* 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>
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Co-authored-by: Amp <amp@ampcode.com>
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>
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.
* 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>
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Signed-off-by: bigcat88 <bigcat88@icloud.com>
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.
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.
* 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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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>
Jedrzej Kosinski
rattus
Daxiong (Lin)
Jukka Seppänen
Alexander Piskun
comfyanonymous
Dr.Lt.Data
blepping
Octopus
Matt Miller
AustinMroz
Comfy Org PR Bot
Abdul Rehman
comfyanonymous
Bedovyy