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Author SHA1 Message Date
Alex Butler
62833446d0
Merge cb213aee66 into 3814bf4454 2026-03-15 22:59:58 +01:00
lostdisc
3814bf4454
Enable Pytorch Attention for gfx1150 (#12973)
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2026-03-15 12:45:30 -07:00
comfyanonymous
d062becb33
Make EmptyLatentImage follow intermediate dtype. (#12974) 2026-03-15 15:37:27 -04:00
rattus
e84a200a3c
ops: opt out of deferred weight init if subclassed (#12967)
If a subclass BYO _load_from_state_dict and doesnt call the super() the
needed default init of these weights is missed and can lead to problems
for uninitialized weights.
2026-03-15 11:49:49 -07:00
Dr.Lt.Data
192cb8eeb9
bump manager version to 4.1b5 (#12957) 2026-03-15 11:48:56 -07:00
Jukka Seppänen
0904cc3fe5
LTXV: Accumulate VAE decode results on intermediate_device (#12955)
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2026-03-14 18:09:09 -07:00
Alex Butler
cb213aee66 Expand AMD ROCm Tips readme section
Add suggestion to disable online tuning
Add miopen info
Add flash attention info
Add vram oom suggestion
2026-02-07 18:03:16 +00:00
6 changed files with 24 additions and 12 deletions

View File

@ -374,7 +374,11 @@ You can enable experimental memory efficient attention on recent pytorch in Comf
```TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 python main.py --use-pytorch-cross-attention```
You can also try setting this env variable `PYTORCH_TUNABLEOP_ENABLED=1` which might speed things up at the cost of a very slow initial run.
You can also try:
* Tunable ops: Setting `PYTORCH_TUNABLEOP_ENABLED=1` which might speed things up at the cost of a very slow initial runs. After running online tuning for a while consider disabling it with `PYTORCH_TUNABLEOP_TUNING=0` to only used the tuned settings and avoid slowdowns.
* MIOpen: Currently disabled by default. Enable with `COMFYUI_ENABLE_MIOPEN=1`. Be aware that miopen will autotune by default, consider disabling it with `MIOPEN_FIND_MODE=FAST` to avoid tuning slowdowns.
* Flash attention: Install from [flash-attention](https://github.com/Dao-AILab/flash-attention) & enable with `FLASH_ATTENTION_TRITON_AMD_ENABLE=TRUE` and arg `--use-flash-attention`. See also notes in the repo on triton autotuning.
* If you are encountering VRAM OOMs `PYTORCH_NO_HIP_MEMORY_CACHING=1` may help.
# Notes

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@ -11,6 +11,7 @@ from .causal_conv3d import CausalConv3d
from .pixel_norm import PixelNorm
from ..model import PixArtAlphaCombinedTimestepSizeEmbeddings
import comfy.ops
import comfy.model_management
from comfy.ldm.modules.diffusionmodules.model import torch_cat_if_needed
ops = comfy.ops.disable_weight_init
@ -536,7 +537,7 @@ class Decoder(nn.Module):
mark_conv3d_ended(self.conv_out)
sample = self.conv_out(sample, causal=self.causal)
if sample is not None and sample.shape[2] > 0:
output.append(sample)
output.append(sample.to(comfy.model_management.intermediate_device()))
return
up_block = self.up_blocks[idx]

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@ -400,7 +400,7 @@ try:
if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
if aotriton_supported(arch): # AMD efficient attention implementation depends on aotriton.
if torch_version_numeric >= (2, 7): # works on 2.6 but doesn't actually seem to improve much
if any((a in arch) for a in ["gfx90a", "gfx942", "gfx950", "gfx1100", "gfx1101", "gfx1151"]): # TODO: more arches, TODO: gfx950
if any((a in arch) for a in ["gfx90a", "gfx942", "gfx950", "gfx1100", "gfx1101", "gfx1150", "gfx1151"]): # TODO: more arches, TODO: gfx950
ENABLE_PYTORCH_ATTENTION = True
if rocm_version >= (7, 0):
if any((a in arch) for a in ["gfx1200", "gfx1201"]):

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@ -336,7 +336,10 @@ class disable_weight_init:
class Linear(torch.nn.Linear, CastWeightBiasOp):
def __init__(self, in_features, out_features, bias=True, device=None, dtype=None):
if not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled:
# don't trust subclasses that BYO state dict loader to call us.
if (not comfy.model_management.WINDOWS
or not comfy.memory_management.aimdo_enabled
or type(self)._load_from_state_dict is not disable_weight_init.Linear._load_from_state_dict):
super().__init__(in_features, out_features, bias, device, dtype)
return
@ -357,7 +360,9 @@ class disable_weight_init:
def _load_from_state_dict(self, state_dict, prefix, local_metadata,
strict, missing_keys, unexpected_keys, error_msgs):
if not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled:
if (not comfy.model_management.WINDOWS
or not comfy.memory_management.aimdo_enabled
or type(self)._load_from_state_dict is not disable_weight_init.Linear._load_from_state_dict):
return super()._load_from_state_dict(state_dict, prefix, local_metadata, strict,
missing_keys, unexpected_keys, error_msgs)
disable_weight_init._lazy_load_from_state_dict(
@ -564,7 +569,10 @@ class disable_weight_init:
def __init__(self, num_embeddings, embedding_dim, padding_idx=None, max_norm=None,
norm_type=2.0, scale_grad_by_freq=False, sparse=False, _weight=None,
_freeze=False, device=None, dtype=None):
if not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled:
# don't trust subclasses that BYO state dict loader to call us.
if (not comfy.model_management.WINDOWS
or not comfy.memory_management.aimdo_enabled
or type(self)._load_from_state_dict is not disable_weight_init.Embedding._load_from_state_dict):
super().__init__(num_embeddings, embedding_dim, padding_idx, max_norm,
norm_type, scale_grad_by_freq, sparse, _weight,
_freeze, device, dtype)
@ -590,7 +598,9 @@ class disable_weight_init:
def _load_from_state_dict(self, state_dict, prefix, local_metadata,
strict, missing_keys, unexpected_keys, error_msgs):
if not comfy.model_management.WINDOWS or not comfy.memory_management.aimdo_enabled:
if (not comfy.model_management.WINDOWS
or not comfy.memory_management.aimdo_enabled
or type(self)._load_from_state_dict is not disable_weight_init.Embedding._load_from_state_dict):
return super()._load_from_state_dict(state_dict, prefix, local_metadata, strict,
missing_keys, unexpected_keys, error_msgs)
disable_weight_init._lazy_load_from_state_dict(

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@ -1 +1 @@
comfyui_manager==4.1b4
comfyui_manager==4.1b5

View File

@ -1211,9 +1211,6 @@ class GLIGENTextBoxApply:
return (c, )
class EmptyLatentImage:
def __init__(self):
self.device = comfy.model_management.intermediate_device()
@classmethod
def INPUT_TYPES(s):
return {
@ -1232,7 +1229,7 @@ class EmptyLatentImage:
SEARCH_ALIASES = ["empty", "empty latent", "new latent", "create latent", "blank latent", "blank"]
def generate(self, width, height, batch_size=1):
latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=self.device)
latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
return ({"samples": latent, "downscale_ratio_spacial": 8}, )