193 lines
6.0 KiB
Python
193 lines
6.0 KiB
Python
# SimpleSyrup - workflow-focused ComfyUI extensions for image generation
|
|
# Copyright (C) 2026 Artificial Sweetener and contributors
|
|
# SPDX-License-Identifier: AGPL-3.0-or-later
|
|
|
|
"""Tests for shared tiled sampling runtime helpers."""
|
|
|
|
from __future__ import annotations
|
|
|
|
import pytest
|
|
import torch
|
|
|
|
from simple_syrup.domain.tiled_diffusion import LatentTile, build_tiled_diffusion_plan
|
|
from simple_syrup.runtime import tiled_sampling
|
|
|
|
|
|
def test_validate_latent_samples_accepts_bchw() -> None:
|
|
"""Standard BCHW latents are valid for tiled samplers."""
|
|
|
|
samples = torch.zeros((1, 4, 8, 8))
|
|
|
|
assert (
|
|
tiled_sampling.validate_latent_samples(
|
|
{"samples": samples},
|
|
sampler_label="TestSampler",
|
|
)
|
|
is samples
|
|
)
|
|
|
|
|
|
def test_validate_latent_samples_accepts_singleton_depth_bcdhw() -> None:
|
|
"""Singleton-depth BCDHW latents are valid for Anima-style models."""
|
|
|
|
samples = torch.zeros((1, 16, 1, 8, 8))
|
|
|
|
assert (
|
|
tiled_sampling.validate_latent_samples(
|
|
{"samples": samples},
|
|
sampler_label="TestSampler",
|
|
)
|
|
is samples
|
|
)
|
|
|
|
|
|
def test_validate_latent_samples_rejects_non_tensor() -> None:
|
|
"""Latent dictionaries must contain tensor samples."""
|
|
|
|
with pytest.raises(ValueError, match="latent samples must be a torch tensor"):
|
|
tiled_sampling.validate_latent_samples(
|
|
{"samples": "not-a-tensor"},
|
|
sampler_label="TestSampler",
|
|
)
|
|
|
|
|
|
def test_validate_tensor_shape_rejects_nested_tensor() -> None:
|
|
"""Nested tensors are rejected before spatial tiling."""
|
|
|
|
samples = torch.nested.nested_tensor([torch.zeros((4, 8, 8))])
|
|
|
|
with pytest.raises(ValueError, match="non-nested latent samples"):
|
|
tiled_sampling.validate_tensor_shape(samples, sampler_label="TestSampler")
|
|
|
|
|
|
def test_validate_tensor_shape_rejects_non_singleton_depth_5d() -> None:
|
|
"""Non-singleton depth 5D latents remain unsupported."""
|
|
|
|
with pytest.raises(ValueError, match="singleton third axis"):
|
|
tiled_sampling.validate_tensor_shape(
|
|
torch.zeros((1, 16, 2, 8, 8)),
|
|
sampler_label="TestSampler",
|
|
)
|
|
|
|
|
|
def test_spatial_tile_slicer_crops_final_axes_for_4d() -> None:
|
|
"""Spatial slicers crop height and width for BCHW tensors."""
|
|
|
|
tensor = torch.arange(1 * 1 * 4 * 6).reshape((1, 1, 4, 6))
|
|
tile = LatentTile(x=2, y=1, width=3, height=2)
|
|
|
|
cropped = tensor[tiled_sampling.spatial_tile_slicer(tile, tensor.ndim)]
|
|
|
|
assert torch.equal(cropped, tensor[:, :, 1:3, 2:5])
|
|
|
|
|
|
def test_spatial_tile_slicer_crops_final_axes_for_5d() -> None:
|
|
"""Spatial slicers preserve singleton depth while cropping BCDHW tensors."""
|
|
|
|
tensor = torch.arange(1 * 2 * 1 * 4 * 6).reshape((1, 2, 1, 4, 6))
|
|
tile = LatentTile(x=1, y=2, width=4, height=2)
|
|
|
|
cropped = tensor[tiled_sampling.spatial_tile_slicer(tile, tensor.ndim)]
|
|
|
|
assert torch.equal(cropped, tensor[:, :, :, 2:4, 1:5])
|
|
|
|
|
|
def test_tile_tensor_crops_spatial_tensor_per_tile() -> None:
|
|
"""Spatial conditioning tensors are cropped and concatenated per tile."""
|
|
|
|
tensor = torch.arange(2 * 1 * 4 * 8, dtype=torch.float32).reshape((2, 1, 4, 8))
|
|
tiles = (LatentTile(0, 0, 4, 4), LatentTile(4, 0, 4, 4))
|
|
|
|
tiled = tiled_sampling.tile_tensor(
|
|
tensor,
|
|
tiles=tiles,
|
|
input_batch_size=2,
|
|
latent_height=4,
|
|
latent_width=8,
|
|
)
|
|
|
|
assert tiled.shape == (4, 1, 4, 4)
|
|
assert torch.equal(tiled[:2], tensor[:, :, :, :4])
|
|
assert torch.equal(tiled[2:], tensor[:, :, :, 4:])
|
|
|
|
|
|
def test_tile_tensor_repeats_matching_batch_tensor() -> None:
|
|
"""Batch-aligned non-spatial tensors repeat once per tile."""
|
|
|
|
tensor = torch.tensor([[1.0], [2.0]])
|
|
tiles = (LatentTile(0, 0, 4, 4), LatentTile(4, 0, 4, 4))
|
|
|
|
tiled = tiled_sampling.tile_tensor(
|
|
tensor,
|
|
tiles=tiles,
|
|
input_batch_size=2,
|
|
latent_height=4,
|
|
latent_width=8,
|
|
)
|
|
|
|
assert torch.equal(tiled, torch.tensor([[1.0], [2.0], [1.0], [2.0]]))
|
|
|
|
|
|
def test_tile_tensor_repeats_singleton_batch_to_tiled_batch_size() -> None:
|
|
"""Singleton-batch tensors expand to the full tiled input batch size."""
|
|
|
|
tensor = torch.tensor([[5.0, 6.0]])
|
|
tiles = (LatentTile(0, 0, 4, 4), LatentTile(4, 0, 4, 4))
|
|
|
|
tiled = tiled_sampling.tile_tensor(
|
|
tensor,
|
|
tiles=tiles,
|
|
input_batch_size=2,
|
|
latent_height=4,
|
|
latent_width=8,
|
|
)
|
|
|
|
assert tiled.shape == (4, 2)
|
|
assert torch.equal(tiled, tensor.repeat((4, 1)))
|
|
|
|
|
|
def test_tile_transformer_options_repeats_model_metadata() -> None:
|
|
"""Transformer metadata aligned to model batches repeats per tile."""
|
|
|
|
timestep = torch.tensor([0.5, 0.75, 0.5, 0.75])
|
|
options = {
|
|
"cond_or_uncond": [0, 1],
|
|
"uuids": ("positive", "negative"),
|
|
"sigmas": torch.tensor([1.0, 0.0]),
|
|
"sample_sigmas": torch.tensor([1.0, 0.0]),
|
|
}
|
|
|
|
tiled = tiled_sampling.tile_transformer_options(
|
|
options,
|
|
tile_count=2,
|
|
tiled_timestep=timestep,
|
|
)
|
|
|
|
assert tiled["cond_or_uncond"] == [0, 1, 0, 1]
|
|
assert tiled["uuids"] == ("positive", "negative", "positive", "negative")
|
|
assert torch.equal(tiled["sigmas"], timestep)
|
|
assert torch.equal(tiled["sample_sigmas"], torch.tensor([1.0, 0.0]))
|
|
|
|
|
|
def test_new_spatial_weight_buffer_broadcasts_over_spatial_axes() -> None:
|
|
"""Spatial weight buffers broadcast over BCHW and BCDHW model outputs."""
|
|
|
|
plan = build_tiled_diffusion_plan(8, 4, 4, 4, 0, 1)
|
|
|
|
assert tiled_sampling.new_spatial_weight_buffer(
|
|
torch.zeros((2, 4, 4, 8)),
|
|
plan,
|
|
).shape == (1, 1, 4, 8)
|
|
assert tiled_sampling.new_spatial_weight_buffer(
|
|
torch.zeros((2, 16, 1, 4, 8)),
|
|
plan,
|
|
).shape == (1, 1, 1, 4, 8)
|
|
|
|
|
|
def test_contains_unsupported_conditioning_key_finds_nested_values() -> None:
|
|
"""Unsupported regional and control keys are detected recursively."""
|
|
|
|
conditioning = [{"model_conds": {"nested": [{"mask": torch.ones((1, 1))}]}}]
|
|
|
|
assert tiled_sampling.contains_unsupported_conditioning_key(conditioning)
|