03493a1c32
- Introduced `generationCancelJob` command ID in `commands/ids.ts`. - Added `GenerationCancelJobPayload` type in `commands/payloads.ts`. - Enhanced job status to include "cancelled" in `editor/state.ts`. - Updated `runGenerationJob` to accept an `AbortSignal` and handle cancellation. - Implemented cancellation logic in `runGenerate` and related functions. - Added tests for job cancellation in `operations/generation/workflow.test.ts`. - Improved layer rendering logic to prevent stack overflow in `editor/document-indexes.ts`. - Added raster size assertions in `platform/browser/rasterLimits.ts` for image processing limits. - Enhanced image file handling to check for size limits in `platform/browser/imageFiles.ts`. - Updated UI components to reflect job cancellation state in `view/GenerationJobStatus.tsx` and `view/bottom-controls/GenerateActionControls.tsx`.
494 lines
22 KiB
TypeScript
494 lines
22 KiB
TypeScript
type GenerateArchitecture = "sdxl" | "z-image" | "z-image-turbo" | "anima";
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type GenerateMode = "text-to-image" | "image-to-image" | "inpaint" | "outpaint";
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type Workflow = Record<string, { class_type: string; inputs: Record<string, unknown> }>;
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export type ComfyGenerateRequest = {
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architecture?: GenerateArchitecture;
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mode: GenerateMode;
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model: string;
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textEncoder?: string;
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vae?: string;
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prompt: string;
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negativePrompt?: string;
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strength?: number;
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steps?: number;
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cfg?: number;
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seed?: number;
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sampler?: string;
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scheduler?: string;
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width?: number;
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height?: number;
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outpaint?: {
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left?: number;
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top?: number;
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right?: number;
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bottom?: number;
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feathering?: number;
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};
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inpaint?: {
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growMaskBy?: number;
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maskBlur?: number;
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maskFeather?: number;
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maskExpand?: number;
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cropPadding?: number;
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maskPolarity?: "hidden" | "revealed";
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maskedContent?: "neutral" | "original" | "originalColor" | "edges";
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crop?: unknown;
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placement?: unknown;
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};
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inputImage?: string;
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maskImage?: string;
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};
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type ComfyObjectInfo = {
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CheckpointLoaderSimple?: { input?: { required?: { ckpt_name?: [string[]] } } };
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KSampler?: { input?: { required?: { sampler_name?: [string[]]; scheduler?: [string[]] } } };
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UNETLoader?: { input?: { required?: { unet_name?: [string[]] } } };
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CLIPLoader?: { input?: { required?: { clip_name?: [string[]] } } };
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VAELoader?: { input?: { required?: { vae_name?: [string[]] } } };
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};
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const comfyBaseUrl = process.env.COMFYUI_URL ?? "http://127.0.0.1:8188";
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const comfyHistoryTimeoutMs = parsePositiveInteger(process.env.COMFYUI_HISTORY_TIMEOUT_MS, 20 * 60 * 1000);
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const comfyHistoryPollIntervalMs = parsePositiveInteger(process.env.COMFYUI_HISTORY_POLL_INTERVAL_MS, 1000);
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const defaultModels: Record<GenerateArchitecture, string> = {
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sdxl: "sd_xl_base_1.0.safetensors",
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"z-image": "z_image_bf16.safetensors",
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"z-image-turbo": "z_image_turbo_bf16.safetensors",
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anima: "anima-base-v1.0.safetensors",
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};
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export async function listGenerationOptions() {
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const response = await fetch(`${comfyBaseUrl}/object_info`);
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if (!response.ok) throw new Error(`ComfyUI option lookup failed: ${response.status}`);
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const info = await response.json() as ComfyObjectInfo;
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const checkpointModels = info.CheckpointLoaderSimple?.input?.required?.ckpt_name?.[0] ?? [];
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const diffusionModels = info.UNETLoader?.input?.required?.unet_name?.[0] ?? [];
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const textEncoders = info.CLIPLoader?.input?.required?.clip_name?.[0] ?? [];
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const vaes = info.VAELoader?.input?.required?.vae_name?.[0] ?? [];
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return {
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models: checkpointModels,
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samplers: info.KSampler?.input?.required?.sampler_name?.[0] ?? [],
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schedulers: info.KSampler?.input?.required?.scheduler?.[0] ?? [],
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diffusionModels,
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textEncoders,
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vaes,
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architectures: [
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{
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value: "sdxl",
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label: "SDXL",
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defaultModel: checkpointModels[0] ?? defaultModels.sdxl,
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models: checkpointModels,
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supportedModes: ["text-to-image", "image-to-image", "inpaint", "outpaint"],
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},
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{
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value: "z-image",
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label: "Z-Image",
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defaultModel: defaultModels["z-image"],
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models: modelsForArchitecture(diffusionModels, defaultModels["z-image"], [/z[_-]?image(?!.*turbo)/i]),
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supportedModes: ["text-to-image"],
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},
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{
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value: "z-image-turbo",
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label: "Z-Image Turbo",
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defaultModel: defaultModels["z-image-turbo"],
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models: modelsForArchitecture(diffusionModels, defaultModels["z-image-turbo"], [/z[_-]?image.*turbo/i]),
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supportedModes: ["text-to-image"],
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},
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{
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value: "anima",
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label: "Anima",
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defaultModel: defaultModels.anima,
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models: modelsForArchitecture(diffusionModels, defaultModels.anima, [], { exclude: [/z[_-]?image/i] }),
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supportedModes: ["text-to-image"],
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},
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],
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};
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}
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async function listCheckpointModels() {
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return (await listGenerationOptions()).models;
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}
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export async function generate(request: ComfyGenerateRequest, signal?: AbortSignal) {
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if (!request.prompt?.trim()) throw new Error("Prompt is required");
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const architecture = normalizeArchitecture(request.architecture);
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if (request.mode !== "text-to-image" && architecture !== "sdxl") throw new Error(`${architectureLabel(architecture)} currently supports text-to-image only`);
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if (!request.model || request.model === "auto") request.model = await defaultModelForArchitecture(architecture);
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const clientId = crypto.randomUUID();
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const uploaded = request.inputImage ? await uploadDataUrl(request.inputImage, `image-studio-${crypto.randomUUID()}.png`, signal) : undefined;
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const mask = request.maskImage ? await uploadDataUrl(request.maskImage, `image-studio-mask-${crypto.randomUUID()}.png`, signal) : undefined;
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if (request.mode === "inpaint" && (!uploaded || !mask)) throw new Error("Inpaint requires normalized input and mask images");
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const prompt = buildComfyWorkflow({ ...request, architecture, inputImage: uploaded, maskImage: mask });
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const queued = await fetch(`${comfyBaseUrl}/prompt`, {
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method: "POST",
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headers: { "content-type": "application/json" },
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body: JSON.stringify({ client_id: clientId, prompt }),
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signal,
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});
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if (!queued.ok) throw new Error(`ComfyUI prompt failed: ${queued.status} ${await queued.text()}`);
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const queuedBody = await queued.json() as { prompt_id?: string; node_errors?: unknown };
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const nodeError = nodeErrorsMessage(queuedBody.node_errors);
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if (nodeError) throw new Error(`ComfyUI rejected the workflow: ${nodeError}`);
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if (!queuedBody.prompt_id) throw new Error("ComfyUI did not return a prompt id");
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const prompt_id = queuedBody.prompt_id;
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let history: unknown;
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try {
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history = await waitForHistory(prompt_id, signal);
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} catch (error) {
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if (signal?.aborted) await cancelComfyPrompt(prompt_id);
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throw error;
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}
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const historyError = historyErrorMessage(history);
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if (historyError) throw new Error(`ComfyUI generation failed: ${historyError}`);
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const image = selectGeneratedOutputImage(history);
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if (!image) throw new Error("ComfyUI did not return an image");
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const imageResponse = await fetch(`${comfyBaseUrl}/view?${new URLSearchParams({ filename: image.filename, subfolder: image.subfolder ?? "", type: image.type ?? "output" })}`, { signal });
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if (!imageResponse.ok) throw new Error(`ComfyUI image fetch failed: ${imageResponse.status}`);
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const bytes = Buffer.from(await imageResponse.arrayBuffer());
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return { source: `data:image/png;base64,${bytes.toString("base64")}`, mimeType: "image/png" };
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}
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async function uploadDataUrl(dataUrl: string, filename: string, signal?: AbortSignal) {
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const match = /^data:([^;]+);base64,(.+)$/.exec(dataUrl);
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if (!match) throw new Error("Expected a base64 data URL image");
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const mimeType = match[1] ?? "image/png";
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const base64 = match[2] ?? "";
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const form = new FormData();
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form.append("image", new File([new Uint8Array(Buffer.from(base64, "base64"))], filename, { type: mimeType }));
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form.append("overwrite", "true");
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const response = await fetch(`${comfyBaseUrl}/upload/image`, { method: "POST", body: form, signal });
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if (!response.ok) throw new Error(`ComfyUI upload failed: ${response.status}`);
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const uploaded = await response.json() as { name: string };
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return uploaded.name;
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}
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async function waitForHistory(promptId: string, signal?: AbortSignal) {
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const startedAt = Date.now();
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let attempts = 0;
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while (Date.now() - startedAt < comfyHistoryTimeoutMs) {
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if (signal?.aborted) {
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await cancelComfyPrompt(promptId);
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throw signal.reason ?? new DOMException("Generation cancelled", "AbortError");
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}
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const response = await fetch(`${comfyBaseUrl}/history/${promptId}`, { signal });
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attempts += 1;
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if (response.ok) {
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const history = await response.json() as Record<string, unknown>;
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if (history[promptId]) return history[promptId];
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}
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await abortableSleep(comfyHistoryPollIntervalMs, signal);
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}
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throw new Error(`Timed out waiting for ComfyUI prompt ${promptId} after ${Math.round(comfyHistoryTimeoutMs / 1000)} seconds and ${attempts} checks`);
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}
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async function cancelComfyPrompt(promptId: string) {
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const queueResponse = await fetch(`${comfyBaseUrl}/queue`).catch(() => undefined);
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const queue = queueResponse?.ok ? await queueResponse.json() as { queue_running?: unknown[][]; queue_pending?: unknown[][] } : undefined;
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const isRunning = queue?.queue_running?.some((entry) => entry.includes(promptId)) ?? false;
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const isPending = queue?.queue_pending?.some((entry) => entry.includes(promptId)) ?? true;
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const requests: Promise<unknown>[] = [];
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if (isPending) requests.push(fetch(`${comfyBaseUrl}/queue`, { method: "POST", headers: { "content-type": "application/json" }, body: JSON.stringify({ delete: [promptId] }) }));
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if (isRunning) requests.push(fetch(`${comfyBaseUrl}/interrupt`, { method: "POST" }));
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await Promise.allSettled(requests);
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}
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function abortableSleep(ms: number, signal?: AbortSignal): Promise<void> {
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if (!signal) return Bun.sleep(ms);
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return new Promise((resolve, reject) => {
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const timeout = setTimeout(resolve, ms);
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signal.addEventListener("abort", () => {
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clearTimeout(timeout);
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reject(signal.reason ?? new DOMException("Generation cancelled", "AbortError"));
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}, { once: true });
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});
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}
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export function selectGeneratedOutputImage(history: unknown): { filename: string; subfolder?: string; type?: string } | undefined {
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const outputs = (history as { outputs?: Record<string, { images?: { filename: string; subfolder?: string; type?: string }[] }> }).outputs ?? {};
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const saveImageOutput = outputs["8"]?.images?.find(isGeneratedImage);
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if (saveImageOutput) return saveImageOutput;
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const prefixedOutput = Object.values(outputs).flatMap((output) => output.images ?? []).find((image) => image.filename.startsWith("image-studio-") && image.type !== "input");
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if (prefixedOutput) return prefixedOutput;
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return Object.values(outputs).flatMap((output) => output.images ?? []).find(isGeneratedImage);
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}
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function isGeneratedImage(image: { filename: string; subfolder?: string; type?: string }) {
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return image.type === undefined || image.type === "output";
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}
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export function buildComfyWorkflow(request: ComfyGenerateRequest): Workflow {
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switch (normalizeArchitecture(request.architecture)) {
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case "z-image":
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return buildZImageWorkflow(request);
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case "z-image-turbo":
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return buildZImageTurboWorkflow(request);
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case "anima":
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return buildAnimaWorkflow(request);
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case "sdxl":
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default:
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return buildSdxlWorkflow(request);
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}
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}
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export function buildSdxlWorkflow(request: ComfyGenerateRequest): Workflow {
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if (!request.width || !request.height) throw new Error("Generation width and height are required");
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const width = Math.max(64, Math.round(request.width));
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const height = Math.max(64, Math.round(request.height));
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const denoise = Math.max(0, Math.min(1, (request.strength ?? 75) / 100));
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const seed = request.seed === undefined || request.seed < 0 ? Math.floor(Math.random() * 2 ** 32) : Math.round(request.seed);
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const steps = Math.max(1, Math.round(request.steps ?? 30));
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const cfg = Math.max(0, request.cfg ?? 7);
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const sampler = request.sampler ?? "euler";
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const scheduler = request.scheduler ?? "normal";
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const positive = request.prompt;
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const negative = request.negativePrompt ?? "";
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const samplerInputs: Record<string, unknown> = { seed, steps, cfg, sampler_name: sampler, scheduler, denoise, model: ["1", 0], positive: ["2", 0], negative: ["3", 0], latent_image: ["5", 0] };
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const workflow: Workflow = {
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"1": { class_type: "CheckpointLoaderSimple", inputs: { ckpt_name: request.model } },
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"2": { class_type: "CLIPTextEncode", inputs: { text: positive, clip: ["1", 1] } },
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"3": { class_type: "CLIPTextEncode", inputs: { text: negative, clip: ["1", 1] } },
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"6": { class_type: "KSampler", inputs: samplerInputs },
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"7": { class_type: "VAEDecode", inputs: { samples: ["6", 0], vae: ["1", 2] } },
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"8": { class_type: "SaveImage", inputs: { filename_prefix: `image-studio-${request.mode}`, images: ["7", 0] } },
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};
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if (request.mode === "text-to-image" || !request.inputImage) {
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workflow["5"] = { class_type: "EmptyLatentImage", inputs: { width, height, batch_size: 1 } };
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return workflow;
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}
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workflow["4"] = { class_type: "LoadImage", inputs: { image: request.inputImage } };
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if (request.mode === "inpaint" && request.maskImage) {
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workflow["9"] = { class_type: "LoadImage", inputs: { image: request.maskImage } };
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workflow["11"] = { class_type: "ImageToMask", inputs: { image: ["9", 0], channel: "red" } };
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if (usesOriginalLatentContent(request)) {
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workflow["5"] = { class_type: "VAEEncode", inputs: { pixels: ["4", 0], vae: ["1", 2] } };
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workflow["12"] = { class_type: "GrowMask", inputs: { mask: ["11", 0], expand: resolveGrowMaskBy(request), tapered_corners: true } };
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workflow["13"] = { class_type: "SetLatentNoiseMask", inputs: { samples: ["5", 0], mask: ["12", 0] } };
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samplerInputs.latent_image = ["13", 0];
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} else {
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workflow["5"] = { class_type: "VAEEncodeForInpaint", inputs: { pixels: ["4", 0], vae: ["1", 2], mask: ["11", 0], grow_mask_by: resolveGrowMaskBy(request) } };
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}
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return workflow;
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}
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if (request.mode === "outpaint") {
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workflow["10"] = { class_type: "ImagePadForOutpaint", inputs: { image: ["4", 0], left: Math.round(request.outpaint?.left ?? 0), top: Math.round(request.outpaint?.top ?? 0), right: Math.round(request.outpaint?.right ?? 0), bottom: Math.round(request.outpaint?.bottom ?? 0), feathering: Math.round(request.outpaint?.feathering ?? 0) } };
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workflow["5"] = { class_type: "VAEEncodeForInpaint", inputs: { pixels: ["10", 0], vae: ["1", 2], mask: ["10", 1], grow_mask_by: resolveGrowMaskBy(request) } };
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return workflow;
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}
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workflow["5"] = { class_type: "VAEEncode", inputs: { pixels: ["4", 0], vae: ["1", 2] } };
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return workflow;
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}
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export function buildZImageWorkflow(request: ComfyGenerateRequest): Workflow {
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return buildSeparatedTextToImageWorkflow(request, {
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architecture: "z-image",
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filenamePrefix: "image-studio-z-image",
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model: defaultModels["z-image"],
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textEncoder: "qwen_3_4b.safetensors",
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vae: "ae.safetensors",
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clipType: "lumina2",
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latentNode: "EmptySD3LatentImage",
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modelSamplingAuraFlow: true,
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negativeMode: "prompt",
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steps: 30,
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cfg: 4,
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sampler: "res_multistep",
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scheduler: "simple",
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});
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}
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export function buildZImageTurboWorkflow(request: ComfyGenerateRequest): Workflow {
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return buildSeparatedTextToImageWorkflow(request, {
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architecture: "z-image-turbo",
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filenamePrefix: "image-studio-z-image-turbo",
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model: defaultModels["z-image-turbo"],
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textEncoder: "qwen_3_4b.safetensors",
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vae: "ae.safetensors",
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clipType: "lumina2",
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latentNode: "EmptySD3LatentImage",
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modelSamplingAuraFlow: true,
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negativeMode: "zero",
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steps: 8,
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cfg: 1,
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sampler: "res_multistep",
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scheduler: "simple",
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});
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}
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export function buildAnimaWorkflow(request: ComfyGenerateRequest): Workflow {
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return buildSeparatedTextToImageWorkflow(request, {
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architecture: "anima",
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filenamePrefix: "image-studio-anima",
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model: defaultModels.anima,
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textEncoder: "qwen_3_06b_base.safetensors",
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vae: "qwen_image_vae.safetensors",
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clipType: "stable_diffusion",
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latentNode: "EmptyLatentImage",
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modelSamplingAuraFlow: false,
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negativeMode: "prompt",
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steps: 30,
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cfg: 4,
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sampler: "er_sde",
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scheduler: "simple",
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});
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}
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function buildSeparatedTextToImageWorkflow(request: ComfyGenerateRequest, config: {
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architecture: GenerateArchitecture;
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filenamePrefix: string;
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model: string;
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textEncoder: string;
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vae: string;
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clipType: string;
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latentNode: "EmptyLatentImage" | "EmptySD3LatentImage";
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modelSamplingAuraFlow: boolean;
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negativeMode: "prompt" | "zero";
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steps: number;
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cfg: number;
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sampler: string;
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scheduler: string;
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}): Workflow {
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if (request.mode !== "text-to-image") throw new Error(`${architectureLabel(config.architecture)} currently supports text-to-image only`);
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const options = resolveSamplerOptions(request, config);
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const modelOutput: [string, number] = config.modelSamplingAuraFlow ? ["7", 0] : ["1", 0];
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const workflow: Workflow = {
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"1": { class_type: "UNETLoader", inputs: { unet_name: request.model && request.model !== "auto" ? request.model : config.model, weight_dtype: "default" } },
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"2": { class_type: "CLIPLoader", inputs: { clip_name: resolveSupportModelName(request.textEncoder, config.textEncoder), type: config.clipType, device: "default" } },
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"3": { class_type: "VAELoader", inputs: { vae_name: resolveSupportModelName(request.vae, config.vae) } },
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"4": { class_type: "CLIPTextEncode", inputs: { text: request.prompt, clip: ["2", 0] } },
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"6": { class_type: config.latentNode, inputs: { width: options.width, height: options.height, batch_size: 1 } },
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"8": { class_type: "KSampler", inputs: { seed: options.seed, steps: options.steps, cfg: options.cfg, sampler_name: options.sampler, scheduler: options.scheduler, denoise: 1, model: modelOutput, positive: ["4", 0], negative: ["5", 0], latent_image: ["6", 0] } },
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"9": { class_type: "VAEDecode", inputs: { samples: ["8", 0], vae: ["3", 0] } },
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"10": { class_type: "SaveImage", inputs: { filename_prefix: config.filenamePrefix, images: ["9", 0] } },
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};
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if (config.modelSamplingAuraFlow) workflow["7"] = { class_type: "ModelSamplingAuraFlow", inputs: { model: ["1", 0], shift: 3 } };
|
|
workflow["5"] = config.negativeMode === "zero"
|
|
? { class_type: "ConditioningZeroOut", inputs: { conditioning: ["4", 0] } }
|
|
: { class_type: "CLIPTextEncode", inputs: { text: request.negativePrompt ?? "", clip: ["2", 0] } };
|
|
|
|
return workflow;
|
|
}
|
|
|
|
function resolveSamplerOptions(request: ComfyGenerateRequest, defaults: { steps: number; cfg: number; sampler: string; scheduler: string }) {
|
|
if (!request.width || !request.height) throw new Error("Generation width and height are required");
|
|
return {
|
|
width: Math.max(64, Math.round(request.width)),
|
|
height: Math.max(64, Math.round(request.height)),
|
|
seed: request.seed === undefined || request.seed < 0 ? Math.floor(Math.random() * 2 ** 32) : Math.round(request.seed),
|
|
steps: Math.max(1, Math.round(request.steps ?? defaults.steps)),
|
|
cfg: Math.max(0, request.cfg ?? defaults.cfg),
|
|
sampler: request.sampler ?? defaults.sampler,
|
|
scheduler: request.scheduler ?? defaults.scheduler,
|
|
};
|
|
}
|
|
|
|
function resolveSupportModelName(value: string | undefined, fallback: string) {
|
|
return value && value !== "auto" ? value : fallback;
|
|
}
|
|
|
|
function resolveGrowMaskBy(request: ComfyGenerateRequest): number {
|
|
const value = request.inpaint?.growMaskBy ?? 6;
|
|
if (!Number.isFinite(value)) return 6;
|
|
return Math.round(Math.max(0, Math.min(256, value)));
|
|
}
|
|
|
|
function usesOriginalLatentContent(request: ComfyGenerateRequest): boolean {
|
|
return request.inpaint?.maskedContent === "original" || request.inpaint?.maskedContent === "originalColor" || request.inpaint?.maskedContent === "edges";
|
|
}
|
|
|
|
async function defaultModelForArchitecture(architecture: GenerateArchitecture) {
|
|
if (architecture !== "sdxl") return defaultModels[architecture];
|
|
const models = await listCheckpointModels();
|
|
return models[0] ?? defaultModels.sdxl;
|
|
}
|
|
|
|
function normalizeArchitecture(architecture: ComfyGenerateRequest["architecture"]): GenerateArchitecture {
|
|
if (architecture === "z-image" || architecture === "z-image-turbo" || architecture === "anima") return architecture;
|
|
return "sdxl";
|
|
}
|
|
|
|
function architectureLabel(architecture: GenerateArchitecture): string {
|
|
switch (architecture) {
|
|
case "z-image":
|
|
return "Z-Image";
|
|
case "z-image-turbo":
|
|
return "Z-Image Turbo";
|
|
case "anima":
|
|
return "Anima";
|
|
case "sdxl":
|
|
default:
|
|
return "SDXL";
|
|
}
|
|
}
|
|
|
|
function modelsForArchitecture(models: string[], defaultModel: string, matchers: RegExp[], options: { exclude?: RegExp[] } = {}) {
|
|
return unique([defaultModel, ...models.filter((model) => {
|
|
if (model === defaultModel) return true;
|
|
if (options.exclude?.some((matcher) => matcher.test(model))) return false;
|
|
if (matchers.length === 0) return true;
|
|
return matchers.some((matcher) => matcher.test(model));
|
|
})]);
|
|
}
|
|
|
|
function unique<T>(values: T[]) {
|
|
return Array.from(new Set(values));
|
|
}
|
|
|
|
function parsePositiveInteger(value: string | undefined, fallback: number) {
|
|
if (!value) return fallback;
|
|
const parsed = Number(value);
|
|
return Number.isFinite(parsed) && parsed > 0 ? Math.round(parsed) : fallback;
|
|
}
|
|
|
|
function nodeErrorsMessage(nodeErrors: unknown): string | undefined {
|
|
if (!nodeErrors) return undefined;
|
|
if (Array.isArray(nodeErrors) && nodeErrors.length === 0) return undefined;
|
|
if (typeof nodeErrors === "object" && Object.keys(nodeErrors).length === 0) return undefined;
|
|
|
|
if (typeof nodeErrors === "string") return nodeErrors;
|
|
try {
|
|
return JSON.stringify(nodeErrors);
|
|
} catch {
|
|
return "Unknown node validation error";
|
|
}
|
|
}
|
|
|
|
function historyErrorMessage(history: unknown): string | undefined {
|
|
const status = (history as { status?: { status_str?: string; completed?: boolean; messages?: unknown[] } }).status;
|
|
if (!status) return undefined;
|
|
if (status.status_str && status.status_str !== "success") return statusMessage(status);
|
|
if (status.completed === false) return statusMessage(status);
|
|
return undefined;
|
|
}
|
|
|
|
function statusMessage(status: { status_str?: string; messages?: unknown[] }) {
|
|
const message = status.messages?.map(formatHistoryMessage).filter(Boolean).join("; ");
|
|
return message || status.status_str || "Unknown execution error";
|
|
}
|
|
|
|
function formatHistoryMessage(message: unknown): string | undefined {
|
|
if (!Array.isArray(message)) return undefined;
|
|
const eventName = typeof message[0] === "string" ? message[0] : undefined;
|
|
const payload = message[1];
|
|
if (payload && typeof payload === "object") {
|
|
const detail = payload as { exception_message?: string; node_type?: string; node_id?: string | number };
|
|
if (detail.exception_message) {
|
|
const node = detail.node_type ? ` in ${detail.node_type}${detail.node_id !== undefined ? ` ${detail.node_id}` : ""}` : "";
|
|
return `${eventName ?? "error"}${node}: ${detail.exception_message}`;
|
|
}
|
|
}
|
|
return eventName;
|
|
}
|