There was an issue where the resample split was too early and dropped one
of the rolling convolutions a frame early. This is most noticable as a
lighting/color change between pixel frames 5->6 (latent 2->3), or as a
lighting change between the first and last frame in an FLF wan flow.
The recent PR that added resize_cond_for_context_window methods to
model classes used inline 'import comfy.context_windows' in each
method body. This moves that import to the top-level import section,
replacing 4 duplicate inline imports with a single top-level one.
* Add slice_cond and per-model context window cond resizing
* Fix cond_value.size() call in context window cond resizing
* Expose additional advanced inputs for ContextWindowsManualNode
Necessary for WanAnimate context windows workflow, which needs cond_retain_index_list = 0 to work properly with its reference input.
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* sd: soft_empty_cache on tiler fallback
This doesnt cost a lot and creates the expected VRAM reduction in
resource monitors when you fallback to tiler.
* wan: vae: Don't recursion in local fns (move run_up)
Moved Decoder3d’s recursive run_up out of forward into a class
method to avoid nested closure self-reference cycles. This avoids
cyclic garbage that delays garbage of tensors which in turn delays
VRAM release before tiled fallback.
* ltx: vae: Don't recursion in local fns (move run_up)
Mov the recursive run_up out of forward into a class
method to avoid nested closure self-reference cycles. This avoids
cyclic garbage that delays garbage of tensors which in turn delays
VRAM release before tiled fallback.
* ltx: vae: add cache state to downsample block
* ltx: vae: Add time stride awareness to causal_conv_3d
* ltx: vae: Automate truncation for encoder
Other VAEs just truncate without error. Do the same.
* sd/ltx: Make chunked_io a flag in its own right
Taking this bi-direcitonal, so make it a for-purpose named flag.
* ltx: vae: implement chunked encoder + CPU IO chunking
People are doing things with big frame counts in LTX including V2V
flows. Implement the time-chunked encoder to keep the VRAM down, with
the converse of the new CPU pre-allocation technique, where the chunks
are brought from the CPU JIT.
* ltx: vae-encode: round chunk sizes more strictly
Only powers of 2 and multiple of 8 are valid due to cache slicing.
On Apple Silicon, `vram_state` is set to `VRAMState.SHARED` because
CPU and GPU share unified memory. However, `text_encoder_device()`
only checked for `HIGH_VRAM` and `NORMAL_VRAM`, causing all text
encoders to fall back to CPU on MPS devices.
Adding `VRAMState.SHARED` to the condition allows non-quantized text
encoders (e.g. bf16 Gemma 3 12B) to run on the MPS GPU, providing
significant speedup for text encoding and prompt generation.
Note: quantized models (fp4/fp8) that use float8_e4m3fn internally
will still fall back to CPU via the `supports_cast()` check in
`CLIP.__init__()`, since MPS does not support fp8 dtypes.
* wan: vae: encoder: Add feature cache layer that corks singles
If a downsample only gives you a single frame, save it to the feature
cache and return nothing to the top level. This increases the
efficiency of cacheability, but also prepares support for going two
by two rather than four by four on the frames.
* wan: remove all concatentation with the feature cache
The loopers are now responsible for ensuring that non-final frames are
processes at least two-by-two, elimiating the need for this cat case.
* wan: vae: recurse and chunk for 2+2 frames on decode
Avoid having to clone off slices of 4 frame chunks and reduce the size
of the big 6 frame convolutions down to 4. Save the VRAMs.
* wan: encode frames 2x2.
Reduce VRAM usage greatly by encoding frames 2 at a time rather than
4.
* wan: vae: remove cloning
The loopers now control the chunking such there is noever more than 2
frames, so just cache these slices directly and avoid the clone
allocations completely.
* wan: vae: free consumer caller tensors on recursion
* wan: vae: restyle a little to match LTX
* ltx: vae: scale the chunk size with the users VRAM
Scale this linearly down for users with low VRAM.
* ltx: vae: free non-chunking recursive intermediates
* ltx: vae: cleanup some intermediates
The conv layer can be the VRAM peak and it does a torch.cat. So cleanup
the pieces of the cat. Also clear our the cache ASAP as each layer detect
its end as this VAE surges in VRAM at the end due to the ended padding
increasing the size of the final frame convolutions off-the-books to
the chunker. So if all the earlier layers free up their cache it can
offset that surge.
Its a fragmentation nightmare, and the chance of it having to recache the
pyt allocator is very high, but you wont OOM.
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.
This is an experimental WIP option that might not work in your workflow but
should lower memory usage if it does.
Currently only the VAE and the load image node will output in fp16 when
this option is turned on.
* Implement seek and read for pins
Source pins from an mmap is pad because its its a CPU->CPU copy that
attempts to fully buffer the same data twice. Instead, use seek and
read which avoids the mmap buffering while usually being a faster
read in the first place (avoiding mmap faulting etc).
* pinned_memory: Use Aimdo pinner
The aimdo pinner bypasses pytorches CPU allocator which can leak
windows commit charge.
* ops: bypass init() of weight for embedding layer
This similarly consumes large commit charge especially for TEs. It can
cause a permanement leaked commit charge which can destabilize on
systems close to the commit ceiling and generally confuses the RAM
stats.
* model_patcher: implement pinned memory counter
Implement a pinned memory counter for better accounting of what volume
of memory pins have.
* implement touch accounting
Implement accounting of touching mmapped tensors.
* mm+mp: add residency mmap getter
* utils: use the aimdo mmap to load sft files
* model_management: Implement tigher RAM pressure semantics
Implement a pressure release on entire MMAPs as windows does perform
faster when mmaps are unloaded and model loads free ramp into fully
unallocated RAM.
Make the concept of freeing for pins a completely separate concept.
Now that pins are loadable directly from original file and don' touch
the mmap, tighten the freeing budget to just the current loaded model
- what you have left over. This still over-frees pins, but its a lot
better than before.
So after the pins are freed with that algorithm, bounce entire MMAPs
to free RAM based on what the model needs, deducting off any known
resident-in-mmap tensors to the free quota to keep it as tight as
possible.
* comfy-aimdo 0.2.11
Comfy aimdo 0.2.11
* mm: Implement file_slice path for QT
* ruff
* ops: put meta-tensors in place to allow custom nodes to check geo
* fix: guard torch.AcceleratorError for compatibility with torch < 2.8.0
torch.AcceleratorError was introduced in PyTorch 2.8.0. Accessing it
directly raises AttributeError on older versions. Use a try/except
fallback at module load time, consistent with the existing pattern used
for OOM_EXCEPTION.
* fix: address review feedback for AcceleratorError compat
- Fall back to RuntimeError instead of type(None) for ACCELERATOR_ERROR,
consistent with OOM_EXCEPTION fallback pattern and valid for except clauses
- Add "out of memory" message introspection for RuntimeError fallback case
- Use RuntimeError directly in discard_cuda_async_error except clause
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Pytorch only filters for OOMs in its own allocators however there are
paths that can OOM on allocators made outside the pytorch allocators.
These manifest as an AllocatorError as pytorch does not have universal
error translation to its OOM type on exception. Handle it. A log I have
for this also shows a double report of the error async, so call the
async discarder to cleanup and make these OOMs look like OOMs.
Sync the compute stream before freeing the cast buffers. This can cause
use after free issues when the cast stream frees the buffer while the
compute stream is behind enough to still needs a casted weight.
* mp: respect model_defined_dtypes in default caster
This is needed for parametrizations when the dtype changes between sd
and model.
* audio_encoders: archive model dtypes
Archive model dtypes to stop the state dict load override the dtypes
defined by the core for compute etc.
