mirror of
https://github.com/comfyanonymous/ComfyUI.git
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9250191c65
DynamicVRAM's on-demand model loading/offloading conflicted with process isolation in three ways: RPC tensor transport stalls from mid-call GPU offload, race conditions between model lifecycle and active RPC operations, and false positive memory leak detection from changed finalizer patterns. - Marshal CUDA tensors to CPU before RPC transport for dynamic models - Add operation state tracking + quiescence waits at workflow boundaries - Distinguish proxy reference release from actual leaks in cleanup_models_gc - Fix init order: DynamicVRAM must initialize before isolation proxies - Add RPC timeouts to prevent indefinite hangs on model unavailability - Prevent proxy-of-proxy chains from DynamicVRAM model reload cycles - Add torch.device/torch.dtype serializers for new DynamicVRAM RPC paths - Guard isolation overhead so non-isolated workflows are unaffected - Migrate env var to PYISOLATE_CHILD
66 lines
2.0 KiB
Python
66 lines
2.0 KiB
Python
from typing_extensions import override
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import torch
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import torch.nn.functional as F
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from comfy_api.latest import ComfyExtension, io
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class Mahiro(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="Mahiro",
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display_name="Positive-Biased Guidance",
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category="_for_testing",
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description="Modify the guidance to scale more on the 'direction' of the positive prompt rather than the difference between the negative prompt.",
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inputs=[
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io.Model.Input("model"),
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],
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outputs=[
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io.Model.Output(display_name="patched_model"),
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],
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is_experimental=True,
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search_aliases=[
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"mahiro",
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"mahiro cfg",
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"similarity-adaptive guidance",
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"positive-biased cfg",
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],
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)
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@classmethod
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def execute(cls, model) -> io.NodeOutput:
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m = model.clone()
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def mahiro_normd(args):
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scale: float = args["cond_scale"]
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cond_p: torch.Tensor = args["cond_denoised"]
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uncond_p: torch.Tensor = args["uncond_denoised"]
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# naive leap
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leap = cond_p * scale
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# sim with uncond leap
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u_leap = uncond_p * scale
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cfg = args["denoised"]
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merge = (leap + cfg) / 2
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normu = torch.sqrt(u_leap.abs()) * u_leap.sign()
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normm = torch.sqrt(merge.abs()) * merge.sign()
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sim = F.cosine_similarity(normu, normm).mean()
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simsc = 2 * (sim + 1)
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wm = (simsc * cfg + (4 - simsc) * leap) / 4
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return wm
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m.set_model_sampler_post_cfg_function(mahiro_normd)
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return io.NodeOutput(m)
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class MahiroExtension(ComfyExtension):
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@override
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async def get_node_list(self) -> list[type[io.ComfyNode]]:
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return [
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Mahiro,
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]
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async def comfy_entrypoint() -> MahiroExtension:
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return MahiroExtension()
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