116 lines
5.5 KiB
TypeScript
116 lines
5.5 KiB
TypeScript
import { describe, expect, test } from "bun:test";
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import { buildAnimaWorkflow, buildSdxlWorkflow, buildZImageTurboWorkflow, buildZImageWorkflow, selectGeneratedOutputImage } from "./comfy";
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describe("Comfy adapter", () => {
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test("selects SaveImage output instead of uploaded input or mask images", () => {
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const image = selectGeneratedOutputImage({
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outputs: {
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"4": { images: [{ filename: "image-studio-input.png", type: "input" }] },
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"9": { images: [{ filename: "image-studio-mask.png", type: "input" }] },
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"8": { images: [{ filename: "image-studio-inpaint_00001_.png", subfolder: "", type: "output" }] },
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},
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});
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expect(image).toEqual({ filename: "image-studio-inpaint_00001_.png", subfolder: "", type: "output" });
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});
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test("falls back to generated filename prefixes when node ids differ", () => {
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const image = selectGeneratedOutputImage({
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outputs: {
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"12": { images: [{ filename: "image-studio-inpaint_00002_.png", type: "output" }] },
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"4": { images: [{ filename: "image-studio-input.png", type: "input" }] },
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},
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});
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expect(image?.filename).toBe("image-studio-inpaint_00002_.png");
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});
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test("builds neutral inpaint with VAEEncodeForInpaint", () => {
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const workflow = buildSdxlWorkflow(inpaintRequest({ maskedContent: "neutral" }));
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expect(workflow["5"]?.class_type).toBe("VAEEncodeForInpaint");
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expect(workflow["5"]?.inputs).toMatchObject({ grow_mask_by: 6, mask: ["11", 0] });
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expect(workflow["6"]?.inputs.latent_image).toEqual(["5", 0]);
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});
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test("builds original-content inpaint with a latent noise mask", () => {
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const workflow = buildSdxlWorkflow(inpaintRequest({ maskedContent: "original", growMaskBy: 12 }));
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expect(workflow["5"]?.class_type).toBe("VAEEncode");
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expect(workflow["12"]?.class_type).toBe("GrowMask");
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expect(workflow["12"]?.inputs).toMatchObject({ mask: ["11", 0], expand: 12 });
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expect(workflow["13"]?.class_type).toBe("SetLatentNoiseMask");
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expect(workflow["13"]?.inputs).toMatchObject({ samples: ["5", 0], mask: ["12", 0] });
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expect(workflow["6"]?.inputs.latent_image).toEqual(["13", 0]);
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});
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test("builds Z-Image text-to-image with separated model loaders", () => {
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const workflow = buildZImageWorkflow(textRequest({ architecture: "z-image", model: "z_image_bf16.safetensors", steps: 30, cfg: 4 }));
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expect(workflow["1"]).toMatchObject({ class_type: "UNETLoader", inputs: { unet_name: "z_image_bf16.safetensors", weight_dtype: "default" } });
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expect(workflow["2"]).toMatchObject({ class_type: "CLIPLoader", inputs: { clip_name: "qwen_3_4b.safetensors", type: "lumina2" } });
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expect(workflow["6"]?.class_type).toBe("EmptySD3LatentImage");
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expect(workflow["7"]).toMatchObject({ class_type: "ModelSamplingAuraFlow", inputs: { shift: 3 } });
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expect(workflow["8"]?.inputs).toMatchObject({ steps: 30, cfg: 4, sampler_name: "res_multistep", scheduler: "simple", model: ["7", 0] });
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expect(workflow["10"]?.class_type).toBe("SaveImage");
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});
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test("builds Z-Image Turbo with zeroed negative conditioning", () => {
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const workflow = buildZImageTurboWorkflow(textRequest({ architecture: "z-image-turbo", model: "z_image_turbo_bf16.safetensors", negativePrompt: "ignored" }));
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expect(workflow["1"]?.inputs.unet_name).toBe("z_image_turbo_bf16.safetensors");
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expect(workflow["5"]).toMatchObject({ class_type: "ConditioningZeroOut", inputs: { conditioning: ["4", 0] } });
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expect(workflow["8"]?.inputs).toMatchObject({ steps: 8, cfg: 1, sampler_name: "res_multistep", scheduler: "simple" });
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});
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test("builds Anima text-to-image workflow", () => {
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const workflow = buildAnimaWorkflow(textRequest({ architecture: "anima", model: "anima-base-v1.0.safetensors" }));
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expect(workflow["1"]).toMatchObject({ class_type: "UNETLoader", inputs: { unet_name: "anima-base-v1.0.safetensors" } });
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expect(workflow["2"]).toMatchObject({ class_type: "CLIPLoader", inputs: { clip_name: "qwen_3_06b_base.safetensors", type: "stable_diffusion" } });
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expect(workflow["3"]).toMatchObject({ class_type: "VAELoader", inputs: { vae_name: "qwen_image_vae.safetensors" } });
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expect(workflow["6"]?.class_type).toBe("EmptyLatentImage");
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expect(workflow["8"]?.inputs).toMatchObject({ steps: 30, cfg: 4, sampler_name: "er_sde", scheduler: "simple", model: ["1", 0] });
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});
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test("builds Anima with model-specific text encoder and VAE", () => {
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const workflow = buildAnimaWorkflow(textRequest({
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architecture: "anima",
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model: "miaomiaoHarem_anima13.safetensors",
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textEncoder: "miaomiaoHarem_anima13_txt.safetensors",
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vae: "qwen_image_vae.safetensors",
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}));
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expect(workflow["1"]?.inputs.unet_name).toBe("miaomiaoHarem_anima13.safetensors");
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expect(workflow["2"]?.inputs.clip_name).toBe("miaomiaoHarem_anima13_txt.safetensors");
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expect(workflow["3"]?.inputs.vae_name).toBe("qwen_image_vae.safetensors");
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});
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});
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function inpaintRequest(inpaint: { maskedContent: "neutral" | "original"; growMaskBy?: number }) {
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return {
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mode: "inpaint" as const,
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model: "model.safetensors",
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prompt: "replace garment",
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width: 128,
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height: 128,
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inputImage: "input.png",
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maskImage: "mask.png",
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inpaint,
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};
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}
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function textRequest(overrides: Partial<Parameters<typeof buildSdxlWorkflow>[0]> = {}) {
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return {
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architecture: "sdxl" as const,
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mode: "text-to-image" as const,
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model: "model.safetensors",
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prompt: "a studio portrait",
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negativePrompt: "low quality",
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width: 1024,
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height: 1024,
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seed: 123,
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...overrides,
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};
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}
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