mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
refactor(ui): update generation tab graphs
This commit is contained in:
psychedelicious
parent
e55192ae2a
commit
aa127b83a3
@ -0,0 +1,131 @@
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import type {
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ControlAdapterData,
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ControlNetData,
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ImageWithDims,
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ProcessorConfig,
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T2IAdapterData,
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} from 'features/controlLayers/store/types';
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import type { ImageField } from 'features/nodes/types/common';
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import { CONTROL_NET_COLLECT, T2I_ADAPTER_COLLECT } from 'features/nodes/util/graph/constants';
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import type { Graph } from 'features/nodes/util/graph/generation/Graph';
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import type { BaseModelType, Invocation } from 'services/api/types';
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import { assert } from 'tsafe';
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export const addControlAdapters = (
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controlAdapters: ControlAdapterData[],
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g: Graph,
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denoise: Invocation<'denoise_latents'>,
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base: BaseModelType
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): ControlAdapterData[] => {
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const validControlAdapters = controlAdapters.filter((ca) => isValidControlAdapter(ca, base));
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for (const ca of validControlAdapters) {
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if (ca.adapterType === 'controlnet') {
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addControlNetToGraph(ca, g, denoise);
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} else {
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addT2IAdapterToGraph(ca, g, denoise);
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}
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}
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return validControlAdapters;
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};
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const addControlNetCollectorSafe = (g: Graph, denoise: Invocation<'denoise_latents'>): Invocation<'collect'> => {
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try {
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// Attempt to retrieve the collector
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const controlNetCollect = g.getNode(CONTROL_NET_COLLECT);
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assert(controlNetCollect.type === 'collect');
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return controlNetCollect;
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} catch {
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// Add the ControlNet collector
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const controlNetCollect = g.addNode({
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id: CONTROL_NET_COLLECT,
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type: 'collect',
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});
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g.addEdge(controlNetCollect, 'collection', denoise, 'control');
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return controlNetCollect;
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}
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};
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const addControlNetToGraph = (ca: ControlNetData, g: Graph, denoise: Invocation<'denoise_latents'>) => {
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const { id, beginEndStepPct, controlMode, image, model, processedImage, processorConfig, weight } = ca;
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assert(model, 'ControlNet model is required');
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const controlImage = buildControlImage(image, processedImage, processorConfig);
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const controlNetCollect = addControlNetCollectorSafe(g, denoise);
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const controlNet = g.addNode({
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id: `control_net_${id}`,
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type: 'controlnet',
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begin_step_percent: beginEndStepPct[0],
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end_step_percent: beginEndStepPct[1],
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control_mode: controlMode,
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resize_mode: 'just_resize',
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control_model: model,
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control_weight: weight,
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image: controlImage,
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});
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g.addEdge(controlNet, 'control', controlNetCollect, 'item');
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};
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const addT2IAdapterCollectorSafe = (g: Graph, denoise: Invocation<'denoise_latents'>): Invocation<'collect'> => {
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try {
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// You see, we've already got one!
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const t2iAdapterCollect = g.getNode(T2I_ADAPTER_COLLECT);
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assert(t2iAdapterCollect.type === 'collect');
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return t2iAdapterCollect;
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} catch {
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const t2iAdapterCollect = g.addNode({
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id: T2I_ADAPTER_COLLECT,
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type: 'collect',
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});
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g.addEdge(t2iAdapterCollect, 'collection', denoise, 't2i_adapter');
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return t2iAdapterCollect;
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}
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};
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const addT2IAdapterToGraph = (ca: T2IAdapterData, g: Graph, denoise: Invocation<'denoise_latents'>) => {
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const { id, beginEndStepPct, image, model, processedImage, processorConfig, weight } = ca;
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assert(model, 'T2I Adapter model is required');
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const controlImage = buildControlImage(image, processedImage, processorConfig);
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const t2iAdapterCollect = addT2IAdapterCollectorSafe(g, denoise);
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const t2iAdapter = g.addNode({
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id: `t2i_adapter_${id}`,
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type: 't2i_adapter',
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begin_step_percent: beginEndStepPct[0],
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end_step_percent: beginEndStepPct[1],
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resize_mode: 'just_resize',
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t2i_adapter_model: model,
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weight: weight,
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image: controlImage,
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});
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g.addEdge(t2iAdapter, 't2i_adapter', t2iAdapterCollect, 'item');
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};
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const buildControlImage = (
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image: ImageWithDims | null,
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processedImage: ImageWithDims | null,
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processorConfig: ProcessorConfig | null
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): ImageField => {
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if (processedImage && processorConfig) {
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// We've processed the image in the app - use it for the control image.
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return {
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image_name: processedImage.name,
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};
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} else if (image) {
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// No processor selected, and we have an image - the user provided a processed image, use it for the control image.
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return {
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image_name: image.name,
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};
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}
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assert(false, 'Attempted to add unprocessed control image');
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};
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const isValidControlAdapter = (ca: ControlAdapterData, base: BaseModelType): boolean => {
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// Must be have a model that matches the current base and must have a control image
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const hasModel = Boolean(ca.model);
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const modelMatchesBase = ca.model?.base === base;
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const hasControlImage = Boolean(ca.image || (ca.processedImage && ca.processorConfig));
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return hasModel && modelMatchesBase && hasControlImage;
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};
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@ -1,617 +0,0 @@
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import { getStore } from 'app/store/nanostores/store';
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import type { RootState } from 'app/store/store';
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import { deepClone } from 'common/util/deepClone';
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import openBase64ImageInTab from 'common/util/openBase64ImageInTab';
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import { blobToDataURL } from 'features/canvas/util/blobToDataURL';
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import { RG_LAYER_NAME } from 'features/controlLayers/konva/naming';
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import { renderers } from 'features/controlLayers/konva/renderers/layers';
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import { regionalGuidanceMaskImageUploaded } from 'features/controlLayers/store/canvasV2Slice';
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import type { InitialImageLayer, LayerData, RegionalGuidanceLayer } from 'features/controlLayers/store/types';
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import {
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isControlAdapterLayer,
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isInitialImageLayer,
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isIPAdapterLayer,
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isRegionalGuidanceLayer,
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} from 'features/controlLayers/store/types';
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import type {
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ControlNetConfigV2,
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ImageWithDims,
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IPAdapterConfigV2,
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ProcessorConfig,
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T2IAdapterConfigV2,
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} from 'features/controlLayers/util/controlAdapters';
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import type { ImageField } from 'features/nodes/types/common';
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import {
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CONTROL_NET_COLLECT,
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IMAGE_TO_LATENTS,
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IP_ADAPTER_COLLECT,
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PROMPT_REGION_INVERT_TENSOR_MASK_PREFIX,
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PROMPT_REGION_MASK_TO_TENSOR_PREFIX,
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PROMPT_REGION_NEGATIVE_COND_PREFIX,
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PROMPT_REGION_POSITIVE_COND_INVERTED_PREFIX,
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PROMPT_REGION_POSITIVE_COND_PREFIX,
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RESIZE,
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T2I_ADAPTER_COLLECT,
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} from 'features/nodes/util/graph/constants';
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import type { Graph } from 'features/nodes/util/graph/generation/Graph';
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import Konva from 'konva';
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import { size } from 'lodash-es';
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import { getImageDTO, imagesApi } from 'services/api/endpoints/images';
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import type { BaseModelType, ImageDTO, Invocation } from 'services/api/types';
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import { assert } from 'tsafe';
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//#region addControlLayers
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/**
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* Adds the control layers to the graph
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* @param state The app root state
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* @param g The graph to add the layers to
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* @param base The base model type
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* @param denoise The main denoise node
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* @param posCond The positive conditioning node
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* @param negCond The negative conditioning node
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* @param posCondCollect The positive conditioning collector
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* @param negCondCollect The negative conditioning collector
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* @param noise The noise node
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* @param vaeSource The VAE source (either seamless, vae_loader, main_model_loader, or sdxl_model_loader)
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* @returns A promise that resolves to the layers that were added to the graph
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*/
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export const addControlLayers = async (
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state: RootState,
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g: Graph,
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base: BaseModelType,
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denoise: Invocation<'denoise_latents'>,
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posCond: Invocation<'compel'> | Invocation<'sdxl_compel_prompt'>,
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negCond: Invocation<'compel'> | Invocation<'sdxl_compel_prompt'>,
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posCondCollect: Invocation<'collect'>,
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negCondCollect: Invocation<'collect'>,
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noise: Invocation<'noise'>,
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vaeSource:
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| Invocation<'seamless'>
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| Invocation<'vae_loader'>
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| Invocation<'main_model_loader'>
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| Invocation<'sdxl_model_loader'>
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): Promise<LayerData[]> => {
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const isSDXL = base === 'sdxl';
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const validLayers = state.canvasV2.layers.filter((l) => isValidLayer(l, base));
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const validControlAdapters = validLayers.filter(isControlAdapterLayer).map((l) => l.controlAdapter);
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for (const ca of validControlAdapters) {
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addGlobalControlAdapterToGraph(ca, g, denoise);
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}
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const validIPAdapters = validLayers.filter(isIPAdapterLayer).map((l) => l.ipAdapter);
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for (const ipAdapter of validIPAdapters) {
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addGlobalIPAdapterToGraph(ipAdapter, g, denoise);
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}
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const initialImageLayers = validLayers.filter(isInitialImageLayer);
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assert(initialImageLayers.length <= 1, 'Only one initial image layer allowed');
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if (initialImageLayers[0]) {
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addInitialImageLayerToGraph(state, g, base, denoise, noise, vaeSource, initialImageLayers[0]);
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}
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// TODO: We should probably just use conditioning collectors by default, and skip all this fanagling with re-routing
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// the existing conditioning nodes.
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const validRGLayers = validLayers.filter(isRegionalGuidanceLayer);
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const layerIds = validRGLayers.map((l) => l.id);
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const blobs = await getRGLayerBlobs(layerIds);
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assert(size(blobs) === size(layerIds), 'Mismatch between layer IDs and blobs');
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for (const layer of validRGLayers) {
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const blob = blobs[layer.id];
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assert(blob, `Blob for layer ${layer.id} not found`);
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// Upload the mask image, or get the cached image if it exists
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const { image_name } = await getMaskImage(layer, blob);
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// The main mask-to-tensor node
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const maskToTensor = g.addNode({
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id: `${PROMPT_REGION_MASK_TO_TENSOR_PREFIX}_${layer.id}`,
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type: 'alpha_mask_to_tensor',
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image: {
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image_name,
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},
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});
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if (layer.positivePrompt) {
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// The main positive conditioning node
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const regionalPosCond = g.addNode(
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isSDXL
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? {
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type: 'sdxl_compel_prompt',
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id: `${PROMPT_REGION_POSITIVE_COND_PREFIX}_${layer.id}`,
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prompt: layer.positivePrompt,
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style: layer.positivePrompt, // TODO: Should we put the positive prompt in both fields?
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}
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: {
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type: 'compel',
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id: `${PROMPT_REGION_POSITIVE_COND_PREFIX}_${layer.id}`,
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prompt: layer.positivePrompt,
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}
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);
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// Connect the mask to the conditioning
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g.addEdge(maskToTensor, 'mask', regionalPosCond, 'mask');
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// Connect the conditioning to the collector
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g.addEdge(regionalPosCond, 'conditioning', posCondCollect, 'item');
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// Copy the connections to the "global" positive conditioning node to the regional cond
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if (posCond.type === 'compel') {
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for (const edge of g.getEdgesTo(posCond, ['clip', 'mask'])) {
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// Clone the edge, but change the destination node to the regional conditioning node
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const clone = deepClone(edge);
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clone.destination.node_id = regionalPosCond.id;
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g.addEdgeFromObj(clone);
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}
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} else {
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for (const edge of g.getEdgesTo(posCond, ['clip', 'clip2', 'mask'])) {
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// Clone the edge, but change the destination node to the regional conditioning node
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const clone = deepClone(edge);
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clone.destination.node_id = regionalPosCond.id;
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g.addEdgeFromObj(clone);
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}
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}
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}
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if (layer.negativePrompt) {
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// The main negative conditioning node
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const regionalNegCond = g.addNode(
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isSDXL
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? {
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type: 'sdxl_compel_prompt',
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id: `${PROMPT_REGION_NEGATIVE_COND_PREFIX}_${layer.id}`,
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prompt: layer.negativePrompt,
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style: layer.negativePrompt,
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}
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: {
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type: 'compel',
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id: `${PROMPT_REGION_NEGATIVE_COND_PREFIX}_${layer.id}`,
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prompt: layer.negativePrompt,
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}
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);
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// Connect the mask to the conditioning
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g.addEdge(maskToTensor, 'mask', regionalNegCond, 'mask');
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// Connect the conditioning to the collector
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g.addEdge(regionalNegCond, 'conditioning', negCondCollect, 'item');
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// Copy the connections to the "global" negative conditioning node to the regional cond
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if (negCond.type === 'compel') {
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for (const edge of g.getEdgesTo(negCond, ['clip', 'mask'])) {
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const clone = deepClone(edge);
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clone.destination.node_id = regionalNegCond.id;
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g.addEdgeFromObj(clone);
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}
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} else {
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for (const edge of g.getEdgesTo(negCond, ['clip', 'clip2', 'mask'])) {
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const clone = deepClone(edge);
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clone.destination.node_id = regionalNegCond.id;
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g.addEdgeFromObj(clone);
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}
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}
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}
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// If we are using the "invert" auto-negative setting, we need to add an additional negative conditioning node
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if (layer.autoNegative === 'invert' && layer.positivePrompt) {
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// We re-use the mask image, but invert it when converting to tensor
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const invertTensorMask = g.addNode({
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id: `${PROMPT_REGION_INVERT_TENSOR_MASK_PREFIX}_${layer.id}`,
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type: 'invert_tensor_mask',
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});
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// Connect the OG mask image to the inverted mask-to-tensor node
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g.addEdge(maskToTensor, 'mask', invertTensorMask, 'mask');
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// Create the conditioning node. It's going to be connected to the negative cond collector, but it uses the positive prompt
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const regionalPosCondInverted = g.addNode(
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isSDXL
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? {
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type: 'sdxl_compel_prompt',
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id: `${PROMPT_REGION_POSITIVE_COND_INVERTED_PREFIX}_${layer.id}`,
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prompt: layer.positivePrompt,
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style: layer.positivePrompt,
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}
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: {
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type: 'compel',
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id: `${PROMPT_REGION_POSITIVE_COND_INVERTED_PREFIX}_${layer.id}`,
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prompt: layer.positivePrompt,
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}
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);
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// Connect the inverted mask to the conditioning
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g.addEdge(invertTensorMask, 'mask', regionalPosCondInverted, 'mask');
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// Connect the conditioning to the negative collector
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g.addEdge(regionalPosCondInverted, 'conditioning', negCondCollect, 'item');
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|
||||||
// Copy the connections to the "global" positive conditioning node to our regional node
|
|
||||||
if (posCond.type === 'compel') {
|
|
||||||
for (const edge of g.getEdgesTo(posCond, ['clip', 'mask'])) {
|
|
||||||
const clone = deepClone(edge);
|
|
||||||
clone.destination.node_id = regionalPosCondInverted.id;
|
|
||||||
g.addEdgeFromObj(clone);
|
|
||||||
}
|
|
||||||
} else {
|
|
||||||
for (const edge of g.getEdgesTo(posCond, ['clip', 'clip2', 'mask'])) {
|
|
||||||
const clone = deepClone(edge);
|
|
||||||
clone.destination.node_id = regionalPosCondInverted.id;
|
|
||||||
g.addEdgeFromObj(clone);
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
const validRegionalIPAdapters: IPAdapterConfigV2[] = layer.ipAdapters.filter((ipa) => isValidIPAdapter(ipa, base));
|
|
||||||
|
|
||||||
for (const ipAdapterConfig of validRegionalIPAdapters) {
|
|
||||||
const ipAdapterCollect = addIPAdapterCollectorSafe(g, denoise);
|
|
||||||
const { id, weight, model, clipVisionModel, method, beginEndStepPct, image } = ipAdapterConfig;
|
|
||||||
assert(model, 'IP Adapter model is required');
|
|
||||||
assert(image, 'IP Adapter image is required');
|
|
||||||
|
|
||||||
const ipAdapter = g.addNode({
|
|
||||||
id: `ip_adapter_${id}`,
|
|
||||||
type: 'ip_adapter',
|
|
||||||
weight,
|
|
||||||
method,
|
|
||||||
ip_adapter_model: model,
|
|
||||||
clip_vision_model: clipVisionModel,
|
|
||||||
begin_step_percent: beginEndStepPct[0],
|
|
||||||
end_step_percent: beginEndStepPct[1],
|
|
||||||
image: {
|
|
||||||
image_name: image.name,
|
|
||||||
},
|
|
||||||
});
|
|
||||||
|
|
||||||
// Connect the mask to the conditioning
|
|
||||||
g.addEdge(maskToTensor, 'mask', ipAdapter, 'mask');
|
|
||||||
g.addEdge(ipAdapter, 'ip_adapter', ipAdapterCollect, 'item');
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
g.upsertMetadata({ control_layers: { layers: validLayers, version: state.canvasV2._version } });
|
|
||||||
return validLayers;
|
|
||||||
};
|
|
||||||
//#endregion
|
|
||||||
|
|
||||||
//#region Control Adapters
|
|
||||||
const addGlobalControlAdapterToGraph = (
|
|
||||||
controlAdapterConfig: ControlNetConfigV2 | T2IAdapterConfigV2,
|
|
||||||
g: Graph,
|
|
||||||
denoise: Invocation<'denoise_latents'>
|
|
||||||
): void => {
|
|
||||||
if (controlAdapterConfig.type === 'controlnet') {
|
|
||||||
addGlobalControlNetToGraph(controlAdapterConfig, g, denoise);
|
|
||||||
}
|
|
||||||
if (controlAdapterConfig.type === 't2i_adapter') {
|
|
||||||
addGlobalT2IAdapterToGraph(controlAdapterConfig, g, denoise);
|
|
||||||
}
|
|
||||||
};
|
|
||||||
|
|
||||||
const addControlNetCollectorSafe = (g: Graph, denoise: Invocation<'denoise_latents'>): Invocation<'collect'> => {
|
|
||||||
try {
|
|
||||||
// Attempt to retrieve the collector
|
|
||||||
const controlNetCollect = g.getNode(CONTROL_NET_COLLECT);
|
|
||||||
assert(controlNetCollect.type === 'collect');
|
|
||||||
return controlNetCollect;
|
|
||||||
} catch {
|
|
||||||
// Add the ControlNet collector
|
|
||||||
const controlNetCollect = g.addNode({
|
|
||||||
id: CONTROL_NET_COLLECT,
|
|
||||||
type: 'collect',
|
|
||||||
});
|
|
||||||
g.addEdge(controlNetCollect, 'collection', denoise, 'control');
|
|
||||||
return controlNetCollect;
|
|
||||||
}
|
|
||||||
};
|
|
||||||
|
|
||||||
const addGlobalControlNetToGraph = (
|
|
||||||
controlNetConfig: ControlNetConfigV2,
|
|
||||||
g: Graph,
|
|
||||||
denoise: Invocation<'denoise_latents'>
|
|
||||||
) => {
|
|
||||||
const { id, beginEndStepPct, controlMode, image, model, processedImage, processorConfig, weight } = controlNetConfig;
|
|
||||||
assert(model, 'ControlNet model is required');
|
|
||||||
const controlImage = buildControlImage(image, processedImage, processorConfig);
|
|
||||||
const controlNetCollect = addControlNetCollectorSafe(g, denoise);
|
|
||||||
|
|
||||||
const controlNet = g.addNode({
|
|
||||||
id: `control_net_${id}`,
|
|
||||||
type: 'controlnet',
|
|
||||||
begin_step_percent: beginEndStepPct[0],
|
|
||||||
end_step_percent: beginEndStepPct[1],
|
|
||||||
control_mode: controlMode,
|
|
||||||
resize_mode: 'just_resize',
|
|
||||||
control_model: model,
|
|
||||||
control_weight: weight,
|
|
||||||
image: controlImage,
|
|
||||||
});
|
|
||||||
g.addEdge(controlNet, 'control', controlNetCollect, 'item');
|
|
||||||
};
|
|
||||||
|
|
||||||
const addT2IAdapterCollectorSafe = (g: Graph, denoise: Invocation<'denoise_latents'>): Invocation<'collect'> => {
|
|
||||||
try {
|
|
||||||
// You see, we've already got one!
|
|
||||||
const t2iAdapterCollect = g.getNode(T2I_ADAPTER_COLLECT);
|
|
||||||
assert(t2iAdapterCollect.type === 'collect');
|
|
||||||
return t2iAdapterCollect;
|
|
||||||
} catch {
|
|
||||||
const t2iAdapterCollect = g.addNode({
|
|
||||||
id: T2I_ADAPTER_COLLECT,
|
|
||||||
type: 'collect',
|
|
||||||
});
|
|
||||||
|
|
||||||
g.addEdge(t2iAdapterCollect, 'collection', denoise, 't2i_adapter');
|
|
||||||
|
|
||||||
return t2iAdapterCollect;
|
|
||||||
}
|
|
||||||
};
|
|
||||||
|
|
||||||
const addGlobalT2IAdapterToGraph = (
|
|
||||||
t2iAdapterConfig: T2IAdapterConfigV2,
|
|
||||||
g: Graph,
|
|
||||||
denoise: Invocation<'denoise_latents'>
|
|
||||||
) => {
|
|
||||||
const { id, beginEndStepPct, image, model, processedImage, processorConfig, weight } = t2iAdapterConfig;
|
|
||||||
assert(model, 'T2I Adapter model is required');
|
|
||||||
const controlImage = buildControlImage(image, processedImage, processorConfig);
|
|
||||||
const t2iAdapterCollect = addT2IAdapterCollectorSafe(g, denoise);
|
|
||||||
|
|
||||||
const t2iAdapter = g.addNode({
|
|
||||||
id: `t2i_adapter_${id}`,
|
|
||||||
type: 't2i_adapter',
|
|
||||||
begin_step_percent: beginEndStepPct[0],
|
|
||||||
end_step_percent: beginEndStepPct[1],
|
|
||||||
resize_mode: 'just_resize',
|
|
||||||
t2i_adapter_model: model,
|
|
||||||
weight: weight,
|
|
||||||
image: controlImage,
|
|
||||||
});
|
|
||||||
|
|
||||||
g.addEdge(t2iAdapter, 't2i_adapter', t2iAdapterCollect, 'item');
|
|
||||||
};
|
|
||||||
|
|
||||||
//#region IP Adapter
|
|
||||||
const addIPAdapterCollectorSafe = (g: Graph, denoise: Invocation<'denoise_latents'>): Invocation<'collect'> => {
|
|
||||||
try {
|
|
||||||
// You see, we've already got one!
|
|
||||||
const ipAdapterCollect = g.getNode(IP_ADAPTER_COLLECT);
|
|
||||||
assert(ipAdapterCollect.type === 'collect');
|
|
||||||
return ipAdapterCollect;
|
|
||||||
} catch {
|
|
||||||
const ipAdapterCollect = g.addNode({
|
|
||||||
id: IP_ADAPTER_COLLECT,
|
|
||||||
type: 'collect',
|
|
||||||
});
|
|
||||||
g.addEdge(ipAdapterCollect, 'collection', denoise, 'ip_adapter');
|
|
||||||
return ipAdapterCollect;
|
|
||||||
}
|
|
||||||
};
|
|
||||||
|
|
||||||
const addGlobalIPAdapterToGraph = (
|
|
||||||
ipAdapterConfig: IPAdapterConfigV2,
|
|
||||||
g: Graph,
|
|
||||||
denoise: Invocation<'denoise_latents'>
|
|
||||||
) => {
|
|
||||||
const { id, weight, model, clipVisionModel, method, beginEndStepPct, image } = ipAdapterConfig;
|
|
||||||
assert(image, 'IP Adapter image is required');
|
|
||||||
assert(model, 'IP Adapter model is required');
|
|
||||||
const ipAdapterCollect = addIPAdapterCollectorSafe(g, denoise);
|
|
||||||
|
|
||||||
const ipAdapter = g.addNode({
|
|
||||||
id: `ip_adapter_${id}`,
|
|
||||||
type: 'ip_adapter',
|
|
||||||
weight,
|
|
||||||
method,
|
|
||||||
ip_adapter_model: model,
|
|
||||||
clip_vision_model: clipVisionModel,
|
|
||||||
begin_step_percent: beginEndStepPct[0],
|
|
||||||
end_step_percent: beginEndStepPct[1],
|
|
||||||
image: {
|
|
||||||
image_name: image.name,
|
|
||||||
},
|
|
||||||
});
|
|
||||||
g.addEdge(ipAdapter, 'ip_adapter', ipAdapterCollect, 'item');
|
|
||||||
};
|
|
||||||
//#endregion
|
|
||||||
|
|
||||||
//#region Initial Image
|
|
||||||
const addInitialImageLayerToGraph = (
|
|
||||||
state: RootState,
|
|
||||||
g: Graph,
|
|
||||||
base: BaseModelType,
|
|
||||||
denoise: Invocation<'denoise_latents'>,
|
|
||||||
noise: Invocation<'noise'>,
|
|
||||||
vaeSource:
|
|
||||||
| Invocation<'seamless'>
|
|
||||||
| Invocation<'vae_loader'>
|
|
||||||
| Invocation<'main_model_loader'>
|
|
||||||
| Invocation<'sdxl_model_loader'>,
|
|
||||||
layer: InitialImageLayer
|
|
||||||
) => {
|
|
||||||
const { vaePrecision } = state.canvasV2.params;
|
|
||||||
const { refinerModel, refinerStart } = state.canvasV2.params;
|
|
||||||
const { width, height } = state.canvasV2.document;
|
|
||||||
assert(layer.isEnabled, 'Initial image layer is not enabled');
|
|
||||||
assert(layer.image, 'Initial image layer has no image');
|
|
||||||
|
|
||||||
const isSDXL = base === 'sdxl';
|
|
||||||
const useRefinerStartEnd = isSDXL && Boolean(refinerModel);
|
|
||||||
|
|
||||||
const { denoisingStrength } = layer;
|
|
||||||
denoise.denoising_start = useRefinerStartEnd ? Math.min(refinerStart, 1 - denoisingStrength) : 1 - denoisingStrength;
|
|
||||||
denoise.denoising_end = useRefinerStartEnd ? refinerStart : 1;
|
|
||||||
|
|
||||||
const i2l = g.addNode({
|
|
||||||
type: 'i2l',
|
|
||||||
id: IMAGE_TO_LATENTS,
|
|
||||||
fp32: vaePrecision === 'fp32',
|
|
||||||
});
|
|
||||||
|
|
||||||
g.addEdge(i2l, 'latents', denoise, 'latents');
|
|
||||||
g.addEdge(vaeSource, 'vae', i2l, 'vae');
|
|
||||||
|
|
||||||
if (layer.image.width !== width || layer.image.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 resize = g.addNode({
|
|
||||||
id: RESIZE,
|
|
||||||
type: 'img_resize',
|
|
||||||
image: {
|
|
||||||
image_name: layer.image.name,
|
|
||||||
},
|
|
||||||
width,
|
|
||||||
height,
|
|
||||||
});
|
|
||||||
|
|
||||||
// The `RESIZE` node then passes its image to `IMAGE_TO_LATENTS`
|
|
||||||
g.addEdge(resize, 'image', i2l, 'image');
|
|
||||||
// The `RESIZE` node also passes its width and height to `NOISE`
|
|
||||||
g.addEdge(resize, 'width', noise, 'width');
|
|
||||||
g.addEdge(resize, 'height', noise, 'height');
|
|
||||||
} else {
|
|
||||||
// We are not resizing, so we need to set the image on the `IMAGE_TO_LATENTS` node explicitly
|
|
||||||
i2l.image = {
|
|
||||||
image_name: layer.image.name,
|
|
||||||
};
|
|
||||||
|
|
||||||
// Pass the image's dimensions to the `NOISE` node
|
|
||||||
g.addEdge(i2l, 'width', noise, 'width');
|
|
||||||
g.addEdge(i2l, 'height', noise, 'height');
|
|
||||||
}
|
|
||||||
|
|
||||||
g.upsertMetadata({ generation_mode: isSDXL ? 'sdxl_img2img' : 'img2img' });
|
|
||||||
};
|
|
||||||
//#endregion
|
|
||||||
|
|
||||||
//#region Layer validators
|
|
||||||
const isValidControlAdapter = (ca: ControlNetConfigV2 | T2IAdapterConfigV2, base: BaseModelType): boolean => {
|
|
||||||
// Must be have a model that matches the current base and must have a control image
|
|
||||||
const hasModel = Boolean(ca.model);
|
|
||||||
const modelMatchesBase = ca.model?.base === base;
|
|
||||||
const hasControlImage = Boolean(ca.image || (ca.processedImage && ca.processorConfig));
|
|
||||||
return hasModel && modelMatchesBase && hasControlImage;
|
|
||||||
};
|
|
||||||
|
|
||||||
const isValidIPAdapter = (ipa: IPAdapterConfigV2, base: BaseModelType): boolean => {
|
|
||||||
// Must be have a model that matches the current base and must have a control image
|
|
||||||
const hasModel = Boolean(ipa.model);
|
|
||||||
const modelMatchesBase = ipa.model?.base === base;
|
|
||||||
const hasImage = Boolean(ipa.image);
|
|
||||||
return hasModel && modelMatchesBase && hasImage;
|
|
||||||
};
|
|
||||||
|
|
||||||
const isValidLayer = (layer: LayerData, base: BaseModelType) => {
|
|
||||||
if (!layer.isEnabled) {
|
|
||||||
return false;
|
|
||||||
}
|
|
||||||
if (isControlAdapterLayer(layer)) {
|
|
||||||
return isValidControlAdapter(layer.controlAdapter, base);
|
|
||||||
}
|
|
||||||
if (isIPAdapterLayer(layer)) {
|
|
||||||
return isValidIPAdapter(layer.ipAdapter, base);
|
|
||||||
}
|
|
||||||
if (isInitialImageLayer(layer)) {
|
|
||||||
const hasImage = Boolean(layer.image);
|
|
||||||
return hasImage;
|
|
||||||
}
|
|
||||||
if (isRegionalGuidanceLayer(layer)) {
|
|
||||||
const hasTextPrompt = Boolean(layer.positivePrompt || layer.negativePrompt);
|
|
||||||
const hasIPAdapter = layer.ipAdapters.filter((ipa) => isValidIPAdapter(ipa, base)).length > 0;
|
|
||||||
return hasTextPrompt || hasIPAdapter;
|
|
||||||
}
|
|
||||||
return false;
|
|
||||||
};
|
|
||||||
//#endregion
|
|
||||||
|
|
||||||
//#region getMaskImage
|
|
||||||
const getMaskImage = async (layer: RegionalGuidanceLayer, blob: Blob): Promise<ImageDTO> => {
|
|
||||||
if (layer.uploadedMaskImage) {
|
|
||||||
const imageDTO = await getImageDTO(layer.uploadedMaskImage.name);
|
|
||||||
if (imageDTO) {
|
|
||||||
return imageDTO;
|
|
||||||
}
|
|
||||||
}
|
|
||||||
const { dispatch } = getStore();
|
|
||||||
// No cached mask, or the cached image no longer exists - we need to upload the mask image
|
|
||||||
const file = new File([blob], `${layer.id}_mask.png`, { type: 'image/png' });
|
|
||||||
const req = dispatch(
|
|
||||||
imagesApi.endpoints.uploadImage.initiate({ file, image_category: 'mask', is_intermediate: true })
|
|
||||||
);
|
|
||||||
req.reset();
|
|
||||||
|
|
||||||
const imageDTO = await req.unwrap();
|
|
||||||
dispatch(regionalGuidanceMaskImageUploaded({ layerId: layer.id, imageDTO }));
|
|
||||||
return imageDTO;
|
|
||||||
};
|
|
||||||
//#endregion
|
|
||||||
|
|
||||||
//#region buildControlImage
|
|
||||||
const buildControlImage = (
|
|
||||||
image: ImageWithDims | null,
|
|
||||||
processedImage: ImageWithDims | null,
|
|
||||||
processorConfig: ProcessorConfig | null
|
|
||||||
): ImageField => {
|
|
||||||
if (processedImage && processorConfig) {
|
|
||||||
// We've processed the image in the app - use it for the control image.
|
|
||||||
return {
|
|
||||||
image_name: processedImage.name,
|
|
||||||
};
|
|
||||||
} else if (image) {
|
|
||||||
// No processor selected, and we have an image - the user provided a processed image, use it for the control image.
|
|
||||||
return {
|
|
||||||
image_name: image.name,
|
|
||||||
};
|
|
||||||
}
|
|
||||||
assert(false, 'Attempted to add unprocessed control image');
|
|
||||||
};
|
|
||||||
//#endregion
|
|
||||||
|
|
||||||
//#region getRGLayerBlobs
|
|
||||||
/**
|
|
||||||
* Get the blobs of all regional prompt layers. Only visible layers are returned.
|
|
||||||
* @param layerIds The IDs of the layers to get blobs for. If not provided, all regional prompt layers are used.
|
|
||||||
* @param preview Whether to open a new tab displaying each layer.
|
|
||||||
* @returns A map of layer IDs to blobs.
|
|
||||||
*/
|
|
||||||
const getRGLayerBlobs = async (layerIds?: string[], preview: boolean = false): Promise<Record<string, Blob>> => {
|
|
||||||
const state = getStore().getState();
|
|
||||||
const { layers } = state.canvasV2;
|
|
||||||
const { width, height } = state.canvasV2.document;
|
|
||||||
const reduxLayers = layers.filter(isRegionalGuidanceLayer);
|
|
||||||
const container = document.createElement('div');
|
|
||||||
const stage = new Konva.Stage({ container, width, height });
|
|
||||||
renderers.renderLayers(stage, reduxLayers, 1, 'brush', getImageDTO);
|
|
||||||
|
|
||||||
const konvaLayers = stage.find<Konva.Layer>(`.${RG_LAYER_NAME}`);
|
|
||||||
const blobs: Record<string, Blob> = {};
|
|
||||||
|
|
||||||
// First remove all layers
|
|
||||||
for (const layer of konvaLayers) {
|
|
||||||
layer.remove();
|
|
||||||
}
|
|
||||||
|
|
||||||
// Next render each layer to a blob
|
|
||||||
for (const layer of konvaLayers) {
|
|
||||||
if (layerIds && !layerIds.includes(layer.id())) {
|
|
||||||
continue;
|
|
||||||
}
|
|
||||||
const reduxLayer = reduxLayers.find((l) => l.id === layer.id());
|
|
||||||
assert(reduxLayer, `Redux layer ${layer.id()} not found`);
|
|
||||||
stage.add(layer);
|
|
||||||
const blob = await new Promise<Blob>((resolve) => {
|
|
||||||
stage.toBlob({
|
|
||||||
callback: (blob) => {
|
|
||||||
assert(blob, 'Blob is null');
|
|
||||||
resolve(blob);
|
|
||||||
},
|
|
||||||
});
|
|
||||||
});
|
|
||||||
|
|
||||||
if (preview) {
|
|
||||||
const base64 = await blobToDataURL(blob);
|
|
||||||
openBase64ImageInTab([
|
|
||||||
{
|
|
||||||
base64,
|
|
||||||
caption: `${reduxLayer.id}: ${reduxLayer.positivePrompt} / ${reduxLayer.negativePrompt}`,
|
|
||||||
},
|
|
||||||
]);
|
|
||||||
}
|
|
||||||
layer.remove();
|
|
||||||
blobs[layer.id()] = blob;
|
|
||||||
}
|
|
||||||
|
|
||||||
return blobs;
|
|
||||||
};
|
|
||||||
//#endregion
|
|
||||||
@ -1,6 +1,7 @@
|
|||||||
import type { RootState } from 'app/store/store';
|
import type { RootState } from 'app/store/store';
|
||||||
import { deepClone } from 'common/util/deepClone';
|
import { deepClone } from 'common/util/deepClone';
|
||||||
import { roundToMultiple } from 'common/util/roundDownToMultiple';
|
import { roundToMultiple } from 'common/util/roundDownToMultiple';
|
||||||
|
import { selectOptimalDimension } from 'features/controlLayers/store/selectors';
|
||||||
import {
|
import {
|
||||||
DENOISE_LATENTS_HRF,
|
DENOISE_LATENTS_HRF,
|
||||||
ESRGAN_HRF,
|
ESRGAN_HRF,
|
||||||
@ -12,7 +13,6 @@ import {
|
|||||||
} from 'features/nodes/util/graph/constants';
|
} from 'features/nodes/util/graph/constants';
|
||||||
import type { Graph } from 'features/nodes/util/graph/generation/Graph';
|
import type { Graph } from 'features/nodes/util/graph/generation/Graph';
|
||||||
import { getBoardField } from 'features/nodes/util/graph/graphBuilderUtils';
|
import { getBoardField } from 'features/nodes/util/graph/graphBuilderUtils';
|
||||||
import { selectOptimalDimension } from 'features/controlLayers/store/selectors';
|
|
||||||
import type { Invocation } from 'services/api/types';
|
import type { Invocation } from 'services/api/types';
|
||||||
|
|
||||||
/**
|
/**
|
||||||
|
|||||||
@ -0,0 +1,64 @@
|
|||||||
|
import type { IPAdapterData } from 'features/controlLayers/store/types';
|
||||||
|
import { IP_ADAPTER_COLLECT } from 'features/nodes/util/graph/constants';
|
||||||
|
import type { Graph } from 'features/nodes/util/graph/generation/Graph';
|
||||||
|
import type { BaseModelType, Invocation } from 'services/api/types';
|
||||||
|
import { assert } from 'tsafe';
|
||||||
|
|
||||||
|
export const addIPAdapters = (
|
||||||
|
ipAdapters: IPAdapterData[],
|
||||||
|
g: Graph,
|
||||||
|
denoise: Invocation<'denoise_latents'>,
|
||||||
|
base: BaseModelType
|
||||||
|
): IPAdapterData[] => {
|
||||||
|
const validIPAdapters = ipAdapters.filter((ipa) => isValidIPAdapter(ipa, base));
|
||||||
|
for (const ipa of validIPAdapters) {
|
||||||
|
addIPAdapter(ipa, g, denoise);
|
||||||
|
}
|
||||||
|
return validIPAdapters;
|
||||||
|
};
|
||||||
|
|
||||||
|
export const addIPAdapterCollectorSafe = (g: Graph, denoise: Invocation<'denoise_latents'>): Invocation<'collect'> => {
|
||||||
|
try {
|
||||||
|
// You see, we've already got one!
|
||||||
|
const ipAdapterCollect = g.getNode(IP_ADAPTER_COLLECT);
|
||||||
|
assert(ipAdapterCollect.type === 'collect');
|
||||||
|
return ipAdapterCollect;
|
||||||
|
} catch {
|
||||||
|
const ipAdapterCollect = g.addNode({
|
||||||
|
id: IP_ADAPTER_COLLECT,
|
||||||
|
type: 'collect',
|
||||||
|
});
|
||||||
|
g.addEdge(ipAdapterCollect, 'collection', denoise, 'ip_adapter');
|
||||||
|
return ipAdapterCollect;
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
const addIPAdapter = (ipa: IPAdapterData, g: Graph, denoise: Invocation<'denoise_latents'>) => {
|
||||||
|
const { id, weight, model, clipVisionModel, method, beginEndStepPct, image } = ipa;
|
||||||
|
assert(image, 'IP Adapter image is required');
|
||||||
|
assert(model, 'IP Adapter model is required');
|
||||||
|
const ipAdapterCollect = addIPAdapterCollectorSafe(g, denoise);
|
||||||
|
|
||||||
|
const ipAdapter = g.addNode({
|
||||||
|
id: `ip_adapter_${id}`,
|
||||||
|
type: 'ip_adapter',
|
||||||
|
weight,
|
||||||
|
method,
|
||||||
|
ip_adapter_model: model,
|
||||||
|
clip_vision_model: clipVisionModel,
|
||||||
|
begin_step_percent: beginEndStepPct[0],
|
||||||
|
end_step_percent: beginEndStepPct[1],
|
||||||
|
image: {
|
||||||
|
image_name: image.name,
|
||||||
|
},
|
||||||
|
});
|
||||||
|
g.addEdge(ipAdapter, 'ip_adapter', ipAdapterCollect, 'item');
|
||||||
|
};
|
||||||
|
|
||||||
|
export const isValidIPAdapter = (ipa: IPAdapterData, base: BaseModelType): boolean => {
|
||||||
|
// Must be have a model that matches the current base and must have a control image
|
||||||
|
const hasModel = Boolean(ipa.model);
|
||||||
|
const modelMatchesBase = ipa.model?.base === base;
|
||||||
|
const hasImage = Boolean(ipa.image);
|
||||||
|
return hasModel && modelMatchesBase && hasImage;
|
||||||
|
};
|
||||||
@ -0,0 +1,303 @@
|
|||||||
|
import { getStore } from 'app/store/nanostores/store';
|
||||||
|
import { deepClone } from 'common/util/deepClone';
|
||||||
|
import openBase64ImageInTab from 'common/util/openBase64ImageInTab';
|
||||||
|
import { blobToDataURL } from 'features/canvas/util/blobToDataURL';
|
||||||
|
import { RG_LAYER_NAME } from 'features/controlLayers/konva/naming';
|
||||||
|
import { renderers } from 'features/controlLayers/konva/renderers/layers';
|
||||||
|
import { rgMaskImageUploaded } from 'features/controlLayers/store/canvasV2Slice';
|
||||||
|
import type { Dimensions, IPAdapterData, RegionalGuidanceData } from 'features/controlLayers/store/types';
|
||||||
|
import {
|
||||||
|
PROMPT_REGION_INVERT_TENSOR_MASK_PREFIX,
|
||||||
|
PROMPT_REGION_MASK_TO_TENSOR_PREFIX,
|
||||||
|
PROMPT_REGION_NEGATIVE_COND_PREFIX,
|
||||||
|
PROMPT_REGION_POSITIVE_COND_INVERTED_PREFIX,
|
||||||
|
PROMPT_REGION_POSITIVE_COND_PREFIX,
|
||||||
|
} from 'features/nodes/util/graph/constants';
|
||||||
|
import { addIPAdapterCollectorSafe, isValidIPAdapter } from 'features/nodes/util/graph/generation/addIPAdapters';
|
||||||
|
import type { Graph } from 'features/nodes/util/graph/generation/Graph';
|
||||||
|
import Konva from 'konva';
|
||||||
|
import type { IRect } from 'konva/lib/types';
|
||||||
|
import { size } from 'lodash-es';
|
||||||
|
import { getImageDTO, imagesApi } from 'services/api/endpoints/images';
|
||||||
|
import type { BaseModelType, ImageDTO, Invocation } from 'services/api/types';
|
||||||
|
import { assert } from 'tsafe';
|
||||||
|
|
||||||
|
/**
|
||||||
|
* Adds regional guidance to the graph
|
||||||
|
* @param regions Array of regions to add
|
||||||
|
* @param g The graph to add the layers to
|
||||||
|
* @param base The base model type
|
||||||
|
* @param denoise The main denoise node
|
||||||
|
* @param posCond The positive conditioning node
|
||||||
|
* @param negCond The negative conditioning node
|
||||||
|
* @param posCondCollect The positive conditioning collector
|
||||||
|
* @param negCondCollect The negative conditioning collector
|
||||||
|
* @returns A promise that resolves to the regions that were successfully added to the graph
|
||||||
|
*/
|
||||||
|
|
||||||
|
export const addRegions = async (
|
||||||
|
regions: RegionalGuidanceData[],
|
||||||
|
g: Graph,
|
||||||
|
documentSize: Dimensions,
|
||||||
|
bbox: IRect,
|
||||||
|
base: BaseModelType,
|
||||||
|
denoise: Invocation<'denoise_latents'>,
|
||||||
|
posCond: Invocation<'compel'> | Invocation<'sdxl_compel_prompt'>,
|
||||||
|
negCond: Invocation<'compel'> | Invocation<'sdxl_compel_prompt'>,
|
||||||
|
posCondCollect: Invocation<'collect'>,
|
||||||
|
negCondCollect: Invocation<'collect'>
|
||||||
|
): Promise<RegionalGuidanceData[]> => {
|
||||||
|
const isSDXL = base === 'sdxl';
|
||||||
|
|
||||||
|
const validRegions = regions.filter((rg) => isValidRegion(rg, base));
|
||||||
|
const blobs = await getRGMaskBlobs(validRegions, documentSize, bbox);
|
||||||
|
assert(size(blobs) === size(validRegions), 'Mismatch between layer IDs and blobs');
|
||||||
|
|
||||||
|
for (const rg of validRegions) {
|
||||||
|
const blob = blobs[rg.id];
|
||||||
|
assert(blob, `Blob for layer ${rg.id} not found`);
|
||||||
|
// Upload the mask image, or get the cached image if it exists
|
||||||
|
const { image_name } = await getMaskImage(rg, blob);
|
||||||
|
|
||||||
|
// The main mask-to-tensor node
|
||||||
|
const maskToTensor = g.addNode({
|
||||||
|
id: `${PROMPT_REGION_MASK_TO_TENSOR_PREFIX}_${rg.id}`,
|
||||||
|
type: 'alpha_mask_to_tensor',
|
||||||
|
image: {
|
||||||
|
image_name,
|
||||||
|
},
|
||||||
|
});
|
||||||
|
|
||||||
|
if (rg.positivePrompt) {
|
||||||
|
// The main positive conditioning node
|
||||||
|
const regionalPosCond = g.addNode(
|
||||||
|
isSDXL
|
||||||
|
? {
|
||||||
|
type: 'sdxl_compel_prompt',
|
||||||
|
id: `${PROMPT_REGION_POSITIVE_COND_PREFIX}_${rg.id}`,
|
||||||
|
prompt: rg.positivePrompt,
|
||||||
|
style: rg.positivePrompt, // TODO: Should we put the positive prompt in both fields?
|
||||||
|
}
|
||||||
|
: {
|
||||||
|
type: 'compel',
|
||||||
|
id: `${PROMPT_REGION_POSITIVE_COND_PREFIX}_${rg.id}`,
|
||||||
|
prompt: rg.positivePrompt,
|
||||||
|
}
|
||||||
|
);
|
||||||
|
// Connect the mask to the conditioning
|
||||||
|
g.addEdge(maskToTensor, 'mask', regionalPosCond, 'mask');
|
||||||
|
// Connect the conditioning to the collector
|
||||||
|
g.addEdge(regionalPosCond, 'conditioning', posCondCollect, 'item');
|
||||||
|
// Copy the connections to the "global" positive conditioning node to the regional cond
|
||||||
|
if (posCond.type === 'compel') {
|
||||||
|
for (const edge of g.getEdgesTo(posCond, ['clip', 'mask'])) {
|
||||||
|
// Clone the edge, but change the destination node to the regional conditioning node
|
||||||
|
const clone = deepClone(edge);
|
||||||
|
clone.destination.node_id = regionalPosCond.id;
|
||||||
|
g.addEdgeFromObj(clone);
|
||||||
|
}
|
||||||
|
} else {
|
||||||
|
for (const edge of g.getEdgesTo(posCond, ['clip', 'clip2', 'mask'])) {
|
||||||
|
// Clone the edge, but change the destination node to the regional conditioning node
|
||||||
|
const clone = deepClone(edge);
|
||||||
|
clone.destination.node_id = regionalPosCond.id;
|
||||||
|
g.addEdgeFromObj(clone);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
if (rg.negativePrompt) {
|
||||||
|
// The main negative conditioning node
|
||||||
|
const regionalNegCond = g.addNode(
|
||||||
|
isSDXL
|
||||||
|
? {
|
||||||
|
type: 'sdxl_compel_prompt',
|
||||||
|
id: `${PROMPT_REGION_NEGATIVE_COND_PREFIX}_${rg.id}`,
|
||||||
|
prompt: rg.negativePrompt,
|
||||||
|
style: rg.negativePrompt,
|
||||||
|
}
|
||||||
|
: {
|
||||||
|
type: 'compel',
|
||||||
|
id: `${PROMPT_REGION_NEGATIVE_COND_PREFIX}_${rg.id}`,
|
||||||
|
prompt: rg.negativePrompt,
|
||||||
|
}
|
||||||
|
);
|
||||||
|
// Connect the mask to the conditioning
|
||||||
|
g.addEdge(maskToTensor, 'mask', regionalNegCond, 'mask');
|
||||||
|
// Connect the conditioning to the collector
|
||||||
|
g.addEdge(regionalNegCond, 'conditioning', negCondCollect, 'item');
|
||||||
|
// Copy the connections to the "global" negative conditioning node to the regional cond
|
||||||
|
if (negCond.type === 'compel') {
|
||||||
|
for (const edge of g.getEdgesTo(negCond, ['clip', 'mask'])) {
|
||||||
|
const clone = deepClone(edge);
|
||||||
|
clone.destination.node_id = regionalNegCond.id;
|
||||||
|
g.addEdgeFromObj(clone);
|
||||||
|
}
|
||||||
|
} else {
|
||||||
|
for (const edge of g.getEdgesTo(negCond, ['clip', 'clip2', 'mask'])) {
|
||||||
|
const clone = deepClone(edge);
|
||||||
|
clone.destination.node_id = regionalNegCond.id;
|
||||||
|
g.addEdgeFromObj(clone);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// If we are using the "invert" auto-negative setting, we need to add an additional negative conditioning node
|
||||||
|
if (rg.autoNegative === 'invert' && rg.positivePrompt) {
|
||||||
|
// We re-use the mask image, but invert it when converting to tensor
|
||||||
|
const invertTensorMask = g.addNode({
|
||||||
|
id: `${PROMPT_REGION_INVERT_TENSOR_MASK_PREFIX}_${rg.id}`,
|
||||||
|
type: 'invert_tensor_mask',
|
||||||
|
});
|
||||||
|
// Connect the OG mask image to the inverted mask-to-tensor node
|
||||||
|
g.addEdge(maskToTensor, 'mask', invertTensorMask, 'mask');
|
||||||
|
// Create the conditioning node. It's going to be connected to the negative cond collector, but it uses the positive prompt
|
||||||
|
const regionalPosCondInverted = g.addNode(
|
||||||
|
isSDXL
|
||||||
|
? {
|
||||||
|
type: 'sdxl_compel_prompt',
|
||||||
|
id: `${PROMPT_REGION_POSITIVE_COND_INVERTED_PREFIX}_${rg.id}`,
|
||||||
|
prompt: rg.positivePrompt,
|
||||||
|
style: rg.positivePrompt,
|
||||||
|
}
|
||||||
|
: {
|
||||||
|
type: 'compel',
|
||||||
|
id: `${PROMPT_REGION_POSITIVE_COND_INVERTED_PREFIX}_${rg.id}`,
|
||||||
|
prompt: rg.positivePrompt,
|
||||||
|
}
|
||||||
|
);
|
||||||
|
// Connect the inverted mask to the conditioning
|
||||||
|
g.addEdge(invertTensorMask, 'mask', regionalPosCondInverted, 'mask');
|
||||||
|
// Connect the conditioning to the negative collector
|
||||||
|
g.addEdge(regionalPosCondInverted, 'conditioning', negCondCollect, 'item');
|
||||||
|
// Copy the connections to the "global" positive conditioning node to our regional node
|
||||||
|
if (posCond.type === 'compel') {
|
||||||
|
for (const edge of g.getEdgesTo(posCond, ['clip', 'mask'])) {
|
||||||
|
const clone = deepClone(edge);
|
||||||
|
clone.destination.node_id = regionalPosCondInverted.id;
|
||||||
|
g.addEdgeFromObj(clone);
|
||||||
|
}
|
||||||
|
} else {
|
||||||
|
for (const edge of g.getEdgesTo(posCond, ['clip', 'clip2', 'mask'])) {
|
||||||
|
const clone = deepClone(edge);
|
||||||
|
clone.destination.node_id = regionalPosCondInverted.id;
|
||||||
|
g.addEdgeFromObj(clone);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
const validRGIPAdapters: IPAdapterData[] = rg.ipAdapters.filter((ipa) => isValidIPAdapter(ipa, base));
|
||||||
|
|
||||||
|
for (const ipa of validRGIPAdapters) {
|
||||||
|
const ipAdapterCollect = addIPAdapterCollectorSafe(g, denoise);
|
||||||
|
const { id, weight, model, clipVisionModel, method, beginEndStepPct, image } = ipa;
|
||||||
|
assert(model, 'IP Adapter model is required');
|
||||||
|
assert(image, 'IP Adapter image is required');
|
||||||
|
|
||||||
|
const ipAdapter = g.addNode({
|
||||||
|
id: `ip_adapter_${id}`,
|
||||||
|
type: 'ip_adapter',
|
||||||
|
weight,
|
||||||
|
method,
|
||||||
|
ip_adapter_model: model,
|
||||||
|
clip_vision_model: clipVisionModel,
|
||||||
|
begin_step_percent: beginEndStepPct[0],
|
||||||
|
end_step_percent: beginEndStepPct[1],
|
||||||
|
image: {
|
||||||
|
image_name: image.name,
|
||||||
|
},
|
||||||
|
});
|
||||||
|
|
||||||
|
// Connect the mask to the conditioning
|
||||||
|
g.addEdge(maskToTensor, 'mask', ipAdapter, 'mask');
|
||||||
|
g.addEdge(ipAdapter, 'ip_adapter', ipAdapterCollect, 'item');
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
g.upsertMetadata({ regions: validRegions });
|
||||||
|
return validRegions;
|
||||||
|
};
|
||||||
|
|
||||||
|
export const isValidRegion = (rg: RegionalGuidanceData, base: BaseModelType) => {
|
||||||
|
const hasTextPrompt = Boolean(rg.positivePrompt || rg.negativePrompt);
|
||||||
|
const hasIPAdapter = rg.ipAdapters.filter((ipa) => isValidIPAdapter(ipa, base)).length > 0;
|
||||||
|
return hasTextPrompt || hasIPAdapter;
|
||||||
|
};
|
||||||
|
|
||||||
|
export const getMaskImage = async (rg: RegionalGuidanceData, blob: Blob): Promise<ImageDTO> => {
|
||||||
|
const { id, imageCache } = rg;
|
||||||
|
if (imageCache) {
|
||||||
|
const imageDTO = await getImageDTO(imageCache.name);
|
||||||
|
if (imageDTO) {
|
||||||
|
return imageDTO;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
const { dispatch } = getStore();
|
||||||
|
// No cached mask, or the cached image no longer exists - we need to upload the mask image
|
||||||
|
const file = new File([blob], `${rg.id}_mask.png`, { type: 'image/png' });
|
||||||
|
const req = dispatch(
|
||||||
|
imagesApi.endpoints.uploadImage.initiate({ file, image_category: 'mask', is_intermediate: true })
|
||||||
|
);
|
||||||
|
req.reset();
|
||||||
|
|
||||||
|
const imageDTO = await req.unwrap();
|
||||||
|
dispatch(rgMaskImageUploaded({ id, imageDTO }));
|
||||||
|
return imageDTO;
|
||||||
|
};
|
||||||
|
|
||||||
|
/**
|
||||||
|
* Get the blobs of all regional prompt layers. Only visible layers are returned.
|
||||||
|
* @param layerIds The IDs of the layers to get blobs for. If not provided, all regional prompt layers are used.
|
||||||
|
* @param preview Whether to open a new tab displaying each layer.
|
||||||
|
* @returns A map of layer IDs to blobs.
|
||||||
|
*/
|
||||||
|
|
||||||
|
export const getRGMaskBlobs = async (
|
||||||
|
regions: RegionalGuidanceData[],
|
||||||
|
documentSize: Dimensions,
|
||||||
|
bbox: IRect,
|
||||||
|
preview: boolean = false
|
||||||
|
): Promise<Record<string, Blob>> => {
|
||||||
|
const container = document.createElement('div');
|
||||||
|
const stage = new Konva.Stage({ container, ...documentSize });
|
||||||
|
renderers.renderLayers(stage, [], [], regions, 1, 'brush', null, getImageDTO);
|
||||||
|
const konvaLayers = stage.find<Konva.Layer>(`.${RG_LAYER_NAME}`);
|
||||||
|
const blobs: Record<string, Blob> = {};
|
||||||
|
|
||||||
|
// First remove all layers
|
||||||
|
for (const layer of konvaLayers) {
|
||||||
|
layer.remove();
|
||||||
|
}
|
||||||
|
|
||||||
|
// Next render each layer to a blob
|
||||||
|
for (const layer of konvaLayers) {
|
||||||
|
const rg = regions.find((l) => l.id === layer.id());
|
||||||
|
if (!rg) {
|
||||||
|
continue;
|
||||||
|
}
|
||||||
|
stage.add(layer);
|
||||||
|
const blob = await new Promise<Blob>((resolve) => {
|
||||||
|
stage.toBlob({
|
||||||
|
callback: (blob) => {
|
||||||
|
assert(blob, 'Blob is null');
|
||||||
|
resolve(blob);
|
||||||
|
},
|
||||||
|
...bbox,
|
||||||
|
});
|
||||||
|
});
|
||||||
|
|
||||||
|
if (preview) {
|
||||||
|
const base64 = await blobToDataURL(blob);
|
||||||
|
openBase64ImageInTab([
|
||||||
|
{
|
||||||
|
base64,
|
||||||
|
caption: `${rg.id}: ${rg.positivePrompt} / ${rg.negativePrompt}`,
|
||||||
|
},
|
||||||
|
]);
|
||||||
|
}
|
||||||
|
layer.remove();
|
||||||
|
blobs[layer.id()] = blob;
|
||||||
|
}
|
||||||
|
|
||||||
|
return blobs;
|
||||||
|
};
|
||||||
@ -1,5 +1,4 @@
|
|||||||
import type { RootState } from 'app/store/store';
|
import type { RootState } from 'app/store/store';
|
||||||
import { isInitialImageLayer, isRegionalGuidanceLayer } from 'features/controlLayers/store/types';
|
|
||||||
import { fetchModelConfigWithTypeGuard } from 'features/metadata/util/modelFetchingHelpers';
|
import { fetchModelConfigWithTypeGuard } from 'features/metadata/util/modelFetchingHelpers';
|
||||||
import {
|
import {
|
||||||
CLIP_SKIP,
|
CLIP_SKIP,
|
||||||
@ -14,8 +13,9 @@ import {
|
|||||||
POSITIVE_CONDITIONING_COLLECT,
|
POSITIVE_CONDITIONING_COLLECT,
|
||||||
VAE_LOADER,
|
VAE_LOADER,
|
||||||
} from 'features/nodes/util/graph/constants';
|
} from 'features/nodes/util/graph/constants';
|
||||||
import { addControlLayers } from 'features/nodes/util/graph/generation/addControlLayers';
|
import { addControlAdapters } from 'features/nodes/util/graph/generation/addControlAdapters';
|
||||||
import { addHRF } from 'features/nodes/util/graph/generation/addHRF';
|
// import { addHRF } from 'features/nodes/util/graph/generation/addHRF';
|
||||||
|
import { addIPAdapters } from 'features/nodes/util/graph/generation/addIPAdapters';
|
||||||
import { addLoRAs } from 'features/nodes/util/graph/generation/addLoRAs';
|
import { addLoRAs } from 'features/nodes/util/graph/generation/addLoRAs';
|
||||||
import { addNSFWChecker } from 'features/nodes/util/graph/generation/addNSFWChecker';
|
import { addNSFWChecker } from 'features/nodes/util/graph/generation/addNSFWChecker';
|
||||||
import { addSeamless } from 'features/nodes/util/graph/generation/addSeamless';
|
import { addSeamless } from 'features/nodes/util/graph/generation/addSeamless';
|
||||||
@ -27,6 +27,8 @@ import type { Invocation } from 'services/api/types';
|
|||||||
import { isNonRefinerMainModelConfig } from 'services/api/types';
|
import { isNonRefinerMainModelConfig } from 'services/api/types';
|
||||||
import { assert } from 'tsafe';
|
import { assert } from 'tsafe';
|
||||||
|
|
||||||
|
import { addRegions } from './addRegions';
|
||||||
|
|
||||||
export const buildGenerationTabGraph = async (state: RootState): Promise<GraphType> => {
|
export const buildGenerationTabGraph = async (state: RootState): Promise<GraphType> => {
|
||||||
const {
|
const {
|
||||||
model,
|
model,
|
||||||
@ -39,6 +41,8 @@ export const buildGenerationTabGraph = async (state: RootState): Promise<GraphTy
|
|||||||
vaePrecision,
|
vaePrecision,
|
||||||
seed,
|
seed,
|
||||||
vae,
|
vae,
|
||||||
|
positivePrompt,
|
||||||
|
negativePrompt,
|
||||||
} = state.canvasV2.params;
|
} = state.canvasV2.params;
|
||||||
const { width, height } = state.canvasV2.document;
|
const { width, height } = state.canvasV2.document;
|
||||||
|
|
||||||
@ -152,23 +156,25 @@ export const buildGenerationTabGraph = async (state: RootState): Promise<GraphTy
|
|||||||
const vaeSource = seamless ?? vaeLoader ?? modelLoader;
|
const vaeSource = seamless ?? vaeLoader ?? modelLoader;
|
||||||
g.addEdge(vaeSource, 'vae', l2i, 'vae');
|
g.addEdge(vaeSource, 'vae', l2i, 'vae');
|
||||||
|
|
||||||
const addedLayers = await addControlLayers(
|
const _addedCAs = addControlAdapters(state.canvasV2.controlAdapters, g, denoise, modelConfig.base);
|
||||||
state,
|
const _addedIPAs = addIPAdapters(state.canvasV2.ipAdapters, g, denoise, modelConfig.base);
|
||||||
|
const _addedRegions = await addRegions(
|
||||||
|
state.canvasV2.regions,
|
||||||
g,
|
g,
|
||||||
|
state.canvasV2.document,
|
||||||
|
state.canvasV2.bbox,
|
||||||
modelConfig.base,
|
modelConfig.base,
|
||||||
denoise,
|
denoise,
|
||||||
posCond,
|
posCond,
|
||||||
negCond,
|
negCond,
|
||||||
posCondCollect,
|
posCondCollect,
|
||||||
negCondCollect,
|
negCondCollect
|
||||||
noise,
|
|
||||||
vaeSource
|
|
||||||
);
|
);
|
||||||
|
|
||||||
const isHRFAllowed = !addedLayers.some((l) => isInitialImageLayer(l) || isRegionalGuidanceLayer(l));
|
// const isHRFAllowed = !addedLayers.some((l) => isInitialImageLayer(l) || isRegionalGuidanceLayer(l));
|
||||||
if (isHRFAllowed && state.hrf.hrfEnabled) {
|
// if (isHRFAllowed && state.hrf.hrfEnabled) {
|
||||||
imageOutput = addHRF(state, g, denoise, noise, l2i, vaeSource);
|
// imageOutput = addHRF(state, g, denoise, noise, l2i, vaeSource);
|
||||||
}
|
// }
|
||||||
|
|
||||||
if (state.system.shouldUseNSFWChecker) {
|
if (state.system.shouldUseNSFWChecker) {
|
||||||
imageOutput = addNSFWChecker(g, imageOutput);
|
imageOutput = addNSFWChecker(g, imageOutput);
|
||||||
|
|||||||
@ -12,7 +12,8 @@ import {
|
|||||||
SDXL_MODEL_LOADER,
|
SDXL_MODEL_LOADER,
|
||||||
VAE_LOADER,
|
VAE_LOADER,
|
||||||
} from 'features/nodes/util/graph/constants';
|
} from 'features/nodes/util/graph/constants';
|
||||||
import { addControlLayers } from 'features/nodes/util/graph/generation/addControlLayers';
|
import { addControlAdapters } from 'features/nodes/util/graph/generation/addControlAdapters';
|
||||||
|
import { addIPAdapters } from 'features/nodes/util/graph/generation/addIPAdapters';
|
||||||
import { addNSFWChecker } from 'features/nodes/util/graph/generation/addNSFWChecker';
|
import { addNSFWChecker } from 'features/nodes/util/graph/generation/addNSFWChecker';
|
||||||
import { addSDXLLoRas } from 'features/nodes/util/graph/generation/addSDXLLoRAs';
|
import { addSDXLLoRas } from 'features/nodes/util/graph/generation/addSDXLLoRAs';
|
||||||
import { addSDXLRefiner } from 'features/nodes/util/graph/generation/addSDXLRefiner';
|
import { addSDXLRefiner } from 'features/nodes/util/graph/generation/addSDXLRefiner';
|
||||||
@ -24,6 +25,8 @@ import type { Invocation, NonNullableGraph } from 'services/api/types';
|
|||||||
import { isNonRefinerMainModelConfig } from 'services/api/types';
|
import { isNonRefinerMainModelConfig } from 'services/api/types';
|
||||||
import { assert } from 'tsafe';
|
import { assert } from 'tsafe';
|
||||||
|
|
||||||
|
import { addRegions } from './addRegions';
|
||||||
|
|
||||||
export const buildGenerationTabSDXLGraph = async (state: RootState): Promise<NonNullableGraph> => {
|
export const buildGenerationTabSDXLGraph = async (state: RootState): Promise<NonNullableGraph> => {
|
||||||
const {
|
const {
|
||||||
model,
|
model,
|
||||||
@ -35,11 +38,13 @@ export const buildGenerationTabSDXLGraph = async (state: RootState): Promise<Non
|
|||||||
shouldUseCpuNoise,
|
shouldUseCpuNoise,
|
||||||
vaePrecision,
|
vaePrecision,
|
||||||
vae,
|
vae,
|
||||||
|
positivePrompt,
|
||||||
|
negativePrompt,
|
||||||
|
refinerModel,
|
||||||
|
refinerStart,
|
||||||
} = state.canvasV2.params;
|
} = state.canvasV2.params;
|
||||||
const { width, height } = state.canvasV2.document;
|
const { width, height } = state.canvasV2.document;
|
||||||
|
|
||||||
const { refinerModel, refinerStart } = state.canvasV2.params;
|
|
||||||
|
|
||||||
assert(model, 'No model found in state');
|
assert(model, 'No model found in state');
|
||||||
|
|
||||||
const { positivePrompt, negativePrompt, positiveStylePrompt, negativeStylePrompt } = getPresetModifiedPrompts(state);
|
const { positivePrompt, negativePrompt, positiveStylePrompt, negativeStylePrompt } = getPresetModifiedPrompts(state);
|
||||||
@ -147,17 +152,19 @@ export const buildGenerationTabSDXLGraph = async (state: RootState): Promise<Non
|
|||||||
await addSDXLRefiner(state, g, denoise, seamless, posCond, negCond, l2i);
|
await addSDXLRefiner(state, g, denoise, seamless, posCond, negCond, l2i);
|
||||||
}
|
}
|
||||||
|
|
||||||
await addControlLayers(
|
const _addedCAs = addControlAdapters(state.canvasV2.controlAdapters, g, denoise, modelConfig.base);
|
||||||
state,
|
const _addedIPAs = addIPAdapters(state.canvasV2.ipAdapters, g, denoise, modelConfig.base);
|
||||||
|
const _addedRegions = await addRegions(
|
||||||
|
state.canvasV2.regions,
|
||||||
g,
|
g,
|
||||||
|
state.canvasV2.document,
|
||||||
|
state.canvasV2.bbox,
|
||||||
modelConfig.base,
|
modelConfig.base,
|
||||||
denoise,
|
denoise,
|
||||||
posCond,
|
posCond,
|
||||||
negCond,
|
negCond,
|
||||||
posCondCollect,
|
posCondCollect,
|
||||||
negCondCollect,
|
negCondCollect
|
||||||
noise,
|
|
||||||
vaeSource
|
|
||||||
);
|
);
|
||||||
|
|
||||||
if (state.system.shouldUseNSFWChecker) {
|
if (state.system.shouldUseNSFWChecker) {
|
||||||
|
|||||||
Loading…
x
Reference in New Issue
Block a user