nit

app/app_settings.py

@@ -9,14 +9,8 @@ class AppSettings():
         self.user_manager = user_manager
 
     def get_settings(self, request):
-        try:
+        file = self.user_manager.get_request_user_filepath(
-            file = self.user_manager.get_request_user_filepath(
+            request, "comfy.settings.json")
-                request,
-                "comfy.settings.json"
-            )
-        except KeyError as e:
-            logging.error("User settings not found.")
-            raise web.HTTPUnauthorized() from e
         if os.path.isfile(file):
             try:
                 with open(file) as f:

comfy/cli_args.py

@@ -79,7 +79,6 @@ fpte_group.add_argument("--fp8_e4m3fn-text-enc", action="store_true", help="Stor
 fpte_group.add_argument("--fp8_e5m2-text-enc", action="store_true", help="Store text encoder weights in fp8 (e5m2 variant).")
 fpte_group.add_argument("--fp16-text-enc", action="store_true", help="Store text encoder weights in fp16.")
 fpte_group.add_argument("--fp32-text-enc", action="store_true", help="Store text encoder weights in fp32.")
-fpte_group.add_argument("--bf16-text-enc", action="store_true", help="Store text encoder weights in bf16.")
 
 parser.add_argument("--force-channels-last", action="store_true", help="Force channels last format when inferencing the models.")
 
@@ -101,7 +100,6 @@ parser.add_argument("--preview-size", type=int, default=512, help="Sets the maxi
 cache_group = parser.add_mutually_exclusive_group()
 cache_group.add_argument("--cache-classic", action="store_true", help="Use the old style (aggressive) caching.")
 cache_group.add_argument("--cache-lru", type=int, default=0, help="Use LRU caching with a maximum of N node results cached. May use more RAM/VRAM.")
-cache_group.add_argument("--cache-none", action="store_true", help="Reduced RAM/VRAM usage at the expense of executing every node for each run.")
 
 attn_group = parser.add_mutually_exclusive_group()
 attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization. Ignored when xformers is used.")
@@ -136,9 +134,8 @@ parser.add_argument("--deterministic", action="store_true", help="Make pytorch u
 class PerformanceFeature(enum.Enum):
     Fp16Accumulation = "fp16_accumulation"
     Fp8MatrixMultiplication = "fp8_matrix_mult"
-    CublasOps = "cublas_ops"
 
-parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: fp16_accumulation fp8_matrix_mult cublas_ops")
+parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: fp16_accumulation fp8_matrix_mult")
 
 parser.add_argument("--dont-print-server", action="store_true", help="Don't print server output.")
 parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test for CI.")

comfy/clip_vision.py

@@ -110,13 +110,9 @@ def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
     elif "vision_model.encoder.layers.30.layer_norm1.weight" in sd:
         json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_h.json")
     elif "vision_model.encoder.layers.22.layer_norm1.weight" in sd:
-        embed_shape = sd["vision_model.embeddings.position_embedding.weight"].shape[0]
         if sd["vision_model.encoder.layers.0.layer_norm1.weight"].shape[0] == 1152:
-            if embed_shape == 729:
+            json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip_384.json")
-                json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip_384.json")
+        elif sd["vision_model.embeddings.position_embedding.weight"].shape[0] == 577:
-            elif embed_shape == 1024:
-                json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip_512.json")
-        elif embed_shape == 577:
             if "multi_modal_projector.linear_1.bias" in sd:
                 json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336_llava.json")
             else:

comfy/clip_vision_siglip_512.json

@@ -1,13 +0,0 @@
-{
-  "num_channels": 3,
-  "hidden_act": "gelu_pytorch_tanh",
-  "hidden_size": 1152,
-  "image_size": 512,
-  "intermediate_size": 4304,
-  "model_type": "siglip_vision_model",
-  "num_attention_heads": 16,
-  "num_hidden_layers": 27,
-  "patch_size": 16,
-  "image_mean": [0.5, 0.5, 0.5],
-  "image_std": [0.5, 0.5, 0.5]
-}

comfy/comfy_types/node_typing.py

@@ -102,13 +102,9 @@ class InputTypeOptions(TypedDict):
     default: bool | str | float | int | list | tuple
     """The default value of the widget"""
     defaultInput: bool
-    """@deprecated in v1.16 frontend. v1.16 frontend allows input socket and widget to co-exist.
+    """Defaults to an input slot rather than a widget"""
-    - defaultInput on required inputs should be dropped.
-    - defaultInput on optional inputs should be replaced with forceInput.
-    Ref: https://github.com/Comfy-Org/ComfyUI_frontend/pull/3364
-    """
     forceInput: bool
-    """Forces the input to be an input slot rather than a widget even a widget is available for the input type."""
+    """`defaultInput` and also don't allow converting to a widget"""
     lazy: bool
     """Declares that this input uses lazy evaluation"""
     rawLink: bool

comfy/k_diffusion/sampling.py

@@ -1422,101 +1422,3 @@ def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None
             x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * (sigmas[i + 1] ** 2 - sigmas[i] ** 2 * r ** 2).sqrt().nan_to_num(nan=0.0)
         old_denoised = denoised
     return x
-
-@torch.no_grad()
-def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=0.5):
-    '''
-    SEEDS-2 - Stochastic Explicit Exponential Derivative-free Solvers (VE Data Prediction) stage 2
-    Arxiv: https://arxiv.org/abs/2305.14267
-    '''
-    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]])
-
-    inject_noise = eta > 0 and s_noise > 0
-
-    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})
-        if sigmas[i + 1] == 0:
-            x = denoised
-        else:
-            t, t_next = -sigmas[i].log(), -sigmas[i + 1].log()
-            h = t_next - t
-            h_eta = h * (eta + 1)
-            s = t + r * h
-            fac = 1 / (2 * r)
-            sigma_s = s.neg().exp()
-
-            coeff_1, coeff_2 = (-r * h_eta).expm1(), (-h_eta).expm1()
-            if inject_noise:
-                noise_coeff_1 = (-2 * r * h * eta).expm1().neg().sqrt()
-                noise_coeff_2 = ((-2 * r * h * eta).expm1() - (-2 * h * eta).expm1()).sqrt()
-                noise_1, noise_2 = noise_sampler(sigmas[i], sigma_s), noise_sampler(sigma_s, sigmas[i + 1])
-
-            # Step 1
-            x_2 = (coeff_1 + 1) * x - coeff_1 * denoised
-            if inject_noise:
-                x_2 = x_2 + sigma_s * (noise_coeff_1 * noise_1) * s_noise
-            denoised_2 = model(x_2, sigma_s * s_in, **extra_args)
-
-            # Step 2
-            denoised_d = (1 - fac) * denoised + fac * denoised_2
-            x = (coeff_2 + 1) * x - coeff_2 * denoised_d
-            if inject_noise:
-                x = x + sigmas[i + 1] * (noise_coeff_2 * noise_1 + noise_coeff_1 * noise_2) * s_noise
-    return x
-
-@torch.no_grad()
-def sample_seeds_3(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r_1=1./3, r_2=2./3):
-    '''
-    SEEDS-3 - Stochastic Explicit Exponential Derivative-free Solvers (VE Data Prediction) stage 3
-    Arxiv: https://arxiv.org/abs/2305.14267
-    '''
-    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]])
-
-    inject_noise = eta > 0 and s_noise > 0
-
-    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})
-        if sigmas[i + 1] == 0:
-            x = denoised
-        else:
-            t, t_next = -sigmas[i].log(), -sigmas[i + 1].log()
-            h = t_next - t
-            h_eta = h * (eta + 1)
-            s_1 = t + r_1 * h
-            s_2 = t + r_2 * h
-            sigma_s_1, sigma_s_2 = s_1.neg().exp(), s_2.neg().exp()
-
-            coeff_1, coeff_2, coeff_3 = (-r_1 * h_eta).expm1(), (-r_2 * h_eta).expm1(), (-h_eta).expm1()
-            if inject_noise:
-                noise_coeff_1 = (-2 * r_1 * h * eta).expm1().neg().sqrt()
-                noise_coeff_2 = ((-2 * r_1 * h * eta).expm1() - (-2 * r_2 * h * eta).expm1()).sqrt()
-                noise_coeff_3 = ((-2 * r_2 * h * eta).expm1() - (-2 * h * eta).expm1()).sqrt()
-                noise_1, noise_2, noise_3 = noise_sampler(sigmas[i], sigma_s_1), noise_sampler(sigma_s_1, sigma_s_2), noise_sampler(sigma_s_2, sigmas[i + 1])
-
-            # Step 1
-            x_2 = (coeff_1 + 1) * x - coeff_1 * denoised
-            if inject_noise:
-                x_2 = x_2 + sigma_s_1 * (noise_coeff_1 * noise_1) * s_noise
-            denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args)
-
-            # Step 2
-            x_3 = (coeff_2 + 1) * x - coeff_2 * denoised + (r_2 / r_1) * (coeff_2 / (r_2 * h_eta) + 1) * (denoised_2 - denoised)
-            if inject_noise:
-                x_3 = x_3 + sigma_s_2 * (noise_coeff_2 * noise_1 + noise_coeff_1 * noise_2) * s_noise
-            denoised_3 = model(x_3, sigma_s_2 * s_in, **extra_args)
-
-            # Step 3
-            x = (coeff_3 + 1) * x - coeff_3 * denoised + (1. / r_2) * (coeff_3 / h_eta + 1) * (denoised_3 - denoised)
-            if inject_noise:
-                x = x + sigmas[i + 1] * (noise_coeff_3 * noise_1 + noise_coeff_2 * noise_2 + noise_coeff_1 * noise_3) * s_noise
-    return x

comfy/ldm/modules/attention.py

@@ -847,7 +847,6 @@ class SpatialTransformer(nn.Module):
         if not isinstance(context, list):
             context = [context] * len(self.transformer_blocks)
         b, c, h, w = x.shape
-        transformer_options["activations_shape"] = list(x.shape)
         x_in = x
         x = self.norm(x)
         if not self.use_linear:
@@ -963,7 +962,6 @@ class SpatialVideoTransformer(SpatialTransformer):
         transformer_options={}
     ) -> torch.Tensor:
         _, _, h, w = x.shape
-        transformer_options["activations_shape"] = list(x.shape)
         x_in = x
         spatial_context = None
         if exists(context):

comfy/lora_convert.py

@@ -1,5 +1,4 @@
 import torch
-import comfy.utils
 
 
 def convert_lora_bfl_control(sd): #BFL loras for Flux
@@ -12,13 +11,7 @@ def convert_lora_bfl_control(sd): #BFL loras for Flux
     return sd_out
 
 
-def convert_lora_wan_fun(sd): #Wan Fun loras
-    return comfy.utils.state_dict_prefix_replace(sd, {"lora_unet__": "lora_unet_"})
-
-
 def convert_lora(sd):
     if "img_in.lora_A.weight" in sd and "single_blocks.0.norm.key_norm.scale" in sd:
         return convert_lora_bfl_control(sd)
-    if "lora_unet__blocks_0_cross_attn_k.lora_down.weight" in sd:
-        return convert_lora_wan_fun(sd)
     return sd

comfy/model_base.py

@@ -992,41 +992,31 @@ class WAN21(BaseModel):
 
     def concat_cond(self, **kwargs):
         noise = kwargs.get("noise", None)
-        extra_channels = self.diffusion_model.patch_embedding.weight.shape[1] - noise.shape[1]
+        if self.diffusion_model.patch_embedding.weight.shape[1] == noise.shape[1]:
-        if extra_channels == 0:
             return None
 
         image = kwargs.get("concat_latent_image", None)
         device = kwargs["device"]
 
         if image is None:
-            shape_image = list(noise.shape)
+            image = torch.zeros_like(noise)
-            shape_image[1] = extra_channels
-            image = torch.zeros(shape_image, dtype=noise.dtype, layout=noise.layout, device=noise.device)
-        else:
-            image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
-            for i in range(0, image.shape[1], 16):
-                image[:, i: i + 16] = self.process_latent_in(image[:, i: i + 16])
-            image = utils.resize_to_batch_size(image, noise.shape[0])
 
-        if not self.image_to_video or extra_channels == image.shape[1]:
+        image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
+        image = self.process_latent_in(image)
+        image = utils.resize_to_batch_size(image, noise.shape[0])
+
+        if not self.image_to_video:
             return image
 
-        if image.shape[1] > (extra_channels - 4):
-            image = image[:, :(extra_channels - 4)]
-
         mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
         if mask is None:
             mask = torch.zeros_like(noise)[:, :4]
         else:
-            if mask.shape[1] != 4:
+            mask = 1.0 - torch.mean(mask, dim=1, keepdim=True)
-                mask = torch.mean(mask, dim=1, keepdim=True)
-            mask = 1.0 - mask
             mask = utils.common_upscale(mask.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
             if mask.shape[-3] < noise.shape[-3]:
                 mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, noise.shape[-3] - mask.shape[-3]), mode='constant', value=0)
-            if mask.shape[1] == 1:
+            mask = mask.repeat(1, 4, 1, 1, 1)
-                mask = mask.repeat(1, 4, 1, 1, 1)
             mask = utils.resize_to_batch_size(mask, noise.shape[0])
 
         return torch.cat((mask, image), dim=1)

comfy/model_management.py

@@ -823,8 +823,6 @@ def text_encoder_dtype(device=None):
         return torch.float8_e5m2
     elif args.fp16_text_enc:
         return torch.float16
-    elif args.bf16_text_enc:
-        return torch.bfloat16
     elif args.fp32_text_enc:
         return torch.float32
 
@@ -1237,8 +1235,6 @@ def soft_empty_cache(force=False):
         torch.xpu.empty_cache()
     elif is_ascend_npu():
         torch.npu.empty_cache()
-    elif is_mlu():
-        torch.mlu.empty_cache()
     elif torch.cuda.is_available():
         torch.cuda.empty_cache()
         torch.cuda.ipc_collect()

comfy/ops.py

@@ -357,25 +357,6 @@ def scaled_fp8_ops(fp8_matrix_mult=False, scale_input=False, override_dtype=None
 
     return scaled_fp8_op
 
-CUBLAS_IS_AVAILABLE = False
-try:
-    from cublas_ops import CublasLinear
-    CUBLAS_IS_AVAILABLE = True
-except ImportError:
-    pass
-
-if CUBLAS_IS_AVAILABLE:
-    class cublas_ops(disable_weight_init):
-        class Linear(CublasLinear, disable_weight_init.Linear):
-            def reset_parameters(self):
-                return None
-
-            def forward_comfy_cast_weights(self, input):
-                return super().forward(input)
-
-            def forward(self, *args, **kwargs):
-                return super().forward(*args, **kwargs)
-
 def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, scaled_fp8=None):
     fp8_compute = comfy.model_management.supports_fp8_compute(load_device)
     if scaled_fp8 is not None:
@@ -388,15 +369,6 @@ def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_
     ):
         return fp8_ops
 
-    if (
-        PerformanceFeature.CublasOps in args.fast and
-        CUBLAS_IS_AVAILABLE and
-        weight_dtype == torch.float16 and
-        (compute_dtype == torch.float16 or compute_dtype is None)
-    ):
-        logging.info("Using cublas ops")
-        return cublas_ops
-
     if compute_dtype is None or weight_dtype == compute_dtype:
         return disable_weight_init
 

comfy/patcher_extension.py

@@ -48,7 +48,6 @@ def get_all_callbacks(call_type: str, transformer_options: dict, is_model_option
 
 class WrappersMP:
     OUTER_SAMPLE = "outer_sample"
-    PREPARE_SAMPLING = "prepare_sampling"
     SAMPLER_SAMPLE = "sampler_sample"
     CALC_COND_BATCH = "calc_cond_batch"
     APPLY_MODEL = "apply_model"

comfy/sampler_helpers.py

@@ -106,13 +106,6 @@ def cleanup_additional_models(models):
 
 
 def prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None):
-    executor = comfy.patcher_extension.WrapperExecutor.new_executor(
-        _prepare_sampling,
-        comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.PREPARE_SAMPLING, model_options, is_model_options=True)
-    )
-    return executor.execute(model, noise_shape, conds, model_options=model_options)
-
-def _prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None):
     real_model: BaseModel = None
     models, inference_memory = get_additional_models(conds, model.model_dtype())
     models += get_additional_models_from_model_options(model_options)

comfy/samplers.py

@@ -710,7 +710,7 @@ 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", "er_sde", "seeds_2", "seeds_3"]
+                  "gradient_estimation", "er_sde"]
 
 class KSAMPLER(Sampler):
     def __init__(self, sampler_function, extra_options={}, inpaint_options={}):

comfy/sd.py

@@ -265,7 +265,6 @@ class VAE:
         self.process_input = lambda image: image * 2.0 - 1.0
         self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)
         self.working_dtypes = [torch.bfloat16, torch.float32]
-        self.disable_offload = False
 
         self.downscale_index_formula = None
         self.upscale_index_formula = None
@@ -338,7 +337,6 @@ class VAE:
                 self.process_output = lambda audio: audio
                 self.process_input = lambda audio: audio
                 self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
-                self.disable_offload = True
             elif "blocks.2.blocks.3.stack.5.weight" in sd or "decoder.blocks.2.blocks.3.stack.5.weight" in sd or "layers.4.layers.1.attn_block.attn.qkv.weight" in sd or "encoder.layers.4.layers.1.attn_block.attn.qkv.weight" in sd: #genmo mochi vae
                 if "blocks.2.blocks.3.stack.5.weight" in sd:
                     sd = comfy.utils.state_dict_prefix_replace(sd, {"": "decoder."})
@@ -517,7 +515,7 @@ class VAE:
         pixel_samples = None
         try:
             memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype)
-            model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload)
+            model_management.load_models_gpu([self.patcher], memory_required=memory_used)
             free_memory = model_management.get_free_memory(self.device)
             batch_number = int(free_memory / memory_used)
             batch_number = max(1, batch_number)
@@ -546,7 +544,7 @@ class VAE:
     def decode_tiled(self, samples, tile_x=None, tile_y=None, overlap=None, tile_t=None, overlap_t=None):
         self.throw_exception_if_invalid()
         memory_used = self.memory_used_decode(samples.shape, self.vae_dtype) #TODO: calculate mem required for tile
-        model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload)
+        model_management.load_models_gpu([self.patcher], memory_required=memory_used)
         dims = samples.ndim - 2
         args = {}
         if tile_x is not None:
@@ -580,7 +578,7 @@ class VAE:
             pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0)
         try:
             memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype)
-            model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload)
+            model_management.load_models_gpu([self.patcher], memory_required=memory_used)
             free_memory = model_management.get_free_memory(self.device)
             batch_number = int(free_memory / max(1, memory_used))
             batch_number = max(1, batch_number)
@@ -614,7 +612,7 @@ class VAE:
             pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0)
 
         memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype)  # TODO: calculate mem required for tile
-        model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload)
+        model_management.load_models_gpu([self.patcher], memory_required=memory_used)
 
         args = {}
         if tile_x is not None:

comfy/supported_models.py

@@ -969,24 +969,12 @@ class WAN21_I2V(WAN21_T2V):
     unet_config = {
         "image_model": "wan2.1",
         "model_type": "i2v",
-        "in_dim": 36,
     }
 
     def get_model(self, state_dict, prefix="", device=None):
         out = model_base.WAN21(self, image_to_video=True, device=device)
         return out
 
-class WAN21_FunControl2V(WAN21_T2V):
-    unet_config = {
-        "image_model": "wan2.1",
-        "model_type": "i2v",
-        "in_dim": 48,
-    }
-
-    def get_model(self, state_dict, prefix="", device=None):
-        out = model_base.WAN21(self, image_to_video=False, device=device)
-        return out
-
 class Hunyuan3Dv2(supported_models_base.BASE):
     unet_config = {
         "image_model": "hunyuan3d2",
@@ -1025,6 +1013,6 @@ class Hunyuan3Dv2mini(Hunyuan3Dv2):
 
     latent_format = latent_formats.Hunyuan3Dv2mini
 
-models = [LotusD, 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, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, Lumina2, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, Hunyuan3Dv2mini, Hunyuan3Dv2]
+models = [LotusD, 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, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, Lumina2, WAN21_T2V, WAN21_I2V, Hunyuan3Dv2mini, Hunyuan3Dv2]
 
 models += [SVD_img2vid]

comfy_execution/caching.py

@@ -316,156 +316,3 @@ class LRUCache(BasicCache):
             self.children[cache_key].append(self.cache_key_set.get_data_key(child_id))
         return self
 
-
-class DependencyAwareCache(BasicCache):
-    """
-    A cache implementation that tracks dependencies between nodes and manages
-    their execution and caching accordingly. It extends the BasicCache class.
-    Nodes are removed from this cache once all of their descendants have been
-    executed.
-    """
-
-    def __init__(self, key_class):
-        """
-        Initialize the DependencyAwareCache.
-
-        Args:
-            key_class: The class used for generating cache keys.
-        """
-        super().__init__(key_class)
-        self.descendants = {}  # Maps node_id -> set of descendant node_ids
-        self.ancestors = {}    # Maps node_id -> set of ancestor node_ids
-        self.executed_nodes = set()  # Tracks nodes that have been executed
-
-    def set_prompt(self, dynprompt, node_ids, is_changed_cache):
-        """
-        Clear the entire cache and rebuild the dependency graph.
-
-        Args:
-            dynprompt: The dynamic prompt object containing node information.
-            node_ids: List of node IDs to initialize the cache for.
-            is_changed_cache: Flag indicating if the cache has changed.
-        """
-        # Clear all existing cache data
-        self.cache.clear()
-        self.subcaches.clear()
-        self.descendants.clear()
-        self.ancestors.clear()
-        self.executed_nodes.clear()
-
-        # Call the parent method to initialize the cache with the new prompt
-        super().set_prompt(dynprompt, node_ids, is_changed_cache)
-
-        # Rebuild the dependency graph
-        self._build_dependency_graph(dynprompt, node_ids)
-
-    def _build_dependency_graph(self, dynprompt, node_ids):
-        """
-        Build the dependency graph for all nodes.
-
-        Args:
-            dynprompt: The dynamic prompt object containing node information.
-            node_ids: List of node IDs to build the graph for.
-        """
-        self.descendants.clear()
-        self.ancestors.clear()
-        for node_id in node_ids:
-            self.descendants[node_id] = set()
-            self.ancestors[node_id] = set()
-
-        for node_id in node_ids:
-            inputs = dynprompt.get_node(node_id)["inputs"]
-            for input_data in inputs.values():
-                if is_link(input_data):  # Check if the input is a link to another node
-                    ancestor_id = input_data[0]
-                    self.descendants[ancestor_id].add(node_id)
-                    self.ancestors[node_id].add(ancestor_id)
-
-    def set(self, node_id, value):
-        """
-        Mark a node as executed and store its value in the cache.
-
-        Args:
-            node_id: The ID of the node to store.
-            value: The value to store for the node.
-        """
-        self._set_immediate(node_id, value)
-        self.executed_nodes.add(node_id)
-        self._cleanup_ancestors(node_id)
-
-    def get(self, node_id):
-        """
-        Retrieve the cached value for a node.
-
-        Args:
-            node_id: The ID of the node to retrieve.
-
-        Returns:
-            The cached value for the node.
-        """
-        return self._get_immediate(node_id)
-
-    def ensure_subcache_for(self, node_id, children_ids):
-        """
-        Ensure a subcache exists for a node and update dependencies.
-
-        Args:
-            node_id: The ID of the parent node.
-            children_ids: List of child node IDs to associate with the parent node.
-
-        Returns:
-            The subcache object for the node.
-        """
-        subcache = super()._ensure_subcache(node_id, children_ids)
-        for child_id in children_ids:
-            self.descendants[node_id].add(child_id)
-            self.ancestors[child_id].add(node_id)
-        return subcache
-
-    def _cleanup_ancestors(self, node_id):
-        """
-        Check if ancestors of a node can be removed from the cache.
-
-        Args:
-            node_id: The ID of the node whose ancestors are to be checked.
-        """
-        for ancestor_id in self.ancestors.get(node_id, []):
-            if ancestor_id in self.executed_nodes:
-                # Remove ancestor if all its descendants have been executed
-                if all(descendant in self.executed_nodes for descendant in self.descendants[ancestor_id]):
-                    self._remove_node(ancestor_id)
-
-    def _remove_node(self, node_id):
-        """
-        Remove a node from the cache.
-
-        Args:
-            node_id: The ID of the node to remove.
-        """
-        cache_key = self.cache_key_set.get_data_key(node_id)
-        if cache_key in self.cache:
-            del self.cache[cache_key]
-        subcache_key = self.cache_key_set.get_subcache_key(node_id)
-        if subcache_key in self.subcaches:
-            del self.subcaches[subcache_key]
-
-    def clean_unused(self):
-        """
-        Clean up unused nodes. This is a no-op for this cache implementation.
-        """
-        pass
-
-    def recursive_debug_dump(self):
-        """
-        Dump the cache and dependency graph for debugging.
-
-        Returns:
-            A list containing the cache state and dependency graph.
-        """
-        result = super().recursive_debug_dump()
-        result.append({
-            "descendants": self.descendants,
-            "ancestors": self.ancestors,
-            "executed_nodes": list(self.executed_nodes),
-        })
-        return result

comfy_execution/graph.py

@@ -1,6 +1,9 @@
-import nodes
+from __future__ import annotations
+from typing import Type, Literal
 
+import nodes
 from comfy_execution.graph_utils import is_link
+from comfy.comfy_types.node_typing import ComfyNodeABC, InputTypeDict, InputTypeOptions
 
 class DependencyCycleError(Exception):
     pass
@@ -54,7 +57,22 @@ class DynamicPrompt:
     def get_original_prompt(self):
         return self.original_prompt
 
-def get_input_info(class_def, input_name, valid_inputs=None):
+def get_input_info(
+    class_def: Type[ComfyNodeABC],
+    input_name: str,
+    valid_inputs: InputTypeDict | None = None
+) -> tuple[str, Literal["required", "optional", "hidden"], InputTypeOptions] | tuple[None, None, None]:
+    """Get the input type, category, and extra info for a given input name.
+
+    Arguments:
+        class_def: The class definition of the node.
+        input_name: The name of the input to get info for.
+        valid_inputs: The valid inputs for the node, or None to use the class_def.INPUT_TYPES().
+
+    Returns:
+        tuple[str, str, dict] | tuple[None, None, None]: The input type, category, and extra info for the input name.
+    """
+
     valid_inputs = valid_inputs or class_def.INPUT_TYPES()
     input_info = None
     input_category = None
@@ -126,7 +144,7 @@ class TopologicalSort:
                     from_node_id, from_socket = value
                     if subgraph_nodes is not None and from_node_id not in subgraph_nodes:
                         continue
-                    input_type, input_category, input_info = self.get_input_info(unique_id, input_name)
+                    _, _, input_info = self.get_input_info(unique_id, input_name)
                     is_lazy = input_info is not None and "lazy" in input_info and input_info["lazy"]
                     if (include_lazy or not is_lazy) and not self.is_cached(from_node_id):
                         node_ids.append(from_node_id)

comfy_extras/nodes_cfg.py

@@ -1,45 +0,0 @@
-import torch
-
-# https://github.com/WeichenFan/CFG-Zero-star
-def optimized_scale(positive, negative):
-    positive_flat = positive.reshape(positive.shape[0], -1)
-    negative_flat = negative.reshape(negative.shape[0], -1)
-
-    # Calculate dot production
-    dot_product = torch.sum(positive_flat * negative_flat, dim=1, keepdim=True)
-
-    # Squared norm of uncondition
-    squared_norm = torch.sum(negative_flat ** 2, dim=1, keepdim=True) + 1e-8
-
-    # st_star = v_cond^T * v_uncond / ||v_uncond||^2
-    st_star = dot_product / squared_norm
-
-    return st_star.reshape([positive.shape[0]] + [1] * (positive.ndim - 1))
-
-class CFGZeroStar:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"model": ("MODEL",),
-                            }}
-    RETURN_TYPES = ("MODEL",)
-    RETURN_NAMES = ("patched_model",)
-    FUNCTION = "patch"
-    CATEGORY = "advanced/guidance"
-
-    def patch(self, model):
-        m = model.clone()
-        def cfg_zero_star(args):
-            guidance_scale = args['cond_scale']
-            x = args['input']
-            cond_p = args['cond_denoised']
-            uncond_p = args['uncond_denoised']
-            out = args["denoised"]
-            alpha = optimized_scale(x - cond_p, x - uncond_p)
-
-            return out + uncond_p * (alpha - 1.0)  + guidance_scale * uncond_p * (1.0 - alpha)
-        m.set_model_sampler_post_cfg_function(cfg_zero_star)
-        return (m, )
-
-NODE_CLASS_MAPPINGS = {
-    "CFGZeroStar": CFGZeroStar
-}

comfy_extras/nodes_hunyuan3d.py

@@ -209,196 +209,6 @@ def voxel_to_mesh(voxels, threshold=0.5, device=None):
     vertices = torch.fliplr(vertices)
     return vertices, faces
 
-def voxel_to_mesh_surfnet(voxels, threshold=0.5, device=None):
-    if device is None:
-        device = torch.device("cpu")
-    voxels = voxels.to(device)
-
-    D, H, W = voxels.shape
-
-    padded = torch.nn.functional.pad(voxels, (1, 1, 1, 1, 1, 1), 'constant', 0)
-    z, y, x = torch.meshgrid(
-        torch.arange(D, device=device),
-        torch.arange(H, device=device),
-        torch.arange(W, device=device),
-        indexing='ij'
-    )
-    cell_positions = torch.stack([z.flatten(), y.flatten(), x.flatten()], dim=1)
-
-    corner_offsets = torch.tensor([
-        [0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0],
-        [0, 0, 1], [1, 0, 1], [0, 1, 1], [1, 1, 1]
-    ], device=device)
-
-    corner_values = torch.zeros((cell_positions.shape[0], 8), device=device)
-    for c, (dz, dy, dx) in enumerate(corner_offsets):
-        corner_values[:, c] = padded[
-            cell_positions[:, 0] + dz,
-            cell_positions[:, 1] + dy,
-            cell_positions[:, 2] + dx
-        ]
-
-    corner_signs = corner_values > threshold
-    has_inside = torch.any(corner_signs, dim=1)
-    has_outside = torch.any(~corner_signs, dim=1)
-    contains_surface = has_inside & has_outside
-
-    active_cells = cell_positions[contains_surface]
-    active_signs = corner_signs[contains_surface]
-    active_values = corner_values[contains_surface]
-
-    if active_cells.shape[0] == 0:
-        return torch.zeros((0, 3), device=device), torch.zeros((0, 3), dtype=torch.long, device=device)
-
-    edges = torch.tensor([
-        [0, 1], [0, 2], [0, 4], [1, 3],
-        [1, 5], [2, 3], [2, 6], [3, 7],
-        [4, 5], [4, 6], [5, 7], [6, 7]
-    ], device=device)
-
-    cell_vertices = {}
-    progress = comfy.utils.ProgressBar(100)
-
-    for edge_idx, (e1, e2) in enumerate(edges):
-        progress.update(1)
-        crossing = active_signs[:, e1] != active_signs[:, e2]
-        if not crossing.any():
-            continue
-
-        cell_indices = torch.nonzero(crossing, as_tuple=True)[0]
-
-        v1 = active_values[cell_indices, e1]
-        v2 = active_values[cell_indices, e2]
-
-        t = torch.zeros_like(v1, device=device)
-        denom = v2 - v1
-        valid = denom != 0
-        t[valid] = (threshold - v1[valid]) / denom[valid]
-        t[~valid] = 0.5
-
-        p1 = corner_offsets[e1].float()
-        p2 = corner_offsets[e2].float()
-
-        intersection = p1.unsqueeze(0) + t.unsqueeze(1) * (p2.unsqueeze(0) - p1.unsqueeze(0))
-
-        for i, point in zip(cell_indices.tolist(), intersection):
-            if i not in cell_vertices:
-                cell_vertices[i] = []
-            cell_vertices[i].append(point)
-
-    # Calculate the final vertices as the average of intersection points for each cell
-    vertices = []
-    vertex_lookup = {}
-
-    vert_progress_mod = round(len(cell_vertices)/50)
-
-    for i, points in cell_vertices.items():
-        if not i % vert_progress_mod:
-            progress.update(1)
-
-        if points:
-            vertex = torch.stack(points).mean(dim=0)
-            vertex = vertex + active_cells[i].float()
-            vertex_lookup[tuple(active_cells[i].tolist())] = len(vertices)
-            vertices.append(vertex)
-
-    if not vertices:
-        return torch.zeros((0, 3), device=device), torch.zeros((0, 3), dtype=torch.long, device=device)
-
-    final_vertices = torch.stack(vertices)
-
-    inside_corners_mask = active_signs
-    outside_corners_mask = ~active_signs
-
-    inside_counts = inside_corners_mask.sum(dim=1, keepdim=True).float()
-    outside_counts = outside_corners_mask.sum(dim=1, keepdim=True).float()
-
-    inside_pos = torch.zeros((active_cells.shape[0], 3), device=device)
-    outside_pos = torch.zeros((active_cells.shape[0], 3), device=device)
-
-    for i in range(8):
-        mask_inside = inside_corners_mask[:, i].unsqueeze(1)
-        mask_outside = outside_corners_mask[:, i].unsqueeze(1)
-        inside_pos += corner_offsets[i].float().unsqueeze(0) * mask_inside
-        outside_pos += corner_offsets[i].float().unsqueeze(0) * mask_outside
-
-    inside_pos /= inside_counts
-    outside_pos /= outside_counts
-    gradients = inside_pos - outside_pos
-
-    pos_dirs = torch.tensor([
-        [1, 0, 0],
-        [0, 1, 0],
-        [0, 0, 1]
-    ], device=device)
-
-    cross_products = [
-        torch.linalg.cross(pos_dirs[i].float(), pos_dirs[j].float())
-        for i in range(3) for j in range(i+1, 3)
-    ]
-
-    faces = []
-    all_keys = set(vertex_lookup.keys())
-
-    face_progress_mod = round(len(active_cells)/38*3)
-
-    for pair_idx, (i, j) in enumerate([(0,1), (0,2), (1,2)]):
-        dir_i = pos_dirs[i]
-        dir_j = pos_dirs[j]
-        cross_product = cross_products[pair_idx]
-
-        ni_positions = active_cells + dir_i
-        nj_positions = active_cells + dir_j
-        diag_positions = active_cells + dir_i + dir_j
-
-        alignments = torch.matmul(gradients, cross_product)
-
-        valid_quads = []
-        quad_indices = []
-
-        for idx, active_cell in enumerate(active_cells):
-            if not idx % face_progress_mod:
-                progress.update(1)
-            cell_key = tuple(active_cell.tolist())
-            ni_key = tuple(ni_positions[idx].tolist())
-            nj_key = tuple(nj_positions[idx].tolist())
-            diag_key = tuple(diag_positions[idx].tolist())
-
-            if cell_key in all_keys and ni_key in all_keys and nj_key in all_keys and diag_key in all_keys:
-                v0 = vertex_lookup[cell_key]
-                v1 = vertex_lookup[ni_key]
-                v2 = vertex_lookup[nj_key]
-                v3 = vertex_lookup[diag_key]
-
-                valid_quads.append((v0, v1, v2, v3))
-                quad_indices.append(idx)
-
-        for q_idx, (v0, v1, v2, v3) in enumerate(valid_quads):
-            cell_idx = quad_indices[q_idx]
-            if alignments[cell_idx] > 0:
-                faces.append(torch.tensor([v0, v1, v3], device=device, dtype=torch.long))
-                faces.append(torch.tensor([v0, v3, v2], device=device, dtype=torch.long))
-            else:
-                faces.append(torch.tensor([v0, v3, v1], device=device, dtype=torch.long))
-                faces.append(torch.tensor([v0, v2, v3], device=device, dtype=torch.long))
-
-    if faces:
-        faces = torch.stack(faces)
-    else:
-        faces = torch.zeros((0, 3), dtype=torch.long, device=device)
-
-    v_min = 0
-    v_max = max(D, H, W)
-
-    final_vertices = final_vertices - (v_min + v_max) / 2
-
-    scale = (v_max - v_min) / 2
-    if scale > 0:
-        final_vertices = final_vertices / scale
-
-    final_vertices = torch.fliplr(final_vertices)
-
-    return final_vertices, faces
 
 class MESH:
     def __init__(self, vertices, faces):
@@ -427,34 +237,6 @@ class VoxelToMeshBasic:
 
         return (MESH(torch.stack(vertices), torch.stack(faces)), )
 
-class VoxelToMesh:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"voxel": ("VOXEL", ),
-                             "algorithm": (["surface net", "basic"], ),
-                             "threshold": ("FLOAT", {"default": 0.6, "min": -1.0, "max": 1.0, "step": 0.01}),
-                             }}
-    RETURN_TYPES = ("MESH",)
-    FUNCTION = "decode"
-
-    CATEGORY = "3d"
-
-    def decode(self, voxel, algorithm, threshold):
-        vertices = []
-        faces = []
-
-        if algorithm == "basic":
-            mesh_function = voxel_to_mesh
-        elif algorithm == "surface net":
-            mesh_function = voxel_to_mesh_surfnet
-
-        for x in voxel.data:
-            v, f = mesh_function(x, threshold=threshold, device=None)
-            vertices.append(v)
-            faces.append(f)
-
-        return (MESH(torch.stack(vertices), torch.stack(faces)), )
-
 
 def save_glb(vertices, faces, filepath, metadata=None):
     """
@@ -462,7 +244,7 @@ def save_glb(vertices, faces, filepath, metadata=None):
 
     Parameters:
     vertices: torch.Tensor of shape (N, 3) - The vertex coordinates
-    faces: torch.Tensor of shape (M, 3) - The face indices (triangle faces)
+    faces: torch.Tensor of shape (M, 4) or (M, 3) - The face indices (quad or triangle faces)
     filepath: str - Output filepath (should end with .glb)
     """
 
@@ -629,6 +411,5 @@ NODE_CLASS_MAPPINGS = {
     "Hunyuan3Dv2ConditioningMultiView": Hunyuan3Dv2ConditioningMultiView,
     "VAEDecodeHunyuan3D": VAEDecodeHunyuan3D,
     "VoxelToMeshBasic": VoxelToMeshBasic,
-    "VoxelToMesh": VoxelToMesh,
     "SaveGLB": SaveGLB,
 }

comfy_extras/nodes_lt.py

@@ -446,9 +446,10 @@ class LTXVPreprocess:
     CATEGORY = "image"
 
     def preprocess(self, image, img_compression):
-        output_images = []
+        if img_compression > 0:
-        for i in range(image.shape[0]):
+            output_images = []
-            output_images.append(preprocess(image[i], img_compression))
+            for i in range(image.shape[0]):
+                output_images.append(preprocess(image[i], img_compression))
         return (torch.stack(output_images),)
 
 

comfy_extras/nodes_mask.py

@@ -2,7 +2,6 @@ import numpy as np
 import scipy.ndimage
 import torch
 import comfy.utils
-import node_helpers
 
 from nodes import MAX_RESOLUTION
 
@@ -88,7 +87,6 @@ class ImageCompositeMasked:
     CATEGORY = "image"
 
     def composite(self, destination, source, x, y, resize_source, mask = None):
-        destination, source = node_helpers.image_alpha_fix(destination, source)
         destination = destination.clone().movedim(-1, 1)
         output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1)
         return (output,)

comfy_extras/nodes_optimalsteps.py

@@ -1,56 +0,0 @@
-# from https://github.com/bebebe666/OptimalSteps
-
-
-import numpy as np
-import torch
-
-def loglinear_interp(t_steps, num_steps):
-    """
-    Performs log-linear interpolation of a given array of decreasing numbers.
-    """
-    xs = np.linspace(0, 1, len(t_steps))
-    ys = np.log(t_steps[::-1])
-
-    new_xs = np.linspace(0, 1, num_steps)
-    new_ys = np.interp(new_xs, xs, ys)
-
-    interped_ys = np.exp(new_ys)[::-1].copy()
-    return interped_ys
-
-
-NOISE_LEVELS = {"FLUX": [0.9968, 0.9886, 0.9819, 0.975, 0.966, 0.9471, 0.9158, 0.8287, 0.5512, 0.2808, 0.001],
-"Wan":[1.0, 0.997, 0.995, 0.993, 0.991, 0.989, 0.987, 0.985, 0.98, 0.975, 0.973, 0.968, 0.96, 0.946, 0.927, 0.902, 0.864, 0.776, 0.539, 0.208, 0.001],
-}
-
-class OptimalStepsScheduler:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required":
-                    {"model_type": (["FLUX", "Wan"], ),
-                     "steps": ("INT", {"default": 20, "min": 3, "max": 1000}),
-                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-                      }
-               }
-    RETURN_TYPES = ("SIGMAS",)
-    CATEGORY = "sampling/custom_sampling/schedulers"
-
-    FUNCTION = "get_sigmas"
-
-    def get_sigmas(self, model_type, steps, denoise):
-        total_steps = steps
-        if denoise < 1.0:
-            if denoise <= 0.0:
-                return (torch.FloatTensor([]),)
-            total_steps = round(steps * denoise)
-
-        sigmas = NOISE_LEVELS[model_type][:]
-        if (steps + 1) != len(sigmas):
-            sigmas = loglinear_interp(sigmas, steps + 1)
-
-        sigmas = sigmas[-(total_steps + 1):]
-        sigmas[-1] = 0
-        return (torch.FloatTensor(sigmas), )
-
-NODE_CLASS_MAPPINGS = {
-    "OptimalStepsScheduler": OptimalStepsScheduler,
-}

comfy_extras/nodes_post_processing.py

@@ -6,7 +6,7 @@ import math
 
 import comfy.utils
 import comfy.model_management
-import node_helpers
+
 
 class Blend:
     def __init__(self):
@@ -34,7 +34,6 @@ class Blend:
     CATEGORY = "image/postprocessing"
 
     def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str):
-        image1, image2 = node_helpers.image_alpha_fix(image1, image2)
         image2 = image2.to(image1.device)
         if image1.shape != image2.shape:
             image2 = image2.permute(0, 3, 1, 2)

comfy_extras/nodes_wan.py

@@ -3,7 +3,6 @@ import node_helpers
 import torch
 import comfy.model_management
 import comfy.utils
-import comfy.latent_formats
 
 
 class WanImageToVideo:
@@ -50,110 +49,6 @@ class WanImageToVideo:
         return (positive, negative, out_latent)
 
 
-class WanFunControlToVideo:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"positive": ("CONDITIONING", ),
-                             "negative": ("CONDITIONING", ),
-                             "vae": ("VAE", ),
-                             "width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
-                             "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
-                             "length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}),
-                             "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
-                },
-                "optional": {"clip_vision_output": ("CLIP_VISION_OUTPUT", ),
-                             "start_image": ("IMAGE", ),
-                             "control_video": ("IMAGE", ),
-                }}
-
-    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
-    RETURN_NAMES = ("positive", "negative", "latent")
-    FUNCTION = "encode"
-
-    CATEGORY = "conditioning/video_models"
-
-    def encode(self, positive, negative, vae, width, height, length, batch_size, start_image=None, clip_vision_output=None, control_video=None):
-        latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
-        concat_latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
-        concat_latent = comfy.latent_formats.Wan21().process_out(concat_latent)
-        concat_latent = concat_latent.repeat(1, 2, 1, 1, 1)
-
-        if start_image is not None:
-            start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
-            concat_latent_image = vae.encode(start_image[:, :, :, :3])
-            concat_latent[:,16:,:concat_latent_image.shape[2]] = concat_latent_image[:,:,:concat_latent.shape[2]]
-
-        if control_video is not None:
-            control_video = comfy.utils.common_upscale(control_video[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
-            concat_latent_image = vae.encode(control_video[:, :, :, :3])
-            concat_latent[:,:16,:concat_latent_image.shape[2]] = concat_latent_image[:,:,:concat_latent.shape[2]]
-
-        positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent})
-        negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent})
-
-        if clip_vision_output is not None:
-            positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output})
-            negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output})
-
-        out_latent = {}
-        out_latent["samples"] = latent
-        return (positive, negative, out_latent)
-
-class WanFunInpaintToVideo:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"positive": ("CONDITIONING", ),
-                             "negative": ("CONDITIONING", ),
-                             "vae": ("VAE", ),
-                             "width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
-                             "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
-                             "length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}),
-                             "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
-                },
-                "optional": {"clip_vision_output": ("CLIP_VISION_OUTPUT", ),
-                             "start_image": ("IMAGE", ),
-                             "end_image": ("IMAGE", ),
-                }}
-
-    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
-    RETURN_NAMES = ("positive", "negative", "latent")
-    FUNCTION = "encode"
-
-    CATEGORY = "conditioning/video_models"
-
-    def encode(self, positive, negative, vae, width, height, length, batch_size, start_image=None, end_image=None, clip_vision_output=None):
-        latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
-        if start_image is not None:
-            start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
-        if end_image is not None:
-            end_image = comfy.utils.common_upscale(end_image[-length:].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
-
-        image = torch.ones((length, height, width, 3)) * 0.5
-        mask = torch.ones((1, 1, latent.shape[2] * 4, latent.shape[-2], latent.shape[-1]))
-
-        if start_image is not None:
-            image[:start_image.shape[0]] = start_image
-            mask[:, :, :start_image.shape[0] + 3] = 0.0
-
-        if end_image is not None:
-            image[-end_image.shape[0]:] = end_image
-            mask[:, :, -end_image.shape[0]:] = 0.0
-
-        concat_latent_image = vae.encode(image[:, :, :, :3])
-        mask = mask.view(1, mask.shape[2] // 4, 4, mask.shape[3], mask.shape[4]).transpose(1, 2)
-        positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent_image, "concat_mask": mask})
-        negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent_image, "concat_mask": mask})
-
-        if clip_vision_output is not None:
-            positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output})
-            negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output})
-
-        out_latent = {}
-        out_latent["samples"] = latent
-        return (positive, negative, out_latent)
-
 NODE_CLASS_MAPPINGS = {
     "WanImageToVideo": WanImageToVideo,
-    "WanFunControlToVideo": WanFunControlToVideo,
-    "WanFunInpaintToVideo": WanFunInpaintToVideo,
 }

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.3.28"
+__version__ = "0.3.27"

execution.py

@@ -15,7 +15,7 @@ import nodes
 import comfy.model_management
 from comfy_execution.graph import get_input_info, ExecutionList, DynamicPrompt, ExecutionBlocker
 from comfy_execution.graph_utils import is_link, GraphBuilder
-from comfy_execution.caching import HierarchicalCache, LRUCache, DependencyAwareCache, CacheKeySetInputSignature, CacheKeySetID
+from comfy_execution.caching import HierarchicalCache, LRUCache, CacheKeySetInputSignature, CacheKeySetID
 from comfy_execution.validation import validate_node_input
 
 class ExecutionResult(Enum):
@@ -59,45 +59,27 @@ class IsChangedCache:
             self.is_changed[node_id] = node["is_changed"]
         return self.is_changed[node_id]
 
-
-class CacheType(Enum):
-    CLASSIC = 0
-    LRU = 1
-    DEPENDENCY_AWARE = 2
-
-
 class CacheSet:
-    def __init__(self, cache_type=None, cache_size=None):
+    def __init__(self, lru_size=None):
-        if cache_type == CacheType.DEPENDENCY_AWARE:
+        if lru_size is None or lru_size == 0:
-            self.init_dependency_aware_cache()
-            logging.info("Disabling intermediate node cache.")
-        elif cache_type == CacheType.LRU:
-            if cache_size is None:
-                cache_size = 0
-            self.init_lru_cache(cache_size)
-            logging.info("Using LRU cache")
-        else:
             self.init_classic_cache()
-
+        else:
+            self.init_lru_cache(lru_size)
         self.all = [self.outputs, self.ui, self.objects]
 
+    # Useful for those with ample RAM/VRAM -- allows experimenting without
+    # blowing away the cache every time
+    def init_lru_cache(self, cache_size):
+        self.outputs = LRUCache(CacheKeySetInputSignature, max_size=cache_size)
+        self.ui = LRUCache(CacheKeySetInputSignature, max_size=cache_size)
+        self.objects = HierarchicalCache(CacheKeySetID)
+
     # Performs like the old cache -- dump data ASAP
     def init_classic_cache(self):
         self.outputs = HierarchicalCache(CacheKeySetInputSignature)
         self.ui = HierarchicalCache(CacheKeySetInputSignature)
         self.objects = HierarchicalCache(CacheKeySetID)
 
-    def init_lru_cache(self, cache_size):
-        self.outputs = LRUCache(CacheKeySetInputSignature, max_size=cache_size)
-        self.ui = LRUCache(CacheKeySetInputSignature, max_size=cache_size)
-        self.objects = HierarchicalCache(CacheKeySetID)
-
-    # only hold cached items while the decendents have not executed
-    def init_dependency_aware_cache(self):
-        self.outputs = DependencyAwareCache(CacheKeySetInputSignature)
-        self.ui = DependencyAwareCache(CacheKeySetInputSignature)
-        self.objects = DependencyAwareCache(CacheKeySetID)
-
     def recursive_debug_dump(self):
         result = {
             "outputs": self.outputs.recursive_debug_dump(),
@@ -111,7 +93,7 @@ def get_input_data(inputs, class_def, unique_id, outputs=None, dynprompt=None, e
     missing_keys = {}
     for x in inputs:
         input_data = inputs[x]
-        input_type, input_category, input_info = get_input_info(class_def, x, valid_inputs)
+        _, input_category, input_info = get_input_info(class_def, x, valid_inputs)
         def mark_missing():
             missing_keys[x] = True
             input_data_all[x] = (None,)
@@ -432,14 +414,13 @@ def execute(server, dynprompt, caches, current_item, extra_data, executed, promp
     return (ExecutionResult.SUCCESS, None, None)
 
 class PromptExecutor:
-    def __init__(self, server, cache_type=False, cache_size=None):
+    def __init__(self, server, lru_size=None):
-        self.cache_size = cache_size
+        self.lru_size = lru_size
-        self.cache_type = cache_type
         self.server = server
         self.reset()
 
     def reset(self):
-        self.caches = CacheSet(cache_type=self.cache_type, cache_size=self.cache_size)
+        self.caches = CacheSet(self.lru_size)
         self.status_messages = []
         self.success = True
 
@@ -574,7 +555,7 @@ def validate_inputs(prompt, item, validated):
     received_types = {}
 
     for x in valid_inputs:
-        type_input, input_category, extra_info = get_input_info(obj_class, x, class_inputs)
+        input_type, input_category, extra_info = get_input_info(obj_class, x, class_inputs)
         assert extra_info is not None
         if x not in inputs:
             if input_category == "required":
@@ -590,7 +571,7 @@ def validate_inputs(prompt, item, validated):
             continue
 
         val = inputs[x]
-        info = (type_input, extra_info)
+        info = (input_type, extra_info)
         if isinstance(val, list):
             if len(val) != 2:
                 error = {
@@ -611,8 +592,8 @@ def validate_inputs(prompt, item, validated):
             r = nodes.NODE_CLASS_MAPPINGS[o_class_type].RETURN_TYPES
             received_type = r[val[1]]
             received_types[x] = received_type
-            if 'input_types' not in validate_function_inputs and not validate_node_input(received_type, type_input):
+            if 'input_types' not in validate_function_inputs and not validate_node_input(received_type, input_type):
-                details = f"{x}, received_type({received_type}) mismatch input_type({type_input})"
+                details = f"{x}, received_type({received_type}) mismatch input_type({input_type})"
                 error = {
                     "type": "return_type_mismatch",
                     "message": "Return type mismatch between linked nodes",
@@ -660,22 +641,22 @@ def validate_inputs(prompt, item, validated):
                     val = val["__value__"]
                     inputs[x] = val
 
-                if type_input == "INT":
+                if input_type == "INT":
                     val = int(val)
                     inputs[x] = val
-                if type_input == "FLOAT":
+                if input_type == "FLOAT":
                     val = float(val)
                     inputs[x] = val
-                if type_input == "STRING":
+                if input_type == "STRING":
                     val = str(val)
                     inputs[x] = val
-                if type_input == "BOOLEAN":
+                if input_type == "BOOLEAN":
                     val = bool(val)
                     inputs[x] = val
             except Exception as ex:
                 error = {
                     "type": "invalid_input_type",
-                    "message": f"Failed to convert an input value to a {type_input} value",
+                    "message": f"Failed to convert an input value to a {input_type} value",
                     "details": f"{x}, {val}, {ex}",
                     "extra_info": {
                         "input_name": x,
@@ -715,18 +696,19 @@ def validate_inputs(prompt, item, validated):
                     errors.append(error)
                     continue
 
-                if isinstance(type_input, list):
+                if isinstance(input_type, list):
-                    if val not in type_input:
+                    combo_options = input_type
+                    if val not in combo_options:
                         input_config = info
                         list_info = ""
 
                         # Don't send back gigantic lists like if they're lots of
                         # scanned model filepaths
-                        if len(type_input) > 20:
+                        if len(combo_options) > 20:
-                            list_info = f"(list of length {len(type_input)})"
+                            list_info = f"(list of length {len(combo_options)})"
                             input_config = None
                         else:
-                            list_info = str(type_input)
+                            list_info = str(combo_options)
 
                         error = {
                             "type": "value_not_in_list",
@@ -794,7 +776,7 @@ def validate_prompt(prompt):
                 "details": f"Node ID '#{x}'",
                 "extra_info": {}
             }
-            return (False, error, [], {})
+            return (False, error, [], [])
 
         class_type = prompt[x]['class_type']
         class_ = nodes.NODE_CLASS_MAPPINGS.get(class_type, None)
@@ -805,7 +787,7 @@ def validate_prompt(prompt):
                 "details": f"Node ID '#{x}'",
                 "extra_info": {}
             }
-            return (False, error, [], {})
+            return (False, error, [], [])
 
         if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE is True:
             outputs.add(x)
@@ -817,7 +799,7 @@ def validate_prompt(prompt):
             "details": "",
             "extra_info": {}
         }
-        return (False, error, [], {})
+        return (False, error, [], [])
 
     good_outputs = set()
     errors = []

folder_paths.py

@@ -85,7 +85,6 @@ cache_helper = CacheHelper()
 
 extension_mimetypes_cache = {
     "webp" : "image",
-    "fbx" : "model",
 }
 
 def map_legacy(folder_name: str) -> str:
@@ -141,14 +140,11 @@ def get_directory_by_type(type_name: str) -> str | None:
         return get_input_directory()
     return None
 
-def filter_files_content_types(files: list[str], content_types: Literal["image", "video", "audio", "model"]) -> list[str]:
+def filter_files_content_types(files: list[str], content_types: Literal["image", "video", "audio"]) -> list[str]:
     """
     Example:
         files = os.listdir(folder_paths.get_input_directory())
-        videos = filter_files_content_types(files, ["video"])
+        filter_files_content_types(files, ["image", "audio", "video"])
-
-    Note:
-        - 'model' in MIME context refers to 3D models, not files containing trained weights and parameters
     """
     global extension_mimetypes_cache
     result = []

main.py

@@ -10,7 +10,6 @@ from app.logger import setup_logger
 import itertools
 import utils.extra_config
 import logging
-import sys
 
 if __name__ == "__main__":
     #NOTE: These do not do anything on core ComfyUI which should already have no communication with the internet, they are for custom nodes.
@@ -157,13 +156,7 @@ def cuda_malloc_warning():
 
 def prompt_worker(q, server_instance):
     current_time: float = 0.0
-    cache_type = execution.CacheType.CLASSIC
+    e = execution.PromptExecutor(server_instance, lru_size=args.cache_lru)
-    if args.cache_lru > 0:
-        cache_type = execution.CacheType.LRU
-    elif args.cache_none:
-        cache_type = execution.CacheType.DEPENDENCY_AWARE
-
-    e = execution.PromptExecutor(server_instance, cache_type=cache_type, cache_size=args.cache_lru)
     last_gc_collect = 0
     need_gc = False
     gc_collect_interval = 10.0
@@ -302,7 +295,6 @@ def start_comfyui(asyncio_loop=None):
 
 if __name__ == "__main__":
     # Running directly, just start ComfyUI.
-    logging.info("Python version: {}".format(sys.version))
     logging.info("ComfyUI version: {}".format(comfyui_version.__version__))
 
     event_loop, _, start_all_func = start_comfyui()

node_helpers.py

@@ -44,11 +44,3 @@ def string_to_torch_dtype(string):
         return torch.float16
     if string == "bf16":
         return torch.bfloat16
-
-def image_alpha_fix(destination, source):
-    if destination.shape[-1] < source.shape[-1]:
-        source = source[...,:destination.shape[-1]]
-    elif destination.shape[-1] > source.shape[-1]:
-        destination = torch.nn.functional.pad(destination, (0, 1))
-        destination[..., -1] = 1.0
-    return destination, source

nodes.py

@@ -786,8 +786,6 @@ class ControlNetLoader:
     def load_controlnet(self, control_net_name):
         controlnet_path = folder_paths.get_full_path_or_raise("controlnet", control_net_name)
         controlnet = comfy.controlnet.load_controlnet(controlnet_path)
-        if controlnet is None:
-            raise RuntimeError("ERROR: controlnet file is invalid and does not contain a valid controlnet model.")
         return (controlnet,)
 
 class DiffControlNetLoader:
@@ -1008,8 +1006,6 @@ class CLIPVisionLoader:
     def load_clip(self, clip_name):
         clip_path = folder_paths.get_full_path_or_raise("clip_vision", clip_name)
         clip_vision = comfy.clip_vision.load(clip_path)
-        if clip_vision is None:
-            raise RuntimeError("ERROR: clip vision file is invalid and does not contain a valid vision model.")
         return (clip_vision,)
 
 class CLIPVisionEncode:
@@ -1654,7 +1650,6 @@ class LoadImage:
     def INPUT_TYPES(s):
         input_dir = folder_paths.get_input_directory()
         files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
-        files = folder_paths.filter_files_content_types(files, ["image"])
         return {"required":
                     {"image": (sorted(files), {"image_upload": True})},
                 }
@@ -1693,9 +1688,6 @@ class LoadImage:
             if 'A' in i.getbands():
                 mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
                 mask = 1. - torch.from_numpy(mask)
-            elif i.mode == 'P' and 'transparency' in i.info:
-                mask = np.array(i.convert('RGBA').getchannel('A')).astype(np.float32) / 255.0
-                mask = 1. - torch.from_numpy(mask)
             else:
                 mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
             output_images.append(image)
@@ -2131,25 +2123,21 @@ def get_module_name(module_path: str) -> str:
 
 
 def load_custom_node(module_path: str, ignore=set(), module_parent="custom_nodes") -> bool:
-    module_name = get_module_name(module_path)
+    module_name = os.path.basename(module_path)
     if os.path.isfile(module_path):
         sp = os.path.splitext(module_path)
         module_name = sp[0]
-        sys_module_name = module_name
-    elif os.path.isdir(module_path):
-        sys_module_name = module_path.replace(".", "_x_")
-
     try:
         logging.debug("Trying to load custom node {}".format(module_path))
         if os.path.isfile(module_path):
-            module_spec = importlib.util.spec_from_file_location(sys_module_name, module_path)
+            module_spec = importlib.util.spec_from_file_location(module_name, module_path)
             module_dir = os.path.split(module_path)[0]
         else:
-            module_spec = importlib.util.spec_from_file_location(sys_module_name, os.path.join(module_path, "__init__.py"))
+            module_spec = importlib.util.spec_from_file_location(module_name, os.path.join(module_path, "__init__.py"))
             module_dir = module_path
 
         module = importlib.util.module_from_spec(module_spec)
-        sys.modules[sys_module_name] = module
+        sys.modules[module_name] = module
         module_spec.loader.exec_module(module)
 
         LOADED_MODULE_DIRS[module_name] = os.path.abspath(module_dir)
@@ -2279,8 +2267,6 @@ def init_builtin_extra_nodes():
         "nodes_lotus.py",
         "nodes_hunyuan3d.py",
         "nodes_primitive.py",
-        "nodes_cfg.py",
-        "nodes_optimalsteps.py"
     ]
 
     import_failed = []

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.3.28"
+version = "0.3.27"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.9"

requirements.txt

@@ -1,4 +1,4 @@
-comfyui-frontend-package==1.15.13
+comfyui-frontend-package==1.14.5
 torch
 torchsde
 torchvision

server.py

@@ -48,7 +48,7 @@ async def send_socket_catch_exception(function, message):
 @web.middleware
 async def cache_control(request: web.Request, handler):
     response: web.Response = await handler(request)
-    if request.path.endswith('.js') or request.path.endswith('.css') or request.path.endswith('index.json'):
+    if request.path.endswith('.js') or request.path.endswith('.css'):
         response.headers.setdefault('Cache-Control', 'no-cache')
     return response
 
@@ -657,13 +657,7 @@ class PromptServer():
                     logging.warning("invalid prompt: {}".format(valid[1]))
                     return web.json_response({"error": valid[1], "node_errors": valid[3]}, status=400)
             else:
-                error = {
+                return web.json_response({"error": "no prompt", "node_errors": []}, status=400)
-                    "type": "no_prompt",
-                    "message": "No prompt provided",
-                    "details": "No prompt provided",
-                    "extra_info": {}
-                }
-                return web.json_response({"error": error, "node_errors": {}}, status=400)
 
         @routes.post("/queue")
         async def post_queue(request):

tests-unit/folder_paths_test/filter_by_content_types_test.py

@@ -1,17 +1,14 @@
 import pytest
 import os
 import tempfile
-from folder_paths import filter_files_content_types, extension_mimetypes_cache
+from folder_paths import filter_files_content_types
-from unittest.mock import patch
-
 
 @pytest.fixture(scope="module")
 def file_extensions():
     return {
         'image': ['gif', 'heif', 'ico', 'jpeg', 'jpg', 'png', 'pnm', 'ppm', 'svg', 'tiff', 'webp', 'xbm', 'xpm'],
         'audio': ['aif', 'aifc', 'aiff', 'au', 'flac', 'm4a', 'mp2', 'mp3', 'ogg', 'snd', 'wav'],
-        'video': ['avi', 'm2v', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ogv', 'qt', 'webm', 'wmv'],
+        'video': ['avi', 'm2v', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ogv', 'qt', 'webm', 'wmv']
-        'model': ['gltf', 'glb', 'obj', 'fbx', 'stl']
     }
 
 
@@ -25,18 +22,7 @@ def mock_dir(file_extensions):
         yield directory
 
 
-@pytest.fixture
+def test_categorizes_all_correctly(mock_dir, file_extensions):
-def patched_mimetype_cache(file_extensions):
-    # Mock model file extensions since they may not be in the test-runner system's mimetype cache
-    new_cache = extension_mimetypes_cache.copy()
-    for extension in file_extensions["model"]:
-        new_cache[extension] = "model"
-
-    with patch("folder_paths.extension_mimetypes_cache", new_cache):
-        yield
-
-
-def test_categorizes_all_correctly(mock_dir, file_extensions, patched_mimetype_cache):
     files = os.listdir(mock_dir)
     for content_type, extensions in file_extensions.items():
         filtered_files = filter_files_content_types(files, [content_type])
@@ -44,7 +30,7 @@ def test_categorizes_all_correctly(mock_dir, file_extensions, patched_mimetype_c
             assert f"sample_{content_type}.{extension}" in filtered_files
 
 
-def test_categorizes_all_uniquely(mock_dir, file_extensions, patched_mimetype_cache):
+def test_categorizes_all_uniquely(mock_dir, file_extensions):
     files = os.listdir(mock_dir)
     for content_type, extensions in file_extensions.items():
         filtered_files = filter_files_content_types(files, [content_type])