convert model_merging and video_model nodes to V3 schema

comfy/ldm/ace/ace_step15.py

@@ -1110,7 +1110,7 @@ class AceStepConditionGenerationModel(nn.Module):
 
         return encoder_hidden, encoder_mask, context_latents
 
-    def forward(self, x, timestep, context, lyric_embed=None, refer_audio=None, audio_codes=None, is_covers=None, replace_with_null_embeds=False, **kwargs):
+    def forward(self, x, timestep, context, lyric_embed=None, refer_audio=None, audio_codes=None, is_covers=None, **kwargs):
         text_attention_mask = None
         lyric_attention_mask = None
         refer_audio_order_mask = None
@@ -1140,9 +1140,6 @@ class AceStepConditionGenerationModel(nn.Module):
             src_latents, chunk_masks, is_covers, precomputed_lm_hints_25Hz=precomputed_lm_hints_25Hz, audio_codes=audio_codes
         )
 
-        if replace_with_null_embeds:
-            enc_hidden[:] = self.null_condition_emb.to(enc_hidden)
-
         out = self.decoder(hidden_states=x,
                            timestep=timestep,
                            timestep_r=timestep,

comfy/ldm/cosmos/predict2.py

@@ -335,7 +335,7 @@ class FinalLayer(nn.Module):
         device=None, dtype=None, operations=None
     ):
         super().__init__()
-        self.layer_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.layer_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
         self.linear = operations.Linear(
             hidden_size, spatial_patch_size * spatial_patch_size * temporal_patch_size * out_channels, bias=False, device=device, dtype=dtype
         )
@@ -463,8 +463,6 @@ class Block(nn.Module):
         extra_per_block_pos_emb: Optional[torch.Tensor] = None,
         transformer_options: Optional[dict] = {},
     ) -> torch.Tensor:
-        residual_dtype = x_B_T_H_W_D.dtype
-        compute_dtype = emb_B_T_D.dtype
         if extra_per_block_pos_emb is not None:
             x_B_T_H_W_D = x_B_T_H_W_D + extra_per_block_pos_emb
 
@@ -514,7 +512,7 @@ class Block(nn.Module):
         result_B_T_H_W_D = rearrange(
             self.self_attn(
                 # normalized_x_B_T_HW_D,
-                rearrange(normalized_x_B_T_H_W_D.to(compute_dtype), "b t h w d -> b (t h w) d"),
+                rearrange(normalized_x_B_T_H_W_D, "b t h w d -> b (t h w) d"),
                 None,
                 rope_emb=rope_emb_L_1_1_D,
                 transformer_options=transformer_options,
@@ -524,7 +522,7 @@ class Block(nn.Module):
             h=H,
             w=W,
         )
-        x_B_T_H_W_D = x_B_T_H_W_D + gate_self_attn_B_T_1_1_D.to(residual_dtype) * result_B_T_H_W_D.to(residual_dtype)
+        x_B_T_H_W_D = x_B_T_H_W_D + gate_self_attn_B_T_1_1_D * result_B_T_H_W_D
 
         def _x_fn(
             _x_B_T_H_W_D: torch.Tensor,
@@ -538,7 +536,7 @@ class Block(nn.Module):
             )
             _result_B_T_H_W_D = rearrange(
                 self.cross_attn(
-                    rearrange(_normalized_x_B_T_H_W_D.to(compute_dtype), "b t h w d -> b (t h w) d"),
+                    rearrange(_normalized_x_B_T_H_W_D, "b t h w d -> b (t h w) d"),
                     crossattn_emb,
                     rope_emb=rope_emb_L_1_1_D,
                     transformer_options=transformer_options,
@@ -557,7 +555,7 @@ class Block(nn.Module):
             shift_cross_attn_B_T_1_1_D,
             transformer_options=transformer_options,
         )
-        x_B_T_H_W_D = result_B_T_H_W_D.to(residual_dtype) * gate_cross_attn_B_T_1_1_D.to(residual_dtype) + x_B_T_H_W_D
+        x_B_T_H_W_D = result_B_T_H_W_D * gate_cross_attn_B_T_1_1_D + x_B_T_H_W_D
 
         normalized_x_B_T_H_W_D = _fn(
             x_B_T_H_W_D,
@@ -565,8 +563,8 @@ class Block(nn.Module):
             scale_mlp_B_T_1_1_D,
             shift_mlp_B_T_1_1_D,
         )
-        result_B_T_H_W_D = self.mlp(normalized_x_B_T_H_W_D.to(compute_dtype))
-        x_B_T_H_W_D = x_B_T_H_W_D + gate_mlp_B_T_1_1_D.to(residual_dtype) * result_B_T_H_W_D.to(residual_dtype)
+        result_B_T_H_W_D = self.mlp(normalized_x_B_T_H_W_D)
+        x_B_T_H_W_D = x_B_T_H_W_D + gate_mlp_B_T_1_1_D * result_B_T_H_W_D
         return x_B_T_H_W_D
 
 
@@ -878,14 +876,6 @@ class MiniTrainDIT(nn.Module):
             "extra_per_block_pos_emb": extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D,
             "transformer_options": kwargs.get("transformer_options", {}),
         }
-
-        # The residual stream for this model has large values. To make fp16 compute_dtype work, we keep the residual stream
-        # in fp32, but run attention and MLP modules in fp16.
-        # An alternate method that clamps fp16 values "works" in the sense that it makes coherent images, but there is noticeable
-        # quality degradation and visual artifacts.
-        if x_B_T_H_W_D.dtype == torch.float16:
-            x_B_T_H_W_D = x_B_T_H_W_D.float()
-
         for block in self.blocks:
             x_B_T_H_W_D = block(
                 x_B_T_H_W_D,
@@ -894,6 +884,6 @@ class MiniTrainDIT(nn.Module):
                 **block_kwargs,
             )
 
-        x_B_T_H_W_O = self.final_layer(x_B_T_H_W_D.to(crossattn_emb.dtype), t_embedding_B_T_D, adaln_lora_B_T_3D=adaln_lora_B_T_3D)
+        x_B_T_H_W_O = self.final_layer(x_B_T_H_W_D, t_embedding_B_T_D, adaln_lora_B_T_3D=adaln_lora_B_T_3D)
         x_B_C_Tt_Hp_Wp = self.unpatchify(x_B_T_H_W_O)[:, :, :orig_shape[-3], :orig_shape[-2], :orig_shape[-1]]
         return x_B_C_Tt_Hp_Wp

comfy/model_base.py

@@ -1552,8 +1552,6 @@ class ACEStep15(BaseModel):
 
         cross_attn = kwargs.get("cross_attn", None)
         if cross_attn is not None:
-            if torch.count_nonzero(cross_attn) == 0:
-                out['replace_with_null_embeds'] = comfy.conds.CONDConstant(True)
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
 
         conditioning_lyrics = kwargs.get("conditioning_lyrics", None)
@@ -1577,10 +1575,6 @@ class ACEStep15(BaseModel):
             else:
                 out['is_covers'] = comfy.conds.CONDConstant(False)
 
-        if refer_audio.shape[2] < noise.shape[2]:
-            pad = comfy.ldm.ace.ace_step15.get_silence_latent(noise.shape[2], device)
-            refer_audio = torch.cat([refer_audio.to(pad), pad[:, :, refer_audio.shape[2]:]], dim=2)
-
         out['refer_audio'] = comfy.conds.CONDRegular(refer_audio)
         return out
 

comfy/supported_models.py

@@ -993,7 +993,7 @@ class CosmosT2IPredict2(supported_models_base.BASE):
 
     memory_usage_factor = 1.0
 
-    supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]
+    supported_inference_dtypes = [torch.bfloat16, torch.float32]
 
     def __init__(self, unet_config):
         super().__init__(unet_config)
@@ -1023,7 +1023,11 @@ class Anima(supported_models_base.BASE):
 
     memory_usage_factor = 1.0
 
-    supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]
+    supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+    def __init__(self, unet_config):
+        super().__init__(unet_config)
+        self.memory_usage_factor = (unet_config.get("model_channels", 2048) / 2048) * 0.95
 
     def get_model(self, state_dict, prefix="", device=None):
         out = model_base.Anima(self, device=device)
@@ -1034,12 +1038,6 @@ class Anima(supported_models_base.BASE):
         detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen3_06b.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.anima.AnimaTokenizer, comfy.text_encoders.anima.te(**detect))
 
-    def set_inference_dtype(self, dtype, manual_cast_dtype, **kwargs):
-        self.memory_usage_factor = (self.unet_config.get("model_channels", 2048) / 2048) * 0.95
-        if dtype is torch.float16:
-            self.memory_usage_factor *= 1.4
-        return super().set_inference_dtype(dtype, manual_cast_dtype, **kwargs)
-
 class CosmosI2VPredict2(CosmosT2IPredict2):
     unet_config = {
         "image_model": "cosmos_predict2",

comfy/text_encoders/anima.py

@@ -23,7 +23,7 @@ class AnimaTokenizer:
     def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
         out = {}
         qwen_ids = self.qwen3_06b.tokenize_with_weights(text, return_word_ids, **kwargs)
-        out["qwen3_06b"] = [[(k[0], 1.0, k[2]) if return_word_ids else (k[0], 1.0) for k in inner_list] for inner_list in qwen_ids]  # Set weights to 1.0
+        out["qwen3_06b"] = [[(token, 1.0) for token, _ in inner_list] for inner_list in qwen_ids]  # Set weights to 1.0
         out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs)
         return out
 

comfy/text_encoders/lt.py

@@ -25,7 +25,7 @@ def ltxv_te(*args, **kwargs):
 class Gemma3_12BTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
         tokenizer = tokenizer_data.get("spiece_model", None)
-        super().__init__(tokenizer, pad_with_end=False, embedding_size=3840, embedding_key='gemma3_12b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, disable_weights=True, tokenizer_args={"add_bos": True, "add_eos": False}, tokenizer_data=tokenizer_data)
+        super().__init__(tokenizer, pad_with_end=False, embedding_size=3840, embedding_key='gemma3_12b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False}, tokenizer_data=tokenizer_data)
 
     def state_dict(self):
         return {"spiece_model": self.tokenizer.serialize_model()}

comfy_extras/nodes_custom_sampler.py

@@ -622,7 +622,6 @@ class SamplerSASolver(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SamplerSASolver",
-            search_aliases=["sde"],
             category="sampling/custom_sampling/samplers",
             inputs=[
                 io.Model.Input("model"),
@@ -667,7 +666,6 @@ class SamplerSEEDS2(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SamplerSEEDS2",
-            search_aliases=["sde", "exp heun"],
             category="sampling/custom_sampling/samplers",
             inputs=[
                 io.Combo.Input("solver_type", options=["phi_1", "phi_2"]),

comfy_extras/nodes_easycache.py

@@ -108,7 +108,7 @@ def lazycache_predict_noise_wrapper(executor, *args, **kwargs):
     easycache: LazyCacheHolder = model_options["transformer_options"]["easycache"]
     if easycache.is_past_end_timestep(timestep):
         return executor(*args, **kwargs)
-    x: torch.Tensor = args[0][:, :easycache.output_channels]
+    x: torch.Tensor = _extract_tensor(args[0], easycache.output_channels)
     # prepare next x_prev
     next_x_prev = x
     input_change = None

comfy_extras/nodes_model_merging.py

@@ -10,146 +10,198 @@ import json
 import os
 
 from comfy.cli_args import args
+from comfy_api.latest import io, ComfyExtension
+from typing_extensions import override
 
-class ModelMergeSimple:
+
+class ModelMergeSimple(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "model1": ("MODEL",),
-                              "model2": ("MODEL",),
-                              "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-                              }}
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "merge"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ModelMergeSimple",
+            category="advanced/model_merging",
+            inputs=[
+                io.Model.Input("model1"),
+                io.Model.Input("model2"),
+                io.Float.Input("ratio", default=1.0, min=0.0, max=1.0, step=0.01),
+            ],
+            outputs=[
+                io.Model.Output(),
+            ],
+        )
 
-    CATEGORY = "advanced/model_merging"
-
-    def merge(self, model1, model2, ratio):
+    @classmethod
+    def execute(cls, model1, model2, ratio) -> io.NodeOutput:
         m = model1.clone()
         kp = model2.get_key_patches("diffusion_model.")
         for k in kp:
             m.add_patches({k: kp[k]}, 1.0 - ratio, ratio)
-        return (m, )
+        return io.NodeOutput(m)
 
-class ModelSubtract:
+    merge = execute  # TODO: remove
+
+
+class ModelSubtract(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "model1": ("MODEL",),
-                              "model2": ("MODEL",),
-                              "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
-                              }}
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "merge"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ModelMergeSubtract",
+            category="advanced/model_merging",
+            inputs=[
+                io.Model.Input("model1"),
+                io.Model.Input("model2"),
+                io.Float.Input("multiplier", default=1.0, min=-10.0, max=10.0, step=0.01),
+            ],
+            outputs=[
+                io.Model.Output(),
+            ],
+        )
 
-    CATEGORY = "advanced/model_merging"
-
-    def merge(self, model1, model2, multiplier):
+    @classmethod
+    def execute(cls, model1, model2, multiplier) -> io.NodeOutput:
         m = model1.clone()
         kp = model2.get_key_patches("diffusion_model.")
         for k in kp:
             m.add_patches({k: kp[k]}, - multiplier, multiplier)
-        return (m, )
+        return io.NodeOutput(m)
 
-class ModelAdd:
+    merge = execute  # TODO: remove
+
+
+class ModelAdd(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "model1": ("MODEL",),
-                              "model2": ("MODEL",),
-                              }}
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "merge"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ModelMergeAdd",
+            category="advanced/model_merging",
+            inputs=[
+                io.Model.Input("model1"),
+                io.Model.Input("model2"),
+            ],
+            outputs=[
+                io.Model.Output(),
+            ],
+        )
 
-    CATEGORY = "advanced/model_merging"
-
-    def merge(self, model1, model2):
+    @classmethod
+    def execute(cls, model1, model2) -> io.NodeOutput:
         m = model1.clone()
         kp = model2.get_key_patches("diffusion_model.")
         for k in kp:
             m.add_patches({k: kp[k]}, 1.0, 1.0)
-        return (m, )
+        return io.NodeOutput(m)
+
+    merge = execute  # TODO: remove
 
 
-class CLIPMergeSimple:
+class CLIPMergeSimple(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "clip1": ("CLIP",),
-                              "clip2": ("CLIP",),
-                              "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-                              }}
-    RETURN_TYPES = ("CLIP",)
-    FUNCTION = "merge"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CLIPMergeSimple",
+            category="advanced/model_merging",
+            inputs=[
+                io.Clip.Input("clip1"),
+                io.Clip.Input("clip2"),
+                io.Float.Input("ratio", default=1.0, min=0.0, max=1.0, step=0.01),
+            ],
+            outputs=[
+                io.Clip.Output(),
+            ],
+        )
 
-    CATEGORY = "advanced/model_merging"
-
-    def merge(self, clip1, clip2, ratio):
+    @classmethod
+    def execute(cls, clip1, clip2, ratio) -> io.NodeOutput:
         m = clip1.clone()
         kp = clip2.get_key_patches()
         for k in kp:
             if k.endswith(".position_ids") or k.endswith(".logit_scale"):
                 continue
             m.add_patches({k: kp[k]}, 1.0 - ratio, ratio)
-        return (m, )
+        return io.NodeOutput(m)
+
+    merge = execute  # TODO: remove
 
 
-class CLIPSubtract:
-    SEARCH_ALIASES = ["clip difference", "text encoder subtract"]
+class CLIPSubtract(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "clip1": ("CLIP",),
-                              "clip2": ("CLIP",),
-                              "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
-                              }}
-    RETURN_TYPES = ("CLIP",)
-    FUNCTION = "merge"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CLIPMergeSubtract",
+            search_aliases=["clip difference", "text encoder subtract"],
+            category="advanced/model_merging",
+            inputs=[
+                io.Clip.Input("clip1"),
+                io.Clip.Input("clip2"),
+                io.Float.Input("multiplier", default=1.0, min=-10.0, max=10.0, step=0.01),
+            ],
+            outputs=[
+                io.Clip.Output(),
+            ],
+        )
 
-    CATEGORY = "advanced/model_merging"
-
-    def merge(self, clip1, clip2, multiplier):
+    @classmethod
+    def execute(cls, clip1, clip2, multiplier) -> io.NodeOutput:
         m = clip1.clone()
         kp = clip2.get_key_patches()
         for k in kp:
             if k.endswith(".position_ids") or k.endswith(".logit_scale"):
                 continue
             m.add_patches({k: kp[k]}, - multiplier, multiplier)
-        return (m, )
+        return io.NodeOutput(m)
+
+    merge = execute  # TODO: remove
 
 
-class CLIPAdd:
-    SEARCH_ALIASES = ["combine clip"]
+class CLIPAdd(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "clip1": ("CLIP",),
-                              "clip2": ("CLIP",),
-                              }}
-    RETURN_TYPES = ("CLIP",)
-    FUNCTION = "merge"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CLIPMergeAdd",
+            search_aliases=["combine clip"],
+            category="advanced/model_merging",
+            inputs=[
+                io.Clip.Input("clip1"),
+                io.Clip.Input("clip2"),
+            ],
+            outputs=[
+                io.Clip.Output(),
+            ],
+        )
 
-    CATEGORY = "advanced/model_merging"
-
-    def merge(self, clip1, clip2):
+    @classmethod
+    def execute(cls, clip1, clip2) -> io.NodeOutput:
         m = clip1.clone()
         kp = clip2.get_key_patches()
         for k in kp:
             if k.endswith(".position_ids") or k.endswith(".logit_scale"):
                 continue
             m.add_patches({k: kp[k]}, 1.0, 1.0)
-        return (m, )
+        return io.NodeOutput(m)
+
+    merge = execute  # TODO: remove
 
 
-class ModelMergeBlocks:
+class ModelMergeBlocks(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "model1": ("MODEL",),
-                              "model2": ("MODEL",),
-                              "input": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-                              "middle": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-                              "out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
-                              }}
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "merge"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ModelMergeBlocks",
+            category="advanced/model_merging",
+            inputs=[
+                io.Model.Input("model1"),
+                io.Model.Input("model2"),
+                io.Float.Input("input", default=1.0, min=0.0, max=1.0, step=0.01),
+                io.Float.Input("middle", default=1.0, min=0.0, max=1.0, step=0.01),
+                io.Float.Input("out", default=1.0, min=0.0, max=1.0, step=0.01),
+            ],
+            outputs=[
+                io.Model.Output(),
+            ],
+        )
 
-    CATEGORY = "advanced/model_merging"
-
-    def merge(self, model1, model2, **kwargs):
+    @classmethod
+    def execute(cls, model1, model2, **kwargs) -> io.NodeOutput:
         m = model1.clone()
         kp = model2.get_key_patches("diffusion_model.")
         default_ratio = next(iter(kwargs.values()))
@@ -165,7 +217,10 @@ class ModelMergeBlocks:
                     last_arg_size = len(arg)
 
             m.add_patches({k: kp[k]}, 1.0 - ratio, ratio)
-        return (m, )
+        return io.NodeOutput(m)
+
+    merge = execute  # TODO: remove
+
 
 def save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefix=None, output_dir=None, prompt=None, extra_pnginfo=None):
     full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, output_dir)
@@ -226,59 +281,65 @@ def save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefi
 
     comfy.sd.save_checkpoint(output_checkpoint, model, clip, vae, clip_vision, metadata=metadata, extra_keys=extra_keys)
 
-class CheckpointSave:
-    SEARCH_ALIASES = ["save model", "export checkpoint", "merge save"]
-    def __init__(self):
-        self.output_dir = folder_paths.get_output_directory()
+
+class CheckpointSave(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CheckpointSave",
+            display_name="Save Checkpoint",
+            search_aliases=["save model", "export checkpoint", "merge save"],
+            category="advanced/model_merging",
+            inputs=[
+                io.Model.Input("model"),
+                io.Clip.Input("clip"),
+                io.Vae.Input("vae"),
+                io.String.Input("filename_prefix", default="checkpoints/ComfyUI"),
+            ],
+            hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
+            is_output_node=True,
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "model": ("MODEL",),
-                              "clip": ("CLIP",),
-                              "vae": ("VAE",),
-                              "filename_prefix": ("STRING", {"default": "checkpoints/ComfyUI"}),},
-                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
-    RETURN_TYPES = ()
-    FUNCTION = "save"
-    OUTPUT_NODE = True
+    def execute(cls, model, clip, vae, filename_prefix) -> io.NodeOutput:
+        save_checkpoint(model, clip=clip, vae=vae, filename_prefix=filename_prefix, output_dir=folder_paths.get_output_directory(), prompt=cls.hidden.prompt, extra_pnginfo=cls.hidden.extra_pnginfo)
+        return io.NodeOutput()
 
-    CATEGORY = "advanced/model_merging"
+    save = execute  # TODO: remove
 
-    def save(self, model, clip, vae, filename_prefix, prompt=None, extra_pnginfo=None):
-        save_checkpoint(model, clip=clip, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo)
-        return {}
 
-class CLIPSave:
-    def __init__(self):
-        self.output_dir = folder_paths.get_output_directory()
+class CLIPSave(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CLIPSave",
+            category="advanced/model_merging",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.String.Input("filename_prefix", default="clip/ComfyUI"),
+            ],
+            hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
+            is_output_node=True,
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "clip": ("CLIP",),
-                              "filename_prefix": ("STRING", {"default": "clip/ComfyUI"}),},
-                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
-    RETURN_TYPES = ()
-    FUNCTION = "save"
-    OUTPUT_NODE = True
-
-    CATEGORY = "advanced/model_merging"
-
-    def save(self, clip, filename_prefix, prompt=None, extra_pnginfo=None):
+    def execute(cls, clip, filename_prefix) -> io.NodeOutput:
         prompt_info = ""
-        if prompt is not None:
-            prompt_info = json.dumps(prompt)
+        if cls.hidden.prompt is not None:
+            prompt_info = json.dumps(cls.hidden.prompt)
 
         metadata = {}
         if not args.disable_metadata:
             metadata["format"] = "pt"
             metadata["prompt"] = prompt_info
-            if extra_pnginfo is not None:
-                for x in extra_pnginfo:
-                    metadata[x] = json.dumps(extra_pnginfo[x])
+            if cls.hidden.extra_pnginfo is not None:
+                for x in cls.hidden.extra_pnginfo:
+                    metadata[x] = json.dumps(cls.hidden.extra_pnginfo[x])
 
         comfy.model_management.load_models_gpu([clip.load_model()], force_patch_weights=True)
         clip_sd = clip.get_sd()
 
+        output_dir = folder_paths.get_output_directory()
         for prefix in ["clip_l.", "clip_g.", "clip_h.", "t5xxl.", "pile_t5xl.", "mt5xl.", "umt5xxl.", "t5base.", "gemma2_2b.", "llama.", "hydit_clip.", ""]:
             k = list(filter(lambda a: a.startswith(prefix), clip_sd.keys()))
             current_clip_sd = {}
@@ -295,7 +356,7 @@ class CLIPSave:
                 replace_prefix[prefix] = ""
             replace_prefix["transformer."] = ""
 
-            full_output_folder, filename, counter, subfolder, filename_prefix_ = folder_paths.get_save_image_path(filename_prefix_, self.output_dir)
+            full_output_folder, filename, counter, subfolder, filename_prefix_ = folder_paths.get_save_image_path(filename_prefix_, output_dir)
 
             output_checkpoint = f"{filename}_{counter:05}_.safetensors"
             output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
@@ -303,76 +364,88 @@ class CLIPSave:
             current_clip_sd = comfy.utils.state_dict_prefix_replace(current_clip_sd, replace_prefix)
 
             comfy.utils.save_torch_file(current_clip_sd, output_checkpoint, metadata=metadata)
-        return {}
+        return io.NodeOutput()
 
-class VAESave:
-    def __init__(self):
-        self.output_dir = folder_paths.get_output_directory()
+    save = execute  # TODO: remove
+
+
+class VAESave(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="VAESave",
+            category="advanced/model_merging",
+            inputs=[
+                io.Vae.Input("vae"),
+                io.String.Input("filename_prefix", default="vae/ComfyUI_vae"),
+            ],
+            hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
+            is_output_node=True,
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "vae": ("VAE",),
-                              "filename_prefix": ("STRING", {"default": "vae/ComfyUI_vae"}),},
-                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
-    RETURN_TYPES = ()
-    FUNCTION = "save"
-    OUTPUT_NODE = True
-
-    CATEGORY = "advanced/model_merging"
-
-    def save(self, vae, filename_prefix, prompt=None, extra_pnginfo=None):
-        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
+    def execute(cls, vae, filename_prefix) -> io.NodeOutput:
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, folder_paths.get_output_directory())
         prompt_info = ""
-        if prompt is not None:
-            prompt_info = json.dumps(prompt)
+        if cls.hidden.prompt is not None:
+            prompt_info = json.dumps(cls.hidden.prompt)
 
         metadata = {}
         if not args.disable_metadata:
             metadata["prompt"] = prompt_info
-            if extra_pnginfo is not None:
-                for x in extra_pnginfo:
-                    metadata[x] = json.dumps(extra_pnginfo[x])
+            if cls.hidden.extra_pnginfo is not None:
+                for x in cls.hidden.extra_pnginfo:
+                    metadata[x] = json.dumps(cls.hidden.extra_pnginfo[x])
 
         output_checkpoint = f"{filename}_{counter:05}_.safetensors"
         output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
 
         comfy.utils.save_torch_file(vae.get_sd(), output_checkpoint, metadata=metadata)
-        return {}
+        return io.NodeOutput()
 
-class ModelSave:
-    SEARCH_ALIASES = ["export model", "checkpoint save"]
-    def __init__(self):
-        self.output_dir = folder_paths.get_output_directory()
+    save = execute  # TODO: remove
+
+
+class ModelSave(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ModelSave",
+            search_aliases=["export model", "checkpoint save"],
+            category="advanced/model_merging",
+            inputs=[
+                io.Model.Input("model"),
+                io.String.Input("filename_prefix", default="diffusion_models/ComfyUI"),
+            ],
+            hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
+            is_output_node=True,
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "model": ("MODEL",),
-                              "filename_prefix": ("STRING", {"default": "diffusion_models/ComfyUI"}),},
-                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
-    RETURN_TYPES = ()
-    FUNCTION = "save"
-    OUTPUT_NODE = True
+    def execute(cls, model, filename_prefix) -> io.NodeOutput:
+        save_checkpoint(model, filename_prefix=filename_prefix, output_dir=folder_paths.get_output_directory(), prompt=cls.hidden.prompt, extra_pnginfo=cls.hidden.extra_pnginfo)
+        return io.NodeOutput()
 
-    CATEGORY = "advanced/model_merging"
+    save = execute  # TODO: remove
 
-    def save(self, model, filename_prefix, prompt=None, extra_pnginfo=None):
-        save_checkpoint(model, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo)
-        return {}
 
-NODE_CLASS_MAPPINGS = {
-    "ModelMergeSimple": ModelMergeSimple,
-    "ModelMergeBlocks": ModelMergeBlocks,
-    "ModelMergeSubtract": ModelSubtract,
-    "ModelMergeAdd": ModelAdd,
-    "CheckpointSave": CheckpointSave,
-    "CLIPMergeSimple": CLIPMergeSimple,
-    "CLIPMergeSubtract": CLIPSubtract,
-    "CLIPMergeAdd": CLIPAdd,
-    "CLIPSave": CLIPSave,
-    "VAESave": VAESave,
-    "ModelSave": ModelSave,
-}
+class ModelMergingExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            ModelMergeSimple,
+            ModelMergeBlocks,
+            ModelSubtract,
+            ModelAdd,
+            CheckpointSave,
+            CLIPMergeSimple,
+            CLIPSubtract,
+            CLIPAdd,
+            CLIPSave,
+            VAESave,
+            ModelSave,
+        ]
 
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "CheckpointSave": "Save Checkpoint",
-}
+
+async def comfy_entrypoint() -> ModelMergingExtension:
+    return ModelMergingExtension()

comfy_extras/nodes_model_merging_model_specific.py

@@ -1,356 +1,455 @@
 import comfy_extras.nodes_model_merging
 
+from comfy_api.latest import io, ComfyExtension
+from typing_extensions import override
+
+
 class ModelMergeSD1(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
     @classmethod
-    def INPUT_TYPES(s):
-        arg_dict = { "model1": ("MODEL",),
-                              "model2": ("MODEL",)}
+    def define_schema(cls):
+        inputs = [
+            io.Model.Input("model1"),
+            io.Model.Input("model2"),
+        ]
 
-        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
+        argument = dict(default=1.0, min=0.0, max=1.0, step=0.01)
 
-        arg_dict["time_embed."] = argument
-        arg_dict["label_emb."] = argument
+        inputs.append(io.Float.Input("time_embed.", **argument))
+        inputs.append(io.Float.Input("label_emb.", **argument))
 
         for i in range(12):
-            arg_dict["input_blocks.{}.".format(i)] = argument
+            inputs.append(io.Float.Input("input_blocks.{}.".format(i), **argument))
 
         for i in range(3):
-            arg_dict["middle_block.{}.".format(i)] = argument
+            inputs.append(io.Float.Input("middle_block.{}.".format(i), **argument))
 
         for i in range(12):
-            arg_dict["output_blocks.{}.".format(i)] = argument
+            inputs.append(io.Float.Input("output_blocks.{}.".format(i), **argument))
 
-        arg_dict["out."] = argument
+        inputs.append(io.Float.Input("out.", **argument))
 
-        return {"required": arg_dict}
+        return io.Schema(
+            node_id="ModelMergeSD1",
+            category="advanced/model_merging/model_specific",
+            inputs=inputs,
+            outputs=[io.Model.Output()],
+        )
+
+
+class ModelMergeSD2(ModelMergeSD1):
+    # SD1 and SD2 have the same blocks
+    @classmethod
+    def define_schema(cls):
+        schema = ModelMergeSD1.define_schema()
+        schema.node_id = "ModelMergeSD2"
+        return schema
 
 
 class ModelMergeSDXL(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
-
     @classmethod
-    def INPUT_TYPES(s):
-        arg_dict = { "model1": ("MODEL",),
-                              "model2": ("MODEL",)}
+    def define_schema(cls):
+        inputs = [
+            io.Model.Input("model1"),
+            io.Model.Input("model2"),
+        ]
 
-        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
+        argument = dict(default=1.0, min=0.0, max=1.0, step=0.01)
 
-        arg_dict["time_embed."] = argument
-        arg_dict["label_emb."] = argument
+        inputs.append(io.Float.Input("time_embed.", **argument))
+        inputs.append(io.Float.Input("label_emb.", **argument))
 
         for i in range(9):
-            arg_dict["input_blocks.{}".format(i)] = argument
+            inputs.append(io.Float.Input("input_blocks.{}".format(i), **argument))
 
         for i in range(3):
-            arg_dict["middle_block.{}".format(i)] = argument
+            inputs.append(io.Float.Input("middle_block.{}".format(i), **argument))
 
         for i in range(9):
-            arg_dict["output_blocks.{}".format(i)] = argument
+            inputs.append(io.Float.Input("output_blocks.{}".format(i), **argument))
 
-        arg_dict["out."] = argument
+        inputs.append(io.Float.Input("out.", **argument))
+
+        return io.Schema(
+            node_id="ModelMergeSDXL",
+            category="advanced/model_merging/model_specific",
+            inputs=inputs,
+            outputs=[io.Model.Output()],
+        )
 
-        return {"required": arg_dict}
 
 class ModelMergeSD3_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
-
     @classmethod
-    def INPUT_TYPES(s):
-        arg_dict = { "model1": ("MODEL",),
-                              "model2": ("MODEL",)}
+    def define_schema(cls):
+        inputs = [
+            io.Model.Input("model1"),
+            io.Model.Input("model2"),
+        ]
 
-        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
+        argument = dict(default=1.0, min=0.0, max=1.0, step=0.01)
 
-        arg_dict["pos_embed."] = argument
-        arg_dict["x_embedder."] = argument
-        arg_dict["context_embedder."] = argument
-        arg_dict["y_embedder."] = argument
-        arg_dict["t_embedder."] = argument
+        inputs.append(io.Float.Input("pos_embed.", **argument))
+        inputs.append(io.Float.Input("x_embedder.", **argument))
+        inputs.append(io.Float.Input("context_embedder.", **argument))
+        inputs.append(io.Float.Input("y_embedder.", **argument))
+        inputs.append(io.Float.Input("t_embedder.", **argument))
 
         for i in range(24):
-            arg_dict["joint_blocks.{}.".format(i)] = argument
+            inputs.append(io.Float.Input("joint_blocks.{}.".format(i), **argument))
 
-        arg_dict["final_layer."] = argument
+        inputs.append(io.Float.Input("final_layer.", **argument))
 
-        return {"required": arg_dict}
+        return io.Schema(
+            node_id="ModelMergeSD3_2B",
+            category="advanced/model_merging/model_specific",
+            inputs=inputs,
+            outputs=[io.Model.Output()],
+        )
 
 
 class ModelMergeAuraflow(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
-
     @classmethod
-    def INPUT_TYPES(s):
-        arg_dict = { "model1": ("MODEL",),
-                              "model2": ("MODEL",)}
+    def define_schema(cls):
+        inputs = [
+            io.Model.Input("model1"),
+            io.Model.Input("model2"),
+        ]
 
-        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
+        argument = dict(default=1.0, min=0.0, max=1.0, step=0.01)
 
-        arg_dict["init_x_linear."] = argument
-        arg_dict["positional_encoding"] = argument
-        arg_dict["cond_seq_linear."] = argument
-        arg_dict["register_tokens"] = argument
-        arg_dict["t_embedder."] = argument
+        inputs.append(io.Float.Input("init_x_linear.", **argument))
+        inputs.append(io.Float.Input("positional_encoding", **argument))
+        inputs.append(io.Float.Input("cond_seq_linear.", **argument))
+        inputs.append(io.Float.Input("register_tokens", **argument))
+        inputs.append(io.Float.Input("t_embedder.", **argument))
 
         for i in range(4):
-            arg_dict["double_layers.{}.".format(i)] = argument
+            inputs.append(io.Float.Input("double_layers.{}.".format(i), **argument))
 
         for i in range(32):
-            arg_dict["single_layers.{}.".format(i)] = argument
+            inputs.append(io.Float.Input("single_layers.{}.".format(i), **argument))
 
-        arg_dict["modF."] = argument
-        arg_dict["final_linear."] = argument
+        inputs.append(io.Float.Input("modF.", **argument))
+        inputs.append(io.Float.Input("final_linear.", **argument))
+
+        return io.Schema(
+            node_id="ModelMergeAuraflow",
+            category="advanced/model_merging/model_specific",
+            inputs=inputs,
+            outputs=[io.Model.Output()],
+        )
 
-        return {"required": arg_dict}
 
 class ModelMergeFlux1(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
-
     @classmethod
-    def INPUT_TYPES(s):
-        arg_dict = { "model1": ("MODEL",),
-                              "model2": ("MODEL",)}
+    def define_schema(cls):
+        inputs = [
+            io.Model.Input("model1"),
+            io.Model.Input("model2"),
+        ]
 
-        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
+        argument = dict(default=1.0, min=0.0, max=1.0, step=0.01)
 
-        arg_dict["img_in."] = argument
-        arg_dict["time_in."] = argument
-        arg_dict["guidance_in"] = argument
-        arg_dict["vector_in."] = argument
-        arg_dict["txt_in."] = argument
+        inputs.append(io.Float.Input("img_in.", **argument))
+        inputs.append(io.Float.Input("time_in.", **argument))
+        inputs.append(io.Float.Input("guidance_in", **argument))
+        inputs.append(io.Float.Input("vector_in.", **argument))
+        inputs.append(io.Float.Input("txt_in.", **argument))
 
         for i in range(19):
-            arg_dict["double_blocks.{}.".format(i)] = argument
+            inputs.append(io.Float.Input("double_blocks.{}.".format(i), **argument))
 
         for i in range(38):
-            arg_dict["single_blocks.{}.".format(i)] = argument
+            inputs.append(io.Float.Input("single_blocks.{}.".format(i), **argument))
 
-        arg_dict["final_layer."] = argument
+        inputs.append(io.Float.Input("final_layer.", **argument))
+
+        return io.Schema(
+            node_id="ModelMergeFlux1",
+            category="advanced/model_merging/model_specific",
+            inputs=inputs,
+            outputs=[io.Model.Output()],
+        )
 
-        return {"required": arg_dict}
 
 class ModelMergeSD35_Large(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
-
     @classmethod
-    def INPUT_TYPES(s):
-        arg_dict = { "model1": ("MODEL",),
-                              "model2": ("MODEL",)}
+    def define_schema(cls):
+        inputs = [
+            io.Model.Input("model1"),
+            io.Model.Input("model2"),
+        ]
 
-        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
+        argument = dict(default=1.0, min=0.0, max=1.0, step=0.01)
 
-        arg_dict["pos_embed."] = argument
-        arg_dict["x_embedder."] = argument
-        arg_dict["context_embedder."] = argument
-        arg_dict["y_embedder."] = argument
-        arg_dict["t_embedder."] = argument
+        inputs.append(io.Float.Input("pos_embed.", **argument))
+        inputs.append(io.Float.Input("x_embedder.", **argument))
+        inputs.append(io.Float.Input("context_embedder.", **argument))
+        inputs.append(io.Float.Input("y_embedder.", **argument))
+        inputs.append(io.Float.Input("t_embedder.", **argument))
 
         for i in range(38):
-            arg_dict["joint_blocks.{}.".format(i)] = argument
+            inputs.append(io.Float.Input("joint_blocks.{}.".format(i), **argument))
 
-        arg_dict["final_layer."] = argument
+        inputs.append(io.Float.Input("final_layer.", **argument))
+
+        return io.Schema(
+            node_id="ModelMergeSD35_Large",
+            category="advanced/model_merging/model_specific",
+            inputs=inputs,
+            outputs=[io.Model.Output()],
+        )
 
-        return {"required": arg_dict}
 
 class ModelMergeMochiPreview(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
-
     @classmethod
-    def INPUT_TYPES(s):
-        arg_dict = { "model1": ("MODEL",),
-                              "model2": ("MODEL",)}
+    def define_schema(cls):
+        inputs = [
+            io.Model.Input("model1"),
+            io.Model.Input("model2"),
+        ]
 
-        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
+        argument = dict(default=1.0, min=0.0, max=1.0, step=0.01)
 
-        arg_dict["pos_frequencies."] = argument
-        arg_dict["t_embedder."] = argument
-        arg_dict["t5_y_embedder."] = argument
-        arg_dict["t5_yproj."] = argument
+        inputs.append(io.Float.Input("pos_frequencies.", **argument))
+        inputs.append(io.Float.Input("t_embedder.", **argument))
+        inputs.append(io.Float.Input("t5_y_embedder.", **argument))
+        inputs.append(io.Float.Input("t5_yproj.", **argument))
 
         for i in range(48):
-            arg_dict["blocks.{}.".format(i)] = argument
+            inputs.append(io.Float.Input("blocks.{}.".format(i), **argument))
 
-        arg_dict["final_layer."] = argument
+        inputs.append(io.Float.Input("final_layer.", **argument))
+
+        return io.Schema(
+            node_id="ModelMergeMochiPreview",
+            category="advanced/model_merging/model_specific",
+            inputs=inputs,
+            outputs=[io.Model.Output()],
+        )
 
-        return {"required": arg_dict}
 
 class ModelMergeLTXV(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
-
     @classmethod
-    def INPUT_TYPES(s):
-        arg_dict = { "model1": ("MODEL",),
-                              "model2": ("MODEL",)}
+    def define_schema(cls):
+        inputs = [
+            io.Model.Input("model1"),
+            io.Model.Input("model2"),
+        ]
 
-        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
+        argument = dict(default=1.0, min=0.0, max=1.0, step=0.01)
 
-        arg_dict["patchify_proj."] = argument
-        arg_dict["adaln_single."] = argument
-        arg_dict["caption_projection."] = argument
+        inputs.append(io.Float.Input("patchify_proj.", **argument))
+        inputs.append(io.Float.Input("adaln_single.", **argument))
+        inputs.append(io.Float.Input("caption_projection.", **argument))
 
         for i in range(28):
-            arg_dict["transformer_blocks.{}.".format(i)] = argument
+            inputs.append(io.Float.Input("transformer_blocks.{}.".format(i), **argument))
 
-        arg_dict["scale_shift_table"] = argument
-        arg_dict["proj_out."] = argument
+        inputs.append(io.Float.Input("scale_shift_table", **argument))
+        inputs.append(io.Float.Input("proj_out.", **argument))
+
+        return io.Schema(
+            node_id="ModelMergeLTXV",
+            category="advanced/model_merging/model_specific",
+            inputs=inputs,
+            outputs=[io.Model.Output()],
+        )
 
-        return {"required": arg_dict}
 
 class ModelMergeCosmos7B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
-
     @classmethod
-    def INPUT_TYPES(s):
-        arg_dict = { "model1": ("MODEL",),
-                              "model2": ("MODEL",)}
+    def define_schema(cls):
+        inputs = [
+            io.Model.Input("model1"),
+            io.Model.Input("model2"),
+        ]
 
-        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
-
-        arg_dict["pos_embedder."] = argument
-        arg_dict["extra_pos_embedder."] = argument
-        arg_dict["x_embedder."] = argument
-        arg_dict["t_embedder."] = argument
-        arg_dict["affline_norm."] = argument
+        argument = dict(default=1.0, min=0.0, max=1.0, step=0.01)
 
+        inputs.append(io.Float.Input("pos_embedder.", **argument))
+        inputs.append(io.Float.Input("extra_pos_embedder.", **argument))
+        inputs.append(io.Float.Input("x_embedder.", **argument))
+        inputs.append(io.Float.Input("t_embedder.", **argument))
+        inputs.append(io.Float.Input("affline_norm.", **argument))
 
         for i in range(28):
-            arg_dict["blocks.block{}.".format(i)] = argument
+            inputs.append(io.Float.Input("blocks.block{}.".format(i), **argument))
 
-        arg_dict["final_layer."] = argument
+        inputs.append(io.Float.Input("final_layer.", **argument))
+
+        return io.Schema(
+            node_id="ModelMergeCosmos7B",
+            category="advanced/model_merging/model_specific",
+            inputs=inputs,
+            outputs=[io.Model.Output()],
+        )
 
-        return {"required": arg_dict}
 
 class ModelMergeCosmos14B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
-
     @classmethod
-    def INPUT_TYPES(s):
-        arg_dict = { "model1": ("MODEL",),
-                              "model2": ("MODEL",)}
+    def define_schema(cls):
+        inputs = [
+            io.Model.Input("model1"),
+            io.Model.Input("model2"),
+        ]
 
-        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
-
-        arg_dict["pos_embedder."] = argument
-        arg_dict["extra_pos_embedder."] = argument
-        arg_dict["x_embedder."] = argument
-        arg_dict["t_embedder."] = argument
-        arg_dict["affline_norm."] = argument
+        argument = dict(default=1.0, min=0.0, max=1.0, step=0.01)
 
+        inputs.append(io.Float.Input("pos_embedder.", **argument))
+        inputs.append(io.Float.Input("extra_pos_embedder.", **argument))
+        inputs.append(io.Float.Input("x_embedder.", **argument))
+        inputs.append(io.Float.Input("t_embedder.", **argument))
+        inputs.append(io.Float.Input("affline_norm.", **argument))
 
         for i in range(36):
-            arg_dict["blocks.block{}.".format(i)] = argument
+            inputs.append(io.Float.Input("blocks.block{}.".format(i), **argument))
 
-        arg_dict["final_layer."] = argument
+        inputs.append(io.Float.Input("final_layer.", **argument))
+
+        return io.Schema(
+            node_id="ModelMergeCosmos14B",
+            category="advanced/model_merging/model_specific",
+            inputs=inputs,
+            outputs=[io.Model.Output()],
+        )
 
-        return {"required": arg_dict}
 
 class ModelMergeWAN2_1(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
-    DESCRIPTION = "1.3B model has 30 blocks, 14B model has 40 blocks. Image to video model has the extra img_emb."
-
     @classmethod
-    def INPUT_TYPES(s):
-        arg_dict = { "model1": ("MODEL",),
-                              "model2": ("MODEL",)}
+    def define_schema(cls):
+        inputs = [
+            io.Model.Input("model1"),
+            io.Model.Input("model2"),
+        ]
 
-        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
+        argument = dict(default=1.0, min=0.0, max=1.0, step=0.01)
 
-        arg_dict["patch_embedding."] = argument
-        arg_dict["time_embedding."] = argument
-        arg_dict["time_projection."] = argument
-        arg_dict["text_embedding."] = argument
-        arg_dict["img_emb."] = argument
+        inputs.append(io.Float.Input("patch_embedding.", **argument))
+        inputs.append(io.Float.Input("time_embedding.", **argument))
+        inputs.append(io.Float.Input("time_projection.", **argument))
+        inputs.append(io.Float.Input("text_embedding.", **argument))
+        inputs.append(io.Float.Input("img_emb.", **argument))
 
         for i in range(40):
-            arg_dict["blocks.{}.".format(i)] = argument
+            inputs.append(io.Float.Input("blocks.{}.".format(i), **argument))
 
-        arg_dict["head."] = argument
+        inputs.append(io.Float.Input("head.", **argument))
+
+        return io.Schema(
+            node_id="ModelMergeWAN2_1",
+            category="advanced/model_merging/model_specific",
+            description="1.3B model has 30 blocks, 14B model has 40 blocks. Image to video model has the extra img_emb.",
+            inputs=inputs,
+            outputs=[io.Model.Output()],
+        )
 
-        return {"required": arg_dict}
 
 class ModelMergeCosmosPredict2_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
-
     @classmethod
-    def INPUT_TYPES(s):
-        arg_dict = { "model1": ("MODEL",),
-                              "model2": ("MODEL",)}
+    def define_schema(cls):
+        inputs = [
+            io.Model.Input("model1"),
+            io.Model.Input("model2"),
+        ]
 
-        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
-
-        arg_dict["pos_embedder."] = argument
-        arg_dict["x_embedder."] = argument
-        arg_dict["t_embedder."] = argument
-        arg_dict["t_embedding_norm."] = argument
+        argument = dict(default=1.0, min=0.0, max=1.0, step=0.01)
 
+        inputs.append(io.Float.Input("pos_embedder.", **argument))
+        inputs.append(io.Float.Input("x_embedder.", **argument))
+        inputs.append(io.Float.Input("t_embedder.", **argument))
+        inputs.append(io.Float.Input("t_embedding_norm.", **argument))
 
         for i in range(28):
-            arg_dict["blocks.{}.".format(i)] = argument
+            inputs.append(io.Float.Input("blocks.{}.".format(i), **argument))
 
-        arg_dict["final_layer."] = argument
+        inputs.append(io.Float.Input("final_layer.", **argument))
+
+        return io.Schema(
+            node_id="ModelMergeCosmosPredict2_2B",
+            category="advanced/model_merging/model_specific",
+            inputs=inputs,
+            outputs=[io.Model.Output()],
+        )
 
-        return {"required": arg_dict}
 
 class ModelMergeCosmosPredict2_14B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
-
     @classmethod
-    def INPUT_TYPES(s):
-        arg_dict = { "model1": ("MODEL",),
-                              "model2": ("MODEL",)}
+    def define_schema(cls):
+        inputs = [
+            io.Model.Input("model1"),
+            io.Model.Input("model2"),
+        ]
 
-        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
-
-        arg_dict["pos_embedder."] = argument
-        arg_dict["x_embedder."] = argument
-        arg_dict["t_embedder."] = argument
-        arg_dict["t_embedding_norm."] = argument
+        argument = dict(default=1.0, min=0.0, max=1.0, step=0.01)
 
+        inputs.append(io.Float.Input("pos_embedder.", **argument))
+        inputs.append(io.Float.Input("x_embedder.", **argument))
+        inputs.append(io.Float.Input("t_embedder.", **argument))
+        inputs.append(io.Float.Input("t_embedding_norm.", **argument))
 
         for i in range(36):
-            arg_dict["blocks.{}.".format(i)] = argument
+            inputs.append(io.Float.Input("blocks.{}.".format(i), **argument))
 
-        arg_dict["final_layer."] = argument
+        inputs.append(io.Float.Input("final_layer.", **argument))
+
+        return io.Schema(
+            node_id="ModelMergeCosmosPredict2_14B",
+            category="advanced/model_merging/model_specific",
+            inputs=inputs,
+            outputs=[io.Model.Output()],
+        )
 
-        return {"required": arg_dict}
 
 class ModelMergeQwenImage(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
-
     @classmethod
-    def INPUT_TYPES(s):
-        arg_dict = { "model1": ("MODEL",),
-                              "model2": ("MODEL",)}
+    def define_schema(cls):
+        inputs = [
+            io.Model.Input("model1"),
+            io.Model.Input("model2"),
+        ]
 
-        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
+        argument = dict(default=1.0, min=0.0, max=1.0, step=0.01)
 
-        arg_dict["pos_embeds."] = argument
-        arg_dict["img_in."] = argument
-        arg_dict["txt_norm."] = argument
-        arg_dict["txt_in."] = argument
-        arg_dict["time_text_embed."] = argument
+        inputs.append(io.Float.Input("pos_embeds.", **argument))
+        inputs.append(io.Float.Input("img_in.", **argument))
+        inputs.append(io.Float.Input("txt_norm.", **argument))
+        inputs.append(io.Float.Input("txt_in.", **argument))
+        inputs.append(io.Float.Input("time_text_embed.", **argument))
 
         for i in range(60):
-            arg_dict["transformer_blocks.{}.".format(i)] = argument
+            inputs.append(io.Float.Input("transformer_blocks.{}.".format(i), **argument))
 
-        arg_dict["proj_out."] = argument
+        inputs.append(io.Float.Input("proj_out.", **argument))
 
-        return {"required": arg_dict}
+        return io.Schema(
+            node_id="ModelMergeQwenImage",
+            category="advanced/model_merging/model_specific",
+            inputs=inputs,
+            outputs=[io.Model.Output()],
+        )
 
-NODE_CLASS_MAPPINGS = {
-    "ModelMergeSD1": ModelMergeSD1,
-    "ModelMergeSD2": ModelMergeSD1, #SD1 and SD2 have the same blocks
-    "ModelMergeSDXL": ModelMergeSDXL,
-    "ModelMergeSD3_2B": ModelMergeSD3_2B,
-    "ModelMergeAuraflow": ModelMergeAuraflow,
-    "ModelMergeFlux1": ModelMergeFlux1,
-    "ModelMergeSD35_Large": ModelMergeSD35_Large,
-    "ModelMergeMochiPreview": ModelMergeMochiPreview,
-    "ModelMergeLTXV": ModelMergeLTXV,
-    "ModelMergeCosmos7B": ModelMergeCosmos7B,
-    "ModelMergeCosmos14B": ModelMergeCosmos14B,
-    "ModelMergeWAN2_1": ModelMergeWAN2_1,
-    "ModelMergeCosmosPredict2_2B": ModelMergeCosmosPredict2_2B,
-    "ModelMergeCosmosPredict2_14B": ModelMergeCosmosPredict2_14B,
-    "ModelMergeQwenImage": ModelMergeQwenImage,
-}
+
+class ModelMergingModelSpecificExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            ModelMergeSD1,
+            ModelMergeSD2,
+            ModelMergeSDXL,
+            ModelMergeSD3_2B,
+            ModelMergeAuraflow,
+            ModelMergeFlux1,
+            ModelMergeSD35_Large,
+            ModelMergeMochiPreview,
+            ModelMergeLTXV,
+            ModelMergeCosmos7B,
+            ModelMergeCosmos14B,
+            ModelMergeWAN2_1,
+            ModelMergeCosmosPredict2_2B,
+            ModelMergeCosmosPredict2_14B,
+            ModelMergeQwenImage,
+        ]
+
+
+async def comfy_entrypoint() -> ModelMergingModelSpecificExtension:
+    return ModelMergingModelSpecificExtension()

comfy_extras/nodes_video_model.py

@@ -6,44 +6,62 @@ import folder_paths
 import comfy_extras.nodes_model_merging
 import node_helpers
 
+from comfy_api.latest import io, ComfyExtension
+from typing_extensions import override
 
-class ImageOnlyCheckpointLoader:
+
+class ImageOnlyCheckpointLoader(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
-                             }}
-    RETURN_TYPES = ("MODEL", "CLIP_VISION", "VAE")
-    FUNCTION = "load_checkpoint"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ImageOnlyCheckpointLoader",
+            display_name="Image Only Checkpoint Loader (img2vid model)",
+            category="loaders/video_models",
+            inputs=[
+                io.Combo.Input("ckpt_name", options=folder_paths.get_filename_list("checkpoints")),
+            ],
+            outputs=[
+                io.Model.Output(),
+                io.ClipVision.Output(),
+                io.Vae.Output(),
+            ],
+        )
 
-    CATEGORY = "loaders/video_models"
-
-    def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
+    @classmethod
+    def execute(cls, ckpt_name, output_vae=True, output_clip=True) -> io.NodeOutput:
         ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)
         out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=False, output_clipvision=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
-        return (out[0], out[3], out[2])
+        return io.NodeOutput(out[0], out[3], out[2])
+
+    load_checkpoint = execute  # TODO: remove
 
 
-class SVD_img2vid_Conditioning:
+class SVD_img2vid_Conditioning(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "clip_vision": ("CLIP_VISION",),
-                              "init_image": ("IMAGE",),
-                              "vae": ("VAE",),
-                              "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
-                              "height": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
-                              "video_frames": ("INT", {"default": 14, "min": 1, "max": 4096}),
-                              "motion_bucket_id": ("INT", {"default": 127, "min": 1, "max": 1023}),
-                              "fps": ("INT", {"default": 6, "min": 1, "max": 1024}),
-                              "augmentation_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01})
-                             }}
-    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
-    RETURN_NAMES = ("positive", "negative", "latent")
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SVD_img2vid_Conditioning",
+            category="conditioning/video_models",
+            inputs=[
+                io.ClipVision.Input("clip_vision"),
+                io.Image.Input("init_image"),
+                io.Vae.Input("vae"),
+                io.Int.Input("width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=8),
+                io.Int.Input("height", default=576, min=16, max=nodes.MAX_RESOLUTION, step=8),
+                io.Int.Input("video_frames", default=14, min=1, max=4096),
+                io.Int.Input("motion_bucket_id", default=127, min=1, max=1023),
+                io.Int.Input("fps", default=6, min=1, max=1024),
+                io.Float.Input("augmentation_level", default=0.0, min=0.0, max=10.0, step=0.01),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
 
-    FUNCTION = "encode"
-
-    CATEGORY = "conditioning/video_models"
-
-    def encode(self, clip_vision, init_image, vae, width, height, video_frames, motion_bucket_id, fps, augmentation_level):
+    @classmethod
+    def execute(cls, clip_vision, init_image, vae, width, height, video_frames, motion_bucket_id, fps, augmentation_level) -> io.NodeOutput:
         output = clip_vision.encode_image(init_image)
         pooled = output.image_embeds.unsqueeze(0)
         pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
@@ -54,20 +72,28 @@ class SVD_img2vid_Conditioning:
         positive = [[pooled, {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "concat_latent_image": t}]]
         negative = [[torch.zeros_like(pooled), {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "concat_latent_image": torch.zeros_like(t)}]]
         latent = torch.zeros([video_frames, 4, height // 8, width // 8])
-        return (positive, negative, {"samples":latent})
+        return io.NodeOutput(positive, negative, {"samples":latent})
 
-class VideoLinearCFGGuidance:
+    encode = execute  # TODO: remove
+
+
+class VideoLinearCFGGuidance(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "model": ("MODEL",),
-                              "min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
-                              }}
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "patch"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="VideoLinearCFGGuidance",
+            category="sampling/video_models",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input("min_cfg", default=1.0, min=0.0, max=100.0, step=0.5, round=0.01),
+            ],
+            outputs=[
+                io.Model.Output(),
+            ],
+        )
 
-    CATEGORY = "sampling/video_models"
-
-    def patch(self, model, min_cfg):
+    @classmethod
+    def execute(cls, model, min_cfg) -> io.NodeOutput:
         def linear_cfg(args):
             cond = args["cond"]
             uncond = args["uncond"]
@@ -78,20 +104,28 @@ class VideoLinearCFGGuidance:
 
         m = model.clone()
         m.set_model_sampler_cfg_function(linear_cfg)
-        return (m, )
+        return io.NodeOutput(m)
 
-class VideoTriangleCFGGuidance:
+    patch = execute  # TODO: remove
+
+
+class VideoTriangleCFGGuidance(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "model": ("MODEL",),
-                              "min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
-                              }}
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "patch"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="VideoTriangleCFGGuidance",
+            category="sampling/video_models",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input("min_cfg", default=1.0, min=0.0, max=100.0, step=0.5, round=0.01),
+            ],
+            outputs=[
+                io.Model.Output(),
+            ],
+        )
 
-    CATEGORY = "sampling/video_models"
-
-    def patch(self, model, min_cfg):
+    @classmethod
+    def execute(cls, model, min_cfg) -> io.NodeOutput:
         def linear_cfg(args):
             cond = args["cond"]
             uncond = args["uncond"]
@@ -105,57 +139,79 @@ class VideoTriangleCFGGuidance:
 
         m = model.clone()
         m.set_model_sampler_cfg_function(linear_cfg)
-        return (m, )
+        return io.NodeOutput(m)
 
-class ImageOnlyCheckpointSave(comfy_extras.nodes_model_merging.CheckpointSave):
-    CATEGORY = "advanced/model_merging"
+    patch = execute  # TODO: remove
+
+
+class ImageOnlyCheckpointSave(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ImageOnlyCheckpointSave",
+            search_aliases=["save model", "export checkpoint", "merge save"],
+            category="advanced/model_merging",
+            inputs=[
+                io.Model.Input("model"),
+                io.ClipVision.Input("clip_vision"),
+                io.Vae.Input("vae"),
+                io.String.Input("filename_prefix", default="checkpoints/ComfyUI"),
+            ],
+            hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
+            is_output_node=True,
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "model": ("MODEL",),
-                              "clip_vision": ("CLIP_VISION",),
-                              "vae": ("VAE",),
-                              "filename_prefix": ("STRING", {"default": "checkpoints/ComfyUI"}),},
-                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
+    def execute(cls, model, clip_vision, vae, filename_prefix) -> io.NodeOutput:
+        comfy_extras.nodes_model_merging.save_checkpoint(model, clip_vision=clip_vision, vae=vae, filename_prefix=filename_prefix, output_dir=folder_paths.get_output_directory(), prompt=cls.hidden.prompt, extra_pnginfo=cls.hidden.extra_pnginfo)
+        return io.NodeOutput()
 
-    def save(self, model, clip_vision, vae, filename_prefix, prompt=None, extra_pnginfo=None):
-        comfy_extras.nodes_model_merging.save_checkpoint(model, clip_vision=clip_vision, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo)
-        return {}
+    save = execute  # TODO: remove
 
 
-class ConditioningSetAreaPercentageVideo:
+class ConditioningSetAreaPercentageVideo(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"conditioning": ("CONDITIONING", ),
-                             "width": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}),
-                             "height": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}),
-                             "temporal": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}),
-                             "x": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}),
-                             "y": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}),
-                             "z": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}),
-                             "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
-                             }}
-    RETURN_TYPES = ("CONDITIONING",)
-    FUNCTION = "append"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ConditioningSetAreaPercentageVideo",
+            category="conditioning",
+            inputs=[
+                io.Conditioning.Input("conditioning"),
+                io.Float.Input("width", default=1.0, min=0.0, max=1.0, step=0.01),
+                io.Float.Input("height", default=1.0, min=0.0, max=1.0, step=0.01),
+                io.Float.Input("temporal", default=1.0, min=0.0, max=1.0, step=0.01),
+                io.Float.Input("x", default=0.0, min=0.0, max=1.0, step=0.01),
+                io.Float.Input("y", default=0.0, min=0.0, max=1.0, step=0.01),
+                io.Float.Input("z", default=0.0, min=0.0, max=1.0, step=0.01),
+                io.Float.Input("strength", default=1.0, min=0.0, max=10.0, step=0.01),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ],
+        )
 
-    CATEGORY = "conditioning"
-
-    def append(self, conditioning, width, height, temporal, x, y, z, strength):
+    @classmethod
+    def execute(cls, conditioning, width, height, temporal, x, y, z, strength) -> io.NodeOutput:
         c = node_helpers.conditioning_set_values(conditioning, {"area": ("percentage", temporal, height, width, z, y, x),
                                                                 "strength": strength,
                                                                 "set_area_to_bounds": False})
-        return (c, )
+        return io.NodeOutput(c)
+
+    append = execute  # TODO: remove
 
 
-NODE_CLASS_MAPPINGS = {
-    "ImageOnlyCheckpointLoader": ImageOnlyCheckpointLoader,
-    "SVD_img2vid_Conditioning": SVD_img2vid_Conditioning,
-    "VideoLinearCFGGuidance": VideoLinearCFGGuidance,
-    "VideoTriangleCFGGuidance": VideoTriangleCFGGuidance,
-    "ImageOnlyCheckpointSave": ImageOnlyCheckpointSave,
-    "ConditioningSetAreaPercentageVideo": ConditioningSetAreaPercentageVideo,
-}
+class VideoModelExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            ImageOnlyCheckpointLoader,
+            SVD_img2vid_Conditioning,
+            VideoLinearCFGGuidance,
+            VideoTriangleCFGGuidance,
+            ImageOnlyCheckpointSave,
+            ConditioningSetAreaPercentageVideo,
+        ]
 
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "ImageOnlyCheckpointLoader": "Image Only Checkpoint Loader (img2vid model)",
-}
+
+async def comfy_entrypoint() -> VideoModelExtension:
+    return VideoModelExtension()

requirements.txt

@@ -1,6 +1,6 @@
 comfyui-frontend-package==1.38.13
 comfyui-workflow-templates==0.8.31
-comfyui-embedded-docs==0.4.1
+comfyui-embedded-docs==0.4.0
 torch
 torchsde
 torchvision