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comfyanony
| Author | SHA1 | Date | |
|---|---|---|---|
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0fecf6f60c |
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|
||||
{"revision": 0, "last_node_id": 13, "last_link_id": 0, "nodes": [{"id": 13, "type": "cf95b747-3e17-46cb-8097-cac60ff9b2e1", "pos": [1120, 330], "size": [240, 58], "flags": {}, "order": 3, "mode": 0, "inputs": [{"localized_name": "video", "name": "video", "type": "VIDEO", "link": null}, {"name": "model_name", "type": "COMBO", "widget": {"name": "model_name"}, "link": null}], "outputs": [{"localized_name": "VIDEO", "name": "VIDEO", "type": "VIDEO", "links": []}], "title": "Video Upscale(GAN x4)", "properties": {"proxyWidgets": [["-1", "model_name"]], "cnr_id": "comfy-core", "ver": "0.14.1"}, "widgets_values": ["RealESRGAN_x4plus.safetensors"]}], "links": [], "version": 0.4, "definitions": {"subgraphs": [{"id": "cf95b747-3e17-46cb-8097-cac60ff9b2e1", "version": 1, "state": {"lastGroupId": 0, "lastNodeId": 13, "lastLinkId": 19, "lastRerouteId": 0}, "revision": 0, "config": {}, "name": "Video Upscale(GAN x4)", "inputNode": {"id": -10, "bounding": [550, 460, 120, 80]}, "outputNode": {"id": -20, "bounding": [1490, 460, 120, 60]}, "inputs": [{"id": "666d633e-93e7-42dc-8d11-2b7b99b0f2a6", "name": "video", "type": "VIDEO", "linkIds": [10], "localized_name": "video", "pos": [650, 480]}, {"id": "2e23a087-caa8-4d65-99e6-662761aa905a", "name": "model_name", "type": "COMBO", "linkIds": [19], "pos": [650, 500]}], "outputs": [{"id": "0c1768ea-3ec2-412f-9af6-8e0fa36dae70", "name": "VIDEO", "type": "VIDEO", "linkIds": [15], "localized_name": "VIDEO", "pos": [1510, 480]}], "widgets": [], "nodes": [{"id": 2, "type": "ImageUpscaleWithModel", "pos": [1110, 450], "size": [320, 46], "flags": {}, "order": 1, "mode": 0, "inputs": [{"localized_name": "upscale_model", "name": "upscale_model", "type": "UPSCALE_MODEL", "link": 1}, {"localized_name": "image", "name": "image", "type": "IMAGE", "link": 14}], "outputs": [{"localized_name": "IMAGE", "name": "IMAGE", "type": "IMAGE", "links": [13]}], "properties": {"cnr_id": "comfy-core", "ver": "0.10.0", "Node name for S&R": "ImageUpscaleWithModel"}}, {"id": 11, "type": "CreateVideo", "pos": [1110, 550], "size": [320, 78], "flags": {}, "order": 3, "mode": 0, "inputs": [{"localized_name": "images", "name": "images", "type": "IMAGE", "link": 13}, {"localized_name": "audio", "name": "audio", "shape": 7, "type": "AUDIO", "link": 16}, {"localized_name": "fps", "name": "fps", "type": "FLOAT", "widget": {"name": "fps"}, "link": 12}], "outputs": [{"localized_name": "VIDEO", "name": "VIDEO", "type": "VIDEO", "links": [15]}], "properties": {"cnr_id": "comfy-core", "ver": "0.10.0", "Node name for S&R": "CreateVideo"}, "widgets_values": [30]}, {"id": 10, "type": "GetVideoComponents", "pos": [1110, 330], "size": [320, 70], "flags": {}, "order": 2, "mode": 0, "inputs": [{"localized_name": "video", "name": "video", "type": "VIDEO", "link": 10}], "outputs": [{"localized_name": "images", "name": "images", "type": "IMAGE", "links": [14]}, {"localized_name": "audio", "name": "audio", "type": "AUDIO", "links": [16]}, {"localized_name": "fps", "name": "fps", "type": "FLOAT", "links": [12]}], "properties": {"cnr_id": "comfy-core", "ver": "0.10.0", "Node name for S&R": "GetVideoComponents"}}, {"id": 1, "type": "UpscaleModelLoader", "pos": [750, 450], "size": [280, 60], "flags": {}, "order": 0, "mode": 0, "inputs": [{"localized_name": "model_name", "name": "model_name", "type": "COMBO", "widget": {"name": "model_name"}, "link": 19}], "outputs": [{"localized_name": "UPSCALE_MODEL", "name": "UPSCALE_MODEL", "type": "UPSCALE_MODEL", "links": [1]}], "properties": {"cnr_id": "comfy-core", "ver": "0.10.0", "Node name for S&R": "UpscaleModelLoader", "models": [{"name": "RealESRGAN_x4plus.safetensors", "url": "https://huggingface.co/Comfy-Org/Real-ESRGAN_repackaged/resolve/main/RealESRGAN_x4plus.safetensors", "directory": "upscale_models"}]}, "widgets_values": ["RealESRGAN_x4plus.safetensors"]}], "groups": [], "links": [{"id": 1, "origin_id": 1, "origin_slot": 0, "target_id": 2, "target_slot": 0, "type": "UPSCALE_MODEL"}, {"id": 14, "origin_id": 10, "origin_slot": 0, "target_id": 2, "target_slot": 1, "type": "IMAGE"}, {"id": 13, "origin_id": 2, "origin_slot": 0, "target_id": 11, "target_slot": 0, "type": "IMAGE"}, {"id": 16, "origin_id": 10, "origin_slot": 1, "target_id": 11, "target_slot": 1, "type": "AUDIO"}, {"id": 12, "origin_id": 10, "origin_slot": 2, "target_id": 11, "target_slot": 2, "type": "FLOAT"}, {"id": 10, "origin_id": -10, "origin_slot": 0, "target_id": 10, "target_slot": 0, "type": "VIDEO"}, {"id": 15, "origin_id": 11, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "VIDEO"}, {"id": 19, "origin_id": -10, "origin_slot": 1, "target_id": 1, "target_slot": 0, "type": "COMBO"}], "extra": {"workflowRendererVersion": "LG"}, "category": "Video generation and editing/Enhance video"}]}, "extra": {}}
|
||||
|
||||
@@ -49,7 +49,7 @@ parser.add_argument("--temp-directory", type=str, default=None, help="Set the Co
|
||||
parser.add_argument("--input-directory", type=str, default=None, help="Set the ComfyUI input directory. Overrides --base-directory.")
|
||||
parser.add_argument("--auto-launch", action="store_true", help="Automatically launch ComfyUI in the default browser.")
|
||||
parser.add_argument("--disable-auto-launch", action="store_true", help="Disable auto launching the browser.")
|
||||
parser.add_argument("--cuda-device", type=str, default=None, metavar="DEVICE_ID", help="Set the ids of cuda devices this instance will use. All other devices will not be visible.")
|
||||
parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use. All other devices will not be visible.")
|
||||
parser.add_argument("--default-device", type=int, default=None, metavar="DEFAULT_DEVICE_ID", help="Set the id of the default device, all other devices will stay visible.")
|
||||
cm_group = parser.add_mutually_exclusive_group()
|
||||
cm_group.add_argument("--cuda-malloc", action="store_true", help="Enable cudaMallocAsync (enabled by default for torch 2.0 and up).")
|
||||
|
||||
@@ -15,14 +15,13 @@
|
||||
You should have received a copy of the GNU General Public License
|
||||
along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
|
||||
import torch
|
||||
from enum import Enum
|
||||
import math
|
||||
import os
|
||||
import logging
|
||||
import copy
|
||||
import comfy.utils
|
||||
import comfy.model_management
|
||||
import comfy.model_detection
|
||||
@@ -39,7 +38,7 @@ import comfy.ldm.hydit.controlnet
|
||||
import comfy.ldm.flux.controlnet
|
||||
import comfy.ldm.qwen_image.controlnet
|
||||
import comfy.cldm.dit_embedder
|
||||
from typing import TYPE_CHECKING, Union
|
||||
from typing import TYPE_CHECKING
|
||||
if TYPE_CHECKING:
|
||||
from comfy.hooks import HookGroup
|
||||
|
||||
@@ -65,18 +64,6 @@ class StrengthType(Enum):
|
||||
CONSTANT = 1
|
||||
LINEAR_UP = 2
|
||||
|
||||
class ControlIsolation:
|
||||
'''Temporarily set a ControlBase object's previous_controlnet to None to prevent cascading calls.'''
|
||||
def __init__(self, control: ControlBase):
|
||||
self.control = control
|
||||
self.orig_previous_controlnet = control.previous_controlnet
|
||||
|
||||
def __enter__(self):
|
||||
self.control.previous_controlnet = None
|
||||
|
||||
def __exit__(self, *args):
|
||||
self.control.previous_controlnet = self.orig_previous_controlnet
|
||||
|
||||
class ControlBase:
|
||||
def __init__(self):
|
||||
self.cond_hint_original = None
|
||||
@@ -90,7 +77,7 @@ class ControlBase:
|
||||
self.compression_ratio = 8
|
||||
self.upscale_algorithm = 'nearest-exact'
|
||||
self.extra_args = {}
|
||||
self.previous_controlnet: Union[ControlBase, None] = None
|
||||
self.previous_controlnet = None
|
||||
self.extra_conds = []
|
||||
self.strength_type = StrengthType.CONSTANT
|
||||
self.concat_mask = False
|
||||
@@ -98,7 +85,6 @@ class ControlBase:
|
||||
self.extra_concat = None
|
||||
self.extra_hooks: HookGroup = None
|
||||
self.preprocess_image = lambda a: a
|
||||
self.multigpu_clones: dict[torch.device, ControlBase] = {}
|
||||
|
||||
def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0), vae=None, extra_concat=[]):
|
||||
self.cond_hint_original = cond_hint
|
||||
@@ -125,38 +111,17 @@ class ControlBase:
|
||||
def cleanup(self):
|
||||
if self.previous_controlnet is not None:
|
||||
self.previous_controlnet.cleanup()
|
||||
for device_cnet in self.multigpu_clones.values():
|
||||
with ControlIsolation(device_cnet):
|
||||
device_cnet.cleanup()
|
||||
|
||||
self.cond_hint = None
|
||||
self.extra_concat = None
|
||||
self.timestep_range = None
|
||||
|
||||
def get_models(self):
|
||||
out = []
|
||||
for device_cnet in self.multigpu_clones.values():
|
||||
out += device_cnet.get_models_only_self()
|
||||
if self.previous_controlnet is not None:
|
||||
out += self.previous_controlnet.get_models()
|
||||
return out
|
||||
|
||||
def get_models_only_self(self):
|
||||
'Calls get_models, but temporarily sets previous_controlnet to None.'
|
||||
with ControlIsolation(self):
|
||||
return self.get_models()
|
||||
|
||||
def get_instance_for_device(self, device):
|
||||
'Returns instance of this Control object intended for selected device.'
|
||||
return self.multigpu_clones.get(device, self)
|
||||
|
||||
def deepclone_multigpu(self, load_device, autoregister=False):
|
||||
'''
|
||||
Create deep clone of Control object where model(s) is set to other devices.
|
||||
|
||||
When autoregister is set to True, the deep clone is also added to multigpu_clones dict.
|
||||
'''
|
||||
raise NotImplementedError("Classes inheriting from ControlBase should define their own deepclone_multigpu funtion.")
|
||||
|
||||
def get_extra_hooks(self):
|
||||
out = []
|
||||
if self.extra_hooks is not None:
|
||||
@@ -165,7 +130,7 @@ class ControlBase:
|
||||
out += self.previous_controlnet.get_extra_hooks()
|
||||
return out
|
||||
|
||||
def copy_to(self, c: ControlBase):
|
||||
def copy_to(self, c):
|
||||
c.cond_hint_original = self.cond_hint_original
|
||||
c.strength = self.strength
|
||||
c.timestep_percent_range = self.timestep_percent_range
|
||||
@@ -319,14 +284,6 @@ class ControlNet(ControlBase):
|
||||
self.copy_to(c)
|
||||
return c
|
||||
|
||||
def deepclone_multigpu(self, load_device, autoregister=False):
|
||||
c = self.copy()
|
||||
c.control_model = copy.deepcopy(c.control_model)
|
||||
c.control_model_wrapped = comfy.model_patcher.ModelPatcher(c.control_model, load_device=load_device, offload_device=comfy.model_management.unet_offload_device())
|
||||
if autoregister:
|
||||
self.multigpu_clones[load_device] = c
|
||||
return c
|
||||
|
||||
def get_models(self):
|
||||
out = super().get_models()
|
||||
out.append(self.control_model_wrapped)
|
||||
@@ -949,14 +906,6 @@ class T2IAdapter(ControlBase):
|
||||
self.copy_to(c)
|
||||
return c
|
||||
|
||||
def deepclone_multigpu(self, load_device, autoregister=False):
|
||||
c = self.copy()
|
||||
c.t2i_model = copy.deepcopy(c.t2i_model)
|
||||
c.device = load_device
|
||||
if autoregister:
|
||||
self.multigpu_clones[load_device] = c
|
||||
return c
|
||||
|
||||
def load_t2i_adapter(t2i_data, model_options={}): #TODO: model_options
|
||||
compression_ratio = 8
|
||||
upscale_algorithm = 'nearest-exact'
|
||||
|
||||
@@ -611,7 +611,6 @@ class AceStepDiTModel(nn.Module):
|
||||
intermediate_size,
|
||||
patch_size,
|
||||
audio_acoustic_hidden_dim,
|
||||
condition_dim=None,
|
||||
layer_types=None,
|
||||
sliding_window=128,
|
||||
rms_norm_eps=1e-6,
|
||||
@@ -641,7 +640,7 @@ class AceStepDiTModel(nn.Module):
|
||||
|
||||
self.time_embed = TimestepEmbedding(256, hidden_size, dtype=dtype, device=device, operations=operations)
|
||||
self.time_embed_r = TimestepEmbedding(256, hidden_size, dtype=dtype, device=device, operations=operations)
|
||||
self.condition_embedder = Linear(condition_dim, hidden_size, dtype=dtype, device=device)
|
||||
self.condition_embedder = Linear(hidden_size, hidden_size, dtype=dtype, device=device)
|
||||
|
||||
if layer_types is None:
|
||||
layer_types = ["full_attention"] * num_layers
|
||||
@@ -1036,9 +1035,6 @@ class AceStepConditionGenerationModel(nn.Module):
|
||||
fsq_dim=2048,
|
||||
fsq_levels=[8, 8, 8, 5, 5, 5],
|
||||
fsq_input_num_quantizers=1,
|
||||
encoder_hidden_size=2048,
|
||||
encoder_intermediate_size=6144,
|
||||
encoder_num_heads=16,
|
||||
audio_model=None,
|
||||
dtype=None,
|
||||
device=None,
|
||||
@@ -1058,24 +1054,24 @@ class AceStepConditionGenerationModel(nn.Module):
|
||||
|
||||
self.decoder = AceStepDiTModel(
|
||||
in_channels, hidden_size, num_dit_layers, num_heads, num_kv_heads, head_dim,
|
||||
intermediate_size, patch_size, audio_acoustic_hidden_dim, condition_dim=encoder_hidden_size,
|
||||
intermediate_size, patch_size, audio_acoustic_hidden_dim,
|
||||
layer_types=layer_types, sliding_window=sliding_window, rms_norm_eps=rms_norm_eps,
|
||||
dtype=dtype, device=device, operations=operations
|
||||
)
|
||||
self.encoder = AceStepConditionEncoder(
|
||||
text_hidden_dim, timbre_hidden_dim, encoder_hidden_size, num_lyric_layers, num_timbre_layers,
|
||||
encoder_num_heads, num_kv_heads, head_dim, encoder_intermediate_size, rms_norm_eps,
|
||||
text_hidden_dim, timbre_hidden_dim, hidden_size, num_lyric_layers, num_timbre_layers,
|
||||
num_heads, num_kv_heads, head_dim, intermediate_size, rms_norm_eps,
|
||||
dtype=dtype, device=device, operations=operations
|
||||
)
|
||||
self.tokenizer = AceStepAudioTokenizer(
|
||||
audio_acoustic_hidden_dim, encoder_hidden_size, pool_window_size, fsq_dim=fsq_dim, fsq_levels=fsq_levels, fsq_input_num_quantizers=fsq_input_num_quantizers, num_layers=num_tokenizer_layers, head_dim=head_dim, rms_norm_eps=rms_norm_eps,
|
||||
audio_acoustic_hidden_dim, hidden_size, pool_window_size, fsq_dim=fsq_dim, fsq_levels=fsq_levels, fsq_input_num_quantizers=fsq_input_num_quantizers, num_layers=num_tokenizer_layers, head_dim=head_dim, rms_norm_eps=rms_norm_eps,
|
||||
dtype=dtype, device=device, operations=operations
|
||||
)
|
||||
self.detokenizer = AudioTokenDetokenizer(
|
||||
encoder_hidden_size, pool_window_size, audio_acoustic_hidden_dim, num_layers=2, head_dim=head_dim,
|
||||
hidden_size, pool_window_size, audio_acoustic_hidden_dim, num_layers=2, head_dim=head_dim,
|
||||
dtype=dtype, device=device, operations=operations
|
||||
)
|
||||
self.null_condition_emb = nn.Parameter(torch.empty(1, 1, encoder_hidden_size, dtype=dtype, device=device))
|
||||
self.null_condition_emb = nn.Parameter(torch.empty(1, 1, hidden_size, dtype=dtype, device=device))
|
||||
|
||||
def prepare_condition(
|
||||
self,
|
||||
|
||||
@@ -155,7 +155,6 @@ class AutoencodingEngineLegacy(AutoencodingEngine):
|
||||
def __init__(self, embed_dim: int, **kwargs):
|
||||
self.max_batch_size = kwargs.pop("max_batch_size", None)
|
||||
ddconfig = kwargs.pop("ddconfig")
|
||||
decoder_ddconfig = kwargs.pop("decoder_ddconfig", ddconfig)
|
||||
super().__init__(
|
||||
encoder_config={
|
||||
"target": "comfy.ldm.modules.diffusionmodules.model.Encoder",
|
||||
@@ -163,7 +162,7 @@ class AutoencodingEngineLegacy(AutoencodingEngine):
|
||||
},
|
||||
decoder_config={
|
||||
"target": "comfy.ldm.modules.diffusionmodules.model.Decoder",
|
||||
"params": decoder_ddconfig,
|
||||
"params": ddconfig,
|
||||
},
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
@@ -696,15 +696,6 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
|
||||
if '{}encoder.lyric_encoder.layers.0.input_layernorm.weight'.format(key_prefix) in state_dict_keys:
|
||||
dit_config = {}
|
||||
dit_config["audio_model"] = "ace1.5"
|
||||
head_dim = 128
|
||||
dit_config["hidden_size"] = state_dict['{}decoder.layers.0.self_attn_norm.weight'.format(key_prefix)].shape[0]
|
||||
dit_config["intermediate_size"] = state_dict['{}decoder.layers.0.mlp.gate_proj.weight'.format(key_prefix)].shape[0]
|
||||
dit_config["num_heads"] = state_dict['{}decoder.layers.0.self_attn.q_proj.weight'.format(key_prefix)].shape[0] // head_dim
|
||||
|
||||
dit_config["encoder_hidden_size"] = state_dict['{}encoder.lyric_encoder.layers.0.input_layernorm.weight'.format(key_prefix)].shape[0]
|
||||
dit_config["encoder_num_heads"] = state_dict['{}encoder.lyric_encoder.layers.0.self_attn.q_proj.weight'.format(key_prefix)].shape[0] // head_dim
|
||||
dit_config["encoder_intermediate_size"] = state_dict['{}encoder.lyric_encoder.layers.0.mlp.gate_proj.weight'.format(key_prefix)].shape[0]
|
||||
dit_config["num_dit_layers"] = count_blocks(state_dict_keys, '{}decoder.layers.'.format(key_prefix) + '{}.')
|
||||
return dit_config
|
||||
|
||||
if '{}encoder.pan_blocks.1.cv4.conv.weight'.format(key_prefix) in state_dict_keys: # RT-DETR_v4
|
||||
|
||||
@@ -15,7 +15,6 @@
|
||||
You should have received a copy of the GNU General Public License
|
||||
along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import psutil
|
||||
import logging
|
||||
@@ -33,11 +32,6 @@ import comfy.memory_management
|
||||
import comfy.utils
|
||||
import comfy.quant_ops
|
||||
|
||||
from typing import TYPE_CHECKING
|
||||
if TYPE_CHECKING:
|
||||
from comfy.model_patcher import ModelPatcher
|
||||
|
||||
|
||||
class VRAMState(Enum):
|
||||
DISABLED = 0 #No vram present: no need to move models to vram
|
||||
NO_VRAM = 1 #Very low vram: enable all the options to save vram
|
||||
@@ -212,25 +206,6 @@ def get_torch_device():
|
||||
else:
|
||||
return torch.device(torch.cuda.current_device())
|
||||
|
||||
def get_all_torch_devices(exclude_current=False):
|
||||
global cpu_state
|
||||
devices = []
|
||||
if cpu_state == CPUState.GPU:
|
||||
if is_nvidia():
|
||||
for i in range(torch.cuda.device_count()):
|
||||
devices.append(torch.device(i))
|
||||
elif is_intel_xpu():
|
||||
for i in range(torch.xpu.device_count()):
|
||||
devices.append(torch.device(i))
|
||||
elif is_ascend_npu():
|
||||
for i in range(torch.npu.device_count()):
|
||||
devices.append(torch.device(i))
|
||||
else:
|
||||
devices.append(get_torch_device())
|
||||
if exclude_current:
|
||||
devices.remove(get_torch_device())
|
||||
return devices
|
||||
|
||||
def get_total_memory(dev=None, torch_total_too=False):
|
||||
global directml_enabled
|
||||
if dev is None:
|
||||
@@ -519,13 +494,9 @@ try:
|
||||
logging.info("Device: {}".format(get_torch_device_name(get_torch_device())))
|
||||
except:
|
||||
logging.warning("Could not pick default device.")
|
||||
try:
|
||||
for device in get_all_torch_devices(exclude_current=True):
|
||||
logging.info("Device: {}".format(get_torch_device_name(device)))
|
||||
except:
|
||||
pass
|
||||
|
||||
current_loaded_models: list[LoadedModel] = []
|
||||
|
||||
current_loaded_models = []
|
||||
|
||||
def module_size(module):
|
||||
module_mem = 0
|
||||
@@ -558,7 +529,7 @@ def module_mmap_residency(module, free=False):
|
||||
return mmap_touched_mem, module_mem
|
||||
|
||||
class LoadedModel:
|
||||
def __init__(self, model: ModelPatcher):
|
||||
def __init__(self, model):
|
||||
self._set_model(model)
|
||||
self.device = model.load_device
|
||||
self.real_model = None
|
||||
@@ -566,7 +537,7 @@ class LoadedModel:
|
||||
self.model_finalizer = None
|
||||
self._patcher_finalizer = None
|
||||
|
||||
def _set_model(self, model: ModelPatcher):
|
||||
def _set_model(self, model):
|
||||
self._model = weakref.ref(model)
|
||||
if model.parent is not None:
|
||||
self._parent_model = weakref.ref(model.parent)
|
||||
@@ -577,7 +548,6 @@ class LoadedModel:
|
||||
model = self._parent_model()
|
||||
if model is not None:
|
||||
self._set_model(model)
|
||||
self.device = model.load_device
|
||||
|
||||
@property
|
||||
def model(self):
|
||||
@@ -1809,34 +1779,7 @@ def soft_empty_cache(force=False):
|
||||
torch.cuda.ipc_collect()
|
||||
|
||||
def unload_all_models():
|
||||
for device in get_all_torch_devices():
|
||||
free_memory(1e30, device)
|
||||
|
||||
def unload_model_and_clones(model: ModelPatcher, unload_additional_models=True, all_devices=False):
|
||||
'Unload only model and its clones - primarily for multigpu cloning purposes.'
|
||||
initial_keep_loaded: list[LoadedModel] = current_loaded_models.copy()
|
||||
additional_models = []
|
||||
if unload_additional_models:
|
||||
additional_models = model.get_nested_additional_models()
|
||||
keep_loaded = []
|
||||
for loaded_model in initial_keep_loaded:
|
||||
if loaded_model.model is not None:
|
||||
if model.clone_base_uuid == loaded_model.model.clone_base_uuid:
|
||||
continue
|
||||
# check additional models if they are a match
|
||||
skip = False
|
||||
for add_model in additional_models:
|
||||
if add_model.clone_base_uuid == loaded_model.model.clone_base_uuid:
|
||||
skip = True
|
||||
break
|
||||
if skip:
|
||||
continue
|
||||
keep_loaded.append(loaded_model)
|
||||
if not all_devices:
|
||||
free_memory(1e30, get_torch_device(), keep_loaded)
|
||||
else:
|
||||
for device in get_all_torch_devices():
|
||||
free_memory(1e30, device, keep_loaded)
|
||||
free_memory(1e30, get_torch_device())
|
||||
|
||||
def debug_memory_summary():
|
||||
if is_amd() or is_nvidia():
|
||||
|
||||
@@ -23,7 +23,6 @@ import inspect
|
||||
import logging
|
||||
import math
|
||||
import uuid
|
||||
import copy
|
||||
from typing import Callable, Optional
|
||||
|
||||
import torch
|
||||
@@ -76,15 +75,12 @@ def set_model_options_pre_cfg_function(model_options, pre_cfg_function, disable_
|
||||
def create_model_options_clone(orig_model_options: dict):
|
||||
return comfy.patcher_extension.copy_nested_dicts(orig_model_options)
|
||||
|
||||
def create_hook_patches_clone(orig_hook_patches, copy_tuples=False):
|
||||
def create_hook_patches_clone(orig_hook_patches):
|
||||
new_hook_patches = {}
|
||||
for hook_ref in orig_hook_patches:
|
||||
new_hook_patches[hook_ref] = {}
|
||||
for k in orig_hook_patches[hook_ref]:
|
||||
new_hook_patches[hook_ref][k] = orig_hook_patches[hook_ref][k][:]
|
||||
if copy_tuples:
|
||||
for i in range(len(new_hook_patches[hook_ref][k])):
|
||||
new_hook_patches[hook_ref][k][i] = tuple(new_hook_patches[hook_ref][k][i])
|
||||
return new_hook_patches
|
||||
|
||||
def wipe_lowvram_weight(m):
|
||||
@@ -276,10 +272,7 @@ class ModelPatcher:
|
||||
self.is_clip = False
|
||||
self.hook_mode = comfy.hooks.EnumHookMode.MaxSpeed
|
||||
|
||||
self.cached_patcher_init: tuple[Callable, tuple] | tuple[Callable, tuple, int] | None = None
|
||||
self.is_multigpu_base_clone = False
|
||||
self.clone_base_uuid = uuid.uuid4()
|
||||
|
||||
self.cached_patcher_init: tuple[Callable, tuple] | None = None
|
||||
if not hasattr(self.model, 'model_loaded_weight_memory'):
|
||||
self.model.model_loaded_weight_memory = 0
|
||||
|
||||
@@ -333,8 +326,6 @@ class ModelPatcher:
|
||||
if self.cached_patcher_init is None:
|
||||
raise RuntimeError("Cannot create non-dynamic delegate: cached_patcher_init is not initialized.")
|
||||
temp_model_patcher = self.cached_patcher_init[0](*self.cached_patcher_init[1], disable_dynamic=True)
|
||||
if len(self.cached_patcher_init) > 2:
|
||||
temp_model_patcher = temp_model_patcher[self.cached_patcher_init[2]]
|
||||
model_override = temp_model_patcher.get_clone_model_override()
|
||||
if model_override is None:
|
||||
model_override = self.get_clone_model_override()
|
||||
@@ -393,98 +384,19 @@ class ModelPatcher:
|
||||
n.hook_mode = self.hook_mode
|
||||
|
||||
n.cached_patcher_init = self.cached_patcher_init
|
||||
n.is_multigpu_base_clone = self.is_multigpu_base_clone
|
||||
n.clone_base_uuid = self.clone_base_uuid
|
||||
|
||||
for callback in self.get_all_callbacks(CallbacksMP.ON_CLONE):
|
||||
callback(self, n)
|
||||
return n
|
||||
|
||||
def deepclone_multigpu(self, new_load_device=None, models_cache: dict[uuid.UUID,ModelPatcher]=None):
|
||||
logging.info(f"Creating deepclone of {self.model.__class__.__name__} for {new_load_device if new_load_device else self.load_device}.")
|
||||
comfy.model_management.unload_model_and_clones(self)
|
||||
n = self.clone()
|
||||
# set load device, if present
|
||||
if new_load_device is not None:
|
||||
n.load_device = new_load_device
|
||||
if self.cached_patcher_init is not None:
|
||||
temp_model_patcher: ModelPatcher | list[ModelPatcher] = self.cached_patcher_init[0](*self.cached_patcher_init[1])
|
||||
if len(self.cached_patcher_init) > 2:
|
||||
temp_model_patcher = temp_model_patcher[self.cached_patcher_init[2]]
|
||||
n.model = temp_model_patcher.model
|
||||
else:
|
||||
n.model = copy.deepcopy(n.model)
|
||||
# unlike for normal clone, backup dicts that shared same ref should not;
|
||||
# otherwise, patchers that have deep copies of base models will erroneously influence each other.
|
||||
n.backup = copy.deepcopy(n.backup)
|
||||
n.object_patches_backup = copy.deepcopy(n.object_patches_backup)
|
||||
n.hook_backup = copy.deepcopy(n.hook_backup)
|
||||
# multigpu clone should not have multigpu additional_models entry
|
||||
n.remove_additional_models("multigpu")
|
||||
# multigpu_clone all stored additional_models; make sure circular references are properly handled
|
||||
if models_cache is None:
|
||||
models_cache = {}
|
||||
for key, model_list in n.additional_models.items():
|
||||
for i in range(len(model_list)):
|
||||
add_model = n.additional_models[key][i]
|
||||
if add_model.clone_base_uuid not in models_cache:
|
||||
models_cache[add_model.clone_base_uuid] = add_model.deepclone_multigpu(new_load_device=new_load_device, models_cache=models_cache)
|
||||
n.additional_models[key][i] = models_cache[add_model.clone_base_uuid]
|
||||
for callback in self.get_all_callbacks(CallbacksMP.ON_DEEPCLONE_MULTIGPU):
|
||||
callback(self, n)
|
||||
return n
|
||||
|
||||
def match_multigpu_clones(self):
|
||||
multigpu_models = self.get_additional_models_with_key("multigpu")
|
||||
if len(multigpu_models) > 0:
|
||||
new_multigpu_models = []
|
||||
for mm in multigpu_models:
|
||||
# clone main model, but bring over relevant props from existing multigpu clone
|
||||
n = self.clone()
|
||||
n.load_device = mm.load_device
|
||||
n.backup = mm.backup
|
||||
n.object_patches_backup = mm.object_patches_backup
|
||||
n.hook_backup = mm.hook_backup
|
||||
n.model = mm.model
|
||||
n.is_multigpu_base_clone = mm.is_multigpu_base_clone
|
||||
n.remove_additional_models("multigpu")
|
||||
orig_additional_models: dict[str, list[ModelPatcher]] = comfy.patcher_extension.copy_nested_dicts(n.additional_models)
|
||||
n.additional_models = comfy.patcher_extension.copy_nested_dicts(mm.additional_models)
|
||||
# figure out which additional models are not present in multigpu clone
|
||||
models_cache = {}
|
||||
for mm_add_model in mm.get_additional_models():
|
||||
models_cache[mm_add_model.clone_base_uuid] = mm_add_model
|
||||
remove_models_uuids = set(list(models_cache.keys()))
|
||||
for key, model_list in orig_additional_models.items():
|
||||
for orig_add_model in model_list:
|
||||
if orig_add_model.clone_base_uuid not in models_cache:
|
||||
models_cache[orig_add_model.clone_base_uuid] = orig_add_model.deepclone_multigpu(new_load_device=n.load_device, models_cache=models_cache)
|
||||
existing_list = n.get_additional_models_with_key(key)
|
||||
existing_list.append(models_cache[orig_add_model.clone_base_uuid])
|
||||
n.set_additional_models(key, existing_list)
|
||||
if orig_add_model.clone_base_uuid in remove_models_uuids:
|
||||
remove_models_uuids.remove(orig_add_model.clone_base_uuid)
|
||||
# remove duplicate additional models
|
||||
for key, model_list in n.additional_models.items():
|
||||
new_model_list = [x for x in model_list if x.clone_base_uuid not in remove_models_uuids]
|
||||
n.set_additional_models(key, new_model_list)
|
||||
for callback in self.get_all_callbacks(CallbacksMP.ON_MATCH_MULTIGPU_CLONES):
|
||||
callback(self, n)
|
||||
new_multigpu_models.append(n)
|
||||
self.set_additional_models("multigpu", new_multigpu_models)
|
||||
|
||||
def is_clone(self, other):
|
||||
if hasattr(other, 'model') and self.model is other.model:
|
||||
return True
|
||||
return False
|
||||
|
||||
def clone_has_same_weights(self, clone: ModelPatcher, allow_multigpu=False):
|
||||
if allow_multigpu:
|
||||
if self.clone_base_uuid != clone.clone_base_uuid:
|
||||
return False
|
||||
else:
|
||||
if not self.is_clone(clone):
|
||||
return False
|
||||
def clone_has_same_weights(self, clone: 'ModelPatcher'):
|
||||
if not self.is_clone(clone):
|
||||
return False
|
||||
|
||||
if self.current_hooks != clone.current_hooks:
|
||||
return False
|
||||
@@ -1255,7 +1167,7 @@ class ModelPatcher:
|
||||
return self.additional_models.get(key, [])
|
||||
|
||||
def get_additional_models(self):
|
||||
all_models: list[ModelPatcher] = []
|
||||
all_models = []
|
||||
for models in self.additional_models.values():
|
||||
all_models.extend(models)
|
||||
return all_models
|
||||
@@ -1309,13 +1221,9 @@ class ModelPatcher:
|
||||
for callback in self.get_all_callbacks(CallbacksMP.ON_PRE_RUN):
|
||||
callback(self)
|
||||
|
||||
def prepare_state(self, timestep, model_options, ignore_multigpu=False):
|
||||
def prepare_state(self, timestep):
|
||||
for callback in self.get_all_callbacks(CallbacksMP.ON_PREPARE_STATE):
|
||||
callback(self, timestep, model_options, ignore_multigpu)
|
||||
if not ignore_multigpu and "multigpu_clones" in model_options:
|
||||
for p in model_options["multigpu_clones"].values():
|
||||
p: ModelPatcher
|
||||
p.prepare_state(timestep, model_options, ignore_multigpu=True)
|
||||
callback(self, timestep)
|
||||
|
||||
def restore_hook_patches(self):
|
||||
if self.hook_patches_backup is not None:
|
||||
@@ -1328,18 +1236,12 @@ class ModelPatcher:
|
||||
def prepare_hook_patches_current_keyframe(self, t: torch.Tensor, hook_group: comfy.hooks.HookGroup, model_options: dict[str]):
|
||||
curr_t = t[0]
|
||||
reset_current_hooks = False
|
||||
multigpu_kf_changed_cache = None
|
||||
transformer_options = model_options.get("transformer_options", {})
|
||||
for hook in hook_group.hooks:
|
||||
changed = hook.hook_keyframe.prepare_current_keyframe(curr_t=curr_t, transformer_options=transformer_options)
|
||||
# if keyframe changed, remove any cached HookGroups that contain hook with the same hook_ref;
|
||||
# this will cause the weights to be recalculated when sampling
|
||||
if changed:
|
||||
# cache changed for multigpu usage
|
||||
if "multigpu_clones" in model_options:
|
||||
if multigpu_kf_changed_cache is None:
|
||||
multigpu_kf_changed_cache = []
|
||||
multigpu_kf_changed_cache.append(hook)
|
||||
# reset current_hooks if contains hook that changed
|
||||
if self.current_hooks is not None:
|
||||
for current_hook in self.current_hooks.hooks:
|
||||
@@ -1351,28 +1253,6 @@ class ModelPatcher:
|
||||
self.cached_hook_patches.pop(cached_group)
|
||||
if reset_current_hooks:
|
||||
self.patch_hooks(None)
|
||||
if "multigpu_clones" in model_options:
|
||||
for p in model_options["multigpu_clones"].values():
|
||||
p: ModelPatcher
|
||||
p._handle_changed_hook_keyframes(multigpu_kf_changed_cache)
|
||||
|
||||
def _handle_changed_hook_keyframes(self, kf_changed_cache: list[comfy.hooks.Hook]):
|
||||
'Used to handle multigpu behavior inside prepare_hook_patches_current_keyframe.'
|
||||
if kf_changed_cache is None:
|
||||
return
|
||||
reset_current_hooks = False
|
||||
# reset current_hooks if contains hook that changed
|
||||
for hook in kf_changed_cache:
|
||||
if self.current_hooks is not None:
|
||||
for current_hook in self.current_hooks.hooks:
|
||||
if current_hook == hook:
|
||||
reset_current_hooks = True
|
||||
break
|
||||
for cached_group in list(self.cached_hook_patches.keys()):
|
||||
if cached_group.contains(hook):
|
||||
self.cached_hook_patches.pop(cached_group)
|
||||
if reset_current_hooks:
|
||||
self.patch_hooks(None)
|
||||
|
||||
def register_all_hook_patches(self, hooks: comfy.hooks.HookGroup, target_dict: dict[str], model_options: dict=None,
|
||||
registered: comfy.hooks.HookGroup = None):
|
||||
|
||||
@@ -1,230 +0,0 @@
|
||||
from __future__ import annotations
|
||||
import queue
|
||||
import threading
|
||||
import torch
|
||||
import logging
|
||||
|
||||
from collections import namedtuple
|
||||
from typing import TYPE_CHECKING
|
||||
if TYPE_CHECKING:
|
||||
from comfy.model_patcher import ModelPatcher
|
||||
import comfy.utils
|
||||
import comfy.patcher_extension
|
||||
import comfy.model_management
|
||||
|
||||
|
||||
class MultiGPUThreadPool:
|
||||
"""Persistent thread pool for multi-GPU work distribution.
|
||||
|
||||
Maintains one worker thread per extra GPU device. Each thread calls
|
||||
torch.cuda.set_device() once at startup so that compiled kernel caches
|
||||
(inductor/triton) stay warm across diffusion steps.
|
||||
"""
|
||||
|
||||
def __init__(self, devices: list[torch.device]):
|
||||
self._workers: list[threading.Thread] = []
|
||||
self._work_queues: dict[torch.device, queue.Queue] = {}
|
||||
self._result_queues: dict[torch.device, queue.Queue] = {}
|
||||
|
||||
for device in devices:
|
||||
wq = queue.Queue()
|
||||
rq = queue.Queue()
|
||||
self._work_queues[device] = wq
|
||||
self._result_queues[device] = rq
|
||||
t = threading.Thread(target=self._worker_loop, args=(device, wq, rq), daemon=True)
|
||||
t.start()
|
||||
self._workers.append(t)
|
||||
|
||||
def _worker_loop(self, device: torch.device, work_q: queue.Queue, result_q: queue.Queue):
|
||||
try:
|
||||
torch.cuda.set_device(device)
|
||||
except Exception as e:
|
||||
logging.error(f"MultiGPUThreadPool: failed to set device {device}: {e}")
|
||||
while True:
|
||||
item = work_q.get()
|
||||
if item is None:
|
||||
return
|
||||
result_q.put((None, e))
|
||||
return
|
||||
while True:
|
||||
item = work_q.get()
|
||||
if item is None:
|
||||
break
|
||||
fn, args, kwargs = item
|
||||
try:
|
||||
result = fn(*args, **kwargs)
|
||||
result_q.put((result, None))
|
||||
except Exception as e:
|
||||
result_q.put((None, e))
|
||||
|
||||
def submit(self, device: torch.device, fn, *args, **kwargs):
|
||||
self._work_queues[device].put((fn, args, kwargs))
|
||||
|
||||
def get_result(self, device: torch.device):
|
||||
return self._result_queues[device].get()
|
||||
|
||||
@property
|
||||
def devices(self) -> list[torch.device]:
|
||||
return list(self._work_queues.keys())
|
||||
|
||||
def shutdown(self):
|
||||
for wq in self._work_queues.values():
|
||||
wq.put(None) # sentinel
|
||||
for t in self._workers:
|
||||
t.join(timeout=5.0)
|
||||
|
||||
|
||||
class GPUOptions:
|
||||
def __init__(self, device_index: int, relative_speed: float):
|
||||
self.device_index = device_index
|
||||
self.relative_speed = relative_speed
|
||||
|
||||
def clone(self):
|
||||
return GPUOptions(self.device_index, self.relative_speed)
|
||||
|
||||
def create_dict(self):
|
||||
return {
|
||||
"relative_speed": self.relative_speed
|
||||
}
|
||||
|
||||
class GPUOptionsGroup:
|
||||
def __init__(self):
|
||||
self.options: dict[int, GPUOptions] = {}
|
||||
|
||||
def add(self, info: GPUOptions):
|
||||
self.options[info.device_index] = info
|
||||
|
||||
def clone(self):
|
||||
c = GPUOptionsGroup()
|
||||
for opt in self.options.values():
|
||||
c.add(opt)
|
||||
return c
|
||||
|
||||
def register(self, model: ModelPatcher):
|
||||
opts_dict = {}
|
||||
# get devices that are valid for this model
|
||||
devices: list[torch.device] = [model.load_device]
|
||||
for extra_model in model.get_additional_models_with_key("multigpu"):
|
||||
extra_model: ModelPatcher
|
||||
devices.append(extra_model.load_device)
|
||||
# create dictionary with actual device mapped to its GPUOptions
|
||||
device_opts_list: list[GPUOptions] = []
|
||||
for device in devices:
|
||||
device_opts = self.options.get(device.index, GPUOptions(device_index=device.index, relative_speed=1.0))
|
||||
opts_dict[device] = device_opts.create_dict()
|
||||
device_opts_list.append(device_opts)
|
||||
# make relative_speed relative to 1.0
|
||||
min_speed = min([x.relative_speed for x in device_opts_list])
|
||||
for value in opts_dict.values():
|
||||
value['relative_speed'] /= min_speed
|
||||
model.model_options['multigpu_options'] = opts_dict
|
||||
|
||||
|
||||
def create_multigpu_deepclones(model: ModelPatcher, max_gpus: int, gpu_options: GPUOptionsGroup=None, reuse_loaded=False):
|
||||
'Prepare ModelPatcher to contain deepclones of its BaseModel and related properties.'
|
||||
model = model.clone()
|
||||
# check if multigpu is already prepared - get the load devices from them if possible to exclude
|
||||
skip_devices = set()
|
||||
multigpu_models = model.get_additional_models_with_key("multigpu")
|
||||
if len(multigpu_models) > 0:
|
||||
for mm in multigpu_models:
|
||||
skip_devices.add(mm.load_device)
|
||||
skip_devices = list(skip_devices)
|
||||
|
||||
full_extra_devices = comfy.model_management.get_all_torch_devices(exclude_current=True)
|
||||
limit_extra_devices = full_extra_devices[:max_gpus-1]
|
||||
extra_devices = limit_extra_devices.copy()
|
||||
# exclude skipped devices
|
||||
for skip in skip_devices:
|
||||
if skip in extra_devices:
|
||||
extra_devices.remove(skip)
|
||||
# create new deepclones
|
||||
if len(extra_devices) > 0:
|
||||
for device in extra_devices:
|
||||
device_patcher = None
|
||||
if reuse_loaded:
|
||||
# check if there are any ModelPatchers currently loaded that could be referenced here after a clone
|
||||
loaded_models: list[ModelPatcher] = comfy.model_management.loaded_models()
|
||||
for lm in loaded_models:
|
||||
if lm.model is not None and lm.clone_base_uuid == model.clone_base_uuid and lm.load_device == device:
|
||||
device_patcher = lm.clone()
|
||||
logging.info(f"Reusing loaded deepclone of {device_patcher.model.__class__.__name__} for {device}")
|
||||
break
|
||||
if device_patcher is None:
|
||||
device_patcher = model.deepclone_multigpu(new_load_device=device)
|
||||
device_patcher.is_multigpu_base_clone = True
|
||||
multigpu_models = model.get_additional_models_with_key("multigpu")
|
||||
multigpu_models.append(device_patcher)
|
||||
model.set_additional_models("multigpu", multigpu_models)
|
||||
model.match_multigpu_clones()
|
||||
if gpu_options is None:
|
||||
gpu_options = GPUOptionsGroup()
|
||||
gpu_options.register(model)
|
||||
else:
|
||||
logging.info("No extra torch devices need initialization, skipping initializing MultiGPU Work Units.")
|
||||
# TODO: only keep model clones that don't go 'past' the intended max_gpu count
|
||||
# multigpu_models = model.get_additional_models_with_key("multigpu")
|
||||
# new_multigpu_models = []
|
||||
# for m in multigpu_models:
|
||||
# if m.load_device in limit_extra_devices:
|
||||
# new_multigpu_models.append(m)
|
||||
# model.set_additional_models("multigpu", new_multigpu_models)
|
||||
# persist skip_devices for use in sampling code
|
||||
# if len(skip_devices) > 0 or "multigpu_skip_devices" in model.model_options:
|
||||
# model.model_options["multigpu_skip_devices"] = skip_devices
|
||||
return model
|
||||
|
||||
|
||||
LoadBalance = namedtuple('LoadBalance', ['work_per_device', 'idle_time'])
|
||||
def load_balance_devices(model_options: dict[str], total_work: int, return_idle_time=False, work_normalized: int=None):
|
||||
'Optimize work assigned to different devices, accounting for their relative speeds and splittable work.'
|
||||
opts_dict = model_options['multigpu_options']
|
||||
devices = list(model_options['multigpu_clones'].keys())
|
||||
speed_per_device = []
|
||||
work_per_device = []
|
||||
# get sum of each device's relative_speed
|
||||
total_speed = 0.0
|
||||
for opts in opts_dict.values():
|
||||
total_speed += opts['relative_speed']
|
||||
# get relative work for each device;
|
||||
# obtained by w = (W*r)/R
|
||||
for device in devices:
|
||||
relative_speed = opts_dict[device]['relative_speed']
|
||||
relative_work = (total_work*relative_speed) / total_speed
|
||||
speed_per_device.append(relative_speed)
|
||||
work_per_device.append(relative_work)
|
||||
# relative work must be expressed in whole numbers, but likely is a decimal;
|
||||
# perform rounding while maintaining total sum equal to total work (sum of relative works)
|
||||
work_per_device = round_preserved(work_per_device)
|
||||
dict_work_per_device = {}
|
||||
for device, relative_work in zip(devices, work_per_device):
|
||||
dict_work_per_device[device] = relative_work
|
||||
if not return_idle_time:
|
||||
return LoadBalance(dict_work_per_device, None)
|
||||
# divide relative work by relative speed to get estimated completion time of said work by each device;
|
||||
# time here is relative and does not correspond to real-world units
|
||||
completion_time = [w/r for w,r in zip(work_per_device, speed_per_device)]
|
||||
# calculate relative time spent by the devices waiting on each other after their work is completed
|
||||
idle_time = abs(min(completion_time) - max(completion_time))
|
||||
# if need to compare work idle time, need to normalize to a common total work
|
||||
if work_normalized:
|
||||
idle_time *= (work_normalized/total_work)
|
||||
|
||||
return LoadBalance(dict_work_per_device, idle_time)
|
||||
|
||||
def round_preserved(values: list[float]):
|
||||
'Round all values in a list, preserving the combined sum of values.'
|
||||
# get floor of values; casting to int does it too
|
||||
floored = [int(x) for x in values]
|
||||
total_floored = sum(floored)
|
||||
# get remainder to distribute
|
||||
remainder = round(sum(values)) - total_floored
|
||||
# pair values with fractional portions
|
||||
fractional = [(i, x-floored[i]) for i, x in enumerate(values)]
|
||||
# sort by fractional part in descending order
|
||||
fractional.sort(key=lambda x: x[1], reverse=True)
|
||||
# distribute the remainder
|
||||
for i in range(remainder):
|
||||
index = fractional[i][0]
|
||||
floored[index] += 1
|
||||
return floored
|
||||
@@ -3,8 +3,6 @@ from typing import Callable
|
||||
|
||||
class CallbacksMP:
|
||||
ON_CLONE = "on_clone"
|
||||
ON_DEEPCLONE_MULTIGPU = "on_deepclone_multigpu"
|
||||
ON_MATCH_MULTIGPU_CLONES = "on_match_multigpu_clones"
|
||||
ON_LOAD = "on_load_after"
|
||||
ON_DETACH = "on_detach_after"
|
||||
ON_CLEANUP = "on_cleanup"
|
||||
|
||||
@@ -20,6 +20,7 @@ try:
|
||||
if cuda_version < (13,):
|
||||
ck.registry.disable("cuda")
|
||||
logging.warning("WARNING: You need pytorch with cu130 or higher to use optimized CUDA operations.")
|
||||
|
||||
ck.registry.disable("triton")
|
||||
for k, v in ck.list_backends().items():
|
||||
logging.info(f"Found comfy_kitchen backend {k}: {v}")
|
||||
|
||||
@@ -1,18 +1,16 @@
|
||||
from __future__ import annotations
|
||||
import torch
|
||||
import uuid
|
||||
import math
|
||||
import collections
|
||||
import comfy.model_management
|
||||
import comfy.conds
|
||||
import comfy.model_patcher
|
||||
import comfy.utils
|
||||
import comfy.hooks
|
||||
import comfy.patcher_extension
|
||||
from typing import TYPE_CHECKING
|
||||
if TYPE_CHECKING:
|
||||
from comfy.model_base import BaseModel
|
||||
from comfy.model_patcher import ModelPatcher
|
||||
from comfy.model_base import BaseModel
|
||||
from comfy.controlnet import ControlBase
|
||||
|
||||
def prepare_mask(noise_mask, shape, device):
|
||||
@@ -120,47 +118,6 @@ def cleanup_additional_models(models):
|
||||
if hasattr(m, 'cleanup'):
|
||||
m.cleanup()
|
||||
|
||||
def preprocess_multigpu_conds(conds: dict[str, list[dict[str]]], model: ModelPatcher, model_options: dict[str]):
|
||||
'''If multigpu acceleration required, creates deepclones of ControlNets and GLIGEN per device.'''
|
||||
multigpu_models: list[ModelPatcher] = model.get_additional_models_with_key("multigpu")
|
||||
if len(multigpu_models) == 0:
|
||||
return
|
||||
extra_devices = [x.load_device for x in multigpu_models]
|
||||
# handle controlnets
|
||||
controlnets: set[ControlBase] = set()
|
||||
for k in conds:
|
||||
for kk in conds[k]:
|
||||
if 'control' in kk:
|
||||
controlnets.add(kk['control'])
|
||||
if len(controlnets) > 0:
|
||||
# first, unload all controlnet clones
|
||||
for cnet in list(controlnets):
|
||||
cnet_models = cnet.get_models()
|
||||
for cm in cnet_models:
|
||||
comfy.model_management.unload_model_and_clones(cm, unload_additional_models=True)
|
||||
|
||||
# next, make sure each controlnet has a deepclone for all relevant devices
|
||||
for cnet in controlnets:
|
||||
curr_cnet = cnet
|
||||
while curr_cnet is not None:
|
||||
for device in extra_devices:
|
||||
if device not in curr_cnet.multigpu_clones:
|
||||
curr_cnet.deepclone_multigpu(device, autoregister=True)
|
||||
curr_cnet = curr_cnet.previous_controlnet
|
||||
# since all device clones are now present, recreate the linked list for cloned cnets per device
|
||||
for cnet in controlnets:
|
||||
curr_cnet = cnet
|
||||
while curr_cnet is not None:
|
||||
prev_cnet = curr_cnet.previous_controlnet
|
||||
for device in extra_devices:
|
||||
device_cnet = curr_cnet.get_instance_for_device(device)
|
||||
prev_device_cnet = None
|
||||
if prev_cnet is not None:
|
||||
prev_device_cnet = prev_cnet.get_instance_for_device(device)
|
||||
device_cnet.set_previous_controlnet(prev_device_cnet)
|
||||
curr_cnet = prev_cnet
|
||||
# potentially handle gligen - since not widely used, ignored for now
|
||||
|
||||
def estimate_memory(model, noise_shape, conds):
|
||||
cond_shapes = collections.defaultdict(list)
|
||||
cond_shapes_min = {}
|
||||
@@ -185,8 +142,7 @@ def prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None
|
||||
return executor.execute(model, noise_shape, conds, model_options=model_options, force_full_load=force_full_load, force_offload=force_offload)
|
||||
|
||||
def _prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None, force_full_load=False, force_offload=False):
|
||||
model.match_multigpu_clones()
|
||||
preprocess_multigpu_conds(conds, model, model_options)
|
||||
real_model: BaseModel = None
|
||||
models, inference_memory = get_additional_models(conds, model.model_dtype())
|
||||
models += get_additional_models_from_model_options(model_options)
|
||||
models += model.get_nested_additional_models() # TODO: does this require inference_memory update?
|
||||
@@ -198,7 +154,7 @@ def _prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=Non
|
||||
memory_required += inference_memory
|
||||
minimum_memory_required += inference_memory
|
||||
comfy.model_management.load_models_gpu([model] + models, memory_required=memory_required, minimum_memory_required=minimum_memory_required, force_full_load=force_full_load)
|
||||
real_model: BaseModel = model.model
|
||||
real_model = model.model
|
||||
|
||||
return real_model, conds, models
|
||||
|
||||
@@ -244,18 +200,3 @@ def prepare_model_patcher(model: ModelPatcher, conds, model_options: dict):
|
||||
comfy.patcher_extension.merge_nested_dicts(to_load_options.setdefault(wc_name, {}), model_options["transformer_options"][wc_name],
|
||||
copy_dict1=False)
|
||||
return to_load_options
|
||||
|
||||
def prepare_model_patcher_multigpu_clones(model_patcher: ModelPatcher, loaded_models: list[ModelPatcher], model_options: dict):
|
||||
'''
|
||||
In case multigpu acceleration is enabled, prep ModelPatchers for each device.
|
||||
'''
|
||||
multigpu_patchers: list[ModelPatcher] = [x for x in loaded_models if x.is_multigpu_base_clone]
|
||||
if len(multigpu_patchers) > 0:
|
||||
multigpu_dict: dict[torch.device, ModelPatcher] = {}
|
||||
multigpu_dict[model_patcher.load_device] = model_patcher
|
||||
for x in multigpu_patchers:
|
||||
x.hook_patches = comfy.model_patcher.create_hook_patches_clone(model_patcher.hook_patches, copy_tuples=True)
|
||||
x.hook_mode = model_patcher.hook_mode # match main model's hook_mode
|
||||
multigpu_dict[x.load_device] = x
|
||||
model_options["multigpu_clones"] = multigpu_dict
|
||||
return multigpu_patchers
|
||||
|
||||
@@ -1,9 +1,7 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import comfy.model_management
|
||||
from .k_diffusion import sampling as k_diffusion_sampling
|
||||
from .extra_samplers import uni_pc
|
||||
from typing import TYPE_CHECKING, Callable, NamedTuple, Any
|
||||
from typing import TYPE_CHECKING, Callable, NamedTuple
|
||||
if TYPE_CHECKING:
|
||||
from comfy.model_patcher import ModelPatcher
|
||||
from comfy.model_base import BaseModel
|
||||
@@ -18,7 +16,6 @@ import comfy.model_patcher
|
||||
import comfy.patcher_extension
|
||||
import comfy.hooks
|
||||
import comfy.context_windows
|
||||
import comfy.multigpu
|
||||
import comfy.utils
|
||||
import scipy.stats
|
||||
import numpy
|
||||
@@ -144,7 +141,7 @@ def can_concat_cond(c1, c2):
|
||||
|
||||
return cond_equal_size(c1.conditioning, c2.conditioning)
|
||||
|
||||
def cond_cat(c_list, device=None):
|
||||
def cond_cat(c_list):
|
||||
temp = {}
|
||||
for x in c_list:
|
||||
for k in x:
|
||||
@@ -156,8 +153,6 @@ def cond_cat(c_list, device=None):
|
||||
for k in temp:
|
||||
conds = temp[k]
|
||||
out[k] = conds[0].concat(conds[1:])
|
||||
if device is not None and hasattr(out[k], 'to'):
|
||||
out[k] = out[k].to(device)
|
||||
|
||||
return out
|
||||
|
||||
@@ -217,9 +212,7 @@ def _calc_cond_batch_outer(model: BaseModel, conds: list[list[dict]], x_in: torc
|
||||
)
|
||||
return executor.execute(model, conds, x_in, timestep, model_options)
|
||||
|
||||
def _calc_cond_batch(model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]):
|
||||
if 'multigpu_clones' in model_options:
|
||||
return _calc_cond_batch_multigpu(model, conds, x_in, timestep, model_options)
|
||||
def _calc_cond_batch(model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep, model_options):
|
||||
out_conds = []
|
||||
out_counts = []
|
||||
# separate conds by matching hooks
|
||||
@@ -251,7 +244,7 @@ def _calc_cond_batch(model: BaseModel, conds: list[list[dict]], x_in: torch.Tens
|
||||
if has_default_conds:
|
||||
finalize_default_conds(model, hooked_to_run, default_conds, x_in, timestep, model_options)
|
||||
|
||||
model.current_patcher.prepare_state(timestep, model_options)
|
||||
model.current_patcher.prepare_state(timestep)
|
||||
|
||||
# run every hooked_to_run separately
|
||||
for hooks, to_run in hooked_to_run.items():
|
||||
@@ -352,220 +345,6 @@ def _calc_cond_batch(model: BaseModel, conds: list[list[dict]], x_in: torch.Tens
|
||||
|
||||
return out_conds
|
||||
|
||||
def _calc_cond_batch_multigpu(model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]):
|
||||
out_conds = []
|
||||
out_counts = []
|
||||
# separate conds by matching hooks
|
||||
hooked_to_run: dict[comfy.hooks.HookGroup,list[tuple[tuple,int]]] = {}
|
||||
default_conds = []
|
||||
has_default_conds = False
|
||||
|
||||
output_device = x_in.device
|
||||
|
||||
for i in range(len(conds)):
|
||||
out_conds.append(torch.zeros_like(x_in))
|
||||
out_counts.append(torch.ones_like(x_in) * 1e-37)
|
||||
|
||||
cond = conds[i]
|
||||
default_c = []
|
||||
if cond is not None:
|
||||
for x in cond:
|
||||
if 'default' in x:
|
||||
default_c.append(x)
|
||||
has_default_conds = True
|
||||
continue
|
||||
p = get_area_and_mult(x, x_in, timestep)
|
||||
if p is None:
|
||||
continue
|
||||
if p.hooks is not None:
|
||||
model.current_patcher.prepare_hook_patches_current_keyframe(timestep, p.hooks, model_options)
|
||||
hooked_to_run.setdefault(p.hooks, list())
|
||||
hooked_to_run[p.hooks] += [(p, i)]
|
||||
default_conds.append(default_c)
|
||||
|
||||
if has_default_conds:
|
||||
finalize_default_conds(model, hooked_to_run, default_conds, x_in, timestep, model_options)
|
||||
|
||||
model.current_patcher.prepare_state(timestep, model_options)
|
||||
|
||||
devices = [dev_m for dev_m in model_options['multigpu_clones'].keys()]
|
||||
device_batched_hooked_to_run: dict[torch.device, list[tuple[comfy.hooks.HookGroup, tuple]]] = {}
|
||||
|
||||
total_conds = 0
|
||||
for to_run in hooked_to_run.values():
|
||||
total_conds += len(to_run)
|
||||
conds_per_device = max(1, math.ceil(total_conds//len(devices)))
|
||||
index_device = 0
|
||||
current_device = devices[index_device]
|
||||
# run every hooked_to_run separately
|
||||
for hooks, to_run in hooked_to_run.items():
|
||||
while len(to_run) > 0:
|
||||
current_device = devices[index_device % len(devices)]
|
||||
batched_to_run = device_batched_hooked_to_run.setdefault(current_device, [])
|
||||
# keep track of conds currently scheduled onto this device
|
||||
batched_to_run_length = 0
|
||||
for btr in batched_to_run:
|
||||
batched_to_run_length += len(btr[1])
|
||||
|
||||
first = to_run[0]
|
||||
first_shape = first[0][0].shape
|
||||
to_batch_temp = []
|
||||
# make sure not over conds_per_device limit when creating temp batch
|
||||
for x in range(len(to_run)):
|
||||
if can_concat_cond(to_run[x][0], first[0]) and len(to_batch_temp) < (conds_per_device - batched_to_run_length):
|
||||
to_batch_temp += [x]
|
||||
|
||||
to_batch_temp.reverse()
|
||||
to_batch = to_batch_temp[:1]
|
||||
|
||||
free_memory = comfy.model_management.get_free_memory(current_device)
|
||||
for i in range(1, len(to_batch_temp) + 1):
|
||||
batch_amount = to_batch_temp[:len(to_batch_temp)//i]
|
||||
input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:]
|
||||
if model.memory_required(input_shape) * 1.5 < free_memory:
|
||||
to_batch = batch_amount
|
||||
break
|
||||
conds_to_batch = []
|
||||
for x in to_batch:
|
||||
conds_to_batch.append(to_run.pop(x))
|
||||
batched_to_run_length += len(conds_to_batch)
|
||||
|
||||
batched_to_run.append((hooks, conds_to_batch))
|
||||
if batched_to_run_length >= conds_per_device:
|
||||
index_device += 1
|
||||
|
||||
class thread_result(NamedTuple):
|
||||
output: Any
|
||||
mult: Any
|
||||
area: Any
|
||||
batch_chunks: int
|
||||
cond_or_uncond: Any
|
||||
error: Exception = None
|
||||
|
||||
def _handle_batch(device: torch.device, batch_tuple: tuple[comfy.hooks.HookGroup, tuple], results: list[thread_result]):
|
||||
try:
|
||||
torch.cuda.set_device(device)
|
||||
model_current: BaseModel = model_options["multigpu_clones"][device].model
|
||||
# run every hooked_to_run separately
|
||||
with torch.no_grad():
|
||||
for hooks, to_batch in batch_tuple:
|
||||
input_x = []
|
||||
mult = []
|
||||
c = []
|
||||
cond_or_uncond = []
|
||||
uuids = []
|
||||
area = []
|
||||
control: ControlBase = None
|
||||
patches = None
|
||||
for x in to_batch:
|
||||
o = x
|
||||
p = o[0]
|
||||
input_x.append(p.input_x)
|
||||
mult.append(p.mult)
|
||||
c.append(p.conditioning)
|
||||
area.append(p.area)
|
||||
cond_or_uncond.append(o[1])
|
||||
uuids.append(p.uuid)
|
||||
control = p.control
|
||||
patches = p.patches
|
||||
|
||||
batch_chunks = len(cond_or_uncond)
|
||||
input_x = torch.cat(input_x).to(device)
|
||||
c = cond_cat(c, device=device)
|
||||
timestep_ = torch.cat([timestep.to(device)] * batch_chunks)
|
||||
|
||||
transformer_options = model_current.current_patcher.apply_hooks(hooks=hooks)
|
||||
if 'transformer_options' in model_options:
|
||||
transformer_options = comfy.patcher_extension.merge_nested_dicts(transformer_options,
|
||||
model_options['transformer_options'],
|
||||
copy_dict1=False)
|
||||
|
||||
if patches is not None:
|
||||
transformer_options["patches"] = comfy.patcher_extension.merge_nested_dicts(
|
||||
transformer_options.get("patches", {}),
|
||||
patches
|
||||
)
|
||||
|
||||
transformer_options["cond_or_uncond"] = cond_or_uncond[:]
|
||||
transformer_options["uuids"] = uuids[:]
|
||||
transformer_options["sigmas"] = timestep.to(device)
|
||||
transformer_options["sample_sigmas"] = transformer_options["sample_sigmas"].to(device)
|
||||
transformer_options["multigpu_thread_device"] = device
|
||||
|
||||
cast_transformer_options(transformer_options, device=device)
|
||||
c['transformer_options'] = transformer_options
|
||||
|
||||
if control is not None:
|
||||
device_control = control.get_instance_for_device(device)
|
||||
c['control'] = device_control.get_control(input_x, timestep_, c, len(cond_or_uncond), transformer_options)
|
||||
|
||||
if 'model_function_wrapper' in model_options:
|
||||
output = model_options['model_function_wrapper'](model_current.apply_model, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).to(output_device).chunk(batch_chunks)
|
||||
else:
|
||||
output = model_current.apply_model(input_x, timestep_, **c).to(output_device).chunk(batch_chunks)
|
||||
results.append(thread_result(output, mult, area, batch_chunks, cond_or_uncond))
|
||||
except Exception as e:
|
||||
results.append(thread_result(None, None, None, None, None, error=e))
|
||||
raise
|
||||
|
||||
|
||||
def _handle_batch_pooled(device, batch_tuple):
|
||||
worker_results = []
|
||||
_handle_batch(device, batch_tuple, worker_results)
|
||||
return worker_results
|
||||
|
||||
results: list[thread_result] = []
|
||||
thread_pool: comfy.multigpu.MultiGPUThreadPool = model_options.get("multigpu_thread_pool")
|
||||
main_device = output_device
|
||||
main_batch_tuple = None
|
||||
|
||||
# Submit extra GPU work to pool first, then run main device on this thread
|
||||
pool_devices = []
|
||||
for device, batch_tuple in device_batched_hooked_to_run.items():
|
||||
if device == main_device and thread_pool is not None:
|
||||
main_batch_tuple = batch_tuple
|
||||
elif thread_pool is not None:
|
||||
thread_pool.submit(device, _handle_batch_pooled, device, batch_tuple)
|
||||
pool_devices.append(device)
|
||||
else:
|
||||
# Fallback: no pool, run everything on main thread
|
||||
_handle_batch(device, batch_tuple, results)
|
||||
|
||||
# Run main device batch on this thread (parallel with pool workers)
|
||||
if main_batch_tuple is not None:
|
||||
_handle_batch(main_device, main_batch_tuple, results)
|
||||
|
||||
# Collect results from pool workers
|
||||
for device in pool_devices:
|
||||
worker_results, error = thread_pool.get_result(device)
|
||||
if error is not None:
|
||||
raise error
|
||||
results.extend(worker_results)
|
||||
|
||||
for output, mult, area, batch_chunks, cond_or_uncond, error in results:
|
||||
if error is not None:
|
||||
raise error
|
||||
for o in range(batch_chunks):
|
||||
cond_index = cond_or_uncond[o]
|
||||
a = area[o]
|
||||
if a is None:
|
||||
out_conds[cond_index] += output[o] * mult[o]
|
||||
out_counts[cond_index] += mult[o]
|
||||
else:
|
||||
out_c = out_conds[cond_index]
|
||||
out_cts = out_counts[cond_index]
|
||||
dims = len(a) // 2
|
||||
for i in range(dims):
|
||||
out_c = out_c.narrow(i + 2, a[i + dims], a[i])
|
||||
out_cts = out_cts.narrow(i + 2, a[i + dims], a[i])
|
||||
out_c += output[o] * mult[o]
|
||||
out_cts += mult[o]
|
||||
|
||||
for i in range(len(out_conds)):
|
||||
out_conds[i] /= out_counts[i]
|
||||
|
||||
return out_conds
|
||||
|
||||
def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): #TODO: remove
|
||||
logging.warning("WARNING: The comfy.samplers.calc_cond_uncond_batch function is deprecated please use the calc_cond_batch one instead.")
|
||||
return tuple(calc_cond_batch(model, [cond, uncond], x_in, timestep, model_options))
|
||||
@@ -870,8 +649,6 @@ def pre_run_control(model, conds):
|
||||
percent_to_timestep_function = lambda a: s.percent_to_sigma(a)
|
||||
if 'control' in x:
|
||||
x['control'].pre_run(model, percent_to_timestep_function)
|
||||
for device_cnet in x['control'].multigpu_clones.values():
|
||||
device_cnet.pre_run(model, percent_to_timestep_function)
|
||||
|
||||
def apply_empty_x_to_equal_area(conds, uncond, name, uncond_fill_func):
|
||||
cond_cnets = []
|
||||
@@ -1114,9 +891,7 @@ def cast_to_load_options(model_options: dict[str], device=None, dtype=None):
|
||||
to_load_options = model_options.get("to_load_options", None)
|
||||
if to_load_options is None:
|
||||
return
|
||||
cast_transformer_options(to_load_options, device, dtype)
|
||||
|
||||
def cast_transformer_options(transformer_options: dict[str], device=None, dtype=None):
|
||||
casts = []
|
||||
if device is not None:
|
||||
casts.append(device)
|
||||
@@ -1125,17 +900,18 @@ def cast_transformer_options(transformer_options: dict[str], device=None, dtype=
|
||||
# if nothing to apply, do nothing
|
||||
if len(casts) == 0:
|
||||
return
|
||||
|
||||
# try to call .to on patches
|
||||
if "patches" in transformer_options:
|
||||
patches = transformer_options["patches"]
|
||||
if "patches" in to_load_options:
|
||||
patches = to_load_options["patches"]
|
||||
for name in patches:
|
||||
patch_list = patches[name]
|
||||
for i in range(len(patch_list)):
|
||||
if hasattr(patch_list[i], "to"):
|
||||
for cast in casts:
|
||||
patch_list[i] = patch_list[i].to(cast)
|
||||
if "patches_replace" in transformer_options:
|
||||
patches = transformer_options["patches_replace"]
|
||||
if "patches_replace" in to_load_options:
|
||||
patches = to_load_options["patches_replace"]
|
||||
for name in patches:
|
||||
patch_list = patches[name]
|
||||
for k in patch_list:
|
||||
@@ -1145,8 +921,8 @@ def cast_transformer_options(transformer_options: dict[str], device=None, dtype=
|
||||
# try to call .to on any wrappers/callbacks
|
||||
wrappers_and_callbacks = ["wrappers", "callbacks"]
|
||||
for wc_name in wrappers_and_callbacks:
|
||||
if wc_name in transformer_options:
|
||||
wc: dict[str, list] = transformer_options[wc_name]
|
||||
if wc_name in to_load_options:
|
||||
wc: dict[str, list] = to_load_options[wc_name]
|
||||
for wc_dict in wc.values():
|
||||
for wc_list in wc_dict.values():
|
||||
for i in range(len(wc_list)):
|
||||
@@ -1154,6 +930,7 @@ def cast_transformer_options(transformer_options: dict[str], device=None, dtype=
|
||||
for cast in casts:
|
||||
wc_list[i] = wc_list[i].to(cast)
|
||||
|
||||
|
||||
class CFGGuider:
|
||||
def __init__(self, model_patcher: ModelPatcher):
|
||||
self.model_patcher = model_patcher
|
||||
@@ -1208,30 +985,16 @@ class CFGGuider:
|
||||
self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds, self.model_options)
|
||||
device = self.model_patcher.load_device
|
||||
|
||||
multigpu_patchers = comfy.sampler_helpers.prepare_model_patcher_multigpu_clones(self.model_patcher, self.loaded_models, self.model_options)
|
||||
|
||||
# Create persistent thread pool for extra GPU devices
|
||||
if multigpu_patchers:
|
||||
extra_devices = [p.load_device for p in multigpu_patchers]
|
||||
self.model_options["multigpu_thread_pool"] = comfy.multigpu.MultiGPUThreadPool(extra_devices)
|
||||
noise = noise.to(device=device, dtype=torch.float32)
|
||||
latent_image = latent_image.to(device=device, dtype=torch.float32)
|
||||
sigmas = sigmas.to(device)
|
||||
cast_to_load_options(self.model_options, device=device, dtype=self.model_patcher.model_dtype())
|
||||
|
||||
try:
|
||||
noise = noise.to(device=device, dtype=torch.float32)
|
||||
latent_image = latent_image.to(device=device, dtype=torch.float32)
|
||||
sigmas = sigmas.to(device)
|
||||
cast_to_load_options(self.model_options, device=device, dtype=self.model_patcher.model_dtype())
|
||||
|
||||
self.model_patcher.pre_run()
|
||||
for multigpu_patcher in multigpu_patchers:
|
||||
multigpu_patcher.pre_run()
|
||||
output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=latent_shapes)
|
||||
finally:
|
||||
thread_pool = self.model_options.pop("multigpu_thread_pool", None)
|
||||
if thread_pool is not None:
|
||||
thread_pool.shutdown()
|
||||
self.model_patcher.cleanup()
|
||||
for multigpu_patcher in multigpu_patchers:
|
||||
multigpu_patcher.cleanup()
|
||||
|
||||
comfy.sampler_helpers.cleanup_models(self.conds, self.loaded_models)
|
||||
del self.inner_model
|
||||
|
||||
16
comfy/sd.py
16
comfy/sd.py
@@ -556,19 +556,12 @@ class VAE:
|
||||
old_memory_used_decode = self.memory_used_decode
|
||||
self.memory_used_decode = lambda shape, dtype: old_memory_used_decode(shape, dtype) * 4.0
|
||||
|
||||
decoder_ch = sd['decoder.conv_in.weight'].shape[0] // ddconfig['ch_mult'][-1]
|
||||
if decoder_ch != ddconfig['ch']:
|
||||
decoder_ddconfig = ddconfig.copy()
|
||||
decoder_ddconfig['ch'] = decoder_ch
|
||||
else:
|
||||
decoder_ddconfig = None
|
||||
|
||||
if 'post_quant_conv.weight' in sd:
|
||||
self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1], **({"decoder_ddconfig": decoder_ddconfig} if decoder_ddconfig is not None else {}))
|
||||
self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1])
|
||||
else:
|
||||
self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"},
|
||||
encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig},
|
||||
decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': decoder_ddconfig if decoder_ddconfig is not None else ddconfig})
|
||||
decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig})
|
||||
elif "decoder.layers.1.layers.0.beta" in sd:
|
||||
config = {}
|
||||
param_key = None
|
||||
@@ -1596,7 +1589,10 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
|
||||
out = load_state_dict_guess_config(sd, output_vae, output_clip, output_clipvision, embedding_directory, output_model, model_options, te_model_options=te_model_options, metadata=metadata, disable_dynamic=disable_dynamic)
|
||||
if out is None:
|
||||
raise RuntimeError("ERROR: Could not detect model type of: {}\n{}".format(ckpt_path, model_detection_error_hint(ckpt_path, sd)))
|
||||
out[0].cached_patcher_init = (load_checkpoint_guess_config, (ckpt_path, False, False, False, embedding_directory, output_model, model_options, te_model_options), 0)
|
||||
if output_model and out[0] is not None:
|
||||
out[0].cached_patcher_init = (load_checkpoint_guess_config_model_only, (ckpt_path, embedding_directory, model_options, te_model_options))
|
||||
if output_clip and out[1] is not None:
|
||||
out[1].patcher.cached_patcher_init = (load_checkpoint_guess_config_clip_only, (ckpt_path, embedding_directory, model_options, te_model_options))
|
||||
return out
|
||||
|
||||
def load_checkpoint_guess_config_model_only(ckpt_path, embedding_directory=None, model_options={}, te_model_options={}, disable_dynamic=False):
|
||||
|
||||
@@ -1,89 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
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
|
||||
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)
|
||||
|
||||
|
||||
class MultiGPUOptionsNode(io.ComfyNode):
|
||||
"""
|
||||
Select the relative speed of GPUs in the special case they have significantly different performance from one another.
|
||||
"""
|
||||
|
||||
@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()
|
||||
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,
|
||||
# MultiGPUOptionsNode,
|
||||
]
|
||||
|
||||
|
||||
async def comfy_entrypoint() -> MultiGPUExtension:
|
||||
return MultiGPUExtension()
|
||||
Reference in New Issue
Block a user