mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Merge branch 'main' into feat/refactor_generation_backend
This commit is contained in:
blessedcoolant
commit
231e665675
@ -161,7 +161,7 @@ the command `npm install -g yarn` if needed)
|
||||
_For Windows/Linux with an NVIDIA GPU:_
|
||||
|
||||
```terminal
|
||||
pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
|
||||
pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu118
|
||||
```
|
||||
|
||||
_For Linux with an AMD GPU:_
|
||||
|
||||
@ -471,7 +471,7 @@ Then type the following commands:
|
||||
|
||||
=== "NVIDIA System"
|
||||
```bash
|
||||
pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu117
|
||||
pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu118
|
||||
pip install xformers
|
||||
```
|
||||
|
||||
|
||||
@ -148,7 +148,7 @@ manager, please follow these steps:
|
||||
=== "CUDA (NVidia)"
|
||||
|
||||
```bash
|
||||
pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
|
||||
pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu118
|
||||
```
|
||||
|
||||
=== "ROCm (AMD)"
|
||||
@ -312,7 +312,7 @@ installation protocol (important!)
|
||||
|
||||
=== "CUDA (NVidia)"
|
||||
```bash
|
||||
pip install -e .[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
|
||||
pip install -e .[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu118
|
||||
```
|
||||
|
||||
=== "ROCm (AMD)"
|
||||
@ -356,7 +356,7 @@ you can do so using this unsupported recipe:
|
||||
mkdir ~/invokeai
|
||||
conda create -n invokeai python=3.10
|
||||
conda activate invokeai
|
||||
pip install InvokeAI[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
|
||||
pip install InvokeAI[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu118
|
||||
invokeai-configure --root ~/invokeai
|
||||
invokeai --root ~/invokeai --web
|
||||
```
|
||||
|
||||
@ -34,11 +34,11 @@ directly from NVIDIA. **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 It is out of date and will cause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.**
|
||||
|
||||
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 operating system,
|
||||
hardware platform, and preferred installation method (e.g. "local"
|
||||
versus "network").
|
||||
Go to [CUDA Toolkit
|
||||
Downloads](https://developer.nvidia.com/cuda-downloads), and use the
|
||||
target selection wizard to choose your operating system, hardware
|
||||
platform, and preferred installation method (e.g. "local" versus
|
||||
"network").
|
||||
|
||||
This will provide you with a downloadable install file or, depending
|
||||
on your choices, a recipe for downloading and running a install shell
|
||||
@ -61,7 +61,7 @@ Runtime Site](https://developer.nvidia.com/nvidia-container-runtime)
|
||||
|
||||
When installing torch and torchvision manually with `pip`, remember to provide
|
||||
the argument `--extra-index-url
|
||||
https://download.pytorch.org/whl/cu117` as described in the [Manual
|
||||
https://download.pytorch.org/whl/cu118` as described in the [Manual
|
||||
Installation Guide](020_INSTALL_MANUAL.md).
|
||||
|
||||
## :simple-amd: ROCm
|
||||
|
||||
@ -28,18 +28,21 @@ command line, then just be sure to activate it's virtual environment.
|
||||
Then run the following three commands:
|
||||
|
||||
```sh
|
||||
pip install xformers==0.0.16rc425
|
||||
pip install triton
|
||||
pip install xformers~=0.0.19
|
||||
pip install triton # WON'T WORK ON WINDOWS
|
||||
python -m xformers.info output
|
||||
```
|
||||
|
||||
The first command installs `xformers`, the second installs the
|
||||
`triton` training accelerator, and the third prints out the `xformers`
|
||||
installation status. If all goes well, you'll see a report like the
|
||||
installation status. On Windows, please omit the `triton` package,
|
||||
which is not available on that platform.
|
||||
|
||||
If all goes well, you'll see a report like the
|
||||
following:
|
||||
|
||||
```sh
|
||||
xFormers 0.0.16rc425
|
||||
xFormers 0.0.20
|
||||
memory_efficient_attention.cutlassF: available
|
||||
memory_efficient_attention.cutlassB: available
|
||||
memory_efficient_attention.flshattF: available
|
||||
@ -48,22 +51,28 @@ memory_efficient_attention.smallkF: available
|
||||
memory_efficient_attention.smallkB: available
|
||||
memory_efficient_attention.tritonflashattF: available
|
||||
memory_efficient_attention.tritonflashattB: available
|
||||
indexing.scaled_index_addF: available
|
||||
indexing.scaled_index_addB: available
|
||||
indexing.index_select: available
|
||||
swiglu.dual_gemm_silu: available
|
||||
swiglu.gemm_fused_operand_sum: available
|
||||
swiglu.fused.p.cpp: available
|
||||
is_triton_available: True
|
||||
is_functorch_available: False
|
||||
pytorch.version: 1.13.1+cu117
|
||||
pytorch.version: 2.0.1+cu118
|
||||
pytorch.cuda: available
|
||||
gpu.compute_capability: 8.6
|
||||
gpu.name: NVIDIA RTX A2000 12GB
|
||||
gpu.compute_capability: 8.9
|
||||
gpu.name: NVIDIA GeForce RTX 4070
|
||||
build.info: available
|
||||
build.cuda_version: 1107
|
||||
build.python_version: 3.10.9
|
||||
build.torch_version: 1.13.1+cu117
|
||||
build.cuda_version: 1108
|
||||
build.python_version: 3.10.11
|
||||
build.torch_version: 2.0.1+cu118
|
||||
build.env.TORCH_CUDA_ARCH_LIST: 5.0+PTX 6.0 6.1 7.0 7.5 8.0 8.6
|
||||
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
|
||||
build.env.XFORMERS_PACKAGE_FROM: wheel-v0.0.20
|
||||
build.nvcc_version: 11.8.89
|
||||
source.privacy: open source
|
||||
```
|
||||
|
||||
@ -83,14 +92,14 @@ installed from source. These instructions were written for a system
|
||||
running Ubuntu 22.04, but other Linux distributions should be able to
|
||||
adapt this recipe.
|
||||
|
||||
#### 1. Install CUDA Toolkit 11.7
|
||||
#### 1. Install CUDA Toolkit 11.8
|
||||
|
||||
You will need the CUDA developer's toolkit in order to compile and
|
||||
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)
|
||||
by NVIDIA itself. Go to [CUDA Toolkit 11.8
|
||||
Downloads](https://developer.nvidia.com/cuda-11-8-0-download-archive)
|
||||
and use the target selection wizard to choose your platform and Linux
|
||||
distribution. Select an installer type of "runfile (local)" at the
|
||||
last step.
|
||||
@ -101,17 +110,17 @@ example, the install script recipe for Ubuntu 22.04 running on a
|
||||
x86_64 system is:
|
||||
|
||||
```
|
||||
wget https://developer.download.nvidia.com/compute/cuda/11.7.0/local_installers/cuda_11.7.0_515.43.04_linux.run
|
||||
sudo sh cuda_11.7.0_515.43.04_linux.run
|
||||
wget https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run
|
||||
sudo sh cuda_11.8.0_520.61.05_linux.run
|
||||
```
|
||||
|
||||
Rather than cut-and-paste this example, We recommend that you walk
|
||||
through the toolkit wizard in order to get the most up to date
|
||||
installer for your system.
|
||||
|
||||
#### 2. Confirm/Install pyTorch 1.13 with CUDA 11.7 support
|
||||
#### 2. Confirm/Install pyTorch 2.01 with CUDA 11.8 support
|
||||
|
||||
If you are using InvokeAI 2.3 or higher, these will already be
|
||||
If you are using InvokeAI 3.0.2 or higher, these will already be
|
||||
installed. If not, you can check whether you have the needed libraries
|
||||
using a quick command. Activate the invokeai virtual environment,
|
||||
either by entering the "developer's console", or manually with a
|
||||
@ -124,7 +133,7 @@ Then run the command:
|
||||
python -c 'exec("import torch\nprint(torch.__version__)")'
|
||||
```
|
||||
|
||||
If it prints __1.13.1+cu117__ you're good. If not, you can install the
|
||||
If it prints __1.13.1+cu118__ you're good. If not, you can install the
|
||||
most up to date libraries with this command:
|
||||
|
||||
```sh
|
||||
|
||||
@ -348,7 +348,7 @@ class InvokeAiInstance:
|
||||
|
||||
introduction()
|
||||
|
||||
from invokeai.frontend.install import invokeai_configure
|
||||
from invokeai.frontend.install.invokeai_configure import invokeai_configure
|
||||
|
||||
# NOTE: currently the config script does its own arg parsing! this means the command-line switches
|
||||
# from the installer will also automatically propagate down to the config script.
|
||||
@ -463,10 +463,10 @@ def get_torch_source() -> (Union[str, None], str):
|
||||
url = "https://download.pytorch.org/whl/cpu"
|
||||
|
||||
if device == "cuda":
|
||||
url = "https://download.pytorch.org/whl/cu117"
|
||||
url = "https://download.pytorch.org/whl/cu118"
|
||||
optional_modules = "[xformers,onnx-cuda]"
|
||||
if device == "cuda_and_dml":
|
||||
url = "https://download.pytorch.org/whl/cu117"
|
||||
url = "https://download.pytorch.org/whl/cu118"
|
||||
optional_modules = "[xformers,onnx-directml]"
|
||||
|
||||
# in all other cases, Torch wheels should be coming from PyPi as of Torch 1.13
|
||||
|
||||
@ -104,8 +104,12 @@ async def update_model(
|
||||
): # model manager moved model path during rename - don't overwrite it
|
||||
info.path = new_info.get("path")
|
||||
|
||||
# replace empty string values with None/null to avoid phenomenon of vae: ''
|
||||
info_dict = info.dict()
|
||||
info_dict = {x: info_dict[x] if info_dict[x] else None for x in info_dict.keys()}
|
||||
|
||||
ApiDependencies.invoker.services.model_manager.update_model(
|
||||
model_name=model_name, base_model=base_model, model_type=model_type, model_attributes=info.dict()
|
||||
model_name=model_name, base_model=base_model, model_type=model_type, model_attributes=info_dict
|
||||
)
|
||||
|
||||
model_raw = ApiDependencies.invoker.services.model_manager.list_model(
|
||||
|
||||
@ -2,6 +2,7 @@ from typing import Literal, Optional, Union
|
||||
|
||||
from pydantic import Field
|
||||
|
||||
from ...version import __version__
|
||||
from invokeai.app.invocations.baseinvocation import (
|
||||
BaseInvocation,
|
||||
BaseInvocationOutput,
|
||||
@ -23,6 +24,7 @@ class LoRAMetadataField(BaseModelExcludeNull):
|
||||
class CoreMetadata(BaseModelExcludeNull):
|
||||
"""Core generation metadata for an image generated in InvokeAI."""
|
||||
|
||||
app_version: str = Field(default=__version__, description="The version of InvokeAI used to generate this image")
|
||||
generation_mode: str = Field(
|
||||
description="The generation mode that output this image",
|
||||
)
|
||||
|
||||
@ -21,7 +21,6 @@ from argparse import Namespace
|
||||
from enum import Enum
|
||||
from pathlib import Path
|
||||
from shutil import get_terminal_size
|
||||
from typing import get_type_hints
|
||||
from urllib import request
|
||||
|
||||
import npyscreen
|
||||
@ -396,13 +395,23 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
|
||||
max_width=80,
|
||||
scroll_exit=True,
|
||||
)
|
||||
self.max_cache_size = self.add_widget_intelligent(
|
||||
IntTitleSlider,
|
||||
self.nextrely += 1
|
||||
self.add_widget_intelligent(
|
||||
npyscreen.TitleFixedText,
|
||||
name="RAM cache size (GB). Make this at least large enough to hold a single full model.",
|
||||
value=old_opts.max_cache_size,
|
||||
out_of=MAX_RAM,
|
||||
lowest=3,
|
||||
begin_entry_at=6,
|
||||
begin_entry_at=0,
|
||||
editable=False,
|
||||
color="CONTROL",
|
||||
scroll_exit=True,
|
||||
)
|
||||
self.nextrely -= 1
|
||||
self.max_cache_size = self.add_widget_intelligent(
|
||||
npyscreen.Slider,
|
||||
value=clip(old_opts.max_cache_size, range=(3.0, MAX_RAM), step=0.5),
|
||||
out_of=round(MAX_RAM),
|
||||
lowest=0.0,
|
||||
step=0.5,
|
||||
relx=8,
|
||||
scroll_exit=True,
|
||||
)
|
||||
if HAS_CUDA:
|
||||
@ -418,7 +427,7 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
|
||||
self.nextrely -= 1
|
||||
self.max_vram_cache_size = self.add_widget_intelligent(
|
||||
npyscreen.Slider,
|
||||
value=old_opts.max_vram_cache_size,
|
||||
value=clip(old_opts.max_vram_cache_size, range=(0, MAX_VRAM), step=0.25),
|
||||
out_of=round(MAX_VRAM * 2) / 2,
|
||||
lowest=0.0,
|
||||
relx=8,
|
||||
@ -596,6 +605,16 @@ def default_user_selections(program_opts: Namespace) -> InstallSelections:
|
||||
)
|
||||
|
||||
|
||||
# -------------------------------------
|
||||
def clip(value: float, range: tuple[float, float], step: float) -> float:
|
||||
minimum, maximum = range
|
||||
if value < minimum:
|
||||
value = minimum
|
||||
if value > maximum:
|
||||
value = maximum
|
||||
return round(value / step) * step
|
||||
|
||||
|
||||
# -------------------------------------
|
||||
def initialize_rootdir(root: Path, yes_to_all: bool = False):
|
||||
logger.info("Initializing InvokeAI runtime directory")
|
||||
|
||||
@ -591,7 +591,6 @@ script, which will perform a full upgrade in place.""",
|
||||
# TODO: revisit - don't rely on invokeai.yaml to exist yet!
|
||||
dest_is_setup = (dest_root / "models/core").exists() and (dest_root / "databases").exists()
|
||||
if not dest_is_setup:
|
||||
import invokeai.frontend.install.invokeai_configure
|
||||
from invokeai.backend.install.invokeai_configure import initialize_rootdir
|
||||
|
||||
initialize_rootdir(dest_root, True)
|
||||
|
||||
@ -143,7 +143,7 @@ class ModelPatcher:
|
||||
# with torch.autocast(device_type="cpu"):
|
||||
layer.to(dtype=torch.float32)
|
||||
layer_scale = layer.alpha / layer.rank if (layer.alpha and layer.rank) else 1.0
|
||||
layer_weight = layer.get_weight() * lora_weight * layer_scale
|
||||
layer_weight = layer.get_weight(original_weights[module_key]) * lora_weight * layer_scale
|
||||
|
||||
if module.weight.shape != layer_weight.shape:
|
||||
# TODO: debug on lycoris
|
||||
@ -361,7 +361,8 @@ class ONNXModelPatcher:
|
||||
|
||||
layer.to(dtype=torch.float32)
|
||||
layer_key = layer_key.replace(prefix, "")
|
||||
layer_weight = layer.get_weight().detach().cpu().numpy() * lora_weight
|
||||
# TODO: rewrite to pass original tensor weight(required by ia3)
|
||||
layer_weight = layer.get_weight(None).detach().cpu().numpy() * lora_weight
|
||||
if layer_key is blended_loras:
|
||||
blended_loras[layer_key] += layer_weight
|
||||
else:
|
||||
|
||||
@ -526,7 +526,7 @@ class ModelManager(object):
|
||||
# Does the config explicitly override the submodel?
|
||||
if submodel_type is not None and hasattr(model_config, submodel_type):
|
||||
submodel_path = getattr(model_config, submodel_type)
|
||||
if submodel_path is not None:
|
||||
if submodel_path is not None and len(submodel_path) > 0:
|
||||
model_path = getattr(model_config, submodel_type)
|
||||
is_submodel_override = True
|
||||
|
||||
|
||||
@ -17,6 +17,7 @@ from .models import (
|
||||
SilenceWarnings,
|
||||
InvalidModelException,
|
||||
)
|
||||
from .util import lora_token_vector_length
|
||||
from .models.base import read_checkpoint_meta
|
||||
|
||||
|
||||
@ -315,38 +316,16 @@ class LoRACheckpointProbe(CheckpointProbeBase):
|
||||
|
||||
def get_base_type(self) -> BaseModelType:
|
||||
checkpoint = self.checkpoint
|
||||
token_vector_length = lora_token_vector_length(checkpoint)
|
||||
|
||||
# SD-2 models are very hard to probe. These probes are brittle and likely to fail in the future
|
||||
# There are also some "SD-2 LoRAs" that have identical keys and shapes to SD-1 and will be
|
||||
# misclassified as SD-1
|
||||
key = "lora_te_text_model_encoder_layers_0_mlp_fc1.lora_down.weight"
|
||||
if key in checkpoint and checkpoint[key].shape[0] == 320:
|
||||
return BaseModelType.StableDiffusion2
|
||||
|
||||
key = "lora_unet_output_blocks_5_1_transformer_blocks_1_ff_net_2.lora_up.weight"
|
||||
if key in checkpoint:
|
||||
return BaseModelType.StableDiffusionXL
|
||||
|
||||
key1 = "lora_te_text_model_encoder_layers_0_mlp_fc1.lora_down.weight"
|
||||
key2 = "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
|
||||
key3 = "lora_te_text_model_encoder_layers_0_self_attn_k_proj.hada_w1_a"
|
||||
|
||||
lora_token_vector_length = (
|
||||
checkpoint[key1].shape[1]
|
||||
if key1 in checkpoint
|
||||
else checkpoint[key2].shape[1]
|
||||
if key2 in checkpoint
|
||||
else checkpoint[key3].shape[0]
|
||||
if key3 in checkpoint
|
||||
else None
|
||||
)
|
||||
|
||||
if lora_token_vector_length == 768:
|
||||
if token_vector_length == 768:
|
||||
return BaseModelType.StableDiffusion1
|
||||
elif lora_token_vector_length == 1024:
|
||||
elif token_vector_length == 1024:
|
||||
return BaseModelType.StableDiffusion2
|
||||
elif token_vector_length == 2048:
|
||||
return BaseModelType.StableDiffusionXL
|
||||
else:
|
||||
raise InvalidModelException(f"Unknown LoRA type")
|
||||
raise InvalidModelException(f"Unknown LoRA type: {self.checkpoint_path}")
|
||||
|
||||
|
||||
class TextualInversionCheckpointProbe(CheckpointProbeBase):
|
||||
|
||||
@ -122,41 +122,7 @@ class LoRALayerBase:
|
||||
self.rank = None # set in layer implementation
|
||||
self.layer_key = layer_key
|
||||
|
||||
def forward(
|
||||
self,
|
||||
module: torch.nn.Module,
|
||||
input_h: Any, # for real looks like Tuple[torch.nn.Tensor] but not sure
|
||||
multiplier: float,
|
||||
):
|
||||
if type(module) == torch.nn.Conv2d:
|
||||
op = torch.nn.functional.conv2d
|
||||
extra_args = dict(
|
||||
stride=module.stride,
|
||||
padding=module.padding,
|
||||
dilation=module.dilation,
|
||||
groups=module.groups,
|
||||
)
|
||||
|
||||
else:
|
||||
op = torch.nn.functional.linear
|
||||
extra_args = {}
|
||||
|
||||
weight = self.get_weight()
|
||||
|
||||
bias = self.bias if self.bias is not None else 0
|
||||
scale = self.alpha / self.rank if (self.alpha and self.rank) else 1.0
|
||||
return (
|
||||
op(
|
||||
*input_h,
|
||||
(weight + bias).view(module.weight.shape),
|
||||
None,
|
||||
**extra_args,
|
||||
)
|
||||
* multiplier
|
||||
* scale
|
||||
)
|
||||
|
||||
def get_weight(self):
|
||||
def get_weight(self, orig_weight: torch.Tensor):
|
||||
raise NotImplementedError()
|
||||
|
||||
def calc_size(self) -> int:
|
||||
@ -197,7 +163,7 @@ class LoRALayer(LoRALayerBase):
|
||||
|
||||
self.rank = self.down.shape[0]
|
||||
|
||||
def get_weight(self):
|
||||
def get_weight(self, orig_weight: torch.Tensor):
|
||||
if self.mid is not None:
|
||||
up = self.up.reshape(self.up.shape[0], self.up.shape[1])
|
||||
down = self.down.reshape(self.down.shape[0], self.down.shape[1])
|
||||
@ -260,7 +226,7 @@ class LoHALayer(LoRALayerBase):
|
||||
|
||||
self.rank = self.w1_b.shape[0]
|
||||
|
||||
def get_weight(self):
|
||||
def get_weight(self, orig_weight: torch.Tensor):
|
||||
if self.t1 is None:
|
||||
weight = (self.w1_a @ self.w1_b) * (self.w2_a @ self.w2_b)
|
||||
|
||||
@ -342,7 +308,7 @@ class LoKRLayer(LoRALayerBase):
|
||||
else:
|
||||
self.rank = None # unscaled
|
||||
|
||||
def get_weight(self):
|
||||
def get_weight(self, orig_weight: torch.Tensor):
|
||||
w1 = self.w1
|
||||
if w1 is None:
|
||||
w1 = self.w1_a @ self.w1_b
|
||||
@ -410,7 +376,7 @@ class FullLayer(LoRALayerBase):
|
||||
|
||||
self.rank = None # unscaled
|
||||
|
||||
def get_weight(self):
|
||||
def get_weight(self, orig_weight: torch.Tensor):
|
||||
return self.weight
|
||||
|
||||
def calc_size(self) -> int:
|
||||
@ -428,6 +394,45 @@ class FullLayer(LoRALayerBase):
|
||||
self.weight = self.weight.to(device=device, dtype=dtype)
|
||||
|
||||
|
||||
class IA3Layer(LoRALayerBase):
|
||||
# weight: torch.Tensor
|
||||
# on_input: torch.Tensor
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
layer_key: str,
|
||||
values: dict,
|
||||
):
|
||||
super().__init__(layer_key, values)
|
||||
|
||||
self.weight = values["weight"]
|
||||
self.on_input = values["on_input"]
|
||||
|
||||
self.rank = None # unscaled
|
||||
|
||||
def get_weight(self, orig_weight: torch.Tensor):
|
||||
weight = self.weight
|
||||
if not self.on_input:
|
||||
weight = weight.reshape(-1, 1)
|
||||
return orig_weight * weight
|
||||
|
||||
def calc_size(self) -> int:
|
||||
model_size = super().calc_size()
|
||||
model_size += self.weight.nelement() * self.weight.element_size()
|
||||
model_size += self.on_input.nelement() * self.on_input.element_size()
|
||||
return model_size
|
||||
|
||||
def to(
|
||||
self,
|
||||
device: Optional[torch.device] = None,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
):
|
||||
super().to(device=device, dtype=dtype)
|
||||
|
||||
self.weight = self.weight.to(device=device, dtype=dtype)
|
||||
self.on_input = self.on_input.to(device=device, dtype=dtype)
|
||||
|
||||
|
||||
# TODO: rename all methods used in model logic with Info postfix and remove here Raw postfix
|
||||
class LoRAModelRaw: # (torch.nn.Module):
|
||||
_name: str
|
||||
@ -547,11 +552,15 @@ class LoRAModelRaw: # (torch.nn.Module):
|
||||
elif "lokr_w1_b" in values or "lokr_w1" in values:
|
||||
layer = LoKRLayer(layer_key, values)
|
||||
|
||||
# diff
|
||||
elif "diff" in values:
|
||||
layer = FullLayer(layer_key, values)
|
||||
|
||||
# ia3
|
||||
elif "weight" in values and "on_input" in values:
|
||||
layer = IA3Layer(layer_key, values)
|
||||
|
||||
else:
|
||||
# TODO: ia3/... format
|
||||
print(f">> Encountered unknown lora layer module in {model.name}: {layer_key} - {list(values.keys())}")
|
||||
raise Exception("Unknown lora format!")
|
||||
|
||||
|
||||
75
invokeai/backend/model_management/util.py
Normal file
75
invokeai/backend/model_management/util.py
Normal file
@ -0,0 +1,75 @@
|
||||
# Copyright (c) 2023 The InvokeAI Development Team
|
||||
"""Utilities used by the Model Manager"""
|
||||
|
||||
|
||||
def lora_token_vector_length(checkpoint: dict) -> int:
|
||||
"""
|
||||
Given a checkpoint in memory, return the lora token vector length
|
||||
|
||||
:param checkpoint: The checkpoint
|
||||
"""
|
||||
|
||||
def _get_shape_1(key, tensor, checkpoint):
|
||||
lora_token_vector_length = None
|
||||
|
||||
if "." not in key:
|
||||
return lora_token_vector_length # wrong key format
|
||||
model_key, lora_key = key.split(".", 1)
|
||||
|
||||
# check lora/locon
|
||||
if lora_key == "lora_down.weight":
|
||||
lora_token_vector_length = tensor.shape[1]
|
||||
|
||||
# check loha (don't worry about hada_t1/hada_t2 as it used only in 4d shapes)
|
||||
elif lora_key in ["hada_w1_b", "hada_w2_b"]:
|
||||
lora_token_vector_length = tensor.shape[1]
|
||||
|
||||
# check lokr (don't worry about lokr_t2 as it used only in 4d shapes)
|
||||
elif "lokr_" in lora_key:
|
||||
if model_key + ".lokr_w1" in checkpoint:
|
||||
_lokr_w1 = checkpoint[model_key + ".lokr_w1"]
|
||||
elif model_key + "lokr_w1_b" in checkpoint:
|
||||
_lokr_w1 = checkpoint[model_key + ".lokr_w1_b"]
|
||||
else:
|
||||
return lora_token_vector_length # unknown format
|
||||
|
||||
if model_key + ".lokr_w2" in checkpoint:
|
||||
_lokr_w2 = checkpoint[model_key + ".lokr_w2"]
|
||||
elif model_key + "lokr_w2_b" in checkpoint:
|
||||
_lokr_w2 = checkpoint[model_key + ".lokr_w2_b"]
|
||||
else:
|
||||
return lora_token_vector_length # unknown format
|
||||
|
||||
lora_token_vector_length = _lokr_w1.shape[1] * _lokr_w2.shape[1]
|
||||
|
||||
elif lora_key == "diff":
|
||||
lora_token_vector_length = tensor.shape[1]
|
||||
|
||||
# ia3 can be detected only by shape[0] in text encoder
|
||||
elif lora_key == "weight" and "lora_unet_" not in model_key:
|
||||
lora_token_vector_length = tensor.shape[0]
|
||||
|
||||
return lora_token_vector_length
|
||||
|
||||
lora_token_vector_length = None
|
||||
lora_te1_length = None
|
||||
lora_te2_length = None
|
||||
for key, tensor in checkpoint.items():
|
||||
if key.startswith("lora_unet_") and ("_attn2_to_k." in key or "_attn2_to_v." in key):
|
||||
lora_token_vector_length = _get_shape_1(key, tensor, checkpoint)
|
||||
elif key.startswith("lora_te") and "_self_attn_" in key:
|
||||
tmp_length = _get_shape_1(key, tensor, checkpoint)
|
||||
if key.startswith("lora_te_"):
|
||||
lora_token_vector_length = tmp_length
|
||||
elif key.startswith("lora_te1_"):
|
||||
lora_te1_length = tmp_length
|
||||
elif key.startswith("lora_te2_"):
|
||||
lora_te2_length = tmp_length
|
||||
|
||||
if lora_te1_length is not None and lora_te2_length is not None:
|
||||
lora_token_vector_length = lora_te1_length + lora_te2_length
|
||||
|
||||
if lora_token_vector_length is not None:
|
||||
break
|
||||
|
||||
return lora_token_vector_length
|
||||
@ -1,6 +1,3 @@
|
||||
"""
|
||||
Initialization file for invokeai.frontend.config
|
||||
"""
|
||||
from .invokeai_configure import main as invokeai_configure
|
||||
from .invokeai_update import main as invokeai_update
|
||||
from .model_install import main as invokeai_model_install
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
"""
|
||||
Wrapper for invokeai.backend.configure.invokeai_configure
|
||||
"""
|
||||
from ...backend.install.invokeai_configure import main
|
||||
from ...backend.install.invokeai_configure import main as invokeai_configure
|
||||
|
||||
@ -382,7 +382,8 @@ def run_cli(args: Namespace):
|
||||
|
||||
def main():
|
||||
args = _parse_args()
|
||||
config.parse_args(["--root", str(args.root_dir)])
|
||||
if args.root_dir:
|
||||
config.parse_args(["--root", str(args.root_dir)])
|
||||
|
||||
try:
|
||||
if args.front_end:
|
||||
|
||||
File diff suppressed because one or more lines are too long
@ -1,4 +1,4 @@
|
||||
import{B as m,g7 as Je,A as y,a5 as Ka,g8 as Xa,af as va,aj as d,g9 as b,ga as t,gb as Ya,gc as h,gd as ua,ge as Ja,gf as Qa,aL as Za,gg as et,ad as rt,gh as at}from"./index-dd054634.js";import{s as fa,n as o,t as tt,o as ha,p as ot,q as ma,v as ga,w as ya,x as it,y as Sa,z as pa,A as xr,B as nt,D as lt,E as st,F as xa,G as $a,H as ka,J as dt,K as _a,L as ct,M as bt,N as vt,O as ut,Q as wa,R as ft,S as ht,T as mt,U as gt,V as yt,W as St,e as pt,X as xt}from"./menu-b42141e3.js";var za=String.raw,Ca=za`
|
||||
import{B as m,g7 as Je,A as y,a5 as Ka,g8 as Xa,af as va,aj as d,g9 as b,ga as t,gb as Ya,gc as h,gd as ua,ge as Ja,gf as Qa,aL as Za,gg as et,ad as rt,gh as at}from"./index-815faab3.js";import{s as fa,n as o,t as tt,o as ha,p as ot,q as ma,v as ga,w as ya,x as it,y as Sa,z as pa,A as xr,B as nt,D as lt,E as st,F as xa,G as $a,H as ka,J as dt,K as _a,L as ct,M as bt,N as vt,O as ut,Q as wa,R as ft,S as ht,T as mt,U as gt,V as yt,W as St,e as pt,X as xt}from"./menu-e9f8a36e.js";var za=String.raw,Ca=za`
|
||||
:root,
|
||||
:host {
|
||||
--chakra-vh: 100vh;
|
||||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
2
invokeai/frontend/web/dist/index.html
vendored
2
invokeai/frontend/web/dist/index.html
vendored
@ -12,7 +12,7 @@
|
||||
margin: 0;
|
||||
}
|
||||
</style>
|
||||
<script type="module" crossorigin src="./assets/index-dd054634.js"></script>
|
||||
<script type="module" crossorigin src="./assets/index-815faab3.js"></script>
|
||||
</head>
|
||||
|
||||
<body dir="ltr">
|
||||
|
||||
@ -1,55 +1,58 @@
|
||||
import { modelChanged } from 'features/parameters/store/generationSlice';
|
||||
import { setActiveTab } from 'features/ui/store/uiSlice';
|
||||
import { forEach } from 'lodash-es';
|
||||
import { NON_REFINER_BASE_MODELS } from 'services/api/constants';
|
||||
import {
|
||||
MainModelConfigEntity,
|
||||
modelsApi,
|
||||
} from 'services/api/endpoints/models';
|
||||
import { mainModelsAdapter, modelsApi } from 'services/api/endpoints/models';
|
||||
import { startAppListening } from '..';
|
||||
|
||||
export const addTabChangedListener = () => {
|
||||
startAppListening({
|
||||
actionCreator: setActiveTab,
|
||||
effect: (action, { getState, dispatch }) => {
|
||||
effect: async (action, { getState, dispatch }) => {
|
||||
const activeTabName = action.payload;
|
||||
if (activeTabName === 'unifiedCanvas') {
|
||||
// grab the models from RTK Query cache
|
||||
const { data } = modelsApi.endpoints.getMainModels.select(
|
||||
NON_REFINER_BASE_MODELS
|
||||
)(getState());
|
||||
const currentBaseModel = getState().generation.model?.base_model;
|
||||
|
||||
if (!data) {
|
||||
// no models yet, so we can't do anything
|
||||
dispatch(modelChanged(null));
|
||||
if (currentBaseModel && ['sd-1', 'sd-2'].includes(currentBaseModel)) {
|
||||
// if we're already on a valid model, no change needed
|
||||
return;
|
||||
}
|
||||
|
||||
// need to filter out all the invalid canvas models (currently, this is just sdxl)
|
||||
const validCanvasModels: MainModelConfigEntity[] = [];
|
||||
try {
|
||||
// just grab fresh models
|
||||
const modelsRequest = dispatch(
|
||||
modelsApi.endpoints.getMainModels.initiate(NON_REFINER_BASE_MODELS)
|
||||
);
|
||||
const models = await modelsRequest.unwrap();
|
||||
// cancel this cache subscription
|
||||
modelsRequest.unsubscribe();
|
||||
|
||||
forEach(data.entities, (entity) => {
|
||||
if (!entity) {
|
||||
if (!models.ids.length) {
|
||||
// no valid canvas models
|
||||
dispatch(modelChanged(null));
|
||||
return;
|
||||
}
|
||||
if (['sd-1', 'sd-2'].includes(entity.base_model)) {
|
||||
validCanvasModels.push(entity);
|
||||
|
||||
// need to filter out all the invalid canvas models (currently sdxl & refiner)
|
||||
const validCanvasModels = mainModelsAdapter
|
||||
.getSelectors()
|
||||
.selectAll(models)
|
||||
.filter((model) => ['sd-1', 'sd-2'].includes(model.base_model));
|
||||
|
||||
const firstValidCanvasModel = validCanvasModels[0];
|
||||
|
||||
if (!firstValidCanvasModel) {
|
||||
// no valid canvas models
|
||||
dispatch(modelChanged(null));
|
||||
return;
|
||||
}
|
||||
});
|
||||
|
||||
// this could still be undefined even tho TS doesn't say so
|
||||
const firstValidCanvasModel = validCanvasModels[0];
|
||||
const { base_model, model_name, model_type } = firstValidCanvasModel;
|
||||
|
||||
if (!firstValidCanvasModel) {
|
||||
// uh oh, we have no models that are valid for canvas
|
||||
dispatch(modelChanged({ base_model, model_name, model_type }));
|
||||
} catch {
|
||||
// network request failed, bail
|
||||
dispatch(modelChanged(null));
|
||||
return;
|
||||
}
|
||||
|
||||
// only store the model name and base model in redux
|
||||
const { base_model, model_name, model_type } = firstValidCanvasModel;
|
||||
|
||||
dispatch(modelChanged({ base_model, model_name, model_type }));
|
||||
}
|
||||
},
|
||||
});
|
||||
|
||||
@ -54,12 +54,7 @@ const ParamLoRASelect = () => {
|
||||
});
|
||||
});
|
||||
|
||||
// Sort Alphabetically
|
||||
data.sort((a, b) =>
|
||||
a.label && b.label ? (a.label?.localeCompare(b.label) ? 1 : -1) : -1
|
||||
);
|
||||
|
||||
return data.sort((a, b) => (a.disabled && !b.disabled ? -1 : 1));
|
||||
return data.sort((a, b) => (a.disabled && !b.disabled ? 1 : -1));
|
||||
}, [loras, loraModels, currentMainModel?.base_model]);
|
||||
|
||||
const handleChange = useCallback(
|
||||
|
||||
@ -365,12 +365,19 @@ export const systemSlice = createSlice({
|
||||
state.statusTranslationKey = 'common.statusConnected';
|
||||
state.progressImage = null;
|
||||
|
||||
let errorDescription = undefined;
|
||||
|
||||
if (action.payload?.status === 422) {
|
||||
errorDescription = 'Validation Error';
|
||||
} else if (action.payload?.error) {
|
||||
errorDescription = action.payload?.error as string;
|
||||
}
|
||||
|
||||
state.toastQueue.push(
|
||||
makeToast({
|
||||
title: t('toast.serverError'),
|
||||
status: 'error',
|
||||
description:
|
||||
action.payload?.status === 422 ? 'Validation Error' : undefined,
|
||||
description: errorDescription,
|
||||
})
|
||||
);
|
||||
});
|
||||
|
||||
@ -60,6 +60,9 @@ type InvokedSessionThunkConfig = {
|
||||
const isErrorWithStatus = (error: unknown): error is { status: number } =>
|
||||
isObject(error) && 'status' in error;
|
||||
|
||||
const isErrorWithDetail = (error: unknown): error is { detail: string } =>
|
||||
isObject(error) && 'detail' in error;
|
||||
|
||||
/**
|
||||
* `SessionsService.invokeSession()` thunk
|
||||
*/
|
||||
@ -85,7 +88,15 @@ export const sessionInvoked = createAsyncThunk<
|
||||
error: (error as any).body.detail,
|
||||
});
|
||||
}
|
||||
return rejectWithValue({ arg, status: response.status, error });
|
||||
if (isErrorWithDetail(error) && response.status === 403) {
|
||||
return rejectWithValue({
|
||||
arg,
|
||||
status: response.status,
|
||||
error: error.detail
|
||||
});
|
||||
}
|
||||
if (error)
|
||||
return rejectWithValue({ arg, status: response.status, error });
|
||||
}
|
||||
});
|
||||
|
||||
|
||||
@ -1 +1 @@
|
||||
__version__ = "3.0.2rc1"
|
||||
__version__ = "3.0.2"
|
||||
|
||||
@ -118,7 +118,7 @@ dependencies = [
|
||||
[project.scripts]
|
||||
|
||||
# legacy entrypoints; provided for backwards compatibility
|
||||
"configure_invokeai.py" = "invokeai.frontend.install:invokeai_configure"
|
||||
"configure_invokeai.py" = "invokeai.frontend.install.invokeai_configure:invokeai_configure"
|
||||
"textual_inversion.py" = "invokeai.frontend.training:invokeai_textual_inversion"
|
||||
|
||||
# shortcut commands to start cli and web
|
||||
@ -130,12 +130,12 @@ dependencies = [
|
||||
"invokeai-web" = "invokeai.app.api_app:invoke_api"
|
||||
|
||||
# full commands
|
||||
"invokeai-configure" = "invokeai.frontend.install:invokeai_configure"
|
||||
"invokeai-configure" = "invokeai.frontend.install.invokeai_configure:invokeai_configure"
|
||||
"invokeai-merge" = "invokeai.frontend.merge:invokeai_merge_diffusers"
|
||||
"invokeai-ti" = "invokeai.frontend.training:invokeai_textual_inversion"
|
||||
"invokeai-model-install" = "invokeai.frontend.install:invokeai_model_install"
|
||||
"invokeai-model-install" = "invokeai.frontend.install.model_install:main"
|
||||
"invokeai-migrate3" = "invokeai.backend.install.migrate_to_3:main"
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"invokeai-update" = "invokeai.frontend.install:invokeai_update"
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"invokeai-update" = "invokeai.frontend.install.invokeai_update:main"
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"invokeai-metadata" = "invokeai.frontend.CLI.sd_metadata:print_metadata"
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"invokeai-node-cli" = "invokeai.app.cli_app:invoke_cli"
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"invokeai-node-web" = "invokeai.app.api_app:invoke_api"
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34
scripts/create_checkpoint_template.py
Executable file
34
scripts/create_checkpoint_template.py
Executable file
@ -0,0 +1,34 @@
|
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#!/usr/bin/env python
|
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"""
|
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Read a checkpoint/safetensors file and write out a template .json file containing
|
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its metadata for use in fast model probing.
|
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"""
|
||||
|
||||
import sys
|
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import argparse
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import json
|
||||
|
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from pathlib import Path
|
||||
|
||||
from invokeai.backend.model_management.models.base import read_checkpoint_meta
|
||||
|
||||
parser = argparse.ArgumentParser(description="Create a .json template from checkpoint/safetensors model")
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||||
parser.add_argument("--checkpoint", "--in", type=Path, help="Path to the input checkpoint/safetensors file")
|
||||
parser.add_argument("--template", "--out", type=Path, help="Path to the output .json file")
|
||||
|
||||
opt = parser.parse_args()
|
||||
ckpt = read_checkpoint_meta(opt.checkpoint)
|
||||
while "state_dict" in ckpt:
|
||||
ckpt = ckpt["state_dict"]
|
||||
|
||||
tmpl = {}
|
||||
|
||||
for key, tensor in ckpt.items():
|
||||
tmpl[key] = list(tensor.shape)
|
||||
|
||||
try:
|
||||
with open(opt.template, "w") as f:
|
||||
json.dump(tmpl, f)
|
||||
print(f"Template written out as {opt.template}")
|
||||
except Exception as e:
|
||||
print(f"An exception occurred while writing template: {str(e)}")
|
||||
37
scripts/verify_checkpoint_template.py
Executable file
37
scripts/verify_checkpoint_template.py
Executable file
@ -0,0 +1,37 @@
|
||||
#!/usr/bin/env python
|
||||
"""
|
||||
Read a checkpoint/safetensors file and compare it to a template .json.
|
||||
Returns True if their metadata match.
|
||||
"""
|
||||
|
||||
import sys
|
||||
import argparse
|
||||
import json
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
from invokeai.backend.model_management.models.base import read_checkpoint_meta
|
||||
|
||||
parser = argparse.ArgumentParser(description="Compare a checkpoint/safetensors file to a JSON metadata template.")
|
||||
parser.add_argument("--checkpoint", "--in", type=Path, help="Path to the input checkpoint/safetensors file")
|
||||
parser.add_argument("--template", "--out", type=Path, help="Path to the template .json file to match against")
|
||||
|
||||
opt = parser.parse_args()
|
||||
ckpt = read_checkpoint_meta(opt.checkpoint)
|
||||
while "state_dict" in ckpt:
|
||||
ckpt = ckpt["state_dict"]
|
||||
|
||||
checkpoint_metadata = {}
|
||||
|
||||
for key, tensor in ckpt.items():
|
||||
checkpoint_metadata[key] = list(tensor.shape)
|
||||
|
||||
with open(opt.template, "r") as f:
|
||||
template = json.load(f)
|
||||
|
||||
if checkpoint_metadata == template:
|
||||
print("True")
|
||||
sys.exit(0)
|
||||
else:
|
||||
print("False")
|
||||
sys.exit(-1)
|
||||
@ -7,6 +7,7 @@ from invokeai.backend import ModelManager, BaseModelType, ModelType, SubModelTyp
|
||||
|
||||
BASIC_MODEL_NAME = ("SDXL base", BaseModelType.StableDiffusionXL, ModelType.Main)
|
||||
VAE_OVERRIDE_MODEL_NAME = ("SDXL with VAE", BaseModelType.StableDiffusionXL, ModelType.Main)
|
||||
VAE_NULL_OVERRIDE_MODEL_NAME = ("SDXL with empty VAE", BaseModelType.StableDiffusionXL, ModelType.Main)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
@ -36,3 +37,11 @@ def test_get_model_path_for_overridden_vae(model_manager: ModelManager, datadir:
|
||||
expected_vae_path = datadir / "models" / "sdxl" / "vae" / "sdxl-vae-fp16-fix"
|
||||
assert vae_model_path == expected_vae_path
|
||||
assert is_override
|
||||
|
||||
|
||||
def test_get_model_path_for_null_overridden_vae(model_manager: ModelManager, datadir: Path):
|
||||
model_config = model_manager._get_model_config(
|
||||
VAE_NULL_OVERRIDE_MODEL_NAME[1], VAE_NULL_OVERRIDE_MODEL_NAME[0], VAE_NULL_OVERRIDE_MODEL_NAME[2]
|
||||
)
|
||||
vae_model_path, is_override = model_manager._get_model_path(model_config, SubModelType.Vae)
|
||||
assert not is_override
|
||||
|
||||
@ -13,3 +13,10 @@ sdxl/main/SDXL with VAE:
|
||||
vae: sdxl/vae/sdxl-vae-fp16-fix/
|
||||
variant: normal
|
||||
format: diffusers
|
||||
|
||||
sdxl/main/SDXL with empty VAE:
|
||||
path: sdxl/main/SDXL base 1_0
|
||||
description: SDXL with customized VAE
|
||||
vae: ''
|
||||
variant: normal
|
||||
format: diffusers
|
||||
|
||||
Loading…
Reference in New Issue
Block a user