bugfix: fix typo in apply_directory for custom_nodes_directory
allow for PATH style ';' delimited custom_node directories.
change delimiter type for seperate folders per platform.
feat(API-nodes): move Rodin3D nodes to new client; removed old api client.py (#10645)
Fix qwen controlnet regression. (#10657)
Enable pinned memory by default on Nvidia. (#10656)
Removed the --fast pinned_memory flag.
You can use --disable-pinned-memory to disable it. Please report if it
causes any issues.
Pinned mem also seems to work on AMD. (#10658)
Remove environment variable.
Removed environment variable fallback for custom nodes directory.
Update documentation for custom nodes directory
Clarified documentation on custom nodes directory argument, removed documentation on environment variable
Clarify release cycle. (#10667)
Tell users they need to upload their logs in bug reports. (#10671)
mm: guard against double pin and unpin explicitly (#10672)
As commented, if you let cuda be the one to detect double pin/unpinning
it actually creates an asyc GPU error.
Only unpin tensor if it was pinned by ComfyUI (#10677)
Make ScaleROPE node work on Flux. (#10686)
Add logging for model unloading. (#10692)
Unload weights if vram usage goes up between runs. (#10690)
ops: Put weight cast on the offload stream (#10697)
This needs to be on the offload stream. This reproduced a black screen
with low resolution images on a slow bus when using FP8.
Update CI workflow to remove dead macOS runner. (#10704)
* Update CI workflow to remove dead macOS runner.
* revert
* revert
Don't pin tensor if not a torch.nn.parameter.Parameter (#10718)
Update README.md for Intel Arc GPU installation, remove IPEX (#10729)
IPEX is no longer needed for Intel Arc GPUs. Removing instruction to setup ipex.
mm/mp: always unload re-used but modified models (#10724)
The partial unloader path in model re-use flow skips straight to the
actual unload without any check of the patching UUID. This means that
if you do an upscale flow with a model patch on an existing model, it
will not apply your patchings.
Fix by delaying the partial_unload until after the uuid checks. This
is done by making partial_unload a model of partial_load where extra_mem
is -ve.
qwen: reduce VRAM usage (#10725)
Clean up a bunch of stacked and no-longer-needed tensors on the QWEN
VRAM peak (currently FFN).
With this I go from OOMing at B=37x1328x1328 to being able to
succesfully run B=47 (RTX5090).
Update Python 3.14 compatibility notes in README (#10730)
Quantized Ops fixes (#10715)
* offload support, bug fixes, remove mixins
* add readme
add PR template for API-Nodes (#10736)
feat: add create_time dict to prompt field in /history and /queue (#10741)
flux: reduce VRAM usage (#10737)
Cleanup a bunch of stack tensors on Flux. This take me from B=19 to B=22
for 1600x1600 on RTX5090.
Better instructions for the portable. (#10743)
Use same code for chroma and flux blocks so that optimizations are shared. (#10746)
Fix custom nodes import error. (#10747)
This should fix the import errors but will break if the custom nodes actually try to use the class.
revert import reordering
revert imports pt 2
Add left padding support to tokenizers. (#10753)
chore(api-nodes): mark OpenAIDalle2 and OpenAIDalle3 nodes as deprecated (#10757)
Revert "chore(api-nodes): mark OpenAIDalle2 and OpenAIDalle3 nodes as deprecated (#10757)" (#10759)
This reverts commit 9a02382568.
Change ROCm nightly install command to 7.1 (#10764)
* mm: factor out the current stream getter
Make this a reusable function.
* ops: sync the offload stream with the consumption of w&b
This sync is nessacary as pytorch will queue cuda async frees on the
same stream as created to tensor. In the case of async offload, this
will be on the offload stream.
Weights and biases can go out of scope in python which then
triggers the pytorch garbage collector to queue the free operation on
the offload stream possible before the compute stream has used the
weight. This causes a use after free on weight data leading to total
corruption of some workflows.
So sync the offload stream with the compute stream after the weight
has been used so the free has to wait for the weight to be used.
The cast_bias_weight is extended in a backwards compatible way with
the new behaviour opt-in on a defaulted parameter. This handles
custom node packs calling cast_bias_weight and defeatures
async-offload for them (as they do not handle the race).
The pattern is now:
cast_bias_weight(... , offloadable=True) #This might be offloaded
thing(weight, bias, ...)
uncast_bias_weight(...)
* controlnet: adopt new cast_bias_weight synchronization scheme
This is nessacary for safe async weight offloading.
* mm: sync the last stream in the queue, not the next
Currently this peeks ahead to sync the next stream in the queue of
streams with the compute stream. This doesnt allow a lot of
parallelization, as then end result is you can only get one weight load
ahead regardless of how many streams you have.
Rotate the loop logic here to synchronize the end of the queue before
returning the next stream. This allows weights to be loaded ahead of the
compute streams position.
When unloading models in load_models_gpu(), the model finalizer was not
being explicitly detached, leading to a memory leak. This caused
linear memory consumption increase over time as models are repeatedly
loaded and unloaded.
This change prevents orphaned finalizer references from accumulating in
memory during model switching operations.
These are not real controlnets but actually a patch on the model so they
will be treated as such.
Put them in the models/model_patches/ folder.
Use the new ModelPatchLoader and QwenImageDiffsynthControlnet nodes.
* Change bf16 check and switch non-blocking to off default with option to force to regain speed on certain classes of iGPUs and refactor xpu check.
* Turn non_blocking off by default for xpu.
* Update README.md for Intel GPUs.
This should speed up the lowvram mode a bit. It currently is only enabled when --async-offload is used but it will be enabled by default in the future if there are no problems.
