* fix: pin SQLAlchemy>=2.0 in requirements.txt (fixes#13036) (#13316)
* Refactor io to IO in nodes_ace.py (#13485)
* Bump comfyui-frontend-package to 1.42.12 (#13489)
* Make the ltx audio vae more native. (#13486)
* feat(api-nodes): add automatic downscaling of videos for ByteDance 2 nodes (#13465)
* Support standalone LTXV audio VAEs (#13499)
* [Partner Nodes] added 4K resolution for Veo models; added Veo 3 Lite model (#13330)
* feat(api nodes): added 4K resolution for Veo models; added Veo 3 Lite model
Signed-off-by: bigcat88 <bigcat88@icloud.com>
* increase poll_interval from 5 to 9
---------
Signed-off-by: bigcat88 <bigcat88@icloud.com>
Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>
* Bump comfyui-frontend-package to 1.42.14 (#13493)
* Add gpt-image-2 as version option (#13501)
* Allow logging in comfy app files. (#13505)
* chore: update workflow templates to v0.9.59 (#13507)
* fix(veo): reject 4K resolution for veo-3.0 models in Veo3VideoGenerationNode (#13504)
The tooltip on the resolution input states that 4K is not available for
veo-3.1-lite or veo-3.0 models, but the execute guard only rejected the
lite combination. Selecting 4K with veo-3.0-generate-001 or
veo-3.0-fast-generate-001 would fall through and hit the upstream API
with an invalid request.
Broaden the guard to match the documented behavior and update the error
message accordingly.
Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>
* feat: RIFE and FILM frame interpolation model support (CORE-29) (#13258)
* initial RIFE support
* Also support FILM
* Better RAM usage, reduce FILM VRAM peak
* Add model folder placeholder
* Fix oom fallback frame loss
* Remove torch.compile for now
* Rename model input
* Shorter input type name
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* fix: use Parameter assignment for Stable_Zero123 cc_projection weights (fixes#13492) (#13518)
On Windows with aimdo enabled, disable_weight_init.Linear uses lazy
initialization that sets weight and bias to None to avoid unnecessary
memory allocation. This caused a crash when copy_() was called on the
None weight attribute in Stable_Zero123.__init__.
Replace copy_() with direct torch.nn.Parameter assignment, which works
correctly on both Windows (aimdo enabled) and other platforms.
* Derive InterruptProcessingException from BaseException (#13523)
* bump manager version to 4.2.1 (#13516)
* ModelPatcherDynamic: force cast stray weights on comfy layers (#13487)
the mixed_precision ops can have input_scale parameters that are used
in tensor math but arent a weight or bias so dont get proper VRAM
management. Treat these as force-castable parameters like the non comfy
weight, random params are buffers already are.
* Update logging level for invalid version format (#13526)
* [Partner Nodes] add SD2 real human support (#13509)
* feat(api-nodes): add SD2 real human support
Signed-off-by: bigcat88 <bigcat88@icloud.com>
* fix: add validation before uploading Assets
Signed-off-by: bigcat88 <bigcat88@icloud.com>
* Add asset_id and group_id displaying on the node
Signed-off-by: bigcat88 <bigcat88@icloud.com>
* extend poll_op to use instead of custom async cycle
Signed-off-by: bigcat88 <bigcat88@icloud.com>
* added the polling for the "Active" status after asset creation
Signed-off-by: bigcat88 <bigcat88@icloud.com>
* updated tooltip for group_id
* allow usage of real human in the ByteDance2FirstLastFrame node
* add reference count limits
* corrected price in status when input assets contain video
Signed-off-by: bigcat88 <bigcat88@icloud.com>
---------
Signed-off-by: bigcat88 <bigcat88@icloud.com>
* feat: SAM (segment anything) 3.1 support (CORE-34) (#13408)
* [Partner Nodes] GPTImage: fix price badges, add new resolutions (#13519)
* fix(api-nodes): fixed price badges, add new resolutions
Signed-off-by: bigcat88 <bigcat88@icloud.com>
* proper calculate the total run cost when "n > 1"
Signed-off-by: bigcat88 <bigcat88@icloud.com>
---------
Signed-off-by: bigcat88 <bigcat88@icloud.com>
* chore: update workflow templates to v0.9.61 (#13533)
* chore: update embedded docs to v0.4.4 (#13535)
* add 4K resolution to Kling nodes (#13536)
Signed-off-by: bigcat88 <bigcat88@icloud.com>
* Fix LTXV Reference Audio node (#13531)
* comfy-aimdo 0.2.14: Hotfix async allocator estimations (#13534)
This was doing an over-estimate of VRAM used by the async allocator when lots
of little small tensors were in play.
Also change the versioning scheme to == so we can roll forward aimdo without
worrying about stable regressions downstream in comfyUI core.
* Disable sageattention for SAM3 (#13529)
Causes Nans
* execution: Add anti-cycle validation (#13169)
Currently if the graph contains a cycle, the just inifitiate recursions,
hits a catch all then throws a generic error against the output node
that seeded the validation. Instead, fail the offending cycling mode
chain and handlng it as an error in its own right.
Co-authored-by: guill <jacob.e.segal@gmail.com>
* chore: update workflow templates to v0.9.62 (#13539)
---------
Signed-off-by: bigcat88 <bigcat88@icloud.com>
Co-authored-by: Octopus <liyuan851277048@icloud.com>
Co-authored-by: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Co-authored-by: Comfy Org PR Bot <snomiao+comfy-pr@gmail.com>
Co-authored-by: Alexander Piskun <13381981+bigcat88@users.noreply.github.com>
Co-authored-by: Jukka Seppänen <40791699+kijai@users.noreply.github.com>
Co-authored-by: AustinMroz <austin@comfy.org>
Co-authored-by: Daxiong (Lin) <contact@comfyui-wiki.com>
Co-authored-by: Matt Miller <matt@miller-media.com>
Co-authored-by: blepping <157360029+blepping@users.noreply.github.com>
Co-authored-by: Dr.Lt.Data <128333288+ltdrdata@users.noreply.github.com>
Co-authored-by: rattus <46076784+rattus128@users.noreply.github.com>
Co-authored-by: guill <jacob.e.segal@gmail.com>
* 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.
* 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.
* 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.
