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Author SHA1 Message Date
DELUXA
65b93b8b37
Merge ed4901d316 into bbe2c13a70 2026-01-30 17:18:48 +02:00
comfyanonymous
bbe2c13a70
Make empty hunyuan latent 1.0 work with the 1.5 model. (#12171)
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2026-01-29 23:52:22 -05:00
Christian Byrne
3aace5c8dc
fix: count non-dict items in outputs_count (#12166)
Move count increment before isinstance(item, dict) check so that
non-dict output items (like text strings from PreviewAny node)
are included in outputs_count.

This aligns OSS Python with Cloud's Go implementation which uses
len(itemsArray) to count ALL items regardless of type.

Amp-Thread-ID: https://ampcode.com/threads/T-019c0bb5-14e0-744f-8808-1e57653f3ae3

Co-authored-by: Amp <amp@ampcode.com>
2026-01-29 17:10:08 -08:00
DELUXA
ed4901d316
Merge branch 'master' into pytorch-attention-vae-rdna4 2026-01-23 18:28:58 +02:00
Your Name
3988f37386 Enable pytorch attention in VAE for AMD RDNA 4 2025-09-20 00:10:20 +03:00
3 changed files with 5 additions and 4 deletions

View File

@ -1252,7 +1252,7 @@ def pytorch_attention_enabled():
return ENABLE_PYTORCH_ATTENTION
def pytorch_attention_enabled_vae():
if is_amd():
if is_amd() and not amd_min_version(device=None, min_rdna_version=4):
return False # enabling pytorch attention on AMD currently causes crash when doing high res
return pytorch_attention_enabled()

View File

@ -171,9 +171,10 @@ def get_outputs_summary(outputs: dict) -> tuple[int, Optional[dict]]:
continue
for item in items:
count += 1
if not isinstance(item, dict):
continue
count += 1
if preview_output is None and is_previewable(media_type, item):
enriched = {

View File

@ -56,7 +56,7 @@ class EmptyHunyuanLatentVideo(io.ComfyNode):
@classmethod
def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput:
latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
return io.NodeOutput({"samples":latent})
return io.NodeOutput({"samples": latent, "downscale_ratio_spacial": 8})
generate = execute # TODO: remove
@ -73,7 +73,7 @@ class EmptyHunyuanVideo15Latent(EmptyHunyuanLatentVideo):
def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput:
# Using scale factor of 16 instead of 8
latent = torch.zeros([batch_size, 32, ((length - 1) // 4) + 1, height // 16, width // 16], device=comfy.model_management.intermediate_device())
return io.NodeOutput({"samples": latent})
return io.NodeOutput({"samples": latent, "downscale_ratio_spacial": 16})
class HunyuanVideo15ImageToVideo(io.ComfyNode):