mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Merge branch 'main' into feat/import-with-vae
This commit is contained in:
Kevin Turner
GitHub
commit
70f8793700
@ -93,9 +93,15 @@ getting InvokeAI up and running on your system. For alternative installation and
|
||||
upgrade instructions, please see:
|
||||
[InvokeAI Installation Overview](installation/)
|
||||
|
||||
Linux users who wish to make use of the PyPatchMatch inpainting functions will
|
||||
need to perform a bit of extra work to enable this module. Instructions can be
|
||||
found at [Installing PyPatchMatch](installation/060_INSTALL_PATCHMATCH.md).
|
||||
Users who wish to make use of the **PyPatchMatch** inpainting functions
|
||||
will need to perform a bit of extra work to enable this
|
||||
module. Instructions can be found at [Installing
|
||||
PyPatchMatch](installation/060_INSTALL_PATCHMATCH.md).
|
||||
|
||||
If you have an NVIDIA card, you can benefit from the significant
|
||||
memory savings and performance benefits provided by Facebook Lab's
|
||||
**xFormers** module. Instructions for Linux and Windows users can be found
|
||||
at [Installing xFormers](installation/070_INSTALL_XFORMERS.md).
|
||||
|
||||
## :fontawesome-solid-computer: Hardware Requirements
|
||||
|
||||
|
||||
206
docs/installation/070_INSTALL_XFORMERS.md
Normal file
206
docs/installation/070_INSTALL_XFORMERS.md
Normal file
@ -0,0 +1,206 @@
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||||
---
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||||
title: Installing xFormers
|
||||
---
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||||
|
||||
# :material-image-size-select-large: Installing xformers
|
||||
|
||||
xFormers is toolbox that integrates with the pyTorch and CUDA
|
||||
libraries to provide accelerated performance and reduced memory
|
||||
consumption for applications using the transformers machine learning
|
||||
architecture. After installing xFormers, InvokeAI users who have
|
||||
CUDA GPUs will see a noticeable decrease in GPU memory consumption and
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||||
an increase in speed.
|
||||
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||||
xFormers can be installed into a working InvokeAI installation without
|
||||
any code changes or other updates. This document explains how to
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||||
install xFormers.
|
||||
|
||||
## Pip Install
|
||||
|
||||
For both Windows and Linux, you can install `xformers` in just a
|
||||
couple of steps from the command line.
|
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||||
If you are used to launching `invoke.sh` or `invoke.bat` to start
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||||
InvokeAI, then run the launcher and select the "developer's console"
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||||
to get to the command line. If you run invoke.py directly from the
|
||||
command line, then just be sure to activate it's virtual environment.
|
||||
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||||
Then run the following three commands:
|
||||
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||||
```sh
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pip install xformers==0.0.16rc425
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pip install triton
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python -m xformers.info output
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||||
```
|
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|
||||
The first command installs `xformers`, the second installs the
|
||||
`triton` training accelerator, and the third prints out the `xformers`
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||||
installation status. If all goes well, you'll see a report like the
|
||||
following:
|
||||
|
||||
```sh
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||||
xFormers 0.0.16rc425
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||||
memory_efficient_attention.cutlassF: available
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||||
memory_efficient_attention.cutlassB: available
|
||||
memory_efficient_attention.flshattF: available
|
||||
memory_efficient_attention.flshattB: available
|
||||
memory_efficient_attention.smallkF: available
|
||||
memory_efficient_attention.smallkB: available
|
||||
memory_efficient_attention.tritonflashattF: available
|
||||
memory_efficient_attention.tritonflashattB: available
|
||||
swiglu.fused.p.cpp: available
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||||
is_triton_available: True
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||||
is_functorch_available: False
|
||||
pytorch.version: 1.13.1+cu117
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||||
pytorch.cuda: available
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||||
gpu.compute_capability: 8.6
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||||
gpu.name: NVIDIA RTX A2000 12GB
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||||
build.info: available
|
||||
build.cuda_version: 1107
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||||
build.python_version: 3.10.9
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build.torch_version: 1.13.1+cu117
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||||
build.env.TORCH_CUDA_ARCH_LIST: 5.0+PTX 6.0 6.1 7.0 7.5 8.0 8.6
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||||
build.env.XFORMERS_BUILD_TYPE: Release
|
||||
build.env.XFORMERS_ENABLE_DEBUG_ASSERTIONS: None
|
||||
build.env.NVCC_FLAGS: None
|
||||
build.env.XFORMERS_PACKAGE_FROM: wheel-v0.0.16rc425
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||||
source.privacy: open source
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```
|
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|
||||
## Source Builds
|
||||
|
||||
`xformers` is currently under active development and at some point you
|
||||
may wish to build it from sourcce to get the latest features and
|
||||
bugfixes.
|
||||
|
||||
### Source Build on Linux
|
||||
|
||||
Note that xFormers only works with true NVIDIA GPUs and will not work
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||||
properly with the ROCm driver for AMD acceleration.
|
||||
|
||||
xFormers is not currently available as a pip binary wheel and must be
|
||||
installed from source. These instructions were written for a system
|
||||
running Ubuntu 22.04, but other Linux distributions should be able to
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||||
adapt this recipe.
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||||
|
||||
#### 1. Install CUDA Toolkit 11.7
|
||||
|
||||
You will need the CUDA developer's toolkit in order to compile and
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||||
install xFormers. **Do not try to install Ubuntu's nvidia-cuda-toolkit
|
||||
package.** It is out of date and will cause conflicts among the NVIDIA
|
||||
driver and binaries. Instead install the CUDA Toolkit package provided
|
||||
by NVIDIA itself. Go to [CUDA Toolkit 11.7
|
||||
Downloads](https://developer.nvidia.com/cuda-11-7-0-download-archive)
|
||||
and use the target selection wizard to choose your platform and Linux
|
||||
distribution. Select an installer type of "runfile (local)" at the
|
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last step.
|
||||
|
||||
This will provide you with a recipe for downloading and running a
|
||||
install shell script that will install the toolkit and drivers. For
|
||||
example, the install script recipe for Ubuntu 22.04 running on a
|
||||
x86_64 system is:
|
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|
||||
```
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||||
wget https://developer.download.nvidia.com/compute/cuda/11.7.0/local_installers/cuda_11.7.0_515.43.04_linux.run
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||||
sudo sh cuda_11.7.0_515.43.04_linux.run
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||||
```
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||||
|
||||
Rather than cut-and-paste this example, We recommend that you walk
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||||
through the toolkit wizard in order to get the most up to date
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||||
installer for your system.
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||||
|
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#### 2. Confirm/Install pyTorch 1.13 with CUDA 11.7 support
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|
||||
If you are using InvokeAI 2.3 or higher, these will already be
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||||
installed. If not, you can check whether you have the needed libraries
|
||||
using a quick command. Activate the invokeai virtual environment,
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either by entering the "developer's console", or manually with a
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||||
command similar to `source ~/invokeai/.venv/bin/activate` (depending
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on where your `invokeai` directory is.
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||||
|
||||
Then run the command:
|
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```sh
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python -c 'exec("import torch\nprint(torch.__version__)")'
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```
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|
||||
If it prints __1.13.1+cu117__ you're good. If not, you can install the
|
||||
most up to date libraries with this command:
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```sh
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pip install --upgrade --force-reinstall torch torchvision
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```
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||||
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||||
#### 3. Install the triton module
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||||
This module isn't necessary for xFormers image inference optimization,
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||||
but avoids a startup warning.
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||||
|
||||
```sh
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||||
pip install triton
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||||
```
|
||||
|
||||
#### 4. Install source code build prerequisites
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||||
|
||||
To build xFormers from source, you will need the `build-essentials`
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||||
package. If you don't have it installed already, run:
|
||||
|
||||
```sh
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sudo apt install build-essential
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```
|
||||
|
||||
#### 5. Build xFormers
|
||||
|
||||
There is no pip wheel package for xFormers at this time (January
|
||||
2023). Although there is a conda package, InvokeAI no longer
|
||||
officially supports conda installations and you're on your own if you
|
||||
wish to try this route.
|
||||
|
||||
Following the recipe provided at the [xFormers GitHub
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||||
page](https://github.com/facebookresearch/xformers), and with the
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||||
InvokeAI virtual environment active (see step 1) run the following
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||||
commands:
|
||||
|
||||
```sh
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||||
pip install ninja
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||||
export TORCH_CUDA_ARCH_LIST="6.0;6.1;6.2;7.0;7.2;7.5;8.0;8.6"
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||||
pip install -v -U git+https://github.com/facebookresearch/xformers.git@main#egg=xformers
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||||
```
|
||||
|
||||
The TORCH_CUDA_ARCH_LIST is a list of GPU architectures to compile
|
||||
xFormer support for. You can speed up compilation by selecting
|
||||
the architecture specific for your system. You'll find the list of
|
||||
GPUs and their architectures at NVIDIA's [GPU Compute
|
||||
Capability](https://developer.nvidia.com/cuda-gpus) table.
|
||||
|
||||
If the compile and install completes successfully, you can check that
|
||||
xFormers is installed with this command:
|
||||
|
||||
```sh
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||||
python -m xformers.info
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||||
```
|
||||
|
||||
If suiccessful, the top of the listing should indicate "available" for
|
||||
each of the `memory_efficient_attention` modules, as shown here:
|
||||
|
||||
```sh
|
||||
memory_efficient_attention.cutlassF: available
|
||||
memory_efficient_attention.cutlassB: available
|
||||
memory_efficient_attention.flshattF: available
|
||||
memory_efficient_attention.flshattB: available
|
||||
memory_efficient_attention.smallkF: available
|
||||
memory_efficient_attention.smallkB: available
|
||||
memory_efficient_attention.tritonflashattF: available
|
||||
memory_efficient_attention.tritonflashattB: available
|
||||
[...]
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||||
```
|
||||
|
||||
You can now launch InvokeAI and enjoy the benefits of xFormers.
|
||||
|
||||
### Windows
|
||||
|
||||
To come
|
||||
|
||||
|
||||
---
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||||
(c) Copyright 2023 Lincoln Stein and the InvokeAI Development Team
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||||
@ -18,7 +18,9 @@ experience and preferences.
|
||||
InvokeAI and its dependencies. We offer two recipes: one suited to
|
||||
those who prefer the `conda` tool, and one suited to those who prefer
|
||||
`pip` and Python virtual environments. In our hands the pip install
|
||||
is faster and more reliable, but your mileage may vary.
|
||||
is faster and more reliable, but your mileage may vary.
|
||||
Note that the conda installation method is currently deprecated and
|
||||
will not be supported at some point in the future.
|
||||
|
||||
This method is recommended for users who have previously used `conda`
|
||||
or `pip` in the past, developers, and anyone who wishes to remain on
|
||||
|
||||
@ -45,6 +45,7 @@ def main():
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||||
Globals.try_patchmatch = args.patchmatch
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||||
Globals.always_use_cpu = args.always_use_cpu
|
||||
Globals.internet_available = args.internet_available and check_internet()
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||||
Globals.disable_xformers = not args.xformers
|
||||
print(f'>> Internet connectivity is {Globals.internet_available}')
|
||||
|
||||
if not args.conf:
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@ -902,7 +903,7 @@ def prepare_image_metadata(
|
||||
try:
|
||||
filename = opt.fnformat.format(**wildcards)
|
||||
except KeyError as e:
|
||||
print(f'** The filename format contains an unknown key \'{e.args[0]}\'. Will use \'{{prefix}}.{{seed}}.png\' instead')
|
||||
print(f'** The filename format contains an unknown key \'{e.args[0]}\'. Will use {{prefix}}.{{seed}}.png\' instead')
|
||||
filename = f'{prefix}.{seed}.png'
|
||||
except IndexError:
|
||||
print(f'** The filename format is broken or complete. Will use \'{{prefix}}.{{seed}}.png\' instead')
|
||||
|
||||
@ -482,6 +482,12 @@ class Args(object):
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||||
action='store_true',
|
||||
help='Force free gpu memory before final decoding',
|
||||
)
|
||||
model_group.add_argument(
|
||||
'--xformers',
|
||||
action=argparse.BooleanOptionalAction,
|
||||
default=True,
|
||||
help='Enable/disable xformers support (default enabled if installed)',
|
||||
)
|
||||
model_group.add_argument(
|
||||
"--always_use_cpu",
|
||||
dest="always_use_cpu",
|
||||
|
||||
@ -21,7 +21,7 @@ import os
|
||||
import re
|
||||
import torch
|
||||
from pathlib import Path
|
||||
from ldm.invoke.globals import Globals
|
||||
from ldm.invoke.globals import Globals, global_cache_dir
|
||||
from safetensors.torch import load_file
|
||||
|
||||
try:
|
||||
@ -637,7 +637,7 @@ def convert_ldm_bert_checkpoint(checkpoint, config):
|
||||
|
||||
|
||||
def convert_ldm_clip_checkpoint(checkpoint):
|
||||
text_model = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14")
|
||||
text_model = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14",cache_dir=global_cache_dir('hub'))
|
||||
|
||||
keys = list(checkpoint.keys())
|
||||
|
||||
@ -677,7 +677,8 @@ textenc_pattern = re.compile("|".join(protected.keys()))
|
||||
|
||||
|
||||
def convert_paint_by_example_checkpoint(checkpoint):
|
||||
config = CLIPVisionConfig.from_pretrained("openai/clip-vit-large-patch14")
|
||||
cache_dir = global_cache_dir('hub')
|
||||
config = CLIPVisionConfig.from_pretrained("openai/clip-vit-large-patch14",cache_dir=cache_dir)
|
||||
model = PaintByExampleImageEncoder(config)
|
||||
|
||||
keys = list(checkpoint.keys())
|
||||
@ -744,7 +745,8 @@ def convert_paint_by_example_checkpoint(checkpoint):
|
||||
|
||||
|
||||
def convert_open_clip_checkpoint(checkpoint):
|
||||
text_model = CLIPTextModel.from_pretrained("stabilityai/stable-diffusion-2", subfolder="text_encoder")
|
||||
cache_dir=global_cache_dir('hub')
|
||||
text_model = CLIPTextModel.from_pretrained("stabilityai/stable-diffusion-2", subfolder="text_encoder", cache_dir=cache_dir)
|
||||
|
||||
keys = list(checkpoint.keys())
|
||||
|
||||
@ -795,6 +797,7 @@ def convert_ckpt_to_diffuser(checkpoint_path:str,
|
||||
):
|
||||
|
||||
checkpoint = load_file(checkpoint_path) if Path(checkpoint_path).suffix == '.safetensors' else torch.load(checkpoint_path)
|
||||
cache_dir = global_cache_dir('hub')
|
||||
|
||||
# Sometimes models don't have the global_step item
|
||||
if "global_step" in checkpoint:
|
||||
@ -904,7 +907,7 @@ def convert_ckpt_to_diffuser(checkpoint_path:str,
|
||||
|
||||
if model_type == "FrozenOpenCLIPEmbedder":
|
||||
text_model = convert_open_clip_checkpoint(checkpoint)
|
||||
tokenizer = CLIPTokenizer.from_pretrained("stabilityai/stable-diffusion-2", subfolder="tokenizer")
|
||||
tokenizer = CLIPTokenizer.from_pretrained("stabilityai/stable-diffusion-2", subfolder="tokenizer",cache_dir=global_cache_dir('diffusers'))
|
||||
pipe = StableDiffusionPipeline(
|
||||
vae=vae,
|
||||
text_encoder=text_model,
|
||||
@ -917,8 +920,8 @@ def convert_ckpt_to_diffuser(checkpoint_path:str,
|
||||
)
|
||||
elif model_type == "PaintByExample":
|
||||
vision_model = convert_paint_by_example_checkpoint(checkpoint)
|
||||
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
|
||||
feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker")
|
||||
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14",cache_dir=cache_dir)
|
||||
feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker",cache_dir=cache_dir)
|
||||
pipe = PaintByExamplePipeline(
|
||||
vae=vae,
|
||||
image_encoder=vision_model,
|
||||
@ -929,9 +932,9 @@ def convert_ckpt_to_diffuser(checkpoint_path:str,
|
||||
)
|
||||
elif model_type in ['FrozenCLIPEmbedder','WeightedFrozenCLIPEmbedder']:
|
||||
text_model = convert_ldm_clip_checkpoint(checkpoint)
|
||||
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
|
||||
safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker")
|
||||
feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker")
|
||||
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14",cache_dir=cache_dir)
|
||||
safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker",cache_dir=cache_dir)
|
||||
feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker",cache_dir=cache_dir)
|
||||
pipe = StableDiffusionPipeline(
|
||||
vae=vae,
|
||||
text_encoder=text_model,
|
||||
@ -944,7 +947,7 @@ def convert_ckpt_to_diffuser(checkpoint_path:str,
|
||||
else:
|
||||
text_config = create_ldm_bert_config(original_config)
|
||||
text_model = convert_ldm_bert_checkpoint(checkpoint, text_config)
|
||||
tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
|
||||
tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased",cache_dir=cache_dir)
|
||||
pipe = LDMTextToImagePipeline(vqvae=vae, bert=text_model, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
|
||||
|
||||
pipe.save_pretrained(
|
||||
|
||||
@ -39,6 +39,7 @@ from diffusers.utils.outputs import BaseOutput
|
||||
from torchvision.transforms.functional import resize as tv_resize
|
||||
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
|
||||
|
||||
from ldm.invoke.globals import Globals
|
||||
from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent, ThresholdSettings
|
||||
from ldm.modules.textual_inversion_manager import TextualInversionManager
|
||||
|
||||
@ -306,7 +307,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
|
||||
textual_inversion_manager=self.textual_inversion_manager
|
||||
)
|
||||
|
||||
if is_xformers_available():
|
||||
if is_xformers_available() and not Globals.disable_xformers:
|
||||
self.enable_xformers_memory_efficient_attention()
|
||||
|
||||
def image_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,
|
||||
|
||||
@ -3,6 +3,7 @@ ldm.invoke.generator.txt2img inherits from ldm.invoke.generator
|
||||
'''
|
||||
|
||||
import math
|
||||
from diffusers.utils.logging import get_verbosity, set_verbosity, set_verbosity_error
|
||||
from typing import Callable, Optional
|
||||
|
||||
import torch
|
||||
@ -66,6 +67,8 @@ class Txt2Img2Img(Generator):
|
||||
|
||||
second_pass_noise = self.get_noise_like(resized_latents)
|
||||
|
||||
verbosity = get_verbosity()
|
||||
set_verbosity_error()
|
||||
pipeline_output = pipeline.img2img_from_latents_and_embeddings(
|
||||
resized_latents,
|
||||
num_inference_steps=steps,
|
||||
@ -73,6 +76,7 @@ class Txt2Img2Img(Generator):
|
||||
strength=strength,
|
||||
noise=second_pass_noise,
|
||||
callback=step_callback)
|
||||
set_verbosity(verbosity)
|
||||
|
||||
return pipeline.numpy_to_pil(pipeline_output.images)[0]
|
||||
|
||||
|
||||
@ -43,6 +43,9 @@ Globals.always_use_cpu = False
|
||||
# The CLI will test connectivity at startup time.
|
||||
Globals.internet_available = True
|
||||
|
||||
# Whether to disable xformers
|
||||
Globals.disable_xformers = False
|
||||
|
||||
# whether we are forcing full precision
|
||||
Globals.full_precision = False
|
||||
|
||||
|
||||
@ -27,6 +27,7 @@ import torch
|
||||
import safetensors
|
||||
import transformers
|
||||
from diffusers import AutoencoderKL, logging as dlogging
|
||||
from diffusers.utils.logging import get_verbosity, set_verbosity, set_verbosity_error
|
||||
from omegaconf import OmegaConf
|
||||
from omegaconf.dictconfig import DictConfig
|
||||
from picklescan.scanner import scan_file_path
|
||||
@ -871,11 +872,11 @@ class ModelManager(object):
|
||||
return model
|
||||
|
||||
# diffusers really really doesn't like us moving a float16 model onto CPU
|
||||
import logging
|
||||
logging.getLogger('diffusers.pipeline_utils').setLevel(logging.CRITICAL)
|
||||
verbosity = get_verbosity()
|
||||
set_verbosity_error()
|
||||
model.cond_stage_model.device = 'cpu'
|
||||
model.to('cpu')
|
||||
logging.getLogger('pipeline_utils').setLevel(logging.INFO)
|
||||
set_verbosity(verbosity)
|
||||
|
||||
for submodel in ('first_stage_model','cond_stage_model','model'):
|
||||
try:
|
||||
|
||||
@ -1,18 +1,16 @@
|
||||
import math
|
||||
import os.path
|
||||
from functools import partial
|
||||
from typing import Optional
|
||||
|
||||
import clip
|
||||
import kornia
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from functools import partial
|
||||
import clip
|
||||
from einops import rearrange, repeat
|
||||
from einops import repeat
|
||||
from transformers import CLIPTokenizer, CLIPTextModel
|
||||
import kornia
|
||||
from ldm.invoke.devices import choose_torch_device
|
||||
from ldm.invoke.globals import Globals, global_cache_dir
|
||||
#from ldm.modules.textual_inversion_manager import TextualInversionManager
|
||||
|
||||
from ldm.invoke.devices import choose_torch_device
|
||||
from ldm.invoke.globals import global_cache_dir
|
||||
from ldm.modules.x_transformer import (
|
||||
Encoder,
|
||||
TransformerWrapper,
|
||||
@ -654,21 +652,22 @@ class WeightedFrozenCLIPEmbedder(FrozenCLIPEmbedder):
|
||||
per_token_weights += [weight] * len(this_fragment_token_ids)
|
||||
|
||||
# leave room for bos/eos
|
||||
if len(all_token_ids) > self.max_length - 2:
|
||||
excess_token_count = len(all_token_ids) - self.max_length - 2
|
||||
max_token_count_without_bos_eos_markers = self.max_length - 2
|
||||
if len(all_token_ids) > max_token_count_without_bos_eos_markers:
|
||||
excess_token_count = len(all_token_ids) - max_token_count_without_bos_eos_markers
|
||||
# TODO build nice description string of how the truncation was applied
|
||||
# this should be done by calling self.tokenizer.convert_ids_to_tokens() then passing the result to
|
||||
# self.tokenizer.convert_tokens_to_string() for the token_ids on each side of the truncation limit.
|
||||
print(f">> Prompt is {excess_token_count} token(s) too long and has been truncated")
|
||||
all_token_ids = all_token_ids[0:self.max_length]
|
||||
per_token_weights = per_token_weights[0:self.max_length]
|
||||
all_token_ids = all_token_ids[0:max_token_count_without_bos_eos_markers]
|
||||
per_token_weights = per_token_weights[0:max_token_count_without_bos_eos_markers]
|
||||
|
||||
# pad out to a 77-entry array: [eos_token, <prompt tokens>, eos_token, ..., eos_token]
|
||||
# pad out to a 77-entry array: [bos_token, <prompt tokens>, eos_token, pad_token…]
|
||||
# (77 = self.max_length)
|
||||
all_token_ids = [self.tokenizer.bos_token_id] + all_token_ids + [self.tokenizer.eos_token_id]
|
||||
per_token_weights = [1.0] + per_token_weights + [1.0]
|
||||
pad_length = self.max_length - len(all_token_ids)
|
||||
all_token_ids += [self.tokenizer.eos_token_id] * pad_length
|
||||
all_token_ids += [self.tokenizer.pad_token_id] * pad_length
|
||||
per_token_weights += [1.0] * pad_length
|
||||
|
||||
all_token_ids_tensor = torch.tensor(all_token_ids, dtype=torch.long).to(self.device)
|
||||
|
||||
@ -3,8 +3,9 @@ import math
|
||||
import torch
|
||||
from transformers import CLIPTokenizer, CLIPTextModel
|
||||
|
||||
from ldm.modules.textual_inversion_manager import TextualInversionManager
|
||||
from ldm.invoke.devices import torch_dtype
|
||||
from ldm.modules.textual_inversion_manager import TextualInversionManager
|
||||
|
||||
|
||||
class WeightedPromptFragmentsToEmbeddingsConverter():
|
||||
|
||||
@ -22,8 +23,8 @@ class WeightedPromptFragmentsToEmbeddingsConverter():
|
||||
return self.tokenizer.model_max_length
|
||||
|
||||
def get_embeddings_for_weighted_prompt_fragments(self,
|
||||
text: list[str],
|
||||
fragment_weights: list[float],
|
||||
text: list[list[str]],
|
||||
fragment_weights: list[list[float]],
|
||||
should_return_tokens: bool = False,
|
||||
device='cpu'
|
||||
) -> torch.Tensor:
|
||||
@ -198,12 +199,12 @@ class WeightedPromptFragmentsToEmbeddingsConverter():
|
||||
all_token_ids = all_token_ids[0:max_token_count_without_bos_eos_markers]
|
||||
per_token_weights = per_token_weights[0:max_token_count_without_bos_eos_markers]
|
||||
|
||||
# pad out to a self.max_length-entry array: [eos_token, <prompt tokens>, eos_token, ..., eos_token]
|
||||
# pad out to a self.max_length-entry array: [bos_token, <prompt tokens>, eos_token, pad_token…]
|
||||
# (typically self.max_length == 77)
|
||||
all_token_ids = [self.tokenizer.bos_token_id] + all_token_ids + [self.tokenizer.eos_token_id]
|
||||
per_token_weights = [1.0] + per_token_weights + [1.0]
|
||||
pad_length = self.max_length - len(all_token_ids)
|
||||
all_token_ids += [self.tokenizer.eos_token_id] * pad_length
|
||||
all_token_ids += [self.tokenizer.pad_token_id] * pad_length
|
||||
per_token_weights += [1.0] * pad_length
|
||||
|
||||
all_token_ids_tensor = torch.tensor(all_token_ids, dtype=torch.long, device=device)
|
||||
|
||||
@ -291,7 +291,7 @@ for more information.
|
||||
|
||||
Visit https://huggingface.co/settings/tokens to generate a token. (Sign up for an account if needed).
|
||||
|
||||
Paste the token below using Ctrl-V on macOS/Linux, or Ctrl-Shift-V or right-click on Windows.
|
||||
Paste the token below using Ctrl-V on macOS/Linux, or Ctrl-Shift-V or right-click on Windows.
|
||||
Alternatively press 'Enter' to skip this step and continue.
|
||||
You may re-run the configuration script again in the future if you do not wish to set the token right now.
|
||||
''')
|
||||
@ -676,7 +676,8 @@ def download_weights(opt:dict) -> Union[str, None]:
|
||||
return
|
||||
|
||||
access_token = authenticate()
|
||||
HfFolder.save_token(access_token)
|
||||
if access_token is not None:
|
||||
HfFolder.save_token(access_token)
|
||||
|
||||
print('\n** DOWNLOADING WEIGHTS **')
|
||||
successfully_downloaded = download_weight_datasets(models, access_token, precision=precision)
|
||||
|
||||
@ -115,6 +115,14 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
|
||||
value=self.precisions.index(saved_args.get('mixed_precision','fp16')),
|
||||
max_height=4,
|
||||
)
|
||||
self.num_train_epochs = self.add_widget_intelligent(
|
||||
npyscreen.TitleSlider,
|
||||
name='Number of training epochs:',
|
||||
out_of=1000,
|
||||
step=50,
|
||||
lowest=1,
|
||||
value=saved_args.get('num_train_epochs',100)
|
||||
)
|
||||
self.max_train_steps = self.add_widget_intelligent(
|
||||
npyscreen.TitleSlider,
|
||||
name='Max Training Steps:',
|
||||
@ -131,6 +139,22 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
|
||||
lowest=1,
|
||||
value=saved_args.get('train_batch_size',8),
|
||||
)
|
||||
self.gradient_accumulation_steps = self.add_widget_intelligent(
|
||||
npyscreen.TitleSlider,
|
||||
name='Gradient Accumulation Steps (may need to decrease this to resume from a checkpoint):',
|
||||
out_of=10,
|
||||
step=1,
|
||||
lowest=1,
|
||||
value=saved_args.get('gradient_accumulation_steps',4)
|
||||
)
|
||||
self.lr_warmup_steps = self.add_widget_intelligent(
|
||||
npyscreen.TitleSlider,
|
||||
name='Warmup Steps:',
|
||||
out_of=100,
|
||||
step=1,
|
||||
lowest=0,
|
||||
value=saved_args.get('lr_warmup_steps',0),
|
||||
)
|
||||
self.learning_rate = self.add_widget_intelligent(
|
||||
npyscreen.TitleText,
|
||||
name="Learning Rate:",
|
||||
@ -154,22 +178,6 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
|
||||
scroll_exit = True,
|
||||
value=self.lr_schedulers.index(saved_args.get('lr_scheduler','constant')),
|
||||
)
|
||||
self.gradient_accumulation_steps = self.add_widget_intelligent(
|
||||
npyscreen.TitleSlider,
|
||||
name='Gradient Accumulation Steps:',
|
||||
out_of=10,
|
||||
step=1,
|
||||
lowest=1,
|
||||
value=saved_args.get('gradient_accumulation_steps',4)
|
||||
)
|
||||
self.lr_warmup_steps = self.add_widget_intelligent(
|
||||
npyscreen.TitleSlider,
|
||||
name='Warmup Steps:',
|
||||
out_of=100,
|
||||
step=1,
|
||||
lowest=0,
|
||||
value=saved_args.get('lr_warmup_steps',0),
|
||||
)
|
||||
|
||||
def initializer_changed(self):
|
||||
placeholder = self.placeholder_token.value
|
||||
@ -236,7 +244,7 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
|
||||
|
||||
# all the integers
|
||||
for attr in ('train_batch_size','gradient_accumulation_steps',
|
||||
'max_train_steps','lr_warmup_steps'):
|
||||
'num_train_epochs','max_train_steps','lr_warmup_steps'):
|
||||
args[attr] = int(getattr(self,attr).value)
|
||||
|
||||
# the floats (just one)
|
||||
@ -324,6 +332,7 @@ if __name__ == '__main__':
|
||||
save_args(args)
|
||||
|
||||
try:
|
||||
print(f'DEBUG: args = {args}')
|
||||
do_textual_inversion_training(**args)
|
||||
copy_to_embeddings_folder(args)
|
||||
except Exception as e:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user