5e4b548ad4
- Implemented ComfyGenerateRequest type and associated functions for generating images using various architectures and modes. - Added functions for listing generation options and handling image uploads. - Created workflows for different generation modes including SDXL, Z-Image, Z-Image Turbo, and Anima. - Introduced GenerationJobStatus component to display the status of ongoing generation jobs. - Developed MaskControls for managing mask operations and displaying mask analysis. - Created palette items for tool selection, layer management, and generation settings.
51 lines
2.6 KiB
TypeScript
51 lines
2.6 KiB
TypeScript
import type { ImageDocument } from "@core/document";
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import type { GenerationCandidate } from "@editor/state";
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import { loadImageCanvas, maskValueFromRgba } from "@platform/browser/maskRaster";
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export async function createMaskedPixelReplacementSource(document: ImageDocument, candidate: GenerationCandidate): Promise<string> {
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if (!candidate.inpaint) throw new Error("Only inpaint candidates can replace masked pixels.");
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const targetAsset = document.assets.find((asset) => asset.id === candidate.inpaint?.sourceAssetId);
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if (!targetAsset) throw new Error("The source layer for this candidate no longer exists.");
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const targetCanvas = await loadImageCanvas(targetAsset.source, targetAsset.intrinsicSize.w, targetAsset.intrinsicSize.h);
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const generatedCanvas = await loadImageCanvas(candidate.source, candidate.width, candidate.height);
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const maskCanvas = await loadImageCanvas(candidate.inpaint.maskImage, candidate.width, candidate.height);
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const targetContext = require2dContext(targetCanvas);
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const generatedContext = require2dContext(generatedCanvas);
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const maskContext = require2dContext(maskCanvas);
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const targetData = targetContext.getImageData(0, 0, targetCanvas.width, targetCanvas.height);
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const generatedData = generatedContext.getImageData(0, 0, generatedCanvas.width, generatedCanvas.height);
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const maskData = maskContext.getImageData(0, 0, maskCanvas.width, maskCanvas.height);
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const crop = candidate.inpaint.crop.assetBounds;
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for (let y = 0; y < candidate.height; y += 1) {
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for (let x = 0; x < candidate.width; x += 1) {
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const targetX = Math.round(crop.x) + x;
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const targetY = Math.round(crop.y) + y;
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if (targetX < 0 || targetY < 0 || targetX >= targetCanvas.width || targetY >= targetCanvas.height) continue;
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const generatedIndex = (y * generatedCanvas.width + x) * 4;
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const targetIndex = (targetY * targetCanvas.width + targetX) * 4;
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const mask = maskValueFromRgba(maskData.data, generatedIndex) / 255;
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if (mask <= 0) continue;
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for (let channel = 0; channel < 4; channel += 1) {
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const previous = targetData.data[targetIndex + channel] ?? 0;
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const next = generatedData.data[generatedIndex + channel] ?? previous;
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targetData.data[targetIndex + channel] = Math.round(previous * (1 - mask) + next * mask);
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}
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}
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}
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targetContext.putImageData(targetData, 0, 0);
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return targetCanvas.toDataURL("image/png");
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}
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function require2dContext(canvas: HTMLCanvasElement): CanvasRenderingContext2D {
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const context = canvas.getContext("2d");
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if (!context) throw new Error("Unable to prepare generated candidate");
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return context;
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}
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