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bring in url download bugfix from PR 2630

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Lincoln Stein 2023-02-16 00:37:17 -05:00
commit fe318775c3
4 changed files with 359 additions and 174 deletions
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65
docs/installation/050_INSTALLING_MODELS.md
View File

@ -80,6 +80,13 @@ only `.safetensors` and `.ckpt` models, but they can be easily loaded
into InvokeAI and/or converted into optimized `diffusers` models. Be
aware that CIVITAI hosts many models that generate NSFW content.
!!! note
InvokeAI 2.3.x does not support directly importing and
running Stable Diffusion version 2 checkpoint models. You may instead
convert them into `diffusers` models using the conversion methods
described below.
## Installation
There are multiple ways to install and manage models:
@ -90,7 +97,7 @@ There are multiple ways to install and manage models:
models files.
3. The web interface (WebUI) has a GUI for importing and managing
models.
models.
### Installation via `invokeai-configure`
@ -106,7 +113,7 @@ confirm that the files are complete.
You can install a new model, including any of the community-supported ones, via
the command-line client's `!import_model` command.
#### Installing `.ckpt` and `.safetensors` models
#### Installing individual `.ckpt` and `.safetensors` models
If the model is already downloaded to your local disk, use
`!import_model /path/to/file.ckpt` to load it. For example:
@ -131,15 +138,40 @@ invoke> !import_model https://example.org/sd_models/martians.safetensors
For this to work, the URL must not be password-protected. Otherwise
you will receive a 404 error.
When you import a legacy model, the CLI will ask you a few questions
about the model, including what size image it was trained on (usually
512x512), what name and description you wish to use for it, what
configuration file to use for it (usually the default
`v1-inference.yaml`), whether you'd like to make this model the
default at startup time, and whether you would like to install a
custom VAE (variable autoencoder) file for the model. For recent
models, the answer to the VAE question is usually "no," but it won't
hurt to answer "yes".
When you import a legacy model, the CLI will first ask you what type
of model this is. You can indicate whether it is a model based on
Stable Diffusion 1.x (1.4 or 1.5), one based on Stable Diffusion 2.x,
or a 1.x inpainting model. Be careful to indicate the correct model
type, or it will not load correctly. You can correct the model type
after the fact using the `!edit_model` command.
The system will then ask you a few other questions about the model,
including what size image it was trained on (usually 512x512), what
name and description you wish to use for it, and whether you would
like to install a custom VAE (variable autoencoder) file for the
model. For recent models, the answer to the VAE question is usually
"no," but it won't hurt to answer "yes".
After importing, the model will load. If this is successful, you will
be asked if you want to keep the model loaded in memory to start
generating immediately. You'll also be asked if you wish to make this
the default model on startup. You can change this later using
`!edit_model`.
#### Importing a batch of `.ckpt` and `.safetensors` models from a directory
You may also point `!import_model` to a directory containing a set of
`.ckpt` or `.safetensors` files. They will be imported _en masse_.
!!! example
```console
invoke> !import_model C:/Users/fred/Downloads/civitai_models/
```
You will be given the option to import all models found in the
directory, or select which ones to import. If there are subfolders
within the directory, they will be searched for models to import.
#### Installing `diffusers` models
@ -284,14 +316,18 @@ up a dialogue that lists the models you have already installed, and
allows you to load, delete or edit them:
<figure markdown>
![model-manager](../assets/installing-models/webui-models-1.png)
</figure>
To add a new model, click on **+ Add New** and select to either a
checkpoint/safetensors model, or a diffusers model:
<figure markdown>
![model-manager-add-new](../assets/installing-models/webui-models-2.png)
</figure>
In this example, we chose **Add Diffusers**. As shown in the figure
@ -302,7 +338,9 @@ choose to enter a path to disk, the system will autocomplete for you
as you type:
<figure markdown>
![model-manager-add-diffusers](../assets/installing-models/webui-models-3.png)
</figure>
Press **Add Model** at the bottom of the dialogue (scrolled out of
@ -317,7 +355,9 @@ directory and press the "Search" icon. This will display the
subfolders, and allow you to choose which ones to import:
<figure markdown>
![model-manager-add-checkpoint](../assets/installing-models/webui-models-4.png)
</figure>
## Model Management Startup Options
@ -342,9 +382,8 @@ invoke.sh --autoconvert /home/fred/stable-diffusion-checkpoints
And here is what the same argument looks like in `invokeai.init`:
```
```bash
--outdir="/home/fred/invokeai/outputs
--no-nsfw_checker
--autoconvert /home/fred/stable-diffusion-checkpoints
```

196
ldm/invoke/CLI.py
View File

@ -1,29 +1,31 @@
import os
import re
import sys
import shlex
import sys
import traceback
from argparse import Namespace
from pathlib import Path
from typing import Optional, Union
from typing import List, Optional, Union
import click
if sys.platform == "darwin":
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
from ldm.invoke.globals import Globals
import pyparsing # type: ignore
import ldm.invoke
from ldm.generate import Generate
from ldm.invoke.prompt_parser import PromptParser
from ldm.invoke.readline import get_completer, Completer
from ldm.invoke.args import Args, metadata_dumps, metadata_from_png, dream_cmd_from_png
from ldm.invoke.pngwriter import PngWriter, retrieve_metadata, write_metadata
from ldm.invoke.args import (Args, dream_cmd_from_png, metadata_dumps,
metadata_from_png)
from ldm.invoke.globals import Globals
from ldm.invoke.image_util import make_grid
from ldm.invoke.log import write_log
from ldm.invoke.model_manager import ModelManager
import click # type: ignore
import ldm.invoke
import pyparsing # type: ignore
from ldm.invoke.pngwriter import PngWriter, retrieve_metadata, write_metadata
from ldm.invoke.prompt_parser import PromptParser
from ldm.invoke.readline import Completer, get_completer
from ldm.util import url_attachment_name
# global used in multiple functions (fix)
infile = None
@ -66,11 +68,11 @@ def main():
print(f'>> InvokeAI runtime directory is "{Globals.root}"')
# loading here to avoid long delays on startup
from ldm.generate import Generate
# these two lines prevent a horrible warning message from appearing
# when the frozen CLIP tokenizer is imported
import transformers # type: ignore
from ldm.generate import Generate
transformers.logging.set_verbosity_error()
import diffusers
diffusers.logging.set_verbosity_error()
@ -574,10 +576,12 @@ def set_default_output_dir(opt:Args, completer:Completer):
def import_model(model_path: str, gen, opt, completer):
'''
model_path can be (1) a URL to a .ckpt file; (2) a local .ckpt file path; or
(3) a huggingface repository id
'''
"""
model_path can be (1) a URL to a .ckpt file; (2) a local .ckpt file path;
(3) a huggingface repository id; or (4) a local directory containing a
diffusers model.
"""
model.path = model_path.replace('\\','/') # windows
model_name = None
if model_path.startswith(('http:','https:','ftp:')):
@ -592,12 +596,8 @@ def import_model(model_path: str, gen, opt, completer):
models = list(Path(model_path).rglob('*.ckpt')) + list(Path(model_path).rglob('*.safetensors'))
if models:
# Only the last model name will be used below.
for model in sorted(models):
if click.confirm(f'Import {model.stem} ?', default=True):
model_name = import_ckpt_model(model, gen, opt, completer)
print()
models = import_checkpoint_list(models, gen, opt, completer)
model_name = models[0] if len(models) == 1 else None
else:
model_name = import_diffuser_model(Path(model_path), gen, opt, completer)
@ -614,14 +614,53 @@ def import_model(model_path: str, gen, opt, completer):
print('** model failed to load. Discarding configuration entry')
gen.model_manager.del_model(model_name)
return
if input('Make this the default model? [n] ').strip() in ('y','Y'):
if click.confirm('Make this the default model?', default=False):
gen.model_manager.set_default_model(model_name)
gen.model_manager.commit(opt.conf)
completer.update_models(gen.model_manager.list_models())
print(f'>> {model_name} successfully installed')
def import_diffuser_model(path_or_repo: Union[Path, str], gen, _, completer) -> Optional[str]:
def import_checkpoint_list(models: List[Path], gen, opt, completer)->List[str]:
'''
Does a mass import of all the checkpoint/safetensors on a path list
'''
model_names = list()
choice = input('** Directory of checkpoint/safetensors models detected. Install <a>ll or <s>elected models? [a] ') or 'a'
do_all = choice.startswith('a')
if do_all:
config_file = _ask_for_config_file(models[0], completer, plural=True)
manager = gen.model_manager
for model in sorted(models):
model_name = f'{model.stem}'
model_description = f'Imported model {model_name}'
if model_name in manager.model_names():
print(f'** {model_name} is already imported. Skipping.')
elif manager.import_ckpt_model(
model,
config = config_file,
model_name = model_name,
model_description = model_description,
commit_to_conf = opt.conf):
model_names.append(model_name)
print(f'>> Model {model_name} imported successfully')
else:
print(f'** Model {model} failed to import')
else:
for model in sorted(models):
if click.confirm(f'Import {model.stem} ?', default=True):
if model_name := import_ckpt_model(model, gen, opt, completer):
print(f'>> Model {model.stem} imported successfully')
model_names.append(model_name)
else:
printf('** Model {model} failed to import')
print()
return model_names
def import_diffuser_model(
path_or_repo: Union[Path, str], gen, _, completer
) -> Optional[str]:
path_or_repo = path_or_repo.replace('\\','/') # windows
manager = gen.model_manager
default_name = Path(path_or_repo).stem
default_description = f'Imported model {default_name}'
@ -632,7 +671,7 @@ def import_diffuser_model(path_or_repo: Union[Path, str], gen, _, completer) ->
model_description=default_description
)
vae = None
if input('Replace this model\'s VAE with "stabilityai/sd-vae-ft-mse"? [n] ').strip() in ('y','Y'):
if click.confirm('Replace this model\'s VAE with "stabilityai/sd-vae-ft-mse"?', default=False):
vae = dict(repo_id='stabilityai/sd-vae-ft-mse')
if not manager.import_diffuser_model(
@ -644,27 +683,22 @@ def import_diffuser_model(path_or_repo: Union[Path, str], gen, _, completer) ->
return None
return model_name
def import_ckpt_model(path_or_url: Union[Path, str], gen, opt, completer) -> Optional[str]:
def import_ckpt_model(
path_or_url: Union[Path, str], gen, opt, completer
) -> Optional[str]:
path_or_url = path_or_url.replace('\\','/')
manager = gen.model_manager
default_name = Path(path_or_url).stem
default_description = f'Imported model {default_name}'
is_a_url = str(path_or_url).startswith(('http:','https:'))
base_name = Path(url_attachment_name(path_or_url)).name if is_a_url else Path(path_or_url).name
default_name = Path(base_name).stem
default_description = f"Imported model {default_name}"
model_name, model_description = _get_model_name_and_desc(
manager,
completer,
model_name=default_name,
model_description=default_description
)
config_file = None
default = Path(Globals.root,'configs/stable-diffusion/v1-inpainting-inference.yaml') \
if re.search('inpaint',default_name, flags=re.IGNORECASE) \
else Path(Globals.root,'configs/stable-diffusion/v1-inference.yaml')
completer.complete_extensions(('.yaml','.yml'))
completer.set_line(str(default))
done = False
while not done:
config_file = input('Configuration file for this model: ').strip()
done = os.path.exists(config_file)
completer.complete_extensions(('.ckpt','.safetensors'))
vae = None
@ -692,10 +726,15 @@ def import_ckpt_model(path_or_url: Union[Path, str], gen, opt, completer) -> Opt
def _verify_load(model_name:str, gen)->bool:
print('>> Verifying that new model loads...')
current_model = gen.model_name
if not gen.model_manager.get_model(model_name):
try:
if not gen.model_manager.get_model(model_name):
return False
except Exception as e:
print(f'** model failed to load: {str(e)}')
print('** note that importing 2.X checkpoints is not supported. Please use !convert_model instead.')
return False
do_switch = input('Keep model loaded? [y] ')
if len(do_switch)==0 or do_switch[0] in ('y','Y'):
if click.confirm('Keep model loaded?', default=True):
gen.set_model(model_name)
else:
print('>> Restoring previous model')
@ -708,16 +747,45 @@ def _get_model_name_and_desc(model_manager,completer,model_name:str='',model_des
model_description = input(f'Description for this model [{model_description}]: ').strip() or model_description
return model_name, model_description
def _is_inpainting(model_name_or_path: str)->bool:
if re.search('inpaint',model_name_or_path, flags=re.IGNORECASE):
return not input('Is this an inpainting model? [y] ').startswith(('n','N'))
else:
return not input('Is this an inpainting model? [n] ').startswith(('y','Y'))
def _ask_for_config_file(model_path: Union[str,Path], completer, plural: bool=False)->Path:
default = '1'
if re.search('inpaint',str(model_path),flags=re.IGNORECASE):
default = '3'
choices={
'1': 'v1-inference.yaml',
'2': 'v2-inference-v.yaml',
'3': 'v1-inpainting-inference.yaml',
}
prompt = '''What type of models are these?:
[1] Models based on Stable Diffusion 1.X
[2] Models based on Stable Diffusion 2.X
[3] Inpainting models based on Stable Diffusion 1.X
[4] Something else''' if plural else '''What type of model is this?:
[1] A model based on Stable Diffusion 1.X
[2] A model based on Stable Diffusion 2.X
[3] An inpainting models based on Stable Diffusion 1.X
[4] Something else'''
print(prompt)
choice = input(f'Your choice: [{default}] ')
choice = choice.strip() or default
if config_file := choices.get(choice,None):
return Path('configs','stable-diffusion',config_file)
def optimize_model(model_name_or_path: str, gen, opt, completer):
# otherwise ask user to select
done = False
completer.complete_extensions(('.yaml','.yml'))
completer.set_line(str(Path(Globals.root,'configs/stable-diffusion/')))
while not done:
config_path = input('Configuration file for this model (leave blank to abort): ').strip()
done = not config_path or os.path.exists(config_path)
return config_path
def optimize_model(model_name_or_path: Union[Path,str], gen, opt, completer):
model_name_or_path = model_name_or_path.replace('\\','/') # windows
manager = gen.model_manager
ckpt_path = None
original_config_file = None
if model_name_or_path == gen.model_name:
print("** Can't convert the active model. !switch to another model first. **")
@ -732,6 +800,9 @@ def optimize_model(model_name_or_path: str, gen, opt, completer):
print(f'** {model_name_or_path} is not a legacy .ckpt weights file')
return
elif os.path.exists(model_name_or_path):
original_config_file = original_config_file or _ask_for_config_file(model_name_or_path, completer)
if not original_config_file:
return
ckpt_path = Path(model_name_or_path)
model_name, model_description = _get_model_name_and_desc(
manager,
@ -739,12 +810,6 @@ def optimize_model(model_name_or_path: str, gen, opt, completer):
ckpt_path.stem,
f'Converted model {ckpt_path.stem}'
)
is_inpainting = _is_inpainting(model_name_or_path)
original_config_file = Path(
'configs',
'stable-diffusion',
'v1-inpainting-inference.yaml' if is_inpainting else 'v1-inference.yaml'
)
else:
print(f'** {model_name_or_path} is neither an existing model nor the path to a .ckpt file')
return
@ -761,7 +826,7 @@ def optimize_model(model_name_or_path: str, gen, opt, completer):
return
vae = None
if input('Replace this model\'s VAE with "stabilityai/sd-vae-ft-mse"? [n] ').strip() in ('y','Y'):
if click.confirm('Replace this model\'s VAE with "stabilityai/sd-vae-ft-mse"?', default=False):
vae = dict(repo_id='stabilityai/sd-vae-ft-mse')
new_config = gen.model_manager.convert_and_import(
@ -777,11 +842,10 @@ def optimize_model(model_name_or_path: str, gen, opt, completer):
return
completer.update_models(gen.model_manager.list_models())
if input(f'Load optimized model {model_name}? [y] ').strip() not in ('n','N'):
if click.confirm(f'Load optimized model {model_name}?', default=True):
gen.set_model(model_name)
response = input(f'Delete the original .ckpt file at ({ckpt_path} ? [n] ')
if response.startswith(('y','Y')):
if click.confirm(f'Delete the original .ckpt file at {ckpt_path}?',default=False):
ckpt_path.unlink(missing_ok=True)
print(f'{ckpt_path} deleted')
@ -794,10 +858,10 @@ def del_config(model_name:str, gen, opt, completer):
print(f"** Unknown model {model_name}")
return
if input(f'Remove {model_name} from the list of models known to InvokeAI? [y] ').strip().startswith(('n','N')):
if not click.confirm(f'Remove {model_name} from the list of models known to InvokeAI?',default=True):
return
delete_completely = input('Completely remove the model file or directory from disk? [n] ').startswith(('y','Y'))
delete_completely = click.confirm('Completely remove the model file or directory from disk?',default=False)
gen.model_manager.del_model(model_name,delete_files=delete_completely)
gen.model_manager.commit(opt.conf)
print(f'** {model_name} deleted')
@ -838,7 +902,7 @@ def edit_model(model_name:str, gen, opt, completer):
# this does the update
manager.add_model(new_name, info, True)
if input('Make this the default model? [n] ').startswith(('y','Y')):
if click.confirm('Make this the default model?',default=False):
manager.set_default_model(new_name)
manager.commit(opt.conf)
completer.update_models(manager.list_models())
@ -1022,6 +1086,7 @@ def get_next_command(infile=None, model_name='no model') -> str: # command stri
def invoke_ai_web_server_loop(gen: Generate, gfpgan, codeformer, esrgan):
print('\n* --web was specified, starting web server...')
from invokeai.backend import InvokeAIWebServer
# Change working directory to the stable-diffusion directory
os.chdir(
os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
@ -1170,8 +1235,7 @@ def report_model_error(opt:Namespace, e:Exception):
if yes_to_all:
print('** Reconfiguration is being forced by environment variable INVOKE_MODEL_RECONFIGURE')
else:
response = input('Do you want to run invokeai-configure script to select and/or reinstall models? [y] ')
if response.startswith(('n', 'N')):
if click.confirm('Do you want to run invokeai-configure script to select and/or reinstall models?', default=True):
return
print('invokeai-configure is launching....\n')

34
ldm/invoke/model_manager.py
View File

@ -32,14 +32,12 @@ from omegaconf import OmegaConf
from omegaconf.dictconfig import DictConfig
from picklescan.scanner import scan_file_path
from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline
from ldm.invoke.globals import (
Globals,
global_autoscan_dir,
global_cache_dir,
global_models_dir,
)
from ldm.util import ask_user, download_with_progress_bar, instantiate_from_config
from ldm.invoke.generator.diffusers_pipeline import \
StableDiffusionGeneratorPipeline
from ldm.invoke.globals import (Globals, global_autoscan_dir, global_cache_dir,
global_models_dir)
from ldm.util import (ask_user, download_with_resume,
url_attachment_name, instantiate_from_config)
DEFAULT_MAX_MODELS = 2
VAE_TO_REPO_ID = { # hack, see note in convert_and_import()
@ -673,15 +671,18 @@ class ModelManager(object):
path to the configuration file, then the new entry will be committed to the
models.yaml file.
"""
if str(weights).startswith(("http:", "https:")):
model_name = model_name or url_attachment_name(weights)
weights_path = self._resolve_path(weights, "models/ldm/stable-diffusion-v1")
config_path = self._resolve_path(config, "configs/stable-diffusion")
config_path = self._resolve_path(config, "configs/stable-diffusion")
if weights_path is None or not weights_path.exists():
return False
if config_path is None or not config_path.exists():
return False
model_name = model_name or Path(weights).stem
model_name = model_name or Path(weights).stem # note this gives ugly pathnames if used on a URL without a Content-Disposition header
model_description = (
model_description or f"imported stable diffusion weights file {model_name}"
)
@ -1042,16 +1043,15 @@ class ModelManager(object):
print("** Migration is done. Continuing...")
def _resolve_path(
self, source: Union[str, Path], dest_directory: str
self, source: Union[str, Path], dest_directory: str
) -> Optional[Path]:
resolved_path = None
if str(source).startswith(("http:", "https:", "ftp:")):
basename = os.path.basename(source)
if not os.path.isabs(dest_directory):
dest_directory = os.path.join(Globals.root, dest_directory)
dest = os.path.join(dest_directory, basename)
if download_with_progress_bar(str(source), Path(dest)):
resolved_path = Path(dest)
dest_directory = Path(dest_directory)
if not dest_directory.is_absolute():
dest_directory = Globals.root / dest_directory
dest_directory.mkdir(parents=True, exist_ok=True)
resolved_path = download_with_resume(str(source), dest_directory)
else:
if not os.path.isabs(source):
source = os.path.join(Globals.root, source)

238
ldm/util.py
View File

@ -1,20 +1,21 @@
import importlib
import math
import multiprocessing as mp
import os
import re
from collections import abc
from inspect import isfunction
from pathlib import Path
from queue import Queue
from threading import Thread
from urllib import request
from tqdm import tqdm
from pathlib import Path
from ldm.invoke.devices import torch_dtype
import numpy as np
import requests
import torch
import os
import traceback
from PIL import Image, ImageDraw, ImageFont
from tqdm import tqdm
from ldm.invoke.devices import torch_dtype
def log_txt_as_img(wh, xc, size=10):
@ -23,18 +24,18 @@ def log_txt_as_img(wh, xc, size=10):
b = len(xc)
txts = list()
for bi in range(b):
txt = Image.new('RGB', wh, color='white')
txt = Image.new("RGB", wh, color="white")
draw = ImageDraw.Draw(txt)
font = ImageFont.load_default()
nc = int(40 * (wh[0] / 256))
lines = '\n'.join(
lines = "\n".join(
xc[bi][start : start + nc] for start in range(0, len(xc[bi]), nc)
)
try:
draw.text((0, 0), lines, fill='black', font=font)
draw.text((0, 0), lines, fill="black", font=font)
except UnicodeEncodeError:
print('Cant encode string for logging. Skipping.')
print("Cant encode string for logging. Skipping.")
txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
txts.append(txt)
@ -77,25 +78,23 @@ def count_params(model, verbose=False):
total_params = sum(p.numel() for p in model.parameters())
if verbose:
print(
f' | {model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.'
f" | {model.__class__.__name__} has {total_params * 1.e-6:.2f} M params."
)
return total_params
def instantiate_from_config(config, **kwargs):
if not 'target' in config:
if config == '__is_first_stage__':
if not "target" in config:
if config == "__is_first_stage__":
return None
elif config == '__is_unconditional__':
elif config == "__is_unconditional__":
return None
raise KeyError('Expected key `target` to instantiate.')
return get_obj_from_str(config['target'])(
**config.get('params', dict()), **kwargs
)
raise KeyError("Expected key `target` to instantiate.")
return get_obj_from_str(config["target"])(**config.get("params", dict()), **kwargs)
def get_obj_from_str(string, reload=False):
module, cls = string.rsplit('.', 1)
module, cls = string.rsplit(".", 1)
if reload:
module_imp = importlib.import_module(module)
importlib.reload(module_imp)
@ -111,14 +110,14 @@ def _do_parallel_data_prefetch(func, Q, data, idx, idx_to_fn=False):
else:
res = func(data)
Q.put([idx, res])
Q.put('Done')
Q.put("Done")
def parallel_data_prefetch(
func: callable,
data,
n_proc,
target_data_type='ndarray',
target_data_type="ndarray",
cpu_intensive=True,
use_worker_id=False,
):
@ -126,21 +125,21 @@ def parallel_data_prefetch(
# raise ValueError(
# "Data, which is passed to parallel_data_prefetch has to be either of type list or ndarray."
# )
if isinstance(data, np.ndarray) and target_data_type == 'list':
raise ValueError('list expected but function got ndarray.')
if isinstance(data, np.ndarray) and target_data_type == "list":
raise ValueError("list expected but function got ndarray.")
elif isinstance(data, abc.Iterable):
if isinstance(data, dict):
print(
f'WARNING:"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.'
'WARNING:"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.'
)
data = list(data.values())
if target_data_type == 'ndarray':
if target_data_type == "ndarray":
data = np.asarray(data)
else:
data = list(data)
else:
raise TypeError(
f'The data, that shall be processed parallel has to be either an np.ndarray or an Iterable, but is actually {type(data)}.'
f"The data, that shall be processed parallel has to be either an np.ndarray or an Iterable, but is actually {type(data)}."
)
if cpu_intensive:
@ -150,7 +149,7 @@ def parallel_data_prefetch(
Q = Queue(1000)
proc = Thread
# spawn processes
if target_data_type == 'ndarray':
if target_data_type == "ndarray":
arguments = [
[func, Q, part, i, use_worker_id]
for i, part in enumerate(np.array_split(data, n_proc))
@ -173,7 +172,7 @@ def parallel_data_prefetch(
processes += [p]
# start processes
print(f'Start prefetching...')
print("Start prefetching...")
import time
start = time.time()
@ -186,13 +185,13 @@ def parallel_data_prefetch(
while k < n_proc:
# get result
res = Q.get()
if res == 'Done':
if res == "Done":
k += 1
else:
gather_res[res[0]] = res[1]
except Exception as e:
print('Exception: ', e)
print("Exception: ", e)
for p in processes:
p.terminate()
@ -200,15 +199,15 @@ def parallel_data_prefetch(
finally:
for p in processes:
p.join()
print(f'Prefetching complete. [{time.time() - start} sec.]')
print(f"Prefetching complete. [{time.time() - start} sec.]")
if target_data_type == 'ndarray':
if target_data_type == "ndarray":
if not isinstance(gather_res[0], np.ndarray):
return np.concatenate([np.asarray(r) for r in gather_res], axis=0)
# order outputs
return np.concatenate(gather_res, axis=0)
elif target_data_type == 'list':
elif target_data_type == "list":
out = []
for r in gather_res:
out.extend(r)
@ -216,49 +215,79 @@ def parallel_data_prefetch(
else:
return gather_res
def rand_perlin_2d(shape, res, device, fade = lambda t: 6*t**5 - 15*t**4 + 10*t**3):
def rand_perlin_2d(
shape, res, device, fade=lambda t: 6 * t**5 - 15 * t**4 + 10 * t**3
):
delta = (res[0] / shape[0], res[1] / shape[1])
d = (shape[0] // res[0], shape[1] // res[1])
grid = torch.stack(torch.meshgrid(torch.arange(0, res[0], delta[0]), torch.arange(0, res[1], delta[1]), indexing='ij'), dim = -1).to(device) % 1
grid = (
torch.stack(
torch.meshgrid(
torch.arange(0, res[0], delta[0]),
torch.arange(0, res[1], delta[1]),
indexing="ij",
),
dim=-1,
).to(device)
% 1
)
rand_val = torch.rand(res[0]+1, res[1]+1)
rand_val = torch.rand(res[0] + 1, res[1] + 1)
angles = 2*math.pi*rand_val
gradients = torch.stack((torch.cos(angles), torch.sin(angles)), dim = -1).to(device)
angles = 2 * math.pi * rand_val
gradients = torch.stack((torch.cos(angles), torch.sin(angles)), dim=-1).to(device)
tile_grads = lambda slice1, slice2: gradients[slice1[0]:slice1[1], slice2[0]:slice2[1]].repeat_interleave(d[0], 0).repeat_interleave(d[1], 1)
tile_grads = (
lambda slice1, slice2: gradients[slice1[0] : slice1[1], slice2[0] : slice2[1]]
.repeat_interleave(d[0], 0)
.repeat_interleave(d[1], 1)
)
dot = lambda grad, shift: (torch.stack((grid[:shape[0],:shape[1],0] + shift[0], grid[:shape[0],:shape[1], 1] + shift[1] ), dim = -1) * grad[:shape[0], :shape[1]]).sum(dim = -1)
dot = lambda grad, shift: (
torch.stack(
(
grid[: shape[0], : shape[1], 0] + shift[0],
grid[: shape[0], : shape[1], 1] + shift[1],
),
dim=-1,
)
* grad[: shape[0], : shape[1]]
).sum(dim=-1)
n00 = dot(tile_grads([0, -1], [0, -1]), [0, 0]).to(device)
n00 = dot(tile_grads([0, -1], [0, -1]), [0, 0]).to(device)
n10 = dot(tile_grads([1, None], [0, -1]), [-1, 0]).to(device)
n01 = dot(tile_grads([0, -1],[1, None]), [0, -1]).to(device)
n11 = dot(tile_grads([1, None], [1, None]), [-1,-1]).to(device)
t = fade(grid[:shape[0], :shape[1]])
noise = math.sqrt(2) * torch.lerp(torch.lerp(n00, n10, t[..., 0]), torch.lerp(n01, n11, t[..., 0]), t[..., 1]).to(device)
n01 = dot(tile_grads([0, -1], [1, None]), [0, -1]).to(device)
n11 = dot(tile_grads([1, None], [1, None]), [-1, -1]).to(device)
t = fade(grid[: shape[0], : shape[1]])
noise = math.sqrt(2) * torch.lerp(
torch.lerp(n00, n10, t[..., 0]), torch.lerp(n01, n11, t[..., 0]), t[..., 1]
).to(device)
return noise.to(dtype=torch_dtype(device))
def ask_user(question: str, answers: list):
from itertools import chain, repeat
user_prompt = f'\n>> {question} {answers}: '
invalid_answer_msg = 'Invalid answer. Please try again.'
pose_question = chain([user_prompt], repeat('\n'.join([invalid_answer_msg, user_prompt])))
user_prompt = f"\n>> {question} {answers}: "
invalid_answer_msg = "Invalid answer. Please try again."
pose_question = chain(
[user_prompt], repeat("\n".join([invalid_answer_msg, user_prompt]))
)
user_answers = map(input, pose_question)
valid_response = next(filter(answers.__contains__, user_answers))
return valid_response
def debug_image(debug_image, debug_text, debug_show=True, debug_result=False, debug_status=False ):
def debug_image(
debug_image, debug_text, debug_show=True, debug_result=False, debug_status=False
):
if not debug_status:
return
image_copy = debug_image.copy().convert("RGBA")
ImageDraw.Draw(image_copy).text(
(5, 5),
debug_text,
(255, 0, 0)
)
ImageDraw.Draw(image_copy).text((5, 5), debug_text, (255, 0, 0))
if debug_show:
image_copy.show()
@ -266,31 +295,84 @@ def debug_image(debug_image, debug_text, debug_show=True, debug_result=False, de
if debug_result:
return image_copy
#-------------------------------------
class ProgressBar():
def __init__(self,model_name='file'):
self.pbar = None
self.name = model_name
def __call__(self, block_num, block_size, total_size):
if not self.pbar:
self.pbar=tqdm(desc=self.name,
initial=0,
unit='iB',
unit_scale=True,
unit_divisor=1000,
total=total_size)
self.pbar.update(block_size)
# -------------------------------------
def download_with_resume(url: str, dest: Path, access_token: str = None) -> Path:
'''
Download a model file.
:param url: https, http or ftp URL
:param dest: A Path object. If path exists and is a directory, then we try to derive the filename
from the URL's Content-Disposition header and copy the URL contents into
dest/filename
:param access_token: Access token to access this resource
'''
resp = requests.get(url, stream=True)
total = int(resp.headers.get("content-length", 0))
if dest.is_dir():
try:
file_name = re.search('filename="(.+)"', resp.headers.get("Content-Disposition")).group(1)
except:
file_name = os.path.basename(url)
dest = dest / file_name
else:
dest.parent.mkdir(parents=True, exist_ok=True)
print(f'DEBUG: after many manipulations, dest={dest}')
header = {"Authorization": f"Bearer {access_token}"} if access_token else {}
open_mode = "wb"
exist_size = 0
if dest.exists():
exist_size = dest.stat().st_size
header["Range"] = f"bytes={exist_size}-"
open_mode = "ab"
if (
resp.status_code == 416
): # "range not satisfiable", which means nothing to return
print(f"* {dest}: complete file found. Skipping.")
return dest
elif resp.status_code != 200:
print(f"** An error occurred during downloading {dest}: {resp.reason}")
elif exist_size > 0:
print(f"* {dest}: partial file found. Resuming...")
else:
print(f"* {dest}: Downloading...")
def download_with_progress_bar(url:str, dest:Path)->bool:
try:
if not dest.exists():
dest.parent.mkdir(parents=True, exist_ok=True)
request.urlretrieve(url,dest,ProgressBar(dest.stem))
return True
else:
return True
except OSError:
print(traceback.format_exc())
return False
if total < 2000:
print(f"*** ERROR DOWNLOADING {url}: {resp.text}")
return None
with open(dest, open_mode) as file, tqdm(
desc=str(dest),
initial=exist_size,
total=total + exist_size,
unit="iB",
unit_scale=True,
unit_divisor=1000,
) as bar:
for data in resp.iter_content(chunk_size=1024):
size = file.write(data)
bar.update(size)
except Exception as e:
print(f"An error occurred while downloading {dest}: {str(e)}")
return None
return dest
def url_attachment_name(url: str) -> dict:
try:
resp = requests.get(url, stream=True)
match = re.search('filename="(.+)"', resp.headers.get("Content-Disposition"))
return match.group(1)
except:
return None
def download_with_progress_bar(url: str, dest: Path) -> bool:
result = download_with_resume(url, dest, access_token=None)
return result is not None