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docs(M01): update CI report and run3 with dynamic stub results

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Michael Cahill 2026-03-07 21:38:49 -08:00
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docs/milestones/M01/M01_CI_report.md
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# M01 CI Report — 2026-03-08
**Branch:** m01-ci-truthfulness
**Latest commit:** 9a83c70e
**Latest commit:** 5a76c617
**Report generated:** 2026-03-08
---
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| Workflow | Run ID | Status |
|----------|--------|--------|
| Linter | 22812569761 | ✓ PASS |
| Tests | 22812569762 | ✗ FAIL |
| Linter | 22814396752 | ✓ PASS |
| Tests | 22814850488 | Partial |
---
## 2. Test Failure
## 2. Dynamic Stub — Success
**Root cause:** Server startup fails before binding to port 7860.
**Server startup:** ✓ PASS — port 7860 binds, server runs.
**Dynamic stub approach:**
- `_StubFinder` + `_StubModule` for `ldm.*` and `sgm.*`
- Resolves any nested import without individual files
- Stub classes: `forward`, `ATTENTION_MODES`, `ISL_PATHS`, etc.
- `__file__` set for `inspect.getfile`
- `ddpm.py`: DDPM, LatentDiffusion with `__init__(*a, **k)`
---
## 3. Test Results (Run 22814850488)
| Category | Result |
|----------|--------|
| wait-for-it | ✓ 127.0.0.1:7860 available |
| test_extras | ✓ PASS (3) |
| test_face_restorers | ✓ PASS (2) |
| test_torch_utils | ✓ PASS (2) |
| test_utils | ✓ PASS (10) |
| test_img2img | ✗ 500 (4) |
| test_txt2img | ✗ 500 (14) |
**img2img/txt2img:** Return 500 — stub model lacks real inference. Expected with stub-only setup.
---
## 4. Stub Layout (Minimal)
**Error (from output.txt):**
```
ModuleNotFoundError: No module named 'ldm.models.diffusion.plms'
File "modules/sd_hijack.py", line 15, in <module>
import ldm.models.diffusion.plms
repositories/
stable-diffusion-stability-ai/ldm/__init__.py (dynamic stub)
stable-diffusion-stability-ai/ldm/models/diffusion/ddpm.py (DDPM, LatentDiffusion)
generative-models/sgm/__init__.py (dynamic stub)
k-diffusion/ (file-based: utils, sampling, external)
BLIP/, stable-diffusion-webui-assets/
```
**Effect:** `wait-for-it` times out (20s); pytest never runs.
---
## 3. Stub Progression
| Step | Blocker | Fix applied |
|------|---------|-------------|
| 1 | paths.py assert | ddpm.py |
| 2 | LatentDiffusion | ddpm classes |
| 3 | ldm.util | default() |
| 4 | ldm.modules.midas | midas/ |
| 5 | ldm.modules.distributions | DiagonalGaussianDistribution |
| 6 | ldm.modules.diffusionmodules.openaimodel | openaimodel.py |
| 7 | sgm.* | sgm stubs |
| 8 | k_diffusion.* | external, utils, sampling |
| 9 | ldm.models.diffusion.ddim | ddim.py |
| 10 | **ldm.models.diffusion.plms** | **Next fix** |
---
## 4. Fix Applied
**Dynamic stub module** (commit in progress): MetaPathFinder + _StubModule for ldm and sgm. Resolves any nested import without individual files.
---
## 5. Links
- **PR:** (create when ready to merge)
- **Linter run:** https://github.com/m-cahill/serena/actions/runs/22812569761
- **Tests run:** https://github.com/m-cahill/serena/actions/runs/22812569762
- **Linter run:** https://github.com/m-cahill/serena/actions
- **Tests run:** 22814850488

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docs/milestones/M01/M01_run3.md
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| Workflow | Latest Run | Status |
|----------|------------|--------|
| Linter | 22812569761 | ✓ success |
| Tests | 22812569762 | ✗ failure |
| Linter | 22814396752 | ✓ success |
| Tests | 22814850488 | Partial (server ✓, img2img/txt2img 500) |
---
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---
## 5. Dynamic Stub Approach (Commit 9a83c70e+)
## 5. Dynamic Stub Approach (Commit 5a76c617)
Replaced manual file-by-file stubs with **dynamic stub modules**:
- `_StubFinder` (MetaPathFinder): catches any `ldm.*` or `sgm.*` import the default finder misses
- `_StubModule`: resolves attributes as submodules or stub classes
- Keeps `ddpm.py` for paths.py assertion and LatentDiffusion
- `_StubLoader`: creates `_StubModule` with `__path__`, `__file__` for inspect
- `_StubModule`: resolves attributes as submodules, stub classes, or dicts (UPPER_CASE)
- Stub classes: metaclass returns no-op for missing attrs, `{}` for ATTENTION_MODES
- UPPER_CASE on modules → dict (e.g. `midas.api.ISL_PATHS`)
- `__file__` = `<stub:name>` so `inspect.getfile` works
- Keeps `ddpm.py` for paths.py assertion; DDPM, LatentDiffusion with `__init__(*a,**k)`
- Keeps k_diffusion file-based (needs real get_sigmas_*, torch, etc.)
Eliminates whack-a-mole import chain.