mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
feat(ui): simplify linear graph creation logic
Instead of manually creating every node and edge, we can simply copy/paste the base graph from node editor, then sub in parameters. This is a much more intelligible process. We still need to handle seed, img2img fit and controlnet separately.
This commit is contained in:
psychedelicious
parent
a01998d095
commit
3c60616b4d
@ -1,10 +1,10 @@
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import { startAppListening } from '..';
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import { buildImageToImageGraph } from 'features/nodes/util/graphBuilders/buildImageToImageGraph';
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import { sessionCreated } from 'services/thunks/session';
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import { log } from 'app/logging/useLogger';
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import { imageToImageGraphBuilt } from 'features/nodes/store/actions';
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import { userInvoked } from 'app/store/actions';
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import { sessionReadyToInvoke } from 'features/system/store/actions';
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import { buildImageToImageGraph } from 'features/nodes/util/graphBuilders/buildImageToImageGraph';
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const moduleLog = log.child({ namespace: 'invoke' });
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@ -1,10 +1,10 @@
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import { startAppListening } from '..';
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import { buildTextToImageGraph } from 'features/nodes/util/graphBuilders/buildTextToImageGraph';
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import { sessionCreated } from 'services/thunks/session';
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import { log } from 'app/logging/useLogger';
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import { textToImageGraphBuilt } from 'features/nodes/store/actions';
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import { userInvoked } from 'app/store/actions';
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import { sessionReadyToInvoke } from 'features/system/store/actions';
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import { buildTextToImageGraph } from 'features/nodes/util/graphBuilders/buildTextToImageGraph';
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const moduleLog = log.child({ namespace: 'invoke' });
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@ -2,8 +2,7 @@ import { RootState } from 'app/store/store';
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import { filter, forEach, size } from 'lodash-es';
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import { CollectInvocation, ControlNetInvocation } from 'services/api';
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import { NonNullableGraph } from '../types/types';
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const CONTROL_NET_COLLECT = 'control_net_collect';
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import { CONTROL_NET_COLLECT } from './graphBuilders/constants';
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export const addControlNetToLinearGraph = (
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graph: NonNullableGraph,
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@ -37,7 +36,7 @@ export const addControlNetToLinearGraph = (
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});
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}
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forEach(controlNets, (controlNet, index) => {
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forEach(controlNets, (controlNet) => {
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const {
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controlNetId,
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isEnabled,
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@ -1,34 +1,30 @@
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import { RootState } from 'app/store/store';
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import {
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CompelInvocation,
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Graph,
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ImageResizeInvocation,
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ImageToLatentsInvocation,
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IterateInvocation,
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LatentsToImageInvocation,
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LatentsToLatentsInvocation,
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NoiseInvocation,
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RandomIntInvocation,
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RangeOfSizeInvocation,
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} from 'services/api';
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import { NonNullableGraph } from 'features/nodes/types/types';
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import { log } from 'app/logging/useLogger';
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import {
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ITERATE,
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LATENTS_TO_IMAGE,
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MODEL_LOADER,
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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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IMAGE_TO_IMAGE_GRAPH,
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IMAGE_TO_LATENTS,
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LATENTS_TO_LATENTS,
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RESIZE,
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} from './constants';
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import { set } from 'lodash-es';
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import { addControlNetToLinearGraph } from '../addControlNetToLinearGraph';
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const moduleLog = log.child({ namespace: 'nodes' });
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const POSITIVE_CONDITIONING = 'positive_conditioning';
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const NEGATIVE_CONDITIONING = 'negative_conditioning';
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const IMAGE_TO_LATENTS = 'image_to_latents';
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const LATENTS_TO_LATENTS = 'latents_to_latents';
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const LATENTS_TO_IMAGE = 'latents_to_image';
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const RESIZE = 'resize_image';
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const NOISE = 'noise';
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const RANDOM_INT = 'rand_int';
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const RANGE_OF_SIZE = 'range_of_size';
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const ITERATE = 'iterate';
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/**
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* Builds the Image to Image tab graph.
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*/
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@ -36,7 +32,7 @@ export const buildImageToImageGraph = (state: RootState): Graph => {
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const {
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positivePrompt,
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negativePrompt,
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model,
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model: model_name,
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cfgScale: cfg_scale,
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scheduler,
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steps,
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@ -50,298 +46,221 @@ export const buildImageToImageGraph = (state: RootState): Graph => {
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shouldRandomizeSeed,
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} = state.generation;
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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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if (!initialImage) {
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moduleLog.error('No initial image found in state');
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throw new Error('No initial image found in state');
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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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nodes: {},
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edges: [],
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};
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// Create the positive conditioning (prompt) node
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const positiveConditioningNode: CompelInvocation = {
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id: POSITIVE_CONDITIONING,
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type: 'compel',
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prompt: positivePrompt,
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model,
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};
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// Negative conditioning
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const negativeConditioningNode: CompelInvocation = {
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id: NEGATIVE_CONDITIONING,
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type: 'compel',
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prompt: negativePrompt,
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model,
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};
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// This will encode the raster image to latents - but it may get its `image` from a resize node,
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// so we do not set its `image` property yet
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const imageToLatentsNode: ImageToLatentsInvocation = {
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id: IMAGE_TO_LATENTS,
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type: 'i2l',
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model,
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};
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// This does the actual img2img inference
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const latentsToLatentsNode: LatentsToLatentsInvocation = {
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id: LATENTS_TO_LATENTS,
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type: 'l2l',
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cfg_scale,
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model,
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scheduler,
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steps,
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strength,
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};
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// Finally we decode the latents back to an image
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const latentsToImageNode: LatentsToImageInvocation = {
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id: LATENTS_TO_IMAGE,
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type: 'l2i',
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model,
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};
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// Add all those nodes to the graph
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graph.nodes[POSITIVE_CONDITIONING] = positiveConditioningNode;
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graph.nodes[NEGATIVE_CONDITIONING] = negativeConditioningNode;
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graph.nodes[IMAGE_TO_LATENTS] = imageToLatentsNode;
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graph.nodes[LATENTS_TO_LATENTS] = latentsToLatentsNode;
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graph.nodes[LATENTS_TO_IMAGE] = latentsToImageNode;
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// Connect the prompt nodes to the imageToLatents node
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graph.edges.push({
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source: { node_id: POSITIVE_CONDITIONING, field: 'conditioning' },
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destination: {
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node_id: LATENTS_TO_LATENTS,
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field: 'positive_conditioning',
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},
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});
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graph.edges.push({
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source: { node_id: NEGATIVE_CONDITIONING, field: 'conditioning' },
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destination: {
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node_id: LATENTS_TO_LATENTS,
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field: 'negative_conditioning',
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},
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});
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// Connect the image-encoding node
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graph.edges.push({
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source: { node_id: IMAGE_TO_LATENTS, field: 'latents' },
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destination: {
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node_id: LATENTS_TO_LATENTS,
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field: 'latents',
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},
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});
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// Connect the image-decoding node
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graph.edges.push({
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source: { node_id: LATENTS_TO_LATENTS, field: 'latents' },
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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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* Now we need to handle iterations and random seeds. There are four possible scenarios:
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* - Single iteration, explicit seed
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* - Single iteration, random seed
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* - Multiple iterations, explicit seed
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* - Multiple iterations, random seed
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*
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* They all have different graphs and connections.
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*/
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// Single iteration, explicit seed
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if (!shouldRandomizeSeed && iterations === 1) {
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// Noise node using the explicit seed
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const noiseNode: NoiseInvocation = {
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id: NOISE,
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type: 'noise',
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seed: seed,
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};
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graph.nodes[NOISE] = noiseNode;
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// Connect noise to l2l
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graph.edges.push({
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source: { node_id: NOISE, field: 'noise' },
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destination: {
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node_id: LATENTS_TO_LATENTS,
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field: 'noise',
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id: IMAGE_TO_IMAGE_GRAPH,
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nodes: {
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[POSITIVE_CONDITIONING]: {
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type: 'compel',
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id: POSITIVE_CONDITIONING,
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prompt: positivePrompt,
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},
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});
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}
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[NEGATIVE_CONDITIONING]: {
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type: 'compel',
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id: NEGATIVE_CONDITIONING,
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prompt: negativePrompt,
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},
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[RANGE_OF_SIZE]: {
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type: 'range_of_size',
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id: RANGE_OF_SIZE,
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// seed - must be connected manually
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// start: 0,
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size: iterations,
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step: 1,
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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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},
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[MODEL_LOADER]: {
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type: 'sd1_model_loader',
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id: MODEL_LOADER,
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model_name,
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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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},
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[ITERATE]: {
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type: 'iterate',
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id: ITERATE,
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},
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[LATENTS_TO_LATENTS]: {
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type: 'l2l',
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id: LATENTS_TO_LATENTS,
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cfg_scale,
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scheduler,
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steps,
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strength,
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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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// 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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},
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edges: [
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{
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source: {
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node_id: 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: 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: MODEL_LOADER,
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field: 'vae',
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},
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destination: {
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node_id: LATENTS_TO_IMAGE,
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field: 'vae',
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},
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},
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{
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source: {
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node_id: RANGE_OF_SIZE,
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field: 'collection',
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},
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destination: {
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node_id: ITERATE,
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field: 'collection',
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},
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},
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{
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source: {
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node_id: ITERATE,
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field: 'item',
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},
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destination: {
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node_id: NOISE,
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field: 'seed',
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},
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},
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{
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source: {
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node_id: LATENTS_TO_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: LATENTS_TO_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: LATENTS_TO_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: MODEL_LOADER,
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field: 'vae',
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},
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destination: {
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node_id: IMAGE_TO_LATENTS,
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field: 'vae',
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},
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},
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{
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source: {
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node_id: MODEL_LOADER,
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field: 'unet',
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},
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destination: {
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node_id: LATENTS_TO_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: NEGATIVE_CONDITIONING,
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field: 'conditioning',
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},
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destination: {
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node_id: LATENTS_TO_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: LATENTS_TO_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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// Single iteration, random seed
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if (shouldRandomizeSeed && iterations === 1) {
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// Random int node to generate the seed
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// handle seed
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if (shouldRandomizeSeed) {
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// Random int node to generate the starting seed
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const randomIntNode: RandomIntInvocation = {
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id: RANDOM_INT,
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type: 'rand_int',
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};
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// Noise node without any seed
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const noiseNode: NoiseInvocation = {
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id: NOISE,
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type: 'noise',
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};
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graph.nodes[RANDOM_INT] = randomIntNode;
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graph.nodes[NOISE] = noiseNode;
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// Connect random int to the seed of the noise node
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graph.edges.push({
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source: { node_id: RANDOM_INT, field: 'a' },
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destination: {
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node_id: NOISE,
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field: 'seed',
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},
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});
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// Connect noise to l2l
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graph.edges.push({
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source: { node_id: NOISE, field: 'noise' },
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destination: {
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node_id: LATENTS_TO_LATENTS,
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field: 'noise',
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},
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});
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}
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// Multiple iterations, explicit seed
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if (!shouldRandomizeSeed && iterations > 1) {
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// Range of size node to generate `iterations` count of seeds - range of size generates a collection
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// of ints from `start` to `start + size`. The `start` is the seed, and the `size` is the number of
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// iterations.
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const rangeOfSizeNode: RangeOfSizeInvocation = {
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id: RANGE_OF_SIZE,
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type: 'range_of_size',
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start: seed,
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size: iterations,
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};
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// Iterate node to iterate over the seeds generated by the range of size node
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const iterateNode: IterateInvocation = {
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id: ITERATE,
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type: 'iterate',
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};
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// Noise node without any seed
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const noiseNode: NoiseInvocation = {
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id: NOISE,
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type: 'noise',
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};
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// Adding to the graph
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graph.nodes[RANGE_OF_SIZE] = rangeOfSizeNode;
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graph.nodes[ITERATE] = iterateNode;
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graph.nodes[NOISE] = noiseNode;
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// Connect range of size to iterate
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graph.edges.push({
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source: { node_id: RANGE_OF_SIZE, field: 'collection' },
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destination: {
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node_id: ITERATE,
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field: 'collection',
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},
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});
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// Connect iterate to noise
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graph.edges.push({
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source: {
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node_id: ITERATE,
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field: 'item',
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},
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destination: {
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node_id: NOISE,
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field: 'seed',
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},
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});
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// Connect noise to l2l
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graph.edges.push({
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source: { node_id: NOISE, field: 'noise' },
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destination: {
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node_id: LATENTS_TO_LATENTS,
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field: 'noise',
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},
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});
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}
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// Multiple iterations, random seed
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if (shouldRandomizeSeed && iterations > 1) {
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// Random int node to generate the seed
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const randomIntNode: RandomIntInvocation = {
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id: RANDOM_INT,
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type: 'rand_int',
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||||
};
|
||||
|
||||
// Range of size node to generate `iterations` count of seeds - range of size generates a collection
|
||||
const rangeOfSizeNode: RangeOfSizeInvocation = {
|
||||
id: RANGE_OF_SIZE,
|
||||
type: 'range_of_size',
|
||||
size: iterations,
|
||||
};
|
||||
|
||||
// Iterate node to iterate over the seeds generated by the range of size node
|
||||
const iterateNode: IterateInvocation = {
|
||||
id: ITERATE,
|
||||
type: 'iterate',
|
||||
};
|
||||
|
||||
// Noise node without any seed
|
||||
const noiseNode: NoiseInvocation = {
|
||||
id: NOISE,
|
||||
type: 'noise',
|
||||
width,
|
||||
height,
|
||||
};
|
||||
|
||||
// Adding to the graph
|
||||
graph.nodes[RANDOM_INT] = randomIntNode;
|
||||
graph.nodes[RANGE_OF_SIZE] = rangeOfSizeNode;
|
||||
graph.nodes[ITERATE] = iterateNode;
|
||||
graph.nodes[NOISE] = noiseNode;
|
||||
|
||||
// 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' },
|
||||
});
|
||||
|
||||
// Connect range of size to iterate
|
||||
graph.edges.push({
|
||||
source: { node_id: RANGE_OF_SIZE, field: 'collection' },
|
||||
destination: {
|
||||
node_id: ITERATE,
|
||||
field: 'collection',
|
||||
},
|
||||
});
|
||||
|
||||
// Connect iterate to noise
|
||||
graph.edges.push({
|
||||
source: {
|
||||
node_id: ITERATE,
|
||||
field: 'item',
|
||||
},
|
||||
destination: {
|
||||
node_id: NOISE,
|
||||
field: 'seed',
|
||||
},
|
||||
});
|
||||
|
||||
// Connect noise to l2l
|
||||
graph.edges.push({
|
||||
source: { node_id: NOISE, field: 'noise' },
|
||||
destination: {
|
||||
node_id: LATENTS_TO_LATENTS,
|
||||
field: 'noise',
|
||||
},
|
||||
});
|
||||
} 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;
|
||||
}
|
||||
|
||||
// handle `fit`
|
||||
if (
|
||||
shouldFitToWidthHeight &&
|
||||
(initialImage.width !== width || initialImage.height !== height)
|
||||
@ -410,6 +329,7 @@ export const buildImageToImageGraph = (state: RootState): Graph => {
|
||||
});
|
||||
}
|
||||
|
||||
// add controlnet
|
||||
addControlNetToLinearGraph(graph, LATENTS_TO_LATENTS, state);
|
||||
|
||||
return graph;
|
||||
|
||||
@ -1,34 +1,29 @@
|
||||
import { RootState } from 'app/store/store';
|
||||
import { NonNullableGraph } from 'features/nodes/types/types';
|
||||
import {
|
||||
CompelInvocation,
|
||||
Graph,
|
||||
IterateInvocation,
|
||||
LatentsToImageInvocation,
|
||||
NoiseInvocation,
|
||||
RandomIntInvocation,
|
||||
RangeOfSizeInvocation,
|
||||
TextToLatentsInvocation,
|
||||
} from 'services/api';
|
||||
import { NonNullableGraph } from 'features/nodes/types/types';
|
||||
import {
|
||||
ITERATE,
|
||||
LATENTS_TO_IMAGE,
|
||||
MODEL_LOADER,
|
||||
NEGATIVE_CONDITIONING,
|
||||
NOISE,
|
||||
POSITIVE_CONDITIONING,
|
||||
RANDOM_INT,
|
||||
RANGE_OF_SIZE,
|
||||
TEXT_TO_IMAGE_GRAPH,
|
||||
TEXT_TO_LATENTS,
|
||||
} from './constants';
|
||||
import { addControlNetToLinearGraph } from '../addControlNetToLinearGraph';
|
||||
|
||||
const POSITIVE_CONDITIONING = 'positive_conditioning';
|
||||
const NEGATIVE_CONDITIONING = 'negative_conditioning';
|
||||
const TEXT_TO_LATENTS = 'text_to_latents';
|
||||
const LATENTS_TO_IMAGE = 'latents_to_image';
|
||||
const NOISE = 'noise';
|
||||
const RANDOM_INT = 'rand_int';
|
||||
const RANGE_OF_SIZE = 'range_of_size';
|
||||
const ITERATE = 'iterate';
|
||||
|
||||
/**
|
||||
* Builds the Text to Image tab graph.
|
||||
*/
|
||||
export const buildTextToImageGraph = (state: RootState): Graph => {
|
||||
const {
|
||||
positivePrompt,
|
||||
negativePrompt,
|
||||
model,
|
||||
model: model_name,
|
||||
cfgScale: cfg_scale,
|
||||
scheduler,
|
||||
steps,
|
||||
@ -39,277 +34,188 @@ export const buildTextToImageGraph = (state: RootState): Graph => {
|
||||
shouldRandomizeSeed,
|
||||
} = state.generation;
|
||||
|
||||
const graph: NonNullableGraph = {
|
||||
nodes: {},
|
||||
edges: [],
|
||||
};
|
||||
|
||||
// Create the conditioning, t2l and l2i nodes
|
||||
const positiveConditioningNode: CompelInvocation = {
|
||||
id: POSITIVE_CONDITIONING,
|
||||
type: 'compel',
|
||||
prompt: positivePrompt,
|
||||
model,
|
||||
};
|
||||
|
||||
const negativeConditioningNode: CompelInvocation = {
|
||||
id: NEGATIVE_CONDITIONING,
|
||||
type: 'compel',
|
||||
prompt: negativePrompt,
|
||||
model,
|
||||
};
|
||||
|
||||
const textToLatentsNode: TextToLatentsInvocation = {
|
||||
id: TEXT_TO_LATENTS,
|
||||
type: 't2l',
|
||||
cfg_scale,
|
||||
model,
|
||||
scheduler,
|
||||
steps,
|
||||
};
|
||||
|
||||
const latentsToImageNode: LatentsToImageInvocation = {
|
||||
id: LATENTS_TO_IMAGE,
|
||||
type: 'l2i',
|
||||
model,
|
||||
};
|
||||
|
||||
// Add to the graph
|
||||
graph.nodes[POSITIVE_CONDITIONING] = positiveConditioningNode;
|
||||
graph.nodes[NEGATIVE_CONDITIONING] = negativeConditioningNode;
|
||||
graph.nodes[TEXT_TO_LATENTS] = textToLatentsNode;
|
||||
graph.nodes[LATENTS_TO_IMAGE] = latentsToImageNode;
|
||||
|
||||
// Connect them
|
||||
graph.edges.push({
|
||||
source: { node_id: POSITIVE_CONDITIONING, field: 'conditioning' },
|
||||
destination: {
|
||||
node_id: TEXT_TO_LATENTS,
|
||||
field: 'positive_conditioning',
|
||||
},
|
||||
});
|
||||
|
||||
graph.edges.push({
|
||||
source: { node_id: NEGATIVE_CONDITIONING, field: 'conditioning' },
|
||||
destination: {
|
||||
node_id: TEXT_TO_LATENTS,
|
||||
field: 'negative_conditioning',
|
||||
},
|
||||
});
|
||||
|
||||
graph.edges.push({
|
||||
source: { node_id: TEXT_TO_LATENTS, field: 'latents' },
|
||||
destination: {
|
||||
node_id: LATENTS_TO_IMAGE,
|
||||
field: 'latents',
|
||||
},
|
||||
});
|
||||
|
||||
/**
|
||||
* Now we need to handle iterations and random seeds. There are four possible scenarios:
|
||||
* - Single iteration, explicit seed
|
||||
* - Single iteration, random seed
|
||||
* - Multiple iterations, explicit seed
|
||||
* - Multiple iterations, random seed
|
||||
* 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.
|
||||
*
|
||||
* They all have different graphs and connections.
|
||||
* 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.
|
||||
*/
|
||||
|
||||
// Single iteration, explicit seed
|
||||
if (!shouldRandomizeSeed && iterations === 1) {
|
||||
// Noise node using the explicit seed
|
||||
const noiseNode: NoiseInvocation = {
|
||||
id: NOISE,
|
||||
type: 'noise',
|
||||
seed: seed,
|
||||
width,
|
||||
height,
|
||||
};
|
||||
|
||||
graph.nodes[NOISE] = noiseNode;
|
||||
|
||||
// Connect noise to l2l
|
||||
graph.edges.push({
|
||||
source: { node_id: NOISE, field: 'noise' },
|
||||
destination: {
|
||||
node_id: TEXT_TO_LATENTS,
|
||||
field: 'noise',
|
||||
// copy-pasted graph from node editor, filled in with state values & friendly node ids
|
||||
const graph: NonNullableGraph = {
|
||||
id: TEXT_TO_IMAGE_GRAPH,
|
||||
nodes: {
|
||||
[POSITIVE_CONDITIONING]: {
|
||||
type: 'compel',
|
||||
id: POSITIVE_CONDITIONING,
|
||||
prompt: positivePrompt,
|
||||
},
|
||||
});
|
||||
}
|
||||
[NEGATIVE_CONDITIONING]: {
|
||||
type: 'compel',
|
||||
id: NEGATIVE_CONDITIONING,
|
||||
prompt: negativePrompt,
|
||||
},
|
||||
[RANGE_OF_SIZE]: {
|
||||
type: 'range_of_size',
|
||||
id: RANGE_OF_SIZE,
|
||||
// start: 0, // seed - must be connected manually
|
||||
size: iterations,
|
||||
step: 1,
|
||||
},
|
||||
[NOISE]: {
|
||||
type: 'noise',
|
||||
id: NOISE,
|
||||
width,
|
||||
height,
|
||||
},
|
||||
[TEXT_TO_LATENTS]: {
|
||||
type: 't2l',
|
||||
id: TEXT_TO_LATENTS,
|
||||
cfg_scale,
|
||||
scheduler,
|
||||
steps,
|
||||
},
|
||||
[MODEL_LOADER]: {
|
||||
type: 'sd1_model_loader',
|
||||
id: MODEL_LOADER,
|
||||
model_name,
|
||||
},
|
||||
[LATENTS_TO_IMAGE]: {
|
||||
type: 'l2i',
|
||||
id: LATENTS_TO_IMAGE,
|
||||
},
|
||||
[ITERATE]: {
|
||||
type: 'iterate',
|
||||
id: ITERATE,
|
||||
},
|
||||
},
|
||||
edges: [
|
||||
{
|
||||
source: {
|
||||
node_id: NEGATIVE_CONDITIONING,
|
||||
field: 'conditioning',
|
||||
},
|
||||
destination: {
|
||||
node_id: TEXT_TO_LATENTS,
|
||||
field: 'negative_conditioning',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: POSITIVE_CONDITIONING,
|
||||
field: 'conditioning',
|
||||
},
|
||||
destination: {
|
||||
node_id: TEXT_TO_LATENTS,
|
||||
field: 'positive_conditioning',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: MODEL_LOADER,
|
||||
field: 'clip',
|
||||
},
|
||||
destination: {
|
||||
node_id: POSITIVE_CONDITIONING,
|
||||
field: 'clip',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: MODEL_LOADER,
|
||||
field: 'clip',
|
||||
},
|
||||
destination: {
|
||||
node_id: NEGATIVE_CONDITIONING,
|
||||
field: 'clip',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: MODEL_LOADER,
|
||||
field: 'unet',
|
||||
},
|
||||
destination: {
|
||||
node_id: TEXT_TO_LATENTS,
|
||||
field: 'unet',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: TEXT_TO_LATENTS,
|
||||
field: 'latents',
|
||||
},
|
||||
destination: {
|
||||
node_id: LATENTS_TO_IMAGE,
|
||||
field: 'latents',
|
||||
},
|
||||
},
|
||||
{
|
||||
source: {
|
||||
node_id: MODEL_LOADER,
|
||||
field: 'vae',
|
||||
},
|
||||
destination: {
|
||||
node_id: LATENTS_TO_IMAGE,
|
||||
field: 'vae',
|
||||
},
|
||||
},
|
||||
{
|
||||
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: NOISE,
|
||||
field: 'noise',
|
||||
},
|
||||
destination: {
|
||||
node_id: TEXT_TO_LATENTS,
|
||||
field: 'noise',
|
||||
},
|
||||
},
|
||||
],
|
||||
};
|
||||
|
||||
// Single iteration, random seed
|
||||
if (shouldRandomizeSeed && iterations === 1) {
|
||||
// Random int node to generate the seed
|
||||
// handle seed
|
||||
if (shouldRandomizeSeed) {
|
||||
// Random int node to generate the starting seed
|
||||
const randomIntNode: RandomIntInvocation = {
|
||||
id: RANDOM_INT,
|
||||
type: 'rand_int',
|
||||
};
|
||||
|
||||
// Noise node without any seed
|
||||
const noiseNode: NoiseInvocation = {
|
||||
id: NOISE,
|
||||
type: 'noise',
|
||||
width,
|
||||
height,
|
||||
};
|
||||
|
||||
graph.nodes[RANDOM_INT] = randomIntNode;
|
||||
graph.nodes[NOISE] = noiseNode;
|
||||
|
||||
// Connect random int to the seed of the noise node
|
||||
graph.edges.push({
|
||||
source: { node_id: RANDOM_INT, field: 'a' },
|
||||
destination: {
|
||||
node_id: NOISE,
|
||||
field: 'seed',
|
||||
},
|
||||
});
|
||||
|
||||
// Connect noise to t2l
|
||||
graph.edges.push({
|
||||
source: { node_id: NOISE, field: 'noise' },
|
||||
destination: {
|
||||
node_id: TEXT_TO_LATENTS,
|
||||
field: 'noise',
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
// Multiple iterations, explicit seed
|
||||
if (!shouldRandomizeSeed && iterations > 1) {
|
||||
// Range of size node to generate `iterations` count of seeds - range of size generates a collection
|
||||
// of ints from `start` to `start + size`. The `start` is the seed, and the `size` is the number of
|
||||
// iterations.
|
||||
const rangeOfSizeNode: RangeOfSizeInvocation = {
|
||||
id: RANGE_OF_SIZE,
|
||||
type: 'range_of_size',
|
||||
start: seed,
|
||||
size: iterations,
|
||||
};
|
||||
|
||||
// Iterate node to iterate over the seeds generated by the range of size node
|
||||
const iterateNode: IterateInvocation = {
|
||||
id: ITERATE,
|
||||
type: 'iterate',
|
||||
};
|
||||
|
||||
// Noise node without any seed
|
||||
const noiseNode: NoiseInvocation = {
|
||||
id: NOISE,
|
||||
type: 'noise',
|
||||
width,
|
||||
height,
|
||||
};
|
||||
|
||||
// Adding to the graph
|
||||
graph.nodes[RANGE_OF_SIZE] = rangeOfSizeNode;
|
||||
graph.nodes[ITERATE] = iterateNode;
|
||||
graph.nodes[NOISE] = noiseNode;
|
||||
|
||||
// Connect range of size to iterate
|
||||
graph.edges.push({
|
||||
source: { node_id: RANGE_OF_SIZE, field: 'collection' },
|
||||
destination: {
|
||||
node_id: ITERATE,
|
||||
field: 'collection',
|
||||
},
|
||||
});
|
||||
|
||||
// Connect iterate to noise
|
||||
graph.edges.push({
|
||||
source: {
|
||||
node_id: ITERATE,
|
||||
field: 'item',
|
||||
},
|
||||
destination: {
|
||||
node_id: NOISE,
|
||||
field: 'seed',
|
||||
},
|
||||
});
|
||||
|
||||
// Connect noise to t2l
|
||||
graph.edges.push({
|
||||
source: { node_id: NOISE, field: 'noise' },
|
||||
destination: {
|
||||
node_id: TEXT_TO_LATENTS,
|
||||
field: 'noise',
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
// Multiple iterations, random seed
|
||||
if (shouldRandomizeSeed && iterations > 1) {
|
||||
// Random int node to generate the seed
|
||||
const randomIntNode: RandomIntInvocation = {
|
||||
id: RANDOM_INT,
|
||||
type: 'rand_int',
|
||||
};
|
||||
|
||||
// Range of size node to generate `iterations` count of seeds - range of size generates a collection
|
||||
const rangeOfSizeNode: RangeOfSizeInvocation = {
|
||||
id: RANGE_OF_SIZE,
|
||||
type: 'range_of_size',
|
||||
size: iterations,
|
||||
};
|
||||
|
||||
// Iterate node to iterate over the seeds generated by the range of size node
|
||||
const iterateNode: IterateInvocation = {
|
||||
id: ITERATE,
|
||||
type: 'iterate',
|
||||
};
|
||||
|
||||
// Noise node without any seed
|
||||
const noiseNode: NoiseInvocation = {
|
||||
id: NOISE,
|
||||
type: 'noise',
|
||||
width,
|
||||
height,
|
||||
};
|
||||
|
||||
// Adding to the graph
|
||||
graph.nodes[RANDOM_INT] = randomIntNode;
|
||||
graph.nodes[RANGE_OF_SIZE] = rangeOfSizeNode;
|
||||
graph.nodes[ITERATE] = iterateNode;
|
||||
graph.nodes[NOISE] = noiseNode;
|
||||
|
||||
// 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' },
|
||||
});
|
||||
|
||||
// Connect range of size to iterate
|
||||
graph.edges.push({
|
||||
source: { node_id: RANGE_OF_SIZE, field: 'collection' },
|
||||
destination: {
|
||||
node_id: ITERATE,
|
||||
field: 'collection',
|
||||
},
|
||||
});
|
||||
|
||||
// Connect iterate to noise
|
||||
graph.edges.push({
|
||||
source: {
|
||||
node_id: ITERATE,
|
||||
field: 'item',
|
||||
},
|
||||
destination: {
|
||||
node_id: NOISE,
|
||||
field: 'seed',
|
||||
},
|
||||
});
|
||||
|
||||
// Connect noise to t2l
|
||||
graph.edges.push({
|
||||
source: { node_id: NOISE, field: 'noise' },
|
||||
destination: {
|
||||
node_id: TEXT_TO_LATENTS,
|
||||
field: 'noise',
|
||||
},
|
||||
});
|
||||
} 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 controlnet
|
||||
addControlNetToLinearGraph(graph, TEXT_TO_LATENTS, state);
|
||||
|
||||
return graph;
|
||||
|
||||
@ -0,0 +1,17 @@
|
||||
export const POSITIVE_CONDITIONING = 'positive_conditioning';
|
||||
export const NEGATIVE_CONDITIONING = 'negative_conditioning';
|
||||
export const TEXT_TO_LATENTS = 'text_to_latents';
|
||||
export const LATENTS_TO_IMAGE = 'latents_to_image';
|
||||
export const NOISE = 'noise';
|
||||
export const RANDOM_INT = 'rand_int';
|
||||
export const RANGE_OF_SIZE = 'range_of_size';
|
||||
export const ITERATE = 'iterate';
|
||||
export const MODEL_LOADER = 'model_loader';
|
||||
export const IMAGE_TO_LATENTS = 'image_to_latents';
|
||||
export const LATENTS_TO_LATENTS = 'latents_to_latents';
|
||||
export const RESIZE = 'resize_image';
|
||||
|
||||
export const TEXT_TO_IMAGE_GRAPH = 'text_to_image_graph';
|
||||
export const IMAGE_TO_IMAGE_GRAPH = 'image_to_image_graph';
|
||||
|
||||
export const CONTROL_NET_COLLECT = 'control_net_collect';
|
||||
Loading…
Reference in New Issue
Block a user