mirror of
https://github.com/invoke-ai/InvokeAI.git
synced 2026-01-21 06:58:02 -05:00
Compare commits
3 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
|
9ecaf60c3a | ||
|
|
3022f4b412 | ||
|
|
dc0c68d018 |
1
.gitattributes
vendored
1
.gitattributes
vendored
@@ -2,4 +2,3 @@
|
||||
# Only affects text files and ignores other file types.
|
||||
# For more info see: https://www.aleksandrhovhannisyan.com/blog/crlf-vs-lf-normalizing-line-endings-in-git/
|
||||
* text=auto
|
||||
docker/** text eol=lf
|
||||
@@ -11,5 +11,5 @@ INVOKEAI_ROOT=
|
||||
# HUGGING_FACE_HUB_TOKEN=
|
||||
|
||||
## optional variables specific to the docker setup.
|
||||
# GPU_DRIVER=cuda # or rocm
|
||||
# CONTAINER_UID=1000
|
||||
# GPU_DRIVER=cuda
|
||||
# CONTAINER_UID=1000
|
||||
@@ -18,8 +18,8 @@ ENV INVOKEAI_SRC=/opt/invokeai
|
||||
ENV VIRTUAL_ENV=/opt/venv/invokeai
|
||||
|
||||
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
|
||||
ARG TORCH_VERSION=2.1.0
|
||||
ARG TORCHVISION_VERSION=0.16
|
||||
ARG TORCH_VERSION=2.0.1
|
||||
ARG TORCHVISION_VERSION=0.15.2
|
||||
ARG GPU_DRIVER=cuda
|
||||
ARG TARGETPLATFORM="linux/amd64"
|
||||
# unused but available
|
||||
@@ -35,7 +35,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
|
||||
if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then \
|
||||
extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cpu"; \
|
||||
elif [ "$GPU_DRIVER" = "rocm" ]; then \
|
||||
extra_index_url_arg="--index-url https://download.pytorch.org/whl/rocm5.6"; \
|
||||
extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/rocm5.4.2"; \
|
||||
else \
|
||||
extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu121"; \
|
||||
fi &&\
|
||||
|
||||
@@ -15,10 +15,6 @@ services:
|
||||
- driver: nvidia
|
||||
count: 1
|
||||
capabilities: [gpu]
|
||||
# For AMD support, comment out the deploy section above and uncomment the devices section below:
|
||||
#devices:
|
||||
# - /dev/kfd:/dev/kfd
|
||||
# - /dev/dri:/dev/dri
|
||||
build:
|
||||
context: ..
|
||||
dockerfile: docker/Dockerfile
|
||||
|
||||
@@ -7,5 +7,5 @@ set -e
|
||||
SCRIPTDIR=$(dirname "${BASH_SOURCE[0]}")
|
||||
cd "$SCRIPTDIR" || exit 1
|
||||
|
||||
docker compose up -d
|
||||
docker compose up --build -d
|
||||
docker compose logs -f
|
||||
|
||||
@@ -150,6 +150,7 @@ Start/End - 0 represents the start of the generation, 1 represents the end. The
|
||||
|
||||
Additionally, each section can be expanded with the "Show Advanced" button in order to manipulate settings for the image pre-processor that adjusts your uploaded image before using it in during the generation process.
|
||||
|
||||
**Note:** T2I-Adapter models and ControlNet models cannot currently be used together.
|
||||
|
||||
## IP-Adapter
|
||||
|
||||
|
||||
@@ -198,7 +198,6 @@ The list of schedulers has been completely revamped and brought up to date:
|
||||
| **dpmpp_2m** | DPMSolverMultistepScheduler | original noise scnedule |
|
||||
| **dpmpp_2m_k** | DPMSolverMultistepScheduler | using karras noise schedule |
|
||||
| **unipc** | UniPCMultistepScheduler | CPU only |
|
||||
| **lcm** | LCMScheduler | |
|
||||
|
||||
Please see [3.0.0 Release Notes](https://github.com/invoke-ai/InvokeAI/releases/tag/v3.0.0) for further details.
|
||||
|
||||
|
||||
@@ -99,14 +99,3 @@ If using an AMD GPU:
|
||||
Use the standard `docker compose up` command, and generally the `docker compose` [CLI](https://docs.docker.com/compose/reference/) as usual.
|
||||
|
||||
Once the container starts up (and configures the InvokeAI root directory if this is a new installation), you can access InvokeAI at [http://localhost:9090](http://localhost:9090)
|
||||
|
||||
## Troubleshooting / FAQ
|
||||
|
||||
- Q: I am running on Windows under WSL2, and am seeing a "no such file or directory" error.
|
||||
- A: Your `docker-entrypoint.sh` file likely has Windows (CRLF) as opposed to Unix (LF) line endings,
|
||||
and you may have cloned this repository before the issue was fixed. To solve this, please change
|
||||
the line endings in the `docker-entrypoint.sh` file to `LF`. You can do this in VSCode
|
||||
(`Ctrl+P` and search for "line endings"), or by using the `dos2unix` utility in WSL.
|
||||
Finally, you may delete `docker-entrypoint.sh` followed by `git pull; git checkout docker/docker-entrypoint.sh`
|
||||
to reset the file to its most recent version.
|
||||
For more information on this issue, please see the [Docker Desktop documentation](https://docs.docker.com/desktop/troubleshoot/topics/#avoid-unexpected-syntax-errors-use-unix-style-line-endings-for-files-in-containers)
|
||||
|
||||
@@ -4,11 +4,7 @@ These are nodes that have been developed by the community, for the community. If
|
||||
|
||||
If you'd like to submit a node for the community, please refer to the [node creation overview](contributingNodes.md).
|
||||
|
||||
To use a node, add the node to the `nodes` folder found in your InvokeAI install location.
|
||||
|
||||
The suggested method is to use `git clone` to clone the repository the node is found in. This allows for easy updates of the node in the future.
|
||||
|
||||
If you'd prefer, you can also just download the `.py` file from the linked repository and add it to the `nodes` folder.
|
||||
To download a node, simply download the `.py` node file from the link and add it to the `invokeai/app/invocations` folder in your Invoke AI install location. If you used the automated installation, this can be found inside the `.venv` folder. Along with the node, an example node graph should be provided to help you get started with the node.
|
||||
|
||||
To use a community workflow, download the the `.json` node graph file and load it into Invoke AI via the **Load Workflow** button in the Workflow Editor.
|
||||
|
||||
@@ -333,9 +329,9 @@ See full docs here: https://github.com/skunkworxdark/XYGrid_nodes/edit/main/READ
|
||||
|
||||
**Description:** This node allows you to do super cool things with InvokeAI.
|
||||
|
||||
**Node Link:** https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/app/invocations/prompt.py
|
||||
**Node Link:** https://github.com/invoke-ai/InvokeAI/fake_node.py
|
||||
|
||||
**Example Workflow:** https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/Prompt_from_File.json
|
||||
**Example Node Graph:** https://github.com/invoke-ai/InvokeAI/fake_node_graph.json
|
||||
|
||||
**Output Examples**
|
||||
|
||||
|
||||
@@ -4,7 +4,7 @@ To learn about the specifics of creating a new node, please visit our [Node crea
|
||||
|
||||
Once you’ve created a node and confirmed that it behaves as expected locally, follow these steps:
|
||||
|
||||
- Make sure the node is contained in a new Python (.py) file. Preferably, the node is in a repo with a README detailing the nodes usage & examples to help others more easily use your node. Including the tag "invokeai-node" in your repository's README can also help other users find it more easily.
|
||||
- Make sure the node is contained in a new Python (.py) file. Preferrably, the node is in a repo with a README detaling the nodes usage & examples to help others more easily use your node.
|
||||
- Submit a pull request with a link to your node(s) repo in GitHub against the `main` branch to add the node to the [Community Nodes](communityNodes.md) list
|
||||
- Make sure you are following the template below and have provided all relevant details about the node and what it does. Example output images and workflows are very helpful for other users looking to use your node.
|
||||
- A maintainer will review the pull request and node. If the node is aligned with the direction of the project, you may be asked for permission to include it in the core project.
|
||||
|
||||
@@ -2,17 +2,13 @@
|
||||
|
||||
We've curated some example workflows for you to get started with Workflows in InvokeAI
|
||||
|
||||
To use them, right click on your desired workflow, follow the link to GitHub and click the "⬇" button to download the raw file. You can then use the "Load Workflow" functionality in InvokeAI to load the workflow and start generating images!
|
||||
To use them, right click on your desired workflow, press "Download Linked File". You can then use the "Load Workflow" functionality in InvokeAI to load the workflow and start generating images!
|
||||
|
||||
If you're interested in finding more workflows, checkout the [#share-your-workflows](https://discord.com/channels/1020123559063990373/1130291608097661000) channel in the InvokeAI Discord.
|
||||
|
||||
* [SD1.5 / SD2 Text to Image](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/Text_to_Image.json)
|
||||
* [SDXL Text to Image](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/SDXL_Text_to_Image.json)
|
||||
* [SDXL Text to Image with Refiner](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/SDXL_w_Refiner_Text_to_Image.json)
|
||||
* [Multi ControlNet (Canny & Depth)](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/Multi_ControlNet_Canny_and_Depth.json)
|
||||
* [Tiled Upscaling with ControlNet](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/ESRGAN_img2img_upscale_w_Canny_ControlNet.json)
|
||||
* [Prompt From File](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/Prompt_from_File.json)
|
||||
* [Face Detailer with IP-Adapter & ControlNet](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/Face_Detailer_with_IP-Adapter_and_Canny.json.json)
|
||||
* [SDXL Text to Image](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/SDXL_Text_to_Image.json)
|
||||
* [SDXL (with Refiner) Text to Image](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/SDXL_Text_to_Image.json)
|
||||
* [Tiled Upscaling with ControlNet](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/ESRGAN_img2img_upscale w_Canny_ControlNet.json)
|
||||
* [FaceMask](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/FaceMask.json)
|
||||
* [FaceOff with 2x Face Scaling](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/FaceOff_FaceScale2x.json)
|
||||
* [QR Code Monster](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/QR_Code_Monster.json)
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -1,985 +0,0 @@
|
||||
{
|
||||
"name": "Multi ControlNet (Canny & Depth)",
|
||||
"author": "Millu",
|
||||
"description": "A sample workflow using canny & depth ControlNets to guide the generation process. ",
|
||||
"version": "0.1.0",
|
||||
"contact": "millun@invoke.ai",
|
||||
"tags": "ControlNet, canny, depth",
|
||||
"notes": "",
|
||||
"exposedFields": [
|
||||
{
|
||||
"nodeId": "54486974-835b-4d81-8f82-05f9f32ce9e9",
|
||||
"fieldName": "model"
|
||||
},
|
||||
{
|
||||
"nodeId": "7ce68934-3419-42d4-ac70-82cfc9397306",
|
||||
"fieldName": "prompt"
|
||||
},
|
||||
{
|
||||
"nodeId": "273e3f96-49ea-4dc5-9d5b-9660390f14e1",
|
||||
"fieldName": "prompt"
|
||||
},
|
||||
{
|
||||
"nodeId": "c4b23e64-7986-40c4-9cad-46327b12e204",
|
||||
"fieldName": "image"
|
||||
},
|
||||
{
|
||||
"nodeId": "8e860e51-5045-456e-bf04-9a62a2a5c49e",
|
||||
"fieldName": "image"
|
||||
}
|
||||
],
|
||||
"meta": {
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"nodes": [
|
||||
{
|
||||
"id": "8e860e51-5045-456e-bf04-9a62a2a5c49e",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "8e860e51-5045-456e-bf04-9a62a2a5c49e",
|
||||
"type": "image",
|
||||
"inputs": {
|
||||
"image": {
|
||||
"id": "189c8adf-68cc-4774-a729-49da89f6fdf1",
|
||||
"name": "image",
|
||||
"type": "ImageField",
|
||||
"fieldKind": "input",
|
||||
"label": "Depth Input Image"
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"image": {
|
||||
"id": "1a31cacd-9d19-4f32-b558-c5e4aa39ce73",
|
||||
"name": "image",
|
||||
"type": "ImageField",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"width": {
|
||||
"id": "12f298fd-1d11-4cca-9426-01240f7ec7cf",
|
||||
"name": "width",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"height": {
|
||||
"id": "c47dabcb-44e8-40c9-992d-81dca59f598e",
|
||||
"name": "height",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 225,
|
||||
"position": {
|
||||
"x": 3617.163483500202,
|
||||
"y": 40.5529847930888
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "a33199c2-8340-401e-b8a2-42ffa875fc1c",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "a33199c2-8340-401e-b8a2-42ffa875fc1c",
|
||||
"type": "controlnet",
|
||||
"inputs": {
|
||||
"image": {
|
||||
"id": "4e0a3172-d3c2-4005-a84c-fa12a404f8a0",
|
||||
"name": "image",
|
||||
"type": "ImageField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"control_model": {
|
||||
"id": "8cb2d998-4086-430a-8b13-94cbc81e3ca3",
|
||||
"name": "control_model",
|
||||
"type": "ControlNetModelField",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": {
|
||||
"model_name": "sd-controlnet-depth",
|
||||
"base_model": "sd-1"
|
||||
}
|
||||
},
|
||||
"control_weight": {
|
||||
"id": "5e32bd8a-9dc8-42d8-9bcc-c2b0460c0b0f",
|
||||
"name": "control_weight",
|
||||
"type": "FloatPolymorphic",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 1
|
||||
},
|
||||
"begin_step_percent": {
|
||||
"id": "c258a276-352a-416c-8358-152f11005c0c",
|
||||
"name": "begin_step_percent",
|
||||
"type": "float",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 0
|
||||
},
|
||||
"end_step_percent": {
|
||||
"id": "43001125-0d70-4f87-8e79-da6603ad6c33",
|
||||
"name": "end_step_percent",
|
||||
"type": "float",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 1
|
||||
},
|
||||
"control_mode": {
|
||||
"id": "d2f14561-9443-4374-9270-e2f05007944e",
|
||||
"name": "control_mode",
|
||||
"type": "enum",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": "balanced"
|
||||
},
|
||||
"resize_mode": {
|
||||
"id": "727ee7d3-8bf6-4c7d-8b8a-43546b3b59cd",
|
||||
"name": "resize_mode",
|
||||
"type": "enum",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": "just_resize"
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"control": {
|
||||
"id": "b034aa0f-4d0d-46e4-b5e3-e25a9588d087",
|
||||
"name": "control",
|
||||
"type": "ControlField",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 508,
|
||||
"position": {
|
||||
"x": 4477.604342844504,
|
||||
"y": -49.39005411272677
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "273e3f96-49ea-4dc5-9d5b-9660390f14e1",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "273e3f96-49ea-4dc5-9d5b-9660390f14e1",
|
||||
"type": "compel",
|
||||
"inputs": {
|
||||
"prompt": {
|
||||
"id": "7c2c4771-2161-4d77-aced-ff8c4b3f1c15",
|
||||
"name": "prompt",
|
||||
"type": "string",
|
||||
"fieldKind": "input",
|
||||
"label": "Negative Prompt",
|
||||
"value": ""
|
||||
},
|
||||
"clip": {
|
||||
"id": "06d59e91-9cca-411d-bf05-86b099b3e8f7",
|
||||
"name": "clip",
|
||||
"type": "ClipField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"conditioning": {
|
||||
"id": "858bc33c-134c-4bf6-8855-f943e1d26f14",
|
||||
"name": "conditioning",
|
||||
"type": "ConditioningField",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 261,
|
||||
"position": {
|
||||
"x": 4444.706437017514,
|
||||
"y": -924.0715320874991
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "54486974-835b-4d81-8f82-05f9f32ce9e9",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "54486974-835b-4d81-8f82-05f9f32ce9e9",
|
||||
"type": "main_model_loader",
|
||||
"inputs": {
|
||||
"model": {
|
||||
"id": "f4a915a5-593e-4b6d-9198-c78eb5cefaed",
|
||||
"name": "model",
|
||||
"type": "MainModelField",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": {
|
||||
"model_name": "stable-diffusion-v1-5",
|
||||
"base_model": "sd-1",
|
||||
"model_type": "main"
|
||||
}
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"unet": {
|
||||
"id": "ee24fb16-da38-4c66-9fbc-e8f296ed40d2",
|
||||
"name": "unet",
|
||||
"type": "UNetField",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"clip": {
|
||||
"id": "f3fb0524-8803-41c1-86db-a61a13ee6a33",
|
||||
"name": "clip",
|
||||
"type": "ClipField",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"vae": {
|
||||
"id": "5c4878a8-b40f-44ab-b146-1c1f42c860b3",
|
||||
"name": "vae",
|
||||
"type": "VaeField",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 226,
|
||||
"position": {
|
||||
"x": 3837.096149678291,
|
||||
"y": -1050.015351148365
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "7ce68934-3419-42d4-ac70-82cfc9397306",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "7ce68934-3419-42d4-ac70-82cfc9397306",
|
||||
"type": "compel",
|
||||
"inputs": {
|
||||
"prompt": {
|
||||
"id": "7c2c4771-2161-4d77-aced-ff8c4b3f1c15",
|
||||
"name": "prompt",
|
||||
"type": "string",
|
||||
"fieldKind": "input",
|
||||
"label": "Positive Prompt",
|
||||
"value": ""
|
||||
},
|
||||
"clip": {
|
||||
"id": "06d59e91-9cca-411d-bf05-86b099b3e8f7",
|
||||
"name": "clip",
|
||||
"type": "ClipField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"conditioning": {
|
||||
"id": "858bc33c-134c-4bf6-8855-f943e1d26f14",
|
||||
"name": "conditioning",
|
||||
"type": "ConditioningField",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 261,
|
||||
"position": {
|
||||
"x": 4449.356038911986,
|
||||
"y": -1201.659695420063
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "d204d184-f209-4fae-a0a1-d152800844e1",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "d204d184-f209-4fae-a0a1-d152800844e1",
|
||||
"type": "controlnet",
|
||||
"inputs": {
|
||||
"image": {
|
||||
"id": "4e0a3172-d3c2-4005-a84c-fa12a404f8a0",
|
||||
"name": "image",
|
||||
"type": "ImageField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"control_model": {
|
||||
"id": "8cb2d998-4086-430a-8b13-94cbc81e3ca3",
|
||||
"name": "control_model",
|
||||
"type": "ControlNetModelField",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": {
|
||||
"model_name": "sd-controlnet-canny",
|
||||
"base_model": "sd-1"
|
||||
}
|
||||
},
|
||||
"control_weight": {
|
||||
"id": "5e32bd8a-9dc8-42d8-9bcc-c2b0460c0b0f",
|
||||
"name": "control_weight",
|
||||
"type": "FloatPolymorphic",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 1
|
||||
},
|
||||
"begin_step_percent": {
|
||||
"id": "c258a276-352a-416c-8358-152f11005c0c",
|
||||
"name": "begin_step_percent",
|
||||
"type": "float",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 0
|
||||
},
|
||||
"end_step_percent": {
|
||||
"id": "43001125-0d70-4f87-8e79-da6603ad6c33",
|
||||
"name": "end_step_percent",
|
||||
"type": "float",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 1
|
||||
},
|
||||
"control_mode": {
|
||||
"id": "d2f14561-9443-4374-9270-e2f05007944e",
|
||||
"name": "control_mode",
|
||||
"type": "enum",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": "balanced"
|
||||
},
|
||||
"resize_mode": {
|
||||
"id": "727ee7d3-8bf6-4c7d-8b8a-43546b3b59cd",
|
||||
"name": "resize_mode",
|
||||
"type": "enum",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": "just_resize"
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"control": {
|
||||
"id": "b034aa0f-4d0d-46e4-b5e3-e25a9588d087",
|
||||
"name": "control",
|
||||
"type": "ControlField",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 508,
|
||||
"position": {
|
||||
"x": 4479.68542130465,
|
||||
"y": -618.4221638099414
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "c4b23e64-7986-40c4-9cad-46327b12e204",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "c4b23e64-7986-40c4-9cad-46327b12e204",
|
||||
"type": "image",
|
||||
"inputs": {
|
||||
"image": {
|
||||
"id": "189c8adf-68cc-4774-a729-49da89f6fdf1",
|
||||
"name": "image",
|
||||
"type": "ImageField",
|
||||
"fieldKind": "input",
|
||||
"label": "Canny Input Image"
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"image": {
|
||||
"id": "1a31cacd-9d19-4f32-b558-c5e4aa39ce73",
|
||||
"name": "image",
|
||||
"type": "ImageField",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"width": {
|
||||
"id": "12f298fd-1d11-4cca-9426-01240f7ec7cf",
|
||||
"name": "width",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"height": {
|
||||
"id": "c47dabcb-44e8-40c9-992d-81dca59f598e",
|
||||
"name": "height",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 225,
|
||||
"position": {
|
||||
"x": 3593.7474460420153,
|
||||
"y": -538.1200472386865
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "ca4d5059-8bfb-447f-b415-da0faba5a143",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "ca4d5059-8bfb-447f-b415-da0faba5a143",
|
||||
"type": "collect",
|
||||
"inputs": {
|
||||
"item": {
|
||||
"id": "b16ae602-8708-4b1b-8d4f-9e0808d429ab",
|
||||
"name": "item",
|
||||
"type": "CollectionItem",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"collection": {
|
||||
"id": "d8987dd8-dec8-4d94-816a-3e356af29884",
|
||||
"name": "collection",
|
||||
"type": "Collection",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "ControlNet Collection",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 104,
|
||||
"position": {
|
||||
"x": 4866.191497139488,
|
||||
"y": -299.0538619537037
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "018b1214-c2af-43a7-9910-fb687c6726d7",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "018b1214-c2af-43a7-9910-fb687c6726d7",
|
||||
"type": "midas_depth_image_processor",
|
||||
"inputs": {
|
||||
"metadata": {
|
||||
"id": "77f91980-c696-4a18-a9ea-6e2fc329a747",
|
||||
"name": "metadata",
|
||||
"type": "MetadataField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"image": {
|
||||
"id": "50710a20-2af5-424d-9d17-aa08167829c6",
|
||||
"name": "image",
|
||||
"type": "ImageField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"a_mult": {
|
||||
"id": "f3b26f9d-2498-415e-9c01-197a8d06c0a5",
|
||||
"name": "a_mult",
|
||||
"type": "float",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 2
|
||||
},
|
||||
"bg_th": {
|
||||
"id": "4b1eb3ae-9d4a-47d6-b0ed-da62501e007f",
|
||||
"name": "bg_th",
|
||||
"type": "float",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 0.1
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"image": {
|
||||
"id": "b4ed637c-c4a0-4fdd-a24e-36d6412e4ccf",
|
||||
"name": "image",
|
||||
"type": "ImageField",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"width": {
|
||||
"id": "6bf9b609-d72c-4239-99bd-390a73cc3a9c",
|
||||
"name": "width",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"height": {
|
||||
"id": "3e8aef09-cf44-4e3e-a490-d3c9e7b23119",
|
||||
"name": "height",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 339,
|
||||
"position": {
|
||||
"x": 4054.229311491893,
|
||||
"y": -31.611411056365725
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "c826ba5e-9676-4475-b260-07b85e88753c",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "c826ba5e-9676-4475-b260-07b85e88753c",
|
||||
"type": "canny_image_processor",
|
||||
"inputs": {
|
||||
"metadata": {
|
||||
"id": "08331ea6-99df-4e61-a919-204d9bfa8fb2",
|
||||
"name": "metadata",
|
||||
"type": "MetadataField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"image": {
|
||||
"id": "33a37284-06ac-459c-ba93-1655e4f69b2d",
|
||||
"name": "image",
|
||||
"type": "ImageField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"low_threshold": {
|
||||
"id": "21ec18a3-50c5-4ba1-9642-f921744d594f",
|
||||
"name": "low_threshold",
|
||||
"type": "integer",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 100
|
||||
},
|
||||
"high_threshold": {
|
||||
"id": "ebeab271-a5ff-4c88-acfd-1d0271ab6ed4",
|
||||
"name": "high_threshold",
|
||||
"type": "integer",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 200
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"image": {
|
||||
"id": "c0caadbf-883f-4cb4-a62d-626b9c81fc4e",
|
||||
"name": "image",
|
||||
"type": "ImageField",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"width": {
|
||||
"id": "df225843-8098-49c0-99d1-3b0b6600559f",
|
||||
"name": "width",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"height": {
|
||||
"id": "e4abe0de-aa16-41f3-9cd7-968b49db5da3",
|
||||
"name": "height",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 339,
|
||||
"position": {
|
||||
"x": 4095.757337055795,
|
||||
"y": -455.63440891935863
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "9db25398-c869-4a63-8815-c6559341ef12",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "9db25398-c869-4a63-8815-c6559341ef12",
|
||||
"type": "l2i",
|
||||
"inputs": {
|
||||
"metadata": {
|
||||
"id": "2f269793-72e5-4ff3-b76c-fab4f93e983f",
|
||||
"name": "metadata",
|
||||
"type": "MetadataField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"latents": {
|
||||
"id": "4aaedd3b-cc77-420c-806e-c7fa74ec4cdf",
|
||||
"name": "latents",
|
||||
"type": "LatentsField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"vae": {
|
||||
"id": "432b066a-2462-4d18-83d9-64620b72df45",
|
||||
"name": "vae",
|
||||
"type": "VaeField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"tiled": {
|
||||
"id": "61f86e0f-7c46-40f8-b3f5-fe2f693595ca",
|
||||
"name": "tiled",
|
||||
"type": "boolean",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": false
|
||||
},
|
||||
"fp32": {
|
||||
"id": "39b6c89a-37ef-4a7e-9509-daeca49d5092",
|
||||
"name": "fp32",
|
||||
"type": "boolean",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": false
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"image": {
|
||||
"id": "6204e9b0-61dd-4250-b685-2092ba0e28e6",
|
||||
"name": "image",
|
||||
"type": "ImageField",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"width": {
|
||||
"id": "b4140649-8d5d-4d2d-bfa6-09e389ede5f9",
|
||||
"name": "width",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"height": {
|
||||
"id": "f3a0c0c8-fc24-4646-8be1-ed8cdd140828",
|
||||
"name": "height",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": false,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 267,
|
||||
"position": {
|
||||
"x": 5678.726701377887,
|
||||
"y": -351.6792416734579
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "ac481b7f-08bf-4a9d-9e0c-3a82ea5243ce",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "ac481b7f-08bf-4a9d-9e0c-3a82ea5243ce",
|
||||
"type": "denoise_latents",
|
||||
"inputs": {
|
||||
"positive_conditioning": {
|
||||
"id": "869cd309-c238-444b-a1a0-5021f99785ba",
|
||||
"name": "positive_conditioning",
|
||||
"type": "ConditioningField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"negative_conditioning": {
|
||||
"id": "343447b4-1e37-4e9e-8ac7-4d04864066af",
|
||||
"name": "negative_conditioning",
|
||||
"type": "ConditioningField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"noise": {
|
||||
"id": "b556571e-0cf9-4e03-8cfc-5caad937d957",
|
||||
"name": "noise",
|
||||
"type": "LatentsField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"steps": {
|
||||
"id": "a3b3d2de-9308-423e-b00d-c209c3e6e808",
|
||||
"name": "steps",
|
||||
"type": "integer",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 10
|
||||
},
|
||||
"cfg_scale": {
|
||||
"id": "b13c50a4-ec7e-4579-b0ef-2fe5df2605ea",
|
||||
"name": "cfg_scale",
|
||||
"type": "FloatPolymorphic",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 7.5
|
||||
},
|
||||
"denoising_start": {
|
||||
"id": "57d5d755-f58f-4347-b991-f0bca4a0ab29",
|
||||
"name": "denoising_start",
|
||||
"type": "float",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 0
|
||||
},
|
||||
"denoising_end": {
|
||||
"id": "323e78a6-880a-4d73-a62c-70faff965aa6",
|
||||
"name": "denoising_end",
|
||||
"type": "float",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 1
|
||||
},
|
||||
"scheduler": {
|
||||
"id": "c25fdc17-a089-43ac-953e-067c45d5c76b",
|
||||
"name": "scheduler",
|
||||
"type": "Scheduler",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": "euler"
|
||||
},
|
||||
"unet": {
|
||||
"id": "6cde662b-e633-4569-b6b4-ec87c52c9c11",
|
||||
"name": "unet",
|
||||
"type": "UNetField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"control": {
|
||||
"id": "276a4df9-bb26-4505-a4d3-a94e18c7b541",
|
||||
"name": "control",
|
||||
"type": "ControlPolymorphic",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"ip_adapter": {
|
||||
"id": "48d40c51-b5e2-4457-a428-eef0696695e8",
|
||||
"name": "ip_adapter",
|
||||
"type": "IPAdapterPolymorphic",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"t2i_adapter": {
|
||||
"id": "75dd8af2-e7d7-48b4-a574-edd9f6e686ad",
|
||||
"name": "t2i_adapter",
|
||||
"type": "T2IAdapterPolymorphic",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"latents": {
|
||||
"id": "9223d67b-1dd7-4b34-a45f-ed0a725d9702",
|
||||
"name": "latents",
|
||||
"type": "LatentsField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"denoise_mask": {
|
||||
"id": "4ee99177-6923-4b7f-8fe0-d721dd7cb05b",
|
||||
"name": "denoise_mask",
|
||||
"type": "DenoiseMaskField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"latents": {
|
||||
"id": "7fb4e326-a974-43e8-9ee7-2e3ab235819d",
|
||||
"name": "latents",
|
||||
"type": "LatentsField",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"width": {
|
||||
"id": "6bb8acd0-8973-4195-a095-e376385dc705",
|
||||
"name": "width",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"height": {
|
||||
"id": "795dea52-1c7d-4e64-99f7-2f60ec6e3ab9",
|
||||
"name": "height",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.4.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 646,
|
||||
"position": {
|
||||
"x": 5274.672987098195,
|
||||
"y": -823.0752416664332
|
||||
}
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"source": "54486974-835b-4d81-8f82-05f9f32ce9e9",
|
||||
"sourceHandle": "clip",
|
||||
"target": "7ce68934-3419-42d4-ac70-82cfc9397306",
|
||||
"targetHandle": "clip",
|
||||
"id": "reactflow__edge-54486974-835b-4d81-8f82-05f9f32ce9e9clip-7ce68934-3419-42d4-ac70-82cfc9397306clip",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "54486974-835b-4d81-8f82-05f9f32ce9e9",
|
||||
"sourceHandle": "clip",
|
||||
"target": "273e3f96-49ea-4dc5-9d5b-9660390f14e1",
|
||||
"targetHandle": "clip",
|
||||
"id": "reactflow__edge-54486974-835b-4d81-8f82-05f9f32ce9e9clip-273e3f96-49ea-4dc5-9d5b-9660390f14e1clip",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "a33199c2-8340-401e-b8a2-42ffa875fc1c",
|
||||
"sourceHandle": "control",
|
||||
"target": "ca4d5059-8bfb-447f-b415-da0faba5a143",
|
||||
"targetHandle": "item",
|
||||
"id": "reactflow__edge-a33199c2-8340-401e-b8a2-42ffa875fc1ccontrol-ca4d5059-8bfb-447f-b415-da0faba5a143item",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "d204d184-f209-4fae-a0a1-d152800844e1",
|
||||
"sourceHandle": "control",
|
||||
"target": "ca4d5059-8bfb-447f-b415-da0faba5a143",
|
||||
"targetHandle": "item",
|
||||
"id": "reactflow__edge-d204d184-f209-4fae-a0a1-d152800844e1control-ca4d5059-8bfb-447f-b415-da0faba5a143item",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "8e860e51-5045-456e-bf04-9a62a2a5c49e",
|
||||
"sourceHandle": "image",
|
||||
"target": "018b1214-c2af-43a7-9910-fb687c6726d7",
|
||||
"targetHandle": "image",
|
||||
"id": "reactflow__edge-8e860e51-5045-456e-bf04-9a62a2a5c49eimage-018b1214-c2af-43a7-9910-fb687c6726d7image",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "018b1214-c2af-43a7-9910-fb687c6726d7",
|
||||
"sourceHandle": "image",
|
||||
"target": "a33199c2-8340-401e-b8a2-42ffa875fc1c",
|
||||
"targetHandle": "image",
|
||||
"id": "reactflow__edge-018b1214-c2af-43a7-9910-fb687c6726d7image-a33199c2-8340-401e-b8a2-42ffa875fc1cimage",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "c4b23e64-7986-40c4-9cad-46327b12e204",
|
||||
"sourceHandle": "image",
|
||||
"target": "c826ba5e-9676-4475-b260-07b85e88753c",
|
||||
"targetHandle": "image",
|
||||
"id": "reactflow__edge-c4b23e64-7986-40c4-9cad-46327b12e204image-c826ba5e-9676-4475-b260-07b85e88753cimage",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "c826ba5e-9676-4475-b260-07b85e88753c",
|
||||
"sourceHandle": "image",
|
||||
"target": "d204d184-f209-4fae-a0a1-d152800844e1",
|
||||
"targetHandle": "image",
|
||||
"id": "reactflow__edge-c826ba5e-9676-4475-b260-07b85e88753cimage-d204d184-f209-4fae-a0a1-d152800844e1image",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "54486974-835b-4d81-8f82-05f9f32ce9e9",
|
||||
"sourceHandle": "vae",
|
||||
"target": "9db25398-c869-4a63-8815-c6559341ef12",
|
||||
"targetHandle": "vae",
|
||||
"id": "reactflow__edge-54486974-835b-4d81-8f82-05f9f32ce9e9vae-9db25398-c869-4a63-8815-c6559341ef12vae",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "ac481b7f-08bf-4a9d-9e0c-3a82ea5243ce",
|
||||
"sourceHandle": "latents",
|
||||
"target": "9db25398-c869-4a63-8815-c6559341ef12",
|
||||
"targetHandle": "latents",
|
||||
"id": "reactflow__edge-ac481b7f-08bf-4a9d-9e0c-3a82ea5243celatents-9db25398-c869-4a63-8815-c6559341ef12latents",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "ca4d5059-8bfb-447f-b415-da0faba5a143",
|
||||
"sourceHandle": "collection",
|
||||
"target": "ac481b7f-08bf-4a9d-9e0c-3a82ea5243ce",
|
||||
"targetHandle": "control",
|
||||
"id": "reactflow__edge-ca4d5059-8bfb-447f-b415-da0faba5a143collection-ac481b7f-08bf-4a9d-9e0c-3a82ea5243cecontrol",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "54486974-835b-4d81-8f82-05f9f32ce9e9",
|
||||
"sourceHandle": "unet",
|
||||
"target": "ac481b7f-08bf-4a9d-9e0c-3a82ea5243ce",
|
||||
"targetHandle": "unet",
|
||||
"id": "reactflow__edge-54486974-835b-4d81-8f82-05f9f32ce9e9unet-ac481b7f-08bf-4a9d-9e0c-3a82ea5243ceunet",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "273e3f96-49ea-4dc5-9d5b-9660390f14e1",
|
||||
"sourceHandle": "conditioning",
|
||||
"target": "ac481b7f-08bf-4a9d-9e0c-3a82ea5243ce",
|
||||
"targetHandle": "negative_conditioning",
|
||||
"id": "reactflow__edge-273e3f96-49ea-4dc5-9d5b-9660390f14e1conditioning-ac481b7f-08bf-4a9d-9e0c-3a82ea5243cenegative_conditioning",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "7ce68934-3419-42d4-ac70-82cfc9397306",
|
||||
"sourceHandle": "conditioning",
|
||||
"target": "ac481b7f-08bf-4a9d-9e0c-3a82ea5243ce",
|
||||
"targetHandle": "positive_conditioning",
|
||||
"id": "reactflow__edge-7ce68934-3419-42d4-ac70-82cfc9397306conditioning-ac481b7f-08bf-4a9d-9e0c-3a82ea5243cepositive_conditioning",
|
||||
"type": "default"
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -1,719 +0,0 @@
|
||||
{
|
||||
"name": "Prompt from File",
|
||||
"author": "InvokeAI",
|
||||
"description": "Sample workflow using prompt from file capabilities of InvokeAI ",
|
||||
"version": "0.1.0",
|
||||
"contact": "millun@invoke.ai",
|
||||
"tags": "text2image, prompt from file, default",
|
||||
"notes": "",
|
||||
"exposedFields": [
|
||||
{
|
||||
"nodeId": "d6353b7f-b447-4e17-8f2e-80a88c91d426",
|
||||
"fieldName": "model"
|
||||
},
|
||||
{
|
||||
"nodeId": "1b7e0df8-8589-4915-a4ea-c0088f15d642",
|
||||
"fieldName": "file_path"
|
||||
}
|
||||
],
|
||||
"meta": {
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"nodes": [
|
||||
{
|
||||
"id": "c2eaf1ba-5708-4679-9e15-945b8b432692",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "c2eaf1ba-5708-4679-9e15-945b8b432692",
|
||||
"type": "compel",
|
||||
"inputs": {
|
||||
"prompt": {
|
||||
"id": "dcdf3f6d-9b96-4bcd-9b8d-f992fefe4f62",
|
||||
"name": "prompt",
|
||||
"type": "string",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": ""
|
||||
},
|
||||
"clip": {
|
||||
"id": "3f1981c9-d8a9-42eb-a739-4f120eb80745",
|
||||
"name": "clip",
|
||||
"type": "ClipField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"conditioning": {
|
||||
"id": "46205e6c-c5e2-44cb-9c82-1cd20b95674a",
|
||||
"name": "conditioning",
|
||||
"type": "ConditioningField",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 261,
|
||||
"position": {
|
||||
"x": 1177.3417789657444,
|
||||
"y": -102.0924766641035
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "1b7e0df8-8589-4915-a4ea-c0088f15d642",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "1b7e0df8-8589-4915-a4ea-c0088f15d642",
|
||||
"type": "prompt_from_file",
|
||||
"inputs": {
|
||||
"file_path": {
|
||||
"id": "37e37684-4f30-4ec8-beae-b333e550f904",
|
||||
"name": "file_path",
|
||||
"type": "string",
|
||||
"fieldKind": "input",
|
||||
"label": "Prompts File Path",
|
||||
"value": ""
|
||||
},
|
||||
"pre_prompt": {
|
||||
"id": "7de02feb-819a-4992-bad3-72a30920ddea",
|
||||
"name": "pre_prompt",
|
||||
"type": "string",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": ""
|
||||
},
|
||||
"post_prompt": {
|
||||
"id": "95f191d8-a282-428e-bd65-de8cb9b7513a",
|
||||
"name": "post_prompt",
|
||||
"type": "string",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": ""
|
||||
},
|
||||
"start_line": {
|
||||
"id": "efee9a48-05ab-4829-8429-becfa64a0782",
|
||||
"name": "start_line",
|
||||
"type": "integer",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 1
|
||||
},
|
||||
"max_prompts": {
|
||||
"id": "abebb428-3d3d-49fd-a482-4e96a16fff08",
|
||||
"name": "max_prompts",
|
||||
"type": "integer",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 1
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"collection": {
|
||||
"id": "77d5d7f1-9877-4ab1-9a8c-33e9ffa9abf3",
|
||||
"name": "collection",
|
||||
"type": "StringCollection",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "Prompts from File",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 589,
|
||||
"position": {
|
||||
"x": 394.181884547075,
|
||||
"y": -423.5345157864633
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "1b89067c-3f6b-42c8-991f-e3055789b251",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "1b89067c-3f6b-42c8-991f-e3055789b251",
|
||||
"type": "iterate",
|
||||
"inputs": {
|
||||
"collection": {
|
||||
"id": "4c564bf8-5ed6-441e-ad2c-dda265d5785f",
|
||||
"name": "collection",
|
||||
"type": "Collection",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": []
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"item": {
|
||||
"id": "36340f9a-e7a5-4afa-b4b5-313f4e292380",
|
||||
"name": "item",
|
||||
"type": "CollectionItem",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 104,
|
||||
"position": {
|
||||
"x": 792.8735298060233,
|
||||
"y": -432.6964953027252
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "d6353b7f-b447-4e17-8f2e-80a88c91d426",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "d6353b7f-b447-4e17-8f2e-80a88c91d426",
|
||||
"type": "main_model_loader",
|
||||
"inputs": {
|
||||
"model": {
|
||||
"id": "3f264259-3418-47d5-b90d-b6600e36ae46",
|
||||
"name": "model",
|
||||
"type": "MainModelField",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": {
|
||||
"model_name": "stable-diffusion-v1-5",
|
||||
"base_model": "sd-1",
|
||||
"model_type": "main"
|
||||
}
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"unet": {
|
||||
"id": "8e182ea2-9d0a-4c02-9407-27819288d4b5",
|
||||
"name": "unet",
|
||||
"type": "UNetField",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"clip": {
|
||||
"id": "d67d9d30-058c-46d5-bded-3d09d6d1aa39",
|
||||
"name": "clip",
|
||||
"type": "ClipField",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"vae": {
|
||||
"id": "89641601-0429-4448-98d5-190822d920d8",
|
||||
"name": "vae",
|
||||
"type": "VaeField",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 226,
|
||||
"position": {
|
||||
"x": -47.66201354137797,
|
||||
"y": -299.218193067033
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "fc9d0e35-a6de-4a19-84e1-c72497c823f6",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "fc9d0e35-a6de-4a19-84e1-c72497c823f6",
|
||||
"type": "compel",
|
||||
"inputs": {
|
||||
"prompt": {
|
||||
"id": "dcdf3f6d-9b96-4bcd-9b8d-f992fefe4f62",
|
||||
"name": "prompt",
|
||||
"type": "string",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": ""
|
||||
},
|
||||
"clip": {
|
||||
"id": "3f1981c9-d8a9-42eb-a739-4f120eb80745",
|
||||
"name": "clip",
|
||||
"type": "ClipField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"conditioning": {
|
||||
"id": "46205e6c-c5e2-44cb-9c82-1cd20b95674a",
|
||||
"name": "conditioning",
|
||||
"type": "ConditioningField",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 261,
|
||||
"position": {
|
||||
"x": 1175.0187896425462,
|
||||
"y": -420.64289413577114
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "0eb5f3f5-1b91-49eb-9ef0-41d67c7eae77",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "0eb5f3f5-1b91-49eb-9ef0-41d67c7eae77",
|
||||
"type": "noise",
|
||||
"inputs": {
|
||||
"seed": {
|
||||
"id": "b722d84a-eeee-484f-bef2-0250c027cb67",
|
||||
"name": "seed",
|
||||
"type": "integer",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 0
|
||||
},
|
||||
"width": {
|
||||
"id": "d5f8ce11-0502-4bfc-9a30-5757dddf1f94",
|
||||
"name": "width",
|
||||
"type": "integer",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 512
|
||||
},
|
||||
"height": {
|
||||
"id": "f187d5ff-38a5-4c3f-b780-fc5801ef34af",
|
||||
"name": "height",
|
||||
"type": "integer",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 512
|
||||
},
|
||||
"use_cpu": {
|
||||
"id": "12f112b8-8b76-4816-b79e-662edc9f9aa5",
|
||||
"name": "use_cpu",
|
||||
"type": "boolean",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": true
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"noise": {
|
||||
"id": "08576ad1-96d9-42d2-96ef-6f5c1961933f",
|
||||
"name": "noise",
|
||||
"type": "LatentsField",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"width": {
|
||||
"id": "f3e1f94a-258d-41ff-9789-bd999bd9f40d",
|
||||
"name": "width",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"height": {
|
||||
"id": "6cefc357-4339-415e-a951-49b9c2be32f4",
|
||||
"name": "height",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 389,
|
||||
"position": {
|
||||
"x": 809.1964864135837,
|
||||
"y": 183.2735123359796
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "dfc20e07-7aef-4fc0-a3a1-7bf68ec6a4e5",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "dfc20e07-7aef-4fc0-a3a1-7bf68ec6a4e5",
|
||||
"type": "rand_int",
|
||||
"inputs": {
|
||||
"low": {
|
||||
"id": "b9fc6cf1-469c-4037-9bf0-04836965826f",
|
||||
"name": "low",
|
||||
"type": "integer",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 0
|
||||
},
|
||||
"high": {
|
||||
"id": "06eac725-0f60-4ba2-b8cd-7ad9f757488c",
|
||||
"name": "high",
|
||||
"type": "integer",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 2147483647
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"value": {
|
||||
"id": "df08c84e-7346-4e92-9042-9e5cb773aaff",
|
||||
"name": "value",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": false,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 218,
|
||||
"position": {
|
||||
"x": 354.19913145404166,
|
||||
"y": 301.86324846905165
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "491ec988-3c77-4c37-af8a-39a0c4e7a2a1",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "491ec988-3c77-4c37-af8a-39a0c4e7a2a1",
|
||||
"type": "l2i",
|
||||
"inputs": {
|
||||
"metadata": {
|
||||
"id": "022e4b33-562b-438d-b7df-41c3fd931f40",
|
||||
"name": "metadata",
|
||||
"type": "MetadataField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"latents": {
|
||||
"id": "67cb6c77-a394-4a66-a6a9-a0a7dcca69ec",
|
||||
"name": "latents",
|
||||
"type": "LatentsField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"vae": {
|
||||
"id": "7b3fd9ad-a4ef-4e04-89fa-3832a9902dbd",
|
||||
"name": "vae",
|
||||
"type": "VaeField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"tiled": {
|
||||
"id": "5ac5680d-3add-4115-8ec0-9ef5bb87493b",
|
||||
"name": "tiled",
|
||||
"type": "boolean",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": false
|
||||
},
|
||||
"fp32": {
|
||||
"id": "db8297f5-55f8-452f-98cf-6572c2582152",
|
||||
"name": "fp32",
|
||||
"type": "boolean",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": false
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"image": {
|
||||
"id": "d8778d0c-592a-4960-9280-4e77e00a7f33",
|
||||
"name": "image",
|
||||
"type": "ImageField",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"width": {
|
||||
"id": "c8b0a75a-f5de-4ff2-9227-f25bb2b97bec",
|
||||
"name": "width",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"height": {
|
||||
"id": "83c05fbf-76b9-49ab-93c4-fa4b10e793e4",
|
||||
"name": "height",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 267,
|
||||
"position": {
|
||||
"x": 2037.861329274915,
|
||||
"y": -329.8393457509562
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "2fb1577f-0a56-4f12-8711-8afcaaaf1d5e",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "2fb1577f-0a56-4f12-8711-8afcaaaf1d5e",
|
||||
"type": "denoise_latents",
|
||||
"inputs": {
|
||||
"positive_conditioning": {
|
||||
"id": "751fb35b-3f23-45ce-af1c-053e74251337",
|
||||
"name": "positive_conditioning",
|
||||
"type": "ConditioningField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"negative_conditioning": {
|
||||
"id": "b9dc06b6-7481-4db1-a8c2-39d22a5eacff",
|
||||
"name": "negative_conditioning",
|
||||
"type": "ConditioningField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"noise": {
|
||||
"id": "6e15e439-3390-48a4-8031-01e0e19f0e1d",
|
||||
"name": "noise",
|
||||
"type": "LatentsField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"steps": {
|
||||
"id": "bfdfb3df-760b-4d51-b17b-0abb38b976c2",
|
||||
"name": "steps",
|
||||
"type": "integer",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 10
|
||||
},
|
||||
"cfg_scale": {
|
||||
"id": "47770858-322e-41af-8494-d8b63ed735f3",
|
||||
"name": "cfg_scale",
|
||||
"type": "FloatPolymorphic",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 7.5
|
||||
},
|
||||
"denoising_start": {
|
||||
"id": "2ba78720-ee02-4130-a348-7bc3531f790b",
|
||||
"name": "denoising_start",
|
||||
"type": "float",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 0
|
||||
},
|
||||
"denoising_end": {
|
||||
"id": "a874dffb-d433-4d1a-9f59-af4367bb05e4",
|
||||
"name": "denoising_end",
|
||||
"type": "float",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 1
|
||||
},
|
||||
"scheduler": {
|
||||
"id": "36e021ad-b762-4fe4-ad4d-17f0291c40b2",
|
||||
"name": "scheduler",
|
||||
"type": "Scheduler",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": "euler"
|
||||
},
|
||||
"unet": {
|
||||
"id": "98d3282d-f9f6-4b5e-b9e8-58658f1cac78",
|
||||
"name": "unet",
|
||||
"type": "UNetField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"control": {
|
||||
"id": "f2ea3216-43d5-42b4-887f-36e8f7166d53",
|
||||
"name": "control",
|
||||
"type": "ControlPolymorphic",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"ip_adapter": {
|
||||
"id": "d0780610-a298-47c8-a54e-70e769e0dfe2",
|
||||
"name": "ip_adapter",
|
||||
"type": "IPAdapterPolymorphic",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"t2i_adapter": {
|
||||
"id": "fdb40970-185e-4ea8-8bb5-88f06f91f46a",
|
||||
"name": "t2i_adapter",
|
||||
"type": "T2IAdapterPolymorphic",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"latents": {
|
||||
"id": "e05b538a-1b5a-4aa5-84b1-fd2361289a81",
|
||||
"name": "latents",
|
||||
"type": "LatentsField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"denoise_mask": {
|
||||
"id": "463a419e-df30-4382-8ffb-b25b25abe425",
|
||||
"name": "denoise_mask",
|
||||
"type": "DenoiseMaskField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"latents": {
|
||||
"id": "559ee688-66cf-4139-8b82-3d3aa69995ce",
|
||||
"name": "latents",
|
||||
"type": "LatentsField",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"width": {
|
||||
"id": "0b4285c2-e8b9-48e5-98f6-0a49d3f98fd2",
|
||||
"name": "width",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"height": {
|
||||
"id": "8b0881b9-45e5-47d5-b526-24b6661de0ee",
|
||||
"name": "height",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.4.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 646,
|
||||
"position": {
|
||||
"x": 1570.9941088179146,
|
||||
"y": -407.6505491604564
|
||||
}
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"source": "1b7e0df8-8589-4915-a4ea-c0088f15d642",
|
||||
"sourceHandle": "collection",
|
||||
"target": "1b89067c-3f6b-42c8-991f-e3055789b251",
|
||||
"targetHandle": "collection",
|
||||
"id": "reactflow__edge-1b7e0df8-8589-4915-a4ea-c0088f15d642collection-1b89067c-3f6b-42c8-991f-e3055789b251collection",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "d6353b7f-b447-4e17-8f2e-80a88c91d426",
|
||||
"sourceHandle": "clip",
|
||||
"target": "fc9d0e35-a6de-4a19-84e1-c72497c823f6",
|
||||
"targetHandle": "clip",
|
||||
"id": "reactflow__edge-d6353b7f-b447-4e17-8f2e-80a88c91d426clip-fc9d0e35-a6de-4a19-84e1-c72497c823f6clip",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "1b89067c-3f6b-42c8-991f-e3055789b251",
|
||||
"sourceHandle": "item",
|
||||
"target": "fc9d0e35-a6de-4a19-84e1-c72497c823f6",
|
||||
"targetHandle": "prompt",
|
||||
"id": "reactflow__edge-1b89067c-3f6b-42c8-991f-e3055789b251item-fc9d0e35-a6de-4a19-84e1-c72497c823f6prompt",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "d6353b7f-b447-4e17-8f2e-80a88c91d426",
|
||||
"sourceHandle": "clip",
|
||||
"target": "c2eaf1ba-5708-4679-9e15-945b8b432692",
|
||||
"targetHandle": "clip",
|
||||
"id": "reactflow__edge-d6353b7f-b447-4e17-8f2e-80a88c91d426clip-c2eaf1ba-5708-4679-9e15-945b8b432692clip",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "dfc20e07-7aef-4fc0-a3a1-7bf68ec6a4e5",
|
||||
"sourceHandle": "value",
|
||||
"target": "0eb5f3f5-1b91-49eb-9ef0-41d67c7eae77",
|
||||
"targetHandle": "seed",
|
||||
"id": "reactflow__edge-dfc20e07-7aef-4fc0-a3a1-7bf68ec6a4e5value-0eb5f3f5-1b91-49eb-9ef0-41d67c7eae77seed",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "fc9d0e35-a6de-4a19-84e1-c72497c823f6",
|
||||
"sourceHandle": "conditioning",
|
||||
"target": "2fb1577f-0a56-4f12-8711-8afcaaaf1d5e",
|
||||
"targetHandle": "positive_conditioning",
|
||||
"id": "reactflow__edge-fc9d0e35-a6de-4a19-84e1-c72497c823f6conditioning-2fb1577f-0a56-4f12-8711-8afcaaaf1d5epositive_conditioning",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "c2eaf1ba-5708-4679-9e15-945b8b432692",
|
||||
"sourceHandle": "conditioning",
|
||||
"target": "2fb1577f-0a56-4f12-8711-8afcaaaf1d5e",
|
||||
"targetHandle": "negative_conditioning",
|
||||
"id": "reactflow__edge-c2eaf1ba-5708-4679-9e15-945b8b432692conditioning-2fb1577f-0a56-4f12-8711-8afcaaaf1d5enegative_conditioning",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "0eb5f3f5-1b91-49eb-9ef0-41d67c7eae77",
|
||||
"sourceHandle": "noise",
|
||||
"target": "2fb1577f-0a56-4f12-8711-8afcaaaf1d5e",
|
||||
"targetHandle": "noise",
|
||||
"id": "reactflow__edge-0eb5f3f5-1b91-49eb-9ef0-41d67c7eae77noise-2fb1577f-0a56-4f12-8711-8afcaaaf1d5enoise",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "d6353b7f-b447-4e17-8f2e-80a88c91d426",
|
||||
"sourceHandle": "unet",
|
||||
"target": "2fb1577f-0a56-4f12-8711-8afcaaaf1d5e",
|
||||
"targetHandle": "unet",
|
||||
"id": "reactflow__edge-d6353b7f-b447-4e17-8f2e-80a88c91d426unet-2fb1577f-0a56-4f12-8711-8afcaaaf1d5eunet",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "2fb1577f-0a56-4f12-8711-8afcaaaf1d5e",
|
||||
"sourceHandle": "latents",
|
||||
"target": "491ec988-3c77-4c37-af8a-39a0c4e7a2a1",
|
||||
"targetHandle": "latents",
|
||||
"id": "reactflow__edge-2fb1577f-0a56-4f12-8711-8afcaaaf1d5elatents-491ec988-3c77-4c37-af8a-39a0c4e7a2a1latents",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "d6353b7f-b447-4e17-8f2e-80a88c91d426",
|
||||
"sourceHandle": "vae",
|
||||
"target": "491ec988-3c77-4c37-af8a-39a0c4e7a2a1",
|
||||
"targetHandle": "vae",
|
||||
"id": "reactflow__edge-d6353b7f-b447-4e17-8f2e-80a88c91d426vae-491ec988-3c77-4c37-af8a-39a0c4e7a2a1vae",
|
||||
"type": "default"
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -1,758 +0,0 @@
|
||||
{
|
||||
"name": "QR Code Monster",
|
||||
"author": "InvokeAI",
|
||||
"description": "Sample workflow for create images with QR code Monster ControlNet",
|
||||
"version": "1.0.1",
|
||||
"contact": "invoke@invoke.ai",
|
||||
"tags": "qrcode, controlnet, default",
|
||||
"notes": "",
|
||||
"exposedFields": [
|
||||
{
|
||||
"nodeId": "a6cc0986-f928-4a7e-8d44-ba2d4b36f54a",
|
||||
"fieldName": "image"
|
||||
},
|
||||
{
|
||||
"nodeId": "aca3b054-bfba-4392-bd20-6476f59504df",
|
||||
"fieldName": "prompt"
|
||||
},
|
||||
{
|
||||
"nodeId": "3db7cee0-31e2-4a3d-94a1-268cb16177dd",
|
||||
"fieldName": "prompt"
|
||||
}
|
||||
],
|
||||
"meta": {
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"nodes": [
|
||||
{
|
||||
"id": "3db7cee0-31e2-4a3d-94a1-268cb16177dd",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "3db7cee0-31e2-4a3d-94a1-268cb16177dd",
|
||||
"type": "compel",
|
||||
"inputs": {
|
||||
"prompt": {
|
||||
"id": "6a1fe244-5656-4f8c-91d1-1fb474e28807",
|
||||
"name": "prompt",
|
||||
"type": "string",
|
||||
"fieldKind": "input",
|
||||
"label": "Negative Prompt",
|
||||
"value": ""
|
||||
},
|
||||
"clip": {
|
||||
"id": "f24688f3-29b8-4a2d-8603-046e5a5c7250",
|
||||
"name": "clip",
|
||||
"type": "ClipField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"conditioning": {
|
||||
"id": "700528eb-3f8b-4745-b540-34f919b5b228",
|
||||
"name": "conditioning",
|
||||
"type": "ConditioningField",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "Prompt",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 261,
|
||||
"position": {
|
||||
"x": 773.0502679628016,
|
||||
"y": 1622.4836086770556
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "610384f1-6f0c-4847-a9a2-37ce7f456ed1",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "610384f1-6f0c-4847-a9a2-37ce7f456ed1",
|
||||
"type": "main_model_loader",
|
||||
"inputs": {
|
||||
"model": {
|
||||
"id": "cb36b6d3-6c1f-4911-a200-646745b0ff74",
|
||||
"name": "model",
|
||||
"type": "MainModelField",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": {
|
||||
"model_name": "stable-diffusion-v1-5",
|
||||
"base_model": "sd-1",
|
||||
"model_type": "main"
|
||||
}
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"unet": {
|
||||
"id": "7246895b-b252-49bc-b952-8d801b4672f7",
|
||||
"name": "unet",
|
||||
"type": "UNetField",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"clip": {
|
||||
"id": "3c2aedb8-30d5-4d4b-99df-d06a0d7bedc6",
|
||||
"name": "clip",
|
||||
"type": "ClipField",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"vae": {
|
||||
"id": "b9743815-5501-4bbb-8bde-8bd6ba298a4e",
|
||||
"name": "vae",
|
||||
"type": "VaeField",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 226,
|
||||
"position": {
|
||||
"x": 211.58866462619744,
|
||||
"y": 1376.0542388105248
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "aca3b054-bfba-4392-bd20-6476f59504df",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "aca3b054-bfba-4392-bd20-6476f59504df",
|
||||
"type": "compel",
|
||||
"inputs": {
|
||||
"prompt": {
|
||||
"id": "6a1fe244-5656-4f8c-91d1-1fb474e28807",
|
||||
"name": "prompt",
|
||||
"type": "string",
|
||||
"fieldKind": "input",
|
||||
"label": "Positive Prompt",
|
||||
"value": ""
|
||||
},
|
||||
"clip": {
|
||||
"id": "f24688f3-29b8-4a2d-8603-046e5a5c7250",
|
||||
"name": "clip",
|
||||
"type": "ClipField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"conditioning": {
|
||||
"id": "700528eb-3f8b-4745-b540-34f919b5b228",
|
||||
"name": "conditioning",
|
||||
"type": "ConditioningField",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 261,
|
||||
"position": {
|
||||
"x": 770.6491131680111,
|
||||
"y": 1316.379247112241
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "a6cc0986-f928-4a7e-8d44-ba2d4b36f54a",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "a6cc0986-f928-4a7e-8d44-ba2d4b36f54a",
|
||||
"type": "image",
|
||||
"inputs": {
|
||||
"image": {
|
||||
"id": "89ba5d58-28c9-4e04-a5df-79fb7a6f3531",
|
||||
"name": "image",
|
||||
"type": "ImageField",
|
||||
"fieldKind": "input",
|
||||
"label": "QR Code / Hidden Image"
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"image": {
|
||||
"id": "54335653-0e17-42da-b9e8-83c5fb5af670",
|
||||
"name": "image",
|
||||
"type": "ImageField",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"width": {
|
||||
"id": "a3c65953-39ea-4d97-8858-d65154ff9d11",
|
||||
"name": "width",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"height": {
|
||||
"id": "2c7db511-ebc9-4286-a46b-bc11e0fd779f",
|
||||
"name": "height",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 225,
|
||||
"position": {
|
||||
"x": 700.5034176864369,
|
||||
"y": 1981.749600549388
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "280fd8a7-3b0c-49fe-8be4-6246e08b6c9a",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "280fd8a7-3b0c-49fe-8be4-6246e08b6c9a",
|
||||
"type": "noise",
|
||||
"inputs": {
|
||||
"seed": {
|
||||
"id": "7c6c76dd-127b-4829-b1ec-430790cb7ed7",
|
||||
"name": "seed",
|
||||
"type": "integer",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 0
|
||||
},
|
||||
"width": {
|
||||
"id": "8ec6a525-a421-40d8-a17e-39e7b6836438",
|
||||
"name": "width",
|
||||
"type": "integer",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 512
|
||||
},
|
||||
"height": {
|
||||
"id": "6af1e58a-e2ee-4ec4-9f06-d8d0412922ca",
|
||||
"name": "height",
|
||||
"type": "integer",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 512
|
||||
},
|
||||
"use_cpu": {
|
||||
"id": "26662e99-5720-43a6-a5d8-06c9dab0e261",
|
||||
"name": "use_cpu",
|
||||
"type": "boolean",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": true
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"noise": {
|
||||
"id": "cb4c4dfc-a744-49eb-af4f-677448e28407",
|
||||
"name": "noise",
|
||||
"type": "LatentsField",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"width": {
|
||||
"id": "97e87be6-e81f-40a3-a522-28ebe4aad0ac",
|
||||
"name": "width",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"height": {
|
||||
"id": "80784420-f1e1-47b0-bd1d-1d381a15e22d",
|
||||
"name": "height",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": false,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 32,
|
||||
"position": {
|
||||
"x": 1182.460291960481,
|
||||
"y": 1759.592972960265
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "2ac03cf6-0326-454a-bed0-d8baef2bf30d",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "2ac03cf6-0326-454a-bed0-d8baef2bf30d",
|
||||
"type": "controlnet",
|
||||
"inputs": {
|
||||
"image": {
|
||||
"id": "1f683889-9f14-40c8-af29-4b991b211a3a",
|
||||
"name": "image",
|
||||
"type": "ImageField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"control_model": {
|
||||
"id": "a933b21d-22c1-4e06-818f-15416b971282",
|
||||
"name": "control_model",
|
||||
"type": "ControlNetModelField",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": {
|
||||
"model_name": "qrcode_monster",
|
||||
"base_model": "sd-1"
|
||||
}
|
||||
},
|
||||
"control_weight": {
|
||||
"id": "198a0825-e55e-4496-bc54-c3d7b02f3d75",
|
||||
"name": "control_weight",
|
||||
"type": "FloatPolymorphic",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 1.4
|
||||
},
|
||||
"begin_step_percent": {
|
||||
"id": "c85ce42f-22af-42a0-8993-676002fb275e",
|
||||
"name": "begin_step_percent",
|
||||
"type": "float",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 0
|
||||
},
|
||||
"end_step_percent": {
|
||||
"id": "a61a65c4-9e6f-4fe2-96a5-1294d17ec6e4",
|
||||
"name": "end_step_percent",
|
||||
"type": "float",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 1
|
||||
},
|
||||
"control_mode": {
|
||||
"id": "1aa45cfa-0249-46b7-bf24-3e38e92f5fa0",
|
||||
"name": "control_mode",
|
||||
"type": "enum",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": "balanced"
|
||||
},
|
||||
"resize_mode": {
|
||||
"id": "a89d3cb9-a141-4cea-bb49-977bf267377b",
|
||||
"name": "resize_mode",
|
||||
"type": "enum",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": "just_resize"
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"control": {
|
||||
"id": "c9a1fc7e-cb25-45a9-adff-1a97c9ff04d6",
|
||||
"name": "control",
|
||||
"type": "ControlField",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 508,
|
||||
"position": {
|
||||
"x": 1165.434407461108,
|
||||
"y": 1862.916856351665
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "28542b66-5a00-4780-a318-0a036d2df914",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "28542b66-5a00-4780-a318-0a036d2df914",
|
||||
"type": "l2i",
|
||||
"inputs": {
|
||||
"metadata": {
|
||||
"id": "a38e8f55-7f2c-4fcc-a71f-d51e2eb0374a",
|
||||
"name": "metadata",
|
||||
"type": "MetadataField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"latents": {
|
||||
"id": "80e97bc8-e716-4175-9115-5b58495aa30c",
|
||||
"name": "latents",
|
||||
"type": "LatentsField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"vae": {
|
||||
"id": "5641bce6-ac2b-47eb-bb32-2f290026b7e1",
|
||||
"name": "vae",
|
||||
"type": "VaeField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"tiled": {
|
||||
"id": "9e75eb16-ae48-47ed-b180-e0409d377436",
|
||||
"name": "tiled",
|
||||
"type": "boolean",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": false
|
||||
},
|
||||
"fp32": {
|
||||
"id": "0518b0ce-ee37-437b-8437-cc2976a3279f",
|
||||
"name": "fp32",
|
||||
"type": "boolean",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": false
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"image": {
|
||||
"id": "ec2ff985-a7eb-401f-92c4-1217cddad6a2",
|
||||
"name": "image",
|
||||
"type": "ImageField",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"width": {
|
||||
"id": "ba1d1720-6d67-4eca-9e9d-b97d08636774",
|
||||
"name": "width",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"height": {
|
||||
"id": "10bcf8f4-6394-422f-b0c0-51680f3bfb25",
|
||||
"name": "height",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 267,
|
||||
"position": {
|
||||
"x": 2110.8415693683014,
|
||||
"y": 1487.253341116115
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "9755ae4c-ef30-4db3-80f6-a31f98979a11",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "9755ae4c-ef30-4db3-80f6-a31f98979a11",
|
||||
"type": "denoise_latents",
|
||||
"inputs": {
|
||||
"positive_conditioning": {
|
||||
"id": "8e6aceaa-a986-4ab2-9c04-5b1027b3daf6",
|
||||
"name": "positive_conditioning",
|
||||
"type": "ConditioningField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"negative_conditioning": {
|
||||
"id": "fbbaa712-ca1a-420b-9016-763f2a29d68c",
|
||||
"name": "negative_conditioning",
|
||||
"type": "ConditioningField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"noise": {
|
||||
"id": "a3b3d5d2-c0f9-4b89-a9b3-8de9418f7bb5",
|
||||
"name": "noise",
|
||||
"type": "LatentsField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"steps": {
|
||||
"id": "e491e664-2f8c-4f49-b3e4-57b051fbb9c5",
|
||||
"name": "steps",
|
||||
"type": "integer",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 10
|
||||
},
|
||||
"cfg_scale": {
|
||||
"id": "f0318abd-ed65-4cad-86a7-48d1c19a6d14",
|
||||
"name": "cfg_scale",
|
||||
"type": "FloatPolymorphic",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 7.5
|
||||
},
|
||||
"denoising_start": {
|
||||
"id": "f7c24c51-496f-44c4-836a-c734e529fec0",
|
||||
"name": "denoising_start",
|
||||
"type": "float",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 0
|
||||
},
|
||||
"denoising_end": {
|
||||
"id": "54f7656a-fb0d-4d9e-a459-f700f7dccd2e",
|
||||
"name": "denoising_end",
|
||||
"type": "float",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 1
|
||||
},
|
||||
"scheduler": {
|
||||
"id": "363ee440-040d-499b-bf84-bf5391b08681",
|
||||
"name": "scheduler",
|
||||
"type": "Scheduler",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": "euler"
|
||||
},
|
||||
"unet": {
|
||||
"id": "5c93d4e5-1064-4700-ab1d-d12e1e9b5ba7",
|
||||
"name": "unet",
|
||||
"type": "UNetField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"control": {
|
||||
"id": "e1948eb3-7407-43b0-93e3-139470f186b7",
|
||||
"name": "control",
|
||||
"type": "ControlPolymorphic",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"ip_adapter": {
|
||||
"id": "5675b2c3-adfb-49ee-b33c-26bdbfab1fed",
|
||||
"name": "ip_adapter",
|
||||
"type": "IPAdapterPolymorphic",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"t2i_adapter": {
|
||||
"id": "89cd4ab3-3bfc-4063-9de5-91d42305c651",
|
||||
"name": "t2i_adapter",
|
||||
"type": "T2IAdapterPolymorphic",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"latents": {
|
||||
"id": "ec01df90-5042-418d-b6d6-86b251c13770",
|
||||
"name": "latents",
|
||||
"type": "LatentsField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"denoise_mask": {
|
||||
"id": "561cde00-cb20-42ae-9bd3-4f477f73fbe1",
|
||||
"name": "denoise_mask",
|
||||
"type": "DenoiseMaskField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"latents": {
|
||||
"id": "f9addefe-efcc-4e01-8945-6ebbc934b002",
|
||||
"name": "latents",
|
||||
"type": "LatentsField",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"width": {
|
||||
"id": "6d48f78b-d681-422a-8677-0111bd0625f1",
|
||||
"name": "width",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"height": {
|
||||
"id": "f25997b8-6316-44ce-b696-b82e4ed51ae5",
|
||||
"name": "height",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.4.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 646,
|
||||
"position": {
|
||||
"x": 1597.9598293300219,
|
||||
"y": 1420.4637727891632
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "59349822-af20-4e0e-a53f-3ba135d00c3f",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "59349822-af20-4e0e-a53f-3ba135d00c3f",
|
||||
"type": "rand_int",
|
||||
"inputs": {
|
||||
"low": {
|
||||
"id": "051f22f9-2d4f-414f-bc51-84af2d626efa",
|
||||
"name": "low",
|
||||
"type": "integer",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 0
|
||||
},
|
||||
"high": {
|
||||
"id": "77206186-f264-4224-9589-f925cf903dc9",
|
||||
"name": "high",
|
||||
"type": "integer",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 2147483647
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"value": {
|
||||
"id": "a7ed9387-3a24-4d34-b7c5-f713bd544ab1",
|
||||
"name": "value",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": false,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": false,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 32,
|
||||
"position": {
|
||||
"x": 1178.16746986153,
|
||||
"y": 1663.9433412808876
|
||||
}
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"source": "59349822-af20-4e0e-a53f-3ba135d00c3f",
|
||||
"target": "280fd8a7-3b0c-49fe-8be4-6246e08b6c9a",
|
||||
"id": "59349822-af20-4e0e-a53f-3ba135d00c3f-280fd8a7-3b0c-49fe-8be4-6246e08b6c9a-collapsed",
|
||||
"type": "collapsed"
|
||||
},
|
||||
{
|
||||
"source": "610384f1-6f0c-4847-a9a2-37ce7f456ed1",
|
||||
"sourceHandle": "clip",
|
||||
"target": "aca3b054-bfba-4392-bd20-6476f59504df",
|
||||
"targetHandle": "clip",
|
||||
"id": "reactflow__edge-610384f1-6f0c-4847-a9a2-37ce7f456ed1clip-aca3b054-bfba-4392-bd20-6476f59504dfclip",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "610384f1-6f0c-4847-a9a2-37ce7f456ed1",
|
||||
"sourceHandle": "clip",
|
||||
"target": "3db7cee0-31e2-4a3d-94a1-268cb16177dd",
|
||||
"targetHandle": "clip",
|
||||
"id": "reactflow__edge-610384f1-6f0c-4847-a9a2-37ce7f456ed1clip-3db7cee0-31e2-4a3d-94a1-268cb16177ddclip",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "a6cc0986-f928-4a7e-8d44-ba2d4b36f54a",
|
||||
"sourceHandle": "image",
|
||||
"target": "2ac03cf6-0326-454a-bed0-d8baef2bf30d",
|
||||
"targetHandle": "image",
|
||||
"id": "reactflow__edge-a6cc0986-f928-4a7e-8d44-ba2d4b36f54aimage-2ac03cf6-0326-454a-bed0-d8baef2bf30dimage",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "610384f1-6f0c-4847-a9a2-37ce7f456ed1",
|
||||
"sourceHandle": "vae",
|
||||
"target": "28542b66-5a00-4780-a318-0a036d2df914",
|
||||
"targetHandle": "vae",
|
||||
"id": "reactflow__edge-610384f1-6f0c-4847-a9a2-37ce7f456ed1vae-28542b66-5a00-4780-a318-0a036d2df914vae",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "280fd8a7-3b0c-49fe-8be4-6246e08b6c9a",
|
||||
"sourceHandle": "noise",
|
||||
"target": "9755ae4c-ef30-4db3-80f6-a31f98979a11",
|
||||
"targetHandle": "noise",
|
||||
"id": "reactflow__edge-280fd8a7-3b0c-49fe-8be4-6246e08b6c9anoise-9755ae4c-ef30-4db3-80f6-a31f98979a11noise",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "3db7cee0-31e2-4a3d-94a1-268cb16177dd",
|
||||
"sourceHandle": "conditioning",
|
||||
"target": "9755ae4c-ef30-4db3-80f6-a31f98979a11",
|
||||
"targetHandle": "negative_conditioning",
|
||||
"id": "reactflow__edge-3db7cee0-31e2-4a3d-94a1-268cb16177ddconditioning-9755ae4c-ef30-4db3-80f6-a31f98979a11negative_conditioning",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "aca3b054-bfba-4392-bd20-6476f59504df",
|
||||
"sourceHandle": "conditioning",
|
||||
"target": "9755ae4c-ef30-4db3-80f6-a31f98979a11",
|
||||
"targetHandle": "positive_conditioning",
|
||||
"id": "reactflow__edge-aca3b054-bfba-4392-bd20-6476f59504dfconditioning-9755ae4c-ef30-4db3-80f6-a31f98979a11positive_conditioning",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "610384f1-6f0c-4847-a9a2-37ce7f456ed1",
|
||||
"sourceHandle": "unet",
|
||||
"target": "9755ae4c-ef30-4db3-80f6-a31f98979a11",
|
||||
"targetHandle": "unet",
|
||||
"id": "reactflow__edge-610384f1-6f0c-4847-a9a2-37ce7f456ed1unet-9755ae4c-ef30-4db3-80f6-a31f98979a11unet",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "2ac03cf6-0326-454a-bed0-d8baef2bf30d",
|
||||
"sourceHandle": "control",
|
||||
"target": "9755ae4c-ef30-4db3-80f6-a31f98979a11",
|
||||
"targetHandle": "control",
|
||||
"id": "reactflow__edge-2ac03cf6-0326-454a-bed0-d8baef2bf30dcontrol-9755ae4c-ef30-4db3-80f6-a31f98979a11control",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "9755ae4c-ef30-4db3-80f6-a31f98979a11",
|
||||
"sourceHandle": "latents",
|
||||
"target": "28542b66-5a00-4780-a318-0a036d2df914",
|
||||
"targetHandle": "latents",
|
||||
"id": "reactflow__edge-9755ae4c-ef30-4db3-80f6-a31f98979a11latents-28542b66-5a00-4780-a318-0a036d2df914latents",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "59349822-af20-4e0e-a53f-3ba135d00c3f",
|
||||
"sourceHandle": "value",
|
||||
"target": "280fd8a7-3b0c-49fe-8be4-6246e08b6c9a",
|
||||
"targetHandle": "seed",
|
||||
"id": "reactflow__edge-59349822-af20-4e0e-a53f-3ba135d00c3fvalue-280fd8a7-3b0c-49fe-8be4-6246e08b6c9aseed",
|
||||
"type": "default"
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -26,6 +26,10 @@
|
||||
{
|
||||
"nodeId": "3193ad09-a7c2-4bf4-a3a9-1c61cc33a204",
|
||||
"fieldName": "style"
|
||||
},
|
||||
{
|
||||
"nodeId": "87ee6243-fb0d-4f77-ad5f-56591659339e",
|
||||
"fieldName": "steps"
|
||||
}
|
||||
],
|
||||
"meta": {
|
||||
@@ -36,6 +40,7 @@
|
||||
"id": "3193ad09-a7c2-4bf4-a3a9-1c61cc33a204",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"version": "1.0.0",
|
||||
"id": "3193ad09-a7c2-4bf4-a3a9-1c61cc33a204",
|
||||
"type": "sdxl_compel_prompt",
|
||||
"inputs": {
|
||||
@@ -130,12 +135,10 @@
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
"isIntermediate": true
|
||||
},
|
||||
"width": 320,
|
||||
"height": 793,
|
||||
"height": 764,
|
||||
"position": {
|
||||
"x": 1275,
|
||||
"y": -350
|
||||
@@ -145,6 +148,7 @@
|
||||
"id": "55705012-79b9-4aac-9f26-c0b10309785b",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"version": "1.0.0",
|
||||
"id": "55705012-79b9-4aac-9f26-c0b10309785b",
|
||||
"type": "noise",
|
||||
"inputs": {
|
||||
@@ -205,9 +209,7 @@
|
||||
"isOpen": false,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
"isIntermediate": true
|
||||
},
|
||||
"width": 320,
|
||||
"height": 32,
|
||||
@@ -216,10 +218,83 @@
|
||||
"y": -300
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"version": "1.0.0",
|
||||
"id": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
|
||||
"type": "l2i",
|
||||
"inputs": {
|
||||
"tiled": {
|
||||
"id": "24f5bc7b-f6a1-425d-8ab1-f50b4db5d0df",
|
||||
"name": "tiled",
|
||||
"type": "boolean",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": false
|
||||
},
|
||||
"fp32": {
|
||||
"id": "b146d873-ffb9-4767-986a-5360504841a2",
|
||||
"name": "fp32",
|
||||
"type": "boolean",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": true
|
||||
},
|
||||
"latents": {
|
||||
"id": "65441abd-7713-4b00-9d8d-3771404002e8",
|
||||
"name": "latents",
|
||||
"type": "LatentsField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"vae": {
|
||||
"id": "a478b833-6e13-4611-9a10-842c89603c74",
|
||||
"name": "vae",
|
||||
"type": "VaeField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"image": {
|
||||
"id": "c87ae925-f858-417a-8940-8708ba9b4b53",
|
||||
"name": "image",
|
||||
"type": "ImageField",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"width": {
|
||||
"id": "4bcb8512-b5a1-45f1-9e52-6e92849f9d6c",
|
||||
"name": "width",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"height": {
|
||||
"id": "23e41c00-a354-48e8-8f59-5875679c27ab",
|
||||
"name": "height",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": true,
|
||||
"isIntermediate": false
|
||||
},
|
||||
"width": 320,
|
||||
"height": 224,
|
||||
"position": {
|
||||
"x": 2025,
|
||||
"y": -250
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "ea94bc37-d995-4a83-aa99-4af42479f2f2",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"version": "1.0.0",
|
||||
"id": "ea94bc37-d995-4a83-aa99-4af42479f2f2",
|
||||
"type": "rand_int",
|
||||
"inputs": {
|
||||
@@ -252,9 +327,7 @@
|
||||
"isOpen": false,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": false,
|
||||
"version": "1.0.0"
|
||||
"isIntermediate": true
|
||||
},
|
||||
"width": 320,
|
||||
"height": 32,
|
||||
@@ -267,6 +340,7 @@
|
||||
"id": "30d3289c-773c-4152-a9d2-bd8a99c8fd22",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"version": "1.0.0",
|
||||
"id": "30d3289c-773c-4152-a9d2-bd8a99c8fd22",
|
||||
"type": "sdxl_model_loader",
|
||||
"inputs": {
|
||||
@@ -277,7 +351,7 @@
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": {
|
||||
"model_name": "stable-diffusion-xl-base-1-0",
|
||||
"model_name": "stable-diffusion-xl-base-1.0",
|
||||
"base_model": "sdxl",
|
||||
"model_type": "main"
|
||||
}
|
||||
@@ -313,12 +387,10 @@
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
"isIntermediate": true
|
||||
},
|
||||
"width": 320,
|
||||
"height": 258,
|
||||
"height": 234,
|
||||
"position": {
|
||||
"x": 475,
|
||||
"y": 25
|
||||
@@ -328,6 +400,7 @@
|
||||
"id": "faf965a4-7530-427b-b1f3-4ba6505c2a08",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"version": "1.0.0",
|
||||
"id": "faf965a4-7530-427b-b1f3-4ba6505c2a08",
|
||||
"type": "sdxl_compel_prompt",
|
||||
"inputs": {
|
||||
@@ -422,143 +495,48 @@
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
"isIntermediate": true
|
||||
},
|
||||
"width": 320,
|
||||
"height": 793,
|
||||
"height": 764,
|
||||
"position": {
|
||||
"x": 900,
|
||||
"y": -350
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "63e91020-83b2-4f35-b174-ad9692aabb48",
|
||||
"id": "87ee6243-fb0d-4f77-ad5f-56591659339e",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "63e91020-83b2-4f35-b174-ad9692aabb48",
|
||||
"type": "l2i",
|
||||
"inputs": {
|
||||
"metadata": {
|
||||
"id": "88971324-3fdb-442d-b8b7-7612478a8622",
|
||||
"name": "metadata",
|
||||
"type": "MetadataField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"latents": {
|
||||
"id": "da0e40cb-c49f-4fa5-9856-338b91a65f6b",
|
||||
"name": "latents",
|
||||
"type": "LatentsField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"vae": {
|
||||
"id": "ae5164ce-1710-4ec5-a83a-6113a0d1b5c0",
|
||||
"name": "vae",
|
||||
"type": "VaeField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"tiled": {
|
||||
"id": "2ccfd535-1a7b-4ecf-84db-9430a64fb3d7",
|
||||
"name": "tiled",
|
||||
"type": "boolean",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": false
|
||||
},
|
||||
"fp32": {
|
||||
"id": "64f07d5a-54a2-429c-8c5b-0c2a3a8e5cd5",
|
||||
"name": "fp32",
|
||||
"type": "boolean",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": false
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"image": {
|
||||
"id": "9b281eaa-6504-407d-a5ca-1e5e8020a4bf",
|
||||
"name": "image",
|
||||
"type": "ImageField",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"width": {
|
||||
"id": "98e545f3-b53b-490d-b94d-bed9418ccc75",
|
||||
"name": "width",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"height": {
|
||||
"id": "4a74bd43-d7f7-4c7f-bb3b-d09bb2992c46",
|
||||
"name": "height",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": false,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 267,
|
||||
"position": {
|
||||
"x": 2112.5626808057173,
|
||||
"y": -174.24042139280238
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "50a36525-3c0a-4cc5-977c-e4bfc3fd6dfb",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "50a36525-3c0a-4cc5-977c-e4bfc3fd6dfb",
|
||||
"version": "1.0.0",
|
||||
"id": "87ee6243-fb0d-4f77-ad5f-56591659339e",
|
||||
"type": "denoise_latents",
|
||||
"inputs": {
|
||||
"positive_conditioning": {
|
||||
"id": "29b73dfa-a06e-4b4a-a844-515b9eb93a81",
|
||||
"name": "positive_conditioning",
|
||||
"type": "ConditioningField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"negative_conditioning": {
|
||||
"id": "a81e6f5b-f4de-4919-b483-b6e2f067465a",
|
||||
"name": "negative_conditioning",
|
||||
"type": "ConditioningField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"noise": {
|
||||
"id": "4ba06bb7-eb45-4fb9-9984-31001b545587",
|
||||
"id": "4884a4b7-cc19-4fea-83c7-1f940e6edd24",
|
||||
"name": "noise",
|
||||
"type": "LatentsField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"steps": {
|
||||
"id": "36ee8a45-ca69-44bc-9bc3-aa881e6045c0",
|
||||
"id": "4c61675c-b6b9-41ac-b187-b5c13b587039",
|
||||
"name": "steps",
|
||||
"type": "integer",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 10
|
||||
"value": 36
|
||||
},
|
||||
"cfg_scale": {
|
||||
"id": "2a2024e0-a736-46ec-933c-c1c1ebe96943",
|
||||
"id": "f8213f35-4637-4a1a-83f4-1f8cfb9ccd2c",
|
||||
"name": "cfg_scale",
|
||||
"type": "FloatPolymorphic",
|
||||
"type": "float",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 7.5
|
||||
},
|
||||
"denoising_start": {
|
||||
"id": "be219d5e-41b7-430a-8fb5-bc21a31ad219",
|
||||
"id": "01e2f30d-0acd-4e21-98b9-a9b8e24c6db2",
|
||||
"name": "denoising_start",
|
||||
"type": "float",
|
||||
"fieldKind": "input",
|
||||
@@ -566,7 +544,7 @@
|
||||
"value": 0
|
||||
},
|
||||
"denoising_end": {
|
||||
"id": "3adfb7ae-c9f7-4a40-b6e0-4c2050bd1a99",
|
||||
"id": "3db95479-a73b-4c75-9b44-08daec16b224",
|
||||
"name": "denoising_end",
|
||||
"type": "float",
|
||||
"fieldKind": "input",
|
||||
@@ -574,71 +552,71 @@
|
||||
"value": 1
|
||||
},
|
||||
"scheduler": {
|
||||
"id": "14423e0d-7215-4ee0-b065-f9e95eaa8d7d",
|
||||
"id": "db8430a9-64c3-4c54-ae38-9f597cf7b6d5",
|
||||
"name": "scheduler",
|
||||
"type": "Scheduler",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": "euler"
|
||||
},
|
||||
"unet": {
|
||||
"id": "e73bbf98-6489-492b-b83c-faed215febac",
|
||||
"name": "unet",
|
||||
"type": "UNetField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"control": {
|
||||
"id": "dab351b3-0c86-4ea5-9782-4e8edbfb0607",
|
||||
"id": "599b49e8-6435-4576-be41-a5155f3a17e3",
|
||||
"name": "control",
|
||||
"type": "ControlPolymorphic",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"ip_adapter": {
|
||||
"id": "192daea0-a90a-43cc-a2ee-0114a8e90318",
|
||||
"name": "ip_adapter",
|
||||
"type": "IPAdapterPolymorphic",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"t2i_adapter": {
|
||||
"id": "ee386a55-d4c7-48c1-ac57-7bc4e3aada7a",
|
||||
"name": "t2i_adapter",
|
||||
"type": "T2IAdapterPolymorphic",
|
||||
"type": "ControlField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"latents": {
|
||||
"id": "3a922c6a-3d8c-4c9e-b3ec-2f4d81cda077",
|
||||
"id": "226f9e91-454e-4159-9fa6-019c0cf29277",
|
||||
"name": "latents",
|
||||
"type": "LatentsField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"denoise_mask": {
|
||||
"id": "cd7ce032-835f-495f-8b45-d57272f33132",
|
||||
"id": "de019cb6-7fb5-45bf-a266-22e20889893f",
|
||||
"name": "denoise_mask",
|
||||
"type": "DenoiseMaskField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"positive_conditioning": {
|
||||
"id": "02fc400a-110d-470e-8411-f404f966a949",
|
||||
"name": "positive_conditioning",
|
||||
"type": "ConditioningField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"negative_conditioning": {
|
||||
"id": "4bd3bdfa-fcf4-42be-8e47-1e314255798f",
|
||||
"name": "negative_conditioning",
|
||||
"type": "ConditioningField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"unet": {
|
||||
"id": "7c2d58a8-b5f1-4e63-8ffd-8ada52c35832",
|
||||
"name": "unet",
|
||||
"type": "UNetField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"latents": {
|
||||
"id": "6260b84f-8361-470a-98d8-5b22a45c2d8c",
|
||||
"id": "6a6fa492-de26-4e95-b1d9-a322fe37eb13",
|
||||
"name": "latents",
|
||||
"type": "LatentsField",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"width": {
|
||||
"id": "aede0ecf-25b6-46be-aa30-b77f79715deb",
|
||||
"id": "a9790729-7d6c-4418-903d-4da961fccf56",
|
||||
"name": "width",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"height": {
|
||||
"id": "519abf62-d475-48ef-ab8f-66136bc0e499",
|
||||
"id": "fa74efe5-7330-4a3c-b256-c82a544585b4",
|
||||
"name": "height",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
@@ -648,15 +626,13 @@
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.4.0"
|
||||
"isIntermediate": true
|
||||
},
|
||||
"width": 320,
|
||||
"height": 646,
|
||||
"height": 558,
|
||||
"position": {
|
||||
"x": 1642.955772577545,
|
||||
"y": -230.2485847594651
|
||||
"x": 1650,
|
||||
"y": -250
|
||||
}
|
||||
}
|
||||
],
|
||||
@@ -710,42 +686,50 @@
|
||||
{
|
||||
"source": "30d3289c-773c-4152-a9d2-bd8a99c8fd22",
|
||||
"sourceHandle": "vae",
|
||||
"target": "63e91020-83b2-4f35-b174-ad9692aabb48",
|
||||
"target": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
|
||||
"targetHandle": "vae",
|
||||
"id": "reactflow__edge-30d3289c-773c-4152-a9d2-bd8a99c8fd22vae-63e91020-83b2-4f35-b174-ad9692aabb48vae",
|
||||
"id": "reactflow__edge-30d3289c-773c-4152-a9d2-bd8a99c8fd22vae-dbcd2f98-d809-48c8-bf64-2635f88a2fe9vae",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "30d3289c-773c-4152-a9d2-bd8a99c8fd22",
|
||||
"sourceHandle": "unet",
|
||||
"target": "50a36525-3c0a-4cc5-977c-e4bfc3fd6dfb",
|
||||
"targetHandle": "unet",
|
||||
"id": "reactflow__edge-30d3289c-773c-4152-a9d2-bd8a99c8fd22unet-50a36525-3c0a-4cc5-977c-e4bfc3fd6dfbunet",
|
||||
"source": "87ee6243-fb0d-4f77-ad5f-56591659339e",
|
||||
"sourceHandle": "latents",
|
||||
"target": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
|
||||
"targetHandle": "latents",
|
||||
"id": "reactflow__edge-87ee6243-fb0d-4f77-ad5f-56591659339elatents-dbcd2f98-d809-48c8-bf64-2635f88a2fe9latents",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "faf965a4-7530-427b-b1f3-4ba6505c2a08",
|
||||
"sourceHandle": "conditioning",
|
||||
"target": "50a36525-3c0a-4cc5-977c-e4bfc3fd6dfb",
|
||||
"target": "87ee6243-fb0d-4f77-ad5f-56591659339e",
|
||||
"targetHandle": "positive_conditioning",
|
||||
"id": "reactflow__edge-faf965a4-7530-427b-b1f3-4ba6505c2a08conditioning-50a36525-3c0a-4cc5-977c-e4bfc3fd6dfbpositive_conditioning",
|
||||
"id": "reactflow__edge-faf965a4-7530-427b-b1f3-4ba6505c2a08conditioning-87ee6243-fb0d-4f77-ad5f-56591659339epositive_conditioning",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "3193ad09-a7c2-4bf4-a3a9-1c61cc33a204",
|
||||
"sourceHandle": "conditioning",
|
||||
"target": "50a36525-3c0a-4cc5-977c-e4bfc3fd6dfb",
|
||||
"target": "87ee6243-fb0d-4f77-ad5f-56591659339e",
|
||||
"targetHandle": "negative_conditioning",
|
||||
"id": "reactflow__edge-3193ad09-a7c2-4bf4-a3a9-1c61cc33a204conditioning-50a36525-3c0a-4cc5-977c-e4bfc3fd6dfbnegative_conditioning",
|
||||
"id": "reactflow__edge-3193ad09-a7c2-4bf4-a3a9-1c61cc33a204conditioning-87ee6243-fb0d-4f77-ad5f-56591659339enegative_conditioning",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "30d3289c-773c-4152-a9d2-bd8a99c8fd22",
|
||||
"sourceHandle": "unet",
|
||||
"target": "87ee6243-fb0d-4f77-ad5f-56591659339e",
|
||||
"targetHandle": "unet",
|
||||
"id": "reactflow__edge-30d3289c-773c-4152-a9d2-bd8a99c8fd22unet-87ee6243-fb0d-4f77-ad5f-56591659339eunet",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "55705012-79b9-4aac-9f26-c0b10309785b",
|
||||
"sourceHandle": "noise",
|
||||
"target": "50a36525-3c0a-4cc5-977c-e4bfc3fd6dfb",
|
||||
"target": "87ee6243-fb0d-4f77-ad5f-56591659339e",
|
||||
"targetHandle": "noise",
|
||||
"id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-50a36525-3c0a-4cc5-977c-e4bfc3fd6dfbnoise",
|
||||
"id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-87ee6243-fb0d-4f77-ad5f-56591659339enoise",
|
||||
"type": "default"
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -18,6 +18,10 @@
|
||||
{
|
||||
"nodeId": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
|
||||
"fieldName": "prompt"
|
||||
},
|
||||
{
|
||||
"nodeId": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
|
||||
"fieldName": "steps"
|
||||
}
|
||||
],
|
||||
"meta": {
|
||||
@@ -28,6 +32,7 @@
|
||||
"id": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"version": "1.0.0",
|
||||
"id": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
|
||||
"type": "compel",
|
||||
"inputs": {
|
||||
@@ -59,21 +64,20 @@
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
"isIntermediate": true
|
||||
},
|
||||
"width": 320,
|
||||
"height": 261,
|
||||
"height": 235,
|
||||
"position": {
|
||||
"x": 995.7263915923627,
|
||||
"y": 239.67783573351227
|
||||
"x": 1400,
|
||||
"y": -75
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "55705012-79b9-4aac-9f26-c0b10309785b",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"version": "1.0.0",
|
||||
"id": "55705012-79b9-4aac-9f26-c0b10309785b",
|
||||
"type": "noise",
|
||||
"inputs": {
|
||||
@@ -134,21 +138,92 @@
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
"isIntermediate": true
|
||||
},
|
||||
"width": 320,
|
||||
"height": 389,
|
||||
"height": 364,
|
||||
"position": {
|
||||
"x": 993.4442117555518,
|
||||
"y": 605.6757415334787
|
||||
"x": 1000,
|
||||
"y": 350
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"version": "1.0.0",
|
||||
"id": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
|
||||
"type": "l2i",
|
||||
"inputs": {
|
||||
"tiled": {
|
||||
"id": "24f5bc7b-f6a1-425d-8ab1-f50b4db5d0df",
|
||||
"name": "tiled",
|
||||
"type": "boolean",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": false
|
||||
},
|
||||
"fp32": {
|
||||
"id": "b146d873-ffb9-4767-986a-5360504841a2",
|
||||
"name": "fp32",
|
||||
"type": "boolean",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": false
|
||||
},
|
||||
"latents": {
|
||||
"id": "65441abd-7713-4b00-9d8d-3771404002e8",
|
||||
"name": "latents",
|
||||
"type": "LatentsField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"vae": {
|
||||
"id": "a478b833-6e13-4611-9a10-842c89603c74",
|
||||
"name": "vae",
|
||||
"type": "VaeField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"image": {
|
||||
"id": "c87ae925-f858-417a-8940-8708ba9b4b53",
|
||||
"name": "image",
|
||||
"type": "ImageField",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"width": {
|
||||
"id": "4bcb8512-b5a1-45f1-9e52-6e92849f9d6c",
|
||||
"name": "width",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"height": {
|
||||
"id": "23e41c00-a354-48e8-8f59-5875679c27ab",
|
||||
"name": "height",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": true,
|
||||
"isIntermediate": false
|
||||
},
|
||||
"width": 320,
|
||||
"height": 266,
|
||||
"position": {
|
||||
"x": 1800,
|
||||
"y": 200
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"version": "1.0.0",
|
||||
"id": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
|
||||
"type": "main_model_loader",
|
||||
"inputs": {
|
||||
@@ -186,24 +261,23 @@
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"isOpen": false,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
"isIntermediate": true
|
||||
},
|
||||
"width": 320,
|
||||
"height": 226,
|
||||
"height": 32,
|
||||
"position": {
|
||||
"x": 163.04436745878343,
|
||||
"y": 254.63156870373479
|
||||
"x": 1000,
|
||||
"y": 200
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"version": "1.0.0",
|
||||
"id": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
|
||||
"type": "compel",
|
||||
"inputs": {
|
||||
@@ -235,21 +309,20 @@
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
"isIntermediate": true
|
||||
},
|
||||
"width": 320,
|
||||
"height": 261,
|
||||
"height": 235,
|
||||
"position": {
|
||||
"x": 595.7263915923627,
|
||||
"y": 239.67783573351227
|
||||
"x": 1000,
|
||||
"y": -75
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "ea94bc37-d995-4a83-aa99-4af42479f2f2",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"version": "1.0.0",
|
||||
"id": "ea94bc37-d995-4a83-aa99-4af42479f2f2",
|
||||
"type": "rand_int",
|
||||
"inputs": {
|
||||
@@ -279,66 +352,51 @@
|
||||
}
|
||||
},
|
||||
"label": "Random Seed",
|
||||
"isOpen": true,
|
||||
"isOpen": false,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": false,
|
||||
"version": "1.0.0"
|
||||
"isIntermediate": true
|
||||
},
|
||||
"width": 320,
|
||||
"height": 218,
|
||||
"height": 32,
|
||||
"position": {
|
||||
"x": 541.094822888628,
|
||||
"y": 694.5704476446829
|
||||
"x": 1000,
|
||||
"y": 275
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
|
||||
"id": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
|
||||
"version": "1.0.0",
|
||||
"id": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
|
||||
"type": "denoise_latents",
|
||||
"inputs": {
|
||||
"positive_conditioning": {
|
||||
"id": "90b7f4f8-ada7-4028-8100-d2e54f192052",
|
||||
"name": "positive_conditioning",
|
||||
"type": "ConditioningField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"negative_conditioning": {
|
||||
"id": "9393779e-796c-4f64-b740-902a1177bf53",
|
||||
"name": "negative_conditioning",
|
||||
"type": "ConditioningField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"noise": {
|
||||
"id": "8e17f1e5-4f98-40b1-b7f4-86aeeb4554c1",
|
||||
"id": "8b18f3eb-40d2-45c1-9a9d-28d6af0dce2b",
|
||||
"name": "noise",
|
||||
"type": "LatentsField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"steps": {
|
||||
"id": "9b63302d-6bd2-42c9-ac13-9b1afb51af88",
|
||||
"id": "0be4373c-46f3-441c-80a7-a4bb6ceb498c",
|
||||
"name": "steps",
|
||||
"type": "integer",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 10
|
||||
"value": 36
|
||||
},
|
||||
"cfg_scale": {
|
||||
"id": "87dd04d3-870e-49e1-98bf-af003a810109",
|
||||
"id": "107267ce-4666-4cd7-94b3-7476b7973ae9",
|
||||
"name": "cfg_scale",
|
||||
"type": "FloatPolymorphic",
|
||||
"type": "float",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": 7.5
|
||||
},
|
||||
"denoising_start": {
|
||||
"id": "f369d80f-4931-4740-9bcd-9f0620719fab",
|
||||
"id": "d2ce9f0f-5fc2-48b2-b917-53442941e9a1",
|
||||
"name": "denoising_start",
|
||||
"type": "float",
|
||||
"fieldKind": "input",
|
||||
@@ -346,7 +404,7 @@
|
||||
"value": 0
|
||||
},
|
||||
"denoising_end": {
|
||||
"id": "747d10e5-6f02-445c-994c-0604d814de8c",
|
||||
"id": "8ad51505-b8d0-422a-beb8-96fc6fc6b65f",
|
||||
"name": "denoising_end",
|
||||
"type": "float",
|
||||
"fieldKind": "input",
|
||||
@@ -354,71 +412,71 @@
|
||||
"value": 1
|
||||
},
|
||||
"scheduler": {
|
||||
"id": "1de84a4e-3a24-4ec8-862b-16ce49633b9b",
|
||||
"id": "53092874-a43b-4623-91a2-76e62fdb1f2e",
|
||||
"name": "scheduler",
|
||||
"type": "Scheduler",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": "euler"
|
||||
},
|
||||
"unet": {
|
||||
"id": "ffa6fef4-3ce2-4bdb-9296-9a834849489b",
|
||||
"name": "unet",
|
||||
"type": "UNetField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"control": {
|
||||
"id": "077b64cb-34be-4fcc-83f2-e399807a02bd",
|
||||
"id": "7abe57cc-469d-437e-ad72-a18efa28215f",
|
||||
"name": "control",
|
||||
"type": "ControlPolymorphic",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"ip_adapter": {
|
||||
"id": "1d6948f7-3a65-4a65-a20c-768b287251aa",
|
||||
"name": "ip_adapter",
|
||||
"type": "IPAdapterPolymorphic",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"t2i_adapter": {
|
||||
"id": "75e67b09-952f-4083-aaf4-6b804d690412",
|
||||
"name": "t2i_adapter",
|
||||
"type": "T2IAdapterPolymorphic",
|
||||
"type": "ControlField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"latents": {
|
||||
"id": "334d4ba3-5a99-4195-82c5-86fb3f4f7d43",
|
||||
"id": "add8bbe5-14d0-42d4-a867-9c65ab8dd129",
|
||||
"name": "latents",
|
||||
"type": "LatentsField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"denoise_mask": {
|
||||
"id": "0d3dbdbf-b014-4e95-8b18-ff2ff9cb0bfa",
|
||||
"id": "f373a190-0fc8-45b7-ae62-c4aa8e9687e1",
|
||||
"name": "denoise_mask",
|
||||
"type": "DenoiseMaskField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"positive_conditioning": {
|
||||
"id": "c7160303-8a23-4f15-9197-855d48802a7f",
|
||||
"name": "positive_conditioning",
|
||||
"type": "ConditioningField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"negative_conditioning": {
|
||||
"id": "fd750efa-1dfc-4d0b-accb-828e905ba320",
|
||||
"name": "negative_conditioning",
|
||||
"type": "ConditioningField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"unet": {
|
||||
"id": "af1f41ba-ce2a-4314-8d7f-494bb5800381",
|
||||
"name": "unet",
|
||||
"type": "UNetField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"latents": {
|
||||
"id": "70fa5bbc-0c38-41bb-861a-74d6d78d2f38",
|
||||
"id": "8508d04d-f999-4a44-94d0-388ab1401d27",
|
||||
"name": "latents",
|
||||
"type": "LatentsField",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"width": {
|
||||
"id": "98ee0e6c-82aa-4e8f-8be5-dc5f00ee47f0",
|
||||
"id": "93dc8287-0a2a-4320-83a4-5e994b7ba23e",
|
||||
"name": "width",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"height": {
|
||||
"id": "e8cb184a-5e1a-47c8-9695-4b8979564f5d",
|
||||
"id": "d9862f5c-0ab5-46fa-8c29-5059bb581d96",
|
||||
"name": "height",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
@@ -428,95 +486,13 @@
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.4.0"
|
||||
"isIntermediate": true
|
||||
},
|
||||
"width": 320,
|
||||
"height": 646,
|
||||
"height": 558,
|
||||
"position": {
|
||||
"x": 1476.5794704734735,
|
||||
"y": 256.80174342731783
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
|
||||
"type": "l2i",
|
||||
"inputs": {
|
||||
"metadata": {
|
||||
"id": "ab375f12-0042-4410-9182-29e30db82c85",
|
||||
"name": "metadata",
|
||||
"type": "MetadataField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"latents": {
|
||||
"id": "3a7e7efd-bff5-47d7-9d48-615127afee78",
|
||||
"name": "latents",
|
||||
"type": "LatentsField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"vae": {
|
||||
"id": "a1f5f7a1-0795-4d58-b036-7820c0b0ef2b",
|
||||
"name": "vae",
|
||||
"type": "VaeField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"tiled": {
|
||||
"id": "da52059a-0cee-4668-942f-519aa794d739",
|
||||
"name": "tiled",
|
||||
"type": "boolean",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": false
|
||||
},
|
||||
"fp32": {
|
||||
"id": "c4841df3-b24e-4140-be3b-ccd454c2522c",
|
||||
"name": "fp32",
|
||||
"type": "boolean",
|
||||
"fieldKind": "input",
|
||||
"label": "",
|
||||
"value": false
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"image": {
|
||||
"id": "72d667d0-cf85-459d-abf2-28bd8b823fe7",
|
||||
"name": "image",
|
||||
"type": "ImageField",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"width": {
|
||||
"id": "c8c907d8-1066-49d1-b9a6-83bdcd53addc",
|
||||
"name": "width",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"height": {
|
||||
"id": "230f359c-b4ea-436c-b372-332d7dcdca85",
|
||||
"name": "height",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": false,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 267,
|
||||
"position": {
|
||||
"x": 2037.9648469717395,
|
||||
"y": 426.10844427600136
|
||||
"x": 1400,
|
||||
"y": 200
|
||||
}
|
||||
}
|
||||
],
|
||||
@@ -546,52 +522,52 @@
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "55705012-79b9-4aac-9f26-c0b10309785b",
|
||||
"sourceHandle": "noise",
|
||||
"target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
|
||||
"targetHandle": "noise",
|
||||
"id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-eea2702a-19fb-45b5-9d75-56b4211ec03cnoise",
|
||||
"source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
|
||||
"sourceHandle": "vae",
|
||||
"target": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
|
||||
"targetHandle": "vae",
|
||||
"id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8vae-dbcd2f98-d809-48c8-bf64-2635f88a2fe9vae",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
|
||||
"sourceHandle": "latents",
|
||||
"target": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
|
||||
"targetHandle": "latents",
|
||||
"id": "reactflow__edge-75899702-fa44-46d2-b2d5-3e17f234c3e7latents-dbcd2f98-d809-48c8-bf64-2635f88a2fe9latents",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
|
||||
"sourceHandle": "conditioning",
|
||||
"target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
|
||||
"target": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
|
||||
"targetHandle": "positive_conditioning",
|
||||
"id": "reactflow__edge-7d8bf987-284f-413a-b2fd-d825445a5d6cconditioning-eea2702a-19fb-45b5-9d75-56b4211ec03cpositive_conditioning",
|
||||
"id": "reactflow__edge-7d8bf987-284f-413a-b2fd-d825445a5d6cconditioning-75899702-fa44-46d2-b2d5-3e17f234c3e7positive_conditioning",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
|
||||
"sourceHandle": "conditioning",
|
||||
"target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
|
||||
"target": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
|
||||
"targetHandle": "negative_conditioning",
|
||||
"id": "reactflow__edge-93dc02a4-d05b-48ed-b99c-c9b616af3402conditioning-eea2702a-19fb-45b5-9d75-56b4211ec03cnegative_conditioning",
|
||||
"id": "reactflow__edge-93dc02a4-d05b-48ed-b99c-c9b616af3402conditioning-75899702-fa44-46d2-b2d5-3e17f234c3e7negative_conditioning",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
|
||||
"sourceHandle": "unet",
|
||||
"target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
|
||||
"target": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
|
||||
"targetHandle": "unet",
|
||||
"id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8unet-eea2702a-19fb-45b5-9d75-56b4211ec03cunet",
|
||||
"id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8unet-75899702-fa44-46d2-b2d5-3e17f234c3e7unet",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
|
||||
"sourceHandle": "latents",
|
||||
"target": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
|
||||
"targetHandle": "latents",
|
||||
"id": "reactflow__edge-eea2702a-19fb-45b5-9d75-56b4211ec03clatents-58c957f5-0d01-41fc-a803-b2bbf0413d4flatents",
|
||||
"type": "default"
|
||||
},
|
||||
{
|
||||
"source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
|
||||
"sourceHandle": "vae",
|
||||
"target": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
|
||||
"targetHandle": "vae",
|
||||
"id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8vae-58c957f5-0d01-41fc-a803-b2bbf0413d4fvae",
|
||||
"source": "55705012-79b9-4aac-9f26-c0b10309785b",
|
||||
"sourceHandle": "noise",
|
||||
"target": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
|
||||
"targetHandle": "noise",
|
||||
"id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-75899702-fa44-46d2-b2d5-3e17f234c3e7noise",
|
||||
"type": "default"
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
@@ -34,4 +34,4 @@ class SocketIO:
|
||||
|
||||
async def _handle_unsub_queue(self, sid, data, *args, **kwargs):
|
||||
if "queue_id" in data:
|
||||
await self.__sio.leave_room(sid, data["queue_id"])
|
||||
await self.__sio.enter_room(sid, data["queue_id"])
|
||||
|
||||
@@ -16,7 +16,6 @@ from pydantic.fields import FieldInfo, _Unset
|
||||
from pydantic_core import PydanticUndefined
|
||||
|
||||
from invokeai.app.services.config.config_default import InvokeAIAppConfig
|
||||
from invokeai.app.shared.fields import FieldDescriptions
|
||||
from invokeai.app.util.misc import uuid_string
|
||||
|
||||
if TYPE_CHECKING:
|
||||
@@ -31,6 +30,70 @@ class InvalidFieldError(TypeError):
|
||||
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"
|
||||
cfg_scale = "Classifier-Free Guidance scale"
|
||||
scheduler = "Scheduler to use during inference"
|
||||
positive_cond = "Positive conditioning tensor"
|
||||
negative_cond = "Negative conditioning tensor"
|
||||
noise = "Noise tensor"
|
||||
clip = "CLIP (tokenizer, text encoder, LoRAs) and skipped layer count"
|
||||
unet = "UNet (scheduler, LoRAs)"
|
||||
vae = "VAE"
|
||||
cond = "Conditioning tensor"
|
||||
controlnet_model = "ControlNet model to load"
|
||||
vae_model = "VAE model to load"
|
||||
lora_model = "LoRA model to load"
|
||||
main_model = "Main model (UNet, VAE, CLIP) to load"
|
||||
sdxl_main_model = "SDXL Main model (UNet, VAE, CLIP1, CLIP2) to load"
|
||||
sdxl_refiner_model = "SDXL Refiner Main Modde (UNet, VAE, CLIP2) to load"
|
||||
onnx_main_model = "ONNX Main model (UNet, VAE, CLIP) to load"
|
||||
lora_weight = "The weight at which the LoRA is applied to each model"
|
||||
compel_prompt = "Prompt to be parsed by Compel to create a conditioning tensor"
|
||||
raw_prompt = "Raw prompt text (no parsing)"
|
||||
sdxl_aesthetic = "The aesthetic score to apply to the conditioning tensor"
|
||||
skipped_layers = "Number of layers to skip in text encoder"
|
||||
seed = "Seed for random number generation"
|
||||
steps = "Number of steps to run"
|
||||
width = "Width of output (px)"
|
||||
height = "Height of output (px)"
|
||||
control = "ControlNet(s) to apply"
|
||||
ip_adapter = "IP-Adapter to apply"
|
||||
t2i_adapter = "T2I-Adapter(s) to apply"
|
||||
denoised_latents = "Denoised latents tensor"
|
||||
latents = "Latents tensor"
|
||||
strength = "Strength of denoising (proportional to steps)"
|
||||
metadata = "Optional metadata to be saved with the image"
|
||||
metadata_collection = "Collection of Metadata"
|
||||
metadata_item_polymorphic = "A single metadata item or collection of metadata items"
|
||||
metadata_item_label = "Label for this metadata item"
|
||||
metadata_item_value = "The value for this metadata item (may be any type)"
|
||||
workflow = "Optional workflow to be saved with the image"
|
||||
interp_mode = "Interpolation mode"
|
||||
torch_antialias = "Whether or not to apply antialiasing (bilinear or bicubic only)"
|
||||
fp32 = "Whether or not to use full float32 precision"
|
||||
precision = "Precision to use"
|
||||
tiled = "Processing using overlapping tiles (reduce memory consumption)"
|
||||
detect_res = "Pixel resolution for detection"
|
||||
image_res = "Pixel resolution for output image"
|
||||
safe_mode = "Whether or not to use safe mode"
|
||||
scribble_mode = "Whether or not to use scribble mode"
|
||||
scale_factor = "The factor by which to scale"
|
||||
blend_alpha = (
|
||||
"Blending factor. 0.0 = use input A only, 1.0 = use input B only, 0.5 = 50% mix of input A and input B."
|
||||
)
|
||||
num_1 = "The first number"
|
||||
num_2 = "The second number"
|
||||
mask = "The mask to use for the operation"
|
||||
board = "The board to save the image to"
|
||||
image = "The image to process"
|
||||
tile_size = "Tile size"
|
||||
inclusive_low = "The inclusive low value"
|
||||
exclusive_high = "The exclusive high value"
|
||||
decimal_places = "The number of decimal places to round to"
|
||||
|
||||
|
||||
class Input(str, Enum):
|
||||
"""
|
||||
The type of input a field accepts.
|
||||
|
||||
@@ -7,7 +7,6 @@ from compel import Compel, ReturnedEmbeddingsType
|
||||
from compel.prompt_parser import Blend, Conjunction, CrossAttentionControlSubstitute, FlattenedPrompt, Fragment
|
||||
|
||||
from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput
|
||||
from invokeai.app.shared.fields import FieldDescriptions
|
||||
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
|
||||
BasicConditioningInfo,
|
||||
ExtraConditioningInfo,
|
||||
@@ -20,6 +19,7 @@ from ...backend.util.devices import torch_dtype
|
||||
from .baseinvocation import (
|
||||
BaseInvocation,
|
||||
BaseInvocationOutput,
|
||||
FieldDescriptions,
|
||||
Input,
|
||||
InputField,
|
||||
InvocationContext,
|
||||
@@ -108,14 +108,13 @@ class CompelInvocation(BaseInvocation):
|
||||
print(f'Warn: trigger: "{trigger}" not found')
|
||||
|
||||
with (
|
||||
ModelPatcher.apply_lora_text_encoder(text_encoder_info.context.model, _lora_loader()),
|
||||
ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
|
||||
tokenizer,
|
||||
ti_manager,
|
||||
),
|
||||
ModelPatcher.apply_clip_skip(text_encoder_info.context.model, self.clip.skipped_layers),
|
||||
text_encoder_info as text_encoder,
|
||||
# Apply the LoRA after text_encoder has been moved to its target device for faster patching.
|
||||
ModelPatcher.apply_lora_text_encoder(text_encoder, _lora_loader()),
|
||||
):
|
||||
compel = Compel(
|
||||
tokenizer=tokenizer,
|
||||
@@ -230,14 +229,13 @@ class SDXLPromptInvocationBase:
|
||||
print(f'Warn: trigger: "{trigger}" not found')
|
||||
|
||||
with (
|
||||
ModelPatcher.apply_lora(text_encoder_info.context.model, _lora_loader(), lora_prefix),
|
||||
ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
|
||||
tokenizer,
|
||||
ti_manager,
|
||||
),
|
||||
ModelPatcher.apply_clip_skip(text_encoder_info.context.model, clip_field.skipped_layers),
|
||||
text_encoder_info as text_encoder,
|
||||
# Apply the LoRA after text_encoder has been moved to its target device for faster patching.
|
||||
ModelPatcher.apply_lora(text_encoder, _lora_loader(), lora_prefix),
|
||||
):
|
||||
compel = Compel(
|
||||
tokenizer=tokenizer,
|
||||
|
||||
@@ -28,12 +28,12 @@ from pydantic import BaseModel, ConfigDict, Field, field_validator
|
||||
|
||||
from invokeai.app.invocations.primitives import ImageField, ImageOutput
|
||||
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
|
||||
from invokeai.app.shared.fields import FieldDescriptions
|
||||
|
||||
from ...backend.model_management import BaseModelType
|
||||
from .baseinvocation import (
|
||||
BaseInvocation,
|
||||
BaseInvocationOutput,
|
||||
FieldDescriptions,
|
||||
Input,
|
||||
InputField,
|
||||
InvocationContext,
|
||||
|
||||
@@ -9,11 +9,19 @@ from PIL import Image, ImageChops, ImageFilter, ImageOps
|
||||
|
||||
from invokeai.app.invocations.primitives import BoardField, ColorField, ImageField, ImageOutput
|
||||
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
|
||||
from invokeai.app.shared.fields import FieldDescriptions
|
||||
from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
|
||||
from invokeai.backend.image_util.safety_checker import SafetyChecker
|
||||
|
||||
from .baseinvocation import BaseInvocation, Input, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
|
||||
from .baseinvocation import (
|
||||
BaseInvocation,
|
||||
FieldDescriptions,
|
||||
Input,
|
||||
InputField,
|
||||
InvocationContext,
|
||||
WithMetadata,
|
||||
WithWorkflow,
|
||||
invocation,
|
||||
)
|
||||
|
||||
|
||||
@invocation("show_image", title="Show Image", tags=["image"], category="image", version="1.0.0")
|
||||
|
||||
@@ -7,6 +7,7 @@ from pydantic import BaseModel, ConfigDict, Field
|
||||
from invokeai.app.invocations.baseinvocation import (
|
||||
BaseInvocation,
|
||||
BaseInvocationOutput,
|
||||
FieldDescriptions,
|
||||
Input,
|
||||
InputField,
|
||||
InvocationContext,
|
||||
@@ -16,7 +17,6 @@ from invokeai.app.invocations.baseinvocation import (
|
||||
invocation_output,
|
||||
)
|
||||
from invokeai.app.invocations.primitives import ImageField
|
||||
from invokeai.app.shared.fields import FieldDescriptions
|
||||
from invokeai.backend.model_management.models.base import BaseModelType, ModelType
|
||||
from invokeai.backend.model_management.models.ip_adapter import get_ip_adapter_image_encoder_model_id
|
||||
|
||||
@@ -67,7 +67,7 @@ class IPAdapterInvocation(BaseInvocation):
|
||||
|
||||
# weight: float = InputField(default=1.0, description="The weight of the IP-Adapter.", ui_type=UIType.Float)
|
||||
weight: Union[float, List[float]] = InputField(
|
||||
default=1, ge=-1, description="The weight given to the IP-Adapter", ui_type=UIType.Float, title="Weight"
|
||||
default=1, ge=0, description="The weight given to the IP-Adapter", ui_type=UIType.Float, title="Weight"
|
||||
)
|
||||
|
||||
begin_step_percent: float = InputField(
|
||||
|
||||
@@ -10,7 +10,7 @@ import torch
|
||||
import torchvision.transforms as T
|
||||
from diffusers import AutoencoderKL, AutoencoderTiny
|
||||
from diffusers.image_processor import VaeImageProcessor
|
||||
from diffusers.models.adapter import T2IAdapter
|
||||
from diffusers.models.adapter import FullAdapterXL, T2IAdapter
|
||||
from diffusers.models.attention_processor import (
|
||||
AttnProcessor2_0,
|
||||
LoRAAttnProcessor2_0,
|
||||
@@ -34,7 +34,6 @@ from invokeai.app.invocations.primitives import (
|
||||
)
|
||||
from invokeai.app.invocations.t2i_adapter import T2IAdapterField
|
||||
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
|
||||
from invokeai.app.shared.fields import FieldDescriptions
|
||||
from invokeai.app.util.controlnet_utils import prepare_control_image
|
||||
from invokeai.app.util.step_callback import stable_diffusion_step_callback
|
||||
from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus
|
||||
@@ -58,6 +57,7 @@ from ...backend.util.devices import choose_precision, choose_torch_device
|
||||
from .baseinvocation import (
|
||||
BaseInvocation,
|
||||
BaseInvocationOutput,
|
||||
FieldDescriptions,
|
||||
Input,
|
||||
InputField,
|
||||
InvocationContext,
|
||||
@@ -562,6 +562,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
|
||||
t2i_adapter_model: T2IAdapter
|
||||
with t2i_adapter_model_info as t2i_adapter_model:
|
||||
total_downscale_factor = t2i_adapter_model.total_downscale_factor
|
||||
if isinstance(t2i_adapter_model.adapter, FullAdapterXL):
|
||||
# HACK(ryand): Work around a bug in FullAdapterXL. This is being addressed upstream in diffusers by
|
||||
# this PR: https://github.com/huggingface/diffusers/pull/5134.
|
||||
total_downscale_factor = total_downscale_factor // 2
|
||||
|
||||
# Resize the T2I-Adapter input image.
|
||||
# We select the resize dimensions so that after the T2I-Adapter's total_downscale_factor is applied, the
|
||||
@@ -707,11 +711,8 @@ class DenoiseLatentsInvocation(BaseInvocation):
|
||||
with (
|
||||
ExitStack() as exit_stack,
|
||||
ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),
|
||||
ModelPatcher.apply_freeu(unet_info.context.model, self.unet.freeu_config),
|
||||
set_seamless(unet_info.context.model, self.unet.seamless_axes),
|
||||
unet_info as unet,
|
||||
# Apply the LoRA after unet has been moved to its target device for faster patching.
|
||||
ModelPatcher.apply_lora_unet(unet, _lora_loader()),
|
||||
):
|
||||
latents = latents.to(device=unet.device, dtype=unet.dtype)
|
||||
if noise is not None:
|
||||
|
||||
@@ -6,9 +6,8 @@ import numpy as np
|
||||
from pydantic import ValidationInfo, field_validator
|
||||
|
||||
from invokeai.app.invocations.primitives import FloatOutput, IntegerOutput
|
||||
from invokeai.app.shared.fields import FieldDescriptions
|
||||
|
||||
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
|
||||
from .baseinvocation import BaseInvocation, FieldDescriptions, InputField, InvocationContext, invocation
|
||||
|
||||
|
||||
@invocation("add", title="Add Integers", tags=["math", "add"], category="math", version="1.0.0")
|
||||
@@ -183,8 +182,8 @@ class IntegerMathInvocation(BaseInvocation):
|
||||
operation: INTEGER_OPERATIONS = InputField(
|
||||
default="ADD", description="The operation to perform", ui_choice_labels=INTEGER_OPERATIONS_LABELS
|
||||
)
|
||||
a: int = InputField(default=1, description=FieldDescriptions.num_1)
|
||||
b: int = InputField(default=1, description=FieldDescriptions.num_2)
|
||||
a: int = InputField(default=0, description=FieldDescriptions.num_1)
|
||||
b: int = InputField(default=0, description=FieldDescriptions.num_2)
|
||||
|
||||
@field_validator("b")
|
||||
def no_unrepresentable_results(cls, v: int, info: ValidationInfo):
|
||||
@@ -257,8 +256,8 @@ class FloatMathInvocation(BaseInvocation):
|
||||
operation: FLOAT_OPERATIONS = InputField(
|
||||
default="ADD", description="The operation to perform", ui_choice_labels=FLOAT_OPERATIONS_LABELS
|
||||
)
|
||||
a: float = InputField(default=1, description=FieldDescriptions.num_1)
|
||||
b: float = InputField(default=1, description=FieldDescriptions.num_2)
|
||||
a: float = InputField(default=0, description=FieldDescriptions.num_1)
|
||||
b: float = InputField(default=0, description=FieldDescriptions.num_2)
|
||||
|
||||
@field_validator("b")
|
||||
def no_unrepresentable_results(cls, v: float, info: ValidationInfo):
|
||||
|
||||
@@ -5,6 +5,7 @@ from pydantic import BaseModel, ConfigDict, Field
|
||||
from invokeai.app.invocations.baseinvocation import (
|
||||
BaseInvocation,
|
||||
BaseInvocationOutput,
|
||||
FieldDescriptions,
|
||||
InputField,
|
||||
InvocationContext,
|
||||
MetadataField,
|
||||
@@ -18,7 +19,6 @@ from invokeai.app.invocations.ip_adapter import IPAdapterModelField
|
||||
from invokeai.app.invocations.model import LoRAModelField, MainModelField, VAEModelField
|
||||
from invokeai.app.invocations.primitives import ImageField
|
||||
from invokeai.app.invocations.t2i_adapter import T2IAdapterField
|
||||
from invokeai.app.shared.fields import FieldDescriptions
|
||||
|
||||
from ...version import __version__
|
||||
|
||||
@@ -160,14 +160,13 @@ class CoreMetadataInvocation(BaseInvocation):
|
||||
)
|
||||
|
||||
# High resolution fix metadata.
|
||||
hrf_enabled: Optional[float] = InputField(
|
||||
hrf_width: Optional[int] = InputField(
|
||||
default=None,
|
||||
description="Whether or not high resolution fix was enabled.",
|
||||
description="The high resolution fix height and width multipler.",
|
||||
)
|
||||
# TODO: should this be stricter or do we just let the UI handle it?
|
||||
hrf_method: Optional[str] = InputField(
|
||||
hrf_height: Optional[int] = InputField(
|
||||
default=None,
|
||||
description="The high resolution fix upscale method.",
|
||||
description="The high resolution fix height and width multipler.",
|
||||
)
|
||||
hrf_strength: Optional[float] = InputField(
|
||||
default=None,
|
||||
|
||||
@@ -3,13 +3,11 @@ from typing import List, Optional
|
||||
|
||||
from pydantic import BaseModel, ConfigDict, Field
|
||||
|
||||
from invokeai.app.shared.fields import FieldDescriptions
|
||||
from invokeai.app.shared.models import FreeUConfig
|
||||
|
||||
from ...backend.model_management import BaseModelType, ModelType, SubModelType
|
||||
from .baseinvocation import (
|
||||
BaseInvocation,
|
||||
BaseInvocationOutput,
|
||||
FieldDescriptions,
|
||||
Input,
|
||||
InputField,
|
||||
InvocationContext,
|
||||
@@ -38,7 +36,6 @@ class UNetField(BaseModel):
|
||||
scheduler: ModelInfo = Field(description="Info to load scheduler submodel")
|
||||
loras: List[LoraInfo] = Field(description="Loras to apply on model loading")
|
||||
seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
|
||||
freeu_config: Optional[FreeUConfig] = Field(default=None, description="FreeU configuration")
|
||||
|
||||
|
||||
class ClipField(BaseModel):
|
||||
@@ -54,32 +51,13 @@ class VaeField(BaseModel):
|
||||
seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
|
||||
|
||||
|
||||
@invocation_output("unet_output")
|
||||
class UNetOutput(BaseInvocationOutput):
|
||||
"""Base class for invocations that output a UNet field"""
|
||||
|
||||
unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet")
|
||||
|
||||
|
||||
@invocation_output("vae_output")
|
||||
class VAEOutput(BaseInvocationOutput):
|
||||
"""Base class for invocations that output a VAE field"""
|
||||
|
||||
vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
|
||||
|
||||
|
||||
@invocation_output("clip_output")
|
||||
class CLIPOutput(BaseInvocationOutput):
|
||||
"""Base class for invocations that output a CLIP field"""
|
||||
|
||||
clip: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP")
|
||||
|
||||
|
||||
@invocation_output("model_loader_output")
|
||||
class ModelLoaderOutput(UNetOutput, CLIPOutput, VAEOutput):
|
||||
class ModelLoaderOutput(BaseInvocationOutput):
|
||||
"""Model loader output"""
|
||||
|
||||
pass
|
||||
unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet")
|
||||
clip: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP")
|
||||
vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
|
||||
|
||||
|
||||
class MainModelField(BaseModel):
|
||||
@@ -388,6 +366,13 @@ class VAEModelField(BaseModel):
|
||||
model_config = ConfigDict(protected_namespaces=())
|
||||
|
||||
|
||||
@invocation_output("vae_loader_output")
|
||||
class VaeLoaderOutput(BaseInvocationOutput):
|
||||
"""VAE output"""
|
||||
|
||||
vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
|
||||
|
||||
|
||||
@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.0")
|
||||
class VaeLoaderInvocation(BaseInvocation):
|
||||
"""Loads a VAE model, outputting a VaeLoaderOutput"""
|
||||
@@ -399,7 +384,7 @@ class VaeLoaderInvocation(BaseInvocation):
|
||||
title="VAE",
|
||||
)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> VAEOutput:
|
||||
def invoke(self, context: InvocationContext) -> VaeLoaderOutput:
|
||||
base_model = self.vae_model.base_model
|
||||
model_name = self.vae_model.model_name
|
||||
model_type = ModelType.Vae
|
||||
@@ -410,7 +395,7 @@ class VaeLoaderInvocation(BaseInvocation):
|
||||
model_type=model_type,
|
||||
):
|
||||
raise Exception(f"Unkown vae name: {model_name}!")
|
||||
return VAEOutput(
|
||||
return VaeLoaderOutput(
|
||||
vae=VaeField(
|
||||
vae=ModelInfo(
|
||||
model_name=model_name,
|
||||
@@ -472,24 +457,3 @@ class SeamlessModeInvocation(BaseInvocation):
|
||||
vae.seamless_axes = seamless_axes_list
|
||||
|
||||
return SeamlessModeOutput(unet=unet, vae=vae)
|
||||
|
||||
|
||||
@invocation("freeu", title="FreeU", tags=["freeu"], category="unet", version="1.0.0")
|
||||
class FreeUInvocation(BaseInvocation):
|
||||
"""
|
||||
Applies FreeU to the UNet. Suggested values (b1/b2/s1/s2):
|
||||
|
||||
SD1.5: 1.2/1.4/0.9/0.2,
|
||||
SD2: 1.1/1.2/0.9/0.2,
|
||||
SDXL: 1.1/1.2/0.6/0.4,
|
||||
"""
|
||||
|
||||
unet: UNetField = InputField(description=FieldDescriptions.unet, input=Input.Connection, title="UNet")
|
||||
b1: float = InputField(default=1.2, ge=-1, le=3, description=FieldDescriptions.freeu_b1)
|
||||
b2: float = InputField(default=1.4, ge=-1, le=3, description=FieldDescriptions.freeu_b2)
|
||||
s1: float = InputField(default=0.9, ge=-1, le=3, description=FieldDescriptions.freeu_s1)
|
||||
s2: float = InputField(default=0.2, ge=-1, le=3, description=FieldDescriptions.freeu_s2)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> UNetOutput:
|
||||
self.unet.freeu_config = FreeUConfig(s1=self.s1, s2=self.s2, b1=self.b1, b2=self.b2)
|
||||
return UNetOutput(unet=self.unet)
|
||||
|
||||
@@ -5,13 +5,13 @@ import torch
|
||||
from pydantic import field_validator
|
||||
|
||||
from invokeai.app.invocations.latent import LatentsField
|
||||
from invokeai.app.shared.fields import FieldDescriptions
|
||||
from invokeai.app.util.misc import SEED_MAX, get_random_seed
|
||||
|
||||
from ...backend.util.devices import choose_torch_device, torch_dtype
|
||||
from .baseinvocation import (
|
||||
BaseInvocation,
|
||||
BaseInvocationOutput,
|
||||
FieldDescriptions,
|
||||
InputField,
|
||||
InvocationContext,
|
||||
OutputField,
|
||||
|
||||
@@ -14,7 +14,6 @@ from tqdm import tqdm
|
||||
|
||||
from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput, ImageField, ImageOutput
|
||||
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
|
||||
from invokeai.app.shared.fields import FieldDescriptions
|
||||
from invokeai.app.util.step_callback import stable_diffusion_step_callback
|
||||
from invokeai.backend import BaseModelType, ModelType, SubModelType
|
||||
|
||||
@@ -24,6 +23,7 @@ from ...backend.util import choose_torch_device
|
||||
from .baseinvocation import (
|
||||
BaseInvocation,
|
||||
BaseInvocationOutput,
|
||||
FieldDescriptions,
|
||||
Input,
|
||||
InputField,
|
||||
InvocationContext,
|
||||
|
||||
@@ -5,11 +5,10 @@ from typing import Optional, Tuple
|
||||
import torch
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from invokeai.app.shared.fields import FieldDescriptions
|
||||
|
||||
from .baseinvocation import (
|
||||
BaseInvocation,
|
||||
BaseInvocationOutput,
|
||||
FieldDescriptions,
|
||||
Input,
|
||||
InputField,
|
||||
InvocationContext,
|
||||
@@ -294,7 +293,7 @@ class DenoiseMaskField(BaseModel):
|
||||
"""An inpaint mask field"""
|
||||
|
||||
mask_name: str = Field(description="The name of the mask image")
|
||||
masked_latents_name: Optional[str] = Field(default=None, description="The name of the masked image latents")
|
||||
masked_latents_name: Optional[str] = Field(description="The name of the masked image latents")
|
||||
|
||||
|
||||
@invocation_output("denoise_mask_output")
|
||||
|
||||
@@ -1,9 +1,8 @@
|
||||
from invokeai.app.shared.fields import FieldDescriptions
|
||||
|
||||
from ...backend.model_management import ModelType, SubModelType
|
||||
from .baseinvocation import (
|
||||
BaseInvocation,
|
||||
BaseInvocationOutput,
|
||||
FieldDescriptions,
|
||||
Input,
|
||||
InputField,
|
||||
InvocationContext,
|
||||
|
||||
@@ -5,6 +5,7 @@ from pydantic import BaseModel, ConfigDict, Field
|
||||
from invokeai.app.invocations.baseinvocation import (
|
||||
BaseInvocation,
|
||||
BaseInvocationOutput,
|
||||
FieldDescriptions,
|
||||
Input,
|
||||
InputField,
|
||||
InvocationContext,
|
||||
@@ -15,7 +16,6 @@ from invokeai.app.invocations.baseinvocation import (
|
||||
)
|
||||
from invokeai.app.invocations.controlnet_image_processors import CONTROLNET_RESIZE_VALUES
|
||||
from invokeai.app.invocations.primitives import ImageField
|
||||
from invokeai.app.shared.fields import FieldDescriptions
|
||||
from invokeai.backend.model_management.models.base import BaseModelType
|
||||
|
||||
|
||||
|
||||
@@ -45,7 +45,6 @@ InvokeAI:
|
||||
ram: 13.5
|
||||
vram: 0.25
|
||||
lazy_offload: true
|
||||
log_memory_usage: false
|
||||
Device:
|
||||
device: auto
|
||||
precision: auto
|
||||
@@ -262,7 +261,6 @@ class InvokeAIAppConfig(InvokeAISettings):
|
||||
ram : float = Field(default=7.5, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
|
||||
vram : float = Field(default=0.25, ge=0, description="Amount of VRAM reserved for model storage (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
|
||||
lazy_offload : bool = Field(default=True, description="Keep models in VRAM until their space is needed", json_schema_extra=Categories.ModelCache, )
|
||||
log_memory_usage : bool = Field(default=False, description="If True, a memory snapshot will be captured before and after every model cache operation, and the result will be logged (at debug level). There is a time cost to capturing the memory snapshots, so it is recommended to only enable this feature if you are actively inspecting the model cache's behaviour.", json_schema_extra=Categories.ModelCache)
|
||||
|
||||
# DEVICE
|
||||
device : Literal["auto", "cpu", "cuda", "cuda:1", "mps"] = Field(default="auto", description="Generation device", json_schema_extra=Categories.Device)
|
||||
|
||||
@@ -57,7 +57,7 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
|
||||
INSERT INTO workflows(workflow)
|
||||
VALUES (?);
|
||||
""",
|
||||
(workflow.model_dump_json(),),
|
||||
(workflow.json(),),
|
||||
)
|
||||
self._conn.commit()
|
||||
except Exception:
|
||||
|
||||
@@ -1,5 +0,0 @@
|
||||
"""
|
||||
This module contains various classes, functions and models which are shared across the app, particularly by invocations.
|
||||
|
||||
Lifting these classes, functions and models into this shared module helps to reduce circular imports.
|
||||
"""
|
||||
@@ -1,66 +0,0 @@
|
||||
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"
|
||||
cfg_scale = "Classifier-Free Guidance scale"
|
||||
scheduler = "Scheduler to use during inference"
|
||||
positive_cond = "Positive conditioning tensor"
|
||||
negative_cond = "Negative conditioning tensor"
|
||||
noise = "Noise tensor"
|
||||
clip = "CLIP (tokenizer, text encoder, LoRAs) and skipped layer count"
|
||||
unet = "UNet (scheduler, LoRAs)"
|
||||
vae = "VAE"
|
||||
cond = "Conditioning tensor"
|
||||
controlnet_model = "ControlNet model to load"
|
||||
vae_model = "VAE model to load"
|
||||
lora_model = "LoRA model to load"
|
||||
main_model = "Main model (UNet, VAE, CLIP) to load"
|
||||
sdxl_main_model = "SDXL Main model (UNet, VAE, CLIP1, CLIP2) to load"
|
||||
sdxl_refiner_model = "SDXL Refiner Main Modde (UNet, VAE, CLIP2) to load"
|
||||
onnx_main_model = "ONNX Main model (UNet, VAE, CLIP) to load"
|
||||
lora_weight = "The weight at which the LoRA is applied to each model"
|
||||
compel_prompt = "Prompt to be parsed by Compel to create a conditioning tensor"
|
||||
raw_prompt = "Raw prompt text (no parsing)"
|
||||
sdxl_aesthetic = "The aesthetic score to apply to the conditioning tensor"
|
||||
skipped_layers = "Number of layers to skip in text encoder"
|
||||
seed = "Seed for random number generation"
|
||||
steps = "Number of steps to run"
|
||||
width = "Width of output (px)"
|
||||
height = "Height of output (px)"
|
||||
control = "ControlNet(s) to apply"
|
||||
ip_adapter = "IP-Adapter to apply"
|
||||
t2i_adapter = "T2I-Adapter(s) to apply"
|
||||
denoised_latents = "Denoised latents tensor"
|
||||
latents = "Latents tensor"
|
||||
strength = "Strength of denoising (proportional to steps)"
|
||||
metadata = "Optional metadata to be saved with the image"
|
||||
metadata_collection = "Collection of Metadata"
|
||||
metadata_item_polymorphic = "A single metadata item or collection of metadata items"
|
||||
metadata_item_label = "Label for this metadata item"
|
||||
metadata_item_value = "The value for this metadata item (may be any type)"
|
||||
workflow = "Optional workflow to be saved with the image"
|
||||
interp_mode = "Interpolation mode"
|
||||
torch_antialias = "Whether or not to apply antialiasing (bilinear or bicubic only)"
|
||||
fp32 = "Whether or not to use full float32 precision"
|
||||
precision = "Precision to use"
|
||||
tiled = "Processing using overlapping tiles (reduce memory consumption)"
|
||||
detect_res = "Pixel resolution for detection"
|
||||
image_res = "Pixel resolution for output image"
|
||||
safe_mode = "Whether or not to use safe mode"
|
||||
scribble_mode = "Whether or not to use scribble mode"
|
||||
scale_factor = "The factor by which to scale"
|
||||
blend_alpha = (
|
||||
"Blending factor. 0.0 = use input A only, 1.0 = use input B only, 0.5 = 50% mix of input A and input B."
|
||||
)
|
||||
num_1 = "The first number"
|
||||
num_2 = "The second number"
|
||||
mask = "The mask to use for the operation"
|
||||
board = "The board to save the image to"
|
||||
image = "The image to process"
|
||||
tile_size = "Tile size"
|
||||
inclusive_low = "The inclusive low value"
|
||||
exclusive_high = "The exclusive high value"
|
||||
decimal_places = "The number of decimal places to round to"
|
||||
freeu_s1 = 'Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to mitigate the "oversmoothing effect" in the enhanced denoising process.'
|
||||
freeu_s2 = 'Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to mitigate the "oversmoothing effect" in the enhanced denoising process.'
|
||||
freeu_b1 = "Scaling factor for stage 1 to amplify the contributions of backbone features."
|
||||
freeu_b2 = "Scaling factor for stage 2 to amplify the contributions of backbone features."
|
||||
@@ -1,16 +0,0 @@
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from invokeai.app.shared.fields import FieldDescriptions
|
||||
|
||||
|
||||
class FreeUConfig(BaseModel):
|
||||
"""
|
||||
Configuration for the FreeU hyperparameters.
|
||||
- https://huggingface.co/docs/diffusers/main/en/using-diffusers/freeu
|
||||
- https://github.com/ChenyangSi/FreeU
|
||||
"""
|
||||
|
||||
s1: float = Field(ge=-1, le=3, description=FieldDescriptions.freeu_s1)
|
||||
s2: float = Field(ge=-1, le=3, description=FieldDescriptions.freeu_s2)
|
||||
b1: float = Field(ge=-1, le=3, description=FieldDescriptions.freeu_b1)
|
||||
b2: float = Field(ge=-1, le=3, description=FieldDescriptions.freeu_b2)
|
||||
@@ -32,7 +32,7 @@ from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionS
|
||||
from huggingface_hub import HfFolder
|
||||
from huggingface_hub import login as hf_hub_login
|
||||
from omegaconf import OmegaConf
|
||||
from pydantic import ValidationError
|
||||
from pydantic.error_wrappers import ValidationError
|
||||
from tqdm import tqdm
|
||||
from transformers import AutoFeatureExtractor, BertTokenizerFast, CLIPTextConfig, CLIPTextModel, CLIPTokenizer
|
||||
|
||||
|
||||
@@ -38,7 +38,6 @@ SAMPLER_CHOICES = [
|
||||
"k_heun",
|
||||
"k_lms",
|
||||
"plms",
|
||||
"lcm",
|
||||
]
|
||||
|
||||
PRECISION_CHOICES = [
|
||||
|
||||
@@ -254,13 +254,7 @@ class ModelInstall(object):
|
||||
elif path.is_dir() and any(
|
||||
[
|
||||
(path / x).exists()
|
||||
for x in {
|
||||
"config.json",
|
||||
"model_index.json",
|
||||
"learned_embeds.bin",
|
||||
"pytorch_lora_weights.bin",
|
||||
"pytorch_lora_weights.safetensors",
|
||||
}
|
||||
for x in {"config.json", "model_index.json", "learned_embeds.bin", "pytorch_lora_weights.bin"}
|
||||
]
|
||||
):
|
||||
models_installed.update({str(model_path_id_or_url): self._install_path(path)})
|
||||
@@ -363,7 +357,7 @@ class ModelInstall(object):
|
||||
for suffix in ["safetensors", "bin"]:
|
||||
if f"{prefix}pytorch_lora_weights.{suffix}" in files:
|
||||
location = self._download_hf_model(
|
||||
repo_id, [f"pytorch_lora_weights.{suffix}"], staging, subfolder=subfolder
|
||||
repo_id, ["pytorch_lora_weights.bin"], staging, subfolder=subfolder
|
||||
) # LoRA
|
||||
break
|
||||
elif (
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import pickle
|
||||
import copy
|
||||
from contextlib import contextmanager
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, List, Optional, Tuple, Union
|
||||
@@ -12,8 +12,6 @@ from diffusers.models import UNet2DConditionModel
|
||||
from safetensors.torch import load_file
|
||||
from transformers import CLIPTextModel, CLIPTokenizer
|
||||
|
||||
from invokeai.app.shared.models import FreeUConfig
|
||||
|
||||
from .models.lora import LoRAModel
|
||||
|
||||
"""
|
||||
@@ -56,6 +54,24 @@ class ModelPatcher:
|
||||
|
||||
return (module_key, module)
|
||||
|
||||
@staticmethod
|
||||
def _lora_forward_hook(
|
||||
applied_loras: List[Tuple[LoRAModel, float]],
|
||||
layer_name: str,
|
||||
):
|
||||
def lora_forward(module, input_h, output):
|
||||
if len(applied_loras) == 0:
|
||||
return output
|
||||
|
||||
for lora, weight in applied_loras:
|
||||
layer = lora.layers.get(layer_name, None)
|
||||
if layer is None:
|
||||
continue
|
||||
output += layer.forward(module, input_h, weight)
|
||||
return output
|
||||
|
||||
return lora_forward
|
||||
|
||||
@classmethod
|
||||
@contextmanager
|
||||
def apply_lora_unet(
|
||||
@@ -113,40 +129,21 @@ class ModelPatcher:
|
||||
if not layer_key.startswith(prefix):
|
||||
continue
|
||||
|
||||
# TODO(ryand): A non-negligible amount of time is currently spent resolving LoRA keys. This
|
||||
# should be improved in the following ways:
|
||||
# 1. The key mapping could be more-efficiently pre-computed. This would save time every time a
|
||||
# LoRA model is applied.
|
||||
# 2. From an API perspective, there's no reason that the `ModelPatcher` should be aware of the
|
||||
# intricacies of Stable Diffusion key resolution. It should just expect the input LoRA
|
||||
# weights to have valid keys.
|
||||
module_key, module = cls._resolve_lora_key(model, layer_key, prefix)
|
||||
|
||||
# All of the LoRA weight calculations will be done on the same device as the module weight.
|
||||
# (Performance will be best if this is a CUDA device.)
|
||||
device = module.weight.device
|
||||
dtype = module.weight.dtype
|
||||
|
||||
if module_key not in original_weights:
|
||||
original_weights[module_key] = module.weight.detach().to(device="cpu", copy=True)
|
||||
|
||||
layer_scale = layer.alpha / layer.rank if (layer.alpha and layer.rank) else 1.0
|
||||
|
||||
# We intentionally move to the target device first, then cast. Experimentally, this was found to
|
||||
# be significantly faster for 16-bit CPU tensors being moved to a CUDA device than doing the
|
||||
# same thing in a single call to '.to(...)'.
|
||||
layer.to(device=device)
|
||||
# enable autocast to calc fp16 loras on cpu
|
||||
# with torch.autocast(device_type="cpu"):
|
||||
layer.to(dtype=torch.float32)
|
||||
# TODO(ryand): Using torch.autocast(...) over explicit casting may offer a speed benefit on CUDA
|
||||
# devices here. Experimentally, it was found to be very slow on CPU. More investigation needed.
|
||||
layer_weight = layer.get_weight(module.weight) * (lora_weight * layer_scale)
|
||||
layer.to(device="cpu")
|
||||
layer_scale = layer.alpha / layer.rank if (layer.alpha and layer.rank) else 1.0
|
||||
layer_weight = layer.get_weight(original_weights[module_key]) * lora_weight * layer_scale
|
||||
|
||||
if module.weight.shape != layer_weight.shape:
|
||||
# TODO: debug on lycoris
|
||||
layer_weight = layer_weight.reshape(module.weight.shape)
|
||||
|
||||
module.weight += layer_weight.to(dtype=dtype)
|
||||
module.weight += layer_weight.to(device=module.weight.device, dtype=module.weight.dtype)
|
||||
|
||||
yield # wait for context manager exit
|
||||
|
||||
@@ -166,25 +163,10 @@ class ModelPatcher:
|
||||
init_tokens_count = None
|
||||
new_tokens_added = None
|
||||
|
||||
# TODO: This is required since Transformers 4.32 see
|
||||
# https://github.com/huggingface/transformers/pull/25088
|
||||
# More information by NVIDIA:
|
||||
# https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html#requirements-tc
|
||||
# This value might need to be changed in the future and take the GPUs model into account as there seem
|
||||
# to be ideal values for different GPUS. This value is temporary!
|
||||
# For references to the current discussion please see https://github.com/invoke-ai/InvokeAI/pull/4817
|
||||
pad_to_multiple_of = 8
|
||||
|
||||
try:
|
||||
# HACK: The CLIPTokenizer API does not include a way to remove tokens after calling add_tokens(...). As a
|
||||
# workaround, we create a full copy of `tokenizer` so that its original behavior can be restored after
|
||||
# exiting this `apply_ti(...)` context manager.
|
||||
#
|
||||
# In a previous implementation, the deep copy was obtained with `ti_tokenizer = copy.deepcopy(tokenizer)`,
|
||||
# but a pickle roundtrip was found to be much faster (1 sec vs. 0.05 secs).
|
||||
ti_tokenizer = pickle.loads(pickle.dumps(tokenizer))
|
||||
ti_tokenizer = copy.deepcopy(tokenizer)
|
||||
ti_manager = TextualInversionManager(ti_tokenizer)
|
||||
init_tokens_count = text_encoder.resize_token_embeddings(None, pad_to_multiple_of).num_embeddings
|
||||
init_tokens_count = text_encoder.resize_token_embeddings(None).num_embeddings
|
||||
|
||||
def _get_trigger(ti_name, index):
|
||||
trigger = ti_name
|
||||
@@ -199,7 +181,7 @@ class ModelPatcher:
|
||||
new_tokens_added += ti_tokenizer.add_tokens(_get_trigger(ti_name, i))
|
||||
|
||||
# modify text_encoder
|
||||
text_encoder.resize_token_embeddings(init_tokens_count + new_tokens_added, pad_to_multiple_of)
|
||||
text_encoder.resize_token_embeddings(init_tokens_count + new_tokens_added)
|
||||
model_embeddings = text_encoder.get_input_embeddings()
|
||||
|
||||
for ti_name, ti in ti_list:
|
||||
@@ -214,9 +196,7 @@ class ModelPatcher:
|
||||
|
||||
if model_embeddings.weight.data[token_id].shape != embedding.shape:
|
||||
raise ValueError(
|
||||
f"Cannot load embedding for {trigger}. It was trained on a model with token dimension"
|
||||
f" {embedding.shape[0]}, but the current model has token dimension"
|
||||
f" {model_embeddings.weight.data[token_id].shape[0]}."
|
||||
f"Cannot load embedding for {trigger}. It was trained on a model with token dimension {embedding.shape[0]}, but the current model has token dimension {model_embeddings.weight.data[token_id].shape[0]}."
|
||||
)
|
||||
|
||||
model_embeddings.weight.data[token_id] = embedding.to(
|
||||
@@ -231,7 +211,7 @@ class ModelPatcher:
|
||||
|
||||
finally:
|
||||
if init_tokens_count and new_tokens_added:
|
||||
text_encoder.resize_token_embeddings(init_tokens_count, pad_to_multiple_of)
|
||||
text_encoder.resize_token_embeddings(init_tokens_count)
|
||||
|
||||
@classmethod
|
||||
@contextmanager
|
||||
@@ -251,25 +231,6 @@ class ModelPatcher:
|
||||
while len(skipped_layers) > 0:
|
||||
text_encoder.text_model.encoder.layers.append(skipped_layers.pop())
|
||||
|
||||
@classmethod
|
||||
@contextmanager
|
||||
def apply_freeu(
|
||||
cls,
|
||||
unet: UNet2DConditionModel,
|
||||
freeu_config: Optional[FreeUConfig] = None,
|
||||
):
|
||||
did_apply_freeu = False
|
||||
try:
|
||||
if freeu_config is not None:
|
||||
unet.enable_freeu(b1=freeu_config.b1, b2=freeu_config.b2, s1=freeu_config.s1, s2=freeu_config.s2)
|
||||
did_apply_freeu = True
|
||||
|
||||
yield
|
||||
|
||||
finally:
|
||||
if did_apply_freeu:
|
||||
unet.disable_freeu()
|
||||
|
||||
|
||||
class TextualInversionModel:
|
||||
embedding: torch.Tensor # [n, 768]|[n, 1280]
|
||||
@@ -296,8 +257,7 @@ class TextualInversionModel:
|
||||
if "string_to_param" in state_dict:
|
||||
if len(state_dict["string_to_param"]) > 1:
|
||||
print(
|
||||
f'Warn: Embedding "{file_path.name}" contains multiple tokens, which is not supported. The first'
|
||||
" token will be used."
|
||||
f'Warn: Embedding "{file_path.name}" contains multiple tokens, which is not supported. The first token will be used.'
|
||||
)
|
||||
|
||||
result.embedding = next(iter(state_dict["string_to_param"].values()))
|
||||
@@ -475,13 +435,7 @@ class ONNXModelPatcher:
|
||||
orig_embeddings = None
|
||||
|
||||
try:
|
||||
# HACK: The CLIPTokenizer API does not include a way to remove tokens after calling add_tokens(...). As a
|
||||
# workaround, we create a full copy of `tokenizer` so that its original behavior can be restored after
|
||||
# exiting this `apply_ti(...)` context manager.
|
||||
#
|
||||
# In a previous implementation, the deep copy was obtained with `ti_tokenizer = copy.deepcopy(tokenizer)`,
|
||||
# but a pickle roundtrip was found to be much faster (1 sec vs. 0.05 secs).
|
||||
ti_tokenizer = pickle.loads(pickle.dumps(tokenizer))
|
||||
ti_tokenizer = copy.deepcopy(tokenizer)
|
||||
ti_manager = TextualInversionManager(ti_tokenizer)
|
||||
|
||||
def _get_trigger(ti_name, index):
|
||||
@@ -516,9 +470,7 @@ class ONNXModelPatcher:
|
||||
|
||||
if embeddings[token_id].shape != embedding.shape:
|
||||
raise ValueError(
|
||||
f"Cannot load embedding for {trigger}. It was trained on a model with token dimension"
|
||||
f" {embedding.shape[0]}, but the current model has token dimension"
|
||||
f" {embeddings[token_id].shape[0]}."
|
||||
f"Cannot load embedding for {trigger}. It was trained on a model with token dimension {embedding.shape[0]}, but the current model has token dimension {embeddings[token_id].shape[0]}."
|
||||
)
|
||||
|
||||
embeddings[token_id] = embedding
|
||||
|
||||
@@ -64,7 +64,7 @@ class MemorySnapshot:
|
||||
return cls(process_ram, vram, malloc_info)
|
||||
|
||||
|
||||
def get_pretty_snapshot_diff(snapshot_1: Optional[MemorySnapshot], snapshot_2: Optional[MemorySnapshot]) -> str:
|
||||
def get_pretty_snapshot_diff(snapshot_1: MemorySnapshot, snapshot_2: MemorySnapshot) -> str:
|
||||
"""Get a pretty string describing the difference between two `MemorySnapshot`s."""
|
||||
|
||||
def get_msg_line(prefix: str, val1: int, val2: int):
|
||||
@@ -73,9 +73,6 @@ def get_pretty_snapshot_diff(snapshot_1: Optional[MemorySnapshot], snapshot_2: O
|
||||
|
||||
msg = ""
|
||||
|
||||
if snapshot_1 is None or snapshot_2 is None:
|
||||
return msg
|
||||
|
||||
msg += get_msg_line("Process RAM", snapshot_1.process_ram, snapshot_2.process_ram)
|
||||
|
||||
if snapshot_1.malloc_info is not None and snapshot_2.malloc_info is not None:
|
||||
|
||||
@@ -117,7 +117,6 @@ class ModelCache(object):
|
||||
lazy_offloading: bool = True,
|
||||
sha_chunksize: int = 16777216,
|
||||
logger: types.ModuleType = logger,
|
||||
log_memory_usage: bool = False,
|
||||
):
|
||||
"""
|
||||
:param max_cache_size: Maximum size of the RAM cache [6.0 GB]
|
||||
@@ -127,10 +126,6 @@ class ModelCache(object):
|
||||
:param lazy_offloading: Keep model in VRAM until another model needs to be loaded
|
||||
:param sequential_offload: Conserve VRAM by loading and unloading each stage of the pipeline sequentially
|
||||
:param sha_chunksize: Chunksize to use when calculating sha256 model hash
|
||||
:param log_memory_usage: If True, a memory snapshot will be captured before and after every model cache
|
||||
operation, and the result will be logged (at debug level). There is a time cost to capturing the memory
|
||||
snapshots, so it is recommended to disable this feature unless you are actively inspecting the model cache's
|
||||
behaviour.
|
||||
"""
|
||||
self.model_infos: Dict[str, ModelBase] = dict()
|
||||
# allow lazy offloading only when vram cache enabled
|
||||
@@ -142,7 +137,6 @@ class ModelCache(object):
|
||||
self.storage_device: torch.device = storage_device
|
||||
self.sha_chunksize = sha_chunksize
|
||||
self.logger = logger
|
||||
self._log_memory_usage = log_memory_usage
|
||||
|
||||
# used for stats collection
|
||||
self.stats = None
|
||||
@@ -150,11 +144,6 @@ class ModelCache(object):
|
||||
self._cached_models = dict()
|
||||
self._cache_stack = list()
|
||||
|
||||
def _capture_memory_snapshot(self) -> Optional[MemorySnapshot]:
|
||||
if self._log_memory_usage:
|
||||
return MemorySnapshot.capture()
|
||||
return None
|
||||
|
||||
def get_key(
|
||||
self,
|
||||
model_path: str,
|
||||
@@ -234,10 +223,10 @@ class ModelCache(object):
|
||||
|
||||
# Load the model from disk and capture a memory snapshot before/after.
|
||||
start_load_time = time.time()
|
||||
snapshot_before = self._capture_memory_snapshot()
|
||||
snapshot_before = MemorySnapshot.capture()
|
||||
with skip_torch_weight_init():
|
||||
model = model_info.get_model(child_type=submodel, torch_dtype=self.precision)
|
||||
snapshot_after = self._capture_memory_snapshot()
|
||||
snapshot_after = MemorySnapshot.capture()
|
||||
end_load_time = time.time()
|
||||
|
||||
self_reported_model_size_after_load = model_info.get_size(submodel)
|
||||
@@ -286,9 +275,9 @@ class ModelCache(object):
|
||||
return
|
||||
|
||||
start_model_to_time = time.time()
|
||||
snapshot_before = self._capture_memory_snapshot()
|
||||
snapshot_before = MemorySnapshot.capture()
|
||||
cache_entry.model.to(target_device)
|
||||
snapshot_after = self._capture_memory_snapshot()
|
||||
snapshot_after = MemorySnapshot.capture()
|
||||
end_model_to_time = time.time()
|
||||
self.logger.debug(
|
||||
f"Moved model '{key}' from {source_device} to"
|
||||
@@ -297,12 +286,7 @@ class ModelCache(object):
|
||||
f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
|
||||
)
|
||||
|
||||
if (
|
||||
snapshot_before is not None
|
||||
and snapshot_after is not None
|
||||
and snapshot_before.vram is not None
|
||||
and snapshot_after.vram is not None
|
||||
):
|
||||
if snapshot_before.vram is not None and snapshot_after.vram is not None:
|
||||
vram_change = abs(snapshot_before.vram - snapshot_after.vram)
|
||||
|
||||
# If the estimated model size does not match the change in VRAM, log a warning.
|
||||
@@ -438,17 +422,12 @@ class ModelCache(object):
|
||||
self.logger.debug(f"Before unloading: cached_models={len(self._cached_models)}")
|
||||
|
||||
pos = 0
|
||||
models_cleared = 0
|
||||
while current_size + bytes_needed > maximum_size and pos < len(self._cache_stack):
|
||||
model_key = self._cache_stack[pos]
|
||||
cache_entry = self._cached_models[model_key]
|
||||
|
||||
refs = sys.getrefcount(cache_entry.model)
|
||||
|
||||
# HACK: This is a workaround for a memory-management issue that we haven't tracked down yet. We are directly
|
||||
# going against the advice in the Python docs by using `gc.get_referrers(...)` in this way:
|
||||
# https://docs.python.org/3/library/gc.html#gc.get_referrers
|
||||
|
||||
# manualy clear local variable references of just finished function calls
|
||||
# for some reason python don't want to collect it even by gc.collect() immidiately
|
||||
if refs > 2:
|
||||
@@ -474,16 +453,15 @@ class ModelCache(object):
|
||||
f" refs: {refs}"
|
||||
)
|
||||
|
||||
# Expected refs:
|
||||
# 2 refs:
|
||||
# 1 from cache_entry
|
||||
# 1 from getrefcount function
|
||||
# 1 from onnx runtime object
|
||||
if not cache_entry.locked and refs <= (3 if "onnx" in model_key else 2):
|
||||
if not cache_entry.locked and refs <= 3 if "onnx" in model_key else 2:
|
||||
self.logger.debug(
|
||||
f"Unloading model {model_key} to free {(model_size/GIG):.2f} GB (-{(cache_entry.size/GIG):.2f} GB)"
|
||||
)
|
||||
current_size -= cache_entry.size
|
||||
models_cleared += 1
|
||||
if self.stats:
|
||||
self.stats.cleared += 1
|
||||
del self._cache_stack[pos]
|
||||
@@ -493,20 +471,7 @@ class ModelCache(object):
|
||||
else:
|
||||
pos += 1
|
||||
|
||||
if models_cleared > 0:
|
||||
# There would likely be some 'garbage' to be collected regardless of whether a model was cleared or not, but
|
||||
# there is a significant time cost to calling `gc.collect()`, so we want to use it sparingly. (The time cost
|
||||
# is high even if no garbage gets collected.)
|
||||
#
|
||||
# Calling gc.collect(...) when a model is cleared seems like a good middle-ground:
|
||||
# - If models had to be cleared, it's a signal that we are close to our memory limit.
|
||||
# - If models were cleared, there's a good chance that there's a significant amount of garbage to be
|
||||
# collected.
|
||||
#
|
||||
# Keep in mind that gc is only responsible for handling reference cycles. Most objects should be cleaned up
|
||||
# immediately when their reference count hits 0.
|
||||
gc.collect()
|
||||
|
||||
gc.collect()
|
||||
torch.cuda.empty_cache()
|
||||
if choose_torch_device() == torch.device("mps"):
|
||||
mps.empty_cache()
|
||||
@@ -526,6 +491,7 @@ class ModelCache(object):
|
||||
vram_in_use = torch.cuda.memory_allocated()
|
||||
self.logger.debug(f"{(vram_in_use/GIG):.2f}GB VRAM used for models; max allowed={(reserved/GIG):.2f}GB")
|
||||
|
||||
gc.collect()
|
||||
torch.cuda.empty_cache()
|
||||
if choose_torch_device() == torch.device("mps"):
|
||||
mps.empty_cache()
|
||||
|
||||
@@ -17,7 +17,7 @@ def skip_torch_weight_init():
|
||||
completely unnecessary if the intent is to load checkpoint weights from disk for the layer. This context manager
|
||||
monkey-patches common torch layers to skip the weight initialization step.
|
||||
"""
|
||||
torch_modules = [torch.nn.Linear, torch.nn.modules.conv._ConvNd, torch.nn.Embedding]
|
||||
torch_modules = [torch.nn.Linear, torch.nn.modules.conv._ConvNd]
|
||||
saved_functions = [m.reset_parameters for m in torch_modules]
|
||||
|
||||
try:
|
||||
|
||||
@@ -351,7 +351,6 @@ class ModelManager(object):
|
||||
precision=precision,
|
||||
sequential_offload=sequential_offload,
|
||||
logger=logger,
|
||||
log_memory_usage=self.app_config.log_memory_usage,
|
||||
)
|
||||
|
||||
self._read_models(config)
|
||||
|
||||
@@ -183,13 +183,12 @@ class ModelProbe(object):
|
||||
if model:
|
||||
class_name = model.__class__.__name__
|
||||
else:
|
||||
for suffix in ["bin", "safetensors"]:
|
||||
if (folder_path / f"learned_embeds.{suffix}").exists():
|
||||
return ModelType.TextualInversion
|
||||
if (folder_path / f"pytorch_lora_weights.{suffix}").exists():
|
||||
return ModelType.Lora
|
||||
if (folder_path / "unet/model.onnx").exists():
|
||||
return ModelType.ONNX
|
||||
if (folder_path / "learned_embeds.bin").exists():
|
||||
return ModelType.TextualInversion
|
||||
if (folder_path / "pytorch_lora_weights.bin").exists():
|
||||
return ModelType.Lora
|
||||
if (folder_path / "image_encoder.txt").exists():
|
||||
return ModelType.IPAdapter
|
||||
|
||||
|
||||
@@ -68,9 +68,8 @@ class LoRAModel(ModelBase):
|
||||
raise ModelNotFoundException()
|
||||
|
||||
if os.path.isdir(path):
|
||||
for ext in ["safetensors", "bin"]:
|
||||
if os.path.exists(os.path.join(path, f"pytorch_lora_weights.{ext}")):
|
||||
return LoRAModelFormat.Diffusers
|
||||
if os.path.exists(os.path.join(path, "pytorch_lora_weights.bin")):
|
||||
return LoRAModelFormat.Diffusers
|
||||
|
||||
if os.path.isfile(path):
|
||||
if any([path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt"]]):
|
||||
@@ -87,10 +86,8 @@ class LoRAModel(ModelBase):
|
||||
base_model: BaseModelType,
|
||||
) -> str:
|
||||
if cls.detect_format(model_path) == LoRAModelFormat.Diffusers:
|
||||
for ext in ["safetensors", "bin"]: # return path to the safetensors file inside the folder
|
||||
path = Path(model_path, f"pytorch_lora_weights.{ext}")
|
||||
if path.exists():
|
||||
return path
|
||||
# TODO: add diffusers lora when it stabilizes a bit
|
||||
raise NotImplementedError("Diffusers lora not supported")
|
||||
else:
|
||||
return model_path
|
||||
|
||||
@@ -443,19 +440,33 @@ class IA3Layer(LoRALayerBase):
|
||||
class LoRAModelRaw: # (torch.nn.Module):
|
||||
_name: str
|
||||
layers: Dict[str, LoRALayer]
|
||||
_device: torch.device
|
||||
_dtype: torch.dtype
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
name: str,
|
||||
layers: Dict[str, LoRALayer],
|
||||
device: torch.device,
|
||||
dtype: torch.dtype,
|
||||
):
|
||||
self._name = name
|
||||
self._device = device or torch.cpu
|
||||
self._dtype = dtype or torch.float32
|
||||
self.layers = layers
|
||||
|
||||
@property
|
||||
def name(self):
|
||||
return self._name
|
||||
|
||||
@property
|
||||
def device(self):
|
||||
return self._device
|
||||
|
||||
@property
|
||||
def dtype(self):
|
||||
return self._dtype
|
||||
|
||||
def to(
|
||||
self,
|
||||
device: Optional[torch.device] = None,
|
||||
@@ -464,6 +475,8 @@ class LoRAModelRaw: # (torch.nn.Module):
|
||||
# TODO: try revert if exception?
|
||||
for key, layer in self.layers.items():
|
||||
layer.to(device=device, dtype=dtype)
|
||||
self._device = device
|
||||
self._dtype = dtype
|
||||
|
||||
def calc_size(self) -> int:
|
||||
model_size = 0
|
||||
@@ -544,6 +557,8 @@ class LoRAModelRaw: # (torch.nn.Module):
|
||||
file_path = Path(file_path)
|
||||
|
||||
model = cls(
|
||||
device=device,
|
||||
dtype=dtype,
|
||||
name=file_path.stem, # TODO:
|
||||
layers=dict(),
|
||||
)
|
||||
|
||||
@@ -546,13 +546,11 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
|
||||
# Handle ControlNet(s) and T2I-Adapter(s)
|
||||
down_block_additional_residuals = None
|
||||
mid_block_additional_residual = None
|
||||
down_intrablock_additional_residuals = None
|
||||
# if control_data is not None and t2i_adapter_data is not None:
|
||||
# TODO(ryand): This is a limitation of the UNet2DConditionModel API, not a fundamental incompatibility
|
||||
# between ControlNets and T2I-Adapters. We will try to fix this upstream in diffusers.
|
||||
# raise Exception("ControlNet(s) and T2I-Adapter(s) cannot be used simultaneously (yet).")
|
||||
# elif control_data is not None:
|
||||
if control_data is not None:
|
||||
if control_data is not None and t2i_adapter_data is not None:
|
||||
# TODO(ryand): This is a limitation of the UNet2DConditionModel API, not a fundamental incompatibility
|
||||
# between ControlNets and T2I-Adapters. We will try to fix this upstream in diffusers.
|
||||
raise Exception("ControlNet(s) and T2I-Adapter(s) cannot be used simultaneously (yet).")
|
||||
elif control_data is not None:
|
||||
down_block_additional_residuals, mid_block_additional_residual = self.invokeai_diffuser.do_controlnet_step(
|
||||
control_data=control_data,
|
||||
sample=latent_model_input,
|
||||
@@ -561,8 +559,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
|
||||
total_step_count=total_step_count,
|
||||
conditioning_data=conditioning_data,
|
||||
)
|
||||
# elif t2i_adapter_data is not None:
|
||||
if t2i_adapter_data is not None:
|
||||
elif t2i_adapter_data is not None:
|
||||
accum_adapter_state = None
|
||||
for single_t2i_adapter_data in t2i_adapter_data:
|
||||
# Determine the T2I-Adapter weights for the current denoising step.
|
||||
@@ -587,8 +584,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
|
||||
for idx, value in enumerate(single_t2i_adapter_data.adapter_state):
|
||||
accum_adapter_state[idx] += value * t2i_adapter_weight
|
||||
|
||||
# down_block_additional_residuals = accum_adapter_state
|
||||
down_intrablock_additional_residuals = accum_adapter_state
|
||||
down_block_additional_residuals = accum_adapter_state
|
||||
|
||||
uc_noise_pred, c_noise_pred = self.invokeai_diffuser.do_unet_step(
|
||||
sample=latent_model_input,
|
||||
@@ -597,9 +593,8 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
|
||||
total_step_count=total_step_count,
|
||||
conditioning_data=conditioning_data,
|
||||
# extra:
|
||||
down_block_additional_residuals=down_block_additional_residuals, # for ControlNet
|
||||
mid_block_additional_residual=mid_block_additional_residual, # for ControlNet
|
||||
down_intrablock_additional_residuals=down_intrablock_additional_residuals, # for T2I-Adapter
|
||||
down_block_additional_residuals=down_block_additional_residuals,
|
||||
mid_block_additional_residual=mid_block_additional_residual,
|
||||
)
|
||||
|
||||
guidance_scale = conditioning_data.guidance_scale
|
||||
|
||||
@@ -260,6 +260,7 @@ class InvokeAIDiffuserComponent:
|
||||
conditioning_data,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
else:
|
||||
(
|
||||
unconditioned_next_x,
|
||||
@@ -409,15 +410,6 @@ class InvokeAIDiffuserComponent:
|
||||
uncond_down_block.append(_uncond_down)
|
||||
cond_down_block.append(_cond_down)
|
||||
|
||||
uncond_down_intrablock, cond_down_intrablock = None, None
|
||||
down_intrablock_additional_residuals = kwargs.pop("down_intrablock_additional_residuals", None)
|
||||
if down_intrablock_additional_residuals is not None:
|
||||
uncond_down_intrablock, cond_down_intrablock = [], []
|
||||
for down_intrablock in down_intrablock_additional_residuals:
|
||||
_uncond_down, _cond_down = down_intrablock.chunk(2)
|
||||
uncond_down_intrablock.append(_uncond_down)
|
||||
cond_down_intrablock.append(_cond_down)
|
||||
|
||||
uncond_mid_block, cond_mid_block = None, None
|
||||
mid_block_additional_residual = kwargs.pop("mid_block_additional_residual", None)
|
||||
if mid_block_additional_residual is not None:
|
||||
@@ -449,7 +441,6 @@ class InvokeAIDiffuserComponent:
|
||||
cross_attention_kwargs=cross_attention_kwargs,
|
||||
down_block_additional_residuals=uncond_down_block,
|
||||
mid_block_additional_residual=uncond_mid_block,
|
||||
down_intrablock_additional_residuals=uncond_down_intrablock,
|
||||
added_cond_kwargs=added_cond_kwargs,
|
||||
**kwargs,
|
||||
)
|
||||
@@ -479,7 +470,6 @@ class InvokeAIDiffuserComponent:
|
||||
cross_attention_kwargs=cross_attention_kwargs,
|
||||
down_block_additional_residuals=cond_down_block,
|
||||
mid_block_additional_residual=cond_mid_block,
|
||||
down_intrablock_additional_residuals=cond_down_intrablock,
|
||||
added_cond_kwargs=added_cond_kwargs,
|
||||
**kwargs,
|
||||
)
|
||||
@@ -504,15 +494,6 @@ class InvokeAIDiffuserComponent:
|
||||
uncond_down_block.append(_uncond_down)
|
||||
cond_down_block.append(_cond_down)
|
||||
|
||||
uncond_down_intrablock, cond_down_intrablock = None, None
|
||||
down_intrablock_additional_residuals = kwargs.pop("down_intrablock_additional_residuals", None)
|
||||
if down_intrablock_additional_residuals is not None:
|
||||
uncond_down_intrablock, cond_down_intrablock = [], []
|
||||
for down_intrablock in down_intrablock_additional_residuals:
|
||||
_uncond_down, _cond_down = down_intrablock.chunk(2)
|
||||
uncond_down_intrablock.append(_uncond_down)
|
||||
cond_down_intrablock.append(_cond_down)
|
||||
|
||||
uncond_mid_block, cond_mid_block = None, None
|
||||
mid_block_additional_residual = kwargs.pop("mid_block_additional_residual", None)
|
||||
if mid_block_additional_residual is not None:
|
||||
@@ -541,7 +522,6 @@ class InvokeAIDiffuserComponent:
|
||||
{"swap_cross_attn_context": cross_attn_processor_context},
|
||||
down_block_additional_residuals=uncond_down_block,
|
||||
mid_block_additional_residual=uncond_mid_block,
|
||||
down_intrablock_additional_residuals=uncond_down_intrablock,
|
||||
added_cond_kwargs=added_cond_kwargs,
|
||||
**kwargs,
|
||||
)
|
||||
@@ -561,7 +541,6 @@ class InvokeAIDiffuserComponent:
|
||||
{"swap_cross_attn_context": cross_attn_processor_context},
|
||||
down_block_additional_residuals=cond_down_block,
|
||||
mid_block_additional_residual=cond_mid_block,
|
||||
down_intrablock_additional_residuals=cond_down_intrablock,
|
||||
added_cond_kwargs=added_cond_kwargs,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
@@ -10,7 +10,6 @@ from diffusers import (
|
||||
HeunDiscreteScheduler,
|
||||
KDPM2AncestralDiscreteScheduler,
|
||||
KDPM2DiscreteScheduler,
|
||||
LCMScheduler,
|
||||
LMSDiscreteScheduler,
|
||||
PNDMScheduler,
|
||||
UniPCMultistepScheduler,
|
||||
@@ -39,5 +38,4 @@ SCHEDULER_MAP = dict(
|
||||
dpmpp_sde=(DPMSolverSDEScheduler, dict(use_karras_sigmas=False, noise_sampler_seed=0)),
|
||||
dpmpp_sde_k=(DPMSolverSDEScheduler, dict(use_karras_sigmas=True, noise_sampler_seed=0)),
|
||||
unipc=(UniPCMultistepScheduler, dict(cpu_only=True)),
|
||||
lcm=(LCMScheduler, dict()),
|
||||
)
|
||||
|
||||
@@ -117,6 +117,9 @@ sd-1/embedding/EasyNegative:
|
||||
recommended: True
|
||||
sd-1/embedding/ahx-beta-453407d:
|
||||
repo_id: sd-concepts-library/ahx-beta-453407d
|
||||
sd-1/lora/LowRA:
|
||||
path: https://civitai.com/api/download/models/63006
|
||||
recommended: True
|
||||
sd-1/lora/Ink scenery:
|
||||
path: https://civitai.com/api/download/models/83390
|
||||
sd-1/ip_adapter/ip_adapter_sd15:
|
||||
|
||||
@@ -50,7 +50,7 @@ def invokeai_is_running() -> bool:
|
||||
return False
|
||||
|
||||
|
||||
def welcome(latest_release: str, latest_prerelease: str):
|
||||
def welcome(versions: dict):
|
||||
@group()
|
||||
def text():
|
||||
yield f"InvokeAI Version: [bold yellow]{__version__}"
|
||||
@@ -61,10 +61,9 @@ def welcome(latest_release: str, latest_prerelease: str):
|
||||
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 [bold]official release[/bold] ([italic]{latest_release}[/italic])
|
||||
[2] Update to the latest [bold]pre-release[/bold] (may be buggy; caveat emptor!) ([italic]{latest_prerelease}[/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"""
|
||||
yield f"""[1] Update to the latest official release ([italic]{versions[0]['tag_name']}[/italic])
|
||||
[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(
|
||||
@@ -92,18 +91,13 @@ def get_extras():
|
||||
|
||||
|
||||
def main():
|
||||
versions = get_versions()
|
||||
released_versions = [x for x in versions if not (x["draft"] or x["prerelease"])]
|
||||
prerelease_versions = [x for x in versions if not x["draft"] and x["prerelease"]]
|
||||
latest_release = released_versions[0]["tag_name"] if len(released_versions) else None
|
||||
latest_prerelease = prerelease_versions[0]["tag_name"] if len(prerelease_versions) else None
|
||||
|
||||
versions = [x for x in get_versions() if not (x['draft'] or x['prerelease'])]
|
||||
if invokeai_is_running():
|
||||
print(":exclamation: [bold red]Please terminate all running instances of InvokeAI before updating.[/red bold]")
|
||||
input("Press any key to continue...")
|
||||
return
|
||||
|
||||
welcome(latest_release, latest_prerelease)
|
||||
welcome(versions)
|
||||
|
||||
tag = None
|
||||
branch = None
|
||||
@@ -111,13 +105,11 @@ def main():
|
||||
choice = Prompt.ask("Choice:", choices=["1", "2", "3", "4"], default="1")
|
||||
|
||||
if choice == "1":
|
||||
release = latest_release
|
||||
release = versions[0]["tag_name"]
|
||||
elif choice == "2":
|
||||
release = latest_prerelease
|
||||
elif choice == "3":
|
||||
while not tag:
|
||||
tag = Prompt.ask("Enter an InvokeAI tag name")
|
||||
elif choice == "4":
|
||||
elif choice == "3":
|
||||
while not branch:
|
||||
branch = Prompt.ask("Enter an InvokeAI branch name")
|
||||
|
||||
|
||||
@@ -274,10 +274,9 @@ class mergeModelsForm(npyscreen.FormMultiPageAction):
|
||||
else:
|
||||
interp = self.interpolations[self.merge_method.value[0]]
|
||||
|
||||
bases = ["sd-1", "sd-2", "sdxl"]
|
||||
args = dict(
|
||||
model_names=models,
|
||||
base_model=BaseModelType(bases[self.base_select.value[0]]),
|
||||
base_model=tuple(BaseModelType)[self.base_select.value[0]],
|
||||
alpha=self.alpha.value,
|
||||
interp=interp,
|
||||
force=self.force.value,
|
||||
|
||||
171
invokeai/frontend/web/dist/assets/App-6d2bea44.js
vendored
Normal file
171
invokeai/frontend/web/dist/assets/App-6d2bea44.js
vendored
Normal file
File diff suppressed because one or more lines are too long
171
invokeai/frontend/web/dist/assets/App-929dadd3.js
vendored
171
invokeai/frontend/web/dist/assets/App-929dadd3.js
vendored
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@@ -1,4 +1,4 @@
|
||||
import{w as s,ib as T,v as l,a1 as I,ic as R,ad as V,id as z,ie as j,ig as D,ih as F,ii as G,ij as W,ik as K,aG as H,il as U,im as Y}from"./index-7ba6700b.js";import{M as Z}from"./MantineProvider-bf828180.js";var P=String.raw,E=P`
|
||||
import{w as s,ia as T,v as l,a2 as I,ib as R,ae as V,ic as z,id as j,ie as D,ig as F,ih as G,ii as W,ij as K,aG as H,ik as U,il as Y}from"./index-6080a931.js";import{M as Z}from"./MantineProvider-b852498d.js";var P=String.raw,E=P`
|
||||
:root,
|
||||
:host {
|
||||
--chakra-vh: 100vh;
|
||||
@@ -277,4 +277,4 @@ import{w as s,ib as T,v as l,a1 as I,ic as R,ad as V,id as z,ie as j,ig as D,ih
|
||||
}
|
||||
|
||||
${E}
|
||||
`}),g={light:"chakra-ui-light",dark:"chakra-ui-dark"};function Q(e={}){const{preventTransition:o=!0}=e,n={setDataset:r=>{const t=o?n.preventTransition():void 0;document.documentElement.dataset.theme=r,document.documentElement.style.colorScheme=r,t==null||t()},setClassName(r){document.body.classList.add(r?g.dark:g.light),document.body.classList.remove(r?g.light:g.dark)},query(){return window.matchMedia("(prefers-color-scheme: dark)")},getSystemTheme(r){var t;return((t=n.query().matches)!=null?t:r==="dark")?"dark":"light"},addListener(r){const t=n.query(),i=a=>{r(a.matches?"dark":"light")};return typeof t.addListener=="function"?t.addListener(i):t.addEventListener("change",i),()=>{typeof t.removeListener=="function"?t.removeListener(i):t.removeEventListener("change",i)}},preventTransition(){const r=document.createElement("style");return r.appendChild(document.createTextNode("*{-webkit-transition:none!important;-moz-transition:none!important;-o-transition:none!important;-ms-transition:none!important;transition:none!important}")),document.head.appendChild(r),()=>{window.getComputedStyle(document.body),requestAnimationFrame(()=>{requestAnimationFrame(()=>{document.head.removeChild(r)})})}}};return n}var X="chakra-ui-color-mode";function L(e){return{ssr:!1,type:"localStorage",get(o){if(!(globalThis!=null&&globalThis.document))return o;let n;try{n=localStorage.getItem(e)||o}catch{}return n||o},set(o){try{localStorage.setItem(e,o)}catch{}}}}var ee=L(X),M=()=>{};function S(e,o){return e.type==="cookie"&&e.ssr?e.get(o):o}function O(e){const{value:o,children:n,options:{useSystemColorMode:r,initialColorMode:t,disableTransitionOnChange:i}={},colorModeManager:a=ee}=e,d=t==="dark"?"dark":"light",[u,p]=l.useState(()=>S(a,d)),[y,b]=l.useState(()=>S(a)),{getSystemTheme:w,setClassName:k,setDataset:x,addListener:$}=l.useMemo(()=>Q({preventTransition:i}),[i]),v=t==="system"&&!u?y:u,c=l.useCallback(m=>{const f=m==="system"?w():m;p(f),k(f==="dark"),x(f),a.set(f)},[a,w,k,x]);I(()=>{t==="system"&&b(w())},[]),l.useEffect(()=>{const m=a.get();if(m){c(m);return}if(t==="system"){c("system");return}c(d)},[a,d,t,c]);const C=l.useCallback(()=>{c(v==="dark"?"light":"dark")},[v,c]);l.useEffect(()=>{if(r)return $(c)},[r,$,c]);const A=l.useMemo(()=>({colorMode:o??v,toggleColorMode:o?M:C,setColorMode:o?M:c,forced:o!==void 0}),[v,C,c,o]);return s.jsx(R.Provider,{value:A,children:n})}O.displayName="ColorModeProvider";var te=["borders","breakpoints","colors","components","config","direction","fonts","fontSizes","fontWeights","letterSpacings","lineHeights","radii","shadows","sizes","space","styles","transition","zIndices"];function re(e){return V(e)?te.every(o=>Object.prototype.hasOwnProperty.call(e,o)):!1}function h(e){return typeof e=="function"}function oe(...e){return o=>e.reduce((n,r)=>r(n),o)}var ne=e=>function(...n){let r=[...n],t=n[n.length-1];return re(t)&&r.length>1?r=r.slice(0,r.length-1):t=e,oe(...r.map(i=>a=>h(i)?i(a):ae(a,i)))(t)},ie=ne(j);function ae(...e){return z({},...e,_)}function _(e,o,n,r){if((h(e)||h(o))&&Object.prototype.hasOwnProperty.call(r,n))return(...t)=>{const i=h(e)?e(...t):e,a=h(o)?o(...t):o;return z({},i,a,_)}}var q=l.createContext({getDocument(){return document},getWindow(){return window}});q.displayName="EnvironmentContext";function N(e){const{children:o,environment:n,disabled:r}=e,t=l.useRef(null),i=l.useMemo(()=>n||{getDocument:()=>{var d,u;return(u=(d=t.current)==null?void 0:d.ownerDocument)!=null?u:document},getWindow:()=>{var d,u;return(u=(d=t.current)==null?void 0:d.ownerDocument.defaultView)!=null?u:window}},[n]),a=!r||!n;return s.jsxs(q.Provider,{value:i,children:[o,a&&s.jsx("span",{id:"__chakra_env",hidden:!0,ref:t})]})}N.displayName="EnvironmentProvider";var se=e=>{const{children:o,colorModeManager:n,portalZIndex:r,resetScope:t,resetCSS:i=!0,theme:a={},environment:d,cssVarsRoot:u,disableEnvironment:p,disableGlobalStyle:y}=e,b=s.jsx(N,{environment:d,disabled:p,children:o});return s.jsx(D,{theme:a,cssVarsRoot:u,children:s.jsxs(O,{colorModeManager:n,options:a.config,children:[i?s.jsx(J,{scope:t}):s.jsx(B,{}),!y&&s.jsx(F,{}),r?s.jsx(G,{zIndex:r,children:b}):b]})})},le=e=>function({children:n,theme:r=e,toastOptions:t,...i}){return s.jsxs(se,{theme:r,...i,children:[s.jsx(W,{value:t==null?void 0:t.defaultOptions,children:n}),s.jsx(K,{...t})]})},de=le(j);const ue=()=>l.useMemo(()=>({colorScheme:"dark",fontFamily:"'Inter Variable', sans-serif",components:{ScrollArea:{defaultProps:{scrollbarSize:10},styles:{scrollbar:{"&:hover":{backgroundColor:"var(--invokeai-colors-baseAlpha-300)"}},thumb:{backgroundColor:"var(--invokeai-colors-baseAlpha-300)"}}}}}),[]),ce=L("@@invokeai-color-mode");function me({children:e}){const{i18n:o}=H(),n=o.dir(),r=l.useMemo(()=>ie({...U,direction:n}),[n]);l.useEffect(()=>{document.body.dir=n},[n]);const t=ue();return s.jsx(Z,{theme:t,children:s.jsx(de,{theme:r,colorModeManager:ce,toastOptions:Y,children:e})})}const ve=l.memo(me);export{ve as default};
|
||||
`}),g={light:"chakra-ui-light",dark:"chakra-ui-dark"};function Q(e={}){const{preventTransition:o=!0}=e,n={setDataset:r=>{const t=o?n.preventTransition():void 0;document.documentElement.dataset.theme=r,document.documentElement.style.colorScheme=r,t==null||t()},setClassName(r){document.body.classList.add(r?g.dark:g.light),document.body.classList.remove(r?g.light:g.dark)},query(){return window.matchMedia("(prefers-color-scheme: dark)")},getSystemTheme(r){var t;return((t=n.query().matches)!=null?t:r==="dark")?"dark":"light"},addListener(r){const t=n.query(),i=a=>{r(a.matches?"dark":"light")};return typeof t.addListener=="function"?t.addListener(i):t.addEventListener("change",i),()=>{typeof t.removeListener=="function"?t.removeListener(i):t.removeEventListener("change",i)}},preventTransition(){const r=document.createElement("style");return r.appendChild(document.createTextNode("*{-webkit-transition:none!important;-moz-transition:none!important;-o-transition:none!important;-ms-transition:none!important;transition:none!important}")),document.head.appendChild(r),()=>{window.getComputedStyle(document.body),requestAnimationFrame(()=>{requestAnimationFrame(()=>{document.head.removeChild(r)})})}}};return n}var X="chakra-ui-color-mode";function L(e){return{ssr:!1,type:"localStorage",get(o){if(!(globalThis!=null&&globalThis.document))return o;let n;try{n=localStorage.getItem(e)||o}catch{}return n||o},set(o){try{localStorage.setItem(e,o)}catch{}}}}var ee=L(X),M=()=>{};function S(e,o){return e.type==="cookie"&&e.ssr?e.get(o):o}function O(e){const{value:o,children:n,options:{useSystemColorMode:r,initialColorMode:t,disableTransitionOnChange:i}={},colorModeManager:a=ee}=e,d=t==="dark"?"dark":"light",[u,p]=l.useState(()=>S(a,d)),[y,b]=l.useState(()=>S(a)),{getSystemTheme:w,setClassName:k,setDataset:x,addListener:$}=l.useMemo(()=>Q({preventTransition:i}),[i]),v=t==="system"&&!u?y:u,c=l.useCallback(h=>{const f=h==="system"?w():h;p(f),k(f==="dark"),x(f),a.set(f)},[a,w,k,x]);I(()=>{t==="system"&&b(w())},[]),l.useEffect(()=>{const h=a.get();if(h){c(h);return}if(t==="system"){c("system");return}c(d)},[a,d,t,c]);const C=l.useCallback(()=>{c(v==="dark"?"light":"dark")},[v,c]);l.useEffect(()=>{if(r)return $(c)},[r,$,c]);const A=l.useMemo(()=>({colorMode:o??v,toggleColorMode:o?M:C,setColorMode:o?M:c,forced:o!==void 0}),[v,C,c,o]);return s.jsx(R.Provider,{value:A,children:n})}O.displayName="ColorModeProvider";var te=["borders","breakpoints","colors","components","config","direction","fonts","fontSizes","fontWeights","letterSpacings","lineHeights","radii","shadows","sizes","space","styles","transition","zIndices"];function re(e){return V(e)?te.every(o=>Object.prototype.hasOwnProperty.call(e,o)):!1}function m(e){return typeof e=="function"}function oe(...e){return o=>e.reduce((n,r)=>r(n),o)}var ne=e=>function(...n){let r=[...n],t=n[n.length-1];return re(t)&&r.length>1?r=r.slice(0,r.length-1):t=e,oe(...r.map(i=>a=>m(i)?i(a):ae(a,i)))(t)},ie=ne(j);function ae(...e){return z({},...e,_)}function _(e,o,n,r){if((m(e)||m(o))&&Object.prototype.hasOwnProperty.call(r,n))return(...t)=>{const i=m(e)?e(...t):e,a=m(o)?o(...t):o;return z({},i,a,_)}}var q=l.createContext({getDocument(){return document},getWindow(){return window}});q.displayName="EnvironmentContext";function N(e){const{children:o,environment:n,disabled:r}=e,t=l.useRef(null),i=l.useMemo(()=>n||{getDocument:()=>{var d,u;return(u=(d=t.current)==null?void 0:d.ownerDocument)!=null?u:document},getWindow:()=>{var d,u;return(u=(d=t.current)==null?void 0:d.ownerDocument.defaultView)!=null?u:window}},[n]),a=!r||!n;return s.jsxs(q.Provider,{value:i,children:[o,a&&s.jsx("span",{id:"__chakra_env",hidden:!0,ref:t})]})}N.displayName="EnvironmentProvider";var se=e=>{const{children:o,colorModeManager:n,portalZIndex:r,resetScope:t,resetCSS:i=!0,theme:a={},environment:d,cssVarsRoot:u,disableEnvironment:p,disableGlobalStyle:y}=e,b=s.jsx(N,{environment:d,disabled:p,children:o});return s.jsx(D,{theme:a,cssVarsRoot:u,children:s.jsxs(O,{colorModeManager:n,options:a.config,children:[i?s.jsx(J,{scope:t}):s.jsx(B,{}),!y&&s.jsx(F,{}),r?s.jsx(G,{zIndex:r,children:b}):b]})})},le=e=>function({children:n,theme:r=e,toastOptions:t,...i}){return s.jsxs(se,{theme:r,...i,children:[s.jsx(W,{value:t==null?void 0:t.defaultOptions,children:n}),s.jsx(K,{...t})]})},de=le(j);const ue=()=>l.useMemo(()=>({colorScheme:"dark",fontFamily:"'Inter Variable', sans-serif",components:{ScrollArea:{defaultProps:{scrollbarSize:10},styles:{scrollbar:{"&:hover":{backgroundColor:"var(--invokeai-colors-baseAlpha-300)"}},thumb:{backgroundColor:"var(--invokeai-colors-baseAlpha-300)"}}}}}),[]),ce=L("@@invokeai-color-mode");function he({children:e}){const{i18n:o}=H(),n=o.dir(),r=l.useMemo(()=>ie({...U,direction:n}),[n]);l.useEffect(()=>{document.body.dir=n},[n]);const t=ue();return s.jsx(Z,{theme:t,children:s.jsx(de,{theme:r,colorModeManager:ce,toastOptions:Y,children:e})})}const ve=l.memo(he);export{ve as default};
|
||||
156
invokeai/frontend/web/dist/assets/index-6080a931.js
vendored
Normal file
156
invokeai/frontend/web/dist/assets/index-6080a931.js
vendored
Normal file
File diff suppressed because one or more lines are too long
156
invokeai/frontend/web/dist/assets/index-7ba6700b.js
vendored
156
invokeai/frontend/web/dist/assets/index-7ba6700b.js
vendored
File diff suppressed because one or more lines are too long
2
invokeai/frontend/web/dist/index.html
vendored
2
invokeai/frontend/web/dist/index.html
vendored
@@ -15,7 +15,7 @@
|
||||
margin: 0;
|
||||
}
|
||||
</style>
|
||||
<script type="module" crossorigin src="./assets/index-7ba6700b.js"></script>
|
||||
<script type="module" crossorigin src="./assets/index-6080a931.js"></script>
|
||||
</head>
|
||||
|
||||
<body dir="ltr">
|
||||
|
||||
212
invokeai/frontend/web/dist/locales/de.json
vendored
212
invokeai/frontend/web/dist/locales/de.json
vendored
@@ -83,14 +83,10 @@
|
||||
"darkMode": "Dunkler Modus",
|
||||
"loading": "Lade",
|
||||
"random": "Zufall",
|
||||
"batch": "Stapel-Manager",
|
||||
"batch": "Batch-Manager",
|
||||
"advanced": "Erweitert",
|
||||
"langBrPortuguese": "Portugiesisch (Brasilien)",
|
||||
"unifiedCanvas": "Einheitliche Leinwand",
|
||||
"openInNewTab": "In einem neuem Tab öffnen",
|
||||
"statusProcessing": "wird bearbeitet",
|
||||
"linear": "Linear",
|
||||
"imagePrompt": "Bild Prompt"
|
||||
"unifiedCanvas": "Einheitliche Leinwand"
|
||||
},
|
||||
"gallery": {
|
||||
"generations": "Erzeugungen",
|
||||
@@ -105,15 +101,7 @@
|
||||
"singleColumnLayout": "Einspaltiges Layout",
|
||||
"allImagesLoaded": "Alle Bilder geladen",
|
||||
"loadMore": "Mehr laden",
|
||||
"noImagesInGallery": "Keine Bilder in der Galerie",
|
||||
"loading": "Lade",
|
||||
"preparingDownload": "bereite Download vor",
|
||||
"preparingDownloadFailed": "Problem beim Download vorbereiten",
|
||||
"deleteImage": "Lösche Bild",
|
||||
"images": "Bilder",
|
||||
"copy": "Kopieren",
|
||||
"download": "Runterladen",
|
||||
"setCurrentImage": "Setze aktuelle Bild"
|
||||
"noImagesInGallery": "Keine Bilder in der Galerie"
|
||||
},
|
||||
"hotkeys": {
|
||||
"keyboardShortcuts": "Tastenkürzel",
|
||||
@@ -122,8 +110,7 @@
|
||||
"galleryHotkeys": "Galerie Tastenkürzel",
|
||||
"unifiedCanvasHotkeys": "Unified Canvas Tastenkürzel",
|
||||
"invoke": {
|
||||
"desc": "Ein Bild erzeugen",
|
||||
"title": "Invoke"
|
||||
"desc": "Ein Bild erzeugen"
|
||||
},
|
||||
"cancel": {
|
||||
"title": "Abbrechen",
|
||||
@@ -320,10 +307,6 @@
|
||||
"acceptStagingImage": {
|
||||
"title": "Staging-Bild akzeptieren",
|
||||
"desc": "Akzeptieren Sie das aktuelle Bild des Staging-Bereichs"
|
||||
},
|
||||
"nodesHotkeys": "Knoten Tastenkürzel",
|
||||
"addNodes": {
|
||||
"title": "Knotenpunkt hinzufügen"
|
||||
}
|
||||
},
|
||||
"modelManager": {
|
||||
@@ -373,7 +356,7 @@
|
||||
"deleteModel": "Model löschen",
|
||||
"deleteConfig": "Konfiguration löschen",
|
||||
"deleteMsg1": "Möchten Sie diesen Model-Eintrag wirklich aus InvokeAI löschen?",
|
||||
"deleteMsg2": "Dadurch WIRD das Modell von der Festplatte gelöscht WENN es im InvokeAI Root Ordner liegt. Wenn es in einem anderem Ordner liegt wird das Modell NICHT von der Festplatte gelöscht.",
|
||||
"deleteMsg2": "Dadurch wird die Modellprüfpunktdatei nicht von Ihrer Festplatte gelöscht. Sie können sie bei Bedarf erneut hinzufügen.",
|
||||
"customConfig": "Benutzerdefinierte Konfiguration",
|
||||
"invokeRoot": "InvokeAI Ordner",
|
||||
"formMessageDiffusersVAELocationDesc": "Falls nicht angegeben, sucht InvokeAI nach der VAE-Datei innerhalb des oben angegebenen Modell Speicherortes.",
|
||||
@@ -419,17 +402,7 @@
|
||||
"v2_768": "v2 (768px)",
|
||||
"none": "Nix",
|
||||
"repoIDValidationMsg": "Online Repo Ihres Modells",
|
||||
"vaeRepoIDValidationMsg": "Online Repo Ihrer VAE",
|
||||
"importModels": "Importiere Modelle",
|
||||
"merge": "Zusammenführen",
|
||||
"addDiffuserModel": "Diffusers hinzufügen",
|
||||
"advanced": "Erweitert",
|
||||
"closeAdvanced": "Schließe Erweitert",
|
||||
"convertingModelBegin": "Konvertiere Modell. Bitte warten.",
|
||||
"customConfigFileLocation": "Benutzerdefinierte Konfiguration Datei Speicherort",
|
||||
"baseModel": "Basis Modell",
|
||||
"convertToDiffusers": "Konvertiere zu Diffusers",
|
||||
"diffusersModels": "Diffusers"
|
||||
"vaeRepoIDValidationMsg": "Online Repo Ihrer VAE"
|
||||
},
|
||||
"parameters": {
|
||||
"images": "Bilder",
|
||||
@@ -449,7 +422,7 @@
|
||||
"type": "Art",
|
||||
"strength": "Stärke",
|
||||
"upscaling": "Hochskalierung",
|
||||
"upscale": "Hochskalieren (Shift + U)",
|
||||
"upscale": "Hochskalieren",
|
||||
"upscaleImage": "Bild hochskalieren",
|
||||
"scale": "Maßstab",
|
||||
"otherOptions": "Andere Optionen",
|
||||
@@ -505,7 +478,7 @@
|
||||
"resetWebUI": "Web-Oberfläche zurücksetzen",
|
||||
"resetWebUIDesc1": "Das Zurücksetzen der Web-Oberfläche setzt nur den lokalen Cache des Browsers mit Ihren Bildern und gespeicherten Einstellungen zurück. Es werden keine Bilder von der Festplatte gelöscht.",
|
||||
"resetWebUIDesc2": "Wenn die Bilder nicht in der Galerie angezeigt werden oder etwas anderes nicht funktioniert, versuchen Sie bitte, die Einstellungen zurückzusetzen, bevor Sie einen Fehler auf GitHub melden.",
|
||||
"resetComplete": "Die Web-Oberfläche wurde zurückgesetzt.",
|
||||
"resetComplete": "Die Web-Oberfläche wurde zurückgesetzt. Aktualisieren Sie die Seite, um sie neu zu laden.",
|
||||
"models": "Modelle",
|
||||
"useSlidersForAll": "Schieberegler für alle Optionen verwenden"
|
||||
},
|
||||
@@ -517,7 +490,7 @@
|
||||
"imageCopied": "Bild kopiert",
|
||||
"imageLinkCopied": "Bildlink kopiert",
|
||||
"imageNotLoaded": "Kein Bild geladen",
|
||||
"imageNotLoadedDesc": "Konnte kein Bild finden",
|
||||
"imageNotLoadedDesc": "Kein Bild gefunden, das an das Bild zu Bild-Modul gesendet werden kann",
|
||||
"imageSavedToGallery": "Bild in die Galerie gespeichert",
|
||||
"canvasMerged": "Leinwand zusammengeführt",
|
||||
"sentToImageToImage": "Gesendet an Bild zu Bild",
|
||||
@@ -612,10 +585,7 @@
|
||||
"betaClear": "Löschen",
|
||||
"betaDarkenOutside": "Außen abdunkeln",
|
||||
"betaLimitToBox": "Begrenzung auf das Feld",
|
||||
"betaPreserveMasked": "Maskiertes bewahren",
|
||||
"antialiasing": "Kantenglättung",
|
||||
"showResultsOn": "Zeige Ergebnisse (An)",
|
||||
"showResultsOff": "Zeige Ergebnisse (Aus)"
|
||||
"betaPreserveMasked": "Maskiertes bewahren"
|
||||
},
|
||||
"accessibility": {
|
||||
"modelSelect": "Model Auswahl",
|
||||
@@ -656,166 +626,6 @@
|
||||
"downloadBoard": "Ordner runterladen",
|
||||
"changeBoard": "Ordner wechseln",
|
||||
"loading": "Laden...",
|
||||
"clearSearch": "Suche leeren",
|
||||
"bottomMessage": "Durch das Löschen dieses Ordners und seiner Bilder werden alle Funktionen zurückgesetzt, die sie derzeit verwenden."
|
||||
},
|
||||
"controlnet": {
|
||||
"showAdvanced": "Zeige Erweitert",
|
||||
"contentShuffleDescription": "Mischt den Inhalt von einem Bild",
|
||||
"addT2IAdapter": "$t(common.t2iAdapter) hinzufügen",
|
||||
"importImageFromCanvas": "Importieren Bild von Zeichenfläche",
|
||||
"lineartDescription": "Konvertiere Bild zu Lineart",
|
||||
"importMaskFromCanvas": "Importiere Maske von Zeichenfläche",
|
||||
"hed": "HED",
|
||||
"hideAdvanced": "Verstecke Erweitert",
|
||||
"contentShuffle": "Inhalt mischen",
|
||||
"controlNetEnabledT2IDisabled": "$t(common.controlNet) ist aktiv, $t(common.t2iAdapter) ist deaktiviert",
|
||||
"ipAdapterModel": "Adapter Modell",
|
||||
"beginEndStepPercent": "Start / Ende Step Prozent",
|
||||
"duplicate": "Kopieren",
|
||||
"f": "F",
|
||||
"h": "H",
|
||||
"depthMidasDescription": "Tiefenmap erstellen mit Midas",
|
||||
"controlnet": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.controlNet))",
|
||||
"t2iEnabledControlNetDisabled": "$t(common.t2iAdapter) ist aktiv, $t(common.controlNet) ist deaktiviert",
|
||||
"weight": "Breite",
|
||||
"selectModel": "Wähle ein Modell",
|
||||
"depthMidas": "Tiefe (Midas)",
|
||||
"w": "W",
|
||||
"addControlNet": "$t(common.controlNet) hinzufügen",
|
||||
"none": "Kein",
|
||||
"incompatibleBaseModel": "Inkompatibles Basismodell:",
|
||||
"enableControlnet": "Aktiviere ControlNet",
|
||||
"detectResolution": "Auflösung erkennen",
|
||||
"controlNetT2IMutexDesc": "$t(common.controlNet) und $t(common.t2iAdapter) zur gleichen Zeit wird nicht unterstützt.",
|
||||
"ip_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.ipAdapter))",
|
||||
"fill": "Füllen",
|
||||
"addIPAdapter": "$t(common.ipAdapter) hinzufügen",
|
||||
"colorMapDescription": "Erstelle eine Farbkarte von diesem Bild",
|
||||
"t2i_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.t2iAdapter))",
|
||||
"imageResolution": "Bild Auflösung",
|
||||
"depthZoe": "Tiefe (Zoe)",
|
||||
"colorMap": "Farbe",
|
||||
"lowThreshold": "Niedrige Schwelle",
|
||||
"highThreshold": "Hohe Schwelle",
|
||||
"toggleControlNet": "Schalten ControlNet um",
|
||||
"delete": "Löschen",
|
||||
"controlAdapter_one": "Control Adapter",
|
||||
"controlAdapter_other": "Control Adapters",
|
||||
"colorMapTileSize": "Tile Größe",
|
||||
"depthZoeDescription": "Tiefenmap erstellen mit Zoe",
|
||||
"setControlImageDimensions": "Setze Control Bild Auflösung auf Breite/Höhe",
|
||||
"handAndFace": "Hand und Gesicht",
|
||||
"enableIPAdapter": "Aktiviere IP Adapter",
|
||||
"resize": "Größe ändern",
|
||||
"resetControlImage": "Zurücksetzen vom Referenz Bild",
|
||||
"balanced": "Ausgewogen",
|
||||
"prompt": "Prompt",
|
||||
"resizeMode": "Größenänderungsmodus",
|
||||
"processor": "Prozessor",
|
||||
"saveControlImage": "Speichere Referenz Bild",
|
||||
"safe": "Speichern",
|
||||
"ipAdapterImageFallback": "Kein IP Adapter Bild ausgewählt",
|
||||
"resetIPAdapterImage": "Zurücksetzen vom IP Adapter Bild"
|
||||
},
|
||||
"queue": {
|
||||
"status": "Status",
|
||||
"cancelTooltip": "Aktuellen Aufgabe abbrechen",
|
||||
"queueEmpty": "Warteschlange leer",
|
||||
"in_progress": "In Arbeit",
|
||||
"queueFront": "An den Anfang der Warteschlange tun",
|
||||
"completed": "Fertig",
|
||||
"queueBack": "In die Warteschlange",
|
||||
"clearFailed": "Probleme beim leeren der Warteschlange",
|
||||
"clearSucceeded": "Warteschlange geleert",
|
||||
"pause": "Pause",
|
||||
"cancelSucceeded": "Auftrag abgebrochen",
|
||||
"queue": "Warteschlange",
|
||||
"batch": "Stapel",
|
||||
"pending": "Ausstehend",
|
||||
"clear": "Leeren",
|
||||
"prune": "Leeren",
|
||||
"total": "Gesamt",
|
||||
"canceled": "Abgebrochen",
|
||||
"clearTooltip": "Abbrechen und alle Aufträge leeren",
|
||||
"current": "Aktuell",
|
||||
"failed": "Fehler",
|
||||
"cancelItem": "Abbruch Auftrag",
|
||||
"next": "Nächste",
|
||||
"cancel": "Abbruch",
|
||||
"session": "Sitzung",
|
||||
"queueTotal": "{{total}} Gesamt",
|
||||
"resume": "Wieder aufnehmen",
|
||||
"item": "Auftrag",
|
||||
"notReady": "Warteschlange noch nicht bereit",
|
||||
"batchValues": "Stapel Werte",
|
||||
"queueCountPrediction": "{{predicted}} zur Warteschlange hinzufügen",
|
||||
"queuedCount": "{{pending}} wartenden Elemente",
|
||||
"clearQueueAlertDialog": "Die Warteschlange leeren, stoppt den aktuellen Prozess und leert die Warteschlange komplett.",
|
||||
"completedIn": "Fertig in",
|
||||
"cancelBatchSucceeded": "Stapel abgebrochen",
|
||||
"cancelBatch": "Stapel stoppen",
|
||||
"enqueueing": "Stapel in der Warteschlange",
|
||||
"queueMaxExceeded": "Maximum von {{max_queue_size}} Elementen erreicht, würde {{skip}} Elemente überspringen",
|
||||
"cancelBatchFailed": "Problem beim Abbruch vom Stapel",
|
||||
"clearQueueAlertDialog2": "bist du sicher die Warteschlange zu leeren?"
|
||||
},
|
||||
"metadata": {
|
||||
"negativePrompt": "Negativ Beschreibung",
|
||||
"metadata": "Meta-Data",
|
||||
"strength": "Bild zu Bild stärke",
|
||||
"imageDetails": "Bild Details",
|
||||
"model": "Modell",
|
||||
"noImageDetails": "Keine Bild Details gefunden",
|
||||
"cfgScale": "CFG-Skala",
|
||||
"fit": "Bild zu Bild passen",
|
||||
"height": "Höhe",
|
||||
"noMetaData": "Keine Meta-Data gefunden",
|
||||
"width": "Breite",
|
||||
"createdBy": "Erstellt von",
|
||||
"steps": "Schritte"
|
||||
},
|
||||
"popovers": {
|
||||
"noiseUseCPU": {
|
||||
"heading": "Nutze Prozessor rauschen"
|
||||
},
|
||||
"paramModel": {
|
||||
"heading": "Modell"
|
||||
},
|
||||
"paramIterations": {
|
||||
"heading": "Iterationen"
|
||||
},
|
||||
"paramCFGScale": {
|
||||
"heading": "CFG-Skala"
|
||||
},
|
||||
"paramSteps": {
|
||||
"heading": "Schritte"
|
||||
},
|
||||
"lora": {
|
||||
"heading": "LoRA Gewichte"
|
||||
},
|
||||
"infillMethod": {
|
||||
"heading": "Füllmethode"
|
||||
},
|
||||
"paramVAE": {
|
||||
"heading": "VAE"
|
||||
}
|
||||
},
|
||||
"ui": {
|
||||
"lockRatio": "Verhältnis sperren",
|
||||
"hideProgressImages": "Verstecke Prozess Bild",
|
||||
"showProgressImages": "Zeige Prozess Bild"
|
||||
},
|
||||
"invocationCache": {
|
||||
"disable": "Deaktivieren",
|
||||
"misses": "Cache Nötig",
|
||||
"hits": "Cache Treffer",
|
||||
"enable": "Aktivieren",
|
||||
"clear": "Leeren"
|
||||
},
|
||||
"embedding": {
|
||||
"noMatchingEmbedding": "Keine passenden Embeddings",
|
||||
"addEmbedding": "Embedding hinzufügen",
|
||||
"incompatibleModel": "Inkompatibles Basismodell:"
|
||||
"clearSearch": "Suche leeren"
|
||||
}
|
||||
}
|
||||
|
||||
37
invokeai/frontend/web/dist/locales/en.json
vendored
37
invokeai/frontend/web/dist/locales/en.json
vendored
@@ -221,19 +221,6 @@
|
||||
"resetIPAdapterImage": "Reset IP Adapter Image",
|
||||
"ipAdapterImageFallback": "No IP Adapter Image Selected"
|
||||
},
|
||||
"hrf": {
|
||||
"hrf": "High Resolution Fix",
|
||||
"enableHrf": "Enable High Resolution Fix",
|
||||
"enableHrfTooltip": "Generate with a lower initial resolution, upscale to the base resolution, then run Image-to-Image.",
|
||||
"upscaleMethod": "Upscale Method",
|
||||
"hrfStrength": "High Resolution Fix Strength",
|
||||
"strengthTooltip": "Lower values result in fewer details, which may reduce potential artifacts.",
|
||||
"metadata": {
|
||||
"enabled": "High Resolution Fix Enabled",
|
||||
"strength": "High Resolution Fix Strength",
|
||||
"method": "High Resolution Fix Method"
|
||||
}
|
||||
},
|
||||
"embedding": {
|
||||
"addEmbedding": "Add Embedding",
|
||||
"incompatibleModel": "Incompatible base model:",
|
||||
@@ -735,9 +722,7 @@
|
||||
"noMatchingModels": "No matching Models",
|
||||
"noModelsAvailable": "No models available",
|
||||
"selectLoRA": "Select a LoRA",
|
||||
"selectModel": "Select a Model",
|
||||
"noLoRAsInstalled": "No LoRAs installed",
|
||||
"noRefinerModelsInstalled": "No SDXL Refiner models installed"
|
||||
"selectModel": "Select a Model"
|
||||
},
|
||||
"nodes": {
|
||||
"addNode": "Add Node",
|
||||
@@ -1271,11 +1256,15 @@
|
||||
},
|
||||
"compositingBlur": {
|
||||
"heading": "Blur",
|
||||
"paragraphs": ["The blur radius of the mask."]
|
||||
"paragraphs": [
|
||||
"The blur radius of the mask."
|
||||
]
|
||||
},
|
||||
"compositingBlurMethod": {
|
||||
"heading": "Blur Method",
|
||||
"paragraphs": ["The method of blur applied to the masked area."]
|
||||
"paragraphs": [
|
||||
"The method of blur applied to the masked area."
|
||||
]
|
||||
},
|
||||
"compositingCoherencePass": {
|
||||
"heading": "Coherence Pass",
|
||||
@@ -1285,7 +1274,9 @@
|
||||
},
|
||||
"compositingCoherenceMode": {
|
||||
"heading": "Mode",
|
||||
"paragraphs": ["The mode of the Coherence Pass."]
|
||||
"paragraphs": [
|
||||
"The mode of the Coherence Pass."
|
||||
]
|
||||
},
|
||||
"compositingCoherenceSteps": {
|
||||
"heading": "Steps",
|
||||
@@ -1303,7 +1294,9 @@
|
||||
},
|
||||
"compositingMaskAdjustments": {
|
||||
"heading": "Mask Adjustments",
|
||||
"paragraphs": ["Adjust the mask."]
|
||||
"paragraphs": [
|
||||
"Adjust the mask."
|
||||
]
|
||||
},
|
||||
"controlNetBeginEnd": {
|
||||
"heading": "Begin / End Step Percentage",
|
||||
@@ -1361,7 +1354,9 @@
|
||||
},
|
||||
"infillMethod": {
|
||||
"heading": "Infill Method",
|
||||
"paragraphs": ["Method to infill the selected area."]
|
||||
"paragraphs": [
|
||||
"Method to infill the selected area."
|
||||
]
|
||||
},
|
||||
"lora": {
|
||||
"heading": "LoRA Weight",
|
||||
|
||||
7
invokeai/frontend/web/dist/locales/it.json
vendored
7
invokeai/frontend/web/dist/locales/it.json
vendored
@@ -1025,8 +1025,7 @@
|
||||
"imageFieldDescription": "Le immagini possono essere passate tra i nodi.",
|
||||
"unableToParseEdge": "Impossibile analizzare il bordo",
|
||||
"latentsCollectionDescription": "Le immagini latenti possono essere passate tra i nodi.",
|
||||
"imageCollection": "Raccolta Immagini",
|
||||
"loRAModelField": "LoRA"
|
||||
"imageCollection": "Raccolta Immagini"
|
||||
},
|
||||
"boards": {
|
||||
"autoAddBoard": "Aggiungi automaticamente bacheca",
|
||||
@@ -1193,9 +1192,7 @@
|
||||
"noLoRAsAvailable": "Nessun LoRA disponibile",
|
||||
"noModelsAvailable": "Nessun modello disponibile",
|
||||
"selectModel": "Seleziona un modello",
|
||||
"selectLoRA": "Seleziona un LoRA",
|
||||
"noRefinerModelsInstalled": "Nessun modello SDXL Refiner installato",
|
||||
"noLoRAsInstalled": "Nessun LoRA installato"
|
||||
"selectLoRA": "Seleziona un LoRA"
|
||||
},
|
||||
"invocationCache": {
|
||||
"disable": "Disabilita",
|
||||
|
||||
10
invokeai/frontend/web/dist/locales/zh_CN.json
vendored
10
invokeai/frontend/web/dist/locales/zh_CN.json
vendored
@@ -866,7 +866,7 @@
|
||||
"version": "版本",
|
||||
"validateConnections": "验证连接和节点图",
|
||||
"inputMayOnlyHaveOneConnection": "输入仅能有一个连接",
|
||||
"notes": "注释",
|
||||
"notes": "节点",
|
||||
"nodeOutputs": "节点输出",
|
||||
"currentImageDescription": "在节点编辑器中显示当前图像",
|
||||
"validateConnectionsHelp": "防止建立无效连接和调用无效节点图",
|
||||
@@ -892,11 +892,11 @@
|
||||
"currentImage": "当前图像",
|
||||
"workflowName": "名称",
|
||||
"cannotConnectInputToInput": "无法将输入连接到输入",
|
||||
"workflowNotes": "注释",
|
||||
"workflowNotes": "节点",
|
||||
"cannotConnectOutputToOutput": "无法将输出连接到输出",
|
||||
"connectionWouldCreateCycle": "连接将创建一个循环",
|
||||
"cannotConnectToSelf": "无法连接自己",
|
||||
"notesDescription": "添加有关您的工作流的注释",
|
||||
"notesDescription": "添加有关您的工作流的节点",
|
||||
"unknownField": "未知",
|
||||
"colorCodeEdges": "边缘颜色编码",
|
||||
"unknownNode": "未知节点",
|
||||
@@ -1231,9 +1231,7 @@
|
||||
"noLoRAsAvailable": "无可用 LoRA",
|
||||
"noModelsAvailable": "无可用模型",
|
||||
"selectModel": "选择一个模型",
|
||||
"selectLoRA": "选择一个 LoRA",
|
||||
"noRefinerModelsInstalled": "无已安装的 SDXL Refiner 模型",
|
||||
"noLoRAsInstalled": "无已安装的 LoRA"
|
||||
"selectLoRA": "选择一个 LoRA"
|
||||
},
|
||||
"boards": {
|
||||
"autoAddBoard": "自动添加面板",
|
||||
|
||||
@@ -54,35 +54,42 @@
|
||||
]
|
||||
},
|
||||
"dependencies": {
|
||||
"@chakra-ui/anatomy": "^2.2.2",
|
||||
"@chakra-ui/anatomy": "^2.2.1",
|
||||
"@chakra-ui/icons": "^2.1.1",
|
||||
"@chakra-ui/react": "^2.8.2",
|
||||
"@chakra-ui/styled-system": "^2.9.2",
|
||||
"@chakra-ui/theme-tools": "^2.1.2",
|
||||
"@chakra-ui/react": "^2.8.1",
|
||||
"@chakra-ui/styled-system": "^2.9.1",
|
||||
"@chakra-ui/theme-tools": "^2.1.1",
|
||||
"@dagrejs/graphlib": "^2.1.13",
|
||||
"@dnd-kit/core": "^6.1.0",
|
||||
"@dnd-kit/utilities": "^3.2.2",
|
||||
"@dnd-kit/core": "^6.0.8",
|
||||
"@dnd-kit/modifiers": "^6.0.1",
|
||||
"@dnd-kit/utilities": "^3.2.1",
|
||||
"@emotion/react": "^11.11.1",
|
||||
"@emotion/styled": "^11.11.0",
|
||||
"@fontsource-variable/inter": "^5.0.15",
|
||||
"@floating-ui/react-dom": "^2.0.2",
|
||||
"@fontsource-variable/inter": "^5.0.13",
|
||||
"@fontsource/inter": "^5.0.13",
|
||||
"@mantine/core": "^6.0.19",
|
||||
"@mantine/form": "^6.0.19",
|
||||
"@mantine/hooks": "^6.0.19",
|
||||
"@nanostores/react": "^0.7.1",
|
||||
"@reduxjs/toolkit": "^1.9.7",
|
||||
"@roarr/browser-log-writer": "^1.3.0",
|
||||
"@stevebel/png": "^1.5.1",
|
||||
"compare-versions": "^6.1.0",
|
||||
"dateformat": "^5.0.3",
|
||||
"formik": "^2.4.5",
|
||||
"framer-motion": "^10.16.4",
|
||||
"i18next": "^23.6.0",
|
||||
"i18next-http-backend": "^2.3.1",
|
||||
"konva": "^9.2.3",
|
||||
"fuse.js": "^6.6.2",
|
||||
"i18next": "^23.5.1",
|
||||
"i18next-browser-languagedetector": "^7.0.2",
|
||||
"i18next-http-backend": "^2.2.2",
|
||||
"konva": "^9.2.2",
|
||||
"lodash-es": "^4.17.21",
|
||||
"nanostores": "^0.9.4",
|
||||
"nanostores": "^0.9.2",
|
||||
"new-github-issue-url": "^1.0.0",
|
||||
"openapi-fetch": "^0.8.1",
|
||||
"overlayscrollbars": "^2.4.4",
|
||||
"overlayscrollbars-react": "^0.5.3",
|
||||
"openapi-fetch": "^0.7.10",
|
||||
"overlayscrollbars": "^2.3.2",
|
||||
"overlayscrollbars-react": "^0.5.2",
|
||||
"patch-package": "^8.0.0",
|
||||
"query-string": "^8.1.0",
|
||||
"react": "^18.2.0",
|
||||
@@ -91,25 +98,26 @@
|
||||
"react-dropzone": "^14.2.3",
|
||||
"react-error-boundary": "^4.0.11",
|
||||
"react-hotkeys-hook": "4.4.1",
|
||||
"react-i18next": "^13.3.1",
|
||||
"react-i18next": "^13.3.0",
|
||||
"react-icons": "^4.11.0",
|
||||
"react-konva": "^18.2.10",
|
||||
"react-redux": "^8.1.3",
|
||||
"react-resizable-panels": "^0.0.55",
|
||||
"react-use": "^17.4.0",
|
||||
"react-virtuoso": "^4.6.2",
|
||||
"reactflow": "^11.9.4",
|
||||
"react-virtuoso": "^4.6.1",
|
||||
"react-zoom-pan-pinch": "^3.2.0",
|
||||
"reactflow": "^11.9.3",
|
||||
"redux-dynamic-middlewares": "^2.2.0",
|
||||
"redux-remember": "^4.0.4",
|
||||
"roarr": "^7.18.3",
|
||||
"roarr": "^7.15.1",
|
||||
"serialize-error": "^11.0.2",
|
||||
"socket.io-client": "^4.7.2",
|
||||
"type-fest": "^4.7.1",
|
||||
"use-debounce": "^10.0.0",
|
||||
"type-fest": "^4.4.0",
|
||||
"use-debounce": "^9.0.4",
|
||||
"use-image": "^1.1.1",
|
||||
"uuid": "^9.0.1",
|
||||
"zod": "^3.22.4",
|
||||
"zod-validation-error": "^2.1.0"
|
||||
"zod-validation-error": "^1.5.0"
|
||||
},
|
||||
"peerDependencies": {
|
||||
"@chakra-ui/cli": "^2.4.0",
|
||||
@@ -120,33 +128,39 @@
|
||||
},
|
||||
"devDependencies": {
|
||||
"@chakra-ui/cli": "^2.4.1",
|
||||
"@types/dateformat": "^5.0.2",
|
||||
"@types/lodash-es": "^4.17.11",
|
||||
"@types/node": "^20.9.0",
|
||||
"@types/react": "^18.2.37",
|
||||
"@types/react-dom": "^18.2.15",
|
||||
"@types/react-redux": "^7.1.30",
|
||||
"@types/uuid": "^9.0.7",
|
||||
"@typescript-eslint/eslint-plugin": "^6.10.0",
|
||||
"@typescript-eslint/parser": "^6.10.0",
|
||||
"@vitejs/plugin-react-swc": "^3.4.1",
|
||||
"concurrently": "^8.2.2",
|
||||
"eslint": "^8.53.0",
|
||||
"@types/dateformat": "^5.0.0",
|
||||
"@types/lodash-es": "^4.17.9",
|
||||
"@types/node": "^20.8.6",
|
||||
"@types/react": "^18.2.28",
|
||||
"@types/react-dom": "^18.2.13",
|
||||
"@types/react-redux": "^7.1.27",
|
||||
"@types/react-transition-group": "^4.4.7",
|
||||
"@types/uuid": "^9.0.5",
|
||||
"@typescript-eslint/eslint-plugin": "^6.7.5",
|
||||
"@typescript-eslint/parser": "^6.7.5",
|
||||
"@vitejs/plugin-react-swc": "^3.4.0",
|
||||
"axios": "^1.5.1",
|
||||
"babel-plugin-transform-imports": "^2.0.0",
|
||||
"concurrently": "^8.2.1",
|
||||
"eslint": "^8.51.0",
|
||||
"eslint-config-prettier": "^9.0.0",
|
||||
"eslint-plugin-prettier": "^5.0.1",
|
||||
"eslint-plugin-react": "^7.33.2",
|
||||
"eslint-plugin-react-hooks": "^4.6.0",
|
||||
"form-data": "^4.0.0",
|
||||
"husky": "^8.0.3",
|
||||
"lint-staged": "^15.0.2",
|
||||
"lint-staged": "^15.0.1",
|
||||
"madge": "^6.1.0",
|
||||
"openapi-types": "^12.1.3",
|
||||
"openapi-typescript": "^6.7.0",
|
||||
"postinstall-postinstall": "^2.1.0",
|
||||
"prettier": "^3.0.3",
|
||||
"rollup-plugin-visualizer": "^5.9.2",
|
||||
"ts-toolbelt": "^9.6.0",
|
||||
"typescript": "^5.2.2",
|
||||
"vite": "^4.5.0",
|
||||
"vite": "^4.4.11",
|
||||
"vite-plugin-css-injected-by-js": "^3.3.0",
|
||||
"vite-plugin-dts": "^3.6.3",
|
||||
"vite-plugin-dts": "^3.6.0",
|
||||
"vite-plugin-eslint": "^1.8.1",
|
||||
"vite-tsconfig-paths": "^4.2.1",
|
||||
"yarn": "^1.22.19"
|
||||
|
||||
@@ -83,14 +83,10 @@
|
||||
"darkMode": "Dunkler Modus",
|
||||
"loading": "Lade",
|
||||
"random": "Zufall",
|
||||
"batch": "Stapel-Manager",
|
||||
"batch": "Batch-Manager",
|
||||
"advanced": "Erweitert",
|
||||
"langBrPortuguese": "Portugiesisch (Brasilien)",
|
||||
"unifiedCanvas": "Einheitliche Leinwand",
|
||||
"openInNewTab": "In einem neuem Tab öffnen",
|
||||
"statusProcessing": "wird bearbeitet",
|
||||
"linear": "Linear",
|
||||
"imagePrompt": "Bild Prompt"
|
||||
"unifiedCanvas": "Einheitliche Leinwand"
|
||||
},
|
||||
"gallery": {
|
||||
"generations": "Erzeugungen",
|
||||
@@ -105,15 +101,7 @@
|
||||
"singleColumnLayout": "Einspaltiges Layout",
|
||||
"allImagesLoaded": "Alle Bilder geladen",
|
||||
"loadMore": "Mehr laden",
|
||||
"noImagesInGallery": "Keine Bilder in der Galerie",
|
||||
"loading": "Lade",
|
||||
"preparingDownload": "bereite Download vor",
|
||||
"preparingDownloadFailed": "Problem beim Download vorbereiten",
|
||||
"deleteImage": "Lösche Bild",
|
||||
"images": "Bilder",
|
||||
"copy": "Kopieren",
|
||||
"download": "Runterladen",
|
||||
"setCurrentImage": "Setze aktuelle Bild"
|
||||
"noImagesInGallery": "Keine Bilder in der Galerie"
|
||||
},
|
||||
"hotkeys": {
|
||||
"keyboardShortcuts": "Tastenkürzel",
|
||||
@@ -122,8 +110,7 @@
|
||||
"galleryHotkeys": "Galerie Tastenkürzel",
|
||||
"unifiedCanvasHotkeys": "Unified Canvas Tastenkürzel",
|
||||
"invoke": {
|
||||
"desc": "Ein Bild erzeugen",
|
||||
"title": "Invoke"
|
||||
"desc": "Ein Bild erzeugen"
|
||||
},
|
||||
"cancel": {
|
||||
"title": "Abbrechen",
|
||||
@@ -320,10 +307,6 @@
|
||||
"acceptStagingImage": {
|
||||
"title": "Staging-Bild akzeptieren",
|
||||
"desc": "Akzeptieren Sie das aktuelle Bild des Staging-Bereichs"
|
||||
},
|
||||
"nodesHotkeys": "Knoten Tastenkürzel",
|
||||
"addNodes": {
|
||||
"title": "Knotenpunkt hinzufügen"
|
||||
}
|
||||
},
|
||||
"modelManager": {
|
||||
@@ -373,7 +356,7 @@
|
||||
"deleteModel": "Model löschen",
|
||||
"deleteConfig": "Konfiguration löschen",
|
||||
"deleteMsg1": "Möchten Sie diesen Model-Eintrag wirklich aus InvokeAI löschen?",
|
||||
"deleteMsg2": "Dadurch WIRD das Modell von der Festplatte gelöscht WENN es im InvokeAI Root Ordner liegt. Wenn es in einem anderem Ordner liegt wird das Modell NICHT von der Festplatte gelöscht.",
|
||||
"deleteMsg2": "Dadurch wird die Modellprüfpunktdatei nicht von Ihrer Festplatte gelöscht. Sie können sie bei Bedarf erneut hinzufügen.",
|
||||
"customConfig": "Benutzerdefinierte Konfiguration",
|
||||
"invokeRoot": "InvokeAI Ordner",
|
||||
"formMessageDiffusersVAELocationDesc": "Falls nicht angegeben, sucht InvokeAI nach der VAE-Datei innerhalb des oben angegebenen Modell Speicherortes.",
|
||||
@@ -419,17 +402,7 @@
|
||||
"v2_768": "v2 (768px)",
|
||||
"none": "Nix",
|
||||
"repoIDValidationMsg": "Online Repo Ihres Modells",
|
||||
"vaeRepoIDValidationMsg": "Online Repo Ihrer VAE",
|
||||
"importModels": "Importiere Modelle",
|
||||
"merge": "Zusammenführen",
|
||||
"addDiffuserModel": "Diffusers hinzufügen",
|
||||
"advanced": "Erweitert",
|
||||
"closeAdvanced": "Schließe Erweitert",
|
||||
"convertingModelBegin": "Konvertiere Modell. Bitte warten.",
|
||||
"customConfigFileLocation": "Benutzerdefinierte Konfiguration Datei Speicherort",
|
||||
"baseModel": "Basis Modell",
|
||||
"convertToDiffusers": "Konvertiere zu Diffusers",
|
||||
"diffusersModels": "Diffusers"
|
||||
"vaeRepoIDValidationMsg": "Online Repo Ihrer VAE"
|
||||
},
|
||||
"parameters": {
|
||||
"images": "Bilder",
|
||||
@@ -449,7 +422,7 @@
|
||||
"type": "Art",
|
||||
"strength": "Stärke",
|
||||
"upscaling": "Hochskalierung",
|
||||
"upscale": "Hochskalieren (Shift + U)",
|
||||
"upscale": "Hochskalieren",
|
||||
"upscaleImage": "Bild hochskalieren",
|
||||
"scale": "Maßstab",
|
||||
"otherOptions": "Andere Optionen",
|
||||
@@ -505,7 +478,7 @@
|
||||
"resetWebUI": "Web-Oberfläche zurücksetzen",
|
||||
"resetWebUIDesc1": "Das Zurücksetzen der Web-Oberfläche setzt nur den lokalen Cache des Browsers mit Ihren Bildern und gespeicherten Einstellungen zurück. Es werden keine Bilder von der Festplatte gelöscht.",
|
||||
"resetWebUIDesc2": "Wenn die Bilder nicht in der Galerie angezeigt werden oder etwas anderes nicht funktioniert, versuchen Sie bitte, die Einstellungen zurückzusetzen, bevor Sie einen Fehler auf GitHub melden.",
|
||||
"resetComplete": "Die Web-Oberfläche wurde zurückgesetzt.",
|
||||
"resetComplete": "Die Web-Oberfläche wurde zurückgesetzt. Aktualisieren Sie die Seite, um sie neu zu laden.",
|
||||
"models": "Modelle",
|
||||
"useSlidersForAll": "Schieberegler für alle Optionen verwenden"
|
||||
},
|
||||
@@ -517,7 +490,7 @@
|
||||
"imageCopied": "Bild kopiert",
|
||||
"imageLinkCopied": "Bildlink kopiert",
|
||||
"imageNotLoaded": "Kein Bild geladen",
|
||||
"imageNotLoadedDesc": "Konnte kein Bild finden",
|
||||
"imageNotLoadedDesc": "Kein Bild gefunden, das an das Bild zu Bild-Modul gesendet werden kann",
|
||||
"imageSavedToGallery": "Bild in die Galerie gespeichert",
|
||||
"canvasMerged": "Leinwand zusammengeführt",
|
||||
"sentToImageToImage": "Gesendet an Bild zu Bild",
|
||||
@@ -612,10 +585,7 @@
|
||||
"betaClear": "Löschen",
|
||||
"betaDarkenOutside": "Außen abdunkeln",
|
||||
"betaLimitToBox": "Begrenzung auf das Feld",
|
||||
"betaPreserveMasked": "Maskiertes bewahren",
|
||||
"antialiasing": "Kantenglättung",
|
||||
"showResultsOn": "Zeige Ergebnisse (An)",
|
||||
"showResultsOff": "Zeige Ergebnisse (Aus)"
|
||||
"betaPreserveMasked": "Maskiertes bewahren"
|
||||
},
|
||||
"accessibility": {
|
||||
"modelSelect": "Model Auswahl",
|
||||
@@ -656,166 +626,6 @@
|
||||
"downloadBoard": "Ordner runterladen",
|
||||
"changeBoard": "Ordner wechseln",
|
||||
"loading": "Laden...",
|
||||
"clearSearch": "Suche leeren",
|
||||
"bottomMessage": "Durch das Löschen dieses Ordners und seiner Bilder werden alle Funktionen zurückgesetzt, die sie derzeit verwenden."
|
||||
},
|
||||
"controlnet": {
|
||||
"showAdvanced": "Zeige Erweitert",
|
||||
"contentShuffleDescription": "Mischt den Inhalt von einem Bild",
|
||||
"addT2IAdapter": "$t(common.t2iAdapter) hinzufügen",
|
||||
"importImageFromCanvas": "Importieren Bild von Zeichenfläche",
|
||||
"lineartDescription": "Konvertiere Bild zu Lineart",
|
||||
"importMaskFromCanvas": "Importiere Maske von Zeichenfläche",
|
||||
"hed": "HED",
|
||||
"hideAdvanced": "Verstecke Erweitert",
|
||||
"contentShuffle": "Inhalt mischen",
|
||||
"controlNetEnabledT2IDisabled": "$t(common.controlNet) ist aktiv, $t(common.t2iAdapter) ist deaktiviert",
|
||||
"ipAdapterModel": "Adapter Modell",
|
||||
"beginEndStepPercent": "Start / Ende Step Prozent",
|
||||
"duplicate": "Kopieren",
|
||||
"f": "F",
|
||||
"h": "H",
|
||||
"depthMidasDescription": "Tiefenmap erstellen mit Midas",
|
||||
"controlnet": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.controlNet))",
|
||||
"t2iEnabledControlNetDisabled": "$t(common.t2iAdapter) ist aktiv, $t(common.controlNet) ist deaktiviert",
|
||||
"weight": "Breite",
|
||||
"selectModel": "Wähle ein Modell",
|
||||
"depthMidas": "Tiefe (Midas)",
|
||||
"w": "W",
|
||||
"addControlNet": "$t(common.controlNet) hinzufügen",
|
||||
"none": "Kein",
|
||||
"incompatibleBaseModel": "Inkompatibles Basismodell:",
|
||||
"enableControlnet": "Aktiviere ControlNet",
|
||||
"detectResolution": "Auflösung erkennen",
|
||||
"controlNetT2IMutexDesc": "$t(common.controlNet) und $t(common.t2iAdapter) zur gleichen Zeit wird nicht unterstützt.",
|
||||
"ip_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.ipAdapter))",
|
||||
"fill": "Füllen",
|
||||
"addIPAdapter": "$t(common.ipAdapter) hinzufügen",
|
||||
"colorMapDescription": "Erstelle eine Farbkarte von diesem Bild",
|
||||
"t2i_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.t2iAdapter))",
|
||||
"imageResolution": "Bild Auflösung",
|
||||
"depthZoe": "Tiefe (Zoe)",
|
||||
"colorMap": "Farbe",
|
||||
"lowThreshold": "Niedrige Schwelle",
|
||||
"highThreshold": "Hohe Schwelle",
|
||||
"toggleControlNet": "Schalten ControlNet um",
|
||||
"delete": "Löschen",
|
||||
"controlAdapter_one": "Control Adapter",
|
||||
"controlAdapter_other": "Control Adapters",
|
||||
"colorMapTileSize": "Tile Größe",
|
||||
"depthZoeDescription": "Tiefenmap erstellen mit Zoe",
|
||||
"setControlImageDimensions": "Setze Control Bild Auflösung auf Breite/Höhe",
|
||||
"handAndFace": "Hand und Gesicht",
|
||||
"enableIPAdapter": "Aktiviere IP Adapter",
|
||||
"resize": "Größe ändern",
|
||||
"resetControlImage": "Zurücksetzen vom Referenz Bild",
|
||||
"balanced": "Ausgewogen",
|
||||
"prompt": "Prompt",
|
||||
"resizeMode": "Größenänderungsmodus",
|
||||
"processor": "Prozessor",
|
||||
"saveControlImage": "Speichere Referenz Bild",
|
||||
"safe": "Speichern",
|
||||
"ipAdapterImageFallback": "Kein IP Adapter Bild ausgewählt",
|
||||
"resetIPAdapterImage": "Zurücksetzen vom IP Adapter Bild"
|
||||
},
|
||||
"queue": {
|
||||
"status": "Status",
|
||||
"cancelTooltip": "Aktuellen Aufgabe abbrechen",
|
||||
"queueEmpty": "Warteschlange leer",
|
||||
"in_progress": "In Arbeit",
|
||||
"queueFront": "An den Anfang der Warteschlange tun",
|
||||
"completed": "Fertig",
|
||||
"queueBack": "In die Warteschlange",
|
||||
"clearFailed": "Probleme beim leeren der Warteschlange",
|
||||
"clearSucceeded": "Warteschlange geleert",
|
||||
"pause": "Pause",
|
||||
"cancelSucceeded": "Auftrag abgebrochen",
|
||||
"queue": "Warteschlange",
|
||||
"batch": "Stapel",
|
||||
"pending": "Ausstehend",
|
||||
"clear": "Leeren",
|
||||
"prune": "Leeren",
|
||||
"total": "Gesamt",
|
||||
"canceled": "Abgebrochen",
|
||||
"clearTooltip": "Abbrechen und alle Aufträge leeren",
|
||||
"current": "Aktuell",
|
||||
"failed": "Fehler",
|
||||
"cancelItem": "Abbruch Auftrag",
|
||||
"next": "Nächste",
|
||||
"cancel": "Abbruch",
|
||||
"session": "Sitzung",
|
||||
"queueTotal": "{{total}} Gesamt",
|
||||
"resume": "Wieder aufnehmen",
|
||||
"item": "Auftrag",
|
||||
"notReady": "Warteschlange noch nicht bereit",
|
||||
"batchValues": "Stapel Werte",
|
||||
"queueCountPrediction": "{{predicted}} zur Warteschlange hinzufügen",
|
||||
"queuedCount": "{{pending}} wartenden Elemente",
|
||||
"clearQueueAlertDialog": "Die Warteschlange leeren, stoppt den aktuellen Prozess und leert die Warteschlange komplett.",
|
||||
"completedIn": "Fertig in",
|
||||
"cancelBatchSucceeded": "Stapel abgebrochen",
|
||||
"cancelBatch": "Stapel stoppen",
|
||||
"enqueueing": "Stapel in der Warteschlange",
|
||||
"queueMaxExceeded": "Maximum von {{max_queue_size}} Elementen erreicht, würde {{skip}} Elemente überspringen",
|
||||
"cancelBatchFailed": "Problem beim Abbruch vom Stapel",
|
||||
"clearQueueAlertDialog2": "bist du sicher die Warteschlange zu leeren?"
|
||||
},
|
||||
"metadata": {
|
||||
"negativePrompt": "Negativ Beschreibung",
|
||||
"metadata": "Meta-Data",
|
||||
"strength": "Bild zu Bild stärke",
|
||||
"imageDetails": "Bild Details",
|
||||
"model": "Modell",
|
||||
"noImageDetails": "Keine Bild Details gefunden",
|
||||
"cfgScale": "CFG-Skala",
|
||||
"fit": "Bild zu Bild passen",
|
||||
"height": "Höhe",
|
||||
"noMetaData": "Keine Meta-Data gefunden",
|
||||
"width": "Breite",
|
||||
"createdBy": "Erstellt von",
|
||||
"steps": "Schritte"
|
||||
},
|
||||
"popovers": {
|
||||
"noiseUseCPU": {
|
||||
"heading": "Nutze Prozessor rauschen"
|
||||
},
|
||||
"paramModel": {
|
||||
"heading": "Modell"
|
||||
},
|
||||
"paramIterations": {
|
||||
"heading": "Iterationen"
|
||||
},
|
||||
"paramCFGScale": {
|
||||
"heading": "CFG-Skala"
|
||||
},
|
||||
"paramSteps": {
|
||||
"heading": "Schritte"
|
||||
},
|
||||
"lora": {
|
||||
"heading": "LoRA Gewichte"
|
||||
},
|
||||
"infillMethod": {
|
||||
"heading": "Füllmethode"
|
||||
},
|
||||
"paramVAE": {
|
||||
"heading": "VAE"
|
||||
}
|
||||
},
|
||||
"ui": {
|
||||
"lockRatio": "Verhältnis sperren",
|
||||
"hideProgressImages": "Verstecke Prozess Bild",
|
||||
"showProgressImages": "Zeige Prozess Bild"
|
||||
},
|
||||
"invocationCache": {
|
||||
"disable": "Deaktivieren",
|
||||
"misses": "Cache Nötig",
|
||||
"hits": "Cache Treffer",
|
||||
"enable": "Aktivieren",
|
||||
"clear": "Leeren"
|
||||
},
|
||||
"embedding": {
|
||||
"noMatchingEmbedding": "Keine passenden Embeddings",
|
||||
"addEmbedding": "Embedding hinzufügen",
|
||||
"incompatibleModel": "Inkompatibles Basismodell:"
|
||||
"clearSearch": "Suche leeren"
|
||||
}
|
||||
}
|
||||
|
||||
@@ -221,19 +221,6 @@
|
||||
"resetIPAdapterImage": "Reset IP Adapter Image",
|
||||
"ipAdapterImageFallback": "No IP Adapter Image Selected"
|
||||
},
|
||||
"hrf": {
|
||||
"hrf": "High Resolution Fix",
|
||||
"enableHrf": "Enable High Resolution Fix",
|
||||
"enableHrfTooltip": "Generate with a lower initial resolution, upscale to the base resolution, then run Image-to-Image.",
|
||||
"upscaleMethod": "Upscale Method",
|
||||
"hrfStrength": "High Resolution Fix Strength",
|
||||
"strengthTooltip": "Lower values result in fewer details, which may reduce potential artifacts.",
|
||||
"metadata": {
|
||||
"enabled": "High Resolution Fix Enabled",
|
||||
"strength": "High Resolution Fix Strength",
|
||||
"method": "High Resolution Fix Method"
|
||||
}
|
||||
},
|
||||
"embedding": {
|
||||
"addEmbedding": "Add Embedding",
|
||||
"incompatibleModel": "Incompatible base model:",
|
||||
@@ -735,9 +722,7 @@
|
||||
"noMatchingModels": "No matching Models",
|
||||
"noModelsAvailable": "No models available",
|
||||
"selectLoRA": "Select a LoRA",
|
||||
"selectModel": "Select a Model",
|
||||
"noLoRAsInstalled": "No LoRAs installed",
|
||||
"noRefinerModelsInstalled": "No SDXL Refiner models installed"
|
||||
"selectModel": "Select a Model"
|
||||
},
|
||||
"nodes": {
|
||||
"addNode": "Add Node",
|
||||
@@ -1271,11 +1256,15 @@
|
||||
},
|
||||
"compositingBlur": {
|
||||
"heading": "Blur",
|
||||
"paragraphs": ["The blur radius of the mask."]
|
||||
"paragraphs": [
|
||||
"The blur radius of the mask."
|
||||
]
|
||||
},
|
||||
"compositingBlurMethod": {
|
||||
"heading": "Blur Method",
|
||||
"paragraphs": ["The method of blur applied to the masked area."]
|
||||
"paragraphs": [
|
||||
"The method of blur applied to the masked area."
|
||||
]
|
||||
},
|
||||
"compositingCoherencePass": {
|
||||
"heading": "Coherence Pass",
|
||||
@@ -1285,7 +1274,9 @@
|
||||
},
|
||||
"compositingCoherenceMode": {
|
||||
"heading": "Mode",
|
||||
"paragraphs": ["The mode of the Coherence Pass."]
|
||||
"paragraphs": [
|
||||
"The mode of the Coherence Pass."
|
||||
]
|
||||
},
|
||||
"compositingCoherenceSteps": {
|
||||
"heading": "Steps",
|
||||
@@ -1303,7 +1294,9 @@
|
||||
},
|
||||
"compositingMaskAdjustments": {
|
||||
"heading": "Mask Adjustments",
|
||||
"paragraphs": ["Adjust the mask."]
|
||||
"paragraphs": [
|
||||
"Adjust the mask."
|
||||
]
|
||||
},
|
||||
"controlNetBeginEnd": {
|
||||
"heading": "Begin / End Step Percentage",
|
||||
@@ -1361,7 +1354,9 @@
|
||||
},
|
||||
"infillMethod": {
|
||||
"heading": "Infill Method",
|
||||
"paragraphs": ["Method to infill the selected area."]
|
||||
"paragraphs": [
|
||||
"Method to infill the selected area."
|
||||
]
|
||||
},
|
||||
"lora": {
|
||||
"heading": "LoRA Weight",
|
||||
|
||||
@@ -1025,8 +1025,7 @@
|
||||
"imageFieldDescription": "Le immagini possono essere passate tra i nodi.",
|
||||
"unableToParseEdge": "Impossibile analizzare il bordo",
|
||||
"latentsCollectionDescription": "Le immagini latenti possono essere passate tra i nodi.",
|
||||
"imageCollection": "Raccolta Immagini",
|
||||
"loRAModelField": "LoRA"
|
||||
"imageCollection": "Raccolta Immagini"
|
||||
},
|
||||
"boards": {
|
||||
"autoAddBoard": "Aggiungi automaticamente bacheca",
|
||||
@@ -1193,9 +1192,7 @@
|
||||
"noLoRAsAvailable": "Nessun LoRA disponibile",
|
||||
"noModelsAvailable": "Nessun modello disponibile",
|
||||
"selectModel": "Seleziona un modello",
|
||||
"selectLoRA": "Seleziona un LoRA",
|
||||
"noRefinerModelsInstalled": "Nessun modello SDXL Refiner installato",
|
||||
"noLoRAsInstalled": "Nessun LoRA installato"
|
||||
"selectLoRA": "Seleziona un LoRA"
|
||||
},
|
||||
"invocationCache": {
|
||||
"disable": "Disabilita",
|
||||
|
||||
@@ -866,7 +866,7 @@
|
||||
"version": "版本",
|
||||
"validateConnections": "验证连接和节点图",
|
||||
"inputMayOnlyHaveOneConnection": "输入仅能有一个连接",
|
||||
"notes": "注释",
|
||||
"notes": "节点",
|
||||
"nodeOutputs": "节点输出",
|
||||
"currentImageDescription": "在节点编辑器中显示当前图像",
|
||||
"validateConnectionsHelp": "防止建立无效连接和调用无效节点图",
|
||||
@@ -892,11 +892,11 @@
|
||||
"currentImage": "当前图像",
|
||||
"workflowName": "名称",
|
||||
"cannotConnectInputToInput": "无法将输入连接到输入",
|
||||
"workflowNotes": "注释",
|
||||
"workflowNotes": "节点",
|
||||
"cannotConnectOutputToOutput": "无法将输出连接到输出",
|
||||
"connectionWouldCreateCycle": "连接将创建一个循环",
|
||||
"cannotConnectToSelf": "无法连接自己",
|
||||
"notesDescription": "添加有关您的工作流的注释",
|
||||
"notesDescription": "添加有关您的工作流的节点",
|
||||
"unknownField": "未知",
|
||||
"colorCodeEdges": "边缘颜色编码",
|
||||
"unknownNode": "未知节点",
|
||||
@@ -1231,9 +1231,7 @@
|
||||
"noLoRAsAvailable": "无可用 LoRA",
|
||||
"noModelsAvailable": "无可用模型",
|
||||
"selectModel": "选择一个模型",
|
||||
"selectLoRA": "选择一个 LoRA",
|
||||
"noRefinerModelsInstalled": "无已安装的 SDXL Refiner 模型",
|
||||
"noLoRAsInstalled": "无已安装的 LoRA"
|
||||
"selectLoRA": "选择一个 LoRA"
|
||||
},
|
||||
"boards": {
|
||||
"autoAddBoard": "自动添加面板",
|
||||
|
||||
@@ -12,7 +12,6 @@ import { addFirstListImagesListener } from './listeners/addFirstListImagesListen
|
||||
import { addAnyEnqueuedListener } from './listeners/anyEnqueued';
|
||||
import { addAppConfigReceivedListener } from './listeners/appConfigReceived';
|
||||
import { addAppStartedListener } from './listeners/appStarted';
|
||||
import { addBatchEnqueuedListener } from './listeners/batchEnqueued';
|
||||
import { addDeleteBoardAndImagesFulfilledListener } from './listeners/boardAndImagesDeleted';
|
||||
import { addBoardIdSelectedListener } from './listeners/boardIdSelected';
|
||||
import { addCanvasCopiedToClipboardListener } from './listeners/canvasCopiedToClipboard';
|
||||
@@ -72,6 +71,8 @@ import { addStagingAreaImageSavedListener } from './listeners/stagingAreaImageSa
|
||||
import { addTabChangedListener } from './listeners/tabChanged';
|
||||
import { addUpscaleRequestedListener } from './listeners/upscaleRequested';
|
||||
import { addWorkflowLoadedListener } from './listeners/workflowLoaded';
|
||||
import { addBatchEnqueuedListener } from './listeners/batchEnqueued';
|
||||
import { addControlAdapterAddedOrEnabledListener } from './listeners/controlAdapterAddedOrEnabled';
|
||||
|
||||
export const listenerMiddleware = createListenerMiddleware();
|
||||
|
||||
@@ -199,3 +200,7 @@ addTabChangedListener();
|
||||
|
||||
// Dynamic prompts
|
||||
addDynamicPromptsListener();
|
||||
|
||||
// Display toast when controlnet or t2i adapter enabled
|
||||
// TODO: Remove when they can both be enabled at same time
|
||||
addControlAdapterAddedOrEnabledListener();
|
||||
|
||||
@@ -0,0 +1,87 @@
|
||||
import { isAnyOf } from '@reduxjs/toolkit';
|
||||
import {
|
||||
controlAdapterAdded,
|
||||
controlAdapterAddedFromImage,
|
||||
controlAdapterIsEnabledChanged,
|
||||
controlAdapterRecalled,
|
||||
selectControlAdapterAll,
|
||||
selectControlAdapterById,
|
||||
} from 'features/controlAdapters/store/controlAdaptersSlice';
|
||||
import { ControlAdapterType } from 'features/controlAdapters/store/types';
|
||||
import { addToast } from 'features/system/store/systemSlice';
|
||||
import i18n from 'i18n';
|
||||
import { startAppListening } from '..';
|
||||
|
||||
const isAnyControlAdapterAddedOrEnabled = isAnyOf(
|
||||
controlAdapterAdded,
|
||||
controlAdapterAddedFromImage,
|
||||
controlAdapterRecalled,
|
||||
controlAdapterIsEnabledChanged
|
||||
);
|
||||
|
||||
/**
|
||||
* Until we can have both controlnet and t2i adapter enabled at once, they are mutually exclusive
|
||||
* This displays a toast when one is enabled and the other is already enabled, or one is added
|
||||
* with the other enabled
|
||||
*/
|
||||
export const addControlAdapterAddedOrEnabledListener = () => {
|
||||
startAppListening({
|
||||
matcher: isAnyControlAdapterAddedOrEnabled,
|
||||
effect: async (action, { dispatch, getOriginalState }) => {
|
||||
const controlAdapters = getOriginalState().controlAdapters;
|
||||
|
||||
const hasEnabledControlNets = selectControlAdapterAll(
|
||||
controlAdapters
|
||||
).some((ca) => ca.isEnabled && ca.type === 'controlnet');
|
||||
|
||||
const hasEnabledT2IAdapters = selectControlAdapterAll(
|
||||
controlAdapters
|
||||
).some((ca) => ca.isEnabled && ca.type === 't2i_adapter');
|
||||
|
||||
let caType: ControlAdapterType | null = null;
|
||||
|
||||
if (controlAdapterAdded.match(action)) {
|
||||
caType = action.payload.type;
|
||||
}
|
||||
|
||||
if (controlAdapterAddedFromImage.match(action)) {
|
||||
caType = action.payload.type;
|
||||
}
|
||||
|
||||
if (controlAdapterRecalled.match(action)) {
|
||||
caType = action.payload.type;
|
||||
}
|
||||
|
||||
if (controlAdapterIsEnabledChanged.match(action)) {
|
||||
const _caType = selectControlAdapterById(
|
||||
controlAdapters,
|
||||
action.payload.id
|
||||
)?.type;
|
||||
if (!_caType) {
|
||||
return;
|
||||
}
|
||||
caType = _caType;
|
||||
}
|
||||
|
||||
if (
|
||||
(caType === 'controlnet' && hasEnabledT2IAdapters) ||
|
||||
(caType === 't2i_adapter' && hasEnabledControlNets)
|
||||
) {
|
||||
const title =
|
||||
caType === 'controlnet'
|
||||
? i18n.t('controlnet.controlNetEnabledT2IDisabled')
|
||||
: i18n.t('controlnet.t2iEnabledControlNetDisabled');
|
||||
|
||||
const description = i18n.t('controlnet.controlNetT2IMutexDesc');
|
||||
|
||||
dispatch(
|
||||
addToast({
|
||||
title,
|
||||
description,
|
||||
status: 'warning',
|
||||
})
|
||||
);
|
||||
}
|
||||
},
|
||||
});
|
||||
};
|
||||
@@ -88,6 +88,61 @@ export const selectValidT2IAdapters = (controlAdapters: ControlAdaptersState) =>
|
||||
(ca.processorType === 'none' && Boolean(ca.controlImage)))
|
||||
);
|
||||
|
||||
// TODO: I think we can safely remove this?
|
||||
// const disableAllIPAdapters = (
|
||||
// state: ControlAdaptersState,
|
||||
// exclude?: string
|
||||
// ) => {
|
||||
// const updates: Update<ControlAdapterConfig>[] = selectAllIPAdapters(state)
|
||||
// .filter((ca) => ca.id !== exclude)
|
||||
// .map((ca) => ({
|
||||
// id: ca.id,
|
||||
// changes: { isEnabled: false },
|
||||
// }));
|
||||
// caAdapter.updateMany(state, updates);
|
||||
// };
|
||||
|
||||
const disableAllControlNets = (
|
||||
state: ControlAdaptersState,
|
||||
exclude?: string
|
||||
) => {
|
||||
const updates: Update<ControlAdapterConfig>[] = selectAllControlNets(state)
|
||||
.filter((ca) => ca.id !== exclude)
|
||||
.map((ca) => ({
|
||||
id: ca.id,
|
||||
changes: { isEnabled: false },
|
||||
}));
|
||||
caAdapter.updateMany(state, updates);
|
||||
};
|
||||
|
||||
const disableAllT2IAdapters = (
|
||||
state: ControlAdaptersState,
|
||||
exclude?: string
|
||||
) => {
|
||||
const updates: Update<ControlAdapterConfig>[] = selectAllT2IAdapters(state)
|
||||
.filter((ca) => ca.id !== exclude)
|
||||
.map((ca) => ({
|
||||
id: ca.id,
|
||||
changes: { isEnabled: false },
|
||||
}));
|
||||
caAdapter.updateMany(state, updates);
|
||||
};
|
||||
|
||||
const disableIncompatibleControlAdapters = (
|
||||
state: ControlAdaptersState,
|
||||
type: ControlAdapterType,
|
||||
exclude?: string
|
||||
) => {
|
||||
if (type === 'controlnet') {
|
||||
// we cannot do controlnet + t2i adapter, if we are enabled a controlnet, disable all t2is
|
||||
disableAllT2IAdapters(state, exclude);
|
||||
}
|
||||
if (type === 't2i_adapter') {
|
||||
// we cannot do controlnet + t2i adapter, if we are enabled a t2i, disable controlnets
|
||||
disableAllControlNets(state, exclude);
|
||||
}
|
||||
};
|
||||
|
||||
export const controlAdaptersSlice = createSlice({
|
||||
name: 'controlAdapters',
|
||||
initialState: initialControlAdapterState,
|
||||
@@ -103,6 +158,7 @@ export const controlAdaptersSlice = createSlice({
|
||||
) => {
|
||||
const { id, type, overrides } = action.payload;
|
||||
caAdapter.addOne(state, buildControlAdapter(id, type, overrides));
|
||||
disableIncompatibleControlAdapters(state, type, id);
|
||||
},
|
||||
prepare: ({
|
||||
type,
|
||||
@@ -119,6 +175,8 @@ export const controlAdaptersSlice = createSlice({
|
||||
action: PayloadAction<ControlAdapterConfig>
|
||||
) => {
|
||||
caAdapter.addOne(state, action.payload);
|
||||
const { type, id } = action.payload;
|
||||
disableIncompatibleControlAdapters(state, type, id);
|
||||
},
|
||||
controlAdapterDuplicated: {
|
||||
reducer: (
|
||||
@@ -138,6 +196,8 @@ export const controlAdaptersSlice = createSlice({
|
||||
isEnabled: true,
|
||||
});
|
||||
caAdapter.addOne(state, newControlAdapter);
|
||||
const { type } = newControlAdapter;
|
||||
disableIncompatibleControlAdapters(state, type, newId);
|
||||
},
|
||||
prepare: (id: string) => {
|
||||
return { payload: { id, newId: uuidv4() } };
|
||||
@@ -157,6 +217,7 @@ export const controlAdaptersSlice = createSlice({
|
||||
state,
|
||||
buildControlAdapter(id, type, { controlImage })
|
||||
);
|
||||
disableIncompatibleControlAdapters(state, type, id);
|
||||
},
|
||||
prepare: (payload: {
|
||||
type: ControlAdapterType;
|
||||
@@ -174,6 +235,12 @@ export const controlAdaptersSlice = createSlice({
|
||||
) => {
|
||||
const { id, isEnabled } = action.payload;
|
||||
caAdapter.updateOne(state, { id, changes: { isEnabled } });
|
||||
if (isEnabled) {
|
||||
// we are enabling a control adapter. due to limitations in the current system, we may need to disable other adapters
|
||||
// TODO: disable when multiple IP adapters are supported
|
||||
const ca = selectControlAdapterById(state, id);
|
||||
ca && disableIncompatibleControlAdapters(state, ca.type, id);
|
||||
}
|
||||
},
|
||||
controlAdapterImageChanged: (
|
||||
state,
|
||||
|
||||
@@ -16,13 +16,15 @@ const ParamDynamicPromptsCollapse = () => {
|
||||
() =>
|
||||
createSelector(stateSelector, ({ dynamicPrompts }) => {
|
||||
const count = dynamicPrompts.prompts.length;
|
||||
if (count > 1) {
|
||||
if (count === 1) {
|
||||
return t('dynamicPrompts.promptsWithCount_one', {
|
||||
count,
|
||||
});
|
||||
} else {
|
||||
return t('dynamicPrompts.promptsWithCount_other', {
|
||||
count,
|
||||
});
|
||||
}
|
||||
|
||||
return;
|
||||
}),
|
||||
[t]
|
||||
);
|
||||
|
||||
@@ -35,9 +35,6 @@ const ImageMetadataActions = (props: Props) => {
|
||||
recallWidth,
|
||||
recallHeight,
|
||||
recallStrength,
|
||||
recallHrfEnabled,
|
||||
recallHrfStrength,
|
||||
recallHrfMethod,
|
||||
recallLoRA,
|
||||
recallControlNet,
|
||||
recallIPAdapter,
|
||||
@@ -84,18 +81,6 @@ const ImageMetadataActions = (props: Props) => {
|
||||
recallStrength(metadata?.strength);
|
||||
}, [metadata?.strength, recallStrength]);
|
||||
|
||||
const handleRecallHrfEnabled = useCallback(() => {
|
||||
recallHrfEnabled(metadata?.hrf_enabled);
|
||||
}, [metadata?.hrf_enabled, recallHrfEnabled]);
|
||||
|
||||
const handleRecallHrfStrength = useCallback(() => {
|
||||
recallHrfStrength(metadata?.hrf_strength);
|
||||
}, [metadata?.hrf_strength, recallHrfStrength]);
|
||||
|
||||
const handleRecallHrfMethod = useCallback(() => {
|
||||
recallHrfMethod(metadata?.hrf_method);
|
||||
}, [metadata?.hrf_method, recallHrfMethod]);
|
||||
|
||||
const handleRecallLoRA = useCallback(
|
||||
(lora: LoRAMetadataItem) => {
|
||||
recallLoRA(lora);
|
||||
@@ -240,27 +225,6 @@ const ImageMetadataActions = (props: Props) => {
|
||||
onClick={handleRecallStrength}
|
||||
/>
|
||||
)}
|
||||
{metadata.hrf_enabled && (
|
||||
<ImageMetadataItem
|
||||
label={t('hrf.metadata.enabled')}
|
||||
value={metadata.hrf_enabled}
|
||||
onClick={handleRecallHrfEnabled}
|
||||
/>
|
||||
)}
|
||||
{metadata.hrf_enabled && metadata.hrf_strength && (
|
||||
<ImageMetadataItem
|
||||
label={t('hrf.metadata.strength')}
|
||||
value={metadata.hrf_strength}
|
||||
onClick={handleRecallHrfStrength}
|
||||
/>
|
||||
)}
|
||||
{metadata.hrf_enabled && metadata.hrf_method && (
|
||||
<ImageMetadataItem
|
||||
label={t('hrf.metadata.method')}
|
||||
value={metadata.hrf_method}
|
||||
onClick={handleRecallHrfMethod}
|
||||
/>
|
||||
)}
|
||||
{metadata.loras &&
|
||||
metadata.loras.map((lora, index) => {
|
||||
if (isValidLoRAModel(lora.lora)) {
|
||||
|
||||
@@ -10,7 +10,6 @@ import { loraAdded } from 'features/lora/store/loraSlice';
|
||||
import { MODEL_TYPE_MAP } from 'features/parameters/types/constants';
|
||||
import { forEach } from 'lodash-es';
|
||||
import { memo, useCallback, useMemo } from 'react';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
import { useGetLoRAModelsQuery } from 'services/api/endpoints/models';
|
||||
|
||||
const selector = createSelector(
|
||||
@@ -25,7 +24,7 @@ const ParamLoRASelect = () => {
|
||||
const dispatch = useAppDispatch();
|
||||
const { loras } = useAppSelector(selector);
|
||||
const { data: loraModels } = useGetLoRAModelsQuery();
|
||||
const { t } = useTranslation();
|
||||
|
||||
const currentMainModel = useAppSelector(
|
||||
(state: RootState) => state.generation.model
|
||||
);
|
||||
@@ -80,7 +79,7 @@ const ParamLoRASelect = () => {
|
||||
return (
|
||||
<Flex sx={{ justifyContent: 'center', p: 2 }}>
|
||||
<Text sx={{ fontSize: 'sm', color: 'base.500', _dark: 'base.700' }}>
|
||||
{t('models.noLoRAsInstalled')}
|
||||
No LoRAs Loaded
|
||||
</Text>
|
||||
</Flex>
|
||||
);
|
||||
|
||||
@@ -1424,9 +1424,6 @@ export const zCoreMetadata = z
|
||||
loras: z.array(zLoRAMetadataItem).nullish().catch(null),
|
||||
vae: zVaeModelField.nullish().catch(null),
|
||||
strength: z.number().nullish().catch(null),
|
||||
hrf_enabled: z.boolean().nullish().catch(null),
|
||||
hrf_strength: z.number().nullish().catch(null),
|
||||
hrf_method: z.string().nullish().catch(null),
|
||||
init_image: z.string().nullish().catch(null),
|
||||
positive_style_prompt: z.string().nullish().catch(null),
|
||||
negative_style_prompt: z.string().nullish().catch(null),
|
||||
|
||||
@@ -1,26 +1,22 @@
|
||||
import { logger } from 'app/logging/logger';
|
||||
import { RootState } from 'app/store/store';
|
||||
import { roundToMultiple } from 'common/util/roundDownToMultiple';
|
||||
import { NonNullableGraph } from 'features/nodes/types/types';
|
||||
import {
|
||||
DenoiseLatentsInvocation,
|
||||
ESRGANInvocation,
|
||||
Edge,
|
||||
LatentsToImageInvocation,
|
||||
NoiseInvocation,
|
||||
ResizeLatentsInvocation,
|
||||
} from 'services/api/types';
|
||||
import {
|
||||
DENOISE_LATENTS,
|
||||
DENOISE_LATENTS_HRF,
|
||||
ESRGAN_HRF,
|
||||
IMAGE_TO_LATENTS_HRF,
|
||||
LATENTS_TO_IMAGE,
|
||||
LATENTS_TO_IMAGE_HRF_HR,
|
||||
LATENTS_TO_IMAGE_HRF_LR,
|
||||
LATENTS_TO_IMAGE_HRF,
|
||||
MAIN_MODEL_LOADER,
|
||||
NOISE,
|
||||
NOISE_HRF,
|
||||
RESIZE_HRF,
|
||||
RESCALE_LATENTS,
|
||||
VAE_LOADER,
|
||||
} from './constants';
|
||||
import { upsertMetadata } from './metadata';
|
||||
@@ -60,52 +56,6 @@ function copyConnectionsToDenoiseLatentsHrf(graph: NonNullableGraph): void {
|
||||
graph.edges = graph.edges.concat(newEdges);
|
||||
}
|
||||
|
||||
/**
|
||||
* Calculates the new resolution for high-resolution features (HRF) based on base model type.
|
||||
* Adjusts the width and height to maintain the aspect ratio and constrains them by the model's dimension limits,
|
||||
* rounding down to the nearest multiple of 8.
|
||||
*
|
||||
* @param {string} baseModel The base model type, which determines the base dimension used in calculations.
|
||||
* @param {number} width The current width to be adjusted for HRF.
|
||||
* @param {number} height The current height to be adjusted for HRF.
|
||||
* @return {{newWidth: number, newHeight: number}} The new width and height, adjusted and rounded as needed.
|
||||
*/
|
||||
function calculateHrfRes(
|
||||
baseModel: string,
|
||||
width: number,
|
||||
height: number
|
||||
): { newWidth: number; newHeight: number } {
|
||||
const aspect = width / height;
|
||||
let dimension;
|
||||
if (baseModel == 'sdxl') {
|
||||
dimension = 1024;
|
||||
} else {
|
||||
dimension = 512;
|
||||
}
|
||||
|
||||
const minDimension = Math.floor(dimension * 0.5);
|
||||
const modelArea = dimension * dimension; // Assuming square images for model_area
|
||||
|
||||
let initWidth;
|
||||
let initHeight;
|
||||
|
||||
if (aspect > 1.0) {
|
||||
initHeight = Math.max(minDimension, Math.sqrt(modelArea / aspect));
|
||||
initWidth = initHeight * aspect;
|
||||
} else {
|
||||
initWidth = Math.max(minDimension, Math.sqrt(modelArea * aspect));
|
||||
initHeight = initWidth / aspect;
|
||||
}
|
||||
// Cap initial height and width to final height and width.
|
||||
initWidth = Math.min(width, initWidth);
|
||||
initHeight = Math.min(height, initHeight);
|
||||
|
||||
const newWidth = roundToMultiple(Math.floor(initWidth), 8);
|
||||
const newHeight = roundToMultiple(Math.floor(initHeight), 8);
|
||||
|
||||
return { newWidth, newHeight };
|
||||
}
|
||||
|
||||
// Adds the high-res fix feature to the given graph.
|
||||
export const addHrfToGraph = (
|
||||
state: RootState,
|
||||
@@ -121,140 +71,105 @@ export const addHrfToGraph = (
|
||||
}
|
||||
const log = logger('txt2img');
|
||||
|
||||
const { vae, hrfStrength, hrfEnabled, hrfMethod } = state.generation;
|
||||
const { vae, hrfWidth, hrfHeight, hrfStrength } = state.generation;
|
||||
const isAutoVae = !vae;
|
||||
const width = state.generation.width;
|
||||
const height = state.generation.height;
|
||||
const baseModel = state.generation.model
|
||||
? state.generation.model.base_model
|
||||
: 'sd1';
|
||||
const { newWidth: hrfWidth, newHeight: hrfHeight } = calculateHrfRes(
|
||||
baseModel,
|
||||
width,
|
||||
height
|
||||
);
|
||||
|
||||
// Pre-existing (original) graph nodes.
|
||||
const originalDenoiseLatentsNode = graph.nodes[DENOISE_LATENTS] as
|
||||
| DenoiseLatentsInvocation
|
||||
| undefined;
|
||||
const originalNoiseNode = graph.nodes[NOISE] as NoiseInvocation | undefined;
|
||||
// Original latents to image should pick this up.
|
||||
const originalLatentsToImageNode = graph.nodes[LATENTS_TO_IMAGE] as
|
||||
| LatentsToImageInvocation
|
||||
| undefined;
|
||||
// Check if originalDenoiseLatentsNode is undefined and log an error
|
||||
if (!originalDenoiseLatentsNode) {
|
||||
log.error('originalDenoiseLatentsNode is undefined');
|
||||
return;
|
||||
}
|
||||
// Check if originalNoiseNode is undefined and log an error
|
||||
if (!originalNoiseNode) {
|
||||
log.error('originalNoiseNode is undefined');
|
||||
return;
|
||||
}
|
||||
|
||||
// Check if originalLatentsToImageNode is undefined and log an error
|
||||
if (!originalLatentsToImageNode) {
|
||||
log.error('originalLatentsToImageNode is undefined');
|
||||
return;
|
||||
}
|
||||
|
||||
// Change height and width of original noise node to initial resolution.
|
||||
if (originalNoiseNode) {
|
||||
originalNoiseNode.width = hrfWidth;
|
||||
originalNoiseNode.height = hrfHeight;
|
||||
}
|
||||
|
||||
// Define new nodes and their connections, roughly in order of operations.
|
||||
graph.nodes[LATENTS_TO_IMAGE_HRF_LR] = {
|
||||
type: 'l2i',
|
||||
id: LATENTS_TO_IMAGE_HRF_LR,
|
||||
fp32: originalLatentsToImageNode?.fp32,
|
||||
is_intermediate: true,
|
||||
// Define new nodes.
|
||||
// Denoise latents node to be run on upscaled latents.
|
||||
const denoiseLatentsHrfNode: DenoiseLatentsInvocation = {
|
||||
type: 'denoise_latents',
|
||||
id: DENOISE_LATENTS_HRF,
|
||||
is_intermediate: originalDenoiseLatentsNode?.is_intermediate,
|
||||
cfg_scale: originalDenoiseLatentsNode?.cfg_scale,
|
||||
scheduler: originalDenoiseLatentsNode?.scheduler,
|
||||
steps: originalDenoiseLatentsNode?.steps,
|
||||
denoising_start: 1 - hrfStrength,
|
||||
denoising_end: 1,
|
||||
};
|
||||
|
||||
// New base resolution noise node.
|
||||
const hrfNoiseNode: NoiseInvocation = {
|
||||
type: 'noise',
|
||||
id: NOISE_HRF,
|
||||
seed: originalNoiseNode?.seed,
|
||||
use_cpu: originalNoiseNode?.use_cpu,
|
||||
is_intermediate: originalNoiseNode?.is_intermediate,
|
||||
};
|
||||
|
||||
const rescaleLatentsNode: ResizeLatentsInvocation = {
|
||||
id: RESCALE_LATENTS,
|
||||
type: 'lresize',
|
||||
width: state.generation.width,
|
||||
height: state.generation.height,
|
||||
};
|
||||
|
||||
// New node to convert latents to image.
|
||||
const latentsToImageHrfNode: LatentsToImageInvocation | undefined =
|
||||
originalLatentsToImageNode
|
||||
? {
|
||||
type: 'l2i',
|
||||
id: LATENTS_TO_IMAGE_HRF,
|
||||
fp32: originalLatentsToImageNode?.fp32,
|
||||
is_intermediate: originalLatentsToImageNode?.is_intermediate,
|
||||
}
|
||||
: undefined;
|
||||
|
||||
// Add new nodes to graph.
|
||||
graph.nodes[LATENTS_TO_IMAGE_HRF] =
|
||||
latentsToImageHrfNode as LatentsToImageInvocation;
|
||||
graph.nodes[DENOISE_LATENTS_HRF] =
|
||||
denoiseLatentsHrfNode as DenoiseLatentsInvocation;
|
||||
graph.nodes[NOISE_HRF] = hrfNoiseNode as NoiseInvocation;
|
||||
graph.nodes[RESCALE_LATENTS] = rescaleLatentsNode as ResizeLatentsInvocation;
|
||||
|
||||
// Connect nodes.
|
||||
graph.edges.push(
|
||||
{
|
||||
// Set up rescale latents.
|
||||
source: {
|
||||
node_id: DENOISE_LATENTS,
|
||||
field: 'latents',
|
||||
},
|
||||
destination: {
|
||||
node_id: LATENTS_TO_IMAGE_HRF_LR,
|
||||
node_id: RESCALE_LATENTS,
|
||||
field: 'latents',
|
||||
},
|
||||
},
|
||||
// Set up new noise node
|
||||
{
|
||||
source: {
|
||||
node_id: isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
|
||||
field: 'vae',
|
||||
},
|
||||
destination: {
|
||||
node_id: LATENTS_TO_IMAGE_HRF_LR,
|
||||
field: 'vae',
|
||||
},
|
||||
}
|
||||
);
|
||||
|
||||
graph.nodes[RESIZE_HRF] = {
|
||||
id: RESIZE_HRF,
|
||||
type: 'img_resize',
|
||||
is_intermediate: true,
|
||||
width: width,
|
||||
height: height,
|
||||
};
|
||||
if (hrfMethod == 'ESRGAN') {
|
||||
let model_name: ESRGANInvocation['model_name'] = 'RealESRGAN_x2plus.pth';
|
||||
if ((width * height) / (hrfWidth * hrfHeight) > 2) {
|
||||
model_name = 'RealESRGAN_x4plus.pth';
|
||||
}
|
||||
graph.nodes[ESRGAN_HRF] = {
|
||||
id: ESRGAN_HRF,
|
||||
type: 'esrgan',
|
||||
model_name,
|
||||
is_intermediate: true,
|
||||
};
|
||||
graph.edges.push(
|
||||
{
|
||||
source: {
|
||||
node_id: LATENTS_TO_IMAGE_HRF_LR,
|
||||
field: 'image',
|
||||
},
|
||||
destination: {
|
||||
node_id: ESRGAN_HRF,
|
||||
field: 'image',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: ESRGAN_HRF,
|
||||
field: 'image',
|
||||
},
|
||||
destination: {
|
||||
node_id: RESIZE_HRF,
|
||||
field: 'image',
|
||||
},
|
||||
}
|
||||
);
|
||||
} else {
|
||||
graph.edges.push({
|
||||
source: {
|
||||
node_id: LATENTS_TO_IMAGE_HRF_LR,
|
||||
field: 'image',
|
||||
},
|
||||
destination: {
|
||||
node_id: RESIZE_HRF,
|
||||
field: 'image',
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
graph.nodes[NOISE_HRF] = {
|
||||
type: 'noise',
|
||||
id: NOISE_HRF,
|
||||
seed: originalNoiseNode?.seed,
|
||||
use_cpu: originalNoiseNode?.use_cpu,
|
||||
is_intermediate: true,
|
||||
};
|
||||
graph.edges.push(
|
||||
{
|
||||
source: {
|
||||
node_id: RESIZE_HRF,
|
||||
node_id: RESCALE_LATENTS,
|
||||
field: 'height',
|
||||
},
|
||||
destination: {
|
||||
@@ -264,58 +179,18 @@ export const addHrfToGraph = (
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: RESIZE_HRF,
|
||||
node_id: RESCALE_LATENTS,
|
||||
field: 'width',
|
||||
},
|
||||
destination: {
|
||||
node_id: NOISE_HRF,
|
||||
field: 'width',
|
||||
},
|
||||
}
|
||||
);
|
||||
|
||||
graph.nodes[IMAGE_TO_LATENTS_HRF] = {
|
||||
type: 'i2l',
|
||||
id: IMAGE_TO_LATENTS_HRF,
|
||||
is_intermediate: true,
|
||||
};
|
||||
graph.edges.push(
|
||||
{
|
||||
source: {
|
||||
node_id: isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
|
||||
field: 'vae',
|
||||
},
|
||||
destination: {
|
||||
node_id: IMAGE_TO_LATENTS_HRF,
|
||||
field: 'vae',
|
||||
},
|
||||
},
|
||||
// Set up new denoise node.
|
||||
{
|
||||
source: {
|
||||
node_id: RESIZE_HRF,
|
||||
field: 'image',
|
||||
},
|
||||
destination: {
|
||||
node_id: IMAGE_TO_LATENTS_HRF,
|
||||
field: 'image',
|
||||
},
|
||||
}
|
||||
);
|
||||
|
||||
graph.nodes[DENOISE_LATENTS_HRF] = {
|
||||
type: 'denoise_latents',
|
||||
id: DENOISE_LATENTS_HRF,
|
||||
is_intermediate: true,
|
||||
cfg_scale: originalDenoiseLatentsNode?.cfg_scale,
|
||||
scheduler: originalDenoiseLatentsNode?.scheduler,
|
||||
steps: originalDenoiseLatentsNode?.steps,
|
||||
denoising_start: 1 - state.generation.hrfStrength,
|
||||
denoising_end: 1,
|
||||
};
|
||||
graph.edges.push(
|
||||
{
|
||||
source: {
|
||||
node_id: IMAGE_TO_LATENTS_HRF,
|
||||
node_id: RESCALE_LATENTS,
|
||||
field: 'latents',
|
||||
},
|
||||
destination: {
|
||||
@@ -332,41 +207,35 @@ export const addHrfToGraph = (
|
||||
node_id: DENOISE_LATENTS_HRF,
|
||||
field: 'noise',
|
||||
},
|
||||
}
|
||||
);
|
||||
copyConnectionsToDenoiseLatentsHrf(graph);
|
||||
|
||||
graph.nodes[LATENTS_TO_IMAGE_HRF_HR] = {
|
||||
type: 'l2i',
|
||||
id: LATENTS_TO_IMAGE_HRF_HR,
|
||||
fp32: originalLatentsToImageNode?.fp32,
|
||||
is_intermediate: true,
|
||||
};
|
||||
graph.edges.push(
|
||||
{
|
||||
source: {
|
||||
node_id: isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
|
||||
field: 'vae',
|
||||
},
|
||||
destination: {
|
||||
node_id: LATENTS_TO_IMAGE_HRF_HR,
|
||||
field: 'vae',
|
||||
},
|
||||
},
|
||||
// Set up new latents to image node.
|
||||
{
|
||||
source: {
|
||||
node_id: DENOISE_LATENTS_HRF,
|
||||
field: 'latents',
|
||||
},
|
||||
destination: {
|
||||
node_id: LATENTS_TO_IMAGE_HRF_HR,
|
||||
node_id: LATENTS_TO_IMAGE_HRF,
|
||||
field: 'latents',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
|
||||
field: 'vae',
|
||||
},
|
||||
destination: {
|
||||
node_id: LATENTS_TO_IMAGE_HRF,
|
||||
field: 'vae',
|
||||
},
|
||||
}
|
||||
);
|
||||
|
||||
upsertMetadata(graph, {
|
||||
hrf_height: hrfHeight,
|
||||
hrf_width: hrfWidth,
|
||||
hrf_strength: hrfStrength,
|
||||
hrf_enabled: hrfEnabled,
|
||||
hrf_method: hrfMethod,
|
||||
});
|
||||
|
||||
copyConnectionsToDenoiseLatentsHrf(graph);
|
||||
};
|
||||
|
||||
@@ -5,7 +5,7 @@ import { SaveImageInvocation } from 'services/api/types';
|
||||
import {
|
||||
CANVAS_OUTPUT,
|
||||
LATENTS_TO_IMAGE,
|
||||
LATENTS_TO_IMAGE_HRF_HR,
|
||||
LATENTS_TO_IMAGE_HRF,
|
||||
NSFW_CHECKER,
|
||||
SAVE_IMAGE,
|
||||
WATERMARKER,
|
||||
@@ -62,10 +62,10 @@ export const addSaveImageNode = (
|
||||
},
|
||||
destination,
|
||||
});
|
||||
} else if (LATENTS_TO_IMAGE_HRF_HR in graph.nodes) {
|
||||
} else if (LATENTS_TO_IMAGE_HRF in graph.nodes) {
|
||||
graph.edges.push({
|
||||
source: {
|
||||
node_id: LATENTS_TO_IMAGE_HRF_HR,
|
||||
node_id: LATENTS_TO_IMAGE_HRF,
|
||||
field: 'image',
|
||||
},
|
||||
destination,
|
||||
|
||||
@@ -4,11 +4,7 @@ export const NEGATIVE_CONDITIONING = 'negative_conditioning';
|
||||
export const DENOISE_LATENTS = 'denoise_latents';
|
||||
export const DENOISE_LATENTS_HRF = 'denoise_latents_hrf';
|
||||
export const LATENTS_TO_IMAGE = 'latents_to_image';
|
||||
export const LATENTS_TO_IMAGE_HRF_HR = 'latents_to_image_hrf_hr';
|
||||
export const LATENTS_TO_IMAGE_HRF_LR = 'latents_to_image_hrf_lr';
|
||||
export const IMAGE_TO_LATENTS_HRF = 'image_to_latents_hrf';
|
||||
export const RESIZE_HRF = 'resize_hrf';
|
||||
export const ESRGAN_HRF = 'esrgan_hrf';
|
||||
export const LATENTS_TO_IMAGE_HRF = 'latents_to_image_hrf';
|
||||
export const SAVE_IMAGE = 'save_image';
|
||||
export const NSFW_CHECKER = 'nsfw_checker';
|
||||
export const WATERMARKER = 'invisible_watermark';
|
||||
@@ -25,6 +21,7 @@ export const CLIP_SKIP = 'clip_skip';
|
||||
export const IMAGE_TO_LATENTS = 'image_to_latents';
|
||||
export const LATENTS_TO_LATENTS = 'latents_to_latents';
|
||||
export const RESIZE = 'resize_image';
|
||||
export const RESCALE_LATENTS = 'rescale_latents';
|
||||
export const IMG2IMG_RESIZE = 'img2img_resize';
|
||||
export const CANVAS_OUTPUT = 'canvas_output';
|
||||
export const INPAINT_IMAGE = 'inpaint_image';
|
||||
|
||||
@@ -28,7 +28,9 @@ export default function ParamAdvancedCollapse() {
|
||||
const activeLabel = useMemo(() => {
|
||||
const activeLabel: string[] = [];
|
||||
|
||||
if (!shouldUseCpuNoise) {
|
||||
if (shouldUseCpuNoise) {
|
||||
activeLabel.push(t('parameters.cpuNoise'));
|
||||
} else {
|
||||
activeLabel.push(t('parameters.gpuNoise'));
|
||||
}
|
||||
|
||||
|
||||
@@ -4,12 +4,12 @@ import { RootState, stateSelector } from 'app/store/store';
|
||||
import { useAppSelector } from 'app/store/storeHooks';
|
||||
import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
|
||||
import IAICollapse from 'common/components/IAICollapse';
|
||||
import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
|
||||
import { useMemo } from 'react';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
import ParamHrfStrength from './ParamHrfStrength';
|
||||
import ParamHrfToggle from './ParamHrfToggle';
|
||||
import ParamHrfMethod from './ParamHrfMethod';
|
||||
import ParamHrfWidth from './ParamHrfWidth';
|
||||
import ParamHrfHeight from './ParamHrfHeight';
|
||||
import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
|
||||
|
||||
const selector = createSelector(
|
||||
stateSelector,
|
||||
@@ -22,25 +22,43 @@ const selector = createSelector(
|
||||
);
|
||||
|
||||
export default function ParamHrfCollapse() {
|
||||
const { t } = useTranslation();
|
||||
const isHRFFeatureEnabled = useFeatureStatus('hrf').isFeatureEnabled;
|
||||
const { hrfEnabled } = useAppSelector(selector);
|
||||
const activeLabel = useMemo(() => {
|
||||
if (hrfEnabled) {
|
||||
return t('common.on');
|
||||
return 'On';
|
||||
} else {
|
||||
return 'Off';
|
||||
}
|
||||
}, [t, hrfEnabled]);
|
||||
}, [hrfEnabled]);
|
||||
|
||||
if (!isHRFFeatureEnabled) {
|
||||
return null;
|
||||
}
|
||||
|
||||
return (
|
||||
<IAICollapse label={t('hrf.hrf')} activeLabel={activeLabel}>
|
||||
<IAICollapse label="High Resolution Fix" activeLabel={activeLabel}>
|
||||
<Flex sx={{ flexDir: 'column', gap: 2 }}>
|
||||
<ParamHrfToggle />
|
||||
<ParamHrfStrength />
|
||||
<ParamHrfMethod />
|
||||
{hrfEnabled && (
|
||||
<Flex
|
||||
sx={{
|
||||
gap: 2,
|
||||
p: 4,
|
||||
borderRadius: 4,
|
||||
flexDirection: 'column',
|
||||
w: 'full',
|
||||
bg: 'base.100',
|
||||
_dark: {
|
||||
bg: 'base.750',
|
||||
},
|
||||
}}
|
||||
>
|
||||
<ParamHrfWidth />
|
||||
<ParamHrfHeight />
|
||||
</Flex>
|
||||
)}
|
||||
{hrfEnabled && <ParamHrfStrength />}
|
||||
</Flex>
|
||||
</IAICollapse>
|
||||
);
|
||||
|
||||
@@ -0,0 +1,87 @@
|
||||
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 IAISlider, { IAIFullSliderProps } from 'common/components/IAISlider';
|
||||
import { roundToMultiple } from 'common/util/roundDownToMultiple';
|
||||
import {
|
||||
setHrfHeight,
|
||||
setHrfWidth,
|
||||
} from 'features/parameters/store/generationSlice';
|
||||
import { memo, useCallback } from 'react';
|
||||
|
||||
function findPrevMultipleOfEight(n: number): number {
|
||||
return Math.floor((n - 1) / 8) * 8;
|
||||
}
|
||||
|
||||
const selector = createSelector(
|
||||
[stateSelector],
|
||||
({ generation, hotkeys, config }) => {
|
||||
const { min, fineStep, coarseStep } = config.sd.height;
|
||||
const { model, height, hrfHeight, aspectRatio, hrfEnabled } = generation;
|
||||
|
||||
const step = hotkeys.shift ? fineStep : coarseStep;
|
||||
|
||||
return {
|
||||
model,
|
||||
height,
|
||||
hrfHeight,
|
||||
min,
|
||||
step,
|
||||
aspectRatio,
|
||||
hrfEnabled,
|
||||
};
|
||||
},
|
||||
defaultSelectorOptions
|
||||
);
|
||||
|
||||
type ParamHeightProps = Omit<
|
||||
IAIFullSliderProps,
|
||||
'label' | 'value' | 'onChange'
|
||||
>;
|
||||
|
||||
const ParamHrfHeight = (props: ParamHeightProps) => {
|
||||
const { height, hrfHeight, min, step, aspectRatio, hrfEnabled } =
|
||||
useAppSelector(selector);
|
||||
const dispatch = useAppDispatch();
|
||||
const maxHrfHeight = Math.max(findPrevMultipleOfEight(height), min);
|
||||
|
||||
const handleChange = useCallback(
|
||||
(v: number) => {
|
||||
dispatch(setHrfHeight(v));
|
||||
if (aspectRatio) {
|
||||
const newWidth = roundToMultiple(v * aspectRatio, 8);
|
||||
dispatch(setHrfWidth(newWidth));
|
||||
}
|
||||
},
|
||||
[dispatch, aspectRatio]
|
||||
);
|
||||
|
||||
const handleReset = useCallback(() => {
|
||||
dispatch(setHrfHeight(maxHrfHeight));
|
||||
if (aspectRatio) {
|
||||
const newWidth = roundToMultiple(maxHrfHeight * aspectRatio, 8);
|
||||
dispatch(setHrfWidth(newWidth));
|
||||
}
|
||||
}, [dispatch, maxHrfHeight, aspectRatio]);
|
||||
|
||||
return (
|
||||
<IAISlider
|
||||
label="Initial Height"
|
||||
value={hrfHeight}
|
||||
min={min}
|
||||
step={step}
|
||||
max={maxHrfHeight}
|
||||
onChange={handleChange}
|
||||
handleReset={handleReset}
|
||||
withInput
|
||||
withReset
|
||||
withSliderMarks
|
||||
sliderNumberInputProps={{ max: maxHrfHeight }}
|
||||
isDisabled={!hrfEnabled}
|
||||
{...props}
|
||||
/>
|
||||
);
|
||||
};
|
||||
|
||||
export default memo(ParamHrfHeight);
|
||||
@@ -1,49 +0,0 @@
|
||||
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 IAIMantineSelect from 'common/components/IAIMantineSelect';
|
||||
import { setHrfMethod } from 'features/parameters/store/generationSlice';
|
||||
import { HrfMethodParam } from 'features/parameters/types/parameterSchemas';
|
||||
import { memo, useCallback } from 'react';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
|
||||
const selector = createSelector(
|
||||
stateSelector,
|
||||
({ generation }) => {
|
||||
const { hrfMethod, hrfEnabled } = generation;
|
||||
return { hrfMethod, hrfEnabled };
|
||||
},
|
||||
defaultSelectorOptions
|
||||
);
|
||||
|
||||
const DATA = ['ESRGAN', 'bilinear'];
|
||||
|
||||
// Dropdown selection for the type of high resolution fix method to use.
|
||||
const ParamHrfMethodSelect = () => {
|
||||
const dispatch = useAppDispatch();
|
||||
const { t } = useTranslation();
|
||||
const { hrfMethod, hrfEnabled } = useAppSelector(selector);
|
||||
|
||||
const handleChange = useCallback(
|
||||
(v: HrfMethodParam | null) => {
|
||||
if (!v) {
|
||||
return;
|
||||
}
|
||||
dispatch(setHrfMethod(v));
|
||||
},
|
||||
[dispatch]
|
||||
);
|
||||
|
||||
return (
|
||||
<IAIMantineSelect
|
||||
label={t('hrf.upscaleMethod')}
|
||||
value={hrfMethod}
|
||||
data={DATA}
|
||||
onChange={handleChange}
|
||||
disabled={!hrfEnabled}
|
||||
/>
|
||||
);
|
||||
};
|
||||
|
||||
export default memo(ParamHrfMethodSelect);
|
||||
@@ -5,8 +5,6 @@ import { memo, useCallback } from 'react';
|
||||
import { stateSelector } from 'app/store/store';
|
||||
import { setHrfStrength } from 'features/parameters/store/generationSlice';
|
||||
import IAISlider from 'common/components/IAISlider';
|
||||
import { Tooltip } from '@chakra-ui/react';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
|
||||
const selector = createSelector(
|
||||
[stateSelector],
|
||||
@@ -33,7 +31,6 @@ const ParamHrfStrength = () => {
|
||||
const { hrfStrength, initial, min, sliderMax, step, hrfEnabled } =
|
||||
useAppSelector(selector);
|
||||
const dispatch = useAppDispatch();
|
||||
const { t } = useTranslation();
|
||||
|
||||
const handleHrfStrengthReset = useCallback(() => {
|
||||
dispatch(setHrfStrength(initial));
|
||||
@@ -47,21 +44,20 @@ const ParamHrfStrength = () => {
|
||||
);
|
||||
|
||||
return (
|
||||
<Tooltip label={t('hrf.strengthTooltip')} placement="right" hasArrow>
|
||||
<IAISlider
|
||||
label={t('parameters.denoisingStrength')}
|
||||
min={min}
|
||||
max={sliderMax}
|
||||
step={step}
|
||||
value={hrfStrength}
|
||||
onChange={handleHrfStrengthChange}
|
||||
withSliderMarks
|
||||
withInput
|
||||
withReset
|
||||
handleReset={handleHrfStrengthReset}
|
||||
isDisabled={!hrfEnabled}
|
||||
/>
|
||||
</Tooltip>
|
||||
<IAISlider
|
||||
label="Denoising Strength"
|
||||
aria-label="High Resolution Denoising Strength"
|
||||
min={min}
|
||||
max={sliderMax}
|
||||
step={step}
|
||||
value={hrfStrength}
|
||||
onChange={handleHrfStrengthChange}
|
||||
withSliderMarks
|
||||
withInput
|
||||
withReset
|
||||
handleReset={handleHrfStrengthReset}
|
||||
isDisabled={!hrfEnabled}
|
||||
/>
|
||||
);
|
||||
};
|
||||
|
||||
|
||||
@@ -3,11 +3,9 @@ import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
|
||||
import IAISwitch from 'common/components/IAISwitch';
|
||||
import { setHrfEnabled } from 'features/parameters/store/generationSlice';
|
||||
import { ChangeEvent, useCallback } from 'react';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
|
||||
export default function ParamHrfToggle() {
|
||||
const dispatch = useAppDispatch();
|
||||
const { t } = useTranslation();
|
||||
|
||||
const hrfEnabled = useAppSelector(
|
||||
(state: RootState) => state.generation.hrfEnabled
|
||||
@@ -21,10 +19,9 @@ export default function ParamHrfToggle() {
|
||||
|
||||
return (
|
||||
<IAISwitch
|
||||
label={t('hrf.enableHrf')}
|
||||
label="Enable High Resolution Fix"
|
||||
isChecked={hrfEnabled}
|
||||
onChange={handleHrfEnabled}
|
||||
tooltip={t('hrf.enableHrfTooltip')}
|
||||
/>
|
||||
);
|
||||
}
|
||||
|
||||
@@ -0,0 +1,84 @@
|
||||
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 IAISlider, { IAIFullSliderProps } from 'common/components/IAISlider';
|
||||
import { roundToMultiple } from 'common/util/roundDownToMultiple';
|
||||
import {
|
||||
setHrfHeight,
|
||||
setHrfWidth,
|
||||
} from 'features/parameters/store/generationSlice';
|
||||
import { memo, useCallback } from 'react';
|
||||
|
||||
function findPrevMultipleOfEight(n: number): number {
|
||||
return Math.floor((n - 1) / 8) * 8;
|
||||
}
|
||||
|
||||
const selector = createSelector(
|
||||
[stateSelector],
|
||||
({ generation, hotkeys, config }) => {
|
||||
const { min, fineStep, coarseStep } = config.sd.width;
|
||||
const { model, width, hrfWidth, aspectRatio, hrfEnabled } = generation;
|
||||
|
||||
const step = hotkeys.shift ? fineStep : coarseStep;
|
||||
|
||||
return {
|
||||
model,
|
||||
width,
|
||||
hrfWidth,
|
||||
min,
|
||||
step,
|
||||
aspectRatio,
|
||||
hrfEnabled,
|
||||
};
|
||||
},
|
||||
defaultSelectorOptions
|
||||
);
|
||||
|
||||
type ParamWidthProps = Omit<IAIFullSliderProps, 'label' | 'value' | 'onChange'>;
|
||||
|
||||
const ParamHrfWidth = (props: ParamWidthProps) => {
|
||||
const { width, hrfWidth, min, step, aspectRatio, hrfEnabled } =
|
||||
useAppSelector(selector);
|
||||
const dispatch = useAppDispatch();
|
||||
const maxHrfWidth = Math.max(findPrevMultipleOfEight(width), min);
|
||||
|
||||
const handleChange = useCallback(
|
||||
(v: number) => {
|
||||
dispatch(setHrfWidth(v));
|
||||
if (aspectRatio) {
|
||||
const newHeight = roundToMultiple(v / aspectRatio, 8);
|
||||
dispatch(setHrfHeight(newHeight));
|
||||
}
|
||||
},
|
||||
[dispatch, aspectRatio]
|
||||
);
|
||||
|
||||
const handleReset = useCallback(() => {
|
||||
dispatch(setHrfWidth(maxHrfWidth));
|
||||
if (aspectRatio) {
|
||||
const newHeight = roundToMultiple(maxHrfWidth / aspectRatio, 8);
|
||||
dispatch(setHrfHeight(newHeight));
|
||||
}
|
||||
}, [dispatch, maxHrfWidth, aspectRatio]);
|
||||
|
||||
return (
|
||||
<IAISlider
|
||||
label="Initial Width"
|
||||
value={hrfWidth}
|
||||
min={min}
|
||||
step={step}
|
||||
max={maxHrfWidth}
|
||||
onChange={handleChange}
|
||||
handleReset={handleReset}
|
||||
withInput
|
||||
withReset
|
||||
withSliderMarks
|
||||
sliderNumberInputProps={{ max: maxHrfWidth }}
|
||||
isDisabled={!hrfEnabled}
|
||||
{...props}
|
||||
/>
|
||||
);
|
||||
};
|
||||
|
||||
export default memo(ParamHrfWidth);
|
||||
@@ -55,9 +55,6 @@ import { initialImageSelected, modelSelected } from '../store/actions';
|
||||
import {
|
||||
setCfgScale,
|
||||
setHeight,
|
||||
setHrfEnabled,
|
||||
setHrfMethod,
|
||||
setHrfStrength,
|
||||
setImg2imgStrength,
|
||||
setNegativePrompt,
|
||||
setPositivePrompt,
|
||||
@@ -70,7 +67,6 @@ import {
|
||||
isValidCfgScale,
|
||||
isValidControlNetModel,
|
||||
isValidHeight,
|
||||
isValidHrfMethod,
|
||||
isValidIPAdapterModel,
|
||||
isValidLoRAModel,
|
||||
isValidMainModel,
|
||||
@@ -87,7 +83,6 @@ import {
|
||||
isValidSteps,
|
||||
isValidStrength,
|
||||
isValidWidth,
|
||||
isValidBoolean,
|
||||
} from '../types/parameterSchemas';
|
||||
|
||||
const selector = createSelector(
|
||||
@@ -366,51 +361,6 @@ export const useRecallParameters = () => {
|
||||
[dispatch, parameterSetToast, parameterNotSetToast]
|
||||
);
|
||||
|
||||
/**
|
||||
* Recall high resolution enabled with toast
|
||||
*/
|
||||
const recallHrfEnabled = useCallback(
|
||||
(hrfEnabled: unknown) => {
|
||||
if (!isValidBoolean(hrfEnabled)) {
|
||||
parameterNotSetToast();
|
||||
return;
|
||||
}
|
||||
dispatch(setHrfEnabled(hrfEnabled));
|
||||
parameterSetToast();
|
||||
},
|
||||
[dispatch, parameterSetToast, parameterNotSetToast]
|
||||
);
|
||||
|
||||
/**
|
||||
* Recall high resolution strength with toast
|
||||
*/
|
||||
const recallHrfStrength = useCallback(
|
||||
(hrfStrength: unknown) => {
|
||||
if (!isValidStrength(hrfStrength)) {
|
||||
parameterNotSetToast();
|
||||
return;
|
||||
}
|
||||
dispatch(setHrfStrength(hrfStrength));
|
||||
parameterSetToast();
|
||||
},
|
||||
[dispatch, parameterSetToast, parameterNotSetToast]
|
||||
);
|
||||
|
||||
/**
|
||||
* Recall high resolution method with toast
|
||||
*/
|
||||
const recallHrfMethod = useCallback(
|
||||
(hrfMethod: unknown) => {
|
||||
if (!isValidHrfMethod(hrfMethod)) {
|
||||
parameterNotSetToast();
|
||||
return;
|
||||
}
|
||||
dispatch(setHrfMethod(hrfMethod));
|
||||
parameterSetToast();
|
||||
},
|
||||
[dispatch, parameterSetToast, parameterNotSetToast]
|
||||
);
|
||||
|
||||
/**
|
||||
* Recall LoRA with toast
|
||||
*/
|
||||
@@ -761,9 +711,6 @@ export const useRecallParameters = () => {
|
||||
steps,
|
||||
width,
|
||||
strength,
|
||||
hrf_enabled,
|
||||
hrf_strength,
|
||||
hrf_method,
|
||||
positive_style_prompt,
|
||||
negative_style_prompt,
|
||||
refiner_model,
|
||||
@@ -782,55 +729,34 @@ export const useRecallParameters = () => {
|
||||
if (isValidCfgScale(cfg_scale)) {
|
||||
dispatch(setCfgScale(cfg_scale));
|
||||
}
|
||||
|
||||
if (isValidMainModel(model)) {
|
||||
dispatch(modelSelected(model));
|
||||
}
|
||||
|
||||
if (isValidPositivePrompt(positive_prompt)) {
|
||||
dispatch(setPositivePrompt(positive_prompt));
|
||||
}
|
||||
|
||||
if (isValidNegativePrompt(negative_prompt)) {
|
||||
dispatch(setNegativePrompt(negative_prompt));
|
||||
}
|
||||
|
||||
if (isValidScheduler(scheduler)) {
|
||||
dispatch(setScheduler(scheduler));
|
||||
}
|
||||
|
||||
if (isValidSeed(seed)) {
|
||||
dispatch(setSeed(seed));
|
||||
}
|
||||
|
||||
if (isValidSteps(steps)) {
|
||||
dispatch(setSteps(steps));
|
||||
}
|
||||
|
||||
if (isValidWidth(width)) {
|
||||
dispatch(setWidth(width));
|
||||
}
|
||||
|
||||
if (isValidHeight(height)) {
|
||||
dispatch(setHeight(height));
|
||||
}
|
||||
|
||||
if (isValidStrength(strength)) {
|
||||
dispatch(setImg2imgStrength(strength));
|
||||
}
|
||||
|
||||
if (isValidBoolean(hrf_enabled)) {
|
||||
dispatch(setHrfEnabled(hrf_enabled));
|
||||
}
|
||||
|
||||
if (isValidStrength(hrf_strength)) {
|
||||
dispatch(setHrfStrength(hrf_strength));
|
||||
}
|
||||
|
||||
if (isValidHrfMethod(hrf_method)) {
|
||||
dispatch(setHrfMethod(hrf_method));
|
||||
}
|
||||
|
||||
if (isValidSDXLPositiveStylePrompt(positive_style_prompt)) {
|
||||
dispatch(setPositiveStylePromptSDXL(positive_style_prompt));
|
||||
}
|
||||
@@ -936,9 +862,6 @@ export const useRecallParameters = () => {
|
||||
recallWidth,
|
||||
recallHeight,
|
||||
recallStrength,
|
||||
recallHrfEnabled,
|
||||
recallHrfStrength,
|
||||
recallHrfMethod,
|
||||
recallLoRA,
|
||||
recallControlNet,
|
||||
recallIPAdapter,
|
||||
|
||||
@@ -11,7 +11,6 @@ import {
|
||||
CanvasCoherenceModeParam,
|
||||
CfgScaleParam,
|
||||
HeightParam,
|
||||
HrfMethodParam,
|
||||
MainModelParam,
|
||||
MaskBlurMethodParam,
|
||||
NegativePromptParam,
|
||||
@@ -28,9 +27,10 @@ import {
|
||||
} from '../types/parameterSchemas';
|
||||
|
||||
export interface GenerationState {
|
||||
hrfHeight: HeightParam;
|
||||
hrfWidth: WidthParam;
|
||||
hrfEnabled: boolean;
|
||||
hrfStrength: StrengthParam;
|
||||
hrfMethod: HrfMethodParam;
|
||||
cfgScale: CfgScaleParam;
|
||||
height: HeightParam;
|
||||
img2imgStrength: StrengthParam;
|
||||
@@ -73,9 +73,10 @@ export interface GenerationState {
|
||||
}
|
||||
|
||||
export const initialGenerationState: GenerationState = {
|
||||
hrfStrength: 0.45,
|
||||
hrfHeight: 64,
|
||||
hrfWidth: 64,
|
||||
hrfStrength: 0.75,
|
||||
hrfEnabled: false,
|
||||
hrfMethod: 'ESRGAN',
|
||||
cfgScale: 7.5,
|
||||
height: 512,
|
||||
img2imgStrength: 0.75,
|
||||
@@ -278,15 +279,18 @@ export const generationSlice = createSlice({
|
||||
setClipSkip: (state, action: PayloadAction<number>) => {
|
||||
state.clipSkip = action.payload;
|
||||
},
|
||||
setHrfHeight: (state, action: PayloadAction<number>) => {
|
||||
state.hrfHeight = action.payload;
|
||||
},
|
||||
setHrfWidth: (state, action: PayloadAction<number>) => {
|
||||
state.hrfWidth = action.payload;
|
||||
},
|
||||
setHrfStrength: (state, action: PayloadAction<number>) => {
|
||||
state.hrfStrength = action.payload;
|
||||
},
|
||||
setHrfEnabled: (state, action: PayloadAction<boolean>) => {
|
||||
state.hrfEnabled = action.payload;
|
||||
},
|
||||
setHrfMethod: (state, action: PayloadAction<HrfMethodParam>) => {
|
||||
state.hrfMethod = action.payload;
|
||||
},
|
||||
shouldUseCpuNoiseChanged: (state, action: PayloadAction<boolean>) => {
|
||||
state.shouldUseCpuNoise = action.payload;
|
||||
},
|
||||
@@ -371,9 +375,10 @@ export const {
|
||||
setSeamlessXAxis,
|
||||
setSeamlessYAxis,
|
||||
setClipSkip,
|
||||
setHrfEnabled,
|
||||
setHrfHeight,
|
||||
setHrfWidth,
|
||||
setHrfStrength,
|
||||
setHrfMethod,
|
||||
setHrfEnabled,
|
||||
shouldUseCpuNoiseChanged,
|
||||
setAspectRatio,
|
||||
setShouldLockAspectRatio,
|
||||
|
||||
@@ -132,7 +132,6 @@ export const zScheduler = z.enum([
|
||||
'lms_k',
|
||||
'euler_a',
|
||||
'kdpm_2_a',
|
||||
'lcm',
|
||||
]);
|
||||
/**
|
||||
* Type alias for scheduler parameter, inferred from its zod schema
|
||||
@@ -167,7 +166,6 @@ export const SCHEDULER_LABEL_MAP: Record<SchedulerParam, string> = {
|
||||
lms_k: 'LMS Karras',
|
||||
euler_a: 'Euler Ancestral',
|
||||
kdpm_2_a: 'KDPM 2 Ancestral',
|
||||
lcm: 'LCM',
|
||||
};
|
||||
|
||||
/**
|
||||
@@ -400,20 +398,6 @@ export type PrecisionParam = z.infer<typeof zPrecision>;
|
||||
export const isValidPrecision = (val: unknown): val is PrecisionParam =>
|
||||
zPrecision.safeParse(val).success;
|
||||
|
||||
/**
|
||||
* Zod schema for a high resolution fix method parameter.
|
||||
*/
|
||||
export const zHrfMethod = z.enum(['ESRGAN', 'bilinear']);
|
||||
/**
|
||||
* Type alias for high resolution fix method parameter, inferred from its zod schema
|
||||
*/
|
||||
export type HrfMethodParam = z.infer<typeof zHrfMethod>;
|
||||
/**
|
||||
* Validates/type-guards a value as a high resolution fix method parameter
|
||||
*/
|
||||
export const isValidHrfMethod = (val: unknown): val is HrfMethodParam =>
|
||||
zHrfMethod.safeParse(val).success;
|
||||
|
||||
/**
|
||||
* Zod schema for SDXL refiner positive aesthetic score parameter
|
||||
*/
|
||||
@@ -496,17 +480,6 @@ export const isValidCoherenceModeParam = (
|
||||
): val is CanvasCoherenceModeParam =>
|
||||
zCanvasCoherenceMode.safeParse(val).success;
|
||||
|
||||
/**
|
||||
* Zod schema for a boolean.
|
||||
*/
|
||||
export const zBoolean = z.boolean();
|
||||
|
||||
/**
|
||||
* Validates/type-guards a value as a boolean parameter
|
||||
*/
|
||||
export const isValidBoolean = (val: unknown): val is boolean =>
|
||||
zBoolean.safeParse(val).success && val !== null && val !== undefined;
|
||||
|
||||
// /**
|
||||
// * Zod schema for BaseModelType
|
||||
// */
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
import { Flex, Text } from '@chakra-ui/react';
|
||||
import { Flex } from '@chakra-ui/react';
|
||||
import { createSelector } from '@reduxjs/toolkit';
|
||||
import { stateSelector } from 'app/store/store';
|
||||
import { useAppSelector } from 'app/store/storeHooks';
|
||||
@@ -14,7 +14,6 @@ import ParamSDXLRefinerStart from './SDXLRefiner/ParamSDXLRefinerStart';
|
||||
import ParamSDXLRefinerSteps from './SDXLRefiner/ParamSDXLRefinerSteps';
|
||||
import ParamUseSDXLRefiner from './SDXLRefiner/ParamUseSDXLRefiner';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
import { useIsRefinerAvailable } from 'services/api/hooks/useIsRefinerAvailable';
|
||||
|
||||
const selector = createSelector(
|
||||
stateSelector,
|
||||
@@ -32,19 +31,6 @@ const selector = createSelector(
|
||||
const ParamSDXLRefinerCollapse = () => {
|
||||
const { activeLabel, shouldUseSliders } = useAppSelector(selector);
|
||||
const { t } = useTranslation();
|
||||
const isRefinerAvailable = useIsRefinerAvailable();
|
||||
|
||||
if (!isRefinerAvailable) {
|
||||
return (
|
||||
<IAICollapse label={t('sdxl.refiner')} activeLabel={activeLabel}>
|
||||
<Flex sx={{ justifyContent: 'center', p: 2 }}>
|
||||
<Text sx={{ fontSize: 'sm', color: 'base.500', _dark: 'base.700' }}>
|
||||
{t('models.noRefinerModelsInstalled')}
|
||||
</Text>
|
||||
</Flex>
|
||||
</IAICollapse>
|
||||
);
|
||||
}
|
||||
|
||||
return (
|
||||
<IAICollapse label={t('sdxl.refiner')} activeLabel={activeLabel}>
|
||||
|
||||
@@ -70,7 +70,7 @@ export const initialConfigState: AppConfig = {
|
||||
coarseStep: 0.05,
|
||||
},
|
||||
hrfStrength: {
|
||||
initial: 0.45,
|
||||
initial: 0.7,
|
||||
min: 0,
|
||||
sliderMax: 1,
|
||||
inputMax: 1,
|
||||
|
||||
242
invokeai/frontend/web/src/services/api/schema.d.ts
vendored
242
invokeai/frontend/web/src/services/api/schema.d.ts
vendored
File diff suppressed because one or more lines are too long
@@ -127,6 +127,7 @@ export type CompelInvocation = s['CompelInvocation'];
|
||||
export type DynamicPromptInvocation = s['DynamicPromptInvocation'];
|
||||
export type NoiseInvocation = s['NoiseInvocation'];
|
||||
export type DenoiseLatentsInvocation = s['DenoiseLatentsInvocation'];
|
||||
export type ResizeLatentsInvocation = s['ResizeLatentsInvocation'];
|
||||
export type ONNXTextToLatentsInvocation = s['ONNXTextToLatentsInvocation'];
|
||||
export type SDXLLoraLoaderInvocation = s['SDXLLoraLoaderInvocation'];
|
||||
export type ImageToLatentsInvocation = s['ImageToLatentsInvocation'];
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1 +1 @@
|
||||
__version__ = "3.4.0rc4"
|
||||
__version__ = "3.4.0rc2"
|
||||
|
||||
@@ -40,7 +40,9 @@ dependencies = [
|
||||
"controlnet-aux>=0.0.6",
|
||||
"timm==0.6.13", # needed to override timm latest in controlnet_aux, see https://github.com/isl-org/ZoeDepth/issues/26
|
||||
"datasets",
|
||||
"diffusers[torch]~=0.23.0",
|
||||
# When bumping diffusers beyond 0.21, make sure to address this:
|
||||
# https://github.com/invoke-ai/InvokeAI/blob/fc09ab7e13cb7ca5389100d149b6422ace7b8ed3/invokeai/app/invocations/latent.py#L513
|
||||
"diffusers[torch]~=0.21.0",
|
||||
"dnspython~=2.4.0",
|
||||
"dynamicprompts",
|
||||
"easing-functions",
|
||||
@@ -78,11 +80,11 @@ dependencies = [
|
||||
"semver~=3.0.1",
|
||||
"send2trash",
|
||||
"test-tube~=0.7.5",
|
||||
"torch~=2.1.0",
|
||||
"torchvision~=0.16",
|
||||
"torch~=2.0.1",
|
||||
"torchvision~=0.15.2",
|
||||
"torchmetrics~=0.11.0",
|
||||
"torchsde~=0.2.5",
|
||||
"transformers~=4.35.0",
|
||||
"transformers~=4.31.0",
|
||||
"uvicorn[standard]~=0.21.1",
|
||||
"windows-curses; sys_platform=='win32'",
|
||||
]
|
||||
@@ -107,8 +109,8 @@ dependencies = [
|
||||
"pytest-datadir",
|
||||
]
|
||||
"xformers" = [
|
||||
"xformers==0.0.22post7; sys_platform!='darwin'",
|
||||
"triton; sys_platform=='linux'",
|
||||
"xformers~=0.0.19; sys_platform!='darwin'",
|
||||
"triton; sys_platform=='linux'",
|
||||
]
|
||||
"onnx" = ["onnxruntime"]
|
||||
"onnx-cuda" = ["onnxruntime-gpu"]
|
||||
@@ -204,7 +206,6 @@ exclude = [
|
||||
"build",
|
||||
"dist",
|
||||
"invokeai/frontend/web/node_modules/",
|
||||
".venv*",
|
||||
]
|
||||
|
||||
[tool.black]
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user