Add SeedVR2 integration coverage

.github/workflows/test-unit.yml

@@ -2,9 +2,9 @@ name: Unit Tests
 
 on:
   push:
-    branches: [ main, master, release/** ]
+    branches: [ main, master, develop, release/** ]
   pull_request:
-    branches: [ main, master, release/** ]
+    branches: [ main, master, develop, release/** ]
 
 jobs:
   test:
@@ -12,7 +12,6 @@ jobs:
       matrix:
         os: [ubuntu-latest, windows-2022, macos-latest]
     runs-on: ${{ matrix.os }}
-    continue-on-error: true
     steps:
     - uses: actions/checkout@v4
     - name: Set up Python      

tests-unit/comfy_test/test_seedvr2_refactor_nodes.py

@@ -0,0 +1,227 @@
+import torch
+
+from comfy.cli_args import args as cli_args
+
+if not torch.cuda.is_available():
+    cli_args.cpu = True
+
+import comfy_extras.nodes_seedvr as nodes_seedvr
+import nodes
+
+
+def test_seedvr2_postprocessing_restores_flat_decoded_batch_time():
+    decoded = torch.arange(6 * 4 * 6 * 1, dtype=torch.float32).reshape(6, 4, 6, 1)
+    original = torch.ones((2, 3, 4, 6, 1), dtype=torch.float32)
+
+    output = nodes_seedvr.SeedVR2PostProcessing.execute(decoded, original, 4, "none").result[0]
+
+    assert output.shape == (6, 4, 6, 1)
+    torch.testing.assert_close(output, decoded)
+
+
+def test_seedvr2_postprocessing_crops_to_resized_original_size():
+    decoded = torch.ones((1, 128, 176, 3), dtype=torch.float32)
+    original = torch.full((1, 1, 120, 169, 3), 0.25, dtype=torch.float32)
+
+    output = nodes_seedvr.SeedVR2PostProcessing.execute(decoded, original, 120, "none").result[0]
+
+    assert output.shape == (1, 120, 168, 3)
+
+
+def test_seedvr2_postprocessing_uses_decoded_size_when_resized_original_is_larger():
+    decoded = torch.ones((1, 128, 160, 3), dtype=torch.float32)
+    original = torch.full((1, 1, 480, 640, 3), 0.25, dtype=torch.float32)
+
+    output = nodes_seedvr.SeedVR2PostProcessing.execute(decoded, original, 480, "none").result[0]
+
+    assert output.shape == (1, 128, 160, 3)
+
+
+def test_seedvr2_postprocessing_does_not_trim_real_black_original_edges():
+    decoded = torch.ones((1, 128, 176, 3), dtype=torch.float32)
+    original = torch.zeros((1, 1, 128, 176, 3), dtype=torch.float32)
+
+    output = nodes_seedvr.SeedVR2PostProcessing.execute(decoded, original, 128, "none").result[0]
+
+    assert output.shape == (1, 128, 176, 3)
+
+
+def test_seedvr2_postprocessing_crops_height_only_to_resized_original_size():
+    decoded = torch.ones((1, 128, 176, 3), dtype=torch.float32)
+    original = torch.full((1, 1, 120, 176, 3), 0.25, dtype=torch.float32)
+
+    output = nodes_seedvr.SeedVR2PostProcessing.execute(decoded, original, 120, "none").result[0]
+
+    assert output.shape == (1, 120, 176, 3)
+
+
+def test_seedvr2_postprocessing_lab_uses_resized_original_size(monkeypatch):
+    decoded = torch.ones((1, 128, 176, 3), dtype=torch.float32)
+    original = torch.full((1, 1, 120, 169, 3), 0.25, dtype=torch.float32)
+    calls = []
+
+    def fake_lab_color_transfer(decoded_flat, reference_flat):
+        calls.append((tuple(decoded_flat.shape), tuple(reference_flat.shape)))
+        return decoded_flat
+
+    monkeypatch.setattr(nodes_seedvr, "lab_color_transfer", fake_lab_color_transfer)
+
+    output = nodes_seedvr.SeedVR2PostProcessing.execute(decoded, original, 120, "lab").result[0]
+
+    assert calls == [((1, 3, 120, 169), (1, 3, 120, 169))]
+    assert output.shape == (1, 120, 168, 3)
+
+
+def test_seedvr2_tiled_decode_node_ignores_seedvr2_sideband_metadata():
+    class FakeVAE:
+        def __init__(self):
+            self.decode_call = None
+
+        def temporal_compression_decode(self):
+            return 4
+
+        def spacial_compression_decode(self):
+            return 8
+
+        def decode_tiled(self, samples, **kwargs):
+            self.decode_call = kwargs
+            return torch.zeros((1, 1, 2, 2, 3), dtype=torch.float32)
+
+    vae = FakeVAE()
+    samples = {
+        "samples": torch.zeros((1, 16, 4, 4, 16), dtype=torch.float32),
+        "seedvr2_channel_last": True,
+    }
+
+    nodes.VAEDecodeTiled().decode(
+        vae,
+        samples,
+        tile_size=64,
+        overlap=0,
+        temporal_size=64,
+        temporal_overlap=8,
+    )
+
+    assert "seedvr2_channel_last" not in vae.decode_call
+
+
+def test_seedvr2_decode_node_ignores_seedvr2_sideband_metadata():
+    class FakeVAE:
+        def __init__(self):
+            self.decode_call = None
+
+        def decode(self, samples, **kwargs):
+            self.decode_call = kwargs
+            return torch.zeros((1, 1, 2, 2, 3), dtype=torch.float32)
+
+    vae = FakeVAE()
+    samples = {
+        "samples": torch.zeros((1, 16, 4, 4, 16), dtype=torch.float32),
+        "seedvr2_channel_last": True,
+    }
+
+    nodes.VAEDecode().decode(vae, samples)
+
+    assert "seedvr2_channel_last" not in vae.decode_call
+
+
+def test_seedvr2_decode_node_leaves_unmarked_ambiguous_latent_unforced():
+    class FakeVAE:
+        def __init__(self):
+            self.decode_call = None
+
+        def decode(self, samples, **kwargs):
+            self.decode_call = kwargs
+            return torch.zeros((1, 1, 2, 2, 3), dtype=torch.float32)
+
+    vae = FakeVAE()
+    samples = {"samples": torch.zeros((1, 16, 4, 4, 16), dtype=torch.float32)}
+
+    nodes.VAEDecode().decode(vae, samples)
+
+    assert "seedvr2_channel_last" not in vae.decode_call
+
+
+def test_seedvr2_encode_node_does_not_mark_model_specific_layout_metadata():
+    class FakeVAE:
+        def encode(self, pixels):
+            return torch.zeros((1, 16, 2, 3, 4), dtype=torch.float32)
+
+    output = nodes.VAEEncode().encode(FakeVAE(), torch.zeros((1, 8, 8, 3)))[0]
+
+    assert set(output) == {"samples"}
+
+
+def test_seedvr2_tiled_encode_node_does_not_mark_model_specific_layout_metadata():
+    class FakeVAE:
+        def encode_tiled(self, pixels, **kwargs):
+            return torch.zeros((1, 16, 2, 3, 4), dtype=torch.float32)
+
+    output = nodes.VAEEncodeTiled().encode(FakeVAE(), torch.zeros((1, 8, 8, 3)), 64, 0)[0]
+
+    assert set(output) == {"samples"}
+
+
+def test_seedvr2_saved_latent_does_not_persist_model_specific_layout_metadata(monkeypatch):
+    saved = {}
+
+    def fake_save_image_path(filename_prefix, output_dir):
+        return output_dir, filename_prefix, 1, "", filename_prefix
+
+    def fake_save_torch_file(output, file, metadata=None):
+        saved.update(output)
+
+    monkeypatch.setattr(nodes.folder_paths, "get_save_image_path", fake_save_image_path)
+    monkeypatch.setattr(nodes.comfy.utils, "save_torch_file", fake_save_torch_file)
+    monkeypatch.setattr(nodes.folder_paths, "get_annotated_filepath", lambda latent: latent)
+    monkeypatch.setattr(nodes.safetensors.torch, "load_file", lambda latent_path, device="cpu": saved)
+
+    original = torch.zeros((1, 16, 4, 4, 16), dtype=torch.float32)
+    nodes.SaveLatent().save({"samples": original, "seedvr2_channel_last": True}, "seedvr2_latent")
+    loaded = nodes.LoadLatent().load("seedvr2_latent")[0]
+
+    assert "seedvr2_channel_last" not in saved
+    assert "seedvr2_channel_last" not in loaded
+    torch.testing.assert_close(loaded["samples"], original)
+
+
+def test_seedvr2_tiled_decode_node_preserves_legacy_decode_tiled_signature():
+    class FakeVAE:
+        def __init__(self):
+            self.decode_call = None
+
+        def temporal_compression_decode(self):
+            return 4
+
+        def spacial_compression_decode(self):
+            return 8
+
+        def decode_tiled(self, samples, tile_x, tile_y, overlap, tile_t, overlap_t):
+            self.decode_call = {
+                "tile_x": tile_x,
+                "tile_y": tile_y,
+                "overlap": overlap,
+                "tile_t": tile_t,
+                "overlap_t": overlap_t,
+            }
+            return torch.zeros((1, 1, 2, 2, 3), dtype=torch.float32)
+
+    vae = FakeVAE()
+    samples = {"samples": torch.zeros((1, 16, 4, 4, 16), dtype=torch.float32)}
+
+    nodes.VAEDecodeTiled().decode(
+        vae,
+        samples,
+        tile_size=64,
+        overlap=0,
+        temporal_size=64,
+        temporal_overlap=8,
+    )
+
+    assert vae.decode_call == {
+        "tile_x": 8,
+        "tile_y": 8,
+        "overlap": 0,
+        "tile_t": 16,
+        "overlap_t": 2,
+    }