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this is release candidate 2.3.3-rc1 (#3033)

Browse Source 
This includes a number of bug fixes described in the draft release
notes.

It also incorporates a modified version of the dialog-based invoke.sh
script suggested by JoshuaKimsey:
https://discord.com/channels/1020123559063990373/1089119602425995304
This commit is contained in:
Lincoln Stein 2023-03-27 12:09:56 -04:00 committed by GitHub
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4 changed files with 711 additions and 312 deletions
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20
docs/installation/050_INSTALLING_MODELS.md
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@ -211,6 +211,26 @@ description for the model, whether to make this the default model that
is loaded at InvokeAI startup time, and whether to replace its
VAE. Generally the answer to the latter question is "no".
### Specifying a configuration file for legacy checkpoints
Some checkpoint files come with instructions to use a specific .yaml
configuration file. For InvokeAI load this file correctly, please put
the config file in the same directory as the corresponding `.ckpt` or
`.safetensors` file and make sure the file has the same basename as
the weights file. Here is an example:
```bash
wonderful-model-v2.ckpt
wonderful-model-v2.yaml
```
Similarly, to use a custom VAE, name the VAE like this:
```bash
wonderful-model-v2.vae.pt
```
### Converting legacy models into `diffusers`
The CLI `!convert_model` will convert a `.safetensors` or `.ckpt`

160
installer/templates/invoke.sh.in
View File

@ -1,5 +1,8 @@
#!/bin/bash
# coauthored by Lincoln Stein, Eugene Brodsky and JoshuaKimsey
# Copyright 2023, The InvokeAI Development Team
####
# This launch script assumes that:
# 1. it is located in the runtime directory,
@ -18,18 +21,109 @@ cd "$scriptdir"
. .venv/bin/activate
export INVOKEAI_ROOT="$scriptdir"
PARAMS=$@
# set required env var for torch on mac MPS
if [ "$(uname -s)" == "Darwin" ]; then
export PYTORCH_ENABLE_MPS_FALLBACK=1
fi
if [ "$0" != "bash" ]; then
do_choice() {
case $1 in
1)
echo "Generate images with a browser-based interface"
clear
invokeai --web $PARAMS
;;
2)
echo "Generate images using a command-line interface"
clear
invokeai $PARAMS
;;
3)
echo "Textual inversion training"
clear
invokeai-ti --gui $PARAMS
;;
4)
echo "Merge models (diffusers type only)"
clear
invokeai-merge --gui $PARAMS
;;
5)
echo "Download and install models"
clear
invokeai-model-install --root ${INVOKEAI_ROOT}
;;
6)
echo "Change InvokeAI startup options"
clear
invokeai-configure --root ${INVOKEAI_ROOT} --skip-sd-weights --skip-support-models
;;
7)
echo "Re-run the configure script to fix a broken install"
clear
invokeai-configure --root ${INVOKEAI_ROOT} --yes --default_only
;;
8)
echo "Open the developer console"
clear
file_name=$(basename "${BASH_SOURCE[0]}")
bash --init-file "$file_name"
;;
9)
echo "Update InvokeAI"
clear
invokeai-update
;;
10)
echo "Command-line help"
clear
invokeai --help
;;
*)
echo "Exiting..."
exit
;;
esac
clear
}
do_dialog() {
while true
do
options=(
1 "Generate images with a browser-based interface"
2 "Generate images using a command-line interface"
3 "Textual inversion training"
4 "Merge models (diffusers type only)"
5 "Download and install models"
6 "Change InvokeAI startup options"
7 "Re-run the configure script to fix a broken install"
8 "Open the developer console"
9 "Update InvokeAI"
10 "Command-line help")
choice=$(dialog --clear \
--backtitle "InvokeAI" \
--title "What would you like to run?" \
--menu "Select an option:" \
0 0 0 \
"${options[@]}" \
2>&1 >/dev/tty) || clear
do_choice "$choice"
done
clear
}
do_line_input() {
echo " ** For a more attractive experience, please install the 'dialog' utility. **"
echo ""
while true
do
echo "Do you want to generate images using the"
echo "1. command-line interface"
echo "2. browser-based UI"
echo "1. browser-based UI"
echo "2. command-line interface"
echo "3. run textual inversion training"
echo "4. merge models (diffusers type only)"
echo "5. download and install models"
@ -40,56 +134,22 @@ if [ "$0" != "bash" ]; then
echo "10. command-line help"
echo "Q - Quit"
echo ""
read -p "Please enter 1-10, Q: [2] " yn
choice=${yn:='2'}
case $choice in
1)
echo "Starting the InvokeAI command-line..."
invokeai $@
;;
2)
echo "Starting the InvokeAI browser-based UI..."
invokeai --web $@
;;
3)
echo "Starting Textual Inversion:"
invokeai-ti --gui $@
;;
4)
echo "Merging Models:"
invokeai-merge --gui $@
;;
5)
invokeai-model-install --root ${INVOKEAI_ROOT}
;;
6)
invokeai-configure --root ${INVOKEAI_ROOT} --skip-sd-weights --skip-support-models
;;
7)
invokeai-configure --root ${INVOKEAI_ROOT} --yes --default_only
;;
8)
echo "Developer Console:"
file_name=$(basename "${BASH_SOURCE[0]}")
bash --init-file "$file_name"
;;
9)
echo "Update:"
invokeai-update
;;
10)
invokeai --help
;;
[qQ])
exit 0
;;
*)
echo "Invalid selection"
exit;;
esac
read -p "Please enter 1-10, Q: [1] " yn
choice=${yn:='1'}
do_choice $choice
done
}
if [ "$0" != "bash" ]; then
# Dialog seems to be a standard installtion for most Linux distros, but this checks to ensure it is present regardless
if command -v dialog &> /dev/null ; then
do_dialog
else
do_line_input
fi
else # in developer console
python --version
echo "Press ^D to exit"
export PS1="(InvokeAI) \u@\h \w> "
fi

611
ldm/invoke/ckpt_to_diffuser.py
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File diff suppressed because it is too large Load Diff

80
ldm/invoke/model_manager.py
View File

@ -19,7 +19,7 @@ import warnings
from enum import Enum
from pathlib import Path
from shutil import move, rmtree
from typing import Any, Optional, Union, Callable
from typing import Any, Callable, Optional, Union
import safetensors
import safetensors.torch
@ -35,12 +35,7 @@ from picklescan.scanner import scan_file_path
from ldm.invoke.devices import CPU_DEVICE
from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline
from ldm.invoke.globals import Globals, global_cache_dir
from ldm.util import (
ask_user,
download_with_resume,
instantiate_from_config,
url_attachment_name,
)
from ldm.util import ask_user, download_with_resume, instantiate_from_config, url_attachment_name
class SDLegacyType(Enum):
@ -384,15 +379,16 @@ class ModelManager(object):
if not os.path.isabs(weights):
weights = os.path.normpath(os.path.join(Globals.root, weights))
# check whether this is a v2 file and force conversion
convert = Globals.ckpt_convert or self.is_v2_config(config)
# if converting automatically to diffusers, then we do the conversion and return
# a diffusers pipeline
if Globals.ckpt_convert:
if convert:
print(
f">> Converting legacy checkpoint {model_name} into a diffusers model..."
)
from ldm.invoke.ckpt_to_diffuser import (
load_pipeline_from_original_stable_diffusion_ckpt,
)
from ldm.invoke.ckpt_to_diffuser import load_pipeline_from_original_stable_diffusion_ckpt
self.offload_model(self.current_model)
if vae_config := self._choose_diffusers_vae(model_name):
@ -547,6 +543,15 @@ class ModelManager(object):
return pipeline, width, height, model_hash
def is_v2_config(self, config: Path) -> bool:
try:
mconfig = OmegaConf.load(config)
return (
mconfig["model"]["params"]["unet_config"]["params"]["context_dim"] > 768
)
except:
return False
def model_name_or_path(self, model_name: Union[str, DictConfig]) -> str | Path:
if isinstance(model_name, DictConfig) or isinstance(model_name, dict):
mconfig = model_name
@ -724,7 +729,7 @@ class ModelManager(object):
SDLegacyType.V2_v (V2 using 'v_prediction' prediction type)
SDLegacyType.UNKNOWN
"""
global_step = checkpoint.get('global_step')
global_step = checkpoint.get("global_step")
state_dict = checkpoint.get("state_dict") or checkpoint
try:
@ -871,6 +876,12 @@ class ModelManager(object):
checkpoint = safetensors.torch.load_file(model_path)
# additional probing needed if no config file provided
if model_config_file is None:
# Is there a like-named .yaml file in the same directory as the
# weights file? If so, we treat this as our model
if model_path.with_suffix(".yaml").exists():
model_config_file = model_path.with_suffix(".yaml")
print(f" | Using config file {model_config_file.name}")
else:
model_type = self.probe_model_type(checkpoint)
if model_type == SDLegacyType.V1:
print(" | SD-v1 model detected")
@ -880,19 +891,16 @@ class ModelManager(object):
elif model_type == SDLegacyType.V1_INPAINT:
print(" | SD-v1 inpainting model detected")
model_config_file = Path(
Globals.root, "configs/stable-diffusion/v1-inpainting-inference.yaml"
Globals.root,
"configs/stable-diffusion/v1-inpainting-inference.yaml",
)
elif model_type == SDLegacyType.V2_v:
print(
" | SD-v2-v model detected"
)
print(" | SD-v2-v model detected")
model_config_file = Path(
Globals.root, "configs/stable-diffusion/v2-inference-v.yaml"
)
elif model_type == SDLegacyType.V2_e:
print(
" | SD-v2-e model detected"
)
print(" | SD-v2-e model detected")
model_config_file = Path(
Globals.root, "configs/stable-diffusion/v2-inference.yaml"
)
@ -910,11 +918,17 @@ class ModelManager(object):
if not model_config_file:
return
if model_config_file.name.startswith('v2'):
if self.is_v2_config(model_config_file):
convert = True
print(
" | This SD-v2 model will be converted to diffusers format for use"
)
print(" | This SD-v2 model will be converted to diffusers format for use")
# look for a custom vae
vae_path = None
for suffix in ["pt", "ckpt", "safetensors"]:
if (model_path.with_suffix(f".vae.{suffix}")).exists():
vae_path = model_path.with_suffix(f".vae.{suffix}")
print(f" | Using VAE file {vae_path.name}")
vae = None if vae_path else dict(repo_id="stabilityai/sd-vae-ft-mse")
if convert:
diffuser_path = Path(
@ -923,7 +937,8 @@ class ModelManager(object):
model_name = self.convert_and_import(
model_path,
diffusers_path=diffuser_path,
vae=dict(repo_id="stabilityai/sd-vae-ft-mse"),
vae=vae,
vae_path=vae_path,
model_name=model_name,
model_description=description,
original_config_file=model_config_file,
@ -941,7 +956,8 @@ class ModelManager(object):
model_name=model_name,
model_description=description,
vae=str(
Path(
vae_path
or Path(
Globals.root,
"models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt",
)
@ -958,7 +974,8 @@ class ModelManager(object):
diffusers_path: Path,
model_name=None,
model_description=None,
vae=None,
vae: dict = None,
vae_path: Path = None,
original_config_file: Path = None,
commit_to_conf: Path = None,
scan_needed: bool = True,
@ -975,7 +992,7 @@ class ModelManager(object):
new_config = None
from ldm.invoke.ckpt_to_diffuser import convert_ckpt_to_diffuser
from ldm.invoke.ckpt_to_diffuser import convert_ckpt_to_diffusers
if diffusers_path.exists():
print(
@ -990,12 +1007,13 @@ class ModelManager(object):
# By passing the specified VAE to the conversion function, the autoencoder
# will be built into the model rather than tacked on afterward via the config file
vae_model = self._load_vae(vae) if vae else None
convert_ckpt_to_diffuser(
convert_ckpt_to_diffusers(
ckpt_path,
diffusers_path,
extract_ema=True,
original_config_file=original_config_file,
vae=vae_model,
vae_path=str(vae_path) if vae_path else None,
scan_needed=scan_needed,
)
print(
@ -1048,7 +1066,7 @@ class ModelManager(object):
# In the event that the original entry is using a custom ckpt VAE, we try to
# map that VAE onto a diffuser VAE using a hard-coded dictionary.
# I would prefer to do this differently: We load the ckpt model into memory, swap the
# VAE in memory, and then pass that to convert_ckpt_to_diffuser() so that the swapped
# VAE in memory, and then pass that to convert_ckpt_to_diffusers() so that the swapped
# VAE is built into the model. However, when I tried this I got obscure key errors.
if vae:
return vae
@ -1134,14 +1152,14 @@ class ModelManager(object):
legacy_locations = [
Path(
models_dir,
"CompVis/stable-diffusion-safety-checker/models--CompVis--stable-diffusion-safety-checker"
"CompVis/stable-diffusion-safety-checker/models--CompVis--stable-diffusion-safety-checker",
),
Path("bert-base-uncased/models--bert-base-uncased"),
Path(
"openai/clip-vit-large-patch14/models--openai--clip-vit-large-patch14"
),
]
legacy_locations.extend(list(global_cache_dir("diffusers").glob('*')))
legacy_locations.extend(list(global_cache_dir("diffusers").glob("*")))
legacy_layout = False
for model in legacy_locations:
legacy_layout = legacy_layout or model.exists()