137 lines
4.6 KiB
Markdown
137 lines
4.6 KiB
Markdown
# Fast Absolute Saver Latent Sidecars Implementation Plan
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> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
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**Goal:** Save unmodified optional latents next to `FastAbsoluteSaver` media outputs and load them back by absolute path.
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**Architecture:** Keep latent save/load behavior in `fast_saver.py` beside the existing saver node. Add small helpers for sidecar path derivation and direct `torch.save`/`torch.load` persistence so media naming and latent naming stay coupled.
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**Tech Stack:** Python 3.10+, PyTorch, pytest, existing ComfyUI node mapping conventions.
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---
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### Task 1: Latent Sidecar Tests
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**Files:**
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- Create: `tests/test_fast_saver_latent.py`
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- Modify: `fast_saver.py`
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- [ ] **Step 1: Write the failing tests**
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```python
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import torch
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from fast_saver import FastAbsoluteSaver
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def test_png_save_writes_matching_latent_sidecar(tmp_path):
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saver = FastAbsoluteSaver()
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images = torch.zeros((1, 2, 2, 3), dtype=torch.float32)
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latent = {"samples": torch.arange(4, dtype=torch.float32).reshape(1, 1, 2, 2), "keep": {"value": 7}}
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saver.save_images_fast(
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images=images,
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output_path=str(tmp_path),
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filename_prefix="frame",
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save_format="png",
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use_timestamp=False,
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auto_increment=False,
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counter_digits=4,
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max_threads=1,
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filename_with_score=False,
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metadata_key="sharpness_score",
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save_workflow_metadata=False,
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save_metadata_png=False,
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webp_lossless=True,
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webp_quality=100,
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webp_method=4,
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video_fps=24,
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video_crf=18,
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video_pixel_format="yuv420p",
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video_bitrate=10,
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prores_profile="hq",
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gif_dither="sierra2_4a",
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latent=latent,
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)
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latent_path = tmp_path / "frame_0000.latent"
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loaded = torch.load(latent_path, map_location="cpu", weights_only=False)
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assert torch.equal(loaded["samples"], latent["samples"])
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assert loaded["keep"] == {"value": 7}
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def test_video_save_writes_latent_sidecar_next_to_video(tmp_path):
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saver = FastAbsoluteSaver()
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images = torch.zeros((2, 2, 2, 3), dtype=torch.float32)
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latent = {"samples": torch.arange(8, dtype=torch.float32).reshape(2, 1, 2, 2)}
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video_path = tmp_path / "clip_0001.mp4"
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def fake_save_video(*args, **kwargs):
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video_path.write_bytes(b"video")
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return str(video_path)
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saver.save_video = fake_save_video
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saver.save_images_fast(
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images=images,
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output_path=str(tmp_path),
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filename_prefix="clip",
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save_format="mp4",
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use_timestamp=False,
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auto_increment=False,
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counter_digits=4,
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max_threads=1,
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filename_with_score=False,
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metadata_key="sharpness_score",
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save_workflow_metadata=False,
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save_metadata_png=False,
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webp_lossless=True,
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webp_quality=100,
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webp_method=4,
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video_fps=24,
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video_crf=18,
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video_pixel_format="yuv420p",
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video_bitrate=10,
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prores_profile="hq",
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gif_dither="sierra2_4a",
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latent=latent,
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)
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loaded = torch.load(tmp_path / "clip_0001.latent", map_location="cpu", weights_only=False)
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assert torch.equal(loaded["samples"], latent["samples"])
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def test_load_latent_absolute_round_trips_saved_object(tmp_path):
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from fast_saver import JDL_LoadLatentAbsolute
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path = tmp_path / "sample.latent"
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latent = {"samples": torch.ones((1, 4, 8, 8)), "noise_mask": torch.zeros((1, 1, 8, 8))}
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torch.save(latent, path)
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loaded, = JDL_LoadLatentAbsolute().load_latent(str(path))
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assert torch.equal(loaded["samples"], latent["samples"])
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assert torch.equal(loaded["noise_mask"], latent["noise_mask"])
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```
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- [ ] **Step 2: Run tests to verify they fail**
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Run: `python -m pytest tests/test_fast_saver_latent.py -q`
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Expected: tests fail because `latent` is not an accepted input and `JDL_LoadLatentAbsolute` does not exist.
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- [ ] **Step 3: Implement minimal production code**
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Add `latent` to `FastAbsoluteSaver.INPUT_TYPES()["optional"]`, accept it in `save_images_fast`, save `.latent` sidecars with `torch.save`, and register `JDL_LoadLatentAbsolute`.
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- [ ] **Step 4: Run focused tests**
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Run: `python -m pytest tests/test_fast_saver_latent.py -q`
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Expected: both tests pass.
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- [ ] **Step 5: Run broader verification**
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Run: `python -m pytest tests/test_fast_saver_latent.py -q && python -m compileall fast_saver.py image_preview.py string_utils.py json_loader_dynamic.py`
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Expected: pytest passes and compileall reports no syntax errors.
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