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cleanup and update changelog

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2025-11-05 12:57:30 -05:00
parent 0c2da8e178
commit 5ffbca9377
2 changed files with 14 additions and 17 deletions
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CHANGELOG.md
+2 -1
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@@ -1,6 +1,6 @@
# Change Log for SD.Next
## Update for 2025-11-04
## Update for 2025-11-05
- **Features**
- **ROCm for Windows** switch to using **TheRock** `torch` builds when available
@@ -49,6 +49,7 @@
- taesd: warn on long decode times
- metadata: keep exif on thumbnail generation
- wildcard: obey seed for reproducible results
- sageattention: handle possible triton issues on some nvidia gpus, thanks @CalamitousFelicitousness
## Update for 2025-10-31
modules/devices.py
+12 -16
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@@ -559,23 +559,12 @@ def set_sdpa_params():
install('sageattention')
from sageattention import sageattn, sageattn_qk_int8_pv_fp16_cuda
# Detect GPU architecture - sm86 confirmed to need CUDA backend workaround as Sage Attention + Triton causes NaNs
# See: https://github.com/vladmandic/sdnext/issues/4235
use_cuda_backend = False
capability = None
if backend == "cuda":
capability = torch.cuda.get_device_capability(device)
# sm80 = compute capability 8.0 (A100/A6000), sm86 = 8.6 (RTX 3090/3090 Ti)
# Issue existing in 8.0 rumored but is not confirmed, enabling only for 8.6 for now
# if capability in [(8, 0), (8, 6)]:
# use_cuda_backend = True
# log.debug(f'Sage Attention: sm{capability[0]}{capability[1]} GPU detected, will use CUDA backend (Triton workaround)')
if capability == (8, 6):
use_cuda_backend = True
log.debug(f'Sage Attention: sm{capability[0]}{capability[1]} GPU detected, will use CUDA backend (Triton workaround)')
if (backend == "cuda") and (torch.cuda.get_device_capability(device) == (8, 6)):
use_cuda_backend = True # Detect GPU architecture - sm86 confirmed to need CUDA backend workaround as Sage Attention + Triton causes NaNs
# Define the sage attention implementation at setup
if use_cuda_backend:
log.debug('Torch attention: type=SageAttention backend=cuda')
def sage_attn_impl(query, key, value, is_causal, scale):
return sageattn_qk_int8_pv_fp16_cuda(
q=query, k=key, v=value,
@@ -583,11 +572,18 @@ def set_sdpa_params():
is_causal=is_causal,
sm_scale=scale,
return_lse=False,
pv_accum_dtype="fp32"
pv_accum_dtype="fp32",
)
else:
log.debug('Torch attention: type=SageAttention backend=auto')
def sage_attn_impl(query, key, value, is_causal, scale):
return sageattn(q=query, k=key, v=value, attn_mask=None, dropout_p=0.0, is_causal=is_causal, scale=scale)
return sageattn(
q=query, k=key, v=value,
attn_mask=None,
dropout_p=0.0,
is_causal=is_causal,
scale=scale,
)
sdpa_pre_sage_atten = torch.nn.functional.scaled_dot_product_attention
@wraps(sdpa_pre_sage_atten)