Make KSampler sigma schedules reachable from custom sampling - #15831
Make KSampler sigma schedules reachable from custom sampling#15831Sravanjangam wants to merge 2 commits into
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KSampler silently builds a different schedule for dpm_2, dpm_2_ancestral, uni_pc and uni_pc_bh2 (one extra step, penultimate sigma dropped), but BasicScheduler had no way to produce that schedule, so identical sampler+scheduler+steps settings gave different images depending on whether sampling went through KSampler or SamplerCustom. Move the adjustment into calculate_sigmas_for_sampler and give BasicScheduler an optional sampler_name input (default none = old behavior) so custom graphs can match KSampler exactly.
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📝 WalkthroughWalkthroughThe change centralizes sampler-specific sigma schedule generation in Merge Risk: ⚪ Minimal · up to This PR aligns custom sampling schedules with KSampler while preserving the existing default behavior; no actionable merge-blocking risk remains after normal checks and review. 🚥 Pre-merge checks | ✅ 5✅ Passed checks (5 passed)
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In `@tests/test_sigma_sampler_consistency.py`:
- Line 39: Move the duplicated function-local BasicScheduler imports to the
module-level import section in tests/test_sigma_sampler_consistency.py,
retaining a single import and removing both local imports while leaving the
surrounding test logic unchanged.
- Around line 43-46: Update the test around BasicScheduler.execute to
instantiate KSampler with the dpm_2 sampler and compare its calculated sigmas
directly with the BasicScheduler output, rather than calling
calculate_sigmas_for_sampler. Also retain coverage for the legacy default
sampler behavior required by the existing test guidance.
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comfy/samplers.py (1)
1399-1423: LGTM!Also applies to: 1439-1439
comfy_extras/nodes_custom_sampler.py (1)
25-26: LGTM!Also applies to: 35-45
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Both findings addressed in 39f6f71: BasicScheduler import moved to module scope, and the parity test now instantiates the real KSampler and asserts BasicScheduler output matches |
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Fixes #4367
Problem
KSampler.calculate_sigmasbuilds a slightly different schedule fordpm_2,dpm_2_ancestral,uni_pcanduni_pc_bh2(steps+1 with the penultimate sigma dropped), butBasicSchedulerhad no way to produce that schedule. Identical sampler+scheduler+steps therefore produced different images depending on whether sampling went through KSampler or SamplerCustom + sigmas nodes.Change
calculate_sigmas_for_sampler()incomfy/samplers.py;KSampler.calculate_sigmasnow delegates to it (behavior unchanged).BasicScheduleran optionalsampler_namecombo (default"none"= exactly the old output). Set it to the downstream sampler to get the same schedule KSampler would build.Tests
New
tests/test_sigma_sampler_consistency.py(5 tests): helper matches the KSampler trim bit-for-bit, non-discard samplers untouched, BasicScheduler with sampler_name=dpm_2 equals the KSampler schedule, default input reproduces old output.Environment: Python 3.12 / torch 2.11 CPU, current master (
82f839f5).