MiniMax H3 Flow-Aligned Regenerate v0.2.0

MiniMax H3 Flow-Aligned Regenerate v0.2.0

v0.2.0 adds an optional learned 3D transfer at the Progressive Target Input handoff boundary, backed by the versioned API-v1 provider from Comfyui_Minimax_h3_latent_Upscaler.

Learned progressive handoff

• Adds handoff_transfer=learned_3d to MiniMax H3 Progressive Handoff (Target Input).
• Keeps handoff_transfer=bicubic as the compatibility default for existing workflows.
• Consumes the API-v1 H3_LATENT_UPSCALER clean-video provider rather than importing sibling-node internals.
• Replaces only the exact-probe clean-video spatial transfer. Deterministic target noise, conditional re-noising, caller audio, masks, target conditioning, sampler/Spectrum history boundaries, and the mandatory first high-grid actual H3 call remain unchanged.
• Learned CNN work is tracked separately and does not increase H3 model-call/NFE counters.

The coordinated provider is released in Comfyui_Minimax_h3_latent_Upscaler v0.2.0. The exact provider revision validated by this release is bdc670e5926bcefbe4022e17fe8b171fbfcf15de.

Decoded-media validation

The learned-transfer path was validated on aggressive progressive transitions around 1 MP rather than only on synthetic shape/contract tests.

• Around a 1152×864 (~0.995 MP) target, an aggressive bicubic handoff from roughly a ~0.55 MP private source showed substantial body/spatial handoff artifacts in the tested difficult prompt.
• Replacing only that boundary with learned_3d fixed the majority of the observed artifacts.
• source_scale=0.70 resolved to 800×608 → 1152×864 and was judged excellent.
• source_scale=0.65 resolved to 736×576 → 1152×864 and began losing reference likeness / tonal stability, so it is not promoted.
• The final higher-resolution gate used source_scale=0.70 at 832×640 → 1184×896 (~1.061 MP). The generated action differed, but decoded quality was again judged very good.
• That final run preserved 38 logical / 28 actual H3 NFE / 10 Spectrum forecast calls across two physical chunks, with 2 exact probes, 6 progressive sampler invocations, 4 history boundaries, copied audio, rebuilt high-grid conditioning, and an actual first high-grid H3 call in both chunks.
• BF16 CUDA learned inference took about 0.60 s and 0.77 s for the two chunks and added zero H3 NFEs.

For this prompt around 1 MP, source_scale=0.70 is the current tested quality/compute point and 0.65 is below the observed fidelity floor. These values are not claimed as universal optima.

Guidance interpretation

The latest learned-transfer media sweep used direction+acceleration. It does not establish an acceleration advantage over direction-only. The earlier matched evidence still supports direction as the conservative preferred guidance mode; acceleration, temporal correspondence, downsample consistency, auto handoff, and resolution-aware refine sigmas remain experimental controls unless future matched media shows a clear benefit.

Compatibility and validation

• Existing bicubic Progressive Target Input workflows retain their prior behavior.
• Full 1-to-0 H3 sigma schedules remain required for progressive handoff.
• CI validates Python 3.10–3.13, Ruff and formatting, 142 unit/synthetic tests, compileall, package build, isolated wheel import, and pinned native/sibling source contracts.
• The source-contract gate pins the merged learned-provider revision and checks its API/kind constants, helper, provider classes, callable surface, and ComfyUI type.