Merge branch 'dev' into master

modules/sd_models.py

@@ -1,22 +1,22 @@
 import collections
 import os.path
 import sys
-import gc
 import threading
 
 import torch
 import re
 import safetensors.torch
-from omegaconf import OmegaConf
+from omegaconf import OmegaConf, ListConfig
 from os import mkdir
 from urllib import request
 import ldm.modules.midas as midas
 
 from ldm.util import instantiate_from_config
 
-from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl, cache, extra_networks, processing, lowvram, sd_hijack
+from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl, cache, extra_networks, processing, lowvram, sd_hijack, patches
 from modules.timer import Timer
 import tomesd
+import numpy as np
 
 model_dir = "Stable-diffusion"
 model_path = os.path.abspath(os.path.join(paths.models_path, model_dir))
@@ -49,11 +49,12 @@ class CheckpointInfo:
     def __init__(self, filename):
         self.filename = filename
         abspath = os.path.abspath(filename)
+        abs_ckpt_dir = os.path.abspath(shared.cmd_opts.ckpt_dir) if shared.cmd_opts.ckpt_dir is not None else None
 
         self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors"
 
-        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, '')
+        if abs_ckpt_dir and abspath.startswith(abs_ckpt_dir):
+            name = abspath.replace(abs_ckpt_dir, '')
         elif abspath.startswith(model_path):
             name = abspath.replace(model_path, '')
         else:
@@ -129,9 +130,12 @@ except Exception:
 
 
 def setup_model():
+    """called once at startup to do various one-time tasks related to SD models"""
+
     os.makedirs(model_path, exist_ok=True)
 
     enable_midas_autodownload()
+    patch_given_betas()
 
 
 def checkpoint_tiles(use_short=False):
@@ -309,6 +313,8 @@ def get_checkpoint_state_dict(checkpoint_info: CheckpointInfo, timer):
     if checkpoint_info in checkpoints_loaded:
         # use checkpoint cache
         print(f"Loading weights [{sd_model_hash}] from cache")
+        # move to end as latest
+        checkpoints_loaded.move_to_end(checkpoint_info)
         return checkpoints_loaded[checkpoint_info]
 
     print(f"Loading weights [{sd_model_hash}] from {checkpoint_info.filename}")
@@ -353,12 +359,12 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
     if model.is_ssd:
         sd_hijack.model_hijack.conv_ssd(model)
         
-    model.load_state_dict(state_dict, strict=False)
-    timer.record("apply weights to model")
-
     if shared.opts.sd_checkpoint_cache > 0:
         # cache newly loaded model
-        checkpoints_loaded[checkpoint_info] = state_dict
+        checkpoints_loaded[checkpoint_info] = state_dict.copy()
+
+    model.load_state_dict(state_dict, strict=False)
+    timer.record("apply weights to model")
 
     del state_dict
 
@@ -456,6 +462,20 @@ def enable_midas_autodownload():
     midas.api.load_model = load_model_wrapper
 
 
+def patch_given_betas():
+    import ldm.models.diffusion.ddpm
+
+    def patched_register_schedule(*args, **kwargs):
+        """a modified version of register_schedule function that converts plain list from Omegaconf into numpy"""
+
+        if isinstance(args[1], ListConfig):
+            args = (args[0], np.array(args[1]), *args[2:])
+
+        original_register_schedule(*args, **kwargs)
+
+    original_register_schedule = patches.patch(__name__, ldm.models.diffusion.ddpm.DDPM, 'register_schedule', patched_register_schedule)
+
+
 def repair_config(sd_config):
 
     if not hasattr(sd_config.model.params, "use_ema"):
@@ -780,17 +800,7 @@ def reload_model_weights(sd_model=None, info=None):
 
 
 def unload_model_weights(sd_model=None, info=None):
-    timer = Timer()
-
-    if model_data.sd_model:
-        model_data.sd_model.to(devices.cpu)
-        sd_hijack.model_hijack.undo_hijack(model_data.sd_model)
-        model_data.sd_model = None
-        sd_model = None
-        gc.collect()
-        devices.torch_gc()
-
-    print(f"Unloaded weights {timer.summary()}.")
+    send_model_to_cpu(sd_model or shared.sd_model)
 
     return sd_model