* feat(assets): add namespaced model type tags
* fix(assets): mark path-derived upload tags automatic
* fix(assets): merge duplicate scan specs
* test(assets): make duplicate path normalization portable
* feat(assets): add loader_path as the authoritative loader locator (#14796)
* fix(assets): filter model_type tags by bucket extension sets
Buckets sharing a base directory (e.g. diffusion_models and a custom
unet_gguf) tagged every file in the directory regardless of whether the
bucket could load it, so .safetensors files were tagged
model_type:unet_gguf and vice versa. Carry each bucket's registered
extension set through get_comfy_models_folders and only emit a
model_type tag when the file extension matches, keeping the empty-set
match-all convention from folder_paths.filter_files_extensions.
Files under a model base matching no bucket now keep only the models
tag instead of every directory-matching model_type tag.
* feat(assets): replace response file_path with persisted loader_path
The old file_path response field was a namespaced storage locator
(models/checkpoints/foo.safetensors): not an absolute path, not unique
identity, and not the value a loader consumes. Nothing needs that shape
on the wire (hash/ID-based locating is the long-term direction), so it
is dropped rather than renamed; the storage-root matching stays internal,
powering display_name.
What loaders DO need is the in-root loader path (category dropped:
models/checkpoints/foo/bar.safetensors -> foo/bar.safetensors). Serve it
as a first-class loader_path field, persisted on asset_references
(migration 0006) and written by every ingest pipeline at insert, so
responses read the column verbatim.
Like the model_type tags, loader_path is a seed-time derivative of the
model folder registry, maintained by the same scan lifecycle (new files seed
fresh values, pruning retires rows whose bucket disappeared). Rows
predating the column serve a null loader_path; databases from before
this stack already need recreating for the base branch's tag changes.
loader_path resolves every registered base including extra_model_paths
entries; display_name only the canonical storage roots. A file can
therefore be loadable with no display name (extra-path models) or the
reverse (unregistered files under the models root), and loader_path is
null exactly when no loader can resolve the file.
* test(assets): lock loader_path matrix (asymmetry, null, persist/read)
Cover the behaviour that has no production change but is easy to regress:
the extra-path asymmetry (loadable but no storage namespace), null
loader_path persistence for orphan files, and the response reading the
stored column with a compute fallback for un-backfilled rows.
* fix(assets): persist subfolder-qualified loader_path for ingested outputs
ingest_existing_file built its seed spec with the file's basename, so
outputs saved into a subfolder persisted loader_path (and the
user_metadata filename that preview URLs split for their subfolder
param) as just the basename: the served locator pointed at a file that
does not exist at that path. Scanner and seeder specs already derive
fname via compute_loader_path; use the same derivation here.
* fix(assets): only extension-matching buckets contribute a loader_path
The model-base match in get_asset_category_and_relative_path ignored
each bucket's extension set, so a file inside a registered base whose
extension the bucket cannot load (e.g. a .txt uploaded into
model_type:checkpoints) advertised a loader_path that no loader list
would ever resolve, while the tag side of the same stack already
excluded it. Apply the extension check used for backend tags (empty set
accepts any extension), keeping loader_path null exactly when no loader
can resolve the file.
* fix(assets): refresh loader_path when re-ingesting an existing reference
upsert_reference only wrote loader_path on the INSERT branch, so
re-ingesting an existing reference (an output overwritten in place, or a
file re-registered after its loader_path derivation changed) kept the
stale or NULL value forever. Write it on the UPDATE branch too, with a
null-safe change guard so a loader_path difference alone is enough to
trigger the update, and identical values stay a no-op.
* fix(assets): repair semantic merge breakage from #14796 and master
Two textually-clean but semantically-broken merges:
- routes.py lost its folder_paths import when #14796's import block
superseded the base's, while the content-type hardening added via the
base's master merge still calls folder_paths.is_dangerous_content_type.
- master's SVG download-hardening test uploads with the pre-namespacing
bare checkpoints tag, which this branch's destination validation
rejects; use model_type:checkpoints.
---------
Co-authored-by: guill <jacob.e.segal@gmail.com>
Make chunking_mode a DynamicCombo on the Chunk SeedVR2 Latent node so frames_per_chunk is shown only when chunking_mode is manual. In auto mode the chunk size is predicted from free VRAM, so frames_per_chunk is irrelevant and is now hidden; temporal_overlap stays visible in both modes. Options are alphabetized (auto, manual).
## Summary
Sampling a long SeedVR2 video in one pass runs out of VRAM: the DiT working set grows linearly with latent frames times pixel area, so a 100 frame clip at 4x upscale needs more memory than any consumer card has. This PR adds two workflow-level nodes that split the latent into overlapping temporal chunks and recombine the sampled chunks with a Hann crossfade. The executor's list handling runs the stock KSampler once per chunk, so the sampler itself is untouched.
- **Chunk SeedVR2 Latent** splits the latent on the temporal axis. `frames_per_chunk` is in pixel frames on the 4n+1 grid, `temporal_overlap` sets how many latent frames adjacent chunks share, and `chunking_mode=auto` solves the chunk size from measured free VRAM and the latent's own dimensions. The node outputs the effective overlap so the merge is wired, not typed.
- **Merge SeedVR2 Latent Chunks** recombines the sampled chunks in order, crossfading each shared region with a Hann window (flat shoulders on the outer thirds, fade across the middle third). Zero overlap is a plain concatenation, bit-identical to `torch.cat`.
## Changes
- Added 2 nodes and 1 crossfade helper to `comfy_extras/nodes_seedvr.py` (+201 lines).
- Added 4 chunk-law constants to `comfy/ldm/seedvr/constants.py` (+10 lines).
- Added pytest unit tests in `tests-unit/comfy_extras_test/test_seedvr2_temporal_chunk.py` (+66 lines): chunk geometry, 4n+1 and mode validation, overlap clamping, temporal noise mask slicing (5-D and 4-D), the auto law including batch scaling, crossfade weights and blend direction, round trip, and metadata handling.
Everything outside the two new nodes is byte-identical to the base branch. The new constants are read only by the chunk node, and workflows that do not use these nodes take no new code path.
## Auto mode calibration
The auto law is `max_latent_frames = (free_GiB - reserved - margin) / (0.30 * megapixels)`, calibrated on an RTX 5090 (32 GB) with the 3b fp16 model: a 17-cell resolution sweep plus temporal bisection located the activation wall and confirmed it is linear in latent frames times pixel area (the same total-voxel budget holds from 1.5:1 through 24:1 aspect ratios and under transposition). The margin is four standard deviations of the measured run-to-run spread, which costs about one latent frame of chunk size and makes an out-of-memory failure a lottery ticket rather than a coin flip. Manual mode bypasses the law entirely.
On a 32 GB card, a 640x480x100 input at 4x upscale sampled as a single chunk allocates past 31 GiB and dies; auto mode picks 49 frame chunks (three chunks with overlap) and the full pipeline completes in about 240 seconds. The same law stands down on small inputs: a 320x240x100 clip runs as a single chunk because it fits.
## Example workflow
Load a video, 4x upscale, auto chunking, temporal overlap 3. Expected output for a 640x480x100 input: 2560x1920, 100 frames, seams invisible at the default overlap.
<details>
<summary>API workflow JSON</summary>
```json
{
"14": {"inputs": {"vae_name": "ema_vae_fp16.safetensors"}, "class_type": "VAELoader"},
"17": {"inputs": {"video": ["24", 0]}, "class_type": "GetVideoComponents"},
"20": {"inputs": {"fps": ["17", 2], "images": ["30", 0], "audio": ["17", 1]}, "class_type": "CreateVideo"},
"21": {"inputs": {"tile_size": 192, "overlap": 64, "temporal_size": 64, "temporal_overlap": 8, "pixels": ["27", 0], "vae": ["14", 0]}, "class_type": "VAEEncodeTiled"},
"22": {"inputs": {"tile_size": 256, "overlap": 32, "temporal_size": 64, "temporal_overlap": 8, "samples": ["33", 0], "vae": ["14", 0]}, "class_type": "VAEDecodeTiled"},
"23": {"inputs": {"unet_name": "seedvr2_3b_fp16.safetensors", "weight_dtype": "default"}, "class_type": "UNETLoader"},
"24": {"inputs": {"file": "input.mp4", "video-preview": ""}, "class_type": "LoadVideo"},
"25": {"inputs": {"filename_prefix": "video/seedvr2_upscale", "format": "auto", "codec": "auto", "video": ["20", 0]}, "class_type": "SaveVideo"},
"26": {"inputs": {"resize_type": "scale by multiplier", "resize_type.multiplier": 4, "scale_method": "bicubic", "input": ["17", 0]}, "class_type": "ResizeImageMaskNode"},
"27": {"inputs": {"resized_images": ["26", 0]}, "class_type": "SeedVR2Preprocess"},
"28": {"inputs": {"model": ["23", 0], "vae_conditioning": ["32", 0]}, "class_type": "SeedVR2Conditioning"},
"29": {"inputs": {"seed": 5770521, "steps": 1, "cfg": 1, "sampler_name": "euler", "scheduler": "simple", "denoise": 1, "model": ["23", 0], "positive": ["28", 0], "negative": ["28", 1], "latent_image": ["32", 0]}, "class_type": "KSampler"},
"30": {"inputs": {"color_correction_method": "lab", "images": ["22", 0], "original_resized_images": ["26", 0]}, "class_type": "SeedVR2PostProcessing"},
"32": {"inputs": {"frames_per_chunk": 21, "temporal_overlap": 3, "chunking_mode": "auto", "latent": ["21", 0]}, "class_type": "SeedVR2TemporalChunk"},
"33": {"inputs": {"temporal_overlap": ["32", 1], "latent_chunks": ["29", 0]}, "class_type": "SeedVR2TemporalMerge"}
}
```
</details>
## Prior art
- Reference implementation: https://github.com/ByteDance-Seed/SeedVR
- Community precedent for temporal chunking with blended reassembly: https://github.com/numz/ComfyUI-SeedVR2_VideoUpscaler
Chunk boundaries are a mathematical compromise: the model attends within a chunk, so different chunkings produce different outputs. The overlap crossfade hides the seam; power users can widen or zero the overlap from the workflow.
