Offload H3 before latent upscale

This commit is contained in:
2026-09-04 09:49:34 +00:00
parent fbfbe4ef5a
commit cbabcf8208
2 changed files with 21 additions and 7 deletions
+3 -3
View File
@@ -6662,6 +6662,9 @@ class H3LongVideos:
parts = _nested_tensor_parts(out_samples)
if not getattr(out_samples, "is_nested", False) or len(parts) < 2:
raise RuntimeError("latent upscale expects a nested AV latent")
if latent_upscale_mode == "model" and str(latent_upscale_param.get("device", "cuda")) == "cuda" and hasattr(model, "clone_base_uuid"):
mm.unload_model_and_clones(model, unload_additional_models=False)
mm.soft_empty_cache()
upscaled_video, up_h, up_w = _upscale_latent_video(parts[0], latent_upscale_param)
full_audio = parts[1]
# Drop the first-pass sampling state before we start the refinement
@@ -6674,9 +6677,6 @@ class H3LongVideos:
target_h = int(up_h) * 16
if target_w <= 0 or target_h <= 0:
raise RuntimeError("latent upscale target size must be positive")
if latent_upscale_mode == "model" and str(latent_upscale_param.get("device", "cuda")) == "cuda" and hasattr(model, "clone_base_uuid"):
mm.unload_model_and_clones(model, unload_additional_models=False)
mm.soft_empty_cache()
upscale_cond, upscale_latent = _build_shot_conditioning(
clip, vae, prompt, target_w, target_h, ln, fps, handoff,
ref_images=refs, ref_image_size=ref_image_size,
+18 -4
View File
@@ -373,6 +373,7 @@ class DumasH3LongVideosHelperTests(unittest.TestCase):
original_cleanup = self.module._deep_cleanup
original_upscale = self.module._upscale_latent_video
original_copy_sample = self.module._copy_sample_latent
original_unload = getattr(self.module.mm, "unload_model_and_clones", None)
original_nested = getattr(self.module.comfy.nested_tensor, "NestedTensor", None)
try:
self.module.comfy.nested_tensor.NestedTensor = FakeNestedTensor
@@ -395,20 +396,28 @@ class DumasH3LongVideosHelperTests(unittest.TestCase):
def cleanup():
order.append("cleanup")
def unload_model_and_clones(*_args, **_kwargs):
order.append("unload_h3")
def upscale_latent_video(video, param):
order.append("upscale")
return FakeTensor("upv"), 8, 16
self.module.nodes.common_ksampler = common_ksampler
self.module._build_shot_conditioning = lambda *_args, **_kwargs: (
"cond",
{"samples": FakeNestedTensor((FakeTensor("basev"), FakeTensor("basea")))},
)
self.module._evict_all_but = lambda *_args, **_kwargs: None
self.module._upscale_latent_video = lambda video, param: (FakeTensor("upv"), 8, 16)
self.module.mm.unload_model_and_clones = unload_model_and_clones
self.module._upscale_latent_video = upscale_latent_video
self.module._copy_sample_latent = lambda sampled: sampled["samples"].unbind()
self.module._decode_video = decode_video
self.module._decode_audio = decode_audio
self.module._deep_cleanup = cleanup
self.module.H3LongVideos()._render(
model=object(),
model=types.SimpleNamespace(clone_base_uuid="h3"),
clip=types.SimpleNamespace(
tokenize=lambda text, **kwargs: text,
encode_from_tokens_scheduled=lambda tokens: tokens,
@@ -427,7 +436,7 @@ class DumasH3LongVideosHelperTests(unittest.TestCase):
latent_upscale_param={
"mode": "model",
"model_name": "upscale.safetensors",
"device": "cpu",
"device": "cuda",
"precision": "fp16",
"sampler_name": "euler_ancestral",
"scheduler": "simple",
@@ -438,7 +447,8 @@ class DumasH3LongVideosHelperTests(unittest.TestCase):
)
self.assertEqual(order[0], "sample")
self.assertEqual(order[1], "latent_upscale_sample")
self.assertLess(order.index("unload_h3"), order.index("upscale"))
self.assertLess(order.index("upscale"), order.index("latent_upscale_sample"))
self.assertLess(order.index("audio"), order.index("video"))
self.assertEqual(order[-1], "cleanup")
finally:
@@ -450,6 +460,10 @@ class DumasH3LongVideosHelperTests(unittest.TestCase):
self.module._deep_cleanup = original_cleanup
self.module._upscale_latent_video = original_upscale
self.module._copy_sample_latent = original_copy_sample
if original_unload is None:
delattr(self.module.mm, "unload_model_and_clones")
else:
self.module.mm.unload_model_and_clones = original_unload
if original_nested is None:
delattr(self.module.comfy.nested_tensor, "NestedTensor")
else: