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merge: preserve batch canvas enhancements on v1.5.4

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2026-09-19 15:48:07 +08:00
parent 0e5fb27f06
commit f9dc1c561b
5 changed files with 1026 additions and 767 deletions
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432
web/src/pages/canvas/use-canvas-batch-table.ts
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@@ -1,58 +1,67 @@
import { useCallback, type Dispatch, type SetStateAction } from "react";
import { App } from "antd";
import { nanoid } from "nanoid";
import { NODE_DEFAULT_SIZE } from "@/constant/canvas";
import { batchInputColumns, batchPromptForRow, batchReferenceColumns, batchReferenceHandleId, createBatchRowsFromColumns, createInheritedBatchRow, moveBatchReferenceCell, removeLastBatchReferenceColumn, reorderBatchReferenceColumns } from "@/lib/canvas/canvas-batch-table";
import { createCanvasNode } from "@/lib/canvas/canvas-project-domain";
import { buildGenerationConfig, resetGenerationTaskMetadata } from "@/lib/canvas/canvas-project-generation";
import { navigateToSettings } from "@/lib/settings-navigation";
import { modelDisplayName, useConfigStore, useEffectiveConfig } from "@/stores/use-config-store";
import { CanvasNodeType, type CanvasBatchRow, type CanvasBatchTableData, type CanvasConnection, type CanvasGenerationBatchMode, type CanvasNodeData } from "@/types/canvas";
type Options = {
nodesRef: { current: CanvasNodeData[] };
connectionsRef: { current: CanvasConnection[] };
setNodes: Dispatch<SetStateAction<CanvasNodeData[]>>;
setConnections: Dispatch<SetStateAction<CanvasConnection[]>>;
setSelectedNodeIds: Dispatch<SetStateAction<Set<string>>>;
enqueueGenerationBatch: (sourceNodeId: string, mode: CanvasGenerationBatchMode, targets: Array<{ rowId: string; nodeId: string }>, options?: { concurrency?: number }) => string | undefined;
};
export function useCanvasBatchTable({ nodesRef, connectionsRef, setNodes, setConnections, setSelectedNodeIds, enqueueGenerationBatch }: Options) {
const { message, modal } = App.useApp();
const effectiveConfig = useEffectiveConfig();
const isAiConfigReady = useConfigStore((state) => state.isAiConfigReady);
const patchTable = useCallback((nodeId: string, patch: Partial<CanvasBatchTableData>) => {
import { useCallback, useMemo, useState, type Dispatch, type SetStateAction } from "react";
import { App } from "antd";
import { nanoid } from "nanoid";
import { NODE_DEFAULT_SIZE } from "@/constant/canvas";
import { MAX_BATCH_REFERENCE_COLUMNS, batchInputColumns, batchPromptForRow, batchReferenceColumns, batchReferenceHandleId, batchTextInputColumns, createBatchRow, createBatchRowsFromColumns, moveBatchReferenceCell, removeLastBatchReferenceColumn, reorderBatchReferenceColumns } from "@/lib/canvas/canvas-batch-table";
import { createCanvasNode } from "@/lib/canvas/canvas-project-domain";
import { buildGenerationConfig, resetGenerationTaskMetadata } from "@/lib/canvas/canvas-project-generation";
import { navigateToSettings } from "@/lib/settings-navigation";
import { useConfigStore, useEffectiveConfig } from "@/stores/use-config-store";
import { CanvasNodeType, type CanvasBatchRow, type CanvasBatchTableData, type CanvasConnection, type CanvasGenerationBatchMode, type CanvasNodeData } from "@/types/canvas";
import type { BatchGenerationSettings } from "@/components/canvas/batch-generation-settings-dialog";
type Options = {
nodesRef: { current: CanvasNodeData[] };
connectionsRef: { current: CanvasConnection[] };
setNodes: Dispatch<SetStateAction<CanvasNodeData[]>>;
setConnections: Dispatch<SetStateAction<CanvasConnection[]>>;
setSelectedNodeIds: Dispatch<SetStateAction<Set<string>>>;
enqueueGenerationBatch: (sourceNodeId: string, mode: CanvasGenerationBatchMode, targets: Array<{ rowId: string; nodeId: string }>, options?: { concurrency?: number }) => string | undefined;
};
type PendingBatchGen = {
nodeId: string;
rows: CanvasBatchRow[];
concurrency: number;
};
export function useCanvasBatchTable({ nodesRef, connectionsRef, setNodes, setConnections, setSelectedNodeIds, enqueueGenerationBatch }: Options) {
const { message } = App.useApp();
const effectiveConfig = useEffectiveConfig();
const isAiConfigReady = useConfigStore((state) => state.isAiConfigReady);
const [batchGenDialog, setBatchGenDialog] = useState<{ open: boolean; pending: PendingBatchGen | null }>({ open: false, pending: null });
const patchTable = useCallback((nodeId: string, patch: Partial<CanvasBatchTableData>) => {
setNodes((current) => current.map((node) => node.id !== nodeId ? node : { ...node, metadata: { ...node.metadata, batchTable: { operation: "try_on", concurrency: 10, rows: [], ...node.metadata?.batchTable, ...patch } } }));
}, [setNodes]);
const updateRow = useCallback((nodeId: string, rowId: string, patch: Partial<CanvasBatchRow>) => {
const node = nodesRef.current.find((item) => item.id === nodeId);
if (!node?.metadata?.batchTable) return;
patchTable(nodeId, { rows: node.metadata.batchTable.rows.map((row) => row.id === rowId ? { ...row, ...patch } : row) });
}, [nodesRef, patchTable]);
const addRow = useCallback((nodeId: string) => {
const table = nodesRef.current.find((item) => item.id === nodeId)?.metadata?.batchTable;
if (!table) return;
patchTable(nodeId, { rows: [...table.rows, createInheritedBatchRow(table.operation, table.rows)] });
}, [nodesRef, patchTable]);
const removeRow = useCallback((nodeId: string, rowId: string) => {
const table = nodesRef.current.find((item) => item.id === nodeId)?.metadata?.batchTable;
if (!table) return;
patchTable(nodeId, { rows: table.rows.filter((row) => row.id !== rowId) });
}, [nodesRef, patchTable]);
}, [setNodes]);
const updateRow = useCallback((nodeId: string, rowId: string, patch: Partial<CanvasBatchRow>) => {
const node = nodesRef.current.find((item) => item.id === nodeId);
if (!node?.metadata?.batchTable) return;
patchTable(nodeId, { rows: node.metadata.batchTable.rows.map((row) => row.id === rowId ? { ...row, ...patch } : row) });
}, [nodesRef, patchTable]);
const addRow = useCallback((nodeId: string) => {
const table = nodesRef.current.find((item) => item.id === nodeId)?.metadata?.batchTable;
if (!table) return;
patchTable(nodeId, { rows: [...table.rows, createBatchRow(table.operation)] });
}, [nodesRef, patchTable]);
const removeRow = useCallback((nodeId: string, rowId: string) => {
const table = nodesRef.current.find((item) => item.id === nodeId)?.metadata?.batchTable;
if (!table) return;
patchTable(nodeId, { rows: table.rows.filter((row) => row.id !== rowId) });
}, [nodesRef, patchTable]);
const addReferenceColumn = useCallback((nodeId: string) => {
const table = nodesRef.current.find((item) => item.id === nodeId)?.metadata?.batchTable;
if (!table) return;
const columns = batchReferenceColumns(table);
if (columns.length >= 6) return message.info("最多支持 6 组参考图");
const nextIndex = columns.length + 1;
patchTable(nodeId, { referenceColumns: [...columns, { id: `reference-${nanoid()}`, label: `参考图 ${nextIndex}` }] });
const table = nodesRef.current.find((item) => item.id === nodeId)?.metadata?.batchTable;
if (!table) return;
const columns = batchReferenceColumns(table);
if (columns.length >= MAX_BATCH_REFERENCE_COLUMNS) return message.info("最多支持 10 组参考图");
const nextIndex = columns.length + 1;
patchTable(nodeId, { referenceColumns: [...columns, { id: `reference-${nanoid()}`, label: `参考图 ${nextIndex}` }] });
}, [message, nodesRef, patchTable]);
const removeReferenceColumn = useCallback((nodeId: string) => {
@@ -61,152 +70,191 @@ export function useCanvasBatchTable({ nodesRef, connectionsRef, setNodes, setCon
const columns = batchReferenceColumns(table);
const nextTable = removeLastBatchReferenceColumn(table);
if (!nextTable) return message.info("至少保留 1 组参考图");
const removed = columns.at(-1);
patchTable(nodeId, nextTable);
const removed = columns.at(-1);
if (removed) {
const handleId = batchReferenceHandleId(removed.id);
setConnections((current) => current.filter((connection) => !(connection.toNodeId === nodeId && connection.toHandleId === handleId)));
}
}, [message, nodesRef, patchTable, setConnections]);
const syncRowsFromConnections = useCallback((nodeId: string, silent = false) => {
const node = nodesRef.current.find((item) => item.id === nodeId);
const table = node?.metadata?.batchTable;
if (!node || !table) return false;
const nodeById = new Map(nodesRef.current.map((item) => [item.id, item]));
const columns = batchInputColumns(node, connectionsRef.current).map((column) => column.filter((inputNodeId) => {
const input = nodeById.get(inputNodeId);
return input?.type === CanvasNodeType.Image && Boolean(input.metadata?.content || input.metadata?.storageKey);
}));
if (!columns.some((column) => column.length)) {
if (!silent) message.warning("请先把图片节点连接到批量创作表");
return false;
}
const rows = createBatchRowsFromColumns(table.operation, columns, table.rows);
if (!rows.length) {
if (!silent) message.warning("批量换装至少需要一张人物图和一张服装图");
return false;
}
if (JSON.stringify(rows) === JSON.stringify(table.rows)) return true;
patchTable(nodeId, { rows });
if (!silent) {
const added = Math.max(0, rows.length - table.rows.length);
message.success(added ? `已同步连线并新增 ${added} 行，原有任务均已保留` : "已同步最新连线，原有任务均已保留");
}
return true;
}, [connectionsRef, message, nodesRef, patchTable]);
const fillRowsFromConnections = useCallback((nodeId: string) => {
syncRowsFromConnections(nodeId);
}, [syncRowsFromConnections]);
const reorderReferenceColumns = useCallback((nodeId: string, fromColumnId: string, toColumnId: string) => {
const table = nodesRef.current.find((item) => item.id === nodeId)?.metadata?.batchTable;
if (!table) return;
const nextTable = reorderBatchReferenceColumns(table, fromColumnId, toColumnId);
if (nextTable !== table) patchTable(nodeId, nextTable);
}, [nodesRef, patchTable]);
const moveReferenceCell = useCallback((nodeId: string, sourceRowId: string, sourceColumnIndex: number, targetRowId: string, targetColumnIndex: number) => {
const table = nodesRef.current.find((item) => item.id === nodeId)?.metadata?.batchTable;
if (!table) return;
const nextTable = moveBatchReferenceCell(table, sourceRowId, sourceColumnIndex, targetRowId, targetColumnIndex);
if (nextTable !== table) patchTable(nodeId, { rows: nextTable.rows });
}, [nodesRef, patchTable]);
const generateRows = useCallback(async (nodeId: string, requestedRowIds?: string[]) => {
const sourceNode = nodesRef.current.find((item) => item.id === nodeId);
const table = sourceNode?.metadata?.batchTable;
if (!sourceNode || !table) return;
const imageModel = effectiveConfig.imageModel || effectiveConfig.model;
if (!isAiConfigReady(effectiveConfig, imageModel)) {
navigateToSettings({ continueCreation: true });
return;
}
const generationConfig = buildGenerationConfig(effectiveConfig, undefined, "image");
const imageResolution = /^(1k|2k|4k)$/i.test(generationConfig.quality) ? generationConfig.quality.toUpperCase() : "由尺寸决定";
const activeNodeIds = new Set((sourceNode.metadata?.generationBatches || []).filter((batch) => batch.mode === "batch_image").flatMap((batch) => batch.items.filter((item) => ["waiting", "submitting", "queued", "running"].includes(item.status)).map((item) => item.nodeId)));
const requested = requestedRowIds?.length ? new Set(requestedRowIds) : null;
const rows = table.rows.filter((row) => {
const inputNodeIds = row.inputNodeIds.filter(Boolean);
if (!row.enabled || (requested && !requested.has(row.id)) || !batchPromptForRow(table, row).trim()) return false;
if (table.operation === "try_on" && inputNodeIds.length < 2) return false;
if (!inputNodeIds.length || inputNodeIds.some((id) => !nodesRef.current.some((node) => node.id === id && node.type === CanvasNodeType.Image && Boolean(node.metadata?.content || node.metadata?.storageKey)))) return false;
const output = row.outputNodeId ? nodesRef.current.find((node) => node.id === row.outputNodeId) : undefined;
if (output && activeNodeIds.has(output.id)) return false;
// 操作列的单行生成允许对已完成结果重新生成；顶部批量按钮仍只提交未完成项。
if (requested) return true;
return !output?.metadata?.content;
});
if (!rows.length) return message.info("没有可提交的未完成任务，请检查参考图和提示词");
const confirmed = await new Promise<boolean>((resolve) => modal.confirm({
title: `确认提交 ${rows.length} 个批量图片任务`,
content: [
`模型：${modelDisplayName(effectiveConfig, generationConfig.model) || generationConfig.model}`,
`出图数量：${rows.length} 张（每行 1 张）`,
`尺寸：${generationConfig.size || "默认"}`,
`质量：${generationConfig.quality || "默认"}`,
`分辨率：${imageResolution}`,
`并发：${table.concurrency}`,
"这些任务可能消耗积分或产生外部模型费用。",
].join("\n"),
okText: "确认生成",
cancelText: "取消",
centered: true,
onOk: () => resolve(true),
onCancel: () => resolve(false),
}));
if (!confirmed) return;
const imageSpec = NODE_DEFAULT_SIZE[CanvasNodeType.Image];
const nextNodes = [...nodesRef.current];
let nextConnections = [...connectionsRef.current];
const outputByRowId = new Map<string, string>();
const targets: Array<{ rowId: string; nodeId: string }> = [];
rows.forEach((row, index) => {
const existingIndex = row.outputNodeId ? nextNodes.findIndex((node) => node.id === row.outputNodeId && node.type === CanvasNodeType.Image) : -1;
const prompt = batchPromptForRow(table, row).trim();
const metadata = {
...(existingIndex >= 0 ? resetGenerationTaskMetadata(nextNodes[existingIndex].metadata) : {}),
prompt,
composerContent: prompt,
model: generationConfig.model,
size: generationConfig.size,
quality: generationConfig.quality,
transparentBackground: generationConfig.transparentBackground,
count: 1,
generationMode: "image" as const,
generationType: "edit" as const,
workflowKind: "final" as const,
workflowTitle: `${table.operation === "try_on" ? "换装" : "创意"}任务 ${index + 1}`,
status: "idle" as const,
batchSourceNodeId: nodeId,
batchRowId: row.id,
batchOperation: table.operation,
const addTextColumn = useCallback((nodeId: string) => {
const table = nodesRef.current.find((item) => item.id === nodeId)?.metadata?.batchTable;
if (!table) return;
const columns = table.textColumns || [];
if (columns.length >= 4) return message.info("最多支持 4 组文字");
patchTable(nodeId, { textColumns: [...columns, { id: `text-${nanoid()}`, label: `文字 ${columns.length + 1}` }] });
}, [message, nodesRef, patchTable]);
const syncRowsFromConnections = useCallback((nodeId: string, silent = false) => {
const node = nodesRef.current.find((item) => item.id === nodeId);
const table = node?.metadata?.batchTable;
if (!node || !table) return false;
const nodeById = new Map(nodesRef.current.map((item) => [item.id, item]));
const columns = batchInputColumns(node, connectionsRef.current).map((column) => column.filter((inputNodeId) => {
const input = nodeById.get(inputNodeId);
return input?.type === CanvasNodeType.Image && Boolean(input.metadata?.content || input.metadata?.storageKey);
}));
if (!columns.some((column) => column.length)) {
if (!silent) message.warning("请先把图片节点连接到批量创作表");
return false;
}
const rows = createBatchRowsFromColumns(table.operation, columns, table.rows);
const textColumns = batchTextInputColumns(node, connectionsRef.current).map((column) => column.filter((inputNodeId) => {
const input = nodeById.get(inputNodeId);
return input?.type === CanvasNodeType.Text && Boolean(input.metadata?.content || input.metadata?.prompt);
}));
const rowsWithText = rows.map((row, index) => ({
...row,
textNodeIds: textColumns.flatMap((column) => {
const input = column.length === 1 ? column[0] : column[index];
return input ? [input] : [];
}),
}));
if (!rows.length) {
if (!silent) message.warning("批量换装至少需要一张人物图和一张服装图");
return false;
}
const rowsChanged = JSON.stringify(table.rows) !== JSON.stringify(rowsWithText);
if (!rowsChanged) return false;
patchTable(nodeId, { rows: rowsWithText });
if (!silent) message.success(`已按连线创建 ${rows.length} 行任务`);
return true;
}, [connectionsRef, message, nodesRef, patchTable]);
const fillRowsFromConnections = useCallback((nodeId: string) => {
syncRowsFromConnections(nodeId);
}, [syncRowsFromConnections]);
const reorderReferenceColumns = useCallback((nodeId: string, fromColumnId: string, toColumnId: string) => {
const table = nodesRef.current.find((item) => item.id === nodeId)?.metadata?.batchTable;
if (!table) return;
const nextTable = reorderBatchReferenceColumns(table, fromColumnId, toColumnId);
if (nextTable !== table) patchTable(nodeId, nextTable);
}, [nodesRef, patchTable]);
const moveReferenceCell = useCallback((nodeId: string, sourceRowId: string, sourceColumnIndex: number, targetRowId: string, targetColumnIndex: number) => {
const table = nodesRef.current.find((item) => item.id === nodeId)?.metadata?.batchTable;
if (!table) return;
const nextTable = moveBatchReferenceCell(table, sourceRowId, sourceColumnIndex, targetRowId, targetColumnIndex);
if (nextTable !== table) patchTable(nodeId, { rows: nextTable.rows });
}, [nodesRef, patchTable]);
const executeBatchGeneration = useCallback((pending: PendingBatchGen, settings: BatchGenerationSettings) => {
const { nodeId, rows, concurrency } = pending;
const sourceNode = nodesRef.current.find((item) => item.id === nodeId);
const table = sourceNode?.metadata?.batchTable;
if (!sourceNode || !table) return;
const mergedConfig = { ...effectiveConfig, ...settings };
const imageSpec = NODE_DEFAULT_SIZE[CanvasNodeType.Image];
const nextNodes = [...nodesRef.current];
let nextConnections = [...connectionsRef.current];
const outputByRowId = new Map<string, string>();
const targets: Array<{ rowId: string; nodeId: string }> = [];
rows.forEach((row, index) => {
const existingIndex = row.outputNodeId ? nextNodes.findIndex((node) => node.id === row.outputNodeId && node.type === CanvasNodeType.Image) : -1;
const prompt = batchPromptForRow(table, row).trim();
const metadata = {
...(existingIndex >= 0 ? resetGenerationTaskMetadata(nextNodes[existingIndex].metadata) : {}),
prompt,
composerContent: [prompt, ...(row.textNodeIds || []).map((textNodeId) => {
const textNode = nodesRef.current.find((node) => node.id === textNodeId);
return textNode?.metadata?.content || textNode?.metadata?.prompt || "";
})].filter(Boolean).join("\n\n"),
model: buildGenerationConfig(mergedConfig, undefined, "image").model,
size: mergedConfig.size,
quality: mergedConfig.quality,
transparentBackground: mergedConfig.transparentBackground,
count: Number(mergedConfig.count) || 1,
generationMode: "image" as const,
generationType: "edit" as const,
workflowKind: "final" as const,
workflowTitle: `${table.operation === "try_on" ? "换装" : "创意"}任务 ${index + 1}`,
status: "idle" as const,
batchSourceNodeId: nodeId,
batchRowId: row.id,
batchOperation: table.operation,
batchInputNodeIds: row.inputNodeIds,
cameraControl: settings.cameraControl,
};
const output = existingIndex >= 0
? { ...nextNodes[existingIndex], metadata }
: createCanvasNode(CanvasNodeType.Image, { x: sourceNode.position.x + sourceNode.width + 120 + imageSpec.width / 2, y: sourceNode.position.y + index * (imageSpec.height + 32) + imageSpec.height / 2 }, metadata);
output.title = `${table.operation === "try_on" ? "换装" : "创意"} · ${index + 1}`;
if (existingIndex >= 0) nextNodes[existingIndex] = output;
else nextNodes.push(output);
nextConnections = nextConnections.filter((connection) => connection.toNodeId !== output.id);
const output = existingIndex >= 0
? { ...nextNodes[existingIndex], metadata }
: createCanvasNode(CanvasNodeType.Image, { x: sourceNode.position.x + sourceNode.width + 120 + imageSpec.width / 2, y: sourceNode.position.y + index * (imageSpec.height + 32) + imageSpec.height / 2 }, metadata);
output.title = `${table.operation === "try_on" ? "换装" : "创意"} · ${index + 1}`;
if (existingIndex >= 0) nextNodes[existingIndex] = output;
else nextNodes.push(output);
nextConnections = nextConnections.filter((connection) => connection.toNodeId !== output.id);
row.inputNodeIds.filter(Boolean).forEach((inputNodeId) => nextConnections.push({ id: nanoid(), fromNodeId: inputNodeId, toNodeId: output.id }));
nextConnections.push({ id: nanoid(), fromNodeId: sourceNode.id, toNodeId: output.id, relation: "batch-output", storyboardRowId: row.id });
outputByRowId.set(row.id, output.id);
targets.push({ rowId: row.id, nodeId: output.id });
});
const sourceIndex = nextNodes.findIndex((node) => node.id === sourceNode.id);
nextNodes[sourceIndex] = { ...sourceNode, metadata: { ...sourceNode.metadata, batchTable: { ...table, rows: table.rows.map((row) => outputByRowId.has(row.id) ? { ...row, outputNodeId: outputByRowId.get(row.id) } : row) } } };
nodesRef.current = nextNodes;
connectionsRef.current = nextConnections;
setNodes(nextNodes);
setConnections(nextConnections);
setSelectedNodeIds(new Set(targets.map((target) => target.nodeId)));
if (enqueueGenerationBatch(nodeId, "batch_image", targets, { concurrency: table.concurrency })) message.success(`${targets.length} 个任务已加入并发队列`);
}, [connectionsRef, effectiveConfig, enqueueGenerationBatch, isAiConfigReady, message, modal, nodesRef, setConnections, setNodes, setSelectedNodeIds]);
return { addReferenceColumn, addRow, fillRowsFromConnections, generateRows, moveReferenceCell, patchTable, removeReferenceColumn, removeRow, reorderReferenceColumns, syncRowsFromConnections, updateRow };
}
nextConnections.push({ id: nanoid(), fromNodeId: sourceNode.id, toNodeId: output.id, relation: "batch-output", storyboardRowId: row.id });
outputByRowId.set(row.id, output.id);
targets.push({ rowId: row.id, nodeId: output.id });
});
const sourceIndex = nextNodes.findIndex((node) => node.id === sourceNode.id);
nextNodes[sourceIndex] = { ...sourceNode, metadata: { ...sourceNode.metadata, batchTable: { ...table, rows: table.rows.map((row) => outputByRowId.has(row.id) ? { ...row, outputNodeId: outputByRowId.get(row.id) } : row) } } };
nodesRef.current = nextNodes;
connectionsRef.current = nextConnections;
setNodes(nextNodes);
setConnections(nextConnections);
setSelectedNodeIds(new Set(targets.map((target) => target.nodeId)));
if (enqueueGenerationBatch(nodeId, "batch_image", targets, { concurrency })) message.success(`${targets.length} 个任务已加入并发队列`);
}, [connectionsRef, effectiveConfig, enqueueGenerationBatch, message, nodesRef, setConnections, setNodes, setSelectedNodeIds]);
const generateRows = useCallback((nodeId: string, requestedRowIds?: string[]) => {
const sourceNode = nodesRef.current.find((item) => item.id === nodeId);
const table = sourceNode?.metadata?.batchTable;
if (!sourceNode || !table) return;
const imageModel = effectiveConfig.imageModel || effectiveConfig.model;
if (!isAiConfigReady(effectiveConfig, imageModel)) {
navigateToSettings({ continueCreation: true });
return;
}
const activeNodeIds = new Set((sourceNode.metadata?.generationBatches || []).filter((batch) => batch.mode === "batch_image").flatMap((batch) => batch.items.filter((item) => ["waiting", "submitting", "queued", "running"].includes(item.status)).map((item) => item.nodeId)));
const requested = requestedRowIds?.length ? new Set(requestedRowIds) : null;
const rows = table.rows.filter((row) => {
if (row.enabled === false || (requested && !requested.has(row.id)) || !batchPromptForRow(table, row).trim()) return false;
if (table.operation === "try_on" && row.inputNodeIds.length < 2) return false;
if (!row.inputNodeIds.length || row.inputNodeIds.some((id) => !nodesRef.current.some((node) => node.id === id && node.type === CanvasNodeType.Image && Boolean(node.metadata?.content || node.metadata?.storageKey)))) return false;
const output = row.outputNodeId ? nodesRef.current.find((node) => node.id === row.outputNodeId) : undefined;
return !output?.metadata?.content && (!output || !activeNodeIds.has(output.id));
});
if (!rows.length) return message.info("没有可提交的未完成任务，请检查参考图和提示词");
setBatchGenDialog({ open: true, pending: { nodeId, rows, concurrency: table.concurrency } });
}, [effectiveConfig, isAiConfigReady, message, nodesRef]);
const closeBatchGenDialog = useCallback(() => {
setBatchGenDialog({ open: false, pending: null });
}, []);
const confirmBatchGenDialog = useCallback((settings: BatchGenerationSettings) => {
if (batchGenDialog.pending) {
executeBatchGeneration(batchGenDialog.pending, settings);
}
setBatchGenDialog({ open: false, pending: null });
}, [batchGenDialog.pending, executeBatchGeneration]);
const dialogConfig = useMemo(() => effectiveConfig, [effectiveConfig]);
return {
addReferenceColumn,
addTextColumn,
addRow,
fillRowsFromConnections,
generateRows,
moveReferenceCell,
patchTable,
removeReferenceColumn,
removeRow,
reorderReferenceColumns,
syncRowsFromConnections,
updateRow,
batchGenDialogOpen: batchGenDialog.open,
batchGenDialogRowCount: batchGenDialog.pending?.rows.length ?? 0,
batchGenDialogConcurrency: batchGenDialog.pending?.concurrency ?? 1,
batchGenDialogConfig: dialogConfig,
closeBatchGenDialog,
confirmBatchGenDialog,
};
}