Vito Sansevero
576efbba41
- Remove debug code and console.log statements - Fix test suite to properly mock folder_paths module - Update test expectations to match actual implementation - Add comprehensive README documentation with feature list - Add placeholder images for documentation screenshots - Include diagnostic scripts for testing embedding paths - All tests passing (338 passed, 2 skipped) Features implemented: - Autocomplete for embeddings, LoRAs, and custom tags - Custom word list loading from URL (with security validation) - Configurable triggers and settings - Auto-insert comma, replace underscores, Tab/Enter selection - Smart scrolling in suggestion list - Secure content validation to prevent XSS attacks Credits to pythongosssss/ComfyUI-Custom-Scripts for inspiration
135 lines
3.6 KiB
Python
135 lines
3.6 KiB
Python
"""
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pytest configuration and fixtures for ComfyUI-KikoTools testing
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Provides mock ComfyUI environments and test data
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"""
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import sys
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import pytest
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import torch
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from unittest.mock import MagicMock
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# Mock folder_paths module before any imports that might use it
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sys.modules["folder_paths"] = MagicMock()
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sys.modules["folder_paths"].get_filename_list = MagicMock(return_value=[])
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sys.modules["folder_paths"].get_folder_paths = MagicMock(return_value=["/mock/path"])
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sys.modules["folder_paths"].base_path = "/mock/base"
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@pytest.fixture
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def mock_image_tensor():
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"""
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Create a mock IMAGE tensor in ComfyUI format
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Shape: [batch, height, width, channels]
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"""
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# Standard SDXL portrait format: 832x1216
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return torch.randn(1, 1216, 832, 3)
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@pytest.fixture
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def mock_image_tensor_square():
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"""
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Create a mock square IMAGE tensor
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Shape: [batch, height, width, channels] - 1024x1024
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"""
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return torch.randn(1, 1024, 1024, 3)
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@pytest.fixture
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def mock_latent_tensor():
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"""
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Create a mock LATENT tensor in ComfyUI format
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ComfyUI latents are dictionaries with 'samples' key
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Latent dimensions are 1/8 of image dimensions
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Shape: [batch, channels, height/8, width/8]
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"""
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# SDXL portrait latent: 104x152 (832/8 x 1216/8)
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samples = torch.randn(1, 4, 152, 104)
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return {"samples": samples}
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@pytest.fixture
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def mock_latent_tensor_square():
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"""
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Create a mock square LATENT tensor
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Shape: [batch, channels, height/8, width/8] - 128x128 (1024/8)
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"""
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samples = torch.randn(1, 4, 128, 128)
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return {"samples": samples}
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@pytest.fixture
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def sample_scale_factors():
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"""
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Common scale factors for testing
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"""
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return [1.0, 1.2, 1.5, 2.0, 2.5, 3.0, 4.0]
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@pytest.fixture
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def expected_sdxl_resolutions():
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"""
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Expected SDXL-optimized resolutions for testing validation
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"""
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return {
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# Square
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"1024x1024": (1024, 1024),
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# Portrait
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"896x1152": (896, 1152),
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"832x1216": (832, 1216),
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"768x1344": (768, 1344),
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"640x1536": (640, 1536),
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# Landscape
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"1152x896": (1152, 896),
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"1216x832": (1216, 832),
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"1344x768": (1344, 768),
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"1536x640": (1536, 640),
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}
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@pytest.fixture
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def mock_comfyui_node():
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"""
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Mock ComfyUI node structure for testing
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"""
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mock_node = MagicMock()
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mock_node.INPUT_TYPES = MagicMock(return_value={"required": {}, "optional": {}})
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mock_node.RETURN_TYPES = ("INT", "INT")
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mock_node.RETURN_NAMES = ("width", "height")
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mock_node.FUNCTION = "calculate_resolution"
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mock_node.CATEGORY = "ComfyAssets"
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return mock_node
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def assert_divisible_by_8(width: int, height: int) -> None:
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"""
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Helper function to assert dimensions are divisible by 8
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ComfyUI requirement for proper tensor operations
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"""
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assert width % 8 == 0, f"Width {width} must be divisible by 8"
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assert height % 8 == 0, f"Height {height} must be divisible by 8"
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def assert_reasonable_dimensions(
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width: int, height: int, min_size: int = 64, max_size: int = 8192
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) -> None:
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"""
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Helper function to assert dimensions are within reasonable bounds
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"""
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assert (
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min_size <= width <= max_size
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), f"Width {width} out of reasonable range [{min_size}, {max_size}]"
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assert (
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min_size <= height <= max_size
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), f"Height {height} out of reasonable range [{min_size}, {max_size}]"
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# Make helper functions available as pytest fixtures
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@pytest.fixture
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def assert_div_by_8():
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return assert_divisible_by_8
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@pytest.fixture
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def assert_reasonable_dims():
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return assert_reasonable_dimensions
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