mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
feat(wip): Add SDXL To Canvas
This commit is contained in:
blessedcoolant
parent
f343ab0302
commit
7293a6036a
@ -12,7 +12,10 @@ export const addTabChangedListener = () => {
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if (activeTabName === 'unifiedCanvas') {
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const currentBaseModel = getState().generation.model?.base_model;
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if (currentBaseModel && ['sd-1', 'sd-2'].includes(currentBaseModel)) {
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if (
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currentBaseModel &&
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['sd-1', 'sd-2', 'sdxl'].includes(currentBaseModel)
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) {
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// if we're already on a valid model, no change needed
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return;
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}
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@ -36,7 +39,9 @@ export const addTabChangedListener = () => {
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const validCanvasModels = mainModelsAdapter
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.getSelectors()
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.selectAll(models)
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.filter((model) => ['sd-1', 'sd-2'].includes(model.base_model));
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.filter((model) =>
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['sd-1', 'sd-2', 'sxdl'].includes(model.base_model)
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);
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const firstValidCanvasModel = validCanvasModels[0];
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@ -3,6 +3,9 @@ import { NonNullableGraph } from 'features/nodes/types/types';
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import { ImageDTO } from 'services/api/types';
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import { buildCanvasImageToImageGraph } from './buildCanvasImageToImageGraph';
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import { buildCanvasInpaintGraph } from './buildCanvasInpaintGraph';
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import { buildCanvasSDXLImageToImageGraph } from './buildCanvasSDXLImageToImageGraph';
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import { buildCanvasSDXLInpaintGraph } from './buildCanvasSDXLInpaintGraph';
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import { buildCanvasSDXLTextToImageGraph } from './buildCanvasSDXLTextToImageGraph';
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import { buildCanvasTextToImageGraph } from './buildCanvasTextToImageGraph';
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export const buildCanvasGraph = (
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@ -14,17 +17,43 @@ export const buildCanvasGraph = (
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let graph: NonNullableGraph;
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if (generationMode === 'txt2img') {
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graph = buildCanvasTextToImageGraph(state);
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if (
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state.generation.model &&
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state.generation.model.base_model === 'sdxl'
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) {
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graph = buildCanvasSDXLTextToImageGraph(state);
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} else {
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graph = buildCanvasTextToImageGraph(state);
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}
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} else if (generationMode === 'img2img') {
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if (!canvasInitImage) {
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throw new Error('Missing canvas init image');
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}
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graph = buildCanvasImageToImageGraph(state, canvasInitImage);
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if (
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state.generation.model &&
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state.generation.model.base_model === 'sdxl'
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) {
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graph = buildCanvasSDXLImageToImageGraph(state, canvasInitImage);
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} else {
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graph = buildCanvasImageToImageGraph(state, canvasInitImage);
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}
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} else {
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if (!canvasInitImage || !canvasMaskImage) {
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throw new Error('Missing canvas init and mask images');
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}
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graph = buildCanvasInpaintGraph(state, canvasInitImage, canvasMaskImage);
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if (
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state.generation.model &&
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state.generation.model.base_model === 'sdxl'
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) {
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graph = buildCanvasSDXLInpaintGraph(
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state,
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canvasInitImage,
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canvasMaskImage
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);
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} else {
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graph = buildCanvasInpaintGraph(state, canvasInitImage, canvasMaskImage);
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}
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}
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return graph;
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@ -0,0 +1,373 @@
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import { logger } from 'app/logging/logger';
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import { RootState } from 'app/store/store';
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import { NonNullableGraph } from 'features/nodes/types/types';
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import { initialGenerationState } from 'features/parameters/store/generationSlice';
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import {
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ImageDTO,
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ImageResizeInvocation,
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ImageToLatentsInvocation,
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} from 'services/api/types';
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import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
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import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
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import { addLoRAsToGraph } from './addLoRAsToGraph';
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import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
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import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
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import { addVAEToGraph } from './addVAEToGraph';
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import { addWatermarkerToGraph } from './addWatermarkerToGraph';
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import {
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DENOISE_LATENTS,
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IMAGE_TO_IMAGE_GRAPH,
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IMAGE_TO_LATENTS,
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LATENTS_TO_IMAGE,
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METADATA_ACCUMULATOR,
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NEGATIVE_CONDITIONING,
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NOISE,
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POSITIVE_CONDITIONING,
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RESIZE,
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SDXL_MODEL_LOADER,
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} from './constants';
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/**
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* Builds the Canvas tab's Image to Image graph.
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*/
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export const buildCanvasSDXLImageToImageGraph = (
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state: RootState,
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initialImage: ImageDTO
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): NonNullableGraph => {
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const log = logger('nodes');
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const {
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positivePrompt,
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negativePrompt,
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model,
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cfgScale: cfg_scale,
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scheduler,
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steps,
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clipSkip,
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shouldUseCpuNoise,
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shouldUseNoiseSettings,
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} = state.generation;
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const {
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positiveStylePrompt,
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negativeStylePrompt,
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shouldConcatSDXLStylePrompt,
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shouldUseSDXLRefiner,
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refinerStart,
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sdxlImg2ImgDenoisingStrength: strength,
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} = state.sdxl;
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// The bounding box determines width and height, not the width and height params
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const { width, height } = state.canvas.boundingBoxDimensions;
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const { shouldAutoSave } = state.canvas;
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if (!model) {
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log.error('No model found in state');
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throw new Error('No model found in state');
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}
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const use_cpu = shouldUseNoiseSettings
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? shouldUseCpuNoise
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: initialGenerationState.shouldUseCpuNoise;
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/**
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* The easiest way to build linear graphs is to do it in the node editor, then copy and paste the
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* full graph here as a template. Then use the parameters from app state and set friendlier node
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* ids.
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*
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* The only thing we need extra logic for is handling randomized seed, control net, and for img2img,
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* the `fit` param. These are added to the graph at the end.
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*/
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// copy-pasted graph from node editor, filled in with state values & friendly node ids
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const graph: NonNullableGraph = {
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id: IMAGE_TO_IMAGE_GRAPH,
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nodes: {
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[SDXL_MODEL_LOADER]: {
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type: 'sdxl_model_loader',
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id: SDXL_MODEL_LOADER,
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model,
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},
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[POSITIVE_CONDITIONING]: {
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type: 'sdxl_compel_prompt',
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id: POSITIVE_CONDITIONING,
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prompt: positivePrompt,
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style: shouldConcatSDXLStylePrompt
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? `${positivePrompt} ${positiveStylePrompt}`
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: positiveStylePrompt,
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},
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[NEGATIVE_CONDITIONING]: {
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type: 'sdxl_compel_prompt',
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id: NEGATIVE_CONDITIONING,
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prompt: negativePrompt,
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style: shouldConcatSDXLStylePrompt
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? `${negativePrompt} ${negativeStylePrompt}`
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: negativeStylePrompt,
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},
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[NOISE]: {
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type: 'noise',
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id: NOISE,
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is_intermediate: true,
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use_cpu,
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},
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[DENOISE_LATENTS]: {
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type: 'denoise_latents',
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id: DENOISE_LATENTS,
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is_intermediate: true,
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cfg_scale,
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scheduler,
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steps,
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denoising_start: shouldUseSDXLRefiner
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? Math.min(refinerStart, 1 - strength)
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: 1 - strength,
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denoising_end: shouldUseSDXLRefiner ? refinerStart : 1,
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},
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[IMAGE_TO_LATENTS]: {
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type: 'i2l',
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id: IMAGE_TO_LATENTS,
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is_intermediate: true,
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// must be set manually later, bc `fit` parameter may require a resize node inserted
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// image: {
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// image_name: initialImage.image_name,
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// },
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},
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[LATENTS_TO_IMAGE]: {
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type: 'l2i',
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id: LATENTS_TO_IMAGE,
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is_intermediate: !shouldAutoSave,
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},
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},
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edges: [
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{
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source: {
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node_id: DENOISE_LATENTS,
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field: 'latents',
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},
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destination: {
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node_id: LATENTS_TO_IMAGE,
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field: 'latents',
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},
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},
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{
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source: {
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node_id: IMAGE_TO_LATENTS,
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field: 'latents',
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},
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destination: {
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node_id: DENOISE_LATENTS,
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field: 'latents',
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},
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},
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{
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source: {
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node_id: NOISE,
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field: 'noise',
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},
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destination: {
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node_id: DENOISE_LATENTS,
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field: 'noise',
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},
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},
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{
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source: {
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node_id: SDXL_MODEL_LOADER,
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field: 'unet',
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},
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destination: {
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node_id: DENOISE_LATENTS,
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field: 'unet',
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},
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},
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{
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source: {
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node_id: SDXL_MODEL_LOADER,
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field: 'clip',
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},
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destination: {
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node_id: POSITIVE_CONDITIONING,
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field: 'clip',
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},
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},
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{
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source: {
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node_id: SDXL_MODEL_LOADER,
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field: 'clip2',
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},
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destination: {
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node_id: POSITIVE_CONDITIONING,
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field: 'clip2',
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},
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},
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{
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source: {
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node_id: SDXL_MODEL_LOADER,
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field: 'clip',
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},
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destination: {
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node_id: NEGATIVE_CONDITIONING,
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field: 'clip',
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},
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},
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{
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source: {
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node_id: SDXL_MODEL_LOADER,
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field: 'clip2',
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},
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destination: {
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node_id: NEGATIVE_CONDITIONING,
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field: 'clip2',
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},
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},
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{
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source: {
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node_id: NEGATIVE_CONDITIONING,
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field: 'conditioning',
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},
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destination: {
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node_id: DENOISE_LATENTS,
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field: 'negative_conditioning',
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},
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},
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{
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source: {
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node_id: POSITIVE_CONDITIONING,
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field: 'conditioning',
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},
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destination: {
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node_id: DENOISE_LATENTS,
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field: 'positive_conditioning',
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},
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},
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],
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};
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// handle `fit`
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if (initialImage.width !== width || initialImage.height !== height) {
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// The init image needs to be resized to the specified width and height before being passed to `IMAGE_TO_LATENTS`
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// Create a resize node, explicitly setting its image
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const resizeNode: ImageResizeInvocation = {
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id: RESIZE,
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type: 'img_resize',
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image: {
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image_name: initialImage.image_name,
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},
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is_intermediate: true,
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width,
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height,
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};
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graph.nodes[RESIZE] = resizeNode;
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// The `RESIZE` node then passes its image to `IMAGE_TO_LATENTS`
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graph.edges.push({
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source: { node_id: RESIZE, field: 'image' },
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destination: {
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node_id: IMAGE_TO_LATENTS,
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field: 'image',
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},
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});
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// The `RESIZE` node also passes its width and height to `NOISE`
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graph.edges.push({
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source: { node_id: RESIZE, field: 'width' },
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destination: {
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node_id: NOISE,
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field: 'width',
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},
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});
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graph.edges.push({
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source: { node_id: RESIZE, field: 'height' },
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destination: {
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node_id: NOISE,
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field: 'height',
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},
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});
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} else {
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// We are not resizing, so we need to set the image on the `IMAGE_TO_LATENTS` node explicitly
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(graph.nodes[IMAGE_TO_LATENTS] as ImageToLatentsInvocation).image = {
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image_name: initialImage.image_name,
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};
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// Pass the image's dimensions to the `NOISE` node
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graph.edges.push({
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source: { node_id: IMAGE_TO_LATENTS, field: 'width' },
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destination: {
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node_id: NOISE,
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field: 'width',
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},
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});
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graph.edges.push({
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source: { node_id: IMAGE_TO_LATENTS, field: 'height' },
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destination: {
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node_id: NOISE,
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field: 'height',
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},
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});
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}
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// add metadata accumulator, which is only mostly populated - some fields are added later
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graph.nodes[METADATA_ACCUMULATOR] = {
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id: METADATA_ACCUMULATOR,
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type: 'metadata_accumulator',
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generation_mode: 'img2img',
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cfg_scale,
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height,
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width,
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positive_prompt: '', // set in addDynamicPromptsToGraph
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negative_prompt: negativePrompt,
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model,
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seed: 0, // set in addDynamicPromptsToGraph
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steps,
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rand_device: use_cpu ? 'cpu' : 'cuda',
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scheduler,
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vae: undefined, // option; set in addVAEToGraph
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controlnets: [], // populated in addControlNetToLinearGraph
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loras: [], // populated in addLoRAsToGraph
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clip_skip: clipSkip,
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strength,
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init_image: initialImage.image_name,
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};
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graph.edges.push({
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source: {
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node_id: METADATA_ACCUMULATOR,
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field: 'metadata',
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},
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destination: {
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node_id: LATENTS_TO_IMAGE,
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field: 'metadata',
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},
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});
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// add LoRA support
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addLoRAsToGraph(state, graph, DENOISE_LATENTS);
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// Add Refiner if enabled
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if (shouldUseSDXLRefiner) {
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addSDXLRefinerToGraph(state, graph, DENOISE_LATENTS);
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}
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// optionally add custom VAE
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addVAEToGraph(state, graph, SDXL_MODEL_LOADER);
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// add dynamic prompts - also sets up core iteration and seed
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addDynamicPromptsToGraph(state, graph);
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// add controlnet, mutating `graph`
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addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
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// NSFW & watermark - must be last thing added to graph
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if (state.system.shouldUseNSFWChecker) {
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// must add before watermarker!
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addNSFWCheckerToGraph(state, graph);
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}
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if (state.system.shouldUseWatermarker) {
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// must add after nsfw checker!
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addWatermarkerToGraph(state, graph);
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}
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return graph;
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};
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@ -0,0 +1,480 @@
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import { logger } from 'app/logging/logger';
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import { RootState } from 'app/store/store';
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import { NonNullableGraph } from 'features/nodes/types/types';
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import {
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ImageDTO,
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InfillPatchmatchInvocation,
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InfillTileInvocation,
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RandomIntInvocation,
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RangeOfSizeInvocation,
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} from 'services/api/types';
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import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
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import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
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import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
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import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
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import { addVAEToGraph } from './addVAEToGraph';
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import { addWatermarkerToGraph } from './addWatermarkerToGraph';
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import {
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COLOR_CORRECT,
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INPAINT,
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INPAINT_FINAL_IMAGE,
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INPAINT_GRAPH,
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INPAINT_IMAGE,
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INPAINT_INFILL,
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ITERATE,
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LATENTS_TO_IMAGE,
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MASK_BLUR,
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MASK_COMBINE,
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MASK_FROM_ALPHA,
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NEGATIVE_CONDITIONING,
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NOISE,
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POSITIVE_CONDITIONING,
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RANDOM_INT,
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RANGE_OF_SIZE,
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SDXL_MODEL_LOADER,
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} from './constants';
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/**
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* Builds the Canvas tab's Inpaint graph.
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*/
|
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export const buildCanvasSDXLInpaintGraph = (
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state: RootState,
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canvasInitImage: ImageDTO,
|
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canvasMaskImage: ImageDTO
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): NonNullableGraph => {
|
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const log = logger('nodes');
|
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const {
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positivePrompt,
|
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negativePrompt,
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model,
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cfgScale: cfg_scale,
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scheduler,
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steps,
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img2imgStrength: strength,
|
||||
shouldFitToWidthHeight,
|
||||
iterations,
|
||||
seed,
|
||||
shouldRandomizeSeed,
|
||||
vaePrecision,
|
||||
shouldUseNoiseSettings,
|
||||
shouldUseCpuNoise,
|
||||
maskBlur,
|
||||
maskBlurMethod,
|
||||
tileSize,
|
||||
infillMethod,
|
||||
} = state.generation;
|
||||
|
||||
const {
|
||||
positiveStylePrompt,
|
||||
negativeStylePrompt,
|
||||
shouldConcatSDXLStylePrompt,
|
||||
shouldUseSDXLRefiner,
|
||||
refinerStart,
|
||||
} = state.sdxl;
|
||||
|
||||
if (!model) {
|
||||
log.error('No model found in state');
|
||||
throw new Error('No model found in state');
|
||||
}
|
||||
|
||||
// The bounding box determines width and height, not the width and height params
|
||||
const { width, height } = state.canvas.boundingBoxDimensions;
|
||||
|
||||
// We may need to set the inpaint width and height to scale the image
|
||||
const {
|
||||
scaledBoundingBoxDimensions,
|
||||
boundingBoxScaleMethod,
|
||||
shouldAutoSave,
|
||||
} = state.canvas;
|
||||
|
||||
const use_cpu = shouldUseNoiseSettings
|
||||
? shouldUseCpuNoise
|
||||
: shouldUseCpuNoise;
|
||||
|
||||
let infillNode: InfillTileInvocation | InfillPatchmatchInvocation = {
|
||||
type: 'infill_tile',
|
||||
id: INPAINT_INFILL,
|
||||
is_intermediate: true,
|
||||
image: canvasInitImage,
|
||||
tile_size: tileSize,
|
||||
};
|
||||
|
||||
if (infillMethod === 'patchmatch') {
|
||||
infillNode = {
|
||||
type: 'infill_patchmatch',
|
||||
id: INPAINT_INFILL,
|
||||
is_intermediate: true,
|
||||
image: canvasInitImage,
|
||||
};
|
||||
}
|
||||
|
||||
const graph: NonNullableGraph = {
|
||||
id: INPAINT_GRAPH,
|
||||
nodes: {
|
||||
[INPAINT]: {
|
||||
type: 'denoise_latents',
|
||||
id: INPAINT,
|
||||
is_intermediate: true,
|
||||
steps: steps,
|
||||
cfg_scale: cfg_scale,
|
||||
scheduler: scheduler,
|
||||
denoising_start: 1 - strength,
|
||||
denoising_end: shouldUseSDXLRefiner ? refinerStart : 1,
|
||||
},
|
||||
[infillNode.id]: infillNode,
|
||||
[MASK_FROM_ALPHA]: {
|
||||
type: 'tomask',
|
||||
id: MASK_FROM_ALPHA,
|
||||
is_intermediate: true,
|
||||
image: canvasInitImage,
|
||||
},
|
||||
[MASK_COMBINE]: {
|
||||
type: 'mask_combine',
|
||||
id: MASK_COMBINE,
|
||||
is_intermediate: true,
|
||||
mask2: canvasMaskImage,
|
||||
},
|
||||
[MASK_BLUR]: {
|
||||
type: 'img_blur',
|
||||
id: MASK_BLUR,
|
||||
is_intermediate: true,
|
||||
radius: maskBlur,
|
||||
blur_type: maskBlurMethod,
|
||||
},
|
||||
[INPAINT_IMAGE]: {
|
||||
type: 'i2l',
|
||||
id: INPAINT_IMAGE,
|
||||
is_intermediate: true,
|
||||
fp32: vaePrecision === 'fp32' ? true : false,
|
||||
},
|
||||
[NOISE]: {
|
||||
type: 'noise',
|
||||
id: NOISE,
|
||||
width,
|
||||
height,
|
||||
use_cpu,
|
||||
is_intermediate: true,
|
||||
},
|
||||
[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,
|
||||
},
|
||||
[SDXL_MODEL_LOADER]: {
|
||||
type: 'sdxl_model_loader',
|
||||
id: SDXL_MODEL_LOADER,
|
||||
model,
|
||||
},
|
||||
[LATENTS_TO_IMAGE]: {
|
||||
type: 'l2i',
|
||||
id: LATENTS_TO_IMAGE,
|
||||
is_intermediate: true,
|
||||
fp32: vaePrecision === 'fp32' ? true : false,
|
||||
},
|
||||
[COLOR_CORRECT]: {
|
||||
type: 'color_correct',
|
||||
id: COLOR_CORRECT,
|
||||
is_intermediate: true,
|
||||
},
|
||||
[INPAINT_FINAL_IMAGE]: {
|
||||
type: 'img_paste',
|
||||
id: INPAINT_FINAL_IMAGE,
|
||||
is_intermediate: true,
|
||||
},
|
||||
[RANGE_OF_SIZE]: {
|
||||
type: 'range_of_size',
|
||||
id: RANGE_OF_SIZE,
|
||||
is_intermediate: true,
|
||||
// seed - must be connected manually
|
||||
// start: 0,
|
||||
size: iterations,
|
||||
step: 1,
|
||||
},
|
||||
[ITERATE]: {
|
||||
type: 'iterate',
|
||||
id: ITERATE,
|
||||
is_intermediate: true,
|
||||
},
|
||||
},
|
||||
edges: [
|
||||
{
|
||||
source: {
|
||||
node_id: SDXL_MODEL_LOADER,
|
||||
field: 'unet',
|
||||
},
|
||||
destination: {
|
||||
node_id: INPAINT,
|
||||
field: 'unet',
|
||||
},
|
||||
},
|
||||
{
|
||||
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: NEGATIVE_CONDITIONING,
|
||||
field: 'conditioning',
|
||||
},
|
||||
destination: {
|
||||
node_id: INPAINT,
|
||||
field: 'negative_conditioning',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: POSITIVE_CONDITIONING,
|
||||
field: 'conditioning',
|
||||
},
|
||||
destination: {
|
||||
node_id: INPAINT,
|
||||
field: 'positive_conditioning',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: NOISE,
|
||||
field: 'noise',
|
||||
},
|
||||
destination: {
|
||||
node_id: INPAINT,
|
||||
field: 'noise',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: INPAINT_INFILL,
|
||||
field: 'image',
|
||||
},
|
||||
destination: {
|
||||
node_id: INPAINT_IMAGE,
|
||||
field: 'image',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: INPAINT_IMAGE,
|
||||
field: 'latents',
|
||||
},
|
||||
destination: {
|
||||
node_id: INPAINT,
|
||||
field: 'latents',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: MASK_FROM_ALPHA,
|
||||
field: 'mask',
|
||||
},
|
||||
destination: {
|
||||
node_id: MASK_COMBINE,
|
||||
field: 'mask1',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: MASK_COMBINE,
|
||||
field: 'image',
|
||||
},
|
||||
destination: {
|
||||
node_id: MASK_BLUR,
|
||||
field: 'image',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: MASK_BLUR,
|
||||
field: 'image',
|
||||
},
|
||||
destination: {
|
||||
node_id: INPAINT,
|
||||
field: 'mask',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: RANGE_OF_SIZE,
|
||||
field: 'collection',
|
||||
},
|
||||
destination: {
|
||||
node_id: ITERATE,
|
||||
field: 'collection',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: ITERATE,
|
||||
field: 'item',
|
||||
},
|
||||
destination: {
|
||||
node_id: NOISE,
|
||||
field: 'seed',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: INPAINT,
|
||||
field: 'latents',
|
||||
},
|
||||
destination: {
|
||||
node_id: LATENTS_TO_IMAGE,
|
||||
field: 'latents',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: INPAINT_INFILL,
|
||||
field: 'image',
|
||||
},
|
||||
destination: {
|
||||
node_id: COLOR_CORRECT,
|
||||
field: 'reference',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: MASK_BLUR,
|
||||
field: 'image',
|
||||
},
|
||||
destination: {
|
||||
node_id: COLOR_CORRECT,
|
||||
field: 'mask',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: LATENTS_TO_IMAGE,
|
||||
field: 'image',
|
||||
},
|
||||
destination: {
|
||||
node_id: COLOR_CORRECT,
|
||||
field: 'image',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: INPAINT_INFILL,
|
||||
field: 'image',
|
||||
},
|
||||
destination: {
|
||||
node_id: INPAINT_FINAL_IMAGE,
|
||||
field: 'base_image',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: MASK_BLUR,
|
||||
field: 'image',
|
||||
},
|
||||
destination: {
|
||||
node_id: INPAINT_FINAL_IMAGE,
|
||||
field: 'mask',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: COLOR_CORRECT,
|
||||
field: 'image',
|
||||
},
|
||||
destination: {
|
||||
node_id: INPAINT_FINAL_IMAGE,
|
||||
field: 'image',
|
||||
},
|
||||
},
|
||||
],
|
||||
};
|
||||
|
||||
// Add Refiner if enabled
|
||||
if (shouldUseSDXLRefiner) {
|
||||
addSDXLRefinerToGraph(state, graph, INPAINT);
|
||||
}
|
||||
|
||||
// Add VAE
|
||||
addVAEToGraph(state, graph, SDXL_MODEL_LOADER);
|
||||
|
||||
// handle seed
|
||||
if (shouldRandomizeSeed) {
|
||||
// Random int node to generate the starting seed
|
||||
const randomIntNode: RandomIntInvocation = {
|
||||
id: RANDOM_INT,
|
||||
type: 'rand_int',
|
||||
};
|
||||
|
||||
graph.nodes[RANDOM_INT] = randomIntNode;
|
||||
|
||||
// Connect random int to the start of the range of size so the range starts on the random first seed
|
||||
graph.edges.push({
|
||||
source: { node_id: RANDOM_INT, field: 'a' },
|
||||
destination: { node_id: RANGE_OF_SIZE, field: 'start' },
|
||||
});
|
||||
} else {
|
||||
// User specified seed, so set the start of the range of size to the seed
|
||||
(graph.nodes[RANGE_OF_SIZE] as RangeOfSizeInvocation).start = seed;
|
||||
}
|
||||
|
||||
// add LoRA support
|
||||
addSDXLLoRAsToGraph(state, graph, INPAINT, SDXL_MODEL_LOADER);
|
||||
|
||||
// add controlnet, mutating `graph`
|
||||
addControlNetToLinearGraph(state, graph, INPAINT);
|
||||
|
||||
// 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;
|
||||
};
|
||||
@ -0,0 +1,304 @@
|
||||
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 {
|
||||
DenoiseLatentsInvocation,
|
||||
ONNXTextToLatentsInvocation,
|
||||
} from 'services/api/types';
|
||||
import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
|
||||
import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
|
||||
import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
|
||||
import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
|
||||
import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
|
||||
import { addVAEToGraph } from './addVAEToGraph';
|
||||
import { addWatermarkerToGraph } from './addWatermarkerToGraph';
|
||||
import {
|
||||
DENOISE_LATENTS,
|
||||
LATENTS_TO_IMAGE,
|
||||
METADATA_ACCUMULATOR,
|
||||
NEGATIVE_CONDITIONING,
|
||||
NOISE,
|
||||
ONNX_MODEL_LOADER,
|
||||
POSITIVE_CONDITIONING,
|
||||
SDXL_MODEL_LOADER,
|
||||
TEXT_TO_IMAGE_GRAPH,
|
||||
} from './constants';
|
||||
|
||||
/**
|
||||
* Builds the Canvas tab's Text to Image graph.
|
||||
*/
|
||||
export const buildCanvasSDXLTextToImageGraph = (
|
||||
state: RootState
|
||||
): NonNullableGraph => {
|
||||
const log = logger('nodes');
|
||||
const {
|
||||
positivePrompt,
|
||||
negativePrompt,
|
||||
model,
|
||||
cfgScale: cfg_scale,
|
||||
scheduler,
|
||||
steps,
|
||||
clipSkip,
|
||||
shouldUseCpuNoise,
|
||||
shouldUseNoiseSettings,
|
||||
} = state.generation;
|
||||
|
||||
// The bounding box determines width and height, not the width and height params
|
||||
const { width, height } = state.canvas.boundingBoxDimensions;
|
||||
|
||||
const { shouldAutoSave } = state.canvas;
|
||||
|
||||
const {
|
||||
positiveStylePrompt,
|
||||
negativeStylePrompt,
|
||||
shouldConcatSDXLStylePrompt,
|
||||
shouldUseSDXLRefiner,
|
||||
refinerStart,
|
||||
} = state.sdxl;
|
||||
|
||||
if (!model) {
|
||||
log.error('No model found in state');
|
||||
throw new Error('No model found in state');
|
||||
}
|
||||
|
||||
const use_cpu = shouldUseNoiseSettings
|
||||
? shouldUseCpuNoise
|
||||
: initialGenerationState.shouldUseCpuNoise;
|
||||
const isUsingOnnxModel = model.model_type === 'onnx';
|
||||
const modelLoaderNodeId = isUsingOnnxModel
|
||||
? ONNX_MODEL_LOADER
|
||||
: SDXL_MODEL_LOADER;
|
||||
const modelLoaderNodeType = isUsingOnnxModel
|
||||
? 'onnx_model_loader'
|
||||
: 'sdxl_model_loader';
|
||||
const t2lNode: DenoiseLatentsInvocation | ONNXTextToLatentsInvocation =
|
||||
isUsingOnnxModel
|
||||
? {
|
||||
type: 't2l_onnx',
|
||||
id: DENOISE_LATENTS,
|
||||
is_intermediate: true,
|
||||
cfg_scale,
|
||||
scheduler,
|
||||
steps,
|
||||
}
|
||||
: {
|
||||
type: 'denoise_latents',
|
||||
id: DENOISE_LATENTS,
|
||||
is_intermediate: true,
|
||||
cfg_scale,
|
||||
scheduler,
|
||||
steps,
|
||||
denoising_start: 0,
|
||||
denoising_end: shouldUseSDXLRefiner ? refinerStart : 1,
|
||||
};
|
||||
/**
|
||||
* 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
|
||||
// TODO: Actually create the graph correctly for ONNX
|
||||
const graph: NonNullableGraph = {
|
||||
id: TEXT_TO_IMAGE_GRAPH,
|
||||
nodes: {
|
||||
[POSITIVE_CONDITIONING]: {
|
||||
type: isUsingOnnxModel ? 'prompt_onnx' : 'sdxl_compel_prompt',
|
||||
id: POSITIVE_CONDITIONING,
|
||||
is_intermediate: true,
|
||||
prompt: positivePrompt,
|
||||
style: shouldConcatSDXLStylePrompt
|
||||
? `${positivePrompt} ${positiveStylePrompt}`
|
||||
: positiveStylePrompt,
|
||||
},
|
||||
[NEGATIVE_CONDITIONING]: {
|
||||
type: isUsingOnnxModel ? 'prompt_onnx' : 'sdxl_compel_prompt',
|
||||
id: NEGATIVE_CONDITIONING,
|
||||
is_intermediate: true,
|
||||
prompt: negativePrompt,
|
||||
style: shouldConcatSDXLStylePrompt
|
||||
? `${negativePrompt} ${negativeStylePrompt}`
|
||||
: negativeStylePrompt,
|
||||
},
|
||||
[NOISE]: {
|
||||
type: 'noise',
|
||||
id: NOISE,
|
||||
is_intermediate: true,
|
||||
width,
|
||||
height,
|
||||
use_cpu,
|
||||
},
|
||||
[t2lNode.id]: t2lNode,
|
||||
[modelLoaderNodeId]: {
|
||||
type: modelLoaderNodeType,
|
||||
id: modelLoaderNodeId,
|
||||
is_intermediate: true,
|
||||
model,
|
||||
},
|
||||
|
||||
[LATENTS_TO_IMAGE]: {
|
||||
type: isUsingOnnxModel ? 'l2i_onnx' : 'l2i',
|
||||
id: LATENTS_TO_IMAGE,
|
||||
is_intermediate: !shouldAutoSave,
|
||||
},
|
||||
},
|
||||
edges: [
|
||||
{
|
||||
source: {
|
||||
node_id: modelLoaderNodeId,
|
||||
field: 'unet',
|
||||
},
|
||||
destination: {
|
||||
node_id: DENOISE_LATENTS,
|
||||
field: 'unet',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: modelLoaderNodeId,
|
||||
field: 'clip',
|
||||
},
|
||||
destination: {
|
||||
node_id: POSITIVE_CONDITIONING,
|
||||
field: 'clip',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: modelLoaderNodeId,
|
||||
field: 'clip2',
|
||||
},
|
||||
destination: {
|
||||
node_id: POSITIVE_CONDITIONING,
|
||||
field: 'clip2',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: modelLoaderNodeId,
|
||||
field: 'clip',
|
||||
},
|
||||
destination: {
|
||||
node_id: NEGATIVE_CONDITIONING,
|
||||
field: 'clip',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: modelLoaderNodeId,
|
||||
field: 'clip2',
|
||||
},
|
||||
destination: {
|
||||
node_id: NEGATIVE_CONDITIONING,
|
||||
field: 'clip2',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: NEGATIVE_CONDITIONING,
|
||||
field: 'conditioning',
|
||||
},
|
||||
destination: {
|
||||
node_id: DENOISE_LATENTS,
|
||||
field: 'negative_conditioning',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: POSITIVE_CONDITIONING,
|
||||
field: 'conditioning',
|
||||
},
|
||||
destination: {
|
||||
node_id: DENOISE_LATENTS,
|
||||
field: 'positive_conditioning',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: DENOISE_LATENTS,
|
||||
field: 'latents',
|
||||
},
|
||||
destination: {
|
||||
node_id: LATENTS_TO_IMAGE,
|
||||
field: 'latents',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: NOISE,
|
||||
field: 'noise',
|
||||
},
|
||||
destination: {
|
||||
node_id: DENOISE_LATENTS,
|
||||
field: 'noise',
|
||||
},
|
||||
},
|
||||
],
|
||||
};
|
||||
|
||||
// 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: '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, // option; set in addVAEToGraph
|
||||
controlnets: [], // populated in addControlNetToLinearGraph
|
||||
loras: [], // populated in addLoRAsToGraph
|
||||
clip_skip: clipSkip,
|
||||
};
|
||||
|
||||
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, DENOISE_LATENTS);
|
||||
}
|
||||
|
||||
// add LoRA support
|
||||
addSDXLLoRAsToGraph(state, graph, DENOISE_LATENTS, modelLoaderNodeId);
|
||||
|
||||
// optionally add custom VAE
|
||||
addVAEToGraph(state, graph, modelLoaderNodeId);
|
||||
|
||||
// add dynamic prompts - also sets up core iteration and seed
|
||||
addDynamicPromptsToGraph(state, graph);
|
||||
|
||||
// add controlnet, mutating `graph`
|
||||
addControlNetToLinearGraph(state, graph, DENOISE_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;
|
||||
};
|
||||
@ -15,11 +15,11 @@ import { modelIdToMainModelParam } from 'features/parameters/util/modelIdToMainM
|
||||
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,
|
||||
useGetOnnxModelsQuery,
|
||||
} from 'services/api/endpoints/models';
|
||||
import { NON_REFINER_BASE_MODELS } from 'services/api/constants';
|
||||
import { useFeatureStatus } from '../../../../system/hooks/useFeatureStatus';
|
||||
|
||||
const selector = createSelector(
|
||||
@ -52,10 +52,7 @@ const ParamMainModelSelect = () => {
|
||||
const data: SelectItem[] = [];
|
||||
|
||||
forEach(mainModels.entities, (model, id) => {
|
||||
if (
|
||||
!model ||
|
||||
(activeTabName === 'unifiedCanvas' && model.base_model === 'sdxl')
|
||||
) {
|
||||
if (!model) {
|
||||
return;
|
||||
}
|
||||
|
||||
|
||||
@ -0,0 +1,29 @@
|
||||
import ParamDynamicPromptsCollapse from 'features/dynamicPrompts/components/ParamDynamicPromptsCollapse';
|
||||
import ParamLoraCollapse from 'features/lora/components/ParamLoraCollapse';
|
||||
import ParamAdvancedCollapse from 'features/parameters/components/Parameters/Advanced/ParamAdvancedCollapse';
|
||||
import ParamInfillAndScalingCollapse from 'features/parameters/components/Parameters/Canvas/InfillAndScaling/ParamInfillAndScalingCollapse';
|
||||
import ParamMaskAdjustmentCollapse from 'features/parameters/components/Parameters/Canvas/MaskAdjustment/ParamMaskAdjustmentCollapse';
|
||||
import ParamControlNetCollapse from 'features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse';
|
||||
import ParamNoiseCollapse from 'features/parameters/components/Parameters/Noise/ParamNoiseCollapse';
|
||||
import ProcessButtons from 'features/parameters/components/ProcessButtons/ProcessButtons';
|
||||
import UnifiedCanvasCoreParameters from 'features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasCoreParameters';
|
||||
import ParamSDXLPromptArea from './ParamSDXLPromptArea';
|
||||
import ParamSDXLRefinerCollapse from './ParamSDXLRefinerCollapse';
|
||||
|
||||
export default function SDXLUnifiedCanvasTabParameters() {
|
||||
return (
|
||||
<>
|
||||
<ParamSDXLPromptArea />
|
||||
<ProcessButtons />
|
||||
<UnifiedCanvasCoreParameters />
|
||||
<ParamSDXLRefinerCollapse />
|
||||
<ParamControlNetCollapse />
|
||||
<ParamLoraCollapse />
|
||||
<ParamDynamicPromptsCollapse />
|
||||
<ParamNoiseCollapse />
|
||||
<ParamMaskAdjustmentCollapse />
|
||||
<ParamInfillAndScalingCollapse />
|
||||
<ParamAdvancedCollapse />
|
||||
</>
|
||||
);
|
||||
}
|
||||
@ -1,14 +1,22 @@
|
||||
import { Flex } from '@chakra-ui/react';
|
||||
import { RootState } from 'app/store/store';
|
||||
import { useAppSelector } from 'app/store/storeHooks';
|
||||
import SDXLUnifiedCanvasTabParameters from 'features/sdxl/components/SDXLUnifiedCanvasTabParameters';
|
||||
import { memo } from 'react';
|
||||
import ParametersPinnedWrapper from '../../ParametersPinnedWrapper';
|
||||
import UnifiedCanvasContent from './UnifiedCanvasContent';
|
||||
import UnifiedCanvasParameters from './UnifiedCanvasParameters';
|
||||
|
||||
const UnifiedCanvasTab = () => {
|
||||
const model = useAppSelector((state: RootState) => state.generation.model);
|
||||
return (
|
||||
<Flex sx={{ gap: 4, w: 'full', h: 'full' }}>
|
||||
<ParametersPinnedWrapper>
|
||||
<UnifiedCanvasParameters />
|
||||
{model && model.base_model === 'sdxl' ? (
|
||||
<SDXLUnifiedCanvasTabParameters />
|
||||
) : (
|
||||
<UnifiedCanvasParameters />
|
||||
)}
|
||||
</ParametersPinnedWrapper>
|
||||
<UnifiedCanvasContent />
|
||||
</Flex>
|
||||
|
||||
Loading…
Reference in New Issue
Block a user