3ef08c988e8b24 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sun, 13 Nov 2022 10:58:15 +0700
Subject: More verbose messages

---
 modules/sd_vae.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

(limited to 'modules/sd_vae.py')

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 8bdb2c17..fa8de905 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -89,15 +89,15 @@ def resolve_vae(checkpoint_file, vae_file="auto"):
     # 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):
+            print(f"VAE provided as function argument doesn't exist: {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")
+            print(f"VAE provided as command line argument doesn't exist: {vae_file}")
     # 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
@@ -105,25 +105,25 @@ def resolve_vae(checkpoint_file, vae_file="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")
+            print(f"Selected VAE doesn't exist: {vae_file}")
     # 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")
+            print(f"Using VAE provided as command line argument: {vae_file}")
     # 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")
+            print(f"Using VAE found similar to selected model: {vae_file}")
     # 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")
+            print(f"Using VAE found similar to selected model: {vae_file}")
     # No more fallbacks for auto
     if vae_file == "auto":
         vae_file = None
-- 
cgit v1.2.3


From 3bf5591efe9a9f219c6088be322a87adc4f48f95 Mon Sep 17 00:00:00 2001
From: Yuval Aboulafia <yuval.abou@gmail.com>
Date: Sat, 24 Dec 2022 21:35:29 +0200
Subject: fix F541 f-string without any placeholders

---
 extensions-builtin/LDSR/ldsr_model_arch.py     | 2 +-
 modules/codeformer/vqgan_arch.py               | 4 ++--
 modules/hypernetworks/hypernetwork.py          | 4 ++--
 modules/images.py                              | 2 +-
 modules/interrogate.py                         | 2 +-
 modules/safe.py                                | 8 ++++----
 modules/sd_models.py                           | 8 ++++----
 modules/sd_vae.py                              | 2 +-
 modules/textual_inversion/textual_inversion.py | 2 +-
 scripts/prompts_from_file.py                   | 2 +-
 10 files changed, 18 insertions(+), 18 deletions(-)

(limited to 'modules/sd_vae.py')

diff --git a/extensions-builtin/LDSR/ldsr_model_arch.py b/extensions-builtin/LDSR/ldsr_model_arch.py
index f5bd8ae4..0ad49f4e 100644
--- a/extensions-builtin/LDSR/ldsr_model_arch.py
+++ b/extensions-builtin/LDSR/ldsr_model_arch.py
@@ -26,7 +26,7 @@ class LDSR:
         global cached_ldsr_model
 
         if shared.opts.ldsr_cached and cached_ldsr_model is not None:
-            print(f"Loading model from cache")
+            print("Loading model from cache")
             model: torch.nn.Module = cached_ldsr_model
         else:
             print(f"Loading model from {self.modelPath}")
diff --git a/modules/codeformer/vqgan_arch.py b/modules/codeformer/vqgan_arch.py
index c06c590c..e7293683 100644
--- a/modules/codeformer/vqgan_arch.py
+++ b/modules/codeformer/vqgan_arch.py
@@ -382,7 +382,7 @@ class VQAutoEncoder(nn.Module):
                 self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
                 logger.info(f'vqgan is loaded from: {model_path} [params]')
             else:
-                raise ValueError(f'Wrong params!')
+                raise ValueError('Wrong params!')
 
 
     def forward(self, x):
@@ -431,7 +431,7 @@ class VQGANDiscriminator(nn.Module):
             elif 'params' in chkpt:
                 self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
             else:
-                raise ValueError(f'Wrong params!')
+                raise ValueError('Wrong params!')
 
     def forward(self, x):
         return self.main(x)
\ No newline at end of file
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index c406ffb3..9d3034ae 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -277,7 +277,7 @@ def load_hypernetwork(filename):
             print(traceback.format_exc(), file=sys.stderr)
     else:
         if shared.loaded_hypernetwork is not None:
-            print(f"Unloading hypernetwork")
+            print("Unloading hypernetwork")
 
         shared.loaded_hypernetwork = None
 
@@ -417,7 +417,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
 
     initial_step = hypernetwork.step or 0
     if initial_step >= steps:
-        shared.state.textinfo = f"Model has already been trained beyond specified max steps"
+        shared.state.textinfo = "Model has already been trained beyond specified max steps"
         return hypernetwork, filename
 
     scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
diff --git a/modules/images.py b/modules/images.py
index 809ad9f7..31d4528d 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -599,7 +599,7 @@ def read_info_from_image(image):
 Negative prompt: {json_info["uc"]}
 Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}, Seed: {json_info["seed"]}, Size: {image.width}x{image.height}, Clip skip: 2, ENSD: 31337"""
         except Exception:
-            print(f"Error parsing NovelAI image generation parameters:", file=sys.stderr)
+            print("Error parsing NovelAI image generation parameters:", file=sys.stderr)
             print(traceback.format_exc(), file=sys.stderr)
 
     return geninfo, items
diff --git a/modules/interrogate.py b/modules/interrogate.py
index 0068b81c..46935210 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -172,7 +172,7 @@ class InterrogateModels:
                             res += ", " + match
 
         except Exception:
-            print(f"Error interrogating", file=sys.stderr)
+            print("Error interrogating", file=sys.stderr)
             print(traceback.format_exc(), file=sys.stderr)
             res += "<error>"
 
diff --git a/modules/safe.py b/modules/safe.py
index 479c8b86..1d4c20b9 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -137,15 +137,15 @@ def load_with_extra(filename, extra_handler=None, *args, **kwargs):
     except pickle.UnpicklingError:
         print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
         print(traceback.format_exc(), file=sys.stderr)
-        print(f"-----> !!!! The file is most likely corrupted !!!! <-----", file=sys.stderr)
-        print(f"You can skip this check with --disable-safe-unpickle commandline argument, but that is not going to help you.\n\n", file=sys.stderr)
+        print("-----> !!!! The file is most likely corrupted !!!! <-----", file=sys.stderr)
+        print("You can skip this check with --disable-safe-unpickle commandline argument, but that is not going to help you.\n\n", file=sys.stderr)
         return None
 
     except Exception:
         print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
         print(traceback.format_exc(), file=sys.stderr)
-        print(f"\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr)
-        print(f"You can skip this check with --disable-safe-unpickle commandline argument.\n\n", file=sys.stderr)
+        print("\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr)
+        print("You can skip this check with --disable-safe-unpickle commandline argument.\n\n", file=sys.stderr)
         return None
 
     return unsafe_torch_load(filename, *args, **kwargs)
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 6ca06211..ecdd91c5 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -117,13 +117,13 @@ def select_checkpoint():
         return checkpoint_info
 
     if len(checkpoints_list) == 0:
-        print(f"No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr)
+        print("No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr)
         if shared.cmd_opts.ckpt is not None:
             print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr)
         print(f" - directory {model_path}", file=sys.stderr)
         if shared.cmd_opts.ckpt_dir is not None:
             print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)
-        print(f"Can't run without a checkpoint. Find and place a .ckpt file into any of those locations. The program will exit.", file=sys.stderr)
+        print("Can't run without a checkpoint. Find and place a .ckpt file into any of those locations. The program will exit.", file=sys.stderr)
         exit(1)
 
     checkpoint_info = next(iter(checkpoints_list.values()))
@@ -324,7 +324,7 @@ def load_model(checkpoint_info=None):
 
     script_callbacks.model_loaded_callback(sd_model)
 
-    print(f"Model loaded.")
+    print("Model loaded.")
     return sd_model
 
 
@@ -359,5 +359,5 @@ def reload_model_weights(sd_model=None, info=None):
     if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
         sd_model.to(devices.device)
 
-    print(f"Weights loaded.")
+    print("Weights loaded.")
     return sd_model
diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 25638a83..3856418e 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -208,5 +208,5 @@ def reload_vae_weights(sd_model=None, vae_file="auto"):
     if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
         sd_model.to(devices.device)
 
-    print(f"VAE Weights loaded.")
+    print("VAE Weights loaded.")
     return sd_model
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index daf3997b..f6112578 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -263,7 +263,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
 
     initial_step = embedding.step or 0
     if initial_step >= steps:
-        shared.state.textinfo = f"Model has already been trained beyond specified max steps"
+        shared.state.textinfo = "Model has already been trained beyond specified max steps"
         return embedding, filename
     scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
 
diff --git a/scripts/prompts_from_file.py b/scripts/prompts_from_file.py
index 6e118ddb..e8386ed2 100644
--- a/scripts/prompts_from_file.py
+++ b/scripts/prompts_from_file.py
@@ -140,7 +140,7 @@ class Script(scripts.Script):
                 try:
                     args = cmdargs(line)
                 except Exception:
-                    print(f"Error parsing line [line] as commandline:", file=sys.stderr)
+                    print(f"Error parsing line {line} as commandline:", file=sys.stderr)
                     print(traceback.format_exc(), file=sys.stderr)
                     args = {"prompt": line}
             else:
-- 
cgit v1.2.3


From 893933e05ad267778111b4fad6d1ecb80937afdf Mon Sep 17 00:00:00 2001
From: hitomi <huahuohe@foxmail.com>
Date: Sun, 25 Dec 2022 20:49:25 +0800
Subject: Add memory cache for VAE weights

---
 modules/sd_vae.py | 31 +++++++++++++++++++++++++------
 modules/shared.py |  1 +
 2 files changed, 26 insertions(+), 6 deletions(-)

(limited to 'modules/sd_vae.py')

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 3856418e..ac71d62d 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -1,5 +1,6 @@
 import torch
 import os
+import collections
 from collections import namedtuple
 from modules import shared, devices, script_callbacks
 from modules.paths import models_path
@@ -30,6 +31,7 @@ base_vae = None
 loaded_vae_file = None
 checkpoint_info = None
 
+checkpoints_loaded = collections.OrderedDict()
 
 def get_base_vae(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
     # save_settings = False
 
+    cache_enabled = shared.opts.sd_vae_checkpoint_cache > 0
+
     if vae_file:
-        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 and vae_file in checkpoints_loaded:
+            # use vae checkpoint cache
+            print(f"Loading VAE weights [{get_filename(vae_file)}] from cache")
+            store_base_vae(model)
+            _load_vae_dict(model, checkpoints_loaded[vae_file])
+        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
         # It will be removed on refresh though
diff --git a/modules/shared.py b/modules/shared.py
index d4ddeea0..671d30e1 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -356,6 +356,7 @@ 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": 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_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_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),
-- 
cgit v1.2.3


From cb255faec6e5f6b47b7632e6b7d450b9e2f6678b Mon Sep 17 00:00:00 2001
From: Lee Bousfield <ljbousfield@gmail.com>
Date: Sun, 8 Jan 2023 10:17:50 -0700
Subject: Add support for loading VAEs from safetensor files

---
 modules/sd_vae.py | 21 ++++++++++++++++++---
 1 file changed, 18 insertions(+), 3 deletions(-)

(limited to 'modules/sd_vae.py')

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index ac71d62d..9fcfd9db 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -1,4 +1,5 @@
 import torch
+import safetensors.torch
 import os
 import collections
 from collections import namedtuple
@@ -72,8 +73,10 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path):
     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(model_path, '**/*.vae.safetensors'), recursive=True),
         *glob.iglob(os.path.join(vae_path, '**/*.ckpt'), recursive=True),
-        *glob.iglob(os.path.join(vae_path, '**/*.pt'), recursive=True)
+        *glob.iglob(os.path.join(vae_path, '**/*.pt'), recursive=True),
+        *glob.iglob(os.path.join(vae_path, '**/*.safetensors'), 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)
@@ -137,6 +140,12 @@ def resolve_vae(checkpoint_file=None, vae_file="auto"):
         if os.path.isfile(vae_file_try):
             vae_file = vae_file_try
             print(f"Using VAE found similar to selected model: {vae_file}")
+    # if still not found, try look for ".vae.safetensors" beside model
+    if vae_file == "auto":
+        vae_file_try = model_path + ".vae.safetensors"
+        if os.path.isfile(vae_file_try):
+            vae_file = vae_file_try
+            print(f"Using VAE found similar to selected model: {vae_file}")
     # No more fallbacks for auto
     if vae_file == "auto":
         vae_file = None
@@ -163,8 +172,14 @@ def load_vae(model, vae_file=None):
             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}
+            _, extension = os.path.splitext(vae_file)
+            if extension.lower() == ".safetensors":
+                vae_ckpt = safetensors.torch.load_file(vae_file, device=shared.weight_load_location)
+            else:
+                vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
+            if "state_dict" in vae_ckpt:
+                vae_ckpt = vae_ckpt["state_dict"]
+            vae_dict_1 = {k: v for k, v in vae_ckpt.items() if k[0:4] != "loss" and k not in vae_ignore_keys}
             _load_vae_dict(model, vae_dict_1)
 
             if cache_enabled:
-- 
cgit v1.2.3


From 49c4509ce2302350210ff650fd26373518c46a79 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 9 Jan 2023 19:58:35 +0300
Subject: use existing function for loading VAE weights from file

---
 modules/sd_vae.py | 13 +++++--------
 1 file changed, 5 insertions(+), 8 deletions(-)

(limited to 'modules/sd_vae.py')

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 9fcfd9db..0a49daa1 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -3,7 +3,7 @@ import safetensors.torch
 import os
 import collections
 from collections import namedtuple
-from modules import shared, devices, script_callbacks
+from modules import shared, devices, script_callbacks, sd_models
 from modules.paths import models_path
 import glob
 from copy import deepcopy
@@ -172,13 +172,8 @@ def load_vae(model, vae_file=None):
             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)
-            _, extension = os.path.splitext(vae_file)
-            if extension.lower() == ".safetensors":
-                vae_ckpt = safetensors.torch.load_file(vae_file, device=shared.weight_load_location)
-            else:
-                vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
-            if "state_dict" in vae_ckpt:
-                vae_ckpt = vae_ckpt["state_dict"]
+
+            vae_ckpt = sd_models.read_state_dict(vae_file, map_location=shared.weight_load_location)
             vae_dict_1 = {k: v for k, v in vae_ckpt.items() if k[0:4] != "loss" and k not in vae_ignore_keys}
             _load_vae_dict(model, vae_dict_1)
 
@@ -210,10 +205,12 @@ def _load_vae_dict(model, vae_dict_1):
     model.first_stage_model.load_state_dict(vae_dict_1)
     model.first_stage_model.to(devices.dtype_vae)
 
+
 def clear_loaded_vae():
     global loaded_vae_file
     loaded_vae_file = None
 
+
 def reload_vae_weights(sd_model=None, vae_file="auto"):
     from modules import lowvram, devices, sd_hijack
 
-- 
cgit v1.2.3