Problem:
After PR #10276 (commit 139addd5) introduced convert_func/set_func for
proper fp8 weight scaling during LoRA application, users with SageAttention
enabled experience 100% reproducible crashes (Exception 0xC0000005
ACCESS_VIOLATION) during KSampler execution.
Root Cause:
PR #10276 added fp8 weight transformations (scale up -> apply LoRA -> scale
down) to fix LoRA quality with Wan 2.1/2.2 14B fp8 models. These
transformations:
1. Convert weights to float32 and create copies (new memory addresses)
2. Invalidate tensor metadata that SageAttention cached
3. Break SageAttention's internal memory references
4. Cause access violation when SageAttention tries to use old pointers
SageAttention expects weights at original memory addresses without
transformations between caching and usage.
Solution:
Add conditional bypass in LowVramPatch.__call__ to detect when
SageAttention is active (via --use-sage-attention flag) and skip
convert_func/set_func calls. This preserves SageAttention's memory
reference stability while maintaining PR #10276 benefits for users
without SageAttention.
Trade-offs:
- When SageAttention is enabled with fp8 models + LoRAs, LoRAs are
applied to scaled weights instead of properly scaled weights
- Potential quality impact unknown (no issues observed in testing)
- Only affects users who explicitly enable SageAttention flag
- Users without SageAttention continue to benefit from PR #10276
Testing Completed:
- RTX 5090, CUDA 12.8, PyTorch 2.7.0, SageAttention 2.1.1
- Wan 2.2 fp8 models with multiple LoRAs
- Crash eliminated, ~40% SageAttention performance benefit preserved
- No visual quality degradation observed
- Non-SageAttention workflows unaffected
Testing Requested:
- Other GPU architectures (RTX 4090, 3090, etc.)
- Different CUDA/PyTorch version combinations
- fp8 LoRA quality comparison with SageAttention enabled
- Edge cases: mixed fp8/non-fp8 workflows
Files Changed:
- comfy/model_patcher.py: LowVramPatch.__call__ method
Related:
- Issue: SageAttention incompatibility with fp8 weight scaling
- Original PR: #10276 (fp8 LoRA quality fix for Wan models)
- SageAttention: https://github.com/thu-ml/SageAttention
## Summary
Fixed incorrect type hint syntax in `MotionEncoder_tc.__init__()` parameter list.
## Changes
- Line 647: Changed `num_heads=int` to `num_heads: int`
- This corrects the parameter annotation from a default value assignment to proper type hint syntax
## Details
The parameter was using assignment syntax (`=`) instead of type annotation syntax (`:`), which would incorrectly set the default value to the `int` class itself rather than annotating the expected type.
If this suffers an exception (such as a VRAM oom) it will leave the
encode() and decode() methods which skips the cleanup of the WAN
feature cache. The comfy node cache then ultimately keeps a reference
this object which is in turn reffing large tensors from the failed
execution.
The feature cache is currently setup at a class variable on the
encoder/decoder however, the encode and decode functions always clear
it on both entry and exit of normal execution.
Its likely the design intent is this is usable as a streaming encoder
where the input comes in batches, however the functions as they are
today don't support that.
So simplify by bringing the cache back to local variable, so that if
it does VRAM OOM the cache itself is properly garbage when the
encode()/decode() functions dissappear from the stack.
When the VAE catches this VRAM OOM, it launches the fallback logic
straight from the exception context.
Python however refs the entire call stack that caused the exception
including any local variables for the sake of exception report and
debugging. In the case of tensors, this can hold on the references
to GBs of VRAM and inhibit the VRAM allocated from freeing them.
So dump the except context completely before going back to the VAE
via the tiler by getting out of the except block with nothing but
a flag.
The greately increases the reliability of the tiler fallback,
especially on low VRAM cards, as with the bug, if the leak randomly
leaked more than the headroom needed for a single tile, the tiler
would fallback would OOM and fail the flow.
* flux: math: Use _addcmul to avoid expensive VRAM intermediate
The rope process can be the VRAM peak and this intermediate
for the addition result before releasing the original can OOM.
addcmul_ it.
* wan: Delete the self attention before cross attention
This saves VRAM when the cross attention and FFN are in play as the
VRAM peak.
When unloading models in load_models_gpu(), the model finalizer was not
being explicitly detached, leading to a memory leak. This caused
linear memory consumption increase over time as models are repeatedly
loaded and unloaded.
This change prevents orphaned finalizer references from accumulating in
memory during model switching operations.
* flux: Do the xq and xk ropes one at a time
This was doing independendent interleaved tensor math on the q and k
tensors, leading to the holding of more than the minimum intermediates
in VRAM. On a bad day, it would VRAM OOM on xk intermediates.
Do everything q and then everything k, so torch can garbage collect
all of qs intermediates before k allocates its intermediates.
This reduces peak VRAM usage for some WAN2.2 inferences (at least).
* wan: Optimize qkv intermediates on attention
As commented. The former logic computed independent pieces of QKV in
parallel which help more inference intermediates in VRAM spiking
VRAM usage. Fully roping Q and garbage collecting the intermediates
before touching K reduces the peak inference VRAM usage.
* Initial Chroma Radiance support
* Minor Chroma Radiance cleanups
* Update Radiance nodes to ensure latents/images are on the intermediate device
* Fix Chroma Radiance memory estimation.
* Increase Chroma Radiance memory usage factor
* Increase Chroma Radiance memory usage factor once again
* Ensure images are multiples of 16 for Chroma Radiance
Add batch dimension and fix channels when necessary in ChromaRadianceImageToLatent node
* Tile Chroma Radiance NeRF to reduce memory consumption, update memory usage factor
* Update Radiance to support conv nerf final head type.
* Allow setting NeRF embedder dtype for Radiance
Bump Radiance nerf tile size to 32
Support EasyCache/LazyCache on Radiance (maybe)
* Add ChromaRadianceStubVAE node
* Crop Radiance image inputs to multiples of 16 instead of erroring to be in line with existing VAE behavior
* Convert Chroma Radiance nodes to V3 schema.
* Add ChromaRadianceOptions node and backend support.
Cleanups/refactoring to reduce code duplication with Chroma.
* Fix overriding the NeRF embedder dtype for Chroma Radiance
* Minor Chroma Radiance cleanups
* Move Chroma Radiance to its own directory in ldm
Minor code cleanups and tooltip improvements
* Fix Chroma Radiance embedder dtype overriding
* Remove Radiance dynamic nerf_embedder dtype override feature
* Unbork Radiance NeRF embedder init
* Remove Chroma Radiance image conversion and stub VAE nodes
Add a chroma_radiance option to the VAELoader builtin node which uses comfy.sd.PixelspaceConversionVAE
Add a PixelspaceConversionVAE to comfy.sd for converting BHWC 0..1 <-> BCHW -1..1
* Looking into a @wrap_attn decorator to look for 'optimized_attention_override' entry in transformer_options
* Created logging code for this branch so that it can be used to track down all the code paths where transformer_options would need to be added
* Fix memory usage issue with inspect
* Made WAN attention receive transformer_options, test node added to wan to test out attention override later
* Added **kwargs to all attention functions so transformer_options could potentially be passed through
* Make sure wrap_attn doesn't make itself recurse infinitely, attempt to load SageAttention and FlashAttention if not enabled so that they can be marked as available or not, create registry for available attention
* Turn off attention logging for now, make AttentionOverrideTestNode have a dropdown with available attention (this is a test node only)
* Make flux work with optimized_attention_override
* Add logs to verify optimized_attention_override is passed all the way into attention function
* Make Qwen work with optimized_attention_override
* Made hidream work with optimized_attention_override
* Made wan patches_replace work with optimized_attention_override
* Made SD3 work with optimized_attention_override
* Made HunyuanVideo work with optimized_attention_override
* Made Mochi work with optimized_attention_override
* Made LTX work with optimized_attention_override
* Made StableAudio work with optimized_attention_override
* Made optimized_attention_override work with ACE Step
* Made Hunyuan3D work with optimized_attention_override
* Make CosmosPredict2 work with optimized_attention_override
* Made CosmosVideo work with optimized_attention_override
* Made Omnigen 2 work with optimized_attention_override
* Made StableCascade work with optimized_attention_override
* Made AuraFlow work with optimized_attention_override
* Made Lumina work with optimized_attention_override
* Made Chroma work with optimized_attention_override
* Made SVD work with optimized_attention_override
* Fix WanI2VCrossAttention so that it expects to receive transformer_options
* Fixed Wan2.1 Fun Camera transformer_options passthrough
* Fixed WAN 2.1 VACE transformer_options passthrough
* Add optimized to get_attention_function
* Disable attention logs for now
* Remove attention logging code
* Remove _register_core_attention_functions, as we wouldn't want someone to call that, just in case
* Satisfy ruff
* Remove AttentionOverrideTest node, that's something to cook up for later
