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Merge branch 'comfyanonymous:master' into master

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patientx 2025-08-13 23:56:43 +03:00 committed by GitHub
commit 804c7097fa
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6 changed files with 29 additions and 6 deletions
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2
.github/workflows/stable-release.yml vendored
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@ -90,7 +90,7 @@ jobs:
cd ..
"C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=512m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable
"C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=768m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable
mv ComfyUI_windows_portable.7z ComfyUI/ComfyUI_windows_portable_nvidia.7z
cd ComfyUI_windows_portable

2
.github/workflows/windows_release_package.yml vendored
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@ -86,7 +86,7 @@ jobs:
cd ..
"C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=512m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable
"C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=768m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable
mv ComfyUI_windows_portable.7z ComfyUI/new_ComfyUI_windows_portable_nvidia_cu${{ inputs.cu }}_or_cpu.7z
cd ComfyUI_windows_portable

2
comfy/ldm/hunyuan3d/vae.py
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@ -178,7 +178,7 @@ class FourierEmbedder(nn.Module):
class CrossAttentionProcessor:
def __call__(self, attn, q, k, v):
out = F.scaled_dot_product_attention(q, k, v)
out = comfy.ops.scaled_dot_product_attention(q, k, v)
return out

4
comfy/ldm/modules/attention.py
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@ -448,7 +448,7 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
mask = mask.unsqueeze(1)
if SDP_BATCH_LIMIT >= b:
out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
if not skip_output_reshape:
out = (
out.transpose(1, 2).reshape(b, -1, heads * dim_head)
@ -461,7 +461,7 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
if mask.shape[0] > 1:
m = mask[i : i + SDP_BATCH_LIMIT]
out[i : i + SDP_BATCH_LIMIT] = torch.nn.functional.scaled_dot_product_attention(
out[i : i + SDP_BATCH_LIMIT] = comfy.ops.scaled_dot_product_attention(
q[i : i + SDP_BATCH_LIMIT],
k[i : i + SDP_BATCH_LIMIT],
v[i : i + SDP_BATCH_LIMIT],

2
comfy/ldm/modules/diffusionmodules/model.py
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@ -285,7 +285,7 @@ def pytorch_attention(q, k, v):
)
try:
out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
out = out.transpose(2, 3).reshape(orig_shape)
except model_management.OOM_EXCEPTION:
logging.warning("scaled_dot_product_attention OOMed: switched to slice attention")

23
comfy/ops.py
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@ -24,6 +24,29 @@ import comfy.float
import comfy.rmsnorm
import contextlib
def scaled_dot_product_attention(q, k, v, *args, **kwargs):
return torch.nn.functional.scaled_dot_product_attention(q, k, v, *args, **kwargs)
try:
if torch.cuda.is_available():
from torch.nn.attention import SDPBackend, sdpa_kernel
SDPA_BACKEND_PRIORITY = [
SDPBackend.FLASH_ATTENTION,
SDPBackend.EFFICIENT_ATTENTION,
SDPBackend.MATH,
]
SDPA_BACKEND_PRIORITY.insert(0, SDPBackend.CUDNN_ATTENTION)
@sdpa_kernel(backends=SDPA_BACKEND_PRIORITY, set_priority=True)
def scaled_dot_product_attention(q, k, v, *args, **kwargs):
return torch.nn.functional.scaled_dot_product_attention(q, k, v, *args, **kwargs)
except (ModuleNotFoundError, TypeError):
logging.warning("Could not set sdpa backend priority.")
cast_to = comfy.model_management.cast_to #TODO: remove once no more references
def cast_to_input(weight, input, non_blocking=False, copy=True):