* 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
---------
* 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>
* Fix Hunyuan 3D 2.1 multi-GPU worksplit: use cond_or_uncond instead of hardcoded chunk(2)
Amp-Thread-ID: https://ampcode.com/threads/T-019da964-2cc8-77f9-9aae-23f65da233db
Co-authored-by: Amp <amp@ampcode.com>
* Add GPU device selection to all loader nodes
- Add get_gpu_device_options() and resolve_gpu_device_option() helpers
in model_management.py for vendor-agnostic GPU device selection
- Add device widget to CheckpointLoaderSimple, UNETLoader, VAELoader
- Expand device options in CLIPLoader, DualCLIPLoader, LTXAVTextEncoderLoader
from [default, cpu] to include gpu:0, gpu:1, etc. on multi-GPU systems
- Wire load_diffusion_model_state_dict and load_state_dict_guess_config
to respect model_options['load_device']
- Graceful fallback: unrecognized devices (e.g. gpu:1 on single-GPU)
silently fall back to default
Amp-Thread-ID: https://ampcode.com/threads/T-019daa41-f394-731a-8955-4cff4f16283a
Co-authored-by: Amp <amp@ampcode.com>
* Add VALIDATE_INPUTS to skip device combo validation for workflow portability
When a workflow saved on a 2-GPU machine (with device=gpu:1) is loaded
on a 1-GPU machine, the combo validation would reject the unknown value.
VALIDATE_INPUTS with the device parameter bypasses combo validation for
that input only, allowing resolve_gpu_device_option to handle the
graceful fallback at runtime.
Amp-Thread-ID: https://ampcode.com/threads/T-019daa41-f394-731a-8955-4cff4f16283a
Co-authored-by: Amp <amp@ampcode.com>
* Set CUDA device context in outer_sample to match model load_device
Custom CUDA kernels (comfy_kitchen fp8 quantization) use
torch.cuda.current_device() for DLPack tensor export. When a model is
loaded on a non-default GPU (e.g. cuda:1), the CUDA context must match
or the kernel fails with 'Can't export tensors on a different CUDA
device index'. Save and restore the previous device around sampling.
Amp-Thread-ID: https://ampcode.com/threads/T-019daa41-f394-731a-8955-4cff4f16283a
Co-authored-by: Amp <amp@ampcode.com>
* Fix code review bugs: negative index guard, CPU offload_device, checkpoint te_model_options
- resolve_gpu_device_option: reject negative indices (gpu:-1)
- UNETLoader: set offload_device when cpu is selected
- CheckpointLoaderSimple: pass te_model_options for CLIP device,
set offload_device for cpu, pass load_device to VAE
- load_diffusion_model_state_dict: respect offload_device from model_options
- load_state_dict_guess_config: respect offload_device, pass load_device to VAE
Amp-Thread-ID: https://ampcode.com/threads/T-019daa41-f394-731a-8955-4cff4f16283a
Co-authored-by: Amp <amp@ampcode.com>
* Fix CUDA device context for CLIP encoding and VAE encode/decode
Add torch.cuda.set_device() calls to match model's load device in:
- CLIP.encode_from_tokens: fixes 'Can't export tensors on a different
CUDA device index' when CLIP is loaded on a non-default GPU
- CLIP.encode_from_tokens_scheduled: same fix for the hooks code path
- CLIP.generate: same fix for text generation
- VAE.decode: fixes VAE decoding on non-default GPU
- VAE.encode: fixes VAE encoding on non-default GPU
Same pattern as the existing outer_sample fix in samplers.py - saves
and restores previous CUDA device in a try/finally block.
Amp-Thread-ID: https://ampcode.com/threads/T-019dabdc-8feb-766f-b4dc-f46ef4d8ff57
Co-authored-by: Amp <amp@ampcode.com>
* Extract cuda_device_context manager, fix tiled VAE methods
Add model_management.cuda_device_context() — a context manager that
saves/restores torch.cuda.current_device when operating on a non-default
GPU. Replaces 6 copies of the manual save/set/restore boilerplate.
Refactored call sites:
- CLIP.encode_from_tokens
- CLIP.encode_from_tokens_scheduled (hooks path)
- CLIP.generate
- VAE.decode
- VAE.encode
- samplers.outer_sample
Bug fixes (newly wrapped):
- VAE.decode_tiled: was missing device context entirely, would fail
on non-default GPU when called from 'VAE Decode (Tiled)' node
- VAE.encode_tiled: same issue for 'VAE Encode (Tiled)' node
Amp-Thread-ID: https://ampcode.com/threads/T-019dabdc-8feb-766f-b4dc-f46ef4d8ff57
Co-authored-by: Amp <amp@ampcode.com>
* Restore CheckpointLoaderSimple, add CheckpointLoaderDevice
Revert CheckpointLoaderSimple to its original form (no device input)
so it remains the simple default loader.
Add new CheckpointLoaderDevice node (advanced/loaders) with separate
model_device, clip_device, and vae_device inputs for per-component
GPU placement in multi-GPU setups.
Amp-Thread-ID: https://ampcode.com/threads/T-019dabdc-8feb-766f-b4dc-f46ef4d8ff57
Co-authored-by: Amp <amp@ampcode.com>
---------
Co-authored-by: Amp <amp@ampcode.com>
Skip unnecessary clone of inference-mode tensors when already inside
torch.inference_mode(), matching the existing guard in set_attr_param.
The unconditional clone introduced in 20561aa9 caused transient VRAM
doubling during model movement for FP8/quantized models.
Benchmarked hybrid (main thread + pool) vs all-pool on 2x RTX 4090
with SD1.5 and NetaYume models. No meaningful performance difference
(within noise). All-pool is simpler: eliminates the main_device
special case, main_batch_tuple deferred execution, and the 3-way
branch in the dispatch loop.
Replace per-step thread create/destroy in _calc_cond_batch_multigpu with a
persistent MultiGPUThreadPool. Each worker thread calls torch.cuda.set_device()
once at startup, preserving compiled kernel caches across diffusion steps.
- Add MultiGPUThreadPool class in comfy/multigpu.py
- Create pool in CFGGuider.outer_sample(), shut down in finally block
- Main thread handles its own device batch directly for zero overhead
- Falls back to sequential execution if no pool is available
Jedrzej Kosinski
rattus
comfyanonymous
Abdul Rehman
Jukka Seppänen
comfyanonymous
Alexander Piskun
Daxiong (Lin)
Bedovyy
Jun Yamog
Comfy Org PR Bot
Terry Jia
huemin