diff options
Diffstat (limited to 'modules/sd_models.py')
| -rw-r--r-- | modules/sd_models.py | 139 |
1 files changed, 117 insertions, 22 deletions
diff --git a/modules/sd_models.py b/modules/sd_models.py index f0cb1240..8e42bfea 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -2,6 +2,8 @@ import collections import os.path
import sys
import gc
+import threading
+
import torch
import re
import safetensors.torch
@@ -13,9 +15,9 @@ 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
-from modules.paths import models_path
from modules.sd_hijack_inpainting import do_inpainting_hijack
from modules.timer import Timer
+import tomesd
model_dir = "Stable-diffusion"
model_path = os.path.abspath(os.path.join(paths.models_path, model_dir))
@@ -45,20 +47,29 @@ class CheckpointInfo: self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
self.hash = model_hash(filename)
- self.sha256 = hashes.sha256_from_cache(self.filename, "checkpoint/" + name)
+ self.sha256 = hashes.sha256_from_cache(self.filename, f"checkpoint/{name}")
self.shorthash = self.sha256[0:10] if self.sha256 else None
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 [])
+ self.metadata = {}
+
+ _, ext = os.path.splitext(self.filename)
+ if ext.lower() == ".safetensors":
+ try:
+ self.metadata = read_metadata_from_safetensors(filename)
+ except Exception as e:
+ errors.display(e, f"reading checkpoint metadata: {filename}")
+
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, "checkpoint/" + self.name)
+ self.sha256 = hashes.sha256(self.filename, f"checkpoint/{self.name}")
if self.sha256 is None:
return
@@ -76,8 +87,7 @@ class CheckpointInfo: try:
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
-
- from transformers import logging, CLIPModel
+ from transformers import logging, CLIPModel # noqa: F401
logging.set_verbosity_error()
except Exception:
@@ -122,7 +132,7 @@ def list_models(): 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:
+ for filename in sorted(model_list, key=str.lower):
checkpoint_info = CheckpointInfo(filename)
checkpoint_info.register()
@@ -156,7 +166,7 @@ def model_hash(filename): def select_checkpoint():
model_checkpoint = shared.opts.sd_model_checkpoint
-
+
checkpoint_info = checkpoint_alisases.get(model_checkpoint, None)
if checkpoint_info is not None:
return checkpoint_info
@@ -178,7 +188,7 @@ def select_checkpoint(): return checkpoint_info
-chckpoint_dict_replacements = {
+checkpoint_dict_replacements = {
'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
@@ -186,7 +196,7 @@ chckpoint_dict_replacements = { def transform_checkpoint_dict_key(k):
- for text, replacement in chckpoint_dict_replacements.items():
+ for text, replacement in checkpoint_dict_replacements.items():
if k.startswith(text):
k = replacement + k[len(text):]
@@ -228,7 +238,7 @@ def read_metadata_from_safetensors(filename): if isinstance(v, str) and v[0:1] == '{':
try:
res[k] = json.loads(v)
- except Exception as e:
+ except Exception:
pass
return res
@@ -363,7 +373,7 @@ def enable_midas_autodownload(): if not os.path.exists(path):
if not os.path.exists(midas_path):
mkdir(midas_path)
-
+
print(f"Downloading midas model weights for {model_type} to {path}")
request.urlretrieve(midas_urls[model_type], path)
print(f"{model_type} downloaded")
@@ -383,17 +393,58 @@ def repair_config(sd_config): elif shared.cmd_opts.upcast_sampling:
sd_config.model.params.unet_config.params.use_fp16 = True
+ if getattr(sd_config.model.params.first_stage_config.params.ddconfig, "attn_type", None) == "vanilla-xformers" and not shared.xformers_available:
+ sd_config.model.params.first_stage_config.params.ddconfig.attn_type = "vanilla"
+
+ # For UnCLIP-L, override the hardcoded karlo directory
+ if hasattr(sd_config.model.params, "noise_aug_config") and hasattr(sd_config.model.params.noise_aug_config.params, "clip_stats_path"):
+ karlo_path = os.path.join(paths.models_path, 'karlo')
+ sd_config.model.params.noise_aug_config.params.clip_stats_path = sd_config.model.params.noise_aug_config.params.clip_stats_path.replace("checkpoints/karlo_models", karlo_path)
+
sd1_clip_weight = 'cond_stage_model.transformer.text_model.embeddings.token_embedding.weight'
sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight'
-def load_model(checkpoint_info=None, already_loaded_state_dict=None, time_taken_to_load_state_dict=None):
+
+class SdModelData:
+ def __init__(self):
+ self.sd_model = None
+ self.was_loaded_at_least_once = False
+ self.lock = threading.Lock()
+
+ def get_sd_model(self):
+ if self.was_loaded_at_least_once:
+ return self.sd_model
+
+ if self.sd_model is None:
+ with self.lock:
+ if self.sd_model is not None or self.was_loaded_at_least_once:
+ return self.sd_model
+
+ try:
+ load_model()
+ except Exception as e:
+ errors.display(e, "loading stable diffusion model")
+ print("", file=sys.stderr)
+ print("Stable diffusion model failed to load", file=sys.stderr)
+ self.sd_model = None
+
+ return self.sd_model
+
+ def set_sd_model(self, v):
+ self.sd_model = v
+
+
+model_data = SdModelData()
+
+
+def load_model(checkpoint_info=None, already_loaded_state_dict=None):
from modules import lowvram, sd_hijack
checkpoint_info = checkpoint_info or select_checkpoint()
- if shared.sd_model:
- sd_hijack.model_hijack.undo_hijack(shared.sd_model)
- shared.sd_model = None
+ if model_data.sd_model:
+ sd_hijack.model_hijack.undo_hijack(model_data.sd_model)
+ model_data.sd_model = None
gc.collect()
devices.torch_gc()
@@ -422,7 +473,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None, time_taken_ try:
with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd):
sd_model = instantiate_from_config(sd_config.model)
- except Exception as e:
+ except Exception:
pass
if sd_model is None:
@@ -447,7 +498,8 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None, time_taken_ timer.record("hijack")
sd_model.eval()
- shared.sd_model = sd_model
+ model_data.sd_model = sd_model
+ model_data.was_loaded_at_least_once = True
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
@@ -467,7 +519,7 @@ def reload_model_weights(sd_model=None, info=None): checkpoint_info = info or select_checkpoint()
if not sd_model:
- sd_model = shared.sd_model
+ sd_model = model_data.sd_model
if sd_model is None: # previous model load failed
current_checkpoint_info = None
@@ -493,13 +545,12 @@ def reload_model_weights(sd_model=None, info=None): 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
+ load_model(checkpoint_info, already_loaded_state_dict=state_dict)
+ return model_data.sd_model
try:
load_model_weights(sd_model, checkpoint_info, state_dict, timer)
- except Exception as e:
+ except Exception:
print("Failed to load checkpoint, restoring previous")
load_model_weights(sd_model, current_checkpoint_info, None, timer)
raise
@@ -517,3 +568,47 @@ def reload_model_weights(sd_model=None, info=None): print(f"Weights loaded in {timer.summary()}.")
return sd_model
+
+
+def unload_model_weights(sd_model=None, info=None):
+ from modules import devices, sd_hijack
+ 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()
+ torch.cuda.empty_cache()
+
+ print(f"Unloaded weights {timer.summary()}.")
+
+ return sd_model
+
+
+def apply_token_merging(sd_model, token_merging_ratio):
+ """
+ Applies speed and memory optimizations from tomesd.
+ """
+
+ current_token_merging_ratio = getattr(sd_model, 'applied_token_merged_ratio', 0)
+
+ if current_token_merging_ratio == token_merging_ratio:
+ return
+
+ if current_token_merging_ratio > 0:
+ tomesd.remove_patch(sd_model)
+
+ if token_merging_ratio > 0:
+ tomesd.apply_patch(
+ sd_model,
+ ratio=token_merging_ratio,
+ use_rand=False, # can cause issues with some samplers
+ merge_attn=True,
+ merge_crossattn=False,
+ merge_mlp=False
+ )
+
+ sd_model.applied_token_merged_ratio = token_merging_ratio
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