Merge branch 'spezialspezial-patch-9' into development

This commit is contained in:
Lincoln Stein 2022-11-02 18:35:00 -04:00
commit 240e5486c8
21 changed files with 2768 additions and 136 deletions

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@ -4,6 +4,7 @@ on:
branches: branches:
- 'main' - 'main'
- 'development' - 'development'
- 'release-candidate-2-1'
jobs: jobs:
mkdocs-material: mkdocs-material:

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@ -43,6 +43,7 @@ title: Manual Installation, Linux
environment named `invokeai` and activate the environment. environment named `invokeai` and activate the environment.
```bash ```bash
(base) rm -rf src # (this is a precaution in case there is already a src directory)
(base) ~/InvokeAI$ conda env create (base) ~/InvokeAI$ conda env create
(base) ~/InvokeAI$ conda activate invokeai (base) ~/InvokeAI$ conda activate invokeai
(invokeai) ~/InvokeAI$ (invokeai) ~/InvokeAI$
@ -51,50 +52,54 @@ title: Manual Installation, Linux
After these steps, your command prompt will be prefixed by `(invokeai)` as shown After these steps, your command prompt will be prefixed by `(invokeai)` as shown
above. above.
6. Load a couple of small machine-learning models required by stable diffusion: 6. Load the big stable diffusion weights files and a couple of smaller machine-learning models:
```bash ```bash
(invokeai) ~/InvokeAI$ python3 scripts/preload_models.py (invokeai) ~/InvokeAI$ python3 scripts/preload_models.py
``` ```
!!! note !!! note
This script will lead you through the process of creating an account on Hugging Face,
accepting the terms and conditions of the Stable Diffusion model license, and
obtaining an access token for downloading. It will then download and install the
weights files for you.
This step is necessary because I modified the original just-in-time Please see [../features/INSTALLING_MODELS.md] for a manual process for doing the
model loading scheme to allow the script to work on GPU machines that are not same thing.
internet connected. See [Preload Models](../features/OTHER.md#preload-models)
7. Install the weights for the stable diffusion model. 7. Start generating images!
- Sign up at https://huggingface.co # Command-line interface
- Go to the [Stable diffusion diffusion model page](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original) (invokeai) python scripts/invoke.py
- Accept the terms and click Access Repository
- Download [v1-5-pruned-emaonly.ckpt (4.27 GB)](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.ckpt)
and move it into this directory under `models/ldm/stable_diffusion_v1/v1-5-pruned-emaonly.ckpt`
There are many other models that you can use. Please see [../features/INSTALLING_MODELS.md] # or run the web interface on localhost:9090!
for details. (invokeai) python scripts/invoke.py --web
8. Start generating images! # or run the web interface on your machine's network interface!
(invokeai) python scripts/invoke.py --web --host 0.0.0.0
```bash To use an alternative model you may invoke the `!switch` command in
# for the pre-release weights use the -l or --liaon400m switch the CLI, or pass `--model <model_name>` during `invoke.py` launch for
(invokeai) ~/InvokeAI$ python3 scripts/invoke.py -l either the CLI or the Web UI. See [Command Line
Client](../features/CLI.md#model-selection-and-importation). The
model names are defined in `configs/models.yaml`.
# for the post-release weights do not use the switch 9. Subsequently, to relaunch the script, be sure to run "conda
(invokeai) ~/InvokeAI$ python3 scripts/invoke.py activate invokeai" (step 5, second command), enter the `InvokeAI`
directory, and then launch the invoke script (step 8). If you forget
# for additional configuration switches and arguments, use -h or --help to activate the 'invokeai' environment, the script will fail with
(invokeai) ~/InvokeAI$ python3 scripts/invoke.py -h multiple `ModuleNotFound` errors.
```
9. Subsequently, to relaunch the script, be sure to run "conda activate invokeai" (step 5, second command), enter the `InvokeAI` directory, and then launch the invoke script (step 8). If you forget to activate the 'invokeai' environment, the script will fail with multiple `ModuleNotFound` errors.
## Updating to newer versions of the script ## Updating to newer versions of the script
This distribution is changing rapidly. If you used the `git clone` method (step 5) to download the InvokeAI directory, then to update to the latest and greatest version, launch the Anaconda window, enter `InvokeAI` and type: This distribution is changing rapidly. If you used the `git clone`
method (step 5) to download the InvokeAI directory, then to update to
the latest and greatest version, launch the Anaconda window, enter
`InvokeAI` and type:
```bash ```bash
(invokeai) ~/InvokeAI$ git pull (invokeai) ~/InvokeAI$ git pull
(invokeai) ~/InvokeAI$ rm -rf src # prevents conda freezing errors
(invokeai) ~/InvokeAI$ conda env update -f environment.yml (invokeai) ~/InvokeAI$ conda env update -f environment.yml
``` ```

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@ -19,24 +19,9 @@ an issue on Github and we will do our best to help.
## Installation ## Installation
First you need to download a large checkpoint file.
1. Sign up at https://huggingface.co
2. Go to the [Stable diffusion diffusion model page](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original)
3. Accept the terms and click Access Repository
4. Download [v1-5-pruned-emaonly.ckpt (4.27 GB)](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.ckpt)
and move it into this directory under `models/ldm/stable_diffusion_v1/v1-5-pruned-emaonly.ckpt`
There are many other models that you can try. Please see [../features/INSTALLING_MODELS.md]
for details.
While that is downloading, open Terminal and run the following
commands one at a time, reading the comments and taking care to run
the appropriate command for your Mac's architecture (Intel or M1).
!!! todo "Homebrew" !!! todo "Homebrew"
If you have no brew installation yet (otherwise skip): First you will install the "brew" package manager. Skip this if brew is already installed.
```bash title="install brew (and Xcode command line tools)" ```bash title="install brew (and Xcode command line tools)"
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)" /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
@ -100,25 +85,6 @@ the appropriate command for your Mac's architecture (Intel or M1).
cd InvokeAI cd InvokeAI
``` ```
!!! todo "Wait until the checkpoint-file download finished, then proceed"
We will leave the big checkpoint wherever you stashed it for long-term storage,
and make a link to it from the repo's folder. This allows you to use it for
other repos, or if you need to delete Invoke AI, you won't have to download it again.
```{.bash .annotate}
# Make the directory in the repo for the symlink
mkdir -p models/ldm/stable-diffusion-v1/
# This is the folder where you put the checkpoint file `sd-v1-4.ckpt`
PATH_TO_CKPT="$HOME/Downloads" # (1)!
# Create a link to the checkpoint
ln -s "$PATH_TO_CKPT/sd-v1-4.ckpt" models/ldm/stable-diffusion-v1/model.ckpt
```
1. replace `$HOME/Downloads` with the Location where you actually stored the Checkppoint (`sd-v1-4.ckpt`)
!!! todo "Create the environment & install packages" !!! todo "Create the environment & install packages"
=== "M1 Mac" === "M1 Mac"
@ -137,25 +103,40 @@ the appropriate command for your Mac's architecture (Intel or M1).
# Activate the environment (you need to do this every time you want to run SD) # Activate the environment (you need to do this every time you want to run SD)
conda activate invokeai conda activate invokeai
# This will download some bits and pieces and make take a while
(invokeai) python scripts/preload_models.py
# Run SD!
(invokeai) python scripts/dream.py
# or run the web interface!
(invokeai) python scripts/invoke.py --web
# The original scripts should work as well.
(invokeai) python scripts/orig_scripts/txt2img.py \
--prompt "a photograph of an astronaut riding a horse" \
--plms
```
!!! info !!! info
`export PIP_EXISTS_ACTION=w` is a precaution to fix `conda env `export PIP_EXISTS_ACTION=w` is a precaution to fix `conda env
create -f environment-mac.yml` never finishing in some situations. So create -f environment-mac.yml` never finishing in some situations. So
it isn't required but wont hurt. it isn't required but won't hurt.
!!! todo "Download the model weight files"
The `preload_models.py` script downloads and installs the model weight
files for you. It will lead you through the process of getting a Hugging Face
account, accepting the Stable Diffusion model weight license agreement, and
creating a download token:
# This will take some time, depending on the speed of your internet connection
# and will consume about 10GB of space
(invokeai) python scripts/preload_models.py
!! todo "Run InvokeAI!"
# Command-line interface
(invokeai) python scripts/invoke.py
# or run the web interface on localhost:9090!
(invokeai) python scripts/invoke.py --web
# or run the web interface on your machine's network interface!
(invokeai) python scripts/invoke.py --web --host 0.0.0.0
To use an alternative model you may invoke the `!switch` command in
the CLI, or pass `--model <model_name>` during `invoke.py` launch for
either the CLI or the Web UI. See [Command Line
Client](../features/CLI.md#model-selection-and-importation). The
model names are defined in `configs/models.yaml`.
--- ---
## Common problems ## Common problems

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@ -69,40 +69,42 @@ in the wiki
environment file isn't specified, conda will default to `environment.yml`. You will need environment file isn't specified, conda will default to `environment.yml`. You will need
to provide the `-f` option if you wish to load a different environment file at any point. to provide the `-f` option if you wish to load a different environment file at any point.
7. Run the command: 7. Load the big stable diffusion weights files and a couple of smaller machine-learning models:
```batch ```bash
python scripts\preload_models.py (invokeai) ~/InvokeAI$ python3 scripts/preload_models.py
``` ```
This installs several machine learning models that stable diffusion requires. !!! note
This script will lead you through the process of creating an account on Hugging Face,
accepting the terms and conditions of the Stable Diffusion model license, and
obtaining an access token for downloading. It will then download and install the
weights files for you.
Note: This step is required. This was done because some users may might be Please see [../features/INSTALLING_MODELS.md] for a manual process for doing the
blocked by firewalls or have limited internet connectivity for the models to same thing.
be downloaded just-in-time.
8. Now you need to install the weights for the big stable diffusion model. 8. Start generating images!
- Sign up at https://huggingface.co # Command-line interface
- Go to the [Stable diffusion diffusion model page](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original) (invokeai) python scripts/invoke.py
- Accept the terms and click Access Repository
- Download [v1-5-pruned-emaonly.ckpt (4.27 GB)](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.ckpt)
and move it into this directory under `models/ldm/stable_diffusion_v1/v1-5-pruned-emaonly.ckpt`
There are many other models that you can use. Please see [../features/INSTALLING_MODELS.md] # or run the web interface on localhost:9090!
for details. (invokeai) python scripts/invoke.py --web
9. Start generating images! # or run the web interface on your machine's network interface!
(invokeai) python scripts/invoke.py --web --host 0.0.0.0
```batch title="for the pre-release weights" To use an alternative model you may invoke the `!switch` command in
python scripts\invoke.py -l the CLI, or pass `--model <model_name>` during `invoke.py` launch for
``` either the CLI or the Web UI. See [Command Line
Client](../features/CLI.md#model-selection-and-importation). The
model names are defined in `configs/models.yaml`.
```batch title="for the post-release weights" 9. Subsequently, to relaunch the script, first activate the Anaconda
python scripts\invoke.py command window (step 3),enter the InvokeAI directory (step 5, `cd
``` \path\to\InvokeAI`), run `conda activate invokeai` (step 6b), and then
launch the invoke script (step 9).
10. Subsequently, to relaunch the script, first activate the Anaconda command window (step 3),enter the InvokeAI directory (step 5, `cd \path\to\InvokeAI`), run `conda activate invokeai` (step 6b), and then launch the invoke script (step 9).
!!! tip "Tildebyte has written an alternative" !!! tip "Tildebyte has written an alternative"

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@ -11,6 +11,7 @@ dependencies:
- numpy=1.19 - numpy=1.19
- imageio=2.9.0 - imageio=2.9.0
- opencv=4.6.0 - opencv=4.6.0
- getpass_asterisk
- pillow=8.* - pillow=8.*
- flask=2.1.* - flask=2.1.*
- flask_cors=3.0.10 - flask_cors=3.0.10

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@ -22,6 +22,7 @@ dependencies:
- diffusers=0.6.0 - diffusers=0.6.0
- einops=0.4.1 - einops=0.4.1
- grpcio=1.46.4 - grpcio=1.46.4
- getpass_asterisk
- humanfriendly=10.0 - humanfriendly=10.0
- imageio=2.21.2 - imageio=2.21.2
- imageio-ffmpeg=0.4.7 - imageio-ffmpeg=0.4.7

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@ -1,14 +1,14 @@
name: invokeai name: invokeai
channels: channels:
- pytorch - pytorch
- nvidia
- defaults - defaults
dependencies: dependencies:
- python>=3.9 - python>=3.9
- pip=20.3 - pip=22.2.2
- cudatoolkit=11.3 - numpy=1.23.3
- pytorch=1.11.0 - torchvision=0.14.0
- torchvision=0.12.0 - pytorch-cuda=11.7
- numpy=1.19.2
- pip: - pip:
- albumentations==0.4.3 - albumentations==0.4.3
- opencv-python==4.5.5.64 - opencv-python==4.5.5.64
@ -33,6 +33,7 @@ dependencies:
- flask_cors==3.0.10 - flask_cors==3.0.10
- dependency_injector==4.40.0 - dependency_injector==4.40.0
- eventlet - eventlet
- getpass_asterisk
- kornia==0.6.0 - kornia==0.6.0
- -e git+https://github.com/openai/CLIP.git@main#egg=clip - -e git+https://github.com/openai/CLIP.git@main#egg=clip
- -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers - -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers

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@ -80,14 +80,36 @@ if not exist ".git" (
call conda activate call conda activate
@rem create the environment @rem create the environment
call conda env remove -n invokeai
call conda env create call conda env create
call conda activate invokeai if "%ERRORLEVEL%" NEQ "0" (
echo ""
echo "Something went wrong while installing Python libraries and cannot continue.
echo "Please visit https://invoke-ai.github.io/InvokeAI/#installation for alternative"
echo "installation methods."
echo "Press any key to continue"
pause
exit /b
)
call conda activate invokeai
@rem preload the models @rem preload the models
call python scripts\preload_models.py call python scripts\preload_models.py
if "%ERRORLEVEL%" NEQ "0" (
echo ""
echo "The preload_models.py script crashed or was cancelled."
echo "InvokeAI is not ready to run. To run preload_models.py again,"
echo "run the command 'update.bat' in this directory."
echo "Press any key to continue"
pause
exit /b
)
@rem tell the user their next steps @rem tell the user their next steps
echo. echo ""
echo "* InvokeAI installed successfully *"
echo "You can now start generating images by double-clicking the 'invoke.bat' file (inside this folder) echo "You can now start generating images by double-clicking the 'invoke.bat' file (inside this folder)
echo "Press any key to continue"
pause pause
exit 0

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@ -99,13 +99,32 @@ conda activate
if [ "$OS_NAME" == "mac" ]; then if [ "$OS_NAME" == "mac" ]; then
PIP_EXISTS_ACTION=w CONDA_SUBDIR=osx-${OS_ARCH} conda env create -f environment-mac.yml PIP_EXISTS_ACTION=w CONDA_SUBDIR=osx-${OS_ARCH} conda env create -f environment-mac.yml
else else
conda env remove -n invokeai
conda env create -f environment.yml conda env create -f environment.yml
fi fi
status=$?
if test $status -ne 0
then
echo "Something went wrong while installing Python libraries and cannot continue."
echo "Please visit https://invoke-ai.github.io/InvokeAI/#installation for alternative"
echo "installation methods"
else
conda activate invokeai conda activate invokeai
# preload the models # preload the models
echo "Calling the preload_models.py script"
python scripts/preload_models.py python scripts/preload_models.py
status=$?
if test $status -ne 0
then
echo "The preload_models.py script crashed or was cancelled."
echo "InvokeAI is not ready to run. Try again by running"
echo "update.sh in this directory."
else
# tell the user their next steps # tell the user their next steps
echo "You can now start generating images by running invoke.sh (inside this folder), using ./invoke.sh" echo "You can now start generating images by running invoke.sh (inside this folder), using ./invoke.sh"
fi
fi
conda activate invokeai

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@ -896,7 +896,7 @@ class Generate:
assert os.path.exists(image_path), '** "{image_path}" not found. Please enter the name of an existing image file to mask **' assert os.path.exists(image_path), '** "{image_path}" not found. Please enter the name of an existing image file to mask **'
basename,_ = os.path.splitext(os.path.basename(image_path)) basename,_ = os.path.splitext(os.path.basename(image_path))
if self.txt2mask is None: if self.txt2mask is None:
self.txt2mask = Txt2Mask(device = self.device) self.txt2mask = Txt2Mask(device = self.device, refined=True)
segmented = self.txt2mask.segment(image_path,prompt) segmented = self.txt2mask.segment(image_path,prompt)
trans = segmented.to_transparent() trans = segmented.to_transparent()
inverse = segmented.to_transparent(invert=True) inverse = segmented.to_transparent(invert=True)

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@ -41,7 +41,7 @@ class Outcrop(object):
height = extended_image.height, height = extended_image.height,
init_img = extended_image, init_img = extended_image,
strength = 0.90, strength = 0.90,
image_callback = wrapped_callback, image_callback = wrapped_callback if image_callback else None,
seam_size = opt.seam_size or 96, seam_size = opt.seam_size or 96,
seam_blur = opt.seam_blur or 16, seam_blur = opt.seam_blur or 16,
seam_strength = opt.seam_strength or 0.7, seam_strength = opt.seam_strength or 0.7,

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@ -36,6 +36,7 @@ from torchvision import transforms
CLIP_VERSION = 'ViT-B/16' CLIP_VERSION = 'ViT-B/16'
CLIPSEG_WEIGHTS = 'src/clipseg/weights/rd64-uni.pth' CLIPSEG_WEIGHTS = 'src/clipseg/weights/rd64-uni.pth'
CLIPSEG_WEIGHTS_REFINED = 'src/clipseg/weights/rd64-uni-refined.pth'
CLIPSEG_SIZE = 352 CLIPSEG_SIZE = 352
class SegmentedGrayscale(object): class SegmentedGrayscale(object):
@ -72,14 +73,14 @@ class Txt2Mask(object):
Create new Txt2Mask object. The optional device argument can be one of Create new Txt2Mask object. The optional device argument can be one of
'cuda', 'mps' or 'cpu'. 'cuda', 'mps' or 'cpu'.
''' '''
def __init__(self,device='cpu'): def __init__(self,device='cpu',refined=False):
print('>> Initializing clipseg model for text to mask inference') print('>> Initializing clipseg model for text to mask inference')
self.device = device self.device = device
self.model = CLIPDensePredT(version=CLIP_VERSION, reduce_dim=64, ) self.model = CLIPDensePredT(version=CLIP_VERSION, reduce_dim=64, complex_trans_conv=refined)
self.model.eval() self.model.eval()
# initially we keep everything in cpu to conserve space # initially we keep everything in cpu to conserve space
self.model.to('cpu') self.model.to('cpu')
self.model.load_state_dict(torch.load(CLIPSEG_WEIGHTS, map_location=torch.device('cpu')), strict=False) self.model.load_state_dict(torch.load(CLIPSEG_WEIGHTS_REFINED if refined else CLIPSEG_WEIGHTS, map_location=torch.device('cpu')), strict=False)
@torch.no_grad() @torch.no_grad()
def segment(self, image, prompt:str) -> SegmentedGrayscale: def segment(self, image, prompt:str) -> SegmentedGrayscale:

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@ -1,6 +1,6 @@
-r requirements.txt -r requirements.txt
protobuf==3.19.6 protobuf==3.19.6
torch torch<1.13.0
torchvision torchvision<0.14.0
-e . -e .

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@ -2,6 +2,7 @@
albumentations albumentations
einops einops
getpass_asterisk
huggingface-hub huggingface-hub
imageio-ffmpeg imageio-ffmpeg
imageio imageio
@ -13,7 +14,7 @@ opencv-python
pillow pillow
pip>=22 pip>=22
pudb pudb
pytorch-lightning=1.7.7 pytorch-lightning==1.7.7
scikit-image>=0.19 scikit-image>=0.19
streamlit streamlit
pyreadline3 pyreadline3

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@ -14,18 +14,21 @@ import warnings
from urllib import request from urllib import request
from tqdm import tqdm from tqdm import tqdm
from omegaconf import OmegaConf from omegaconf import OmegaConf
from huggingface_hub import HfFolder, hf_hub_url
from pathlib import Path from pathlib import Path
from getpass_asterisk import getpass_asterisk
import traceback import traceback
import getpass
import requests import requests
import clip import clip
import transformers import transformers
import torch import torch
transformers.logging.set_verbosity_error() transformers.logging.set_verbosity_error()
# deferred loading so that help message can be printed quickly import warnings
def load_libs(): warnings.filterwarnings('ignore')
pass #warnings.simplefilter('ignore')
#warnings.filterwarnings('ignore',category=DeprecationWarning)
#warnings.filterwarnings('ignore',category=UserWarning)
#--------------------------globals-- #--------------------------globals--
Model_dir = './models/ldm/stable-diffusion-v1/' Model_dir = './models/ldm/stable-diffusion-v1/'
@ -98,7 +101,7 @@ this program and resume later.\n'''
#-------------------------------------------- #--------------------------------------------
def postscript(): def postscript():
print( print(
'''You're all set! You may now launch InvokeAI using one of these two commands: '''\n** Model Installation Successful **\nYou're all set! You may now launch InvokeAI using one of these two commands:
Web version: Web version:
python scripts/invoke.py --web (connect to http://localhost:9090) python scripts/invoke.py --web (connect to http://localhost:9090)
@ -220,10 +223,18 @@ This involves a few easy steps.
''' '''
) )
input('Press <enter> when you are ready to continue:') input('Press <enter> when you are ready to continue:')
<<<<<<< HEAD
from huggingface_hub import HfFolder from huggingface_hub import HfFolder
=======
print('(Fetching Hugging Face token from cache...',end='')
>>>>>>> spezialspezial-patch-9
access_token = HfFolder.get_token() access_token = HfFolder.get_token()
if access_token is not None:
print('found')
if access_token is None: if access_token is None:
print('not found')
print(''' print('''
4. Thank you! The last step is to enter your HuggingFace access token so that 4. Thank you! The last step is to enter your HuggingFace access token so that
this script is authorized to initiate the download. Go to the access tokens this script is authorized to initiate the download. Go to the access tokens
@ -237,8 +248,7 @@ This involves a few easy steps.
Now copy the token to your clipboard and paste it here: ''' Now copy the token to your clipboard and paste it here: '''
) )
access_token = getpass.getpass() access_token = getpass_asterisk.getpass_asterisk()
HfFolder.save_token(access_token)
return access_token return access_token
#--------------------------------------------- #---------------------------------------------
@ -268,14 +278,26 @@ def download_weight_datasets(models:dict, access_token:str):
) )
if success: if success:
successful[mod] = True successful[mod] = True
if len(successful) < len(models):
print(f'\n\n** There were errors downloading one or more files. **')
print('Please double-check your license agreements, and your access token.')
HfFolder.delete_token()
print('Press any key to try again. Type ^C to quit.\n')
input()
return None
HfFolder.save_token(access_token)
keys = ', '.join(successful.keys()) keys = ', '.join(successful.keys())
print(f'Successfully installed {keys}') print(f'Successfully installed {keys}')
return successful return successful
#--------------------------------------------- #---------------------------------------------
def download_with_resume(repo_id:str, model_name:str, access_token:str)->bool: def download_with_resume(repo_id:str, model_name:str, access_token:str)->bool:
<<<<<<< HEAD
from huggingface_hub import hf_hub_url from huggingface_hub import hf_hub_url
=======
>>>>>>> spezialspezial-patch-9
model_dest = os.path.join(Model_dir, model_name) model_dest = os.path.join(Model_dir, model_name)
os.makedirs(os.path.dirname(model_dest), exist_ok=True) os.makedirs(os.path.dirname(model_dest), exist_ok=True)
url = hf_hub_url(repo_id, model_name) url = hf_hub_url(repo_id, model_name)
@ -295,6 +317,8 @@ def download_with_resume(repo_id:str, model_name:str, access_token:str)->bool:
if resp.status_code==416: # "range not satisfiable", which means nothing to return if resp.status_code==416: # "range not satisfiable", which means nothing to return
print(f'* {model_name}: complete file found. Skipping.') print(f'* {model_name}: complete file found. Skipping.')
return True return True
elif resp.status_code != 200:
print(f'** An error occurred during downloading {model_name}: {resp.reason}')
elif exist_size > 0: elif exist_size > 0:
print(f'* {model_name}: partial file found. Resuming...') print(f'* {model_name}: partial file found. Resuming...')
else: else:
@ -302,7 +326,7 @@ def download_with_resume(repo_id:str, model_name:str, access_token:str)->bool:
try: try:
if total < 2000: if total < 2000:
print(f'* {model_name}: {resp.text}') print(f'*** ERROR DOWNLOADING {model_name}: {resp.text}')
return False return False
with open(model_dest, open_mode) as file, tqdm( with open(model_dest, open_mode) as file, tqdm(
@ -329,7 +353,11 @@ def update_config_file(successfully_downloaded:dict,opt:dict):
try: try:
if os.path.exists(Config_file): if os.path.exists(Config_file):
<<<<<<< HEAD
print(f'** {Config_file} exists. Renaming to {Config_file}.orig') print(f'** {Config_file} exists. Renaming to {Config_file}.orig')
=======
print(f'* {Config_file} exists. Renaming to {Config_file}.orig')
>>>>>>> spezialspezial-patch-9
os.rename(Config_file,f'{Config_file}.orig') os.rename(Config_file,f'{Config_file}.orig')
tmpfile = os.path.join(os.path.dirname(Config_file),'new_config.tmp') tmpfile = os.path.join(os.path.dirname(Config_file),'new_config.tmp')
with open(tmpfile, 'w') as outfile: with open(tmpfile, 'w') as outfile:
@ -383,26 +411,35 @@ def new_config_file_contents(successfully_downloaded:dict, Config_file:str)->str
# this will preload the Bert tokenizer fles # this will preload the Bert tokenizer fles
def download_bert(): def download_bert():
print('Installing bert tokenizer (ignore deprecation errors)...', end='') print('Installing bert tokenizer (ignore deprecation errors)...', end='')
<<<<<<< HEAD
from transformers import BertTokenizerFast, AutoFeatureExtractor from transformers import BertTokenizerFast, AutoFeatureExtractor
=======
sys.stdout.flush()
>>>>>>> spezialspezial-patch-9
with warnings.catch_warnings(): with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=DeprecationWarning) warnings.filterwarnings('ignore', category=DeprecationWarning)
from transformers import BertTokenizerFast, AutoFeatureExtractor
tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased') tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
print('...success') print('...success')
sys.stdout.flush()
#--------------------------------------------- #---------------------------------------------
# this will download requirements for Kornia # this will download requirements for Kornia
def download_kornia(): def download_kornia():
print('Installing Kornia requirements...', end='') print('Installing Kornia requirements (ignore deprecation errors)...', end='')
with warnings.catch_warnings(): sys.stdout.flush()
warnings.filterwarnings('ignore', category=DeprecationWarning)
import kornia import kornia
print('...success') print('...success')
#--------------------------------------------- #---------------------------------------------
def download_clip(): def download_clip():
print('Loading CLIP model...',end='') print('Loading CLIP model...',end='')
<<<<<<< HEAD
from transformers import CLIPTokenizer, CLIPTextModel from transformers import CLIPTokenizer, CLIPTextModel
=======
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=DeprecationWarning)
from transformers import CLIPTokenizer, CLIPTextModel
>>>>>>> spezialspezial-patch-9
sys.stdout.flush() sys.stdout.flush()
version = 'openai/clip-vit-large-patch14' version = 'openai/clip-vit-large-patch14'
tokenizer = CLIPTokenizer.from_pretrained(version) tokenizer = CLIPTokenizer.from_pretrained(version)
@ -531,7 +568,6 @@ if __name__ == '__main__':
default='./configs/models.yaml', default='./configs/models.yaml',
help='path to configuration file to create') help='path to configuration file to create')
opt = parser.parse_args() opt = parser.parse_args()
load_libs()
try: try:
if opt.interactive: if opt.interactive:

View File

@ -8,6 +8,12 @@ if exist ".git" (
call git pull call git pull
) )
conda env update
conda env update
conda activate invokeai
python scripts/preload_models.py
echo "Press any key to continue"
pause pause
exit 0

View File

@ -20,3 +20,5 @@ case "${OS_NAME}" in
Darwin*) conda env update -f environment-mac.yml;; Darwin*) conda env update -f environment-mac.yml;;
*) echo "Unknown OS: $OS_NAME! This script runs only on Linux or Mac" && exit *) echo "Unknown OS: $OS_NAME! This script runs only on Linux or Mac" && exit
esac esac
python scripts/preload_models.py