Merge remote-tracking branch 'upstream/master' into sam3d_body

.ci/windows_amd_base_files/README_VERY_IMPORTANT.txt

@@ -1,28 +1,27 @@
-As of the time of writing this you need this driver for best results:
-https://www.amd.com/en/resources/support-articles/release-notes/RN-AMDGPU-WINDOWS-PYTORCH-7-1-1.html
-
-HOW TO RUN:
-
-If you have a AMD gpu:
-
-run_amd_gpu.bat
-
-If you have memory issues you can try disabling the smart memory management by running comfyui with:
-
-run_amd_gpu_disable_smart_memory.bat
-
-IF YOU GET A RED ERROR IN THE UI MAKE SURE YOU HAVE A MODEL/CHECKPOINT IN: ComfyUI\models\checkpoints
-
-You can download the stable diffusion XL one from: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors
-
-
-RECOMMENDED WAY TO UPDATE:
-To update the ComfyUI code: update\update_comfyui.bat
-
-
-TO SHARE MODELS BETWEEN COMFYUI AND ANOTHER UI:
-In the ComfyUI directory you will find a file: extra_model_paths.yaml.example
-Rename this file to: extra_model_paths.yaml and edit it with your favorite text editor.
-
-
-
+As of the time of writing this you need a recent driver. Updating to the latest driver is recommended.
+
+HOW TO RUN:
+
+If you have a AMD gpu:
+
+run_amd_gpu.bat
+
+If you have memory issues you can try enabling the new dynamic memory management by running comfyui with:
+
+run_amd_gpu_enable_dynamic_vram.bat
+
+IF YOU GET A RED ERROR IN THE UI MAKE SURE YOU HAVE A MODEL/CHECKPOINT IN: ComfyUI\models\checkpoints
+
+You can download the stable diffusion XL one from: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors
+
+
+RECOMMENDED WAY TO UPDATE:
+To update the ComfyUI code: update\update_comfyui.bat
+
+
+TO SHARE MODELS BETWEEN COMFYUI AND ANOTHER UI:
+In the ComfyUI directory you will find a file: extra_model_paths.yaml.example
+Rename this file to: extra_model_paths.yaml and edit it with your favorite text editor.
+
+
+

comfy/background_removal/birefnet.py

@@ -105,7 +105,7 @@ class WindowAttention(nn.Module):
 
         relative_position_bias = self.relative_position_bias_table[self.relative_position_index.long().view(-1)].view(
             self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+        relative_position_bias = comfy.ops.cast_to_input(relative_position_bias.permute(2, 0, 1).contiguous(), attn)  # nH, Wh*Ww, Wh*Ww
         attn = attn + relative_position_bias.unsqueeze(0)
 
         if mask is not None:

comfy/clip_vision.py

@@ -9,6 +9,7 @@ import comfy.model_management
 import comfy.utils
 import comfy.clip_model
 import comfy.image_encoders.dino2
+import comfy.image_encoders.dino3
 
 class Output:
     def __getitem__(self, key):
@@ -23,12 +24,16 @@ IMAGE_ENCODERS = {
     "siglip_vision_model": comfy.clip_model.CLIPVisionModelProjection,
     "siglip2_vision_model": comfy.clip_model.CLIPVisionModelProjection,
     "dinov2": comfy.image_encoders.dino2.Dinov2Model,
+    "dinov3": comfy.image_encoders.dino3.DINOv3ViTModel,
 }
 
 class ClipVisionModel():
     def __init__(self, json_config):
-        with open(json_config) as f:
-            config = json.load(f)
+        if isinstance(json_config, dict):
+            config = json_config
+        else:
+            with open(json_config) as f:
+                config = json.load(f)
 
         self.image_size = config.get("image_size", 224)
         self.image_mean = config.get("image_mean", [0.48145466, 0.4578275, 0.40821073])
@@ -134,6 +139,8 @@ def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
         json_config = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "image_encoders"), "dino2_giant.json")
     elif 'encoder.layer.23.layer_scale2.lambda1' in sd:
         json_config = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "image_encoders"), "dino2_large.json")
+    elif 'layer.0.mlp.gate_proj.weight' in sd and 'layer.31.norm1.weight' in sd: # Dinov3 ViT-H/16+ (SwiGLU gated MLP, 32 layers)
+        json_config = comfy.image_encoders.dino3.DINOV3_VITH_CONFIG
     else:
         return None
 

comfy/image_encoders/dino3.py

@@ -0,0 +1,259 @@
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import comfy.ops
+from comfy.ldm.modules.attention import optimized_attention_for_device
+from comfy.image_encoders.dino2 import LayerScale as DINOv3ViTLayerScale
+
+
+# DINOv3 ViT-H/16+ (SwiGLU)
+DINOV3_VITH_CONFIG = {
+    "model_type": "dinov3",
+    "num_hidden_layers": 32,
+    "hidden_size": 1280,
+    "num_attention_heads": 20,
+    "num_register_tokens": 4,
+    "intermediate_size": 5120,
+    "layer_norm_eps": 1e-5,
+    "num_channels": 3,
+    "patch_size": 16,
+    "rope_theta": 100.0,
+    "use_gated_mlp": True,
+    "gated_mlp_act": "silu",
+    "image_size": 1024,
+    "image_mean": [0.485, 0.456, 0.406],
+    "image_std": [0.229, 0.224, 0.225],
+}
+
+
+class DINOv3ViTMLP(nn.Module):
+    def __init__(self, hidden_size, intermediate_size, mlp_bias, device, dtype, operations):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.up_proj = operations.Linear(self.hidden_size, self.intermediate_size, bias=mlp_bias, device=device, dtype=dtype)
+        self.down_proj = operations.Linear(self.intermediate_size, self.hidden_size, bias=mlp_bias, device=device, dtype=dtype)
+        self.act_fn = torch.nn.GELU()
+
+    def forward(self, x):
+        return self.down_proj(self.act_fn(self.up_proj(x)))
+
+
+def rotate_half(x):
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, **kwargs):
+    num_tokens = q.shape[-2]
+    num_patches = sin.shape[-2]
+    num_prefix_tokens = num_tokens - num_patches
+
+    q_prefix_tokens, q_patches = q.split((num_prefix_tokens, num_patches), dim=-2)
+    k_prefix_tokens, k_patches = k.split((num_prefix_tokens, num_patches), dim=-2)
+
+    q_patches = (q_patches * cos) + (rotate_half(q_patches) * sin)
+    k_patches = (k_patches * cos) + (rotate_half(k_patches) * sin)
+
+    q = torch.cat((q_prefix_tokens, q_patches), dim=-2)
+    k = torch.cat((k_prefix_tokens, k_patches), dim=-2)
+
+    return q, k
+
+
+class DINOv3ViTAttention(nn.Module):
+    def __init__(self, hidden_size, num_attention_heads, device, dtype, operations):
+        super().__init__()
+        self.embed_dim = hidden_size
+        self.num_heads = num_attention_heads
+        self.head_dim = self.embed_dim // self.num_heads
+
+        self.k_proj = operations.Linear(self.embed_dim, self.embed_dim, bias=False, device=device, dtype=dtype)  # key_bias = False
+        self.v_proj = operations.Linear(self.embed_dim, self.embed_dim, bias=True, device=device, dtype=dtype)
+        self.q_proj = operations.Linear(self.embed_dim, self.embed_dim, bias=True, device=device, dtype=dtype)
+        self.o_proj = operations.Linear(self.embed_dim, self.embed_dim, bias=True, device=device, dtype=dtype)
+
+    def forward(self, hidden_states, attention_mask=None, position_embeddings=None, **kwargs):
+        batch_size, patches, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(batch_size, patches, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(batch_size, patches, self.num_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(batch_size, patches, self.num_heads, self.head_dim).transpose(1, 2)
+
+        if position_embeddings is not None:
+            cos, sin = position_embeddings
+            query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        attn = optimized_attention_for_device(query_states.device, mask=False)
+        attn_output = attn(
+            query_states, key_states, value_states, self.num_heads, attention_mask,
+            skip_reshape=True, skip_output_reshape=True, low_precision_attention=False,
+        )
+
+        attn_output = attn_output.transpose(1, 2)
+        attn_output = attn_output.reshape(batch_size, patches, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output
+
+
+class DINOv3ViTGatedMLP(nn.Module):
+    def __init__(self, hidden_size, intermediate_size, mlp_bias, device, dtype, operations, act="silu"):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.gate_proj = operations.Linear(self.hidden_size, self.intermediate_size, bias=mlp_bias, device=device, dtype=dtype)
+        self.up_proj = operations.Linear(self.hidden_size, self.intermediate_size, bias=mlp_bias, device=device, dtype=dtype)
+        self.down_proj = operations.Linear(self.intermediate_size, self.hidden_size, bias=mlp_bias, device=device, dtype=dtype)
+        self.act_fn = torch.nn.SiLU() if act == "silu" else torch.nn.GELU()
+
+    def forward(self, x):
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+def get_patches_center_coordinates(num_patches_h, num_patches_w, dtype, device):
+    coords_h = torch.arange(0.5, num_patches_h, dtype=dtype, device=device)
+    coords_w = torch.arange(0.5, num_patches_w, dtype=dtype, device=device)
+    coords_h = coords_h / num_patches_h
+    coords_w = coords_w / num_patches_w
+    coords = torch.stack(torch.meshgrid(coords_h, coords_w, indexing="ij"), dim=-1)
+    coords = coords.flatten(0, 1)
+    coords = 2.0 * coords - 1.0
+    return coords
+
+
+class DINOv3ViTRopePositionEmbedding(nn.Module):
+    inv_freq: torch.Tensor
+
+    def __init__(self, rope_theta, hidden_size, num_attention_heads, patch_size, device, dtype):
+        super().__init__()
+        self.base = rope_theta
+        self.head_dim = hidden_size // num_attention_heads
+        self.patch_size = patch_size
+
+        inv_freq = 1 / self.base ** torch.arange(0, 1, 4 / self.head_dim, dtype=torch.float32, device=device)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+    def forward(self, pixel_values):
+        _, _, height, width = pixel_values.shape
+        num_patches_h = height // self.patch_size
+        num_patches_w = width // self.patch_size
+
+        patch_coords = get_patches_center_coordinates(num_patches_h, num_patches_w, dtype=torch.float32, device=pixel_values.device)
+        self.inv_freq = self.inv_freq.to(pixel_values.device)
+        angles = 2 * math.pi * patch_coords[:, :, None] * self.inv_freq[None, None, :]
+        angles = angles.flatten(1, 2)
+        angles = angles.tile(2)
+        cos = torch.cos(angles).to(dtype=pixel_values.dtype)
+        sin = torch.sin(angles).to(dtype=pixel_values.dtype)
+        return cos, sin
+
+
+class DINOv3ViTEmbeddings(nn.Module):
+    def __init__(self, hidden_size, num_register_tokens, num_channels, patch_size, dtype, device, operations):
+        super().__init__()
+        self.cls_token = nn.Parameter(torch.empty(1, 1, hidden_size, device=device, dtype=dtype))
+        self.mask_token = nn.Parameter(torch.empty(1, 1, hidden_size, device=device, dtype=dtype))
+        self.register_tokens = nn.Parameter(torch.empty(1, num_register_tokens, hidden_size, device=device, dtype=dtype))
+        self.patch_embeddings = operations.Conv2d(
+            num_channels, hidden_size, kernel_size=patch_size, stride=patch_size, device=device, dtype=dtype
+        )
+
+    def forward(self, pixel_values, bool_masked_pos=None):
+        batch_size = pixel_values.shape[0]
+
+        patch_embeddings = self.patch_embeddings(pixel_values)
+        patch_embeddings = patch_embeddings.flatten(2).transpose(1, 2)
+
+        if bool_masked_pos is not None:
+            mask_token = comfy.ops.cast_to_input(self.mask_token, patch_embeddings)
+            patch_embeddings = torch.where(bool_masked_pos.unsqueeze(-1), mask_token, patch_embeddings)
+
+        cls_token = comfy.ops.cast_to_input(self.cls_token.expand(batch_size, -1, -1), patch_embeddings)
+        register_tokens = comfy.ops.cast_to_input(self.register_tokens.expand(batch_size, -1, -1), patch_embeddings)
+        embeddings = torch.cat([cls_token, register_tokens, patch_embeddings], dim=1)
+        return embeddings
+
+
+class DINOv3ViTLayer(nn.Module):
+    def __init__(self, hidden_size, layer_norm_eps, use_gated_mlp, mlp_bias, intermediate_size,
+                 num_attention_heads, device, dtype, operations, gated_mlp_act="silu"):
+        super().__init__()
+        self.norm1 = operations.LayerNorm(hidden_size, eps=layer_norm_eps, device=device, dtype=dtype)
+        self.attention = DINOv3ViTAttention(hidden_size, num_attention_heads, device=device, dtype=dtype, operations=operations)
+        self.layer_scale1 = DINOv3ViTLayerScale(hidden_size, device=device, dtype=dtype, operations=None)
+
+        self.norm2 = operations.LayerNorm(hidden_size, eps=layer_norm_eps, device=device, dtype=dtype)
+        if use_gated_mlp:
+            self.mlp = DINOv3ViTGatedMLP(hidden_size, intermediate_size, mlp_bias, device=device, dtype=dtype, operations=operations, act=gated_mlp_act)
+        else:
+            self.mlp = DINOv3ViTMLP(hidden_size, intermediate_size=intermediate_size, mlp_bias=mlp_bias, device=device, dtype=dtype, operations=operations)
+        self.layer_scale2 = DINOv3ViTLayerScale(hidden_size, device=device, dtype=dtype, operations=None)
+
+    def forward(self, hidden_states, attention_mask=None, position_embeddings=None):
+        residual = hidden_states
+        hidden_states = self.norm1(hidden_states)
+        hidden_states = self.attention(hidden_states, attention_mask=attention_mask, position_embeddings=position_embeddings)
+        hidden_states = self.layer_scale1(hidden_states)
+        hidden_states = hidden_states + residual
+
+        residual = hidden_states
+        hidden_states = self.norm2(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = self.layer_scale2(hidden_states)
+        hidden_states = hidden_states + residual
+        return hidden_states
+
+
+class DINOv3ViTModel(nn.Module):
+    def __init__(self, config, dtype, device, operations):
+        super().__init__()
+        num_hidden_layers = config["num_hidden_layers"]
+        hidden_size = config["hidden_size"]
+        num_attention_heads = config["num_attention_heads"]
+        num_register_tokens = config["num_register_tokens"]
+        intermediate_size = config["intermediate_size"]
+        layer_norm_eps = config["layer_norm_eps"]
+        num_channels = config["num_channels"]
+        patch_size = config["patch_size"]
+        rope_theta = config["rope_theta"]
+        use_gated_mlp = config.get("use_gated_mlp", False)
+        gated_mlp_act = config.get("gated_mlp_act", "silu")
+
+        self.embeddings = DINOv3ViTEmbeddings(
+            hidden_size, num_register_tokens, num_channels=num_channels, patch_size=patch_size,
+            dtype=dtype, device=device, operations=operations
+        )
+        self.rope_embeddings = DINOv3ViTRopePositionEmbedding(
+            rope_theta, hidden_size, num_attention_heads, patch_size=patch_size, dtype=dtype, device=device
+        )
+        self.layer = nn.ModuleList([
+            DINOv3ViTLayer(hidden_size, layer_norm_eps, use_gated_mlp=use_gated_mlp, mlp_bias=True,
+                           intermediate_size=intermediate_size, num_attention_heads=num_attention_heads,
+                           dtype=dtype, device=device, operations=operations, gated_mlp_act=gated_mlp_act)
+            for _ in range(num_hidden_layers)])
+        self.norm = operations.LayerNorm(hidden_size, eps=layer_norm_eps, dtype=dtype, device=device)
+
+    def get_input_embeddings(self):
+        return self.embeddings.patch_embeddings
+
+    def forward(self, pixel_values, bool_masked_pos=None, **kwargs):
+        hidden_states = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos)
+        position_embeddings = self.rope_embeddings(pixel_values)
+
+        for layer_module in self.layer:
+            hidden_states = layer_module(hidden_states, position_embeddings=position_embeddings)
+
+        if kwargs.get("skip_norm_elementwise", False):
+            sequence_output = F.layer_norm(hidden_states, hidden_states.shape[-1:])
+        else:
+            norm = self.norm.to(hidden_states.device)
+            sequence_output = norm(hidden_states)
+        pooled_output = sequence_output[:, 0, :]
+        return sequence_output, None, pooled_output, None

comfy/latent_formats.py

@@ -239,6 +239,16 @@ class Flux2(LatentFormat):
     def process_out(self, latent):
         return latent
 
+class TripoSplat(LatentFormat):
+    # Sequence latent (B, 8192, 16) the camera token rides alongside as a second nested latent
+    latent_channels = 16
+
+    def process_in(self, latent):
+        return latent
+
+    def process_out(self, latent):
+        return latent
+
 class Mochi(LatentFormat):
     latent_channels = 12
     latent_dimensions = 3

comfy/ldm/chroma_radiance/model.py

@@ -38,6 +38,8 @@ class ChromaRadianceParams(ChromaParams):
     # None means use the same dtype as the model.
     nerf_embedder_dtype: Optional[torch.dtype]
     use_x0: bool
+    # Use sequential txt_ids instead of zeros
+    use_sequential_txt_ids: bool
 
 class ChromaRadiance(Chroma):
     """
@@ -162,6 +164,9 @@ class ChromaRadiance(Chroma):
         if params.use_x0:
             self.register_buffer("__x0__", torch.tensor([]))
 
+        if params.use_sequential_txt_ids:
+            self.register_buffer("__sequential__", torch.tensor([]))
+
     @property
     def _nerf_final_layer(self) -> nn.Module:
         if self.params.nerf_final_head_type == "linear":
@@ -313,6 +318,9 @@ class ChromaRadiance(Chroma):
         img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
         img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
         txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
+        # Radiance after 2026-05-22 uses sequential txt_ids instead of zeros
+        if params.use_sequential_txt_ids:
+            txt_ids[:, :, 0] = torch.arange(context.shape[1], device=x.device, dtype=x.dtype).unsqueeze(0).expand(bs, -1)
 
         img_out = self.forward_orig(
             img,

comfy/ldm/flux/math.py

@@ -4,7 +4,7 @@ from torch import Tensor
 
 from comfy.ldm.modules.attention import optimized_attention
 import comfy.model_management
-import logging
+import comfy.quant_ops
 
 
 def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None, transformer_options={}) -> Tensor:
@@ -44,21 +44,15 @@ def _apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor):
     return apply_rope1(xq, freqs_cis), apply_rope1(xk, freqs_cis)
 
 
-try:
-    import comfy.quant_ops
-    q_apply_rope = comfy.quant_ops.ck.apply_rope
-    q_apply_rope1 = comfy.quant_ops.ck.apply_rope1
-    def apply_rope(xq, xk, freqs_cis):
-        if comfy.model_management.in_training:
-            return _apply_rope(xq, xk, freqs_cis)
-        else:
-            return apply_rope1(xq, freqs_cis), apply_rope1(xk, freqs_cis)
-    def apply_rope1(x, freqs_cis):
-        if comfy.model_management.in_training:
-            return _apply_rope1(x, freqs_cis)
-        else:
-            return q_apply_rope1(x, freqs_cis)
-except:
-    logging.warning("No comfy kitchen, using old apply_rope functions.")
-    apply_rope = _apply_rope
-    apply_rope1 = _apply_rope1
+def apply_rope(xq, xk, freqs_cis):
+    if comfy.model_management.in_training:
+        return _apply_rope(xq, xk, freqs_cis)
+    else:
+        return comfy.quant_ops.ck.apply_rope(xq, xk, freqs_cis)
+
+
+def apply_rope1(x, freqs_cis):
+    if comfy.model_management.in_training:
+        return _apply_rope1(x, freqs_cis)
+    else:
+        return comfy.quant_ops.ck.apply_rope1(x, freqs_cis)

comfy/ldm/ideogram4/model.py

@@ -0,0 +1,297 @@
+"""
+The Ideogram 4 transformer is a NextDiT/Lumina2-family single-stream model
+consumes Qwen3-VL hidden-state features (concatenated from 13 layers -> 53248 dims)
+packs ``[text tokens, image tokens]`` into one sequence with block-diagonal segment attention and 3D interleaved MRoPE.
+"""
+
+from __future__ import annotations
+
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import comfy.patcher_extension
+from comfy.ldm.lumina.model import FeedForward
+from comfy.ldm.modules.attention import optimized_attention_masked
+from comfy.text_encoders.llama import apply_rope, precompute_freqs_cis
+
+# Per-token role indicators
+SEQUENCE_PADDING_INDICATOR = -1
+OUTPUT_IMAGE_INDICATOR = 2
+LLM_TOKEN_INDICATOR = 3
+# Image grid coordinates are offset so they never collide with text positions
+IMAGE_POSITION_OFFSET = 65536
+
+
+class Ideogram4Attention(nn.Module):
+    def __init__(self, hidden_size, num_heads, eps=1e-5, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = hidden_size // num_heads
+        self.hidden_size = hidden_size
+
+        self.qkv = operations.Linear(hidden_size, hidden_size * 3, bias=False, dtype=dtype, device=device)
+        self.norm_q = operations.RMSNorm(self.head_dim, eps=eps, elementwise_affine=True, dtype=dtype, device=device)
+        self.norm_k = operations.RMSNorm(self.head_dim, eps=eps, elementwise_affine=True, dtype=dtype, device=device)
+        self.o = operations.Linear(hidden_size, hidden_size, bias=False, dtype=dtype, device=device)
+
+    def forward(self, x, attn_mask, freqs_cis, transformer_options={}):
+        batch_size, seq_len, _ = x.shape
+        qkv = self.qkv(x).view(batch_size, seq_len, 3, self.num_heads, self.head_dim)
+        q, k, v = qkv.unbind(dim=2)
+
+        q = self.norm_q(q)
+        k = self.norm_k(k)
+
+        # (B, heads, L, head_dim)
+        q = q.transpose(1, 2)
+        k = k.transpose(1, 2)
+        v = v.transpose(1, 2)
+
+        q, k = apply_rope(q, k, freqs_cis)
+
+        out = optimized_attention_masked(q, k, v, self.num_heads, attn_mask, skip_reshape=True, transformer_options=transformer_options)
+        return self.o(out)
+
+
+class Ideogram4TransformerBlock(nn.Module):
+    def __init__(self, hidden_size, intermediate_size, num_heads, norm_eps, adaln_dim, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.attention = Ideogram4Attention(hidden_size, num_heads, eps=1e-5, dtype=dtype, device=device, operations=operations)
+        self.feed_forward = FeedForward(
+            dim=hidden_size, hidden_dim=intermediate_size, multiple_of=1, ffn_dim_multiplier=None,
+            operation_settings={"operations": operations, "dtype": dtype, "device": device},
+        )
+
+        self.attention_norm1 = operations.RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=True, dtype=dtype, device=device)
+        self.ffn_norm1 = operations.RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=True, dtype=dtype, device=device)
+        self.attention_norm2 = operations.RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=True, dtype=dtype, device=device)
+        self.ffn_norm2 = operations.RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=True, dtype=dtype, device=device)
+
+        self.adaln_modulation = operations.Linear(adaln_dim, 4 * hidden_size, bias=True, dtype=dtype, device=device)
+
+    def forward(self, x, attn_mask, freqs_cis, adaln_input, transformer_options={}):
+        mod = self.adaln_modulation(adaln_input)
+        scale_msa, gate_msa, scale_mlp, gate_mlp = mod.chunk(4, dim=-1)
+        gate_msa = torch.tanh(gate_msa)
+        gate_mlp = torch.tanh(gate_mlp)
+        scale_msa = 1.0 + scale_msa
+        scale_mlp = 1.0 + scale_mlp
+
+        attn_out = self.attention(self.attention_norm1(x) * scale_msa, attn_mask, freqs_cis, transformer_options=transformer_options)
+        x = x + gate_msa * self.attention_norm2(attn_out)
+        x = x + gate_mlp * self.ffn_norm2(self.feed_forward(self.ffn_norm1(x) * scale_mlp))
+        return x
+
+
+def _sinusoidal_embedding(t, dim, scale=1e4):
+    t = t.to(torch.float32)
+    half = dim // 2
+    freq = math.log(scale) / (half - 1)
+    freq = torch.exp(torch.arange(half, dtype=torch.float32, device=t.device) * -freq)
+    emb = t.unsqueeze(-1) * freq
+    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
+    if dim % 2 == 1:
+        emb = F.pad(emb, (0, 1))
+    return emb
+
+
+class Ideogram4EmbedScalar(nn.Module):
+    def __init__(self, dim, input_range=(0.0, 1.0), dtype=None, device=None, operations=None):
+        super().__init__()
+        self.dim = dim
+        self.range_min, self.range_max = input_range
+        self.mlp_in = operations.Linear(dim, dim, bias=True, dtype=dtype, device=device)
+        self.mlp_out = operations.Linear(dim, dim, bias=True, dtype=dtype, device=device)
+
+    def forward(self, x):
+        x = x.to(torch.float32)
+        scaled = 1e4 * (x - self.range_min) / (self.range_max - self.range_min)
+        emb = _sinusoidal_embedding(scaled, self.dim)
+        emb = emb.to(self.mlp_in.weight.dtype)
+        emb = F.silu(self.mlp_in(emb))
+        return self.mlp_out(emb)
+
+
+class Ideogram4FinalLayer(nn.Module):
+    def __init__(self, hidden_size, out_channels, adaln_dim, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.norm_final = operations.LayerNorm(hidden_size, eps=1e-6, elementwise_affine=False, dtype=dtype, device=device)
+        self.linear = operations.Linear(hidden_size, out_channels, bias=True, dtype=dtype, device=device)
+        self.adaln_modulation = operations.Linear(adaln_dim, hidden_size, bias=True, dtype=dtype, device=device)
+
+    def forward(self, x, c):
+        scale = 1.0 + self.adaln_modulation(F.silu(c))
+        return self.linear(self.norm_final(x) * scale)
+
+
+class Ideogram4Transformer(nn.Module):
+    """A single Ideogram 4 backbone operating on a packed token sequence."""
+
+    def __init__(self, emb_dim, num_layers, num_heads, intermediate_size, adaln_dim,
+                 in_channels, llm_features_dim, rope_theta, mrope_section, norm_eps,
+                 dtype=None, device=None, operations=None):
+        super().__init__()
+        self.head_dim = emb_dim // num_heads
+        self.rope_theta = rope_theta
+        self.mrope_section = tuple(mrope_section)
+
+        self.input_proj = operations.Linear(in_channels, emb_dim, bias=True, dtype=dtype, device=device)
+        self.llm_cond_norm = operations.RMSNorm(llm_features_dim, eps=1e-6, elementwise_affine=True, dtype=dtype, device=device)
+        self.llm_cond_proj = operations.Linear(llm_features_dim, emb_dim, bias=True, dtype=dtype, device=device)
+        self.t_embedding = Ideogram4EmbedScalar(emb_dim, input_range=(0.0, 1.0), dtype=dtype, device=device, operations=operations)
+        self.adaln_proj = operations.Linear(emb_dim, adaln_dim, bias=True, dtype=dtype, device=device)
+
+        self.embed_image_indicator = operations.Embedding(2, emb_dim, dtype=dtype, device=device)
+
+        self.layers = nn.ModuleList([
+            Ideogram4TransformerBlock(emb_dim, intermediate_size, num_heads, norm_eps, adaln_dim,
+                                      dtype=dtype, device=device, operations=operations)
+            for _ in range(num_layers)
+        ])
+
+        self.final_layer = Ideogram4FinalLayer(emb_dim, in_channels, adaln_dim, dtype=dtype, device=device, operations=operations)
+
+    def _backbone(self, llm_features, x, t, position_ids, attn_mask, indicator, transformer_options={}):
+        indicator = indicator.to(torch.long)
+        output_image_mask = (indicator == OUTPUT_IMAGE_INDICATOR).to(x.dtype).unsqueeze(-1)
+
+        x = x * output_image_mask
+        h = self.input_proj(x) * output_image_mask
+
+        t_cond = self.t_embedding(t)
+        if t.dim() == 1:
+            t_cond = t_cond.unsqueeze(1)
+        adaln_input = F.silu(self.adaln_proj(t_cond))
+
+        # h is zero on the text rows (content lives only on image rows), add writes the text features in place
+        if llm_features is not None:
+            L_text = llm_features.shape[1]
+            text_mask = (indicator[:, :L_text] == LLM_TOKEN_INDICATOR).to(x.dtype).unsqueeze(-1)
+            llm = self.llm_cond_norm(llm_features * text_mask)
+            llm = self.llm_cond_proj(llm) * text_mask
+            h[:, :L_text] = h[:, :L_text] + llm
+
+        h = h + self.embed_image_indicator((indicator == OUTPUT_IMAGE_INDICATOR).to(torch.long), out_dtype=h.dtype)
+
+        # Qwen3-VL interleaved MRoPE; position_ids (B, L, 3) -> (3, L) (same across batch).
+        freqs_cis = precompute_freqs_cis(
+            self.head_dim, position_ids[0].transpose(0, 1), self.rope_theta,
+            rope_dims=self.mrope_section, interleaved_mrope=True, device=position_ids.device,
+        )
+
+        if attn_mask is not None and attn_mask.dtype == torch.bool:
+            attn_mask = torch.zeros_like(attn_mask, dtype=h.dtype).masked_fill_(~attn_mask, -torch.finfo(h.dtype).max)
+
+        for layer in self.layers:
+            h = layer(h, attn_mask, freqs_cis, adaln_input, transformer_options=transformer_options)
+
+        return self.final_layer(h, adaln_input)
+
+
+class Ideogram4Transformer2DModel(Ideogram4Transformer):
+    """Ideogram 4 single-stream DiT.
+
+    Runs a packed ``[text, image]`` sequence when text context is supplied, or an image-only sequence when ``context is None``.
+    """
+
+    def __init__(self, image_model=None, in_channels=128, num_layers=34, num_attention_heads=18, attention_head_dim=256, intermediate_size=12288,
+                 adaln_dim=512, llm_features_dim=53248, rope_theta=5000000, mrope_section=(24, 20, 20), norm_eps=1e-5,
+                 dtype=None, device=None, operations=None, **kwargs):
+        emb_dim = num_attention_heads * attention_head_dim
+        super().__init__(
+            emb_dim=emb_dim, num_layers=num_layers, num_heads=num_attention_heads,
+            intermediate_size=intermediate_size, adaln_dim=adaln_dim, in_channels=in_channels,
+            llm_features_dim=llm_features_dim, rope_theta=rope_theta, mrope_section=mrope_section,
+            norm_eps=norm_eps, dtype=dtype, device=device, operations=operations)
+        self.dtype = dtype
+        self.in_channels = in_channels
+        self.out_channels = in_channels
+        # 128-dim token = patch (2x2) * ae_channels (32).
+        self.patch_size = 2
+        self.ae_channels = in_channels // (self.patch_size * self.patch_size)
+
+    def _img_to_tokens(self, x):
+        B, C, gh, gw = x.shape
+        x = x.view(B, self.ae_channels, self.patch_size, self.patch_size, gh, gw)
+        x = x.permute(0, 4, 5, 2, 3, 1)  # (B, gh, gw, pi, pj, c)
+        return x.reshape(B, gh * gw, C)
+
+    def _tokens_to_img(self, tokens, gh, gw):
+        B = tokens.shape[0]
+        C = tokens.shape[-1]
+        x = tokens.reshape(B, gh, gw, self.patch_size, self.patch_size, self.ae_channels)
+        x = x.permute(0, 5, 3, 4, 1, 2)  # (B, c, pi, pj, gh, gw)
+        return x.reshape(B, C, gh, gw)
+
+    def _image_position_ids(self, gh, gw, device):
+        h_idx = torch.arange(gh, device=device).view(-1, 1).expand(gh, gw).reshape(-1)
+        w_idx = torch.arange(gw, device=device).view(1, -1).expand(gh, gw).reshape(-1)
+        t_idx = torch.zeros_like(h_idx)
+        return torch.stack([t_idx, h_idx, w_idx], dim=1) + IMAGE_POSITION_OFFSET  # (L_img, 3)
+
+    def _run_conditional(self, x_chunk, context_chunk, attn_mask_chunk, t_chunk, gh, gw, transformer_options):
+        B = x_chunk.shape[0]
+        device = x_chunk.device
+        img_tokens = self._img_to_tokens(x_chunk)
+        L_img = img_tokens.shape[1]
+        L_text = context_chunk.shape[1]
+        L = L_text + L_img
+        latent_dim = img_tokens.shape[-1]
+
+        x_full = torch.zeros(B, L, latent_dim, dtype=img_tokens.dtype, device=device)
+        x_full[:, L_text:] = img_tokens
+
+        text_pos = torch.arange(L_text, device=device).view(-1, 1).expand(L_text, 3)
+        img_pos = self._image_position_ids(gh, gw, device)
+        position_ids = torch.cat([text_pos, img_pos], dim=0).unsqueeze(0).expand(B, L, 3)
+
+        indicator = torch.empty(B, L, dtype=torch.long, device=device)
+        indicator[:, :L_text] = LLM_TOKEN_INDICATOR
+        indicator[:, L_text:] = OUTPUT_IMAGE_INDICATOR
+
+        attn_mask = None
+        if attn_mask_chunk is not None:
+            segment_ids = torch.ones(B, L, dtype=torch.long, device=device)
+            pad = (attn_mask_chunk == 0)
+            segment_ids[:, :L_text][pad] = SEQUENCE_PADDING_INDICATOR
+            indicator[:, :L_text][pad] = 0
+            # Block-diagonal mask from segment ids: (B, 1, L, L), True = attend.
+            attn_mask = (segment_ids.unsqueeze(2) == segment_ids.unsqueeze(1)).unsqueeze(1)
+
+        out = self._backbone(context_chunk, x_full, t_chunk, position_ids, attn_mask, indicator,
+                             transformer_options=transformer_options)
+        return self._tokens_to_img(out[:, L_text:], gh, gw)
+
+    def _run_image_only(self, x_chunk, t_chunk, gh, gw, transformer_options):
+        B = x_chunk.shape[0]
+        device = x_chunk.device
+        img_tokens = self._img_to_tokens(x_chunk)
+        L_img = img_tokens.shape[1]
+
+        position_ids = self._image_position_ids(gh, gw, device).unsqueeze(0).expand(B, L_img, 3)
+        indicator = torch.full((B, L_img), OUTPUT_IMAGE_INDICATOR, dtype=torch.long, device=device)
+
+        # Image-only sequence is a single segment -> no mask, full attention, no LLM context.
+        out = self._backbone(None, img_tokens, t_chunk, position_ids, None, indicator, transformer_options=transformer_options)
+        return self._tokens_to_img(out, gh, gw)
+
+    def forward(self, x, timesteps, context=None, attention_mask=None, transformer_options={}, **kwargs):
+        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
+            self._forward,
+            self,
+            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options),
+        ).execute(x, timesteps, context, attention_mask, transformer_options, **kwargs)
+
+    def _forward(self, x, timesteps, context=None, attention_mask=None, transformer_options={}, **kwargs):
+        bs, c, gh, gw = x.shape
+
+        timesteps = 1.0 - timesteps
+
+        # unconditional pass
+        if context is None:
+            return -self._run_image_only(x, timesteps, gh, gw, transformer_options)
+
+        return -self._run_conditional(x, context, attention_mask, timesteps, gh, gw, transformer_options)

comfy/ldm/triposplat/gaussian.py

@@ -0,0 +1,199 @@
+# TripoSplat 3D gaussian container. Operates on already-decoded
+# tensors and exposes them as render-ready tensors (render_tensors) for the generic SPLAT type.
+import torch
+import torch.nn.functional as F
+
+import comfy.model_management
+
+
+class GaussianModel:
+    def __init__(self, aabb: list, sh_degree: int = 0, mininum_kernel_size: float = 0.0,
+                 scaling_bias: float = 0.01, opacity_bias: float = 0.1,
+                 scaling_activation: str = "exp", device=None):
+        self.sh_degree = sh_degree
+        self.mininum_kernel_size = mininum_kernel_size
+        self.scaling_bias = scaling_bias
+        self.opacity_bias = opacity_bias
+        self.device = device
+        self.aabb = torch.tensor(aabb, dtype=torch.float32, device=device)
+
+        if scaling_activation == "exp":
+            self._scaling_activation = torch.exp
+            self._inverse_scaling_activation = torch.log
+        elif scaling_activation == "softplus":
+            self._scaling_activation = F.softplus
+            self._inverse_scaling_activation = lambda x: x + torch.log(-torch.expm1(-x))
+
+        self._opacity_activation = torch.sigmoid
+        self._inverse_opacity_activation = lambda x: torch.log(x / (1 - x))
+
+        self.scale_bias = self._inverse_scaling_activation(torch.tensor(self.scaling_bias)).to(self.device)
+        self.rots_bias = torch.zeros(4, device=self.device)
+        self.rots_bias[0] = 1
+        self.opacity_bias_val = self._inverse_opacity_activation(torch.tensor(self.opacity_bias)).to(self.device)
+
+        self._storage = {}
+
+    def _get_store(self, name):
+        return self._storage.get(name)
+
+    def _set_store(self, name, value):
+        self._storage[name] = value
+
+    @property
+    def _xyz(self):
+        return self._get_store("_xyz")
+    @_xyz.setter
+    def _xyz(self, value):
+        if value is None:
+            self._set_store("_xyz", None)
+            self._set_store("xyz", None)
+            return
+        self._set_store("_xyz", value)
+        self._set_store("xyz", value * self.aabb[None, 3:] + self.aabb[None, :3])
+
+    @property
+    def get_xyz(self):
+        return self._get_store("xyz")
+
+    @property
+    def _features_dc(self):
+        return self._get_store("_features_dc")
+    @_features_dc.setter
+    def _features_dc(self, value):
+        self._set_store("_features_dc", value)
+
+    @property
+    def _opacity(self):
+        return self._get_store("_opacity")
+    @_opacity.setter
+    def _opacity(self, value):
+        if value is None:
+            self._set_store("_opacity", None)
+            self._set_store("opacity", None)
+            return
+        self._set_store("_opacity", value)
+        self._set_store("opacity", self._opacity_activation(value + self.opacity_bias_val))
+
+    @property
+    def get_opacity(self):
+        return self._get_store("opacity")
+
+    @property
+    def _scaling(self):
+        return self._get_store("_scaling")
+    @_scaling.setter
+    def _scaling(self, value):
+        if value is None:
+            self._set_store("_scaling", None)
+            self._set_store("scaling", None)
+            return
+        self._set_store("_scaling", value)
+        s = self._scaling_activation(value + self.scale_bias)
+        s = torch.square(s) + self.mininum_kernel_size ** 2
+        self._set_store("scaling", torch.sqrt(s))
+
+    @property
+    def get_scaling(self):
+        return self._get_store("scaling")
+
+    @property
+    def _rotation(self):
+        return self._get_store("_rotation")
+    @_rotation.setter
+    def _rotation(self, value):
+        self._set_store("_rotation", value)
+
+    _DEFAULT_TRANSFORM = [[1, 0, 0], [0, 0, -1], [0, 1, 0]]
+
+    def render_tensors(self):
+        # Render-ready (activated, world-space) tensors for the generic SPLAT type. The axis transform
+        # (a 3x3 rotation, object frame -> viewer Y-up) is baked into positions and rotations.
+        # Returns float tensors on the intermediate device: positions (N,3), scales (N,3) linear,
+        # rotations (N,4) wxyz, opacities (N,1) in [0,1], sh (N,K,3) coefficients.
+        xyz = self.get_xyz.float()
+        scaling = self.get_scaling.float()
+        opacity = self.get_opacity.float()
+        rotation = (self._rotation + self.rots_bias[None, :]).float()
+        sh = self._features_dc.float()  # (N, K, 3)
+        T = torch.as_tensor(self._DEFAULT_TRANSFORM, dtype=torch.float32, device=xyz.device)
+        xyz = xyz @ T.T
+        rotation = _matrix_to_quat(torch.matmul(T, _quat_to_matrix(rotation)))
+        rotation = rotation / torch.linalg.norm(rotation, dim=-1, keepdim=True)
+        out_device = comfy.model_management.intermediate_device()
+        return (
+            xyz.to(out_device).contiguous(), scaling.to(out_device).contiguous(),
+            rotation.to(out_device).contiguous(), opacity.to(out_device).contiguous(),
+            sh.to(out_device).contiguous(),
+        )
+
+
+def _quat_to_matrix(q):
+    q = q / torch.linalg.norm(q, dim=-1, keepdim=True)
+    w, x, y, z = q[:, 0], q[:, 1], q[:, 2], q[:, 3]
+    R = torch.stack([
+        1 - 2*(y*y + z*z), 2*(x*y - w*z),     2*(x*z + w*y),
+        2*(x*y + w*z),     1 - 2*(x*x + z*z), 2*(y*z - w*x),
+        2*(x*z - w*y),     2*(y*z + w*x),     1 - 2*(x*x + y*y),
+    ], dim=-1).reshape(-1, 3, 3)
+    return R
+
+
+def _matrix_to_quat(R):
+    trace = R[:, 0, 0] + R[:, 1, 1] + R[:, 2, 2]
+    q = torch.zeros((R.shape[0], 4), dtype=R.dtype, device=R.device)
+    s = torch.sqrt(torch.clamp(trace + 1, min=0)) * 2
+    q[:, 0] = 0.25 * s
+    denom = torch.where(s != 0, s, torch.ones_like(s))
+    q[:, 1] = (R[:, 2, 1] - R[:, 1, 2]) / denom
+    q[:, 2] = (R[:, 0, 2] - R[:, 2, 0]) / denom
+    q[:, 3] = (R[:, 1, 0] - R[:, 0, 1]) / denom
+    m01 = (R[:, 0, 0] >= R[:, 1, 1]) & (R[:, 0, 0] >= R[:, 2, 2]) & (s == 0)
+    s1 = torch.sqrt(torch.clamp(1 + R[:, 0, 0] - R[:, 1, 1] - R[:, 2, 2], min=0)) * 2
+    q[m01, 0] = (R[m01, 2, 1] - R[m01, 1, 2]) / s1[m01]
+    q[m01, 1] = 0.25 * s1[m01]
+    q[m01, 2] = (R[m01, 0, 1] + R[m01, 1, 0]) / s1[m01]
+    q[m01, 3] = (R[m01, 0, 2] + R[m01, 2, 0]) / s1[m01]
+    m11 = (R[:, 1, 1] > R[:, 0, 0]) & (R[:, 1, 1] >= R[:, 2, 2]) & (s == 0)
+    s2 = torch.sqrt(torch.clamp(1 + R[:, 1, 1] - R[:, 0, 0] - R[:, 2, 2], min=0)) * 2
+    q[m11, 0] = (R[m11, 0, 2] - R[m11, 2, 0]) / s2[m11]
+    q[m11, 1] = (R[m11, 0, 1] + R[m11, 1, 0]) / s2[m11]
+    q[m11, 2] = 0.25 * s2[m11]
+    q[m11, 3] = (R[m11, 1, 2] + R[m11, 2, 1]) / s2[m11]
+    m21 = (R[:, 2, 2] > R[:, 0, 0]) & (R[:, 2, 2] > R[:, 1, 1]) & (s == 0)
+    s3 = torch.sqrt(torch.clamp(1 + R[:, 2, 2] - R[:, 0, 0] - R[:, 1, 1], min=0)) * 2
+    q[m21, 0] = (R[m21, 1, 0] - R[m21, 0, 1]) / s3[m21]
+    q[m21, 1] = (R[m21, 0, 2] + R[m21, 2, 0]) / s3[m21]
+    q[m21, 2] = (R[m21, 1, 2] + R[m21, 2, 1]) / s3[m21]
+    q[m21, 3] = 0.25 * s3[m21]
+    return q / torch.linalg.norm(q, dim=-1, keepdim=True)
+
+
+def build_gaussian_models(decoder, points_pred: dict, pred: dict):
+    # Assemble GaussianModels from the elastic decoder layout. decoder is the ElasticGaussianFixedlenDecoder
+    # (carries layout / rep_config / _get_offset)
+    x = points_pred
+    offset = decoder._get_offset(pred['features'])
+    h = pred["features"]
+    ret = []
+    for i in range(h.shape[0]):
+        g = GaussianModel(
+            sh_degree=0,
+            aabb=[-0.5, -0.5, -0.5, 1.0, 1.0, 1.0],
+            mininum_kernel_size=decoder.rep_config['filter_kernel_size_3d'],
+            scaling_bias=decoder.rep_config['scaling_bias'],
+            opacity_bias=decoder.rep_config['opacity_bias'],
+            scaling_activation=decoder.rep_config['scaling_activation'],
+            device=h.device,
+        )
+        _x = x["points"][i, :, None, :]
+        for k, v in decoder.layout.items():
+            if k == '_xyz':
+                setattr(g, k, (offset[i] + _x).flatten(0, 1))
+            elif k in ('_xyz_center', '_offset_scale'):
+                continue
+            else:
+                feats = h[i][:, v['range'][0]:v['range'][1]].reshape(-1, *v['shape']).flatten(0, 1)
+                setattr(g, k, feats * decoder.rep_config['lr'][k])
+        ret.append(g)
+    return ret

comfy/ldm/triposplat/model.py

@@ -0,0 +1,326 @@
+# TripoSplat flow-matching denoiser (LatentSeqMMFlowModel). Registered as a ModelType.FLOW arch and
+# driven by the standard KSampler; jointly denoises the (B, 8192, 16) latent and a (B, 1, 5) camera token
+# carried as a 2-element nested latent.
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import comfy.model_management
+import comfy.patcher_extension
+import comfy.rmsnorm
+from comfy.ldm.modules.attention import optimized_attention
+from comfy.ldm.flux.math import apply_rope
+
+
+class MultiHeadRMSNorm(nn.Module):
+    def __init__(self, dim, heads, dtype=None, device=None):
+        super().__init__()
+        self.gamma = nn.Parameter(torch.empty(heads, dim, dtype=dtype, device=device))
+
+    def forward(self, x):
+        x = comfy.rmsnorm.rms_norm(x)
+        return x * comfy.model_management.cast_to(self.gamma, x.dtype, x.device)
+
+
+# Positional embeddings
+
+class RePo3DRotaryEmbedding(nn.Module):
+    def __init__(self, model_channels, num_heads, head_dim, repo_hidden_ratio=0.125, max_freq=16.0,
+                 dtype=None, device=None, operations=None):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+        repo_hidden_size = int(model_channels * repo_hidden_ratio)
+        self.norm = operations.LayerNorm(model_channels, dtype=dtype, device=device)
+        self.gate_map = operations.Linear(model_channels, repo_hidden_size, bias=False, dtype=dtype, device=device)
+        self.content_map = operations.Linear(model_channels, repo_hidden_size, bias=False, dtype=dtype, device=device)
+        self.act = nn.SiLU()
+        self.final_map = operations.Linear(repo_hidden_size, 3 * num_heads, bias=False, dtype=dtype, device=device)
+        self.dim_0 = 2 * (head_dim // 6)
+        self.dim_1 = 2 * (head_dim // 6)
+        self.dim_2 = head_dim - self.dim_0 - self.dim_1
+        dims = [self.dim_0, self.dim_1, self.dim_2]
+        freqs_list = []
+        for d in dims:
+            freq_dim = d // 2
+            freqs_list.append(torch.linspace(1.0, float(max_freq), steps=freq_dim, dtype=torch.float32))
+        self.freqs_0 = nn.Parameter(freqs_list[0])
+        self.freqs_1 = nn.Parameter(freqs_list[1])
+        self.freqs_2 = nn.Parameter(freqs_list[2])
+
+    def forward(self, hidden_states):
+        h = self.norm(hidden_states)
+        feat = self.act(self.gate_map(h)) * self.content_map(h)
+        out = self.final_map(feat)
+        B, L, _ = out.shape
+        delta_pos = out.reshape(B, L, self.num_heads, 3)
+        f0 = comfy.model_management.cast_to(self.freqs_0, torch.float32, out.device)
+        f1 = comfy.model_management.cast_to(self.freqs_1, torch.float32, out.device)
+        f2 = comfy.model_management.cast_to(self.freqs_2, torch.float32, out.device)
+        ang_0 = delta_pos[..., 0].unsqueeze(-1) * f0 * torch.pi
+        ang_1 = delta_pos[..., 1].unsqueeze(-1) * f1 * torch.pi
+        ang_2 = delta_pos[..., 2].unsqueeze(-1) * f2 * torch.pi
+        ang = torch.cat([ang_0, ang_1, ang_2], dim=-1).float()  # (B, L, heads, head_dim/2)
+        cos, sin = ang.cos(), ang.sin()
+        return torch.stack([cos, -sin, sin, cos], dim=-1).reshape(*ang.shape, 2, 2)
+
+
+class PcdAbsolutePositionEmbedder(nn.Module):
+    # Sinusoidal absolute position embedding. Two fixed schedules are used in TripoSplat:
+    # "pow2" (flow-model latent anchors) and "log2" (octree / gaussian decoders).
+    def __init__(self, channels: int, in_channels: int = 3, max_res: int = 16, schedule: str = "pow2"):
+        super().__init__()
+        self.channels = channels
+        self.in_channels = in_channels
+        self.max_res = max_res
+        self.schedule = schedule
+        self.freq_dim = channels // in_channels // 2
+
+    def _freqs(self, device):
+        if self.schedule == "pow2":
+            freqs_2exp = torch.arange(self.max_res, dtype=torch.float32, device=device)
+            res_dim = max(0, self.freq_dim - self.max_res)
+            freqs_res = (torch.arange(res_dim, dtype=torch.float32, device=device) / max(res_dim, 1) * self.max_res
+                         if res_dim > 0 else torch.empty(0, device=device))
+            freqs = torch.cat([freqs_2exp, freqs_res], dim=0)[:self.freq_dim]
+            return torch.pow(2.0, freqs) * 2.0  # *2 folds this schedule's 2*pi into the shared *pi below
+        logs = torch.linspace(0.0, float(self.max_res), steps=self.freq_dim, dtype=torch.float32, device=device)
+        return torch.pow(2.0, logs)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        orig_dtype = x.dtype
+        x = x.float()
+        *dims, D = x.shape
+        out = torch.outer(x.reshape(-1), self._freqs(x.device)) * torch.pi
+        out = torch.cat([out.sin(), out.cos()], dim=-1).reshape(*dims, -1)
+        if out.shape[-1] < self.channels:
+            out = torch.cat([out, torch.zeros(*dims, self.channels - out.shape[-1],
+                                              device=out.device, dtype=out.dtype)], dim=-1)
+        return out.to(orig_dtype)
+
+
+def attention(q, k, v, transformer_options=None):
+    # q, k, v: (B, L, heads, dim) -> (B, L, heads, dim). Shared optimized_attention call convention.
+    out = optimized_attention(q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), heads=q.shape[2],
+                              skip_reshape=True, skip_output_reshape=True, low_precision_attention=False,
+                              transformer_options=transformer_options)
+    return out.transpose(1, 2)
+
+
+# Transformer building blocks
+
+class MLP(nn.Module):
+    def __init__(self, in_channels, hidden_channels, out_channels, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            operations.Linear(in_channels, hidden_channels, dtype=dtype, device=device),
+            nn.GELU(approximate="tanh"),
+            operations.Linear(hidden_channels, out_channels, dtype=dtype, device=device),
+        )
+
+    def forward(self, x):
+        return self.mlp(x)
+
+
+class RopeMultiHeadAttention(nn.Module):
+    def __init__(self, channels, num_heads, qkv_bias=True, qk_rms_norm=False, use_rope=False,
+                 dtype=None, device=None, operations=None):
+        super().__init__()
+        self.channels = channels
+        self.num_heads = num_heads
+        self.head_dim = channels // num_heads
+        self.qk_rms_norm = qk_rms_norm
+        self.use_rope = use_rope
+        self.qkv = operations.Linear(channels, channels * 3, bias=qkv_bias, dtype=dtype, device=device)
+        if self.qk_rms_norm:
+            self.q_norm = MultiHeadRMSNorm(self.head_dim, num_heads, dtype=dtype, device=device)
+            self.k_norm = MultiHeadRMSNorm(self.head_dim, num_heads, dtype=dtype, device=device)
+        self.out = operations.Linear(channels, channels, dtype=dtype, device=device)
+
+    def forward(self, x, rope_emb=None, transformer_options=None):
+        B, L, C = x.shape
+        qkv = self.qkv(x).reshape(B, L, 3, self.num_heads, self.head_dim)
+        q, k, v = qkv.unbind(2)
+        if self.use_rope:
+            q, k = apply_rope(q, k, rope_emb)
+        if self.qk_rms_norm:
+            q = self.q_norm(q)
+            k = self.k_norm(k)
+        h = attention(q, k, v, transformer_options)  # (B, L, heads, dim)
+        return self.out(h.reshape(B, L, C))
+
+
+class UnifiedTransformerBlock(nn.Module):
+    def __init__(self, channels, num_heads, mlp_ratio=4.0,
+                 use_rope=False, qk_rms_norm=False, qkv_bias=True,
+                 modulation=True, share_mod=False,
+                 dtype=None, device=None, operations=None):
+        super().__init__()
+        self.modulation = modulation
+        self.share_mod = share_mod
+        self.norm1 = operations.LayerNorm(channels, elementwise_affine=not modulation, eps=1e-6, dtype=dtype, device=device)
+        self.norm2 = operations.LayerNorm(channels, elementwise_affine=not modulation, eps=1e-6, dtype=dtype, device=device)
+        self.attn = RopeMultiHeadAttention(channels, num_heads=num_heads,
+                                           qkv_bias=qkv_bias, use_rope=use_rope, qk_rms_norm=qk_rms_norm,
+                                           dtype=dtype, device=device, operations=operations)
+        self.mlp = MLP(channels, int(channels * mlp_ratio), channels, dtype=dtype, device=device, operations=operations)
+        if modulation:
+            if not share_mod:
+                self.adaLN_modulation = nn.Sequential(
+                    nn.SiLU(), operations.Linear(channels, 6 * channels, bias=True, dtype=dtype, device=device))
+            self.shift_table = nn.Parameter(torch.empty(1, 6 * channels, dtype=dtype, device=device))
+
+    def forward(self, x, mod=None, rotary_emb=None, transformer_options=None):
+        if self.modulation:
+            if not self.share_mod:
+                mod = self.adaLN_modulation(mod)
+            mod = mod + comfy.model_management.cast_to(self.shift_table, mod.dtype, mod.device)
+            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = mod.chunk(6, dim=1)
+            h = torch.addcmul(shift_msa.unsqueeze(1), self.norm1(x), 1 + scale_msa.unsqueeze(1))
+            x = torch.addcmul(x, self.attn(h, rope_emb=rotary_emb, transformer_options=transformer_options), gate_msa.unsqueeze(1))
+            h = torch.addcmul(shift_mlp.unsqueeze(1), self.norm2(x), 1 + scale_mlp.unsqueeze(1))
+            x = torch.addcmul(x, self.mlp(h), gate_mlp.unsqueeze(1))
+        else:
+            x = x + self.attn(self.norm1(x), rope_emb=rotary_emb, transformer_options=transformer_options)
+            x = x + self.mlp(self.norm2(x))
+        return x
+
+
+class TimestepEmbedder(nn.Module):
+    def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
+        )
+        self.frequency_embedding_size = frequency_embedding_size
+
+    @staticmethod
+    def timestep_embedding(t, dim, max_period=10000):
+        half = dim // 2
+        freqs = torch.exp(-np.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(device=t.device)
+        args = t[:, None].float() * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+        return embedding
+
+    def forward(self, t):
+        emb = self.timestep_embedding(t, self.frequency_embedding_size)
+        return self.mlp(emb.to(self.mlp[0].weight.dtype))
+
+
+class LatentSeqMMFlowModel(nn.Module):
+    def __init__(self, image_model=None, q_token_length=8192, in_channels=16, model_channels=1024,
+                 cond_channels=1280, out_channels=16, num_blocks=24, num_refiner_blocks=2,
+                 num_heads=None, num_head_channels=64, cam_channels=5, cond2_channels=128,
+                 mlp_ratio=4, share_mod=True, qk_rms_norm=True,
+                 dtype=None, device=None, operations=None, **kwargs):
+        super().__init__()
+        self.dtype = dtype
+        self.q_token_length = q_token_length
+        self.in_channels = in_channels
+        self.cam_channels = cam_channels
+        self.model_channels = model_channels
+        self.cond_channels = cond_channels
+        self.cond2_channels = cond2_channels
+        self.out_channels = out_channels
+        self.num_blocks = num_blocks
+        self.num_refiner_blocks = num_refiner_blocks
+        self.num_heads = num_heads or model_channels // num_head_channels
+        self.mlp_ratio = mlp_ratio
+        self.share_mod = share_mod
+        self.qk_rms_norm = qk_rms_norm
+
+        factory_kwargs = dict(dtype=dtype, device=device)
+        op_kwargs = dict(operations=operations, **factory_kwargs)
+
+        self.t_embedder = TimestepEmbedder(model_channels, **op_kwargs)
+        if share_mod:
+            self.adaLN_modulation = nn.Sequential(nn.SiLU(), operations.Linear(model_channels, 6 * model_channels, bias=True, **factory_kwargs))
+
+        self.input_layer = operations.Linear(in_channels, model_channels, **factory_kwargs)
+        self.cond_embedder = operations.Linear(cond_channels, model_channels, **factory_kwargs)
+        self.cond_embedder2 = operations.Linear(cond2_channels, model_channels, **factory_kwargs) if cond2_channels is not None else None
+
+        # Fixed Sobol (low-discrepancy) 3D anchor positions for the latent tokens, used as positional encoding.
+        # The embedder is parameter-free and the anchors are fixed, precompute once.
+        sobol_seq = torch.quasirandom.SobolEngine(dimension=3, scramble=True, seed=123).draw(q_token_length)
+        pos_emb = PcdAbsolutePositionEmbedder(model_channels)(sobol_seq.unsqueeze(0))
+        self.register_buffer("pos_emb", pos_emb, persistent=False)
+
+        # RePo3DRotaryEmbedding layers for the refiner and main blocks
+        repo_kwargs = dict(num_heads=self.num_heads, head_dim=num_head_channels, **op_kwargs)
+        self.noise_repo_layers = nn.ModuleList(
+            [RePo3DRotaryEmbedding(model_channels, **repo_kwargs) for _ in range(num_refiner_blocks)])
+        self.context_repo_layers = nn.ModuleList(
+            [RePo3DRotaryEmbedding(model_channels, **repo_kwargs) for _ in range(num_refiner_blocks)])
+        self.repo_layers = nn.ModuleList(
+            [RePo3DRotaryEmbedding(model_channels, **repo_kwargs) for _ in range(num_blocks)])
+
+        # Refiner blocks
+        block_kwargs = dict(num_heads=self.num_heads, mlp_ratio=self.mlp_ratio, use_rope=True, qk_rms_norm=self.qk_rms_norm, **op_kwargs)
+        self.noise_refiner = nn.ModuleList(
+            [UnifiedTransformerBlock(model_channels, modulation=True, share_mod=self.share_mod, **block_kwargs) for _ in range(num_refiner_blocks)])
+        self.context_refiner = nn.ModuleList(
+            [UnifiedTransformerBlock(model_channels, modulation=False, **block_kwargs) for _ in range(num_refiner_blocks)])
+
+        self.cam_refiner = MLP(self.cam_channels, model_channels, model_channels, **op_kwargs)
+
+        self.blocks = nn.ModuleList(
+            [UnifiedTransformerBlock(model_channels, modulation=True, share_mod=self.share_mod, **block_kwargs) for _ in range(num_blocks)])
+
+        self.shift_table = nn.Parameter(torch.empty(1, 2, model_channels, **factory_kwargs))
+        self.out_layer = operations.Linear(model_channels, out_channels, **factory_kwargs)
+        self.cam_out_layer = operations.Linear(model_channels, cam_channels, **factory_kwargs)
+
+    def forward(self, x, t, context=None, ref_latents=None, transformer_options={}, **kwargs):
+        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
+            self._forward,
+            self,
+            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)
+        ).execute(x, t, context, ref_latents, transformer_options, **kwargs)
+
+    def _forward(self, x, t, context=None, ref_latents=None, transformer_options={}, **kwargs):
+        # x is the unpacked nested latent: [latent (B,8192,in_channels), camera (B,1,cam_channels)].
+        # context == feature1.
+        z, camera = x[0], x[1]
+        feat1 = context
+
+        h_x = self.input_layer(z)
+        h_cond = self.cond_embedder(feat1)
+        if ref_latents is not None and self.cond_embedder2 is not None:
+            # Flatten the Flux2 VAE latent (B,128,h,w) to a token sequence and front-pad to feat1's length
+            # (the pad count = feat1's prefix tokens: DINOv3 cls + registers), then add to the context.
+            feat2 = ref_latents[0].flatten(2).transpose(1, 2)
+            feat2 = F.pad(feat2, (0, 0, feat1.shape[1] - feat2.shape[1], 0))
+            h_cond = h_cond + self.cond_embedder2(feat2.to(h_cond.dtype))
+        t_emb = self.t_embedder(t)
+        t_mod = self.adaLN_modulation(t_emb) if self.share_mod else t_emb
+
+        h_x = h_x + self.pos_emb.to(z)
+
+        for i, block in enumerate(self.noise_refiner):
+            h_x = block(h_x, mod=t_mod, rotary_emb=self.noise_repo_layers[i](h_x), transformer_options=transformer_options)
+
+        for i, block in enumerate(self.context_refiner):
+            h_cond = block(h_cond, mod=None, rotary_emb=self.context_repo_layers[i](h_cond), transformer_options=transformer_options)
+
+        cam = camera.to(z)
+        h_cam = self.cam_refiner(cam)
+        h = torch.cat([h_x, h_cond, h_cam], dim=1)
+
+        for i, block in enumerate(self.blocks):
+            h = block(h, mod=t_mod, rotary_emb=self.repo_layers[i](h), transformer_options=transformer_options)
+
+        h_x = F.layer_norm(h[:, :z.shape[1]].float(), h.shape[-1:]).to(z)
+        h_cam = F.layer_norm(h[:, -cam.shape[1]:].float(), h.shape[-1:]).to(z)
+
+        shift, scale = (comfy.model_management.cast_to(self.shift_table, t_emb.dtype, t_emb.device) + t_emb.unsqueeze(1)).chunk(2, dim=1)
+        scale = 1 + scale
+        h_x = torch.addcmul(shift, h_x, scale)
+        h_cam = torch.addcmul(shift, h_cam, scale)
+
+        return self.out_layer(h_x), self.cam_out_layer(h_cam)

comfy/ldm/triposplat/preview.py

@@ -0,0 +1,91 @@
+# Live preview for TripoSplat: decode an x0 estimate into a coarse gaussian splat and render it with a perspective orbit camera.
+import numpy as np
+from PIL import Image
+
+_C0 = 0.28209479177387814
+_LATENT_TOKENS = 8192  # q_token_length
+_LATENT_CH = 16        # in_channels
+_OBJECT_TO_VIEWER = np.array([[1, 0, 0], [0, 0, -1], [0, 1, 0]], np.float32)  # object frame -> viewer Y-up frame
+
+
+def _view_matrix(yaw_deg, pitch_deg):
+    y, p = np.radians(yaw_deg), np.radians(pitch_deg)
+    Ry = np.array([[np.cos(y), 0, np.sin(y)], [0, 1, 0], [-np.sin(y), 0, np.cos(y)]], np.float32)
+    Rx = np.array([[1, 0, 0], [0, np.cos(p), -np.sin(p)], [0, np.sin(p), np.cos(p)]], np.float32)
+    return Rx @ Ry
+
+
+def render_splat(xyz, rgb, scale, opacity=None, yaw=35.0, pitch=30.0, size=320, min_px=2, gain=1.0,
+                 max_px=9, min_opacity=0.0, fov=35.0, dist=2.2):
+    # Project gaussian centers with a perspective camera and paint each as a filled disk whose screen
+    # radius follows the gaussian's world-space scale, composited with a nearest-wins z-buffer.
+    # gain scales the footprint (≈ std spanned), `min_px`/`max_px` clamp the on-screen radius.
+
+    pts = xyz.astype(np.float32) @ _OBJECT_TO_VIEWER.T
+    v = pts @ _view_matrix(yaw, pitch).T
+    zc = v[:, 2] + dist
+    keep = zc > 1e-2
+    if opacity is not None and min_opacity > 0.0:  # culls gaussians with very low opacity
+        keep = keep & (opacity > min_opacity)
+    v, zc, scale = v[keep], zc[keep], scale[keep]
+    col = (np.clip(rgb, 0, 1)[:, :3] * 255).astype(np.uint8)[keep]
+    if v.shape[0] == 0:
+        return Image.fromarray(np.zeros((size, size, 3), np.uint8))
+    f = (size / 2) / np.tan(np.radians(fov) / 2)
+    cx = size / 2 + f * v[:, 0] / zc
+    cy = size / 2 + f * v[:, 1] / zc
+    radius = np.clip(np.round(f * scale / zc * gain), min_px, max_px).astype(np.int32)
+
+    # Expand each splat to its disk pixels, bucketed by integer radius so it stays vectorized.
+    px, py, pz, pc = [], [], [], []
+    for r in range(int(radius.min()), int(radius.max()) + 1):
+        m = radius == r
+        if not m.any():
+            continue
+        dy, dx = np.mgrid[-r:r + 1, -r:r + 1]
+        disk = (dx * dx + dy * dy) <= r * r
+        ox, oy = dx[disk], dy[disk]
+        px.append((cx[m, None] + ox).ravel())
+        py.append((cy[m, None] + oy).ravel())
+        pz.append(np.repeat(zc[m], ox.size))
+        pc.append(np.repeat(col[m], ox.size, axis=0))
+    px, py = np.concatenate(px), np.concatenate(py)
+    pz, pc = np.concatenate(pz), np.concatenate(pc)
+    xi = np.clip(px, 0, size - 1).astype(np.int64)
+    yi = np.clip(py, 0, size - 1).astype(np.int64)
+
+    # Nearest-wins z-buffer: pack (quantized depth, source index), per-pixel min picks the closest
+    # splat, then decode the winning index back to its color.
+    pid = yi * size + xi
+    q = np.clip((pz * 1024.0).astype(np.int64), 0, (1 << 20) - 1)  # near = small
+    key = (q << 32) | np.arange(pid.size, dtype=np.int64)
+    buf = np.full(size * size, 1 << 62, np.int64)
+    np.minimum.at(buf, pid, key)
+    img = np.zeros((size * size, 3), np.uint8)
+    hit = buf < (1 << 62)
+    img[hit] = pc[buf[hit] & 0xFFFFFFFF]
+    return Image.fromarray(img.reshape(size, size, 3))
+
+
+def _extract_latent(x0):
+    # x0 from the sampler callback is the nested latent packed to (B, 1, TOKENS*CH + 1*5);
+    # the plain single-latent case is (B, TOKENS, CH). Return the (B, TOKENS, CH) latent stream.
+    if x0.ndim == 3 and x0.shape[1] == _LATENT_TOKENS and x0.shape[2] == _LATENT_CH:
+        return x0
+    flat = x0.reshape(x0.shape[0], -1)
+    return flat[:, :_LATENT_TOKENS * _LATENT_CH].reshape(x0.shape[0], _LATENT_TOKENS, _LATENT_CH)
+
+
+def decode_x0_to_image(decoder, x0, cfg):
+    # Decode x0 at a coarse octree level / few gaussians and render a preview image.
+    latent = _extract_latent(x0)
+    fsm = decoder.first_stage_model
+    gaussian = fsm.decode(latent.to(decoder.device, decoder.vae_dtype),
+                          num_gaussians=cfg.get("gaussians", 16384), level=cfg.get("level", 5))[0]
+    xyz = gaussian.get_xyz.float().cpu().numpy()
+    rgb = gaussian._features_dc.float().cpu().numpy()[:, 0, :] * _C0 + 0.5
+    scale = gaussian.get_scaling.float().cpu().numpy().max(axis=1)  # per-splat world radius (largest axis)
+    opacity = gaussian.get_opacity.float().cpu().numpy()[:, 0]
+    return render_splat(xyz, rgb, scale, opacity=opacity, yaw=cfg.get("yaw", 35.0), pitch=cfg.get("pitch", 30.0),
+                        size=cfg.get("size", 320), min_px=1, gain=1.0, max_px=cfg.get("point_size", 3),
+                        min_opacity=0.01)

comfy/ldm/triposplat/vae.py

@@ -0,0 +1,382 @@
+# TripoSplat gaussian decoder ("VAE"): an octree probability decoder picks point coords, then an
+# elastic-gaussian decoder predicts per-point gaussian params. OctreeGaussianDecoder.decode() returns
+# a Gaussian. The octree sampler uses the global torch RNG (no generator) like upstream, so seed it for repeatable decodes.
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import comfy.model_management
+import comfy.ops
+from .gaussian import build_gaussian_models
+from .model import MultiHeadRMSNorm, MLP, PcdAbsolutePositionEmbedder, attention
+
+
+# Quasi-random sampling utilities (pure functions, dtype/device-agnostic)
+
+PRIMES = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53]
+
+
+def radical_inverse(base, n):
+    val = 0
+    inv_base = 1.0 / base
+    inv_base_n = inv_base
+    while n > 0:
+        digit = n % base
+        val += digit * inv_base_n
+        n //= base
+        inv_base_n *= inv_base
+    return val
+
+
+def halton_sequence(dim, n):
+    return [radical_inverse(PRIMES[i], n) for i in range(dim)]
+
+
+def hammersley_sequence(dim, n, num_samples):
+    return [n / num_samples] + halton_sequence(dim - 1, n)
+
+
+def sample_probs(probs, counts, generator=None):
+    # Systematic resampling: distribute counts[r] draws across the P bins of row r
+    batch_shape = counts.shape
+    R = counts.numel()
+    P = probs.size(-1)
+    device = probs.device
+    probs = probs.reshape(R, P).to(torch.float32).clamp_min(0)
+    counts = counts.reshape(R).to(device=device, dtype=torch.long)
+
+    row_sums = probs.sum(1, keepdim=True)
+    probs = torch.where(row_sums == 0, probs.new_tensor(1.0 / P), probs / row_sums.clamp_min(1))
+    cdf = probs.cumsum(dim=1).clamp(max=1.0 - 1e-12)
+
+    Nmax = int(counts.max())
+    if Nmax == 0:
+        return counts.new_zeros(*batch_shape, P)
+    cnt = counts.clamp_min(1).float().unsqueeze(1)                              # (R, 1)
+    grid = torch.arange(Nmax, device=device, dtype=torch.float32).unsqueeze(0)  # (1, Nmax)
+    u = (torch.rand(R, 1, generator=generator).to(device) + grid) / cnt         # (R, Nmax) systematic samples (CPU-seeded)
+    idx = torch.searchsorted(cdf, u.clamp(max=1.0 - 1e-12)).clamp_max(P - 1)
+    weight = (grid < counts.unsqueeze(1)).to(cdf.dtype)                         # mask out j >= counts[r]
+    out = torch.zeros(R, P, dtype=torch.float32, device=device)
+    out.scatter_add_(1, idx, weight)
+    return out.to(torch.long).view(*batch_shape, P)
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(self, channels, num_heads, ctx_channels=None, type="self", qkv_bias=True, qk_rms_norm=False,
+                 dtype=None, device=None, operations=None):
+        super().__init__()
+        assert channels % num_heads == 0
+        self.channels = channels
+        self.head_dim = channels // num_heads
+        self.ctx_channels = ctx_channels if ctx_channels is not None else channels
+        self.num_heads = num_heads
+        self._type = type
+        self.qk_rms_norm = qk_rms_norm
+        if self._type == "self":
+            self.to_qkv = operations.Linear(channels, channels * 3, bias=qkv_bias, dtype=dtype, device=device)
+        else:
+            self.to_q = operations.Linear(channels, channels, bias=qkv_bias, dtype=dtype, device=device)
+            self.to_kv = operations.Linear(self.ctx_channels, channels * 2, bias=qkv_bias, dtype=dtype, device=device)
+        if self.qk_rms_norm:
+            self.q_rms_norm = MultiHeadRMSNorm(self.head_dim, num_heads, dtype=dtype, device=device)
+            self.k_rms_norm = MultiHeadRMSNorm(self.head_dim, num_heads, dtype=dtype, device=device)
+        self.to_out = operations.Linear(channels, channels, dtype=dtype, device=device)
+
+    def forward(self, x, context=None):
+        B, L, C = x.shape
+        if self._type == "self":
+            q, k, v = self.to_qkv(x).reshape(B, L, 3, self.num_heads, -1).unbind(dim=2)
+        else:
+            Lkv = context.shape[1]
+            q = self.to_q(x).reshape(B, L, self.num_heads, -1)
+            k, v = self.to_kv(context).reshape(B, Lkv, 2, self.num_heads, -1).unbind(dim=2)
+        if self.qk_rms_norm:
+            q = self.q_rms_norm(q)
+            k = self.k_rms_norm(k)
+        h = attention(q, k, v)
+        return self.to_out(h.reshape(B, L, -1))
+
+
+# Octree probability decoder
+
+class LevelEmbedder(nn.Module):
+    def __init__(self, hidden_size, frequency_embedding_size=256, max_period=1024,
+                 dtype=None, device=None, operations=None):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
+        )
+        self.frequency_embedding_size = frequency_embedding_size
+        self.max_period = max_period
+
+    @staticmethod
+    def level_embedding(t, dim, max_period=1024):
+        half = dim // 2
+        freqs = torch.exp(-np.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(device=t.device)
+        args = t[:, None].float() * freqs[None] * 2 * torch.pi
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+        return embedding
+
+    def forward(self, t):
+        emb = self.level_embedding(t, self.frequency_embedding_size, self.max_period)
+        return self.mlp(emb.to(self.mlp[0].weight.dtype))
+
+
+class ModulatedTransformerCrossOnlyBlock(nn.Module):
+    def __init__(self, channels, ctx_channels, num_heads, mlp_ratio=4.0, share_mod=False,
+                 qk_rms_norm_cross=True, qkv_bias=True, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.share_mod = share_mod
+        self.norm1 = operations.LayerNorm(channels, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.norm2 = operations.LayerNorm(channels, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.cross_attn = MultiHeadAttention(channels, ctx_channels=ctx_channels, num_heads=num_heads,
+                                             type="cross", qkv_bias=qkv_bias,
+                                             qk_rms_norm=qk_rms_norm_cross, dtype=dtype, device=device, operations=operations)
+        self.mlp = MLP(channels, int(channels * mlp_ratio), channels, dtype=dtype, device=device, operations=operations)
+        if not share_mod:
+            self.adaLN_modulation = nn.Sequential(
+                nn.SiLU(), operations.Linear(channels, 6 * channels, bias=True, dtype=dtype, device=device))
+
+    def forward(self, x, mod, context):
+        if self.share_mod:
+            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = mod.chunk(6, dim=1)
+        else:
+            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(mod).chunk(6, dim=1)
+        h = torch.addcmul(shift_msa.unsqueeze(1), self.norm1(x), 1 + scale_msa.unsqueeze(1))
+        x = torch.addcmul(x, self.cross_attn(h, context), gate_msa.unsqueeze(1))
+        h = torch.addcmul(shift_mlp.unsqueeze(1), self.norm2(x), 1 + scale_mlp.unsqueeze(1))
+        x = torch.addcmul(x, self.mlp(h), gate_mlp.unsqueeze(1))
+        return x
+
+
+class OctreeProbabilityFixedlenDecoder(nn.Module):
+    # Cross-attention transformer over octree coords -> per-node 8-way child occupancy logits.
+    def __init__(self, model_channels=1024, cond_channels=16, num_blocks=4, num_heads=16,
+                 num_head_channels=64, mlp_ratio=4.0, share_mod=True,
+                 qk_rms_norm_cross=True, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.model_channels = model_channels
+        self.cond_channels = cond_channels
+        self.num_blocks = num_blocks
+        self.num_heads = num_heads or model_channels // num_head_channels
+        self.mlp_ratio = mlp_ratio
+        self.share_mod = share_mod
+        self.qk_rms_norm_cross = qk_rms_norm_cross
+        self.input_layer = operations.Linear(model_channels, model_channels, dtype=dtype, device=device)
+        self.l_embedder = LevelEmbedder(model_channels, dtype=dtype, device=device, operations=operations)
+        if share_mod:
+            self.adaLN_modulation = nn.Sequential(
+                nn.SiLU(), operations.Linear(model_channels, 6 * model_channels, bias=True, dtype=dtype, device=device))
+        if cond_channels is not None:
+            self.blocks = nn.ModuleList([
+                ModulatedTransformerCrossOnlyBlock(
+                    model_channels, ctx_channels=cond_channels, num_heads=self.num_heads,
+                    mlp_ratio=self.mlp_ratio, qk_rms_norm_cross=self.qk_rms_norm_cross,
+                    share_mod=self.share_mod, dtype=dtype, device=device, operations=operations)
+                for _ in range(num_blocks)
+            ])
+        self.out_proj = operations.Linear(model_channels, 8, dtype=dtype, device=device)
+        self.in_proj = operations.Linear(3, model_channels, dtype=dtype, device=device)
+        self.pos_embedder = PcdAbsolutePositionEmbedder(channels=model_channels, in_channels=3, max_res=10, schedule="log2")
+
+    def forward(self, x, l, cond):
+        d = next(self.parameters()).dtype
+        B, L, _ = x.shape
+        h = self.in_proj(x.to(d)) + self.pos_embedder(x.reshape(-1, 3)).reshape(B, L, -1).to(d)
+        h = self.input_layer(h)
+        l_emb = self.l_embedder(l)
+        if self.share_mod:
+            l_emb = self.adaLN_modulation(l_emb)
+        cond = cond.to(d)
+        for block in self.blocks:
+            h = block(h, l_emb, cond)
+        h = F.layer_norm(h.float(), h.shape[-1:]).to(d)
+        logits = self.out_proj(h)
+        return {"logits": logits, "probs": torch.softmax(logits, dim=-1)}
+
+    @staticmethod
+    def sample(model, cond, num_points, level, temperature=1.0, generator=None):
+        B = cond.shape[0]
+        device = cond.device
+        child_offset = torch.tensor([[i, j, k] for k in [0, 1] for j in [0, 1] for i in [0, 1]],
+                                    dtype=torch.long, device=device)
+        prev_coords_int = torch.zeros(B, 1, 3, dtype=torch.long, device=device)
+        prev_counts = torch.full((B, 1), num_points, dtype=torch.long, device=device)
+        prev_log_probs = torch.zeros(B, 1, dtype=torch.float32, device=device)
+        batch_indices_range = torch.arange(B, device=device).unsqueeze(1)
+
+        for lv in range(1, level + 1):
+            res_p = 1 << (lv - 1)
+            res = 1 << lv
+            parent_coords_norm = (prev_coords_int.to(torch.float32) + 0.5) / res_p
+            res_tensor = torch.full((B,), res, dtype=torch.long, device=device)
+            pred_logits = model(parent_coords_norm, res_tensor, cond)["logits"] / temperature
+            pred_probs = torch.softmax(pred_logits, dim=-1)
+            pred_log_probs = torch.log_softmax(pred_logits, dim=-1)
+            sampled = sample_probs(pred_probs, prev_counts, generator=generator).flatten(1, 2)
+            pred_log_probs = pred_log_probs.flatten(1, 2)
+            prev_log_probs_expanded = prev_log_probs.repeat_interleave(8, dim=1)
+            child_coords_int = (prev_coords_int[:, :, None, :] * 2 + child_offset[None, None, :, :]).flatten(1, 2)
+            mask = sampled > 0
+            max_valid = mask.sum(dim=1).max().item()
+            scatter_indices = mask.cumsum(dim=1) - 1
+            valid_scatter_indices = scatter_indices[mask]
+            valid_batch_indices = batch_indices_range.expand_as(mask)[mask]
+            next_prev_coords_int = torch.zeros(B, max_valid, 3, dtype=child_coords_int.dtype, device=device)
+            next_prev_coords_int[valid_batch_indices, valid_scatter_indices] = child_coords_int[mask]
+            next_prev_counts = torch.zeros(B, max_valid, dtype=sampled.dtype, device=device)
+            next_prev_counts[valid_batch_indices, valid_scatter_indices] = sampled[mask]
+            next_prev_log_probs = torch.zeros(B, max_valid, dtype=prev_log_probs.dtype, device=device)
+            next_prev_log_probs[valid_batch_indices, valid_scatter_indices] = (prev_log_probs_expanded + pred_log_probs)[mask]
+            prev_coords_int = next_prev_coords_int
+            prev_counts = next_prev_counts
+            prev_log_probs = next_prev_log_probs
+
+        res = 1 << level
+        prev_log_probs = torch.repeat_interleave(prev_log_probs.flatten(0, 1), prev_counts.flatten(0, 1), dim=0).reshape(B, num_points)
+        coords_int = torch.repeat_interleave(prev_coords_int.flatten(0, 1), prev_counts.flatten(0, 1), dim=0).reshape(B, num_points, -1)
+        rand = torch.rand(coords_int.shape, dtype=torch.float32, generator=generator).to(device)
+        coords_norm = (coords_int.to(torch.float32) + rand) / res
+        return {"points": coords_norm, "log_probs": prev_log_probs}
+
+
+# Elastic gaussian decoder
+
+class TransformerCrossBlock(nn.Module):
+    def __init__(self, channels, ctx_channels, num_heads, mlp_ratio=4.0,
+                 qk_rms_norm=True, qk_rms_norm_cross=True, qkv_bias=True,
+                 dtype=None, device=None, operations=None):
+        super().__init__()
+        self.norm1 = operations.LayerNorm(channels, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.norm2 = operations.LayerNorm(channels, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device)
+        self.norm3 = operations.LayerNorm(channels, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.self_attn = MultiHeadAttention(channels, num_heads=num_heads, type="self", qkv_bias=qkv_bias,
+                                            qk_rms_norm=qk_rms_norm, dtype=dtype, device=device, operations=operations)
+        self.cross_attn = MultiHeadAttention(channels, ctx_channels=ctx_channels, num_heads=num_heads, type="cross",
+                                             qkv_bias=qkv_bias, qk_rms_norm=qk_rms_norm_cross, dtype=dtype, device=device, operations=operations)
+        self.mlp = MLP(channels, int(channels * mlp_ratio), channels, dtype=dtype, device=device, operations=operations)
+
+    def forward(self, x, context):
+        x = x + self.self_attn(self.norm1(x))
+        x = x + self.cross_attn(self.norm2(x), context)
+        x = x + self.mlp(self.norm3(x))
+        return x
+
+
+class ElasticGaussianFixedlenDecoder(nn.Module):
+    # Cross-attention transformer over sampled octree points -> per-point gaussian params.
+    def __init__(self, in_channels=3, model_channels=1024, cond_channels=16, num_blocks=16, num_heads=16,
+                 num_head_channels=64, mlp_ratio=4.0, *, representation_config=None,
+                 qk_rms_norm=True, qk_rms_norm_cross=True, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.rep_config = representation_config or dict(
+            lr=dict(_xyz=1.0, _features_dc=1.0, _opacity=1.0, _scaling=1.0, _rotation=0.1),
+            perturb_offset=True, perturbe_size=1.5, offset_scale=0.05, num_gaussians=32,
+            filter_kernel_size_3d=0.0009, scaling_bias=0.004, opacity_bias=0.1,
+            scaling_activation="softplus",
+        )
+        self.out_channels = self._calc_layout()
+        self.model_channels = model_channels
+        self.cond_channels = cond_channels
+        self.num_blocks = num_blocks
+        self.num_heads = num_heads or model_channels // num_head_channels
+        self.mlp_ratio = mlp_ratio
+        self.input_layer = operations.Linear(model_channels, model_channels, dtype=dtype, device=device)
+        if cond_channels is not None:
+            self.blocks = nn.ModuleList([
+                TransformerCrossBlock(model_channels, ctx_channels=cond_channels,
+                                      num_heads=self.num_heads, mlp_ratio=self.mlp_ratio,
+                                      qk_rms_norm=qk_rms_norm, qk_rms_norm_cross=qk_rms_norm_cross,
+                                      dtype=dtype, device=device, operations=operations)
+                for _ in range(num_blocks)
+            ])
+        self.in_proj = operations.Linear(in_channels, model_channels, dtype=dtype, device=device)
+        self.pos_embedder = PcdAbsolutePositionEmbedder(channels=model_channels, in_channels=3, max_res=10, schedule="log2")
+        self.out_proj = operations.Linear(model_channels, self.out_channels, dtype=dtype, device=device)
+        self._build_perturbation()
+
+    def _calc_layout(self):
+        ng = self.rep_config['num_gaussians']
+        self.layout = {
+            '_xyz':         {'shape': (ng, 3),    'size': ng * 3},
+            '_features_dc': {'shape': (ng, 1, 3), 'size': ng * 3},
+            '_scaling':     {'shape': (ng, 3),    'size': ng * 3},
+            '_rotation':    {'shape': (ng, 4),    'size': ng * 4},
+            '_opacity':     {'shape': (ng, 1),    'size': ng},
+        }
+        self.layout['_offset_scale'] = {'shape': (ng, 1), 'size': ng}
+        start = 0
+        for k, v in self.layout.items():
+            v['range'] = (start, start + v['size'])
+            start += v['size']
+        return start
+
+    def _build_perturbation(self):
+        ng = self.rep_config['num_gaussians']
+        perturbation = torch.tensor([hammersley_sequence(3, i, ng) for i in range(ng)]).float()
+        perturbation = torch.atanh((perturbation * 2 - 1) / self.rep_config['perturbe_size'])
+        self.register_buffer('points_offset_perturbation', perturbation)
+        base = torch.tensor(self.rep_config['offset_scale'])
+        self.register_buffer('base_offset_scale', torch.log(torch.exp(base) - 1.0))
+
+    def _get_offset(self, h):
+        B = h.shape[0]
+        r = self.layout['_offset_scale']['range']
+        _offset_scale = F.softplus(
+            h[:, :, r[0]:r[1]].reshape(B, -1, *self.layout['_offset_scale']['shape'])
+            + comfy.model_management.cast_to(self.base_offset_scale, h.dtype, h.device))
+
+        r = self.layout['_xyz']['range']
+        offset = h[:, :, r[0]:r[1]].reshape(B, -1, *self.layout['_xyz']['shape'])
+        offset = offset * self.rep_config['lr']['_xyz']
+        if self.rep_config['perturb_offset']:
+            offset = offset + comfy.model_management.cast_to(self.points_offset_perturbation, offset.dtype, offset.device)
+        offset = torch.tanh(offset) * 0.5 * self.rep_config['perturbe_size']
+        offset = offset * _offset_scale
+        return offset
+
+    def forward(self, x=None, cond=None):
+        pcd = x["points"]
+        d = next(self.parameters()).dtype
+        B, L, _ = pcd.shape
+        h = self.in_proj(pcd.to(d)) + self.pos_embedder(pcd.reshape(-1, 3)).reshape(B, L, -1).to(d)
+        h = self.input_layer(h)
+        cond = cond.to(d)
+        for block in self.blocks:
+            h = block(h, cond)
+        h = F.layer_norm(h.float(), h.shape[-1:]).to(h.dtype)
+        return {"features": self.out_proj(h)}
+
+
+# Combined octree gaussian decoder (comfy first-stage model)
+
+class OctreeGaussianDecoder(nn.Module):
+    _MAX_VOXEL_LEVEL = 8
+
+    def __init__(self, dtype=None, device=None, operations=None):
+        super().__init__()
+        if operations is None:
+            operations = comfy.ops.disable_weight_init
+        self.octree = OctreeProbabilityFixedlenDecoder(dtype=dtype, device=device, operations=operations)
+        self.gs = ElasticGaussianFixedlenDecoder(dtype=dtype, device=device, operations=operations)
+
+    @property
+    def gaussians_per_point(self) -> int:
+        return self.gs.rep_config['num_gaussians']
+
+    def decode(self, latent: torch.Tensor, num_gaussians: int, level: int = None, generator=None):
+        # level defaults to the full octree depth, a lower level is cheaper (coarser) for live previews.
+        # generator (a CPU torch.Generator) makes the octree sampling reproducible without touching global RNG.
+        level = self._MAX_VOXEL_LEVEL if level is None else level
+        num_decoder_tokens = max(1, num_gaussians // self.gaussians_per_point)
+        points_pred = OctreeProbabilityFixedlenDecoder.sample(
+            self.octree, latent, num_points=num_decoder_tokens, level=level, temperature=1.0, generator=generator,
+        )
+        pred = self.gs(x=points_pred, cond=latent)
+        return build_gaussian_models(self.gs, points_pred, pred)  # one GaussianModel per batch item

comfy/model_base.py

@@ -46,6 +46,7 @@ import comfy.ldm.wan.model_animate
 import comfy.ldm.wan.ar_model
 import comfy.ldm.wan.model_wandancer
 import comfy.ldm.hunyuan3d.model
+import comfy.ldm.triposplat.model
 import comfy.ldm.hidream.model
 import comfy.ldm.chroma.model
 import comfy.ldm.chroma_radiance.model
@@ -54,6 +55,7 @@ import comfy.ldm.pixeldit.pid
 import comfy.ldm.ace.model
 import comfy.ldm.omnigen.omnigen2
 import comfy.ldm.qwen_image.model
+import comfy.ldm.ideogram4.model
 import comfy.ldm.kandinsky5.model
 import comfy.ldm.anima.model
 import comfy.ldm.ace.ace_step15
@@ -1806,6 +1808,24 @@ class Hunyuan3Dv2_1(BaseModel):
             out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))
         return out
 
+class TripoSplat(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.triposplat.model.LatentSeqMMFlowModel)
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        cross_attn = kwargs.get("cross_attn", None) # DINOv3 token sequence -> cross-attention context.
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+        ref_latents = kwargs.get("reference_latents", None) # Flux2 VAE image latent -> additive second conditioning.
+        if ref_latents is not None:
+            out['ref_latents'] = comfy.conds.CONDList(list(ref_latents))
+        latent_shapes = kwargs.get("latent_shapes", None) # {latent, camera} nested latent
+        if latent_shapes is not None:
+            out['latent_shapes'] = comfy.conds.CONDConstant(latent_shapes)
+        return out
+
+
 class HiDream(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hidream.model.HiDreamImageTransformer2DModel)
@@ -1999,6 +2019,21 @@ class QwenImage(BaseModel):
             out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16])
         return out
 
+class Ideogram4(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.ideogram4.model.Ideogram4Transformer2DModel)
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        attention_mask = kwargs.get("attention_mask", None)
+        if attention_mask is not None:
+            if torch.numel(attention_mask) != attention_mask.sum():
+                out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+        return out
+
 class HunyuanImage21(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan_video.model.HunyuanVideo)

comfy/model_detection.py

@@ -313,6 +313,10 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
                     dit_config["use_x0"] = True
                 else:
                     dit_config["use_x0"] = False
+                if "{}__sequential__".format(key_prefix) in state_dict_keys: # sequential txt_ids
+                    dit_config["use_sequential_txt_ids"] = True
+                else:
+                    dit_config["use_sequential_txt_ids"] = False
         else:
             dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys
             dit_config["yak_mlp"] = '{}double_blocks.0.img_mlp.gate_proj.weight'.format(key_prefix) in state_dict_keys
@@ -676,6 +680,9 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         dit_config["guidance_cond_proj_dim"] = None#f"{key_prefix}t_embedder.cond_proj.weight" in state_dict_keys
         return dit_config
 
+    if '{}cam_out_layer.weight'.format(key_prefix) in state_dict_keys and '{}repo_layers.0.final_map.weight'.format(key_prefix) in state_dict_keys:  # TripoSplat
+        return {"image_model": "triposplat"}
+
     if '{}t_embedder1.mlp.0.weight'.format(key_prefix) in state_dict_keys and '{}x_embedder.proj1.weight'.format(key_prefix) in state_dict_keys:  # HiDream-O1
         return {"image_model": "hidream_o1"}
 
@@ -808,6 +815,13 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
             dit_config["default_ref_method"] = "negative_index"
         return dit_config
 
+    if '{}embed_image_indicator.weight'.format(key_prefix) in state_dict_keys:  # Ideogram 4
+        dit_config = {}
+        dit_config["image_model"] = "ideogram4"
+        dit_config["in_channels"] = state_dict['{}input_proj.weight'.format(key_prefix)].shape[1]
+        dit_config["num_layers"] = count_blocks(state_dict_keys, '{}layers.'.format(key_prefix) + '{}.')
+        return dit_config
+
     if '{}visual_transformer_blocks.0.cross_attention.key_norm.weight'.format(key_prefix) in state_dict_keys: # Kandinsky 5
         dit_config = {}
         model_dim = state_dict['{}visual_embeddings.in_layer.bias'.format(key_prefix)].shape[0]

comfy/model_management.py

@@ -651,8 +651,7 @@ def ensure_pin_budget(size, evict_active=False):
     to_free = shortfall + PIN_PRESSURE_HYSTERESIS
     return free_pins(to_free, evict_active=evict_active) >= shortfall
 
-def ensure_pin_registerable(size, evict_active=True):
-    shortfall = TOTAL_PINNED_MEMORY + size - MAX_PINNED_MEMORY
+def free_registrations(shortfall, evict_active=True):
     if MAX_PINNED_MEMORY <= 0:
         return False
     if shortfall <= 0:
@@ -674,6 +673,9 @@ def ensure_pin_registerable(size, evict_active=True):
                     return True
     return shortfall <= REGISTERABLE_PIN_HYSTERESIS
 
+def ensure_pin_registerable(size, evict_active=True):
+    return free_registrations(TOTAL_PINNED_MEMORY + size - MAX_PINNED_MEMORY, evict_active=evict_active)
+
 class LoadedModel:
     def __init__(self, model: ModelPatcher):
         self._set_model(model)

comfy/multigpu.py

@@ -54,6 +54,8 @@ class MultiGPUThreadPool:
             try:
                 result = fn(*args, **kwargs)
                 result_q.put((result, None))
+            except comfy.model_management.InterruptProcessingException as e:
+                result_q.put((None, e))
             except Exception as e:
                 result_q.put((None, e))
 

comfy/pinned_memory.py

@@ -89,13 +89,26 @@ def pin_memory(module, subset="weights", size=None):
         not comfy.model_management.ensure_pin_registerable(registerable_size)):
         return _steal_pin(module, stack, buckets, size, priority)
 
+    extended = False
     try:
-        hostbuf.extend(size=size)
+        hostbuf.extend(size=size, register=False)
+        extended = True
+        pin = comfy_aimdo.torch.hostbuf_to_tensor(hostbuf)[offset:offset + size]
+        pin.untyped_storage()._comfy_hostbuf = hostbuf
+        if torch.cuda.cudart().cudaHostRegister(pin.data_ptr(), size, 1) != 0:
+            comfy.model_management.discard_cuda_async_error()
+            comfy.model_management.free_registrations(size)
+            if torch.cuda.cudart().cudaHostRegister(pin.data_ptr(), size, 1) != 0:
+                comfy.model_management.discard_cuda_async_error()
+                del pin
+                hostbuf.truncate(offset, do_unregister=False)
+                return _steal_pin(module, stack, buckets, size, priority)
     except RuntimeError:
+        if extended:
+            hostbuf.truncate(offset, do_unregister=False)
         return _steal_pin(module, stack, buckets, size, priority)
 
-    module._pin = comfy_aimdo.torch.hostbuf_to_tensor(hostbuf)[offset:offset + size]
-    module._pin.untyped_storage()._comfy_hostbuf = hostbuf
+    module._pin = pin
     stack.append((module, offset))
     module._pin_registered = True
     module._pin_stack_index = len(stack) - 1

comfy/sd.py

@@ -16,6 +16,7 @@ import comfy.ldm.cosmos.vae
 import comfy.ldm.wan.vae
 import comfy.ldm.wan.vae2_2
 import comfy.ldm.hunyuan3d.vae
+import comfy.ldm.triposplat.vae
 import comfy.ldm.ace.vae.music_dcae_pipeline
 import comfy.ldm.cogvideo.vae
 import comfy.ldm.hunyuan_video.vae
@@ -57,6 +58,7 @@ import comfy.text_encoders.omnigen2
 import comfy.text_encoders.qwen_image
 import comfy.text_encoders.hunyuan_image
 import comfy.text_encoders.z_image
+import comfy.text_encoders.ideogram4
 import comfy.text_encoders.ovis
 import comfy.text_encoders.kandinsky5
 import comfy.text_encoders.jina_clip_2
@@ -894,6 +896,16 @@ class VAE:
                 #Force cast it for --disable-dynamic-vram users until there is a true core fix.
                 if not comfy.memory_management.aimdo_enabled:
                     self.disable_offload = True
+            elif "gs.base_offset_scale" in sd and "octree.out_proj.weight" in sd:  # TripoSplat octree gaussian decoder
+                self.first_stage_model = comfy.ldm.triposplat.vae.OctreeGaussianDecoder()
+                self.latent_channels = 16
+                self.latent_dim = 1
+                self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+                # The generic VAE.encode/decode path isn't used: VAEDecodeTripoSplat calls the gaussian
+                # decoder directly (structured GaussianSplat objects, not a tensor and reserves VRAM itself from num_gaussians.
+                def _no_generic_io(*args, **kwargs):
+                    raise RuntimeError("TripoSplat gaussian decoder: use the 'TripoSplat Decode' (VAEDecodeTripoSplat)")
+                self.memory_used_encode = self.memory_used_decode = _no_generic_io
             else:
                 logging.warning("WARNING: No VAE weights detected, VAE not initalized.")
                 self.first_stage_model = None
@@ -1287,6 +1299,7 @@ class CLIPType(Enum):
     COGVIDEOX = 27
     LENS = 28
     PIXELDIT = 29
+    IDEOGRAM4 = 30
 
 
 
@@ -1585,8 +1598,12 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
             clip_target.clip = comfy.text_encoders.ovis.te(**llama_detect(clip_data))
             clip_target.tokenizer = comfy.text_encoders.ovis.OvisTokenizer
         elif te_model == TEModel.QWEN3_8B:
-            clip_target.clip = comfy.text_encoders.flux.klein_te(**llama_detect(clip_data), model_type="qwen3_8b")
-            clip_target.tokenizer = comfy.text_encoders.flux.KleinTokenizer8B
+            if clip_type == CLIPType.IDEOGRAM4:
+                clip_target.clip = comfy.text_encoders.ideogram4.te(**llama_detect(clip_data))
+                clip_target.tokenizer = comfy.text_encoders.ideogram4.Ideogram4Tokenizer
+            else:
+                clip_target.clip = comfy.text_encoders.flux.klein_te(**llama_detect(clip_data), model_type="qwen3_8b")
+                clip_target.tokenizer = comfy.text_encoders.flux.KleinTokenizer8B
         elif te_model == TEModel.JINA_CLIP_2:
             clip_target.clip = comfy.text_encoders.jina_clip_2.JinaClip2TextModelWrapper
             clip_target.tokenizer = comfy.text_encoders.jina_clip_2.JinaClip2TokenizerWrapper

comfy/supported_models.py

@@ -24,6 +24,7 @@ import comfy.text_encoders.qwen_image
 import comfy.text_encoders.hunyuan_image
 import comfy.text_encoders.kandinsky5
 import comfy.text_encoders.z_image
+import comfy.text_encoders.ideogram4
 import comfy.text_encoders.anima
 import comfy.text_encoders.ace15
 import comfy.text_encoders.longcat_image
@@ -1538,6 +1539,30 @@ class Hunyuan3Dv2mini(Hunyuan3Dv2):
 
     latent_format = latent_formats.Hunyuan3Dv2mini
 
+class TripoSplat(supported_models_base.BASE):
+    # Image -> 3D gaussian splat flow denoiser
+    unet_config = {
+        "image_model": "triposplat",
+    }
+
+    unet_extra_config = {}
+
+    sampling_settings = {
+        "shift": 3.0,
+    }
+
+    memory_usage_factor = 0.6
+
+    latent_format = latent_formats.TripoSplat
+
+    supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+
+    def get_model(self, state_dict, prefix="", device=None):
+        return model_base.TripoSplat(self, device=device)
+
+    def clip_target(self, state_dict={}):
+        return None
+
 class HiDream(supported_models_base.BASE):
     unet_config = {
         "image_model": "hidream",
@@ -1722,6 +1747,44 @@ class Omnigen2(supported_models_base.BASE):
         hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_3b.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.omnigen2.Omnigen2Tokenizer, comfy.text_encoders.omnigen2.te(**hunyuan_detect))
 
+class Ideogram4(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "ideogram4",
+    }
+
+    sampling_settings = {
+        "multiplier": 1.0,
+        "shift": 1.0,
+    }
+
+    memory_usage_factor = 11.6
+
+    unet_extra_config = {
+        "num_attention_heads": 18,
+        "attention_head_dim": 256,
+        "intermediate_size": 12288,
+        "adaln_dim": 512,
+        "llm_features_dim": 53248,
+        "rope_theta": 5000000,
+        "mrope_section": [24, 20, 20],
+        "norm_eps": 1e-5,
+    }
+    latent_format = latent_formats.Flux2
+
+    supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+    vae_key_prefix = ["vae."]
+    text_encoder_key_prefix = ["text_encoders."]
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.Ideogram4(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        pref = self.text_encoder_key_prefix[0]
+        hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen3vl_8b.transformer.".format(pref))
+        return supported_models_base.ClipTarget(comfy.text_encoders.ideogram4.Ideogram4Tokenizer, comfy.text_encoders.ideogram4.te(**hunyuan_detect))
+
 class QwenImage(supported_models_base.BASE):
     unet_config = {
         "image_model": "qwen_image",
@@ -2200,6 +2263,7 @@ models = [
     Hunyuan3Dv2mini,
     Hunyuan3Dv2,
     Hunyuan3Dv2_1,
+    TripoSplat,
     HiDream,
     HiDreamO1,
     Chroma,
@@ -2208,6 +2272,7 @@ models = [
     ACEStep15,
     Omnigen2,
     QwenImage,
+    Ideogram4,
     Flux2,
     Lens,
     Kandinsky5Image,

comfy/text_encoders/ideogram4.py

@@ -0,0 +1,77 @@
+"""Ideogram 4 text encoder: Qwen3-VL-8B language model, 13-layer tap.
+
+Ideogram 4 conditions on the concatenation of hidden states from 13 layers of
+Qwen3-VL (layers 0,3,...,33,35), giving a 4096*13 = 53248-dim feature per token.
+"""
+
+import os
+
+from transformers import Qwen2Tokenizer
+
+import comfy.text_encoders.llama
+from comfy import sd1_clip
+
+# Reference taps outputs of layers (0,3,...,35); comfy captures layer inputs, offset by +1.
+IDEOGRAM4_TAP_LAYERS = [1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 36]
+
+
+class Qwen3VLTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer")
+        super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory,
+                         embedding_size=4096, embedding_key='qwen3vl_8b', tokenizer_class=Qwen2Tokenizer,
+                         has_start_token=False, has_end_token=False, pad_to_max_length=False,
+                         max_length=99999999, min_length=1, pad_token=151643, tokenizer_data=tokenizer_data)
+
+
+class Ideogram4Tokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data,
+                         name="qwen3vl_8b", tokenizer=Qwen3VLTokenizer)
+
+        self.llama_template = "<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
+
+    def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, **kwargs):
+        if llama_template is None:
+            llama_text = self.llama_template.format(text)
+        else:
+            llama_text = llama_template.format(text)
+        return super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs)
+
+
+# Qwen3-VL-8B = 5e6 (vs plain Qwen3-8B's 1e6)
+# final_norm/lm_head off -> Ideogram only reads raw tapped hidden states
+QWEN3VL_8B_CONFIG = {"rope_theta": 5000000.0, "final_norm": False, "lm_head": False}
+
+
+class Qwen3VL8BModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="hidden", layer_idx=None, dtype=None, attention_mask=True, model_options={}):
+        super().__init__(device=device, layer=IDEOGRAM4_TAP_LAYERS, layer_idx=None,
+                         textmodel_json_config=dict(QWEN3VL_8B_CONFIG),
+                         dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False,
+                         model_class=comfy.text_encoders.llama.Qwen3_8B,
+                         enable_attention_masks=attention_mask, return_attention_masks=attention_mask,
+                         model_options=model_options)
+
+
+class Ideogram4TEModel(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__(device=device, dtype=dtype, name="qwen3vl_8b", clip_model=Qwen3VL8BModel, model_options=model_options)
+
+    def encode_token_weights(self, token_weight_pairs):
+        out, pooled, extra = super().encode_token_weights(token_weight_pairs)
+        b, n, seq, h = out.shape # (B, n_taps=13, seq, 4096) stacked in ascending layer order.
+        out = out.permute(0, 2, 3, 1).reshape(b, seq, h * n) # (B, seq, 4096*13). permute -> (B, seq, H, taps).
+        return out, pooled, extra
+
+
+def te(dtype_llama=None, llama_quantization_metadata=None):
+    class Ideogram4TEModel_(Ideogram4TEModel):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if dtype_llama is not None:
+                dtype = dtype_llama
+            if llama_quantization_metadata is not None:
+                model_options = model_options.copy()
+                model_options["quantization_metadata"] = llama_quantization_metadata
+            super().__init__(device=device, dtype=dtype, model_options=model_options)
+    return Ideogram4TEModel_

comfy_api/latest/_input/video_types.py

@@ -65,6 +65,12 @@ class VideoInput(ABC):
         buffer.seek(0)
         return buffer
 
+    def get_active_trim_window(self) -> tuple[float, float]:
+        """Return the active trim as ``(start_time, duration)`` in seconds (start_time normalized
+        to ``>= 0``; ``duration == 0`` means "until the end"). Default: no trim; trimmable subclasses override.
+        """
+        return 0.0, 0.0
+
     # Provide a default implementation, but subclasses can provide optimized versions
     # if possible.
     def get_dimensions(self) -> tuple[int, int]:

comfy_api/latest/_input_impl/video_types.py

@@ -75,6 +75,12 @@ class VideoFromFile(VideoInput):
             self.__file.seek(0)
         return self.__file
 
+    def get_active_trim_window(self) -> tuple[float, float]:
+        start_time = self.__start_time
+        if start_time < 0:
+            start_time = max(self._get_raw_duration() + start_time, 0.0)
+        return float(start_time), float(self.__duration)
+
     def get_dimensions(self) -> tuple[int, int]:
         """
         Returns the dimensions of the video input.

comfy_api/latest/_io.py

@@ -755,6 +755,18 @@ class File3DKSPLAT(ComfyTypeIO):
     Type = File3D
 
 
+@comfytype(io_type="FILE_3D_SPLAT_ANY")
+class File3DSplatAny(ComfyTypeIO):
+    """General 3D Gaussian splat file type - accepts any supported splat container (.ply / .spz / .splat / .ksplat)."""
+    Type = File3D
+
+
+@comfytype(io_type="FILE_3D_POINT_CLOUD_ANY")
+class File3DPointCloudAny(ComfyTypeIO):
+    """General point cloud file type - accepts any supported point cloud container (currently .ply)."""
+    Type = File3D
+
+
 @comfytype(io_type="HOOKS")
 class Hooks(ComfyTypeIO):
     if TYPE_CHECKING:
@@ -2336,6 +2348,8 @@ __all__ = [
     "File3DSPLAT",
     "File3DSPZ",
     "File3DKSPLAT",
+    "File3DSplatAny",
+    "File3DPointCloudAny",
     "Hooks",
     "HookKeyframes",
     "TimestepsRange",

comfy_api/latest/_ui.py

@@ -285,7 +285,7 @@ class AudioSaveHelper:
         results = []
         for batch_number, waveform in enumerate(audio["waveform"].cpu()):
             filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
-            file = f"{filename_with_batch_num}_{counter:05}_.{format}"
+            file = f"{filename_with_batch_num}_{counter:05}.{format}"
             output_path = os.path.join(full_output_folder, file)
 
             # Use original sample rate initially

comfy_api_nodes/apis/bfl.py

@@ -1,71 +1,72 @@
 from enum import Enum
-from typing import Any, Dict, Optional
+from typing import Any
 
-from pydantic import BaseModel, Field, confloat, conint
-
-
-class BFLOutputFormat(str, Enum):
-    png = 'png'
-    jpeg = 'jpeg'
+from pydantic import BaseModel, Field
 
 
 class BFLFluxExpandImageRequest(BaseModel):
-    prompt: str = Field(..., description='The description of the changes you want to make. This text guides the expansion process, allowing you to specify features, styles, or modifications for the expanded areas.')
-    prompt_upsampling: Optional[bool] = Field(
-        None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.'
-    )
-    seed: Optional[int] = Field(None, description='The seed value for reproducibility.')
-    top: conint(ge=0, le=2048) = Field(..., description='Number of pixels to expand at the top of the image')
-    bottom: conint(ge=0, le=2048) = Field(..., description='Number of pixels to expand at the bottom of the image')
-    left: conint(ge=0, le=2048) = Field(..., description='Number of pixels to expand at the left side of the image')
-    right: conint(ge=0, le=2048) = Field(..., description='Number of pixels to expand at the right side of the image')
-    steps: conint(ge=15, le=50) = Field(..., description='Number of steps for the image generation process')
-    guidance: confloat(ge=1.5, le=100) = Field(..., description='Guidance strength for the image generation process')
-    safety_tolerance: Optional[conint(ge=0, le=6)] = Field(
-        6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.'
-    )
-    output_format: Optional[BFLOutputFormat] = Field(
-        BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png']
-    )
-    image: str = Field(None, description='A Base64-encoded string representing the image you wish to expand')
+    prompt: str = Field(...)
+    prompt_upsampling: bool | None = Field(None)
+    seed: int | None = Field(None)
+    top: int = Field(...)
+    bottom: int = Field(...)
+    left: int = Field(...)
+    right: int = Field(...)
+    steps: int = Field(...)
+    guidance: float = Field(...)
+    safety_tolerance: int = Field(6)
+    output_format: str = Field("png")
+    image: str = Field(None, description="A Base64-encoded string representing the image you wish to expand")
 
 
 class BFLFluxFillImageRequest(BaseModel):
-    prompt: str = Field(..., description='The description of the changes you want to make. This text guides the expansion process, allowing you to specify features, styles, or modifications for the expanded areas.')
-    prompt_upsampling: Optional[bool] = Field(
-        None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.'
+    prompt: str = Field(...)
+    prompt_upsampling: bool | None = Field(None)
+    seed: int | None = Field(None)
+    steps: int = Field(...)
+    guidance: float = Field(...)
+    safety_tolerance: int = Field(6)
+    output_format: str = Field("png")
+    image: str = Field(
+        None, description="Base64-encoded string representing the image to modify. Can contain alpha mask if desired.",
     )
-    seed: Optional[int] = Field(None, description='The seed value for reproducibility.')
-    steps: conint(ge=15, le=50) = Field(..., description='Number of steps for the image generation process')
-    guidance: confloat(ge=1.5, le=100) = Field(..., description='Guidance strength for the image generation process')
-    safety_tolerance: Optional[conint(ge=0, le=6)] = Field(
-        6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.'
+    mask: str = Field(
+        None, description="Base64-encoded string representing the mask of the areas you wish to modify."
     )
-    output_format: Optional[BFLOutputFormat] = Field(
-        BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png']
+
+
+class BFLFluxEraseRequest(BaseModel):
+    image: str = Field(..., description="A Base64-encoded string representing the image to erase from.")
+    mask: str = Field(
+        ...,
+        description="A Base64-encoded black/white mask matching the input dimensions; "
+        "white (255) marks areas to remove, black (0) marks areas to preserve.",
     )
-    image: str = Field(None, description='A Base64-encoded string representing the image you wish to modify. Can contain alpha mask if desired.')
-    mask: str = Field(None, description='A Base64-encoded string representing the mask of the areas you with to modify.')
+    dilate_pixels: int = Field(10)
+    seed: int | None = Field(None)
+    output_format: str = Field("png")
+
+
+class BFLFluxVTORequest(BaseModel):
+    prompt: str = Field(
+        ..., description="Natural-language styling instruction. Required field, but may be an empty string."
+    )
+    person: str = Field(..., description="A Base64-encoded string representing the person image.")
+    garment: str = Field(..., description="A Base64-encoded string representing the garment reference image.")
+    seed: int | None = Field(None)
+    safety_tolerance: int = Field(5)
+    output_format: str = Field("png")
 
 
 class BFLFluxProGenerateRequest(BaseModel):
-    prompt: str = Field(..., description='The text prompt for image generation.')
-    prompt_upsampling: Optional[bool] = Field(
-        None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.'
-    )
-    seed: Optional[int] = Field(None, description='The seed value for reproducibility.')
-    width: conint(ge=256, le=1440) = Field(1024, description='Width of the generated image in pixels. Must be a multiple of 32.')
-    height: conint(ge=256, le=1440) = Field(768, description='Height of the generated image in pixels. Must be a multiple of 32.')
-    safety_tolerance: Optional[conint(ge=0, le=6)] = Field(
-        6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.'
-    )
-    output_format: Optional[BFLOutputFormat] = Field(
-        BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png']
-    )
-    image_prompt: Optional[str] = Field(None, description='Optional image to remix in base64 format')
-    # image_prompt_strength: Optional[confloat(ge=0.0, le=1.0)] = Field(
-    #     None, description='Blend between the prompt and the image prompt.'
-    # )
+    prompt: str = Field(...)
+    prompt_upsampling: bool | None = Field(None)
+    seed: int | None = Field(None)
+    width: int = Field(1024, description="Must be a multiple of 32.")
+    height: int = Field(768, description="Must be a multiple of 32.")
+    safety_tolerance: int = Field(6)
+    output_format: str = Field("png")
+    image_prompt: str | None = Field(None, description="Optional image to remix in base64 format")
 
 
 class Flux2ProGenerateRequest(BaseModel):
@@ -83,55 +84,37 @@ class Flux2ProGenerateRequest(BaseModel):
     input_image_7: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
     input_image_8: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
     input_image_9: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
-    safety_tolerance: int | None = Field(
-        5, description="Tolerance level for input and output moderation. Value 0 being most strict.", ge=0, le=5
-    )
-    output_format: str | None = Field(
-        "png", description="Output format for the generated image. Can be 'jpeg' or 'png'."
-    )
+    safety_tolerance: int = Field(5)
+    output_format: str = Field("png")
 
 
 class BFLFluxKontextProGenerateRequest(BaseModel):
-    prompt: str = Field(..., description='The text prompt for what you wannt to edit.')
-    input_image: Optional[str] = Field(None, description='Image to edit in base64 format')
-    seed: Optional[int] = Field(None, description='The seed value for reproducibility.')
-    guidance: confloat(ge=0.1, le=99.0) = Field(..., description='Guidance strength for the image generation process')
-    steps: conint(ge=1, le=150) = Field(..., description='Number of steps for the image generation process')
-    safety_tolerance: Optional[conint(ge=0, le=2)] = Field(
-        2, description='Tolerance level for input and output moderation. Between 0 and 2, 0 being most strict, 6 being least strict. Defaults to 2.'
-    )
-    output_format: Optional[BFLOutputFormat] = Field(
-        BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png']
-    )
-    aspect_ratio: Optional[str] = Field(None, description='Aspect ratio of the image between 21:9 and 9:21.')
-    prompt_upsampling: Optional[bool] = Field(
-        None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.'
-    )
+    prompt: str = Field(...)
+    input_image: str | None = Field(None, description="Image to edit in base64 format")
+    seed: int | None = Field(None)
+    guidance: float = Field(...)
+    steps: int = Field(...)
+    safety_tolerance: int = Field(2)
+    output_format: str = Field("png")
+    aspect_ratio: str | None = Field(None)
+    prompt_upsampling: bool | None = Field(None)
 
 
 class BFLFluxProUltraGenerateRequest(BaseModel):
-    prompt: str = Field(..., description='The text prompt for image generation.')
-    prompt_upsampling: Optional[bool] = Field(
-        None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.'
-    )
-    seed: Optional[int] = Field(None, description='The seed value for reproducibility.')
-    aspect_ratio: Optional[str] = Field(None, description='Aspect ratio of the image between 21:9 and 9:21.')
-    safety_tolerance: Optional[conint(ge=0, le=6)] = Field(
-        6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.'
-    )
-    output_format: Optional[BFLOutputFormat] = Field(
-        BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png']
-    )
-    raw: Optional[bool] = Field(None, description='Generate less processed, more natural-looking images.')
-    image_prompt: Optional[str] = Field(None, description='Optional image to remix in base64 format')
-    image_prompt_strength: Optional[confloat(ge=0.0, le=1.0)] = Field(
-        None, description='Blend between the prompt and the image prompt.'
-    )
+    prompt: str = Field(...)
+    prompt_upsampling: bool | None = Field(None)
+    seed: int | None = Field(None)
+    aspect_ratio: str | None = Field(None)
+    safety_tolerance: int = Field(6)
+    output_format: str = Field("png")
+    raw: bool | None = Field(None)
+    image_prompt: str | None = Field(None, description="Optional image to remix in base64 format")
+    image_prompt_strength: float | None = Field(None)
 
 
 class BFLFluxProGenerateResponse(BaseModel):
-    id: str = Field(..., description="The unique identifier for the generation task.")
-    polling_url: str = Field(..., description="URL to poll for the generation result.")
+    id: str = Field(...)
+    polling_url: str = Field(...)
     cost: float | None = Field(None, description="Price in cents")
 
 
@@ -145,7 +128,7 @@ class BFLStatus(str, Enum):
 
 
 class BFLFluxStatusResponse(BaseModel):
-    id: str = Field(..., description="The unique identifier for the generation task.")
-    status: BFLStatus = Field(..., description="The status of the task.")
-    result: Optional[Dict[str, Any]] = Field(None, description="The result of the task (null if not completed).")
-    progress: Optional[float] = Field(None, description="The progress of the task (0.0 to 1.0).", ge=0.0, le=1.0)
+    id: str = Field(...)
+    status: BFLStatus = Field(...)
+    result: dict[str, Any] | None = Field(None)
+    progress: float | None = Field(None, ge=0.0, le=1.0)

comfy_api_nodes/apis/bria.py

@@ -97,3 +97,28 @@ class BriaRemoveVideoBackgroundResult(BaseModel):
 class BriaRemoveVideoBackgroundResponse(BaseModel):
     status: str = Field(...)
     result: BriaRemoveVideoBackgroundResult | None = Field(None)
+
+
+class BriaVideoGreenScreenRequest(BaseModel):
+    video: str = Field(..., description="Publicly accessible URL of the input video.")
+    green_shade: str = Field(
+        default="broadcast_green",
+        description="Solid chroma-key shade applied behind the foreground "
+        "(broadcast_green, chroma_green, or blue_screen).",
+    )
+    output_container_and_codec: str = Field(...)
+    preserve_audio: bool = Field(True)
+    seed: int = Field(...)
+
+
+class BriaVideoReplaceBackgroundRequest(BaseModel):
+    video: str = Field(..., description="Publicly accessible URL of the input (foreground) video.")
+    background_url: str = Field(
+        ...,
+        description="Publicly accessible URL of the background image or video to composite behind "
+        "the foreground. Stretched to the foreground frame; match its aspect ratio for "
+        "undistorted results.",
+    )
+    output_container_and_codec: str = Field(...)
+    preserve_audio: bool = Field(True)
+    seed: int = Field(...)

comfy_api_nodes/apis/gemini.py

@@ -108,13 +108,19 @@ class GeminiVideoMetadata(BaseModel):
     startOffset: GeminiOffset | None = Field(None)
 
 
+class GeminiThinkingConfig(BaseModel):
+    includeThoughts: bool | None = Field(None)
+    thinkingLevel: str = Field(...)
+
+
 class GeminiGenerationConfig(BaseModel):
-    maxOutputTokens: int | None = Field(None, ge=16, le=8192)
+    maxOutputTokens: int | None = Field(None, ge=16, le=65536)
     seed: int | None = Field(None)
     stopSequences: list[str] | None = Field(None)
     temperature: float | None = Field(None, ge=0.0, le=2.0)
     topK: int | None = Field(None, ge=1)
     topP: float | None = Field(None, ge=0.0, le=1.0)
+    thinkingConfig: GeminiThinkingConfig | None = Field(None)
 
 
 class GeminiImageOutputOptions(BaseModel):
@@ -128,11 +134,6 @@ class GeminiImageConfig(BaseModel):
     imageOutputOptions: GeminiImageOutputOptions = Field(default_factory=GeminiImageOutputOptions)
 
 
-class GeminiThinkingConfig(BaseModel):
-    includeThoughts: bool | None = Field(None)
-    thinkingLevel: str = Field(...)
-
-
 class GeminiImageGenerationConfig(GeminiGenerationConfig):
     responseModalities: list[str] | None = Field(None)
     imageConfig: GeminiImageConfig | None = Field(None)

comfy_api_nodes/apis/ideogram.py

@@ -290,3 +290,19 @@ class IdeogramV3Request(BaseModel):
         None,
         description='Optional masks for character reference images. When provided, must match the number of character_reference_images. Each mask should be a grayscale image of the same dimensions as the corresponding character reference image. The images should be in JPEG, PNG or WebP format.'
     )
+
+
+class IdeogramV4Request(BaseModel):
+    text_prompt: str | None = Field(
+        None,
+        description="Natural-language prompt; Magic Prompt is applied automatically. "
+        "Supply exactly one of text_prompt or json_prompt.",
+    )
+    json_prompt: dict[str, Any] | None = Field(
+        None,
+        description="Structured V4 prompt object consumed directly (disables Magic Prompt). "
+        "Supply exactly one of text_prompt or json_prompt.",
+    )
+    resolution: str | None = Field(None, description="Output resolution in WIDTHxHEIGHT (e.g. '2048x2048').")
+    rendering_speed: str | None = Field(None, description="Rendering speed: 'TURBO', 'DEFAULT', or 'QUALITY'.")
+    enable_copyright_detection: bool | None = Field(None, description="Opt into post-generation copyright detection.")

comfy_api_nodes/nodes_anthropic.py

@@ -155,7 +155,7 @@ class ClaudeNode(IO.ComfyNode):
         return IO.Schema(
             node_id="ClaudeNode",
             display_name="Anthropic Claude",
-            category="text/partner/Anthropic",
+            category="partner/text/Anthropic",
             essentials_category="Text Generation",
             description="Generate text responses with Anthropic's Claude models. "
             "Provide a text prompt and optionally one or more images for multimodal context.",

comfy_api_nodes/nodes_beeble.py

@@ -206,7 +206,7 @@ class BeebleSwitchXVideoEdit(IO.ComfyNode):
         return IO.Schema(
             node_id="BeebleSwitchXVideoEdit",
             display_name="Beeble SwitchX Video Edit",
-            category="video/partner/Beeble",
+            category="partner/video/Beeble",
             description=(
                 "Edit a video with Beeble SwitchX. Switches anything in the scene (background, "
                 "lighting, costume) while preserving the original subject's pixels and motion. "
@@ -302,7 +302,7 @@ class BeebleSwitchXImageEdit(IO.ComfyNode):
         return IO.Schema(
             node_id="BeebleSwitchXImageEdit",
             display_name="Beeble SwitchX Image Edit",
-            category="image/partner/Beeble",
+            category="partner/image/Beeble",
             description=(
                 "Edit a single image with Beeble SwitchX. Switches anything in the scene "
                 "(background, lighting, costume) while preserving the original subject's pixels. "

comfy_api_nodes/nodes_bfl.py

@@ -4,17 +4,20 @@ from typing_extensions import override
 
 from comfy_api.latest import IO, ComfyExtension, Input
 from comfy_api_nodes.apis.bfl import (
+    BFLFluxEraseRequest,
     BFLFluxExpandImageRequest,
     BFLFluxFillImageRequest,
     BFLFluxKontextProGenerateRequest,
     BFLFluxProGenerateResponse,
     BFLFluxProUltraGenerateRequest,
     BFLFluxStatusResponse,
+    BFLFluxVTORequest,
     BFLStatus,
     Flux2ProGenerateRequest,
 )
 from comfy_api_nodes.util import (
     ApiEndpoint,
+    convert_mask_to_image,
     download_url_to_image_tensor,
     get_number_of_images,
     poll_op,
@@ -22,19 +25,11 @@ from comfy_api_nodes.util import (
     sync_op,
     tensor_to_base64_string,
     validate_aspect_ratio_string,
+    validate_image_dimensions,
     validate_string,
 )
 
 
-def convert_mask_to_image(mask: Input.Image):
-    """
-    Make mask have the expected amount of dims (4) and channels (3) to be recognized as an image.
-    """
-    mask = mask.unsqueeze(-1)
-    mask = torch.cat([mask] * 3, dim=-1)
-    return mask
-
-
 class FluxProUltraImageNode(IO.ComfyNode):
 
     @classmethod
@@ -42,7 +37,7 @@ class FluxProUltraImageNode(IO.ComfyNode):
         return IO.Schema(
             node_id="FluxProUltraImageNode",
             display_name="Flux 1.1 [pro] Ultra Image",
-            category="image/partner/BFL",
+            category="partner/image/BFL",
             description="Generates images using Flux Pro 1.1 Ultra via api based on prompt and resolution.",
             inputs=[
                 IO.String.Input(
@@ -160,7 +155,7 @@ class FluxKontextProImageNode(IO.ComfyNode):
         return IO.Schema(
             node_id=cls.NODE_ID,
             display_name=cls.DISPLAY_NAME,
-            category="image/partner/BFL",
+            category="partner/image/BFL",
             description="Edits images using Flux.1 Kontext [pro] via api based on prompt and aspect ratio.",
             inputs=[
                 IO.String.Input(
@@ -282,7 +277,7 @@ class FluxProExpandNode(IO.ComfyNode):
         return IO.Schema(
             node_id="FluxProExpandNode",
             display_name="Flux.1 Expand Image",
-            category="image/partner/BFL",
+            category="partner/image/BFL",
             description="Outpaints image based on prompt.",
             inputs=[
                 IO.Image.Input("image"),
@@ -419,7 +414,7 @@ class FluxProFillNode(IO.ComfyNode):
         return IO.Schema(
             node_id="FluxProFillNode",
             display_name="Flux.1 Fill Image",
-            category="image/partner/BFL",
+            category="partner/image/BFL",
             description="Inpaints image based on mask and prompt.",
             inputs=[
                 IO.Image.Input("image"),
@@ -519,6 +514,174 @@ class FluxProFillNode(IO.ComfyNode):
         return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"]))
 
 
+class FluxEraseNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="FluxEraseNode",
+            display_name="Flux Erase Image",
+            category="partner/image/BFL",
+            description="Removes the masked object from an image and reconstructs the background. "
+            "Paint the mask over what you want to erase.",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.Mask.Input("mask", tooltip="White areas are removed; black areas are preserved."),
+                IO.Int.Input(
+                    "dilate_pixels",
+                    default=10,
+                    min=0,
+                    max=25,
+                    tooltip="Expands the mask boundaries to ensure clean coverage of the object's edges.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="The random seed used for creating the noise.",
+                    optional=True,
+                ),
+            ],
+            outputs=[IO.Image.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"range_usd","min_usd":0.03,"max_usd":0.06,"format":{"approximate":true}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        mask: Input.Image,
+        dilate_pixels: int = 10,
+        seed: int = 0,
+    ) -> IO.NodeOutput:
+        validate_image_dimensions(image, min_width=256, min_height=256)
+        mask = resize_mask_to_image(mask, image)
+        mask = tensor_to_base64_string(convert_mask_to_image(mask))
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/bfl/v1/flux-tools/erase-v1", method="POST"),
+            response_model=BFLFluxProGenerateResponse,
+            data=BFLFluxEraseRequest(
+                image=tensor_to_base64_string(image[:, :, :, :3]),  # make sure image will have alpha channel removed
+                mask=mask,
+                dilate_pixels=dilate_pixels,
+                seed=seed,
+            ),
+        )
+
+        def price_extractor(_r: BaseModel) -> float | None:
+            return None if initial_response.cost is None else initial_response.cost / 100
+
+        response = await poll_op(
+            cls,
+            ApiEndpoint(initial_response.polling_url),
+            response_model=BFLFluxStatusResponse,
+            status_extractor=lambda r: r.status,
+            progress_extractor=lambda r: r.progress,
+            price_extractor=price_extractor,
+            completed_statuses=[BFLStatus.ready],
+            failed_statuses=[
+                BFLStatus.request_moderated,
+                BFLStatus.content_moderated,
+                BFLStatus.error,
+                BFLStatus.task_not_found,
+            ],
+            queued_statuses=[],
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"]))
+
+
+class FluxVTONode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="FluxVTONode",
+            display_name="Flux Virtual Try-On",
+            category="partner/image/BFL",
+            description="Virtual try-on: dresses the person in the provided garment.",
+            inputs=[
+                IO.Image.Input("person", tooltip="Image of the person to dress."),
+                IO.Image.Input("garment", tooltip="Image of the garment to apply."),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Optional natural-language styling instruction (e.g. how the garment should fit).",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="The random seed used for creating the noise.",
+                ),
+            ],
+            outputs=[IO.Image.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"range_usd","min_usd":0.0375,"max_usd":0.075,"format":{"approximate":true}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        person: Input.Image,
+        garment: Input.Image,
+        prompt: str = "",
+        seed: int = 0,
+    ) -> IO.NodeOutput:
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/bfl/v1/flux-tools/vto-v1", method="POST"),
+            response_model=BFLFluxProGenerateResponse,
+            data=BFLFluxVTORequest(
+                prompt=prompt,
+                person=tensor_to_base64_string(person[:, :, :, :3]),
+                garment=tensor_to_base64_string(garment[:, :, :, :3]),
+                seed=seed,
+            ),
+        )
+
+        def price_extractor(_r: BaseModel) -> float | None:
+            return None if initial_response.cost is None else initial_response.cost / 100
+
+        response = await poll_op(
+            cls,
+            ApiEndpoint(initial_response.polling_url),
+            response_model=BFLFluxStatusResponse,
+            status_extractor=lambda r: r.status,
+            progress_extractor=lambda r: r.progress,
+            price_extractor=price_extractor,
+            completed_statuses=[BFLStatus.ready],
+            failed_statuses=[
+                BFLStatus.request_moderated,
+                BFLStatus.content_moderated,
+                BFLStatus.error,
+                BFLStatus.task_not_found,
+            ],
+            queued_statuses=[],
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"]))
+
+
 class Flux2ProImageNode(IO.ComfyNode):
 
     NODE_ID = "Flux2ProImageNode"
@@ -545,7 +708,7 @@ class Flux2ProImageNode(IO.ComfyNode):
         return IO.Schema(
             node_id=cls.NODE_ID,
             display_name=cls.DISPLAY_NAME,
-            category="image/partner/BFL",
+            category="partner/image/BFL",
             description="Generates images synchronously based on prompt and resolution.",
             inputs=[
                 IO.String.Input(
@@ -716,7 +879,7 @@ class Flux2ImageNode(IO.ComfyNode):
         return IO.Schema(
             node_id="Flux2ImageNode",
             display_name="Flux.2 Image",
-            category="image/partner/BFL",
+            category="partner/image/BFL",
             description="Generate images via Flux.2 [pro] or Flux.2 [max] from a prompt and optional reference images.",
             inputs=[
                 IO.String.Input(
@@ -853,6 +1016,8 @@ class BFLExtension(ComfyExtension):
             FluxKontextMaxImageNode,
             FluxProExpandNode,
             FluxProFillNode,
+            FluxEraseNode,
+            FluxVTONode,
             Flux2ProImageNode,
             Flux2MaxImageNode,
             Flux2ImageNode,

comfy_api_nodes/nodes_bria.py

@@ -1,14 +1,19 @@
+import av
+import torch
+from av.codec import CodecContext
 from typing_extensions import override
 
 from comfy_api.latest import IO, ComfyExtension, Input
 from comfy_api_nodes.apis.bria import (
     BriaEditImageRequest,
+    BriaImageEditResponse,
     BriaRemoveBackgroundRequest,
     BriaRemoveBackgroundResponse,
     BriaRemoveVideoBackgroundRequest,
     BriaRemoveVideoBackgroundResponse,
-    BriaImageEditResponse,
     BriaStatusResponse,
+    BriaVideoGreenScreenRequest,
+    BriaVideoReplaceBackgroundRequest,
     InputModerationSettings,
 )
 from comfy_api_nodes.util import (
@@ -31,7 +36,7 @@ class BriaImageEditNode(IO.ComfyNode):
         return IO.Schema(
             node_id="BriaImageEditNode",
             display_name="Bria FIBO Image Edit",
-            category="image/partner/Bria",
+            category="partner/image/Bria",
             description="Edit images using Bria latest model",
             inputs=[
                 IO.Combo.Input("model", options=["FIBO"]),
@@ -169,7 +174,7 @@ class BriaRemoveImageBackground(IO.ComfyNode):
         return IO.Schema(
             node_id="BriaRemoveImageBackground",
             display_name="Bria Remove Image Background",
-            category="image/partner/Bria",
+            category="partner/image/Bria",
             description="Remove the background from an image using Bria RMBG 2.0.",
             inputs=[
                 IO.Image.Input("image"),
@@ -245,7 +250,7 @@ class BriaRemoveVideoBackground(IO.ComfyNode):
         return IO.Schema(
             node_id="BriaRemoveVideoBackground",
             display_name="Bria Remove Video Background",
-            category="video/partner/Bria",
+            category="partner/video/Bria",
             description="Remove the background from a video using Bria. ",
             inputs=[
                 IO.Video.Input("video"),
@@ -316,6 +321,248 @@ class BriaRemoveVideoBackground(IO.ComfyNode):
         return IO.NodeOutput(await download_url_to_video_output(response.result.video_url))
 
 
+class BriaVideoGreenScreen(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="BriaVideoGreenScreen",
+            display_name="Bria Video Green Screen",
+            category="partner/video/Bria",
+            description="Replace a video's background with a solid chroma-key screen using Bria.",
+            inputs=[
+                IO.Video.Input("video"),
+                IO.Combo.Input(
+                    "green_shade",
+                    options=["broadcast_green", "chroma_green", "blue_screen"],
+                    tooltip="Solid chroma-key shade applied behind the foreground: "
+                    "broadcast_green (#00B140), chroma_green (#00FF00), or blue_screen (#0000FF).",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.14,"format":{"suffix":"/second"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: Input.Video,
+        green_shade: str,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_video_duration(video, max_duration=60.0)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/bria/v2/video/edit/green_screen", method="POST"),
+            data=BriaVideoGreenScreenRequest(
+                video=await upload_video_to_comfyapi(cls, video),
+                green_shade=green_shade,
+                output_container_and_codec="mp4_h264",
+                seed=seed,
+            ),
+            response_model=BriaStatusResponse,
+        )
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/bria/v2/status/{response.request_id}"),
+            status_extractor=lambda r: r.status,
+            response_model=BriaRemoveVideoBackgroundResponse,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.result.video_url))
+
+
+class BriaVideoReplaceBackground(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="BriaVideoReplaceBackground",
+            display_name="Bria Video Replace Background",
+            category="partner/video/Bria",
+            description="Replace a video's background with a supplied image or video using Bria. "
+            "The output keeps the foreground's resolution and frame rate; a background with a "
+            "different aspect ratio is stretched to fit, so match it for undistorted results.",
+            inputs=[
+                IO.Video.Input("video", tooltip="Foreground video whose background is replaced."),
+                IO.Image.Input(
+                    "background_image",
+                    optional=True,
+                    tooltip="Background image to composite behind the foreground. "
+                    "Provide either a background image or a background video, not both.",
+                ),
+                IO.Video.Input(
+                    "background_video",
+                    optional=True,
+                    tooltip="Background video to composite behind the foreground. "
+                    "Provide either a background image or a background video, not both.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.14,"format":{"suffix":"/second"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: Input.Video,
+        seed: int,
+        background_image: Input.Image | None = None,
+        background_video: Input.Video | None = None,
+    ) -> IO.NodeOutput:
+        if (background_image is None) == (background_video is None):
+            raise ValueError("Provide either a background image or a background video, not both.")
+        validate_video_duration(video, max_duration=60.0)
+        if background_video is not None:
+            validate_video_duration(background_video, max_duration=60.0)
+            background_url = await upload_video_to_comfyapi(cls, background_video, wait_label="Uploading background")
+        else:
+            background_url = await upload_image_to_comfyapi(cls, background_image, wait_label="Uploading background")
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/bria/v2/video/edit/replace_background", method="POST"),
+            data=BriaVideoReplaceBackgroundRequest(
+                video=await upload_video_to_comfyapi(cls, video),
+                background_url=background_url,
+                output_container_and_codec="mp4_h264",
+                seed=seed,
+            ),
+            response_model=BriaStatusResponse,
+        )
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/bria/v2/status/{response.request_id}"),
+            status_extractor=lambda r: r.status,
+            response_model=BriaRemoveVideoBackgroundResponse,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.result.video_url))
+
+
+def _video_to_images_and_mask(video: Input.Video) -> tuple[Input.Image, Input.Mask]:
+    """Decode a transparent webm (VP9 + alpha) into image frames and an alpha mask.
+
+    VP9 keeps its alpha in a side layer that PyAV's default vp9 decoder drops, so the frames
+    are decoded with libvpx-vp9. Returns RGB images [B,H,W,3] in 0..1 and a mask [B,H,W]
+    following the Load Image convention (1 = transparent) for compositing or Save WEBM.
+    """
+    rgb_frames: list[torch.Tensor] = []
+    alpha_frames: list[torch.Tensor] = []
+    with av.open(video.get_stream_source(), mode="r") as container:
+        stream = container.streams.video[0]
+        decoder = CodecContext.create("libvpx-vp9", "r") if stream.codec_context.name == "vp9" else None
+        for packet in container.demux(stream):
+            for frame in (decoder.decode(packet) if decoder is not None else packet.decode()):
+                rgba = torch.from_numpy(frame.to_ndarray(format="rgba")).float() / 255.0
+                rgb_frames.append(rgba[..., :3])
+                alpha_frames.append(rgba[..., 3])
+    images = torch.stack(rgb_frames) if rgb_frames else torch.zeros(0, 0, 0, 3)
+    mask = (1.0 - torch.stack(alpha_frames)) if alpha_frames else torch.zeros((images.shape[0], 64, 64))
+    return images, mask
+
+
+class BriaTransparentVideoBackground(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="BriaTransparentVideoBackground",
+            display_name="Bria Remove Video Background (Transparent)",
+            category="partner/video/Bria",
+            description="Remove the background from a video using Bria and return the cut-out frames "
+            "plus an alpha mask. Connect both to a compositing node, or feed them to Save WEBM to "
+            "write a transparent video.",
+            inputs=[
+                IO.Video.Input("video"),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(display_name="images"),
+                IO.Mask.Output(display_name="mask"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.14,"format":{"suffix":"/second"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: Input.Video,
+        seed: int,
+    ) -> IO.NodeOutput:
+        validate_video_duration(video, max_duration=60.0)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/bria/v2/video/edit/remove_background", method="POST"),
+            data=BriaRemoveVideoBackgroundRequest(
+                video=await upload_video_to_comfyapi(cls, video),
+                background_color="Transparent",
+                output_container_and_codec="webm_vp9",
+                seed=seed,
+            ),
+            response_model=BriaStatusResponse,
+        )
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/bria/v2/status/{response.request_id}"),
+            status_extractor=lambda r: r.status,
+            response_model=BriaRemoveVideoBackgroundResponse,
+        )
+        video_out = await download_url_to_video_output(response.result.video_url)
+        images, mask = _video_to_images_and_mask(video_out)
+        return IO.NodeOutput(images, mask)
+
+
 class BriaExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -323,6 +570,9 @@ class BriaExtension(ComfyExtension):
             BriaImageEditNode,
             BriaRemoveImageBackground,
             BriaRemoveVideoBackground,
+            BriaVideoGreenScreen,
+            # BriaVideoReplaceBackground,  # server returns Status 500 when we pass background video
+            BriaTransparentVideoBackground,
         ]
 
 

comfy_api_nodes/nodes_bytedance.py

@@ -7,6 +7,7 @@ from io import BytesIO
 import torch
 from typing_extensions import override
 
+from comfy.utils import common_upscale
 from comfy_api.latest import IO, ComfyExtension, Input, Types
 from comfy_api_nodes.apis.bytedance import (
     RECOMMENDED_PRESETS,
@@ -131,6 +132,44 @@ def _prepare_seedance_image(image: Input.Image) -> Input.Image:
     return image
 
 
+# Supported output aspect ratios, used to pre-size FLF frames to matching pixel pair to avoid the 1080p stretch jump.
+SEEDANCE2_RATIO_WH = {
+    "16:9": (16, 9),
+    "4:3": (4, 3),
+    "1:1": (1, 1),
+    "3:4": (3, 4),
+    "9:16": (9, 16),
+    "21:9": (21, 9),
+}
+SEEDANCE2_RES_SHORT_SIDE = {"480p": 480, "720p": 720, "1080p": 1080}
+
+
+def _seedance2_target_dims(resolution: str, ratio: str, image: torch.Tensor) -> tuple[int, int]:
+    """Exact supported output (width, height) for (resolution, ratio).
+
+    The shorter side equals the resolution number (e.g. 1080p 16:9 -> 1920x1080). For ratio
+    "adaptive" (or any unexpected value) the ratio is derived from the image's own aspect, snapped
+    to the nearest supported ratio, so the output keeps the frame's orientation.
+    """
+    short = SEEDANCE2_RES_SHORT_SIDE[resolution]
+    if ratio not in SEEDANCE2_RATIO_WH:
+        aspect = image.shape[-2] / image.shape[-3]  # W / H; tensor is (B, H, W, C)
+        ratio = min(SEEDANCE2_RATIO_WH, key=lambda k: abs(SEEDANCE2_RATIO_WH[k][0] / SEEDANCE2_RATIO_WH[k][1] - aspect))
+    rw, rh = SEEDANCE2_RATIO_WH[ratio]
+    if rw >= rh:  # landscape or square: shorter side is the height
+        out_w, out_h = round(short * rw / rh), short
+    else:  # portrait: shorter side is the width
+        out_w, out_h = short, round(short * rh / rw)
+    return out_w - out_w % 2, out_h - out_h % 2
+
+
+def _resize_to_exact(image: torch.Tensor, width: int, height: int) -> torch.Tensor:
+    """Center-crop to the target aspect and resize to exactly width x height (lanczos)."""
+    samples = image.movedim(-1, 1)  # (B, H, W, C) -> (B, C, H, W)
+    resized = common_upscale(samples, width, height, "lanczos", "center")
+    return resized.movedim(1, -1)
+
+
 async def _resolve_reference_assets(
     cls: type[IO.ComfyNode],
     asset_ids: list[str],
@@ -368,7 +407,7 @@ class ByteDanceImageNode(IO.ComfyNode):
         return IO.Schema(
             node_id="ByteDanceImageNode",
             display_name="ByteDance Image",
-            category="image/partner/ByteDance",
+            category="partner/image/ByteDance",
             description="Generate images using ByteDance models via api based on prompt",
             inputs=[
                 IO.Combo.Input("model", options=["seedream-3-0-t2i-250415"]),
@@ -492,7 +531,7 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
         return IO.Schema(
             node_id="ByteDanceSeedreamNode",
             display_name="ByteDance Seedream 4.5 & 5.0",
-            category="image/partner/ByteDance",
+            category="partner/image/ByteDance",
             description="Unified text-to-image generation and precise single-sentence editing at up to 4K resolution.",
             inputs=[
                 IO.Combo.Input(
@@ -754,7 +793,7 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode):
         return IO.Schema(
             node_id="ByteDanceSeedreamNodeV2",
             display_name="ByteDance Seedream 4.5 & 5.0",
-            category="image/partner/ByteDance",
+            category="partner/image/ByteDance",
             description="Unified text-to-image generation and precise single-sentence editing at up to 4K resolution.",
             inputs=[
                 IO.String.Input(
@@ -920,7 +959,7 @@ class ByteDanceTextToVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="ByteDanceTextToVideoNode",
             display_name="ByteDance Text to Video",
-            category="video/partner/ByteDance",
+            category="partner/video/ByteDance",
             description="Generate video using ByteDance models via api based on prompt",
             inputs=[
                 IO.Combo.Input(
@@ -1048,7 +1087,7 @@ class ByteDanceImageToVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="ByteDanceImageToVideoNode",
             display_name="ByteDance Image to Video",
-            category="video/partner/ByteDance",
+            category="partner/video/ByteDance",
             description="Generate video using ByteDance models via api based on image and prompt",
             inputs=[
                 IO.Combo.Input(
@@ -1185,7 +1224,7 @@ class ByteDanceFirstLastFrameNode(IO.ComfyNode):
         return IO.Schema(
             node_id="ByteDanceFirstLastFrameNode",
             display_name="ByteDance First-Last-Frame to Video",
-            category="video/partner/ByteDance",
+            category="partner/video/ByteDance",
             description="Generate video using prompt and first and last frames.",
             inputs=[
                 IO.Combo.Input(
@@ -1333,7 +1372,7 @@ class ByteDanceImageReferenceNode(IO.ComfyNode):
         return IO.Schema(
             node_id="ByteDanceImageReferenceNode",
             display_name="ByteDance Reference Images to Video",
-            category="video/partner/ByteDance",
+            category="partner/video/ByteDance",
             description="Generate video using prompt and reference images.",
             inputs=[
                 IO.Combo.Input(
@@ -1576,7 +1615,7 @@ class ByteDance2TextToVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="ByteDance2TextToVideoNode",
             display_name="ByteDance Seedance 2.0 Text to Video",
-            category="video/partner/ByteDance",
+            category="partner/video/ByteDance",
             description="Generate video using Seedance 2.0 models based on a text prompt.",
             inputs=[
                 IO.DynamicCombo.Input(
@@ -1677,7 +1716,7 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode):
         return IO.Schema(
             node_id="ByteDance2FirstLastFrameNode",
             display_name="ByteDance Seedance 2.0 First-Last-Frame to Video",
-            category="video/partner/ByteDance",
+            category="partner/video/ByteDance",
             description="Generate video using Seedance 2.0 from a first frame image and optional last frame image.",
             inputs=[
                 IO.DynamicCombo.Input(
@@ -1790,10 +1829,28 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode):
         if last_frame is not None and last_frame_asset_id:
             raise ValueError("Provide only one of last_frame or last_frame_asset_id, not both.")
 
-        if first_frame is not None:
-            first_frame = _prepare_seedance_image(first_frame)
-        if last_frame is not None:
-            last_frame = _prepare_seedance_image(last_frame)
+        request_ratio = model["ratio"]
+        if first_frame_asset_id or last_frame_asset_id:
+            if first_frame is not None:
+                first_frame = _prepare_seedance_image(first_frame)
+            if last_frame is not None:
+                last_frame = _prepare_seedance_image(last_frame)
+        else:
+            # The 1080p FLF stretch fix (pre-size frames to a supported pixel pair + submit ratio="adaptive")
+            # only applies to local image inputs we can resize.
+            request_ratio = "adaptive"
+            target_dims: tuple[int, int] | None = None
+            if first_frame is not None:
+                validate_image_aspect_ratio(first_frame, (2, 5), (5, 2), strict=False)  # 0.4 to 2.5
+                validate_image_dimensions(first_frame, min_width=300, min_height=300)
+                target_dims = _seedance2_target_dims(model["resolution"], model["ratio"], first_frame)
+                first_frame = _resize_to_exact(first_frame, *target_dims)
+            if last_frame is not None:
+                validate_image_aspect_ratio(last_frame, (2, 5), (5, 2), strict=False)  # 0.4 to 2.5
+                validate_image_dimensions(last_frame, min_width=300, min_height=300)
+                if target_dims is None:
+                    target_dims = _seedance2_target_dims(model["resolution"], model["ratio"], last_frame)
+                last_frame = _resize_to_exact(last_frame, *target_dims)
 
         asset_ids_to_resolve = [a for a in (first_frame_asset_id, last_frame_asset_id) if a]
         image_assets: dict[str, str] = {}
@@ -1844,7 +1901,7 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode):
                 content=content,
                 generate_audio=model["generate_audio"],
                 resolution=model["resolution"],
-                ratio=model["ratio"],
+                ratio=request_ratio,
                 duration=model["duration"],
                 seed=seed,
                 watermark=watermark,
@@ -1944,7 +2001,7 @@ class ByteDance2ReferenceNode(IO.ComfyNode):
         return IO.Schema(
             node_id="ByteDance2ReferenceNode",
             display_name="ByteDance Seedance 2.0 Reference to Video",
-            category="video/partner/ByteDance",
+            category="partner/video/ByteDance",
             description="Generate, edit, or extend video using Seedance 2.0 with reference images, "
             "videos, and audio. Supports multimodal reference, video editing, and video extension.",
             inputs=[
@@ -2241,7 +2298,7 @@ class ByteDanceCreateImageAsset(IO.ComfyNode):
         return IO.Schema(
             node_id="ByteDanceCreateImageAsset",
             display_name="ByteDance Create Image Asset",
-            category="image/partner/ByteDance",
+            category="partner/image/ByteDance",
             description=(
                 "Create a Seedance 2.0 personal image asset. Uploads the input image and "
                 "registers it in the given asset group. If group_id is empty, runs a real-person "
@@ -2308,7 +2365,7 @@ class ByteDanceCreateVideoAsset(IO.ComfyNode):
         return IO.Schema(
             node_id="ByteDanceCreateVideoAsset",
             display_name="ByteDance Create Video Asset",
-            category="video/partner/ByteDance",
+            category="partner/video/ByteDance",
             description=(
                 "Create a Seedance 2.0 personal video asset. Uploads the input video and "
                 "registers it in the given asset group. If group_id is empty, runs a real-person "

comfy_api_nodes/nodes_bytedance_llm.py

@@ -144,7 +144,7 @@ class ByteDanceSeedNode(IO.ComfyNode):
         return IO.Schema(
             node_id="ByteDanceSeedNode",
             display_name="ByteDance Seed",
-            category="text/partner/ByteDance",
+            category="partner/text/ByteDance",
             essentials_category="Text Generation",
             description="Generate text responses with ByteDance's Seed 2.0 models. "
             "Provide a text prompt and optionally one or more images or videos for multimodal context.",

comfy_api_nodes/nodes_elevenlabs.py

@@ -69,7 +69,7 @@ class ElevenLabsSpeechToText(IO.ComfyNode):
         return IO.Schema(
             node_id="ElevenLabsSpeechToText",
             display_name="ElevenLabs Speech to Text",
-            category="audio/partner/ElevenLabs",
+            category="partner/audio/ElevenLabs",
             description="Transcribe audio to text. "
             "Supports automatic language detection, speaker diarization, and audio event tagging.",
             inputs=[
@@ -210,7 +210,7 @@ class ElevenLabsVoiceSelector(IO.ComfyNode):
         return IO.Schema(
             node_id="ElevenLabsVoiceSelector",
             display_name="ElevenLabs Voice Selector",
-            category="audio/partner/ElevenLabs",
+            category="partner/audio/ElevenLabs",
             description="Select a predefined ElevenLabs voice for text-to-speech generation.",
             inputs=[
                 IO.Combo.Input(
@@ -239,7 +239,7 @@ class ElevenLabsTextToSpeech(IO.ComfyNode):
         return IO.Schema(
             node_id="ElevenLabsTextToSpeech",
             display_name="ElevenLabs Text to Speech",
-            category="audio/partner/ElevenLabs",
+            category="partner/audio/ElevenLabs",
             description="Convert text to speech.",
             inputs=[
                 IO.Custom(ELEVENLABS_VOICE).Input(
@@ -414,7 +414,7 @@ class ElevenLabsAudioIsolation(IO.ComfyNode):
         return IO.Schema(
             node_id="ElevenLabsAudioIsolation",
             display_name="ElevenLabs Voice Isolation",
-            category="audio/partner/ElevenLabs",
+            category="partner/audio/ElevenLabs",
             description="Remove background noise from audio, isolating vocals or speech.",
             inputs=[
                 IO.Audio.Input(
@@ -459,7 +459,7 @@ class ElevenLabsTextToSoundEffects(IO.ComfyNode):
         return IO.Schema(
             node_id="ElevenLabsTextToSoundEffects",
             display_name="ElevenLabs Text to Sound Effects",
-            category="audio/partner/ElevenLabs",
+            category="partner/audio/ElevenLabs",
             description="Generate sound effects from text descriptions.",
             inputs=[
                 IO.String.Input(
@@ -555,7 +555,7 @@ class ElevenLabsInstantVoiceClone(IO.ComfyNode):
         return IO.Schema(
             node_id="ElevenLabsInstantVoiceClone",
             display_name="ElevenLabs Instant Voice Clone",
-            category="audio/partner/ElevenLabs",
+            category="partner/audio/ElevenLabs",
             description="Create a cloned voice from audio samples. "
             "Provide 1-8 audio recordings of the voice to clone.",
             inputs=[
@@ -658,7 +658,7 @@ class ElevenLabsSpeechToSpeech(IO.ComfyNode):
         return IO.Schema(
             node_id="ElevenLabsSpeechToSpeech",
             display_name="ElevenLabs Speech to Speech",
-            category="audio/partner/ElevenLabs",
+            category="partner/audio/ElevenLabs",
             description="Transform speech from one voice to another while preserving the original content and emotion.",
             inputs=[
                 IO.Custom(ELEVENLABS_VOICE).Input(
@@ -793,7 +793,7 @@ class ElevenLabsTextToDialogue(IO.ComfyNode):
         return IO.Schema(
             node_id="ElevenLabsTextToDialogue",
             display_name="ElevenLabs Text to Dialogue",
-            category="audio/partner/ElevenLabs",
+            category="partner/audio/ElevenLabs",
             description="Generate multi-speaker dialogue from text. Each dialogue entry has its own text and voice.",
             inputs=[
                 IO.Float.Input(

comfy_api_nodes/nodes_gemini.py

@@ -8,7 +8,7 @@ import os
 from enum import Enum
 from fnmatch import fnmatch
 from io import BytesIO
-from typing import Literal
+from typing import Any, Literal
 
 import torch
 from typing_extensions import override
@@ -19,6 +19,7 @@ from comfy_api_nodes.apis.gemini import (
     GeminiContent,
     GeminiFileData,
     GeminiGenerateContentRequest,
+    GeminiGenerationConfig,
     GeminiGenerateContentResponse,
     GeminiImageConfig,
     GeminiImageGenerateContentRequest,
@@ -40,13 +41,18 @@ from comfy_api_nodes.util import (
     get_number_of_images,
     sync_op,
     tensor_to_base64_string,
+    upload_audio_to_comfyapi,
+    upload_image_to_comfyapi,
     upload_images_to_comfyapi,
+    upload_video_to_comfyapi,
     validate_string,
     video_to_base64_string,
 )
 
 GEMINI_BASE_ENDPOINT = "/proxy/vertexai/gemini"
 GEMINI_MAX_INPUT_FILE_SIZE = 20 * 1024 * 1024  # 20 MB
+GEMINI_URL_INPUT_BUDGET = 10
+GEMINI_MAX_INLINE_BYTES = 18 * 1024 * 1024
 GEMINI_IMAGE_SYS_PROMPT = (
     "You are an expert image-generation engine. You must ALWAYS produce an image.\n"
     "Interpret all user input—regardless of "
@@ -285,6 +291,140 @@ def calculate_tokens_price(response: GeminiGenerateContentResponse) -> float | N
     return final_price / 1_000_000.0
 
 
+def create_video_parts(video_input: Input.Video) -> list[GeminiPart]:
+    """Convert a single video input to Gemini API compatible parts (inline MP4/H.264)."""
+    base_64_string = video_to_base64_string(
+        video_input, container_format=Types.VideoContainer.MP4, codec=Types.VideoCodec.H264
+    )
+    return [
+        GeminiPart(
+            inlineData=GeminiInlineData(
+                mimeType=GeminiMimeType.video_mp4,
+                data=base_64_string,
+            )
+        )
+    ]
+
+
+def create_audio_parts(audio_input: Input.Audio) -> list[GeminiPart]:
+    """Convert an audio input to Gemini API compatible parts (one inline MP3 part per batch item)."""
+    audio_parts: list[GeminiPart] = []
+    for batch_index in range(audio_input["waveform"].shape[0]):
+        # Recreate an IO.AUDIO object for the given batch dimension index
+        audio_at_index = Input.Audio(
+            waveform=audio_input["waveform"][batch_index].unsqueeze(0),
+            sample_rate=audio_input["sample_rate"],
+        )
+        # Convert to MP3 format for compatibility with Gemini API
+        audio_bytes = audio_to_base64_string(
+            audio_at_index,
+            container_format="mp3",
+            codec_name="libmp3lame",
+        )
+        audio_parts.append(
+            GeminiPart(
+                inlineData=GeminiInlineData(
+                    mimeType=GeminiMimeType.audio_mp3,
+                    data=audio_bytes,
+                )
+            )
+        )
+    return audio_parts
+
+
+def _flatten_images(images: list[Input.Image]) -> list[torch.Tensor]:
+    """Expand any batched image tensors into individual (H, W, C) frames, preserving order."""
+    frames: list[torch.Tensor] = []
+    for img in images:
+        if len(img.shape) == 4:
+            frames.extend(img[i] for i in range(img.shape[0]))
+        else:
+            frames.append(img)
+    return frames
+
+
+def _flatten_audio(audios: list[Input.Audio]) -> list[Input.Audio]:
+    """Expand any batched audio inputs into individual single-clip audio inputs, preserving order."""
+    clips: list[Input.Audio] = []
+    for audio in audios:
+        waveform = audio["waveform"]
+        for i in range(waveform.shape[0]):
+            clips.append(Input.Audio(waveform=waveform[i].unsqueeze(0), sample_rate=audio["sample_rate"]))
+    return clips
+
+
+async def _media_url_part(cls: type[IO.ComfyNode], kind: str, payload: Any) -> GeminiPart:
+    """Upload a single media unit to ComfyAPI storage and return a fileData (URL) part."""
+    if kind == "image":
+        url = await upload_image_to_comfyapi(cls, payload, mime_type="image/png", wait_label="Uploading image")
+        return GeminiPart(fileData=GeminiFileData(mimeType=GeminiMimeType.image_png, fileUri=url))
+    if kind == "audio":
+        url = await upload_audio_to_comfyapi(
+            cls, payload, container_format="mp3", codec_name="libmp3lame", mime_type="audio/mp3"
+        )
+        return GeminiPart(fileData=GeminiFileData(mimeType=GeminiMimeType.audio_mp3, fileUri=url))
+    url = await upload_video_to_comfyapi(cls, payload, wait_label="Uploading video")
+    return GeminiPart(fileData=GeminiFileData(mimeType=GeminiMimeType.video_mp4, fileUri=url))
+
+
+def _media_inline_part(kind: str, payload: Any) -> tuple[GeminiPart, int]:
+    """Encode a single media unit as an inline base64 part; returns (part, base64_length)."""
+    if kind == "image":
+        data = tensor_to_base64_string(payload, mime_type="image/webp")
+        mime = GeminiMimeType.image_webp
+    elif kind == "audio":
+        data = audio_to_base64_string(payload, container_format="mp3", codec_name="libmp3lame")
+        mime = GeminiMimeType.audio_mp3
+    else:
+        data = video_to_base64_string(
+            payload, container_format=Types.VideoContainer.MP4, codec=Types.VideoCodec.H264
+        )
+        mime = GeminiMimeType.video_mp4
+    return GeminiPart(inlineData=GeminiInlineData(mimeType=mime, data=data)), len(data)
+
+
+async def build_gemini_media_parts(
+    cls: type[IO.ComfyNode],
+    images: list[Input.Image],
+    audios: list[Input.Audio],
+    videos: list[Input.Video],
+    *,
+    url_budget: int = GEMINI_URL_INPUT_BUDGET,
+    max_inline_bytes: int = GEMINI_MAX_INLINE_BYTES,
+) -> list[GeminiPart]:
+    """Build Gemini parts for multimodal inputs (images, audio, video).
+
+    fileData URLs are preferred for every media type: the upload is fetched directly by the
+    model, keeping the request body tiny regardless of media size. The URL budget is shared
+    across all media and assigned largest-first (video, then audio, then images), so that if it
+    is ever exhausted the inline-base64 overflow is limited to the smallest items. Total inline
+    payload is capped by `max_inline_bytes`.
+    """
+    units: list[tuple[str, Any]] = (
+        [("video", v) for v in videos]
+        + [("audio", a) for a in _flatten_audio(audios)]
+        + [("image", f) for f in _flatten_images(images)]
+    )
+
+    parts: list[GeminiPart] = []
+    url_used = 0
+    inline_bytes = 0
+    for kind, payload in units:
+        if url_used < url_budget:
+            parts.append(await _media_url_part(cls, kind, payload))
+            url_used += 1
+            continue
+        part, nbytes = _media_inline_part(kind, payload)
+        inline_bytes += nbytes
+        if inline_bytes > max_inline_bytes:
+            raise ValueError(
+                f"Too much media to send inline (over {max_inline_bytes // (1024 * 1024)}MB after the first "
+                f"{url_budget} inputs are uploaded as URLs). Reduce the number or size of attached media."
+            )
+        parts.append(part)
+    return parts
+
+
 class GeminiNode(IO.ComfyNode):
     """
     Node to generate text responses from a Gemini model.
@@ -300,7 +440,7 @@ class GeminiNode(IO.ComfyNode):
         return IO.Schema(
             node_id="GeminiNode",
             display_name="Google Gemini",
-            category="text/partner/Gemini",
+            category="partner/text/Gemini",
             description="Generate text responses with Google's Gemini AI model. "
             "You can provide multiple types of inputs (text, images, audio, video) "
             "as context for generating more relevant and meaningful responses.",
@@ -407,58 +547,9 @@ class GeminiNode(IO.ComfyNode):
                 )
                 """,
             ),
+            is_deprecated=True,
         )
 
-    @classmethod
-    def create_video_parts(cls, video_input: Input.Video) -> list[GeminiPart]:
-        """Convert video input to Gemini API compatible parts."""
-
-        base_64_string = video_to_base64_string(
-            video_input, container_format=Types.VideoContainer.MP4, codec=Types.VideoCodec.H264
-        )
-        return [
-            GeminiPart(
-                inlineData=GeminiInlineData(
-                    mimeType=GeminiMimeType.video_mp4,
-                    data=base_64_string,
-                )
-            )
-        ]
-
-    @classmethod
-    def create_audio_parts(cls, audio_input: Input.Audio) -> list[GeminiPart]:
-        """
-        Convert audio input to Gemini API compatible parts.
-
-        Args:
-            audio_input: Audio input from ComfyUI, containing waveform tensor and sample rate.
-
-        Returns:
-            List of GeminiPart objects containing the encoded audio.
-        """
-        audio_parts: list[GeminiPart] = []
-        for batch_index in range(audio_input["waveform"].shape[0]):
-            # Recreate an IO.AUDIO object for the given batch dimension index
-            audio_at_index = Input.Audio(
-                waveform=audio_input["waveform"][batch_index].unsqueeze(0),
-                sample_rate=audio_input["sample_rate"],
-            )
-            # Convert to MP3 format for compatibility with Gemini API
-            audio_bytes = audio_to_base64_string(
-                audio_at_index,
-                container_format="mp3",
-                codec_name="libmp3lame",
-            )
-            audio_parts.append(
-                GeminiPart(
-                    inlineData=GeminiInlineData(
-                        mimeType=GeminiMimeType.audio_mp3,
-                        data=audio_bytes,
-                    )
-                )
-            )
-        return audio_parts
-
     @classmethod
     async def execute(
         cls,
@@ -482,9 +573,9 @@ class GeminiNode(IO.ComfyNode):
         if images is not None:
             parts.extend(await create_image_parts(cls, images))
         if audio is not None:
-            parts.extend(cls.create_audio_parts(audio))
+            parts.extend(create_audio_parts(audio))
         if video is not None:
-            parts.extend(cls.create_video_parts(video))
+            parts.extend(create_video_parts(video))
         if files is not None:
             parts.extend(files)
 
@@ -512,6 +603,210 @@ class GeminiNode(IO.ComfyNode):
         return IO.NodeOutput(output_text or "Empty response from Gemini model...")
 
 
+GEMINI_V2_MODELS: dict[str, str] = {
+    "Gemini 3.1 Pro": "gemini-3.1-pro-preview",
+    "Gemini 3.1 Flash-Lite": "gemini-3.1-flash-lite-preview",
+}
+
+
+def _gemini_text_model_inputs(thinking_default: str) -> list[Input]:
+    """Per-model inputs revealed by the model DynamicCombo (shared media + sampling controls)."""
+    return [
+        IO.Autogrow.Input(
+            "images",
+            template=IO.Autogrow.TemplateNames(
+                IO.Image.Input("image"),
+                names=[f"image_{i}" for i in range(1, 17)],
+                min=0,
+            ),
+            tooltip="Optional image(s) to use as context for the model. Up to 16 images.",
+        ),
+        IO.Autogrow.Input(
+            "audio",
+            template=IO.Autogrow.TemplateNames(
+                IO.Audio.Input("audio"),
+                names=["audio_1"],
+                min=0,
+            ),
+            tooltip="Optional audio clip to use as context for the model.",
+        ),
+        IO.Autogrow.Input(
+            "video",
+            template=IO.Autogrow.TemplateNames(
+                IO.Video.Input("video"),
+                names=["video_1"],
+                min=0,
+            ),
+            tooltip="Optional video clip to use as context for the model.",
+        ),
+        IO.Custom("GEMINI_INPUT_FILES").Input(
+            "files",
+            optional=True,
+            tooltip="Optional file(s) to use as context for the model. "
+            "Accepts inputs from the Gemini Input Files node.",
+        ),
+        IO.Combo.Input(
+            "thinking_level",
+            options=["LOW", "HIGH"],
+            default=thinking_default,
+            tooltip="How hard the model reasons internally before answering. "
+            "HIGH improves quality on difficult tasks but costs more (thinking) tokens and is slower.",
+        ),
+        IO.Float.Input(
+            "temperature",
+            default=1.0,
+            min=0.0,
+            max=2.0,
+            step=0.01,
+            tooltip="Controls randomness. Lower is more focused/deterministic, higher is more creative.",
+            advanced=True,
+        ),
+        IO.Float.Input(
+            "top_p",
+            default=0.95,
+            min=0.0,
+            max=1.0,
+            step=0.01,
+            tooltip="Nucleus sampling: sample from the smallest token set whose cumulative probability reaches top_p.",
+            advanced=True,
+        ),
+        IO.Int.Input(
+            "max_output_tokens",
+            default=32768,
+            min=16,
+            max=65536,
+            tooltip="Maximum tokens to generate, including the model's internal thinking. "
+            "With thinking_level HIGH, a low value can leave no room for the answer; raise this if "
+            "responses come back empty or truncated. The model stops early when finished, so a higher "
+            "cap costs nothing extra for short replies.",
+            advanced=True,
+        ),
+    ]
+
+
+class GeminiNodeV2(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="GeminiNodeV2",
+            display_name="Google Gemini",
+            category="partner/text/Gemini",
+            essentials_category="Text Generation",
+            description="Generate text responses with Google's Gemini models. Provide a text prompt and, "
+            "optionally, one or more images, audio clips, videos, or files as multimodal context.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text input to the model. Include detailed instructions, questions, or context.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option("Gemini 3.1 Pro", _gemini_text_model_inputs("HIGH")),
+                        IO.DynamicCombo.Option("Gemini 3.1 Flash-Lite", _gemini_text_model_inputs("LOW")),
+                    ],
+                    tooltip="The Gemini model used to generate the response.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed for sampling. Set to 0 for a random seed. Deterministic output isn't guaranteed.",
+                ),
+                IO.String.Input(
+                    "system_prompt",
+                    multiline=True,
+                    default="",
+                    optional=True,
+                    advanced=True,
+                    tooltip="Foundational instructions that dictate the model's behavior.",
+                ),
+            ],
+            outputs=[
+                IO.String.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model"]),
+                expr="""
+                (
+                  $m := widgets.model;
+                  $contains($m, "lite") ? {
+                    "type": "list_usd",
+                    "usd": [0.00025, 0.0015],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  } : {
+                    "type": "list_usd",
+                    "usd": [0.002, 0.012],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: dict,
+        seed: int,
+        system_prompt: str = "",
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        model_id = GEMINI_V2_MODELS[model["model"]]
+
+        parts: list[GeminiPart] = [GeminiPart(text=prompt)]
+        images = [t for t in (model.get("images") or {}).values() if t is not None]
+        audios = [a for a in (model.get("audio") or {}).values() if a is not None]
+        videos = [v for v in (model.get("video") or {}).values() if v is not None]
+        if images or audios or videos:
+            parts.extend(await build_gemini_media_parts(cls, images, audios, videos))
+        files = model.get("files")
+        if files is not None:
+            parts.extend(files)
+
+        gemini_system_prompt = None
+        if system_prompt:
+            gemini_system_prompt = GeminiSystemInstructionContent(parts=[GeminiTextPart(text=system_prompt)], role=None)
+
+        response = await sync_op(
+            cls,
+            endpoint=ApiEndpoint(path=f"{GEMINI_BASE_ENDPOINT}/{model_id}", method="POST"),
+            data=GeminiGenerateContentRequest(
+                contents=[
+                    GeminiContent(
+                        role=GeminiRole.user,
+                        parts=parts,
+                    )
+                ],
+                generationConfig=GeminiGenerationConfig(
+                    temperature=model["temperature"],
+                    topP=model["top_p"],
+                    maxOutputTokens=model["max_output_tokens"],
+                    seed=seed if seed > 0 else None,
+                    thinkingConfig=GeminiThinkingConfig(thinkingLevel=model["thinking_level"]),
+                ),
+                systemInstruction=gemini_system_prompt,
+            ),
+            response_model=GeminiGenerateContentResponse,
+            price_extractor=calculate_tokens_price,
+        )
+
+        output_text = get_text_from_response(response)
+        return IO.NodeOutput(output_text or "Empty response from Gemini model...")
+
+
 class GeminiInputFiles(IO.ComfyNode):
     """
     Loads and formats input files for use with the Gemini API.
@@ -541,7 +836,7 @@ class GeminiInputFiles(IO.ComfyNode):
         return IO.Schema(
             node_id="GeminiInputFiles",
             display_name="Gemini Input Files",
-            category="text/partner/Gemini",
+            category="partner/text/Gemini",
             description="Loads and prepares input files to include as inputs for Gemini LLM nodes. "
             "The files will be read by the Gemini model when generating a response. "
             "The contents of the text file count toward the token limit. "
@@ -598,7 +893,7 @@ class GeminiImage(IO.ComfyNode):
         return IO.Schema(
             node_id="GeminiImageNode",
             display_name="Nano Banana (Google Gemini Image)",
-            category="image/partner/Gemini",
+            category="partner/image/Gemini",
             description="Edit images synchronously via Google API.",
             inputs=[
                 IO.String.Input(
@@ -731,7 +1026,7 @@ class GeminiImage2(IO.ComfyNode):
         return IO.Schema(
             node_id="GeminiImage2Node",
             display_name="Nano Banana Pro (Google Gemini Image)",
-            category="image/partner/Gemini",
+            category="partner/image/Gemini",
             description="Generate or edit images synchronously via Google Vertex API.",
             inputs=[
                 IO.String.Input(
@@ -869,7 +1164,7 @@ class GeminiNanoBanana2(IO.ComfyNode):
         return IO.Schema(
             node_id="GeminiNanoBanana2",
             display_name="Nano Banana 2",
-            category="image/partner/Gemini",
+            category="partner/image/Gemini",
             description="Generate or edit images synchronously via Google Vertex API.",
             inputs=[
                 IO.String.Input(
@@ -1085,7 +1380,7 @@ class GeminiNanoBanana2V2(IO.ComfyNode):
         return IO.Schema(
             node_id="GeminiNanoBanana2V2",
             display_name="Nano Banana 2",
-            category="image/partner/Gemini",
+            category="partner/image/Gemini",
             description="Generate or edit images synchronously via Google Vertex API.",
             inputs=[
                 IO.String.Input(
@@ -1129,6 +1424,26 @@ class GeminiNanoBanana2V2(IO.ComfyNode):
                     tooltip="Foundational instructions that dictate an AI's behavior.",
                     advanced=True,
                 ),
+                IO.Float.Input(
+                    "temperature",
+                    default=1.0,
+                    min=0.0,
+                    max=2.0,
+                    step=0.01,
+                    optional=True,
+                    tooltip="Controls randomness in generation. Lower is more focused/deterministic.",
+                    advanced=True,
+                ),
+                IO.Float.Input(
+                    "top_p",
+                    default=0.95,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    optional=True,
+                    tooltip="Nucleus sampling threshold. Lower is more focused, higher more diverse.",
+                    advanced=True,
+                ),
             ],
             outputs=[
                 IO.Image.Output(),
@@ -1165,6 +1480,8 @@ class GeminiNanoBanana2V2(IO.ComfyNode):
         seed: int,
         response_modalities: str,
         system_prompt: str = "",
+        temperature: float = 1.0,
+        top_p: float = 0.95,
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=True, min_length=1)
         model_choice = model["model"]
@@ -1204,6 +1521,8 @@ class GeminiNanoBanana2V2(IO.ComfyNode):
                     responseModalities=(["IMAGE"] if response_modalities == "IMAGE" else ["TEXT", "IMAGE"]),
                     imageConfig=image_config,
                     thinkingConfig=GeminiThinkingConfig(thinkingLevel=model["thinking_level"]),
+                    temperature=temperature,
+                    topP=top_p,
                 ),
                 systemInstruction=gemini_system_prompt,
             ),
@@ -1222,6 +1541,7 @@ class GeminiExtension(ComfyExtension):
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
         return [
             GeminiNode,
+            GeminiNodeV2,
             GeminiImage,
             GeminiImage2,
             GeminiNanoBanana2,

comfy_api_nodes/nodes_grok.py

@@ -29,6 +29,11 @@ from comfy_api_nodes.util import (
 )
 
 
+_GROK_VIDEO_MODEL_API_IDS = {
+    "grok-imagine-video-1.5": "grok-imagine-video-1.5-preview",
+}
+
+
 def _extract_grok_price(response) -> float | None:
     if response.usage and response.usage.cost_in_usd_ticks is not None:
         return response.usage.cost_in_usd_ticks / 10_000_000_000
@@ -49,7 +54,7 @@ class GrokImageNode(IO.ComfyNode):
         return IO.Schema(
             node_id="GrokImageNode",
             display_name="Grok Image",
-            category="image/partner/Grok",
+            category="partner/image/Grok",
             description="Generate images using Grok based on a text prompt",
             inputs=[
                 IO.Combo.Input(
@@ -223,7 +228,7 @@ class GrokImageEditNode(IO.ComfyNode):
         return IO.Schema(
             node_id="GrokImageEditNode",
             display_name="Grok Image Edit",
-            category="image/partner/Grok",
+            category="partner/image/Grok",
             description="Modify an existing image based on a text prompt",
             inputs=[
                 IO.Combo.Input(
@@ -364,7 +369,7 @@ class GrokImageEditNodeV2(IO.ComfyNode):
         return IO.Schema(
             node_id="GrokImageEditNodeV2",
             display_name="Grok Image Edit",
-            category="image/partner/Grok",
+            category="partner/image/Grok",
             description="Modify an existing image based on a text prompt",
             inputs=[
                 IO.String.Input(
@@ -501,10 +506,14 @@ class GrokVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="GrokVideoNode",
             display_name="Grok Video",
-            category="video/partner/Grok",
+            category="partner/video/Grok",
             description="Generate video from a prompt or an image",
             inputs=[
-                IO.Combo.Input("model", options=["grok-imagine-video"]),
+                IO.Combo.Input(
+                    "model",
+                    options=["grok-imagine-video", "grok-imagine-video-1.5"],
+                    tooltip="grok-imagine-video-1.5 currently always requires an input image.",
+                ),
                 IO.String.Input(
                     "prompt",
                     multiline=True,
@@ -540,7 +549,11 @@ class GrokVideoNode(IO.ComfyNode):
                     tooltip="Seed to determine if node should re-run; "
                     "actual results are nondeterministic regardless of seed.",
                 ),
-                IO.Image.Input("image", optional=True),
+                IO.Image.Input(
+                    "image",
+                    optional=True,
+                    tooltip="Optional starting image for grok-imagine-video. Required for grok-imagine-video-1.5.",
+                ),
             ],
             outputs=[
                 IO.Video.Output(),
@@ -552,12 +565,16 @@ class GrokVideoNode(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["duration", "resolution"], inputs=["image"]),
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "duration", "resolution"], inputs=["image"]),
                 expr="""
                 (
-                  $rate := widgets.resolution = "720p" ? 0.07 : 0.05;
+                  $is15 := $contains(widgets.model, "1.5");
+                  $rate := $is15
+                    ? (widgets.resolution = "720p" ? 0.2002 : 0.1144)
+                    : (widgets.resolution = "720p" ? 0.07 : 0.05);
+                  $imgCost := $is15 ? 0.0143 : 0.002;
                   $base := $rate * widgets.duration;
-                  {"type":"usd","usd": inputs.image.connected ? $base + 0.002 : $base}
+                  {"type":"usd","usd": inputs.image.connected ? $base + $imgCost : $base}
                 )
                 """,
             ),
@@ -574,6 +591,8 @@ class GrokVideoNode(IO.ComfyNode):
         seed: int,
         image: Input.Image | None = None,
     ) -> IO.NodeOutput:
+        if image is None and model == "grok-imagine-video-1.5":
+            raise ValueError(f"The '{model}' model requires an input image; connect one to the 'image' input.")
         image_url = None
         if image is not None:
             if get_number_of_images(image) != 1:
@@ -584,7 +603,7 @@ class GrokVideoNode(IO.ComfyNode):
             cls,
             ApiEndpoint(path="/proxy/xai/v1/videos/generations", method="POST"),
             data=VideoGenerationRequest(
-                model=model,
+                model=_GROK_VIDEO_MODEL_API_IDS.get(model, model),
                 image=image_url,
                 prompt=prompt,
                 resolution=resolution,
@@ -599,7 +618,7 @@ class GrokVideoNode(IO.ComfyNode):
             ApiEndpoint(path=f"/proxy/xai/v1/videos/{initial_response.request_id}"),
             status_extractor=lambda r: r.status if r.status is not None else "complete",
             response_model=VideoStatusResponse,
-            price_extractor=_extract_grok_price,
+            price_extractor=_extract_grok_video_price if model == "grok-imagine-video-1.5" else _extract_grok_price,
         )
         return IO.NodeOutput(await download_url_to_video_output(response.video.url))
 
@@ -611,7 +630,7 @@ class GrokVideoEditNode(IO.ComfyNode):
         return IO.Schema(
             node_id="GrokVideoEditNode",
             display_name="Grok Video Edit",
-            category="video/partner/Grok",
+            category="partner/video/Grok",
             description="Edit an existing video based on a text prompt.",
             inputs=[
                 IO.Combo.Input("model", options=["grok-imagine-video"]),
@@ -689,7 +708,7 @@ class GrokVideoReferenceNode(IO.ComfyNode):
         return IO.Schema(
             node_id="GrokVideoReferenceNode",
             display_name="Grok Reference-to-Video",
-            category="video/partner/Grok",
+            category="partner/video/Grok",
             description="Generate video guided by reference images as style and content references.",
             inputs=[
                 IO.String.Input(
@@ -822,7 +841,7 @@ class GrokVideoExtendNode(IO.ComfyNode):
         return IO.Schema(
             node_id="GrokVideoExtendNode",
             display_name="Grok Video Extend",
-            category="video/partner/Grok",
+            category="partner/video/Grok",
             description="Extend an existing video with a seamless continuation based on a text prompt.",
             inputs=[
                 IO.String.Input(

comfy_api_nodes/nodes_hitpaw.py

@@ -71,7 +71,7 @@ class HitPawGeneralImageEnhance(IO.ComfyNode):
         return IO.Schema(
             node_id="HitPawGeneralImageEnhance",
             display_name="HitPaw General Image Enhance",
-            category="image/partner/HitPaw",
+            category="partner/image/HitPaw",
             description="Upscale low-resolution images to super-resolution, eliminate artifacts and noise. "
             f"Maximum output: {MAX_MP_GENERATIVE} megapixels.",
             inputs=[
@@ -201,7 +201,7 @@ class HitPawVideoEnhance(IO.ComfyNode):
         return IO.Schema(
             node_id="HitPawVideoEnhance",
             display_name="HitPaw Video Enhance",
-            category="video/partner/HitPaw",
+            category="partner/video/HitPaw",
             description="Upscale low-resolution videos to high resolution, eliminate artifacts and noise. "
             "Prices shown are per second of video.",
             inputs=[

comfy_api_nodes/nodes_hunyuan3d.py

@@ -123,7 +123,7 @@ class TencentTextToModelNode(IO.ComfyNode):
         return IO.Schema(
             node_id="TencentTextToModelNode",
             display_name="Hunyuan3D: Text to Model",
-            category="3d/partner/Tencent",
+            category="partner/3d/Tencent",
             essentials_category="3D",
             inputs=[
                 IO.Combo.Input(
@@ -242,7 +242,7 @@ class TencentImageToModelNode(IO.ComfyNode):
         return IO.Schema(
             node_id="TencentImageToModelNode",
             display_name="Hunyuan3D: Image(s) to Model",
-            category="3d/partner/Tencent",
+            category="partner/3d/Tencent",
             essentials_category="3D",
             inputs=[
                 IO.Combo.Input(
@@ -415,7 +415,7 @@ class TencentModelTo3DUVNode(IO.ComfyNode):
         return IO.Schema(
             node_id="TencentModelTo3DUVNode",
             display_name="Hunyuan3D: Model to UV",
-            category="3d/partner/Tencent",
+            category="partner/3d/Tencent",
             description="Perform UV unfolding on a 3D model to generate UV texture. "
             "Input model must have less than 30000 faces.",
             inputs=[
@@ -505,7 +505,7 @@ class Tencent3DTextureEditNode(IO.ComfyNode):
         return IO.Schema(
             node_id="Tencent3DTextureEditNode",
             display_name="Hunyuan3D: 3D Texture Edit",
-            category="3d/partner/Tencent",
+            category="partner/3d/Tencent",
             description="After inputting the 3D model, perform 3D model texture redrawing.",
             inputs=[
                 IO.MultiType.Input(
@@ -594,7 +594,7 @@ class Tencent3DPartNode(IO.ComfyNode):
         return IO.Schema(
             node_id="Tencent3DPartNode",
             display_name="Hunyuan3D: 3D Part",
-            category="3d/partner/Tencent",
+            category="partner/3d/Tencent",
             description="Automatically perform component identification and generation based on the model structure.",
             inputs=[
                 IO.MultiType.Input(
@@ -666,7 +666,7 @@ class TencentSmartTopologyNode(IO.ComfyNode):
         return IO.Schema(
             node_id="TencentSmartTopologyNode",
             display_name="Hunyuan3D: Smart Topology",
-            category="3d/partner/Tencent",
+            category="partner/3d/Tencent",
             description="Perform smart retopology on a 3D model. "
             "Supports GLB/OBJ formats; max 200MB; recommended for high-poly models.",
             inputs=[

comfy_api_nodes/nodes_ideogram.py

@@ -10,6 +10,7 @@ from comfy_api_nodes.apis.ideogram import (
     ImageRequest,
     IdeogramV3Request,
     IdeogramV3EditRequest,
+    IdeogramV4Request,
 )
 from comfy_api_nodes.util import (
     ApiEndpoint,
@@ -17,6 +18,7 @@ from comfy_api_nodes.util import (
     download_url_as_bytesio,
     resize_mask_to_image,
     sync_op,
+    validate_string,
 )
 
 V1_V1_RES_MAP = {
@@ -234,7 +236,7 @@ class IdeogramV1(IO.ComfyNode):
         return IO.Schema(
             node_id="IdeogramV1",
             display_name="Ideogram V1",
-            category="image/partner/Ideogram",
+            category="partner/image/Ideogram",
             description="Generates images using the Ideogram V1 model.",
             inputs=[
                 IO.String.Input(
@@ -360,7 +362,7 @@ class IdeogramV2(IO.ComfyNode):
         return IO.Schema(
             node_id="IdeogramV2",
             display_name="Ideogram V2",
-            category="image/partner/Ideogram",
+            category="partner/image/Ideogram",
             description="Generates images using the Ideogram V2 model.",
             inputs=[
                 IO.String.Input(
@@ -526,7 +528,7 @@ class IdeogramV3(IO.ComfyNode):
         return IO.Schema(
             node_id="IdeogramV3",
             display_name="Ideogram V3",
-            category="image/partner/Ideogram",
+            category="partner/image/Ideogram",
             description="Generates images using the Ideogram V3 model. "
                         "Supports both regular image generation from text prompts and image editing with mask.",
             inputs=[
@@ -798,6 +800,119 @@ class IdeogramV3(IO.ComfyNode):
         return IO.NodeOutput(await download_and_process_images(image_urls))
 
 
+class IdeogramV4(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="IdeogramV4",
+            display_name="Ideogram V4",
+            category="partner/image/Ideogram",
+            description="Generates images using the Ideogram 4.0 model from a text prompt.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text prompt for the image generation.",
+                ),
+                IO.Combo.Input(
+                    "resolution",
+                    options=[
+                        "Auto",
+                        "2048x2048 (1:1)",
+                        "1440x2880 (1:2)",
+                        "2880x1440 (2:1)",
+                        "1664x2496 (2:3)",
+                        "2496x1664 (3:2)",
+                        "1792x2240 (4:5)",
+                        "2240x1792 (5:4)",
+                        "1440x2560 (9:16)",
+                        "2560x1440 (16:9)",
+                        "1600x2560 (5:8)",
+                        "2560x1600 (8:5)",
+                        "1728x2304 (3:4)",
+                        "2304x1728 (4:3)",
+                        "1296x3168 (9:22)",
+                        "3168x1296 (22:9)",
+                        "1152x2944 (9:23)",
+                        "2944x1152 (23:9)",
+                        "1248x3328 (3:8)",
+                        "3328x1248 (8:3)",
+                        "1280x3072 (5:12)",
+                        "3072x1280 (12:5)",
+                    ],
+                    default="Auto",
+                ),
+                IO.Combo.Input(
+                    "rendering_speed",
+                    options=["DEFAULT", "TURBO", "QUALITY"],
+                    default="DEFAULT",
+                    tooltip="Controls the trade-off between generation speed and quality.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    control_after_generate=True,
+                    display_mode=IO.NumberDisplay.number,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["rendering_speed"]),
+                expr="""
+                (
+                  $speed := widgets.rendering_speed;
+                  $price :=
+                    $contains($speed,"turbo") ? 0.0429 :
+                    $contains($speed,"quality") ? 0.143 :
+                    0.0858;
+                  {"type":"usd","usd": $price}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        resolution: str,
+        rendering_speed: str,
+        seed: int,
+    ):
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/ideogram/ideogram-v4/generate", method="POST"),
+            response_model=IdeogramGenerateResponse,
+            data=IdeogramV4Request(
+                text_prompt=prompt,
+                resolution=resolution.split(" ")[0] if resolution != "Auto" else None,
+                rendering_speed=rendering_speed,
+            ),
+            max_retries=1,
+        )
+
+        if not response.data or len(response.data) == 0:
+            raise Exception("No images were generated in the response")
+        image_urls = [image_data.url for image_data in response.data if image_data.url]
+        if not image_urls:
+            raise Exception("No image URLs were generated in the response")
+        return IO.NodeOutput(await download_and_process_images(image_urls))
+
+
 class IdeogramExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -805,6 +920,7 @@ class IdeogramExtension(ComfyExtension):
             IdeogramV1,
             IdeogramV2,
             IdeogramV3,
+            IdeogramV4,
         ]
 
 

comfy_api_nodes/nodes_kling.py

@@ -642,7 +642,7 @@ class KlingCameraControls(IO.ComfyNode):
         return IO.Schema(
             node_id="KlingCameraControls",
             display_name="Kling Camera Controls",
-            category="video/partner/Kling",
+            category="partner/video/Kling",
             description="Allows specifying configuration options for Kling Camera Controls and motion control effects.",
             inputs=[
                 IO.Combo.Input("camera_control_type", options=KlingCameraControlType),
@@ -762,7 +762,7 @@ class KlingTextToVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="KlingTextToVideoNode",
             display_name="Kling Text to Video",
-            category="video/partner/Kling",
+            category="partner/video/Kling",
             description="Kling Text to Video Node",
             inputs=[
                 IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"),
@@ -849,7 +849,7 @@ class OmniProTextToVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="KlingOmniProTextToVideoNode",
             display_name="Kling 3.0 Omni Text to Video",
-            category="video/partner/Kling",
+            category="partner/video/Kling",
             description="Use text prompts to generate videos with the latest Kling model.",
             inputs=[
                 IO.Combo.Input("model_name", options=["kling-v3-omni", "kling-video-o1"]),
@@ -998,7 +998,7 @@ class OmniProFirstLastFrameNode(IO.ComfyNode):
         return IO.Schema(
             node_id="KlingOmniProFirstLastFrameNode",
             display_name="Kling 3.0 Omni First-Last-Frame to Video",
-            category="video/partner/Kling",
+            category="partner/video/Kling",
             description="Use a start frame, an optional end frame, or reference images with the latest Kling model.",
             inputs=[
                 IO.Combo.Input("model_name", options=["kling-v3-omni", "kling-video-o1"]),
@@ -1205,7 +1205,7 @@ class OmniProImageToVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="KlingOmniProImageToVideoNode",
             display_name="Kling 3.0 Omni Image to Video",
-            category="video/partner/Kling",
+            category="partner/video/Kling",
             description="Use up to 7 reference images to generate a video with the latest Kling model.",
             inputs=[
                 IO.Combo.Input("model_name", options=["kling-v3-omni", "kling-video-o1"]),
@@ -1374,7 +1374,7 @@ class OmniProVideoToVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="KlingOmniProVideoToVideoNode",
             display_name="Kling 3.0 Omni Video to Video",
-            category="video/partner/Kling",
+            category="partner/video/Kling",
             description="Use a video and up to 4 reference images to generate a video with the latest Kling model.",
             inputs=[
                 IO.Combo.Input("model_name", options=["kling-v3-omni", "kling-video-o1"]),
@@ -1485,7 +1485,7 @@ class OmniProEditVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="KlingOmniProEditVideoNode",
             display_name="Kling 3.0 Omni Edit Video",
-            category="video/partner/Kling",
+            category="partner/video/Kling",
             essentials_category="Video Generation",
             description="Edit an existing video with the latest model from Kling.",
             inputs=[
@@ -1593,7 +1593,7 @@ class OmniProImageNode(IO.ComfyNode):
         return IO.Schema(
             node_id="KlingOmniProImageNode",
             display_name="Kling 3.0 Omni Image",
-            category="image/partner/Kling",
+            category="partner/image/Kling",
             description="Create or edit images with the latest model from Kling.",
             inputs=[
                 IO.Combo.Input("model_name", options=["kling-v3-omni", "kling-image-o1"]),
@@ -1721,7 +1721,7 @@ class KlingCameraControlT2VNode(IO.ComfyNode):
         return IO.Schema(
             node_id="KlingCameraControlT2VNode",
             display_name="Kling Text to Video (Camera Control)",
-            category="video/partner/Kling",
+            category="partner/video/Kling",
             description="Transform text into cinematic videos with professional camera movements that simulate real-world cinematography. Control virtual camera actions including zoom, rotation, pan, tilt, and first-person view, while maintaining focus on your original text.",
             inputs=[
                 IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"),
@@ -1783,7 +1783,7 @@ class KlingImage2VideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="KlingImage2VideoNode",
             display_name="Kling Image(First Frame) to Video",
-            category="video/partner/Kling",
+            category="partner/video/Kling",
             inputs=[
                 IO.Image.Input("start_frame", tooltip="The reference image used to generate the video."),
                 IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"),
@@ -1882,7 +1882,7 @@ class KlingCameraControlI2VNode(IO.ComfyNode):
         return IO.Schema(
             node_id="KlingCameraControlI2VNode",
             display_name="Kling Image to Video (Camera Control)",
-            category="video/partner/Kling",
+            category="partner/video/Kling",
             description="Transform still images into cinematic videos with professional camera movements that simulate real-world cinematography. Control virtual camera actions including zoom, rotation, pan, tilt, and first-person view, while maintaining focus on your original image.",
             inputs=[
                 IO.Image.Input(
@@ -1953,7 +1953,7 @@ class KlingStartEndFrameNode(IO.ComfyNode):
         return IO.Schema(
             node_id="KlingStartEndFrameNode",
             display_name="Kling Start-End Frame to Video",
-            category="video/partner/Kling",
+            category="partner/video/Kling",
             description="Generate a video sequence that transitions between your provided start and end images. The node creates all frames in between, producing a smooth transformation from the first frame to the last.",
             inputs=[
                 IO.Image.Input(
@@ -2047,7 +2047,7 @@ class KlingVideoExtendNode(IO.ComfyNode):
         return IO.Schema(
             node_id="KlingVideoExtendNode",
             display_name="Kling Video Extend",
-            category="video/partner/Kling",
+            category="partner/video/Kling",
             description="Kling Video Extend Node. Extend videos made by other Kling nodes. The video_id is created by using other Kling Nodes.",
             inputs=[
                 IO.String.Input(
@@ -2128,7 +2128,7 @@ class KlingDualCharacterVideoEffectNode(IO.ComfyNode):
         return IO.Schema(
             node_id="KlingDualCharacterVideoEffectNode",
             display_name="Kling Dual Character Video Effects",
-            category="video/partner/Kling",
+            category="partner/video/Kling",
             description="Achieve different special effects when generating a video based on the effect_scene. First image will be positioned on left side, second on right side of the composite.",
             inputs=[
                 IO.Image.Input("image_left", tooltip="Left side image"),
@@ -2218,7 +2218,7 @@ class KlingSingleImageVideoEffectNode(IO.ComfyNode):
         return IO.Schema(
             node_id="KlingSingleImageVideoEffectNode",
             display_name="Kling Video Effects",
-            category="video/partner/Kling",
+            category="partner/video/Kling",
             description="Achieve different special effects when generating a video based on the effect_scene.",
             inputs=[
                 IO.Image.Input(
@@ -2291,7 +2291,7 @@ class KlingLipSyncAudioToVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="KlingLipSyncAudioToVideoNode",
             display_name="Kling Lip Sync Video with Audio",
-            category="video/partner/Kling",
+            category="partner/video/Kling",
             essentials_category="Video Generation",
             description="Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file. When using, ensure that the audio contains clearly distinguishable vocals and that the video contains a distinct face. The audio file should not be larger than 5MB. The video file should not be larger than 100MB, should have height/width between 720px and 1920px, and should be between 2s and 10s in length.",
             inputs=[
@@ -2343,7 +2343,7 @@ class KlingLipSyncTextToVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="KlingLipSyncTextToVideoNode",
             display_name="Kling Lip Sync Video with Text",
-            category="video/partner/Kling",
+            category="partner/video/Kling",
             description="Kling Lip Sync Text to Video Node. Syncs mouth movements in a video file to a text prompt. The video file should not be larger than 100MB, should have height/width between 720px and 1920px, and should be between 2s and 10s in length.",
             inputs=[
                 IO.Video.Input("video"),
@@ -2411,7 +2411,7 @@ class KlingVirtualTryOnNode(IO.ComfyNode):
         return IO.Schema(
             node_id="KlingVirtualTryOnNode",
             display_name="Kling Virtual Try On",
-            category="image/partner/Kling",
+            category="partner/image/Kling",
             description="Kling Virtual Try On Node. Input a human image and a cloth image to try on the cloth on the human. You can merge multiple clothing item pictures into one image with a white background.",
             inputs=[
                 IO.Image.Input("human_image"),
@@ -2478,7 +2478,7 @@ class KlingImageGenerationNode(IO.ComfyNode):
         return IO.Schema(
             node_id="KlingImageGenerationNode",
             display_name="Kling 3.0 Image",
-            category="image/partner/Kling",
+            category="partner/image/Kling",
             description="Kling Image Generation Node. Generate an image from a text prompt with an optional reference image.",
             inputs=[
                 IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"),
@@ -2615,7 +2615,7 @@ class TextToVideoWithAudio(IO.ComfyNode):
         return IO.Schema(
             node_id="KlingTextToVideoWithAudio",
             display_name="Kling 2.6 Text to Video with Audio",
-            category="video/partner/Kling",
+            category="partner/video/Kling",
             inputs=[
                 IO.Combo.Input("model_name", options=["kling-v2-6"]),
                 IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt."),
@@ -2683,7 +2683,7 @@ class ImageToVideoWithAudio(IO.ComfyNode):
         return IO.Schema(
             node_id="KlingImageToVideoWithAudio",
             display_name="Kling 2.6 Image(First Frame) to Video with Audio",
-            category="video/partner/Kling",
+            category="partner/video/Kling",
             inputs=[
                 IO.Combo.Input("model_name", options=["kling-v2-6"]),
                 IO.Image.Input("start_frame"),
@@ -2753,7 +2753,7 @@ class MotionControl(IO.ComfyNode):
         return IO.Schema(
             node_id="KlingMotionControl",
             display_name="Kling Motion Control",
-            category="video/partner/Kling",
+            category="partner/video/Kling",
             inputs=[
                 IO.String.Input("prompt", multiline=True),
                 IO.Image.Input("reference_image"),
@@ -2854,7 +2854,7 @@ class KlingVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="KlingVideoNode",
             display_name="Kling 3.0 Video",
-            category="video/partner/Kling",
+            category="partner/video/Kling",
             description="Generate videos with Kling V3. "
             "Supports text-to-video and image-to-video with optional storyboard multi-prompt and audio generation.",
             inputs=[
@@ -3077,7 +3077,7 @@ class KlingFirstLastFrameNode(IO.ComfyNode):
         return IO.Schema(
             node_id="KlingFirstLastFrameNode",
             display_name="Kling 3.0 First-Last-Frame to Video",
-            category="video/partner/Kling",
+            category="partner/video/Kling",
             description="Generate videos with Kling V3 using first and last frames.",
             inputs=[
                 IO.String.Input("prompt", multiline=True, default=""),
@@ -3202,7 +3202,7 @@ class KlingAvatarNode(IO.ComfyNode):
         return IO.Schema(
             node_id="KlingAvatarNode",
             display_name="Kling Avatar 2.0",
-            category="video/partner/Kling",
+            category="partner/video/Kling",
             description="Generate broadcast-style digital human videos from a single photo and an audio file.",
             inputs=[
                 IO.Image.Input(

comfy_api_nodes/nodes_krea.py

@@ -42,9 +42,11 @@ async def _upload_image_to_krea_assets(cls: type[IO.ComfyNode], image: Input.Ima
 
 
 _MODEL_MEDIUM = "Krea 2 Medium"
+_MODEL_MEDIUM_TURBO = "Krea 2 Medium Turbo"
 _MODEL_LARGE = "Krea 2 Large"
 _MODEL_ENDPOINTS: dict[str, str] = {
     _MODEL_MEDIUM: "/proxy/krea/generate/image/krea/krea-2/medium",
+    _MODEL_MEDIUM_TURBO: "/proxy/krea/generate/image/krea/krea-2/medium-turbo",
     _MODEL_LARGE: "/proxy/krea/generate/image/krea/krea-2/large",
 }
 
@@ -57,7 +59,7 @@ _UUID_RE = re.compile(r"^[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F
 
 
 def _krea_model_inputs() -> list:
-    """Nested inputs shared by both Krea 2 Medium and Large under the DynamicCombo."""
+    """Nested inputs shared by Krea 2 Medium, Medium Turbo and Large under the DynamicCombo."""
     return [
         IO.Combo.Input(
             "aspect_ratio",
@@ -106,7 +108,7 @@ class Krea2ImageNode(IO.ComfyNode):
         return IO.Schema(
             node_id="Krea2ImageNode",
             display_name="Krea 2 Image",
-            category="image/partner/Krea",
+            category="partner/image/Krea",
             description=(
                 "Generate images via Krea 2 — pick Medium (expressive illustrations) or "
                 "Large (expressive photorealism). Supports an optional moodboard and up "
@@ -123,6 +125,7 @@ class Krea2ImageNode(IO.ComfyNode):
                     "model",
                     options=[
                         IO.DynamicCombo.Option(_MODEL_MEDIUM, _krea_model_inputs()),
+                        IO.DynamicCombo.Option(_MODEL_MEDIUM_TURBO, _krea_model_inputs()),
                         IO.DynamicCombo.Option(_MODEL_LARGE, _krea_model_inputs()),
                     ],
                     tooltip="Krea 2 Medium is best for expressive illustrations; "
@@ -151,14 +154,15 @@ class Krea2ImageNode(IO.ComfyNode):
                 ),
                 expr="""
                 (
-                  $isLarge := widgets.model = "krea 2 large";
+                  $rates := {
+                    "krea 2 medium turbo": {"text": 0.015, "style": 0.0175, "moodboard": 0.02},
+                    "krea 2 medium": {"text": 0.03, "style": 0.035, "moodboard": 0.04},
+                    "krea 2 large": {"text": 0.06, "style": 0.065, "moodboard": 0.07}
+                  };
+                  $r := $lookup($rates, widgets.model);
                   $hasMoodboard := $length($lookup(widgets, "model.moodboard_id")) > 0;
                   $hasStyle := $lookup(inputs, "model.style_reference").connected;
-                  $usd := $hasMoodboard
-                    ? ($isLarge ? 0.07 : 0.04)
-                    : ($hasStyle
-                        ? ($isLarge ? 0.065 : 0.035)
-                        : ($isLarge ? 0.06 : 0.03));
+                  $usd := $hasMoodboard ? $r.moodboard : ($hasStyle ? $r.style : $r.text);
                   {"type":"usd","usd": $usd}
                 )
                 """,
@@ -229,7 +233,7 @@ class Krea2StyleReferenceNode(IO.ComfyNode):
         return IO.Schema(
             node_id="Krea2StyleReferenceNode",
             display_name="Krea 2 Style Reference",
-            category="image/partner/Krea",
+            category="partner/image/Krea",
             description=(
                 "Add an image style reference to a Krea 2 generation. Chain multiple Krea 2 "
                 "Style Reference nodes (max 10) and feed the final `style_reference` output "

comfy_api_nodes/nodes_ltxv.py

@@ -50,7 +50,7 @@ class TextToVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="LtxvApiTextToVideo",
             display_name="LTXV Text To Video",
-            category="video/partner/LTXV",
+            category="partner/video/LTXV",
             description="Professional-quality videos with customizable duration and resolution.",
             inputs=[
                 IO.Combo.Input("model", options=list(MODELS_MAP.keys())),
@@ -127,7 +127,7 @@ class ImageToVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="LtxvApiImageToVideo",
             display_name="LTXV Image To Video",
-            category="video/partner/LTXV",
+            category="partner/video/LTXV",
             description="Professional-quality videos with customizable duration and resolution based on start image.",
             inputs=[
                 IO.Image.Input("image", tooltip="First frame to be used for the video."),

comfy_api_nodes/nodes_luma.py

@@ -46,7 +46,7 @@ class LumaReferenceNode(IO.ComfyNode):
         return IO.Schema(
             node_id="LumaReferenceNode",
             display_name="Luma Reference",
-            category="image/partner/Luma",
+            category="partner/image/Luma",
             description="Holds an image and weight for use with Luma Generate Image node.",
             inputs=[
                 IO.Image.Input(
@@ -85,7 +85,7 @@ class LumaConceptsNode(IO.ComfyNode):
         return IO.Schema(
             node_id="LumaConceptsNode",
             display_name="Luma Concepts",
-            category="video/partner/Luma",
+            category="partner/video/Luma",
             description="Camera Concepts for use with Luma Text to Video and Luma Image to Video nodes.",
             inputs=[
                 IO.Combo.Input(
@@ -134,7 +134,7 @@ class LumaImageGenerationNode(IO.ComfyNode):
         return IO.Schema(
             node_id="LumaImageNode",
             display_name="Luma Text to Image",
-            category="image/partner/Luma",
+            category="partner/image/Luma",
             description="Generates images synchronously based on prompt and aspect ratio.",
             inputs=[
                 IO.String.Input(
@@ -278,7 +278,7 @@ class LumaImageModifyNode(IO.ComfyNode):
         return IO.Schema(
             node_id="LumaImageModifyNode",
             display_name="Luma Image to Image",
-            category="image/partner/Luma",
+            category="partner/image/Luma",
             description="Modifies images synchronously based on prompt and aspect ratio.",
             inputs=[
                 IO.Image.Input(
@@ -371,7 +371,7 @@ class LumaTextToVideoGenerationNode(IO.ComfyNode):
         return IO.Schema(
             node_id="LumaVideoNode",
             display_name="Luma Text to Video",
-            category="video/partner/Luma",
+            category="partner/video/Luma",
             description="Generates videos synchronously based on prompt and output_size.",
             inputs=[
                 IO.String.Input(
@@ -472,7 +472,7 @@ class LumaImageToVideoGenerationNode(IO.ComfyNode):
         return IO.Schema(
             node_id="LumaImageToVideoNode",
             display_name="Luma Image to Video",
-            category="video/partner/Luma",
+            category="partner/video/Luma",
             description="Generates videos synchronously based on prompt, input images, and output_size.",
             inputs=[
                 IO.String.Input(
@@ -724,7 +724,7 @@ class LumaImageNode(IO.ComfyNode):
         return IO.Schema(
             node_id="LumaImageNode2",
             display_name="Luma UNI-1 Image",
-            category="image/partner/Luma",
+            category="partner/image/Luma",
             description="Generate images from text using the Luma UNI-1 model.",
             inputs=[
                 IO.String.Input(
@@ -853,7 +853,7 @@ class LumaImageEditNode(IO.ComfyNode):
         return IO.Schema(
             node_id="LumaImageEditNode2",
             display_name="Luma UNI-1 Image Edit",
-            category="image/partner/Luma",
+            category="partner/image/Luma",
             description="Edit an existing image with a text prompt using the Luma UNI-1 model.",
             inputs=[
                 IO.Image.Input(

comfy_api_nodes/nodes_magnific.py

@@ -61,7 +61,7 @@ class MagnificImageUpscalerCreativeNode(IO.ComfyNode):
         return IO.Schema(
             node_id="MagnificImageUpscalerCreativeNode",
             display_name="Magnific Image Upscale (Creative)",
-            category="image/partner/Magnific",
+            category="partner/image/Magnific",
             description="Prompt‑guided enhancement, stylization, and 2x/4x/8x/16x upscaling. "
             "Maximum output: 25.3 megapixels.",
             inputs=[
@@ -240,7 +240,7 @@ class MagnificImageUpscalerPreciseV2Node(IO.ComfyNode):
         return IO.Schema(
             node_id="MagnificImageUpscalerPreciseV2Node",
             display_name="Magnific Image Upscale (Precise V2)",
-            category="image/partner/Magnific",
+            category="partner/image/Magnific",
             description="High-fidelity upscaling with fine control over sharpness, grain, and detail. "
             "Maximum output: 10060×10060 pixels.",
             inputs=[
@@ -400,7 +400,7 @@ class MagnificImageStyleTransferNode(IO.ComfyNode):
         return IO.Schema(
             node_id="MagnificImageStyleTransferNode",
             display_name="Magnific Image Style Transfer",
-            category="image/partner/Magnific",
+            category="partner/image/Magnific",
             description="Transfer the style from a reference image to your input image.",
             inputs=[
                 IO.Image.Input("image", tooltip="The image to apply style transfer to."),
@@ -549,7 +549,7 @@ class MagnificImageRelightNode(IO.ComfyNode):
         return IO.Schema(
             node_id="MagnificImageRelightNode",
             display_name="Magnific Image Relight",
-            category="image/partner/Magnific",
+            category="partner/image/Magnific",
             description="Relight an image with lighting adjustments and optional reference-based light transfer.",
             inputs=[
                 IO.Image.Input("image", tooltip="The image to relight."),
@@ -789,7 +789,7 @@ class MagnificImageSkinEnhancerNode(IO.ComfyNode):
         return IO.Schema(
             node_id="MagnificImageSkinEnhancerNode",
             display_name="Magnific Image Skin Enhancer",
-            category="image/partner/Magnific",
+            category="partner/image/Magnific",
             description="Skin enhancement for portraits with multiple processing modes.",
             inputs=[
                 IO.Image.Input("image", tooltip="The portrait image to enhance."),

comfy_api_nodes/nodes_meshy.py

@@ -33,7 +33,7 @@ class MeshyTextToModelNode(IO.ComfyNode):
         return IO.Schema(
             node_id="MeshyTextToModelNode",
             display_name="Meshy: Text to Model",
-            category="3d/partner/Meshy",
+            category="partner/3d/Meshy",
             inputs=[
                 IO.Combo.Input("model", options=["latest"]),
                 IO.String.Input("prompt", multiline=True, default=""),
@@ -145,7 +145,7 @@ class MeshyRefineNode(IO.ComfyNode):
         return IO.Schema(
             node_id="MeshyRefineNode",
             display_name="Meshy: Refine Draft Model",
-            category="3d/partner/Meshy",
+            category="partner/3d/Meshy",
             description="Refine a previously created draft model.",
             inputs=[
                 IO.Combo.Input("model", options=["latest"]),
@@ -240,7 +240,7 @@ class MeshyImageToModelNode(IO.ComfyNode):
         return IO.Schema(
             node_id="MeshyImageToModelNode",
             display_name="Meshy: Image to Model",
-            category="3d/partner/Meshy",
+            category="partner/3d/Meshy",
             inputs=[
                 IO.Combo.Input("model", options=["latest"]),
                 IO.Image.Input("image"),
@@ -405,7 +405,7 @@ class MeshyMultiImageToModelNode(IO.ComfyNode):
         return IO.Schema(
             node_id="MeshyMultiImageToModelNode",
             display_name="Meshy: Multi-Image to Model",
-            category="3d/partner/Meshy",
+            category="partner/3d/Meshy",
             inputs=[
                 IO.Combo.Input("model", options=["latest"]),
                 IO.Autogrow.Input(
@@ -575,7 +575,7 @@ class MeshyRigModelNode(IO.ComfyNode):
         return IO.Schema(
             node_id="MeshyRigModelNode",
             display_name="Meshy: Rig Model",
-            category="3d/partner/Meshy",
+            category="partner/3d/Meshy",
             description="Provides a rigged character in standard formats. "
             "Auto-rigging is currently not suitable for untextured meshes, non-humanoid assets, "
             "or humanoid assets with unclear limb and body structure.",
@@ -656,7 +656,7 @@ class MeshyAnimateModelNode(IO.ComfyNode):
         return IO.Schema(
             node_id="MeshyAnimateModelNode",
             display_name="Meshy: Animate Model",
-            category="3d/partner/Meshy",
+            category="partner/3d/Meshy",
             description="Apply a specific animation action to a previously rigged character.",
             inputs=[
                 IO.Custom("MESHY_RIGGED_TASK_ID").Input("rig_task_id"),
@@ -722,7 +722,7 @@ class MeshyTextureNode(IO.ComfyNode):
         return IO.Schema(
             node_id="MeshyTextureNode",
             display_name="Meshy: Texture Model",
-            category="3d/partner/Meshy",
+            category="partner/3d/Meshy",
             inputs=[
                 IO.Combo.Input("model", options=["latest"]),
                 IO.Custom("MESHY_TASK_ID").Input("meshy_task_id"),

comfy_api_nodes/nodes_minimax.py

@@ -101,7 +101,7 @@ class MinimaxTextToVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="MinimaxTextToVideoNode",
             display_name="MiniMax Text to Video",
-            category="video/partner/MiniMax",
+            category="partner/video/MiniMax",
             description="Generates videos synchronously based on a prompt, and optional parameters.",
             inputs=[
                 IO.String.Input(
@@ -163,7 +163,7 @@ class MinimaxImageToVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="MinimaxImageToVideoNode",
             display_name="MiniMax Image to Video",
-            category="video/partner/MiniMax",
+            category="partner/video/MiniMax",
             description="Generates videos synchronously based on an image and prompt, and optional parameters.",
             inputs=[
                 IO.Image.Input(
@@ -230,7 +230,7 @@ class MinimaxSubjectToVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="MinimaxSubjectToVideoNode",
             display_name="MiniMax Subject to Video",
-            category="video/partner/MiniMax",
+            category="partner/video/MiniMax",
             description="Generates videos synchronously based on an image and prompt, and optional parameters.",
             inputs=[
                 IO.Image.Input(
@@ -294,7 +294,7 @@ class MinimaxHailuoVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="MinimaxHailuoVideoNode",
             display_name="MiniMax Hailuo Video",
-            category="video/partner/MiniMax",
+            category="partner/video/MiniMax",
             description="Generates videos from prompt, with optional start frame using the new MiniMax Hailuo-02 model.",
             inputs=[
                 IO.String.Input(

comfy_api_nodes/nodes_openai.py

@@ -99,7 +99,7 @@ class OpenAIDalle2(IO.ComfyNode):
         return IO.Schema(
             node_id="OpenAIDalle2",
             display_name="OpenAI DALL·E 2",
-            category="image/partner/OpenAI",
+            category="partner/image/OpenAI",
             description="Generates images synchronously via OpenAI's DALL·E 2 endpoint.",
             inputs=[
                 IO.String.Input(
@@ -249,7 +249,7 @@ class OpenAIDalle3(IO.ComfyNode):
         return IO.Schema(
             node_id="OpenAIDalle3",
             display_name="OpenAI DALL·E 3",
-            category="image/partner/OpenAI",
+            category="partner/image/OpenAI",
             description="Generates images synchronously via OpenAI's DALL·E 3 endpoint.",
             inputs=[
                 IO.String.Input(
@@ -371,7 +371,7 @@ class OpenAIGPTImage1(IO.ComfyNode):
         return IO.Schema(
             node_id="OpenAIGPTImage1",
             display_name="OpenAI GPT Image 2",
-            category="image/partner/OpenAI",
+            category="partner/image/OpenAI",
             description="Generates images synchronously via OpenAI's GPT Image endpoint.",
             is_deprecated=True,
             inputs=[
@@ -695,7 +695,7 @@ class OpenAIGPTImageNodeV2(IO.ComfyNode):
         return IO.Schema(
             node_id="OpenAIGPTImageNodeV2",
             display_name="OpenAI GPT Image 2",
-            category="image/partner/OpenAI",
+            category="partner/image/OpenAI",
             description="Generates images via OpenAI's GPT Image endpoint.",
             inputs=[
                 IO.String.Input(
@@ -962,7 +962,7 @@ class OpenAIChatNode(IO.ComfyNode):
         return IO.Schema(
             node_id="OpenAIChatNode",
             display_name="OpenAI ChatGPT",
-            category="text/partner/OpenAI",
+            category="partner/text/OpenAI",
             essentials_category="Text Generation",
             description="Generate text responses from an OpenAI model.",
             inputs=[
@@ -1201,7 +1201,7 @@ class OpenAIInputFiles(IO.ComfyNode):
         return IO.Schema(
             node_id="OpenAIInputFiles",
             display_name="OpenAI ChatGPT Input Files",
-            category="text/partner/OpenAI",
+            category="partner/text/OpenAI",
             description="Loads and prepares input files (text, pdf, etc.) to include as inputs for the OpenAI Chat Node. The files will be read by the OpenAI model when generating a response. 🛈 TIP: Can be chained together with other OpenAI Input File nodes.",
             inputs=[
                 IO.Combo.Input(
@@ -1248,7 +1248,7 @@ class OpenAIChatConfig(IO.ComfyNode):
         return IO.Schema(
             node_id="OpenAIChatConfig",
             display_name="OpenAI ChatGPT Advanced Options",
-            category="text/partner/OpenAI",
+            category="partner/text/OpenAI",
             description="Allows specifying advanced configuration options for the OpenAI Chat Nodes.",
             inputs=[
                 IO.Combo.Input(

comfy_api_nodes/nodes_openrouter.py

@@ -265,7 +265,7 @@ class OpenRouterLLMNode(IO.ComfyNode):
         return IO.Schema(
             node_id="OpenRouterLLMNode",
             display_name="OpenRouter LLM",
-            category="text/partner/OpenRouter",
+            category="partner/text/OpenRouter",
             essentials_category="Text Generation",
             description=(
                 "Generate text responses through OpenRouter. Routes to a curated set of popular "

comfy_api_nodes/nodes_pixverse.py

@@ -53,7 +53,7 @@ class PixverseTemplateNode(IO.ComfyNode):
         return IO.Schema(
             node_id="PixverseTemplateNode",
             display_name="PixVerse Template",
-            category="video/partner/PixVerse",
+            category="partner/video/PixVerse",
             inputs=[
                 IO.Combo.Input("template", options=list(pixverse_templates.keys())),
             ],
@@ -74,7 +74,7 @@ class PixverseTextToVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="PixverseTextToVideoNode",
             display_name="PixVerse Text to Video",
-            category="video/partner/PixVerse",
+            category="partner/video/PixVerse",
             description="Generates videos based on prompt and output_size.",
             inputs=[
                 IO.String.Input(
@@ -192,7 +192,7 @@ class PixverseImageToVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="PixverseImageToVideoNode",
             display_name="PixVerse Image to Video",
-            category="video/partner/PixVerse",
+            category="partner/video/PixVerse",
             description="Generates videos based on prompt and output_size.",
             inputs=[
                 IO.Image.Input("image"),
@@ -310,7 +310,7 @@ class PixverseTransitionVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="PixverseTransitionVideoNode",
             display_name="PixVerse Transition Video",
-            category="video/partner/PixVerse",
+            category="partner/video/PixVerse",
             description="Generates videos based on prompt and output_size.",
             inputs=[
                 IO.Image.Input("first_frame"),

comfy_api_nodes/nodes_quiver.py

@@ -62,7 +62,7 @@ class QuiverTextToSVGNode(IO.ComfyNode):
         return IO.Schema(
             node_id="QuiverTextToSVGNode",
             display_name="Quiver Text to SVG",
-            category="image/partner/Quiver",
+            category="partner/image/Quiver",
             description="Generate an SVG from a text prompt using Quiver AI.",
             inputs=[
                 IO.String.Input(
@@ -177,7 +177,7 @@ class QuiverImageToSVGNode(IO.ComfyNode):
         return IO.Schema(
             node_id="QuiverImageToSVGNode",
             display_name="Quiver Image to SVG",
-            category="image/partner/Quiver",
+            category="partner/image/Quiver",
             description="Vectorize a raster image into SVG using Quiver AI.",
             inputs=[
                 IO.Image.Input(

comfy_api_nodes/nodes_recraft.py

@@ -178,7 +178,7 @@ class RecraftColorRGBNode(IO.ComfyNode):
         return IO.Schema(
             node_id="RecraftColorRGB",
             display_name="Recraft Color RGB",
-            category="image/partner/Recraft",
+            category="partner/image/Recraft",
             description="Create Recraft Color by choosing specific RGB values.",
             inputs=[
                 IO.Int.Input("r", default=0, min=0, max=255, tooltip="Red value of color."),
@@ -204,7 +204,7 @@ class RecraftControlsNode(IO.ComfyNode):
         return IO.Schema(
             node_id="RecraftControls",
             display_name="Recraft Controls",
-            category="image/partner/Recraft",
+            category="partner/image/Recraft",
             description="Create Recraft Controls for customizing Recraft generation.",
             inputs=[
                 IO.Custom(RecraftIO.COLOR).Input("colors", optional=True),
@@ -228,7 +228,7 @@ class RecraftStyleV3RealisticImageNode(IO.ComfyNode):
         return IO.Schema(
             node_id="RecraftStyleV3RealisticImage",
             display_name="Recraft Style - Realistic Image",
-            category="image/partner/Recraft",
+            category="partner/image/Recraft",
             description="Select realistic_image style and optional substyle.",
             inputs=[
                 IO.Combo.Input("substyle", options=get_v3_substyles(cls.RECRAFT_STYLE)),
@@ -253,7 +253,7 @@ class RecraftStyleV3DigitalIllustrationNode(RecraftStyleV3RealisticImageNode):
         return IO.Schema(
             node_id="RecraftStyleV3DigitalIllustration",
             display_name="Recraft Style - Digital Illustration",
-            category="image/partner/Recraft",
+            category="partner/image/Recraft",
             description="Select realistic_image style and optional substyle.",
             inputs=[
                 IO.Combo.Input("substyle", options=get_v3_substyles(cls.RECRAFT_STYLE)),
@@ -272,7 +272,7 @@ class RecraftStyleV3VectorIllustrationNode(RecraftStyleV3RealisticImageNode):
         return IO.Schema(
             node_id="RecraftStyleV3VectorIllustrationNode",
             display_name="Recraft Style - Realistic Image",
-            category="image/partner/Recraft",
+            category="partner/image/Recraft",
             description="Select realistic_image style and optional substyle.",
             inputs=[
                 IO.Combo.Input("substyle", options=get_v3_substyles(cls.RECRAFT_STYLE)),
@@ -291,7 +291,7 @@ class RecraftStyleV3LogoRasterNode(RecraftStyleV3RealisticImageNode):
         return IO.Schema(
             node_id="RecraftStyleV3LogoRaster",
             display_name="Recraft Style - Logo Raster",
-            category="image/partner/Recraft",
+            category="partner/image/Recraft",
             description="Select realistic_image style and optional substyle.",
             inputs=[
                 IO.Combo.Input("substyle", options=get_v3_substyles(cls.RECRAFT_STYLE, include_none=False)),
@@ -308,7 +308,7 @@ class RecraftStyleInfiniteStyleLibrary(IO.ComfyNode):
         return IO.Schema(
             node_id="RecraftStyleV3InfiniteStyleLibrary",
             display_name="Recraft Style - Infinite Style Library",
-            category="image/partner/Recraft",
+            category="partner/image/Recraft",
             description="Choose style based on preexisting UUID from Recraft's Infinite Style Library.",
             inputs=[
                 IO.String.Input("style_id", default="", tooltip="UUID of style from Infinite Style Library."),
@@ -331,7 +331,7 @@ class RecraftCreateStyleNode(IO.ComfyNode):
         return IO.Schema(
             node_id="RecraftCreateStyleNode",
             display_name="Recraft Create Style",
-            category="image/partner/Recraft",
+            category="partner/image/Recraft",
             description="Create a custom style from reference images. "
             "Upload 1-5 images to use as style references. "
             "Total size of all images is limited to 5 MB.",
@@ -400,7 +400,7 @@ class RecraftTextToImageNode(IO.ComfyNode):
         return IO.Schema(
             node_id="RecraftTextToImageNode",
             display_name="Recraft Text to Image",
-            category="image/partner/Recraft",
+            category="partner/image/Recraft",
             description="Generates images synchronously based on prompt and resolution.",
             inputs=[
                 IO.String.Input("prompt", multiline=True, default="", tooltip="Prompt for the image generation."),
@@ -512,7 +512,7 @@ class RecraftImageToImageNode(IO.ComfyNode):
         return IO.Schema(
             node_id="RecraftImageToImageNode",
             display_name="Recraft Image to Image",
-            category="image/partner/Recraft",
+            category="partner/image/Recraft",
             description="Modify image based on prompt and strength.",
             inputs=[
                 IO.Image.Input("image"),
@@ -630,7 +630,7 @@ class RecraftImageInpaintingNode(IO.ComfyNode):
         return IO.Schema(
             node_id="RecraftImageInpaintingNode",
             display_name="Recraft Image Inpainting",
-            category="image/partner/Recraft",
+            category="partner/image/Recraft",
             description="Modify image based on prompt and mask.",
             inputs=[
                 IO.Image.Input("image"),
@@ -732,7 +732,7 @@ class RecraftTextToVectorNode(IO.ComfyNode):
         return IO.Schema(
             node_id="RecraftTextToVectorNode",
             display_name="Recraft Text to Vector",
-            category="image/partner/Recraft",
+            category="partner/image/Recraft",
             description="Generates SVG synchronously based on prompt and resolution.",
             inputs=[
                 IO.String.Input("prompt", default="", tooltip="Prompt for the image generation.", multiline=True),
@@ -832,7 +832,7 @@ class RecraftVectorizeImageNode(IO.ComfyNode):
         return IO.Schema(
             node_id="RecraftVectorizeImageNode",
             display_name="Recraft Vectorize Image",
-            category="image/partner/Recraft",
+            category="partner/image/Recraft",
             essentials_category="Image Tools",
             description="Generates SVG synchronously from an input image.",
             inputs=[
@@ -876,7 +876,7 @@ class RecraftReplaceBackgroundNode(IO.ComfyNode):
         return IO.Schema(
             node_id="RecraftReplaceBackgroundNode",
             display_name="Recraft Replace Background",
-            category="image/partner/Recraft",
+            category="partner/image/Recraft",
             description="Replace background on image, based on provided prompt.",
             inputs=[
                 IO.Image.Input("image"),
@@ -963,7 +963,7 @@ class RecraftRemoveBackgroundNode(IO.ComfyNode):
         return IO.Schema(
             node_id="RecraftRemoveBackgroundNode",
             display_name="Recraft Remove Background",
-            category="image/partner/Recraft",
+            category="partner/image/Recraft",
             essentials_category="Image Tools",
             description="Remove background from image, and return processed image and mask.",
             inputs=[
@@ -1012,7 +1012,7 @@ class RecraftCrispUpscaleNode(IO.ComfyNode):
         return IO.Schema(
             node_id="RecraftCrispUpscaleNode",
             display_name="Recraft Crisp Upscale Image",
-            category="image/partner/Recraft",
+            category="partner/image/Recraft",
             description="Upscale image synchronously.\n"
             "Enhances a given raster image using ‘crisp upscale’ tool, "
             "increasing image resolution, making the image sharper and cleaner.",
@@ -1058,7 +1058,7 @@ class RecraftCreativeUpscaleNode(RecraftCrispUpscaleNode):
         return IO.Schema(
             node_id="RecraftCreativeUpscaleNode",
             display_name="Recraft Creative Upscale Image",
-            category="image/partner/Recraft",
+            category="partner/image/Recraft",
             description="Upscale image synchronously.\n"
             "Enhances a given raster image using ‘creative upscale’ tool, "
             "boosting resolution with a focus on refining small details and faces.",
@@ -1086,7 +1086,7 @@ class RecraftV4TextToImageNode(IO.ComfyNode):
         return IO.Schema(
             node_id="RecraftV4TextToImageNode",
             display_name="Recraft V4 Text to Image",
-            category="image/partner/Recraft",
+            category="partner/image/Recraft",
             description="Generates images using Recraft V4 or V4 Pro models.",
             inputs=[
                 IO.String.Input(
@@ -1210,7 +1210,7 @@ class RecraftV4TextToVectorNode(IO.ComfyNode):
         return IO.Schema(
             node_id="RecraftV4TextToVectorNode",
             display_name="Recraft V4 Text to Vector",
-            category="image/partner/Recraft",
+            category="partner/image/Recraft",
             description="Generates SVG using Recraft V4 or V4 Pro models.",
             inputs=[
                 IO.String.Input(

comfy_api_nodes/nodes_reve.py

@@ -109,7 +109,7 @@ class ReveImageCreateNode(IO.ComfyNode):
         return IO.Schema(
             node_id="ReveImageCreateNode",
             display_name="Reve Image Create",
-            category="image/partner/Reve",
+            category="partner/image/Reve",
             description="Generate images from text descriptions using Reve.",
             inputs=[
                 IO.String.Input(
@@ -200,7 +200,7 @@ class ReveImageEditNode(IO.ComfyNode):
         return IO.Schema(
             node_id="ReveImageEditNode",
             display_name="Reve Image Edit",
-            category="image/partner/Reve",
+            category="partner/image/Reve",
             description="Edit images using natural language instructions with Reve.",
             inputs=[
                 IO.Image.Input("image", tooltip="The image to edit."),
@@ -300,7 +300,7 @@ class ReveImageRemixNode(IO.ComfyNode):
         return IO.Schema(
             node_id="ReveImageRemixNode",
             display_name="Reve Image Remix",
-            category="image/partner/Reve",
+            category="partner/image/Reve",
             description="Combine reference images with text prompts to create new images using Reve.",
             inputs=[
                 IO.Autogrow.Input(

comfy_api_nodes/nodes_rodin.py

@@ -230,7 +230,7 @@ class Rodin3D_Regular(IO.ComfyNode):
         return IO.Schema(
             node_id="Rodin3D_Regular",
             display_name="Rodin 3D Generate - Regular Generate",
-            category="3d/partner/Rodin",
+            category="partner/3d/Rodin",
             description=cleandoc(cls.__doc__ or ""),
             inputs=[
                 IO.Image.Input("Images"),
@@ -289,7 +289,7 @@ class Rodin3D_Detail(IO.ComfyNode):
         return IO.Schema(
             node_id="Rodin3D_Detail",
             display_name="Rodin 3D Generate - Detail Generate",
-            category="3d/partner/Rodin",
+            category="partner/3d/Rodin",
             description=cleandoc(cls.__doc__ or ""),
             inputs=[
                 IO.Image.Input("Images"),
@@ -348,7 +348,7 @@ class Rodin3D_Smooth(IO.ComfyNode):
         return IO.Schema(
             node_id="Rodin3D_Smooth",
             display_name="Rodin 3D Generate - Smooth Generate",
-            category="3d/partner/Rodin",
+            category="partner/3d/Rodin",
             description=cleandoc(cls.__doc__ or ""),
             inputs=[
                 IO.Image.Input("Images"),
@@ -406,7 +406,7 @@ class Rodin3D_Sketch(IO.ComfyNode):
         return IO.Schema(
             node_id="Rodin3D_Sketch",
             display_name="Rodin 3D Generate - Sketch Generate",
-            category="3d/partner/Rodin",
+            category="partner/3d/Rodin",
             description=cleandoc(cls.__doc__ or ""),
             inputs=[
                 IO.Image.Input("Images"),
@@ -468,7 +468,7 @@ class Rodin3D_Gen2(IO.ComfyNode):
         return IO.Schema(
             node_id="Rodin3D_Gen2",
             display_name="Rodin 3D Generate - Gen-2 Generate",
-            category="3d/partner/Rodin",
+            category="partner/3d/Rodin",
             description=cleandoc(cls.__doc__ or ""),
             inputs=[
                 IO.Image.Input("Images"),
@@ -941,7 +941,7 @@ class Rodin3D_Gen25_Image(IO.ComfyNode):
         return IO.Schema(
             node_id="Rodin3D_Gen25_Image",
             display_name="Rodin 3D Gen-2.5 - Image to 3D",
-            category="3d/partner/Rodin",
+            category="partner/3d/Rodin",
             description=(
                 "Generate a 3D model from 1-5 reference images via Rodin Gen-2.5. "
                 "Pick a mode (Fast / Regular / Extreme-High) to tune quality vs. cost."
@@ -1035,7 +1035,7 @@ class Rodin3D_Gen25_Text(IO.ComfyNode):
         return IO.Schema(
             node_id="Rodin3D_Gen25_Text",
             display_name="Rodin 3D Gen-2.5 - Text to 3D",
-            category="3d/partner/Rodin",
+            category="partner/3d/Rodin",
             description=(
                 "Generate a 3D model from a text prompt via Rodin Gen-2.5. "
                 "Pick a mode (Fast / Regular / Extreme-High) to tune quality vs. cost."

comfy_api_nodes/nodes_runway.py

@@ -140,7 +140,7 @@ class RunwayImageToVideoNodeGen3a(IO.ComfyNode):
         return IO.Schema(
             node_id="RunwayImageToVideoNodeGen3a",
             display_name="Runway Image to Video (Gen3a Turbo)",
-            category="video/partner/Runway",
+            category="partner/video/Runway",
             description="Generate a video from a single starting frame using Gen3a Turbo model. "
             "Before diving in, review these best practices to ensure that "
             "your input selections will set your generation up for success: "
@@ -234,7 +234,7 @@ class RunwayImageToVideoNodeGen4(IO.ComfyNode):
         return IO.Schema(
             node_id="RunwayImageToVideoNodeGen4",
             display_name="Runway Image to Video (Gen4 Turbo)",
-            category="video/partner/Runway",
+            category="partner/video/Runway",
             description="Generate a video from a single starting frame using Gen4 Turbo model. "
             "Before diving in, review these best practices to ensure that "
             "your input selections will set your generation up for success: "
@@ -329,7 +329,7 @@ class RunwayFirstLastFrameNode(IO.ComfyNode):
         return IO.Schema(
             node_id="RunwayFirstLastFrameNode",
             display_name="Runway First-Last-Frame to Video",
-            category="video/partner/Runway",
+            category="partner/video/Runway",
             description="Upload first and last keyframes, draft a prompt, and generate a video. "
             "More complex transitions, such as cases where the Last frame is completely different "
             "from the First frame, may benefit from the longer 10s duration. "
@@ -440,7 +440,7 @@ class RunwayTextToImageNode(IO.ComfyNode):
         return IO.Schema(
             node_id="RunwayTextToImageNode",
             display_name="Runway Text to Image",
-            category="image/partner/Runway",
+            category="partner/image/Runway",
             description="Generate an image from a text prompt using Runway's Gen 4 model. "
             "You can also include reference image to guide the generation.",
             inputs=[

comfy_api_nodes/nodes_sonilo.py

@@ -34,7 +34,7 @@ class SoniloVideoToMusic(IO.ComfyNode):
         return IO.Schema(
             node_id="SoniloVideoToMusic",
             display_name="Sonilo Video to Music",
-            category="audio/partner/Sonilo",
+            category="partner/audio/Sonilo",
             description="Generate music from video content using Sonilo's AI model. "
             "Analyzes the video and creates matching music.",
             inputs=[
@@ -99,7 +99,7 @@ class SoniloTextToMusic(IO.ComfyNode):
         return IO.Schema(
             node_id="SoniloTextToMusic",
             display_name="Sonilo Text to Music",
-            category="audio/partner/Sonilo",
+            category="partner/audio/Sonilo",
             description="Generate music from a text prompt using Sonilo's AI model. "
             "Leave duration at 0 to let the model infer it from the prompt.",
             inputs=[

comfy_api_nodes/nodes_sora.py

@@ -34,7 +34,7 @@ class OpenAIVideoSora2(IO.ComfyNode):
         return IO.Schema(
             node_id="OpenAIVideoSora2",
             display_name="OpenAI Sora - Video (DEPRECATED)",
-            category="video/partner/Sora",
+            category="partner/video/Sora",
             description=(
                 "OpenAI video and audio generation.\n\n"
                 "DEPRECATION NOTICE: OpenAI will stop serving the Sora v2 API in September 2026. "

comfy_api_nodes/nodes_stability.py

@@ -62,7 +62,7 @@ class StabilityStableImageUltraNode(IO.ComfyNode):
         return IO.Schema(
             node_id="StabilityStableImageUltraNode",
             display_name="Stability AI Stable Image Ultra",
-            category="image/partner/Stability AI",
+            category="partner/image/Stability AI",
             description=cleandoc(cls.__doc__ or ""),
             inputs=[
                 IO.String.Input(
@@ -197,7 +197,7 @@ class StabilityStableImageSD_3_5Node(IO.ComfyNode):
         return IO.Schema(
             node_id="StabilityStableImageSD_3_5Node",
             display_name="Stability AI Stable Diffusion 3.5 Image",
-            category="image/partner/Stability AI",
+            category="partner/image/Stability AI",
             description=cleandoc(cls.__doc__ or ""),
             inputs=[
                 IO.String.Input(
@@ -354,7 +354,7 @@ class StabilityUpscaleConservativeNode(IO.ComfyNode):
         return IO.Schema(
             node_id="StabilityUpscaleConservativeNode",
             display_name="Stability AI Upscale Conservative",
-            category="image/partner/Stability AI",
+            category="partner/image/Stability AI",
             description=cleandoc(cls.__doc__ or ""),
             inputs=[
                 IO.Image.Input("image"),
@@ -457,7 +457,7 @@ class StabilityUpscaleCreativeNode(IO.ComfyNode):
         return IO.Schema(
             node_id="StabilityUpscaleCreativeNode",
             display_name="Stability AI Upscale Creative",
-            category="image/partner/Stability AI",
+            category="partner/image/Stability AI",
             description=cleandoc(cls.__doc__ or ""),
             inputs=[
                 IO.Image.Input("image"),
@@ -578,7 +578,7 @@ class StabilityUpscaleFastNode(IO.ComfyNode):
         return IO.Schema(
             node_id="StabilityUpscaleFastNode",
             display_name="Stability AI Upscale Fast",
-            category="image/partner/Stability AI",
+            category="partner/image/Stability AI",
             description=cleandoc(cls.__doc__ or ""),
             inputs=[
                 IO.Image.Input("image"),
@@ -630,7 +630,7 @@ class StabilityTextToAudio(IO.ComfyNode):
         return IO.Schema(
             node_id="StabilityTextToAudio",
             display_name="Stability AI Text To Audio",
-            category="audio/partner/Stability AI",
+            category="partner/audio/Stability AI",
             essentials_category="Audio",
             description=cleandoc(cls.__doc__ or ""),
             inputs=[
@@ -708,7 +708,7 @@ class StabilityAudioToAudio(IO.ComfyNode):
         return IO.Schema(
             node_id="StabilityAudioToAudio",
             display_name="Stability AI Audio To Audio",
-            category="audio/partner/Stability AI",
+            category="partner/audio/Stability AI",
             description=cleandoc(cls.__doc__ or ""),
             inputs=[
                 IO.Combo.Input(
@@ -802,7 +802,7 @@ class StabilityAudioInpaint(IO.ComfyNode):
         return IO.Schema(
             node_id="StabilityAudioInpaint",
             display_name="Stability AI Audio Inpaint",
-            category="audio/partner/Stability AI",
+            category="partner/audio/Stability AI",
             description=cleandoc(cls.__doc__ or ""),
             inputs=[
                 IO.Combo.Input(

comfy_api_nodes/nodes_topaz.py

@@ -52,7 +52,7 @@ class TopazImageEnhance(IO.ComfyNode):
         return IO.Schema(
             node_id="TopazImageEnhance",
             display_name="Topaz Image Enhance",
-            category="image/partner/Topaz",
+            category="partner/image/Topaz",
             description="Industry-standard upscaling and image enhancement.",
             inputs=[
                 IO.Combo.Input("model", options=["Reimagine"]),
@@ -235,7 +235,7 @@ class TopazVideoEnhance(IO.ComfyNode):
         return IO.Schema(
             node_id="TopazVideoEnhance",
             display_name="Topaz Video Enhance (Legacy)",
-            category="video/partner/Topaz",
+            category="partner/video/Topaz",
             description="Breathe new life into video with powerful upscaling and recovery technology.",
             inputs=[
                 IO.Video.Input("video"),
@@ -475,7 +475,7 @@ class TopazVideoEnhanceV2(IO.ComfyNode):
         return IO.Schema(
             node_id="TopazVideoEnhanceV2",
             display_name="Topaz Video Enhance",
-            category="video/partner/Topaz",
+            category="partner/video/Topaz",
             description="Breathe new life into video with powerful upscaling and recovery technology.",
             inputs=[
                 IO.Video.Input("video"),

comfy_api_nodes/nodes_tripo.py

@@ -83,7 +83,7 @@ class TripoTextToModelNode(IO.ComfyNode):
         return IO.Schema(
             node_id="TripoTextToModelNode",
             display_name="Tripo: Text to Model",
-            category="3d/partner/Tripo",
+            category="partner/3d/Tripo",
             inputs=[
                 IO.String.Input("prompt", multiline=True),
                 IO.String.Input("negative_prompt", multiline=True, optional=True),
@@ -210,7 +210,7 @@ class TripoImageToModelNode(IO.ComfyNode):
         return IO.Schema(
             node_id="TripoImageToModelNode",
             display_name="Tripo: Image to Model",
-            category="3d/partner/Tripo",
+            category="partner/3d/Tripo",
             inputs=[
                 IO.Image.Input("image"),
                 IO.Combo.Input(
@@ -358,7 +358,7 @@ class TripoMultiviewToModelNode(IO.ComfyNode):
         return IO.Schema(
             node_id="TripoMultiviewToModelNode",
             display_name="Tripo: Multiview to Model",
-            category="3d/partner/Tripo",
+            category="partner/3d/Tripo",
             inputs=[
                 IO.Image.Input("image"),
                 IO.Image.Input("image_left", optional=True),
@@ -518,7 +518,7 @@ class TripoTextureNode(IO.ComfyNode):
         return IO.Schema(
             node_id="TripoTextureNode",
             display_name="Tripo: Texture model",
-            category="3d/partner/Tripo",
+            category="partner/3d/Tripo",
             inputs=[
                 IO.Custom("MODEL_TASK_ID").Input("model_task_id"),
                 IO.Boolean.Input("texture", default=True, optional=True),
@@ -595,7 +595,7 @@ class TripoRefineNode(IO.ComfyNode):
         return IO.Schema(
             node_id="TripoRefineNode",
             display_name="Tripo: Refine Draft model",
-            category="3d/partner/Tripo",
+            category="partner/3d/Tripo",
             description="Refine a draft model created by v1.4 Tripo models only.",
             inputs=[
                 IO.Custom("MODEL_TASK_ID").Input("model_task_id", tooltip="Must be a v1.4 Tripo model"),
@@ -635,7 +635,7 @@ class TripoRigNode(IO.ComfyNode):
         return IO.Schema(
             node_id="TripoRigNode",
             display_name="Tripo: Rig model",
-            category="3d/partner/Tripo",
+            category="partner/3d/Tripo",
             inputs=[IO.Custom("MODEL_TASK_ID").Input("original_model_task_id")],
             outputs=[
                 IO.String.Output(display_name="model_file"),  # for backward compatibility only
@@ -672,7 +672,7 @@ class TripoRetargetNode(IO.ComfyNode):
         return IO.Schema(
             node_id="TripoRetargetNode",
             display_name="Tripo: Retarget rigged model",
-            category="3d/partner/Tripo",
+            category="partner/3d/Tripo",
             inputs=[
                 IO.Custom("RIG_TASK_ID").Input("original_model_task_id"),
                 IO.Combo.Input(
@@ -737,7 +737,7 @@ class TripoConversionNode(IO.ComfyNode):
         return IO.Schema(
             node_id="TripoConversionNode",
             display_name="Tripo: Convert model",
-            category="3d/partner/Tripo",
+            category="partner/3d/Tripo",
             inputs=[
                 IO.Custom("MODEL_TASK_ID,RIG_TASK_ID,RETARGET_TASK_ID").Input("original_model_task_id"),
                 IO.Combo.Input("format", options=["GLTF", "USDZ", "FBX", "OBJ", "STL", "3MF"]),
@@ -1051,7 +1051,7 @@ class TripoP1TextToModelNode(IO.ComfyNode):
         return IO.Schema(
             node_id="TripoP1TextToModelNode",
             display_name="Tripo P1: Text to Model",
-            category="3d/partner/Tripo",
+            category="partner/3d/Tripo",
             description="Tripo P1 text-to-3D. Optimized for low-poly, game-ready meshes with stable topology.",
             inputs=[
                 IO.String.Input("prompt", multiline=True, tooltip="Up to 1024 characters."),
@@ -1122,7 +1122,7 @@ class TripoP1ImageToModelNode(IO.ComfyNode):
         return IO.Schema(
             node_id="TripoP1ImageToModelNode",
             display_name="Tripo P1: Image to Model",
-            category="3d/partner/Tripo",
+            category="partner/3d/Tripo",
             description="Tripo P1 image-to-3D. Optimized for low-poly, game-ready meshes.",
             inputs=[
                 IO.Image.Input("image"),
@@ -1202,7 +1202,7 @@ class TripoP1MultiviewToModelNode(IO.ComfyNode):
         return IO.Schema(
             node_id="TripoP1MultiviewToModelNode",
             display_name="Tripo P1: Multiview to Model",
-            category="3d/partner/Tripo",
+            category="partner/3d/Tripo",
             description="Tripo P1 multiview-to-3D from 2-4 reference images in [front, left, back, right] order. "
             "Front is required; any combination of the other three may be omitted.",
             inputs=[

comfy_api_nodes/nodes_veo2.py

@@ -45,7 +45,7 @@ class VeoVideoGenerationNode(IO.ComfyNode):
         return IO.Schema(
             node_id="VeoVideoGenerationNode",
             display_name="Google Veo 2 Video Generation",
-            category="video/partner/Veo",
+            category="partner/video/Veo",
             description="Generates videos from text prompts using Google's Veo 2 API",
             inputs=[
                 IO.String.Input(
@@ -256,7 +256,7 @@ class Veo3VideoGenerationNode(IO.ComfyNode):
         return IO.Schema(
             node_id="Veo3VideoGenerationNode",
             display_name="Google Veo 3 Video Generation",
-            category="video/partner/Veo",
+            category="partner/video/Veo",
             description="Generates videos from text prompts using Google's Veo 3 API",
             inputs=[
                 IO.String.Input(
@@ -468,7 +468,7 @@ class Veo3FirstLastFrameNode(IO.ComfyNode):
         return IO.Schema(
             node_id="Veo3FirstLastFrameNode",
             display_name="Google Veo 3 First-Last-Frame to Video",
-            category="video/partner/Veo",
+            category="partner/video/Veo",
             description="Generate video using prompt and first and last frames.",
             inputs=[
                 IO.String.Input(

comfy_api_nodes/nodes_vidu.py

@@ -71,7 +71,7 @@ class ViduTextToVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="ViduTextToVideoNode",
             display_name="Vidu Text To Video Generation",
-            category="video/partner/Vidu",
+            category="partner/video/Vidu",
             description="Generate video from a text prompt",
             inputs=[
                 IO.Combo.Input("model", options=["viduq1"], tooltip="Model name"),
@@ -169,7 +169,7 @@ class ViduImageToVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="ViduImageToVideoNode",
             display_name="Vidu Image To Video Generation",
-            category="video/partner/Vidu",
+            category="partner/video/Vidu",
             description="Generate video from image and optional prompt",
             inputs=[
                 IO.Combo.Input("model", options=["viduq1"], tooltip="Model name"),
@@ -273,7 +273,7 @@ class ViduReferenceVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="ViduReferenceVideoNode",
             display_name="Vidu Reference To Video Generation",
-            category="video/partner/Vidu",
+            category="partner/video/Vidu",
             description="Generate video from multiple images and a prompt",
             inputs=[
                 IO.Combo.Input("model", options=["viduq1"], tooltip="Model name"),
@@ -388,7 +388,7 @@ class ViduStartEndToVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="ViduStartEndToVideoNode",
             display_name="Vidu Start End To Video Generation",
-            category="video/partner/Vidu",
+            category="partner/video/Vidu",
             description="Generate a video from start and end frames and a prompt",
             inputs=[
                 IO.Combo.Input("model", options=["viduq1"], tooltip="Model name"),
@@ -492,7 +492,7 @@ class Vidu2TextToVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="Vidu2TextToVideoNode",
             display_name="Vidu2 Text-to-Video Generation",
-            category="video/partner/Vidu",
+            category="partner/video/Vidu",
             description="Generate video from a text prompt",
             inputs=[
                 IO.Combo.Input("model", options=["viduq2"]),
@@ -584,7 +584,7 @@ class Vidu2ImageToVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="Vidu2ImageToVideoNode",
             display_name="Vidu2 Image-to-Video Generation",
-            category="video/partner/Vidu",
+            category="partner/video/Vidu",
             description="Generate a video from an image and an optional prompt.",
             inputs=[
                 IO.Combo.Input("model", options=["viduq2-pro-fast", "viduq2-pro", "viduq2-turbo"]),
@@ -714,7 +714,7 @@ class Vidu2ReferenceVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="Vidu2ReferenceVideoNode",
             display_name="Vidu2 Reference-to-Video Generation",
-            category="video/partner/Vidu",
+            category="partner/video/Vidu",
             description="Generate a video from multiple reference images and a prompt.",
             inputs=[
                 IO.Combo.Input("model", options=["viduq2"]),
@@ -849,7 +849,7 @@ class Vidu2StartEndToVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="Vidu2StartEndToVideoNode",
             display_name="Vidu2 Start/End Frame-to-Video Generation",
-            category="video/partner/Vidu",
+            category="partner/video/Vidu",
             description="Generate a video from a start frame, an end frame, and a prompt.",
             inputs=[
                 IO.Combo.Input("model", options=["viduq2-pro-fast", "viduq2-pro", "viduq2-turbo"]),
@@ -969,7 +969,7 @@ class ViduExtendVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="ViduExtendVideoNode",
             display_name="Vidu Video Extension",
-            category="video/partner/Vidu",
+            category="partner/video/Vidu",
             description="Extend an existing video by generating additional frames.",
             inputs=[
                 IO.DynamicCombo.Input(
@@ -1138,7 +1138,7 @@ class ViduMultiFrameVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="ViduMultiFrameVideoNode",
             display_name="Vidu Multi-Frame Video Generation",
-            category="video/partner/Vidu",
+            category="partner/video/Vidu",
             description="Generate a video with multiple keyframe transitions.",
             inputs=[
                 IO.Combo.Input("model", options=["viduq2-pro", "viduq2-turbo"]),
@@ -1284,7 +1284,7 @@ class Vidu3TextToVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="Vidu3TextToVideoNode",
             display_name="Vidu Q3 Text-to-Video Generation",
-            category="video/partner/Vidu",
+            category="partner/video/Vidu",
             description="Generate video from a text prompt.",
             inputs=[
                 IO.DynamicCombo.Input(
@@ -1429,7 +1429,7 @@ class Vidu3ImageToVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="Vidu3ImageToVideoNode",
             display_name="Vidu Q3 Image-to-Video Generation",
-            category="video/partner/Vidu",
+            category="partner/video/Vidu",
             description="Generate a video from an image and an optional prompt.",
             inputs=[
                 IO.DynamicCombo.Input(
@@ -1571,7 +1571,7 @@ class Vidu3StartEndToVideoNode(IO.ComfyNode):
         return IO.Schema(
             node_id="Vidu3StartEndToVideoNode",
             display_name="Vidu Q3 Start/End Frame-to-Video Generation",
-            category="video/partner/Vidu",
+            category="partner/video/Vidu",
             description="Generate a video from a start frame, an end frame, and a prompt.",
             inputs=[
                 IO.DynamicCombo.Input(

comfy_api_nodes/nodes_wan.py

@@ -61,7 +61,7 @@ class WanTextToImageApi(IO.ComfyNode):
         return IO.Schema(
             node_id="WanTextToImageApi",
             display_name="Wan Text to Image",
-            category="image/partner/Wan",
+            category="partner/image/Wan",
             description="Generates an image based on a text prompt.",
             inputs=[
                 IO.Combo.Input(
@@ -184,7 +184,7 @@ class WanImageToImageApi(IO.ComfyNode):
         return IO.Schema(
             node_id="WanImageToImageApi",
             display_name="Wan Image to Image",
-            category="image/partner/Wan",
+            category="partner/image/Wan",
             description="Generates an image from one or two input images and a text prompt. "
             "The output image is currently fixed at 1.6 MP, and its aspect ratio matches the input image(s).",
             inputs=[
@@ -312,7 +312,7 @@ class WanTextToVideoApi(IO.ComfyNode):
         return IO.Schema(
             node_id="WanTextToVideoApi",
             display_name="Wan Text to Video",
-            category="video/partner/Wan",
+            category="partner/video/Wan",
             description="Generates a video based on a text prompt.",
             inputs=[
                 IO.Combo.Input(
@@ -495,7 +495,7 @@ class WanImageToVideoApi(IO.ComfyNode):
         return IO.Schema(
             node_id="WanImageToVideoApi",
             display_name="Wan Image to Video",
-            category="video/partner/Wan",
+            category="partner/video/Wan",
             description="Generates a video from the first frame and a text prompt.",
             inputs=[
                 IO.Combo.Input(
@@ -674,7 +674,7 @@ class WanReferenceVideoApi(IO.ComfyNode):
         return IO.Schema(
             node_id="WanReferenceVideoApi",
             display_name="Wan Reference to Video",
-            category="video/partner/Wan",
+            category="partner/video/Wan",
             description="Use the character and voice from input videos, combined with a prompt, "
             "to generate a new video that maintains character consistency.",
             inputs=[
@@ -828,7 +828,7 @@ class Wan2TextToVideoApi(IO.ComfyNode):
         return IO.Schema(
             node_id="Wan2TextToVideoApi",
             display_name="Wan 2.7 Text to Video",
-            category="video/partner/Wan",
+            category="partner/video/Wan",
             description="Generates a video based on a text prompt using the Wan 2.7 model.",
             inputs=[
                 IO.DynamicCombo.Input(
@@ -981,7 +981,7 @@ class Wan2ImageToVideoApi(IO.ComfyNode):
         return IO.Schema(
             node_id="Wan2ImageToVideoApi",
             display_name="Wan 2.7 Image to Video",
-            category="video/partner/Wan",
+            category="partner/video/Wan",
             description="Generate a video from a first-frame image, with optional last-frame image and audio.",
             inputs=[
                 IO.DynamicCombo.Input(
@@ -1152,7 +1152,7 @@ class Wan2VideoContinuationApi(IO.ComfyNode):
         return IO.Schema(
             node_id="Wan2VideoContinuationApi",
             display_name="Wan 2.7 Video Continuation",
-            category="video/partner/Wan",
+            category="partner/video/Wan",
             description="Continue a video from where it left off, with optional last-frame control.",
             inputs=[
                 IO.DynamicCombo.Input(
@@ -1319,7 +1319,7 @@ class Wan2VideoEditApi(IO.ComfyNode):
         return IO.Schema(
             node_id="Wan2VideoEditApi",
             display_name="Wan 2.7 Video Edit",
-            category="video/partner/Wan",
+            category="partner/video/Wan",
             description="Edit a video using text instructions, reference images, or style transfer.",
             inputs=[
                 IO.DynamicCombo.Input(
@@ -1477,7 +1477,7 @@ class Wan2ReferenceVideoApi(IO.ComfyNode):
         return IO.Schema(
             node_id="Wan2ReferenceVideoApi",
             display_name="Wan 2.7 Reference to Video",
-            category="video/partner/Wan",
+            category="partner/video/Wan",
             description="Generate a video featuring a person or object from reference materials. "
             "Supports single-character performances and multi-character interactions.",
             inputs=[
@@ -1651,7 +1651,7 @@ class HappyHorseTextToVideoApi(IO.ComfyNode):
         return IO.Schema(
             node_id="HappyHorseTextToVideoApi",
             display_name="HappyHorse Text to Video",
-            category="video/partner/Wan",
+            category="partner/video/Wan",
             description="Generates a video based on a text prompt using the HappyHorse model.",
             inputs=[
                 IO.DynamicCombo.Input(
@@ -1775,7 +1775,7 @@ class HappyHorseImageToVideoApi(IO.ComfyNode):
         return IO.Schema(
             node_id="HappyHorseImageToVideoApi",
             display_name="HappyHorse Image to Video",
-            category="video/partner/Wan",
+            category="partner/video/Wan",
             description="Generate a video from a first-frame image using the HappyHorse model.",
             inputs=[
                 IO.DynamicCombo.Input(
@@ -1905,7 +1905,7 @@ class HappyHorseVideoEditApi(IO.ComfyNode):
         return IO.Schema(
             node_id="HappyHorseVideoEditApi",
             display_name="HappyHorse Video Edit",
-            category="video/partner/Wan",
+            category="partner/video/Wan",
             description="Edit a video using text instructions or reference images with the HappyHorse model. "
             "Output duration is 3-15s and matches the input video; inputs longer than 15s are truncated.",
             inputs=[
@@ -2046,7 +2046,7 @@ class HappyHorseReferenceVideoApi(IO.ComfyNode):
         return IO.Schema(
             node_id="HappyHorseReferenceVideoApi",
             display_name="HappyHorse Reference to Video",
-            category="video/partner/Wan",
+            category="partner/video/Wan",
             description="Generate a video featuring a person or object from reference materials with the HappyHorse "
             "model. Supports single-character performances and multi-character interactions.",
             inputs=[

comfy_api_nodes/nodes_wavespeed.py

@@ -27,7 +27,7 @@ class WavespeedFlashVSRNode(IO.ComfyNode):
         return IO.Schema(
             node_id="WavespeedFlashVSRNode",
             display_name="FlashVSR Video Upscale",
-            category="video/partner/WaveSpeed",
+            category="partner/video/WaveSpeed",
             description="Fast, high-quality video upscaler that "
             "boosts resolution and restores clarity for low-resolution or blurry footage.",
             inputs=[
@@ -98,7 +98,7 @@ class WavespeedImageUpscaleNode(IO.ComfyNode):
         return IO.Schema(
             node_id="WavespeedImageUpscaleNode",
             display_name="WaveSpeed Image Upscale",
-            category="image/partner/WaveSpeed",
+            category="partner/image/WaveSpeed",
             description="Boost image resolution and quality, upscaling photos to 4K or 8K for sharp, detailed results.",
             inputs=[
                 IO.Combo.Input("model", options=["SeedVR2", "Ultimate"]),

comfy_api_nodes/util/conversions.py

@@ -469,6 +469,11 @@ def _apply_video_scale(video: Input.Video, scale_dims: tuple[int, int]) -> Input
     input_container = None
     output_container = None
 
+    # get_stream_source() is untrimmed, so apply the trim window in this same pass.
+    # start_time is normalized (>= 0); duration == 0 means "until the end".
+    start_time, duration = video.get_active_trim_window()
+    trimming = bool(start_time or duration)
+
     try:
         input_source = video.get_stream_source()
         input_container = av.open(input_source, mode="r")
@@ -487,16 +492,45 @@ def _apply_video_scale(video: Input.Video, scale_dims: tuple[int, int]) -> Input
                 audio_stream.layout = stream.layout
                 break
 
+        in_video = input_container.streams.video[0]
+        start_pts = int(start_time / in_video.time_base) if trimming else 0
+        end_pts = int((start_time + duration) / in_video.time_base) if duration else None
+        if start_pts:
+            input_container.seek(start_pts, stream=in_video)
+
+        encoded = 0
         for frame in input_container.decode(video=0):
+            if trimming:
+                if frame.pts is None or frame.pts < start_pts:
+                    continue
+                if end_pts is not None and frame.pts >= end_pts:
+                    break
             frame = frame.reformat(width=out_w, height=out_h, format="yuv420p")
+            # Re-wrap as a fresh frame: dropping irregular source timestamps (VFR/AVI/GIF/...)
+            # lets the encoder assign clean ones and avoids mp4 muxer errors.
+            frame = av.VideoFrame.from_ndarray(frame.to_ndarray(format="yuv420p"), format="yuv420p")
             for packet in video_stream.encode(frame):
                 output_container.mux(packet)
+            encoded += 1
         for packet in video_stream.encode():
             output_container.mux(packet)
 
+        if encoded == 0:
+            raise ValueError(
+                f"resize produced no frames (start_time={start_time}, duration={duration} "
+                "selected nothing from the source)"
+            )
+
         if audio_stream is not None:
             input_container.seek(0)
             for audio_frame in input_container.decode(audio=0):
+                if trimming:
+                    if audio_frame.time is None or audio_frame.time < start_time:
+                        continue
+                    if duration and audio_frame.time > start_time + duration:
+                        break
+                # Carry odd audio time bases the mp4 muxer rejects; reset pts, encoder assigns clean ones (MP3-in-AVI)
+                audio_frame.pts = None
                 for packet in audio_stream.encode(audio_frame):
                     output_container.mux(packet)
             for packet in audio_stream.encode():

comfy_extras/nodes_audio.py

@@ -158,7 +158,7 @@ class SaveAudio(IO.ComfyNode):
         return IO.Schema(
             node_id="SaveAudio",
             search_aliases=["export flac"],
-            display_name="Save Audio (FLAC)",
+            display_name="Save Audio (FLAC) (Deprecated)",
             category="audio",
             essentials_category="Audio",
             inputs=[
@@ -167,6 +167,7 @@ class SaveAudio(IO.ComfyNode):
             ],
             hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
             is_output_node=True,
+            is_deprecated=True,
         )
 
     @classmethod
@@ -186,7 +187,7 @@ class SaveAudioMP3(IO.ComfyNode):
         return IO.Schema(
             node_id="SaveAudioMP3",
             search_aliases=["export mp3"],
-            display_name="Save Audio (MP3)",
+            display_name="Save Audio (MP3) (Deprecated)",
             category="audio",
             essentials_category="Audio",
             inputs=[
@@ -196,6 +197,7 @@ class SaveAudioMP3(IO.ComfyNode):
             ],
             hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
             is_output_node=True,
+            is_deprecated=True,
         )
 
     @classmethod
@@ -217,7 +219,7 @@ class SaveAudioOpus(IO.ComfyNode):
         return IO.Schema(
             node_id="SaveAudioOpus",
             search_aliases=["export opus"],
-            display_name="Save Audio (Opus)",
+            display_name="Save Audio (Opus) (Deprecated)",
             category="audio",
             inputs=[
                 IO.Audio.Input("audio"),
@@ -226,6 +228,7 @@ class SaveAudioOpus(IO.ComfyNode):
             ],
             hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
             is_output_node=True,
+            is_deprecated=True,
         )
 
     @classmethod
@@ -241,6 +244,54 @@ class SaveAudioOpus(IO.ComfyNode):
     save_opus = execute  # TODO: remove
 
 
+class SaveAudioAdvanced(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SaveAudioAdvanced",
+            search_aliases=["save audio", "export audio", "output audio", "write audio", "flac", "mp3", "opus"],
+            display_name="Save Audio (Advanced)",
+            description="Saves the input audio to your ComfyUI output directory.",
+            category="audio",
+            inputs=[
+                IO.Audio.Input("audio", tooltip="The audio to save."),
+                IO.String.Input(
+                    "filename_prefix",
+                    default="audio/ComfyUI",
+                    tooltip=(
+                        "The prefix for the file to save. May include formatting tokens "
+                        "such as %date:yyyy-MM-dd%."
+                    ),
+                ),
+                IO.DynamicCombo.Input(
+                    "format",
+                    options=[
+                        IO.DynamicCombo.Option("flac", []),
+                        IO.DynamicCombo.Option("mp3", [
+                            IO.Combo.Input("quality", options=["V0", "128k", "320k"], default="V0"),
+                        ]),
+                        IO.DynamicCombo.Option("opus", [
+                            IO.Combo.Input("quality", options=["64k", "96k", "128k", "192k", "320k"], default="128k"),
+                        ]),
+                    ],
+                    tooltip="The file format in which to save the audio.",
+                ),
+            ],
+            hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, audio, filename_prefix: str, format: dict) -> IO.NodeOutput:
+        file_format = format.get("format", None)
+        quality = format.get("quality", None)
+        if quality:
+            ui=UI.AudioSaveHelper.get_save_audio_ui(audio, filename_prefix=filename_prefix, cls=cls, format=file_format, quality=quality)
+        else:
+            ui=UI.AudioSaveHelper.get_save_audio_ui(audio, filename_prefix=filename_prefix, cls=cls, format=file_format)
+        return IO.NodeOutput(ui=ui)
+
+
 class PreviewAudio(IO.ComfyNode):
     @classmethod
     def define_schema(cls):
@@ -822,6 +873,7 @@ class AudioExtension(ComfyExtension):
             SaveAudio,
             SaveAudioMP3,
             SaveAudioOpus,
+            SaveAudioAdvanced,
             LoadAudio,
             PreviewAudio,
             ConditioningStableAudio,

comfy_extras/nodes_chroma_radiance.py

@@ -65,6 +65,12 @@ class ChromaRadianceOptions(io.ComfyNode):
                     tooltip="Allows overriding the default NeRF tile size. -1 means use the default (32). 0 means use non-tiling mode (may require a lot of VRAM).",
                     advanced=True,
                 ),
+                io.Boolean.Input(
+                    id="force_sequential_txt_ids",
+                    default=False,
+                    tooltip="Force usage of sequential text token IDs instead of zeroes. Should be used for checkpoints from 2026-05-22 to 2026-06-01 that are trained in this way but do not contain the __sequential__ key in the state dict.",
+                    advanced=True,
+                ),
             ],
             outputs=[io.Model.Output()],
         )
@@ -78,11 +84,15 @@ class ChromaRadianceOptions(io.ComfyNode):
         start_sigma: float,
         end_sigma: float,
         nerf_tile_size: int,
+        force_sequential_txt_ids: bool,
     ) -> io.NodeOutput:
         radiance_options = {}
         if nerf_tile_size >= 0:
             radiance_options["nerf_tile_size"] = nerf_tile_size
 
+        if force_sequential_txt_ids:
+            radiance_options["use_sequential_txt_ids"] = True
+
         if not radiance_options:
             return io.NodeOutput(model)
 

comfy_extras/nodes_custom_sampler.py

@@ -1,5 +1,7 @@
 import math
 import comfy.samplers
+import comfy.sampler_helpers
+import comfy.patcher_extension
 import comfy.sample
 from comfy.k_diffusion import sampling as k_diffusion_sampling
 from comfy.k_diffusion import sa_solver
@@ -894,6 +896,85 @@ class DualCFGGuider(io.ComfyNode):
 
     get_guider = execute
 
+class Guider_DualModel(comfy.samplers.CFGGuider):
+    # Runs the positive (cond) pass on the main model and the negative (uncond) pass on a separate model
+    def __init__(self, model_patcher, uncond_model_patcher):
+        super().__init__(model_patcher)
+        self.uncond_model_patcher = uncond_model_patcher
+        self.uncond_inner = None
+
+    def outer_sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None, latent_shapes=None):
+        self.uncond_inner = None
+        self.uncond_loaded = []
+        self._uncond_neg = None
+        # skip at cfg 1.0
+        if not math.isclose(self.cfg, 1.0):
+            uc = {"negative": list(map(lambda a: a.copy(), self.conds["negative"]))}
+            self.uncond_inner, uc, self.uncond_loaded = comfy.sampler_helpers.prepare_sampling(
+                self.uncond_model_patcher, noise.shape, uc, self.uncond_model_patcher.model_options)
+            self._uncond_neg = uc["negative"]
+            self.uncond_model_patcher.pre_run()
+        try:
+            return super().outer_sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=latent_shapes)
+        finally:
+            if self.uncond_inner is not None:
+                self.uncond_model_patcher.cleanup()
+                comfy.sampler_helpers.cleanup_models({"negative": self._uncond_neg}, self.uncond_loaded)
+                self.uncond_inner = None
+
+    def inner_sample(self, noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=None):
+        if self.uncond_inner is not None:
+            li = latent_image
+            if li is not None and torch.count_nonzero(li) > 0:
+                li = self.uncond_inner.process_latent_in(li)
+            self._uncond_conds = comfy.samplers.process_conds(
+                self.uncond_inner, noise, {"negative": self._uncond_neg}, device, li, denoise_mask, seed, latent_shapes=latent_shapes)["negative"]
+        return super().inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=latent_shapes)
+
+    def predict_noise(self, x, timestep, model_options={}, seed=None):
+        positive = self.conds.get("positive", None)
+        cond = comfy.samplers.calc_cond_batch(self.inner_model, [positive], x, timestep, model_options)[0]
+        # uncond model not loaded (base cfg==1/no negative), or cfg driven to 1.0 this step -> single model, cond only
+        if self.uncond_inner is None or (math.isclose(self.cfg, 1.0) and not model_options.get("disable_cfg1_optimization", False)):
+            return cond
+
+        uncond_model_options = model_options
+        if "multigpu_clones" in model_options: # TODO: support multigpu instead of just running uncond on a single GPU
+            uncond_model_options = {k: v for k, v in model_options.items() if k != "multigpu_clones"}
+        uncond = comfy.samplers.calc_cond_batch(self.uncond_inner, [self._uncond_conds], x, timestep, uncond_model_options)[0]
+        return comfy.samplers.cfg_function(self.inner_model, cond, uncond, self.cfg, x, timestep,
+                                           model_options=model_options, cond=positive, uncond=self._uncond_conds)
+
+class DualModelGuider(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DualModelGuider",
+            display_name="Dual Model CFG Guider",
+            category="model/sampling/guiders",
+            is_experimental=True,
+            inputs=[
+                io.Model.Input("model", tooltip="Model used for the positive (conditional) pass."),
+                io.Model.Input("model_negative", optional=True, tooltip="Model used for the negative (unconditional) pass. Use the same model for ordinary CFG."),
+                io.Conditioning.Input("positive"),
+                io.Float.Input("cfg", default=4.0, min=0.0, max=100.0, step=0.1, round=0.01),
+                io.Conditioning.Input("negative", optional=True, tooltip="Negative conditioning run on the negative model. Leave unconnected for a text-free (image-only) unconditional pass."),
+            ],
+            outputs=[io.Guider.Output()],
+        )
+
+    @classmethod
+    def execute(cls, model, positive, cfg, model_negative=None, negative=None) -> io.NodeOutput:
+        if negative is None:
+            negative = [[None, {}]]  # null cond -> no cross_attn -> model runs image-only
+
+        guider = Guider_DualModel(model, model_negative) if model_negative is not None else comfy.samplers.CFGGuider(model)
+        guider.set_conds(positive, negative)
+        guider.set_cfg(cfg)
+        return io.NodeOutput(guider)
+
+    get_guider = execute
+
 class DisableNoise(io.ComfyNode):
     @classmethod
     def define_schema(cls):
@@ -1054,11 +1135,53 @@ class ManualSigmas(io.ComfyNode):
         sigmas = torch.FloatTensor(sigmas)
         return io.NodeOutput(sigmas)
 
+class CFGOverride(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="CFGOverride",
+            display_name="CFG Override",
+            description="Override cfg to a fixed value over a [start, end] percent (sigma) range. "
+                        "With multiple overrides, the one nearest the sampler wins on overlap.",
+            category="sampling/custom_sampling",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input("cfg", default=1.0, min=0.0, max=100.0, step=0.1, round=0.01),
+                io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001),
+                io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001),
+            ],
+            outputs=[io.Model.Output()],
+        )
+
+    @classmethod
+    def execute(cls, model, cfg, start_percent, end_percent) -> io.NodeOutput:
+        ms = model.get_model_object("model_sampling")
+        sigma_hi = ms.percent_to_sigma(start_percent)  # percent->sigma decreasing, so hi >= lo
+        sigma_lo = ms.percent_to_sigma(end_percent)
+
+        def predict_noise_wrapper(executor, *args, **kwargs):
+            sigma = float(args[1].flatten()[0])        # args = (x, timestep, model_options, seed)
+            if not (sigma_lo <= sigma <= sigma_hi):
+                return executor(*args, **kwargs)
+            guider = executor.class_obj                # guider.cfg feeds cond_scale
+            saved = guider.cfg
+            guider.cfg = cfg
+            try:
+                return executor(*args, **kwargs)
+            finally:
+                guider.cfg = saved                     # restore for other steps/overrides
+
+        m = model.clone()
+        m.add_wrapper(comfy.patcher_extension.WrappersMP.PREDICT_NOISE, predict_noise_wrapper)
+        return io.NodeOutput(m)
+
+
 class CustomSamplersExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[io.ComfyNode]]:
         return [
             SamplerCustom,
+            CFGOverride,
             BasicScheduler,
             KarrasScheduler,
             ExponentialScheduler,
@@ -1087,6 +1210,7 @@ class CustomSamplersExtension(ComfyExtension):
             SamplingPercentToSigma,
             CFGGuider,
             DualCFGGuider,
+            DualModelGuider,
             BasicGuider,
             RandomNoise,
             DisableNoise,

comfy_extras/nodes_dataset.py

@@ -411,6 +411,21 @@ class ImageProcessingNode(io.ComfyNode):
 
         return has_group
 
+    @classmethod
+    def _ensure_image_list(cls, images):
+        """Normalize to a flat list of [1, H, W, C] tensors."""
+        if isinstance(images, torch.Tensor):
+            if images.ndim != 4:
+                raise ValueError(f"Expected 4D image tensor, got shape {tuple(images.shape)}")
+            return [images[i:i+1] for i in range(images.shape[0])]
+
+        flat = []
+        for item in images:
+            if not isinstance(item, torch.Tensor) or item.ndim != 4:
+                raise ValueError(f"Expected 4D image tensor, got {type(item).__name__} shape {getattr(item, 'shape', None)}")
+            flat.extend([item[i:i+1] for i in range(item.shape[0])])
+        return flat
+
     @classmethod
     def define_schema(cls):
         if cls.node_id is None:
@@ -458,6 +473,9 @@ class ImageProcessingNode(io.ComfyNode):
         """Execute the node. Routes to _process or _group_process based on mode."""
         is_group = cls._detect_processing_mode()
 
+        if is_group:
+            images = cls._ensure_image_list(images)
+
         # Extract scalar values from lists for parameters
         params = {}
         for k, v in kwargs.items():

comfy_extras/nodes_gaussian_splat.py

@@ -488,7 +488,7 @@ class SplatToFile3D(IO.ComfyNode):
                                        "spz: Niantic gzip-compressed (~10x smaller), base color only "
                                        ),
             ],
-            outputs=[IO.File3DAny.Output(display_name="model_3d")],
+            outputs=[IO.File3DSplatAny.Output(display_name="model_3d")],
         )
 
     @classmethod
@@ -516,7 +516,7 @@ class File3DToSplat(IO.ComfyNode):
             inputs=[
                 IO.MultiType.Input(
                     IO.File3DAny.Input("model_3d"),
-                    types=[IO.File3DPLY, IO.File3DSPLAT, IO.File3DKSPLAT, IO.File3DSPZ],
+                    types=[IO.File3DSplatAny, IO.File3DPLY, IO.File3DSPLAT, IO.File3DKSPLAT, IO.File3DSPZ],
                     tooltip="A gaussian splat 3D file",
                 ),
             ],
@@ -968,7 +968,8 @@ class RenderSplat(IO.ComfyNode):
         bg = _hex_to_rgb(background)
         bg_imgs = None
         if bg_image is not None:  # resize the plate(s) to the render size: (B,H,W,3)
-            bi = comfy.utils.common_upscale(bg_image.movedim(-1, 1), width, height, "bicubic", "disabled")
+            bi = bg_image[... , :3].movedim(-1, 1)  # (B,3,H,W)
+            bi = comfy.utils.common_upscale(bi, width, height, "bicubic", "disabled")
             bg_imgs = bi.movedim(1, -1).clamp(0, 1)
         n_frames = abs(int(frames)) or 1         # magnitude = frame count (0 -> single still)
         orbit_dir = -1.0 if frames < 0 else 1.0  # sign = orbit direction

comfy_extras/nodes_ideogram4.py

@@ -0,0 +1,64 @@
+"""Ideogram 4 sampling helper
+"""
+
+import math
+
+import torch
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+
+_LOGSNR_MIN = -15.0
+_LOGSNR_MAX = 18.0
+
+
+def _logit_normal_schedule(u, mean, std):
+    # Reference time (0=noise..1=clean) via the probit/ndtri quantile.
+    u = torch.as_tensor(u, dtype=torch.float64)
+    t = 1.0 - torch.special.expit(mean + std * torch.special.ndtri(u))
+    t_min = 1.0 / (1.0 + math.exp(0.5 * _LOGSNR_MAX))
+    t_max = 1.0 / (1.0 + math.exp(0.5 * _LOGSNR_MIN))
+    return t.clamp(t_min, t_max)
+
+
+def ideogram4_sigmas(num_steps, width, height, mu, std):
+    """Descending sigmas (len num_steps+1) for the reference schedule.
+
+    mu + the resolution term form the logSNR shift; std is the spread.
+    """
+    mean = mu + 0.5 * math.log((width * height) / (512 * 512))
+    u = torch.linspace(0.0, 1.0, num_steps + 1, dtype=torch.float64)
+    sigmas = (1.0 - _logit_normal_schedule(u, mean, std)).flip(0)
+    sigmas[-1] = 0.0                                      # clamp leaves ~6e-4; force full denoise
+    return sigmas.to(torch.float32)
+
+
+class Ideogram4Scheduler(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="Ideogram4Scheduler",
+            display_name="Ideogram 4 Scheduler",
+            category="sampling/custom_sampling/schedulers",
+            inputs=[
+                io.Int.Input("steps", default=20, min=1, max=200),
+                io.Int.Input("width", default=1024, min=256, max=8192, step=16),
+                io.Int.Input("height", default=1024, min=256, max=8192, step=16),
+                io.Float.Input("mu", default=0.0, min=-10.0, max=10.0, step=0.05),
+                io.Float.Input("std", default=1.75, min=0.1, max=5.0, step=0.05),
+            ],
+            outputs=[io.Sigmas.Output()],
+        )
+
+    @classmethod
+    def execute(cls, steps, width, height, mu, std) -> io.NodeOutput:
+        return io.NodeOutput(ideogram4_sigmas(steps, width, height, mu, std))
+
+
+class Ideogram4Extension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [Ideogram4Scheduler]
+
+
+async def comfy_entrypoint() -> Ideogram4Extension:
+    return Ideogram4Extension()

comfy_extras/nodes_load_3d.py

@@ -51,6 +51,14 @@ class Load3D(IO.ComfyNode):
             ],
         )
 
+    @classmethod
+    def validate_inputs(cls, model_file, **kwargs) -> bool | str:
+        if not model_file or model_file == "none":
+            return True
+        if not folder_paths.exists_annotated_filepath(model_file):
+            return f"Invalid 3D model file: {model_file}"
+        return True
+
     @classmethod
     def execute(cls, model_file, image, **kwargs) -> IO.NodeOutput:
         image_path = folder_paths.get_annotated_filepath(image['image'])
@@ -136,7 +144,7 @@ class Preview3DAdvanced(IO.ComfyNode):
             is_output_node=True,
             inputs=[
                 IO.MultiType.Input(
-                    "model_file",
+                    "model_3d",
                     types=[
                         IO.File3DGLB,
                         IO.File3DGLTF,
@@ -148,34 +156,161 @@ class Preview3DAdvanced(IO.ComfyNode):
                     ],
                     tooltip="3D model file from an upstream 3D node.",
                 ),
-                IO.Load3D.Input("image"),
-                IO.Load3DCamera.Input("camera_info", optional=True, advanced=True),
                 IO.Load3DModelInfo.Input("model_3d_info", optional=True, advanced=True),
+                IO.Load3D.Input("viewport_state"),
+                IO.Load3DCamera.Input("camera_info", optional=True, advanced=True),
                 IO.Int.Input("width", default=1024, min=1, max=4096, step=1),
                 IO.Int.Input("height", default=1024, min=1, max=4096, step=1),
             ],
             outputs=[
-                IO.File3DAny.Output(display_name="model_file"),
-                IO.Load3DCamera.Output(display_name="camera_info"),
+                IO.File3DAny.Output(display_name="model_3d"),
                 IO.Load3DModelInfo.Output(display_name="model_3d_info"),
+                IO.Load3DCamera.Output(display_name="camera_info"),
                 IO.Int.Output(display_name="width"),
                 IO.Int.Output(display_name="height"),
             ],
         )
 
     @classmethod
-    def execute(cls, model_file: Types.File3D, image, width: int, height: int, **kwargs) -> IO.NodeOutput:
-        filename = f"preview3d_advanced_{uuid.uuid4().hex}.{model_file.format}"
-        model_file.save_to(os.path.join(folder_paths.get_output_directory(), filename))
+    def execute(cls, model_3d: Types.File3D, viewport_state, width: int, height: int, **kwargs) -> IO.NodeOutput:
+        filename = f"preview3d_advanced_{uuid.uuid4().hex}.{model_3d.format}"
+        model_3d.save_to(os.path.join(folder_paths.get_temp_directory(), filename))
 
         camera_info_input = kwargs.get("camera_info", None)
-        camera_info = camera_info_input if camera_info_input is not None else image['camera_info']
+        camera_info = camera_info_input if camera_info_input is not None else viewport_state['camera_info']
         model_3d_info_input = kwargs.get("model_3d_info", None)
-        model_3d_info = model_3d_info_input if model_3d_info_input is not None else image.get('model_3d_info', [])
+        model_3d_info = model_3d_info_input if model_3d_info_input is not None else viewport_state.get('model_3d_info', [])
         return IO.NodeOutput(
-            model_file,
-            camera_info,
+            model_3d,
             model_3d_info,
+            camera_info,
+            width,
+            height,
+            ui=UI.PreviewUI3DAdvanced(filename, camera_info, model_3d_info),
+        )
+
+
+class PreviewGaussianSplat(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="PreviewGaussianSplat",
+            display_name="Preview Splat",
+            category="3d",
+            is_experimental=True,
+            is_output_node=True,
+            search_aliases=[
+                "view splat",
+                "view gaussian",
+                "view gaussian splat",
+                "preview gaussian",
+                "preview gaussian splat",
+                "view 3dgs",
+                "preview 3dgs",
+                "preview ply",
+                "preview spz",
+                "preview splat",
+                "preview ksplat",
+            ],
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[
+                        IO.File3DSplatAny,
+                        IO.File3DPLY,
+                        IO.File3DSPLAT,
+                        IO.File3DSPZ,
+                        IO.File3DKSPLAT,
+                    ],
+                    tooltip="A gaussian splat 3D file.",
+                ),
+                IO.Load3DModelInfo.Input("model_3d_info", optional=True, advanced=True),
+                IO.Load3D.Input("viewport_state"),
+                IO.Load3DCamera.Input("camera_info", optional=True, advanced=True),
+                IO.Int.Input("width", default=1024, min=1, max=4096, step=1),
+                IO.Int.Input("height", default=1024, min=1, max=4096, step=1),
+            ],
+            outputs=[
+                IO.File3DSplatAny.Output(display_name="model_3d"),
+                IO.Load3DModelInfo.Output(display_name="model_3d_info"),
+                IO.Load3DCamera.Output(display_name="camera_info"),
+                IO.Int.Output(display_name="width"),
+                IO.Int.Output(display_name="height"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model_3d: Types.File3D, viewport_state, width: int, height: int, **kwargs) -> IO.NodeOutput:
+        filename = f"preview_splat_{uuid.uuid4().hex}.{model_3d.format}"
+        model_3d.save_to(os.path.join(folder_paths.get_temp_directory(), filename))
+
+        camera_info_input = kwargs.get("camera_info", None)
+        camera_info = camera_info_input if camera_info_input is not None else viewport_state['camera_info']
+        model_3d_info_input = kwargs.get("model_3d_info", None)
+        model_3d_info = model_3d_info_input if model_3d_info_input is not None else viewport_state.get('model_3d_info', [])
+        return IO.NodeOutput(
+            model_3d,
+            model_3d_info,
+            camera_info,
+            width,
+            height,
+            ui=UI.PreviewUI3DAdvanced(filename, camera_info, model_3d_info),
+        )
+
+
+class PreviewPointCloud(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="PreviewPointCloud",
+            display_name="Preview Point Cloud",
+            category="3d",
+            is_experimental=True,
+            is_output_node=True,
+            search_aliases=[
+                "view point cloud",
+                "view pointcloud",
+                "preview point cloud",
+                "preview pointcloud",
+                "preview ply",
+            ],
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[
+                        IO.File3DPointCloudAny,
+                        IO.File3DPLY,
+                    ],
+                    tooltip="Point cloud file (.ply)",
+                ),
+                IO.Load3DModelInfo.Input("model_3d_info", optional=True, advanced=True),
+                IO.Load3D.Input("viewport_state"),
+                IO.Load3DCamera.Input("camera_info", optional=True, advanced=True),
+                IO.Int.Input("width", default=1024, min=1, max=4096, step=1),
+                IO.Int.Input("height", default=1024, min=1, max=4096, step=1),
+            ],
+            outputs=[
+                IO.File3DPointCloudAny.Output(display_name="model_3d"),
+                IO.Load3DModelInfo.Output(display_name="model_3d_info"),
+                IO.Load3DCamera.Output(display_name="camera_info"),
+                IO.Int.Output(display_name="width"),
+                IO.Int.Output(display_name="height"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model_3d: Types.File3D, viewport_state, width: int, height: int, **kwargs) -> IO.NodeOutput:
+        filename = f"preview_pointcloud_{uuid.uuid4().hex}.{model_3d.format}"
+        model_3d.save_to(os.path.join(folder_paths.get_temp_directory(), filename))
+
+        camera_info_input = kwargs.get("camera_info", None)
+        camera_info = camera_info_input if camera_info_input is not None else viewport_state['camera_info']
+        model_3d_info_input = kwargs.get("model_3d_info", None)
+        model_3d_info = model_3d_info_input if model_3d_info_input is not None else viewport_state.get('model_3d_info', [])
+        return IO.NodeOutput(
+            model_3d,
+            model_3d_info,
+            camera_info,
             width,
             height,
             ui=UI.PreviewUI3DAdvanced(filename, camera_info, model_3d_info),
@@ -189,6 +324,8 @@ class Load3DExtension(ComfyExtension):
             Load3D,
             Preview3D,
             Preview3DAdvanced,
+            PreviewGaussianSplat,
+            PreviewPointCloud,
         ]
 
 

comfy_extras/nodes_math.py

@@ -102,11 +102,18 @@ class MathExpressionNode(io.ComfyNode):
                 f"Math Expression '{expression}' must evaluate to a numeric result, "
                 f"got {type(result).__name__}: {result!r}"
             )
-        if not math.isfinite(result):
+        try:
+            float_result = float(result)
+        except OverflowError:
+            raise ValueError(
+                f"Math Expression '{expression}' produced a result too large to "
+                f"represent as a float: {result}"
+            ) from None
+        if not math.isfinite(float_result):
             raise ValueError(
                 f"Math Expression '{expression}' produced a non-finite result: {result}"
             )
-        return io.NodeOutput(float(result), int(result), bool(result))
+        return io.NodeOutput(float_result, int(result), bool(result))
 
 
 class MathExtension(ComfyExtension):

comfy_extras/nodes_pid.py

@@ -21,8 +21,8 @@ class PiDConditioning(io.ComfyNode):
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Latent.Input("latent", tooltip="latent (from VAEEncode or a KSampler)."),
-                io.Combo.Input("latent_format", options=["flux", "sd3"], default="flux",
-                               tooltip="Flux1 and Flux2 latents auto-detected from channel dim, sd3 has to be selected manually."),
+                io.Combo.Input("latent_format", options=["flux", "sd3", "sdxl", "qwenimage"], default="flux",
+                               tooltip="Flux1 (16-ch) and Flux2 (128-ch) latents are auto-detected from channel dim under 'flux'. For SD3 (16-ch), SDXL (4-ch), or QwenImage (16-ch), select manually."),
                 io.Float.Input(
                     "degrade_sigma", default=0.0, min=0.0, max=1.0, step=0.01,
                     tooltip="0 = clean latent. Increase to denoise corrupted latent outputs.",
@@ -36,9 +36,17 @@ class PiDConditioning(io.ComfyNode):
         samples = latent["samples"]
         if latent_format == "flux":
             fmt_cls = comfy.latent_formats.Flux2 if samples.shape[1] == 128 else comfy.latent_formats.Flux
-        else:
+        elif latent_format == "sd3":
             fmt_cls = comfy.latent_formats.SD3
+        elif latent_format == "sdxl":
+            fmt_cls = comfy.latent_formats.SDXL
+        elif latent_format == "qwenimage":
+            fmt_cls = comfy.latent_formats.Wan21
+        else:
+            raise ValueError(f"Unknown latent_format: {latent_format}")
         lq_latent = fmt_cls().process_in(samples)
+        if lq_latent.ndim == 5:
+            lq_latent = lq_latent[:, :, 0]
         sigma_t = torch.tensor([float(degrade_sigma)], dtype=torch.float32)
         return io.NodeOutput(node_helpers.conditioning_set_values(
             positive, {"lq_latent": lq_latent, "degrade_sigma": sigma_t},

comfy_extras/nodes_save_3d.py

@@ -348,6 +348,12 @@ class SaveGLB(IO.ComfyNode):
                         IO.File3DFBX,
                         IO.File3DSTL,
                         IO.File3DUSDZ,
+                        IO.File3DPLY,
+                        IO.File3DSPLAT,
+                        IO.File3DSPZ,
+                        IO.File3DKSPLAT,
+                        IO.File3DSplatAny,
+                        IO.File3DPointCloudAny,
                         IO.File3DAny,
                     ],
                     tooltip="Mesh or 3D file to save",

comfy_extras/nodes_triposplat.py

@@ -0,0 +1,270 @@
+# TripoSplat nodes: image -> 3D gaussian splat
+
+import logging
+
+import torch
+import torch.nn.functional as F
+from typing_extensions import override
+
+import comfy.model_management
+import comfy.nested_tensor
+import comfy.patcher_extension
+import comfy.utils
+from comfy_api.latest import ComfyExtension, IO, Types
+
+
+_Q_TOKEN_LENGTH = 8192
+_LATENT_CHANNELS = 16
+_CAM_CHANNELS = 5
+_DINOV3_MEAN = [0.485, 0.456, 0.406]
+_DINOV3_STD = [0.229, 0.224, 0.225]
+_NUM_GAUSSIANS_MIN = 32768
+_NUM_GAUSSIANS_MAX = 1048576
+
+
+def _preprocess(image: torch.Tensor, mask: torch.Tensor, erode_radius: int, size: int) -> torch.Tensor:
+    # Match original preprocessing:
+    # resize min side to `size` -> erode alpha -> alpha bbox -> 1.2x square crop -> resize -> composite on black.
+    rgb = image[..., :3].clamp(0, 1).movedim(-1, 0)        # (3, H, W)
+    alpha = mask.clamp(0, 1)[None]                         # (1, H, W)
+    rgba = torch.cat([rgb, alpha], 0)[None]                # (1, 4, H, W)
+
+    h, w = rgba.shape[-2:]
+    s = size / min(w, h)
+    rgba = comfy.utils.common_upscale(rgba, max(1, round(w * s)), max(1, round(h * s)), "lanczos", "disabled").clamp(0, 1)
+
+    a = rgba[:, 3:4]
+    if erode_radius > 0:
+        # min filter over a (2r+1) window == morphological erosion of the alpha matte.
+        a = -F.max_pool2d(-a, 2 * erode_radius + 1, stride=1, padding=erode_radius)
+        rgba = torch.cat([rgba[:, :3], a], 1)
+
+    ys, xs = torch.nonzero(a[0, 0] > 0, as_tuple=True)
+    if xs.numel() == 0:
+        raise ValueError("TripoSplatPreprocessImage: mask is empty (no foreground pixels).")
+    x0, x1 = int(xs.min()), int(xs.max())
+    y0, y1 = int(ys.min()), int(ys.max())
+    cx, cy = (x0 + x1) / 2, (y0 + y1) / 2
+    half = max(x1 - x0, y1 - y0) / 2 * 1.2
+    left, upper, right, lower = int(cx - half), int(cy - half), int(cx + half), int(cy + half)
+
+    H, W = rgba.shape[-2:]
+    crop = rgba.new_zeros((1, 4, lower - upper, right - left))  # out-of-bounds stays 0, matching PIL.crop
+    sx0, sy0, sx1, sy1 = max(left, 0), max(upper, 0), min(right, W), min(lower, H)
+    if sx1 > sx0 and sy1 > sy0:
+        crop[:, :, sy0 - upper:sy1 - upper, sx0 - left:sx1 - left] = rgba[:, :, sy0:sy1, sx0:sx1]
+
+    crop = comfy.utils.common_upscale(crop, size, size, "lanczos", "disabled").clamp(0, 1)
+    out = (crop[:, :3] * crop[:, 3:4])[0].movedim(0, -1)   # composite over black == rgb * alpha
+    return out.unsqueeze(0)  # (1, 1024, 1024, 3)
+
+
+class TripoSplatPreprocessImage(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TripoSplatPreprocessImage",
+            display_name="TripoSplat Preprocess Image",
+            category="3d/conditioning",
+            description="Crop center each image to a square canvas on a black background and add padding.",
+            inputs=[
+                IO.Image.Input("image"),
+                IO.Mask.Input("mask"),
+                IO.Int.Input("erode_radius", default=1, min=0, max=16,
+                             tooltip="Erode the alpha matte by this pixel radius before cropping (avoids border bleed)."),
+                IO.Int.Input("size", default=1024, min=256, max=4096, step=16,
+                             tooltip="Square image size. The model is trained at 1024; other sizes run but are off-distribution."),
+            ],
+            outputs=[IO.Image.Output(display_name="image")],
+        )
+
+    @classmethod
+    def execute(cls, image, mask, erode_radius, size) -> IO.NodeOutput:
+        size = max(16, (int(size) // 16) * 16)  # DINOv3 patch / Flux2 VAE stride is 16
+        if mask.shape[0] != image.shape[0]:
+            mask = comfy.utils.repeat_to_batch_size(mask, image.shape[0])
+        if tuple(mask.shape[1:]) != tuple(image.shape[1:3]):
+            mask = F.interpolate(mask[:, None].float(), size=tuple(image.shape[1:3]), mode="bilinear", align_corners=False)[:, 0]
+        prepared = torch.cat([_preprocess(image[i], mask[i], erode_radius, size) for i in range(image.shape[0])], dim=0)
+        return IO.NodeOutput(prepared)
+
+
+class TripoSplatConditioning(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TripoSplatConditioning",
+            display_name="TripoSplat Conditioning",
+            category="3d/conditioning",
+            description="Encode the image with DINOv3 and the Flux2 VAE into TripoSplat positive/negative "
+                        "conditioning, and create the fixed size noise target (latent + camera) for the KSampler",
+            inputs=[
+                IO.ClipVision.Input("clip_vision", tooltip="DINOv3 ViT-H/16+ image encoder"),
+                IO.Vae.Input("vae", tooltip="Flux2 VAE"),
+                IO.Image.Input("image"),
+            ],
+            outputs=[
+                IO.Conditioning.Output(display_name="positive"),
+                IO.Conditioning.Output(display_name="negative"),
+                IO.Latent.Output(display_name="latent", tooltip="The fixed size noise target (latent +camera)."),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, clip_vision, vae, image) -> IO.NodeOutput:
+        # feature1: DINOv3 token sequence (cls + registers + patches), ImageNet-normalized, with a final non-affine layer norm on top
+        comfy.model_management.load_model_gpu(clip_vision.patcher)
+        device = clip_vision.load_device
+        img = image.movedim(-1, 1).to(device)  # (B,3,H,W) in [0,1]
+        mean = torch.tensor(_DINOV3_MEAN, device=device).view(1, 3, 1, 1)
+        std = torch.tensor(_DINOV3_STD, device=device).view(1, 3, 1, 1)
+        img = (img - mean) / std
+        seq = clip_vision.model(pixel_values=img.float())[0]
+        feature1 = F.layer_norm(seq.float(), seq.shape[-1:]).to(comfy.model_management.intermediate_device())
+
+        # Second conditioning: the Flux2 VAE latent of the image, carried as a standard reference_latents entry
+        ref = vae.encode(image).to(comfy.model_management.intermediate_device())  # (B, 128, H, W)
+        b = ref.shape[0]
+
+        positive = [[feature1, {"reference_latents": [ref]}]]
+        negative = [[torch.zeros_like(feature1), {"reference_latents": [torch.zeros_like(ref)]}]]
+
+        # Fixed noise target: the latent is a constant-shape (8192, 16) shape-code + a (1, 5) camera token
+        dev = comfy.model_management.intermediate_device()
+        latent_seq = torch.zeros([b, _Q_TOKEN_LENGTH, _LATENT_CHANNELS], device=dev)
+        camera = torch.zeros([b, 1, _CAM_CHANNELS], device=dev)
+        samples = comfy.nested_tensor.NestedTensor((latent_seq, camera))
+        return IO.NodeOutput(positive, negative, {"samples": samples})
+
+
+class VAEDecodeTripoSplat(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="VAEDecodeTripoSplat",
+            display_name="TripoSplat Decode",
+            category="3d/latent",
+            description="Decode the sampled TripoSplat latent into a 3D gaussian splat. "
+                        "Modify the number of gaussians to vary the density.",
+            inputs=[
+                IO.Latent.Input("samples"),
+                IO.Vae.Input("vae", tooltip="TripoSplat VAE decoder"),
+                IO.Int.Input("num_gaussians", default=262144, min=_NUM_GAUSSIANS_MIN, max=_NUM_GAUSSIANS_MAX, step=32,
+                             tooltip="Number of gaussians to produce (rounded to a multiple of 32). "
+                                     "262144 matches the octree's point density; higher oversamples the same points "
+                                     "(denser, but no new detail) and costs proportionally more VRAM/time."),
+                IO.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff,
+                             tooltip="Seeds the octree point sampler (global RNG) for deterministic decodes."),
+            ],
+            outputs=[IO.Splat.Output(display_name="splat")],
+        )
+
+    @classmethod
+    def execute(cls, samples, vae, num_gaussians, seed) -> IO.NodeOutput:
+        s = samples["samples"]
+        latent = s.unbind()[0] if getattr(s, "is_nested", False) else s  # take the latent stream, drop camera
+
+        decoder = vae.first_stage_model
+        gpp = decoder.gaussians_per_point
+        n = max(_NUM_GAUSSIANS_MIN, min(_NUM_GAUSSIANS_MAX, int(num_gaussians)))
+        if n % gpp != 0:
+            n = round(n / gpp) * gpp
+
+        dtype_size = comfy.model_management.dtype_size(vae.vae_dtype)
+        hidden = decoder.gs.model_channels
+        cond_tokens = latent.shape[1]
+        memory_required = (cond_tokens * 4 + (n // gpp) * 10) * hidden * dtype_size
+        comfy.model_management.load_models_gpu([vae.patcher], memory_required=memory_required)
+        latent = latent.to(device=vae.device, dtype=vae.vae_dtype)
+        generator = torch.Generator(device="cpu").manual_seed(seed)
+        parts = [g.render_tensors() for g in decoder.decode(latent, num_gaussians=n, generator=generator)]
+        positions, scales, rotations, opacities, sh = (torch.stack(t) for t in zip(*parts))
+        return IO.NodeOutput(Types.SPLAT(positions, scales, rotations, opacities, sh))
+
+
+class TripoSplatSamplingPreview(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TripoSplatSamplingPreview",
+            display_name="TripoSplat Sampling Preview",
+            category="3d/latent",
+            description="Patch the TripoSplat model for the standard Ksampler node to show a live decoded "
+                        "gaussian splat preview at each step.",
+            inputs=[
+                IO.Model.Input("model"),
+                IO.Vae.Input("vae", tooltip="TripoSplat VAE decoder"),
+                IO.Int.Input("octree_level", default=5, min=2, max=8, advanced=True,
+                             tooltip="Octree depth for the preview decode (lower = cheaper/coarser)."),
+                IO.Int.Input("num_gaussians", default=16384, min=1024, max=262144, step=32,
+                             tooltip="Number of gaussians to produce for the preview (rounded to a multiple of 32)."),
+                IO.Float.Input("yaw", default=90.0, min=-360.0, max=360.0, step=1.0, tooltip="Preview camera yaw in degrees.", advanced=True,),
+                IO.Float.Input("pitch", default=15.0, min=-89.0, max=89.0, step=1.0, tooltip="Preview camera pitch in degrees.", advanced=True,),
+                IO.Int.Input("point_size", default=3, min=1, max=16,
+                             tooltip="Maximum splat radius in pixels. Each gaussian is sized from its scale and capped here; "
+                                     "lower = finer/pointier, higher = chunkier."),
+            ],
+            outputs=[IO.Model.Output()],
+        )
+
+    @classmethod
+    def execute(cls, model, vae, octree_level, num_gaussians, yaw, pitch, point_size) -> IO.NodeOutput:
+        from comfy.ldm.triposplat.preview import decode_x0_to_image
+        cfg = {"gaussians": num_gaussians, "level": octree_level, "yaw": yaw, "pitch": pitch,
+               "point_size": point_size}
+
+        fsm = vae.first_stage_model
+        cond_tokens = model.model.diffusion_model.q_token_length
+        memory_required = (cond_tokens * 4 + (num_gaussians // fsm.gaussians_per_point) * 10) * fsm.gs.model_channels * comfy.model_management.dtype_size(vae.vae_dtype)
+
+        # Live preview via WrappersMP.OUTER_SAMPLE + ProgressBar
+        # The wrapper augments the sampler's own callback to decode x0 -> gaussian splat -> preview image each step
+        def outer_sample_wrapper(executor, *args, **kwargs):
+            args = list(args)
+            cb_idx = 5  # outer_sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
+            orig_cb = args[cb_idx] if len(args) > cb_idx else kwargs.get("callback")
+            state = {"ok": True, "pbar": None, "loaded": False}
+
+            def callback(step, x0, x, total_steps):
+                if orig_cb is not None:
+                    orig_cb(step, x0, x, total_steps)
+                if not state["ok"]:
+                    return
+                try:
+                    if not state["loaded"]:
+                        loaded_models = comfy.model_management.loaded_models(only_currently_used=True)
+                        loaded_models.append(vae.patcher)
+                        comfy.model_management.load_models_gpu(loaded_models, memory_required=memory_required)
+                        state["loaded"] = True
+                    img = decode_x0_to_image(vae, x0, cfg)
+                    if state["pbar"] is None:
+                        state["pbar"] = comfy.utils.ProgressBar(total_steps)
+                    state["pbar"].update_absolute(step + 1, total_steps, ("JPEG", img, 512))
+                except Exception as e:
+                    logging.warning("TripoSplatSamplingPreview: preview failed, disabling ({})".format(e))
+                    state["ok"] = False
+
+            if len(args) > cb_idx:
+                args[cb_idx] = callback
+            else:
+                kwargs["callback"] = callback
+            return executor(*args, **kwargs)
+
+        m = model.clone()
+        m.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, "triposplat_sampling_preview", outer_sample_wrapper)
+        return IO.NodeOutput(m)
+
+
+class TripoSplatExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            TripoSplatPreprocessImage,
+            TripoSplatConditioning,
+            VAEDecodeTripoSplat,
+            TripoSplatSamplingPreview,
+        ]
+
+
+async def comfy_entrypoint() -> TripoSplatExtension:
+    return TripoSplatExtension()

comfy_extras/nodes_video.py

@@ -19,7 +19,7 @@ class SaveWEBM(io.ComfyNode):
             category="video",
             is_experimental=True,
             inputs=[
-                io.Image.Input("images"),
+                io.Image.Input("images", tooltip="RGBA images are saved with their alpha channel as transparency (vp9 codec only)."),
                 io.String.Input("filename_prefix", default="ComfyUI"),
                 io.Combo.Input("codec", options=["vp9", "av1"]),
                 io.Float.Input("fps", default=24.0, min=0.01, max=1000.0, step=0.01),
@@ -45,18 +45,25 @@ class SaveWEBM(io.ComfyNode):
             for x in cls.hidden.extra_pnginfo:
                 container.metadata[x] = json.dumps(cls.hidden.extra_pnginfo[x])
 
+        # Save transparency when the images carry an alpha channel (RGBA) and the codec supports it.
+        # vp9 -> yuva420p; other codecs have no usable alpha path, so the alpha is ignored.
+        save_alpha = images.shape[-1] == 4 and codec == "vp9"
+
         codec_map = {"vp9": "libvpx-vp9", "av1": "libsvtav1"}
         stream = container.add_stream(codec_map[codec], rate=Fraction(round(fps * 1000), 1000))
         stream.width = images.shape[-2]
         stream.height = images.shape[-3]
-        stream.pix_fmt = "yuv420p10le" if codec == "av1" else "yuv420p"
+        stream.pix_fmt = "yuva420p" if save_alpha else ("yuv420p10le" if codec == "av1" else "yuv420p")
         stream.bit_rate = 0
         stream.options = {'crf': str(crf)}
         if codec == "av1":
             stream.options["preset"] = "6"
 
         for frame in images:
-            frame = av.VideoFrame.from_ndarray(torch.clamp(frame[..., :3] * 255, min=0, max=255).to(device=torch.device("cpu"), dtype=torch.uint8).numpy(), format="rgb24")
+            if save_alpha:
+                frame = av.VideoFrame.from_ndarray(torch.clamp(frame[..., :4] * 255, min=0, max=255).to(device=torch.device("cpu"), dtype=torch.uint8).numpy(), format="rgba")
+            else:
+                frame = av.VideoFrame.from_ndarray(torch.clamp(frame[..., :3] * 255, min=0, max=255).to(device=torch.device("cpu"), dtype=torch.uint8).numpy(), format="rgb24")
             for packet in stream.encode(frame):
                 container.mux(packet)
         container.mux(stream.encode())

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.22.0"
+__version__ = "0.24.0"

main.py

@@ -464,13 +464,6 @@ def start_comfyui(asyncio_loop=None):
         folder_paths.set_temp_directory(temp_dir)
     cleanup_temp()
 
-    if args.windows_standalone_build:
-        try:
-            import new_updater
-            new_updater.update_windows_updater()
-        except:
-            pass
-
     if not asyncio_loop:
         asyncio_loop = asyncio.new_event_loop()
         asyncio.set_event_loop(asyncio_loop)

new_updater.py

@@ -1,35 +0,0 @@
-import os
-import shutil
-
-base_path = os.path.dirname(os.path.realpath(__file__))
-
-
-def update_windows_updater():
-    top_path = os.path.dirname(base_path)
-    updater_path = os.path.join(base_path, ".ci/update_windows/update.py")
-    bat_path = os.path.join(base_path, ".ci/update_windows/update_comfyui.bat")
-
-    dest_updater_path = os.path.join(top_path, "update/update.py")
-    dest_bat_path = os.path.join(top_path, "update/update_comfyui.bat")
-    dest_bat_deps_path = os.path.join(top_path, "update/update_comfyui_and_python_dependencies.bat")
-
-    try:
-        with open(dest_bat_path, 'rb') as f:
-            contents = f.read()
-    except:
-        return
-
-    if not contents.startswith(b"..\\python_embeded\\python.exe .\\update.py"):
-        return
-
-    shutil.copy(updater_path, dest_updater_path)
-    try:
-        with open(dest_bat_deps_path, 'rb') as f:
-            contents = f.read()
-            contents = contents.replace(b'..\\python_embeded\\python.exe .\\update.py ..\\ComfyUI\\', b'call update_comfyui.bat nopause')
-        with open(dest_bat_deps_path, 'wb') as f:
-            f.write(contents)
-    except:
-        pass
-    shutil.copy(bat_path, dest_bat_path)
-    print("Updated the windows standalone package updater.")  # noqa: T201

nodes.py

@@ -969,7 +969,7 @@ class CLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "clip_name": (folder_paths.get_filename_list("text_encoders"), ),
-                              "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace", "omnigen2", "qwen_image", "hunyuan_image", "flux2", "ovis", "longcat_image", "cogvideox", "lens", "pixeldit"], ),
+                              "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace", "omnigen2", "qwen_image", "hunyuan_image", "flux2", "ovis", "longcat_image", "cogvideox", "lens", "pixeldit", "ideogram4"], ),
                               },
                 "optional": {
                               "device": (["default", "cpu"], {"advanced": True}),
@@ -2362,6 +2362,7 @@ async def init_builtin_extra_nodes():
         "nodes_model_downscale.py",
         "nodes_images.py",
         "nodes_video_model.py",
+        "nodes_ideogram4.py",
         "nodes_train.py",
         "nodes_dataset.py",
         "nodes_sag.py",
@@ -2456,6 +2457,7 @@ async def init_builtin_extra_nodes():
         "nodes_moge.py",
         "nodes_mediapipe.py",
         "nodes_gaussian_splat.py",
+        "nodes_triposplat.py"
         "nodes_sam3d_body.py",
     ]
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.22.0"
+version = "0.24.0"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"

requirements.txt

@@ -1,5 +1,5 @@
-comfyui-frontend-package==1.44.19
-comfyui-workflow-templates==0.9.91
+comfyui-frontend-package==1.45.15
+comfyui-workflow-templates==0.9.98
 comfyui-embedded-docs==0.5.2
 torch
 torchsde
@@ -23,7 +23,7 @@ SQLAlchemy>=2.0.0
 filelock
 av>=16.0.0
 comfy-kitchen==0.2.10
-comfy-aimdo==0.4.7
+comfy-aimdo==0.4.9
 requests
 simpleeval>=1.0.0
 blake3

tests-unit/comfy_extras_test/nodes_math_test.py

@@ -197,3 +197,10 @@ class TestMathExpressionExecute:
     def test_pow_huge_exponent_raises(self):
         with pytest.raises(ValueError, match="Exponent .* exceeds maximum"):
             self._exec("pow(a, b)", a=10, b=10000000)
+
+    def test_huge_int_result_raises_value_error(self):
+        # Exponent is within the allowed MAX_EXPONENT range, so the result is a
+        # finite Python int that is nonetheless too large to convert to float.
+        # This must raise a clean ValueError, not an uncaught OverflowError.
+        with pytest.raises(ValueError, match="too large to represent as a float"):
+            self._exec("2 ** 3999")