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>
- 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>
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
When a multigpu clone ModelPatcher is garbage collected, LoadedModel._switch_parent
switches the weakref to point at the parent (main) ModelPatcher. However, it was not
updating LoadedModel.device, leaving it with the old clone's device (e.g., cuda:1).
On subsequent runs, this stale device was passed to ModelPatcherDynamic.load(), causing
an assertion failure (device_to != self.load_device).
Amp-Thread-ID: https://ampcode.com/threads/T-019d3f5c-28c5-72c9-abed-34681f1b54ba
Co-authored-by: Amp <amp@ampcode.com>
* mm: Lower windows pin threshold
Some workflows have more extranous use of shared GPU memory than is
accounted for in the 5% pin headroom. Lower this for safety.
* mm: Remove pin count clearing threshold.
TOTAL_PINNED_MEMORY is shared between the legacy and aimdo pinning
systems, however this catch-all assumes only the legacy system exists.
Remove the catch-all as the PINNED_MEMORY buffer is coherent already.
There was an issue where the resample split was too early and dropped one
of the rolling convolutions a frame early. This is most noticable as a
lighting/color change between pixel frames 5->6 (latent 2->3), or as a
lighting change between the first and last frame in an FLF wan flow.
The recent PR that added resize_cond_for_context_window methods to
model classes used inline 'import comfy.context_windows' in each
method body. This moves that import to the top-level import section,
replacing 4 duplicate inline imports with a single top-level one.
* Add slice_cond and per-model context window cond resizing
* Fix cond_value.size() call in context window cond resizing
* Expose additional advanced inputs for ContextWindowsManualNode
Necessary for WanAnimate context windows workflow, which needs cond_retain_index_list = 0 to work properly with its reference input.
---------
* 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.
Jedrzej Kosinski
comfyanonymous
Jukka Seppänen
Bedovyy
rattus
Jun Yamog
huemin
Kohaku-Blueleaf
Talmaj
drozbay