CODEOWNERS

@@ -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

app/frontend_management.py

@@ -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()

app/logger.py

@@ -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()

comfy/comfy_types/node_typing.py

@@ -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):

comfy/k_diffusion/sampling.py

@@ -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

comfy/ldm/flux/layers.py

@@ -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)

comfy/ldm/hunyuan_video/model.py

@@ -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)

comfy/model_base.py

@@ -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]

comfy/model_management.py

@@ -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
 

comfy/model_patcher.py

@@ -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()

comfy/samplers.py

@@ -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={}):

comfy_extras/nodes_load_3d.py

@@ -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

comfy_extras/nodes_lt.py

@@ -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])

comfyui_version.py

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

execution.py

@@ -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

main.py

@@ -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")

nodes.py

@@ -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:

pyproject.toml

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

tests-unit/app_test/frontend_manager_test.py

@@ -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()