diff --git a/server/comfy.test.ts b/server/comfy.test.ts index 4fb652f..fddde40 100644 --- a/server/comfy.test.ts +++ b/server/comfy.test.ts @@ -65,6 +65,14 @@ describe("Comfy adapter", () => { expect(workflow["6"]?.inputs).toMatchObject({ latent_image: ["19", 0], positive: ["22", 0], negative: ["22", 1] }); }); + test("uses Xinsir-compatible pose rendering for pose ControlNet", () => { + const workflow = buildSdxlWorkflow({ + ...inpaintRequest({ maskedContent: "neutral" }), + inpaint: { maskedContent: "neutral", structureControl: "pose", controlModel: "controlnet-openpose-sdxl-1.0.safetensors" }, + }); + expect(workflow["21"]).toMatchObject({ class_type: "OpenposePreprocessor", inputs: { detect_hand: "enable", detect_body: "enable", detect_face: "enable", scale_stick_for_xinsr_cn: "enable" } }); + }); + test("adds an optional low-denoise detail pass", () => { const workflow = buildSdxlWorkflow({ ...inpaintRequest({ maskedContent: "neutral" }), refinePass: true, refineStrength: 18, seed: 40 }); expect(workflow["30"]).toMatchObject({ class_type: "KSampler", inputs: { seed: 41, denoise: 0.18, latent_image: ["6", 0] } }); @@ -73,7 +81,7 @@ describe("Comfy adapter", () => { test("builds native SAM3 point selection as a mask output", () => { const workflow = buildSemanticSelectionWorkflow({ inputImage: "input.png", model: "sam3.safetensors", x: 24.4, y: 18.6 }); - expect(workflow["1"]).toMatchObject({ class_type: "UNETLoader", inputs: { unet_name: "sam3.safetensors" } }); + expect(workflow["1"]).toMatchObject({ class_type: "CheckpointLoaderSimple", inputs: { ckpt_name: "sam3.safetensors" } }); expect(workflow["3"]).toMatchObject({ class_type: "SAM3_Detect", inputs: { positive_coords: '[{"x":24,"y":19}]', refine_iterations: 2 } }); expect(workflow["4"]).toMatchObject({ class_type: "MaskToImage", inputs: { mask: ["3", 0] } }); }); @@ -123,7 +131,7 @@ describe("Comfy adapter", () => { test("lists branded non-Z diffusion models under Anima", async () => { const originalFetch = globalThis.fetch; const mockFetch: typeof fetch = Object.assign(async () => new Response(JSON.stringify({ - CheckpointLoaderSimple: { input: { required: { ckpt_name: [["sd_xl_base_1.0.safetensors"]] } } }, + CheckpointLoaderSimple: { input: { required: { ckpt_name: [["sd_xl_base_1.0.safetensors", "sam3.1_multiplex_fp16.safetensors"]] } } }, KSampler: { input: { required: { sampler_name: [["euler"]], scheduler: [["normal"]] } } }, UNETLoader: { input: { @@ -140,12 +148,29 @@ describe("Comfy adapter", () => { }, CLIPLoader: { input: { required: { clip_name: [["qwen_3_06b_base.safetensors"]] } } }, VAELoader: { input: { required: { vae_name: [["qwen_image_vae.safetensors"]] } } }, + ControlNetLoader: { input: { required: { control_net_name: [[ + "controlnet-canny-sdxl-1.0-fp16.safetensors", + "controlnet-depth-sdxl-1.0-fp16.safetensors", + "controlnet-openpose-sdxl-1.0.safetensors", + ]] } } }, + Canny: {}, + "MiDaS-DepthMapPreprocessor": {}, + OpenposePreprocessor: {}, + ControlNetApplyAdvanced: {}, + SAM3_Detect: {}, + MaskToImage: {}, }), { headers: { "content-type": "application/json" } }), { preconnect: originalFetch.preconnect }); globalThis.fetch = mockFetch; try { const response = await handleComfyApi(new Request("http://image-studio.test/api/comfy/models")); - const body = await response.json() as { architectures: { value: string; models: string[] }[] }; + const body = await response.json() as { + architectures: { value: string; models: string[] }[]; + controlModels: string[]; + structureControls: string[]; + semanticSelection: boolean; + sam3Models: string[]; + }; const anima = body.architectures.find((architecture) => architecture.value === "anima"); expect(response.status).toBe(200); @@ -153,6 +178,10 @@ describe("Comfy adapter", () => { expect(anima?.models).toContain("miaomiaoHarem_anima13.safetensors"); expect(anima?.models).not.toContain("z_image_bf16.safetensors"); expect(anima?.models).not.toContain("z_image_turbo_bf16.safetensors"); + expect(body.structureControls).toEqual(["canny", "depth", "pose"]); + expect(body.controlModels).toHaveLength(3); + expect(body.semanticSelection).toBeTrue(); + expect(body.sam3Models).toEqual(["sam3.1_multiplex_fp16.safetensors"]); } finally { globalThis.fetch = originalFetch; } diff --git a/server/comfy.ts b/server/comfy.ts index 10ab83c..5918596 100644 --- a/server/comfy.ts +++ b/server/comfy.ts @@ -95,8 +95,8 @@ export async function listGenerationOptions() { ...(info["MiDaS-DepthMapPreprocessor"] && info.ControlNetApplyAdvanced && info.ControlNetLoader ? ["depth"] : []), ...(info.OpenposePreprocessor && info.ControlNetApplyAdvanced && info.ControlNetLoader ? ["pose"] : []), ], - semanticSelection: Boolean(info.SAM3_Detect && info.MaskToImage && diffusionModels.some((model) => /sam.?3/i.test(model))), - sam3Models: diffusionModels.filter((model) => /sam.?3/i.test(model)), + semanticSelection: Boolean(info.SAM3_Detect && info.MaskToImage && checkpointModels.some((model) => /sam.?3/i.test(model))), + sam3Models: checkpointModels.filter((model) => /sam.?3/i.test(model)), architectures: [ { value: "sdxl", @@ -134,7 +134,7 @@ export async function segment(request: ComfySegmentRequest, signal?: AbortSignal if (!request.inputImage) throw new Error("Semantic selection requires an image"); if (!Number.isFinite(request.x) || !Number.isFinite(request.y)) throw new Error("Semantic selection requires a valid point"); const info = await fetchObjectInfo(); - const models = info.UNETLoader?.input?.required?.unet_name?.[0] ?? []; + const models = info.CheckpointLoaderSimple?.input?.required?.ckpt_name?.[0] ?? []; const model = request.model && request.model !== "auto" ? request.model : models.find((candidate) => /sam.?3/i.test(candidate)); 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."); const uploaded = await uploadDataUrl(request.inputImage, `image-studio-segment-${crypto.randomUUID()}.png`, signal); @@ -158,7 +158,7 @@ export async function segment(request: ComfySegmentRequest, signal?: AbortSignal export function buildSemanticSelectionWorkflow(request: ComfySegmentRequest & { model: string }): Workflow { return { - "1": { class_type: "UNETLoader", inputs: { unet_name: request.model, weight_dtype: "default" } }, + "1": { class_type: "CheckpointLoaderSimple", inputs: { ckpt_name: request.model } }, "2": { class_type: "LoadImage", inputs: { image: request.inputImage } }, "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 } }, "4": { class_type: "MaskToImage", inputs: { mask: ["3", 0] } }, @@ -379,7 +379,7 @@ function finalizeSdxlWorkflow(workflow: Workflow, samplerInputs: Record