from __future__ import annotations import copy import logging from inspect import cleandoc from typing import TYPE_CHECKING from typing_extensions import override from comfy_api.latest import ComfyExtension, io if TYPE_CHECKING: from comfy.model_patcher import ModelPatcher from comfy.sd import CLIP, VAE import torch import comfy.model_management import comfy.multigpu class MultiGPUCFGSplitNode(io.ComfyNode): """ Prepares model to have sampling accelerated via splitting work units. Should be placed after nodes that modify the model object itself, such as compile or attention-switch nodes. Other than those exceptions, this node can be placed in any order. """ @classmethod def define_schema(cls): return io.Schema( node_id="MultiGPU_WorkUnits", display_name="MultiGPU CFG Split", category="advanced/multigpu", description=cleandoc(cls.__doc__), inputs=[ io.Model.Input("model"), io.Int.Input("max_gpus", default=2, min=1, step=1), ], outputs=[ io.Model.Output(), ], ) @classmethod def execute(cls, model: ModelPatcher, max_gpus: int) -> io.NodeOutput: model = comfy.multigpu.create_multigpu_deepclones(model, max_gpus, reuse_loaded=True) return io.NodeOutput(model) def _force_supported_compute_dtype(patcher: ModelPatcher, device: torch.device): """Cast compute dtype to one the device supports; no-op if already supported.""" weight_dtype = patcher.model_dtype() cast_dtype = comfy.model_management.unet_manual_cast(weight_dtype, device) if cast_dtype is None: return logging.info(f"Select Model Device: using {cast_dtype} compute dtype on {device} (model weight dtype was {weight_dtype}).") patcher.set_model_compute_dtype(cast_dtype) def _remember_base_devices(patcher: ModelPatcher): """Stash the original load/offload device on the underlying model. Stored on patcher.model (which is shared with the input patcher), so later "default" selections can recover the loader's original routing. Only the first Select on a given chain writes these attrs; subsequent deepclones inherit them onto their freshly-loaded model below. """ if not hasattr(patcher.model, "_select_base_load_device"): patcher.model._select_base_load_device = patcher.load_device patcher.model._select_base_offload_device = patcher.offload_device def _propagate_base_devices(src_model, dst_model): """Carry the loader-original device attrs onto the freshly-deepcloned model.""" if hasattr(src_model, "_select_base_load_device") and not hasattr(dst_model, "_select_base_load_device"): dst_model._select_base_load_device = src_model._select_base_load_device dst_model._select_base_offload_device = src_model._select_base_offload_device def _retarget_patcher(patcher: ModelPatcher, target_load_device, target_offload_device): """Return a patcher whose actual model weights live on *target_load_device*. If *patcher* is already on *target_load_device* we just retarget the (already-cloned) patcher's metadata in place. Otherwise we call :meth:`ModelPatcher.deepclone_multigpu` to spawn a fresh model from the loader's ``cached_patcher_init`` factory -- the only safe way to move weights that may already be partially loaded onto another device. NOTE: reusing the input patcher's model when the requested device matches its current load_device is a deliberate fast path. Anything that has already mutated the original model (e.g. a prior KSampler invocation on the same model) will be observed here. This is by design and documented on the SelectXDeviceNode docstrings -- placing Select X Device after a node that consumes the same model is not recommended. """ if patcher.load_device == target_load_device: # Fast path: weights already on the desired device, just update offload. patcher.offload_device = target_offload_device return patcher src_model = patcher.model patcher = patcher.deepclone_multigpu(new_load_device=target_load_device) patcher.offload_device = target_offload_device _propagate_base_devices(src_model, patcher.model) if hasattr(patcher, "register_load_device"): patcher.register_load_device(patcher.load_device) return patcher def _apply_patcher_device(patcher: ModelPatcher, resolved, base_offload_override=None): """Resolve the requested device and produce a patcher routed there. For "default" we restore the loader's original load/offload pair. For CPU we pin both load and offload to CPU (and, on a dynamic patcher, downgrade to a plain ModelPatcher so the dynamic-only code paths are bypassed). For an explicit GPU we keep the loader's original offload but target the requested load device; if that differs from the current load device the patcher is deepcloned onto the new device. """ _remember_base_devices(patcher) base_load = patcher.model._select_base_load_device base_offload = base_offload_override if base_offload_override is not None else patcher.model._select_base_offload_device if resolved is None: # "default" -> route back to the loader's original devices. return _retarget_patcher(patcher, base_load, base_offload) if resolved.type == "cpu": if patcher.is_dynamic(): # clone(disable_dynamic=True) requires cached_patcher_init; let the # exception surface to the caller (Select*DeviceNode.execute), which # will translate it into a passthrough+log so unsupported loaders # don't hard-fail the workflow. patcher = patcher.clone(disable_dynamic=True) patcher.load_device = resolved patcher.offload_device = resolved return patcher return _retarget_patcher(patcher, resolved, base_offload) def _prune_multigpu_collision(model: ModelPatcher, primary_device): """Drop any multigpu clone whose load_device matches *primary_device*. Without pruning, MultiGPU CFG Split would have stacked a clone on the same device the primary now occupies (i.e. the workflow places MultiGPU CFG Split before Select Model Device). Keeps the clone set consistent with the new primary placement. """ multigpu_models = model.get_additional_models_with_key("multigpu") if not multigpu_models: return filtered = [m for m in multigpu_models if m.load_device != primary_device] if len(filtered) != len(multigpu_models): logging.info(f"Select Model Device: pruning MultiGPU clone on {primary_device} that now collides with the primary model.") model.set_additional_models("multigpu", filtered) if hasattr(model, "match_multigpu_clones"): model.match_multigpu_clones() class SelectModelDeviceNode(io.ComfyNode): """ Place the diffusion model on a specific device (default / cpu / gpu:N). - "default" restores the device assigned by the loader (even after a prior Select Model Device call). - "cpu" pins both the load and offload device to CPU. - "gpu:N" pins the load device to the Nth available GPU; the offload device is restored to the loader's original choice. When the requested device differs from the device the input model is already on, a fresh model is spawned via the loader's reload factory (cached_patcher_init) so the new patcher owns independent weights on the new device. Loaders that don't support multigpu (no factory) will cause the node to pass through unchanged with a warning. If the workflow already has MultiGPU CFG Split applied and the chosen GPU collides with one of the existing multigpu clones, that clone is dropped so two patchers don't end up bound to the same device. When the selected device does not exist on the current machine (e.g. a workflow built on a 2-GPU box opened on a 1-GPU box), the node passes the model through unchanged and logs a message instead of failing. NOTE: Placing Select Model Device *after* a node that has already consumed the same model (e.g. a KSampler that ran on this model on the original device) is not recommended -- any state the prior consumer mutated on the original model will be observed when the selected device matches the original (fast path). Place Select Model Device before any consumer of the model. """ @classmethod def define_schema(cls): return io.Schema( node_id="SelectModelDevice", display_name="Select Model Device", category="advanced/multigpu", description=cleandoc(cls.__doc__), inputs=[ io.Model.Input("model"), io.Combo.Input("device", options=comfy.model_management.get_gpu_device_options()), ], outputs=[ io.Model.Output(), ], ) @classmethod def validate_inputs(cls, device="default"): # Allow unknown gpu:N values so portable workflows do not error # at validation time; runtime fallback will handle them. return True @classmethod def execute(cls, model: ModelPatcher, device: str = "default") -> io.NodeOutput: model = model.clone() resolved = comfy.model_management.resolve_gpu_device_option(device) if resolved is None and device not in (None, "default"): logging.info(f"Select Model Device: requested device '{device}' not available, passing through unchanged.") return io.NodeOutput(model) try: model = _apply_patcher_device(model, resolved) except RuntimeError as e: logging.warning(f"Select Model Device: cannot retarget model, passing through unchanged. ({e})") return io.NodeOutput(model) if resolved is not None: _force_supported_compute_dtype(model, resolved) _prune_multigpu_collision(model, model.load_device) return io.NodeOutput(model) class SelectCLIPDeviceNode(io.ComfyNode): """ Place the CLIP text encoder on a specific device (default / cpu / gpu:N). - "default" restores the device assigned by the loader. - "cpu" pins both the load and offload device to CPU. - "gpu:N" pins the load device to the Nth available GPU. When the selected device does not exist on the current machine (e.g. a workflow built on a 2-GPU box opened on a 1-GPU box), the node passes the CLIP through unchanged and logs a message instead of failing. """ @classmethod def define_schema(cls): return io.Schema( node_id="SelectCLIPDevice", display_name="Select CLIP Device", category="advanced/multigpu", description=cleandoc(cls.__doc__), inputs=[ io.Clip.Input("clip"), io.Combo.Input("device", options=comfy.model_management.get_gpu_device_options()), ], outputs=[ io.Clip.Output(), ], ) @classmethod def validate_inputs(cls, device="default"): return True @classmethod def execute(cls, clip: CLIP, device: str = "default") -> io.NodeOutput: clip = clip.clone() resolved = comfy.model_management.resolve_gpu_device_option(device) if resolved is None and device not in (None, "default"): logging.info(f"Select CLIP Device: requested device '{device}' not available, passing through unchanged.") return io.NodeOutput(clip) try: clip.patcher = _apply_patcher_device(clip.patcher, resolved) except RuntimeError as e: logging.warning(f"Select CLIP Device: cannot retarget CLIP, passing through unchanged. ({e})") return io.NodeOutput(clip) class SelectVAEDeviceNode(io.ComfyNode): """ Place the VAE on a specific device (default / gpu:N). - "default" restores the device assigned by the loader. - "gpu:N" pins the load device to the Nth available GPU; the offload device is set to the standard VAE offload device. CPU is intentionally not exposed in the UI for the VAE; if a workflow supplies "cpu" anyway (e.g. opened from another machine), the request is dropped with a log message and the VAE is passed through unchanged. When the selected device does not exist on the current machine (e.g. a workflow built on a 2-GPU box opened on a 1-GPU box), the node passes the VAE through unchanged and logs a message instead of failing. """ @classmethod def define_schema(cls): return io.Schema( node_id="SelectVAEDevice", display_name="Select VAE Device", category="advanced/multigpu", description=cleandoc(cls.__doc__), inputs=[ io.Vae.Input("vae"), io.Combo.Input("device", options=comfy.model_management.get_gpu_device_options_no_cpu()), ], outputs=[ io.Vae.Output(), ], ) @classmethod def validate_inputs(cls, device="default"): return True @classmethod def execute(cls, vae: VAE, device: str = "default") -> io.NodeOutput: # VAE has no .clone(); shallow-copy the wrapper and clone the patcher # so we can retarget load/offload device without affecting the input VAE. vae = copy.copy(vae) vae.patcher = vae.patcher.clone() resolved = comfy.model_management.resolve_gpu_device_option(device) if resolved is None and device not in (None, "default"): logging.info(f"Select VAE Device: requested device '{device}' not available, passing through unchanged.") return io.NodeOutput(vae) if resolved is not None and resolved.type == "cpu": logging.info("Select VAE Device: CPU is not a supported choice, passing through unchanged.") return io.NodeOutput(vae) if not hasattr(vae, "_select_base_device"): vae._select_base_device = vae.device try: vae.patcher = _apply_patcher_device( vae.patcher, resolved, base_offload_override=comfy.model_management.vae_offload_device(), ) except RuntimeError as e: logging.warning(f"Select VAE Device: cannot retarget VAE, passing through unchanged. ({e})") return io.NodeOutput(vae) # Keep VAE wrapper in sync with whatever model the patcher now owns; # deepclone_multigpu may have produced a fresh first_stage_model. vae.first_stage_model = vae.patcher.model vae.device = vae._select_base_device if resolved is None else resolved return io.NodeOutput(vae) class MultiGPUOptionsNode(io.ComfyNode): """ Select the relative speed of GPUs in the special case they have significantly different performance from one another. NOTE (not registered yet, see MultiGPUExtension.get_node_list below): The output GPUOptionsGroup is plumbed through create_multigpu_deepclones() and stored on model.model_options['multigpu_options'] via GPUOptionsGroup.register(), but the cond scheduler in comfy/samplers.py (calc_cond_batch_outer_multigpu) does NOT yet consult relative_speed when distributing conds across devices; it uses a uniform conds_per_device round-robin via next_available_device(). Before re-enabling this node, wire its relative_speed into the scheduler (e.g. via comfy.multigpu.load_balance_devices(), which already implements the proportional split) so the input actually affects work distribution. """ @classmethod def define_schema(cls): return io.Schema( node_id="MultiGPU_Options", display_name="MultiGPU Options", category="advanced/multigpu", description=cleandoc(cls.__doc__), inputs=[ io.Int.Input("device_index", default=0, min=0, max=64), io.Float.Input("relative_speed", default=1.0, min=0.0, step=0.01), io.Custom("GPU_OPTIONS").Input("gpu_options", optional=True), ], outputs=[ io.Custom("GPU_OPTIONS").Output(), ], ) @classmethod def execute(cls, device_index: int, relative_speed: float, gpu_options: comfy.multigpu.GPUOptionsGroup = None) -> io.NodeOutput: if not gpu_options: gpu_options = comfy.multigpu.GPUOptionsGroup() else: gpu_options = gpu_options.clone() opt = comfy.multigpu.GPUOptions(device_index=device_index, relative_speed=relative_speed) gpu_options.add(opt) return io.NodeOutput(gpu_options) class MultiGPUExtension(ComfyExtension): @override async def get_node_list(self) -> list[type[io.ComfyNode]]: return [ MultiGPUCFGSplitNode, SelectModelDeviceNode, SelectCLIPDeviceNode, SelectVAEDeviceNode, # MultiGPUOptionsNode, ] async def comfy_entrypoint() -> MultiGPUExtension: return MultiGPUExtension()