* causal_video_ae: Remove attention ResNet
This attention_head_dim argument does not exist on this constructor so
this is dead code. Remove as generic attention mid VAE conflicts with
temporal roll.
* ltx-vae: consoldate causal/non-causal code paths
* ltx-vae: add cache rolling adder
* ltx-vae: use cached adder for resnet
* ltx-vae: Implement rolling VAE
Implement a temporal rolling VAE for the LTX2 VAE.
Usually when doing temporal rolling VAEs you can just chunk on time relying
on causality and cache behind you as you go. The LTX VAE is however
non-causal.
So go whole hog and implement per layer run ahead and backpressure between
the decoder layers using recursive state beween the layers.
Operations are ammended with temporal_cache_state{} which they can use to
hold any state then need for partial execution. Convolutions cache their
inputs behind the up to N-1 frames, and skip connections need to cache the
mismatch between convolution input and output that happens due to missing
future (non-causal) input.
Each call to run_up() processes a layer accross a range on input that
may or may not be complete. It goes depth first to process as much as
possible to try and digest frames to the final output ASAP. If layers run
out of input due to convolution losses, they simply return without action
effectively applying back-pressure to the earlier layers. As the earlier
layers do more work and caller deeper, the partial states are reconciled
and output continues to digest depth first as much as possible.
Chunking is done using a size quota rather than a fixed frame length and
any layer can initiate chunking, and multiple layers can chunk at different
granulatiries. This remove the old limitation of always having to process
1 latent frame to entirety and having to hold 8 full decoded frames as
the VRAM peak.
For LTX Audio VAE, remove normalization of audio during MEL spectrogram creation.
This aligs inference with training and prevents loud audio from being attenuated.
* 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
* Attempting a universal implementation of EasyCache, starting with flux as test; I screwed up the math a bit, but when I set it just right it works.
* Fixed math to make threshold work as expected, refactored code to use EasyCacheHolder instead of a dict wrapped by object
* Use sigmas from transformer_options instead of timesteps to be compatible with a greater amount of models, make end_percent work
* Make log statement when not skipping useful, preparing for per-cond caching
* Added DIFFUSION_MODEL wrapper around forward function for wan model
* Add subsampling for heuristic inputs
* Add subsampling to output_prev (output_prev_subsampled now)
* Properly consider conds in EasyCache logic
* Created SuperEasyCache to test what happens if caching and reuse is moved outside the scope of conds, added PREDICT_NOISE wrapper to facilitate this test
* Change max reuse_threshold to 3.0
* Mark EasyCache/SuperEasyCache as experimental (beta)
* Make Lumina2 compatible with EasyCache
* Add EasyCache support for Qwen Image
* Fix missing comma, curse you Cursor
* Add EasyCache support to AceStep
* Add EasyCache support to Chroma
* Added EasyCache support to Cosmos Predict t2i
* Make EasyCache not crash with Cosmos Predict ImagToVideo latents, but does not work well at all
* Add EasyCache support to hidream
* Added EasyCache support to hunyuan video
* Added EasyCache support to hunyuan3d
* Added EasyCache support to LTXV (not very good, but does not crash)
* Implemented EasyCache for aura_flow
* Renamed SuperEasyCache to LazyCache, hardcoded subsample_factor to 8 on nodes
* Eatra logging when verbose is true for EasyCache
