converted 6 more files

comfy_extras/v3/nodes_audio.py

@@ -51,7 +51,7 @@ class EmptyLatentAudio(io.ComfyNode):
             inputs=[
                 io.Float.Input("seconds", default=47.6, min=1.0, max=1000.0, step=0.1),
                 io.Int.Input(
-                    id="batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."
+                    "batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."
                 ),
             ],
             outputs=[io.Latent.Output()],

comfy_extras/v3/nodes_hunyuan3d.py

@@ -0,0 +1,672 @@
+from __future__ import annotations
+
+import json
+import os
+import struct
+
+import numpy as np
+import torch
+
+import comfy.model_management
+import folder_paths
+from comfy.cli_args import args
+from comfy.ldm.modules.diffusionmodules.mmdit import (
+    get_1d_sincos_pos_embed_from_grid_torch,
+)
+from comfy_api.latest import io
+
+
+class VOXEL:
+    def __init__(self, data):
+        self.data = data
+
+
+class MESH:
+    def __init__(self, vertices, faces):
+        self.vertices = vertices
+        self.faces = faces
+
+
+def voxel_to_mesh(voxels, threshold=0.5, device=None):
+    if device is None:
+        device = torch.device("cpu")
+    voxels = voxels.to(device)
+
+    binary = (voxels > threshold).float()
+    padded = torch.nn.functional.pad(binary, (1, 1, 1, 1, 1, 1), 'constant', 0)
+
+    D, H, W = binary.shape
+
+    neighbors = torch.tensor([
+        [0, 0, 1],
+        [0, 0, -1],
+        [0, 1, 0],
+        [0, -1, 0],
+        [1, 0, 0],
+        [-1, 0, 0]
+    ], device=device)
+
+    z, y, x = torch.meshgrid(
+        torch.arange(D, device=device),
+        torch.arange(H, device=device),
+        torch.arange(W, device=device),
+        indexing='ij'
+    )
+    voxel_indices = torch.stack([z.flatten(), y.flatten(), x.flatten()], dim=1)
+
+    solid_mask = binary.flatten() > 0
+    solid_indices = voxel_indices[solid_mask]
+
+    corner_offsets = [
+        torch.tensor([
+            [0, 0, 1], [0, 1, 1], [1, 1, 1], [1, 0, 1]
+        ], device=device),
+        torch.tensor([
+            [0, 0, 0], [1, 0, 0], [1, 1, 0], [0, 1, 0]
+        ], device=device),
+        torch.tensor([
+            [0, 1, 0], [1, 1, 0], [1, 1, 1], [0, 1, 1]
+        ], device=device),
+        torch.tensor([
+            [0, 0, 0], [0, 0, 1], [1, 0, 1], [1, 0, 0]
+        ], device=device),
+        torch.tensor([
+            [1, 0, 1], [1, 1, 1], [1, 1, 0], [1, 0, 0]
+        ], device=device),
+        torch.tensor([
+            [0, 1, 0], [0, 1, 1], [0, 0, 1], [0, 0, 0]
+        ], device=device)
+    ]
+
+    all_vertices = []
+    all_indices = []
+
+    vertex_count = 0
+
+    for face_idx, offset in enumerate(neighbors):
+        neighbor_indices = solid_indices + offset
+
+        padded_indices = neighbor_indices + 1
+
+        is_exposed = padded[
+            padded_indices[:, 0],
+            padded_indices[:, 1],
+            padded_indices[:, 2]
+        ] == 0
+
+        if not is_exposed.any():
+            continue
+
+        exposed_indices = solid_indices[is_exposed]
+
+        corners = corner_offsets[face_idx].unsqueeze(0)
+
+        face_vertices = exposed_indices.unsqueeze(1) + corners
+
+        all_vertices.append(face_vertices.reshape(-1, 3))
+
+        num_faces = exposed_indices.shape[0]
+        face_indices = torch.arange(
+            vertex_count,
+            vertex_count + 4 * num_faces,
+            device=device
+        ).reshape(-1, 4)
+
+        all_indices.append(torch.stack([face_indices[:, 0], face_indices[:, 1], face_indices[:, 2]], dim=1))
+        all_indices.append(torch.stack([face_indices[:, 0], face_indices[:, 2], face_indices[:, 3]], dim=1))
+
+        vertex_count += 4 * num_faces
+
+    if len(all_vertices) > 0:
+        vertices = torch.cat(all_vertices, dim=0)
+        faces = torch.cat(all_indices, dim=0)
+    else:
+        vertices = torch.zeros((1, 3))
+        faces = torch.zeros((1, 3))
+
+    v_min = 0
+    v_max = max(voxels.shape)
+
+    vertices = vertices - (v_min + v_max) / 2
+
+    scale = (v_max - v_min) / 2
+    if scale > 0:
+        vertices = vertices / scale
+
+    vertices = torch.fliplr(vertices)
+    return vertices, faces
+
+def voxel_to_mesh_surfnet(voxels, threshold=0.5, device=None):
+    if device is None:
+        device = torch.device("cpu")
+    voxels = voxels.to(device)
+
+    D, H, W = voxels.shape
+
+    padded = torch.nn.functional.pad(voxels, (1, 1, 1, 1, 1, 1), 'constant', 0)
+    z, y, x = torch.meshgrid(
+        torch.arange(D, device=device),
+        torch.arange(H, device=device),
+        torch.arange(W, device=device),
+        indexing='ij'
+    )
+    cell_positions = torch.stack([z.flatten(), y.flatten(), x.flatten()], dim=1)
+
+    corner_offsets = torch.tensor([
+        [0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0],
+        [0, 0, 1], [1, 0, 1], [0, 1, 1], [1, 1, 1]
+    ], device=device)
+
+    corner_values = torch.zeros((cell_positions.shape[0], 8), device=device)
+    for c, (dz, dy, dx) in enumerate(corner_offsets):
+        corner_values[:, c] = padded[
+            cell_positions[:, 0] + dz,
+            cell_positions[:, 1] + dy,
+            cell_positions[:, 2] + dx
+        ]
+
+    corner_signs = corner_values > threshold
+    has_inside = torch.any(corner_signs, dim=1)
+    has_outside = torch.any(~corner_signs, dim=1)
+    contains_surface = has_inside & has_outside
+
+    active_cells = cell_positions[contains_surface]
+    active_signs = corner_signs[contains_surface]
+    active_values = corner_values[contains_surface]
+
+    if active_cells.shape[0] == 0:
+        return torch.zeros((0, 3), device=device), torch.zeros((0, 3), dtype=torch.long, device=device)
+
+    edges = torch.tensor([
+        [0, 1], [0, 2], [0, 4], [1, 3],
+        [1, 5], [2, 3], [2, 6], [3, 7],
+        [4, 5], [4, 6], [5, 7], [6, 7]
+    ], device=device)
+
+    cell_vertices = {}
+    progress = comfy.utils.ProgressBar(100)
+
+    for edge_idx, (e1, e2) in enumerate(edges):
+        progress.update(1)
+        crossing = active_signs[:, e1] != active_signs[:, e2]
+        if not crossing.any():
+            continue
+
+        cell_indices = torch.nonzero(crossing, as_tuple=True)[0]
+
+        v1 = active_values[cell_indices, e1]
+        v2 = active_values[cell_indices, e2]
+
+        t = torch.zeros_like(v1, device=device)
+        denom = v2 - v1
+        valid = denom != 0
+        t[valid] = (threshold - v1[valid]) / denom[valid]
+        t[~valid] = 0.5
+
+        p1 = corner_offsets[e1].float()
+        p2 = corner_offsets[e2].float()
+
+        intersection = p1.unsqueeze(0) + t.unsqueeze(1) * (p2.unsqueeze(0) - p1.unsqueeze(0))
+
+        for i, point in zip(cell_indices.tolist(), intersection):
+            if i not in cell_vertices:
+                cell_vertices[i] = []
+            cell_vertices[i].append(point)
+
+    # Calculate the final vertices as the average of intersection points for each cell
+    vertices = []
+    vertex_lookup = {}
+
+    vert_progress_mod = round(len(cell_vertices)/50)
+
+    for i, points in cell_vertices.items():
+        if not i % vert_progress_mod:
+            progress.update(1)
+
+        if points:
+            vertex = torch.stack(points).mean(dim=0)
+            vertex = vertex + active_cells[i].float()
+            vertex_lookup[tuple(active_cells[i].tolist())] = len(vertices)
+            vertices.append(vertex)
+
+    if not vertices:
+        return torch.zeros((0, 3), device=device), torch.zeros((0, 3), dtype=torch.long, device=device)
+
+    final_vertices = torch.stack(vertices)
+
+    inside_corners_mask = active_signs
+    outside_corners_mask = ~active_signs
+
+    inside_counts = inside_corners_mask.sum(dim=1, keepdim=True).float()
+    outside_counts = outside_corners_mask.sum(dim=1, keepdim=True).float()
+
+    inside_pos = torch.zeros((active_cells.shape[0], 3), device=device)
+    outside_pos = torch.zeros((active_cells.shape[0], 3), device=device)
+
+    for i in range(8):
+        mask_inside = inside_corners_mask[:, i].unsqueeze(1)
+        mask_outside = outside_corners_mask[:, i].unsqueeze(1)
+        inside_pos += corner_offsets[i].float().unsqueeze(0) * mask_inside
+        outside_pos += corner_offsets[i].float().unsqueeze(0) * mask_outside
+
+    inside_pos /= inside_counts
+    outside_pos /= outside_counts
+    gradients = inside_pos - outside_pos
+
+    pos_dirs = torch.tensor([
+        [1, 0, 0],
+        [0, 1, 0],
+        [0, 0, 1]
+    ], device=device)
+
+    cross_products = [
+        torch.linalg.cross(pos_dirs[i].float(), pos_dirs[j].float())
+        for i in range(3) for j in range(i+1, 3)
+    ]
+
+    faces = []
+    all_keys = set(vertex_lookup.keys())
+
+    face_progress_mod = round(len(active_cells)/38*3)
+
+    for pair_idx, (i, j) in enumerate([(0,1), (0,2), (1,2)]):
+        dir_i = pos_dirs[i]
+        dir_j = pos_dirs[j]
+        cross_product = cross_products[pair_idx]
+
+        ni_positions = active_cells + dir_i
+        nj_positions = active_cells + dir_j
+        diag_positions = active_cells + dir_i + dir_j
+
+        alignments = torch.matmul(gradients, cross_product)
+
+        valid_quads = []
+        quad_indices = []
+
+        for idx, active_cell in enumerate(active_cells):
+            if not idx % face_progress_mod:
+                progress.update(1)
+            cell_key = tuple(active_cell.tolist())
+            ni_key = tuple(ni_positions[idx].tolist())
+            nj_key = tuple(nj_positions[idx].tolist())
+            diag_key = tuple(diag_positions[idx].tolist())
+
+            if cell_key in all_keys and ni_key in all_keys and nj_key in all_keys and diag_key in all_keys:
+                v0 = vertex_lookup[cell_key]
+                v1 = vertex_lookup[ni_key]
+                v2 = vertex_lookup[nj_key]
+                v3 = vertex_lookup[diag_key]
+
+                valid_quads.append((v0, v1, v2, v3))
+                quad_indices.append(idx)
+
+        for q_idx, (v0, v1, v2, v3) in enumerate(valid_quads):
+            cell_idx = quad_indices[q_idx]
+            if alignments[cell_idx] > 0:
+                faces.append(torch.tensor([v0, v1, v3], device=device, dtype=torch.long))
+                faces.append(torch.tensor([v0, v3, v2], device=device, dtype=torch.long))
+            else:
+                faces.append(torch.tensor([v0, v3, v1], device=device, dtype=torch.long))
+                faces.append(torch.tensor([v0, v2, v3], device=device, dtype=torch.long))
+
+    if faces:
+        faces = torch.stack(faces)
+    else:
+        faces = torch.zeros((0, 3), dtype=torch.long, device=device)
+
+    v_min = 0
+    v_max = max(D, H, W)
+
+    final_vertices = final_vertices - (v_min + v_max) / 2
+
+    scale = (v_max - v_min) / 2
+    if scale > 0:
+        final_vertices = final_vertices / scale
+
+    final_vertices = torch.fliplr(final_vertices)
+
+    return final_vertices, faces
+
+
+def save_glb(vertices, faces, filepath, metadata=None):
+    """
+    Save PyTorch tensor vertices and faces as a GLB file without external dependencies.
+
+    Parameters:
+    vertices: torch.Tensor of shape (N, 3) - The vertex coordinates
+    faces: torch.Tensor of shape (M, 3) - The face indices (triangle faces)
+    filepath: str - Output filepath (should end with .glb)
+    """
+
+    # Convert tensors to numpy arrays
+    vertices_np = vertices.cpu().numpy().astype(np.float32)
+    faces_np = faces.cpu().numpy().astype(np.uint32)
+
+    vertices_buffer = vertices_np.tobytes()
+    indices_buffer = faces_np.tobytes()
+
+    def pad_to_4_bytes(buffer):
+        padding_length = (4 - (len(buffer) % 4)) % 4
+        return buffer + b'\x00' * padding_length
+
+    vertices_buffer_padded = pad_to_4_bytes(vertices_buffer)
+    indices_buffer_padded = pad_to_4_bytes(indices_buffer)
+
+    buffer_data = vertices_buffer_padded + indices_buffer_padded
+
+    vertices_byte_length = len(vertices_buffer)
+    vertices_byte_offset = 0
+    indices_byte_length = len(indices_buffer)
+    indices_byte_offset = len(vertices_buffer_padded)
+
+    gltf = {
+        "asset": {"version": "2.0", "generator": "ComfyUI"},
+        "buffers": [
+            {
+                "byteLength": len(buffer_data)
+            }
+        ],
+        "bufferViews": [
+            {
+                "buffer": 0,
+                "byteOffset": vertices_byte_offset,
+                "byteLength": vertices_byte_length,
+                "target": 34962  # ARRAY_BUFFER
+            },
+            {
+                "buffer": 0,
+                "byteOffset": indices_byte_offset,
+                "byteLength": indices_byte_length,
+                "target": 34963  # ELEMENT_ARRAY_BUFFER
+            }
+        ],
+        "accessors": [
+            {
+                "bufferView": 0,
+                "byteOffset": 0,
+                "componentType": 5126,  # FLOAT
+                "count": len(vertices_np),
+                "type": "VEC3",
+                "max": vertices_np.max(axis=0).tolist(),
+                "min": vertices_np.min(axis=0).tolist()
+            },
+            {
+                "bufferView": 1,
+                "byteOffset": 0,
+                "componentType": 5125,  # UNSIGNED_INT
+                "count": faces_np.size,
+                "type": "SCALAR"
+            }
+        ],
+        "meshes": [
+            {
+                "primitives": [
+                    {
+                        "attributes": {
+                            "POSITION": 0
+                        },
+                        "indices": 1,
+                        "mode": 4  # TRIANGLES
+                    }
+                ]
+            }
+        ],
+        "nodes": [
+            {
+                "mesh": 0
+            }
+        ],
+        "scenes": [
+            {
+                "nodes": [0]
+            }
+        ],
+        "scene": 0
+    }
+
+    if metadata is not None:
+        gltf["asset"]["extras"] = metadata
+
+    # Convert the JSON to bytes
+    gltf_json = json.dumps(gltf).encode('utf8')
+
+    def pad_json_to_4_bytes(buffer):
+        padding_length = (4 - (len(buffer) % 4)) % 4
+        return buffer + b' ' * padding_length
+
+    gltf_json_padded = pad_json_to_4_bytes(gltf_json)
+
+    # Create the GLB header
+    # Magic glTF
+    glb_header = struct.pack('<4sII', b'glTF', 2, 12 + 8 + len(gltf_json_padded) + 8 + len(buffer_data))
+
+    # Create JSON chunk header (chunk type 0)
+    json_chunk_header = struct.pack('<II', len(gltf_json_padded), 0x4E4F534A)  # "JSON" in little endian
+
+    # Create BIN chunk header (chunk type 1)
+    bin_chunk_header = struct.pack('<II', len(buffer_data), 0x004E4942)  # "BIN\0" in little endian
+
+    # Write the GLB file
+    with open(filepath, 'wb') as f:
+        f.write(glb_header)
+        f.write(json_chunk_header)
+        f.write(gltf_json_padded)
+        f.write(bin_chunk_header)
+        f.write(buffer_data)
+
+    return filepath
+
+
+class EmptyLatentHunyuan3Dv2(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="EmptyLatentHunyuan3Dv2_V3",
+            category="latent/3d",
+            inputs=[
+                io.Int.Input("resolution", default=3072, min=1, max=8192),
+                io.Int.Input("batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch.")
+            ],
+            outputs=[
+                io.Latent.Output()
+            ]
+        )
+
+    @classmethod
+    def execute(cls, resolution, batch_size):
+        latent = torch.zeros([batch_size, 64, resolution], device=comfy.model_management.intermediate_device())
+        return io.NodeOutput({"samples": latent, "type": "hunyuan3dv2"})
+
+
+class Hunyuan3Dv2Conditioning(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="Hunyuan3Dv2Conditioning_V3",
+            category="conditioning/video_models",
+            inputs=[
+                io.ClipVisionOutput.Input("clip_vision_output")
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative")
+            ]
+        )
+
+    @classmethod
+    def execute(cls, clip_vision_output):
+        embeds = clip_vision_output.last_hidden_state
+        positive = [[embeds, {}]]
+        negative = [[torch.zeros_like(embeds), {}]]
+        return io.NodeOutput(positive, negative)
+
+
+class Hunyuan3Dv2ConditioningMultiView(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="Hunyuan3Dv2ConditioningMultiView_V3",
+            category="conditioning/video_models",
+            inputs=[
+                io.ClipVisionOutput.Input("front", optional=True),
+                io.ClipVisionOutput.Input("left", optional=True),
+                io.ClipVisionOutput.Input("back", optional=True),
+                io.ClipVisionOutput.Input("right", optional=True)
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative")
+            ]
+        )
+
+    @classmethod
+    def execute(cls, front=None, left=None, back=None, right=None):
+        all_embeds = [front, left, back, right]
+        out = []
+        pos_embeds = None
+        for i, e in enumerate(all_embeds):
+            if e is not None:
+                if pos_embeds is None:
+                    pos_embeds = get_1d_sincos_pos_embed_from_grid_torch(e.last_hidden_state.shape[-1], torch.arange(4))
+                out.append(e.last_hidden_state + pos_embeds[i].reshape(1, 1, -1))
+
+        embeds = torch.cat(out, dim=1)
+        positive = [[embeds, {}]]
+        negative = [[torch.zeros_like(embeds), {}]]
+        return io.NodeOutput(positive, negative)
+
+
+class SaveGLB(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SaveGLB_V3",
+            category="3d",
+            is_output_node=True,
+            inputs=[
+                io.Mesh.Input("mesh"),
+                io.String.Input("filename_prefix", default="mesh/ComfyUI")
+            ],
+            outputs=[],
+            hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo]
+        )
+
+    @classmethod
+    def execute(cls, mesh, filename_prefix):
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, folder_paths.get_output_directory())
+        results = []
+
+        metadata = {}
+        if not args.disable_metadata:
+            if cls.hidden.prompt is not None:
+                metadata["prompt"] = json.dumps(cls.hidden.prompt)
+            if cls.hidden.extra_pnginfo is not None:
+                for x in cls.hidden.extra_pnginfo:
+                    metadata[x] = json.dumps(cls.hidden.extra_pnginfo[x])
+
+        for i in range(mesh.vertices.shape[0]):
+            f = f"{filename}_{counter:05}_.glb"
+            save_glb(mesh.vertices[i], mesh.faces[i], os.path.join(full_output_folder, f), metadata)
+            results.append({
+                "filename": f,
+                "subfolder": subfolder,
+                "type": "output"
+            })
+            counter += 1
+
+        return io.NodeOutput(ui={"ui": {"3d": results}})  # TODO: do we need an additional type of preview for this?
+
+
+class VAEDecodeHunyuan3D(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="VAEDecodeHunyuan3D_V3",
+            category="latent/3d",
+            inputs=[
+                io.Latent.Input("samples"),
+                io.Vae.Input("vae"),
+                io.Int.Input("num_chunks", default=8000, min=1000, max=500000),
+                io.Int.Input("octree_resolution", default=256, min=16, max=512)
+            ],
+            outputs=[
+                io.Voxel.Output()
+            ]
+        )
+
+    @classmethod
+    def execute(cls, vae, samples, num_chunks, octree_resolution):
+        voxels = VOXEL(vae.decode(samples["samples"], vae_options={"num_chunks": num_chunks, "octree_resolution": octree_resolution}))
+        return io.NodeOutput(voxels)
+
+
+class VoxelToMesh(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="VoxelToMesh_V3",
+            category="3d",
+            inputs=[
+                io.Voxel.Input("voxel"),
+                io.Combo.Input("algorithm", options=["surface net", "basic"]),
+                io.Float.Input("threshold", default=0.6, min=-1.0, max=1.0, step=0.01)
+            ],
+            outputs=[
+                io.Mesh.Output()
+            ]
+        )
+
+    @classmethod
+    def execute(cls, voxel, algorithm, threshold):
+        vertices = []
+        faces = []
+
+        if algorithm == "basic":
+            mesh_function = voxel_to_mesh
+        elif algorithm == "surface net":
+            mesh_function = voxel_to_mesh_surfnet
+
+        for x in voxel.data:
+            v, f = mesh_function(x, threshold=threshold, device=None)
+            vertices.append(v)
+            faces.append(f)
+
+        return io.NodeOutput(MESH(torch.stack(vertices), torch.stack(faces)))
+
+
+class VoxelToMeshBasic(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="VoxelToMeshBasic_V3",
+            category="3d",
+            inputs=[
+                io.Voxel.Input("voxel"),
+                io.Float.Input("threshold", default=0.6, min=-1.0, max=1.0, step=0.01)
+            ],
+            outputs=[
+                io.Mesh.Output()
+            ]
+        )
+
+    @classmethod
+    def execute(cls, voxel, threshold):
+        vertices = []
+        faces = []
+        for x in voxel.data:
+            v, f = voxel_to_mesh(x, threshold=threshold, device=None)
+            vertices.append(v)
+            faces.append(f)
+
+        return io.NodeOutput(MESH(torch.stack(vertices), torch.stack(faces)))
+
+
+NODES_LIST = [
+    EmptyLatentHunyuan3Dv2,
+    Hunyuan3Dv2Conditioning,
+    Hunyuan3Dv2ConditioningMultiView,
+    SaveGLB,
+    VAEDecodeHunyuan3D,
+    VoxelToMesh,
+    VoxelToMeshBasic,
+]

comfy_extras/v3/nodes_lt.py

@@ -127,12 +127,12 @@ class LTXVAddGuide(io.ComfyNode):
                 io.Vae.Input("vae"),
                 io.Latent.Input("latent"),
                 io.Image.Input(
-                    id="image",
+                    "image",
                     tooltip="Image or video to condition the latent video on. Must be 8*n + 1 frames. "
                             "If the video is not 8*n + 1 frames, it will be cropped to the nearest 8*n + 1 frames.",
                 ),
                 io.Int.Input(
-                    id="frame_idx",
+                    "frame_idx",
                     default=0,
                     min=-9999,
                     max=9999,

comfy_extras/v3/nodes_mahiro.py

@@ -0,0 +1,51 @@
+from __future__ import annotations
+
+import torch
+import torch.nn.functional as F
+
+from comfy_api.latest import io
+
+
+class Mahiro(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="Mahiro_V3",
+            display_name="Mahiro is so cute that she deserves a better guidance function!! (。・ω・。) _V3",
+            category="_for_testing",
+            description="Modify the guidance to scale more on the 'direction' of the positive prompt rather than the difference between the negative prompt.",
+            is_experimental=True,
+            inputs=[
+                io.Model.Input("model")
+            ],
+            outputs=[
+                io.Model.Output(display_name="patched_model")
+            ]
+        )
+
+    @classmethod
+    def execute(cls, model):
+        m = model.clone()
+        def mahiro_normd(args):
+            scale: float = args['cond_scale']
+            cond_p: torch.Tensor = args['cond_denoised']
+            uncond_p: torch.Tensor = args['uncond_denoised']
+            #naive leap
+            leap = cond_p * scale
+            #sim with uncond leap
+            u_leap = uncond_p * scale
+            cfg = args["denoised"]
+            merge = (leap + cfg) / 2
+            normu = torch.sqrt(u_leap.abs()) * u_leap.sign()
+            normm = torch.sqrt(merge.abs()) * merge.sign()
+            sim = F.cosine_similarity(normu, normm).mean()
+            simsc = 2 * (sim+1)
+            wm = (simsc*cfg + (4-simsc)*leap) / 4
+            return wm
+        m.set_model_sampler_post_cfg_function(mahiro_normd)
+        return io.NodeOutput(m)
+
+
+NODES_LIST = [
+    Mahiro,
+]

comfy_extras/v3/nodes_model_advanced.py

@@ -17,7 +17,7 @@ class LCM(comfy.model_sampling.EPS):
         x0 = model_input - model_output * sigma
 
         sigma_data = 0.5
-        scaled_timestep = timestep * 10.0 #timestep_scaling
+        scaled_timestep = timestep * 10.0 # timestep_scaling
 
         c_skip = sigma_data**2 / (scaled_timestep**2 + sigma_data**2)
         c_out = scaled_timestep / (scaled_timestep**2 + sigma_data**2) ** 0.5

comfy_extras/v3/nodes_model_merging.py

@@ -0,0 +1,422 @@
+from __future__ import annotations
+
+import json
+import os
+
+import torch
+
+import comfy.model_base
+import comfy.model_management
+import comfy.model_sampling
+import comfy.sd
+import comfy.utils
+import folder_paths
+from comfy.cli_args import args
+from comfy_api.latest import io
+
+
+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)
+    prompt_info = ""
+    if prompt is not None:
+        prompt_info = json.dumps(prompt)
+
+    metadata = {}
+
+    enable_modelspec = True
+    if isinstance(model.model, comfy.model_base.SDXL):
+        if isinstance(model.model, comfy.model_base.SDXL_instructpix2pix):
+            metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-edit"
+        else:
+            metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-base"
+    elif isinstance(model.model, comfy.model_base.SDXLRefiner):
+        metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-refiner"
+    elif isinstance(model.model, comfy.model_base.SVD_img2vid):
+        metadata["modelspec.architecture"] = "stable-video-diffusion-img2vid-v1"
+    elif isinstance(model.model, comfy.model_base.SD3):
+        metadata["modelspec.architecture"] = "stable-diffusion-v3-medium" #TODO: other SD3 variants
+    else:
+        enable_modelspec = False
+
+    if enable_modelspec:
+        metadata["modelspec.sai_model_spec"] = "1.0.0"
+        metadata["modelspec.implementation"] = "sgm"
+        metadata["modelspec.title"] = "{} {}".format(filename, counter)
+
+    #TODO:
+    # "stable-diffusion-v1", "stable-diffusion-v1-inpainting", "stable-diffusion-v2-512",
+    # "stable-diffusion-v2-768-v", "stable-diffusion-v2-unclip-l", "stable-diffusion-v2-unclip-h",
+    # "v2-inpainting"
+
+    extra_keys = {}
+    model_sampling = model.get_model_object("model_sampling")
+    if isinstance(model_sampling, comfy.model_sampling.ModelSamplingContinuousEDM):
+        if isinstance(model_sampling, comfy.model_sampling.V_PREDICTION):
+            extra_keys["edm_vpred.sigma_max"] = torch.tensor(model_sampling.sigma_max).float()
+            extra_keys["edm_vpred.sigma_min"] = torch.tensor(model_sampling.sigma_min).float()
+
+    if model.model.model_type == comfy.model_base.ModelType.EPS:
+        metadata["modelspec.predict_key"] = "epsilon"
+    elif model.model.model_type == comfy.model_base.ModelType.V_PREDICTION:
+        metadata["modelspec.predict_key"] = "v"
+        extra_keys["v_pred"] = torch.tensor([])
+        if getattr(model_sampling, "zsnr", False):
+            extra_keys["ztsnr"] = torch.tensor([])
+
+    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])
+
+    output_checkpoint = f"{filename}_{counter:05}_.safetensors"
+    output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
+
+    comfy.sd.save_checkpoint(output_checkpoint, model, clip, vae, clip_vision, metadata=metadata, extra_keys=extra_keys)
+
+
+class CheckpointSave(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CheckpointSave_V3",
+            display_name="Save Checkpoint _V3",
+            category="advanced/model_merging",
+            is_output_node=True,
+            inputs=[
+                io.Model.Input("model"),
+                io.Clip.Input("clip"),
+                io.Vae.Input("vae"),
+                io.String.Input("filename_prefix", default="checkpoints/ComfyUI")
+            ],
+            outputs=[],
+            hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo]
+        )
+
+    @classmethod
+    def execute(cls, model, clip, vae, filename_prefix):
+        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()
+
+
+class CLIPAdd(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CLIPMergeAdd_V3",
+            category="advanced/model_merging",
+            inputs=[
+                io.Clip.Input("clip1"),
+                io.Clip.Input("clip2")
+            ],
+            outputs=[
+                io.Clip.Output()
+            ]
+        )
+
+    @classmethod
+    def execute(cls, clip1, clip2):
+        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 io.NodeOutput(m)
+
+
+class CLIPMergeSimple(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CLIPMergeSimple_V3",
+            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()
+            ]
+        )
+
+    @classmethod
+    def execute(cls, clip1, clip2, ratio):
+        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 io.NodeOutput(m)
+
+
+class CLIPSave(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CLIPSave_V3",
+            category="advanced/model_merging",
+            is_output_node=True,
+            inputs=[
+                io.Clip.Input("clip"),
+                io.String.Input("filename_prefix", default="clip/ComfyUI")
+            ],
+            outputs=[],
+            hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo]
+        )
+
+    @classmethod
+    def execute(cls, clip, filename_prefix):
+        prompt_info = ""
+        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 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()
+
+        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 = {}
+            for x in k:
+                current_clip_sd[x] = clip_sd.pop(x)
+            if len(current_clip_sd) == 0:
+                continue
+
+            p = prefix[:-1]
+            replace_prefix = {}
+            filename_prefix_ = filename_prefix
+            if len(p) > 0:
+                filename_prefix_ = "{}_{}".format(filename_prefix_, p)
+                replace_prefix[prefix] = ""
+            replace_prefix["transformer."] = ""
+
+            full_output_folder, filename, counter, subfolder, filename_prefix_ = folder_paths.get_save_image_path(filename_prefix_, folder_paths.get_output_directory())
+
+            output_checkpoint = f"{filename}_{counter:05}_.safetensors"
+            output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
+
+            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 io.NodeOutput()
+
+
+class CLIPSubtract(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CLIPMergeSubtract_V3",
+            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()
+            ]
+        )
+
+    @classmethod
+    def execute(cls, clip1, clip2, multiplier):
+        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 io.NodeOutput(m)
+
+
+class ModelAdd(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ModelMergeAdd_V3",
+            category="advanced/model_merging",
+            inputs=[
+                io.Model.Input("model1"),
+                io.Model.Input("model2")
+            ],
+            outputs=[
+                io.Model.Output()
+            ]
+        )
+
+    @classmethod
+    def execute(cls, model1, model2):
+        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 io.NodeOutput(m)
+
+
+class ModelMergeBlocks(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ModelMergeBlocks_V3",
+            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()
+            ]
+        )
+
+    @classmethod
+    def execute(cls, model1, model2, **kwargs):
+        m = model1.clone()
+        kp = model2.get_key_patches("diffusion_model.")
+        default_ratio = next(iter(kwargs.values()))
+
+        for k in kp:
+            ratio = default_ratio
+            k_unet = k[len("diffusion_model."):]
+
+            last_arg_size = 0
+            for arg in kwargs:
+                if k_unet.startswith(arg) and last_arg_size < len(arg):
+                    ratio = kwargs[arg]
+                    last_arg_size = len(arg)
+
+            m.add_patches({k: kp[k]}, 1.0 - ratio, ratio)
+        return io.NodeOutput(m)
+
+
+class ModelMergeSimple(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ModelMergeSimple_V3",
+            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()
+            ]
+        )
+
+    @classmethod
+    def execute(cls, model1, model2, ratio):
+        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 io.NodeOutput(m)
+
+
+class ModelSave(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ModelSave_V3",
+            category="advanced/model_merging",
+            is_output_node=True,
+            inputs=[
+                io.Model.Input("model"),
+                io.String.Input("filename_prefix", default="diffusion_models/ComfyUI")
+            ],
+            outputs=[],
+            hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo]
+        )
+
+    @classmethod
+    def execute(cls, model, filename_prefix):
+        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()
+
+
+class ModelSubtract(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ModelMergeSubtract_V3",
+            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()
+            ]
+        )
+
+    @classmethod
+    def execute(cls, model1, model2, multiplier):
+        m = model1.clone()
+        kp = model2.get_key_patches("diffusion_model.")
+        for k in kp:
+            m.add_patches({k: kp[k]}, - multiplier, multiplier)
+        return io.NodeOutput(m)
+
+
+class VAESave(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="VAESave_V3",
+            category="advanced/model_merging",
+            is_output_node=True,
+            inputs=[
+                io.Vae.Input("vae"),
+                io.String.Input("filename_prefix", default="vae/ComfyUI_vae")
+            ],
+            outputs=[],
+            hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo]
+        )
+
+    @classmethod
+    def execute(cls, vae, filename_prefix):
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, folder_paths.get_output_directory())
+        prompt_info = ""
+        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 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 io.NodeOutput()
+
+
+NODES_LIST = [
+    CheckpointSave,
+    CLIPAdd,
+    CLIPMergeSimple,
+    CLIPSave,
+    CLIPSubtract,
+    ModelAdd,
+    ModelMergeBlocks,
+    ModelMergeSimple,
+    ModelSave,
+    ModelSubtract,
+    VAESave,
+]

comfy_extras/v3/nodes_model_merging_model_specific.py

@@ -0,0 +1,399 @@
+from __future__ import annotations
+
+from comfy_api.latest import io
+from comfy_extras.v3.nodes_model_merging import ModelMergeBlocks
+
+
+class ModelMergeAuraflow(ModelMergeBlocks):
+    @classmethod
+    def define_schema(cls):
+        inputs = [
+            io.Model.Input("model1"),
+            io.Model.Input("model2"),
+            io.Float.Input("init_x_linear.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("positional_encoding", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("cond_seq_linear.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("register_tokens", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("t_embedder.", default=1.0, min=0.0, max=1.0, step=0.01)
+        ]
+
+        for i in range(4):
+            inputs.append(io.Float.Input(f"double_layers.{i}.", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        for i in range(32):
+            inputs.append(io.Float.Input(f"single_layers.{i}.", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        inputs.extend([
+            io.Float.Input("modF.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("final_linear.", default=1.0, min=0.0, max=1.0, step=0.01)
+        ])
+
+        return io.Schema(
+            node_id="ModelMergeAuraflow_V3",
+            category="advanced/model_merging/model_specific",
+            inputs=inputs,
+            outputs=[
+                io.Model.Output(),
+            ]
+        )
+
+
+class ModelMergeCosmos14B(ModelMergeBlocks):
+    @classmethod
+    def define_schema(cls):
+        inputs = [
+            io.Model.Input("model1"),
+            io.Model.Input("model2"),
+            io.Float.Input("pos_embedder.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("extra_pos_embedder.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("x_embedder.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("t_embedder.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("affline_norm.", default=1.0, min=0.0, max=1.0, step=0.01)
+        ]
+
+        for i in range(36):
+            inputs.append(io.Float.Input(f"blocks.block{i}.", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        inputs.append(io.Float.Input("final_layer.", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        return io.Schema(
+            node_id="ModelMergeCosmos14B_V3",
+            category="advanced/model_merging/model_specific",
+            inputs=inputs,
+            outputs=[
+                io.Model.Output(),
+            ]
+        )
+
+
+class ModelMergeCosmos7B(ModelMergeBlocks):
+    @classmethod
+    def define_schema(cls):
+        inputs = [
+            io.Model.Input("model1"),
+            io.Model.Input("model2"),
+            io.Float.Input("pos_embedder.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("extra_pos_embedder.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("x_embedder.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("t_embedder.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("affline_norm.", default=1.0, min=0.0, max=1.0, step=0.01)
+        ]
+
+        for i in range(28):
+            inputs.append(io.Float.Input(f"blocks.block{i}.", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        inputs.append(io.Float.Input("final_layer.", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        return io.Schema(
+            node_id="ModelMergeCosmos7B_V3",
+            category="advanced/model_merging/model_specific",
+            inputs=inputs,
+            outputs=[
+                io.Model.Output(),
+            ]
+        )
+
+
+class ModelMergeCosmosPredict2_14B(ModelMergeBlocks):
+    @classmethod
+    def define_schema(cls):
+        inputs = [
+            io.Model.Input("model1"),
+            io.Model.Input("model2"),
+            io.Float.Input("pos_embedder.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("x_embedder.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("t_embedder.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("t_embedding_norm.", default=1.0, min=0.0, max=1.0, step=0.01)
+        ]
+
+        for i in range(36):
+            inputs.append(io.Float.Input(f"blocks.{i}.", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        inputs.append(io.Float.Input("final_layer.", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        return io.Schema(
+            node_id="ModelMergeCosmosPredict2_14B_V3",
+            category="advanced/model_merging/model_specific",
+            inputs=inputs,
+            outputs=[
+                io.Model.Output(),
+            ]
+        )
+
+
+class ModelMergeCosmosPredict2_2B(ModelMergeBlocks):
+    @classmethod
+    def define_schema(cls):
+        inputs = [
+            io.Model.Input("model1"),
+            io.Model.Input("model2"),
+            io.Float.Input("pos_embedder.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("x_embedder.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("t_embedder.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("t_embedding_norm.", default=1.0, min=0.0, max=1.0, step=0.01)
+        ]
+
+        for i in range(28):
+            inputs.append(io.Float.Input(f"blocks.{i}.", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        inputs.append(io.Float.Input("final_layer.", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        return io.Schema(
+            node_id="ModelMergeCosmosPredict2_2B_V3",
+            category="advanced/model_merging/model_specific",
+            inputs=inputs,
+            outputs=[
+                io.Model.Output(),
+            ]
+        )
+
+
+class ModelMergeFlux1(ModelMergeBlocks):
+    @classmethod
+    def define_schema(cls):
+        inputs = [
+            io.Model.Input("model1"),
+            io.Model.Input("model2"),
+            io.Float.Input("img_in.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("time_in.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("guidance_in", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("vector_in.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("txt_in.", default=1.0, min=0.0, max=1.0, step=0.01)
+        ]
+
+        for i in range(19):
+            inputs.append(io.Float.Input(f"double_blocks.{i}.", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        for i in range(38):
+            inputs.append(io.Float.Input(f"single_blocks.{i}.", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        inputs.append(io.Float.Input("final_layer.", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        return io.Schema(
+            node_id="ModelMergeFlux1_V3",
+            category="advanced/model_merging/model_specific",
+            inputs=inputs,
+            outputs=[
+                io.Model.Output(),
+            ]
+        )
+
+
+class ModelMergeLTXV(ModelMergeBlocks):
+    @classmethod
+    def define_schema(cls):
+        inputs = [
+            io.Model.Input("model1"),
+            io.Model.Input("model2"),
+            io.Float.Input("patchify_proj.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("adaln_single.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("caption_projection.", default=1.0, min=0.0, max=1.0, step=0.01)
+        ]
+
+        for i in range(28):
+            inputs.append(io.Float.Input(f"transformer_blocks.{i}.", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        inputs.extend([
+            io.Float.Input("scale_shift_table", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("proj_out.", default=1.0, min=0.0, max=1.0, step=0.01)
+        ])
+
+        return io.Schema(
+            node_id="ModelMergeLTXV_V3",
+            category="advanced/model_merging/model_specific",
+            inputs=inputs,
+            outputs=[
+                io.Model.Output(),
+            ]
+        )
+
+
+class ModelMergeMochiPreview(ModelMergeBlocks):
+    @classmethod
+    def define_schema(cls):
+        inputs = [
+            io.Model.Input("model1"),
+            io.Model.Input("model2"),
+            io.Float.Input("pos_frequencies.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("t_embedder.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("t5_y_embedder.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("t5_yproj.", default=1.0, min=0.0, max=1.0, step=0.01)
+        ]
+
+        for i in range(48):
+            inputs.append(io.Float.Input(f"blocks.{i}.", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        inputs.append(io.Float.Input("final_layer.", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        return io.Schema(
+            node_id="ModelMergeMochiPreview_V3",
+            category="advanced/model_merging/model_specific",
+            inputs=inputs,
+            outputs=[
+                io.Model.Output(),
+            ]
+        )
+
+
+class ModelMergeSD1(ModelMergeBlocks):
+    @classmethod
+    def define_schema(cls):
+        inputs = [
+            io.Model.Input("model1"),
+            io.Model.Input("model2"),
+            io.Float.Input("time_embed.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("label_emb.", default=1.0, min=0.0, max=1.0, step=0.01)
+        ]
+
+        for i in range(12):
+            inputs.append(io.Float.Input(f"input_blocks.{i}.", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        for i in range(3):
+            inputs.append(io.Float.Input(f"middle_block.{i}.", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        for i in range(12):
+            inputs.append(io.Float.Input(f"output_blocks.{i}.", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        inputs.append(io.Float.Input("out.", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        return io.Schema(
+            node_id="ModelMergeSD1_V3",
+            category="advanced/model_merging/model_specific",
+            inputs=inputs,
+            outputs=[
+                io.Model.Output(),
+            ]
+        )
+
+
+class ModelMergeSD3_2B(ModelMergeBlocks):
+    @classmethod
+    def define_schema(cls):
+        inputs = [
+            io.Model.Input("model1"),
+            io.Model.Input("model2"),
+            io.Float.Input("pos_embed.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("x_embedder.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("context_embedder.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("y_embedder.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("t_embedder.", default=1.0, min=0.0, max=1.0, step=0.01)
+        ]
+
+        for i in range(24):
+            inputs.append(io.Float.Input(f"joint_blocks.{i}.", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        inputs.append(io.Float.Input("final_layer.", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        return io.Schema(
+            node_id="ModelMergeSD3_2B_V3",
+            category="advanced/model_merging/model_specific",
+            inputs=inputs,
+            outputs=[
+                io.Model.Output(),
+            ]
+        )
+
+
+class ModelMergeSD35_Large(ModelMergeBlocks):
+    @classmethod
+    def define_schema(cls):
+        inputs = [
+            io.Model.Input("model1"),
+            io.Model.Input("model2"),
+            io.Float.Input("pos_embed.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("x_embedder.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("context_embedder.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("y_embedder.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("t_embedder.", default=1.0, min=0.0, max=1.0, step=0.01)
+        ]
+
+        for i in range(38):
+            inputs.append(io.Float.Input(f"joint_blocks.{i}.", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        inputs.append(io.Float.Input("final_layer.", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        return io.Schema(
+            node_id="ModelMergeSD35_Large_V3",
+            category="advanced/model_merging/model_specific",
+            inputs=inputs,
+            outputs=[
+                io.Model.Output(),
+            ]
+        )
+
+
+class ModelMergeSDXL(ModelMergeBlocks):
+    @classmethod
+    def define_schema(cls):
+        inputs = [
+            io.Model.Input("model1"),
+            io.Model.Input("model2"),
+            io.Float.Input("time_embed.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("label_emb.", default=1.0, min=0.0, max=1.0, step=0.01)
+        ]
+
+        for i in range(9):
+            inputs.append(io.Float.Input(f"input_blocks.{i}", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        for i in range(3):
+            inputs.append(io.Float.Input(f"middle_block.{i}", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        for i in range(9):
+            inputs.append(io.Float.Input(f"output_blocks.{i}", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        inputs.append(io.Float.Input("out.", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        return io.Schema(
+            node_id="ModelMergeSDXL_V3",
+            category="advanced/model_merging/model_specific",
+            inputs=inputs,
+            outputs=[
+                io.Model.Output(),
+            ]
+        )
+
+
+class ModelMergeWAN2_1(ModelMergeBlocks):
+    @classmethod
+    def define_schema(cls):
+        inputs = [
+            io.Model.Input("model1"),
+            io.Model.Input("model2"),
+            io.Float.Input("patch_embedding.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("time_embedding.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("time_projection.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("text_embedding.", default=1.0, min=0.0, max=1.0, step=0.01),
+            io.Float.Input("img_emb.", default=1.0, min=0.0, max=1.0, step=0.01)
+        ]
+
+        for i in range(40):
+            inputs.append(io.Float.Input(f"blocks.{i}.", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        inputs.append(io.Float.Input("head.", default=1.0, min=0.0, max=1.0, step=0.01))
+
+        return io.Schema(
+            node_id="ModelMergeWAN2_1_V3",
+            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(),
+            ]
+        )
+
+
+NODES_LIST = [
+    ModelMergeAuraflow,
+    ModelMergeCosmos14B,
+    ModelMergeCosmos7B,
+    ModelMergeCosmosPredict2_14B,
+    ModelMergeCosmosPredict2_2B,
+    ModelMergeFlux1,
+    ModelMergeLTXV,
+    ModelMergeMochiPreview,
+    ModelMergeSD1,
+    ModelMergeSD3_2B,
+    ModelMergeSD35_Large,
+    ModelMergeSDXL,
+    ModelMergeWAN2_1,
+]

comfy_extras/v3/nodes_stable3d.py

@@ -0,0 +1,165 @@
+from __future__ import annotations
+
+import torch
+
+import comfy.utils
+import nodes
+from comfy_api.latest import io
+
+
+def camera_embeddings(elevation, azimuth):
+    elevation = torch.as_tensor([elevation])
+    azimuth = torch.as_tensor([azimuth])
+    embeddings = torch.stack(
+        [
+                torch.deg2rad(
+                    (90 - elevation) - 90
+                ),  # Zero123 polar is 90-elevation
+                torch.sin(torch.deg2rad(azimuth)),
+                torch.cos(torch.deg2rad(azimuth)),
+                torch.deg2rad(
+                    90 - torch.full_like(elevation, 0)
+                ),
+        ], dim=-1).unsqueeze(1)
+
+    return embeddings
+
+
+class StableZero123_Conditioning(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="StableZero123_Conditioning_V3",
+            category="conditioning/3d_models",
+            inputs=[
+                io.ClipVision.Input("clip_vision"),
+                io.Image.Input("init_image"),
+                io.Vae.Input("vae"),
+                io.Int.Input("width", default=256, min=16, max=nodes.MAX_RESOLUTION, step=8),
+                io.Int.Input("height", default=256, min=16, max=nodes.MAX_RESOLUTION, step=8),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+                io.Float.Input("elevation", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
+                io.Float.Input("azimuth", default=0.0, min=-180.0, max=180.0, step=0.1, round=False)
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent")
+            ]
+        )
+
+    @classmethod
+    def execute(cls, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth):
+        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)
+        encode_pixels = pixels[:,:,:,:3]
+        t = vae.encode(encode_pixels)
+        cam_embeds = camera_embeddings(elevation, azimuth)
+        cond = torch.cat([pooled, cam_embeds.to(pooled.device).repeat((pooled.shape[0], 1, 1))], dim=-1)
+
+        positive = [[cond, {"concat_latent_image": t}]]
+        negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]]
+        latent = torch.zeros([batch_size, 4, height // 8, width // 8])
+        return io.NodeOutput(positive, negative, {"samples":latent})
+
+
+class StableZero123_Conditioning_Batched(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="StableZero123_Conditioning_Batched_V3",
+            category="conditioning/3d_models",
+            inputs=[
+                io.ClipVision.Input("clip_vision"),
+                io.Image.Input("init_image"),
+                io.Vae.Input("vae"),
+                io.Int.Input("width", default=256, min=16, max=nodes.MAX_RESOLUTION, step=8),
+                io.Int.Input("height", default=256, min=16, max=nodes.MAX_RESOLUTION, step=8),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+                io.Float.Input("elevation", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
+                io.Float.Input("azimuth", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
+                io.Float.Input("elevation_batch_increment", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
+                io.Float.Input("azimuth_batch_increment", default=0.0, min=-180.0, max=180.0, step=0.1, round=False)
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent")
+            ]
+        )
+
+    @classmethod
+    def execute(cls, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth, elevation_batch_increment, azimuth_batch_increment):
+        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)
+        encode_pixels = pixels[:,:,:,:3]
+        t = vae.encode(encode_pixels)
+
+        cam_embeds = []
+        for i in range(batch_size):
+            cam_embeds.append(camera_embeddings(elevation, azimuth))
+            elevation += elevation_batch_increment
+            azimuth += azimuth_batch_increment
+
+        cam_embeds = torch.cat(cam_embeds, dim=0)
+        cond = torch.cat([comfy.utils.repeat_to_batch_size(pooled, batch_size), cam_embeds], dim=-1)
+
+        positive = [[cond, {"concat_latent_image": t}]]
+        negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]]
+        latent = torch.zeros([batch_size, 4, height // 8, width // 8])
+        return io.NodeOutput(positive, negative, {"samples":latent, "batch_index": [0] * batch_size})
+
+
+class SV3D_Conditioning(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SV3D_Conditioning_V3",
+            category="conditioning/3d_models",
+            inputs=[
+                io.ClipVision.Input("clip_vision"),
+                io.Image.Input("init_image"),
+                io.Vae.Input("vae"),
+                io.Int.Input("width", default=576, 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=21, min=1, max=4096),
+                io.Float.Input("elevation", default=0.0, min=-90.0, max=90.0, step=0.1, round=False)
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent")
+            ]
+        )
+
+    @classmethod
+    def execute(cls, clip_vision, init_image, vae, width, height, video_frames, elevation):
+        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)
+        encode_pixels = pixels[:,:,:,:3]
+        t = vae.encode(encode_pixels)
+
+        azimuth = 0
+        azimuth_increment = 360 / (max(video_frames, 2) - 1)
+
+        elevations = []
+        azimuths = []
+        for i in range(video_frames):
+            elevations.append(elevation)
+            azimuths.append(azimuth)
+            azimuth += azimuth_increment
+
+        positive = [[pooled, {"concat_latent_image": t, "elevation": elevations, "azimuth": azimuths}]]
+        negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t), "elevation": elevations, "azimuth": azimuths}]]
+        latent = torch.zeros([video_frames, 4, height // 8, width // 8])
+        return io.NodeOutput(positive, negative, {"samples":latent})
+
+
+NODES_LIST = [
+    StableZero123_Conditioning,
+    StableZero123_Conditioning_Batched,
+    SV3D_Conditioning,
+]

comfy_extras/v3/nodes_string.py

@@ -0,0 +1,380 @@
+from __future__ import annotations
+
+import re
+
+from comfy_api.latest import io
+
+
+class CaseConverter(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CaseConverter_V3",
+            display_name="Case Converter _V3",
+            category="utils/string",
+            inputs=[
+                io.String.Input("string", multiline=True),
+                io.Combo.Input("mode", options=["UPPERCASE", "lowercase", "Capitalize", "Title Case"])
+            ],
+            outputs=[
+                io.String.Output()
+            ]
+        )
+
+    @classmethod
+    def execute(cls, string, mode):
+        if mode == "UPPERCASE":
+            result = string.upper()
+        elif mode == "lowercase":
+            result = string.lower()
+        elif mode == "Capitalize":
+            result = string.capitalize()
+        elif mode == "Title Case":
+            result = string.title()
+        else:
+            result = string
+
+        return io.NodeOutput(result)
+
+
+class RegexExtract(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="RegexExtract_V3",
+            display_name="Regex Extract _V3",
+            category="utils/string",
+            inputs=[
+                io.String.Input("string", multiline=True),
+                io.String.Input("regex_pattern", multiline=True),
+                io.Combo.Input("mode", options=["First Match", "All Matches", "First Group", "All Groups"]),
+                io.Boolean.Input("case_insensitive", default=True),
+                io.Boolean.Input("multiline", default=False),
+                io.Boolean.Input("dotall", default=False),
+                io.Int.Input("group_index", default=1, min=0, max=100)
+            ],
+            outputs=[
+                io.String.Output()
+            ]
+        )
+
+    @classmethod
+    def execute(cls, string, regex_pattern, mode, case_insensitive, multiline, dotall, group_index):
+        join_delimiter = "\n"
+
+        flags = 0
+        if case_insensitive:
+            flags |= re.IGNORECASE
+        if multiline:
+            flags |= re.MULTILINE
+        if dotall:
+            flags |= re.DOTALL
+
+        try:
+            if mode == "First Match":
+                match = re.search(regex_pattern, string, flags)
+                if match:
+                    result = match.group(0)
+                else:
+                    result = ""
+
+            elif mode == "All Matches":
+                matches = re.findall(regex_pattern, string, flags)
+                if matches:
+                    if isinstance(matches[0], tuple):
+                        result = join_delimiter.join([m[0] for m in matches])
+                    else:
+                        result = join_delimiter.join(matches)
+                else:
+                    result = ""
+
+            elif mode == "First Group":
+                match = re.search(regex_pattern, string, flags)
+                if match and len(match.groups()) >= group_index:
+                    result = match.group(group_index)
+                else:
+                    result = ""
+
+            elif mode == "All Groups":
+                matches = re.finditer(regex_pattern, string, flags)
+                results = []
+                for match in matches:
+                    if match.groups() and len(match.groups()) >= group_index:
+                        results.append(match.group(group_index))
+                result = join_delimiter.join(results)
+            else:
+                result = ""
+
+        except re.error:
+            result = ""
+
+        return io.NodeOutput(result)
+
+
+class RegexMatch(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="RegexMatch_V3",
+            display_name="Regex Match _V3",
+            category="utils/string",
+            inputs=[
+                io.String.Input("string", multiline=True),
+                io.String.Input("regex_pattern", multiline=True),
+                io.Boolean.Input("case_insensitive", default=True),
+                io.Boolean.Input("multiline", default=False),
+                io.Boolean.Input("dotall", default=False)
+            ],
+            outputs=[
+                io.Boolean.Output(display_name="matches")
+            ]
+        )
+
+    @classmethod
+    def execute(cls, string, regex_pattern, case_insensitive, multiline, dotall):
+        flags = 0
+
+        if case_insensitive:
+            flags |= re.IGNORECASE
+        if multiline:
+            flags |= re.MULTILINE
+        if dotall:
+            flags |= re.DOTALL
+
+        try:
+            match = re.search(regex_pattern, string, flags)
+            result = match is not None
+
+        except re.error:
+            result = False
+
+        return io.NodeOutput(result)
+
+
+class RegexReplace(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="RegexReplace_V3",
+            display_name="Regex Replace _V3",
+            category="utils/string",
+            description="Find and replace text using regex patterns.",
+            inputs=[
+                io.String.Input("string", multiline=True),
+                io.String.Input("regex_pattern", multiline=True),
+                io.String.Input("replace", multiline=True),
+                io.Boolean.Input("case_insensitive", default=True, optional=True),
+                io.Boolean.Input("multiline", default=False, optional=True),
+                io.Boolean.Input("dotall", default=False, optional=True, tooltip="When enabled, the dot (.) character will match any character including newline characters. When disabled, dots won't match newlines."),
+                io.Int.Input("count", default=0, min=0, max=100, optional=True, tooltip="Maximum number of replacements to make. Set to 0 to replace all occurrences (default). Set to 1 to replace only the first match, 2 for the first two matches, etc.")
+            ],
+            outputs=[
+                io.String.Output()
+            ]
+        )
+
+    @classmethod
+    def execute(cls, string, regex_pattern, replace, case_insensitive=True, multiline=False, dotall=False, count=0):
+        flags = 0
+
+        if case_insensitive:
+            flags |= re.IGNORECASE
+        if multiline:
+            flags |= re.MULTILINE
+        if dotall:
+            flags |= re.DOTALL
+        result = re.sub(regex_pattern, replace, string, count=count, flags=flags)
+        return io.NodeOutput(result)
+
+
+class StringCompare(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="StringCompare_V3",
+            display_name="Compare _V3",
+            category="utils/string",
+            inputs=[
+                io.String.Input("string_a", multiline=True),
+                io.String.Input("string_b", multiline=True),
+                io.Combo.Input("mode", options=["Starts With", "Ends With", "Equal"]),
+                io.Boolean.Input("case_sensitive", default=True)
+            ],
+            outputs=[
+                io.Boolean.Output()
+            ]
+        )
+
+    @classmethod
+    def execute(cls, string_a, string_b, mode, case_sensitive):
+        if case_sensitive:
+            a = string_a
+            b = string_b
+        else:
+            a = string_a.lower()
+            b = string_b.lower()
+
+        if mode == "Equal":
+            return io.NodeOutput(a == b)
+        elif mode == "Starts With":
+            return io.NodeOutput(a.startswith(b))
+        elif mode == "Ends With":
+            return io.NodeOutput(a.endswith(b))
+
+
+class StringConcatenate(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="StringConcatenate_V3",
+            display_name="Concatenate _V3",
+            category="utils/string",
+            inputs=[
+                io.String.Input("string_a", multiline=True),
+                io.String.Input("string_b", multiline=True),
+                io.String.Input("delimiter", multiline=False, default="")
+            ],
+            outputs=[
+                io.String.Output()
+            ]
+        )
+
+    @classmethod
+    def execute(cls, string_a, string_b, delimiter):
+        return io.NodeOutput(delimiter.join((string_a, string_b)))
+
+
+class StringContains(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="StringContains_V3",
+            display_name="Contains _V3",
+            category="utils/string",
+            inputs=[
+                io.String.Input("string", multiline=True),
+                io.String.Input("substring", multiline=True),
+                io.Boolean.Input("case_sensitive", default=True)
+            ],
+            outputs=[
+                io.Boolean.Output(display_name="contains")
+            ]
+        )
+
+    @classmethod
+    def execute(cls, string, substring, case_sensitive):
+        if case_sensitive:
+            contains = substring in string
+        else:
+            contains = substring.lower() in string.lower()
+
+        return io.NodeOutput(contains)
+
+
+class StringLength(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="StringLength_V3",
+            display_name="Length _V3",
+            category="utils/string",
+            inputs=[
+                io.String.Input("string", multiline=True)
+            ],
+            outputs=[
+                io.Int.Output(display_name="length")
+            ]
+        )
+
+    @classmethod
+    def execute(cls, string):
+        return io.NodeOutput(len(string))
+
+
+class StringReplace(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="StringReplace_V3",
+            display_name="Replace _V3",
+            category="utils/string",
+            inputs=[
+                io.String.Input("string", multiline=True),
+                io.String.Input("find", multiline=True),
+                io.String.Input("replace", multiline=True)
+            ],
+            outputs=[
+                io.String.Output()
+            ]
+        )
+
+    @classmethod
+    def execute(cls, string, find, replace):
+        return io.NodeOutput(string.replace(find, replace))
+
+
+class StringSubstring(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="StringSubstring_V3",
+            display_name="Substring _V3",
+            category="utils/string",
+            inputs=[
+                io.String.Input("string", multiline=True),
+                io.Int.Input("start"),
+                io.Int.Input("end")
+            ],
+            outputs=[
+                io.String.Output()
+            ]
+        )
+
+    @classmethod
+    def execute(cls, string, start, end):
+        return io.NodeOutput(string[start:end])
+
+
+class StringTrim(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="StringTrim_V3",
+            display_name="Trim _V3",
+            category="utils/string",
+            inputs=[
+                io.String.Input("string", multiline=True),
+                io.Combo.Input("mode", options=["Both", "Left", "Right"])
+            ],
+            outputs=[
+                io.String.Output()
+            ]
+        )
+
+    @classmethod
+    def execute(cls, string, mode):
+        if mode == "Both":
+            result = string.strip()
+        elif mode == "Left":
+            result = string.lstrip()
+        elif mode == "Right":
+            result = string.rstrip()
+        else:
+            result = string
+
+        return io.NodeOutput(result)
+
+
+NODES_LIST = [
+    CaseConverter,
+    RegexExtract,
+    RegexMatch,
+    RegexReplace,
+    StringCompare,
+    StringConcatenate,
+    StringContains,
+    StringLength,
+    StringReplace,
+    StringSubstring,
+    StringTrim,
+]

nodes.py

@@ -2316,6 +2316,7 @@ async def init_builtin_extra_nodes():
         "v3/nodes_cond.py",
         "v3/nodes_controlnet.py",
         "v3/nodes_cosmos.py",
+        # "v3/nodes_custom_sampler.py",
         "v3/nodes_differential_diffusion.py",
         "v3/nodes_edit_model.py",
         "v3/nodes_flux.py",
@@ -2323,7 +2324,9 @@ async def init_builtin_extra_nodes():
         "v3/nodes_fresca.py",
         "v3/nodes_gits.py",
         "v3/nodes_hidream.py",
+        # "v3/nodes_hooks.py",
         "v3/nodes_hunyuan.py",
+        "v3/nodes_hunyuan3d.py",
         "v3/nodes_hypernetwork.py",
         "v3/nodes_hypertile.py",
         "v3/nodes_images.py",
@@ -2334,10 +2337,13 @@ async def init_builtin_extra_nodes():
         "v3/nodes_lotus.py",
         "v3/nodes_lt.py",
         "v3/nodes_lumina2.py",
+        "v3/nodes_mahiro.py",
         "v3/nodes_mask.py",
         "v3/nodes_mochi.py",
         "v3/nodes_model_advanced.py",
         "v3/nodes_model_downscale.py",
+        "v3/nodes_model_merging.py",
+        "v3/nodes_model_merging_model_specific.py",
         "v3/nodes_morphology.py",
         "v3/nodes_optimalsteps.py",
         "v3/nodes_pag.py",
@@ -2352,7 +2358,9 @@ async def init_builtin_extra_nodes():
         "v3/nodes_sd3.py",
         "v3/nodes_sdupscale.py",
         "v3/nodes_slg.py",
+        "v3/nodes_stable3d.py",
         "v3/nodes_stable_cascade.py",
+        "v3/nodes_string.py",
         "v3/nodes_tcfg.py",
         "v3/nodes_tomesd.py",
         "v3/nodes_torch_compile.py",