5e57024336f7f00f3516884583e65d3c5dfc2934
4
Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 aszc
|
d90546b6bb |
feat(extraction): split conversion into the coreml-diffusion package (E0–E5) (#63)
* docs(extraction): E0 seam inventory + correct stale spec assumptions Resolve all pre-flight greps for the converter-extraction seam: - conversion/* and lcm/unet.py confirmed comfy-free - converter.py: only folder_paths reach-in is get_out_path - lcm/converter.py: folder_paths + comfy.model_management to cut; dup helpers and SimianLuo HF hardcode confirmed - no attention module-global; already per-call Correct two stale assumptions verified against current source: - ml-stable-diffusion is already fully removed (#58); CoreMLModel is a local coremltools wrapper, not Apple's. Drop the dep-pinning blocker and the package/suite dep lines that assumed it. - model_version discovery must emit .name (node reverses via ModelVersion[...]); the .value form in the draft would KeyError on every saved workflow. * feat(extraction): E1 coreml_diffusion package + discovery API Stand up the framework-free coreml_diffusion namespace and freeze its versioned discovery contract. The package re-exports from already comfy-free coreml_suite sources (model_version, attention, core.naming); the conversion implementation moves in E2. - list_model_versions/list_attention_impls/list_quant_modes return today's exact dropdown strings, so wiring the node onto them (E3) changes no value and breaks no saved workflow. - Status/_MODEL_STATUS registry gates VERIFIED vs EXPERIMENTAL in the package, so promoting a model expands the node dropdown with no Suite change (additive-only contract; CONTRACT_VERSION=1.0). - Tier-0 test pins the contract and proves comfy/diffusers/coremltools are not pulled on import. Node untouched; zero behavior change. * refactor(extraction): E2 move conversion mechanics into coreml_diffusion Physically relocate the framework-free conversion code into the package and collapse the duplicated LCM/main helpers, behavior-preserving. - coreml_suite/conversion/ -> coreml_diffusion/conversion/ (attention, shapes, trace, unet) - coreml_suite/core/naming.py -> coreml_diffusion/naming.py (the cache-key contract now lives with the package; tests re-pointed) - coreml_suite/converter.py logic -> coreml_diffusion/convert.py, with convert() made keyword-only past (ckpt_path, model_version, out_path) per the interface contract; out_path is injected (no folder_paths) - dedup: load_coreml_model / convert_to_coreml / get_coreml_inputs / add_cnet_support / get_encoder_hidden_states_shape / inputs-spec now defined once in the package; get_sample_input gains an optional scheduler arg so the LCM path shares it (same keys/order/dtypes) - coreml_suite.{converter,lcm.converter} reduced to comfy-side shims: folder_paths path resolution and the LCM scheduler's comfy.model_management stay here; the package imports neither - __init__ keeps discovery + compose_out_name eager; convert is lazy via __getattr__ so 'import coreml_diffusion' stays Tier-0 pure Nodes untouched (E3 thins them onto the package). Tier-0 (109) and smoke (3, real coremltools conversion) green; [M2-ANE] golden pending a server. * refactor(extraction): E3 thin nodes onto coreml_diffusion + discovery dropdowns The CoreMLConverter node now calls coreml_diffusion directly instead of the coreml_suite.converter shim, and its dropdowns are populated at runtime from the package's discovery API. - INPUT_TYPES dropdowns (model_version / attention_implementation / quantize_nbits) now come from a fail-soft _discover() that calls coreml_diffusion.list_*; a missing/old package falls back to a literal list and logs a warning instead of de-registering the node. Installing a newer coreml_diffusion surfaces new conversion types with no Suite change. - folder_paths path resolution moved inline into the node; the package's convert() takes the output path as an injected positional. - compose_out_name / lora_names_from_params now imported from coreml_diffusion (lazily, inside convert) — no node-side copy. - deleted the dead coreml_suite/converter.py and coreml_suite/core/naming.py shims (no remaining importers). Field names, RETURN_TYPES/NAMES and NODE_*_MAPPINGS unchanged; dropdown values are a superset of the prior literals (additive-only). Tier-0 (109) and smoke (3) green; [M2-ANE] golden re-runs on push. * refactor(extraction): E5 depend on external coreml-diffusion package Conversion code now lives in the standalone coreml-diffusion repo. The Suite deletes its in-tree copy and depends on the package instead. - removed coreml_diffusion/ (whole package), coreml_suite/model_version.py and coreml_suite/attention.py (moved to the package as its source of truth), and the tests that moved with them (discovery, conversion_helpers, out_name; smoke synthetic_unet + split_einsum) - re-pointed ModelVersion imports (config.py, nodes.py, lcm/converter.py) to coreml_diffusion - pyproject: drop the coreml_diffusion package include and the conversion-only deps (peft/omegaconf/transformers, now transitive via coreml-diffusion); add coreml-diffusion as a dependency with a local path source until it is published (switch to git tag/PyPI once the repo exists, so CI can resolve it) Suite Tier-0 green (75); conversion code fully absent from the Suite. The comfy node still imports coreml_diffusion (installed package) for ModelVersion + the discovery dropdowns + convert. * build(extraction): pin coreml-diffusion to git tag v0.1.0 Switch the coreml-diffusion source from a local path to the published git tag so CI can resolve it. Suite Tier-0 green resolving from the tag. * ci(extraction): drop Suite smoke tier (moved to coreml-diffusion) The conversion smoke tests moved to the coreml-diffusion repo, which runs its own Tier 1. The Suite's smoke lane had no tests left (pytest exit 5). The Suite keeps Tier 0 (inference units) and the m2 golden e2e. * chore(release): v2.1.0; wire coreml-diffusion into requirements.txt Minor bump: the conversion path moved to the external coreml-diffusion package (node graph + artifact cache keys unchanged, golden-verified). requirements.txt (used by ComfyUI Manager) now installs coreml-diffusion from the v0.1.0 tag and drops the conversion-only deps now provided transitively. |
||
|
aszc
|
65a2de2fab |
feat!: modernize toolchain and replace apple/ml-stable-diffusion with native diffusers conversion (#58)
* feat(deps): support ComfyUI's numpy 2 toolchain; make conversion optional The runtime package now installs and runs under numpy 2 / coremltools 9 / torch 2.7 — matching current ComfyUI — without apple/ml-stable-diffusion. - Drop the heavy converter stack (ml-stable-diffusion, diffusers, peft, omegaconf, overrides, transformers) from runtime dependencies; require numpy>=2. - Vendor the runtime pieces: a slim CoreMLModel wrapper around coremltools and the attention-implementation constants. - Lazy-import the converters; the Convert nodes raise a clear error when the legacy conversion dependencies are absent. Loading and sampling existing Core ML models no longer needs them. - CI: Tier 0 tracks the numpy 2 / torch 2.7 toolchain; drop the Tier 2 golden-image lane (it converts at runtime, which now requires the legacy stack) and its fixtures. * feat(conversion): replace apple/ml-stable-diffusion with native diffusers path Reimplement Core ML UNet conversion on top of diffusers instead of the apple/ml-stable-diffusion git dependency, so the full suite (including conversion) installs through ComfyUI Manager without extras on the NumPy 2 toolchain. - Add coreml_suite/conversion package: split-einsum attention processors, a conv2d output-shape helper, Transformer2D trace patches, and a UNet input-adapter wrapper preserving the historical Core ML I/O contract. - Drop python_coreml_stable_diffusion and overrides; route SD15, SDXL, SDXL refiner, and LCM conversion through diffusers UNet2DConditionModel. - Declare diffusers, peft, omegaconf, and transformers as runtime deps. - Add characterization tests asserting split-einsum matches reference attention math; extend the synthetic-UNet smoke test for the wrapper. - Bump to 1.1.0 and set requires-comfyui to a semver constraint (>=0.3.27) so the Comfy Registry publish succeeds. * refactor(conversion)!: native diffusers context layout; drop legacy fallbacks Address PR review feedback: - Drop the legacy converter ImportError fallbacks and LEGACY_CONVERTER_MODULES guards in nodes.py and lcm/nodes.py. Conversion dependencies are mandatory in pyproject, so the indirection is dead code. - Tier 0 CI resolves its toolchain from pyproject via uv (uv sync + uv run) instead of hand-pinned pip installs, removing duplicated version maintenance. - Document the conversion lineage: credit apple/ml-stable-diffusion as the origin, note the implementation has diverged to a native diffusers path, and state the intent to iterate independently. Fix stale README links that pointed users to apple/ml-stable-diffusion for conversion. - Drop the unused `sources` argument from CoreMLModel. BREAKING CHANGE: the converted Core ML UNet now takes encoder_hidden_states in the native diffusers layout (batch, tokens, hidden) instead of (batch, hidden, 1, tokens). This removes the boundary transposes in CoreMLUNetWrapper and CoreMLInputs. Core ML models converted with earlier versions are incompatible and must be re-converted. Bump to 2.0.0. * test: widen split-einsum allclose tolerance for cross-platform float drift The split-einsum attention reorders float32 reductions relative to the reference, so equality holds only up to rounding. The default allclose atol (1e-8) is too tight on Linux x86 BLAS and failed Tier 0 CI; use atol=1e-6 to match the existing chunked-path characterization test. * ci: run macOS smoke tier on the self-hosted Apple Silicon runner GitHub-hosted macOS carries a 10x minute multiplier and exhausts the included Actions minutes too quickly. Move the Tier 1 smoke job onto the self-hosted Apple Silicon runner ([self-hosted, macOS, ARM64, coreml]) so macOS coverage no longer consumes hosted minutes. Tier 0 stays on hosted ubuntu (1x). * ci: fix uv setup for both tiers astral-sh/setup-uv@v3 was retagged and its old commit garbage-collected, so codeload 404s when Actions resolves the stale SHA. Bump Tier 0 (ubuntu) to setup-uv@v7, and drop the action entirely from Tier 1 since the self-hosted runner already provides uv. * test(ci): restore golden-image correctness gate on the self-hosted runner The Tier 2 end-to-end correctness check (real SD1.5 -> Core ML -> image, gated on SHA/PSNR vs a golden) was dropped during the modernization. With the breaking 3D-context change, the synthetic smoke and shape/attention characterization tests no longer cover real-model conversion correctness. Restore tier2.yml (on the [self-hosted, macOS, ARM64, coreml] runner shared with Tier 1), the golden-image test, and the e2e workflow. Adapt the pinned ComfyUI resolution to the requires-comfyui semver tag (vX.Y.Z) instead of a commit SHA, and re-register the m2 marker. The golden is intentionally not committed: the first self-hosted run regenerates it under the new 3D contract and fails for review, per the test's documented bootstrap. * test(ci): add golden image for SD1.5 seed 42 under the 3D-context contract Generated by the first Tier 2 self-hosted run after the native diffusers conversion change. The decoded image is a coherent SD1.5 generation, confirming the (batch, tokens, hidden) Core ML contract produces correct output end-to-end. Subsequent runs gate on this golden (SHA-strict, PSNR fallback). * test(ci): force fresh conversion in Tier 2; drop stale golden The converter skips conversion when a same-named model already exists, keyed on conversion parameters but not the conversion code/toolchain. The self-hosted runner held a pre-existing v1-5 .mlmodelc (4D-context, old toolchain), so the Tier 2 runs reused it (~8-30s) instead of converting — the gate validated a stale model, not the new native diffusers path. Purge the cached Core ML UNets before running so every Tier 2 run does a real convert -> compile -> sample. Drop the golden generated from the stale cache; the next run regenerates it from a genuine 3D-contract conversion and fails for review. * test(ci): add golden image from a genuine 3D-contract conversion Regenerated by a Tier 2 run with the model cache purged, so the converter actually ran (62s, not a cache hit). The fresh model exposes the new 3D encoder_hidden_states input [1, 77, 768], and its decoded SD1.5 seed-42 image is byte-identical to the prior baseline — confirming the native diffusers conversion is behavior-preserving end to end. |
||
|
aszc
|
02b6e8ece3 |
feat: modernize toolchain, refactor core, add tiered CI and opt-in quantization
Modernizes ComfyUI-CoreMLSuite onto Python 3.12 / torch 2.7 / coremltools 9 with a characterization-test safety net. The default conversion path is unchanged; existing saved workflows produce identical output. - Toolchain bump (Python 3.12, torch 2.7, coremltools 9, numpy <2) with the blocking upstream pins overridden. - Framework-free logic moved into coreml_suite/core/ (no comfy/coremltools imports); old module paths re-export from there. - Opt-in quantize_nbits dropdown (none|8|6|4) for k-means weight palettization; default none is byte-for-byte identical to before. - Tiered CI: Tier 0 (Linux unit), Tier 1 (macOS-ARM smoke), Tier 2 (self-hosted Apple Silicon golden-image check on the ANE). |
||
|
aszc-dev
|
3224d62342 | Restructure tests directory |