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Support nested AV latents in SaveLatent and LoadLatent

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Amp-Thread-ID: https://ampcode.com/threads/T-019ff272-8eec-75bb-9297-07d66ae9c739
Co-authored-by: Amp <amp@ampcode.com>
This commit is contained in:
Jedrzej Kosinski
2026-08-12 07:33:22 -07:00
parent 43b9661906
commit b06a7195fd
1 changed files with 15 additions and 2 deletions
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17
nodes.py
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@@ -25,6 +25,7 @@ import comfy.samplers
import comfy.sample
import comfy.sd
import comfy.utils
import comfy.nested_tensor
import comfy.controlnet
from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict, FileLocator
from comfy_api.internal import register_versions, ComfyAPIWithVersion
@@ -543,7 +544,13 @@ class SaveLatent:
file = os.path.join(full_output_folder, file)
output = {}
output["latent_tensor"] = samples["samples"].contiguous()
latent = samples["samples"]
if getattr(latent, "is_nested", False):
# nested AV latents (e.g. MiniMax H3): one indexed key per stream
for i, t in enumerate(latent.unbind()):
output["latent_tensor_{}".format(i)] = t.contiguous()
else:
output["latent_tensor"] = latent.contiguous()
output["latent_format_version_0"] = torch.tensor([])
comfy.utils.save_torch_file(output, file, metadata=metadata)
@@ -570,7 +577,13 @@ class LoadLatent:
multiplier = 1.0
if "latent_format_version_0" not in latent:
multiplier = 1.0 / 0.18215
samples = {"samples": latent["latent_tensor"].float() * multiplier}
if "latent_tensor" in latent:
samples = {"samples": latent["latent_tensor"].float() * multiplier}
else:
tensors = []
while "latent_tensor_{}".format(len(tensors)) in latent:
tensors.append(latent["latent_tensor_{}".format(len(tensors))].float())
samples = {"samples": comfy.nested_tensor.NestedTensor(tensors)}
return (samples, )
@classmethod