From 5ba04f9ec050a66e918571f07e8863f157f05b44 Mon Sep 17 00:00:00 2001
From: Nicolas Patry <patry.nicolas@protonmail.com>
Date: Wed, 21 Dec 2022 13:45:58 +0100
Subject: Attempting to solve slow loads for `safetensors`.

Fixes #5893
---
 modules/sd_models.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

(limited to 'modules/sd_models.py')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index ecdd91c5..cd938656 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -168,7 +168,10 @@ def get_state_dict_from_checkpoint(pl_sd):
 def read_state_dict(checkpoint_file, print_global_state=False, map_location=None):
     _, extension = os.path.splitext(checkpoint_file)
     if extension.lower() == ".safetensors":
-        pl_sd = safetensors.torch.load_file(checkpoint_file, device=map_location or shared.weight_load_location)
+        device = map_location or shared.weight_load_location
+        if device is None:
+            device = "cuda:0" if torch.cuda.is_available() else "cpu"
+        pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
     else:
         pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location)
 
-- 
cgit v1.2.3


From f55ac33d446185680604e872ceda2ae858821d5c Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sat, 31 Dec 2022 11:27:02 -0500
Subject: validate textual inversion embeddings

---
 modules/sd_models.py                           |  3 ++
 modules/textual_inversion/textual_inversion.py | 43 +++++++++++++++++++++++---
 modules/ui.py                                  |  2 --
 3 files changed, 41 insertions(+), 7 deletions(-)

(limited to 'modules/sd_models.py')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index ecdd91c5..ebd4dff7 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -325,6 +325,9 @@ def load_model(checkpoint_info=None):
     script_callbacks.model_loaded_callback(sd_model)
 
     print("Model loaded.")
+
+    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload = True) # Reload embeddings after model load as they may or may not fit the model
+
     return sd_model
 
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index f6112578..103ace60 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -23,6 +23,8 @@ class Embedding:
         self.vec = vec
         self.name = name
         self.step = step
+        self.shape = None
+        self.vectors = 0
         self.cached_checksum = None
         self.sd_checkpoint = None
         self.sd_checkpoint_name = None
@@ -57,8 +59,10 @@ class EmbeddingDatabase:
     def __init__(self, embeddings_dir):
         self.ids_lookup = {}
         self.word_embeddings = {}
+        self.skipped_embeddings = []
         self.dir_mtime = None
         self.embeddings_dir = embeddings_dir
+        self.expected_shape = -1
 
     def register_embedding(self, embedding, model):
 
@@ -75,14 +79,35 @@ class EmbeddingDatabase:
 
         return embedding
 
-    def load_textual_inversion_embeddings(self):
+    def get_expected_shape(self):
+        expected_shape = -1 # initialize with unknown
+        idx = torch.tensor(0).to(shared.device)
+        if expected_shape == -1:
+            try: # matches sd15 signature
+                first_embedding = shared.sd_model.cond_stage_model.wrapped.transformer.text_model.embeddings.token_embedding.wrapped(idx)
+                expected_shape = first_embedding.shape[0]
+            except:
+                pass
+        if expected_shape == -1:
+            try: # matches sd20 signature
+                first_embedding = shared.sd_model.cond_stage_model.wrapped.model.token_embedding.wrapped(idx)
+                expected_shape = first_embedding.shape[0]
+            except:
+                pass
+        if expected_shape == -1:
+            print('Could not determine expected embeddings shape from model')
+        return expected_shape
+
+    def load_textual_inversion_embeddings(self, force_reload = False):
         mt = os.path.getmtime(self.embeddings_dir)
-        if self.dir_mtime is not None and mt <= self.dir_mtime:
+        if not force_reload and self.dir_mtime is not None and mt <= self.dir_mtime:
             return
 
         self.dir_mtime = mt
         self.ids_lookup.clear()
         self.word_embeddings.clear()
+        self.skipped_embeddings = []
+        self.expected_shape = self.get_expected_shape()
 
         def process_file(path, filename):
             name = os.path.splitext(filename)[0]
@@ -122,7 +147,14 @@ class EmbeddingDatabase:
             embedding.step = data.get('step', None)
             embedding.sd_checkpoint = data.get('sd_checkpoint', None)
             embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)
-            self.register_embedding(embedding, shared.sd_model)
+            embedding.vectors = vec.shape[0]
+            embedding.shape = vec.shape[-1]
+
+            if (self.expected_shape == -1) or (self.expected_shape == embedding.shape):
+                self.register_embedding(embedding, shared.sd_model)
+            else:
+                self.skipped_embeddings.append(name)
+                # print('Skipping embedding {name}: shape was {shape} expected {expected}'.format(name = name, shape = embedding.shape, expected = self.expected_shape))
 
         for fn in os.listdir(self.embeddings_dir):
             try:
@@ -137,8 +169,9 @@ class EmbeddingDatabase:
                 print(traceback.format_exc(), file=sys.stderr)
                 continue
 
-        print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.")
-        print("Embeddings:", ', '.join(self.word_embeddings.keys()))
+        print("Textual inversion embeddings {num} loaded: {val}".format(num = len(self.word_embeddings), val = ', '.join(self.word_embeddings.keys())))
+        if (len(self.skipped_embeddings) > 0):
+            print("Textual inversion embeddings {num} skipped: {val}".format(num = len(self.skipped_embeddings), val = ', '.join(self.skipped_embeddings)))
 
     def find_embedding_at_position(self, tokens, offset):
         token = tokens[offset]
diff --git a/modules/ui.py b/modules/ui.py
index 57ee0465..397dd804 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1157,8 +1157,6 @@ def create_ui():
             with gr.Column(variant='panel'):
                 submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
 
-    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
-
     with gr.Blocks(analytics_enabled=False) as train_interface:
         with gr.Row().style(equal_height=False):
             gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
-- 
cgit v1.2.3


From 311354c0bb8930ea939d6aa6b3edd50c69301320 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 2 Jan 2023 00:38:09 +0300
Subject: fix the issue with training on SD2.0

---
 modules/sd_models.py                           | 2 ++
 modules/textual_inversion/textual_inversion.py | 3 +--
 2 files changed, 3 insertions(+), 2 deletions(-)

(limited to 'modules/sd_models.py')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index ebd4dff7..bff8d6c9 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -228,6 +228,8 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
     model.sd_model_checkpoint = checkpoint_file
     model.sd_checkpoint_info = checkpoint_info
 
+    model.logvar = model.logvar.to(devices.device)  # fix for training
+
     sd_vae.delete_base_vae()
     sd_vae.clear_loaded_vae()
     vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 66f40367..1e5722e7 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -282,7 +282,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
         return embedding, filename
     scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
 
-   # dataset loading may take a while, so input validations and early returns should be done before this
+    # dataset loading may take a while, so input validations and early returns should be done before this
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     old_parallel_processing_allowed = shared.parallel_processing_allowed
 
@@ -310,7 +310,6 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
     loss_step = 0
     _loss_step = 0 #internal
 
-
     last_saved_file = "<none>"
     last_saved_image = "<none>"
     forced_filename = "<none>"
-- 
cgit v1.2.3


From 8f96f9289981a66741ba770d14f3d27ce335a0fb Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 3 Jan 2023 18:39:14 +0300
Subject: call script callbacks for reloaded model after loading embeddings

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

(limited to 'modules/sd_models.py')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index bff8d6c9..b98b05fc 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -324,12 +324,12 @@ def load_model(checkpoint_info=None):
     sd_model.eval()
     shared.sd_model = sd_model
 
+    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True)  # Reload embeddings after model load as they may or may not fit the model
+
     script_callbacks.model_loaded_callback(sd_model)
 
     print("Model loaded.")
 
-    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload = True) # Reload embeddings after model load as they may or may not fit the model
-
     return sd_model
 
 
-- 
cgit v1.2.3


From 02d7abf5141431b9a3a8a189bb3136c71abd5e79 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 4 Jan 2023 12:35:07 +0300
Subject: helpful error message when trying to load 2.0 without config failing
 to load model weights from settings won't break generation for currently
 loaded model anymore

---
 modules/errors.py    | 25 +++++++++++++++++++++++--
 modules/sd_models.py | 26 ++++++++++++++++++--------
 modules/shared.py    |  9 +++++++--
 webui.py             | 12 ++++++++++--
 4 files changed, 58 insertions(+), 14 deletions(-)

(limited to 'modules/sd_models.py')

diff --git a/modules/errors.py b/modules/errors.py
index 372dc51a..a668c014 100644
--- a/modules/errors.py
+++ b/modules/errors.py
@@ -2,9 +2,30 @@ import sys
 import traceback
 
 
+def print_error_explanation(message):
+    lines = message.strip().split("\n")
+    max_len = max([len(x) for x in lines])
+
+    print('=' * max_len, file=sys.stderr)
+    for line in lines:
+        print(line, file=sys.stderr)
+    print('=' * max_len, file=sys.stderr)
+
+
+def display(e: Exception, task):
+    print(f"{task or 'error'}: {type(e).__name__}", file=sys.stderr)
+    print(traceback.format_exc(), file=sys.stderr)
+
+    message = str(e)
+    if "copying a param with shape torch.Size([640, 1024]) from checkpoint, the shape in current model is torch.Size([640, 768])" in message:
+        print_error_explanation("""
+The most likely cause of this is you are trying to load Stable Diffusion 2.0 model without specifying its connfig file.
+See https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20 for how to solve this.
+        """)
+
+
 def run(code, task):
     try:
         code()
     except Exception as e:
-        print(f"{task}: {type(e).__name__}", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
+        display(task, e)
diff --git a/modules/sd_models.py b/modules/sd_models.py
index b98b05fc..6846b74a 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -278,6 +278,7 @@ def enable_midas_autodownload():
 
     midas.api.load_model = load_model_wrapper
 
+
 def load_model(checkpoint_info=None):
     from modules import lowvram, sd_hijack
     checkpoint_info = checkpoint_info or select_checkpoint()
@@ -312,6 +313,7 @@ def load_model(checkpoint_info=None):
         sd_config.model.params.unet_config.params.use_fp16 = False
 
     sd_model = instantiate_from_config(sd_config.model)
+
     load_model_weights(sd_model, checkpoint_info)
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
@@ -336,10 +338,12 @@ 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
 
+    current_checkpoint_info = sd_model.sd_checkpoint_info
+
     if sd_model.sd_model_checkpoint == checkpoint_info.filename:
         return
 
@@ -356,13 +360,19 @@ def reload_model_weights(sd_model=None, info=None):
 
     sd_hijack.model_hijack.undo_hijack(sd_model)
 
-    load_model_weights(sd_model, checkpoint_info)
-
-    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)
+    try:
+        load_model_weights(sd_model, checkpoint_info)
+    except Exception as e:
+        print("Failed to load checkpoint, restoring previous")
+        load_model_weights(sd_model, current_checkpoint_info)
+        raise
+    finally:
+        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("Weights loaded.")
+
     return sd_model
diff --git a/modules/shared.py b/modules/shared.py
index 23657a93..7588c47b 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -14,7 +14,7 @@ import modules.interrogate
 import modules.memmon
 import modules.styles
 import modules.devices as devices
-from modules import localization, sd_vae, extensions, script_loading
+from modules import localization, sd_vae, extensions, script_loading, errors
 from modules.paths import models_path, script_path, sd_path
 
 
@@ -494,7 +494,12 @@ class Options:
             return False
 
         if self.data_labels[key].onchange is not None:
-            self.data_labels[key].onchange()
+            try:
+                self.data_labels[key].onchange()
+            except Exception as e:
+                errors.display(e, f"changing setting {key} to {value}")
+                setattr(self, key, oldval)
+                return False
 
         return True
 
diff --git a/webui.py b/webui.py
index c7d55a97..13375e71 100644
--- a/webui.py
+++ b/webui.py
@@ -9,7 +9,7 @@ from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.middleware.gzip import GZipMiddleware
 
-from modules import import_hook
+from modules import import_hook, errors
 from modules.call_queue import wrap_queued_call, queue_lock, wrap_gradio_gpu_call
 from modules.paths import script_path
 
@@ -61,7 +61,15 @@ def initialize():
     modelloader.load_upscalers()
 
     modules.sd_vae.refresh_vae_list()
-    modules.sd_models.load_model()
+
+    try:
+        modules.sd_models.load_model()
+    except Exception as e:
+        errors.display(e, "loading stable diffusion model")
+        print("", file=sys.stderr)
+        print("Stable diffusion model failed to load, exiting", file=sys.stderr)
+        exit(1)
+
     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)
-- 
cgit v1.2.3


From 8d8a05a3bbb50fdfeab51679a919d2487bd97976 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 4 Jan 2023 12:47:42 +0300
Subject: find configs for models at runtime rather than when starting

---
 modules/sd_hijack_inpainting.py |  5 ++++-
 modules/sd_models.py            | 31 ++++++++++++++++++-------------
 2 files changed, 22 insertions(+), 14 deletions(-)

(limited to 'modules/sd_models.py')

diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index 3c214a35..31d2c898 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -97,8 +97,11 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F
 
 
 def should_hijack_inpainting(checkpoint_info):
+    from modules import sd_models
+
     ckpt_basename = os.path.basename(checkpoint_info.filename).lower()
-    cfg_basename = os.path.basename(checkpoint_info.config).lower()
+    cfg_basename = os.path.basename(sd_models.find_checkpoint_config(checkpoint_info)).lower()
+
     return "inpainting" in ckpt_basename and not "inpainting" in cfg_basename
 
 
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 6846b74a..6dca4ddf 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -20,7 +20,7 @@ from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inp
 model_dir = "Stable-diffusion"
 model_path = os.path.abspath(os.path.join(models_path, model_dir))
 
-CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config'])
+CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name'])
 checkpoints_list = {}
 checkpoints_loaded = collections.OrderedDict()
 
@@ -48,6 +48,14 @@ def checkpoint_tiles():
     return sorted([x.title for x in checkpoints_list.values()], key = alphanumeric_key)
 
 
+def find_checkpoint_config(info):
+    config = os.path.splitext(info.filename)[0] + ".yaml"
+    if os.path.exists(config):
+        return config
+
+    return shared.cmd_opts.config
+
+
 def list_models():
     checkpoints_list.clear()
     model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"])
@@ -73,7 +81,7 @@ def list_models():
     if os.path.exists(cmd_ckpt):
         h = model_hash(cmd_ckpt)
         title, short_model_name = modeltitle(cmd_ckpt, h)
-        checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name, shared.cmd_opts.config)
+        checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name)
         shared.opts.data['sd_model_checkpoint'] = title
     elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
         print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
@@ -81,12 +89,7 @@ def list_models():
         h = model_hash(filename)
         title, short_model_name = modeltitle(filename, h)
 
-        basename, _ = os.path.splitext(filename)
-        config = basename + ".yaml"
-        if not os.path.exists(config):
-            config = shared.cmd_opts.config
-
-        checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name, config)
+        checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name)
 
 
 def get_closet_checkpoint_match(searchString):
@@ -282,9 +285,10 @@ def enable_midas_autodownload():
 def load_model(checkpoint_info=None):
     from modules import lowvram, sd_hijack
     checkpoint_info = checkpoint_info or select_checkpoint()
+    checkpoint_config = find_checkpoint_config(checkpoint_info)
 
-    if checkpoint_info.config != shared.cmd_opts.config:
-        print(f"Loading config from: {checkpoint_info.config}")
+    if checkpoint_config != shared.cmd_opts.config:
+        print(f"Loading config from: {checkpoint_config}")
 
     if shared.sd_model:
         sd_hijack.model_hijack.undo_hijack(shared.sd_model)
@@ -292,7 +296,7 @@ def load_model(checkpoint_info=None):
         gc.collect()
         devices.torch_gc()
 
-    sd_config = OmegaConf.load(checkpoint_info.config)
+    sd_config = OmegaConf.load(checkpoint_config)
     
     if should_hijack_inpainting(checkpoint_info):
         # Hardcoded config for now...
@@ -302,7 +306,7 @@ def load_model(checkpoint_info=None):
         sd_config.model.params.finetune_keys = None
 
         # Create a "fake" config with a different name so that we know to unload it when switching models.
-        checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))
+        checkpoint_info = checkpoint_info._replace(config=checkpoint_config.replace(".yaml", "-inpainting.yaml"))
 
     if not hasattr(sd_config.model.params, "use_ema"):
         sd_config.model.params.use_ema = False
@@ -343,11 +347,12 @@ def reload_model_weights(sd_model=None, info=None):
         sd_model = shared.sd_model
 
     current_checkpoint_info = sd_model.sd_checkpoint_info
+    checkpoint_config = find_checkpoint_config(current_checkpoint_info)
 
     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):
+    if checkpoint_config != find_checkpoint_config(checkpoint_info) or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
         del sd_model
         checkpoints_loaded.clear()
         load_model(checkpoint_info)
-- 
cgit v1.2.3


From 0cd6399b8b1699b8b7acad6f0ad2988111fe618e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 4 Jan 2023 14:29:13 +0300
Subject: fix broken inpainting model

---
 modules/sd_models.py | 3 ---
 1 file changed, 3 deletions(-)

(limited to 'modules/sd_models.py')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 6dca4ddf..a568823d 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -305,9 +305,6 @@ def load_model(checkpoint_info=None):
         sd_config.model.params.unet_config.params.in_channels = 9
         sd_config.model.params.finetune_keys = None
 
-        # Create a "fake" config with a different name so that we know to unload it when switching models.
-        checkpoint_info = checkpoint_info._replace(config=checkpoint_config.replace(".yaml", "-inpainting.yaml"))
-
     if not hasattr(sd_config.model.params, "use_ema"):
         sd_config.model.params.use_ema = False
 
-- 
cgit v1.2.3


From 642142556d8ecdea9beb86d7618b628b1803ab98 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 4 Jan 2023 15:09:53 +0300
Subject: use commandline-supplied cuda device name instead of cuda:0 for
 safetensors PR that doesn't fix anything

---
 modules/sd_models.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules/sd_models.py')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index ee918f24..76a89e88 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -173,7 +173,7 @@ def read_state_dict(checkpoint_file, print_global_state=False, map_location=None
     if extension.lower() == ".safetensors":
         device = map_location or shared.weight_load_location
         if device is None:
-            device = "cuda:0" if torch.cuda.is_available() else "cpu"
+            device = devices.get_cuda_device_string() if torch.cuda.is_available() else "cpu"
         pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
     else:
         pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location)
-- 
cgit v1.2.3


From 552d7b90bf483c160cd20740f7acd7fccbc02e6f Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 9 Jan 2023 18:34:26 -0500
Subject: allow model load if previous model failed

---
 modules/sd_models.py | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

(limited to 'modules/sd_models.py')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 76a89e88..0a6d55ca 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -49,6 +49,9 @@ def checkpoint_tiles():
 
 
 def find_checkpoint_config(info):
+    if info is None:
+        return shared.cmd_opts.config
+
     config = os.path.splitext(info.filename)[0] + ".yaml"
     if os.path.exists(config):
         return config
@@ -345,14 +348,16 @@ def reload_model_weights(sd_model=None, info=None):
 
     if not sd_model:
         sd_model = shared.sd_model
+    if sd_model is None: # previous model load failed
+        current_checkpoint_info = None
+    else:
+        current_checkpoint_info = sd_model.sd_checkpoint_info
+        if sd_model.sd_model_checkpoint == checkpoint_info.filename:
+            return
 
-    current_checkpoint_info = sd_model.sd_checkpoint_info
     checkpoint_config = find_checkpoint_config(current_checkpoint_info)
 
-    if sd_model.sd_model_checkpoint == checkpoint_info.filename:
-        return
-
-    if checkpoint_config != find_checkpoint_config(checkpoint_info) or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
+    if current_checkpoint_info is None or checkpoint_config != find_checkpoint_config(checkpoint_info) or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
         del sd_model
         checkpoints_loaded.clear()
         load_model(checkpoint_info)
-- 
cgit v1.2.3


From 0c3feb202c5714abd50d879c1db2cd9a71ce93e3 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 10 Jan 2023 14:08:29 +0300
Subject: disable torch weight initialization and CLIP downloading/reading
 checkpoint to speedup creating sd model from config

---
 modules/sd_disable_initialization.py | 44 ++++++++++++++++++++++++++++++++++++
 modules/sd_models.py                 |  5 ++--
 2 files changed, 47 insertions(+), 2 deletions(-)
 create mode 100644 modules/sd_disable_initialization.py

(limited to 'modules/sd_models.py')

diff --git a/modules/sd_disable_initialization.py b/modules/sd_disable_initialization.py
new file mode 100644
index 00000000..c9a3b5e4
--- /dev/null
+++ b/modules/sd_disable_initialization.py
@@ -0,0 +1,44 @@
+import ldm.modules.encoders.modules
+import open_clip
+import torch
+
+
+class DisableInitialization:
+    """
+    When an object of this class enters a `with` block, it starts preventing torch's layer initialization
+    functions from working, and changes CLIP and OpenCLIP to not download model weights. When it leaves,
+    reverts everything to how it was.
+
+    Use like this:
+    ```
+    with DisableInitialization():
+        do_things()
+    ```
+    """
+
+    def __enter__(self):
+        def do_nothing(*args, **kwargs):
+            pass
+
+        def create_model_and_transforms_without_pretrained(*args, pretrained=None, **kwargs):
+            return self.create_model_and_transforms(*args, pretrained=None, **kwargs)
+
+        def CLIPTextModel_from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs):
+            return self.CLIPTextModel_from_pretrained(None, *model_args, config=pretrained_model_name_or_path, state_dict={}, **kwargs)
+
+        self.init_kaiming_uniform = torch.nn.init.kaiming_uniform_
+        self.init_no_grad_normal = torch.nn.init._no_grad_normal_
+        self.create_model_and_transforms = open_clip.create_model_and_transforms
+        self.CLIPTextModel_from_pretrained = ldm.modules.encoders.modules.CLIPTextModel.from_pretrained
+
+        torch.nn.init.kaiming_uniform_ = do_nothing
+        torch.nn.init._no_grad_normal_ = do_nothing
+        open_clip.create_model_and_transforms = create_model_and_transforms_without_pretrained
+        ldm.modules.encoders.modules.CLIPTextModel.from_pretrained = CLIPTextModel_from_pretrained
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        torch.nn.init.kaiming_uniform_ = self.init_kaiming_uniform
+        torch.nn.init._no_grad_normal_ = self.init_no_grad_normal
+        open_clip.create_model_and_transforms = self.create_model_and_transforms
+        ldm.modules.encoders.modules.CLIPTextModel.from_pretrained = self.CLIPTextModel_from_pretrained
+
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 0a6d55ca..ee241032 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -13,7 +13,7 @@ import ldm.modules.midas as midas
 
 from ldm.util import instantiate_from_config
 
-from modules import shared, modelloader, devices, script_callbacks, sd_vae
+from modules import shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization
 from modules.paths import models_path
 from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
 
@@ -319,7 +319,8 @@ def load_model(checkpoint_info=None):
     if shared.cmd_opts.no_half:
         sd_config.model.params.unet_config.params.use_fp16 = False
 
-    sd_model = instantiate_from_config(sd_config.model)
+    with sd_disable_initialization.DisableInitialization():
+        sd_model = instantiate_from_config(sd_config.model)
 
     load_model_weights(sd_model, checkpoint_info)
 
-- 
cgit v1.2.3


From ce3f639ec8758ce2bc90483336361d2dc25acd3a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 10 Jan 2023 16:51:04 +0300
Subject: add more stuff to ignore when creating model from config prevent
 .vae.safetensors files from being listed as stable diffusion models

---
 modules/modelloader.py               |  4 +++-
 modules/sd_disable_initialization.py | 29 +++++++++++++++++++++++++----
 modules/sd_models.py                 | 32 ++++++++++++++++++++++++++++----
 3 files changed, 56 insertions(+), 9 deletions(-)

(limited to 'modules/sd_models.py')

diff --git a/modules/modelloader.py b/modules/modelloader.py
index 6a1a7ac8..e9aa514e 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -10,7 +10,7 @@ from modules.upscaler import Upscaler
 from modules.paths import script_path, models_path
 
 
-def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None) -> list:
+def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None, ext_blacklist=None) -> list:
     """
     A one-and done loader to try finding the desired models in specified directories.
 
@@ -45,6 +45,8 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
                     full_path = file
                     if os.path.isdir(full_path):
                         continue
+                    if ext_blacklist is not None and any([full_path.endswith(x) for x in ext_blacklist]):
+                        continue
                     if len(ext_filter) != 0:
                         model_name, extension = os.path.splitext(file)
                         if extension not in ext_filter:
diff --git a/modules/sd_disable_initialization.py b/modules/sd_disable_initialization.py
index c9a3b5e4..9942bd7e 100644
--- a/modules/sd_disable_initialization.py
+++ b/modules/sd_disable_initialization.py
@@ -1,15 +1,19 @@
 import ldm.modules.encoders.modules
 import open_clip
 import torch
+import transformers.utils.hub
 
 
 class DisableInitialization:
     """
-    When an object of this class enters a `with` block, it starts preventing torch's layer initialization
-    functions from working, and changes CLIP and OpenCLIP to not download model weights. When it leaves,
-    reverts everything to how it was.
+    When an object of this class enters a `with` block, it starts:
+    - preventing torch's layer initialization functions from working
+    - changes CLIP and OpenCLIP to not download model weights
+    - changes CLIP to not make requests to check if there is a new version of a file you already have
 
-    Use like this:
+    When it leaves the block, it reverts everything to how it was before.
+
+    Use it like this:
     ```
     with DisableInitialization():
         do_things()
@@ -26,19 +30,36 @@ class DisableInitialization:
         def CLIPTextModel_from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs):
             return self.CLIPTextModel_from_pretrained(None, *model_args, config=pretrained_model_name_or_path, state_dict={}, **kwargs)
 
+        def transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs):
+
+            # this file is always 404, prevent making request
+            if url == 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json':
+                raise transformers.utils.hub.EntryNotFoundError
+
+            try:
+                return self.transformers_utils_hub_get_from_cache(url, *args, local_files_only=True, **kwargs)
+            except Exception as e:
+                return self.transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs)
+
         self.init_kaiming_uniform = torch.nn.init.kaiming_uniform_
         self.init_no_grad_normal = torch.nn.init._no_grad_normal_
+        self.init_no_grad_uniform_ = torch.nn.init._no_grad_uniform_
         self.create_model_and_transforms = open_clip.create_model_and_transforms
         self.CLIPTextModel_from_pretrained = ldm.modules.encoders.modules.CLIPTextModel.from_pretrained
+        self.transformers_utils_hub_get_from_cache = transformers.utils.hub.get_from_cache
 
         torch.nn.init.kaiming_uniform_ = do_nothing
         torch.nn.init._no_grad_normal_ = do_nothing
+        torch.nn.init._no_grad_uniform_ = do_nothing
         open_clip.create_model_and_transforms = create_model_and_transforms_without_pretrained
         ldm.modules.encoders.modules.CLIPTextModel.from_pretrained = CLIPTextModel_from_pretrained
+        transformers.utils.hub.get_from_cache = transformers_utils_hub_get_from_cache
 
     def __exit__(self, exc_type, exc_val, exc_tb):
         torch.nn.init.kaiming_uniform_ = self.init_kaiming_uniform
         torch.nn.init._no_grad_normal_ = self.init_no_grad_normal
+        torch.nn.init._no_grad_uniform_ = self.init_no_grad_uniform_
         open_clip.create_model_and_transforms = self.create_model_and_transforms
         ldm.modules.encoders.modules.CLIPTextModel.from_pretrained = self.CLIPTextModel_from_pretrained
+        transformers.utils.hub.get_from_cache = self.transformers_utils_hub_get_from_cache
 
diff --git a/modules/sd_models.py b/modules/sd_models.py
index ee241032..1bb9088b 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -2,6 +2,7 @@ import collections
 import os.path
 import sys
 import gc
+import time
 from collections import namedtuple
 import torch
 import re
@@ -61,7 +62,7 @@ def find_checkpoint_config(info):
 
 def list_models():
     checkpoints_list.clear()
-    model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"])
+    model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], ext_blacklist=[".vae.safetensors"])
 
     def modeltitle(path, shorthash):
         abspath = os.path.abspath(path)
@@ -288,6 +289,17 @@ def enable_midas_autodownload():
     midas.api.load_model = load_model_wrapper
 
 
+class Timer:
+    def __init__(self):
+        self.start = time.time()
+
+    def elapsed(self):
+        end = time.time()
+        res = end - self.start
+        self.start = end
+        return res
+
+
 def load_model(checkpoint_info=None):
     from modules import lowvram, sd_hijack
     checkpoint_info = checkpoint_info or select_checkpoint()
@@ -319,11 +331,17 @@ def load_model(checkpoint_info=None):
     if shared.cmd_opts.no_half:
         sd_config.model.params.unet_config.params.use_fp16 = False
 
+    timer = Timer()
+
     with sd_disable_initialization.DisableInitialization():
         sd_model = instantiate_from_config(sd_config.model)
 
+    elapsed_create = timer.elapsed()
+
     load_model_weights(sd_model, checkpoint_info)
 
+    elapsed_load_weights = timer.elapsed()
+
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
         lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
     else:
@@ -338,7 +356,9 @@ def load_model(checkpoint_info=None):
 
     script_callbacks.model_loaded_callback(sd_model)
 
-    print("Model loaded.")
+    elapsed_the_rest = timer.elapsed()
+
+    print(f"Model loaded in {elapsed_create + elapsed_load_weights + elapsed_the_rest:.1f}s ({elapsed_create:.1f}s create model, {elapsed_load_weights:.1f}s load weights).")
 
     return sd_model
 
@@ -349,7 +369,7 @@ def reload_model_weights(sd_model=None, info=None):
 
     if not sd_model:
         sd_model = shared.sd_model
-    if sd_model is None: # previous model load failed
+    if sd_model is None:  # previous model load failed
         current_checkpoint_info = None
     else:
         current_checkpoint_info = sd_model.sd_checkpoint_info
@@ -371,6 +391,8 @@ def reload_model_weights(sd_model=None, info=None):
 
     sd_hijack.model_hijack.undo_hijack(sd_model)
 
+    timer = Timer()
+
     try:
         load_model_weights(sd_model, checkpoint_info)
     except Exception as e:
@@ -384,6 +406,8 @@ 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("Weights loaded.")
+    elapsed = timer.elapsed()
+
+    print(f"Weights loaded in {elapsed:.1f}s.")
 
     return sd_model
-- 
cgit v1.2.3


From 0f8603a55988d22616b17140e6c4a7e9d0736af5 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 10 Jan 2023 17:46:59 +0300
Subject: add support for transformers==4.25.1 add fallback for when quick
 model creation fails

---
 modules/sd_disable_initialization.py | 42 ++++++++++++++++++++++++++++++------
 modules/sd_models.py                 |  8 +++++--
 2 files changed, 42 insertions(+), 8 deletions(-)

(limited to 'modules/sd_models.py')

diff --git a/modules/sd_disable_initialization.py b/modules/sd_disable_initialization.py
index 9942bd7e..088ac24b 100644
--- a/modules/sd_disable_initialization.py
+++ b/modules/sd_disable_initialization.py
@@ -30,30 +30,53 @@ class DisableInitialization:
         def CLIPTextModel_from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs):
             return self.CLIPTextModel_from_pretrained(None, *model_args, config=pretrained_model_name_or_path, state_dict={}, **kwargs)
 
-        def transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs):
+        def transformers_modeling_utils_load_pretrained_model(*args, **kwargs):
+            args = args[0:3] + ('/', ) + args[4:]  # resolved_archive_file; must set it to something to prevent what seems to be a bug
+            return self.transformers_modeling_utils_load_pretrained_model(*args, **kwargs)
+
+        def transformers_utils_hub_get_file_from_cache(original, url, *args, **kwargs):
 
             # this file is always 404, prevent making request
             if url == 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json':
                 raise transformers.utils.hub.EntryNotFoundError
 
             try:
-                return self.transformers_utils_hub_get_from_cache(url, *args, local_files_only=True, **kwargs)
+                return original(url, *args, local_files_only=True, **kwargs)
             except Exception as e:
-                return self.transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs)
+                return original(url, *args, local_files_only=False, **kwargs)
+
+        def transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs):
+            return transformers_utils_hub_get_file_from_cache(self.transformers_utils_hub_get_from_cache, url, *args, **kwargs)
+
+        def transformers_tokenization_utils_base_cached_file(url, *args, local_files_only=False, **kwargs):
+            return transformers_utils_hub_get_file_from_cache(self.transformers_tokenization_utils_base_cached_file, url, *args, **kwargs)
+
+        def transformers_configuration_utils_cached_file(url, *args, local_files_only=False, **kwargs):
+            return transformers_utils_hub_get_file_from_cache(self.transformers_configuration_utils_cached_file, url, *args, **kwargs)
 
         self.init_kaiming_uniform = torch.nn.init.kaiming_uniform_
         self.init_no_grad_normal = torch.nn.init._no_grad_normal_
         self.init_no_grad_uniform_ = torch.nn.init._no_grad_uniform_
         self.create_model_and_transforms = open_clip.create_model_and_transforms
         self.CLIPTextModel_from_pretrained = ldm.modules.encoders.modules.CLIPTextModel.from_pretrained
-        self.transformers_utils_hub_get_from_cache = transformers.utils.hub.get_from_cache
+        self.transformers_modeling_utils_load_pretrained_model = getattr(transformers.modeling_utils.PreTrainedModel, '_load_pretrained_model', None)
+        self.transformers_tokenization_utils_base_cached_file = getattr(transformers.tokenization_utils_base, 'cached_file', None)
+        self.transformers_configuration_utils_cached_file = getattr(transformers.configuration_utils, 'cached_file', None)
+        self.transformers_utils_hub_get_from_cache = getattr(transformers.utils.hub, 'get_from_cache', None)
 
         torch.nn.init.kaiming_uniform_ = do_nothing
         torch.nn.init._no_grad_normal_ = do_nothing
         torch.nn.init._no_grad_uniform_ = do_nothing
         open_clip.create_model_and_transforms = create_model_and_transforms_without_pretrained
         ldm.modules.encoders.modules.CLIPTextModel.from_pretrained = CLIPTextModel_from_pretrained
-        transformers.utils.hub.get_from_cache = transformers_utils_hub_get_from_cache
+        if self.transformers_modeling_utils_load_pretrained_model is not None:
+            transformers.modeling_utils.PreTrainedModel._load_pretrained_model = transformers_modeling_utils_load_pretrained_model
+        if self.transformers_tokenization_utils_base_cached_file is not None:
+            transformers.tokenization_utils_base.cached_file = transformers_tokenization_utils_base_cached_file
+        if self.transformers_configuration_utils_cached_file is not None:
+            transformers.configuration_utils.cached_file = transformers_configuration_utils_cached_file
+        if self.transformers_utils_hub_get_from_cache is not None:
+            transformers.utils.hub.get_from_cache = transformers_utils_hub_get_from_cache
 
     def __exit__(self, exc_type, exc_val, exc_tb):
         torch.nn.init.kaiming_uniform_ = self.init_kaiming_uniform
@@ -61,5 +84,12 @@ class DisableInitialization:
         torch.nn.init._no_grad_uniform_ = self.init_no_grad_uniform_
         open_clip.create_model_and_transforms = self.create_model_and_transforms
         ldm.modules.encoders.modules.CLIPTextModel.from_pretrained = self.CLIPTextModel_from_pretrained
-        transformers.utils.hub.get_from_cache = self.transformers_utils_hub_get_from_cache
+        if self.transformers_modeling_utils_load_pretrained_model is not None:
+            transformers.modeling_utils.PreTrainedModel._load_pretrained_model = self.transformers_modeling_utils_load_pretrained_model
+        if self.transformers_tokenization_utils_base_cached_file is not None:
+            transformers.utils.hub.cached_file = self.transformers_tokenization_utils_base_cached_file
+        if self.transformers_configuration_utils_cached_file is not None:
+            transformers.utils.hub.cached_file = self.transformers_configuration_utils_cached_file
+        if self.transformers_utils_hub_get_from_cache is not None:
+            transformers.utils.hub.get_from_cache = self.transformers_utils_hub_get_from_cache
 
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 1bb9088b..b5bc12f0 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -14,7 +14,7 @@ import ldm.modules.midas as midas
 
 from ldm.util import instantiate_from_config
 
-from modules import shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization
+from modules import shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors
 from modules.paths import models_path
 from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
 
@@ -333,7 +333,11 @@ def load_model(checkpoint_info=None):
 
     timer = Timer()
 
-    with sd_disable_initialization.DisableInitialization():
+    try:
+        with sd_disable_initialization.DisableInitialization():
+            sd_model = instantiate_from_config(sd_config.model)
+    except Exception as e:
+        print('Failed to create model quickly; will retry using slow method.', file=sys.stderr)
         sd_model = instantiate_from_config(sd_config.model)
 
     elapsed_create = timer.elapsed()
-- 
cgit v1.2.3


From 4fdacd31e48c6a7a35c1c25c559932585e8addde Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 11 Jan 2023 10:24:56 +0300
Subject: possible fix for fallback for fast model creation from config

---
 modules/sd_models.py | 3 +++
 1 file changed, 3 insertions(+)

(limited to 'modules/sd_models.py')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index b5bc12f0..a0a8a909 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -337,6 +337,9 @@ def load_model(checkpoint_info=None):
         with sd_disable_initialization.DisableInitialization():
             sd_model = instantiate_from_config(sd_config.model)
     except Exception as e:
+        pass
+
+    if sd_model is None:
         print('Failed to create model quickly; will retry using slow method.', file=sys.stderr)
         sd_model = instantiate_from_config(sd_config.model)
 
-- 
cgit v1.2.3


From 1a23dc32ac5e16fac10115cafd0b841abd06e59f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 11 Jan 2023 10:34:36 +0300
Subject: possible fix for fallback for fast model creation from config,
 attempt 2

---
 modules/sd_models.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules/sd_models.py')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index a0a8a909..084ba7fa 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -333,6 +333,7 @@ def load_model(checkpoint_info=None):
 
     timer = Timer()
 
+    sd_model = None
     try:
         with sd_disable_initialization.DisableInitialization():
             sd_model = instantiate_from_config(sd_config.model)
-- 
cgit v1.2.3


From 4bd490727e156ff53107d53416d6b89be86f2a62 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 11 Jan 2023 18:54:04 +0300
Subject: fix for an error caused by skipping initialization, for realsies this
 time: TypeError: expected str, bytes or os.PathLike object, not NoneType

---
 modules/sd_disable_initialization.py | 71 ++++++++++++++++--------------------
 modules/sd_models.py                 |  1 +
 2 files changed, 33 insertions(+), 39 deletions(-)

(limited to 'modules/sd_models.py')

diff --git a/modules/sd_disable_initialization.py b/modules/sd_disable_initialization.py
index 088ac24b..c72d8efc 100644
--- a/modules/sd_disable_initialization.py
+++ b/modules/sd_disable_initialization.py
@@ -20,6 +20,19 @@ class DisableInitialization:
     ```
     """
 
+    def __init__(self):
+        self.replaced = []
+
+    def replace(self, obj, field, func):
+        original = getattr(obj, field, None)
+        if original is None:
+            return None
+
+        self.replaced.append((obj, field, original))
+        setattr(obj, field, func)
+
+        return original
+
     def __enter__(self):
         def do_nothing(*args, **kwargs):
             pass
@@ -37,11 +50,14 @@ class DisableInitialization:
         def transformers_utils_hub_get_file_from_cache(original, url, *args, **kwargs):
 
             # this file is always 404, prevent making request
-            if url == 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json':
-                raise transformers.utils.hub.EntryNotFoundError
+            if url == 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json' or url == 'openai/clip-vit-large-patch14' and args[0] == 'added_tokens.json':
+                return None
 
             try:
-                return original(url, *args, local_files_only=True, **kwargs)
+                res = original(url, *args, local_files_only=True, **kwargs)
+                if res is None:
+                    res = original(url, *args, local_files_only=False, **kwargs)
+                return res
             except Exception as e:
                 return original(url, *args, local_files_only=False, **kwargs)
 
@@ -54,42 +70,19 @@ class DisableInitialization:
         def transformers_configuration_utils_cached_file(url, *args, local_files_only=False, **kwargs):
             return transformers_utils_hub_get_file_from_cache(self.transformers_configuration_utils_cached_file, url, *args, **kwargs)
 
-        self.init_kaiming_uniform = torch.nn.init.kaiming_uniform_
-        self.init_no_grad_normal = torch.nn.init._no_grad_normal_
-        self.init_no_grad_uniform_ = torch.nn.init._no_grad_uniform_
-        self.create_model_and_transforms = open_clip.create_model_and_transforms
-        self.CLIPTextModel_from_pretrained = ldm.modules.encoders.modules.CLIPTextModel.from_pretrained
-        self.transformers_modeling_utils_load_pretrained_model = getattr(transformers.modeling_utils.PreTrainedModel, '_load_pretrained_model', None)
-        self.transformers_tokenization_utils_base_cached_file = getattr(transformers.tokenization_utils_base, 'cached_file', None)
-        self.transformers_configuration_utils_cached_file = getattr(transformers.configuration_utils, 'cached_file', None)
-        self.transformers_utils_hub_get_from_cache = getattr(transformers.utils.hub, 'get_from_cache', None)
-
-        torch.nn.init.kaiming_uniform_ = do_nothing
-        torch.nn.init._no_grad_normal_ = do_nothing
-        torch.nn.init._no_grad_uniform_ = do_nothing
-        open_clip.create_model_and_transforms = create_model_and_transforms_without_pretrained
-        ldm.modules.encoders.modules.CLIPTextModel.from_pretrained = CLIPTextModel_from_pretrained
-        if self.transformers_modeling_utils_load_pretrained_model is not None:
-            transformers.modeling_utils.PreTrainedModel._load_pretrained_model = transformers_modeling_utils_load_pretrained_model
-        if self.transformers_tokenization_utils_base_cached_file is not None:
-            transformers.tokenization_utils_base.cached_file = transformers_tokenization_utils_base_cached_file
-        if self.transformers_configuration_utils_cached_file is not None:
-            transformers.configuration_utils.cached_file = transformers_configuration_utils_cached_file
-        if self.transformers_utils_hub_get_from_cache is not None:
-            transformers.utils.hub.get_from_cache = transformers_utils_hub_get_from_cache
+        self.replace(torch.nn.init, 'kaiming_uniform_', do_nothing)
+        self.replace(torch.nn.init, '_no_grad_normal_', do_nothing)
+        self.replace(torch.nn.init, '_no_grad_uniform_', do_nothing)
+        self.create_model_and_transforms = self.replace(open_clip, 'create_model_and_transforms', create_model_and_transforms_without_pretrained)
+        self.CLIPTextModel_from_pretrained = self.replace(ldm.modules.encoders.modules.CLIPTextModel, 'from_pretrained', CLIPTextModel_from_pretrained)
+        self.transformers_modeling_utils_load_pretrained_model = self.replace(transformers.modeling_utils.PreTrainedModel, '_load_pretrained_model', transformers_modeling_utils_load_pretrained_model)
+        self.transformers_tokenization_utils_base_cached_file = self.replace(transformers.tokenization_utils_base, 'cached_file', transformers_tokenization_utils_base_cached_file)
+        self.transformers_configuration_utils_cached_file = self.replace(transformers.configuration_utils, 'cached_file', transformers_configuration_utils_cached_file)
+        self.transformers_utils_hub_get_from_cache = self.replace(transformers.utils.hub, 'get_from_cache', transformers_utils_hub_get_from_cache)
 
     def __exit__(self, exc_type, exc_val, exc_tb):
-        torch.nn.init.kaiming_uniform_ = self.init_kaiming_uniform
-        torch.nn.init._no_grad_normal_ = self.init_no_grad_normal
-        torch.nn.init._no_grad_uniform_ = self.init_no_grad_uniform_
-        open_clip.create_model_and_transforms = self.create_model_and_transforms
-        ldm.modules.encoders.modules.CLIPTextModel.from_pretrained = self.CLIPTextModel_from_pretrained
-        if self.transformers_modeling_utils_load_pretrained_model is not None:
-            transformers.modeling_utils.PreTrainedModel._load_pretrained_model = self.transformers_modeling_utils_load_pretrained_model
-        if self.transformers_tokenization_utils_base_cached_file is not None:
-            transformers.utils.hub.cached_file = self.transformers_tokenization_utils_base_cached_file
-        if self.transformers_configuration_utils_cached_file is not None:
-            transformers.utils.hub.cached_file = self.transformers_configuration_utils_cached_file
-        if self.transformers_utils_hub_get_from_cache is not None:
-            transformers.utils.hub.get_from_cache = self.transformers_utils_hub_get_from_cache
+        for obj, field, original in self.replaced:
+            setattr(obj, field, original)
+
+        self.replaced.clear()
 
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 084ba7fa..c466f273 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -334,6 +334,7 @@ def load_model(checkpoint_info=None):
     timer = Timer()
 
     sd_model = None
+
     try:
         with sd_disable_initialization.DisableInitialization():
             sd_model = instantiate_from_config(sd_config.model)
-- 
cgit v1.2.3


From a95f1353089bdeaccd7c266b40cdd79efedfe632 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 14 Jan 2023 09:56:59 +0300
Subject: change hash to sha256

---
 .gitignore                                     |   1 +
 modules/api/api.py                             |   2 +-
 modules/api/models.py                          |   3 +-
 modules/hashes.py                              |  72 +++++++++++++++
 modules/hypernetworks/hypernetwork.py          |   4 +-
 modules/sd_models.py                           | 116 ++++++++++++++++---------
 modules/shared.py                              |   2 +-
 modules/textual_inversion/textual_inversion.py |   6 +-
 webui.py                                       |   2 +
 9 files changed, 158 insertions(+), 50 deletions(-)
 create mode 100644 modules/hashes.py

(limited to 'modules/sd_models.py')

diff --git a/.gitignore b/.gitignore
index 21fa26a7..0b1d17ca 100644
--- a/.gitignore
+++ b/.gitignore
@@ -32,3 +32,4 @@ notification.mp3
 /extensions
 /test/stdout.txt
 /test/stderr.txt
+/cache.json
diff --git a/modules/api/api.py b/modules/api/api.py
index 5767ba90..9814bbc2 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -371,7 +371,7 @@ class Api:
         return upscalers
 
     def get_sd_models(self):
-        return [{"title":x.title, "model_name":x.model_name, "hash":x.hash, "filename": x.filename, "config": find_checkpoint_config(x)} for x in checkpoints_list.values()]
+        return [{"title": x.title, "model_name": x.model_name, "hash": x.shorthash, "sha256": x.sha256, "filename": x.filename, "config": find_checkpoint_config(x)} for x in checkpoints_list.values()]
 
     def get_hypernetworks(self):
         return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks]
diff --git a/modules/api/models.py b/modules/api/models.py
index c78095ca..1eb1fcf1 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -224,7 +224,8 @@ class UpscalerItem(BaseModel):
 class SDModelItem(BaseModel):
     title: str = Field(title="Title")
     model_name: str = Field(title="Model Name")
-    hash: str = Field(title="Hash")
+    hash: Optional[str] = Field(title="Short hash")
+    sha256: Optional[str] = Field(title="sha256 hash")
     filename: str = Field(title="Filename")
     config: str = Field(title="Config file")
 
diff --git a/modules/hashes.py b/modules/hashes.py
new file mode 100644
index 00000000..ebfbd90c
--- /dev/null
+++ b/modules/hashes.py
@@ -0,0 +1,72 @@
+import hashlib
+import json
+import os.path
+
+import filelock
+
+
+cache_filename = "cache.json"
+cache_data = None
+
+
+def dump_cache():
+    with filelock.FileLock(cache_filename+".lock"):
+        with open(cache_filename, "w", encoding="utf8") as file:
+            json.dump(cache_data, file, indent=4)
+
+
+def cache(subsection):
+    global cache_data
+
+    if cache_data is None:
+        with filelock.FileLock(cache_filename+".lock"):
+            if not os.path.isfile(cache_filename):
+                cache_data = {}
+            else:
+                with open(cache_filename, "r", encoding="utf8") as file:
+                    cache_data = json.load(file)
+
+    s = cache_data.get(subsection, {})
+    cache_data[subsection] = s
+
+    return s
+
+
+def calculate_sha256(filename):
+    hash_sha256 = hashlib.sha256()
+
+    with open(filename, "rb") as f:
+        for chunk in iter(lambda: f.read(4096), b""):
+            hash_sha256.update(chunk)
+
+    return hash_sha256.hexdigest()
+
+
+def sha256(filename, title):
+    hashes = cache("hashes")
+    ondisk_mtime = os.path.getmtime(filename)
+
+    if title in hashes:
+        cached_sha256 = hashes[title].get("sha256", None)
+        cached_mtime = hashes[title].get("mtime", 0)
+
+        if ondisk_mtime <= cached_mtime and cached_sha256 is not None:
+            return cached_sha256
+
+    print(f"Calculating sha256 for {filename}: ", end='')
+    sha256_value = calculate_sha256(filename)
+    print(f"{sha256_value}")
+
+    hashes[title] = {
+        "mtime": ondisk_mtime,
+        "sha256": sha256_value,
+    }
+
+    dump_cache()
+
+    return sha256_value
+
+
+
+
+
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 83cbb4f0..9b5f2e79 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -509,7 +509,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
 
     if shared.opts.save_training_settings_to_txt:
         saved_params = dict(
-            model_name=checkpoint.model_name, model_hash=checkpoint.hash, num_of_dataset_images=len(ds),
+            model_name=checkpoint.model_name, model_hash=checkpoint.shorthash, num_of_dataset_images=len(ds),
             **{field: getattr(hypernetwork, field) for field in ['layer_structure', 'activation_func', 'weight_init', 'add_layer_norm', 'use_dropout', ]}
         )
         logging.save_settings_to_file(log_directory, {**saved_params, **locals()})
@@ -737,7 +737,7 @@ def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):
     old_sd_checkpoint = hypernetwork.sd_checkpoint if hasattr(hypernetwork, "sd_checkpoint") else None
     old_sd_checkpoint_name = hypernetwork.sd_checkpoint_name if hasattr(hypernetwork, "sd_checkpoint_name") else None
     try:
-        hypernetwork.sd_checkpoint = checkpoint.hash
+        hypernetwork.sd_checkpoint = checkpoint.shorthash
         hypernetwork.sd_checkpoint_name = checkpoint.model_name
         hypernetwork.name = hypernetwork_name
         hypernetwork.save(filename)
diff --git a/modules/sd_models.py b/modules/sd_models.py
index c466f273..7babb9ae 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -14,17 +14,56 @@ import ldm.modules.midas as midas
 
 from ldm.util import instantiate_from_config
 
-from modules import shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors
+from modules import shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes
 from modules.paths import models_path
 from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
 
 model_dir = "Stable-diffusion"
 model_path = os.path.abspath(os.path.join(models_path, model_dir))
 
-CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name'])
 checkpoints_list = {}
+checkpoint_alisases = {}
 checkpoints_loaded = collections.OrderedDict()
 
+
+class CheckpointInfo:
+    def __init__(self, filename):
+        self.filename = filename
+        abspath = os.path.abspath(filename)
+
+        if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir):
+            name = abspath.replace(shared.cmd_opts.ckpt_dir, '')
+        elif abspath.startswith(model_path):
+            name = abspath.replace(model_path, '')
+        else:
+            name = os.path.basename(filename)
+
+        if name.startswith("\\") or name.startswith("/"):
+            name = name[1:]
+
+        self.title = name
+        self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
+        self.hash = model_hash(filename)
+        self.ids = [self.hash, self.model_name, self.title, f'{name} [{self.hash}]']
+        self.shorthash = None
+        self.sha256 = None
+
+    def register(self):
+        checkpoints_list[self.title] = self
+        for id in self.ids:
+            checkpoint_alisases[id] = self
+
+    def calculate_shorthash(self):
+        self.sha256 = hashes.sha256(self.filename, self.title)
+        self.shorthash = self.sha256[0:10]
+
+        if self.shorthash not in self.ids:
+            self.ids += [self.shorthash, self.sha256]
+            self.register()
+
+        return self.shorthash
+
+
 try:
     # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
 
@@ -43,10 +82,14 @@ def setup_model():
     enable_midas_autodownload()
 
 
-def checkpoint_tiles(): 
-    convert = lambda name: int(name) if name.isdigit() else name.lower() 
-    alphanumeric_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)] 
-    return sorted([x.title for x in checkpoints_list.values()], key = alphanumeric_key)
+def checkpoint_tiles():
+    def convert(name):
+        return int(name) if name.isdigit() else name.lower()
+
+    def alphanumeric_key(key):
+        return [convert(c) for c in re.split('([0-9]+)', key)]
+
+    return sorted([x.title for x in checkpoints_list.values()], key=alphanumeric_key)
 
 
 def find_checkpoint_config(info):
@@ -62,48 +105,38 @@ def find_checkpoint_config(info):
 
 def list_models():
     checkpoints_list.clear()
+    checkpoint_alisases.clear()
     model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], ext_blacklist=[".vae.safetensors"])
 
-    def modeltitle(path, shorthash):
-        abspath = os.path.abspath(path)
-
-        if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir):
-            name = abspath.replace(shared.cmd_opts.ckpt_dir, '')
-        elif abspath.startswith(model_path):
-            name = abspath.replace(model_path, '')
-        else:
-            name = os.path.basename(path)
-
-        if name.startswith("\\") or name.startswith("/"):
-            name = name[1:]
-
-        shortname = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
-
-        return f'{name} [{shorthash}]', shortname
-
     cmd_ckpt = shared.cmd_opts.ckpt
     if os.path.exists(cmd_ckpt):
-        h = model_hash(cmd_ckpt)
-        title, short_model_name = modeltitle(cmd_ckpt, h)
-        checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name)
-        shared.opts.data['sd_model_checkpoint'] = title
+        checkpoint_info = CheckpointInfo(cmd_ckpt)
+        checkpoint_info.register()
+
+        shared.opts.data['sd_model_checkpoint'] = checkpoint_info.title
     elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
         print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
+
     for filename in model_list:
-        h = model_hash(filename)
-        title, short_model_name = modeltitle(filename, h)
+        checkpoint_info = CheckpointInfo(filename)
+        checkpoint_info.register()
+
 
-        checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name)
+def get_closet_checkpoint_match(search_string):
+    checkpoint_info = checkpoint_alisases.get(search_string, None)
+    if checkpoint_info is not None:
+        return
 
+    found = sorted([info for info in checkpoints_list.values() if search_string in info.title], key=lambda x: len(x.title))
+    if found:
+        return found[0]
 
-def get_closet_checkpoint_match(searchString):
-    applicable = sorted([info for info in checkpoints_list.values() if searchString in info.title], key = lambda x:len(x.title))
-    if len(applicable) > 0:
-        return applicable[0]
     return None
 
 
 def model_hash(filename):
+    """old hash that only looks at a small part of the file and is prone to collisions"""
+
     try:
         with open(filename, "rb") as file:
             import hashlib
@@ -119,7 +152,7 @@ def model_hash(filename):
 def select_checkpoint():
     model_checkpoint = shared.opts.sd_model_checkpoint
         
-    checkpoint_info = checkpoints_list.get(model_checkpoint, None)
+    checkpoint_info = checkpoint_alisases.get(model_checkpoint, None)
     if checkpoint_info is not None:
         return checkpoint_info
 
@@ -189,9 +222,8 @@ def read_state_dict(checkpoint_file, print_global_state=False, map_location=None
     return sd
 
 
-def load_model_weights(model, checkpoint_info, vae_file="auto"):
-    checkpoint_file = checkpoint_info.filename
-    sd_model_hash = checkpoint_info.hash
+def load_model_weights(model, checkpoint_info: CheckpointInfo, vae_file="auto"):
+    sd_model_hash = checkpoint_info.calculate_shorthash()
 
     cache_enabled = shared.opts.sd_checkpoint_cache > 0
 
@@ -201,9 +233,9 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
         model.load_state_dict(checkpoints_loaded[checkpoint_info])
     else:
         # load from file
-        print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
+        print(f"Loading weights [{sd_model_hash}] from {checkpoint_info.filename}")
 
-        sd = read_state_dict(checkpoint_file)
+        sd = read_state_dict(checkpoint_info.filename)
         model.load_state_dict(sd, strict=False)
         del sd
         
@@ -235,14 +267,14 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
             checkpoints_loaded.popitem(last=False)  # LRU
 
     model.sd_model_hash = sd_model_hash
-    model.sd_model_checkpoint = checkpoint_file
+    model.sd_model_checkpoint = checkpoint_info.filename
     model.sd_checkpoint_info = checkpoint_info
 
     model.logvar = model.logvar.to(devices.device)  # fix for training
 
     sd_vae.delete_base_vae()
     sd_vae.clear_loaded_vae()
-    vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
+    vae_file = sd_vae.resolve_vae(checkpoint_info.filename, vae_file=vae_file)
     sd_vae.load_vae(model, vae_file)
 
 
diff --git a/modules/shared.py b/modules/shared.py
index b90ded52..d74c069d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -428,7 +428,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "return_grid": OptionInfo(True, "Show grid in results for web"),
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
     "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
-    "add_model_name_to_info": OptionInfo(False, "Add model name to generation information"),
+    "add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
     "disable_weights_auto_swap": OptionInfo(False, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
     "send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
     "send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 6939efcc..63935878 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -407,7 +407,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
     ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize)
 
     if shared.opts.save_training_settings_to_txt:
-        save_settings_to_file(log_directory, {**dict(model_name=checkpoint.model_name, model_hash=checkpoint.hash, num_of_dataset_images=len(ds), num_vectors_per_token=len(embedding.vec)), **locals()})
+        save_settings_to_file(log_directory, {**dict(model_name=checkpoint.model_name, model_hash=checkpoint.shorthash, num_of_dataset_images=len(ds), num_vectors_per_token=len(embedding.vec)), **locals()})
 
     latent_sampling_method = ds.latent_sampling_method
 
@@ -584,7 +584,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
 
                         checkpoint = sd_models.select_checkpoint()
                         footer_left = checkpoint.model_name
-                        footer_mid = '[{}]'.format(checkpoint.hash)
+                        footer_mid = '[{}]'.format(checkpoint.shorthash)
                         footer_right = '{}v {}s'.format(vectorSize, steps_done)
 
                         captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
@@ -626,7 +626,7 @@ def save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, r
     old_sd_checkpoint_name = embedding.sd_checkpoint_name if hasattr(embedding, "sd_checkpoint_name") else None
     old_cached_checksum = embedding.cached_checksum if hasattr(embedding, "cached_checksum") else None
     try:
-        embedding.sd_checkpoint = checkpoint.hash
+        embedding.sd_checkpoint = checkpoint.shorthash
         embedding.sd_checkpoint_name = checkpoint.model_name
         if remove_cached_checksum:
             embedding.cached_checksum = None
diff --git a/webui.py b/webui.py
index 47d372c7..1fff80da 100644
--- a/webui.py
+++ b/webui.py
@@ -78,6 +78,8 @@ def initialize():
         print("Stable diffusion model failed to load, exiting", file=sys.stderr)
         exit(1)
 
+    shared.opts.data["sd_model_checkpoint"] = shared.sd_model.sd_checkpoint_info.title
+
     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)
-- 
cgit v1.2.3


From f9ac3352cb66ce2bc0aa4325130fc7267fb35e4f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 14 Jan 2023 10:25:21 +0300
Subject: change hypernets to use sha256 hashes

---
 modules/hypernetworks/hypernetwork.py | 40 ++++++++++++++++++++---------------
 modules/processing.py                 |  2 +-
 modules/sd_models.py                  |  2 +-
 modules/shared.py                     |  1 +
 4 files changed, 26 insertions(+), 19 deletions(-)

(limited to 'modules/sd_models.py')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 9b5f2e79..3aebefa8 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -12,7 +12,7 @@ import torch
 import tqdm
 from einops import rearrange, repeat
 from ldm.util import default
-from modules import devices, processing, sd_models, shared, sd_samplers
+from modules import devices, processing, sd_models, shared, sd_samplers, hashes
 from modules.textual_inversion import textual_inversion, logging
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 from torch import einsum
@@ -225,7 +225,7 @@ class Hypernetwork:
 
         torch.save(state_dict, filename)
         if shared.opts.save_optimizer_state and self.optimizer_state_dict:
-            optimizer_saved_dict['hash'] = sd_models.model_hash(filename)
+            optimizer_saved_dict['hash'] = self.shorthash()
             optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict
             torch.save(optimizer_saved_dict, filename + '.optim')
 
@@ -237,32 +237,33 @@ class Hypernetwork:
         state_dict = torch.load(filename, map_location='cpu')
 
         self.layer_structure = state_dict.get('layer_structure', [1, 2, 1])
-        print(self.layer_structure)
-        optional_info = state_dict.get('optional_info', None)
-        if optional_info is not None:
-            print(f"INFO:\n {optional_info}\n")
-            self.optional_info = optional_info
+        self.optional_info = state_dict.get('optional_info', None)
         self.activation_func = state_dict.get('activation_func', None)
-        print(f"Activation function is {self.activation_func}")
         self.weight_init = state_dict.get('weight_initialization', 'Normal')
-        print(f"Weight initialization is {self.weight_init}")
         self.add_layer_norm = state_dict.get('is_layer_norm', False)
-        print(f"Layer norm is set to {self.add_layer_norm}")
         self.dropout_structure = state_dict.get('dropout_structure', None)
         self.use_dropout = True if self.dropout_structure is not None and any(self.dropout_structure) else state_dict.get('use_dropout', False)
-        print(f"Dropout usage is set to {self.use_dropout}" )
         self.activate_output = state_dict.get('activate_output', True)
-        print(f"Activate last layer is set to {self.activate_output}")
         self.last_layer_dropout = state_dict.get('last_layer_dropout', False)
         # Dropout structure should have same length as layer structure, Every digits should be in [0,1), and last digit must be 0.
         if self.dropout_structure is None:
-            print("Using previous dropout structure")
             self.dropout_structure = parse_dropout_structure(self.layer_structure, self.use_dropout, self.last_layer_dropout)
-        print(f"Dropout structure is set to {self.dropout_structure}")
 
-        optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {}
+        if shared.opts.print_hypernet_extra:
+            if self.optional_info is not None:
+                print(f"  INFO:\n {self.optional_info}\n")
 
-        if sd_models.model_hash(filename) == optimizer_saved_dict.get('hash', None):
+            print(f"  Layer structure: {self.layer_structure}")
+            print(f"  Activation function: {self.activation_func}")
+            print(f"  Weight initialization: {self.weight_init}")
+            print(f"  Layer norm: {self.add_layer_norm}")
+            print(f"  Dropout usage: {self.use_dropout}" )
+            print(f"  Activate last layer: {self.activate_output}")
+            print(f"  Dropout structure: {self.dropout_structure}")
+
+        optimizer_saved_dict = torch.load(self.filename + '.optim', map_location='cpu') if os.path.exists(self.filename + '.optim') else {}
+
+        if self.shorthash() == optimizer_saved_dict.get('hash', None):
             self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
         else:
             self.optimizer_state_dict = None
@@ -289,6 +290,11 @@ class Hypernetwork:
         self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)
         self.eval()
 
+    def shorthash(self):
+        sha256 = hashes.sha256(self.filename, f'hypernet/{self.name}')
+
+        return sha256[0:10]
+
 
 def list_hypernetworks(path):
     res = {}
@@ -296,7 +302,7 @@ def list_hypernetworks(path):
         name = os.path.splitext(os.path.basename(filename))[0]
         # Prevent a hypothetical "None.pt" from being listed.
         if name != "None":
-            res[name + f"({sd_models.model_hash(filename)})"] = filename
+            res[name] = filename
     return res
 
 
diff --git a/modules/processing.py b/modules/processing.py
index ae04cab7..849f6b19 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -437,7 +437,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
         "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
         "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name),
-        "Hypernet hash": (None if shared.loaded_hypernetwork is None else sd_models.model_hash(shared.loaded_hypernetwork.filename)),
+        "Hypernet hash": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.shorthash()),
         "Hypernet strength": (None if shared.loaded_hypernetwork is None or shared.opts.sd_hypernetwork_strength >= 1 else shared.opts.sd_hypernetwork_strength),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 7babb9ae..8f00191c 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -125,7 +125,7 @@ def list_models():
 def get_closet_checkpoint_match(search_string):
     checkpoint_info = checkpoint_alisases.get(search_string, None)
     if checkpoint_info is not None:
-        return
+        return checkpoint_info
 
     found = sorted([info for info in checkpoints_list.values() if search_string in info.title], key=lambda x: len(x.title))
     if found:
diff --git a/modules/shared.py b/modules/shared.py
index d74c069d..a6c61db3 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -361,6 +361,7 @@ options_templates.update(options_section(('system', "System"), {
     "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}),
     "samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),
     "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
+    "print_hypernet_extra": OptionInfo(False, "Print extra hypernetwork information to console."),
 }))
 
 options_templates.update(options_section(('training', "Training"), {
-- 
cgit v1.2.3


From febd2b722e80959b89a0e5966a159b4eb430c5a5 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 14 Jan 2023 13:37:55 +0300
Subject: update key to use with checkpoints' sha256 in cache

---
 modules/sd_models.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules/sd_models.py')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 8f00191c..1fe6d11b 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -54,7 +54,7 @@ class CheckpointInfo:
             checkpoint_alisases[id] = self
 
     def calculate_shorthash(self):
-        self.sha256 = hashes.sha256(self.filename, self.title)
+        self.sha256 = hashes.sha256(self.filename, "checkpoint/" + self.title)
         self.shorthash = self.sha256[0:10]
 
         if self.shorthash not in self.ids:
-- 
cgit v1.2.3


From 08c6f009a5ee92dd3218a942c08e8337c26352be Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 14 Jan 2023 15:55:40 +0300
Subject: load hashes from cache for checkpoints that have them add checkpoint
 hash to footer

---
 javascript/ui.js     | 25 ++++++++++++++++---------
 modules/hashes.py    | 26 +++++++++++++++++++-------
 modules/sd_models.py |  9 ++++++---
 modules/shared.py    |  1 +
 modules/ui.py        |  2 ++
 script.js            |  4 ++++
 6 files changed, 48 insertions(+), 19 deletions(-)

(limited to 'modules/sd_models.py')

diff --git a/javascript/ui.js b/javascript/ui.js
index a41dd26f..1e04a8f4 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -143,14 +143,6 @@ function confirm_clear_prompt(prompt, negative_prompt) {
 
 
 opts = {}
-function apply_settings(jsdata){
-    console.log(jsdata)
-
-    opts = JSON.parse(jsdata)
-
-    return jsdata
-}
-
 onUiUpdate(function(){
 	if(Object.keys(opts).length != 0) return;
 
@@ -160,7 +152,7 @@ onUiUpdate(function(){
     textarea = json_elem.querySelector('textarea')
     jsdata = textarea.value
     opts = JSON.parse(jsdata)
-
+    executeCallbacks(optionsChangedCallbacks);
 
     Object.defineProperty(textarea, 'value', {
         set: function(newValue) {
@@ -171,6 +163,8 @@ onUiUpdate(function(){
             if (oldValue != newValue) {
                 opts = JSON.parse(textarea.value)
             }
+
+            executeCallbacks(optionsChangedCallbacks);
         },
         get: function() {
             var valueProp = Object.getOwnPropertyDescriptor(HTMLTextAreaElement.prototype, 'value');
@@ -201,6 +195,19 @@ onUiUpdate(function(){
     }
 })
 
+
+onOptionsChanged(function(){
+    elem = gradioApp().getElementById('sd_checkpoint_hash')
+    sd_checkpoint_hash = opts.sd_checkpoint_hash || ""
+    shorthash = sd_checkpoint_hash.substr(0,10)
+
+	if(elem && elem.textContent != shorthash){
+	    elem.textContent = shorthash
+	    elem.title = sd_checkpoint_hash
+	    elem.href = "https://google.com/search?q=" + sd_checkpoint_hash
+	}
+})
+
 let txt2img_textarea, img2img_textarea = undefined;
 let wait_time = 800
 let token_timeout;
diff --git a/modules/hashes.py b/modules/hashes.py
index ebfbd90c..14231771 100644
--- a/modules/hashes.py
+++ b/modules/hashes.py
@@ -42,23 +42,35 @@ def calculate_sha256(filename):
     return hash_sha256.hexdigest()
 
 
-def sha256(filename, title):
+def sha256_from_cache(filename, title):
     hashes = cache("hashes")
     ondisk_mtime = os.path.getmtime(filename)
 
-    if title in hashes:
-        cached_sha256 = hashes[title].get("sha256", None)
-        cached_mtime = hashes[title].get("mtime", 0)
+    if title not in hashes:
+        return None
+
+    cached_sha256 = hashes[title].get("sha256", None)
+    cached_mtime = hashes[title].get("mtime", 0)
+
+    if ondisk_mtime > cached_mtime or cached_sha256 is None:
+        return None
+
+    return cached_sha256
+
+
+def sha256(filename, title):
+    hashes = cache("hashes")
 
-        if ondisk_mtime <= cached_mtime and cached_sha256 is not None:
-            return cached_sha256
+    sha256_value = sha256_from_cache(filename, title)
+    if sha256_value is not None:
+        return sha256_value
 
     print(f"Calculating sha256 for {filename}: ", end='')
     sha256_value = calculate_sha256(filename)
     print(f"{sha256_value}")
 
     hashes[title] = {
-        "mtime": ondisk_mtime,
+        "mtime": os.path.getmtime(filename),
         "sha256": sha256_value,
     }
 
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 1fe6d11b..e5a0bc63 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -44,9 +44,11 @@ class CheckpointInfo:
         self.title = name
         self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
         self.hash = model_hash(filename)
-        self.ids = [self.hash, self.model_name, self.title, f'{name} [{self.hash}]']
-        self.shorthash = None
-        self.sha256 = None
+
+        self.sha256 = hashes.sha256_from_cache(self.filename, "checkpoint/" + self.title)
+        self.shorthash = self.sha256[0:10] if self.sha256 else None
+
+        self.ids = [self.hash, self.model_name, self.title, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256] if self.shorthash else [])
 
     def register(self):
         checkpoints_list[self.title] = self
@@ -269,6 +271,7 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, vae_file="auto"):
     model.sd_model_hash = sd_model_hash
     model.sd_model_checkpoint = checkpoint_info.filename
     model.sd_checkpoint_info = checkpoint_info
+    shared.opts.data["sd_checkpoint_hash"] = checkpoint_info.sha256
 
     model.logvar = model.logvar.to(devices.device)  # fix for training
 
diff --git a/modules/shared.py b/modules/shared.py
index a6c61db3..c9988d4d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -458,6 +458,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
 
 options_templates.update(options_section((None, "Hidden options"), {
     "disabled_extensions": OptionInfo([], "Disable those extensions"),
+    "sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
 }))
 
 options_templates.update()
diff --git a/modules/ui.py b/modules/ui.py
index e86a624b..2625ae32 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1841,4 +1841,6 @@ xformers: {xformers_version}
 gradio: {gr.__version__}
  • 
 commit: <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/{commit}">{short_commit}</a>
+ • 
+checkpoint: <a id="sd_checkpoint_hash">N/A</a>
 """
diff --git a/script.js b/script.js
index 21960d91..3345e32b 100644
--- a/script.js
+++ b/script.js
@@ -14,6 +14,7 @@ function get_uiCurrentTabContent() {
 
 uiUpdateCallbacks = []
 uiTabChangeCallbacks = []
+optionsChangedCallbacks = []
 let uiCurrentTab = null
 
 function onUiUpdate(callback){
@@ -22,6 +23,9 @@ function onUiUpdate(callback){
 function onUiTabChange(callback){
     uiTabChangeCallbacks.push(callback)
 }
+function onOptionsChanged(callback){
+    optionsChangedCallbacks.push(callback)
+}
 
 function runCallback(x, m){
     try {
-- 
cgit v1.2.3


From a5bbcd215304e0c83ab2b9fe7f172f88536d7629 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 14 Jan 2023 19:56:09 +0300
Subject: fix bug with "Ignore selected VAE for..." option completely disabling
 VAE election rework VAE resolving code to be more simple

---
 modules/sd_models.py |   6 +-
 modules/sd_vae.py    | 194 ++++++++++++++++++++-------------------------------
 modules/shared.py    |   4 +-
 scripts/xy_grid.py   |  27 ++++---
 4 files changed, 95 insertions(+), 136 deletions(-)

(limited to 'modules/sd_models.py')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index e5a0bc63..6a681cef 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -224,7 +224,7 @@ def read_state_dict(checkpoint_file, print_global_state=False, map_location=None
     return sd
 
 
-def load_model_weights(model, checkpoint_info: CheckpointInfo, vae_file="auto"):
+def load_model_weights(model, checkpoint_info: CheckpointInfo):
     sd_model_hash = checkpoint_info.calculate_shorthash()
 
     cache_enabled = shared.opts.sd_checkpoint_cache > 0
@@ -277,8 +277,8 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, vae_file="auto"):
 
     sd_vae.delete_base_vae()
     sd_vae.clear_loaded_vae()
-    vae_file = sd_vae.resolve_vae(checkpoint_info.filename, vae_file=vae_file)
-    sd_vae.load_vae(model, vae_file)
+    vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename)
+    sd_vae.load_vae(model, vae_file, vae_source)
 
 
 def enable_midas_autodownload():
diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 0a49daa1..6ea92711 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -9,23 +9,9 @@ import glob
 from copy import deepcopy
 
 
-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_path = os.path.abspath(os.path.join(models_path, "VAE"))
 vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
-
-
-default_vae_dict = {"auto": "auto", "None": None, 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
+vae_dict = {}
 
 
 base_vae = None
@@ -64,100 +50,69 @@ def restore_base_vae(model):
 
 
 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(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, '**/*.safetensors'), recursive=True),
+    return os.path.basename(filepath)
+
+
+def refresh_vae_list():
+    vae_dict.clear()
+
+    paths = [
+        os.path.join(sd_models.model_path, '**/*.vae.ckpt'),
+        os.path.join(sd_models.model_path, '**/*.vae.pt'),
+        os.path.join(sd_models.model_path, '**/*.vae.safetensors'),
+        os.path.join(vae_path, '**/*.ckpt'),
+        os.path.join(vae_path, '**/*.pt'),
+        os.path.join(vae_path, '**/*.safetensors'),
     ]
-    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)
+
+    if shared.cmd_opts.ckpt_dir is not None and os.path.isdir(shared.cmd_opts.ckpt_dir):
+        paths += [
+            os.path.join(shared.cmd_opts.ckpt_dir, '**/*.vae.ckpt'),
+            os.path.join(shared.cmd_opts.ckpt_dir, '**/*.vae.pt'),
+            os.path.join(shared.cmd_opts.ckpt_dir, '**/*.vae.safetensors'),
+        ]
+
+    candidates = []
+    for path in paths:
+        candidates += glob.iglob(path, recursive=True)
+
     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 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(f"Selected VAE doesn't exist: {vae_file}")
-    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
-    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"
-    # 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(f"VAE provided as command line argument doesn't exist: {vae_file}")
-    # 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):
-            vae_file = shared.cmd_opts.vae_path
-            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(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(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
-    # 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
+        vae_dict[name] = filepath
+
+
+def find_vae_near_checkpoint(checkpoint_file):
+    checkpoint_path = os.path.splitext(checkpoint_file)[0]
+    for vae_location in [checkpoint_path + ".vae.pt", checkpoint_path + ".vae.ckpt", checkpoint_path + ".vae.safetensors"]:
+        if os.path.isfile(vae_location):
+            return vae_location
+
+    return None
+
+
+def resolve_vae(checkpoint_file):
+    if shared.cmd_opts.vae_path is not None:
+        return shared.cmd_opts.vae_path, 'from commandline argument'
+
+    vae_near_checkpoint = find_vae_near_checkpoint(checkpoint_file)
+    if vae_near_checkpoint is not None and (shared.opts.sd_vae_as_default or shared.opts.sd_vae == "auto"):
+        return vae_near_checkpoint, 'found near the checkpoint'
+
+    if shared.opts.sd_vae == "None":
+        return None, None
+
+    vae_from_options = vae_dict.get(shared.opts.sd_vae, None)
+    if vae_from_options is not None:
+        return vae_from_options, 'specified in settings'
+
+    if shared.opts.sd_vae != "Automatic":
+        print(f"Couldn't find VAE named {shared.opts.sd_vae}; using None instead")
+
+    return None, None
+
+
+def load_vae(model, vae_file=None, vae_source="from unknown source"):
+    global vae_dict, loaded_vae_file
     # save_settings = False
 
     cache_enabled = shared.opts.sd_vae_checkpoint_cache > 0
@@ -165,12 +120,12 @@ def load_vae(model, vae_file=None):
     if vae_file:
         if cache_enabled and vae_file in checkpoints_loaded:
             # use vae checkpoint cache
-            print(f"Loading VAE weights [{get_filename(vae_file)}] from cache")
+            print(f"Loading VAE weights {vae_source}: cached {get_filename(vae_file)}")
             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}")
+            assert os.path.isfile(vae_file), f"VAE {vae_source} doesn't exist: {vae_file}"
+            print(f"Loading VAE weights {vae_source}: {vae_file}")
             store_base_vae(model)
 
             vae_ckpt = sd_models.read_state_dict(vae_file, map_location=shared.weight_load_location)
@@ -191,14 +146,12 @@ def load_vae(model, vae_file=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)
+
     elif loaded_vae_file:
         restore_base_vae(model)
 
     loaded_vae_file = vae_file
 
-    first_load = False
-
 
 # don't call this from outside
 def _load_vae_dict(model, vae_dict_1):
@@ -211,7 +164,10 @@ def clear_loaded_vae():
     loaded_vae_file = None
 
 
-def reload_vae_weights(sd_model=None, vae_file="auto"):
+unspecified = object()
+
+
+def reload_vae_weights(sd_model=None, vae_file=unspecified):
     from modules import lowvram, devices, sd_hijack
 
     if not sd_model:
@@ -219,7 +175,11 @@ def reload_vae_weights(sd_model=None, vae_file="auto"):
 
     checkpoint_info = sd_model.sd_checkpoint_info
     checkpoint_file = checkpoint_info.filename
-    vae_file = resolve_vae(checkpoint_file, vae_file=vae_file)
+
+    if vae_file == unspecified:
+        vae_file, vae_source = resolve_vae(checkpoint_file)
+    else:
+        vae_source = "from function argument"
 
     if loaded_vae_file == vae_file:
         return
@@ -231,7 +191,7 @@ def reload_vae_weights(sd_model=None, vae_file="auto"):
 
     sd_hijack.model_hijack.undo_hijack(sd_model)
 
-    load_vae(sd_model, vae_file)
+    load_vae(sd_model, vae_file, vae_source)
 
     sd_hijack.model_hijack.hijack(sd_model)
     script_callbacks.model_loaded_callback(sd_model)
@@ -239,5 +199,5 @@ 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("VAE Weights loaded.")
+    print("VAE weights loaded.")
     return sd_model
diff --git a/modules/shared.py b/modules/shared.py
index e0ec3136..9756adea 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -83,7 +83,7 @@ parser.add_argument("--theme", type=str, help="launches the UI with light or dar
 parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
 parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
 parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
-parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
+parser.add_argument('--vae-path', type=str, help='Checkpoint to use as VAE; setting this argument disables all settings related to VAE', default=None)
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
 parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)")
 parser.add_argument("--api-auth", type=str, help='Set authentication for API like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
@@ -383,7 +383,7 @@ 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": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": ["Automatic", "None"] + list(sd_vae.vae_dict)}, 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),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index f04d9b7e..bd3087d4 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -125,24 +125,21 @@ def apply_upscale_latent_space(p, x, xs):
 
 
 def find_vae(name: str):
-    if name.lower() in ['auto', 'none']:
-        return name
+    if name.lower() in ['auto', 'automatic']:
+        return modules.sd_vae.unspecified
+    if name.lower() == 'none':
+        return None
     else:
-        vae_path = os.path.abspath(os.path.join(paths.models_path, 'VAE'))
-        found = glob.glob(os.path.join(vae_path, f'**/{name}.*pt'), recursive=True)
-        if found:
-            return found[0]
+        choices = [x for x in sorted(modules.sd_vae.vae_dict, key=lambda x: len(x)) if name.lower().strip() in x.lower()]
+        if len(choices) == 0:
+            print(f"No VAE found for {name}; using automatic")
+            return modules.sd_vae.unspecified
         else:
-            return 'auto'
+            return modules.sd_vae.vae_dict[choices[0]]
 
 
 def apply_vae(p, x, xs):
-    if x.lower().strip() == 'none':
-        modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file='None')
-    else:
-        found = find_vae(x)
-        if found:
-            v = modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file=found)
+    modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file=find_vae(x))
 
 
 def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _):
@@ -271,7 +268,9 @@ class SharedSettingsStackHelper(object):
   
     def __exit__(self, exc_type, exc_value, tb):
         modules.sd_models.reload_model_weights(self.model)
-        modules.sd_vae.reload_vae_weights(self.model, vae_file=find_vae(self.vae))
+
+        opts.data["sd_vae"] = self.vae
+        modules.sd_vae.reload_vae_weights(self.model)
 
         hypernetwork.load_hypernetwork(self.hypernetwork)
         hypernetwork.apply_strength()
-- 
cgit v1.2.3


From c1928cdd6194928af0f53f70c51d59479b7025e2 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 19 Jan 2023 18:58:08 +0300
Subject: bring back short hashes to sd checkpoint selection

---
 modules/sd_models.py | 15 +++++++++++----
 modules/ui.py        | 23 ++++++++++++-----------
 2 files changed, 23 insertions(+), 15 deletions(-)

(limited to 'modules/sd_models.py')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 6a681cef..12083848 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -41,14 +41,16 @@ class CheckpointInfo:
         if name.startswith("\\") or name.startswith("/"):
             name = name[1:]
 
-        self.title = name
+        self.name = name
         self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
         self.hash = model_hash(filename)
 
-        self.sha256 = hashes.sha256_from_cache(self.filename, "checkpoint/" + self.title)
+        self.sha256 = hashes.sha256_from_cache(self.filename, "checkpoint/" + name)
         self.shorthash = self.sha256[0:10] if self.sha256 else None
 
-        self.ids = [self.hash, self.model_name, self.title, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256] if self.shorthash else [])
+        self.title = name if self.shorthash is None else f'{name} [{self.shorthash}]'
+
+        self.ids = [self.hash, self.model_name, self.title, name, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]'] if self.shorthash else [])
 
     def register(self):
         checkpoints_list[self.title] = self
@@ -56,13 +58,15 @@ class CheckpointInfo:
             checkpoint_alisases[id] = self
 
     def calculate_shorthash(self):
-        self.sha256 = hashes.sha256(self.filename, "checkpoint/" + self.title)
+        self.sha256 = hashes.sha256(self.filename, "checkpoint/" + self.name)
         self.shorthash = self.sha256[0:10]
 
         if self.shorthash not in self.ids:
             self.ids += [self.shorthash, self.sha256]
             self.register()
 
+        self.title = f'{self.name} [{self.shorthash}]'
+
         return self.shorthash
 
 
@@ -225,7 +229,10 @@ def read_state_dict(checkpoint_file, print_global_state=False, map_location=None
 
 
 def load_model_weights(model, checkpoint_info: CheckpointInfo):
+    title = checkpoint_info.title
     sd_model_hash = checkpoint_info.calculate_shorthash()
+    if checkpoint_info.title != title:
+        shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title
 
     cache_enabled = shared.opts.sd_checkpoint_cache > 0
 
diff --git a/modules/ui.py b/modules/ui.py
index 0c5ba358..13d80ae2 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -439,7 +439,7 @@ def apply_setting(key, value):
         opts.data_labels[key].onchange()
 
     opts.save(shared.config_filename)
-    return value
+    return getattr(opts, key)
 
 
 def update_generation_info(generation_info, html_info, img_index):
@@ -597,6 +597,16 @@ def ordered_ui_categories():
         yield category
 
 
+def get_value_for_setting(key):
+    value = getattr(opts, key)
+
+    info = opts.data_labels[key]
+    args = info.component_args() if callable(info.component_args) else info.component_args or {}
+    args = {k: v for k, v in args.items() if k not in {'precision'}}
+
+    return gr.update(value=value, **args)
+
+
 def create_ui():
     import modules.img2img
     import modules.txt2img
@@ -1600,7 +1610,7 @@ def create_ui():
 
         opts.save(shared.config_filename)
 
-        return gr.update(value=value), opts.dumpjson()
+        return get_value_for_setting(key), opts.dumpjson()
 
     with gr.Blocks(analytics_enabled=False) as settings_interface:
         with gr.Row():
@@ -1771,15 +1781,6 @@ def create_ui():
 
         component_keys = [k for k in opts.data_labels.keys() if k in component_dict]
 
-        def get_value_for_setting(key):
-            value = getattr(opts, key)
-
-            info = opts.data_labels[key]
-            args = info.component_args() if callable(info.component_args) else info.component_args or {}
-            args = {k: v for k, v in args.items() if k not in {'precision'}}
-
-            return gr.update(value=value, **args)
-
         def get_settings_values():
             return [get_value_for_setting(key) for key in component_keys]
 
-- 
cgit v1.2.3


From 84d9ce30cb427759547bc7876ed80ab91787d175 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Tue, 24 Jan 2023 23:51:45 -0500
Subject: Add option for float32 sampling with float16 UNet

This also handles type casting so that ROCm and MPS torch devices work correctly without --no-half. One cast is required for deepbooru in deepbooru_model.py, some explicit casting is required for img2img and inpainting. depth_model can't be converted to float16 or it won't work correctly on some systems (it's known to have issues on MPS) so in sd_models.py model.depth_model is removed for model.half().
---
 README.md                  |  1 +
 modules/deepbooru_model.py |  4 +++-
 modules/devices.py         |  2 ++
 modules/processing.py      | 15 ++++++++-------
 modules/sd_hijack_unet.py  | 29 +++++++++++++++++++++++++++++
 modules/sd_hijack_utils.py | 28 ++++++++++++++++++++++++++++
 modules/sd_models.py       | 10 ++++++++++
 modules/shared.py          |  1 +
 8 files changed, 82 insertions(+), 8 deletions(-)
 create mode 100644 modules/sd_hijack_utils.py

(limited to 'modules/sd_models.py')

diff --git a/README.md b/README.md
index 9c0cd1ef..a5611671 100644
--- a/README.md
+++ b/README.md
@@ -157,4 +157,5 @@ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and al
 - DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru
 - Security advice - RyotaK
 - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
+- Sampling in float32 precision from a float16 UNet - marunine for the idea, Birch-san for the example Diffusers implementation (https://github.com/Birch-san/diffusers-play/tree/92feee6)
 - (You)
diff --git a/modules/deepbooru_model.py b/modules/deepbooru_model.py
index edd40c81..83d2ff09 100644
--- a/modules/deepbooru_model.py
+++ b/modules/deepbooru_model.py
@@ -2,6 +2,8 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
+from modules import devices
+
 # see https://github.com/AUTOMATIC1111/TorchDeepDanbooru for more
 
 
@@ -196,7 +198,7 @@ class DeepDanbooruModel(nn.Module):
         t_358, = inputs
         t_359 = t_358.permute(*[0, 3, 1, 2])
         t_359_padded = F.pad(t_359, [2, 3, 2, 3], value=0)
-        t_360 = self.n_Conv_0(t_359_padded)
+        t_360 = self.n_Conv_0(t_359_padded.to(self.n_Conv_0.bias.dtype) if devices.unet_needs_upcast else t_359_padded)
         t_361 = F.relu(t_360)
         t_361 = F.pad(t_361, [0, 1, 0, 1], value=float('-inf'))
         t_362 = self.n_MaxPool_0(t_361)
diff --git a/modules/devices.py b/modules/devices.py
index 524ec7af..0981ef80 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -79,6 +79,8 @@ cpu = torch.device("cpu")
 device = device_interrogate = device_gfpgan = device_esrgan = device_codeformer = None
 dtype = torch.float16
 dtype_vae = torch.float16
+dtype_unet = torch.float16
+unet_needs_upcast = False
 
 
 def randn(seed, shape):
diff --git a/modules/processing.py b/modules/processing.py
index bc541e2f..2d186ba0 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -172,7 +172,8 @@ class StableDiffusionProcessing:
         midas_in = torch.from_numpy(transformed["midas_in"][None, ...]).to(device=shared.device)
         midas_in = repeat(midas_in, "1 ... -> n ...", n=self.batch_size)
 
-        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image))
+        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image.to(devices.dtype_unet) if devices.unet_needs_upcast else source_image))
+        conditioning_image = conditioning_image.float() if devices.unet_needs_upcast else conditioning_image
         conditioning = torch.nn.functional.interpolate(
             self.sd_model.depth_model(midas_in),
             size=conditioning_image.shape[2:],
@@ -203,7 +204,7 @@ class StableDiffusionProcessing:
 
         # Create another latent image, this time with a masked version of the original input.
         # Smoothly interpolate between the masked and unmasked latent conditioning image using a parameter.
-        conditioning_mask = conditioning_mask.to(source_image.device).to(source_image.dtype)
+        conditioning_mask = conditioning_mask.to(device=source_image.device, dtype=source_image.dtype)
         conditioning_image = torch.lerp(
             source_image,
             source_image * (1.0 - conditioning_mask),
@@ -211,7 +212,7 @@ class StableDiffusionProcessing:
         )
 
         # Encode the new masked image using first stage of network.
-        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
+        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image.to(devices.dtype_unet) if devices.unet_needs_upcast else conditioning_image))
 
         # Create the concatenated conditioning tensor to be fed to `c_concat`
         conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=latent_image.shape[-2:])
@@ -225,10 +226,10 @@ class StableDiffusionProcessing:
         # HACK: Using introspection as the Depth2Image model doesn't appear to uniquely
         # identify itself with a field common to all models. The conditioning_key is also hybrid.
         if isinstance(self.sd_model, LatentDepth2ImageDiffusion):
-            return self.depth2img_image_conditioning(source_image)
+            return self.depth2img_image_conditioning(source_image.float() if devices.unet_needs_upcast else source_image)
 
         if self.sampler.conditioning_key in {'hybrid', 'concat'}:
-            return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
+            return self.inpainting_image_conditioning(source_image.float() if devices.unet_needs_upcast else source_image, latent_image, image_mask=image_mask)
 
         # Dummy zero conditioning if we're not using inpainting or depth model.
         return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
@@ -610,7 +611,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if p.n_iter > 1:
                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"
 
-            with devices.autocast():
+            with devices.autocast(disable=devices.unet_needs_upcast):
                 samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
 
             x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]
@@ -988,7 +989,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
         image = torch.from_numpy(batch_images)
         image = 2. * image - 1.
-        image = image.to(shared.device)
+        image = image.to(device=shared.device, dtype=devices.dtype_unet if devices.unet_needs_upcast else None)
 
         self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
 
diff --git a/modules/sd_hijack_unet.py b/modules/sd_hijack_unet.py
index 18daf8c1..88c94e54 100644
--- a/modules/sd_hijack_unet.py
+++ b/modules/sd_hijack_unet.py
@@ -1,4 +1,8 @@
 import torch
+from packaging import version
+
+from modules import devices
+from modules.sd_hijack_utils import CondFunc
 
 
 class TorchHijackForUnet:
@@ -28,3 +32,28 @@ class TorchHijackForUnet:
 
 
 th = TorchHijackForUnet()
+
+
+# Below are monkey patches to enable upcasting a float16 UNet for float32 sampling
+def apply_model(orig_func, self, x_noisy, t, cond, **kwargs):
+    for y in cond.keys():
+        cond[y] = [x.to(devices.dtype_unet) if isinstance(x, torch.Tensor) else x for x in cond[y]]
+    with devices.autocast():
+        return orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs).float()
+
+class GELUHijack(torch.nn.GELU, torch.nn.Module):
+    def __init__(self, *args, **kwargs):
+        torch.nn.GELU.__init__(self, *args, **kwargs)
+    def forward(self, x):
+        if devices.unet_needs_upcast:
+            return torch.nn.GELU.forward(self.float(), x.float()).to(devices.dtype_unet)
+        else:
+            return torch.nn.GELU.forward(self, x)
+
+unet_needs_upcast = lambda *args, **kwargs: devices.unet_needs_upcast
+CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
+CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).to(devices.dtype_unet), unet_needs_upcast)
+if version.parse(torch.__version__) <= version.parse("1.13.1"):
+    CondFunc('ldm.modules.diffusionmodules.util.GroupNorm32.forward', lambda orig_func, self, *args, **kwargs: orig_func(self.float(), *args, **kwargs), unet_needs_upcast)
+    CondFunc('ldm.modules.attention.GEGLU.forward', lambda orig_func, self, x: orig_func(self.float(), x.float()).to(devices.dtype_unet), unet_needs_upcast)
+    CondFunc('open_clip.transformer.ResidualAttentionBlock.__init__', lambda orig_func, *args, **kwargs: kwargs.update({'act_layer': GELUHijack}) and False or orig_func(*args, **kwargs), lambda _, *args, **kwargs: kwargs.get('act_layer') is None or kwargs['act_layer'] == torch.nn.GELU)
diff --git a/modules/sd_hijack_utils.py b/modules/sd_hijack_utils.py
new file mode 100644
index 00000000..f81b169a
--- /dev/null
+++ b/modules/sd_hijack_utils.py
@@ -0,0 +1,28 @@
+import importlib
+
+class CondFunc:
+    def __new__(cls, orig_func, sub_func, cond_func):
+        self = super(CondFunc, cls).__new__(cls)
+        if isinstance(orig_func, str):
+            func_path = orig_func.split('.')
+            for i in range(len(func_path)-2, -1, -1):
+                try:
+                    resolved_obj = importlib.import_module('.'.join(func_path[:i]))
+                    break
+                except ImportError:
+                    pass
+            for attr_name in func_path[i:-1]:
+                resolved_obj = getattr(resolved_obj, attr_name)
+            orig_func = getattr(resolved_obj, func_path[-1])
+            setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
+        self.__init__(orig_func, sub_func, cond_func)
+        return lambda *args, **kwargs: self(*args, **kwargs)
+    def __init__(self, orig_func, sub_func, cond_func):
+        self.__orig_func = orig_func
+        self.__sub_func = sub_func
+        self.__cond_func = cond_func
+    def __call__(self, *args, **kwargs):
+        if not self.__cond_func or self.__cond_func(self.__orig_func, *args, **kwargs):
+            return self.__sub_func(self.__orig_func, *args, **kwargs)
+        else:
+            return self.__orig_func(*args, **kwargs)
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 12083848..7c98991a 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -257,16 +257,24 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo):
 
         if not shared.cmd_opts.no_half:
             vae = model.first_stage_model
+            depth_model = getattr(model, 'depth_model', None)
 
             # with --no-half-vae, remove VAE from model when doing half() to prevent its weights from being converted to float16
             if shared.cmd_opts.no_half_vae:
                 model.first_stage_model = None
+            # with --upcast-sampling, don't convert the depth model weights to float16
+            if shared.cmd_opts.upcast_sampling and depth_model:
+                model.depth_model = None
 
             model.half()
             model.first_stage_model = vae
+            if depth_model:
+                model.depth_model = depth_model
 
         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
+        devices.dtype_unet = model.model.diffusion_model.dtype
+        devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
 
         model.first_stage_model.to(devices.dtype_vae)
 
@@ -372,6 +380,8 @@ def load_model(checkpoint_info=None):
 
     if shared.cmd_opts.no_half:
         sd_config.model.params.unet_config.params.use_fp16 = False
+    elif shared.cmd_opts.upcast_sampling:
+        sd_config.model.params.unet_config.params.use_fp16 = True
 
     timer = Timer()
 
diff --git a/modules/shared.py b/modules/shared.py
index 5f713bee..4ce1209b 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -45,6 +45,7 @@ parser.add_argument("--lowram", action='store_true', help="load stable diffusion
 parser.add_argument("--always-batch-cond-uncond", action='store_true', help="disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram")
 parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
 parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
+parser.add_argument("--upcast-sampling", action='store_true', help="upcast sampling. No effect with --no-half. Usually produces similar results to --no-half with better performance while using less memory.")
 parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
 parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
 parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us")
-- 
cgit v1.2.3


From ee0a0da3244123cb6d2ba4097a54a1e9caccb687 Mon Sep 17 00:00:00 2001
From: Kyle <zerouex@gmail.com>
Date: Wed, 25 Jan 2023 08:53:23 -0500
Subject: Add instruct-pix2pix hijack

Allows loading instruct-pix2pix models via same method as inpainting models in sd_models.py and sd_hijack_ip2p.py

Adds ddpm_edit.py necessary for instruct-pix2pix
---
 modules/models/diffusion/ddpm_edit.py | 1459 +++++++++++++++++++++++++++++++++
 modules/sd_hijack_ip2p.py             |   13 +
 modules/sd_models.py                  |   12 +-
 3 files changed, 1483 insertions(+), 1 deletion(-)
 create mode 100644 modules/models/diffusion/ddpm_edit.py
 create mode 100644 modules/sd_hijack_ip2p.py

(limited to 'modules/sd_models.py')

diff --git a/modules/models/diffusion/ddpm_edit.py b/modules/models/diffusion/ddpm_edit.py
new file mode 100644
index 00000000..f3d49c44
--- /dev/null
+++ b/modules/models/diffusion/ddpm_edit.py
@@ -0,0 +1,1459 @@
+"""
+wild mixture of
+https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
+https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
+https://github.com/CompVis/taming-transformers
+-- merci
+"""
+
+# File modified by authors of InstructPix2Pix from original (https://github.com/CompVis/stable-diffusion).
+# See more details in LICENSE.
+
+import torch
+import torch.nn as nn
+import numpy as np
+import pytorch_lightning as pl
+from torch.optim.lr_scheduler import LambdaLR
+from einops import rearrange, repeat
+from contextlib import contextmanager
+from functools import partial
+from tqdm import tqdm
+from torchvision.utils import make_grid
+from pytorch_lightning.utilities.distributed import rank_zero_only
+
+from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
+from ldm.modules.ema import LitEma
+from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
+from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
+from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
+from ldm.models.diffusion.ddim import DDIMSampler
+
+
+__conditioning_keys__ = {'concat': 'c_concat',
+                         'crossattn': 'c_crossattn',
+                         'adm': 'y'}
+
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+
+def uniform_on_device(r1, r2, shape, device):
+    return (r1 - r2) * torch.rand(*shape, device=device) + r2
+
+
+class DDPM(pl.LightningModule):
+    # classic DDPM with Gaussian diffusion, in image space
+    def __init__(self,
+                 unet_config,
+                 timesteps=1000,
+                 beta_schedule="linear",
+                 loss_type="l2",
+                 ckpt_path=None,
+                 ignore_keys=[],
+                 load_only_unet=False,
+                 monitor="val/loss",
+                 use_ema=True,
+                 first_stage_key="image",
+                 image_size=256,
+                 channels=3,
+                 log_every_t=100,
+                 clip_denoised=True,
+                 linear_start=1e-4,
+                 linear_end=2e-2,
+                 cosine_s=8e-3,
+                 given_betas=None,
+                 original_elbo_weight=0.,
+                 v_posterior=0.,  # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
+                 l_simple_weight=1.,
+                 conditioning_key=None,
+                 parameterization="eps",  # all assuming fixed variance schedules
+                 scheduler_config=None,
+                 use_positional_encodings=False,
+                 learn_logvar=False,
+                 logvar_init=0.,
+                 load_ema=True,
+                 ):
+        super().__init__()
+        assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
+        self.parameterization = parameterization
+        print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
+        self.cond_stage_model = None
+        self.clip_denoised = clip_denoised
+        self.log_every_t = log_every_t
+        self.first_stage_key = first_stage_key
+        self.image_size = image_size  # try conv?
+        self.channels = channels
+        self.use_positional_encodings = use_positional_encodings
+        self.model = DiffusionWrapper(unet_config, conditioning_key)
+        count_params(self.model, verbose=True)
+        self.use_ema = use_ema
+
+        self.use_scheduler = scheduler_config is not None
+        if self.use_scheduler:
+            self.scheduler_config = scheduler_config
+
+        self.v_posterior = v_posterior
+        self.original_elbo_weight = original_elbo_weight
+        self.l_simple_weight = l_simple_weight
+
+        if monitor is not None:
+            self.monitor = monitor
+
+        if self.use_ema and load_ema:
+            self.model_ema = LitEma(self.model)
+            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
+
+            # If initialing from EMA-only checkpoint, create EMA model after loading.
+            if self.use_ema and not load_ema:
+                self.model_ema = LitEma(self.model)
+                print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+        self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
+                               linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
+
+        self.loss_type = loss_type
+
+        self.learn_logvar = learn_logvar
+        self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
+        if self.learn_logvar:
+            self.logvar = nn.Parameter(self.logvar, requires_grad=True)
+
+
+    def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
+                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+        if exists(given_betas):
+            betas = given_betas
+        else:
+            betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
+                                       cosine_s=cosine_s)
+        alphas = 1. - betas
+        alphas_cumprod = np.cumprod(alphas, axis=0)
+        alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
+
+        timesteps, = betas.shape
+        self.num_timesteps = int(timesteps)
+        self.linear_start = linear_start
+        self.linear_end = linear_end
+        assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
+
+        to_torch = partial(torch.tensor, dtype=torch.float32)
+
+        self.register_buffer('betas', to_torch(betas))
+        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
+        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
+        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
+        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
+        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
+
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
+                    1. - alphas_cumprod) + self.v_posterior * betas
+        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+        self.register_buffer('posterior_variance', to_torch(posterior_variance))
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+        self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
+        self.register_buffer('posterior_mean_coef1', to_torch(
+            betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
+        self.register_buffer('posterior_mean_coef2', to_torch(
+            (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
+
+        if self.parameterization == "eps":
+            lvlb_weights = self.betas ** 2 / (
+                        2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
+        elif self.parameterization == "x0":
+            lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
+        else:
+            raise NotImplementedError("mu not supported")
+        # TODO how to choose this term
+        lvlb_weights[0] = lvlb_weights[1]
+        self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
+        assert not torch.isnan(self.lvlb_weights).all()
+
+    @contextmanager
+    def ema_scope(self, context=None):
+        if self.use_ema:
+            self.model_ema.store(self.model.parameters())
+            self.model_ema.copy_to(self.model)
+            if context is not None:
+                print(f"{context}: Switched to EMA weights")
+        try:
+            yield None
+        finally:
+            if self.use_ema:
+                self.model_ema.restore(self.model.parameters())
+                if context is not None:
+                    print(f"{context}: Restored training weights")
+
+    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
+        sd = torch.load(path, map_location="cpu")
+        if "state_dict" in list(sd.keys()):
+            sd = sd["state_dict"]
+        keys = list(sd.keys())
+
+        # Our model adds additional channels to the first layer to condition on an input image.
+        # For the first layer, copy existing channel weights and initialize new channel weights to zero.
+        input_keys = [
+            "model.diffusion_model.input_blocks.0.0.weight",
+            "model_ema.diffusion_modelinput_blocks00weight",
+        ]
+
+        self_sd = self.state_dict()
+        for input_key in input_keys:
+            if input_key not in sd or input_key not in self_sd:
+                continue
+
+            input_weight = self_sd[input_key]
+
+            if input_weight.size() != sd[input_key].size():
+                print(f"Manual init: {input_key}")
+                input_weight.zero_()
+                input_weight[:, :4, :, :].copy_(sd[input_key])
+                ignore_keys.append(input_key)
+
+        for k in keys:
+            for ik in ignore_keys:
+                if k.startswith(ik):
+                    print("Deleting key {} from state_dict.".format(k))
+                    del sd[k]
+        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
+            sd, strict=False)
+        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+        if len(missing) > 0:
+            print(f"Missing Keys: {missing}")
+        if len(unexpected) > 0:
+            print(f"Unexpected Keys: {unexpected}")
+
+    def q_mean_variance(self, x_start, t):
+        """
+        Get the distribution q(x_t | x_0).
+        :param x_start: the [N x C x ...] tensor of noiseless inputs.
+        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
+        """
+        mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
+        variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
+        log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
+        return mean, variance, log_variance
+
+    def predict_start_from_noise(self, x_t, t, noise):
+        return (
+                extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
+                extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
+        )
+
+    def q_posterior(self, x_start, x_t, t):
+        posterior_mean = (
+                extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
+                extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
+        )
+        posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
+        posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+    def p_mean_variance(self, x, t, clip_denoised: bool):
+        model_out = self.model(x, t)
+        if self.parameterization == "eps":
+            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+        elif self.parameterization == "x0":
+            x_recon = model_out
+        if clip_denoised:
+            x_recon.clamp_(-1., 1.)
+
+        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+        return model_mean, posterior_variance, posterior_log_variance
+
+    @torch.no_grad()
+    def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
+        b, *_, device = *x.shape, x.device
+        model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
+        noise = noise_like(x.shape, device, repeat_noise)
+        # no noise when t == 0
+        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+    @torch.no_grad()
+    def p_sample_loop(self, shape, return_intermediates=False):
+        device = self.betas.device
+        b = shape[0]
+        img = torch.randn(shape, device=device)
+        intermediates = [img]
+        for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
+            img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
+                                clip_denoised=self.clip_denoised)
+            if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
+                intermediates.append(img)
+        if return_intermediates:
+            return img, intermediates
+        return img
+
+    @torch.no_grad()
+    def sample(self, batch_size=16, return_intermediates=False):
+        image_size = self.image_size
+        channels = self.channels
+        return self.p_sample_loop((batch_size, channels, image_size, image_size),
+                                  return_intermediates=return_intermediates)
+
+    def q_sample(self, x_start, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+        return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
+                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
+
+    def get_loss(self, pred, target, mean=True):
+        if self.loss_type == 'l1':
+            loss = (target - pred).abs()
+            if mean:
+                loss = loss.mean()
+        elif self.loss_type == 'l2':
+            if mean:
+                loss = torch.nn.functional.mse_loss(target, pred)
+            else:
+                loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
+        else:
+            raise NotImplementedError("unknown loss type '{loss_type}'")
+
+        return loss
+
+    def p_losses(self, x_start, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+        model_out = self.model(x_noisy, t)
+
+        loss_dict = {}
+        if self.parameterization == "eps":
+            target = noise
+        elif self.parameterization == "x0":
+            target = x_start
+        else:
+            raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported")
+
+        loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
+
+        log_prefix = 'train' if self.training else 'val'
+
+        loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
+        loss_simple = loss.mean() * self.l_simple_weight
+
+        loss_vlb = (self.lvlb_weights[t] * loss).mean()
+        loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
+
+        loss = loss_simple + self.original_elbo_weight * loss_vlb
+
+        loss_dict.update({f'{log_prefix}/loss': loss})
+
+        return loss, loss_dict
+
+    def forward(self, x, *args, **kwargs):
+        # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
+        # assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
+        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+        return self.p_losses(x, t, *args, **kwargs)
+
+    def get_input(self, batch, k):
+        return batch[k]
+
+    def shared_step(self, batch):
+        x = self.get_input(batch, self.first_stage_key)
+        loss, loss_dict = self(x)
+        return loss, loss_dict
+
+    def training_step(self, batch, batch_idx):
+        loss, loss_dict = self.shared_step(batch)
+
+        self.log_dict(loss_dict, prog_bar=True,
+                      logger=True, on_step=True, on_epoch=True)
+
+        self.log("global_step", self.global_step,
+                 prog_bar=True, logger=True, on_step=True, on_epoch=False)
+
+        if self.use_scheduler:
+            lr = self.optimizers().param_groups[0]['lr']
+            self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
+
+        return loss
+
+    @torch.no_grad()
+    def validation_step(self, batch, batch_idx):
+        _, loss_dict_no_ema = self.shared_step(batch)
+        with self.ema_scope():
+            _, loss_dict_ema = self.shared_step(batch)
+            loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
+        self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
+        self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
+
+    def on_train_batch_end(self, *args, **kwargs):
+        if self.use_ema:
+            self.model_ema(self.model)
+
+    def _get_rows_from_list(self, samples):
+        n_imgs_per_row = len(samples)
+        denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
+        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
+        return denoise_grid
+
+    @torch.no_grad()
+    def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
+        log = dict()
+        x = self.get_input(batch, self.first_stage_key)
+        N = min(x.shape[0], N)
+        n_row = min(x.shape[0], n_row)
+        x = x.to(self.device)[:N]
+        log["inputs"] = x
+
+        # get diffusion row
+        diffusion_row = list()
+        x_start = x[:n_row]
+
+        for t in range(self.num_timesteps):
+            if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+                t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+                t = t.to(self.device).long()
+                noise = torch.randn_like(x_start)
+                x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+                diffusion_row.append(x_noisy)
+
+        log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
+
+        if sample:
+            # get denoise row
+            with self.ema_scope("Plotting"):
+                samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
+
+            log["samples"] = samples
+            log["denoise_row"] = self._get_rows_from_list(denoise_row)
+
+        if return_keys:
+            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
+                return log
+            else:
+                return {key: log[key] for key in return_keys}
+        return log
+
+    def configure_optimizers(self):
+        lr = self.learning_rate
+        params = list(self.model.parameters())
+        if self.learn_logvar:
+            params = params + [self.logvar]
+        opt = torch.optim.AdamW(params, lr=lr)
+        return opt
+
+
+class LatentDiffusion(DDPM):
+    """main class"""
+    def __init__(self,
+                 first_stage_config,
+                 cond_stage_config,
+                 num_timesteps_cond=None,
+                 cond_stage_key="image",
+                 cond_stage_trainable=False,
+                 concat_mode=True,
+                 cond_stage_forward=None,
+                 conditioning_key=None,
+                 scale_factor=1.0,
+                 scale_by_std=False,
+                 load_ema=True,
+                 *args, **kwargs):
+        self.num_timesteps_cond = default(num_timesteps_cond, 1)
+        self.scale_by_std = scale_by_std
+        assert self.num_timesteps_cond <= kwargs['timesteps']
+        # for backwards compatibility after implementation of DiffusionWrapper
+        if conditioning_key is None:
+            conditioning_key = 'concat' if concat_mode else 'crossattn'
+        if cond_stage_config == '__is_unconditional__':
+            conditioning_key = None
+        ckpt_path = kwargs.pop("ckpt_path", None)
+        ignore_keys = kwargs.pop("ignore_keys", [])
+        super().__init__(conditioning_key=conditioning_key, *args, load_ema=load_ema, **kwargs)
+        self.concat_mode = concat_mode
+        self.cond_stage_trainable = cond_stage_trainable
+        self.cond_stage_key = cond_stage_key
+        try:
+            self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
+        except:
+            self.num_downs = 0
+        if not scale_by_std:
+            self.scale_factor = scale_factor
+        else:
+            self.register_buffer('scale_factor', torch.tensor(scale_factor))
+        self.instantiate_first_stage(first_stage_config)
+        self.instantiate_cond_stage(cond_stage_config)
+        self.cond_stage_forward = cond_stage_forward
+        self.clip_denoised = False
+        self.bbox_tokenizer = None
+
+        self.restarted_from_ckpt = False
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys)
+            self.restarted_from_ckpt = True
+
+            if self.use_ema and not load_ema:
+                self.model_ema = LitEma(self.model)
+                print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+    def make_cond_schedule(self, ):
+        self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
+        ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
+        self.cond_ids[:self.num_timesteps_cond] = ids
+
+    @rank_zero_only
+    @torch.no_grad()
+    def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
+        # only for very first batch
+        if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
+            assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
+            # set rescale weight to 1./std of encodings
+            print("### USING STD-RESCALING ###")
+            x = super().get_input(batch, self.first_stage_key)
+            x = x.to(self.device)
+            encoder_posterior = self.encode_first_stage(x)
+            z = self.get_first_stage_encoding(encoder_posterior).detach()
+            del self.scale_factor
+            self.register_buffer('scale_factor', 1. / z.flatten().std())
+            print(f"setting self.scale_factor to {self.scale_factor}")
+            print("### USING STD-RESCALING ###")
+
+    def register_schedule(self,
+                          given_betas=None, beta_schedule="linear", timesteps=1000,
+                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+        super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
+
+        self.shorten_cond_schedule = self.num_timesteps_cond > 1
+        if self.shorten_cond_schedule:
+            self.make_cond_schedule()
+
+    def instantiate_first_stage(self, config):
+        model = instantiate_from_config(config)
+        self.first_stage_model = model.eval()
+        self.first_stage_model.train = disabled_train
+        for param in self.first_stage_model.parameters():
+            param.requires_grad = False
+
+    def instantiate_cond_stage(self, config):
+        if not self.cond_stage_trainable:
+            if config == "__is_first_stage__":
+                print("Using first stage also as cond stage.")
+                self.cond_stage_model = self.first_stage_model
+            elif config == "__is_unconditional__":
+                print(f"Training {self.__class__.__name__} as an unconditional model.")
+                self.cond_stage_model = None
+                # self.be_unconditional = True
+            else:
+                model = instantiate_from_config(config)
+                self.cond_stage_model = model.eval()
+                self.cond_stage_model.train = disabled_train
+                for param in self.cond_stage_model.parameters():
+                    param.requires_grad = False
+        else:
+            assert config != '__is_first_stage__'
+            assert config != '__is_unconditional__'
+            model = instantiate_from_config(config)
+            self.cond_stage_model = model
+
+    def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
+        denoise_row = []
+        for zd in tqdm(samples, desc=desc):
+            denoise_row.append(self.decode_first_stage(zd.to(self.device),
+                                                            force_not_quantize=force_no_decoder_quantization))
+        n_imgs_per_row = len(denoise_row)
+        denoise_row = torch.stack(denoise_row)  # n_log_step, n_row, C, H, W
+        denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
+        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
+        return denoise_grid
+
+    def get_first_stage_encoding(self, encoder_posterior):
+        if isinstance(encoder_posterior, DiagonalGaussianDistribution):
+            z = encoder_posterior.sample()
+        elif isinstance(encoder_posterior, torch.Tensor):
+            z = encoder_posterior
+        else:
+            raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
+        return self.scale_factor * z
+
+    def get_learned_conditioning(self, c):
+        if self.cond_stage_forward is None:
+            if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
+                c = self.cond_stage_model.encode(c)
+                if isinstance(c, DiagonalGaussianDistribution):
+                    c = c.mode()
+            else:
+                c = self.cond_stage_model(c)
+        else:
+            assert hasattr(self.cond_stage_model, self.cond_stage_forward)
+            c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
+        return c
+
+    def meshgrid(self, h, w):
+        y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
+        x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
+
+        arr = torch.cat([y, x], dim=-1)
+        return arr
+
+    def delta_border(self, h, w):
+        """
+        :param h: height
+        :param w: width
+        :return: normalized distance to image border,
+         wtith min distance = 0 at border and max dist = 0.5 at image center
+        """
+        lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
+        arr = self.meshgrid(h, w) / lower_right_corner
+        dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
+        dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
+        edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
+        return edge_dist
+
+    def get_weighting(self, h, w, Ly, Lx, device):
+        weighting = self.delta_border(h, w)
+        weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
+                               self.split_input_params["clip_max_weight"], )
+        weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
+
+        if self.split_input_params["tie_braker"]:
+            L_weighting = self.delta_border(Ly, Lx)
+            L_weighting = torch.clip(L_weighting,
+                                     self.split_input_params["clip_min_tie_weight"],
+                                     self.split_input_params["clip_max_tie_weight"])
+
+            L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
+            weighting = weighting * L_weighting
+        return weighting
+
+    def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1):  # todo load once not every time, shorten code
+        """
+        :param x: img of size (bs, c, h, w)
+        :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
+        """
+        bs, nc, h, w = x.shape
+
+        # number of crops in image
+        Ly = (h - kernel_size[0]) // stride[0] + 1
+        Lx = (w - kernel_size[1]) // stride[1] + 1
+
+        if uf == 1 and df == 1:
+            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+            unfold = torch.nn.Unfold(**fold_params)
+
+            fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
+
+            weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
+            normalization = fold(weighting).view(1, 1, h, w)  # normalizes the overlap
+            weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
+
+        elif uf > 1 and df == 1:
+            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+            unfold = torch.nn.Unfold(**fold_params)
+
+            fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
+                                dilation=1, padding=0,
+                                stride=(stride[0] * uf, stride[1] * uf))
+            fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
+
+            weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
+            normalization = fold(weighting).view(1, 1, h * uf, w * uf)  # normalizes the overlap
+            weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
+
+        elif df > 1 and uf == 1:
+            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+            unfold = torch.nn.Unfold(**fold_params)
+
+            fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
+                                dilation=1, padding=0,
+                                stride=(stride[0] // df, stride[1] // df))
+            fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
+
+            weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
+            normalization = fold(weighting).view(1, 1, h // df, w // df)  # normalizes the overlap
+            weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
+
+        else:
+            raise NotImplementedError
+
+        return fold, unfold, normalization, weighting
+
+    @torch.no_grad()
+    def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
+                  cond_key=None, return_original_cond=False, bs=None, uncond=0.05):
+        x = super().get_input(batch, k)
+        if bs is not None:
+            x = x[:bs]
+        x = x.to(self.device)
+        encoder_posterior = self.encode_first_stage(x)
+        z = self.get_first_stage_encoding(encoder_posterior).detach()
+        cond_key = cond_key or self.cond_stage_key
+        xc = super().get_input(batch, cond_key)
+        if bs is not None:
+            xc["c_crossattn"] = xc["c_crossattn"][:bs]
+            xc["c_concat"] = xc["c_concat"][:bs]
+        cond = {}
+
+        # To support classifier-free guidance, randomly drop out only text conditioning 5%, only image conditioning 5%, and both 5%.
+        random = torch.rand(x.size(0), device=x.device)
+        prompt_mask = rearrange(random < 2 * uncond, "n -> n 1 1")
+        input_mask = 1 - rearrange((random >= uncond).float() * (random < 3 * uncond).float(), "n -> n 1 1 1")
+
+        null_prompt = self.get_learned_conditioning([""])
+        cond["c_crossattn"] = [torch.where(prompt_mask, null_prompt, self.get_learned_conditioning(xc["c_crossattn"]).detach())]
+        cond["c_concat"] = [input_mask * self.encode_first_stage((xc["c_concat"].to(self.device))).mode().detach()]
+
+        out = [z, cond]
+        if return_first_stage_outputs:
+            xrec = self.decode_first_stage(z)
+            out.extend([x, xrec])
+        if return_original_cond:
+            out.append(xc)
+        return out
+
+    @torch.no_grad()
+    def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
+        if predict_cids:
+            if z.dim() == 4:
+                z = torch.argmax(z.exp(), dim=1).long()
+            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
+            z = rearrange(z, 'b h w c -> b c h w').contiguous()
+
+        z = 1. / self.scale_factor * z
+
+        if hasattr(self, "split_input_params"):
+            if self.split_input_params["patch_distributed_vq"]:
+                ks = self.split_input_params["ks"]  # eg. (128, 128)
+                stride = self.split_input_params["stride"]  # eg. (64, 64)
+                uf = self.split_input_params["vqf"]
+                bs, nc, h, w = z.shape
+                if ks[0] > h or ks[1] > w:
+                    ks = (min(ks[0], h), min(ks[1], w))
+                    print("reducing Kernel")
+
+                if stride[0] > h or stride[1] > w:
+                    stride = (min(stride[0], h), min(stride[1], w))
+                    print("reducing stride")
+
+                fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
+
+                z = unfold(z)  # (bn, nc * prod(**ks), L)
+                # 1. Reshape to img shape
+                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
+
+                # 2. apply model loop over last dim
+                if isinstance(self.first_stage_model, VQModelInterface):
+                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
+                                                                 force_not_quantize=predict_cids or force_not_quantize)
+                                   for i in range(z.shape[-1])]
+                else:
+
+                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
+                                   for i in range(z.shape[-1])]
+
+                o = torch.stack(output_list, axis=-1)  # # (bn, nc, ks[0], ks[1], L)
+                o = o * weighting
+                # Reverse 1. reshape to img shape
+                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
+                # stitch crops together
+                decoded = fold(o)
+                decoded = decoded / normalization  # norm is shape (1, 1, h, w)
+                return decoded
+            else:
+                if isinstance(self.first_stage_model, VQModelInterface):
+                    return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+                else:
+                    return self.first_stage_model.decode(z)
+
+        else:
+            if isinstance(self.first_stage_model, VQModelInterface):
+                return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+            else:
+                return self.first_stage_model.decode(z)
+
+    # same as above but without decorator
+    def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
+        if predict_cids:
+            if z.dim() == 4:
+                z = torch.argmax(z.exp(), dim=1).long()
+            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
+            z = rearrange(z, 'b h w c -> b c h w').contiguous()
+
+        z = 1. / self.scale_factor * z
+
+        if hasattr(self, "split_input_params"):
+            if self.split_input_params["patch_distributed_vq"]:
+                ks = self.split_input_params["ks"]  # eg. (128, 128)
+                stride = self.split_input_params["stride"]  # eg. (64, 64)
+                uf = self.split_input_params["vqf"]
+                bs, nc, h, w = z.shape
+                if ks[0] > h or ks[1] > w:
+                    ks = (min(ks[0], h), min(ks[1], w))
+                    print("reducing Kernel")
+
+                if stride[0] > h or stride[1] > w:
+                    stride = (min(stride[0], h), min(stride[1], w))
+                    print("reducing stride")
+
+                fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
+
+                z = unfold(z)  # (bn, nc * prod(**ks), L)
+                # 1. Reshape to img shape
+                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
+
+                # 2. apply model loop over last dim
+                if isinstance(self.first_stage_model, VQModelInterface):
+                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
+                                                                 force_not_quantize=predict_cids or force_not_quantize)
+                                   for i in range(z.shape[-1])]
+                else:
+
+                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
+                                   for i in range(z.shape[-1])]
+
+                o = torch.stack(output_list, axis=-1)  # # (bn, nc, ks[0], ks[1], L)
+                o = o * weighting
+                # Reverse 1. reshape to img shape
+                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
+                # stitch crops together
+                decoded = fold(o)
+                decoded = decoded / normalization  # norm is shape (1, 1, h, w)
+                return decoded
+            else:
+                if isinstance(self.first_stage_model, VQModelInterface):
+                    return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+                else:
+                    return self.first_stage_model.decode(z)
+
+        else:
+            if isinstance(self.first_stage_model, VQModelInterface):
+                return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+            else:
+                return self.first_stage_model.decode(z)
+
+    @torch.no_grad()
+    def encode_first_stage(self, x):
+        if hasattr(self, "split_input_params"):
+            if self.split_input_params["patch_distributed_vq"]:
+                ks = self.split_input_params["ks"]  # eg. (128, 128)
+                stride = self.split_input_params["stride"]  # eg. (64, 64)
+                df = self.split_input_params["vqf"]
+                self.split_input_params['original_image_size'] = x.shape[-2:]
+                bs, nc, h, w = x.shape
+                if ks[0] > h or ks[1] > w:
+                    ks = (min(ks[0], h), min(ks[1], w))
+                    print("reducing Kernel")
+
+                if stride[0] > h or stride[1] > w:
+                    stride = (min(stride[0], h), min(stride[1], w))
+                    print("reducing stride")
+
+                fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)
+                z = unfold(x)  # (bn, nc * prod(**ks), L)
+                # Reshape to img shape
+                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
+
+                output_list = [self.first_stage_model.encode(z[:, :, :, :, i])
+                               for i in range(z.shape[-1])]
+
+                o = torch.stack(output_list, axis=-1)
+                o = o * weighting
+
+                # Reverse reshape to img shape
+                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
+                # stitch crops together
+                decoded = fold(o)
+                decoded = decoded / normalization
+                return decoded
+
+            else:
+                return self.first_stage_model.encode(x)
+        else:
+            return self.first_stage_model.encode(x)
+
+    def shared_step(self, batch, **kwargs):
+        x, c = self.get_input(batch, self.first_stage_key)
+        loss = self(x, c)
+        return loss
+
+    def forward(self, x, c, *args, **kwargs):
+        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+        if self.model.conditioning_key is not None:
+            assert c is not None
+            if self.cond_stage_trainable:
+                c = self.get_learned_conditioning(c)
+            if self.shorten_cond_schedule:  # TODO: drop this option
+                tc = self.cond_ids[t].to(self.device)
+                c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
+        return self.p_losses(x, c, t, *args, **kwargs)
+
+    def _rescale_annotations(self, bboxes, crop_coordinates):  # TODO: move to dataset
+        def rescale_bbox(bbox):
+            x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
+            y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
+            w = min(bbox[2] / crop_coordinates[2], 1 - x0)
+            h = min(bbox[3] / crop_coordinates[3], 1 - y0)
+            return x0, y0, w, h
+
+        return [rescale_bbox(b) for b in bboxes]
+
+    def apply_model(self, x_noisy, t, cond, return_ids=False):
+
+        if isinstance(cond, dict):
+            # hybrid case, cond is exptected to be a dict
+            pass
+        else:
+            if not isinstance(cond, list):
+                cond = [cond]
+            key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
+            cond = {key: cond}
+
+        if hasattr(self, "split_input_params"):
+            assert len(cond) == 1  # todo can only deal with one conditioning atm
+            assert not return_ids
+            ks = self.split_input_params["ks"]  # eg. (128, 128)
+            stride = self.split_input_params["stride"]  # eg. (64, 64)
+
+            h, w = x_noisy.shape[-2:]
+
+            fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
+
+            z = unfold(x_noisy)  # (bn, nc * prod(**ks), L)
+            # Reshape to img shape
+            z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
+            z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
+
+            if self.cond_stage_key in ["image", "LR_image", "segmentation",
+                                       'bbox_img'] and self.model.conditioning_key:  # todo check for completeness
+                c_key = next(iter(cond.keys()))  # get key
+                c = next(iter(cond.values()))  # get value
+                assert (len(c) == 1)  # todo extend to list with more than one elem
+                c = c[0]  # get element
+
+                c = unfold(c)
+                c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
+
+                cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
+
+            elif self.cond_stage_key == 'coordinates_bbox':
+                assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
+
+                # assuming padding of unfold is always 0 and its dilation is always 1
+                n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
+                full_img_h, full_img_w = self.split_input_params['original_image_size']
+                # as we are operating on latents, we need the factor from the original image size to the
+                # spatial latent size to properly rescale the crops for regenerating the bbox annotations
+                num_downs = self.first_stage_model.encoder.num_resolutions - 1
+                rescale_latent = 2 ** (num_downs)
+
+                # get top left postions of patches as conforming for the bbbox tokenizer, therefore we
+                # need to rescale the tl patch coordinates to be in between (0,1)
+                tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
+                                         rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
+                                        for patch_nr in range(z.shape[-1])]
+
+                # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
+                patch_limits = [(x_tl, y_tl,
+                                 rescale_latent * ks[0] / full_img_w,
+                                 rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
+                # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
+
+                # tokenize crop coordinates for the bounding boxes of the respective patches
+                patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)
+                                      for bbox in patch_limits]  # list of length l with tensors of shape (1, 2)
+                print(patch_limits_tknzd[0].shape)
+                # cut tknzd crop position from conditioning
+                assert isinstance(cond, dict), 'cond must be dict to be fed into model'
+                cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
+                print(cut_cond.shape)
+
+                adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
+                adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
+                print(adapted_cond.shape)
+                adapted_cond = self.get_learned_conditioning(adapted_cond)
+                print(adapted_cond.shape)
+                adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
+                print(adapted_cond.shape)
+
+                cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
+
+            else:
+                cond_list = [cond for i in range(z.shape[-1])]  # Todo make this more efficient
+
+            # apply model by loop over crops
+            output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
+            assert not isinstance(output_list[0],
+                                  tuple)  # todo cant deal with multiple model outputs check this never happens
+
+            o = torch.stack(output_list, axis=-1)
+            o = o * weighting
+            # Reverse reshape to img shape
+            o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
+            # stitch crops together
+            x_recon = fold(o) / normalization
+
+        else:
+            x_recon = self.model(x_noisy, t, **cond)
+
+        if isinstance(x_recon, tuple) and not return_ids:
+            return x_recon[0]
+        else:
+            return x_recon
+
+    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
+        return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
+               extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
+
+    def _prior_bpd(self, x_start):
+        """
+        Get the prior KL term for the variational lower-bound, measured in
+        bits-per-dim.
+        This term can't be optimized, as it only depends on the encoder.
+        :param x_start: the [N x C x ...] tensor of inputs.
+        :return: a batch of [N] KL values (in bits), one per batch element.
+        """
+        batch_size = x_start.shape[0]
+        t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
+        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
+        kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
+        return mean_flat(kl_prior) / np.log(2.0)
+
+    def p_losses(self, x_start, cond, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+        model_output = self.apply_model(x_noisy, t, cond)
+
+        loss_dict = {}
+        prefix = 'train' if self.training else 'val'
+
+        if self.parameterization == "x0":
+            target = x_start
+        elif self.parameterization == "eps":
+            target = noise
+        else:
+            raise NotImplementedError()
+
+        loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
+        loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
+
+        logvar_t = self.logvar[t].to(self.device)
+        loss = loss_simple / torch.exp(logvar_t) + logvar_t
+        # loss = loss_simple / torch.exp(self.logvar) + self.logvar
+        if self.learn_logvar:
+            loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
+            loss_dict.update({'logvar': self.logvar.data.mean()})
+
+        loss = self.l_simple_weight * loss.mean()
+
+        loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
+        loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
+        loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
+        loss += (self.original_elbo_weight * loss_vlb)
+        loss_dict.update({f'{prefix}/loss': loss})
+
+        return loss, loss_dict
+
+    def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
+                        return_x0=False, score_corrector=None, corrector_kwargs=None):
+        t_in = t
+        model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
+
+        if score_corrector is not None:
+            assert self.parameterization == "eps"
+            model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
+
+        if return_codebook_ids:
+            model_out, logits = model_out
+
+        if self.parameterization == "eps":
+            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+        elif self.parameterization == "x0":
+            x_recon = model_out
+        else:
+            raise NotImplementedError()
+
+        if clip_denoised:
+            x_recon.clamp_(-1., 1.)
+        if quantize_denoised:
+            x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
+        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+        if return_codebook_ids:
+            return model_mean, posterior_variance, posterior_log_variance, logits
+        elif return_x0:
+            return model_mean, posterior_variance, posterior_log_variance, x_recon
+        else:
+            return model_mean, posterior_variance, posterior_log_variance
+
+    @torch.no_grad()
+    def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
+                 return_codebook_ids=False, quantize_denoised=False, return_x0=False,
+                 temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
+        b, *_, device = *x.shape, x.device
+        outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
+                                       return_codebook_ids=return_codebook_ids,
+                                       quantize_denoised=quantize_denoised,
+                                       return_x0=return_x0,
+                                       score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+        if return_codebook_ids:
+            raise DeprecationWarning("Support dropped.")
+            model_mean, _, model_log_variance, logits = outputs
+        elif return_x0:
+            model_mean, _, model_log_variance, x0 = outputs
+        else:
+            model_mean, _, model_log_variance = outputs
+
+        noise = noise_like(x.shape, device, repeat_noise) * temperature
+        if noise_dropout > 0.:
+            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+        # no noise when t == 0
+        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+
+        if return_codebook_ids:
+            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
+        if return_x0:
+            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
+        else:
+            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+    @torch.no_grad()
+    def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
+                              img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
+                              score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
+                              log_every_t=None):
+        if not log_every_t:
+            log_every_t = self.log_every_t
+        timesteps = self.num_timesteps
+        if batch_size is not None:
+            b = batch_size if batch_size is not None else shape[0]
+            shape = [batch_size] + list(shape)
+        else:
+            b = batch_size = shape[0]
+        if x_T is None:
+            img = torch.randn(shape, device=self.device)
+        else:
+            img = x_T
+        intermediates = []
+        if cond is not None:
+            if isinstance(cond, dict):
+                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
+                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+            else:
+                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
+
+        if start_T is not None:
+            timesteps = min(timesteps, start_T)
+        iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
+                        total=timesteps) if verbose else reversed(
+            range(0, timesteps))
+        if type(temperature) == float:
+            temperature = [temperature] * timesteps
+
+        for i in iterator:
+            ts = torch.full((b,), i, device=self.device, dtype=torch.long)
+            if self.shorten_cond_schedule:
+                assert self.model.conditioning_key != 'hybrid'
+                tc = self.cond_ids[ts].to(cond.device)
+                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+
+            img, x0_partial = self.p_sample(img, cond, ts,
+                                            clip_denoised=self.clip_denoised,
+                                            quantize_denoised=quantize_denoised, return_x0=True,
+                                            temperature=temperature[i], noise_dropout=noise_dropout,
+                                            score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+            if mask is not None:
+                assert x0 is not None
+                img_orig = self.q_sample(x0, ts)
+                img = img_orig * mask + (1. - mask) * img
+
+            if i % log_every_t == 0 or i == timesteps - 1:
+                intermediates.append(x0_partial)
+            if callback: callback(i)
+            if img_callback: img_callback(img, i)
+        return img, intermediates
+
+    @torch.no_grad()
+    def p_sample_loop(self, cond, shape, return_intermediates=False,
+                      x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
+                      mask=None, x0=None, img_callback=None, start_T=None,
+                      log_every_t=None):
+
+        if not log_every_t:
+            log_every_t = self.log_every_t
+        device = self.betas.device
+        b = shape[0]
+        if x_T is None:
+            img = torch.randn(shape, device=device)
+        else:
+            img = x_T
+
+        intermediates = [img]
+        if timesteps is None:
+            timesteps = self.num_timesteps
+
+        if start_T is not None:
+            timesteps = min(timesteps, start_T)
+        iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
+            range(0, timesteps))
+
+        if mask is not None:
+            assert x0 is not None
+            assert x0.shape[2:3] == mask.shape[2:3]  # spatial size has to match
+
+        for i in iterator:
+            ts = torch.full((b,), i, device=device, dtype=torch.long)
+            if self.shorten_cond_schedule:
+                assert self.model.conditioning_key != 'hybrid'
+                tc = self.cond_ids[ts].to(cond.device)
+                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+
+            img = self.p_sample(img, cond, ts,
+                                clip_denoised=self.clip_denoised,
+                                quantize_denoised=quantize_denoised)
+            if mask is not None:
+                img_orig = self.q_sample(x0, ts)
+                img = img_orig * mask + (1. - mask) * img
+
+            if i % log_every_t == 0 or i == timesteps - 1:
+                intermediates.append(img)
+            if callback: callback(i)
+            if img_callback: img_callback(img, i)
+
+        if return_intermediates:
+            return img, intermediates
+        return img
+
+    @torch.no_grad()
+    def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
+               verbose=True, timesteps=None, quantize_denoised=False,
+               mask=None, x0=None, shape=None,**kwargs):
+        if shape is None:
+            shape = (batch_size, self.channels, self.image_size, self.image_size)
+        if cond is not None:
+            if isinstance(cond, dict):
+                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
+                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+            else:
+                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
+        return self.p_sample_loop(cond,
+                                  shape,
+                                  return_intermediates=return_intermediates, x_T=x_T,
+                                  verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
+                                  mask=mask, x0=x0)
+
+    @torch.no_grad()
+    def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
+
+        if ddim:
+            ddim_sampler = DDIMSampler(self)
+            shape = (self.channels, self.image_size, self.image_size)
+            samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size,
+                                                        shape,cond,verbose=False,**kwargs)
+
+        else:
+            samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
+                                                 return_intermediates=True,**kwargs)
+
+        return samples, intermediates
+
+
+    @torch.no_grad()
+    def log_images(self, batch, N=4, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
+                   quantize_denoised=True, inpaint=False, plot_denoise_rows=False, plot_progressive_rows=False,
+                   plot_diffusion_rows=False, **kwargs):
+
+        use_ddim = False
+
+        log = dict()
+        z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
+                                           return_first_stage_outputs=True,
+                                           force_c_encode=True,
+                                           return_original_cond=True,
+                                           bs=N, uncond=0)
+        N = min(x.shape[0], N)
+        n_row = min(x.shape[0], n_row)
+        log["inputs"] = x
+        log["reals"] = xc["c_concat"]
+        log["reconstruction"] = xrec
+        if self.model.conditioning_key is not None:
+            if hasattr(self.cond_stage_model, "decode"):
+                xc = self.cond_stage_model.decode(c)
+                log["conditioning"] = xc
+            elif self.cond_stage_key in ["caption"]:
+                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"])
+                log["conditioning"] = xc
+            elif self.cond_stage_key == 'class_label':
+                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
+                log['conditioning'] = xc
+            elif isimage(xc):
+                log["conditioning"] = xc
+            if ismap(xc):
+                log["original_conditioning"] = self.to_rgb(xc)
+
+        if plot_diffusion_rows:
+            # get diffusion row
+            diffusion_row = list()
+            z_start = z[:n_row]
+            for t in range(self.num_timesteps):
+                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+                    t = t.to(self.device).long()
+                    noise = torch.randn_like(z_start)
+                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
+                    diffusion_row.append(self.decode_first_stage(z_noisy))
+
+            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
+            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
+            diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
+            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
+            log["diffusion_row"] = diffusion_grid
+
+        if sample:
+            # get denoise row
+            with self.ema_scope("Plotting"):
+                samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
+                                                         ddim_steps=ddim_steps,eta=ddim_eta)
+                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
+            x_samples = self.decode_first_stage(samples)
+            log["samples"] = x_samples
+            if plot_denoise_rows:
+                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
+                log["denoise_row"] = denoise_grid
+
+            if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
+                    self.first_stage_model, IdentityFirstStage):
+                # also display when quantizing x0 while sampling
+                with self.ema_scope("Plotting Quantized Denoised"):
+                    samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
+                                                             ddim_steps=ddim_steps,eta=ddim_eta,
+                                                             quantize_denoised=True)
+                    # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
+                    #                                      quantize_denoised=True)
+                x_samples = self.decode_first_stage(samples.to(self.device))
+                log["samples_x0_quantized"] = x_samples
+
+            if inpaint:
+                # make a simple center square
+                b, h, w = z.shape[0], z.shape[2], z.shape[3]
+                mask = torch.ones(N, h, w).to(self.device)
+                # zeros will be filled in
+                mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
+                mask = mask[:, None, ...]
+                with self.ema_scope("Plotting Inpaint"):
+
+                    samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta,
+                                                ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+                x_samples = self.decode_first_stage(samples.to(self.device))
+                log["samples_inpainting"] = x_samples
+                log["mask"] = mask
+
+                # outpaint
+                with self.ema_scope("Plotting Outpaint"):
+                    samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta,
+                                                ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+                x_samples = self.decode_first_stage(samples.to(self.device))
+                log["samples_outpainting"] = x_samples
+
+        if plot_progressive_rows:
+            with self.ema_scope("Plotting Progressives"):
+                img, progressives = self.progressive_denoising(c,
+                                                               shape=(self.channels, self.image_size, self.image_size),
+                                                               batch_size=N)
+            prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
+            log["progressive_row"] = prog_row
+
+        if return_keys:
+            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
+                return log
+            else:
+                return {key: log[key] for key in return_keys}
+        return log
+
+    def configure_optimizers(self):
+        lr = self.learning_rate
+        params = list(self.model.parameters())
+        if self.cond_stage_trainable:
+            print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
+            params = params + list(self.cond_stage_model.parameters())
+        if self.learn_logvar:
+            print('Diffusion model optimizing logvar')
+            params.append(self.logvar)
+        opt = torch.optim.AdamW(params, lr=lr)
+        if self.use_scheduler:
+            assert 'target' in self.scheduler_config
+            scheduler = instantiate_from_config(self.scheduler_config)
+
+            print("Setting up LambdaLR scheduler...")
+            scheduler = [
+                {
+                    'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
+                    'interval': 'step',
+                    'frequency': 1
+                }]
+            return [opt], scheduler
+        return opt
+
+    @torch.no_grad()
+    def to_rgb(self, x):
+        x = x.float()
+        if not hasattr(self, "colorize"):
+            self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
+        x = nn.functional.conv2d(x, weight=self.colorize)
+        x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
+        return x
+
+
+class DiffusionWrapper(pl.LightningModule):
+    def __init__(self, diff_model_config, conditioning_key):
+        super().__init__()
+        self.diffusion_model = instantiate_from_config(diff_model_config)
+        self.conditioning_key = conditioning_key
+        assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm']
+
+    def forward(self, x, t, c_concat: list = None, c_crossattn: list = None):
+        if self.conditioning_key is None:
+            out = self.diffusion_model(x, t)
+        elif self.conditioning_key == 'concat':
+            xc = torch.cat([x] + c_concat, dim=1)
+            out = self.diffusion_model(xc, t)
+        elif self.conditioning_key == 'crossattn':
+            cc = torch.cat(c_crossattn, 1)
+            out = self.diffusion_model(x, t, context=cc)
+        elif self.conditioning_key == 'hybrid':
+            xc = torch.cat([x] + c_concat, dim=1)
+            cc = torch.cat(c_crossattn, 1)
+            out = self.diffusion_model(xc, t, context=cc)
+        elif self.conditioning_key == 'adm':
+            cc = c_crossattn[0]
+            out = self.diffusion_model(x, t, y=cc)
+        else:
+            raise NotImplementedError()
+
+        return out
+
+
+class Layout2ImgDiffusion(LatentDiffusion):
+    # TODO: move all layout-specific hacks to this class
+    def __init__(self, cond_stage_key, *args, **kwargs):
+        assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
+        super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs)
+
+    def log_images(self, batch, N=8, *args, **kwargs):
+        logs = super().log_images(batch=batch, N=N, *args, **kwargs)
+
+        key = 'train' if self.training else 'validation'
+        dset = self.trainer.datamodule.datasets[key]
+        mapper = dset.conditional_builders[self.cond_stage_key]
+
+        bbox_imgs = []
+        map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno))
+        for tknzd_bbox in batch[self.cond_stage_key][:N]:
+            bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256))
+            bbox_imgs.append(bboximg)
+
+        cond_img = torch.stack(bbox_imgs, dim=0)
+        logs['bbox_image'] = cond_img
+        return logs
diff --git a/modules/sd_hijack_ip2p.py b/modules/sd_hijack_ip2p.py
new file mode 100644
index 00000000..635f015f
--- /dev/null
+++ b/modules/sd_hijack_ip2p.py
@@ -0,0 +1,13 @@
+import collections
+import os.path
+import sys
+import gc
+import time
+
+def should_hijack_ip2p(checkpoint_info):
+    from modules import sd_models
+
+    ckpt_basename = os.path.basename(checkpoint_info.filename).lower()
+    cfg_basename = os.path.basename(sd_models.find_checkpoint_config(checkpoint_info)).lower()
+
+    return "pix2pix" in ckpt_basename and not "pix2pix" in cfg_basename
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 12083848..cddc2343 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -17,6 +17,7 @@ from ldm.util import instantiate_from_config
 from modules import shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes
 from modules.paths import models_path
 from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
+from modules.sd_hijack_ip2p import should_hijack_ip2p
 
 model_dir = "Stable-diffusion"
 model_path = os.path.abspath(os.path.join(models_path, model_dir))
@@ -365,6 +366,15 @@ def load_model(checkpoint_info=None):
         sd_config.model.params.unet_config.params.in_channels = 9
         sd_config.model.params.finetune_keys = None
 
+    if should_hijack_ip2p(checkpoint_info):
+        sd_config.model.target = "modules.models.diffusion.ddpm_edit.LatentDiffusion"
+        sd_config.model.params.conditioning_key = "hybrid"
+        sd_config.model.params.first_stage_key = "edited"
+        sd_config.model.params.cond_stage_key = "edit"
+        sd_config.model.params.image_size = 16
+        sd_config.model.params.unet_config.params.in_channels = 8
+        sd_config.model.params.unet_config.params.out_channels = 4
+
     if not hasattr(sd_config.model.params, "use_ema"):
         sd_config.model.params.use_ema = False
 
@@ -429,7 +439,7 @@ def reload_model_weights(sd_model=None, info=None):
 
     checkpoint_config = find_checkpoint_config(current_checkpoint_info)
 
-    if current_checkpoint_info is None or checkpoint_config != find_checkpoint_config(checkpoint_info) or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
+    if current_checkpoint_info is None or checkpoint_config != find_checkpoint_config(checkpoint_info) or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info) or should_hijack_ip2p(checkpoint_info) != should_hijack_ip2p(sd_model.sd_checkpoint_info):
         del sd_model
         checkpoints_loaded.clear()
         load_model(checkpoint_info)
-- 
cgit v1.2.3


From d2ac95fa7b2a8d0bcc5361ee16dba9cbb81ff8b2 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 27 Jan 2023 11:28:12 +0300
Subject: remove the need to place configs near models

---
 configs/instruct-pix2pix.yaml        |  99 +++++++++++++++
 configs/v1-inpainting-inference.yaml |  70 +++++++++++
 modules/api/api.py                   |   5 +-
 modules/devices.py                   |  12 +-
 modules/sd_hijack_inpainting.py      |   9 --
 modules/sd_models.py                 | 228 +++++++++++++++++------------------
 modules/sd_models_config.py          |  65 ++++++++++
 modules/shared.py                    |   7 +-
 modules/shared_items.py              |  15 ++-
 modules/timer.py                     |  35 ++++++
 v2-inference-v.yaml                  |  68 -----------
 11 files changed, 411 insertions(+), 202 deletions(-)
 create mode 100644 configs/instruct-pix2pix.yaml
 create mode 100644 configs/v1-inpainting-inference.yaml
 create mode 100644 modules/sd_models_config.py
 create mode 100644 modules/timer.py
 delete mode 100644 v2-inference-v.yaml

(limited to 'modules/sd_models.py')

diff --git a/configs/instruct-pix2pix.yaml b/configs/instruct-pix2pix.yaml
new file mode 100644
index 00000000..437ddcef
--- /dev/null
+++ b/configs/instruct-pix2pix.yaml
@@ -0,0 +1,99 @@
+# File modified by authors of InstructPix2Pix from original (https://github.com/CompVis/stable-diffusion).
+# See more details in LICENSE.
+
+model:
+  base_learning_rate: 1.0e-04
+  target: modules.models.diffusion.ddpm_edit.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: edited
+    cond_stage_key: edit
+    # image_size: 64
+    # image_size: 32
+    image_size: 16
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: hybrid
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: true
+    load_ema: true
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 0 ]
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # unused
+        in_channels: 8
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
+
+data:
+  target: main.DataModuleFromConfig
+  params:
+    batch_size: 128
+    num_workers: 1
+    wrap: false
+    validation:
+      target: edit_dataset.EditDataset
+      params:
+        path: data/clip-filtered-dataset
+        cache_dir:  data/
+        cache_name: data_10k
+        split: val
+        min_text_sim: 0.2
+        min_image_sim: 0.75
+        min_direction_sim: 0.2
+        max_samples_per_prompt: 1
+        min_resize_res: 512
+        max_resize_res: 512
+        crop_res: 512
+        output_as_edit: False
+        real_input: True
diff --git a/configs/v1-inpainting-inference.yaml b/configs/v1-inpainting-inference.yaml
new file mode 100644
index 00000000..f9eec37d
--- /dev/null
+++ b/configs/v1-inpainting-inference.yaml
@@ -0,0 +1,70 @@
+model:
+  base_learning_rate: 7.5e-05
+  target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: hybrid   # important
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    finetune_keys: null
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 2500 ] # NOTE for resuming. use 10000 if starting from scratch
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # unused
+        in_channels: 9  # 4 data + 4 downscaled image + 1 mask
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
diff --git a/modules/api/api.py b/modules/api/api.py
index 25c65e57..eb7b1da5 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -18,7 +18,8 @@ from modules.textual_inversion.textual_inversion import create_embedding, train_
 from modules.textual_inversion.preprocess import preprocess
 from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
 from PIL import PngImagePlugin,Image
-from modules.sd_models import checkpoints_list, find_checkpoint_config
+from modules.sd_models import checkpoints_list
+from modules.sd_models_config import find_checkpoint_config_near_filename
 from modules.realesrgan_model import get_realesrgan_models
 from modules import devices
 from typing import List
@@ -387,7 +388,7 @@ class Api:
         ]
 
     def get_sd_models(self):
-        return [{"title": x.title, "model_name": x.model_name, "hash": x.shorthash, "sha256": x.sha256, "filename": x.filename, "config": find_checkpoint_config(x)} for x in checkpoints_list.values()]
+        return [{"title": x.title, "model_name": x.model_name, "hash": x.shorthash, "sha256": x.sha256, "filename": x.filename, "config": find_checkpoint_config_near_filename(x)} for x in checkpoints_list.values()]
 
     def get_hypernetworks(self):
         return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks]
diff --git a/modules/devices.py b/modules/devices.py
index 6b36622c..2d5f797a 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -34,14 +34,18 @@ def get_cuda_device_string():
     return "cuda"
 
 
-def get_optimal_device():
+def get_optimal_device_name():
     if torch.cuda.is_available():
-        return torch.device(get_cuda_device_string())
+        return get_cuda_device_string()
 
     if has_mps():
-        return torch.device("mps")
+        return "mps"
+
+    return "cpu"
 
-    return cpu
+
+def get_optimal_device():
+    return torch.device(get_optimal_device_name())
 
 
 def get_device_for(task):
diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index 31d2c898..478cd499 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -96,15 +96,6 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F
     return x_prev, pred_x0, e_t
 
 
-def should_hijack_inpainting(checkpoint_info):
-    from modules import sd_models
-
-    ckpt_basename = os.path.basename(checkpoint_info.filename).lower()
-    cfg_basename = os.path.basename(sd_models.find_checkpoint_config(checkpoint_info)).lower()
-
-    return "inpainting" in ckpt_basename and not "inpainting" in cfg_basename
-
-
 def do_inpainting_hijack():
     # p_sample_plms is needed because PLMS can't work with dicts as conditionings
 
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 7072eb2e..fa208728 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -2,8 +2,6 @@ import collections
 import os.path
 import sys
 import gc
-import time
-from collections import namedtuple
 import torch
 import re
 import safetensors.torch
@@ -14,10 +12,10 @@ import ldm.modules.midas as midas
 
 from ldm.util import instantiate_from_config
 
-from modules import shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes
+from modules import shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config
 from modules.paths import models_path
-from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
-from modules.sd_hijack_ip2p import should_hijack_ip2p
+from modules.sd_hijack_inpainting import do_inpainting_hijack
+from modules.timer import Timer
 
 model_dir = "Stable-diffusion"
 model_path = os.path.abspath(os.path.join(models_path, model_dir))
@@ -99,17 +97,6 @@ def checkpoint_tiles():
     return sorted([x.title for x in checkpoints_list.values()], key=alphanumeric_key)
 
 
-def find_checkpoint_config(info):
-    if info is None:
-        return shared.cmd_opts.config
-
-    config = os.path.splitext(info.filename)[0] + ".yaml"
-    if os.path.exists(config):
-        return config
-
-    return shared.cmd_opts.config
-
-
 def list_models():
     checkpoints_list.clear()
     checkpoint_alisases.clear()
@@ -215,9 +202,7 @@ def get_state_dict_from_checkpoint(pl_sd):
 def read_state_dict(checkpoint_file, print_global_state=False, map_location=None):
     _, extension = os.path.splitext(checkpoint_file)
     if extension.lower() == ".safetensors":
-        device = map_location or shared.weight_load_location
-        if device is None:
-            device = devices.get_cuda_device_string() if torch.cuda.is_available() else "cpu"
+        device = map_location or shared.weight_load_location or devices.get_optimal_device_name()
         pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
     else:
         pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location)
@@ -229,60 +214,74 @@ def read_state_dict(checkpoint_file, print_global_state=False, map_location=None
     return sd
 
 
-def load_model_weights(model, checkpoint_info: CheckpointInfo):
+def get_checkpoint_state_dict(checkpoint_info: CheckpointInfo, timer):
+    sd_model_hash = checkpoint_info.calculate_shorthash()
+    timer.record("calculate hash")
+
+    if checkpoint_info in checkpoints_loaded:
+        # use checkpoint cache
+        print(f"Loading weights [{sd_model_hash}] from cache")
+        return checkpoints_loaded[checkpoint_info]
+
+    print(f"Loading weights [{sd_model_hash}] from {checkpoint_info.filename}")
+    res = read_state_dict(checkpoint_info.filename)
+    timer.record("load weights from disk")
+
+    return res
+
+
+def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer):
     title = checkpoint_info.title
     sd_model_hash = checkpoint_info.calculate_shorthash()
+    timer.record("calculate hash")
+
     if checkpoint_info.title != title:
         shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title
 
-    cache_enabled = shared.opts.sd_checkpoint_cache > 0
+    if state_dict is None:
+        state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
 
-    if cache_enabled and checkpoint_info in checkpoints_loaded:
-        # use checkpoint cache
-        print(f"Loading weights [{sd_model_hash}] from cache")
-        model.load_state_dict(checkpoints_loaded[checkpoint_info])
-    else:
-        # load from file
-        print(f"Loading weights [{sd_model_hash}] from {checkpoint_info.filename}")
+    model.load_state_dict(state_dict, strict=False)
+    del state_dict
+    timer.record("apply weights to model")
 
-        sd = read_state_dict(checkpoint_info.filename)
-        model.load_state_dict(sd, strict=False)
-        del sd
-        
-        if cache_enabled:
-            # cache newly loaded model
-            checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
+    if shared.opts.sd_checkpoint_cache > 0:
+        # cache newly loaded model
+        checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
+
+    if shared.cmd_opts.opt_channelslast:
+        model.to(memory_format=torch.channels_last)
+        timer.record("apply channels_last")
 
-        if shared.cmd_opts.opt_channelslast:
-            model.to(memory_format=torch.channels_last)
+    if not shared.cmd_opts.no_half:
+        vae = model.first_stage_model
+        depth_model = getattr(model, 'depth_model', None)
 
-        if not shared.cmd_opts.no_half:
-            vae = model.first_stage_model
-            depth_model = getattr(model, 'depth_model', None)
+        # with --no-half-vae, remove VAE from model when doing half() to prevent its weights from being converted to float16
+        if shared.cmd_opts.no_half_vae:
+            model.first_stage_model = None
+        # with --upcast-sampling, don't convert the depth model weights to float16
+        if shared.cmd_opts.upcast_sampling and depth_model:
+            model.depth_model = None
 
-            # with --no-half-vae, remove VAE from model when doing half() to prevent its weights from being converted to float16
-            if shared.cmd_opts.no_half_vae:
-                model.first_stage_model = None
-            # with --upcast-sampling, don't convert the depth model weights to float16
-            if shared.cmd_opts.upcast_sampling and depth_model:
-                model.depth_model = None
+        model.half()
+        model.first_stage_model = vae
+        if depth_model:
+            model.depth_model = depth_model
 
-            model.half()
-            model.first_stage_model = vae
-            if depth_model:
-                model.depth_model = depth_model
+        timer.record("apply half()")
 
-        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
-        devices.dtype_unet = model.model.diffusion_model.dtype
-        devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
+    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
+    devices.dtype_unet = model.model.diffusion_model.dtype
+    devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
 
-        model.first_stage_model.to(devices.dtype_vae)
+    model.first_stage_model.to(devices.dtype_vae)
+    timer.record("apply dtype to VAE")
 
     # clean up cache if limit is reached
-    if cache_enabled:
-        while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache + 1: # we need to count the current model
-            checkpoints_loaded.popitem(last=False)  # LRU
+    while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
+        checkpoints_loaded.popitem(last=False)
 
     model.sd_model_hash = sd_model_hash
     model.sd_model_checkpoint = checkpoint_info.filename
@@ -295,6 +294,7 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo):
     sd_vae.clear_loaded_vae()
     vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename)
     sd_vae.load_vae(model, vae_file, vae_source)
+    timer.record("load VAE")
 
 
 def enable_midas_autodownload():
@@ -340,24 +340,20 @@ def enable_midas_autodownload():
     midas.api.load_model = load_model_wrapper
 
 
-class Timer:
-    def __init__(self):
-        self.start = time.time()
+def repair_config(sd_config):
 
-    def elapsed(self):
-        end = time.time()
-        res = end - self.start
-        self.start = end
-        return res
+    if not hasattr(sd_config.model.params, "use_ema"):
+        sd_config.model.params.use_ema = False
 
+    if shared.cmd_opts.no_half:
+        sd_config.model.params.unet_config.params.use_fp16 = False
+    elif shared.cmd_opts.upcast_sampling:
+        sd_config.model.params.unet_config.params.use_fp16 = True
 
-def load_model(checkpoint_info=None):
+
+def load_model(checkpoint_info=None, already_loaded_state_dict=None, time_taken_to_load_state_dict=None):
     from modules import lowvram, sd_hijack
     checkpoint_info = checkpoint_info or select_checkpoint()
-    checkpoint_config = find_checkpoint_config(checkpoint_info)
-
-    if checkpoint_config != shared.cmd_opts.config:
-        print(f"Loading config from: {checkpoint_config}")
 
     if shared.sd_model:
         sd_hijack.model_hijack.undo_hijack(shared.sd_model)
@@ -365,38 +361,27 @@ def load_model(checkpoint_info=None):
         gc.collect()
         devices.torch_gc()
 
-    sd_config = OmegaConf.load(checkpoint_config)
-    
-    if should_hijack_inpainting(checkpoint_info):
-        # Hardcoded config for now...
-        sd_config.model.target = "ldm.models.diffusion.ddpm.LatentInpaintDiffusion"
-        sd_config.model.params.conditioning_key = "hybrid"
-        sd_config.model.params.unet_config.params.in_channels = 9
-        sd_config.model.params.finetune_keys = None
-
-    if should_hijack_ip2p(checkpoint_info):
-        sd_config.model.target = "modules.models.diffusion.ddpm_edit.LatentDiffusion"
-        sd_config.model.params.conditioning_key = "hybrid"
-        sd_config.model.params.first_stage_key = "edited"
-        sd_config.model.params.cond_stage_key = "edit"
-        sd_config.model.params.image_size = 16
-        sd_config.model.params.unet_config.params.in_channels = 8
-        sd_config.model.params.unet_config.params.out_channels = 4
+    do_inpainting_hijack()
 
-    if not hasattr(sd_config.model.params, "use_ema"):
-        sd_config.model.params.use_ema = False
+    timer = Timer()
 
-    do_inpainting_hijack()
+    if already_loaded_state_dict is not None:
+        state_dict = already_loaded_state_dict
+    else:
+        state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
 
-    if shared.cmd_opts.no_half:
-        sd_config.model.params.unet_config.params.use_fp16 = False
-    elif shared.cmd_opts.upcast_sampling:
-        sd_config.model.params.unet_config.params.use_fp16 = True
+    checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
 
-    timer = Timer()
+    timer.record("find config")
 
-    sd_model = None
+    sd_config = OmegaConf.load(checkpoint_config)
+    repair_config(sd_config)
+
+    timer.record("load config")
+
+    print(f"Creating model from config: {checkpoint_config}")
 
+    sd_model = None
     try:
         with sd_disable_initialization.DisableInitialization():
             sd_model = instantiate_from_config(sd_config.model)
@@ -407,29 +392,35 @@ def load_model(checkpoint_info=None):
         print('Failed to create model quickly; will retry using slow method.', file=sys.stderr)
         sd_model = instantiate_from_config(sd_config.model)
 
-    elapsed_create = timer.elapsed()
+    sd_model.used_config = checkpoint_config
 
-    load_model_weights(sd_model, checkpoint_info)
+    timer.record("create model")
 
-    elapsed_load_weights = timer.elapsed()
+    load_model_weights(sd_model, checkpoint_info, state_dict, timer)
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
         lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
     else:
         sd_model.to(shared.device)
 
+    timer.record("move model to device")
+
     sd_hijack.model_hijack.hijack(sd_model)
 
+    timer.record("hijack")
+
     sd_model.eval()
     shared.sd_model = sd_model
 
     sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True)  # Reload embeddings after model load as they may or may not fit the model
 
+    timer.record("load textual inversion embeddings")
+
     script_callbacks.model_loaded_callback(sd_model)
 
-    elapsed_the_rest = timer.elapsed()
+    timer.record("scripts callbacks")
 
-    print(f"Model loaded in {elapsed_create + elapsed_load_weights + elapsed_the_rest:.1f}s ({elapsed_create:.1f}s create model, {elapsed_load_weights:.1f}s load weights).")
+    print(f"Model loaded in {timer.summary()}.")
 
     return sd_model
 
@@ -440,6 +431,7 @@ def reload_model_weights(sd_model=None, info=None):
 
     if not sd_model:
         sd_model = shared.sd_model
+
     if sd_model is None:  # previous model load failed
         current_checkpoint_info = None
     else:
@@ -447,14 +439,6 @@ def reload_model_weights(sd_model=None, info=None):
         if sd_model.sd_model_checkpoint == checkpoint_info.filename:
             return
 
-    checkpoint_config = find_checkpoint_config(current_checkpoint_info)
-
-    if current_checkpoint_info is None or checkpoint_config != find_checkpoint_config(checkpoint_info) or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info) or should_hijack_ip2p(checkpoint_info) != should_hijack_ip2p(sd_model.sd_checkpoint_info):
-        del sd_model
-        checkpoints_loaded.clear()
-        load_model(checkpoint_info)
-        return shared.sd_model
-
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
         lowvram.send_everything_to_cpu()
     else:
@@ -464,21 +448,35 @@ def reload_model_weights(sd_model=None, info=None):
 
     timer = Timer()
 
+    state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
+
+    checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
+
+    timer.record("find config")
+
+    if sd_model is None or checkpoint_config != sd_model.used_config:
+        del sd_model
+        checkpoints_loaded.clear()
+        load_model(checkpoint_info, already_loaded_state_dict=state_dict, time_taken_to_load_state_dict=timer.records["load weights from disk"])
+        return shared.sd_model
+
     try:
-        load_model_weights(sd_model, checkpoint_info)
+        load_model_weights(sd_model, checkpoint_info, state_dict, timer)
     except Exception as e:
         print("Failed to load checkpoint, restoring previous")
-        load_model_weights(sd_model, current_checkpoint_info)
+        load_model_weights(sd_model, current_checkpoint_info, None, timer)
         raise
     finally:
         sd_hijack.model_hijack.hijack(sd_model)
+        timer.record("hijack")
+
         script_callbacks.model_loaded_callback(sd_model)
+        timer.record("script callbacks")
 
         if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
             sd_model.to(devices.device)
+            timer.record("move model to device")
 
-    elapsed = timer.elapsed()
-
-    print(f"Weights loaded in {elapsed:.1f}s.")
+    print(f"Weights loaded in {timer.summary()}.")
 
     return sd_model
diff --git a/modules/sd_models_config.py b/modules/sd_models_config.py
new file mode 100644
index 00000000..ea773a10
--- /dev/null
+++ b/modules/sd_models_config.py
@@ -0,0 +1,65 @@
+import re
+import os
+
+from modules import shared, paths
+
+sd_configs_path = shared.sd_configs_path
+sd_repo_configs_path = os.path.join(paths.paths['Stable Diffusion'], "configs", "stable-diffusion")
+
+
+config_default = shared.sd_default_config
+config_sd2 = os.path.join(sd_repo_configs_path, "v2-inference.yaml")
+config_sd2v = os.path.join(sd_repo_configs_path, "v2-inference-v.yaml")
+config_inpainting = os.path.join(sd_configs_path, "v1-inpainting-inference.yaml")
+config_instruct_pix2pix = os.path.join(sd_configs_path, "instruct-pix2pix.yaml")
+config_alt_diffusion = os.path.join(sd_configs_path, "alt-diffusion-inference.yaml")
+
+re_parametrization_v = re.compile(r'-v\b')
+
+
+def guess_model_config_from_state_dict(sd, filename):
+    fn = os.path.basename(filename)
+
+    sd2_cond_proj_weight = sd.get('cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight', None)
+    diffusion_model_input = sd.get('model.diffusion_model.input_blocks.0.0.weight', None)
+    roberta_weight = sd.get('cond_stage_model.roberta.embeddings.word_embeddings.weight', None)
+
+    if sd2_cond_proj_weight is not None and sd2_cond_proj_weight.shape[1] == 1024:
+        if re.search(re_parametrization_v, fn) or "v2-1_768" in fn:
+            return config_sd2v
+        else:
+            return config_sd2
+
+    if diffusion_model_input is not None:
+        if diffusion_model_input.shape[1] == 9:
+            return config_inpainting
+        if diffusion_model_input.shape[1] == 8:
+            return config_instruct_pix2pix
+
+    if roberta_weight is not None:
+        return config_alt_diffusion
+
+    return config_default
+
+
+def find_checkpoint_config(state_dict, info):
+    if info is None:
+        return guess_model_config_from_state_dict(state_dict, "")
+
+    config = find_checkpoint_config_near_filename(info)
+    if config is not None:
+        return config
+
+    return guess_model_config_from_state_dict(state_dict, info.filename)
+
+
+def find_checkpoint_config_near_filename(info):
+    if info is None:
+        return None
+
+    config = os.path.splitext(info.filename)[0] + ".yaml"
+    if os.path.exists(config):
+        return config
+
+    return None
+
diff --git a/modules/shared.py b/modules/shared.py
index cdeed55d..14be993d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -13,13 +13,14 @@ import modules.interrogate
 import modules.memmon
 import modules.styles
 import modules.devices as devices
-from modules import localization, sd_vae, extensions, script_loading, errors, ui_components, shared_items
+from modules import localization, extensions, script_loading, errors, ui_components, shared_items
 from modules.paths import models_path, script_path
 
 
 demo = None
 
-sd_default_config = os.path.join(script_path, "configs/v1-inference.yaml")
+sd_configs_path = os.path.join(script_path, "configs")
+sd_default_config = os.path.join(sd_configs_path, "v1-inference.yaml")
 sd_model_file = os.path.join(script_path, 'model.ckpt')
 default_sd_model_file = sd_model_file
 
@@ -391,7 +392,7 @@ 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("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": ["Automatic", "None"] + list(sd_vae.vae_dict)}, refresh=sd_vae.refresh_vae_list),
+    "sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list),
     "sd_vae_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
     "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01}),
diff --git a/modules/shared_items.py b/modules/shared_items.py
index b5d480c9..8b5ec96d 100644
--- a/modules/shared_items.py
+++ b/modules/shared_items.py
@@ -4,7 +4,20 @@ def realesrgan_models_names():
     import modules.realesrgan_model
     return [x.name for x in modules.realesrgan_model.get_realesrgan_models(None)]
 
+
 def postprocessing_scripts():
     import modules.scripts
 
-    return modules.scripts.scripts_postproc.scripts
\ No newline at end of file
+    return modules.scripts.scripts_postproc.scripts
+
+
+def sd_vae_items():
+    import modules.sd_vae
+
+    return ["Automatic", "None"] + list(modules.sd_vae.vae_dict)
+
+
+def refresh_vae_list():
+    import modules.sd_vae
+
+    return modules.sd_vae.refresh_vae_list
diff --git a/modules/timer.py b/modules/timer.py
new file mode 100644
index 00000000..57a4f17a
--- /dev/null
+++ b/modules/timer.py
@@ -0,0 +1,35 @@
+import time
+
+
+class Timer:
+    def __init__(self):
+        self.start = time.time()
+        self.records = {}
+        self.total = 0
+
+    def elapsed(self):
+        end = time.time()
+        res = end - self.start
+        self.start = end
+        return res
+
+    def record(self, category, extra_time=0):
+        e = self.elapsed()
+        if category not in self.records:
+            self.records[category] = 0
+
+        self.records[category] += e + extra_time
+        self.total += e + extra_time
+
+    def summary(self):
+        res = f"{self.total:.1f}s"
+
+        additions = [x for x in self.records.items() if x[1] >= 0.1]
+        if not additions:
+            return res
+
+        res += " ("
+        res += ", ".join([f"{category}: {time_taken:.1f}s" for category, time_taken in additions])
+        res += ")"
+
+        return res
diff --git a/v2-inference-v.yaml b/v2-inference-v.yaml
deleted file mode 100644
index 513cd635..00000000
--- a/v2-inference-v.yaml
+++ /dev/null
@@ -1,68 +0,0 @@
-model:
-  base_learning_rate: 1.0e-4
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    parameterization: "v"
-    linear_start: 0.00085
-    linear_end: 0.0120
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: "jpg"
-    cond_stage_key: "txt"
-    image_size: 64
-    channels: 4
-    cond_stage_trainable: false
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    scale_factor: 0.18215
-    use_ema: False # we set this to false because this is an inference only config
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        use_checkpoint: True
-        use_fp16: True
-        image_size: 32 # unused
-        in_channels: 4
-        out_channels: 4
-        model_channels: 320
-        attention_resolutions: [ 4, 2, 1 ]
-        num_res_blocks: 2
-        channel_mult: [ 1, 2, 4, 4 ]
-        num_head_channels: 64 # need to fix for flash-attn
-        use_spatial_transformer: True
-        use_linear_in_transformer: True
-        transformer_depth: 1
-        context_dim: 1024
-        legacy: False
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: val/rec_loss
-        ddconfig:
-          #attn_type: "vanilla-xformers"
-          double_z: true
-          z_channels: 4
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 4
-          - 4
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
-      params:
-        freeze: True
-        layer: "penultimate"
\ No newline at end of file
-- 
cgit v1.2.3


From 6f31d2210c189f8db118e6f95add7ba2a64f0238 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 27 Jan 2023 11:54:19 +0300
Subject: support detecting midas model fix broken api for checkpoint list

---
 modules/api/models.py       |  2 +-
 modules/sd_models.py        | 10 +++++-----
 modules/sd_models_config.py |  7 +++++--
 3 files changed, 11 insertions(+), 8 deletions(-)

(limited to 'modules/sd_models.py')

diff --git a/modules/api/models.py b/modules/api/models.py
index 805bd8f7..cba43d3b 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -228,7 +228,7 @@ class SDModelItem(BaseModel):
     hash: Optional[str] = Field(title="Short hash")
     sha256: Optional[str] = Field(title="sha256 hash")
     filename: str = Field(title="Filename")
-    config: str = Field(title="Config file")
+    config: Optional[str] = Field(title="Config file")
 
 class HypernetworkItem(BaseModel):
     name: str = Field(title="Name")
diff --git a/modules/sd_models.py b/modules/sd_models.py
index fa208728..37dad18d 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -439,12 +439,12 @@ def reload_model_weights(sd_model=None, info=None):
         if sd_model.sd_model_checkpoint == checkpoint_info.filename:
             return
 
-    if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
-        lowvram.send_everything_to_cpu()
-    else:
-        sd_model.to(devices.cpu)
+        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)
+        sd_hijack.model_hijack.undo_hijack(sd_model)
 
     timer = Timer()
 
diff --git a/modules/sd_models_config.py b/modules/sd_models_config.py
index ea773a10..4d1e92e1 100644
--- a/modules/sd_models_config.py
+++ b/modules/sd_models_config.py
@@ -10,6 +10,7 @@ sd_repo_configs_path = os.path.join(paths.paths['Stable Diffusion'], "configs",
 config_default = shared.sd_default_config
 config_sd2 = os.path.join(sd_repo_configs_path, "v2-inference.yaml")
 config_sd2v = os.path.join(sd_repo_configs_path, "v2-inference-v.yaml")
+config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml")
 config_inpainting = os.path.join(sd_configs_path, "v1-inpainting-inference.yaml")
 config_instruct_pix2pix = os.path.join(sd_configs_path, "instruct-pix2pix.yaml")
 config_alt_diffusion = os.path.join(sd_configs_path, "alt-diffusion-inference.yaml")
@@ -22,7 +23,9 @@ def guess_model_config_from_state_dict(sd, filename):
 
     sd2_cond_proj_weight = sd.get('cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight', None)
     diffusion_model_input = sd.get('model.diffusion_model.input_blocks.0.0.weight', None)
-    roberta_weight = sd.get('cond_stage_model.roberta.embeddings.word_embeddings.weight', None)
+
+    if sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None:
+        return config_depth_model
 
     if sd2_cond_proj_weight is not None and sd2_cond_proj_weight.shape[1] == 1024:
         if re.search(re_parametrization_v, fn) or "v2-1_768" in fn:
@@ -36,7 +39,7 @@ def guess_model_config_from_state_dict(sd, filename):
         if diffusion_model_input.shape[1] == 8:
             return config_instruct_pix2pix
 
-    if roberta_weight is not None:
+    if sd.get('cond_stage_model.roberta.embeddings.word_embeddings.weight', None) is not None:
         return config_alt_diffusion
 
     return config_default
-- 
cgit v1.2.3


From 5eee2ac39863f9e44591b50d0710dd2615416a13 Mon Sep 17 00:00:00 2001
From: Max Audron <audron@cocaine.farm>
Date: Wed, 25 Jan 2023 17:15:42 +0100
Subject: add data-dir flag and set all user data directories based on it

---
 modules/extensions.py                      |  2 +-
 modules/generation_parameters_copypaste.py |  4 ++--
 modules/gfpgan_model.py                    |  5 ++---
 modules/hashes.py                          |  4 +++-
 modules/interrogate.py                     |  2 +-
 modules/paths.py                           | 10 +++++++++-
 modules/processing.py                      |  3 ++-
 modules/sd_models.py                       |  6 +++---
 modules/sd_vae.py                          |  5 ++---
 modules/shared.py                          | 11 ++++++-----
 modules/textual_inversion/preprocess.py    |  5 ++---
 modules/ui.py                              |  6 +++---
 modules/ui_extensions.py                   |  2 +-
 modules/upscaler.py                        |  5 ++---
 14 files changed, 39 insertions(+), 31 deletions(-)

(limited to 'modules/sd_models.py')

diff --git a/modules/extensions.py b/modules/extensions.py
index b522125c..92ee8144 100644
--- a/modules/extensions.py
+++ b/modules/extensions.py
@@ -7,7 +7,7 @@ import git
 from modules import paths, shared
 
 extensions = []
-extensions_dir = os.path.join(paths.script_path, "extensions")
+extensions_dir = os.path.join(paths.data_path, "extensions")
 extensions_builtin_dir = os.path.join(paths.script_path, "extensions-builtin")
 
 
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 46e12dc6..35f72808 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -6,7 +6,7 @@ import re
 from pathlib import Path
 
 import gradio as gr
-from modules.shared import script_path
+from modules.paths import data_path, script_path
 from modules import shared, ui_tempdir, script_callbacks
 import tempfile
 from PIL import Image
@@ -289,7 +289,7 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
 def connect_paste(button, paste_fields, input_comp, jsfunc=None):
     def paste_func(prompt):
         if not prompt and not shared.cmd_opts.hide_ui_dir_config:
-            filename = os.path.join(script_path, "params.txt")
+            filename = os.path.join(data_path, "params.txt")
             if os.path.exists(filename):
                 with open(filename, "r", encoding="utf8") as file:
                     prompt = file.read()
diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py
index 1e2dbc32..fbe6215a 100644
--- a/modules/gfpgan_model.py
+++ b/modules/gfpgan_model.py
@@ -6,12 +6,11 @@ import facexlib
 import gfpgan
 
 import modules.face_restoration
-from modules import shared, devices, modelloader
-from modules.paths import models_path
+from modules import paths, shared, devices, modelloader
 
 model_dir = "GFPGAN"
 user_path = None
-model_path = os.path.join(models_path, model_dir)
+model_path = os.path.join(paths.models_path, model_dir)
 model_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth"
 have_gfpgan = False
 loaded_gfpgan_model = None
diff --git a/modules/hashes.py b/modules/hashes.py
index b85a7580..819362a3 100644
--- a/modules/hashes.py
+++ b/modules/hashes.py
@@ -4,8 +4,10 @@ import os.path
 
 import filelock
 
+from modules.paths import data_path
 
-cache_filename = "cache.json"
+
+cache_filename = os.path.join(data_path, "cache.json")
 cache_data = None
 
 
diff --git a/modules/interrogate.py b/modules/interrogate.py
index c72ff694..cbb80683 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -12,7 +12,7 @@ from torchvision import transforms
 from torchvision.transforms.functional import InterpolationMode
 
 import modules.shared as shared
-from modules import devices, paths, lowvram, modelloader, errors
+from modules import devices, paths, shared, lowvram, modelloader, errors
 
 blip_image_eval_size = 384
 clip_model_name = 'ViT-L/14'
diff --git a/modules/paths.py b/modules/paths.py
index 20b3e4d8..08e6f9b9 100644
--- a/modules/paths.py
+++ b/modules/paths.py
@@ -4,7 +4,15 @@ import sys
 import modules.safe
 
 script_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
-models_path = os.path.join(script_path, "models")
+
+# Parse the --data-dir flag first so we can use it as a base for our other argument default values
+parser = argparse.ArgumentParser()
+parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored",)
+cmd_opts_pre = parser.parse_known_args()[0]
+data_path = cmd_opts_pre.data_dir
+models_path = os.path.join(data_path, "models")
+
+# data_path = cmd_opts_pre.data
 sys.path.insert(0, script_path)
 
 # search for directory of stable diffusion in following places
diff --git a/modules/processing.py b/modules/processing.py
index 262806a1..5072fc40 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -17,6 +17,7 @@ from modules import devices, prompt_parser, masking, sd_samplers, lowvram, gener
 from modules.sd_hijack import model_hijack
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
+import modules.paths as paths
 import modules.face_restoration
 import modules.images as images
 import modules.styles
@@ -584,7 +585,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if not p.disable_extra_networks:
                 extra_networks.activate(p, extra_network_data)
 
-        with open(os.path.join(shared.script_path, "params.txt"), "w", encoding="utf8") as file:
+        with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
             processed = Processed(p, [], p.seed, "")
             file.write(processed.infotext(p, 0))
 
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 37dad18d..b2d48a51 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -12,13 +12,13 @@ import ldm.modules.midas as midas
 
 from ldm.util import instantiate_from_config
 
-from modules import shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config
+from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config
 from modules.paths import models_path
 from modules.sd_hijack_inpainting import do_inpainting_hijack
 from modules.timer import Timer
 
 model_dir = "Stable-diffusion"
-model_path = os.path.abspath(os.path.join(models_path, model_dir))
+model_path = os.path.abspath(os.path.join(paths.models_path, model_dir))
 
 checkpoints_list = {}
 checkpoint_alisases = {}
@@ -307,7 +307,7 @@ def enable_midas_autodownload():
     location automatically.
     """
 
-    midas_path = os.path.join(models_path, 'midas')
+    midas_path = os.path.join(paths.models_path, 'midas')
 
     # stable-diffusion-stability-ai hard-codes the midas model path to
     # a location that differs from where other scripts using this model look.
diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 4ce238b8..9b00f76e 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -3,13 +3,12 @@ import safetensors.torch
 import os
 import collections
 from collections import namedtuple
-from modules import shared, devices, script_callbacks, sd_models
-from modules.paths import models_path
+from modules import paths, shared, devices, script_callbacks, sd_models
 import glob
 from copy import deepcopy
 
 
-vae_path = os.path.abspath(os.path.join(models_path, "VAE"))
+vae_path = os.path.abspath(os.path.join(paths.models_path, "VAE"))
 vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
 vae_dict = {}
 
diff --git a/modules/shared.py b/modules/shared.py
index 14be993d..474fcc42 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -14,7 +14,7 @@ import modules.memmon
 import modules.styles
 import modules.devices as devices
 from modules import localization, extensions, script_loading, errors, ui_components, shared_items
-from modules.paths import models_path, script_path
+from modules.paths import models_path, script_path, data_path
 
 
 demo = None
@@ -25,6 +25,7 @@ sd_model_file = os.path.join(script_path, 'model.ckpt')
 default_sd_model_file = sd_model_file
 
 parser = argparse.ArgumentParser()
+parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored",)
 parser.add_argument("--config", type=str, default=sd_default_config, help="path to config which constructs model",)
 parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
 parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
@@ -35,7 +36,7 @@ parser.add_argument("--no-half", action='store_true', help="do not switch the mo
 parser.add_argument("--no-half-vae", action='store_true', help="do not switch the VAE model to 16-bit floats")
 parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
 parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
-parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
+parser.add_argument("--embeddings-dir", type=str, default=os.path.join(data_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
 parser.add_argument("--textual-inversion-templates-dir", type=str, default=os.path.join(script_path, 'textual_inversion_templates'), help="directory with textual inversion templates")
 parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
 parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory")
@@ -74,16 +75,16 @@ parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for sp
 parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
 parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
 parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
-parser.add_argument("--ui-config-file", type=str, help="filename to use for ui configuration", default=os.path.join(script_path, 'ui-config.json'))
+parser.add_argument("--ui-config-file", type=str, help="filename to use for ui configuration", default=os.path.join(data_path, 'ui-config.json'))
 parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide directory configuration from webui", default=False)
 parser.add_argument("--freeze-settings", action='store_true', help="disable editing settings", default=False)
-parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(script_path, 'config.json'))
+parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(data_path, 'config.json'))
 parser.add_argument("--gradio-debug",  action='store_true', help="launch gradio with --debug option")
 parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
 parser.add_argument("--gradio-img2img-tool", type=str, help='does not do anything')
 parser.add_argument("--gradio-inpaint-tool", type=str, help="does not do anything")
 parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
-parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
+parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(data_path, 'styles.csv'))
 parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
 parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None)
 parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index c0ac11d3..2239cb84 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -6,8 +6,7 @@ import sys
 import tqdm
 import time
 
-from modules import shared, images, deepbooru
-from modules.paths import models_path
+from modules import paths, shared, images, deepbooru
 from modules.shared import opts, cmd_opts
 from modules.textual_inversion import autocrop
 
@@ -199,7 +198,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
 
             dnn_model_path = None
             try:
-                dnn_model_path = autocrop.download_and_cache_models(os.path.join(models_path, "opencv"))
+                dnn_model_path = autocrop.download_and_cache_models(os.path.join(paths.models_path, "opencv"))
             except Exception as e:
                 print("Unable to load face detection model for auto crop selection. Falling back to lower quality haar method.", e)
 
diff --git a/modules/ui.py b/modules/ui.py
index 85ae62c7..0117df3e 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -21,7 +21,7 @@ from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_grad
 
 from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, postprocessing, ui_components, ui_common, ui_postprocessing
 from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
-from modules.paths import script_path
+from modules.paths import script_path, data_path
 
 from modules.shared import opts, cmd_opts, restricted_opts
 
@@ -1497,8 +1497,8 @@ def create_ui():
         with open(cssfile, "r", encoding="utf8") as file:
             css += file.read() + "\n"
 
-    if os.path.exists(os.path.join(script_path, "user.css")):
-        with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file:
+    if os.path.exists(os.path.join(data_path, "user.css")):
+        with open(os.path.join(data_path, "user.css"), "r", encoding="utf8") as file:
             css += file.read() + "\n"
 
     if not cmd_opts.no_progressbar_hiding:
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index 742e745e..66a41865 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -132,7 +132,7 @@ def install_extension_from_url(dirname, url):
     normalized_url = normalize_git_url(url)
     assert len([x for x in extensions.extensions if normalize_git_url(x.remote) == normalized_url]) == 0, 'Extension with this URL is already installed'
 
-    tmpdir = os.path.join(paths.script_path, "tmp", dirname)
+    tmpdir = os.path.join(paths.data_path, "tmp", dirname)
 
     try:
         shutil.rmtree(tmpdir, True)
diff --git a/modules/upscaler.py b/modules/upscaler.py
index a5bf5acb..e2eaa730 100644
--- a/modules/upscaler.py
+++ b/modules/upscaler.py
@@ -11,7 +11,6 @@ from modules import modelloader, shared
 
 LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
 NEAREST = (Image.Resampling.NEAREST if hasattr(Image, 'Resampling') else Image.NEAREST)
-from modules.paths import models_path
 
 
 class Upscaler:
@@ -39,7 +38,7 @@ class Upscaler:
         self.mod_scale = None
 
         if self.model_path is None and self.name:
-            self.model_path = os.path.join(models_path, self.name)
+            self.model_path = os.path.join(shared.models_path, self.name)
         if self.model_path and create_dirs:
             os.makedirs(self.model_path, exist_ok=True)
 
@@ -143,4 +142,4 @@ class UpscalerNearest(Upscaler):
     def __init__(self, dirname=None):
         super().__init__(False)
         self.name = "Nearest"
-        self.scalers = [UpscalerData("Nearest", None, self)]
\ No newline at end of file
+        self.scalers = [UpscalerData("Nearest", None, self)]
-- 
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