mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Merge branch 'main' into feat/onnx
This commit is contained in:
Brandon Rising
commit
f5ac73b091
@ -394,7 +394,7 @@ rm .\.venv -r -force
|
||||
python -mvenv .venv
|
||||
.\.venv\Scripts\activate
|
||||
pip install invokeai
|
||||
invokeai-configure --root .
|
||||
invokeai-configure --yes --root .
|
||||
```
|
||||
|
||||
If you see anything marked as an error during this process please stop
|
||||
|
||||
@ -14,20 +14,25 @@ The nodes linked below have been developed and contributed by members of the Inv
|
||||
|
||||
## List of Nodes
|
||||
|
||||
### Face Mask
|
||||
### FaceTools
|
||||
|
||||
**Description:** This node autodetects a face in the image using MediaPipe and masks it by making it transparent. Via outpainting you can swap faces with other faces, or invert the mask and swap things around the face with other things. Additionally, you can supply X and Y offset values to scale/change the shape of the mask for finer control. The node also outputs an all-white mask in the same dimensions as the input image. This is needed by the inpaint node (and unified canvas) for outpainting.
|
||||
**Description:** FaceTools is a collection of nodes created to manipulate faces as you would in Unified Canvas. It includes FaceMask, FaceOff, and FacePlace. FaceMask autodetects a face in the image using MediaPipe and creates a mask from it. FaceOff similarly detects a face, then takes the face off of the image by adding a square bounding box around it and cropping/scaling it. FacePlace puts the bounded face image from FaceOff back onto the original image. Using these nodes with other inpainting node(s), you can put new faces on existing things, put new things around existing faces, and work closer with a face as a bounded image. Additionally, you can supply X and Y offset values to scale/change the shape of the mask for finer control on FaceMask and FaceOff. See GitHub repository below for usage examples.
|
||||
|
||||
**Node Link:** https://github.com/ymgenesis/InvokeAI/blob/facemaskmediapipe/invokeai/app/invocations/facemask.py
|
||||
**Node Link:** https://github.com/ymgenesis/FaceTools/
|
||||
|
||||
**Example Node Graph:** https://www.mediafire.com/file/gohn5sb1bfp8use/21-July_2023-FaceMask.json/file
|
||||
**FaceMask Output Examples**
|
||||
|
||||
**Output Examples**
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
<hr>
|
||||
|
||||
### Ideal Size
|
||||
|
||||
**Description:** This node calculates an ideal image size for a first pass of a multi-pass upscaling. The aim is to avoid duplication that results from choosing a size larger than the model is capable of.
|
||||
|
||||
**Node Link:** https://github.com/JPPhoto/ideal-size-node
|
||||
|
||||
--------------------------------
|
||||
### Super Cool Node Template
|
||||
@ -42,11 +47,5 @@ The nodes linked below have been developed and contributed by members of the Inv
|
||||
|
||||
|
||||
|
||||
### Ideal Size
|
||||
|
||||
**Description:** This node calculates an ideal image size for a first pass of a multi-pass upscaling. The aim is to avoid duplication that results from choosing a size larger than the model is capable of.
|
||||
|
||||
**Node Link:** https://github.com/JPPhoto/ideal-size-node
|
||||
|
||||
## Help
|
||||
If you run into any issues with a node, please post in the [InvokeAI Discord](https://discord.gg/ZmtBAhwWhy).
|
||||
|
||||
25
flake.lock
Normal file
25
flake.lock
Normal file
@ -0,0 +1,25 @@
|
||||
{
|
||||
"nodes": {
|
||||
"nixpkgs": {
|
||||
"locked": {
|
||||
"lastModified": 1690630721,
|
||||
"narHash": "sha256-Y04onHyBQT4Erfr2fc82dbJTfXGYrf4V0ysLUYnPOP8=",
|
||||
"owner": "NixOS",
|
||||
"repo": "nixpkgs",
|
||||
"rev": "d2b52322f35597c62abf56de91b0236746b2a03d",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
"id": "nixpkgs",
|
||||
"type": "indirect"
|
||||
}
|
||||
},
|
||||
"root": {
|
||||
"inputs": {
|
||||
"nixpkgs": "nixpkgs"
|
||||
}
|
||||
}
|
||||
},
|
||||
"root": "root",
|
||||
"version": 7
|
||||
}
|
||||
81
flake.nix
Normal file
81
flake.nix
Normal file
@ -0,0 +1,81 @@
|
||||
# Important note: this flake does not attempt to create a fully isolated, 'pure'
|
||||
# Python environment for InvokeAI. Instead, it depends on local invocations of
|
||||
# virtualenv/pip to install the required (binary) packages, most importantly the
|
||||
# prebuilt binary pytorch packages with CUDA support.
|
||||
# ML Python packages with CUDA support, like pytorch, are notoriously expensive
|
||||
# to compile so it's purposefuly not what this flake does.
|
||||
|
||||
{
|
||||
description = "An (impure) flake to develop on InvokeAI.";
|
||||
|
||||
outputs = { self, nixpkgs }:
|
||||
let
|
||||
system = "x86_64-linux";
|
||||
pkgs = import nixpkgs {
|
||||
inherit system;
|
||||
config.allowUnfree = true;
|
||||
};
|
||||
|
||||
python = pkgs.python310;
|
||||
|
||||
mkShell = { dir, install }:
|
||||
let
|
||||
setupScript = pkgs.writeScript "setup-invokai" ''
|
||||
# This must be sourced using 'source', not executed.
|
||||
${python}/bin/python -m venv ${dir}
|
||||
${dir}/bin/python -m pip install ${install}
|
||||
# ${dir}/bin/python -c 'import torch; assert(torch.cuda.is_available())'
|
||||
source ${dir}/bin/activate
|
||||
'';
|
||||
in
|
||||
pkgs.mkShell rec {
|
||||
buildInputs = with pkgs; [
|
||||
# Backend: graphics, CUDA.
|
||||
cudaPackages.cudnn
|
||||
cudaPackages.cuda_nvrtc
|
||||
cudatoolkit
|
||||
freeglut
|
||||
glib
|
||||
gperf
|
||||
procps
|
||||
libGL
|
||||
libGLU
|
||||
linuxPackages.nvidia_x11
|
||||
python
|
||||
stdenv.cc
|
||||
stdenv.cc.cc.lib
|
||||
xorg.libX11
|
||||
xorg.libXext
|
||||
xorg.libXi
|
||||
xorg.libXmu
|
||||
xorg.libXrandr
|
||||
xorg.libXv
|
||||
zlib
|
||||
|
||||
# Pre-commit hooks.
|
||||
black
|
||||
|
||||
# Frontend.
|
||||
yarn
|
||||
nodejs
|
||||
];
|
||||
LD_LIBRARY_PATH = pkgs.lib.makeLibraryPath buildInputs;
|
||||
CUDA_PATH = pkgs.cudatoolkit;
|
||||
EXTRA_LDFLAGS = "-L${pkgs.linuxPackages.nvidia_x11}/lib";
|
||||
shellHook = ''
|
||||
if [[ -f "${dir}/bin/activate" ]]; then
|
||||
source "${dir}/bin/activate"
|
||||
echo "Using Python: $(which python)"
|
||||
else
|
||||
echo "Use 'source ${setupScript}' to set up the environment."
|
||||
fi
|
||||
'';
|
||||
};
|
||||
in
|
||||
{
|
||||
devShells.${system} = rec {
|
||||
develop = mkShell { dir = "venv"; install = "-e '.[xformers]' --extra-index-url https://download.pytorch.org/whl/cu118"; };
|
||||
default = develop;
|
||||
};
|
||||
};
|
||||
}
|
||||
@ -13,7 +13,7 @@ from pathlib import Path
|
||||
from tempfile import TemporaryDirectory
|
||||
from typing import Union
|
||||
|
||||
SUPPORTED_PYTHON = ">=3.9.0,<3.11"
|
||||
SUPPORTED_PYTHON = ">=3.9.0,<=3.11.100"
|
||||
INSTALLER_REQS = ["rich", "semver", "requests", "plumbum", "prompt-toolkit"]
|
||||
BOOTSTRAP_VENV_PREFIX = "invokeai-installer-tmp"
|
||||
|
||||
@ -149,7 +149,7 @@ class Installer:
|
||||
return venv_dir
|
||||
|
||||
def install(
|
||||
self, root: str = "~/invokeai-3", version: str = "latest", yes_to_all=False, find_links: Path = None
|
||||
self, root: str = "~/invokeai", version: str = "latest", yes_to_all=False, find_links: Path = None
|
||||
) -> None:
|
||||
"""
|
||||
Install the InvokeAI application into the given runtime path
|
||||
@ -168,7 +168,8 @@ class Installer:
|
||||
|
||||
messages.welcome()
|
||||
|
||||
self.dest = Path(root).expanduser().resolve() if yes_to_all else messages.dest_path(root)
|
||||
default_path = os.environ.get("INVOKEAI_ROOT") or Path(root).expanduser().resolve()
|
||||
self.dest = default_path if yes_to_all else messages.dest_path(root)
|
||||
|
||||
# create the venv for the app
|
||||
self.venv = self.app_venv()
|
||||
@ -248,6 +249,9 @@ class InvokeAiInstance:
|
||||
pip[
|
||||
"install",
|
||||
"--require-virtualenv",
|
||||
"numpy~=1.24.0", # choose versions that won't be uninstalled during phase 2
|
||||
"urllib3~=1.26.0",
|
||||
"requests~=2.28.0",
|
||||
"torch~=2.0.0",
|
||||
"torchmetrics==0.11.4",
|
||||
"torchvision>=0.14.1",
|
||||
|
||||
@ -3,6 +3,7 @@ InvokeAI Installer
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import os
|
||||
from pathlib import Path
|
||||
from installer import Installer
|
||||
|
||||
@ -15,7 +16,7 @@ if __name__ == "__main__":
|
||||
dest="root",
|
||||
type=str,
|
||||
help="Destination path for installation",
|
||||
default="~/invokeai",
|
||||
default=os.environ.get("INVOKEAI_ROOT") or "~/invokeai",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-y",
|
||||
|
||||
@ -41,7 +41,7 @@ IF /I "%choice%" == "1" (
|
||||
python .venv\Scripts\invokeai-configure.exe --skip-sd-weight --skip-support-models
|
||||
) ELSE IF /I "%choice%" == "7" (
|
||||
echo Running invokeai-configure...
|
||||
python .venv\Scripts\invokeai-configure.exe --yes --default_only
|
||||
python .venv\Scripts\invokeai-configure.exe --yes --skip-sd-weight
|
||||
) ELSE IF /I "%choice%" == "8" (
|
||||
echo Developer Console
|
||||
echo Python command is:
|
||||
|
||||
@ -82,7 +82,7 @@ do_choice() {
|
||||
7)
|
||||
clear
|
||||
printf "Re-run the configure script to fix a broken install or to complete a major upgrade\n"
|
||||
invokeai-configure --root ${INVOKEAI_ROOT} --yes --default_only
|
||||
invokeai-configure --root ${INVOKEAI_ROOT} --yes --default_only --skip-sd-weights
|
||||
;;
|
||||
8)
|
||||
clear
|
||||
|
||||
@ -4,6 +4,8 @@ from typing import Literal
|
||||
|
||||
from pydantic import Field
|
||||
|
||||
from invokeai.app.invocations.prompt import PromptOutput
|
||||
|
||||
from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
|
||||
from .math import FloatOutput, IntOutput
|
||||
|
||||
@ -64,3 +66,18 @@ class ParamStringInvocation(BaseInvocation):
|
||||
|
||||
def invoke(self, context: InvocationContext) -> StringOutput:
|
||||
return StringOutput(text=self.text)
|
||||
|
||||
|
||||
class ParamPromptInvocation(BaseInvocation):
|
||||
"""A prompt input parameter"""
|
||||
|
||||
type: Literal["param_prompt"] = "param_prompt"
|
||||
prompt: str = Field(default="", description="The prompt value")
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {"tags": ["param", "prompt"], "title": "Prompt"},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> PromptOutput:
|
||||
return PromptOutput(prompt=self.prompt)
|
||||
|
||||
@ -171,7 +171,6 @@ from pydantic import BaseSettings, Field, parse_obj_as
|
||||
from typing import ClassVar, Dict, List, Set, Literal, Union, get_origin, get_type_hints, get_args
|
||||
|
||||
INIT_FILE = Path("invokeai.yaml")
|
||||
MODEL_CORE = Path("models/core")
|
||||
DB_FILE = Path("invokeai.db")
|
||||
LEGACY_INIT_FILE = Path("invokeai.init")
|
||||
|
||||
@ -275,7 +274,7 @@ class InvokeAISettings(BaseSettings):
|
||||
@classmethod
|
||||
def _excluded(self) -> List[str]:
|
||||
# internal fields that shouldn't be exposed as command line options
|
||||
return ["type", "initconf"]
|
||||
return ["type", "initconf", "cached_root"]
|
||||
|
||||
@classmethod
|
||||
def _excluded_from_yaml(self) -> List[str]:
|
||||
@ -291,6 +290,7 @@ class InvokeAISettings(BaseSettings):
|
||||
"restore",
|
||||
"root",
|
||||
"nsfw_checker",
|
||||
"cached_root",
|
||||
]
|
||||
|
||||
class Config:
|
||||
@ -357,7 +357,7 @@ def _find_root() -> Path:
|
||||
venv = Path(os.environ.get("VIRTUAL_ENV") or ".")
|
||||
if os.environ.get("INVOKEAI_ROOT"):
|
||||
root = Path(os.environ.get("INVOKEAI_ROOT")).resolve()
|
||||
elif any([(venv.parent / x).exists() for x in [INIT_FILE, LEGACY_INIT_FILE, MODEL_CORE]]):
|
||||
elif any([(venv.parent / x).exists() for x in [INIT_FILE, LEGACY_INIT_FILE]]):
|
||||
root = (venv.parent).resolve()
|
||||
else:
|
||||
root = Path("~/invokeai").expanduser().resolve()
|
||||
@ -424,6 +424,7 @@ class InvokeAIAppConfig(InvokeAISettings):
|
||||
log_level : Literal[tuple(["debug","info","warning","error","critical"])] = Field(default="info", description="Emit logging messages at this level or higher", category="Logging")
|
||||
|
||||
version : bool = Field(default=False, description="Show InvokeAI version and exit", category="Other")
|
||||
cached_root : Path = Field(default=None, description="internal use only", category="DEPRECATED")
|
||||
# fmt: on
|
||||
|
||||
def parse_args(self, argv: List[str] = None, conf: DictConfig = None, clobber=False):
|
||||
@ -471,10 +472,15 @@ class InvokeAIAppConfig(InvokeAISettings):
|
||||
"""
|
||||
Path to the runtime root directory
|
||||
"""
|
||||
if self.root:
|
||||
return Path(self.root).expanduser().absolute()
|
||||
# we cache value of root to protect against it being '.' and the cwd changing
|
||||
if self.cached_root:
|
||||
root = self.cached_root
|
||||
elif self.root:
|
||||
root = Path(self.root).expanduser().absolute()
|
||||
else:
|
||||
return self.find_root()
|
||||
root = self.find_root()
|
||||
self.cached_root = root
|
||||
return self.cached_root
|
||||
|
||||
@property
|
||||
def root_dir(self) -> Path:
|
||||
|
||||
@ -181,7 +181,7 @@ def download_with_progress_bar(model_url: str, model_dest: str, label: str = "th
|
||||
|
||||
|
||||
def download_conversion_models():
|
||||
target_dir = config.root_path / "models/core/convert"
|
||||
target_dir = config.models_path / "core/convert"
|
||||
kwargs = dict() # for future use
|
||||
try:
|
||||
logger.info("Downloading core tokenizers and text encoders")
|
||||
|
||||
@ -7,7 +7,7 @@ import warnings
|
||||
from dataclasses import dataclass, field
|
||||
from pathlib import Path
|
||||
from tempfile import TemporaryDirectory
|
||||
from typing import List, Dict, Callable, Union, Set
|
||||
from typing import List, Dict, Callable, Union, Set, Optional
|
||||
|
||||
import requests
|
||||
from diffusers import DiffusionPipeline
|
||||
@ -129,7 +129,9 @@ class ModelInstall(object):
|
||||
model_dict[key] = ModelLoadInfo(**value)
|
||||
|
||||
# supplement with entries in models.yaml
|
||||
installed_models = self.mgr.list_models()
|
||||
installed_models = [x for x in self.mgr.list_models()]
|
||||
# suppresses autoloaded models
|
||||
# installed_models = [x for x in self.mgr.list_models() if not self._is_autoloaded(x)]
|
||||
|
||||
for md in installed_models:
|
||||
base = md["base_model"]
|
||||
@ -148,6 +150,17 @@ class ModelInstall(object):
|
||||
)
|
||||
return {x: model_dict[x] for x in sorted(model_dict.keys(), key=lambda y: model_dict[y].name.lower())}
|
||||
|
||||
def _is_autoloaded(self, model_info: dict) -> bool:
|
||||
path = model_info.get("path")
|
||||
if not path:
|
||||
return False
|
||||
for autodir in ["autoimport_dir", "lora_dir", "embedding_dir", "controlnet_dir"]:
|
||||
if autodir_path := getattr(self.config, autodir):
|
||||
autodir_path = self.config.root_path / autodir_path
|
||||
if Path(path).is_relative_to(autodir_path):
|
||||
return True
|
||||
return False
|
||||
|
||||
def list_models(self, model_type):
|
||||
installed = self.mgr.list_models(model_type=model_type)
|
||||
print(f"Installed models of type `{model_type}`:")
|
||||
@ -274,6 +287,7 @@ class ModelInstall(object):
|
||||
logger.error(f"Unable to download {url}. Skipping.")
|
||||
info = ModelProbe().heuristic_probe(location)
|
||||
dest = self.config.models_path / info.base_type.value / info.model_type.value / location.name
|
||||
dest.parent.mkdir(parents=True, exist_ok=True)
|
||||
models_path = shutil.move(location, dest)
|
||||
|
||||
# staged version will be garbage-collected at this time
|
||||
@ -349,7 +363,7 @@ class ModelInstall(object):
|
||||
if key in self.datasets:
|
||||
description = self.datasets[key].get("description") or description
|
||||
|
||||
rel_path = self.relative_to_root(path)
|
||||
rel_path = self.relative_to_root(path, self.config.models_path)
|
||||
|
||||
attributes = dict(
|
||||
path=str(rel_path),
|
||||
@ -389,8 +403,8 @@ class ModelInstall(object):
|
||||
attributes.update(dict(config=str(legacy_conf)))
|
||||
return attributes
|
||||
|
||||
def relative_to_root(self, path: Path) -> Path:
|
||||
root = self.config.root_path
|
||||
def relative_to_root(self, path: Path, root: Optional[Path] = None) -> Path:
|
||||
root = root or self.config.root_path
|
||||
if path.is_relative_to(root):
|
||||
return path.relative_to(root)
|
||||
else:
|
||||
|
||||
@ -63,7 +63,7 @@ from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionS
|
||||
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
|
||||
|
||||
from invokeai.backend.util.logging import InvokeAILogger
|
||||
from invokeai.app.services.config import InvokeAIAppConfig, MODEL_CORE
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
|
||||
from picklescan.scanner import scan_file_path
|
||||
from .models import BaseModelType, ModelVariantType
|
||||
@ -81,7 +81,7 @@ if is_accelerate_available():
|
||||
from accelerate.utils import set_module_tensor_to_device
|
||||
|
||||
logger = InvokeAILogger.getLogger(__name__)
|
||||
CONVERT_MODEL_ROOT = InvokeAIAppConfig.get_config().root_path / MODEL_CORE / "convert"
|
||||
CONVERT_MODEL_ROOT = InvokeAIAppConfig.get_config().models_path / "core/convert"
|
||||
|
||||
|
||||
def shave_segments(path, n_shave_prefix_segments=1):
|
||||
@ -1070,7 +1070,7 @@ def convert_controlnet_checkpoint(
|
||||
extract_ema,
|
||||
use_linear_projection=None,
|
||||
cross_attention_dim=None,
|
||||
precision: torch.dtype = torch.float32,
|
||||
precision: Optional[torch.dtype] = None,
|
||||
):
|
||||
ctrlnet_config = create_unet_diffusers_config(original_config, image_size=image_size, controlnet=True)
|
||||
ctrlnet_config["upcast_attention"] = upcast_attention
|
||||
@ -1111,7 +1111,6 @@ def convert_controlnet_checkpoint(
|
||||
return controlnet.to(precision)
|
||||
|
||||
|
||||
# TO DO - PASS PRECISION
|
||||
def download_from_original_stable_diffusion_ckpt(
|
||||
checkpoint_path: str,
|
||||
model_version: BaseModelType,
|
||||
@ -1121,7 +1120,7 @@ def download_from_original_stable_diffusion_ckpt(
|
||||
prediction_type: str = None,
|
||||
model_type: str = None,
|
||||
extract_ema: bool = False,
|
||||
precision: torch.dtype = torch.float32,
|
||||
precision: Optional[torch.dtype] = None,
|
||||
scheduler_type: str = "pndm",
|
||||
num_in_channels: Optional[int] = None,
|
||||
upcast_attention: Optional[bool] = None,
|
||||
@ -1194,6 +1193,8 @@ def download_from_original_stable_diffusion_ckpt(
|
||||
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer)
|
||||
to use. If this parameter is `None`, the function will load a new instance of [CLIPTokenizer] by itself, if
|
||||
needed.
|
||||
precision (`torch.dtype`, *optional*, defauts to `None`):
|
||||
If not provided the precision will be set to the precision of the original file.
|
||||
return: A StableDiffusionPipeline object representing the passed-in `.ckpt`/`.safetensors` file.
|
||||
"""
|
||||
|
||||
@ -1252,6 +1253,10 @@ def download_from_original_stable_diffusion_ckpt(
|
||||
|
||||
logger.debug(f"model_type = {model_type}; original_config_file = {original_config_file}")
|
||||
|
||||
precision_probing_key = "model.diffusion_model.input_blocks.0.0.bias"
|
||||
logger.debug(f"original checkpoint precision == {checkpoint[precision_probing_key].dtype}")
|
||||
precision = precision or checkpoint[precision_probing_key].dtype
|
||||
|
||||
if original_config_file is None:
|
||||
key_name_v2_1 = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
|
||||
key_name_sd_xl_base = "conditioner.embedders.1.model.transformer.resblocks.9.mlp.c_proj.bias"
|
||||
@ -1279,9 +1284,12 @@ def download_from_original_stable_diffusion_ckpt(
|
||||
original_config_file = BytesIO(requests.get(config_url).content)
|
||||
|
||||
original_config = OmegaConf.load(original_config_file)
|
||||
if original_config["model"]["params"].get("use_ema") is not None:
|
||||
extract_ema = original_config["model"]["params"]["use_ema"]
|
||||
|
||||
if (
|
||||
model_version == BaseModelType.StableDiffusion2
|
||||
and original_config["model"]["params"]["parameterization"] == "v"
|
||||
and original_config["model"]["params"].get("parameterization") == "v"
|
||||
):
|
||||
prediction_type = "v_prediction"
|
||||
upcast_attention = True
|
||||
@ -1447,7 +1455,7 @@ def download_from_original_stable_diffusion_ckpt(
|
||||
if controlnet:
|
||||
pipe = pipeline_class(
|
||||
vae=vae.to(precision),
|
||||
text_encoder=text_model,
|
||||
text_encoder=text_model.to(precision),
|
||||
tokenizer=tokenizer,
|
||||
unet=unet.to(precision),
|
||||
scheduler=scheduler,
|
||||
@ -1459,7 +1467,7 @@ def download_from_original_stable_diffusion_ckpt(
|
||||
else:
|
||||
pipe = pipeline_class(
|
||||
vae=vae.to(precision),
|
||||
text_encoder=text_model,
|
||||
text_encoder=text_model.to(precision),
|
||||
tokenizer=tokenizer,
|
||||
unet=unet.to(precision),
|
||||
scheduler=scheduler,
|
||||
@ -1484,8 +1492,8 @@ def download_from_original_stable_diffusion_ckpt(
|
||||
image_noising_scheduler=image_noising_scheduler,
|
||||
# regular denoising components
|
||||
tokenizer=tokenizer,
|
||||
text_encoder=text_model,
|
||||
unet=unet,
|
||||
text_encoder=text_model.to(precision),
|
||||
unet=unet.to(precision),
|
||||
scheduler=scheduler,
|
||||
# vae
|
||||
vae=vae,
|
||||
@ -1560,7 +1568,7 @@ def download_from_original_stable_diffusion_ckpt(
|
||||
if controlnet:
|
||||
pipe = pipeline_class(
|
||||
vae=vae.to(precision),
|
||||
text_encoder=text_model,
|
||||
text_encoder=text_model.to(precision),
|
||||
tokenizer=tokenizer,
|
||||
unet=unet.to(precision),
|
||||
controlnet=controlnet,
|
||||
@ -1571,7 +1579,7 @@ def download_from_original_stable_diffusion_ckpt(
|
||||
else:
|
||||
pipe = pipeline_class(
|
||||
vae=vae.to(precision),
|
||||
text_encoder=text_model,
|
||||
text_encoder=text_model.to(precision),
|
||||
tokenizer=tokenizer,
|
||||
unet=unet.to(precision),
|
||||
scheduler=scheduler,
|
||||
@ -1594,9 +1602,9 @@ def download_from_original_stable_diffusion_ckpt(
|
||||
|
||||
pipe = StableDiffusionXLPipeline(
|
||||
vae=vae.to(precision),
|
||||
text_encoder=text_encoder,
|
||||
text_encoder=text_encoder.to(precision),
|
||||
tokenizer=tokenizer,
|
||||
text_encoder_2=text_encoder_2,
|
||||
text_encoder_2=text_encoder_2.to(precision),
|
||||
tokenizer_2=tokenizer_2,
|
||||
unet=unet.to(precision),
|
||||
scheduler=scheduler,
|
||||
@ -1639,7 +1647,7 @@ def download_controlnet_from_original_ckpt(
|
||||
original_config_file: str,
|
||||
image_size: int = 512,
|
||||
extract_ema: bool = False,
|
||||
precision: torch.dtype = torch.float32,
|
||||
precision: Optional[torch.dtype] = None,
|
||||
num_in_channels: Optional[int] = None,
|
||||
upcast_attention: Optional[bool] = None,
|
||||
device: str = None,
|
||||
@ -1680,6 +1688,12 @@ def download_controlnet_from_original_ckpt(
|
||||
while "state_dict" in checkpoint:
|
||||
checkpoint = checkpoint["state_dict"]
|
||||
|
||||
# use original precision
|
||||
precision_probing_key = "input_blocks.0.0.bias"
|
||||
ckpt_precision = checkpoint[precision_probing_key].dtype
|
||||
logger.debug(f"original controlnet precision = {ckpt_precision}")
|
||||
precision = precision or ckpt_precision
|
||||
|
||||
original_config = OmegaConf.load(original_config_file)
|
||||
|
||||
if num_in_channels is not None:
|
||||
@ -1699,7 +1713,7 @@ def download_controlnet_from_original_ckpt(
|
||||
cross_attention_dim=cross_attention_dim,
|
||||
)
|
||||
|
||||
return controlnet
|
||||
return controlnet.to(precision)
|
||||
|
||||
|
||||
def convert_ldm_vae_to_diffusers(checkpoint, vae_config: DictConfig, image_size: int) -> AutoencoderKL:
|
||||
|
||||
@ -187,7 +187,9 @@ class ModelCache(object):
|
||||
# TODO: lock for no copies on simultaneous calls?
|
||||
cache_entry = self._cached_models.get(key, None)
|
||||
if cache_entry is None:
|
||||
self.logger.info(f"Loading model {model_path}, type {base_model}:{model_type}:{submodel}")
|
||||
self.logger.info(
|
||||
f"Loading model {model_path}, type {base_model.value}:{model_type.value}:{submodel.value if submodel else ''}"
|
||||
)
|
||||
|
||||
# this will remove older cached models until
|
||||
# there is sufficient room to load the requested model
|
||||
|
||||
@ -423,7 +423,7 @@ class ModelManager(object):
|
||||
return (model_name, base_model, model_type)
|
||||
|
||||
def _get_model_cache_path(self, model_path):
|
||||
return self.app_config.models_path / ".cache" / hashlib.md5(str(model_path).encode()).hexdigest()
|
||||
return self.resolve_model_path(Path(".cache") / hashlib.md5(str(model_path).encode()).hexdigest())
|
||||
|
||||
@classmethod
|
||||
def initialize_model_config(cls, config_path: Path):
|
||||
@ -456,7 +456,7 @@ class ModelManager(object):
|
||||
raise ModelNotFoundException(f"Model not found - {model_key}")
|
||||
|
||||
model_config = self.models[model_key]
|
||||
model_path = self.app_config.root_path / model_config.path
|
||||
model_path = self.resolve_model_path(model_config.path)
|
||||
|
||||
if not model_path.exists():
|
||||
if model_class.save_to_config:
|
||||
@ -586,7 +586,7 @@ class ModelManager(object):
|
||||
|
||||
# expose paths as absolute to help web UI
|
||||
if path := model_dict.get("path"):
|
||||
model_dict["path"] = str(self.app_config.root_path / path)
|
||||
model_dict["path"] = str(self.resolve_model_path(path))
|
||||
models.append(model_dict)
|
||||
|
||||
return models
|
||||
@ -623,7 +623,7 @@ class ModelManager(object):
|
||||
self.cache.uncache_model(cache_id)
|
||||
|
||||
# if model inside invoke models folder - delete files
|
||||
model_path = self.app_config.root_path / model_cfg.path
|
||||
model_path = self.resolve_model_path(model_cfg.path)
|
||||
cache_path = self._get_model_cache_path(model_path)
|
||||
if cache_path.exists():
|
||||
rmtree(str(cache_path))
|
||||
@ -654,10 +654,9 @@ class ModelManager(object):
|
||||
The returned dict has the same format as the dict returned by
|
||||
model_info().
|
||||
"""
|
||||
# relativize paths as they go in - this makes it easier to move the root directory around
|
||||
# relativize paths as they go in - this makes it easier to move the models directory around
|
||||
if path := model_attributes.get("path"):
|
||||
if Path(path).is_relative_to(self.app_config.root_path):
|
||||
model_attributes["path"] = str(Path(path).relative_to(self.app_config.root_path))
|
||||
model_attributes["path"] = str(self.relative_model_path(Path(path)))
|
||||
|
||||
model_class = MODEL_CLASSES[base_model][model_type]
|
||||
model_config = model_class.create_config(**model_attributes)
|
||||
@ -715,7 +714,7 @@ class ModelManager(object):
|
||||
if not model_cfg:
|
||||
raise ModelNotFoundException(f"Unknown model: {model_key}")
|
||||
|
||||
old_path = self.app_config.root_path / model_cfg.path
|
||||
old_path = self.resolve_model_path(model_cfg.path)
|
||||
new_name = new_name or model_name
|
||||
new_base = new_base or base_model
|
||||
new_key = self.create_key(new_name, new_base, model_type)
|
||||
@ -724,15 +723,15 @@ class ModelManager(object):
|
||||
|
||||
# if this is a model file/directory that we manage ourselves, we need to move it
|
||||
if old_path.is_relative_to(self.app_config.models_path):
|
||||
new_path = (
|
||||
self.app_config.root_path
|
||||
/ "models"
|
||||
/ BaseModelType(new_base).value
|
||||
/ ModelType(model_type).value
|
||||
/ new_name
|
||||
new_path = self.resolve_model_path(
|
||||
Path(
|
||||
BaseModelType(new_base).value,
|
||||
ModelType(model_type).value,
|
||||
new_name,
|
||||
)
|
||||
)
|
||||
move(old_path, new_path)
|
||||
model_cfg.path = str(new_path.relative_to(self.app_config.root_path))
|
||||
model_cfg.path = str(new_path.relative_to(self.app_config.models_path))
|
||||
|
||||
# clean up caches
|
||||
old_model_cache = self._get_model_cache_path(old_path)
|
||||
@ -782,7 +781,7 @@ class ModelManager(object):
|
||||
**submodel,
|
||||
)
|
||||
checkpoint_path = self.app_config.root_path / info["path"]
|
||||
old_diffusers_path = self.app_config.models_path / model.location
|
||||
old_diffusers_path = self.resolve_model_path(model.location)
|
||||
new_diffusers_path = (
|
||||
dest_directory or self.app_config.models_path / base_model.value / model_type.value
|
||||
) / model_name
|
||||
@ -795,7 +794,7 @@ class ModelManager(object):
|
||||
info["path"] = (
|
||||
str(new_diffusers_path)
|
||||
if dest_directory
|
||||
else str(new_diffusers_path.relative_to(self.app_config.root_path))
|
||||
else str(new_diffusers_path.relative_to(self.app_config.models_path))
|
||||
)
|
||||
info.pop("config")
|
||||
|
||||
@ -810,6 +809,15 @@ class ModelManager(object):
|
||||
|
||||
return result
|
||||
|
||||
def resolve_model_path(self, path: Union[Path, str]) -> Path:
|
||||
"""return relative paths based on configured models_path"""
|
||||
return self.app_config.models_path / path
|
||||
|
||||
def relative_model_path(self, model_path: Path) -> Path:
|
||||
if model_path.is_relative_to(self.app_config.models_path):
|
||||
model_path = model_path.relative_to(self.app_config.models_path)
|
||||
return model_path
|
||||
|
||||
def search_models(self, search_folder):
|
||||
self.logger.info(f"Finding Models In: {search_folder}")
|
||||
models_folder_ckpt = Path(search_folder).glob("**/*.ckpt")
|
||||
@ -883,10 +891,17 @@ class ModelManager(object):
|
||||
new_models_found = False
|
||||
|
||||
self.logger.info(f"Scanning {self.app_config.models_path} for new models")
|
||||
with Chdir(self.app_config.root_path):
|
||||
with Chdir(self.app_config.models_path):
|
||||
for model_key, model_config in list(self.models.items()):
|
||||
model_name, cur_base_model, cur_model_type = self.parse_key(model_key)
|
||||
model_path = self.app_config.root_path.absolute() / model_config.path
|
||||
|
||||
# Patch for relative path bug in older models.yaml - paths should not
|
||||
# be starting with a hard-coded 'models'. This will also fix up
|
||||
# models.yaml when committed.
|
||||
if model_config.path.startswith("models"):
|
||||
model_config.path = str(Path(*Path(model_config.path).parts[1:]))
|
||||
|
||||
model_path = self.resolve_model_path(model_config.path).absolute()
|
||||
if not model_path.exists():
|
||||
model_class = MODEL_CLASSES[cur_base_model][cur_model_type]
|
||||
if model_class.save_to_config:
|
||||
@ -905,7 +920,7 @@ class ModelManager(object):
|
||||
if model_type is not None and cur_model_type != model_type:
|
||||
continue
|
||||
model_class = MODEL_CLASSES[cur_base_model][cur_model_type]
|
||||
models_dir = self.app_config.models_path / cur_base_model.value / cur_model_type.value
|
||||
models_dir = self.resolve_model_path(Path(cur_base_model.value, cur_model_type.value))
|
||||
|
||||
if not models_dir.exists():
|
||||
continue # TODO: or create all folders?
|
||||
@ -919,9 +934,7 @@ class ModelManager(object):
|
||||
if model_key in self.models:
|
||||
raise DuplicateModelException(f"Model with key {model_key} added twice")
|
||||
|
||||
if model_path.is_relative_to(self.app_config.root_path):
|
||||
model_path = model_path.relative_to(self.app_config.root_path)
|
||||
|
||||
model_path = self.relative_model_path(model_path)
|
||||
model_config: ModelConfigBase = model_class.probe_config(str(model_path))
|
||||
self.models[model_key] = model_config
|
||||
new_models_found = True
|
||||
@ -932,12 +945,11 @@ class ModelManager(object):
|
||||
except NotImplementedError as e:
|
||||
self.logger.warning(e)
|
||||
|
||||
imported_models = self.autoimport()
|
||||
|
||||
imported_models = self.scan_autoimport_directory()
|
||||
if (new_models_found or imported_models) and self.config_path:
|
||||
self.commit()
|
||||
|
||||
def autoimport(self) -> Dict[str, AddModelResult]:
|
||||
def scan_autoimport_directory(self) -> Dict[str, AddModelResult]:
|
||||
"""
|
||||
Scan the autoimport directory (if defined) and import new models, delete defunct models.
|
||||
"""
|
||||
@ -971,7 +983,7 @@ class ModelManager(object):
|
||||
# LS: hacky
|
||||
# Patch in the SD VAE from core so that it is available for use by the UI
|
||||
try:
|
||||
self.heuristic_import({config.root_path / "models/core/convert/sd-vae-ft-mse"})
|
||||
self.heuristic_import({self.resolve_model_path("core/convert/sd-vae-ft-mse")})
|
||||
except:
|
||||
pass
|
||||
|
||||
|
||||
@ -17,6 +17,7 @@ from .base import (
|
||||
ModelNotFoundException,
|
||||
)
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
import invokeai.backend.util.logging as logger
|
||||
|
||||
|
||||
class ControlNetModelFormat(str, Enum):
|
||||
@ -66,7 +67,7 @@ class ControlNetModel(ModelBase):
|
||||
child_type: Optional[SubModelType] = None,
|
||||
):
|
||||
if child_type is not None:
|
||||
raise Exception("There is no child models in controlnet model")
|
||||
raise Exception("There are no child models in controlnet model")
|
||||
|
||||
model = None
|
||||
for variant in ["fp16", None]:
|
||||
@ -124,9 +125,7 @@ class ControlNetModel(ModelBase):
|
||||
return model_path
|
||||
|
||||
|
||||
@classmethod
|
||||
def _convert_controlnet_ckpt_and_cache(
|
||||
cls,
|
||||
model_path: str,
|
||||
output_path: str,
|
||||
base_model: BaseModelType,
|
||||
@ -141,6 +140,7 @@ def _convert_controlnet_ckpt_and_cache(
|
||||
weights = app_config.root_path / model_path
|
||||
output_path = Path(output_path)
|
||||
|
||||
logger.info(f"Converting {weights} to diffusers format")
|
||||
# return cached version if it exists
|
||||
if output_path.exists():
|
||||
return output_path
|
||||
|
||||
@ -123,6 +123,7 @@ class StableDiffusion1Model(DiffusersModel):
|
||||
return _convert_ckpt_and_cache(
|
||||
version=BaseModelType.StableDiffusion1,
|
||||
model_config=config,
|
||||
load_safety_checker=False,
|
||||
output_path=output_path,
|
||||
)
|
||||
else:
|
||||
@ -259,7 +260,7 @@ def _convert_ckpt_and_cache(
|
||||
"""
|
||||
app_config = InvokeAIAppConfig.get_config()
|
||||
|
||||
weights = app_config.root_path / model_config.path
|
||||
weights = app_config.models_path / model_config.path
|
||||
config_file = app_config.root_path / model_config.config
|
||||
output_path = Path(output_path)
|
||||
|
||||
|
||||
@ -112,7 +112,7 @@ def main():
|
||||
|
||||
extras = get_extras()
|
||||
|
||||
print(f":crossed_fingers: Upgrading to [yellow]{tag if tag else release}[/yellow]")
|
||||
print(f":crossed_fingers: Upgrading to [yellow]{tag or release or branch}[/yellow]")
|
||||
if release:
|
||||
cmd = f'pip install "invokeai{extras} @ {INVOKE_AI_SRC}/{release}.zip" --use-pep517 --upgrade'
|
||||
elif tag:
|
||||
|
||||
@ -58,6 +58,9 @@ logger = InvokeAILogger.getLogger()
|
||||
# from https://stackoverflow.com/questions/92438/stripping-non-printable-characters-from-a-string-in-python
|
||||
NOPRINT_TRANS_TABLE = {i: None for i in range(0, sys.maxunicode + 1) if not chr(i).isprintable()}
|
||||
|
||||
# maximum number of installed models we can display before overflowing vertically
|
||||
MAX_OTHER_MODELS = 72
|
||||
|
||||
|
||||
def make_printable(s: str) -> str:
|
||||
"""Replace non-printable characters in a string"""
|
||||
@ -102,7 +105,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
|
||||
SingleSelectColumns,
|
||||
values=[
|
||||
"STARTER MODELS",
|
||||
"MORE MODELS",
|
||||
"MAIN MODELS",
|
||||
"CONTROLNETS",
|
||||
"LORA/LYCORIS",
|
||||
"TEXTUAL INVERSION",
|
||||
@ -153,7 +156,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
|
||||
BufferBox,
|
||||
name="Log Messages",
|
||||
editable=False,
|
||||
max_height=8,
|
||||
max_height=15,
|
||||
)
|
||||
|
||||
self.nextrely += 1
|
||||
@ -253,6 +256,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
|
||||
model_labels = [self.model_labels[x] for x in model_list]
|
||||
|
||||
show_recommended = len(self.installed_models) == 0
|
||||
truncated = False
|
||||
if len(model_list) > 0:
|
||||
max_width = max([len(x) for x in model_labels])
|
||||
columns = window_width // (max_width + 8) # 8 characters for "[x] " and padding
|
||||
@ -271,6 +275,10 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
|
||||
)
|
||||
)
|
||||
|
||||
if len(model_labels) > MAX_OTHER_MODELS:
|
||||
model_labels = model_labels[0:MAX_OTHER_MODELS]
|
||||
truncated = True
|
||||
|
||||
widgets.update(
|
||||
models_selected=self.add_widget_intelligent(
|
||||
MultiSelectColumns,
|
||||
@ -289,6 +297,16 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
|
||||
models=model_list,
|
||||
)
|
||||
|
||||
if truncated:
|
||||
widgets.update(
|
||||
warning_message=self.add_widget_intelligent(
|
||||
npyscreen.FixedText,
|
||||
value=f"Too many models to display (max={MAX_OTHER_MODELS}). Some are not displayed.",
|
||||
editable=False,
|
||||
color="CAUTION",
|
||||
)
|
||||
)
|
||||
|
||||
self.nextrely += 1
|
||||
widgets.update(
|
||||
download_ids=self.add_widget_intelligent(
|
||||
@ -313,7 +331,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
|
||||
widgets = self.add_model_widgets(
|
||||
model_type=model_type,
|
||||
window_width=window_width,
|
||||
install_prompt=f"Additional {model_type.value.title()} models already installed.",
|
||||
install_prompt=f"Installed {model_type.value.title()} models. Unchecked models in the InvokeAI root directory will be deleted. Enter URLs, paths or repo_ids to import.",
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
@ -399,7 +417,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
|
||||
self.ok_button.hidden = True
|
||||
self.display()
|
||||
|
||||
# for communication with the subprocess
|
||||
# TO DO: Spawn a worker thread, not a subprocess
|
||||
parent_conn, child_conn = Pipe()
|
||||
p = Process(
|
||||
target=process_and_execute,
|
||||
@ -414,7 +432,6 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
|
||||
self.subprocess_connection = parent_conn
|
||||
self.subprocess = p
|
||||
app.install_selections = InstallSelections()
|
||||
# process_and_execute(app.opt, app.install_selections)
|
||||
|
||||
def on_back(self):
|
||||
self.parentApp.switchFormPrevious()
|
||||
@ -489,8 +506,6 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
|
||||
|
||||
# rebuild the form, saving and restoring some of the fields that need to be preserved.
|
||||
saved_messages = self.monitor.entry_widget.values
|
||||
# autoload_dir = str(config.root_path / self.pipeline_models['autoload_directory'].value)
|
||||
# autoscan = self.pipeline_models['autoscan_on_startup'].value
|
||||
|
||||
app.main_form = app.addForm(
|
||||
"MAIN",
|
||||
@ -544,12 +559,6 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
|
||||
if downloads := section.get("download_ids"):
|
||||
selections.install_models.extend(downloads.value.split())
|
||||
|
||||
# load directory and whether to scan on startup
|
||||
# if self.parentApp.autoload_pending:
|
||||
# selections.scan_directory = str(config.root_path / self.pipeline_models['autoload_directory'].value)
|
||||
# self.parentApp.autoload_pending = False
|
||||
# selections.autoscan_on_startup = self.pipeline_models['autoscan_on_startup'].value
|
||||
|
||||
|
||||
class AddModelApplication(npyscreen.NPSAppManaged):
|
||||
def __init__(self, opt):
|
||||
@ -639,6 +648,11 @@ def process_and_execute(
|
||||
selections: InstallSelections,
|
||||
conn_out: Connection = None,
|
||||
):
|
||||
# need to reinitialize config in subprocess
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
args = ["--root", opt.root] if opt.root else []
|
||||
config.parse_args(args)
|
||||
|
||||
# set up so that stderr is sent to conn_out
|
||||
if conn_out:
|
||||
translator = StderrToMessage(conn_out)
|
||||
@ -656,38 +670,11 @@ def process_and_execute(
|
||||
conn_out.close()
|
||||
|
||||
|
||||
def do_listings(opt) -> bool:
|
||||
"""List installed models of various sorts, and return
|
||||
True if any were requested."""
|
||||
model_manager = ModelManager(config.model_conf_path)
|
||||
if opt.list_models == "diffusers":
|
||||
print("Diffuser models:")
|
||||
model_manager.print_models()
|
||||
elif opt.list_models == "controlnets":
|
||||
print("Installed Controlnet Models:")
|
||||
cnm = model_manager.list_controlnet_models()
|
||||
print(textwrap.indent("\n".join([x for x in cnm if cnm[x]]), prefix=" "))
|
||||
elif opt.list_models == "loras":
|
||||
print("Installed LoRA/LyCORIS Models:")
|
||||
cnm = model_manager.list_lora_models()
|
||||
print(textwrap.indent("\n".join([x for x in cnm if cnm[x]]), prefix=" "))
|
||||
elif opt.list_models == "tis":
|
||||
print("Installed Textual Inversion Embeddings:")
|
||||
cnm = model_manager.list_ti_models()
|
||||
print(textwrap.indent("\n".join([x for x in cnm if cnm[x]]), prefix=" "))
|
||||
else:
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
# --------------------------------------------------------
|
||||
def select_and_download_models(opt: Namespace):
|
||||
precision = "float32" if opt.full_precision else choose_precision(torch.device(choose_torch_device()))
|
||||
config.precision = precision
|
||||
helper = lambda x: ask_user_for_prediction_type(x)
|
||||
# if do_listings(opt):
|
||||
# pass
|
||||
|
||||
installer = ModelInstall(config, prediction_type_helper=helper)
|
||||
if opt.list_models:
|
||||
installer.list_models(opt.list_models)
|
||||
@ -706,8 +693,6 @@ def select_and_download_models(opt: Namespace):
|
||||
# needed to support the probe() method running under a subprocess
|
||||
torch.multiprocessing.set_start_method("spawn")
|
||||
|
||||
# the third argument is needed in the Windows 11 environment in
|
||||
# order to launch and resize a console window running this program
|
||||
set_min_terminal_size(MIN_COLS, MIN_LINES)
|
||||
installApp = AddModelApplication(opt)
|
||||
try:
|
||||
|
||||
@ -320,7 +320,7 @@ class mergeModelsForm(npyscreen.FormMultiPageAction):
|
||||
|
||||
def get_model_names(self, base_model: BaseModelType = None) -> List[str]:
|
||||
model_names = [
|
||||
info["name"]
|
||||
info["model_name"]
|
||||
for info in self.model_manager.list_models(model_type=ModelType.Main, base_model=base_model)
|
||||
if info["model_format"] == "diffusers"
|
||||
]
|
||||
|
||||
169
invokeai/frontend/web/dist/assets/App-d6f88f50.js
vendored
169
invokeai/frontend/web/dist/assets/App-d6f88f50.js
vendored
File diff suppressed because one or more lines are too long
169
invokeai/frontend/web/dist/assets/App-ea7b7298.js
vendored
Normal file
169
invokeai/frontend/web/dist/assets/App-ea7b7298.js
vendored
Normal file
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
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-bad7ff83.js"></script>
|
||||
<script type="module" crossorigin src="./assets/index-9bb68e3a.js"></script>
|
||||
</head>
|
||||
|
||||
<body dir="ltr">
|
||||
|
||||
@ -139,8 +139,19 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
|
||||
useHotkeys('s', handleUseSeed, [imageDTO]);
|
||||
|
||||
const handleUsePrompt = useCallback(() => {
|
||||
recallBothPrompts(metadata?.positive_prompt, metadata?.negative_prompt);
|
||||
}, [metadata?.negative_prompt, metadata?.positive_prompt, recallBothPrompts]);
|
||||
recallBothPrompts(
|
||||
metadata?.positive_prompt,
|
||||
metadata?.negative_prompt,
|
||||
metadata?.positive_style_prompt,
|
||||
metadata?.negative_style_prompt
|
||||
);
|
||||
}, [
|
||||
metadata?.negative_prompt,
|
||||
metadata?.positive_prompt,
|
||||
metadata?.positive_style_prompt,
|
||||
metadata?.negative_style_prompt,
|
||||
recallBothPrompts,
|
||||
]);
|
||||
|
||||
useHotkeys('p', handleUsePrompt, [imageDTO]);
|
||||
|
||||
|
||||
@ -102,8 +102,19 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
|
||||
|
||||
// Recall parameters handlers
|
||||
const handleRecallPrompt = useCallback(() => {
|
||||
recallBothPrompts(metadata?.positive_prompt, metadata?.negative_prompt);
|
||||
}, [metadata?.negative_prompt, metadata?.positive_prompt, recallBothPrompts]);
|
||||
recallBothPrompts(
|
||||
metadata?.positive_prompt,
|
||||
metadata?.negative_prompt,
|
||||
metadata?.positive_style_prompt,
|
||||
metadata?.negative_style_prompt
|
||||
);
|
||||
}, [
|
||||
metadata?.negative_prompt,
|
||||
metadata?.positive_prompt,
|
||||
metadata?.positive_style_prompt,
|
||||
metadata?.negative_style_prompt,
|
||||
recallBothPrompts,
|
||||
]);
|
||||
|
||||
const handleRecallSeed = useCallback(() => {
|
||||
recallSeed(metadata?.seed);
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
import { Input } from '@chakra-ui/react';
|
||||
import { Input, Textarea } from '@chakra-ui/react';
|
||||
import { useAppDispatch } from 'app/store/storeHooks';
|
||||
import { fieldValueChanged } from 'features/nodes/store/nodesSlice';
|
||||
import {
|
||||
@ -12,10 +12,11 @@ const StringInputFieldComponent = (
|
||||
props: FieldComponentProps<StringInputFieldValue, StringInputFieldTemplate>
|
||||
) => {
|
||||
const { nodeId, field } = props;
|
||||
|
||||
const dispatch = useAppDispatch();
|
||||
|
||||
const handleValueChanged = (e: ChangeEvent<HTMLInputElement>) => {
|
||||
const handleValueChanged = (
|
||||
e: ChangeEvent<HTMLInputElement | HTMLTextAreaElement>
|
||||
) => {
|
||||
dispatch(
|
||||
fieldValueChanged({
|
||||
nodeId,
|
||||
@ -25,7 +26,11 @@ const StringInputFieldComponent = (
|
||||
);
|
||||
};
|
||||
|
||||
return <Input onChange={handleValueChanged} value={field.value}></Input>;
|
||||
return ['prompt', 'style'].includes(field.name.toLowerCase()) ? (
|
||||
<Textarea onChange={handleValueChanged} value={field.value} rows={2} />
|
||||
) : (
|
||||
<Input onChange={handleValueChanged} value={field.value} />
|
||||
);
|
||||
};
|
||||
|
||||
export default memo(StringInputFieldComponent);
|
||||
|
||||
@ -1,5 +1,15 @@
|
||||
import { useAppToaster } from 'app/components/Toaster';
|
||||
import { useAppDispatch } from 'app/store/storeHooks';
|
||||
import {
|
||||
refinerModelChanged,
|
||||
setNegativeStylePromptSDXL,
|
||||
setPositiveStylePromptSDXL,
|
||||
setRefinerAestheticScore,
|
||||
setRefinerCFGScale,
|
||||
setRefinerScheduler,
|
||||
setRefinerStart,
|
||||
setRefinerSteps,
|
||||
} from 'features/sdxl/store/sdxlSlice';
|
||||
import { useCallback } from 'react';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
import { UnsafeImageMetadata } from 'services/api/endpoints/images';
|
||||
@ -22,6 +32,10 @@ import {
|
||||
isValidMainModel,
|
||||
isValidNegativePrompt,
|
||||
isValidPositivePrompt,
|
||||
isValidSDXLNegativeStylePrompt,
|
||||
isValidSDXLPositiveStylePrompt,
|
||||
isValidSDXLRefinerAestheticScore,
|
||||
isValidSDXLRefinerStart,
|
||||
isValidScheduler,
|
||||
isValidSeed,
|
||||
isValidSteps,
|
||||
@ -74,17 +88,34 @@ export const useRecallParameters = () => {
|
||||
* Recall both prompts with toast
|
||||
*/
|
||||
const recallBothPrompts = useCallback(
|
||||
(positivePrompt: unknown, negativePrompt: unknown) => {
|
||||
(
|
||||
positivePrompt: unknown,
|
||||
negativePrompt: unknown,
|
||||
positiveStylePrompt: unknown,
|
||||
negativeStylePrompt: unknown
|
||||
) => {
|
||||
if (
|
||||
isValidPositivePrompt(positivePrompt) ||
|
||||
isValidNegativePrompt(negativePrompt)
|
||||
isValidNegativePrompt(negativePrompt) ||
|
||||
isValidSDXLPositiveStylePrompt(positiveStylePrompt) ||
|
||||
isValidSDXLNegativeStylePrompt(negativeStylePrompt)
|
||||
) {
|
||||
if (isValidPositivePrompt(positivePrompt)) {
|
||||
dispatch(setPositivePrompt(positivePrompt));
|
||||
}
|
||||
|
||||
if (isValidNegativePrompt(negativePrompt)) {
|
||||
dispatch(setNegativePrompt(negativePrompt));
|
||||
}
|
||||
|
||||
if (isValidSDXLPositiveStylePrompt(positiveStylePrompt)) {
|
||||
dispatch(setPositiveStylePromptSDXL(positiveStylePrompt));
|
||||
}
|
||||
|
||||
if (isValidSDXLPositiveStylePrompt(negativeStylePrompt)) {
|
||||
dispatch(setNegativeStylePromptSDXL(negativeStylePrompt));
|
||||
}
|
||||
|
||||
parameterSetToast();
|
||||
return;
|
||||
}
|
||||
@ -123,6 +154,36 @@ export const useRecallParameters = () => {
|
||||
[dispatch, parameterSetToast, parameterNotSetToast]
|
||||
);
|
||||
|
||||
/**
|
||||
* Recall SDXL Positive Style Prompt with toast
|
||||
*/
|
||||
const recallSDXLPositiveStylePrompt = useCallback(
|
||||
(positiveStylePrompt: unknown) => {
|
||||
if (!isValidSDXLPositiveStylePrompt(positiveStylePrompt)) {
|
||||
parameterNotSetToast();
|
||||
return;
|
||||
}
|
||||
dispatch(setPositiveStylePromptSDXL(positiveStylePrompt));
|
||||
parameterSetToast();
|
||||
},
|
||||
[dispatch, parameterSetToast, parameterNotSetToast]
|
||||
);
|
||||
|
||||
/**
|
||||
* Recall SDXL Negative Style Prompt with toast
|
||||
*/
|
||||
const recallSDXLNegativeStylePrompt = useCallback(
|
||||
(negativeStylePrompt: unknown) => {
|
||||
if (!isValidSDXLNegativeStylePrompt(negativeStylePrompt)) {
|
||||
parameterNotSetToast();
|
||||
return;
|
||||
}
|
||||
dispatch(setNegativeStylePromptSDXL(negativeStylePrompt));
|
||||
parameterSetToast();
|
||||
},
|
||||
[dispatch, parameterSetToast, parameterNotSetToast]
|
||||
);
|
||||
|
||||
/**
|
||||
* Recall seed with toast
|
||||
*/
|
||||
@ -271,6 +332,14 @@ export const useRecallParameters = () => {
|
||||
steps,
|
||||
width,
|
||||
strength,
|
||||
positive_style_prompt,
|
||||
negative_style_prompt,
|
||||
refiner_model,
|
||||
refiner_cfg_scale,
|
||||
refiner_steps,
|
||||
refiner_scheduler,
|
||||
refiner_aesthetic_store,
|
||||
refiner_start,
|
||||
} = metadata;
|
||||
|
||||
if (isValidCfgScale(cfg_scale)) {
|
||||
@ -304,6 +373,38 @@ export const useRecallParameters = () => {
|
||||
dispatch(setImg2imgStrength(strength));
|
||||
}
|
||||
|
||||
if (isValidSDXLPositiveStylePrompt(positive_style_prompt)) {
|
||||
dispatch(setPositiveStylePromptSDXL(positive_style_prompt));
|
||||
}
|
||||
|
||||
if (isValidSDXLNegativeStylePrompt(negative_style_prompt)) {
|
||||
dispatch(setNegativeStylePromptSDXL(negative_style_prompt));
|
||||
}
|
||||
|
||||
if (isValidMainModel(refiner_model)) {
|
||||
dispatch(refinerModelChanged(refiner_model));
|
||||
}
|
||||
|
||||
if (isValidSteps(refiner_steps)) {
|
||||
dispatch(setRefinerSteps(refiner_steps));
|
||||
}
|
||||
|
||||
if (isValidCfgScale(refiner_cfg_scale)) {
|
||||
dispatch(setRefinerCFGScale(refiner_cfg_scale));
|
||||
}
|
||||
|
||||
if (isValidScheduler(refiner_scheduler)) {
|
||||
dispatch(setRefinerScheduler(refiner_scheduler));
|
||||
}
|
||||
|
||||
if (isValidSDXLRefinerAestheticScore(refiner_aesthetic_store)) {
|
||||
dispatch(setRefinerAestheticScore(refiner_aesthetic_store));
|
||||
}
|
||||
|
||||
if (isValidSDXLRefinerStart(refiner_start)) {
|
||||
dispatch(setRefinerStart(refiner_start));
|
||||
}
|
||||
|
||||
allParameterSetToast();
|
||||
},
|
||||
[allParameterNotSetToast, allParameterSetToast, dispatch]
|
||||
@ -313,6 +414,8 @@ export const useRecallParameters = () => {
|
||||
recallBothPrompts,
|
||||
recallPositivePrompt,
|
||||
recallNegativePrompt,
|
||||
recallSDXLPositiveStylePrompt,
|
||||
recallSDXLNegativeStylePrompt,
|
||||
recallSeed,
|
||||
recallCfgScale,
|
||||
recallModel,
|
||||
|
||||
@ -324,6 +324,39 @@ export type PrecisionParam = z.infer<typeof zPrecision>;
|
||||
export const isValidPrecision = (val: unknown): val is PrecisionParam =>
|
||||
zPrecision.safeParse(val).success;
|
||||
|
||||
/**
|
||||
* Zod schema for SDXL refiner aesthetic score parameter
|
||||
*/
|
||||
export const zSDXLRefinerAestheticScore = z.number().min(1).max(10);
|
||||
/**
|
||||
* Type alias for SDXL refiner aesthetic score parameter, inferred from its zod schema
|
||||
*/
|
||||
export type SDXLRefinerAestheticScoreParam = z.infer<
|
||||
typeof zSDXLRefinerAestheticScore
|
||||
>;
|
||||
/**
|
||||
* Validates/type-guards a value as a SDXL refiner aesthetic score parameter
|
||||
*/
|
||||
export const isValidSDXLRefinerAestheticScore = (
|
||||
val: unknown
|
||||
): val is SDXLRefinerAestheticScoreParam =>
|
||||
zSDXLRefinerAestheticScore.safeParse(val).success;
|
||||
|
||||
/**
|
||||
* Zod schema for SDXL start parameter
|
||||
*/
|
||||
export const zSDXLRefinerstart = z.number().min(0).max(1);
|
||||
/**
|
||||
* Type alias for SDXL start, inferred from its zod schema
|
||||
*/
|
||||
export type SDXLRefinerStartParam = z.infer<typeof zSDXLRefinerstart>;
|
||||
/**
|
||||
* Validates/type-guards a value as a SDXL refiner aesthetic score parameter
|
||||
*/
|
||||
export const isValidSDXLRefinerStart = (
|
||||
val: unknown
|
||||
): val is SDXLRefinerStartParam => zSDXLRefinerstart.safeParse(val).success;
|
||||
|
||||
// /**
|
||||
// * Zod schema for BaseModelType
|
||||
// */
|
||||
|
||||
@ -21,8 +21,8 @@ export default function ParamSDXLConcatButton() {
|
||||
|
||||
return (
|
||||
<IAIIconButton
|
||||
aria-label="Concat"
|
||||
tooltip="Concatenates Basic Prompt with Style (Recommended)"
|
||||
aria-label="Concatenate Prompt & Style"
|
||||
tooltip="Concatenate Prompt & Style"
|
||||
variant="outline"
|
||||
isChecked={shouldConcatSDXLStylePrompt}
|
||||
onClick={handleShouldConcatPromptChange}
|
||||
|
||||
@ -1381,7 +1381,7 @@ export type components = {
|
||||
* @description The nodes in this graph
|
||||
*/
|
||||
nodes?: {
|
||||
[key: string]: (components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]) | undefined;
|
||||
[key: string]: (components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]) | undefined;
|
||||
};
|
||||
/**
|
||||
* Edges
|
||||
@ -1424,7 +1424,7 @@ export type components = {
|
||||
* @description The results of node executions
|
||||
*/
|
||||
results: {
|
||||
[key: string]: (components["schemas"]["ImageOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["LoraLoaderOutput"] | components["schemas"]["VaeLoaderOutput"] | components["schemas"]["MetadataAccumulatorOutput"] | components["schemas"]["PromptOutput"] | components["schemas"]["PromptCollectionOutput"] | components["schemas"]["IntOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["CompelOutput"] | components["schemas"]["ClipSkipInvocationOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["IntCollectionOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["ONNXModelLoaderOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["GraphInvocationOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["CollectInvocationOutput"]) | undefined;
|
||||
[key: string]: (components["schemas"]["ImageOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["LoraLoaderOutput"] | components["schemas"]["VaeLoaderOutput"] | components["schemas"]["MetadataAccumulatorOutput"] | components["schemas"]["IntCollectionOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["CompelOutput"] | components["schemas"]["ClipSkipInvocationOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["IntOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["PromptOutput"] | components["schemas"]["PromptCollectionOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["GraphInvocationOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["CollectInvocationOutput"]) | undefined;
|
||||
};
|
||||
/**
|
||||
* Errors
|
||||
@ -4265,6 +4265,35 @@ export type components = {
|
||||
*/
|
||||
a?: number;
|
||||
};
|
||||
/**
|
||||
* ParamPromptInvocation
|
||||
* @description A prompt input parameter
|
||||
*/
|
||||
ParamPromptInvocation: {
|
||||
/**
|
||||
* Id
|
||||
* @description The id of this node. Must be unique among all nodes.
|
||||
*/
|
||||
id: string;
|
||||
/**
|
||||
* Is Intermediate
|
||||
* @description Whether or not this node is an intermediate node.
|
||||
* @default false
|
||||
*/
|
||||
is_intermediate?: boolean;
|
||||
/**
|
||||
* Type
|
||||
* @default param_prompt
|
||||
* @enum {string}
|
||||
*/
|
||||
type?: "param_prompt";
|
||||
/**
|
||||
* Prompt
|
||||
* @description The prompt value
|
||||
* @default
|
||||
*/
|
||||
prompt?: string;
|
||||
};
|
||||
/**
|
||||
* ParamStringInvocation
|
||||
* @description A string parameter
|
||||
@ -5874,24 +5903,18 @@ export type components = {
|
||||
* @enum {string}
|
||||
*/
|
||||
StableDiffusionXLModelFormat: "checkpoint" | "diffusers";
|
||||
/**
|
||||
* ControlNetModelFormat
|
||||
* @description An enumeration.
|
||||
* @enum {string}
|
||||
*/
|
||||
ControlNetModelFormat: "checkpoint" | "diffusers";
|
||||
/**
|
||||
* StableDiffusion2ModelFormat
|
||||
* @description An enumeration.
|
||||
* @enum {string}
|
||||
*/
|
||||
StableDiffusion2ModelFormat: "checkpoint" | "diffusers";
|
||||
/**
|
||||
* StableDiffusion1ModelFormat
|
||||
* @description An enumeration.
|
||||
* @enum {string}
|
||||
*/
|
||||
StableDiffusion1ModelFormat: "checkpoint" | "diffusers";
|
||||
/**
|
||||
* ControlNetModelFormat
|
||||
* @description An enumeration.
|
||||
* @enum {string}
|
||||
*/
|
||||
ControlNetModelFormat: "checkpoint" | "diffusers";
|
||||
};
|
||||
responses: never;
|
||||
parameters: never;
|
||||
@ -6002,7 +6025,7 @@ export type operations = {
|
||||
};
|
||||
requestBody: {
|
||||
content: {
|
||||
"application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
|
||||
"application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
|
||||
};
|
||||
};
|
||||
responses: {
|
||||
@ -6039,7 +6062,7 @@ export type operations = {
|
||||
};
|
||||
requestBody: {
|
||||
content: {
|
||||
"application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
|
||||
"application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
|
||||
};
|
||||
};
|
||||
responses: {
|
||||
|
||||
@ -1 +1 @@
|
||||
__version__ = "3.0.1"
|
||||
__version__ = "3.0.1post3"
|
||||
|
||||
@ -1,281 +1,283 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "ycYWcsEKc6w7"
|
||||
},
|
||||
"source": [
|
||||
"# Stable Diffusion AI Notebook (Release 2.0.0)\n",
|
||||
"\n",
|
||||
"<img src=\"https://user-images.githubusercontent.com/60411196/186547976-d9de378a-9de8-4201-9c25-c057a9c59bad.jpeg\" alt=\"stable-diffusion-ai\" width=\"170px\"/> <br>\n",
|
||||
"#### Instructions:\n",
|
||||
"1. Execute each cell in order to mount a Dream bot and create images from text. <br>\n",
|
||||
"2. Once cells 1-8 were run correctly you'll be executing a terminal in cell #9, you'll need to enter `python scripts/dream.py` command to run Dream bot.<br> \n",
|
||||
"3. After launching dream bot, you'll see: <br> `Dream > ` in terminal. <br> Insert a command, eg. `Dream > Astronaut floating in a distant galaxy`, or type `-h` for help.\n",
|
||||
"3. After completion you'll see your generated images in path `stable-diffusion/outputs/img-samples/`, you can also show last generated images in cell #10.\n",
|
||||
"4. To quit Dream bot use `q` command. <br> \n",
|
||||
"---\n",
|
||||
"<font color=\"red\">Note:</font> It takes some time to load, but after installing all dependencies you can use the bot all time you want while colab instance is up. <br>\n",
|
||||
"<font color=\"red\">Requirements:</font> For this notebook to work you need to have [Stable-Diffusion-v-1-4](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original) stored in your Google Drive, it will be needed in cell #7\n",
|
||||
"##### For more details visit Github repository: [invoke-ai/InvokeAI](https://github.com/invoke-ai/InvokeAI)\n",
|
||||
"---\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "dr32VLxlnouf"
|
||||
},
|
||||
"source": [
|
||||
"## ◢ Installation"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "a2Z5Qu_o8VtQ"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#@title 1. Check current GPU assigned\n",
|
||||
"!nvidia-smi -L\n",
|
||||
"!nvidia-smi"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "vbI9ZsQHzjqF"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#@title 2. Download stable-diffusion Repository\n",
|
||||
"from os.path import exists\n",
|
||||
"\n",
|
||||
"!git clone --quiet https://github.com/invoke-ai/InvokeAI.git # Original repo\n",
|
||||
"%cd /content/InvokeAI/\n",
|
||||
"!git checkout --quiet tags/v2.0.0"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "QbXcGXYEFSNB"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#@title 3. Install dependencies\n",
|
||||
"import gc\n",
|
||||
"\n",
|
||||
"!wget https://raw.githubusercontent.com/invoke-ai/InvokeAI/development/environments-and-requirements/requirements-base.txt\n",
|
||||
"!wget https://raw.githubusercontent.com/invoke-ai/InvokeAI/development/environments-and-requirements/requirements-win-colab-cuda.txt\n",
|
||||
"!pip install colab-xterm\n",
|
||||
"!pip install -r requirements-lin-win-colab-CUDA.txt\n",
|
||||
"!pip install clean-fid torchtext\n",
|
||||
"!pip install transformers\n",
|
||||
"gc.collect()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "8rSMhgnAttQa"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#@title 4. Restart Runtime\n",
|
||||
"exit()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "ChIDWxLVHGGJ"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#@title 5. Load small ML models required\n",
|
||||
"import gc\n",
|
||||
"%cd /content/InvokeAI/\n",
|
||||
"!python scripts/preload_models.py\n",
|
||||
"gc.collect()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "795x1tMoo8b1"
|
||||
},
|
||||
"source": [
|
||||
"## ◢ Configuration"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "YEWPV-sF1RDM"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#@title 6. Mount google Drive\n",
|
||||
"from google.colab import drive\n",
|
||||
"drive.mount('/content/drive')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "zRTJeZ461WGu"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#@title 7. Drive Path to model\n",
|
||||
"#@markdown Path should start with /content/drive/path-to-your-file <br>\n",
|
||||
"#@markdown <font color=\"red\">Note:</font> Model should be downloaded from https://huggingface.co <br>\n",
|
||||
"#@markdown Lastest release: [Stable-Diffusion-v-1-4](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original)\n",
|
||||
"from os.path import exists\n",
|
||||
"\n",
|
||||
"model_path = \"\" #@param {type:\"string\"}\n",
|
||||
"if exists(model_path):\n",
|
||||
" print(\"✅ Valid directory\")\n",
|
||||
"else: \n",
|
||||
" print(\"❌ File doesn't exist\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "UY-NNz4I8_aG"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#@title 8. Symlink to model\n",
|
||||
"\n",
|
||||
"from os.path import exists\n",
|
||||
"import os \n",
|
||||
"\n",
|
||||
"# Folder creation if it doesn't exist\n",
|
||||
"if exists(\"/content/InvokeAI/models/ldm/stable-diffusion-v1\"):\n",
|
||||
" print(\"❗ Dir stable-diffusion-v1 already exists\")\n",
|
||||
"else:\n",
|
||||
" %mkdir /content/InvokeAI/models/ldm/stable-diffusion-v1\n",
|
||||
" print(\"✅ Dir stable-diffusion-v1 created\")\n",
|
||||
"\n",
|
||||
"# Symbolic link if it doesn't exist\n",
|
||||
"if exists(\"/content/InvokeAI/models/ldm/stable-diffusion-v1/model.ckpt\"):\n",
|
||||
" print(\"❗ Symlink already created\")\n",
|
||||
"else: \n",
|
||||
" src = model_path\n",
|
||||
" dst = '/content/InvokeAI/models/ldm/stable-diffusion-v1/model.ckpt'\n",
|
||||
" os.symlink(src, dst) \n",
|
||||
" print(\"✅ Symbolic link created successfully\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "Mc28N0_NrCQH"
|
||||
},
|
||||
"source": [
|
||||
"## ◢ Execution"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "ir4hCrMIuUpl"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#@title 9. Run Terminal and Execute Dream bot\n",
|
||||
"#@markdown <font color=\"blue\">Steps:</font> <br>\n",
|
||||
"#@markdown 1. Execute command `python scripts/invoke.py` to run InvokeAI.<br>\n",
|
||||
"#@markdown 2. After initialized you'll see `Dream>` line.<br>\n",
|
||||
"#@markdown 3. Example text: `Astronaut floating in a distant galaxy` <br>\n",
|
||||
"#@markdown 4. To quit Dream bot use: `q` command.<br>\n",
|
||||
"\n",
|
||||
"%load_ext colabxterm\n",
|
||||
"%xterm\n",
|
||||
"gc.collect()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "qnLohSHmKoGk"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#@title 10. Show the last 15 generated images\n",
|
||||
"import glob\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import matplotlib.image as mpimg\n",
|
||||
"%matplotlib inline\n",
|
||||
"\n",
|
||||
"images = []\n",
|
||||
"for img_path in sorted(glob.glob('/content/InvokeAI/outputs/img-samples/*.png'), reverse=True):\n",
|
||||
" images.append(mpimg.imread(img_path))\n",
|
||||
"\n",
|
||||
"images = images[:15] \n",
|
||||
"\n",
|
||||
"plt.figure(figsize=(20,10))\n",
|
||||
"\n",
|
||||
"columns = 5\n",
|
||||
"for i, image in enumerate(images):\n",
|
||||
" ax = plt.subplot(len(images) / columns + 1, columns, i + 1)\n",
|
||||
" ax.axes.xaxis.set_visible(False)\n",
|
||||
" ax.axes.yaxis.set_visible(False)\n",
|
||||
" ax.axis('off')\n",
|
||||
" plt.imshow(image)\n",
|
||||
" gc.collect()\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"accelerator": "GPU",
|
||||
"colab": {
|
||||
"collapsed_sections": [],
|
||||
"private_outputs": true,
|
||||
"provenance": []
|
||||
},
|
||||
"gpuClass": "standard",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.9.12 64-bit",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"name": "python",
|
||||
"version": "3.9.12"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "4e870c5c5fe42db7e2c5647ae5af656ff3391bf8c2b729cbf7fa0e16ca8cb5af"
|
||||
}
|
||||
}
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "ycYWcsEKc6w7"
|
||||
},
|
||||
"source": [
|
||||
"# Stable Diffusion AI Notebook (Release 2.0.0)\n",
|
||||
"\n",
|
||||
"<img src=\"https://user-images.githubusercontent.com/60411196/186547976-d9de378a-9de8-4201-9c25-c057a9c59bad.jpeg\" alt=\"stable-diffusion-ai\" width=\"170px\"/> <br>\n",
|
||||
"#### Instructions:\n",
|
||||
"1. Execute each cell in order to mount a Dream bot and create images from text. <br>\n",
|
||||
"2. Once cells 1-8 were run correctly you'll be executing a terminal in cell #9, you'll need to enter `python scripts/dream.py` command to run Dream bot.<br> \n",
|
||||
"3. After launching dream bot, you'll see: <br> `Dream > ` in terminal. <br> Insert a command, eg. `Dream > Astronaut floating in a distant galaxy`, or type `-h` for help.\n",
|
||||
"3. After completion you'll see your generated images in path `stable-diffusion/outputs/img-samples/`, you can also show last generated images in cell #10.\n",
|
||||
"4. To quit Dream bot use `q` command. <br> \n",
|
||||
"---\n",
|
||||
"<font color=\"red\">Note:</font> It takes some time to load, but after installing all dependencies you can use the bot all time you want while colab instance is up. <br>\n",
|
||||
"<font color=\"red\">Requirements:</font> For this notebook to work you need to have [Stable-Diffusion-v-1-4](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original) stored in your Google Drive, it will be needed in cell #7\n",
|
||||
"##### For more details visit Github repository: [invoke-ai/InvokeAI](https://github.com/invoke-ai/InvokeAI)\n",
|
||||
"---\n"
|
||||
]
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "dr32VLxlnouf"
|
||||
},
|
||||
"source": [
|
||||
"## ◢ Installation"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "a2Z5Qu_o8VtQ"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# @title 1. Check current GPU assigned\n",
|
||||
"!nvidia-smi -L\n",
|
||||
"!nvidia-smi"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "vbI9ZsQHzjqF"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# @title 2. Download stable-diffusion Repository\n",
|
||||
"from os.path import exists\n",
|
||||
"\n",
|
||||
"!git clone --quiet https://github.com/invoke-ai/InvokeAI.git # Original repo\n",
|
||||
"%cd /content/InvokeAI/\n",
|
||||
"!git checkout --quiet tags/v2.0.0"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "QbXcGXYEFSNB"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# @title 3. Install dependencies\n",
|
||||
"import gc\n",
|
||||
"\n",
|
||||
"!wget https://raw.githubusercontent.com/invoke-ai/InvokeAI/development/environments-and-requirements/requirements-base.txt\n",
|
||||
"!wget https://raw.githubusercontent.com/invoke-ai/InvokeAI/development/environments-and-requirements/requirements-win-colab-cuda.txt\n",
|
||||
"!pip install colab-xterm\n",
|
||||
"!pip install -r requirements-lin-win-colab-CUDA.txt\n",
|
||||
"!pip install clean-fid torchtext\n",
|
||||
"!pip install transformers\n",
|
||||
"gc.collect()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "8rSMhgnAttQa"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# @title 4. Restart Runtime\n",
|
||||
"exit()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "ChIDWxLVHGGJ"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# @title 5. Load small ML models required\n",
|
||||
"import gc\n",
|
||||
"\n",
|
||||
"%cd /content/InvokeAI/\n",
|
||||
"!python scripts/preload_models.py\n",
|
||||
"gc.collect()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "795x1tMoo8b1"
|
||||
},
|
||||
"source": [
|
||||
"## ◢ Configuration"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "YEWPV-sF1RDM"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# @title 6. Mount google Drive\n",
|
||||
"from google.colab import drive\n",
|
||||
"\n",
|
||||
"drive.mount(\"/content/drive\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "zRTJeZ461WGu"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# @title 7. Drive Path to model\n",
|
||||
"# @markdown Path should start with /content/drive/path-to-your-file <br>\n",
|
||||
"# @markdown <font color=\"red\">Note:</font> Model should be downloaded from https://huggingface.co <br>\n",
|
||||
"# @markdown Lastest release: [Stable-Diffusion-v-1-4](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original)\n",
|
||||
"from os.path import exists\n",
|
||||
"\n",
|
||||
"model_path = \"\" # @param {type:\"string\"}\n",
|
||||
"if exists(model_path):\n",
|
||||
" print(\"✅ Valid directory\")\n",
|
||||
"else:\n",
|
||||
" print(\"❌ File doesn't exist\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "UY-NNz4I8_aG"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# @title 8. Symlink to model\n",
|
||||
"\n",
|
||||
"from os.path import exists\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"# Folder creation if it doesn't exist\n",
|
||||
"if exists(\"/content/InvokeAI/models/ldm/stable-diffusion-v1\"):\n",
|
||||
" print(\"❗ Dir stable-diffusion-v1 already exists\")\n",
|
||||
"else:\n",
|
||||
" %mkdir /content/InvokeAI/models/ldm/stable-diffusion-v1\n",
|
||||
" print(\"✅ Dir stable-diffusion-v1 created\")\n",
|
||||
"\n",
|
||||
"# Symbolic link if it doesn't exist\n",
|
||||
"if exists(\"/content/InvokeAI/models/ldm/stable-diffusion-v1/model.ckpt\"):\n",
|
||||
" print(\"❗ Symlink already created\")\n",
|
||||
"else:\n",
|
||||
" src = model_path\n",
|
||||
" dst = \"/content/InvokeAI/models/ldm/stable-diffusion-v1/model.ckpt\"\n",
|
||||
" os.symlink(src, dst)\n",
|
||||
" print(\"✅ Symbolic link created successfully\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "Mc28N0_NrCQH"
|
||||
},
|
||||
"source": [
|
||||
"## ◢ Execution"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "ir4hCrMIuUpl"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# @title 9. Run Terminal and Execute Dream bot\n",
|
||||
"# @markdown <font color=\"blue\">Steps:</font> <br>\n",
|
||||
"# @markdown 1. Execute command `python scripts/invoke.py` to run InvokeAI.<br>\n",
|
||||
"# @markdown 2. After initialized you'll see `Dream>` line.<br>\n",
|
||||
"# @markdown 3. Example text: `Astronaut floating in a distant galaxy` <br>\n",
|
||||
"# @markdown 4. To quit Dream bot use: `q` command.<br>\n",
|
||||
"\n",
|
||||
"%load_ext colabxterm\n",
|
||||
"%xterm\n",
|
||||
"gc.collect()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "qnLohSHmKoGk"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#@title 10. Show the last 15 generated images\n",
|
||||
"import glob\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import matplotlib.image as mpimg\n",
|
||||
"%matplotlib inline\n",
|
||||
"\n",
|
||||
"images = []\n",
|
||||
"for img_path in sorted(glob.glob('/content/InvokeAI/outputs/img-samples/*.png'), reverse=True):\n",
|
||||
" images.append(mpimg.imread(img_path))\n",
|
||||
"\n",
|
||||
"images = images[:15] \n",
|
||||
"\n",
|
||||
"plt.figure(figsize=(20,10))\n",
|
||||
"\n",
|
||||
"columns = 5\n",
|
||||
"for i, image in enumerate(images):\n",
|
||||
" ax = plt.subplot(len(images) / columns + 1, columns, i + 1)\n",
|
||||
" ax.axes.xaxis.set_visible(False)\n",
|
||||
" ax.axes.yaxis.set_visible(False)\n",
|
||||
" ax.axis('off')\n",
|
||||
" plt.imshow(image)\n",
|
||||
" gc.collect()\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"accelerator": "GPU",
|
||||
"colab": {
|
||||
"collapsed_sections": [],
|
||||
"private_outputs": true,
|
||||
"provenance": []
|
||||
},
|
||||
"gpuClass": "standard",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.9.12 64-bit",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"name": "python",
|
||||
"version": "3.9.12"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "4e870c5c5fe42db7e2c5647ae5af656ff3391bf8c2b729cbf7fa0e16ca8cb5af"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
|
||||
@ -58,15 +58,15 @@ dependencies = [
|
||||
"invisible-watermark~=0.2.0", # needed to install SDXL base and refiner using their repo_ids
|
||||
"matplotlib", # needed for plotting of Penner easing functions
|
||||
"mediapipe", # needed for "mediapipeface" controlnet model
|
||||
"numpy",
|
||||
"npyscreen",
|
||||
"numpy==1.24.4",
|
||||
"omegaconf",
|
||||
"onnx",
|
||||
"opencv-python",
|
||||
"pydantic==1.*",
|
||||
"picklescan",
|
||||
"pillow",
|
||||
"prompt-toolkit",
|
||||
"pydantic==1.10.10",
|
||||
"pympler~=1.0.1",
|
||||
"pypatchmatch",
|
||||
'pyperclip',
|
||||
@ -82,7 +82,7 @@ dependencies = [
|
||||
"test-tube~=0.7.5",
|
||||
"torch~=2.0.1",
|
||||
"torchvision~=0.15.2",
|
||||
"torchmetrics~=1.0.1",
|
||||
"torchmetrics~=0.11.0",
|
||||
"torchsde~=0.2.5",
|
||||
"transformers~=4.31.0",
|
||||
"uvicorn[standard]~=0.21.1",
|
||||
|
||||
@ -52,17 +52,17 @@
|
||||
"name": "stdout",
|
||||
"text": [
|
||||
"Cloning into 'latent-diffusion'...\n",
|
||||
"remote: Enumerating objects: 992, done.\u001B[K\n",
|
||||
"remote: Counting objects: 100% (695/695), done.\u001B[K\n",
|
||||
"remote: Compressing objects: 100% (397/397), done.\u001B[K\n",
|
||||
"remote: Total 992 (delta 375), reused 564 (delta 253), pack-reused 297\u001B[K\n",
|
||||
"remote: Enumerating objects: 992, done.\u001b[K\n",
|
||||
"remote: Counting objects: 100% (695/695), done.\u001b[K\n",
|
||||
"remote: Compressing objects: 100% (397/397), done.\u001b[K\n",
|
||||
"remote: Total 992 (delta 375), reused 564 (delta 253), pack-reused 297\u001b[K\n",
|
||||
"Receiving objects: 100% (992/992), 30.78 MiB | 29.43 MiB/s, done.\n",
|
||||
"Resolving deltas: 100% (510/510), done.\n",
|
||||
"Cloning into 'taming-transformers'...\n",
|
||||
"remote: Enumerating objects: 1335, done.\u001B[K\n",
|
||||
"remote: Counting objects: 100% (525/525), done.\u001B[K\n",
|
||||
"remote: Compressing objects: 100% (493/493), done.\u001B[K\n",
|
||||
"remote: Total 1335 (delta 58), reused 481 (delta 30), pack-reused 810\u001B[K\n",
|
||||
"remote: Enumerating objects: 1335, done.\u001b[K\n",
|
||||
"remote: Counting objects: 100% (525/525), done.\u001b[K\n",
|
||||
"remote: Compressing objects: 100% (493/493), done.\u001b[K\n",
|
||||
"remote: Total 1335 (delta 58), reused 481 (delta 30), pack-reused 810\u001b[K\n",
|
||||
"Receiving objects: 100% (1335/1335), 412.35 MiB | 30.53 MiB/s, done.\n",
|
||||
"Resolving deltas: 100% (267/267), done.\n",
|
||||
"Obtaining file:///content/taming-transformers\n",
|
||||
@ -73,23 +73,24 @@
|
||||
"Installing collected packages: taming-transformers\n",
|
||||
" Running setup.py develop for taming-transformers\n",
|
||||
"Successfully installed taming-transformers-0.0.1\n",
|
||||
"\u001B[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
|
||||
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
|
||||
"tensorflow 2.8.0 requires tf-estimator-nightly==2.8.0.dev2021122109, which is not installed.\n",
|
||||
"arviz 0.11.4 requires typing-extensions<4,>=3.7.4.3, but you have typing-extensions 4.1.1 which is incompatible.\u001B[0m\n"
|
||||
"arviz 0.11.4 requires typing-extensions<4,>=3.7.4.3, but you have typing-extensions 4.1.1 which is incompatible.\u001b[0m\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#@title Installation\n",
|
||||
"# @title Installation\n",
|
||||
"!git clone https://github.com/CompVis/latent-diffusion.git\n",
|
||||
"!git clone https://github.com/CompVis/taming-transformers\n",
|
||||
"!pip install -e ./taming-transformers\n",
|
||||
"!pip install omegaconf>=2.0.0 pytorch-lightning>=1.0.8 torch-fidelity einops\n",
|
||||
"\n",
|
||||
"import sys\n",
|
||||
"\n",
|
||||
"sys.path.append(\".\")\n",
|
||||
"sys.path.append('./taming-transformers')\n",
|
||||
"from taming.models import vqgan "
|
||||
"sys.path.append(\"./taming-transformers\")\n",
|
||||
"from taming.models import vqgan"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -104,11 +105,11 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"#@title Download\n",
|
||||
"%cd latent-diffusion/ \n",
|
||||
"# @title Download\n",
|
||||
"%cd latent-diffusion/\n",
|
||||
"\n",
|
||||
"!mkdir -p models/ldm/cin256-v2/\n",
|
||||
"!wget -O models/ldm/cin256-v2/model.ckpt https://ommer-lab.com/files/latent-diffusion/nitro/cin/model.ckpt "
|
||||
"!wget -O models/ldm/cin256-v2/model.ckpt https://ommer-lab.com/files/latent-diffusion/nitro/cin/model.ckpt"
|
||||
],
|
||||
"metadata": {
|
||||
"colab": {
|
||||
@ -203,7 +204,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"#@title loading utils\n",
|
||||
"# @title loading utils\n",
|
||||
"import torch\n",
|
||||
"from omegaconf import OmegaConf\n",
|
||||
"\n",
|
||||
@ -212,7 +213,7 @@
|
||||
"\n",
|
||||
"def load_model_from_config(config, ckpt):\n",
|
||||
" print(f\"Loading model from {ckpt}\")\n",
|
||||
" pl_sd = torch.load(ckpt)#, map_location=\"cpu\")\n",
|
||||
" pl_sd = torch.load(ckpt) # , map_location=\"cpu\")\n",
|
||||
" sd = pl_sd[\"state_dict\"]\n",
|
||||
" model = instantiate_from_config(config.model)\n",
|
||||
" m, u = model.load_state_dict(sd, strict=False)\n",
|
||||
@ -222,7 +223,7 @@
|
||||
"\n",
|
||||
"\n",
|
||||
"def get_model():\n",
|
||||
" config = OmegaConf.load(\"configs/latent-diffusion/cin256-v2.yaml\") \n",
|
||||
" config = OmegaConf.load(\"configs/latent-diffusion/cin256-v2.yaml\")\n",
|
||||
" model = load_model_from_config(config, \"models/ldm/cin256-v2/model.ckpt\")\n",
|
||||
" return model"
|
||||
],
|
||||
@ -276,18 +277,18 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"import numpy as np \n",
|
||||
"import numpy as np\n",
|
||||
"from PIL import Image\n",
|
||||
"from einops import rearrange\n",
|
||||
"from torchvision.utils import make_grid\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"classes = [25, 187, 448, 992] # define classes to be sampled here\n",
|
||||
"classes = [25, 187, 448, 992] # define classes to be sampled here\n",
|
||||
"n_samples_per_class = 6\n",
|
||||
"\n",
|
||||
"ddim_steps = 20\n",
|
||||
"ddim_eta = 0.0\n",
|
||||
"scale = 3.0 # for unconditional guidance\n",
|
||||
"scale = 3.0 # for unconditional guidance\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"all_samples = list()\n",
|
||||
@ -295,36 +296,39 @@
|
||||
"with torch.no_grad():\n",
|
||||
" with model.ema_scope():\n",
|
||||
" uc = model.get_learned_conditioning(\n",
|
||||
" {model.cond_stage_key: torch.tensor(n_samples_per_class*[1000]).to(model.device)}\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" {model.cond_stage_key: torch.tensor(n_samples_per_class * [1000]).to(model.device)}\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" for class_label in classes:\n",
|
||||
" print(f\"rendering {n_samples_per_class} examples of class '{class_label}' in {ddim_steps} steps and using s={scale:.2f}.\")\n",
|
||||
" xc = torch.tensor(n_samples_per_class*[class_label])\n",
|
||||
" print(\n",
|
||||
" f\"rendering {n_samples_per_class} examples of class '{class_label}' in {ddim_steps} steps and using s={scale:.2f}.\"\n",
|
||||
" )\n",
|
||||
" xc = torch.tensor(n_samples_per_class * [class_label])\n",
|
||||
" c = model.get_learned_conditioning({model.cond_stage_key: xc.to(model.device)})\n",
|
||||
" \n",
|
||||
" samples_ddim, _ = sampler.sample(S=ddim_steps,\n",
|
||||
" conditioning=c,\n",
|
||||
" batch_size=n_samples_per_class,\n",
|
||||
" shape=[3, 64, 64],\n",
|
||||
" verbose=False,\n",
|
||||
" unconditional_guidance_scale=scale,\n",
|
||||
" unconditional_conditioning=uc, \n",
|
||||
" eta=ddim_eta)\n",
|
||||
"\n",
|
||||
" samples_ddim, _ = sampler.sample(\n",
|
||||
" S=ddim_steps,\n",
|
||||
" conditioning=c,\n",
|
||||
" batch_size=n_samples_per_class,\n",
|
||||
" shape=[3, 64, 64],\n",
|
||||
" verbose=False,\n",
|
||||
" unconditional_guidance_scale=scale,\n",
|
||||
" unconditional_conditioning=uc,\n",
|
||||
" eta=ddim_eta,\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" x_samples_ddim = model.decode_first_stage(samples_ddim)\n",
|
||||
" x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, \n",
|
||||
" min=0.0, max=1.0)\n",
|
||||
" x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)\n",
|
||||
" all_samples.append(x_samples_ddim)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# display as grid\n",
|
||||
"grid = torch.stack(all_samples, 0)\n",
|
||||
"grid = rearrange(grid, 'n b c h w -> (n b) c h w')\n",
|
||||
"grid = rearrange(grid, \"n b c h w -> (n b) c h w\")\n",
|
||||
"grid = make_grid(grid, nrow=n_samples_per_class)\n",
|
||||
"\n",
|
||||
"# to image\n",
|
||||
"grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()\n",
|
||||
"grid = 255.0 * rearrange(grid, \"c h w -> h w c\").cpu().numpy()\n",
|
||||
"Image.fromarray(grid.astype(np.uint8))"
|
||||
],
|
||||
"metadata": {
|
||||
|
||||
Loading…
Reference in New Issue
Block a user