* hunyuan upsampler: rework imports
Remove the transitive import of VideoConv3d and Resnet and takes these
from actual implementation source.
* model: remove unused give_pre_end
According to git grep, this is not used now, and was not used in the
initial commit that introduced it (see below).
This semantic is difficult to implement temporal roll VAE for (and would
defeat the purpose). Rather than implement the complex if, just delete
the unused feature.
(venv) rattus@rattus-box2:~/ComfyUI$ git log --oneline
220afe33 (HEAD) Initial commit.
(venv) rattus@rattus-box2:~/ComfyUI$ git grep give_pre
comfy/ldm/modules/diffusionmodules/model.py: resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
comfy/ldm/modules/diffusionmodules/model.py: self.give_pre_end = give_pre_end
comfy/ldm/modules/diffusionmodules/model.py: if self.give_pre_end:
(venv) rattus@rattus-box2:~/ComfyUI$ git co origin/master
Previous HEAD position was 220afe33 Initial commit.
HEAD is now at 9d8a8179 Enable async offloading by default on Nvidia. (#10953)
(venv) rattus@rattus-box2:~/ComfyUI$ git grep give_pre
comfy/ldm/modules/diffusionmodules/model.py: resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
comfy/ldm/modules/diffusionmodules/model.py: self.give_pre_end = give_pre_end
comfy/ldm/modules/diffusionmodules/model.py: if self.give_pre_end:
* move refiner VAE temporal roller to core
Move the carrying conv op to the common VAE code and give it a better
name. Roll the carry implementation logic for Resnet into the base
class and scrap the Hunyuan specific subclass.
* model: Add temporal roll to main VAE decoder
If there are no attention layers, its a standard resnet and VideoConv3d
is asked for, substitute in the temporal rolloing VAE algorithm. This
reduces VAE usage by the temporal dimension (can be huge VRAM savings).
* model: Add temporal roll to main VAE encoder
If there are no attention layers, its a standard resnet and VideoConv3d
is asked for, substitute in the temporal rolling VAE algorithm. This
reduces VAE usage by the temporal dimension (can be huge VRAM savings).
* init
* update
* Update model.py
* Update model.py
* remove print
* Fix text encoding
* Prevent empty negative prompt
Really doesn't work otherwise
* fp16 works
* I2V
* Update model_base.py
* Update nodes_hunyuan.py
* Better latent rgb factors
* Use the correct sigclip output...
* Support HunyuanVideo1.5 SR model
* whitespaces...
* Proper latent channel count
* SR model fixes
This also still needs timesteps scheduling based on the noise scale, can be used with two samplers too already
* vae_refiner: roll the convolution through temporal
Work in progress.
Roll the convolution through time using 2-latent-frame chunks and a
FIFO queue for the convolution seams.
* Support HunyuanVideo15 latent resampler
* fix
* Some cleanup
Co-Authored-By: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
* Proper hyvid15 I2V channels
Co-Authored-By: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
* Fix TokenRefiner for fp16
Otherwise x.sum has infs, just in case only casting if input is fp16, I don't know if necessary.
* Bugfix for the HunyuanVideo15 SR model
* vae_refiner: roll the convolution through temporal II
Roll the convolution through time using 2-latent-frame chunks and a
FIFO queue for the convolution seams.
Added support for encoder, lowered to 1 latent frame to save more
VRAM, made work for Hunyuan Image 3.0 (as code shared).
Fixed names, cleaned up code.
* Allow any number of input frames in VAE.
* Better VAE encode mem estimation.
* Lowvram fix.
* Fix hunyuan image 2.1 refiner.
* Fix mistake.
* Name changes.
* Rename.
* Whitespace.
* Fix.
* Fix.
---------
Co-authored-by: kijai <40791699+kijai@users.noreply.github.com>
Co-authored-by: Rattus <rattus128@gmail.com>
* 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
