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https://github.com/Comfy-Org/ComfyUI_frontend.git
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83f4e7060a
## What Adds `AssetHelper` — a builder-pattern helper for mocking asset-related API endpoints in Playwright E2E tests, plus deterministic fixture data. ## Why 12+ asset-related API endpoints need mocking for asset browser tests (PNL-02), cloud dialog testing (DLG-08), and other asset-dependent E2E scenarios. Random mock data from existing `createMockAssets()` is unsuitable for deterministic E2E assertions. ## What's included ### `AssetHelper.ts` (307 LOC) - Fluent builder API: `assetHelper.withModels(3).withImages(5).mock()` - Stateful mock store (Map) for upload→verify→delete flows - Endpoint coverage: GET/POST/PUT/DELETE `/assets`, download progress - `mockError()` for error state testing - `clearMocks()` cleanup matching QueueHelper/FeatureFlagHelper pattern ### `assetFixtures.ts` (304 LOC) - 11 stable named constants (checkpoints, loras, VAE, embedding, inputs, outputs) - Factory functions: `generateModels()`, `generateInputFiles()`, `generateOutputAssets()` - Fixed IDs/dates/sizes — no randomness, safe for screenshot comparisons ### ComfyPage integration - Available as `comfyPage.assets` in all tests ## Testing - TypeScript compiles clean - Follows existing QueueHelper/FeatureFlagHelper conventions ## Unblocks - PNL-02: Asset browser tests (@Jaewon Yoon) - DLG-08: Assets modal / cloud dialog testing Part of: Test Coverage Q2 Overhaul ┆Issue is synchronized with this [Notion page](https://www.notion.so/PR-10545-test-infra-AssetHelper-with-builder-pattern-deterministic-fixtures-32f6d73d365081d3985ef079ff3dbede) by [Unito](https://www.unito.io) --------- Co-authored-by: GitHub Action <action@github.com>
307 lines
8.5 KiB
TypeScript
307 lines
8.5 KiB
TypeScript
import type { Asset } from '@comfyorg/ingest-types'
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function createModelAsset(overrides: Partial<Asset> = {}): Asset {
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return {
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id: 'test-model-001',
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name: 'model.safetensors',
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asset_hash:
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'blake3:0000000000000000000000000000000000000000000000000000000000000000',
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size: 2_147_483_648,
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mime_type: 'application/octet-stream',
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tags: ['models', 'checkpoints'],
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created_at: '2025-01-15T10:00:00Z',
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updated_at: '2025-01-15T10:00:00Z',
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last_access_time: '2025-01-15T10:00:00Z',
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user_metadata: { base_model: 'sd15' },
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...overrides
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}
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}
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function createInputAsset(overrides: Partial<Asset> = {}): Asset {
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return {
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id: 'test-input-001',
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name: 'input.png',
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asset_hash:
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'blake3:1111111111111111111111111111111111111111111111111111111111111111',
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size: 2_048_576,
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mime_type: 'image/png',
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tags: ['input'],
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created_at: '2025-03-01T09:00:00Z',
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updated_at: '2025-03-01T09:00:00Z',
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last_access_time: '2025-03-01T09:00:00Z',
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...overrides
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}
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}
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function createOutputAsset(overrides: Partial<Asset> = {}): Asset {
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return {
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id: 'test-output-001',
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name: 'output_00001.png',
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asset_hash:
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'blake3:2222222222222222222222222222222222222222222222222222222222222222',
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size: 4_194_304,
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mime_type: 'image/png',
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tags: ['output'],
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created_at: '2025-03-10T12:00:00Z',
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updated_at: '2025-03-10T12:00:00Z',
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last_access_time: '2025-03-10T12:00:00Z',
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...overrides
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}
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}
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export const STABLE_CHECKPOINT: Asset = createModelAsset({
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id: 'test-checkpoint-001',
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name: 'sd_xl_base_1.0.safetensors',
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size: 6_938_078_208,
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tags: ['models', 'checkpoints'],
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user_metadata: {
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base_model: 'sdxl',
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description: 'Stable Diffusion XL Base 1.0'
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},
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created_at: '2025-01-15T10:30:00Z',
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updated_at: '2025-01-15T10:30:00Z'
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})
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export const STABLE_CHECKPOINT_2: Asset = createModelAsset({
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id: 'test-checkpoint-002',
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name: 'v1-5-pruned-emaonly.safetensors',
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size: 4_265_146_304,
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tags: ['models', 'checkpoints'],
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user_metadata: {
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base_model: 'sd15',
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description: 'Stable Diffusion 1.5 Pruned EMA-Only'
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},
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created_at: '2025-01-20T08:00:00Z',
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updated_at: '2025-01-20T08:00:00Z'
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})
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export const STABLE_LORA: Asset = createModelAsset({
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id: 'test-lora-001',
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name: 'detail_enhancer_v1.2.safetensors',
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size: 184_549_376,
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tags: ['models', 'loras'],
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user_metadata: {
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base_model: 'sdxl',
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description: 'Detail Enhancement LoRA'
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},
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created_at: '2025-02-20T14:00:00Z',
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updated_at: '2025-02-20T14:00:00Z'
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})
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export const STABLE_LORA_2: Asset = createModelAsset({
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id: 'test-lora-002',
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name: 'add_detail_v2.safetensors',
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size: 226_492_416,
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tags: ['models', 'loras'],
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user_metadata: {
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base_model: 'sd15',
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description: 'Add Detail LoRA v2'
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},
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created_at: '2025-02-25T11:00:00Z',
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updated_at: '2025-02-25T11:00:00Z'
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})
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export const STABLE_VAE: Asset = createModelAsset({
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id: 'test-vae-001',
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name: 'sdxl_vae.safetensors',
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size: 334_641_152,
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tags: ['models', 'vae'],
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user_metadata: {
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base_model: 'sdxl',
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description: 'SDXL VAE'
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},
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created_at: '2025-01-18T16:00:00Z',
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updated_at: '2025-01-18T16:00:00Z'
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})
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export const STABLE_EMBEDDING: Asset = createModelAsset({
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id: 'test-embedding-001',
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name: 'bad_prompt_v2.pt',
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size: 32_768,
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mime_type: 'application/x-pytorch',
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tags: ['models', 'embeddings'],
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user_metadata: {
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base_model: 'sd15',
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description: 'Negative Embedding: Bad Prompt v2'
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},
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created_at: '2025-02-01T09:30:00Z',
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updated_at: '2025-02-01T09:30:00Z'
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})
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export const STABLE_INPUT_IMAGE: Asset = createInputAsset({
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id: 'test-input-001',
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name: 'reference_photo.png',
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size: 2_048_576,
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mime_type: 'image/png',
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tags: ['input'],
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created_at: '2025-03-01T09:00:00Z',
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updated_at: '2025-03-01T09:00:00Z'
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})
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export const STABLE_INPUT_IMAGE_2: Asset = createInputAsset({
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id: 'test-input-002',
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name: 'mask_layer.png',
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size: 1_048_576,
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mime_type: 'image/png',
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tags: ['input'],
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created_at: '2025-03-05T10:00:00Z',
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updated_at: '2025-03-05T10:00:00Z'
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})
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export const STABLE_INPUT_VIDEO: Asset = createInputAsset({
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id: 'test-input-003',
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name: 'clip_720p.mp4',
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size: 15_728_640,
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mime_type: 'video/mp4',
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tags: ['input'],
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created_at: '2025-03-08T14:30:00Z',
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updated_at: '2025-03-08T14:30:00Z'
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})
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export const STABLE_OUTPUT: Asset = createOutputAsset({
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id: 'test-output-001',
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name: 'ComfyUI_00001_.png',
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size: 4_194_304,
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mime_type: 'image/png',
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tags: ['output'],
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created_at: '2025-03-10T12:00:00Z',
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updated_at: '2025-03-10T12:00:00Z'
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})
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export const STABLE_OUTPUT_2: Asset = createOutputAsset({
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id: 'test-output-002',
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name: 'ComfyUI_00002_.png',
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size: 3_670_016,
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mime_type: 'image/png',
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tags: ['output'],
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created_at: '2025-03-10T12:05:00Z',
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updated_at: '2025-03-10T12:05:00Z'
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})
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export const ALL_MODEL_FIXTURES: Asset[] = [
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STABLE_CHECKPOINT,
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STABLE_CHECKPOINT_2,
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STABLE_LORA,
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STABLE_LORA_2,
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STABLE_VAE,
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STABLE_EMBEDDING
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]
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export const ALL_INPUT_FIXTURES: Asset[] = [
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STABLE_INPUT_IMAGE,
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STABLE_INPUT_IMAGE_2,
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STABLE_INPUT_VIDEO
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]
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export const ALL_OUTPUT_FIXTURES: Asset[] = [STABLE_OUTPUT, STABLE_OUTPUT_2]
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const CHECKPOINT_NAMES = [
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'sd_xl_base_1.0.safetensors',
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'v1-5-pruned-emaonly.safetensors',
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'sd_xl_refiner_1.0.safetensors',
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'dreamshaper_8.safetensors',
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'realisticVision_v51.safetensors',
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'deliberate_v3.safetensors',
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'anything_v5.safetensors',
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'counterfeit_v3.safetensors',
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'revAnimated_v122.safetensors',
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'majicmixRealistic_v7.safetensors'
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]
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const LORA_NAMES = [
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'detail_enhancer_v1.2.safetensors',
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'add_detail_v2.safetensors',
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'epi_noiseoffset_v2.safetensors',
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'lcm_lora_sdxl.safetensors',
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'film_grain_v1.safetensors',
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'sharpness_fix_v2.safetensors',
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'better_hands_v1.safetensors',
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'smooth_skin_v3.safetensors',
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'color_pop_v1.safetensors',
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'bokeh_effect_v2.safetensors'
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]
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const INPUT_NAMES = [
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'reference_photo.png',
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'mask_layer.png',
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'clip_720p.mp4',
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'depth_map.png',
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'control_pose.png',
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'sketch_input.jpg',
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'inpainting_mask.png',
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'style_reference.png',
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'batch_001.png',
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'batch_002.png'
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]
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const EXTENSION_MIME_MAP: Record<string, string> = {
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png: 'image/png',
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jpg: 'image/jpeg',
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jpeg: 'image/jpeg',
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mp4: 'video/mp4',
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webm: 'video/webm',
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mov: 'video/quicktime',
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mp3: 'audio/mpeg',
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wav: 'audio/wav',
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ogg: 'audio/ogg',
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flac: 'audio/flac'
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}
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function getMimeType(filename: string): string {
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const ext = filename.split('.').pop()?.toLowerCase() ?? ''
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return EXTENSION_MIME_MAP[ext] ?? 'application/octet-stream'
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}
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/**
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* Generate N deterministic model assets of a given category.
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* Uses sequential IDs and fixed names for screenshot stability.
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*/
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export function generateModels(
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count: number,
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category: 'checkpoints' | 'loras' | 'vae' | 'embeddings' = 'checkpoints'
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): Asset[] {
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const names = category === 'loras' ? LORA_NAMES : CHECKPOINT_NAMES
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return Array.from({ length: Math.min(count, names.length) }, (_, i) =>
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createModelAsset({
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id: `gen-${category}-${String(i + 1).padStart(3, '0')}`,
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name: names[i % names.length],
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size: 2_000_000_000 + i * 500_000_000,
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tags: ['models', category],
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user_metadata: { base_model: i % 2 === 0 ? 'sdxl' : 'sd15' },
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created_at: `2025-01-${String(15 + i).padStart(2, '0')}T10:00:00Z`,
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updated_at: `2025-01-${String(15 + i).padStart(2, '0')}T10:00:00Z`
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})
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)
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}
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/**
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* Generate N deterministic input file assets.
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*/
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export function generateInputFiles(count: number): Asset[] {
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return Array.from({ length: Math.min(count, INPUT_NAMES.length) }, (_, i) => {
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const name = INPUT_NAMES[i % INPUT_NAMES.length]
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return createInputAsset({
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id: `gen-input-${String(i + 1).padStart(3, '0')}`,
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name,
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size: 1_000_000 + i * 500_000,
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mime_type: getMimeType(name),
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tags: ['input'],
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created_at: `2025-03-${String(1 + i).padStart(2, '0')}T09:00:00Z`,
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updated_at: `2025-03-${String(1 + i).padStart(2, '0')}T09:00:00Z`
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})
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})
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}
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/**
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* Generate N deterministic output assets.
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*/
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export function generateOutputAssets(count: number): Asset[] {
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return Array.from({ length: count }, (_, i) =>
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createOutputAsset({
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id: `gen-output-${String(i + 1).padStart(3, '0')}`,
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name: `ComfyUI_${String(i + 1).padStart(5, '0')}_.png`,
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size: 3_000_000 + i * 200_000,
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mime_type: 'image/png',
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tags: ['output'],
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created_at: `2025-03-10T${String((12 + Math.floor(i / 60)) % 24).padStart(2, '0')}:${String(i % 60).padStart(2, '0')}:00Z`,
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updated_at: `2025-03-10T${String((12 + Math.floor(i / 60)) % 24).padStart(2, '0')}:${String(i % 60).padStart(2, '0')}:00Z`
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})
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)
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}
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