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from comfy.ldm.modules import attention as comfy_attention
import logging
import comfy.model_patcher
import comfy.utils
import comfy.sd
import torch
import comfy.model_management as mm
from comfy.cli_args import args
sageattn_modes = [
"disabled",
"auto",
"auto_speed",
"auto_quality",
"sageattn_qk_int8_pv_fp16_cuda",
"sageattn_qk_int8_pv_fp16_triton",
"sageattn_qk_int8_pv_fp8_cuda",
"sageattn_qk_int8_pv_fp8_cuda++",
]
_initialized = False
# Avoid spamming logs each attention call
_sage_warned_once = False
_sage_generic_warned_once = False
_original_functions = {}
# Runtime override knobs (may be set by other nodes, e.g., CADE2 Beta)
# CURRENT_PV_ACCUM can be None, "fp32+fp16" or "fp32+fp32"
CURRENT_PV_ACCUM = None
# Lightweight attention-entropy probe (for AQClip Attn-mode)
_attn_entropy_enabled = False
_attn_entropy_last = None # torch.Tensor | None, shape (B,1,h',w') in [0,1]
_attn_probe_heads_cap = 4
_attn_probe_tokens_cap = 1024
def enable_attention_entropy_capture(enable: bool, max_tokens: int = 1024, max_heads: int = 4):
"""Toggle capturing a tiny attention entropy map during optimized_attention.
Stores a normalized map per forward pass; consumer may upsample to latent size.
"""
global _attn_entropy_enabled, _attn_probe_tokens_cap, _attn_probe_heads_cap, _attn_entropy_last
_attn_entropy_enabled = bool(enable)
_attn_probe_tokens_cap = int(max(128, min(16384, max_tokens)))
_attn_probe_heads_cap = int(max(1, min(32, max_heads)))
if not _attn_entropy_enabled:
_attn_entropy_last = None
def get_attention_entropy_map(clear: bool = False):
"""Return last captured attention entropy map (B,1,h',w') in [0,1] or None."""
global _attn_entropy_last
out = _attn_entropy_last
if clear:
_attn_entropy_last = None
return out
# ------------------------ KV pruning (self-attention) ------------------------
_kv_prune_enabled = False
_kv_prune_keep = 0.85
_kv_prune_min_tokens = 128
def set_kv_prune(enable: bool, keep: float = 0.85, min_tokens: int = 128):
"""Enable lightweight K/V token pruning inside optimized attention.
- Applies only to self-attention (len(Q)==len(K)).
- Keeps top-`keep` fraction of keys/values by L2 energy of K, averaged over heads.
- Skips pruning when an attention mask is provided (shape mismatch risk).
"""
global _kv_prune_enabled, _kv_prune_keep, _kv_prune_min_tokens
_kv_prune_enabled = bool(enable)
try:
_kv_prune_keep = float(max(0.5, min(1.0, keep)))
except Exception:
_kv_prune_keep = 0.85
try:
_kv_prune_min_tokens = int(max(1, min_tokens))
except Exception:
_kv_prune_min_tokens = 128
if not _initialized:
_original_functions["orig_attention"] = comfy_attention.optimized_attention
_original_functions["original_patch_model"] = comfy.model_patcher.ModelPatcher.patch_model
_original_functions["original_load_lora_for_models"] = comfy.sd.load_lora_for_models
_initialized = True
class MGSagpuBaseLoader:
original_linear = None
cublas_patched = False
@torch.compiler.disable()
def _patch_modules(self, patch_cublaslinear, sage_attention):
from comfy.ops import disable_weight_init, CastWeightBiasOp, cast_bias_weight
if sage_attention != "disabled":
print("Patching comfy attention to use sageattn")
try:
from sageattention import sageattn
from sageattention import (
sageattn_qk_int8_pv_fp16_cuda,
sageattn_qk_int8_pv_fp16_triton,
sageattn_qk_int8_pv_fp8_cuda,
sageattn_qk_int8_pv_fp8_cuda_sm90,
)
except ImportError:
from SageAttention import sageattn
from SageAttention import (
sageattn_qk_int8_pv_fp16_cuda,
sageattn_qk_int8_pv_fp16_triton,
sageattn_qk_int8_pv_fp8_cuda,
sageattn_qk_int8_pv_fp8_cuda_sm90,
)
def set_sage_func(sage_attention):
# Helper: pick best kernel for current GPU
def select_auto(quality: bool):
def _auto(q, k, v, is_causal=False, attn_mask=None, tensor_layout="NHD"):
major, minor = torch.cuda.get_device_capability(torch.cuda.current_device()) if torch.cuda.is_available() else (0, 0)
try:
if major == 12 and minor == 0:
# RTX 50 series
pv = "fp32+fp32" if quality else "fp32+fp16"
return sageattn_qk_int8_pv_fp8_cuda(q, k, v, is_causal=is_causal, attn_mask=attn_mask, pv_accum_dtype=pv, tensor_layout=tensor_layout)
elif major == 9:
# H100 family
pv = "fp32+fp32" if quality else "fp32+fp32"
return sageattn_qk_int8_pv_fp8_cuda_sm90(q, k, v, is_causal=is_causal, attn_mask=attn_mask, pv_accum_dtype=pv, tensor_layout=tensor_layout)
elif major == 8 and minor == 9:
pv = "fp32+fp32" if quality else "fp32+fp16"
return sageattn_qk_int8_pv_fp8_cuda(q, k, v, is_causal=is_causal, attn_mask=attn_mask, pv_accum_dtype=pv, tensor_layout=tensor_layout)
elif major == 8 and minor in (0, 6):
# Ampere
# Prefer CUDA kernel when possible
return sageattn_qk_int8_pv_fp16_cuda(q, k, v, is_causal=is_causal, attn_mask=attn_mask, pv_accum_dtype="fp32", tensor_layout=tensor_layout)
except Exception:
pass
# Generic auto (library decides), works across arch when available
return sageattn(q, k, v, is_causal=is_causal, attn_mask=attn_mask, tensor_layout=tensor_layout)
return _auto
if sage_attention == "auto":
return select_auto(quality=False)
if sage_attention == "auto_speed":
return select_auto(quality=False)
if sage_attention == "auto_quality":
return select_auto(quality=True)
elif sage_attention == "sageattn_qk_int8_pv_fp16_cuda":
def func(q, k, v, is_causal=False, attn_mask=None, tensor_layout="NHD"):
return sageattn_qk_int8_pv_fp16_cuda(q, k, v, is_causal=is_causal, attn_mask=attn_mask, pv_accum_dtype="fp32", tensor_layout=tensor_layout)
return func
elif sage_attention == "sageattn_qk_int8_pv_fp16_triton":
def func(q, k, v, is_causal=False, attn_mask=None, tensor_layout="NHD"):
return sageattn_qk_int8_pv_fp16_triton(q, k, v, is_causal=is_causal, attn_mask=attn_mask, tensor_layout=tensor_layout)
return func
elif sage_attention == "sageattn_qk_int8_pv_fp8_cuda":
def func(q, k, v, is_causal=False, attn_mask=None, tensor_layout="NHD"):
return sageattn_qk_int8_pv_fp8_cuda(q, k, v, is_causal=is_causal, attn_mask=attn_mask, pv_accum_dtype="fp32+fp32", tensor_layout=tensor_layout)
return func
elif sage_attention == "sageattn_qk_int8_pv_fp8_cuda++":
# using imported sageattn_qk_int8_pv_fp8_cuda above (name alias consistent for both module names)
# This variant requires SM89 (Ada 8.9). On newer GPUs (e.g., SM90),
# fall back to generic auto selection to avoid kernel assertion.
try:
if torch.cuda.is_available():
major, minor = torch.cuda.get_device_capability(torch.cuda.current_device())
if not (major == 8 and minor == 9):
logging.warning(f"sageattn_qk_int8_pv_fp8_cuda++ requires SM89, but detected SM{major}{minor}. Falling back to auto kernel selection.")
def func(q, k, v, is_causal=False, attn_mask=None, tensor_layout="NHD"):
return sageattn(q, k, v, is_causal=is_causal, attn_mask=attn_mask, tensor_layout=tensor_layout)
return func
except Exception:
pass
def func(q, k, v, is_causal=False, attn_mask=None, tensor_layout="NHD"):
return sageattn_qk_int8_pv_fp8_cuda(q, k, v, is_causal=is_causal, attn_mask=attn_mask, pv_accum_dtype="fp32+fp16", tensor_layout=tensor_layout)
return func
sage_func = set_sage_func(sage_attention)
@torch.compiler.disable()
def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, transformer_options=None, **kwargs):
if skip_reshape:
b, _, _, dim_head = q.shape
tensor_layout="HND"
else:
b, _, dim_head = q.shape
dim_head //= heads
q, k, v = map(
lambda t: t.view(b, -1, heads, dim_head),
(q, k, v),
)
tensor_layout="NHD"
if mask is not None:
# add a batch dimension if there isn't already one
if mask.ndim == 2:
mask = mask.unsqueeze(0)
# add a heads dimension if there isn't already one
if mask.ndim == 3:
mask = mask.unsqueeze(1)
# Prefer trying sage kernels; allow runtime overrides via transformer_options or CURRENT_PV_ACCUM
# Optional K/V pruning for self-attention (token-level top-k)
try:
if _kv_prune_enabled and (mask is None):
import math
if tensor_layout == "NHD":
# q,k,v: B,N,H,D
Bn, Nq, Hn, Dh = q.shape
Nk = k.shape[1]
if Nq == Nk and Nk >= _kv_prune_min_tokens:
keep = max(1, int(math.ceil(float(_kv_prune_keep) * Nk)))
if keep < Nk:
# importance: mean over heads of L2 norm of K per token
imp = (k.pow(2).sum(dim=-1)).mean(dim=2) # B,N
top = torch.topk(imp, k=keep, dim=1, largest=True, sorted=False).indices
idx = top.unsqueeze(-1).unsqueeze(-1).expand(Bn, keep, Hn, Dh)
k = torch.gather(k, dim=1, index=idx)
v = torch.gather(v, dim=1, index=idx)
else:
# HND: q,k,v: B,H,N,D
Bb, Hn, Nq, Dh = q.shape
Nk = k.shape[2]
if Nq == Nk and Nk >= _kv_prune_min_tokens:
keep = max(1, int(math.ceil(float(_kv_prune_keep) * Nk)))
if keep < Nk:
imp = (k.pow(2).sum(dim=-1)).mean(dim=1) # B,N
top = torch.topk(imp, k=keep, dim=1, largest=True, sorted=False).indices
idx = top.unsqueeze(1).unsqueeze(-1).expand(Bb, Hn, keep, Dh)
k = torch.gather(k, dim=2, index=idx)
v = torch.gather(v, dim=2, index=idx)
except Exception:
# On any issue, skip pruning silently
pass
try:
pv_override = None
if transformer_options and isinstance(transformer_options, dict):
so = transformer_options.get("sageattn")
if isinstance(so, dict):
pv_override = so.get("pv_accum_dtype", None)
if pv_override is None:
pv_override = CURRENT_PV_ACCUM
if pv_override is not None:
out = sageattn(q, k, v, attn_mask=mask, is_causal=False, tensor_layout=tensor_layout, pv_accum_dtype=pv_override)
else:
out = sage_func(q, k, v, attn_mask=mask, is_causal=False, tensor_layout=tensor_layout)
except Exception as e:
global _sage_generic_warned_once
if not _sage_generic_warned_once:
logging.warning(f"Error running sage attention: {e}. Falling back.")
_sage_generic_warned_once = True
try:
out = sageattn(q, k, v, attn_mask=mask, is_causal=False, tensor_layout=tensor_layout)
except Exception:
# Final fallback to PyTorch attention, silent after first warning
if tensor_layout == "NHD":
q, k, v = map(lambda t: t.transpose(1, 2), (q, k, v))
return comfy_attention.attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=True, skip_output_reshape=skip_output_reshape, transformer_options=transformer_options, **kwargs)
# Optional tiny attention-entropy probe (avoid heavy compute)
try:
if _attn_entropy_enabled:
import torch
with torch.inference_mode():
if tensor_layout == "HND":
# q: B,H,N,D -> B,N,H,D for uniform handling
q_probe = q.transpose(1, 2)
k_probe = k.transpose(1, 2)
else:
q_probe = q
k_probe = k
B_, N_, H_, Dh = q_probe.shape
# Cap heads and tokens
h_cap = min(H_, _attn_probe_heads_cap)
step = max(1, N_ // _attn_probe_tokens_cap)
q_s = q_probe[:, ::step, :h_cap, :].transpose(1, 2) # B,h,q,d
k_s = k_probe[:, ::step, :h_cap, :].transpose(1, 2) # B,h,k,d
scale = (float(Dh) ** -0.5)
# logits: B,h,q,k
logits = torch.matmul(q_s * scale, k_s.transpose(-1, -2))
p = torch.softmax(logits, dim=-1)
# entropy per query
eps = 1e-9
Hq = -(p * (p.clamp_min(eps).log())).sum(dim=-1) # B,h,q
Hq = Hq.mean(dim=1) # B,q
# reshape to approx grid
import math
Q = Hq.shape[-1]
w = int(math.sqrt(Q))
w = max(1, w)
h = max(1, Q // w)
if h * w > Q:
Hq = Hq[..., : (h * w)]
elif h * w < Q:
# pad with last
pad = (h * w) - Q
if pad > 0:
Hq = torch.cat([Hq, Hq[..., -1:].expand(B_, pad)], dim=-1)
Hmap = Hq.reshape(B_, 1, h, w)
# normalize per-sample to [0,1]
Hmin = Hmap.amin(dim=(2, 3), keepdim=True)
Hmax = Hmap.amax(dim=(2, 3), keepdim=True)
Hn = (Hmap - Hmin) / (Hmax - Hmin + 1e-6)
global _attn_entropy_last
_attn_entropy_last = Hn.detach()
except Exception:
pass
if tensor_layout == "HND":
if not skip_output_reshape:
out = (
out.transpose(1, 2).reshape(b, -1, heads * dim_head)
)
else:
if skip_output_reshape:
out = out.transpose(1, 2)
else:
out = out.reshape(b, -1, heads * dim_head)
return out
comfy_attention.optimized_attention = attention_sage
comfy.ldm.hunyuan_video.model.optimized_attention = attention_sage
comfy.ldm.flux.math.optimized_attention = attention_sage
comfy.ldm.genmo.joint_model.asymm_models_joint.optimized_attention = attention_sage
comfy.ldm.cosmos.blocks.optimized_attention = attention_sage
comfy.ldm.wan.model.optimized_attention = attention_sage
else:
print("Restoring initial comfy attention")
comfy_attention.optimized_attention = _original_functions.get("orig_attention")
comfy.ldm.hunyuan_video.model.optimized_attention = _original_functions.get("orig_attention")
comfy.ldm.flux.math.optimized_attention = _original_functions.get("orig_attention")
comfy.ldm.genmo.joint_model.asymm_models_joint.optimized_attention = _original_functions.get("orig_attention")
comfy.ldm.cosmos.blocks.optimized_attention = _original_functions.get("orig_attention")
comfy.ldm.wan.model.optimized_attention = _original_functions.get("orig_attention")
if patch_cublaslinear:
if not MGSagpuBaseLoader.cublas_patched:
MGSagpuBaseLoader.original_linear = disable_weight_init.Linear
try:
from cublas_ops import CublasLinear
except ImportError:
raise Exception("Can't import 'torch-cublas-hgemm', install it from here https://github.com/aredden/torch-cublas-hgemm")
class PatchedLinear(CublasLinear, CastWeightBiasOp):
def reset_parameters(self):
pass
def forward_comfy_cast_weights(self, input):
weight, bias = cast_bias_weight(self, input)
return torch.nn.functional.linear(input, weight, bias)
def forward(self, *args, **kwargs):
if self.comfy_cast_weights:
return self.forward_comfy_cast_weights(*args, **kwargs)
else:
return super().forward(*args, **kwargs)
disable_weight_init.Linear = PatchedLinear
MGSagpuBaseLoader.cublas_patched = True
else:
if MGSagpuBaseLoader.cublas_patched:
disable_weight_init.Linear = MGSagpuBaseLoader.original_linear
MGSagpuBaseLoader.cublas_patched = False
from comfy.patcher_extension import CallbacksMP
class MGSagpuAttention(MGSagpuBaseLoader):
@classmethod
def INPUT_TYPES(s):
return {"required": {
"model": ("MODEL",),
"sage_attention": (sageattn_modes, {"default": False, "tooltip": "Global patch comfy attention to use sageattn, once patched to revert back to normal you would need to run this node again with disabled option."}),
}}
RETURN_TYPES = ("MODEL", )
FUNCTION = "patch"
DESCRIPTION = "Node for patching attention mode. This doesn't use the model patching system and thus can't be disabled without running the node again with 'disabled' option."
EXPERIMENTAL = False
CATEGORY = "MagicNodes"
def patch(self, model, sage_attention):
model_clone = model.clone()
@torch.compiler.disable()
def patch_attention_enable(model):
self._patch_modules(False, sage_attention)
@torch.compiler.disable()
def patch_attention_disable(model):
self._patch_modules(False, "disabled")
model_clone.add_callback(CallbacksMP.ON_PRE_RUN, patch_attention_enable)
model_clone.add_callback(CallbacksMP.ON_CLEANUP, patch_attention_disable)
return model_clone,
# Legacy compile helpers removed
# Legacy video helpers removed
import inspect as _inspect
try:
from comfy.ldm.modules import attention as _cm_attn
except Exception as _e:
_cm_attn = None
_nag_patch_active = False
_nag_params = {"scale": 5.0, "tau": 2.5, "alpha": 0.25}
_original_functions.setdefault("orig_crossattn_forward", None)
_original_functions.setdefault("orig_crossattn_sig", None)
def _call_orig_crossattn(self, x, context=None, **kwargs):
#\"\"\"Call the original CrossAttention.forward with kwargs filtered to its signature.\"\"\"
f = _original_functions.get("orig_crossattn_forward", None)
if f is None:
# Should not happen; just try current method
return self.__class__.forward(self, x, context=context, **kwargs)
sig = _original_functions.get("orig_crossattn_sig", None)
if sig is None:
try:
sig = _inspect.signature(f)
_original_functions["orig_crossattn_sig"] = sig
except Exception:
sig = None
if sig is not None:
allowed = set(sig.parameters.keys())
fkwargs = {k: v for k, v in kwargs.items() if k in allowed}
else:
fkwargs = kwargs
try:
return f(self, x, context=context, **fkwargs)
except TypeError:
# Some builds have (x, context=None, value=None, mask=None) only
fkwargs.pop("attn_precision", None)
fkwargs.pop("transformer_options", None)
try:
return f(self, x, context=context, **fkwargs)
except Exception:
# Give up; call current method (unpatched) to avoid crashing
return self.__class__.forward(self, x, context=context, **kwargs)
def _kj_crossattn_forward_nag(self, x, context=None, value=None, mask=None, **kwargs):
# If patch not active or context not having cond/uncond, defer to original.
if (not _nag_patch_active) or (_cm_attn is None):
return _call_orig_crossattn(self, x, context=context, value=value, mask=mask, **kwargs)
try:
if context is None or not torch.is_tensor(context):
return _call_orig_crossattn(self, x, context=context, value=value, mask=mask, **kwargs)
# Expect batch 2 with [uncond, cond]; if not, fall back
if context.shape[0] < 2:
return _call_orig_crossattn(self, x, context=context, value=value, mask=mask, **kwargs)
# Split branches. In most samplers order is [uncond, cond].
# If x has batch==2, split it likewise; else use the same x for both calls.
x_has_pair = (torch.is_tensor(x) and x.shape[0] == 2)
x_u = x[0:1] if x_has_pair else x
x_c = x[1:2] if x_has_pair else x
c_u, c_c = context[0:1], context[1:2]
# value may also be batched
v = kwargs.get("value", value)
if torch.is_tensor(v) and v.shape[0] == 2:
v_u, v_c = v[0:1], v[1:2]
else:
v_u = v_c = v
# Get per-branch outputs using the ORIGINAL forward
# - Neg branch (for real uncond stream)
out_u = _call_orig_crossattn(self, x_u, context=c_u, value=v_u, mask=mask, **kwargs)
# - Pos branch
z_pos = _call_orig_crossattn(self, x_c, context=c_c, value=v_c, mask=mask, **kwargs)
# - "Neg guidance" term computed with *positive query but negative context*
z_neg = _call_orig_crossattn(self, x_c, context=c_u, value=v_u, mask=mask, **kwargs)
# NAG mixing in the attention output space
phi = float(_nag_params.get("scale", 5.0))
tau = float(_nag_params.get("tau", 2.5))
alpha = float(_nag_params.get("alpha", 0.25))
g = z_pos * phi - z_neg * (phi - 1.0)
# L1-norm based clipping to limit deviation from Z+
def _l1_norm(t):
return torch.sum(torch.abs(t), dim=-1, keepdim=True).clamp_min(1e-6)
s_pos = _l1_norm(z_pos)
s_g = _l1_norm(g)
scale = (s_pos * tau) / s_g
g = torch.where((s_g > s_pos * tau), g * scale, g)
z_guided = g * alpha + z_pos * (1.0 - alpha)
if x_has_pair:
return torch.cat([out_u, z_guided], dim=0)
else:
return z_guided
except Exception as e:
# If anything goes wrong, use the original forward.
return _call_orig_crossattn(self, x, context=context, value=value, mask=mask, **kwargs)
def enable_crossattention_nag_patch(enable: bool, nag_scale: float = 5.0, nag_tau: float = 2.5, nag_alpha: float = 0.25):
#\"\"\"Enable/disable a safe CrossAttention forward wrapper that applies NAG to the positive branch only.
#This does not modify model weights and is fully reversible. The wrapper preserves
#unknown kwargs (filters per-signature) to avoid errors on older Comfy builds.
#\"\"\"
global _nag_patch_active, _nag_params
if _cm_attn is None:
return False
if enable:
_nag_params = {"scale": float(nag_scale), "tau": float(nag_tau), "alpha": float(nag_alpha)}
if _original_functions.get("orig_crossattn_forward", None) is None:
try:
_original_functions["orig_crossattn_forward"] = _cm_attn.CrossAttention.forward
try:
_original_functions["orig_crossattn_sig"] = _inspect.signature(_cm_attn.CrossAttention.forward)
except Exception:
_original_functions["orig_crossattn_sig"] = None
except Exception:
return False
# Patch in our wrapper
try:
_cm_attn.CrossAttention.forward = _kj_crossattn_forward_nag
_nag_patch_active = True
return True
except Exception:
return False
else:
# Restore original if we have it
if _original_functions.get("orig_crossattn_forward", None) is not None:
try:
_cm_attn.CrossAttention.forward = _original_functions["orig_crossattn_forward"]
except Exception:
pass
_nag_patch_active = False
return True
# ===============================================================================
PatchSageAttention = MGSagpuAttention
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