Merge pull request #2573 from raefu/ckpt-cache

add --ckpt-cache option for faster model switching

2 files changed, 33 insertions, 26 deletions

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 3a01c93d..3aa21ec1 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -1,4 +1,4 @@
-import glob

+import collections

 import os.path

 import sys

 from collections import namedtuple

@@ -15,6 +15,7 @@ model_path = os.path.abspath(os.path.join(models_path, model_dir))
 

 CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config'])

 checkpoints_list = {}

+checkpoints_loaded = collections.OrderedDict()

 

 try:

     # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.

@@ -132,41 +133,45 @@ def load_model_weights(model, checkpoint_info):
     checkpoint_file = checkpoint_info.filename

     sd_model_hash = checkpoint_info.hash

 

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

+    if checkpoint_info not in checkpoints_loaded:

+        print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")

 

-    pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)

+        pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)

+        if "global_step" in pl_sd:

+            print(f"Global Step: {pl_sd['global_step']}")

 

-    if "global_step" in pl_sd:

-        print(f"Global Step: {pl_sd['global_step']}")

+        sd = get_state_dict_from_checkpoint(pl_sd)

+        model.load_state_dict(sd, strict=False)

 

-    sd = get_state_dict_from_checkpoint(pl_sd)

+        if shared.cmd_opts.opt_channelslast:

+            model.to(memory_format=torch.channels_last)

 

-    model.load_state_dict(sd, strict=False)

+        if not shared.cmd_opts.no_half:

+            model.half()

 

-    if shared.cmd_opts.opt_channelslast:

-        model.to(memory_format=torch.channels_last)

+        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

 

-    if not shared.cmd_opts.no_half:

-        model.half()

+        vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"

 

-    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

+        if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None:

+            vae_file = shared.cmd_opts.vae_path

 

-    vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"

+        if os.path.exists(vae_file):

+            print(f"Loading VAE weights from: {vae_file}")

+            vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)

+            vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}

+            model.first_stage_model.load_state_dict(vae_dict)

 

-    if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None:

-        vae_file = shared.cmd_opts.vae_path

+        model.first_stage_model.to(devices.dtype_vae)

 

-    if os.path.exists(vae_file):

-        print(f"Loading VAE weights from: {vae_file}")

-

-        vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)

-

-        vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}

-

-        model.first_stage_model.load_state_dict(vae_dict)

-

-    model.first_stage_model.to(devices.dtype_vae)

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

+        while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:

+            checkpoints_loaded.popitem(last=False)  # LRU

+    else:

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

+        checkpoints_loaded.move_to_end(checkpoint_info)

+        model.load_state_dict(checkpoints_loaded[checkpoint_info])

 

     model.sd_model_hash = sd_model_hash

     model.sd_model_checkpoint = checkpoint_file

@@ -205,6 +210,7 @@ def reload_model_weights(sd_model, info=None):
         return

 

     if sd_model.sd_checkpoint_info.config != checkpoint_info.config:

+        checkpoints_loaded.clear()

         shared.sd_model = load_model()

         return shared.sd_model

 

diff --git a/modules/shared.py b/modules/shared.py
index d41a7ab3..aa69bedf 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -242,6 +242,7 @@ options_templates.update(options_section(('training', "Training"), {
 

 options_templates.update(options_section(('sd', "Stable Diffusion"), {

     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),

+    "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),

     "sd_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}),

     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),