Merge branch 'master' into worksplit-multigpu

README.md

@@ -62,6 +62,7 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
    - [HunyuanDiT](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_dit/)
    - [Flux](https://comfyanonymous.github.io/ComfyUI_examples/flux/)
    - [Lumina Image 2.0](https://comfyanonymous.github.io/ComfyUI_examples/lumina2/)
+   - [HiDream](https://comfyanonymous.github.io/ComfyUI_examples/hidream/)
 - Video Models
    - [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/)
    - [Mochi](https://comfyanonymous.github.io/ComfyUI_examples/mochi/)

app/frontend_management.py

@@ -184,6 +184,27 @@ comfyui-frontend-package is not installed.
             )
             sys.exit(-1)
 
+    @classmethod
+    def templates_path(cls) -> str:
+        try:
+            import comfyui_workflow_templates
+
+            return str(
+                importlib.resources.files(comfyui_workflow_templates) / "templates"
+            )
+        except ImportError:
+            logging.error(
+                f"""
+********** ERROR ***********
+
+comfyui-workflow-templates is not installed.
+
+{frontend_install_warning_message()}
+
+********** ERROR ***********
+""".strip()
+            )
+
     @classmethod
     def parse_version_string(cls, value: str) -> tuple[str, str, str]:
         """

comfy/ldm/wan/model.py

@@ -83,7 +83,7 @@ class WanSelfAttention(nn.Module):
 
 class WanT2VCrossAttention(WanSelfAttention):
 
-    def forward(self, x, context):
+    def forward(self, x, context, **kwargs):
         r"""
         Args:
             x(Tensor): Shape [B, L1, C]
@@ -116,14 +116,14 @@ class WanI2VCrossAttention(WanSelfAttention):
         # self.alpha = nn.Parameter(torch.zeros((1, )))
         self.norm_k_img = RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
 
-    def forward(self, x, context):
+    def forward(self, x, context, context_img_len):
         r"""
         Args:
             x(Tensor): Shape [B, L1, C]
             context(Tensor): Shape [B, L2, C]
         """
-        context_img = context[:, :257]
+        context_img = context[:, :context_img_len]
-        context = context[:, 257:]
+        context = context[:, context_img_len:]
 
         # compute query, key, value
         q = self.norm_q(self.q(x))
@@ -193,6 +193,7 @@ class WanAttentionBlock(nn.Module):
         e,
         freqs,
         context,
+        context_img_len=257,
     ):
         r"""
         Args:
@@ -213,7 +214,7 @@ class WanAttentionBlock(nn.Module):
         x = x + y * e[2]
 
         # cross-attention & ffn
-        x = x + self.cross_attn(self.norm3(x), context)
+        x = x + self.cross_attn(self.norm3(x), context, context_img_len=context_img_len)
         y = self.ffn(self.norm2(x) * (1 + e[4]) + e[3])
         x = x + y * e[5]
         return x
@@ -250,7 +251,7 @@ class Head(nn.Module):
 
 class MLPProj(torch.nn.Module):
 
-    def __init__(self, in_dim, out_dim, operation_settings={}):
+    def __init__(self, in_dim, out_dim, flf_pos_embed_token_number=None, operation_settings={}):
         super().__init__()
 
         self.proj = torch.nn.Sequential(
@@ -258,7 +259,15 @@ class MLPProj(torch.nn.Module):
             torch.nn.GELU(), operation_settings.get("operations").Linear(in_dim, out_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")),
             operation_settings.get("operations").LayerNorm(out_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
 
+        if flf_pos_embed_token_number is not None:
+            self.emb_pos = nn.Parameter(torch.empty((1, flf_pos_embed_token_number, in_dim), device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
+        else:
+            self.emb_pos = None
+
     def forward(self, image_embeds):
+        if self.emb_pos is not None:
+            image_embeds = image_embeds[:, :self.emb_pos.shape[1]] + comfy.model_management.cast_to(self.emb_pos[:, :image_embeds.shape[1]], dtype=image_embeds.dtype, device=image_embeds.device)
+
         clip_extra_context_tokens = self.proj(image_embeds)
         return clip_extra_context_tokens
 
@@ -284,6 +293,7 @@ class WanModel(torch.nn.Module):
                  qk_norm=True,
                  cross_attn_norm=True,
                  eps=1e-6,
+                 flf_pos_embed_token_number=None,
                  image_model=None,
                  device=None,
                  dtype=None,
@@ -373,7 +383,7 @@ class WanModel(torch.nn.Module):
         self.rope_embedder = EmbedND(dim=d, theta=10000.0, axes_dim=[d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)])
 
         if model_type == 'i2v':
-            self.img_emb = MLPProj(1280, dim, operation_settings=operation_settings)
+            self.img_emb = MLPProj(1280, dim, flf_pos_embed_token_number=flf_pos_embed_token_number, operation_settings=operation_settings)
         else:
             self.img_emb = None
 
@@ -420,9 +430,12 @@ class WanModel(torch.nn.Module):
         # context
         context = self.text_embedding(context)
 
-        if clip_fea is not None and self.img_emb is not None:
+        context_img_len = None
-            context_clip = self.img_emb(clip_fea)  # bs x 257 x dim
+        if clip_fea is not None:
-            context = torch.concat([context_clip, context], dim=1)
+            if self.img_emb is not None:
+                context_clip = self.img_emb(clip_fea)  # bs x 257 x dim
+                context = torch.concat([context_clip, context], dim=1)
+            context_img_len = clip_fea.shape[-2]
 
         patches_replace = transformer_options.get("patches_replace", {})
         blocks_replace = patches_replace.get("dit", {})
@@ -430,12 +443,12 @@ class WanModel(torch.nn.Module):
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
-                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"])
+                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len)
                     return out
                 out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap})
                 x = out["img"]
             else:
-                x = block(x, e=e0, freqs=freqs, context=context)
+                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len)
 
         # head
         x = self.head(x, e)

comfy/model_detection.py

@@ -321,6 +321,9 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
             dit_config["model_type"] = "i2v"
         else:
             dit_config["model_type"] = "t2v"
+        flf_weight = state_dict.get('{}img_emb.emb_pos'.format(key_prefix))
+        if flf_weight is not None:
+            dit_config["flf_pos_embed_token_number"] = flf_weight.shape[1]
         return dit_config
 
     if '{}latent_in.weight'.format(key_prefix) in state_dict_keys:  # Hunyuan 3D

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_fresca.py

@@ -0,0 +1,100 @@
+# Code based on https://github.com/WikiChao/FreSca (MIT License)
+import torch
+import torch.fft as fft
+
+
+def Fourier_filter(x, scale_low=1.0, scale_high=1.5, freq_cutoff=20):
+    """
+    Apply frequency-dependent scaling to an image tensor using Fourier transforms.
+
+    Parameters:
+        x:           Input tensor of shape (B, C, H, W)
+        scale_low:   Scaling factor for low-frequency components (default: 1.0)
+        scale_high:  Scaling factor for high-frequency components (default: 1.5)
+        freq_cutoff: Number of frequency indices around center to consider as low-frequency (default: 20)
+
+    Returns:
+        x_filtered: Filtered version of x in spatial domain with frequency-specific scaling applied.
+    """
+    # Preserve input dtype and device
+    dtype, device = x.dtype, x.device
+
+    # Convert to float32 for FFT computations
+    x = x.to(torch.float32)
+
+    # 1) Apply FFT and shift low frequencies to center
+    x_freq = fft.fftn(x, dim=(-2, -1))
+    x_freq = fft.fftshift(x_freq, dim=(-2, -1))
+
+    # Initialize mask with high-frequency scaling factor
+    mask = torch.ones(x_freq.shape, device=device) * scale_high
+    m = mask
+    for d in range(len(x_freq.shape) - 2):
+        dim = d + 2
+        cc = x_freq.shape[dim] // 2
+        f_c = min(freq_cutoff, cc)
+        m = m.narrow(dim, cc - f_c, f_c * 2)
+
+    # Apply low-frequency scaling factor to center region
+    m[:] = scale_low
+
+    # 3) Apply frequency-specific scaling
+    x_freq = x_freq * mask
+
+    # 4) Convert back to spatial domain
+    x_freq = fft.ifftshift(x_freq, dim=(-2, -1))
+    x_filtered = fft.ifftn(x_freq, dim=(-2, -1)).real
+
+    # 5) Restore original dtype
+    x_filtered = x_filtered.to(dtype)
+
+    return x_filtered
+
+
+class FreSca:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "scale_low": ("FLOAT", {"default": 1.0, "min": 0, "max": 10, "step": 0.01,
+                                        "tooltip": "Scaling factor for low-frequency components"}),
+                "scale_high": ("FLOAT", {"default": 1.25, "min": 0, "max": 10, "step": 0.01,
+                                        "tooltip": "Scaling factor for high-frequency components"}),
+                "freq_cutoff": ("INT", {"default": 20, "min": 1, "max": 10000, "step": 1,
+                                        "tooltip": "Number of frequency indices around center to consider as low-frequency"}),
+            }
+        }
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+    CATEGORY = "_for_testing"
+    DESCRIPTION = "Applies frequency-dependent scaling to the guidance"
+    def patch(self, model, scale_low, scale_high, freq_cutoff):
+        def custom_cfg_function(args):
+            cond = args["conds_out"][0]
+            uncond = args["conds_out"][1]
+
+            guidance = cond - uncond
+            filtered_guidance = Fourier_filter(
+                guidance,
+                scale_low=scale_low,
+                scale_high=scale_high,
+                freq_cutoff=freq_cutoff,
+            )
+            filtered_cond = filtered_guidance + uncond
+
+            return [filtered_cond, uncond]
+
+        m = model.clone()
+        m.set_model_sampler_pre_cfg_function(custom_cfg_function)
+
+        return (m,)
+
+
+NODE_CLASS_MAPPINGS = {
+    "FreSca": FreSca,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "FreSca": "FreSca",
+}

comfy_extras/nodes_load_3d.py

@@ -21,8 +21,8 @@ class Load3D():
             "height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
         }}
 
-    RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE", "IMAGE")
+    RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE", "IMAGE", "LOAD3D_CAMERA")
-    RETURN_NAMES = ("image", "mask", "mesh_path", "normal", "lineart")
+    RETURN_NAMES = ("image", "mask", "mesh_path", "normal", "lineart", "camera_info")
 
     FUNCTION = "process"
     EXPERIMENTAL = True
@@ -41,7 +41,7 @@ class Load3D():
         normal_image, ignore_mask2 = load_image_node.load_image(image=normal_path)
         lineart_image, ignore_mask3 = load_image_node.load_image(image=lineart_path)
 
-        return output_image, output_mask, model_file, normal_image, lineart_image
+        return output_image, output_mask, model_file, normal_image, lineart_image, image['camera_info']
 
 class Load3DAnimation():
     @classmethod
@@ -59,8 +59,8 @@ class Load3DAnimation():
             "height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
         }}
 
-    RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE")
+    RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE", "LOAD3D_CAMERA")
-    RETURN_NAMES = ("image", "mask", "mesh_path", "normal")
+    RETURN_NAMES = ("image", "mask", "mesh_path", "normal", "camera_info")
 
     FUNCTION = "process"
     EXPERIMENTAL = True
@@ -77,13 +77,16 @@ class Load3DAnimation():
         ignore_image, output_mask = load_image_node.load_image(image=mask_path)
         normal_image, ignore_mask2 = load_image_node.load_image(image=normal_path)
 
-        return output_image, output_mask, model_file, normal_image
+        return output_image, output_mask, model_file, normal_image, image['camera_info']
 
 class Preview3D():
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
             "model_file": ("STRING", {"default": "", "multiline": False}),
+        },
+        "optional": {
+            "camera_info": ("LOAD3D_CAMERA", {})
         }}
 
     OUTPUT_NODE = True
@@ -95,13 +98,22 @@ class Preview3D():
     EXPERIMENTAL = True
 
     def process(self, model_file, **kwargs):
-        return {"ui": {"model_file": [model_file]}, "result": ()}
+        camera_info = kwargs.get("camera_info", None)
+
+        return {
+            "ui": {
+                "result": [model_file, camera_info]
+            }
+        }
 
 class Preview3DAnimation():
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
             "model_file": ("STRING", {"default": "", "multiline": False}),
+        },
+        "optional": {
+            "camera_info": ("LOAD3D_CAMERA", {})
         }}
 
     OUTPUT_NODE = True
@@ -113,7 +125,13 @@ class Preview3DAnimation():
     EXPERIMENTAL = True
 
     def process(self, model_file, **kwargs):
-        return {"ui": {"model_file": [model_file]}, "result": ()}
+        camera_info = kwargs.get("camera_info", None)
+
+        return {
+            "ui": {
+                "result": [model_file, camera_info]
+            }
+        }
 
 NODE_CLASS_MAPPINGS = {
     "Load3D": Load3D,

comfy_extras/nodes_wan.py

@@ -4,6 +4,7 @@ import torch
 import comfy.model_management
 import comfy.utils
 import comfy.latent_formats
+import comfy.clip_vision
 
 
 class WanImageToVideo:
@@ -99,6 +100,72 @@ class WanFunControlToVideo:
         out_latent["samples"] = latent
         return (positive, negative, out_latent)
 
+class WanFirstLastFrameToVideo:
+    @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_start_image": ("CLIP_VISION_OUTPUT", ),
+                             "clip_vision_end_image": ("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_start_image=None, clip_vision_end_image=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_start_image is not None:
+            clip_vision_output = clip_vision_start_image
+
+        if clip_vision_end_image is not None:
+            if clip_vision_output is not None:
+                states = torch.cat([clip_vision_output.penultimate_hidden_states, clip_vision_end_image.penultimate_hidden_states], dim=-2)
+                clip_vision_output = comfy.clip_vision.Output()
+                clip_vision_output.penultimate_hidden_states = states
+            else:
+                clip_vision_output = clip_vision_end_image
+
+        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):
@@ -122,38 +189,13 @@ class WanFunInpaintToVideo:
     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())
+        flfv = WanFirstLastFrameToVideo()
-        if start_image is not None:
+        return flfv.encode(positive, negative, vae, width, height, length, batch_size, start_image=start_image, end_image=end_image, clip_vision_start_image=clip_vision_output)
-            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,
+    "WanFirstLastFrameToVideo": WanFirstLastFrameToVideo,
 }

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__ = "0.3.29"

execution.py

@@ -111,7 +111,7 @@ 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,)
@@ -574,7 +574,7 @@ 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 +590,7 @@ 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 +611,8 @@ 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 +660,22 @@ 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 +715,19 @@ 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",

nodes.py

@@ -930,26 +930,7 @@ class CLIPLoader:
     DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 xxl/ clip-g / clip-l\nstable_audio: t5 base\nmochi: t5 xxl\ncosmos: old t5 xxl\nlumina2: gemma 2 2B\nwan: umt5 xxl"
 
     def load_clip(self, clip_name, type="stable_diffusion", device="default"):
-        if type == "stable_cascade":
+        clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
-            clip_type = comfy.sd.CLIPType.STABLE_CASCADE
-        elif type == "sd3":
-            clip_type = comfy.sd.CLIPType.SD3
-        elif type == "stable_audio":
-            clip_type = comfy.sd.CLIPType.STABLE_AUDIO
-        elif type == "mochi":
-            clip_type = comfy.sd.CLIPType.MOCHI
-        elif type == "ltxv":
-            clip_type = comfy.sd.CLIPType.LTXV
-        elif type == "pixart":
-            clip_type = comfy.sd.CLIPType.PIXART
-        elif type == "cosmos":
-            clip_type = comfy.sd.CLIPType.COSMOS
-        elif type == "lumina2":
-            clip_type = comfy.sd.CLIPType.LUMINA2
-        elif type == "wan":
-            clip_type = comfy.sd.CLIPType.WAN
-        else:
-            clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
 
         model_options = {}
         if device == "cpu":
@@ -977,16 +958,10 @@ class DualCLIPLoader:
     DESCRIPTION = "[Recipes]\n\nsdxl: clip-l, clip-g\nsd3: clip-l, clip-g / clip-l, t5 / clip-g, t5\nflux: clip-l, t5"
 
     def load_clip(self, clip_name1, clip_name2, type, device="default"):
+        clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
+
         clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", clip_name1)
         clip_path2 = folder_paths.get_full_path_or_raise("text_encoders", clip_name2)
-        if type == "sdxl":
-            clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
-        elif type == "sd3":
-            clip_type = comfy.sd.CLIPType.SD3
-        elif type == "flux":
-            clip_type = comfy.sd.CLIPType.FLUX
-        elif type == "hunyuan_video":
-            clip_type = comfy.sd.CLIPType.HUNYUAN_VIDEO
 
         model_options = {}
         if device == "cpu":
@@ -2282,7 +2257,8 @@ def init_builtin_extra_nodes():
         "nodes_primitive.py",
         "nodes_cfg.py",
         "nodes_optimalsteps.py",
-        "nodes_hidream.py"
+        "nodes_hidream.py",
+        "nodes_fresca.py",
     ]
 
     import_failed = []

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.3.28"
+version = "0.3.29"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.9"

requirements.txt

@@ -1,4 +1,5 @@
-comfyui-frontend-package==1.15.13
+comfyui-frontend-package==1.16.9
+comfyui-workflow-templates==0.1.1
 torch
 torchsde
 torchvision

server.py

@@ -736,6 +736,12 @@ class PromptServer():
         for name, dir in nodes.EXTENSION_WEB_DIRS.items():
             self.app.add_routes([web.static('/extensions/' + name, dir)])
 
+        workflow_templates_path = FrontendManager.templates_path()
+        if workflow_templates_path:
+            self.app.add_routes([
+                web.static('/templates', workflow_templates_path)
+            ])
+
         self.app.add_routes([
             web.static('/', self.web_root),
         ])