Vito Sansevero
5485aa8c19
BREAKING CHANGE: Node categories now use emoji-based organization Add 6 new xyz-helper nodes adapted from comfyui-essentials-nodes: - FluxSamplerParams: FLUX-optimized parameter generator with batch support - LoRAFolderBatch: Batch process multiple LoRAs from folders - PlotParameters: Visualize parameter effects with graphs - SamplerSelectHelper: Intelligent sampler selection with recommendations - SchedulerSelectHelper: Optimal scheduler selection for samplers - TextEncodeSamplerParams: Combined text encoding and parameter management Changes: - Port and enhance nodes from comfyui-essentials (now in maintenance mode) - Add comprehensive documentation with attribution to original author (cubiq) - Create example workflows for xyz-helpers tools - Update all node categories to use emoji-based organization - Fix all unit tests to pass with new category system - Update README with xyz-helpers section and attribution Attribution: xyz-helpers adapted from github.com/cubiq/ComfyUI_essentials All tests passing (318 pass, 2 skip)
227 lines
9.1 KiB
Python
227 lines
9.1 KiB
Python
"""Tests for Empty Latent Batch node and logic."""
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import pytest
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import torch
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from kikotools.tools.empty_latent_batch.node import EmptyLatentBatchNode
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from kikotools.tools.empty_latent_batch.logic import (
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create_empty_latent_batch,
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validate_dimensions,
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sanitize_dimensions,
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)
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class TestEmptyLatentBatchLogic:
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"""Test the logic functions for empty latent batch creation."""
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def test_create_empty_latent_batch_basic(self):
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"""Test basic empty latent creation."""
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result = create_empty_latent_batch(512, 512, 1)
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assert "samples" in result
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samples = result["samples"]
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assert isinstance(samples, torch.Tensor)
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assert samples.shape == (1, 4, 64, 64) # 512/8 = 64
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assert torch.all(samples == 0) # Should be all zeros
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def test_create_empty_latent_batch_with_batch_size(self):
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"""Test empty latent creation with larger batch size."""
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batch_size = 4
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result = create_empty_latent_batch(1024, 768, batch_size)
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assert "samples" in result
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samples = result["samples"]
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assert isinstance(samples, torch.Tensor)
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assert samples.shape == (4, 4, 96, 128) # 768/8=96, 1024/8=128
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assert torch.all(samples == 0)
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def test_create_empty_latent_batch_invalid_dimensions(self):
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"""Test error handling for invalid dimensions."""
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with pytest.raises(ValueError, match="Width and height must be positive"):
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create_empty_latent_batch(0, 512, 1)
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with pytest.raises(ValueError, match="Width and height must be positive"):
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create_empty_latent_batch(512, -100, 1)
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def test_create_empty_latent_batch_not_divisible_by_8(self):
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"""Test error handling for dimensions not divisible by 8."""
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with pytest.raises(ValueError, match="must be divisible by 8"):
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create_empty_latent_batch(513, 512, 1)
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with pytest.raises(ValueError, match="must be divisible by 8"):
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create_empty_latent_batch(512, 515, 1)
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def test_create_empty_latent_batch_invalid_batch_size(self):
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"""Test error handling for invalid batch size."""
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with pytest.raises(ValueError, match="Batch size must be positive"):
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create_empty_latent_batch(512, 512, 0)
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with pytest.raises(ValueError, match="Batch size must be positive"):
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create_empty_latent_batch(512, 512, -1)
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def test_validate_dimensions_valid(self):
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"""Test dimension validation with valid inputs."""
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assert validate_dimensions(512, 512) is True
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assert validate_dimensions(1024, 768) is True
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assert validate_dimensions(64, 64) is True # Minimum size
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assert validate_dimensions(8192, 8192) is True # Maximum size
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def test_validate_dimensions_invalid(self):
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"""Test dimension validation with invalid inputs."""
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assert validate_dimensions(0, 512) is False # Zero dimension
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assert validate_dimensions(512, -100) is False # Negative dimension
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assert validate_dimensions(513, 512) is False # Not divisible by 8
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assert validate_dimensions(32, 32) is False # Too small
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assert validate_dimensions(8200, 8200) is False # Too large
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def test_sanitize_dimensions_basic(self):
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"""Test basic dimension sanitization."""
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width, height = sanitize_dimensions(512, 512)
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assert width == 512
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assert height == 512
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def test_sanitize_dimensions_not_divisible_by_8(self):
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"""Test sanitization of dimensions not divisible by 8."""
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width, height = sanitize_dimensions(513, 515)
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assert width == 520 # Rounds up to nearest multiple of 8
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assert height == 520
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width, height = sanitize_dimensions(517, 519)
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assert width == 520 # Rounds up to nearest multiple of 8
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assert height == 520
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def test_sanitize_dimensions_too_small(self):
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"""Test sanitization of dimensions that are too small."""
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width, height = sanitize_dimensions(32, 16)
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assert width == 64 # Minimum size
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assert height == 64
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def test_sanitize_dimensions_too_large(self):
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"""Test sanitization of dimensions that are too large."""
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width, height = sanitize_dimensions(10000, 9000)
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assert width == 8192 # Maximum size
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assert height == 8192
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class TestEmptyLatentBatchNode:
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"""Test the EmptyLatentBatchNode ComfyUI node."""
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def setup_method(self):
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"""Set up test fixtures."""
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self.node = EmptyLatentBatchNode()
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def test_input_types_structure(self):
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"""Test that INPUT_TYPES returns proper structure."""
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input_types = EmptyLatentBatchNode.INPUT_TYPES()
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assert "required" in input_types
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required = input_types["required"]
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assert "width" in required
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assert "height" in required
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assert "batch_size" in required
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# Check width parameter
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width_spec = required["width"]
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assert width_spec[0] == "INT"
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assert width_spec[1]["default"] == 1024
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assert width_spec[1]["min"] == 64
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assert width_spec[1]["max"] == 8192
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assert width_spec[1]["step"] == 8
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def test_node_attributes(self):
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"""Test node class attributes."""
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assert EmptyLatentBatchNode.RETURN_TYPES == ("LATENT", "INT", "INT")
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assert EmptyLatentBatchNode.RETURN_NAMES == ("latent", "width", "height")
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assert EmptyLatentBatchNode.FUNCTION == "create_empty_latent"
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assert EmptyLatentBatchNode.CATEGORY == "ComfyAssets/📦 Latents"
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def test_create_empty_latent_basic(self):
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"""Test basic empty latent creation through node."""
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result = self.node.create_empty_latent("custom", 512, 512, 1)
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assert isinstance(result, tuple)
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assert len(result) == 3 # Now returns (latent, width, height)
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latent_dict, width, height = result
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assert isinstance(latent_dict, dict)
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assert "samples" in latent_dict
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assert width == 512
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assert height == 512
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samples = latent_dict["samples"]
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assert isinstance(samples, torch.Tensor)
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assert samples.shape == (1, 4, 64, 64)
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def test_create_empty_latent_with_batch(self):
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"""Test empty latent creation with batch size."""
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batch_size = 3
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result = self.node.create_empty_latent("custom", 1024, 768, batch_size)
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latent_dict, width, height = result
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assert width == 1024
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assert height == 768
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samples = latent_dict["samples"]
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assert samples.shape == (3, 4, 96, 128) # batch=3, 768/8=96, 1024/8=128
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def test_create_empty_latent_dimension_adjustment(self):
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"""Test that dimensions are adjusted when not divisible by 8."""
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# Input dimensions not divisible by 8
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result = self.node.create_empty_latent("custom", 513, 515, 1)
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latent_dict, width, height = result
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# Dimensions should be rounded UP to nearest multiple of 8
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assert width == 520 # 513 -> 520
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assert height == 520 # 515 -> 520
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samples = latent_dict["samples"]
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# Should be adjusted to 520x520 -> 65x65 latent
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assert samples.shape == (1, 4, 65, 65)
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def test_validate_inputs_valid(self):
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"""Test input validation with valid parameters."""
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assert self.node.validate_inputs("custom", 512, 512, 1) is True
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assert self.node.validate_inputs("custom", 1024, 768, 4) is True
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def test_validate_inputs_invalid_batch_size(self):
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"""Test input validation with invalid batch size."""
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assert self.node.validate_inputs("custom", 512, 512, 0) is False
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assert self.node.validate_inputs("custom", 512, 512, 100) is False # Too large
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def test_get_latent_info(self):
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"""Test latent info generation."""
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info = self.node.get_latent_info(512, 512, 2)
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assert "Empty latent batch" in info
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assert "2 × 4 × 64 × 64" in info
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assert "512×512" in info
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def test_get_memory_estimate(self):
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"""Test memory estimation."""
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estimate = self.node.get_memory_estimate(512, 512, 1)
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assert "KB" in estimate or "MB" in estimate
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# Larger batch should show larger estimate
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large_estimate = self.node.get_memory_estimate(1024, 1024, 8)
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assert "MB" in large_estimate
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def test_node_registration_mappings(self):
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"""Test that node registration mappings are properly defined."""
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from kikotools.tools.empty_latent_batch.node import (
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NODE_CLASS_MAPPINGS,
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NODE_DISPLAY_NAME_MAPPINGS,
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)
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assert "EmptyLatentBatch" in NODE_CLASS_MAPPINGS
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assert NODE_CLASS_MAPPINGS["EmptyLatentBatch"] == EmptyLatentBatchNode
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assert "EmptyLatentBatch" in NODE_DISPLAY_NAME_MAPPINGS
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assert NODE_DISPLAY_NAME_MAPPINGS["EmptyLatentBatch"] == "Empty Latent Batch"
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def test_node_inheritance(self):
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"""Test that node properly inherits from base class."""
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from kikotools.base.base_node import ComfyAssetsBaseNode
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assert isinstance(self.node, ComfyAssetsBaseNode)
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assert hasattr(self.node, "handle_error")
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assert hasattr(self.node, "log_info")
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assert hasattr(self.node, "validate_inputs")
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