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Merge branch 'main' into lstein-improve-ti-frontend

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Lincoln Stein 2023-01-18 22:22:30 -05:00 committed by GitHub
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1
.github/workflows/test-invoke-pip.yml vendored
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@ -119,6 +119,7 @@ jobs:
run: >
configure_invokeai.py
--yes
--default_only
--full-precision # can't use fp16 weights without a GPU
- name: Run the tests

49
configs/INITIAL_MODELS.yaml
View File

@ -1,18 +1,32 @@
stable-diffusion-2.1-768:
description: Stable Diffusion version 2.1 diffusers model, trained on 768x768 images (5.21 GB)
repo_id: stabilityai/stable-diffusion-2-1
format: diffusers
recommended: True
stable-diffusion-2.1-base:
description: Stable Diffusion version 2.1 diffusers base model, trained on 512x512 images (5.21 GB)
repo_id: stabilityai/stable-diffusion-2-1-base
format: diffusers
recommended: False
stable-diffusion-1.5:
description: Stable Diffusion version 1.5 weight file (4.27 GB)
repo_id: runwayml/stable-diffusion-v1-5
format: diffusers
recommended: True
default: True
vae:
repo_id: stabilityai/sd-vae-ft-mse
default: True
stable-diffusion-2.1:
description: Stable Diffusion version 2.1 diffusers model (5.21 GB)
repo_id: stabilityai/stable-diffusion-2-1
stable-diffusion-1.4:
description: The original Stable Diffusion version 1.4 weight file (4.27 GB)
repo_id: CompVis/stable-diffusion-v1-4
recommended: False
format: diffusers
recommended: True
vae:
repo_id: stabilityai/sd-vae-ft-mse
width: 512
height: 512
inpainting-1.5:
description: RunwayML SD 1.5 model optimized for inpainting (4.27 GB)
description: RunwayML SD 1.5 model optimized for inpainting (ckpt version) (4.27 GB)
repo_id: runwayml/stable-diffusion-inpainting
config: v1-inpainting-inference.yaml
file: sd-v1-5-inpainting.ckpt
@ -23,19 +37,13 @@ inpainting-1.5:
recommended: True
width: 512
height: 512
stable-diffusion-1.4:
description: The original Stable Diffusion version 1.4 weight file (4.27 GB)
repo_id: CompVis/stable-diffusion-v1-4
recommended: False
format: diffusers
vae:
repo_id: stabilityai/sd-vae-ft-mse
waifu-diffusion-1.4:
description: Waifu diffusion 1.4
description: Latest waifu diffusion 1.4 (diffusers version)
format: diffusers
repo_id: hakurei/waifu-diffusion
recommended: True
waifu-diffusion-1.3:
description: Stable Diffusion 1.4 fine tuned on anime-styled images (4.27 GB)
description: Stable Diffusion 1.4 fine tuned on anime-styled images (ckpt version) (4.27 GB)
repo_id: hakurei/waifu-diffusion-v1-3
config: v1-inference.yaml
file: model-epoch09-float32.ckpt
@ -53,8 +61,8 @@ trinart-2.0:
recommended: False
vae:
repo_id: stabilityai/sd-vae-ft-mse
trinart_characters-2.0:
description: An SD model finetuned with 19.2M anime/manga style images (4.27 GB)
trinart_characters-2_0:
description: An SD model finetuned with 19.2M anime/manga style images (ckpt version) (4.27 GB)
repo_id: naclbit/trinart_derrida_characters_v2_stable_diffusion
config: v1-inference.yaml
file: derrida_final.ckpt
@ -65,6 +73,11 @@ trinart_characters-2.0:
recommended: False
width: 512
height: 512
anything-4.0:
description: High-quality, highly detailed anime style images with just a few prompts
format: diffusers
repo_id: andite/anything-v4.0
recommended: False
papercut-1.0:
description: SD 1.5 fine-tuned for papercut art (use "PaperCut" in your prompts) (2.13 GB)
repo_id: Fictiverse/Stable_Diffusion_PaperCut_Model
@ -72,8 +85,6 @@ papercut-1.0:
vae:
repo_id: stabilityai/sd-vae-ft-mse
recommended: False
width: 512
height: 512
voxel_art-1.0:
description: Stable Diffusion trained on voxel art (use "VoxelArt" in your prompts) (4.27 GB)
repo_id: Fictiverse/Stable_Diffusion_VoxelArt_Model

2
docs/installation/010_INSTALL_AUTOMATED.md
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@ -52,7 +52,7 @@ version of InvokeAI with the option to upgrade to experimental versions later.
find python, then open the Python installer again and choose
"Modify" existing installation.
- Installation requires an up to date version of the Microsoft Visual C libraries. Please install the 2015-2022 libraries available here: https://learn.microsoft.com/en-us/cpp/windows/deploying-native-desktop-applications-visual-cpp?view=msvc-170
- Installation requires an up to date version of the Microsoft Visual C libraries. Please install the 2015-2022 libraries available here: https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist?view=msvc-170
=== "Mac users"

43
docs/installation/020_INSTALL_MANUAL.md
View File

@ -12,17 +12,18 @@ title: Installing Manually
## Introduction
You have two choices for manual installation, the [first
one](#PIP_method) uses basic Python virtual environment (`venv`)
commands and the PIP package manager. The [second one](#Conda_method)
based on the Anaconda3 package manager (`conda`). Both methods require
you to enter commands on the terminal, also known as the "console".
You have two choices for manual installation.
The [first one](#pip-Install) uses basic Python virtual environment (`venv`)
command and `pip` package manager.
The [second one](#Conda-method) uses Anaconda3 package manager (`conda`).
Both methods require you to enter commands on the terminal, also known as the
"console".
Note that the conda install method is currently deprecated and will not
be supported at some point in the future.
Note that the `conda` installation method is currently deprecated and will
not be supported at some point in the future.
On Windows systems you are encouraged to install and use the
[Powershell](https://learn.microsoft.com/en-us/powershell/scripting/install/installing-powershell-on-windows?view=powershell-7.3),
On Windows systems, you are encouraged to install and use the
[PowerShell](https://learn.microsoft.com/en-us/powershell/scripting/install/installing-powershell-on-windows?view=powershell-7.3),
which provides compatibility with Linux and Mac shells and nice
features such as command-line completion.
@ -37,7 +38,7 @@ manager, please follow these steps:
```bash
python -V
```
2. Clone the [InvokeAI](https://github.com/invoke-ai/InvokeAI) source code from
GitHub:
@ -52,15 +53,15 @@ manager, please follow these steps:
environment named `invokeai`:
```bash
python -mvenv invokeai
python -m venv invokeai
source invokeai/bin/activate
```
4. Make sure that pip is installed in your virtual environment an up to date:
4. Make sure that pip is installed in your virtual environment an up to date:
```bash
python -mensurepip --upgrade
python -mpip install --upgrade pip
python -m ensurepip --upgrade
python -m pip install --upgrade pip
```
5. Pick the correct `requirements*.txt` file for your hardware and operating
@ -199,24 +200,24 @@ manager, please follow these steps:
You can permanently set the location of the runtime directory by setting the environment variable INVOKEAI_ROOT to the path of the directory.
9. Render away!
9. Render away!
Browse the [features](../features/CLI.md) section to learn about all the things you
can do with InvokeAI.
Note that some GPUs are slow to warm up. In particular, when using an AMD
card with the ROCm driver, you may have to wait for over a minute the first
time you try to generate an image. Fortunately, after the warm up period
time you try to generate an image. Fortunately, after the warm-up period
rendering will be fast.
10. Subsequently, to relaunch the script, be sure to run "conda activate
invokeai", enter the `InvokeAI` directory, and then launch the invoke
script. If you forget to activate the 'invokeai' environment, the script
will fail with multiple `ModuleNotFound` errors.
10. Subsequently, to relaunch the script, be sure to enter `InvokeAI` directory,
activate the virtual environment, and then launch `invoke.py` script.
If you forget to activate the virtual environment,
the script will fail with multiple `ModuleNotFound` errors.
!!! tip
Do not move the source code repository after installation. The virtual environment directory has absolute paths in it that get confused if the directory is moved.
Do not move the source code repository after installation. The virtual environment directory has absolute paths in it that get confused if the directory is moved.
---

2
environments-and-requirements/requirements-lin-amd.txt
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@ -1,6 +1,6 @@
-r environments-and-requirements/requirements-base.txt
# Get hardware-appropriate torch/torchvision
--extra-index-url https://download.pytorch.org/whl/rocm5.1.1 --trusted-host https://download.pytorch.org
--extra-index-url https://download.pytorch.org/whl/rocm5.2 --trusted-host https://download.pytorch.org
torch>=1.13.1
torchvision>=0.14.1
-e .

2
environments-and-requirements/requirements-lin-cuda.txt
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@ -1,4 +1,4 @@
--extra-index-url https://download.pytorch.org/whl/cu116 --trusted-host https://download.pytorch.org
--trusted-host https://download.pytorch.org
-r environments-and-requirements/requirements-base.txt
torch>=1.13.1
torchvision>=0.14.1

2
environments-and-requirements/requirements-win-colab-cuda.txt
View File

@ -1,6 +1,6 @@
-r environments-and-requirements/requirements-base.txt
# Get hardware-appropriate torch/torchvision
--extra-index-url https://download.pytorch.org/whl/cu116 --trusted-host https://download.pytorch.org
--extra-index-url https://download.pytorch.org/whl/cu117 --trusted-host https://download.pytorch.org
torch==1.13.1
torchvision==0.14.1
-e .

3
ldm/generate.py
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@ -29,7 +29,7 @@ from ldm.invoke.concepts_lib import HuggingFaceConceptsLibrary
from ldm.invoke.conditioning import get_uc_and_c_and_ec
from ldm.invoke.devices import choose_torch_device, choose_precision
from ldm.invoke.generator.inpaint import infill_methods
from ldm.invoke.globals import global_cache_dir
from ldm.invoke.globals import global_cache_dir, Globals
from ldm.invoke.image_util import InitImageResizer
from ldm.invoke.model_manager import ModelManager
from ldm.invoke.pngwriter import PngWriter
@ -201,6 +201,7 @@ class Generate:
self.precision = 'float32'
if self.precision == 'auto':
self.precision = choose_precision(self.device)
Globals.full_precision = self.precision=='float32'
# model caching system for fast switching
self.model_manager = ModelManager(mconfig,self.device,self.precision,max_loaded_models=max_loaded_models)

7
ldm/invoke/CLI.py
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@ -613,8 +613,6 @@ def import_diffuser_model(path_or_repo:str, gen, opt, completer)->str:
description = model_description):
print('** model failed to import')
return None
if input('Make this the default model? [n] ').startswith(('y','Y')):
manager.set_default_model(model_name)
return model_name
def import_ckpt_model(path_or_url:str, gen, opt, completer)->str:
@ -647,8 +645,6 @@ def import_ckpt_model(path_or_url:str, gen, opt, completer)->str:
print('** model failed to import')
return None
if input('Make this the default model? [n] ').startswith(('y','Y')):
manager.set_model_default(model_name)
return model_name
def _verify_load(model_name:str, gen)->bool:
@ -726,6 +722,9 @@ def del_config(model_name:str, gen, opt, completer):
if model_name == current_model:
print("** Can't delete active model. !switch to another model first. **")
return
if model_name not in gen.model_manager.config:
print(f"** Unknown model {model_name}")
return
gen.model_manager.del_model(model_name)
gen.model_manager.commit(opt.conf)
print(f'** {model_name} deleted')

3
ldm/invoke/ckpt_generator/base.py
View File

@ -335,4 +335,5 @@ class CkptGenerator():
os.makedirs(dirname, exist_ok=True)
image.save(filepath,'PNG')
def torch_dtype(self)->torch.dtype:
return torch.float16 if self.precision == 'float16' else torch.float32

18
ldm/invoke/ckpt_generator/txt2img.py
View File

@ -72,16 +72,18 @@ class CkptTxt2Img(CkptGenerator):
device = self.model.device
if self.use_mps_noise or device.type == 'mps':
x = torch.randn([1,
self.latent_channels,
height // self.downsampling_factor,
width // self.downsampling_factor],
device='cpu').to(device)
self.latent_channels,
height // self.downsampling_factor,
width // self.downsampling_factor],
dtype=self.torch_dtype(),
device='cpu').to(device)
else:
x = torch.randn([1,
self.latent_channels,
height // self.downsampling_factor,
width // self.downsampling_factor],
device=device)
self.latent_channels,
height // self.downsampling_factor,
width // self.downsampling_factor],
dtype=self.torch_dtype(),
device=device)
if self.perlin > 0.0:
x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(width // self.downsampling_factor, height // self.downsampling_factor)
return x

9
ldm/invoke/devices.py
View File

@ -21,10 +21,19 @@ def choose_precision(device) -> str:
return 'float16'
return 'float32'
def torch_dtype(device) -> torch.dtype:
if Globals.full_precision:
return torch.float32
if choose_precision(device) == 'float16':
return torch.float16
else:
return torch.float32
def choose_autocast(precision):
'''Returns an autocast context or nullcontext for the given precision string'''
# float16 currently requires autocast to avoid errors like:
# 'expected scalar type Half but found Float'
print(f'DEBUG: choose_autocast() called')
if precision == 'autocast' or precision == 'float16':
return autocast
return nullcontext

11
ldm/invoke/generator/base.py
View File

@ -8,6 +8,7 @@ import os
import os.path as osp
import random
import traceback
from contextlib import nullcontext
import cv2
import numpy as np
@ -18,8 +19,6 @@ from einops import rearrange
from pytorch_lightning import seed_everything
from tqdm import trange
from ldm.invoke.devices import choose_autocast
from ldm.models.diffusion.cross_attention_map_saving import AttentionMapSaver
from ldm.models.diffusion.ddpm import DiffusionWrapper
from ldm.util import rand_perlin_2d
@ -64,7 +63,7 @@ class Generator:
image_callback=None, step_callback=None, threshold=0.0, perlin=0.0,
safety_checker:dict=None,
**kwargs):
scope = choose_autocast(self.precision)
scope = nullcontext
self.safety_checker = safety_checker
attention_maps_images = []
attention_maps_callback = lambda saver: attention_maps_images.append(saver.get_stacked_maps_image())
@ -236,7 +235,8 @@ class Generator:
def get_perlin_noise(self,width,height):
fixdevice = 'cpu' if (self.model.device.type == 'mps') else self.model.device
return torch.stack([rand_perlin_2d((height, width), (8, 8), device = self.model.device).to(fixdevice) for _ in range(self.latent_channels)], dim=0).to(self.model.device)
noise = torch.stack([rand_perlin_2d((height, width), (8, 8), device = self.model.device).to(fixdevice) for _ in range(self.latent_channels)], dim=0).to(self.model.device)
return noise
def new_seed(self):
self.seed = random.randrange(0, np.iinfo(np.uint32).max)
@ -341,3 +341,6 @@ class Generator:
image.save(filepath,'PNG')
def torch_dtype(self)->torch.dtype:
return torch.float16 if self.precision == 'float16' else torch.float32

8
ldm/invoke/generator/diffusers_pipeline.py
View File

@ -391,7 +391,9 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
for i, t in enumerate(self.progress_bar(timesteps)):
batched_t.fill_(t)
step_output = self.step(batched_t, latents, conditioning_data,
i, additional_guidance=additional_guidance)
step_index=i,
total_step_count=len(timesteps),
additional_guidance=additional_guidance)
latents = step_output.prev_sample
predicted_original = getattr(step_output, 'pred_original_sample', None)
@ -410,7 +412,8 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
@torch.inference_mode()
def step(self, t: torch.Tensor, latents: torch.Tensor,
conditioning_data: ConditioningData,
step_index:int | None = None, additional_guidance: List[Callable] = None):
step_index:int, total_step_count:int,
additional_guidance: List[Callable] = None):
# invokeai_diffuser has batched timesteps, but diffusers schedulers expect a single value
timestep = t[0]
@ -427,6 +430,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
conditioning_data.unconditioned_embeddings, conditioning_data.text_embeddings,
conditioning_data.guidance_scale,
step_index=step_index,
total_step_count=total_step_count,
threshold=conditioning_data.threshold
)

21
ldm/invoke/generator/txt2img.py
View File

@ -36,10 +36,9 @@ class Txt2Img(Generator):
threshold = ThresholdSettings(threshold, warmup=0.2) if threshold else None)
.add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta))
def make_image(x_T) -> PIL.Image.Image:
pipeline_output = pipeline.image_from_embeddings(
latents=torch.zeros_like(x_T),
latents=torch.zeros_like(x_T,dtype=self.torch_dtype()),
noise=x_T,
num_inference_steps=steps,
conditioning_data=conditioning_data,
@ -59,16 +58,18 @@ class Txt2Img(Generator):
input_channels = min(self.latent_channels, 4)
if self.use_mps_noise or device.type == 'mps':
x = torch.randn([1,
input_channels,
height // self.downsampling_factor,
width // self.downsampling_factor],
device='cpu').to(device)
input_channels,
height // self.downsampling_factor,
width // self.downsampling_factor],
dtype=self.torch_dtype(),
device='cpu').to(device)
else:
x = torch.randn([1,
input_channels,
height // self.downsampling_factor,
width // self.downsampling_factor],
device=device)
input_channels,
height // self.downsampling_factor,
width // self.downsampling_factor],
dtype=self.torch_dtype(),
device=device)
if self.perlin > 0.0:
x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(width // self.downsampling_factor, height // self.downsampling_factor)
return x

10
ldm/invoke/generator/txt2img2img.py
View File

@ -90,9 +90,9 @@ class Txt2Img2Img(Generator):
def get_noise_like(self, like: torch.Tensor):
device = like.device
if device.type == 'mps':
x = torch.randn_like(like, device='cpu').to(device)
x = torch.randn_like(like, device='cpu', dtype=self.torch_dtype()).to(device)
else:
x = torch.randn_like(like, device=device)
x = torch.randn_like(like, device=device, dtype=self.torch_dtype())
if self.perlin > 0.0:
shape = like.shape
x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(shape[3], shape[2])
@ -117,10 +117,12 @@ class Txt2Img2Img(Generator):
self.latent_channels,
scaled_height // self.downsampling_factor,
scaled_width // self.downsampling_factor],
device='cpu').to(device)
dtype=self.torch_dtype(),
device='cpu').to(device)
else:
return torch.randn([1,
self.latent_channels,
scaled_height // self.downsampling_factor,
scaled_width // self.downsampling_factor],
device=device)
dtype=self.torch_dtype(),
device=device)

17
ldm/invoke/globals.py
View File

@ -43,6 +43,9 @@ Globals.always_use_cpu = False
# The CLI will test connectivity at startup time.
Globals.internet_available = True
# whether we are forcing full precision
Globals.full_precision = False
def global_config_dir()->Path:
return Path(Globals.root, Globals.config_dir)
@ -59,11 +62,21 @@ def global_cache_dir(subdir:Union[str,Path]='')->Path:
'''
Returns Path to the model cache directory. If a subdirectory
is provided, it will be appended to the end of the path, allowing
for huggingface-style conventions:
for huggingface-style conventions:
global_cache_dir('diffusers')
global_cache_dir('transformers')
'''
if (home := os.environ.get('HF_HOME')):
home: str = os.getenv('HF_HOME')
if home is None:
home = os.getenv('XDG_CACHE_HOME')
if home is not None:
# Set `home` to $XDG_CACHE_HOME/huggingface, which is the default location mentioned in HuggingFace Hub Client Library.
# See: https://huggingface.co/docs/huggingface_hub/main/en/package_reference/environment_variables#xdgcachehome
home += os.sep + 'huggingface'
if home is not None:
return Path(home,subdir)
else:
return Path(Globals.root,'models',subdir)

67
ldm/invoke/model_manager.py
View File

@ -166,7 +166,7 @@ class ModelManager(object):
# don't include VAEs in listing (legacy style)
if 'config' in stanza and '/VAE/' in stanza['config']:
continue
models[name] = dict()
format = stanza.get('format','ckpt') # Determine Format
@ -183,7 +183,7 @@ class ModelManager(object):
format = format,
status = status,
)
# Checkpoint Config Parse
if format == 'ckpt':
models[name].update(
@ -193,7 +193,7 @@ class ModelManager(object):
width = str(stanza.get('width', 512)),
height = str(stanza.get('height', 512)),
)
# Diffusers Config Parse
if (vae := stanza.get('vae',None)):
if isinstance(vae,DictConfig):
@ -202,14 +202,14 @@ class ModelManager(object):
path = str(vae.get('path',None)),
subfolder = str(vae.get('subfolder',None))
)
if format == 'diffusers':
models[name].update(
vae = vae,
repo_id = str(stanza.get('repo_id', None)),
path = str(stanza.get('path',None)),
)
return models
def print_models(self) -> None:
@ -230,6 +230,9 @@ class ModelManager(object):
Delete the named model.
'''
omega = self.config
if model_name not in omega:
print(f'** Unknown model {model_name}')
return
del omega[model_name]
if model_name in self.stack:
self.stack.remove(model_name)
@ -253,9 +256,8 @@ class ModelManager(object):
assert (clobber or model_name not in omega), f'attempt to overwrite existing model definition "{model_name}"'
if model_name not in omega:
omega[model_name] = dict()
OmegaConf.update(omega,model_name,model_attributes,merge=False)
omega[model_name] = model_attributes
if 'weights' in omega[model_name]:
omega[model_name]['weights'].replace('\\','/')
@ -349,7 +351,7 @@ class ModelManager(object):
if self.precision == 'float16':
print(' | Using faster float16 precision')
model.to(torch.float16)
model = model.to(torch.float16)
else:
print(' | Using more accurate float32 precision')
@ -552,12 +554,12 @@ class ModelManager(object):
'''
Attempts to install the indicated ckpt file and returns True if successful.
"weights" can be either a path-like object corresponding to a local .ckpt file
"weights" can be either a path-like object corresponding to a local .ckpt file
or a http/https URL pointing to a remote model.
"config" is the model config file to use with this ckpt file. It defaults to
v1-inference.yaml. If a URL is provided, the config will be downloaded.
You can optionally provide a model name and/or description. If not provided,
then these will be derived from the weight file name. If you provide a commit_to_conf
path to the configuration file, then the new entry will be committed to the
@ -570,7 +572,7 @@ class ModelManager(object):
return False
if config_path is None or not config_path.exists():
return False
model_name = model_name or Path(weights).stem
model_description = model_description or f'imported stable diffusion weights file {model_name}'
new_config = dict(
@ -585,7 +587,7 @@ class ModelManager(object):
if commit_to_conf:
self.commit(commit_to_conf)
return True
def autoconvert_weights(
self,
conf_path:Path,
@ -658,7 +660,7 @@ class ModelManager(object):
except Exception as e:
print(f'** Conversion failed: {str(e)}')
traceback.print_exc()
print('done.')
return new_config
@ -753,19 +755,31 @@ class ModelManager(object):
print('** Legacy version <= 2.2.5 model directory layout detected. Reorganizing.')
print('** This is a quick one-time operation.')
from shutil import move
from shutil import move, rmtree
# transformer files get moved into the hub directory
hub = models_dir / 'hub'
if cls._is_huggingface_hub_directory_present():
hub = global_cache_dir('hub')
else:
hub = models_dir / 'hub'
os.makedirs(hub, exist_ok=True)
for model in legacy_locations:
source = models_dir /model
source = models_dir / model
dest = hub / model.stem
print(f'** {source} => {dest}')
if source.exists():
print(f'DEBUG: Moving {models_dir / model} into hub')
move(models_dir / model, hub)
if dest.exists():
rmtree(source)
else:
move(source, dest)
# anything else gets moved into the diffusers directory
diffusers = models_dir / 'diffusers'
if cls._is_huggingface_hub_directory_present():
diffusers = global_cache_dir('diffusers')
else:
diffusers = models_dir / 'diffusers'
os.makedirs(diffusers, exist_ok=True)
for root, dirs, _ in os.walk(models_dir, topdown=False):
for dir in dirs:
@ -773,7 +787,12 @@ class ModelManager(object):
if full_path.is_relative_to(hub) or full_path.is_relative_to(diffusers):
continue
if Path(dir).match('models--*--*'):
move(full_path,diffusers)
dest = diffusers / dir
print(f'** {full_path} => {dest}')
if dest.exists():
rmtree(full_path)
else:
move(full_path,dest)
# now clean up by removing any empty directories
empty = [root for root, dirs, files, in os.walk(models_dir) if not len(dirs) and not len(files)]
@ -951,3 +970,7 @@ class ModelManager(object):
print(f'** Could not load VAE {name_or_path}: {str(deferred_error)}')
return vae
@staticmethod
def _is_huggingface_hub_directory_present() -> bool:
return os.getenv('HF_HOME') is not None or os.getenv('XDG_CACHE_HOME') is not None

3
ldm/models/diffusion/cross_attention_control.py
View File

@ -7,6 +7,7 @@ import torch
import diffusers
from torch import nn
from diffusers.models.unet_2d_condition import UNet2DConditionModel
from ldm.invoke.devices import torch_dtype
# adapted from bloc97's CrossAttentionControl colab
# https://github.com/bloc97/CrossAttentionControl
@ -383,7 +384,7 @@ def inject_attention_function(unet, context: Context):
remapped_saved_attention_slice = torch.index_select(saved_attention_slice, -1, index_map)
this_attention_slice = suggested_attention_slice
mask = context.cross_attention_mask
mask = context.cross_attention_mask.to(torch_dtype(suggested_attention_slice.device))
saved_mask = mask
this_mask = 1 - mask
attention_slice = remapped_saved_attention_slice * saved_mask + \

11
ldm/models/diffusion/shared_invokeai_diffusion.py
View File

@ -89,6 +89,7 @@ class InvokeAIDiffuserComponent:
conditioning: Union[torch.Tensor,dict],
unconditional_guidance_scale: float,
step_index: Optional[int]=None,
total_step_count: Optional[int]=None,
threshold: Optional[ThresholdSettings]=None,
):
"""
@ -106,7 +107,15 @@ class InvokeAIDiffuserComponent:
cross_attention_control_types_to_do = []
context: Context = self.cross_attention_control_context
if self.cross_attention_control_context is not None:
percent_through = self.estimate_percent_through(step_index, sigma)
if step_index is not None and total_step_count is not None:
# 🧨diffusers codepath
percent_through = step_index / total_step_count # will never reach 1.0 - this is deliberate
else:
# legacy compvis codepath
# TODO remove when compvis codepath support is dropped
if step_index is None and sigma is None:
raise ValueError(f"Either step_index or sigma is required when doing cross attention control, but both are None.")
percent_through = self.estimate_percent_through(step_index, sigma)
cross_attention_control_types_to_do = context.get_active_cross_attention_control_types_for_step(percent_through)
wants_cross_attention_control = (len(cross_attention_control_types_to_do) > 0)

4
ldm/modules/prompt_to_embeddings_converter.py
View File

@ -4,7 +4,7 @@ import torch
from transformers import CLIPTokenizer, CLIPTextModel
from ldm.modules.textual_inversion_manager import TextualInversionManager
from ldm.invoke.devices import torch_dtype
class WeightedPromptFragmentsToEmbeddingsConverter():
@ -207,7 +207,7 @@ class WeightedPromptFragmentsToEmbeddingsConverter():
per_token_weights += [1.0] * pad_length
all_token_ids_tensor = torch.tensor(all_token_ids, dtype=torch.long, device=device)
per_token_weights_tensor = torch.tensor(per_token_weights, dtype=torch.float32, device=device)
per_token_weights_tensor = torch.tensor(per_token_weights, dtype=torch_dtype(self.text_encoder.device), device=device)
#print(f"assembled all_token_ids_tensor with shape {all_token_ids_tensor.shape}")
return all_token_ids_tensor, per_token_weights_tensor

1
ldm/modules/textual_inversion_manager.py
View File

@ -111,7 +111,6 @@ class TextualInversionManager():
if ti.trigger_token_id is not None:
raise ValueError(f"Tokens already injected for textual inversion with trigger '{ti.trigger_string}'")
print(f'DEBUG: Injecting token {ti.trigger_string}')
trigger_token_id = self._get_or_create_token_id_and_assign_embedding(ti.trigger_string, ti.embedding[0])
if ti.embedding_vector_length > 1:

4
ldm/util.py
View File

@ -8,6 +8,7 @@ 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 torch
@ -235,7 +236,8 @@ def rand_perlin_2d(shape, res, device, fade = lambda t: 6*t**5 - 15*t**4 + 10*t*
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]])
return math.sqrt(2) * torch.lerp(torch.lerp(n00, n10, t[..., 0]), torch.lerp(n01, n11, t[..., 0]), t[..., 1]).to(device)
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

13
scripts/configure_invokeai.py
View File

@ -197,6 +197,14 @@ def recommended_datasets()->dict:
datasets[ds]=True
return datasets
#---------------------------------------------
def default_dataset()->dict:
datasets = dict()
for ds in Datasets.keys():
if Datasets[ds].get('default',False):
datasets[ds]=True
return datasets
#---------------------------------------------
def all_datasets()->dict:
datasets = dict()
@ -646,7 +654,7 @@ def download_weights(opt:dict) -> Union[str, None]:
precision = 'float32' if opt.full_precision else choose_precision(torch.device(choose_torch_device()))
if opt.yes_to_all:
models = recommended_datasets()
models = default_dataset() if opt.default_only else recommended_datasets()
access_token = authenticate(opt.yes_to_all)
if len(models)>0:
successfully_downloaded = download_weight_datasets(models, access_token, precision=precision)
@ -808,6 +816,9 @@ def main():
dest='yes_to_all',
action='store_true',
help='answer "yes" to all prompts')
parser.add_argument('--default_only',
action='store_true',
help='when --yes specified, only install the default model')
parser.add_argument('--config_file',
'-c',
dest='config_file',