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https://github.com/invoke-ai/InvokeAI
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chore(ui): lint
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a3a6449786
commit
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@ -214,18 +214,12 @@ export const zControlNetConfigV2 = zControlAdapterBase.extend({
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});
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});
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export type ControlNetConfigV2 = z.infer<typeof zControlNetConfigV2>;
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export type ControlNetConfigV2 = z.infer<typeof zControlNetConfigV2>;
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export const isControlNetConfigV2 = (ca: ControlNetConfigV2 | T2IAdapterConfigV2): ca is ControlNetConfigV2 =>
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zControlNetConfigV2.safeParse(ca).success;
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export const zT2IAdapterConfigV2 = zControlAdapterBase.extend({
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export const zT2IAdapterConfigV2 = zControlAdapterBase.extend({
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type: z.literal('t2i_adapter'),
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type: z.literal('t2i_adapter'),
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model: zModelIdentifierField.nullable(),
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model: zModelIdentifierField.nullable(),
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});
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});
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export type T2IAdapterConfigV2 = z.infer<typeof zT2IAdapterConfigV2>;
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export type T2IAdapterConfigV2 = z.infer<typeof zT2IAdapterConfigV2>;
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export const isT2IAdapterConfigV2 = (ca: ControlNetConfigV2 | T2IAdapterConfigV2): ca is T2IAdapterConfigV2 =>
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zT2IAdapterConfigV2.safeParse(ca).success;
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const zCLIPVisionModelV2 = z.enum(['ViT-H', 'ViT-G']);
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const zCLIPVisionModelV2 = z.enum(['ViT-H', 'ViT-G']);
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export type CLIPVisionModelV2 = z.infer<typeof zCLIPVisionModelV2>;
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export type CLIPVisionModelV2 = z.infer<typeof zCLIPVisionModelV2>;
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export const isCLIPVisionModelV2 = (v: unknown): v is CLIPVisionModelV2 => zCLIPVisionModelV2.safeParse(v).success;
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export const isCLIPVisionModelV2 = (v: unknown): v is CLIPVisionModelV2 => zCLIPVisionModelV2.safeParse(v).success;
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@ -1,133 +0,0 @@
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import type { RootState } from 'app/store/store';
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import { isInitialImageLayer } from 'features/controlLayers/store/controlLayersSlice';
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import { upsertMetadata } from 'features/nodes/util/graph/metadata';
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import type { ImageResizeInvocation, ImageToLatentsInvocation, NonNullableGraph } from 'services/api/types';
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import { assert } from 'tsafe';
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import { IMAGE_TO_LATENTS, NOISE, RESIZE } from './constants';
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/**
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* Returns true if an initial image was added, false if not.
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*/
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export const addInitialImageToLinearGraph = (
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state: RootState,
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graph: NonNullableGraph,
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denoiseNodeId: string
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): boolean => {
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// Remove Existing UNet Connections
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const { vaePrecision, model } = state.generation;
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const { refinerModel, refinerStart } = state.sdxl;
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const { width, height } = state.controlLayers.present.size;
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const initialImageLayer = state.controlLayers.present.layers.find(isInitialImageLayer);
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const initialImage = initialImageLayer?.isEnabled ? initialImageLayer?.image : null;
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if (!initialImage || !initialImageLayer) {
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return false;
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}
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const isSDXL = model?.base === 'sdxl';
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const useRefinerStartEnd = isSDXL && Boolean(refinerModel);
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const denoiseNode = graph.nodes[denoiseNodeId];
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assert(denoiseNode?.type === 'denoise_latents', `Missing denoise node or incorrect type: ${denoiseNode?.type}`);
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const { denoisingStrength } = initialImageLayer;
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denoiseNode.denoising_start = useRefinerStartEnd
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? Math.min(refinerStart, 1 - denoisingStrength)
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: 1 - denoisingStrength;
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denoiseNode.denoising_end = useRefinerStartEnd ? refinerStart : 1;
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// We conditionally hook the image in depending on if a resize is needed
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const i2lNode: ImageToLatentsInvocation = {
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type: 'i2l',
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id: IMAGE_TO_LATENTS,
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is_intermediate: true,
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use_cache: true,
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fp32: vaePrecision === 'fp32',
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};
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graph.nodes[i2lNode.id] = i2lNode;
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graph.edges.push({
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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: denoiseNode.id,
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field: 'latents',
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},
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});
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if (initialImage.width !== width || initialImage.height !== height) {
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// The init image needs to be resized to the specified width and height before being passed to `IMAGE_TO_LATENTS`
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// Create a resize node, explicitly setting its image
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const resizeNode: ImageResizeInvocation = {
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id: RESIZE,
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type: 'img_resize',
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image: {
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image_name: initialImage.name,
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},
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is_intermediate: true,
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width,
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height,
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};
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graph.nodes[RESIZE] = resizeNode;
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// The `RESIZE` node then passes its image to `IMAGE_TO_LATENTS`
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graph.edges.push({
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source: { node_id: RESIZE, field: 'image' },
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destination: {
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node_id: IMAGE_TO_LATENTS,
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field: 'image',
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},
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});
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// The `RESIZE` node also passes its width and height to `NOISE`
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graph.edges.push({
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source: { node_id: RESIZE, field: 'width' },
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destination: {
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node_id: NOISE,
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field: 'width',
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},
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});
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graph.edges.push({
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source: { node_id: RESIZE, field: 'height' },
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destination: {
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node_id: NOISE,
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field: 'height',
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},
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});
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} else {
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// We are not resizing, so we need to set the image on the `IMAGE_TO_LATENTS` node explicitly
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i2lNode.image = {
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image_name: initialImage.name,
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};
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// Pass the image's dimensions to the `NOISE` node
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graph.edges.push({
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source: { node_id: IMAGE_TO_LATENTS, field: 'width' },
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destination: {
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node_id: NOISE,
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field: 'width',
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},
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});
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graph.edges.push({
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source: { node_id: IMAGE_TO_LATENTS, field: 'height' },
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destination: {
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node_id: NOISE,
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field: 'height',
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},
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});
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}
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upsertMetadata(graph, {
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generation_mode: isSDXL ? 'sdxl_img2img' : 'img2img',
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strength: denoisingStrength,
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init_image: initialImage.name,
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});
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return true;
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};
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