Add tests for sampler lifecycle callbacks

Covers the three paths raised in review: no registered callbacks (legacy per-step callback unchanged), start/step/end delivery with expected payloads, and end delivery with samples_shape=None when sampling raises.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

tests-unit/comfy_test/sampler_callbacks_test.py

@@ -0,0 +1,118 @@
+import pytest
+import torch
+
+import comfy.patcher_extension as patcher_extension
+from comfy.samplers import KSAMPLER
+
+SIGMAS = torch.tensor([14.6, 7.0, 0.0])
+NOISE = torch.zeros(1, 4, 8, 8)
+LATENT = torch.zeros(1, 4, 8, 8)
+
+
+class _ModelSampling:
+    sigma_max = 14.6
+
+    def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
+        return noise
+
+    def inverse_noise_scaling(self, sigma, latent):
+        return latent
+
+
+class _InnerModel:
+    def __init__(self):
+        self.model_sampling = _ModelSampling()
+
+
+class _ModelPatcher:
+    def __init__(self):
+        self.model = _InnerModel()
+
+
+class _ModelWrap:
+    """Minimal stand-in for CFGGuider: only what KSAMPLER.sample and the first-step log touch."""
+
+    def __init__(self):
+        self.inner_model = _InnerModel()
+        self.model_patcher = _ModelPatcher()
+        self.cfg = 8.0
+
+
+def _sampler_function(model, noise, sigmas, extra_args=None, callback=None, disable=False, **kwargs):
+    for i in range(len(sigmas) - 1):
+        if callback is not None:
+            callback({"i": i, "denoised": noise, "x": noise, "sigma": sigmas[i]})
+    return noise
+
+
+def _failing_sampler_function(model, noise, sigmas, extra_args=None, callback=None, disable=False, **kwargs):
+    raise RuntimeError("sampling failed")
+
+
+@pytest.fixture
+def extra_args():
+    return {"model_options": {}}
+
+
+def _register(extra_args, call_type, callback):
+    patcher_extension.add_callback(call_type, callback, extra_args["model_options"], is_model_options=True)
+
+
+def test_sampling_unchanged_without_lifecycle_callbacks(extra_args):
+    """No registered lifecycle callbacks: the legacy per-step callback still fires and the result is returned."""
+    legacy_steps = []
+    sampler = KSAMPLER(_sampler_function)
+
+    samples = sampler.sample(
+        _ModelWrap(), SIGMAS, extra_args,
+        lambda i, denoised, x, total_steps: legacy_steps.append((i, total_steps)),
+        NOISE, latent_image=LATENT,
+    )
+
+    assert samples.shape == NOISE.shape
+    assert legacy_steps == [(0, 2), (1, 2)]
+
+
+def test_start_step_end_callbacks_are_delivered(extra_args):
+    started, stepped, ended = [], [], []
+    _register(extra_args, patcher_extension.CallbacksMP.ON_SAMPLER_START, started.append)
+    _register(extra_args, patcher_extension.CallbacksMP.ON_SAMPLER_STEP, stepped.append)
+    _register(extra_args, patcher_extension.CallbacksMP.ON_SAMPLER_END, ended.append)
+
+    KSAMPLER(_sampler_function).sample(
+        _ModelWrap(), SIGMAS, extra_args, None, NOISE, latent_image=LATENT,
+    )
+
+    assert len(started) == 1
+    assert started[0]["total_steps"] == 2
+    assert started[0]["noise_shape"] == tuple(NOISE.shape)
+    assert started[0]["latent_shape"] == tuple(LATENT.shape)
+    assert started[0]["sampler_function"] == "_sampler_function"
+
+    assert [s["step"] for s in stepped] == [0, 1]
+    assert [s["total_steps"] for s in stepped] == [2, 2]
+    assert float(stepped[0]["sigma"]) == pytest.approx(float(SIGMAS[0]))
+    assert float(stepped[0]["sigma_next"]) == pytest.approx(float(SIGMAS[1]))
+    assert float(stepped[1]["sigma_next"]) == pytest.approx(float(SIGMAS[2]))
+    assert stepped[0]["x_shape"] == tuple(NOISE.shape)
+    assert stepped[0]["denoised_shape"] == tuple(NOISE.shape)
+
+    assert len(ended) == 1
+    assert ended[0]["total_steps"] == 2
+    assert ended[0]["samples_shape"] == tuple(NOISE.shape)
+    assert ended[0]["sampler_function"] == "_sampler_function"
+
+
+def test_end_callback_runs_when_sampling_raises(extra_args):
+    ended = []
+    _register(extra_args, patcher_extension.CallbacksMP.ON_SAMPLER_END, ended.append)
+
+    with pytest.raises(RuntimeError, match="sampling failed"):
+        KSAMPLER(_failing_sampler_function).sample(
+            _ModelWrap(), SIGMAS, extra_args, None, NOISE, latent_image=LATENT,
+        )
+
+    assert len(ended) == 1
+    assert ended[0]["samples_shape"] is None
+    assert ended[0]["total_steps"] == 2
+    assert ended[0]["sampler_function"] == "_failing_sampler_function"