feat: add inpaint region functionality and related tools
- Enhanced cursor behavior for new tools: semantic select, mask lasso, and mask rectangle. - Updated mask edit state to include mask asset ID and kind. - Implemented inpaint region commands for adding, applying, and removing inpaint regions. - Introduced new operations for lasso and semantic selection tools. - Created UI components for candidate review and inpaint region management. - Added tests for inpaint region commands to ensure functionality. - Updated various components to support new inpaint features and improve user experience.
This commit is contained in:
syntaxbullet
201
server/comfy.ts
201
server/comfy.ts
@@ -18,6 +18,9 @@ export type ComfyGenerateRequest = {
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scheduler?: string;
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width?: number;
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height?: number;
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batchSize?: number;
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refinePass?: boolean;
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refineStrength?: number;
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outpaint?: {
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left?: number;
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top?: number;
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@@ -35,17 +38,30 @@ export type ComfyGenerateRequest = {
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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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structureControl?: "none" | "canny" | "depth" | "pose";
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controlStrength?: number;
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controlModel?: string;
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};
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inputImage?: string;
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maskImage?: string;
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};
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export type ComfyProgress = { progress: number; detail: string };
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export type ComfySegmentRequest = { inputImage: string; x: number; y: number; model?: string };
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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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ControlNetLoader?: { input?: { required?: { control_net_name?: [string[]] } } };
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Canny?: unknown;
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"MiDaS-DepthMapPreprocessor"?: unknown;
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OpenposePreprocessor?: unknown;
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ControlNetApplyAdvanced?: unknown;
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SAM3_Detect?: unknown;
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MaskToImage?: unknown;
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};
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const comfyBaseUrl = process.env.COMFYUI_URL ?? "http://127.0.0.1:8188";
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@@ -59,9 +75,7 @@ const defaultModels: Record<GenerateArchitecture, string> = {
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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 info = await fetchObjectInfo();
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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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@@ -69,11 +83,20 @@ export async function listGenerationOptions() {
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return {
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models: checkpointModels,
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inpaintModels: checkpointModels.filter((model) => /inpaint|fill/i.test(model)),
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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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controlModels: info.ControlNetLoader?.input?.required?.control_net_name?.[0] ?? [],
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structureControls: [
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...(info.Canny && info.ControlNetApplyAdvanced && info.ControlNetLoader ? ["canny"] : []),
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...(info["MiDaS-DepthMapPreprocessor"] && info.ControlNetApplyAdvanced && info.ControlNetLoader ? ["depth"] : []),
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...(info.OpenposePreprocessor && info.ControlNetApplyAdvanced && info.ControlNetLoader ? ["pose"] : []),
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],
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semanticSelection: Boolean(info.SAM3_Detect && info.MaskToImage && diffusionModels.some((model) => /sam.?3/i.test(model))),
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sam3Models: diffusionModels.filter((model) => /sam.?3/i.test(model)),
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architectures: [
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{
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value: "sdxl",
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@@ -107,21 +130,60 @@ export async function listGenerationOptions() {
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};
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}
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export async function segment(request: ComfySegmentRequest, signal?: AbortSignal) {
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if (!request.inputImage) throw new Error("Semantic selection requires an image");
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if (!Number.isFinite(request.x) || !Number.isFinite(request.y)) throw new Error("Semantic selection requires a valid point");
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const info = await fetchObjectInfo();
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const models = info.UNETLoader?.input?.required?.unet_name?.[0] ?? [];
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const model = request.model && request.model !== "auto" ? request.model : models.find((candidate) => /sam.?3/i.test(candidate));
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if (!info.SAM3_Detect || !info.MaskToImage || !model || !models.includes(model)) throw new Error("SAM3 semantic selection is not installed in ComfyUI. Install a SAM3 model and enable the native SAM3 nodes.");
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const uploaded = await uploadDataUrl(request.inputImage, `image-studio-segment-${crypto.randomUUID()}.png`, signal);
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const prompt = buildSemanticSelectionWorkflow({ ...request, model, inputImage: uploaded });
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const queued = await fetch(`${comfyBaseUrl}/prompt`, { method: "POST", headers: { "content-type": "application/json" }, body: JSON.stringify({ client_id: crypto.randomUUID(), prompt }), signal });
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if (!queued.ok) throw new Error(`ComfyUI semantic selection 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 semantic selection: ${nodeError}`);
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if (!queuedBody.prompt_id) throw new Error("ComfyUI did not return a semantic selection prompt id");
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const history = await waitForHistory(queuedBody.prompt_id, signal);
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const historyError = historyErrorMessage(history);
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if (historyError) throw new Error(`ComfyUI semantic selection failed: ${historyError}`);
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const image = selectGeneratedOutputImage(history);
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if (!image) throw new Error("ComfyUI did not return a semantic selection mask");
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const response = await fetch(`${comfyBaseUrl}/view?${new URLSearchParams({ filename: image.filename, subfolder: image.subfolder ?? "", type: image.type ?? "output" })}`, { signal });
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if (!response.ok) throw new Error(`ComfyUI mask fetch failed: ${response.status}`);
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const bytes = Buffer.from(await response.arrayBuffer());
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return { source: `data:image/png;base64,${bytes.toString("base64")}`, mimeType: "image/png" };
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}
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export function buildSemanticSelectionWorkflow(request: ComfySegmentRequest & { model: string }): Workflow {
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return {
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"1": { class_type: "UNETLoader", inputs: { unet_name: request.model, weight_dtype: "default" } },
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"2": { class_type: "LoadImage", inputs: { image: request.inputImage } },
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"3": { class_type: "SAM3_Detect", inputs: { model: ["1", 0], image: ["2", 0], positive_coords: JSON.stringify([{ x: Math.round(request.x), y: Math.round(request.y) }]), threshold: 0.5, refine_iterations: 2, individual_masks: false } },
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"4": { class_type: "MaskToImage", inputs: { mask: ["3", 0] } },
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"8": { class_type: "SaveImage", inputs: { filename_prefix: "image-studio-segment", images: ["4", 0] } },
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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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export async function generate(request: ComfyGenerateRequest, signal?: AbortSignal, onProgress?: (event: ComfyProgress) => void) {
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if (!request.prompt?.trim()) throw new Error("Prompt is required");
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onProgress?.({ progress: 0.03, detail: "Preparing workflow" });
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const architecture = normalizeArchitecture(request.architecture);
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if (request.mode !== "text-to-image" && architecture !== "sdxl") throw new Error(`${architectureLabel(architecture)} currently supports text-to-image only`);
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if (!request.model || request.model === "auto") request.model = await defaultModelForArchitecture(architecture);
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if (!request.model || request.model === "auto") request.model = await defaultModelForArchitecture(architecture, request.mode);
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const clientId = crypto.randomUUID();
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const uploaded = request.inputImage ? await uploadDataUrl(request.inputImage, `image-studio-${crypto.randomUUID()}.png`, signal) : undefined;
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const mask = request.maskImage ? await uploadDataUrl(request.maskImage, `image-studio-mask-${crypto.randomUUID()}.png`, signal) : undefined;
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onProgress?.({ progress: 0.16, detail: "Inputs uploaded" });
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if (request.mode === "inpaint" && (!uploaded || !mask)) throw new Error("Inpaint requires normalized input and mask images");
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const prompt = buildComfyWorkflow({ ...request, architecture, inputImage: uploaded, maskImage: mask });
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const resolvedRequest = await resolveStructureControl({ ...request, architecture, inputImage: uploaded, maskImage: mask });
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const prompt = buildComfyWorkflow(resolvedRequest);
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const queued = await fetch(`${comfyBaseUrl}/prompt`, {
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method: "POST",
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@@ -134,23 +196,28 @@ export async function generate(request: ComfyGenerateRequest, signal?: AbortSign
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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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onProgress?.({ progress: 0.24, detail: "Queued in ComfyUI" });
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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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history = await waitForHistory(prompt_id, signal, onProgress);
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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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const images = selectGeneratedOutputImages(history);
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if (images.length === 0) throw new Error("ComfyUI did not return an image");
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onProgress?.({ progress: 0.9, detail: "Downloading results" });
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const results = await Promise.all(images.map(async (image, index) => {
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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", seed: Math.round((resolvedRequest.seed ?? 0) + index) };
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}));
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onProgress?.({ progress: 1, detail: "Results ready" });
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return { results };
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}
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async function uploadDataUrl(dataUrl: string, filename: string, signal?: AbortSignal) {
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@@ -167,7 +234,7 @@ async function uploadDataUrl(dataUrl: string, filename: string, signal?: AbortSi
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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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async function waitForHistory(promptId: string, signal?: AbortSignal, onProgress?: (event: ComfyProgress) => void) {
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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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@@ -177,6 +244,8 @@ async function waitForHistory(promptId: string, signal?: AbortSignal) {
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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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const elapsedRatio = Math.min(1, (Date.now() - startedAt) / comfyHistoryTimeoutMs);
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onProgress?.({ progress: 0.25 + elapsedRatio * 0.6, detail: attempts <= 1 ? "Generating" : `Generating · check ${attempts}` });
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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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@@ -209,14 +278,18 @@ function abortableSleep(ms: number, signal?: AbortSignal): Promise<void> {
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}
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export function selectGeneratedOutputImage(history: unknown): { filename: string; subfolder?: string; type?: string } | undefined {
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return selectGeneratedOutputImages(history)[0];
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}
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export function selectGeneratedOutputImages(history: unknown): Array<{ filename: string; subfolder?: string; type?: string }> {
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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 saveImageOutput = outputs["8"]?.images?.filter(isGeneratedImage) ?? [];
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if (saveImageOutput.length > 0) 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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const prefixedOutput = Object.values(outputs).flatMap((output) => output.images ?? []).filter((image) => image.filename.startsWith("image-studio-") && image.type !== "input");
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if (prefixedOutput.length > 0) return prefixedOutput;
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return Object.values(outputs).flatMap((output) => output.images ?? []).find(isGeneratedImage);
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return Object.values(outputs).flatMap((output) => output.images ?? []).filter(isGeneratedImage);
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}
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function isGeneratedImage(image: { filename: string; subfolder?: string; type?: string }) {
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@@ -260,8 +333,8 @@ export function buildSdxlWorkflow(request: ComfyGenerateRequest): Workflow {
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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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workflow["5"] = { class_type: "EmptyLatentImage", inputs: { width, height, batch_size: resolveBatchSize(request) } };
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return finalizeSdxlWorkflow(workflow, samplerInputs, request);
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}
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workflow["4"] = { class_type: "LoadImage", inputs: { image: request.inputImage } };
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@@ -277,19 +350,96 @@ export function buildSdxlWorkflow(request: ComfyGenerateRequest): Workflow {
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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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return finalizeSdxlWorkflow(workflow, samplerInputs, request);
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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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return finalizeSdxlWorkflow(workflow, samplerInputs, request);
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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 finalizeSdxlWorkflow(workflow, samplerInputs, request);
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}
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function finalizeSdxlWorkflow(workflow: Workflow, samplerInputs: Record<string, unknown>, request: ComfyGenerateRequest): Workflow {
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const batchSize = resolveBatchSize(request);
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if (batchSize > 1 && workflow["5"]?.class_type !== "EmptyLatentImage") {
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const latent = samplerInputs.latent_image;
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workflow["19"] = { class_type: "RepeatLatentBatch", inputs: { samples: latent, amount: batchSize } };
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samplerInputs.latent_image = ["19", 0];
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}
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const control = request.inpaint?.structureControl ?? "none";
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if (request.mode !== "inpaint" || control === "none" || !request.inpaint?.controlModel || request.inpaint.controlModel === "auto") return addRefinementPass(workflow, samplerInputs, request);
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workflow["20"] = { class_type: "ControlNetLoader", inputs: { control_net_name: request.inpaint.controlModel } };
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if (control === "canny") {
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workflow["21"] = { class_type: "Canny", inputs: { image: ["4", 0], low_threshold: 100, high_threshold: 200 } };
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} else if (control === "depth") {
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workflow["21"] = { class_type: "MiDaS-DepthMapPreprocessor", inputs: { image: ["4", 0], a: 6.283, bg_threshold: 0.1, resolution: Math.max(request.width ?? 512, request.height ?? 512) } };
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} else {
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workflow["21"] = { class_type: "OpenposePreprocessor", inputs: { image: ["4", 0], detect_hand: "enable", detect_body: "enable", detect_face: "enable", resolution: Math.max(request.width ?? 512, request.height ?? 512) } };
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}
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workflow["22"] = {
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class_type: "ControlNetApplyAdvanced",
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inputs: {
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positive: ["2", 0],
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negative: ["3", 0],
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control_net: ["20", 0],
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image: ["21", 0],
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strength: Math.max(0, Math.min(1, request.inpaint.controlStrength ?? 0.55)),
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start_percent: 0,
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end_percent: 0.85,
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vae: ["1", 2],
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},
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};
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samplerInputs.positive = ["22", 0];
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samplerInputs.negative = ["22", 1];
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return addRefinementPass(workflow, samplerInputs, request);
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}
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function addRefinementPass(workflow: Workflow, samplerInputs: Record<string, unknown>, request: ComfyGenerateRequest): Workflow {
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if (!request.refinePass) return workflow;
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workflow["30"] = {
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class_type: "KSampler",
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inputs: {
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...samplerInputs,
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seed: Math.round((request.seed ?? 0) + 1),
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steps: Math.max(6, Math.round((request.steps ?? 30) / 3)),
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denoise: Math.max(0, Math.min(1, (request.refineStrength ?? 20) / 100)),
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latent_image: ["6", 0],
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},
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};
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if (workflow["7"]) workflow["7"].inputs.samples = ["30", 0];
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return workflow;
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}
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function resolveBatchSize(request: ComfyGenerateRequest) {
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return Math.round(Math.max(1, Math.min(8, request.batchSize ?? 1)));
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}
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async function fetchObjectInfo(): Promise<ComfyObjectInfo> {
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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: ${response.status}`);
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return response.json() as Promise<ComfyObjectInfo>;
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}
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async function resolveStructureControl(request: ComfyGenerateRequest): Promise<ComfyGenerateRequest> {
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const control = request.inpaint?.structureControl ?? "none";
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if (request.mode !== "inpaint" || control === "none") return request;
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const info = await fetchObjectInfo();
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const requiredPreprocessor = control === "canny" ? info.Canny : control === "depth" ? info["MiDaS-DepthMapPreprocessor"] : info.OpenposePreprocessor;
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if (!requiredPreprocessor || !info.ControlNetLoader || !info.ControlNetApplyAdvanced) {
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throw new Error(`${control === "canny" ? "Canny" : control === "depth" ? "Depth" : "Pose"} structural control is not installed in ComfyUI.`);
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}
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const models = info.ControlNetLoader.input?.required?.control_net_name?.[0] ?? [];
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const requested = request.inpaint?.controlModel;
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const model = requested && requested !== "auto" ? requested : models.find((candidate) => candidate.toLowerCase().includes(control));
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if (!model || !models.includes(model)) throw new Error(`Install or select a ${control} ControlNet model before using structural control.`);
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return { ...request, inpaint: { ...request.inpaint, controlModel: model } };
|
||||
}
|
||||
|
||||
export function buildZImageWorkflow(request: ComfyGenerateRequest): Workflow {
|
||||
return buildSeparatedTextToImageWorkflow(request, {
|
||||
architecture: "z-image",
|
||||
@@ -408,9 +558,10 @@ function usesOriginalLatentContent(request: ComfyGenerateRequest): boolean {
|
||||
return request.inpaint?.maskedContent === "original" || request.inpaint?.maskedContent === "originalColor" || request.inpaint?.maskedContent === "edges";
|
||||
}
|
||||
|
||||
async function defaultModelForArchitecture(architecture: GenerateArchitecture) {
|
||||
async function defaultModelForArchitecture(architecture: GenerateArchitecture, mode: GenerateMode) {
|
||||
if (architecture !== "sdxl") return defaultModels[architecture];
|
||||
const models = await listCheckpointModels();
|
||||
if (mode === "inpaint") return models.find((model) => /inpaint|fill/i.test(model)) ?? models[0] ?? defaultModels.sdxl;
|
||||
return models[0] ?? defaultModels.sdxl;
|
||||
}
|
||||
|
||||
|
||||
Reference in New Issue
Block a user