diff --git a/.github/workflows/close-inactive-issues.yml b/.github/workflows/close-inactive-issues.yml index 58ec78c0b8..9636911b2e 100644 --- a/.github/workflows/close-inactive-issues.yml +++ b/.github/workflows/close-inactive-issues.yml @@ -1,11 +1,11 @@ name: Close inactive issues on: schedule: - - cron: "00 6 * * *" + - cron: "00 4 * * *" env: - DAYS_BEFORE_ISSUE_STALE: 14 - DAYS_BEFORE_ISSUE_CLOSE: 28 + DAYS_BEFORE_ISSUE_STALE: 30 + DAYS_BEFORE_ISSUE_CLOSE: 14 jobs: close-issues: @@ -14,7 +14,7 @@ jobs: issues: write pull-requests: write steps: - - uses: actions/stale@v5 + - uses: actions/stale@v8 with: days-before-issue-stale: ${{ env.DAYS_BEFORE_ISSUE_STALE }} days-before-issue-close: ${{ env.DAYS_BEFORE_ISSUE_CLOSE }} @@ -23,5 +23,6 @@ jobs: close-issue-message: "Due to inactivity, this issue was automatically closed. If you are still experiencing the issue, please recreate the issue." days-before-pr-stale: -1 days-before-pr-close: -1 + exempt-issue-labels: "Active Issue" repo-token: ${{ secrets.GITHUB_TOKEN }} operations-per-run: 500 diff --git a/docs/assets/nodes/groupsallscale.png b/docs/assets/nodes/groupsallscale.png new file mode 100644 index 0000000000..aa67db9a3e Binary files /dev/null and b/docs/assets/nodes/groupsallscale.png differ diff --git a/docs/assets/nodes/groupsconditioning.png b/docs/assets/nodes/groupsconditioning.png new file mode 100644 index 0000000000..b988cee78c Binary files /dev/null and b/docs/assets/nodes/groupsconditioning.png differ diff --git a/docs/assets/nodes/groupscontrol.png b/docs/assets/nodes/groupscontrol.png new file mode 100644 index 0000000000..ad696c3087 Binary files /dev/null and b/docs/assets/nodes/groupscontrol.png differ diff --git a/docs/assets/nodes/groupsimgvae.png b/docs/assets/nodes/groupsimgvae.png new file mode 100644 index 0000000000..c60bf40d67 Binary files /dev/null and b/docs/assets/nodes/groupsimgvae.png differ diff --git a/docs/assets/nodes/groupsiterate.png b/docs/assets/nodes/groupsiterate.png new file mode 100644 index 0000000000..9c1cd15bc2 Binary files /dev/null and b/docs/assets/nodes/groupsiterate.png differ diff --git a/docs/assets/nodes/groupslora.png b/docs/assets/nodes/groupslora.png new file mode 100644 index 0000000000..befcee6490 Binary files /dev/null and b/docs/assets/nodes/groupslora.png differ diff --git a/docs/assets/nodes/groupsmultigenseeding.png b/docs/assets/nodes/groupsmultigenseeding.png new file mode 100644 index 0000000000..a644146c86 Binary files /dev/null and b/docs/assets/nodes/groupsmultigenseeding.png differ diff --git a/docs/assets/nodes/groupsnoise.png b/docs/assets/nodes/groupsnoise.png new file mode 100644 index 0000000000..afb1e23e81 Binary files /dev/null and b/docs/assets/nodes/groupsnoise.png differ diff --git a/docs/assets/nodes/groupsrandseed.png b/docs/assets/nodes/groupsrandseed.png new file mode 100644 index 0000000000..06430cdee4 Binary files /dev/null and b/docs/assets/nodes/groupsrandseed.png differ diff --git a/docs/assets/nodes/nodescontrol.png b/docs/assets/nodes/nodescontrol.png new file mode 100644 index 0000000000..8b179e43ac Binary files /dev/null and b/docs/assets/nodes/nodescontrol.png differ diff --git a/docs/assets/nodes/nodesi2i.png b/docs/assets/nodes/nodesi2i.png new file mode 100644 index 0000000000..9908833804 Binary files /dev/null and b/docs/assets/nodes/nodesi2i.png differ diff --git a/docs/assets/nodes/nodest2i.png b/docs/assets/nodes/nodest2i.png new file mode 100644 index 0000000000..7e882dbf1b Binary files /dev/null and b/docs/assets/nodes/nodest2i.png differ diff --git a/docs/features/CONFIGURATION.md b/docs/features/CONFIGURATION.md index 68c5ced59b..ccb23ae79a 100644 --- a/docs/features/CONFIGURATION.md +++ b/docs/features/CONFIGURATION.md @@ -136,19 +136,16 @@ command-line options by giving the `--help` argument: ``` (.venv) > invokeai-web --help -usage: InvokeAI [-h] [--host HOST] [--port PORT] [--allow_origins [ALLOW_ORIGINS ...]] [--allow_credentials | --no-allow_credentials] - [--allow_methods [ALLOW_METHODS ...]] [--allow_headers [ALLOW_HEADERS ...]] [--esrgan | --no-esrgan] - [--internet_available | --no-internet_available] [--log_tokenization | --no-log_tokenization] - [--nsfw_checker | --no-nsfw_checker] [--patchmatch | --no-patchmatch] [--restore | --no-restore] - [--always_use_cpu | --no-always_use_cpu] [--free_gpu_mem | --no-free_gpu_mem] [--max_cache_size MAX_CACHE_SIZE] - [--max_vram_cache_size MAX_VRAM_CACHE_SIZE] [--precision {auto,float16,float32,autocast}] - [--sequential_guidance | --no-sequential_guidance] [--xformers_enabled | --no-xformers_enabled] - [--tiled_decode | --no-tiled_decode] [--root ROOT] [--autoimport_dir AUTOIMPORT_DIR] [--lora_dir LORA_DIR] - [--embedding_dir EMBEDDING_DIR] [--controlnet_dir CONTROLNET_DIR] [--conf_path CONF_PATH] [--models_dir MODELS_DIR] - [--legacy_conf_dir LEGACY_CONF_DIR] [--db_dir DB_DIR] [--outdir OUTDIR] [--from_file FROM_FILE] - [--use_memory_db | --no-use_memory_db] [--model MODEL] [--log_handlers [LOG_HANDLERS ...]] - [--log_format {plain,color,syslog,legacy}] [--log_level {debug,info,warning,error,critical}] -... +usage: InvokeAI [-h] [--host HOST] [--port PORT] [--allow_origins [ALLOW_ORIGINS ...]] [--allow_credentials | --no-allow_credentials] [--allow_methods [ALLOW_METHODS ...]] + [--allow_headers [ALLOW_HEADERS ...]] [--esrgan | --no-esrgan] [--internet_available | --no-internet_available] [--log_tokenization | --no-log_tokenization] + [--nsfw_checker | --no-nsfw_checker] [--invisible_watermark | --no-invisible_watermark] [--patchmatch | --no-patchmatch] [--restore | --no-restore] + [--always_use_cpu | --no-always_use_cpu] [--free_gpu_mem | --no-free_gpu_mem] [--max_loaded_models MAX_LOADED_MODELS] [--max_cache_size MAX_CACHE_SIZE] + [--max_vram_cache_size MAX_VRAM_CACHE_SIZE] [--gpu_mem_reserved GPU_MEM_RESERVED] [--precision {auto,float16,float32,autocast}] + [--sequential_guidance | --no-sequential_guidance] [--xformers_enabled | --no-xformers_enabled] [--tiled_decode | --no-tiled_decode] [--root ROOT] + [--autoimport_dir AUTOIMPORT_DIR] [--lora_dir LORA_DIR] [--embedding_dir EMBEDDING_DIR] [--controlnet_dir CONTROLNET_DIR] [--conf_path CONF_PATH] + [--models_dir MODELS_DIR] [--legacy_conf_dir LEGACY_CONF_DIR] [--db_dir DB_DIR] [--outdir OUTDIR] [--from_file FROM_FILE] + [--use_memory_db | --no-use_memory_db] [--model MODEL] [--log_handlers [LOG_HANDLERS ...]] [--log_format {plain,color,syslog,legacy}] + [--log_level {debug,info,warning,error,critical}] [--version | --no-version] ``` ## The Configuration Settings @@ -179,6 +176,7 @@ These configuration settings allow you to enable and disable various InvokeAI fe | `internet_available` | `true` | When a resource is not available locally, try to fetch it via the internet | | `log_tokenization` | `false` | Before each text2image generation, print a color-coded representation of the prompt to the console; this can help understand why a prompt is not working as expected | | `nsfw_checker` | `true` | Activate the NSFW checker to blur out risque images | +| `invisible_watermark` | `true` | Write an invisible watermark 'InvokeAI' into generated images for use by AI image detectors | | `patchmatch` | `true` | Activate the "patchmatch" algorithm for improved inpainting | | `restore` | `true` | Activate the facial restoration features (DEPRECATED; restoration features will be removed in 3.0.0) | diff --git a/docs/features/NODES.md b/docs/features/NODES.md index 1f276d2012..eef71eb974 100644 --- a/docs/features/NODES.md +++ b/docs/features/NODES.md @@ -61,11 +61,13 @@ A noise scheduler (eg. DPM++ 2M Karras) schedules the subtraction of noise from | ImageInverseLerp | Inverse linear interpolation of all pixels of an image | | ImageLerp | Linear interpolation of all pixels of an image | | ImageMultiply | Multiplies two images together using `PIL.ImageChops.Multiply()` | +| ImageNSFWBlurInvocation | Detects and blurs images that may contain sexually explicit content | | ImagePaste | Pastes an image into another image | | ImageProcessor | Base class for invocations that reprocess images for ControlNet | | ImageResize | Resizes an image to specific dimensions | | ImageScale | Scales an image by a factor | | ImageToLatents | Scales latents by a given factor | +| ImageWatermarkInvocation | Adds an invisible watermark to images | | InfillColor | Infills transparent areas of an image with a solid color | | InfillPatchMatch | Infills transparent areas of an image using the PatchMatch algorithm | | InfillTile | Infills transparent areas of an image with tiles of the image | @@ -116,49 +118,49 @@ There are several node grouping concepts that can be examined with a narrow focu As described, an initial noise tensor is necessary for the latent diffusion process. As a result, all non-image *ToLatents nodes require a noise node input. -groupsnoise +![groupsnoise](../assets/nodes/groupsnoise.png) ### Conditioning As described, conditioning is necessary for the latent diffusion process, whether empty or not. As a result, all non-image *ToLatents nodes require positive and negative conditioning inputs. Conditioning is reliant on a CLIP tokenizer provided by the Model Loader node. -groupsconditioning +![groupsconditioning](../assets/nodes/groupsconditioning.png) ### Image Space & VAE The ImageToLatents node doesn't require a noise node input, but requires a VAE input to convert the image from image space into latent space. In reverse, the LatentsToImage node requires a VAE input to convert from latent space back into image space. -groupsimgvae +![groupsimgvae](../assets/nodes/groupsimgvae.png) ### Defined & Random Seeds It is common to want to use both the same seed (for continuity) and random seeds (for variance). To define a seed, simply enter it into the 'Seed' field on a noise node. Conversely, the RandomInt node generates a random integer between 'Low' and 'High', and can be used as input to the 'Seed' edge point on a noise node to randomize your seed. -groupsrandseed +![groupsrandseed](../assets/nodes/groupsrandseed.png) ### Control Control means to guide the diffusion process to adhere to a defined input or structure. Control can be provided as input to non-image *ToLatents nodes from ControlNet nodes. ControlNet nodes usually require an image processor which converts an input image for use with ControlNet. -groupscontrol +![groupscontrol](../assets/nodes/groupscontrol.png) ### LoRA The Lora Loader node lets you load a LoRA (say that ten times fast) and pass it as output to both the Prompt (Compel) and non-image *ToLatents nodes. A model's CLIP tokenizer is passed through the LoRA into Prompt (Compel), where it affects conditioning. A model's U-Net is also passed through the LoRA into a non-image *ToLatents node, where it affects noise prediction. -groupslora +![groupslora](../assets/nodes/groupslora.png) ### Scaling Use the ImageScale, ScaleLatents, and Upscale nodes to upscale images and/or latent images. The chosen method differs across contexts. However, be aware that latents are already noisy and compressed at their original resolution; scaling an image could produce more detailed results. -groupsallscale +![groupsallscale](../assets/nodes/groupsallscale.png) ### Iteration + Multiple Images as Input Iteration is a common concept in any processing, and means to repeat a process with given input. In nodes, you're able to use the Iterate node to iterate through collections usually gathered by the Collect node. The Iterate node has many potential uses, from processing a collection of images one after another, to varying seeds across multiple image generations and more. This screenshot demonstrates how to collect several images and pass them out one at a time. -groupsiterate +![groupsiterate](../assets/nodes/groupsiterate.png) ### Multiple Image Generation + Random Seeds @@ -166,7 +168,7 @@ Multiple image generation in the node editor is done using the RandomRange node. To control seeds across generations takes some care. The first row in the screenshot will generate multiple images with different seeds, but using the same RandomRange parameters across invocations will result in the same group of random seeds being used across the images, producing repeatable results. In the second row, adding the RandomInt node as input to RandomRange's 'Seed' edge point will ensure that seeds are varied across all images across invocations, producing varied results. -groupsmultigenseeding +![groupsmultigenseeding](../assets/nodes/groupsmultigenseeding.png) ## Examples @@ -174,7 +176,7 @@ With our knowledge of node grouping and the diffusion process, let’s break dow ### Basic text-to-image Node Graph -nodest2i +![nodest2i](../assets/nodes/nodest2i.png) - Model Loader: A necessity to generating images (as we’ve read above). We choose our model from the dropdown. It outputs a U-Net, CLIP tokenizer, and VAE. - Prompt (Compel): Another necessity. Two prompt nodes are created. One will output positive conditioning (what you want, ‘dog’), one will output negative (what you don’t want, ‘cat’). They both input the CLIP tokenizer that the Model Loader node outputs. @@ -184,7 +186,7 @@ With our knowledge of node grouping and the diffusion process, let’s break dow ### Basic image-to-image Node Graph -nodesi2i +![nodesi2i](../assets/nodes/nodesi2i.png) - Model Loader: Choose a model from the dropdown. - Prompt (Compel): Two prompt nodes. One positive (dog), one negative (dog). Same CLIP inputs from the Model Loader node as before. @@ -195,7 +197,7 @@ With our knowledge of node grouping and the diffusion process, let’s break dow ### Basic ControlNet Node Graph -nodescontrol +![nodescontrol](../assets/nodes/nodescontrol.png) - Model Loader - Prompt (Compel) diff --git a/docs/features/OTHER.md b/docs/features/OTHER.md index 2401f644ba..00bfd05e14 100644 --- a/docs/features/OTHER.md +++ b/docs/features/OTHER.md @@ -16,21 +16,24 @@ Output Example: --- -## **Seamless Tiling** +## **Invisible Watermark** -The seamless tiling mode causes generated images to seamlessly tile -with itself creating repetitive wallpaper-like patterns. To use it, -activate the Seamless Tiling option in the Web GUI and then select -whether to tile on the X (horizontal) and/or Y (vertical) axes. Tiling -will then be active for the next set of generations. +In keeping with the principles for responsible AI generation, and to +help AI researchers avoid synthetic images contaminating their +training sets, InvokeAI adds an invisible watermark to each of the +final images it generates. The watermark consists of the text +"InvokeAI" and can be viewed using the +[invisible-watermarks](https://github.com/ShieldMnt/invisible-watermark) +tool. -A nice prompt to test seamless tiling with is: +Watermarking is controlled using the `invisible-watermark` setting in +`invokeai.yaml`. To turn it off, add the following line under the `Features` +category. ``` -pond garden with lotus by claude monet" +invisible_watermark: false ``` ---- ## **Weighted Prompts** @@ -39,34 +42,10 @@ priority to them, by adding `:` to the end of the section you wish to u example consider this prompt: ```bash -tabby cat:0.25 white duck:0.75 hybrid +(tabby cat):0.25 (white duck):0.75 hybrid ``` This will tell the sampler to invest 25% of its effort on the tabby cat aspect of the image and 75% on the white duck aspect (surprisingly, this example actually works). The prompt weights can use any combination of integers and floating point numbers, and they do not need to add up to 1. -## **Thresholding and Perlin Noise Initialization Options** - -Under the Noise section of the Web UI, you will find two options named -Perlin Noise and Noise Threshold. [Perlin -noise](https://en.wikipedia.org/wiki/Perlin_noise) is a type of -structured noise used to simulate terrain and other natural -textures. The slider controls the percentage of perlin noise that will -be mixed into the image at the beginning of generation. Adding a little -perlin noise to a generation will alter the image substantially. - -The noise threshold limits the range of the latent values during -sampling and helps combat the oversharpening seem with higher CFG -scale values. - -For better intuition into what these options do in practice: - -![here is a graphic demonstrating them both](../assets/truncation_comparison.jpg) - -In generating this graphic, perlin noise at initialization was -programmatically varied going across on the diagram by values 0.0, -0.1, 0.2, 0.4, 0.5, 0.6, 0.8, 0.9, 1.0; and the threshold was varied -going down from 0, 1, 2, 3, 4, 5, 10, 20, 100. The other options are -fixed using the prompt "a portrait of a beautiful young lady" a CFG of -20, 100 steps, and a seed of 1950357039. diff --git a/invokeai/app/api/routers/app_info.py b/invokeai/app/api/routers/app_info.py index e1bfeda4a1..e37184a77b 100644 --- a/invokeai/app/api/routers/app_info.py +++ b/invokeai/app/api/routers/app_info.py @@ -1,9 +1,15 @@ +import typing from enum import Enum from fastapi import Body from fastapi.routing import APIRouter +from pathlib import Path from pydantic import BaseModel, Field from invokeai.backend.image_util.patchmatch import PatchMatch +from invokeai.backend.image_util.safety_checker import SafetyChecker +from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark +from invokeai.app.invocations.upscale import ESRGAN_MODELS + from invokeai.version import __version__ from ..dependencies import ApiDependencies @@ -16,6 +22,10 @@ class LogLevel(int, Enum): Warning = logging.WARNING Error = logging.ERROR Critical = logging.CRITICAL + +class Upscaler(BaseModel): + upscaling_method: str = Field(description="Name of upscaling method") + upscaling_models: list[str] = Field(description="List of upscaling models for this method") app_router = APIRouter(prefix="/v1/app", tags=["app"]) @@ -30,6 +40,9 @@ class AppConfig(BaseModel): """App Config Response""" infill_methods: list[str] = Field(description="List of available infill methods") + upscaling_methods: list[Upscaler] = Field(description="List of upscaling methods") + nsfw_methods: list[str] = Field(description="List of NSFW checking methods") + watermarking_methods: list[str] = Field(description="List of invisible watermark methods") @app_router.get( @@ -46,7 +59,30 @@ async def get_config() -> AppConfig: infill_methods = ['tile'] if PatchMatch.patchmatch_available(): infill_methods.append('patchmatch') - return AppConfig(infill_methods=infill_methods) + + + upscaling_models = [] + for model in typing.get_args(ESRGAN_MODELS): + upscaling_models.append(str(Path(model).stem)) + upscaler = Upscaler( + upscaling_method = 'esrgan', + upscaling_models = upscaling_models + ) + + nsfw_methods = [] + if SafetyChecker.safety_checker_available(): + nsfw_methods.append('nsfw_checker') + + watermarking_methods = [] + if InvisibleWatermark.invisible_watermark_available(): + watermarking_methods.append('invisible_watermark') + + return AppConfig( + infill_methods=infill_methods, + upscaling_methods=[upscaler], + nsfw_methods=nsfw_methods, + watermarking_methods=watermarking_methods, + ) @app_router.get( "/logging", diff --git a/invokeai/app/api_app.py b/invokeai/app/api_app.py index 824b697d54..044271779c 100644 --- a/invokeai/app/api_app.py +++ b/invokeai/app/api_app.py @@ -203,7 +203,10 @@ def invoke_api(): return find_port(port=port + 1) else: return port - + + from invokeai.backend.install.check_root import check_invokeai_root + check_invokeai_root(app_config) # note, may exit with an exception if root not set up + port = find_port(app_config.port) if port != app_config.port: logger.warn(f"Port {app_config.port} in use, using port {port}") diff --git a/invokeai/assets/web/caution.png b/invokeai/app/assets/images/caution.png similarity index 100% rename from invokeai/assets/web/caution.png rename to invokeai/app/assets/images/caution.png diff --git a/invokeai/app/invocations/compel.py b/invokeai/app/invocations/compel.py index fa1b6939d2..6aadbf509d 100644 --- a/invokeai/app/invocations/compel.py +++ b/invokeai/app/invocations/compel.py @@ -95,7 +95,7 @@ class CompelInvocation(BaseInvocation): def _lora_loader(): for lora in self.clip.loras: lora_info = context.services.model_manager.get_model( - **lora.dict(exclude={"weight"})) + **lora.dict(exclude={"weight"}), context=context) yield (lora_info.context.model, lora.weight) del lora_info return @@ -171,16 +171,16 @@ class CompelInvocation(BaseInvocation): class SDXLPromptInvocationBase: def run_clip_raw(self, context, clip_field, prompt, get_pooled): tokenizer_info = context.services.model_manager.get_model( - **clip_field.tokenizer.dict(), + **clip_field.tokenizer.dict(), context=context, ) text_encoder_info = context.services.model_manager.get_model( - **clip_field.text_encoder.dict(), + **clip_field.text_encoder.dict(), context=context, ) def _lora_loader(): for lora in clip_field.loras: lora_info = context.services.model_manager.get_model( - **lora.dict(exclude={"weight"})) + **lora.dict(exclude={"weight"}), context=context) yield (lora_info.context.model, lora.weight) del lora_info return @@ -196,6 +196,7 @@ class SDXLPromptInvocationBase: model_name=name, base_model=clip_field.text_encoder.base_model, model_type=ModelType.TextualInversion, + context=context, ).context.model ) except ModelNotFoundException: @@ -240,16 +241,16 @@ class SDXLPromptInvocationBase: def run_clip_compel(self, context, clip_field, prompt, get_pooled): tokenizer_info = context.services.model_manager.get_model( - **clip_field.tokenizer.dict(), + **clip_field.tokenizer.dict(), context=context, ) text_encoder_info = context.services.model_manager.get_model( - **clip_field.text_encoder.dict(), + **clip_field.text_encoder.dict(), context=context, ) def _lora_loader(): for lora in clip_field.loras: lora_info = context.services.model_manager.get_model( - **lora.dict(exclude={"weight"})) + **lora.dict(exclude={"weight"}), context=context) yield (lora_info.context.model, lora.weight) del lora_info return @@ -265,6 +266,7 @@ class SDXLPromptInvocationBase: model_name=name, base_model=clip_field.text_encoder.base_model, model_type=ModelType.TextualInversion, + context=context, ).context.model ) except ModelNotFoundException: diff --git a/invokeai/app/invocations/controlnet_image_processors.py b/invokeai/app/invocations/controlnet_image_processors.py index 911fede8fb..2087594b7b 100644 --- a/invokeai/app/invocations/controlnet_image_processors.py +++ b/invokeai/app/invocations/controlnet_image_processors.py @@ -20,7 +20,7 @@ from ...backend.model_management import BaseModelType, ModelType from ..models.image import ImageCategory, ImageField, ResourceOrigin from .baseinvocation import (BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext) -from .image import ImageOutput, PILInvocationConfig +from ..models.image import ImageOutput, PILInvocationConfig CONTROLNET_DEFAULT_MODELS = [ ########################################### diff --git a/invokeai/app/invocations/image.py b/invokeai/app/invocations/image.py index 8405aa6daf..26ee9e391d 100644 --- a/invokeai/app/invocations/image.py +++ b/invokeai/app/invocations/image.py @@ -4,61 +4,21 @@ from typing import Literal, Optional import numpy from PIL import Image, ImageFilter, ImageOps, ImageChops -from pydantic import BaseModel, Field +from pydantic import Field +from pathlib import Path from typing import Union - -from ..models.image import ImageCategory, ImageField, ResourceOrigin +from invokeai.app.invocations.metadata import CoreMetadata +from ..models.image import ( + ImageCategory, ImageField, ResourceOrigin, + PILInvocationConfig, ImageOutput, MaskOutput, +) from .baseinvocation import ( BaseInvocation, - BaseInvocationOutput, InvocationContext, InvocationConfig, ) - - -class PILInvocationConfig(BaseModel): - """Helper class to provide all PIL invocations with additional config""" - - class Config(InvocationConfig): - schema_extra = { - "ui": { - "tags": ["PIL", "image"], - }, - } - - -class ImageOutput(BaseInvocationOutput): - """Base class for invocations that output an image""" - - # fmt: off - type: Literal["image_output"] = "image_output" - image: ImageField = Field(default=None, description="The output image") - width: int = Field(description="The width of the image in pixels") - height: int = Field(description="The height of the image in pixels") - # fmt: on - - class Config: - schema_extra = {"required": ["type", "image", "width", "height"]} - - -class MaskOutput(BaseInvocationOutput): - """Base class for invocations that output a mask""" - - # fmt: off - type: Literal["mask"] = "mask" - mask: ImageField = Field(default=None, description="The output mask") - width: int = Field(description="The width of the mask in pixels") - height: int = Field(description="The height of the mask in pixels") - # fmt: on - - class Config: - schema_extra = { - "required": [ - "type", - "mask", - ] - } - +from invokeai.backend.image_util.safety_checker import SafetyChecker +from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark class LoadImageInvocation(BaseInvocation): """Load an image and provide it as output.""" @@ -397,7 +357,6 @@ class ImageConvertInvocation(BaseInvocation, PILInvocationConfig): height=image_dto.height, ) - class ImageBlurInvocation(BaseInvocation, PILInvocationConfig): """Blurs an image""" @@ -602,7 +561,6 @@ class ImageLerpInvocation(BaseInvocation, PILInvocationConfig): height=image_dto.height, ) - class ImageInverseLerpInvocation(BaseInvocation, PILInvocationConfig): """Inverse linear interpolation of all pixels of an image""" @@ -650,3 +608,97 @@ class ImageInverseLerpInvocation(BaseInvocation, PILInvocationConfig): width=image_dto.width, height=image_dto.height, ) + +class ImageNSFWBlurInvocation(BaseInvocation, PILInvocationConfig): + """Add blur to NSFW-flagged images""" + + # fmt: off + type: Literal["img_nsfw"] = "img_nsfw" + + # Inputs + image: Optional[ImageField] = Field(default=None, description="The image to check") + metadata: Optional[CoreMetadata] = Field(default=None, description="Optional core metadata to be written to the image") + # fmt: on + + class Config(InvocationConfig): + schema_extra = { + "ui": { + "title": "Blur NSFW Images", + "tags": ["image", "nsfw", "checker"] + }, + } + + def invoke(self, context: InvocationContext) -> ImageOutput: + image = context.services.images.get_pil_image(self.image.image_name) + + logger = context.services.logger + logger.debug("Running NSFW checker") + if SafetyChecker.has_nsfw_concept(image): + logger.info("A potentially NSFW image has been detected. Image will be blurred.") + blurry_image = image.filter(filter=ImageFilter.GaussianBlur(radius=32)) + caution = self._get_caution_img() + blurry_image.paste(caution,(0,0),caution) + image = blurry_image + + image_dto = context.services.images.create( + image=image, + image_origin=ResourceOrigin.INTERNAL, + image_category=ImageCategory.GENERAL, + node_id=self.id, + session_id=context.graph_execution_state_id, + is_intermediate=self.is_intermediate, + metadata=self.metadata.dict() if self.metadata else None, + ) + + return ImageOutput( + image=ImageField(image_name=image_dto.image_name), + width=image_dto.width, + height=image_dto.height, + ) + + def _get_caution_img(self)->Image: + import invokeai.app.assets.images as image_assets + caution = Image.open(Path(image_assets.__path__[0]) / 'caution.png') + return caution.resize((caution.width // 2, caution.height //2)) + +class ImageWatermarkInvocation(BaseInvocation, PILInvocationConfig): + """ Add an invisible watermark to an image """ + + # fmt: off + type: Literal["img_watermark"] = "img_watermark" + + # Inputs + image: Optional[ImageField] = Field(default=None, description="The image to check") + text: str = Field(default='InvokeAI', description="Watermark text") + metadata: Optional[CoreMetadata] = Field(default=None, description="Optional core metadata to be written to the image") + # fmt: on + + class Config(InvocationConfig): + schema_extra = { + "ui": { + "title": "Add Invisible Watermark", + "tags": ["image", "watermark", "invisible"] + }, + } + + def invoke(self, context: InvocationContext) -> ImageOutput: + image = context.services.images.get_pil_image(self.image.image_name) + new_image = InvisibleWatermark.add_watermark(image, self.text) + image_dto = context.services.images.create( + image=new_image, + image_origin=ResourceOrigin.INTERNAL, + image_category=ImageCategory.GENERAL, + node_id=self.id, + session_id=context.graph_execution_state_id, + is_intermediate=self.is_intermediate, + metadata=self.metadata.dict() if self.metadata else None, + ) + + return ImageOutput( + image=ImageField(image_name=image_dto.image_name), + width=image_dto.width, + height=image_dto.height, + ) + + + diff --git a/invokeai/app/invocations/latent.py b/invokeai/app/invocations/latent.py index 6082057bd3..69dffef09f 100644 --- a/invokeai/app/invocations/latent.py +++ b/invokeai/app/invocations/latent.py @@ -501,7 +501,7 @@ class LatentsToImageInvocation(BaseInvocation): vae: VaeField = Field(default=None, description="Vae submodel") tiled: bool = Field( default=False, - description="Decode latents by overlaping tiles(less memory consumption)") + description="Decode latents by overlapping tiles(less memory consumption)") fp32: bool = Field(DEFAULT_PRECISION=='float32', description="Decode in full precision") metadata: Optional[CoreMetadata] = Field(default=None, description="Optional core metadata to be written to the image") diff --git a/invokeai/app/invocations/metadata.py b/invokeai/app/invocations/metadata.py index 836ab7d0af..b0742d0419 100644 --- a/invokeai/app/invocations/metadata.py +++ b/invokeai/app/invocations/metadata.py @@ -2,16 +2,18 @@ from typing import Literal, Optional, Union from pydantic import BaseModel, Field -from invokeai.app.invocations.baseinvocation import (BaseInvocation, - BaseInvocationOutput, InvocationConfig, - InvocationContext) +from invokeai.app.invocations.baseinvocation import ( + BaseInvocation, + BaseInvocationOutput, + InvocationConfig, + InvocationContext, +) from invokeai.app.invocations.controlnet_image_processors import ControlField -from invokeai.app.invocations.model import (LoRAModelField, MainModelField, - VAEModelField) - +from invokeai.app.invocations.model import LoRAModelField, MainModelField, VAEModelField class LoRAMetadataField(BaseModel): """LoRA metadata for an image generated in InvokeAI.""" + lora: LoRAModelField = Field(description="The LoRA model") weight: float = Field(description="The weight of the LoRA model") @@ -19,7 +21,9 @@ class LoRAMetadataField(BaseModel): class CoreMetadata(BaseModel): """Core generation metadata for an image generated in InvokeAI.""" - generation_mode: str = Field(description="The generation mode that output this image",) + generation_mode: str = Field( + description="The generation mode that output this image", + ) positive_prompt: str = Field(description="The positive prompt parameter") negative_prompt: str = Field(description="The negative prompt parameter") width: int = Field(description="The width parameter") @@ -29,10 +33,20 @@ class CoreMetadata(BaseModel): cfg_scale: float = Field(description="The classifier-free guidance scale parameter") steps: int = Field(description="The number of steps used for inference") scheduler: str = Field(description="The scheduler used for inference") - clip_skip: int = Field(description="The number of skipped CLIP layers",) + clip_skip: int = Field( + description="The number of skipped CLIP layers", + ) model: MainModelField = Field(description="The main model used for inference") - controlnets: list[ControlField]= Field(description="The ControlNets used for inference") + controlnets: list[ControlField] = Field( + description="The ControlNets used for inference" + ) loras: list[LoRAMetadataField] = Field(description="The LoRAs used for inference") + vae: Union[VAEModelField, None] = Field( + default=None, + description="The VAE used for decoding, if the main model's default was not used", + ) + + # Latents-to-Latents strength: Union[float, None] = Field( default=None, description="The strength used for latents-to-latents", @@ -40,9 +54,34 @@ class CoreMetadata(BaseModel): init_image: Union[str, None] = Field( default=None, description="The name of the initial image" ) - vae: Union[VAEModelField, None] = Field( + + # SDXL + positive_style_prompt: Union[str, None] = Field( + default=None, description="The positive style prompt parameter" + ) + negative_style_prompt: Union[str, None] = Field( + default=None, description="The negative style prompt parameter" + ) + + # SDXL Refiner + refiner_model: Union[MainModelField, None] = Field( + default=None, description="The SDXL Refiner model used" + ) + refiner_cfg_scale: Union[float, None] = Field( default=None, - description="The VAE used for decoding, if the main model's default was not used", + description="The classifier-free guidance scale parameter used for the refiner", + ) + refiner_steps: Union[int, None] = Field( + default=None, description="The number of steps used for the refiner" + ) + refiner_scheduler: Union[str, None] = Field( + default=None, description="The scheduler used for the refiner" + ) + refiner_aesthetic_store: Union[float, None] = Field( + default=None, description="The aesthetic score used for the refiner" + ) + refiner_start: Union[float, None] = Field( + default=None, description="The start value used for refiner denoising" ) @@ -71,7 +110,9 @@ class MetadataAccumulatorInvocation(BaseInvocation): type: Literal["metadata_accumulator"] = "metadata_accumulator" - generation_mode: str = Field(description="The generation mode that output this image",) + generation_mode: str = Field( + description="The generation mode that output this image", + ) positive_prompt: str = Field(description="The positive prompt parameter") negative_prompt: str = Field(description="The negative prompt parameter") width: int = Field(description="The width parameter") @@ -81,9 +122,13 @@ class MetadataAccumulatorInvocation(BaseInvocation): cfg_scale: float = Field(description="The classifier-free guidance scale parameter") steps: int = Field(description="The number of steps used for inference") scheduler: str = Field(description="The scheduler used for inference") - clip_skip: int = Field(description="The number of skipped CLIP layers",) + clip_skip: int = Field( + description="The number of skipped CLIP layers", + ) model: MainModelField = Field(description="The main model used for inference") - controlnets: list[ControlField]= Field(description="The ControlNets used for inference") + controlnets: list[ControlField] = Field( + description="The ControlNets used for inference" + ) loras: list[LoRAMetadataField] = Field(description="The LoRAs used for inference") strength: Union[float, None] = Field( default=None, @@ -97,36 +142,44 @@ class MetadataAccumulatorInvocation(BaseInvocation): description="The VAE used for decoding, if the main model's default was not used", ) + # SDXL + positive_style_prompt: Union[str, None] = Field( + default=None, description="The positive style prompt parameter" + ) + negative_style_prompt: Union[str, None] = Field( + default=None, description="The negative style prompt parameter" + ) + + # SDXL Refiner + refiner_model: Union[MainModelField, None] = Field( + default=None, description="The SDXL Refiner model used" + ) + refiner_cfg_scale: Union[float, None] = Field( + default=None, + description="The classifier-free guidance scale parameter used for the refiner", + ) + refiner_steps: Union[int, None] = Field( + default=None, description="The number of steps used for the refiner" + ) + refiner_scheduler: Union[str, None] = Field( + default=None, description="The scheduler used for the refiner" + ) + refiner_aesthetic_store: Union[float, None] = Field( + default=None, description="The aesthetic score used for the refiner" + ) + refiner_start: Union[float, None] = Field( + default=None, description="The start value used for refiner denoising" + ) + class Config(InvocationConfig): schema_extra = { "ui": { "title": "Metadata Accumulator", - "tags": ["image", "metadata", "generation"] + "tags": ["image", "metadata", "generation"], }, } - def invoke(self, context: InvocationContext) -> MetadataAccumulatorOutput: """Collects and outputs a CoreMetadata object""" - return MetadataAccumulatorOutput( - metadata=CoreMetadata( - generation_mode=self.generation_mode, - positive_prompt=self.positive_prompt, - negative_prompt=self.negative_prompt, - width=self.width, - height=self.height, - seed=self.seed, - rand_device=self.rand_device, - cfg_scale=self.cfg_scale, - steps=self.steps, - scheduler=self.scheduler, - model=self.model, - strength=self.strength, - init_image=self.init_image, - vae=self.vae, - controlnets=self.controlnets, - loras=self.loras, - clip_skip=self.clip_skip, - ) - ) + return MetadataAccumulatorOutput(metadata=CoreMetadata(**self.dict())) diff --git a/invokeai/app/invocations/sdxl.py b/invokeai/app/invocations/sdxl.py index f877b22924..3b4d3a9d86 100644 --- a/invokeai/app/invocations/sdxl.py +++ b/invokeai/app/invocations/sdxl.py @@ -138,7 +138,7 @@ class SDXLRefinerModelLoaderInvocation(BaseInvocation): "ui": { "title": "SDXL Refiner Model Loader", "tags": ["model", "loader", "sdxl_refiner"], - "type_hints": {"model": "model"}, + "type_hints": {"model": "refiner_model"}, }, } @@ -295,7 +295,7 @@ class SDXLTextToLatentsInvocation(BaseInvocation): unet_info = context.services.model_manager.get_model( - **self.unet.unet.dict() + **self.unet.unet.dict(), context=context ) do_classifier_free_guidance = True cross_attention_kwargs = None @@ -463,8 +463,8 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation): unet: UNetField = Field(default=None, description="UNet submodel") latents: Optional[LatentsField] = Field(description="Initial latents") - denoising_start: float = Field(default=0.0, ge=0, lt=1, description="") - denoising_end: float = Field(default=1.0, gt=0, le=1, description="") + denoising_start: float = Field(default=0.0, ge=0, le=1, description="") + denoising_end: float = Field(default=1.0, ge=0, le=1, description="") #control: Union[ControlField, list[ControlField]] = Field(default=None, description="The control to use") #seamless: bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", ) @@ -549,13 +549,13 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation): num_inference_steps = num_inference_steps - t_start # apply noise(if provided) - if self.noise is not None: + if self.noise is not None and timesteps.shape[0] > 0: noise = context.services.latents.get(self.noise.latents_name) latents = scheduler.add_noise(latents, noise, timesteps[:1]) del noise unet_info = context.services.model_manager.get_model( - **self.unet.unet.dict() + **self.unet.unet.dict(), context=context, ) do_classifier_free_guidance = True cross_attention_kwargs = None diff --git a/invokeai/app/models/image.py b/invokeai/app/models/image.py index 988a3e1447..1183cabc54 100644 --- a/invokeai/app/models/image.py +++ b/invokeai/app/models/image.py @@ -1,9 +1,80 @@ from enum import Enum -from typing import Optional, Tuple +from typing import Optional, Tuple, Literal from pydantic import BaseModel, Field from invokeai.app.util.metaenum import MetaEnum +from ..invocations.baseinvocation import ( + BaseInvocationOutput, + InvocationConfig, +) +class ImageField(BaseModel): + """An image field used for passing image objects between invocations""" + + image_name: Optional[str] = Field(default=None, description="The name of the image") + + class Config: + schema_extra = {"required": ["image_name"]} + + +class ColorField(BaseModel): + r: int = Field(ge=0, le=255, description="The red component") + g: int = Field(ge=0, le=255, description="The green component") + b: int = Field(ge=0, le=255, description="The blue component") + a: int = Field(ge=0, le=255, description="The alpha component") + + def tuple(self) -> Tuple[int, int, int, int]: + return (self.r, self.g, self.b, self.a) + + +class ProgressImage(BaseModel): + """The progress image sent intermittently during processing""" + + width: int = Field(description="The effective width of the image in pixels") + height: int = Field(description="The effective height of the image in pixels") + dataURL: str = Field(description="The image data as a b64 data URL") + +class PILInvocationConfig(BaseModel): + """Helper class to provide all PIL invocations with additional config""" + + class Config(InvocationConfig): + schema_extra = { + "ui": { + "tags": ["PIL", "image"], + }, + } + +class ImageOutput(BaseInvocationOutput): + """Base class for invocations that output an image""" + + # fmt: off + type: Literal["image_output"] = "image_output" + image: ImageField = Field(default=None, description="The output image") + width: int = Field(description="The width of the image in pixels") + height: int = Field(description="The height of the image in pixels") + # fmt: on + + class Config: + schema_extra = {"required": ["type", "image", "width", "height"]} + + +class MaskOutput(BaseInvocationOutput): + """Base class for invocations that output a mask""" + + # fmt: off + type: Literal["mask"] = "mask" + mask: ImageField = Field(default=None, description="The output mask") + width: int = Field(description="The width of the mask in pixels") + height: int = Field(description="The height of the mask in pixels") + # fmt: on + + class Config: + schema_extra = { + "required": [ + "type", + "mask", + ] + } class ResourceOrigin(str, Enum, metaclass=MetaEnum): """The origin of a resource (eg image). @@ -63,28 +134,3 @@ class InvalidImageCategoryException(ValueError): super().__init__(message) -class ImageField(BaseModel): - """An image field used for passing image objects between invocations""" - - image_name: Optional[str] = Field(default=None, description="The name of the image") - - class Config: - schema_extra = {"required": ["image_name"]} - - -class ColorField(BaseModel): - r: int = Field(ge=0, le=255, description="The red component") - g: int = Field(ge=0, le=255, description="The green component") - b: int = Field(ge=0, le=255, description="The blue component") - a: int = Field(ge=0, le=255, description="The alpha component") - - def tuple(self) -> Tuple[int, int, int, int]: - return (self.r, self.g, self.b, self.a) - - -class ProgressImage(BaseModel): - """The progress image sent intermittently during processing""" - - width: int = Field(description="The effective width of the image in pixels") - height: int = Field(description="The effective height of the image in pixels") - dataURL: str = Field(description="The image data as a b64 data URL") diff --git a/invokeai/app/services/config.py b/invokeai/app/services/config.py index b1e4e7ed5d..dfcff86ca0 100644 --- a/invokeai/app/services/config.py +++ b/invokeai/app/services/config.py @@ -28,7 +28,6 @@ InvokeAI: always_use_cpu: false free_gpu_mem: false Features: - nsfw_checker: true restore: true esrgan: true patchmatch: true @@ -92,18 +91,18 @@ Typical usage at the top level file: from invokeai.app.services.config import InvokeAIAppConfig - # get global configuration and print its nsfw_checker value + # get global configuration and print its cache size conf = InvokeAIAppConfig.get_config() conf.parse_args() - print(conf.nsfw_checker) + print(conf.max_cache_size) Typical usage in a backend module: from invokeai.app.services.config import InvokeAIAppConfig - # get global configuration and print its nsfw_checker value + # get global configuration and print its cache size value conf = InvokeAIAppConfig.get_config() - print(conf.nsfw_checker) + print(conf.max_cache_size) Computed properties: @@ -277,7 +276,7 @@ class InvokeAISettings(BaseSettings): @classmethod def _excluded_from_yaml(self)->List[str]: # combination of deprecated parameters and internal ones that shouldn't be exposed as invokeai.yaml options - return ['type','initconf', 'gpu_mem_reserved', 'max_loaded_models', 'version', 'from_file', 'model', 'restore', 'root'] + return ['type','initconf', 'gpu_mem_reserved', 'max_loaded_models', 'version', 'from_file', 'model', 'restore', 'root', 'nsfw_checker'] class Config: env_file_encoding = 'utf-8' @@ -364,7 +363,6 @@ setting environment variables INVOKEAI_. esrgan : bool = Field(default=True, description="Enable/disable upscaling code", category='Features') internet_available : bool = Field(default=True, description="If true, attempt to download models on the fly; otherwise only use local models", category='Features') log_tokenization : bool = Field(default=False, description="Enable logging of parsed prompt tokens.", category='Features') - nsfw_checker : bool = Field(default=True, description="Enable/disable the NSFW checker", category='Features') patchmatch : bool = Field(default=True, description="Enable/disable patchmatch inpaint code", category='Features') restore : bool = Field(default=True, description="Enable/disable face restoration code (DEPRECATED)", category='DEPRECATED') @@ -374,6 +372,7 @@ setting environment variables INVOKEAI_. max_cache_size : float = Field(default=6.0, gt=0, description="Maximum memory amount used by model cache for rapid switching", category='Memory/Performance') max_vram_cache_size : float = Field(default=2.75, ge=0, description="Amount of VRAM reserved for model storage", category='Memory/Performance') gpu_mem_reserved : float = Field(default=2.75, ge=0, description="DEPRECATED: use max_vram_cache_size. Amount of VRAM reserved for model storage", category='DEPRECATED') + nsfw_checker : bool = Field(default=True, description="DEPRECATED: use Web settings to enable/disable", category='DEPRECATED') precision : Literal[tuple(['auto','float16','float32','autocast'])] = Field(default='auto',description='Floating point precision', category='Memory/Performance') sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", category='Memory/Performance') xformers_enabled : bool = Field(default=True, description="Enable/disable memory-efficient attention", category='Memory/Performance') @@ -525,6 +524,16 @@ setting environment variables INVOKEAI_. """Return true if patchmatch true""" return self.patchmatch + @property + def nsfw_checker(self)->bool: + """ NSFW node is always active and disabled from Web UIe""" + return True + + @property + def invisible_watermark(self)->bool: + """ invisible watermark node is always active and disabled from Web UIe""" + return True + @staticmethod def find_root()->Path: ''' diff --git a/invokeai/app/services/default_graphs.py b/invokeai/app/services/default_graphs.py index 92263751b7..22e35d1d6b 100644 --- a/invokeai/app/services/default_graphs.py +++ b/invokeai/app/services/default_graphs.py @@ -1,4 +1,5 @@ from ..invocations.latent import LatentsToImageInvocation, TextToLatentsInvocation +from ..invocations.image import ImageNSFWBlurInvocation from ..invocations.noise import NoiseInvocation from ..invocations.compel import CompelInvocation from ..invocations.params import ParamIntInvocation @@ -24,6 +25,7 @@ def create_text_to_image() -> LibraryGraph: '5': CompelInvocation(id='5'), '6': TextToLatentsInvocation(id='6'), '7': LatentsToImageInvocation(id='7'), + '8': ImageNSFWBlurInvocation(id='8'), }, edges=[ Edge(source=EdgeConnection(node_id='width', field='a'), destination=EdgeConnection(node_id='3', field='width')), @@ -33,6 +35,7 @@ def create_text_to_image() -> LibraryGraph: Edge(source=EdgeConnection(node_id='6', field='latents'), destination=EdgeConnection(node_id='7', field='latents')), Edge(source=EdgeConnection(node_id='4', field='conditioning'), destination=EdgeConnection(node_id='6', field='positive_conditioning')), Edge(source=EdgeConnection(node_id='5', field='conditioning'), destination=EdgeConnection(node_id='6', field='negative_conditioning')), + Edge(source=EdgeConnection(node_id='7', field='image'), destination=EdgeConnection(node_id='8', field='image')), ] ), exposed_inputs=[ @@ -43,7 +46,7 @@ def create_text_to_image() -> LibraryGraph: ExposedNodeInput(node_path='seed', field='a', alias='seed'), ], exposed_outputs=[ - ExposedNodeOutput(node_path='7', field='image', alias='image') + ExposedNodeOutput(node_path='8', field='image', alias='image') ]) diff --git a/invokeai/app/services/images.py b/invokeai/app/services/images.py index dc1b18babe..007f4021d4 100644 --- a/invokeai/app/services/images.py +++ b/invokeai/app/services/images.py @@ -216,16 +216,13 @@ class ImageService(ImageServiceABC): metadata=metadata, session_id=session_id, ) - if board_id is not None: self._services.board_image_records.add_image_to_board( board_id=board_id, image_name=image_name ) - self._services.image_files.save( image_name=image_name, image=image, metadata=metadata, graph=graph ) - image_dto = self.get_dto(image_name) return image_dto @@ -236,7 +233,7 @@ class ImageService(ImageServiceABC): self._services.logger.error("Failed to save image file") raise except Exception as e: - self._services.logger.error("Problem saving image record and file") + self._services.logger.error(f"Problem saving image record and file: {str(e)}") raise e def update( diff --git a/invokeai/backend/__init__.py b/invokeai/backend/__init__.py index ff8b4bc8c5..a33b70ceb4 100644 --- a/invokeai/backend/__init__.py +++ b/invokeai/backend/__init__.py @@ -12,4 +12,4 @@ from .model_management import ( ModelManager, ModelCache, BaseModelType, ModelType, SubModelType, ModelInfo ) -from .safety_checker import SafetyChecker +from .model_management.models import SilenceWarnings diff --git a/invokeai/backend/generator/base.py b/invokeai/backend/generator/base.py index 99a63d56a3..8af8d399d5 100644 --- a/invokeai/backend/generator/base.py +++ b/invokeai/backend/generator/base.py @@ -28,7 +28,6 @@ from diffusers.schedulers import SchedulerMixin as Scheduler import invokeai.backend.util.logging as logger from ..image_util import configure_model_padding from ..util.util import rand_perlin_2d -from ..safety_checker import SafetyChecker from ..stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeline from ..stable_diffusion.schedulers import SCHEDULER_MAP @@ -52,7 +51,6 @@ class InvokeAIGeneratorBasicParams: v_symmetry_time_pct: Optional[float]=None variation_amount: float = 0.0 with_variations: list=field(default_factory=list) - safety_checker: Optional[SafetyChecker]=None @dataclass class InvokeAIGeneratorOutput: @@ -240,7 +238,6 @@ class Generator: self.seed = None self.latent_channels = model.unet.config.in_channels self.downsampling_factor = downsampling # BUG: should come from model or config - self.safety_checker = None self.perlin = 0.0 self.threshold = 0 self.variation_amount = 0 @@ -277,12 +274,10 @@ class Generator: perlin=0.0, h_symmetry_time_pct=None, v_symmetry_time_pct=None, - safety_checker: SafetyChecker=None, free_gpu_mem: bool = False, **kwargs, ): scope = nullcontext - self.safety_checker = safety_checker self.free_gpu_mem = free_gpu_mem attention_maps_images = [] attention_maps_callback = lambda saver: attention_maps_images.append( @@ -329,9 +324,6 @@ class Generator: # Pass on the seed in case a layer beneath us needs to generate noise on its own. image = make_image(x_T, seed) - if self.safety_checker is not None: - image = self.safety_checker.check(image) - results.append([image, seed, attention_maps_images]) if image_callback is not None: diff --git a/invokeai/backend/image_util/invisible_watermark.py b/invokeai/backend/image_util/invisible_watermark.py new file mode 100644 index 0000000000..0fb72b095d --- /dev/null +++ b/invokeai/backend/image_util/invisible_watermark.py @@ -0,0 +1,34 @@ +""" +This module defines a singleton object, "invisible_watermark" that +wraps the invisible watermark model. It respects the global "invisible_watermark" +configuration variable, that allows the watermarking to be supressed. +""" +import numpy as np +import cv2 +from PIL import Image +from imwatermark import WatermarkEncoder +from invokeai.app.services.config import InvokeAIAppConfig +import invokeai.backend.util.logging as logger +config = InvokeAIAppConfig.get_config() + +class InvisibleWatermark: + """ + Wrapper around InvisibleWatermark module. + """ + + @classmethod + def invisible_watermark_available(self) -> bool: + return config.invisible_watermark + + @classmethod + def add_watermark(self, image: Image, watermark_text:str) -> Image: + if not self.invisible_watermark_available(): + return image + logger.debug(f'Applying invisible watermark "{watermark_text}"') + bgr = cv2.cvtColor(np.array(image.convert("RGB")), cv2.COLOR_RGB2BGR) + encoder = WatermarkEncoder() + encoder.set_watermark('bytes', watermark_text.encode('utf-8')) + bgr_encoded = encoder.encode(bgr, 'dwtDct') + return Image.fromarray( + cv2.cvtColor(bgr_encoded, cv2.COLOR_BGR2RGB) + ).convert("RGBA") diff --git a/invokeai/backend/image_util/safety_checker.py b/invokeai/backend/image_util/safety_checker.py new file mode 100644 index 0000000000..7a31c9422d --- /dev/null +++ b/invokeai/backend/image_util/safety_checker.py @@ -0,0 +1,63 @@ +""" +This module defines a singleton object, "safety_checker" that +wraps the safety_checker model. It respects the global "nsfw_checker" +configuration variable, that allows the checker to be supressed. +""" +import numpy as np +from PIL import Image +from invokeai.backend import SilenceWarnings +from invokeai.app.services.config import InvokeAIAppConfig +from invokeai.backend.util.devices import choose_torch_device +import invokeai.backend.util.logging as logger +config = InvokeAIAppConfig.get_config() + +CHECKER_PATH = 'core/convert/stable-diffusion-safety-checker' + +class SafetyChecker: + """ + Wrapper around SafetyChecker model. + """ + safety_checker = None + feature_extractor = None + tried_load: bool = False + + @classmethod + def _load_safety_checker(self): + if self.tried_load: + return + + if config.nsfw_checker: + try: + from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker + from transformers import AutoFeatureExtractor + self.safety_checker = StableDiffusionSafetyChecker.from_pretrained( + config.models_path / CHECKER_PATH + ) + self.feature_extractor = AutoFeatureExtractor.from_pretrained( + config.models_path / CHECKER_PATH) + logger.info('NSFW checker initialized') + except Exception as e: + logger.warning(f'Could not load NSFW checker: {str(e)}') + else: + logger.info('NSFW checker loading disabled') + self.tried_load = True + + @classmethod + def safety_checker_available(self) -> bool: + self._load_safety_checker() + return self.safety_checker is not None + + @classmethod + def has_nsfw_concept(self, image: Image) -> bool: + if not self.safety_checker_available(): + return False + + device = choose_torch_device() + features = self.feature_extractor([image], return_tensors="pt") + features.to(device) + self.safety_checker.to(device) + x_image = np.array(image).astype(np.float32) / 255.0 + x_image = x_image[None].transpose(0, 3, 1, 2) + with SilenceWarnings(): + checked_image, has_nsfw_concept = self.safety_checker(images=x_image, clip_input=features.pixel_values) + return has_nsfw_concept[0] diff --git a/invokeai/backend/install/check_root.py b/invokeai/backend/install/check_root.py new file mode 100644 index 0000000000..0c24034dc7 --- /dev/null +++ b/invokeai/backend/install/check_root.py @@ -0,0 +1,31 @@ +""" +Check that the invokeai_root is correctly configured and exit if not. +""" +import sys +from invokeai.app.services.config import ( + InvokeAIAppConfig, +) + +def check_invokeai_root(config: InvokeAIAppConfig): + try: + assert config.model_conf_path.exists() + assert config.db_path.exists() + assert config.models_path.exists() + for model in [ + 'CLIP-ViT-bigG-14-laion2B-39B-b160k', + 'bert-base-uncased', + 'clip-vit-large-patch14', + 'sd-vae-ft-mse', + 'stable-diffusion-2-clip', + 'stable-diffusion-safety-checker']: + assert (config.models_path / f'core/convert/{model}').exists() + except: + print() + print('== STARTUP ABORTED ==') + print('** One or more necessary files is missing from your InvokeAI root directory **') + print('** Please rerun the configuration script to fix this problem. **') + print('** From the launcher, selection option [7]. **') + print('** From the command line, activate the virtual environment and run "invokeai-configure --yes --skip-sd-weights" **') + input('Press any key to continue...') + sys.exit(0) + diff --git a/invokeai/backend/install/invokeai_configure.py b/invokeai/backend/install/invokeai_configure.py index fa22376ab4..3a6dacd79a 100755 --- a/invokeai/backend/install/invokeai_configure.py +++ b/invokeai/backend/install/invokeai_configure.py @@ -32,6 +32,7 @@ from omegaconf import OmegaConf from tqdm import tqdm from transformers import ( CLIPTextModel, + CLIPTextConfig, CLIPTokenizer, AutoFeatureExtractor, BertTokenizerFast, @@ -56,6 +57,7 @@ from invokeai.frontend.install.widgets import ( from invokeai.backend.install.legacy_arg_parsing import legacy_parser from invokeai.backend.install.model_install_backend import ( hf_download_from_pretrained, + hf_download_with_resume, InstallSelections, ModelInstall, ) @@ -205,6 +207,15 @@ def download_conversion_models(): pipeline = CLIPTextModel.from_pretrained(repo_id, subfolder="text_encoder", **kwargs) pipeline.save_pretrained(target_dir / 'stable-diffusion-2-clip' / 'text_encoder', safe_serialization=True) + # sd-xl - tokenizer_2 + repo_id = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k" + _, model_name = repo_id.split('/') + pipeline = CLIPTokenizer.from_pretrained(repo_id, **kwargs) + pipeline.save_pretrained(target_dir / model_name, safe_serialization=True) + + pipeline = CLIPTextConfig.from_pretrained(repo_id, **kwargs) + pipeline.save_pretrained(target_dir / model_name, safe_serialization=True) + # VAE logger.info('Downloading stable diffusion VAE') vae = AutoencoderKL.from_pretrained('stabilityai/sd-vae-ft-mse', **kwargs) @@ -288,13 +299,6 @@ Use cursor arrows to make a checkbox selection, and space to toggle. color="CONTROL", ) - self.nextrely += 1 - self.nsfw_checker = self.add_widget_intelligent( - npyscreen.Checkbox, - name="Activate the NSFW checker to blur images showing potential sexual imagery", - value=old_opts.nsfw_checker, - scroll_exit=True, - ) self.nextrely += 1 label = """HuggingFace access token (OPTIONAL) for automatic model downloads. See https://huggingface.co/settings/tokens.""" for line in textwrap.wrap(label,width=window_width-6): @@ -390,7 +394,7 @@ Use cursor arrows to make a checkbox selection, and space to toggle. must_exist=False, use_two_lines=False, labelColor="GOOD", - begin_entry_at=68, + begin_entry_at=40, max_height=3, scroll_exit=True, ) @@ -475,7 +479,6 @@ https://huggingface.co/spaces/CompVis/stable-diffusion-license for attr in [ "outdir", - "nsfw_checker", "free_gpu_mem", "max_cache_size", "xformers_enabled", @@ -519,7 +522,7 @@ class EditOptApplication(npyscreen.NPSAppManaged): addModelsForm, name="Install Stable Diffusion Models", multipage=True, - cycle_widgets=True, + cycle_widgets=False, ) def new_opts(self): @@ -533,8 +536,6 @@ def edit_opts(program_opts: Namespace, invokeai_opts: Namespace) -> argparse.Nam def default_startup_options(init_file: Path) -> Namespace: opts = InvokeAIAppConfig.get_config() - if not init_file.exists(): - opts.nsfw_checker = True return opts def default_user_selections(program_opts: Namespace) -> InstallSelections: @@ -557,7 +558,7 @@ def default_user_selections(program_opts: Namespace) -> InstallSelections: # ------------------------------------- def initialize_rootdir(root: Path, yes_to_all: bool = False): - logger.info("** INITIALIZING INVOKEAI RUNTIME DIRECTORY **") + logger.info("Initializing InvokeAI runtime directory") for name in ( "models", "databases", @@ -658,7 +659,6 @@ def migrate_init_file(legacy_format:Path): # a few places where the field names have changed and we have to # manually add in the new names/values - new.nsfw_checker = old.safety_checker new.xformers_enabled = old.xformers new.conf_path = old.conf new.root = legacy_format.parent.resolve() @@ -767,8 +767,8 @@ def main(): if migrate_if_needed(opt, config.root_path): sys.exit(0) - if not config.model_conf_path.exists(): - initialize_rootdir(config.root_path, opt.yes_to_all) + # run this unconditionally in case new directories need to be added + initialize_rootdir(config.root_path, opt.yes_to_all) models_to_download = default_user_selections(opt) new_init_file = config.root_path / 'invokeai.yaml' @@ -788,15 +788,14 @@ def main(): sys.exit(0) if opt.skip_support_models: - logger.info("SKIPPING SUPPORT MODEL DOWNLOADS PER USER REQUEST") + logger.info("Skipping support models at user's request") else: - logger.info("CHECKING/UPDATING SUPPORT MODELS") + logger.info("Installing support models") download_support_models() if opt.skip_sd_weights: - logger.warning("SKIPPING DIFFUSION WEIGHTS DOWNLOAD PER USER REQUEST") + logger.warning("Skipping diffusion weights download per user request") elif models_to_download: - logger.info("DOWNLOADING DIFFUSION WEIGHTS") process_and_execute(opt, models_to_download) postscript(errors=errors) diff --git a/invokeai/backend/install/model_install_backend.py b/invokeai/backend/install/model_install_backend.py index 0537d6069a..26d37a7cbf 100644 --- a/invokeai/backend/install/model_install_backend.py +++ b/invokeai/backend/install/model_install_backend.py @@ -58,7 +58,15 @@ LEGACY_CONFIGS = { SchedulerPredictionType.Epsilon: 'v2-inpainting-inference.yaml', SchedulerPredictionType.VPrediction: 'v2-inpainting-inference-v.yaml', } - } + }, + + BaseModelType.StableDiffusionXL: { + ModelVariantType.Normal: 'sd_xl_base.yaml', + }, + + BaseModelType.StableDiffusionXLRefiner: { + ModelVariantType.Normal: 'sd_xl_refiner.yaml', + }, } @dataclass @@ -141,16 +149,17 @@ class ModelInstall(object): for i in installed: print(f"{i['model_name']}\t{i['base_model']}\t{i['path']}") - def starter_models(self)->Set[str]: + # logic here a little reversed to maintain backward compatibility + def starter_models(self, all_models: bool=False)->Set[str]: models = set() for key, value in self.datasets.items(): name,base,model_type = ModelManager.parse_key(key) - if model_type==ModelType.Main: + if all_models or model_type==ModelType.Main: models.add(key) return models def recommended_models(self)->Set[str]: - starters = self.starter_models() + starters = self.starter_models(all_models=True) return set([x for x in starters if self.datasets[x].get('recommended',False)]) def default_model(self)->str: @@ -329,6 +338,7 @@ class ModelInstall(object): description = str(description), model_format = info.format, ) + legacy_conf = None if info.model_type == ModelType.Main: attributes.update(dict(variant = info.variant_type,)) if info.format=="checkpoint": @@ -343,11 +353,17 @@ class ModelInstall(object): except KeyError: legacy_conf = Path(self.config.legacy_conf_dir, 'v1-inference.yaml') # best guess - attributes.update( - dict( - config = str(legacy_conf) - ) + if info.model_type == ModelType.ControlNet and info.format=="checkpoint": + possible_conf = path.with_suffix('.yaml') + if possible_conf.exists(): + legacy_conf = str(self.relative_to_root(possible_conf)) + + if legacy_conf: + attributes.update( + dict( + config = str(legacy_conf) ) + ) return attributes def relative_to_root(self, path: Path)->Path: diff --git a/invokeai/backend/model_management/convert_ckpt_to_diffusers.py b/invokeai/backend/model_management/convert_ckpt_to_diffusers.py index d9d4262e47..0124da7f56 100644 --- a/invokeai/backend/model_management/convert_ckpt_to_diffusers.py +++ b/invokeai/backend/model_management/convert_ckpt_to_diffusers.py @@ -1,5 +1,5 @@ # coding=utf-8 -# Copyright 2022 The HuggingFace Inc. team. +# Copyright 2023 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -13,23 +13,60 @@ # See the License for the specific language governing permissions and # limitations under the License. # -# Adapted for use as a module by Lincoln Stein -# Original file at: https://github.com/huggingface/diffusers/blob/main/scripts/convert_ldm_original_checkpoint_to_diffusers.py -""" Conversion script for the LDM checkpoints. """ +# Adapted for use in InvokeAI by Lincoln Stein, July 2023 +# +""" Conversion script for the Stable Diffusion checkpoints.""" import re -import warnings +from contextlib import nullcontext +from io import BytesIO +from typing import Optional, Union from pathlib import Path -from typing import Union -from packaging import version +import requests import torch -from safetensors.torch import load_file +from transformers import ( + AutoFeatureExtractor, + BertTokenizerFast, + CLIPImageProcessor, + CLIPTextConfig, + CLIPTextModel, + CLIPTextModelWithProjection, + CLIPTokenizer, + CLIPVisionConfig, + CLIPVisionModelWithProjection, +) -import invokeai.backend.util.logging as logger -from invokeai.app.services.config import InvokeAIAppConfig +from diffusers.models import ( + AutoencoderKL, + ControlNetModel, + PriorTransformer, + UNet2DConditionModel, +) +from diffusers.schedulers import ( + DDIMScheduler, + DDPMScheduler, + DPMSolverMultistepScheduler, + EulerAncestralDiscreteScheduler, + EulerDiscreteScheduler, + HeunDiscreteScheduler, + LMSDiscreteScheduler, + PNDMScheduler, + UnCLIPScheduler, +) +from diffusers.utils import is_accelerate_available, is_omegaconf_available, is_safetensors_available +from diffusers.utils.import_utils import BACKENDS_MAPPING +from diffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import ( + LDMBertConfig, LDMBertModel +) +from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder +from diffusers.pipelines.pipeline_utils import DiffusionPipeline +from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker +from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer + +from invokeai.backend.util.logging import InvokeAILogger +from invokeai.app.services.config import InvokeAIAppConfig, MODEL_CORE -from .model_manager import ModelManager from picklescan.scanner import scan_file_path from .models import BaseModelType, ModelVariantType @@ -41,43 +78,12 @@ except ImportError: "OmegaConf is required to convert the LDM checkpoints. Please install it with `pip install OmegaConf`." ) -from diffusers import ( - AutoencoderKL, - DDIMScheduler, - DPMSolverMultistepScheduler, - EulerAncestralDiscreteScheduler, - EulerDiscreteScheduler, - HeunDiscreteScheduler, - LDMTextToImagePipeline, - LMSDiscreteScheduler, - PNDMScheduler, - UniPCMultistepScheduler, - StableDiffusionPipeline, - UNet2DConditionModel, -) -from diffusers import logging as dlogging -from diffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import ( - LDMBertConfig, - LDMBertModel, -) -from diffusers.pipelines.stable_diffusion.safety_checker import ( - StableDiffusionSafetyChecker, -) -from diffusers.utils import is_safetensors_available -import transformers -from transformers import ( - AutoFeatureExtractor, - BertTokenizerFast, - CLIPTextModel, - CLIPTokenizer, - CLIPVisionConfig, -) +if is_accelerate_available(): + from accelerate import init_empty_weights + from accelerate.utils import set_module_tensor_to_device -from ..stable_diffusion import StableDiffusionGeneratorPipeline - -# TODO: redo in future -#CONVERT_MODEL_ROOT = InvokeAIAppConfig.get_config().models_path / "core" / "convert" -CONVERT_MODEL_ROOT = InvokeAIAppConfig.get_config().root_path / "models" / "core" / "convert" +logger = InvokeAILogger.getLogger(__name__) +CONVERT_MODEL_ROOT = InvokeAIAppConfig.get_config().root_path / MODEL_CORE / "convert" def shave_segments(path, n_shave_prefix_segments=1): """ @@ -104,9 +110,7 @@ def renew_resnet_paths(old_list, n_shave_prefix_segments=0): new_item = new_item.replace("emb_layers.1", "time_emb_proj") new_item = new_item.replace("skip_connection", "conv_shortcut") - new_item = shave_segments( - new_item, n_shave_prefix_segments=n_shave_prefix_segments - ) + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) mapping.append({"old": old_item, "new": new_item}) @@ -122,9 +126,7 @@ def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0): new_item = old_item new_item = new_item.replace("nin_shortcut", "conv_shortcut") - new_item = shave_segments( - new_item, n_shave_prefix_segments=n_shave_prefix_segments - ) + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) mapping.append({"old": old_item, "new": new_item}) @@ -175,9 +177,7 @@ def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0): new_item = new_item.replace("proj_out.weight", "to_out.0.weight") new_item = new_item.replace("proj_out.bias", "to_out.0.bias") - new_item = shave_segments( - new_item, n_shave_prefix_segments=n_shave_prefix_segments - ) + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) mapping.append({"old": old_item, "new": new_item}) @@ -185,26 +185,40 @@ def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0): def assign_to_checkpoint( - paths, - checkpoint, - old_checkpoint, - additional_replacements=None, - config=None, + paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None ): """ - This does the final conversion step: take locally converted weights and apply a global renaming - to them. It splits attention layers, and takes into account additional replacements - that may arise. + This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits + attention layers, and takes into account additional replacements that may arise. Assigns the weights to the new checkpoint. """ - assert isinstance( - paths, list - ), "Paths should be a list of dicts containing 'old' and 'new' keys." + assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." + + # Splits the attention layers into three variables. + if attention_paths_to_split is not None: + for path, path_map in attention_paths_to_split.items(): + old_tensor = old_checkpoint[path] + channels = old_tensor.shape[0] // 3 + + target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) + + num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3 + + old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) + query, key, value = old_tensor.split(channels // num_heads, dim=1) + + checkpoint[path_map["query"]] = query.reshape(target_shape) + checkpoint[path_map["key"]] = key.reshape(target_shape) + checkpoint[path_map["value"]] = value.reshape(target_shape) for path in paths: new_path = path["new"] + # These have already been assigned + if attention_paths_to_split is not None and new_path in attention_paths_to_split: + continue + # Global renaming happens here new_path = new_path.replace("middle_block.0", "mid_block.resnets.0") new_path = new_path.replace("middle_block.1", "mid_block.attentions.0") @@ -215,82 +229,124 @@ def assign_to_checkpoint( new_path = new_path.replace(replacement["old"], replacement["new"]) # proj_attn.weight has to be converted from conv 1D to linear - if "proj_attn.weight" in new_path: + is_attn_weight = "proj_attn.weight" in new_path or ("attentions" in new_path and "to_" in new_path) + shape = old_checkpoint[path["old"]].shape + if is_attn_weight and len(shape) == 3: checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0] + elif is_attn_weight and len(shape) == 4: + checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0, 0] else: checkpoint[new_path] = old_checkpoint[path["old"]] def conv_attn_to_linear(checkpoint): keys = list(checkpoint.keys()) - attn_keys = ["to_q.weight", "to_k.weight", "to_v.weight"] + attn_keys = ["query.weight", "key.weight", "value.weight"] for key in keys: if ".".join(key.split(".")[-2:]) in attn_keys: if checkpoint[key].ndim > 2: checkpoint[key] = checkpoint[key][:, :, 0, 0] - elif "to_out.0.weight" in key: + elif "proj_attn.weight" in key: if checkpoint[key].ndim > 2: - checkpoint[key] = checkpoint[key][:, :, 0, 0] + checkpoint[key] = checkpoint[key][:, :, 0] -def create_unet_diffusers_config(original_config, image_size: int): +def create_unet_diffusers_config(original_config, image_size: int, controlnet=False): """ Creates a config for the diffusers based on the config of the LDM model. """ - unet_params = original_config.model.params.unet_config.params + if controlnet: + unet_params = original_config.model.params.control_stage_config.params + else: + if "unet_config" in original_config.model.params and original_config.model.params.unet_config is not None: + unet_params = original_config.model.params.unet_config.params + else: + unet_params = original_config.model.params.network_config.params + vae_params = original_config.model.params.first_stage_config.params.ddconfig - block_out_channels = [ - unet_params.model_channels * mult for mult in unet_params.channel_mult - ] + block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult] down_block_types = [] resolution = 1 for i in range(len(block_out_channels)): - block_type = ( - "CrossAttnDownBlock2D" - if resolution in unet_params.attention_resolutions - else "DownBlock2D" - ) + block_type = "CrossAttnDownBlock2D" if resolution in unet_params.attention_resolutions else "DownBlock2D" down_block_types.append(block_type) if i != len(block_out_channels) - 1: resolution *= 2 up_block_types = [] for i in range(len(block_out_channels)): - block_type = ( - "CrossAttnUpBlock2D" - if resolution in unet_params.attention_resolutions - else "UpBlock2D" - ) + block_type = "CrossAttnUpBlock2D" if resolution in unet_params.attention_resolutions else "UpBlock2D" up_block_types.append(block_type) resolution //= 2 + if unet_params.transformer_depth is not None: + transformer_layers_per_block = ( + unet_params.transformer_depth + if isinstance(unet_params.transformer_depth, int) + else list(unet_params.transformer_depth) + ) + else: + transformer_layers_per_block = 1 + vae_scale_factor = 2 ** (len(vae_params.ch_mult) - 1) head_dim = unet_params.num_heads if "num_heads" in unet_params else None use_linear_projection = ( - unet_params.use_linear_in_transformer - if "use_linear_in_transformer" in unet_params - else False + unet_params.use_linear_in_transformer if "use_linear_in_transformer" in unet_params else False ) if use_linear_projection: # stable diffusion 2-base-512 and 2-768 if head_dim is None: - head_dim = [5, 10, 20, 20] + head_dim_mult = unet_params.model_channels // unet_params.num_head_channels + head_dim = [head_dim_mult * c for c in list(unet_params.channel_mult)] - config = dict( - sample_size=image_size // vae_scale_factor, - in_channels=unet_params.in_channels, - out_channels=unet_params.out_channels, - down_block_types=tuple(down_block_types), - up_block_types=tuple(up_block_types), - block_out_channels=tuple(block_out_channels), - layers_per_block=unet_params.num_res_blocks, - cross_attention_dim=unet_params.context_dim, - attention_head_dim=head_dim, - use_linear_projection=use_linear_projection, - ) + class_embed_type = None + addition_embed_type = None + addition_time_embed_dim = None + projection_class_embeddings_input_dim = None + context_dim = None + + if unet_params.context_dim is not None: + context_dim = ( + unet_params.context_dim if isinstance(unet_params.context_dim, int) else unet_params.context_dim[0] + ) + + if "num_classes" in unet_params: + if unet_params.num_classes == "sequential": + if context_dim in [2048, 1280]: + # SDXL + addition_embed_type = "text_time" + addition_time_embed_dim = 256 + else: + class_embed_type = "projection" + assert "adm_in_channels" in unet_params + projection_class_embeddings_input_dim = unet_params.adm_in_channels + else: + raise NotImplementedError(f"Unknown conditional unet num_classes config: {unet_params.num_classes}") + + config = { + "sample_size": image_size // vae_scale_factor, + "in_channels": unet_params.in_channels, + "down_block_types": tuple(down_block_types), + "block_out_channels": tuple(block_out_channels), + "layers_per_block": unet_params.num_res_blocks, + "cross_attention_dim": context_dim, + "attention_head_dim": head_dim, + "use_linear_projection": use_linear_projection, + "class_embed_type": class_embed_type, + "addition_embed_type": addition_embed_type, + "addition_time_embed_dim": addition_time_embed_dim, + "projection_class_embeddings_input_dim": projection_class_embeddings_input_dim, + "transformer_layers_per_block": transformer_layers_per_block, + } + + if controlnet: + config["conditioning_channels"] = unet_params.hint_channels + else: + config["out_channels"] = unet_params.out_channels + config["up_block_types"] = tuple(up_block_types) return config @@ -306,16 +362,16 @@ def create_vae_diffusers_config(original_config, image_size: int): down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels) up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels) - config = dict( - sample_size=image_size, - in_channels=vae_params.in_channels, - out_channels=vae_params.out_ch, - down_block_types=tuple(down_block_types), - up_block_types=tuple(up_block_types), - block_out_channels=tuple(block_out_channels), - latent_channels=vae_params.z_channels, - layers_per_block=vae_params.num_res_blocks, - ) + config = { + "sample_size": image_size, + "in_channels": vae_params.in_channels, + "out_channels": vae_params.out_ch, + "down_block_types": tuple(down_block_types), + "up_block_types": tuple(up_block_types), + "block_out_channels": tuple(block_out_channels), + "latent_channels": vae_params.z_channels, + "layers_per_block": vae_params.num_res_blocks, + } return config @@ -330,7 +386,7 @@ def create_diffusers_schedular(original_config): def create_ldm_bert_config(original_config): - bert_params = original_config.model.params.cond_stage_config.params + bert_params = original_config.model.parms.cond_stage_config.params config = LDMBertConfig( d_model=bert_params.n_embed, encoder_layers=bert_params.n_layer, @@ -339,103 +395,96 @@ def create_ldm_bert_config(original_config): return config -def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False): +def convert_ldm_unet_checkpoint( + checkpoint, config, path=None, extract_ema=False, controlnet=False, skip_extract_state_dict=False +): """ Takes a state dict and a config, and returns a converted checkpoint. """ - # extract state_dict for UNet - unet_state_dict = {} - keys = list(checkpoint.keys()) + if skip_extract_state_dict: + unet_state_dict = checkpoint + else: + # extract state_dict for UNet + unet_state_dict = {} + keys = list(checkpoint.keys()) - unet_key = "model.diffusion_model." - # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA - if sum(k.startswith("model_ema") for k in keys) > 100: - logger.debug(f"Checkpoint {path} has both EMA and non-EMA weights.") - if extract_ema: - logger.debug("Extracting EMA weights (usually better for inference)") + if controlnet: + unet_key = "control_model." + else: + unet_key = "model.diffusion_model." + + # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA + if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema: + logger.warning(f"Checkpoint {path} has both EMA and non-EMA weights.") + logger.warning( + "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA" + " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag." + ) for key in keys: if key.startswith("model.diffusion_model"): flat_ema_key = "model_ema." + "".join(key.split(".")[1:]) - flat_ema_key_alt = "model_ema." + "".join(key.split(".")[2:]) - if flat_ema_key in checkpoint: - unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop( - flat_ema_key - ) - elif flat_ema_key_alt in checkpoint: - unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop( - flat_ema_key_alt - ) - else: - unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop( - key - ) + unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key) else: - logger.debug( - "Extracting only the non-EMA weights (usually better for fine-tuning)" - ) + if sum(k.startswith("model_ema") for k in keys) > 100: + logger.warning( + "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA" + " weights (usually better for inference), please make sure to add the `--extract_ema` flag." + ) - for key in keys: - if key.startswith("model.diffusion_model") and key in checkpoint: - unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key) + for key in keys: + if key.startswith(unet_key): + unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key) new_checkpoint = {} - new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict[ - "time_embed.0.weight" - ] - new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict[ - "time_embed.0.bias" - ] - new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict[ - "time_embed.2.weight" - ] - new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict[ - "time_embed.2.bias" - ] + new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] + new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] + new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] + new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] + + if config["class_embed_type"] is None: + # No parameters to port + ... + elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection": + new_checkpoint["class_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"] + new_checkpoint["class_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"] + new_checkpoint["class_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"] + new_checkpoint["class_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"] + else: + raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}") + + if config["addition_embed_type"] == "text_time": + new_checkpoint["add_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"] + new_checkpoint["add_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"] + new_checkpoint["add_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"] + new_checkpoint["add_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"] new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] - new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] - new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] - new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] - new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] + if not controlnet: + new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] + new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] + new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] + new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] # Retrieves the keys for the input blocks only - num_input_blocks = len( - { - ".".join(layer.split(".")[:2]) - for layer in unet_state_dict - if "input_blocks" in layer - } - ) + num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) input_blocks = { layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key] for layer_id in range(num_input_blocks) } # Retrieves the keys for the middle blocks only - num_middle_blocks = len( - { - ".".join(layer.split(".")[:2]) - for layer in unet_state_dict - if "middle_block" in layer - } - ) + num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) middle_blocks = { layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key] for layer_id in range(num_middle_blocks) } # Retrieves the keys for the output blocks only - num_output_blocks = len( - { - ".".join(layer.split(".")[:2]) - for layer in unet_state_dict - if "output_blocks" in layer - } - ) + num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) output_blocks = { layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key] for layer_id in range(num_output_blocks) @@ -446,45 +495,29 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) resnets = [ - key - for key in input_blocks[i] - if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key + key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key ] attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] if f"input_blocks.{i}.0.op.weight" in unet_state_dict: - new_checkpoint[ - f"down_blocks.{block_id}.downsamplers.0.conv.weight" - ] = unet_state_dict.pop(f"input_blocks.{i}.0.op.weight") - new_checkpoint[ - f"down_blocks.{block_id}.downsamplers.0.conv.bias" - ] = unet_state_dict.pop(f"input_blocks.{i}.0.op.bias") + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( + f"input_blocks.{i}.0.op.weight" + ) + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( + f"input_blocks.{i}.0.op.bias" + ) paths = renew_resnet_paths(resnets) - meta_path = { - "old": f"input_blocks.{i}.0", - "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}", - } + meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} assign_to_checkpoint( - paths, - new_checkpoint, - unet_state_dict, - additional_replacements=[meta_path], - config=config, + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config ) if len(attentions): paths = renew_attention_paths(attentions) - meta_path = { - "old": f"input_blocks.{i}.1", - "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}", - } + meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"} assign_to_checkpoint( - paths, - new_checkpoint, - unet_state_dict, - additional_replacements=[meta_path], - config=config, + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config ) resnet_0 = middle_blocks[0] @@ -500,11 +533,7 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False attentions_paths = renew_attention_paths(attentions) meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} assign_to_checkpoint( - attentions_paths, - new_checkpoint, - unet_state_dict, - additional_replacements=[meta_path], - config=config, + attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config ) for i in range(num_output_blocks): @@ -522,36 +551,25 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False if len(output_block_list) > 1: resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] - attentions = [ - key for key in output_blocks[i] if f"output_blocks.{i}.1" in key - ] + attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key] resnet_0_paths = renew_resnet_paths(resnets) paths = renew_resnet_paths(resnets) - meta_path = { - "old": f"output_blocks.{i}.0", - "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}", - } + meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} assign_to_checkpoint( - paths, - new_checkpoint, - unet_state_dict, - additional_replacements=[meta_path], - config=config, + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config ) output_block_list = {k: sorted(v) for k, v in output_block_list.items()} if ["conv.bias", "conv.weight"] in output_block_list.values(): - index = list(output_block_list.values()).index( - ["conv.bias", "conv.weight"] - ) - new_checkpoint[ - f"up_blocks.{block_id}.upsamplers.0.conv.weight" - ] = unet_state_dict[f"output_blocks.{i}.{index}.conv.weight"] - new_checkpoint[ - f"up_blocks.{block_id}.upsamplers.0.conv.bias" - ] = unet_state_dict[f"output_blocks.{i}.{index}.conv.bias"] + index = list(output_block_list.values()).index(["conv.bias", "conv.weight"]) + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ + f"output_blocks.{i}.{index}.conv.weight" + ] + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ + f"output_blocks.{i}.{index}.conv.bias" + ] # Clear attentions as they have been attributed above. if len(attentions) == 2: @@ -564,82 +582,85 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}", } assign_to_checkpoint( - paths, - new_checkpoint, - unet_state_dict, - additional_replacements=[meta_path], - config=config, + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config ) else: - resnet_0_paths = renew_resnet_paths( - output_block_layers, n_shave_prefix_segments=1 - ) + resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) for path in resnet_0_paths: old_path = ".".join(["output_blocks", str(i), path["old"]]) - new_path = ".".join( - [ - "up_blocks", - str(block_id), - "resnets", - str(layer_in_block_id), - path["new"], - ] - ) + new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) new_checkpoint[new_path] = unet_state_dict[old_path] + if controlnet: + # conditioning embedding + + orig_index = 0 + + new_checkpoint["controlnet_cond_embedding.conv_in.weight"] = unet_state_dict.pop( + f"input_hint_block.{orig_index}.weight" + ) + new_checkpoint["controlnet_cond_embedding.conv_in.bias"] = unet_state_dict.pop( + f"input_hint_block.{orig_index}.bias" + ) + + orig_index += 2 + + diffusers_index = 0 + + while diffusers_index < 6: + new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_index}.weight"] = unet_state_dict.pop( + f"input_hint_block.{orig_index}.weight" + ) + new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_index}.bias"] = unet_state_dict.pop( + f"input_hint_block.{orig_index}.bias" + ) + diffusers_index += 1 + orig_index += 2 + + new_checkpoint["controlnet_cond_embedding.conv_out.weight"] = unet_state_dict.pop( + f"input_hint_block.{orig_index}.weight" + ) + new_checkpoint["controlnet_cond_embedding.conv_out.bias"] = unet_state_dict.pop( + f"input_hint_block.{orig_index}.bias" + ) + + # down blocks + for i in range(num_input_blocks): + new_checkpoint[f"controlnet_down_blocks.{i}.weight"] = unet_state_dict.pop(f"zero_convs.{i}.0.weight") + new_checkpoint[f"controlnet_down_blocks.{i}.bias"] = unet_state_dict.pop(f"zero_convs.{i}.0.bias") + + # mid block + new_checkpoint["controlnet_mid_block.weight"] = unet_state_dict.pop("middle_block_out.0.weight") + new_checkpoint["controlnet_mid_block.bias"] = unet_state_dict.pop("middle_block_out.0.bias") + return new_checkpoint + def convert_ldm_vae_checkpoint(checkpoint, config): - # Extract state dict for VAE. Works both with burnt-in - # VAEs, and with standalone VAEs. + # extract state dict for VAE + vae_state_dict = {} + keys = list(checkpoint.keys()) + vae_key = "first_stage_model." if any(k.startswith("first_stage_model.") for k in keys) else "" + for key in keys: + if key.startswith(vae_key): + vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key) - # checkpoint can either be a all-in-one stable diffusion - # model, or an isolated vae .ckpt. This tests for - # a key that will be present in the all-in-one model - # that isn't present in the isolated ckpt. - probe_key = "first_stage_model.encoder.conv_in.weight" - if probe_key in checkpoint: - vae_state_dict = {} - vae_key = "first_stage_model." - keys = list(checkpoint.keys()) - for key in keys: - if key.startswith(vae_key): - vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key) - else: - vae_state_dict = checkpoint - - new_checkpoint = convert_ldm_vae_state_dict(vae_state_dict, config) - return new_checkpoint - -def convert_ldm_vae_state_dict(vae_state_dict, config): new_checkpoint = {} new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"] new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"] - new_checkpoint["encoder.conv_out.weight"] = vae_state_dict[ - "encoder.conv_out.weight" - ] + new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"] new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"] - new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict[ - "encoder.norm_out.weight" - ] - new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict[ - "encoder.norm_out.bias" - ] + new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"] + new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"] new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"] new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"] - new_checkpoint["decoder.conv_out.weight"] = vae_state_dict[ - "decoder.conv_out.weight" - ] + new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"] new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"] - new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict[ - "decoder.norm_out.weight" - ] - new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict[ - "decoder.norm_out.bias" - ] + new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"] + new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"] new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"] new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"] @@ -647,55 +668,31 @@ def convert_ldm_vae_state_dict(vae_state_dict, config): new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"] # Retrieves the keys for the encoder down blocks only - num_down_blocks = len( - { - ".".join(layer.split(".")[:3]) - for layer in vae_state_dict - if "encoder.down" in layer - } - ) + num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer}) down_blocks = { - layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] - for layer_id in range(num_down_blocks) + layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) } # Retrieves the keys for the decoder up blocks only - num_up_blocks = len( - { - ".".join(layer.split(".")[:3]) - for layer in vae_state_dict - if "decoder.up" in layer - } - ) + num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer}) up_blocks = { - layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] - for layer_id in range(num_up_blocks) + layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks) } for i in range(num_down_blocks): - resnets = [ - key - for key in down_blocks[i] - if f"down.{i}" in key and f"down.{i}.downsample" not in key - ] + resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key] if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: - new_checkpoint[ - f"encoder.down_blocks.{i}.downsamplers.0.conv.weight" - ] = vae_state_dict.pop(f"encoder.down.{i}.downsample.conv.weight") - new_checkpoint[ - f"encoder.down_blocks.{i}.downsamplers.0.conv.bias" - ] = vae_state_dict.pop(f"encoder.down.{i}.downsample.conv.bias") + new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop( + f"encoder.down.{i}.downsample.conv.weight" + ) + new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop( + f"encoder.down.{i}.downsample.conv.bias" + ) paths = renew_vae_resnet_paths(resnets) meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"} - assign_to_checkpoint( - paths, - new_checkpoint, - vae_state_dict, - additional_replacements=[meta_path], - config=config, - ) + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key] num_mid_res_blocks = 2 @@ -704,51 +701,31 @@ def convert_ldm_vae_state_dict(vae_state_dict, config): paths = renew_vae_resnet_paths(resnets) meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} - assign_to_checkpoint( - paths, - new_checkpoint, - vae_state_dict, - additional_replacements=[meta_path], - config=config, - ) + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key] paths = renew_vae_attention_paths(mid_attentions) meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} - assign_to_checkpoint( - paths, - new_checkpoint, - vae_state_dict, - additional_replacements=[meta_path], - config=config, - ) + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) conv_attn_to_linear(new_checkpoint) for i in range(num_up_blocks): block_id = num_up_blocks - 1 - i resnets = [ - key - for key in up_blocks[block_id] - if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key + key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key ] if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: - new_checkpoint[ - f"decoder.up_blocks.{i}.upsamplers.0.conv.weight" - ] = vae_state_dict[f"decoder.up.{block_id}.upsample.conv.weight"] - new_checkpoint[ - f"decoder.up_blocks.{i}.upsamplers.0.conv.bias" - ] = vae_state_dict[f"decoder.up.{block_id}.upsample.conv.bias"] + new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[ + f"decoder.up.{block_id}.upsample.conv.weight" + ] + new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[ + f"decoder.up.{block_id}.upsample.conv.bias" + ] paths = renew_vae_resnet_paths(resnets) meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"} - assign_to_checkpoint( - paths, - new_checkpoint, - vae_state_dict, - additional_replacements=[meta_path], - config=config, - ) + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key] num_mid_res_blocks = 2 @@ -757,24 +734,12 @@ def convert_ldm_vae_state_dict(vae_state_dict, config): paths = renew_vae_resnet_paths(resnets) meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} - assign_to_checkpoint( - paths, - new_checkpoint, - vae_state_dict, - additional_replacements=[meta_path], - config=config, - ) + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key] paths = renew_vae_attention_paths(mid_attentions) meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} - assign_to_checkpoint( - paths, - new_checkpoint, - vae_state_dict, - additional_replacements=[meta_path], - config=config, - ) + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) conv_attn_to_linear(new_checkpoint) return new_checkpoint @@ -816,9 +781,7 @@ def convert_ldm_bert_checkpoint(checkpoint, config): # copy embeds hf_model.model.embed_tokens.weight = checkpoint.transformer.token_emb.weight - hf_model.model.embed_positions.weight.data = ( - checkpoint.transformer.pos_emb.emb.weight - ) + hf_model.model.embed_positions.weight.data = checkpoint.transformer.pos_emb.emb.weight # copy layer norm _copy_linear(hf_model.model.layer_norm, checkpoint.transformer.norm) @@ -831,43 +794,40 @@ def convert_ldm_bert_checkpoint(checkpoint, config): return hf_model -def convert_ldm_clip_checkpoint(checkpoint): - text_model = CLIPTextModel.from_pretrained(CONVERT_MODEL_ROOT / 'clip-vit-large-patch14') +def convert_ldm_clip_checkpoint(checkpoint, local_files_only=False, text_encoder=None): + if text_encoder is None: + config = CLIPTextConfig.from_pretrained(CONVERT_MODEL_ROOT / 'clip-vit-large-patch14') + + ctx = init_empty_weights if is_accelerate_available() else nullcontext + with ctx(): + text_model = CLIPTextModel(config) + keys = list(checkpoint.keys()) text_model_dict = {} + remove_prefixes = ["cond_stage_model.transformer", "conditioner.embedders.0.transformer"] + for key in keys: - if key.startswith("cond_stage_model.transformer"): - text_model_dict[key[len("cond_stage_model.transformer.") :]] = checkpoint[ - key - ] + for prefix in remove_prefixes: + if key.startswith(prefix): + text_model_dict[key[len(prefix + ".") :]] = checkpoint[key] - # transformers 4.31.0 and higher - this key no longer in state dict - if version.parse(transformers.__version__) >= version.parse("4.31.0"): - position_ids = text_model_dict.pop("text_model.embeddings.position_ids", None) - text_model.load_state_dict(text_model_dict) - if position_ids is not None: - text_model.text_model.embeddings.position_ids.copy_(position_ids) - - # transformers 4.30.2 and lower - position_ids is part of state_dict + if is_accelerate_available(): + for param_name, param in text_model_dict.items(): + set_module_tensor_to_device(text_model, param_name, "cpu", value=param) else: - text_model.load_state_dict(text_model_dict) + text_model.load_state_dict(text_model_dict) return text_model textenc_conversion_lst = [ - ( - "cond_stage_model.model.positional_embedding", - "text_model.embeddings.position_embedding.weight", - ), - ( - "cond_stage_model.model.token_embedding.weight", - "text_model.embeddings.token_embedding.weight", - ), - ("cond_stage_model.model.ln_final.weight", "text_model.final_layer_norm.weight"), - ("cond_stage_model.model.ln_final.bias", "text_model.final_layer_norm.bias"), + ("positional_embedding", "text_model.embeddings.position_embedding.weight"), + ("token_embedding.weight", "text_model.embeddings.token_embedding.weight"), + ("ln_final.weight", "text_model.final_layer_norm.weight"), + ("ln_final.bias", "text_model.final_layer_norm.bias"), + ("text_projection", "text_projection.weight"), ] textenc_conversion_map = {x[0]: x[1] for x in textenc_conversion_lst} @@ -880,106 +840,851 @@ textenc_transformer_conversion_lst = [ (".c_proj.", ".fc2."), (".attn", ".self_attn"), ("ln_final.", "transformer.text_model.final_layer_norm."), - ( - "token_embedding.weight", - "transformer.text_model.embeddings.token_embedding.weight", - ), - ( - "positional_embedding", - "transformer.text_model.embeddings.position_embedding.weight", - ), + ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"), + ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"), ] protected = {re.escape(x[0]): x[1] for x in textenc_transformer_conversion_lst} textenc_pattern = re.compile("|".join(protected.keys())) -def convert_open_clip_checkpoint(checkpoint): - text_model = CLIPTextModel.from_pretrained( - CONVERT_MODEL_ROOT / 'stable-diffusion-2-clip', - subfolder='text_encoder', - ) +def convert_paint_by_example_checkpoint(checkpoint): + config = CLIPVisionConfig.from_pretrained(CONVERT_MODEL_ROOT / "clip-vit-large-patch14") + model = PaintByExampleImageEncoder(config) keys = list(checkpoint.keys()) text_model_dict = {} - if "cond_stage_model.model.text_projection" in keys: - d_model = int(checkpoint["cond_stage_model.model.text_projection"].shape[0]) - elif "cond_stage_model.model.ln_final.bias" in keys: - d_model = int(checkpoint["cond_stage_model.model.ln_final.bias"].shape[0]) - else: - raise KeyError( - 'Expected key "cond_stage_model.model.text_projection" not found in model' - ) + for key in keys: + if key.startswith("cond_stage_model.transformer"): + text_model_dict[key[len("cond_stage_model.transformer.") :]] = checkpoint[key] - text_model_dict[ - "text_model.embeddings.position_ids" - ] = text_model.text_model.embeddings.get_buffer("position_ids") + # load clip vision + model.model.load_state_dict(text_model_dict) + + # load mapper + keys_mapper = { + k[len("cond_stage_model.mapper.res") :]: v + for k, v in checkpoint.items() + if k.startswith("cond_stage_model.mapper") + } + + MAPPING = { + "attn.c_qkv": ["attn1.to_q", "attn1.to_k", "attn1.to_v"], + "attn.c_proj": ["attn1.to_out.0"], + "ln_1": ["norm1"], + "ln_2": ["norm3"], + "mlp.c_fc": ["ff.net.0.proj"], + "mlp.c_proj": ["ff.net.2"], + } + + mapped_weights = {} + for key, value in keys_mapper.items(): + prefix = key[: len("blocks.i")] + suffix = key.split(prefix)[-1].split(".")[-1] + name = key.split(prefix)[-1].split(suffix)[0][1:-1] + mapped_names = MAPPING[name] + + num_splits = len(mapped_names) + for i, mapped_name in enumerate(mapped_names): + new_name = ".".join([prefix, mapped_name, suffix]) + shape = value.shape[0] // num_splits + mapped_weights[new_name] = value[i * shape : (i + 1) * shape] + + model.mapper.load_state_dict(mapped_weights) + + # load final layer norm + model.final_layer_norm.load_state_dict( + { + "bias": checkpoint["cond_stage_model.final_ln.bias"], + "weight": checkpoint["cond_stage_model.final_ln.weight"], + } + ) + + # load final proj + model.proj_out.load_state_dict( + { + "bias": checkpoint["proj_out.bias"], + "weight": checkpoint["proj_out.weight"], + } + ) + + # load uncond vector + model.uncond_vector.data = torch.nn.Parameter(checkpoint["learnable_vector"]) + return model + + +def convert_open_clip_checkpoint( + checkpoint, config_name, prefix="cond_stage_model.model.", has_projection=False, **config_kwargs +): + # text_model = CLIPTextModel.from_pretrained("stabilityai/stable-diffusion-2", subfolder="text_encoder") + # text_model = CLIPTextModelWithProjection.from_pretrained( + # "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", projection_dim=1280 + # ) + config = CLIPTextConfig.from_pretrained(config_name, **config_kwargs) + + ctx = init_empty_weights if is_accelerate_available() else nullcontext + with ctx(): + text_model = CLIPTextModelWithProjection(config) if has_projection else CLIPTextModel(config) + + keys = list(checkpoint.keys()) + + keys_to_ignore = [] + if config_name == "stabilityai/stable-diffusion-2" and config.num_hidden_layers == 23: + # make sure to remove all keys > 22 + keys_to_ignore += [k for k in keys if k.startswith("cond_stage_model.model.transformer.resblocks.23")] + keys_to_ignore += ["cond_stage_model.model.text_projection"] + + text_model_dict = {} + + if prefix + "text_projection" in checkpoint: + d_model = int(checkpoint[prefix + "text_projection"].shape[0]) + else: + d_model = 1024 + + text_model_dict["text_model.embeddings.position_ids"] = text_model.text_model.embeddings.get_buffer("position_ids") for key in keys: - if ( - "resblocks.23" in key - ): # Diffusers drops the final layer and only uses the penultimate layer + if key in keys_to_ignore: continue - if key in textenc_conversion_map: - text_model_dict[textenc_conversion_map[key]] = checkpoint[key] - if key.startswith("cond_stage_model.model.transformer."): - new_key = key[len("cond_stage_model.model.transformer.") :] + if key[len(prefix) :] in textenc_conversion_map: + if key.endswith("text_projection"): + value = checkpoint[key].T.contiguous() + else: + value = checkpoint[key] + + text_model_dict[textenc_conversion_map[key[len(prefix) :]]] = value + + if key.startswith(prefix + "transformer."): + new_key = key[len(prefix + "transformer.") :] if new_key.endswith(".in_proj_weight"): new_key = new_key[: -len(".in_proj_weight")] - new_key = textenc_pattern.sub( - lambda m: protected[re.escape(m.group(0))], new_key - ) - text_model_dict[new_key + ".q_proj.weight"] = checkpoint[key][ - :d_model, : - ] - text_model_dict[new_key + ".k_proj.weight"] = checkpoint[key][ - d_model : d_model * 2, : - ] - text_model_dict[new_key + ".v_proj.weight"] = checkpoint[key][ - d_model * 2 :, : - ] + new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key) + text_model_dict[new_key + ".q_proj.weight"] = checkpoint[key][:d_model, :] + text_model_dict[new_key + ".k_proj.weight"] = checkpoint[key][d_model : d_model * 2, :] + text_model_dict[new_key + ".v_proj.weight"] = checkpoint[key][d_model * 2 :, :] elif new_key.endswith(".in_proj_bias"): new_key = new_key[: -len(".in_proj_bias")] - new_key = textenc_pattern.sub( - lambda m: protected[re.escape(m.group(0))], new_key - ) + new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key) text_model_dict[new_key + ".q_proj.bias"] = checkpoint[key][:d_model] - text_model_dict[new_key + ".k_proj.bias"] = checkpoint[key][ - d_model : d_model * 2 - ] - text_model_dict[new_key + ".v_proj.bias"] = checkpoint[key][ - d_model * 2 : - ] + text_model_dict[new_key + ".k_proj.bias"] = checkpoint[key][d_model : d_model * 2] + text_model_dict[new_key + ".v_proj.bias"] = checkpoint[key][d_model * 2 :] else: - new_key = textenc_pattern.sub( - lambda m: protected[re.escape(m.group(0))], new_key - ) + new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key) text_model_dict[new_key] = checkpoint[key] - # transformers 4.31.0 and higher - this key no longer in state dict - if version.parse(transformers.__version__) >= version.parse("4.31.0"): - position_ids = text_model_dict.pop("text_model.embeddings.position_ids", None) - text_model.load_state_dict(text_model_dict) - if position_ids is not None: - text_model.text_model.embeddings.position_ids.copy_(position_ids) - - # transformers 4.30.2 and lower - position_ids is part of state_dict + if is_accelerate_available(): + for param_name, param in text_model_dict.items(): + set_module_tensor_to_device(text_model, param_name, "cpu", value=param) else: - text_model.load_state_dict(text_model_dict) + text_model.load_state_dict(text_model_dict) return text_model -def replace_checkpoint_vae(checkpoint, vae_path: str): - if vae_path.endswith(".safetensors"): - vae_ckpt = load_file(vae_path) + +def stable_unclip_image_encoder(original_config): + """ + Returns the image processor and clip image encoder for the img2img unclip pipeline. + + We currently know of two types of stable unclip models which separately use the clip and the openclip image + encoders. + """ + + image_embedder_config = original_config.model.params.embedder_config + + sd_clip_image_embedder_class = image_embedder_config.target + sd_clip_image_embedder_class = sd_clip_image_embedder_class.split(".")[-1] + + if sd_clip_image_embedder_class == "ClipImageEmbedder": + clip_model_name = image_embedder_config.params.model + + if clip_model_name == "ViT-L/14": + feature_extractor = CLIPImageProcessor() + image_encoder = CLIPVisionModelWithProjection.from_pretrained(CONVERT_MODEL_ROOT / "clip-vit-large-patch14") + else: + raise NotImplementedError(f"Unknown CLIP checkpoint name in stable diffusion checkpoint {clip_model_name}") + + elif sd_clip_image_embedder_class == "FrozenOpenCLIPImageEmbedder": + feature_extractor = CLIPImageProcessor() + # InvokeAI doesn't use CLIPVisionModelWithProjection so it isn't in the core - if this code is hit a download will occur + image_encoder = CLIPVisionModelWithProjection.from_pretrained(CONVERT_MODEL_ROOT / "CLIP-ViT-H-14-laion2B-s32B-b79K") else: - vae_ckpt = torch.load(vae_path, map_location="cpu") - state_dict = vae_ckpt['state_dict'] if "state_dict" in vae_ckpt else vae_ckpt - for vae_key in state_dict: - new_key = f'first_stage_model.{vae_key}' - checkpoint[new_key] = state_dict[vae_key] + raise NotImplementedError( + f"Unknown CLIP image embedder class in stable diffusion checkpoint {sd_clip_image_embedder_class}" + ) + + return feature_extractor, image_encoder + + +def stable_unclip_image_noising_components( + original_config, clip_stats_path: Optional[str] = None, device: Optional[str] = None +): + """ + Returns the noising components for the img2img and txt2img unclip pipelines. + + Converts the stability noise augmentor into + 1. a `StableUnCLIPImageNormalizer` for holding the CLIP stats + 2. a `DDPMScheduler` for holding the noise schedule + + If the noise augmentor config specifies a clip stats path, the `clip_stats_path` must be provided. + """ + noise_aug_config = original_config.model.params.noise_aug_config + noise_aug_class = noise_aug_config.target + noise_aug_class = noise_aug_class.split(".")[-1] + + if noise_aug_class == "CLIPEmbeddingNoiseAugmentation": + noise_aug_config = noise_aug_config.params + embedding_dim = noise_aug_config.timestep_dim + max_noise_level = noise_aug_config.noise_schedule_config.timesteps + beta_schedule = noise_aug_config.noise_schedule_config.beta_schedule + + image_normalizer = StableUnCLIPImageNormalizer(embedding_dim=embedding_dim) + image_noising_scheduler = DDPMScheduler(num_train_timesteps=max_noise_level, beta_schedule=beta_schedule) + + if "clip_stats_path" in noise_aug_config: + if clip_stats_path is None: + raise ValueError("This stable unclip config requires a `clip_stats_path`") + + clip_mean, clip_std = torch.load(clip_stats_path, map_location=device) + clip_mean = clip_mean[None, :] + clip_std = clip_std[None, :] + + clip_stats_state_dict = { + "mean": clip_mean, + "std": clip_std, + } + + image_normalizer.load_state_dict(clip_stats_state_dict) + else: + raise NotImplementedError(f"Unknown noise augmentor class: {noise_aug_class}") + + return image_normalizer, image_noising_scheduler + + +def convert_controlnet_checkpoint( + checkpoint, + original_config, + checkpoint_path, + image_size, + upcast_attention, + extract_ema, + use_linear_projection=None, + cross_attention_dim=None, + precision: torch.dtype=torch.float32, +): + ctrlnet_config = create_unet_diffusers_config(original_config, image_size=image_size, controlnet=True) + ctrlnet_config["upcast_attention"] = upcast_attention + + ctrlnet_config.pop("sample_size") + original_config = ctrlnet_config.copy() + + ctrlnet_config.pop('addition_embed_type') + ctrlnet_config.pop('addition_time_embed_dim') + ctrlnet_config.pop('transformer_layers_per_block') + + if use_linear_projection is not None: + ctrlnet_config["use_linear_projection"] = use_linear_projection + + if cross_attention_dim is not None: + ctrlnet_config["cross_attention_dim"] = cross_attention_dim + + controlnet = ControlNetModel(**ctrlnet_config) + + # Some controlnet ckpt files are distributed independently from the rest of the + # model components i.e. https://huggingface.co/thibaud/controlnet-sd21/ + if "time_embed.0.weight" in checkpoint: + skip_extract_state_dict = True + else: + skip_extract_state_dict = False + + converted_ctrl_checkpoint = convert_ldm_unet_checkpoint( + checkpoint, + original_config, + path=checkpoint_path, + extract_ema=extract_ema, + controlnet=True, + skip_extract_state_dict=skip_extract_state_dict, + ) + + controlnet.load_state_dict(converted_ctrl_checkpoint) + + return controlnet.to(precision) + +# TO DO - PASS PRECISION +def download_from_original_stable_diffusion_ckpt( + checkpoint_path: str, + model_version: BaseModelType, + model_variant: ModelVariantType, + original_config_file: str = None, + image_size: Optional[int] = None, + prediction_type: str = None, + model_type: str = None, + extract_ema: bool = False, + precision: torch.dtype = torch.float32, + scheduler_type: str = "pndm", + num_in_channels: Optional[int] = None, + upcast_attention: Optional[bool] = None, + device: str = None, + from_safetensors: bool = False, + stable_unclip: Optional[str] = None, + stable_unclip_prior: Optional[str] = None, + clip_stats_path: Optional[str] = None, + controlnet: Optional[bool] = None, + load_safety_checker: bool = True, + pipeline_class: DiffusionPipeline = None, + local_files_only=False, + vae_path=None, + text_encoder=None, + tokenizer=None, + scan_needed: bool = True, +) -> DiffusionPipeline: + """ + Load a Stable Diffusion pipeline object from a CompVis-style `.ckpt`/`.safetensors` file and (ideally) a `.yaml` + config file. + + Although many of the arguments can be automatically inferred, some of these rely on brittle checks against the + global step count, which will likely fail for models that have undergone further fine-tuning. Therefore, it is + recommended that you override the default values and/or supply an `original_config_file` wherever possible. + + Args: + checkpoint_path (`str`): Path to `.ckpt` file. + original_config_file (`str`): + Path to `.yaml` config file corresponding to the original architecture. If `None`, will be automatically + inferred by looking for a key that only exists in SD2.0 models. + image_size (`int`, *optional*, defaults to 512): + The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Diffusion v2 + Base. Use 768 for Stable Diffusion v2. + prediction_type (`str`, *optional*): + The prediction type that the model was trained on. Use `'epsilon'` for Stable Diffusion v1.X and Stable + Diffusion v2 Base. Use `'v_prediction'` for Stable Diffusion v2. + num_in_channels (`int`, *optional*, defaults to None): + The number of input channels. If `None`, it will be automatically inferred. + scheduler_type (`str`, *optional*, defaults to 'pndm'): + Type of scheduler to use. Should be one of `["pndm", "lms", "heun", "euler", "euler-ancestral", "dpm", + "ddim"]`. + model_type (`str`, *optional*, defaults to `None`): + The pipeline type. `None` to automatically infer, or one of `["FrozenOpenCLIPEmbedder", + "FrozenCLIPEmbedder", "PaintByExample"]`. + is_img2img (`bool`, *optional*, defaults to `False`): + Whether the model should be loaded as an img2img pipeline. + extract_ema (`bool`, *optional*, defaults to `False`): Only relevant for + checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights or not. Defaults to + `False`. Pass `True` to extract the EMA weights. EMA weights usually yield higher quality images for + inference. Non-EMA weights are usually better to continue fine-tuning. + upcast_attention (`bool`, *optional*, defaults to `None`): + Whether the attention computation should always be upcasted. This is necessary when running stable + diffusion 2.1. + device (`str`, *optional*, defaults to `None`): + The device to use. Pass `None` to determine automatically. + from_safetensors (`str`, *optional*, defaults to `False`): + If `checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch. + load_safety_checker (`bool`, *optional*, defaults to `True`): + Whether to load the safety checker or not. Defaults to `True`. + pipeline_class (`str`, *optional*, defaults to `None`): + The pipeline class to use. Pass `None` to determine automatically. + local_files_only (`bool`, *optional*, defaults to `False`): + Whether or not to only look at local files (i.e., do not try to download the model). + text_encoder (`CLIPTextModel`, *optional*, defaults to `None`): + An instance of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel) + to use, specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) + variant. If this parameter is `None`, the function will load a new instance of [CLIP] by itself, if needed. + tokenizer (`CLIPTokenizer`, *optional*, defaults to `None`): + An instance of + [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer) + to use. If this parameter is `None`, the function will load a new instance of [CLIPTokenizer] by itself, if + needed. + return: A StableDiffusionPipeline object representing the passed-in `.ckpt`/`.safetensors` file. + """ + + # import pipelines here to avoid circular import error when using from_single_file method + from diffusers import ( + LDMTextToImagePipeline, + PaintByExamplePipeline, + StableDiffusionControlNetPipeline, + StableDiffusionInpaintPipeline, + StableDiffusionPipeline, + StableDiffusionXLPipeline, + StableDiffusionXLImg2ImgPipeline, + StableUnCLIPImg2ImgPipeline, + StableUnCLIPPipeline, + ) + + if pipeline_class is None: + pipeline_class = StableDiffusionPipeline if not controlnet else StableDiffusionControlNetPipeline + + if prediction_type == "v-prediction": + prediction_type = "v_prediction" + + if not is_omegaconf_available(): + raise ValueError(BACKENDS_MAPPING["omegaconf"][1]) + + if from_safetensors: + if not is_safetensors_available(): + raise ValueError(BACKENDS_MAPPING["safetensors"][1]) + + from safetensors.torch import load_file as safe_load + + checkpoint = safe_load(checkpoint_path, device="cpu") + else: + if scan_needed: + # scan model + scan_result = scan_file_path(checkpoint_path) + if scan_result.infected_files != 0: + raise "The model {checkpoint_path} is potentially infected by malware. Aborting import." + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + checkpoint = torch.load(checkpoint_path, map_location=device) + else: + checkpoint = torch.load(checkpoint_path, map_location=device) + + # Sometimes models don't have the global_step item + if "global_step" in checkpoint: + global_step = checkpoint["global_step"] + else: + logger.debug("global_step key not found in model") + global_step = None + + # NOTE: this while loop isn't great but this controlnet checkpoint has one additional + # "state_dict" key https://huggingface.co/thibaud/controlnet-canny-sd21 + while "state_dict" in checkpoint: + checkpoint = checkpoint["state_dict"] + + logger.debug(f'model_type = {model_type}; original_config_file = {original_config_file}') + + if original_config_file is None: + key_name_v2_1 = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight" + key_name_sd_xl_base = "conditioner.embedders.1.model.transformer.resblocks.9.mlp.c_proj.bias" + key_name_sd_xl_refiner = "conditioner.embedders.0.model.transformer.resblocks.9.mlp.c_proj.bias" + + # model_type = "v1" + config_url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml" + + if key_name_v2_1 in checkpoint and checkpoint[key_name_v2_1].shape[-1] == 1024: + # model_type = "v2" + config_url = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml" + + if global_step == 110000: + # v2.1 needs to upcast attention + upcast_attention = True + elif key_name_sd_xl_base in checkpoint: + # only base xl has two text embedders + config_url = "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml" + elif key_name_sd_xl_refiner in checkpoint: + # only refiner xl has embedder and one text embedders + config_url = "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_refiner.yaml" + + original_config_file = BytesIO(requests.get(config_url).content) + + original_config = OmegaConf.load(original_config_file) + if model_version == BaseModelType.StableDiffusion2 and original_config["model"]["params"]["parameterization"] == "v": + prediction_type = "v_prediction" + upcast_attention = True + image_size = 768 + else: + prediction_type = "epsilon" + upcast_attention = False + image_size = 512 + + # Convert the text model. + if ( + model_type is None + and "cond_stage_config" in original_config.model.params + and original_config.model.params.cond_stage_config is not None + ): + model_type = original_config.model.params.cond_stage_config.target.split(".")[-1] + logger.debug(f"no `model_type` given, `model_type` inferred as: {model_type}") + elif model_type is None and original_config.model.params.network_config is not None: + if original_config.model.params.network_config.params.context_dim == 2048: + model_type = "SDXL" + else: + model_type = "SDXL-Refiner" + if image_size is None: + image_size = 1024 + + if num_in_channels is None and pipeline_class == StableDiffusionInpaintPipeline: + num_in_channels = 9 + elif num_in_channels is None: + num_in_channels = 4 + + if "unet_config" in original_config.model.params: + original_config["model"]["params"]["unet_config"]["params"]["in_channels"] = num_in_channels + + if ( + "parameterization" in original_config["model"]["params"] + and original_config["model"]["params"]["parameterization"] == "v" + ): + if prediction_type is None: + # NOTE: For stable diffusion 2 base it is recommended to pass `prediction_type=="epsilon"` + # as it relies on a brittle global step parameter here + prediction_type = "epsilon" if global_step == 875000 else "v_prediction" + if image_size is None: + # NOTE: For stable diffusion 2 base one has to pass `image_size==512` + # as it relies on a brittle global step parameter here + image_size = 512 if global_step == 875000 else 768 + else: + if prediction_type is None: + prediction_type = "epsilon" + if image_size is None: + image_size = 512 + + if controlnet is None and "control_stage_config" in original_config.model.params: + controlnet = convert_controlnet_checkpoint( + checkpoint, original_config, checkpoint_path, image_size, upcast_attention, extract_ema + ) + + num_train_timesteps = getattr(original_config.model.params, "timesteps", None) or 1000 + + if model_type in ["SDXL", "SDXL-Refiner"]: + scheduler_dict = { + "beta_schedule": "scaled_linear", + "beta_start": 0.00085, + "beta_end": 0.012, + "interpolation_type": "linear", + "num_train_timesteps": num_train_timesteps, + "prediction_type": "epsilon", + "sample_max_value": 1.0, + "set_alpha_to_one": False, + "skip_prk_steps": True, + "steps_offset": 1, + "timestep_spacing": "leading", + } + scheduler = EulerDiscreteScheduler.from_config(scheduler_dict) + scheduler_type = "euler" + else: + beta_start = getattr(original_config.model.params, "linear_start", None) or 0.02 + beta_end = getattr(original_config.model.params, "linear_end", None) or 0.085 + scheduler = DDIMScheduler( + beta_end=beta_end, + beta_schedule="scaled_linear", + beta_start=beta_start, + num_train_timesteps=num_train_timesteps, + steps_offset=1, + clip_sample=False, + set_alpha_to_one=False, + prediction_type=prediction_type, + ) + # make sure scheduler works correctly with DDIM + scheduler.register_to_config(clip_sample=False) + + if scheduler_type == "pndm": + config = dict(scheduler.config) + config["skip_prk_steps"] = True + scheduler = PNDMScheduler.from_config(config) + elif scheduler_type == "lms": + scheduler = LMSDiscreteScheduler.from_config(scheduler.config) + elif scheduler_type == "heun": + scheduler = HeunDiscreteScheduler.from_config(scheduler.config) + elif scheduler_type == "euler": + scheduler = EulerDiscreteScheduler.from_config(scheduler.config) + elif scheduler_type == "euler-ancestral": + scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler.config) + elif scheduler_type == "dpm": + scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config) + elif scheduler_type == "ddim": + scheduler = scheduler + else: + raise ValueError(f"Scheduler of type {scheduler_type} doesn't exist!") + + # Convert the UNet2DConditionModel model. + unet_config = create_unet_diffusers_config(original_config, image_size=image_size) + unet_config["upcast_attention"] = upcast_attention + converted_unet_checkpoint = convert_ldm_unet_checkpoint( + checkpoint, unet_config, path=checkpoint_path, extract_ema=extract_ema + ) + + ctx = init_empty_weights if is_accelerate_available() else nullcontext + with ctx(): + unet = UNet2DConditionModel(**unet_config) + + if is_accelerate_available(): + for param_name, param in converted_unet_checkpoint.items(): + set_module_tensor_to_device(unet, param_name, "cpu", value=param) + else: + unet.load_state_dict(converted_unet_checkpoint) + + # Convert the VAE model. + if vae_path is None: + vae_config = create_vae_diffusers_config(original_config, image_size=image_size) + converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config) + + if ( + "model" in original_config + and "params" in original_config.model + and "scale_factor" in original_config.model.params + ): + vae_scaling_factor = original_config.model.params.scale_factor + else: + vae_scaling_factor = 0.18215 # default SD scaling factor + + vae_config["scaling_factor"] = vae_scaling_factor + + ctx = init_empty_weights if is_accelerate_available() else nullcontext + with ctx(): + vae = AutoencoderKL(**vae_config) + + if is_accelerate_available(): + for param_name, param in converted_vae_checkpoint.items(): + set_module_tensor_to_device(vae, param_name, "cpu", value=param) + else: + vae.load_state_dict(converted_vae_checkpoint) + else: + vae = AutoencoderKL.from_pretrained(vae_path) + + if model_type == "FrozenOpenCLIPEmbedder": + config_name = "stabilityai/stable-diffusion-2" + config_kwargs = {"subfolder": "text_encoder"} + + text_model = convert_open_clip_checkpoint(checkpoint, config_name, **config_kwargs) + tokenizer = CLIPTokenizer.from_pretrained(CONVERT_MODEL_ROOT / 'stable-diffusion-2-clip', subfolder="tokenizer") + + if stable_unclip is None: + if controlnet: + pipe = pipeline_class( + vae=vae.to(precision), + text_encoder=text_model, + tokenizer=tokenizer, + unet=unet.to(precision), + scheduler=scheduler, + controlnet=controlnet, + safety_checker=None, + feature_extractor=None, + requires_safety_checker=False, + ) + else: + pipe = pipeline_class( + vae=vae.to(precision), + text_encoder=text_model, + tokenizer=tokenizer, + unet=unet.to(precision), + scheduler=scheduler, + safety_checker=None, + feature_extractor=None, + requires_safety_checker=False, + ) + else: + image_normalizer, image_noising_scheduler = stable_unclip_image_noising_components( + original_config, clip_stats_path=clip_stats_path, device=device + ) + + if stable_unclip == "img2img": + feature_extractor, image_encoder = stable_unclip_image_encoder(original_config) + + pipe = StableUnCLIPImg2ImgPipeline( + # image encoding components + feature_extractor=feature_extractor, + image_encoder=image_encoder, + # image noising components + image_normalizer=image_normalizer, + image_noising_scheduler=image_noising_scheduler, + # regular denoising components + tokenizer=tokenizer, + text_encoder=text_model, + unet=unet, + scheduler=scheduler, + # vae + vae=vae, + ) + elif stable_unclip == "txt2img": + if stable_unclip_prior is None or stable_unclip_prior == "karlo": + karlo_model = "kakaobrain/karlo-v1-alpha" + prior = PriorTransformer.from_pretrained(karlo_model, subfolder="prior") + + prior_tokenizer = CLIPTokenizer.from_pretrained(CONVERT_MODEL_ROOT / "clip-vit-large-patch14") + prior_text_model = CLIPTextModelWithProjection.from_pretrained(CONVERT_MODEL_ROOT / "clip-vit-large-patch14") + + prior_scheduler = UnCLIPScheduler.from_pretrained(karlo_model, subfolder="prior_scheduler") + prior_scheduler = DDPMScheduler.from_config(prior_scheduler.config) + else: + raise NotImplementedError(f"unknown prior for stable unclip model: {stable_unclip_prior}") + + pipe = StableUnCLIPPipeline( + # prior components + prior_tokenizer=prior_tokenizer, + prior_text_encoder=prior_text_model, + prior=prior, + prior_scheduler=prior_scheduler, + # image noising components + image_normalizer=image_normalizer, + image_noising_scheduler=image_noising_scheduler, + # regular denoising components + tokenizer=tokenizer, + text_encoder=text_model, + unet=unet, + scheduler=scheduler, + # vae + vae=vae, + ) + else: + raise NotImplementedError(f"unknown `stable_unclip` type: {stable_unclip}") + elif model_type == "PaintByExample": + vision_model = convert_paint_by_example_checkpoint(checkpoint) + tokenizer = CLIPTokenizer.from_pretrained(CONVERT_MODEL_ROOT / "clip-vit-large-patch14") + feature_extractor = AutoFeatureExtractor.from_pretrained(CONVERT_MODEL_ROOT / "stable-diffusion-safety-checker") + pipe = PaintByExamplePipeline( + vae=vae, + image_encoder=vision_model, + unet=unet, + scheduler=scheduler, + safety_checker=None, + feature_extractor=feature_extractor, + ) + elif model_type == "FrozenCLIPEmbedder": + text_model = convert_ldm_clip_checkpoint( + checkpoint, local_files_only=local_files_only, text_encoder=text_encoder + ) + tokenizer = CLIPTokenizer.from_pretrained(CONVERT_MODEL_ROOT / "clip-vit-large-patch14") if tokenizer is None else tokenizer + + if load_safety_checker: + safety_checker = StableDiffusionSafetyChecker.from_pretrained(CONVERT_MODEL_ROOT / "stable-diffusion-safety-checker") + feature_extractor = AutoFeatureExtractor.from_pretrained(CONVERT_MODEL_ROOT / "stable-diffusion-safety-checker") + else: + safety_checker = None + feature_extractor = None + + if controlnet: + pipe = pipeline_class( + vae=vae.to(precision), + text_encoder=text_model, + tokenizer=tokenizer, + unet=unet.to(precision), + controlnet=controlnet, + scheduler=scheduler, + safety_checker=safety_checker, + feature_extractor=feature_extractor, + ) + else: + pipe = pipeline_class( + vae=vae.to(precision), + text_encoder=text_model, + tokenizer=tokenizer, + unet=unet.to(precision), + scheduler=scheduler, + safety_checker=safety_checker, + feature_extractor=feature_extractor, + ) + elif model_type in ["SDXL", "SDXL-Refiner"]: + if model_type == "SDXL": + tokenizer = CLIPTokenizer.from_pretrained(CONVERT_MODEL_ROOT / "clip-vit-large-patch14") + text_encoder = convert_ldm_clip_checkpoint(checkpoint, local_files_only=local_files_only) + + tokenizer_name = CONVERT_MODEL_ROOT / "CLIP-ViT-bigG-14-laion2B-39B-b160k" + tokenizer_2 = CLIPTokenizer.from_pretrained(tokenizer_name, pad_token="!") + + config_name = tokenizer_name + config_kwargs = {"projection_dim": 1280} + text_encoder_2 = convert_open_clip_checkpoint( + checkpoint, config_name, prefix="conditioner.embedders.1.model.", has_projection=True, **config_kwargs + ) + + pipe = StableDiffusionXLPipeline ( + vae=vae.to(precision), + text_encoder=text_encoder, + tokenizer=tokenizer, + text_encoder_2=text_encoder_2, + tokenizer_2=tokenizer_2, + unet=unet.to(precision), + scheduler=scheduler, + force_zeros_for_empty_prompt=True, + ) + else: + tokenizer = None + text_encoder = None + tokenizer_name = CONVERT_MODEL_ROOT / "CLIP-ViT-bigG-14-laion2B-39B-b160k" + tokenizer_2 = CLIPTokenizer.from_pretrained(tokenizer_name, pad_token="!") + + config_name = tokenizer_name + config_kwargs = {"projection_dim": 1280} + text_encoder_2 = convert_open_clip_checkpoint( + checkpoint, config_name, prefix="conditioner.embedders.0.model.", has_projection=True, **config_kwargs + ) + + pipe = StableDiffusionXLImg2ImgPipeline( + vae=vae.to(precision), + text_encoder=text_encoder, + tokenizer=tokenizer, + text_encoder_2=text_encoder_2, + tokenizer_2=tokenizer_2, + unet=unet.to(precision), + scheduler=scheduler, + requires_aesthetics_score=True, + force_zeros_for_empty_prompt=False, + ) + else: + text_config = create_ldm_bert_config(original_config) + text_model = convert_ldm_bert_checkpoint(checkpoint, text_config) + tokenizer = BertTokenizerFast.from_pretrained(CONVERT_MODEL_ROOT / "bert-base-uncased") + pipe = LDMTextToImagePipeline(vqvae=vae, bert=text_model, tokenizer=tokenizer, unet=unet, scheduler=scheduler) + + return pipe + + +def download_controlnet_from_original_ckpt( + checkpoint_path: str, + original_config_file: str, + image_size: int = 512, + extract_ema: bool = False, + precision: torch.dtype = torch.float32, + num_in_channels: Optional[int] = None, + upcast_attention: Optional[bool] = None, + device: str = None, + from_safetensors: bool = False, + use_linear_projection: Optional[bool] = None, + cross_attention_dim: Optional[bool] = None, + scan_needed: bool = False, +) -> DiffusionPipeline: + if not is_omegaconf_available(): + raise ValueError(BACKENDS_MAPPING["omegaconf"][1]) + + from omegaconf import OmegaConf + + if from_safetensors: + if not is_safetensors_available(): + raise ValueError(BACKENDS_MAPPING["safetensors"][1]) + + from safetensors import safe_open + + checkpoint = {} + with safe_open(checkpoint_path, framework="pt", device="cpu") as f: + for key in f.keys(): + checkpoint[key] = f.get_tensor(key) + else: + if scan_needed: + # scan model + scan_result = scan_file_path(checkpoint_path) + if scan_result.infected_files != 0: + raise "The model {checkpoint_path} is potentially infected by malware. Aborting import." + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + checkpoint = torch.load(checkpoint_path, map_location=device) + else: + checkpoint = torch.load(checkpoint_path, map_location=device) + + # NOTE: this while loop isn't great but this controlnet checkpoint has one additional + # "state_dict" key https://huggingface.co/thibaud/controlnet-canny-sd21 + while "state_dict" in checkpoint: + checkpoint = checkpoint["state_dict"] + + original_config = OmegaConf.load(original_config_file) + + if num_in_channels is not None: + original_config["model"]["params"]["unet_config"]["params"]["in_channels"] = num_in_channels + + if "control_stage_config" not in original_config.model.params: + raise ValueError("`control_stage_config` not present in original config") + + controlnet = convert_controlnet_checkpoint( + checkpoint, + original_config, + checkpoint_path, + image_size, + upcast_attention, + extract_ema, + use_linear_projection=use_linear_projection, + cross_attention_dim=cross_attention_dim, + ) + + return controlnet def convert_ldm_vae_to_diffusers(checkpoint, vae_config: DictConfig, image_size: int) -> AutoencoderKL: vae_config = create_vae_diffusers_config( @@ -994,175 +1699,35 @@ def convert_ldm_vae_to_diffusers(checkpoint, vae_config: DictConfig, image_size: vae.load_state_dict(converted_vae_checkpoint) return vae -def load_pipeline_from_original_stable_diffusion_ckpt( - checkpoint_path: str, - model_version: BaseModelType, - model_variant: ModelVariantType, - original_config_file: str, - extract_ema: bool = True, - precision: torch.dtype = torch.float32, - scan_needed: bool = True, -) -> StableDiffusionPipeline: - """ - Load a Stable Diffusion pipeline object from a CompVis-style `.ckpt`/`.safetensors` file and (ideally) a `.yaml` - config file. - - Although many of the arguments can be automatically inferred, some of these rely on brittle checks against the - global step count, which will likely fail for models that have undergone further fine-tuning. Therefore, it is - recommended that you override the default values and/or supply an `original_config_file` wherever possible. - - :param checkpoint_path: Path to `.ckpt` file. - :param original_config_file: Path to `.yaml` config file corresponding to the original architecture. - If `None`, will be automatically inferred by looking for a key that only exists in SD2.0 models. - :param scheduler_type: Type of scheduler to use. Should be one of `["pndm", "lms", "heun", "euler", - "euler-ancestral", "dpm", "ddim"]`. :param model_type: The pipeline type. `None` to automatically infer, or one of - `["FrozenOpenCLIPEmbedder", "FrozenCLIPEmbedder"]`. :param extract_ema: Only relevant for - checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights - or not. Defaults to `False`. Pass `True` to extract the EMA weights. EMA weights usually yield higher - quality images for inference. Non-EMA weights are usually better to continue fine-tuning. - :param precision: precision to use - torch.float16, torch.float32 or torch.autocast - """ - if not isinstance(checkpoint_path, Path): - checkpoint_path = Path(checkpoint_path) - - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - verbosity = dlogging.get_verbosity() - dlogging.set_verbosity_error() - - if checkpoint_path.suffix == ".safetensors": - checkpoint = load_file(checkpoint_path) - else: - if scan_needed: - # scan model - scan_result = scan_file_path(checkpoint_path) - if scan_result.infected_files != 0: - raise "The model {checkpoint_path} is potentially infected by malware. Aborting import." - checkpoint = torch.load(checkpoint_path) - - # sometimes there is a state_dict key and sometimes not - if "state_dict" in checkpoint: - checkpoint = checkpoint["state_dict"] - - original_config = OmegaConf.load(original_config_file) - - if model_version == BaseModelType.StableDiffusion2 and original_config["model"]["params"]["parameterization"] == "v": - prediction_type = "v_prediction" - upcast_attention = True - image_size = 768 - else: - prediction_type = "epsilon" - upcast_attention = False - image_size = 512 - - # - # convert scheduler - # - - num_train_timesteps = original_config.model.params.timesteps - beta_start = original_config.model.params.linear_start - beta_end = original_config.model.params.linear_end - - scheduler = PNDMScheduler( - beta_end=beta_end, - beta_schedule="scaled_linear", - beta_start=beta_start, - num_train_timesteps=num_train_timesteps, - steps_offset=1, - set_alpha_to_one=False, - prediction_type=prediction_type, - skip_prk_steps=True - ) - # make sure scheduler works correctly with DDIM - scheduler.register_to_config(clip_sample=False) - - # - # convert unet - # - - unet_config = create_unet_diffusers_config( - original_config, image_size=image_size - ) - unet_config["upcast_attention"] = upcast_attention - unet = UNet2DConditionModel(**unet_config) - - converted_unet_checkpoint = convert_ldm_unet_checkpoint( - checkpoint, unet_config, path=checkpoint_path, extract_ema=extract_ema - ) - - unet.load_state_dict(converted_unet_checkpoint) - - # - # convert vae - # - - vae = convert_ldm_vae_to_diffusers( - checkpoint, - original_config, - image_size, - ) - - # Convert the text model. - model_type = original_config.model.params.cond_stage_config.target.split(".")[-1] - if model_type == "FrozenOpenCLIPEmbedder": - text_model = convert_open_clip_checkpoint(checkpoint) - tokenizer = CLIPTokenizer.from_pretrained( - CONVERT_MODEL_ROOT / 'stable-diffusion-2-clip', - subfolder='tokenizer', - ) - pipe = StableDiffusionPipeline( - vae=vae.to(precision), - text_encoder=text_model.to(precision), - tokenizer=tokenizer, - unet=unet.to(precision), - scheduler=scheduler, - safety_checker=None, - feature_extractor=None, - requires_safety_checker=False, - ) - - elif model_type in ["FrozenCLIPEmbedder", "WeightedFrozenCLIPEmbedder"]: - text_model = convert_ldm_clip_checkpoint(checkpoint) - tokenizer = CLIPTokenizer.from_pretrained(CONVERT_MODEL_ROOT / 'clip-vit-large-patch14') - safety_checker = StableDiffusionSafetyChecker.from_pretrained(CONVERT_MODEL_ROOT / 'stable-diffusion-safety-checker') - feature_extractor = AutoFeatureExtractor.from_pretrained(CONVERT_MODEL_ROOT / 'stable-diffusion-safety-checker') - pipe = StableDiffusionPipeline( - vae=vae.to(precision), - text_encoder=text_model.to(precision), - tokenizer=tokenizer, - unet=unet.to(precision), - scheduler=scheduler, - safety_checker=safety_checker.to(precision), - feature_extractor=feature_extractor, - ) - - else: - text_config = create_ldm_bert_config(original_config) - text_model = convert_ldm_bert_checkpoint(checkpoint, text_config) - tokenizer = BertTokenizerFast.from_pretrained(CONVERT_MODEL_ROOT / "bert-base-uncased") - pipe = LDMTextToImagePipeline( - vqvae=vae, - bert=text_model, - tokenizer=tokenizer, - unet=unet, - scheduler=scheduler, - ) - dlogging.set_verbosity(verbosity) - - return pipe - - def convert_ckpt_to_diffusers( + checkpoint_path: Union[str, Path], + dump_path: Union[str, Path], + use_safetensors: bool=True, + **kwargs, +): + """ + Takes all the arguments of download_from_original_stable_diffusion_ckpt(), + and in addition a path-like object indicating the location of the desired diffusers + model to be written. + """ + pipe = download_from_original_stable_diffusion_ckpt(checkpoint_path, **kwargs) + + pipe.save_pretrained( + dump_path, + safe_serialization=use_safetensors and is_safetensors_available(), + ) + +def convert_controlnet_to_diffusers( checkpoint_path: Union[str, Path], dump_path: Union[str, Path], **kwargs, ): """ - Takes all the arguments of load_pipeline_from_original_stable_diffusion_ckpt(), + Takes all the arguments of download_controlnet_from_original_ckpt(), and in addition a path-like object indicating the location of the desired diffusers model to be written. """ - pipe = load_pipeline_from_original_stable_diffusion_ckpt(checkpoint_path, **kwargs) + pipe = download_controlnet_from_original_ckpt(checkpoint_path, **kwargs) pipe.save_pretrained( dump_path, diff --git a/invokeai/backend/model_management/model_manager.py b/invokeai/backend/model_management/model_manager.py index a0b3e6d625..894fca9d74 100644 --- a/invokeai/backend/model_management/model_manager.py +++ b/invokeai/backend/model_management/model_manager.py @@ -673,6 +673,7 @@ class ModelManager(object): self.models[model_key] = model_config self.commit() + return AddModelResult( name = model_name, model_type = model_type, @@ -840,7 +841,7 @@ class ModelManager(object): Returns the preamble for the config file. """ return textwrap.dedent( - """\ + """ # This file describes the alternative machine learning models # available to InvokeAI script. # diff --git a/invokeai/backend/model_management/model_probe.py b/invokeai/backend/model_management/model_probe.py index 6a65401675..f768417506 100644 --- a/invokeai/backend/model_management/model_probe.py +++ b/invokeai/backend/model_management/model_probe.py @@ -253,10 +253,13 @@ class PipelineCheckpointProbe(CheckpointProbeBase): return BaseModelType.StableDiffusion1 if key_name in state_dict and state_dict[key_name].shape[-1] == 1024: return BaseModelType.StableDiffusion2 - # TODO: Verify that this is correct! Need an XL checkpoint file for this. + key_name = 'model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_k.weight' if key_name in state_dict and state_dict[key_name].shape[-1] == 2048: return BaseModelType.StableDiffusionXL - raise InvalidModelException("Cannot determine base type") + elif key_name in state_dict and state_dict[key_name].shape[-1] == 1280: + return BaseModelType.StableDiffusionXLRefiner + else: + raise InvalidModelException("Cannot determine base type") def get_scheduler_prediction_type(self)->SchedulerPredictionType: type = self.get_base_type() diff --git a/invokeai/backend/model_management/models/controlnet.py b/invokeai/backend/model_management/models/controlnet.py index 178fea4d13..41952af5d9 100644 --- a/invokeai/backend/model_management/models/controlnet.py +++ b/invokeai/backend/model_management/models/controlnet.py @@ -1,7 +1,8 @@ import os import torch from enum import Enum -from typing import Optional +from pathlib import Path +from typing import Optional, Literal from .base import ( ModelBase, ModelConfigBase, @@ -15,6 +16,7 @@ from .base import ( InvalidModelException, ModelNotFoundException, ) +from invokeai.app.services.config import InvokeAIAppConfig class ControlNetModelFormat(str, Enum): Checkpoint = "checkpoint" @@ -24,8 +26,12 @@ class ControlNetModel(ModelBase): #model_class: Type #model_size: int - class Config(ModelConfigBase): - model_format: ControlNetModelFormat + class DiffusersConfig(ModelConfigBase): + model_format: Literal[ControlNetModelFormat.Diffusers] + + class CheckpointConfig(ModelConfigBase): + model_format: Literal[ControlNetModelFormat.Checkpoint] + config: str def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType): assert model_type == ModelType.ControlNet @@ -99,13 +105,51 @@ class ControlNetModel(ModelBase): @classmethod def convert_if_required( + cls, + model_path: str, + output_path: str, + config: ModelConfigBase, + base_model: BaseModelType, + ) -> str: + if cls.detect_format(model_path) == ControlNetModelFormat.Checkpoint: + return _convert_controlnet_ckpt_and_cache( + model_path = model_path, + model_config = config.config, + output_path = output_path, + base_model = base_model, + ) + else: + return model_path + +@classmethod +def _convert_controlnet_ckpt_and_cache( cls, model_path: str, output_path: str, - config: ModelConfigBase, # empty config or config of parent model base_model: BaseModelType, - ) -> str: - if cls.detect_format(model_path) != ControlNetModelFormat.Diffusers: - raise NotImplementedError("Checkpoint controlnet models currently unsupported") - else: - return model_path + model_config: ControlNetModel.CheckpointConfig, +) -> str: + """ + Convert the controlnet from checkpoint format to diffusers format, + cache it to disk, and return Path to converted + file. If already on disk then just returns Path. + """ + app_config = InvokeAIAppConfig.get_config() + weights = app_config.root_path / model_path + output_path = Path(output_path) + + # return cached version if it exists + if output_path.exists(): + return output_path + + # to avoid circular import errors + from ..convert_ckpt_to_diffusers import convert_controlnet_to_diffusers + convert_controlnet_to_diffusers( + weights, + output_path, + original_config_file = app_config.root_path / model_config, + image_size = 512, + scan_needed = True, + from_safetensors = weights.suffix == ".safetensors" + ) + return output_path diff --git a/invokeai/backend/model_management/models/sdxl.py b/invokeai/backend/model_management/models/sdxl.py index 76cabcdc62..ef0dd4c720 100644 --- a/invokeai/backend/model_management/models/sdxl.py +++ b/invokeai/backend/model_management/models/sdxl.py @@ -1,5 +1,6 @@ import os import json +import invokeai.backend.util.logging as logger from enum import Enum from pydantic import Field from typing import Literal, Optional @@ -48,7 +49,7 @@ class StableDiffusionXLModel(DiffusersModel): if model_format == StableDiffusionXLModelFormat.Checkpoint: if ckpt_config_path: ckpt_config = OmegaConf.load(ckpt_config_path) - ckpt_config["model"]["params"]["unet_config"]["params"]["in_channels"] + in_channels = ckpt_config["model"]["params"]["unet_config"]["params"]["in_channels"] else: checkpoint = read_checkpoint_meta(path) @@ -108,7 +109,20 @@ class StableDiffusionXLModel(DiffusersModel): config: ModelConfigBase, base_model: BaseModelType, ) -> str: + # The convert script adapted from the diffusers package uses + # strings for the base model type. To avoid making too many + # source code changes, we simply translate here + model_base_to_model_type = {BaseModelType.StableDiffusionXL: 'SDXL', + BaseModelType.StableDiffusionXLRefiner: 'SDXL-Refiner', + } if isinstance(config, cls.CheckpointConfig): - raise NotImplementedError('conversion of SDXL checkpoint models to diffusers format is not yet supported') + from invokeai.backend.model_management.models.stable_diffusion import _convert_ckpt_and_cache + return _convert_ckpt_and_cache( + version=base_model, + model_config=config, + output_path=output_path, + model_type=model_base_to_model_type[base_model], + use_safetensors=False, # corrupts sdxl models for some reason + ) else: return model_path diff --git a/invokeai/backend/model_management/models/stable_diffusion.py b/invokeai/backend/model_management/models/stable_diffusion.py index a90a72ba30..64dbd1c9ee 100644 --- a/invokeai/backend/model_management/models/stable_diffusion.py +++ b/invokeai/backend/model_management/models/stable_diffusion.py @@ -14,10 +14,14 @@ from .base import ( read_checkpoint_meta, classproperty, InvalidModelException, + ModelNotFoundException, ) +from .sdxl import StableDiffusionXLModel +import invokeai.backend.util.logging as logger from invokeai.app.services.config import InvokeAIAppConfig from omegaconf import OmegaConf + class StableDiffusion1ModelFormat(str, Enum): Checkpoint = "checkpoint" Diffusers = "diffusers" @@ -235,42 +239,17 @@ class StableDiffusion2Model(DiffusersModel): else: return model_path -def _select_ckpt_config(version: BaseModelType, variant: ModelVariantType): - ckpt_configs = { - BaseModelType.StableDiffusion1: { - ModelVariantType.Normal: "v1-inference.yaml", - ModelVariantType.Inpaint: "v1-inpainting-inference.yaml", - }, - BaseModelType.StableDiffusion2: { - ModelVariantType.Normal: "v2-inference-v.yaml", # best guess, as we can't differentiate with base(512) - ModelVariantType.Inpaint: "v2-inpainting-inference.yaml", - ModelVariantType.Depth: "v2-midas-inference.yaml", - }, - # note that these .yaml files don't yet exist! - BaseModelType.StableDiffusionXL: { - ModelVariantType.Normal: "xl-inference-v.yaml", - ModelVariantType.Inpaint: "xl-inpainting-inference.yaml", - ModelVariantType.Depth: "xl-midas-inference.yaml", - } - } - - app_config = InvokeAIAppConfig.get_config() - try: - config_path = app_config.legacy_conf_path / ckpt_configs[version][variant] - if config_path.is_relative_to(app_config.root_path): - config_path = config_path.relative_to(app_config.root_path) - return str(config_path) - - except: - return None - - # TODO: rework -# Note that convert_ckpt_to_diffuses does not currently support conversion of SDXL models +# pass precision - currently defaulting to fp16 def _convert_ckpt_and_cache( - version: BaseModelType, - model_config: Union[StableDiffusion1Model.CheckpointConfig, StableDiffusion2Model.CheckpointConfig], - output_path: str, + version: BaseModelType, + model_config: Union[StableDiffusion1Model.CheckpointConfig, + StableDiffusion2Model.CheckpointConfig, + StableDiffusionXLModel.CheckpointConfig, + ], + output_path: str, + use_save_model: bool=False, + **kwargs, ) -> str: """ Convert the checkpoint model indicated in mconfig into a @@ -289,14 +268,60 @@ def _convert_ckpt_and_cache( # to avoid circular import errors from ..convert_ckpt_to_diffusers import convert_ckpt_to_diffusers + from ...util.devices import choose_torch_device, torch_dtype + + model_base_to_model_type = {BaseModelType.StableDiffusion1: 'FrozenCLIPEmbedder', + BaseModelType.StableDiffusion2: 'FrozenOpenCLIPEmbedder', + } + + logger.info(f'Converting {weights} to diffusers format') with SilenceWarnings(): convert_ckpt_to_diffusers( weights, output_path, + model_type=model_base_to_model_type[version], model_version=version, model_variant=model_config.variant, original_config_file=config_file, extract_ema=True, scan_needed=True, + from_safetensors = weights.suffix == ".safetensors", + precision = torch_dtype(choose_torch_device()), + **kwargs, ) return output_path + +def _select_ckpt_config(version: BaseModelType, variant: ModelVariantType): + ckpt_configs = { + BaseModelType.StableDiffusion1: { + ModelVariantType.Normal: "v1-inference.yaml", + ModelVariantType.Inpaint: "v1-inpainting-inference.yaml", + }, + BaseModelType.StableDiffusion2: { + ModelVariantType.Normal: "v2-inference-v.yaml", # best guess, as we can't differentiate with base(512) + ModelVariantType.Inpaint: "v2-inpainting-inference.yaml", + ModelVariantType.Depth: "v2-midas-inference.yaml", + }, + BaseModelType.StableDiffusionXL: { + ModelVariantType.Normal: "sd_xl_base.yaml", + ModelVariantType.Inpaint: None, + ModelVariantType.Depth: None, + }, + BaseModelType.StableDiffusionXLRefiner: { + ModelVariantType.Normal: "sd_xl_refiner.yaml", + ModelVariantType.Inpaint: None, + ModelVariantType.Depth: None, + }, + } + + app_config = InvokeAIAppConfig.get_config() + try: + config_path = app_config.legacy_conf_path / ckpt_configs[version][variant] + if config_path.is_relative_to(app_config.root_path): + config_path = config_path.relative_to(app_config.root_path) + return str(config_path) + + except: + return None + + diff --git a/invokeai/backend/safety_checker.py b/invokeai/backend/safety_checker.py deleted file mode 100644 index 0a943ad7b6..0000000000 --- a/invokeai/backend/safety_checker.py +++ /dev/null @@ -1,77 +0,0 @@ -''' -SafetyChecker class - checks images against the StabilityAI NSFW filter -and blurs images that contain potential NSFW content. -''' -import diffusers -import numpy as np -import torch -import traceback -from diffusers.pipelines.stable_diffusion.safety_checker import ( - StableDiffusionSafetyChecker, -) -from pathlib import Path -from PIL import Image, ImageFilter -from transformers import AutoFeatureExtractor - -import invokeai.assets.web as web_assets -import invokeai.backend.util.logging as logger -from invokeai.app.services.config import InvokeAIAppConfig -from .util import CPU_DEVICE - -config = InvokeAIAppConfig.get_config() - -class SafetyChecker(object): - CAUTION_IMG = "caution.png" - - def __init__(self, device: torch.device): - path = Path(web_assets.__path__[0]) / self.CAUTION_IMG - caution = Image.open(path) - self.caution_img = caution.resize((caution.width // 2, caution.height // 2)) - self.device = device - - try: - safety_model_id = config.models_path / 'core/convert/stable-diffusion-safety-checker' - feature_extractor_id = config.models_path / 'core/convert/stable-diffusion-safety-checker-extractor' - self.safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id) - self.safety_feature_extractor = AutoFeatureExtractor.from_pretrained(feature_extractor_id) - except Exception: - logger.error( - "An error was encountered while installing the safety checker:" - ) - print(traceback.format_exc()) - - def check(self, image: Image.Image): - """ - Check provided image against the StabilityAI safety checker and return - - """ - - self.safety_checker.to(self.device) - features = self.safety_feature_extractor([image], return_tensors="pt") - features.to(self.device) - - # unfortunately checker requires the numpy version, so we have to convert back - x_image = np.array(image).astype(np.float32) / 255.0 - x_image = x_image[None].transpose(0, 3, 1, 2) - - diffusers.logging.set_verbosity_error() - checked_image, has_nsfw_concept = self.safety_checker( - images=x_image, clip_input=features.pixel_values - ) - self.safety_checker.to(CPU_DEVICE) # offload - if has_nsfw_concept[0]: - logger.warning( - "An image with potential non-safe content has been detected. A blurred image will be returned." - ) - return self.blur(image) - else: - return image - - def blur(self, input): - blurry = input.filter(filter=ImageFilter.GaussianBlur(radius=32)) - try: - if caution := self.caution_img: - blurry.paste(caution, (0, 0), caution) - except FileNotFoundError: - pass - return blurry diff --git a/invokeai/backend/util/logging.py b/invokeai/backend/util/logging.py index 09ae600633..d06c036506 100644 --- a/invokeai/backend/util/logging.py +++ b/invokeai/backend/util/logging.py @@ -1,7 +1,7 @@ # Copyright (c) 2023 Lincoln D. Stein and The InvokeAI Development Team """ -invokeai.util.logging +invokeai.backend.util.logging Logging class for InvokeAI that produces console messages diff --git a/invokeai/configs/INITIAL_MODELS.yaml b/invokeai/configs/INITIAL_MODELS.yaml index 1a785cac13..36c9346754 100644 --- a/invokeai/configs/INITIAL_MODELS.yaml +++ b/invokeai/configs/INITIAL_MODELS.yaml @@ -16,13 +16,13 @@ sd-2/main/stable-diffusion-2-inpainting: description: Stable Diffusion version 2.0 inpainting model (5.21 GB) repo_id: stabilityai/stable-diffusion-2-inpainting recommended: False -sdxl/main/stable-diffusion-xl-base-0-9: - description: Stable Diffusion XL base model (12 GB; access token required) - repo_id: stabilityai/stable-diffusion-xl-base-0.9 +sdxl/main/stable-diffusion-xl-base-1-0: + description: Stable Diffusion XL base model (12 GB) + repo_id: stabilityai/stable-diffusion-xl-base-1.0 recommended: False -sdxl-refiner/main/stable-diffusion-xl-refiner-0-9: - description: Stable Diffusion XL refiner model (12 GB; access token required) - repo_id: stabilityai/stable-diffusion-xl-refiner-0.9 +sdxl-refiner/main/stable-diffusion-xl-refiner-1-0: + description: Stable Diffusion XL refiner model (12 GB) + repo_id: stabilityai/stable-diffusion-xl-refiner-1.0 recommended: False sd-1/main/Analog-Diffusion: description: An SD-1.5 model trained on diverse analog photographs (2.13 GB) diff --git a/invokeai/configs/stable-diffusion/sd_xl_base.yaml b/invokeai/configs/stable-diffusion/sd_xl_base.yaml new file mode 100644 index 0000000000..2022dac950 --- /dev/null +++ b/invokeai/configs/stable-diffusion/sd_xl_base.yaml @@ -0,0 +1,98 @@ +model: + target: sgm.models.diffusion.DiffusionEngine + params: + scale_factor: 0.13025 + disable_first_stage_autocast: True + + denoiser_config: + target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser + params: + num_idx: 1000 + + weighting_config: + target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting + scaling_config: + target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling + discretization_config: + target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization + + network_config: + target: sgm.modules.diffusionmodules.openaimodel.UNetModel + params: + adm_in_channels: 2816 + num_classes: sequential + use_checkpoint: True + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [4, 2] + num_res_blocks: 2 + channel_mult: [1, 2, 4] + num_head_channels: 64 + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: [1, 2, 10] # note: the first is unused (due to attn_res starting at 2) 32, 16, 8 --> 64, 32, 16 + context_dim: 2048 + spatial_transformer_attn_type: softmax-xformers + legacy: False + + conditioner_config: + target: sgm.modules.GeneralConditioner + params: + emb_models: + # crossattn cond + - is_trainable: False + input_key: txt + target: sgm.modules.encoders.modules.FrozenCLIPEmbedder + params: + layer: hidden + layer_idx: 11 + # crossattn and vector cond + - is_trainable: False + input_key: txt + target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2 + params: + arch: ViT-bigG-14 + version: laion2b_s39b_b160k + freeze: True + layer: penultimate + always_return_pooled: True + legacy: False + # vector cond + - is_trainable: False + input_key: original_size_as_tuple + target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND + params: + outdim: 256 # multiplied by two + # vector cond + - is_trainable: False + input_key: crop_coords_top_left + target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND + params: + outdim: 256 # multiplied by two + # vector cond + - is_trainable: False + input_key: target_size_as_tuple + target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND + params: + outdim: 256 # multiplied by two + + first_stage_config: + target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + attn_type: vanilla-xformers + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: [1, 2, 4, 4] + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity \ No newline at end of file diff --git a/invokeai/configs/stable-diffusion/sd_xl_refiner.yaml b/invokeai/configs/stable-diffusion/sd_xl_refiner.yaml new file mode 100644 index 0000000000..cab5fe283d --- /dev/null +++ b/invokeai/configs/stable-diffusion/sd_xl_refiner.yaml @@ -0,0 +1,91 @@ +model: + target: sgm.models.diffusion.DiffusionEngine + params: + scale_factor: 0.13025 + disable_first_stage_autocast: True + + denoiser_config: + target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser + params: + num_idx: 1000 + + weighting_config: + target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting + scaling_config: + target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling + discretization_config: + target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization + + network_config: + target: sgm.modules.diffusionmodules.openaimodel.UNetModel + params: + adm_in_channels: 2560 + num_classes: sequential + use_checkpoint: True + in_channels: 4 + out_channels: 4 + model_channels: 384 + attention_resolutions: [4, 2] + num_res_blocks: 2 + channel_mult: [1, 2, 4, 4] + num_head_channels: 64 + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 4 + context_dim: [1280, 1280, 1280, 1280] # 1280 + spatial_transformer_attn_type: softmax-xformers + legacy: False + + conditioner_config: + target: sgm.modules.GeneralConditioner + params: + emb_models: + # crossattn and vector cond + - is_trainable: False + input_key: txt + target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2 + params: + arch: ViT-bigG-14 + version: laion2b_s39b_b160k + legacy: False + freeze: True + layer: penultimate + always_return_pooled: True + # vector cond + - is_trainable: False + input_key: original_size_as_tuple + target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND + params: + outdim: 256 # multiplied by two + # vector cond + - is_trainable: False + input_key: crop_coords_top_left + target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND + params: + outdim: 256 # multiplied by two + # vector cond + - is_trainable: False + input_key: aesthetic_score + target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND + params: + outdim: 256 # multiplied by one + + first_stage_config: + target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + attn_type: vanilla-xformers + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: [1, 2, 4, 4] + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity diff --git a/invokeai/frontend/web/dist/assets/App-06ea4e5e.js 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Jre(){return{animate:La(!0),whileInView:La(),whileHover:La(),whileTap:La(),whileDrag:La(),whileFocus:La(),exit:La()}}class eie extends Ea{constructor(t){super(t),t.animationState||(t.animationState=Qre(t))}updateAnimationControlsSubscription(){const{animate:t}=this.node.getProps();this.unmount(),Av(t)&&(this.unmount=t.subscribe(this.node))}mount(){this.updateAnimationControlsSubscription()}update(){const{animate:t}=this.node.getProps(),{animate:n}=this.node.prevProps||{};t!==n&&this.updateAnimationControlsSubscription()}unmount(){}}let tie=0;class nie extends Ea{constructor(){super(...arguments),this.id=tie++}update(){if(!this.node.presenceContext)return;const{isPresent:t,onExitComplete:n,custom:r}=this.node.presenceContext,{isPresent:i}=this.node.prevPresenceContext||{};if(!this.node.animationState||t===i)return;const o=this.node.animationState.setActive("exit",!t,{custom:r??this.node.getProps().custom});n&&!t&&o.then(()=>n(this.id))}mount(){const{register:t}=this.node.presenceContext||{};t&&(this.unmount=t(this.id))}unmount(){}}const rie={animation:{Feature:eie},exit:{Feature:nie}},yP=(e,t)=>Math.abs(e-t);function iie(e,t){const n=yP(e.x,t.x),r=yP(e.y,t.y);return Math.sqrt(n**2+r**2)}class WM{constructor(t,n,{transformPagePoint:r}={}){if(this.startEvent=null,this.lastMoveEvent=null,this.lastMoveEventInfo=null,this.handlers={},this.updatePoint=()=>{if(!(this.lastMoveEvent&&this.lastMoveEventInfo))return;const u=jS(this.lastMoveEventInfo,this.history),c=this.startEvent!==null,d=iie(u.offset,{x:0,y:0})>=3;if(!c&&!d)return;const{point:f}=u,{timestamp:h}=Xn;this.history.push({...f,timestamp:h});const{onStart:p,onMove:m}=this.handlers;c||(p&&p(this.lastMoveEvent,u),this.startEvent=this.lastMoveEvent),m&&m(this.lastMoveEvent,u)},this.handlePointerMove=(u,c)=>{this.lastMoveEvent=u,this.lastMoveEventInfo=BS(c,this.transformPagePoint),kt.update(this.updatePoint,!0)},this.handlePointerUp=(u,c)=>{if(this.end(),!(this.lastMoveEvent&&this.lastMoveEventInfo))return;const{onEnd:d,onSessionEnd:f}=this.handlers,h=jS(u.type==="pointercancel"?this.lastMoveEventInfo:BS(c,this.transformPagePoint),this.history);this.startEvent&&d&&d(u,h),f&&f(u,h)},!uM(t))return;this.handlers=n,this.transformPagePoint=r;const i=Iv(t),o=BS(i,this.transformPagePoint),{point:s}=o,{timestamp:a}=Xn;this.history=[{...s,timestamp:a}];const{onSessionStart:l}=n;l&&l(t,jS(o,this.history)),this.removeListeners=ea(es(window,"pointermove",this.handlePointerMove),es(window,"pointerup",this.handlePointerUp),es(window,"pointercancel",this.handlePointerUp))}updateHandlers(t){this.handlers=t}end(){this.removeListeners&&this.removeListeners(),cs(this.updatePoint)}}function BS(e,t){return t?{point:t(e.point)}:e}function vP(e,t){return{x:e.x-t.x,y:e.y-t.y}}function jS({point:e},t){return{point:e,delta:vP(e,KM(t)),offset:vP(e,oie(t)),velocity:sie(t,.1)}}function oie(e){return e[0]}function KM(e){return e[e.length-1]}function sie(e,t){if(e.length<2)return{x:0,y:0};let n=e.length-1,r=null;const i=KM(e);for(;n>=0&&(r=e[n],!(i.timestamp-r.timestamp>ta(t)));)n--;if(!r)return{x:0,y:0};const o=ts(i.timestamp-r.timestamp);if(o===0)return{x:0,y:0};const s={x:(i.x-r.x)/o,y:(i.y-r.y)/o};return s.x===1/0&&(s.x=0),s.y===1/0&&(s.y=0),s}function Hr(e){return e.max-e.min}function fw(e,t=0,n=.01){return Math.abs(e-t)<=n}function bP(e,t,n,r=.5){e.origin=r,e.originPoint=Dt(t.min,t.max,e.origin),e.scale=Hr(n)/Hr(t),(fw(e.scale,1,1e-4)||isNaN(e.scale))&&(e.scale=1),e.translate=Dt(n.min,n.max,e.origin)-e.originPoint,(fw(e.translate)||isNaN(e.translate))&&(e.translate=0)}function df(e,t,n,r){bP(e.x,t.x,n.x,r?r.originX:void 0),bP(e.y,t.y,n.y,r?r.originY:void 0)}function SP(e,t,n){e.min=n.min+t.min,e.max=e.min+Hr(t)}function aie(e,t,n){SP(e.x,t.x,n.x),SP(e.y,t.y,n.y)}function _P(e,t,n){e.min=t.min-n.min,e.max=e.min+Hr(t)}function ff(e,t,n){_P(e.x,t.x,n.x),_P(e.y,t.y,n.y)}function lie(e,{min:t,max:n},r){return t!==void 0&&en&&(e=r?Dt(n,e,r.max):Math.min(e,n)),e}function wP(e,t,n){return{min:t!==void 0?e.min+t:void 0,max:n!==void 0?e.max+n-(e.max-e.min):void 0}}function uie(e,{top:t,left:n,bottom:r,right:i}){return{x:wP(e.x,n,i),y:wP(e.y,t,r)}}function xP(e,t){let n=t.min-e.min,r=t.max-e.max;return t.max-t.minr?n=nh(t.min,t.max-r,e.min):r>i&&(n=nh(e.min,e.max-i,t.min)),fa(0,1,n)}function fie(e,t){const n={};return t.min!==void 0&&(n.min=t.min-e.min),t.max!==void 0&&(n.max=t.max-e.min),n}const hw=.35;function hie(e=hw){return e===!1?e=0:e===!0&&(e=hw),{x:CP(e,"left","right"),y:CP(e,"top","bottom")}}function CP(e,t,n){return{min:TP(e,t),max:TP(e,n)}}function TP(e,t){return typeof e=="number"?e:e[t]||0}const EP=()=>({translate:0,scale:1,origin:0,originPoint:0}),Mu=()=>({x:EP(),y:EP()}),PP=()=>({min:0,max:0}),en=()=>({x:PP(),y:PP()});function Zi(e){return[e("x"),e("y")]}function YM({top:e,left:t,right:n,bottom:r}){return{x:{min:t,max:n},y:{min:e,max:r}}}function pie({x:e,y:t}){return{top:t.min,right:e.max,bottom:t.max,left:e.min}}function gie(e,t){if(!t)return e;const n=t({x:e.left,y:e.top}),r=t({x:e.right,y:e.bottom});return{top:n.y,left:n.x,bottom:r.y,right:r.x}}function VS(e){return e===void 0||e===1}function pw({scale:e,scaleX:t,scaleY:n}){return!VS(e)||!VS(t)||!VS(n)}function Ga(e){return pw(e)||XM(e)||e.z||e.rotate||e.rotateX||e.rotateY}function XM(e){return kP(e.x)||kP(e.y)}function kP(e){return e&&e!=="0%"}function Cy(e,t,n){const r=e-n,i=t*r;return n+i}function AP(e,t,n,r,i){return i!==void 0&&(e=Cy(e,i,r)),Cy(e,n,r)+t}function gw(e,t=0,n=1,r,i){e.min=AP(e.min,t,n,r,i),e.max=AP(e.max,t,n,r,i)}function QM(e,{x:t,y:n}){gw(e.x,t.translate,t.scale,t.originPoint),gw(e.y,n.translate,n.scale,n.originPoint)}function mie(e,t,n,r=!1){const i=n.length;if(!i)return;t.x=t.y=1;let o,s;for(let a=0;a1.0000000000001||e<.999999999999?e:1}function Ns(e,t){e.min=e.min+t,e.max=e.max+t}function RP(e,t,[n,r,i]){const o=t[i]!==void 0?t[i]:.5,s=Dt(e.min,e.max,o);gw(e,t[n],t[r],s,t.scale)}const yie=["x","scaleX","originX"],vie=["y","scaleY","originY"];function Nu(e,t){RP(e.x,t,yie),RP(e.y,t,vie)}function ZM(e,t){return YM(gie(e.getBoundingClientRect(),t))}function bie(e,t,n){const r=ZM(e,n),{scroll:i}=t;return i&&(Ns(r.x,i.offset.x),Ns(r.y,i.offset.y)),r}const Sie=new WeakMap;class _ie{constructor(t){this.openGlobalLock=null,this.isDragging=!1,this.currentDirection=null,this.originPoint={x:0,y:0},this.constraints=!1,this.hasMutatedConstraints=!1,this.elastic=en(),this.visualElement=t}start(t,{snapToCursor:n=!1}={}){const{presenceContext:r}=this.visualElement;if(r&&r.isPresent===!1)return;const i=l=>{this.stopAnimation(),n&&this.snapToCursor(Iv(l,"page").point)},o=(l,u)=>{const{drag:c,dragPropagation:d,onDragStart:f}=this.getProps();if(c&&!d&&(this.openGlobalLock&&this.openGlobalLock(),this.openGlobalLock=dM(c),!this.openGlobalLock))return;this.isDragging=!0,this.currentDirection=null,this.resolveConstraints(),this.visualElement.projection&&(this.visualElement.projection.isAnimationBlocked=!0,this.visualElement.projection.target=void 0),Zi(p=>{let m=this.getAxisMotionValue(p).get()||0;if(bo.test(m)){const{projection:S}=this.visualElement;if(S&&S.layout){const v=S.layout.layoutBox[p];v&&(m=Hr(v)*(parseFloat(m)/100))}}this.originPoint[p]=m}),f&&kt.update(()=>f(l,u),!1,!0);const{animationState:h}=this.visualElement;h&&h.setActive("whileDrag",!0)},s=(l,u)=>{const{dragPropagation:c,dragDirectionLock:d,onDirectionLock:f,onDrag:h}=this.getProps();if(!c&&!this.openGlobalLock)return;const{offset:p}=u;if(d&&this.currentDirection===null){this.currentDirection=wie(p),this.currentDirection!==null&&f&&f(this.currentDirection);return}this.updateAxis("x",u.point,p),this.updateAxis("y",u.point,p),this.visualElement.render(),h&&h(l,u)},a=(l,u)=>this.stop(l,u);this.panSession=new WM(t,{onSessionStart:i,onStart:o,onMove:s,onSessionEnd:a},{transformPagePoint:this.visualElement.getTransformPagePoint()})}stop(t,n){const r=this.isDragging;if(this.cancel(),!r)return;const{velocity:i}=n;this.startAnimation(i);const{onDragEnd:o}=this.getProps();o&&kt.update(()=>o(t,n))}cancel(){this.isDragging=!1;const{projection:t,animationState:n}=this.visualElement;t&&(t.isAnimationBlocked=!1),this.panSession&&this.panSession.end(),this.panSession=void 0;const{dragPropagation:r}=this.getProps();!r&&this.openGlobalLock&&(this.openGlobalLock(),this.openGlobalLock=null),n&&n.setActive("whileDrag",!1)}updateAxis(t,n,r){const{drag:i}=this.getProps();if(!r||!ag(t,i,this.currentDirection))return;const o=this.getAxisMotionValue(t);let s=this.originPoint[t]+r[t];this.constraints&&this.constraints[t]&&(s=lie(s,this.constraints[t],this.elastic[t])),o.set(s)}resolveConstraints(){const{dragConstraints:t,dragElastic:n}=this.getProps(),{layout:r}=this.visualElement.projection||{},i=this.constraints;t&&Ru(t)?this.constraints||(this.constraints=this.resolveRefConstraints()):t&&r?this.constraints=uie(r.layoutBox,t):this.constraints=!1,this.elastic=hie(n),i!==this.constraints&&r&&this.constraints&&!this.hasMutatedConstraints&&Zi(o=>{this.getAxisMotionValue(o)&&(this.constraints[o]=fie(r.layoutBox[o],this.constraints[o]))})}resolveRefConstraints(){const{dragConstraints:t,onMeasureDragConstraints:n}=this.getProps();if(!t||!Ru(t))return!1;const r=t.current,{projection:i}=this.visualElement;if(!i||!i.layout)return!1;const o=bie(r,i.root,this.visualElement.getTransformPagePoint());let s=cie(i.layout.layoutBox,o);if(n){const a=n(pie(s));this.hasMutatedConstraints=!!a,a&&(s=YM(a))}return s}startAnimation(t){const{drag:n,dragMomentum:r,dragElastic:i,dragTransition:o,dragSnapToOrigin:s,onDragTransitionEnd:a}=this.getProps(),l=this.constraints||{},u=Zi(c=>{if(!ag(c,n,this.currentDirection))return;let d=l&&l[c]||{};s&&(d={min:0,max:0});const f=i?200:1e6,h=i?40:1e7,p={type:"inertia",velocity:r?t[c]:0,bounceStiffness:f,bounceDamping:h,timeConstant:750,restDelta:1,restSpeed:10,...o,...d};return this.startAxisValueAnimation(c,p)});return Promise.all(u).then(a)}startAxisValueAnimation(t,n){const r=this.getAxisMotionValue(t);return r.start(R5(t,r,0,n))}stopAnimation(){Zi(t=>this.getAxisMotionValue(t).stop())}getAxisMotionValue(t){const n="_drag"+t.toUpperCase(),r=this.visualElement.getProps(),i=r[n];return i||this.visualElement.getValue(t,(r.initial?r.initial[t]:void 0)||0)}snapToCursor(t){Zi(n=>{const{drag:r}=this.getProps();if(!ag(n,r,this.currentDirection))return;const{projection:i}=this.visualElement,o=this.getAxisMotionValue(n);if(i&&i.layout){const{min:s,max:a}=i.layout.layoutBox[n];o.set(t[n]-Dt(s,a,.5))}})}scalePositionWithinConstraints(){if(!this.visualElement.current)return;const{drag:t,dragConstraints:n}=this.getProps(),{projection:r}=this.visualElement;if(!Ru(n)||!r||!this.constraints)return;this.stopAnimation();const i={x:0,y:0};Zi(s=>{const a=this.getAxisMotionValue(s);if(a){const l=a.get();i[s]=die({min:l,max:l},this.constraints[s])}});const{transformTemplate:o}=this.visualElement.getProps();this.visualElement.current.style.transform=o?o({},""):"none",r.root&&r.root.updateScroll(),r.updateLayout(),this.resolveConstraints(),Zi(s=>{if(!ag(s,t,null))return;const a=this.getAxisMotionValue(s),{min:l,max:u}=this.constraints[s];a.set(Dt(l,u,i[s]))})}addListeners(){if(!this.visualElement.current)return;Sie.set(this.visualElement,this);const t=this.visualElement.current,n=es(t,"pointerdown",l=>{const{drag:u,dragListener:c=!0}=this.getProps();u&&c&&this.start(l)}),r=()=>{const{dragConstraints:l}=this.getProps();Ru(l)&&(this.constraints=this.resolveRefConstraints())},{projection:i}=this.visualElement,o=i.addEventListener("measure",r);i&&!i.layout&&(i.root&&i.root.updateScroll(),i.updateLayout()),r();const s=Qo(window,"resize",()=>this.scalePositionWithinConstraints()),a=i.addEventListener("didUpdate",({delta:l,hasLayoutChanged:u})=>{this.isDragging&&u&&(Zi(c=>{const d=this.getAxisMotionValue(c);d&&(this.originPoint[c]+=l[c].translate,d.set(d.get()+l[c].translate))}),this.visualElement.render())});return()=>{s(),n(),o(),a&&a()}}getProps(){const t=this.visualElement.getProps(),{drag:n=!1,dragDirectionLock:r=!1,dragPropagation:i=!1,dragConstraints:o=!1,dragElastic:s=hw,dragMomentum:a=!0}=t;return{...t,drag:n,dragDirectionLock:r,dragPropagation:i,dragConstraints:o,dragElastic:s,dragMomentum:a}}}function ag(e,t,n){return(t===!0||t===e)&&(n===null||n===e)}function wie(e,t=10){let n=null;return Math.abs(e.y)>t?n="y":Math.abs(e.x)>t&&(n="x"),n}class xie extends Ea{constructor(t){super(t),this.removeGroupControls=on,this.removeListeners=on,this.controls=new _ie(t)}mount(){const{dragControls:t}=this.node.getProps();t&&(this.removeGroupControls=t.subscribe(this.controls)),this.removeListeners=this.controls.addListeners()||on}unmount(){this.removeGroupControls(),this.removeListeners()}}const IP=e=>(t,n)=>{e&&kt.update(()=>e(t,n))};class Cie extends Ea{constructor(){super(...arguments),this.removePointerDownListener=on}onPointerDown(t){this.session=new WM(t,this.createPanHandlers(),{transformPagePoint:this.node.getTransformPagePoint()})}createPanHandlers(){const{onPanSessionStart:t,onPanStart:n,onPan:r,onPanEnd:i}=this.node.getProps();return{onSessionStart:IP(t),onStart:IP(n),onMove:r,onEnd:(o,s)=>{delete this.session,i&&kt.update(()=>i(o,s))}}}mount(){this.removePointerDownListener=es(this.node.current,"pointerdown",t=>this.onPointerDown(t))}update(){this.session&&this.session.updateHandlers(this.createPanHandlers())}unmount(){this.removePointerDownListener(),this.session&&this.session.end()}}function Tie(){const e=E.useContext(rp);if(e===null)return[!0,null];const{isPresent:t,onExitComplete:n,register:r}=e,i=E.useId();return E.useEffect(()=>r(i),[]),!t&&n?[!1,()=>n&&n(i)]:[!0]}function Eie(){return Pie(E.useContext(rp))}function Pie(e){return e===null?!0:e.isPresent}const om={hasAnimatedSinceResize:!0,hasEverUpdated:!1};function MP(e,t){return t.max===t.min?0:e/(t.max-t.min)*100}const bd={correct:(e,t)=>{if(!t.target)return e;if(typeof e=="string")if(we.test(e))e=parseFloat(e);else return e;const n=MP(e,t.target.x),r=MP(e,t.target.y);return`${n}% ${r}%`}},kie={correct:(e,{treeScale:t,projectionDelta:n})=>{const r=e,i=ha.parse(e);if(i.length>5)return r;const o=ha.createTransformer(e),s=typeof i[0]!="number"?1:0,a=n.x.scale*t.x,l=n.y.scale*t.y;i[0+s]/=a,i[1+s]/=l;const u=Dt(a,l,.5);return typeof i[2+s]=="number"&&(i[2+s]/=u),typeof i[3+s]=="number"&&(i[3+s]/=u),o(i)}};class Aie extends ze.Component{componentDidMount(){const{visualElement:t,layoutGroup:n,switchLayoutGroup:r,layoutId:i}=this.props,{projection:o}=t;Dte(Oie),o&&(n.group&&n.group.add(o),r&&r.register&&i&&r.register(o),o.root.didUpdate(),o.addEventListener("animationComplete",()=>{this.safeToRemove()}),o.setOptions({...o.options,onExitComplete:()=>this.safeToRemove()})),om.hasEverUpdated=!0}getSnapshotBeforeUpdate(t){const{layoutDependency:n,visualElement:r,drag:i,isPresent:o}=this.props,s=r.projection;return s&&(s.isPresent=o,i||t.layoutDependency!==n||n===void 0?s.willUpdate():this.safeToRemove(),t.isPresent!==o&&(o?s.promote():s.relegate()||kt.postRender(()=>{const a=s.getStack();(!a||!a.members.length)&&this.safeToRemove()}))),null}componentDidUpdate(){const{projection:t}=this.props.visualElement;t&&(t.root.didUpdate(),queueMicrotask(()=>{!t.currentAnimation&&t.isLead()&&this.safeToRemove()}))}componentWillUnmount(){const{visualElement:t,layoutGroup:n,switchLayoutGroup:r}=this.props,{projection:i}=t;i&&(i.scheduleCheckAfterUnmount(),n&&n.group&&n.group.remove(i),r&&r.deregister&&r.deregister(i))}safeToRemove(){const{safeToRemove:t}=this.props;t&&t()}render(){return null}}function JM(e){const[t,n]=Tie(),r=E.useContext(y5);return ze.createElement(Aie,{...e,layoutGroup:r,switchLayoutGroup:E.useContext(K7),isPresent:t,safeToRemove:n})}const Oie={borderRadius:{...bd,applyTo:["borderTopLeftRadius","borderTopRightRadius","borderBottomLeftRadius","borderBottomRightRadius"]},borderTopLeftRadius:bd,borderTopRightRadius:bd,borderBottomLeftRadius:bd,borderBottomRightRadius:bd,boxShadow:kie},eN=["TopLeft","TopRight","BottomLeft","BottomRight"],Rie=eN.length,NP=e=>typeof e=="string"?parseFloat(e):e,DP=e=>typeof e=="number"||we.test(e);function Iie(e,t,n,r,i,o){i?(e.opacity=Dt(0,n.opacity!==void 0?n.opacity:1,Mie(r)),e.opacityExit=Dt(t.opacity!==void 0?t.opacity:1,0,Nie(r))):o&&(e.opacity=Dt(t.opacity!==void 0?t.opacity:1,n.opacity!==void 0?n.opacity:1,r));for(let s=0;srt?1:n(nh(e,t,r))}function $P(e,t){e.min=t.min,e.max=t.max}function ei(e,t){$P(e.x,t.x),$P(e.y,t.y)}function FP(e,t,n,r,i){return e-=t,e=Cy(e,1/n,r),i!==void 0&&(e=Cy(e,1/i,r)),e}function Die(e,t=0,n=1,r=.5,i,o=e,s=e){if(bo.test(t)&&(t=parseFloat(t),t=Dt(s.min,s.max,t/100)-s.min),typeof t!="number")return;let a=Dt(o.min,o.max,r);e===o&&(a-=t),e.min=FP(e.min,t,n,a,i),e.max=FP(e.max,t,n,a,i)}function BP(e,t,[n,r,i],o,s){Die(e,t[n],t[r],t[i],t.scale,o,s)}const Lie=["x","scaleX","originX"],$ie=["y","scaleY","originY"];function jP(e,t,n,r){BP(e.x,t,Lie,n?n.x:void 0,r?r.x:void 0),BP(e.y,t,$ie,n?n.y:void 0,r?r.y:void 0)}function VP(e){return e.translate===0&&e.scale===1}function nN(e){return VP(e.x)&&VP(e.y)}function mw(e,t){return e.x.min===t.x.min&&e.x.max===t.x.max&&e.y.min===t.y.min&&e.y.max===t.y.max}function zP(e){return Hr(e.x)/Hr(e.y)}class Fie{constructor(){this.members=[]}add(t){I5(this.members,t),t.scheduleRender()}remove(t){if(M5(this.members,t),t===this.prevLead&&(this.prevLead=void 0),t===this.lead){const n=this.members[this.members.length-1];n&&this.promote(n)}}relegate(t){const n=this.members.findIndex(i=>t===i);if(n===0)return!1;let r;for(let i=n;i>=0;i--){const o=this.members[i];if(o.isPresent!==!1){r=o;break}}return r?(this.promote(r),!0):!1}promote(t,n){const r=this.lead;if(t!==r&&(this.prevLead=r,this.lead=t,t.show(),r)){r.instance&&r.scheduleRender(),t.scheduleRender(),t.resumeFrom=r,n&&(t.resumeFrom.preserveOpacity=!0),r.snapshot&&(t.snapshot=r.snapshot,t.snapshot.latestValues=r.animationValues||r.latestValues),t.root&&t.root.isUpdating&&(t.isLayoutDirty=!0);const{crossfade:i}=t.options;i===!1&&r.hide()}}exitAnimationComplete(){this.members.forEach(t=>{const{options:n,resumingFrom:r}=t;n.onExitComplete&&n.onExitComplete(),r&&r.options.onExitComplete&&r.options.onExitComplete()})}scheduleRender(){this.members.forEach(t=>{t.instance&&t.scheduleRender(!1)})}removeLeadSnapshot(){this.lead&&this.lead.snapshot&&(this.lead.snapshot=void 0)}}function UP(e,t,n){let r="";const i=e.x.translate/t.x,o=e.y.translate/t.y;if((i||o)&&(r=`translate3d(${i}px, ${o}px, 0) `),(t.x!==1||t.y!==1)&&(r+=`scale(${1/t.x}, ${1/t.y}) `),n){const{rotate:l,rotateX:u,rotateY:c}=n;l&&(r+=`rotate(${l}deg) `),u&&(r+=`rotateX(${u}deg) `),c&&(r+=`rotateY(${c}deg) `)}const s=e.x.scale*t.x,a=e.y.scale*t.y;return(s!==1||a!==1)&&(r+=`scale(${s}, ${a})`),r||"none"}const Bie=(e,t)=>e.depth-t.depth;class jie{constructor(){this.children=[],this.isDirty=!1}add(t){I5(this.children,t),this.isDirty=!0}remove(t){M5(this.children,t),this.isDirty=!0}forEach(t){this.isDirty&&this.children.sort(Bie),this.isDirty=!1,this.children.forEach(t)}}function Vie(e,t){const n=performance.now(),r=({timestamp:i})=>{const o=i-n;o>=t&&(cs(r),e(o-t))};return kt.read(r,!0),()=>cs(r)}function zie(e){window.MotionDebug&&window.MotionDebug.record(e)}function Uie(e){return e instanceof SVGElement&&e.tagName!=="svg"}function Gie(e,t,n){const r=Nr(e)?e:_c(e);return r.start(R5("",r,t,n)),r.animation}const GP=["","X","Y","Z"],HP=1e3;let Hie=0;const Ha={type:"projectionFrame",totalNodes:0,resolvedTargetDeltas:0,recalculatedProjection:0};function rN({attachResizeListener:e,defaultParent:t,measureScroll:n,checkIsScrollRoot:r,resetTransform:i}){return class{constructor(s={},a=t==null?void 0:t()){this.id=Hie++,this.animationId=0,this.children=new Set,this.options={},this.isTreeAnimating=!1,this.isAnimationBlocked=!1,this.isLayoutDirty=!1,this.isProjectionDirty=!1,this.isSharedProjectionDirty=!1,this.isTransformDirty=!1,this.updateManuallyBlocked=!1,this.updateBlockedByResize=!1,this.isUpdating=!1,this.isSVG=!1,this.needsReset=!1,this.shouldResetTransform=!1,this.treeScale={x:1,y:1},this.eventHandlers=new Map,this.hasTreeAnimated=!1,this.updateScheduled=!1,this.checkUpdateFailed=()=>{this.isUpdating&&(this.isUpdating=!1,this.clearAllSnapshots())},this.updateProjection=()=>{Ha.totalNodes=Ha.resolvedTargetDeltas=Ha.recalculatedProjection=0,this.nodes.forEach(Kie),this.nodes.forEach(Jie),this.nodes.forEach(eoe),this.nodes.forEach(Yie),zie(Ha)},this.hasProjected=!1,this.isVisible=!0,this.animationProgress=0,this.sharedNodes=new Map,this.latestValues=s,this.root=a?a.root||a:this,this.path=a?[...a.path,a]:[],this.parent=a,this.depth=a?a.depth+1:0;for(let l=0;lthis.root.updateBlockedByResize=!1;e(s,()=>{this.root.updateBlockedByResize=!0,d&&d(),d=Vie(f,250),om.hasAnimatedSinceResize&&(om.hasAnimatedSinceResize=!1,this.nodes.forEach(WP))})}l&&this.root.registerSharedNode(l,this),this.options.animate!==!1&&c&&(l||u)&&this.addEventListener("didUpdate",({delta:d,hasLayoutChanged:f,hasRelativeTargetChanged:h,layout:p})=>{if(this.isTreeAnimationBlocked()){this.target=void 0,this.relativeTarget=void 0;return}const m=this.options.transition||c.getDefaultTransition()||ooe,{onLayoutAnimationStart:S,onLayoutAnimationComplete:v}=c.getProps(),y=!this.targetLayout||!mw(this.targetLayout,p)||h,g=!f&&h;if(this.options.layoutRoot||this.resumeFrom&&this.resumeFrom.instance||g||f&&(y||!this.currentAnimation)){this.resumeFrom&&(this.resumingFrom=this.resumeFrom,this.resumingFrom.resumingFrom=void 0),this.setAnimationOrigin(d,g);const b={...UM(m,"layout"),onPlay:S,onComplete:v};(c.shouldReduceMotion||this.options.layoutRoot)&&(b.delay=0,b.type=!1),this.startAnimation(b)}else f||WP(this),this.isLead()&&this.options.onExitComplete&&this.options.onExitComplete();this.targetLayout=p})}unmount(){this.options.layoutId&&this.willUpdate(),this.root.nodes.remove(this);const s=this.getStack();s&&s.remove(this),this.parent&&this.parent.children.delete(this),this.instance=void 0,cs(this.updateProjection)}blockUpdate(){this.updateManuallyBlocked=!0}unblockUpdate(){this.updateManuallyBlocked=!1}isUpdateBlocked(){return this.updateManuallyBlocked||this.updateBlockedByResize}isTreeAnimationBlocked(){return this.isAnimationBlocked||this.parent&&this.parent.isTreeAnimationBlocked()||!1}startUpdate(){this.isUpdateBlocked()||(this.isUpdating=!0,this.nodes&&this.nodes.forEach(toe),this.animationId++)}getTransformTemplate(){const{visualElement:s}=this.options;return s&&s.getProps().transformTemplate}willUpdate(s=!0){if(this.root.hasTreeAnimated=!0,this.root.isUpdateBlocked()){this.options.onExitComplete&&this.options.onExitComplete();return}if(!this.root.isUpdating&&this.root.startUpdate(),this.isLayoutDirty)return;this.isLayoutDirty=!0;for(let c=0;cthis.update()))}clearAllSnapshots(){this.nodes.forEach(Xie),this.sharedNodes.forEach(noe)}scheduleUpdateProjection(){kt.preRender(this.updateProjection,!1,!0)}scheduleCheckAfterUnmount(){kt.postRender(()=>{this.isLayoutDirty?this.root.didUpdate():this.root.checkUpdateFailed()})}updateSnapshot(){this.snapshot||!this.instance||(this.snapshot=this.measure())}updateLayout(){if(!this.instance||(this.updateScroll(),!(this.options.alwaysMeasureLayout&&this.isLead())&&!this.isLayoutDirty))return;if(this.resumeFrom&&!this.resumeFrom.instance)for(let l=0;l{const _=b/1e3;KP(d.x,s.x,_),KP(d.y,s.y,_),this.setTargetDelta(d),this.relativeTarget&&this.relativeTargetOrigin&&this.layout&&this.relativeParent&&this.relativeParent.layout&&(ff(f,this.layout.layoutBox,this.relativeParent.layout.layoutBox),roe(this.relativeTarget,this.relativeTargetOrigin,f,_),g&&mw(this.relativeTarget,g)&&(this.isProjectionDirty=!1),g||(g=en()),ei(g,this.relativeTarget)),m&&(this.animationValues=c,Iie(c,u,this.latestValues,_,y,v)),this.root.scheduleUpdateProjection(),this.scheduleRender(),this.animationProgress=_},this.mixTargetDelta(this.options.layoutRoot?1e3:0)}startAnimation(s){this.notifyListeners("animationStart"),this.currentAnimation&&this.currentAnimation.stop(),this.resumingFrom&&this.resumingFrom.currentAnimation&&this.resumingFrom.currentAnimation.stop(),this.pendingAnimation&&(cs(this.pendingAnimation),this.pendingAnimation=void 0),this.pendingAnimation=kt.update(()=>{om.hasAnimatedSinceResize=!0,this.currentAnimation=Gie(0,HP,{...s,onUpdate:a=>{this.mixTargetDelta(a),s.onUpdate&&s.onUpdate(a)},onComplete:()=>{s.onComplete&&s.onComplete(),this.completeAnimation()}}),this.resumingFrom&&(this.resumingFrom.currentAnimation=this.currentAnimation),this.pendingAnimation=void 0})}completeAnimation(){this.resumingFrom&&(this.resumingFrom.currentAnimation=void 0,this.resumingFrom.preserveOpacity=void 0);const s=this.getStack();s&&s.exitAnimationComplete(),this.resumingFrom=this.currentAnimation=this.animationValues=void 0,this.notifyListeners("animationComplete")}finishAnimation(){this.currentAnimation&&(this.mixTargetDelta&&this.mixTargetDelta(HP),this.currentAnimation.stop()),this.completeAnimation()}applyTransformsToTarget(){const s=this.getLead();let{targetWithTransforms:a,target:l,layout:u,latestValues:c}=s;if(!(!a||!l||!u)){if(this!==s&&this.layout&&u&&iN(this.options.animationType,this.layout.layoutBox,u.layoutBox)){l=this.target||en();const d=Hr(this.layout.layoutBox.x);l.x.min=s.target.x.min,l.x.max=l.x.min+d;const f=Hr(this.layout.layoutBox.y);l.y.min=s.target.y.min,l.y.max=l.y.min+f}ei(a,l),Nu(a,c),df(this.projectionDeltaWithTransform,this.layoutCorrected,a,c)}}registerSharedNode(s,a){this.sharedNodes.has(s)||this.sharedNodes.set(s,new Fie),this.sharedNodes.get(s).add(a);const u=a.options.initialPromotionConfig;a.promote({transition:u?u.transition:void 0,preserveFollowOpacity:u&&u.shouldPreserveFollowOpacity?u.shouldPreserveFollowOpacity(a):void 0})}isLead(){const s=this.getStack();return s?s.lead===this:!0}getLead(){var s;const{layoutId:a}=this.options;return a?((s=this.getStack())===null||s===void 0?void 0:s.lead)||this:this}getPrevLead(){var s;const{layoutId:a}=this.options;return a?(s=this.getStack())===null||s===void 0?void 0:s.prevLead:void 0}getStack(){const{layoutId:s}=this.options;if(s)return this.root.sharedNodes.get(s)}promote({needsReset:s,transition:a,preserveFollowOpacity:l}={}){const u=this.getStack();u&&u.promote(this,l),s&&(this.projectionDelta=void 0,this.needsReset=!0),a&&this.setOptions({transition:a})}relegate(){const s=this.getStack();return s?s.relegate(this):!1}resetRotation(){const{visualElement:s}=this.options;if(!s)return;let a=!1;const{latestValues:l}=s;if((l.rotate||l.rotateX||l.rotateY||l.rotateZ)&&(a=!0),!a)return;const u={};for(let c=0;c{var a;return(a=s.currentAnimation)===null||a===void 0?void 0:a.stop()}),this.root.nodes.forEach(qP),this.root.sharedNodes.clear()}}}function qie(e){e.updateLayout()}function Wie(e){var t;const n=((t=e.resumeFrom)===null||t===void 0?void 0:t.snapshot)||e.snapshot;if(e.isLead()&&e.layout&&n&&e.hasListeners("didUpdate")){const{layoutBox:r,measuredBox:i}=e.layout,{animationType:o}=e.options,s=n.source!==e.layout.source;o==="size"?Zi(d=>{const f=s?n.measuredBox[d]:n.layoutBox[d],h=Hr(f);f.min=r[d].min,f.max=f.min+h}):iN(o,n.layoutBox,r)&&Zi(d=>{const f=s?n.measuredBox[d]:n.layoutBox[d],h=Hr(r[d]);f.max=f.min+h,e.relativeTarget&&!e.currentAnimation&&(e.isProjectionDirty=!0,e.relativeTarget[d].max=e.relativeTarget[d].min+h)});const a=Mu();df(a,r,n.layoutBox);const l=Mu();s?df(l,e.applyTransform(i,!0),n.measuredBox):df(l,r,n.layoutBox);const u=!nN(a);let c=!1;if(!e.resumeFrom){const d=e.getClosestProjectingParent();if(d&&!d.resumeFrom){const{snapshot:f,layout:h}=d;if(f&&h){const p=en();ff(p,n.layoutBox,f.layoutBox);const m=en();ff(m,r,h.layoutBox),mw(p,m)||(c=!0),d.options.layoutRoot&&(e.relativeTarget=m,e.relativeTargetOrigin=p,e.relativeParent=d)}}}e.notifyListeners("didUpdate",{layout:r,snapshot:n,delta:l,layoutDelta:a,hasLayoutChanged:u,hasRelativeTargetChanged:c})}else if(e.isLead()){const{onExitComplete:r}=e.options;r&&r()}e.options.transition=void 0}function Kie(e){Ha.totalNodes++,e.parent&&(e.isProjecting()||(e.isProjectionDirty=e.parent.isProjectionDirty),e.isSharedProjectionDirty||(e.isSharedProjectionDirty=!!(e.isProjectionDirty||e.parent.isProjectionDirty||e.parent.isSharedProjectionDirty)),e.isTransformDirty||(e.isTransformDirty=e.parent.isTransformDirty))}function Yie(e){e.isProjectionDirty=e.isSharedProjectionDirty=e.isTransformDirty=!1}function Xie(e){e.clearSnapshot()}function qP(e){e.clearMeasurements()}function Qie(e){e.isLayoutDirty=!1}function Zie(e){const{visualElement:t}=e.options;t&&t.getProps().onBeforeLayoutMeasure&&t.notify("BeforeLayoutMeasure"),e.resetTransform()}function WP(e){e.finishAnimation(),e.targetDelta=e.relativeTarget=e.target=void 0,e.isProjectionDirty=!0}function Jie(e){e.resolveTargetDelta()}function eoe(e){e.calcProjection()}function toe(e){e.resetRotation()}function noe(e){e.removeLeadSnapshot()}function KP(e,t,n){e.translate=Dt(t.translate,0,n),e.scale=Dt(t.scale,1,n),e.origin=t.origin,e.originPoint=t.originPoint}function YP(e,t,n,r){e.min=Dt(t.min,n.min,r),e.max=Dt(t.max,n.max,r)}function roe(e,t,n,r){YP(e.x,t.x,n.x,r),YP(e.y,t.y,n.y,r)}function ioe(e){return e.animationValues&&e.animationValues.opacityExit!==void 0}const ooe={duration:.45,ease:[.4,0,.1,1]};function XP(e){e.min=Math.round(e.min*2)/2,e.max=Math.round(e.max*2)/2}function soe(e){XP(e.x),XP(e.y)}function iN(e,t,n){return e==="position"||e==="preserve-aspect"&&!fw(zP(t),zP(n),.2)}const aoe=rN({attachResizeListener:(e,t)=>Qo(e,"resize",t),measureScroll:()=>({x:document.documentElement.scrollLeft||document.body.scrollLeft,y:document.documentElement.scrollTop||document.body.scrollTop}),checkIsScrollRoot:()=>!0}),zS={current:void 0},oN=rN({measureScroll:e=>({x:e.scrollLeft,y:e.scrollTop}),defaultParent:()=>{if(!zS.current){const e=new aoe({});e.mount(window),e.setOptions({layoutScroll:!0}),zS.current=e}return zS.current},resetTransform:(e,t)=>{e.style.transform=t!==void 0?t:"none"},checkIsScrollRoot:e=>window.getComputedStyle(e).position==="fixed"}),loe={pan:{Feature:Cie},drag:{Feature:xie,ProjectionNode:oN,MeasureLayout:JM}},uoe=/var\((--[a-zA-Z0-9-_]+),? ?([a-zA-Z0-9 ()%#.,-]+)?\)/;function coe(e){const t=uoe.exec(e);if(!t)return[,];const[,n,r]=t;return[n,r]}function yw(e,t,n=1){const[r,i]=coe(e);if(!r)return;const o=window.getComputedStyle(t).getPropertyValue(r);return o?o.trim():ow(i)?yw(i,t,n+1):i}function doe(e,{...t},n){const r=e.current;if(!(r instanceof Element))return{target:t,transitionEnd:n};n&&(n={...n}),e.values.forEach(i=>{const o=i.get();if(!ow(o))return;const s=yw(o,r);s&&i.set(s)});for(const i in t){const o=t[i];if(!ow(o))continue;const s=yw(o,r);s&&(t[i]=s,n||(n={}),n[i]===void 0&&(n[i]=o))}return{target:t,transitionEnd:n}}const foe=new Set(["width","height","top","left","right","bottom","x","y","translateX","translateY"]),sN=e=>foe.has(e),hoe=e=>Object.keys(e).some(sN),QP=e=>e===Il||e===we,ZP=(e,t)=>parseFloat(e.split(", ")[t]),JP=(e,t)=>(n,{transform:r})=>{if(r==="none"||!r)return 0;const i=r.match(/^matrix3d\((.+)\)$/);if(i)return ZP(i[1],t);{const o=r.match(/^matrix\((.+)\)$/);return o?ZP(o[1],e):0}},poe=new Set(["x","y","z"]),goe=ip.filter(e=>!poe.has(e));function moe(e){const t=[];return goe.forEach(n=>{const r=e.getValue(n);r!==void 0&&(t.push([n,r.get()]),r.set(n.startsWith("scale")?1:0))}),t.length&&e.render(),t}const wc={width:({x:e},{paddingLeft:t="0",paddingRight:n="0"})=>e.max-e.min-parseFloat(t)-parseFloat(n),height:({y:e},{paddingTop:t="0",paddingBottom:n="0"})=>e.max-e.min-parseFloat(t)-parseFloat(n),top:(e,{top:t})=>parseFloat(t),left:(e,{left:t})=>parseFloat(t),bottom:({y:e},{top:t})=>parseFloat(t)+(e.max-e.min),right:({x:e},{left:t})=>parseFloat(t)+(e.max-e.min),x:JP(4,13),y:JP(5,14)};wc.translateX=wc.x;wc.translateY=wc.y;const yoe=(e,t,n)=>{const r=t.measureViewportBox(),i=t.current,o=getComputedStyle(i),{display:s}=o,a={};s==="none"&&t.setStaticValue("display",e.display||"block"),n.forEach(u=>{a[u]=wc[u](r,o)}),t.render();const l=t.measureViewportBox();return n.forEach(u=>{const c=t.getValue(u);c&&c.jump(a[u]),e[u]=wc[u](l,o)}),e},voe=(e,t,n={},r={})=>{t={...t},r={...r};const i=Object.keys(t).filter(sN);let o=[],s=!1;const a=[];if(i.forEach(l=>{const u=e.getValue(l);if(!e.hasValue(l))return;let c=n[l],d=vd(c);const f=t[l];let h;if(by(f)){const p=f.length,m=f[0]===null?1:0;c=f[m],d=vd(c);for(let S=m;S=0?window.pageYOffset:null,u=yoe(t,e,a);return o.length&&o.forEach(([c,d])=>{e.getValue(c).set(d)}),e.render(),kv&&l!==null&&window.scrollTo({top:l}),{target:u,transitionEnd:r}}else return{target:t,transitionEnd:r}};function boe(e,t,n,r){return hoe(t)?voe(e,t,n,r):{target:t,transitionEnd:r}}const Soe=(e,t,n,r)=>{const i=doe(e,t,r);return t=i.target,r=i.transitionEnd,boe(e,t,n,r)},vw={current:null},aN={current:!1};function _oe(){if(aN.current=!0,!!kv)if(window.matchMedia){const e=window.matchMedia("(prefers-reduced-motion)"),t=()=>vw.current=e.matches;e.addListener(t),t()}else vw.current=!1}function woe(e,t,n){const{willChange:r}=t;for(const i in t){const o=t[i],s=n[i];if(Nr(o))e.addValue(i,o),xy(r)&&r.add(i);else if(Nr(s))e.addValue(i,_c(o,{owner:e})),xy(r)&&r.remove(i);else if(s!==o)if(e.hasValue(i)){const a=e.getValue(i);!a.hasAnimated&&a.set(o)}else{const a=e.getStaticValue(i);e.addValue(i,_c(a!==void 0?a:o,{owner:e}))}}for(const i in n)t[i]===void 0&&e.removeValue(i);return t}const e6=new WeakMap,lN=Object.keys(th),xoe=lN.length,t6=["AnimationStart","AnimationComplete","Update","BeforeLayoutMeasure","LayoutMeasure","LayoutAnimationStart","LayoutAnimationComplete"],Coe=m5.length;class Toe{constructor({parent:t,props:n,presenceContext:r,reducedMotionConfig:i,visualState:o},s={}){this.current=null,this.children=new Set,this.isVariantNode=!1,this.isControllingVariants=!1,this.shouldReduceMotion=null,this.values=new Map,this.features={},this.valueSubscriptions=new Map,this.prevMotionValues={},this.events={},this.propEventSubscriptions={},this.notifyUpdate=()=>this.notify("Update",this.latestValues),this.render=()=>{this.current&&(this.triggerBuild(),this.renderInstance(this.current,this.renderState,this.props.style,this.projection))},this.scheduleRender=()=>kt.render(this.render,!1,!0);const{latestValues:a,renderState:l}=o;this.latestValues=a,this.baseTarget={...a},this.initialValues=n.initial?{...a}:{},this.renderState=l,this.parent=t,this.props=n,this.presenceContext=r,this.depth=t?t.depth+1:0,this.reducedMotionConfig=i,this.options=s,this.isControllingVariants=Ov(n),this.isVariantNode=W7(n),this.isVariantNode&&(this.variantChildren=new Set),this.manuallyAnimateOnMount=!!(t&&t.current);const{willChange:u,...c}=this.scrapeMotionValuesFromProps(n,{});for(const d in c){const f=c[d];a[d]!==void 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Boe={initial:e=>{const{position:t}=e,n=["top","bottom"].includes(t)?"y":"x";let r=["top-right","bottom-right"].includes(t)?1:-1;return t==="bottom"&&(r=1),{opacity:0,[n]:r*24}},animate:{opacity:1,y:0,x:0,scale:1,transition:{duration:.4,ease:[.4,0,.2,1]}},exit:{opacity:0,scale:.85,transition:{duration:.2,ease:[.4,0,1,1]}}},dN=E.memo(e=>{const{id:t,message:n,onCloseComplete:r,onRequestRemove:i,requestClose:o=!1,position:s="bottom",duration:a=5e3,containerStyle:l,motionVariants:u=Boe,toastSpacing:c="0.5rem"}=e,[d,f]=E.useState(a),h=Eie();gy(()=>{h||r==null||r()},[h]),gy(()=>{f(a)},[a]);const p=()=>f(null),m=()=>f(a),S=()=>{h&&i()};E.useEffect(()=>{h&&o&&i()},[h,o,i]),Cte(S,d);const v=E.useMemo(()=>({pointerEvents:"auto",maxWidth:560,minWidth:300,margin:c,...l}),[l,c]),y=E.useMemo(()=>wte(s),[s]);return j.jsx(D5.div,{layout:!0,className:"chakra-toast",variants:u,initial:"initial",animate:"animate",exit:"exit",onHoverStart:p,onHoverEnd:m,custom:{position:s},style:y,children:j.jsx(Ae.div,{role:"status","aria-atomic":"true",className:"chakra-toast__inner",__css:v,children:uo(n,{id:t,onClose:S})})})});dN.displayName="ToastComponent";function joe(e,t){var n;const r=e??"bottom",o={"top-start":{ltr:"top-left",rtl:"top-right"},"top-end":{ltr:"top-right",rtl:"top-left"},"bottom-start":{ltr:"bottom-left",rtl:"bottom-right"},"bottom-end":{ltr:"bottom-right",rtl:"bottom-left"}}[r];return(n=o==null?void 0:o[t])!=null?n:r}var 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Re{constructor(t){this.spa=this.safeParseAsync,this._def=t,this.parse=this.parse.bind(this),this.safeParse=this.safeParse.bind(this),this.parseAsync=this.parseAsync.bind(this),this.safeParseAsync=this.safeParseAsync.bind(this),this.spa=this.spa.bind(this),this.refine=this.refine.bind(this),this.refinement=this.refinement.bind(this),this.superRefine=this.superRefine.bind(this),this.optional=this.optional.bind(this),this.nullable=this.nullable.bind(this),this.nullish=this.nullish.bind(this),this.array=this.array.bind(this),this.promise=this.promise.bind(this),this.or=this.or.bind(this),this.and=this.and.bind(this),this.transform=this.transform.bind(this),this.brand=this.brand.bind(this),this.default=this.default.bind(this),this.catch=this.catch.bind(this),this.describe=this.describe.bind(this),this.pipe=this.pipe.bind(this),this.isNullable=this.isNullable.bind(this),this.isOptional=this.isOptional.bind(this)}get description(){return this._def.description}_getType(t){return js(t.data)}_getOrReturnCtx(t,n){return n||{common:t.parent.common,data:t.data,parsedType:js(t.data),schemaErrorMap:this._def.errorMap,path:t.path,parent:t.parent}}_processInputParams(t){return{status:new er,ctx:{common:t.parent.common,data:t.data,parsedType:js(t.data),schemaErrorMap:this._def.errorMap,path:t.path,parent:t.parent}}}_parseSync(t){const n=this._parse(t);if(Ay(n))throw new Error("Synchronous parse encountered promise.");return n}_parseAsync(t){const n=this._parse(t);return Promise.resolve(n)}parse(t,n){const r=this.safeParse(t,n);if(r.success)return r.data;throw r.error}safeParse(t,n){var r;const i={common:{issues:[],async:(r=n==null?void 0:n.async)!==null&&r!==void 0?r:!1,contextualErrorMap:n==null?void 0:n.errorMap},path:(n==null?void 0:n.path)||[],schemaErrorMap:this._def.errorMap,parent:null,data:t,parsedType:js(t)},o=this._parseSync({data:t,path:i.path,parent:i});return O6(i,o)}async parseAsync(t,n){const r=await this.safeParseAsync(t,n);if(r.success)return r.data;throw r.error}async safeParseAsync(t,n){const r={common:{issues:[],contextualErrorMap:n==null?void 0:n.errorMap,async:!0},path:(n==null?void 0:n.path)||[],schemaErrorMap:this._def.errorMap,parent:null,data:t,parsedType:js(t)},i=this._parse({data:t,path:r.path,parent:r}),o=await(Ay(i)?i:Promise.resolve(i));return O6(r,o)}refine(t,n){const r=i=>typeof n=="string"||typeof n>"u"?{message:n}:typeof n=="function"?n(i):n;return this._refinement((i,o)=>{const s=t(i),a=()=>o.addIssue({code:re.custom,...r(i)});return typeof Promise<"u"&&s instanceof Promise?s.then(l=>l?!0:(a(),!1)):s?!0:(a(),!1)})}refinement(t,n){return this._refinement((r,i)=>t(r)?!0:(i.addIssue(typeof n=="function"?n(r,i):n),!1))}_refinement(t){return new Ui({schema:this,typeName:xe.ZodEffects,effect:{type:"refinement",refinement:t}})}superRefine(t){return this._refinement(t)}optional(){return ns.create(this,this._def)}nullable(){return vl.create(this,this._def)}nullish(){return this.nullable().optional()}array(){return Fi.create(this,this._def)}promise(){return kc.create(this,this._def)}or(t){return uh.create([this,t],this._def)}and(t){return ch.create(this,t,this._def)}transform(t){return new Ui({...Oe(this._def),schema:this,typeName:xe.ZodEffects,effect:{type:"transform",transform:t}})}default(t){const n=typeof t=="function"?t:()=>t;return new gh({...Oe(this._def),innerType:this,defaultValue:n,typeName:xe.ZodDefault})}brand(){return new hD({typeName:xe.ZodBranded,type:this,...Oe(this._def)})}catch(t){const n=typeof t=="function"?t:()=>t;return new My({...Oe(this._def),innerType:this,catchValue:n,typeName:xe.ZodCatch})}describe(t){const n=this.constructor;return new n({...this._def,description:t})}pipe(t){return dp.create(this,t)}isOptional(){return this.safeParse(void 0).success}isNullable(){return this.safeParse(null).success}}const pue=/^c[^\s-]{8,}$/i,gue=/^[a-z][a-z0-9]*$/,mue=/[0-9A-HJKMNP-TV-Z]{26}/,yue=/^([a-f0-9]{8}-[a-f0-9]{4}-[1-5][a-f0-9]{3}-[a-f0-9]{4}-[a-f0-9]{12}|00000000-0000-0000-0000-000000000000)$/i,vue=/^(([^<>()[\]\\.,;:\s@\"]+(\.[^<>()[\]\\.,;:\s@\"]+)*)|(\".+\"))@((\[(((25[0-5])|(2[0-4][0-9])|(1[0-9]{2})|([0-9]{1,2}))\.){3}((25[0-5])|(2[0-4][0-9])|(1[0-9]{2})|([0-9]{1,2}))\])|(\[IPv6:(([a-f0-9]{1,4}:){7}|::([a-f0-9]{1,4}:){0,6}|([a-f0-9]{1,4}:){1}:([a-f0-9]{1,4}:){0,5}|([a-f0-9]{1,4}:){2}:([a-f0-9]{1,4}:){0,4}|([a-f0-9]{1,4}:){3}:([a-f0-9]{1,4}:){0,3}|([a-f0-9]{1,4}:){4}:([a-f0-9]{1,4}:){0,2}|([a-f0-9]{1,4}:){5}:([a-f0-9]{1,4}:){0,1})([a-f0-9]{1,4}|(((25[0-5])|(2[0-4][0-9])|(1[0-9]{2})|([0-9]{1,2}))\.){3}((25[0-5])|(2[0-4][0-9])|(1[0-9]{2})|([0-9]{1,2})))\])|([A-Za-z0-9]([A-Za-z0-9-]*[A-Za-z0-9])*(\.[A-Za-z]{2,})+))$/,bue=/^(\p{Extended_Pictographic}|\p{Emoji_Component})+$/u,Sue=/^(((25[0-5])|(2[0-4][0-9])|(1[0-9]{2})|([0-9]{1,2}))\.){3}((25[0-5])|(2[0-4][0-9])|(1[0-9]{2})|([0-9]{1,2}))$/,_ue=/^(([a-f0-9]{1,4}:){7}|::([a-f0-9]{1,4}:){0,6}|([a-f0-9]{1,4}:){1}:([a-f0-9]{1,4}:){0,5}|([a-f0-9]{1,4}:){2}:([a-f0-9]{1,4}:){0,4}|([a-f0-9]{1,4}:){3}:([a-f0-9]{1,4}:){0,3}|([a-f0-9]{1,4}:){4}:([a-f0-9]{1,4}:){0,2}|([a-f0-9]{1,4}:){5}:([a-f0-9]{1,4}:){0,1})([a-f0-9]{1,4}|(((25[0-5])|(2[0-4][0-9])|(1[0-9]{2})|([0-9]{1,2}))\.){3}((25[0-5])|(2[0-4][0-9])|(1[0-9]{2})|([0-9]{1,2})))$/,wue=e=>e.precision?e.offset?new RegExp(`^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}\\.\\d{${e.precision}}(([+-]\\d{2}(:?\\d{2})?)|Z)$`):new RegExp(`^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}\\.\\d{${e.precision}}Z$`):e.precision===0?e.offset?new RegExp("^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}(([+-]\\d{2}(:?\\d{2})?)|Z)$"):new RegExp("^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}Z$"):e.offset?new RegExp("^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}(\\.\\d+)?(([+-]\\d{2}(:?\\d{2})?)|Z)$"):new RegExp("^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}(\\.\\d+)?Z$");function xue(e,t){return!!((t==="v4"||!t)&&Sue.test(e)||(t==="v6"||!t)&&_ue.test(e))}class Ii extends Re{constructor(){super(...arguments),this._regex=(t,n,r)=>this.refinement(i=>t.test(i),{validation:n,code:re.invalid_string,...be.errToObj(r)}),this.nonempty=t=>this.min(1,be.errToObj(t)),this.trim=()=>new Ii({...this._def,checks:[...this._def.checks,{kind:"trim"}]}),this.toLowerCase=()=>new Ii({...this._def,checks:[...this._def.checks,{kind:"toLowerCase"}]}),this.toUpperCase=()=>new Ii({...this._def,checks:[...this._def.checks,{kind:"toUpperCase"}]})}_parse(t){if(this._def.coerce&&(t.data=String(t.data)),this._getType(t)!==le.string){const o=this._getOrReturnCtx(t);return ce(o,{code:re.invalid_type,expected:le.string,received:o.parsedType}),Ce}const r=new er;let i;for(const o of this._def.checks)if(o.kind==="min")t.data.lengtho.value&&(i=this._getOrReturnCtx(t,i),ce(i,{code:re.too_big,maximum:o.value,type:"string",inclusive:!0,exact:!1,message:o.message}),r.dirty());else if(o.kind==="length"){const s=t.data.length>o.value,a=t.data.length"u"?null:t==null?void 0:t.precision,offset:(n=t==null?void 0:t.offset)!==null&&n!==void 0?n:!1,...be.errToObj(t==null?void 0:t.message)})}regex(t,n){return this._addCheck({kind:"regex",regex:t,...be.errToObj(n)})}includes(t,n){return this._addCheck({kind:"includes",value:t,position:n==null?void 0:n.position,...be.errToObj(n==null?void 0:n.message)})}startsWith(t,n){return this._addCheck({kind:"startsWith",value:t,...be.errToObj(n)})}endsWith(t,n){return this._addCheck({kind:"endsWith",value:t,...be.errToObj(n)})}min(t,n){return this._addCheck({kind:"min",value:t,...be.errToObj(n)})}max(t,n){return this._addCheck({kind:"max",value:t,...be.errToObj(n)})}length(t,n){return this._addCheck({kind:"length",value:t,...be.errToObj(n)})}get isDatetime(){return!!this._def.checks.find(t=>t.kind==="datetime")}get isEmail(){return!!this._def.checks.find(t=>t.kind==="email")}get isURL(){return!!this._def.checks.find(t=>t.kind==="url")}get isEmoji(){return!!this._def.checks.find(t=>t.kind==="emoji")}get isUUID(){return!!this._def.checks.find(t=>t.kind==="uuid")}get isCUID(){return!!this._def.checks.find(t=>t.kind==="cuid")}get isCUID2(){return!!this._def.checks.find(t=>t.kind==="cuid2")}get isULID(){return!!this._def.checks.find(t=>t.kind==="ulid")}get isIP(){return!!this._def.checks.find(t=>t.kind==="ip")}get minLength(){let t=null;for(const n of this._def.checks)n.kind==="min"&&(t===null||n.value>t)&&(t=n.value);return t}get maxLength(){let t=null;for(const n of this._def.checks)n.kind==="max"&&(t===null||n.value{var t;return new Ii({checks:[],typeName:xe.ZodString,coerce:(t=e==null?void 0:e.coerce)!==null&&t!==void 0?t:!1,...Oe(e)})};function Cue(e,t){const n=(e.toString().split(".")[1]||"").length,r=(t.toString().split(".")[1]||"").length,i=n>r?n:r,o=parseInt(e.toFixed(i).replace(".","")),s=parseInt(t.toFixed(i).replace(".",""));return o%s/Math.pow(10,i)}class pa extends Re{constructor(){super(...arguments),this.min=this.gte,this.max=this.lte,this.step=this.multipleOf}_parse(t){if(this._def.coerce&&(t.data=Number(t.data)),this._getType(t)!==le.number){const o=this._getOrReturnCtx(t);return ce(o,{code:re.invalid_type,expected:le.number,received:o.parsedType}),Ce}let r;const i=new er;for(const o of this._def.checks)o.kind==="int"?Qe.isInteger(t.data)||(r=this._getOrReturnCtx(t,r),ce(r,{code:re.invalid_type,expected:"integer",received:"float",message:o.message}),i.dirty()):o.kind==="min"?(o.inclusive?t.datao.value:t.data>=o.value)&&(r=this._getOrReturnCtx(t,r),ce(r,{code:re.too_big,maximum:o.value,type:"number",inclusive:o.inclusive,exact:!1,message:o.message}),i.dirty()):o.kind==="multipleOf"?Cue(t.data,o.value)!==0&&(r=this._getOrReturnCtx(t,r),ce(r,{code:re.not_multiple_of,multipleOf:o.value,message:o.message}),i.dirty()):o.kind==="finite"?Number.isFinite(t.data)||(r=this._getOrReturnCtx(t,r),ce(r,{code:re.not_finite,message:o.message}),i.dirty()):Qe.assertNever(o);return{status:i.value,value:t.data}}gte(t,n){return this.setLimit("min",t,!0,be.toString(n))}gt(t,n){return this.setLimit("min",t,!1,be.toString(n))}lte(t,n){return this.setLimit("max",t,!0,be.toString(n))}lt(t,n){return this.setLimit("max",t,!1,be.toString(n))}setLimit(t,n,r,i){return new pa({...this._def,checks:[...this._def.checks,{kind:t,value:n,inclusive:r,message:be.toString(i)}]})}_addCheck(t){return new pa({...this._def,checks:[...this._def.checks,t]})}int(t){return this._addCheck({kind:"int",message:be.toString(t)})}positive(t){return this._addCheck({kind:"min",value:0,inclusive:!1,message:be.toString(t)})}negative(t){return this._addCheck({kind:"max",value:0,inclusive:!1,message:be.toString(t)})}nonpositive(t){return this._addCheck({kind:"max",value:0,inclusive:!0,message:be.toString(t)})}nonnegative(t){return this._addCheck({kind:"min",value:0,inclusive:!0,message:be.toString(t)})}multipleOf(t,n){return this._addCheck({kind:"multipleOf",value:t,message:be.toString(n)})}finite(t){return this._addCheck({kind:"finite",message:be.toString(t)})}safe(t){return this._addCheck({kind:"min",inclusive:!0,value:Number.MIN_SAFE_INTEGER,message:be.toString(t)})._addCheck({kind:"max",inclusive:!0,value:Number.MAX_SAFE_INTEGER,message:be.toString(t)})}get minValue(){let t=null;for(const n of this._def.checks)n.kind==="min"&&(t===null||n.value>t)&&(t=n.value);return t}get maxValue(){let t=null;for(const n of this._def.checks)n.kind==="max"&&(t===null||n.valuet.kind==="int"||t.kind==="multipleOf"&&Qe.isInteger(t.value))}get isFinite(){let t=null,n=null;for(const r of this._def.checks){if(r.kind==="finite"||r.kind==="int"||r.kind==="multipleOf")return!0;r.kind==="min"?(n===null||r.value>n)&&(n=r.value):r.kind==="max"&&(t===null||r.valuenew pa({checks:[],typeName:xe.ZodNumber,coerce:(e==null?void 0:e.coerce)||!1,...Oe(e)});class ga extends Re{constructor(){super(...arguments),this.min=this.gte,this.max=this.lte}_parse(t){if(this._def.coerce&&(t.data=BigInt(t.data)),this._getType(t)!==le.bigint){const o=this._getOrReturnCtx(t);return ce(o,{code:re.invalid_type,expected:le.bigint,received:o.parsedType}),Ce}let r;const i=new er;for(const o of this._def.checks)o.kind==="min"?(o.inclusive?t.datao.value:t.data>=o.value)&&(r=this._getOrReturnCtx(t,r),ce(r,{code:re.too_big,type:"bigint",maximum:o.value,inclusive:o.inclusive,message:o.message}),i.dirty()):o.kind==="multipleOf"?t.data%o.value!==BigInt(0)&&(r=this._getOrReturnCtx(t,r),ce(r,{code:re.not_multiple_of,multipleOf:o.value,message:o.message}),i.dirty()):Qe.assertNever(o);return{status:i.value,value:t.data}}gte(t,n){return this.setLimit("min",t,!0,be.toString(n))}gt(t,n){return this.setLimit("min",t,!1,be.toString(n))}lte(t,n){return this.setLimit("max",t,!0,be.toString(n))}lt(t,n){return this.setLimit("max",t,!1,be.toString(n))}setLimit(t,n,r,i){return new ga({...this._def,checks:[...this._def.checks,{kind:t,value:n,inclusive:r,message:be.toString(i)}]})}_addCheck(t){return new ga({...this._def,checks:[...this._def.checks,t]})}positive(t){return this._addCheck({kind:"min",value:BigInt(0),inclusive:!1,message:be.toString(t)})}negative(t){return this._addCheck({kind:"max",value:BigInt(0),inclusive:!1,message:be.toString(t)})}nonpositive(t){return this._addCheck({kind:"max",value:BigInt(0),inclusive:!0,message:be.toString(t)})}nonnegative(t){return this._addCheck({kind:"min",value:BigInt(0),inclusive:!0,message:be.toString(t)})}multipleOf(t,n){return this._addCheck({kind:"multipleOf",value:t,message:be.toString(n)})}get minValue(){let t=null;for(const n of this._def.checks)n.kind==="min"&&(t===null||n.value>t)&&(t=n.value);return t}get maxValue(){let t=null;for(const n of this._def.checks)n.kind==="max"&&(t===null||n.value{var t;return new ga({checks:[],typeName:xe.ZodBigInt,coerce:(t=e==null?void 0:e.coerce)!==null&&t!==void 0?t:!1,...Oe(e)})};class sh extends Re{_parse(t){if(this._def.coerce&&(t.data=!!t.data),this._getType(t)!==le.boolean){const r=this._getOrReturnCtx(t);return ce(r,{code:re.invalid_type,expected:le.boolean,received:r.parsedType}),Ce}return pr(t.data)}}sh.create=e=>new sh({typeName:xe.ZodBoolean,coerce:(e==null?void 0:e.coerce)||!1,...Oe(e)});class ml extends Re{_parse(t){if(this._def.coerce&&(t.data=new Date(t.data)),this._getType(t)!==le.date){const o=this._getOrReturnCtx(t);return ce(o,{code:re.invalid_type,expected:le.date,received:o.parsedType}),Ce}if(isNaN(t.data.getTime())){const o=this._getOrReturnCtx(t);return ce(o,{code:re.invalid_date}),Ce}const r=new er;let i;for(const o of this._def.checks)o.kind==="min"?t.data.getTime()o.value&&(i=this._getOrReturnCtx(t,i),ce(i,{code:re.too_big,message:o.message,inclusive:!0,exact:!1,maximum:o.value,type:"date"}),r.dirty()):Qe.assertNever(o);return{status:r.value,value:new Date(t.data.getTime())}}_addCheck(t){return new ml({...this._def,checks:[...this._def.checks,t]})}min(t,n){return this._addCheck({kind:"min",value:t.getTime(),message:be.toString(n)})}max(t,n){return this._addCheck({kind:"max",value:t.getTime(),message:be.toString(n)})}get minDate(){let t=null;for(const n of this._def.checks)n.kind==="min"&&(t===null||n.value>t)&&(t=n.value);return t!=null?new Date(t):null}get maxDate(){let t=null;for(const n of this._def.checks)n.kind==="max"&&(t===null||n.valuenew ml({checks:[],coerce:(e==null?void 0:e.coerce)||!1,typeName:xe.ZodDate,...Oe(e)});class Oy extends Re{_parse(t){if(this._getType(t)!==le.symbol){const r=this._getOrReturnCtx(t);return ce(r,{code:re.invalid_type,expected:le.symbol,received:r.parsedType}),Ce}return pr(t.data)}}Oy.create=e=>new Oy({typeName:xe.ZodSymbol,...Oe(e)});class ah extends Re{_parse(t){if(this._getType(t)!==le.undefined){const r=this._getOrReturnCtx(t);return ce(r,{code:re.invalid_type,expected:le.undefined,received:r.parsedType}),Ce}return pr(t.data)}}ah.create=e=>new ah({typeName:xe.ZodUndefined,...Oe(e)});class lh extends Re{_parse(t){if(this._getType(t)!==le.null){const r=this._getOrReturnCtx(t);return ce(r,{code:re.invalid_type,expected:le.null,received:r.parsedType}),Ce}return pr(t.data)}}lh.create=e=>new lh({typeName:xe.ZodNull,...Oe(e)});class Pc extends Re{constructor(){super(...arguments),this._any=!0}_parse(t){return pr(t.data)}}Pc.create=e=>new Pc({typeName:xe.ZodAny,...Oe(e)});class ol extends Re{constructor(){super(...arguments),this._unknown=!0}_parse(t){return pr(t.data)}}ol.create=e=>new ol({typeName:xe.ZodUnknown,...Oe(e)});class fs extends Re{_parse(t){const n=this._getOrReturnCtx(t);return ce(n,{code:re.invalid_type,expected:le.never,received:n.parsedType}),Ce}}fs.create=e=>new fs({typeName:xe.ZodNever,...Oe(e)});class Ry extends Re{_parse(t){if(this._getType(t)!==le.undefined){const r=this._getOrReturnCtx(t);return ce(r,{code:re.invalid_type,expected:le.void,received:r.parsedType}),Ce}return pr(t.data)}}Ry.create=e=>new Ry({typeName:xe.ZodVoid,...Oe(e)});class Fi extends Re{_parse(t){const{ctx:n,status:r}=this._processInputParams(t),i=this._def;if(n.parsedType!==le.array)return ce(n,{code:re.invalid_type,expected:le.array,received:n.parsedType}),Ce;if(i.exactLength!==null){const s=n.data.length>i.exactLength.value,a=n.data.lengthi.maxLength.value&&(ce(n,{code:re.too_big,maximum:i.maxLength.value,type:"array",inclusive:!0,exact:!1,message:i.maxLength.message}),r.dirty()),n.common.async)return Promise.all([...n.data].map((s,a)=>i.type._parseAsync(new xo(n,s,n.path,a)))).then(s=>er.mergeArray(r,s));const o=[...n.data].map((s,a)=>i.type._parseSync(new xo(n,s,n.path,a)));return er.mergeArray(r,o)}get element(){return this._def.type}min(t,n){return new Fi({...this._def,minLength:{value:t,message:be.toString(n)}})}max(t,n){return new Fi({...this._def,maxLength:{value:t,message:be.toString(n)}})}length(t,n){return new Fi({...this._def,exactLength:{value:t,message:be.toString(n)}})}nonempty(t){return this.min(1,t)}}Fi.create=(e,t)=>new Fi({type:e,minLength:null,maxLength:null,exactLength:null,typeName:xe.ZodArray,...Oe(t)});function fu(e){if(e instanceof It){const t={};for(const n in e.shape){const r=e.shape[n];t[n]=ns.create(fu(r))}return new It({...e._def,shape:()=>t})}else return e instanceof Fi?new Fi({...e._def,type:fu(e.element)}):e instanceof ns?ns.create(fu(e.unwrap())):e instanceof vl?vl.create(fu(e.unwrap())):e instanceof Co?Co.create(e.items.map(t=>fu(t))):e}class It extends Re{constructor(){super(...arguments),this._cached=null,this.nonstrict=this.passthrough,this.augment=this.extend}_getCached(){if(this._cached!==null)return this._cached;const t=this._def.shape(),n=Qe.objectKeys(t);return this._cached={shape:t,keys:n}}_parse(t){if(this._getType(t)!==le.object){const u=this._getOrReturnCtx(t);return ce(u,{code:re.invalid_type,expected:le.object,received:u.parsedType}),Ce}const{status:r,ctx:i}=this._processInputParams(t),{shape:o,keys:s}=this._getCached(),a=[];if(!(this._def.catchall instanceof fs&&this._def.unknownKeys==="strip"))for(const u in i.data)s.includes(u)||a.push(u);const l=[];for(const u of s){const c=o[u],d=i.data[u];l.push({key:{status:"valid",value:u},value:c._parse(new xo(i,d,i.path,u)),alwaysSet:u in i.data})}if(this._def.catchall instanceof fs){const u=this._def.unknownKeys;if(u==="passthrough")for(const c of a)l.push({key:{status:"valid",value:c},value:{status:"valid",value:i.data[c]}});else if(u==="strict")a.length>0&&(ce(i,{code:re.unrecognized_keys,keys:a}),r.dirty());else if(u!=="strip")throw new Error("Internal ZodObject error: invalid unknownKeys value.")}else{const u=this._def.catchall;for(const c of a){const d=i.data[c];l.push({key:{status:"valid",value:c},value:u._parse(new xo(i,d,i.path,c)),alwaysSet:c in i.data})}}return i.common.async?Promise.resolve().then(async()=>{const u=[];for(const c of l){const d=await c.key;u.push({key:d,value:await c.value,alwaysSet:c.alwaysSet})}return u}).then(u=>er.mergeObjectSync(r,u)):er.mergeObjectSync(r,l)}get shape(){return this._def.shape()}strict(t){return be.errToObj,new It({...this._def,unknownKeys:"strict",...t!==void 0?{errorMap:(n,r)=>{var i,o,s,a;const l=(s=(o=(i=this._def).errorMap)===null||o===void 0?void 0:o.call(i,n,r).message)!==null&&s!==void 0?s:r.defaultError;return n.code==="unrecognized_keys"?{message:(a=be.errToObj(t).message)!==null&&a!==void 0?a:l}:{message:l}}}:{}})}strip(){return new It({...this._def,unknownKeys:"strip"})}passthrough(){return new It({...this._def,unknownKeys:"passthrough"})}extend(t){return new It({...this._def,shape:()=>({...this._def.shape(),...t})})}merge(t){return new It({unknownKeys:t._def.unknownKeys,catchall:t._def.catchall,shape:()=>({...this._def.shape(),...t._def.shape()}),typeName:xe.ZodObject})}setKey(t,n){return this.augment({[t]:n})}catchall(t){return new It({...this._def,catchall:t})}pick(t){const n={};return Qe.objectKeys(t).forEach(r=>{t[r]&&this.shape[r]&&(n[r]=this.shape[r])}),new It({...this._def,shape:()=>n})}omit(t){const n={};return Qe.objectKeys(this.shape).forEach(r=>{t[r]||(n[r]=this.shape[r])}),new It({...this._def,shape:()=>n})}deepPartial(){return fu(this)}partial(t){const n={};return Qe.objectKeys(this.shape).forEach(r=>{const i=this.shape[r];t&&!t[r]?n[r]=i:n[r]=i.optional()}),new It({...this._def,shape:()=>n})}required(t){const n={};return Qe.objectKeys(this.shape).forEach(r=>{if(t&&!t[r])n[r]=this.shape[r];else{let o=this.shape[r];for(;o instanceof ns;)o=o._def.innerType;n[r]=o}}),new It({...this._def,shape:()=>n})}keyof(){return fD(Qe.objectKeys(this.shape))}}It.create=(e,t)=>new It({shape:()=>e,unknownKeys:"strip",catchall:fs.create(),typeName:xe.ZodObject,...Oe(t)});It.strictCreate=(e,t)=>new It({shape:()=>e,unknownKeys:"strict",catchall:fs.create(),typeName:xe.ZodObject,...Oe(t)});It.lazycreate=(e,t)=>new It({shape:e,unknownKeys:"strip",catchall:fs.create(),typeName:xe.ZodObject,...Oe(t)});class uh extends Re{_parse(t){const{ctx:n}=this._processInputParams(t),r=this._def.options;function i(o){for(const a of o)if(a.result.status==="valid")return a.result;for(const a of o)if(a.result.status==="dirty")return n.common.issues.push(...a.ctx.common.issues),a.result;const s=o.map(a=>new $i(a.ctx.common.issues));return ce(n,{code:re.invalid_union,unionErrors:s}),Ce}if(n.common.async)return Promise.all(r.map(async o=>{const s={...n,common:{...n.common,issues:[]},parent:null};return{result:await o._parseAsync({data:n.data,path:n.path,parent:s}),ctx:s}})).then(i);{let o;const s=[];for(const l of r){const u={...n,common:{...n.common,issues:[]},parent:null},c=l._parseSync({data:n.data,path:n.path,parent:u});if(c.status==="valid")return c;c.status==="dirty"&&!o&&(o={result:c,ctx:u}),u.common.issues.length&&s.push(u.common.issues)}if(o)return n.common.issues.push(...o.ctx.common.issues),o.result;const a=s.map(l=>new $i(l));return ce(n,{code:re.invalid_union,unionErrors:a}),Ce}}get options(){return this._def.options}}uh.create=(e,t)=>new uh({options:e,typeName:xe.ZodUnion,...Oe(t)});const fm=e=>e instanceof fh?fm(e.schema):e instanceof Ui?fm(e.innerType()):e instanceof hh?[e.value]:e instanceof ma?e.options:e instanceof ph?Object.keys(e.enum):e instanceof gh?fm(e._def.innerType):e instanceof ah?[void 0]:e instanceof lh?[null]:null;class jv extends Re{_parse(t){const{ctx:n}=this._processInputParams(t);if(n.parsedType!==le.object)return ce(n,{code:re.invalid_type,expected:le.object,received:n.parsedType}),Ce;const r=this.discriminator,i=n.data[r],o=this.optionsMap.get(i);return o?n.common.async?o._parseAsync({data:n.data,path:n.path,parent:n}):o._parseSync({data:n.data,path:n.path,parent:n}):(ce(n,{code:re.invalid_union_discriminator,options:Array.from(this.optionsMap.keys()),path:[r]}),Ce)}get discriminator(){return this._def.discriminator}get options(){return this._def.options}get optionsMap(){return this._def.optionsMap}static create(t,n,r){const i=new Map;for(const o of n){const s=fm(o.shape[t]);if(!s)throw new Error(`A discriminator value for key \`${t}\` could not be extracted from all schema options`);for(const a of s){if(i.has(a))throw new Error(`Discriminator property ${String(t)} has duplicate value ${String(a)}`);i.set(a,o)}}return new jv({typeName:xe.ZodDiscriminatedUnion,discriminator:t,options:n,optionsMap:i,...Oe(r)})}}function Iw(e,t){const n=js(e),r=js(t);if(e===t)return{valid:!0,data:e};if(n===le.object&&r===le.object){const i=Qe.objectKeys(t),o=Qe.objectKeys(e).filter(a=>i.indexOf(a)!==-1),s={...e,...t};for(const a of o){const l=Iw(e[a],t[a]);if(!l.valid)return{valid:!1};s[a]=l.data}return{valid:!0,data:s}}else if(n===le.array&&r===le.array){if(e.length!==t.length)return{valid:!1};const i=[];for(let o=0;o{if(Ow(o)||Ow(s))return Ce;const a=Iw(o.value,s.value);return a.valid?((Rw(o)||Rw(s))&&n.dirty(),{status:n.value,value:a.data}):(ce(r,{code:re.invalid_intersection_types}),Ce)};return r.common.async?Promise.all([this._def.left._parseAsync({data:r.data,path:r.path,parent:r}),this._def.right._parseAsync({data:r.data,path:r.path,parent:r})]).then(([o,s])=>i(o,s)):i(this._def.left._parseSync({data:r.data,path:r.path,parent:r}),this._def.right._parseSync({data:r.data,path:r.path,parent:r}))}}ch.create=(e,t,n)=>new ch({left:e,right:t,typeName:xe.ZodIntersection,...Oe(n)});class Co extends Re{_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.parsedType!==le.array)return ce(r,{code:re.invalid_type,expected:le.array,received:r.parsedType}),Ce;if(r.data.lengththis._def.items.length&&(ce(r,{code:re.too_big,maximum:this._def.items.length,inclusive:!0,exact:!1,type:"array"}),n.dirty());const o=[...r.data].map((s,a)=>{const l=this._def.items[a]||this._def.rest;return l?l._parse(new xo(r,s,r.path,a)):null}).filter(s=>!!s);return r.common.async?Promise.all(o).then(s=>er.mergeArray(n,s)):er.mergeArray(n,o)}get items(){return this._def.items}rest(t){return new Co({...this._def,rest:t})}}Co.create=(e,t)=>{if(!Array.isArray(e))throw new Error("You must pass an array of schemas to z.tuple([ ... ])");return new Co({items:e,typeName:xe.ZodTuple,rest:null,...Oe(t)})};class dh extends Re{get keySchema(){return this._def.keyType}get valueSchema(){return this._def.valueType}_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.parsedType!==le.object)return ce(r,{code:re.invalid_type,expected:le.object,received:r.parsedType}),Ce;const i=[],o=this._def.keyType,s=this._def.valueType;for(const a in r.data)i.push({key:o._parse(new xo(r,a,r.path,a)),value:s._parse(new xo(r,r.data[a],r.path,a))});return r.common.async?er.mergeObjectAsync(n,i):er.mergeObjectSync(n,i)}get element(){return this._def.valueType}static create(t,n,r){return n instanceof Re?new dh({keyType:t,valueType:n,typeName:xe.ZodRecord,...Oe(r)}):new dh({keyType:Ii.create(),valueType:t,typeName:xe.ZodRecord,...Oe(n)})}}class Iy extends Re{_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.parsedType!==le.map)return ce(r,{code:re.invalid_type,expected:le.map,received:r.parsedType}),Ce;const i=this._def.keyType,o=this._def.valueType,s=[...r.data.entries()].map(([a,l],u)=>({key:i._parse(new xo(r,a,r.path,[u,"key"])),value:o._parse(new xo(r,l,r.path,[u,"value"]))}));if(r.common.async){const a=new Map;return Promise.resolve().then(async()=>{for(const l of s){const u=await l.key,c=await l.value;if(u.status==="aborted"||c.status==="aborted")return Ce;(u.status==="dirty"||c.status==="dirty")&&n.dirty(),a.set(u.value,c.value)}return{status:n.value,value:a}})}else{const a=new Map;for(const l of s){const u=l.key,c=l.value;if(u.status==="aborted"||c.status==="aborted")return Ce;(u.status==="dirty"||c.status==="dirty")&&n.dirty(),a.set(u.value,c.value)}return{status:n.value,value:a}}}}Iy.create=(e,t,n)=>new Iy({valueType:t,keyType:e,typeName:xe.ZodMap,...Oe(n)});class yl extends Re{_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.parsedType!==le.set)return ce(r,{code:re.invalid_type,expected:le.set,received:r.parsedType}),Ce;const i=this._def;i.minSize!==null&&r.data.sizei.maxSize.value&&(ce(r,{code:re.too_big,maximum:i.maxSize.value,type:"set",inclusive:!0,exact:!1,message:i.maxSize.message}),n.dirty());const o=this._def.valueType;function s(l){const u=new Set;for(const c of l){if(c.status==="aborted")return Ce;c.status==="dirty"&&n.dirty(),u.add(c.value)}return{status:n.value,value:u}}const a=[...r.data.values()].map((l,u)=>o._parse(new xo(r,l,r.path,u)));return r.common.async?Promise.all(a).then(l=>s(l)):s(a)}min(t,n){return new yl({...this._def,minSize:{value:t,message:be.toString(n)}})}max(t,n){return new yl({...this._def,maxSize:{value:t,message:be.toString(n)}})}size(t,n){return this.min(t,n).max(t,n)}nonempty(t){return this.min(1,t)}}yl.create=(e,t)=>new yl({valueType:e,minSize:null,maxSize:null,typeName:xe.ZodSet,...Oe(t)});class Qu extends Re{constructor(){super(...arguments),this.validate=this.implement}_parse(t){const{ctx:n}=this._processInputParams(t);if(n.parsedType!==le.function)return ce(n,{code:re.invalid_type,expected:le.function,received:n.parsedType}),Ce;function r(a,l){return Py({data:a,path:n.path,errorMaps:[n.common.contextualErrorMap,n.schemaErrorMap,Ey(),oh].filter(u=>!!u),issueData:{code:re.invalid_arguments,argumentsError:l}})}function i(a,l){return Py({data:a,path:n.path,errorMaps:[n.common.contextualErrorMap,n.schemaErrorMap,Ey(),oh].filter(u=>!!u),issueData:{code:re.invalid_return_type,returnTypeError:l}})}const o={errorMap:n.common.contextualErrorMap},s=n.data;return this._def.returns instanceof kc?pr(async(...a)=>{const l=new $i([]),u=await this._def.args.parseAsync(a,o).catch(f=>{throw l.addIssue(r(a,f)),l}),c=await s(...u);return await this._def.returns._def.type.parseAsync(c,o).catch(f=>{throw l.addIssue(i(c,f)),l})}):pr((...a)=>{const l=this._def.args.safeParse(a,o);if(!l.success)throw new $i([r(a,l.error)]);const u=s(...l.data),c=this._def.returns.safeParse(u,o);if(!c.success)throw new $i([i(u,c.error)]);return c.data})}parameters(){return this._def.args}returnType(){return this._def.returns}args(...t){return new Qu({...this._def,args:Co.create(t).rest(ol.create())})}returns(t){return new Qu({...this._def,returns:t})}implement(t){return this.parse(t)}strictImplement(t){return this.parse(t)}static create(t,n,r){return new Qu({args:t||Co.create([]).rest(ol.create()),returns:n||ol.create(),typeName:xe.ZodFunction,...Oe(r)})}}class fh extends Re{get schema(){return this._def.getter()}_parse(t){const{ctx:n}=this._processInputParams(t);return this._def.getter()._parse({data:n.data,path:n.path,parent:n})}}fh.create=(e,t)=>new fh({getter:e,typeName:xe.ZodLazy,...Oe(t)});class hh extends Re{_parse(t){if(t.data!==this._def.value){const n=this._getOrReturnCtx(t);return ce(n,{received:n.data,code:re.invalid_literal,expected:this._def.value}),Ce}return{status:"valid",value:t.data}}get value(){return this._def.value}}hh.create=(e,t)=>new hh({value:e,typeName:xe.ZodLiteral,...Oe(t)});function fD(e,t){return new ma({values:e,typeName:xe.ZodEnum,...Oe(t)})}class ma extends Re{_parse(t){if(typeof t.data!="string"){const n=this._getOrReturnCtx(t),r=this._def.values;return ce(n,{expected:Qe.joinValues(r),received:n.parsedType,code:re.invalid_type}),Ce}if(this._def.values.indexOf(t.data)===-1){const n=this._getOrReturnCtx(t),r=this._def.values;return ce(n,{received:n.data,code:re.invalid_enum_value,options:r}),Ce}return pr(t.data)}get options(){return this._def.values}get enum(){const t={};for(const n of this._def.values)t[n]=n;return t}get Values(){const t={};for(const n of this._def.values)t[n]=n;return t}get Enum(){const t={};for(const n of this._def.values)t[n]=n;return t}extract(t){return ma.create(t)}exclude(t){return ma.create(this.options.filter(n=>!t.includes(n)))}}ma.create=fD;class ph extends Re{_parse(t){const n=Qe.getValidEnumValues(this._def.values),r=this._getOrReturnCtx(t);if(r.parsedType!==le.string&&r.parsedType!==le.number){const i=Qe.objectValues(n);return ce(r,{expected:Qe.joinValues(i),received:r.parsedType,code:re.invalid_type}),Ce}if(n.indexOf(t.data)===-1){const i=Qe.objectValues(n);return ce(r,{received:r.data,code:re.invalid_enum_value,options:i}),Ce}return pr(t.data)}get enum(){return this._def.values}}ph.create=(e,t)=>new ph({values:e,typeName:xe.ZodNativeEnum,...Oe(t)});class kc extends Re{unwrap(){return this._def.type}_parse(t){const{ctx:n}=this._processInputParams(t);if(n.parsedType!==le.promise&&n.common.async===!1)return ce(n,{code:re.invalid_type,expected:le.promise,received:n.parsedType}),Ce;const r=n.parsedType===le.promise?n.data:Promise.resolve(n.data);return pr(r.then(i=>this._def.type.parseAsync(i,{path:n.path,errorMap:n.common.contextualErrorMap})))}}kc.create=(e,t)=>new kc({type:e,typeName:xe.ZodPromise,...Oe(t)});class Ui extends Re{innerType(){return this._def.schema}sourceType(){return this._def.schema._def.typeName===xe.ZodEffects?this._def.schema.sourceType():this._def.schema}_parse(t){const{status:n,ctx:r}=this._processInputParams(t),i=this._def.effect||null;if(i.type==="preprocess"){const s=i.transform(r.data);return r.common.async?Promise.resolve(s).then(a=>this._def.schema._parseAsync({data:a,path:r.path,parent:r})):this._def.schema._parseSync({data:s,path:r.path,parent:r})}const o={addIssue:s=>{ce(r,s),s.fatal?n.abort():n.dirty()},get path(){return r.path}};if(o.addIssue=o.addIssue.bind(o),i.type==="refinement"){const s=a=>{const l=i.refinement(a,o);if(r.common.async)return Promise.resolve(l);if(l instanceof Promise)throw new Error("Async refinement encountered during synchronous parse operation. Use .parseAsync instead.");return a};if(r.common.async===!1){const a=this._def.schema._parseSync({data:r.data,path:r.path,parent:r});return a.status==="aborted"?Ce:(a.status==="dirty"&&n.dirty(),s(a.value),{status:n.value,value:a.value})}else return this._def.schema._parseAsync({data:r.data,path:r.path,parent:r}).then(a=>a.status==="aborted"?Ce:(a.status==="dirty"&&n.dirty(),s(a.value).then(()=>({status:n.value,value:a.value}))))}if(i.type==="transform")if(r.common.async===!1){const s=this._def.schema._parseSync({data:r.data,path:r.path,parent:r});if(!ky(s))return s;const a=i.transform(s.value,o);if(a instanceof Promise)throw new Error("Asynchronous transform encountered during synchronous parse operation. Use .parseAsync instead.");return{status:n.value,value:a}}else return this._def.schema._parseAsync({data:r.data,path:r.path,parent:r}).then(s=>ky(s)?Promise.resolve(i.transform(s.value,o)).then(a=>({status:n.value,value:a})):s);Qe.assertNever(i)}}Ui.create=(e,t,n)=>new Ui({schema:e,typeName:xe.ZodEffects,effect:t,...Oe(n)});Ui.createWithPreprocess=(e,t,n)=>new Ui({schema:t,effect:{type:"preprocess",transform:e},typeName:xe.ZodEffects,...Oe(n)});class ns extends Re{_parse(t){return this._getType(t)===le.undefined?pr(void 0):this._def.innerType._parse(t)}unwrap(){return this._def.innerType}}ns.create=(e,t)=>new ns({innerType:e,typeName:xe.ZodOptional,...Oe(t)});class vl extends Re{_parse(t){return this._getType(t)===le.null?pr(null):this._def.innerType._parse(t)}unwrap(){return this._def.innerType}}vl.create=(e,t)=>new vl({innerType:e,typeName:xe.ZodNullable,...Oe(t)});class gh extends Re{_parse(t){const{ctx:n}=this._processInputParams(t);let r=n.data;return n.parsedType===le.undefined&&(r=this._def.defaultValue()),this._def.innerType._parse({data:r,path:n.path,parent:n})}removeDefault(){return this._def.innerType}}gh.create=(e,t)=>new gh({innerType:e,typeName:xe.ZodDefault,defaultValue:typeof t.default=="function"?t.default:()=>t.default,...Oe(t)});class My extends Re{_parse(t){const{ctx:n}=this._processInputParams(t),r={...n,common:{...n.common,issues:[]}},i=this._def.innerType._parse({data:r.data,path:r.path,parent:{...r}});return Ay(i)?i.then(o=>({status:"valid",value:o.status==="valid"?o.value:this._def.catchValue({get error(){return new $i(r.common.issues)},input:r.data})})):{status:"valid",value:i.status==="valid"?i.value:this._def.catchValue({get error(){return new $i(r.common.issues)},input:r.data})}}removeCatch(){return this._def.innerType}}My.create=(e,t)=>new My({innerType:e,typeName:xe.ZodCatch,catchValue:typeof t.catch=="function"?t.catch:()=>t.catch,...Oe(t)});class Ny extends Re{_parse(t){if(this._getType(t)!==le.nan){const r=this._getOrReturnCtx(t);return ce(r,{code:re.invalid_type,expected:le.nan,received:r.parsedType}),Ce}return{status:"valid",value:t.data}}}Ny.create=e=>new Ny({typeName:xe.ZodNaN,...Oe(e)});const Tue=Symbol("zod_brand");class hD extends Re{_parse(t){const{ctx:n}=this._processInputParams(t),r=n.data;return this._def.type._parse({data:r,path:n.path,parent:n})}unwrap(){return this._def.type}}class dp extends Re{_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.common.async)return(async()=>{const o=await this._def.in._parseAsync({data:r.data,path:r.path,parent:r});return o.status==="aborted"?Ce:o.status==="dirty"?(n.dirty(),dD(o.value)):this._def.out._parseAsync({data:o.value,path:r.path,parent:r})})();{const i=this._def.in._parseSync({data:r.data,path:r.path,parent:r});return i.status==="aborted"?Ce:i.status==="dirty"?(n.dirty(),{status:"dirty",value:i.value}):this._def.out._parseSync({data:i.value,path:r.path,parent:r})}}static create(t,n){return new dp({in:t,out:n,typeName:xe.ZodPipeline})}}const pD=(e,t={},n)=>e?Pc.create().superRefine((r,i)=>{var o,s;if(!e(r)){const a=typeof t=="function"?t(r):typeof t=="string"?{message:t}:t,l=(s=(o=a.fatal)!==null&&o!==void 0?o:n)!==null&&s!==void 0?s:!0,u=typeof a=="string"?{message:a}:a;i.addIssue({code:"custom",...u,fatal:l})}}):Pc.create(),Eue={object:It.lazycreate};var 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0?n.returnDetails:this.options.returnDetails,o=n.keySeparator!==void 0?n.keySeparator:this.options.keySeparator,{key:s,namespaces:a}=this.extractFromKey(t[t.length-1],n),l=a[a.length-1],u=n.lng||this.language,c=n.appendNamespaceToCIMode||this.options.appendNamespaceToCIMode;if(u&&u.toLowerCase()==="cimode"){if(c){const b=n.nsSeparator||this.options.nsSeparator;return i?{res:`${l}${b}${s}`,usedKey:s,exactUsedKey:s,usedLng:u,usedNS:l}:`${l}${b}${s}`}return i?{res:s,usedKey:s,exactUsedKey:s,usedLng:u,usedNS:l}:s}const d=this.resolve(t,n);let f=d&&d.res;const h=d&&d.usedKey||s,p=d&&d.exactUsedKey||s,m=Object.prototype.toString.apply(f),S=["[object Number]","[object Function]","[object RegExp]"],v=n.joinArrays!==void 0?n.joinArrays:this.options.joinArrays,y=!this.i18nFormat||this.i18nFormat.handleAsObject;if(y&&f&&(typeof f!="string"&&typeof f!="boolean"&&typeof f!="number")&&S.indexOf(m)<0&&!(typeof v=="string"&&m==="[object Array]")){if(!n.returnObjects&&!this.options.returnObjects){this.options.returnedObjectHandler||this.logger.warn("accessing an object - but returnObjects options is not enabled!");const b=this.options.returnedObjectHandler?this.options.returnedObjectHandler(h,f,{...n,ns:a}):`key '${s} (${this.language})' returned an object instead of string.`;return i?(d.res=b,d):b}if(o){const b=m==="[object Array]",_=b?[]:{},w=b?p:h;for(const x in f)if(Object.prototype.hasOwnProperty.call(f,x)){const C=`${w}${o}${x}`;_[x]=this.translate(C,{...n,joinArrays:!1,ns:a}),_[x]===C&&(_[x]=f[x])}f=_}}else if(y&&typeof v=="string"&&m==="[object Array]")f=f.join(v),f&&(f=this.extendTranslation(f,t,n,r));else{let b=!1,_=!1;const w=n.count!==void 0&&typeof n.count!="string",x=i0.hasDefaultValue(n),C=w?this.pluralResolver.getSuffix(u,n.count,n):"",k=n.ordinal&&w?this.pluralResolver.getSuffix(u,n.count,{ordinal:!1}):"",P=n[`defaultValue${C}`]||n[`defaultValue${k}`]||n.defaultValue;!this.isValidLookup(f)&&x&&(b=!0,f=P),this.isValidLookup(f)||(_=!0,f=s);const $=(n.missingKeyNoValueFallbackToKey||this.options.missingKeyNoValueFallbackToKey)&&_?void 0:f,N=x&&P!==f&&this.options.updateMissing;if(_||b||N){if(this.logger.log(N?"updateKey":"missingKey",u,l,s,N?P:f),o){const M=this.resolve(s,{...n,keySeparator:!1});M&&M.res&&this.logger.warn("Seems the loaded translations were in flat JSON format instead of nested. Either set keySeparator: false on init or make sure your translations are published in nested format.")}let T=[];const O=this.languageUtils.getFallbackCodes(this.options.fallbackLng,n.lng||this.language);if(this.options.saveMissingTo==="fallback"&&O&&O[0])for(let M=0;M{const L=x&&D!==f?D:$;this.options.missingKeyHandler?this.options.missingKeyHandler(M,l,R,L,N,n):this.backendConnector&&this.backendConnector.saveMissing&&this.backendConnector.saveMissing(M,l,R,L,N,n),this.emit("missingKey",M,l,R,f)};this.options.saveMissing&&(this.options.saveMissingPlurals&&w?T.forEach(M=>{this.pluralResolver.getSuffixes(M,n).forEach(R=>{I([M],s+R,n[`defaultValue${R}`]||P)})}):I(T,s,P))}f=this.extendTranslation(f,t,n,d,r),_&&f===s&&this.options.appendNamespaceToMissingKey&&(f=`${l}:${s}`),(_||b)&&this.options.parseMissingKeyHandler&&(this.options.compatibilityAPI!=="v1"?f=this.options.parseMissingKeyHandler(this.options.appendNamespaceToMissingKey?`${l}:${s}`:s,b?f:void 0):f=this.options.parseMissingKeyHandler(f))}return i?(d.res=f,d):f}extendTranslation(t,n,r,i,o){var s=this;if(this.i18nFormat&&this.i18nFormat.parse)t=this.i18nFormat.parse(t,{...this.options.interpolation.defaultVariables,...r},i.usedLng,i.usedNS,i.usedKey,{resolved:i});else if(!r.skipInterpolation){r.interpolation&&this.interpolator.init({...r,interpolation:{...this.options.interpolation,...r.interpolation}});const u=typeof t=="string"&&(r&&r.interpolation&&r.interpolation.skipOnVariables!==void 0?r.interpolation.skipOnVariables:this.options.interpolation.skipOnVariables);let c;if(u){const f=t.match(this.interpolator.nestingRegexp);c=f&&f.length}let d=r.replace&&typeof r.replace!="string"?r.replace:r;if(this.options.interpolation.defaultVariables&&(d={...this.options.interpolation.defaultVariables,...d}),t=this.interpolator.interpolate(t,d,r.lng||this.language,r),u){const f=t.match(this.interpolator.nestingRegexp),h=f&&f.length;c1&&arguments[1]!==void 0?arguments[1]:{},r,i,o,s,a;return typeof t=="string"&&(t=[t]),t.forEach(l=>{if(this.isValidLookup(r))return;const u=this.extractFromKey(l,n),c=u.key;i=c;let d=u.namespaces;this.options.fallbackNS&&(d=d.concat(this.options.fallbackNS));const f=n.count!==void 0&&typeof n.count!="string",h=f&&!n.ordinal&&n.count===0&&this.pluralResolver.shouldUseIntlApi(),p=n.context!==void 0&&(typeof n.context=="string"||typeof n.context=="number")&&n.context!=="",m=n.lngs?n.lngs:this.languageUtils.toResolveHierarchy(n.lng||this.language,n.fallbackLng);d.forEach(S=>{this.isValidLookup(r)||(a=S,!ck[`${m[0]}-${S}`]&&this.utils&&this.utils.hasLoadedNamespace&&!this.utils.hasLoadedNamespace(a)&&(ck[`${m[0]}-${S}`]=!0,this.logger.warn(`key "${i}" for languages "${m.join(", ")}" won't get resolved as namespace "${a}" was not yet loaded`,"This means something IS WRONG in your setup. You access the t function before i18next.init / i18next.loadNamespace / i18next.changeLanguage was done. Wait for the callback or Promise to resolve before accessing it!!!")),m.forEach(v=>{if(this.isValidLookup(r))return;s=v;const y=[c];if(this.i18nFormat&&this.i18nFormat.addLookupKeys)this.i18nFormat.addLookupKeys(y,c,v,S,n);else{let b;f&&(b=this.pluralResolver.getSuffix(v,n.count,n));const _=`${this.options.pluralSeparator}zero`,w=`${this.options.pluralSeparator}ordinal${this.options.pluralSeparator}`;if(f&&(y.push(c+b),n.ordinal&&b.indexOf(w)===0&&y.push(c+b.replace(w,this.options.pluralSeparator)),h&&y.push(c+_)),p){const x=`${c}${this.options.contextSeparator}${n.context}`;y.push(x),f&&(y.push(x+b),n.ordinal&&b.indexOf(w)===0&&y.push(x+b.replace(w,this.options.pluralSeparator)),h&&y.push(x+_))}}let g;for(;g=y.pop();)this.isValidLookup(r)||(o=g,r=this.getResource(v,S,g,n))}))})}),{res:r,usedKey:i,exactUsedKey:o,usedLng:s,usedNS:a}}isValidLookup(t){return t!==void 0&&!(!this.options.returnNull&&t===null)&&!(!this.options.returnEmptyString&&t==="")}getResource(t,n,r){let i=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{};return this.i18nFormat&&this.i18nFormat.getResource?this.i18nFormat.getResource(t,n,r,i):this.resourceStore.getResource(t,n,r,i)}static hasDefaultValue(t){const n="defaultValue";for(const r in t)if(Object.prototype.hasOwnProperty.call(t,r)&&n===r.substring(0,n.length)&&t[r]!==void 0)return!0;return!1}}function m_(e){return e.charAt(0).toUpperCase()+e.slice(1)}class dk{constructor(t){this.options=t,this.supportedLngs=this.options.supportedLngs||!1,this.logger=ho.create("languageUtils")}getScriptPartFromCode(t){if(t=r0(t),!t||t.indexOf("-")<0)return null;const n=t.split("-");return n.length===2||(n.pop(),n[n.length-1].toLowerCase()==="x")?null:this.formatLanguageCode(n.join("-"))}getLanguagePartFromCode(t){if(t=r0(t),!t||t.indexOf("-")<0)return t;const n=t.split("-");return this.formatLanguageCode(n[0])}formatLanguageCode(t){if(typeof t=="string"&&t.indexOf("-")>-1){const n=["hans","hant","latn","cyrl","cans","mong","arab"];let r=t.split("-");return this.options.lowerCaseLng?r=r.map(i=>i.toLowerCase()):r.length===2?(r[0]=r[0].toLowerCase(),r[1]=r[1].toUpperCase(),n.indexOf(r[1].toLowerCase())>-1&&(r[1]=m_(r[1].toLowerCase()))):r.length===3&&(r[0]=r[0].toLowerCase(),r[1].length===2&&(r[1]=r[1].toUpperCase()),r[0]!=="sgn"&&r[2].length===2&&(r[2]=r[2].toUpperCase()),n.indexOf(r[1].toLowerCase())>-1&&(r[1]=m_(r[1].toLowerCase())),n.indexOf(r[2].toLowerCase())>-1&&(r[2]=m_(r[2].toLowerCase()))),r.join("-")}return this.options.cleanCode||this.options.lowerCaseLng?t.toLowerCase():t}isSupportedCode(t){return(this.options.load==="languageOnly"||this.options.nonExplicitSupportedLngs)&&(t=this.getLanguagePartFromCode(t)),!this.supportedLngs||!this.supportedLngs.length||this.supportedLngs.indexOf(t)>-1}getBestMatchFromCodes(t){if(!t)return null;let n;return t.forEach(r=>{if(n)return;const i=this.formatLanguageCode(r);(!this.options.supportedLngs||this.isSupportedCode(i))&&(n=i)}),!n&&this.options.supportedLngs&&t.forEach(r=>{if(n)return;const i=this.getLanguagePartFromCode(r);if(this.isSupportedCode(i))return n=i;n=this.options.supportedLngs.find(o=>{if(o===i)return o;if(!(o.indexOf("-")<0&&i.indexOf("-")<0)&&o.indexOf(i)===0)return o})}),n||(n=this.getFallbackCodes(this.options.fallbackLng)[0]),n}getFallbackCodes(t,n){if(!t)return[];if(typeof t=="function"&&(t=t(n)),typeof t=="string"&&(t=[t]),Object.prototype.toString.apply(t)==="[object Array]")return t;if(!n)return t.default||[];let r=t[n];return r||(r=t[this.getScriptPartFromCode(n)]),r||(r=t[this.formatLanguageCode(n)]),r||(r=t[this.getLanguagePartFromCode(n)]),r||(r=t.default),r||[]}toResolveHierarchy(t,n){const r=this.getFallbackCodes(n||this.options.fallbackLng||[],t),i=[],o=s=>{s&&(this.isSupportedCode(s)?i.push(s):this.logger.warn(`rejecting language code not found in supportedLngs: ${s}`))};return typeof t=="string"&&(t.indexOf("-")>-1||t.indexOf("_")>-1)?(this.options.load!=="languageOnly"&&o(this.formatLanguageCode(t)),this.options.load!=="languageOnly"&&this.options.load!=="currentOnly"&&o(this.getScriptPartFromCode(t)),this.options.load!=="currentOnly"&&o(this.getLanguagePartFromCode(t))):typeof t=="string"&&o(this.formatLanguageCode(t)),r.forEach(s=>{i.indexOf(s)<0&&o(this.formatLanguageCode(s))}),i}}let d1e=[{lngs:["ach","ak","am","arn","br","fil","gun","ln","mfe","mg","mi","oc","pt","pt-BR","tg","tl","ti","tr","uz","wa"],nr:[1,2],fc:1},{lngs:["af","an","ast","az","bg","bn","ca","da","de","dev","el","en","eo","es","et","eu","fi","fo","fur","fy","gl","gu","ha","hi","hu","hy","ia","it","kk","kn","ku","lb","mai","ml","mn","mr","nah","nap","nb","ne","nl","nn","no","nso","pa","pap","pms","ps","pt-PT","rm","sco","se","si","so","son","sq","sv","sw","ta","te","tk","ur","yo"],nr:[1,2],fc:2},{lngs:["ay","bo","cgg","fa","ht","id","ja","jbo","ka","km","ko","ky","lo","ms","sah","su","th","tt","ug","vi","wo","zh"],nr:[1],fc:3},{lngs:["be","bs","cnr","dz","hr","ru","sr","uk"],nr:[1,2,5],fc:4},{lngs:["ar"],nr:[0,1,2,3,11,100],fc:5},{lngs:["cs","sk"],nr:[1,2,5],fc:6},{lngs:["csb","pl"],nr:[1,2,5],fc:7},{lngs:["cy"],nr:[1,2,3,8],fc:8},{lngs:["fr"],nr:[1,2],fc:9},{lngs:["ga"],nr:[1,2,3,7,11],fc:10},{lngs:["gd"],nr:[1,2,3,20],fc:11},{lngs:["is"],nr:[1,2],fc:12},{lngs:["jv"],nr:[0,1],fc:13},{lngs:["kw"],nr:[1,2,3,4],fc:14},{lngs:["lt"],nr:[1,2,10],fc:15},{lngs:["lv"],nr:[1,2,0],fc:16},{lngs:["mk"],nr:[1,2],fc:17},{lngs:["mnk"],nr:[0,1,2],fc:18},{lngs:["mt"],nr:[1,2,11,20],fc:19},{lngs:["or"],nr:[2,1],fc:2},{lngs:["ro"],nr:[1,2,20],fc:20},{lngs:["sl"],nr:[5,1,2,3],fc:21},{lngs:["he","iw"],nr:[1,2,20,21],fc:22}],f1e={1:function(e){return+(e>1)},2:function(e){return+(e!=1)},3:function(e){return 0},4:function(e){return e%10==1&&e%100!=11?0:e%10>=2&&e%10<=4&&(e%100<10||e%100>=20)?1:2},5:function(e){return e==0?0:e==1?1:e==2?2:e%100>=3&&e%100<=10?3:e%100>=11?4:5},6:function(e){return e==1?0:e>=2&&e<=4?1:2},7:function(e){return e==1?0:e%10>=2&&e%10<=4&&(e%100<10||e%100>=20)?1:2},8:function(e){return e==1?0:e==2?1:e!=8&&e!=11?2:3},9:function(e){return+(e>=2)},10:function(e){return e==1?0:e==2?1:e<7?2:e<11?3:4},11:function(e){return e==1||e==11?0:e==2||e==12?1:e>2&&e<20?2:3},12:function(e){return+(e%10!=1||e%100==11)},13:function(e){return+(e!==0)},14:function(e){return e==1?0:e==2?1:e==3?2:3},15:function(e){return e%10==1&&e%100!=11?0:e%10>=2&&(e%100<10||e%100>=20)?1:2},16:function(e){return e%10==1&&e%100!=11?0:e!==0?1:2},17:function(e){return e==1||e%10==1&&e%100!=11?0:1},18:function(e){return e==0?0:e==1?1:2},19:function(e){return e==1?0:e==0||e%100>1&&e%100<11?1:e%100>10&&e%100<20?2:3},20:function(e){return e==1?0:e==0||e%100>0&&e%100<20?1:2},21:function(e){return e%100==1?1:e%100==2?2:e%100==3||e%100==4?3:0},22:function(e){return e==1?0:e==2?1:(e<0||e>10)&&e%10==0?2:3}};const h1e=["v1","v2","v3"],p1e=["v4"],fk={zero:0,one:1,two:2,few:3,many:4,other:5};function g1e(){const e={};return d1e.forEach(t=>{t.lngs.forEach(n=>{e[n]={numbers:t.nr,plurals:f1e[t.fc]}})}),e}class m1e{constructor(t){let n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};this.languageUtils=t,this.options=n,this.logger=ho.create("pluralResolver"),(!this.options.compatibilityJSON||p1e.includes(this.options.compatibilityJSON))&&(typeof Intl>"u"||!Intl.PluralRules)&&(this.options.compatibilityJSON="v3",this.logger.error("Your environment seems not to be Intl API compatible, use an Intl.PluralRules polyfill. Will fallback to the compatibilityJSON v3 format handling.")),this.rules=g1e()}addRule(t,n){this.rules[t]=n}getRule(t){let n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};if(this.shouldUseIntlApi())try{return new Intl.PluralRules(r0(t),{type:n.ordinal?"ordinal":"cardinal"})}catch{return}return this.rules[t]||this.rules[this.languageUtils.getLanguagePartFromCode(t)]}needsPlural(t){let n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};const r=this.getRule(t,n);return this.shouldUseIntlApi()?r&&r.resolvedOptions().pluralCategories.length>1:r&&r.numbers.length>1}getPluralFormsOfKey(t,n){let r=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{};return this.getSuffixes(t,r).map(i=>`${n}${i}`)}getSuffixes(t){let n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};const r=this.getRule(t,n);return r?this.shouldUseIntlApi()?r.resolvedOptions().pluralCategories.sort((i,o)=>fk[i]-fk[o]).map(i=>`${this.options.prepend}${n.ordinal?`ordinal${this.options.prepend}`:""}${i}`):r.numbers.map(i=>this.getSuffix(t,i,n)):[]}getSuffix(t,n){let r=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{};const i=this.getRule(t,r);return i?this.shouldUseIntlApi()?`${this.options.prepend}${r.ordinal?`ordinal${this.options.prepend}`:""}${i.select(n)}`:this.getSuffixRetroCompatible(i,n):(this.logger.warn(`no plural rule found for: ${t}`),"")}getSuffixRetroCompatible(t,n){const r=t.noAbs?t.plurals(n):t.plurals(Math.abs(n));let i=t.numbers[r];this.options.simplifyPluralSuffix&&t.numbers.length===2&&t.numbers[0]===1&&(i===2?i="plural":i===1&&(i=""));const o=()=>this.options.prepend&&i.toString()?this.options.prepend+i.toString():i.toString();return this.options.compatibilityJSON==="v1"?i===1?"":typeof i=="number"?`_plural_${i.toString()}`:o():this.options.compatibilityJSON==="v2"||this.options.simplifyPluralSuffix&&t.numbers.length===2&&t.numbers[0]===1?o():this.options.prepend&&r.toString()?this.options.prepend+r.toString():r.toString()}shouldUseIntlApi(){return!h1e.includes(this.options.compatibilityJSON)}}function hk(e,t,n){let r=arguments.length>3&&arguments[3]!==void 0?arguments[3]:".",i=arguments.length>4&&arguments[4]!==void 0?arguments[4]:!0,o=s1e(e,t,n);return!o&&i&&typeof n=="string"&&(o=n0(e,n,r),o===void 0&&(o=n0(t,n,r))),o}class y1e{constructor(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};this.logger=ho.create("interpolator"),this.options=t,this.format=t.interpolation&&t.interpolation.format||(n=>n),this.init(t)}init(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};t.interpolation||(t.interpolation={escapeValue:!0});const n=t.interpolation;this.escape=n.escape!==void 0?n.escape:l1e,this.escapeValue=n.escapeValue!==void 0?n.escapeValue:!0,this.useRawValueToEscape=n.useRawValueToEscape!==void 0?n.useRawValueToEscape:!1,this.prefix=n.prefix?ou(n.prefix):n.prefixEscaped||"{{",this.suffix=n.suffix?ou(n.suffix):n.suffixEscaped||"}}",this.formatSeparator=n.formatSeparator?n.formatSeparator:n.formatSeparator||",",this.unescapePrefix=n.unescapeSuffix?"":n.unescapePrefix||"-",this.unescapeSuffix=this.unescapePrefix?"":n.unescapeSuffix||"",this.nestingPrefix=n.nestingPrefix?ou(n.nestingPrefix):n.nestingPrefixEscaped||ou("$t("),this.nestingSuffix=n.nestingSuffix?ou(n.nestingSuffix):n.nestingSuffixEscaped||ou(")"),this.nestingOptionsSeparator=n.nestingOptionsSeparator?n.nestingOptionsSeparator:n.nestingOptionsSeparator||",",this.maxReplaces=n.maxReplaces?n.maxReplaces:1e3,this.alwaysFormat=n.alwaysFormat!==void 0?n.alwaysFormat:!1,this.resetRegExp()}reset(){this.options&&this.init(this.options)}resetRegExp(){const t=`${this.prefix}(.+?)${this.suffix}`;this.regexp=new RegExp(t,"g");const n=`${this.prefix}${this.unescapePrefix}(.+?)${this.unescapeSuffix}${this.suffix}`;this.regexpUnescape=new RegExp(n,"g");const r=`${this.nestingPrefix}(.+?)${this.nestingSuffix}`;this.nestingRegexp=new RegExp(r,"g")}interpolate(t,n,r,i){let o,s,a;const l=this.options&&this.options.interpolation&&this.options.interpolation.defaultVariables||{};function u(p){return p.replace(/\$/g,"$$$$")}const c=p=>{if(p.indexOf(this.formatSeparator)<0){const y=hk(n,l,p,this.options.keySeparator,this.options.ignoreJSONStructure);return this.alwaysFormat?this.format(y,void 0,r,{...i,...n,interpolationkey:p}):y}const m=p.split(this.formatSeparator),S=m.shift().trim(),v=m.join(this.formatSeparator).trim();return this.format(hk(n,l,S,this.options.keySeparator,this.options.ignoreJSONStructure),v,r,{...i,...n,interpolationkey:S})};this.resetRegExp();const d=i&&i.missingInterpolationHandler||this.options.missingInterpolationHandler,f=i&&i.interpolation&&i.interpolation.skipOnVariables!==void 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t=e.toLowerCase().trim();const n={};if(e.indexOf("(")>-1){const r=e.split("(");t=r[0].toLowerCase().trim();const i=r[1].substring(0,r[1].length-1);t==="currency"&&i.indexOf(":")<0?n.currency||(n.currency=i.trim()):t==="relativetime"&&i.indexOf(":")<0?n.range||(n.range=i.trim()):i.split(";").forEach(s=>{if(!s)return;const[a,...l]=s.split(":"),u=l.join(":").trim().replace(/^'+|'+$/g,"");n[a.trim()]||(n[a.trim()]=u),u==="false"&&(n[a.trim()]=!1),u==="true"&&(n[a.trim()]=!0),isNaN(u)||(n[a.trim()]=parseInt(u,10))})}return{formatName:t,formatOptions:n}}function su(e){const t={};return function(r,i,o){const s=i+JSON.stringify(o);let a=t[s];return a||(a=e(r0(i),o),t[s]=a),a(r)}}class b1e{constructor(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};this.logger=ho.create("formatter"),this.options=t,this.formats={number:su((n,r)=>{const i=new Intl.NumberFormat(n,{...r});return o=>i.format(o)}),currency:su((n,r)=>{const i=new Intl.NumberFormat(n,{...r,style:"currency"});return o=>i.format(o)}),datetime:su((n,r)=>{const i=new Intl.DateTimeFormat(n,{...r});return o=>i.format(o)}),relativetime:su((n,r)=>{const i=new Intl.RelativeTimeFormat(n,{...r});return o=>i.format(o,r.range||"day")}),list:su((n,r)=>{const i=new Intl.ListFormat(n,{...r});return o=>i.format(o)})},this.init(t)}init(t){const r=(arguments.length>1&&arguments[1]!==void 0?arguments[1]:{interpolation:{}}).interpolation;this.formatSeparator=r.formatSeparator?r.formatSeparator:r.formatSeparator||","}add(t,n){this.formats[t.toLowerCase().trim()]=n}addCached(t,n){this.formats[t.toLowerCase().trim()]=su(n)}format(t,n,r){let i=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{};return n.split(this.formatSeparator).reduce((a,l)=>{const{formatName:u,formatOptions:c}=v1e(l);if(this.formats[u]){let d=a;try{const f=i&&i.formatParams&&i.formatParams[i.interpolationkey]||{},h=f.locale||f.lng||i.locale||i.lng||r;d=this.formats[u](a,h,{...c,...i,...f})}catch(f){this.logger.warn(f)}return d}else 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0&&(l[d]=!0)))}),c||(a[u]=!0)}),(Object.keys(o).length||Object.keys(s).length)&&this.queue.push({pending:s,pendingCount:Object.keys(s).length,loaded:{},errors:[],callback:i}),{toLoad:Object.keys(o),pending:Object.keys(s),toLoadLanguages:Object.keys(a),toLoadNamespaces:Object.keys(l)}}loaded(t,n,r){const i=t.split("|"),o=i[0],s=i[1];n&&this.emit("failedLoading",o,s,n),r&&this.store.addResourceBundle(o,s,r),this.state[t]=n?-1:2;const a={};this.queue.forEach(l=>{o1e(l.loaded,[o],s),S1e(l,t),n&&l.errors.push(n),l.pendingCount===0&&!l.done&&(Object.keys(l.loaded).forEach(u=>{a[u]||(a[u]={});const c=l.loaded[u];c.length&&c.forEach(d=>{a[u][d]===void 0&&(a[u][d]=!0)})}),l.done=!0,l.errors.length?l.callback(l.errors):l.callback())}),this.emit("loaded",a),this.queue=this.queue.filter(l=>!l.done)}read(t,n,r){let i=arguments.length>3&&arguments[3]!==void 0?arguments[3]:0,o=arguments.length>4&&arguments[4]!==void 0?arguments[4]:this.retryTimeout,s=arguments.length>5?arguments[5]:void 0;if(!t.length)return s(null,{});if(this.readingCalls>=this.maxParallelReads){this.waitingReads.push({lng:t,ns:n,fcName:r,tried:i,wait:o,callback:s});return}this.readingCalls++;const a=(u,c)=>{if(this.readingCalls--,this.waitingReads.length>0){const d=this.waitingReads.shift();this.read(d.lng,d.ns,d.fcName,d.tried,d.wait,d.callback)}if(u&&c&&i{this.read.call(this,t,n,r,i+1,o*2,s)},o);return}s(u,c)},l=this.backend[r].bind(this.backend);if(l.length===2){try{const u=l(t,n);u&&typeof u.then=="function"?u.then(c=>a(null,c)).catch(a):a(null,u)}catch(u){a(u)}return}return l(t,n,a)}prepareLoading(t,n){let r=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{},i=arguments.length>3?arguments[3]:void 0;if(!this.backend)return this.logger.warn("No backend was added via i18next.use. 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You access the t function before i18next.init / i18next.loadNamespace / i18next.changeLanguage was done. Wait for the callback or Promise to resolve before accessing it!!!");return}if(!(r==null||r==="")){if(this.backend&&this.backend.create){const l={...s,isUpdate:o},u=this.backend.create.bind(this.backend);if(u.length<6)try{let c;u.length===5?c=u(t,n,r,i,l):c=u(t,n,r,i),c&&typeof c.then=="function"?c.then(d=>a(null,d)).catch(a):a(null,c)}catch(c){a(c)}else u(t,n,r,i,a,l)}!t||!t[0]||this.store.addResource(t[0],n,r,i)}}}function pk(){return{debug:!1,initImmediate:!0,ns:["translation"],defaultNS:["translation"],fallbackLng:["dev"],fallbackNS:!1,supportedLngs:!1,nonExplicitSupportedLngs:!1,load:"all",preload:!1,simplifyPluralSuffix:!0,keySeparator:".",nsSeparator:":",pluralSeparator:"_",contextSeparator:"_",partialBundledLanguages:!1,saveMissing:!1,updateMissing:!1,saveMissingTo:"fallback",saveMissingPlurals:!0,missingKeyHandler:!1,missingInterpolationHandler:!1,postProcess:!1,postProcessPassResolved:!1,returnNull:!1,returnEmptyString:!0,returnObjects:!1,joinArrays:!1,returnedObjectHandler:!1,parseMissingKeyHandler:!1,appendNamespaceToMissingKey:!1,appendNamespaceToCIMode:!1,overloadTranslationOptionHandler:function(t){let n={};if(typeof t[1]=="object"&&(n=t[1]),typeof t[1]=="string"&&(n.defaultValue=t[1]),typeof t[2]=="string"&&(n.tDescription=t[2]),typeof t[2]=="object"||typeof t[3]=="object"){const r=t[3]||t[2];Object.keys(r).forEach(i=>{n[i]=r[i]})}return n},interpolation:{escapeValue:!0,format:(e,t,n,r)=>e,prefix:"{{",suffix:"}}",formatSeparator:",",unescapePrefix:"-",nestingPrefix:"$t(",nestingSuffix:")",nestingOptionsSeparator:",",maxReplaces:1e3,skipOnVariables:!0}}}function gk(e){return typeof e.ns=="string"&&(e.ns=[e.ns]),typeof e.fallbackLng=="string"&&(e.fallbackLng=[e.fallbackLng]),typeof e.fallbackNS=="string"&&(e.fallbackNS=[e.fallbackNS]),e.supportedLngs&&e.supportedLngs.indexOf("cimode")<0&&(e.supportedLngs=e.supportedLngs.concat(["cimode"])),e}function Eg(){}function w1e(e){Object.getOwnPropertyNames(Object.getPrototypeOf(e)).forEach(n=>{typeof e[n]=="function"&&(e[n]=e[n].bind(e))})}class kh extends v1{constructor(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},n=arguments.length>1?arguments[1]:void 0;if(super(),this.options=gk(t),this.services={},this.logger=ho,this.modules={external:[]},w1e(this),n&&!this.isInitialized&&!t.isClone){if(!this.options.initImmediate)return this.init(t,n),this;setTimeout(()=>{this.init(t,n)},0)}}init(){var t=this;let n=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},r=arguments.length>1?arguments[1]:void 0;typeof n=="function"&&(r=n,n={}),!n.defaultNS&&n.defaultNS!==!1&&n.ns&&(typeof n.ns=="string"?n.defaultNS=n.ns:n.ns.indexOf("translation")<0&&(n.defaultNS=n.ns[0]));const i=pk();this.options={...i,...this.options,...gk(n)},this.options.compatibilityAPI!=="v1"&&(this.options.interpolation={...i.interpolation,...this.options.interpolation}),n.keySeparator!==void 0&&(this.options.userDefinedKeySeparator=n.keySeparator),n.nsSeparator!==void 0&&(this.options.userDefinedNsSeparator=n.nsSeparator);function o(c){return c?typeof c=="function"?new c:c:null}if(!this.options.isClone){this.modules.logger?ho.init(o(this.modules.logger),this.options):ho.init(null,this.options);let c;this.modules.formatter?c=this.modules.formatter:typeof Intl<"u"&&(c=b1e);const d=new dk(this.options);this.store=new uk(this.options.resources,this.options);const f=this.services;f.logger=ho,f.resourceStore=this.store,f.languageUtils=d,f.pluralResolver=new m1e(d,{prepend:this.options.pluralSeparator,compatibilityJSON:this.options.compatibilityJSON,simplifyPluralSuffix:this.options.simplifyPluralSuffix}),c&&(!this.options.interpolation.format||this.options.interpolation.format===i.interpolation.format)&&(f.formatter=o(c),f.formatter.init(f,this.options),this.options.interpolation.format=f.formatter.format.bind(f.formatter)),f.interpolator=new y1e(this.options),f.utils={hasLoadedNamespace:this.hasLoadedNamespace.bind(this)},f.backendConnector=new _1e(o(this.modules.backend),f.resourceStore,f,this.options),f.backendConnector.on("*",function(h){for(var p=arguments.length,m=new Array(p>1?p-1:0),S=1;S1?p-1:0),S=1;S{h.init&&h.init(this)})}if(this.format=this.options.interpolation.format,r||(r=Eg),this.options.fallbackLng&&!this.services.languageDetector&&!this.options.lng){const c=this.services.languageUtils.getFallbackCodes(this.options.fallbackLng);c.length>0&&c[0]!=="dev"&&(this.options.lng=c[0])}!this.services.languageDetector&&!this.options.lng&&this.logger.warn("init: no languageDetector is used and no lng is defined"),["getResource","hasResourceBundle","getResourceBundle","getDataByLanguage"].forEach(c=>{this[c]=function(){return t.store[c](...arguments)}}),["addResource","addResources","addResourceBundle","removeResourceBundle"].forEach(c=>{this[c]=function(){return t.store[c](...arguments),t}});const l=Ad(),u=()=>{const c=(d,f)=>{this.isInitialized&&!this.initializedStoreOnce&&this.logger.warn("init: i18next is already initialized. 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Please check the object you are passing to i18next.use()");if(!t.type)throw new Error("You are passing a wrong module! Please check the object you are passing to i18next.use()");return t.type==="backend"&&(this.modules.backend=t),(t.type==="logger"||t.log&&t.warn&&t.error)&&(this.modules.logger=t),t.type==="languageDetector"&&(this.modules.languageDetector=t),t.type==="i18nFormat"&&(this.modules.i18nFormat=t),t.type==="postProcessor"&&TF.addPostProcessor(t),t.type==="formatter"&&(this.modules.formatter=t),t.type==="3rdParty"&&this.modules.external.push(t),this}setResolvedLanguage(t){if(!(!t||!this.languages)&&!(["cimode","dev"].indexOf(t)>-1))for(let n=0;n-1)&&this.store.hasLanguageSomeTranslations(r)){this.resolvedLanguage=r;break}}}changeLanguage(t,n){var r=this;this.isLanguageChangingTo=t;const i=Ad();this.emit("languageChanging",t);const o=l=>{this.language=l,this.languages=this.services.languageUtils.toResolveHierarchy(l),this.resolvedLanguage=void 0,this.setResolvedLanguage(l)},s=(l,u)=>{u?(o(u),this.translator.changeLanguage(u),this.isLanguageChangingTo=void 0,this.emit("languageChanged",u),this.logger.log("languageChanged",u)):this.isLanguageChangingTo=void 0,i.resolve(function(){return r.t(...arguments)}),n&&n(l,function(){return r.t(...arguments)})},a=l=>{!t&&!l&&this.services.languageDetector&&(l=[]);const u=typeof l=="string"?l:this.services.languageUtils.getBestMatchFromCodes(l);u&&(this.language||o(u),this.translator.language||this.translator.changeLanguage(u),this.services.languageDetector&&this.services.languageDetector.cacheUserLanguage&&this.services.languageDetector.cacheUserLanguage(u)),this.loadResources(u,c=>{s(c,u)})};return!t&&this.services.languageDetector&&!this.services.languageDetector.async?a(this.services.languageDetector.detect()):!t&&this.services.languageDetector&&this.services.languageDetector.async?this.services.languageDetector.detect.length===0?this.services.languageDetector.detect().then(a):this.services.languageDetector.detect(a):a(t),i}getFixedT(t,n,r){var i=this;const o=function(s,a){let l;if(typeof 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this._cache.has(ja)}getClientRect(t){throw new Error('abstract "getClientRect" method call')}_transformedRect(t,n){var r=[{x:t.x,y:t.y},{x:t.x+t.width,y:t.y},{x:t.x+t.width,y:t.y+t.height},{x:t.x,y:t.y+t.height}],i,o,s,a,l=this.getAbsoluteTransform(n);return r.forEach(function(u){var c=l.point(u);i===void 0&&(i=s=c.x,o=a=c.y),i=Math.min(i,c.x),o=Math.min(o,c.y),s=Math.max(s,c.x),a=Math.max(a,c.y)}),{x:i,y:o,width:s-i,height:a-o}}_drawCachedSceneCanvas(t){t.save(),t._applyOpacity(this),t._applyGlobalCompositeOperation(this);const n=this._getCanvasCache();t.translate(n.x,n.y);var r=this._getCachedSceneCanvas(),i=r.pixelRatio;t.drawImage(r._canvas,0,0,r.width/i,r.height/i),t.restore()}_drawCachedHitCanvas(t){var n=this._getCanvasCache(),r=n.hit;t.save(),t.translate(n.x,n.y),t.drawImage(r._canvas,0,0,r.width/r.pixelRatio,r.height/r.pixelRatio),t.restore()}_getCachedSceneCanvas(){var t=this.filters(),n=this._getCanvasCache(),r=n.scene,i=n.filter,o=i.getContext(),s,a,l,u;if(t){if(!this._filterUpToDate){var c=r.pixelRatio;i.setSize(r.width/r.pixelRatio,r.height/r.pixelRatio);try{for(s=t.length,o.clear(),o.drawImage(r._canvas,0,0,r.getWidth()/c,r.getHeight()/c),a=o.getImageData(0,0,i.getWidth(),i.getHeight()),l=0;l{var n,r;if(!t)return this;for(n in t)n!==s_e&&(r=Lk+je.Util._capitalize(n),je.Util._isFunction(this[r])?this[r](t[n]):this._setAttr(n,t[n]))}),this}isListening(){return this._getCache(lx,this._isListening)}_isListening(t){if(!this.listening())return!1;const r=this.getParent();return r&&r!==t&&this!==t?r._isListening(t):!0}isVisible(){return this._getCache(ux,this._isVisible)}_isVisible(t){if(!this.visible())return!1;const r=this.getParent();return r&&r!==t&&this!==t?r._isVisible(t):!0}shouldDrawHit(t,n=!1){if(t)return this._isVisible(t)&&this._isListening(t);var r=this.getLayer(),i=!1;ti.DD._dragElements.forEach(s=>{s.dragStatus==="dragging"&&(s.node.nodeType==="Stage"||s.node.getLayer()===r)&&(i=!0)});var o=!n&&!Ba.Konva.hitOnDragEnabled&&i;return this.isListening()&&this.isVisible()&&!o}show(){return this.visible(!0),this}hide(){return this.visible(!1),this}getZIndex(){return this.index||0}getAbsoluteZIndex(){var t=this.getDepth(),n=this,r=0,i,o,s,a;function l(u){for(i=[],o=u.length,s=0;s0&&i[0].getDepth()<=t&&l(i)}return n.nodeType!==l_e&&l(n.getStage().getChildren()),r}getDepth(){for(var t=0,n=this.parent;n;)t++,n=n.parent;return t}_batchTransformChanges(t){this._batchingTransformChange=!0,t(),this._batchingTransformChange=!1,this._needClearTransformCache&&(this._clearCache(Os),this._clearSelfAndDescendantCache(Uo)),this._needClearTransformCache=!1}setPosition(t){return this._batchTransformChanges(()=>{this.x(t.x),this.y(t.y)}),this}getPosition(){return{x:this.x(),y:this.y()}}getRelativePointerPosition(){if(!this.getStage())return null;var t=this.getStage().getPointerPosition();if(!t)return null;var n=this.getAbsoluteTransform().copy();return n.invert(),n.point(t)}getAbsolutePosition(t){let n=!1,r=this.parent;for(;r;){if(r.isCached()){n=!0;break}r=r.parent}n&&!t&&(t=!0);var i=this.getAbsoluteTransform(t).getMatrix(),o=new je.Transform,s=this.offset();return o.m=i.slice(),o.translate(s.x,s.y),o.getTranslation()}setAbsolutePosition(t){var n=this._clearTransform();this.attrs.x=n.x,this.attrs.y=n.y,delete n.x,delete n.y,this._clearCache(Os);var r=this._getAbsoluteTransform().copy();return r.invert(),r.translate(t.x,t.y),t={x:this.attrs.x+r.getTranslation().x,y:this.attrs.y+r.getTranslation().y},this._setTransform(n),this.setPosition({x:t.x,y:t.y}),this._clearCache(Os),this._clearSelfAndDescendantCache(Uo),this}_setTransform(t){var n;for(n in t)this.attrs[n]=t[n]}_clearTransform(){var t={x:this.x(),y:this.y(),rotation:this.rotation(),scaleX:this.scaleX(),scaleY:this.scaleY(),offsetX:this.offsetX(),offsetY:this.offsetY(),skewX:this.skewX(),skewY:this.skewY()};return this.attrs.x=0,this.attrs.y=0,this.attrs.rotation=0,this.attrs.scaleX=1,this.attrs.scaleY=1,this.attrs.offsetX=0,this.attrs.offsetY=0,this.attrs.skewX=0,this.attrs.skewY=0,t}move(t){var n=t.x,r=t.y,i=this.x(),o=this.y();return n!==void 0&&(i+=n),r!==void 0&&(o+=r),this.setPosition({x:i,y:o}),this}_eachAncestorReverse(t,n){var r=[],i=this.getParent(),o,s;if(!(n&&n._id===this._id)){for(r.unshift(this);i&&(!n||i._id!==n._id);)r.unshift(i),i=i.parent;for(o=r.length,s=0;s0?(this.parent.children.splice(t,1),this.parent.children.splice(t-1,0,this),this.parent._setChildrenIndices(),!0):!1}moveToBottom(){if(!this.parent)return je.Util.warn("Node has no parent. moveToBottom function is ignored."),!1;var t=this.index;return t>0?(this.parent.children.splice(t,1),this.parent.children.unshift(this),this.parent._setChildrenIndices(),!0):!1}setZIndex(t){if(!this.parent)return je.Util.warn("Node has no parent. zIndex parameter is ignored."),this;(t<0||t>=this.parent.children.length)&&je.Util.warn("Unexpected value "+t+" for zIndex property. zIndex is just index of a node in children of its parent. Expected value is from 0 to "+(this.parent.children.length-1)+".");var n=this.index;return this.parent.children.splice(n,1),this.parent.children.splice(t,0,this),this.parent._setChildrenIndices(),this}getAbsoluteOpacity(){return this._getCache(_m,this._getAbsoluteOpacity)}_getAbsoluteOpacity(){var t=this.opacity(),n=this.getParent();return n&&!n._isUnderCache&&(t*=n.getAbsoluteOpacity()),t}moveTo(t){return this.getParent()!==t&&(this._remove(),t.add(this)),this}toObject(){var t={},n=this.getAttrs(),r,i,o,s,a;t.attrs={};for(r in n)i=n[r],a=je.Util.isObject(i)&&!je.Util._isPlainObject(i)&&!je.Util._isArray(i),!a&&(o=typeof this[r]=="function"&&this[r],delete n[r],s=o?o.call(this):null,n[r]=i,s!==i&&(t.attrs[r]=i));return t.className=this.getClassName(),je.Util._prepareToStringify(t)}toJSON(){return JSON.stringify(this.toObject())}getParent(){return this.parent}findAncestors(t,n,r){var i=[];n&&this._isMatch(t)&&i.push(this);for(var o=this.parent;o;){if(o===r)return i;o._isMatch(t)&&i.push(o),o=o.parent}return i}isAncestorOf(t){return!1}findAncestor(t,n,r){return this.findAncestors(t,n,r)[0]}_isMatch(t){if(!t)return!1;if(typeof t=="function")return t(this);var n=t.replace(/ /g,"").split(","),r=n.length,i,o;for(i=0;i{try{const i=t==null?void 0:t.callback;i&&delete t.callback,je.Util._urlToImage(this.toDataURL(t),function(o){n(o),i==null||i(o)})}catch(i){r(i)}})}toBlob(t){return new Promise((n,r)=>{try{const i=t==null?void 0:t.callback;i&&delete t.callback,this.toCanvas(t).toBlob(o=>{n(o),i==null||i(o)})}catch(i){r(i)}})}setSize(t){return this.width(t.width),this.height(t.height),this}getSize(){return{width:this.width(),height:this.height()}}getClassName(){return this.className||this.nodeType}getType(){return this.nodeType}getDragDistance(){return this.attrs.dragDistance!==void 0?this.attrs.dragDistance:this.parent?this.parent.getDragDistance():Ba.Konva.dragDistance}_off(t,n,r){var i=this.eventListeners[t],o,s,a;for(o=0;o=0;if(r&&!this.isDragging()){var i=!1;ti.DD._dragElements.forEach(o=>{this.isAncestorOf(o.node)&&(i=!0)}),i||this._createDragElement(t)}})}_dragChange(){if(this.attrs.draggable)this._listenDrag();else{this._dragCleanup();var t=this.getStage();if(!t)return;const n=ti.DD._dragElements.get(this._id),r=n&&n.dragStatus==="dragging",i=n&&n.dragStatus==="ready";r?this.stopDrag():i&&ti.DD._dragElements.delete(this._id)}}_dragCleanup(){this.off("mousedown.konva"),this.off("touchstart.konva")}isClientRectOnScreen(t={x:0,y:0}){const n=this.getStage();if(!n)return!1;const r={x:-t.x,y:-t.y,width:n.width()+2*t.x,height:n.height()+2*t.y};return je.Util.haveIntersection(r,this.getClientRect())}static create(t,n){return je.Util._isString(t)&&(t=JSON.parse(t)),this._createNode(t,n)}static _createNode(t,n){var r=cx.prototype.getClassName.call(t),i=t.children,o,s,a;n&&(t.attrs.container=n),Ba.Konva[r]||(je.Util.warn('Can not find a node with class name "'+r+'". Fallback to "Shape".'),r="Shape");const l=Ba.Konva[r];if(o=new l(t.attrs),i)for(s=i.length,a=0;a0}removeChildren(){return this.getChildren().forEach(t=>{t.parent=null,t.index=0,t.remove()}),this.children=[],this._requestDraw(),this}destroyChildren(){return this.getChildren().forEach(t=>{t.parent=null,t.index=0,t.destroy()}),this.children=[],this._requestDraw(),this}add(...t){if(t.length===0)return this;if(t.length>1){for(var n=0;n0?n[0]:void 0}_generalFind(t,n){var r=[];return this._descendants(i=>{const o=i._isMatch(t);return o&&r.push(i),!!(o&&n)}),r}_descendants(t){let n=!1;const r=this.getChildren();for(const i of r){if(n=t(i),n)return!0;if(i.hasChildren()&&(n=i._descendants(t),n))return!0}return!1}toObject(){var t=b_.Node.prototype.toObject.call(this);return t.children=[],this.getChildren().forEach(n=>{t.children.push(n.toObject())}),t}isAncestorOf(t){for(var n=t.getParent();n;){if(n._id===this._id)return!0;n=n.getParent()}return!1}clone(t){var n=b_.Node.prototype.clone.call(this,t);return this.getChildren().forEach(function(r){n.add(r.clone())}),n}getAllIntersections(t){var n=[];return this.find("Shape").forEach(function(r){r.isVisible()&&r.intersects(t)&&n.push(r)}),n}_clearSelfAndDescendantCache(t){var n;super._clearSelfAndDescendantCache(t),!this.isCached()&&((n=this.children)===null||n===void 0||n.forEach(function(r){r._clearSelfAndDescendantCache(t)}))}_setChildrenIndices(){var t;(t=this.children)===null||t===void 0||t.forEach(function(n,r){n.index=r}),this._requestDraw()}drawScene(t,n){var r=this.getLayer(),i=t||r&&r.getCanvas(),o=i&&i.getContext(),s=this._getCanvasCache(),a=s&&s.scene,l=i&&i.isCache;if(!this.isVisible()&&!l)return this;if(a){o.save();var u=this.getAbsoluteTransform(n).getMatrix();o.transform(u[0],u[1],u[2],u[3],u[4],u[5]),this._drawCachedSceneCanvas(o),o.restore()}else this._drawChildren("drawScene",i,n);return this}drawHit(t,n){if(!this.shouldDrawHit(n))return this;var r=this.getLayer(),i=t||r&&r.hitCanvas,o=i&&i.getContext(),s=this._getCanvasCache(),a=s&&s.hit;if(a){o.save();var l=this.getAbsoluteTransform(n).getMatrix();o.transform(l[0],l[1],l[2],l[3],l[4],l[5]),this._drawCachedHitCanvas(o),o.restore()}else this._drawChildren("drawHit",i,n);return this}_drawChildren(t,n,r){var i,o=n&&n.getContext(),s=this.clipWidth(),a=this.clipHeight(),l=this.clipFunc(),u=s&&a||l;const c=r===this;if(u){o.save();var d=this.getAbsoluteTransform(r),f=d.getMatrix();o.transform(f[0],f[1],f[2],f[3],f[4],f[5]),o.beginPath();let S;if(l)S=l.call(this,o,this);else{var h=this.clipX(),p=this.clipY();o.rect(h,p,s,a)}o.clip.apply(o,S),f=d.copy().invert().getMatrix(),o.transform(f[0],f[1],f[2],f[3],f[4],f[5])}var m=!c&&this.globalCompositeOperation()!=="source-over"&&t==="drawScene";m&&(o.save(),o._applyGlobalCompositeOperation(this)),(i=this.children)===null||i===void 0||i.forEach(function(S){S[t](n,r)}),m&&o.restore(),u&&o.restore()}getClientRect(t){var n;t=t||{};var r=t.skipTransform,i=t.relativeTo,o,s,a,l,u={x:1/0,y:1/0,width:0,height:0},c=this;(n=this.children)===null||n===void 0||n.forEach(function(m){if(m.visible()){var S=m.getClientRect({relativeTo:c,skipShadow:t.skipShadow,skipStroke:t.skipStroke});S.width===0&&S.height===0||(o===void 0?(o=S.x,s=S.y,a=S.x+S.width,l=S.y+S.height):(o=Math.min(o,S.x),s=Math.min(s,S.y),a=Math.max(a,S.x+S.width),l=Math.max(l,S.y+S.height)))}});for(var d=this.find("Shape"),f=!1,h=0;hX.indexOf("pointer")>=0?"pointer":X.indexOf("touch")>=0?"touch":"mouse",z=X=>{const F=V(X);if(F==="pointer")return i.Konva.pointerEventsEnabled&&L.pointer;if(F==="touch")return L.touch;if(F==="mouse")return L.mouse};function U(X={}){return(X.clipFunc||X.clipWidth||X.clipHeight)&&t.Util.warn("Stage does not support clipping. Please use clip for Layers or Groups."),X}const K="Pointer position is missing and not registered by the stage. Looks like it is outside of the stage container. You can set it manually from event: stage.setPointersPositions(event);";e.stages=[];class W extends r.Container{constructor(F){super(U(F)),this._pointerPositions=[],this._changedPointerPositions=[],this._buildDOM(),this._bindContentEvents(),e.stages.push(this),this.on("widthChange.konva heightChange.konva",this._resizeDOM),this.on("visibleChange.konva",this._checkVisibility),this.on("clipWidthChange.konva clipHeightChange.konva clipFuncChange.konva",()=>{U(this.attrs)}),this._checkVisibility()}_validateAdd(F){const q=F.getType()==="Layer",Q=F.getType()==="FastLayer";q||Q||t.Util.throw("You may only add layers to the stage.")}_checkVisibility(){if(!this.content)return;const F=this.visible()?"":"none";this.content.style.display=F}setContainer(F){if(typeof F===c){if(F.charAt(0)==="."){var q=F.slice(1);F=document.getElementsByClassName(q)[0]}else{var Q;F.charAt(0)!=="#"?Q=F:Q=F.slice(1),F=document.getElementById(Q)}if(!F)throw"Can not find container in document with id "+Q}return this._setAttr("container",F),this.content&&(this.content.parentElement&&this.content.parentElement.removeChild(this.content),F.appendChild(this.content)),this}shouldDrawHit(){return!0}clear(){var F=this.children,q=F.length,Q;for(Q=0;Q-1&&e.stages.splice(q,1),t.Util.releaseCanvas(this.bufferCanvas._canvas,this.bufferHitCanvas._canvas),this}getPointerPosition(){const F=this._pointerPositions[0]||this._changedPointerPositions[0];return F?{x:F.x,y:F.y}:(t.Util.warn(K),null)}_getPointerById(F){return this._pointerPositions.find(q=>q.id===F)}getPointersPositions(){return this._pointerPositions}getStage(){return this}getContent(){return this.content}_toKonvaCanvas(F){F=F||{},F.x=F.x||0,F.y=F.y||0,F.width=F.width||this.width(),F.height=F.height||this.height();var q=new o.SceneCanvas({width:F.width,height:F.height,pixelRatio:F.pixelRatio||1}),Q=q.getContext()._context,J=this.children;return(F.x||F.y)&&Q.translate(-1*F.x,-1*F.y),J.forEach(function(ne){if(ne.isVisible()){var Z=ne._toKonvaCanvas(F);Q.drawImage(Z._canvas,F.x,F.y,Z.getWidth()/Z.getPixelRatio(),Z.getHeight()/Z.getPixelRatio())}}),q}getIntersection(F){if(!F)return null;var q=this.children,Q=q.length,J=Q-1,ne;for(ne=J;ne>=0;ne--){const Z=q[ne].getIntersection(F);if(Z)return Z}return null}_resizeDOM(){var F=this.width(),q=this.height();this.content&&(this.content.style.width=F+d,this.content.style.height=q+d),this.bufferCanvas.setSize(F,q),this.bufferHitCanvas.setSize(F,q),this.children.forEach(Q=>{Q.setSize({width:F,height:q}),Q.draw()})}add(F,...q){if(arguments.length>1){for(var Q=0;QR&&t.Util.warn("The stage has "+J+" layers. Recommended maximum number of layers is 3-5. Adding more layers into the stage may drop the performance. Rethink your tree structure, you can use Konva.Group."),F.setSize({width:this.width(),height:this.height()}),F.draw(),i.Konva.isBrowser&&this.content.appendChild(F.canvas._canvas),this}getParent(){return null}getLayer(){return null}hasPointerCapture(F){return l.hasPointerCapture(F,this)}setPointerCapture(F){l.setPointerCapture(F,this)}releaseCapture(F){l.releaseCapture(F,this)}getLayers(){return this.children}_bindContentEvents(){i.Konva.isBrowser&&D.forEach(([F,q])=>{this.content.addEventListener(F,Q=>{this[q](Q)},{passive:!1})})}_pointerenter(F){this.setPointersPositions(F);const q=z(F.type);this._fire(q.pointerenter,{evt:F,target:this,currentTarget:this})}_pointerover(F){this.setPointersPositions(F);const q=z(F.type);this._fire(q.pointerover,{evt:F,target:this,currentTarget:this})}_getTargetShape(F){let q=this[F+"targetShape"];return q&&!q.getStage()&&(q=null),q}_pointerleave(F){const q=z(F.type),Q=V(F.type);if(q){this.setPointersPositions(F);var J=this._getTargetShape(Q),ne=!s.DD.isDragging||i.Konva.hitOnDragEnabled;J&&ne?(J._fireAndBubble(q.pointerout,{evt:F}),J._fireAndBubble(q.pointerleave,{evt:F}),this._fire(q.pointerleave,{evt:F,target:this,currentTarget:this}),this[Q+"targetShape"]=null):ne&&(this._fire(q.pointerleave,{evt:F,target:this,currentTarget:this}),this._fire(q.pointerout,{evt:F,target:this,currentTarget:this})),this.pointerPos=void 0,this._pointerPositions=[]}}_pointerdown(F){const q=z(F.type),Q=V(F.type);if(q){this.setPointersPositions(F);var J=!1;this._changedPointerPositions.forEach(ne=>{var Z=this.getIntersection(ne);if(s.DD.justDragged=!1,i.Konva["_"+Q+"ListenClick"]=!0,!(Z&&Z.isListening()))return;i.Konva.capturePointerEventsEnabled&&Z.setPointerCapture(ne.id),this[Q+"ClickStartShape"]=Z,Z._fireAndBubble(q.pointerdown,{evt:F,pointerId:ne.id}),J=!0;const ae=F.type.indexOf("touch")>=0;Z.preventDefault()&&F.cancelable&&ae&&F.preventDefault()}),J||this._fire(q.pointerdown,{evt:F,target:this,currentTarget:this,pointerId:this._pointerPositions[0].id})}}_pointermove(F){const q=z(F.type),Q=V(F.type);if(!q)return;s.DD.isDragging&&s.DD.node.preventDefault()&&F.cancelable&&F.preventDefault(),this.setPointersPositions(F);var J=!s.DD.isDragging||i.Konva.hitOnDragEnabled;if(!J)return;var ne={};let Z=!1;var ge=this._getTargetShape(Q);this._changedPointerPositions.forEach(ae=>{const oe=l.getCapturedShape(ae.id)||this.getIntersection(ae),Te=ae.id,se={evt:F,pointerId:Te};var We=ge!==oe;if(We&&ge&&(ge._fireAndBubble(q.pointerout,Object.assign({},se),oe),ge._fireAndBubble(q.pointerleave,Object.assign({},se),oe)),oe){if(ne[oe._id])return;ne[oe._id]=!0}oe&&oe.isListening()?(Z=!0,We&&(oe._fireAndBubble(q.pointerover,Object.assign({},se),ge),oe._fireAndBubble(q.pointerenter,Object.assign({},se),ge),this[Q+"targetShape"]=oe),oe._fireAndBubble(q.pointermove,Object.assign({},se))):ge&&(this._fire(q.pointerover,{evt:F,target:this,currentTarget:this,pointerId:Te}),this[Q+"targetShape"]=null)}),Z||this._fire(q.pointermove,{evt:F,target:this,currentTarget:this,pointerId:this._changedPointerPositions[0].id})}_pointerup(F){const q=z(F.type),Q=V(F.type);if(!q)return;this.setPointersPositions(F);const J=this[Q+"ClickStartShape"],ne=this[Q+"ClickEndShape"];var Z={};let ge=!1;this._changedPointerPositions.forEach(ae=>{const oe=l.getCapturedShape(ae.id)||this.getIntersection(ae);if(oe){if(oe.releaseCapture(ae.id),Z[oe._id])return;Z[oe._id]=!0}const Te=ae.id,se={evt:F,pointerId:Te};let We=!1;i.Konva["_"+Q+"InDblClickWindow"]?(We=!0,clearTimeout(this[Q+"DblTimeout"])):s.DD.justDragged||(i.Konva["_"+Q+"InDblClickWindow"]=!0,clearTimeout(this[Q+"DblTimeout"])),this[Q+"DblTimeout"]=setTimeout(function(){i.Konva["_"+Q+"InDblClickWindow"]=!1},i.Konva.dblClickWindow),oe&&oe.isListening()?(ge=!0,this[Q+"ClickEndShape"]=oe,oe._fireAndBubble(q.pointerup,Object.assign({},se)),i.Konva["_"+Q+"ListenClick"]&&J&&J===oe&&(oe._fireAndBubble(q.pointerclick,Object.assign({},se)),We&&ne&&ne===oe&&oe._fireAndBubble(q.pointerdblclick,Object.assign({},se)))):(this[Q+"ClickEndShape"]=null,i.Konva["_"+Q+"ListenClick"]&&this._fire(q.pointerclick,{evt:F,target:this,currentTarget:this,pointerId:Te}),We&&this._fire(q.pointerdblclick,{evt:F,target:this,currentTarget:this,pointerId:Te}))}),ge||this._fire(q.pointerup,{evt:F,target:this,currentTarget:this,pointerId:this._changedPointerPositions[0].id}),i.Konva["_"+Q+"ListenClick"]=!1,F.cancelable&&Q!=="touch"&&F.preventDefault()}_contextmenu(F){this.setPointersPositions(F);var q=this.getIntersection(this.getPointerPosition());q&&q.isListening()?q._fireAndBubble($,{evt:F}):this._fire($,{evt:F,target:this,currentTarget:this})}_wheel(F){this.setPointersPositions(F);var q=this.getIntersection(this.getPointerPosition());q&&q.isListening()?q._fireAndBubble(M,{evt:F}):this._fire(M,{evt:F,target:this,currentTarget:this})}_pointercancel(F){this.setPointersPositions(F);const q=l.getCapturedShape(F.pointerId)||this.getIntersection(this.getPointerPosition());q&&q._fireAndBubble(_,l.createEvent(F)),l.releaseCapture(F.pointerId)}_lostpointercapture(F){l.releaseCapture(F.pointerId)}setPointersPositions(F){var q=this._getContentPosition(),Q=null,J=null;F=F||window.event,F.touches!==void 0?(this._pointerPositions=[],this._changedPointerPositions=[],Array.prototype.forEach.call(F.touches,ne=>{this._pointerPositions.push({id:ne.identifier,x:(ne.clientX-q.left)/q.scaleX,y:(ne.clientY-q.top)/q.scaleY})}),Array.prototype.forEach.call(F.changedTouches||F.touches,ne=>{this._changedPointerPositions.push({id:ne.identifier,x:(ne.clientX-q.left)/q.scaleX,y:(ne.clientY-q.top)/q.scaleY})})):(Q=(F.clientX-q.left)/q.scaleX,J=(F.clientY-q.top)/q.scaleY,this.pointerPos={x:Q,y:J},this._pointerPositions=[{x:Q,y:J,id:t.Util._getFirstPointerId(F)}],this._changedPointerPositions=[{x:Q,y:J,id:t.Util._getFirstPointerId(F)}])}_setPointerPosition(F){t.Util.warn('Method _setPointerPosition is deprecated. Use "stage.setPointersPositions(event)" instead.'),this.setPointersPositions(F)}_getContentPosition(){if(!this.content||!this.content.getBoundingClientRect)return{top:0,left:0,scaleX:1,scaleY:1};var F=this.content.getBoundingClientRect();return{top:F.top,left:F.left,scaleX:F.width/this.content.clientWidth||1,scaleY:F.height/this.content.clientHeight||1}}_buildDOM(){if(this.bufferCanvas=new o.SceneCanvas({width:this.width(),height:this.height()}),this.bufferHitCanvas=new o.HitCanvas({pixelRatio:1,width:this.width(),height:this.height()}),!!i.Konva.isBrowser){var F=this.container();if(!F)throw"Stage has no container. A container is required.";F.innerHTML="",this.content=document.createElement("div"),this.content.style.position="relative",this.content.style.userSelect="none",this.content.className="konvajs-content",this.content.setAttribute("role","presentation"),F.appendChild(this.content),this._resizeDOM()}}cache(){return t.Util.warn("Cache function is not allowed for stage. You may use cache only for layers, groups and shapes."),this}clearCache(){return this}batchDraw(){return this.getChildren().forEach(function(F){F.batchDraw()}),this}}e.Stage=W,W.prototype.nodeType=u,(0,a._registerNode)(W),n.Factory.addGetterSetter(W,"container")})(WF);var xp={},pn={};(function(e){Object.defineProperty(e,"__esModule",{value:!0}),e.Shape=e.shapes=void 0;const t=Le,n=Bt,r=Ie,i=At,o=fe,s=Le,a=Br;var l="hasShadow",u="shadowRGBA",c="patternImage",d="linearGradient",f="radialGradient";let h;function p(){return h||(h=n.Util.createCanvasElement().getContext("2d"),h)}e.shapes={};function m(k){const P=this.attrs.fillRule;P?k.fill(P):k.fill()}function S(k){k.stroke()}function v(k){k.fill()}function y(k){k.stroke()}function g(){this._clearCache(l)}function b(){this._clearCache(u)}function _(){this._clearCache(c)}function w(){this._clearCache(d)}function x(){this._clearCache(f)}class C extends i.Node{constructor(P){super(P);let A;for(;A=n.Util.getRandomColor(),!(A&&!(A in e.shapes)););this.colorKey=A,e.shapes[A]=this}getContext(){return n.Util.warn("shape.getContext() method is deprecated. Please do not use it."),this.getLayer().getContext()}getCanvas(){return n.Util.warn("shape.getCanvas() method is deprecated. Please do not use it."),this.getLayer().getCanvas()}getSceneFunc(){return this.attrs.sceneFunc||this._sceneFunc}getHitFunc(){return this.attrs.hitFunc||this._hitFunc}hasShadow(){return this._getCache(l,this._hasShadow)}_hasShadow(){return this.shadowEnabled()&&this.shadowOpacity()!==0&&!!(this.shadowColor()||this.shadowBlur()||this.shadowOffsetX()||this.shadowOffsetY())}_getFillPattern(){return this._getCache(c,this.__getFillPattern)}__getFillPattern(){if(this.fillPatternImage()){var P=p();const A=P.createPattern(this.fillPatternImage(),this.fillPatternRepeat()||"repeat");if(A&&A.setTransform){const $=new n.Transform;$.translate(this.fillPatternX(),this.fillPatternY()),$.rotate(t.Konva.getAngle(this.fillPatternRotation())),$.scale(this.fillPatternScaleX(),this.fillPatternScaleY()),$.translate(-1*this.fillPatternOffsetX(),-1*this.fillPatternOffsetY());const N=$.getMatrix(),T=typeof DOMMatrix>"u"?{a:N[0],b:N[1],c:N[2],d:N[3],e:N[4],f:N[5]}:new DOMMatrix(N);A.setTransform(T)}return A}}_getLinearGradient(){return this._getCache(d,this.__getLinearGradient)}__getLinearGradient(){var P=this.fillLinearGradientColorStops();if(P){for(var A=p(),$=this.fillLinearGradientStartPoint(),N=this.fillLinearGradientEndPoint(),T=A.createLinearGradient($.x,$.y,N.x,N.y),O=0;Othis.fillEnabled()&&!!(this.fill()||this.fillPatternImage()||this.fillLinearGradientColorStops()||this.fillRadialGradientColorStops()))}hasStroke(){return this._calculate("hasStroke",["strokeEnabled","strokeWidth","stroke","strokeLinearGradientColorStops"],()=>this.strokeEnabled()&&this.strokeWidth()&&!!(this.stroke()||this.strokeLinearGradientColorStops()))}hasHitStroke(){const P=this.hitStrokeWidth();return P==="auto"?this.hasStroke():this.strokeEnabled()&&!!P}intersects(P){var A=this.getStage(),$=A.bufferHitCanvas,N;return $.getContext().clear(),this.drawHit($,null,!0),N=$.context.getImageData(Math.round(P.x),Math.round(P.y),1,1).data,N[3]>0}destroy(){return i.Node.prototype.destroy.call(this),delete e.shapes[this.colorKey],delete this.colorKey,this}_useBufferCanvas(P){var A;if(!this.getStage()||!((A=this.attrs.perfectDrawEnabled)!==null&&A!==void 0?A:!0))return!1;const N=P||this.hasFill(),T=this.hasStroke(),O=this.getAbsoluteOpacity()!==1;if(N&&T&&O)return!0;const I=this.hasShadow(),M=this.shadowForStrokeEnabled();return!!(N&&T&&I&&M)}setStrokeHitEnabled(P){n.Util.warn("strokeHitEnabled property is deprecated. Please use hitStrokeWidth instead."),P?this.hitStrokeWidth("auto"):this.hitStrokeWidth(0)}getStrokeHitEnabled(){return this.hitStrokeWidth()!==0}getSelfRect(){var P=this.size();return{x:this._centroid?-P.width/2:0,y:this._centroid?-P.height/2:0,width:P.width,height:P.height}}getClientRect(P={}){const A=P.skipTransform,$=P.relativeTo,N=this.getSelfRect(),O=!P.skipStroke&&this.hasStroke()&&this.strokeWidth()||0,I=N.width+O,M=N.height+O,R=!P.skipShadow&&this.hasShadow(),D=R?this.shadowOffsetX():0,L=R?this.shadowOffsetY():0,V=I+Math.abs(D),z=M+Math.abs(L),U=R&&this.shadowBlur()||0,K=V+U*2,W=z+U*2,X={width:K,height:W,x:-(O/2+U)+Math.min(D,0)+N.x,y:-(O/2+U)+Math.min(L,0)+N.y};return A?X:this._transformedRect(X,$)}drawScene(P,A){var $=this.getLayer(),N=P||$.getCanvas(),T=N.getContext(),O=this._getCanvasCache(),I=this.getSceneFunc(),M=this.hasShadow(),R,D,L,V=N.isCache,z=A===this;if(!this.isVisible()&&!z)return this;if(O){T.save();var U=this.getAbsoluteTransform(A).getMatrix();return T.transform(U[0],U[1],U[2],U[3],U[4],U[5]),this._drawCachedSceneCanvas(T),T.restore(),this}if(!I)return this;if(T.save(),this._useBufferCanvas()&&!V){R=this.getStage(),D=R.bufferCanvas,L=D.getContext(),L.clear(),L.save(),L._applyLineJoin(this);var K=this.getAbsoluteTransform(A).getMatrix();L.transform(K[0],K[1],K[2],K[3],K[4],K[5]),I.call(this,L,this),L.restore();var W=D.pixelRatio;M&&T._applyShadow(this),T._applyOpacity(this),T._applyGlobalCompositeOperation(this),T.drawImage(D._canvas,0,0,D.width/W,D.height/W)}else{if(T._applyLineJoin(this),!z){var K=this.getAbsoluteTransform(A).getMatrix();T.transform(K[0],K[1],K[2],K[3],K[4],K[5]),T._applyOpacity(this),T._applyGlobalCompositeOperation(this)}M&&T._applyShadow(this),I.call(this,T,this)}return T.restore(),this}drawHit(P,A,$=!1){if(!this.shouldDrawHit(A,$))return this;var N=this.getLayer(),T=P||N.hitCanvas,O=T&&T.getContext(),I=this.hitFunc()||this.sceneFunc(),M=this._getCanvasCache(),R=M&&M.hit;if(this.colorKey||n.Util.warn("Looks like your canvas has a destroyed shape in it. Do not reuse shape after you destroyed it. If you want to reuse shape you should call remove() instead of destroy()"),R){O.save();var D=this.getAbsoluteTransform(A).getMatrix();return O.transform(D[0],D[1],D[2],D[3],D[4],D[5]),this._drawCachedHitCanvas(O),O.restore(),this}if(!I)return this;if(O.save(),O._applyLineJoin(this),!(this===A)){var V=this.getAbsoluteTransform(A).getMatrix();O.transform(V[0],V[1],V[2],V[3],V[4],V[5])}return I.call(this,O,this),O.restore(),this}drawHitFromCache(P=0){var A=this._getCanvasCache(),$=this._getCachedSceneCanvas(),N=A.hit,T=N.getContext(),O=N.getWidth(),I=N.getHeight(),M,R,D,L,V,z;T.clear(),T.drawImage($._canvas,0,0,O,I);try{for(M=T.getImageData(0,0,O,I),R=M.data,D=R.length,L=n.Util._hexToRgb(this.colorKey),V=0;VP?(R[V]=L.r,R[V+1]=L.g,R[V+2]=L.b,R[V+3]=255):R[V+3]=0;T.putImageData(M,0,0)}catch(U){n.Util.error("Unable to draw hit graph from cached scene canvas. "+U.message)}return this}hasPointerCapture(P){return a.hasPointerCapture(P,this)}setPointerCapture(P){a.setPointerCapture(P,this)}releaseCapture(P){a.releaseCapture(P,this)}}e.Shape=C,C.prototype._fillFunc=m,C.prototype._strokeFunc=S,C.prototype._fillFuncHit=v,C.prototype._strokeFuncHit=y,C.prototype._centroid=!1,C.prototype.nodeType="Shape",(0,s._registerNode)(C),C.prototype.eventListeners={},C.prototype.on.call(C.prototype,"shadowColorChange.konva shadowBlurChange.konva shadowOffsetChange.konva shadowOpacityChange.konva shadowEnabledChange.konva",g),C.prototype.on.call(C.prototype,"shadowColorChange.konva shadowOpacityChange.konva shadowEnabledChange.konva",b),C.prototype.on.call(C.prototype,"fillPriorityChange.konva fillPatternImageChange.konva fillPatternRepeatChange.konva fillPatternScaleXChange.konva fillPatternScaleYChange.konva fillPatternOffsetXChange.konva fillPatternOffsetYChange.konva fillPatternXChange.konva fillPatternYChange.konva fillPatternRotationChange.konva",_),C.prototype.on.call(C.prototype,"fillPriorityChange.konva fillLinearGradientColorStopsChange.konva fillLinearGradientStartPointXChange.konva fillLinearGradientStartPointYChange.konva fillLinearGradientEndPointXChange.konva fillLinearGradientEndPointYChange.konva",w),C.prototype.on.call(C.prototype,"fillPriorityChange.konva fillRadialGradientColorStopsChange.konva fillRadialGradientStartPointXChange.konva fillRadialGradientStartPointYChange.konva fillRadialGradientEndPointXChange.konva fillRadialGradientEndPointYChange.konva fillRadialGradientStartRadiusChange.konva fillRadialGradientEndRadiusChange.konva",x),r.Factory.addGetterSetter(C,"stroke",void 0,(0,o.getStringOrGradientValidator)()),r.Factory.addGetterSetter(C,"strokeWidth",2,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(C,"fillAfterStrokeEnabled",!1),r.Factory.addGetterSetter(C,"hitStrokeWidth","auto",(0,o.getNumberOrAutoValidator)()),r.Factory.addGetterSetter(C,"strokeHitEnabled",!0,(0,o.getBooleanValidator)()),r.Factory.addGetterSetter(C,"perfectDrawEnabled",!0,(0,o.getBooleanValidator)()),r.Factory.addGetterSetter(C,"shadowForStrokeEnabled",!0,(0,o.getBooleanValidator)()),r.Factory.addGetterSetter(C,"lineJoin"),r.Factory.addGetterSetter(C,"lineCap"),r.Factory.addGetterSetter(C,"sceneFunc"),r.Factory.addGetterSetter(C,"hitFunc"),r.Factory.addGetterSetter(C,"dash"),r.Factory.addGetterSetter(C,"dashOffset",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(C,"shadowColor",void 0,(0,o.getStringValidator)()),r.Factory.addGetterSetter(C,"shadowBlur",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(C,"shadowOpacity",1,(0,o.getNumberValidator)()),r.Factory.addComponentsGetterSetter(C,"shadowOffset",["x","y"]),r.Factory.addGetterSetter(C,"shadowOffsetX",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(C,"shadowOffsetY",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(C,"fillPatternImage"),r.Factory.addGetterSetter(C,"fill",void 0,(0,o.getStringOrGradientValidator)()),r.Factory.addGetterSetter(C,"fillPatternX",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(C,"fillPatternY",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(C,"fillLinearGradientColorStops"),r.Factory.addGetterSetter(C,"strokeLinearGradientColorStops"),r.Factory.addGetterSetter(C,"fillRadialGradientStartRadius",0),r.Factory.addGetterSetter(C,"fillRadialGradientEndRadius",0),r.Factory.addGetterSetter(C,"fillRadialGradientColorStops"),r.Factory.addGetterSetter(C,"fillPatternRepeat","repeat"),r.Factory.addGetterSetter(C,"fillEnabled",!0),r.Factory.addGetterSetter(C,"strokeEnabled",!0),r.Factory.addGetterSetter(C,"shadowEnabled",!0),r.Factory.addGetterSetter(C,"dashEnabled",!0),r.Factory.addGetterSetter(C,"strokeScaleEnabled",!0),r.Factory.addGetterSetter(C,"fillPriority","color"),r.Factory.addComponentsGetterSetter(C,"fillPatternOffset",["x","y"]),r.Factory.addGetterSetter(C,"fillPatternOffsetX",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(C,"fillPatternOffsetY",0,(0,o.getNumberValidator)()),r.Factory.addComponentsGetterSetter(C,"fillPatternScale",["x","y"]),r.Factory.addGetterSetter(C,"fillPatternScaleX",1,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(C,"fillPatternScaleY",1,(0,o.getNumberValidator)()),r.Factory.addComponentsGetterSetter(C,"fillLinearGradientStartPoint",["x","y"]),r.Factory.addComponentsGetterSetter(C,"strokeLinearGradientStartPoint",["x","y"]),r.Factory.addGetterSetter(C,"fillLinearGradientStartPointX",0),r.Factory.addGetterSetter(C,"strokeLinearGradientStartPointX",0),r.Factory.addGetterSetter(C,"fillLinearGradientStartPointY",0),r.Factory.addGetterSetter(C,"strokeLinearGradientStartPointY",0),r.Factory.addComponentsGetterSetter(C,"fillLinearGradientEndPoint",["x","y"]),r.Factory.addComponentsGetterSetter(C,"strokeLinearGradientEndPoint",["x","y"]),r.Factory.addGetterSetter(C,"fillLinearGradientEndPointX",0),r.Factory.addGetterSetter(C,"strokeLinearGradientEndPointX",0),r.Factory.addGetterSetter(C,"fillLinearGradientEndPointY",0),r.Factory.addGetterSetter(C,"strokeLinearGradientEndPointY",0),r.Factory.addComponentsGetterSetter(C,"fillRadialGradientStartPoint",["x","y"]),r.Factory.addGetterSetter(C,"fillRadialGradientStartPointX",0),r.Factory.addGetterSetter(C,"fillRadialGradientStartPointY",0),r.Factory.addComponentsGetterSetter(C,"fillRadialGradientEndPoint",["x","y"]),r.Factory.addGetterSetter(C,"fillRadialGradientEndPointX",0),r.Factory.addGetterSetter(C,"fillRadialGradientEndPointY",0),r.Factory.addGetterSetter(C,"fillPatternRotation",0),r.Factory.addGetterSetter(C,"fillRule",void 0,(0,o.getStringValidator)()),r.Factory.backCompat(C,{dashArray:"dash",getDashArray:"getDash",setDashArray:"getDash",drawFunc:"sceneFunc",getDrawFunc:"getSceneFunc",setDrawFunc:"setSceneFunc",drawHitFunc:"hitFunc",getDrawHitFunc:"getHitFunc",setDrawHitFunc:"setHitFunc"})})(pn);Object.defineProperty(xp,"__esModule",{value:!0});xp.Layer=void 0;const jo=Bt,S_=$l,au=At,N3=Ie,Bk=Bi,g_e=fe,m_e=pn,y_e=Le;var v_e="#",b_e="beforeDraw",S_e="draw",XF=[{x:0,y:0},{x:-1,y:-1},{x:1,y:-1},{x:1,y:1},{x:-1,y:1}],__e=XF.length;class Xc extends S_.Container{constructor(t){super(t),this.canvas=new Bk.SceneCanvas,this.hitCanvas=new Bk.HitCanvas({pixelRatio:1}),this._waitingForDraw=!1,this.on("visibleChange.konva",this._checkVisibility),this._checkVisibility(),this.on("imageSmoothingEnabledChange.konva",this._setSmoothEnabled),this._setSmoothEnabled()}createPNGStream(){return this.canvas._canvas.createPNGStream()}getCanvas(){return this.canvas}getNativeCanvasElement(){return this.canvas._canvas}getHitCanvas(){return this.hitCanvas}getContext(){return this.getCanvas().getContext()}clear(t){return this.getContext().clear(t),this.getHitCanvas().getContext().clear(t),this}setZIndex(t){super.setZIndex(t);var n=this.getStage();return n&&n.content&&(n.content.removeChild(this.getNativeCanvasElement()),t{this.draw(),this._waitingForDraw=!1})),this}getIntersection(t){if(!this.isListening()||!this.isVisible())return null;for(var n=1,r=!1;;){for(let i=0;i<__e;i++){const o=XF[i],s=this._getIntersection({x:t.x+o.x*n,y:t.y+o.y*n}),a=s.shape;if(a)return a;if(r=!!s.antialiased,!s.antialiased)break}if(r)n+=1;else return null}}_getIntersection(t){const n=this.hitCanvas.pixelRatio,r=this.hitCanvas.context.getImageData(Math.round(t.x*n),Math.round(t.y*n),1,1).data,i=r[3];if(i===255){const o=jo.Util._rgbToHex(r[0],r[1],r[2]),s=m_e.shapes[v_e+o];return s?{shape:s}:{antialiased:!0}}else if(i>0)return{antialiased:!0};return{}}drawScene(t,n){var r=this.getLayer(),i=t||r&&r.getCanvas();return this._fire(b_e,{node:this}),this.clearBeforeDraw()&&i.getContext().clear(),S_.Container.prototype.drawScene.call(this,i,n),this._fire(S_e,{node:this}),this}drawHit(t,n){var r=this.getLayer(),i=t||r&&r.hitCanvas;return r&&r.clearBeforeDraw()&&r.getHitCanvas().getContext().clear(),S_.Container.prototype.drawHit.call(this,i,n),this}enableHitGraph(){return this.hitGraphEnabled(!0),this}disableHitGraph(){return this.hitGraphEnabled(!1),this}setHitGraphEnabled(t){jo.Util.warn("hitGraphEnabled method is deprecated. Please use layer.listening() instead."),this.listening(t)}getHitGraphEnabled(t){return jo.Util.warn("hitGraphEnabled method is deprecated. Please use layer.listening() instead."),this.listening()}toggleHitCanvas(){if(!(!this.parent||!this.parent.content)){var t=this.parent,n=!!this.hitCanvas._canvas.parentNode;n?t.content.removeChild(this.hitCanvas._canvas):t.content.appendChild(this.hitCanvas._canvas)}}destroy(){return jo.Util.releaseCanvas(this.getNativeCanvasElement(),this.getHitCanvas()._canvas),super.destroy()}}xp.Layer=Xc;Xc.prototype.nodeType="Layer";(0,y_e._registerNode)(Xc);N3.Factory.addGetterSetter(Xc,"imageSmoothingEnabled",!0);N3.Factory.addGetterSetter(Xc,"clearBeforeDraw",!0);N3.Factory.addGetterSetter(Xc,"hitGraphEnabled",!0,(0,g_e.getBooleanValidator)());var T1={};Object.defineProperty(T1,"__esModule",{value:!0});T1.FastLayer=void 0;const w_e=Bt,x_e=xp,C_e=Le;class D3 extends x_e.Layer{constructor(t){super(t),this.listening(!1),w_e.Util.warn('Konva.Fast layer is deprecated. Please use "new Konva.Layer({ listening: false })" instead.')}}T1.FastLayer=D3;D3.prototype.nodeType="FastLayer";(0,C_e._registerNode)(D3);var Qc={};Object.defineProperty(Qc,"__esModule",{value:!0});Qc.Group=void 0;const T_e=Bt,E_e=$l,P_e=Le;class L3 extends E_e.Container{_validateAdd(t){var n=t.getType();n!=="Group"&&n!=="Shape"&&T_e.Util.throw("You may only add groups and shapes to groups.")}}Qc.Group=L3;L3.prototype.nodeType="Group";(0,P_e._registerNode)(L3);var Zc={};Object.defineProperty(Zc,"__esModule",{value:!0});Zc.Animation=void 0;const __=Le,jk=Bt;var w_=function(){return __.glob.performance&&__.glob.performance.now?function(){return __.glob.performance.now()}:function(){return new Date().getTime()}}();class ao{constructor(t,n){this.id=ao.animIdCounter++,this.frame={time:0,timeDiff:0,lastTime:w_(),frameRate:0},this.func=t,this.setLayers(n)}setLayers(t){var n=[];return t?t.length>0?n=t:n=[t]:n=[],this.layers=n,this}getLayers(){return this.layers}addLayer(t){var n=this.layers,r=n.length,i;for(i=0;ithis.duration?this.yoyo?(this._time=this.duration,this.reverse()):this.finish():p<0?this.yoyo?(this._time=0,this.play()):this.reset():(this._time=p,this.update())}getTime(){return this._time}setPosition(p){this.prevPos=this._pos,this.propFunc(p),this._pos=p}getPosition(p){return p===void 0&&(p=this._time),this.func(p,this.begin,this._change,this.duration)}play(){this.state=a,this._startTime=this.getTimer()-this._time,this.onEnterFrame(),this.fire("onPlay")}reverse(){this.state=l,this._time=this.duration-this._time,this._startTime=this.getTimer()-this._time,this.onEnterFrame(),this.fire("onReverse")}seek(p){this.pause(),this._time=p,this.update(),this.fire("onSeek")}reset(){this.pause(),this._time=0,this.update(),this.fire("onReset")}finish(){this.pause(),this._time=this.duration,this.update(),this.fire("onFinish")}update(){this.setPosition(this.getPosition(this._time)),this.fire("onUpdate")}onEnterFrame(){var p=this.getTimer()-this._startTime;this.state===a?this.setTime(p):this.state===l&&this.setTime(this.duration-p)}pause(){this.state=s,this.fire("onPause")}getTimer(){return new Date().getTime()}}class f{constructor(p){var m=this,S=p.node,v=S._id,y,g=p.easing||e.Easings.Linear,b=!!p.yoyo,_;typeof p.duration>"u"?y=.3:p.duration===0?y=.001:y=p.duration,this.node=S,this._id=u++;var w=S.getLayer()||(S instanceof i.Konva.Stage?S.getLayers():null);w||t.Util.error("Tween constructor have `node` that is not in a layer. Please add node into layer first."),this.anim=new n.Animation(function(){m.tween.onEnterFrame()},w),this.tween=new d(_,function(x){m._tweenFunc(x)},g,0,1,y*1e3,b),this._addListeners(),f.attrs[v]||(f.attrs[v]={}),f.attrs[v][this._id]||(f.attrs[v][this._id]={}),f.tweens[v]||(f.tweens[v]={});for(_ in p)o[_]===void 0&&this._addAttr(_,p[_]);this.reset(),this.onFinish=p.onFinish,this.onReset=p.onReset,this.onUpdate=p.onUpdate}_addAttr(p,m){var S=this.node,v=S._id,y,g,b,_,w,x,C,k;if(b=f.tweens[v][p],b&&delete f.attrs[v][b][p],y=S.getAttr(p),t.Util._isArray(m))if(g=[],w=Math.max(m.length,y.length),p==="points"&&m.length!==y.length&&(m.length>y.length?(C=y,y=t.Util._prepareArrayForTween(y,m,S.closed())):(x=m,m=t.Util._prepareArrayForTween(m,y,S.closed()))),p.indexOf("fill")===0)for(_=0;_{this.anim.start()},this.tween.onReverse=()=>{this.anim.start()},this.tween.onPause=()=>{this.anim.stop()},this.tween.onFinish=()=>{var p=this.node,m=f.attrs[p._id][this._id];m.points&&m.points.trueEnd&&p.setAttr("points",m.points.trueEnd),this.onFinish&&this.onFinish.call(this)},this.tween.onReset=()=>{var p=this.node,m=f.attrs[p._id][this._id];m.points&&m.points.trueStart&&p.points(m.points.trueStart),this.onReset&&this.onReset()},this.tween.onUpdate=()=>{this.onUpdate&&this.onUpdate.call(this)}}play(){return this.tween.play(),this}reverse(){return this.tween.reverse(),this}reset(){return this.tween.reset(),this}seek(p){return this.tween.seek(p*1e3),this}pause(){return this.tween.pause(),this}finish(){return this.tween.finish(),this}destroy(){var p=this.node._id,m=this._id,S=f.tweens[p],v;this.pause();for(v in S)delete f.tweens[p][v];delete f.attrs[p][m]}}e.Tween=f,f.attrs={},f.tweens={},r.Node.prototype.to=function(h){var p=h.onFinish;h.node=this,h.onFinish=function(){this.destroy(),p&&p()};var m=new f(h);m.play()},e.Easings={BackEaseIn(h,p,m,S){var v=1.70158;return m*(h/=S)*h*((v+1)*h-v)+p},BackEaseOut(h,p,m,S){var v=1.70158;return m*((h=h/S-1)*h*((v+1)*h+v)+1)+p},BackEaseInOut(h,p,m,S){var v=1.70158;return(h/=S/2)<1?m/2*(h*h*(((v*=1.525)+1)*h-v))+p:m/2*((h-=2)*h*(((v*=1.525)+1)*h+v)+2)+p},ElasticEaseIn(h,p,m,S,v,y){var g=0;return h===0?p:(h/=S)===1?p+m:(y||(y=S*.3),!v||v0?t:n),c=s*n,d=a*(a>0?t:n),f=l*(l>0?n:t);return{x:u,y:r?-1*f:d,width:c-u,height:f-d}}}E1.Arc=bs;bs.prototype._centroid=!0;bs.prototype.className="Arc";bs.prototype._attrsAffectingSize=["innerRadius","outerRadius"];(0,A_e._registerNode)(bs);P1.Factory.addGetterSetter(bs,"innerRadius",0,(0,k1.getNumberValidator)());P1.Factory.addGetterSetter(bs,"outerRadius",0,(0,k1.getNumberValidator)());P1.Factory.addGetterSetter(bs,"angle",0,(0,k1.getNumberValidator)());P1.Factory.addGetterSetter(bs,"clockwise",!1,(0,k1.getBooleanValidator)());var A1={},Cp={};Object.defineProperty(Cp,"__esModule",{value:!0});Cp.Line=void 0;const O1=Ie,O_e=pn,ZF=fe,R_e=Le;function dx(e,t,n,r,i,o,s){var a=Math.sqrt(Math.pow(n-e,2)+Math.pow(r-t,2)),l=Math.sqrt(Math.pow(i-n,2)+Math.pow(o-r,2)),u=s*a/(a+l),c=s*l/(a+l),d=n-u*(i-e),f=r-u*(o-t),h=n+c*(i-e),p=r+c*(o-t);return[d,f,h,p]}function zk(e,t){var n=e.length,r=[],i,o;for(i=2;i4){for(a=this.getTensionPoints(),l=a.length,u=o?0:4,o||t.quadraticCurveTo(a[0],a[1],a[2],a[3]);u{let u,c,d;u=l/2,c=0;for(let h=0;h<20;h++)d=u*e.tValues[20][h]+u,c+=e.cValues[20][h]*r(s,a,d);return u*c};e.getCubicArcLength=t;const n=(s,a,l)=>{l===void 0&&(l=1);const u=s[0]-2*s[1]+s[2],c=a[0]-2*a[1]+a[2],d=2*s[1]-2*s[0],f=2*a[1]-2*a[0],h=4*(u*u+c*c),p=4*(u*d+c*f),m=d*d+f*f;if(h===0)return l*Math.sqrt(Math.pow(s[2]-s[0],2)+Math.pow(a[2]-a[0],2));const S=p/(2*h),v=m/h,y=l+S,g=v-S*S,b=y*y+g>0?Math.sqrt(y*y+g):0,_=S*S+g>0?Math.sqrt(S*S+g):0,w=S+Math.sqrt(S*S+g)!==0?g*Math.log(Math.abs((y+b)/(S+_))):0;return Math.sqrt(h)/2*(y*b-S*_+w)};e.getQuadraticArcLength=n;function r(s,a,l){const u=i(1,l,s),c=i(1,l,a),d=u*u+c*c;return Math.sqrt(d)}const i=(s,a,l)=>{const u=l.length-1;let c,d;if(u===0)return 0;if(s===0){d=0;for(let f=0;f<=u;f++)d+=e.binomialCoefficients[u][f]*Math.pow(1-a,u-f)*Math.pow(a,f)*l[f];return d}else{c=new Array(u);for(let f=0;f{let u=1,c=s/a,d=(s-l(c))/a,f=0;for(;u>.001;){const h=l(c+d),p=Math.abs(s-h)/a;if(p500)break}return c};e.t2length=o})(JF);Object.defineProperty(Jc,"__esModule",{value:!0});Jc.Path=void 0;const I_e=Ie,M_e=pn,N_e=Le,lu=JF;class ln extends M_e.Shape{constructor(t){super(t),this.dataArray=[],this.pathLength=0,this._readDataAttribute(),this.on("dataChange.konva",function(){this._readDataAttribute()})}_readDataAttribute(){this.dataArray=ln.parsePathData(this.data()),this.pathLength=ln.getPathLength(this.dataArray)}_sceneFunc(t){var n=this.dataArray;t.beginPath();for(var r=!1,i=0;ic?u:c,S=u>c?1:u/c,v=u>c?c/u:1;t.translate(a,l),t.rotate(h),t.scale(S,v),t.arc(0,0,m,d,d+f,1-p),t.scale(1/S,1/v),t.rotate(-h),t.translate(-a,-l);break;case"z":r=!0,t.closePath();break}}!r&&!this.hasFill()?t.strokeShape(this):t.fillStrokeShape(this)}getSelfRect(){var t=[];this.dataArray.forEach(function(u){if(u.command==="A"){var c=u.points[4],d=u.points[5],f=u.points[4]+d,h=Math.PI/180;if(Math.abs(c-f)f;p-=h){const m=ln.getPointOnEllipticalArc(u.points[0],u.points[1],u.points[2],u.points[3],p,0);t.push(m.x,m.y)}else for(let p=c+h;pn[i].pathLength;)t-=n[i].pathLength,++i;if(i===o)return r=n[i-1].points.slice(-2),{x:r[0],y:r[1]};if(t<.01)return r=n[i].points.slice(0,2),{x:r[0],y:r[1]};var s=n[i],a=s.points;switch(s.command){case"L":return ln.getPointOnLine(t,s.start.x,s.start.y,a[0],a[1]);case"C":return ln.getPointOnCubicBezier((0,lu.t2length)(t,ln.getPathLength(n),m=>(0,lu.getCubicArcLength)([s.start.x,a[0],a[2],a[4]],[s.start.y,a[1],a[3],a[5]],m)),s.start.x,s.start.y,a[0],a[1],a[2],a[3],a[4],a[5]);case"Q":return ln.getPointOnQuadraticBezier((0,lu.t2length)(t,ln.getPathLength(n),m=>(0,lu.getQuadraticArcLength)([s.start.x,a[0],a[2]],[s.start.y,a[1],a[3]],m)),s.start.x,s.start.y,a[0],a[1],a[2],a[3]);case"A":var l=a[0],u=a[1],c=a[2],d=a[3],f=a[4],h=a[5],p=a[6];return f+=h*t/s.pathLength,ln.getPointOnEllipticalArc(l,u,c,d,f,p)}return null}static getPointOnLine(t,n,r,i,o,s,a){s===void 0&&(s=n),a===void 0&&(a=r);var l=(o-r)/(i-n+1e-8),u=Math.sqrt(t*t/(1+l*l));i0&&!isNaN(p[0]);){var y=null,g=[],b=l,_=u,w,x,C,k,P,A,$,N,T,O;switch(h){case"l":l+=p.shift(),u+=p.shift(),y="L",g.push(l,u);break;case"L":l=p.shift(),u=p.shift(),g.push(l,u);break;case"m":var I=p.shift(),M=p.shift();if(l+=I,u+=M,y="M",s.length>2&&s[s.length-1].command==="z"){for(var R=s.length-2;R>=0;R--)if(s[R].command==="M"){l=s[R].points[0]+I,u=s[R].points[1]+M;break}}g.push(l,u),h="l";break;case"M":l=p.shift(),u=p.shift(),y="M",g.push(l,u),h="L";break;case"h":l+=p.shift(),y="L",g.push(l,u);break;case"H":l=p.shift(),y="L",g.push(l,u);break;case"v":u+=p.shift(),y="L",g.push(l,u);break;case"V":u=p.shift(),y="L",g.push(l,u);break;case"C":g.push(p.shift(),p.shift(),p.shift(),p.shift()),l=p.shift(),u=p.shift(),g.push(l,u);break;case"c":g.push(l+p.shift(),u+p.shift(),l+p.shift(),u+p.shift()),l+=p.shift(),u+=p.shift(),y="C",g.push(l,u);break;case"S":x=l,C=u,w=s[s.length-1],w.command==="C"&&(x=l+(l-w.points[2]),C=u+(u-w.points[3])),g.push(x,C,p.shift(),p.shift()),l=p.shift(),u=p.shift(),y="C",g.push(l,u);break;case"s":x=l,C=u,w=s[s.length-1],w.command==="C"&&(x=l+(l-w.points[2]),C=u+(u-w.points[3])),g.push(x,C,l+p.shift(),u+p.shift()),l+=p.shift(),u+=p.shift(),y="C",g.push(l,u);break;case"Q":g.push(p.shift(),p.shift()),l=p.shift(),u=p.shift(),g.push(l,u);break;case"q":g.push(l+p.shift(),u+p.shift()),l+=p.shift(),u+=p.shift(),y="Q",g.push(l,u);break;case"T":x=l,C=u,w=s[s.length-1],w.command==="Q"&&(x=l+(l-w.points[0]),C=u+(u-w.points[1])),l=p.shift(),u=p.shift(),y="Q",g.push(x,C,l,u);break;case"t":x=l,C=u,w=s[s.length-1],w.command==="Q"&&(x=l+(l-w.points[0]),C=u+(u-w.points[1])),l+=p.shift(),u+=p.shift(),y="Q",g.push(x,C,l,u);break;case"A":k=p.shift(),P=p.shift(),A=p.shift(),$=p.shift(),N=p.shift(),T=l,O=u,l=p.shift(),u=p.shift(),y="A",g=this.convertEndpointToCenterParameterization(T,O,l,u,$,N,k,P,A);break;case"a":k=p.shift(),P=p.shift(),A=p.shift(),$=p.shift(),N=p.shift(),T=l,O=u,l+=p.shift(),u+=p.shift(),y="A",g=this.convertEndpointToCenterParameterization(T,O,l,u,$,N,k,P,A);break}s.push({command:y||h,points:g,start:{x:b,y:_},pathLength:this.calcLength(b,_,y||h,g)})}(h==="z"||h==="Z")&&s.push({command:"z",points:[],start:void 0,pathLength:0})}return s}static calcLength(t,n,r,i){var o,s,a,l,u=ln;switch(r){case"L":return u.getLineLength(t,n,i[0],i[1]);case"C":return(0,lu.getCubicArcLength)([t,i[0],i[2],i[4]],[n,i[1],i[3],i[5]],1);case"Q":return(0,lu.getQuadraticArcLength)([t,i[0],i[2]],[n,i[1],i[3]],1);case"A":o=0;var c=i[4],d=i[5],f=i[4]+d,h=Math.PI/180;if(Math.abs(c-f)f;l-=h)a=u.getPointOnEllipticalArc(i[0],i[1],i[2],i[3],l,0),o+=u.getLineLength(s.x,s.y,a.x,a.y),s=a;else for(l=c+h;l1&&(a*=Math.sqrt(h),l*=Math.sqrt(h));var p=Math.sqrt((a*a*(l*l)-a*a*(f*f)-l*l*(d*d))/(a*a*(f*f)+l*l*(d*d)));o===s&&(p*=-1),isNaN(p)&&(p=0);var m=p*a*f/l,S=p*-l*d/a,v=(t+r)/2+Math.cos(c)*m-Math.sin(c)*S,y=(n+i)/2+Math.sin(c)*m+Math.cos(c)*S,g=function(P){return Math.sqrt(P[0]*P[0]+P[1]*P[1])},b=function(P,A){return(P[0]*A[0]+P[1]*A[1])/(g(P)*g(A))},_=function(P,A){return(P[0]*A[1]=1&&(k=0),s===0&&k>0&&(k=k-2*Math.PI),s===1&&k<0&&(k=k+2*Math.PI),[v,y,a,l,w,k,c,s]}}Jc.Path=ln;ln.prototype.className="Path";ln.prototype._attrsAffectingSize=["data"];(0,N_e._registerNode)(ln);I_e.Factory.addGetterSetter(ln,"data");Object.defineProperty(A1,"__esModule",{value:!0});A1.Arrow=void 0;const R1=Ie,D_e=Cp,eB=fe,L_e=Le,Uk=Jc;class Bl extends D_e.Line{_sceneFunc(t){super._sceneFunc(t);var n=Math.PI*2,r=this.points(),i=r,o=this.tension()!==0&&r.length>4;o&&(i=this.getTensionPoints());var s=this.pointerLength(),a=r.length,l,u;if(o){const f=[i[i.length-4],i[i.length-3],i[i.length-2],i[i.length-1],r[a-2],r[a-1]],h=Uk.Path.calcLength(i[i.length-4],i[i.length-3],"C",f),p=Uk.Path.getPointOnQuadraticBezier(Math.min(1,1-s/h),f[0],f[1],f[2],f[3],f[4],f[5]);l=r[a-2]-p.x,u=r[a-1]-p.y}else l=r[a-2]-r[a-4],u=r[a-1]-r[a-3];var c=(Math.atan2(u,l)+n)%n,d=this.pointerWidth();this.pointerAtEnding()&&(t.save(),t.beginPath(),t.translate(r[a-2],r[a-1]),t.rotate(c),t.moveTo(0,0),t.lineTo(-s,d/2),t.lineTo(-s,-d/2),t.closePath(),t.restore(),this.__fillStroke(t)),this.pointerAtBeginning()&&(t.save(),t.beginPath(),t.translate(r[0],r[1]),o?(l=(i[0]+i[2])/2-r[0],u=(i[1]+i[3])/2-r[1]):(l=r[2]-r[0],u=r[3]-r[1]),t.rotate((Math.atan2(-u,-l)+n)%n),t.moveTo(0,0),t.lineTo(-s,d/2),t.lineTo(-s,-d/2),t.closePath(),t.restore(),this.__fillStroke(t))}__fillStroke(t){var n=this.dashEnabled();n&&(this.attrs.dashEnabled=!1,t.setLineDash([])),t.fillStrokeShape(this),n&&(this.attrs.dashEnabled=!0)}getSelfRect(){const t=super.getSelfRect(),n=this.pointerWidth()/2;return{x:t.x-n,y:t.y-n,width:t.width+n*2,height:t.height+n*2}}}A1.Arrow=Bl;Bl.prototype.className="Arrow";(0,L_e._registerNode)(Bl);R1.Factory.addGetterSetter(Bl,"pointerLength",10,(0,eB.getNumberValidator)());R1.Factory.addGetterSetter(Bl,"pointerWidth",10,(0,eB.getNumberValidator)());R1.Factory.addGetterSetter(Bl,"pointerAtBeginning",!1);R1.Factory.addGetterSetter(Bl,"pointerAtEnding",!0);var I1={};Object.defineProperty(I1,"__esModule",{value:!0});I1.Circle=void 0;const $_e=Ie,F_e=pn,B_e=fe,j_e=Le;class ed extends F_e.Shape{_sceneFunc(t){t.beginPath(),t.arc(0,0,this.attrs.radius||0,0,Math.PI*2,!1),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.radius()*2}getHeight(){return this.radius()*2}setWidth(t){this.radius()!==t/2&&this.radius(t/2)}setHeight(t){this.radius()!==t/2&&this.radius(t/2)}}I1.Circle=ed;ed.prototype._centroid=!0;ed.prototype.className="Circle";ed.prototype._attrsAffectingSize=["radius"];(0,j_e._registerNode)(ed);$_e.Factory.addGetterSetter(ed,"radius",0,(0,B_e.getNumberValidator)());var M1={};Object.defineProperty(M1,"__esModule",{value:!0});M1.Ellipse=void 0;const $3=Ie,V_e=pn,tB=fe,z_e=Le;class Oa extends V_e.Shape{_sceneFunc(t){var n=this.radiusX(),r=this.radiusY();t.beginPath(),t.save(),n!==r&&t.scale(1,r/n),t.arc(0,0,n,0,Math.PI*2,!1),t.restore(),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.radiusX()*2}getHeight(){return this.radiusY()*2}setWidth(t){this.radiusX(t/2)}setHeight(t){this.radiusY(t/2)}}M1.Ellipse=Oa;Oa.prototype.className="Ellipse";Oa.prototype._centroid=!0;Oa.prototype._attrsAffectingSize=["radiusX","radiusY"];(0,z_e._registerNode)(Oa);$3.Factory.addComponentsGetterSetter(Oa,"radius",["x","y"]);$3.Factory.addGetterSetter(Oa,"radiusX",0,(0,tB.getNumberValidator)());$3.Factory.addGetterSetter(Oa,"radiusY",0,(0,tB.getNumberValidator)());var N1={};Object.defineProperty(N1,"__esModule",{value:!0});N1.Image=void 0;const x_=Bt,jl=Ie,U_e=pn,G_e=Le,Tp=fe;let Ro=class nB extends U_e.Shape{constructor(t){super(t),this.on("imageChange.konva",()=>{this._setImageLoad()}),this._setImageLoad()}_setImageLoad(){const t=this.image();t&&t.complete||t&&t.readyState===4||t&&t.addEventListener&&t.addEventListener("load",()=>{this._requestDraw()})}_useBufferCanvas(){return super._useBufferCanvas(!0)}_sceneFunc(t){const n=this.getWidth(),r=this.getHeight(),i=this.cornerRadius(),o=this.attrs.image;let s;if(o){const a=this.attrs.cropWidth,l=this.attrs.cropHeight;a&&l?s=[o,this.cropX(),this.cropY(),a,l,0,0,n,r]:s=[o,0,0,n,r]}(this.hasFill()||this.hasStroke()||i)&&(t.beginPath(),i?x_.Util.drawRoundedRectPath(t,n,r,i):t.rect(0,0,n,r),t.closePath(),t.fillStrokeShape(this)),o&&(i&&t.clip(),t.drawImage.apply(t,s))}_hitFunc(t){var n=this.width(),r=this.height(),i=this.cornerRadius();t.beginPath(),i?x_.Util.drawRoundedRectPath(t,n,r,i):t.rect(0,0,n,r),t.closePath(),t.fillStrokeShape(this)}getWidth(){var t,n;return(t=this.attrs.width)!==null&&t!==void 0?t:(n=this.image())===null||n===void 0?void 0:n.width}getHeight(){var t,n;return(t=this.attrs.height)!==null&&t!==void 0?t:(n=this.image())===null||n===void 0?void 0:n.height}static fromURL(t,n,r=null){var i=x_.Util.createImageElement();i.onload=function(){var o=new nB({image:i});n(o)},i.onerror=r,i.crossOrigin="Anonymous",i.src=t}};N1.Image=Ro;Ro.prototype.className="Image";(0,G_e._registerNode)(Ro);jl.Factory.addGetterSetter(Ro,"cornerRadius",0,(0,Tp.getNumberOrArrayOfNumbersValidator)(4));jl.Factory.addGetterSetter(Ro,"image");jl.Factory.addComponentsGetterSetter(Ro,"crop",["x","y","width","height"]);jl.Factory.addGetterSetter(Ro,"cropX",0,(0,Tp.getNumberValidator)());jl.Factory.addGetterSetter(Ro,"cropY",0,(0,Tp.getNumberValidator)());jl.Factory.addGetterSetter(Ro,"cropWidth",0,(0,Tp.getNumberValidator)());jl.Factory.addGetterSetter(Ro,"cropHeight",0,(0,Tp.getNumberValidator)());var Dc={};Object.defineProperty(Dc,"__esModule",{value:!0});Dc.Tag=Dc.Label=void 0;const D1=Ie,H_e=pn,q_e=Qc,F3=fe,rB=Le;var iB=["fontFamily","fontSize","fontStyle","padding","lineHeight","text","width","height","pointerDirection","pointerWidth","pointerHeight"],W_e="Change.konva",K_e="none",fx="up",hx="right",px="down",gx="left",Y_e=iB.length;class B3 extends q_e.Group{constructor(t){super(t),this.on("add.konva",function(n){this._addListeners(n.child),this._sync()})}getText(){return this.find("Text")[0]}getTag(){return this.find("Tag")[0]}_addListeners(t){var n=this,r,i=function(){n._sync()};for(r=0;r{n=Math.min(n,s.x),r=Math.max(r,s.x),i=Math.min(i,s.y),o=Math.max(o,s.y)}),{x:n,y:i,width:r-n,height:o-i}}getWidth(){return this.radius()*2}getHeight(){return this.radius()*2}setWidth(t){this.radius(t/2)}setHeight(t){this.radius(t/2)}}$1.RegularPolygon=zl;zl.prototype.className="RegularPolygon";zl.prototype._centroid=!0;zl.prototype._attrsAffectingSize=["radius"];(0,n2e._registerNode)(zl);oB.Factory.addGetterSetter(zl,"radius",0,(0,sB.getNumberValidator)());oB.Factory.addGetterSetter(zl,"sides",0,(0,sB.getNumberValidator)());var F1={};Object.defineProperty(F1,"__esModule",{value:!0});F1.Ring=void 0;const aB=Ie,r2e=pn,lB=fe,i2e=Le;var Gk=Math.PI*2;class Ul extends r2e.Shape{_sceneFunc(t){t.beginPath(),t.arc(0,0,this.innerRadius(),0,Gk,!1),t.moveTo(this.outerRadius(),0),t.arc(0,0,this.outerRadius(),Gk,0,!0),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.outerRadius()*2}getHeight(){return this.outerRadius()*2}setWidth(t){this.outerRadius(t/2)}setHeight(t){this.outerRadius(t/2)}}F1.Ring=Ul;Ul.prototype.className="Ring";Ul.prototype._centroid=!0;Ul.prototype._attrsAffectingSize=["innerRadius","outerRadius"];(0,i2e._registerNode)(Ul);aB.Factory.addGetterSetter(Ul,"innerRadius",0,(0,lB.getNumberValidator)());aB.Factory.addGetterSetter(Ul,"outerRadius",0,(0,lB.getNumberValidator)());var B1={};Object.defineProperty(B1,"__esModule",{value:!0});B1.Sprite=void 0;const Gl=Ie,o2e=pn,s2e=Zc,uB=fe,a2e=Le;class Io extends o2e.Shape{constructor(t){super(t),this._updated=!0,this.anim=new s2e.Animation(()=>{var n=this._updated;return this._updated=!1,n}),this.on("animationChange.konva",function(){this.frameIndex(0)}),this.on("frameIndexChange.konva",function(){this._updated=!0}),this.on("frameRateChange.konva",function(){this.anim.isRunning()&&(clearInterval(this.interval),this._setInterval())})}_sceneFunc(t){var n=this.animation(),r=this.frameIndex(),i=r*4,o=this.animations()[n],s=this.frameOffsets(),a=o[i+0],l=o[i+1],u=o[i+2],c=o[i+3],d=this.image();if((this.hasFill()||this.hasStroke())&&(t.beginPath(),t.rect(0,0,u,c),t.closePath(),t.fillStrokeShape(this)),d)if(s){var f=s[n],h=r*2;t.drawImage(d,a,l,u,c,f[h+0],f[h+1],u,c)}else t.drawImage(d,a,l,u,c,0,0,u,c)}_hitFunc(t){var n=this.animation(),r=this.frameIndex(),i=r*4,o=this.animations()[n],s=this.frameOffsets(),a=o[i+2],l=o[i+3];if(t.beginPath(),s){var u=s[n],c=r*2;t.rect(u[c+0],u[c+1],a,l)}else t.rect(0,0,a,l);t.closePath(),t.fillShape(this)}_useBufferCanvas(){return super._useBufferCanvas(!0)}_setInterval(){var t=this;this.interval=setInterval(function(){t._updateIndex()},1e3/this.frameRate())}start(){if(!this.isRunning()){var t=this.getLayer();this.anim.setLayers(t),this._setInterval(),this.anim.start()}}stop(){this.anim.stop(),clearInterval(this.interval)}isRunning(){return this.anim.isRunning()}_updateIndex(){var t=this.frameIndex(),n=this.animation(),r=this.animations(),i=r[n],o=i.length/4;t{t=t.trim();const n=t.indexOf(" ")>=0,r=t.indexOf('"')>=0||t.indexOf("'")>=0;return n&&!r&&(t=`"${t}"`),t}).join(", ")}var Rg;function T_(){return Rg||(Rg=mx.Util.createCanvasElement().getContext(p2e),Rg)}function T2e(e){e.fillText(this._partialText,this._partialTextX,this._partialTextY)}function E2e(e){e.setAttr("miterLimit",2),e.strokeText(this._partialText,this._partialTextX,this._partialTextY)}function P2e(e){return e=e||{},!e.fillLinearGradientColorStops&&!e.fillRadialGradientColorStops&&!e.fillPatternImage&&(e.fill=e.fill||"black"),e}class Xt extends c2e.Shape{constructor(t){super(P2e(t)),this._partialTextX=0,this._partialTextY=0;for(var n=0;n1&&(v+=s)}}}_hitFunc(t){var n=this.getWidth(),r=this.getHeight();t.beginPath(),t.rect(0,0,n,r),t.closePath(),t.fillStrokeShape(this)}setText(t){var n=mx.Util._isString(t)?t:t==null?"":t+"";return this._setAttr(g2e,n),this}getWidth(){var t=this.attrs.width===uu||this.attrs.width===void 0;return t?this.getTextWidth()+this.padding()*2:this.attrs.width}getHeight(){var t=this.attrs.height===uu||this.attrs.height===void 0;return t?this.fontSize()*this.textArr.length*this.lineHeight()+this.padding()*2:this.attrs.height}getTextWidth(){return this.textWidth}getTextHeight(){return mx.Util.warn("text.getTextHeight() method is deprecated. Use text.height() - for full height and text.fontSize() - for one line height."),this.textHeight}measureSize(t){var n=T_(),r=this.fontSize(),i;return n.save(),n.font=this._getContextFont(),i=n.measureText(t),n.restore(),{width:i.width,height:r}}_getContextFont(){return this.fontStyle()+Og+this.fontVariant()+Og+(this.fontSize()+b2e)+C2e(this.fontFamily())}_addTextLine(t){this.align()===Rd&&(t=t.trim());var r=this._getTextWidth(t);return this.textArr.push({text:t,width:r,lastInParagraph:!1})}_getTextWidth(t){var n=this.letterSpacing(),r=t.length;return T_().measureText(t).width+(r?n*(r-1):0)}_setTextData(){var t=this.text().split(` -`),n=+this.fontSize(),r=0,i=this.lineHeight()*n,o=this.attrs.width,s=this.attrs.height,a=o!==uu&&o!==void 0,l=s!==uu&&s!==void 0,u=this.padding(),c=o-u*2,d=s-u*2,f=0,h=this.wrap(),p=h!==Wk,m=h!==w2e&&p,S=this.ellipsis();this.textArr=[],T_().font=this._getContextFont();for(var v=S?this._getTextWidth(C_):0,y=0,g=t.length;yc)for(;b.length>0;){for(var w=0,x=b.length,C="",k=0;w>>1,A=b.slice(0,P+1),$=this._getTextWidth(A)+v;$<=c?(w=P+1,C=A,k=$):x=P}if(C){if(m){var N,T=b[C.length],O=T===Og||T===Hk;O&&k<=c?N=C.length:N=Math.max(C.lastIndexOf(Og),C.lastIndexOf(Hk))+1,N>0&&(w=N,C=C.slice(0,w),k=this._getTextWidth(C))}C=C.trimRight(),this._addTextLine(C),r=Math.max(r,k),f+=i;var I=this._shouldHandleEllipsis(f);if(I){this._tryToAddEllipsisToLastLine();break}if(b=b.slice(w),b=b.trimLeft(),b.length>0&&(_=this._getTextWidth(b),_<=c)){this._addTextLine(b),f+=i,r=Math.max(r,_);break}}else break}else this._addTextLine(b),f+=i,r=Math.max(r,_),this._shouldHandleEllipsis(f)&&yd)break}this.textHeight=n,this.textWidth=r}_shouldHandleEllipsis(t){var n=+this.fontSize(),r=this.lineHeight()*n,i=this.attrs.height,o=i!==uu&&i!==void 0,s=this.padding(),a=i-s*2,l=this.wrap(),u=l!==Wk;return!u||o&&t+r>a}_tryToAddEllipsisToLastLine(){var t=this.attrs.width,n=t!==uu&&t!==void 0,r=this.padding(),i=t-r*2,o=this.ellipsis(),s=this.textArr[this.textArr.length-1];if(!(!s||!o)){if(n){var a=this._getTextWidth(s.text+C_)n?null:Id.Path.getPointAtLengthOfDataArray(t,this.dataArray)}_readDataAttribute(){this.dataArray=Id.Path.parsePathData(this.attrs.data),this.pathLength=this._getTextPathLength()}_sceneFunc(t){t.setAttr("font",this._getContextFont()),t.setAttr("textBaseline",this.textBaseline()),t.setAttr("textAlign","left"),t.save();var n=this.textDecoration(),r=this.fill(),i=this.fontSize(),o=this.glyphInfo;n==="underline"&&t.beginPath();for(var s=0;s=1){var r=n[0].p0;t.moveTo(r.x,r.y)}for(var i=0;ie+`.${vB}`).join(" "),Xk="nodesRect",D2e=["widthChange","heightChange","scaleXChange","scaleYChange","skewXChange","skewYChange","rotationChange","offsetXChange","offsetYChange","transformsEnabledChange","strokeWidthChange"],L2e={"top-left":-45,"top-center":0,"top-right":45,"middle-right":-90,"middle-left":90,"bottom-left":-135,"bottom-center":180,"bottom-right":135};const $2e="ontouchstart"in Ci.Konva._global;function F2e(e,t){if(e==="rotater")return"crosshair";t+=at.Util.degToRad(L2e[e]||0);var n=(at.Util.radToDeg(t)%360+360)%360;return at.Util._inRange(n,315+22.5,360)||at.Util._inRange(n,0,22.5)?"ns-resize":at.Util._inRange(n,45-22.5,45+22.5)?"nesw-resize":at.Util._inRange(n,90-22.5,90+22.5)?"ew-resize":at.Util._inRange(n,135-22.5,135+22.5)?"nwse-resize":at.Util._inRange(n,180-22.5,180+22.5)?"ns-resize":at.Util._inRange(n,225-22.5,225+22.5)?"nesw-resize":at.Util._inRange(n,270-22.5,270+22.5)?"ew-resize":at.Util._inRange(n,315-22.5,315+22.5)?"nwse-resize":(at.Util.error("Transformer has unknown angle for cursor detection: "+n),"pointer")}var a0=["top-left","top-center","top-right","middle-right","middle-left","bottom-left","bottom-center","bottom-right"],Qk=1e8;function B2e(e){return{x:e.x+e.width/2*Math.cos(e.rotation)+e.height/2*Math.sin(-e.rotation),y:e.y+e.height/2*Math.cos(e.rotation)+e.width/2*Math.sin(e.rotation)}}function bB(e,t,n){const r=n.x+(e.x-n.x)*Math.cos(t)-(e.y-n.y)*Math.sin(t),i=n.y+(e.x-n.x)*Math.sin(t)+(e.y-n.y)*Math.cos(t);return Object.assign(Object.assign({},e),{rotation:e.rotation+t,x:r,y:i})}function j2e(e,t){const n=B2e(e);return bB(e,t,n)}function V2e(e,t,n){let r=t;for(let i=0;ii.isAncestorOf(this)?(at.Util.error("Konva.Transformer cannot be an a child of the node you are trying to attach"),!1):!0);this._nodes=t=n,t.length===1&&this.useSingleNodeRotation()?this.rotation(t[0].getAbsoluteRotation()):this.rotation(0),this._nodes.forEach(i=>{const o=()=>{this.nodes().length===1&&this.useSingleNodeRotation()&&this.rotation(this.nodes()[0].getAbsoluteRotation()),this._resetTransformCache(),!this._transforming&&!this.isDragging()&&this.update()},s=i._attrsAffectingSize.map(a=>a+"Change."+this._getEventNamespace()).join(" ");i.on(s,o),i.on(D2e.map(a=>a+`.${this._getEventNamespace()}`).join(" "),o),i.on(`absoluteTransformChange.${this._getEventNamespace()}`,o),this._proxyDrag(i)}),this._resetTransformCache();var r=!!this.findOne(".top-left");return r&&this.update(),this}_proxyDrag(t){let n;t.on(`dragstart.${this._getEventNamespace()}`,r=>{n=t.getAbsolutePosition(),!this.isDragging()&&t!==this.findOne(".back")&&this.startDrag(r,!1)}),t.on(`dragmove.${this._getEventNamespace()}`,r=>{if(!n)return;const i=t.getAbsolutePosition(),o=i.x-n.x,s=i.y-n.y;this.nodes().forEach(a=>{if(a===t||a.isDragging())return;const l=a.getAbsolutePosition();a.setAbsolutePosition({x:l.x+o,y:l.y+s}),a.startDrag(r)}),n=null})}getNodes(){return this._nodes||[]}getActiveAnchor(){return this._movingAnchorName}detach(){this._nodes&&this._nodes.forEach(t=>{t.off("."+this._getEventNamespace())}),this._nodes=[],this._resetTransformCache()}_resetTransformCache(){this._clearCache(Xk),this._clearCache("transform"),this._clearSelfAndDescendantCache("absoluteTransform")}_getNodeRect(){return this._getCache(Xk,this.__getNodeRect)}__getNodeShape(t,n=this.rotation(),r){var i=t.getClientRect({skipTransform:!0,skipShadow:!0,skipStroke:this.ignoreStroke()}),o=t.getAbsoluteScale(r),s=t.getAbsolutePosition(r),a=i.x*o.x-t.offsetX()*o.x,l=i.y*o.y-t.offsetY()*o.y;const u=(Ci.Konva.getAngle(t.getAbsoluteRotation())+Math.PI*2)%(Math.PI*2),c={x:s.x+a*Math.cos(u)+l*Math.sin(-u),y:s.y+l*Math.cos(u)+a*Math.sin(u),width:i.width*o.x,height:i.height*o.y,rotation:u};return bB(c,-Ci.Konva.getAngle(n),{x:0,y:0})}__getNodeRect(){var t=this.getNode();if(!t)return{x:-Qk,y:-Qk,width:0,height:0,rotation:0};const n=[];this.nodes().map(u=>{const c=u.getClientRect({skipTransform:!0,skipShadow:!0,skipStroke:this.ignoreStroke()});var d=[{x:c.x,y:c.y},{x:c.x+c.width,y:c.y},{x:c.x+c.width,y:c.y+c.height},{x:c.x,y:c.y+c.height}],f=u.getAbsoluteTransform();d.forEach(function(h){var p=f.point(h);n.push(p)})});const r=new at.Transform;r.rotate(-Ci.Konva.getAngle(this.rotation()));var i,o,s,a;n.forEach(function(u){var c=r.point(u);i===void 0&&(i=s=c.x,o=a=c.y),i=Math.min(i,c.x),o=Math.min(o,c.y),s=Math.max(s,c.x),a=Math.max(a,c.y)}),r.invert();const l=r.point({x:i,y:o});return{x:l.x,y:l.y,width:s-i,height:a-o,rotation:Ci.Konva.getAngle(this.rotation())}}getX(){return this._getNodeRect().x}getY(){return this._getNodeRect().y}getWidth(){return this._getNodeRect().width}getHeight(){return this._getNodeRect().height}_createElements(){this._createBack(),a0.forEach((function(t){this._createAnchor(t)}).bind(this)),this._createAnchor("rotater")}_createAnchor(t){var n=new I2e.Rect({stroke:"rgb(0, 161, 255)",fill:"white",strokeWidth:1,name:t+" _anchor",dragDistance:0,draggable:!0,hitStrokeWidth:$2e?10:"auto"}),r=this;n.on("mousedown touchstart",function(i){r._handleMouseDown(i)}),n.on("dragstart",i=>{n.stopDrag(),i.cancelBubble=!0}),n.on("dragend",i=>{i.cancelBubble=!0}),n.on("mouseenter",()=>{var i=Ci.Konva.getAngle(this.rotation()),o=F2e(t,i);n.getStage().content&&(n.getStage().content.style.cursor=o),this._cursorChange=!0}),n.on("mouseout",()=>{n.getStage().content&&(n.getStage().content.style.cursor=""),this._cursorChange=!1}),this.add(n)}_createBack(){var t=new R2e.Shape({name:"back",width:0,height:0,draggable:!0,sceneFunc(n){var r=this.getParent(),i=r.padding();n.beginPath(),n.rect(-i,-i,this.width()+i*2,this.height()+i*2),n.moveTo(this.width()/2,-i),r.rotateEnabled()&&n.lineTo(this.width()/2,-r.rotateAnchorOffset()*at.Util._sign(this.height())-i),n.fillStrokeShape(this)},hitFunc:(n,r)=>{if(this.shouldOverdrawWholeArea()){var i=this.padding();n.beginPath(),n.rect(-i,-i,r.width()+i*2,r.height()+i*2),n.fillStrokeShape(r)}}});this.add(t),this._proxyDrag(t),t.on("dragstart",n=>{n.cancelBubble=!0}),t.on("dragmove",n=>{n.cancelBubble=!0}),t.on("dragend",n=>{n.cancelBubble=!0}),this.on("dragmove",n=>{this.update()})}_handleMouseDown(t){this._movingAnchorName=t.target.name().split(" ")[0];var n=this._getNodeRect(),r=n.width,i=n.height,o=Math.sqrt(Math.pow(r,2)+Math.pow(i,2));this.sin=Math.abs(i/o),this.cos=Math.abs(r/o),typeof window<"u"&&(window.addEventListener("mousemove",this._handleMouseMove),window.addEventListener("touchmove",this._handleMouseMove),window.addEventListener("mouseup",this._handleMouseUp,!0),window.addEventListener("touchend",this._handleMouseUp,!0)),this._transforming=!0;var s=t.target.getAbsolutePosition(),a=t.target.getStage().getPointerPosition();this._anchorDragOffset={x:a.x-s.x,y:a.y-s.y},this._fire("transformstart",{evt:t.evt,target:this.getNode()}),this._nodes.forEach(l=>{l._fire("transformstart",{evt:t.evt,target:l})})}_handleMouseMove(t){var n,r,i,o=this.findOne("."+this._movingAnchorName),s=o.getStage();s.setPointersPositions(t);const a=s.getPointerPosition();let l={x:a.x-this._anchorDragOffset.x,y:a.y-this._anchorDragOffset.y};const u=o.getAbsolutePosition();this.anchorDragBoundFunc()&&(l=this.anchorDragBoundFunc()(u,l,t)),o.setAbsolutePosition(l);const c=o.getAbsolutePosition();if(!(u.x===c.x&&u.y===c.y)){if(this._movingAnchorName==="rotater"){var d=this._getNodeRect();n=o.x()-d.width/2,r=-o.y()+d.height/2;let N=Math.atan2(-r,n)+Math.PI/2;d.height<0&&(N-=Math.PI);var f=Ci.Konva.getAngle(this.rotation());const T=f+N,O=Ci.Konva.getAngle(this.rotationSnapTolerance()),M=V2e(this.rotationSnaps(),T,O)-d.rotation,R=j2e(d,M);this._fitNodesInto(R,t);return}var h=this.shiftBehavior(),p;h==="inverted"?p=this.keepRatio()&&!t.shiftKey:h==="none"?p=this.keepRatio():p=this.keepRatio()||t.shiftKey;var g=this.centeredScaling()||t.altKey;if(this._movingAnchorName==="top-left"){if(p){var m=g?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".bottom-right").x(),y:this.findOne(".bottom-right").y()};i=Math.sqrt(Math.pow(m.x-o.x(),2)+Math.pow(m.y-o.y(),2));var S=this.findOne(".top-left").x()>m.x?-1:1,v=this.findOne(".top-left").y()>m.y?-1:1;n=i*this.cos*S,r=i*this.sin*v,this.findOne(".top-left").x(m.x-n),this.findOne(".top-left").y(m.y-r)}}else if(this._movingAnchorName==="top-center")this.findOne(".top-left").y(o.y());else if(this._movingAnchorName==="top-right"){if(p){var m=g?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".bottom-left").x(),y:this.findOne(".bottom-left").y()};i=Math.sqrt(Math.pow(o.x()-m.x,2)+Math.pow(m.y-o.y(),2));var S=this.findOne(".top-right").x()m.y?-1:1;n=i*this.cos*S,r=i*this.sin*v,this.findOne(".top-right").x(m.x+n),this.findOne(".top-right").y(m.y-r)}var y=o.position();this.findOne(".top-left").y(y.y),this.findOne(".bottom-right").x(y.x)}else if(this._movingAnchorName==="middle-left")this.findOne(".top-left").x(o.x());else if(this._movingAnchorName==="middle-right")this.findOne(".bottom-right").x(o.x());else if(this._movingAnchorName==="bottom-left"){if(p){var m=g?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".top-right").x(),y:this.findOne(".top-right").y()};i=Math.sqrt(Math.pow(m.x-o.x(),2)+Math.pow(o.y()-m.y,2));var S=m.x{r._fire("transformend",{evt:t,target:r})}),this._movingAnchorName=null}}_fitNodesInto(t,n){var r=this._getNodeRect();const i=1;if(at.Util._inRange(t.width,-this.padding()*2-i,i)){this.update();return}if(at.Util._inRange(t.height,-this.padding()*2-i,i)){this.update();return}const o=this.flipEnabled();var s=new at.Transform;if(s.rotate(Ci.Konva.getAngle(this.rotation())),this._movingAnchorName&&t.width<0&&this._movingAnchorName.indexOf("left")>=0){const d=s.point({x:-this.padding()*2,y:0});if(t.x+=d.x,t.y+=d.y,t.width+=this.padding()*2,this._movingAnchorName=this._movingAnchorName.replace("left","right"),this._anchorDragOffset.x-=d.x,this._anchorDragOffset.y-=d.y,!o){this.update();return}}else if(this._movingAnchorName&&t.width<0&&this._movingAnchorName.indexOf("right")>=0){const d=s.point({x:this.padding()*2,y:0});if(this._movingAnchorName=this._movingAnchorName.replace("right","left"),this._anchorDragOffset.x-=d.x,this._anchorDragOffset.y-=d.y,t.width+=this.padding()*2,!o){this.update();return}}if(this._movingAnchorName&&t.height<0&&this._movingAnchorName.indexOf("top")>=0){const d=s.point({x:0,y:-this.padding()*2});if(t.x+=d.x,t.y+=d.y,this._movingAnchorName=this._movingAnchorName.replace("top","bottom"),this._anchorDragOffset.x-=d.x,this._anchorDragOffset.y-=d.y,t.height+=this.padding()*2,!o){this.update();return}}else if(this._movingAnchorName&&t.height<0&&this._movingAnchorName.indexOf("bottom")>=0){const d=s.point({x:0,y:this.padding()*2});if(this._movingAnchorName=this._movingAnchorName.replace("bottom","top"),this._anchorDragOffset.x-=d.x,this._anchorDragOffset.y-=d.y,t.height+=this.padding()*2,!o){this.update();return}}if(this.boundBoxFunc()){const d=this.boundBoxFunc()(r,t);d?t=d:at.Util.warn("boundBoxFunc returned falsy. You should return new bound rect from it!")}const a=1e7,l=new at.Transform;l.translate(r.x,r.y),l.rotate(r.rotation),l.scale(r.width/a,r.height/a);const u=new at.Transform;u.translate(t.x,t.y),u.rotate(t.rotation),u.scale(t.width/a,t.height/a);const c=u.multiply(l.invert());this._nodes.forEach(d=>{var f;const h=d.getParent().getAbsoluteTransform(),p=d.getTransform().copy();p.translate(d.offsetX(),d.offsetY());const m=new at.Transform;m.multiply(h.copy().invert()).multiply(c).multiply(h).multiply(p);const S=m.decompose();d.setAttrs(S),this._fire("transform",{evt:n,target:d}),d._fire("transform",{evt:n,target:d}),(f=d.getLayer())===null||f===void 0||f.batchDraw()}),this.rotation(at.Util._getRotation(t.rotation)),this._resetTransformCache(),this.update(),this.getLayer().batchDraw()}forceUpdate(){this._resetTransformCache(),this.update()}_batchChangeChild(t,n){this.findOne(t).setAttrs(n)}update(){var t,n=this._getNodeRect();this.rotation(at.Util._getRotation(n.rotation));var r=n.width,i=n.height,o=this.enabledAnchors(),s=this.resizeEnabled(),a=this.padding(),l=this.anchorSize();const u=this.find("._anchor");u.forEach(d=>{d.setAttrs({width:l,height:l,offsetX:l/2,offsetY:l/2,stroke:this.anchorStroke(),strokeWidth:this.anchorStrokeWidth(),fill:this.anchorFill(),cornerRadius:this.anchorCornerRadius()})}),this._batchChangeChild(".top-left",{x:0,y:0,offsetX:l/2+a,offsetY:l/2+a,visible:s&&o.indexOf("top-left")>=0}),this._batchChangeChild(".top-center",{x:r/2,y:0,offsetY:l/2+a,visible:s&&o.indexOf("top-center")>=0}),this._batchChangeChild(".top-right",{x:r,y:0,offsetX:l/2-a,offsetY:l/2+a,visible:s&&o.indexOf("top-right")>=0}),this._batchChangeChild(".middle-left",{x:0,y:i/2,offsetX:l/2+a,visible:s&&o.indexOf("middle-left")>=0}),this._batchChangeChild(".middle-right",{x:r,y:i/2,offsetX:l/2-a,visible:s&&o.indexOf("middle-right")>=0}),this._batchChangeChild(".bottom-left",{x:0,y:i,offsetX:l/2+a,offsetY:l/2-a,visible:s&&o.indexOf("bottom-left")>=0}),this._batchChangeChild(".bottom-center",{x:r/2,y:i,offsetY:l/2-a,visible:s&&o.indexOf("bottom-center")>=0}),this._batchChangeChild(".bottom-right",{x:r,y:i,offsetX:l/2-a,offsetY:l/2-a,visible:s&&o.indexOf("bottom-right")>=0}),this._batchChangeChild(".rotater",{x:r/2,y:-this.rotateAnchorOffset()*at.Util._sign(i)-a,visible:this.rotateEnabled()}),this._batchChangeChild(".back",{width:r,height:i,visible:this.borderEnabled(),stroke:this.borderStroke(),strokeWidth:this.borderStrokeWidth(),dash:this.borderDash(),x:0,y:0});const c=this.anchorStyleFunc();c&&u.forEach(d=>{c(d)}),(t=this.getLayer())===null||t===void 0||t.batchDraw()}isTransforming(){return this._transforming}stopTransform(){if(this._transforming){this._removeEvents();var t=this.findOne("."+this._movingAnchorName);t&&t.stopDrag()}}destroy(){return this.getStage()&&this._cursorChange&&this.getStage().content&&(this.getStage().content.style.cursor=""),Yk.Group.prototype.destroy.call(this),this.detach(),this._removeEvents(),this}toObject(){return Kk.Node.prototype.toObject.call(this)}clone(t){var n=Kk.Node.prototype.clone.call(this,t);return n}getClientRect(){return this.nodes().length>0?super.getClientRect():{x:0,y:0,width:0,height:0}}}z1.Transformer=Je;function z2e(e){return e instanceof Array||at.Util.warn("enabledAnchors value should be an array"),e instanceof Array&&e.forEach(function(t){a0.indexOf(t)===-1&&at.Util.warn("Unknown anchor name: "+t+". 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255)");st.Factory.addGetterSetter(Je,"anchorStrokeWidth",1,(0,Ma.getNumberValidator)());st.Factory.addGetterSetter(Je,"anchorFill","white");st.Factory.addGetterSetter(Je,"anchorCornerRadius",0,(0,Ma.getNumberValidator)());st.Factory.addGetterSetter(Je,"borderStroke","rgb(0, 161, 255)");st.Factory.addGetterSetter(Je,"borderStrokeWidth",1,(0,Ma.getNumberValidator)());st.Factory.addGetterSetter(Je,"borderDash");st.Factory.addGetterSetter(Je,"keepRatio",!0);st.Factory.addGetterSetter(Je,"shiftBehavior","default");st.Factory.addGetterSetter(Je,"centeredScaling",!1);st.Factory.addGetterSetter(Je,"ignoreStroke",!1);st.Factory.addGetterSetter(Je,"padding",0,(0,Ma.getNumberValidator)());st.Factory.addGetterSetter(Je,"node");st.Factory.addGetterSetter(Je,"nodes");st.Factory.addGetterSetter(Je,"boundBoxFunc");st.Factory.addGetterSetter(Je,"anchorDragBoundFunc");st.Factory.addGetterSetter(Je,"anchorStyleFunc");st.Factory.addGetterSetter(Je,"shouldOverdrawWholeArea",!1);st.Factory.addGetterSetter(Je,"useSingleNodeRotation",!0);st.Factory.backCompat(Je,{lineEnabled:"borderEnabled",rotateHandlerOffset:"rotateAnchorOffset",enabledHandlers:"enabledAnchors"});var U1={};Object.defineProperty(U1,"__esModule",{value:!0});U1.Wedge=void 0;const G1=Ie,U2e=pn,G2e=Le,SB=fe,H2e=Le;class 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X2e(e,t){var n=e.data,r=e.width,i=e.height,o,s,a,l,u,c,d,f,h,p,m,S,v,y,g,b,_,w,x,C,k,P,A,$,N=t+t+1,T=r-1,O=i-1,I=t+1,M=I*(I+1)/2,R=new Jk,D=null,L=R,V=null,z=null,U=K2e[t],K=Y2e[t];for(a=1;a>K,A!==0?(A=255/A,n[c]=(f*U>>K)*A,n[c+1]=(h*U>>K)*A,n[c+2]=(p*U>>K)*A):n[c]=n[c+1]=n[c+2]=0,f-=S,h-=v,p-=y,m-=g,S-=V.r,v-=V.g,y-=V.b,g-=V.a,l=d+((l=o+t+1)>K,A>0?(A=255/A,n[l]=(f*U>>K)*A,n[l+1]=(h*U>>K)*A,n[l+2]=(p*U>>K)*A):n[l]=n[l+1]=n[l+2]=0,f-=S,h-=v,p-=y,m-=g,S-=V.r,v-=V.g,y-=V.b,g-=V.a,l=o+((l=s+I)0&&X2e(t,n)};H1.Blur=Q2e;Zk.Factory.addGetterSetter(q2e.Node,"blurRadius",0,(0,W2e.getNumberValidator)(),Zk.Factory.afterSetFilter);var q1={};Object.defineProperty(q1,"__esModule",{value:!0});q1.Brighten=void 0;const eA=Ie,Z2e=At,J2e=fe,ewe=function(e){var t=this.brightness()*255,n=e.data,r=n.length,i;for(i=0;i255?255:i,o=o<0?0:o>255?255:o,s=s<0?0:s>255?255:s,n[a]=i,n[a+1]=o,n[a+2]=s};W1.Contrast=rwe;tA.Factory.addGetterSetter(twe.Node,"contrast",0,(0,nwe.getNumberValidator)(),tA.Factory.afterSetFilter);var K1={};Object.defineProperty(K1,"__esModule",{value:!0});K1.Emboss=void 0;const ba=Ie,Y1=At,iwe=Bt,_B=fe,owe=function(e){var t=this.embossStrength()*10,n=this.embossWhiteLevel()*255,r=this.embossDirection(),i=this.embossBlend(),o=0,s=0,a=e.data,l=e.width,u=e.height,c=l*4,d=u;switch(r){case"top-left":o=-1,s=-1;break;case"top":o=-1,s=0;break;case"top-right":o=-1,s=1;break;case"right":o=0,s=1;break;case"bottom-right":o=1,s=1;break;case"bottom":o=1,s=0;break;case"bottom-left":o=1,s=-1;break;case"left":o=0,s=-1;break;default:iwe.Util.error("Unknown emboss direction: "+r)}do{var f=(d-1)*c,h=o;d+h<1&&(h=0),d+h>u&&(h=0);var p=(d-1+h)*l*4,m=l;do{var S=f+(m-1)*4,v=s;m+v<1&&(v=0),m+v>l&&(v=0);var 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t=e.data,n=t.length,r=t[0],i=r,o,s=t[1],a=s,l,u=t[2],c=u,d,f,h=this.enhance();if(h!==0){for(f=0;fi&&(i=o),l=t[f+1],la&&(a=l),d=t[f+2],dc&&(c=d);i===r&&(i=255,r=0),a===s&&(a=255,s=0),c===u&&(c=255,u=0);var p,m,S,v,y,g,b,_,w;for(h>0?(m=i+h*(255-i),S=r-h*(r-0),y=a+h*(255-a),g=s-h*(s-0),_=c+h*(255-c),w=u-h*(u-0)):(p=(i+r)*.5,m=i+h*(i-p),S=r+h*(r-p),v=(a+s)*.5,y=a+h*(a-v),g=s+h*(s-v),b=(c+u)*.5,_=c+h*(c-b),w=u+h*(u-b)),f=0;fv?S:v;var y=s,g=o,b,_,w=360/g*Math.PI/180,x,C;for(_=0;_g?y:g;var b=s,_=o,w,x,C=n.polarRotation||0,k,P;for(c=0;ct&&(b=g,_=0,w=-1),i=0;i=0&&h=0&&p=0&&h=0&&p=255*4?255:0}return s}function xwe(e,t,n){for(var r=[.1111111111111111,.1111111111111111,.1111111111111111,.1111111111111111,.1111111111111111,.1111111111111111,.1111111111111111,.1111111111111111,.1111111111111111],i=Math.round(Math.sqrt(r.length)),o=Math.floor(i/2),s=[],a=0;a=0&&h=0&&p=n))for(o=m;o=r||(s=(n*o+i)*4,a+=b[s+0],l+=b[s+1],u+=b[s+2],c+=b[s+3],g+=1);for(a=a/g,l=l/g,u=u/g,c=c/g,i=h;i=n))for(o=m;o=r||(s=(n*o+i)*4,b[s+0]=a,b[s+1]=l,b[s+2]=u,b[s+3]=c)}};ib.Pixelate=Rwe;sA.Factory.addGetterSetter(Awe.Node,"pixelSize",8,(0,Owe.getNumberValidator)(),sA.Factory.afterSetFilter);var ob={};Object.defineProperty(ob,"__esModule",{value:!0});ob.Posterize=void 0;const aA=Ie,Iwe=At,Mwe=fe,Nwe=function(e){var t=Math.round(this.levels()*254)+1,n=e.data,r=n.length,i=255/t,o;for(o=0;o255?255:e<0?0:Math.round(e)});u0.Factory.addGetterSetter(q3.Node,"green",0,function(e){return this._filterUpToDate=!1,e>255?255:e<0?0:Math.round(e)});u0.Factory.addGetterSetter(q3.Node,"blue",0,Dwe.RGBComponent,u0.Factory.afterSetFilter);var 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addEventListener=="function"&&(this.opts.closeOnBeforeunload&&(this.beforeunloadEventListener=()=>{this.transport&&(this.transport.removeAllListeners(),this.transport.close())},addEventListener("beforeunload",this.beforeunloadEventListener,!1)),this.hostname!=="localhost"&&(this.offlineEventListener=()=>{this.onClose("transport close",{description:"network connection lost"})},addEventListener("offline",this.offlineEventListener,!1))),this.open()}createTransport(t){const n=Object.assign({},this.opts.query);n.EIO=VB,n.transport=t,this.id&&(n.sid=this.id);const r=Object.assign({},this.opts,{query:n,socket:this,hostname:this.hostname,secure:this.secure,port:this.port},this.opts.transportOptions[t]);return new E5e[t](r)}open(){let t;if(this.opts.rememberUpgrade&&mu.priorWebsocketSuccess&&this.transports.indexOf("websocket")!==-1)t="websocket";else if(this.transports.length===0){this.setTimeoutFn(()=>{this.emitReserved("error","No transports available")},0);return}else t=this.transports[0];this.readyState="opening";try{t=this.createTransport(t)}catch{this.transports.shift(),this.open();return}t.open(),this.setTransport(t)}setTransport(t){this.transport&&this.transport.removeAllListeners(),this.transport=t,t.on("drain",this.onDrain.bind(this)).on("packet",this.onPacket.bind(this)).on("error",this.onError.bind(this)).on("close",n=>this.onClose("transport close",n))}probe(t){let n=this.createTransport(t),r=!1;mu.priorWebsocketSuccess=!1;const i=()=>{r||(n.send([{type:"ping",data:"probe"}]),n.once("packet",d=>{if(!r)if(d.type==="pong"&&d.data==="probe"){if(this.upgrading=!0,this.emitReserved("upgrading",n),!n)return;mu.priorWebsocketSuccess=n.name==="websocket",this.transport.pause(()=>{r||this.readyState!=="closed"&&(c(),this.setTransport(n),n.send([{type:"upgrade"}]),this.emitReserved("upgrade",n),n=null,this.upgrading=!1,this.flush())})}else{const f=new Error("probe error");f.transport=n.name,this.emitReserved("upgradeError",f)}}))};function o(){r||(r=!0,c(),n.close(),n=null)}const s=d=>{const f=new Error("probe error: "+d);f.transport=n.name,o(),this.emitReserved("upgradeError",f)};function a(){s("transport closed")}function l(){s("socket closed")}function u(d){n&&d.name!==n.name&&o()}const c=()=>{n.removeListener("open",i),n.removeListener("error",s),n.removeListener("close",a),this.off("close",l),this.off("upgrading",u)};n.once("open",i),n.once("error",s),n.once("close",a),this.once("close",l),this.once("upgrading",u),this.upgrades.indexOf("webtransport")!==-1&&t!=="webtransport"?this.setTimeoutFn(()=>{r||n.open()},200):n.open()}onOpen(){if(this.readyState="open",mu.priorWebsocketSuccess=this.transport.name==="websocket",this.emitReserved("open"),this.flush(),this.readyState==="open"&&this.opts.upgrade){let t=0;const n=this.upgrades.length;for(;t{this.onClose("ping timeout")},this.pingInterval+this.pingTimeout),this.opts.autoUnref&&this.pingTimeoutTimer.unref()}onDrain(){this.writeBuffer.splice(0,this.prevBufferLen),this.prevBufferLen=0,this.writeBuffer.length===0?this.emitReserved("drain"):this.flush()}flush(){if(this.readyState!=="closed"&&this.transport.writable&&!this.upgrading&&this.writeBuffer.length){const t=this.getWritablePackets();this.transport.send(t),this.prevBufferLen=t.length,this.emitReserved("flush")}}getWritablePackets(){if(!(this.maxPayload&&this.transport.name==="polling"&&this.writeBuffer.length>1))return this.writeBuffer;let n=1;for(let r=0;r0&&n>this.maxPayload)return this.writeBuffer.slice(0,r);n+=2}return this.writeBuffer}write(t,n,r){return this.sendPacket("message",t,n,r),this}send(t,n,r){return this.sendPacket("message",t,n,r),this}sendPacket(t,n,r,i){if(typeof n=="function"&&(i=n,n=void 0),typeof r=="function"&&(i=r,r=null),this.readyState==="closing"||this.readyState==="closed")return;r=r||{},r.compress=r.compress!==!1;const o={type:t,data:n,options:r};this.emitReserved("packetCreate",o),this.writeBuffer.push(o),i&&this.once("flush",i),this.flush()}close(){const t=()=>{this.onClose("forced close"),this.transport.close()},n=()=>{this.off("upgrade",n),this.off("upgradeError",n),t()},r=()=>{this.once("upgrade",n),this.once("upgradeError",n)};return(this.readyState==="opening"||this.readyState==="open")&&(this.readyState="closing",this.writeBuffer.length?this.once("drain",()=>{this.upgrading?r():t()}):this.upgrading?r():t()),this}onError(t){mu.priorWebsocketSuccess=!1,this.emitReserved("error",t),this.onClose("transport error",t)}onClose(t,n){(this.readyState==="opening"||this.readyState==="open"||this.readyState==="closing")&&(this.clearTimeoutFn(this.pingTimeoutTimer),this.transport.removeAllListeners("close"),this.transport.close(),this.transport.removeAllListeners(),typeof removeEventListener=="function"&&(removeEventListener("beforeunload",this.beforeunloadEventListener,!1),removeEventListener("offline",this.offlineEventListener,!1)),this.readyState="closed",this.id=null,this.emitReserved("close",t,n),this.writeBuffer=[],this.prevBufferLen=0)}filterUpgrades(t){const n=[];let r=0;const i=t.length;for(;rtypeof ArrayBuffer.isView=="function"?ArrayBuffer.isView(e):e.buffer instanceof ArrayBuffer,KB=Object.prototype.toString,N5e=typeof Blob=="function"||typeof Blob<"u"&&KB.call(Blob)==="[object BlobConstructor]",D5e=typeof File=="function"||typeof File<"u"&&KB.call(File)==="[object FileConstructor]";function Z3(e){return I5e&&(e instanceof ArrayBuffer||M5e(e))||N5e&&e instanceof Blob||D5e&&e instanceof File}function Tm(e,t){if(!e||typeof e!="object")return!1;if(Array.isArray(e)){for(let n=0,r=e.length;n=0&&e.num{delete this.acks[t];for(let s=0;s{this.io.clearTimeoutFn(o),n.apply(this,[null,...s])}}emitWithAck(t,...n){const r=this.flags.timeout!==void 0||this._opts.ackTimeout!==void 0;return new Promise((i,o)=>{n.push((s,a)=>r?s?o(s):i(a):i(s)),this.emit(t,...n)})}_addToQueue(t){let n;typeof t[t.length-1]=="function"&&(n=t.pop());const r={id:this._queueSeq++,tryCount:0,pending:!1,args:t,flags:Object.assign({fromQueue:!0},this.flags)};t.push((i,...o)=>r!==this._queue[0]?void 0:(i!==null?r.tryCount>this._opts.retries&&(this._queue.shift(),n&&n(i)):(this._queue.shift(),n&&n(null,...o)),r.pending=!1,this._drainQueue())),this._queue.push(r),this._drainQueue()}_drainQueue(t=!1){if(!this.connected||this._queue.length===0)return;const n=this._queue[0];n.pending&&!t||(n.pending=!0,n.tryCount++,this.flags=n.flags,this.emit.apply(this,n.args))}packet(t){t.nsp=this.nsp,this.io._packet(t)}onopen(){typeof this.auth=="function"?this.auth(t=>{this._sendConnectPacket(t)}):this._sendConnectPacket(this.auth)}_sendConnectPacket(t){this.packet({type:He.CONNECT,data:this._pid?Object.assign({pid:this._pid,offset:this._lastOffset},t):t})}onerror(t){this.connected||this.emitReserved("connect_error",t)}onclose(t,n){this.connected=!1,delete this.id,this.emitReserved("disconnect",t,n)}onpacket(t){if(t.nsp===this.nsp)switch(t.type){case He.CONNECT:t.data&&t.data.sid?this.onconnect(t.data.sid,t.data.pid):this.emitReserved("connect_error",new Error("It seems you are trying to reach a Socket.IO server in v2.x with a v3.x client, but they are not compatible (more information here: https://socket.io/docs/v3/migrating-from-2-x-to-3-0/)"));break;case He.EVENT:case He.BINARY_EVENT:this.onevent(t);break;case He.ACK:case He.BINARY_ACK:this.onack(t);break;case He.DISCONNECT:this.ondisconnect();break;case He.CONNECT_ERROR:this.destroy();const r=new Error(t.data.message);r.data=t.data.data,this.emitReserved("connect_error",r);break}}onevent(t){const n=t.data||[];t.id!=null&&n.push(this.ack(t.id)),this.connected?this.emitEvent(n):this.receiveBuffer.push(Object.freeze(n))}emitEvent(t){if(this._anyListeners&&this._anyListeners.length){const n=this._anyListeners.slice();for(const r of n)r.apply(this,t)}super.emit.apply(this,t),this._pid&&t.length&&typeof t[t.length-1]=="string"&&(this._lastOffset=t[t.length-1])}ack(t){const n=this;let r=!1;return function(...i){r||(r=!0,n.packet({type:He.ACK,id:t,data:i}))}}onack(t){const n=this.acks[t.id];typeof n=="function"&&(n.apply(this,t.data),delete this.acks[t.id])}onconnect(t,n){this.id=t,this.recovered=n&&this._pid===n,this._pid=n,this.connected=!0,this.emitBuffered(),this.emitReserved("connect"),this._drainQueue(!0)}emitBuffered(){this.receiveBuffer.forEach(t=>this.emitEvent(t)),this.receiveBuffer=[],this.sendBuffer.forEach(t=>{this.notifyOutgoingListeners(t),this.packet(t)}),this.sendBuffer=[]}ondisconnect(){this.destroy(),this.onclose("io server disconnect")}destroy(){this.subs&&(this.subs.forEach(t=>t()),this.subs=void 0),this.io._destroy(this)}disconnect(){return this.connected&&this.packet({type:He.DISCONNECT}),this.destroy(),this.connected&&this.onclose("io client disconnect"),this}close(){return this.disconnect()}compress(t){return this.flags.compress=t,this}get volatile(){return this.flags.volatile=!0,this}timeout(t){return this.flags.timeout=t,this}onAny(t){return this._anyListeners=this._anyListeners||[],this._anyListeners.push(t),this}prependAny(t){return this._anyListeners=this._anyListeners||[],this._anyListeners.unshift(t),this}offAny(t){if(!this._anyListeners)return this;if(t){const n=this._anyListeners;for(let r=0;r0&&e.jitter<=1?e.jitter:0,this.attempts=0}td.prototype.duration=function(){var e=this.ms*Math.pow(this.factor,this.attempts++);if(this.jitter){var t=Math.random(),n=Math.floor(t*this.jitter*e);e=Math.floor(t*10)&1?e+n:e-n}return Math.min(e,this.max)|0};td.prototype.reset=function(){this.attempts=0};td.prototype.setMin=function(e){this.ms=e};td.prototype.setMax=function(e){this.max=e};td.prototype.setJitter=function(e){this.jitter=e};class Tx extends sn{constructor(t,n){var r;super(),this.nsps={},this.subs=[],t&&typeof t=="object"&&(n=t,t=void 0),n=n||{},n.path=n.path||"/socket.io",this.opts=n,pb(this,n),this.reconnection(n.reconnection!==!1),this.reconnectionAttempts(n.reconnectionAttempts||1/0),this.reconnectionDelay(n.reconnectionDelay||1e3),this.reconnectionDelayMax(n.reconnectionDelayMax||5e3),this.randomizationFactor((r=n.randomizationFactor)!==null&&r!==void 0?r:.5),this.backoff=new td({min:this.reconnectionDelay(),max:this.reconnectionDelayMax(),jitter:this.randomizationFactor()}),this.timeout(n.timeout==null?2e4:n.timeout),this._readyState="closed",this.uri=t;const i=n.parser||z5e;this.encoder=new i.Encoder,this.decoder=new i.Decoder,this._autoConnect=n.autoConnect!==!1,this._autoConnect&&this.open()}reconnection(t){return arguments.length?(this._reconnection=!!t,this):this._reconnection}reconnectionAttempts(t){return t===void 0?this._reconnectionAttempts:(this._reconnectionAttempts=t,this)}reconnectionDelay(t){var n;return t===void 0?this._reconnectionDelay:(this._reconnectionDelay=t,(n=this.backoff)===null||n===void 0||n.setMin(t),this)}randomizationFactor(t){var n;return t===void 0?this._randomizationFactor:(this._randomizationFactor=t,(n=this.backoff)===null||n===void 0||n.setJitter(t),this)}reconnectionDelayMax(t){var n;return t===void 0?this._reconnectionDelayMax:(this._reconnectionDelayMax=t,(n=this.backoff)===null||n===void 0||n.setMax(t),this)}timeout(t){return arguments.length?(this._timeout=t,this):this._timeout}maybeReconnectOnOpen(){!this._reconnecting&&this._reconnection&&this.backoff.attempts===0&&this.reconnect()}open(t){if(~this._readyState.indexOf("open"))return this;this.engine=new WB(this.uri,this.opts);const n=this.engine,r=this;this._readyState="opening",this.skipReconnect=!1;const i=Pi(n,"open",function(){r.onopen(),t&&t()}),o=a=>{this.cleanup(),this._readyState="closed",this.emitReserved("error",a),t?t(a):this.maybeReconnectOnOpen()},s=Pi(n,"error",o);if(this._timeout!==!1){const a=this._timeout,l=this.setTimeoutFn(()=>{i(),o(new Error("timeout")),n.close()},a);this.opts.autoUnref&&l.unref(),this.subs.push(()=>{this.clearTimeoutFn(l)})}return this.subs.push(i),this.subs.push(s),this}connect(t){return this.open(t)}onopen(){this.cleanup(),this._readyState="open",this.emitReserved("open");const t=this.engine;this.subs.push(Pi(t,"ping",this.onping.bind(this)),Pi(t,"data",this.ondata.bind(this)),Pi(t,"error",this.onerror.bind(this)),Pi(t,"close",this.onclose.bind(this)),Pi(this.decoder,"decoded",this.ondecoded.bind(this)))}onping(){this.emitReserved("ping")}ondata(t){try{this.decoder.add(t)}catch(n){this.onclose("parse error",n)}}ondecoded(t){Q3(()=>{this.emitReserved("packet",t)},this.setTimeoutFn)}onerror(t){this.emitReserved("error",t)}socket(t,n){let r=this.nsps[t];return r?this._autoConnect&&!r.active&&r.connect():(r=new YB(this,t,n),this.nsps[t]=r),r}_destroy(t){const n=Object.keys(this.nsps);for(const r of n)if(this.nsps[r].active)return;this._close()}_packet(t){const n=this.encoder.encode(t);for(let r=0;rt()),this.subs.length=0,this.decoder.destroy()}_close(){this.skipReconnect=!0,this._reconnecting=!1,this.onclose("forced close"),this.engine&&this.engine.close()}disconnect(){return this._close()}onclose(t,n){this.cleanup(),this.backoff.reset(),this._readyState="closed",this.emitReserved("close",t,n),this._reconnection&&!this.skipReconnect&&this.reconnect()}reconnect(){if(this._reconnecting||this.skipReconnect)return this;const t=this;if(this.backoff.attempts>=this._reconnectionAttempts)this.backoff.reset(),this.emitReserved("reconnect_failed"),this._reconnecting=!1;else{const n=this.backoff.duration();this._reconnecting=!0;const r=this.setTimeoutFn(()=>{t.skipReconnect||(this.emitReserved("reconnect_attempt",t.backoff.attempts),!t.skipReconnect&&t.open(i=>{i?(t._reconnecting=!1,t.reconnect(),this.emitReserved("reconnect_error",i)):t.onreconnect()}))},n);this.opts.autoUnref&&r.unref(),this.subs.push(()=>{this.clearTimeoutFn(r)})}}onreconnect(){const t=this.backoff.attempts;this._reconnecting=!1,this.backoff.reset(),this.emitReserved("reconnect",t)}}const Ld={};function Em(e,t){typeof e=="object"&&(t=e,e=void 0),t=t||{};const n=R5e(e,t.path||"/socket.io"),r=n.source,i=n.id,o=n.path,s=Ld[i]&&o in Ld[i].nsps,a=t.forceNew||t["force new connection"]||t.multiplex===!1||s;let l;return a?l=new Tx(r,t):(Ld[i]||(Ld[i]=new Tx(r,t)),l=Ld[i]),n.query&&!t.query&&(t.query=n.queryKey),l.socket(n.path,t)}Object.assign(Em,{Manager:Tx,Socket:YB,io:Em,connect:Em});var G5e=/d{1,4}|D{3,4}|m{1,4}|yy(?:yy)?|([HhMsTt])\1?|W{1,2}|[LlopSZN]|"[^"]*"|'[^']*'/g,H5e=/\b(?:[A-Z]{1,3}[A-Z][TC])(?:[-+]\d{4})?|((?:Australian 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r?"GMT":n?"UTC":X5e(e)},o:function(){return(p()>0?"-":"+")+$r(Math.floor(Math.abs(p())/60)*100+Math.abs(p())%60,4)},p:function(){return(p()>0?"-":"+")+$r(Math.floor(Math.abs(p())/60),2)+":"+$r(Math.floor(Math.abs(p())%60),2)},S:function(){return["th","st","nd","rd"][s()%10>3?0:(s()%100-s()%10!=10)*s()%10]},W:function(){return m()},WW:function(){return $r(m())},N:function(){return S()}};return t.replace(G5e,function(y){return y in v?v[y]():y.slice(1,y.length-1)})}var OA={default:"ddd mmm dd yyyy HH:MM:ss",shortDate:"m/d/yy",paddedShortDate:"mm/dd/yyyy",mediumDate:"mmm d, yyyy",longDate:"mmmm d, yyyy",fullDate:"dddd, mmmm d, yyyy",shortTime:"h:MM TT",mediumTime:"h:MM:ss TT",longTime:"h:MM:ss TT Z",isoDate:"yyyy-mm-dd",isoTime:"HH:MM:ss",isoDateTime:"yyyy-mm-dd'T'HH:MM:sso",isoUtcDateTime:"UTC:yyyy-mm-dd'T'HH:MM:ss'Z'",expiresHeaderFormat:"ddd, dd mmm yyyy HH:MM:ss 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d[o+"FullYear"]()};return p()===n&&h()===r&&f()===i?l?"Tdy":"Today":v()===n&&S()===r&&m()===i?l?"Ysd":"Yesterday":b()===n&&g()===r&&y()===i?l?"Tmw":"Tomorrow":s},K5e=function(t){var n=new Date(t.getFullYear(),t.getMonth(),t.getDate());n.setDate(n.getDate()-(n.getDay()+6)%7+3);var r=new Date(n.getFullYear(),0,4);r.setDate(r.getDate()-(r.getDay()+6)%7+3);var i=n.getTimezoneOffset()-r.getTimezoneOffset();n.setHours(n.getHours()-i);var o=(n-r)/(864e5*7);return 1+Math.floor(o)},Y5e=function(t){var n=t.getDay();return n===0&&(n=7),n},X5e=function(t){return(String(t).match(H5e)||[""]).pop().replace(q5e,"").replace(/GMT\+0000/g,"UTC")};const ri=()=>W5e(new 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mf(vr(s)),this.initialCoordinates=(i=Nh(o))!=null?i:Gi,this.handleStart=this.handleStart.bind(this),this.handleMove=this.handleMove.bind(this),this.handleEnd=this.handleEnd.bind(this),this.handleCancel=this.handleCancel.bind(this),this.handleKeydown=this.handleKeydown.bind(this),this.removeTextSelection=this.removeTextSelection.bind(this),this.attach()}attach(){const{events:t,props:{options:{activationConstraint:n}}}=this;if(this.listeners.add(t.move.name,this.handleMove,{passive:!1}),this.listeners.add(t.end.name,this.handleEnd),this.windowListeners.add(ii.Resize,this.handleCancel),this.windowListeners.add(ii.DragStart,LA),this.windowListeners.add(ii.VisibilityChange,this.handleCancel),this.windowListeners.add(ii.ContextMenu,LA),this.documentListeners.add(ii.Keydown,this.handleKeydown),n){if($A(n))return;if(FA(n)){this.timeoutId=setTimeout(this.handleStart,n.delay);return}}this.handleStart()}detach(){this.listeners.removeAll(),this.windowListeners.removeAll(),setTimeout(this.documentListeners.removeAll,50),this.timeoutId!==null&&(clearTimeout(this.timeoutId),this.timeoutId=null)}handleStart(){const{initialCoordinates:t}=this,{onStart:n}=this.props;t&&(this.activated=!0,this.documentListeners.add(ii.Click,j3e,{capture:!0}),this.removeTextSelection(),this.documentListeners.add(ii.SelectionChange,this.removeTextSelection),n(t))}handleMove(t){var n;const{activated:r,initialCoordinates:i,props:o}=this,{onMove:s,options:{activationConstraint:a}}=o;if(!i)return;const l=(n=Nh(t))!=null?n:Gi,u=g0(i,l);if(!r&&a){if(FA(a))return D_(u,a.tolerance)?this.handleCancel():void 0;if($A(a))return a.tolerance!=null&&D_(u,a.tolerance)?this.handleCancel():D_(u,a.distance)?this.handleStart():void 0}t.cancelable&&t.preventDefault(),s(l)}handleEnd(){const{onEnd:t}=this.props;this.detach(),t()}handleCancel(){const{onCancel:t}=this.props;this.detach(),t()}handleKeydown(t){t.code===gt.Esc&&this.handleCancel()}removeTextSelection(){var t;(t=this.document.getSelection())==null||t.removeAllRanges()}}const z3e={move:{name:"pointermove"},end:{name:"pointerup"}};class uj extends o4{constructor(t){const{event:n}=t,r=rd(n.target);super(t,z3e,r)}}uj.activators=[{eventName:"onPointerDown",handler:(e,t)=>{let{nativeEvent:n}=e,{onActivation:r}=t;return!n.isPrimary||n.button!==0?!1:(r==null||r({event:n}),!0)}}];const U3e={move:{name:"mousemove"},end:{name:"mouseup"}};var Px;(function(e){e[e.RightClick=2]="RightClick"})(Px||(Px={}));class cj extends o4{constructor(t){super(t,U3e,rd(t.event.target))}}cj.activators=[{eventName:"onMouseDown",handler:(e,t)=>{let{nativeEvent:n}=e,{onActivation:r}=t;return n.button===Px.RightClick?!1:(r==null||r({event:n}),!0)}}];const L_={move:{name:"touchmove"},end:{name:"touchend"}};class dj extends o4{constructor(t){super(t,L_)}static setup(){return window.addEventListener(L_.move.name,t,{capture:!1,passive:!1}),function(){window.removeEventListener(L_.move.name,t)};function t(){}}}dj.activators=[{eventName:"onTouchStart",handler:(e,t)=>{let{nativeEvent:n}=e,{onActivation:r}=t;const{touches:i}=n;return i.length>1?!1:(r==null||r({event:n}),!0)}}];var yf;(function(e){e[e.Pointer=0]="Pointer",e[e.DraggableRect=1]="DraggableRect"})(yf||(yf={}));var y0;(function(e){e[e.TreeOrder=0]="TreeOrder",e[e.ReversedTreeOrder=1]="ReversedTreeOrder"})(y0||(y0={}));function 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Lh;(function(e){e[e.Always=0]="Always",e[e.BeforeDragging=1]="BeforeDragging",e[e.WhileDragging=2]="WhileDragging"})(Lh||(Lh={}));var kx;(function(e){e.Optimized="optimized"})(kx||(kx={}));const BA=new Map;function Y3e(e,t){let{dragging:n,dependencies:r,config:i}=t;const[o,s]=E.useState(null),{frequency:a,measure:l,strategy:u}=i,c=E.useRef(e),d=S(),f=Mh(d),h=E.useCallback(function(v){v===void 0&&(v=[]),!f.current&&s(y=>y===null?v:y.concat(v.filter(g=>!y.includes(g))))},[f]),p=E.useRef(null),m=Op(v=>{if(d&&!n)return BA;if(!v||v===BA||c.current!==e||o!=null){const y=new Map;for(let g of e){if(!g)continue;if(o&&o.length>0&&!o.includes(g.id)&&g.rect.current){y.set(g.id,g.rect.current);continue}const b=g.node.current,_=b?new i4(l(b),b):null;g.rect.current=_,_&&y.set(g.id,_)}return y}return v},[e,o,n,d,l]);return E.useEffect(()=>{c.current=e},[e]),E.useEffect(()=>{d||h()},[n,d]),E.useEffect(()=>{o&&o.length>0&&s(null)},[JSON.stringify(o)]),E.useEffect(()=>{d||typeof 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0&&(t=Rp);const[n]=e,r=fj(n?vr(n):null),[i,o]=E.useReducer(a,$_),s=vb({callback:o});return e.length>0&&i===$_&&o(),Po(()=>{e.length?e.forEach(l=>s==null?void 0:s.observe(l)):(s==null||s.disconnect(),o())},[e]),i;function a(){return e.length?e.map(l=>rj(l)?r:new i4(t(l),l)):$_}}function hj(e){if(!e)return null;if(e.children.length>1)return e;const t=e.children[0];return Ap(t)?t:e}function o4e(e){let{measure:t}=e;const[n,r]=E.useState(null),i=E.useCallback(u=>{for(const{target:c}of u)if(Ap(c)){r(d=>{const f=t(c);return d?{...d,width:f.width,height:f.height}:f});break}},[t]),o=vb({callback:i}),s=E.useCallback(u=>{const c=hj(u);o==null||o.disconnect(),c&&(o==null||o.observe(c)),r(c?t(c):null)},[t,o]),[a,l]=h0(s);return E.useMemo(()=>({nodeRef:a,rect:n,setRef:l}),[n,a,l])}const s4e=[{sensor:uj,options:{}},{sensor:lj,options:{}}],a4e={current:{}},Pm={draggable:{measure:DA},droppable:{measure:DA,strategy:Lh.WhileDragging,frequency:kx.Optimized},dragOverlay:{measure:Rp}};class vf extends Map{get(t){var n;return t!=null&&(n=super.get(t))!=null?n:void 0}toArray(){return Array.from(this.values())}getEnabled(){return this.toArray().filter(t=>{let{disabled:n}=t;return!n})}getNodeFor(t){var n,r;return(n=(r=this.get(t))==null?void 0:r.node.current)!=null?n:void 0}}const l4e={activatorEvent:null,active:null,activeNode:null,activeNodeRect:null,collisions:null,containerNodeRect:null,draggableNodes:new Map,droppableRects:new Map,droppableContainers:new vf,over:null,dragOverlay:{nodeRef:{current:null},rect:null,setRef:m0},scrollableAncestors:[],scrollableAncestorRects:[],measuringConfiguration:Pm,measureDroppableContainers:m0,windowRect:null,measuringScheduled:!1},pj={activatorEvent:null,activators:[],active:null,activeNodeRect:null,ariaDescribedById:{draggable:""},dispatch:m0,draggableNodes:new Map,over:null,measureDroppableContainers:m0},Ip=E.createContext(pj),gj=E.createContext(l4e);function u4e(){return{draggable:{active:null,initialCoordinates:{x:0,y:0},nodes:new 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C,L5 as D,D5 as E,Ev as F,Ao as G,yTe as H,Ml as I,TN as J,Fn as K,Zf as L,gy as M,Ou as N,aJ as O,sTe as P,am as Q,Yu as R,Wf as S,uo as T,Cl as U,_G as V,ze as W,vse as X,l6 as Y,qee as Z,Zm as _,UC as a,K6 as a$,lse as a0,vTe as a1,Ol as a2,RN as a3,Tse as a4,STe as a5,bTe as a6,Yae as a7,Wae as a8,wv as a9,tr as aA,Oi as aB,c0e as aC,h0e as aD,Dr as aE,ia as aF,Ji as aG,Y6e as aH,F$ as aI,T0e as aJ,tve as aK,Sme as aL,Nc as aM,Vn as aN,wce as aO,n7 as aP,V5 as aQ,z5 as aR,xw as aS,tke as aT,Kc as aU,cy as aV,IEe as aW,h8e as aX,sD as aY,x6e as aZ,Hv as a_,tp as aa,Tie as ab,yN as ac,$n as ad,vo as ae,Dv as af,uTe as ag,tw as ah,iTe as ai,sm as aj,s6 as ak,kle as al,mse as am,a6 as an,ale as ao,Cle as ap,HS as aq,Ql as ar,qS as as,ug as at,lle as au,_Te as av,cp as aw,_i as ax,Kt as ay,nc as az,YC as b,jTe as b$,vPe as b0,li as b1,d5 as b2,Hj as b3,X4e as b4,K4e as b5,Y4e as b6,De as b7,Q5 as b8,bN as b9,C7 as bA,XZ as bB,b4 as bC,T6 as bD,wN as bE,xN as bF,cue as bG,d6e as bH,c6e as bI,Nv as bJ,W4e as bK,IQ as bL,CTe as bM,TTe as bN,qTe as bO,WTe as bP,ATe as bQ,JTe as bR,PTe as bS,BTe as bT,ITe as bU,mbe as bV,kTe as bW,KTe as bX,ETe as bY,iEe as bZ,OTe as b_,Au as ba,o6e as bb,KL as bc,r6e as bd,i6e as be,oue as bf,t6e as bg,lue as bh,qc as bi,z6e as bj,SN as bk,Jxe as bl,nke as bm,ike as bn,_6e as bo,w6e as bp,p6e as bq,e6e as br,Fw as bs,v6e as bt,n6e as bu,B5 as bv,$Ce as bw,CEe as bx,KZ as by,u5 as bz,Uq as c,ye as c$,RTe as c0,VTe as c1,DTe as c2,UTe as c3,gbe as c4,me as c5,V4e as c6,b6e as c7,Tpe as c8,T6e as c9,REe as cA,P8e as cB,g6e as cC,E6e as cD,zL as cE,eL as cF,Vt as cG,Q4e as cH,Z4e as cI,AEe as cJ,C8e as cK,eke as cL,mCe as cM,x8e as cN,k8e as cO,Tce as cP,bEe as cQ,kEe as cR,E8e as cS,Cce as cT,MEe as cU,v8e as cV,bD as cW,gEe as cX,d8e as cY,hCe as cZ,yEe as c_,tL as ca,sPe as cb,xce as cc,Bw as cd,rke as ce,rTe as cf,h6e as cg,l6e as ch,u6e as ci,ra as cj,rbe as ck,m6e as cl,y6e as cm,s6e as cn,Cse as co,Uy as cp,D8e as cq,wEe as cr,Xo as cs,Ppe as ct,g8e as cu,G6e as cv,WL as cw,YL as cx,R8e as cy,nr as cz,PQ as d,GTe as d$,lr as d0,L8e as d1,V6e as d2,F6e as d3,B6e as d4,j6e as d5,q6e as d6,W6e as d7,QL as d8,B8e as d9,k6e as dA,j8e as dB,so as dC,LTe as dD,_p as dE,pEe as dF,oEe as dG,dEe as dH,HTe as dI,fEe as dJ,hEe as dK,uEe as dL,cEe as dM,lEe as dN,aEe as dO,nEe as dP,eEe as dQ,b8e as dR,Dl as dS,_8e as dT,_w as dU,UN as dV,HN as dW,GN as dX,KN as dY,WN as dZ,wTe as d_,bc as da,H6e as db,c7 as dc,xTe as dd,Se as de,NTe as df,F8e as dg,y8 as dh,I6e as di,f3 as dj,ype as dk,gu as dl,v8 as dm,M6e as dn,N6e as dp,IN as dq,D6e as dr,vpe as ds,L6e as dt,VL as du,A6e as dv,R6e as dw,P6e as dx,$6e as dy,Lxe as dz,KI as e,iPe as e$,zv as e0,K8e as e1,sa as e2,$8e as e3,yce as e4,V8e as e5,SD as e6,xD as e7,io as e8,zTe as e9,xR as eA,u8e as eB,n8e as eC,r8e as eD,i8e as eE,a8e as eF,l8e as eG,Ph as eH,o5 as eI,_F as eJ,c8e as eK,Z6e as eL,tCe as eM,X6e as eN,Q6e as eO,e8e as eP,J6e as eQ,t8e as eR,s8e as eS,Zve as eT,yF as eU,UF as eV,hV as eW,Le as eX,TPe as eY,GPe as eZ,UPe as e_,tEe as ea,J4e as eb,yZ as ec,rEe as ed,X8e as ee,H8e as ef,O8e as eg,G8e as eh,Y8e as ei,A8e as ej,W8e as ek,UQ as el,q8e as em,z8e as en,U8e as eo,aF as ep,vv as eq,Q8e as er,Jy as es,ve as et,Z8e as eu,ox as ev,pZ as ew,MTe as ex,eTe as ey,gR as ez,CI as f,D1e as f$,NPe as f0,qPe as f1,B1e as f2,XEe as f3,SPe as f4,Nd as f5,mPe as f6,QEe as f7,b1 as f8,bPe as f9,FEe as fA,BEe as fB,M8e as fC,N8e as fD,gPe as fE,aPe as fF,PPe as fG,MPe as fH,kPe as fI,rPe as fJ,YEe as fK,jPe as fL,BPe as fM,RPe as fN,APe as fO,OPe as fP,QPe as fQ,$Pe as fR,ZPe as fS,pPe as fT,hPe as fU,qEe as fV,HEe as fW,YPe as fX,oPe as fY,$1e as fZ,N1e as f_,WEe as fa,yPe as fb,KEe as fc,ePe as fd,$Ee as fe,LEe as ff,DEe as fg,HPe as fh,QD as fi,mh as fj,zEe as fk,UEe as fl,GEe as fm,zPe as fn,nPe as fo,tPe as fp,Zfe as fq,VPe as fr,F1e as fs,ZEe as ft,lPe as fu,uPe as fv,zd as fw,EPe as fx,CPe as fy,_Pe as fz,SQ as g,L1e as g0,jEe as g1,mEe as g2,I8e as g3,Sce as g4,sEe as g5,VEe as g6,dPe as g7,XPe as g8,YTe as g9,mTe as gA,XTe as ga,QTe as gb,ZTe as gc,fPe as gd,xEe as ge,EEe as gf,TEe as gg,PEe as gh,NEe as gi,vZ as gj,O1e as gk,f8e as gl,nTe as gm,O7 as gn,fTe as go,$ee as gp,cTe as gq,aTe as gr,dTe as gs,co as gt,lTe as gu,tTe as gv,Gee as gw,Iee as gx,dJ as gy,gTe as gz,MI as h,Jn as i,Uc as j,mv as k,En as l,Jh as m,Qh as n,zi as o,Ta as p,TI as q,ko as r,E as s,xS as t,Ze as u,j as v,Si as w,fi as x,Ae as y,Gn as z}; diff --git a/invokeai/frontend/web/dist/index.html b/invokeai/frontend/web/dist/index.html index 802c1f222f..315bad4316 100644 --- a/invokeai/frontend/web/dist/index.html +++ b/invokeai/frontend/web/dist/index.html @@ -12,7 +12,7 @@ margin: 0; } - + diff --git a/invokeai/frontend/web/dist/locales/en.json b/invokeai/frontend/web/dist/locales/en.json index 59cf87fbda..0640ab9ef0 100644 --- a/invokeai/frontend/web/dist/locales/en.json +++ b/invokeai/frontend/web/dist/locales/en.json @@ -102,8 +102,7 @@ "openInNewTab": "Open in New Tab", "dontAskMeAgain": "Don't ask me again", "areYouSure": "Are you sure?", - "imagePrompt": "Image Prompt", - "clearNodes": "Are you sure you want to clear all nodes?" + "imagePrompt": "Image Prompt" }, "gallery": { "generations": "Generations", @@ -615,6 +614,11 @@ "initialImageNotSetDesc": "Could not load initial image", "nodesSaved": "Nodes Saved", "nodesLoaded": "Nodes Loaded", + "nodesNotValidGraph": "Not a valid InvokeAI Node Graph", + "nodesNotValidJSON": "Not a valid JSON", + "nodesCorruptedGraph": "Cannot load. Graph seems to be corrupted.", + "nodesUnrecognizedTypes": "Cannot load. Graph has unrecognized types", + "nodesBrokenConnections": "Cannot load. Some connections are broken.", "nodesLoadedFailed": "Failed To Load Nodes", "nodesCleared": "Nodes Cleared" }, @@ -700,9 +704,10 @@ }, "nodes": { "reloadSchema": "Reload Schema", - "saveNodes": "Save Nodes", - "loadNodes": "Load Nodes", - "clearNodes": "Clear Nodes", + "saveGraph": "Save Graph", + "loadGraph": "Load Graph (saved from Node Editor) (Do not copy-paste metadata)", + "clearGraph": "Clear Graph", + "clearGraphDesc": "Are you sure you want to clear all nodes?", "zoomInNodes": "Zoom In", "zoomOutNodes": "Zoom Out", "fitViewportNodes": "Fit View", diff --git a/invokeai/frontend/web/package.json b/invokeai/frontend/web/package.json index c58e7bdc97..481ee8f9e7 100644 --- a/invokeai/frontend/web/package.json +++ b/invokeai/frontend/web/package.json @@ -53,11 +53,11 @@ ] }, "dependencies": { - "@chakra-ui/anatomy": "^2.1.1", + "@chakra-ui/anatomy": "^2.2.0", "@chakra-ui/icons": "^2.0.19", - "@chakra-ui/react": "^2.7.1", + "@chakra-ui/react": "^2.8.0", "@chakra-ui/styled-system": "^2.9.1", - "@chakra-ui/theme-tools": "^2.0.18", + "@chakra-ui/theme-tools": "^2.1.0", "@dagrejs/graphlib": "^2.1.13", "@dnd-kit/core": "^6.0.8", "@dnd-kit/modifiers": "^6.0.1", diff --git a/invokeai/frontend/web/public/locales/en.json b/invokeai/frontend/web/public/locales/en.json index 0640ab9ef0..b9f75ac30f 100644 --- a/invokeai/frontend/web/public/locales/en.json +++ b/invokeai/frontend/web/public/locales/en.json @@ -23,6 +23,7 @@ "menu": "Menu" }, "common": { + "communityLabel": "Community", "hotkeysLabel": "Hotkeys", "darkMode": "Dark Mode", "lightMode": "Light Mode", diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/index.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/index.ts index 5adc4f5e5e..f06c324bc6 100644 --- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/index.ts +++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/index.ts @@ -65,18 +65,19 @@ import { addGeneratorProgressEventListener as addGeneratorProgressListener } fro import { addGraphExecutionStateCompleteEventListener as addGraphExecutionStateCompleteListener } from './listeners/socketio/socketGraphExecutionStateComplete'; import { addInvocationCompleteEventListener as addInvocationCompleteListener } from './listeners/socketio/socketInvocationComplete'; import { addInvocationErrorEventListener as addInvocationErrorListener } from './listeners/socketio/socketInvocationError'; +import { addInvocationRetrievalErrorEventListener } from './listeners/socketio/socketInvocationRetrievalError'; import { addInvocationStartedEventListener as addInvocationStartedListener } from './listeners/socketio/socketInvocationStarted'; import { addModelLoadEventListener } from './listeners/socketio/socketModelLoad'; +import { addSessionRetrievalErrorEventListener } from './listeners/socketio/socketSessionRetrievalError'; import { addSocketSubscribedEventListener as addSocketSubscribedListener } from './listeners/socketio/socketSubscribed'; import { addSocketUnsubscribedEventListener as addSocketUnsubscribedListener } from './listeners/socketio/socketUnsubscribed'; import { addStagingAreaImageSavedListener } from './listeners/stagingAreaImageSaved'; +import { addTabChangedListener } from './listeners/tabChanged'; import { addUpscaleRequestedListener } from './listeners/upscaleRequested'; import { addUserInvokedCanvasListener } from './listeners/userInvokedCanvas'; import { addUserInvokedImageToImageListener } from './listeners/userInvokedImageToImage'; import { addUserInvokedNodesListener } from './listeners/userInvokedNodes'; import { addUserInvokedTextToImageListener } from './listeners/userInvokedTextToImage'; -import { addSessionRetrievalErrorEventListener } from './listeners/socketio/socketSessionRetrievalError'; -import { addInvocationRetrievalErrorEventListener } from './listeners/socketio/socketInvocationRetrievalError'; export const listenerMiddleware = createListenerMiddleware(); @@ -201,3 +202,6 @@ addFirstListImagesListener(); // Ad-hoc upscale workflwo addUpscaleRequestedListener(); + +// Tab Change +addTabChangedListener(); diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/appConfigReceived.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/appConfigReceived.ts index 68148a192f..ef81377f99 100644 --- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/appConfigReceived.ts +++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/appConfigReceived.ts @@ -1,4 +1,8 @@ import { setInfillMethod } from 'features/parameters/store/generationSlice'; +import { + shouldUseNSFWCheckerChanged, + shouldUseWatermarkerChanged, +} from 'features/system/store/systemSlice'; import { appInfoApi } from 'services/api/endpoints/appInfo'; import { startAppListening } from '..'; @@ -6,12 +10,21 @@ export const addAppConfigReceivedListener = () => { startAppListening({ matcher: appInfoApi.endpoints.getAppConfig.matchFulfilled, effect: async (action, { getState, dispatch }) => { - const { infill_methods } = action.payload; + const { infill_methods, nsfw_methods, watermarking_methods } = + action.payload; const infillMethod = getState().generation.infillMethod; if (!infill_methods.includes(infillMethod)) { dispatch(setInfillMethod(infill_methods[0])); } + + if (!nsfw_methods.includes('nsfw_checker')) { + dispatch(shouldUseNSFWCheckerChanged(false)); + } + + if (!watermarking_methods.includes('invisible_watermark')) { + dispatch(shouldUseWatermarkerChanged(false)); + } }, }); }; diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/modelsLoaded.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/modelsLoaded.ts index 57981918d8..1e0b3dbc61 100644 --- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/modelsLoaded.ts +++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/modelsLoaded.ts @@ -9,13 +9,19 @@ import { zMainModel, zVaeModel, } from 'features/parameters/types/parameterSchemas'; +import { + refinerModelChanged, + setShouldUseSDXLRefiner, +} from 'features/sdxl/store/sdxlSlice'; import { forEach, some } from 'lodash-es'; import { modelsApi } from 'services/api/endpoints/models'; import { startAppListening } from '..'; export const addModelsLoadedListener = () => { startAppListening({ - matcher: modelsApi.endpoints.getMainModels.matchFulfilled, + predicate: (state, action) => + modelsApi.endpoints.getMainModels.matchFulfilled(action) && + !action.meta.arg.originalArgs.includes('sdxl-refiner'), effect: async (action, { getState, dispatch }) => { // models loaded, we need to ensure the selected model is available and if not, select the first one const log = logger('models'); @@ -59,6 +65,54 @@ export const addModelsLoadedListener = () => { dispatch(modelChanged(result.data)); }, }); + startAppListening({ + predicate: (state, action) => + modelsApi.endpoints.getMainModels.matchFulfilled(action) && + action.meta.arg.originalArgs.includes('sdxl-refiner'), + effect: async (action, { getState, dispatch }) => { + // models loaded, we need to ensure the selected model is available and if not, select the first one + const log = logger('models'); + log.info( + { models: action.payload.entities }, + `SDXL Refiner models loaded (${action.payload.ids.length})` + ); + + const currentModel = getState().sdxl.refinerModel; + + const isCurrentModelAvailable = some( + action.payload.entities, + (m) => + m?.model_name === currentModel?.model_name && + m?.base_model === currentModel?.base_model + ); + + if (isCurrentModelAvailable) { + return; + } + + const firstModelId = action.payload.ids[0]; + const firstModel = action.payload.entities[firstModelId]; + + if (!firstModel) { + // No models loaded at all + dispatch(refinerModelChanged(null)); + dispatch(setShouldUseSDXLRefiner(false)); + return; + } + + const result = zMainModel.safeParse(firstModel); + + if (!result.success) { + log.error( + { error: result.error.format() }, + 'Failed to parse SDXL Refiner Model' + ); + return; + } + + dispatch(refinerModelChanged(result.data)); + }, + }); startAppListening({ matcher: modelsApi.endpoints.getVaeModels.matchFulfilled, effect: async (action, { getState, dispatch }) => { diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/socketio/socketConnected.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/socketio/socketConnected.ts index ae0f049ae7..32a6cce203 100644 --- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/socketio/socketConnected.ts +++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/socketio/socketConnected.ts @@ -1,4 +1,6 @@ import { logger } from 'app/logging/logger'; +import { LIST_TAG } from 'services/api'; +import { appInfoApi } from 'services/api/endpoints/appInfo'; import { modelsApi } from 'services/api/endpoints/models'; import { receivedOpenAPISchema } from 'services/api/thunks/schema'; import { appSocketConnected, socketConnected } from 'services/events/actions'; @@ -24,11 +26,18 @@ export const addSocketConnectedEventListener = () => { dispatch(appSocketConnected(action.payload)); // update all server state - dispatch(modelsApi.endpoints.getMainModels.initiate()); - dispatch(modelsApi.endpoints.getControlNetModels.initiate()); - dispatch(modelsApi.endpoints.getLoRAModels.initiate()); - dispatch(modelsApi.endpoints.getTextualInversionModels.initiate()); - dispatch(modelsApi.endpoints.getVaeModels.initiate()); + dispatch( + modelsApi.util.invalidateTags([ + { type: 'MainModel', id: LIST_TAG }, + { type: 'SDXLRefinerModel', id: LIST_TAG }, + { type: 'LoRAModel', id: LIST_TAG }, + { type: 'ControlNetModel', id: LIST_TAG }, + { type: 'VaeModel', id: LIST_TAG }, + { type: 'TextualInversionModel', id: LIST_TAG }, + { type: 'ScannedModels', id: LIST_TAG }, + ]) + ); + dispatch(appInfoApi.util.invalidateTags(['AppConfig', 'AppVersion'])); }, }); }; diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationStarted.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationStarted.ts index 15447e5350..4e77811762 100644 --- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationStarted.ts +++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationStarted.ts @@ -21,7 +21,10 @@ export const addInvocationStartedEventListener = () => { return; } - log.debug(action.payload, 'Invocation started'); + log.debug( + action.payload, + `Invocation started (${action.payload.data.node.type})` + ); dispatch(appSocketInvocationStarted(action.payload)); }, }); diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/tabChanged.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/tabChanged.ts new file mode 100644 index 0000000000..578241573c --- /dev/null +++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/tabChanged.ts @@ -0,0 +1,56 @@ +import { modelChanged } from 'features/parameters/store/generationSlice'; +import { setActiveTab } from 'features/ui/store/uiSlice'; +import { forEach } from 'lodash-es'; +import { NON_REFINER_BASE_MODELS } from 'services/api/constants'; +import { + MainModelConfigEntity, + modelsApi, +} from 'services/api/endpoints/models'; +import { startAppListening } from '..'; + +export const addTabChangedListener = () => { + startAppListening({ + actionCreator: setActiveTab, + effect: (action, { getState, dispatch }) => { + const activeTabName = action.payload; + if (activeTabName === 'unifiedCanvas') { + // grab the models from RTK Query cache + const { data } = modelsApi.endpoints.getMainModels.select( + NON_REFINER_BASE_MODELS + )(getState()); + + if (!data) { + // no models yet, so we can't do anything + dispatch(modelChanged(null)); + return; + } + + // need to filter out all the invalid canvas models (currently, this is just sdxl) + const validCanvasModels: MainModelConfigEntity[] = []; + + forEach(data.entities, (entity) => { + if (!entity) { + return; + } + if (['sd-1', 'sd-2'].includes(entity.base_model)) { + validCanvasModels.push(entity); + } + }); + + // this could still be undefined even tho TS doesn't say so + const firstValidCanvasModel = validCanvasModels[0]; + + if (!firstValidCanvasModel) { + // uh oh, we have no models that are valid for canvas + dispatch(modelChanged(null)); + return; + } + + // only store the model name and base model in redux + const { base_model, model_name } = firstValidCanvasModel; + + dispatch(modelChanged({ base_model, model_name })); + } + }, + }); +}; diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/userInvokedImageToImage.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/userInvokedImageToImage.ts index 8101530eea..b0172e693b 100644 --- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/userInvokedImageToImage.ts +++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/userInvokedImageToImage.ts @@ -3,6 +3,7 @@ import { userInvoked } from 'app/store/actions'; import { parseify } from 'common/util/serialize'; import { imageToImageGraphBuilt } from 'features/nodes/store/actions'; import { buildLinearImageToImageGraph } from 'features/nodes/util/graphBuilders/buildLinearImageToImageGraph'; +import { buildLinearSDXLImageToImageGraph } from 'features/nodes/util/graphBuilders/buildLinearSDXLImageToImageGraph'; import { sessionReadyToInvoke } from 'features/system/store/actions'; import { sessionCreated } from 'services/api/thunks/session'; import { startAppListening } from '..'; @@ -14,8 +15,16 @@ export const addUserInvokedImageToImageListener = () => { effect: async (action, { getState, dispatch, take }) => { const log = logger('session'); const state = getState(); + const model = state.generation.model; + + let graph; + + if (model && model.base_model === 'sdxl') { + graph = buildLinearSDXLImageToImageGraph(state); + } else { + graph = buildLinearImageToImageGraph(state); + } - const graph = buildLinearImageToImageGraph(state); dispatch(imageToImageGraphBuilt(graph)); log.debug({ graph: parseify(graph) }, 'Image to Image graph built'); diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/userInvokedTextToImage.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/userInvokedTextToImage.ts index a9e9fe1ad8..f1cdabcabd 100644 --- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/userInvokedTextToImage.ts +++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/userInvokedTextToImage.ts @@ -2,6 +2,7 @@ import { logger } from 'app/logging/logger'; import { userInvoked } from 'app/store/actions'; import { parseify } from 'common/util/serialize'; import { textToImageGraphBuilt } from 'features/nodes/store/actions'; +import { buildLinearSDXLTextToImageGraph } from 'features/nodes/util/graphBuilders/buildLinearSDXLTextToImageGraph'; import { buildLinearTextToImageGraph } from 'features/nodes/util/graphBuilders/buildLinearTextToImageGraph'; import { sessionReadyToInvoke } from 'features/system/store/actions'; import { sessionCreated } from 'services/api/thunks/session'; @@ -14,8 +15,15 @@ export const addUserInvokedTextToImageListener = () => { effect: async (action, { getState, dispatch, take }) => { const log = logger('session'); const state = getState(); + const model = state.generation.model; - const graph = buildLinearTextToImageGraph(state); + let graph; + + if (model && model.base_model === 'sdxl') { + graph = buildLinearSDXLTextToImageGraph(state); + } else { + graph = buildLinearTextToImageGraph(state); + } dispatch(textToImageGraphBuilt(graph)); diff --git a/invokeai/frontend/web/src/app/store/store.ts b/invokeai/frontend/web/src/app/store/store.ts index dd80e1e378..d71a147913 100644 --- a/invokeai/frontend/web/src/app/store/store.ts +++ b/invokeai/frontend/web/src/app/store/store.ts @@ -15,6 +15,7 @@ import loraReducer from 'features/lora/store/loraSlice'; import nodesReducer from 'features/nodes/store/nodesSlice'; import generationReducer from 'features/parameters/store/generationSlice'; import postprocessingReducer from 'features/parameters/store/postprocessingSlice'; +import sdxlReducer from 'features/sdxl/store/sdxlSlice'; import configReducer from 'features/system/store/configSlice'; import systemReducer from 'features/system/store/systemSlice'; import modelmanagerReducer from 'features/ui/components/tabs/ModelManager/store/modelManagerSlice'; @@ -47,6 +48,7 @@ const allReducers = { imageDeletion: imageDeletionReducer, lora: loraReducer, modelmanager: modelmanagerReducer, + sdxl: sdxlReducer, [api.reducerPath]: api.reducer, }; @@ -58,6 +60,7 @@ const rememberedKeys: (keyof typeof allReducers)[] = [ 'canvas', 'gallery', 'generation', + 'sdxl', 'nodes', 'postprocessing', 'system', diff --git a/invokeai/frontend/web/src/app/types/invokeai.ts b/invokeai/frontend/web/src/app/types/invokeai.ts index be642a6435..b38790e0c9 100644 --- a/invokeai/frontend/web/src/app/types/invokeai.ts +++ b/invokeai/frontend/web/src/app/types/invokeai.ts @@ -95,7 +95,8 @@ export type AppFeature = | 'localization' | 'consoleLogging' | 'dynamicPrompting' - | 'batches'; + | 'batches' + | 'syncModels'; /** * A disable-able Stable Diffusion feature diff --git a/invokeai/frontend/web/src/common/components/IAISlider.tsx b/invokeai/frontend/web/src/common/components/IAISlider.tsx index 53a195c7b7..fd3eed754f 100644 --- a/invokeai/frontend/web/src/common/components/IAISlider.tsx +++ b/invokeai/frontend/web/src/common/components/IAISlider.tsx @@ -114,6 +114,11 @@ const IAISlider = (props: IAIFullSliderProps) => { setLocalInputValue(value); }, [value]); + const numberInputMin = useMemo( + () => (sliderNumberInputProps?.min ? sliderNumberInputProps.min : min), + [min, sliderNumberInputProps?.min] + ); + const numberInputMax = useMemo( () => (sliderNumberInputProps?.max ? sliderNumberInputProps.max : max), [max, sliderNumberInputProps?.max] @@ -129,24 +134,23 @@ const IAISlider = (props: IAIFullSliderProps) => { const handleInputBlur = useCallback( (e: FocusEvent) => { if (e.target.value === '') { - e.target.value = String(min); + e.target.value = String(numberInputMin); } const clamped = clamp( isInteger ? Math.floor(Number(e.target.value)) : Number(localInputValue), - min, + numberInputMin, numberInputMax ); const quantized = roundDownToMultiple(clamped, step); onChange(quantized); setLocalInputValue(quantized); }, - [isInteger, localInputValue, min, numberInputMax, onChange, step] + [isInteger, localInputValue, numberInputMin, numberInputMax, onChange, step] ); const handleInputChange = useCallback((v: number | string) => { - console.log('input'); setLocalInputValue(v); }, []); @@ -310,7 +314,7 @@ const IAISlider = (props: IAIFullSliderProps) => { {withInput && ( { }); }); + // Sort Alphabetically + data.sort((a, b) => + a.label && b.label ? (a.label?.localeCompare(b.label) ? -1 : 1) : -1 + ); + return data.sort((a, b) => (a.disabled && !b.disabled ? 1 : -1)); }, [embeddingQueryData, currentMainModel?.base_model]); diff --git a/invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/SingleSelectionMenuItems.tsx b/invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/SingleSelectionMenuItems.tsx index 74fea27927..237a38ca33 100644 --- a/invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/SingleSelectionMenuItems.tsx +++ b/invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/SingleSelectionMenuItems.tsx @@ -1,5 +1,6 @@ -import { Link, MenuItem } from '@chakra-ui/react'; +import { MenuItem } from '@chakra-ui/react'; import { createSelector } from '@reduxjs/toolkit'; +import { skipToken } from '@reduxjs/toolkit/dist/query'; import { useAppToaster } from 'app/components/Toaster'; import { stateSelector } from 'app/store/store'; import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; @@ -33,10 +34,9 @@ import { useRemoveImageFromBoardMutation, } from 'services/api/endpoints/images'; import { ImageDTO } from 'services/api/types'; +import { useDebounce } from 'use-debounce'; import { AddImageToBoardContext } from '../../../../app/contexts/AddImageToBoardContext'; import { sentImageToCanvas, sentImageToImg2Img } from '../../store/actions'; -import { useDebounce } from 'use-debounce'; -import { skipToken } from '@reduxjs/toolkit/dist/query'; type SingleSelectionMenuItemsProps = { imageDTO: ImageDTO; @@ -154,21 +154,29 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => { return ( <> - - }> - {t('common.openInNewTab')} - - + } + > + {t('common.openInNewTab')} + {isClipboardAPIAvailable && ( } onClickCapture={handleCopyImage}> {t('parameters.copyImage')} )} - - } w="100%"> - {t('parameters.downloadImage')} - - + } + w="100%" + > + {t('parameters.downloadImage')} + } onClickCapture={handleRecallPrompt} diff --git a/invokeai/frontend/web/src/features/lora/components/ParamLora.tsx b/invokeai/frontend/web/src/features/lora/components/ParamLora.tsx index a1584ca13a..3432838ec0 100644 --- a/invokeai/frontend/web/src/features/lora/components/ParamLora.tsx +++ b/invokeai/frontend/web/src/features/lora/components/ParamLora.tsx @@ -48,6 +48,7 @@ const ParamLora = (props: Props) => { handleReset={handleReset} withSliderMarks sliderMarks={[-1, 0, 1, 2]} + sliderNumberInputProps={{ min: -50, max: 50 }} /> { }); }); - return data.sort((a, b) => (a.disabled && !b.disabled ? 1 : -1)); + // Sort Alphabetically + data.sort((a, b) => + a.label && b.label ? (a.label?.localeCompare(b.label) ? 1 : -1) : -1 + ); + + return data.sort((a, b) => (a.disabled && !b.disabled ? -1 : 1)); }, [loras, loraModels, currentMainModel?.base_model]); const handleChange = useCallback( diff --git a/invokeai/frontend/web/src/features/nodes/components/InputFieldComponent.tsx b/invokeai/frontend/web/src/features/nodes/components/InputFieldComponent.tsx index 23effc5375..0ecc43ef9c 100644 --- a/invokeai/frontend/web/src/features/nodes/components/InputFieldComponent.tsx +++ b/invokeai/frontend/web/src/features/nodes/components/InputFieldComponent.tsx @@ -20,6 +20,7 @@ import StringInputFieldComponent from './fields/StringInputFieldComponent'; import UnetInputFieldComponent from './fields/UnetInputFieldComponent'; import VaeInputFieldComponent from './fields/VaeInputFieldComponent'; import VaeModelInputFieldComponent from './fields/VaeModelInputFieldComponent'; +import RefinerModelInputFieldComponent from './fields/RefinerModelInputFieldComponent'; type InputFieldComponentProps = { nodeId: string; @@ -155,6 +156,16 @@ const InputFieldComponent = (props: InputFieldComponentProps) => { ); } + if (type === 'refiner_model' && template.type === 'refiner_model') { + return ( + + ); + } + if (type === 'vae_model' && template.type === 'vae_model') { return ( @@ -24,8 +26,11 @@ const ModelInputFieldComponent = ( const dispatch = useAppDispatch(); const { t } = useTranslation(); + const isSyncModelEnabled = useFeatureStatus('syncModels').isFeatureEnabled; - const { data: mainModels, isLoading } = useGetMainModelsQuery(); + const { data: mainModels, isLoading } = useGetMainModelsQuery( + NON_REFINER_BASE_MODELS + ); const data = useMemo(() => { if (!mainModels) { @@ -103,9 +108,11 @@ const ModelInputFieldComponent = ( disabled={data.length === 0} onChange={handleChangeModel} /> - - - + {isSyncModelEnabled && ( + + + + )} ); }; diff --git a/invokeai/frontend/web/src/features/nodes/components/fields/RefinerModelInputFieldComponent.tsx b/invokeai/frontend/web/src/features/nodes/components/fields/RefinerModelInputFieldComponent.tsx new file mode 100644 index 0000000000..28c6567e8d --- /dev/null +++ b/invokeai/frontend/web/src/features/nodes/components/fields/RefinerModelInputFieldComponent.tsx @@ -0,0 +1,120 @@ +import { Box, Flex } from '@chakra-ui/react'; +import { SelectItem } from '@mantine/core'; +import { useAppDispatch } from 'app/store/storeHooks'; +import IAIMantineSearchableSelect from 'common/components/IAIMantineSearchableSelect'; +import { fieldValueChanged } from 'features/nodes/store/nodesSlice'; +import { + RefinerModelInputFieldTemplate, + RefinerModelInputFieldValue, +} from 'features/nodes/types/types'; +import { MODEL_TYPE_MAP } from 'features/parameters/types/constants'; +import { modelIdToMainModelParam } from 'features/parameters/util/modelIdToMainModelParam'; +import SyncModelsButton from 'features/ui/components/tabs/ModelManager/subpanels/ModelManagerSettingsPanel/SyncModelsButton'; +import { forEach } from 'lodash-es'; +import { memo, useCallback, useMemo } from 'react'; +import { useTranslation } from 'react-i18next'; +import { REFINER_BASE_MODELS } from 'services/api/constants'; +import { useGetMainModelsQuery } from 'services/api/endpoints/models'; +import { FieldComponentProps } from './types'; +import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus'; + +const RefinerModelInputFieldComponent = ( + props: FieldComponentProps< + RefinerModelInputFieldValue, + RefinerModelInputFieldTemplate + > +) => { + const { nodeId, field } = props; + + const dispatch = useAppDispatch(); + const { t } = useTranslation(); + const isSyncModelEnabled = useFeatureStatus('syncModels').isFeatureEnabled; + const { data: refinerModels, isLoading } = + useGetMainModelsQuery(REFINER_BASE_MODELS); + + const data = useMemo(() => { + if (!refinerModels) { + return []; + } + + const data: SelectItem[] = []; + + forEach(refinerModels.entities, (model, id) => { + if (!model) { + return; + } + + data.push({ + value: id, + label: model.model_name, + group: MODEL_TYPE_MAP[model.base_model], + }); + }); + + return data; + }, [refinerModels]); + + // grab the full model entity from the RTK Query cache + // TODO: maybe we should just store the full model entity in state? + const selectedModel = useMemo( + () => + refinerModels?.entities[ + `${field.value?.base_model}/main/${field.value?.model_name}` + ] ?? null, + [field.value?.base_model, field.value?.model_name, refinerModels?.entities] + ); + + const handleChangeModel = useCallback( + (v: string | null) => { + if (!v) { + return; + } + + const newModel = modelIdToMainModelParam(v); + + if (!newModel) { + return; + } + + dispatch( + fieldValueChanged({ + nodeId, + fieldName: field.name, + value: newModel, + }) + ); + }, + [dispatch, field.name, nodeId] + ); + + return isLoading ? ( + + ) : ( + + 0 ? 'Select a model' : 'No models available'} + data={data} + error={data.length === 0} + disabled={data.length === 0} + onChange={handleChangeModel} + /> + {isSyncModelEnabled && ( + + + + )} + + ); +}; + +export default memo(RefinerModelInputFieldComponent); diff --git a/invokeai/frontend/web/src/features/nodes/types/constants.ts b/invokeai/frontend/web/src/features/nodes/types/constants.ts index c07efc9c27..d83d240847 100644 --- a/invokeai/frontend/web/src/features/nodes/types/constants.ts +++ b/invokeai/frontend/web/src/features/nodes/types/constants.ts @@ -17,6 +17,7 @@ export const FIELD_TYPE_MAP: Record = { ClipField: 'clip', VaeField: 'vae', model: 'model', + refiner_model: 'refiner_model', vae_model: 'vae_model', lora_model: 'lora_model', controlnet_model: 'controlnet_model', @@ -120,6 +121,12 @@ export const FIELDS: Record = { title: 'Model', description: 'Models are models.', }, + refiner_model: { + color: 'teal', + colorCssVar: getColorTokenCssVariable('teal'), + title: 'Refiner Model', + description: 'Models are models.', + }, vae_model: { color: 'teal', colorCssVar: getColorTokenCssVariable('teal'), diff --git a/invokeai/frontend/web/src/features/nodes/types/types.ts b/invokeai/frontend/web/src/features/nodes/types/types.ts index f111155a39..157b990b96 100644 --- a/invokeai/frontend/web/src/features/nodes/types/types.ts +++ b/invokeai/frontend/web/src/features/nodes/types/types.ts @@ -70,6 +70,7 @@ export type FieldType = | 'vae' | 'control' | 'model' + | 'refiner_model' | 'vae_model' | 'lora_model' | 'controlnet_model' @@ -100,6 +101,7 @@ export type InputFieldValue = | ControlInputFieldValue | EnumInputFieldValue | MainModelInputFieldValue + | RefinerModelInputFieldValue | VaeModelInputFieldValue | LoRAModelInputFieldValue | ControlNetModelInputFieldValue @@ -128,6 +130,7 @@ export type InputFieldTemplate = | ControlInputFieldTemplate | EnumInputFieldTemplate | ModelInputFieldTemplate + | RefinerModelInputFieldTemplate | VaeModelInputFieldTemplate | LoRAModelInputFieldTemplate | ControlNetModelInputFieldTemplate @@ -243,6 +246,11 @@ export type MainModelInputFieldValue = FieldValueBase & { value?: MainModelParam; }; +export type RefinerModelInputFieldValue = FieldValueBase & { + type: 'refiner_model'; + value?: MainModelParam; +}; + export type VaeModelInputFieldValue = FieldValueBase & { type: 'vae_model'; value?: VaeModelParam; @@ -367,6 +375,11 @@ export type ModelInputFieldTemplate = InputFieldTemplateBase & { type: 'model'; }; +export type RefinerModelInputFieldTemplate = InputFieldTemplateBase & { + default: string; + type: 'refiner_model'; +}; + export type VaeModelInputFieldTemplate = InputFieldTemplateBase & { default: string; type: 'vae_model'; diff --git a/invokeai/frontend/web/src/features/nodes/util/fieldTemplateBuilders.ts b/invokeai/frontend/web/src/features/nodes/util/fieldTemplateBuilders.ts index 9c01deded6..83692533f7 100644 --- a/invokeai/frontend/web/src/features/nodes/util/fieldTemplateBuilders.ts +++ b/invokeai/frontend/web/src/features/nodes/util/fieldTemplateBuilders.ts @@ -22,6 +22,7 @@ import { LoRAModelInputFieldTemplate, ModelInputFieldTemplate, OutputFieldTemplate, + RefinerModelInputFieldTemplate, StringInputFieldTemplate, TypeHints, UNetInputFieldTemplate, @@ -178,6 +179,21 @@ const buildModelInputFieldTemplate = ({ return template; }; +const buildRefinerModelInputFieldTemplate = ({ + schemaObject, + baseField, +}: BuildInputFieldArg): RefinerModelInputFieldTemplate => { + const template: RefinerModelInputFieldTemplate = { + ...baseField, + type: 'refiner_model', + inputRequirement: 'always', + inputKind: 'direct', + default: schemaObject.default ?? undefined, + }; + + return template; +}; + const buildVaeModelInputFieldTemplate = ({ schemaObject, baseField, @@ -492,6 +508,9 @@ export const buildInputFieldTemplate = ( if (['model'].includes(fieldType)) { return buildModelInputFieldTemplate({ schemaObject, baseField }); } + if (['refiner_model'].includes(fieldType)) { + return buildRefinerModelInputFieldTemplate({ schemaObject, baseField }); + } if (['vae_model'].includes(fieldType)) { return buildVaeModelInputFieldTemplate({ schemaObject, baseField }); } diff --git a/invokeai/frontend/web/src/features/nodes/util/fieldValueBuilders.ts b/invokeai/frontend/web/src/features/nodes/util/fieldValueBuilders.ts index f54a7640bd..3c6850a88a 100644 --- a/invokeai/frontend/web/src/features/nodes/util/fieldValueBuilders.ts +++ b/invokeai/frontend/web/src/features/nodes/util/fieldValueBuilders.ts @@ -76,6 +76,10 @@ export const buildInputFieldValue = ( fieldValue.value = undefined; } + if (template.type === 'refiner_model') { + fieldValue.value = undefined; + } + if (template.type === 'vae_model') { fieldValue.value = undefined; } diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addNSFWCheckerToGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addNSFWCheckerToGraph.ts new file mode 100644 index 0000000000..35e7f3ac38 --- /dev/null +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addNSFWCheckerToGraph.ts @@ -0,0 +1,70 @@ +import { RootState } from 'app/store/store'; +import { NonNullableGraph } from 'features/nodes/types/types'; +import { activeTabNameSelector } from 'features/ui/store/uiSelectors'; +import { + ImageNSFWBlurInvocation, + LatentsToImageInvocation, + MetadataAccumulatorInvocation, +} from 'services/api/types'; +import { + LATENTS_TO_IMAGE, + METADATA_ACCUMULATOR, + NSFW_CHECKER, +} from './constants'; + +export const addNSFWCheckerToGraph = ( + state: RootState, + graph: NonNullableGraph, + nodeIdToAddTo = LATENTS_TO_IMAGE +): void => { + const activeTabName = activeTabNameSelector(state); + + const is_intermediate = + activeTabName === 'unifiedCanvas' ? !state.canvas.shouldAutoSave : false; + + const nodeToAddTo = graph.nodes[nodeIdToAddTo] as + | LatentsToImageInvocation + | undefined; + + const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as + | MetadataAccumulatorInvocation + | undefined; + + if (!nodeToAddTo) { + // something has gone terribly awry + return; + } + + nodeToAddTo.is_intermediate = true; + + const nsfwCheckerNode: ImageNSFWBlurInvocation = { + id: NSFW_CHECKER, + type: 'img_nsfw', + is_intermediate, + }; + + graph.nodes[NSFW_CHECKER] = nsfwCheckerNode; + graph.edges.push({ + source: { + node_id: nodeIdToAddTo, + field: 'image', + }, + destination: { + node_id: NSFW_CHECKER, + field: 'image', + }, + }); + + if (metadataAccumulator) { + graph.edges.push({ + source: { + node_id: METADATA_ACCUMULATOR, + field: 'metadata', + }, + destination: { + node_id: NSFW_CHECKER, + field: 'metadata', + }, + }); + } +}; diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSDXLRefinerToGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSDXLRefinerToGraph.ts new file mode 100644 index 0000000000..c47c7be8b4 --- /dev/null +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSDXLRefinerToGraph.ts @@ -0,0 +1,186 @@ +import { RootState } from 'app/store/store'; +import { MetadataAccumulatorInvocation } from 'services/api/types'; +import { NonNullableGraph } from '../../types/types'; +import { + IMAGE_TO_LATENTS, + LATENTS_TO_IMAGE, + METADATA_ACCUMULATOR, + SDXL_LATENTS_TO_LATENTS, + SDXL_MODEL_LOADER, + SDXL_REFINER_LATENTS_TO_LATENTS, + SDXL_REFINER_MODEL_LOADER, + SDXL_REFINER_NEGATIVE_CONDITIONING, + SDXL_REFINER_POSITIVE_CONDITIONING, +} from './constants'; + +export const addSDXLRefinerToGraph = ( + state: RootState, + graph: NonNullableGraph, + baseNodeId: string +): void => { + const { positivePrompt, negativePrompt } = state.generation; + const { + refinerModel, + refinerAestheticScore, + positiveStylePrompt, + negativeStylePrompt, + refinerSteps, + refinerScheduler, + refinerCFGScale, + refinerStart, + } = state.sdxl; + + if (!refinerModel) return; + + const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as + | MetadataAccumulatorInvocation + | undefined; + + if (metadataAccumulator) { + metadataAccumulator.refiner_model = refinerModel; + metadataAccumulator.refiner_aesthetic_store = refinerAestheticScore; + metadataAccumulator.refiner_cfg_scale = refinerCFGScale; + metadataAccumulator.refiner_scheduler = refinerScheduler; + metadataAccumulator.refiner_start = refinerStart; + metadataAccumulator.refiner_steps = refinerSteps; + } + + // Unplug SDXL Latents Generation To Latents To Image + graph.edges = graph.edges.filter( + (e) => + !(e.source.node_id === baseNodeId && ['latents'].includes(e.source.field)) + ); + + graph.edges = graph.edges.filter( + (e) => + !( + e.source.node_id === SDXL_MODEL_LOADER && + ['vae'].includes(e.source.field) + ) + ); + + // connect the VAE back to the i2l, which we just removed in the filter + // but only if we are doing l2l + if (baseNodeId === SDXL_LATENTS_TO_LATENTS) { + graph.edges.push({ + source: { + node_id: SDXL_MODEL_LOADER, + field: 'vae', + }, + destination: { + node_id: IMAGE_TO_LATENTS, + field: 'vae', + }, + }); + } + + graph.nodes[SDXL_REFINER_MODEL_LOADER] = { + type: 'sdxl_refiner_model_loader', + id: SDXL_REFINER_MODEL_LOADER, + model: refinerModel, + }; + graph.nodes[SDXL_REFINER_POSITIVE_CONDITIONING] = { + type: 'sdxl_refiner_compel_prompt', + id: SDXL_REFINER_POSITIVE_CONDITIONING, + style: `${positivePrompt} ${positiveStylePrompt}`, + aesthetic_score: refinerAestheticScore, + }; + graph.nodes[SDXL_REFINER_NEGATIVE_CONDITIONING] = { + type: 'sdxl_refiner_compel_prompt', + id: SDXL_REFINER_NEGATIVE_CONDITIONING, + style: `${negativePrompt} ${negativeStylePrompt}`, + aesthetic_score: refinerAestheticScore, + }; + graph.nodes[SDXL_REFINER_LATENTS_TO_LATENTS] = { + type: 'l2l_sdxl', + id: SDXL_REFINER_LATENTS_TO_LATENTS, + cfg_scale: refinerCFGScale, + steps: refinerSteps / (1 - Math.min(refinerStart, 0.99)), + scheduler: refinerScheduler, + denoising_start: refinerStart, + denoising_end: 1, + }; + + graph.edges.push( + { + source: { + node_id: SDXL_REFINER_MODEL_LOADER, + field: 'unet', + }, + destination: { + node_id: SDXL_REFINER_LATENTS_TO_LATENTS, + field: 'unet', + }, + }, + { + source: { + node_id: SDXL_REFINER_MODEL_LOADER, + field: 'vae', + }, + destination: { + node_id: LATENTS_TO_IMAGE, + field: 'vae', + }, + }, + { + source: { + node_id: SDXL_REFINER_MODEL_LOADER, + field: 'clip2', + }, + destination: { + node_id: SDXL_REFINER_POSITIVE_CONDITIONING, + field: 'clip2', + }, + }, + { + source: { + node_id: SDXL_REFINER_MODEL_LOADER, + field: 'clip2', + }, + destination: { + node_id: SDXL_REFINER_NEGATIVE_CONDITIONING, + field: 'clip2', + }, + }, + { + source: { + node_id: SDXL_REFINER_POSITIVE_CONDITIONING, + field: 'conditioning', + }, + destination: { + node_id: SDXL_REFINER_LATENTS_TO_LATENTS, + field: 'positive_conditioning', + }, + }, + { + source: { + node_id: SDXL_REFINER_NEGATIVE_CONDITIONING, + field: 'conditioning', + }, + destination: { + node_id: SDXL_REFINER_LATENTS_TO_LATENTS, + field: 'negative_conditioning', + }, + }, + { + source: { + node_id: baseNodeId, + field: 'latents', + }, + destination: { + node_id: SDXL_REFINER_LATENTS_TO_LATENTS, + field: 'latents', + }, + }, + { + source: { + node_id: SDXL_REFINER_LATENTS_TO_LATENTS, + field: 'latents', + }, + destination: { + node_id: LATENTS_TO_IMAGE, + field: 'latents', + }, + } + ); +}; diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addWatermarkerToGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addWatermarkerToGraph.ts new file mode 100644 index 0000000000..f2e8a0aeca --- /dev/null +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addWatermarkerToGraph.ts @@ -0,0 +1,95 @@ +import { RootState } from 'app/store/store'; +import { NonNullableGraph } from 'features/nodes/types/types'; +import { activeTabNameSelector } from 'features/ui/store/uiSelectors'; +import { + ImageNSFWBlurInvocation, + ImageWatermarkInvocation, + LatentsToImageInvocation, + MetadataAccumulatorInvocation, +} from 'services/api/types'; +import { + LATENTS_TO_IMAGE, + METADATA_ACCUMULATOR, + NSFW_CHECKER, + WATERMARKER, +} from './constants'; + +export const addWatermarkerToGraph = ( + state: RootState, + graph: NonNullableGraph, + nodeIdToAddTo = LATENTS_TO_IMAGE +): void => { + const activeTabName = activeTabNameSelector(state); + + const is_intermediate = + activeTabName === 'unifiedCanvas' ? !state.canvas.shouldAutoSave : false; + + const nodeToAddTo = graph.nodes[nodeIdToAddTo] as + | LatentsToImageInvocation + | undefined; + + const nsfwCheckerNode = graph.nodes[NSFW_CHECKER] as + | ImageNSFWBlurInvocation + | undefined; + + const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as + | MetadataAccumulatorInvocation + | undefined; + + if (!nodeToAddTo) { + // something has gone terribly awry + return; + } + + const watermarkerNode: ImageWatermarkInvocation = { + id: WATERMARKER, + type: 'img_watermark', + is_intermediate, + }; + + graph.nodes[WATERMARKER] = watermarkerNode; + + // no matter the situation, we want the l2i node to be intermediate + nodeToAddTo.is_intermediate = true; + + if (nsfwCheckerNode) { + // if we are using NSFW checker, we need to "disable" it output by marking it intermediate, + // then connect it to the watermark node + nsfwCheckerNode.is_intermediate = true; + graph.edges.push({ + source: { + node_id: NSFW_CHECKER, + field: 'image', + }, + destination: { + node_id: WATERMARKER, + field: 'image', + }, + }); + } else { + // otherwise we just connect to the watermark node + graph.edges.push({ + source: { + node_id: nodeIdToAddTo, + field: 'image', + }, + destination: { + node_id: WATERMARKER, + field: 'image', + }, + }); + } + + if (metadataAccumulator) { + graph.edges.push({ + source: { + node_id: METADATA_ACCUMULATOR, + field: 'metadata', + }, + destination: { + node_id: WATERMARKER, + field: 'metadata', + }, + }); + } +}; diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasImageToImageGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasImageToImageGraph.ts index 4ef3ff77d2..81e99e0dd0 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasImageToImageGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasImageToImageGraph.ts @@ -10,7 +10,9 @@ import { import { addControlNetToLinearGraph } from './addControlNetToLinearGraph'; import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph'; import { addLoRAsToGraph } from './addLoRAsToGraph'; +import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph'; import { addVAEToGraph } from './addVAEToGraph'; +import { addWatermarkerToGraph } from './addWatermarkerToGraph'; import { CLIP_SKIP, IMAGE_TO_IMAGE_GRAPH, @@ -103,11 +105,6 @@ export const buildCanvasImageToImageGraph = ( is_intermediate: true, skipped_layers: clipSkip, }, - [LATENTS_TO_IMAGE]: { - is_intermediate: !shouldAutoSave, - type: 'l2i', - id: LATENTS_TO_IMAGE, - }, [LATENTS_TO_LATENTS]: { type: 'l2l', id: LATENTS_TO_LATENTS, @@ -126,6 +123,11 @@ export const buildCanvasImageToImageGraph = ( // image_name: initialImage.image_name, // }, }, + [LATENTS_TO_IMAGE]: { + type: 'l2i', + id: LATENTS_TO_IMAGE, + is_intermediate: !shouldAutoSave, + }, }, edges: [ { @@ -310,17 +312,6 @@ export const buildCanvasImageToImageGraph = ( init_image: initialImage.image_name, }; - graph.edges.push({ - source: { - node_id: METADATA_ACCUMULATOR, - field: 'metadata', - }, - destination: { - node_id: LATENTS_TO_IMAGE, - field: 'metadata', - }, - }); - // add LoRA support addLoRAsToGraph(state, graph, LATENTS_TO_LATENTS); @@ -333,5 +324,16 @@ export const buildCanvasImageToImageGraph = ( // add controlnet, mutating `graph` addControlNetToLinearGraph(state, graph, LATENTS_TO_LATENTS); + // NSFW & watermark - must be last thing added to graph + if (state.system.shouldUseNSFWChecker) { + // must add before watermarker! + addNSFWCheckerToGraph(state, graph); + } + + if (state.system.shouldUseWatermarker) { + // must add after nsfw checker! + addWatermarkerToGraph(state, graph); + } + return graph; }; diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasInpaintGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasInpaintGraph.ts index fa6e20aaa4..4154c1b5eb 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasInpaintGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasInpaintGraph.ts @@ -20,6 +20,8 @@ import { RANDOM_INT, RANGE_OF_SIZE, } from './constants'; +import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph'; +import { addWatermarkerToGraph } from './addWatermarkerToGraph'; /** * Builds the Canvas tab's Inpaint graph. @@ -249,5 +251,16 @@ export const buildCanvasInpaintGraph = ( (graph.nodes[RANGE_OF_SIZE] as RangeOfSizeInvocation).start = seed; } + // NSFW & watermark - must be last thing added to graph + if (state.system.shouldUseNSFWChecker) { + // must add before watermarker! + addNSFWCheckerToGraph(state, graph, INPAINT); + } + + if (state.system.shouldUseWatermarker) { + // must add after nsfw checker! + addWatermarkerToGraph(state, graph, INPAINT); + } + return graph; }; diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasTextToImageGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasTextToImageGraph.ts index d0eabbc14b..597a643367 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasTextToImageGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasTextToImageGraph.ts @@ -5,7 +5,9 @@ import { initialGenerationState } from 'features/parameters/store/generationSlic import { addControlNetToLinearGraph } from './addControlNetToLinearGraph'; import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph'; import { addLoRAsToGraph } from './addLoRAsToGraph'; +import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph'; import { addVAEToGraph } from './addVAEToGraph'; +import { addWatermarkerToGraph } from './addWatermarkerToGraph'; import { CLIP_SKIP, LATENTS_TO_IMAGE, @@ -215,17 +217,6 @@ export const buildCanvasTextToImageGraph = ( clip_skip: clipSkip, }; - graph.edges.push({ - source: { - node_id: METADATA_ACCUMULATOR, - field: 'metadata', - }, - destination: { - node_id: LATENTS_TO_IMAGE, - field: 'metadata', - }, - }); - // add LoRA support addLoRAsToGraph(state, graph, TEXT_TO_LATENTS); @@ -238,5 +229,16 @@ export const buildCanvasTextToImageGraph = ( // add controlnet, mutating `graph` addControlNetToLinearGraph(state, graph, TEXT_TO_LATENTS); + // NSFW & watermark - must be last thing added to graph + if (state.system.shouldUseNSFWChecker) { + // must add before watermarker! + addNSFWCheckerToGraph(state, graph); + } + + if (state.system.shouldUseWatermarker) { + // must add after nsfw checker! + addWatermarkerToGraph(state, graph); + } + return graph; }; diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearImageToImageGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearImageToImageGraph.ts index f5f5e670c9..d78e2e1356 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearImageToImageGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearImageToImageGraph.ts @@ -9,7 +9,9 @@ import { import { addControlNetToLinearGraph } from './addControlNetToLinearGraph'; import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph'; import { addLoRAsToGraph } from './addLoRAsToGraph'; +import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph'; import { addVAEToGraph } from './addVAEToGraph'; +import { addWatermarkerToGraph } from './addWatermarkerToGraph'; import { CLIP_SKIP, IMAGE_TO_IMAGE_GRAPH, @@ -46,6 +48,7 @@ export const buildLinearImageToImageGraph = ( clipSkip, shouldUseCpuNoise, shouldUseNoiseSettings, + vaePrecision, } = state.generation; // TODO: add batch functionality @@ -113,6 +116,7 @@ export const buildLinearImageToImageGraph = ( [LATENTS_TO_IMAGE]: { type: 'l2i', id: LATENTS_TO_IMAGE, + fp32: vaePrecision === 'fp32' ? true : false, }, [LATENTS_TO_LATENTS]: { type: 'l2l', @@ -129,6 +133,7 @@ export const buildLinearImageToImageGraph = ( // image: { // image_name: initialImage.image_name, // }, + fp32: vaePrecision === 'fp32' ? true : false, }, }, edges: [ @@ -294,42 +299,6 @@ export const buildLinearImageToImageGraph = ( }); } - // TODO: add batch functionality - // if (isBatchEnabled && asInitialImage && batchImageNames.length > 0) { - // // we are going to connect an iterate up to the init image - // delete (graph.nodes[IMAGE_TO_LATENTS] as ImageToLatentsInvocation).image; - - // const imageCollection: ImageCollectionInvocation = { - // id: IMAGE_COLLECTION, - // type: 'image_collection', - // images: batchImageNames.map((image_name) => ({ image_name })), - // }; - - // const imageCollectionIterate: IterateInvocation = { - // id: IMAGE_COLLECTION_ITERATE, - // type: 'iterate', - // }; - - // graph.nodes[IMAGE_COLLECTION] = imageCollection; - // graph.nodes[IMAGE_COLLECTION_ITERATE] = imageCollectionIterate; - - // graph.edges.push({ - // source: { node_id: IMAGE_COLLECTION, field: 'collection' }, - // destination: { - // node_id: IMAGE_COLLECTION_ITERATE, - // field: 'collection', - // }, - // }); - - // graph.edges.push({ - // source: { node_id: IMAGE_COLLECTION_ITERATE, field: 'item' }, - // destination: { - // node_id: IMAGE_TO_LATENTS, - // field: 'image', - // }, - // }); - // } - // add metadata accumulator, which is only mostly populated - some fields are added later graph.nodes[METADATA_ACCUMULATOR] = { id: METADATA_ACCUMULATOR, @@ -353,17 +322,6 @@ export const buildLinearImageToImageGraph = ( init_image: initialImage.imageName, }; - graph.edges.push({ - source: { - node_id: METADATA_ACCUMULATOR, - field: 'metadata', - }, - destination: { - node_id: LATENTS_TO_IMAGE, - field: 'metadata', - }, - }); - // add LoRA support addLoRAsToGraph(state, graph, LATENTS_TO_LATENTS); @@ -376,5 +334,16 @@ export const buildLinearImageToImageGraph = ( // add controlnet, mutating `graph` addControlNetToLinearGraph(state, graph, LATENTS_TO_LATENTS); + // NSFW & watermark - must be last thing added to graph + if (state.system.shouldUseNSFWChecker) { + // must add before watermarker! + addNSFWCheckerToGraph(state, graph); + } + + if (state.system.shouldUseWatermarker) { + // must add after nsfw checker! + addWatermarkerToGraph(state, graph); + } + return graph; }; diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLImageToImageGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLImageToImageGraph.ts new file mode 100644 index 0000000000..a260dbc467 --- /dev/null +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLImageToImageGraph.ts @@ -0,0 +1,387 @@ +import { logger } from 'app/logging/logger'; +import { RootState } from 'app/store/store'; +import { NonNullableGraph } from 'features/nodes/types/types'; +import { initialGenerationState } from 'features/parameters/store/generationSlice'; +import { + ImageResizeInvocation, + ImageToLatentsInvocation, +} from 'services/api/types'; +import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph'; +import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph'; +import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph'; +import { addWatermarkerToGraph } from './addWatermarkerToGraph'; +import { + IMAGE_TO_LATENTS, + LATENTS_TO_IMAGE, + METADATA_ACCUMULATOR, + NEGATIVE_CONDITIONING, + NOISE, + POSITIVE_CONDITIONING, + RESIZE, + SDXL_IMAGE_TO_IMAGE_GRAPH, + SDXL_LATENTS_TO_LATENTS, + SDXL_MODEL_LOADER, +} from './constants'; + +/** + * Builds the Image to Image tab graph. + */ +export const buildLinearSDXLImageToImageGraph = ( + state: RootState +): NonNullableGraph => { + const log = logger('nodes'); + const { + positivePrompt, + negativePrompt, + model, + cfgScale: cfg_scale, + scheduler, + steps, + initialImage, + shouldFitToWidthHeight, + width, + height, + clipSkip, + shouldUseCpuNoise, + shouldUseNoiseSettings, + vaePrecision, + } = state.generation; + + const { + positiveStylePrompt, + negativeStylePrompt, + shouldConcatSDXLStylePrompt, + shouldUseSDXLRefiner, + refinerStart, + sdxlImg2ImgDenoisingStrength: strength, + } = state.sdxl; + + /** + * The easiest way to build linear graphs is to do it in the node editor, then copy and paste the + * full graph here as a template. Then use the parameters from app state and set friendlier node + * ids. + * + * The only thing we need extra logic for is handling randomized seed, control net, and for img2img, + * the `fit` param. These are added to the graph at the end. + */ + + if (!initialImage) { + log.error('No initial image found in state'); + throw new Error('No initial image found in state'); + } + + if (!model) { + log.error('No model found in state'); + throw new Error('No model found in state'); + } + + const use_cpu = shouldUseNoiseSettings + ? shouldUseCpuNoise + : initialGenerationState.shouldUseCpuNoise; + + // copy-pasted graph from node editor, filled in with state values & friendly node ids + const graph: NonNullableGraph = { + id: SDXL_IMAGE_TO_IMAGE_GRAPH, + nodes: { + [SDXL_MODEL_LOADER]: { + type: 'sdxl_model_loader', + id: SDXL_MODEL_LOADER, + model, + }, + [POSITIVE_CONDITIONING]: { + type: 'sdxl_compel_prompt', + id: POSITIVE_CONDITIONING, + prompt: positivePrompt, + style: shouldConcatSDXLStylePrompt + ? `${positivePrompt} ${positiveStylePrompt}` + : positiveStylePrompt, + }, + [NEGATIVE_CONDITIONING]: { + type: 'sdxl_compel_prompt', + id: NEGATIVE_CONDITIONING, + prompt: negativePrompt, + style: shouldConcatSDXLStylePrompt + ? `${negativePrompt} ${negativeStylePrompt}` + : negativeStylePrompt, + }, + [NOISE]: { + type: 'noise', + id: NOISE, + use_cpu, + }, + [LATENTS_TO_IMAGE]: { + type: 'l2i', + id: LATENTS_TO_IMAGE, + fp32: vaePrecision === 'fp32' ? true : false, + }, + [SDXL_LATENTS_TO_LATENTS]: { + type: 'l2l_sdxl', + id: SDXL_LATENTS_TO_LATENTS, + cfg_scale, + scheduler, + steps, + denoising_start: shouldUseSDXLRefiner + ? Math.min(refinerStart, 1 - strength) + : 1 - strength, + denoising_end: shouldUseSDXLRefiner ? refinerStart : 1, + }, + [IMAGE_TO_LATENTS]: { + type: 'i2l', + id: IMAGE_TO_LATENTS, + // must be set manually later, bc `fit` parameter may require a resize node inserted + // image: { + // image_name: initialImage.image_name, + // }, + fp32: vaePrecision === 'fp32' ? true : false, + }, + }, + edges: [ + { + source: { + node_id: SDXL_MODEL_LOADER, + field: 'unet', + }, + destination: { + node_id: SDXL_LATENTS_TO_LATENTS, + field: 'unet', + }, + }, + { + source: { + node_id: SDXL_MODEL_LOADER, + field: 'vae', + }, + destination: { + node_id: LATENTS_TO_IMAGE, + field: 'vae', + }, + }, + { + source: { + node_id: SDXL_MODEL_LOADER, + field: 'vae', + }, + destination: { + node_id: IMAGE_TO_LATENTS, + field: 'vae', + }, + }, + { + source: { + node_id: SDXL_MODEL_LOADER, + field: 'clip', + }, + destination: { + node_id: POSITIVE_CONDITIONING, + field: 'clip', + }, + }, + { + source: { + node_id: SDXL_MODEL_LOADER, + field: 'clip2', + }, + destination: { + node_id: POSITIVE_CONDITIONING, + field: 'clip2', + }, + }, + { + source: { + node_id: SDXL_MODEL_LOADER, + field: 'clip', + }, + destination: { + node_id: NEGATIVE_CONDITIONING, + field: 'clip', + }, + }, + { + source: { + node_id: SDXL_MODEL_LOADER, + field: 'clip2', + }, + destination: { + node_id: NEGATIVE_CONDITIONING, + field: 'clip2', + }, + }, + { + source: { + node_id: SDXL_LATENTS_TO_LATENTS, + field: 'latents', + }, + destination: { + node_id: LATENTS_TO_IMAGE, + field: 'latents', + }, + }, + { + source: { + node_id: IMAGE_TO_LATENTS, + field: 'latents', + }, + destination: { + node_id: SDXL_LATENTS_TO_LATENTS, + field: 'latents', + }, + }, + { + source: { + node_id: NOISE, + field: 'noise', + }, + destination: { + node_id: SDXL_LATENTS_TO_LATENTS, + field: 'noise', + }, + }, + { + source: { + node_id: POSITIVE_CONDITIONING, + field: 'conditioning', + }, + destination: { + node_id: SDXL_LATENTS_TO_LATENTS, + field: 'positive_conditioning', + }, + }, + { + source: { + node_id: NEGATIVE_CONDITIONING, + field: 'conditioning', + }, + destination: { + node_id: SDXL_LATENTS_TO_LATENTS, + field: 'negative_conditioning', + }, + }, + ], + }; + + // handle `fit` + if ( + shouldFitToWidthHeight && + (initialImage.width !== width || initialImage.height !== height) + ) { + // The init image needs to be resized to the specified width and height before being passed to `IMAGE_TO_LATENTS` + + // Create a resize node, explicitly setting its image + const resizeNode: ImageResizeInvocation = { + id: RESIZE, + type: 'img_resize', + image: { + image_name: initialImage.imageName, + }, + is_intermediate: true, + width, + height, + }; + + graph.nodes[RESIZE] = resizeNode; + + // The `RESIZE` node then passes its image to `IMAGE_TO_LATENTS` + graph.edges.push({ + source: { node_id: RESIZE, field: 'image' }, + destination: { + node_id: IMAGE_TO_LATENTS, + field: 'image', + }, + }); + + // The `RESIZE` node also passes its width and height to `NOISE` + graph.edges.push({ + source: { node_id: RESIZE, field: 'width' }, + destination: { + node_id: NOISE, + field: 'width', + }, + }); + + graph.edges.push({ + source: { node_id: RESIZE, field: 'height' }, + destination: { + node_id: NOISE, + field: 'height', + }, + }); + } else { + // We are not resizing, so we need to set the image on the `IMAGE_TO_LATENTS` node explicitly + (graph.nodes[IMAGE_TO_LATENTS] as ImageToLatentsInvocation).image = { + image_name: initialImage.imageName, + }; + + // Pass the image's dimensions to the `NOISE` node + graph.edges.push({ + source: { node_id: IMAGE_TO_LATENTS, field: 'width' }, + destination: { + node_id: NOISE, + field: 'width', + }, + }); + graph.edges.push({ + source: { node_id: IMAGE_TO_LATENTS, field: 'height' }, + destination: { + node_id: NOISE, + field: 'height', + }, + }); + } + + // add metadata accumulator, which is only mostly populated - some fields are added later + graph.nodes[METADATA_ACCUMULATOR] = { + id: METADATA_ACCUMULATOR, + type: 'metadata_accumulator', + generation_mode: 'sdxl_img2img', + cfg_scale, + height, + width, + positive_prompt: '', // set in addDynamicPromptsToGraph + negative_prompt: negativePrompt, + model, + seed: 0, // set in addDynamicPromptsToGraph + steps, + rand_device: use_cpu ? 'cpu' : 'cuda', + scheduler, + vae: undefined, + controlnets: [], + loras: [], + clip_skip: clipSkip, + strength: strength, + init_image: initialImage.imageName, + positive_style_prompt: positiveStylePrompt, + negative_style_prompt: negativeStylePrompt, + }; + + graph.edges.push({ + source: { + node_id: METADATA_ACCUMULATOR, + field: 'metadata', + }, + destination: { + node_id: LATENTS_TO_IMAGE, + field: 'metadata', + }, + }); + + // Add Refiner if enabled + if (shouldUseSDXLRefiner) { + addSDXLRefinerToGraph(state, graph, SDXL_LATENTS_TO_LATENTS); + } + + // add dynamic prompts - also sets up core iteration and seed + addDynamicPromptsToGraph(state, graph); + + // NSFW & watermark - must be last thing added to graph + if (state.system.shouldUseNSFWChecker) { + // must add before watermarker! + addNSFWCheckerToGraph(state, graph); + } + + if (state.system.shouldUseWatermarker) { + // must add after nsfw checker! + addWatermarkerToGraph(state, graph); + } + + return graph; +}; diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLTextToImageGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLTextToImageGraph.ts new file mode 100644 index 0000000000..c10e7831d3 --- /dev/null +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLTextToImageGraph.ts @@ -0,0 +1,269 @@ +import { logger } from 'app/logging/logger'; +import { RootState } from 'app/store/store'; +import { NonNullableGraph } from 'features/nodes/types/types'; +import { initialGenerationState } from 'features/parameters/store/generationSlice'; +import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph'; +import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph'; +import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph'; +import { addWatermarkerToGraph } from './addWatermarkerToGraph'; +import { + LATENTS_TO_IMAGE, + METADATA_ACCUMULATOR, + NEGATIVE_CONDITIONING, + NOISE, + POSITIVE_CONDITIONING, + SDXL_MODEL_LOADER, + SDXL_TEXT_TO_IMAGE_GRAPH, + SDXL_TEXT_TO_LATENTS, +} from './constants'; + +export const buildLinearSDXLTextToImageGraph = ( + state: RootState +): NonNullableGraph => { + const log = logger('nodes'); + const { + positivePrompt, + negativePrompt, + model, + cfgScale: cfg_scale, + scheduler, + steps, + width, + height, + clipSkip, + shouldUseCpuNoise, + shouldUseNoiseSettings, + vaePrecision, + } = state.generation; + + const { + positiveStylePrompt, + negativeStylePrompt, + shouldConcatSDXLStylePrompt, + shouldUseSDXLRefiner, + refinerStart, + } = state.sdxl; + + const use_cpu = shouldUseNoiseSettings + ? shouldUseCpuNoise + : initialGenerationState.shouldUseCpuNoise; + + if (!model) { + log.error('No model found in state'); + throw new Error('No model found in state'); + } + + /** + * The easiest way to build linear graphs is to do it in the node editor, then copy and paste the + * full graph here as a template. Then use the parameters from app state and set friendlier node + * ids. + * + * The only thing we need extra logic for is handling randomized seed, control net, and for img2img, + * the `fit` param. These are added to the graph at the end. + */ + + // copy-pasted graph from node editor, filled in with state values & friendly node ids + const graph: NonNullableGraph = { + id: SDXL_TEXT_TO_IMAGE_GRAPH, + nodes: { + [SDXL_MODEL_LOADER]: { + type: 'sdxl_model_loader', + id: SDXL_MODEL_LOADER, + model, + }, + [POSITIVE_CONDITIONING]: { + type: 'sdxl_compel_prompt', + id: POSITIVE_CONDITIONING, + prompt: positivePrompt, + style: shouldConcatSDXLStylePrompt + ? `${positivePrompt} ${positiveStylePrompt}` + : positiveStylePrompt, + }, + [NEGATIVE_CONDITIONING]: { + type: 'sdxl_compel_prompt', + id: NEGATIVE_CONDITIONING, + prompt: negativePrompt, + style: shouldConcatSDXLStylePrompt + ? `${negativePrompt} ${negativeStylePrompt}` + : negativeStylePrompt, + }, + [NOISE]: { + type: 'noise', + id: NOISE, + width, + height, + use_cpu, + }, + [SDXL_TEXT_TO_LATENTS]: { + type: 't2l_sdxl', + id: SDXL_TEXT_TO_LATENTS, + cfg_scale, + scheduler, + steps, + denoising_end: shouldUseSDXLRefiner ? refinerStart : 1, + }, + [LATENTS_TO_IMAGE]: { + type: 'l2i', + id: LATENTS_TO_IMAGE, + fp32: vaePrecision === 'fp32' ? true : false, + }, + }, + edges: [ + { + source: { + node_id: SDXL_MODEL_LOADER, + field: 'unet', + }, + destination: { + node_id: SDXL_TEXT_TO_LATENTS, + field: 'unet', + }, + }, + { + source: { + node_id: SDXL_MODEL_LOADER, + field: 'vae', + }, + destination: { + node_id: LATENTS_TO_IMAGE, + field: 'vae', + }, + }, + { + source: { + node_id: SDXL_MODEL_LOADER, + field: 'clip', + }, + destination: { + node_id: POSITIVE_CONDITIONING, + field: 'clip', + }, + }, + { + source: { + node_id: SDXL_MODEL_LOADER, + field: 'clip2', + }, + destination: { + node_id: POSITIVE_CONDITIONING, + field: 'clip2', + }, + }, + { + source: { + node_id: SDXL_MODEL_LOADER, + field: 'clip', + }, + destination: { + node_id: NEGATIVE_CONDITIONING, + field: 'clip', + }, + }, + { + source: { + node_id: SDXL_MODEL_LOADER, + field: 'clip2', + }, + destination: { + node_id: NEGATIVE_CONDITIONING, + field: 'clip2', + }, + }, + { + source: { + node_id: POSITIVE_CONDITIONING, + field: 'conditioning', + }, + destination: { + node_id: SDXL_TEXT_TO_LATENTS, + field: 'positive_conditioning', + }, + }, + { + source: { + node_id: NEGATIVE_CONDITIONING, + field: 'conditioning', + }, + destination: { + node_id: SDXL_TEXT_TO_LATENTS, + field: 'negative_conditioning', + }, + }, + { + source: { + node_id: NOISE, + field: 'noise', + }, + destination: { + node_id: SDXL_TEXT_TO_LATENTS, + field: 'noise', + }, + }, + { + source: { + node_id: SDXL_TEXT_TO_LATENTS, + field: 'latents', + }, + destination: { + node_id: LATENTS_TO_IMAGE, + field: 'latents', + }, + }, + ], + }; + + // add metadata accumulator, which is only mostly populated - some fields are added later + graph.nodes[METADATA_ACCUMULATOR] = { + id: METADATA_ACCUMULATOR, + type: 'metadata_accumulator', + generation_mode: 'sdxl_txt2img', + cfg_scale, + height, + width, + positive_prompt: '', // set in addDynamicPromptsToGraph + negative_prompt: negativePrompt, + model, + seed: 0, // set in addDynamicPromptsToGraph + steps, + rand_device: use_cpu ? 'cpu' : 'cuda', + scheduler, + vae: undefined, + controlnets: [], + loras: [], + clip_skip: clipSkip, + positive_style_prompt: positiveStylePrompt, + negative_style_prompt: negativeStylePrompt, + }; + + graph.edges.push({ + source: { + node_id: METADATA_ACCUMULATOR, + field: 'metadata', + }, + destination: { + node_id: LATENTS_TO_IMAGE, + field: 'metadata', + }, + }); + + // Add Refiner if enabled + if (shouldUseSDXLRefiner) { + addSDXLRefinerToGraph(state, graph, SDXL_TEXT_TO_LATENTS); + } + + // add dynamic prompts - also sets up core iteration and seed + addDynamicPromptsToGraph(state, graph); + + // NSFW & watermark - must be last thing added to graph + if (state.system.shouldUseNSFWChecker) { + // must add before watermarker! + addNSFWCheckerToGraph(state, graph); + } + + if (state.system.shouldUseWatermarker) { + // must add after nsfw checker! + addWatermarkerToGraph(state, graph); + } + + return graph; +}; diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearTextToImageGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearTextToImageGraph.ts index c8529f95d3..94f35feba0 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearTextToImageGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearTextToImageGraph.ts @@ -5,7 +5,9 @@ import { initialGenerationState } from 'features/parameters/store/generationSlic import { addControlNetToLinearGraph } from './addControlNetToLinearGraph'; import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph'; import { addLoRAsToGraph } from './addLoRAsToGraph'; +import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph'; import { addVAEToGraph } from './addVAEToGraph'; +import { addWatermarkerToGraph } from './addWatermarkerToGraph'; import { CLIP_SKIP, LATENTS_TO_IMAGE, @@ -34,6 +36,7 @@ export const buildLinearTextToImageGraph = ( clipSkip, shouldUseCpuNoise, shouldUseNoiseSettings, + vaePrecision, } = state.generation; const use_cpu = shouldUseNoiseSettings @@ -95,6 +98,7 @@ export const buildLinearTextToImageGraph = ( [LATENTS_TO_IMAGE]: { type: 'l2i', id: LATENTS_TO_IMAGE, + fp32: vaePrecision === 'fp32' ? true : false, }, }, edges: [ @@ -202,17 +206,6 @@ export const buildLinearTextToImageGraph = ( clip_skip: clipSkip, }; - graph.edges.push({ - source: { - node_id: METADATA_ACCUMULATOR, - field: 'metadata', - }, - destination: { - node_id: LATENTS_TO_IMAGE, - field: 'metadata', - }, - }); - // add LoRA support addLoRAsToGraph(state, graph, TEXT_TO_LATENTS); @@ -225,5 +218,16 @@ export const buildLinearTextToImageGraph = ( // add controlnet, mutating `graph` addControlNetToLinearGraph(state, graph, TEXT_TO_LATENTS); + // NSFW & watermark - must be last thing added to graph + if (state.system.shouldUseNSFWChecker) { + // must add before watermarker! + addNSFWCheckerToGraph(state, graph); + } + + if (state.system.shouldUseWatermarker) { + // must add after nsfw checker! + addWatermarkerToGraph(state, graph); + } + return graph; }; diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/constants.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/constants.ts index e01fcf2c70..8cb9c6d50d 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/constants.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/constants.ts @@ -3,6 +3,8 @@ export const POSITIVE_CONDITIONING = 'positive_conditioning'; export const NEGATIVE_CONDITIONING = 'negative_conditioning'; export const TEXT_TO_LATENTS = 'text_to_latents'; export const LATENTS_TO_IMAGE = 'latents_to_image'; +export const NSFW_CHECKER = 'nsfw_checker'; +export const WATERMARKER = 'invisible_watermark'; export const NOISE = 'noise'; export const RANDOM_INT = 'rand_int'; export const RANGE_OF_SIZE = 'range_of_size'; @@ -23,8 +25,19 @@ export const METADATA_ACCUMULATOR = 'metadata_accumulator'; export const REALESRGAN = 'esrgan'; export const DIVIDE = 'divide'; export const SCALE = 'scale_image'; +export const SDXL_MODEL_LOADER = 'sdxl_model_loader'; +export const SDXL_TEXT_TO_LATENTS = 't2l_sdxl'; +export const SDXL_LATENTS_TO_LATENTS = 'l2l_sdxl'; +export const SDXL_REFINER_MODEL_LOADER = 'sdxl_refiner_model_loader'; +export const SDXL_REFINER_POSITIVE_CONDITIONING = + 'sdxl_refiner_positive_conditioning'; +export const SDXL_REFINER_NEGATIVE_CONDITIONING = + 'sdxl_refiner_negative_conditioning'; +export const SDXL_REFINER_LATENTS_TO_LATENTS = 'l2l_sdxl_refiner'; // friendly graph ids export const TEXT_TO_IMAGE_GRAPH = 'text_to_image_graph'; +export const SDXL_TEXT_TO_IMAGE_GRAPH = 'sdxl_text_to_image_graph'; +export const SDXL_IMAGE_TO_IMAGE_GRAPH = 'sxdl_image_to_image_graph'; export const IMAGE_TO_IMAGE_GRAPH = 'image_to_image_graph'; export const INPAINT_GRAPH = 'inpaint_graph'; diff --git a/invokeai/frontend/web/src/features/nodes/util/parseSchema.ts b/invokeai/frontend/web/src/features/nodes/util/parseSchema.ts index bedf932b50..3a9cf233b5 100644 --- a/invokeai/frontend/web/src/features/nodes/util/parseSchema.ts +++ b/invokeai/frontend/web/src/features/nodes/util/parseSchema.ts @@ -13,7 +13,12 @@ import { buildOutputFieldTemplates, } from './fieldTemplateBuilders'; -const RESERVED_FIELD_NAMES = ['id', 'type', 'is_intermediate', 'metadata']; +const getReservedFieldNames = (type: string): string[] => { + if (type === 'l2i') { + return ['id', 'type', 'metadata']; + } + return ['id', 'type', 'is_intermediate', 'metadata']; +}; const invocationDenylist = [ 'Graph', @@ -21,11 +26,11 @@ const invocationDenylist = [ 'MetadataAccumulatorInvocation', ]; -export const parseSchema = (openAPI: OpenAPIV3.Document) => { - // filter out non-invocation schemas, plus some tricky invocations for now +export const parseSchema = ( + openAPI: OpenAPIV3.Document +): Record => { const filteredSchemas = filter( - // eslint-disable-next-line @typescript-eslint/no-non-null-assertion - openAPI.components!.schemas, + openAPI.components?.schemas, (schema, key) => key.includes('Invocation') && !key.includes('InvocationOutput') && @@ -35,21 +40,17 @@ export const parseSchema = (openAPI: OpenAPIV3.Document) => { const invocations = filteredSchemas.reduce< Record >((acc, schema) => { - // only want SchemaObjects if (isInvocationSchemaObject(schema)) { const type = schema.properties.type.default; + const RESERVED_FIELD_NAMES = getReservedFieldNames(type); const title = schema.ui?.title ?? schema.title.replace('Invocation', ''); - const typeHints = schema.ui?.type_hints; const inputs: Record = {}; if (type === 'collect') { - const itemProperty = schema.properties[ - 'item' - ] as InvocationSchemaObject; - // Handle the special Collect node + const itemProperty = schema.properties.item as InvocationSchemaObject; inputs.item = { type: 'item', name: 'item', @@ -60,10 +61,8 @@ export const parseSchema = (openAPI: OpenAPIV3.Document) => { default: undefined, }; } else if (type === 'iterate') { - const itemProperty = schema.properties[ - 'collection' - ] as InvocationSchemaObject; - + const itemProperty = schema.properties + .collection as InvocationSchemaObject; inputs.collection = { type: 'array', name: 'collection', @@ -74,18 +73,18 @@ export const parseSchema = (openAPI: OpenAPIV3.Document) => { inputKind: 'connection', }; } else { - // All other nodes reduce( schema.properties, (inputsAccumulator, property, propertyName) => { if ( - // `type` and `id` are not valid inputs/outputs !RESERVED_FIELD_NAMES.includes(propertyName) && isSchemaObject(property) ) { - const field: InputFieldTemplate | undefined = - buildInputFieldTemplate(property, propertyName, typeHints); - + const field = buildInputFieldTemplate( + property, + propertyName, + typeHints + ); if (field) { inputsAccumulator[propertyName] = field; } @@ -97,22 +96,17 @@ export const parseSchema = (openAPI: OpenAPIV3.Document) => { } const rawOutput = (schema as InvocationSchemaObject).output; - let outputs: Record; - // some special handling is needed for collect, iterate and range nodes if (type === 'iterate') { - // this is guaranteed to be a SchemaObject - // eslint-disable-next-line @typescript-eslint/no-non-null-assertion - const iterationOutput = openAPI.components!.schemas![ + const iterationOutput = openAPI.components?.schemas?.[ 'IterateInvocationOutput' ] as OpenAPIV3.SchemaObject; - outputs = { item: { name: 'item', - title: iterationOutput.title ?? '', - description: iterationOutput.description ?? '', + title: iterationOutput?.title ?? '', + description: iterationOutput?.description ?? '', type: 'array', }, }; diff --git a/invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamAspectRatio.tsx b/invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamAspectRatio.tsx index 657034e362..41be41ec28 100644 --- a/invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamAspectRatio.tsx +++ b/invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamAspectRatio.tsx @@ -7,9 +7,9 @@ import { activeTabNameSelector } from '../../../../ui/store/uiSelectors'; const aspectRatios = [ { name: 'Free', value: null }, - { name: 'Portrait', value: 0.67 / 1 }, - { name: 'Wide', value: 16 / 9 }, - { name: 'Square', value: 1 / 1 }, + { name: '2:3', value: 2 / 3 }, + { name: '16:9', value: 16 / 9 }, + { name: '1:1', value: 1 / 1 }, ]; export default function ParamAspectRatio() { diff --git a/invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamModelandVAEandScheduler.tsx b/invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamModelandVAEandScheduler.tsx index 74418de1d3..37c5eb30c4 100644 --- a/invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamModelandVAEandScheduler.tsx +++ b/invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamModelandVAEandScheduler.tsx @@ -4,6 +4,7 @@ import { memo } from 'react'; import ParamMainModelSelect from '../MainModel/ParamMainModelSelect'; import ParamVAEModelSelect from '../VAEModel/ParamVAEModelSelect'; import ParamScheduler from './ParamScheduler'; +import ParamVAEPrecision from '../VAEModel/ParamVAEPrecision'; const ParamModelandVAEandScheduler = () => { const isVaeEnabled = useFeatureStatus('vae').isFeatureEnabled; @@ -13,16 +14,15 @@ const ParamModelandVAEandScheduler = () => { - - {isVaeEnabled && ( - - - - )} - - - - + + + + {isVaeEnabled && ( + + + + + )} ); }; diff --git a/invokeai/frontend/web/src/features/parameters/components/Parameters/MainModel/ParamMainModelSelect.tsx b/invokeai/frontend/web/src/features/parameters/components/Parameters/MainModel/ParamMainModelSelect.tsx index 75f1bc8bd9..d380da60bf 100644 --- a/invokeai/frontend/web/src/features/parameters/components/Parameters/MainModel/ParamMainModelSelect.tsx +++ b/invokeai/frontend/web/src/features/parameters/components/Parameters/MainModel/ParamMainModelSelect.tsx @@ -13,8 +13,11 @@ import { modelSelected } from 'features/parameters/store/actions'; import { MODEL_TYPE_MAP } from 'features/parameters/types/constants'; import { modelIdToMainModelParam } from 'features/parameters/util/modelIdToMainModelParam'; import SyncModelsButton from 'features/ui/components/tabs/ModelManager/subpanels/ModelManagerSettingsPanel/SyncModelsButton'; +import { activeTabNameSelector } from 'features/ui/store/uiSelectors'; import { forEach } from 'lodash-es'; +import { NON_REFINER_BASE_MODELS } from 'services/api/constants'; import { useGetMainModelsQuery } from 'services/api/endpoints/models'; +import { useFeatureStatus } from '../../../../system/hooks/useFeatureStatus'; const selector = createSelector( stateSelector, @@ -28,7 +31,12 @@ const ParamMainModelSelect = () => { const { model } = useAppSelector(selector); - const { data: mainModels, isLoading } = useGetMainModelsQuery(); + const isSyncModelEnabled = useFeatureStatus('syncModels').isFeatureEnabled; + const { data: mainModels, isLoading } = useGetMainModelsQuery( + NON_REFINER_BASE_MODELS + ); + + const activeTabName = useAppSelector(activeTabNameSelector); const data = useMemo(() => { if (!mainModels) { @@ -38,7 +46,10 @@ const ParamMainModelSelect = () => { const data: SelectItem[] = []; forEach(mainModels.entities, (model, id) => { - if (!model || ['sdxl', 'sdxl-refiner'].includes(model.base_model)) { + if ( + !model || + (activeTabName === 'unifiedCanvas' && model.base_model === 'sdxl') + ) { return; } @@ -50,7 +61,7 @@ const ParamMainModelSelect = () => { }); return data; - }, [mainModels]); + }, [mainModels, activeTabName]); // grab the full model entity from the RTK Query cache // TODO: maybe we should just store the full model entity in state? @@ -86,7 +97,7 @@ const ParamMainModelSelect = () => { data={[]} /> ) : ( - + { onChange={handleChangeModel} w="100%" /> - - - + {isSyncModelEnabled && ( + + + + )} ); }; diff --git a/invokeai/frontend/web/src/features/parameters/components/Parameters/Seed/ParamSeed.tsx b/invokeai/frontend/web/src/features/parameters/components/Parameters/Seed/ParamSeed.tsx index d5ced67d0e..481fe27964 100644 --- a/invokeai/frontend/web/src/features/parameters/components/Parameters/Seed/ParamSeed.tsx +++ b/invokeai/frontend/web/src/features/parameters/components/Parameters/Seed/ParamSeed.tsx @@ -32,11 +32,6 @@ export default function ParamSeed() { isInvalid={seed < 0 && shouldGenerateVariations} onChange={handleChangeSeed} value={seed} - formControlProps={{ - display: 'flex', - alignItems: 'center', - gap: 3, // really this should work with 2 but seems to need to be 3 to match gap 2? - }} /> ); } diff --git a/invokeai/frontend/web/src/features/parameters/components/Parameters/Seed/ParamSeedFull.tsx b/invokeai/frontend/web/src/features/parameters/components/Parameters/Seed/ParamSeedFull.tsx index 75a5d189ae..a1887ec896 100644 --- a/invokeai/frontend/web/src/features/parameters/components/Parameters/Seed/ParamSeedFull.tsx +++ b/invokeai/frontend/web/src/features/parameters/components/Parameters/Seed/ParamSeedFull.tsx @@ -6,7 +6,7 @@ import ParamSeedRandomize from './ParamSeedRandomize'; const ParamSeedFull = () => { return ( - + diff --git a/invokeai/frontend/web/src/features/parameters/components/Parameters/VAEModel/ParamVAEPrecision.tsx b/invokeai/frontend/web/src/features/parameters/components/Parameters/VAEModel/ParamVAEPrecision.tsx new file mode 100644 index 0000000000..c57cdc1132 --- /dev/null +++ b/invokeai/frontend/web/src/features/parameters/components/Parameters/VAEModel/ParamVAEPrecision.tsx @@ -0,0 +1,46 @@ +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 { vaePrecisionChanged } from 'features/parameters/store/generationSlice'; +import { PrecisionParam } from 'features/parameters/types/parameterSchemas'; +import { memo, useCallback } from 'react'; + +const selector = createSelector( + stateSelector, + ({ generation }) => { + const { vaePrecision } = generation; + return { vaePrecision }; + }, + defaultSelectorOptions +); + +const DATA = ['fp16', 'fp32']; + +const ParamVAEModelSelect = () => { + const dispatch = useAppDispatch(); + const { vaePrecision } = useAppSelector(selector); + + const handleChange = useCallback( + (v: string | null) => { + if (!v) { + return; + } + + dispatch(vaePrecisionChanged(v as PrecisionParam)); + }, + [dispatch] + ); + + return ( + + ); +}; + +export default memo(ParamVAEModelSelect); diff --git a/invokeai/frontend/web/src/features/parameters/store/generationSlice.ts b/invokeai/frontend/web/src/features/parameters/store/generationSlice.ts index 971558335b..a2ddb569dd 100644 --- a/invokeai/frontend/web/src/features/parameters/store/generationSlice.ts +++ b/invokeai/frontend/web/src/features/parameters/store/generationSlice.ts @@ -11,6 +11,7 @@ import { MainModelParam, NegativePromptParam, PositivePromptParam, + PrecisionParam, SchedulerParam, SeedParam, StepsParam, @@ -51,6 +52,7 @@ export interface GenerationState { verticalSymmetrySteps: number; model: MainModelField | null; vae: VaeModelParam | null; + vaePrecision: PrecisionParam; seamlessXAxis: boolean; seamlessYAxis: boolean; clipSkip: number; @@ -89,6 +91,7 @@ export const initialGenerationState: GenerationState = { verticalSymmetrySteps: 0, model: null, vae: null, + vaePrecision: 'fp32', seamlessXAxis: false, seamlessYAxis: false, clipSkip: 0, @@ -241,6 +244,9 @@ export const generationSlice = createSlice({ // null is a valid VAE! state.vae = action.payload; }, + vaePrecisionChanged: (state, action: PayloadAction) => { + state.vaePrecision = action.payload; + }, setClipSkip: (state, action: PayloadAction) => { state.clipSkip = action.payload; }, @@ -327,6 +333,7 @@ export const { shouldUseCpuNoiseChanged, setShouldShowAdvancedOptions, setAspectRatio, + vaePrecisionChanged, } = generationSlice.actions; export default generationSlice.reducer; diff --git a/invokeai/frontend/web/src/features/parameters/types/parameterSchemas.ts b/invokeai/frontend/web/src/features/parameters/types/parameterSchemas.ts index cea5fb9987..64f4665c5f 100644 --- a/invokeai/frontend/web/src/features/parameters/types/parameterSchemas.ts +++ b/invokeai/frontend/web/src/features/parameters/types/parameterSchemas.ts @@ -42,6 +42,42 @@ export const isValidNegativePrompt = ( val: unknown ): val is NegativePromptParam => zNegativePrompt.safeParse(val).success; +/** + * Zod schema for SDXL positive style prompt parameter + */ +export const zPositiveStylePromptSDXL = z.string(); +/** + * Type alias for SDXL positive style prompt parameter, inferred from its zod schema + */ +export type PositiveStylePromptSDXLParam = z.infer< + typeof zPositiveStylePromptSDXL +>; +/** + * Validates/type-guards a value as a SDXL positive style prompt parameter + */ +export const isValidSDXLPositiveStylePrompt = ( + val: unknown +): val is PositiveStylePromptSDXLParam => + zPositiveStylePromptSDXL.safeParse(val).success; + +/** + * Zod schema for SDXL negative style prompt parameter + */ +export const zNegativeStylePromptSDXL = z.string(); +/** + * Type alias for SDXL negative style prompt parameter, inferred from its zod schema + */ +export type NegativeStylePromptSDXLParam = z.infer< + typeof zNegativeStylePromptSDXL +>; +/** + * Validates/type-guards a value as a SDXL negative style prompt parameter + */ +export const isValidSDXLNegativeStylePrompt = ( + val: unknown +): val is NegativeStylePromptSDXLParam => + zNegativeStylePromptSDXL.safeParse(val).success; + /** * Zod schema for steps parameter */ @@ -260,6 +296,20 @@ export type StrengthParam = z.infer; export const isValidStrength = (val: unknown): val is StrengthParam => zStrength.safeParse(val).success; +/** + * Zod schema for a precision parameter + */ +export const zPrecision = z.enum(['fp16', 'fp32']); +/** + * Type alias for precision parameter, inferred from its zod schema + */ +export type PrecisionParam = z.infer; +/** + * Validates/type-guards a value as a precision parameter + */ +export const isValidPrecision = (val: unknown): val is PrecisionParam => + zPrecision.safeParse(val).success; + // /** // * Zod schema for BaseModelType // */ diff --git a/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLConcatPrompt.tsx b/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLConcatPrompt.tsx new file mode 100644 index 0000000000..89e67be652 --- /dev/null +++ b/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLConcatPrompt.tsx @@ -0,0 +1,33 @@ +import { Box } from '@chakra-ui/react'; +import { RootState } from 'app/store/store'; +import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; +import IAISwitch from 'common/components/IAISwitch'; +import { ChangeEvent } from 'react'; +import { setShouldConcatSDXLStylePrompt } from '../store/sdxlSlice'; + +export default function ParamSDXLConcatPrompt() { + const shouldConcatSDXLStylePrompt = useAppSelector( + (state: RootState) => state.sdxl.shouldConcatSDXLStylePrompt + ); + + const dispatch = useAppDispatch(); + + const handleShouldConcatPromptChange = (e: ChangeEvent) => { + dispatch(setShouldConcatSDXLStylePrompt(e.target.checked)); + }; + + return ( + + + + ); +} diff --git a/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLImg2ImgDenoisingStrength.tsx b/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLImg2ImgDenoisingStrength.tsx new file mode 100644 index 0000000000..52d7567339 --- /dev/null +++ b/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLImg2ImgDenoisingStrength.tsx @@ -0,0 +1,53 @@ +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 from 'common/components/IAISlider'; +import { memo, useCallback } from 'react'; +import { useTranslation } from 'react-i18next'; +import { setSDXLImg2ImgDenoisingStrength } from '../store/sdxlSlice'; + +const selector = createSelector( + [stateSelector], + ({ sdxl }) => { + const { sdxlImg2ImgDenoisingStrength } = sdxl; + + return { + sdxlImg2ImgDenoisingStrength, + }; + }, + defaultSelectorOptions +); + +const ParamSDXLImg2ImgDenoisingStrength = () => { + const { sdxlImg2ImgDenoisingStrength } = useAppSelector(selector); + const dispatch = useAppDispatch(); + const { t } = useTranslation(); + + const handleChange = useCallback( + (v: number) => dispatch(setSDXLImg2ImgDenoisingStrength(v)), + [dispatch] + ); + + const handleReset = useCallback(() => { + dispatch(setSDXLImg2ImgDenoisingStrength(0.7)); + }, [dispatch]); + + return ( + + ); +}; + +export default memo(ParamSDXLImg2ImgDenoisingStrength); diff --git a/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLNegativeStyleConditioning.tsx b/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLNegativeStyleConditioning.tsx new file mode 100644 index 0000000000..e2d5c5b8e4 --- /dev/null +++ b/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLNegativeStyleConditioning.tsx @@ -0,0 +1,149 @@ +import { Box, FormControl, useDisclosure } from '@chakra-ui/react'; +import { stateSelector } from 'app/store/store'; +import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; +import { ChangeEvent, KeyboardEvent, useCallback, useRef } from 'react'; + +import { createSelector } from '@reduxjs/toolkit'; +import { clampSymmetrySteps } from 'features/parameters/store/generationSlice'; +import { activeTabNameSelector } from 'features/ui/store/uiSelectors'; + +import { userInvoked } from 'app/store/actions'; +import IAITextarea from 'common/components/IAITextarea'; +import { useIsReadyToInvoke } from 'common/hooks/useIsReadyToInvoke'; +import AddEmbeddingButton from 'features/embedding/components/AddEmbeddingButton'; +import ParamEmbeddingPopover from 'features/embedding/components/ParamEmbeddingPopover'; +import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus'; +import { isEqual } from 'lodash-es'; +import { flushSync } from 'react-dom'; +import { setNegativeStylePromptSDXL } from '../store/sdxlSlice'; + +const promptInputSelector = createSelector( + [stateSelector, activeTabNameSelector], + ({ sdxl }, activeTabName) => { + return { + prompt: sdxl.negativeStylePrompt, + activeTabName, + }; + }, + { + memoizeOptions: { + resultEqualityCheck: isEqual, + }, + } +); + +/** + * Prompt input text area. + */ +const ParamSDXLNegativeStyleConditioning = () => { + const dispatch = useAppDispatch(); + const { prompt, activeTabName } = useAppSelector(promptInputSelector); + const isReady = useIsReadyToInvoke(); + const promptRef = useRef(null); + const { isOpen, onClose, onOpen } = useDisclosure(); + + const handleChangePrompt = useCallback( + (e: ChangeEvent) => { + dispatch(setNegativeStylePromptSDXL(e.target.value)); + }, + [dispatch] + ); + + const handleSelectEmbedding = useCallback( + (v: string) => { + if (!promptRef.current) { + return; + } + + // this is where we insert the TI trigger + const caret = promptRef.current.selectionStart; + + if (caret === undefined) { + return; + } + + let newPrompt = prompt.slice(0, caret); + + if (newPrompt[newPrompt.length - 1] !== '<') { + newPrompt += '<'; + } + + newPrompt += `${v}>`; + + // we insert the cursor after the `>` + const finalCaretPos = newPrompt.length; + + newPrompt += prompt.slice(caret); + + // must flush dom updates else selection gets reset + flushSync(() => { + dispatch(setNegativeStylePromptSDXL(newPrompt)); + }); + + // set the caret position to just after the TI trigger + promptRef.current.selectionStart = finalCaretPos; + promptRef.current.selectionEnd = finalCaretPos; + onClose(); + }, + [dispatch, onClose, prompt] + ); + + const isEmbeddingEnabled = useFeatureStatus('embedding').isFeatureEnabled; + + const handleKeyDown = useCallback( + (e: KeyboardEvent) => { + if (e.key === 'Enter' && e.shiftKey === false && isReady) { + e.preventDefault(); + dispatch(clampSymmetrySteps()); + dispatch(userInvoked(activeTabName)); + } + if (isEmbeddingEnabled && e.key === '<') { + onOpen(); + } + }, + [isReady, dispatch, activeTabName, onOpen, isEmbeddingEnabled] + ); + + // const handleSelect = (e: MouseEvent) => { + // const target = e.target as HTMLTextAreaElement; + // setCaret({ start: target.selectionStart, end: target.selectionEnd }); + // }; + + return ( + + + + + + + {!isOpen && isEmbeddingEnabled && ( + + + + )} + + ); +}; + +export default ParamSDXLNegativeStyleConditioning; diff --git a/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLPositiveStyleConditioning.tsx b/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLPositiveStyleConditioning.tsx new file mode 100644 index 0000000000..8512aedd68 --- /dev/null +++ b/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLPositiveStyleConditioning.tsx @@ -0,0 +1,148 @@ +import { Box, FormControl, useDisclosure } from '@chakra-ui/react'; +import { stateSelector } from 'app/store/store'; +import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; +import { ChangeEvent, KeyboardEvent, useCallback, useRef } from 'react'; + +import { createSelector } from '@reduxjs/toolkit'; +import { clampSymmetrySteps } from 'features/parameters/store/generationSlice'; +import { activeTabNameSelector } from 'features/ui/store/uiSelectors'; + +import { userInvoked } from 'app/store/actions'; +import IAITextarea from 'common/components/IAITextarea'; +import { useIsReadyToInvoke } from 'common/hooks/useIsReadyToInvoke'; +import AddEmbeddingButton from 'features/embedding/components/AddEmbeddingButton'; +import ParamEmbeddingPopover from 'features/embedding/components/ParamEmbeddingPopover'; +import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus'; +import { isEqual } from 'lodash-es'; +import { flushSync } from 'react-dom'; +import { setPositiveStylePromptSDXL } from '../store/sdxlSlice'; + +const promptInputSelector = createSelector( + [stateSelector, activeTabNameSelector], + ({ sdxl }, activeTabName) => { + return { + prompt: sdxl.positiveStylePrompt, + activeTabName, + }; + }, + { + memoizeOptions: { + resultEqualityCheck: isEqual, + }, + } +); + +/** + * Prompt input text area. + */ +const ParamSDXLPositiveStyleConditioning = () => { + const dispatch = useAppDispatch(); + const { prompt, activeTabName } = useAppSelector(promptInputSelector); + const isReady = useIsReadyToInvoke(); + const promptRef = useRef(null); + const { isOpen, onClose, onOpen } = useDisclosure(); + + const handleChangePrompt = useCallback( + (e: ChangeEvent) => { + dispatch(setPositiveStylePromptSDXL(e.target.value)); + }, + [dispatch] + ); + + const handleSelectEmbedding = useCallback( + (v: string) => { + if (!promptRef.current) { + return; + } + + // this is where we insert the TI trigger + const caret = promptRef.current.selectionStart; + + if (caret === undefined) { + return; + } + + let newPrompt = prompt.slice(0, caret); + + if (newPrompt[newPrompt.length - 1] !== '<') { + newPrompt += '<'; + } + + newPrompt += `${v}>`; + + // we insert the cursor after the `>` + const finalCaretPos = newPrompt.length; + + newPrompt += prompt.slice(caret); + + // must flush dom updates else selection gets reset + flushSync(() => { + dispatch(setPositiveStylePromptSDXL(newPrompt)); + }); + + // set the caret position to just after the TI trigger + promptRef.current.selectionStart = finalCaretPos; + promptRef.current.selectionEnd = finalCaretPos; + onClose(); + }, + [dispatch, onClose, prompt] + ); + + const isEmbeddingEnabled = useFeatureStatus('embedding').isFeatureEnabled; + + const handleKeyDown = useCallback( + (e: KeyboardEvent) => { + if (e.key === 'Enter' && e.shiftKey === false && isReady) { + e.preventDefault(); + dispatch(clampSymmetrySteps()); + dispatch(userInvoked(activeTabName)); + } + if (isEmbeddingEnabled && e.key === '<') { + onOpen(); + } + }, + [isReady, dispatch, activeTabName, onOpen, isEmbeddingEnabled] + ); + + // const handleSelect = (e: MouseEvent) => { + // const target = e.target as HTMLTextAreaElement; + // setCaret({ start: target.selectionStart, end: target.selectionEnd }); + // }; + + return ( + + + + + + + {!isOpen && isEmbeddingEnabled && ( + + + + )} + + ); +}; + +export default ParamSDXLPositiveStyleConditioning; diff --git a/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLRefinerCollapse.tsx b/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLRefinerCollapse.tsx new file mode 100644 index 0000000000..37e1718dc6 --- /dev/null +++ b/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLRefinerCollapse.tsx @@ -0,0 +1,48 @@ +import { Flex } from '@chakra-ui/react'; +import { createSelector } from '@reduxjs/toolkit'; +import { stateSelector } from 'app/store/store'; +import { useAppSelector } from 'app/store/storeHooks'; +import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions'; +import IAICollapse from 'common/components/IAICollapse'; +import ParamSDXLRefinerAestheticScore from './SDXLRefiner/ParamSDXLRefinerAestheticScore'; +import ParamSDXLRefinerCFGScale from './SDXLRefiner/ParamSDXLRefinerCFGScale'; +import ParamSDXLRefinerModelSelect from './SDXLRefiner/ParamSDXLRefinerModelSelect'; +import ParamSDXLRefinerScheduler from './SDXLRefiner/ParamSDXLRefinerScheduler'; +import ParamSDXLRefinerStart from './SDXLRefiner/ParamSDXLRefinerStart'; +import ParamSDXLRefinerSteps from './SDXLRefiner/ParamSDXLRefinerSteps'; +import ParamUseSDXLRefiner from './SDXLRefiner/ParamUseSDXLRefiner'; + +const selector = createSelector( + stateSelector, + (state) => { + const { shouldUseSDXLRefiner } = state.sdxl; + const { shouldUseSliders } = state.ui; + return { + activeLabel: shouldUseSDXLRefiner ? 'Enabled' : undefined, + shouldUseSliders, + }; + }, + defaultSelectorOptions +); + +const ParamSDXLRefinerCollapse = () => { + const { activeLabel, shouldUseSliders } = useAppSelector(selector); + + return ( + + + + + + + + + + + + + + ); +}; + +export default ParamSDXLRefinerCollapse; diff --git a/invokeai/frontend/web/src/features/sdxl/components/SDXLImageToImageTabCoreParameters.tsx b/invokeai/frontend/web/src/features/sdxl/components/SDXLImageToImageTabCoreParameters.tsx new file mode 100644 index 0000000000..4d7c919655 --- /dev/null +++ b/invokeai/frontend/web/src/features/sdxl/components/SDXLImageToImageTabCoreParameters.tsx @@ -0,0 +1,78 @@ +import { Box, Flex } from '@chakra-ui/react'; +import { createSelector } from '@reduxjs/toolkit'; +import { useAppSelector } from 'app/store/storeHooks'; +import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions'; +import IAICollapse from 'common/components/IAICollapse'; +import ParamCFGScale from 'features/parameters/components/Parameters/Core/ParamCFGScale'; +import ParamIterations from 'features/parameters/components/Parameters/Core/ParamIterations'; +import ParamModelandVAEandScheduler from 'features/parameters/components/Parameters/Core/ParamModelandVAEandScheduler'; +import ParamSize from 'features/parameters/components/Parameters/Core/ParamSize'; +import ParamSteps from 'features/parameters/components/Parameters/Core/ParamSteps'; +import ImageToImageFit from 'features/parameters/components/Parameters/ImageToImage/ImageToImageFit'; +import ParamSeedFull from 'features/parameters/components/Parameters/Seed/ParamSeedFull'; +import { generationSelector } from 'features/parameters/store/generationSelectors'; +import { uiSelector } from 'features/ui/store/uiSelectors'; +import { memo } from 'react'; +import ParamSDXLImg2ImgDenoisingStrength from './ParamSDXLImg2ImgDenoisingStrength'; + +const selector = createSelector( + [uiSelector, generationSelector], + (ui, generation) => { + const { shouldUseSliders } = ui; + const { shouldRandomizeSeed } = generation; + + const activeLabel = !shouldRandomizeSeed ? 'Manual Seed' : undefined; + + return { shouldUseSliders, activeLabel }; + }, + defaultSelectorOptions +); + +const SDXLImageToImageTabCoreParameters = () => { + const { shouldUseSliders, activeLabel } = useAppSelector(selector); + + return ( + + + {shouldUseSliders ? ( + <> + + + + + + + + + + ) : ( + <> + + + + + + + + + + + + )} + + + + + ); +}; + +export default memo(SDXLImageToImageTabCoreParameters); diff --git a/invokeai/frontend/web/src/features/sdxl/components/SDXLImageToImageTabParameters.tsx b/invokeai/frontend/web/src/features/sdxl/components/SDXLImageToImageTabParameters.tsx new file mode 100644 index 0000000000..05aca62b79 --- /dev/null +++ b/invokeai/frontend/web/src/features/sdxl/components/SDXLImageToImageTabParameters.tsx @@ -0,0 +1,41 @@ +import { Flex } from '@chakra-ui/react'; +import ParamDynamicPromptsCollapse from 'features/dynamicPrompts/components/ParamDynamicPromptsCollapse'; +import ParamNegativeConditioning from 'features/parameters/components/Parameters/Core/ParamNegativeConditioning'; +import ParamPositiveConditioning from 'features/parameters/components/Parameters/Core/ParamPositiveConditioning'; +import ParamNoiseCollapse from 'features/parameters/components/Parameters/Noise/ParamNoiseCollapse'; +import ProcessButtons from 'features/parameters/components/ProcessButtons/ProcessButtons'; +import ParamSDXLConcatPrompt from './ParamSDXLConcatPrompt'; +import ParamSDXLNegativeStyleConditioning from './ParamSDXLNegativeStyleConditioning'; +import ParamSDXLPositiveStyleConditioning from './ParamSDXLPositiveStyleConditioning'; +import ParamSDXLRefinerCollapse from './ParamSDXLRefinerCollapse'; +import SDXLImageToImageTabCoreParameters from './SDXLImageToImageTabCoreParameters'; + +const SDXLImageToImageTabParameters = () => { + return ( + <> + + + + + + + + + + + + + + ); +}; + +export default SDXLImageToImageTabParameters; diff --git a/invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerAestheticScore.tsx b/invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerAestheticScore.tsx new file mode 100644 index 0000000000..9c9c4b2f89 --- /dev/null +++ b/invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerAestheticScore.tsx @@ -0,0 +1,60 @@ +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 from 'common/components/IAISlider'; +import { setRefinerAestheticScore } from 'features/sdxl/store/sdxlSlice'; +import { memo, useCallback } from 'react'; +import { useIsRefinerAvailable } from 'services/api/hooks/useIsRefinerAvailable'; + +const selector = createSelector( + [stateSelector], + ({ sdxl, hotkeys }) => { + const { refinerAestheticScore } = sdxl; + const { shift } = hotkeys; + + return { + refinerAestheticScore, + shift, + }; + }, + defaultSelectorOptions +); + +const ParamSDXLRefinerAestheticScore = () => { + const { refinerAestheticScore, shift } = useAppSelector(selector); + + const isRefinerAvailable = useIsRefinerAvailable(); + + const dispatch = useAppDispatch(); + + const handleChange = useCallback( + (v: number) => dispatch(setRefinerAestheticScore(v)), + [dispatch] + ); + + const handleReset = useCallback( + () => dispatch(setRefinerAestheticScore(6)), + [dispatch] + ); + + return ( + + ); +}; + +export default memo(ParamSDXLRefinerAestheticScore); diff --git a/invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerCFGScale.tsx b/invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerCFGScale.tsx new file mode 100644 index 0000000000..dd678ac0f7 --- /dev/null +++ b/invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerCFGScale.tsx @@ -0,0 +1,75 @@ +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 IAINumberInput from 'common/components/IAINumberInput'; +import IAISlider from 'common/components/IAISlider'; +import { setRefinerCFGScale } from 'features/sdxl/store/sdxlSlice'; +import { memo, useCallback } from 'react'; +import { useTranslation } from 'react-i18next'; +import { useIsRefinerAvailable } from 'services/api/hooks/useIsRefinerAvailable'; + +const selector = createSelector( + [stateSelector], + ({ sdxl, ui, hotkeys }) => { + const { refinerCFGScale } = sdxl; + const { shouldUseSliders } = ui; + const { shift } = hotkeys; + + return { + refinerCFGScale, + shouldUseSliders, + shift, + }; + }, + defaultSelectorOptions +); + +const ParamSDXLRefinerCFGScale = () => { + const { refinerCFGScale, shouldUseSliders, shift } = useAppSelector(selector); + const isRefinerAvailable = useIsRefinerAvailable(); + const dispatch = useAppDispatch(); + const { t } = useTranslation(); + + const handleChange = useCallback( + (v: number) => dispatch(setRefinerCFGScale(v)), + [dispatch] + ); + + const handleReset = useCallback( + () => dispatch(setRefinerCFGScale(7)), + [dispatch] + ); + + return shouldUseSliders ? ( + + ) : ( + + ); +}; + +export default memo(ParamSDXLRefinerCFGScale); diff --git a/invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerModelSelect.tsx b/invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerModelSelect.tsx new file mode 100644 index 0000000000..cae40bbff3 --- /dev/null +++ b/invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerModelSelect.tsx @@ -0,0 +1,111 @@ +import { Box, Flex } from '@chakra-ui/react'; +import { SelectItem } from '@mantine/core'; +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 IAIMantineSearchableSelect from 'common/components/IAIMantineSearchableSelect'; +import { MODEL_TYPE_MAP } from 'features/parameters/types/constants'; +import { modelIdToMainModelParam } from 'features/parameters/util/modelIdToMainModelParam'; +import { refinerModelChanged } from 'features/sdxl/store/sdxlSlice'; +import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus'; +import SyncModelsButton from 'features/ui/components/tabs/ModelManager/subpanels/ModelManagerSettingsPanel/SyncModelsButton'; +import { forEach } from 'lodash-es'; +import { memo, useCallback, useMemo } from 'react'; +import { REFINER_BASE_MODELS } from 'services/api/constants'; +import { useGetMainModelsQuery } from 'services/api/endpoints/models'; + +const selector = createSelector( + stateSelector, + (state) => ({ model: state.sdxl.refinerModel }), + defaultSelectorOptions +); + +const ParamSDXLRefinerModelSelect = () => { + const dispatch = useAppDispatch(); + const isSyncModelEnabled = useFeatureStatus('syncModels').isFeatureEnabled; + + const { model } = useAppSelector(selector); + + const { data: refinerModels, isLoading } = + useGetMainModelsQuery(REFINER_BASE_MODELS); + + const data = useMemo(() => { + if (!refinerModels) { + return []; + } + + const data: SelectItem[] = []; + + forEach(refinerModels.entities, (model, id) => { + if (!model) { + return; + } + + data.push({ + value: id, + label: model.model_name, + group: MODEL_TYPE_MAP[model.base_model], + }); + }); + + return data; + }, [refinerModels]); + + // grab the full model entity from the RTK Query cache + // TODO: maybe we should just store the full model entity in state? + const selectedModel = useMemo( + () => + refinerModels?.entities[ + `${model?.base_model}/main/${model?.model_name}` + ] ?? null, + [refinerModels?.entities, model] + ); + + const handleChangeModel = useCallback( + (v: string | null) => { + if (!v) { + return; + } + + const newModel = modelIdToMainModelParam(v); + + if (!newModel) { + return; + } + + dispatch(refinerModelChanged(newModel)); + }, + [dispatch] + ); + + return isLoading ? ( + + ) : ( + + 0 ? 'Select a model' : 'No models available'} + data={data} + error={data.length === 0} + disabled={data.length === 0} + onChange={handleChangeModel} + w="100%" + /> + {isSyncModelEnabled && ( + + + + )} + + ); +}; + +export default memo(ParamSDXLRefinerModelSelect); diff --git a/invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerScheduler.tsx b/invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerScheduler.tsx new file mode 100644 index 0000000000..e14eb0b5f8 --- /dev/null +++ b/invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerScheduler.tsx @@ -0,0 +1,65 @@ +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 IAIMantineSearchableSelect from 'common/components/IAIMantineSearchableSelect'; +import { + SCHEDULER_LABEL_MAP, + SchedulerParam, +} from 'features/parameters/types/parameterSchemas'; +import { setRefinerScheduler } from 'features/sdxl/store/sdxlSlice'; +import { map } from 'lodash-es'; +import { memo, useCallback } from 'react'; +import { useTranslation } from 'react-i18next'; +import { useIsRefinerAvailable } from 'services/api/hooks/useIsRefinerAvailable'; + +const selector = createSelector( + stateSelector, + ({ ui, sdxl }) => { + const { refinerScheduler } = sdxl; + const { favoriteSchedulers: enabledSchedulers } = ui; + + const data = map(SCHEDULER_LABEL_MAP, (label, name) => ({ + value: name, + label: label, + group: enabledSchedulers.includes(name as SchedulerParam) + ? 'Favorites' + : undefined, + })).sort((a, b) => a.label.localeCompare(b.label)); + + return { + refinerScheduler, + data, + }; + }, + defaultSelectorOptions +); + +const ParamSDXLRefinerScheduler = () => { + const dispatch = useAppDispatch(); + const { t } = useTranslation(); + const { refinerScheduler, data } = useAppSelector(selector); + const isRefinerAvailable = useIsRefinerAvailable(); + const handleChange = useCallback( + (v: string | null) => { + if (!v) { + return; + } + dispatch(setRefinerScheduler(v as SchedulerParam)); + }, + [dispatch] + ); + + return ( + + ); +}; + +export default memo(ParamSDXLRefinerScheduler); diff --git a/invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerStart.tsx b/invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerStart.tsx new file mode 100644 index 0000000000..a98259203c --- /dev/null +++ b/invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerStart.tsx @@ -0,0 +1,53 @@ +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 from 'common/components/IAISlider'; +import { setRefinerStart } from 'features/sdxl/store/sdxlSlice'; +import { memo, useCallback } from 'react'; +import { useIsRefinerAvailable } from 'services/api/hooks/useIsRefinerAvailable'; + +const selector = createSelector( + [stateSelector], + ({ sdxl }) => { + const { refinerStart } = sdxl; + return { + refinerStart, + }; + }, + defaultSelectorOptions +); + +const ParamSDXLRefinerStart = () => { + const { refinerStart } = useAppSelector(selector); + const dispatch = useAppDispatch(); + const isRefinerAvailable = useIsRefinerAvailable(); + const handleChange = useCallback( + (v: number) => dispatch(setRefinerStart(v)), + [dispatch] + ); + + const handleReset = useCallback( + () => dispatch(setRefinerStart(0.7)), + [dispatch] + ); + + return ( + + ); +}; + +export default memo(ParamSDXLRefinerStart); diff --git a/invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerSteps.tsx b/invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerSteps.tsx new file mode 100644 index 0000000000..456cbb5d3a --- /dev/null +++ b/invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerSteps.tsx @@ -0,0 +1,72 @@ +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 IAINumberInput from 'common/components/IAINumberInput'; +import IAISlider from 'common/components/IAISlider'; +import { setRefinerSteps } from 'features/sdxl/store/sdxlSlice'; +import { memo, useCallback } from 'react'; +import { useTranslation } from 'react-i18next'; +import { useIsRefinerAvailable } from 'services/api/hooks/useIsRefinerAvailable'; + +const selector = createSelector( + [stateSelector], + ({ sdxl, ui }) => { + const { refinerSteps } = sdxl; + const { shouldUseSliders } = ui; + + return { + refinerSteps, + shouldUseSliders, + }; + }, + defaultSelectorOptions +); + +const ParamSDXLRefinerSteps = () => { + const { refinerSteps, shouldUseSliders } = useAppSelector(selector); + const isRefinerAvailable = useIsRefinerAvailable(); + + const dispatch = useAppDispatch(); + const { t } = useTranslation(); + + const handleChange = useCallback( + (v: number) => { + dispatch(setRefinerSteps(v)); + }, + [dispatch] + ); + const handleReset = useCallback(() => { + dispatch(setRefinerSteps(20)); + }, [dispatch]); + + return shouldUseSliders ? ( + + ) : ( + + ); +}; + +export default memo(ParamSDXLRefinerSteps); diff --git a/invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamUseSDXLRefiner.tsx b/invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamUseSDXLRefiner.tsx new file mode 100644 index 0000000000..1649f95e9a --- /dev/null +++ b/invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamUseSDXLRefiner.tsx @@ -0,0 +1,28 @@ +import { RootState } from 'app/store/store'; +import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; +import IAISwitch from 'common/components/IAISwitch'; +import { setShouldUseSDXLRefiner } from 'features/sdxl/store/sdxlSlice'; +import { ChangeEvent } from 'react'; +import { useIsRefinerAvailable } from 'services/api/hooks/useIsRefinerAvailable'; + +export default function ParamUseSDXLRefiner() { + const shouldUseSDXLRefiner = useAppSelector( + (state: RootState) => state.sdxl.shouldUseSDXLRefiner + ); + const isRefinerAvailable = useIsRefinerAvailable(); + + const dispatch = useAppDispatch(); + + const handleUseSDXLRefinerChange = (e: ChangeEvent) => { + dispatch(setShouldUseSDXLRefiner(e.target.checked)); + }; + + return ( + + ); +} diff --git a/invokeai/frontend/web/src/features/sdxl/components/SDXLTextToImageTabParameters.tsx b/invokeai/frontend/web/src/features/sdxl/components/SDXLTextToImageTabParameters.tsx new file mode 100644 index 0000000000..eddc0b8891 --- /dev/null +++ b/invokeai/frontend/web/src/features/sdxl/components/SDXLTextToImageTabParameters.tsx @@ -0,0 +1,41 @@ +import { Flex } from '@chakra-ui/react'; +import ParamDynamicPromptsCollapse from 'features/dynamicPrompts/components/ParamDynamicPromptsCollapse'; +import ParamNegativeConditioning from 'features/parameters/components/Parameters/Core/ParamNegativeConditioning'; +import ParamPositiveConditioning from 'features/parameters/components/Parameters/Core/ParamPositiveConditioning'; +import ParamNoiseCollapse from 'features/parameters/components/Parameters/Noise/ParamNoiseCollapse'; +import ProcessButtons from 'features/parameters/components/ProcessButtons/ProcessButtons'; +import TextToImageTabCoreParameters from 'features/ui/components/tabs/TextToImage/TextToImageTabCoreParameters'; +import ParamSDXLConcatPrompt from './ParamSDXLConcatPrompt'; +import ParamSDXLNegativeStyleConditioning from './ParamSDXLNegativeStyleConditioning'; +import ParamSDXLPositiveStyleConditioning from './ParamSDXLPositiveStyleConditioning'; +import ParamSDXLRefinerCollapse from './ParamSDXLRefinerCollapse'; + +const SDXLTextToImageTabParameters = () => { + return ( + <> + + + + + + + + + + + + + + ); +}; + +export default SDXLTextToImageTabParameters; diff --git a/invokeai/frontend/web/src/features/sdxl/store/sdxlSlice.ts b/invokeai/frontend/web/src/features/sdxl/store/sdxlSlice.ts new file mode 100644 index 0000000000..7a8ccffa97 --- /dev/null +++ b/invokeai/frontend/web/src/features/sdxl/store/sdxlSlice.ts @@ -0,0 +1,95 @@ +import { PayloadAction, createSlice } from '@reduxjs/toolkit'; +import { + MainModelParam, + NegativeStylePromptSDXLParam, + PositiveStylePromptSDXLParam, + SchedulerParam, +} from 'features/parameters/types/parameterSchemas'; +import { MainModelField } from 'services/api/types'; + +type SDXLInitialState = { + positiveStylePrompt: PositiveStylePromptSDXLParam; + negativeStylePrompt: NegativeStylePromptSDXLParam; + shouldConcatSDXLStylePrompt: boolean; + shouldUseSDXLRefiner: boolean; + sdxlImg2ImgDenoisingStrength: number; + refinerModel: MainModelField | null; + refinerSteps: number; + refinerCFGScale: number; + refinerScheduler: SchedulerParam; + refinerAestheticScore: number; + refinerStart: number; +}; + +const sdxlInitialState: SDXLInitialState = { + positiveStylePrompt: '', + negativeStylePrompt: '', + shouldConcatSDXLStylePrompt: true, + shouldUseSDXLRefiner: false, + sdxlImg2ImgDenoisingStrength: 0.7, + refinerModel: null, + refinerSteps: 20, + refinerCFGScale: 7.5, + refinerScheduler: 'euler', + refinerAestheticScore: 6, + refinerStart: 0.7, +}; + +const sdxlSlice = createSlice({ + name: 'sdxl', + initialState: sdxlInitialState, + reducers: { + setPositiveStylePromptSDXL: (state, action: PayloadAction) => { + state.positiveStylePrompt = action.payload; + }, + setNegativeStylePromptSDXL: (state, action: PayloadAction) => { + state.negativeStylePrompt = action.payload; + }, + setShouldConcatSDXLStylePrompt: (state, action: PayloadAction) => { + state.shouldConcatSDXLStylePrompt = action.payload; + }, + setShouldUseSDXLRefiner: (state, action: PayloadAction) => { + state.shouldUseSDXLRefiner = action.payload; + }, + setSDXLImg2ImgDenoisingStrength: (state, action: PayloadAction) => { + state.sdxlImg2ImgDenoisingStrength = action.payload; + }, + refinerModelChanged: ( + state, + action: PayloadAction + ) => { + state.refinerModel = action.payload; + }, + setRefinerSteps: (state, action: PayloadAction) => { + state.refinerSteps = action.payload; + }, + setRefinerCFGScale: (state, action: PayloadAction) => { + state.refinerCFGScale = action.payload; + }, + setRefinerScheduler: (state, action: PayloadAction) => { + state.refinerScheduler = action.payload; + }, + setRefinerAestheticScore: (state, action: PayloadAction) => { + state.refinerAestheticScore = action.payload; + }, + setRefinerStart: (state, action: PayloadAction) => { + state.refinerStart = action.payload; + }, + }, +}); + +export const { + setPositiveStylePromptSDXL, + setNegativeStylePromptSDXL, + setShouldConcatSDXLStylePrompt, + setShouldUseSDXLRefiner, + setSDXLImg2ImgDenoisingStrength, + refinerModelChanged, + setRefinerSteps, + setRefinerCFGScale, + setRefinerScheduler, + setRefinerAestheticScore, + setRefinerStart, +} = sdxlSlice.actions; + +export default sdxlSlice.reducer; diff --git a/invokeai/frontend/web/src/features/system/components/ColorModeButton.tsx b/invokeai/frontend/web/src/features/system/components/ColorModeButton.tsx deleted file mode 100644 index 9bf748146c..0000000000 --- a/invokeai/frontend/web/src/features/system/components/ColorModeButton.tsx +++ /dev/null @@ -1,32 +0,0 @@ -import { useColorMode } from '@chakra-ui/react'; -import IAIIconButton from 'common/components/IAIIconButton'; -import { useTranslation } from 'react-i18next'; -import { FaMoon, FaSun } from 'react-icons/fa'; - -const ColorModeButton = () => { - const { colorMode, toggleColorMode } = useColorMode(); - const { t } = useTranslation(); - - return ( - - ) : ( - - ) - } - onClick={toggleColorMode} - variant="link" - /> - ); -}; - -export default ColorModeButton; diff --git a/invokeai/frontend/web/src/features/system/components/LanguagePicker.tsx b/invokeai/frontend/web/src/features/system/components/LanguagePicker.tsx deleted file mode 100644 index 0a88ac9215..0000000000 --- a/invokeai/frontend/web/src/features/system/components/LanguagePicker.tsx +++ /dev/null @@ -1,50 +0,0 @@ -import { - IconButton, - Menu, - MenuButton, - MenuItemOption, - MenuList, - MenuOptionGroup, - Tooltip, -} from '@chakra-ui/react'; -import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; -import { map } from 'lodash-es'; -import { useTranslation } from 'react-i18next'; -import { IoLanguage } from 'react-icons/io5'; -import { LANGUAGES } from '../store/constants'; -import { languageSelector } from '../store/systemSelectors'; -import { languageChanged } from '../store/systemSlice'; - -export default function LanguagePicker() { - const { t } = useTranslation(); - const dispatch = useAppDispatch(); - const language = useAppSelector(languageSelector); - - return ( - - - } - variant="link" - aria-label={t('common.languagePickerLabel')} - fontSize={22} - minWidth={8} - /> - - - - {map(LANGUAGES, (languageName, l: keyof typeof LANGUAGES) => ( - dispatch(languageChanged(l))} - > - {languageName} - - ))} - - - - ); -} diff --git a/invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsModal.tsx b/invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsModal.tsx index 2deccfa46d..af810f7836 100644 --- a/invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsModal.tsx +++ b/invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsModal.tsx @@ -10,6 +10,7 @@ import { ModalOverlay, Text, useDisclosure, + useColorMode, } from '@chakra-ui/react'; import { createSelector } from '@reduxjs/toolkit'; import { VALID_LOG_LEVELS } from 'app/logging/logger'; @@ -26,6 +27,8 @@ import { setShouldConfirmOnDelete, shouldAntialiasProgressImageChanged, shouldLogToConsoleChanged, + shouldUseNSFWCheckerChanged, + shouldUseWatermarkerChanged, } from 'features/system/store/systemSlice'; import { setShouldShowProgressInViewer, @@ -42,10 +45,15 @@ import { } from 'react'; import { useTranslation } from 'react-i18next'; import { LogLevelName } from 'roarr'; +import { useGetAppConfigQuery } from 'services/api/endpoints/appInfo'; import SettingSwitch from './SettingSwitch'; import SettingsClearIntermediates from './SettingsClearIntermediates'; import SettingsSchedulers from './SettingsSchedulers'; import StyledFlex from './StyledFlex'; +import { useFeatureStatus } from '../../hooks/useFeatureStatus'; +import { LANGUAGES } from '../../store/constants'; +import { languageChanged } from '../../store/systemSlice'; +import { languageSelector } from '../../store/systemSelectors'; const selector = createSelector( [stateSelector], @@ -57,6 +65,8 @@ const selector = createSelector( shouldLogToConsole, shouldAntialiasProgressImage, isNodesEnabled, + shouldUseNSFWChecker, + shouldUseWatermarker, } = system; const { @@ -78,6 +88,8 @@ const selector = createSelector( shouldAntialiasProgressImage, shouldShowAdvancedOptions, isNodesEnabled, + shouldUseNSFWChecker, + shouldUseWatermarker, }; }, { @@ -92,6 +104,7 @@ type ConfigOptions = { shouldShowAdvancedOptionsSettings: boolean; shouldShowClearIntermediates: boolean; shouldShowNodesToggle: boolean; + shouldShowLocalizationToggle: boolean; }; type SettingsModalProps = { @@ -113,6 +126,8 @@ const SettingsModal = ({ children, config }: SettingsModalProps) => { const shouldShowClearIntermediates = config?.shouldShowClearIntermediates ?? true; const shouldShowNodesToggle = config?.shouldShowNodesToggle ?? true; + const shouldShowLocalizationToggle = + config?.shouldShowLocalizationToggle ?? true; useEffect(() => { if (!shouldShowDeveloperSettings) { @@ -120,6 +135,16 @@ const SettingsModal = ({ children, config }: SettingsModalProps) => { } }, [shouldShowDeveloperSettings, dispatch]); + const { isNSFWCheckerAvailable, isWatermarkerAvailable } = + useGetAppConfigQuery(undefined, { + selectFromResult: ({ data }) => ({ + isNSFWCheckerAvailable: + data?.nsfw_methods.includes('nsfw_checker') ?? false, + isWatermarkerAvailable: + data?.watermarking_methods.includes('invisible_watermark') ?? false, + }), + }); + const { isOpen: isSettingsModalOpen, onOpen: onSettingsModalOpen, @@ -143,6 +168,8 @@ const SettingsModal = ({ children, config }: SettingsModalProps) => { shouldAntialiasProgressImage, shouldShowAdvancedOptions, isNodesEnabled, + shouldUseNSFWChecker, + shouldUseWatermarker, } = useAppSelector(selector); const handleClickResetWebUI = useCallback(() => { @@ -166,6 +193,13 @@ const SettingsModal = ({ children, config }: SettingsModalProps) => { [dispatch] ); + const handleLanguageChanged = useCallback( + (l: string) => { + dispatch(languageChanged(l as keyof typeof LANGUAGES)); + }, + [dispatch] + ); + const handleLogToConsoleChanged = useCallback( (e: ChangeEvent) => { dispatch(shouldLogToConsoleChanged(e.target.checked)); @@ -180,6 +214,12 @@ const SettingsModal = ({ children, config }: SettingsModalProps) => { [dispatch] ); + const { colorMode, toggleColorMode } = useColorMode(); + + const isLocalizationEnabled = + useFeatureStatus('localization').isFeatureEnabled; + const language = useAppSelector(languageSelector); + return ( <> {cloneElement(children, { @@ -221,10 +261,31 @@ const SettingsModal = ({ children, config }: SettingsModalProps) => { {t('settings.generation')} + ) => + dispatch(shouldUseNSFWCheckerChanged(e.target.checked)) + } + /> + ) => + dispatch(shouldUseWatermarkerChanged(e.target.checked)) + } + /> {t('settings.ui')} + { onChange={handleToggleNodes} /> )} + {shouldShowLocalizationToggle && ( + ({ + value, + label, + }))} + onChange={handleLanguageChanged} + /> + )} {shouldShowDeveloperSettings && ( diff --git a/invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsSchedulers.tsx b/invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsSchedulers.tsx index 6d2e963b6a..0be58a8815 100644 --- a/invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsSchedulers.tsx +++ b/invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsSchedulers.tsx @@ -10,9 +10,9 @@ import { map } from 'lodash-es'; import { useCallback } from 'react'; import { useTranslation } from 'react-i18next'; -const data = map(SCHEDULER_LABEL_MAP, (value, label) => ({ - value, - label, +const data = map(SCHEDULER_LABEL_MAP, (label, name) => ({ + value: name, + label: label, })).sort((a, b) => a.label.localeCompare(b.label)); export default function SettingsSchedulers() { diff --git a/invokeai/frontend/web/src/features/system/components/SiteHeader.tsx b/invokeai/frontend/web/src/features/system/components/SiteHeader.tsx index 758f03f19a..70f4b12859 100644 --- a/invokeai/frontend/web/src/features/system/components/SiteHeader.tsx +++ b/invokeai/frontend/web/src/features/system/components/SiteHeader.tsx @@ -1,28 +1,40 @@ -import { Flex, Spacer } from '@chakra-ui/react'; -import { memo } from 'react'; -import StatusIndicator from './StatusIndicator'; - -import { Link } from '@chakra-ui/react'; +import { + Flex, + Menu, + MenuButton, + MenuGroup, + MenuItem, + MenuList, + Spacer, +} from '@chakra-ui/react'; import IAIIconButton from 'common/components/IAIIconButton'; +import { memo } from 'react'; import { useTranslation } from 'react-i18next'; -import { FaBug, FaDiscord, FaGithub, FaKeyboard } from 'react-icons/fa'; -import { MdSettings } from 'react-icons/md'; +import { + FaBars, + FaBug, + FaCog, + FaDiscord, + FaGithub, + FaKeyboard, +} from 'react-icons/fa'; +import { menuListMotionProps } from 'theme/components/menu'; import { useFeatureStatus } from '../hooks/useFeatureStatus'; -import ColorModeButton from './ColorModeButton'; import HotkeysModal from './HotkeysModal/HotkeysModal'; import InvokeAILogoComponent from './InvokeAILogoComponent'; -import LanguagePicker from './LanguagePicker'; import SettingsModal from './SettingsModal/SettingsModal'; +import StatusIndicator from './StatusIndicator'; const SiteHeader = () => { const { t } = useTranslation(); - const isLocalizationEnabled = - useFeatureStatus('localization').isFeatureEnabled; const isBugLinkEnabled = useFeatureStatus('bugLink').isFeatureEnabled; const isDiscordLinkEnabled = useFeatureStatus('discordLink').isFeatureEnabled; const isGithubLinkEnabled = useFeatureStatus('githubLink').isFeatureEnabled; + const githubLink = 'http://github.com/invoke-ai/InvokeAI'; + const discordLink = 'https://discord.gg/ZmtBAhwWhy'; + return ( { - - + } + aria-label={t('accessibility.menu')} + icon={} + sx={{ boxSize: 8 }} /> - - - {isLocalizationEnabled && } - - {isBugLinkEnabled && ( - - } - /> - - )} - - {isGithubLinkEnabled && ( - - } - /> - - )} - - {isDiscordLinkEnabled && ( - - } - /> - - )} - - - - - } - /> - + + + {isGithubLinkEnabled && ( + } + > + {t('common.githubLabel')} + + )} + {isBugLinkEnabled && ( + } + > + {t('common.reportBugLabel')} + + )} + {isDiscordLinkEnabled && ( + } + > + {t('common.discordLabel')} + + )} + + + + }> + {t('common.hotkeysLabel')} + + + + }> + {t('common.settingsLabel')} + + + + + ); }; diff --git a/invokeai/frontend/web/src/features/system/components/SiteHeaderMenu.tsx b/invokeai/frontend/web/src/features/system/components/SiteHeaderMenu.tsx deleted file mode 100644 index ba135be8d1..0000000000 --- a/invokeai/frontend/web/src/features/system/components/SiteHeaderMenu.tsx +++ /dev/null @@ -1,111 +0,0 @@ -import { Flex, Link } from '@chakra-ui/react'; -import { useTranslation } from 'react-i18next'; -import { FaBug, FaDiscord, FaGithub, FaKeyboard } from 'react-icons/fa'; -import { MdSettings } from 'react-icons/md'; -import HotkeysModal from './HotkeysModal/HotkeysModal'; -import LanguagePicker from './LanguagePicker'; -import SettingsModal from './SettingsModal/SettingsModal'; - -import IAIIconButton from 'common/components/IAIIconButton'; -import { useFeatureStatus } from '../hooks/useFeatureStatus'; - -const SiteHeaderMenu = () => { - const { t } = useTranslation(); - - const isLocalizationEnabled = - useFeatureStatus('localization').isFeatureEnabled; - const isBugLinkEnabled = useFeatureStatus('bugLink').isFeatureEnabled; - const isDiscordLinkEnabled = useFeatureStatus('discordLink').isFeatureEnabled; - const isGithubLinkEnabled = useFeatureStatus('githubLink').isFeatureEnabled; - - return ( - - - } - /> - - - {isLocalizationEnabled && } - - {isBugLinkEnabled && ( - - } - /> - - )} - - {isGithubLinkEnabled && ( - - } - /> - - )} - - {isDiscordLinkEnabled && ( - - } - /> - - )} - - - } - /> - - - ); -}; - -SiteHeaderMenu.displayName = 'SiteHeaderMenu'; -export default SiteHeaderMenu; diff --git a/invokeai/frontend/web/src/features/system/store/systemSlice.ts b/invokeai/frontend/web/src/features/system/store/systemSlice.ts index b7a5e606e2..fba980131e 100644 --- a/invokeai/frontend/web/src/features/system/store/systemSlice.ts +++ b/invokeai/frontend/web/src/features/system/store/systemSlice.ts @@ -87,6 +87,8 @@ export interface SystemState { language: keyof typeof LANGUAGES; isUploading: boolean; isNodesEnabled: boolean; + shouldUseNSFWChecker: boolean; + shouldUseWatermarker: boolean; } export const initialSystemState: SystemState = { @@ -119,6 +121,8 @@ export const initialSystemState: SystemState = { language: 'en', isUploading: false, isNodesEnabled: false, + shouldUseNSFWChecker: false, + shouldUseWatermarker: false, }; export const systemSlice = createSlice({ @@ -194,6 +198,12 @@ export const systemSlice = createSlice({ setIsNodesEnabled(state, action: PayloadAction) { state.isNodesEnabled = action.payload; }, + shouldUseNSFWCheckerChanged(state, action: PayloadAction) { + state.shouldUseNSFWChecker = action.payload; + }, + shouldUseWatermarkerChanged(state, action: PayloadAction) { + state.shouldUseWatermarker = action.payload; + }, }, extraReducers(builder) { /** @@ -409,6 +419,8 @@ export const { languageChanged, progressImageSet, setIsNodesEnabled, + shouldUseNSFWCheckerChanged, + shouldUseWatermarkerChanged, } = systemSlice.actions; export default systemSlice.reducer; diff --git a/invokeai/frontend/web/src/features/ui/components/InvokeTabs.tsx b/invokeai/frontend/web/src/features/ui/components/InvokeTabs.tsx index 6c683470e7..26d06adfb3 100644 --- a/invokeai/frontend/web/src/features/ui/components/InvokeTabs.tsx +++ b/invokeai/frontend/web/src/features/ui/components/InvokeTabs.tsx @@ -16,7 +16,7 @@ import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; import { requestCanvasRescale } from 'features/canvas/store/thunks/requestCanvasScale'; import ImageGalleryContent from 'features/gallery/components/ImageGalleryContent'; import { configSelector } from 'features/system/store/configSelectors'; -import { InvokeTabName } from 'features/ui/store/tabMap'; +import { InvokeTabName, tabMap } from 'features/ui/store/tabMap'; import { setActiveTab, togglePanels } from 'features/ui/store/uiSlice'; import { ResourceKey } from 'i18next'; import { isEqual } from 'lodash-es'; @@ -172,13 +172,22 @@ const InvokeTabs = () => { const { ref: galleryPanelRef, minSizePct: galleryMinSizePct } = useMinimumPanelSize(MIN_GALLERY_WIDTH, DEFAULT_GALLERY_PCT, 'app'); + const handleTabChange = useCallback( + (index: number) => { + const activeTabName = tabMap[index]; + if (!activeTabName) { + return; + } + dispatch(setActiveTab(activeTabName)); + }, + [dispatch] + ); + return ( { - dispatch(setActiveTab(index)); - }} + onChange={handleTabChange} sx={{ flexGrow: 1, gap: 4, diff --git a/invokeai/frontend/web/src/features/ui/components/ParametersDrawer.tsx b/invokeai/frontend/web/src/features/ui/components/ParametersDrawer.tsx index 89400c09e5..393cb5bb34 100644 --- a/invokeai/frontend/web/src/features/ui/components/ParametersDrawer.tsx +++ b/invokeai/frontend/web/src/features/ui/components/ParametersDrawer.tsx @@ -1,7 +1,10 @@ import { Flex } from '@chakra-ui/react'; import { createSelector } from '@reduxjs/toolkit'; +import { RootState } from 'app/store/store'; import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions'; +import SDXLImageToImageTabParameters from 'features/sdxl/components/SDXLImageToImageTabParameters'; +import SDXLTextToImageTabParameters from 'features/sdxl/components/SDXLTextToImageTabParameters'; import InvokeAILogoComponent from 'features/system/components/InvokeAILogoComponent'; import { activeTabNameSelector, @@ -39,13 +42,23 @@ const ParametersDrawer = () => { dispatch(setShouldShowParametersPanel(false)); }; + const model = useAppSelector((state: RootState) => state.generation.model); + const drawerContent = useMemo(() => { if (activeTabName === 'txt2img') { - return ; + return model && model.base_model === 'sdxl' ? ( + + ) : ( + + ); } if (activeTabName === 'img2img') { - return ; + return model && model.base_model === 'sdxl' ? ( + + ) : ( + + ); } if (activeTabName === 'unifiedCanvas') { @@ -53,7 +66,7 @@ const ParametersDrawer = () => { } return null; - }, [activeTabName]); + }, [activeTabName, model]); if (shouldPinParametersPanel) { return null; diff --git a/invokeai/frontend/web/src/features/ui/components/tabs/ImageToImage/ImageToImageTab.tsx b/invokeai/frontend/web/src/features/ui/components/tabs/ImageToImage/ImageToImageTab.tsx index a0ec95d72d..d58630d1b9 100644 --- a/invokeai/frontend/web/src/features/ui/components/tabs/ImageToImage/ImageToImageTab.tsx +++ b/invokeai/frontend/web/src/features/ui/components/tabs/ImageToImage/ImageToImageTab.tsx @@ -1,7 +1,9 @@ import { Box, Flex } from '@chakra-ui/react'; -import { useAppDispatch } from 'app/store/storeHooks'; +import { RootState } from 'app/store/store'; +import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; import { requestCanvasRescale } from 'features/canvas/store/thunks/requestCanvasScale'; import InitialImageDisplay from 'features/parameters/components/Parameters/ImageToImage/InitialImageDisplay'; +import SDXLImageToImageTabParameters from 'features/sdxl/components/SDXLImageToImageTabParameters'; import { memo, useCallback, useRef } from 'react'; import { ImperativePanelGroupHandle, @@ -16,6 +18,7 @@ import ImageToImageTabParameters from './ImageToImageTabParameters'; const ImageToImageTab = () => { const dispatch = useAppDispatch(); const panelGroupRef = useRef(null); + const model = useAppSelector((state: RootState) => state.generation.model); const handleDoubleClickHandle = useCallback(() => { if (!panelGroupRef.current) { @@ -28,7 +31,11 @@ const ImageToImageTab = () => { return ( - + {model && model.base_model === 'sdxl' ? ( + + ) : ( + + )} (''); // Get paths of models that are already installed - const { data: installedModels } = useGetMainModelsQuery(); + const { data: installedModels } = useGetMainModelsQuery(ALL_BASE_MODELS); // Get all model paths from a given directory const { foundModels, alreadyInstalled, filteredModels } = diff --git a/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/MergeModelsPanel.tsx b/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/MergeModelsPanel.tsx index 19ca10e240..4ad8fbaba6 100644 --- a/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/MergeModelsPanel.tsx +++ b/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/MergeModelsPanel.tsx @@ -1,5 +1,4 @@ import { Flex, Radio, RadioGroup, Text, Tooltip } from '@chakra-ui/react'; -import { makeToast } from 'features/system/util/makeToast'; import { useAppDispatch } from 'app/store/storeHooks'; import IAIButton from 'common/components/IAIButton'; import IAIInput from 'common/components/IAIInput'; @@ -8,9 +7,11 @@ import IAIMantineSelect from 'common/components/IAIMantineSelect'; import IAISimpleCheckbox from 'common/components/IAISimpleCheckbox'; import IAISlider from 'common/components/IAISlider'; import { addToast } from 'features/system/store/systemSlice'; +import { makeToast } from 'features/system/util/makeToast'; import { pickBy } from 'lodash-es'; import { useMemo, useState } from 'react'; import { useTranslation } from 'react-i18next'; +import { ALL_BASE_MODELS } from 'services/api/constants'; import { useGetMainModelsQuery, useMergeMainModelsMutation, @@ -32,7 +33,7 @@ export default function MergeModelsPanel() { const { t } = useTranslation(); const dispatch = useAppDispatch(); - const { data } = useGetMainModelsQuery(); + const { data } = useGetMainModelsQuery(ALL_BASE_MODELS); const [mergeModels, { isLoading }] = useMergeMainModelsMutation(); diff --git a/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel.tsx b/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel.tsx index f49294cfb0..87eb918564 100644 --- a/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel.tsx +++ b/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel.tsx @@ -8,10 +8,11 @@ import { import CheckpointModelEdit from './ModelManagerPanel/CheckpointModelEdit'; import DiffusersModelEdit from './ModelManagerPanel/DiffusersModelEdit'; import ModelList from './ModelManagerPanel/ModelList'; +import { ALL_BASE_MODELS } from 'services/api/constants'; export default function ModelManagerPanel() { const [selectedModelId, setSelectedModelId] = useState(); - const { model } = useGetMainModelsQuery(undefined, { + const { model } = useGetMainModelsQuery(ALL_BASE_MODELS, { selectFromResult: ({ data }) => ({ model: selectedModelId ? data?.entities[selectedModelId] : undefined, }), diff --git a/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/CheckpointModelEdit.tsx b/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/CheckpointModelEdit.tsx index cb297d3afe..6c3ebf2530 100644 --- a/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/CheckpointModelEdit.tsx +++ b/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/CheckpointModelEdit.tsx @@ -1,6 +1,5 @@ import { Badge, Divider, Flex, Text } from '@chakra-ui/react'; import { useForm } from '@mantine/form'; -import { makeToast } from 'features/system/util/makeToast'; import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; import IAIButton from 'common/components/IAIButton'; import IAIMantineTextInput from 'common/components/IAIMantineInput'; @@ -8,6 +7,7 @@ import IAISimpleCheckbox from 'common/components/IAISimpleCheckbox'; import { MODEL_TYPE_MAP } from 'features/parameters/types/constants'; import { selectIsBusy } from 'features/system/store/systemSelectors'; import { addToast } from 'features/system/store/systemSlice'; +import { makeToast } from 'features/system/util/makeToast'; import { useCallback, useEffect, useState } from 'react'; import { useTranslation } from 'react-i18next'; import { @@ -115,7 +115,7 @@ export default function CheckpointModelEdit(props: CheckpointModelEditProps) { {MODEL_TYPE_MAP[model.base_model]} Model - {!['sdxl', 'sdxl-refiner'].includes(model.base_model) ? ( + {![''].includes(model.base_model) ? ( ) : ( { const [modelFormatFilter, setModelFormatFilter] = useState('images'); - const { filteredDiffusersModels } = useGetMainModelsQuery(undefined, { + const { filteredDiffusersModels } = useGetMainModelsQuery(ALL_BASE_MODELS, { selectFromResult: ({ data }) => ({ filteredDiffusersModels: modelsFilter(data, 'diffusers', nameFilter), }), }); - const { filteredCheckpointModels } = useGetMainModelsQuery(undefined, { + const { filteredCheckpointModels } = useGetMainModelsQuery(ALL_BASE_MODELS, { selectFromResult: ({ data }) => ({ filteredCheckpointModels: modelsFilter(data, 'checkpoint', nameFilter), }), diff --git a/invokeai/frontend/web/src/features/ui/components/tabs/TextToImage/TextToImageTab.tsx b/invokeai/frontend/web/src/features/ui/components/tabs/TextToImage/TextToImageTab.tsx index 90141af785..7e6e02ff5a 100644 --- a/invokeai/frontend/web/src/features/ui/components/tabs/TextToImage/TextToImageTab.tsx +++ b/invokeai/frontend/web/src/features/ui/components/tabs/TextToImage/TextToImageTab.tsx @@ -1,14 +1,22 @@ import { Flex } from '@chakra-ui/react'; +import { RootState } from 'app/store/store'; +import { useAppSelector } from 'app/store/storeHooks'; +import SDXLTextToImageTabParameters from 'features/sdxl/components/SDXLTextToImageTabParameters'; import { memo } from 'react'; import ParametersPinnedWrapper from '../../ParametersPinnedWrapper'; import TextToImageTabMain from './TextToImageTabMain'; import TextToImageTabParameters from './TextToImageTabParameters'; const TextToImageTab = () => { + const model = useAppSelector((state: RootState) => state.generation.model); return ( - + {model && model.base_model === 'sdxl' ? ( + + ) : ( + + )} diff --git a/invokeai/frontend/web/src/features/ui/store/uiSlice.ts b/invokeai/frontend/web/src/features/ui/store/uiSlice.ts index 81243aa03f..e487f08067 100644 --- a/invokeai/frontend/web/src/features/ui/store/uiSlice.ts +++ b/invokeai/frontend/web/src/features/ui/store/uiSlice.ts @@ -26,7 +26,7 @@ export const uiSlice = createSlice({ name: 'ui', initialState: initialUIState, reducers: { - setActiveTab: (state, action: PayloadAction) => { + setActiveTab: (state, action: PayloadAction) => { setActiveTabReducer(state, action.payload); }, setShouldPinParametersPanel: (state, action: PayloadAction) => { diff --git a/invokeai/frontend/web/src/index.ts b/invokeai/frontend/web/src/index.ts index add4999b6d..57dbeab0e5 100644 --- a/invokeai/frontend/web/src/index.ts +++ b/invokeai/frontend/web/src/index.ts @@ -3,7 +3,6 @@ export type { PartialAppConfig } from './app/types/invokeai'; export { default as IAIIconButton } from './common/components/IAIIconButton'; export { default as IAIPopover } from './common/components/IAIPopover'; export { default as ParamMainModelSelect } from './features/parameters/components/Parameters/MainModel/ParamMainModelSelect'; -export { default as ColorModeButton } from './features/system/components/ColorModeButton'; export { default as InvokeAiLogoComponent } from './features/system/components/InvokeAILogoComponent'; export { default as SettingsModal } from './features/system/components/SettingsModal/SettingsModal'; export { default as StatusIndicator } from './features/system/components/StatusIndicator'; diff --git a/invokeai/frontend/web/src/services/api/constants.ts b/invokeai/frontend/web/src/services/api/constants.ts new file mode 100644 index 0000000000..8bf35d0198 --- /dev/null +++ b/invokeai/frontend/web/src/services/api/constants.ts @@ -0,0 +1,16 @@ +import { BaseModelType } from './types'; + +export const ALL_BASE_MODELS: BaseModelType[] = [ + 'sd-1', + 'sd-2', + 'sdxl', + 'sdxl-refiner', +]; + +export const NON_REFINER_BASE_MODELS: BaseModelType[] = [ + 'sd-1', + 'sd-2', + 'sdxl', +]; + +export const REFINER_BASE_MODELS: BaseModelType[] = ['sdxl-refiner']; diff --git a/invokeai/frontend/web/src/services/api/endpoints/appInfo.ts b/invokeai/frontend/web/src/services/api/endpoints/appInfo.ts index f76b56761c..2d3537998d 100644 --- a/invokeai/frontend/web/src/services/api/endpoints/appInfo.ts +++ b/invokeai/frontend/web/src/services/api/endpoints/appInfo.ts @@ -8,6 +8,7 @@ export const appInfoApi = api.injectEndpoints({ url: `app/version`, method: 'GET', }), + providesTags: ['AppVersion'], keepUnusedDataFor: 86400000, // 1 day }), getAppConfig: build.query({ @@ -15,6 +16,7 @@ export const appInfoApi = api.injectEndpoints({ url: `app/config`, method: 'GET', }), + providesTags: ['AppConfig'], keepUnusedDataFor: 86400000, // 1 day }), }), diff --git a/invokeai/frontend/web/src/services/api/endpoints/models.ts b/invokeai/frontend/web/src/services/api/endpoints/models.ts index ff82bc2802..3d0013a62c 100644 --- a/invokeai/frontend/web/src/services/api/endpoints/models.ts +++ b/invokeai/frontend/web/src/services/api/endpoints/models.ts @@ -144,8 +144,19 @@ const createModelEntities = ( export const modelsApi = api.injectEndpoints({ endpoints: (build) => ({ - getMainModels: build.query, void>({ - query: () => ({ url: 'models/', params: { model_type: 'main' } }), + getMainModels: build.query< + EntityState, + BaseModelType[] + >({ + query: (base_models) => { + const params = { + model_type: 'main', + base_models, + }; + + const query = queryString.stringify(params, { arrayFormat: 'none' }); + return `models/?${query}`; + }, providesTags: (result, error, arg) => { const tags: ApiFullTagDescription[] = [ { type: 'MainModel', id: LIST_TAG }, @@ -187,7 +198,10 @@ export const modelsApi = api.injectEndpoints({ body: body, }; }, - invalidatesTags: [{ type: 'MainModel', id: LIST_TAG }], + invalidatesTags: [ + { type: 'MainModel', id: LIST_TAG }, + { type: 'SDXLRefinerModel', id: LIST_TAG }, + ], }), importMainModels: build.mutation< ImportMainModelResponse, @@ -200,7 +214,10 @@ export const modelsApi = api.injectEndpoints({ body: body, }; }, - invalidatesTags: [{ type: 'MainModel', id: LIST_TAG }], + invalidatesTags: [ + { type: 'MainModel', id: LIST_TAG }, + { type: 'SDXLRefinerModel', id: LIST_TAG }, + ], }), addMainModels: build.mutation({ query: ({ body }) => { @@ -210,7 +227,10 @@ export const modelsApi = api.injectEndpoints({ body: body, }; }, - invalidatesTags: [{ type: 'MainModel', id: LIST_TAG }], + invalidatesTags: [ + { type: 'MainModel', id: LIST_TAG }, + { type: 'SDXLRefinerModel', id: LIST_TAG }, + ], }), deleteMainModels: build.mutation< DeleteMainModelResponse, @@ -222,7 +242,10 @@ export const modelsApi = api.injectEndpoints({ method: 'DELETE', }; }, - invalidatesTags: [{ type: 'MainModel', id: LIST_TAG }], + invalidatesTags: [ + { type: 'MainModel', id: LIST_TAG }, + { type: 'SDXLRefinerModel', id: LIST_TAG }, + ], }), convertMainModels: build.mutation< ConvertMainModelResponse, @@ -235,7 +258,10 @@ export const modelsApi = api.injectEndpoints({ params: params, }; }, - invalidatesTags: [{ type: 'MainModel', id: LIST_TAG }], + invalidatesTags: [ + { type: 'MainModel', id: LIST_TAG }, + { type: 'SDXLRefinerModel', id: LIST_TAG }, + ], }), mergeMainModels: build.mutation({ query: ({ base_model, body }) => { @@ -245,7 +271,10 @@ export const modelsApi = api.injectEndpoints({ body: body, }; }, - invalidatesTags: [{ type: 'MainModel', id: LIST_TAG }], + invalidatesTags: [ + { type: 'MainModel', id: LIST_TAG }, + { type: 'SDXLRefinerModel', id: LIST_TAG }, + ], }), syncModels: build.mutation({ query: () => { @@ -254,7 +283,10 @@ export const modelsApi = api.injectEndpoints({ method: 'POST', }; }, - invalidatesTags: [{ type: 'MainModel', id: LIST_TAG }], + invalidatesTags: [ + { type: 'MainModel', id: LIST_TAG }, + { type: 'SDXLRefinerModel', id: LIST_TAG }, + ], }), getLoRAModels: build.query, void>({ query: () => ({ url: 'models/', params: { model_type: 'lora' } }), diff --git a/invokeai/frontend/web/src/services/api/hooks/useIsRefinerAvailable.ts b/invokeai/frontend/web/src/services/api/hooks/useIsRefinerAvailable.ts new file mode 100644 index 0000000000..4cb4891be4 --- /dev/null +++ b/invokeai/frontend/web/src/services/api/hooks/useIsRefinerAvailable.ts @@ -0,0 +1,12 @@ +import { REFINER_BASE_MODELS } from 'services/api/constants'; +import { useGetMainModelsQuery } from 'services/api/endpoints/models'; + +export const useIsRefinerAvailable = () => { + const { isRefinerAvailable } = useGetMainModelsQuery(REFINER_BASE_MODELS, { + selectFromResult: ({ data }) => ({ + isRefinerAvailable: data ? data.ids.length > 0 : false, + }), + }); + + return isRefinerAvailable; +}; diff --git a/invokeai/frontend/web/src/services/api/schema.d.ts b/invokeai/frontend/web/src/services/api/schema.d.ts index 0fce960703..c8e87ef8c5 100644 --- a/invokeai/frontend/web/src/services/api/schema.d.ts +++ b/invokeai/frontend/web/src/services/api/schema.d.ts @@ -318,6 +318,21 @@ export type components = { * @description List of available infill methods */ infill_methods: (string)[]; + /** + * Upscaling Methods + * @description List of upscaling methods + */ + upscaling_methods: (components["schemas"]["Upscaler"])[]; + /** + * Nsfw Methods + * @description List of NSFW checking methods + */ + nsfw_methods: (string)[]; + /** + * Watermarking Methods + * @description List of invisible watermark methods + */ + watermarking_methods: (string)[]; }; /** * AppVersion @@ -1014,6 +1029,11 @@ export type components = { * @description The LoRAs used for inference */ loras: (components["schemas"]["LoRAMetadataField"])[]; + /** + * Vae + * @description The VAE used for decoding, if the main model's default was not used + */ + vae?: components["schemas"]["VAEModelField"]; /** * Strength * @description The strength used for latents-to-latents @@ -1025,10 +1045,45 @@ export type components = { */ init_image?: string; /** - * Vae - * @description The VAE used for decoding, if the main model's default was not used + * Positive Style Prompt + * @description The positive style prompt parameter */ - vae?: components["schemas"]["VAEModelField"]; + positive_style_prompt?: string; + /** + * Negative Style Prompt + * @description The negative style prompt parameter + */ + negative_style_prompt?: string; + /** + * Refiner Model + * @description The SDXL Refiner model used + */ + refiner_model?: components["schemas"]["MainModelField"]; + /** + * Refiner Cfg Scale + * @description The classifier-free guidance scale parameter used for the refiner + */ + refiner_cfg_scale?: number; + /** + * Refiner Steps + * @description The number of steps used for the refiner + */ + refiner_steps?: number; + /** + * Refiner Scheduler + * @description The scheduler used for the refiner + */ + refiner_scheduler?: string; + /** + * Refiner Aesthetic Store + * @description The aesthetic score used for the refiner + */ + refiner_aesthetic_store?: number; + /** + * Refiner Start + * @description The start value used for refiner denoising + */ + refiner_start?: number; }; /** * CvInpaintInvocation @@ -1305,7 +1360,7 @@ export type components = { * @description The nodes in this graph */ nodes?: { - [key: string]: (components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]) | undefined; + [key: string]: (components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]) | undefined; }; /** * Edges @@ -1895,6 +1950,39 @@ export type components = { */ image2?: components["schemas"]["ImageField"]; }; + /** + * ImageNSFWBlurInvocation + * @description Add blur to NSFW-flagged images + */ + ImageNSFWBlurInvocation: { + /** + * Id + * @description The id of this node. Must be unique among all nodes. + */ + id: string; + /** + * Is Intermediate + * @description Whether or not this node is an intermediate node. + * @default false + */ + is_intermediate?: boolean; + /** + * Type + * @default img_nsfw + * @enum {string} + */ + type?: "img_nsfw"; + /** + * Image + * @description The image to check + */ + image?: components["schemas"]["ImageField"]; + /** + * Metadata + * @description Optional core metadata to be written to the image + */ + metadata?: components["schemas"]["CoreMetadata"]; + }; /** * ImageOutput * @description Base class for invocations that output an image @@ -2175,6 +2263,45 @@ export type components = { */ thumbnail_url: string; }; + /** + * ImageWatermarkInvocation + * @description Add an invisible watermark to an image + */ + ImageWatermarkInvocation: { + /** + * Id + * @description The id of this node. Must be unique among all nodes. + */ + id: string; + /** + * Is Intermediate + * @description Whether or not this node is an intermediate node. + * @default false + */ + is_intermediate?: boolean; + /** + * Type + * @default img_watermark + * @enum {string} + */ + type?: "img_watermark"; + /** + * Image + * @description The image to check + */ + image?: components["schemas"]["ImageField"]; + /** + * Text + * @description Watermark text + * @default InvokeAI + */ + text?: string; + /** + * Metadata + * @description Optional core metadata to be written to the image + */ + metadata?: components["schemas"]["CoreMetadata"]; + }; /** * InfillColorInvocation * @description Infills transparent areas of an image with a solid color @@ -2604,7 +2731,7 @@ export type components = { vae?: components["schemas"]["VaeField"]; /** * Tiled - * @description Decode latents by overlaping tiles(less memory consumption) + * @description Decode latents by overlapping tiles(less memory consumption) * @default false */ tiled?: boolean; @@ -3268,6 +3395,46 @@ export type components = { * @description The VAE used for decoding, if the main model's default was not used */ vae?: components["schemas"]["VAEModelField"]; + /** + * Positive Style Prompt + * @description The positive style prompt parameter + */ + positive_style_prompt?: string; + /** + * Negative Style Prompt + * @description The negative style prompt parameter + */ + negative_style_prompt?: string; + /** + * Refiner Model + * @description The SDXL Refiner model used + */ + refiner_model?: components["schemas"]["MainModelField"]; + /** + * Refiner Cfg Scale + * @description The classifier-free guidance scale parameter used for the refiner + */ + refiner_cfg_scale?: number; + /** + * Refiner Steps + * @description The number of steps used for the refiner + */ + refiner_steps?: number; + /** + * Refiner Scheduler + * @description The scheduler used for the refiner + */ + refiner_scheduler?: string; + /** + * Refiner Aesthetic Store + * @description The aesthetic score used for the refiner + */ + refiner_aesthetic_store?: number; + /** + * Refiner Start + * @description The start value used for refiner denoising + */ + refiner_start?: number; }; /** * MetadataAccumulatorOutput @@ -5229,6 +5396,19 @@ export type components = { */ loras: (components["schemas"]["LoraInfo"])[]; }; + /** Upscaler */ + Upscaler: { + /** + * Upscaling Method + * @description Name of upscaling method + */ + upscaling_method: string; + /** + * Upscaling Models + * @description List of upscaling models for this method + */ + upscaling_models: (string)[]; + }; /** * VAEModelField * @description Vae model field @@ -5355,6 +5535,12 @@ export type components = { */ image?: components["schemas"]["ImageField"]; }; + /** + * StableDiffusion1ModelFormat + * @description An enumeration. + * @enum {string} + */ + StableDiffusion1ModelFormat: "checkpoint" | "diffusers"; /** * StableDiffusion2ModelFormat * @description An enumeration. @@ -5367,12 +5553,6 @@ export type components = { * @enum {string} */ StableDiffusionXLModelFormat: "checkpoint" | "diffusers"; - /** - * StableDiffusion1ModelFormat - * @description An enumeration. - * @enum {string} - */ - StableDiffusion1ModelFormat: "checkpoint" | "diffusers"; }; responses: never; parameters: never; @@ -5483,7 +5663,7 @@ export type operations = { }; requestBody: { content: { - "application/json": components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]; + "application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]; }; }; responses: { @@ -5520,7 +5700,7 @@ export type operations = { }; requestBody: { content: { - "application/json": components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]; + "application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]; }; }; responses: { diff --git a/invokeai/frontend/web/src/services/api/types.d.ts b/invokeai/frontend/web/src/services/api/types.d.ts index 3e945691f1..7d9040321c 100644 --- a/invokeai/frontend/web/src/services/api/types.d.ts +++ b/invokeai/frontend/web/src/services/api/types.d.ts @@ -134,6 +134,12 @@ export type ESRGANInvocation = TypeReq< export type DivideInvocation = TypeReq< components['schemas']['DivideInvocation'] >; +export type ImageNSFWBlurInvocation = TypeReq< + components['schemas']['ImageNSFWBlurInvocation'] +>; +export type ImageWatermarkInvocation = TypeReq< + components['schemas']['ImageWatermarkInvocation'] +>; // ControlNet Nodes export type ControlNetInvocation = TypeReq< diff --git a/invokeai/frontend/web/src/theme/components/menu.ts b/invokeai/frontend/web/src/theme/components/menu.ts index 563c5ce6a9..4ab323bdb5 100644 --- a/invokeai/frontend/web/src/theme/components/menu.ts +++ b/invokeai/frontend/web/src/theme/components/menu.ts @@ -33,12 +33,16 @@ const invokeAI = definePartsStyle((props) => ({ bg: mode('base.200', 'base.800')(props), _hover: { bg: mode('base.300', 'base.700')(props), + svg: { + opacity: 1, + }, }, _focus: { bg: mode('base.400', 'base.600')(props), }, svg: { - opacity: 0.5, + opacity: 0.7, + fontSize: 14, }, }, })); diff --git a/invokeai/frontend/web/yarn.lock b/invokeai/frontend/web/yarn.lock index 35208003d0..17517ce4e0 100644 --- a/invokeai/frontend/web/yarn.lock +++ b/invokeai/frontend/web/yarn.lock @@ -71,56 +71,51 @@ "@babel/helper-validator-identifier" "^7.22.5" to-fast-properties "^2.0.0" -"@chakra-ui/accordion@2.2.0": +"@chakra-ui/accordion@2.3.0": + version "2.3.0" + resolved "https://registry.yarnpkg.com/@chakra-ui/accordion/-/accordion-2.3.0.tgz#2c85fd2d2734b176f019f8db9f4e075007b4e1fb" + integrity sha512-A4TkRw3Jnt+Fam6dSSJ62rskdrvjF3JGctYcfXlojfFIpHPuIw4pDwfZgNAxlaxWkcj0e7JJKlQ88dnZW+QfFg== + dependencies: + "@chakra-ui/descendant" "3.1.0" + "@chakra-ui/icon" "3.1.0" + "@chakra-ui/react-context" "2.1.0" + "@chakra-ui/react-use-controllable-state" "2.1.0" + "@chakra-ui/react-use-merge-refs" "2.1.0" + "@chakra-ui/shared-utils" "2.0.5" + "@chakra-ui/transition" "2.1.0" + 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"@chakra-ui/shared-utils" "2.0.5" -"@chakra-ui/breadcrumb@2.1.5": - version "2.1.5" - resolved "https://registry.yarnpkg.com/@chakra-ui/breadcrumb/-/breadcrumb-2.1.5.tgz#a43b22cc8005291a615696a8c88efc37064562f3" - integrity sha512-p3eQQrHQBkRB69xOmNyBJqEdfCrMt+e0eOH+Pm/DjFWfIVIbnIaFbmDCeWClqlLa21Ypc6h1hR9jEmvg8kmOog== +"@chakra-ui/breadcrumb@2.2.0": + version "2.2.0" + resolved "https://registry.yarnpkg.com/@chakra-ui/breadcrumb/-/breadcrumb-2.2.0.tgz#751bc48498f3c403f97b5d9aae528ebfd405ef48" + integrity sha512-4cWCG24flYBxjruRi4RJREWTGF74L/KzI2CognAW/d/zWR0CjiScuJhf37Am3LFbCySP6WSoyBOtTIoTA4yLEA== dependencies: "@chakra-ui/react-children-utils" "2.0.6" - "@chakra-ui/react-context" "2.0.8" + "@chakra-ui/react-context" "2.1.0" "@chakra-ui/shared-utils" "2.0.5" "@chakra-ui/breakpoint-utils@2.0.8": @@ -130,39 +125,39 @@ dependencies: "@chakra-ui/shared-utils" "2.0.5" -"@chakra-ui/button@2.0.18": - version "2.0.18" - resolved 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sha512-0j2gZq8HiZ51z4zNnSkF1iSkqlwRDvdH+son3wHdoz+7IUdMN/5Exd4TxMJ+gq2Of1DiXReYLL9qqh2PdQ4wgA== +"@zag-js/element-size@0.10.5": + version "0.10.5" + resolved "https://registry.yarnpkg.com/@zag-js/element-size/-/element-size-0.10.5.tgz#a24bad2eeb7e2c8709e32be5336e158e1a1a174f" + integrity sha512-uQre5IidULANvVkNOBQ1tfgwTQcGl4hliPSe69Fct1VfYb2Fd0jdAcGzqQgPhfrXFpR62MxLPB7erxJ/ngtL8w== + +"@zag-js/focus-visible@0.10.5": + version "0.10.5" + resolved "https://registry.yarnpkg.com/@zag-js/focus-visible/-/focus-visible-0.10.5.tgz#643e196ea768bea5ce54771102fae22f5e9ba3c7" + integrity sha512-EhDHKLutMtvLFCjBjyIY6h1JoJJNXG3KJz7Dj1sh4tj4LWAqo/TqLvgHyUTB29XMHwoslFHDJHKVWmLGMi+ULQ== + dependencies: + "@zag-js/dom-query" "0.10.5" acorn-jsx@^5.3.2: version "5.3.2" diff --git a/tests/nodes/test_node_graph.py b/tests/nodes/test_node_graph.py index cd995141ab..e2a4c8b343 100644 --- a/tests/nodes/test_node_graph.py +++ b/tests/nodes/test_node_graph.py @@ -462,16 +462,16 @@ def test_graph_subgraph_t2i(): n4 = ShowImageInvocation(id = "4") g.add_node(n4) - g.add_edge(create_edge("1.7","image","4","image")) + g.add_edge(create_edge("1.8","image","4","image")) # Validate dg = g.nx_graph_flat() - assert set(dg.nodes) == set(['1.width', '1.height', '1.seed', '1.3', '1.4', '1.5', '1.6', '1.7', '2', '3', '4']) + assert set(dg.nodes) == set(['1.width', '1.height', '1.seed', '1.3', '1.4', '1.5', '1.6', '1.7', '1.8', '2', '3', '4']) expected_edges = [(f'1.{e.source.node_id}',f'1.{e.destination.node_id}') for e in lg.graph.edges] expected_edges.extend([ ('2','1.width'), ('3','1.height'), - ('1.7','4') + ('1.8','4') ]) print(expected_edges) print(list(dg.edges)) diff --git a/tests/test_config.py b/tests/test_config.py index d6c9d95d7e..498a47748e 100644 --- a/tests/test_config.py +++ b/tests/test_config.py @@ -13,9 +13,10 @@ init1 = OmegaConf.create( ''' InvokeAI: Features: - nsfw_checker: False + always_use_cpu: false Memory/Performance: max_cache_size: 5 + tiled_decode: false ''' ) @@ -23,9 +24,10 @@ init2 = OmegaConf.create( ''' InvokeAI: Features: - nsfw_checker: true + always_use_cpu: true Memory/Performance: max_cache_size: 2 + tiled_decode: true ''' ) @@ -36,20 +38,21 @@ def test_use_init(): conf1 = InvokeAIAppConfig.get_config() assert conf1 conf1.parse_args(conf=init1,argv=[]) + assert not conf1.tiled_decode assert conf1.max_cache_size==5 - assert not conf1.nsfw_checker + assert not conf1.always_use_cpu conf2 = InvokeAIAppConfig.get_config() assert conf2 conf2.parse_args(conf=init2,argv=[]) - assert conf2.nsfw_checker + assert conf2.tiled_decode assert conf2.max_cache_size==2 assert not hasattr(conf2,'invalid_attribute') def test_argv_override(): conf = InvokeAIAppConfig.get_config() - conf.parse_args(conf=init1,argv=['--nsfw_checker','--max_cache=10']) - assert conf.nsfw_checker + conf.parse_args(conf=init1,argv=['--always_use_cpu','--max_cache=10']) + assert conf.always_use_cpu assert conf.max_cache_size==10 assert conf.outdir==Path('outputs') # this is the default @@ -57,10 +60,10 @@ def test_env_override(): # argv overrides conf = InvokeAIAppConfig() conf.parse_args(conf=init1,argv=['--max_cache=10']) - assert conf.nsfw_checker==False - os.environ['INVOKEAI_nsfw_checker'] = 'True' + assert conf.always_use_cpu==False + os.environ['INVOKEAI_always_use_cpu'] = 'True' conf.parse_args(conf=init1,argv=['--max_cache=10']) - assert conf.nsfw_checker==True + assert conf.always_use_cpu==True # environment variables should be case insensitive os.environ['InvokeAI_Max_Cache_Size'] = '15' @@ -69,8 +72,8 @@ def test_env_override(): assert conf.max_cache_size == 15 conf = InvokeAIAppConfig() - conf.parse_args(conf=init1,argv=['--no-nsfw_checker','--max_cache=10']) - assert conf.nsfw_checker==False + conf.parse_args(conf=init1,argv=['--no-always_use_cpu','--max_cache=10']) + assert conf.always_use_cpu==False assert conf.max_cache_size==10 conf = InvokeAIAppConfig.get_config(max_cache_size=20) diff --git a/tests/test_path.py b/tests/test_path.py index 6076c6554f..52936142c7 100644 --- a/tests/test_path.py +++ b/tests/test_path.py @@ -9,7 +9,7 @@ from PIL import Image import invokeai.frontend.web.dist as frontend import invokeai.configs as configs -import invokeai.assets.web as assets_web +import invokeai.app.assets.images as image_assets class ConfigsTestCase(unittest.TestCase): """Test the configuration related imports and objects""" @@ -35,7 +35,7 @@ class ConfigsTestCase(unittest.TestCase): def test_caution_img(self): """Verify the caution image""" - caution_img = Image.open(osp.join(assets_web.__path__[0], "caution.png")) + caution_img = Image.open(osp.join(image_assets.__path__[0], "caution.png")) assert caution_img.width == int(500) assert caution_img.height == int(441) assert caution_img.format == str("PNG")