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36
.github/CODEOWNERS vendored
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@ -1,34 +1,34 @@
# continuous integration
/.github/workflows/ @lstein @blessedcoolant
/.github/workflows/ @lstein @blessedcoolant @hipsterusername
# documentation
/docs/ @lstein @blessedcoolant @hipsterusername @Millu
/mkdocs.yml @lstein @blessedcoolant
/mkdocs.yml @lstein @blessedcoolant @hipsterusername @Millu
# nodes
/invokeai/app/ @Kyle0654 @blessedcoolant @psychedelicious @brandonrising
/invokeai/app/ @Kyle0654 @blessedcoolant @psychedelicious @brandonrising @hipsterusername
# installation and configuration
/pyproject.toml @lstein @blessedcoolant
/docker/ @lstein @blessedcoolant
/scripts/ @ebr @lstein
/installer/ @lstein @ebr
/invokeai/assets @lstein @ebr
/invokeai/configs @lstein
/invokeai/version @lstein @blessedcoolant
/pyproject.toml @lstein @blessedcoolant @hipsterusername
/docker/ @lstein @blessedcoolant @hipsterusername
/scripts/ @ebr @lstein @hipsterusername
/installer/ @lstein @ebr @hipsterusername
/invokeai/assets @lstein @ebr @hipsterusername
/invokeai/configs @lstein @hipsterusername
/invokeai/version @lstein @blessedcoolant @hipsterusername
# web ui
/invokeai/frontend @blessedcoolant @psychedelicious @lstein @maryhipp
/invokeai/backend @blessedcoolant @psychedelicious @lstein @maryhipp
/invokeai/frontend @blessedcoolant @psychedelicious @lstein @maryhipp @hipsterusername
/invokeai/backend @blessedcoolant @psychedelicious @lstein @maryhipp @hipsterusername
# generation, model management, postprocessing
/invokeai/backend @damian0815 @lstein @blessedcoolant @gregghelt2 @StAlKeR7779 @brandonrising @ryanjdick
/invokeai/backend @damian0815 @lstein @blessedcoolant @gregghelt2 @StAlKeR7779 @brandonrising @ryanjdick @hipsterusername
# front ends
/invokeai/frontend/CLI @lstein
/invokeai/frontend/install @lstein @ebr
/invokeai/frontend/merge @lstein @blessedcoolant
/invokeai/frontend/training @lstein @blessedcoolant
/invokeai/frontend/web @psychedelicious @blessedcoolant @maryhipp
/invokeai/frontend/CLI @lstein @hipsterusername
/invokeai/frontend/install @lstein @ebr @hipsterusername
/invokeai/frontend/merge @lstein @blessedcoolant @hipsterusername
/invokeai/frontend/training @lstein @blessedcoolant @hipsterusername
/invokeai/frontend/web @psychedelicious @blessedcoolant @maryhipp @hipsterusername

12
.github/ISSUE_TEMPLATE/FEATURE_REQUEST.yml vendored
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@ -1,5 +1,5 @@
name: Feature Request
description: Commit a idea or Request a new feature
description: Contribute a idea or request a new feature
title: '[enhancement]: '
labels: ['enhancement']
# assignees:
@ -9,14 +9,14 @@ body:
- type: markdown
attributes:
value: |
Thanks for taking the time to fill out this Feature request!
Thanks for taking the time to fill out this feature request!
- type: checkboxes
attributes:
label: Is there an existing issue for this?
description: |
Please make use of the [search function](https://github.com/invoke-ai/InvokeAI/labels/enhancement)
to see if a simmilar issue already exists for the feature you want to request
to see if a similar issue already exists for the feature you want to request
options:
- label: I have searched the existing issues
required: true
@ -36,7 +36,7 @@ body:
label: What should this feature add?
description: Please try to explain the functionality this feature should add
placeholder: |
Instead of one huge textfield, it would be nice to have forms for bug-reports, feature-requests, ...
Instead of one huge text field, it would be nice to have forms for bug-reports, feature-requests, ...
Great benefits with automatic labeling, assigning and other functionalitys not available in that form
via old-fashioned markdown-templates. I would also love to see the use of a moderator bot 🤖 like
https://github.com/marketplace/actions/issue-moderator-with-commands to auto close old issues and other things
@ -51,6 +51,6 @@ body:
- type: textarea
attributes:
label: Aditional Content
label: Additional Content
description: Add any other context or screenshots about the feature request here.
placeholder: This is a Mockup of the design how I imagine it <screenshot>
placeholder: This is a mockup of the design how I imagine it <screenshot>

8
docs/contributing/INVOCATIONS.md
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@ -244,8 +244,12 @@ copy-paste the template above.
We can use the `@invocation` decorator to provide some additional info to the
UI, like a custom title, tags and category.
We also encourage providing a version. This must be a
[semver](https://semver.org/) version string ("$MAJOR.$MINOR.$PATCH"). The UI
will let users know if their workflow is using a mismatched version of the node.
```python
@invocation("resize", title="My Resizer", tags=["resize", "image"], category="My Invocations")
@invocation("resize", title="My Resizer", tags=["resize", "image"], category="My Invocations", version="1.0.0")
class ResizeInvocation(BaseInvocation):
"""Resizes an image"""
@ -279,8 +283,6 @@ take a look a at our [contributing nodes overview](contributingNodes).
## Advanced
-->
### Custom Output Types
Like with custom inputs, sometimes you might find yourself needing custom

6
docs/index.md
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@ -15,7 +15,8 @@ title: Home
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.2.1/css/fontawesome.min.css">
<style>
.button {
width: 300px;
width: 100%;
max-width: 100%;
height: 50px;
background-color: #448AFF;
color: #fff;
@ -27,8 +28,9 @@ title: Home
.button-container {
display: grid;
grid-template-columns: repeat(3, 300px);
grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
gap: 20px;
justify-content: center;
}
.button:hover {

24
docs/installation/030_INSTALL_CUDA_AND_ROCM.md
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@ -57,6 +57,30 @@ familiar with containerization technologies such as Docker.
For downloads and instructions, visit the [NVIDIA CUDA Container
Runtime Site](https://developer.nvidia.com/nvidia-container-runtime)
### cuDNN Installation for 40/30 Series Optimization* (Optional)
1. Find the InvokeAI folder
2. Click on .venv folder - e.g., YourInvokeFolderHere\\.venv
3. Click on Lib folder - e.g., YourInvokeFolderHere\\.venv\Lib
4. Click on site-packages folder - e.g., YourInvokeFolderHere\\.venv\Lib\site-packages
5. Click on Torch directory - e.g., YourInvokeFolderHere\InvokeAI\\.venv\Lib\site-packages\torch
6. Click on the lib folder - e.g., YourInvokeFolderHere\\.venv\Lib\site-packages\torch\lib
7. Copy everything inside the folder and save it elsewhere as a backup.
8. Go to __https://developer.nvidia.com/cudnn__
9. Login or create an Account.
10. Choose the newer version of cuDNN. **Note:**
There are two versions, 11.x or 12.x for the differents architectures(Turing,Maxwell Etc...) of GPUs.
You can find which version you should download from [this link](https://docs.nvidia.com/deeplearning/cudnn/support-matrix/index.html).
13. Download the latest version and extract it from the download location
14. Find the bin folder E\cudnn-windows-x86_64-__Whatever Version__\bin
15. Copy and paste the .dll files into YourInvokeFolderHere\\.venv\Lib\site-packages\torch\lib **Make sure to copy, and not move the files**
16. If prompted, replace any existing files
**Notes:**
* If no change is seen or any issues are encountered, follow the same steps as above and paste the torch/lib backup folder you made earlier and replace it. If you didn't make a backup, you can also uninstall and reinstall torch through the command line to repair this folder.
* This optimization is intended for the newer version of graphics card (40/30 series) but results have been seen with older graphics card.
### Torch Installation
When installing torch and torchvision manually with `pip`, remember to provide

99
docs/nodes/communityNodes.md
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@ -22,12 +22,26 @@ To use a community node graph, download the the `.json` node graph file and load
![b920b710-1882-49a0-8d02-82dff2cca907](https://github.com/invoke-ai/InvokeAI/assets/25252829/7660c1ed-bf7d-4d0a-947f-1fc1679557ba)
![71a91805-fda5-481c-b380-264665703133](https://github.com/invoke-ai/InvokeAI/assets/25252829/f8f6a2ee-2b68-4482-87da-b90221d5c3e2)
--------------------------------
### 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
--------------------------------
### Film Grain
**Description:** This node adds a film grain effect to the input image based on the weights, seeds, and blur radii parameters. It works with RGB input images only.
**Node Link:** https://github.com/JPPhoto/film-grain-node
--------------------------------
### Image Picker
**Description:** This InvokeAI node takes in a collection of images and randomly chooses one. This can be useful when you have a number of poses to choose from for a ControlNet node, or a number of input images for another purpose.
**Node Link:** https://github.com/JPPhoto/image-picker-node
--------------------------------
### Retroize
@ -95,6 +109,91 @@ a Text-Generation-Webui instance (might work remotely too, but I never tried it)
This node works best with SDXL models, especially as the style can be described independantly of the LLM's output.
--------------------------------
### Depth Map from Wavefront OBJ
**Description:** Render depth maps from Wavefront .obj files (triangulated) using this simple 3D renderer utilizing numpy and matplotlib to compute and color the scene. There are simple parameters to change the FOV, camera position, and model orientation.
To be imported, an .obj must use triangulated meshes, so make sure to enable that option if exporting from a 3D modeling program. This renderer makes each triangle a solid color based on its average depth, so it will cause anomalies if your .obj has large triangles. In Blender, the Remesh modifier can be helpful to subdivide a mesh into small pieces that work well given these limitations.
**Node Link:** https://github.com/dwringer/depth-from-obj-node
**Example Usage:**
![depth from obj usage graph](https://raw.githubusercontent.com/dwringer/depth-from-obj-node/main/depth_from_obj_usage.jpg)
--------------------------------
### Enhance Image (simple adjustments)
**Description:** Boost or reduce color saturation, contrast, brightness, sharpness, or invert colors of any image at any stage with this simple wrapper for pillow [PIL]'s ImageEnhance module.
Color inversion is toggled with a simple switch, while each of the four enhancer modes are activated by entering a value other than 1 in each corresponding input field. Values less than 1 will reduce the corresponding property, while values greater than 1 will enhance it.
**Node Link:** https://github.com/dwringer/image-enhance-node
**Example Usage:**
![enhance image usage graph](https://raw.githubusercontent.com/dwringer/image-enhance-node/main/image_enhance_usage.jpg)
--------------------------------
### Generative Grammar-Based Prompt Nodes
**Description:** This set of 3 nodes generates prompts from simple user-defined grammar rules (loaded from custom files - examples provided below). The prompts are made by recursively expanding a special template string, replacing nonterminal "parts-of-speech" until no more nonterminal terms remain in the string.
This includes 3 Nodes:
- *Lookup Table from File* - loads a YAML file "prompt" section (or of a whole folder of YAML's) into a JSON-ified dictionary (Lookups output)
- *Lookups Entry from Prompt* - places a single entry in a new Lookups output under the specified heading
- *Prompt from Lookup Table* - uses a Collection of Lookups as grammar rules from which to randomly generate prompts.
**Node Link:** https://github.com/dwringer/generative-grammar-prompt-nodes
**Example Usage:**
![lookups usage example graph](https://raw.githubusercontent.com/dwringer/generative-grammar-prompt-nodes/main/lookuptables_usage.jpg)
--------------------------------
### Image and Mask Composition Pack
**Description:** This is a pack of nodes for composing masks and images, including a simple text mask creator and both image and latent offset nodes. The offsets wrap around, so these can be used in conjunction with the Seamless node to progressively generate centered on different parts of the seamless tiling.
This includes 4 Nodes:
- *Text Mask (simple 2D)* - create and position a white on black (or black on white) line of text using any font locally available to Invoke.
- *Image Compositor* - Take a subject from an image with a flat backdrop and layer it on another image using a chroma key or flood select background removal.
- *Offset Latents* - Offset a latents tensor in the vertical and/or horizontal dimensions, wrapping it around.
- *Offset Image* - Offset an image in the vertical and/or horizontal dimensions, wrapping it around.
**Node Link:** https://github.com/dwringer/composition-nodes
**Example Usage:**
![composition nodes usage graph](https://raw.githubusercontent.com/dwringer/composition-nodes/main/composition_nodes_usage.jpg)
--------------------------------
### Size Stepper Nodes
**Description:** This is a set of nodes for calculating the necessary size increments for doing upscaling workflows. Use the *Final Size & Orientation* node to enter your full size dimensions and orientation (portrait/landscape/random), then plug that and your initial generation dimensions into the *Ideal Size Stepper* and get 1, 2, or 3 intermediate pairs of dimensions for upscaling. Note this does not output the initial size or full size dimensions: the 1, 2, or 3 outputs of this node are only the intermediate sizes.
A third node is included, *Random Switch (Integers)*, which is just a generic version of Final Size with no orientation selection.
**Node Link:** https://github.com/dwringer/size-stepper-nodes
**Example Usage:**
![size stepper usage graph](https://raw.githubusercontent.com/dwringer/size-stepper-nodes/main/size_nodes_usage.jpg)
--------------------------------
### Text font to Image
**Description:** text font to text image node for InvokeAI, download a font to use (or if in font cache uses it from there), the text is always resized to the image size, but can control that with padding, optional 2nd line
**Node Link:** https://github.com/mickr777/textfontimage
**Output Examples**
![a3609d48-d9b7-41f0-b280-063d857986fb](https://github.com/mickr777/InvokeAI/assets/115216705/c21b0af3-d9c6-4c16-9152-846a23effd36)
Results after using the depth controlnet
![9133eabb-bcda-4326-831e-1b641228b178](https://github.com/mickr777/InvokeAI/assets/115216705/915f1a53-968e-43eb-aa61-07cd8f1a733a)
![4f9a3fa8-9be9-4236-8a3e-fcec66decd2a](https://github.com/mickr777/InvokeAI/assets/115216705/821ef89e-8a60-44f5-b94e-471a9d8690cc)
![babd69c4-9d60-4a55-a834-5e8397f62610](https://github.com/mickr777/InvokeAI/assets/115216705/2befcb6d-49f4-4bfd-b5fc-1fee19274f89)
--------------------------------
### Example Node Template

4
docs/nodes/defaultNodes.md
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@ -35,13 +35,13 @@ The table below contains a list of the default nodes shipped with InvokeAI and t
|Inverse Lerp Image | Inverse linear interpolation of all pixels of an image|
|Image Primitive | An image primitive value|
|Lerp Image | Linear interpolation of all pixels of an image|
|Image Luminosity Adjustment | Adjusts the Luminosity (Value) of an image.|
|Offset Image Channel | Add to or subtract from an image color channel by a uniform value.|
|Multiply Image Channel | Multiply or Invert an image color channel by a scalar value.|
|Multiply Images | Multiplies two images together using `PIL.ImageChops.multiply()`.|
|Blur NSFW Image | Add blur to NSFW-flagged images|
|Paste Image | Pastes an image into another image.|
|ImageProcessor | Base class for invocations that preprocess images for ControlNet|
|Resize Image | Resizes an image to specific dimensions|
|Image Saturation Adjustment | Adjusts the Saturation of an image.|
|Scale Image | Scales an image by a factor|
|Image to Latents | Encodes an image into latents.|
|Add Invisible Watermark | Add an invisible watermark to an image|

12
invokeai/app/api/routers/app_info.py
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@ -1,19 +1,19 @@
import typing
from enum import Enum
from pathlib import Path
from fastapi import Body
from fastapi.routing import APIRouter
from pathlib import Path
from pydantic import BaseModel, Field
from invokeai.app.invocations.upscale import ESRGAN_MODELS
from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
from invokeai.backend.image_util.patchmatch import PatchMatch
from invokeai.backend.image_util.safety_checker import SafetyChecker
from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
from invokeai.app.invocations.upscale import ESRGAN_MODELS
from invokeai.backend.util.logging import logging
from invokeai.version import __version__
from ..dependencies import ApiDependencies
from invokeai.backend.util.logging import logging
class LogLevel(int, Enum):
@ -55,7 +55,7 @@ async def get_version() -> AppVersion:
@app_router.get("/config", operation_id="get_config", status_code=200, response_model=AppConfig)
async def get_config() -> AppConfig:
infill_methods = ["tile", "lama"]
infill_methods = ["tile", "lama", "cv2"]
if PatchMatch.patchmatch_available():
infill_methods.append("patchmatch")

76
invokeai/app/api_app.py
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@ -1,45 +1,47 @@
# Copyright (c) 2022-2023 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
import asyncio
import logging
import socket
from inspect import signature
from pathlib import Path
import uvicorn
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html
from fastapi.openapi.utils import get_openapi
from fastapi.staticfiles import StaticFiles
from fastapi_events.handlers.local import local_handler
from fastapi_events.middleware import EventHandlerASGIMiddleware
from pydantic.schema import schema
from .services.config import InvokeAIAppConfig
from ..backend.util.logging import InvokeAILogger
from invokeai.version.invokeai_version import __version__
import invokeai.frontend.web as web_dir
import mimetypes
from .api.dependencies import ApiDependencies
from .api.routers import sessions, models, images, boards, board_images, app_info
from .api.sockets import SocketIO
from .invocations.baseinvocation import BaseInvocation, _InputField, _OutputField, UIConfigBase
import torch
# noinspection PyUnresolvedReferences
import invokeai.backend.util.hotfixes # noqa: F401 (monkeypatching on import)
if torch.backends.mps.is_available():
# noinspection PyUnresolvedReferences
import invokeai.backend.util.mps_fixes # noqa: F401 (monkeypatching on import)
# parse_args() must be called before any other imports. if it is not called first, consumers of the config
# which are imported/used before parse_args() is called will get the default config values instead of the
# values from the command line or config file.
app_config = InvokeAIAppConfig.get_config()
app_config.parse_args()
if True: # hack to make flake8 happy with imports coming after setting up the config
import asyncio
import logging
import mimetypes
import socket
from inspect import signature
from pathlib import Path
import torch
import uvicorn
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html
from fastapi.openapi.utils import get_openapi
from fastapi.staticfiles import StaticFiles
from fastapi_events.handlers.local import local_handler
from fastapi_events.middleware import EventHandlerASGIMiddleware
from pydantic.schema import schema
# noinspection PyUnresolvedReferences
import invokeai.backend.util.hotfixes # noqa: F401 (monkeypatching on import)
import invokeai.frontend.web as web_dir
from invokeai.version.invokeai_version import __version__
from ..backend.util.logging import InvokeAILogger
from .api.dependencies import ApiDependencies
from .api.routers import app_info, board_images, boards, images, models, sessions
from .api.sockets import SocketIO
from .invocations.baseinvocation import BaseInvocation, UIConfigBase, _InputField, _OutputField
if torch.backends.mps.is_available():
# noinspection PyUnresolvedReferences
import invokeai.backend.util.mps_fixes # noqa: F401 (monkeypatching on import)
logger = InvokeAILogger.getLogger(config=app_config)
# fix for windows mimetypes registry entries being borked

113
invokeai/app/cli_app.py
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@ -1,67 +1,64 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
import argparse
import re
import shlex
import sys
import time
from typing import Union, get_type_hints, Optional
from pydantic import BaseModel, ValidationError
from pydantic.fields import Field
# This should come early so that the logger can pick up its configuration options
from .services.config import InvokeAIAppConfig
from invokeai.backend.util.logging import InvokeAILogger
from invokeai.version.invokeai_version import __version__
from invokeai.app.services.board_image_record_storage import (
SqliteBoardImageRecordStorage,
)
from invokeai.app.services.board_images import (
BoardImagesService,
BoardImagesServiceDependencies,
)
from invokeai.app.services.board_record_storage import SqliteBoardRecordStorage
from invokeai.app.services.boards import BoardService, BoardServiceDependencies
from invokeai.app.services.image_record_storage import SqliteImageRecordStorage
from invokeai.app.services.images import ImageService, ImageServiceDependencies
from invokeai.app.services.resource_name import SimpleNameService
from invokeai.app.services.urls import LocalUrlService
from invokeai.app.services.invocation_stats import InvocationStatsService
from .services.default_graphs import default_text_to_image_graph_id, create_system_graphs
from .services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
from .cli.commands import BaseCommand, CliContext, ExitCli, SortedHelpFormatter, add_graph_parsers, add_parsers
from .cli.completer import set_autocompleter
from .invocations.baseinvocation import BaseInvocation
from .services.events import EventServiceBase
from .services.graph import (
Edge,
EdgeConnection,
GraphExecutionState,
GraphInvocation,
LibraryGraph,
are_connection_types_compatible,
)
from .services.image_file_storage import DiskImageFileStorage
from .services.invocation_queue import MemoryInvocationQueue
from .services.invocation_services import InvocationServices
from .services.invoker import Invoker
from .services.model_manager_service import ModelManagerService
from .services.processor import DefaultInvocationProcessor
from .services.sqlite import SqliteItemStorage
import torch
import invokeai.backend.util.hotfixes # noqa: F401 (monkeypatching on import)
if torch.backends.mps.is_available():
import invokeai.backend.util.mps_fixes # noqa: F401 (monkeypatching on import)
# parse_args() must be called before any other imports. if it is not called first, consumers of the config
# which are imported/used before parse_args() is called will get the default config values instead of the
# values from the command line or config file.
config = InvokeAIAppConfig.get_config()
config.parse_args()
if True: # hack to make flake8 happy with imports coming after setting up the config
import argparse
import re
import shlex
import sys
import time
from typing import Optional, Union, get_type_hints
import torch
from pydantic import BaseModel, ValidationError
from pydantic.fields import Field
import invokeai.backend.util.hotfixes # noqa: F401 (monkeypatching on import)
from invokeai.app.services.board_image_record_storage import SqliteBoardImageRecordStorage
from invokeai.app.services.board_images import BoardImagesService, BoardImagesServiceDependencies
from invokeai.app.services.board_record_storage import SqliteBoardRecordStorage
from invokeai.app.services.boards import BoardService, BoardServiceDependencies
from invokeai.app.services.image_record_storage import SqliteImageRecordStorage
from invokeai.app.services.images import ImageService, ImageServiceDependencies
from invokeai.app.services.invocation_stats import InvocationStatsService
from invokeai.app.services.resource_name import SimpleNameService
from invokeai.app.services.urls import LocalUrlService
from invokeai.backend.util.logging import InvokeAILogger
from invokeai.version.invokeai_version import __version__
from .cli.commands import BaseCommand, CliContext, ExitCli, SortedHelpFormatter, add_graph_parsers, add_parsers
from .cli.completer import set_autocompleter
from .invocations.baseinvocation import BaseInvocation
from .services.default_graphs import create_system_graphs, default_text_to_image_graph_id
from .services.events import EventServiceBase
from .services.graph import (
Edge,
EdgeConnection,
GraphExecutionState,
GraphInvocation,
LibraryGraph,
are_connection_types_compatible,
)
from .services.image_file_storage import DiskImageFileStorage
from .services.invocation_queue import MemoryInvocationQueue
from .services.invocation_services import InvocationServices
from .services.invoker import Invoker
from .services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
from .services.model_manager_service import ModelManagerService
from .services.processor import DefaultInvocationProcessor
from .services.sqlite import SqliteItemStorage
if torch.backends.mps.is_available():
import invokeai.backend.util.mps_fixes # noqa: F401 (monkeypatching on import)
logger = InvokeAILogger().getLogger(config=config)

106
invokeai/app/invocations/baseinvocation.py
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@ -26,11 +26,18 @@ from typing import (
from pydantic import BaseModel, Field, validator
from pydantic.fields import Undefined, ModelField
from pydantic.typing import NoArgAnyCallable
import semver
from invokeai.app.services.config.invokeai_config import InvokeAIAppConfig
if TYPE_CHECKING:
from ..services.invocation_services import InvocationServices
class InvalidVersionError(ValueError):
pass
class FieldDescriptions:
denoising_start = "When to start denoising, expressed a percentage of total steps"
denoising_end = "When to stop denoising, expressed a percentage of total steps"
@ -105,24 +112,39 @@ class UIType(str, Enum):
"""
# region Primitives
Integer = "integer"
Float = "float"
Boolean = "boolean"
String = "string"
Array = "array"
Image = "ImageField"
Latents = "LatentsField"
Color = "ColorField"
Conditioning = "ConditioningField"
Control = "ControlField"
Color = "ColorField"
ImageCollection = "ImageCollection"
ConditioningCollection = "ConditioningCollection"
ColorCollection = "ColorCollection"
LatentsCollection = "LatentsCollection"
IntegerCollection = "IntegerCollection"
FloatCollection = "FloatCollection"
StringCollection = "StringCollection"
Float = "float"
Image = "ImageField"
Integer = "integer"
Latents = "LatentsField"
String = "string"
# endregion
# region Collection Primitives
BooleanCollection = "BooleanCollection"
ColorCollection = "ColorCollection"
ConditioningCollection = "ConditioningCollection"
ControlCollection = "ControlCollection"
FloatCollection = "FloatCollection"
ImageCollection = "ImageCollection"
IntegerCollection = "IntegerCollection"
LatentsCollection = "LatentsCollection"
StringCollection = "StringCollection"
# endregion
# region Polymorphic Primitives
BooleanPolymorphic = "BooleanPolymorphic"
ColorPolymorphic = "ColorPolymorphic"
ConditioningPolymorphic = "ConditioningPolymorphic"
ControlPolymorphic = "ControlPolymorphic"
FloatPolymorphic = "FloatPolymorphic"
ImagePolymorphic = "ImagePolymorphic"
IntegerPolymorphic = "IntegerPolymorphic"
LatentsPolymorphic = "LatentsPolymorphic"
StringPolymorphic = "StringPolymorphic"
# endregion
# region Models
@ -176,6 +198,7 @@ class _InputField(BaseModel):
ui_type: Optional[UIType]
ui_component: Optional[UIComponent]
ui_order: Optional[int]
item_default: Optional[Any]
class _OutputField(BaseModel):
@ -223,6 +246,7 @@ def InputField(
ui_component: Optional[UIComponent] = None,
ui_hidden: bool = False,
ui_order: Optional[int] = None,
item_default: Optional[Any] = None,
**kwargs: Any,
) -> Any:
"""
@ -249,6 +273,11 @@ def InputField(
For this case, you could provide `UIComponent.Textarea`.
: param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI.
: param int ui_order: [None] Specifies the order in which this field should be rendered in the UI. \
: param bool item_default: [None] Specifies the default item value, if this is a collection input. \
Ignored for non-collection fields..
"""
return Field(
*args,
@ -282,6 +311,7 @@ def InputField(
ui_component=ui_component,
ui_hidden=ui_hidden,
ui_order=ui_order,
item_default=item_default,
**kwargs,
)
@ -332,6 +362,8 @@ def OutputField(
`UIType.SDXLMainModelField` to indicate that the field is an SDXL main model field.
: param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI. \
: param int ui_order: [None] Specifies the order in which this field should be rendered in the UI. \
"""
return Field(
*args,
@ -376,6 +408,9 @@ class UIConfigBase(BaseModel):
tags: Optional[list[str]] = Field(default_factory=None, description="The node's tags")
title: Optional[str] = Field(default=None, description="The node's display name")
category: Optional[str] = Field(default=None, description="The node's category")
version: Optional[str] = Field(
default=None, description='The node\'s version. Should be a valid semver string e.g. "1.0.0" or "3.8.13".'
)
class InvocationContext:
@ -437,6 +472,7 @@ class BaseInvocation(ABC, BaseModel):
@classmethod
def get_all_subclasses(cls):
app_config = InvokeAIAppConfig.get_config()
subclasses = []
toprocess = [cls]
while len(toprocess) > 0:
@ -444,7 +480,23 @@ class BaseInvocation(ABC, BaseModel):
next_subclasses = next.__subclasses__()
subclasses.extend(next_subclasses)
toprocess.extend(next_subclasses)
return subclasses
allowed_invocations = []
for sc in subclasses:
is_in_allowlist = (
sc.__fields__.get("type").default in app_config.allow_nodes
if isinstance(app_config.allow_nodes, list)
else True
)
is_in_denylist = (
sc.__fields__.get("type").default in app_config.deny_nodes
if isinstance(app_config.deny_nodes, list)
else False
)
if is_in_allowlist and not is_in_denylist:
allowed_invocations.append(sc)
return allowed_invocations
@classmethod
def get_invocations(cls):
@ -474,6 +526,8 @@ class BaseInvocation(ABC, BaseModel):
schema["tags"] = uiconfig.tags
if uiconfig and hasattr(uiconfig, "category"):
schema["category"] = uiconfig.category
if uiconfig and hasattr(uiconfig, "version"):
schema["version"] = uiconfig.version
if "required" not in schema or not isinstance(schema["required"], list):
schema["required"] = list()
schema["required"].extend(["type", "id"])
@ -542,7 +596,11 @@ GenericBaseInvocation = TypeVar("GenericBaseInvocation", bound=BaseInvocation)
def invocation(
invocation_type: str, title: Optional[str] = None, tags: Optional[list[str]] = None, category: Optional[str] = None
invocation_type: str,
title: Optional[str] = None,
tags: Optional[list[str]] = None,
category: Optional[str] = None,
version: Optional[str] = None,
) -> Callable[[Type[GenericBaseInvocation]], Type[GenericBaseInvocation]]:
"""
Adds metadata to an invocation.
@ -569,6 +627,12 @@ def invocation(
cls.UIConfig.tags = tags
if category is not None:
cls.UIConfig.category = category
if version is not None:
try:
semver.Version.parse(version)
except ValueError as e:
raise InvalidVersionError(f'Invalid version string for node "{invocation_type}": "{version}"') from e
cls.UIConfig.version = version
# Add the invocation type to the pydantic model of the invocation
invocation_type_annotation = Literal[invocation_type] # type: ignore
@ -580,8 +644,9 @@ def invocation(
config=cls.__config__,
)
cls.__fields__.update({"type": invocation_type_field})
cls.__annotations__.update({"type": invocation_type_annotation})
# to support 3.9, 3.10 and 3.11, as described in https://docs.python.org/3/howto/annotations.html
if annotations := cls.__dict__.get("__annotations__", None):
annotations.update({"type": invocation_type_annotation})
return cls
return wrapper
@ -615,7 +680,10 @@ def invocation_output(
config=cls.__config__,
)
cls.__fields__.update({"type": output_type_field})
cls.__annotations__.update({"type": output_type_annotation})
# to support 3.9, 3.10 and 3.11, as described in https://docs.python.org/3/howto/annotations.html
if annotations := cls.__dict__.get("__annotations__", None):
annotations.update({"type": output_type_annotation})
return cls

6
invokeai/app/invocations/collections.py
View File

@ -10,7 +10,9 @@ from invokeai.app.util.misc import SEED_MAX, get_random_seed
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
@invocation("range", title="Integer Range", tags=["collection", "integer", "range"], category="collections")
@invocation(
"range", title="Integer Range", tags=["collection", "integer", "range"], category="collections", version="1.0.0"
)
class RangeInvocation(BaseInvocation):
"""Creates a range of numbers from start to stop with step"""
@ -33,6 +35,7 @@ class RangeInvocation(BaseInvocation):
title="Integer Range of Size",
tags=["collection", "integer", "size", "range"],
category="collections",
version="1.0.0",
)
class RangeOfSizeInvocation(BaseInvocation):
"""Creates a range from start to start + size with step"""
@ -50,6 +53,7 @@ class RangeOfSizeInvocation(BaseInvocation):
title="Random Range",
tags=["range", "integer", "random", "collection"],
category="collections",
version="1.0.0",
)
class RandomRangeInvocation(BaseInvocation):
"""Creates a collection of random numbers"""

6
invokeai/app/invocations/compel.py
View File

@ -44,7 +44,7 @@ class ConditioningFieldData:
# PerpNeg = "perp_neg"
@invocation("compel", title="Prompt", tags=["prompt", "compel"], category="conditioning")
@invocation("compel", title="Prompt", tags=["prompt", "compel"], category="conditioning", version="1.0.0")
class CompelInvocation(BaseInvocation):
"""Parse prompt using compel package to conditioning."""
@ -267,6 +267,7 @@ class SDXLPromptInvocationBase:
title="SDXL Prompt",
tags=["sdxl", "compel", "prompt"],
category="conditioning",
version="1.0.0",
)
class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
"""Parse prompt using compel package to conditioning."""
@ -351,6 +352,7 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
title="SDXL Refiner Prompt",
tags=["sdxl", "compel", "prompt"],
category="conditioning",
version="1.0.0",
)
class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
"""Parse prompt using compel package to conditioning."""
@ -403,7 +405,7 @@ class ClipSkipInvocationOutput(BaseInvocationOutput):
clip: ClipField = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
@invocation("clip_skip", title="CLIP Skip", tags=["clipskip", "clip", "skip"], category="conditioning")
@invocation("clip_skip", title="CLIP Skip", tags=["clipskip", "clip", "skip"], category="conditioning", version="1.0.0")
class ClipSkipInvocation(BaseInvocation):
"""Skip layers in clip text_encoder model."""

31
invokeai/app/invocations/controlnet_image_processors.py
View File

@ -95,14 +95,12 @@ class ControlOutput(BaseInvocationOutput):
control: ControlField = OutputField(description=FieldDescriptions.control)
@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet")
@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.0.0")
class ControlNetInvocation(BaseInvocation):
"""Collects ControlNet info to pass to other nodes"""
image: ImageField = InputField(description="The control image")
control_model: ControlNetModelField = InputField(
default="lllyasviel/sd-controlnet-canny", description=FieldDescriptions.controlnet_model, input=Input.Direct
)
control_model: ControlNetModelField = InputField(description=FieldDescriptions.controlnet_model, input=Input.Direct)
control_weight: Union[float, List[float]] = InputField(
default=1.0, description="The weight given to the ControlNet", ui_type=UIType.Float
)
@ -129,7 +127,9 @@ class ControlNetInvocation(BaseInvocation):
)
@invocation("image_processor", title="Base Image Processor", tags=["controlnet"], category="controlnet")
@invocation(
"image_processor", title="Base Image Processor", tags=["controlnet"], category="controlnet", version="1.0.0"
)
class ImageProcessorInvocation(BaseInvocation):
"""Base class for invocations that preprocess images for ControlNet"""
@ -173,6 +173,7 @@ class ImageProcessorInvocation(BaseInvocation):
title="Canny Processor",
tags=["controlnet", "canny"],
category="controlnet",
version="1.0.0",
)
class CannyImageProcessorInvocation(ImageProcessorInvocation):
"""Canny edge detection for ControlNet"""
@ -195,6 +196,7 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation):
title="HED (softedge) Processor",
tags=["controlnet", "hed", "softedge"],
category="controlnet",
version="1.0.0",
)
class HedImageProcessorInvocation(ImageProcessorInvocation):
"""Applies HED edge detection to image"""
@ -223,6 +225,7 @@ class HedImageProcessorInvocation(ImageProcessorInvocation):
title="Lineart Processor",
tags=["controlnet", "lineart"],
category="controlnet",
version="1.0.0",
)
class LineartImageProcessorInvocation(ImageProcessorInvocation):
"""Applies line art processing to image"""
@ -244,6 +247,7 @@ class LineartImageProcessorInvocation(ImageProcessorInvocation):
title="Lineart Anime Processor",
tags=["controlnet", "lineart", "anime"],
category="controlnet",
version="1.0.0",
)
class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
"""Applies line art anime processing to image"""
@ -266,6 +270,7 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
title="Openpose Processor",
tags=["controlnet", "openpose", "pose"],
category="controlnet",
version="1.0.0",
)
class OpenposeImageProcessorInvocation(ImageProcessorInvocation):
"""Applies Openpose processing to image"""
@ -290,6 +295,7 @@ class OpenposeImageProcessorInvocation(ImageProcessorInvocation):
title="Midas Depth Processor",
tags=["controlnet", "midas"],
category="controlnet",
version="1.0.0",
)
class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
"""Applies Midas depth processing to image"""
@ -316,6 +322,7 @@ class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
title="Normal BAE Processor",
tags=["controlnet"],
category="controlnet",
version="1.0.0",
)
class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
"""Applies NormalBae processing to image"""
@ -331,7 +338,9 @@ class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
return processed_image
@invocation("mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet")
@invocation(
"mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.0.0"
)
class MlsdImageProcessorInvocation(ImageProcessorInvocation):
"""Applies MLSD processing to image"""
@ -352,7 +361,9 @@ class MlsdImageProcessorInvocation(ImageProcessorInvocation):
return processed_image
@invocation("pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet")
@invocation(
"pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.0.0"
)
class PidiImageProcessorInvocation(ImageProcessorInvocation):
"""Applies PIDI processing to image"""
@ -378,6 +389,7 @@ class PidiImageProcessorInvocation(ImageProcessorInvocation):
title="Content Shuffle Processor",
tags=["controlnet", "contentshuffle"],
category="controlnet",
version="1.0.0",
)
class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
"""Applies content shuffle processing to image"""
@ -407,6 +419,7 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
title="Zoe (Depth) Processor",
tags=["controlnet", "zoe", "depth"],
category="controlnet",
version="1.0.0",
)
class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
"""Applies Zoe depth processing to image"""
@ -422,6 +435,7 @@ class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
title="Mediapipe Face Processor",
tags=["controlnet", "mediapipe", "face"],
category="controlnet",
version="1.0.0",
)
class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
"""Applies mediapipe face processing to image"""
@ -444,6 +458,7 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
title="Leres (Depth) Processor",
tags=["controlnet", "leres", "depth"],
category="controlnet",
version="1.0.0",
)
class LeresImageProcessorInvocation(ImageProcessorInvocation):
"""Applies leres processing to image"""
@ -472,6 +487,7 @@ class LeresImageProcessorInvocation(ImageProcessorInvocation):
title="Tile Resample Processor",
tags=["controlnet", "tile"],
category="controlnet",
version="1.0.0",
)
class TileResamplerProcessorInvocation(ImageProcessorInvocation):
"""Tile resampler processor"""
@ -511,6 +527,7 @@ class TileResamplerProcessorInvocation(ImageProcessorInvocation):
title="Segment Anything Processor",
tags=["controlnet", "segmentanything"],
category="controlnet",
version="1.0.0",
)
class SegmentAnythingProcessorInvocation(ImageProcessorInvocation):
"""Applies segment anything processing to image"""

7
invokeai/app/invocations/cv.py
View File

@ -10,12 +10,7 @@ from invokeai.app.models.image import ImageCategory, ResourceOrigin
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
@invocation(
"cv_inpaint",
title="OpenCV Inpaint",
tags=["opencv", "inpaint"],
category="inpaint",
)
@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.0.0")
class CvInpaintInvocation(BaseInvocation):
"""Simple inpaint using opencv."""

205
invokeai/app/invocations/image.py
View File

@ -16,7 +16,7 @@ from ..models.image import ImageCategory, ResourceOrigin
from .baseinvocation import BaseInvocation, FieldDescriptions, InputField, InvocationContext, invocation
@invocation("show_image", title="Show Image", tags=["image"], category="image")
@invocation("show_image", title="Show Image", tags=["image"], category="image", version="1.0.0")
class ShowImageInvocation(BaseInvocation):
"""Displays a provided image using the OS image viewer, and passes it forward in the pipeline."""
@ -36,7 +36,7 @@ class ShowImageInvocation(BaseInvocation):
)
@invocation("blank_image", title="Blank Image", tags=["image"], category="image")
@invocation("blank_image", title="Blank Image", tags=["image"], category="image", version="1.0.0")
class BlankImageInvocation(BaseInvocation):
"""Creates a blank image and forwards it to the pipeline"""
@ -65,7 +65,7 @@ class BlankImageInvocation(BaseInvocation):
)
@invocation("img_crop", title="Crop Image", tags=["image", "crop"], category="image")
@invocation("img_crop", title="Crop Image", tags=["image", "crop"], category="image", version="1.0.0")
class ImageCropInvocation(BaseInvocation):
"""Crops an image to a specified box. The box can be outside of the image."""
@ -98,7 +98,7 @@ class ImageCropInvocation(BaseInvocation):
)
@invocation("img_paste", title="Paste Image", tags=["image", "paste"], category="image")
@invocation("img_paste", title="Paste Image", tags=["image", "paste"], category="image", version="1.0.0")
class ImagePasteInvocation(BaseInvocation):
"""Pastes an image into another image."""
@ -146,7 +146,7 @@ class ImagePasteInvocation(BaseInvocation):
)
@invocation("tomask", title="Mask from Alpha", tags=["image", "mask"], category="image")
@invocation("tomask", title="Mask from Alpha", tags=["image", "mask"], category="image", version="1.0.0")
class MaskFromAlphaInvocation(BaseInvocation):
"""Extracts the alpha channel of an image as a mask."""
@ -177,7 +177,7 @@ class MaskFromAlphaInvocation(BaseInvocation):
)
@invocation("img_mul", title="Multiply Images", tags=["image", "multiply"], category="image")
@invocation("img_mul", title="Multiply Images", tags=["image", "multiply"], category="image", version="1.0.0")
class ImageMultiplyInvocation(BaseInvocation):
"""Multiplies two images together using `PIL.ImageChops.multiply()`."""
@ -210,7 +210,7 @@ class ImageMultiplyInvocation(BaseInvocation):
IMAGE_CHANNELS = Literal["A", "R", "G", "B"]
@invocation("img_chan", title="Extract Image Channel", tags=["image", "channel"], category="image")
@invocation("img_chan", title="Extract Image Channel", tags=["image", "channel"], category="image", version="1.0.0")
class ImageChannelInvocation(BaseInvocation):
"""Gets a channel from an image."""
@ -242,7 +242,7 @@ class ImageChannelInvocation(BaseInvocation):
IMAGE_MODES = Literal["L", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F"]
@invocation("img_conv", title="Convert Image Mode", tags=["image", "convert"], category="image")
@invocation("img_conv", title="Convert Image Mode", tags=["image", "convert"], category="image", version="1.0.0")
class ImageConvertInvocation(BaseInvocation):
"""Converts an image to a different mode."""
@ -271,7 +271,7 @@ class ImageConvertInvocation(BaseInvocation):
)
@invocation("img_blur", title="Blur Image", tags=["image", "blur"], category="image")
@invocation("img_blur", title="Blur Image", tags=["image", "blur"], category="image", version="1.0.0")
class ImageBlurInvocation(BaseInvocation):
"""Blurs an image"""
@ -325,7 +325,7 @@ PIL_RESAMPLING_MAP = {
}
@invocation("img_resize", title="Resize Image", tags=["image", "resize"], category="image")
@invocation("img_resize", title="Resize Image", tags=["image", "resize"], category="image", version="1.0.0")
class ImageResizeInvocation(BaseInvocation):
"""Resizes an image to specific dimensions"""
@ -365,7 +365,7 @@ class ImageResizeInvocation(BaseInvocation):
)
@invocation("img_scale", title="Scale Image", tags=["image", "scale"], category="image")
@invocation("img_scale", title="Scale Image", tags=["image", "scale"], category="image", version="1.0.0")
class ImageScaleInvocation(BaseInvocation):
"""Scales an image by a factor"""
@ -406,7 +406,7 @@ class ImageScaleInvocation(BaseInvocation):
)
@invocation("img_lerp", title="Lerp Image", tags=["image", "lerp"], category="image")
@invocation("img_lerp", title="Lerp Image", tags=["image", "lerp"], category="image", version="1.0.0")
class ImageLerpInvocation(BaseInvocation):
"""Linear interpolation of all pixels of an image"""
@ -439,7 +439,7 @@ class ImageLerpInvocation(BaseInvocation):
)
@invocation("img_ilerp", title="Inverse Lerp Image", tags=["image", "ilerp"], category="image")
@invocation("img_ilerp", title="Inverse Lerp Image", tags=["image", "ilerp"], category="image", version="1.0.0")
class ImageInverseLerpInvocation(BaseInvocation):
"""Inverse linear interpolation of all pixels of an image"""
@ -472,7 +472,7 @@ class ImageInverseLerpInvocation(BaseInvocation):
)
@invocation("img_nsfw", title="Blur NSFW Image", tags=["image", "nsfw"], category="image")
@invocation("img_nsfw", title="Blur NSFW Image", tags=["image", "nsfw"], category="image", version="1.0.0")
class ImageNSFWBlurInvocation(BaseInvocation):
"""Add blur to NSFW-flagged images"""
@ -517,7 +517,9 @@ class ImageNSFWBlurInvocation(BaseInvocation):
return caution.resize((caution.width // 2, caution.height // 2))
@invocation("img_watermark", title="Add Invisible Watermark", tags=["image", "watermark"], category="image")
@invocation(
"img_watermark", title="Add Invisible Watermark", tags=["image", "watermark"], category="image", version="1.0.0"
)
class ImageWatermarkInvocation(BaseInvocation):
"""Add an invisible watermark to an image"""
@ -548,7 +550,7 @@ class ImageWatermarkInvocation(BaseInvocation):
)
@invocation("mask_edge", title="Mask Edge", tags=["image", "mask", "inpaint"], category="image")
@invocation("mask_edge", title="Mask Edge", tags=["image", "mask", "inpaint"], category="image", version="1.0.0")
class MaskEdgeInvocation(BaseInvocation):
"""Applies an edge mask to an image"""
@ -561,7 +563,7 @@ class MaskEdgeInvocation(BaseInvocation):
)
def invoke(self, context: InvocationContext) -> ImageOutput:
mask = context.services.images.get_pil_image(self.image.image_name)
mask = context.services.images.get_pil_image(self.image.image_name).convert("L")
npimg = numpy.asarray(mask, dtype=numpy.uint8)
npgradient = numpy.uint8(255 * (1.0 - numpy.floor(numpy.abs(0.5 - numpy.float32(npimg) / 255.0) * 2.0)))
@ -593,7 +595,9 @@ class MaskEdgeInvocation(BaseInvocation):
)
@invocation("mask_combine", title="Combine Masks", tags=["image", "mask", "multiply"], category="image")
@invocation(
"mask_combine", title="Combine Masks", tags=["image", "mask", "multiply"], category="image", version="1.0.0"
)
class MaskCombineInvocation(BaseInvocation):
"""Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`."""
@ -623,7 +627,7 @@ class MaskCombineInvocation(BaseInvocation):
)
@invocation("color_correct", title="Color Correct", tags=["image", "color"], category="image")
@invocation("color_correct", title="Color Correct", tags=["image", "color"], category="image", version="1.0.0")
class ColorCorrectInvocation(BaseInvocation):
"""
Shifts the colors of a target image to match the reference image, optionally
@ -696,8 +700,13 @@ class ColorCorrectInvocation(BaseInvocation):
# Blur the mask out (into init image) by specified amount
if self.mask_blur_radius > 0:
nm = numpy.asarray(pil_init_mask, dtype=numpy.uint8)
inverted_nm = 255 - nm
dilation_size = int(round(self.mask_blur_radius) + 20)
dilating_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (dilation_size, dilation_size))
inverted_dilated_nm = cv2.dilate(inverted_nm, dilating_kernel)
dilated_nm = 255 - inverted_dilated_nm
nmd = cv2.erode(
nm,
dilated_nm,
kernel=numpy.ones((3, 3), dtype=numpy.uint8),
iterations=int(self.mask_blur_radius / 2),
)
@ -728,7 +737,7 @@ class ColorCorrectInvocation(BaseInvocation):
)
@invocation("img_hue_adjust", title="Adjust Image Hue", tags=["image", "hue"], category="image")
@invocation("img_hue_adjust", title="Adjust Image Hue", tags=["image", "hue"], category="image", version="1.0.0")
class ImageHueAdjustmentInvocation(BaseInvocation):
"""Adjusts the Hue of an image."""
@ -769,38 +778,95 @@ class ImageHueAdjustmentInvocation(BaseInvocation):
)
COLOR_CHANNELS = Literal[
"Red (RGBA)",
"Green (RGBA)",
"Blue (RGBA)",
"Alpha (RGBA)",
"Cyan (CMYK)",
"Magenta (CMYK)",
"Yellow (CMYK)",
"Black (CMYK)",
"Hue (HSV)",
"Saturation (HSV)",
"Value (HSV)",
"Luminosity (LAB)",
"A (LAB)",
"B (LAB)",
"Y (YCbCr)",
"Cb (YCbCr)",
"Cr (YCbCr)",
]
CHANNEL_FORMATS = {
"Red (RGBA)": ("RGBA", 0),
"Green (RGBA)": ("RGBA", 1),
"Blue (RGBA)": ("RGBA", 2),
"Alpha (RGBA)": ("RGBA", 3),
"Cyan (CMYK)": ("CMYK", 0),
"Magenta (CMYK)": ("CMYK", 1),
"Yellow (CMYK)": ("CMYK", 2),
"Black (CMYK)": ("CMYK", 3),
"Hue (HSV)": ("HSV", 0),
"Saturation (HSV)": ("HSV", 1),
"Value (HSV)": ("HSV", 2),
"Luminosity (LAB)": ("LAB", 0),
"A (LAB)": ("LAB", 1),
"B (LAB)": ("LAB", 2),
"Y (YCbCr)": ("YCbCr", 0),
"Cb (YCbCr)": ("YCbCr", 1),
"Cr (YCbCr)": ("YCbCr", 2),
}
@invocation(
"img_luminosity_adjust",
title="Adjust Image Luminosity",
tags=["image", "luminosity", "hsl"],
"img_channel_offset",
title="Offset Image Channel",
tags=[
"image",
"offset",
"red",
"green",
"blue",
"alpha",
"cyan",
"magenta",
"yellow",
"black",
"hue",
"saturation",
"luminosity",
"value",
],
category="image",
version="1.0.0",
)
class ImageLuminosityAdjustmentInvocation(BaseInvocation):
"""Adjusts the Luminosity (Value) of an image."""
class ImageChannelOffsetInvocation(BaseInvocation):
"""Add or subtract a value from a specific color channel of an image."""
image: ImageField = InputField(description="The image to adjust")
luminosity: float = InputField(
default=1.0, ge=0, le=1, description="The factor by which to adjust the luminosity (value)"
)
channel: COLOR_CHANNELS = InputField(description="Which channel to adjust")
offset: int = InputField(default=0, ge=-255, le=255, description="The amount to adjust the channel by")
def invoke(self, context: InvocationContext) -> ImageOutput:
pil_image = context.services.images.get_pil_image(self.image.image_name)
# Convert PIL image to OpenCV format (numpy array), note color channel
# ordering is changed from RGB to BGR
image = numpy.array(pil_image.convert("RGB"))[:, :, ::-1]
# extract the channel and mode from the input and reference tuple
mode = CHANNEL_FORMATS[self.channel][0]
channel_number = CHANNEL_FORMATS[self.channel][1]
# Convert image to HSV color space
hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# Convert PIL image to new format
converted_image = numpy.array(pil_image.convert(mode)).astype(int)
image_channel = converted_image[:, :, channel_number]
# Adjust the luminosity (value)
hsv_image[:, :, 2] = numpy.clip(hsv_image[:, :, 2] * self.luminosity, 0, 255)
# Adjust the value, clipping to 0..255
image_channel = numpy.clip(image_channel + self.offset, 0, 255)
# Convert image back to BGR color space
image = cv2.cvtColor(hsv_image, cv2.COLOR_HSV2BGR)
# Put the channel back into the image
converted_image[:, :, channel_number] = image_channel
# Convert back to PIL format and to original color mode
pil_image = Image.fromarray(image[:, :, ::-1], "RGB").convert("RGBA")
# Convert back to RGBA format and output
pil_image = Image.fromarray(converted_image.astype(numpy.uint8), mode=mode).convert("RGBA")
image_dto = context.services.images.create(
image=pil_image,
@ -822,35 +888,60 @@ class ImageLuminosityAdjustmentInvocation(BaseInvocation):
@invocation(
"img_saturation_adjust",
title="Adjust Image Saturation",
tags=["image", "saturation", "hsl"],
"img_channel_multiply",
title="Multiply Image Channel",
tags=[
"image",
"invert",
"scale",
"multiply",
"red",
"green",
"blue",
"alpha",
"cyan",
"magenta",
"yellow",
"black",
"hue",
"saturation",
"luminosity",
"value",
],
category="image",
version="1.0.0",
)
class ImageSaturationAdjustmentInvocation(BaseInvocation):
"""Adjusts the Saturation of an image."""
class ImageChannelMultiplyInvocation(BaseInvocation):
"""Scale a specific color channel of an image."""
image: ImageField = InputField(description="The image to adjust")
saturation: float = InputField(default=1.0, ge=0, le=1, description="The factor by which to adjust the saturation")
channel: COLOR_CHANNELS = InputField(description="Which channel to adjust")
scale: float = InputField(default=1.0, ge=0.0, description="The amount to scale the channel by.")
invert_channel: bool = InputField(default=False, description="Invert the channel after scaling")
def invoke(self, context: InvocationContext) -> ImageOutput:
pil_image = context.services.images.get_pil_image(self.image.image_name)
# Convert PIL image to OpenCV format (numpy array), note color channel
# ordering is changed from RGB to BGR
image = numpy.array(pil_image.convert("RGB"))[:, :, ::-1]
# extract the channel and mode from the input and reference tuple
mode = CHANNEL_FORMATS[self.channel][0]
channel_number = CHANNEL_FORMATS[self.channel][1]
# Convert image to HSV color space
hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# Convert PIL image to new format
converted_image = numpy.array(pil_image.convert(mode)).astype(float)
image_channel = converted_image[:, :, channel_number]
# Adjust the saturation
hsv_image[:, :, 1] = numpy.clip(hsv_image[:, :, 1] * self.saturation, 0, 255)
# Adjust the value, clipping to 0..255
image_channel = numpy.clip(image_channel * self.scale, 0, 255)
# Convert image back to BGR color space
image = cv2.cvtColor(hsv_image, cv2.COLOR_HSV2BGR)
# Invert the channel if requested
if self.invert_channel:
image_channel = 255 - image_channel
# Convert back to PIL format and to original color mode
pil_image = Image.fromarray(image[:, :, ::-1], "RGB").convert("RGBA")
# Put the channel back into the image
converted_image[:, :, channel_number] = image_channel
# Convert back to RGBA format and output
pil_image = Image.fromarray(converted_image.astype(numpy.uint8), mode=mode).convert("RGBA")
image_dto = context.services.images.create(
image=pil_image,

73
invokeai/app/invocations/infill.py
View File

@ -8,19 +8,17 @@ from PIL import Image, ImageOps
from invokeai.app.invocations.primitives import ColorField, ImageField, ImageOutput
from invokeai.app.util.misc import SEED_MAX, get_random_seed
from invokeai.backend.image_util.cv2_inpaint import cv2_inpaint
from invokeai.backend.image_util.lama import LaMA
from invokeai.backend.image_util.patchmatch import PatchMatch
from ..models.image import ImageCategory, ResourceOrigin
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
from .image import PIL_RESAMPLING_MAP, PIL_RESAMPLING_MODES
def infill_methods() -> list[str]:
methods = [
"tile",
"solid",
"lama",
]
methods = ["tile", "solid", "lama", "cv2"]
if PatchMatch.patchmatch_available():
methods.insert(0, "patchmatch")
return methods
@ -49,6 +47,10 @@ def infill_patchmatch(im: Image.Image) -> Image.Image:
return im_patched
def infill_cv2(im: Image.Image) -> Image.Image:
return cv2_inpaint(im)
def get_tile_images(image: np.ndarray, width=8, height=8):
_nrows, _ncols, depth = image.shape
_strides = image.strides
@ -116,7 +118,7 @@ def tile_fill_missing(im: Image.Image, tile_size: int = 16, seed: Optional[int]
return si
@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint")
@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
class InfillColorInvocation(BaseInvocation):
"""Infills transparent areas of an image with a solid color"""
@ -151,7 +153,7 @@ class InfillColorInvocation(BaseInvocation):
)
@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint")
@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
class InfillTileInvocation(BaseInvocation):
"""Infills transparent areas of an image with tiles of the image"""
@ -187,20 +189,42 @@ class InfillTileInvocation(BaseInvocation):
)
@invocation("infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint")
@invocation(
"infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0"
)
class InfillPatchMatchInvocation(BaseInvocation):
"""Infills transparent areas of an image using the PatchMatch algorithm"""
image: ImageField = InputField(description="The image to infill")
downscale: float = InputField(default=2.0, gt=0, description="Run patchmatch on downscaled image to speedup infill")
resample_mode: PIL_RESAMPLING_MODES = InputField(default="bicubic", description="The resampling mode")
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
image = context.services.images.get_pil_image(self.image.image_name).convert("RGBA")
resample_mode = PIL_RESAMPLING_MAP[self.resample_mode]
infill_image = image.copy()
width = int(image.width / self.downscale)
height = int(image.height / self.downscale)
infill_image = infill_image.resize(
(width, height),
resample=resample_mode,
)
if PatchMatch.patchmatch_available():
infilled = infill_patchmatch(image.copy())
infilled = infill_patchmatch(infill_image)
else:
raise ValueError("PatchMatch is not available on this system")
infilled = infilled.resize(
(image.width, image.height),
resample=resample_mode,
)
infilled.paste(image, (0, 0), mask=image.split()[-1])
# image.paste(infilled, (0, 0), mask=image.split()[-1])
image_dto = context.services.images.create(
image=infilled,
image_origin=ResourceOrigin.INTERNAL,
@ -218,7 +242,7 @@ class InfillPatchMatchInvocation(BaseInvocation):
)
@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint")
@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
class LaMaInfillInvocation(BaseInvocation):
"""Infills transparent areas of an image using the LaMa model"""
@ -243,3 +267,30 @@ class LaMaInfillInvocation(BaseInvocation):
width=image_dto.width,
height=image_dto.height,
)
@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint")
class CV2InfillInvocation(BaseInvocation):
"""Infills transparent areas of an image using OpenCV Inpainting"""
image: ImageField = InputField(description="The image to infill")
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
infilled = infill_cv2(image.copy())
image_dto = context.services.images.create(
image=infilled,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
)
return ImageOutput(
image=ImageField(image_name=image_dto.image_name),
width=image_dto.width,
height=image_dto.height,
)

31
invokeai/app/invocations/latent.py
View File

@ -76,7 +76,7 @@ class SchedulerOutput(BaseInvocationOutput):
scheduler: SAMPLER_NAME_VALUES = OutputField(description=FieldDescriptions.scheduler, ui_type=UIType.Scheduler)
@invocation("scheduler", title="Scheduler", tags=["scheduler"], category="latents")
@invocation("scheduler", title="Scheduler", tags=["scheduler"], category="latents", version="1.0.0")
class SchedulerInvocation(BaseInvocation):
"""Selects a scheduler."""
@ -88,7 +88,9 @@ class SchedulerInvocation(BaseInvocation):
return SchedulerOutput(scheduler=self.scheduler)
@invocation("create_denoise_mask", title="Create Denoise Mask", tags=["mask", "denoise"], category="latents")
@invocation(
"create_denoise_mask", title="Create Denoise Mask", tags=["mask", "denoise"], category="latents", version="1.0.0"
)
class CreateDenoiseMaskInvocation(BaseInvocation):
"""Creates mask for denoising model run."""
@ -188,6 +190,7 @@ def get_scheduler(
title="Denoise Latents",
tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
category="latents",
version="1.0.0",
)
class DenoiseLatentsInvocation(BaseInvocation):
"""Denoises noisy latents to decodable images"""
@ -210,12 +213,14 @@ class DenoiseLatentsInvocation(BaseInvocation):
)
unet: UNetField = InputField(description=FieldDescriptions.unet, input=Input.Connection, title="UNet", ui_order=2)
control: Union[ControlField, list[ControlField]] = InputField(
default=None, description=FieldDescriptions.control, input=Input.Connection, ui_order=5
default=None,
description=FieldDescriptions.control,
input=Input.Connection,
ui_order=5,
)
latents: Optional[LatentsField] = InputField(description=FieldDescriptions.latents, input=Input.Connection)
denoise_mask: Optional[DenoiseMaskField] = InputField(
default=None,
description=FieldDescriptions.mask,
default=None, description=FieldDescriptions.mask, input=Input.Connection, ui_order=6
)
@validator("cfg_scale")
@ -319,7 +324,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
context: InvocationContext,
# really only need model for dtype and device
model: StableDiffusionGeneratorPipeline,
control_input: List[ControlField],
control_input: Union[ControlField, List[ControlField]],
latents_shape: List[int],
exit_stack: ExitStack,
do_classifier_free_guidance: bool = True,
@ -544,7 +549,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
return build_latents_output(latents_name=name, latents=result_latents, seed=seed)
@invocation("l2i", title="Latents to Image", tags=["latents", "image", "vae", "l2i"], category="latents")
@invocation(
"l2i", title="Latents to Image", tags=["latents", "image", "vae", "l2i"], category="latents", version="1.0.0"
)
class LatentsToImageInvocation(BaseInvocation):
"""Generates an image from latents."""
@ -650,7 +657,7 @@ class LatentsToImageInvocation(BaseInvocation):
LATENTS_INTERPOLATION_MODE = Literal["nearest", "linear", "bilinear", "bicubic", "trilinear", "area", "nearest-exact"]
@invocation("lresize", title="Resize Latents", tags=["latents", "resize"], category="latents")
@invocation("lresize", title="Resize Latents", tags=["latents", "resize"], category="latents", version="1.0.0")
class ResizeLatentsInvocation(BaseInvocation):
"""Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8."""
@ -694,7 +701,7 @@ class ResizeLatentsInvocation(BaseInvocation):
return build_latents_output(latents_name=name, latents=resized_latents, seed=self.latents.seed)
@invocation("lscale", title="Scale Latents", tags=["latents", "resize"], category="latents")
@invocation("lscale", title="Scale Latents", tags=["latents", "resize"], category="latents", version="1.0.0")
class ScaleLatentsInvocation(BaseInvocation):
"""Scales latents by a given factor."""
@ -730,7 +737,9 @@ class ScaleLatentsInvocation(BaseInvocation):
return build_latents_output(latents_name=name, latents=resized_latents, seed=self.latents.seed)
@invocation("i2l", title="Image to Latents", tags=["latents", "image", "vae", "i2l"], category="latents")
@invocation(
"i2l", title="Image to Latents", tags=["latents", "image", "vae", "i2l"], category="latents", version="1.0.0"
)
class ImageToLatentsInvocation(BaseInvocation):
"""Encodes an image into latents."""
@ -830,7 +839,7 @@ class ImageToLatentsInvocation(BaseInvocation):
return vae.encode(image_tensor).latents
@invocation("lblend", title="Blend Latents", tags=["latents", "blend"], category="latents")
@invocation("lblend", title="Blend Latents", tags=["latents", "blend"], category="latents", version="1.0.0")
class BlendLatentsInvocation(BaseInvocation):
"""Blend two latents using a given alpha. Latents must have same size."""

10
invokeai/app/invocations/math.py
View File

@ -7,7 +7,7 @@ from invokeai.app.invocations.primitives import IntegerOutput
from .baseinvocation import BaseInvocation, FieldDescriptions, InputField, InvocationContext, invocation
@invocation("add", title="Add Integers", tags=["math", "add"], category="math")
@invocation("add", title="Add Integers", tags=["math", "add"], category="math", version="1.0.0")
class AddInvocation(BaseInvocation):
"""Adds two numbers"""
@ -18,7 +18,7 @@ class AddInvocation(BaseInvocation):
return IntegerOutput(value=self.a + self.b)
@invocation("sub", title="Subtract Integers", tags=["math", "subtract"], category="math")
@invocation("sub", title="Subtract Integers", tags=["math", "subtract"], category="math", version="1.0.0")
class SubtractInvocation(BaseInvocation):
"""Subtracts two numbers"""
@ -29,7 +29,7 @@ class SubtractInvocation(BaseInvocation):
return IntegerOutput(value=self.a - self.b)
@invocation("mul", title="Multiply Integers", tags=["math", "multiply"], category="math")
@invocation("mul", title="Multiply Integers", tags=["math", "multiply"], category="math", version="1.0.0")
class MultiplyInvocation(BaseInvocation):
"""Multiplies two numbers"""
@ -40,7 +40,7 @@ class MultiplyInvocation(BaseInvocation):
return IntegerOutput(value=self.a * self.b)
@invocation("div", title="Divide Integers", tags=["math", "divide"], category="math")
@invocation("div", title="Divide Integers", tags=["math", "divide"], category="math", version="1.0.0")
class DivideInvocation(BaseInvocation):
"""Divides two numbers"""
@ -51,7 +51,7 @@ class DivideInvocation(BaseInvocation):
return IntegerOutput(value=int(self.a / self.b))
@invocation("rand_int", title="Random Integer", tags=["math", "random"], category="math")
@invocation("rand_int", title="Random Integer", tags=["math", "random"], category="math", version="1.0.0")
class RandomIntInvocation(BaseInvocation):
"""Outputs a single random integer."""

4
invokeai/app/invocations/metadata.py
View File

@ -98,7 +98,9 @@ class MetadataAccumulatorOutput(BaseInvocationOutput):
metadata: CoreMetadata = OutputField(description="The core metadata for the image")
@invocation("metadata_accumulator", title="Metadata Accumulator", tags=["metadata"], category="metadata")
@invocation(
"metadata_accumulator", title="Metadata Accumulator", tags=["metadata"], category="metadata", version="1.0.0"
)
class MetadataAccumulatorInvocation(BaseInvocation):
"""Outputs a Core Metadata Object"""

10
invokeai/app/invocations/model.py
View File

@ -73,7 +73,7 @@ class LoRAModelField(BaseModel):
base_model: BaseModelType = Field(description="Base model")
@invocation("main_model_loader", title="Main Model", tags=["model"], category="model")
@invocation("main_model_loader", title="Main Model", tags=["model"], category="model", version="1.0.0")
class MainModelLoaderInvocation(BaseInvocation):
"""Loads a main model, outputting its submodels."""
@ -173,7 +173,7 @@ class LoraLoaderOutput(BaseInvocationOutput):
clip: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
@invocation("lora_loader", title="LoRA", tags=["model"], category="model")
@invocation("lora_loader", title="LoRA", tags=["model"], category="model", version="1.0.0")
class LoraLoaderInvocation(BaseInvocation):
"""Apply selected lora to unet and text_encoder."""
@ -244,7 +244,7 @@ class SDXLLoraLoaderOutput(BaseInvocationOutput):
clip2: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP 2")
@invocation("sdxl_lora_loader", title="SDXL LoRA", tags=["lora", "model"], category="model")
@invocation("sdxl_lora_loader", title="SDXL LoRA", tags=["lora", "model"], category="model", version="1.0.0")
class SDXLLoraLoaderInvocation(BaseInvocation):
"""Apply selected lora to unet and text_encoder."""
@ -338,7 +338,7 @@ class VaeLoaderOutput(BaseInvocationOutput):
vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model")
@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.0")
class VaeLoaderInvocation(BaseInvocation):
"""Loads a VAE model, outputting a VaeLoaderOutput"""
@ -376,7 +376,7 @@ class SeamlessModeOutput(BaseInvocationOutput):
vae: Optional[VaeField] = OutputField(description=FieldDescriptions.vae, title="VAE")
@invocation("seamless", title="Seamless", tags=["seamless", "model"], category="model")
@invocation("seamless", title="Seamless", tags=["seamless", "model"], category="model", version="1.0.0")
class SeamlessModeInvocation(BaseInvocation):
"""Applies the seamless transformation to the Model UNet and VAE."""

2
invokeai/app/invocations/noise.py
View File

@ -78,7 +78,7 @@ def build_noise_output(latents_name: str, latents: torch.Tensor, seed: int):
)
@invocation("noise", title="Noise", tags=["latents", "noise"], category="latents")
@invocation("noise", title="Noise", tags=["latents", "noise"], category="latents", version="1.0.0")
class NoiseInvocation(BaseInvocation):
"""Generates latent noise."""

6
invokeai/app/invocations/onnx.py
View File

@ -56,7 +56,7 @@ ORT_TO_NP_TYPE = {
PRECISION_VALUES = Literal[tuple(list(ORT_TO_NP_TYPE.keys()))]
@invocation("prompt_onnx", title="ONNX Prompt (Raw)", tags=["prompt", "onnx"], category="conditioning")
@invocation("prompt_onnx", title="ONNX Prompt (Raw)", tags=["prompt", "onnx"], category="conditioning", version="1.0.0")
class ONNXPromptInvocation(BaseInvocation):
prompt: str = InputField(default="", description=FieldDescriptions.raw_prompt, ui_component=UIComponent.Textarea)
clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection)
@ -143,6 +143,7 @@ class ONNXPromptInvocation(BaseInvocation):
title="ONNX Text to Latents",
tags=["latents", "inference", "txt2img", "onnx"],
category="latents",
version="1.0.0",
)
class ONNXTextToLatentsInvocation(BaseInvocation):
"""Generates latents from conditionings."""
@ -319,6 +320,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
title="ONNX Latents to Image",
tags=["latents", "image", "vae", "onnx"],
category="image",
version="1.0.0",
)
class ONNXLatentsToImageInvocation(BaseInvocation):
"""Generates an image from latents."""
@ -403,7 +405,7 @@ class OnnxModelField(BaseModel):
model_type: ModelType = Field(description="Model Type")
@invocation("onnx_model_loader", title="ONNX Main Model", tags=["onnx", "model"], category="model")
@invocation("onnx_model_loader", title="ONNX Main Model", tags=["onnx", "model"], category="model", version="1.0.0")
class OnnxModelLoaderInvocation(BaseInvocation):
"""Loads a main model, outputting its submodels."""

4
invokeai/app/invocations/param_easing.py
View File

@ -45,7 +45,7 @@ from invokeai.app.invocations.primitives import FloatCollectionOutput
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
@invocation("float_range", title="Float Range", tags=["math", "range"], category="math")
@invocation("float_range", title="Float Range", tags=["math", "range"], category="math", version="1.0.0")
class FloatLinearRangeInvocation(BaseInvocation):
"""Creates a range"""
@ -96,7 +96,7 @@ EASING_FUNCTION_KEYS = Literal[tuple(list(EASING_FUNCTIONS_MAP.keys()))]
# actually I think for now could just use CollectionOutput (which is list[Any]
@invocation("step_param_easing", title="Step Param Easing", tags=["step", "easing"], category="step")
@invocation("step_param_easing", title="Step Param Easing", tags=["step", "easing"], category="step", version="1.0.0")
class StepParamEasingInvocation(BaseInvocation):
"""Experimental per-step parameter easing for denoising steps"""

78
invokeai/app/invocations/primitives.py
View File

@ -14,7 +14,6 @@ from .baseinvocation import (
InvocationContext,
OutputField,
UIComponent,
UIType,
invocation,
invocation_output,
)
@ -40,10 +39,14 @@ class BooleanOutput(BaseInvocationOutput):
class BooleanCollectionOutput(BaseInvocationOutput):
"""Base class for nodes that output a collection of booleans"""
collection: list[bool] = OutputField(description="The output boolean collection", ui_type=UIType.BooleanCollection)
collection: list[bool] = OutputField(
description="The output boolean collection",
)
@invocation("boolean", title="Boolean Primitive", tags=["primitives", "boolean"], category="primitives")
@invocation(
"boolean", title="Boolean Primitive", tags=["primitives", "boolean"], category="primitives", version="1.0.0"
)
class BooleanInvocation(BaseInvocation):
"""A boolean primitive value"""
@ -58,13 +61,12 @@ class BooleanInvocation(BaseInvocation):
title="Boolean Collection Primitive",
tags=["primitives", "boolean", "collection"],
category="primitives",
version="1.0.0",
)
class BooleanCollectionInvocation(BaseInvocation):
"""A collection of boolean primitive values"""
collection: list[bool] = InputField(
default_factory=list, description="The collection of boolean values", ui_type=UIType.BooleanCollection
)
collection: list[bool] = InputField(default_factory=list, description="The collection of boolean values")
def invoke(self, context: InvocationContext) -> BooleanCollectionOutput:
return BooleanCollectionOutput(collection=self.collection)
@ -86,10 +88,14 @@ class IntegerOutput(BaseInvocationOutput):
class IntegerCollectionOutput(BaseInvocationOutput):
"""Base class for nodes that output a collection of integers"""
collection: list[int] = OutputField(description="The int collection", ui_type=UIType.IntegerCollection)
collection: list[int] = OutputField(
description="The int collection",
)
@invocation("integer", title="Integer Primitive", tags=["primitives", "integer"], category="primitives")
@invocation(
"integer", title="Integer Primitive", tags=["primitives", "integer"], category="primitives", version="1.0.0"
)
class IntegerInvocation(BaseInvocation):
"""An integer primitive value"""
@ -104,13 +110,12 @@ class IntegerInvocation(BaseInvocation):
title="Integer Collection Primitive",
tags=["primitives", "integer", "collection"],
category="primitives",
version="1.0.0",
)
class IntegerCollectionInvocation(BaseInvocation):
"""A collection of integer primitive values"""
collection: list[int] = InputField(
default_factory=list, description="The collection of integer values", ui_type=UIType.IntegerCollection
)
collection: list[int] = InputField(default_factory=list, description="The collection of integer values")
def invoke(self, context: InvocationContext) -> IntegerCollectionOutput:
return IntegerCollectionOutput(collection=self.collection)
@ -132,10 +137,12 @@ class FloatOutput(BaseInvocationOutput):
class FloatCollectionOutput(BaseInvocationOutput):
"""Base class for nodes that output a collection of floats"""
collection: list[float] = OutputField(description="The float collection", ui_type=UIType.FloatCollection)
collection: list[float] = OutputField(
description="The float collection",
)
@invocation("float", title="Float Primitive", tags=["primitives", "float"], category="primitives")
@invocation("float", title="Float Primitive", tags=["primitives", "float"], category="primitives", version="1.0.0")
class FloatInvocation(BaseInvocation):
"""A float primitive value"""
@ -150,13 +157,12 @@ class FloatInvocation(BaseInvocation):
title="Float Collection Primitive",
tags=["primitives", "float", "collection"],
category="primitives",
version="1.0.0",
)
class FloatCollectionInvocation(BaseInvocation):
"""A collection of float primitive values"""
collection: list[float] = InputField(
default_factory=list, description="The collection of float values", ui_type=UIType.FloatCollection
)
collection: list[float] = InputField(default_factory=list, description="The collection of float values")
def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
return FloatCollectionOutput(collection=self.collection)
@ -178,10 +184,12 @@ class StringOutput(BaseInvocationOutput):
class StringCollectionOutput(BaseInvocationOutput):
"""Base class for nodes that output a collection of strings"""
collection: list[str] = OutputField(description="The output strings", ui_type=UIType.StringCollection)
collection: list[str] = OutputField(
description="The output strings",
)
@invocation("string", title="String Primitive", tags=["primitives", "string"], category="primitives")
@invocation("string", title="String Primitive", tags=["primitives", "string"], category="primitives", version="1.0.0")
class StringInvocation(BaseInvocation):
"""A string primitive value"""
@ -196,13 +204,12 @@ class StringInvocation(BaseInvocation):
title="String Collection Primitive",
tags=["primitives", "string", "collection"],
category="primitives",
version="1.0.0",
)
class StringCollectionInvocation(BaseInvocation):
"""A collection of string primitive values"""
collection: list[str] = InputField(
default_factory=list, description="The collection of string values", ui_type=UIType.StringCollection
)
collection: list[str] = InputField(default_factory=list, description="The collection of string values")
def invoke(self, context: InvocationContext) -> StringCollectionOutput:
return StringCollectionOutput(collection=self.collection)
@ -232,10 +239,12 @@ class ImageOutput(BaseInvocationOutput):
class ImageCollectionOutput(BaseInvocationOutput):
"""Base class for nodes that output a collection of images"""
collection: list[ImageField] = OutputField(description="The output images", ui_type=UIType.ImageCollection)
collection: list[ImageField] = OutputField(
description="The output images",
)
@invocation("image", title="Image Primitive", tags=["primitives", "image"], category="primitives")
@invocation("image", title="Image Primitive", tags=["primitives", "image"], category="primitives", version="1.0.0")
class ImageInvocation(BaseInvocation):
"""An image primitive value"""
@ -256,13 +265,12 @@ class ImageInvocation(BaseInvocation):
title="Image Collection Primitive",
tags=["primitives", "image", "collection"],
category="primitives",
version="1.0.0",
)
class ImageCollectionInvocation(BaseInvocation):
"""A collection of image primitive values"""
collection: list[ImageField] = InputField(
default_factory=list, description="The collection of image values", ui_type=UIType.ImageCollection
)
collection: list[ImageField] = InputField(description="The collection of image values")
def invoke(self, context: InvocationContext) -> ImageCollectionOutput:
return ImageCollectionOutput(collection=self.collection)
@ -316,11 +324,12 @@ class LatentsCollectionOutput(BaseInvocationOutput):
collection: list[LatentsField] = OutputField(
description=FieldDescriptions.latents,
ui_type=UIType.LatentsCollection,
)
@invocation("latents", title="Latents Primitive", tags=["primitives", "latents"], category="primitives")
@invocation(
"latents", title="Latents Primitive", tags=["primitives", "latents"], category="primitives", version="1.0.0"
)
class LatentsInvocation(BaseInvocation):
"""A latents tensor primitive value"""
@ -337,12 +346,13 @@ class LatentsInvocation(BaseInvocation):
title="Latents Collection Primitive",
tags=["primitives", "latents", "collection"],
category="primitives",
version="1.0.0",
)
class LatentsCollectionInvocation(BaseInvocation):
"""A collection of latents tensor primitive values"""
collection: list[LatentsField] = InputField(
description="The collection of latents tensors", ui_type=UIType.LatentsCollection
description="The collection of latents tensors",
)
def invoke(self, context: InvocationContext) -> LatentsCollectionOutput:
@ -385,10 +395,12 @@ class ColorOutput(BaseInvocationOutput):
class ColorCollectionOutput(BaseInvocationOutput):
"""Base class for nodes that output a collection of colors"""
collection: list[ColorField] = OutputField(description="The output colors", ui_type=UIType.ColorCollection)
collection: list[ColorField] = OutputField(
description="The output colors",
)
@invocation("color", title="Color Primitive", tags=["primitives", "color"], category="primitives")
@invocation("color", title="Color Primitive", tags=["primitives", "color"], category="primitives", version="1.0.0")
class ColorInvocation(BaseInvocation):
"""A color primitive value"""
@ -422,7 +434,6 @@ class ConditioningCollectionOutput(BaseInvocationOutput):
collection: list[ConditioningField] = OutputField(
description="The output conditioning tensors",
ui_type=UIType.ConditioningCollection,
)
@ -431,6 +442,7 @@ class ConditioningCollectionOutput(BaseInvocationOutput):
title="Conditioning Primitive",
tags=["primitives", "conditioning"],
category="primitives",
version="1.0.0",
)
class ConditioningInvocation(BaseInvocation):
"""A conditioning tensor primitive value"""
@ -446,6 +458,7 @@ class ConditioningInvocation(BaseInvocation):
title="Conditioning Collection Primitive",
tags=["primitives", "conditioning", "collection"],
category="primitives",
version="1.0.0",
)
class ConditioningCollectionInvocation(BaseInvocation):
"""A collection of conditioning tensor primitive values"""
@ -453,7 +466,6 @@ class ConditioningCollectionInvocation(BaseInvocation):
collection: list[ConditioningField] = InputField(
default_factory=list,
description="The collection of conditioning tensors",
ui_type=UIType.ConditioningCollection,
)
def invoke(self, context: InvocationContext) -> ConditioningCollectionOutput:

4
invokeai/app/invocations/prompt.py
View File

@ -10,7 +10,7 @@ from invokeai.app.invocations.primitives import StringCollectionOutput
from .baseinvocation import BaseInvocation, InputField, InvocationContext, UIComponent, invocation
@invocation("dynamic_prompt", title="Dynamic Prompt", tags=["prompt", "collection"], category="prompt")
@invocation("dynamic_prompt", title="Dynamic Prompt", tags=["prompt", "collection"], category="prompt", version="1.0.0")
class DynamicPromptInvocation(BaseInvocation):
"""Parses a prompt using adieyal/dynamicprompts' random or combinatorial generator"""
@ -29,7 +29,7 @@ class DynamicPromptInvocation(BaseInvocation):
return StringCollectionOutput(collection=prompts)
@invocation("prompt_from_file", title="Prompts from File", tags=["prompt", "file"], category="prompt")
@invocation("prompt_from_file", title="Prompts from File", tags=["prompt", "file"], category="prompt", version="1.0.0")
class PromptsFromFileInvocation(BaseInvocation):
"""Loads prompts from a text file"""

3
invokeai/app/invocations/sdxl.py
View File

@ -33,7 +33,7 @@ class SDXLRefinerModelLoaderOutput(BaseInvocationOutput):
vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
@invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model")
@invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model", version="1.0.0")
class SDXLModelLoaderInvocation(BaseInvocation):
"""Loads an sdxl base model, outputting its submodels."""
@ -119,6 +119,7 @@ class SDXLModelLoaderInvocation(BaseInvocation):
title="SDXL Refiner Model",
tags=["model", "sdxl", "refiner"],
category="model",
version="1.0.0",
)
class SDXLRefinerModelLoaderInvocation(BaseInvocation):
"""Loads an sdxl refiner model, outputting its submodels."""

2
invokeai/app/invocations/upscale.py
View File

@ -23,7 +23,7 @@ ESRGAN_MODELS = Literal[
]
@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan")
@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.0.0")
class ESRGANInvocation(BaseInvocation):
"""Upscales an image using RealESRGAN."""

4
invokeai/app/services/config/base.py
View File

@ -42,7 +42,9 @@ class InvokeAISettings(BaseSettings):
def parse_args(self, argv: list = sys.argv[1:]):
parser = self.get_parser()
opt = parser.parse_args(argv)
opt, unknown_opts = parser.parse_known_args(argv)
if len(unknown_opts) > 0:
print("Unknown args:", unknown_opts)
for name in self.__fields__:
if name not in self._excluded():
value = getattr(opt, name)

4
invokeai/app/services/config/invokeai_config.py
View File

@ -254,6 +254,10 @@ class InvokeAIAppConfig(InvokeAISettings):
attention_slice_size: Literal[tuple(["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8])] = Field(default="auto", description='Slice size, valid when attention_type=="sliced"', category="Generation", )
force_tiled_decode: bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category="Generation",)
# NODES
allow_nodes : Optional[List[str]] = Field(default=None, description="List of nodes to allow. Omit to allow all.", category="Nodes")
deny_nodes : Optional[List[str]] = Field(default=None, description="List of nodes to deny. Omit to deny none.", category="Nodes")
# DEPRECATED FIELDS - STILL HERE IN ORDER TO OBTAN VALUES FROM PRE-3.1 CONFIG FILES
always_use_cpu : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", category='Memory/Performance')
free_gpu_mem : Optional[bool] = Field(default=None, description="If true, purge model from GPU after each generation.", category='Memory/Performance')

8
invokeai/app/services/graph.py
View File

@ -112,6 +112,10 @@ def are_connection_types_compatible(from_type: Any, to_type: Any) -> bool:
if to_type in get_args(from_type):
return True
# allow int -> float, pydantic will cast for us
if from_type is int and to_type is float:
return True
# if not issubclass(from_type, to_type):
if not is_union_subtype(from_type, to_type):
return False
@ -178,7 +182,7 @@ class IterateInvocationOutput(BaseInvocationOutput):
# TODO: Fill this out and move to invocations
@invocation("iterate")
@invocation("iterate", version="1.0.0")
class IterateInvocation(BaseInvocation):
"""Iterates over a list of items"""
@ -199,7 +203,7 @@ class CollectInvocationOutput(BaseInvocationOutput):
)
@invocation("collect")
@invocation("collect", version="1.0.0")
class CollectInvocation(BaseInvocation):
"""Collects values into a collection"""

20
invokeai/backend/image_util/cv2_inpaint.py Normal file
View File

@ -0,0 +1,20 @@
import cv2
import numpy as np
from PIL import Image
def cv2_inpaint(image: Image.Image) -> Image.Image:
# Prepare Image
image_array = np.array(image.convert("RGB"))
image_cv = cv2.cvtColor(image_array, cv2.COLOR_RGB2BGR)
# Prepare Mask From Alpha Channel
mask = image.split()[3].convert("RGB")
mask_array = np.array(mask)
mask_cv = cv2.cvtColor(mask_array, cv2.COLOR_BGR2GRAY)
mask_inv = cv2.bitwise_not(mask_cv)
# Inpaint Image
inpainted_result = cv2.inpaint(image_cv, mask_inv, 3, cv2.INPAINT_TELEA)
inpainted_image = Image.fromarray(cv2.cvtColor(inpainted_result, cv2.COLOR_BGR2RGB))
return inpainted_image

4
invokeai/backend/image_util/lama.py
View File

@ -5,6 +5,7 @@ import numpy as np
import torch
from PIL import Image
import invokeai.backend.util.logging as logger
from invokeai.app.services.config import get_invokeai_config
from invokeai.backend.util.devices import choose_torch_device
@ -19,7 +20,7 @@ def norm_img(np_img):
def load_jit_model(url_or_path, device):
model_path = url_or_path
print(f"Loading model from: {model_path}")
logger.info(f"Loading model from: {model_path}")
model = torch.jit.load(model_path, map_location="cpu").to(device)
model.eval()
return model
@ -52,5 +53,6 @@ class LaMA:
del model
gc.collect()
torch.cuda.empty_cache()
return infilled_image

16
invokeai/backend/install/invokeai_configure.py
View File

@ -290,9 +290,20 @@ def download_realesrgan():
download_with_progress_bar(model["url"], config.models_path / model["dest"], model["description"])
# ---------------------------------------------
def download_lama():
logger.info("Installing lama infill model")
download_with_progress_bar(
"https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt",
config.models_path / "core/misc/lama/lama.pt",
"lama infill model",
)
# ---------------------------------------------
def download_support_models():
download_realesrgan()
download_lama()
download_conversion_models()
@ -496,7 +507,7 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
scroll_exit=True,
)
else:
self.vram_cache_size = DummyWidgetValue.zero
self.vram = DummyWidgetValue.zero
self.nextrely += 1
self.outdir = self.add_widget_intelligent(
FileBox,
@ -594,7 +605,8 @@ https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENS
"vram",
"outdir",
]:
setattr(new_opts, attr, getattr(self, attr).value)
if hasattr(self, attr):
setattr(new_opts, attr, getattr(self, attr).value)
for attr in self.autoimport_dirs:
directory = Path(self.autoimport_dirs[attr].value)

6
invokeai/backend/stable_diffusion/image_degradation/__init__.py
View File

@ -1,6 +0,0 @@
from ldm.modules.image_degradation.bsrgan import ( # noqa: F401
degradation_bsrgan_variant as degradation_fn_bsr,
)
from ldm.modules.image_degradation.bsrgan_light import ( # noqa: F401
degradation_bsrgan_variant as degradation_fn_bsr_light,
)

794
invokeai/backend/stable_diffusion/image_degradation/bsrgan.py
View File

@ -1,794 +0,0 @@
# -*- coding: utf-8 -*-
"""
# --------------------------------------------
# Super-Resolution
# --------------------------------------------
#
# Kai Zhang (cskaizhang@gmail.com)
# https://github.com/cszn
# From 2019/03--2021/08
# --------------------------------------------
"""
import random
from functools import partial
import albumentations
import cv2
import ldm.modules.image_degradation.utils_image as util
import numpy as np
import scipy
import scipy.stats as ss
import torch
from scipy import ndimage
from scipy.interpolate import interp2d
from scipy.linalg import orth
def modcrop_np(img, sf):
"""
Args:
img: numpy image, WxH or WxHxC
sf: scale factor
Return:
cropped image
"""
w, h = img.shape[:2]
im = np.copy(img)
return im[: w - w % sf, : h - h % sf, ...]
"""
# --------------------------------------------
# anisotropic Gaussian kernels
# --------------------------------------------
"""
def analytic_kernel(k):
"""Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
k_size = k.shape[0]
# Calculate the big kernels size
big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
# Loop over the small kernel to fill the big one
for r in range(k_size):
for c in range(k_size):
big_k[2 * r : 2 * r + k_size, 2 * c : 2 * c + k_size] += k[r, c] * k
# Crop the edges of the big kernel to ignore very small values and increase run time of SR
crop = k_size // 2
cropped_big_k = big_k[crop:-crop, crop:-crop]
# Normalize to 1
return cropped_big_k / cropped_big_k.sum()
def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
"""generate an anisotropic Gaussian kernel
Args:
ksize : e.g., 15, kernel size
theta : [0, pi], rotation angle range
l1 : [0.1,50], scaling of eigenvalues
l2 : [0.1,l1], scaling of eigenvalues
If l1 = l2, will get an isotropic Gaussian kernel.
Returns:
k : kernel
"""
v = np.dot(
np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]),
np.array([1.0, 0.0]),
)
V = np.array([[v[0], v[1]], [v[1], -v[0]]])
D = np.array([[l1, 0], [0, l2]])
Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
return k
def gm_blur_kernel(mean, cov, size=15):
center = size / 2.0 + 0.5
k = np.zeros([size, size])
for y in range(size):
for x in range(size):
cy = y - center + 1
cx = x - center + 1
k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
k = k / np.sum(k)
return k
def shift_pixel(x, sf, upper_left=True):
"""shift pixel for super-resolution with different scale factors
Args:
x: WxHxC or WxH
sf: scale factor
upper_left: shift direction
"""
h, w = x.shape[:2]
shift = (sf - 1) * 0.5
xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
if upper_left:
x1 = xv + shift
y1 = yv + shift
else:
x1 = xv - shift
y1 = yv - shift
x1 = np.clip(x1, 0, w - 1)
y1 = np.clip(y1, 0, h - 1)
if x.ndim == 2:
x = interp2d(xv, yv, x)(x1, y1)
if x.ndim == 3:
for i in range(x.shape[-1]):
x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
return x
def blur(x, k):
"""
x: image, NxcxHxW
k: kernel, Nx1xhxw
"""
n, c = x.shape[:2]
p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode="replicate")
k = k.repeat(1, c, 1, 1)
k = k.view(-1, 1, k.shape[2], k.shape[3])
x = x.view(1, -1, x.shape[2], x.shape[3])
x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
x = x.view(n, c, x.shape[2], x.shape[3])
return x
def gen_kernel(
k_size=np.array([15, 15]),
scale_factor=np.array([4, 4]),
min_var=0.6,
max_var=10.0,
noise_level=0,
):
""" "
# modified version of https://github.com/assafshocher/BlindSR_dataset_generator
# Kai Zhang
# min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var
# max_var = 2.5 * sf
"""
# Set random eigen-vals (lambdas) and angle (theta) for COV matrix
lambda_1 = min_var + np.random.rand() * (max_var - min_var)
lambda_2 = min_var + np.random.rand() * (max_var - min_var)
theta = np.random.rand() * np.pi # random theta
noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
# Set COV matrix using Lambdas and Theta
LAMBDA = np.diag([lambda_1, lambda_2])
Q = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]])
SIGMA = Q @ LAMBDA @ Q.T
INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
# Set expectation position (shifting kernel for aligned image)
MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2)
MU = MU[None, None, :, None]
# Create meshgrid for Gaussian
[X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
Z = np.stack([X, Y], 2)[:, :, :, None]
# Calcualte Gaussian for every pixel of the kernel
ZZ = Z - MU
ZZ_t = ZZ.transpose(0, 1, 3, 2)
raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
# shift the kernel so it will be centered
# raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
# Normalize the kernel and return
# kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
kernel = raw_kernel / np.sum(raw_kernel)
return kernel
def fspecial_gaussian(hsize, sigma):
hsize = [hsize, hsize]
siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
std = sigma
[x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
arg = -(x * x + y * y) / (2 * std * std)
h = np.exp(arg)
h[h < scipy.finfo(float).eps * h.max()] = 0
sumh = h.sum()
if sumh != 0:
h = h / sumh
return h
def fspecial_laplacian(alpha):
alpha = max([0, min([alpha, 1])])
h1 = alpha / (alpha + 1)
h2 = (1 - alpha) / (alpha + 1)
h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
h = np.array(h)
return h
def fspecial(filter_type, *args, **kwargs):
"""
python code from:
https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
"""
if filter_type == "gaussian":
return fspecial_gaussian(*args, **kwargs)
if filter_type == "laplacian":
return fspecial_laplacian(*args, **kwargs)
"""
# --------------------------------------------
# degradation models
# --------------------------------------------
"""
def bicubic_degradation(x, sf=3):
"""
Args:
x: HxWxC image, [0, 1]
sf: down-scale factor
Return:
bicubicly downsampled LR image
"""
x = util.imresize_np(x, scale=1 / sf)
return x
def srmd_degradation(x, k, sf=3):
"""blur + bicubic downsampling
Args:
x: HxWxC image, [0, 1]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
Reference:
@inproceedings{zhang2018learning,
title={Learning a single convolutional super-resolution network for multiple degradations},
author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
pages={3262--3271},
year={2018}
}
"""
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode="wrap") # 'nearest' | 'mirror'
x = bicubic_degradation(x, sf=sf)
return x
def dpsr_degradation(x, k, sf=3):
"""bicubic downsampling + blur
Args:
x: HxWxC image, [0, 1]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
Reference:
@inproceedings{zhang2019deep,
title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
pages={1671--1681},
year={2019}
}
"""
x = bicubic_degradation(x, sf=sf)
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode="wrap")
return x
def classical_degradation(x, k, sf=3):
"""blur + downsampling
Args:
x: HxWxC image, [0, 1]/[0, 255]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
"""
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode="wrap")
# x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
st = 0
return x[st::sf, st::sf, ...]
def add_sharpening(img, weight=0.5, radius=50, threshold=10):
"""USM sharpening. borrowed from real-ESRGAN
Input image: I; Blurry image: B.
1. K = I + weight * (I - B)
2. Mask = 1 if abs(I - B) > threshold, else: 0
3. Blur mask:
4. Out = Mask * K + (1 - Mask) * I
Args:
img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
weight (float): Sharp weight. Default: 1.
radius (float): Kernel size of Gaussian blur. Default: 50.
threshold (int):
"""
if radius % 2 == 0:
radius += 1
blur = cv2.GaussianBlur(img, (radius, radius), 0)
residual = img - blur
mask = np.abs(residual) * 255 > threshold
mask = mask.astype("float32")
soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
K = img + weight * residual
K = np.clip(K, 0, 1)
return soft_mask * K + (1 - soft_mask) * img
def add_blur(img, sf=4):
wd2 = 4.0 + sf
wd = 2.0 + 0.2 * sf
if random.random() < 0.5:
l1 = wd2 * random.random()
l2 = wd2 * random.random()
k = anisotropic_Gaussian(
ksize=2 * random.randint(2, 11) + 3,
theta=random.random() * np.pi,
l1=l1,
l2=l2,
)
else:
k = fspecial("gaussian", 2 * random.randint(2, 11) + 3, wd * random.random())
img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode="mirror")
return img
def add_resize(img, sf=4):
rnum = np.random.rand()
if rnum > 0.8: # up
sf1 = random.uniform(1, 2)
elif rnum < 0.7: # down
sf1 = random.uniform(0.5 / sf, 1)
else:
sf1 = 1.0
img = cv2.resize(
img,
(int(sf1 * img.shape[1]), int(sf1 * img.shape[0])),
interpolation=random.choice([1, 2, 3]),
)
img = np.clip(img, 0.0, 1.0)
return img
# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
# noise_level = random.randint(noise_level1, noise_level2)
# rnum = np.random.rand()
# if rnum > 0.6: # add color Gaussian noise
# img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
# elif rnum < 0.4: # add grayscale Gaussian noise
# img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
# else: # add noise
# L = noise_level2 / 255.
# D = np.diag(np.random.rand(3))
# U = orth(np.random.rand(3, 3))
# conv = np.dot(np.dot(np.transpose(U), D), U)
# img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
# img = np.clip(img, 0.0, 1.0)
# return img
def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
noise_level = random.randint(noise_level1, noise_level2)
rnum = np.random.rand()
if rnum > 0.6: # add color Gaussian noise
img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
elif rnum < 0.4: # add grayscale Gaussian noise
img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
else: # add noise
L = noise_level2 / 255.0
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L**2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
def add_speckle_noise(img, noise_level1=2, noise_level2=25):
noise_level = random.randint(noise_level1, noise_level2)
img = np.clip(img, 0.0, 1.0)
rnum = random.random()
if rnum > 0.6:
img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
elif rnum < 0.4:
img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
else:
L = noise_level2 / 255.0
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L**2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
def add_Poisson_noise(img):
img = np.clip((img * 255.0).round(), 0, 255) / 255.0
vals = 10 ** (2 * random.random() + 2.0) # [2, 4]
if random.random() < 0.5:
img = np.random.poisson(img * vals).astype(np.float32) / vals
else:
img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.0
noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
img += noise_gray[:, :, np.newaxis]
img = np.clip(img, 0.0, 1.0)
return img
def add_JPEG_noise(img):
quality_factor = random.randint(30, 95)
img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
result, encimg = cv2.imencode(".jpg", img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
img = cv2.imdecode(encimg, 1)
img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
return img
def random_crop(lq, hq, sf=4, lq_patchsize=64):
h, w = lq.shape[:2]
rnd_h = random.randint(0, h - lq_patchsize)
rnd_w = random.randint(0, w - lq_patchsize)
lq = lq[rnd_h : rnd_h + lq_patchsize, rnd_w : rnd_w + lq_patchsize, :]
rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
hq = hq[
rnd_h_H : rnd_h_H + lq_patchsize * sf,
rnd_w_H : rnd_w_H + lq_patchsize * sf,
:,
]
return lq, hq
def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
"""
This is the degradation model of BSRGAN from the paper
"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
----------
img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
sf: scale factor
isp_model: camera ISP model
Returns
-------
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
"""
isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
sf_ori = sf
h1, w1 = img.shape[:2]
img = img.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...] # mod crop
h, w = img.shape[:2]
if h < lq_patchsize * sf or w < lq_patchsize * sf:
raise ValueError(f"img size ({h1}X{w1}) is too small!")
hq = img.copy()
if sf == 4 and random.random() < scale2_prob: # downsample1
if np.random.rand() < 0.5:
img = cv2.resize(
img,
(int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
interpolation=random.choice([1, 2, 3]),
)
else:
img = util.imresize_np(img, 1 / 2, True)
img = np.clip(img, 0.0, 1.0)
sf = 2
shuffle_order = random.sample(range(7), 7)
idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
if idx1 > idx2: # keep downsample3 last
shuffle_order[idx1], shuffle_order[idx2] = (
shuffle_order[idx2],
shuffle_order[idx1],
)
for i in shuffle_order:
if i == 0:
img = add_blur(img, sf=sf)
elif i == 1:
img = add_blur(img, sf=sf)
elif i == 2:
a, b = img.shape[1], img.shape[0]
# downsample2
if random.random() < 0.75:
sf1 = random.uniform(1, 2 * sf)
img = cv2.resize(
img,
(int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
interpolation=random.choice([1, 2, 3]),
)
else:
k = fspecial("gaussian", 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode="mirror")
img = img[0::sf, 0::sf, ...] # nearest downsampling
img = np.clip(img, 0.0, 1.0)
elif i == 3:
# downsample3
img = cv2.resize(
img,
(int(1 / sf * a), int(1 / sf * b)),
interpolation=random.choice([1, 2, 3]),
)
img = np.clip(img, 0.0, 1.0)
elif i == 4:
# add Gaussian noise
img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
elif i == 5:
# add JPEG noise
if random.random() < jpeg_prob:
img = add_JPEG_noise(img)
elif i == 6:
# add processed camera sensor noise
if random.random() < isp_prob and isp_model is not None:
with torch.no_grad():
img, hq = isp_model.forward(img.copy(), hq)
# add final JPEG compression noise
img = add_JPEG_noise(img)
# random crop
img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
return img, hq
# todo no isp_model?
def degradation_bsrgan_variant(image, sf=4, isp_model=None):
"""
This is the degradation model of BSRGAN from the paper
"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
----------
sf: scale factor
isp_model: camera ISP model
Returns
-------
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
"""
image = util.uint2single(image)
jpeg_prob, scale2_prob = 0.9, 0.25
# isp_prob = 0.25 # uncomment with `if i== 6` block below
# sf_ori = sf # uncomment with `if i== 6` block below
h1, w1 = image.shape[:2]
image = image.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...] # mod crop
h, w = image.shape[:2]
# hq = image.copy() # uncomment with `if i== 6` block below
if sf == 4 and random.random() < scale2_prob: # downsample1
if np.random.rand() < 0.5:
image = cv2.resize(
image,
(int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
interpolation=random.choice([1, 2, 3]),
)
else:
image = util.imresize_np(image, 1 / 2, True)
image = np.clip(image, 0.0, 1.0)
sf = 2
shuffle_order = random.sample(range(7), 7)
idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
if idx1 > idx2: # keep downsample3 last
shuffle_order[idx1], shuffle_order[idx2] = (
shuffle_order[idx2],
shuffle_order[idx1],
)
for i in shuffle_order:
if i == 0:
image = add_blur(image, sf=sf)
elif i == 1:
image = add_blur(image, sf=sf)
elif i == 2:
a, b = image.shape[1], image.shape[0]
# downsample2
if random.random() < 0.75:
sf1 = random.uniform(1, 2 * sf)
image = cv2.resize(
image,
(
int(1 / sf1 * image.shape[1]),
int(1 / sf1 * image.shape[0]),
),
interpolation=random.choice([1, 2, 3]),
)
else:
k = fspecial("gaussian", 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode="mirror")
image = image[0::sf, 0::sf, ...] # nearest downsampling
image = np.clip(image, 0.0, 1.0)
elif i == 3:
# downsample3
image = cv2.resize(
image,
(int(1 / sf * a), int(1 / sf * b)),
interpolation=random.choice([1, 2, 3]),
)
image = np.clip(image, 0.0, 1.0)
elif i == 4:
# add Gaussian noise
image = add_Gaussian_noise(image, noise_level1=2, noise_level2=25)
elif i == 5:
# add JPEG noise
if random.random() < jpeg_prob:
image = add_JPEG_noise(image)
# elif i == 6:
# # add processed camera sensor noise
# if random.random() < isp_prob and isp_model is not None:
# with torch.no_grad():
# img, hq = isp_model.forward(img.copy(), hq)
# add final JPEG compression noise
image = add_JPEG_noise(image)
image = util.single2uint(image)
example = {"image": image}
return example
# TODO incase there is a pickle error one needs to replace a += x with a = a + x in add_speckle_noise etc...
def degradation_bsrgan_plus(
img,
sf=4,
shuffle_prob=0.5,
use_sharp=True,
lq_patchsize=64,
isp_model=None,
):
"""
This is an extended degradation model by combining
the degradation models of BSRGAN and Real-ESRGAN
----------
img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
sf: scale factor
use_shuffle: the degradation shuffle
use_sharp: sharpening the img
Returns
-------
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
"""
h1, w1 = img.shape[:2]
img = img.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...] # mod crop
h, w = img.shape[:2]
if h < lq_patchsize * sf or w < lq_patchsize * sf:
raise ValueError(f"img size ({h1}X{w1}) is too small!")
if use_sharp:
img = add_sharpening(img)
hq = img.copy()
if random.random() < shuffle_prob:
shuffle_order = random.sample(range(13), 13)
else:
shuffle_order = list(range(13))
# local shuffle for noise, JPEG is always the last one
shuffle_order[2:6] = random.sample(shuffle_order[2:6], len(range(2, 6)))
shuffle_order[9:13] = random.sample(shuffle_order[9:13], len(range(9, 13)))
poisson_prob, speckle_prob, isp_prob = 0.1, 0.1, 0.1
for i in shuffle_order:
if i == 0:
img = add_blur(img, sf=sf)
elif i == 1:
img = add_resize(img, sf=sf)
elif i == 2:
img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
elif i == 3:
if random.random() < poisson_prob:
img = add_Poisson_noise(img)
elif i == 4:
if random.random() < speckle_prob:
img = add_speckle_noise(img)
elif i == 5:
if random.random() < isp_prob and isp_model is not None:
with torch.no_grad():
img, hq = isp_model.forward(img.copy(), hq)
elif i == 6:
img = add_JPEG_noise(img)
elif i == 7:
img = add_blur(img, sf=sf)
elif i == 8:
img = add_resize(img, sf=sf)
elif i == 9:
img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
elif i == 10:
if random.random() < poisson_prob:
img = add_Poisson_noise(img)
elif i == 11:
if random.random() < speckle_prob:
img = add_speckle_noise(img)
elif i == 12:
if random.random() < isp_prob and isp_model is not None:
with torch.no_grad():
img, hq = isp_model.forward(img.copy(), hq)
else:
print("check the shuffle!")
# resize to desired size
img = cv2.resize(
img,
(int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])),
interpolation=random.choice([1, 2, 3]),
)
# add final JPEG compression noise
img = add_JPEG_noise(img)
# random crop
img, hq = random_crop(img, hq, sf, lq_patchsize)
return img, hq
if __name__ == "__main__":
print("hey")
img = util.imread_uint("utils/test.png", 3)
print(img)
img = util.uint2single(img)
print(img)
img = img[:448, :448]
h = img.shape[0] // 4
print("resizing to", h)
sf = 4
deg_fn = partial(degradation_bsrgan_variant, sf=sf)
for i in range(20):
print(i)
img_lq = deg_fn(img)
print(img_lq)
img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)["image"]
print(img_lq.shape)
print("bicubic", img_lq_bicubic.shape)
# print(img_hq.shape)
lq_nearest = cv2.resize(
util.single2uint(img_lq),
(int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
interpolation=0,
)
lq_bicubic_nearest = cv2.resize(
util.single2uint(img_lq_bicubic),
(int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
interpolation=0,
)
# img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest], axis=1)
util.imsave(img_concat, str(i) + ".png")

704
invokeai/backend/stable_diffusion/image_degradation/bsrgan_light.py
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@ -1,704 +0,0 @@
# -*- coding: utf-8 -*-
import random
from functools import partial
import albumentations
import cv2
import ldm.modules.image_degradation.utils_image as util
import numpy as np
import scipy
import scipy.stats as ss
import torch
from scipy import ndimage
from scipy.interpolate import interp2d
from scipy.linalg import orth
"""
# --------------------------------------------
# Super-Resolution
# --------------------------------------------
#
# Kai Zhang (cskaizhang@gmail.com)
# https://github.com/cszn
# From 2019/03--2021/08
# --------------------------------------------
"""
def modcrop_np(img, sf):
"""
Args:
img: numpy image, WxH or WxHxC
sf: scale factor
Return:
cropped image
"""
w, h = img.shape[:2]
im = np.copy(img)
return im[: w - w % sf, : h - h % sf, ...]
"""
# --------------------------------------------
# anisotropic Gaussian kernels
# --------------------------------------------
"""
def analytic_kernel(k):
"""Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
k_size = k.shape[0]
# Calculate the big kernels size
big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
# Loop over the small kernel to fill the big one
for r in range(k_size):
for c in range(k_size):
big_k[2 * r : 2 * r + k_size, 2 * c : 2 * c + k_size] += k[r, c] * k
# Crop the edges of the big kernel to ignore very small values and increase run time of SR
crop = k_size // 2
cropped_big_k = big_k[crop:-crop, crop:-crop]
# Normalize to 1
return cropped_big_k / cropped_big_k.sum()
def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
"""generate an anisotropic Gaussian kernel
Args:
ksize : e.g., 15, kernel size
theta : [0, pi], rotation angle range
l1 : [0.1,50], scaling of eigenvalues
l2 : [0.1,l1], scaling of eigenvalues
If l1 = l2, will get an isotropic Gaussian kernel.
Returns:
k : kernel
"""
v = np.dot(
np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]),
np.array([1.0, 0.0]),
)
V = np.array([[v[0], v[1]], [v[1], -v[0]]])
D = np.array([[l1, 0], [0, l2]])
Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
return k
def gm_blur_kernel(mean, cov, size=15):
center = size / 2.0 + 0.5
k = np.zeros([size, size])
for y in range(size):
for x in range(size):
cy = y - center + 1
cx = x - center + 1
k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
k = k / np.sum(k)
return k
def shift_pixel(x, sf, upper_left=True):
"""shift pixel for super-resolution with different scale factors
Args:
x: WxHxC or WxH
sf: scale factor
upper_left: shift direction
"""
h, w = x.shape[:2]
shift = (sf - 1) * 0.5
xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
if upper_left:
x1 = xv + shift
y1 = yv + shift
else:
x1 = xv - shift
y1 = yv - shift
x1 = np.clip(x1, 0, w - 1)
y1 = np.clip(y1, 0, h - 1)
if x.ndim == 2:
x = interp2d(xv, yv, x)(x1, y1)
if x.ndim == 3:
for i in range(x.shape[-1]):
x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
return x
def blur(x, k):
"""
x: image, NxcxHxW
k: kernel, Nx1xhxw
"""
n, c = x.shape[:2]
p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode="replicate")
k = k.repeat(1, c, 1, 1)
k = k.view(-1, 1, k.shape[2], k.shape[3])
x = x.view(1, -1, x.shape[2], x.shape[3])
x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
x = x.view(n, c, x.shape[2], x.shape[3])
return x
def gen_kernel(
k_size=np.array([15, 15]),
scale_factor=np.array([4, 4]),
min_var=0.6,
max_var=10.0,
noise_level=0,
):
""" "
# modified version of https://github.com/assafshocher/BlindSR_dataset_generator
# Kai Zhang
# min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var
# max_var = 2.5 * sf
"""
# Set random eigen-vals (lambdas) and angle (theta) for COV matrix
lambda_1 = min_var + np.random.rand() * (max_var - min_var)
lambda_2 = min_var + np.random.rand() * (max_var - min_var)
theta = np.random.rand() * np.pi # random theta
noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
# Set COV matrix using Lambdas and Theta
LAMBDA = np.diag([lambda_1, lambda_2])
Q = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]])
SIGMA = Q @ LAMBDA @ Q.T
INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
# Set expectation position (shifting kernel for aligned image)
MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2)
MU = MU[None, None, :, None]
# Create meshgrid for Gaussian
[X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
Z = np.stack([X, Y], 2)[:, :, :, None]
# Calcualte Gaussian for every pixel of the kernel
ZZ = Z - MU
ZZ_t = ZZ.transpose(0, 1, 3, 2)
raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
# shift the kernel so it will be centered
# raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
# Normalize the kernel and return
# kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
kernel = raw_kernel / np.sum(raw_kernel)
return kernel
def fspecial_gaussian(hsize, sigma):
hsize = [hsize, hsize]
siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
std = sigma
[x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
arg = -(x * x + y * y) / (2 * std * std)
h = np.exp(arg)
h[h < scipy.finfo(float).eps * h.max()] = 0
sumh = h.sum()
if sumh != 0:
h = h / sumh
return h
def fspecial_laplacian(alpha):
alpha = max([0, min([alpha, 1])])
h1 = alpha / (alpha + 1)
h2 = (1 - alpha) / (alpha + 1)
h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
h = np.array(h)
return h
def fspecial(filter_type, *args, **kwargs):
"""
python code from:
https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
"""
if filter_type == "gaussian":
return fspecial_gaussian(*args, **kwargs)
if filter_type == "laplacian":
return fspecial_laplacian(*args, **kwargs)
"""
# --------------------------------------------
# degradation models
# --------------------------------------------
"""
def bicubic_degradation(x, sf=3):
"""
Args:
x: HxWxC image, [0, 1]
sf: down-scale factor
Return:
bicubicly downsampled LR image
"""
x = util.imresize_np(x, scale=1 / sf)
return x
def srmd_degradation(x, k, sf=3):
"""blur + bicubic downsampling
Args:
x: HxWxC image, [0, 1]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
Reference:
@inproceedings{zhang2018learning,
title={Learning a single convolutional super-resolution network for multiple degradations},
author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
pages={3262--3271},
year={2018}
}
"""
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode="wrap") # 'nearest' | 'mirror'
x = bicubic_degradation(x, sf=sf)
return x
def dpsr_degradation(x, k, sf=3):
"""bicubic downsampling + blur
Args:
x: HxWxC image, [0, 1]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
Reference:
@inproceedings{zhang2019deep,
title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
pages={1671--1681},
year={2019}
}
"""
x = bicubic_degradation(x, sf=sf)
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode="wrap")
return x
def classical_degradation(x, k, sf=3):
"""blur + downsampling
Args:
x: HxWxC image, [0, 1]/[0, 255]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
"""
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode="wrap")
# x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
st = 0
return x[st::sf, st::sf, ...]
def add_sharpening(img, weight=0.5, radius=50, threshold=10):
"""USM sharpening. borrowed from real-ESRGAN
Input image: I; Blurry image: B.
1. K = I + weight * (I - B)
2. Mask = 1 if abs(I - B) > threshold, else: 0
3. Blur mask:
4. Out = Mask * K + (1 - Mask) * I
Args:
img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
weight (float): Sharp weight. Default: 1.
radius (float): Kernel size of Gaussian blur. Default: 50.
threshold (int):
"""
if radius % 2 == 0:
radius += 1
blur = cv2.GaussianBlur(img, (radius, radius), 0)
residual = img - blur
mask = np.abs(residual) * 255 > threshold
mask = mask.astype("float32")
soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
K = img + weight * residual
K = np.clip(K, 0, 1)
return soft_mask * K + (1 - soft_mask) * img
def add_blur(img, sf=4):
wd2 = 4.0 + sf
wd = 2.0 + 0.2 * sf
wd2 = wd2 / 4
wd = wd / 4
if random.random() < 0.5:
l1 = wd2 * random.random()
l2 = wd2 * random.random()
k = anisotropic_Gaussian(
ksize=random.randint(2, 11) + 3,
theta=random.random() * np.pi,
l1=l1,
l2=l2,
)
else:
k = fspecial("gaussian", random.randint(2, 4) + 3, wd * random.random())
img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode="mirror")
return img
def add_resize(img, sf=4):
rnum = np.random.rand()
if rnum > 0.8: # up
sf1 = random.uniform(1, 2)
elif rnum < 0.7: # down
sf1 = random.uniform(0.5 / sf, 1)
else:
sf1 = 1.0
img = cv2.resize(
img,
(int(sf1 * img.shape[1]), int(sf1 * img.shape[0])),
interpolation=random.choice([1, 2, 3]),
)
img = np.clip(img, 0.0, 1.0)
return img
# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
# noise_level = random.randint(noise_level1, noise_level2)
# rnum = np.random.rand()
# if rnum > 0.6: # add color Gaussian noise
# img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
# elif rnum < 0.4: # add grayscale Gaussian noise
# img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
# else: # add noise
# L = noise_level2 / 255.
# D = np.diag(np.random.rand(3))
# U = orth(np.random.rand(3, 3))
# conv = np.dot(np.dot(np.transpose(U), D), U)
# img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
# img = np.clip(img, 0.0, 1.0)
# return img
def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
noise_level = random.randint(noise_level1, noise_level2)
rnum = np.random.rand()
if rnum > 0.6: # add color Gaussian noise
img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
elif rnum < 0.4: # add grayscale Gaussian noise
img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
else: # add noise
L = noise_level2 / 255.0
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L**2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
def add_speckle_noise(img, noise_level1=2, noise_level2=25):
noise_level = random.randint(noise_level1, noise_level2)
img = np.clip(img, 0.0, 1.0)
rnum = random.random()
if rnum > 0.6:
img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
elif rnum < 0.4:
img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
else:
L = noise_level2 / 255.0
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L**2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
def add_Poisson_noise(img):
img = np.clip((img * 255.0).round(), 0, 255) / 255.0
vals = 10 ** (2 * random.random() + 2.0) # [2, 4]
if random.random() < 0.5:
img = np.random.poisson(img * vals).astype(np.float32) / vals
else:
img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.0
noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
img += noise_gray[:, :, np.newaxis]
img = np.clip(img, 0.0, 1.0)
return img
def add_JPEG_noise(img):
quality_factor = random.randint(80, 95)
img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
result, encimg = cv2.imencode(".jpg", img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
img = cv2.imdecode(encimg, 1)
img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
return img
def random_crop(lq, hq, sf=4, lq_patchsize=64):
h, w = lq.shape[:2]
rnd_h = random.randint(0, h - lq_patchsize)
rnd_w = random.randint(0, w - lq_patchsize)
lq = lq[rnd_h : rnd_h + lq_patchsize, rnd_w : rnd_w + lq_patchsize, :]
rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
hq = hq[
rnd_h_H : rnd_h_H + lq_patchsize * sf,
rnd_w_H : rnd_w_H + lq_patchsize * sf,
:,
]
return lq, hq
def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
"""
This is the degradation model of BSRGAN from the paper
"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
----------
img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
sf: scale factor
isp_model: camera ISP model
Returns
-------
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
"""
isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
sf_ori = sf
h1, w1 = img.shape[:2]
img = img.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...] # mod crop
h, w = img.shape[:2]
if h < lq_patchsize * sf or w < lq_patchsize * sf:
raise ValueError(f"img size ({h1}X{w1}) is too small!")
hq = img.copy()
if sf == 4 and random.random() < scale2_prob: # downsample1
if np.random.rand() < 0.5:
img = cv2.resize(
img,
(int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
interpolation=random.choice([1, 2, 3]),
)
else:
img = util.imresize_np(img, 1 / 2, True)
img = np.clip(img, 0.0, 1.0)
sf = 2
shuffle_order = random.sample(range(7), 7)
idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
if idx1 > idx2: # keep downsample3 last
shuffle_order[idx1], shuffle_order[idx2] = (
shuffle_order[idx2],
shuffle_order[idx1],
)
for i in shuffle_order:
if i == 0:
img = add_blur(img, sf=sf)
elif i == 1:
img = add_blur(img, sf=sf)
elif i == 2:
a, b = img.shape[1], img.shape[0]
# downsample2
if random.random() < 0.75:
sf1 = random.uniform(1, 2 * sf)
img = cv2.resize(
img,
(int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
interpolation=random.choice([1, 2, 3]),
)
else:
k = fspecial("gaussian", 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode="mirror")
img = img[0::sf, 0::sf, ...] # nearest downsampling
img = np.clip(img, 0.0, 1.0)
elif i == 3:
# downsample3
img = cv2.resize(
img,
(int(1 / sf * a), int(1 / sf * b)),
interpolation=random.choice([1, 2, 3]),
)
img = np.clip(img, 0.0, 1.0)
elif i == 4:
# add Gaussian noise
img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8)
elif i == 5:
# add JPEG noise
if random.random() < jpeg_prob:
img = add_JPEG_noise(img)
elif i == 6:
# add processed camera sensor noise
if random.random() < isp_prob and isp_model is not None:
with torch.no_grad():
img, hq = isp_model.forward(img.copy(), hq)
# add final JPEG compression noise
img = add_JPEG_noise(img)
# random crop
img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
return img, hq
# todo no isp_model?
def degradation_bsrgan_variant(image, sf=4, isp_model=None):
"""
This is the degradation model of BSRGAN from the paper
"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
----------
sf: scale factor
isp_model: camera ISP model
Returns
-------
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
"""
image = util.uint2single(image)
jpeg_prob, scale2_prob = 0.9, 0.25
# isp_prob = 0.25 # uncomment with `if i== 6` block below
# sf_ori = sf # uncomment with `if i== 6` block below
h1, w1 = image.shape[:2]
image = image.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...] # mod crop
h, w = image.shape[:2]
# hq = image.copy() # uncomment with `if i== 6` block below
if sf == 4 and random.random() < scale2_prob: # downsample1
if np.random.rand() < 0.5:
image = cv2.resize(
image,
(int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
interpolation=random.choice([1, 2, 3]),
)
else:
image = util.imresize_np(image, 1 / 2, True)
image = np.clip(image, 0.0, 1.0)
sf = 2
shuffle_order = random.sample(range(7), 7)
idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
if idx1 > idx2: # keep downsample3 last
shuffle_order[idx1], shuffle_order[idx2] = (
shuffle_order[idx2],
shuffle_order[idx1],
)
for i in shuffle_order:
if i == 0:
image = add_blur(image, sf=sf)
# elif i == 1:
# image = add_blur(image, sf=sf)
if i == 0:
pass
elif i == 2:
a, b = image.shape[1], image.shape[0]
# downsample2
if random.random() < 0.8:
sf1 = random.uniform(1, 2 * sf)
image = cv2.resize(
image,
(
int(1 / sf1 * image.shape[1]),
int(1 / sf1 * image.shape[0]),
),
interpolation=random.choice([1, 2, 3]),
)
else:
k = fspecial("gaussian", 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode="mirror")
image = image[0::sf, 0::sf, ...] # nearest downsampling
image = np.clip(image, 0.0, 1.0)
elif i == 3:
# downsample3
image = cv2.resize(
image,
(int(1 / sf * a), int(1 / sf * b)),
interpolation=random.choice([1, 2, 3]),
)
image = np.clip(image, 0.0, 1.0)
elif i == 4:
# add Gaussian noise
image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2)
elif i == 5:
# add JPEG noise
if random.random() < jpeg_prob:
image = add_JPEG_noise(image)
#
# elif i == 6:
# # add processed camera sensor noise
# if random.random() < isp_prob and isp_model is not None:
# with torch.no_grad():
# img, hq = isp_model.forward(img.copy(), hq)
# add final JPEG compression noise
image = add_JPEG_noise(image)
image = util.single2uint(image)
example = {"image": image}
return example
if __name__ == "__main__":
print("hey")
img = util.imread_uint("utils/test.png", 3)
img = img[:448, :448]
h = img.shape[0] // 4
print("resizing to", h)
sf = 4
deg_fn = partial(degradation_bsrgan_variant, sf=sf)
for i in range(20):
print(i)
img_hq = img
img_lq = deg_fn(img)["image"]
img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq)
print(img_lq)
img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)[
"image"
]
print(img_lq.shape)
print("bicubic", img_lq_bicubic.shape)
print(img_hq.shape)
lq_nearest = cv2.resize(
util.single2uint(img_lq),
(int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
interpolation=0,
)
lq_bicubic_nearest = cv2.resize(
util.single2uint(img_lq_bicubic),
(int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
interpolation=0,
)
img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
util.imsave(img_concat, str(i) + ".png")

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import math
import os
import random
from datetime import datetime
import cv2
import numpy as np
import torch
from torchvision.utils import make_grid
import invokeai.backend.util.logging as logger
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
"""
# --------------------------------------------
# Kai Zhang (github: https://github.com/cszn)
# 03/Mar/2019
# --------------------------------------------
# https://github.com/twhui/SRGAN-pyTorch
# https://github.com/xinntao/BasicSR
# --------------------------------------------
"""
IMG_EXTENSIONS = [
".jpg",
".JPG",
".jpeg",
".JPEG",
".png",
".PNG",
".ppm",
".PPM",
".bmp",
".BMP",
".tif",
]
def is_image_file(filename):
return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
def get_timestamp():
return datetime.now().strftime("%y%m%d-%H%M%S")
def imshow(x, title=None, cbar=False, figsize=None):
import matplotlib.pyplot as plt
plt.figure(figsize=figsize)
plt.imshow(np.squeeze(x), interpolation="nearest", cmap="gray")
if title:
plt.title(title)
if cbar:
plt.colorbar()
plt.show()
def surf(Z, cmap="rainbow", figsize=None):
import matplotlib.pyplot as plt
plt.figure(figsize=figsize)
ax3 = plt.axes(projection="3d")
w, h = Z.shape[:2]
xx = np.arange(0, w, 1)
yy = np.arange(0, h, 1)
X, Y = np.meshgrid(xx, yy)
ax3.plot_surface(X, Y, Z, cmap=cmap)
# ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap)
plt.show()
"""
# --------------------------------------------
# get image pathes
# --------------------------------------------
"""
def get_image_paths(dataroot):
paths = None # return None if dataroot is None
if dataroot is not None:
paths = sorted(_get_paths_from_images(dataroot))
return paths
def _get_paths_from_images(path):
assert os.path.isdir(path), "{:s} is not a valid directory".format(path)
images = []
for dirpath, _, fnames in sorted(os.walk(path, followlinks=True)):
for fname in sorted(fnames):
if is_image_file(fname):
img_path = os.path.join(dirpath, fname)
images.append(img_path)
assert images, "{:s} has no valid image file".format(path)
return images
"""
# --------------------------------------------
# split large images into small images
# --------------------------------------------
"""
def patches_from_image(img, p_size=512, p_overlap=64, p_max=800):
w, h = img.shape[:2]
patches = []
if w > p_max and h > p_max:
w1 = list(np.arange(0, w - p_size, p_size - p_overlap, dtype=np.int))
h1 = list(np.arange(0, h - p_size, p_size - p_overlap, dtype=np.int))
w1.append(w - p_size)
h1.append(h - p_size)
# print(w1)
# print(h1)
for i in w1:
for j in h1:
patches.append(img[i : i + p_size, j : j + p_size, :])
else:
patches.append(img)
return patches
def imssave(imgs, img_path):
"""
imgs: list, N images of size WxHxC
"""
img_name, ext = os.path.splitext(os.path.basename(img_path))
for i, img in enumerate(imgs):
if img.ndim == 3:
img = img[:, :, [2, 1, 0]]
new_path = os.path.join(
os.path.dirname(img_path),
img_name + str("_s{:04d}".format(i)) + ".png",
)
cv2.imwrite(new_path, img)
def split_imageset(
original_dataroot,
taget_dataroot,
n_channels=3,
p_size=800,
p_overlap=96,
p_max=1000,
):
"""
split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size),
and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max)
will be splitted.
Args:
original_dataroot:
taget_dataroot:
p_size: size of small images
p_overlap: patch size in training is a good choice
p_max: images with smaller size than (p_max)x(p_max) keep unchanged.
"""
paths = get_image_paths(original_dataroot)
for img_path in paths:
# img_name, ext = os.path.splitext(os.path.basename(img_path))
img = imread_uint(img_path, n_channels=n_channels)
patches = patches_from_image(img, p_size, p_overlap, p_max)
imssave(patches, os.path.join(taget_dataroot, os.path.basename(img_path)))
# if original_dataroot == taget_dataroot:
# del img_path
"""
# --------------------------------------------
# makedir
# --------------------------------------------
"""
def mkdir(path):
if not os.path.exists(path):
os.makedirs(path)
def mkdirs(paths):
if isinstance(paths, str):
mkdir(paths)
else:
for path in paths:
mkdir(path)
def mkdir_and_rename(path):
if os.path.exists(path):
new_name = path + "_archived_" + get_timestamp()
logger.error("Path already exists. Rename it to [{:s}]".format(new_name))
os.replace(path, new_name)
os.makedirs(path)
"""
# --------------------------------------------
# read image from path
# opencv is fast, but read BGR numpy image
# --------------------------------------------
"""
# --------------------------------------------
# get uint8 image of size HxWxn_channles (RGB)
# --------------------------------------------
def imread_uint(path, n_channels=3):
# input: path
# output: HxWx3(RGB or GGG), or HxWx1 (G)
if n_channels == 1:
img = cv2.imread(path, 0) # cv2.IMREAD_GRAYSCALE
img = np.expand_dims(img, axis=2) # HxWx1
elif n_channels == 3:
img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # BGR or G
if img.ndim == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) # GGG
else:
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # RGB
return img
# --------------------------------------------
# matlab's imwrite
# --------------------------------------------
def imsave(img, img_path):
img = np.squeeze(img)
if img.ndim == 3:
img = img[:, :, [2, 1, 0]]
cv2.imwrite(img_path, img)
def imwrite(img, img_path):
img = np.squeeze(img)
if img.ndim == 3:
img = img[:, :, [2, 1, 0]]
cv2.imwrite(img_path, img)
# --------------------------------------------
# get single image of size HxWxn_channles (BGR)
# --------------------------------------------
def read_img(path):
# read image by cv2
# return: Numpy float32, HWC, BGR, [0,1]
img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # cv2.IMREAD_GRAYSCALE
img = img.astype(np.float32) / 255.0
if img.ndim == 2:
img = np.expand_dims(img, axis=2)
# some images have 4 channels
if img.shape[2] > 3:
img = img[:, :, :3]
return img
"""
# --------------------------------------------
# image format conversion
# --------------------------------------------
# numpy(single) <---> numpy(unit)
# numpy(single) <---> tensor
# numpy(unit) <---> tensor
# --------------------------------------------
"""
# --------------------------------------------
# numpy(single) [0, 1] <---> numpy(unit)
# --------------------------------------------
def uint2single(img):
return np.float32(img / 255.0)
def single2uint(img):
return np.uint8((img.clip(0, 1) * 255.0).round())
def uint162single(img):
return np.float32(img / 65535.0)
def single2uint16(img):
return np.uint16((img.clip(0, 1) * 65535.0).round())
# --------------------------------------------
# numpy(unit) (HxWxC or HxW) <---> tensor
# --------------------------------------------
# convert uint to 4-dimensional torch tensor
def uint2tensor4(img):
if img.ndim == 2:
img = np.expand_dims(img, axis=2)
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.0).unsqueeze(0)
# convert uint to 3-dimensional torch tensor
def uint2tensor3(img):
if img.ndim == 2:
img = np.expand_dims(img, axis=2)
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.0)
# convert 2/3/4-dimensional torch tensor to uint
def tensor2uint(img):
img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy()
if img.ndim == 3:
img = np.transpose(img, (1, 2, 0))
return np.uint8((img * 255.0).round())
# --------------------------------------------
# numpy(single) (HxWxC) <---> tensor
# --------------------------------------------
# convert single (HxWxC) to 3-dimensional torch tensor
def single2tensor3(img):
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float()
# convert single (HxWxC) to 4-dimensional torch tensor
def single2tensor4(img):
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0)
# convert torch tensor to single
def tensor2single(img):
img = img.data.squeeze().float().cpu().numpy()
if img.ndim == 3:
img = np.transpose(img, (1, 2, 0))
return img
# convert torch tensor to single
def tensor2single3(img):
img = img.data.squeeze().float().cpu().numpy()
if img.ndim == 3:
img = np.transpose(img, (1, 2, 0))
elif img.ndim == 2:
img = np.expand_dims(img, axis=2)
return img
def single2tensor5(img):
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0)
def single32tensor5(img):
return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0)
def single42tensor4(img):
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float()
# from skimage.io import imread, imsave
def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
"""
Converts a torch Tensor into an image Numpy array of BGR channel order
Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
"""
tensor = tensor.squeeze().float().cpu().clamp_(*min_max) # squeeze first, then clamp
tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0]) # to range [0,1]
n_dim = tensor.dim()
if n_dim == 4:
n_img = len(tensor)
img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR
elif n_dim == 3:
img_np = tensor.numpy()
img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR
elif n_dim == 2:
img_np = tensor.numpy()
else:
raise TypeError("Only support 4D, 3D and 2D tensor. But received with dimension: {:d}".format(n_dim))
if out_type == np.uint8:
img_np = (img_np * 255.0).round()
# Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
return img_np.astype(out_type)
"""
# --------------------------------------------
# Augmentation, flipe and/or rotate
# --------------------------------------------
# The following two are enough.
# (1) augmet_img: numpy image of WxHxC or WxH
# (2) augment_img_tensor4: tensor image 1xCxWxH
# --------------------------------------------
"""
def augment_img(img, mode=0):
"""Kai Zhang (github: https://github.com/cszn)"""
if mode == 0:
return img
elif mode == 1:
return np.flipud(np.rot90(img))
elif mode == 2:
return np.flipud(img)
elif mode == 3:
return np.rot90(img, k=3)
elif mode == 4:
return np.flipud(np.rot90(img, k=2))
elif mode == 5:
return np.rot90(img)
elif mode == 6:
return np.rot90(img, k=2)
elif mode == 7:
return np.flipud(np.rot90(img, k=3))
def augment_img_tensor4(img, mode=0):
"""Kai Zhang (github: https://github.com/cszn)"""
if mode == 0:
return img
elif mode == 1:
return img.rot90(1, [2, 3]).flip([2])
elif mode == 2:
return img.flip([2])
elif mode == 3:
return img.rot90(3, [2, 3])
elif mode == 4:
return img.rot90(2, [2, 3]).flip([2])
elif mode == 5:
return img.rot90(1, [2, 3])
elif mode == 6:
return img.rot90(2, [2, 3])
elif mode == 7:
return img.rot90(3, [2, 3]).flip([2])
def augment_img_tensor(img, mode=0):
"""Kai Zhang (github: https://github.com/cszn)"""
img_size = img.size()
img_np = img.data.cpu().numpy()
if len(img_size) == 3:
img_np = np.transpose(img_np, (1, 2, 0))
elif len(img_size) == 4:
img_np = np.transpose(img_np, (2, 3, 1, 0))
img_np = augment_img(img_np, mode=mode)
img_tensor = torch.from_numpy(np.ascontiguousarray(img_np))
if len(img_size) == 3:
img_tensor = img_tensor.permute(2, 0, 1)
elif len(img_size) == 4:
img_tensor = img_tensor.permute(3, 2, 0, 1)
return img_tensor.type_as(img)
def augment_img_np3(img, mode=0):
if mode == 0:
return img
elif mode == 1:
return img.transpose(1, 0, 2)
elif mode == 2:
return img[::-1, :, :]
elif mode == 3:
img = img[::-1, :, :]
img = img.transpose(1, 0, 2)
return img
elif mode == 4:
return img[:, ::-1, :]
elif mode == 5:
img = img[:, ::-1, :]
img = img.transpose(1, 0, 2)
return img
elif mode == 6:
img = img[:, ::-1, :]
img = img[::-1, :, :]
return img
elif mode == 7:
img = img[:, ::-1, :]
img = img[::-1, :, :]
img = img.transpose(1, 0, 2)
return img
def augment_imgs(img_list, hflip=True, rot=True):
# horizontal flip OR rotate
hflip = hflip and random.random() < 0.5
vflip = rot and random.random() < 0.5
rot90 = rot and random.random() < 0.5
def _augment(img):
if hflip:
img = img[:, ::-1, :]
if vflip:
img = img[::-1, :, :]
if rot90:
img = img.transpose(1, 0, 2)
return img
return [_augment(img) for img in img_list]
"""
# --------------------------------------------
# modcrop and shave
# --------------------------------------------
"""
def modcrop(img_in, scale):
# img_in: Numpy, HWC or HW
img = np.copy(img_in)
if img.ndim == 2:
H, W = img.shape
H_r, W_r = H % scale, W % scale
img = img[: H - H_r, : W - W_r]
elif img.ndim == 3:
H, W, C = img.shape
H_r, W_r = H % scale, W % scale
img = img[: H - H_r, : W - W_r, :]
else:
raise ValueError("Wrong img ndim: [{:d}].".format(img.ndim))
return img
def shave(img_in, border=0):
# img_in: Numpy, HWC or HW
img = np.copy(img_in)
h, w = img.shape[:2]
img = img[border : h - border, border : w - border]
return img
"""
# --------------------------------------------
# image processing process on numpy image
# channel_convert(in_c, tar_type, img_list):
# rgb2ycbcr(img, only_y=True):
# bgr2ycbcr(img, only_y=True):
# ycbcr2rgb(img):
# --------------------------------------------
"""
def rgb2ycbcr(img, only_y=True):
"""same as matlab rgb2ycbcr
only_y: only return Y channel
Input:
uint8, [0, 255]
float, [0, 1]
"""
in_img_type = img.dtype
img.astype(np.float32)
if in_img_type != np.uint8:
img *= 255.0
# convert
if only_y:
rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
else:
rlt = np.matmul(
img,
[
[65.481, -37.797, 112.0],
[128.553, -74.203, -93.786],
[24.966, 112.0, -18.214],
],
) / 255.0 + [16, 128, 128]
if in_img_type == np.uint8:
rlt = rlt.round()
else:
rlt /= 255.0
return rlt.astype(in_img_type)
def ycbcr2rgb(img):
"""same as matlab ycbcr2rgb
Input:
uint8, [0, 255]
float, [0, 1]
"""
in_img_type = img.dtype
img.astype(np.float32)
if in_img_type != np.uint8:
img *= 255.0
# convert
rlt = np.matmul(
img,
[
[0.00456621, 0.00456621, 0.00456621],
[0, -0.00153632, 0.00791071],
[0.00625893, -0.00318811, 0],
],
) * 255.0 + [-222.921, 135.576, -276.836]
if in_img_type == np.uint8:
rlt = rlt.round()
else:
rlt /= 255.0
return rlt.astype(in_img_type)
def bgr2ycbcr(img, only_y=True):
"""bgr version of rgb2ycbcr
only_y: only return Y channel
Input:
uint8, [0, 255]
float, [0, 1]
"""
in_img_type = img.dtype
img.astype(np.float32)
if in_img_type != np.uint8:
img *= 255.0
# convert
if only_y:
rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
else:
rlt = np.matmul(
img,
[
[24.966, 112.0, -18.214],
[128.553, -74.203, -93.786],
[65.481, -37.797, 112.0],
],
) / 255.0 + [16, 128, 128]
if in_img_type == np.uint8:
rlt = rlt.round()
else:
rlt /= 255.0
return rlt.astype(in_img_type)
def channel_convert(in_c, tar_type, img_list):
# conversion among BGR, gray and y
if in_c == 3 and tar_type == "gray": # BGR to gray
gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list]
return [np.expand_dims(img, axis=2) for img in gray_list]
elif in_c == 3 and tar_type == "y": # BGR to y
y_list = [bgr2ycbcr(img, only_y=True) for img in img_list]
return [np.expand_dims(img, axis=2) for img in y_list]
elif in_c == 1 and tar_type == "RGB": # gray/y to BGR
return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list]
else:
return img_list
"""
# --------------------------------------------
# metric, PSNR and SSIM
# --------------------------------------------
"""
# --------------------------------------------
# PSNR
# --------------------------------------------
def calculate_psnr(img1, img2, border=0):
# img1 and img2 have range [0, 255]
# img1 = img1.squeeze()
# img2 = img2.squeeze()
if not img1.shape == img2.shape:
raise ValueError("Input images must have the same dimensions.")
h, w = img1.shape[:2]
img1 = img1[border : h - border, border : w - border]
img2 = img2[border : h - border, border : w - border]
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
mse = np.mean((img1 - img2) ** 2)
if mse == 0:
return float("inf")
return 20 * math.log10(255.0 / math.sqrt(mse))
# --------------------------------------------
# SSIM
# --------------------------------------------
def calculate_ssim(img1, img2, border=0):
"""calculate SSIM
the same outputs as MATLAB's
img1, img2: [0, 255]
"""
# img1 = img1.squeeze()
# img2 = img2.squeeze()
if not img1.shape == img2.shape:
raise ValueError("Input images must have the same dimensions.")
h, w = img1.shape[:2]
img1 = img1[border : h - border, border : w - border]
img2 = img2[border : h - border, border : w - border]
if img1.ndim == 2:
return ssim(img1, img2)
elif img1.ndim == 3:
if img1.shape[2] == 3:
ssims = []
for i in range(3):
ssims.append(ssim(img1[:, :, i], img2[:, :, i]))
return np.array(ssims).mean()
elif img1.shape[2] == 1:
return ssim(np.squeeze(img1), np.squeeze(img2))
else:
raise ValueError("Wrong input image dimensions.")
def ssim(img1, img2):
C1 = (0.01 * 255) ** 2
C2 = (0.03 * 255) ** 2
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
kernel = cv2.getGaussianKernel(11, 1.5)
window = np.outer(kernel, kernel.transpose())
mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid
mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
mu1_sq = mu1**2
mu2_sq = mu2**2
mu1_mu2 = mu1 * mu2
sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
return ssim_map.mean()
"""
# --------------------------------------------
# matlab's bicubic imresize (numpy and torch) [0, 1]
# --------------------------------------------
"""
# matlab 'imresize' function, now only support 'bicubic'
def cubic(x):
absx = torch.abs(x)
absx2 = absx**2
absx3 = absx**3
return (1.5 * absx3 - 2.5 * absx2 + 1) * ((absx <= 1).type_as(absx)) + (
-0.5 * absx3 + 2.5 * absx2 - 4 * absx + 2
) * (((absx > 1) * (absx <= 2)).type_as(absx))
def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing):
if (scale < 1) and (antialiasing):
# Use a modified kernel to simultaneously interpolate and antialias- larger kernel width
kernel_width = kernel_width / scale
# Output-space coordinates
x = torch.linspace(1, out_length, out_length)
# Input-space coordinates. Calculate the inverse mapping such that 0.5
# in output space maps to 0.5 in input space, and 0.5+scale in output
# space maps to 1.5 in input space.
u = x / scale + 0.5 * (1 - 1 / scale)
# What is the left-most pixel that can be involved in the computation?
left = torch.floor(u - kernel_width / 2)
# What is the maximum number of pixels that can be involved in the
# computation? Note: it's OK to use an extra pixel here; if the
# corresponding weights are all zero, it will be eliminated at the end
# of this function.
P = math.ceil(kernel_width) + 2
# The indices of the input pixels involved in computing the k-th output
# pixel are in row k of the indices matrix.
indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view(1, P).expand(
out_length, P
)
# The weights used to compute the k-th output pixel are in row k of the
# weights matrix.
distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices
# apply cubic kernel
if (scale < 1) and (antialiasing):
weights = scale * cubic(distance_to_center * scale)
else:
weights = cubic(distance_to_center)
# Normalize the weights matrix so that each row sums to 1.
weights_sum = torch.sum(weights, 1).view(out_length, 1)
weights = weights / weights_sum.expand(out_length, P)
# If a column in weights is all zero, get rid of it. only consider the first and last column.
weights_zero_tmp = torch.sum((weights == 0), 0)
if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
indices = indices.narrow(1, 1, P - 2)
weights = weights.narrow(1, 1, P - 2)
if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
indices = indices.narrow(1, 0, P - 2)
weights = weights.narrow(1, 0, P - 2)
weights = weights.contiguous()
indices = indices.contiguous()
sym_len_s = -indices.min() + 1
sym_len_e = indices.max() - in_length
indices = indices + sym_len_s - 1
return weights, indices, int(sym_len_s), int(sym_len_e)
# --------------------------------------------
# imresize for tensor image [0, 1]
# --------------------------------------------
def imresize(img, scale, antialiasing=True):
# Now the scale should be the same for H and W
# input: img: pytorch tensor, CHW or HW [0,1]
# output: CHW or HW [0,1] w/o round
need_squeeze = True if img.dim() == 2 else False
if need_squeeze:
img.unsqueeze_(0)
in_C, in_H, in_W = img.size()
out_C, out_H, out_W = (
in_C,
math.ceil(in_H * scale),
math.ceil(in_W * scale),
)
kernel_width = 4
kernel = "cubic"
# Return the desired dimension order for performing the resize. The
# strategy is to perform the resize first along the dimension with the
# smallest scale factor.
# Now we do not support this.
# get weights and indices
weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
in_H, out_H, scale, kernel, kernel_width, antialiasing
)
weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
in_W, out_W, scale, kernel, kernel_width, antialiasing
)
# process H dimension
# symmetric copying
img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W)
img_aug.narrow(1, sym_len_Hs, in_H).copy_(img)
sym_patch = img[:, :sym_len_Hs, :]
inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(1, inv_idx)
img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv)
sym_patch = img[:, -sym_len_He:, :]
inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(1, inv_idx)
img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
out_1 = torch.FloatTensor(in_C, out_H, in_W)
kernel_width = weights_H.size(1)
for i in range(out_H):
idx = int(indices_H[i][0])
for j in range(out_C):
out_1[j, i, :] = img_aug[j, idx : idx + kernel_width, :].transpose(0, 1).mv(weights_H[i])
# process W dimension
# symmetric copying
out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We)
out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1)
sym_patch = out_1[:, :, :sym_len_Ws]
inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(2, inv_idx)
out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv)
sym_patch = out_1[:, :, -sym_len_We:]
inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(2, inv_idx)
out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
out_2 = torch.FloatTensor(in_C, out_H, out_W)
kernel_width = weights_W.size(1)
for i in range(out_W):
idx = int(indices_W[i][0])
for j in range(out_C):
out_2[j, :, i] = out_1_aug[j, :, idx : idx + kernel_width].mv(weights_W[i])
if need_squeeze:
out_2.squeeze_()
return out_2
# --------------------------------------------
# imresize for numpy image [0, 1]
# --------------------------------------------
def imresize_np(img, scale, antialiasing=True):
# Now the scale should be the same for H and W
# input: img: Numpy, HWC or HW [0,1]
# output: HWC or HW [0,1] w/o round
img = torch.from_numpy(img)
need_squeeze = True if img.dim() == 2 else False
if need_squeeze:
img.unsqueeze_(2)
in_H, in_W, in_C = img.size()
out_C, out_H, out_W = (
in_C,
math.ceil(in_H * scale),
math.ceil(in_W * scale),
)
kernel_width = 4
kernel = "cubic"
# Return the desired dimension order for performing the resize. The
# strategy is to perform the resize first along the dimension with the
# smallest scale factor.
# Now we do not support this.
# get weights and indices
weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
in_H, out_H, scale, kernel, kernel_width, antialiasing
)
weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
in_W, out_W, scale, kernel, kernel_width, antialiasing
)
# process H dimension
# symmetric copying
img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C)
img_aug.narrow(0, sym_len_Hs, in_H).copy_(img)
sym_patch = img[:sym_len_Hs, :, :]
inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(0, inv_idx)
img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv)
sym_patch = img[-sym_len_He:, :, :]
inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(0, inv_idx)
img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
out_1 = torch.FloatTensor(out_H, in_W, in_C)
kernel_width = weights_H.size(1)
for i in range(out_H):
idx = int(indices_H[i][0])
for j in range(out_C):
out_1[i, :, j] = img_aug[idx : idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i])
# process W dimension
# symmetric copying
out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C)
out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1)
sym_patch = out_1[:, :sym_len_Ws, :]
inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(1, inv_idx)
out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv)
sym_patch = out_1[:, -sym_len_We:, :]
inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(1, inv_idx)
out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
out_2 = torch.FloatTensor(out_H, out_W, in_C)
kernel_width = weights_W.size(1)
for i in range(out_W):
idx = int(indices_W[i][0])
for j in range(out_C):
out_2[:, i, j] = out_1_aug[:, idx : idx + kernel_width, j].mv(weights_W[i])
if need_squeeze:
out_2.squeeze_()
return out_2.numpy()
if __name__ == "__main__":
print("---")
# img = imread_uint('test.bmp', 3)
# img = uint2single(img)
# img_bicubic = imresize_np(img, 1/4)

1
invokeai/backend/util/__init__.py
View File

@ -10,7 +10,6 @@ from .devices import ( # noqa: F401
normalize_device,
torch_dtype,
)
from .log import write_log # noqa: F401
from .util import ( # noqa: F401
ask_user,
download_with_resume,

18
invokeai/frontend/install/invokeai_update.py
View File

@ -54,13 +54,15 @@ def welcome(versions: dict):
def text():
yield f"InvokeAI Version: [bold yellow]{__version__}"
yield ""
yield "This script will update InvokeAI to the latest release, or to a development version of your choice."
yield "This script will update InvokeAI to the latest release, or to the development version of your choice."
yield ""
yield "When updating to an arbitrary tag or branch, be aware that the front end may be mismatched to the backend,"
yield "making the web frontend unusable. Please downgrade to the latest release if this happens."
yield ""
yield "[bold yellow]Options:"
yield f"""[1] Update to the latest official release ([italic]{versions[0]['tag_name']}[/italic])
[2] Update to the bleeding-edge development version ([italic]main[/italic])
[3] Manually enter the [bold]tag name[/bold] for the version you wish to update to
[4] Manually enter the [bold]branch name[/bold] for the version you wish to update to"""
[2] Manually enter the [bold]tag name[/bold] for the version you wish to update to
[3] Manually enter the [bold]branch name[/bold] for the version you wish to update to"""
console.rule()
print(
@ -104,11 +106,11 @@ def main():
if choice == "1":
release = versions[0]["tag_name"]
elif choice == "2":
release = "main"
while not tag:
tag = Prompt.ask("Enter an InvokeAI tag name")
elif choice == "3":
tag = Prompt.ask("Enter an InvokeAI tag name")
elif choice == "4":
branch = Prompt.ask("Enter an InvokeAI branch name")
while not branch:
branch = Prompt.ask("Enter an InvokeAI branch name")
extras = get_extras()

171
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invokeai/frontend/web/dist/assets/App-d1567775.js vendored Normal file
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12
invokeai/frontend/web/dist/assets/ThemeLocaleProvider-707a230a.js → invokeai/frontend/web/dist/assets/ThemeLocaleProvider-374b1ae5.js vendored
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126
invokeai/frontend/web/dist/assets/index-08cda350.js vendored
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128
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1
invokeai/frontend/web/dist/assets/menu-31376327.js vendored Normal file
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1
invokeai/frontend/web/dist/assets/menu-3d10c968.js vendored
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2
invokeai/frontend/web/dist/index.html vendored
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@ -12,7 +12,7 @@
margin: 0;
}
</style>
<script type="module" crossorigin src="./assets/index-08cda350.js"></script>
<script type="module" crossorigin src="./assets/index-f83c2c5c.js"></script>
</head>
<body dir="ltr">

2
invokeai/frontend/web/dist/locales/en.json vendored
View File

@ -511,6 +511,7 @@
"maskBlur": "Blur",
"maskBlurMethod": "Blur Method",
"coherencePassHeader": "Coherence Pass",
"coherenceMode": "Mode",
"coherenceSteps": "Steps",
"coherenceStrength": "Strength",
"seamLowThreshold": "Low",
@ -520,6 +521,7 @@
"scaledHeight": "Scaled H",
"infillMethod": "Infill Method",
"tileSize": "Tile Size",
"patchmatchDownScaleSize": "Downscale",
"boundingBoxHeader": "Bounding Box",
"seamCorrectionHeader": "Seam Correction",
"infillScalingHeader": "Infill and Scaling",

1
invokeai/frontend/web/package.json
View File

@ -75,6 +75,7 @@
"@reduxjs/toolkit": "^1.9.5",
"@roarr/browser-log-writer": "^1.1.5",
"@stevebel/png": "^1.5.1",
"compare-versions": "^6.1.0",
"dateformat": "^5.0.3",
"formik": "^2.4.3",
"framer-motion": "^10.16.1",

2
invokeai/frontend/web/public/locales/en.json
View File

@ -511,6 +511,7 @@
"maskBlur": "Blur",
"maskBlurMethod": "Blur Method",
"coherencePassHeader": "Coherence Pass",
"coherenceMode": "Mode",
"coherenceSteps": "Steps",
"coherenceStrength": "Strength",
"seamLowThreshold": "Low",
@ -520,6 +521,7 @@
"scaledHeight": "Scaled H",
"infillMethod": "Infill Method",
"tileSize": "Tile Size",
"patchmatchDownScaleSize": "Downscale",
"boundingBoxHeader": "Bounding Box",
"seamCorrectionHeader": "Seam Correction",
"infillScalingHeader": "Infill and Scaling",

4
invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/index.ts
View File

@ -84,6 +84,7 @@ import { addUserInvokedCanvasListener } from './listeners/userInvokedCanvas';
import { addUserInvokedImageToImageListener } from './listeners/userInvokedImageToImage';
import { addUserInvokedNodesListener } from './listeners/userInvokedNodes';
import { addUserInvokedTextToImageListener } from './listeners/userInvokedTextToImage';
import { addWorkflowLoadedListener } from './listeners/workflowLoaded';
export const listenerMiddleware = createListenerMiddleware();
@ -202,6 +203,9 @@ addBoardIdSelectedListener();
// Node schemas
addReceivedOpenAPISchemaListener();
// Workflows
addWorkflowLoadedListener();
// DND
addImageDroppedListener();

10
invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/receivedOpenAPISchema.ts
View File

@ -9,13 +9,17 @@ import { startAppListening } from '..';
export const addReceivedOpenAPISchemaListener = () => {
startAppListening({
actionCreator: receivedOpenAPISchema.fulfilled,
effect: (action, { dispatch }) => {
effect: (action, { dispatch, getState }) => {
const log = logger('system');
const schemaJSON = action.payload;
log.debug({ schemaJSON }, 'Received OpenAPI schema');
const nodeTemplates = parseSchema(schemaJSON);
const { nodesAllowlist, nodesDenylist } = getState().config;
const nodeTemplates = parseSchema(
schemaJSON,
nodesAllowlist,
nodesDenylist
);
log.debug(
{ nodeTemplates: parseify(nodeTemplates) },

55
invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/workflowLoaded.ts Normal file
View File

@ -0,0 +1,55 @@
import { logger } from 'app/logging/logger';
import { workflowLoadRequested } from 'features/nodes/store/actions';
import { workflowLoaded } from 'features/nodes/store/nodesSlice';
import { $flow } from 'features/nodes/store/reactFlowInstance';
import { validateWorkflow } from 'features/nodes/util/validateWorkflow';
import { addToast } from 'features/system/store/systemSlice';
import { makeToast } from 'features/system/util/makeToast';
import { setActiveTab } from 'features/ui/store/uiSlice';
import { startAppListening } from '..';
export const addWorkflowLoadedListener = () => {
startAppListening({
actionCreator: workflowLoadRequested,
effect: (action, { dispatch, getState }) => {
const log = logger('nodes');
const workflow = action.payload;
const nodeTemplates = getState().nodes.nodeTemplates;
const { workflow: validatedWorkflow, errors } = validateWorkflow(
workflow,
nodeTemplates
);
dispatch(workflowLoaded(validatedWorkflow));
if (!errors.length) {
dispatch(
addToast(
makeToast({
title: 'Workflow Loaded',
status: 'success',
})
)
);
} else {
dispatch(
addToast(
makeToast({
title: 'Workflow Loaded with Warnings',
status: 'warning',
})
)
);
errors.forEach(({ message, ...rest }) => {
log.warn(rest, message);
});
}
dispatch(setActiveTab('nodes'));
requestAnimationFrame(() => {
$flow.get()?.fitView();
});
},
});
};

3
invokeai/frontend/web/src/app/types/invokeai.ts
View File

@ -45,10 +45,13 @@ export type AppConfig = {
* Whether or not we should update image urls when image loading errors
*/
shouldUpdateImagesOnConnect: boolean;
shouldFetchMetadataFromApi: boolean;
disabledTabs: InvokeTabName[];
disabledFeatures: AppFeature[];
disabledSDFeatures: SDFeature[];
canRestoreDeletedImagesFromBin: boolean;
nodesAllowlist: string[] | undefined;
nodesDenylist: string[] | undefined;
sd: {
defaultModel?: string;
disabledControlNetModels: string[];

76
invokeai/frontend/web/src/common/hooks/useIsReadyToInvoke.ts
View File

@ -31,48 +31,54 @@ const selector = createSelector(
reasons.push('No initial image selected');
}
if (activeTabName === 'nodes' && nodes.shouldValidateGraph) {
if (!nodes.nodes.length) {
reasons.push('No nodes in graph');
}
nodes.nodes.forEach((node) => {
if (!isInvocationNode(node)) {
return;
if (activeTabName === 'nodes') {
if (nodes.shouldValidateGraph) {
if (!nodes.nodes.length) {
reasons.push('No nodes in graph');
}
const nodeTemplate = nodes.nodeTemplates[node.data.type];
if (!nodeTemplate) {
// Node type not found
reasons.push('Missing node template');
return;
}
const connectedEdges = getConnectedEdges([node], nodes.edges);
forEach(node.data.inputs, (field) => {
const fieldTemplate = nodeTemplate.inputs[field.name];
const hasConnection = connectedEdges.some(
(edge) =>
edge.target === node.id && edge.targetHandle === field.name
);
if (!fieldTemplate) {
reasons.push('Missing field template');
nodes.nodes.forEach((node) => {
if (!isInvocationNode(node)) {
return;
}
if (fieldTemplate.required && !field.value && !hasConnection) {
reasons.push(
`${node.data.label || nodeTemplate.title} -> ${
field.label || fieldTemplate.title
} missing input`
const nodeTemplate = nodes.nodeTemplates[node.data.type];
if (!nodeTemplate) {
// Node type not found
reasons.push('Missing node template');
return;
}
const connectedEdges = getConnectedEdges([node], nodes.edges);
forEach(node.data.inputs, (field) => {
const fieldTemplate = nodeTemplate.inputs[field.name];
const hasConnection = connectedEdges.some(
(edge) =>
edge.target === node.id && edge.targetHandle === field.name
);
return;
}
if (!fieldTemplate) {
reasons.push('Missing field template');
return;
}
if (
fieldTemplate.required &&
field.value === undefined &&
!hasConnection
) {
reasons.push(
`${node.data.label || nodeTemplate.title} -> ${
field.label || fieldTemplate.title
} missing input`
);
return;
}
});
});
});
}
} else {
if (!model) {
reasons.push('No model selected');

2
invokeai/frontend/web/src/common/util/colorTokenToCssVar.ts
View File

@ -1,2 +1,2 @@
export const colorTokenToCssVar = (colorToken: string) =>
`var(--invokeai-colors-${colorToken.split('.').join('-')}`;
`var(--invokeai-colors-${colorToken.split('.').join('-')})`;

6
invokeai/frontend/web/src/features/canvas/components/IAICanvasToolbar/IAICanvasToolChooserOptions.tsx
View File

@ -118,7 +118,11 @@ const IAICanvasToolChooserOptions = () => {
useHotkeys(
['BracketLeft'],
() => {
dispatch(setBrushSize(Math.max(brushSize - 5, 5)));
if (brushSize - 5 <= 5) {
dispatch(setBrushSize(Math.max(brushSize - 1, 1)));
} else {
dispatch(setBrushSize(Math.max(brushSize - 5, 1)));
}
},
{
enabled: () => !isStaging,

12
invokeai/frontend/web/src/features/canvas/store/canvasSlice.ts
View File

@ -235,10 +235,18 @@ export const canvasSlice = createSlice({
state.boundingBoxDimensions.width,
state.boundingBoxDimensions.height,
];
const [currScaledWidth, currScaledHeight] = [
state.scaledBoundingBoxDimensions.width,
state.scaledBoundingBoxDimensions.height,
];
state.boundingBoxDimensions = {
width: currHeight,
height: currWidth,
};
state.scaledBoundingBoxDimensions = {
width: currScaledHeight,
height: currScaledWidth,
};
},
setBoundingBoxCoordinates: (state, action: PayloadAction<Vector2d>) => {
state.boundingBoxCoordinates = floorCoordinates(action.payload);
@ -788,6 +796,10 @@ export const canvasSlice = createSlice({
state.boundingBoxDimensions.width / ratio,
64
);
state.scaledBoundingBoxDimensions.height = roundToMultiple(
state.scaledBoundingBoxDimensions.width / ratio,
64
);
}
});
},

34
invokeai/frontend/web/src/features/gallery/components/CurrentImage/CurrentImageButtons.tsx
View File

@ -17,20 +17,17 @@ import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
import IAIIconButton from 'common/components/IAIIconButton';
import { DeleteImageButton } from 'features/deleteImageModal/components/DeleteImageButton';
import { imagesToDeleteSelected } from 'features/deleteImageModal/store/slice';
import { workflowLoaded } from 'features/nodes/store/nodesSlice';
import { workflowLoadRequested } from 'features/nodes/store/actions';
import ParamUpscalePopover from 'features/parameters/components/Parameters/Upscale/ParamUpscaleSettings';
import { useRecallParameters } from 'features/parameters/hooks/useRecallParameters';
import { initialImageSelected } from 'features/parameters/store/actions';
import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
import { addToast } from 'features/system/store/systemSlice';
import { makeToast } from 'features/system/util/makeToast';
import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
import {
setActiveTab,
setShouldShowImageDetails,
setShouldShowProgressInViewer,
} from 'features/ui/store/uiSlice';
import { memo, useCallback } from 'react';
import { memo, useCallback, useMemo } from 'react';
import { useHotkeys } from 'react-hotkeys-hook';
import { useTranslation } from 'react-i18next';
import {
@ -52,7 +49,7 @@ import SingleSelectionMenuItems from '../ImageContextMenu/SingleSelectionMenuIte
const currentImageButtonsSelector = createSelector(
[stateSelector, activeTabNameSelector],
({ gallery, system, ui }, activeTabName) => {
({ gallery, system, ui, config }, activeTabName) => {
const { isProcessing, isConnected, shouldConfirmOnDelete, progressImage } =
system;
@ -62,6 +59,8 @@ const currentImageButtonsSelector = createSelector(
shouldShowProgressInViewer,
} = ui;
const { shouldFetchMetadataFromApi } = config;
const lastSelectedImage = gallery.selection[gallery.selection.length - 1];
return {
@ -75,6 +74,7 @@ const currentImageButtonsSelector = createSelector(
shouldHidePreview,
shouldShowProgressInViewer,
lastSelectedImage,
shouldFetchMetadataFromApi,
};
},
{
@ -95,6 +95,7 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
shouldShowImageDetails,
lastSelectedImage,
shouldShowProgressInViewer,
shouldFetchMetadataFromApi,
} = useAppSelector(currentImageButtonsSelector);
const isUpscalingEnabled = useFeatureStatus('upscaling').isFeatureEnabled;
@ -109,8 +110,16 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
lastSelectedImage?.image_name ?? skipToken
);
const getMetadataArg = useMemo(() => {
if (lastSelectedImage) {
return { image: lastSelectedImage, shouldFetchMetadataFromApi };
} else {
return skipToken;
}
}, [lastSelectedImage, shouldFetchMetadataFromApi]);
const { metadata, workflow, isLoading } = useGetImageMetadataFromFileQuery(
lastSelectedImage ?? skipToken,
getMetadataArg,
{
selectFromResult: (res) => ({
isLoading: res.isFetching,
@ -124,16 +133,7 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
if (!workflow) {
return;
}
dispatch(workflowLoaded(workflow));
dispatch(setActiveTab('nodes'));
dispatch(
addToast(
makeToast({
title: 'Workflow Loaded',
status: 'success',
})
)
);
dispatch(workflowLoadRequested(workflow));
}, [dispatch, workflow]);
const handleClickUseAllParameters = useCallback(() => {

21
invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/SingleSelectionMenuItems.tsx
View File

@ -1,18 +1,15 @@
import { Flex, MenuItem, Spinner } from '@chakra-ui/react';
import { useAppToaster } from 'app/components/Toaster';
import { useAppDispatch } from 'app/store/storeHooks';
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice';
import {
imagesToChangeSelected,
isModalOpenChanged,
} from 'features/changeBoardModal/store/slice';
import { imagesToDeleteSelected } from 'features/deleteImageModal/store/slice';
import { workflowLoaded } from 'features/nodes/store/nodesSlice';
import { useRecallParameters } from 'features/parameters/hooks/useRecallParameters';
import { initialImageSelected } from 'features/parameters/store/actions';
import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
import { addToast } from 'features/system/store/systemSlice';
import { makeToast } from 'features/system/util/makeToast';
import { useCopyImageToClipboard } from 'features/ui/hooks/useCopyImageToClipboard';
import { setActiveTab } from 'features/ui/store/uiSlice';
import { memo, useCallback } from 'react';
@ -36,6 +33,8 @@ import {
} from 'services/api/endpoints/images';
import { ImageDTO } from 'services/api/types';
import { sentImageToCanvas, sentImageToImg2Img } from '../../store/actions';
import { workflowLoadRequested } from 'features/nodes/store/actions';
import { configSelector } from '../../../system/store/configSelectors';
type SingleSelectionMenuItemsProps = {
imageDTO: ImageDTO;
@ -50,9 +49,10 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
const toaster = useAppToaster();
const isCanvasEnabled = useFeatureStatus('unifiedCanvas').isFeatureEnabled;
const { shouldFetchMetadataFromApi } = useAppSelector(configSelector);
const { metadata, workflow, isLoading } = useGetImageMetadataFromFileQuery(
imageDTO,
{ image: imageDTO, shouldFetchMetadataFromApi },
{
selectFromResult: (res) => ({
isLoading: res.isFetching,
@ -102,16 +102,7 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
if (!workflow) {
return;
}
dispatch(workflowLoaded(workflow));
dispatch(setActiveTab('nodes'));
dispatch(
addToast(
makeToast({
title: 'Workflow Loaded',
status: 'success',
})
)
);
dispatch(workflowLoadRequested(workflow));
}, [dispatch, workflow]);
const handleSendToImageToImage = useCallback(() => {

19
invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataViewer.tsx
View File

@ -15,6 +15,8 @@ import { useGetImageMetadataFromFileQuery } from 'services/api/endpoints/images'
import { ImageDTO } from 'services/api/types';
import DataViewer from './DataViewer';
import ImageMetadataActions from './ImageMetadataActions';
import { useAppSelector } from '../../../../app/store/storeHooks';
import { configSelector } from '../../../system/store/configSelectors';
type ImageMetadataViewerProps = {
image: ImageDTO;
@ -27,12 +29,17 @@ const ImageMetadataViewer = ({ image }: ImageMetadataViewerProps) => {
// dispatch(setShouldShowImageDetails(false));
// });
const { metadata, workflow } = useGetImageMetadataFromFileQuery(image, {
selectFromResult: (res) => ({
metadata: res?.currentData?.metadata,
workflow: res?.currentData?.workflow,
}),
});
const { shouldFetchMetadataFromApi } = useAppSelector(configSelector);
const { metadata, workflow } = useGetImageMetadataFromFileQuery(
{ image, shouldFetchMetadataFromApi },
{
selectFromResult: (res) => ({
metadata: res?.currentData?.metadata,
workflow: res?.currentData?.workflow,
}),
}
);
return (
<Flex

8
invokeai/frontend/web/src/features/nodes/components/flow/Flow.tsx
View File

@ -3,6 +3,7 @@ import { createSelector } from '@reduxjs/toolkit';
import { stateSelector } from 'app/store/store';
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
import { $flow } from 'features/nodes/store/reactFlowInstance';
import { contextMenusClosed } from 'features/ui/store/uiSlice';
import { useCallback } from 'react';
import { useHotkeys } from 'react-hotkeys-hook';
@ -13,6 +14,7 @@ import {
OnConnectStart,
OnEdgesChange,
OnEdgesDelete,
OnInit,
OnMoveEnd,
OnNodesChange,
OnNodesDelete,
@ -147,6 +149,11 @@ export const Flow = () => {
dispatch(contextMenusClosed());
}, [dispatch]);
const onInit: OnInit = useCallback((flow) => {
$flow.set(flow);
flow.fitView();
}, []);
useHotkeys(['Ctrl+c', 'Meta+c'], (e) => {
e.preventDefault();
dispatch(selectionCopied());
@ -170,6 +177,7 @@ export const Flow = () => {
edgeTypes={edgeTypes}
nodes={nodes}
edges={edges}
onInit={onInit}
onNodesChange={onNodesChange}
onEdgesChange={onEdgesChange}
onEdgesDelete={onEdgesDelete}

54
invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/InvocationNodeNotes.tsx
View File

@ -12,6 +12,7 @@ import {
Tooltip,
useDisclosure,
} from '@chakra-ui/react';
import { compare } from 'compare-versions';
import { useNodeData } from 'features/nodes/hooks/useNodeData';
import { useNodeLabel } from 'features/nodes/hooks/useNodeLabel';
import { useNodeTemplate } from 'features/nodes/hooks/useNodeTemplate';
@ -20,6 +21,7 @@ import { isInvocationNodeData } from 'features/nodes/types/types';
import { memo, useMemo } from 'react';
import { FaInfoCircle } from 'react-icons/fa';
import NotesTextarea from './NotesTextarea';
import { useDoNodeVersionsMatch } from 'features/nodes/hooks/useDoNodeVersionsMatch';
interface Props {
nodeId: string;
@ -29,6 +31,7 @@ const InvocationNodeNotes = ({ nodeId }: Props) => {
const { isOpen, onOpen, onClose } = useDisclosure();
const label = useNodeLabel(nodeId);
const title = useNodeTemplateTitle(nodeId);
const doVersionsMatch = useDoNodeVersionsMatch(nodeId);
return (
<>
@ -50,7 +53,11 @@ const InvocationNodeNotes = ({ nodeId }: Props) => {
>
<Icon
as={FaInfoCircle}
sx={{ boxSize: 4, w: 8, color: 'base.400' }}
sx={{
boxSize: 4,
w: 8,
color: doVersionsMatch ? 'base.400' : 'error.400',
}}
/>
</Flex>
</Tooltip>
@ -92,16 +99,59 @@ const TooltipContent = memo(({ nodeId }: { nodeId: string }) => {
return 'Unknown Node';
}, [data, nodeTemplate]);
const versionComponent = useMemo(() => {
if (!isInvocationNodeData(data) || !nodeTemplate) {
return null;
}
if (!data.version) {
return (
<Text as="span" sx={{ color: 'error.500' }}>
Version unknown
</Text>
);
}
if (!nodeTemplate.version) {
return (
<Text as="span" sx={{ color: 'error.500' }}>
Version {data.version} (unknown template)
</Text>
);
}
if (compare(data.version, nodeTemplate.version, '<')) {
return (
<Text as="span" sx={{ color: 'error.500' }}>
Version {data.version} (update node)
</Text>
);
}
if (compare(data.version, nodeTemplate.version, '>')) {
return (
<Text as="span" sx={{ color: 'error.500' }}>
Version {data.version} (update app)
</Text>
);
}
return <Text as="span">Version {data.version}</Text>;
}, [data, nodeTemplate]);
if (!isInvocationNodeData(data)) {
return <Text sx={{ fontWeight: 600 }}>Unknown Node</Text>;
}
return (
<Flex sx={{ flexDir: 'column' }}>
<Text sx={{ fontWeight: 600 }}>{title}</Text>
<Text as="span" sx={{ fontWeight: 600 }}>
{title}
</Text>
<Text sx={{ opacity: 0.7, fontStyle: 'oblique 5deg' }}>
{nodeTemplate?.description}
</Text>
{versionComponent}
{data?.notes && <Text>{data.notes}</Text>}
</Flex>
);

23
invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/FieldHandle.tsx
View File

@ -1,8 +1,11 @@
import { Tooltip } from '@chakra-ui/react';
import { colorTokenToCssVar } from 'common/util/colorTokenToCssVar';
import {
COLLECTION_TYPES,
FIELDS,
HANDLE_TOOLTIP_OPEN_DELAY,
MODEL_TYPES,
POLYMORPHIC_TYPES,
} from 'features/nodes/types/constants';
import {
InputFieldTemplate,
@ -18,6 +21,7 @@ export const handleBaseStyles: CSSProperties = {
borderWidth: 0,
zIndex: 1,
};
``;
export const inputHandleStyles: CSSProperties = {
left: '-1rem',
@ -44,15 +48,25 @@ const FieldHandle = (props: FieldHandleProps) => {
connectionError,
} = props;
const { name, type } = fieldTemplate;
const { color, title } = FIELDS[type];
const { color: typeColor, title } = FIELDS[type];
const styles: CSSProperties = useMemo(() => {
const isCollectionType = COLLECTION_TYPES.includes(type);
const isPolymorphicType = POLYMORPHIC_TYPES.includes(type);
const isModelType = MODEL_TYPES.includes(type);
const color = colorTokenToCssVar(typeColor);
const s: CSSProperties = {
backgroundColor: colorTokenToCssVar(color),
backgroundColor:
isCollectionType || isPolymorphicType
? 'var(--invokeai-colors-base-900)'
: color,
position: 'absolute',
width: '1rem',
height: '1rem',
borderWidth: 0,
borderWidth: isCollectionType || isPolymorphicType ? 4 : 0,
borderStyle: 'solid',
borderColor: color,
borderRadius: isModelType ? 4 : '100%',
zIndex: 1,
};
@ -78,11 +92,12 @@ const FieldHandle = (props: FieldHandleProps) => {
return s;
}, [
color,
connectionError,
handleType,
isConnectionInProgress,
isConnectionStartField,
type,
typeColor,
]);
const tooltip = useMemo(() => {

1
invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/InputField.tsx
View File

@ -75,6 +75,7 @@ const InputField = ({ nodeId, fieldName }: Props) => {
sx={{
display: 'flex',
alignItems: 'center',
h: 'full',
mb: 0,
px: 1,
gap: 2,

134
invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/InputFieldRenderer.tsx
View File

@ -3,18 +3,10 @@ import { useFieldData } from 'features/nodes/hooks/useFieldData';
import { useFieldTemplate } from 'features/nodes/hooks/useFieldTemplate';
import { memo } from 'react';
import BooleanInputField from './inputs/BooleanInputField';
import ClipInputField from './inputs/ClipInputField';
import CollectionInputField from './inputs/CollectionInputField';
import CollectionItemInputField from './inputs/CollectionItemInputField';
import ColorInputField from './inputs/ColorInputField';
import ConditioningInputField from './inputs/ConditioningInputField';
import ControlInputField from './inputs/ControlInputField';
import ControlNetModelInputField from './inputs/ControlNetModelInputField';
import DenoiseMaskInputField from './inputs/DenoiseMaskInputField';
import EnumInputField from './inputs/EnumInputField';
import ImageCollectionInputField from './inputs/ImageCollectionInputField';
import ImageInputField from './inputs/ImageInputField';
import LatentsInputField from './inputs/LatentsInputField';
import LoRAModelInputField from './inputs/LoRAModelInputField';
import MainModelInputField from './inputs/MainModelInputField';
import NumberInputField from './inputs/NumberInputField';
@ -22,8 +14,6 @@ import RefinerModelInputField from './inputs/RefinerModelInputField';
import SDXLMainModelInputField from './inputs/SDXLMainModelInputField';
import SchedulerInputField from './inputs/SchedulerInputField';
import StringInputField from './inputs/StringInputField';
import UnetInputField from './inputs/UnetInputField';
import VaeInputField from './inputs/VaeInputField';
import VaeModelInputField from './inputs/VaeModelInputField';
type InputFieldProps = {
@ -31,7 +21,6 @@ type InputFieldProps = {
fieldName: string;
};
// build an individual input element based on the schema
const InputFieldRenderer = ({ nodeId, fieldName }: InputFieldProps) => {
const field = useFieldData(nodeId, fieldName);
const fieldTemplate = useFieldTemplate(nodeId, fieldName, 'input');
@ -93,88 +82,6 @@ const InputFieldRenderer = ({ nodeId, fieldName }: InputFieldProps) => {
);
}
if (
field?.type === 'LatentsField' &&
fieldTemplate?.type === 'LatentsField'
) {
return (
<LatentsInputField
nodeId={nodeId}
field={field}
fieldTemplate={fieldTemplate}
/>
);
}
if (
field?.type === 'DenoiseMaskField' &&
fieldTemplate?.type === 'DenoiseMaskField'
) {
return (
<DenoiseMaskInputField
nodeId={nodeId}
field={field}
fieldTemplate={fieldTemplate}
/>
);
}
if (
field?.type === 'ConditioningField' &&
fieldTemplate?.type === 'ConditioningField'
) {
return (
<ConditioningInputField
nodeId={nodeId}
field={field}
fieldTemplate={fieldTemplate}
/>
);
}
if (field?.type === 'UNetField' && fieldTemplate?.type === 'UNetField') {
return (
<UnetInputField
nodeId={nodeId}
field={field}
fieldTemplate={fieldTemplate}
/>
);
}
if (field?.type === 'ClipField' && fieldTemplate?.type === 'ClipField') {
return (
<ClipInputField
nodeId={nodeId}
field={field}
fieldTemplate={fieldTemplate}
/>
);
}
if (field?.type === 'VaeField' && fieldTemplate?.type === 'VaeField') {
return (
<VaeInputField
nodeId={nodeId}
field={field}
fieldTemplate={fieldTemplate}
/>
);
}
if (
field?.type === 'ControlField' &&
fieldTemplate?.type === 'ControlField'
) {
return (
<ControlInputField
nodeId={nodeId}
field={field}
fieldTemplate={fieldTemplate}
/>
);
}
if (
field?.type === 'MainModelField' &&
fieldTemplate?.type === 'MainModelField'
@ -240,29 +147,6 @@ const InputFieldRenderer = ({ nodeId, fieldName }: InputFieldProps) => {
);
}
if (field?.type === 'Collection' && fieldTemplate?.type === 'Collection') {
return (
<CollectionInputField
nodeId={nodeId}
field={field}
fieldTemplate={fieldTemplate}
/>
);
}
if (
field?.type === 'CollectionItem' &&
fieldTemplate?.type === 'CollectionItem'
) {
return (
<CollectionItemInputField
nodeId={nodeId}
field={field}
fieldTemplate={fieldTemplate}
/>
);
}
if (field?.type === 'ColorField' && fieldTemplate?.type === 'ColorField') {
return (
<ColorInputField
@ -273,19 +157,6 @@ const InputFieldRenderer = ({ nodeId, fieldName }: InputFieldProps) => {
);
}
if (
field?.type === 'ImageCollection' &&
fieldTemplate?.type === 'ImageCollection'
) {
return (
<ImageCollectionInputField
nodeId={nodeId}
field={field}
fieldTemplate={fieldTemplate}
/>
);
}
if (
field?.type === 'SDXLMainModelField' &&
fieldTemplate?.type === 'SDXLMainModelField'
@ -309,6 +180,11 @@ const InputFieldRenderer = ({ nodeId, fieldName }: InputFieldProps) => {
);
}
if (field && fieldTemplate) {
// Fallback for when there is no component for the type
return null;
}
return (
<Box p={1}>
<Text

7
invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/inputs/ControlInputField.tsx
View File

@ -1,12 +1,17 @@
import {
ControlInputFieldTemplate,
ControlInputFieldValue,
ControlPolymorphicInputFieldTemplate,
ControlPolymorphicInputFieldValue,
FieldComponentProps,
} from 'features/nodes/types/types';
import { memo } from 'react';
const ControlInputFieldComponent = (
_props: FieldComponentProps<ControlInputFieldValue, ControlInputFieldTemplate>
_props: FieldComponentProps<
ControlInputFieldValue | ControlPolymorphicInputFieldValue,
ControlInputFieldTemplate | ControlPolymorphicInputFieldTemplate
>
) => {
return null;
};

2
invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/inputs/ImageInputField.tsx
View File

@ -9,9 +9,9 @@ import {
} from 'features/dnd/types';
import { fieldImageValueChanged } from 'features/nodes/store/nodesSlice';
import {
FieldComponentProps,
ImageInputFieldTemplate,
ImageInputFieldValue,
FieldComponentProps,
} from 'features/nodes/types/types';
import { memo, useCallback, useMemo } from 'react';
import { FaUndo } from 'react-icons/fa';

7
invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/inputs/LatentsInputField.tsx
View File

@ -2,11 +2,16 @@ import {
LatentsInputFieldTemplate,
LatentsInputFieldValue,
FieldComponentProps,
LatentsPolymorphicInputFieldValue,
LatentsPolymorphicInputFieldTemplate,
} from 'features/nodes/types/types';
import { memo } from 'react';
const LatentsInputFieldComponent = (
_props: FieldComponentProps<LatentsInputFieldValue, LatentsInputFieldTemplate>
_props: FieldComponentProps<
LatentsInputFieldValue | LatentsPolymorphicInputFieldValue,
LatentsInputFieldTemplate | LatentsPolymorphicInputFieldTemplate
>
) => {
return null;
};

2
invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/inputs/NumberInputField.tsx
View File

@ -9,11 +9,11 @@ import { useAppDispatch } from 'app/store/storeHooks';
import { numberStringRegex } from 'common/components/IAINumberInput';
import { fieldNumberValueChanged } from 'features/nodes/store/nodesSlice';
import {
FieldComponentProps,
FloatInputFieldTemplate,
FloatInputFieldValue,
IntegerInputFieldTemplate,
IntegerInputFieldValue,
FieldComponentProps,
} from 'features/nodes/types/types';
import { memo, useEffect, useMemo, useState } from 'react';

21
invokeai/frontend/web/src/features/nodes/components/flow/nodes/common/NodeWrapper.tsx
View File

@ -9,13 +9,20 @@ import { stateSelector } from 'app/store/store';
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
import NodeSelectionOverlay from 'common/components/NodeSelectionOverlay';
import { useMouseOverNode } from 'features/nodes/hooks/useMouseOverNode';
import { nodeExclusivelySelected } from 'features/nodes/store/nodesSlice';
import {
DRAG_HANDLE_CLASSNAME,
NODE_WIDTH,
} from 'features/nodes/types/constants';
import { NodeStatus } from 'features/nodes/types/types';
import { contextMenusClosed } from 'features/ui/store/uiSlice';
import { PropsWithChildren, memo, useCallback, useMemo } from 'react';
import {
MouseEvent,
PropsWithChildren,
memo,
useCallback,
useMemo,
} from 'react';
type NodeWrapperProps = PropsWithChildren & {
nodeId: string;
@ -57,9 +64,15 @@ const NodeWrapper = (props: NodeWrapperProps) => {
const opacity = useAppSelector((state) => state.nodes.nodeOpacity);
const handleClick = useCallback(() => {
dispatch(contextMenusClosed());
}, [dispatch]);
const handleClick = useCallback(
(e: MouseEvent<HTMLDivElement>) => {
if (!e.ctrlKey && !e.altKey && !e.metaKey && !e.shiftKey) {
dispatch(nodeExclusivelySelected(nodeId));
}
dispatch(contextMenusClosed());
},
[dispatch, nodeId]
);
return (
<Box

7
invokeai/frontend/web/src/features/nodes/hooks/useBuildNodeData.ts
View File

@ -138,13 +138,14 @@ export const useBuildNodeData = () => {
data: {
id: nodeId,
type,
inputs,
outputs,
isOpen: true,
version: template.version,
label: '',
notes: '',
isOpen: true,
embedWorkflow: false,
isIntermediate: true,
inputs,
outputs,
},
};

33
invokeai/frontend/web/src/features/nodes/hooks/useDoNodeVersionsMatch.ts Normal file
View File

@ -0,0 +1,33 @@
import { createSelector } from '@reduxjs/toolkit';
import { stateSelector } from 'app/store/store';
import { useAppSelector } from 'app/store/storeHooks';
import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
import { compareVersions } from 'compare-versions';
import { useMemo } from 'react';
import { isInvocationNode } from '../types/types';
export const useDoNodeVersionsMatch = (nodeId: string) => {
const selector = useMemo(
() =>
createSelector(
stateSelector,
({ nodes }) => {
const node = nodes.nodes.find((node) => node.id === nodeId);
if (!isInvocationNode(node)) {
return false;
}
const nodeTemplate = nodes.nodeTemplates[node?.data.type ?? ''];
if (!nodeTemplate?.version || !node.data?.version) {
return false;
}
return compareVersions(nodeTemplate.version, node.data.version) === 0;
},
defaultSelectorOptions
),
[nodeId]
);
const nodeTemplate = useAppSelector(selector);
return nodeTemplate;
};

2
invokeai/frontend/web/src/features/nodes/hooks/useDoesInputHaveValue.ts
View File

@ -15,7 +15,7 @@ export const useDoesInputHaveValue = (nodeId: string, fieldName: string) => {
if (!isInvocationNode(node)) {
return;
}
return Boolean(node?.data.inputs[fieldName]?.value);
return node?.data.inputs[fieldName]?.value !== undefined;
},
defaultSelectorOptions
),

94
invokeai/frontend/web/src/features/nodes/hooks/useIsValidConnection.ts
View File

@ -3,9 +3,19 @@ import graphlib from '@dagrejs/graphlib';
import { useAppSelector } from 'app/store/storeHooks';
import { useCallback } from 'react';
import { Connection, Edge, Node, useReactFlow } from 'reactflow';
import { COLLECTION_TYPES } from '../types/constants';
import {
COLLECTION_MAP,
COLLECTION_TYPES,
POLYMORPHIC_TO_SINGLE_MAP,
POLYMORPHIC_TYPES,
} from '../types/constants';
import { InvocationNodeData } from '../types/types';
/**
* NOTE: The logic here must be duplicated in `invokeai/frontend/web/src/features/nodes/store/util/makeIsConnectionValidSelector.ts`
* TODO: Figure out how to do this without duplicating all the logic
*/
export const useIsValidConnection = () => {
const flow = useReactFlow();
const shouldValidateGraph = useAppSelector(
@ -42,6 +52,19 @@ export const useIsValidConnection = () => {
return false;
}
if (
edges
.filter((edge) => {
return edge.target === target && edge.targetHandle === targetHandle;
})
.find((edge) => {
edge.source === source && edge.sourceHandle === sourceHandle;
})
) {
// We already have a connection from this source to this target
return false;
}
// Connection is invalid if target already has a connection
if (
edges.find((edge) => {
@ -53,21 +76,62 @@ export const useIsValidConnection = () => {
return false;
}
// Connection types must be the same for a connection
if (
sourceType !== targetType &&
sourceType !== 'CollectionItem' &&
targetType !== 'CollectionItem'
) {
if (
!(
COLLECTION_TYPES.includes(targetType) &&
COLLECTION_TYPES.includes(sourceType)
)
) {
return false;
}
/**
* Connection types must be the same for a connection, with exceptions:
* - CollectionItem can connect to any non-Collection
* - Non-Collections can connect to CollectionItem
* - Anything (non-Collections, Collections, Polymorphics) can connect to Polymorphics of the same base type
* - Generic Collection can connect to any other Collection or Polymorphic
* - Any Collection can connect to a Generic Collection
*/
if (sourceType !== targetType) {
const isCollectionItemToNonCollection =
sourceType === 'CollectionItem' &&
!COLLECTION_TYPES.includes(targetType);
const isNonCollectionToCollectionItem =
targetType === 'CollectionItem' &&
!COLLECTION_TYPES.includes(sourceType) &&
!POLYMORPHIC_TYPES.includes(sourceType);
const isAnythingToPolymorphicOfSameBaseType =
POLYMORPHIC_TYPES.includes(targetType) &&
(() => {
if (!POLYMORPHIC_TYPES.includes(targetType)) {
return false;
}
const baseType =
POLYMORPHIC_TO_SINGLE_MAP[
targetType as keyof typeof POLYMORPHIC_TO_SINGLE_MAP
];
const collectionType =
COLLECTION_MAP[baseType as keyof typeof COLLECTION_MAP];
return sourceType === baseType || sourceType === collectionType;
})();
const isGenericCollectionToAnyCollectionOrPolymorphic =
sourceType === 'Collection' &&
(COLLECTION_TYPES.includes(targetType) ||
POLYMORPHIC_TYPES.includes(targetType));
const isCollectionToGenericCollection =
targetType === 'Collection' && COLLECTION_TYPES.includes(sourceType);
const isIntToFloat = sourceType === 'integer' && targetType === 'float';
return (
isCollectionItemToNonCollection ||
isNonCollectionToCollectionItem ||
isAnythingToPolymorphicOfSameBaseType ||
isGenericCollectionToAnyCollectionOrPolymorphic ||
isCollectionToGenericCollection ||
isIntToFloat
);
}
// Graphs much be acyclic (no loops!)
return getIsGraphAcyclic(source, target, nodes, edges);
},

32
invokeai/frontend/web/src/features/nodes/hooks/useLoadWorkflowFromFile.tsx
View File

@ -2,13 +2,13 @@ import { ListItem, Text, UnorderedList } from '@chakra-ui/react';
import { useLogger } from 'app/logging/useLogger';
import { useAppDispatch } from 'app/store/storeHooks';
import { parseify } from 'common/util/serialize';
import { workflowLoaded } from 'features/nodes/store/nodesSlice';
import { zValidatedWorkflow } from 'features/nodes/types/types';
import { zWorkflow } from 'features/nodes/types/types';
import { addToast } from 'features/system/store/systemSlice';
import { makeToast } from 'features/system/util/makeToast';
import { memo, useCallback } from 'react';
import { ZodError } from 'zod';
import { fromZodError, fromZodIssue } from 'zod-validation-error';
import { workflowLoadRequested } from '../store/actions';
export const useLoadWorkflowFromFile = () => {
const dispatch = useAppDispatch();
@ -24,7 +24,7 @@ export const useLoadWorkflowFromFile = () => {
try {
const parsedJSON = JSON.parse(String(rawJSON));
const result = zValidatedWorkflow.safeParse(parsedJSON);
const result = zWorkflow.safeParse(parsedJSON);
if (!result.success) {
const { message } = fromZodError(result.error, {
@ -45,32 +45,8 @@ export const useLoadWorkflowFromFile = () => {
reader.abort();
return;
}
dispatch(workflowLoaded(result.data.workflow));
if (!result.data.warnings.length) {
dispatch(
addToast(
makeToast({
title: 'Workflow Loaded',
status: 'success',
})
)
);
reader.abort();
return;
}
dispatch(
addToast(
makeToast({
title: 'Workflow Loaded with Warnings',
status: 'warning',
})
)
);
result.data.warnings.forEach(({ message, ...rest }) => {
logger.warn(rest, message);
});
dispatch(workflowLoadRequested(result.data));
reader.abort();
} catch {

5
invokeai/frontend/web/src/features/nodes/store/actions.ts
View File

@ -1,5 +1,6 @@
import { createAction, isAnyOf } from '@reduxjs/toolkit';
import { Graph } from 'services/api/types';
import { Workflow } from '../types/types';
export const textToImageGraphBuilt = createAction<Graph>(
'nodes/textToImageGraphBuilt'
@ -16,3 +17,7 @@ export const isAnyGraphBuilt = isAnyOf(
canvasGraphBuilt,
nodesGraphBuilt
);
export const workflowLoadRequested = createAction<Workflow>(
'nodes/workflowLoadRequested'
);

12
invokeai/frontend/web/src/features/nodes/store/nodesSlice.ts
View File

@ -443,6 +443,17 @@ const nodesSlice = createSlice({
}
node.data.notes = notes;
},
nodeExclusivelySelected: (state, action: PayloadAction<string>) => {
const nodeId = action.payload;
state.nodes = applyNodeChanges(
state.nodes.map((n) => ({
id: n.id,
type: 'select',
selected: n.id === nodeId ? true : false,
})),
state.nodes
);
},
selectedNodesChanged: (state, action: PayloadAction<string[]>) => {
state.selectedNodes = action.payload;
},
@ -892,6 +903,7 @@ export const {
nodeEmbedWorkflowChanged,
nodeIsIntermediateChanged,
mouseOverNodeChanged,
nodeExclusivelySelected,
} = nodesSlice.actions;
export default nodesSlice.reducer;

4
invokeai/frontend/web/src/features/nodes/store/reactFlowInstance.ts Normal file
View File

@ -0,0 +1,4 @@
import { atom } from 'nanostores';
import { ReactFlowInstance } from 'reactflow';
export const $flow = atom<ReactFlowInstance | null>(null);

102
invokeai/frontend/web/src/features/nodes/store/util/makeIsConnectionValidSelector.ts
View File

@ -1,10 +1,20 @@
import { createSelector } from '@reduxjs/toolkit';
import { stateSelector } from 'app/store/store';
import { getIsGraphAcyclic } from 'features/nodes/hooks/useIsValidConnection';
import { COLLECTION_TYPES } from 'features/nodes/types/constants';
import {
COLLECTION_MAP,
COLLECTION_TYPES,
POLYMORPHIC_TO_SINGLE_MAP,
POLYMORPHIC_TYPES,
} from 'features/nodes/types/constants';
import { FieldType } from 'features/nodes/types/types';
import { HandleType } from 'reactflow';
/**
* NOTE: The logic here must be duplicated in `invokeai/frontend/web/src/features/nodes/hooks/useIsValidConnection.ts`
* TODO: Figure out how to do this without duplicating all the logic
*/
export const makeConnectionErrorSelector = (
nodeId: string,
fieldName: string,
@ -19,11 +29,6 @@ export const makeConnectionErrorSelector = (
const { currentConnectionFieldType, connectionStartParams, nodes, edges } =
state.nodes;
if (!state.nodes.shouldValidateGraph) {
// manual override!
return null;
}
if (!connectionStartParams || !currentConnectionFieldType) {
return 'No connection in progress';
}
@ -38,9 +43,9 @@ export const makeConnectionErrorSelector = (
return 'No connection data';
}
const targetFieldType =
const targetType =
handleType === 'target' ? fieldType : currentConnectionFieldType;
const sourceFieldType =
const sourceType =
handleType === 'source' ? fieldType : currentConnectionFieldType;
if (nodeId === connectionNodeId) {
@ -55,30 +60,73 @@ export const makeConnectionErrorSelector = (
}
if (
fieldType !== currentConnectionFieldType &&
fieldType !== 'CollectionItem' &&
currentConnectionFieldType !== 'CollectionItem'
) {
if (
!(
COLLECTION_TYPES.includes(targetFieldType) &&
COLLECTION_TYPES.includes(sourceFieldType)
)
) {
// except for collection items, field types must match
return 'Field types must match';
}
}
if (
handleType === 'target' &&
edges.find((edge) => {
return edge.target === nodeId && edge.targetHandle === fieldName;
}) &&
// except CollectionItem inputs can have multiples
targetFieldType !== 'CollectionItem'
targetType !== 'CollectionItem'
) {
return 'Inputs may only have one connection';
return 'Input may only have one connection';
}
/**
* Connection types must be the same for a connection, with exceptions:
* - CollectionItem can connect to any non-Collection
* - Non-Collections can connect to CollectionItem
* - Anything (non-Collections, Collections, Polymorphics) can connect to Polymorphics of the same base type
* - Generic Collection can connect to any other Collection or Polymorphic
* - Any Collection can connect to a Generic Collection
*/
if (sourceType !== targetType) {
const isCollectionItemToNonCollection =
sourceType === 'CollectionItem' &&
!COLLECTION_TYPES.includes(targetType);
const isNonCollectionToCollectionItem =
targetType === 'CollectionItem' &&
!COLLECTION_TYPES.includes(sourceType) &&
!POLYMORPHIC_TYPES.includes(sourceType);
const isAnythingToPolymorphicOfSameBaseType =
POLYMORPHIC_TYPES.includes(targetType) &&
(() => {
if (!POLYMORPHIC_TYPES.includes(targetType)) {
return false;
}
const baseType =
POLYMORPHIC_TO_SINGLE_MAP[
targetType as keyof typeof POLYMORPHIC_TO_SINGLE_MAP
];
const collectionType =
COLLECTION_MAP[baseType as keyof typeof COLLECTION_MAP];
return sourceType === baseType || sourceType === collectionType;
})();
const isGenericCollectionToAnyCollectionOrPolymorphic =
sourceType === 'Collection' &&
(COLLECTION_TYPES.includes(targetType) ||
POLYMORPHIC_TYPES.includes(targetType));
const isCollectionToGenericCollection =
targetType === 'Collection' && COLLECTION_TYPES.includes(sourceType);
const isIntToFloat = sourceType === 'integer' && targetType === 'float';
if (
!(
isCollectionItemToNonCollection ||
isNonCollectionToCollectionItem ||
isAnythingToPolymorphicOfSameBaseType ||
isGenericCollectionToAnyCollectionOrPolymorphic ||
isCollectionToGenericCollection ||
isIntToFloat
)
) {
return 'Field types must match';
}
}
const isGraphAcyclic = getIsGraphAcyclic(

377
invokeai/frontend/web/src/features/nodes/types/constants.ts
View File

@ -17,176 +17,297 @@ export const KIND_MAP = {
export const COLLECTION_TYPES: FieldType[] = [
'Collection',
'IntegerCollection',
'BooleanCollection',
'FloatCollection',
'StringCollection',
'BooleanCollection',
'ImageCollection',
'LatentsCollection',
'ConditioningCollection',
'ControlCollection',
'ColorCollection',
];
export const POLYMORPHIC_TYPES = [
'IntegerPolymorphic',
'BooleanPolymorphic',
'FloatPolymorphic',
'StringPolymorphic',
'ImagePolymorphic',
'LatentsPolymorphic',
'ConditioningPolymorphic',
'ControlPolymorphic',
'ColorPolymorphic',
];
export const MODEL_TYPES = [
'ControlNetModelField',
'LoRAModelField',
'MainModelField',
'ONNXModelField',
'SDXLMainModelField',
'SDXLRefinerModelField',
'VaeModelField',
'UNetField',
'VaeField',
'ClipField',
];
export const COLLECTION_MAP = {
integer: 'IntegerCollection',
boolean: 'BooleanCollection',
number: 'FloatCollection',
float: 'FloatCollection',
string: 'StringCollection',
ImageField: 'ImageCollection',
LatentsField: 'LatentsCollection',
ConditioningField: 'ConditioningCollection',
ControlField: 'ControlCollection',
ColorField: 'ColorCollection',
};
export const isCollectionItemType = (
itemType: string | undefined
): itemType is keyof typeof COLLECTION_MAP =>
Boolean(itemType && itemType in COLLECTION_MAP);
export const SINGLE_TO_POLYMORPHIC_MAP = {
integer: 'IntegerPolymorphic',
boolean: 'BooleanPolymorphic',
number: 'FloatPolymorphic',
float: 'FloatPolymorphic',
string: 'StringPolymorphic',
ImageField: 'ImagePolymorphic',
LatentsField: 'LatentsPolymorphic',
ConditioningField: 'ConditioningPolymorphic',
ControlField: 'ControlPolymorphic',
ColorField: 'ColorPolymorphic',
};
export const POLYMORPHIC_TO_SINGLE_MAP = {
IntegerPolymorphic: 'integer',
BooleanPolymorphic: 'boolean',
FloatPolymorphic: 'float',
StringPolymorphic: 'string',
ImagePolymorphic: 'ImageField',
LatentsPolymorphic: 'LatentsField',
ConditioningPolymorphic: 'ConditioningField',
ControlPolymorphic: 'ControlField',
ColorPolymorphic: 'ColorField',
};
export const isPolymorphicItemType = (
itemType: string | undefined
): itemType is keyof typeof SINGLE_TO_POLYMORPHIC_MAP =>
Boolean(itemType && itemType in SINGLE_TO_POLYMORPHIC_MAP);
export const FIELDS: Record<FieldType, FieldUIConfig> = {
integer: {
title: 'Integer',
description: 'Integers are whole numbers, without a decimal point.',
color: 'red.500',
},
float: {
title: 'Float',
description: 'Floats are numbers with a decimal point.',
color: 'orange.500',
},
string: {
title: 'String',
description: 'Strings are text.',
color: 'yellow.500',
},
boolean: {
title: 'Boolean',
color: 'green.500',
description: 'Booleans are true or false.',
title: 'Boolean',
},
enum: {
title: 'Enum',
description: 'Enums are values that may be one of a number of options.',
color: 'blue.500',
BooleanCollection: {
color: 'green.500',
description: 'A collection of booleans.',
title: 'Boolean Collection',
},
array: {
title: 'Array',
description: 'Enums are values that may be one of a number of options.',
color: 'base.500',
},
ImageField: {
title: 'Image',
description: 'Images may be passed between nodes.',
color: 'purple.500',
},
DenoiseMaskField: {
title: 'Denoise Mask',
description: 'Denoise Mask may be passed between nodes',
color: 'base.500',
},
LatentsField: {
title: 'Latents',
description: 'Latents may be passed between nodes.',
color: 'pink.500',
},
LatentsCollection: {
title: 'Latents Collection',
description: 'Latents may be passed between nodes.',
color: 'pink.500',
},
ConditioningField: {
color: 'cyan.500',
title: 'Conditioning',
description: 'Conditioning may be passed between nodes.',
},
ConditioningCollection: {
color: 'cyan.500',
title: 'Conditioning Collection',
description: 'Conditioning may be passed between nodes.',
},
ImageCollection: {
title: 'Image Collection',
description: 'A collection of images.',
color: 'base.300',
},
UNetField: {
color: 'red.500',
title: 'UNet',
description: 'UNet submodel.',
BooleanPolymorphic: {
color: 'green.500',
description: 'A collection of booleans.',
title: 'Boolean Polymorphic',
},
ClipField: {
color: 'green.500',
title: 'Clip',
description: 'Tokenizer and text_encoder submodels.',
},
VaeField: {
color: 'blue.500',
title: 'Vae',
description: 'Vae submodel.',
},
ControlField: {
color: 'cyan.500',
title: 'Control',
description: 'Control info passed between nodes.',
},
MainModelField: {
color: 'teal.500',
title: 'Model',
description: 'TODO',
},
SDXLRefinerModelField: {
color: 'teal.500',
title: 'Refiner Model',
description: 'TODO',
},
VaeModelField: {
color: 'teal.500',
title: 'VAE',
description: 'TODO',
},
LoRAModelField: {
color: 'teal.500',
title: 'LoRA',
description: 'TODO',
},
ControlNetModelField: {
color: 'teal.500',
title: 'ControlNet',
description: 'TODO',
},
Scheduler: {
color: 'base.500',
title: 'Scheduler',
description: 'TODO',
title: 'Clip',
},
Collection: {
color: 'base.500',
title: 'Collection',
description: 'TODO',
title: 'Collection',
},
CollectionItem: {
color: 'base.500',
title: 'Collection Item',
description: 'TODO',
title: 'Collection Item',
},
ColorCollection: {
color: 'pink.300',
description: 'A collection of colors.',
title: 'Color Collection',
},
ColorField: {
title: 'Color',
color: 'pink.300',
description: 'A RGBA color.',
color: 'base.500',
title: 'Color',
},
BooleanCollection: {
title: 'Boolean Collection',
description: 'A collection of booleans.',
color: 'green.500',
ColorPolymorphic: {
color: 'pink.300',
description: 'A collection of colors.',
title: 'Color Polymorphic',
},
IntegerCollection: {
title: 'Integer Collection',
description: 'A collection of integers.',
color: 'red.500',
ConditioningCollection: {
color: 'cyan.500',
description: 'Conditioning may be passed between nodes.',
title: 'Conditioning Collection',
},
ConditioningField: {
color: 'cyan.500',
description: 'Conditioning may be passed between nodes.',
title: 'Conditioning',
},
ConditioningPolymorphic: {
color: 'cyan.500',
description: 'Conditioning may be passed between nodes.',
title: 'Conditioning Polymorphic',
},
ControlCollection: {
color: 'teal.500',
description: 'Control info passed between nodes.',
title: 'Control Collection',
},
ControlField: {
color: 'teal.500',
description: 'Control info passed between nodes.',
title: 'Control',
},
ControlNetModelField: {
color: 'teal.500',
description: 'TODO',
title: 'ControlNet',
},
ControlPolymorphic: {
color: 'teal.500',
description: 'Control info passed between nodes.',
title: 'Control Polymorphic',
},
DenoiseMaskField: {
color: 'blue.300',
description: 'Denoise Mask may be passed between nodes',
title: 'Denoise Mask',
},
enum: {
color: 'blue.500',
description: 'Enums are values that may be one of a number of options.',
title: 'Enum',
},
float: {
color: 'orange.500',
description: 'Floats are numbers with a decimal point.',
title: 'Float',
},
FloatCollection: {
color: 'orange.500',
title: 'Float Collection',
description: 'A collection of floats.',
title: 'Float Collection',
},
ColorCollection: {
color: 'base.500',
title: 'Color Collection',
description: 'A collection of colors.',
FloatPolymorphic: {
color: 'orange.500',
description: 'A collection of floats.',
title: 'Float Polymorphic',
},
ImageCollection: {
color: 'purple.500',
description: 'A collection of images.',
title: 'Image Collection',
},
ImageField: {
color: 'purple.500',
description: 'Images may be passed between nodes.',
title: 'Image',
},
ImagePolymorphic: {
color: 'purple.500',
description: 'A collection of images.',
title: 'Image Polymorphic',
},
integer: {
color: 'red.500',
description: 'Integers are whole numbers, without a decimal point.',
title: 'Integer',
},
IntegerCollection: {
color: 'red.500',
description: 'A collection of integers.',
title: 'Integer Collection',
},
IntegerPolymorphic: {
color: 'red.500',
description: 'A collection of integers.',
title: 'Integer Polymorphic',
},
LatentsCollection: {
color: 'pink.500',
description: 'Latents may be passed between nodes.',
title: 'Latents Collection',
},
LatentsField: {
color: 'pink.500',
description: 'Latents may be passed between nodes.',
title: 'Latents',
},
LatentsPolymorphic: {
color: 'pink.500',
description: 'Latents may be passed between nodes.',
title: 'Latents Polymorphic',
},
LoRAModelField: {
color: 'teal.500',
description: 'TODO',
title: 'LoRA',
},
MainModelField: {
color: 'teal.500',
description: 'TODO',
title: 'Model',
},
ONNXModelField: {
color: 'base.500',
title: 'ONNX Model',
color: 'teal.500',
description: 'ONNX model field.',
title: 'ONNX Model',
},
Scheduler: {
color: 'base.500',
description: 'TODO',
title: 'Scheduler',
},
SDXLMainModelField: {
color: 'base.500',
title: 'SDXL Model',
color: 'teal.500',
description: 'SDXL model field.',
title: 'SDXL Model',
},
SDXLRefinerModelField: {
color: 'teal.500',
description: 'TODO',
title: 'Refiner Model',
},
string: {
color: 'yellow.500',
description: 'Strings are text.',
title: 'String',
},
StringCollection: {
color: 'yellow.500',
title: 'String Collection',
description: 'A collection of strings.',
title: 'String Collection',
},
StringPolymorphic: {
color: 'yellow.500',
description: 'A collection of strings.',
title: 'String Polymorphic',
},
UNetField: {
color: 'red.500',
description: 'UNet submodel.',
title: 'UNet',
},
VaeField: {
color: 'blue.500',
description: 'Vae submodel.',
title: 'Vae',
},
VaeModelField: {
color: 'teal.500',
description: 'TODO',
title: 'VAE',
},
};

522
invokeai/frontend/web/src/features/nodes/types/types.ts
View File

@ -11,7 +11,7 @@ import { keyBy } from 'lodash-es';
import { OpenAPIV3 } from 'openapi-types';
import { RgbaColor } from 'react-colorful';
import { Node } from 'reactflow';
import { Graph, ImageDTO, _InputField, _OutputField } from 'services/api/types';
import { Graph, _InputField, _OutputField } from 'services/api/types';
import {
AnyInvocationType,
AnyResult,
@ -52,6 +52,10 @@ export type InvocationTemplate = {
* The type of this node's output
*/
outputType: string; // TODO: generate a union of output types
/**
* The invocation's version.
*/
version?: string;
};
export type FieldUIConfig = {
@ -62,50 +66,48 @@ export type FieldUIConfig = {
// TODO: Get this from the OpenAPI schema? may be tricky...
export const zFieldType = z.enum([
// region Primitives
'integer',
'float',
'boolean',
'string',
'array',
'ImageField',
'DenoiseMaskField',
'LatentsField',
'ConditioningField',
'ControlField',
'ColorField',
'ImageCollection',
'ConditioningCollection',
'ColorCollection',
'LatentsCollection',
'IntegerCollection',
'FloatCollection',
'StringCollection',
'BooleanCollection',
// endregion
// region Models
'MainModelField',
'SDXLMainModelField',
'SDXLRefinerModelField',
'ONNXModelField',
'VaeModelField',
'LoRAModelField',
'ControlNetModelField',
'UNetField',
'VaeField',
'BooleanPolymorphic',
'ClipField',
// endregion
// region Iterate/Collect
'Collection',
'CollectionItem',
// endregion
// region Misc
'ColorCollection',
'ColorField',
'ColorPolymorphic',
'ConditioningCollection',
'ConditioningField',
'ConditioningPolymorphic',
'ControlCollection',
'ControlField',
'ControlNetModelField',
'ControlPolymorphic',
'DenoiseMaskField',
'enum',
'float',
'FloatCollection',
'FloatPolymorphic',
'ImageCollection',
'ImageField',
'ImagePolymorphic',
'integer',
'IntegerCollection',
'IntegerPolymorphic',
'LatentsCollection',
'LatentsField',
'LatentsPolymorphic',
'LoRAModelField',
'MainModelField',
'ONNXModelField',
'Scheduler',
// endregion
'SDXLMainModelField',
'SDXLRefinerModelField',
'string',
'StringCollection',
'StringPolymorphic',
'UNetField',
'VaeField',
'VaeModelField',
]);
export type FieldType = z.infer<typeof zFieldType>;
@ -122,38 +124,6 @@ export const isFieldType = (value: unknown): value is FieldType =>
zFieldType.safeParse(value).success ||
zReservedFieldType.safeParse(value).success;
/**
* An input field template is generated on each page load from the OpenAPI schema.
*
* The template provides the field type and other field metadata (e.g. title, description,
* maximum length, pattern to match, etc).
*/
export type InputFieldTemplate =
| IntegerInputFieldTemplate
| FloatInputFieldTemplate
| StringInputFieldTemplate
| BooleanInputFieldTemplate
| ImageInputFieldTemplate
| DenoiseMaskInputFieldTemplate
| LatentsInputFieldTemplate
| ConditioningInputFieldTemplate
| UNetInputFieldTemplate
| ClipInputFieldTemplate
| VaeInputFieldTemplate
| ControlInputFieldTemplate
| EnumInputFieldTemplate
| MainModelInputFieldTemplate
| SDXLMainModelInputFieldTemplate
| SDXLRefinerModelInputFieldTemplate
| VaeModelInputFieldTemplate
| LoRAModelInputFieldTemplate
| ControlNetModelInputFieldTemplate
| CollectionInputFieldTemplate
| CollectionItemInputFieldTemplate
| ColorInputFieldTemplate
| ImageCollectionInputFieldTemplate
| SchedulerInputFieldTemplate;
/**
* Indicates the kind of input(s) this field may have.
*/
@ -232,24 +202,88 @@ export const zIntegerInputFieldValue = zInputFieldValueBase.extend({
});
export type IntegerInputFieldValue = z.infer<typeof zIntegerInputFieldValue>;
export const zIntegerCollectionInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('IntegerCollection'),
value: z.array(z.number().int()).optional(),
});
export type IntegerCollectionInputFieldValue = z.infer<
typeof zIntegerCollectionInputFieldValue
>;
export const zIntegerPolymorphicInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('IntegerPolymorphic'),
value: z.union([z.number().int(), z.array(z.number().int())]).optional(),
});
export type IntegerPolymorphicInputFieldValue = z.infer<
typeof zIntegerPolymorphicInputFieldValue
>;
export const zFloatInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('float'),
value: z.number().optional(),
});
export type FloatInputFieldValue = z.infer<typeof zFloatInputFieldValue>;
export const zFloatCollectionInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('FloatCollection'),
value: z.array(z.number()).optional(),
});
export type FloatCollectionInputFieldValue = z.infer<
typeof zFloatCollectionInputFieldValue
>;
export const zFloatPolymorphicInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('FloatPolymorphic'),
value: z.union([z.number(), z.array(z.number())]).optional(),
});
export type FloatPolymorphicInputFieldValue = z.infer<
typeof zFloatPolymorphicInputFieldValue
>;
export const zStringInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('string'),
value: z.string().optional(),
});
export type StringInputFieldValue = z.infer<typeof zStringInputFieldValue>;
export const zStringCollectionInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('StringCollection'),
value: z.array(z.string()).optional(),
});
export type StringCollectionInputFieldValue = z.infer<
typeof zStringCollectionInputFieldValue
>;
export const zStringPolymorphicInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('StringPolymorphic'),
value: z.union([z.string(), z.array(z.string())]).optional(),
});
export type StringPolymorphicInputFieldValue = z.infer<
typeof zStringPolymorphicInputFieldValue
>;
export const zBooleanInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('boolean'),
value: z.boolean().optional(),
});
export type BooleanInputFieldValue = z.infer<typeof zBooleanInputFieldValue>;
export const zBooleanCollectionInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('BooleanCollection'),
value: z.array(z.boolean()).optional(),
});
export type BooleanCollectionInputFieldValue = z.infer<
typeof zBooleanCollectionInputFieldValue
>;
export const zBooleanPolymorphicInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('BooleanPolymorphic'),
value: z.union([z.boolean(), z.array(z.boolean())]).optional(),
});
export type BooleanPolymorphicInputFieldValue = z.infer<
typeof zBooleanPolymorphicInputFieldValue
>;
export const zEnumInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('enum'),
value: z.union([z.string(), z.number()]).optional(),
@ -262,6 +296,22 @@ export const zLatentsInputFieldValue = zInputFieldValueBase.extend({
});
export type LatentsInputFieldValue = z.infer<typeof zLatentsInputFieldValue>;
export const zLatentsCollectionInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('LatentsCollection'),
value: z.array(zLatentsField).optional(),
});
export type LatentsCollectionInputFieldValue = z.infer<
typeof zLatentsCollectionInputFieldValue
>;
export const zLatentsPolymorphicInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('LatentsPolymorphic'),
value: z.union([zLatentsField, z.array(zLatentsField)]).optional(),
});
export type LatentsPolymorphicInputFieldValue = z.infer<
typeof zLatentsPolymorphicInputFieldValue
>;
export const zDenoiseMaskInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('DenoiseMaskField'),
value: zDenoiseMaskField.optional(),
@ -278,6 +328,26 @@ export type ConditioningInputFieldValue = z.infer<
typeof zConditioningInputFieldValue
>;
export const zConditioningCollectionInputFieldValue =
zInputFieldValueBase.extend({
type: z.literal('ConditioningCollection'),
value: z.array(zConditioningField).optional(),
});
export type ConditioningCollectionInputFieldValue = z.infer<
typeof zConditioningCollectionInputFieldValue
>;
export const zConditioningPolymorphicInputFieldValue =
zInputFieldValueBase.extend({
type: z.literal('ConditioningPolymorphic'),
value: z
.union([zConditioningField, z.array(zConditioningField)])
.optional(),
});
export type ConditioningPolymorphicInputFieldValue = z.infer<
typeof zConditioningPolymorphicInputFieldValue
>;
export const zControlNetModel = zModelIdentifier;
export type ControlNetModel = z.infer<typeof zControlNetModel>;
@ -302,6 +372,22 @@ export const zControlInputFieldValue = zInputFieldValueBase.extend({
});
export type ControlInputFieldValue = z.infer<typeof zControlInputFieldValue>;
export const zControlPolymorphicInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('ControlPolymorphic'),
value: z.union([zControlField, z.array(zControlField)]).optional(),
});
export type ControlPolymorphicInputFieldValue = z.infer<
typeof zControlPolymorphicInputFieldValue
>;
export const zControlCollectionInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('ControlCollection'),
value: z.array(zControlField).optional(),
});
export type ControlCollectionInputFieldValue = z.infer<
typeof zControlCollectionInputFieldValue
>;
export const zModelType = z.enum([
'onnx',
'main',
@ -381,6 +467,14 @@ export const zImageInputFieldValue = zInputFieldValueBase.extend({
});
export type ImageInputFieldValue = z.infer<typeof zImageInputFieldValue>;
export const zImagePolymorphicInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('ImagePolymorphic'),
value: z.union([zImageField, z.array(zImageField)]).optional(),
});
export type ImagePolymorphicInputFieldValue = z.infer<
typeof zImagePolymorphicInputFieldValue
>;
export const zImageCollectionInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('ImageCollection'),
value: z.array(zImageField).optional(),
@ -473,6 +567,22 @@ export const zColorInputFieldValue = zInputFieldValueBase.extend({
});
export type ColorInputFieldValue = z.infer<typeof zColorInputFieldValue>;
export const zColorCollectionInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('ColorCollection'),
value: z.array(zColorField).optional(),
});
export type ColorCollectionInputFieldValue = z.infer<
typeof zColorCollectionInputFieldValue
>;
export const zColorPolymorphicInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('ColorPolymorphic'),
value: z.union([zColorField, z.array(zColorField)]).optional(),
});
export type ColorPolymorphicInputFieldValue = z.infer<
typeof zColorPolymorphicInputFieldValue
>;
export const zSchedulerInputFieldValue = zInputFieldValueBase.extend({
type: z.literal('Scheduler'),
value: zScheduler.optional(),
@ -482,30 +592,47 @@ export type SchedulerInputFieldValue = z.infer<
>;
export const zInputFieldValue = z.discriminatedUnion('type', [
zIntegerInputFieldValue,
zFloatInputFieldValue,
zStringInputFieldValue,
zBooleanCollectionInputFieldValue,
zBooleanInputFieldValue,
zImageInputFieldValue,
zLatentsInputFieldValue,
zDenoiseMaskInputFieldValue,
zConditioningInputFieldValue,
zUNetInputFieldValue,
zBooleanPolymorphicInputFieldValue,
zClipInputFieldValue,
zVaeInputFieldValue,
zControlInputFieldValue,
zEnumInputFieldValue,
zMainModelInputFieldValue,
zSDXLMainModelInputFieldValue,
zSDXLRefinerModelInputFieldValue,
zVaeModelInputFieldValue,
zLoRAModelInputFieldValue,
zControlNetModelInputFieldValue,
zCollectionInputFieldValue,
zCollectionItemInputFieldValue,
zColorInputFieldValue,
zColorCollectionInputFieldValue,
zColorPolymorphicInputFieldValue,
zConditioningInputFieldValue,
zConditioningCollectionInputFieldValue,
zConditioningPolymorphicInputFieldValue,
zControlInputFieldValue,
zControlNetModelInputFieldValue,
zControlCollectionInputFieldValue,
zControlPolymorphicInputFieldValue,
zDenoiseMaskInputFieldValue,
zEnumInputFieldValue,
zFloatCollectionInputFieldValue,
zFloatInputFieldValue,
zFloatPolymorphicInputFieldValue,
zImageCollectionInputFieldValue,
zImagePolymorphicInputFieldValue,
zImageInputFieldValue,
zIntegerCollectionInputFieldValue,
zIntegerPolymorphicInputFieldValue,
zIntegerInputFieldValue,
zLatentsInputFieldValue,
zLatentsCollectionInputFieldValue,
zLatentsPolymorphicInputFieldValue,
zLoRAModelInputFieldValue,
zMainModelInputFieldValue,
zSchedulerInputFieldValue,
zSDXLMainModelInputFieldValue,
zSDXLRefinerModelInputFieldValue,
zStringCollectionInputFieldValue,
zStringPolymorphicInputFieldValue,
zStringInputFieldValue,
zUNetInputFieldValue,
zVaeInputFieldValue,
zVaeModelInputFieldValue,
]);
export type InputFieldValue = z.infer<typeof zInputFieldValue>;
@ -514,7 +641,6 @@ export type InputFieldTemplateBase = {
name: string;
title: string;
description: string;
type: FieldType;
required: boolean;
fieldKind: 'input';
} & _InputField;
@ -529,6 +655,19 @@ export type IntegerInputFieldTemplate = InputFieldTemplateBase & {
exclusiveMinimum?: boolean;
};
export type IntegerCollectionInputFieldTemplate = InputFieldTemplateBase & {
type: 'IntegerCollection';
default: number[];
item_default?: number;
};
export type IntegerPolymorphicInputFieldTemplate = Omit<
IntegerInputFieldTemplate,
'type'
> & {
type: 'IntegerPolymorphic';
};
export type FloatInputFieldTemplate = InputFieldTemplateBase & {
type: 'float';
default: number;
@ -539,6 +678,19 @@ export type FloatInputFieldTemplate = InputFieldTemplateBase & {
exclusiveMinimum?: boolean;
};
export type FloatCollectionInputFieldTemplate = InputFieldTemplateBase & {
type: 'FloatCollection';
default: number[];
item_default?: number;
};
export type FloatPolymorphicInputFieldTemplate = Omit<
FloatInputFieldTemplate,
'type'
> & {
type: 'FloatPolymorphic';
};
export type StringInputFieldTemplate = InputFieldTemplateBase & {
type: 'string';
default: string;
@ -547,19 +699,53 @@ export type StringInputFieldTemplate = InputFieldTemplateBase & {
pattern?: string;
};
export type StringCollectionInputFieldTemplate = InputFieldTemplateBase & {
type: 'StringCollection';
default: string[];
item_default?: string;
};
export type StringPolymorphicInputFieldTemplate = Omit<
StringInputFieldTemplate,
'type'
> & {
type: 'StringPolymorphic';
};
export type BooleanInputFieldTemplate = InputFieldTemplateBase & {
default: boolean;
type: 'boolean';
};
export type BooleanCollectionInputFieldTemplate = InputFieldTemplateBase & {
type: 'BooleanCollection';
default: boolean[];
item_default?: boolean;
};
export type BooleanPolymorphicInputFieldTemplate = Omit<
BooleanInputFieldTemplate,
'type'
> & {
type: 'BooleanPolymorphic';
};
export type ImageInputFieldTemplate = InputFieldTemplateBase & {
default: ImageDTO;
default: ImageField;
type: 'ImageField';
};
export type ImageCollectionInputFieldTemplate = InputFieldTemplateBase & {
default: ImageField[];
type: 'ImageCollection';
item_default?: ImageField;
};
export type ImagePolymorphicInputFieldTemplate = Omit<
ImageInputFieldTemplate,
'type'
> & {
type: 'ImagePolymorphic';
};
export type DenoiseMaskInputFieldTemplate = InputFieldTemplateBase & {
@ -568,15 +754,40 @@ export type DenoiseMaskInputFieldTemplate = InputFieldTemplateBase & {
};
export type LatentsInputFieldTemplate = InputFieldTemplateBase & {
default: string;
default: LatentsField;
type: 'LatentsField';
};
export type LatentsCollectionInputFieldTemplate = InputFieldTemplateBase & {
default: LatentsField[];
type: 'LatentsCollection';
item_default?: LatentsField;
};
export type LatentsPolymorphicInputFieldTemplate = InputFieldTemplateBase & {
default: LatentsField;
type: 'LatentsPolymorphic';
};
export type ConditioningInputFieldTemplate = InputFieldTemplateBase & {
default: undefined;
type: 'ConditioningField';
};
export type ConditioningCollectionInputFieldTemplate =
InputFieldTemplateBase & {
default: ConditioningField[];
type: 'ConditioningCollection';
item_default?: ConditioningField;
};
export type ConditioningPolymorphicInputFieldTemplate = Omit<
ConditioningInputFieldTemplate,
'type'
> & {
type: 'ConditioningPolymorphic';
};
export type UNetInputFieldTemplate = InputFieldTemplateBase & {
default: undefined;
type: 'UNetField';
@ -597,6 +808,19 @@ export type ControlInputFieldTemplate = InputFieldTemplateBase & {
type: 'ControlField';
};
export type ControlCollectionInputFieldTemplate = InputFieldTemplateBase & {
default: undefined;
type: 'ControlCollection';
item_default?: ControlField;
};
export type ControlPolymorphicInputFieldTemplate = Omit<
ControlInputFieldTemplate,
'type'
> & {
type: 'ControlPolymorphic';
};
export type EnumInputFieldTemplate = InputFieldTemplateBase & {
default: string | number;
type: 'enum';
@ -649,6 +873,18 @@ export type ColorInputFieldTemplate = InputFieldTemplateBase & {
type: 'ColorField';
};
export type ColorPolymorphicInputFieldTemplate = Omit<
ColorInputFieldTemplate,
'type'
> & {
type: 'ColorPolymorphic';
};
export type ColorCollectionInputFieldTemplate = InputFieldTemplateBase & {
default: [];
type: 'ColorCollection';
};
export type SchedulerInputFieldTemplate = InputFieldTemplateBase & {
default: SchedulerParam;
type: 'Scheduler';
@ -659,6 +895,55 @@ export type WorkflowInputFieldTemplate = InputFieldTemplateBase & {
type: 'WorkflowField';
};
/**
* An input field template is generated on each page load from the OpenAPI schema.
*
* The template provides the field type and other field metadata (e.g. title, description,
* maximum length, pattern to match, etc).
*/
export type InputFieldTemplate =
| BooleanCollectionInputFieldTemplate
| BooleanPolymorphicInputFieldTemplate
| BooleanInputFieldTemplate
| ClipInputFieldTemplate
| CollectionInputFieldTemplate
| CollectionItemInputFieldTemplate
| ColorInputFieldTemplate
| ColorCollectionInputFieldTemplate
| ColorPolymorphicInputFieldTemplate
| ConditioningInputFieldTemplate
| ConditioningCollectionInputFieldTemplate
| ConditioningPolymorphicInputFieldTemplate
| ControlInputFieldTemplate
| ControlCollectionInputFieldTemplate
| ControlNetModelInputFieldTemplate
| ControlPolymorphicInputFieldTemplate
| DenoiseMaskInputFieldTemplate
| EnumInputFieldTemplate
| FloatCollectionInputFieldTemplate
| FloatInputFieldTemplate
| FloatPolymorphicInputFieldTemplate
| ImageCollectionInputFieldTemplate
| ImagePolymorphicInputFieldTemplate
| ImageInputFieldTemplate
| IntegerCollectionInputFieldTemplate
| IntegerPolymorphicInputFieldTemplate
| IntegerInputFieldTemplate
| LatentsInputFieldTemplate
| LatentsCollectionInputFieldTemplate
| LatentsPolymorphicInputFieldTemplate
| LoRAModelInputFieldTemplate
| MainModelInputFieldTemplate
| SchedulerInputFieldTemplate
| SDXLMainModelInputFieldTemplate
| SDXLRefinerModelInputFieldTemplate
| StringCollectionInputFieldTemplate
| StringPolymorphicInputFieldTemplate
| StringInputFieldTemplate
| UNetInputFieldTemplate
| VaeInputFieldTemplate
| VaeModelInputFieldTemplate;
export const isInputFieldValue = (
field?: InputFieldValue | OutputFieldValue
): field is InputFieldValue => Boolean(field && field.fieldKind === 'input');
@ -681,6 +966,7 @@ export type InvocationSchemaExtra = {
title: string;
category?: string;
tags?: string[];
version?: string;
properties: Omit<
NonNullable<OpenAPIV3.SchemaObject['properties']> &
(_InputField | _OutputField),
@ -731,8 +1017,22 @@ export type InvocationSchemaObject = (
) & { class: 'invocation' };
export const isSchemaObject = (
obj: OpenAPIV3.ReferenceObject | OpenAPIV3.SchemaObject
): obj is OpenAPIV3.SchemaObject => !('$ref' in obj);
obj: OpenAPIV3.ReferenceObject | OpenAPIV3.SchemaObject | undefined
): obj is OpenAPIV3.SchemaObject => Boolean(obj && !('$ref' in obj));
export const isArraySchemaObject = (
obj: OpenAPIV3.ReferenceObject | OpenAPIV3.SchemaObject | undefined
): obj is OpenAPIV3.ArraySchemaObject =>
Boolean(obj && !('$ref' in obj) && obj.type === 'array');
export const isNonArraySchemaObject = (
obj: OpenAPIV3.ReferenceObject | OpenAPIV3.SchemaObject | undefined
): obj is OpenAPIV3.NonArraySchemaObject =>
Boolean(obj && !('$ref' in obj) && obj.type !== 'array');
export const isRefObject = (
obj: OpenAPIV3.ReferenceObject | OpenAPIV3.SchemaObject | undefined
): obj is OpenAPIV3.ReferenceObject => Boolean(obj && '$ref' in obj);
export const isInvocationSchemaObject = (
obj:
@ -800,6 +1100,29 @@ export const zCoreMetadata = z
export type CoreMetadata = z.infer<typeof zCoreMetadata>;
export const zSemVer = z.string().refine((val) => {
const [major, minor, patch] = val.split('.');
return (
major !== undefined &&
Number.isInteger(Number(major)) &&
minor !== undefined &&
Number.isInteger(Number(minor)) &&
patch !== undefined &&
Number.isInteger(Number(patch))
);
});
export const zParsedSemver = zSemVer.transform((val) => {
const [major, minor, patch] = val.split('.');
return {
major: Number(major),
minor: Number(minor),
patch: Number(patch),
};
});
export type SemVer = z.infer<typeof zSemVer>;
export const zInvocationNodeData = z.object({
id: z.string().trim().min(1),
// no easy way to build this dynamically, and we don't want to anyways, because this will be used
@ -812,6 +1135,7 @@ export const zInvocationNodeData = z.object({
notes: z.string(),
embedWorkflow: z.boolean(),
isIntermediate: z.boolean(),
version: zSemVer.optional(),
});
// Massage this to get better type safety while developing
@ -900,20 +1224,6 @@ export const zFieldIdentifier = z.object({
export type FieldIdentifier = z.infer<typeof zFieldIdentifier>;
export const zSemVer = z.string().refine((val) => {
const [major, minor, patch] = val.split('.');
return (
major !== undefined &&
minor !== undefined &&
patch !== undefined &&
Number.isInteger(Number(major)) &&
Number.isInteger(Number(minor)) &&
Number.isInteger(Number(patch))
);
});
export type SemVer = z.infer<typeof zSemVer>;
export type WorkflowWarning = {
message: string;
issues: string[];

635
invokeai/frontend/web/src/features/nodes/util/fieldTemplateBuilders.ts
View File

@ -1,5 +1,14 @@
import { isBoolean, isInteger, isNumber, isString } from 'lodash-es';
import { OpenAPIV3 } from 'openapi-types';
import {
COLLECTION_MAP,
POLYMORPHIC_TYPES,
SINGLE_TO_POLYMORPHIC_MAP,
isCollectionItemType,
isPolymorphicItemType,
} from '../types/constants';
import {
BooleanCollectionInputFieldTemplate,
BooleanInputFieldTemplate,
ClipInputFieldTemplate,
CollectionInputFieldTemplate,
@ -11,10 +20,13 @@ import {
DenoiseMaskInputFieldTemplate,
EnumInputFieldTemplate,
FieldType,
FloatCollectionInputFieldTemplate,
FloatPolymorphicInputFieldTemplate,
FloatInputFieldTemplate,
ImageCollectionInputFieldTemplate,
ImageInputFieldTemplate,
InputFieldTemplateBase,
IntegerCollectionInputFieldTemplate,
IntegerInputFieldTemplate,
InvocationFieldSchema,
InvocationSchemaObject,
@ -24,11 +36,32 @@ import {
SDXLMainModelInputFieldTemplate,
SDXLRefinerModelInputFieldTemplate,
SchedulerInputFieldTemplate,
StringCollectionInputFieldTemplate,
StringInputFieldTemplate,
UNetInputFieldTemplate,
VaeInputFieldTemplate,
VaeModelInputFieldTemplate,
isArraySchemaObject,
isNonArraySchemaObject,
isRefObject,
isSchemaObject,
ControlPolymorphicInputFieldTemplate,
ColorPolymorphicInputFieldTemplate,
ColorCollectionInputFieldTemplate,
IntegerPolymorphicInputFieldTemplate,
StringPolymorphicInputFieldTemplate,
BooleanPolymorphicInputFieldTemplate,
ImagePolymorphicInputFieldTemplate,
LatentsPolymorphicInputFieldTemplate,
LatentsCollectionInputFieldTemplate,
ConditioningPolymorphicInputFieldTemplate,
ConditioningCollectionInputFieldTemplate,
ControlCollectionInputFieldTemplate,
ImageField,
LatentsField,
ConditioningField,
} from '../types/types';
import { ControlField } from 'services/api/types';
export type BaseFieldProperties = 'name' | 'title' | 'description';
@ -45,15 +78,8 @@ export type BuildInputFieldArg = {
* @example
* refObjectToFieldType({ "$ref": "#/components/schemas/ImageField" }) --> 'ImageField'
*/
export const refObjectToFieldType = (
refObject: OpenAPIV3.ReferenceObject
): FieldType => {
const name = refObject.$ref.split('/').slice(-1)[0];
if (!name) {
throw `Unknown field type: ${name}`;
}
return name as FieldType;
};
export const refObjectToSchemaName = (refObject: OpenAPIV3.ReferenceObject) =>
refObject.$ref.split('/').slice(-1)[0];
const buildIntegerInputFieldTemplate = ({
schemaObject,
@ -88,6 +114,57 @@ const buildIntegerInputFieldTemplate = ({
return template;
};
const buildIntegerPolymorphicInputFieldTemplate = ({
schemaObject,
baseField,
}: BuildInputFieldArg): IntegerPolymorphicInputFieldTemplate => {
const template: IntegerPolymorphicInputFieldTemplate = {
...baseField,
type: 'IntegerPolymorphic',
default: schemaObject.default ?? 0,
};
if (schemaObject.multipleOf !== undefined) {
template.multipleOf = schemaObject.multipleOf;
}
if (schemaObject.maximum !== undefined) {
template.maximum = schemaObject.maximum;
}
if (schemaObject.exclusiveMaximum !== undefined) {
template.exclusiveMaximum = schemaObject.exclusiveMaximum;
}
if (schemaObject.minimum !== undefined) {
template.minimum = schemaObject.minimum;
}
if (schemaObject.exclusiveMinimum !== undefined) {
template.exclusiveMinimum = schemaObject.exclusiveMinimum;
}
return template;
};
const buildIntegerCollectionInputFieldTemplate = ({
schemaObject,
baseField,
}: BuildInputFieldArg): IntegerCollectionInputFieldTemplate => {
const item_default =
isNumber(schemaObject.item_default) && isInteger(schemaObject.item_default)
? schemaObject.item_default
: 0;
const template: IntegerCollectionInputFieldTemplate = {
...baseField,
type: 'IntegerCollection',
default: schemaObject.default ?? [],
item_default,
};
return template;
};
const buildFloatInputFieldTemplate = ({
schemaObject,
baseField,
@ -121,6 +198,54 @@ const buildFloatInputFieldTemplate = ({
return template;
};
const buildFloatPolymorphicInputFieldTemplate = ({
schemaObject,
baseField,
}: BuildInputFieldArg): FloatPolymorphicInputFieldTemplate => {
const template: FloatPolymorphicInputFieldTemplate = {
...baseField,
type: 'FloatPolymorphic',
default: schemaObject.default ?? 0,
};
if (schemaObject.multipleOf !== undefined) {
template.multipleOf = schemaObject.multipleOf;
}
if (schemaObject.maximum !== undefined) {
template.maximum = schemaObject.maximum;
}
if (schemaObject.exclusiveMaximum !== undefined) {
template.exclusiveMaximum = schemaObject.exclusiveMaximum;
}
if (schemaObject.minimum !== undefined) {
template.minimum = schemaObject.minimum;
}
if (schemaObject.exclusiveMinimum !== undefined) {
template.exclusiveMinimum = schemaObject.exclusiveMinimum;
}
return template;
};
const buildFloatCollectionInputFieldTemplate = ({
schemaObject,
baseField,
}: BuildInputFieldArg): FloatCollectionInputFieldTemplate => {
const item_default = isNumber(schemaObject.item_default)
? schemaObject.item_default
: 0;
const template: FloatCollectionInputFieldTemplate = {
...baseField,
type: 'FloatCollection',
default: schemaObject.default ?? [],
item_default,
};
return template;
};
const buildStringInputFieldTemplate = ({
schemaObject,
baseField,
@ -146,6 +271,48 @@ const buildStringInputFieldTemplate = ({
return template;
};
const buildStringPolymorphicInputFieldTemplate = ({
schemaObject,
baseField,
}: BuildInputFieldArg): StringPolymorphicInputFieldTemplate => {
const template: StringPolymorphicInputFieldTemplate = {
...baseField,
type: 'StringPolymorphic',
default: schemaObject.default ?? '',
};
if (schemaObject.minLength !== undefined) {
template.minLength = schemaObject.minLength;
}
if (schemaObject.maxLength !== undefined) {
template.maxLength = schemaObject.maxLength;
}
if (schemaObject.pattern !== undefined) {
template.pattern = schemaObject.pattern;
}
return template;
};
const buildStringCollectionInputFieldTemplate = ({
schemaObject,
baseField,
}: BuildInputFieldArg): StringCollectionInputFieldTemplate => {
const item_default = isString(schemaObject.item_default)
? schemaObject.item_default
: '';
const template: StringCollectionInputFieldTemplate = {
...baseField,
type: 'StringCollection',
default: schemaObject.default ?? [],
item_default,
};
return template;
};
const buildBooleanInputFieldTemplate = ({
schemaObject,
baseField,
@ -159,6 +326,37 @@ const buildBooleanInputFieldTemplate = ({
return template;
};
const buildBooleanPolymorphicInputFieldTemplate = ({
schemaObject,
baseField,
}: BuildInputFieldArg): BooleanPolymorphicInputFieldTemplate => {
const template: BooleanPolymorphicInputFieldTemplate = {
...baseField,
type: 'BooleanPolymorphic',
default: schemaObject.default ?? false,
};
return template;
};
const buildBooleanCollectionInputFieldTemplate = ({
schemaObject,
baseField,
}: BuildInputFieldArg): BooleanCollectionInputFieldTemplate => {
const item_default =
schemaObject.item_default && isBoolean(schemaObject.item_default)
? schemaObject.item_default
: false;
const template: BooleanCollectionInputFieldTemplate = {
...baseField,
type: 'BooleanCollection',
default: schemaObject.default ?? [],
item_default,
};
return template;
};
const buildMainModelInputFieldTemplate = ({
schemaObject,
baseField,
@ -250,6 +448,19 @@ const buildImageInputFieldTemplate = ({
return template;
};
const buildImagePolymorphicInputFieldTemplate = ({
schemaObject,
baseField,
}: BuildInputFieldArg): ImagePolymorphicInputFieldTemplate => {
const template: ImagePolymorphicInputFieldTemplate = {
...baseField,
type: 'ImagePolymorphic',
default: schemaObject.default ?? undefined,
};
return template;
};
const buildImageCollectionInputFieldTemplate = ({
schemaObject,
baseField,
@ -257,7 +468,8 @@ const buildImageCollectionInputFieldTemplate = ({
const template: ImageCollectionInputFieldTemplate = {
...baseField,
type: 'ImageCollection',
default: schemaObject.default ?? undefined,
default: schemaObject.default ?? [],
item_default: (schemaObject.item_default as ImageField) ?? undefined,
};
return template;
@ -289,6 +501,33 @@ const buildLatentsInputFieldTemplate = ({
return template;
};
const buildLatentsPolymorphicInputFieldTemplate = ({
schemaObject,
baseField,
}: BuildInputFieldArg): LatentsPolymorphicInputFieldTemplate => {
const template: LatentsPolymorphicInputFieldTemplate = {
...baseField,
type: 'LatentsPolymorphic',
default: schemaObject.default ?? undefined,
};
return template;
};
const buildLatentsCollectionInputFieldTemplate = ({
schemaObject,
baseField,
}: BuildInputFieldArg): LatentsCollectionInputFieldTemplate => {
const template: LatentsCollectionInputFieldTemplate = {
...baseField,
type: 'LatentsCollection',
default: schemaObject.default ?? [],
item_default: (schemaObject.item_default as LatentsField) ?? undefined,
};
return template;
};
const buildConditioningInputFieldTemplate = ({
schemaObject,
baseField,
@ -302,6 +541,33 @@ const buildConditioningInputFieldTemplate = ({
return template;
};
const buildConditioningPolymorphicInputFieldTemplate = ({
schemaObject,
baseField,
}: BuildInputFieldArg): ConditioningPolymorphicInputFieldTemplate => {
const template: ConditioningPolymorphicInputFieldTemplate = {
...baseField,
type: 'ConditioningPolymorphic',
default: schemaObject.default ?? undefined,
};
return template;
};
const buildConditioningCollectionInputFieldTemplate = ({
schemaObject,
baseField,
}: BuildInputFieldArg): ConditioningCollectionInputFieldTemplate => {
const template: ConditioningCollectionInputFieldTemplate = {
...baseField,
type: 'ConditioningCollection',
default: schemaObject.default ?? [],
item_default: (schemaObject.item_default as ConditioningField) ?? undefined,
};
return template;
};
const buildUNetInputFieldTemplate = ({
schemaObject,
baseField,
@ -355,6 +621,33 @@ const buildControlInputFieldTemplate = ({
return template;
};
const buildControlPolymorphicInputFieldTemplate = ({
schemaObject,
baseField,
}: BuildInputFieldArg): ControlPolymorphicInputFieldTemplate => {
const template: ControlPolymorphicInputFieldTemplate = {
...baseField,
type: 'ControlPolymorphic',
default: schemaObject.default ?? undefined,
};
return template;
};
const buildControlCollectionInputFieldTemplate = ({
schemaObject,
baseField,
}: BuildInputFieldArg): ControlCollectionInputFieldTemplate => {
const template: ControlCollectionInputFieldTemplate = {
...baseField,
type: 'ControlCollection',
default: schemaObject.default ?? [],
item_default: (schemaObject.item_default as ControlField) ?? undefined,
};
return template;
};
const buildEnumInputFieldTemplate = ({
schemaObject,
baseField,
@ -408,6 +701,32 @@ const buildColorInputFieldTemplate = ({
return template;
};
const buildColorPolymorphicInputFieldTemplate = ({
schemaObject,
baseField,
}: BuildInputFieldArg): ColorPolymorphicInputFieldTemplate => {
const template: ColorPolymorphicInputFieldTemplate = {
...baseField,
type: 'ColorPolymorphic',
default: schemaObject.default ?? { r: 127, g: 127, b: 127, a: 255 },
};
return template;
};
const buildColorCollectionInputFieldTemplate = ({
schemaObject,
baseField,
}: BuildInputFieldArg): ColorCollectionInputFieldTemplate => {
const template: ColorCollectionInputFieldTemplate = {
...baseField,
type: 'ColorCollection',
default: schemaObject.default ?? [],
};
return template;
};
const buildSchedulerInputFieldTemplate = ({
schemaObject,
baseField,
@ -421,45 +740,138 @@ const buildSchedulerInputFieldTemplate = ({
return template;
};
export const getFieldType = (schemaObject: InvocationFieldSchema): string => {
let fieldType = '';
const { ui_type } = schemaObject;
if (ui_type) {
fieldType = ui_type;
export const getFieldType = (
schemaObject: InvocationFieldSchema
): string | undefined => {
if (schemaObject?.ui_type) {
return schemaObject.ui_type;
} else if (!schemaObject.type) {
// console.log('refObject', schemaObject);
// if schemaObject has no type, then it should have one of allOf, anyOf, oneOf
if (schemaObject.allOf) {
fieldType = refObjectToFieldType(
// eslint-disable-next-line @typescript-eslint/no-non-null-assertion
schemaObject.allOf![0] as OpenAPIV3.ReferenceObject
);
const allOf = schemaObject.allOf;
if (allOf && allOf[0] && isRefObject(allOf[0])) {
return refObjectToSchemaName(allOf[0]);
}
} else if (schemaObject.anyOf) {
fieldType = refObjectToFieldType(
// eslint-disable-next-line @typescript-eslint/no-non-null-assertion
schemaObject.anyOf![0] as OpenAPIV3.ReferenceObject
);
} else if (schemaObject.oneOf) {
fieldType = refObjectToFieldType(
// eslint-disable-next-line @typescript-eslint/no-non-null-assertion
schemaObject.oneOf![0] as OpenAPIV3.ReferenceObject
);
const anyOf = schemaObject.anyOf;
/**
* Handle Polymorphic inputs, eg string | string[]. In OpenAPI, this is:
* - an `anyOf` with two items
* - one is an `ArraySchemaObject` with a single `SchemaObject or ReferenceObject` of type T in its `items`
* - the other is a `SchemaObject` or `ReferenceObject` of type T
*
* Any other cases we ignore.
*/
let firstType: string | undefined;
let secondType: string | undefined;
if (isArraySchemaObject(anyOf[0])) {
// first is array, second is not
const first = anyOf[0].items;
const second = anyOf[1];
if (isRefObject(first) && isRefObject(second)) {
firstType = refObjectToSchemaName(first);
secondType = refObjectToSchemaName(second);
} else if (
isNonArraySchemaObject(first) &&
isNonArraySchemaObject(second)
) {
firstType = first.type;
secondType = second.type;
}
} else if (isArraySchemaObject(anyOf[1])) {
// first is not array, second is
const first = anyOf[0];
const second = anyOf[1].items;
if (isRefObject(first) && isRefObject(second)) {
firstType = refObjectToSchemaName(first);
secondType = refObjectToSchemaName(second);
} else if (
isNonArraySchemaObject(first) &&
isNonArraySchemaObject(second)
) {
firstType = first.type;
secondType = second.type;
}
}
if (firstType === secondType && isPolymorphicItemType(firstType)) {
return SINGLE_TO_POLYMORPHIC_MAP[firstType];
}
}
} else if (schemaObject.enum) {
fieldType = 'enum';
return 'enum';
} else if (schemaObject.type) {
if (schemaObject.type === 'number') {
// floats are "number" in OpenAPI, while ints are "integer"
fieldType = 'float';
// floats are "number" in OpenAPI, while ints are "integer" - we need to distinguish them
return 'float';
} else if (schemaObject.type === 'array') {
const itemType = isSchemaObject(schemaObject.items)
? schemaObject.items.type
: refObjectToSchemaName(schemaObject.items);
if (isCollectionItemType(itemType)) {
return COLLECTION_MAP[itemType];
}
return;
} else {
fieldType = schemaObject.type;
return schemaObject.type;
}
}
return fieldType;
return;
};
const TEMPLATE_BUILDER_MAP = {
boolean: buildBooleanInputFieldTemplate,
BooleanCollection: buildBooleanCollectionInputFieldTemplate,
BooleanPolymorphic: buildBooleanPolymorphicInputFieldTemplate,
ClipField: buildClipInputFieldTemplate,
Collection: buildCollectionInputFieldTemplate,
CollectionItem: buildCollectionItemInputFieldTemplate,
ColorCollection: buildColorCollectionInputFieldTemplate,
ColorField: buildColorInputFieldTemplate,
ColorPolymorphic: buildColorPolymorphicInputFieldTemplate,
ConditioningCollection: buildConditioningCollectionInputFieldTemplate,
ConditioningField: buildConditioningInputFieldTemplate,
ConditioningPolymorphic: buildConditioningPolymorphicInputFieldTemplate,
ControlCollection: buildControlCollectionInputFieldTemplate,
ControlField: buildControlInputFieldTemplate,
ControlNetModelField: buildControlNetModelInputFieldTemplate,
ControlPolymorphic: buildControlPolymorphicInputFieldTemplate,
DenoiseMaskField: buildDenoiseMaskInputFieldTemplate,
enum: buildEnumInputFieldTemplate,
float: buildFloatInputFieldTemplate,
FloatCollection: buildFloatCollectionInputFieldTemplate,
FloatPolymorphic: buildFloatPolymorphicInputFieldTemplate,
ImageCollection: buildImageCollectionInputFieldTemplate,
ImageField: buildImageInputFieldTemplate,
ImagePolymorphic: buildImagePolymorphicInputFieldTemplate,
integer: buildIntegerInputFieldTemplate,
IntegerCollection: buildIntegerCollectionInputFieldTemplate,
IntegerPolymorphic: buildIntegerPolymorphicInputFieldTemplate,
LatentsCollection: buildLatentsCollectionInputFieldTemplate,
LatentsField: buildLatentsInputFieldTemplate,
LatentsPolymorphic: buildLatentsPolymorphicInputFieldTemplate,
LoRAModelField: buildLoRAModelInputFieldTemplate,
MainModelField: buildMainModelInputFieldTemplate,
Scheduler: buildSchedulerInputFieldTemplate,
SDXLMainModelField: buildSDXLMainModelInputFieldTemplate,
SDXLRefinerModelField: buildRefinerModelInputFieldTemplate,
string: buildStringInputFieldTemplate,
StringCollection: buildStringCollectionInputFieldTemplate,
StringPolymorphic: buildStringPolymorphicInputFieldTemplate,
UNetField: buildUNetInputFieldTemplate,
VaeField: buildVaeInputFieldTemplate,
VaeModelField: buildVaeModelInputFieldTemplate,
};
const isTemplatedFieldType = (
fieldType: string | undefined
): fieldType is keyof typeof TEMPLATE_BUILDER_MAP =>
Boolean(fieldType && fieldType in TEMPLATE_BUILDER_MAP);
/**
* Builds an input field from an invocation schema property.
* @param fieldSchema The schema object
@ -474,7 +886,8 @@ export const buildInputFieldTemplate = (
const { input, ui_hidden, ui_component, ui_type, ui_order } = fieldSchema;
const extra = {
input,
// TODO: Can we support polymorphic inputs in the UI?
input: POLYMORPHIC_TYPES.includes(fieldType) ? 'connection' : input,
ui_hidden,
ui_component,
ui_type,
@ -490,146 +903,12 @@ export const buildInputFieldTemplate = (
...extra,
};
if (fieldType === 'ImageField') {
return buildImageInputFieldTemplate({
schemaObject: fieldSchema,
baseField,
});
if (!isTemplatedFieldType(fieldType)) {
return;
}
if (fieldType === 'ImageCollection') {
return buildImageCollectionInputFieldTemplate({
schemaObject: fieldSchema,
baseField,
});
}
if (fieldType === 'DenoiseMaskField') {
return buildDenoiseMaskInputFieldTemplate({
schemaObject: fieldSchema,
baseField,
});
}
if (fieldType === 'LatentsField') {
return buildLatentsInputFieldTemplate({
schemaObject: fieldSchema,
baseField,
});
}
if (fieldType === 'ConditioningField') {
return buildConditioningInputFieldTemplate({
schemaObject: fieldSchema,
baseField,
});
}
if (fieldType === 'UNetField') {
return buildUNetInputFieldTemplate({
schemaObject: fieldSchema,
baseField,
});
}
if (fieldType === 'ClipField') {
return buildClipInputFieldTemplate({
schemaObject: fieldSchema,
baseField,
});
}
if (fieldType === 'VaeField') {
return buildVaeInputFieldTemplate({ schemaObject: fieldSchema, baseField });
}
if (fieldType === 'ControlField') {
return buildControlInputFieldTemplate({
schemaObject: fieldSchema,
baseField,
});
}
if (fieldType === 'MainModelField') {
return buildMainModelInputFieldTemplate({
schemaObject: fieldSchema,
baseField,
});
}
if (fieldType === 'SDXLRefinerModelField') {
return buildRefinerModelInputFieldTemplate({
schemaObject: fieldSchema,
baseField,
});
}
if (fieldType === 'SDXLMainModelField') {
return buildSDXLMainModelInputFieldTemplate({
schemaObject: fieldSchema,
baseField,
});
}
if (fieldType === 'VaeModelField') {
return buildVaeModelInputFieldTemplate({
schemaObject: fieldSchema,
baseField,
});
}
if (fieldType === 'LoRAModelField') {
return buildLoRAModelInputFieldTemplate({
schemaObject: fieldSchema,
baseField,
});
}
if (fieldType === 'ControlNetModelField') {
return buildControlNetModelInputFieldTemplate({
schemaObject: fieldSchema,
baseField,
});
}
if (fieldType === 'enum') {
return buildEnumInputFieldTemplate({
schemaObject: fieldSchema,
baseField,
});
}
if (fieldType === 'integer') {
return buildIntegerInputFieldTemplate({
schemaObject: fieldSchema,
baseField,
});
}
if (fieldType === 'float') {
return buildFloatInputFieldTemplate({
schemaObject: fieldSchema,
baseField,
});
}
if (fieldType === 'string') {
return buildStringInputFieldTemplate({
schemaObject: fieldSchema,
baseField,
});
}
if (fieldType === 'boolean') {
return buildBooleanInputFieldTemplate({
schemaObject: fieldSchema,
baseField,
});
}
if (fieldType === 'Collection') {
return buildCollectionInputFieldTemplate({
schemaObject: fieldSchema,
baseField,
});
}
if (fieldType === 'CollectionItem') {
return buildCollectionItemInputFieldTemplate({
schemaObject: fieldSchema,
baseField,
});
}
if (fieldType === 'ColorField') {
return buildColorInputFieldTemplate({
schemaObject: fieldSchema,
baseField,
});
}
if (fieldType === 'Scheduler') {
return buildSchedulerInputFieldTemplate({
schemaObject: fieldSchema,
baseField,
});
}
return;
return TEMPLATE_BUILDER_MAP[fieldType]({
schemaObject: fieldSchema,
baseField,
});
};

143
invokeai/frontend/web/src/features/nodes/util/fieldValueBuilders.ts
View File

@ -1,104 +1,79 @@
import { InputFieldTemplate, InputFieldValue } from '../types/types';
const FIELD_VALUE_FALLBACK_MAP = {
'enum.number': 0,
'enum.string': '',
boolean: false,
BooleanCollection: [],
BooleanPolymorphic: false,
ClipField: undefined,
Collection: [],
CollectionItem: undefined,
ColorCollection: [],
ColorField: undefined,
ColorPolymorphic: undefined,
ConditioningCollection: [],
ConditioningField: undefined,
ConditioningPolymorphic: undefined,
ControlCollection: [],
ControlField: undefined,
ControlNetModelField: undefined,
ControlPolymorphic: undefined,
DenoiseMaskField: undefined,
float: 0,
FloatCollection: [],
FloatPolymorphic: 0,
ImageCollection: [],
ImageField: undefined,
ImagePolymorphic: undefined,
integer: 0,
IntegerCollection: [],
IntegerPolymorphic: 0,
LatentsCollection: [],
LatentsField: undefined,
LatentsPolymorphic: undefined,
LoRAModelField: undefined,
MainModelField: undefined,
ONNXModelField: undefined,
Scheduler: 'euler',
SDXLMainModelField: undefined,
SDXLRefinerModelField: undefined,
string: '',
StringCollection: [],
StringPolymorphic: '',
UNetField: undefined,
VaeField: undefined,
VaeModelField: undefined,
};
export const buildInputFieldValue = (
id: string,
template: InputFieldTemplate
): InputFieldValue => {
const fieldValue: InputFieldValue = {
// TODO: this should be `fieldValue: InputFieldValue`, but that introduces a TS issue I couldn't
// resolve - for some reason, it doesn't like `template.type`, which is the discriminant for both
// `InputFieldTemplate` union. It is (type-structurally) equal to the discriminant for the
// `InputFieldValue` union, but TS doesn't seem to like it...
const fieldValue = {
id,
name: template.name,
type: template.type,
label: '',
fieldKind: 'input',
};
if (template.type === 'string') {
fieldValue.value = template.default ?? '';
}
if (template.type === 'integer') {
fieldValue.value = template.default ?? 0;
}
if (template.type === 'float') {
fieldValue.value = template.default ?? 0;
}
if (template.type === 'boolean') {
fieldValue.value = template.default ?? false;
}
} as InputFieldValue;
if (template.type === 'enum') {
if (template.enumType === 'number') {
fieldValue.value = template.default ?? 0;
fieldValue.value =
template.default ?? FIELD_VALUE_FALLBACK_MAP['enum.number'];
}
if (template.enumType === 'string') {
fieldValue.value = template.default ?? '';
fieldValue.value =
template.default ?? FIELD_VALUE_FALLBACK_MAP['enum.string'];
}
}
if (template.type === 'Collection') {
fieldValue.value = template.default ?? 1;
}
if (template.type === 'ImageField') {
fieldValue.value = undefined;
}
if (template.type === 'ImageCollection') {
fieldValue.value = [];
}
if (template.type === 'DenoiseMaskField') {
fieldValue.value = undefined;
}
if (template.type === 'LatentsField') {
fieldValue.value = undefined;
}
if (template.type === 'ConditioningField') {
fieldValue.value = undefined;
}
if (template.type === 'UNetField') {
fieldValue.value = undefined;
}
if (template.type === 'ClipField') {
fieldValue.value = undefined;
}
if (template.type === 'VaeField') {
fieldValue.value = undefined;
}
if (template.type === 'ControlField') {
fieldValue.value = undefined;
}
if (template.type === 'MainModelField') {
fieldValue.value = undefined;
}
if (template.type === 'SDXLRefinerModelField') {
fieldValue.value = undefined;
}
if (template.type === 'VaeModelField') {
fieldValue.value = undefined;
}
if (template.type === 'LoRAModelField') {
fieldValue.value = undefined;
}
if (template.type === 'ControlNetModelField') {
fieldValue.value = undefined;
}
if (template.type === 'Scheduler') {
fieldValue.value = 'euler';
} else {
fieldValue.value =
template.default ?? FIELD_VALUE_FALLBACK_MAP[template.type];
}
return fieldValue;

9
invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSDXLRefinerToGraph.ts
View File

@ -10,7 +10,8 @@ import {
CANVAS_OUTPUT,
INPAINT_IMAGE_RESIZE_UP,
LATENTS_TO_IMAGE,
MASK_BLUR,
MASK_COMBINE,
MASK_RESIZE_UP,
METADATA_ACCUMULATOR,
SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH,
SDXL_CANVAS_INPAINT_GRAPH,
@ -46,6 +47,8 @@ export const addSDXLRefinerToGraph = (
const { seamlessXAxis, seamlessYAxis, vaePrecision } = state.generation;
const { boundingBoxScaleMethod } = state.canvas;
const fp32 = vaePrecision === 'fp32';
const isUsingScaledDimensions = ['auto', 'manual'].includes(
boundingBoxScaleMethod
);
@ -231,7 +234,7 @@ export const addSDXLRefinerToGraph = (
type: 'create_denoise_mask',
id: SDXL_REFINER_INPAINT_CREATE_MASK,
is_intermediate: true,
fp32: vaePrecision === 'fp32' ? true : false,
fp32,
};
if (isUsingScaledDimensions) {
@ -257,7 +260,7 @@ export const addSDXLRefinerToGraph = (
graph.edges.push(
{
source: {
node_id: MASK_BLUR,
node_id: isUsingScaledDimensions ? MASK_RESIZE_UP : MASK_COMBINE,
field: 'image',
},
destination: {

17
invokeai/frontend/web/src/features/nodes/util/graphBuilders/addVAEToGraph.ts
View File

@ -2,6 +2,7 @@ import { RootState } from 'app/store/store';
import { NonNullableGraph } from 'features/nodes/types/types';
import { MetadataAccumulatorInvocation } from 'services/api/types';
import {
CANVAS_COHERENCE_INPAINT_CREATE_MASK,
CANVAS_IMAGE_TO_IMAGE_GRAPH,
CANVAS_INPAINT_GRAPH,
CANVAS_OUTPAINT_GRAPH,
@ -31,7 +32,7 @@ export const addVAEToGraph = (
graph: NonNullableGraph,
modelLoaderNodeId: string = MAIN_MODEL_LOADER
): void => {
const { vae } = state.generation;
const { vae, canvasCoherenceMode } = state.generation;
const { boundingBoxScaleMethod } = state.canvas;
const { shouldUseSDXLRefiner } = state.sdxl;
@ -146,6 +147,20 @@ export const addVAEToGraph = (
},
}
);
// Handle Coherence Mode
if (canvasCoherenceMode !== 'unmasked') {
graph.edges.push({
source: {
node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER,
field: isAutoVae && isOnnxModel ? 'vae_decoder' : 'vae',
},
destination: {
node_id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
field: 'vae',
},
});
}
}
if (shouldUseSDXLRefiner) {

6
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasImageToImageGraph.ts
View File

@ -59,6 +59,8 @@ export const buildCanvasImageToImageGraph = (
shouldAutoSave,
} = state.canvas;
const fp32 = vaePrecision === 'fp32';
const isUsingScaledDimensions = ['auto', 'manual'].includes(
boundingBoxScaleMethod
);
@ -245,7 +247,7 @@ export const buildCanvasImageToImageGraph = (
id: LATENTS_TO_IMAGE,
type: 'l2i',
is_intermediate: true,
fp32: vaePrecision === 'fp32' ? true : false,
fp32,
};
graph.nodes[CANVAS_OUTPUT] = {
id: CANVAS_OUTPUT,
@ -292,7 +294,7 @@ export const buildCanvasImageToImageGraph = (
type: 'l2i',
id: CANVAS_OUTPUT,
is_intermediate: !shouldAutoSave,
fp32: vaePrecision === 'fp32' ? true : false,
fp32,
};
(graph.nodes[IMAGE_TO_LATENTS] as ImageToLatentsInvocation).image =

140
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasInpaintGraph.ts
View File

@ -6,6 +6,7 @@ import {
ImageBlurInvocation,
ImageDTO,
ImageToLatentsInvocation,
MaskEdgeInvocation,
NoiseInvocation,
RandomIntInvocation,
RangeOfSizeInvocation,
@ -18,6 +19,8 @@ import { addVAEToGraph } from './addVAEToGraph';
import { addWatermarkerToGraph } from './addWatermarkerToGraph';
import {
CANVAS_COHERENCE_DENOISE_LATENTS,
CANVAS_COHERENCE_INPAINT_CREATE_MASK,
CANVAS_COHERENCE_MASK_EDGE,
CANVAS_COHERENCE_NOISE,
CANVAS_COHERENCE_NOISE_INCREMENT,
CANVAS_INPAINT_GRAPH,
@ -67,6 +70,7 @@ export const buildCanvasInpaintGraph = (
shouldUseCpuNoise,
maskBlur,
maskBlurMethod,
canvasCoherenceMode,
canvasCoherenceSteps,
canvasCoherenceStrength,
clipSkip,
@ -89,6 +93,12 @@ export const buildCanvasInpaintGraph = (
shouldAutoSave,
} = state.canvas;
const fp32 = vaePrecision === 'fp32';
const isUsingScaledDimensions = ['auto', 'manual'].includes(
boundingBoxScaleMethod
);
let modelLoaderNodeId = MAIN_MODEL_LOADER;
const use_cpu = shouldUseNoiseSettings
@ -133,13 +143,7 @@ export const buildCanvasInpaintGraph = (
type: 'i2l',
id: INPAINT_IMAGE,
is_intermediate: true,
fp32: vaePrecision === 'fp32' ? true : false,
},
[INPAINT_CREATE_MASK]: {
type: 'create_denoise_mask',
id: INPAINT_CREATE_MASK,
is_intermediate: true,
fp32: vaePrecision === 'fp32' ? true : false,
fp32,
},
[NOISE]: {
type: 'noise',
@ -147,6 +151,12 @@ export const buildCanvasInpaintGraph = (
use_cpu,
is_intermediate: true,
},
[INPAINT_CREATE_MASK]: {
type: 'create_denoise_mask',
id: INPAINT_CREATE_MASK,
is_intermediate: true,
fp32,
},
[DENOISE_LATENTS]: {
type: 'denoise_latents',
id: DENOISE_LATENTS,
@ -171,7 +181,7 @@ export const buildCanvasInpaintGraph = (
},
[CANVAS_COHERENCE_DENOISE_LATENTS]: {
type: 'denoise_latents',
id: DENOISE_LATENTS,
id: CANVAS_COHERENCE_DENOISE_LATENTS,
is_intermediate: true,
steps: canvasCoherenceSteps,
cfg_scale: cfg_scale,
@ -183,7 +193,7 @@ export const buildCanvasInpaintGraph = (
type: 'l2i',
id: LATENTS_TO_IMAGE,
is_intermediate: true,
fp32: vaePrecision === 'fp32' ? true : false,
fp32,
},
[CANVAS_OUTPUT]: {
type: 'color_correct',
@ -418,7 +428,7 @@ export const buildCanvasInpaintGraph = (
};
// Handle Scale Before Processing
if (['auto', 'manual'].includes(boundingBoxScaleMethod)) {
if (isUsingScaledDimensions) {
const scaledWidth: number = scaledBoundingBoxDimensions.width;
const scaledHeight: number = scaledBoundingBoxDimensions.height;
@ -581,6 +591,116 @@ export const buildCanvasInpaintGraph = (
);
}
// Handle Coherence Mode
if (canvasCoherenceMode !== 'unmasked') {
// Create Mask If Coherence Mode Is Not Full
graph.nodes[CANVAS_COHERENCE_INPAINT_CREATE_MASK] = {
type: 'create_denoise_mask',
id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
is_intermediate: true,
fp32,
};
// Handle Image Input For Mask Creation
if (isUsingScaledDimensions) {
graph.edges.push({
source: {
node_id: INPAINT_IMAGE_RESIZE_UP,
field: 'image',
},
destination: {
node_id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
field: 'image',
},
});
} else {
graph.nodes[CANVAS_COHERENCE_INPAINT_CREATE_MASK] = {
...(graph.nodes[
CANVAS_COHERENCE_INPAINT_CREATE_MASK
] as CreateDenoiseMaskInvocation),
image: canvasInitImage,
};
}
// Create Mask If Coherence Mode Is Mask
if (canvasCoherenceMode === 'mask') {
if (isUsingScaledDimensions) {
graph.edges.push({
source: {
node_id: MASK_RESIZE_UP,
field: 'image',
},
destination: {
node_id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
field: 'mask',
},
});
} else {
graph.nodes[CANVAS_COHERENCE_INPAINT_CREATE_MASK] = {
...(graph.nodes[
CANVAS_COHERENCE_INPAINT_CREATE_MASK
] as CreateDenoiseMaskInvocation),
mask: canvasMaskImage,
};
}
}
// Create Mask Edge If Coherence Mode Is Edge
if (canvasCoherenceMode === 'edge') {
graph.nodes[CANVAS_COHERENCE_MASK_EDGE] = {
type: 'mask_edge',
id: CANVAS_COHERENCE_MASK_EDGE,
is_intermediate: true,
edge_blur: maskBlur,
edge_size: maskBlur * 2,
low_threshold: 100,
high_threshold: 200,
};
// Handle Scaled Dimensions For Mask Edge
if (isUsingScaledDimensions) {
graph.edges.push({
source: {
node_id: MASK_RESIZE_UP,
field: 'image',
},
destination: {
node_id: CANVAS_COHERENCE_MASK_EDGE,
field: 'image',
},
});
} else {
graph.nodes[CANVAS_COHERENCE_MASK_EDGE] = {
...(graph.nodes[CANVAS_COHERENCE_MASK_EDGE] as MaskEdgeInvocation),
image: canvasMaskImage,
};
}
graph.edges.push({
source: {
node_id: CANVAS_COHERENCE_MASK_EDGE,
field: 'image',
},
destination: {
node_id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
field: 'mask',
},
});
}
// Plug Denoise Mask To Coherence Denoise Latents
graph.edges.push({
source: {
node_id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
field: 'denoise_mask',
},
destination: {
node_id: CANVAS_COHERENCE_DENOISE_LATENTS,
field: 'denoise_mask',
},
});
}
// Handle Seed
if (shouldRandomizeSeed) {
// Random int node to generate the starting seed

209
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasOutpaintGraph.ts
View File

@ -2,7 +2,6 @@ import { logger } from 'app/logging/logger';
import { RootState } from 'app/store/store';
import { NonNullableGraph } from 'features/nodes/types/types';
import {
ImageBlurInvocation,
ImageDTO,
ImageToLatentsInvocation,
InfillPatchMatchInvocation,
@ -19,6 +18,8 @@ import { addVAEToGraph } from './addVAEToGraph';
import { addWatermarkerToGraph } from './addWatermarkerToGraph';
import {
CANVAS_COHERENCE_DENOISE_LATENTS,
CANVAS_COHERENCE_INPAINT_CREATE_MASK,
CANVAS_COHERENCE_MASK_EDGE,
CANVAS_COHERENCE_NOISE,
CANVAS_COHERENCE_NOISE_INCREMENT,
CANVAS_OUTPAINT_GRAPH,
@ -34,7 +35,6 @@ import {
ITERATE,
LATENTS_TO_IMAGE,
MAIN_MODEL_LOADER,
MASK_BLUR,
MASK_COMBINE,
MASK_FROM_ALPHA,
MASK_RESIZE_DOWN,
@ -71,10 +71,11 @@ export const buildCanvasOutpaintGraph = (
shouldUseNoiseSettings,
shouldUseCpuNoise,
maskBlur,
maskBlurMethod,
canvasCoherenceMode,
canvasCoherenceSteps,
canvasCoherenceStrength,
tileSize,
infillTileSize,
infillPatchmatchDownscaleSize,
infillMethod,
clipSkip,
seamlessXAxis,
@ -96,6 +97,12 @@ export const buildCanvasOutpaintGraph = (
shouldAutoSave,
} = state.canvas;
const fp32 = vaePrecision === 'fp32';
const isUsingScaledDimensions = ['auto', 'manual'].includes(
boundingBoxScaleMethod
);
let modelLoaderNodeId = MAIN_MODEL_LOADER;
const use_cpu = shouldUseNoiseSettings
@ -141,18 +148,11 @@ export const buildCanvasOutpaintGraph = (
is_intermediate: true,
mask2: canvasMaskImage,
},
[MASK_BLUR]: {
type: 'img_blur',
id: MASK_BLUR,
is_intermediate: true,
radius: maskBlur,
blur_type: maskBlurMethod,
},
[INPAINT_IMAGE]: {
type: 'i2l',
id: INPAINT_IMAGE,
is_intermediate: true,
fp32: vaePrecision === 'fp32' ? true : false,
fp32,
},
[NOISE]: {
type: 'noise',
@ -164,7 +164,7 @@ export const buildCanvasOutpaintGraph = (
type: 'create_denoise_mask',
id: INPAINT_CREATE_MASK,
is_intermediate: true,
fp32: vaePrecision === 'fp32' ? true : false,
fp32,
},
[DENOISE_LATENTS]: {
type: 'denoise_latents',
@ -202,7 +202,7 @@ export const buildCanvasOutpaintGraph = (
type: 'l2i',
id: LATENTS_TO_IMAGE,
is_intermediate: true,
fp32: vaePrecision === 'fp32' ? true : false,
fp32,
},
[CANVAS_OUTPUT]: {
type: 'color_correct',
@ -333,7 +333,7 @@ export const buildCanvasOutpaintGraph = (
// Create Inpaint Mask
{
source: {
node_id: MASK_BLUR,
node_id: isUsingScaledDimensions ? MASK_RESIZE_UP : MASK_COMBINE,
field: 'image',
},
destination: {
@ -443,6 +443,16 @@ export const buildCanvasOutpaintGraph = (
field: 'latents',
},
},
{
source: {
node_id: INPAINT_INFILL,
field: 'image',
},
destination: {
node_id: INPAINT_CREATE_MASK,
field: 'image',
},
},
// Decode the result from Inpaint
{
source: {
@ -463,6 +473,7 @@ export const buildCanvasOutpaintGraph = (
type: 'infill_patchmatch',
id: INPAINT_INFILL,
is_intermediate: true,
downscale: infillPatchmatchDownscaleSize,
};
}
@ -474,17 +485,25 @@ export const buildCanvasOutpaintGraph = (
};
}
if (infillMethod === 'cv2') {
graph.nodes[INPAINT_INFILL] = {
type: 'infill_cv2',
id: INPAINT_INFILL,
is_intermediate: true,
};
}
if (infillMethod === 'tile') {
graph.nodes[INPAINT_INFILL] = {
type: 'infill_tile',
id: INPAINT_INFILL,
is_intermediate: true,
tile_size: tileSize,
tile_size: infillTileSize,
};
}
// Handle Scale Before Processing
if (['auto', 'manual'].includes(boundingBoxScaleMethod)) {
if (isUsingScaledDimensions) {
const scaledWidth: number = scaledBoundingBoxDimensions.width;
const scaledHeight: number = scaledBoundingBoxDimensions.height;
@ -546,16 +565,6 @@ export const buildCanvasOutpaintGraph = (
field: 'image',
},
},
{
source: {
node_id: INPAINT_INFILL,
field: 'image',
},
destination: {
node_id: INPAINT_CREATE_MASK,
field: 'image',
},
},
// Take combined mask and resize and then blur
{
source: {
@ -567,16 +576,7 @@ export const buildCanvasOutpaintGraph = (
field: 'image',
},
},
{
source: {
node_id: MASK_RESIZE_UP,
field: 'image',
},
destination: {
node_id: MASK_BLUR,
field: 'image',
},
},
// Resize Results Down
{
source: {
@ -658,32 +658,8 @@ export const buildCanvasOutpaintGraph = (
...(graph.nodes[INPAINT_IMAGE] as ImageToLatentsInvocation),
image: canvasInitImage,
};
graph.nodes[MASK_BLUR] = {
...(graph.nodes[MASK_BLUR] as ImageBlurInvocation),
};
graph.edges.push(
// Take combined mask and plug it to blur
{
source: {
node_id: MASK_COMBINE,
field: 'image',
},
destination: {
node_id: MASK_BLUR,
field: 'image',
},
},
{
source: {
node_id: INPAINT_INFILL,
field: 'image',
},
destination: {
node_id: INPAINT_CREATE_MASK,
field: 'image',
},
},
// Color Correct The Inpainted Result
{
source: {
@ -707,7 +683,7 @@ export const buildCanvasOutpaintGraph = (
},
{
source: {
node_id: MASK_BLUR,
node_id: MASK_COMBINE,
field: 'image',
},
destination: {
@ -718,6 +694,115 @@ export const buildCanvasOutpaintGraph = (
);
}
// Handle Coherence Mode
if (canvasCoherenceMode !== 'unmasked') {
graph.nodes[CANVAS_COHERENCE_INPAINT_CREATE_MASK] = {
type: 'create_denoise_mask',
id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
is_intermediate: true,
fp32,
};
// Handle Image Input For Mask Creation
graph.edges.push({
source: {
node_id: INPAINT_INFILL,
field: 'image',
},
destination: {
node_id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
field: 'image',
},
});
// Create Mask If Coherence Mode Is Mask
if (canvasCoherenceMode === 'mask') {
if (isUsingScaledDimensions) {
graph.edges.push({
source: {
node_id: MASK_RESIZE_UP,
field: 'image',
},
destination: {
node_id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
field: 'mask',
},
});
} else {
graph.edges.push({
source: {
node_id: MASK_COMBINE,
field: 'image',
},
destination: {
node_id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
field: 'mask',
},
});
}
}
if (canvasCoherenceMode === 'edge') {
graph.nodes[CANVAS_COHERENCE_MASK_EDGE] = {
type: 'mask_edge',
id: CANVAS_COHERENCE_MASK_EDGE,
is_intermediate: true,
edge_blur: maskBlur,
edge_size: maskBlur * 2,
low_threshold: 100,
high_threshold: 200,
};
// Handle Scaled Dimensions For Mask Edge
if (isUsingScaledDimensions) {
graph.edges.push({
source: {
node_id: MASK_RESIZE_UP,
field: 'image',
},
destination: {
node_id: CANVAS_COHERENCE_MASK_EDGE,
field: 'image',
},
});
} else {
graph.edges.push({
source: {
node_id: MASK_COMBINE,
field: 'image',
},
destination: {
node_id: CANVAS_COHERENCE_MASK_EDGE,
field: 'image',
},
});
}
graph.edges.push({
source: {
node_id: CANVAS_COHERENCE_MASK_EDGE,
field: 'image',
},
destination: {
node_id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
field: 'mask',
},
});
}
// Plug Denoise Mask To Coherence Denoise Latents
graph.edges.push({
source: {
node_id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
field: 'denoise_mask',
},
destination: {
node_id: CANVAS_COHERENCE_DENOISE_LATENTS,
field: 'denoise_mask',
},
});
}
// Handle Seed
if (shouldRandomizeSeed) {
// Random int node to generate the starting seed

8
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLImageToImageGraph.ts
View File

@ -67,6 +67,8 @@ export const buildCanvasSDXLImageToImageGraph = (
shouldAutoSave,
} = state.canvas;
const fp32 = vaePrecision === 'fp32';
const isUsingScaledDimensions = ['auto', 'manual'].includes(
boundingBoxScaleMethod
);
@ -133,7 +135,7 @@ export const buildCanvasSDXLImageToImageGraph = (
type: 'i2l',
id: IMAGE_TO_LATENTS,
is_intermediate: true,
fp32: vaePrecision === 'fp32' ? true : false,
fp32,
},
[SDXL_DENOISE_LATENTS]: {
type: 'denoise_latents',
@ -258,7 +260,7 @@ export const buildCanvasSDXLImageToImageGraph = (
id: LATENTS_TO_IMAGE,
type: 'l2i',
is_intermediate: true,
fp32: vaePrecision === 'fp32' ? true : false,
fp32,
};
graph.nodes[CANVAS_OUTPUT] = {
id: CANVAS_OUTPUT,
@ -305,7 +307,7 @@ export const buildCanvasSDXLImageToImageGraph = (
type: 'l2i',
id: CANVAS_OUTPUT,
is_intermediate: !shouldAutoSave,
fp32: vaePrecision === 'fp32' ? true : false,
fp32,
};
(graph.nodes[IMAGE_TO_LATENTS] as ImageToLatentsInvocation).image =

130
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLInpaintGraph.ts
View File

@ -6,6 +6,7 @@ import {
ImageBlurInvocation,
ImageDTO,
ImageToLatentsInvocation,
MaskEdgeInvocation,
NoiseInvocation,
RandomIntInvocation,
RangeOfSizeInvocation,
@ -19,6 +20,8 @@ import { addVAEToGraph } from './addVAEToGraph';
import { addWatermarkerToGraph } from './addWatermarkerToGraph';
import {
CANVAS_COHERENCE_DENOISE_LATENTS,
CANVAS_COHERENCE_INPAINT_CREATE_MASK,
CANVAS_COHERENCE_MASK_EDGE,
CANVAS_COHERENCE_NOISE,
CANVAS_COHERENCE_NOISE_INCREMENT,
CANVAS_OUTPUT,
@ -68,6 +71,7 @@ export const buildCanvasSDXLInpaintGraph = (
shouldUseCpuNoise,
maskBlur,
maskBlurMethod,
canvasCoherenceMode,
canvasCoherenceSteps,
canvasCoherenceStrength,
seamlessXAxis,
@ -96,6 +100,12 @@ export const buildCanvasSDXLInpaintGraph = (
shouldAutoSave,
} = state.canvas;
const fp32 = vaePrecision === 'fp32';
const isUsingScaledDimensions = ['auto', 'manual'].includes(
boundingBoxScaleMethod
);
let modelLoaderNodeId = SDXL_MODEL_LOADER;
const use_cpu = shouldUseNoiseSettings
@ -137,7 +147,7 @@ export const buildCanvasSDXLInpaintGraph = (
type: 'i2l',
id: INPAINT_IMAGE,
is_intermediate: true,
fp32: vaePrecision === 'fp32' ? true : false,
fp32,
},
[NOISE]: {
type: 'noise',
@ -149,7 +159,7 @@ export const buildCanvasSDXLInpaintGraph = (
type: 'create_denoise_mask',
id: INPAINT_CREATE_MASK,
is_intermediate: true,
fp32: vaePrecision === 'fp32' ? true : false,
fp32,
},
[SDXL_DENOISE_LATENTS]: {
type: 'denoise_latents',
@ -177,7 +187,7 @@ export const buildCanvasSDXLInpaintGraph = (
},
[CANVAS_COHERENCE_DENOISE_LATENTS]: {
type: 'denoise_latents',
id: SDXL_DENOISE_LATENTS,
id: CANVAS_COHERENCE_DENOISE_LATENTS,
is_intermediate: true,
steps: canvasCoherenceSteps,
cfg_scale: cfg_scale,
@ -189,7 +199,7 @@ export const buildCanvasSDXLInpaintGraph = (
type: 'l2i',
id: LATENTS_TO_IMAGE,
is_intermediate: true,
fp32: vaePrecision === 'fp32' ? true : false,
fp32,
},
[CANVAS_OUTPUT]: {
type: 'color_correct',
@ -433,7 +443,7 @@ export const buildCanvasSDXLInpaintGraph = (
};
// Handle Scale Before Processing
if (['auto', 'manual'].includes(boundingBoxScaleMethod)) {
if (isUsingScaledDimensions) {
const scaledWidth: number = scaledBoundingBoxDimensions.width;
const scaledHeight: number = scaledBoundingBoxDimensions.height;
@ -596,6 +606,116 @@ export const buildCanvasSDXLInpaintGraph = (
);
}
// Handle Coherence Mode
if (canvasCoherenceMode !== 'unmasked') {
// Create Mask If Coherence Mode Is Not Full
graph.nodes[CANVAS_COHERENCE_INPAINT_CREATE_MASK] = {
type: 'create_denoise_mask',
id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
is_intermediate: true,
fp32,
};
// Handle Image Input For Mask Creation
if (isUsingScaledDimensions) {
graph.edges.push({
source: {
node_id: INPAINT_IMAGE_RESIZE_UP,
field: 'image',
},
destination: {
node_id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
field: 'image',
},
});
} else {
graph.nodes[CANVAS_COHERENCE_INPAINT_CREATE_MASK] = {
...(graph.nodes[
CANVAS_COHERENCE_INPAINT_CREATE_MASK
] as CreateDenoiseMaskInvocation),
image: canvasInitImage,
};
}
// Create Mask If Coherence Mode Is Mask
if (canvasCoherenceMode === 'mask') {
if (isUsingScaledDimensions) {
graph.edges.push({
source: {
node_id: MASK_RESIZE_UP,
field: 'image',
},
destination: {
node_id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
field: 'mask',
},
});
} else {
graph.nodes[CANVAS_COHERENCE_INPAINT_CREATE_MASK] = {
...(graph.nodes[
CANVAS_COHERENCE_INPAINT_CREATE_MASK
] as CreateDenoiseMaskInvocation),
mask: canvasMaskImage,
};
}
}
// Create Mask Edge If Coherence Mode Is Edge
if (canvasCoherenceMode === 'edge') {
graph.nodes[CANVAS_COHERENCE_MASK_EDGE] = {
type: 'mask_edge',
id: CANVAS_COHERENCE_MASK_EDGE,
is_intermediate: true,
edge_blur: maskBlur,
edge_size: maskBlur * 2,
low_threshold: 100,
high_threshold: 200,
};
// Handle Scaled Dimensions For Mask Edge
if (isUsingScaledDimensions) {
graph.edges.push({
source: {
node_id: MASK_RESIZE_UP,
field: 'image',
},
destination: {
node_id: CANVAS_COHERENCE_MASK_EDGE,
field: 'image',
},
});
} else {
graph.nodes[CANVAS_COHERENCE_MASK_EDGE] = {
...(graph.nodes[CANVAS_COHERENCE_MASK_EDGE] as MaskEdgeInvocation),
image: canvasMaskImage,
};
}
graph.edges.push({
source: {
node_id: CANVAS_COHERENCE_MASK_EDGE,
field: 'image',
},
destination: {
node_id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
field: 'mask',
},
});
}
// Plug Denoise Mask To Coherence Denoise Latents
graph.edges.push({
source: {
node_id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
field: 'denoise_mask',
},
destination: {
node_id: CANVAS_COHERENCE_DENOISE_LATENTS,
field: 'denoise_mask',
},
});
}
// Handle Seed
if (shouldRandomizeSeed) {
// Random int node to generate the starting seed

215
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLOutpaintGraph.ts
View File

@ -2,7 +2,6 @@ import { logger } from 'app/logging/logger';
import { RootState } from 'app/store/store';
import { NonNullableGraph } from 'features/nodes/types/types';
import {
ImageBlurInvocation,
ImageDTO,
ImageToLatentsInvocation,
InfillPatchMatchInvocation,
@ -20,6 +19,8 @@ import { addVAEToGraph } from './addVAEToGraph';
import { addWatermarkerToGraph } from './addWatermarkerToGraph';
import {
CANVAS_COHERENCE_DENOISE_LATENTS,
CANVAS_COHERENCE_INPAINT_CREATE_MASK,
CANVAS_COHERENCE_MASK_EDGE,
CANVAS_COHERENCE_NOISE,
CANVAS_COHERENCE_NOISE_INCREMENT,
CANVAS_OUTPUT,
@ -31,7 +32,6 @@ import {
INPAINT_INFILL_RESIZE_DOWN,
ITERATE,
LATENTS_TO_IMAGE,
MASK_BLUR,
MASK_COMBINE,
MASK_FROM_ALPHA,
MASK_RESIZE_DOWN,
@ -72,10 +72,11 @@ export const buildCanvasSDXLOutpaintGraph = (
shouldUseNoiseSettings,
shouldUseCpuNoise,
maskBlur,
maskBlurMethod,
canvasCoherenceMode,
canvasCoherenceSteps,
canvasCoherenceStrength,
tileSize,
infillTileSize,
infillPatchmatchDownscaleSize,
infillMethod,
seamlessXAxis,
seamlessYAxis,
@ -103,6 +104,12 @@ export const buildCanvasSDXLOutpaintGraph = (
shouldAutoSave,
} = state.canvas;
const fp32 = vaePrecision === 'fp32';
const isUsingScaledDimensions = ['auto', 'manual'].includes(
boundingBoxScaleMethod
);
let modelLoaderNodeId = SDXL_MODEL_LOADER;
const use_cpu = shouldUseNoiseSettings
@ -145,18 +152,11 @@ export const buildCanvasSDXLOutpaintGraph = (
is_intermediate: true,
mask2: canvasMaskImage,
},
[MASK_BLUR]: {
type: 'img_blur',
id: MASK_BLUR,
is_intermediate: true,
radius: maskBlur,
blur_type: maskBlurMethod,
},
[INPAINT_IMAGE]: {
type: 'i2l',
id: INPAINT_IMAGE,
is_intermediate: true,
fp32: vaePrecision === 'fp32' ? true : false,
fp32,
},
[NOISE]: {
type: 'noise',
@ -168,7 +168,7 @@ export const buildCanvasSDXLOutpaintGraph = (
type: 'create_denoise_mask',
id: INPAINT_CREATE_MASK,
is_intermediate: true,
fp32: vaePrecision === 'fp32' ? true : false,
fp32,
},
[SDXL_DENOISE_LATENTS]: {
type: 'denoise_latents',
@ -208,7 +208,7 @@ export const buildCanvasSDXLOutpaintGraph = (
type: 'l2i',
id: LATENTS_TO_IMAGE,
is_intermediate: true,
fp32: vaePrecision === 'fp32' ? true : false,
fp32,
},
[CANVAS_OUTPUT]: {
type: 'color_correct',
@ -348,7 +348,7 @@ export const buildCanvasSDXLOutpaintGraph = (
// Create Inpaint Mask
{
source: {
node_id: MASK_BLUR,
node_id: isUsingScaledDimensions ? MASK_RESIZE_UP : MASK_COMBINE,
field: 'image',
},
destination: {
@ -410,7 +410,7 @@ export const buildCanvasSDXLOutpaintGraph = (
},
{
source: {
node_id: SDXL_MODEL_LOADER,
node_id: modelLoaderNodeId,
field: 'unet',
},
destination: {
@ -458,6 +458,16 @@ export const buildCanvasSDXLOutpaintGraph = (
field: 'latents',
},
},
{
source: {
node_id: INPAINT_INFILL,
field: 'image',
},
destination: {
node_id: INPAINT_CREATE_MASK,
field: 'image',
},
},
// Decode inpainted latents to image
{
source: {
@ -473,12 +483,12 @@ export const buildCanvasSDXLOutpaintGraph = (
};
// Add Infill Nodes
if (infillMethod === 'patchmatch') {
graph.nodes[INPAINT_INFILL] = {
type: 'infill_patchmatch',
id: INPAINT_INFILL,
is_intermediate: true,
downscale: infillPatchmatchDownscaleSize,
};
}
@ -490,17 +500,25 @@ export const buildCanvasSDXLOutpaintGraph = (
};
}
if (infillMethod === 'cv2') {
graph.nodes[INPAINT_INFILL] = {
type: 'infill_cv2',
id: INPAINT_INFILL,
is_intermediate: true,
};
}
if (infillMethod === 'tile') {
graph.nodes[INPAINT_INFILL] = {
type: 'infill_tile',
id: INPAINT_INFILL,
is_intermediate: true,
tile_size: tileSize,
tile_size: infillTileSize,
};
}
// Handle Scale Before Processing
if (['auto', 'manual'].includes(boundingBoxScaleMethod)) {
if (isUsingScaledDimensions) {
const scaledWidth: number = scaledBoundingBoxDimensions.width;
const scaledHeight: number = scaledBoundingBoxDimensions.height;
@ -562,16 +580,7 @@ export const buildCanvasSDXLOutpaintGraph = (
field: 'image',
},
},
{
source: {
node_id: INPAINT_INFILL,
field: 'image',
},
destination: {
node_id: INPAINT_CREATE_MASK,
field: 'image',
},
},
// Take combined mask and resize and then blur
{
source: {
@ -583,16 +592,7 @@ export const buildCanvasSDXLOutpaintGraph = (
field: 'image',
},
},
{
source: {
node_id: MASK_RESIZE_UP,
field: 'image',
},
destination: {
node_id: MASK_BLUR,
field: 'image',
},
},
// Resize Results Down
{
source: {
@ -674,32 +674,8 @@ export const buildCanvasSDXLOutpaintGraph = (
...(graph.nodes[INPAINT_IMAGE] as ImageToLatentsInvocation),
image: canvasInitImage,
};
graph.nodes[MASK_BLUR] = {
...(graph.nodes[MASK_BLUR] as ImageBlurInvocation),
};
graph.edges.push(
// Take combined mask and plug it to blur
{
source: {
node_id: MASK_COMBINE,
field: 'image',
},
destination: {
node_id: MASK_BLUR,
field: 'image',
},
},
{
source: {
node_id: INPAINT_INFILL,
field: 'image',
},
destination: {
node_id: INPAINT_CREATE_MASK,
field: 'image',
},
},
// Color Correct The Inpainted Result
{
source: {
@ -723,7 +699,7 @@ export const buildCanvasSDXLOutpaintGraph = (
},
{
source: {
node_id: MASK_BLUR,
node_id: MASK_COMBINE,
field: 'image',
},
destination: {
@ -734,7 +710,116 @@ export const buildCanvasSDXLOutpaintGraph = (
);
}
// Handle seed
// Handle Coherence Mode
if (canvasCoherenceMode !== 'unmasked') {
graph.nodes[CANVAS_COHERENCE_INPAINT_CREATE_MASK] = {
type: 'create_denoise_mask',
id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
is_intermediate: true,
fp32,
};
// Handle Image Input For Mask Creation
graph.edges.push({
source: {
node_id: INPAINT_INFILL,
field: 'image',
},
destination: {
node_id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
field: 'image',
},
});
// Create Mask If Coherence Mode Is Mask
if (canvasCoherenceMode === 'mask') {
if (isUsingScaledDimensions) {
graph.edges.push({
source: {
node_id: MASK_RESIZE_UP,
field: 'image',
},
destination: {
node_id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
field: 'mask',
},
});
} else {
graph.edges.push({
source: {
node_id: MASK_COMBINE,
field: 'image',
},
destination: {
node_id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
field: 'mask',
},
});
}
}
if (canvasCoherenceMode === 'edge') {
graph.nodes[CANVAS_COHERENCE_MASK_EDGE] = {
type: 'mask_edge',
id: CANVAS_COHERENCE_MASK_EDGE,
is_intermediate: true,
edge_blur: maskBlur,
edge_size: maskBlur * 2,
low_threshold: 100,
high_threshold: 200,
};
// Handle Scaled Dimensions For Mask Edge
if (isUsingScaledDimensions) {
graph.edges.push({
source: {
node_id: MASK_RESIZE_UP,
field: 'image',
},
destination: {
node_id: CANVAS_COHERENCE_MASK_EDGE,
field: 'image',
},
});
} else {
graph.edges.push({
source: {
node_id: MASK_COMBINE,
field: 'image',
},
destination: {
node_id: CANVAS_COHERENCE_MASK_EDGE,
field: 'image',
},
});
}
graph.edges.push({
source: {
node_id: CANVAS_COHERENCE_MASK_EDGE,
field: 'image',
},
destination: {
node_id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
field: 'mask',
},
});
}
// Plug Denoise Mask To Coherence Denoise Latents
graph.edges.push({
source: {
node_id: CANVAS_COHERENCE_INPAINT_CREATE_MASK,
field: 'denoise_mask',
},
destination: {
node_id: CANVAS_COHERENCE_DENOISE_LATENTS,
field: 'denoise_mask',
},
});
}
// Handle Seed
if (shouldRandomizeSeed) {
// Random int node to generate the starting seed
const randomIntNode: RandomIntInvocation = {

6
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLTextToImageGraph.ts
View File

@ -61,6 +61,8 @@ export const buildCanvasSDXLTextToImageGraph = (
shouldAutoSave,
} = state.canvas;
const fp32 = vaePrecision === 'fp32';
const isUsingScaledDimensions = ['auto', 'manual'].includes(
boundingBoxScaleMethod
);
@ -252,7 +254,7 @@ export const buildCanvasSDXLTextToImageGraph = (
id: LATENTS_TO_IMAGE,
type: isUsingOnnxModel ? 'l2i_onnx' : 'l2i',
is_intermediate: true,
fp32: vaePrecision === 'fp32' ? true : false,
fp32,
};
graph.nodes[CANVAS_OUTPUT] = {
@ -290,7 +292,7 @@ export const buildCanvasSDXLTextToImageGraph = (
type: isUsingOnnxModel ? 'l2i_onnx' : 'l2i',
id: CANVAS_OUTPUT,
is_intermediate: !shouldAutoSave,
fp32: vaePrecision === 'fp32' ? true : false,
fp32,
};
graph.edges.push({

6
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasTextToImageGraph.ts
View File

@ -59,6 +59,8 @@ export const buildCanvasTextToImageGraph = (
shouldAutoSave,
} = state.canvas;
const fp32 = vaePrecision === 'fp32';
const isUsingScaledDimensions = ['auto', 'manual'].includes(
boundingBoxScaleMethod
);
@ -238,7 +240,7 @@ export const buildCanvasTextToImageGraph = (
id: LATENTS_TO_IMAGE,
type: isUsingOnnxModel ? 'l2i_onnx' : 'l2i',
is_intermediate: true,
fp32: vaePrecision === 'fp32' ? true : false,
fp32,
};
graph.nodes[CANVAS_OUTPUT] = {
@ -276,7 +278,7 @@ export const buildCanvasTextToImageGraph = (
type: isUsingOnnxModel ? 'l2i_onnx' : 'l2i',
id: CANVAS_OUTPUT,
is_intermediate: !shouldAutoSave,
fp32: vaePrecision === 'fp32' ? true : false,
fp32,
};
graph.edges.push({

6
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearImageToImageGraph.ts
View File

@ -84,6 +84,8 @@ export const buildLinearImageToImageGraph = (
throw new Error('No model found in state');
}
const fp32 = vaePrecision === 'fp32';
let modelLoaderNodeId = MAIN_MODEL_LOADER;
const use_cpu = shouldUseNoiseSettings
@ -122,7 +124,7 @@ export const buildLinearImageToImageGraph = (
[LATENTS_TO_IMAGE]: {
type: 'l2i',
id: LATENTS_TO_IMAGE,
fp32: vaePrecision === 'fp32' ? true : false,
fp32,
},
[DENOISE_LATENTS]: {
type: 'denoise_latents',
@ -140,7 +142,7 @@ export const buildLinearImageToImageGraph = (
// image: {
// image_name: initialImage.image_name,
// },
fp32: vaePrecision === 'fp32' ? true : false,
fp32,
},
},
edges: [

6
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLImageToImageGraph.ts
View File

@ -84,6 +84,8 @@ export const buildLinearSDXLImageToImageGraph = (
throw new Error('No model found in state');
}
const fp32 = vaePrecision === 'fp32';
// Model Loader ID
let modelLoaderNodeId = SDXL_MODEL_LOADER;
@ -124,7 +126,7 @@ export const buildLinearSDXLImageToImageGraph = (
[LATENTS_TO_IMAGE]: {
type: 'l2i',
id: LATENTS_TO_IMAGE,
fp32: vaePrecision === 'fp32' ? true : false,
fp32,
},
[SDXL_DENOISE_LATENTS]: {
type: 'denoise_latents',
@ -144,7 +146,7 @@ export const buildLinearSDXLImageToImageGraph = (
// image: {
// image_name: initialImage.image_name,
// },
fp32: vaePrecision === 'fp32' ? true : false,
fp32,
},
},
edges: [

4
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLTextToImageGraph.ts
View File

@ -62,6 +62,8 @@ export const buildLinearSDXLTextToImageGraph = (
throw new Error('No model found in state');
}
const fp32 = vaePrecision === 'fp32';
// Construct Style Prompt
const { craftedPositiveStylePrompt, craftedNegativeStylePrompt } =
craftSDXLStylePrompt(state, shouldConcatSDXLStylePrompt);
@ -118,7 +120,7 @@ export const buildLinearSDXLTextToImageGraph = (
[LATENTS_TO_IMAGE]: {
type: 'l2i',
id: LATENTS_TO_IMAGE,
fp32: vaePrecision === 'fp32' ? true : false,
fp32,
},
},
edges: [

4
invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearTextToImageGraph.ts
View File

@ -57,6 +57,8 @@ export const buildLinearTextToImageGraph = (
throw new Error('No model found in state');
}
const fp32 = vaePrecision === 'fp32';
const isUsingOnnxModel = model.model_type === 'onnx';
let modelLoaderNodeId = isUsingOnnxModel
@ -139,7 +141,7 @@ export const buildLinearTextToImageGraph = (
[LATENTS_TO_IMAGE]: {
type: isUsingOnnxModel ? 'l2i_onnx' : 'l2i',
id: LATENTS_TO_IMAGE,
fp32: vaePrecision === 'fp32' ? true : false,
fp32,
},
},
edges: [

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