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Merge branch 'master' into worksplit-multigpu

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This commit is contained in:
Jedrzej Kosinski 2025-03-13 20:59:11 -05:00
commit cc928a786d
19 changed files with 259 additions and 100 deletions
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5
CODEOWNERS
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@ -19,5 +19,6 @@
/app/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata
/utils/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata
# Extra nodes
/comfy_extras/ @yoland68 @robinjhuang @huchenlei @pythongosssss @ltdrdata @Kosinkadink
# Node developers
/comfy_extras/ @yoland68 @robinjhuang @huchenlei @pythongosssss @ltdrdata @Kosinkadink @webfiltered
/comfy/comfy_types/ @yoland68 @robinjhuang @huchenlei @pythongosssss @ltdrdata @Kosinkadink @webfiltered

51
app/frontend_management.py
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@ -11,33 +11,44 @@ from dataclasses import dataclass
from functools import cached_property
from pathlib import Path
from typing import TypedDict, Optional
from importlib.metadata import version
import requests
from typing_extensions import NotRequired
from comfy.cli_args import DEFAULT_VERSION_STRING
import app.logger
# The path to the requirements.txt file
req_path = Path(__file__).parents[1] / "requirements.txt"
def frontend_install_warning_message():
req_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'requirements.txt'))
"""The warning message to display when the frontend version is not up to date."""
extra = ""
if sys.flags.no_user_site:
extra = "-s "
return f"Please install the updated requirements.txt file by running:\n{sys.executable} {extra}-m pip install -r {req_path}\n\nThis error is happening because the ComfyUI frontend is no longer shipped as part of the main repo but as a pip package instead.\n\nIf you are on the portable package you can run: update\\update_comfyui.bat to solve this problem"
try:
import comfyui_frontend_package
except ImportError:
# TODO: Remove the check after roll out of 0.3.16
logging.error(f"\n\n********** ERROR ***********\n\ncomfyui-frontend-package is not installed. {frontend_install_warning_message()}\n********** ERROR **********\n")
exit(-1)
def check_frontend_version():
"""Check if the frontend version is up to date."""
def parse_version(version: str) -> tuple[int, int, int]:
return tuple(map(int, version.split(".")))
try:
frontend_version_str = version("comfyui-frontend-package")
frontend_version = parse_version(frontend_version_str)
with open(req_path, "r", encoding="utf-8") as f:
required_frontend = parse_version(f.readline().split("=")[-1])
if frontend_version < required_frontend:
app.logger.log_startup_warning("________________________________________________________________________\nWARNING WARNING WARNING WARNING WARNING\n\nInstalled frontend version {} is lower than the recommended version {}.\n\n{}\n________________________________________________________________________".format('.'.join(map(str, frontend_version)), '.'.join(map(str, required_frontend)), frontend_install_warning_message()))
else:
logging.info("ComfyUI frontend version: {}".format(frontend_version_str))
except Exception as e:
logging.error(f"Failed to check frontend version: {e}")
try:
frontend_version = tuple(map(int, comfyui_frontend_package.__version__.split(".")))
except:
frontend_version = (0,)
pass
REQUEST_TIMEOUT = 10 # seconds
@ -133,9 +144,17 @@ def download_release_asset_zip(release: Release, destination_path: str) -> None:
class FrontendManager:
DEFAULT_FRONTEND_PATH = str(importlib.resources.files(comfyui_frontend_package) / "static")
CUSTOM_FRONTENDS_ROOT = str(Path(__file__).parents[1] / "web_custom_versions")
@classmethod
def default_frontend_path(cls) -> str:
try:
import comfyui_frontend_package
return str(importlib.resources.files(comfyui_frontend_package) / "static")
except ImportError:
logging.error(f"\n\n********** ERROR ***********\n\ncomfyui-frontend-package is not installed. {frontend_install_warning_message()}\n********** ERROR **********\n")
sys.exit(-1)
@classmethod
def parse_version_string(cls, value: str) -> tuple[str, str, str]:
"""
@ -172,7 +191,8 @@ class FrontendManager:
main error source might be request timeout or invalid URL.
"""
if version_string == DEFAULT_VERSION_STRING:
return cls.DEFAULT_FRONTEND_PATH
check_frontend_version()
return cls.default_frontend_path()
repo_owner, repo_name, version = cls.parse_version_string(version_string)
@ -225,4 +245,5 @@ class FrontendManager:
except Exception as e:
logging.error("Failed to initialize frontend: %s", e)
logging.info("Falling back to the default frontend.")
return cls.DEFAULT_FRONTEND_PATH
check_frontend_version()
return cls.default_frontend_path()

14
app/logger.py
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@ -82,3 +82,17 @@ def setup_logger(log_level: str = 'INFO', capacity: int = 300, use_stdout: bool
logger.addHandler(stdout_handler)
logger.addHandler(stream_handler)
STARTUP_WARNINGS = []
def log_startup_warning(msg):
logging.warning(msg)
STARTUP_WARNINGS.append(msg)
def print_startup_warnings():
for s in STARTUP_WARNINGS:
logging.warning(s)
STARTUP_WARNINGS.clear()

26
comfy/comfy_types/node_typing.py
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@ -2,6 +2,7 @@
from __future__ import annotations
from typing import Literal, TypedDict
from typing_extensions import NotRequired
from abc import ABC, abstractmethod
from enum import Enum
@ -26,6 +27,7 @@ class IO(StrEnum):
BOOLEAN = "BOOLEAN"
INT = "INT"
FLOAT = "FLOAT"
COMBO = "COMBO"
CONDITIONING = "CONDITIONING"
SAMPLER = "SAMPLER"
SIGMAS = "SIGMAS"
@ -66,6 +68,7 @@ class IO(StrEnum):
b = frozenset(value.split(","))
return not (b.issubset(a) or a.issubset(b))
class RemoteInputOptions(TypedDict):
route: str
"""The route to the remote source."""
@ -80,6 +83,14 @@ class RemoteInputOptions(TypedDict):
refresh: int
"""The TTL of the remote input's value in milliseconds. Specifies the interval at which the remote input's value is refreshed."""
class MultiSelectOptions(TypedDict):
placeholder: NotRequired[str]
"""The placeholder text to display in the multi-select widget when no items are selected."""
chip: NotRequired[bool]
"""Specifies whether to use chips instead of comma separated values for the multi-select widget."""
class InputTypeOptions(TypedDict):
"""Provides type hinting for the return type of the INPUT_TYPES node function.
@ -133,9 +144,22 @@ class InputTypeOptions(TypedDict):
"""Specifies which folder to get preview images from if the input has the ``image_upload`` flag.
"""
remote: RemoteInputOptions
"""Specifies the configuration for a remote input."""
"""Specifies the configuration for a remote input.
Available after ComfyUI frontend v1.9.7
https://github.com/Comfy-Org/ComfyUI_frontend/pull/2422"""
control_after_generate: bool
"""Specifies whether a control widget should be added to the input, adding options to automatically change the value after each prompt is queued. Currently only used for INT and COMBO types."""
options: NotRequired[list[str | int | float]]
"""COMBO type only. Specifies the selectable options for the combo widget.
Prefer:
["COMBO", {"options": ["Option 1", "Option 2", "Option 3"]}]
Over:
[["Option 1", "Option 2", "Option 3"]]
"""
multi_select: NotRequired[MultiSelectOptions]
"""COMBO type only. Specifies the configuration for a multi-select widget.
Available after ComfyUI frontend v1.13.4
https://github.com/Comfy-Org/ComfyUI_frontend/pull/2987"""
class HiddenInputTypeDict(TypedDict):

68
comfy/k_diffusion/sampling.py
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@ -688,10 +688,10 @@ def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=N
if len(sigmas) <= 1:
return x
extra_args = {} if extra_args is None else extra_args
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
seed = extra_args.get("seed", None)
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones([x.shape[0]])
sigma_fn = lambda t: t.neg().exp()
t_fn = lambda sigma: sigma.log().neg()
@ -762,10 +762,10 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
if solver_type not in {'heun', 'midpoint'}:
raise ValueError('solver_type must be \'heun\' or \'midpoint\'')
extra_args = {} if extra_args is None else extra_args
seed = extra_args.get("seed", None)
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones([x.shape[0]])
old_denoised = None
@ -808,10 +808,10 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
if len(sigmas) <= 1:
return x
extra_args = {} if extra_args is None else extra_args
seed = extra_args.get("seed", None)
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones([x.shape[0]])
denoised_1, denoised_2 = None, None
@ -858,7 +858,7 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
if len(sigmas) <= 1:
return x
extra_args = {} if extra_args is None else extra_args
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
return sample_dpmpp_3m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler)
@ -867,7 +867,7 @@ def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, di
def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
if len(sigmas) <= 1:
return x
extra_args = {} if extra_args is None else extra_args
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type)
@ -876,7 +876,7 @@ def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, di
def sample_dpmpp_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
if len(sigmas) <= 1:
return x
extra_args = {} if extra_args is None else extra_args
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
return sample_dpmpp_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=r)
@ -1366,3 +1366,59 @@ def sample_gradient_estimation(model, x, sigmas, extra_args=None, callback=None,
x = x + d_bar * dt
old_d = d
return x
@torch.no_grad()
def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None, noise_scaler=None, max_stage=3):
"""
Extended Reverse-Time SDE solver (VE ER-SDE-Solver-3). Arxiv: https://arxiv.org/abs/2309.06169.
Code reference: https://github.com/QinpengCui/ER-SDE-Solver/blob/main/er_sde_solver.py.
"""
extra_args = {} if extra_args is None else extra_args
seed = extra_args.get("seed", None)
noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
s_in = x.new_ones([x.shape[0]])
def default_noise_scaler(sigma):
return sigma * ((sigma ** 0.3).exp() + 10.0)
noise_scaler = default_noise_scaler if noise_scaler is None else noise_scaler
num_integration_points = 200.0
point_indice = torch.arange(0, num_integration_points, dtype=torch.float32, device=x.device)
old_denoised = None
old_denoised_d = None
for i in trange(len(sigmas) - 1, disable=disable):
denoised = model(x, sigmas[i] * s_in, **extra_args)
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
stage_used = min(max_stage, i + 1)
if sigmas[i + 1] == 0:
x = denoised
elif stage_used == 1:
r = noise_scaler(sigmas[i + 1]) / noise_scaler(sigmas[i])
x = r * x + (1 - r) * denoised
else:
r = noise_scaler(sigmas[i + 1]) / noise_scaler(sigmas[i])
x = r * x + (1 - r) * denoised
dt = sigmas[i + 1] - sigmas[i]
sigma_step_size = -dt / num_integration_points
sigma_pos = sigmas[i + 1] + point_indice * sigma_step_size
scaled_pos = noise_scaler(sigma_pos)
# Stage 2
s = torch.sum(1 / scaled_pos) * sigma_step_size
denoised_d = (denoised - old_denoised) / (sigmas[i] - sigmas[i - 1])
x = x + (dt + s * noise_scaler(sigmas[i + 1])) * denoised_d
if stage_used >= 3:
# Stage 3
s_u = torch.sum((sigma_pos - sigmas[i]) / scaled_pos) * sigma_step_size
denoised_u = (denoised_d - old_denoised_d) / ((sigmas[i] - sigmas[i - 2]) / 2)
x = x + ((dt ** 2) / 2 + s_u * noise_scaler(sigmas[i + 1])) * denoised_u
old_denoised_d = denoised_d
if s_noise != 0 and sigmas[i + 1] > 0:
x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * (sigmas[i + 1] ** 2 - sigmas[i] ** 2 * r ** 2).sqrt()
old_denoised = denoised
return x

14
comfy/ldm/flux/layers.py
View File

@ -159,20 +159,20 @@ class DoubleStreamBlock(nn.Module):
)
self.flipped_img_txt = flipped_img_txt
def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims=None):
def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims_img=None, modulation_dims_txt=None):
img_mod1, img_mod2 = self.img_mod(vec)
txt_mod1, txt_mod2 = self.txt_mod(vec)
# prepare image for attention
img_modulated = self.img_norm1(img)
img_modulated = apply_mod(img_modulated, (1 + img_mod1.scale), img_mod1.shift, modulation_dims)
img_modulated = apply_mod(img_modulated, (1 + img_mod1.scale), img_mod1.shift, modulation_dims_img)
img_qkv = self.img_attn.qkv(img_modulated)
img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
# prepare txt for attention
txt_modulated = self.txt_norm1(txt)
txt_modulated = apply_mod(txt_modulated, (1 + txt_mod1.scale), txt_mod1.shift, modulation_dims)
txt_modulated = apply_mod(txt_modulated, (1 + txt_mod1.scale), txt_mod1.shift, modulation_dims_txt)
txt_qkv = self.txt_attn.qkv(txt_modulated)
txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
@ -195,12 +195,12 @@ class DoubleStreamBlock(nn.Module):
txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1]:]
# calculate the img bloks
img = img + apply_mod(self.img_attn.proj(img_attn), img_mod1.gate, None, modulation_dims)
img = img + apply_mod(self.img_mlp(apply_mod(self.img_norm2(img), (1 + img_mod2.scale), img_mod2.shift, modulation_dims)), img_mod2.gate, None, modulation_dims)
img = img + apply_mod(self.img_attn.proj(img_attn), img_mod1.gate, None, modulation_dims_img)
img = img + apply_mod(self.img_mlp(apply_mod(self.img_norm2(img), (1 + img_mod2.scale), img_mod2.shift, modulation_dims_img)), img_mod2.gate, None, modulation_dims_img)
# calculate the txt bloks
txt += apply_mod(self.txt_attn.proj(txt_attn), txt_mod1.gate, None, modulation_dims)
txt += apply_mod(self.txt_mlp(apply_mod(self.txt_norm2(txt), (1 + txt_mod2.scale), txt_mod2.shift, modulation_dims)), txt_mod2.gate, None, modulation_dims)
txt += apply_mod(self.txt_attn.proj(txt_attn), txt_mod1.gate, None, modulation_dims_txt)
txt += apply_mod(self.txt_mlp(apply_mod(self.txt_norm2(txt), (1 + txt_mod2.scale), txt_mod2.shift, modulation_dims_txt)), txt_mod2.gate, None, modulation_dims_txt)
if txt.dtype == torch.float16:
txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504)

12
comfy/ldm/hunyuan_video/model.py
View File

@ -244,9 +244,11 @@ class HunyuanVideo(nn.Module):
vec = torch.cat([(vec_ + token_replace_vec).unsqueeze(1), (vec_ + vec).unsqueeze(1)], dim=1)
frame_tokens = (initial_shape[-1] // self.patch_size[-1]) * (initial_shape[-2] // self.patch_size[-2])
modulation_dims = [(0, frame_tokens, 0), (frame_tokens, None, 1)]
modulation_dims_txt = [(0, None, 1)]
else:
vec = vec + self.vector_in(y[:, :self.params.vec_in_dim])
modulation_dims = None
modulation_dims_txt = None
if self.params.guidance_embed:
if guidance is not None:
@ -273,14 +275,14 @@ class HunyuanVideo(nn.Module):
if ("double_block", i) in blocks_replace:
def block_wrap(args):
out = {}
out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"])
out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims_img=args["modulation_dims_img"], modulation_dims_txt=args["modulation_dims_txt"])
return out
out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe, "attention_mask": attn_mask}, {"original_block": block_wrap})
out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims_img': modulation_dims, 'modulation_dims_txt': modulation_dims_txt}, {"original_block": block_wrap})
txt = out["txt"]
img = out["img"]
else:
img, txt = block(img=img, txt=txt, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims=modulation_dims)
img, txt = block(img=img, txt=txt, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims_img=modulation_dims, modulation_dims_txt=modulation_dims_txt)
if control is not None: # Controlnet
control_i = control.get("input")
@ -295,10 +297,10 @@ class HunyuanVideo(nn.Module):
if ("single_block", i) in blocks_replace:
def block_wrap(args):
out = {}
out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"])
out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims=args["modulation_dims"])
return out
out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe, "attention_mask": attn_mask}, {"original_block": block_wrap})
out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims': modulation_dims}, {"original_block": block_wrap})
img = out["img"]
else:
img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims=modulation_dims)

7
comfy/model_base.py
View File

@ -973,11 +973,11 @@ class WAN21(BaseModel):
self.image_to_video = image_to_video
def concat_cond(self, **kwargs):
if not self.image_to_video:
noise = kwargs.get("noise", None)
if self.diffusion_model.patch_embedding.weight.shape[1] == noise.shape[1]:
return None
image = kwargs.get("concat_latent_image", None)
noise = kwargs.get("noise", None)
device = kwargs["device"]
if image is None:
@ -987,6 +987,9 @@ class WAN21(BaseModel):
image = self.process_latent_in(image)
image = utils.resize_to_batch_size(image, noise.shape[0])
if not self.image_to_video:
return image
mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
if mask is None:
mask = torch.zeros_like(noise)[:, :4]

15
comfy/model_management.py
View File

@ -210,12 +210,21 @@ def get_total_memory(dev=None, torch_total_too=False):
else:
return mem_total
def mac_version():
try:
return tuple(int(n) for n in platform.mac_ver()[0].split("."))
except:
return None
total_vram = get_total_memory(get_torch_device()) / (1024 * 1024)
total_ram = psutil.virtual_memory().total / (1024 * 1024)
logging.info("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram))
try:
logging.info("pytorch version: {}".format(torch_version))
mac_ver = mac_version()
if mac_ver is not None:
logging.info("Mac Version {}".format(mac_ver))
except:
pass
@ -997,12 +1006,6 @@ def pytorch_attention_flash_attention():
return True #if you have pytorch attention enabled on AMD it probably supports at least mem efficient attention
return False
def mac_version():
try:
return tuple(int(n) for n in platform.mac_ver()[0].split("."))
except:
return None
def force_upcast_attention_dtype():
upcast = args.force_upcast_attention

23
comfy/model_patcher.py
View File

@ -1201,7 +1201,6 @@ class ModelPatcher:
def patch_hooks(self, hooks: comfy.hooks.HookGroup):
with self.use_ejected():
self.unpatch_hooks()
if hooks is not None:
model_sd_keys = list(self.model_state_dict().keys())
memory_counter = None
@ -1212,12 +1211,16 @@ class ModelPatcher:
# if have cached weights for hooks, use it
cached_weights = self.cached_hook_patches.get(hooks, None)
if cached_weights is not None:
model_sd_keys_set = set(model_sd_keys)
for key in cached_weights:
if key not in model_sd_keys:
logging.warning(f"Cached hook could not patch. Key does not exist in model: {key}")
continue
self.patch_cached_hook_weights(cached_weights=cached_weights, key=key, memory_counter=memory_counter)
model_sd_keys_set.remove(key)
self.unpatch_hooks(model_sd_keys_set)
else:
self.unpatch_hooks()
relevant_patches = self.get_combined_hook_patches(hooks=hooks)
original_weights = None
if len(relevant_patches) > 0:
@ -1228,6 +1231,8 @@ class ModelPatcher:
continue
self.patch_hook_weight_to_device(hooks=hooks, combined_patches=relevant_patches, key=key, original_weights=original_weights,
memory_counter=memory_counter)
else:
self.unpatch_hooks()
self.current_hooks = hooks
def patch_cached_hook_weights(self, cached_weights: dict, key: str, memory_counter: MemoryCounter):
@ -1284,17 +1289,23 @@ class ModelPatcher:
del out_weight
del weight
def unpatch_hooks(self) -> None:
def unpatch_hooks(self, whitelist_keys_set: set[str]=None) -> None:
with self.use_ejected():
if len(self.hook_backup) == 0:
self.current_hooks = None
return
keys = list(self.hook_backup.keys())
for k in keys:
comfy.utils.copy_to_param(self.model, k, self.hook_backup[k][0].to(device=self.hook_backup[k][1]))
if whitelist_keys_set:
for k in keys:
if k in whitelist_keys_set:
comfy.utils.copy_to_param(self.model, k, self.hook_backup[k][0].to(device=self.hook_backup[k][1]))
self.hook_backup.pop(k)
else:
for k in keys:
comfy.utils.copy_to_param(self.model, k, self.hook_backup[k][0].to(device=self.hook_backup[k][1]))
self.hook_backup.clear()
self.current_hooks = None
self.hook_backup.clear()
self.current_hooks = None
def clean_hooks(self):
self.unpatch_hooks()

2
comfy/samplers.py
View File

@ -903,7 +903,7 @@ KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_c
"lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_2s_ancestral_cfg_pp", "dpmpp_sde", "dpmpp_sde_gpu",
"dpmpp_2m", "dpmpp_2m_cfg_pp", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
"ipndm", "ipndm_v", "deis", "res_multistep", "res_multistep_cfg_pp", "res_multistep_ancestral", "res_multistep_ancestral_cfg_pp",
"gradient_estimation"]
"gradient_estimation", "er_sde"]
class KSAMPLER(Sampler):
def __init__(self, sampler_function, extra_options={}, inpaint_options={}):

8
comfy_extras/nodes_load_3d.py
View File

@ -19,8 +19,6 @@ class Load3D():
"image": ("LOAD_3D", {}),
"width": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
"height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
"material": (["original", "normal", "wireframe", "depth"],),
"up_direction": (["original", "-x", "+x", "-y", "+y", "-z", "+z"],),
}}
RETURN_TYPES = ("IMAGE", "MASK", "STRING")
@ -55,8 +53,6 @@ class Load3DAnimation():
"image": ("LOAD_3D_ANIMATION", {}),
"width": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
"height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
"material": (["original", "normal", "wireframe", "depth"],),
"up_direction": (["original", "-x", "+x", "-y", "+y", "-z", "+z"],),
}}
RETURN_TYPES = ("IMAGE", "MASK", "STRING")
@ -82,8 +78,6 @@ class Preview3D():
def INPUT_TYPES(s):
return {"required": {
"model_file": ("STRING", {"default": "", "multiline": False}),
"material": (["original", "normal", "wireframe", "depth"],),
"up_direction": (["original", "-x", "+x", "-y", "+y", "-z", "+z"],),
}}
OUTPUT_NODE = True
@ -102,8 +96,6 @@ class Preview3DAnimation():
def INPUT_TYPES(s):
return {"required": {
"model_file": ("STRING", {"default": "", "multiline": False}),
"material": (["original", "normal", "wireframe", "depth"],),
"up_direction": (["original", "-x", "+x", "-y", "+y", "-z", "+z"],),
}}
OUTPUT_NODE = True

18
comfy_extras/nodes_lt.py
View File

@ -99,12 +99,13 @@ class LTXVAddGuide:
"negative": ("CONDITIONING", ),
"vae": ("VAE",),
"latent": ("LATENT",),
"image": ("IMAGE", {"tooltip": "Image or video to condition the latent video on. Must be 8*n + 1 frames." \
"image": ("IMAGE", {"tooltip": "Image or video to condition the latent video on. Must be 8*n + 1 frames."
"If the video is not 8*n + 1 frames, it will be cropped to the nearest 8*n + 1 frames."}),
"frame_idx": ("INT", {"default": 0, "min": -9999, "max": 9999,
"tooltip": "Frame index to start the conditioning at. Must be divisible by 8. " \
"If a frame is not divisible by 8, it will be rounded down to the nearest multiple of 8. " \
"Negative values are counted from the end of the video."}),
"tooltip": "Frame index to start the conditioning at. For single-frame images or "
"videos with 1-8 frames, any frame_idx value is acceptable. For videos with 9+ "
"frames, frame_idx must be divisible by 8, otherwise it will be rounded down to "
"the nearest multiple of 8. Negative values are counted from the end of the video."}),
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}
}
@ -127,12 +128,13 @@ class LTXVAddGuide:
t = vae.encode(encode_pixels)
return encode_pixels, t
def get_latent_index(self, cond, latent_length, frame_idx, scale_factors):
def get_latent_index(self, cond, latent_length, guide_length, frame_idx, scale_factors):
time_scale_factor, _, _ = scale_factors
_, num_keyframes = get_keyframe_idxs(cond)
latent_count = latent_length - num_keyframes
frame_idx = frame_idx if frame_idx >= 0 else max((latent_count - 1) * 8 + 1 + frame_idx, 0)
frame_idx = frame_idx // time_scale_factor * time_scale_factor # frame index must be divisible by 8
frame_idx = frame_idx if frame_idx >= 0 else max((latent_count - 1) * time_scale_factor + 1 + frame_idx, 0)
if guide_length > 1:
frame_idx = frame_idx // time_scale_factor * time_scale_factor # frame index must be divisible by 8
latent_idx = (frame_idx + time_scale_factor - 1) // time_scale_factor
@ -191,7 +193,7 @@ class LTXVAddGuide:
_, _, latent_length, latent_height, latent_width = latent_image.shape
image, t = self.encode(vae, latent_width, latent_height, image, scale_factors)
frame_idx, latent_idx = self.get_latent_index(positive, latent_length, frame_idx, scale_factors)
frame_idx, latent_idx = self.get_latent_index(positive, latent_length, len(image), frame_idx, scale_factors)
assert latent_idx + t.shape[2] <= latent_length, "Conditioning frames exceed the length of the latent sequence."
num_prefix_frames = min(self._num_prefix_frames, t.shape[2])

2
comfyui_version.py
View File

@ -1,3 +1,3 @@
# This file is automatically generated by the build process when version is
# updated in pyproject.toml.
__version__ = "0.3.24"
__version__ = "0.3.26"

7
execution.py
View File

@ -634,6 +634,13 @@ def validate_inputs(prompt, item, validated):
continue
else:
try:
# Unwraps values wrapped in __value__ key. This is used to pass
# list widget value to execution, as by default list value is
# reserved to represent the connection between nodes.
if isinstance(val, dict) and "__value__" in val:
val = val["__value__"]
inputs[x] = val
if type_input == "INT":
val = int(val)
inputs[x] = val

18
main.py
View File

@ -139,7 +139,7 @@ from server import BinaryEventTypes
import nodes
import comfy.model_management
import comfyui_version
import app.frontend_management
import app.logger
def cuda_malloc_warning():
@ -293,28 +293,14 @@ def start_comfyui(asyncio_loop=None):
return asyncio_loop, prompt_server, start_all
def warn_frontend_version(frontend_version):
try:
required_frontend = (0,)
req_path = os.path.join(os.path.dirname(__file__), 'requirements.txt')
with open(req_path, 'r') as f:
required_frontend = tuple(map(int, f.readline().split('=')[-1].split('.')))
if frontend_version < required_frontend:
logging.warning("________________________________________________________________________\nWARNING WARNING WARNING WARNING WARNING\n\nInstalled frontend version {} is lower than the recommended version {}.\n\n{}\n________________________________________________________________________".format('.'.join(map(str, frontend_version)), '.'.join(map(str, required_frontend)), app.frontend_management.frontend_install_warning_message()))
except:
pass
if __name__ == "__main__":
# Running directly, just start ComfyUI.
logging.info("ComfyUI version: {}".format(comfyui_version.__version__))
frontend_version = app.frontend_management.frontend_version
logging.info("ComfyUI frontend version: {}".format('.'.join(map(str, frontend_version))))
event_loop, _, start_all_func = start_comfyui()
try:
x = start_all_func()
warn_frontend_version(frontend_version)
app.logger.print_startup_warnings()
event_loop.run_until_complete(x)
except KeyboardInterrupt:
logging.info("\nStopped server")

11
nodes.py
View File

@ -489,7 +489,7 @@ class SaveLatent:
file = os.path.join(full_output_folder, file)
output = {}
output["latent_tensor"] = samples["samples"]
output["latent_tensor"] = samples["samples"].contiguous()
output["latent_format_version_0"] = torch.tensor([])
comfy.utils.save_torch_file(output, file, metadata=metadata)
@ -1785,14 +1785,7 @@ class LoadImageOutput(LoadImage):
DESCRIPTION = "Load an image from the output folder. When the refresh button is clicked, the node will update the image list and automatically select the first image, allowing for easy iteration."
EXPERIMENTAL = True
FUNCTION = "load_image_output"
def load_image_output(self, image):
return self.load_image(f"{image} [output]")
@classmethod
def VALIDATE_INPUTS(s, image):
return True
FUNCTION = "load_image"
class ImageScale:

2
pyproject.toml
View File

@ -1,6 +1,6 @@
[project]
name = "ComfyUI"
version = "0.3.24"
version = "0.3.26"
readme = "README.md"
license = { file = "LICENSE" }
requires-python = ">=3.9"

56
tests-unit/app_test/frontend_manager_test.py
View File

@ -70,7 +70,7 @@ def test_get_release_invalid_version(mock_provider):
def test_init_frontend_default():
version_string = DEFAULT_VERSION_STRING
frontend_path = FrontendManager.init_frontend(version_string)
assert frontend_path == FrontendManager.DEFAULT_FRONTEND_PATH
assert frontend_path == FrontendManager.default_frontend_path()
def test_init_frontend_invalid_version():
@ -84,24 +84,29 @@ def test_init_frontend_invalid_provider():
with pytest.raises(HTTPError):
FrontendManager.init_frontend_unsafe(version_string)
@pytest.fixture
def mock_os_functions():
with patch('app.frontend_management.os.makedirs') as mock_makedirs, \
patch('app.frontend_management.os.listdir') as mock_listdir, \
patch('app.frontend_management.os.rmdir') as mock_rmdir:
with (
patch("app.frontend_management.os.makedirs") as mock_makedirs,
patch("app.frontend_management.os.listdir") as mock_listdir,
patch("app.frontend_management.os.rmdir") as mock_rmdir,
):
mock_listdir.return_value = [] # Simulate empty directory
yield mock_makedirs, mock_listdir, mock_rmdir
@pytest.fixture
def mock_download():
with patch('app.frontend_management.download_release_asset_zip') as mock:
with patch("app.frontend_management.download_release_asset_zip") as mock:
mock.side_effect = Exception("Download failed") # Simulate download failure
yield mock
def test_finally_block(mock_os_functions, mock_download, mock_provider):
# Arrange
mock_makedirs, mock_listdir, mock_rmdir = mock_os_functions
version_string = 'test-owner/test-repo@1.0.0'
version_string = "test-owner/test-repo@1.0.0"
# Act & Assert
with pytest.raises(Exception):
@ -128,3 +133,42 @@ def test_parse_version_string_invalid():
version_string = "invalid"
with pytest.raises(argparse.ArgumentTypeError):
FrontendManager.parse_version_string(version_string)
def test_init_frontend_default_with_mocks():
# Arrange
version_string = DEFAULT_VERSION_STRING
# Act
with (
patch("app.frontend_management.check_frontend_version") as mock_check,
patch.object(
FrontendManager, "default_frontend_path", return_value="/mocked/path"
),
):
frontend_path = FrontendManager.init_frontend(version_string)
# Assert
assert frontend_path == "/mocked/path"
mock_check.assert_called_once()
def test_init_frontend_fallback_on_error():
# Arrange
version_string = "test-owner/test-repo@1.0.0"
# Act
with (
patch.object(
FrontendManager, "init_frontend_unsafe", side_effect=Exception("Test error")
),
patch("app.frontend_management.check_frontend_version") as mock_check,
patch.object(
FrontendManager, "default_frontend_path", return_value="/default/path"
),
):
frontend_path = FrontendManager.init_frontend(version_string)
# Assert
assert frontend_path == "/default/path"
mock_check.assert_called_once()