Æmotion StudioandClaude Opus 5
3af34338ab
The Direct node now dispatches: "standard" (default) runs the new pipeline, "legacy" runs the frozen pre-2.0 one. Measured through the node with a recording sampler, 20 steps: standard, 1 stage 1 call x 20 steps, from sigma 14.61 standard, 2 stages 2 calls x 10 steps, from 14.61 then 1.48 standard, denoise 0.6 1 call x 20 steps, from 2.23 standard, 3 injections 4 calls x 5 steps, 14.61 / 3.87 / 1.48 / 0.60 legacy, 2 stages 2 calls, each rebuilding a full schedule The 1.48 is the fix: stages continue one trajectory instead of restarting at maximum noise, where flow models discard the previous stage entirely. The 2.23 is denoise finally reaching the schedule. Also on the node: - sequential_distribution, injection_distribution and fast_high_channel_noise become real optional inputs. As V1 `hidden` tuple inputs ComfyUI never delivered them, so they were stuck at their defaults. - the debug/visualisation hidden inputs are gone; they drove stub no-ops. - IS_CHANGED is removed: it only restated widget values that are already part of the cache key, and would have rejected the new input. - new widgets are appended last, so saved workflows keep their widget order. web/src/sampling_mode_migration.ts switches nodes loaded from pre-2.0 workflows to "legacy", recognising them by the absence of the snk_version property, so existing seeds keep reproducing. 186 Python tests and 85 web tests pass; the 11 legacy goldens now exercise the legacy branch through this dispatch. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
107 lines
4.1 KiB
Python
107 lines
4.1 KiB
Python
"""
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The node routes to the right pipeline, and standard mode samples one trajectory.
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Measured through the node with a recording sampler:
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standard, 1 stage 1 call x 20 steps, from sigma 14.61
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standard, 2 stages 2 calls x 10 steps, from 14.61 then 1.48
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standard, denoise 0.6 1 call x 20 steps, from 2.23
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standard, 3 injections 4 calls x 5 steps, 14.61 / 3.87 / 1.48 / 0.60
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legacy, 2 stages 2 calls, each building its own full schedule
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The second sigma in the two-stage standard run is the point: legacy restarted
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every stage at 14.61, which on flow models discards the previous stage entirely.
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"""
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from unittest import mock
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import pytest
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import torch
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import comfy.sample
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from helpers import FakeModel
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from snk.direct_shader_ksampler import DirectShaderNoiseKSampler
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@pytest.fixture
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def sampler_calls():
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calls = []
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def fake_sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative,
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latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None,
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force_full_denoise=False, noise_mask=None, sigmas=None, callback=None,
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disable_pbar=False, seed=None):
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calls.append({
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"steps": steps,
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"sigmas": None if sigmas is None else sigmas.detach().clone(),
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"denoise": denoise,
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})
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result = latent_image + 0.1 * noise
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if callback is not None:
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callback(max(steps - 1, 0), result * 0.5, result, steps)
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return result
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with mock.patch.object(comfy.sample, "sample", fake_sample):
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yield calls
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def run_node(**overrides):
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kwargs = dict(
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model=FakeModel("eps"), seed=8888, steps=20, cfg=7.0, sampler_name="euler",
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scheduler="normal", positive=[], negative=[],
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latent_image={"samples": torch.zeros(1, 4, 16, 16)}, denoise=1.0,
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sequential_stages=1, injection_stages=0, shader_strength=0.3, blend_mode="multiply",
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noise_transform="none", use_temporal_coherence=False, shader_type="domain_warp",
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shape_type="none", color_scheme="none", noise_scale=1.0, octaves=1.0,
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warp_strength=0.5, shape_mask_strength=1.0, phase_shift=0.5, color_intensity=0.8,
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)
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kwargs.update(overrides)
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return DirectShaderNoiseKSampler().sample(**kwargs)
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@pytest.mark.parametrize("mode", ["standard", "legacy"])
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def test_both_modes_return_a_latent(sampler_calls, mode):
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result = run_node(sampling_mode=mode)
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assert "result" in result and isinstance(result["result"], tuple)
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assert result["result"][0]["samples"].shape == (1, 4, 16, 16)
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def test_standard_mode_samples_one_schedule(sampler_calls):
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run_node(sampling_mode="standard", sequential_stages=2)
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assert len(sampler_calls) == 2
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first, second = sampler_calls
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assert first["steps"] == second["steps"] == 10
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assert first["sigmas"] is not None
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# The second segment continues where the first stopped instead of restarting.
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assert float(second["sigmas"][0]) < float(first["sigmas"][0])
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assert torch.equal(first["sigmas"][-1], second["sigmas"][0])
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def test_legacy_mode_still_builds_its_own_schedule(sampler_calls):
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"""The frozen path passes no sigmas, so KSampler rebuilds a full one per stage."""
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run_node(sampling_mode="legacy", sequential_stages=2)
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assert len(sampler_calls) == 2
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assert all(call["sigmas"] is None for call in sampler_calls)
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def test_denoise_only_reaches_the_schedule_in_standard_mode(sampler_calls):
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run_node(sampling_mode="standard", denoise=1.0)
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full_start = float(sampler_calls[0]["sigmas"][0])
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sampler_calls.clear()
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run_node(sampling_mode="standard", denoise=0.6)
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assert float(sampler_calls[0]["sigmas"][0]) < full_start
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def test_injection_stages_never_leave_a_one_step_segment(sampler_calls):
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run_node(sampling_mode="standard", injection_stages=3)
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assert sum(call["steps"] for call in sampler_calls) == 20
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assert all(call["steps"] >= 2 for call in sampler_calls)
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def test_standard_mode_is_the_default(sampler_calls):
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run_node(sequential_stages=2)
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assert all(call["sigmas"] is not None for call in sampler_calls)
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