Merge branch 'master' into multigpu_support

.github/workflows/stable-release.yml

@@ -22,7 +22,7 @@ on:
         description: 'Python patch version'
         required: true
         type: string
-        default: "7"
+        default: "8"
 
 
 jobs:

.github/workflows/test-build.yml

@@ -18,7 +18,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}
@@ -28,4 +28,4 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install -r requirements.txt
+          pip install -r requirements.txt

.github/workflows/windows_release_dependencies.yml

@@ -29,7 +29,7 @@ on:
         description: 'python patch version'
         required: true
         type: string
-        default: "7"
+        default: "8"
 #  push:
 #    branches:
 #      - master

.github/workflows/windows_release_package.yml

@@ -19,7 +19,7 @@ on:
         description: 'python patch version'
         required: true
         type: string
-        default: "7"
+        default: "8"
 #  push:
 #    branches:
 #      - master

comfy/k_diffusion/res.py

@@ -1,258 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Copied from Nvidia Cosmos code.
-
-import torch
-from torch import Tensor
-from typing import Callable, List, Tuple, Optional, Any
-import math
-from tqdm.auto import trange
-
-
-def common_broadcast(x: Tensor, y: Tensor) -> tuple[Tensor, Tensor]:
-    ndims1 = x.ndim
-    ndims2 = y.ndim
-
-    if ndims1 < ndims2:
-        x = x.reshape(x.shape + (1,) * (ndims2 - ndims1))
-    elif ndims2 < ndims1:
-        y = y.reshape(y.shape + (1,) * (ndims1 - ndims2))
-
-    return x, y
-
-
-def batch_mul(x: Tensor, y: Tensor) -> Tensor:
-    x, y = common_broadcast(x, y)
-    return x * y
-
-
-def phi1(t: torch.Tensor) -> torch.Tensor:
-    """
-    Compute the first order phi function: (exp(t) - 1) / t.
-
-    Args:
-        t: Input tensor.
-
-    Returns:
-        Tensor: Result of phi1 function.
-    """
-    input_dtype = t.dtype
-    t = t.to(dtype=torch.float32)
-    return (torch.expm1(t) / t).to(dtype=input_dtype)
-
-
-def phi2(t: torch.Tensor) -> torch.Tensor:
-    """
-    Compute the second order phi function: (phi1(t) - 1) / t.
-
-    Args:
-        t: Input tensor.
-
-    Returns:
-        Tensor: Result of phi2 function.
-    """
-    input_dtype = t.dtype
-    t = t.to(dtype=torch.float32)
-    return ((phi1(t) - 1.0) / t).to(dtype=input_dtype)
-
-
-def res_x0_rk2_step(
-    x_s: torch.Tensor,
-    t: torch.Tensor,
-    s: torch.Tensor,
-    x0_s: torch.Tensor,
-    s1: torch.Tensor,
-    x0_s1: torch.Tensor,
-) -> torch.Tensor:
-    """
-    Perform a residual-based 2nd order Runge-Kutta step.
-
-    Args:
-        x_s: Current state tensor.
-        t: Target time tensor.
-        s: Current time tensor.
-        x0_s: Prediction at current time.
-        s1: Intermediate time tensor.
-        x0_s1: Prediction at intermediate time.
-
-    Returns:
-        Tensor: Updated state tensor.
-
-    Raises:
-        AssertionError: If step size is too small.
-    """
-    s = -torch.log(s)
-    t = -torch.log(t)
-    m = -torch.log(s1)
-
-    dt = t - s
-    assert not torch.any(torch.isclose(dt, torch.zeros_like(dt), atol=1e-6)), "Step size is too small"
-    assert not torch.any(torch.isclose(m - s, torch.zeros_like(dt), atol=1e-6)), "Step size is too small"
-
-    c2 = (m - s) / dt
-    phi1_val, phi2_val = phi1(-dt), phi2(-dt)
-
-    # Handle edge case where t = s = m
-    b1 = torch.nan_to_num(phi1_val - 1.0 / c2 * phi2_val, nan=0.0)
-    b2 = torch.nan_to_num(1.0 / c2 * phi2_val, nan=0.0)
-
-    return batch_mul(torch.exp(-dt), x_s) + batch_mul(dt, batch_mul(b1, x0_s) + batch_mul(b2, x0_s1))
-
-
-def reg_x0_euler_step(
-    x_s: torch.Tensor,
-    s: torch.Tensor,
-    t: torch.Tensor,
-    x0_s: torch.Tensor,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    """
-    Perform a regularized Euler step based on x0 prediction.
-
-    Args:
-        x_s: Current state tensor.
-        s: Current time tensor.
-        t: Target time tensor.
-        x0_s: Prediction at current time.
-
-    Returns:
-        Tuple[Tensor, Tensor]: Updated state tensor and current prediction.
-    """
-    coef_x0 = (s - t) / s
-    coef_xs = t / s
-    return batch_mul(coef_x0, x0_s) + batch_mul(coef_xs, x_s), x0_s
-
-
-def order2_fn(
-    x_s: torch.Tensor, s: torch.Tensor, t: torch.Tensor, x0_s: torch.Tensor, x0_preds: torch.Tensor
-) -> Tuple[torch.Tensor, List[torch.Tensor]]:
-    """
-    impl the second order multistep method in https://arxiv.org/pdf/2308.02157
-    Adams Bashforth approach!
-    """
-    if x0_preds:
-        x0_s1, s1 = x0_preds[0]
-        x_t = res_x0_rk2_step(x_s, t, s, x0_s, s1, x0_s1)
-    else:
-        x_t = reg_x0_euler_step(x_s, s, t, x0_s)[0]
-    return x_t, [(x0_s, s)]
-
-
-class SolverConfig:
-    is_multi: bool = True
-    rk: str = "2mid"
-    multistep: str = "2ab"
-    s_churn: float = 0.0
-    s_t_max: float = float("inf")
-    s_t_min: float = 0.0
-    s_noise: float = 1.0
-
-
-def fori_loop(lower: int, upper: int, body_fun: Callable[[int, Any], Any], init_val: Any, disable=None) -> Any:
-    """
-    Implements a for loop with a function.
-
-    Args:
-        lower: Lower bound of the loop (inclusive).
-        upper: Upper bound of the loop (exclusive).
-        body_fun: Function to be applied in each iteration.
-        init_val: Initial value for the loop.
-
-    Returns:
-        The final result after all iterations.
-    """
-    val = init_val
-    for i in trange(lower, upper, disable=disable):
-        val = body_fun(i, val)
-    return val
-
-
-def differential_equation_solver(
-    x0_fn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor],
-    sigmas_L: torch.Tensor,
-    solver_cfg: SolverConfig,
-    noise_sampler,
-    callback=None,
-    disable=None,
-) -> Callable[[torch.Tensor], torch.Tensor]:
-    """
-    Creates a differential equation solver function.
-
-    Args:
-        x0_fn: Function to compute x0 prediction.
-        sigmas_L: Tensor of sigma values with shape [L,].
-        solver_cfg: Configuration for the solver.
-
-    Returns:
-        A function that solves the differential equation.
-    """
-    num_step = len(sigmas_L) - 1
-
-    # if solver_cfg.is_multi:
-    #     update_step_fn = get_multi_step_fn(solver_cfg.multistep)
-    # else:
-    #     update_step_fn = get_runge_kutta_fn(solver_cfg.rk)
-    update_step_fn = order2_fn
-
-    eta = min(solver_cfg.s_churn / (num_step + 1), math.sqrt(1.2) - 1)
-
-    def sample_fn(input_xT_B_StateShape: torch.Tensor) -> torch.Tensor:
-        """
-        Samples from the differential equation.
-
-        Args:
-            input_xT_B_StateShape: Input tensor with shape [B, StateShape].
-
-        Returns:
-            Output tensor with shape [B, StateShape].
-        """
-        ones_B = torch.ones(input_xT_B_StateShape.size(0), device=input_xT_B_StateShape.device, dtype=torch.float32)
-
-        def step_fn(
-            i_th: int, state: Tuple[torch.Tensor, Optional[List[torch.Tensor]]]
-        ) -> Tuple[torch.Tensor, Optional[List[torch.Tensor]]]:
-            input_x_B_StateShape, x0_preds = state
-            sigma_cur_0, sigma_next_0 = sigmas_L[i_th], sigmas_L[i_th + 1]
-
-            if sigma_next_0 == 0:
-                output_x_B_StateShape = x0_pred_B_StateShape = x0_fn(input_x_B_StateShape, sigma_cur_0 * ones_B)
-            else:
-                # algorithm 2: line 4-6
-                if solver_cfg.s_t_min < sigma_cur_0 < solver_cfg.s_t_max and eta > 0:
-                    hat_sigma_cur_0 = sigma_cur_0 + eta * sigma_cur_0
-                    input_x_B_StateShape = input_x_B_StateShape + (
-                        hat_sigma_cur_0**2 - sigma_cur_0**2
-                    ).sqrt() * solver_cfg.s_noise * noise_sampler(sigma_cur_0, sigma_next_0)  # torch.randn_like(input_x_B_StateShape)
-                    sigma_cur_0 = hat_sigma_cur_0
-
-                if solver_cfg.is_multi:
-                    x0_pred_B_StateShape = x0_fn(input_x_B_StateShape, sigma_cur_0 * ones_B)
-                    output_x_B_StateShape, x0_preds = update_step_fn(
-                        input_x_B_StateShape, sigma_cur_0 * ones_B, sigma_next_0 * ones_B, x0_pred_B_StateShape, x0_preds
-                    )
-                else:
-                    output_x_B_StateShape, x0_preds = update_step_fn(
-                        input_x_B_StateShape, sigma_cur_0 * ones_B, sigma_next_0 * ones_B, x0_fn
-                    )
-
-            if callback is not None:
-                callback({'x': input_x_B_StateShape, 'i': i_th, 'sigma': sigma_cur_0, 'sigma_hat': sigma_cur_0, 'denoised': x0_pred_B_StateShape})
-
-            return output_x_B_StateShape, x0_preds
-
-        x_at_eps, _ = fori_loop(0, num_step, step_fn, [input_xT_B_StateShape, None], disable=disable)
-        return x_at_eps
-
-    return sample_fn

comfy/k_diffusion/sampling.py

@@ -8,7 +8,6 @@ from tqdm.auto import trange, tqdm
 
 from . import utils
 from . import deis
-from . import res
 import comfy.model_patcher
 import comfy.model_sampling
 
@@ -1268,18 +1267,72 @@ def sample_dpmpp_2m_cfg_pp(model, x, sigmas, extra_args=None, callback=None, dis
     return x
 
 @torch.no_grad()
-def sample_res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1., noise_sampler=None):
+def res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1., noise_sampler=None, cfg_pp=False):
     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]])
+    sigma_fn = lambda t: t.neg().exp()
+    t_fn = lambda sigma: sigma.log().neg()
+    phi1_fn = lambda t: torch.expm1(t) / t
+    phi2_fn = lambda t: (phi1_fn(t) - 1.0) / t
 
-    x0_func = lambda x, sigma: model(x, sigma, **extra_args)
+    old_denoised = None
+    uncond_denoised = None
+    def post_cfg_function(args):
+        nonlocal uncond_denoised
+        uncond_denoised = args["uncond_denoised"]
+        return args["denoised"]
 
-    solver_cfg = res.SolverConfig()
+    if cfg_pp:
-    solver_cfg.s_churn = s_churn
+        model_options = extra_args.get("model_options", {}).copy()
-    solver_cfg.s_t_max = s_tmax
+        extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
-    solver_cfg.s_t_min = s_tmin
-    solver_cfg.s_noise = s_noise
 
-    x = res.differential_equation_solver(x0_func, sigmas, solver_cfg, noise_sampler, callback=callback, disable=disable)(x)
+    for i in trange(len(sigmas) - 1, disable=disable):
+        if s_churn > 0:
+            gamma = min(s_churn / (len(sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.0
+            sigma_hat = sigmas[i] * (gamma + 1)
+        else:
+            gamma = 0
+            sigma_hat = sigmas[i]
+
+        if gamma > 0:
+            eps = torch.randn_like(x) * s_noise
+            x = x + eps * (sigma_hat**2 - sigmas[i] ** 2) ** 0.5
+        denoised = model(x, sigma_hat * s_in, **extra_args)
+        if callback is not None:
+            callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigma_hat, "denoised": denoised})
+        if sigmas[i + 1] == 0 or old_denoised is None:
+            # Euler method
+            if cfg_pp:
+                d = to_d(x, sigma_hat, uncond_denoised)
+                x = denoised + d * sigmas[i + 1]
+            else:
+                d = to_d(x, sigma_hat, denoised)
+                dt = sigmas[i + 1] - sigma_hat
+                x = x + d * dt
+        else:
+            # Second order multistep method in https://arxiv.org/pdf/2308.02157
+            t, t_next, t_prev = t_fn(sigmas[i]), t_fn(sigmas[i + 1]), t_fn(sigmas[i - 1])
+            h = t_next - t
+            c2 = (t_prev - t) / h
+
+            phi1_val, phi2_val = phi1_fn(-h), phi2_fn(-h)
+            b1 = torch.nan_to_num(phi1_val - 1.0 / c2 * phi2_val, nan=0.0)
+            b2 = torch.nan_to_num(1.0 / c2 * phi2_val, nan=0.0)
+
+            if cfg_pp:
+                x = x + (denoised - uncond_denoised)
+
+            x = (sigma_fn(t_next) / sigma_fn(t)) * x + h * (b1 * denoised + b2 * old_denoised)
+
+        old_denoised = denoised
     return x
+
+@torch.no_grad()
+def sample_res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1., noise_sampler=None):
+    return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_churn=s_churn, s_tmin=s_tmin, s_tmax=s_tmax, s_noise=s_noise, noise_sampler=noise_sampler, cfg_pp=False)
+
+@torch.no_grad()
+def sample_res_multistep_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1., noise_sampler=None):
+    return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_churn=s_churn, s_tmin=s_tmin, s_tmax=s_tmax, s_noise=s_noise, noise_sampler=noise_sampler, cfg_pp=True)

comfy/model_base.py

@@ -189,9 +189,10 @@ class BaseModel(torch.nn.Module):
 
             if denoise_mask is not None:
                 if len(denoise_mask.shape) == len(noise.shape):
-                    denoise_mask = denoise_mask[:,:1]
+                    denoise_mask = denoise_mask[:, :1]
 
-                denoise_mask = denoise_mask.reshape((-1, 1, denoise_mask.shape[-2], denoise_mask.shape[-1]))
+                num_dim = noise.ndim - 2
+                denoise_mask = denoise_mask.reshape((-1, 1) + tuple(denoise_mask.shape[-num_dim:]))
                 if denoise_mask.shape[-2:] != noise.shape[-2:]:
                     denoise_mask = utils.common_upscale(denoise_mask, noise.shape[-1], noise.shape[-2], "bilinear", "center")
                 denoise_mask = utils.resize_to_batch_size(denoise_mask.round(), noise.shape[0])
@@ -201,12 +202,16 @@ class BaseModel(torch.nn.Module):
                     if ck == "mask":
                         cond_concat.append(denoise_mask.to(device))
                     elif ck == "masked_image":
-                        cond_concat.append(concat_latent_image.to(device)) #NOTE: the latent_image should be masked by the mask in pixel space
+                        cond_concat.append(concat_latent_image.to(device))  # NOTE: the latent_image should be masked by the mask in pixel space
+                    elif ck == "mask_inverted":
+                        cond_concat.append(1.0 - denoise_mask.to(device))
                 else:
                     if ck == "mask":
-                        cond_concat.append(torch.ones_like(noise)[:,:1])
+                        cond_concat.append(torch.ones_like(noise)[:, :1])
                     elif ck == "masked_image":
                         cond_concat.append(self.blank_inpaint_image_like(noise))
+                    elif ck == "mask_inverted":
+                        cond_concat.append(torch.zeros_like(noise)[:, :1])
             data = torch.cat(cond_concat, dim=1)
             return data
         return None
@@ -294,6 +299,9 @@ class BaseModel(torch.nn.Module):
             return blank_image
         self.blank_inpaint_image_like = blank_inpaint_image_like
 
+    def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):
+        return self.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1)), noise, latent_image)
+
     def memory_required(self, input_shape):
         if comfy.model_management.xformers_enabled() or comfy.model_management.pytorch_attention_flash_attention():
             dtype = self.get_dtype()
@@ -859,8 +867,11 @@ class HunyuanVideo(BaseModel):
         return out
 
 class CosmosVideo(BaseModel):
-    def __init__(self, model_config, model_type=ModelType.EDM, device=None):
+    def __init__(self, model_config, model_type=ModelType.EDM, image_to_video=False, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.cosmos.model.GeneralDIT)
+        self.image_to_video = image_to_video
+        if self.image_to_video:
+            self.concat_keys = ("mask_inverted",)
 
     def extra_conds(self, **kwargs):
         out = super().extra_conds(**kwargs)
@@ -873,3 +884,11 @@ class CosmosVideo(BaseModel):
 
         out['fps'] = comfy.conds.CONDConstant(kwargs.get("frame_rate", None))
         return out
+
+    def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):
+        sigma = sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1))
+        sigma_noise_augmentation = 0 #TODO
+        if sigma_noise_augmentation != 0:
+            latent_image = latent_image + noise
+        latent_image = self.model_sampling.calculate_input(torch.tensor([sigma_noise_augmentation], device=latent_image.device, dtype=latent_image.dtype), latent_image)
+        return latent_image * ((sigma ** 2 + self.model_sampling.sigma_data ** 2) ** 0.5)

comfy/model_detection.py

@@ -245,13 +245,14 @@ def detect_unet_config(state_dict, key_prefix):
         dit_config["max_img_h"] = 240
         dit_config["max_img_w"] = 240
         dit_config["max_frames"] = 128
-        dit_config["in_channels"] = 16
+        concat_padding_mask = True
+        dit_config["in_channels"] = (state_dict['{}x_embedder.proj.1.weight'.format(key_prefix)].shape[1] // 4) - int(concat_padding_mask)
         dit_config["out_channels"] = 16
         dit_config["patch_spatial"] = 2
         dit_config["patch_temporal"] = 1
         dit_config["model_channels"] = state_dict['{}blocks.block0.blocks.0.block.attn.to_q.0.weight'.format(key_prefix)].shape[0]
         dit_config["block_config"] = "FA-CA-MLP"
-        dit_config["concat_padding_mask"] = True
+        dit_config["concat_padding_mask"] = concat_padding_mask
         dit_config["pos_emb_cls"] = "rope3d"
         dit_config["pos_emb_learnable"] = False
         dit_config["pos_emb_interpolation"] = "crop"

comfy/samplers.py

@@ -548,7 +548,7 @@ class KSamplerX0Inpaint:
             if "denoise_mask_function" in model_options:
                 denoise_mask = model_options["denoise_mask_function"](sigma, denoise_mask, extra_options={"model": self.inner_model, "sigmas": self.sigmas})
             latent_mask = 1. - denoise_mask
-            x = x * denoise_mask + self.inner_model.inner_model.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1)), self.noise, self.latent_image) * latent_mask
+            x = x * denoise_mask + self.inner_model.inner_model.scale_latent_inpaint(x=x, sigma=sigma, noise=self.noise, latent_image=self.latent_image) * latent_mask
         out = self.inner_model(x, sigma, model_options=model_options, seed=seed)
         if denoise_mask is not None:
             out = out * denoise_mask + self.latent_image * latent_mask
@@ -859,7 +859,7 @@ class Sampler:
 KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_cfg_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
                   "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"]
+                  "ipndm", "ipndm_v", "deis", "res_multistep", "res_multistep_cfg_pp"]
 
 class KSAMPLER(Sampler):
     def __init__(self, sampler_function, extra_options={}, inpaint_options={}):

comfy/sd.py

@@ -534,7 +534,7 @@ class VAE:
     def encode(self, pixel_samples):
         pixel_samples = self.vae_encode_crop_pixels(pixel_samples)
         pixel_samples = pixel_samples.movedim(-1, 1)
-        if self.latent_dim == 3:
+        if self.latent_dim == 3 and pixel_samples.ndim < 5:
             pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0)
         try:
             memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype)

comfy/sd1_clip.py

@@ -388,13 +388,10 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No
             import safetensors.torch
             embed = safetensors.torch.load_file(embed_path, device="cpu")
         else:
-            if 'weights_only' in torch.load.__code__.co_varnames:
+            try:
-                try:
+                embed = torch.load(embed_path, weights_only=True, map_location="cpu")
-                    embed = torch.load(embed_path, weights_only=True, map_location="cpu")
+            except:
-                except:
+                embed_out = safe_load_embed_zip(embed_path)
-                    embed_out = safe_load_embed_zip(embed_path)
-            else:
-                embed = torch.load(embed_path, map_location="cpu")
     except Exception:
         logging.warning("{}\n\nerror loading embedding, skipping loading: {}".format(traceback.format_exc(), embedding_name))
         return None

comfy/supported_models.py

@@ -824,9 +824,10 @@ class HunyuanVideo(supported_models_base.BASE):
         hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}llama.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.hunyuan_video.HunyuanVideoTokenizer, comfy.text_encoders.hunyuan_video.hunyuan_video_clip(**hunyuan_detect))
 
-class Cosmos(supported_models_base.BASE):
+class CosmosT2V(supported_models_base.BASE):
     unet_config = {
         "image_model": "cosmos",
+        "in_channels": 16,
     }
 
     sampling_settings = {
@@ -854,7 +855,16 @@ class Cosmos(supported_models_base.BASE):
         t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.cosmos.CosmosT5Tokenizer, comfy.text_encoders.cosmos.te(**t5_detect))
 
+class CosmosI2V(CosmosT2V):
+    unet_config = {
+        "image_model": "cosmos",
+        "in_channels": 17,
+    }
 
-models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideo, Cosmos]
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.CosmosVideo(self, image_to_video=True, device=device)
+        return out
+
+models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideo, CosmosT2V, CosmosI2V]
 
 models += [SVD_img2vid]

comfy/utils.py

@@ -29,17 +29,29 @@ import itertools
 from torch.nn.functional import interpolate
 from einops import rearrange
 
+ALWAYS_SAFE_LOAD = False
+if hasattr(torch.serialization, "add_safe_globals"):  # TODO: this was added in pytorch 2.4, the unsafe path should be removed once earlier versions are deprecated
+    class ModelCheckpoint:
+        pass
+    ModelCheckpoint.__module__ = "pytorch_lightning.callbacks.model_checkpoint"
+
+    from numpy.core.multiarray import scalar
+    from numpy import dtype
+    from numpy.dtypes import Float64DType
+    from _codecs import encode
+
+    torch.serialization.add_safe_globals([ModelCheckpoint, scalar, dtype, Float64DType, encode])
+    ALWAYS_SAFE_LOAD = True
+    logging.info("Checkpoint files will always be loaded safely.")
+
+
 def load_torch_file(ckpt, safe_load=False, device=None):
     if device is None:
         device = torch.device("cpu")
     if ckpt.lower().endswith(".safetensors") or ckpt.lower().endswith(".sft"):
         sd = safetensors.torch.load_file(ckpt, device=device.type)
     else:
-        if safe_load:
+        if safe_load or ALWAYS_SAFE_LOAD:
-            if not 'weights_only' in torch.load.__code__.co_varnames:
-                logging.warning("Warning torch.load doesn't support weights_only on this pytorch version, loading unsafely.")
-                safe_load = False
-        if safe_load:
             pl_sd = torch.load(ckpt, map_location=device, weights_only=True)
         else:
             pl_sd = torch.load(ckpt, map_location=device, pickle_module=comfy.checkpoint_pickle)

comfy_extras/nodes_cosmos.py

@@ -1,6 +1,8 @@
 import nodes
 import torch
 import comfy.model_management
+import comfy.utils
+
 
 class EmptyCosmosLatentVideo:
     @classmethod
@@ -16,8 +18,65 @@ class EmptyCosmosLatentVideo:
 
     def generate(self, width, height, length, batch_size=1):
         latent = torch.zeros([batch_size, 16, ((length - 1) // 8) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
-        return ({"samples":latent}, )
+        return ({"samples": latent}, )
+
+
+def vae_encode_with_padding(vae, image, width, height, length, padding=0):
+    pixels = comfy.utils.common_upscale(image[..., :3].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
+    pixel_len = min(pixels.shape[0], length)
+    padded_length = min(length, (((pixel_len - 1) // 8) + 1 + padding) * 8 - 7)
+    padded_pixels = torch.ones((padded_length, height, width, 3)) * 0.5
+    padded_pixels[:pixel_len] = pixels[:pixel_len]
+    latent_len = ((pixel_len - 1) // 8) + 1
+    latent_temp = vae.encode(padded_pixels)
+    return latent_temp[:, :, :latent_len]
+
+
+class CosmosImageToVideoLatent:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"vae": ("VAE", ),
+                             "width": ("INT", {"default": 1280, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
+                             "height": ("INT", {"default": 704, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
+                             "length": ("INT", {"default": 121, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 8}),
+                             "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
+                },
+                "optional": {"start_image": ("IMAGE", ),
+                             "end_image": ("IMAGE", ),
+                }}
+
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "encode"
+
+    CATEGORY = "conditioning/inpaint"
+
+    def encode(self, vae, width, height, length, batch_size, start_image=None, end_image=None):
+        latent = torch.zeros([1, 16, ((length - 1) // 8) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+        if start_image is None and end_image is None:
+            out_latent = {}
+            out_latent["samples"] = latent
+            return (out_latent,)
+
+        mask = torch.ones([latent.shape[0], 1, ((length - 1) // 8) + 1, latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device())
+
+        if start_image is not None:
+            latent_temp = vae_encode_with_padding(vae, start_image, width, height, length, padding=1)
+            latent[:, :, :latent_temp.shape[-3]] = latent_temp
+            mask[:, :, :latent_temp.shape[-3]] *= 0.0
+
+        if end_image is not None:
+            latent_temp = vae_encode_with_padding(vae, end_image, width, height, length, padding=0)
+            latent[:, :, -latent_temp.shape[-3]:] = latent_temp
+            mask[:, :, -latent_temp.shape[-3]:] *= 0.0
+
+        out_latent = {}
+        out_latent["samples"] = latent.repeat((batch_size, ) + (1,) * (latent.ndim - 1))
+        out_latent["noise_mask"] = mask.repeat((batch_size, ) + (1,) * (mask.ndim - 1))
+        return (out_latent,)
+
 
 NODE_CLASS_MAPPINGS = {
     "EmptyCosmosLatentVideo": EmptyCosmosLatentVideo,
+    "CosmosImageToVideoLatent": CosmosImageToVideoLatent,
 }

comfy_extras/nodes_custom_sampler.py

@@ -231,6 +231,24 @@ class FlipSigmas:
             sigmas[0] = 0.0001
         return (sigmas,)
 
+class SetFirstSigma:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"sigmas": ("SIGMAS", ),
+                     "sigma": ("FLOAT", {"default": 136.0, "min": 0.0, "max": 20000.0, "step": 0.001, "round": False}),
+                    }
+               }
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "sampling/custom_sampling/sigmas"
+
+    FUNCTION = "set_first_sigma"
+
+    def set_first_sigma(self, sigmas, sigma):
+        sigmas = sigmas.clone()
+        sigmas[0] = sigma
+        return (sigmas, )
+
 class KSamplerSelect:
     @classmethod
     def INPUT_TYPES(s):
@@ -710,6 +728,7 @@ NODE_CLASS_MAPPINGS = {
     "SplitSigmas": SplitSigmas,
     "SplitSigmasDenoise": SplitSigmasDenoise,
     "FlipSigmas": FlipSigmas,
+    "SetFirstSigma": SetFirstSigma,
 
     "CFGGuider": CFGGuider,
     "DualCFGGuider": DualCFGGuider,

requirements.txt

@@ -2,6 +2,7 @@ torch
 torchsde
 torchvision
 torchaudio
+numpy>=1.25.0
 einops
 transformers>=4.28.1
 tokenizers>=0.13.3